Infectious Disease and the Diversification of the Human Genome

The human immune system is under great pathogen-mediated selective pressure. A combination of divergent infectious disease pathogenesis across human populations, and the overrepresentation of “immune genes” in genomic regions with signatures of positive selection suggests that pathogens have significantly altered the human genome. However, important features of the human immune system can confound searches for and interpretations of signatures of pathogen-mediated evolution. Immune system redundancy, immune gene pleiotropy, host ability to acquire immunity and alter the immune repertoire of their offspring through “priming”, and host microbiome complicate evolutionary interpretations of host- pathogen interactions. The overall promiscuity and sensitivity of the immune system to local environments can also muddy assumptions about the origins of a selective pressure on a given set of genes. This review addresses how features of the immune system, the primary buffer between a pathogen and the human genome, affect evolutionary signal. Here, considerations that must be made when assessing how pathogens have contributed to human diversification are addressed

Primate innate immune responses to bacterial and viral pathogens reveals an evolutionary trade-off between strength and specificity

Despite their close genetic relatedness, apes and African and Asian monkeys (AAMs) differ in their susceptibility to severe bacterial and viral infections that are important causes of human disease. Such differences between humans and other primates are thought to be a result, at least in part, of interspecies differences in immune response to infection. However, because of the lack of comparative functional data across species, it remains unclear in what ways the immune systems of humans and other primates differ. Here, we report the whole-genome transcriptomic responses of ape species (human and chimpanzee) and AAMs (rhesus macaque and baboon) to bacterial and viral stimulation. We find stark differences in the responsiveness of these groups, with apes mounting a markedly stronger early transcriptional response to both viral and bacterial stimulation, altering the transcription of ∼40% more genes than AAMs. Additionally, we find that genes involved in the regulation of inflammatory and interferon responses show the most divergent early transcriptional responses across primates and that this divergence is attenuated over time. Finally, we find that relative to AAMs, apes engage a much less specific immune response to different classes of pathogens during the early hours of infection, up-regulating genes typical of anti-viral and anti-bacterial responses regardless of the nature of the stimulus. Overall, these findings suggest apes exhibit increased sensitivity to bacterial and viral immune stimulation, activating a broader array of defense molecules that may be beneficial for early pathogen killing at the potential cost of increased energy expenditure and tissue damage

Immune System Promiscuity in Human and Nonhuman Primate Evolution

Many genes that respond to infection have functions outside of immunity and have been found to be under natural selection. Pathogens may therefore incidentally alter nonimmune physiology through engagement with immune system genes. This raises a logical question of how genetically promiscuous the immune system is, here defined as how heavily cross-referenced the immune system is into other physiological systems. This work examined immune gene promiscuity across physiological systems in primates by assessing the baseline (unperturbed) expression of key tissue and cell types for differences, and primate genomes for signatures of selection. These efforts revealed “immune” gene expression to be cross-referenced extensively in other physiological systems in primates. When immune and nonimmune tissues diverge in expression, the differentially expressed genes at baseline are enriched for cell biological activities not immediately identifiable as immune function based. Individual comparisons of immune and nonimmune tissues in primates revealed low divergence in gene expression between tissues, with the exception of whole blood. Immune gene promiscuity increases over evolutionary time, with hominoids exhibiting the most cross-referencing of such genes among primates. An assessment of genetic sequences also found positive selection in the coding regions of differentially expressed genes between tissues functionally associated with immunity. This suggests that, with increasing promiscuity, divergent gene expression between the immune system and other physiological systems tends to be adaptive and enriched for immune functions in hominoids

Widespread Shortening of 3’ Untranslated Regions and Increased Exon Inclusion Are Evolutionarily Conserved Features of Innate Immune Responses to Infection

The contribution of pre-mRNA processing mechanisms to the regulation of immune responses remains poorly studied despite emerging examples of their role as regulators of immune defenses. We sought to investigate the role of mRNA processing in the cellular responses of human macrophages to live bacterial infections. Here, we used mRNA sequencing to quantify gene expression and isoform abundances in primary macrophages from 60 individuals, before and after infection with Listeria monocytogenes and Salmonella typhimurium. In response to both bacteria we identified thousands of genes that significantly change isoform usage in response to infection, characterized by an overall increase in isoform diversity after infection. In response to both bacteria, we found global shifts towards (i) the inclusion of cassette exons and (ii) shorter 3’ UTRs, with near-universal shifts towards usage of more upstream polyadenylation sites. Using complementary data collected in non-human primates, we show that these features are evolutionarily conserved among primates. Following infection, we identify candidate RNA processing factors whose expression is associated with individual-specific variation in isoform abundance. Finally, by profiling microRNA levels, we show that 3’ UTRs with reduced abundance after infection are significantly enriched for target sites for particular miRNAs. These results suggest that the pervasive usage of shorter 3’ UTRs is a mechanism for particular genes to evade repression by immune-activated miRNAs. Collectively, our results suggest that dynamic changes in RNA processing may play key roles in the regulation of innate immune responses

Widespread Shortening of 3’ Untranslated Regions and Increased Exon Inclusion Are Evolutionarily Conserved Features of Innate Immune Responses to Infection

