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Abstract Investigating the evolution ofEscherichia coliin microgravity offers valuable insights into microbial adaptation to extreme environments. Here the effects of simulated microgravity (SµG) on gene expression and genome evolution ofE. coliREL606, a strain evolved terrestrially for 35 years, is explored. The transcriptomic changes for glucose-limited and glucose-replete conditions over 24 h illustrate that SµG increased the expression of genes involved in stress response, biofilm, and metabolism. A greater number of differentially expressed genes related to the general stress response (GSR) and biofilm formation is observed in simulated microgravity cultures under glucose-limited conditions in comparison to glucose-replete conditions. Longer term SµG culture under glucose-limited conditions led to the accumulation of unique mutations when compared to control cultures, particularly in themraZ/fruRintergenic region and theelyC gene, suggesting changes in peptidoglycan and enterobacterial common antigen (ECA) production. These findings highlight the physiological and genomic adaptations ofE. colito microgravity, offering a foundation for future research into the long-term effects of space conditions on bacterial evolution. 

Abstract Marine microbes like diatoms make up the base of marine food webs and drive global nutrient cycles. Despite their key roles in ecology, biogeochemistry, and biotechnology, we have limited empirical data on how forces other than adaptation may drive diatom diversification, especially in the absence of environmental change. One key feature of diatom populations is frequent extreme reductions in population size, which can occur both in situ and ex situ as part of bloom-and-bust growth dynamics. This can drive divergence between closely related lineages, even in the absence of environmental differences. Here, we combine experimental evolution and transcriptome landscapes (t-scapes) to reveal repeated evolutionary divergence within several species of diatoms in a constant environment. We show that most of the transcriptional divergence can be captured on a reduced set of axes, and that repeatable evolution can occur along a single major axis of variation defined by core ortholog expression comprising common metabolic pathways. Previous work has associated specific transcriptional changes in gene networks with environmental factors. Here, we find that these same gene networks diverge in the absence of environmental change, suggesting these pathways may be central in generating phenotypic diversity as a result of both selective and random evolutionary forces. If this is the case, these genes and the functions they encode may represent universal axes of variation. Such axes that capture suites of interacting transcriptional changes during diversification improve our understanding of both global patterns in local adaptation and microdiversity, as well as evolutionary forces shaping algal cultivation. 

Background: With the advent of metagenomics, the importance of microorganisms and how their interactions are relevant to ecosystem resilience, sustainability, and human health has become evident. Cataloging and preserving biodiversity is para- mount not only for the Earth’s natural systems but also for discovering solutions to challenges that we face as a growing civilization. Metagenomics pertains to the in silico study of all microorganisms within an ecological community in situ, however, many software suites recover only prokaryotes and have limited to no support for viruses and eukaryotes. Results: In this study, we introduce the Viral Eukaryotic Bacterial Archaeal (VEBA) open- source software suite developed to recover genomes from all domains. To our knowl- edge, VEBA is the first end-to-end metagenomics suite that can directly recover, quality assess, and classify prokaryotic, eukaryotic, and viral genomes from metagenomes. VEBA implements a novel iterative binning procedure and hybrid sample-specific/ multi-sample framework that yields more genomes than any existing methodology alone. VEBA includes a consensus microeukaryotic database containing proteins from existing databases to optimize microeukaryotic gene modeling and taxonomic classifi- cation. VEBA also provides a unique clustering-based dereplication strategy allowing for sample-specific genomes and genes to be directly compared across non-overlapping biological samples. Finally, VEBA is the only pipeline that automates the detection of candidate phyla radiation bacteria and implements the appropriate genome quality assessments. VEBA’s capabilities are demonstrated by reanalyzing 3 existing public datasets which recovered a total of 948 MAGs (458 prokaryotic, 8 eukaryotic, and 482 viral) including several uncharacterized organisms and organisms with no public genome representatives. Conclusions: The VEBA software suite allows for the in silico recovery of microorgan- isms from all domains of life by integrating cutting edge algorithms in novel ways. VEBA fully integrates both end-to-end and task-specific metagenomic analysis in a modular architecture that minimizes dependencies and maximizes productivity. The contributions of VEBA to the metagenomics community includes seamless end-to-end metagenomics analysis but also provides users with the frexibility to perform specific analytical tasks. VEBA allos for the automation of several metagenomics steps and shows that new information can be recovered from existing datasets.