Integrating Genomic Data Sets for Knowledge Discovery: An Informed Approach to Management of Captive Endangered Species

Many endangered captive populations exhibit reduced genetic diversity resulting in health issues that impact reproductive fitness and quality of life. Numerous cost effective genomic sequencing and genotyping technologies provide unparalleled opportunity for incorporating genomics knowledge in management of endangered species. Genomic data, such as sequence data, transcriptome data, and genotyping data, provide critical information about a captive population that, when leveraged correctly, can be utilized to maximize population genetic variation while simultaneously reducing unintended introduction or propagation of undesirable phenotypes. Current approaches aimed at managing endangered captive populations utilize species survival plans (SSPs) that rely upon mean kinship estimates to maximize genetic diversity while simultaneously avoiding artificial selection in the breeding program. However, as genomic resources increase for each endangered species, the potential knowledge available for management also increases. Unlike model organisms in which considerable scientific resources are used to experimentally validate genotype-phenotype relationships, endangered species typically lack the necessary sample sizes and economic resources required for such studies. Even so, in the absence of experimentally verified genetic discoveries, genomics data still provides value. In fact, bioinformatics and comparative genomics approaches offer mechanisms for translating these raw genomics data sets into integrated knowledge that enable an informed approach to endangered species management

RNA sequencing demonstrates large-scale temporal dysregulation of gene expression in stimulated macrophages derived from MHC-defined chicken haplotypes

<div><p>Discovering genetic biomarkers associated with disease resistance and enhanced immunity is critical to developing advanced strategies for controlling viral and bacterial infections in different species. Macrophages, important cells of innate immunity, are directly involved in cellular interactions with pathogens, the release of cytokines activating other immune cells and antigen presentation to cells of the adaptive immune response. IFNγ is a potent activator of macrophages and increased production has been associated with disease resistance in several species. This study characterizes the molecular basis for dramatically different nitric oxide production and immune function between the B2 and the B19 haplotype chicken macrophages.A large-scale RNA sequencing approach was employed to sequence the RNA of purified macrophages from each haplotype group (B2 vs. B19) during differentiation and after stimulation. Our results demonstrate that a large number of genes exhibit divergent expression between B2 and B19 haplotype cells both prior and after stimulation. These differences in gene expression appear to be regulated by complex epigenetic mechanisms that need further investigation.</p></div

Sequencing reads across biological samples and time points.

<p>Sequencing reads across biological samples and time points.</p

Gene enrichment analysis—Highlights from gene ontology, KEGG pathways, reactome pathways.

<p>Gene enrichment analysis—Highlights from gene ontology, KEGG pathways, reactome pathways.</p

Pattern of 13,618 genes expressed across haplotypes and timepoints.

<p>Visual representation of genes within B2 and B19 haplotypes at each of the time points. Figure includes genes expressed in common, genes expressed only in B2, genes expressed only in B19, all genes expressed in B2, all genes expressed in B19, and total non-redundant genes expressed in either B2 or B19 haplotypes.</p

Distinct temporal gene expression patterns in B2 versus B19 monocytes/macrophages.

<p>B-locus haplotypes in chickens provide a mechanism for genetically perturbing the cluster of immunologically important genes on chromosome 16 and producing phenotypic variation affecting infectious disease susceptibility and resistance. The heat map allows visualization of gene expression between the two genetically distinct haplotypes. Each row represents a gene within the B-locus (listed on the right) and each column corresponds to a particular time point when cells were collected for RNA sequencing. Black pixels indicate zero gene expression for a particular gene at a specific point in time, and dark blue corresponds to very low expression, while brighter blue indicates the next higher levels. Dark purple represents higher expression levels than blue colors, and pink represents the highest levels of gene expression. Monocytes were obtained from each haplotype of chicken and allowed to differentiate into macrophages in vitro for seven, days beginning on day minus 6 (t-6). RNA was sampled on day t-6, day t-3, and again three days later which is denoted as 0 hours (t0), when IFNγ was initially added to the cultures. On t0, RNA was sampled immediately before stimulation with IFNγ. Subsequent time points correspond to the time following interferon stimulation, in hours (1 hour, 2 hours, 4 hours, 8 hours, 16 hours and 24 hours). As visible on the heat map, there are distinct differences in gene expression between the B2 and B19 cells. The most dramatic difference occurs on day t-6. B2 cells exhibit a rapid burst of gene expression, indicated as a single column of pink on the left most edge of the heat map. In contrast, the B19 cells appear to undergo a much slower and prolonged gene expression program that was not as rapidly down regulated as in genes in the B2 cells. Additional gene expression data for a number of proteins involved in cell growth and apoptosis, is shown in the bottom half of the figure to highlight a similar pattern in gene expression and kinetics. The green border indicates the B2 haplotype expression pattern and the red border corresponds to the B19 expression pattern.</p

Identification of divergent gene expression patterns between B2 and B19 macrophages.

<p>Visualization of divergent gene expression patterns between the B2 and B19 haplotypes. A subset of genes exhibiting divergent gene expression were identified and visualized in heat following hierarchical clustering of the genes (rows), but not the time points (columns). The genes cluster into four major clades (clade1, clade2, clade3, and clade4) with a singleton gene (labelled clade 5). Among these genes, represented in clade1 and clade2, are a number of miRNAs exhibiting strong expression in B2 cells (mir-147, mir-146b, mir-1618, mir-200a, mir-1649, and mir-1648a) compared to the B19 samples. Likewise, miRNAs contained in clade3 and clade4 exhibit greater expression in B19 cells (mir-1627, mir-222b, mir-1633, and mir-19a). Additionally, a number of small nucleolar RNAs (snoRNAs) exhibit similarly dichotomous gene expression patterns (clade4) such that SNORd24, snoZ40, SNORD74, SNORA17, and SNORD12 exhibit substantially higher levels of expression in B19 cells on day -6 compared to B2 cells while B2 cells express such as snoU2_19 (clade2).</p