bioRxivGenomicsPreprint
Immune gene variation associated with chromosome-scale differences among individual zebrafish genomes
Sean C. McConnelliD, Kyle M. HernandeziD, Jorge AndradeiD, Jill L. O. de JongiD
Abstract
Immune genes have evolved to maintain exceptional diversity, offering robust defense against pathogens. We performed genomic sequencing and assembly to examine immune gene variation among three zebrafish individuals. We identified remarkably high levels of sequence divergence as well as presence/absence variation among these zebrafish genomes, particularly when compared with the level of variation in human genomes. Gene pathway analysis identified zebrafish immune genes as significantly enriched among genes with evidence of positive selection. A large subset of genes was absent from analysis of coding sequences due to apparent lack of reads, prompting us to examine genes overlapping zero coverage regions (ZCRs), defined as 2kb stretches without mapped reads. Zebrafish immune genes were also identified as highly enriched within ZCRs, including over 60% of zebrafish major histocompatibility complex (MHC) genes and NOD-like receptor (NLR) genes, mediators of direct and indirect pathogen recognition. This variation was most highly concentrated throughout one arm of zebrafish chromosome 4 carrying a large cluster of NLR genes, associated with large-scale structural variation covering more than half of a vertebrate chromosome. While previous studies have shown marked variation in NLR genes between vertebrate species, our study highlights extensive variation between individuals of the same species. Our genomic assemblies also provide sequences for alternative haplotypes and distinct complements of immune genes among individual zebrafish, including the MHC Class II locus. Taken together, these findings provide evidence of immune gene variation on a scale previously unknown in other vertebrate species and raise questions about potential impact on immune function.
- organism: zebrafish
- assay: whole-genome sequencing
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 1
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