Skip to main content

The effect of TGF-β1 polymorphisms on pulmonary disease progression in patients with cystic fibrosis



Transforming Growth Factor-β1 (TGF-β1) is a genetic modifier in patients with cystic fibrosis (CF). Several single nucleotide polymorphisms (SNPs) of TGF-β1 are associated with neutrophilic inflammation, lung fibrosis and loss of pulmonary function.


The aim of this study was to assess the relationship between genetic TGF-β1 polymorphisms and pulmonary disease progression in CF patients. Furthermore, the effect of TGF-β1 polymorphisms on inflammatory cytokines in sputum was investigated.


56 CF-patients and 62 controls were genotyped for three relevant SNPs in their TGF-β1 sequence using the SNaPshot® technique. Individual “slopes” in forced expiratory volume in 1 s (FEV1) for all patients were calculated by using documented lung function values of the previous five years. The status of Pseudomonas aeruginosa (Pa) infection was determined. Sputum concentrations of the protease elastase, the serine protease inhibitor elafin and the cytokines IL-1β, IL-8, IL-6, TNF-α were measured after a standardized sputum induction and processing.


The homozygous TT genotype at codon 10 was associated with a lower rate of chronic Pa infection (p < 0.05). The heterozygous GC genotype at codon 25 was associated with lower lung function decline (p < 0.05). Patients with homozygous TT genotype at the promotor SNP showed higher levels of TNF-α (p < 0,05). Higher levels of TGF-β1 in plasma were associated with a more rapid FEV1 decline over five years (p < 0.05).


Our results suggest that polymorphisms in the TGF-β1 gene have an effect on lung function decline, Pa infection as well as levels of inflammatory cytokines. Genotyping these polymorphisms could potentially be used to identify CF patients with higher risk of disease progression. TGF-β1 inhibition could potentially be developed as a new therapeutic option to modulate CF lung disease.

Peer Review reports


Cystic fibrosis (CF) is an autosomal recessive, genetic disorder that affects approximately 85,000 individuals worldwide [1]. This multisystemic disorder is caused by mutations affecting the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) in the epithelial membrane of exocrine glands, which lead to dysfunctional fluid and ion-transport causing a production of thickened mucus [2]. Pathological mucociliary clearance leads to CF lung disease, which over time becomes the major life-shortening factor for CF patients [1]. Chronic plugging of bronchioles with secretions, recurring bacterial infections and pulmonary exacerbations instigate the development and retention of a hostile inflammatory environment in the lungs, leading to tissue breakdown and irreversible lung damage [3]. The most relevant microorganism in CF lungs, Pseudomonas aeruginosa (Pa), provokes a vigorous inflammatory response with neutrophilic infiltration of airways and subsequent damage by the release of proteases and oxidants [4]. This dysregulated chronic state of inflammation in CF airways is sustained by a variety of proinflammatory mediators including TNF-α, IL-1β, IL-6 and IL-8 and leads to a decline in lung function caused by bronchiectasis and irreversible fibrotic remodeling of lung tissue [4]. 98% of all CF patients die of progressive respiratory insufficiency [5].

Whilst prevalent CFTR mutations are an important determinator for the severity of CF lung disease, the genotype–phenotype correlation between the genetically determined loss of CFTR function and lung function decline is approximately 60% [6, 7]. This suggests that other non-CFTR related factors, such as genetic modifiers with a regulatory effect on the inflammatory response in CF lungs, may also have a significant impact on lung function decline in CF patients.

TGF-β1 has been identified as such a genetic modifier for CF lung disease [6]. Produced by bronchial epithelial cell, this growth factor acts with a localized, modulatory role in the recruitment and activation of neutrophilic granulocytes within a complex network of inflammatory and anti-inflammatory cytokines, thereby regulating inflammatory processes, specifically in context of chronic pulmonary disease [8]. TGF-β1 inhibits the degradation of extracellular matrix by stimulating protease-inhibitors leading to fibrotic reconstruction of lung tissue [9]. Furthermore, it promotes smooth muscle cell hypertrophy and hyperplasia [4, 10].

In a recent study, Sagwal et al. have shown that levels of serum TGF-β1 were increased in pulmonary exacerbation phases, in infection with Pa and in subjects with a ΔF508 mutation [11]. TGF-β1 levels decreased significantly after antibiotic treatment of pulmonary exacerbations [11].

