Biofiltration in drinking water treatment (BDWT) are popular as it holds promise as an alternative to chemical treatments, yet our understanding of the key drivers and trends underlying bacterial evolution within this process remains limited. While plasmids and phages are recognized as the main vectors of horizontal gene transfer (HGT), their roles in shaping bacterial evolution in BDWT remain largely unknown. Here we leverage global metagenomic data to unravel the primary forces driving bacterial evolution in BDWT. Our results revealed that the primary vector of HGT varies depending on the type of source water (groundwater and surface water). Both plasmids and phages accelerated bacterial evolution in BDWT by enhancing genetic diversity within species, but they drove contrasting evolutionary trends in functional redundancy in different source water types. Specifically, trends towards and away from functional redundancy (indicated as gene-protein ratio) were observed in surface-water and groundwater biofilters, respectively. Virulent phages drove bacterial evolution through synergistic interactions with bacterial species capable of natural transformation and with certain natural compounds that disrupt bacterial cytoplasmic membranes. Genes relating to water purification (such as Mn(II)-oxidizing genes), microbial risks (antibiotic resistance genes), and chemical risk (polycyclic aromatic hydrocarbons) were enriched via HGT in BDWT, highlighting the necessity for heighted focus on these useful and risky objects. Overall, these discoveries enhance our understanding of bacterial evolution in BDWT and have implications for the optimization of water treatment strategies.

Continuous gain and loss of genes are the primary driving forces of bacterial evolution and environmental adaptation. Studying bacterial evolution in terms of protein domain, which is the fundamental function and evolutionary unit of proteins, can provide a more comprehensive understanding of bacterial differentiation and phenotypic adaptation processes. Therefore, we proposed a phylogenetic tree-based method for detecting genetic gain and loss events in terms of protein domains. Specifically, the method focuses on a single domain to trace its evolution process or on multiple domains to investigate their co-evolution principles. This novel method was validated using 122 Shigella isolates. We found that the loss of a significant number of domains was likely the main driving force behind the evolution of Shigella, which could reduce energy expenditure and preserve only the most essential functions. Additionally, we observed that simultaneously gained and lost domains were often functionally related, which can facilitate and accelerate phenotypic evolutionary adaptation to the environment. All results obtained using our method agree with those of previous studies, which validates our proposed method.

Many nonsporulating bacterial species survive prolonged resource exhaustion, by entering a state termed long-term stationary phase. Here, we performed long-term stationary phase evolutionary experiments on the bacterium Pseudomonas putida, followed by whole-genome sequencing of evolved clones. We show that P. putida is able to persist and adapt genetically under long-term stationary phase. We observed an accumulation of mutations within the evolving P. putida populations. Within each population, independently evolving lineages are established early on and persist throughout the 4-month-long experiment. Mutations accumulate in a highly convergent manner, with similar loci being mutated across independently evolving populations. Across populations, mutators emerge, that due to mutations within mismatch repair genes developed a much higher rate of mutation than other clones with which they coexisted within their respective populations. While these general dynamics of the adaptive process are quite similar to those we previously observed in the model bacterium Escherichia coli, the specific loci that are involved in adaptation only partially overlap between P. putida and E. coli.

Salmonella enterica serovar Typhi and Paratyphi A are the cause of typhoid and paratyphoid fever in humans, which are systemic life-threatening illnesses. Both serovars are exclusively adapted to the human host, where they can cause life-long persistent infection. A distinct feature of these serovars is the presence of a relatively high number of degraded coding sequences coding for metabolic pathways, most likely a consequence of their adaptation to a single host. As a result of convergent evolution, these serovars shared many of the degraded coding sequences although often affecting different genes in the same metabolic pathway. However, there are several coding sequences that appear intact in one serovar while clearly degraded in the other, suggesting differences in their metabolic capabilities. Here, we examined the functionality of metabolic pathways that appear intact in S. Typhi but that show clear signs of degradation in S. Paratyphi A. We found that, in all cases, the existence of single amino acid substitutions in S. Typhi metabolic enzymes, transporters, or transcription regulators resulted in the inactivation of these metabolic pathways. Thus, the inability of S. Typhi to metabolize Glucose-6-Phosphate or 3-phosphoglyceric acid is due to the silencing of the expression of the genes encoding the transporters for these compounds due to point mutations in the transcriptional regulatory proteins. In contrast, its inability to utilize glucarate or galactarate is due to the presence of point mutations in the transporter and enzymes necessary for the metabolism of these sugars. These studies provide additional support for the concept of adaptive convergent evolution of these two human-adapted S. enterica serovars and highlight a limitation of bioinformatic approaches to predict metabolic capabilities.
OBJECTIVE: Salmonella enterica serovar Typhi and Paratyphi A are the cause of typhoid and paratyphoid fever in humans, which are systemic life-threatening illnesses. Both serovars can only infect the human host, where they can cause life-long persistent infection. Because of their adaptation to the human host, these bacterial pathogens have changed their metabolism, leading to the loss of their ability to utilize certain nutrients. In this study we examined the functionality of metabolic pathways that appear intact in S. Typhi but that show clear signs of degradation in S. Paratyphi A. We found that, in all cases, the existence of single amino acid substitutions in S. Typhi metabolic enzymes, transporters, or transcription regulators resulted in the inactivation of these metabolic pathways. These studies provide additional support for the concept of adaptive convergent evolution of these two human-adapted S. enterica serovars.

