Analysis of 772 complete severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes from early in the Boston-area epidemic revealed numerous introductions of the virus, a small number of which led to most cases. The data revealed two superspreading events. One, in a skilled nursing facility, led to rapid transmission and significant mortality in this vulnerable population but little broader spread, whereas other introductions into the facility had little effect. The second, at an international business conference, produced sustained community transmission and was exported, resulting in extensive regional, national, and international spread. The two events also differed substantially in the genetic variation they generated, suggesting varying transmission dynamics in superspreading events. Our results show how genomic epidemiology can help to understand the link between individual clusters and wider community spread.

Highlights d A family of muscle-tropic capsids identified by directed evolution in mice and primates d MyoAAV transduction is dependent on integrin heterodimers in mouse and human cells d MyoAAV administration at low dose results in therapeutic efficacy in disease models d Systemically administrated MyoAAV transduces primate muscles highly efficiently

Eicosanoids and related species are critical, small bioactive mediators of human physiology and inflammation. While ~1100 distinct eicosanoids have been predicted to exist, to date, less than 150 of these molecules have been measured in humans, limiting our understanding of eicosanoids and their role in human biology. Using a directed non-targeted mass spectrometry approach in conjunction with computational chemical networking of spectral fragmentation patterns, we find over 500 discrete chemical signals highly consistent with known and putative eicosanoids in human plasma, including 46 putative novel molecules not previously described, thereby greatly expanding the breath of prior analytical strategies. In plasma samples from 1500 individuals, we find members of this expanded eicosanoid library hold close association with markers of inflammation, as well as clinical characteristics linked with inflammation, including advancing age and obesity. These experimental and computational approaches enable discovery of new chemical entities and will shed important insight into the role of bioactive molecules in human disease.

SummaryLongevity in mammals is influenced by sex, and lifespan extension in response to anti‐aging interventions is often sex‐specific, although the mechanisms underlying these sexual dimorphisms are largely unknown. Treatment of mice with 17‐α estradiol (17aE2) results in sex‐specific lifespan extension, with an increase in median survival in males of 19% and no survival effect in females. Given the links between lifespan extension and metabolism, we performed untargeted metabolomics analysis of liver, skeletal muscle and plasma from male and female mice treated with 17aE2 for eight months. We find that 17aE2 generates distinct sex‐specific changes in the metabolomic profile of liver and plasma. In males, 17aE2 treatment raised the abundance of several amino acids in the liver, and this was further associated with elevations in metabolites involved in urea cycling, suggesting altered amino acid metabolism. In females, amino acids and urea cycling metabolites were unaffected by 17aE2. 17aE2 also results in male‐specific elevations in a second estrogenic steroid—estriol‐3‐sulfate—suggesting different metabolism of this drug in males and females. To understand the underlying endocrine causes for these sexual dimorphisms, we castrated males and ovariectomized females prior to 17aE2 treatment, and found that virtually all the male‐specific metabolite responses to 17aE2 are inhibited or reduced by male castration. These results suggest novel metabolic pathways linked to male‐specific lifespan extension and show that the male‐specific metabolomic response to 17aE2 depends on the production of testicular hormones in adult life.

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High-throughput metabolomics investigations, when conducted in large human cohorts, represent a potentially powerful tool for elucidating the biochemical diversity underlying human health and disease. Large-scale metabolomics data sources, generated using either targeted or nontargeted platforms, are becoming more common. Appropriate statistical analysis of these complex high-dimensional data will be critical for extracting meaningful results from such large-scale human metabolomics studies. Therefore, we consider the statistical analytical approaches that have been employed in prior human metabolomics studies. Based on the lessons learned and collective experience to date in the field, we offer a step-by-step framework for pursuing statistical analyses of cohort-based human metabolomics data, with a focus on feature selection. We discuss the range of options and approaches that may be employed at each stage of data management, analysis, and interpretation and offer guidance on the analytical decisions that need to be considered over the course of implementing a data analysis workflow. Certain pervasive analytical challenges facing the field warrant ongoing focused research. Addressing these challenges, particularly those related to analyzing human metabolomics data, will allow for more standardization of as well as advances in how research in the field is practiced. In turn, such major analytical advances will lead to substantial improvements in the overall contributions of human metabolomics investigations.

Background In addition to serving as building blocks for protein synthesis, amino acids also provide energy and precursors that are used by cells through catabolism. Mechanistic target of rapamycin complex 1 (mTORC1) is a central coordinator of cellular metabolism. However, little is known regarding the function of mTORC1 in amino acid catabolism. The aims of this study were to explore the mechanism by which mTORC1 controls the conversion of glutamate to α-ketoglutarate and ornithine to putrescine, and mTORC1 regulates the expression amino acid catabolism-related genes in hepatocyte. Methods HL-7702 hepatocytes were treated with glutamate, ornithine, rapamycin or SC75741, alone or in combination; the plasmids pRNAT-U6.1/Neo-shRaptor and pIRES2-EGFP-Rheb were transfected into HL-7702 cells to silencing Raptor or overexpressing Rheb. The intracellular content of glutamate, oxaloacetate, α-ketoglutaric acid, and aspartic acid, and the intracellular level of aspartate aminotransferase (AST), ornithine decarboxylase (ODC), glutamate dehydrogenase (GDH), and glutamic acid decarboxylase (GAD) were measured by ELISA. The concentrations of intracellular ornithine and putrescine were measured by HPLC. The mRNA level of amino acid catabolism-related genes was detected by qRT-PCR, and the protein level of mTORC1 and NF-κB was investigated by western blot. Results Our data showed that rapamycin inhibits the utilization of glutamate and ornithine in HL-7702 hepatocytes. mTORC1 regulates the expression of AST and ODC through the transcription factor NF-κB in response to glutamate or ornithine. Further, inactivated mTORC1 by Raptor silencing downregulated the expression of AST , ODC , GDH and GAD , while enhanced mTORC1 by Rheb overexpression upregulated NF-κB activation and the indicated genes expression in hepatocytes. Inhibited NF-κB by inhibitor SC75741 decreased the AST , ODC , GDH , and GAD expression. Conclusions 4 Our results demonstrate that mTORC1 regulates amino acid catabolism by inducing the expression of AST , ODC , GDH , and GAD , which is mediated by NF-κB. This finding constitutes a novel mechanism by which amino acid catabolism is regulated in hepatocytes.

To assist with management and interpretation of human metabolomics data, which are rapidly increasing in quantity and complexity, we need better visualization tools. Using a dataset of several hundred metabolite measures profiled in a cohort of ~1500 individuals sampled from a population-based community study, we performed association analyses with eight demographic and clinical traits and outcomes. We compared frequently used existing graphical approaches with a novel ‘rain plot’ approach to display the results of these analyses. The ‘rain plot’ combines features of a raindrop plot and a conventional heatmap to convey results of multiple association analyses. A rain plot can simultaneously indicate effect size, directionality, and statistical significance of associations between metabolites and several traits. This approach enables visual comparison features of all metabolites examined with a given trait. The rain plot extends prior approaches and offers complementary information for data interpretation. Additional work is needed in data visualizations for metabolomics to assist investigators in the process of understanding and convey large-scale analysis results effectively, feasibly, and practically.