Moreover, it has been shown that TGF-β1 has an inhibitory effect on the biogenesis of CFTR and prevents the functional rescue of delF508-CFTR [10]. In a recent study by Mitash et al., TGF-β1 has been associated with degradation of CFTR mRNA in human bronchial epithelial cells via recruitment of microRNAs to an RNA-induced silencing complex [12]. Snodgrass et al. have shown that TGF-β1 was associated with CFTR inhibition and prevention of functional rescue in human epithelial cells [10].

However, in vivo levels of TGF-β1 are dependent on specific polymorphisms in the TGF-β1 gene [13]. So far, few studies have investigated the effects of genetic polymorphisms of TGF-β1 on lung function. In the context of CF, three single nucleotide polymorphisms (= SNPs) have previously been investigated. Each of these polymorphisms, i.e. rs1800469 located in the promotor region and both rs1800470 and rs1800471 located in Exon 1 of the TGF-β1 gene, result in a change in the primary amino acid sequence of the TGF-β1 [6, 13, 14].

In previous studies it was shown that some of these TGF-β1 polymorphic genotypes are associated with higher TGF-β1 expression, a steeper decline in pulmonary function (FEV1) as well as increased pulmonary fibrosis [6, 13, 15, 16]. However, some of the results among these studies are contradictory, as different genotypes were associated with a decrease in pulmonary function and worse clinical status. Furthermore, very little is known about the impact of a TGF-β1 polymorphism-related dysregulation of the signal pathway of TGF-β1 on the complex inflammatory response of the CF airways. It has to be noted, however, that immunological factors contributing to or perhaps even enabling the onset of bacterial infection with Pa, one of the major predictors for mortality and morbidity for CF patients, could not yet be identified [17].

The primary aim of this study was to investigate whether TGF-β1 SNP genotypes, as modifiers of CF lung disease, can be associated with a faster decline in pulmonary function. To our knowledge, there is no data correlating TGF-β1 phenotypes with the individual FEV1 slopes of CF patients. FEV1 correlates with morbidity and mortality of CF-patients and is a gold standard outcome parameter in routine diagnostics to assess disease progression as well as in clinical studies to investigate the efficacy of new drugs [18]. Furthermore, we wanted to investigate whether TGF-β1 polymorphisms are associated with higher TGF-β1 expression, higher Pa infection rates and elevated levels of proinflammatory cytokines in sputum.

Materials and methods

Study population

The TGF-β1 genotypes for all three SNPs were determined in 56 CF-patients and 62 healthy controls. All CF patients had a confirmed diagnosis of CF according to the consensus guidelines of the Cystic Fibrosis Foundation [19]. Inclusion criteria were a signed informed consent and the ability of patients to expectorate sputum. Exocrine pancreatic insufficiency was diagnosed by repeated pancreas elastase testing of patients´ stools and confirmed by repeated levels < 200ug/g. The genomic DNA of CF patients was isolated from a whole blood sample obtained during the routine yearly blood sample collection. The DNA samples of 62 randomly chosen, healthy controls were obtained from paternity test samples at the Institute of Legal Medicine, to which the individuals had given their consent when these samples were obtained [20]. Patients with an acute pulmonary exacerbation at the study visit were excluded. All pulmonary function tests performed during the 5-year interval between 2010 and 2014 were reviewed. The best FEV1 value for every year was obtained for FEV1 slope calculation. 15 patients underwent changes in CF therapy (e.g. start of CFTR-modulatory therapy) or lung transplantation within this period of time. For these, a different five-year time span, prior to their new therapy, was chosen for calculation of the individual FEV1 slope.

TGF-β 1 polymorphism genotyping

DNA was extracted from whole blood and diluted to a standard concentration of 1 ng/\(\mu\) l. The DNA was then amplified using Polymerase Chain Reaction (PCR) with specific primers designed to amplify two separate targets of the genome, containing the relevant SNPs, using Primer3Plus, BLAST and NCBI Electronic PCR-Software [21, 22]. Primers were produced and shipped by [23]. PCR primer sequences used are available on request. Agarose gel electrophoresis tests of the amplicons were performed to monitor the correct amplification of the two targets. Enzymatic purification of samples followed using Exonuclease and Shrimp Alkaline Phosphate (SAP). According to instructions of the SNaPshot™ Multiplex Kit (Applied Biosystems), a Single Base Extension (SBE) with didesoxyribonucleosid-triphosphates (ddNTPs), marked with four different fluorescent signals, QIAGEN Mastermix (containing DNA Polymerase AmpliTaq©, reaction puffer) was performed in a thermocycler (Gene AMP PCR System 2720 thermocycler, Applied Biosystems) [24,25,26]. SNP typing primer sequences were GGCAACAGGACACCTGA(A/G) for SNP rs1800469, CAGCGGTAGCAGGAGC(G/A) for Codon 10 SNP rs1800470 and GTGCTGACGCCTGGCC(G/C) for Codon 25 rs1800471.

