Synthetic cross-phyla gene replacement and evolutionary assimilation of major enzymes

Evolution and engineering of pathways for aromatic O-demethylation in Pseudomonas putida KT2440

Biological conversion of lignin from biomass offers a promising strategy for sustainable production of fuels and chemicals. However, aromatic compounds derived from lignin commonly contain methoxy groups, and O-demethylation of these substrates is often a rate-limiting reaction that influences catabolic efficiency. Several enzyme families catalyze aromatic O-demethylation, but they are rarely compared in vivo to determine an optimal biocatalytic strategy. Here, two pathways for aromatic O-demethylation were compared in Pseudomonas putida KT2440. The native Rieske non-heme iron monooxygenase (VanAB) and, separately, a heterologous tetrahydrofolate-dependent demethylase (LigM) were constitutively expressed in P. putida, and the strains were optimized via adaptive laboratory evolution (ALE) with vanillate as a model substrate. All evolved strains displayed improved growth phenotypes, with the evolved strains harboring the native VanAB pathway exhibiting growth rates ∼1.8x faster than those harboring the heterologous LigM pathway. Enzyme kinetics and transcriptomics studies investigated the contribution of selected mutations toward enhanced utilization of vanillate. The VanAB-overexpressing strains contained the most impactful mutations, including those in VanB, the reductase for vanillate O-demethylase, PP_3494, a global regulator of vanillate catabolism, and fghA, involved in formaldehyde detoxification. These three mutations were combined into a single strain, which exhibited approximately 5x faster vanillate consumption than the wild-type strain in the first 8 h of cultivation. Overall, this study illuminates the details of vanillate catabolism in the context of two distinct enzymatic mechanisms, yielding a platform strain for efficient O-demethylation of lignin-related aromatic compounds to value-added products.

The hallmarks of a tradeoff in transcriptomes that balances stress and growth functions

Fast growth phenotypes are achieved through optimal transcriptomic allocation, in which cells must balance tradeoffs in resource allocation between diverse functions. One such balance between stress readiness and unbridled growth in E. coli has been termed the fear versus greed (f/g) tradeoff. Two specific RNA polymerase (RNAP) mutations observed in adaptation to fast growth have been previously shown to affect the f/g tradeoff, suggesting that genetic adaptations may be primed to control f/g resource allocation. Here, we conduct a greatly expanded study of the genetic control of the f/g tradeoff across diverse conditions. We introduced 12 RNA polymerase (RNAP) mutations commonly acquired during adaptive laboratory evolution (ALE) and obtained expression profiles of each. We found that these single RNAP mutation strains resulted in large shifts in the f/g tradeoff primarily in the RpoS regulon and ribosomal genes, likely through modifying RNAP-DNA interactions. Two of these mutations additionally caused condition-specific transcriptional adaptations. While this tradeoff was previously characterized by the RpoS regulon and ribosomal expression, we find that the GAD regulon plays an important role in stress readiness and ppGpp in translation activity, expanding the scope of the tradeoff. A phylogenetic analysis found the greed-related genes of the tradeoff present in numerous bacterial species. The results suggest that the f/g tradeoff represents a general principle of transcriptome allocation in bacteria where small genetic changes can result in large phenotypic adaptations to growth conditions.IMPORTANCETo increase growth, E. coli must raise ribosomal content at the expense of non-growth functions. Previous studies have linked RNAP mutations to this transcriptional shift and increased growth but were focused on only two mutations found in the protein's central region. RNAP mutations, however, commonly occur over a large structural range. To explore RNAP mutations' impact, we have introduced 12 RNAP mutations found in laboratory evolution experiments and obtained expression profiles of each. The mutations nearly universally increased growth rates by adjusting said tradeoff away from non-growth functions. In addition to this shift, a few caused condition-specific adaptations. We explored the prevalence of this tradeoff across phylogeny and found it to be a widespread and conserved trend among bacteria.

A multi-scale expression and regulation knowledge base for Escherichia coli

Transcriptomic data is accumulating rapidly; thus, scalable methods for extracting knowledge from this data are critical. Here, we assembled a top-down expression and regulation knowledge base for Escherichia coli. The expression component is a 1035-sample, high-quality RNA-seq compendium consisting of data generated in our lab using a single experimental protocol. The compendium contains diverse growth conditions, including: 9 media; 39 supplements, including antibiotics; 42 heterologous proteins; and 76 gene knockouts. Using this resource, we elucidated global expression patterns. We used machine learning to extract 201 modules that account for 86% of known regulatory interactions, creating the regulatory component. With these modules, we identified two novel regulons and quantified systems-level regulatory responses. We also integrated 1675 curated, publicly-available transcriptomes into the resource. We demonstrated workflows for analyzing new data against this knowledge base via deconstruction of regulation during aerobic transition. This resource illuminates the E. coli transcriptome at scale and provides a blueprint for top-down transcriptomic analysis of non-model organisms.

