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Cystic fibrosis and survival to 40 years: a case–control study

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The clinical course of patients with cystic fibrosis (CF) is variable and probably determined by many interacting factors. We aimed to examine the influence of early social and clinical factors on long-term survival.

A case–control study of adult CF patients was used to compare long-term survivors (aged ≥40 yrs) with patients who died before reaching 30 yrs of age. Each case (n = 78) was matched by birth date with at least one control (n = 152), after exclusion of “late diagnosis” patients. Probability-weighted logistic regression models were used to identify influences on survival.

Factors resulting in increased probabilities of survival included high body mass index (OR 1.76, 95% CI 1.40–2.22), forced expiratory volume in 1 s (OR per 5% increase 1.54, 95% CI 1.32–1.80), and forced vital capacity (OR per 5% increase 1.54, 95% CI 1.33–1.78) at transfer to the adult clinic and the exclusive use of oral antibiotics (OR 8.31, 95% CI 3.02–22.88). Factors resulting in decreased probabilities of survival were Pseudomonas aeruginosa acquisition (OR 0.18, 95% 0.05–0.65) or pneumothorax before transfer to the adult clinic (OR 0.02, 95% CI 0.004–0.08) and referral from a paediatric clinic in a deprived area (OR 0.13, 95% CI 0.04–0.38).

Long-term survival is associated with the clinical features present by the time of referral to an adult clinic. Even “early-diagnosis” disease appears to have different phenotypes, possibly independent of CF gene function, that have different survival patterns.

• cystic fibrosis

The life expectancy of patients with cystic fibrosis (CF) has been steadily increasing despite the lack of a cure for the underlying cellular defect. Patients born today are expected to have a median survival into their 6th decade 1 . The improvement has been explained in several ways including through the introduction of pancreatic enzymes, better nutrition, specialist-centre care, improved physiotherapy and more intensive antimicrobial treatment 2 – 4 .

CF covers a wide spectrum of disease, from milder phenotypes with “non-classic” disease (with pancreatic sufficiency, milder lung disease and a later diagnosis), to more severe cases with a “classic” phenotype 5 . However, even within different groups there is variation in the rate of disease progression; some patients with features of classic disease run a mild course and indeed an important proportion of patients with the common “severe” δF508 mutation survive beyond 40 yrs of age with relatively well-maintained lung function and weight 6 , 7 .

Thus, it has been hypothesised that other factors influence survival in CF. These include variations in the function of the responsible gene, the cystic fibrosis transmembrane conductance regulator ( CFTR ), and other independent genetic factors (“modifier” genes). None, however, has yet been shown directly to influence survival 8 . Other potential, nongenetic determinants of survival are so-called environmental influences; these cover a diverse range of factors, broadly divided into biological effectors ( e.g. microorganisms, nutrition, sex and pollutants), social and cultural influences ( e.g. socioeconomic status and adherence to treatment) and healthcare-related factors, such as access to care and interclinic treatment variations 9 . Evidence for or against these factors is variable and when they are most influential, or when an individual is most vulnerable to them, is not well understood. In view of this, we conducted a case–control study of long-term survival among patients registered with a specialist adult CF clinic with the aim of identifying early potential influences of long-term survival in patients diagnosed with CF in childhood.

Since 1965, details of all patients referred to the adult unit at Royal Brompton Hospital (RBH; London, UK) and confirmed to have CF have been entered onto a database. The diagnosis is based on clinical features and a positive sweat sodium (>70 mmol·L −1 ) or chloride (>60 mmol·L −1 ) test or, in cases with a borderline or negative sweat test result, the presence of a known disease-causing mutation on each CFTR gene, or of an abnormal nasal potential difference measurement. Patients were referred as adults from an adult physician or by their general practitioner, or directly through transition from paediatric clinics (at ∼15 yrs of age). Clinical and demographic details are collected at the first consultation and are subsequently updated at annual review.

We studied only patients with a diagnosis of CF before the age of 17 yrs. These were identified from the database and classified as cases or controls as follows. Cases (long-term survivors) were all patients with complete records who had reached 40 yrs of age without transplantation by December 31, 2004. Controls were selected from all patients with complete records who had died before 30 yrs of age or required transplantation at <30 yrs of age by December 31, 2004. We excluded controls (n = 27) who had died from a non-CF related cause ( e.g. road traffic accident).

80 cases and 400 controls were identified from the original population. To ensure that cases and controls were similar in terms of era of birth, as it is likely that this would have influenced the nature of care received, cases were matched by date of birth (±365 days) to all eligible controls. Of the 80 cases identified, 78 were matched to at least one control. Each control was matched with as many cases as eligible and controls could be matched to more than one case. Of the 400 controls identified, 152 were matched to at least one case.

Information on source of referral, guardian's occupation, genotype and clinical state (weight, height, lung function, sputum microbiology, diabetic status, use of pancreatic enzymes, previous pneumothoraces, episodes of major haemoptysis and number of previous hospital admissions or antibiotic courses) prior to and at referral was collected from the initial assessment at the adult clinic; the remaining data were collected from annual reviews (school disruption, number of Advanced (“A”)-level school examinations and number of siblings). Antibiotic treatments before first attendance at the adult clinic were categorised as oral, aerosolised or i.v.

Statistical analysis

Differences between cases and controls were described by frequencies and proportions for categorical variables, and medians and interquartile ranges for continuous variables. Development of CF-related diabetes (CFRD) and the acquisition of Staphylococcus aureus , Pseudomonas aeruginosa and Haemophilus influenzae were assessed in terms of whether the patient developed these conditions before the age of 16 yrs. As such, analyses of these variables were limited to those who arrived at RBH by 16 yrs of age (69 cases and 109 controls). Physical measurements at initial assessment, history of antibiotic use and number of hospital admissions prior to initial assessment were limited to those arriving at RBH by the age of 15 yrs (73 cases and 131 controls).

We used probability-weighted logistic regression models to assess the association between possible predictors and survival to 40 yrs of age (case status). Using this method, controls were weighted according to the cases to which they were matched; thus, making the distribution of the matching variable (date of birth) similar in both groups. Each control was weighted by the sum, across its matched case, of 1/(number of controls to which the case is matched). Cases were allocated a weight of 1. Model results are presented as OR and 95% CI. Since patients were transferred to the adult clinic at varying ages, ORs for physical measures and medical history prior to initial assessment (use of antibiotics, prior hospital admissions, history of pneumothorax and major haemoptysis prior to initial assessment) were adjusted for age at assessment. ORs for physical measures were also adjusted for sex. Analyses were conducted in SAS v9.1 (SAS Institute, Cary, NC, USA) or STATA (StataCorp LP, College Station, TX, USA).

All patients consented for their anonymised data to be included in the database for research purposes. The study was approved by the RBH Research Ethics Committee.

Clinical characteristics

Half of the participants were born between 1960 and 1965 and most (80.4%) were diagnosed with CF before the age of 5 yrs ( table 1 ). 70% were first seen in the adult clinic before 21 yrs of age. 97% had pancreatic insufficiency and there were similar proportions of males in cases (long-term survivors) and controls. Genotyping was only possible for patients surviving beyond 1989 ( i.e. the year CFTR was discovered); therefore, genetic data were available for 74 patients (67 cases). Of the long-term survivors genotyped (86%), 32 (48%) were homozygous for δF508, 13 (19%) were compound heterozygous for δF508 and 19 (28%) were heterozygous for δF508 (with an unidentifiable second CF mutation). The remaining three cases were 621+1G→T, R553X (both with unidentifiable second genes) and R347P/3659delC. The seven controls genotyped were homozygous δF508.

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Features significantly associated with case status ( i.e. long-term survivors) included diagnosis after 5 yrs of age. Patients whose initial presentation had been with respiratory disease were significantly less likely to be cases. Patients who had suffered a pneumothorax prior to referral to the adult clinic were significantly less likely to be cases after adjusting for age at first attendance. There was little heterogeneity in the distributions of pancreatic insufficiency, haemoptysis and CFRD prior to referral; none was associated with case status. After adjusting for age at initial assessment and sex, the probability of survival to 40 yrs increased with increasing height, weight, body mass index (BMI), forced expiratory volume in 1 s (FEV 1 ) and forced vital capacity as recorded at the initial assessment in the adult clinic.

Sociodemographic factors and patients’ educational background

Associations between long-term survival and measures of socio-economic status and educational attainment are shown in table 2 . Patients referred from paediatric clinic B (paediatric clinic in a low social economic status area) were less likely to be cases. Those whose guardians were in managerial or manual (skilled or unskilled) occupations were more likely to be cases than those in professional occupations, but the difference was not statistically significant. Patients classified as having “mildly” or “grossly” disrupted schooling were statistically more likely to be controls, but there was no association between case status and the number of A-levels achieved. We found no association between sibling number (with or without CF) and case status.

Sputum microbiology, antibiotic courses and hospital admissions

Table 3 displays the association between long-term survival and sputum microbiology, antibiotic courses and hospital admissions prior to referral to the adult clinic. Acquiring P. aeruginosa, but not H. influenza e or S. aureus, in the sputum prior to 16 yrs of age, was associated with a reduced probability of being a case.

Patients who had received oral antibiotics (as intermittent courses and/or long-term/prophylaxis), and had not received aerosolised or i.v. antibiotics, were significantly more likely to be cases than those who had not taken oral antibiotics. Conversely, the prior use of aerosolised or i.v. antibiotics was inversely associated with case status. Patients requiring annual or more frequent hospital admissions were significantly less likely to be cases.

This carefully matched case–control study is the first to report on the potential early influences of long-term survival in patients diagnosed with CF in childhood. Patients with a later diagnosis ( i.e. at 5–16 yrs of age), those whose CF did not present with respiratory disease and those with higher weight, height, BMI and lung function (% predicted) at the time of their first assessment at the adult clinic were statistically more likely to reach 40 yrs of age. Acquiring P. aeruginosa , but not H. influenza e or S. aureus , in the sputum prior to 16 yrs of age, was associated with a reduced probability of long-term survival. Factors that did not influence long-term survival included sex, parental occupation and major haemoptysis or the development of diabetes before 16 yrs of age. These findings suggest that the long-term survival of adults diagnosed with CF in childhood is determined predominantly by an intrinsically severe phenotype in early life, with little evidence of major modification by socioeconomic influences, and that maintaining good health in childhood is an important determinant of long-term survival.

We elected to study only patients whose disease had been diagnosed during childhood, and thus remove the bias associated with the good prognosis of disease when diagnosed in adulthood 10 , 11 . Moreover, by studying long-term survivors under the care of a single institution and by matching them with “controls” born within a year of their birth date, we reduced the effects of different adult treatment strategies between centres and changing strategies over time, each of which may have independent effects on survival 9 . We may, in this way, have “over-matched” patients, leaving insufficient heterogeneity of exposure to examine some important determinants of survival. For example, it is widely accepted that socioeconomic factors have a strong influence on prognosis 9 , 12 , 13 but our findings demonstrated only limited evidence of this. In contrast to a previous UK study in 1989, we found no correlation of parental occupation (an index of family socioeconomic status) with long-term survival 14 . The association of poor survival with referral from paediatric clinic B (situated in an area of relatively low socioeconomic status) may reflect differences in resources and provision of care, as well as patients’ sociodemographics.

However, the present study provides an important extra dimension to published studies on predictors of mortality. The earliest, observational, studies recognised the association of poor nutritional status and low FEV 1 with a worse outcome 15 – 17 . Since then, more robust epidemiological studies have confirmed this correlation, including a large population study of the Canadian Patient Data Registry 3 . More recently, an Irish study investigated factors relating to mortality in their adult patients, concluding that lower FEV 1 and BMI, and higher infection rates of P. aeruginosa and Burkholderia cepacia were associated with patients who had died 18 . They assessed differences in predetermined clinical parameters between patients who died during a 10-yr period and those who remained alive, therefore making it difficult to draw conclusions about the timing of the events ( i.e. when they were most influential). Our study adds to this by clearly showing the importance of these factors at an early stage.

The present study demonstrated a worse outcome in patients diagnosed with CF early (before 5 yrs of age) and also in those with an initial disease presentation of respiratory symptoms. This supports the findings of a US registry-based study, demonstrating variable survival among patients with inherently different degrees of baseline risk, reflected by their age at diagnosis and their degree of disease severity at presentation 19 . They also showed that meconium ileus was associated with reduced survival, which provides an explanation for the lack of correlation found in our study, as only a few patients presenting with meconium ileus survived to adulthood. Contrary to their findings, we found that sex did not predict survival, which, in part, might be explained by the historical higher mortality among CF females, particularly around puberty, taking its toll, thus leaving those who have a predetermined survival advantage to progress through to the adult clinic 20 . However, others have argued that the so-called “gender gap” does not exist, highlighting the complex interaction of this much-debated relationship 21 . Patients with an increased baseline risk are predisposed to developing worse lung disease and an accelerated decline in their general health. Consequently, they develop more complications and ultimately require more hospital admissions and i.v. antibiotic courses, as demonstrated by the strong correlation of these factors with control status in our study.

The negative impact on survival of P. aeruginosa infection is consistent with previous studies and, although there is still some controversy regarding causality and ascertainment bias, it should be regarded as a poor prognostic factor 22 , 23 . The insignificant impact of H. influenzae and S. aureus is consistent with other studies. A European cross-sectional study demonstrated that S. aureus was not associated with worse pulmonary status and others have shown a deleterious effect on symptoms only, including the risk of massive haemoptysis 24 – 26 . The finding of a survival benefit for patients receiving oral antibiotics (without aerosolised or i.v. antibiotics) is interesting, as oral flucloxicillin is usually given as long-term prophylactic anti-staphylococcal treatment, suggesting indirectly that S. aureus may be relevant to survival, although this association may also be an indicator of milder disease 27 .

We were unable to explore the impact on survival of specific CFTR mutations, as the majority of controls died before the discovery of the CF gene in 1989, making regression analysis impossible 28 . However, as 48% of the long-term survivors were homozygous for δF508 (compared with 50% in the total UK adult CF population 29 ), their survival advantage cannot be attributed to “milder” genotypes with less severe disease expression. We chose to use 17 yrs of age as our age criterion, as it has been demonstrated previously that this differentiates two distinct phenotypes of long-term survivors 11 . We acknowledge that we cannot be certain that all non-classic phenotypes have been excluded but combined with the genotype data and the fact that 97% of the total study population had pancreatic insufficiency, bias from genuine non-classic disease would have been minimal. Additionally, the use of a younger age of diagnosis would have further selected out “mild” cases; but with the recognition of significant disease heterogeneity even for homozygous δF508, reducing the age would have excluded patients with “classic” disease genotypes that follow a milder disease course ( e.g. due to gene modifiers), i.e. the group of patients of particular interest to this study.

There are several limitations to our findings. The incidence of complications such as CFRD and major haemoptysis increase with age 24 , thus numbers were small in both groups at the time of assessment in the adult clinic, limiting the likelihood of finding an effect on survival. We were unable to assess the impact of B. cepacia complex infection as the importance of this pathogen in CF became apparent only in the mid-1980s 30 . Asymptomatic patients, diagnosed at birth through neonatal screening, are also not included in this study, as such programmes have only recently been introduced. The study was further limited by the data available to us and, therefore, in some instances, proxy markers ( e.g. parental occupation) had to be used and patient numbers were small, making interpretation difficult. The information on socioeconomic status was therefore limited, as the broad category of “parental occupation” and the recognised limitations of “source of referral” do not allow for definitive conclusions to be made.

In summary, this study demonstrates the importance for long-term survival of achieving optimal growth and lung health by the time a patient attends an adult clinic. Effective clinical care is needed to facilitate this but, from our findings, we conclude that longevity is determined early, possibly by factors independent of CFTR function ( e.g. gene modifiers) that determine early phenotype, disease severity and, ultimately, the probability of long-term survival.

Statement of interest

None declared.

• Received January 5, 2010.
• Accepted March 27, 2010.
• Stanton M ,
• Mahadeva R ,
• Westerbeek RC ,
• De Boeck K ,
• Wilschanski M ,
• Castellani C ,
• Simmonds NJ ,
• Cullinan P ,
• Hodson ME ,
• Warwick WJ ,
• Konstan MW ,
• Schluchter MD ,
• Schechter MS
• Rodman DM ,
• Heltshe SL ,
• Schechter MS ,
• Margolis PA
• Shelton BJ ,
• Margolis PA ,
• Kraemer R ,
• Rudeberg A ,
• McLaughlin FJ ,
• Williams M ,
• Schidlow DV ,
• Szatrowski TH ,
• Courtney JM ,
• Bradley J ,
• Mccaughan J ,
• Rosenfeld M ,
• FitzSimmons S ,
• Emerson J ,
• McNamara S ,
• Mellis CM ,
• Yankaskas JR ,
• Ebeling M ,
• Watkin SL ,
• Elborn JS ,
• Cordon SM ,
• Navarro J ,
• Rainisio M ,
• Rommens JM ,
• Buchanan JA ,
• ↵ UK Cystic Fibrosis Database . Annual Data Report 2004. University of Dundee 2006 . www.cystic-fibrosis.org.uk/pdfs/annualreport/AuditReport2004.pdf Date last accessed: October 4, 2010. Date last updated: April, 2: 2010 .
• Maclusky I ,
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• Published: 25 August 2024

Lentiviral vector gene therapy and CFTR modulators show comparable effectiveness in cystic fibrosis rat airway models

• Alexandra McCarron   ORCID: orcid.org/0000-0002-6045-3998 1 , 2 , 3 ,
• Kak-Ming Ling 4 , 5 ,
• Samuel T. Montgomery 4 , 5 ,
• Kelly M. Martinovich 4 , 6 ,
• Patricia Cmielewski   ORCID: orcid.org/0000-0002-2236-9410 1 , 2 , 3 ,
• Nathan Rout-Pitt 1 , 2 , 3 ,
• Anthony Kicic 4 , 5 , 7 , 8 ,
• David Parsons   ORCID: orcid.org/0000-0003-1746-3290 1 , 2 , 3 &
• Martin Donnelley   ORCID: orcid.org/0000-0002-5320-7756 1 , 2 , 3  

Gene Therapy ( 2024 ) Cite this article

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• Genetic vectors
• Respiratory tract diseases

Mutation-agnostic treatments such as airway gene therapy have the potential to treat any individual with cystic fibrosis (CF), irrespective of their CF transmembrane conductance regulator ( CFTR ) gene variants. The aim of this study was to employ two CF rat models, Phe508del and CFTR knockout (KO), to assess the comparative effectiveness of CFTR modulators and lentiviral (LV) vector-mediated gene therapy. Cells were isolated from the tracheas of rats and used to establish air-liquid interface (ALI) cultures. Phe508del rat ALIs were treated with the modulator combination, elexacaftor-tezacaftor-ivacaftor (ETI), and separate groups of Phe508del and KO tracheal epithelial cells were treated with LV-CFTR followed by differentiation at ALI. Ussing chamber measurements were performed to assess CFTR function. ETI-treated Phe508del ALI cultures demonstrated CFTR function that was 59% of wild-type level, while gene-addition therapy restored Phe508del to 68% and KO to 47% of wild-type level, respectively. Our findings show that rat Phe508del-CFTR protein can be successfully rescued with ETI treatment, and that CFTR gene-addition therapy provides significant CFTR correction in Phe508del and KO ALI cultures to levels that were comparable to ETI. These findings highlight the potential of an LV vector-based gene therapy for the treatment of CF lung disease.

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Introduction.

Cystic fibrosis (CF) is a genetic disorder that arises from pathogenic variants in the CF transmembrane conductance regulator ( CFTR ) gene. The CFTR gene encodes for an ion channel that is responsible for the transport of chloride and bicarbonate ions in epithelial cells. In CF, this ion channel is dysfunctional, leading to disease manifestations in a range of organs, including the lungs. Pulmonary disease is characterised by an imbalance in ion and water transport, subsequent dehydration of the airway surface liquid, and increased viscosity of the overlying mucus. Mucus-stasis creates an environment that is highly susceptible to colonisation by microbes. Over time, persistent infections produce a state of chronic inflammation, leading to tissue damage and impaired lung function [ 1 ].

Research is ongoing to further elucidate the mechanisms underlying CF lung disease pathogenesis and to develop effective treatment strategies. Prior to human assessment and to facilitate these investigations, genetically modified animals with defects in the CFTR gene have been generated to model CF disease including mice [ 2 ], rats [ 3 , 4 , 5 , 6 ], ferrets [ 7 ] and pigs [ 8 ], each having their own benefits and limitations [ 9 , 10 ]. We have generated and characterised two CF rat models, one with a CFTR knockout (KO; Class I) and another with the common Phe508del (Class II) mutation. These rat models demonstrate CF phenotypes including intestinal obstruction, malformation of the male reproductive tract, and electrophysiological defects in the nasal epithelium [ 5 ]. However, they do not demonstrate histopathological evidence of lung disease. Moreover, it is currently unknown whether the CF electrophysiological defects observed in the nasal epithelium are replicated in the trachea, and if so, whether treatment with available modulator drugs can restore CFTR function.

CFTR modulators are a highly effective therapy for most people with CF. However, thousands of individuals are unable to benefit from these medications for a range of reasons including having refractory CFTR variants that are not amenable to pharmacological rescue (~10%), poor tolerability due to side effects, minimal clinical benefit despite having suitable variants, or lack affordable access due to location [ 11 ]. Modulators are small molecule compounds that act to either increase the amount of mature CFTR protein that is trafficked to the cell surface (corrector) or restore ion conductance by increasing opening of the CFTR channel (potentiator) [ 11 ]. Elexacaftor-tezacaftor-ivacaftor (ETI) is a triple combination modulator therapy consisting of two corrector molecules and a potentiator. Treatment with this modulator combination has proven to be highly effective in patients with at least one copy of the Phe508del variant and has resulted in improved lung function and reduced frequency of pulmonary exacerbations [ 12 , 13 ]. As CFTR modulators are now considered the gold standard treatment for most people with CF, all new therapies will likely need to demonstrate comparable effectiveness in their ability to restore CFTR function.

Alternative therapeutic strategies are under development for individuals that cannot receive modulator therapies, including genetic-based therapies that target the airway epithelium. Airway gene-addition therapy is one approach that involves delivering wild-type copies of the CFTR gene to the airway cells, with the intention of restoring CFTR ion channel function. Viral vectors such as adeno-associated virus (AAV) vectors and lentiviral (LV) vectors are considered the leading candidates for CF airway gene therapy, and their advantages and disadvantages have been reviewed in detail previously [ 14 , 15 , 16 ]. Using an LV vector approach, we have demonstrated restoration of CFTR function in the nasal epithelium of CF mice and CF rats [ 17 , 18 ], while others have shown promising results using LV vectors to restore CFTR activity in human CF cell culture systems [ 19 , 20 ] and the lower airways of CF pigs [ 21 ]. While we have shown correction in the upper nasal airways, we need to examine gene-correction in the lower airways, which more accurately recapacitate the target for CF gene therapy. The functional correction levels that can be achieved with gene-addition therapy in comparison to CFTR modulators also remains unknown.

The aim of this study was to characterise CFTR function in air-liquid interface (ALI) cultures derived from the trachea of Phe508del and KO rats, and to determine if Phe508del rat airway cells are responsive to ETI. Following this, we sought to compare CFTR restoration levels following LV vector gene-addition therapy or ETI.

Materials and methods

This study was approved by the University of Adelaide animal ethics committee under application M-2019-038 and was conducted in accordance with ARRIVE guidelines. Male and female wild-type, Phe508del and CFTR knockout rats >8 week of age were employed. CF rats were maintained using previously described husbandry measures [ 5 ].

Rat tracheal epithelial cell isolation and air-liquid interface (ALI) culture

Rats were humanely killed by CO 2 asphyxiation. Tracheal epithelial cells were isolated by pronase dissociation as previously described [ 22 ]. Briefly, tracheas were cannulated, excised, and infused with 1% pronase (Roche, Switzerland) solution in Ham’s F12 media (Sigma-Aldrich, MO, USA) supplemented with 1% PenStrep, 0.05 mg/mL Gentamicin, and 0.25 µg/mL Amphotericin B. Tracheas were incubated for 18-24 hours at 4 °C. On day two, epithelial cells were retrieved by flushing the tracheas with 30 mL Ham’s F12 media. Cells were collected by centrifugation (500 x g , 4 °C, and 10 mins) and incubated on ice with 0.5 mg/mL DNase (Sigma-Aldrich) and 10 mg/mL bovine serum albumin (BSA) (Sigma-Aldrich) to reduce cell clumping. Cells were spun down, resuspended in PneumaCult Ex-Plus media (#5040, StemCell Technologies, Canada) and assessed for number and viability. Cells were seeded directly onto 6-well Snapwell inserts (#CLS3407, Corning, NY, USA) pre-coated with 1:100 rat-tail collagen type I in phosphate-buffered saline (PBS) at a density of 300,000 cells per well. Once confluent, the growth media was removed from both apical and basolateral compartments and the basolateral compartment only was replaced with PneumaCult ALI media (#5001, StemCell Technologies). Media in the basolateral chamber was refreshed every 48 hours.

Immunohistochemistry (IHC)

Inserts were fixed in 10% neutral buffered formalin for 15 minutes, embedded in paraffin wax, and sectioned at 5 μm. Sections were deparaffinised and stained with hematoxylin and eosin (H&E), or processed for IHC as follows. Heat-mediated antigen retrieval was performed in sodium citrate buffer (pH 6.0) for 20 minutes. Sections were blocked with 1% BSA and 0.05% Tween-20 for one hour at room temperature. Primary antibodies were prepared in a dilution buffer consisting of 1% BSA and 0.1% Triton X-100 in PBS. Antibodies that were used included acetyl-alpha tubulin (5335, Cell Signalling Technology, MA, USA) at 1:500 dilution, cytokeratin 5 (ab52635, Abcam, United Kingdom) at 1:400 dilution and MUC5AC (MA5-12178, Invitrogen, MA, USA) at 1:200 dilution. Sections were incubated with primary antibodies overnight at 4 °C. Slides were washed thoroughly and incubated at room temperature for one hour with secondary antibodies donkey anti-rabbit (IgG) Alexa Fluor 488 (ab150073, Abcam) or goat anti-mouse (IgG) Alexa Fluor 568 (A-11004, Invitrogen) at a 1:200 dilution in antibody dilution buffer. The secondary antibody was washed and a DAPI nuclear counterstain was applied (4083S, Cell Signalling Technology). Slides were washed and mounted with ProLong Diamond Antifade mountant (P36970,  Invitrogen). Bright field images were captured on a Nikon Eclipse E400 microscope and fluorescence images on a Nikon Eclipse Ts2 with DS-Fi3 camera and NIS-elements D software version 4.20.02.

Transepithelial electrical resistance (TEER) measurements

At 7-10 days post air-lift, multilayered cells, mucus and cilia were all visible under light microscopy suggesting terminal differentiation of cultures. TEER measurements were then performed to ensure the appropriate formation of cell junctions and cell integrity. Using an epithelial voltammeter (Millicell-ERS voltmeter, Millipore) with silver chloride “chopstick” electrodes, triplicate measurements of TEER were obtained for each well followed by Ussing chamber measurements. Resistance obtained from a cell-free culture was subtracted from that measured across each culture and corrected for surface areas of inserts (1.1 cm 2 ) to yield the TEER of the epithelial cells with values expressed in Ω/cm 2 .

Short-circuit current (I sc ) measurements

Baseline CFTR function was assessed in wild-type ( n  = 14), Phe508del ( n  = 13) and KO ( n  = 12) rat ALI cultures that were generated from individual animals. At 7–10 days post air-lift the transepithelial short-circuit current (I sc ) was measured using an Ussing chamber (VCC MC6, Physiologic Instruments, NV, USA) as previously described [ 23 ]. Cells grown on Snapwell inserts were mounted into sliders (P2302, Physiologic Instruments) in the Ussing chamber and submerged in Krebs Ringer Buffer (KRB) that was bubbled with carbogen (95% O 2 , 5% CO 2 ). After compensating for voltage offsets, the transepithelial voltage was clamped at 0 mV and current and resistance were recorded with the Acquire and Analyze software (Physiologic Instruments). To assess chloride ion transport, sodium transport was blocked with the addition of amiloride bilaterally (10 µM final concentration) before stimulation of cAMP-mediated chloride transport with the addition of forskolin (10 µM final concentration) and blocking of CFTR transport using CFTR inh -172 (30 µM final concentration). Appropriate periods of equilibration between each addition were used. To calculate changes in I sc , the average current measured over the 60 seconds following amiloride addition was subtracted from the average current after forskolin measurement.

CFTR modulator treatment of rat ALI cultures

Differentiated rat ALI cultures generated from individual animals ( n  = 8) were treated via the basolateral chamber with modulators 24 hours prior to short-circuit current measurements. Cells were treated with 3 µM elexacaftor (VX-445), 18 µM tezacaftor (VX-661), and 1 µM ivacaftor (VX-770) (Selleck Chemicals, TX, USA) as previously described [ 24 ]. A DMSO vehicle control was included with no differences observed to the untreated controls (data not shown). Following treatment, TEER and short-circuit current measurements were performed as described above.

