Project description:We investigated the individual phenotypic predisposition to developing uncomplicated infection or sepsis in a large cohort of non-infected patients undergoing major elective surgery. Whole blood RNA sequencing analysis was performed on preoperative samples taken from 267 patients. These comprised patients who developed postoperative infection with (n=77) or without (n=49) sepsis, non-infectious systemic inflammatory response (n=31), or an uncomplicated postoperative course (n=110). Machine learning classification models built on preoperative transcriptomic signatures predicted postoperative outcomes including sepsis.
Project description:We investigated the individual phenotypic predisposition to developing uncomplicated infection or sepsis in a large cohort of non-infected patients undergoing major elective surgery. We built machine learning classification models on preoperative transcriptomic signatures to predict postoperative outcomes including sepsis. To test the predictive capability of these models for ongoing infection, whole blood RNA sequencing analysis on 61 independent patients with COVID-19 (10 mild, 51 severe cases) was performed.
Project description:A growing body of evidence suggests interplay between the gut microbiota and the pathogenesis of nonalcoholic fatty liver disease (NAFLD). However, the role of the gut microbiome in early detection of NAFLD is unclear. Prospective studies are necessary for identifying reliable, microbiome markers for early NAFLD. We evaluated 2487 individuals in a community-based cohort who were followed up 4.6 years after initial clinical examination and biospecimen sampling. Metagenomic and metabolomic characterizations using stool and serum samples taken at baseline were performed for 90 participants who progressed to NAFLD and 90 controls who remained NAFLD free at the follow-up visit. Cases and controls were matched for gender, age, body mass index (BMI) at baseline and follow-up, and 4-year BMI change. Machine learning models integrating baseline microbial signatures (14 features) correctly classified participants (auROCs of 0.72 to 0.80) based on their NAFLD status and liver fat accumulation at the 4-year follow up, outperforming other prognostic clinical models (auROCs of 0.58 to 0.60). We confirmed the biological relevance of the microbiome features by testing their diagnostic ability in four external NAFLD case-control cohorts examined by biopsy or magnetic resonance spectroscopy, from Asia, Europe, and the United States. Our findings raise the possibility of using gut microbiota for early clinical warning of NAFLD development.
Project description:Autism Spectrum Disorder (ASD) presents a wide, and often varied, behavioral phenotype. Impulsivity and improper assessment of risks has been widely reported among individuals diagnosed with ASD. However, there is little knowledge of the molecular underpinnings of the impaired risk-assessment phenotype. In this study, we have identified impaired risk-assessment activity in multiple male ASD mouse models. By performing network-based analysis of striatal whole transcriptome data from each of these ASD models, we have identified a cluster of glutamate receptor–associated genes that correlate with the risk-assessment phenotype. Furthermore, pharmacological inhibition of striatal glutamatergic receptors was able to mimic the dysregulation in risk-assessment. Therefore, this study has identified a molecular mechanism that may underlie impulsivity and risk-assessment dysregulation in ASD.
Project description:The mortality risk from cancer-associated sepsis is differentially affected by individual cancer sites and host responses to sepsis are heterogenous. Native Hawaiians have a 2-fold higher mortality risk from cancer-associated sepsis than Whites and also higher mortality risk from colorectal cancer (CRC). We investigated this disparity by examining ethnic variation in the transcriptome of patients with CRC-associated sepsis and its relation to survival and genetic diversity. We conducted transcriptomic profiling of CRC tumors and adjacent non-tumor tissue obtained from adult patients of Native Hawaiian and Japanese ethnicity who died from cancer-associated sepsis. We examined differential gene expression in relation to patient survival and sepsis disease etiology.
Project description:The temporal evolution of sepsis was monitored by transcriptional profiling of five critically ill children with meningococcal sepsis and sepsis-induced multiple organ failure. Blood was sampled at 6 time points during the first 48 hours of their admission to pediatric intensive care, where the children received standard clinical treatment including organ support and antimicrobial therapy. Striking transcript instability was observed over the 48 hours, with increasing numbers of regulated genes over time. Most notably, proposed biomarkers for sepsis risk stratification also showed expression instability, with varied expression levels over 48 hours. This study demonstrates the extent of the complexity of temporal changes in gene expression that occur during the evolution of sepsis-induced multiple organ failure. Importantly, stratification tools that propose expression of biomarkers must take into account the temporal changes, over the use of single snapshots that may be less informative.
Project description:This study aimed to compare gene expression profiles between patients with sepsis and healthy volunteers, to determine the accuracy of these profiles in diagnosing sepsis, and to predict sepsis outcomes by combining bioinformatics data with molecular experiments and clinical information.