<i>SLC6A14</i> Is a Genetic Modifier of Cystic Fibrosis That Regulates <i>Pseudomonas aeruginosa</i> Attachment to Human Bronchial Epithelial Cells
Bibliographic record
Abstract
ABSTRACT Cystic fibrosis (CF) is caused by mutations in the CFTR gene and is associated with progressive and ultimately fatal infectious lung disease. There can be considerable variability in disease severity among individuals with the same CFTR mutations, and recent genome-wide association studies have identified secondary genetic factors that contribute to this. One of these modifier genes is SLC6A14 , which encodes an amino acid transporter. Importantly, variants of this gene have been associated with age at first acquisition of Pseudomonas aeruginosa . In this study, we aimed to determine the function of SLC6A14 in airway epithelia and how it might affect colonization by P. aeruginosa . We show that SLC6A14 is expressed in respiratory epithelial cells and transports l -arginine out of the airway surface liquid (ASL). Exposure of airway epithelia to flagellin from P. aeruginosa led to upregulation of SLC6A14 expression and increased SLC6A14-dependent uptake of l -arginine from the ASL. In support of the hypothesis that l -arginine affects P. aeruginosa attachment, we showed that l -arginine supplementation promoted P. aeruginosa attachment to an abiotic surface in a dose-dependent manner. In a coculture model, we found that inhibition of SLC6A14-dependent l -arginine transport enhanced P. aeruginosa attachment. In Slc6a14 −/y (knockout) mice, P. aeruginosa attachment to lung tissue was also significantly enhanced. Together, these findings suggest that SLC6A14 activity plays a role in the modification of the initial stages of airway infection by altering the level of l -arginine in the ASL, which in turn affects the attachment of P. aeruginosa . IMPORTANCE CF patients with shared CFTR gene mutations show significant variability in their clinical presentation of infectious lung disease. Genome-wide association studies have been used to identify secondary genetic factors that may explain the variable susceptibility to infection by opportunistic pathogens, including P. aeruginosa , the leading cause of pathogen-induced lung damage in nonpediatric CF patients. Once identified and characterized, these secondary genetic modifiers may allow for the development of personalized medicine for patients and ultimately the extension of life. In this study, we interrogated the biological role of one of these modifiers, SLC6A14 , and showed that it contributes to host defense by depleting extracellular arginine (an attachment-promoting metabolite for P. aeruginosa ) from the airway surface liquid.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".