LV-GFP vector transduction of rat airway epithelial cells

The VSV-G (vesicular stomatitis virus glycoprotein) pseudotyped EF1α-3XFLAG-fLuc-eGFP LV vector was produced in-house and was titered using flow cytometric detection of green fluorescent protein (GFP) as previously described [ 25 ]. Wild-type rat airway epithelial cells were seeded into collagen-coated 6-well plates (#152034, Thermo Fisher Scientific, MA, USA) at 25,000 cells per well and were maintained in F-medium containing ROCK inhibitor as previously described [ 23 ]. Following adhesion, cells were transduced at a multiplicity of infection (MOI) of 1, 10 or 100 with the appropriate volume of EF1α-3XFLAG-fLuc-eGFP vector at a titre of 1 ×10 9 transducing units (TU)/mL. Each MOI was tested in triplicate over two separate experiments ( n  = 6 per group in total). The following day, the media was refreshed. Three days post-transduction cells were imaged via fluorescent microscopy (Nikon Eclipse Ts2). Cells were harvested for flow cytometry to determine the proportion of GFP positive cells and to confirm their identity as basal cells. Cells were fixed in 4% paraformaldehyde, permeabilised in 0.1% saponin and stained with anti-cytokeratin 5 antibody (ab52635, Abcam) at a 1:100 dilution for 30 minutes. Cells were washed and stained with Goat anti-rabbit IgG Alexa Fluor 568 (A-11011, Invitrogen) at 1:500 dilution for 30 minutes. Cells were washed and stored overnight at 4 °C in PBS. Samples were measured using an LSRFortessa (BD biosciences, NJ, USA) and analysed using FlowJo software (v10).

LV-CFTR vector treatment of CF rat airway epithelial cells

The VSV-G pseudotyped EF1α-V5-CFTR LV vector was produced by the Functional Genomics South Australia Core Facility (FGSA) at the University of Adelaide. Vector titration was performed by transducing HEK 293T cells and detecting the V5 epitope tag by flow cytometry, as previously described [ 18 ]. The titre of the vector employed was 1.5 ×10 8 TU/mL. Tracheal cells were isolated from Phe508del ( n  = 5) and KO rats ( n  = 5) and were seeded onto inserts at a density of 300,000 cells per well. After 24 hours, reference wells were used to determine the average number of cells for calculating the MOI. An appropriate volume of LV vector was applied to each well to achieve an MOI of 1, and cells were incubated for approximately 24 hours. The following day the media was replaced, and the cells were airlifted. Following differentiation, TEER and short-circuit current measurements were performed as described above.

CFTR gene expression analyses by qPCR

Differentiated ALI cultures were lysed in PureLink lysis buffer (Thermo Fisher Scientific) and total RNA was extracted using the RNeasy® kit (Qiagen, Germany) following the manufacturer’s instructions. The quantification and evaluation of the purity of RNA samples was assessed using the NanoDrop™ Lite spectrophotometer (Thermo Fisher Scientific). Reverse transcription of RNA to synthesise complementary DNA was performed using the QuantiTect Reverse Transcription Kit (Qiagen) following the manufacturer’s guidelines. qPCR was performed using the Fast SYBR™ Green Master Mix (Thermo Fisher Scientific) following the manufacturer’s instructions, in line with the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines [ 26 ]. Primer sequences are included in the supplementary information (Table  S1 ). The ability of the primers to specifically amplify either rat or human CFTR is shown in the supplementary figures (Figures  S1 and S2 ). PCR samples were heated for 10 minutes at 50 °C followed by 3 minutes at 95 °C, then qPCR reactions were run for 40 cycles of 95 °C for 10 seconds (denaturation) and 56 °C or 60 °C for 60 seconds (combined annealing/extension) using a CFX Connect Real-Time PCR Detection System (Bio-Rad, CA, USA). Samples were run alongside a standard curve of known copy numbers for Cyclophilin A  ( CycA ), rat CFTR and human LV-derived  CFTR . The calculated copy number for rat CFTR and human  CFTR was normalised to CycA copy number. PCR products were assessed by gel electrophoresis using a 1% agarose gel with a 100 bp DNA ladder (AXYM-DNA-100bp, Axygen, CA, USA) (Figure  S3 ).

Data was analysed using GraphPad Prism (v9). Where appropriate, data were assessed for normality using the Shapiro-Wilk test, and the Brown-Forsythe and Bartlett’s tests was used determine equal variances between groups. Data were analysed for statistically significant differences using one-way ANOVA with Tukey’s multiple comparisons test. A p -value of <0.05 was considered statistically significant. Graphs show the mean ± standard error of the mean (SEM) and reported in-text values are the mean ± standard deviation (SD).

ALI cultures can be successfully generated from CF rat trachea cells

Rat-derived ALI cultures from all genotypes developed into a pseudostratified epithelium (Fig.  1 ). Additionally, the presence of mucus and cilia-beating was observed upon light microscopy, and TEER measurements (data not shown) confirmed the integrity and permeability of the culture. Immunohistochemical staining identified the presence of respiratory cell types including ciliated (ɑ-tubulin), goblet (MUC5AC), and basal cells (cytokeratin 5), with no differences observed in the cellular composition between the rat genotypes.

Cellular markers of differentiated cell types identified via immunohistochemistry, including α-tubulin for ciliated cells, MUC5AC for goblet cells, and CK5 for basal cells. Representative images are from a wild-type rat ALI culture at 15-days post air-lift. Scale bar = 10 µM.

Rat airway basal cells can be successfully transduced with LV vector

Rat airway epithelial cell cultures were shown to be permissive to transduction by a VSV-G pseudotyped LV-GFP vector. Flow cytometry performed on the cultured cells confirmed their identity as predominantly basal cells, with 81% staining positive for cytokeratin 5. Cells transduced with the LV-GFP vector at MOI of 1, 10, and 100 showed significant dose-related increases in transduction levels ( p  < 0.0001) (Fig.  2 ). Flow cytometric quantification of GFP-positive cells showed that an MOI of 1 produced an average of 1.2 ± 0.08% GFP-positive cells, an MOI of 10, 12.6 ± 1.5% and an MOI of 100, 70.8 ± 3.4%.

A Wild-type rat airway cells transduced with LV-GFP at MOIs of 1, 10 and 100 show dose-related increases in the proportion of cells with green fluorescence. B Corresponding flow cytometry quantification of the percentage of GFP-positive cells. C Graph showing the average percentage of GFP-positive cells at the respective MOIs. D Immunostaining with cytokeratin 5 confirmed the identity of the rat airway epithelial cells as predominantly basal. Data are represented as mean ± SEM, one-way ANOVA, Tukey’s multiple comparison test, **** p  < 0.0001, n  = 6 technical replicates per group (performed over two independent experiments).

Phe508del and KO rats have reduced CFTR function in the tracheal airways

Using chamber measurements performed on rat ALI cultures revealed a significant reduction in the delta short-circuit current (ΔI sc ) response to forskolin in both Phe508del (23.25 ± 6.95 µA/cm 2 , p  < 0.0001) and KO (4.52 ± 1.84 µA/cm 2 , p  < 0.0001) rats when compared to wild-type (71.66 ± 16.96 µA/cm 2 ) (Fig.  3A, E ). KO rats had a significantly blunted response to forskolin that was only 6% of wild-type level, while the Phe508del rats had a small response that was 32% of the wild-type, indicating low-level residual CFTR function.

Representative I sc traces from A ALIs derived from wild-type, Phe508del and KO airway epithelial cells, B untreated Phe508del and ETI-treated rat ALI cultures, C Phe508del LV-CFTR treated ALI culture compared to untreated Phe508del and D KO LV-CFTR treated ALI culture compared to untreated KO. E ΔI sc forskolin response in wild-type, Phe508del, KO, Phe508del treated with ETI, and Phe508del and KO treated with LV-CFTR. Data are represented as mean ± SEM, one-way ANOVA, Tukey’s multiple comparison test, ns: not significant, ** p  < 0.01, *** p  < 0.001, **** p  < 0.0001, n  = 5-14 per group. Each data point represents an individual animal. F508; Phe508del.

Phe508del rat airway cells respond to ETI treatment

Rat ALI cultures were treated with the CFTR modulator combination ETI 24 hours prior to assessment. Phe508del ALIs were found to be responsive to modulator treatment indicated by improved CFTR function. Phe508del treated cultures demonstrated a significant increase in the ΔI sc forskolin response (42.17 ± 8.32 µA/cm 2 , p  = 0.0034) that was corrected to 59% of wild-type level (Fig.  3B, E ).

LV-CFTR gene-addition therapy restores CFTR function in Phe508del and KO rat ALIs

Treatment of CF rat airway epithelial cells with LV-CFTR gene vector at an MOI of 1 produced significant CFTR function upon Ussing chamber measurement of differentiated ALI cultures. Treated Phe508del (48.74 ± 12.03 µA/cm 2 , p  = 0.0005) and KO (33.72 ± 10.68 µA/cm 2 , p  < 0.0001) cells exhibited significantly increased ΔI sc forskolin responses when compared to untreated cultures (Fig.  3C–E ). On average, the Phe508del rats demonstrated restoration that was 68% of wild-type level, and the KO rats reached 47%. There were no statistically significant differences between the Phe508del ETI-treated group and the Phe508del or KO LV-CFTR treated groups.

Human CFTR transcripts were detected following LV-CFTR treatment

Quantitative PCR (qPCR) showed the presence of human CFTR transcripts in Phe508del ( p  < 0.0001) and KO ( p  = 0.078) rat cultures that were treated with LV-CFTR gene-addition therapy, while no h CFTR transcripts were detected in the untreated samples (Fig.  4A ). The LV-CFTR treated Phe508del cells had significantly higher levels of hCFTR mRNA than the treated KO cells ( p  = 0.0004). Treatment with LV-CFTR did not have any effect on the endogenous rat CFTR mRNA expression levels (Fig.  4B ).

Absolute quantification of A human and B rat CFTR mRNA in untreated and LV-CFTR treated rat ALIs. Total copy number is expressed relative to the total copy number of the housekeeping gene CycA . Data are represented as mean ± SEM, one-way ANOVA, Tukey’s multiple comparison test, ns: not significant, *** p  < 0.001, **** p  < 0.0001, n  = 2–5 per group. F508; Phe508del.

Gene-addition therapy is a promising strategy for tackling CF lung disease and may provide an effective alternative for those without adequate treatment options. Our study demonstrated restoration of CFTR function in ALI cultures derived from Phe508del and KO rat airways, highlighting that a gene-addition approach is effective irrespective of mutation type. This work supports our previous studies that showed improved CFTR function in the nasal airways of CF mice [ 17 , 27 ] and CF rats [ 18 ] following LV-mediated gene therapy when assessed by nasal potential difference (NPD). Here we also revealed that LV gene-addition therapy produces similar levels of CFTR restoration to ETI, an important finding given that modulator drugs are now the benchmark for which all new CF therapies will be compared.

CF rats are proving to be useful models for research investigations. In particular, the development of rat models with human CF-causing variants introduced into the CFTR gene such as Phe508del provides the opportunity to test mutation-specific therapies in vivo including CFTR modulators, antisense oligonucleotides, and gene editing strategies [ 4 , 5 ]. In a separate study, we recently showed that oral treatment of Phe508del rats with ETI produces significant improvements in CFTR function in the nasal airways following NPD assessment [ 28 ]. Mouse and rat models have also been generated to express human CFTR under control of the endogenous promoter, enabling the gene to be expressed at physiological levels in the appropriate tissues. A rat model expressing human CFTR with the Gly551Asp mutation has previously been found to be responsive to ivacaftor with measures including electrophysiology, airway surface and periciliary liquid depth, mucus transport rates, and mucus viscosity showing normalisation following treatment [ 29 ].

Our study demonstrated that CF rat trachea-derived ALI cultures could be successfully established, with immunohistochemical staining confirming the presence of cell types including ciliated, basal, and goblet cells. Ussing chamber assessment of CFTR ion channel function revealed that Phe508del and KO rat ALIs had significantly diminished responses to the CFTR agonist forskolin, with KO rats more severely affected. Similarly, ALI cultures derived from CF G542X rat tracheal cells were previously found to have significantly reduced forskolin-stimulated I sc when compared to wild-type [ 6 ]. Studies conducted on excised CF rat tracheal tissue also demonstrate reduced CFTR function [ 6 , 29 ]. Comparatively, studies characterising CFTR ion channel function in excised CF mouse tracheal tissue demonstrate a significant chloride secretory response to forskolin. This finding has been attributed to the presence of alternative chloride secretory pathways that are more dominant in murine airways and compensate for the lack of CFTR function [ 30 ]. The presence of defective CFTR-mediated chloride secretion in the tracheal airways of CF rat models suggests that rats are superior to mice when studying aspects related to airway physiology.

Phe508del rat ALI cultures were found to be responsive to the modulator combination ETI, with treated cells demonstrating a forskolin response that was restored to 59% of the wild-type level. In a previous study conducted on Phe508del rat nasal-derived ALIs treated with lumacaftor-ivacaftor, the forskolin response was similarly improved to 47% of wild-type level [ 4 ]. In comparison, the improvements in CFTR activity in human cells appear to be more substantial. One study conducted on nasal brushing-derived ALIs from CF patients with Phe508del/unknown variants showed that ETI restored the forskolin response to 83% of the non-CF level [ 31 ].

The lower-level CFTR restoration observed in modulator-treated Phe508del CF rat airway cells both here and previously [ 4 ] may be attributed to differences in the rat CFTR protein structure when compared to humans. Previous studies have shown that species-dependent differences in the CFTR protein can alter the response to CFTR modulator drugs. Corrector molecules such as lumacaftor have been found to rescue misfolded mouse Phe508del-Cftr protein, whereas potentiator molecules such as ivacaftor completely fail to augment mouse Gly551Asp-Cftr or Phe508del-Cftr function [ 32 , 33 ]. In contrast, these results suggest that rat Phe508del-CFTR is amenable to potentiation by ivacaftor, as an improved response to forskolin stimulation would not have otherwise been observed. While ETI did not completely restore CFTR function in the Phe508del rat ALI cultures, our validation of a beneficial effect underscores the utility of this CF rat model and has already enabled studies assessing phenotype normalisation following in vivo treatment with modulators [ 28 ].

Rat airway epithelial cells were successfully transduced with VSV-G pseudotyped LV-GFP vector at MOIs of 1, 10, and 100, resulting in dose-dependent increases in transduction efficacy. Rat basal cells transduced with LV-CFTR at an MOI of 1 demonstrated that as little as 1% corrected cells could significantly restore CFTR function. Others have shown similar restoration of CFTR function in CF human airway epithelial cells treated with LV-CFTR vector followed by differentiation at ALI [ 20 ]. In this study, we went on to demonstrate comparable levels of CFTR restoration with gene therapy and ETI treatment, with gene-addition producing 47% and 68% increase toward wild-type in KO and Phe508del respectively, and ETI achieving 59% improvement. Use of higher MOI such as 10 or 100 is likely to have resulted in further increases in CFTR function, however, higher MOIs are not expected to be achievable in vivo or in clinical situations, and therefore were not tested in this study.

Assessment of mRNA expression via qPCR confirmed the presence of h CFTR transcripts in the LV-treated rat ALI cultures, providing clear evidence that the improvements in ion transport were a direct result of the LV-CFTR gene-addition. The Phe508del-treated group had significantly higher h CFTR mRNA expression than the KO treated group, potentially suggesting that Phe508del cells are more transducible than KO cells, although further investigation is required to confirm this. The number of h CFTR transcripts in the treated samples were approximately ten times lower than the wild-type endogenous rat CFTR levels, suggesting only small amounts of wild-type CFTR mRNA are needed to significantly restore CFTR function. The levels of rat CFTR mRNA transcripts were not altered following treatment, indicating that LV-CFTR gene addition does not affect endogenous transcription.

One limitation of this study was that the basal cells were transduced with LV-CFTR rather than differentiated airway cultures, which is more representative of an in vivo setting and is likely to require a much higher MOI when compared to the MOI of 1 that was used here. However, there is value in this approach as the basis of an airway-directed stem cell therapy where basal cells are transduced ex vivo and then delivered into the airways [ 15 ]. Thus, determining the transduction efficiency of basal cells and the effects on differentiation of the epithelium are still of significant value. Another limitation is that we utilised the GFP reporter gene to determine which MOI to use for the LV-CFTR studies. Although we have no evidence to suggest that this is not a reliable approach, there is potential for some discrepancy in the expression levels between the two transgenes.

This investigation has laid the groundwork for future studies that will assess the in vivo effectiveness of LV-CFTR gene therapy in the lungs of CF rats. We have previously shown corrected CFTR function in the nasal airways of CF rats [ 18 ] and CF mice [ 17 , 27 ] however, the target region of gene therapy for people with CF will be the small airways of the lungs as this is where the disease initiates. Future work will include treating the lungs of CF rats with gene therapy and assessing improvement in CFTR function via lower airway transepithelial potential difference or Ussing chamber measurements on excised airway tissue. Assessment of lung function with measures such as respiratory mechanics (e.g. flexiVent) or functional lung imaging (e.g. X-ray velocimetry) may also prove valuable in assessing the efficacy of LV vector-mediated gene therapy [ 34 ].

In summary, our findings highlight the potential of a LV vector-based gene therapy for the treatment of CF lung disease. We showed that Phe508del rat ALI cultures had improved CFTR ion transport following treatment with ETI, demonstrating that rat Phe508del-CFTR protein trafficking and gating can be rescued with this modulator combination. LV-CFTR treatment of Phe508del and KO rat cultures produced comparable CFTR function restoration to ETI, illustrating the effectiveness of this therapeutic approach. This work provides further support for the continued development of a gene-addition therapy for CF and offers the possibility to provide a mutation-agonistic treatment for those without modulator therapies.

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Turcios NL. Cystic fibrosis lung disease: an overview. Respir Care. 2020;65:233–51.

Article   PubMed   Google Scholar  

Guilbault C, Saeed Z, Downey GP, Radzioch D. Cystic fibrosis mouse models. Am J Respir Cell Mol Biol. 2007;36:1–7.

Article   CAS   PubMed   Google Scholar  

Tuggle KL, Birket SE, Cui X, Hong J, Warren J, Reid L, et al. Characterization of defects in ion transport and tissue development in cystic fibrosis transmembrane conductance regulator (CFTR)-knockout rats. PLoS One. 2014;9:e91253.

Article   PubMed   PubMed Central   Google Scholar  

Dreano E, Bacchetta M, Simonin J, Galmiche L, Usal C, Slimani L, et al. Characterization of two rat models of cystic fibrosis-KO and F508del CFTR-Generated by Crispr-Cas9. Anim Model Exp Med. 2019;2:297–311.

Article   Google Scholar  

McCarron A, Cmielewski P, Reyne N, McIntyre C, Finnie J, Craig F, et al. Phenotypic characterization and comparison of cystic fibrosis rat models generated using CRISPR/Cas9 gene editing. Am J Pathol. 2020;190:977–93.

Sharma J, Abbott J, Klaskala L, Zhao G, Birket SE, Rowe SM. A Novel G542X CFTR rat model of cystic fibrosis is sensitive to nonsense mediated decay. Front Physiol. 2020;11:611294.

Sun X, Olivier AK, Liang B, Yi Y, Sui H, Evans TIA, et al. Lung phenotype of juvenile and adult cystic fibrosis transmembrane conductance regulator–knockout ferrets. Am J Respir Cell Mol Biol. 2013;50:502–12.

Stoltz DA, Meyerholz DK, Pezzulo AA, Ramachandran S, Rogan MP, Davis GJ, et al. Cystic fibrosis pigs develop lung disease and exhibit defective bacterial eradication at birth. Sci Transl Med. 2010;2:29ra31.

McCarron A, Donnelley M, Parsons D. Airway disease phenotypes in animal models of cystic fibrosis. Respir Res. 2018;19:54.

McCarron A, Parsons D, Donnelley M. Animal and cell culture models for cystic fibrosis: which model is right for your application? Am J Pathol. 2021;191:228–42.

Lopes-Pacheco M. CFTR modulators: the changing face of cystic fibrosis in the era of precision medicine. Front Pharm. 2020;10:1662.

Middleton PG, Mall MA, Dřevínek P, Lands LC, McKone EF, Polineni D, et al. Elexacaftor–Tezacaftor–Ivacaftor for cystic fibrosis with a single Phe508del Allele. N. Engl J Med. 2019;381:1809–19.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Heijerman HGM, McKone EF, Downey DG, Van Braeckel E, Rowe SM, Tullis E, et al. Efficacy and safety of the elexacaftor plus tezacaftor plus ivacaftor combination regimen in people with cystic fibrosis homozygous for the F508del mutation: a double-blind, randomised, phase 3 trial. Lancet. 2019;394:1940–8.

Cooney AL, McCray PB, Sinn PL. Cystic fibrosis gene therapy: looking back, looking forward. In: Genes , 2018.

Allan KM, Farrow N, Donnelley M, Jaffe A, Waters SA. Treatment of cystic fibrosis: from gene- to cell-based therapies. Front Pharm. 2021;12:639475.

Article   CAS   Google Scholar  

Sui H, Xu X, Su Y, Gong Z, Yao M, Liu X, et al. Gene therapy for cystic fibrosis: Challenges and prospects. Front Pharm. 2022;13:1015926.

Cmielewski P, Donnelley M, Parsons DW. Long-term therapeutic and reporter gene expression in lentiviral vector treated cystic fibrosis mice. J Gene Med. 2014;16:291–9.

Reyne N, Cmielewski P, McCarron A, Delhove J, Parsons D, Donnelley M. Single-dose lentiviral mediated gene therapy recovers CFTR function in cystic fibrosis knockout rats. Front Pharm. 2021;12:682299.

Alton EWFW, Beekman JM, Boyd AC, Brand J, Carlon MS, Connolly MM, et al. Preparation for a first-in-man lentivirus trial in patients with cystic fibrosis. Thorax. 2017;72:137.

Cooney AL, Thurman AL, McCray PB Jr, Pezzulo AA, Sinn PL. Lentiviral vectors transduce lung stem cells without disrupting plasticity. Mol Ther Nucleic Acids. 2021;25:293–301.

Cooney AL, Abou Alaiwa MH, Shah VS, Bouzek DC, Stroik MR, Powers LS, et al. Lentiviral-mediated phenotypic correction of cystic fibrosis pigs. JCI insight. 2016;1:e88730.

Kaartinen L, Nettesheim P, Adler KB, Randell SH. Rat tracheal epithelial cell differentiation in vitro. In Vitro cellular & developmental biology. Animal 1993;29:481–92.

CAS   Google Scholar  

Martinovich KM, Iosifidis T, Buckley AG, Looi K, Ling K-M, Sutanto EN, et al. Conditionally reprogrammed primary airway epithelial cells maintain morphology, lineage and disease specific functional characteristics. Sci Rep. 2017;7:17971.

Veit G, Roldan A, Hancock MA, Da Fonte DF, Xu H, Hussein M, et al. Allosteric folding correction of F508del and rare CFTR mutants by elexacaftor-tezacaftor-ivacaftor (Trikafta) combination. JCI insight. 2020;5:e139983.

McCarron A, Donnelley M, McIntyre C, Parsons D. Transient lentiviral vector production using a packed-bed bioreactor system. Hum Gene Ther methods. 2019;30:93–101.

Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M, et al. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem. 2009;55:611–22.

Cmielewski P, Delhove J, Donnelley M, Parsons D. Assessment of lentiviral vector mediated CFTR correction in mice using an improved rapid in vivo nasal potential difference measurement protocol. Front Pharm. 2021;12:714452.

Reyne N, Cmielewski P, McCarron A, Smith R, Eikelis N, Pirakalathanan P, et al. Effect of elexacaftor-tezacaftor-ivacaftor on nasal potential difference and lung function in Phe508del rats. Front Pharm. 2024;15:1362325.

Birket SE, Davis JM, Fernandez-Petty CM, Henderson AG, Oden AM, Tang L, et al. Ivacaftor reverses airway mucus abnormalities in a rat model harboring a humanized G551D-CFTR. Am J Respir Crit care Med. 2020;202:1271–82.

Grubb BR, Boucher RC. Pathophysiology of gene-targeted mouse models for cystic fibrosis. Physiol Rev. 1999;79:S193–214.

Comegna M, Terlizzi V, Salvatore D, Colangelo C, Di Lullo AM, Zollo I, et al. Elexacaftor-Tezacaftor-Ivacaftor therapy for cystic fibrosis patients with The F508del/Unknown genotype. Antibiotics. 2021;10:828.

Bose SJ, Bijvelds MJC, Wang Y, Liu J, Cai Z, Bot AGM, et al. Differential thermostability and response to cystic fibrosis transmembrane conductance regulator potentiators of human and mouse F508del-CFTR. Am J Physiol Lung Cell Mol Physiol. 2019;317:L71–l86.

Van Goor F, Hadida S, Grootenhuis PD, Burton B, Cao D, Neuberger T, et al. Rescue of CF airway epithelial cell function in vitro by a CFTR potentiator, VX-770. Proc Natl Acad Sci USA. 2009;106:18825–30.

Donnelley M, Parsons DW. Gene therapy for cystic fibrosis lung disease: overcoming the barriers to translation to the clinic. Front Pharm. 2018;9:1381.

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Funding for this work was provided by the National Health and Medical Research Council (GNT1160011), Medical Research Future Fund (RFRHPSI000013), and Cystic Fibrosis Foundation (DONNEL21GO). Open Access funding enabled and organized by CAUL and its Member Institutions.

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Alexandra McCarron, Patricia Cmielewski, Nathan Rout-Pitt, David Parsons & Martin Donnelley

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Kak-Ming Ling, Samuel T. Montgomery, Kelly M. Martinovich & Anthony Kicic

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Conceptualisation was performed by DP, MD, AK, and AM. Data gathering was performed by AM, KML, SM, KM, PC, and NRP. Data analysis was conducted by AM, KML, SM, KM, and NRP. Funding was acquired by DP and MD. Manuscript writing was conducted by AM, KML and SM. Manuscript was edited and reviewed by all authors.

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McCarron, A., Ling, KM., Montgomery, S.T. et al. Lentiviral vector gene therapy and CFTR modulators show comparable effectiveness in cystic fibrosis rat airway models. Gene Ther (2024). https://doi.org/10.1038/s41434-024-00480-y

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Received : 24 May 2024

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Published : 25 August 2024

DOI : https://doi.org/10.1038/s41434-024-00480-y

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Clinical implications of innate immune exhaustion in cystic fibrosis

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Objectives Lung disease progression in people with cystic fibrosis (pwCF) varies from one individual to another. Different immunological characteristics have been suggested to explain this variation, and we hypothesised that lung capacity may be associated with the innate immune response in pwCF. In an exploratory study, we aimed to investigate potential links between the innate immune response and lung function in pwCF using the standardised immune function assay TruCulture.

Methods In a single-centre study with combined cross-sectional and longitudinal data before and after intravenous antibiotics, blood was sampled from Pseudomonas aeruginosa -infected pwCF. Whole blood was analysed by TruCulture to reveal the unstimulated and stimulated cytokine release. Tobit regressions and Spearman's correlations were used to estimate the associations between lung function and cytokine release.

Results We included 52 pwCF in the cross-sectional study and 24 in the longitudinal study. In the cross-sectional study, we found that compared to a healthy population, the release of toll-like receptor (TLR)3, TLR4- and TLR7/8-stimulated interferon-γ, and interleukin (IL)-12p40 was reduced. Although TLR3-stimulated IL-1β and IL-6 release increased with lung function, overall, cytokine release did not correlate well with lung function. In the longitudinal study, the cytokine release was modified by antibiotic treatment, but the cytokine release before antibiotic treatment did not associate with changes in lung function after treatment.

Conclusion The stimulated cytokine release could not predict lung function levels or changes in pwCF, but our data indicate that pwCF experience exhaustion in the innate immune response after years of chronic bacterial infection.

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Immune function is affected in CF, possibly altering disease progression. This study reports evidence of immune exhaustion, an immunomodulatory effect of intravenous antibiotic treatment, and a potential role of the TLR3 pathway in CF lung disease. https://bit.ly/3U7Sxjx

• Introduction

Persistent lung infection and inflammation remain the most significant challenge and cause of morbidity and mortality in cystic fibrosis (CF) [ 1 ]. Although the treatment of CF airway infections has improved for decades [ 2 ], the life expectancy among people with CF (pwCF) is still far from the life expectancy in the general population [ 3 ]. For poorly understood reasons, some pwCF show a steep lung function decline at a very early age [ 4 ], while others have a normal lung function for decades [ 5 ]. Although some pwCF maintain a stable lung function over time, multiple daily inhalations and occasionally harsh intravenous antibiotic treatments are prescribed to most pwCF [ 3 , 5 ]. Understanding and monitoring the mechanisms underlying the heterogeneous disease progression in CF is of critical clinical importance, as it could pave the way for personalised administration of antibiotics [ 6 ].

To explain the heterogeneity in lung function deterioration in CF, previous studies have searched for possible immunological defects in pwCF. For example, a more pronounced type 2 T-helper cell (Th2) immune response has been linked with deleterious outcomes in people with Pseudomonas aeruginosa infections, while a Th1-skewed immune response seems more protective [ 7 , 8 ]. The direction of the adaptive immune response might be stimulated and influenced by cytokines released from innate pathways. Consistently, a reduced stimulated toll-like receptor (TLR) mediated cytokine response has been associated with a lower % predicted forced expiratory volume in 1 s (FEV 1 % pred) [ 9 ], as well as to a higher frequency of P. aeruginosa infections [ 10 ]. Furthermore, former studies have reported that interleukin (IL)-17A levels in the airways were positively correlated to clinical exacerbations [ 11 ], and unstimulated IL-8 levels in the blood were positively correlated to more severe disease [ 12 ]. Hence, we hypothesised that severe CF airway disease is associated with high unstimulated cytokine release causing reduced responsiveness of TLRs, which further leads to a disturbed or impaired immune response. In addition, baseline levels and changes in cytokine release during antibiotic treatment might be useful as biomarkers, which could indicate reversible and treatable flares in lung inflammation.

