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Extracellular vesicles (EVs) transport biological content between cells to mediate physiological processes. The association between EVs and resilience, the ability to cope with stress, is unknown. Using unbiased machine learning approaches, we aimed to identify a biological profile of resilience. Twenty servicemen (27.8 ± 5.9 years) completed the Connor Davidson Resilience (CD‐RISC) questionnaire and were exposed to daily physical and cognitive exertion with 48‐hr sleep and caloric restriction. Blood samples from baseline and the second day of stress were analyzed for neuroendocrine biomarkers impacted by military stress. EVs were isolated from plasma and stained with antibodies associated with exosomes (CD63), microvesicles (VAMP3), and apoptotic bodies (THSD1). Individuals were separated into high (n = 10, CD‐RISC > 90) and low (n = 10, CD‐RISC < 79) resilience. EV features were stratified by size, then down‐selected using regression trees and compared between groups. Diagnostic accuracy was assessed using receiver operating characteristic curves. Compared to low resilience, high resilience demonstrated a greater increase in variability of THSD1 local bright spot intensities among large‐sized EVs in response to stress (p = 0.002, Hedges’ g = 1.59). Among medium‐sized EVs, high resilience exhibited a greater decrease in side scatter intensity (p = 0.014, Hedges’ g = 1.17). Both features demonstrated high to moderate diagnostic accuracy for high resilience (AUC = 0.90 and 0.79). In contrast, neuroendocrine biomarker concentrations were similar between groups. The increase in variability among THSD1 + EVs in high, but not low, resilient individuals following stress may suggest high resilience is accompanied by stress‐triggered apoptotic adaptations to the environment that are not detected in neuroendocrine biomarkers. Individuals with high self‐report resilience demonstrated extracellular vesicle adaptations during a multifactorial stress environment that were not observed in low‐resilient individuals. Extracellular vesicles may be more sensitive than circulating neuroendocrine biomarkers, as no differences were observed between groups at the hormone/peptide level.

Heterochronic blood exchange (HBE) has demonstrated that circulating factors restore youthful features to aged tissues. However, the systemic mediators of those rejuvenating effects remain poorly defined. We show here that the beneficial effect of young blood on aged muscle regeneration was diminished when serum was depleted of extracellular vesicles (EVs). Whereas EVs from young animals rejuvenate aged cell bioenergetics and skeletal muscle regeneration, aging shifts EV subpopulation heterogeneity and compromises downstream benefits on recipient cells. Machine learning classifiers revealed that aging shifts the nucleic acid, but not protein, fingerprint of circulating EVs. Alterations in sub-population heterogeneity were accompanied by declines in transcript levels of the pro-longevity protein, α-Klotho, and injection of EVs improved muscle regeneration in a Klotho mRNA-dependent manner. These studies demonstrate that EVs play a key role in the rejuvenating effects of HBE and that Klotho transcripts within EVs phenocopy the effects of young serum on aged skeletal muscle.

Protein structures can provide invaluable information, both for reasoning about biological processes and for enabling interventions such as structure-based drug development or targeted mutagenesis. After decades of effort, 17% of the total residues in human protein sequences are covered by an experimentally determined structure 1 . Here we markedly expand the structural coverage of the proteome by applying the state-of-the-art machine learning method, AlphaFold 2 , at a scale that covers almost the entire human proteome (98.5% of human proteins). The resulting dataset covers 58% of residues with a confident prediction, of which a subset (36% of all residues) have very high confidence. We introduce several metrics developed by building on the AlphaFold model and use them to interpret the dataset, identifying strong multi-domain predictions as well as regions that are likely to be disordered. Finally, we provide some case studies to illustrate how high-quality predictions could be used to generate biological hypotheses. We are making our predictions freely available to the community and anticipate that routine large-scale and high-accuracy structure prediction will become an important tool that will allow new questions to be addressed from a structural perspective. AlphaFold is used to predict the structures of almost all of the proteins in the human proteome—the availability of high-confidence predicted structures could enable new avenues of investigation from a structural perspective.

