Lappalainen, Tuuli

Abstract Rationale. Bronchoscopy can directly assess cells in airway epithelium in participants with COPD, but it is invasive, expensive, difficult to perform at scale, and contraindicated in severe disease. Blood is much more easily available but contains only a limited subset of cell types, many of which are less relevant to lung biology. Recent advances in RNA-sequencing technology have allowed for reliable measurement of transcriptomics in a wider variety of more accessible tissues, including hair follicle cells. We collected and performed bulk RNA-seq on hair follicles as an ancillary study within the NIH-funded SubPopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS) and investigated their cell type composition to better understand the opportunities and limitations of this tissue for the study of lung disease. Methods. SPIROMICS recruited individuals with and without COPD aged 40-80 years with ≥ 20 pack-years smoking history. Participant at several sites underwent collection of approximately 10 hair follicles from the head, which were immediately frozen at -80ºC in 2018-21. mRNA in hair follicles was measured by bulk RNA-seq, and cell type deconvolution was performed using GEDIT v1.7 with the provided BlueCodeV1.0.tsv reference matrix. Cell types were collapsed into broader categories by summing the estimated cell type proportions. Results. The cell type deconvolution of the hair follicle bulk RNA-seq from 80 participants with and without COPD estimated that the tissue was largely composed of immune cells (median 36%, including B cells, T cells, neutrophils, macrophages, NK cells, and monocytes) and epithelial cell types (median 31%), with the largest subgroup of epithelial cell types identified as hair follicular keratinocytes (Figure 1). The next largest component was fibroblasts (median 9%), followed by endothelial cells (median 8%). Signals for myocytes (median 5%), melanocytes (median 5%), and chondrocytes (median 5%) were also present. Conclusion. This initial study demonstrated promising signals for a variety of lung-relevant cell types in hair follicles in SPIROMICS, including immune and epithelial cells in addition to fibroblasts and endothelial cells that are not otherwise easily accessible. Hair follicle gene expression profiling shows promise for understanding numerous facets of lung biology and disease that both complements and exceeds that of other noninvasive tissues such as blood.

Abstract RATIONALE: Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of mortality worldwide. We previously published the polygenic transcriptome risk score (PTRS) which uses the cumulative effect of predicted gene expression to construct genetic predictors for complex diseases, and we demonstrated the value of PTRS for COPD built on Genotype-Tissue Expression (GTEx) Lung tissue with significantly improved cross-ancestry portability. However, the postmortem collection and lack of lung disease status on GTEx samples likely affect results. We hypothesized that the performance of PTRS will be improved by using disease-relevant expression quantitative trait loci (eQTLs) to construct the score. Here, we aimed to improve performance of PTRS by leveraging disease-relevant eQTLs. METHODS: We constructed two transcriptome prediction models, Elastic Net (EN) and Prediction Using Models Informed by Chromatin conformation and Epigenomics (PUMICE), using TOPMed lung RNA-seq data from the Lung Tissue Research Consortium (LTRC), which included COPD patients undergoing lung surgery. We compared their performance with two published models built on RNA-seq from GTEx Lung tissue, GTEx-EN and GTEx-PUMICE. We first integrated each of four transcriptome models with multi-ancestry GWAS of FEV1/FVC ratio (Shrine et al. 2023) to generate transcriptome-wide association study (TWAS) results, which produced trait-associated genes. Pathway analysis was then conducted on significant TWAS genes. Finally, the PTRS was computed on TWAS genes that were included in the top 20 significantly enriched pathways. We tested performance of PTRS in race-stratified analysis for moderate-to-severe and severe COPD in COPDGene. RESULTS: Our models produced more significant TWAS genes (FDR<0.05) and had more overlaps with genes identified for FEV1/FVC ratio from Shrine et al. 2023 compared to two GTEx models (Fig. A). Additionally, our models had significantly higher AUC for predicting both moderate-to-severe and severe COPD in race-stratified analysis. The mean AUC from LTRC-EN for predicting severe COPD was 0.57 and 0.55 respectively for EUR and AFA, which was significantly higher than mean AUC of 0.54 and 0.51 from GTEx-EN (Delong p-value=8.15x10-10 for EUR and 1.42x10-3 for AFA) (Fig. B). Similarly, the PTRS derived from our models produced higher Odds Ratios (OR) per standard deviation. The mean OR from LTRC-PUMICE for moderate-to-severe COPD was 1.25 and 1.18 respectively for EUR and AFA, while it was 1.17 and 1.12 from GTEx-PUMICE (Fig. C). CONCLUSIONS: Our study demonstrates the value of leveraging disease-relevant RNA-seq to construct transcriptome prediction models and their improvement on identifying lung function genes and predicting COPD across ancestries.

Abstract Exonic variants present some of the strongest links between genotype and phenotype. However, these variants can have significant inter-individual pathogenicity differences, known as variable penetrance. In this study, we propose a model where genetically controlled mRNA splicing modulates the pathogenicity of exonic variants. By first cataloging exonic inclusion from RNA-sequencing data in GTEx V8, we find that pathogenic alleles are depleted on highly included exons. Using a large-scale phased whole genome sequencing data from the TOPMed consortium, we observe that this effect may be driven by common splice-regulatory genetic variants, and that natural selection acts on haplotype configurations that reduce the transcript inclusion of putatively pathogenic variants, especially when limiting to haploinsufficient genes. Finally, we test if this effect may be relevant for autism risk using families from the Simons Simplex Collection, but find that splicing of pathogenic alleles has a penetrance reducing effect here as well. Overall, our results indicate that common splice-regulatory variants may play a role in reducing the damaging effects of rare exonic variants.