Search bioRxiv⌕ Search

Biology subjects

Forrest, I. S.

Publications and source records attributed to Forrest, I. S..

2 recordsLinked to original sources

CD36 homologs determine microbial resistance to the Lyme disease spirochete

Pattern recognition receptors sense pathogens in arthropods and mammals through distinct immune processes. Whether these molecules share a similar function and recognize the same microbe in evolutionarily distant species remain ill-defined. Here, we establish that the CD36 superfamily is required for Borrelia burgdorferi resistance in both the arthropod vector and humans. Using the blacklegged tick Ixodes scapularis and an electronic health record-linked biobank, we demonstrate that CD36 members elicit immunity to the Lyme disease spirochete. In ticks, the CD36-like protein Croquemort recognizes lipids and initiates the immune deficiency and jun N-terminal kinase pathways against B. burgdorferi. In humans, exome sequencing and clinical information reveal that individuals with CD36 loss-of-function variants have increased prevalence of Lyme disease. Altogether, we discovered a conserved mechanism of anti-bacterial immunity. One Sentence SummaryLipid receptors belonging to the CD36 superfamily exhibit a shared immune function in both ticks and humans.

immunology↗

A tissue-level phenome-wide network map of colocalized genes and phenotypes in the UK Biobank

BackgroundPhenome-wide association studies conducted in electronic health record (EHR)-linked biobanks have uncovered a large number of genomic loci associated with traits and diseases. However, interpretation of the complex relationships of associated genes and phenotypes is challenging. ResultsWe constructed a tissue-level phenome-wide network map of colocalized genes and phenotypes. First, we generated colocalized expression quantitative trait loci from 48 tissues of the Genotype-Tissue Expression project and from publicly available genome-wide association study summary statistics from the UK Biobank. We identified 9,151 colocalized genes for 1,411 phenotypes across 48 tissues. Then, we constructed a bipartite network using the colocalized signals to establish links between genes and phenotypes in each tissue. The majority of links are observed in a single tissue whereas only a few are present in all tissues. Finally, we applied the biLouvain clustering algorithm in each tissue-specific bipartite network to identify co-clusters of non-overlapping genes and phenotypes. The majority of co-clusters contains a small number of genes and phenotypes, and 88.6% of co-clusters are found in only one tissue. To demonstrate functionality of the phenome-wide map, we tested if these co-clusters were enriched with known biological and functional gene classes and observed several significant enrichments. Furthermore, we observed that tissue-specific co-clusters are enriched with reported drug side effects for the corresponding drug target genes in clinical trial data. ConclusionsThe phenome-wide map provides links between genes, phenotypes and tissues across a wide spectrum of biological classes and can yield biological and clinical discoveries. The phenome-wide map is publicly available at https://rstudio-connect.hpc.mssm.edu/biPheMap/.

genomics↗