Specialized pro-resolving mediators (SPMs), synthesized from PUFAs, resolve inflammation and return damaged tissue to homeostasis. Thus, increasing metabolites of the SPM biosynthetic pathway may have potential health benefits for select clinical populations, such as subjects with obesity who display dysregulation of SPM metabolism. However, the concentrations of SPMs and their metabolic intermediates in humans with obesity remains unclear. The primary objective of this study was to determine if a marine oil supplement increased specific metabolites of the SPM biosynthetic pathway in adults with obesity. The second objective was to determine if the supplement changed the relative abundance of key immune cell populations. Finally, given the critical role of antibodies in inflammation, we determined if ex vivo CD19 + B-cell antibody production was modified by marine oil intervention. Twenty-three subjects [median age: 56 y; BMI (in kg/m²): 33.1] consumed 2 g/d of a marine oil supplement for 28–30 d. The supplement was particularly enriched with 18-hydroxyeicosapentaenoic (HEPE), 14-hydroxydocosahexaenoic acid (14-HDHA), and 17-HDHA. Blood was collected pre- and postsupplementation for plasma mass spectrometry oxylipin and fatty acid analyses, flow cytometry, and B-cell isolation. Paired t-tests and Wilcoxon tests were used for statistical analyses. Relative to preintervention, the supplement increased 6 different HEPEs and HDHAs accompanied by changes in plasma PUFAs. Resolvin E1 and docosapentaenoic acid–derived maresin 1 concentrations were increased 3.5- and 4.7-fold upon intervention, respectively. The supplement did not increase the concentration of D-series resolvins and had no effect on the abundance of immune cells. Ex vivo B-cell IgG but not IgM concentrations were lowered postsupplementation. A marine oil supplement increased select SPMs and their metabolic intermediates in adults with obesity. Additional studies are needed to determine if increased concentrations of specific SPMs control the resolution of inflammation in humans with obesity. This trial was registered at clinicaltrials.gov as NCT04701138.

Wastewater treatment plants (WWTPs) are thought to be potential incubators of antibiotic resistance. Persistence of commonly used antibiotics in wastewater may increase the potential for selection of resistance genes transferred between bacterial populations, some of which might pose a threat to human health. In this study, we measured the concentrations of ten antibiotics in wastewater plant influents and effluents, and in surface waters up- and downstream from two Charlotte area treatment facilities. We performed Illumina shotgun sequencing to assay the microbial community and resistome compositions at each site across four time points from late winter to mid-summer of 2016. Antibiotics are present throughout wastewater treatment, and elevated concentrations of multiple antibiotics are maintained in moving stream water downstream of effluent release. While some human gut and activated sludge associated taxa are detectable downstream, these seem to attenuate with distance while the core microbial community of the stream remains fairly consistent. We observe the slight suppression of functional pathways in the downstream microbial communities, including amino acid, carbohydrate, and nucleic acid metabolism, as well as nucleotide and amino acid scavenging. Nearly all antibiotic resistance genes (ARGs) and potentially pathogenic taxa are removed in the treatment process, though a few ARG markers are elevated downstream of effluent release. Taken together, these results represent baseline measurements that future studies can utilize to help to determine which factors control the movement of antibiotics and resistance genes through aquatic urban ecosystems before, during, and after wastewater treatment.

Gestational alcohol exposure causes fetal alcohol spectrum disorder (FASD) and is a prominent cause of neurodevelopmental disability. Whole transcriptome sequencing (RNA-Seq) offer insights into mechanisms underlying FASD, but gene-level analysis provides limited information regarding complex transcriptional processes such as alternative splicing and non-coding RNAs. Moreover, traditional analytical approaches that use multiple hypothesis testing with a false discovery rate adjustment prioritize genes based on an adjusted p-value, which is not always biologically relevant. We address these limitations with a novel approach and implemented an unsupervised machine learning model, which we applied to an exon-level analysis to reduce data complexity to the most likely functionally relevant exons, without loss of novel information. This was performed on an RNA-Seq paired-end dataset derived from alcohol-exposed neural fold-stage chick crania, wherein alcohol causes facial deficits recapitulating those of FASD. A principal component analysis along with k-means clustering was utilized to extract exons that deviated from baseline expression. This identified 6857 differentially expressed exons representing 1251 geneIDs; 391 of these genes were identified in a prior gene-level analysis of this dataset. It also identified exons encoding 23 microRNAs (miRNAs) having significantly differential expression profiles in response to alcohol. We developed an RDAVID pipeline to identify KEGG pathways represented by these exons, and separately identified predicted KEGG pathways targeted by these miRNAs. Several of these (ribosome biogenesis, oxidative phosphorylation) were identified in our prior gene-level analysis. Other pathways are crucial to facial morphogenesis and represent both novel (focal adhesion, FoxO signaling, insulin signaling) and known (Wnt signaling) alcohol targets. Importantly, there was substantial overlap between the exomes themselves and the predicted miRNA targets, suggesting these miRNAs contribute to the gene-level expression changes. Our novel application of unsupervised machine learning in conjunction with statistical analyses facilitated the discovery of signaling pathways and miRNAs that inform mechanisms underlying FASD. Genomic research often yields an overwhelming amount of information. Accurate models for predicting and validating multivariate big data in genomics distill complex relationships and interactions. A prime example is fetal alcohol spectrum disorders, the largest known cause of neurodevelopmental disability affecting nearly 5% of children in the United States. Alcohol exposure during pregnancy leads to complex epigenetic and transcriptomic modifications, subsequently impairing signaling pathways in neural and morphologic development. Identifying transcriptomic mechanisms regulating alcohol’s teratogenicity during embryonic development is crucial for understanding variable phenotypic outcomes. This allows for the advancement of future therapeutic interventions that may mediate alcohol’s effects. Most genomic studies do not incorporate various levels of transcriptomic analysis, spanning gene, exon, and splicing variants, because it is difficult to meaningfully consolidate all those analyses. Therefore, enhancing machine learning approaches that corroborate traditional statistical methods can yield novel relationships, and is important for robust functional experiments that proceed from such genomic studies.

Background: Obesity increases influenza disease risk in millions of adults worldwide. In this study, we investigated the effect of diet-induced obesity on pulmonary CD8 ⁺ T cell metabolism and function as a mechanism of impairment. Methods: Male C57BL/6J mice were fed either control (10% kcal/g) or high-fat (60% kcal/g) diet. Sub-lethal A/PR/8/34 influenza virus infection generated a robust pulmonary immune response. T cell metabolism and function were assessed at day 10 and day 24 post infection. Results: At day 10 post infection, CD8 ⁺ T cells from obese mice had impaired oxidative and glycolytic metabolism, greater fatty acid uptake, and decreased effector populations and cytokine production. At infection resolution, obese mice had lower numbers of naïve and central memory CD8 ⁺ T cell populations in the lungs. Conclusion: Diet-induced obesity increases influenza virus pathogenesis through CD8 ⁺ T cell mediated metabolic reprogramming resulting in suppressed effector CD8 ⁺ T cell function. Summary: Diet-induced obesity impairs the metabolism of pulmonary CD8 ⁺ T cells resulting in reduced effector CD8 ⁺ T cells and cytokine production following primary influenza infection.