Studying and evaluating the CF immune system in clinical settings remains challenging, as non-standardised methods are the only available tools. This might explain why studies of the CF immune response have found varying and at times inconsistent tendencies [ 9 , 12 ]. A new standardised immune assay (TruCulture; Myriad RBM, Austin, TX, USA) has been developed and tested as a predictive and diagnostic tool to link systemic immune responses to clinical parameters in several clinical cohorts, including people undergoing surgery, people with cancer and people with neuroborreliosis [ 13 – 16 ]. TruCulture estimates unstimulated and stimulated cytokine release to TLR agonists in whole blood using test tubes containing standardised concentrations of different agonists. As such, the in vitro stimulation of whole blood with these agonists mimics the in vivo immune response to external stimuli [ 16 ]. To our knowledge, TruCulture has never been used to study the immunological responses in CF.

This exploratory study evaluated the TruCulture assay in whole blood as a research and clinical tool in pwCF. In particular, we tested the ability of TruCulture to predict and explain differences in lung function, measured as FEV 1 % pred. Further, to investigate the use of TruCulture as a clinical tool, we assessed the immunomodulatory effects of systemic antibiotic treatment and the assay's ability to predict and explain the clinical effect of a 14-day course of intravenous antibiotic treatment in pwCF.

Study design

This single-centre study combined prospective and longitudinal data collected between 2019 and 2020 at the Copenhagen CF Centre, Rigshospitalet, Denmark. The cross-sectional arm enrolled patients attending routine consultations, and the longitudinal arm enrolled patients at the start of intravenous antibiotic treatments. In the Copenhagen CF Centre, pwCF with chronic P. aeruginosa infection are treated with quarterly elective 14-day treatments and with urgent treatments in case of exacerbations [ 17 ]. Blood samples were collected during routine consultations and at the start and end of intravenous antibiotic treatment. Data from the first visit of all participants were included in the cross-sectional analyses.

Participants

pwCF older than 10 years with chronic P. aeruginosa infections were eligible for the study. Chronic infection with P. aeruginosa was confirmed by routine microbiology culturing either from sputum samples or endolaryngeal suction samples from the monthly clinical controls. Infection was defined as chronic when present in ≥50% of at least three microbiological cultures within the last year [ 18 ]. However, due to limited sputum sampling during the COVID-19 pandemic, we allowed individuals who had met these criteria in previous years, and if whole genome sequencing and levels of P. aeruginosa antibodies supported the diagnosis.

To ensure the inclusion of participants with different levels of inflammation in the study samples, we enrolled pwCF of different ages, sex and lung function as assessed by FEV 1 % pred. Furthermore, we also included six adolescents without P. aeruginosa to enable the inclusion of participants with less lung inflammation ( table 1 ). Lung transplant recipients were excluded.

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Baseline data of 52 people with cystic fibrosis (CF)

TruCulture assay

Blood samples were collected in lithium heparin tubes. 60 min after sampling, 1 mL of whole blood was added to each TruCulture test tube. Our assay included three different TLR agonists: polyinosinic:polycytidylic acid (Poly I:C), a TLR3 agonist, which mimics double-stranded RNA often found in viruses [ 19 ]; lipopolysaccharide [ 20 ] (LPS), a TLR4 agonist, which is an endotoxin from Escherichia coli , O111:B4; and Resiquimod 848 [ 21 ] (R848), a TLR7/8 agonist, which mimics viral single-stranded RNA [ 22 ]. A fourth tube contained only a test medium with no stimulating effects (NULL). The tubes were incubated for 22 h at 37°C, after which release of tumour necrosis factor (TNF)-α, IL-1β, IL-6, IL-8, IL-10, IL-12p40, IL-17A, interferon (IFN)-α and IFN-γ was measured in the supernatant by a multiplex Luminex 9-plex assay, as indicators of immune function.

Biochemistry

Additional blood samples to determine levels of haemoglobin (absorption photometry, Sysmex XN; Sysmex Co., Kobe, Japan), thrombocytes (particle count, resistance measurement, Sysmex XN) and leukocytes including differential count (particle count, flowcytometry, Sysmex XN) were analysed on the same day as the sample for TruCulture.

Lung function and baseline data

During clinical consultations, lung function was measured with spirometry (Intramedic VyntusTM SPIRO, reference: Standard EU-GLI [ 23 ]). Baseline data included demographic data (age, sex), clinical data (FEV 1 % pred, CF mutation class, CF-related diabetes and pancreatic insufficiency status (y/n) and body mass index) and microbiological data (duration of P. aeruginosa infection and chronic infection with other CF-related pathogens) ( table 1 ).

Statistical analysis

Correlations between FEV 1 % pred and monocytes, neutrophils and lymphocytes were tested with Spearman's correlation analyses. The cytokine profiles in the different TLR assays were illustrated in box plots including interquartile ranges (IQR: 25th–75th percentile) based on an internal reference from healthy individuals. With one-sample Wilcoxon signed-rank tests, we tested whether the cytokine release in the CF population was below or above the interquartile range of the reference population. Associations between log-transformed cytokine release and baseline (before antibiotic treatment) FEV 1 % pred were evaluated in univariate and multivariate models adjusted for neutrophils, monocytes, lymphocytes and the TruCulture batch, as the batches were changed at 12-month intervals, due to expiry. In some cases, the cytokine release was below the detection threshold, and therefore we used Tobit regression models, which accounts for left censoring of the data by adding detection threshold values to the model. Changes in the cytokine release after antibiotic treatment was presented as the pseudo-median of the symmetric percentage change and tested with one-sample Wilcoxon signed-rank test. Symmetric percentage changes in cytokine profiles, FEV 1 % pred and differential counts after a 14-day intravenous antibiotic course were correlated with Spearman's correlation and presented in heat maps. A p-value <0.05 was considered significant. In supplementary analyses, we used principal component analyses to investigate the relation between cytokine release profiles and age, lung function, sex or modulator treatment. Moreover, in boxplots we showed differences in cytokine release in pwCF with and without P. aeruginosa infections.

The study was approved by the Ethical Committee of the Capital Region of Denmark (H-19001151), and by the local data protection agency in the Capital Region of Denmark (p-2020–1191). Oral and written consent were given before sample collection by the participants.

The baseline characteristics of the 52 participants in the cross-sectional (all) and longitudinal study (n=24, 46% of total) are presented in table 1 . The median age was 36 years (IQR: 19–46), and based on FEV 1 % pred at baseline, 23 (44%) had mild (FEV 1 >70% pred), 22 (42%) had moderate (FEV 1 40–70% pred) and 7 (13%) had severe lung disease (FEV 1 <40% pred). The genotype was classified as severe in 90% of participants (class I–III), and 14 (27%) of the participants had chronic lung infections with pathogens other than P. aeruginosa , e.g. , Burkholderia spp. and Achromobacter spp. In the longitudinal study group, the median FEV 1 % pred at follow-up was lower than at baseline. Most patients (79%) were on CFTR modulator therapy, but only 10% had started elexacaftor/tezacaftor/ivacaftor (ETI) at enrolment. At baseline, FEV 1 % pred correlated negatively with the count of neutrophils (rho= −0.3, p=0.03) but did not correlate with lymphocytes (rho=0.2, p=0.15) and monocytes (rho= −0.15, p=0.29). There was no apparent variation in the cytokine release profiles depending on age, lung function, sex and modulator treatment in principal component analyses ( supplementary figure S1 ).

Cytokine release in pwCF compared to healthy people: the cross-sectional study

Figure 1 shows the unstimulated and stimulated cytokine release in the CF population compared to reference values. While the unstimulated cytokine release did not differ between pwCF and healthy individuals, a high proportion of TLR-stimulated cytokine release was lower in pwCF. When stimulating TLR3, TLR4 and TLR7/8, the release of IFN-γ (p<0.001, p<0.001 and p<0.001) and IL-12p40 (p=0.007, p=0.045 and p<0.001) was below the lower quartile range of the healthy individuals. Similarly, IFN-α release was reduced after TLR4 and TLR7/8 stimulation (p<0.001 and p<0.001) and IL-1β release was reduced after TLR3 and TLR4 stimulation (p=0.009 and p<0.001). IL-6 (p<0.001), and TNF-α (p<0.001) release was also reduced in the TLR3-stimulated samples and IL-10 (p<0.001) release was reduced in the TLR7/8-stimulated samples. In a sensitivity analysis, pwCF without P. aeruginosa seemed to have increased TLR3-stimulated cytokine release compared to pwCF with chronic P. aeruginosa infection ( supplementary figure S2 ).

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a–d) Cytokine release in 52 people with cystic fibrosis (CF) (coloured boxes) compared to the normal reference level (black boxes). The figure shows cytokine release in response to different stimuli in TruCulture test tubes from 523 people with CF. Coloured boxes represent 25th percentiles, medians and 75th percentiles. Similarly, the black boxes show the reference values, with the boxes representing 25th percentiles, medians and 75th percentiles. Cytokine release is shown in picograms per millilitre on a logarithmic scale. IFN: interferon; IL: interleukin; Poly I:C: polyinosinic:polycytidylic acid; TLR: Toll-like receptor; R848: Resiquimod 848; TNF: tumor necrosis factor.

The relation between cytokine release and lung function in pwCF: the cross-sectional study

The correlations between cytokine release and FEV 1 % pred are shown in table 2 . In the unadjusted models, positive associations were found between FEV 1 % pred and TLR3-stimulated release of IL-1β, IL-6 and TNF-α. The associations with TLR3-stimulated IL-1β and IL-6 remained significant in the adjusted model (p=0.02 and p=0.01, respectively). Furthermore, in the adjusted model, FEV 1 % pred was positively associated with TLR4-stimulated IL-6 and negatively associated with unstimulated IL-17A.

Correlations between cytokine release and forced expiratory volume in 1 s (FEV 1 ) % predicted in 52 people with cystic fibrosis

Changes in cytokine release after antibiotic treatment in pwCF: the longitudinal study

Figure 2 shows the changes in TLR-stimulated cytokine release after 14 days of intravenous antibiotic treatment. The most significant change after treatment was found in the TLR3-stimulated and unstimulated samples. The TLR3-stimulated cytokine release tended to be stronger after treatment, and IL-17A and IL-1β increased significantly (p=0.023 and p=0.046, respectively). In contrast, the unstimulated samples tended to have lower cytokine release after 14 days of antibiotics, and IFN-γ and IL-6 declined significantly (p=0.011 and p=0.044, respectively).

Change in cytokine release after 14 days of intravenous antibiotic treatment in 24 people with cystic fibrosis. Data are shown as the pseudo-median of the change in cytokine release after 14 days of intravenous antibiotics. The 14-day changes were calculated as the symmetric % change. Values below the detection threshold were fixed at the threshold value. IFN: interferon; IL: interleukin; TNF: tumour necrosis factor; TLR: Toll-like receptor; Poly I:C: polyinosinic:polycytidylic acid; LPS: lipopolysaccharide; R848: Resiquimod 848. *: p<0.05 in the Wilcoxon rank-sum test.

The relation between cytokine release and the change in lung function in pwCF: the longitudinal study

The correlations between baseline cytokine release and the corresponding changes in lung function and differential leukocyte counts after a 14-day intravenous antibiotic course are shown in figure 3a . Except for a negative correlation between the baseline TLR4-stimulated IFN-γ release and unstimulated IFN-α and the change in FEV 1 % pred, no associations were identified between baseline cytokine release and lung function. Figure 3b shows correlations between changes in all measures: cytokine release, lung function and differential leukocyte counts. Increasing release of TLR7/8-stimulated IL-8 correlated with decreased lung function, whereas increased release of TLR3-stimulated IFN-α and IL-6 and unstimulated IFN-α were associated with increased FEV 1 % pred. In general, changes in the TLR7/8-stimulated cytokines seemed to correlate with changes in leukocyte and neutrophil counts observed after 14 days of intravenous antibiotics.

Correlations between baseline and delta values of cytokine release and forced expiratory volume in 1 s (FEV 1 ) % pred after 14 days treatment with intravenous antibiotics among 24 people with cystic fibrosis. Spearman's correlation between a) the cytokine release at baseline and the change in lung function and the different leukocytes after 14 days of intravenous antibiotics, and b) the change in cytokine release and the change in lung function and the different leukocytes after 14 days of intravenous antibiotics. The 14-day changes were calculated as the symmetric % change. Values below the detection threshold were fixed at the threshold value. Only significant correlations are coloured. FVC: forced vital capacity; IFN: interferon; IL: interleukin; TNF: tumour necrosis factor; Δ: delta/change; LFT: lung function test; TLR: Toll-like receptor; Poly I:C: polyinosinic:polycytidylic acid; LPS: lipopolysaccharide; R848: Resiquimod 848.

We evaluated lung function and immune response in 52 pwCF using the TruCulture assay. Herein, we found that pwCF had normal release of cytokines in the unstimulated samples. However, a lower level of cytokines compared to a healthy population could be detected upon stimulation, and this was especially clear for Th1-related cytokines such as IFN-γ, IL-12p40 and IL-1β. In the cross-sectional study, TLR3-stimulated IL-1β and IL-6 were positively associated with FEV 1 % pred in all statistical models. In the longitudinal study, TLR3-stimulated IL-17A and IL-1β increased by ∼37% and 45% over the course of intravenous antibiotic treatment, and unstimulated IFN-γ and IL-6 decreased by 93% and 66%, respectively. However, cytokine release at the baseline visit did not seem to be associated with changes in lung function during the antibiotic treatment.

Cross-sectional study

In the cross-sectional part of our study, we used TruCulture as an explorative tool to look for immunological deviations that might explain inter-individual differences in the progression of CF lung disease. Comparing cytokine release from pwCF to samples from a healthy population, we found that the unstimulated samples were similar in pwCF and healthy controls, but a high proportion of the stimulated cytokines were significantly lower in pwCF than in healthy controls. This was particularly clear for IFN-γ in all TLR-stimulated assays, where >75% of the CF population had cytokine release below the lower quartile range of healthy controls. Similarly, IL-12p40 was significantly lower in all stimulated assays, and in the TLR3-stimulated assay IL-1β, IL-6 and TNF-α were also reduced. Furthermore, a consistent pattern was found when comparing pwCF with and without chronic P. aeruginosa , as pwCF with chronic infection seemed to have a lower stimulated cytokine release. However, the sample size was too small to draw firm conclusions. It has previously been described that high ex vivo unstimulated cytokine release and low stimulated cytokine release may reflect exhaustion [ 14 ]. Thus, our data indicate that the immune system in CF might be exhausted or impaired after long-term persistent infection and inflammation [ 24 ], similar to what is seen in other types of inflammatory diseases [ 25 ]. This could be explained by chronic infections or tissue injury, which negatively regulates the TLR-stimulated immune response [ 26 ]. Though the new highly effective modulator therapy (ETI) might mitigate some of this exhaustion by, for example, improving inflammation, this was beyond the scope of this paper to investigate. Nevertheless, exhaustion was evident in this cohort, where the majority were treated with the first-generation modulators.

Immune exhaustion assessed with TruCulture has also been found in people infected with Borrelia burgdorferi [ 15 ]. In this study, similar indicators of immune exhaustion with generally reduced cytokine release profiles were found in the Borrelia group compared to the healthy group. Though CF and neuroborreliosis have very different manifestations, both diseases may be characterised by long-term, low-grade inflammation, possibly explaining their low TLR response.

In previous studies, it has been suggested that immune exhaustion may be linked with clinical status in CF: high unstimulated release of IL-8 was associated with more severe disease [ 12 ], and at the same time, low release of TLR-stimulated IL-8 was associated with low FEV 1 % pred [ 9 ]. In addition, B razova et al . showed that low release of LPS-stimulated IFN-γ and IL-4 in children with CF predicted impaired lung function at 3-year follow-up visits [ 10 ], thus supporting a link between immunological exhaustion and clinical parameters in CF. In our cross-sectional study, a few findings were compatible with these observations. For instance, unstimulated IL-17A was negatively associated with FEV 1 % pred in the adjusted model, whereas TLR3-stimulated IL-1β and IL-6 were positively associated with FEV 1 % pred in both statistical models ( table 2 ). In summary, our cross-sectional study found a pattern of an exhausted immune system and signs that this may be associated with lower FEV 1 % pred. This may imply that a weak TLR-dependent response indicates inflammation and poor clinical status in CF, as hypothesised. However, while some of our findings may support our initial hypothesis, there was not a clear association between cytokine release and FEV 1 % pred, in contrast to previous studies [ 9 , 12 ].

Longitudinal study

In the longitudinal part of the study, we tested TruCulture as a clinical tool to predict and explain the effect of a 14-day treatment with intravenous antibiotics, reflected by the FEV 1 % pred. Figure 2 shows the changes in cytokine release after 14 days, where the unstimulated release of IFN-γ and IL-6 were significantly decreased after antibiotic treatment. The TLR3-stimulated cytokine release was also noticeable in the longitudinal study, with cytokine release tending to be enhanced after antibiotic treatment and significantly increased for IL-17A and IL-1β ( figure 2 ). With a tendency of increased stimulated cytokine release and lower unstimulated release, these changes may reflect a recovering immune system after antibiotic treatment. Unfortunately, the changes did not appear to correlate with changes in lung function after 14 days ( figure 3 ). However, we acknowledge that the follow-up time may have been too short. In figure 3a , we observed that a few cytokine releases (low baseline release of TLR4-stimulated IFN-γ and unstimulated IFN-α) were associated with a treatment-related increase in FEV 1 % pred. However, based on our findings we question whether TruCulture would be useful as a clinical tool to predict the effect of intravenous treatment in pwCF. In figure 3b , a treatment-related increase in TLR3-stimulated IFN-α and IL-6 were associated with an increase in FEV 1 % pred, which seems to be in line with our initial theory. On the other hand, and in contrast to our expectations, a decrease in TLR7/8-stimulated IL-8 and an increase in unstimulated IFN-α were associated with higher FEV 1 % pred after 14 days of intravenous antibiotics. We speculate whether these unexpected correlations and the decline in FEV 1 % pred after treatment may represent a treatment-related immunological flare due to release of increased cell debris.

Another interesting finding of our study was the apparent significance of TLR3-stimulated cytokines, which were related to both lung function levels ( table 2 ) and affected by antibiotic treatment ( figure 2 ). TLR3 is usually activated by double-stranded RNA, but also seems to respond to other heterogeneous RNA species found in necrotic cell debris [ 27 ]. This may suggest that high amounts of cellular debris in severely injured lungs or other organs cause downregulation of the TLR3-stimulated cytokine release in pwCF. However, the causality cannot be confirmed in this study, and another explanation could therefore be that low TLR3-stimulated cytokine release leads to worse lung disease in CF. Pursuing TLR3-stimulated cytokines in future studies may be highly interesting, since other studies have favoured using the TLR4 agonist LPS when examining the stimulated innate immune response in the CF lungs [ 10 , 12 ].

Strengths and limitations

An important strength of this study is that it is one of the few studies that thoroughly explore the innate immune system in CF. Moreover, we included pwCF receiving elective antibiotic treatments. This suggests that our study population was clinically stable, with few outliers due to acute exacerbations. However, our study also has some limitations. First, it is essential to note that FEV 1 % pred did not increase in our study population after antibiotic treatment. Thus, our population did not respond as expected to the 14-day intravenous treatment, suggesting that the study may have been underpowered. The effect may have been further attenuated by a high mean age in our study population, where lung function may be less influenced by elective antibiotic treatment due to long-term chronic inflammation. In our regression analyses comparing baseline lung function and cytokine release, only few associations had similar estimates in the adjusted and unadjusted model. There is currently no gold standard for confounder adjustment in TruCulture. Hence, the associations which were only found after adjusting should be interpreted with caution, while associations persisting in both models may be considered more robust and reliable. Furthermore, TruCulture is based on circulating blood, which is why we might have overlooked some aberrant patterns in the CF immune system, as the CF inflammation is often less systemic and more localised in the lungs. This could also explain the scarce signals without a systematic pattern in this study. However, another possible explanation may simply lie in the choice of stimuli and cytokines assessed, thus a different combination of TLR stimuli and cytokines within the TruCulture framework could potentially have revealed important immunological patterns in CF. To improve interpretability of future findings, future studies may benefit from combined assessments of systemic and local cytokine release, repeated measurements, longer follow-up time and larger assay panels of innate immunological pathways.

In this first study of TruCulture immune response in pwCF and chronic infection with P. aeruginosa , we found that the TLR-stimulated cytokine release appeared lower in pwCF compared to healthy individuals. This likely reflects immune exhaustion, i.e. , an acquired or inherent immune deficiency in CF. Antibiotic treatment seemed to affect the pattern of cytokine release, which may reflect recovery of the immune response. However, the cytokine release neither convincingly predicted FEV 1 % pred nor the change in FEV 1 % pred after antibiotic treatment in our CF population, and we did not identify any important immunological cause of variation in CF lung disease progression. This could have been due to systemic, rather than local, examination of an incorrect selection of cytokines. Thus, based on our initial findings, TruCulture may not be an optimal clinical tool to predict changes in lung function in pwCF. However, the TLR3-stimulated cytokine release was identified as a potential marker of clinical variations in CF, though no causal pathways were identified. This suggests further research on the TLR3 response in CF is required.

• Supplementary material

Supplementary Material

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Supplementary material 00256-2024.SUPPLEMENT

• Acknowledgements

The authors thank Camilla de Gier (Department of Clinical Microbiology, Copenhagen University Hospital) for participating in data collection for the study.

Provenance: Submitted article, peer reviewed.

Ethics statement: Participants were recruited at routine visits, and gave oral and written informed consent to participate. 6 mL blood was required for participation, apart from routine tests. All data was stored pseudoanonymised in secured databases and data folders. The study was approved by the ethical committee in the Capital Region of Denmark.

Conflict of interest:

Conflict of interest: E. Rossi discloses a “Biomedical research conducted by young researchers” grant (2020-3581), payment to Università degli Studi di Milano, for contracts from Cariplo Foundation; disclosure made outside the submitted work.

Conflict of interest: The remaining authors have nothing to disclose.

Support statement: H.K. Johansen and S.R. Ostrowski are funded by The Novo Nordisk Foundation (grant number: NNF18OC0052776) and the Danish Research Council (DFF-9039-00037A). H.K. Johansen is supported by a Challenge Grant (ref. number: NNF19OC0056411). S.R. Ostrowski is supported by The Novo Nordisk Foundation (grant number: NNF20OC0059288). E. Rossi is supported by a Cariplo Foundation “Biomedical research conducted by young researchers” (grant number: 2020-3581). Funding information for this article has been deposited with the Crossref Funder Registry .

• Received March 19, 2024.
• Accepted April 11, 2024.
• Copyright ©The authors 2024

This version is distributed under the terms of the Creative Commons Attribution Non-Commercial Licence 4.0. For commercial reproduction rights and permissions contact permissions{at}ersnet.org

• Castellani C ,
• Bell SC , et al.
• Charman S ,
• McClenaghan E ,
• Cosgriff R , et al.
• Szczesniak RD ,
• Su W , et al.
• Cystic Fibrosis Foundation.
• La Rosa R ,
• Bartell JA , et al.
• Jensen PØ ,
• Pressler T , et al.
• Johansen HK
• Hisert KB ,
• Dmyterko V , et al.
• Brazova J ,
• Pospisilova D , et al.
• Tiringer K ,
• Fucik P , et al.
• Schmitt-Grohé S ,
• Naujoks C ,
• Bargon J , et al.
• Aasvang EK ,
• Hansen CP , et al.
• Egebjerg K ,
• Nielsen SD , et al.
• Gynthersen RMM ,
• Mens H , et al.
• Rouilly V ,
• Libri V , et al.
• Nielsen BU ,
• Olesen HV , et al.
• Brownlee KG ,
• Conway SP , et al.
• Alexopoulou L ,
• Medzhitov R , et al.
• Poltorak A ,
• Smirnova I , et al.
• Vollmer J , et al.
• Sato A , et al.
• Quanjer PH ,
• Stanojevic S ,
• Cole TJ , et al.
• Nielsen B , et al.
• Torres MI ,
• López-Casado MA ,
• de León CP , et al.
• Brint EK , et al.
• Capodici J , et al.

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Leukocyte telomere length and attrition in association with disease severity in cystic fibrosis patients, dries s. martens 1 , * , , elise j. lammertyn 2 , * , , pieter c. goeminne 3 , , kristine colpaert 4 , , marijke proesmans 5 , , bart m. vanaudenaerde 2 , , tim s. nawrot 1 , 6 , , lieven j. dupont 2 , 4 , ,.

• 1 Centre for Environmental Sciences, Hasselt University, Hasselt, Belgium
• 2 Department of Chronic Diseases and Metabolism (CHROMETA), Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), KU Leuven, Leuven, Belgium
• 3 Hospital VITAZ Sint-Niklaas, Sint-Niklaas, Belgium
• 4 Department of Respiratory Diseases, University Hospitals Leuven, Leuven, Belgium
• 5 Department of Pediatrics, Pediatric Pulmonology, University Hospital of Leuven, Leuven, Flanders, Belgium
• 6 Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium

Received: December 19, 2023       Accepted: July 15, 2024       Published: August 29, 2024      

Cite this article, how to cite.

Martens DS , Lammertyn EJ , Goeminne PC , Colpaert K , Proesmans M , Vanaudenaerde BM , Nawrot TS , Dupont LJ , . Leukocyte telomere length and attrition in association with disease severity in cystic fibrosis patients. Aging (Albany NY). 2024 Aug 29; . https://doi.org/10.18632/aging.206093 [Epub ahead of print]

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Copyright: © 2024 Martens et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Cystic fibrosis (CF) is characterized by chronic airway inflammation and premature aging. The link with leukocyte telomere length (LTL) as a marker of biological aging is unclear. We studied disease severity and LTL in 168 CF patients of which 85 patients had a second retrospective LTL assessment. A higher FEV 1 was associated with longer LTL, with a stronger effect in men (5.08% longer LTL) compared to women (0.41% longer LTL). A higher FEV 1 /FVC ratio was associated with 7.05% ( P =0.017) longer LTL in men. CF asthma, as defined by the treatment with inhaled corticosteroids, was associated with -6.65% shorter LTL ( P =0.028). Men homozygous for the ΔF508 genotype showed a –10.48% ( P =0.026) shorter LTL compared to heterozygotes. A genotype-specific non-linear association between LTL shortening and chronological age was observed. Stronger age-related LTL shortening was observed in patients homozygous for the ΔF508 genotype ( P -interaction= 0.044). This work showed that disease severity in CF patients negatively influences LTL, with slightly more pronounced effects in men. The homozygous genotype for ΔF508 may play a role in LTL attrition in CF patients. Understanding factors in CF patients that accelerate biological aging provides insights into mechanisms that can extend the overall life quality in CF-diseased.

• Introduction

Aging is a complex biological process characterized by the progressive weakening of almost all physiological functions resulting in a time-dependent increase in mortality [ 1 , 2 ]. The molecular mechanisms behind aging are being explored including cellular senescence [ 2 ]. Senescence encompasses a process that imposes permanent proliferative arrest on aged cells that have been chronically accumulating damage over the years until they eventually reach a threshold of cellular stress [ 3 ]. Possible inducers of cellular senescence include oxidative stress, DNA damage, and telomere shortening [ 4 ]. Telomeres are stretches of repetitive DNA (tandem TTAGGG repeats) that cap the ends of chromosomes, protecting them from unscheduled DNA repair and degradation. In somatic cells, telomeres undergo attrition at each cell replication until they become dysfunctional and trigger a DNA damage response causing cellular senescence [ 5 , 6 ].

A growing body of research illustrates the association between chronic, age-related respiratory diseases and telomere shortening as it has been shown that patients suffering from chronic asthma, chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) all demonstrate shorter telomeres than age-matched controls [ 7 – 11 ]. Furthermore, there is evidence for the presence of increased cellular senescence in the cystic fibrosis (CF) airways. Fischer et al. demonstrated an elevated expression of the neutrophil elastase-induced senescence marker p16 and DNA damage response markers in airway epithelial cells of CF patients [ 12 ]. However, telomere length (TL) in these cells did not differ when compared with controls, although some CF subjects did exhibit telomere shortening. Furthermore, no difference in TL was observed in CF lung tissue compared with healthy tissue [ 9 ] and in leukocytes of CF patients when compared with age-matched controls [ 13 ]. In this latter study, comorbidities, a higher number of hospitalization days and inhaled corticosteroids treatment (ICS) were associated with shorter leukocyte telomere length [ 13 ].

Based on the European patient registry report, CF patients make up a quite young population with a mean age across all Western European countries of 21.2 years. CF used to be a predominantly pediatric disease with a life expectancy of about 29 years at the beginning of the 1990’s. Currently, 54% of patients reach adulthood and have a median predicted survival age that has risen to 41 years. Therefore, clinicians become increasingly challenged by aging and age-related diseases such as osteopenia and osteoporosis, and malignancies of the gastrointestinal tract [ 14 , 15 ]. These cancers have a higher frequency in CF patients and occur at a younger age compared to the general population [ 16 ].

We hypothesized that CF patients with more severe disease characteristics exhibit shorter leukocyte telomere length (LTL) and greater LTL attrition. This suggests increased cellular senescence, which may contribute to accelerated aging and a higher incidence of age-related diseases. Therefore, we determined LTL and LTL attrition (change in LTL measured at two different timepoints) in our CF population consisting of both adults and children and correlated our measurements with clinical characteristics.