Lastly, after enzymatic purification of the Single Base Extension reaction (SBE) reaction products, capillary electrophoresis (using ABI Prism 3130 Genetic Analyzers) was performed to determine the genotype of each SNP for all patients and controls using the software Genemapper 4.0 (Applied Biosystems). An exemplary capillary electrophoresis result of one patient’s genotype for all three polymorphisms is shown in Additional file 1: Figure A. Materials, concentrations, PCR primers sequences and exact reaction conditions for PCR and Single Base Extension (SBE) are available on request.


Spirometric measurements were performed according to the ATS guidelines using GLI references and assessed before any other study assessment with Master Screen Body (Jaeger, Heidelberg, Germany) and SentrySuite™ version 2.19 software (Carefusion, Becton Dickinson, Franklin Lakes, New Jersey, USA) [27]. For each measurement, the best FEV1 value was used for analysis. The best yearly FEV1 value was used in a linear regression model to calculate individual FEV1 slope values for every patient. Patient results were also analyzed within different FEV1 subgroups and FEV1 slope subgroups, according to FEV1 progression over time.

Pseudomonas aeruginosa (Pa) infection

The status of Pa infection, defined by clinically established Leeds criteria, was obtained from the patients’ files and is described according to the following three groups: Pa positive (= chronic infection), Pa naïve (= never infected) or Pa negative (infected in the past, currently not infected after eradication therapy) [28].

Sputum analysis of TGF-β 1 and other cytokines

As part of the regular outpatient visits, patients induced their sputum by inhalation of hypertonic saline during a routine physiotherapist session. This sputum was processed according to the standard operating procedure (SOP) of the TDN (Therapeutic Drug Development Network, USA). Concentrations of elastase and elafin in sputum were determined by specific ELISA assays (EnzChek® Elastase Assay Kit,—Molecular Probes Europe, Leiden, Netherlands; Elafin/Skalp Human ELISA Kit—abcam, Cambridge, UK). Pro-inflammatory cytokine concentrations in sputum were assessed using a human inflammatory cytokine ELISA-kit (BD Cytometric Bead Array Humane Inflammatory Cytokine Kit, San Jose, CA, USA). TGF-β1 levels in sputum and plasma were determined by a TGF- β1 specific ELISA-kit (Quantikine®ELISA Human TGF-β1, R&D systems, Minneapolis, MN, USA).

Statistical analysis

IBM SPSS Statistics 24 was used for statistical analysis. To compare two metric variables, we correlated using Pearson’s test. For correlation between one metric and one discontinuous variable, we used the Kruskal–Wallis test. For tests correlating two discontinuous variables we used cross-classified tables with exact Fisher’s test. For all tests, a p-value < 0,05 was considered statistically significant.

For a detailed analysis of FEV1 slopes, different patient subgroups were formed as summarized in Additional file 1: Figure B. One categorization involved a comparison between patients with positive FEV1 slope and those with negative slopes (Categorization 1). Two further categorizations were used to compare patients with steepest decline in FEV1 to patients with a relatively steady FEV1 (with only little decline or even small improvements) and patients with clear improvements in FEV1 over the period of investigation (Categorization 2 & 3). These categories were formed to investigate inflammatory status in different stages of CF lung disease and to determine the role of TGF-β1 in this process.

Additionally, for statistical analysis, patient subgroups were also formed according to patients’ absolute, best FEV1 at the end of the observed 5 year-period. Here patients were analyzed in different FEV1 subgroups in order to investigate patients who finished their 5-year FEV1 slope in a “normal” FEV1 group (> 80% predicted), an “intermediate” FEV1 group (40–80% predicted) or a “low” FEV1 group (< 40% predicted). A summary of these subgroups can be found in Additional file 1: Figure C.

For more detailed analysis of TGF-β1 genotypes, for some statistical investigations we used subgroups of combined genotypes to explore the impact of a heterozygous genotype when compared to homozygous genotypes (e.g. CT vs TT/CC).


