Project description:Two molecular phenotypes of sepsis and acute respiratory distress syndrome, termed hyperinflammatory and hypoinflammatory, have been consistently identified by latent class analysis in numerous cohorts, with widely divergent clinical outcomes and differential responses to some treatments; however, the key biological differences between these phenotypes remain poorly understood. We used host and microbe metagenomic sequencing data from blood to deepen our understanding of biological differences between latent class analysis-derived phenotypes and to assess concordance between the latent class analysis-derived phenotypes and phenotypes reported by other investigative groups (e.g., SRS1-2, MARS1-4, reactive/uninflamed). We analyzed data from 113 hypoinflammatory and 76 hyperinflammatory sepsis patients enrolled in a two-hospital prospective cohort study. Molecular phenotypes had been previously assigned using latent class analysis. The hyperinflammatory and hypoinflammatory phenotypes of sepsis had distinct gene expression signatures, with 5,755 genes (31%) differentially expressed. The hyperinflammatory phenotype was associated with elevated expression of innate immune response genes, while the hypoinflammatory phenotype was associated with elevated expression of adaptive immune response genes, and notably, T-cell response genes. Plasma metagenomic analysis identified differences in prevalence of bacteremia, bacterial DNA abundance and composition between the phenotypes, with an increased presence and abundance of Enterobacteriaceae in the hyperinflammatory phenotype. Significant overlap was observed between these phenotypes and previously identified transcriptional subtypes of acute respiratory distress syndrome (reactive/uninflamed) and sepsis (SRS1-2). Analysis of data from the VANISH trial indicated that corticosteroids might have a detrimental effect in hypoinflammatory patients. The hyperinflammatory and hypoinflammatory phenotypes have distinct transcriptional and metagenomic features that could be leveraged for precision treatment strategies.
Project description:Here we developed a new approach to sepsis diagnosis that integrates host transcriptional profiling with metagenomic broad-range pathogen detection from cell-free plasma RNA and DNA.
Project description:Here we developed a new approach to sepsis diagnosis that integrates host transcriptional profiling with metagenomic broad-range pathogen detection from cell-free plasma RNA and DNA.
Project description:Recent progress in unbiased metagenomic next-generation sequencing (mNGS) allows simultaneous examination of microbial and host genetic material in a single test. Leveraging affordable bronchoalveolar lavage fluid (BALF) mNGS data, we employed machine learning to create a diagnostic approach distinguishing lung cancer from pulmonary infections, conditions prone to misdiagnosis in clinical settings. This prospective study analyzed BALF-mNGS data from lung cancer and pulmonary infection patients, delineating differences in DNA/RNA microbial composition, bacteriophage abundances, and host responses, including gene expression, transposable element levels, immune cell composition, and tumor fraction derived from copy number variation (CNV). Integrating these metrics into a host/microbe metagenomics-driven machine learning model (Model VI) demonstrated robustness, achieving an AUC of 0.87 (95% CI = 0.857-0.883), sensitivity = 73.8%, and specificity = 84.5% in the training cohort, and an AUC of 0.831 (95% CI = 0.819-0.843), sensitivity = 67.1%, and specificity = 94.4% in the validation cohort for distinguishing lung cancer from pulmonary infections. The application of a rule-in and rule-out strategy-based composite predictive model significantly enhances accuracy (ACC) in distinguishing between lung cancer and tuberculosis (ACC=0.913), fungal infection (ACC=0.955), and bacterial infection (ACC=0.836). These findings highlight the potential of cost-effective mNGS-based analysis as a valuable tool for early differentiation between lung cancer and pulmonary infections, offering significant benefits through a single comprehensive testing.
Project description:The microbial biogeochemical processes occurring in marine sediment in Antarctica remain underexplored due to limited access. Further, these polar habitats are unique, as they are being exposed to significant changes in their climate. To explore how microbes drive biogeochemistry in these sediments, we performed a shotgun metagenomic survey of marine surficial sediment (0 to 3 cm of the seafloor) collected from 13 locations in western Antarctica and assembled 16 high-quality metagenome assembled genomes for focused interrogation of the lifestyles of some abundant lineages. We observe an abundance of genes from pathways for the utilization of reduced carbon, sulfur, and nitrogen sources. Although organotrophy is pervasive, nitrification and sulfide oxidation are the dominant lithotrophic pathways and likely fuel carbon fixation via the reverse tricarboxylic acid and Calvin cycles. Oxygen-dependent terminal oxidases are common, and genes for reduction of oxidized nitrogen are sporadically present in our samples. Our results suggest that the underlying benthic communities are well primed for the utilization of settling organic matter, which is consistent with findings from highly productive surface water. Despite the genetic potential for nitrate reduction, the net catabolic pathway in our samples remains aerobic respiration, likely coupled to the oxidation of sulfur and nitrogen imported from the highly productive Antarctic water column above. IMPORTANCE The impacts of climate change in polar regions, like Antarctica, have the potential to alter numerous ecosystems and biogeochemical cycles. Increasing temperature and freshwater runoff from melting ice can have profound impacts on the cycling of organic and inorganic nutrients between the pelagic and benthic ecosystems. Within the benthos, sediment microbial communities play a critical role in carbon mineralization and the cycles of essential nutrients like nitrogen and sulfur. Metagenomic data collected from sediment samples from the continental shelf of western Antarctica help to examine this unique system and document the metagenomic potential for lithotrophic metabolisms and the cycles of both nitrogen and sulfur, which support not only benthic microbes but also life in the pelagic zone.
Project description:Next-Generation-Sequencing (NGS) technologies have led to important improvement in the detection of new or unrecognized infective agents, related to infectious diseases. In this context, NGS high-throughput technology can be used to achieve a comprehensive and unbiased sequencing of the nucleic acids present in a clinical sample (i.e. tissues). Metagenomic shotgun sequencing has emerged as powerful high-throughput approaches to analyze and survey microbial composition in the field of infectious diseases. By directly sequencing millions of nucleic acid molecules in a sample and matching the sequences to those available in databases, pathogens of an infectious disease can be inferred. Despite the large amount of metagenomic shotgun data produced, there is a lack of a comprehensive and easy-use pipeline for data analysis that avoid annoying and complicated bioinformatics steps. Here we present HOME-BIO, a modular and exhaustive pipeline for analysis of biological entity estimation, specific designed for shotgun sequenced clinical samples. HOME-BIO analysis provides comprehensive taxonomy classification by querying different source database and carry out main steps in metagenomic investigation. HOME-BIO is a powerful tool in the hand of biologist without computational experience, which are focused on metagenomic analysis. Its easy-to-use intrinsic characteristic allows users to simply import raw sequenced reads file and obtain taxonomy profile of their samples.