Patient characteristics

Demographic and clinical patient characteristics are summarized in Table 1 . The study population consisted of 168 CF patients, of which data was collected between 2014 and 2015. Of this population, 30% were below 18 years of age, resulting in a mean age of 23.8 years (range: 4-55) and 47% were females. All patients, except one, were of European descent. Mean FEV 1 76.8% predicted (range: 25-151) and mean BMI was 20.8 kg/m 2 (range: 14-38). In total 42% of included patients were treated with ICS, 14% had a history of smoking of whom 10% used to smoke, 2% actively smoked and 2% were passively exposed to smoke in the household. From 85 patients out of the 168 included patients, a historical DNA sample and data was retrospectively obtained between 1990 and 2013. The data retrospectively collected (n=85) are referred to as timepoint 1 (TP1) and the data collected for the 168 patients between 2014 and 2015 are referred to as timepoint 2 (TP2), see Supplementary Figure 1 . Characteristics of this subgroup (n=85) at TP2 did not differ from the overall study group at TP2 (n=85). These patients had a mean age of 9.5 years (range: 0-39) at TP1. The mean follow-up time between TP1 and TP2 was 11.5 years (range: 1.8-23.6).

Table 1. Demographic and clinical characteristics of all cystic fibrosis diagnosed patients at timepoint 1 and timepoint 2.

General demographic determinants of LTL in CF patients

LTL decreased with chronological age at TP1 and TP2 ( Figure 1A , 1C , respectively). Each year increment was associated with –1.22% (95% CI: –0.74 to –0.33%; P P Figure 1B , 1D , respectively). At TP2 women had 10.5% (95% CI: 4.0 to 17.4%; P =0.0015) longer LTL compared to men, after age adjustment. LTL showed a clear tracking in 85 CF patients between TP1 and TP2 as shown by the LTL correlation within the same subjects (r=0.57, P Figure 1E ). LTL ranged from 0.77 to 2.51 in blood samples at TP1 and from 0.44 to 1.46 in samples at TP2, resulting in a significantly lower LTL at TP2 compared with TP1 ( P Figure 1F .

Figure 1. General demographic determinants of leukocyte telomere length at timepoint 1 (n=85) and at timepoint 2 (n=168) in CF patients. ( A ) and ( C ) Pearson correlation of LTL with age at TP1 and TP2, respectively. ( B ) and ( D ) Sex differences in LTL at TP1 and TP2, respectively. ( E ) Pearson correlation of LTL at TP1 and TP2, representing LTL tracking over time. ( F ) Decline in LTL for 85 participants from TP1 to TP2. Abbreviations: TP1: timepoint 1; TP2: timepoint 2; LTL: leukocyte telomere length.

Association of clinical CF predictors with LTL

In unadjusted and after adjustment for age, sex, BMI, and smoking history, FEV 1 and the FEV 1 /FVC ratio were positively associated with LTL ( Table 2 ). Each SD increment in FEV 1 and FEV1/FVC ratio was associated with 4.13% (95%CI: 0.33 to 8.08%; P =0.033) and 3.98% (95%CI: –0.02 to 8.14%; P =0.051) longer LTL, respectively. These associations were observed in men (6.14%; p=0.023 and 8.46%; P =0.0028, longer LTL for each SD increment in FEV 1 and FEV 1 /FVC ratio, respectively) but not in women (0.76%; P =0.79 and –2.74%; P =0.36, difference in LTL for each SD increment in FEV 1 and FEV 1 /FVC ratio, respectively). For the association between FEV1/FVC ratio and LTL a significant sex-specific association was observed ( P -interaction=0.014). Further adjusting these models for ICS use and genotype, slightly attenuated the effects but were confirmative. FVC was not associated with LTL in CF patients ( Table 2 ). Patients using ICS as part of their daily maintenance therapy (n=70), demonstrated –7.19% (95% CI: –14.41 to –0.43%; P =0.037) shorter LTL compared with patients not using ICS ( Table 2 ). Effects were comparable for men and women, and further adjustment for genotype did not alter these results ( Table 2 ). Lastly, genotype, as a surrogate parameter for disease severity, was not associated with LTL. After stratifying for sex, male homozygous CF patients (n=48) had –10.46% (95% CI: –20.66 to –1.12%; P =0.028) shorter LTL compared with male heterozygotes (n=41). No association was observed in women, however no interaction ( P =0.14) between genotype and sex was observed ( Table 2 ). Additionally, adjusting for ICS did not alter these results.

Table 2. Association of clinical cystic fibrosis predictors with leukocyte telomere length in cystic fibrosis patients (n=168).

Predictors of leukocyte telomere attrition in CF patients

In 85 CF patients, telomere attrition could be evaluated. Figure 2 shows the unadjusted association between age at TP1 as a predictor for LTL attrition (ΔLTL). We observed a non-linear (quadratic) age effect on LTL attrition with stronger attrition at younger ages. This was significantly different according to genotype. After adjusting for sex and time between TP1 and TP2, this non-linear age association with LTL attrition remained different according to genotype ( P -interaction=0.044) ( Table 3 ). In CF patients homozygous for the ΔF508 mutation, the LTL attrition was highest in very young children and declined throughout childhood and adolescence. At the age of 1, each year increment in age showed less strong attrition of 0.052 T/S ratio (95%CI: 0.023 to 0.081; P P =0.0021) and was not observed at the age of 17 ( Table 3 ). For patients heterozygous for the ΔF508 mutation, no significant association between age and LTL attrition was observed ( Table 3 ). In models correcting for the regression of the mean phenomenon, these results were confirmed ( Supplementary Table 2 ).

Figure 2. Difference in age-dependent leukocyte telomere length change from timepoint 1 to timepoint 2 in ΔF508 homozygous vs. heterozygous. Non-linear (quadratic) association of age with the LTL attrition in CF patients homozygous for the ΔF508 mutation (n=44) compared with heterozygous patients (n=41) P-interaction between the quadratic term of age at TP1 and genotype ( P =0.044), reflecting the genotype-specific non-linear LTL attrition-age association. Abbreviations: TP1: timepoint 1; TP2: timepoint 2; LTL: leukocyte telomere length.

Table 3. Genotype-specific non-linear age association with leukocyte telomere length attrition in cystic fibrosis patients (n=85).

The present study demonstrated that CF patients suffering from more severe lung disease, homozygous for the ΔF508 mutation, or diagnosed with CF asthma (as defined by the use of ICS), have shorter LTL. The associations of FEV 1 , FEV 1 /FVC ratio, and genotype with shorter LTL were potentially sex-specific. Furthermore, there was a non-linear effect of age on the LTL attrition which was significantly modified by genotype. Homozygous patients demonstrated a stronger LTL attrition during childhood, decreasing into adolescence, steady during adulthood, and eventually increasing again at an older age. In contrast, heterozygous patients showed a less strong association between LTL attrition and age, resulting in steady LTL shortening throughout life.

Our findings on the age-dependent LTL attrition in homozygous patients are in line with previous findings of rapid LTL attrition during the first 20 years of life [ 17 ], resulting in a virtually fixed ranking of an individual’s LTL compared to the general population for the rest of the adult life [ 18 , 19 ]. We have demonstrated a positive intra-individual correlation between LTL at the two different evaluated timepoints l, indicating that patients having long or short LTL at a given timepoint will still have a long or short LTL at a later timepoint in life, respectively. Taken together, our data suggest that the ΔF508 homozygous genotype, resulting in the full absence of cystic fibrosis transmembrane conductance regulator (CFTR) channels on the cell surface and therefore giving rise to a severe phenotype [ 20 ], exerts its effect on LTL attrition already early in life and maybe even already intra-uterine, resulting in shorter LTL remaining anchored for the rest of the adult life.

These observations on telomere biology in CF patients may have important clinical implications. Early diagnosis, preferably with genetic analysis, and rapid start-up of appropriate therapy is key for the evolution of the disease in later life. Widespread implementation of systematic newborn screening for CF may lead to faster diagnosis. Recently, two phase 3 clinical trials have been reported during which a combination of small molecule therapy targeting the CFTR protein dysfunction under the form of ivacaftor, a CFTR potentiator, and lumacaftor, a CFTR corrector was administered to patients 12 years of age or older homozygous for the ΔF508 mutation [ 21 ]. Compared with placebo, this combination improved FEV 1 and reduced the frequency of pulmonary exacerbations. Our findings suggest that small molecule therapy may also be beneficial to younger children as it may partly restore CFTR function possibly leading to a reduced LTL attrition.

Female CF patients appeared to be less sensitive to the associations of FEV 1 and genotype with shorter LTL. A possible explanation could be that women in general have intrinsically longer telomeres compared with men [ 22 ], which may indicate a higher protective capacity against telomere-influencing factors and therefore may reduce their effect. Two potential mechanisms are described which may contribute to longer LTL in females. Firstly, the female sex hormone estrogen diminishes oxidative stress, which is known to induce telomere shortening [ 23 ]. Besides, estrogen stimulates the transcription of the gene encoding for telomerase. Telomerase maintains TL in gametes and stem cells by adding guanine-rich repetitive sequences [ 24 , 25 ]. Subsequently, stem cells give rise to highly proliferative cell populations including blood cells, explaining the longer LTL compared with men. In our study, LTL attrition is especially apparent in pre-pubertal children and progresses more steadily during adulthood, suggesting that the influence of sex hormones such as estrogen on this process is limited. This may explain the lack of a gender-specific genotype-modified effect of age on LTL attrition. Secondly, Skewed X inactivation is a process during which the inactivation of the second X chromosome does not occur randomly, but after the selection of the parental X chromosome that provides a survival advantage [ 23 ]. As there is evidence that gene variance on the X chromosome strongly influences TL [ 26 ], the X chromosome giving rise to cells with longer telomeres will be selected as they might produce greater tissue reserves. Taken together, these mechanisms may protect female CF patients against the consequences of shorter LTL associated with lower FEV 1 and the homozygous genotype.

CF patients using ICS demonstrated significantly shorter LTL compared with patients who were not treated with ICS. These observations are in line with recent findings in an independent smaller study of CF-patients [ 13 ]. The use of ICS originated during the 1970’s as an alternative to oral corticosteroids for the treatment of asthma and they were soon after prescribed as an anti-inflammatory therapy to CF patients as well, in order to reduce bronchial hyperresponsiveness, wheezing, chronic cough, and bronchospasms. Although the indication of ICS in CF is not straightforward, it is estimated that about 19% of CF patients present with clear symptoms of airway reactivity and are therefore diagnosed with CF asthma [ 27 ]. In this study, 41% of patients were prescribed ICS because of the presence of at least an asthmatic component worsening their lung disease. Recently, Belsky et al. demonstrated that patients suffering from chronic, life-course-persistent asthma had shorter LTL compared with gender- and age-matched healthy individuals [ 8 ]. Our data suggest that a diagnosis of CF asthma or an asthmatic component complicating the CF lung pathology is associated with increased cellular senescence in CF patients.

Our study has several limitations. First, we did not dispose of DNA of matched healthy individuals, so we could not compare LTL and LTL attrition between CF patients and the general population. In this regard, previous studies did not observe differences in lung TL or leukocyte TL measured in CF patients and healthy controls [ 9 , 13 ]. In the current study, our aim was to evaluate the link between LTL as a body-wide marker of biological aging in relation to disease severity in a specific CF patient cohort. Intra-individual differences in TL between leukocytes and lung tissue exists due to proliferative differences, nevertheless, TL displays a high intra-individual synchrony and correlation across somatic cells such as leukocytes, lung cells, muscle cells, and fat cells [ 28 , 29 ]. The latter motivates the idea that leukocyte TL may serve as a body-wide circulatory marker of biological aging in humans, and it has indeed been shown that average relative LTL is causally related to lifespan and age-related diseases [ 30 ]. Nevertheless, we acknowledge that tissue-specific and chromosome-specific differences in TL may exert specific disease phenotypes, which was beyond the scope of the current analysis [ 31 ]. Second, our sample size of patients having a DNA sample at both evaluated timepoints is limited, and therefore our findings on longitudinal changes in LTL should be interpreted with caution. Nevertheless, this is however inherent to the very specific patient population and the sometimes-wide window of time between both samples. Nevertheless, these are unique samples with equal gender distribution and a homogeneous age range, as is the case for samples collected at TP2. Third, our population included patients from all ages, and developmental trajectories across childhood and adolescence may be different from adults that might have an impact on the observed associations. However, our study is limited in sample size to further evaluated these developmental differences in e.g. age subgroup analyses. Finally, due to unavailability of the sometimes very old patient files, we were not able to retrieve other clinical patient characteristics at TP1, implicating that we were not able to study additional effects on LTL attrition.

• Conclusions

In CF patients, characteristics of more severe disease were associated with LTL, possibly resulting in an increased rate of aging affecting their other bodily functions and making them more prone to age-related diseases. These effects are probably already established during childhood, stressing the need for early diagnosis of CF and appropriate therapy from an early age onwards.

• Materials and Methods

Study population

176 CF patients (120 adults and 56 children and adolescents younger than 18 years) were included in this study. They were recruited at the University Hospitals Leuven outpatient clinic between April 2014 and September 2015. This recruitment moment is referred to as timepoint 2 (TP2). Written informed consent or written informed parental consent (and written informed assent in case of a minor above the age of twelve) was obtained for all participants. Because of missing genotype determination (n=3) and FEV 1 (n=5), the final sample size consisted of 168 CF patients (117 adults and 51 children aged under 18). Next, with the consent of participants, we consulted the Centre for Human Genetics of the KU Leuven to explore the availability of existing DNA samples extracted at the time of genotype determination of our study participants. This second, historical DNA sample was available for 107 of the 176 participants (68 adults and 39 children). However, due to improper DNA quality, 22 samples were discarded, resulting in 85 patients with a historical DNA sample (36 adults and 49 children). The moment of historical DNA extraction (between April 1990 and May 2013) is referred to as timepoint 1 (TP1). Supplementary Figure 1 shows a comprehensive timeline and flow chart of the included participants. Clinical characteristics were collected via the electronic patient files. This study was approved by the local ethics committee and the University Hospitals Leuven biobanking centre (S56319).

Lung function tests were performed according to current American Thoracic Society (ATS)/European Respiratory Society (ERS) recommendations [ 32 , 33 ]. Because absolute forced vital capacity (FVC) and forced expiratory volume in 1 second (FEV1) expressed in liters are highly dependent on age, and considering the wide age range of patients at TP2, FVC, and FEV1 expressed as the percentage of the predicted value and calculated according to the 2005 ATS/ERS guidelines, was used to assess the effect of lung function on LTL.

DNA extraction and sample processing

Peripheral blood was collected using 10 ml EDTA vacutainers. After ten minutes of centrifugation at 370 rcf, plasma was removed and the remaining buffy coat was isolated. Buffy coat DNA was extracted using the QIAamp DNA mini kit (Qiagen Inc., Venlo, The Netherlands). DNA concentration and purity were determined using NanoDrop (Thermo Scientific NanoDrop Technologies, Wilmington, DE, USA). DNA integrity was assessed using 1.5% agarose gel electrophoresis to evaluate potential DNA degradation. In total, 22 DNA samples from TP1 showed severe DNA degradation, as evidenced by DNA smears on gels and no intense band of intact DNA, and therefore were excluded.

CF genotyping

Patients were screened for the ∆F508 mutation in the CFTR gene using the pan-European cystic fibrosis Elucigene CF-EU2v1 kit (Elucigene Diagnostics, Manchester, United Kingdom). This kit detects point mutations, insertions or deletions in the CFTR gene, using a method based on fluorescent amplification refractory mutation system (ARMS). The Elucigene CF-EU2v1 kit can detect the Tn and the TGn polymorphisms and the 50 most common mutations found across the European population, including the ∆F508 mutation. Details on other mutations are provided in Supplementary Material 1 . The kit is a multiplexed test which includes two polymerase chain reactions (PCR), A and B. In PCR A the mutant sequences will be detected, together with the Tn and TGn polymorphisms and the wild type sequence for the most common CFTR mutation in Caucasians, ∆F508. In PCR B the corresponding wild type sequences, with exception of the ∆F508 wild type, will be detected. Both PCR reactions include the same non-CFTR internal amplification control markers. DNA was amplified with the following program: 94° C for 20 minutes, followed by 30 cycles consisting of denaturation at 94° C for 1 minute, primer annealing at 58° C for 2 minutes and extension at 72° C for 1 minute. At the end of the program, an additional extension stage at 72° C lasting 20 minutes was included. PCR products were carried out by capillary electrophoresis on the ABI 3730xl Genetic Analyzer (Thermo Fisher Scientific Inc., Waltham, MA, USA) and analysis of the data was performed in the GeneMapper 5 (Thermo Fisher Scientific Inc.) software.

Leukocyte telomere length measurement

Average relative LTL was measured in triplicate for each sample using a modified qPCR protocol as described previously [ 34 ] and in the Supplementary Material 2 . All telomeres were measured in triplicate on a 7900HT Fast Real-Time PCR System (Applied Biosystems, Lennik, Belgium) in a 384-well format. Patients that had 2 DNA samples at the 2 timepoints were matched on the same qPCR plate. A 6-point serial dilution of pooled buffy-coat DNA was included to assess PCR efficiency and six inter-run calibrators were included to account for inter-run variability. Relative average LTLs were expressed as the ratio of telomere copy number to single-copy gene number (T/S ratio) relative to the average T/S ratio of the entire sample set (n=261) using the qBase software (for mathematical details we refer to Hellemans [ 35 ]). The precision of the assay is evaluated using an intra- and inter-assay intraclass correlation coefficient (ICC). The intra-assay ICC was calculated using all (n=253) triplicate LTL measures (ICC = 0.90; 95%CI: 0.88 to 0.92) and the inter-assay ICC was calculated for a set of samples (n=10) that were analyzed twice within a 1-week interval (ICC = 0.93; 95%CI: 0.71 to 0.98).

Statistical analysis

All statistical analyses were performed using the SAS 9.4 statistical software (SAS Institute Inc., Cary, NC, USA). LTLs were log 10 transformed to improve normality. Pearson correlation and student’s t-test were performed to evaluate the association between general demographic determinants and predictors of LTL at both timepoints. Using multivariable-adjusted linear models, clinical disease severity characteristics of CF patients in relation to LTL at TP2 were evaluated. First, the association between FEV 1 , FVC, the FEV 1 /FVC ratio, and LTL was evaluated. Second, the association between the use of ICS (classified as yes or no) and LTL was evaluated. Third, we evaluated whether genotype (homozygous for the ∆F508 mutation or heterozygous) was associated with LTL. All models were adjusted for age, sex, BMI, and smoking history. In additional models, we adjusted the spirometry models for ICS use and genotype. The ICS model was additionally adjusted for genotype and the genotype model for ICS use. As a secondary analysis, all models were stratified for sex and the predictor*sex interaction was evaluated. Estimates are presented as %difference with 95%CI in LTL for a standard deviation (SD) increment in FEV 1 , FVC, FEV 1 /FVC, or the use of ICS vs. non-users and the homozygous vs. heterozygous genotype. Finally, in 85 CF patients with two LTL assessments, telomere attrition was studied. LTL attrition was calculated by the difference in LTL between TP2 and TP1 (ΔLTL=LTL TP2 –LTL TP1 ). We assessed the non-linear effect of age (modeled as a quadratic term) at TP1 on telomere attrition. Genotype-specific differences were evaluated by including an interaction between genotype and the quadratic age term. Models were adjusted for sex and timing between TP1 and TP2. As a sensitivity analysis, we evaluated whether the results on telomere attrition were robust by considering the potential regression to the mean effect. This was done by replacing ΔLTL with the D score proposed by Verhulst and colleagues [ 36 ]. This D score adjusts the difference of consecutive TL measurements by subtracting the change expected from the regression to the mean effect.

• Supplementary Materials

Supplementary Figure 1

Supplementary tables.

• Abbreviations

CF: Cystic Fibrosis; CFTR: Cystic Fibrosis Transmembrane Conductance Regulator; COPD: Chronic Obstructive Pulmonary Disease; FEV 1 : Forced Expiratory Volume in 1 second; FVC: Forced Vital Capacity; ICS: Inhaled Corticosteroids; IPF: Idiopathic Pulmonary Disease; LTL: Leukocyte Telomere Length; SD: Standard deviation; TP1: Timepoint 1; TP2: Timepoint 2.

• Author Contributions

DSM performed LTL measurements, drafted the manuscript, and performed statistical analyses. EJL collected all blood samples, performed all DNA extractions, and drafted the manuscript. KC and MP included the patients at the University Hospitals Leuven outpatient clinic. PCG, BMV, TSN, and LJD participated in the design and coordination of the study and helped to draft the manuscript. All authors have read and approved the manuscript.

• Conflicts of Interest

The authors declare that they have no conflicts of interest.

• Ethical Statement and Consent

This study was approved by the local Ethics Committee and the University Hospitals Leuven biobanking centre (S56319). Written informed consent or written informed parental consent (and written informed assent in case of a minor above the age of twelve) was obtained for all participants.

This research was funded by the Alphonse and Jean Forton Award of the King Baudoin Foundation, a C2 project from the KU Leuven (C24/15/30), the 7 th Framework Programme of the European Union (grant agreement no 603038) and Research Foundation Flanders (FWO grant G048420N). Dries Martens holds a postdoctoral grant from the Research Foundation Flanders (FWO grant 12X9623N). Tim Nawrot is supported by the Methusalem funding programme.

• 1. Lenart P, Krejci L. DNA, the central molecule of aging. Mutat Res. 2016; 786:1–7. https://doi.org/10.1016/j.mrfmmm.2016.01.007 [ PubMed ]
• 2. López-Otín C, Blasco MA, Partridge L, Serrano M, Kroemer G. The hallmarks of aging. Cell. 2013; 153:1194–217. https://doi.org/10.1016/j.cell.2013.05.039 [ PubMed ]
• 3. Childs BG, Durik M, Baker DJ, van Deursen JM. Cellular senescence in aging and age-related disease: from mechanisms to therapy. Nat Med. 2015; 21:1424–35. https://doi.org/10.1038/nm.4000 [ PubMed ]
• 4. Collado M, Serrano M. The power and the promise of oncogene-induced senescence markers. Nat Rev Cancer. 2006; 6:472–6. https://doi.org/10.1038/nrc1884 [ PubMed ]
• 5. Blackburn EH, Epel ES, Lin J. Human telomere biology: A contributory and interactive factor in aging, disease risks, and protection. Science. 2015; 350:1193–8. https://doi.org/10.1126/science.aab3389 [ PubMed ]
• 6. O’Sullivan RJ, Kubicek S, Schreiber SL, Karlseder J. Reduced histone biosynthesis and chromatin changes arising from a damage signal at telomeres. Nat Struct Mol Biol. 2010; 17:1218–25. https://doi.org/10.1038/nsmb.1897 [ PubMed ]
• 7. Armanios M. Telomerase and idiopathic pulmonary fibrosis. Mutat Res. 2012; 730:52–8. https://doi.org/10.1016/j.mrfmmm.2011.10.013 [ PubMed ]
• 8. Belsky DW, Shalev I, Sears MR, Hancox RJ, Lee Harrington H, Houts R, Moffitt TE, Sugden K, Williams B, Poulton R, Caspi A. Is chronic asthma associated with shorter leukocyte telomere length at midlife? Am J Respir Crit Care Med. 2014; 190:384–91. https://doi.org/10.1164/rccm.201402-0370OC [ PubMed ]
• 9. Everaerts S, Lammertyn EJ, Martens DS, De Sadeleer LJ, Maes K, van Batenburg AA, Goldschmeding R, van Moorsel CH, Dupont LJ, Wuyts WA, Vos R, Gayan-Ramirez G, Kaminski N, et al. The aging lung: tissue telomere shortening in health and disease. Respir Res. 2018; 19:95. https://doi.org/10.1186/s12931-018-0794-z [ PubMed ]
• 10. McDonough JE, Martens DS, Tanabe N, Ahangari F, Verleden SE, Maes K, Verleden GM, Kaminski N, Hogg JC, Nawrot TS, Wuyts WA, Vanaudenaerde BM. A role for telomere length and chromosomal damage in idiopathic pulmonary fibrosis. Respir Res. 2018; 19:132. https://doi.org/10.1186/s12931-018-0838-4 [ PubMed ]
• 11. Savale L, Chaouat A, Bastuji-Garin S, Marcos E, Boyer L, Maitre B, Sarni M, Housset B, Weitzenblum E, Matrat M, Le Corvoisier P, Rideau D, Boczkowski J, et al. Shortened telomeres in circulating leukocytes of patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2009; 179:566–71. https://doi.org/10.1164/rccm.200809-1398OC [ PubMed ]
• 12. Fischer BM, Wong JK, Degan S, Kummarapurugu AB, Zheng S, Haridass P, Voynow JA. Increased expression of senescence markers in cystic fibrosis airways. Am J Physiol Lung Cell Mol Physiol. 2013; 304:L394–400. https://doi.org/10.1152/ajplung.00091.2012 [ PubMed ]
• 13. Glapa-Nowak A, Mutt SJ, Lisowska A, Sapiejka E, Goździk-Spychalska J, Wieczorek-Filipiak M, Drzymała-Czyż S, Nowak JK, Thalmann O, Herzig KH, Walkowiak J. Leukocyte Telomere Length Is Not Reduced in Children and Adults with Cystic Fibrosis but Associates with Clinical Characteristics-A Cross-Sectional Study. J Clin Med. 2021; 10:590. https://doi.org/10.3390/jcm10040590 [ PubMed ]
• 14. Demeyer S, De Boeck K, Witters P, Cosaert K. Beyond pancreatic insufficiency and liver disease in cystic fibrosis. Eur J Pediatr. 2016; 175:881–94. https://doi.org/10.1007/s00431-016-2719-5 [ PubMed ]
• 15. Plant BJ, Goss CH, Plant WD, Bell SC. Management of comorbidities in older patients with cystic fibrosis. Lancet Respir Med. 2013; 1:164–74. https://doi.org/10.1016/S2213-2600(13)70025-0 [ PubMed ]
• 16. Neglia JP, FitzSimmons SC, Maisonneuve P, Schöni MH, Schöni-Affolter F, Corey M, Lowenfels AB. The risk of cancer among patients with cystic fibrosis. Cystic Fibrosis and Cancer Study Group. N Engl J Med. 1995; 332:494–9. https://doi.org/10.1056/NEJM199502233320803 [ PubMed ]
• 17. Aubert G, Baerlocher GM, Vulto I, Poon SS, Lansdorp PM. Collapse of telomere homeostasis in hematopoietic cells caused by heterozygous mutations in telomerase genes. PLoS Genet. 2012; 8:e1002696. https://doi.org/10.1371/journal.pgen.1002696 [ PubMed ]
• 18. Benetos A, Kark JD, Susser E, Kimura M, Sinnreich R, Chen W, Steenstrup T, Christensen K, Herbig U, von Bornemann Hjelmborg J, Srinivasan SR, Berenson GS, Labat C, Aviv A. Tracking and fixed ranking of leukocyte telomere length across the adult life course. Aging Cell. 2013; 12:615–21. https://doi.org/10.1111/acel.12086 [ PubMed ]
• 19. Martens DS, Van Der Stukken C, Derom C, Thiery E, Bijnens EM, Nawrot TS. Newborn telomere length predicts later life telomere length: Tracking telomere length from birth to child- and adulthood. EBioMedicine. 2021; 63:103164. https://doi.org/10.1016/j.ebiom.2020.103164 [ PubMed ]
• 20. Elborn JS. Cystic fibrosis. Lancet. 2016; 388:2519–31. https://doi.org/10.1016/S0140-6736(16)00576-6 [ PubMed ]
• 21. Wainwright CE, Elborn JS, Ramsey BW. Lumacaftor-Ivacaftor in Patients with Cystic Fibrosis Homozygous for Phe508del CFTR. N Engl J Med. 2015; 373:1783–4. https://doi.org/10.1056/NEJMc1510466 [ PubMed ]
• 22. Gardner M, Bann D, Wiley L, Cooper R, Hardy R, Nitsch D, Martin-Ruiz C, Shiels P, Sayer AA, Barbieri M, Bekaert S, Bischoff C, Brooks-Wilson A, et al, and Halcyon study team. Gender and telomere length: systematic review and meta-analysis. Exp Gerontol. 2014; 51:15–27. https://doi.org/10.1016/j.exger.2013.12.004 [ PubMed ]
• 23. Aviv A, Shay J, Christensen K, Wright W. The longevity gender gap: are telomeres the explanation? Sci Aging Knowledge Environ. 2005; 2005:pe16. https://doi.org/10.1126/sageke.2005.23.pe16 [ PubMed ]
• 24. Dalgård C, Benetos A, Verhulst S, Labat C, Kark JD, Christensen K, Kimura M, Kyvik KO, Aviv A. Leukocyte telomere length dynamics in women and men: menopause vs age effects. Int J Epidemiol. 2015; 44:1688–95. https://doi.org/10.1093/ije/dyv165 [ PubMed ]
• 25. Kyo S, Takakura M, Kanaya T, Zhuo W, Fujimoto K, Nishio Y, Orimo A, Inoue M. Estrogen activates telomerase. Cancer Res. 1999; 59:5917–21. [ PubMed ]
• 26. Nawrot TS, Staessen JA, Gardner JP, Aviv A. Telomere length and possible link to X chromosome. Lancet. 2004; 363:507–10. https://doi.org/10.1016/S0140-6736(04)15535-9 [ PubMed ]
• 27. Ross KR, Chmiel JF, Konstan MW. The role of inhaled corticosteroids in the management of cystic fibrosis. Paediatr Drugs. 2009; 11:101–13. https://doi.org/10.2165/00148581-200911020-00002 [ PubMed ]
• 28. Daniali L, Benetos A, Susser E, Kark JD, Labat C, Kimura M, Desai K, Granick M, Aviv A. Telomeres shorten at equivalent rates in somatic tissues of adults. Nat Commun. 2013; 4:1597. https://doi.org/10.1038/ncomms2602 [ PubMed ]
• 29. Demanelis K, Jasmine F, Chen LS, Chernoff M, Tong L, Delgado D, Zhang C, Shinkle J, Sabarinathan M, Lin H, Ramirez E, Oliva M, Kim-Hellmuth S, et al, and GTEx Consortium. Determinants of telomere length across human tissues. Science. 2020; 369:eaaz6876. https://doi.org/10.1126/science.aaz6876 [ PubMed ]
• 30. Codd V, Wang Q, Allara E, Musicha C, Kaptoge S, Stoma S, Jiang T, Hamby SE, Braund PS, Bountziouka V, Budgeon CA, Denniff M, Swinfield C, et al. Polygenic basis and biomedical consequences of telomere length variation. Nat Genet. 2021; 53:1425–33. https://doi.org/10.1038/s41588-021-00944-6 [ PubMed ]
• 31. Karimian K, Groot A, Huso V, Kahidi R, Tan KT, Sholes S, Keener R, McDyer JF, Alder JK, Li H, Rechtsteiner A, Greider CW. Human telomere length is chromosome end-specific and conserved across individuals. Science. 2024; 384:533–9. https://doi.org/10.1126/science.ado0431 [ PubMed ]
• 32. Pellegrino R, Viegi G, Brusasco V, Crapo RO, Burgos F, Casaburi R, Coates A, van der Grinten CP, Gustafsson P, Hankinson J, Jensen R, Johnson DC, MacIntyre N, et al. Interpretative strategies for lung function tests. Eur Respir J. 2005; 26:948–68. https://doi.org/10.1183/09031936.05.00035205 [ PubMed ]
• 33. Wanger J, Clausen JL, Coates A, Pedersen OF, Brusasco V, Burgos F, Casaburi R, Crapo R, Enright P, van der Grinten CP, Gustafsson P, Hankinson J, Jensen R, et al. Standardisation of the measurement of lung volumes. Eur Respir J. 2005; 26:511–22. https://doi.org/10.1183/09031936.05.00035005 [ PubMed ]
• 34. Martens DS, Plusquin M, Gyselaers W, De Vivo I, Nawrot TS. Maternal pre-pregnancy body mass index and newborn telomere length. BMC Med. 2016; 14:148. https://doi.org/10.1186/s12916-016-0689-0 [ PubMed ]
• 35. Hellemans J, Mortier G, De Paepe A, Speleman F, Vandesompele J. qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data. Genome Biol. 2007; 8:R19. https://doi.org/10.1186/gb-2007-8-2-r19 [ PubMed ]
• 36. Verhulst S, Aviv A, Benetos A, Berenson GS, Kark JD. Do leukocyte telomere length dynamics depend on baseline telomere length? An analysis that corrects for ‘regression to the mean’. Eur J Epidemiol. 2013; 28:859–66. https://doi.org/10.1007/s10654-013-9845-4 [ PubMed ]

Corresponding Author

Dries S. Martens [email protected]

Paper Sections

• Open access
• Published: 28 August 2024

Severe bronchiectasis is associated with increased carotid intima-media thickness

• Wang Chun Kwok 1   na1 ,
• Kui Kai Lau 1 , 2   na1 ,
• Kay Cheong Teo 1 ,
• Sze Him Isaac Leung 3 ,
• Chung Ki Tsui 1 ,
• Matthew S.S. Hsu 4 ,
• Kkts Pijarnvanit 1 ,
• Carman Nga-Man Cheung 1 ,
• Yick Hin Chow 1 &
• James Chung Man Ho 1  

BMC Cardiovascular Disorders volume  24 , Article number:  457 ( 2024 ) Cite this article

Metrics details

Although bronchiectasis has been shown to be associated with cardiovascular disease, there is limited evidence of an association with subclinical atherosclerosis, especially carotid intima-media thickness (CIMT).