Study population

The mean age of CF-patients was 21 years (SD ± 11.1 years). The mean FEV1 at the time of blood sampling was 74.26% predicted (SD ± 25.36% predicted). The mean FEV1 slope of patients was -1.81%FEV1 change per year (SD ± 3.20%FEV1 change). 33.9% of patients were chronically infected with Pa. In our CF cohort, 29 patients (approx. 52%) were F508del homozygous, whilst 21 patients (approx. 37%) were F508del heterozygous and 6 patients (approx. 11%) carried two other CF-causing mutations. Furthermore, 53 patients (approx. 95%) in our cohort showed exocrine pancreatic insufficiency.

The demographic results of our cohort are presented in Table 1.

Table 1 Clinical data of CF patients

As shown in Table 2, there was no significant difference in the distribution of genotypes or alleles in the investigated cohort of 56 CF patients compared to 62 healthy controls. The genotype results were successfully tested for conforming to expected distributions according to the Hardy Weinberg Equilibrium.

Table 2 Distribution of genotypes in CF-patients and controls

TGF- β 1 polymorphisms and pulmonary status

Codon 10

The homozygous TT genotype at codon 10 was significantly associated with a lower Pa infection rate, as demonstrated in Fig. 1. 16.7% of patients with this genotype were infected with Pa, compared to 42.1% in the combined CC/CT genotype group (p = 0.047). Of all patients with chronic Pa infection in our cohort (n = 19), 15.2% showed the TT genotype. No significant associations between the FEV1 slope or FEV1 slope subgroups and genetic polymorphisms at codon 10 were found.

Fig. 1
figure 1

TGF- β1 codon 10 SNP genotypes and prevalence of Pa infection status within the genotype group

Codon 25

For the polymorphism at codon 25, we found no significant association with Pa infection rate. However, we were able to show that the group of patients with the heterozygous GC genotype contained a significantly higher proportion of patients with positive FEV1 slopes, with positive FEV1 change greater than 1.0% per year (p = 0.003) according to FEV1 slope categorization 2. As shown in Table 3, of all patients with the GC genotype, 66.7%, exhibited an FEV1 slope greater than + 1.0% FEV1 change/year, compared to only 10.2% of patients with a GG genotype. Baseline FEV1 in the GC-group was 72,7%, compared to 84,1% in the GG group. Also, according to FEV1 slope categorization 2, 100% of patients with stable FEV1 slopes (between -1%/year and + 1%/year) and 93% of the patients with declining FEV1 slopes (< -1%/year) presented with a GG genotype at codon 25.

Table 3 Prevalence of FEV1-slopes >  + 1%/year for patients with different Codon 25 genotypes

No significant associations of Codon 25 polymorphisms with absolute FEV1, FEV1 slope or FEV1 slope subgroups were found in our cohort.


We did not find a significant association between polymorphisms in the TGF-β1 promotor and patients’ Pa infection status, their FEV1 slopes or their FEV1 slope subgroups.

A summary of mean average FEV1 slope results for different SNP groups is shown in Additional file 1: Figure D.

TGF-β 1 concentration and FEV 1 slopes

Higher TGF-β1 concentrations in patients’ plasma significantly correlated with a steeper decline in FEV1 slope (p = 0.045). This is demonstrated in Fig. 2. Patients with FEV1 slopes that were below than -2%/year (Categorization 3) showed a higher TGF-β1 concentration (25,332 pg/ml) in plasma compared to patients with a positive FEV1 slope of greater than + 2%/year (16,754 pg/ml).

Fig. 2
figure 2

TGF- β1 plasma concentration and FEV1 slope of CF patients

No correlation between TGF-β1 levels in plasma and FEV1 slope subgroups was found.

In our study, a higher concentration of TGF-β1 in the patients’ sputum significantly correlated with a positive FEV1 slope (> 0% FEV1 change/year) according to slope categorization 1 (p = 0.010). The median TGF-β1 concentration in sputum was 66.0 pg/ml higher in patients with a positive FEV1 slope (> 0%/year) compared to patients with a negative FEV1 slope (< 0%/year). TGF-β1 sputum levels showed no significant associations with other investigated FEV1 slope categorizations.

However, patients with intermediate FEV1 values (40–80% predicted) at the end of their five year observation period, showed significantly higher concentrations of TGF-β1 in sputum compared to patients with normal (> 80%) or low (< 40%) FEV1 values (p = 0.01). These results are demonstrated in Fig. 3a, b.