The contribution of pre-mRNA processing mechanisms to the regulation of immune responses remains poorly studied despite emerging examples of their role as regulators of immune defenses. We sought to investigate the role of mRNA processing in the cellular responses of human macrophages to live bacterial infections. Here, we used mRNA sequencing to quantify gene expression and isoform abundances in primary macrophages from 60 individuals, before and after infection with Listeria monocytogenes and Salmonella typhimurium. In response to both bacteria we identified thousands of genes that significantly change isoform usage in response to infection, characterized by an overall increase in isoform diversity after infection. In response to both bacteria, we found global shifts towards (i) the inclusion of cassette exons and (ii) shorter 3’ UTRs, with near-universal shifts towards usage of more upstream polyadenylation sites. Using complementary data collected in non-human primates, we show that these features are evolutionarily conserved among primates. Following infection, we identify candidate RNA processing factors whose expression is associated with individual-specific variation in isoform abundance. Finally, by profiling microRNA levels, we show that 3’ UTRs with reduced abundance after infection are significantly enriched for target sites for particular miRNAs. These results suggest that the pervasive usage of shorter 3’ UTRs is a mechanism for particular genes to evade repression by immune-activated miRNAs. Collectively, our results suggest that dynamic changes in RNA processing may play key roles in the regulation of innate immune responses

Behavioral immune system activity predicts downregulation of chronic basal inflammation.

Here, we present a mechanistically grounded theory detailing a novel function of the behavioral immune system (BIS), the psychological system that prompts pathogen avoidance behaviors. We propose that BIS activity allows the body to downregulate basal inflammation, preventing resultant oxidative damage to DNA and promoting longevity. Study 1 investigated the relationship between a trait measure of pathogen avoidance motivation and in vitro and in vivo proinflammatory cytokine production. Study 2 examined the relationship between this same predictor and DNA damage often associated with prolonged inflammation. Results revealed that greater trait pathogen avoidance motivation predicts a) lower levels of spontaneous (but not stimulated) proinflammatory cytokine release by peripheral blood mononuclear cells (PBMCs), b) lower plasma levels of the proinflammatory cytokine interleukin-6 (IL-6), and c) lower levels of oxidative DNA damage. Thus, the BIS may promote health by protecting the body from the deleterious effects of inflammation and oxidative stress

Tandem 3’ UTR shortening allows evasion of regulation by miRNAs.

<p>(A) Distribution of frequency of miRNA target sites per nucleotide in the extended regions of Tandem UTRs that either show no change after infection (<i>grey</i>) or significantly change after infection (<i>blue</i>). (B) Significantly enriched miRNA target sites (FDR ≤ 10%, |FC| > 1.5) in the extended regions of significantly changing Tandem UTRs after infection with <i>Listeria</i>-only (<i>top</i>), with <i>Salmonella</i>-only (<i>middle</i>), or both bacteria (<i>bottom</i>). For each bacteria, the barplots in the left panels show the fold enrichment (<i>x-axis</i>, log₂ scale) of target sites in the extended regions. White bars represent non-significant enrichments. Panels on the right show the fold change in miRNA expression (<i>x-axis</i>, log₂ scale with standard error bars) after either 2 hours of infection (<i>light colors</i>) or 24 hours of infection (<i>dark colors</i>).</p

3’RNA sequencing shows increased usage of upstream polyadenylation sites upon infection.

<p>(A) Meta-gene distributions of 3’RNA-seq read densities at the upstream polyA sites (core regions, <i>left</i>) and downstream polyA sites (extended regions, <i>right</i>) of Tandem 3’ UTRs after infection with <i>Listeria</i> or <i>Salmonella</i> (<i>top</i> and <i>bottom</i>, respectively). Shown are the read distributions for non-infected samples across all Tandem 3’ UTRs (<i>black</i>) and infected samples at Tandem 3’ UTRs that significantly change after infection (<i>yellow</i>) or show no change after infection (<i>blue</i>), as called by the RNA-seq data. (B) Distribution of ΔΨ values calculated from 3’RNA-seq data for Tandem 3’ UTRs. We observe significant shifts (<i>P</i> < 2.2 × 10⁻¹⁶ for both <i>Listeria</i> and <i>Salmonella</i>) towards negative ΔΨ values in Tandem UTRs that are identified as significantly changing in RNA-seq data (<i>yellow</i>) relative to Tandem UTRs without any change after infection (<i>blue</i>).</p

Gene expression and isoform proportion differences in response to bacterial infection.

<p>(A) Principal component analysis of gene expression data from all samples (PC1 and PC2 on the <i>x-</i> and <i>y-axis</i>, respectively). (B) <i>IL24</i>, a gene with significant changes in isoform usage upon infection with <i>Listeria</i> and <i>Salmonella</i>. For each <i>IL24</i> isoform in response to infection, plotted are the average relative isoform usages across samples (<i>left panel</i>, <i>top</i>) with their isoform structures (<i>left panel</i>, <i>bottom</i>) and corresponding fold changes (<i>right panel</i>; log₂ scale; with standard error bars). Isoforms are ordered by relative abundance in non-infected samples, and colored circles (<i>right panel</i>) correspond to colors in barplot (<i>left panel</i>). (C) Number of genes showing only DIU, only DGE, and both DIU and DGE upon infection with <i>Listeria</i> and <i>Salmonella</i>, (11,353 genes tested).</p

RNA processing changes in response to bacterial infection.

<p>(A) Proportion of events for RNA processing category that are significantly changing after infection with <i>Listeria</i> (<i>left</i>), <i>Salmonella</i> (<i>middle</i>), or variation between non-infected samples as a control (<i>right</i>). Numbers indicate the number of significant changes per category. (B) Significantly Gene Ontology categories for genes with any significant RNA processing change (FDR ≤ 10%). (C) Distribution of ΔΨ values for each RNA processing category. Negative values represent less inclusion, while positive values represent more inclusion, as defined by the schematic exon representations.</p