This prospective study compared CIMT among patients with and without bronchiectasis, and among bronchiectatic patients classified according to disease severity using the FACED score. The study was carried out at a major regional hospital and tertiary respiratory referral centre in Hong Kong.

Total 155 Chinese patients with non-cystic fibrosis (CF) bronchiectasis and 512 controls were recruited. The mean CIMT was 0.58 ± 0.10 mm, 0.63 ± 0.11 mm and 0.66 ± 0.08 mm respectively among controls, patients with mild-to-moderate bronchiectasis and patients with severe bronchiectasis. There was no statistically significant difference in CIMT between patients with mild-to-moderate bronchiectasis and controls. Multivariate linear regression revealed that CIMT was significantly increased in patients with severe bronchiectasis relative to controls. The same phenomenon was observed among patients without a history of cardiovascular disease or cardiovascular risk factors.

Conclusions

CIMT was significantly increased in patients with severe bronchiectasis compared with controls without bronchiectasis, but not among patients with mild-to-moderate bronchiectasis, which suggested the subclinical atherosclerosis to be more prevalent among patients with severe bronchiectasis.

Peer Review reports

Introduction

Bronchiectasis is characterized by airway inflammation, abnormal mucus clearance and bacterial colonization with consequent progressive airway destruction and distortion. There is accumulating evidence that airway inflammation and immune dysregulation play a central role in the evolution of non-CF bronchiectasis [ 1 ].

An association of systemic inflammation with cardiovascular diseases has been demonstrated, while baseline C-reactive protein (CRP) level has been shown to predict the long-term risk of a first myocardial infarction, ischemic stroke, and peripheral artery disease [ 2 , 3 , 4 ]. Guidelines suggest measurement of high-sensitivity CRP in patients at intermediate risk of coronary heart disease (CHD) [ 5 , 6 , 7 ]. Other inflammatory markers such as interleukin-6, [ 8 , 9 ] leukocyte enzyme myeloperoxidase, [ 7 , 10 , 11 , 12 , 13 ] white blood cell count, erythrocyte sedimentation rate, IL-18, tumor necrosis factor alpha, transforming growth factor beta, soluble intercellular adhesion molecule-1, P-selectin, cathepsin S, and lipoprotein-associated phospholipase A2 have also been reported as markers of increased CHD risk [ 12 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ].

There is growing evidence of an association between bronchiectasis and cardiovascular diseases [ 21 , 22 , 23 , 24 , 25 , 26 ]. Bronchiectasis was associated with the development of cardiovascular disease in a population-based study conducted in the United Kingdom, as well as increased risks for coronary heart disease and stroke [ 22 ].

To assess underlying atherosclerotic burden and predict adverse cardiovascular events, various non-invasive functional and structural surrogate markers of vascular health can be measured. These include assessment of endothelial function by brachial-artery flow-mediated dilatation (FMD), and measurement of carotid intima-media thickness (CIMT) and arterial stiffness and ankle-brachial index (ABI). A case control study also showed that patients with bronchiectasis were at greater risk of endothelial dysfunction as measured by FMD but not CIMT [ 27 ]. Nonetheless this study comprised only 80 patients with bronchiectasis and 80 controls. The study also did not stratify patients with bronchiectasis according to disease severity.

A large-scale study to compare CIMT in patients with bronchiectasis and healthy subjects is warranted to assess the risks of subclinical atherosclerosis, taking into account disease severity. In view of this knowledge gap, we conducted this study with the objective being assessing burden of subclinical atherosclerosis as measured by CIMT among patients with bronchiectasis of different severity, as well as comparing CIMT between patients with bronchiectasis and healthy controls.

Materials and methods

A prospective study was conducted at the University Department of Medicine, Queen Mary Hospital (QMH). The Divisions of Respiratory Medicine and Neurology at Queen Mary Hospital are tertiary referral centers for the territory as well as major receiving units for patients with various respiratory diseases (including bronchiectasis) and neurological diseases in the Hong Kong West Cluster. Subjects above the age of 18 years old with a confirmed diagnosis of bronchiectasis based on high-resolution computed tomography (HRCT) scan were included. Those with co-existent systemic inflammatory diseases (Rheumatological diseases, inflammatory bowel diseases and other autoimmune diseases) and respiratory co-morbidities (asthma, chronic obstructive pulmonary disease and interstitial lung diseases) were excluded. HRCT and lung function tests were performed within 12 months prior to recruitment to the study. Severity of bronchiectasis was defined according to the FACED [forced expiratory volume in 1 s (FEV 1 ), age, chronic colonization, extension, and dyspnea score] score, with mild, moderate and severe bronchiectasis defined as a FACED score of 0–2, 3–4, and 5–7 points respectively [ 28 ]. Subjects with bronchiectasis were recruited from 1st October to 31st December 2021 from the bronchiectasis clinic of QMH. Controls without bronchiectasis, as assessed by symptom questionnaires and checking of electronic patient records of the Hospital Authority of Hong Kong, were recruited between July 2018 and June 2020 from the community by open advertisements.

As gender and smoking status are the important factors that could contribute to differences in CIMT, while they are also significantly different in the two groups, they are chosen to be factors for propensity score matching. Control patients were individually matched by gender and smoking status using propensity score matching with a 1:2 matching ratio and caliper of 0.2. Covariates that were not well-matched were adjusted in multivariate analysis.

Vascular ultrasound examination of carotid intima-medial thickness (CIMT) was performed by a standard B-mode ultrasound examination with a 7.5 MHz linear array transducer and a high-resolution ultrasound system in accordance with the Mannheim Carotid Intima-Media Thickness and Plaque Consensus [ 29 , 30 , 31 ]. In patients with bronchiectasis, ultrasound examination was performed by a single experienced operator (Kwok Wang Chun) using General Electric LOGIQ e. In controls, scans were performed by one of four operators (Matthew SS Hsu, Kkts Pijarnvanit, Carman Nga-Man Cheung, Yick Hin Chow) using a Samsung Ultrasound RS80A. All subjects were examined in a supine position. Ultrasound scans of the right and left common carotid artery in three different projections (anterior, lateral, and posterior) were performed. CIMT was determined by measuring the distance between the lumen-intima and media-adventitia border at a 10 mm straight arterial segment near the bulb at the far wall of both common carotid arteries. CIMT was calculated using automated IMT software. CIMT was measured at each projection (anterior, lateral, and posterior) from each side giving six measurements for each patient and the mean value calculated.

Seventeen randomly selected controls underwent CIMT re-measurement, performed with General Electric LOGIQ e by the same operator who assessed bronchiectasis patients to identify any inter-machine variability.

Automated IMT function was used to measure CIMT in both bronchiectasis patients and control, which has been demonstrated to have high accuracy and reproducibility [ 32 , 33 , 34 ]. Automated IMT measurement allows automated contour detection of lumen-intima and media-adventitia vessel walls and calculation of IMT quality index. The advantage of having automated IMT measurement is that it avoided some of the problems from manual measurement of CIMT, including being user-dependent, measurement not fully standardized, subjective, time-consuming and prone to errors [ 35 ].

The primary outcome was the difference in CIMT between patients with bronchiectasis and controls, and among patients with bronchiectasis of different severity as determined by FACED score. The study was approved by the Institutional Review Board of the University of Hong Kong and Hospital Authority Hong Kong West Cluster (UW19-624).

Statistical analysis

The demographic and clinical data are presented as actual frequency, mean ± standard deviation (SD) or median (interquartile range, IQR) as appropriate. Categorical variables were compared using χ2 test. For continuous variables, two-group comparisons were performed using unpaired t test or Mann-Whitney U test as appropriate. One-way ANOVA was employed to compare multi-group continuous variables. Multivariate linear regression analyses were performed to adjust for cofounders that included age, gender, smoking history, body mass index (BMI), cardiovascular risk factors and history of cardiovascular diseases. Statistical significance was determined at the level of p  = 0.05. All statistical analyses were performed using R V.4.2.2 (R Foundation for Statistical Computing) statistical software.

A total of 155 Chinese patients with non-CF bronchiectasis managed at QMH and 512 controls were included. The baseline characteristics of the patients with bronchiectasis and the controls are summarized in Table  1 .

Correlation of CIMT among controls using different ultrasound machines

Both the General Electric LOGIQ e and Samsung Ultrasound RS80A were used to measure CIMT in 17 controls. All scans were performed by the same investigator. Measurements of CIMT by both machines had a high Pearson Correlation Coefficient of 0.856 (95% confidence interval [CI] = 0.627–0.949, p  < 0.001).

Interrater variability of CIMT among controls

Ultrasound measurement of CIMT in controls was performed by four operators. The interrater variability was determined by repeat ultrasound CIMT measurement in 11 controls by different investigators. The intraclass correlation coefficient was 0.977.

Whole cohort with 155 patients with bronchiectasis and 512 controls

Cimt in patients with bronchiectasis and controls (whole cohort, n  = 667).

Patients with bronchiectasis had significantly increased CIMT compared with controls (0.64 ± 0.11 mm vs. 0.58 ± 0.10 mm respectively, p  < 0.001). The association was statistically significant after adjusting for age, gender, BMI, smoking status, any cardiovascular risk factors and any history of cardiovascular diseases ( p  = 0.020) (Supplementary Table S1 ).

CIMT in patients with bronchiectasis and controls without a history of cardiovascular disease or cardiovascular risk factors (Bronchiectasis , n  = 92; control, n  = 384)

The mean CIMT, as compared by unpaired t test, among patients with bronchiectasis and controls was 0.61 ± 0.10 mm and 0.57 ± 0.10 mm respectively ( p  < 0.001). The association was statistically significant after adjusting for age, gender, BMI, smoking status, any cardiovascular risk factors, and any history of cardiovascular disease in multivariate linear regression ( p  = 0.007) (Supplementary Table S1 ).

CIMT in patients with bronchiectasis of different severity and controls (Mild-to-moderate bronchiectasis , n  = 126; Severe bronchiectasis, n  = 29; control, n  = 512)

The CIMT, compared by one-way ANOVA, was 0.58 ± 0.10 mm, 0.63 ± 0.11 mm and 0.66 ± 0.08 mm among controls, patients with mild-to-moderate bronchiectasis and patients with severe bronchiectasis respectively (Fig.  1 ). The association was statistically significant after adjusting for age, gender, BMI, smoking status, any cardiovascular risk factors, and any history of cardiovascular disease in multivariate linear regression ( p  = 0.021) (Supplementary Table S1 ).

Mean CIMT among all patients with mild-to-moderate bronchiectasis, severe bronchiectasis, and controls in the whole cohort. CIMT are compared by one-way ANOVA. CIMT: Carotid intima-media thickness

CIMT in patients with bronchiectasis of different severity and controls with no history of cardiovascular disease or cardiovascular risk factors (Mild-to-moderate bronchiectasis , n  = 76; Severe bronchiectasis, n  = 16; control, n  = 384)

The CIMT, compared by one-way ANOVA, was 0.57 ± 0.10 mm, 0.60 ± 0.10 mm and 0.69 ± 0.06 mm among controls, patients with mild-to-moderate bronchiectasis and patients with severe bronchiectasis (Fig.  2 ). The association was statistically significant after adjusting for age, gender, BMI, smoking status, any cardiovascular risk factor, and any history of cardiovascular disease in multivariate linear regression ( p  < 0.001) (Supplementary Table S1 ).

Mean CIMT among patients with mild-to-moderate bronchiectasis, severe bronchiectasis, and controls with no cardiovascular risk factors or history of cardiovascular diseases in the whole cohort. CIMT are compared by one-way ANOVA. CIMT: Carotid intima-media thickness

Matched cohort ( n  = 465)

There were 155 subjects with bronchiectasis and 310 controls in the propensity score matched cohort (Table  2 ).

CIMT in patients with bronchiectasis and controls

Patients with bronchiectasis had significantly increased CIMT compared with controls (0.64 ± 0.11 mm and 0.58 ± 0.10 mm respectively, p  < 0.001), as compared by unpaired t-test. The association was statistically significant after adjusting for age, gender, BMI, smoking status, any cardiovascular risk factor, and any history of cardiovascular disease in multivariate linear regression ( p  = 0.020) (Supplementary Table S2 ).

CIMT in patients with bronchiectasis and controls with no history of cardiovascular disease or cardiovascular risk factors (Bronchiectasis , n  = 92; control, n  = 235)

The mean CIMT, compared by unpaired t-test, among patients with bronchiectasis and controls was 0.61 ± 0.10 mm and 0.57 ± 0.09 mm respectively ( p  < 0.001). The association was statistically significant after adjusting for age, gender, BMI, smoking status, any cardiovascular risk factor and any history of cardiovascular disease in multivariate linear regression ( p  = 0.017) (Supplementary Table S2 ).

CIMT in patients with bronchiectasis of different severity and controls (Mild-to-moderate bronchiectasis , n  = 126; Severe bronchiectasis, n  = 29; control, n  = 310)

The mean CIMT, as compared by one-way ANOVA, was 0.58 ± 0.10 mm, 0.63 ± 0.11 mm and 0.66 ± 0.08 mm among controls, patients with mild-to-moderate and severe bronchiectasis respectively. The association was statistically significant after adjusting for age, gender, BMI, smoking status, any cardiovascular risk factor and any history of cardiovascular disease in multivariate linear regression ( p  = 0.004) (Supplementary Table S2 ).

CIMT in patients with bronchiectasis of different severity and controls with no history of cardiovascular disease or cardiovascular risk factors (Mild-to-moderate bronchiectasis , n  = 76; Severe bronchiectasis, n  = 16; control, n  = 235)

The mean CIMT was 0.56 ± 0.09 mm, 0.60 ± 0.10 mm and 0.69 ± 0.06 mm respectively among controls, patients with mild-to-moderate and patients with severe bronchiectasis. The association was statistically significant after adjusting for age, gender, BMI, smoking status, any cardiovascular risk factor, and any history of cardiovascular disease ( p  < 0.001) (Supplementary Table S2 ).

Subgroup analysis – age > 60

There were 126 subjects with bronchiectasis and 102 controls above the age of 60 years in the propensity score matched cohort. Age is reported to be associated with CIMT [ 36 ], with age 60 years a commonly used cut-off to define age group in CIMT studies [ 37 , 38 , 39 , 40 ]. The results in this subgroup show consistent results as in the primary analysis. The findings of subgroup analysis were summarized in Supplementary Table S3 and S4 .

To the best of our knowledge, this is the first report of a significant association of severe bronchiectasis with CIMT, but not mild-to-moderate bronchiectasis. The findings were consistent among patients with or without cardiovascular risk factors or cardiovascular disease. Among patients with severe bronchiectasis, defined by a FACED score 5 or above, CIMT was increased compared with controls.

There is growing evidence of an association of adverse cardiovascular outcomes with bronchiectasis. A possible link between the two is chronic inflammation, a hallmark of bronchiectasis. Nonetheless no previous study has suggested that CIMT is increased in patients with bronchiectasis. A small-scale case control study identified only a difference in FMD, not CIMT. This may have been due to the small sample size of only 80 patients with bronchiectasis and 80 controls. Our study has a larger sample size so overcomes the potential problem of the previous case control study that lacked statistical power to detect CIMT differences. In addition, previous study did not take account of disease severity. In our study, bronchiectasis as a whole group (a group with varying severity) may not account for the differences in CIMT. Nonetheless based on analysis according to disease severity, we determined that severe bronchiectasis, not mild-to-moderate, was associated with increased CIMT. We postulate that there is an interplay of various factors that underlie the association of bronchiectasis and CIMT. This finding is consistent with the proposal that CIMT, as a marker of subclinical atherosclerosis, is related to the degree of inflammation. The more inflamed the airway is, the thicker the CIMT. This finding is supported by studies in other inflammatory diseases. In rheumatoid arthritis, features of more severe disease, including extra-articular manifestations, erosions, high inflammatory parameters, and long disease duration, were associated with greater CIMT [ 41 ]. A similar phenomenon has been observed in systemic lupus erythematosus and psoriasis [ 42 , 43 ]. This may explain why CIMT is increased in patients with severe bronchiectasis but not mild-to-moderate cases. We postulate that as an inflammatory disease mainly affecting the respiratory tract, the severity of bronchiectasis is closely related to the degree of systemic inflammation and hence CIMT. For patients with mild-to-moderate bronchiectasis, the degree of systemic inflammation is low and CIMT is not increased.

CIMT was also being assessed in other respiratory diseases before. In chronic obstructive pulmonary disease (COPD), 32% of patients with mild COPD and 36% with moderate to severe COPD had increased CIMT, compared with 23% in patients without COPD [ 44 ]. CIMT was also found to be positively correlated with exacerbation rate in past year and negatively correlated with FEV 1 among patients with COPD, which suggested that CIMT is related to COPD severity [ 45 ]. CIMT was also reported to be increased among patients with asthma, [ 46 ] which may have differences in different phenotypes. In Atherosclerosis Risk in Communities (ARIC) study, CIMT was increased among women with adult-onset asthma but not childhood-onset asthma [ 47 ]. This could be related to the severity of asthma as adult-onset asthma which is well reported to be associated with higher morbidity and mortality [ 48 , 49 ]. In cystic fibrosis, CIMT was increased among patients who are pancreatic insufficient but not those who are pancreatic sufficient [ 50 ].

In patients with severe bronchiectasis, as defined by FACED score, they are more likely to have Pseudomonas colonization , worse lung function, and more symptomatic as measured by mMRC dyspnoea scale. They are also older with more lobes of the lungs being involved. By having Pseudomonas colonization , this will lead to chronic low-grade airway inflammation. The chronic inflammation can contribute to an increase in CIMT as in other inflammatory diseases. Worse lung function by FEV 1 , higher mMRC dyspnoea scale and more extensive disease could reflect the airway damage from chronic airway inflammation, which is linked to the increase in CIMT. The chronic inflammatory state in these patients with severe bronchiectasis could eventually lead to subclinical atherosclerosis with an increase in CIMT. The constellation of all these factors, as reflected in FACED score, ultimately translate into increase in CIMT, mediated through heightened inflammatory state in these patients.

Our findings provide a pathophysiological basis for the observed association of bronchiectasis with cardiovascular diseases. Although systemic inflammatory diseases have been reported to be associated with cardiovascular diseases, the evidence for bronchiectasis is weaker, especially from a pathogenic aspect. The only evidence for subclinical atherosclerosis derived from a small-scale study of FMD [ 27 ] and brachial-ankle pulse wave velocity (baPWV), a measure of arterial stiffness [ 51 ]. Our study is the first to show evidence of an association of bronchiectasis with increased CIMT in a severity-dependent manner. Together with the previous reports on FMD and baPWV, [ 27 , 51 ] these findings provide a stronger pathogenic basis to support the reported association of bronchiectasis with cardiovascular diseases. In previous literature, bronchiectasis has been shown to be associated the adverse cardiovascular outcomes. But as a heterogenous disease, the exact pathophysiological mechanism is not established. This could be attributed by smoking, advanced age and co-existing diseases. Our study provides data to suggest that the association of bronchiectasis and cardiovascular diseases could be contributed by bronchiectasis itself, if severe enough. As bronchiectasis is associated with cardiovascular diseases, we believe that using CIMT as a non-invasive tool can help to identify at risk population, especially patients with severe bronchiectasis and underlying cardiovascular risk factors. The findings from our study also call for the screening and monitoring of cardiovascular health in patients with bronchiectasis, especially if it is a severe one. CIMT is one of the options while other clinical parameters such as blood pressure, glucose and lipid level shall also be assessed. These patients may need better control of bronchiectasis and cardiovascular risk factors, for example with smoking cessation, lowering of lipid level and achieve optimal blood pressure control, to prevent future cardiovascular events. Although bronchiectasis cannot be reversed, yet, it can be controlled with various medications like macrolide and inhaled antibiotics, which may help to prevent bronchiectasis exacerbation and probable subsequent cardiovascular events after bronchiectasis exacerbation.

In bronchiectasis, one of the goals of pharmacotherapy is immunomodulation, such as by macrolide and inhaled corticosteroid (ICS). Whether these treatments can have effect on CIMT and subsequent cardiovascular events worth further research, Macrolide has been shown to reduce the frequency of exacerbations and improve quality of life in bronchiectasis [ 52 ]. It acts by suppressing bacterial infection and reducing airway inflammation. ICS works in selected group of patients stratified by the blood eosinophil count [ 53 ]. ICS exerts the effect through altering macrophage gene expression, decreasing interferon (IFN)-γ expression and upregulating chemokine production [ 54 ]. The clinical benefits of these treatments in bronchiectasis are mainly on the respiratory outcomes such as bronchiectasis exacerbation. It is interesting to know if they can offer cardiovascular protective effect through dampening down the degree of airway inflammation and later reduction in CIMT, which is a marker of subclinical atherosclerosis.

There are some limitations to our study. First, it involved only a single centre with all the patients included being Chinese. This could have potential implication of generalizability of the findings in different ethnic groups, in which the etiology of bronchiectasis could be different. Also, as a single centre study, the sample size is relatively small, especially for patients with severe bronchiectasis. Nonetheless as a tertiary medical centre, the respiratory unit receives referrals from all other health care facilities across the territory. Patients diagnosed with bronchiectasis are managed in a designated bronchiectasis clinic at our centre. And the association of CIMT and severity of bronchiectasis can be demonstrated in this study with statistical significance. A large-scale multi-centre study involving different ethnic group will be worthwhile to conduct to assess if the same phenomenon is observed across different ethnic groups. Second, CIMT was measured using different ultrasound machines for patients with bronchiectasis and controls. Although there may be inter-machine variability for measurement of CIMT, we validated the findings in 17 controls and demonstrated a good correlation for measurements obtained by the two ultrasound machines. In propensity score matching, ideally, all factors that are significantly different should be matched. However, given a relatively small sample size, especially in the severe bronchiectasis group. Matching all the factors that are significantly different such as age and co-morbidities would lead to loss of some of the patients with severe bronchiectasis which cannot be matched. As such, we matched only the gender and smoking status, while adjusting the other factors that are significantly different but not matched are included in multi-variate analysis as confounder. Another limitation is that lung function and blood test results available. They were identified by symptom questionnaire and electronic health record. This may potentially include some patients with minute lung function abnormality or any systemic disorder. Yet, they have been screened for any underlying respiratory diseases by questionnaire and their electronic health record. As they are free of respiratory symptoms without physician diagnosed respiratory diseases, the possibility of erroneously recruiting healthy controls with major respiratory or systemic diseases that affect the CIMT is very low. Also, in the bronchiectasis group, the mean baseline FEV 1 was 86.3 ± 24.5%, with 104 patients with baseline FEV 1 > 70%. The baseline neutrophil and lymphocyte count in the bronchiectasis group is also relatively normal. The impact from lung function and abnormal blood count on the result is considered to be minute, if there is any.

Data availability

Dataset supporting the conclusion of this article is included within this article and no additional data will be provided. Research data is not shared.

Abbreviations

Brachial index

Ankle pulse wave velocity

Body mass index

Coronary heart disease

Carotid intima-media thickness

C-reactive protein

Flow-mediated dilatation

High-resolution computed tomography

Interquartile range

Standard deviation

Boyton RJ, Altmann DM. Bronchiectasis: current concepts in Pathogenesis, Immunology, and Microbiology. Annu Rev Pathol. 2016;11:523–54.

Article   CAS   PubMed   Google Scholar  

Koenig W, Sund M, Frohlich M, et al. C-Reactive protein, a sensitive marker of inflammation, predicts future risk of coronary heart disease in initially healthy middle-aged men: results from the MONICA (Monitoring trends and determinants in Cardiovascular Disease) Augsburg Cohort Study, 1984 to 1992. Circulation. 1999;99(2):237–42.

Ridker PM, Buring JE, Shih J, Matias M, Hennekens CH. Prospective study of C-reactive protein and the risk of future cardiovascular events among apparently healthy women. Circulation. 1998;98(8):731–3.

Ridker PM, Glynn RJ, Hennekens CH. C-reactive protein adds to the predictive value of total and HDL cholesterol in determining risk of first myocardial infarction. Circulation. 1998;97(20):2007–11.

Force USPST, Curry SJ, Krist AH, et al. Risk Assessment for Cardiovascular Disease with nontraditional risk factors: US Preventive Services Task Force Recommendation Statement. JAMA. 2018;320(3):272–80.

Article   Google Scholar  

Genest J, McPherson R, Frohlich J, et al. 2009 Canadian Cardiovascular Society/Canadian guidelines for the diagnosis and treatment of dyslipidemia and prevention of cardiovascular disease in the adult – 2009 recommendations. Can J Cardiol. 2009;25(10):567–79.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Pearson TA, Mensah GA, Alexander RW, et al. Markers of inflammation and cardiovascular disease: application to clinical and public health practice: a statement for healthcare professionals from the Centers for Disease Control and Prevention and the American Heart Association. Circulation. 2003;107(3):499–511.

Article   PubMed   Google Scholar  

Collaboration IRGCERF, Sarwar N, Butterworth AS, et al. Interleukin-6 receptor pathways in coronary heart disease: a collaborative meta-analysis of 82 studies. Lancet. 2012;379(9822):1205–13.

Interleukin-6 Receptor Mendelian Randomisation, Analysis C, Swerdlow DI, Holmes MV, et al. The interleukin-6 receptor as a target for prevention of coronary heart disease: a mendelian randomisation analysis. Lancet. 2012;379(9822):1214–24.

Brennan ML, Penn MS, Van Lente F, et al. Prognostic value of myeloperoxidase in patients with chest pain. N Engl J Med. 2003;349(17):1595–604.

Zhang R, Brennan ML, Fu X, et al. Association between myeloperoxidase levels and risk of coronary artery disease. JAMA. 2001;286(17):2136–42.

Zheng L, Nukuna B, Brennan ML, et al. Apolipoprotein A-I is a selective target for myeloperoxidase-catalyzed oxidation and functional impairment in subjects with cardiovascular disease. J Clin Invest. 2004;114(4):529–41.

