A community resource for paired genomic and metabolomic data mining

Genomics and metabolomics are widely used to explore specialized metabolite diversity. The Paired Omics Data Platform is a community initiative to systematically document links between metabolome and (meta)genome data, aiding identification of natural product biosynthetic origins and metabolite structures.Peer reviewe

Vertical Inheritance Facilitates Interspecies Diversification in Biosynthetic Gene Clusters and Specialized Metabolites.

While specialized metabolites are thought to mediate ecological interactions, the evolutionary processes driving chemical diversification, particularly among closely related lineages, remain poorly understood. Here, we examine the evolutionary dynamics governing the distribution of natural product biosynthetic gene clusters (BGCs) among 118 strains representing all nine currently named species of the marine actinobacterial genus Salinispora. While much attention has been given to the role of horizontal gene transfer (HGT) in structuring BGC distributions, we find that vertical descent facilitates interspecies BGC diversification over evolutionary timescales. Moreover, we identified a distinct phylogenetic signal among Salinispora species at both the BGC and metabolite level, indicating that specialized metabolism represents a conserved phylogenetic trait. Using a combination of genomic analyses and liquid chromatography-high-resolution tandem mass spectrometry (LC-MS/MS) targeting nine experimentally characterized BGCs and their small molecule products, we identified gene gain/loss events, constrained interspecies recombination, and other evolutionary processes associated with vertical inheritance as major contributors to BGC diversification. These evolutionary dynamics had direct consequences for the compounds produced, as exemplified by species-level differences in salinosporamide production. Together, our results support the concept that specialized metabolites, and their cognate BGCs, can represent phylogenetically conserved functional traits with chemical diversification proceeding in species-specific patterns over evolutionary time frames. IMPORTANCE Microbial natural products are traditionally exploited for their pharmaceutical potential, yet our understanding of the evolutionary processes driving BGC evolution and compound diversification remain poorly developed. While HGT is recognized as an integral driver of BGC distributions, we find that the effects of vertical inheritance on BGC diversification had direct implications for species-level specialized metabolite production. As such, understanding the degree of genetic variation that corresponds to species delineations can enhance natural product discovery efforts. Resolving the evolutionary relationships between closely related strains and specialized metabolism can also facilitate our understanding of the ecological roles of small molecules in structuring the environmental distribution of microbes

The Alleviation of Dextran Sulfate Sodium (DSS)-Induced Colitis Correlate with the logP Values of Food-Derived Electrophilic Compounds

Food-derived electrophilic compounds (FECs) are small molecules with electrophilic groups with potential cytoprotective effects. This study investigated the differential effects of six prevalent FECs on colitis in dextran sodium sulfate (DSS)-induced mice and the underlying relationship with molecular characteristics. Fumaric acid (FMA), isoliquiritigenin (ISO), cinnamaldehyde (CA), ferulic acid (FA), sulforaphane (SFN), and chlorogenic acid (CGA) exhibited varying improvements in colitis on clinical signs, colonic histopathology, inflammatory and oxidative indicators, and Nrf2 pathway in a sequence of SFN, ISO > FA, CA > FMA, CGA. Representative molecular characteristics of the “penetration-affinity–covalent binding” procedure, logP value, Keap1 affinity energy, and electrophilic index of FECs were theoretically calculated, among which logP value revealed a strong correlation with colitis improvements, which was related to the expression of Nrf2 and its downstream proteins. Above all, SFN and ISO possessed high logP values and effectively improving DSS-induced colitis by activating the Keap1–Nrf2 pathway to alleviate oxidative stress and inflammatory responses

Small molecule in situ resin capture provides a compound first approach to natural product discovery