Fig. 3
figure 3

a TGF- β1 sputum concentration and FEV1 slope group, b TGF- β1 sputum concentration and FEV1 group over five years

TGF-β 1 polymorphism and concentration of inflammatory markers

We found a significantly higher concentration of sputum TNF-α in patients with a homozygous TT genotype at the TGF-β1 promotor polymorphism (p = 0.019), as shown in Fig. 4. For all other inflammatory parameters, there was no significant correlation to TGF-β1 concentration. A summary of all polymorphisms and corresponding levels of inflammatory markers are listed in Additional file 1: Figure E.

Fig. 4
figure 4

TGF-β1 Promotor SNP genotype group and TNF-α concentration in sputum


The aim of this study was to investigate whether TGF-β1 SNP genotypes, as modifiers of CF lung disease, can be associated with a faster decline in pulmonary function, higher TGF-β1 expression, higher Pa infection rates and elevated levels of proinflammatory cytokines in sputum. As expected, we found no difference in the genotype frequency distributions of TGF- β1 polymorphisms between CF patients and healthy controls. The genotype frequencies for each TGF-β1 polymorphism corresponded with known allele frequencies for these polymorphisms as documented following a PubMed dbSNP search [29]. This distribution of the various genotypes demonstrates that the patient cohort is representative for a randomly selected population sample.

We showed that codon 10 TGF- β1 polymorphism genotypes (CC or CT) are associated with a higher rate of Pa infection (p = 0.047). The observed significant correlation between TGF- β1 genotype TT in codon 10 and less chronic Pa infection is a finding of special interest. However, it contrasts the findings of Arkwright et al., who found no significant difference between Pa infection and TGF- β1 codon 10 TT genotype [15]. As shown in studies by Emerson et al. and Thomassen et al., chronic Pa infection leads to an activation of profibrotic inflammatory mediators in CF patients and increased pulmonary inflammation resulting in higher mortality rates. [17, 30]. In a recent study by Sagwal et al., increased levels of TGF- β1 in serum were found in all types of bacterial infections, with comparably more increase in patients infected with Pa [11]. In our cohort, only 16.7% of patients with the homozygous codon 10 TT-genotype were chronically infected with Pa, compared to a 42.1% infection-rate in the group of patients with either the CT or CC genotype. Specifically, the high chronic infection rate of 57.1% in the CC group was noticeable at this context. Of all the patients with chronic Pa infection in our cohort (n = 19), only 15.2% showed the TT genotype. There might be an unclear mechanism, by which the immunological protection against this pathogen is affected. The noticeably higher infection rate of patients with the CC genotype for this polymorphism might be supported by Drumm et al.’s observed association of this genotype with a worsened lung function, as it also correlates with increased gene expression and circulating TGF- β1 levels in their study [13]. Whether TGF- β1 with a TT genotype at codon 10 has protective antimicrobial properties, e.g. by contributing to a generally more controlled pulmonary inflammation that significantly lowers the rate of Pa infection in this subgroup, or whether the TGF- β1 CC-genotype at codon 10 acts as an immunological predisposition for onset of Pa infection, is yet to be investigated.

Our results show a significant association between the TGF- β1 polymorphism genotypes at the promotor SNP of TGF- β1 and the concentration of TNF-α in the patients’ sputum (p = 0,019). Interestingly, the concentration of TNF-α for patients with the homozygous TT promotor genotype at this polymorphism was four times higher than in patients with other genotypes. TNF-α acts as a signal cytokine, that activates the acute phase proteins [31]. Yang et al. hypothesized that colonization with flagellated bacteria, such as Pa, may lead to a higher expression of TGF- β1 via MAP kinases [32]. In addition to this, Eickmeier et al. found a higher co-expression of TGF-β1 and TNF-α in patients with microbiological evidence of at least one type of bacterial infection [3]. In a more recent case–control study by Oueslati et al., CF patients with a TT promoter genotype were associated with worse lung symptoms than patients with other genotypes at this SNP [33]. In summary, our result partially corresponds with the hypothesis of a TGF-β1/TNF-α co-expression, as the homozygous TGF-β1 promotor genotype TT was significantly associated with a higher expression of TNF-α, despite there being no significant difference in TGF-β1 concentration in sputum.

