AN INTEGRATIVE APPROACH FOR TRANSLATING RESOLVIN E1 TO IMPROVE OBESITY-RELATED OUTCOMES

Chronic inflammation contributes toward the pathogenesis of numerous diseases including, but not limited to, obesity, autoimmunity, cardiovascular diseases, and cancers. The discovery of specialized pro-resolving mediators (SPMs), critical molecules for resolving inflammation, initiated investigation into targeting pathways of inflammation resolution to improve physiological outcomes. To determine mechanisms by which SPMs target inflammation resolution in obesity we conducted RNA-sequencing on hepatic tissue on mice treated with resolvin E1 (RvE1), an SPM synthesized from eicosapentaenoic (EPA) that we previously showed attenuates hyperinsulinemia and hyperglycemia in obese mice. We included mice that are either wildtype or knockouts of the ChemR23 gene, the receptor for RvE1. We were able to establish gene and exon-level splicing variants that altered the hepatic genomic landscape, providing potential novel mechanisms of action for RvE1 and its metabolic/inflammatory effects. Next, we conducted studies that focused on the translation of RvE1 for obesity-related metabolic outcomes. We studied RvE1 with diversity outbred mice fed a high-fat diet to model the heterogeneity of obesity in the human population. We first found SNPs that significantly increased risk of weight gain with diet-induced obesity and then showed that upon RvE1 treatment there were positive and negative responders to RvE1 for insulin, glucose, glucagon, resistin, leptin, and gastric inhibitory peptide. RvE1’s effects were most pronounced with positive responders for RvE1 in the lowest fat mass in relation to hyperleptinemia. Lastly, to translate our findings at the human level, we conducted a non-randomized uncontrolled clinical trial where we gave subjects with obesity an enriched marine oil dietary supplement that contains the parent molecule to RvE1, 18-hydroxyeicosapentaenoic. After participants took 2g of the supplement for one month, we found an increase in several SPMs including a 3-fold increase in plasma RvE1. Here we showed that SPM bioavailability in individuals with obesity, who are at higher risk for SPM deficiencies, can be improved upon supplementation. These results establish integrative approaches for translating RvE1 and other SPM therapeutics for improvement of obesity-related outcomes.Doctor of Philosoph

Resolvin E1 Derived from Eicosapentaenoic Acid Prevents Hyperinsulinemia and Hyperglycemia in a Host Genetic Manner

The FASEB Journal published by Wiley Periodicals LLC on behalf of Federation of American Societies for Experimental Biology Eicosapentaenoic acid (EPA) has garnered attention after the success of the REDUCE-IT trial, which contradicted previous conclusions on EPA for cardiovascular disease risk. Here we first investigated EPA\u27s preventative role on hyperglycemia and hyperinsulinemia. EPA ethyl esters prevented obesity-induced glucose intolerance, hyperinsulinemia, and hyperglycemia in C57BL/6J mice. Supporting NHANES analyses showed that fasting glucose levels of obese adults were inversely related to EPA intake. We next investigated how EPA improved murine hyperinsulinemia and hyperglycemia. EPA overturned the obesity-driven decrement in the concentration of 18-hydroxyeicosapentaenoic acid (18-HEPE) in white adipose tissue and liver. Treatment of obese inbred mice with RvE1, the downstream immunoresolvant metabolite of 18-HEPE, but not 18-HEPE itself, reversed hyperinsulinemia and hyperglycemia through the G-protein coupled receptor ERV1/ChemR23. To translate the findings, we determined if the effects of RvE1 were dependent on host genetics. RvE1\u27s effects on hyperinsulinemia and hyperglycemia were divergent in diversity outbred mice that model human genetic variation. Secondary SNP analyses further confirmed extensive genetic variation in human RvE1/EPA-metabolizing genes. Collectively, the data suggest EPA prevents hyperinsulinemia and hyperglycemia, in part, through RvE1\u27s activation of ERV1/ChemR23 in a host genetic manner. The studies underscore the need for personalized administration of RvE1 based on genetic/metabolic enzyme profiles

Exon level machine learning analyses elucidate novel candidate miRNA targets in an avian model of fetal alcohol spectrum disorder.

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