Project description:Consumer-resource interactions are a central issue in evolutionary and community ecology because they play important roles in selection and population regulation. Most consumers encounter resource variation at multiple scales, and respond through phenotypic plasticity in the short term or evolutionary divergence in the long term. The key traits for these responses may influence resource acquisition, assimilation and/or allocation. To identify candidate genes, we experimentally assayed genome-wide gene expression in pond and lake Daphnia ecotypes exposed to alternate resource environments. One was a simple, high-quality laboratory diet, Ankistrodesmus falcatus. The other was the complex natural seston from a large lake. In temporary ponds, Daphnia generally experience high-quality, abundant resources, whereas lakes provide low-quality, seasonally shifting resources that are chronically limiting. For both ecotypes, we used replicate clones drawn from a number of separate populations. We compared gene expression in whole Daphnia pulex that had been raised in the lab for 10 days, and then exposed to alternate resource environments for 24 hours. One resource environment was a 24 hour continuation of the lab resource, a satiating level of Ankistrodesmus falcatus. The alternate environment was the natural seston present in the epilimnion of Lake Murray, South Carolina. Two ecotypes were analyzed, one adapted to large lakes, and one adapted to temporary ponds. For each ecotype, eight replicate clones were used. Clones of the lake ecotype were isolated from eight independent lakes, clones of the pond ecotype were isolated from six different ponds. The total number of arrays is 16 (8 replicate clones x 2 ecotypes) x 2 resource environments). Total RNA was extracted from eight whole organisms pooled together. Pools were then converted to cDNA and labelled with a single round of amplification. For array hybridizations, samples from the two resource environments were paired for each clone, and dyes were swapped across clones.

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.

Dataset Information

NECC-Metagenomics

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