We also found, that the GC genotype of the TGF- β1 codon 25 polymorphism correlates significantly with better FEV1 slopes in CF patients (p = 0.003). 66.6% of patients with this genotype showed a positive FEV1 slope >  + 1% FEV1 change/year, compared to merely 10.2% of the patients in the group with homozygous GG genotypes, despite a lower average baseline FEV1 in the GC-group. This correlation has not been described previously in other studies before. Although Arkwright’s study on TGF-β1 polymorphisms included the combined TT/GG genotype (Codon 10/Codon 25) in the “high-producer” categorization of patients, no correlation was found between codon 25 genotype and measures of survival (age of death/transplantation), Pa infection or lung function [15]. In our study, plasma and sputum levels of TGF-β1 showed no significant difference between codon 25 genotype groups.

We were able to demonstrate that high TGF-β1 plasma levels are associated with a more rapid decline in lung function over a five-year period (p = 0.045). The highest TGF-β1 plasma concentrations were found in patients with a more severe FEV1 decline over time. This correlation has also been described by Brazova et al. [6] and can be explained by the adverse effects of chronic, systemic inflammation in CF, potentially regulated by TGF-β1 in plasma. However, an association of very low TGF-β1 concentration with a more deteriorated lung function was also demonstrated in their study, which our results do not confirm.

Interestingly, we found that the highest local TGF-β1 concentrations in sputum were associated with intermediate FEV1 values (40–80% predicted) of CF patients over 5 years (p = 0.01), whereas for patients with both high (> 80% predicted) or low (< 40% predicted) FEV1 values over 5 years, the TGF-β1 concentrations were significantly lower. In patients with normal and stable FEV1 values above 80% predicted, inflammatory processes might not be activated to the same level as in patients with intermediate FEV1 values showing a higher rate of decline. Patients with highly impaired lung function (FEV1 < 40% predicted) might show more activation in pulmonary tissue remodeling and pulmonary fibrosis and less active inflammation; this could explain the < 40% FEV1 group showing lower TGF-β1 concentrations in their sputa. Zemel et al. also showed that an initially high FEV1 value in CF patients is linked to worse FEV1 progression over time, which may be linked to a less aggressive anti-inflammatory and antimicrobial treatment in children with an initially higher FEV1 [34]. This might suggest that the impact of local TGF- β1 (in sputum) on pulmonary function is significant, especially in those patients with intermediate FEV1 values, when chronic inflammation reaches its maximum during a phase of steady pulmonary function decline of 1–3% FEV1/year.

Limitations of this study

  • Despite several statistical comparisons made with SNPs, inflammatory marker concentrations and pulmonary outcomes, we did not conduct a correction for multiple comparisons, as this was an exploratory study.

  • The statistically significant correlation between TGF- β1 plasma levels vs. FEV1 slope, shown in Fig. 2, was not adjusted for baseline differences in FEV1 or other markers of disease severity.

  • Biomarker concentrations were not transformed prior to analysis to account for possible skew.

  • Although FEV1 serves as gold standard parameter for evaluation of CF lung disease progression, in patients starting with higher FEV1 values (> 80%) small changes in lung function could have been analysed more closely using LCI measurement. This was not performed at the time of the study, as LCI measurement had not yet been established within clinical routine at our CF center at the time of data collection.


In conclusion our results demonstrate the relevance of the multifunctional cytokine TGF-β1 as a genetic modifier in patients with CF. We showed that genetic polymorphisms in the TGF-β1 sequence have an impact on pulmonary function, rates of chronic Pa infection as well as the concentration of inflammatory cytokines, such as TNF-α. TGF-β1 polymorphisms might therefore be used to identify patients with a high risk for disease progression. Furthermore, TGF- β1 inhibition could be used as a therapeutic target to prevent the effects of a dysregulated signal pathway leading to higher levels of pulmonary inflammation for certain TGF-β1 polymorphisms.

