Metatranscriptomics reveals a shift in microbial community composition and function during summer months in a coastal marine environment

Abstract

Temperate coastal marine waters are often thermally stratified from spring through fall but can be dynamic and disrupted by tidal currents and wind‐driven upwelling. These mixing events introduce deeper, cooler water with a higher partial pressure of CO2 (pCO2) and its associated microbial communities to the surface. Anecdotally, these events impact shellfish hatcheries and farms, warranting improved understanding of changes in composition and activity of marine microbial communities in relation to environmental processes. To characterize both compositional and functional changes associated with abiotic factors, here, we generate a reference metatranscriptome from the Strait of Georgia over representative seasons and analyze metatranscriptomic profiles of the microorganisms present within intake water containing different pCO2 levels at a shellfish hatchery in British Columbia from June through October. Abiotic factors studied include pH, temperature, alkalinity, aragonite, calcite, and pCO2. Community composition changes were observed to occur at broad taxonomic levels and most notably to vary with temperature and pCO2. Functional gene expression profiles indicated a strong difference between early (June–July) and late summer (August–October) associated with viral activity. The taxonomic data suggest this could be due to the termination of cyanobacteria and phytoplankton blooms by viral lysis in the late season. Functional analysis indicated fewer differentially expressed transcripts associated with abiotic variables (e.g., pCO2) than with the temporal effect. Microbial composition and activity in these waters vary with both short‐term effects observed alongside abiotic variation and long‐term effects observed across seasons. The analysis of both taxonomy and functional gene expression simultaneously in the same samples by environmental RNA (eRNA metatranscriptomics) provided a more comprehensive view for monitoring water bodies than either would in isolation.

Beyond species detection—leveraging environmental DNA and environmental RNA to push beyond presence/absence applications

“…They show that eDNA metabarcoding may accurately and efficiently define the spatial and temporal characteristics of fish spawning activity in lentic environments. Sutherland et al (2023) conduct a unique trial using eRNA for taxonomic assignments of microbes and gene expression analysis at the same time. Water samples were collected in a shellfish hatchery in British Columbia, Canada, and they detected temporal changes in the microbial community composition and functional gene differences associated with abiotic factors and time.…”

Deep6: Classification of Metatranscriptomic Sequences into Cellular Empires and Viral Realms Using Deep Learning Models

“…Viruses play key roles in marine ecosystems ( 1 ), but studying viral diversity and activity remains challenging. Recently, metatranscriptomes are being used to simultaneously profile biological functions and taxonomic information of cellular and viral origin in the same samples ( 2 ). Machine learning tools such as DeepVirFinder ( 3 ), DeepMicrobeFinder ( 4 ), and VirSorter2 ( 5 ) were designed to compensate for incomplete viral reference databases.…”

Effects of ancient anthropogenic clam gardens on the growth, survival, and transcriptome of Pacific littleneck clams (Leukoma staminea)

“…Gene-expression data can then be linked with survival, growth, and environmental information and used to predict possible physiological effects (Evans et al 2011, Miller et al 2017). Transcriptomics can not only yield faster results but may also be used for taxonomic identifications (Bourlat et al 2013, Sutherland et al 2022). The present study aims to determine whether Pacific littleneck clam health and productivity, as assessed by growth and survival as well as transcriptomic profiling, are affected by the presence of unmaintained clam gardens and various sediment characteristics (e.g., grain-size distribution, organics, and carbonates).…”