Culture-based microbial natural product discovery strategies fail to realize the extraordinary biosynthetic potential detected across earth's microbiomes. Here we introduce Small Molecule In situ Resin Capture (SMIRC), a culture-independent method to obtain natural products directly from the environments in which they are produced. We use SMIRC to capture numerous compounds including two new carbon skeletons that were characterized using NMR and contain structural features that are, to the best of our knowledge, unprecedented among natural products. Applications across diverse marine habitats reveal biome-specific metabolomic signatures and levels of chemical diversity in concordance with sequence-based predictions. Expanded deployments, in situ cultivation, and metagenomics facilitate compound discovery, enhance yields, and link compounds to candidate producing organisms, although microbial community complexity creates challenges for the later. This compound-first approach to natural product discovery provides access to poorly explored chemical space and has implications for drug discovery and the detection of chemically mediated biotic interactions

Statistical Machines for Trauma Hospital Outcomes Research: Application to the PRospective, Observational, Multi-Center Major Trauma Transfusion (PROMMTT) Study

<div><p>Improving the treatment of trauma, a leading cause of death worldwide, is of great clinical and public health interest. This analysis introduces flexible statistical methods for estimating center-level effects on individual outcomes in the context of highly variable patient populations, such as those of the PRospective, Observational, Multi-center Major Trauma Transfusion study. Ten US level I trauma centers enrolled a total of 1,245 trauma patients who survived at least 30 minutes after admission and received at least one unit of red blood cells. Outcomes included death, multiple organ failure, substantial bleeding, and transfusion of blood products. The centers involved were classified as either large or small-volume based on the number of massive transfusion patients enrolled during the study period. We focused on estimation of parameters inspired by causal inference, specifically estimated impacts on patient outcomes related to the volume of the trauma hospital that treated them. We defined this association as the change in mean outcomes of interest that would be observed if, contrary to fact, subjects from large-volume sites were treated at small-volume sites (the effect of treatment among the treated). We estimated this parameter using three different methods, some of which use data-adaptive machine learning tools to derive the outcome models, minimizing residual confounding by reducing model misspecification. Differences between unadjusted and adjusted estimators sometimes differed dramatically, demonstrating the need to account for differences in patient characteristics in clinic comparisons. In addition, the estimators based on robust adjustment methods showed potential impacts of hospital volume. For instance, we estimated a survival benefit for patients who were treated at large-volume sites, which was not apparent in simpler, unadjusted comparisons. By removing arbitrary modeling decisions from the estimation process and concentrating on parameters that have more direct policy implications, these potentially automated approaches allow methodological standardization across similar comparativeness effectiveness studies.</p></div

Heatmap of matched data set with hierarchical clustering.

<p>A visualization of the scaled covariate values in the matched data set with hierarchical clustering of the individual revealed little clustering of the individuals by site type, indicated in the bar on the left, suggesting that balance in the covariates was achieved. The dendrogram on the right is the result of hierarchical clustering of the individuals based on their covariate values. The color bar on the left indicates the site size where each individual was treated (purple corresponds to small-volume sites and blue to large-volume sites).</p

Summary statistics.

<p>^{<b>^</b>}<b>p</b>-values derived from Mann-Whitney U and Z-tests for continuous and binary variables respectively.</p><p>* <b>p</b>-value significant (α = 0.05).</p><p>BMI: body mass index; ISS: injury severity score; BP: blood pressure; BPM: beats per minute; INR: international normalized ratio; RBC: red blood cell; U: units.</p><p>Summaries are presented as percent where indicated, and mean (standard deviation) otherwise.</p

“Counterfactual” residual plot for binary outcomes.

<p>Residuals (see <i>Diagnostics</i>) for each outcome were plotted against probabilities of overall mortality for large (dotted line) and small-volume (solid line) site patients for binary outcomes using loess smooths.</p

Difference in means, and adjusted ETT estimates (simple substitution, TMLE, and propensity score matching).

<p>95% confidence intervals are included in parentheses after each estimate. Asymptotic (normal-based) confidence intervals were calculated using the standard error of the Unadjusted, Targeted Maximum-Likelihood, and Matching estimators. The nonparametric bootstrap was used to generate 95% confidence intervals in the case of the Simple Substitution estimator.</p