Data availability

The datasets generated and analyzed during the current study are not publicly available due to further studies being conducted with the data. The datasets and materials used are available from the corresponding author on reasonable request. The genetic data of the investigated, previously known TGF-β1 polymorphisms can be found under the following web links of the dbSNP database: rs1800469— rs1800470— rs1800471—



American thoracic society






Cystic fibrosis


Cystic fibrosis transmembrane conductance regulator


Dideoxynucleotide triphosphate


Deoxyribonucleic acid

FEV1 :

Forced expiratory volume in 1 s


Guanine; IL-1β: Interleukine 1 Beta


Interleukine 8


Interleukine 6

Pa :

Pseudomonas aeruginosa


Polymerase chain reaction




Shrimp alkaline phosphate


Single base extension


Standard deviation


Single nucleotide polymorphism



TGF-β1 :

Transforming growth factor beta 1; TNF-α: Tumor necrosis factor alpha


  1. De Boeck K. Progress in therapies for cystic fibrosis. Lancet Respir Med. 2016;2016(4):662–74.

    Article  Google Scholar 

  2. Rafeeq MM. Cystic fibrosis: current therapeutic targets and future approaches. J Transl Med. 2017;15(1):84.

    Article  Google Scholar 

  3. Eickmeier O (2013) Transforming growth factor b1 genotypes in relation to tgfb1, IL-8, TNF-alpha in induced sputum and blood in cystic fibrosis. Mediators Inflamm.

  4. Nichols D. Inflammation and its genesis in cystic fibrosis. Pediatr Pulmonol. 2015;50:39–56.

    Article  Google Scholar 

  5. Jaurent GJ. Encyclopedia of respiratory medicine. Academic Press; 2006.

    Google Scholar 

  6. Brazova J. Polymorphisms of TGF-beta 1 in cystic fibrosis patients. Clin Immunol. 2006;121:350–7.

    Article  CAS  Google Scholar 

  7. Hull J. Contribution of genetic factors other than CFTR to disease severity in cystic fibrosis. Thorax. 1998;53:1018–21.

    Article  CAS  Google Scholar 

  8. Gagliardo R. The Role of transforming growth factor-beta1 in airway inflammation of childhood asthma. Int J Immunopathol Pharmacol, Jul-Sep. 2013;26(3):725–38.

    Article  CAS  Google Scholar 

  9. van Obberghen-Schilling E. Transforming growth factor beta1 positively regulates its own expression in normal and transformed cells. J o Biol Chem. 1988;263(16):7741–6.

    Article  Google Scholar 

  10. Snodgrass SM. TGF-beta1 inhibits CFTR biogenesis and prevents functional rescue of delF08-CFTR in primary differentiated human bronchial epithelial cells. PLoS ONE. 2013;8(5):e63167.

    Article  CAS  Google Scholar 

  11. Sagwal S. Association of serum TGF-β1 levels with different clinical phenotypes of cystic fibrosis exacerbation. Lung. 2020;198:377–83.

    Article  CAS  Google Scholar 

  12. Mitash N (2019). Transforming growth factor-β1 selectively recruits microRNA to the RNA-induced silencing complex and degrades CFTR mRNA under permissive conditions in human bronchial epithelial cells: Int J Mol Sci; 4933.

  13. Drumm ML. Genetic modifiers of lung disease in cystic fibrosis. N Engl J Med. 2005;353:1443–53.

    Article  CAS  Google Scholar 

  14. Kim SJ. Characterization of the promoter region of the human transforming growth factor-(beta)1 gene. J Biol Chem. 1989;264:402–8.

    Article  CAS  Google Scholar 

  15. Arkwright PD. TGFbeta(1) genotype and accelerated decline in lung function of patients with cystic fibrosis. Thorax. 2000;55:459–62.

    Article  CAS  Google Scholar 

  16. Awad MR. Genotypic variation in the transforming growth factor-1 gene. Transplantation. 1988;66:1014–20.

    Article  Google Scholar 

  17. Emerson J. Pseudomonas aeruginosa and other predictors of mortality and morbidity in young children with cystic fibrosis. Pediatr Pulmonol. 2002;34(2):91–100.

    Article  Google Scholar 

  18. Amadori A. Recurrent exacerbations affect FEV1 decline in adult patients with cystic fibrosis. Resp Med. 2009;103:407–13.

    Article  Google Scholar 

  19. Farrell PM. Guidelines for diagnosis of cystic fibrosis in newborns through older adults: cystic fibrosis foundation consensus report. J Pediatr. 2008;153(2):4–14.

