Multi-Omic Profiling of the Liver Across Diets and Age in a Diverse Mouse Population

Abstract

Systems biology approaches often use networks of gene expression and metabolite data to identify regulatory factors and pathways connected with phenotypic variance. Separating upstream causal mechanisms, downstream biomarkers, and incidental correlations remains a significant challenge, yet it is essential for designing mechanistic experiments. To address this, we first designed a population following 2157 individual mice from 89 isogenic strains of BXD mice across their lifespans to identify molecular interactions between genotype, environment, age (GxExA) and metabolic fitness. Each strain was separated into two cohorts, fed low fat (6% cal/fat) or high fat (60% cal/fat) diets. One-third of individuals (662) were sacrificed at ~6, 12, 18, or 24 months-of-age, with the remainder monitored until natural death. Transcriptome, proteome, and metabolome profiles were generated from liver samples. These multi-omic measurements were deconvolved into metabolic networks, where we observed varying network connectivity as a function of GxExA. The multiple independent study variables permitted causal inference analysis for the network variants using stability selection. This calculates the strength and directionality of the interactions between molecular measurements and metabolic networks as a function of age, diet, and genotype, and assigns each gene a score for its relative position to the target pathway. At 1% FDR, 94% of novel connections were stable across age and diet, such as the connection between Rdh11 with cholesterol biosynthesis and Mut with mitochondrial translation. 6% of discovered candidate genes were unstable, indicating a clear causal relationship between the segregating independent variable, the gene, and the pathway. For instance, age drives variation in proteasomal genes (e.g. Psmb3, Psmb4), which in turn drive changes in the mitochondrial ribosome. Conversely, COX7A2L malformation drives variation in OXPHOS genes, but both are downstream of changes in mitochondrial translation. Finally, we examined all data for connections with the longevity and known longevity-related pathways, identifying several dozen novel candidate genes. Specific C. elegans orthologs for the top two candidates, Ctsd and St7, were knocked down with RNAi and found to reduce longevity both in wildtype worms and in mutant long-lived strains.

Genetic Loci and Metabolic States Associated With Murine Epigenetic Aging

“…To narrow the list, we applied two selection criteria: genes that (1) contain missense and/or stop variants, and/or (2) contain non-coding variants and regulated by a cis-acting expression QTL (eQTL). For the eQTL analysis, we utilized an existing liver transcriptome data from the same aging cohort 19 . We identified 24 positional candidates in Eaaq11 that includes Stxbp4 , Erbb2 ( Her-2 oncogenic gene), and Grb7 (growth factor receptor binding) ( Data S4 ).…”

“…Next, we performed the correlational analysis using liver proteomic data that was available for 164 of the BXDs. The proteome data quantifies over 32000 protein variants from only 3940 unique genes 19 . We took the top 2000 protein correlates of univ.EAA (|r| ≥ 0.27, p ≤ 6.0E-04) ( Data S7 ).…”

“…Here, we examine these novel clocks, lifespan predictor, and methylome entropy in a cohort of mice belonging to the BXD family that were maintained on either normal chow or high-fat diet (HFD) 18,19 . The BXDs are a well-established mouse genetic reference panel that were first created as a family of recombinant inbred (RI) strains by crossing two inbred progenitors: C57BL/6J (B6) and DBA/2J (D2).…”

“…The family has been expanded to ~150 fully sequenced progeny strains 20,21 . Members of the BXD family vary greatly in their metabolic profiles, aging rates, and natural life expectancy 18,19,22–24 . The genetic variation, and the availability of accompanying deep -omic data make the BXDs a unique experimental population for dissecting the genetic modulators of epigenetic aging.…”

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“…Rodents, especially mice, have been the species most prominently used for biomedically relevant traits. Amongst these, the BXD family of recombinant inbred (RI) mouse strains, have been extensively used for almost 50 years in fields such as neuropharmacology [4,5,6,7,8,9,10], immunology [11,12,13,14,15], behavior [16,17,18,19,20,21,22,23], aging [24,25,26,27,28], neurodegeneration [29,30,31,32,33], and gut microbiome-host interactions [34].…”