Karakas M, Koenig W, Zierer A, et al. Myeloperoxidase is associated with incident coronary heart disease independently of traditional risk factors: results from the MONICA/KORA Augsburg study. J Intern Med. 2012;271(1):43–50.

Blankenberg S, Rupprecht HJ, Bickel C, et al. Circulating cell adhesion molecules and death in patients with coronary artery disease. Circulation. 2001;104(12):1336–42.

Blankenberg S, Tiret L, Bickel C, et al. Interleukin-18 is a strong predictor of cardiovascular death in stable and unstable angina. Circulation. 2002;106(1):24–30.

Oei HH, van der Meer IM, Hofman A, et al. Lipoprotein-associated phospholipase A2 activity is associated with risk of coronary heart disease and ischemic stroke: the Rotterdam Study. Circulation. 2005;111(5):570–5.

Ridker PM, Buring JE, Rifai N. Soluble P-selectin and the risk of future cardiovascular events. Circulation. 2001;103(4):491–5.

Roldan V, Marin F, Lip GY, Blann AD. Soluble E-selectin in cardiovascular disease and its risk factors. A review of the literature. Thromb Haemost. 2003;90(6):1007–20.

Tiret L, Godefroy T, Lubos E, et al. Genetic analysis of the interleukin-18 system highlights the role of the interleukin-18 gene in cardiovascular disease. Circulation. 2005;112(5):643–50.

Valgimigli M, Ceconi C, Malagutti P, et al. Tumor necrosis factor-alpha receptor 1 is a major predictor of mortality and new-onset heart failure in patients with acute myocardial infarction: the cytokine-activation and long-term prognosis in myocardial infarction (C-ALPHA) study. Circulation. 2005;111(7):863–70.

Saleh AD, Kwok B, Brown JS, Hurst JR. Correlates and assessment of excess cardiovascular risk in bronchiectasis. Eur Respir J 2017;50(5).

Navaratnam V, Millett ER, Hurst JR, et al. Bronchiectasis and the risk of cardiovascular disease: a population-based study. Thorax. 2017;72(2):161–6.

Mendez R, Feced L, Alcaraz-Serrano V, et al. Cardiovascular events during and after bronchiectasis exacerbations and long-term mortality. Chest. 2022;161(3):629–36.

Huang JT, Kuzmanova E, Dicker AJ, et al. Serum desmosine is Associated with Long-Term all-cause and Cardiovascular Mortality in Bronchiectasis. Am J Respir Crit Care Med. 2020;202(6):897–9.

Navaratnam V, Root AA, Douglas I, Smeeth L, Hubbard RB, Quint JK. Cardiovascular outcomes after a respiratory tract infection among adults with non-cystic fibrosis bronchiectasis: a General Population-based study. Ann Am Thorac Soc. 2018;15(3):315–21.

Article   PubMed   PubMed Central   Google Scholar  

Choi H, Kim SH, Han K, et al. Association between exercise and risk of cardiovascular diseases in patients with non-cystic fibrosis bronchiectasis. Respir Res. 2022;23(1):288.

Gao YH, Liu SX, Cui JJ, et al. Subclinical atherosclerosis in adults with steady-state bronchiectasis: a case-control study. Respir Med. 2018;134:110–6.

Costa JC, Machado JN, Ferreira C, Gama J, Rodrigues C. The Bronchiectasis Severity Index and FACED score for assessment of the severity of bronchiectasis. Pulmonology. 2018.

Lau KK, Chan YH, Wong YK, et al. Garlic intake is an independent predictor of endothelial function in patients with ischemic stroke. J Nutr Health Aging. 2013;17(7):600–4.

Lau KK, Chan YH, Yiu KH, et al. Burden of carotid atherosclerosis in patients with stroke: relationships with circulating endothelial progenitor cells and hypertension. J Hum Hypertens. 2007;21(6):445–51.

Touboul PJ, Hennerici MG, Meairs S et al. Mannheim carotid intima-media thickness and plaque consensus (2004-2006-2011). An update on behalf of the advisory board of the 3rd, 4th and 5th watching the risk symposia, at the 13th, 15th and 20th European Stroke Conferences, Mannheim, Germany, 2004, Brussels, Belgium, 2006, and Hamburg, Germany, 2011. Cerebrovasc Dis. 2012;34(4):290–296.

Molinari F, Meiburger KM, Saba L, et al. Ultrasound IMT measurement on a multi-ethnic and multi-institutional database: our review and experience using four fully automated and one semi-automated methods. Comput Methods Programs Biomed. 2012;108(3):946–60.

Molinari F, Meiburger KM, Saba L et al. Automated carotid IMT measurement and its validation in low contrast ultrasound database of 885 patient Indian population epidemiological study: results of AtheroEdge ® software. Multi-modality Atherosclerosis Imaging Diagnosis 2014:209–19.

Saba L, Montisci R, Famiglietti L, et al. Automated analysis of intima-media thickness: analysis and performance of CARES 3.0. J Ultrasound Med. 2013;32(7):1127–35.

Naqvi T. Ultrasound vascular screening for cardiovascular risk assessment. Why, when and how? Minerva Cardioangiol. 2006;54(1):53–67.

CAS   PubMed   Google Scholar  

van den Munckhof ICL, Jones H, Hopman MTE, et al. Relation between age and carotid artery intima-medial thickness: a systematic review. Clin Cardiol. 2018;41(5):698–704.

Kuswardhani RT, Wiradharma KG, Kandarini Y, Widiana GR, Martadiani ED. Factors associated with carotid intima-media thickness in patients on maintenance hemodialysis. Int J Gen Med. 2019;12:1–6.

Ma M, Wang L, Zhong X, et al. Age and gender differences between carotid intima-media thickness and serum uric acid. Am J Cardiol. 2022;172:137–43.

Chang CC, Chang ML, Huang CH, Chou PC, Ong ET, Chin CH. Carotid intima-media thickness and plaque occurrence in predicting stable angiographic coronary artery disease. Clin Interv Aging. 2013;8:1283–8.

PubMed   PubMed Central   Google Scholar  

Loboz-Rudnicka M, Jaroch J, Bociaga Z, et al. Impact of cardiovascular risk factors on carotid intima-media thickness: sex differences. Clin Interv Aging. 2016;11:721–31.

CAS   PubMed   PubMed Central   Google Scholar  

Targonska-Stepniak B, Drelich-Zbroja A, Majdan M. The relationship between carotid intima-media thickness and the activity of rheumatoid arthritis. J Clin Rheumatol. 2011;17(5):249–55.

Medeiros PBS, Salomao RG, Teixeira SR, et al. Disease activity index is associated with subclinical atherosclerosis in childhood-onset systemic lupus erythematosus. Pediatr Rheumatol Online J. 2021;19(1):35.

Eder L, Jayakar J, Shanmugarajah S, et al. The burden of carotid artery plaques is higher in patients with psoriatic arthritis compared with those with psoriasis alone. Ann Rheum Dis. 2013;72(5):715–20.

Van Gestel YR, Flu W-J, van Kuijk J-P, et al. Association of COPD with carotid wall intima-media thickness in vascular surgery patients. Respir Med. 2010;104(5):712–6.

Gulbas G, Turan O, Sarioglu N, et al. Carotid intima-media thickness in chronic obstructive pulmonary disease and survival: a multicenter prospective study. Clin Respir J. 2019;13(6):391–9.

Yılmaz M, Bozkurt Yılmaz HE, Şen N, Altın C, Tekin A, Müderrisoğlu H. Investigation of the relationship between asthma and subclinical atherosclerosis by carotid/femoral intima media and epicardial fat thickness measurement. J Asthma. 2018;55(1):50–6.

Onufrak S, Abramson J, Vaccarino V. Adult-onset asthma is associated with increased carotid atherosclerosis among women in the atherosclerosis risk in communities (ARIC) study. Atherosclerosis. 2007;195(1):129–37.

Tsai C-L, Delclos GL, Huang JS, Hanania NA, Camargo CA Jr. Age-related differences in asthma outcomes in the United States, 1988–2006. Ann Allergy Asthma Immunol. 2013;110(4):240–6. e241.

Enright PL, Kronmal RA, Higgins MW, Schenker MB, Haponik EF. Prevalence and correlates of respiratory symptoms and disease in the elderly. Chest. 1994;106(3):827–34.

Nowak JK, Wykrętowicz A, Mądry E, et al. Preclinical atherosclerosis in cystic fibrosis: two distinct presentations are related to pancreatic status. J Cyst Fibros. 2022;21(1):26–33.

Gao YH, Cui JJ, Wang LY, et al. Arterial stiffness in adults with steady-state bronchiectasis: association with clinical indices and disease severity. Respir Res. 2018;19(1):86.

Kelly C, Chalmers JD, Crossingham I et al. Macrolide antibiotics for bronchiectasis. Cochrane Database Syst Reviews 2018(3).

Kwok WC, Tam TCC, Lam DCL, Ip MSM, Ho JCM. Blood eosinophil percentage as a predictor of response to inhaled corticosteroid in bronchiectasis. Clin Respir J 2023.

Martínez-García MÁ, Oscullo G, García-Ortega A, Matera MG, Rogliani P, Cazzola M. Inhaled corticosteroids in adults with non-cystic fibrosis bronchiectasis: from bench to Bedside. A narrative review. Drugs. 2022;82(14):1453–68.

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Acknowledgements

The study was supported by a Hong Kong College of Physicians Young Investigator Research Grant.

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Wang Chun Kwok and Kui Kai Lau contributed equally to this work.

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Department of Medicine, The University of Hong Kong, Queen Mary Hospital, 102 Pokfulam Road, Hong Kong, Pokfulam, Hong Kong SAR, China

Wang Chun Kwok, Kui Kai Lau, Kay Cheong Teo, Chung Ki Tsui, Kkts Pijarnvanit, Carman Nga-Man Cheung, Yick Hin Chow & James Chung Man Ho

State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China

Kui Kai Lau

Department of Statistics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China

Sze Him Isaac Leung

Department of Pathology, The University of Hong Kong, Queen Mary Hospital, Pokfulam, Hong Kong SAR, China

Matthew S.S. Hsu

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Dr. Wang Chun Kwok and Gary Kui Kai Lau were involved with study concept and design, analysis and interpretation of data, acquisition of data, drafting of manuscript, and approval of the final version of the manuscript. Dr. Kay Cheong Teo, Chung Ki Tsui and Sze Him Isaac Leung, Matthew SS Hsu, Kkts Pijarnvanit, Mr. Yick Hin Chow and Ms. Carman Nga-Man Cheung were involved with critical revision of the manuscript for important intellectual content and approval of the final version. Dr. James Chung Man Ho was involved with the study concept and design, drafting of manuscript, critical revision of the manuscript for important intellectual content, study supervision, and approval of the final version. All the authors had full access to the data, contributed to the study, approved the final version of the publication and took all the responsibility of its accuracy and integrity.

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Kwok, W.C., Lau, K.K., Teo, K.C. et al. Severe bronchiectasis is associated with increased carotid intima-media thickness. BMC Cardiovasc Disord 24 , 457 (2024). https://doi.org/10.1186/s12872-024-04129-x

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Metabolic specialization drives reduced pathogenicity in Pseudomonas aeruginosa isolates from cystic fibrosis patients

Roles Data curation, Formal analysis, Investigation, Visualization, Writing – original draft

Affiliation The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark

Roles Investigation

Affiliation Department of Clinical Microbiology 9301, Rigshospitalet, Copenhagen, Denmark

Roles Methodology, Resources, Writing – review & editing

Affiliation Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom

Roles Funding acquisition, Resources, Writing – review & editing

Affiliations Department of Clinical Microbiology 9301, Rigshospitalet, Copenhagen, Denmark, Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

Roles Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Writing – review & editing

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Supervision, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark, Department of Clinical Microbiology 9301, Rigshospitalet, Copenhagen, Denmark

• Bjarke Haldrup Pedersen, 
• Filipa Bica Simões, 
• Ivan Pogrebnyakov, 
• Martin Welch, 
• Helle Krogh Johansen, 
• Søren Molin, 
• Ruggero La Rosa

• Published: August 23, 2024
• https://doi.org/10.1371/journal.pbio.3002781
• Peer Review
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Metabolism provides the foundation for all cellular functions. During persistent infections, in adapted pathogenic bacteria metabolism functions radically differently compared with more naïve strains. Whether this is simply a necessary accommodation to the persistence phenotype or if metabolism plays a direct role in achieving persistence in the host is still unclear. Here, we characterize a convergent shift in metabolic function(s) linked with the persistence phenotype during Pseudomonas aeruginosa colonization in the airways of people with cystic fibrosis. We show that clinically relevant mutations in the key metabolic enzyme, pyruvate dehydrogenase, lead to a host-specialized metabolism together with a lower virulence and immune response recruitment. These changes in infection phenotype are mediated by impaired type III secretion system activity and by secretion of the antioxidant metabolite, pyruvate, respectively. Our results show how metabolic adaptations directly impinge on persistence and pathogenicity in this organism.

Citation: Pedersen BH, Simões FB, Pogrebnyakov I, Welch M, Johansen HK, Molin S, et al. (2024) Metabolic specialization drives reduced pathogenicity in Pseudomonas aeruginosa isolates from cystic fibrosis patients. PLoS Biol 22(8): e3002781. https://doi.org/10.1371/journal.pbio.3002781

Academic Editor: Alice Prince, Columbia University, UNITED STATES OF AMERICA

Received: January 17, 2024; Accepted: August 1, 2024; Published: August 23, 2024

Copyright: © 2024 Pedersen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The authors declare that all data necessary for supporting the findings of this study are enclosed in this paper ( S1 – S4 Data). All genomic data is publicly available through the SRA database and has been published previously by Marvig et al., (2015), doi: 10.1038/ng.3148 .

Funding: The work at the Novo Nordisk Foundation Center for Biosustainability is supported by the Novo Nordisk Foundation www.novonordiskfonden.dk (grant number NNF20CC0035580). This work was supported by the UK Cystic Fibrosis Trust www.cysticfibrosis.org.uk (grant number SRC017 - MW, SM, HKJ) and the Independent Research Fund Denmark/Natural Sciences www.dff.dk (grant number 9040-00106B - SM). HKL was supported by the Novo Nordisk Foundation www.novonordiskfonden.dk (Challenge grant NNF19OC0056411). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Abbreviations: ALI, air–liquid interface; COG, Clusters of Orthologous Groups; HCA, hierarchical cluster analysis; HCN, hydrogen cyanide; LPS, lipopolysaccharide; PCA, principal component analysis; PDHc, pyruvate dehydrogenase complex; PFA, paraformaldehyde; PQS, Pseudomonas quinolone signal; pwCF, people with cystic fibrosis; ROS, reactive oxygen species; SCFM2, synthetic cystic fibrosis medium 2; T3SS, type III secretion system; TEER, transepithelial electrical resistance

Introduction

Difficult to treat bacterial infections are increasing around the world [ 1 ]. While antibiotic resistance is a major cause of treatment failure, other less characterized mechanisms rooted in the complexity of the host–pathogen interactions are also substantial contributors to persistence [ 2 ]. Genetic variants with high tolerance to stresses, host and immune evasion capabilities, and low virulence are often specifically selected for in persistent infections since they provide higher within-host fitness [ 3 , 4 ]. In people with cystic fibrosis (pwCF), for example, opportunistic pathogens such as Pseudomonas aeruginosa colonize the airways and establish persistent infections that can last for more than 30 years. During the infection, the bacteria differentiate into heterogeneous populations specifically evolved for the host microenvironment [ 5 – 8 ]. Surprisingly, antibiotic resistance is not the initial driver of persistence, since bacteria retain antibiotic susceptibility for years after colonization [ 9 ]. Seemingly, these populations use mechanisms of persistence such as biofilm formation, loss of flagella and virulence factors, growth rate reduction, immune escape, and metabolic specialization to “hide” from the immune system and withstand the antibiotic treatment [ 2 ]. These mechanisms usually fall below the detection radar since, in simple laboratory conditions, without the intrinsic complexity of the host environment and its interactions with the bacteria, no assumptions on the persistence of such populations can be made. However, these mechanisms are relevant for the overall persistence of the infecting population, possibly being the main drivers of the initial host colonization before the insurgence of antibiotic resistance.

One hypothesis is that metabolic specialization strongly influences the host–pathogen interactions and leads to persistence [ 10 , 11 ]. In many infections, the nutrient composition of the host microenvironment provides an environmental cue for bacterial pathogens to activate their virulence repertoire [ 11 ]. Abnormalities in calcium homeostasis, nutrient limitation, or change in pH can, for example, trigger the type III secretion system (T3SS) cascade in several bacterial species, activating the secretion of virulence effectors promoting colonization of the host [ 12 – 15 ]. In P . aeruginosa , the T3SS has a key role during infection, since through its injectosome and secreted factors, it subverts the host cell machinery influencing both invasion, growth, and host immune response [ 12 , 16 , 17 ]. Similarly, metabolism-dependent processes such as biofilm production and mucoidy provide shielding from antibiotics and immune cells and are generally associated with worse prognoses [ 18 – 20 ]. Furthermore, crosstalk between bacteria and immune cells through exchange of bacterial and host metabolites has additionally revealed the importance of metabolism in determining the outcome of an infection [ 21 – 23 ]. Importantly, metabolism is not a static function; it dynamically changes to support cell growth and virulence specifically for each host and type of infection. Clinical strains of P . aeruginosa that infect pwCF have been shown to modify their metabolic preference to accommodate the specific nutrient composition of the airways [ 24 , 25 ]. Auxotrophy, specialized assimilation of carbon sources, secretion of high value metabolites and differential oxygen requirements of adapted clinical strains ensure appropriate functionality of the cell and support the phenotype requirements in the host [ 10 ]. However, it is still unclear if and how metabolic specialization directly contribute(s) to persistence [ 24 , 26 ]. Furthermore, the specific selective forces (for example, antibiotic treatment or the immune system) leading to metabolic specialization still remain uncharacterized. A few examples of laboratory studies in P . aeruginosa and Escherichia coli have suggested the involvement of specific metabolic mutations with changes in antibiotic susceptibility and virulence [ 27 – 31 ]. However, limited knowledge is available on their relevance in clinical isolates of P . aeruginosa during an infection. Moreover, the extent to which metabolic specialization per se provides a specific fitness advantage or if it is merely a downstream effect of accommodating other essential phenotypes still remains unknown. For example, it has been shown that overexpression of multidrug efflux pumps causing antibiotic resistance, can lead to rewired metabolism, thereby compensating for the associated fitness cost [ 32 ]. However, if such a mechanism is generalizable and whether it specifically contributes to persistence remains still unexplored. Importantly, previous metabolic characterizations of clinical strains were carried out on only a limited number of isolates and/or on bacterial cultures at one specific growth phase (exponential or stationary phase) lacking the dynamics of metabolic processes [ 24 , 33 , 34 ]. Because of technical challenges related to the complexity of dynamic metabolomic analysis and its interpretation, large-scale analyses of populations of clinical isolates that account for the inherent dynamics of metabolism have so far been lacking. Moreover, the effect of metabolic specialization on the host–pathogen interactions remains unclear, limiting the understanding of its contribution to persistence [ 2 , 35 ]. It is, therefore, crucial to systematically identify and characterize new mechanisms of metabolic specialization to build a comprehensive understanding of the contribution of metabolism to persistence. Such efforts will provide new understanding of treatment failure and unravel new pathogen vulnerabilities and therapeutic options, which are currently overshadowed by the focus on increasing antibiotic resistance.

Here, we identify and characterize molecular mechanisms of metabolic specialization occurring in clinical strains of P . aeruginosa associated with persistence in pwCF. We further show that these mechanisms have an impact on the relationship between the host and the pathogen. By analyzing, in detail, the metabolic and proteomic profiles of clinical strains from pwCF at different stages of within-host evolution, we identified distinct metabolic configurations characterized by CF-specific nutrient assimilation and secretion patterns. These changes in metabolism and proteome allocation are directly linked with mutations affecting key metabolic genes. We also characterize 1 specific mechanism of metabolic specialization involving the pyruvate dehydrogenase complex (PDHc), which is essential for the processive flux of pyruvate through central carbon metabolism. Surprisingly, recombinant strains containing single mutations in the PDHc show decreased infection capabilities and are associated with inflammation in an air–liquid interface (ALI) infection model system. These strains display a chronic infection phenotype and an increased secretion of pyruvate, which is an important scavenger of reactive oxygen species (ROS) and has known anti-inflammatory properties [ 36 – 38 ]. Importantly, we show that these mutations are widespread in clinical isolates of P . aeruginosa from different patients and infection scenarios. This suggests that metabolic specialization might be specifically selected for during the early stages of an infection to limit host-dependent inflammation. Altogether, these results provide a rationale for metabolic specialization during CF airway infections.

Strain collection for metabolomic analyses

To identify molecular mechanisms of metabolic specialization which modify the interactions between the host and the pathogen occurring during the infection of pwCF, we selected from our longitudinal collection of 474 P . aeruginosa clinical strains [ 39 ] pairs of early (*_E; first P . aeruginosa isolated from the person) and late (*_L) clinical isolates, longitudinally isolated from eight different pwCF ( Fig 1A ). Previous studies have shown that slow growth is an important phenotype associated with long term adaptive evolution to the CF airways, appearing within the first 2 to 3 years of the infection [ 9 ]. Therefore, we used growth rate as a proxy for within-host evolution. This allowed us to increase the odds of selecting strains presenting unexplored mechanisms of metabolic specialization. Accordingly, we investigated metabolic adaptation in a diverse collection of 16 clinical strains from 8 distinct pwCF ( Fig 1A ).

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(A) Growth rate (hour -1 ) of early (circles), late (triangles), and PAO1 (square) strains colored by their clone type. The fold change of growth rate from early to late is shown as bar chart on the right y axis and as number value at the base of each bar. (B) The maximal optical density (maxOD) of all isolates, grouped by early vs. late comparison. Statistical significance was calculated by unpaired Welch t test and indicated as *** ( p < 0.001). (C) Growth rate (hour -1 ) of each strain in M9 minimal media containing 20 mM of a single carbon source glucose (Glu), lactate (Lac), or succinate (Scc). The data underlying this figure can be found in S4 Data . maxOD, maximal optical density.

https://doi.org/10.1371/journal.pbio.3002781.g001

As by selection criteria, when grown in synthetic cystic fibrosis medium 2 (SCFM2) (which has been formulated to resemble the CF mucus composition [ 40 ]), early isolates showed a high growth rate, comparable with the reference strain, PAO1. Conversely, late isolates showed a 2.2- to 5.1-fold reduction in growth rate, in addition to a lower maximal optical density (paired t test, p = 0.002) ( Fig 1A and 1B ). Moreover, some late isolates were unable to grow in minimal medium in presence of single carbon sources such as glucose, lactate, or succinate, indicative of a metabolic constraint such as auxotrophy [ 41 ] ( Fig 1C ).

The genome of each isolate had been previously sequenced, and their genotype and phylogenetic relationship determined [ 39 ]. Specifically, each pair of early and late clinical isolates are phylogenetically related, share a common ancestor, and belong to the same “clone type” (defined as genomes sharing more than 10,000 SNPs) ( Fig 1A ). Each pwCF was colonized by a distinct clone type with evolutionary histories spanning between 1.4 and 7 years ( S1 Table ). On average, the difference in mutations between early and late strains was 46 ± 5 with the exception of strains of the DK36 clone type which show an increased number of mutations (402 mutation differences) due to hypermutation ( S1 Table ) [ 39 ]. As previously reported [ 42 ], each single isolate represents the most abundant representative of the population from a sputum sample. These selection criteria, therefore, allowed us to analyze isolates from different individuals with distinct clinical and evolutionary histories, rather than focusing on the heterogeneous population present in pwCF.

Within-host evolution selects for specialized metabotypes

To evaluate the degree of metabolic specialization in the clinical isolates, dynamic exo-metabolomics was performed on cells growing in SCFM2 ( S1 Data ). This approach allowed for analysis of the assimilation and secretion patterns of specific metabolites during the growth of each isolate, providing a dynamic profile of their metabolic activity. Principal component analysis (PCA), k-means clustering, and hierarchical cluster analysis (HCA) of the extracellular metabolomes revealed the presence of 3 distinct specialized metabolic configurations in late strains, hereafter defined as “adapted metabotypes” (DK15 and DK55 metabotype 1; DK12 metabotype 2; DK03, DK13, and DK36 metabotype 3). These metabotypes separate the late isolates from each respective early isolate and from those strains not exhibiting metabolic changes, hereafter referred to as “naïve metabotypes” ( Fig 2A ). Note that whereas PCA emphasizes the largest differences in the metabolomic profiles, the HCA clearly separates the early strains from the late strains, indicating a certain degree of metabolic specialization in all late isolates ( Fig 2B ). Indeed, both the hierarchy of assimilation (metabolite half-lives, OD 50 ) which represents the order of assimilation of the available carbon sources, and the secretion of metabolites differ between early and late isolates ( Fig 2C and 2D ). Of note, late strains of DK12, DK36, and DK55 secreted high amounts of pyruvate which is a key metabolite connecting glucose metabolism to the TCA cycle. Detailed information on the specific metabolic preferences of each metabotype is presented in S1 Text . Furthermore, the net balance between assimilated and secreted metabolites (total mM) varies between early and late strains ( Fig 2E ), which positively correlates with the lower biomass of the late isolates (Pearson’s r = 0.9; p = 0.0023) (Figs 1B and 2E ). In other words, a high percentage of assimilated carbon sources are catabolized in the late isolates and secreted back into the culture medium, thus limiting their availability for biomass production. This suggests either a specific metabolic configuration for the late isolates—the objective of which is not biomass accumulation—or an apparent inefficient metabolism.

(A) PCA showing separation of strains based on their time-resolved exo-metabolomes. Strains are shown as circles (early), triangles (late), and square (PAO1), with dashed arrows indicating the most notable trajectories from early to late, colored by clone type. Metabotypes were designated based on iterative k-means clustering analysis and HCA. See Material and methods for details. (B) HCA of the exo-metabolomics data, showing a general separation between early and late strains. Branches for early isolates are indicated in blue, late isolates in red and PAO1 in black. Accuracy of the HCA was tested by bootstrapping where gray values within the branches represent the % of bootstrap values for 10,000 replicates. (C) Assimilation hierarchies of the analyzed metabolites. Each symbol represents the half-life (OD 50 ) of a specific metabolite while each line connects the same metabolite for each pair of early and late isolates. Missing connecting curves indicates that either the strain did not assimilate the metabolite, or that the OD 50 was outside the analyzed assimilation window. (D) Secretion plots showing variations in the concentration of acetate, pyruvate, and formate (mM) relative to normalized growth (OD 600 ). Shaded areas indicate the 95% confidence intervals. (E) Table showing the total amount (mM) of carbon sources assimilated for each strain. Secreted metabolites were subtracted from the total to account for their excretion in the medium. Clone types are separated by gray boxes with “E” indicating early and “L” indicating late isolate. The data underlying this figure can be found in S1 and S4 Data. HCA, hierarchical cluster analysis; PCA, principal component analysis.

https://doi.org/10.1371/journal.pbio.3002781.g002

Changes in proteome allocation supports the metabolic specialization of clinical isolates

To test the hypothesis that the observed metabolic specialization is rooted in changes in expression of proteins involved in cellular metabolism, we analyzed the proteome of each clinical strain. To this end, we performed whole cell proteomics to compare protein expression profiles between PAO1, early, and late strains (comparisons being early versus PAO1, late versus PAO1, and late versus early). This allowed us to evaluate proteome changes in the clinical isolates relative to a laboratory reference strain (early versus PAO1, late versus PAO1) and to identify in the clinical isolates possible molecular mechanism(s) underpinning the metabolic specialization we observed (late versus early). Of the 2,061 proteins identified, 740 were differentially expressed in at least 1 comparison (early versus PAO1 or late versus early) ( S2 Data ). Similar to the metabolomic analysis, when comparing the normalized expression profile of all identified proteins, PCA readily separated the proteome of the late isolates from that of the early isolates, suggesting that changes in metabolite profiles might be rooted in the proteome ( Fig 3A and 3B ). Most of the early and some of the late proteomes co-localized with the reference strain PAO1, indicating little or no changes in proteome allocation ( Fig 3A and 3B ). However, the proteome of the late isolates of lineages DK12, DK17, DK36, and DK55 separated from the respective early strains to form a cluster of adapted proteomes ( Fig 3A and 3B ). Notably, the DK03 strains formed an independent cluster, indicative of a lineage-specific proteomic signature (Figs 1A , 3A , and 3B ).

(A) PCA showing separation of strains based on their mid-exponential proteomes. Strains are shown as circles (early), triangles (late), and square (PAO1), with dashed arrows indicating most notable trajectories from early to late, colored by clone type. (B) HCA of the proteomic data containing 2,061 proteins identified, showing a general separation between early and late strains. Branches for early isolates are indicated in blue, late isolates in red, and PAO1 in black. Accuracy of the HCA was tested by bootstrapping where gray values within the branches represent the % of values for 10,000 replicates. (C) HCA of the 235 differentially expressed proteins when comparing late vs. early strains within the COG categories related to metabolism. Proteomes are represented by dashed lines for “cluster A” and continuous lines for “cluster B.” Accuracy of the HCA was tested by bootstrapping where gray values within the branches represent the % of bootstrap values for 10,000 replicates. (D) Parallel plots of the number of DE proteins within the COG categories related to metabolism in cluster A (blue, shaded) and B (red, transparent). For each category, the mean of each cluster is indicated. The difference in the total number of DE proteins between clusters when matching by category was computed by two-tailed paired t test where ** indicates p = 0.0047. The data underlying this figure can be found in S2 Data . COG, Clusters of Orthologous Groups; DE, differentially expressed; HCA, hierarchical cluster analysis; PCA, principal component analysis.