    Article  Google Scholar 

  20. Gendiagnostik-Kommission (2011). Richtlinie der Gendiagnostik-Kommission (GEKO) zu den Anforderungen an die Inhalte der Aufklärung gemäß § 23 Abs. 2 Nr. 3 GenDG bei genetischen Untersuchungen zur Klärung der Abstammung. Bundesgesundheitsbl 54:1242–1247; DOI

  21. Last date accessed: 14.02.2022.

  22. Last date accessed: 14.02.2022

  23. Last date accessed: 14.02.2022.

  24. Last date accessed: 14.02.22.

  25. Last date accessed: 14.02.2022.

  26. Last date accessed: 14.02.2022.

  27. Laszlo G. (2006). Standardisation of lung function testing: helpful guidance from the ATS/ERS Task Force. Thorax 774–776.

  28. Lee T. Evaluation of a new definition for chronic Pseudomonas aeuruginosa infection in cystic fibrosis patients. J Cystic Fibrosis. 2003;2:29–34.

    Article  Google Scholar 

  29. NCBI - dbSNP Short Genetic Variations: Last date Accessed: 14.02.2022.

  30. Thomassen JC. Reduced neutrophil elastase inhibitor elafin and elevated TGFβ1 are linked to inflammatory response in sputum of CF patients with Pseudomonas aeruginosa. ERJ Open Research. 2021;7:00636–2020.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Chena Y. Serum TNF-a concentrations in type 2 diabetes mellitus patients and diabetic nephropathy patients: A systematic review and meta-analysis. Immunol Letters. 2017;186:52–8.

    Article  Google Scholar 

  32. Yang D. Flagellin of Pseudomonas aeruginosa induces transforming growth factor β 1 expression in normal bronchial epithelial cells through mitogen activated protein kinase cascades. Chin Med J. 2011;124(4):599–605.

    CAS  PubMed  Google Scholar 

  33. Oueslati S. Association of TGFB1 -509C/T polymorphism gene with clinical variability in cystic fibrosis patients: A case-control study. Pathol Biol (Paris). 2015;63(4–5):175–8.

    Article  CAS  Google Scholar 

  34. Zemel BS. Longitudinal relationship among growth, nutritional status, and pulmonary function in children with cystic fibrosis: Analysis of the Cystic Fibrosis Foundation National CF Patient Registry. J Pediatr. 2000;137:374–80.

    Article  CAS  Google Scholar 

Download references


The authors would like to appreciate the staff of the Division of Forensic Genetics at the Institute of Legal Medicine, Faculty of Medicine, University of Cologne, for their excellent collaboration.


Open Access funding enabled and organized by Projekt DEAL. This article is based on a doctoral thesis by the author and was supported by Wilsing Stiftung, Cologne and the CF Center, Faculty of Medicine, University of Cologne.We acknowledge support for the Article Processing Charge from the DFG (German Research Foundation, 491454339).

Author information

Authors and Affiliations



MvM, GF, SvK collected sputum and performed sputum processing. MvM, GF, MA and JCT collected data regarding the analysis of sputum and plasma TGF-β1 levels. TT and PMS collected data regarding genotypes of TGF-β1 polymorphisms. TT and SvK collected data regarding clinical data of patients. TT, SvK, ER, MA and PMS designed the study and interpreted the data. SvK and ER contributed in manuscript writing. All authors read and approved the final manuscript.

Corresponding author

Correspondence to T. Trojan.

Ethics declarations

Ethical approval and consent to participants

Human guidelines of good clinical practice and the declaration of Helsinki (1964) and Edinburgh (2000) were followed in the conduct of the trial. Ethical approval was obtained from the Medical Ethical Committee of the University Hospital Cologne. Written informed consent was obtained from all parents and all patients older than eight years of age, as well as from all healthy donors of control DNA samples.

Consent to publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1.

 Contains Figures A, B, C, D and E as referred to in the manuscript text above. Figure A shows an exemplary capillary electrophoresis result of a CF-patient in our cohort used to determine the genotype at all three investigated TGF-β1 polymorphism loci. Figure B shows a summary of all FEV1slope subgroups and categorizations used for more detailed analysis of slope associations with SNPs and TGF-β1 levels. Figure C shows a summary of FEV1 subgroups, according to best FEV1 in the final year of their 5-year observation period. Figure D shows a summary of mean average FEV1 slope for different SNP genotype groups at all three investigated TGF-β1 SNP loci. Figure E shows a summary of all TGF-β1 SNP genotypes and mean average concentrations of investigated inflammatory markers. 

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Trojan, T., Alejandre Alcazar, M.A., Fink, G. et al. The effect of TGF-β1 polymorphisms on pulmonary disease progression in patients with cystic fibrosis. BMC Pulm Med 22, 183 (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: