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Independent component analysis (ICA) of bacterial transcriptomes has emerged as a powerful tool for obtaining co-regulated, independently-modulated gene sets (iModulons), inferring their activities across a range of conditions, and enabling their association to known genetic regulators. By grouping and analyzing genes based on observations from big data alone, iModulons can provide a novel perspective into how the composition of the transcriptome adapts to environmental conditions. Here, we present iModulonDB (imodulondb.org), a knowledgebase of prokaryotic transcriptional regulation computed from high-quality transcriptomic datasets using ICA. Users select an organism from the home page and then search or browse the curated iModulons that make up its transcriptome. Each iModulon and gene has its own interactive dashboard, featuring plots and tables with clickable, hoverable, and downloadable features. This site enhances research by presenting scientists of all backgrounds with co-expressed gene sets and their activity levels, which lead to improved understanding of regulator-gene relationships, discovery of transcription factors, and the elucidation of unexpected relationships between conditions and genetic regulatory activity. The current release of iModulonDB covers three organisms (Escherichia coli, Staphylococcus aureus and Bacillus subtilis) with 204 iModulons, and can be expanded to cover many additional organisms.

BackgroundAdaptive Laboratory Evolution (ALE) has emerged as an experimental approach to discover mutations that confer phenotypic functions of interest. However, the task of finding and understanding all beneficial mutations of an ALE experiment remains an open challenge for the field. To provide for better results than traditional methods of ALE mutation analysis, this work applied enrichment methods to mutations described by a multiscale annotation framework and a consolidated set of ALE experiment conditions. A total of 25,321 unique genome annotations from various sources were leveraged to describe multiple scales of mutated features in a set of 35 Escherichia coli based ALE experiments. These experiments totalled 208 independent evolutions and 2641 mutations. Additionally, mutated features were statistically associated across a total of 43 unique experimental conditions to aid in deconvoluting mutation selection pressures.ResultsIdentifying potentially beneficial, or key, mutations was enhanced by seeking coding and non-coding genome features significantly enriched by mutations across multiple ALE replicates and scales of genome annotations. The median proportion of ALE experiment key mutations increased from 62%, with only small coding and non-coding features, to 71% with larger aggregate features. Understanding key mutations was enhanced by considering the functions of broader annotation types and the significantly associated conditions for key mutated features. The approaches developed here were used to find and characterize novel key mutations in two ALE experiments: one previously unpublished with Escherichia coli grown on glycerol as a carbon source and one previously published with Escherichia coli tolerized to high concentrations of L-serine.ConclusionsThe emergent adaptive strategies represented by sets of ALE mutations became more clear upon observing the aggregation of mutated features across small to large scale genome annotations. The clarification of mutation selection pressures among the many experimental conditions also helped bring these strategies to light. This work demonstrates how multiscale genome annotation frameworks and data-driven methods can help better characterize ALE mutations, and thus help elucidate the genotype-to-phenotype relationship of the studied organism.

The ability of DNA to produce a functional protein even after transfer to a foreign host is of fundamental importance in both evolutionary biology and biotechnology, enabling horizontal gene transfer in the wild and heterologous expression in the lab. However, the influence of genetic particulars on DNA functionality in a new host is poorly understood, as are the evolutionary mechanisms of assimilation and refinement. Here, we describe an automation-enabled large-scale experiment wherein Escherichia coli strains were evolved in parallel after replacement of the genes pgi or tpiA with orthologous DNA from donor species spanning all domains of life, from humans to hyperthermophilic archaea. Via analysis of hundreds of clones evolved for 50,000+ cumulative generations across dozens of independent lineages, we show that orthogene-upregulating mutations can completely mitigate fitness defects that result from initial non-functionality, with coding sequence changes unnecessary. Gene target, donor species and genomic location of the swap all influenced outcomes-both the nature of adaptive mutations (often synonymous) and the frequency with which strains successfully evolved to assimilate the foreign DNA. Additionally, time series DNA sequencing and replay evolution experiments revealed transient copy number expansions, the contingency of lineage outcome on first-step mutations and the ability for strains to escape from suboptimal local fitness maxima. Overall, this study establishes the influence of various DNA and protein features on cross-species genetic interchangeability and evolutionary outcomes, with implications for both horizontal gene transfer and rational strain design.

Enzyme turnover numbers (k cats) are essential for a quantitative understanding of cells. Because k cats are traditionally measured in low-throughput assays, they can be inconsistent, labor-intensive to obtain, and can miss in vivo effects. We use a data-driven approach to estimate in vivo k cats using metabolic specialist Escherichia coli strains that resulted from gene knockouts in central metabolism followed by metabolic optimization via laboratory evolution. By combining absolute proteomics with fluxomics data, we find that in vivo k cats are robust against genetic perturbations, suggesting that metabolic adaptation to gene loss is mostly achieved through other mechanisms, like gene-regulatory changes. Combining machine learning and genome-scale metabolic models, we show that the obtained in vivo k cats predict unseen proteomics data with much higher precision than in vitro k cats. The results demonstrate that in vivo k cats can solve the problem of inconsistent and low-coverage parameterizations of genome-scale cellular models.

Evolution fine-tunes biological pathways to achieve a robust cellular physiology. Two and a half billion years ago, rapidly rising levels of oxygen as a byproduct of blooming cyanobacterial photosynthesis resulted in a redox upshift in microbial energetics. The appearance of higher-redox-potential respiratory quinone, ubiquinone (UQ), is believed to be an adaptive response to this environmental transition. However, the majority of bacterial species are still dependent on the ancient respiratory quinone, naphthoquinone (NQ). Gammaproteobacteria can biosynthesize both of these respiratory quinones, where UQ has been associated with aerobic lifestyle and NQ with anaerobic lifestyle. We engineered an obligate NQ-dependent γ-proteobacterium, Escherichia coli ΔubiC, and performed adaptive laboratory evolution to understand the selection against the use of NQ in an oxic environment and also the adaptation required to support the NQ-driven aerobic electron transport chain. A comparative systems-level analysis of pre- and postevolved NQ-dependent strains revealed a clear shift from fermentative to oxidative metabolism enabled by higher periplasmic superoxide defense. This metabolic shift was driven by the concerted activity of 3 transcriptional regulators (PdhR, RpoS, and Fur). Analysis of these findings using a genome-scale model suggested that resource allocation to reactive oxygen species (ROS) mitigation results in lower growth rates. These results provide a direct elucidation of a resource allocation tradeoff between growth rate and ROS mitigation costs associated with NQ usage under oxygen-replete condition.

Adaptive Laboratory Evolution (ALE) has emerged as an experimental approach to discover causal mutations that confer desired phenotypic functions. ALE not only represents a controllable experimental approach to systematically discover genotype-phenotype relationships, but also allows for the revelation of the series of genetic alterations required to acquire the new phenotype. Numerous ALE studies have been published, providing a strong impetus for developing databases to warehouse experimental evolution information and make it retrievable for large-scale analysis. Here, the first step towards establishing this resource is presented: ALEdb (http://aledb.org). This initial release contains over 11 000 mutations that have been discovered from eleven ALE publications. ALEdb (i) is a web-based platform that comprehensively reports on ALE acquired mutations and their conditions, (ii) reports key mutations using previously established trends, (iii) enables a search-driven workflow to enhance user mutation functional analysis through mutation cross-reference, (iv) allows exporting of mutation query results for custom analysis, (v) includes a bibliome describing the databased experiment publications and (vi) contains experimental evolution mutations from multiple model organisms. Thus, ALEdb is an informative platform which will become increasingly revealing as the number of reported ALE experiments and identified mutations continue to expand.

Understanding the fundamental characteristics of microbial communities could have far reaching implications for human health and applied biotechnology. Despite this, much is still unknown regarding the genetic basis and evolutionary strategies underlying the formation of viable synthetic communities. By pairing auxotrophic mutants in co-culture, it has been demonstrated that viable nascent E. coli communities can be established where the mutant strains are metabolically coupled. A novel algorithm, OptAux, was constructed to design 61 unique multi-knockout E. coli auxotrophic strains that require significant metabolite uptake to grow. These predicted knockouts included a diverse set of novel non-specific auxotrophs that result from inhibition of major biosynthetic subsystems. Three OptAux predicted non-specific auxotrophic strains-with diverse metabolic deficiencies-were co-cultured with an L-histidine auxotroph and optimized via adaptive laboratory evolution (ALE). Time-course sequencing revealed the genetic changes employed by each strain to achieve higher community growth rates and provided insight into mechanisms for adapting to the syntrophic niche. A community model of metabolism and gene expression was utilized to predict the relative community composition and fundamental characteristics of the evolved communities. This work presents new insight into the genetic strategies underlying viable nascent community formation and a cutting-edge computational method to elucidate metabolic changes that empower the creation of cooperative communities.