The chronology and phylogeny of bacterial evolution are difficult to reconstruct due to a scarce fossil record. The analysis of bacterial genomes remains challenging because of large sequence divergence, the plasticity of bacterial genomes due to frequent gene loss, horizontal gene transfer, and differences in selective pressure from one locus to another. Therefore, taking advantage of the rich and rapidly accumulating genomic data requires accurate modeling of genome evolution. An important technical consideration is that loci with high effective mutation rates may diverge beyond the detection limit of the alignment algorithms used, biasing the genome-wide divergence estimates toward smaller divergences. In this article, we propose a novel method to gain insight into bacterial evolution based on statistical properties of genome comparisons. We find that the length distribution of sequence matches is shaped by the effective mutation rates of different loci, by the horizontal transfers, and by the aligner sensitivity. Based on these inputs, we build a model and show that it accounts for the empirically observed distributions, taking the Enterobacteriaceae family as an example. Our method allows to distinguish segments of vertical and horizontal origins and to estimate the time divergence and exchange rate between any pair of taxa from genome-wide alignments. Based on the estimated time divergences, we construct a time-calibrated phylogenetic tree to demonstrate the accuracy of the method.

Gene transfer agents (GTAs) are genetic elements derived from ancestral bacteriophages that have become domesticated by the host. GTAs are present in diverse prokaryotic organisms, where they can facilitate horizontal gene transfer under certain conditions. Unlike typical bacteriophages, GTAs do not exhibit any preference for the replication or transfer of the genes encoding them; instead, they exhibit a remarkable capacity to package chromosomal, and sometimes extrachromosomal, DNA into virus-like capsids and disseminate it to neighboring cells. Because GTAs resemble defective prophages, identification of novel GTAs is not trivial. The detection of candidates relies on the genetic similarity to known GTAs, which has been fruitful in α-proteobacterial lineages but challenging in more distant bacteria. Here we consider several fundamental questions: What is the true prevalence of GTAs in prokaryote genomes? Given there are high costs for GTA production, what advantage do GTAs provide to the bacterial host to justify their maintenance? How is the bacterial chromosome recognized and processed for inclusion in GTA particles? This article highlights the challenges in comprehensively understanding GTAs\' prevalence, function and DNA packaging method. Going forward, broad study of atypical GTAs and use of ecologically relevant conditions are required to uncover their true impact on bacterial chromosome evolution.

Two-component regulatory systems (TCSs) are a major mechanism used by bacteria to sense and respond to their environments. Many of the same TCSs are used by biologically diverse organisms with different regulatory needs, suggesting that the functions of TCS must evolve. To explore this topic, we analysed the amino acid sequence divergence patterns of a large set of broadly conserved TCS across different branches of Enterobacteriaceae, a family of Gram-negative bacteria that includes biomedically important genera such as Salmonella, Escherichia, Klebsiella and others. Our analysis revealed trends in how TCS sequences change across different proteins or functional domains of the TCS, and across different lineages. Based on these trends, we identified individual TCS that exhibit atypical evolutionary patterns. We observed that the relative extent to which the sequence of a given TCS varies across different lineages is generally well conserved, unveiling a hierarchy of TCS sequence conservation with EnvZ/OmpR as the most conserved TCS. We provide evidence that, for the most divergent of the TCS analysed, PmrA/PmrB, different alleles were horizontally acquired by different branches of this family, and that different PmrA/PmrB sequence variants have highly divergent signal-sensing domains. Collectively, this study sheds light on how TCS evolve, and serves as a compendium for how the sequences of the TCS in this family have diverged over the course of evolution.