Adaptive evolution of a minimal organism with a synthetic genome

The bacterial strain JCVI-syn3.0 stands as the first example of a living organism with a minimized synthetic genome, derived from the Mycoplasma mycoides genome and chemically synthesized in vitro. Here, we report the experimental evolution of a syn3.0- derived strain. Ten independent replicates were evolved for several hundred generations, leading to growth rate improvements of > 15%. Endpoint strains possessed an average of 8 mutations composed of indels and SNPs, with a pronounced C/G- > A/T transversion bias. Multiple genes were repeated mutational targets across the independent lineages, including phase variable lipoprotein activation, 5 distinct; nonsynonymous substitutions in the same membrane transporter protein, and inactivation of an uncharacterized gene. Transcriptomic analysis revealed an overall tradeoff reflected in upregulated ribosomal proteins and downregulated DNA and RNA related proteins during adaptation. This work establishes the suitability of synthetic, minimal strains for laboratory evolution, providing a means to optimize strain growth characteristics and elucidate gene functionality.

The hallmarks of a tradeoff in transcriptomes that balances stress and growth functions

Fit phenotypes are achieved through optimal transcriptomic allocation. Here, we performed a high-resolution, multi-scale study of the transcriptomic tradeoff between two key fitness phenotypes, stress response (fear) and growth (greed), in Escherichia coli. We introduced twelve RNA polymerase (RNAP) mutations commonly acquired during adaptive laboratory evolution (ALE) and found that single mutations resulted in large shifts in the fear vs. greed tradeoff, likely through destabilizing the rpoB-rpoC interface. RpoS and GAD regulons drive the fear response while ribosomal proteins and the ppGpp regulon underlie greed. Growth rate selection pressure during ALE results in endpoint strains that often have RNAP mutations, with synergistic mutations reflective of particular conditions. A phylogenetic analysis found the tradeoff in numerous bacteria species. The results suggest that the fear vs. greed tradeoff represents a general principle of transcriptome allocation in bacteria where small genetic changes can result in large phenotypic adaptations to growth conditions.

Distribution of fitness effects of cross-species transformation reveals potential for fast adaptive evolution

Bacterial transformation, a common mechanism of horizontal gene transfer, can speed up adaptive evolution. How its costs and benefits depend on the growth environment is poorly understood. Here, we characterize the distributions of fitness effects (DFE) of transformation in different conditions and test whether they predict in which condition transformation is beneficial. To determine the DFEs, we generate hybrid libraries between the recipient Bacillus subtilis and different donor species and measure the selection coefficient of each hybrid strain. In complex medium, the donor Bacillus vallismortis confers larger fitness effects than the more closely related donor Bacillus spizizenii. For both donors, the DFEs show strong effect beneficial transfers, indicating potential for fast adaptive evolution. While some transfers of B. vallismortis DNA show pleiotropic effects, various transfers are beneficial only under a single growth condition, indicating that the recipient can benefit from a variety of donor genes to adapt to varying growth conditions. We scrutinize the predictive value of the DFEs by laboratory evolution under different growth conditions and show that the DFEs correctly predict the condition at which transformation confers a benefit. We conclude that transformation has a strong potential for speeding up adaptation to varying environments by profiting from a gene pool shared between closely related species.

Transcriptional Potential Determines the Adaptability of Escherichia coli Strains with Different Fitness Backgrounds

Adaptation through the fitness landscape may be influenced by the gene pool or expression network. However, genetic factors that determine the contribution of beneficial mutations during adaptive evolution are poorly understood. In this study, we experimentally evolved wild-type Escherichia coli K-12 MG1655 and its isogenic derivative that has two additional replication origins and shows higher background fitness. During the short time of experimental evolution, the fitness gains of the two E. coli strains with different fitness backgrounds converged. Populational genome sequencing revealed various mutations with different allele frequencies in evolved populations. Several mutations occurred in genes affecting transcriptional regulation (e.g., RNA polymerase subunit, RNase, ppGpp synthetase, and transcription termination/antitermination factor genes). When we introduced mutations into the ancestral E. coli strains, beneficial effects tended to be lower in the ancestor with higher initial fitness. Replication rate analysis showed that the various replication indices do not correlate with the growth rate. Transcriptome profiling showed that gene expression and gene ontology are markedly enriched in populations with lower background fitness after experimental evolution. Further, the degree of transcriptional change was proportional to the fitness gain. Thus, the evolutionary trajectories of bacteria with different fitness backgrounds can be complex and counterintuitive. Notably, transcriptional change is a major contributor to adaptability. IMPORTANCE Predicting the adaptive potential of bacterial populations can be difficult due to their complexity and dynamic environmental conditions. Also, epistatic interaction between mutations affects the adaptive trajectory. Nevertheless, next-generation sequencing sheds light on understanding evolutionary dynamics through high-throughput genome and transcriptome information. Experimental evolution of two E. coli strains with different background fitness showed that the trajectories of fitness gain, which slowed down during the later stages of evolution, became convergent. This suggests that the adaptability of bacteria can be counterintuitive and that predicting the evolutionary path of bacteria can be difficult even in a constant environment. In addition, transcriptional change is associated with fitness gain during the evolutionary process. Thus, the adaptability of cells depends on their intrinsic genetic capacity for a given evolutionary period. This should be considered when genetically engineered bacteria are optimized through adaptive evolution.

