Social status is one of the strongest predictors of human disease risk and mortality, and it also influences Darwinian fitness in social mammals more generally. To understand the biological basis of these effects, we combined genomics with a social status manipulation in female rhesus macaques to investigate how status alters immune function. We demonstrate causal but largely plastic social status effects on immune cell proportions, cell type–specific gene expression levels, and the gene expression response to immune challenge. Further, we identify specific transcription factor signaling pathways that explain these differences, including low-status–associated polarization of the Toll-like receptor 4 signaling pathway toward a proinflammatory response. Our findings provide insight into the direct biological effects of social inequality on immune function, thus improving our understanding of social gradients in health.

Drosophila suzukii recently invaded North America and Europe. Populations in Hawaii, California, New York and Nova Scotia are polymorphic for Wolbachia, typically with <20% infection frequency. The Wolbachia in D. suzukii, denoted wSuz, is closely related to wRi, the variant prevalent in continental populations of D. simulans. wSuz is also nearly identical to Wolbachia found in D. subpulchrella, plausibly D. suzukii's sister species. This suggests vertical Wolbachia transmission through cladogenesis (“cladogenic transmission”). The widespread occurrence of 7-20% infection frequencies indicates a stable polymorphism. wSuz is imperfectly maternally transmitted, with wild infected females producing on average 5-10% uninfected progeny. As expected from its low frequency, wSuz produces no cytoplasmic incompatibility (CI), i.e., no elevated embryo mortality when infected males mate with uninfected females, and no appreciable sex-ratio distortion. The persistence of wSuz despite imperfect maternal transmission suggests positive fitness effects. Assuming a balance between selection and imperfect transmission, we expect a fitness advantage on the order of 20%. Unexpectedly, Wolbachia-infected females produce fewer progeny than do uninfected females. We do not yet understand the maintenance of wSuz in D. suzukii. The absence of detectable CI in D. suzukii and D. subpulchrella makes it unlikely that CI-based mechanisms could be used to control this species without transinfection using novel Wolbachia. Contrary to their reputation as horizontally transmitted reproductive parasites, many Wolbachia infections are acquired through introgression or cladogenesis and many cause no appreciable reproductive manipulation. Such infections, likely to be mutualistic, may be central to understanding the pervasiveness of Wolbachia among arthropods.

Research on the genetics of natural populations was revolutionized in the 1990s by methods for genotyping noninvasively collected samples. However, these methods have remained largely unchanged for the past 20 years and lag far behind the genomics era. To close this gap, here we report an optimized laboratory protocol for genome-wide capture of endogenous DNA from noninvasively collected samples, coupled with a novel computational approach to reconstruct pedigree links from the resulting low-coverage data. We validated both methods using fecal samples from 62 wild baboons, including 48 from an independently constructed extended pedigree. We enriched fecal-derived DNA samples up to 40-fold for endogenous baboon DNA and reconstructed near-perfect pedigree relationships even with extremely low-coverage sequencing. We anticipate that these methods will be broadly applicable to the many research systems for which only noninvasive samples are available. The lab protocol and software (“WHODAD”) are freely available at www.tung-lab.org/protocols-and-software.html and www.xzlab.org/software.html, respectively.

Ion mobility (IM) spectrometry provides semiorthogonal data to mass spectrometry (MS), showing promise for identifying unknown metabolites in complex non-targeted metabolomics data sets. While current literature has showcased IM−MS for identifying unknowns under near ideal circumstances, less work has been conducted to evaluate the performance of this approach in metabolomics studies involving highly complex samples with difficult matrices. Here, we present a workflow incorporating de novo molecular formula annotation and MS/MS structure elucidation using SIRIUS 4 with experimental IM collision cross-section (CCS) measurements and machine learning CCS predictions to identify differential unknown metabolites in mutant strains of Caenorhabditis elegans. For many of those ion features, this workflow enabled the successful filtering of candidate structures generated by in silico MS/MS predictions, though in some cases, annotations were challenged by significant hurdles in instrumentation performance and data analysis. While for 37% of differential features we were able to successfully collect both MS/MS and CCS data, fewer than half of these features benefited from a reduction in the number of possible candidate structures using CCS filtering due to poor matching of the machine learning training sets, limited accuracy of experimental and predicted CCS values, and lack of candidate structures resulting from the MS/MS data. When using a CCS error cutoff of ±3%, on average, 28% of candidate structures could be successfully filtered. Herein, we identify and describe the bottlenecks and limitations associated with the identification of unknowns in non-targeted metabolomics using IM−MS to focus and provide insights into areas requiring further improvement.

The use of quality control samples in metabolomics ensures data quality, reproducibility, and comparability between studies, analytical platforms, and laboratories. Long-term, stable, and sustainable reference materials (RMs) are a critical component of the quality assurance/quality control (QA/QC) system; however, the limited selection of currently available matrix-matched RMs reduces their applicability for widespread use. To produce an RM in any context, for any matrix that is robust to changes over the course of time, we developed iterative batch averaging method (IBAT). To illustrate this method, we generated 11 independently grown Escherichia coli batches and made an RM over the course of 10 IBAT iterations. We measured the variance of these materials by nuclear magnetic resonance (NMR) and showed that IBAT produces a stable and sustainable RM over time. This E. coli RM was then used as a food source to produce a Caenorhabditis elegans RM for a metabolomics experiment. The metabolite extraction of this material, alongside 41 independently grown individual C. elegans samples of the same genotype, allowed us to estimate the proportion of sample variation in preanalytical steps. From the NMR data, we found that 40% of the metabolite variance is due to the metabolite extraction process and analysis and 60% is due to sample-to-sample variance. The availability of RMs in untargeted metabolomics is one of the predominant needs of the metabolomics community that reach beyond quality control practices. IBAT addresses this need by facilitating the production of biologically relevant RMs and increasing their widespread use.