A year in the life of a thrombolite: comparative metatranscriptomics reveals dynamic metabolic changes over diel and seasonal cycles

Abstract

Microbialites are one of the oldest known ecosystems on Earth and the coordinated metabolisms and activities of these mineral-depositing communities have had a profound impact on the habitability of the planet. Despite efforts to understand the diversity and metabolic potential of these systems, there has not been a systematic molecular analysis of the transcriptional changes that occur within a living microbialite over time. In this study, we generated metatranscriptomic libraries from actively growing thrombolites, a type of microbialite, throughout diel and seasonal cycles and observed dynamic shifts in the population and metabolic transcriptional activity. The most transcribed genes in all seasons were associated with photosynthesis, but only transcripts associated with photosystem II exhibited diel cycling. Photosystem I transcripts were constitutively expressed at all time points including midnight and sunrise. Transcripts associated with nitrogen fixation, methanogenesis and dissimilatory sulfate reduction exhibited diel cycling, and variability between seasons. Networking analysis of the metatranscriptomes showed correlated expression patterns helping to elucidate how metabolic interactions are coordinated within the thrombolite community. These findings have identified distinctive temporal patterns within the thrombolites and will serve an important foundation to understand the mechanisms by which these communities form and respond to changes in their environment.

Comparative Metagenomics Provides Insight Into the Ecosystem Functioning of the Shark Bay Stromatolites, Western Australia

“…Despite the similar metabolic profiles between the two subtidal stromatolite-forming mat types, each of the associated carbonate build-ups exhibited different surface morphologies, with the colloform mats exhibiting a convoluted surface and the smooth mats having a flat, even surface. These differences could reflect differential gene expression between the two mats, as other metatranscriptomic analyses of Bahamian thrombolites have shown that the metatranscriptome can vary significantly throughout diel and seasonal cycles and does not fully reflect the metagenome ( Mobberley et al, 2015 ; Louyakis et al, 2018 ).…”

“…Additionally, recent metagenomic and metatranscriptomic examination of the unlaminated thrombolites of Highborne Cay, The Bahamas have shown that the filamentous Dichothrix spp. and its associated coccoid cyanobacteria are the most transcriptionally active taxa within the thrombolite communities, with most transcripts associated with photosynthesis ( Mobberley et al, 2015 ; Louyakis et al, 2017 , 2018 ). These results coupled with stable isotope analyses of the calcium carbonate have revealed that the primary metabolism driving precipitation in the intertidal Bahamian thrombolites is photosynthesis ( Louyakis et al, 2017 ).…”

Functional Gene Expression in Shark Bay Hypersaline Microbial Mats: Adaptive Responses

“…This was also observed with the active microbial community distributions, which indicates that physiochemical conditions that shape microbial community structures as well as seasonal variations are significant factors influencing functional gene expression in these ecosystems. However, the combination of functional genes into the respective pathways indicated that PSI and PSII, as well as the Calvin-Benson cycle were transcribed more during the day time when peak oxygen production is known to occur (Louyakis et al, 2018). A potential reason for not observing diel changes in individual functional genes might be due to their stability.…”

Evaluating the lettuce metatranscriptome with MinION sequencing for future spaceflight food production applications

“…In addition, transcripts derived from taxa assigned to Cyanobacteria, which represented a substantial number of reads (Supplementary Dataset 1 ), as well as transcripts associated with photosynthesis genes (Supplementary Dataset 2 ), were excluded to prevent the inclusion of possible misassigned chloroplast transcripts. Abundance of transcripts that had KEGG annotations 79 were normalized using gene-length corrected transcripts per million method followed by Trimmed Mean of M -values method as implemented in EdgeR 80 . KEGG-annotated association with their respective pathways were performed using the R package KEGGREST 81 .…”