The impact of microbial communities, also known as the microbiome, on human health and the environment is receiving increased attention. Studying translated gene products (proteins) and comparing metaproteomic profiles may elucidate how microbiomes respond to specific environmental stimuli, and interact with host organisms. Characterizing proteins expressed by a complex microbiome and interpreting their functional signature requires sophisticated informatics tools and workflows tailored to metaproteomics. Additionally, there is a need to disseminate these informatics resources to researchers undertaking metaproteomic studies, who could use them to make new and important discoveries in microbiome research. The Galaxy for proteomics platform (Galaxy-P) offers an open source, web-based bioinformatics platform for disseminating metaproteomics software and workflows. Within this platform, we have developed easily-accessible and documented metaproteomic software tools and workflows aimed at training researchers in their operation and disseminating the tools for more widespread use. The modular workflows encompass the core requirements of metaproteomic informatics: (a) database generation; (b) peptide spectral matching; (c) taxonomic analysis and (d) functional analysis. Much of the software available via the Galaxy-P platform was selected, packaged and deployed through an online metaproteomics “Contribution Fest“ undertaken by a unique consortium of expert software developers and users from the metaproteomics research community, who have co-authored this manuscript. These resources are documented on GitHub and freely available through the Galaxy Toolshed, as well as a publicly accessible metaproteomics gateway Galaxy instance. These documented workflows are well suited for the training of novice metaproteomics researchers, through online resources such as the Galaxy Training Network, as well as hands-on training workshops. Here, we describe the metaproteomics tools available within these Galaxy-based resources, as well as the process by which they were selected and implemented in our community-based work. We hope this description will increase access to and utilization of metaproteomics tools, as well as offer a framework for continued community-based development and dissemination of cutting edge metaproteomics software.

The immune system plays a crucial role in cancer surveillance. Previous studies have shown that lymphopenia associated with radiotherapy (RT) portends a poor prognosis. We sought to differentiate the effects of proton and photon RT on changes in absolute lymphocyte count (ALC) for patients with hepatocellular carcinoma (HCC). Patients with HCC treated with definitive RT from 2006 to 2016 were studied. Serial ALCs were graded according to CTCAE v4.0. Overall survival (OS), disease-free survival, and distant metastasis-free survival were analyzed using the Kaplan-Meier method. Univariable and multivariable Cox-proportional hazards analyses were used to identify predictors of OS. A cohort analysis matched for treatment volume was performed to investigate differences in ALC dynamics between photon and proton therapy. Of 143 patients identified, the median age was 66 (range, 19-90) years. The treatment modality was photon in 103 (72%) and proton in 40 (28%). Median follow-up was 17 months (95% confidence interval, 13-25 months). The median time to ALC nadir after initiation of RT was 17 days with a median relative decrease of 67%. Those who received proton RT had a higher median ALC nadir (0.41 vs 0.32 k/µL, p=0.002) and longer median OS (33 vs 13 months, p=0.002) than those who received photon RT. Matched cohort analyses revealed a larger low-dose liver volume in the photon group, which correlated with lower ALC. On multivariable Cox analysis, Grade 3 or higher lymphopenia prior to or after RT, portal venous tumor thrombus, larger planning target volumes, Child-Pugh (CP) Class B, and increased CP score after RT were associated with a higher risk of death, whereas the use of proton therapy was associated with lower risk. Grade 3 or higher lymphopenia may be associated with poorer outcomes in patients receiving RT for HCC. Protons may mitigate lymphopenia compared with photons, potentially due to reduced dose exposure of sites of lymphopoiesis.

The heaviest stable nuclei in the universe owe their existence to quantum shell structure, the grouping of protons and neutrons into discrete energy levels separated by gaps. The largest known neutron shell gap in stable nuclei, at \\(N=126\\), stabilizes doubly-magic \\(^208\\)Pb and is responsible for the characteristic abundance peak of heavy elements near gold and platinum produced by the rapid neutron-capture process (r-process). Whether this shell gap persists as protons are removed from lead is a question central to both nuclear structure and the modeling of heavy-element synthesis, yet it has remained unanswered due to the extraordinary difficulty of producing the relevant neutron-rich nuclei. Direct experimental knowledge in this region was essentially absent. Here we report the first precision mass measurements of \\(^203,204\\)Pt and \\(^204,205,206\\)Au, performed at GSI using a novel combination of Schottky and isochronous mass spectrometry in a heavy-ion storage ring. The \\(N=126\\) isotones \\(^204\\)Pt and \\(^205\\)Au are more strongly bound than the extrapolated trend of the previously known mass surface by 403 and 464~keV, respectively, revealing an unexpectedly enhanced \\(N=126\\) shell strength below doubly-magic \\(^208\\)Pb. Furthermore, the proton-neutron interaction strength exhibits a hitherto unobserved bifurcation at \\(N=126\\) as protons are removed from \\(^208\\)Pb. Our results redefine the nuclear mass surface in the neutron-rich heavy-element region and provide direct experimental benchmarks for theoretical models whose extrapolations toward more exotic nuclei are essential for r-process nucleosynthesis calculations.

We present Bioconda (https://bioconda.github.io), a distribution of bioinformatics software for the lightweight, multi-platform and language-agnostic package manager Conda. Currently, Bioconda offers a collection of over 3000 software packages, which is continuously maintained, updated, and extended by a growing global community of more than 200 contributors. Bioconda improves analysis reproducibility by allowing users to define isolated environments with defined software versions, all of which are easily installed and managed without administrative privileges.