Search for: All records

ABSTRACT Early life environments can have long-lasting impacts on health and fitness, but the evolutionary significance of these effects remains debated. Two major classes of explanations have been proposed: developmental constraints (DC) explanations posit that early life adversity limits optimal development, leading to long-term costs, while predictive adaptive response (PAR) explanations posit that organisms use early life cues to predict adult conditions, resulting in detriments when adult environments do not match expectations. We tested these hypotheses using anthropological and biomedical data for the Orang Asli—the Indigenous peoples of Peninsular Malaysia—who are undergoing a rapid but heterogenous transition from non-industrial, subsistence-based livelihoods to more industrialized, market-integrated conditions. Using questionnaire data, we show that this shift creates natural variation in the degree of similarity between early life and adult environments. Using anthropometric and health data, we find that, more rural, subsistence-based early life environments are associated with shorter stature but better adult cardiometabolic health. Applying a quadratic regression framework, we find support for DC but not PAR in explaining adult cardiometabolic health, echoing findings and conclusions from other long-lived species. Overall, our results suggest that early life conditions can provide additive protection against common health issues associated with urban, industrialized lifestyle exposure. 

Age and early life adversity (ELA) are both key determinants of health, but whether they target similar physiological mechanisms across the body is unknown due to limited multi-tissue datasets from well-characterized cohorts. We generated DNA methylation (DNAm) profiles across 14 tissues in 237 semi-free ranging rhesus macaques, with records of naturally occurring ELA. We show that age-associated DNAm variation is predominantly tissue-dependent, yet tissue-specific epigenetic clocks reveal that the pace of epigenetic aging is relatively consistent within individuals. ELA effects on loci are adversity-dependent, but a given ELA has a coordinated impact across tissues. Finally, ELA targeted many of the same loci as age, but the direction of these effects varied, indicating that ELA does not uniformly contribute to accelerated age in the epigenome. ELA thus imprints a coordinated, tissue-spanning epigenetic signature that is both distinct from and intertwined with age-related change, advancing our understanding of how early environments sculpt the molecular foundations of aging and disease. 

Characterizing DNA methylation patterns is important for addressing key questions in evolutionary biology, development, geroscience, and medical genomics. While costs are decreasing, whole-genome DNA methylation profiling remains prohibitively expensive for most population-scale studies, creating a need for cost-effective, reduced representation approaches (i.e., assays that rely on microarrays, enzyme digests, or sequence capture to target a subset of the genome). Most common whole genome and reduced representation techniques rely on bisulfite conversion, which can damage DNA resulting in DNA loss and sequencing biases. Enzymatic methyl sequencing (EM-seq) was recently proposed to overcome these issues, but thorough benchmarking of EM-seq combined with cost-effective, reduced representation strategies is currently lacking. To address this gap, we optimized the Targeted Methylation Sequencing protocol (TMS)—which profiles ~4 million CpG sites—for miniaturization, flexibility, and multispecies use. First, we tested modifications to increase throughput and reduce cost, including increasing multiplexing, decreasing DNA input, and using enzymatic rather than mechanical fragmentation to prepare DNA. Second, we compared our optimized TMS protocol to commonly used techniques, specifically the Infinium MethylationEPIC BeadChip (n = 55 paired samples) and whole genome bisulfite sequencing (n = 6 paired samples). In both cases, we found strong agreement between technologies (R² = 0.97 and 0.99, respectively). Third, we tested the optimized TMS protocol in three non-human primate species (rhesus macaques, geladas, and capuchins). We captured a high percentage (mean = 77.1%) of targeted CpG sites and produced methylation level estimates that agreed with those generated from reduced representation bisulfite sequencing (R² = 0.98). Finally, we confirmed that estimates of 1) epigenetic age and 2) tissue-specific DNA methylation patterns are strongly recapitulated using data generated from TMS versus other technologies. Altogether, our optimized TMS protocol will enable cost-effective, population-scale studies of genome-wide DNA methylation levels across human and non-human primate species. 

ABSTRACT Characterizing DNA methylation patterns is important for addressing key questions in evolutionary biology, geroscience, and medical genomics. While costs are decreasing, whole-genome DNA methylation profiling remains prohibitively expensive for most population-scale studies, creating a need for cost-effective, reduced representation approaches (i.e., assays that rely on microarrays, enzyme digests, or sequence capture to target a subset of the genome). Most common whole genome and reduced representation techniques rely on bisulfite conversion, which can damage DNA resulting in DNA loss and sequencing biases. Enzymatic methyl sequencing (EM-seq) was recently proposed to overcome these issues, but thorough benchmarking of EM-seq combined with cost-effective, reduced representation strategies has not yet been performed. To do so, we optimized Targeted Methylation Sequencing protocol (TMS)—which profiles ∼4 million CpG sites—for miniaturization, flexibility, and multispecies use at a cost of ∼$80. First, we tested modifications to increase throughput and reduce cost, including increasing multiplexing, decreasing DNA input, and using enzymatic rather than mechanical fragmentation to prepare DNA. Second, we compared our optimized TMS protocol to commonly used techniques, specifically the Infinium MethylationEPIC BeadChip (n=55 paired samples) and whole genome bisulfite sequencing (n=6 paired samples). In both cases, we found strong agreement between technologies (R² = 0.97 and 0.99, respectively). Third, we tested the optimized TMS protocol in three non-human primate species (rhesus macaques, geladas, and capuchins). We captured a high percentage (mean=77.1%) of targeted CpG sites and produced methylation level estimates that agreed with those generated from reduced representation bisulfite sequencing (R² = 0.98). Finally, we applied our protocol to profile age-associated DNA methylation variation in two subsistence-level populations—the Tsimane of lowland Bolivia and the Orang Asli of Peninsular Malaysia—and found age-methylation patterns that were strikingly similar to those reported in high income cohorts, despite known differences in age-health relationships between lifestyle contexts. Altogether, our optimized TMS protocol will enable cost-effective, population-scale studies of genome-wide DNA methylation levels across human and non-human primate species. 

Identifying biomarkers of age-related changes in immune system functioning that can be measured non-invasively is a significant step in progressing research on immunosenescence and inflammaging in free-ranging and wild animal populations. In the present study, we aimed to investigate the suitability of two urinary compounds, neopterin and suPAR, as biomarkers of age-related changes in immune activation and inflammation in a free-ranging rhesus macaque ( Macaca mulatta ) population. We also investigated age-associated variation in gene transcription from blood samples to understand the underlying proximate mechanisms that drive age-related changes in urinary neopterin or suPAR. Neopterin was significantly positively correlated with age, and had a moderate within-individual repeatability, indicating it is applicable as a biomarker of age-related changes. The age-related changes in urinary neopterin are not apparently driven by an age-related increase in the primary signaler of neopterin, IFN-y, but may be driven instead by an age-related increase in both CD14+ and CD14− monocytes. suPAR was not correlated with age, and had low repeatability within-individuals, indicating that it is likely better suited to measure acute inflammation rather than chronic age-related increases in inflammation (i.e., “inflammaging”). Neopterin and suPAR had a correlation of 25%, indicating that they likely often signal different processes, which if disentangled could provide a nuanced picture of immune-system function and inflammation when measured in tandem.