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A similar relationship between the proteomes is obtained through HCA, specifically when considering the 235 differentially expressed proteins (late versus early comparison) belonging to the Clusters of Orthologous Groups (COG) categories involved in metabolism ( Fig 3C ). When comparing differentially expressed proteins between late versus early strains, 2 clusters of proteomes are identified. Cluster A represents proteomic changes in clone types without any apparent metabolic specialization. In contrast, cluster B represents proteomic changes in strains which underwent metabolic specialization in the adaptive evolution process from early to late (DK12, DK36, and DK55). This result supports our hypothesis that metabolic specialization is likely rooted in changes in the expression of proteins involved in cellular metabolism ( Fig 3C ). Notably, cluster B is characterized by a significantly higher number of differentially expressed proteins in the COG categories involved in metabolism relative to cluster A (two-tailed paired t test p = 0.0047) (Figs 3D and S1A ). Similarly, several categories of proteins involved in the metabolism of amino acids, fatty acids, and sugars are statistically enriched in early and late strains ( S1B Fig ) providing a molecular explanation for the altered hierarchy of assimilation of the carbon sources and the reduced growth rate of the adapted metabotypes ( Fig 2C and 2D ). Detailed information on the convergent expression at the pathway level of metabolic proteins is presented in S1 Text ( S2 Fig ).

Although late strains of DK12, DK36, and DK55 belong to different metabotypes, their proteomes move in the same direction ( Fig 3A–3C ). This indicates that largely similar proteomes can sustain distinct metabolic configurations which ultimately depend on the metabolic fluxes thorough specific pathways and on their regulation. We note that, in the case of the DK17 strains comparison (cluster B) ( Fig 3A–3C ), although our metabolomic analysis did not detect any major metabolic rewiring ( Fig 2A and 2B ), proteome reorganization could be related to other metabolic processes which were not analyzed in our study.

Changes in virulence traits during within patient evolution

To evaluate the relationship between metabolic specialization and virulence, we analyzed which differentially expressed proteins were enriched when comparing late versus early isolates. Interestingly, most of the changes (based on their KEGG and GO categories) were related to adaptation to the infection environment, redox balance, and virulence (Figs 4A and S1B ). For example, most of the lineages showed expression changes in phenazine biosynthesis (KEGG) and secondary metabolite(s) biosynthetic process (GO), both of which are deeply involved in redox-balance, cell homeostasis, metabolism, and virulence [ 43 , 44 ].

(A) Enrichment analysis for KEGG and GO terms by the specific comparisons (Early vs. PAO1; Late vs. PAO1; Late vs. Early). Icons represent individual lineages and are colored by fold-enrichment. (B) Differential expression of virulence factors in early clinical strains vs. PAO1. Only differentially expressed proteins are represented and are colored by clone type. (C) HCA of differentially expressed proteins of virulence factors in late vs. early clinical strains. The data underlying this figure can be found in S2 Data . HCA, hierarchical cluster analysis.

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Several changes in protein expression are already apparent in early strains relative to PAO1 indicating a different response of clinical strains to the airway-like conditions accompanying growth in SCFM2 ( Fig 4A ). Specifically, early strains show increased expression of proteins involved in alginate production, hydrogen cyanide (HCN), phenazine biosynthesis, and PQS (the P seudomonas q uinolone s ignal), and decreased expression of proteins involved in flagella biogenesis, lipopolysaccharide (LPS) O-antigen metabolism, and pyoverdine production, which are all hallmarks of an acute infection phenotype ( Fig 4B ). Early stage colonization and acute infection are thought to require expression of several virulence factors promoting host tissue injury and immune response impairment [ 45 ]. By contrast, when comparing late versus early strains, the adapted metabotypes—and specifically strains belonging to cluster B (DK12, DK17, DK36, and DK55)—show convergent up-regulation of proteins related to alginate, phenazine, PQS, and the type VI secretion system (T6SS), suggesting that metabolism plays an active role in regulating virulence and the chronic infection phenotype (Figs 4C and S4 ). Reduced virulence and cytotoxicity are advantageous for long-term infection and the establishment of a persistent infection [ 46 ]. Interestingly, the DK12 late strain also shows lower expression (-3-fold) of the T3SS toxin, ExoT, which is known to play a role in preventing phagocytosis, in the induction of cytoskeletal reorganization, and in host cell apoptosis [ 16 ]. Overall, the identified pattern of proteomic changes suggests that early isolates show reduced expression of virulence factors relative to PAO1, with an even greater reduction being associated with the late isolates. This is consistent with their persistence during CF airway infections [ 46 ].

aceE and aceF mutations leads to metabolic specialization and impaired virulence

From the previously published genome sequences [ 39 ], we searched for mutations in the genome of each clinical isolate which might explain the secretion of pyruvate and the metabolic specialization of late isolates. In particular, we focused on those genes encoding the phospho enol pyruvate-pyruvate-oxaloacetate node ( S2 Fig ), since such mutations might be expected to lead to elevated pyruvate secretion. This led to the identification of mutations in genes encoding the PDHc, aceE and aceF , in late strains of DK12 and DK36, respectively. The PDHc catalyzes the conversion of pyruvate to acetyl-CoA, connecting sugar metabolism with the TCA cycle ( S2 Fig ). Interestingly, the aceE and aceF genes are candidate pathoadaptive genes, suggesting positive selection for such mutations in pwCF [ 39 ]. In our strain collection of 474 isolates from 34 pwCF [ 39 ], we identified the presence of 18 different and independent aceE / aceF mutations (4 indels and 14 SNPs, of which 3 were synonymous) in 18 separate lineages. These lineages were present in more than half of the patients, supporting the hypothesis that modulation of the PDHc might be selected for during airway infections ( S5 Fig ). Moreover, in situ expression of the aceE and aceF genes is reduced in sputum samples collected from chronically infected pwCF [ 47 ]. This suggests an undescribed role of the PDHc activity which connects central carbon metabolism and pathogenicity during infection.

The DK12_L and DK36_L strains contained a +TCCC duplication at position 813 in aceF and a T→C transition at position 551 in aceE , respectively ( Fig 5A ). To evaluate the contribution of these mutations to the bacterial phenotype, independent of the underlying historical contingency of the clinical strains, we generated recombinant PAO1 derivative strains containing the same mutations. The aceF mutation leads to a frameshift starting from Lys 273, whereas the aceE mutation leads to a Phe→Ser amino acid change at position 184. The recombinant aceE and aceF mutant strains show reduced growth rate ( Fig 5B ) and increased secretion of pyruvate (maximal pyruvate accumulation by the aceE mutant was 0.5 mM, whereas for aceF , the value was 13 mM) ( Fig 5C ). By comparison, maximal pyruvate accumulation by the DK12_L and DK36_L late strains was 8.5 mM and 4.6 mM, respectively ( Fig 2D ). The PAO1-derived aceE mutant strain clearly has a milder phenotype (a smaller reduction in growth rate and lower pyruvate secretion) compared with the aceF mutant strain, suggesting partial functionality of the PDHc. As previously reported, in P . aeruginosa , acetate is catabolized into acetyl-CoA, and can, therefore, metabolically complement the growth defects of PDH mutants [ 31 , 48 , 49 ]. Indeed, we were able to fully restore the growth phenotype of the aceE strain by supplementing the bacterial culture with acetate to replenish the pool of acetyl-CoA, whereas this only partially restored growth in the aceF mutant strain ( Fig 5B ). A similar result was obtained by expressing a wild-type copy of the aceE gene under its native promoter which complements the growth defect of the strain ( S6A Fig ). These data confirm that the frameshift in aceF has a much greater impact on PDHc activity than the SNP in aceE . Surprisingly, in laboratory conditions, phenotypes such as biofilm formation, motility, redox susceptibility, pyoverdine production, and antibiotic susceptibility showed no statistical difference between the PAO1 wild-type strain and the aceE and aceF mutant strains, except for a slight decrease in twitching motility and mildly increased tobramycin susceptibility ( S6 Fig ).

(A) Schematic of the aceE and aceF genes including location and type of mutation found in the DK36 ( aceE ) and DK12 ( aceF ) late strains. (B) Growth rate (hour -1 ) in SCFM2 (blue) and SCFM2 supplemented with 5 mM acetate (red) for PAO1, aceE and aceF mutant strains, and DK12 and DK36 late clinical isolates. Bars indicate mean ± SEM, with icons representing biological replicates. Statistical significance is assessed by two-tailed unpaired parametric Welch t test and indicated as * ( p < 0.05), ** ( p < 0.01), or **** ( p < 0.0001). (C) Pyruvate secretion (mM) for PAO1 wt (yellow), aceE (cyan), and aceF (magenta) mutant strains over 24 h. Icons represent biological replicates. (D) PCA and HCA of whole-cell proteomics for PAO1 wt and aceE and aceF mutant strains. Filled icons indicate without (−) and unfilled icons indicate with (+) 5 mM acetate. E Number of differentially expressed proteins for mutant strain vs. PAO1 wt comparisons: in orange in absence of acetate (+/− mutation) and in green in presence of acetate (+/− mutation (ace)). In purple, the number of differentially expressed proteins for either the PAO1 wt or the aceE or aceF mutant strain in presence vs. absence of acetate (+/− acetate). (F) Metabolic map of enzymes related to pyruvate and acetyl-CoA metabolism for mutant strains vs. PAO1 in absence (left) or presence (right) of acetate. Reactions are colored by their pathway if the underlining enzyme is differentially expressed (dashed if down-regulated or continuous if up-regulated). Enzymes responsible for each reaction are indicated by numbered circles. For details on individual enzymes, see S7 Fig . (G) Differentially expressed proteins involved in virulence in aceE and aceF mutant strains vs. PAO1 in absence of acetate. Jittered icons indicate specific proteins that are up-regulated (red plus) or down-regulated (blue circle). The data underlying this figure can be found in S3 and S4 Data. HCA, hierarchical cluster analysis; PCA, principal component analysis; SCFM2, synthetic cystic fibrosis medium 2.

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To characterize the effect of the aceE and aceF mutations more broadly, we performed whole cell proteomics on the aceE and aceF recombinant strains (and on the PAO1 progenitor) in presence and absence of acetate. In total, we quantified 3,246 proteins and identified 449 as differentially expressed in at least 1 comparison of either the mutant versus wild-type or of the mutant in presence versus absence of acetate ( S3 Data ). In the absence of acetate, the proteome of the aceE and aceF mutants was clearly different compared with that of the wild-type progenitor, PAO1 ( Fig 5D ). PC1, which encompasses >50% of the variance in the data set, separated the aceF mutant proteome from that of the wild type, whereas the proteome of the aceE mutant was more similar to that of the wild type, separating along PC2 (accounting for just 20% of the total variance in the data set, Fig 5D ). As previously noted, supplementation of the growth medium with acetate altered both the aceE and aceF mutant proteomes, with both moving closer to that of the wild type ( Fig 5D ). This effect was greater in the aceF mutant, where growth in acetate decreased the number of up-regulated proteins ( cf . PAO1) by more than half (from 301 to 126) ( Fig 5E ). Unsurprisingly, the expression of several enzymes involved in pyruvate and acetyl-CoA metabolism (e.g., lactate dehydrogenase (41), acetyl-coenzyme A synthetase (6), citrate synthase (28)) return to wild-type levels following the addition of acetate (Figs 5F and S7A ). Similarly, for the clinical isolates DK12_L and DK36_L, the categories of proteins involved in amino acid/lipid metabolism and energy conversion presented the largest number of differentially expressed proteins ( S7B Fig ). Moreover, proteins involved in terpene, propionate, isoprenoid and branched-chain amino acid metabolism (all of which are directly connected to pyruvate and acetyl-CoA metabolism) were statistically enriched in the PAO1-derived aceF mutant, indicating a reorganization of both central and peripherical pathways to cope with reduced synthesis of acetyl-CoA ( S7C Fig ). The PAO1-derived aceE mutant also shows an extreme down-regulation of the oxygen-sensing transcriptional regulator Dnr, which is known to be required for denitrification but also regulates acetate metabolism and the T6SS [ 50 – 52 ]. This mirrors the down-regulation seen specifically for cluster B of the clinical isolates ( Fig 3C ), which may suggest a role for Dnr down-regulation in metabolic specialization ( S7D Fig ).

Importantly, the aceE and aceF mutants showed increased expression of proteins involved in alginate production and T6SS, as well as decreased expression of proteins involved in the T3SS, including the secreted factor ExoT (in strains aceF and DK12_L) ( Fig 5G and S3 Data ). This indicates that the aceE and aceF mutations contribute to the expression profile of virulence determinants shown by the late clinical isolates of DK12 and DK36 (Figs 4 and 5G and S3 Data ).

Pyruvate dehydrogenase mutations modulate pathogenicity in ALI culture infections

To test whether the secretion of pyruvate and the reduced expression of virulence determinants in the PAO1-derived aceF and aceE mutants lead to reduced infectivity, we performed host-bacteria infections using an ALI infection model system. This model system is composed of mucociliated differentiated airway epithelial cells which represent the airways and provides insights into the host response including epithelial damage and recruitment of the immune system. Overall, the aceF mutant displayed a broad suppression of virulence, including reduced epithelium damage and innate immune recognition during the infection ( Fig 6 ). The transepithelial electrical resistance (TEER) which quantifies the integrity and permeability of the epithelial layer, LDH release which quantifies the epithelium cellular damage, and the bacterial count which quantifies the growth and penetration of the bacteria through the epithelium to the basolateral side of the ALI transwells, all showed reduced values after 14 h of infection by the aceF mutant, compared with the PAO1 progenitor ( Fig 6A ). This is in line with the behavior of a Δ pscC mutant defective in T3SS, which also shows severely reduced virulence ( Fig 6A ). By contrast, the aceE mutant elicited epithelial damage similar to PAO1, indicating that the mutation does not influence bacterial penetration ( Fig 6A ). This is consistent with the proteomic analysis showing no significant differential expression in this strain of proteins involved in the T3SS, which is the leading cause of epithelial damage ( Fig 5G ). However, both the aceE and aceF mutants, together with the Δ pscC mutant, elicit lower interleukin 8 (IL-8) release, a cytokine that is secreted by epithelial cells and is necessary for the recruitment of the immune system at the site of infection ( Fig 6B ). These results are corroborated by confocal microscopy data, which show a similar colonization profile on the airway epithelium of the wild-type and aceE mutant, and of the aceF and Δ pscC mutant, respectively. It is worth noting that nuclei shedding, seen as nuclei (in blue) being pushed to the apical side of the epithelium (in red), was observed only for PAO1 and not for the aceE mutant ( Fig 6C ), which may suggest a slight reduction in the aggressiveness of the infection. This mechanism of reduced cellular damage and reduced inflammation seems not to depend on the growth defect associated with the aceF mutant, since the Δ pscC mutant shows comparable aggressiveness to the aceF mutant but a wild-type–like growth rate.

(A) Mean ± SEM of TEER (Ω∙cm 2 ), LDH release, and CFUs in the apical and basolateral ALI compartments after 14 h of infection in fully differentiated BCi-NS1.1 cells. (B) IL-8 release into the basolateral media indicating inflammation caused by the invading bacteria. Icons represent each biological replicates. Mock represents un-infected control cells. Statistical significance was determined by two-way ANOVA for TEER and CFU measurements and one-way ANOVA for LDH and IL-8 measurements and indicated as * ( p < 0.05), ** ( p < 0.01), *** ( p < 0.001), and **** ( p < 0.0001). (C) Confocal images of ALI transwells following infection with P . aeruginosa in green (GFP), epithelium in red (Phalloidin), and nuclei in blue (To-pro). Scale bar = 40 μm. The data underlying this figure can be found in S4 Data . ALI, air–liquid interface; CFU, colony-forming unit; TEER, transepithelial electrical resistance.

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Importantly, in our longitudinal collection of clinical P . aeruginosa strains, 18 different lineages (specific clone types obtained from different pwCF; 89 isolates) harbored mutations in the aceE and aceF genes and/or carried additional mutations in T3SS genes [ 39 ]. In 9 lineages, PDHc mutations are present alone or anticipate additional T3SS mutations. In 7 lineages, T3SS mutations precede PDHc mutation or both systems are mutated at the same time (4 lineages) already in the first isolate of the lineage ( S8 Fig ).

Altogether, these results suggest a role for the aceE and aceF mutations in persistent infections and a link between pyruvate metabolism and virulence.

Metabolism constitutes a central process which is specifically regulated and optimized according to the nutritional resources available and the phenotypic requirements of the cell. Several studies have shown that metabolic specialization occurs in clinical isolates of P . aeruginosa [ 5 , 10 , 24 , 25 , 53 ]. However, little is known about whether this metabolic specialization is simply an accommodation for other significant fitness gains, i.e., metabolic compensation for phenotypes with high fitness cost, or whether it is specifically selected to enable increased persistence in the host.

By analyzing a range of longitudinal clinical isolates of P . aeruginosa , we found that all infection lineages underwent a substantial metabolic rewiring. This includes CF-specific patterns of carbon source (amino acids, sugars, and organic acids) assimilation and fermentation (acetate secretion). These metabolic changes, in turn, are associated with changes in proteome allocation potentially driven by mutations in metabolic genes. Adapted strains harboring mutations in genes ( aceE and aceF ) encoding the PDHc are characterized by a chronic-like phenotype (T3SS down-regulation, up-regulation of the T6SS, phenazines, the alginate biosynthetic pathway, and slow growth) suggesting that these mutations confer a selective advantage in the host [ 4 ]. Moreover, adapted strains secrete large amounts of pyruvate, which is an important mediator of inflammation in the host [ 37 ]. Reconstitution of clinical aceE and aceF mutations in a defined laboratory strain (PAO1) revealed that these mutations elicit reduced stimulation of the immune system and reduced epithelial layer penetration when compared with the isogenic wild-type progenitor strain (PAO1), which exhibits a highly virulent phenotype similar to that associated with acute infection. Importantly, this phenotype is only evident in the ALI infection model system and not in standard laboratory conditions since virulence and pathogenicity are only elicited in the presence of the host. These findings are consistent with the notion that strains with reduced virulence are selected during within-patient evolution in pwCF to reduce host tissue damage and limit the activity of the immune system [ 54 ]. Such reduced aggressiveness in the aceF mutant appears to be mediated by lower expression of the T3SS effector, ExoT, which induces apoptosis in host cells [ 16 , 17 ]. This is consistent with previous studies showing that mutations in the PDHc-encoding genes lead to repression of T3S in P . aeruginosa [ 31 , 55 ]. Mutations in the enzyme iso citrate lyase, involved in the glyoxylate shunt of the TCA cycle, have also been associated with reduced T3SS activity, suggestive of a direct correlation of metabolism to virulence [ 29 ]. This is the case for the DK15 late isolate in our collection, which shows reduced expression of iso citrate lyase together with suppression of the ExoT exotoxin. Given the tendency in our collection for aceE and aceF mutations to precede mutations in the T3SS-encoding genes, it is interesting to consider if mutations in the PDHc and/or modified PDHc activity could play a broader role in promoting a persistence phenotype during evolution in the host by reducing T3SS-dependent virulence. However, further investigation would be needed to establish such a mechanism. Nevertheless, the diversity of aceE and aceF mutations indicate that the activity of the PDHc can be fine-tuned accordingly to the specific host environment and consequently lead to different infection outcomes. Indeed, while the aceE mutant (carrying just an SNP) shows comparable aggressiveness to PAO1 but reduced recruitment of the immune system (IL-8 secretion), the aceF mutant (carrying a frameshifting indel) combines the effect of the reduced aggressiveness and that of the reduced immune recruitment.

It is worth noting that pyruvate, in addition to its crucial role as intermediate of central carbon metabolism, is a potent scavenger of ROS which use has been suggested as an anti-inflammatory treatment in several diseases [ 36 , 56 ]. Due to the sustained activation of neutrophils and other defects in the homeostatic processes of pulmonary epithelia in CF airways, an abnormal flux of ROS is present which can be alleviated by the bacterial pyruvate secretion [ 57 ]. During exacerbation events, the concentration of pyruvate increases (0.3 mM) which might counterbalance the increased inflammation [ 58 ]. Indeed, it has been shown that treatment of human lung epithelial cells with pyruvate reduces the inflammatory response by reducing the production of IL-8 [ 59 , 60 ]. It induces a potent shutdown of the response to LPS in dendritic cells [ 61 ] and reduces alveolar tissue destruction in a COPD model system [ 62 ]. Similarly, inhibition of the PDHc and redirection of the pyruvate flux has been suggested both as a potential anti-inflammation therapy for chronic metabolic diseases [ 63 ] and as a cancer treatment [ 64 ]. Moreover, fine-tuning of the flux through the mitochondrial PDHc in macrophages and dendritic cells is key for regulating their polarization and thus the balancing between pro- and anti-inflammatory responses [ 38 , 65 ]. LPS-induced polarization of macrophages is prevented by pharmacological inhibition of pyruvate import into the mitochondria [ 66 ]. Moreover, flux through the PDHc, possibly altered by the pyruvate secreted by the aceE and aceF mutants, controls the production of the antibacterial immunometabolite, itaconate, by macrophages [ 67 – 70 ]. Therefore, the reduced recognition from the epithelial layer of the aceE and aceF mutants in this study may depend on the secretion of bacterial pyruvate, which is known to mediate an important inflammatory response in both the epithelium and the immune cells [ 37 , 38 , 56 ].

The metabolic specialization and specifically the PDHc dysregulation seen in the clinical isolates may, therefore, serve as a mechanism for ensuring active pyruvate secretion and suppression of the immune system by a mechanism of cross-feeding. The evolutionary benefit of such a mechanism of host–pathogen crosstalk during persistent infections may explain why, in many cases, ROS produced in the CF airways appear to select for metabolic specialization in P . aeruginosa [ 71 ]. Moreover, the consequent phenotypic change of metabolically adapted strains limits epithelial damage to avoid any further inflammation. Indeed, changes in the T3SS, the T6SS, phenazines, alginate biosynthesis, and growth physiology are all hallmarks of a transition from an acute (high virulence) to a chronic (low virulence) phenotype which are specifically selected for during colonization of pwCF [ 4 , 54 ]. These combined mechanisms of cross-feeding and reduced virulence in turn promote persistence in the host by a change in the infection microenvironment which can benefit the entire population of infecting bacteria. Moreover, they can provide a rationale for the broad accumulation and maintenance of PDHc mutations in P . aeruginosa clinical CF isolates and the reduced expression of the PDHc in sputum samples [ 39 , 47 ]. This suggests a highly complex and near universal role of the pyruvate node in regulation of both host and pathogen (and their respective functions in vivo) through a conserved central metabolite. Such a mechanism might be engaged years before the development of antibiotic resistance, because of its high relevance for the establishment of a chronic phenotype.

The mechanism of metabolic specialization presented here is only one of many different mechanisms that P . aeruginosa might use for long-term survival in the host. However, the connection between metabolic behaviors and virulence phenotype (acute or chronic) are still poorly characterized, limiting our capacity to design treatments that counteract such processes. While our work focuses on a limited collection of clinical strains, a systematic analysis of the heterogeneous populations infecting pwCF is necessary to fully evaluate the role of metabolic specialization in persistence. Designing new methods, e.g., for high-throughput metabolomics analyses under in vivo-like conditions, such as those recreated by ALI or organoid cultures [ 71 ] which go beyond the limitation of laboratory conditions, will allow the characterization of new persistence mechanisms by clarifying the relationship between the host, the pathogen, and other microbial species of the host microbiota. Indeed, laboratory conditions and classical phenotype screening approaches are inadequate to unravel complex host–pathogen interactions. Still, it is important to note that the pyruvate secretion and the associated phenotypic changes described here might be beneficial not only for the PDHc mutated population but also for other nearby “wild-type” cells, since they alleviate the airway inflammation and limit the recruitment of the immune system. Importantly, metabolic specialization might be equally relevant in other persistent infections such as those caused by Escherichia coli , Staphylococcus aureus , and Mycobacterium tuberculosis which are similarly threatening and difficult to tackle [ 2 ]. Finally, understanding specific mechanisms linking metabolism, energy balance and virulence, and most importantly how the relationship between the host and the pathogen changes during an infection, could provide new opportunities for more efficient and/or complimentary treatments, beyond the classical antibiotic treatment, which are greatly needed.

Strains and media

The collection of clinical isolates used in this study is a subset of the collection published in Marvig and colleagues and Bartell and colleagues [ 9 , 39 ]. The local ethics committee at the Capital Region of Denmark (Region Hovedstaden) approved the use of the stored P . aeruginosa isolates (registration number H-21078844). For information about specific clinical isolates, see S1 Table .

For construction of aceE , aceF , and Δ pscC mutant strains, derivatives of pACRISPR were constructed with the Uracil Specific Excision Reagent (USER) cloning [ 72 ]. The plasmids and primers used are listed in S1 and S2 Tables, respectively. Target DNA fragments were amplified from gDNA of clinical strains DK12 Late and DK36 Late using the Phusion U polymerase kit (Thermo Fisher Scientific, United States of America). For the Δ pscC mutant strain primers were designed to create matching short overlaps between the DNA fragments that should be stitched together in the final plasmid. The resulting products were treated with FastDigest DpnI enzyme (Thermo Fisher Scientific, USA) and ligated with the USER Enzyme (New England Biolabs, USA). PCR was performed on random E . coli DH5alpha colonies using OneTaq 2X Master Mix (New England Biolabs, USA) to confirm the correct insertions and further sequenced with Sanger method (Eurofins Scientific, Luxembourg). aceE , aceF , and Δ pscC mutations in the genome of P . aeruginosa PAO1 were introduced using the previously developed CRISPR-Cas9 system and following the indicated protocol [ 73 ] with the addition of 0.2% arabinose in the growth medium during transformation. To confirm the presence of desired mutations, PCR fragment of the genome around the mutations was amplified and sequenced with Sanger sequencing (Eurofins Scientific, Luxembourg). To perform confocal microscopy analyses, strains were tagged with GFP using 4 parental mating using a mini-Tn7 delivery method [ 74 ] but for strains aceE and aceF with the additional supplementation of 5 mM acetate to ensure growth of PDHc dysregulated target strains. Transformants were identified by green fluorescence and validated by comparing growth rate to the untagged target strain.

To complement the mutation in the PDH ( aceE gene), we constructed a complementation strain aceE(rev) containing a wild-type copy of the aceE gene under the control of its native promoter and integrated it into the genome of the aceE mutant using the mini-Tn7 delivery method [ 74 ] as above. The plasmid pIP281 carrying the aceE gene and the Tn7 system was assembled with USER cloning [ 72 ]. The plasmids and primers used are listed in S1 and S2 Tables, respectively.

Bacteria were grown in Synthetic Cystic Fibrosis Media 2 (SCFM2) [ 75 ]. To reduce viscosity and allow for HPLC analysis, DNA and mucins were excluded [ 76 ]. Cultures were grown at 37°C and 250 rpm.

Dynamic exo-metabolomics analyses

Sampling of supernatants was performed in 96-well deep well plates (Cat. No. 0030502302; Eppendorf, Hamburg, Germany) with an air:liquid ratio of 1:1 using a high-throughput dilution-based growth method as previously described [ 77 ]. Immediately before sampling, OD 600 of the cultures were measured in a Synergy MX microtiter plate reader (BioTek Instruments, Winooski, Vermont, USA). Supernatants were stored at −80°C until HPLC-analysis. For organic acids and sugars (glucose, lactate, formate, acetate, and pyruvate), a Dionex Ultimate 3000 system (Thermo Scientific, Waltham, USA) with a HPx87H ion exclusion column (125–0140, Aminex, Dublin, Ireland), equipped with a guard column (125–0129, BioRad, Hercules, California, USA) and guard column holder (125–0131, BioRad, Hercules, California, USA) was used. Samples were injected with an injection volume of 20 μl and eluted, using a 5 mM H 2 SO 4 mobile phase, at an isocratic flow of 0.6 ml min −1 at 45°C for 30 min. Pyruvate was analyzed by UV detection at a wavelength of 210 nm, using a System Gold 166 UV-detector (Beckman Coulter, Brea, USA), while the rest of the metabolites were analyzed by RI detection, using a Smartline RI detector 2300 (KNAUER Wissenschaftliche Geräte, Berlin, Germany). For amino acids (aspartic acid, glutamic acid, serine, histidine, glycine, threonine, arginine, alanine, tyrosine, valine, phenylalanine, isoleucine, leucine, lysine, and proline), 20 μl of the thawed sample was diluted 1:10 by mixing with 80 μl Ultrapure MilliQ water and 100 μl of internal standard (20 μg/ml 2-aminobutyric acid and sarcosine) before injection of 56.5 μl into the instrument. Prior to injection into the column, derivatization of amino acids was performed in the HPLC-instrument by automatic mixing with the following eluents: (i) 0.5% (v/v) 3-mercaptopropionic acid in borate buffer 0.4 M at pH 10.2; (ii) 120 mM iodoacetic acid in 140 mM NaOH; (iii) OPA reagent (10 mg/ml o -phtalaldehyde and 3-mercaptopropionic acid in 0.4 M borate buffer); (iv) FMOC reagent (2.5 mg/ml 9-fluorenylmethyl chloroformate in acetonitrile); and (v) buffer A (40 mM Na 2 HPO 4 , 0.02% (w/v) NaN 3 at pH = 7). Following derivatization, the samples were separated isocratically on a Dionex Ultimater 3000 HPLC with fluorescence detector (Thermo Scientific, Waltham, USA) through a Gemini C18 column (00F-4439-E0, Phenomenex, Værløse, Denmark) equipped with a SecurityGuard Gemini C18 guard column (AJ0-7597, Phenomenex, Værløse, Denmark) with 5 mM H 2 SO 4 at a flowrate of 1 ml min −1 at 37°C for 31 min. Amino acids were detected using an UltiMate 3000 Fluorescence variable wavelength UV detector (FLD-3400RS, Waltham, Massachusetts, USA).