AB5-type toxins are a diverse family of protein toxins composed of an enzymatic active (A) subunit and a pentameric delivery (B) subunit. Salmonella enterica serovar Typhi\'s typhoid toxin features two A subunits, CdtB and PltA, in complex with the B subunit PltB. Recently, it was shown that S. Typhi encodes a horizontally acquired B subunit, PltC, that also assembles with PltA/CdtB to produce a second form of typhoid toxin. S. Typhi therefore produces two AB5 toxins with the same A subunits but distinct B subunits, an evolutionary twist that is unique to typhoid toxin. Here, we show that, remarkably, the Salmonella bongori species independently evolved an analogous capacity to produce two typhoid toxins with distinct B subunits. S. bongori\'s alternate B subunit, PltD, is evolutionarily distant from both PltB and PltC and outcompetes PltB to form the predominant toxin. We show that, surprisingly, S. bongori elicits similar levels of CdtB-mediated intoxication as S. Typhi during infection of cultured human epithelial cells. This toxicity is exclusively due to the PltB toxin, and strains lacking pltD produce increased amounts of PltB toxin and exhibit increased toxicity compared to the wild type, suggesting that the acquisition of the PltD subunit potentially made S. bongori less virulent toward humans. Collectively, this study unveils a striking example of convergent evolution that highlights the importance of the poorly understood \"two-toxin\" paradigm for typhoid toxin biology and, more broadly, illustrates how the flexibility of A-B interactions has fueled the evolutionary diversification and expansion of AB5-type toxins.
OBJECTIVE: Typhoid toxin is an important Salmonella Typhi virulence factor and an attractive target for therapeutic interventions to combat typhoid fever. The recent discovery of a second version of this toxin has substantial implications for understanding S. Typhi pathogenesis and combating typhoid fever. In this study, we discover that a remarkably similar two-toxin paradigm evolved independently in Salmonella bongori, which strongly suggests that this is a critical aspect of typhoid toxin biology. We observe significant parallels between how the two toxins assemble and their capacity to intoxicate host cells during infection in S. Typhi and S. bongori, which provides clues to the biological significance of this unusual toxin arrangement. More broadly, AB5 toxins with diverse activities and mechanisms are essential virulence factors for numerous important bacterial pathogens. This study illustrates the capacity for novel A-B interactions to evolve and thus provides insight into how such a diverse arsenal of toxins might have emerged.

Mathematical models of cancer and bacterial evolution have generally stemmed from a gene-centric framework, assuming clonal evolution via acquisition of resistance-conferring mutations and selection of their corresponding subpopulations. More recently, the role of phenotypic plasticity has been recognized and models accounting for phenotypic switching between discrete cell states (e.g., epithelial and mesenchymal) have been developed. However, seldom do models incorporate both plasticity and mutationally driven resistance, particularly when the state space is continuous and resistance evolves in a continuous fashion. In this paper, we develop a framework to model plastic and mutational mechanisms of acquiring resistance in a continuous gradual fashion. We use this framework to examine ways in which cancer and bacterial populations can respond to stress and consider implications for therapeutic strategies. Although we primarily discuss our framework in the context of cancer and bacteria, it applies broadly to any system capable of evolving via plasticity and genetic evolution.

Bacterial growth and metabolic rates are often closely related. However, under antibiotic selection, a paradox in this relationship arises: antibiotic efficacy decreases when bacteria are metabolically dormant, yet antibiotics select for resistant cells that grow fastest during treatment. That is, antibiotic selection counterintuitively favors bacteria with fast growth but slow metabolism. Despite this apparent contradiction, antibiotic resistant cells have historically been characterized primarily in the context of growth, whereas the extent of analogous changes in metabolism is comparatively unknown. Here, we observed that previously evolved antibiotic-resistant strains exhibited a unique relationship between growth and metabolism whereby nutrient utilization became more efficient, regardless of the growth rate. To better understand this unexpected phenomenon, we used a simplified model to simulate bacterial populations adapting to sub-inhibitory antibiotic selection through successive bottlenecking events. Simulations predicted that sub-inhibitory bactericidal antibiotic concentrations could select for enhanced metabolic efficiency, defined based on nutrient utilization: drug-adapted cells are able to achieve the same biomass while utilizing less substrate, even in the absence of treatment. Moreover, simulations predicted that restoring metabolic efficiency would re-sensitize resistant bacteria exhibiting metabolic-dependent resistance; we confirmed this result using adaptive laboratory evolutions of Escherichia coli under carbenicillin treatment. Overall, these results indicate that metabolic efficiency is under direct selective pressure during antibiotic treatment and that differences in evolutionary context may determine both the efficacy of different antibiotics and corresponding re-sensitization approaches.IMPORTANCEThe sustained emergence of antibiotic-resistant pathogens combined with the stalled drug discovery pipelines highlights the critical need to better understand the underlying evolution mechanisms of antibiotic resistance. To this end, bacterial growth and metabolic rates are often closely related, and resistant cells have historically been characterized exclusively in the context of growth. However, under antibiotic selection, antibiotics counterintuitively favor cells with fast growth, and slow metabolism. Through an integrated approach of mathematical modeling and experiments, this study thereby addresses the significant knowledge gap of whether antibiotic selection drives changes in metabolism that complement, and/or act independently, of antibiotic resistance phenotypes.