Genetic Architecture and Fitness of Bacterial Interspecies Hybrids

Integration of a conjugative plasmid into a bacterial chromosome can promote the transfer of chromosomal DNA to other bacteria. Intraspecies chromosomal conjugation is believed responsible for creating the global pathogens Klebsiella pneumoniae ST258 and Escherichia coli ST1193. Interspecies conjugation is also possible but little is known about the genetic architecture or fitness of such hybrids. To study this, we generated by conjugation 14 hybrids of E. coli and Salmonella enterica. These species belong to different genera, diverged from a common ancestor >100 Ma, and share a conserved order of orthologous genes with ∼15% nucleotide divergence. Genomic analysis revealed that all but one hybrid had acquired a contiguous segment of donor E. coli DNA, replacing a homologous region of recipient Salmonella chromosome, and ranging in size from ∼100 to >4,000 kb. Recombination joints occurred in sequences with higher-than-average nucleotide identity. Most hybrid strains suffered a large reduction in growth rate, but the magnitude of this cost did not correlate with the length of foreign DNA. Compensatory evolution to ameliorate the cost of low-fitness hybrids pointed towards disruption of complex genetic networks as a cause. Most interestingly, 4 of the 14 hybrids, in which from 45% to 90% of the Salmonella chromosome was replaced with E. coli DNA, showed no significant reduction in growth fitness. These data suggest that the barriers to creating high-fitness interspecies hybrids may be significantly lower than generally appreciated with implications for the creation of novel species.

Genome-scale reconstructions to assess metabolic phylogeny and organism clustering

Approaches for systematizing information of relatedness between organisms is important in biology. Phylogenetic analyses based on sets of highly conserved genes are currently the basis for the Tree of Life. Genome-scale metabolic reconstructions contain high-quality information regarding the metabolic capability of an organism and are typically restricted to metabolically active enzyme-encoding genes. While there are many tools available to generate draft reconstructions, expert-level knowledge is still required to generate and manually curate high-quality genome-scale metabolic models and to fill gaps in their reaction networks. Here, we use the tool AutoKEGGRec to construct 975 genome-scale metabolic draft reconstructions encoded in the KEGG database without further curation. The organisms are selected across all three domains, and their metabolic networks serve as basis for generating phylogenetic trees. We find that using all reactions encoded, these metabolism-based comparisons give rise to a phylogenetic tree with close similarity to the Tree of Life. While this tree is quite robust to reasonable levels of noise in the metabolic reaction content of an organism, we find a significant heterogeneity in how much noise an organism may tolerate before it is incorrectly placed in the tree. Furthermore, by using the protein sequences for particular metabolic functions and pathway sets, such as central carbon-, nitrogen-, and sulfur-metabolism, as basis for the organism comparisons, we generate highly specific phylogenetic trees. We believe the generation of phylogenetic trees based on metabolic reaction content, in particular when focused on specific functions and pathways, could aid the identification of functionally important metabolic enzymes and be of value for genome-scale metabolic modellers and enzyme-engineers.

Evolutionary History of Bioessential Elements Can Guide the Search for Life in the Universe

Our understanding of life in the universe comes from one sample, life on Earth. Current and next-generation space missions will target exoplanets as well as planets and moons in our own solar system with the primary goal of detecting, interpreting and characterizing indications of possible biological activity. Thus, understanding life's fundamental characteristics is increasingly critical for detecting and interpreting potential biological signatures elsewhere in the universe. Astrobiologists have outlined the essential roles of carbon and water for life, but we have yet to decipher the rules governing the evolution of how living organisms use bioessential elements. Does the suite of life's essential chemical elements on Earth constitute only one possible evolutionary outcome? Are some elements so essential for biological functions that evolution will select for them despite low availability? How would this play out on other worlds that have different relative element abundances? When we look for life in the universe, or the conditions that could give rise to life, we must learn how to recognize it in extremely different chemical and environmental conditions from those on Earth. We argue that by exposing self-organizing biotic chemistries to different combinations of abiotic materials, and by mapping the evolutionary history of metalloenzyme biochemistry onto geological availabilities of metals, alternative element choices that are very different from life's present-day molecular structure might result. A greater understanding of the paleomolecular evolutionary history of life on Earth will create a predictive capacity for detecting and assessing life's existence on worlds where alternate evolutionary paths might have been taken.