Proteomic analyses

To maintain a 1:1 air:liquid ratio, as in the metabolomic analysis, 25 ml SCFM2 cultures were inoculated from ON cultures in 50 ml Falcon tubes at OD 600 of 0.05 and then incubated in an orbital shaker at 37°C and 250 rpm until sampling by centrifugation at mid-exponential phase. Five biological replicates were analyzed for PAO1 and the clinical isolates while 3 biological replicates for the PAO1 derivative strains aceE and aceF both in presence and absence of 5 mM acetate. Pellets were washed twice with PBS and stored at −80°C until protein extraction. No specific preparation or enrichments were performed to identify secreted proteins. For experiment with clinical isolates, protein extraction was done in Lysis buffer A (100 mM Tris, 50 mM NaCl, 1 mM tris(2-carboxyethyl)phosphine (TCEP), 10% glycerol, pH = 7.5) with cOmplete Mini protease inhibitor (Roche) by sonication at amplitude 10 for 3 × 10 s cycles with 20 s cooling between, followed by acetone precipitation and resuspension in Lysis buffer B (6 M Guanidinium hydrochloride, 5 mM TCEP, 10 mM chloroacetamide, 100 mM Tris-HCl, pH = 8.5). For experiment with PAO1 derivative mutant strains, protein extraction was done in Lysis buffer B by bead beating with 3-mm zirconium oxide beads at 99°C for 5 min in a Tissuelyzer (Retsch, MM 400) at 25 Hz, then boiled, still at 99°C, in heat block (Eppendorf Thermomixer C) for 10 min while shaking/mixing at 2,000 rpm. In both cases, protein concentrations were determined by micro BCA Protein Assay Kit (Thermo Scientific, prod #23235) and 100 μg protein was used for trypsin digest in Trypsin/Lys-C Buffer (0.1 μg/μl trypsin, 50 mM Ammonium Bicarbonate). The reaction was stopped by addition of 10 μl 10% TFA and samples were desalted by stagetipping with C18 filter plugs (Solaμ HRP 2 mg/1 ml 96-well plate, Thermo Scientific). Peptide samples were stored in 40 μl of 0.1% formic acid at 4°C until LC-MS analysis.

For the experiment with clinical isolates, LC-MS/MS was carried out using a CapLC system (Thermo Fisher Scientific, Waltham, USA) coupled to an Orbitrap Q-exactive HF-X mass spectrometer (Thermo Fisher Scientific, Waltham, USA). The first samples were captured at a flow of 10 μl/min on a precolumn (μ-pre-column C18 PepMap 100, 5 μm, 100 Å) and then at a flow of 1.2 μl/min. Peptides were separated on a 15 cm C18 easy spray column (PepMap RSLC C18 2 μm, 100Å, 150 μm × 15 cm). The applied gradient went from 4% acetonitrile in water to 76% over a total of 60 min. MS-level scans were performed with Orbitrap resolution set to 60,000; AGC Target 3.0e6; maximum injection time 50 ms; intensity threshold 5.0e3; dynamic exclusion 25 s. Data-dependent MS2 selection was performed in Top 20 Speed mode with HCD collision energy set to 28% (AGC target 1.0e4, maximum injection time 22 ms, Isolation window 1.2 m/z). For the experiment with the aceE and aceF mutants, peptides were loaded onto a 2 cm C18 trap column (Thermo Fisher 164946), connected in-line to a 15 cm C18 reverse-phase analytical column (Thermo EasySpray ES904) using 100% Buffer A (0.1% formic acid in water) at 750 bar, using the Thermo EasyLC 1200 HPLC system, and the column oven operating at 30°C. Peptides were eluted over a 70 min gradient ranging from 10% to 60% of 80% acetonitrile, 0.1% formic acid at 250 nL/min, and the Orbitrap Exploris instrument (Thermo Fisher Scientific) was run in DIA mode with FAIMS Pro Interface (Thermo Fisher Scientific) with CV of −45 V. Full MS spectra were collected at a resolution of 120,000, with an AGC target of 300% or maximum injection time set to “auto” and a scan range of 400 to 1,000 m/z. The MS2 spectra were obtained in DIA mode in the orbitrap operating at a resolution of 60.000, with an AGC target 1,000% or maximum injection time set to “auto,” and a normalized HCD collision energy of 32. The isolation window was set to 6 m/z with a 1 m/z overlap and window placement on. Each DIA experiment covered a range of 200 m/z resulting in 3 DIA experiments (400 to 600 m/z, 600 to 800 m/z, and 800 to 1,000 m/z). Between the DIA experiments a full MS scan is performed. MS performance was verified for consistency by running complex cell lysate quality control standards, and chromatography was monitored to check for reproducibility.

Phenotypic characterizations

Growth curves were performed by inoculating 1 μl of overnight culture in 149 μl of media, using 96-well microtiter plates (Cat. No. 650001; Greiner Bio-One, Kremsmünster, Austria), covered with plate seals (Ref. 4306311, Thermo Fisher Scientific, United Kingdom) and incubated at 37°C and 250 rpm in a BioTek ELx808 Absorbance Microtiter Reader (BioTek Instruments, Winooski, Vermont, USA) for 24 to 48 h. Antibiotic MICs were determined by microdilution. ON cultures were standardized to OD 600 = 0.5 and diluted 1:2,500 to reach 5 × 10 5 CFU/ml in fresh SCFM2 media (LB for Azithromycin). Growth assays were performed at increasing antibiotic concentrations and MIC determined based on final OD. Resistance to oxidative stress was measured as the diameter of clearance zones around diffusion disks saturated with 5 μl of fresh 30% H 2 O 2 after 24 h incubation at 37°C on LB agar plates cast with 3 ml overlay agar containing 100 μl of LB ON culture standardized to OD 600 = 1. Pyoverdine production was measured as the fluorescence at 400/460 nm excitation/emission on a Synergy H1 Hybrid Multi-Mode Reader (BioTek Instruments, Winooski, Vermont, USA) of supernatants normalized against OD 600 of ON cultures in King’s B medium. Biofilm formation assay was done as previously described for NUNC peg lids [ 9 ] (NUNC cat no. 445497). Motility was measured as the diameter of the motility zone around single colonies deposited in middle layer of 0.3% (swimming), surface layer of 0.6% (swarming), or bottom layer of 1.5% (twitching) LB agar motility plates, after incubation at 37°C for 24 to 48 h. For PAO1 wild-type and aceEF mutant strains, pyruvate secretion was determined from supernatants of samples taken after 0, 4, 8, and 24 h of growth in SCFM2. Supernatants were stored at −80°C and analyzed by the HPLC method also used for exo-metabolomics.

Infection of ALI cultures

For the ALI infections, the BCi-NS1.1 cells were used [ 78 ]. Cells were cultured in Pneumacult-Ex Plus medium (STEMCELL Technologies, 05040) supplemented with Pneumacult-Ex 50x supplement (STEMCELL Technologies, 05008), 96 ng/ml hydrocortisone (STEMCELL Technologies, 07925), and 10 μm Y-27632 ROCK inhibitor (Bio-Techne #1254/10) in a 37°C, 5% CO 2 humidified incubator. Following expansion, 1.5 × 10 5 cells were seeded onto 6.5-diameter-size transwells with 0.4 μm pore polyester membrane inserts (Corning Incorporated, 3470) previously coated with human type I collagen (Gibco, A1048301). ALI was established once cells reached full confluency by removing media from the apical chamber and replacing media in the basolateral chamber with Pneumacult-ALI maintenance medium (STEMCELL Technologies, 05001). Pneumacult-ALI maintenance medium was supplemented with Pneumacult-ALI 10× supplement (STEMCELL Technologies, 05003), Pneumacult-ALI maintenance supplement (STEMCELL Technologies, 05006), 480 ng/ml hydrocortisone, and 4 μg/ml heparin (STEMCELL Technologies, 07980). ALI cultures were grown in a 37°C, 5% CO 2 humidified incubator for 30 days, with media replacement every 2 to 3 days. Epithelial polarization was monitored by measurements of the TEER using a chopstick electrode (STX2; World Precision Instruments). Following 15 days under ALI conditions, the apical surface was washed with PBS every media change to remove accumulated mucus. Biological replicates of bacterial strains were obtained from single colonies on LB agar plates grown ON as precultures in LB and then diluted to an OD 600 of 0.05 before sampling at mid-exponential phase by centrifugation, washing with PBS and resuspending in PBS at a density of 10 5 CFU/ml. Fully differentiated BCi-NS1.1 cells were inoculated with 10 3 CFU from the apical side, diluted in 10 μl PBS. Control wells were incubated with bacteria-free PBS. Cells were incubated for 14 h at 37°C, followed by addition of 200 μl PBS to the apical side and measurement of the TEER. CFUs were determined by platting 10 μl of 6-fold serial dilutions on LB-agar plates in technical triplicates both for the initial inoculum, as well as for the apical and basolateral solutions following TEER measurements. The basolateral media was also used for measurements of LDH and IL-8 release in technical triplicates, using the Invitrogen CyQUANT LDH Cytotoxicity Assay Kit (Invitrogen, C20301) and Human IL-8/CXCL8 DuoSet ELISA Kit (R&D Systems, DY208) according to the manufacturer’s instructions. BCi-NS1.1 cells on transwell inserts were rinsed once with PBS and fixed by adding 4% (wt/vol) paraformaldehyde (PFA) to both apical and basolateral chambers for 20 min at 4°C. After washing, cells were permeabilized and blocked for 1 h with a buffer containing 3% BSA, 1% Saponin, and 1% Triton X-100 in PBS. Cells were stained on the apical side with Phalloidin-AF488 (Invitrogen, 65-0863-14) and TO-PRO3 (Biolegend, 304008) diluted in a staining buffer (3% BSA and 1% Saponin in PBS) at a 1:500 dilution for 2 h at room temperature. Transwells were removed from their supports with a scalpel and mounted on glass slides with VECTASHIELD Antifade Mounting Medium (VWR, VECTH-1000). Images were acquired with a Carl Zeiss LSM 510 Confocal Laser Scanning Microscope (40× magnification, 1.3 oil) and analyzed using the ImageJ software.

Data analysis

For exometabolomics, all chromatograms were analyzed and used to construct standard curves constructed for absolute quantification of concentrations for all 20 metabolites, using software Chromeleon v7.2.9. All other analysis of exo-metabolomic data was done in JMP Pro 15.0. To compare between different strains and media batch-effects, OD 600 was normalized against final OD 600 of that strain in the experiment and concentrations were normalized against specific concentrations in SCFM2 controls for each batch. Missing values were replaced with 20% of lowest value detected for any metabolite in any sample. PCA and HCA were done on C metabolite of all quantified metabolites for 16 samples (8 time points from biological duplicates) of each strain. PCA and iterative k-means clustering was done on covariance using JMP Pro 15.0 software. HCA was performed using the “average” clustering method and “correlation” for distance using the R package pvclust 2.2. Reliability of the clusters were analyzed by 10,000 bootstraps. Metabolite half-life (OD 50 ), defined as the normalized OD 600 -value, where 50% of the starting concentration of a metabolite is present, was calculated from the sigmoidal mechanistic growth model (Equation: a(1 –bExp(–cx)) where a = asymptote, b = scale, and c = rate) [ 79 ]. Naïve and adapted metabotypes were designated based on PCA, iterative k-means clustering (range 3 to 10) followed by curation based on HCA. Specifically, iterative k-means clustering readily identified adapted metabotypes 2 and 3 based on 3 clusters and the highest Cubic Cluster Criterion statistical analysis. Cluster 1 is determined based on 5 k-means and agrees with the grouping generated by HCA followed by bootstrapping. Differences of metabolic preferences between metabotypes were analyzed by comparing the assimilation and secretion profiles between strains using OD 50 values. The net balance of carbon sources assimilated during the growth was calculated by summing the amount (mM concentration) of assimilated metabolites and subtracting the secreted ones. Metabolomics data are enclosed in S1 Data .

For the experiment with clinical isolates, proteomic raw files were analyzed using Proteome discoverer 2.4 software with the following settings: Fixed modifications: Carbamidomethyl (C) and Variable modifications: oxidation of methionine residues. First search mass tolerance 20 ppm and a MS/MS tolerance of 20 ppm. Trypsin as enzyme and allowing 1 missed cleavage. FDR was set at 0.1%. The Match between runs window was set to 0.7 min. Quantification was only based on unique peptides and normalization between samples was based on total peptide amount. For the experiment with mutant strains, raw files were analyzed using Spectronaut (version 16.2). Dynamic modifications were set as Oxidation (M) and Acetyl on protein N-termini. Cysteine carbamidomethyl was set as a static modification. All results were filtered to a 1% FDR, and protein quantitation done on the MS1 level. The data was normalized by RT dependent local regression model [ 80 ] and protein groups were inferred by IDPicker. In both cases, spectra were matched against the P . aeruginosa PAO1 reference proteome (UniProt Proteome ID UP000002438). In both experiments, any protein that was not quantified in at least 3 of 5 or 2 of 3 biological replicates for all strains were excluded from the analysis. Using JMP Pro 15.0, abundances were normalized by Log 2 -transformation and biological replicates used for missing value imputation. PCA Wide was done on correlations using JMP Pro 15.0 and HCA was performed on mean Log 2 (abundance) and Log 2 (Fold change) for the relevant comparisons. HCA was performed using the “average” clustering method and “correlation” for distance using the R package pvclust 2.2. Reliability of the clusters were analyzed by 10,000 bootstraps. Differential expression was determined by two-way ANOVA with Tukey’s multiple comparisons test, using GraphPad Prism 9.3.1, and defined as those protein-comparisons where adjusted P value ≤0.05 and Log 2 (Fold Change) ≥ |0.6|. Enrichment analysis was done using the DAVID Functional Annotation Bioinformatics Microarray Analysis tool from lists of Locus Tags of proteins that were differentially expressed, separated into lists of up-regulated and down-regulated proteins, respectively, for each of the relevant strain comparisons. The reference genome was set as Pseudomonas aeruginosa . Proteomics data are enclosed in S2 and S3 Data.

Growth rates and maxOD were calculated in JMP Pro 15.0. Blanks were first subtracted from OD 600 -measurements and values converted to cm -1 (using Greiner dimensions for pathlength). The stationary phase was excluded, and growth rates were calculated by fitting the Exponential 3P model to the exponential phase (r 2 > 0.99). Mean ± SD was calculated from biological replicates. GraphPad Prism 9.3.1 was used for statistical analysis of biofilm formation, motility, redox sensitivity, pyoverdine production, and MICs (see S6 Fig for details), as well as for ALI infection experiments. ALI data are represented as mean ± SEM. Replicates represent independent experiments performed with cells from different passages. Statistical comparisons were calculated using two-way ANOVA for TEER and CFU measurements and one-way ANOVA for LDH and IL-8 measurements. Statistical significance was considered for p value < 0.05. All figures were finalized in Adobe Illustrator Artwork 27.0.

Supporting information

(A) Number of differentially expressed proteins (top) and Log 2 (Fold change) of individual differentially expressed proteins in late vs. early clinical isolates, separated by lineage and on the x-axis by COG categories. (B) Complete enrichment analysis showing fold-enrichments separated by comparison and lineage on x-axis and by KEGG and GO terms on y-axis. The data underlying this figure can be found in S2 Data .

https://doi.org/10.1371/journal.pbio.3002781.s001

Metabolic maps showing the differences between early clinical strains vs. PAO1 (left) and late vs. early clinical strains (right) in pathways related to the catabolism of nutrients present in SCFM2 through central carbon metabolism. Arrows represent individual metabolite-conversions colored by their pathway. Reactions are colored if the underlining enzyme is differentially expressed in more than one clone type—either with dashed (down-regulated) or full (up-regulated) lines, or both. Enzymes responsible for each reaction are indicated by numbered circles. For details on individual enzymes, see S3 Fig . Transporters are shown at the bottom as arrows crossing bacterial cell membrane with their specific transported metabolites. Asp, Glu, and acetate are labeled with * to indicate that they are represented in 2 places on the map, due to their involvement in different parts of central carbon metabolism. The data underlying this figure can be found in S2 Data .

https://doi.org/10.1371/journal.pbio.3002781.s002

(A) Metabolic map in the same style as S2 Fig for late clinical strains vs. PAO1. (B) Table of genes/Locus Tags of all metabolic enzymes included in the map. (C, D) Bar charts showing Log 2 (Fold change) of all differentially expressed proteins highlighted on the metabolic maps. Icons colored by lineage and separated into 3 comparisons on x-axis (Early vs. PAO1; Late vs. Early; Late vs. PAO1). Panel c contains metabolic enzymes and panel d contains transporters. The data underlying this figure can be found in S2 Data .

https://doi.org/10.1371/journal.pbio.3002781.s003

(A) Hierarchical cluster analysis in the same style as Fig 4C , showing differential expression in late vs. early clinical strains for several more virulence categories. (B) The Log 2 (Fold change) of differentially expressed proteins in late vs. early clinical strains separated into the relevant virulence categories and further separated into the 2 main clusters from panel a. Icons are colored by lineage and lines depict the difference in mean Log 2 (Fold change) between the 2 clusters. The data underlying this figure can be found in S2 Data .

https://doi.org/10.1371/journal.pbio.3002781.s004

Schematic of all 18 unique mutations observed for aceE (top) and aceF (bottom) in our collection of clinical isolates of P . aeruginosa . Gray lines indicate mutation site and letters indicate nucleotide-sequence colored by type of mutation. The data underlying this figure can be found in Marvig and colleagues [ 39 ].

https://doi.org/10.1371/journal.pbio.3002781.s005

(A) Complementation analysis of aceE mutant. Growth rate (hour -1 ) in SCFM for PAO1, aceE mutant and complementation strain aceE(rev) . Bars indicate mean ± SEM, with icons representing biological replicates. Statistical significance was assessed by one-way ANOVA with Tukey’s multiple comparisons test and indicated as * ( p < 0.05). (B) Potential for biofilm formation (surface attachment) of PAO1 wt (blue), as well as aceE (red) and aceF (green) mutant strains and DK12 and DK36 late clinical strains after 24 and 48 h, respectively. For the clinical isolates, strain is indicated on the x-axes and incubation time is indicated as blue (24 h) and red (48 h). Attachment is measured as the ratio of surface-attached cells (OD 590 ) to total number of cells (OD 600 ) after incubation. For PAO1 wt and aceE and aceF mutants, all strains were compared by one-way ANOVA. For clinical isolates, each strain was compared to itself after 24 and 48 h of incubation, respectively, using two-tailed unpaired parametric Welch t test. Significance is indicated as “ns” ( p > 0.05), * ( p < 0.05), ** ( p < 0.01), *** ( p < 0.001). Same coloring and statistical analysis for PAO1 and mutant strains in panels B–D. (C) Motility measured as the diameter (mm) of the zone of growth in motility plates (LB agar). Swimming motility was clearly visible after 24 h of incubation. Swarming and twitching plates required 48 h of incubation. (D) Redox sensitivity measured as the diameter of the clearance zone (mm) on bacterial lawns (LB agar) of each strain from H 2 O 2 diffusion disks after 24 h. (E) Pyoverdine production measured as relative fluorescence (F/OD 600 ) of each strain after 24 h of growth in King’s B medium. For panels A–D, icons indicate biological replicates and bars represent the mean ± SEM. (F) MICs of PAO1 wt and aceE and aceF mutant strains. Each cell represents the maxOD of growth curves under the given condition, following the color gradient to the right of each heatmap. The MIC is the concentration where no growth is observed (white). Each heat map shows the MIC for all 3 strains for a given antibiotic (Ceftazidime, Meropenem, Piperacillin, Azithromycin, Chloramphenicol, Ciprofloxacin, and Tobramycin). Concentrations (μg/ml) increase 2-fold downward on the vertical axis and the specific strain is given on the horizontal axis. Azithromycin MICs were determined in LB, while all other MICs were determined in SCFM2. Piperacillin was used in combination with the β-lactamase inhibitor Tazobactam. The data underlying this figure can be found in S4 Data .

https://doi.org/10.1371/journal.pbio.3002781.s006

(A) Metabolic map in the same style as S2 Fig for aceF mutant strain vs. PAO1 reference in SCFM2 in absence (left) and presence (right) of acetate. (B) Parallel plot showing the number of differentially expressed proteins, separated by metabolic COG categories, in aceE (cyan) and aceF (magenta) mutant strains vs. PAO1. (C) Enrichment analysis showing fold enrichment on x-axis separated by KEGG and GO terms on y-axis for aceE (left) and aceF (right) mutant strains vs. PAO1. (D) The -Log2(Fold change) of the Dnr transcriptional regulator in late vs. early clinical isolates (same holds for late strains vs. PAO1) for lineages in Cluster A (left) and B (middle), as well as in the aceE mutant strain (right) when compared to PAO1 wt in SCFM2 in absence (+/− mutation) and presence (Ace) of acetate or when compared to itself in presence of acetate (+/− Acetate). The data underlying this figure can be found in S3 Data .

https://doi.org/10.1371/journal.pbio.3002781.s007

S8 Fig. Longitudinal collection of P . aeruginosa clinical isolates from CF airways.

Icons indicate isolates carrying mutations in genes encoding PDHc (red squares) and/or T3SS (purple triangles) proteins, as well as isolates with no mutations in either (gray dots). Isolates are separated by Clone Type and further separated by patient-specific lineages within Clone Types (A, B, C). The x-axis shows the length of infection (years) since the first isolate of the given lineage. The data underlying this figure can be found in Marvig and colleagues [ 39 ].

https://doi.org/10.1371/journal.pbio.3002781.s008

S1 Data. Dynamic exometabolomics data.

Values correspond to mM concentrations of the analyzed metabolites by their collection OD and strain.

https://doi.org/10.1371/journal.pbio.3002781.s009

S2 Data. Proteomic data for the clinical isolates.

Values correspond to the Log 2 (Fold-change) for the indicated comparison. Statistical significance is reported by q values.

https://doi.org/10.1371/journal.pbio.3002781.s010

S3 Data. Proteomic data for the aceE and aceF recombinant strains.

https://doi.org/10.1371/journal.pbio.3002781.s011

S4 Data. Individual numerical values underlying all figures.

https://doi.org/10.1371/journal.pbio.3002781.s012

S1 Table. List of strains and plasmid.

https://doi.org/10.1371/journal.pbio.3002781.s013

S2 Table. List of primers.

https://doi.org/10.1371/journal.pbio.3002781.s014

S1 Text. Detailed information on metabolic preferences and convergent proteomic changes.

https://doi.org/10.1371/journal.pbio.3002781.s015

Acknowledgments

The Basal Cell Immortalized Non-Smoker 1.1 (BCi-NS1.1) cell line was a kind gift from Professor Ronald G. Cristal (Weil Cornell Medical College, New York, USA).

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Depression Symptoms in Patients with Cystic Fibrosis Fluctuate at Baseline and Improve with Elexacaftor/Tezacaftor/Ivacaftor Therapy

Linus piehler.

1 Department of Pediatric Respiratory Medicine, Immunology and Critical Care Medicine and Cystic Fibrosis Center and

Ralf Thalemann

Christine lehmann, mirjam stahl.

2 Berlin Institute of Health, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; and

3 German Center for Lung Research (DZL), associated partner site, Berlin, Germany

Marcus A. Mall

Simon y. graeber.

To the Editor :

With great interest, we read the editorial by Tim W. R. Lee and Alistair J. A. Duff highlighting the need for more research on depression symptoms in patients with cystic fibrosis (CF) who are treated with the triple-combination CFTR (cystic fibrosis transmembrane conductance regulator) modulator therapy elexacaftor/tezacaftor/ivacaftor (ETI) ( 1 ). Although we agree that research on the impact of ETI on mental health is important, we disagree with the authors’ interpretation of the results of our recently published prospective study on effects of ETI on symptoms of anxiety and depression in patients with CF ( 2 ). In their editorial, Lee and Duff reprinted our original data on the effects of ETI on symptoms of depression ( Figure 1A ) ( 2 ), as measured by the Patient Health Questionnaire-9 (PHQ-9), a validated questionnaire for symptoms of depression, and they claimed that “there was certainly a proportion of subjects who had worse PHQ-9 scores after commencing ELX/TEZ/IVA therapy; indeed PHQ-9 score increased by 17% in the group with minimal depression symptoms at baseline” (p. 239) ( 1 ). It is correct that the group with minimal depression symptoms increased by 17%. However, this statement is misleading, as the increase in the percentage of this group is not because of patients with worse PHQ-9 scores after initiation of ETI, as stated by Lee and Duff. In contrast, the increase in the group with minimal symptoms of depression is caused by a decrease in PHQ-9 scores in patients who had higher PHQ-9 scores at baseline and changed from mild, moderate, or severe PHQ-9 scores at baseline to minimal PHQ-9 scores on ETI ( Figure 1A ). Furthermore, it is important to note that patients with no symptoms of depression (PHQ-9 score of 0) are included in the group with minimal depression symptoms because of the definition of the PHQ-9 score and increased from 5.6% at baseline to 14.1% after the initiation of ETI.

Changes in symptoms of depression after initiation of elexacaftor/tezacaftor/ivacaftor (ETI) and in the pre-ETI era. Alluvial graphics of paired assessment of the Patient Health Questionnaire-9 (PHQ-9) ( A ) from a prospective observational trial at baseline and 3 months after initiation of ETI ( n  = 70) ( 2 ) and ( B ) from the yearly mental health screening at the Cystic Fibrosis Center at Charité – University Medicine Berlin in the years 2017 to 2020 derived from the German Cystic Fibrosis Registry (Mukoviszidose e.V.) ( n  = 68). Categories represent no or minimal (scores = 0–3; blue), mild (scores = 4–6; green), moderate (scores = 7–9; yellow) and severe (scores = 10–21; red) symptoms of depression as assessed by the PHQ-9. ( A ) is adapted from our previous study ( 2 ) to highlight the course of PHQ-9 scores of the 67.6% patients who reported no or minimal symptoms of depression after the initiation of ETI.

However, our study did not include a control group. To assess for background variability of the PHQ-9 score in our patient population, we analyzed registry data from our CF center of the yearly mental health screening in the years 2017 to 2020; that is, before the approval of ETI and the start of the coronavirus disease (COVID-19) pandemic ( Figure 1B ). Of note, these data show evidence of variability in yearly PHQ-9 scores in individual patients in real life before the approval of ETI, as well as an overall increase in PHQ-9 scores after one year (median change = 2.0, P  < 0.05; Wilcoxon matched-pairs signed rank test). These findings further support that the improvements in symptoms of depression observed in our prospective observational study ( 2 ) are caused by the initiation of ETI therapy in adult patients with CF.

These data are consistent with the assessment by Ramsey and colleagues, who observed an increase in prevalence of depression in the pre-ETI era ( 3 ). However, the prevalence of depression continued to increase post-ETI in the U.S. registry cohort of a postauthorization safety study. Nevertheless, it is not surprising that the substantial improvement in CFTR function, lung function, and quality of life that was observed after the initiation of ETI ( 4 , 5 ) does not directly lead to an improvement in mental health to the same extent. Depression in CF is multifactorial, and starting a life-altering therapy could itself contribute to symptoms of depression and anxiety in patients with CF after modulator therapy ( 6 ).

Nevertheless, we agree with Lee and Duff ( 1 ) that there might be a small proportion of individuals who are particularly prone to mental health issues after the initiation of ETI, and it is important to identify these patients in advance to potentially prevent symptoms of depression. In this context, it is noteworthy that we observed a gender-specific effect on symptoms of depression, with female patients not showing an improvement compared with male patients ( 2 ). Future studies that are powered for gender-specific differences and controlled for baseline fluctuations of symptoms of depression and anxiety will be necessary to receive more answers to these pressing questions.

Originally Published in Press as DOI: 10.1164/rccm.202404-0787LE on June 6, 2024

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Factors associated with cystic fibrosis mortality before the age of 30: retrospective analysis of a cohort in southern Brazil

Affiliations.

• 1 Programa de Pós-Graduação em Ciências Pneumológicas, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brasil.
• 2 Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brasil.
• PMID: 39194031
• PMCID: PMC11349154
• DOI: 10.1590/1414-431X2024e13476

The aim of this study was to retrospectively evaluate the factors associated with mortality before the age of 30 in adults with cystic fibrosis (CF) followed up at a referral center in southern Brazil. This study included individuals over 18 years of age. Clinical data related to childhood and the period of transition to an adult healthcare of individuals with CF were recorded, as well as spirometric and mortality data of individuals between 18 and 30 years of age. A total of 48 patients were included in this study, of which 28 (58.3%) were male. Comparing groups, we observed a higher prevalence of homozygosis for the F508del mutation (P=0.028), massive hemoptysis before the age of 18 (P=0.027), and lower values of pulmonary function, forced expiratory volume in the first second (FEV1) (%) (P=0.002), forced vital capacity (FVC) (%) (P=0.01), and FEV1/FVC (%) (P=0.001) in the group that died before age 30. F508del homozygosis, episodes of massive hemoptysis in childhood, and lower FEV1 values at age 18 were related to mortality before age 30 in a cohort of individuals with CF in southern Brazil.

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