Hur, Benjamin

Abstract Summary We recently introduced the Gut Microbiome Wellness Index (GMWI), a stool metagenome-based indicator for assessing health by determining the likelihood of disease given the state of one’s gut microbiome. The calculation of our wellness index depends on the relative abundances of health-prevalent and health-scarce species. Encouragingly, GMWI has already been utilized in various studies focusing on differences in the gut microbiome between cases and controls. Herein, we introduce the GMWI-webtool, a user-friendly browser application that computes GMWI, health-prevalent/-scarce species’ relative abundances, and α-diversities from stool shotgun metagenome taxonomic profiles. Users of our interactive online tool can visualize their results and compare them side-by-side with those from our pooled reference dataset of metagenomes, as well as export data in.csv format and high-resolution figures. Availability and implementation GMWI-webtool is freely available here: https://gmwi-webtool.github.io/. Supplementary information Supplementary data are available at Bioinformatics online.

Chemotherapy-induced cognitive impairment (CICI) has emerged as a significant medical problem without therapeutic options. Using the platinum-based chemotherapy cisplatin to model CICI, we revealed robust elevations in the adenosine A 2A receptor (A 2A R) and its downstream effectors, cAMP and CREB, by cisplatin in the adult mouse hippocampus, a critical brain structure for learning and memory. Notably, A 2A R inhibition by the Food and Drug Administration–approved A 2A R antagonist KW-6002 prevented cisplatin-induced impairments in neural progenitor proliferation and dendrite morphogenesis of adult-born neurons, while improving memory and anxiety-like behavior, without affecting tumor growth or cisplatin’s antitumor activity. Collectively, our study identifies A 2A R signaling as a key pathway that can be therapeutically targeted to prevent cisplatin-induced cognitive impairments.

ObjectiveTo identify hallmark genes and biomolecular processes in aortitis using high‐throughput gene expression profiling, and to provide a range of potentially new drug targets (genes) and therapeutics from a pharmacogenomic network analysis.MethodsBulk RNA sequencing was performed on surgically resected ascending aortic tissues from inflammatory aneurysms (giant cell arteritis [GCA] with or without polymyalgia rheumatica, n = 8; clinically isolated aortitis [CIA], n = 17) and noninflammatory aneurysms (n = 25) undergoing surgical aortic repair. Differentially expressed genes (DEGs) between the 2 patient groups were identified while controlling for clinical covariates. A protein–protein interaction model, drug–gene target information, and the DEGs were used to construct a pharmacogenomic network for identifying promising drug targets and potentially new treatment strategies in aortitis.ResultsOverall, tissue gene expression patterns were the most associated with disease state than with any other clinical characteristic. We identified 159 and 93 genes that were significantly up‐regulated and down‐regulated, respectively, in inflammatory aortic aneurysms compared to noninflammatory aortic aneurysms. We found that the up‐regulated genes were enriched in immune‐related functions, whereas the down‐regulated genes were enriched in neuronal processes. Notably, gene expression profiles of inflammatory aortic aneurysms from patients with GCA were no different than those from patients with CIA. Finally, our pharmacogenomic network analysis identified genes that could potentially be targeted by immunosuppressive drugs currently approved for other inflammatory diseases.ConclusionWe performed the first global transcriptomics analysis in inflammatory aortic aneurysms from surgically resected aortic tissues. We identified signature genes and biomolecular processes, while finding that CIA may be a limited presentation of GCA. Moreover, our computational network analysis revealed potential novel strategies for pharmacologic interventions and suggests future biomarker discovery directions for the precise diagnosis and treatment of aortitis.

Abstract Problem Prenatal exposure to metabolic dysregulation arising from maternal obesity can have negative health consequences in post‐natal life. To date, the specific effects of maternal obesity on fetal immunity at a cellular level have not been well characterized. Method of Study Using cord blood mononuclear cells (CBMCs) and cord plasma (n = 9/group) isolated from infants born to women with a high body mass index (BMI>25kg/m 2 ) compared to women with a normal BMI (18‐25kg/m 2 ), we evaluated differences in immune cell populations using single‐cell mass cytometry (CyTOF). CBMCs were matched according to potentially confounding variables, such as maternal and gestational age, ethnicity, smoking status, and gravidity. Statistical results were adjusted for fetal sex. Data were analyzed by viSNE and FlowSOM softwares in Cytobank ™ . Results In newborn CBMCs from women with high BMI, we observed changes in frequency and phenotype of immune cell populations, including significant increases in CD4 + T cells and decreases in myeloid cell populations. IL‐12p40 and MDC concentrations were significantly elevated in the high BMI group compared to control. Conclusion This study demonstrates an association between maternal obesity and fetal immunity. Our results warrant following long‐term immunologic outcomes and associated clinical risks in children born to women with a high pre‐pregnancy BMI.