The FAM13A Long Isoform Regulates Cilia Movement and Co-ordination in Airway Mucociliary Transport.

SNPs in the FAM13A locus are amongst the most commonly reported risk alleles associated with chronic obstructive pulmonary disease (COPD) and other respiratory diseases, however the physiological role of FAM13A is unclear. In humans, two major protein isoforms are expressed at the FAM13A locus: 'long' and 'short', but their functions remain unknown, partly due to a lack of isoform conservation in mice. We performed in-depth characterisation of organotypic primary human airway epithelial cell subsets and show that multiciliated cells predominantly express the FAM13A long isoform containing a putative N-terminal Rho GTPase activating protein (RhoGAP) domain. Using purified proteins, we directly demonstrate RhoGAP activity of this domain. In Xenopus laevis, which conserve the long isoform, Fam13a-deficiency impaired cilia-dependent embryo motility. In human primary epithelial cells, long isoform deficiency did not affect multiciliogenesis but reduced cilia co-ordination in mucociliary transport assays. This is the first demonstration that FAM13A isoforms are differentially expressed within the airway epithelium, with implications for the assessment and interpretation of SNP effects on FAM13A expression levels. We also show that the long FAM13A isoform co-ordinates cilia-driven movement, suggesting that FAM13A risk alleles may affect susceptibility to respiratory diseases through deficiencies in mucociliary clearance. This article is open access and distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).

Proteomic signatures improve risk prediction for common and rare diseases.

For many diseases there are delays in diagnosis due to a lack of objective biomarkers for disease onset. Here, in 41,931 individuals from the United Kingdom Biobank Pharma Proteomics Project, we integrated measurements of ~3,000 plasma proteins with clinical information to derive sparse prediction models for the 10-year incidence of 218 common and rare diseases (81-6,038 cases). We then compared prediction models developed using proteomic data with models developed using either basic clinical information alone or clinical information combined with data from 37 clinical assays. The predictive performance of sparse models including as few as 5 to 20 proteins was superior to the performance of models developed using basic clinical information for 67 pathologically diverse diseases (median delta C-index = 0.07; range = 0.02-0.31). Sparse protein models further outperformed models developed using basic information combined with clinical assay data for 52 diseases, including multiple myeloma, non-Hodgkin lymphoma, motor neuron disease, pulmonary fibrosis and dilated cardiomyopathy. For multiple myeloma, single-cell RNA sequencing from bone marrow in newly diagnosed patients showed that four of the five predictor proteins were expressed specifically in plasma cells, consistent with the strong predictive power of these proteins. External replication of sparse protein models in the EPIC-Norfolk study showed good generalizability for prediction of the six diseases tested. These findings show that sparse plasma protein signatures, including both disease-specific proteins and protein predictors shared across several diseases, offer clinically useful prediction of common and rare diseases.