Bacterial sepsis triggers stronger transcriptomic immune responses in larger primates.
Ryan McMindsRays H Y JiangSwamy R AdapaEmily Cornelius RuhsRachel A MundsJennifer W LeidingCynthia J DownsLynn B MartinPublished in: Proceedings. Biological sciences (2024)
Empirical data relating body mass to immune defence against infections remain limited. Although the metabolic theory of ecology predicts that larger organisms would have weaker immune responses, recent studies have suggested that the opposite may be true. These discoveries have led to the safety factor hypothesis, which proposes that larger organisms have evolved stronger immune defences because they carry greater risks of exposure to pathogens and parasites. In this study, we simulated sepsis by exposing blood from nine primate species to a bacterial lipopolysaccharide (LPS), measured the relative expression of immune and other genes using RNAseq, and fitted phylogenetic models to determine how gene expression was related to body mass. In contrast to non-immune-annotated genes, we discovered hypermetric scaling in the LPS-induced expression of innate immune genes, such that large primates had a disproportionately greater increase in gene expression of immune genes compared to small primates. Hypermetric immune gene expression appears to support the safety factor hypothesis, though this pattern may represent a balanced evolutionary mechanism to compensate for lower per-transcript immunological effectiveness. This study contributes to the growing body of immune allometry research, highlighting its importance in understanding the complex interplay between body size and immunity over evolutionary timescales.
Keyphrases
- gene expression
- genome wide
- immune response
- lps induced
- dna methylation
- inflammatory response
- intensive care unit
- poor prognosis
- innate immune
- gram negative
- dendritic cells
- randomized controlled trial
- magnetic resonance
- computed tomography
- risk assessment
- machine learning
- bioinformatics analysis
- genome wide identification
- climate change
- contrast enhanced
- big data
- genome wide analysis
- deep learning