Mouse Tracheal Epithelial Cells (mTECs) as a Tool to Study the Role of BPIFA1 in Influenza A Infection
Bibliographic record
Abstract
Abstract The respiratory epithelium is a major physical barrier to infections and provides a robust innate defensive shield through the concerted actions of the mucociliary epithelial layer and its secreted chemical components. Influenza A virus (IAV) is a major human pathogen that overcomes these defences to cause disease. The mechanism that the virus uses to infect the airway remains to be fully elucidated. We have employed primary airway cells grown in 3D cultures as models to understanding the role of epithelial cells in homeostasis and infectious disease. In these cells differentiation occurs when the confluent cell layer develops on the semi-permeable insert with the cells fed with media underneath, termed an air liquid interface (ALI) condition. Studies using mice tracheal epithelial cells (mTECs) at the ALI have recently shown that BPIFA1 (Bacterial Permeability increasing fold containing Family A member 1), a respiratory tract secreted protein, protects the airway from IAV infection. The mechanism for this remains unknown and it was assumed that the infection was occurring through ciliated cells. In this thesis I have established and validated mTECs grown at an ALI as a tool for infection studies. I conducted a genome-wide transcriptional analysis to investigate global alterations in gene expression as the cells transitioned from isolated cells, through a basal cell intermediate phenotype, to full mucociliary differentiation. The cultures represent a model of the native tracheal epithelium. This analysis identified multiple genes as being up regulated during this process and serves as a resource for target gene identification. Using published single cell RNAseq data we could show that Bpifa1 expression is seen in several cell types with by far the highest expression being seen in the secretory cells. A comparative expression analysis showed that Bpifa1 was the most highly expressed member of the larger Bpif gene family in mTECs and confirmed that the closest murine paralog of the gene, Bpifa5, was not expressed in these cells, and would not be likely to serve a similar function. Using IF microscopy I confirmed that IAV did not infect BPIFA1 positive cells in mTEC cultures and was not commonly associated with ciliated cells at the early stages of infection. To address issues of cell specificity, I infected undifferentiated mTECs (lacking the mature epithelial phenotype) and could show that these were infected, despite the absence of ciliated cells. Levels of infection 2 | Page were less than was seen in differentiated mTECs. A genome wide transcriptional study showed cells of both phenotypes (basal cell intermediate, and full mucociliary differentiation phenotypes) upregulated multiple interferons stimulated genes (ISGs), albeit with lower response in the undifferentiated cells. I identified the gut antimicrobial protein gene Lypd8, as a potential novel ISG. I employed this infection model to establish an assay for IAV infection of undifferentiated mTECs, which can be utilized to examine the role of BPIFA1 in IAV anti-viral responses. To investigate this further, I generated several recombinant BPIFA1 protein expression constructs that exhibit sequence differences in a presumptive functional domain at the N-terminus of the protein for use in this infection assay. Comparative analysis showed that this repeat region is highly variable between species and is longer in rodents. I also generated a series of short peptides corresponding to the repeat region in this domain. Both sets of BPIFA1 derived reagents could be investigated for their ability to modulate IAV infection and to define more fully the functional mechanism that BPIFA1 employs against IAV infection. My results show that mTECs are a good model for studies investigating IAV infections. Undifferentiated mTECs can be used in a simple quantitative infection assay to unravel the contributions of specific regions of BPIFA1 in regulating IAV infection.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".