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Late in 2020, two genetically-distinct clusters of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with mutations of biological concern were reported, one in the United Kingdom and one in South Africa. Using a combination of data from routine surveillance, genomic sequencing and international travel we track the international dispersal of lineages B.1.1.7 and B.1.351 (variant 501YV2). We account for potential biases in genomic surveillance efforts by including passenger volumes from location of where the lineage was first reported, London and South Africa respectively. Using the software tool grinch (global report investigating novel coronavirus haplotypes), we track the international spread of lineages of concern with automated daily reports, Further, we have built a custom tracking website (cov-lineages.org/global_report.html) which hosts this daily report and will continue to include novel SARS-CoV-2 lineages of concern as they are detected., Wellcome Open Research, 6

Escherichia coli is commonly considered a host-associated bacterium. However, there is evidence that some strains occupy environmental (non-host-associated) niches. Here, we report the complete genomes of 47 Escherichia coli environmental isolates. These will be useful for understanding the dynamics of plasmids, phages, and other repetitive genetic elements.

Population genetic structure in the marine environment can be influenced by life‐history traits such as developmental mode (biphasic, with distinct adult and larval morphology, and direct development, in which larvae resemble adults) or habitat specificity, as well as geography and selection. Developmental mode is thought to significantly influence dispersal, with direct developers expected to have much lower dispersal potential. However, this prediction can be complicated by the presence of geophysical barriers to dispersal. In this study, we use a panel of 8,020 SNPs to investigate population structure and biogeography over multiple spatial scales for a direct‐developing species, the New Zealand endemic marine isopod Isocladus armatus. Because our sampling range is intersected by two well‐known biogeographic barriers (the East Cape and the Cook Strait), our study provides an opportunity to understand how such barriers influence dispersal in direct developers. On a small spatial scale (20 km), gene flow between locations is extremely high, suggestive of an island model of migration. However, over larger spatial scales (600 km), populations exhibit a clear pattern of isolation‐by‐distance. Our results indicate that I. armatus exhibits significant migration across the hypothesized barriers and suggest that large‐scale ocean currents associated with these locations do not present a barrier to dispersal. Interestingly, we find evidence of a north‐south population genetic break occurring between Māhia and Wellington. While no known geophysical barrier is apparent in this area, it coincides with the location of a proposed border between bioregions. Analysis of loci under selection revealed that both isolation‐by‐distance and adaption may be contributing to the degree of population structure we have observed here. We conclude that developmental life history largely predicts dispersal in the intertidal isopod I. armatus. However, localized biogeographic processes can disrupt this expectation, and this may explain the potential meta‐population detected in the Auckland region., We show, for a New Zealand isopod, a strong relationship between geographic and genetic relationships. Isocladus armatus exhibits both isolation‐by‐distance and geographic concordance with a PCA. Unusually, we find evidence of high and ongoing gene flow across a well studied biogeographic break, which in other species hinders connectivity.

Background The first step in understanding ecological community diversity and dynamics is quantifying community membership. An increasingly common method for doing so is through metagenomics. Because of the rapidly increasing popularity of this approach, a large number of computational tools and pipelines are available for analysing metagenomic data. However, the majority of these tools have been designed and benchmarked using highly accurate short read data (i.e. Illumina), with few studies benchmarking classification accuracy for long error-prone reads (PacBio or Oxford Nanopore). In addition, few tools have been benchmarked for non-microbial communities. Results Here we compare simulated long reads from Oxford Nanopore and Pacific Biosciences (PacBio) with high accuracy Illumina read sets to systematically investigate the effects of sequence length and taxon type on classification accuracy for metagenomic data from both microbial and non-microbial communities. We show that very generally, classification accuracy is far lower for non-microbial communities, even at low taxonomic resolution (e.g. family rather than genus). We then show that for two popular taxonomic classifiers, long reads can significantly increase classification accuracy, and this is most pronounced for non-microbial communities. Conclusions This work provides insight on the expected accuracy for metagenomic analyses for different taxonomic groups, and establishes the point at which read length becomes more important than error rate for assigning the correct taxon.

Determining the molecular changes that give rise to functional innovations is a major unresolved problem in biology. The paucity of examples has served as a significant hindrance in furthering our understanding of this process. Here we used experimental evolution with the bacterium Escherichia coli to quantify the molecular changes underlying functional innovation in 68 independent instances ranging over 22 different metabolic functions. Using whole-genome sequencing, we show that the relative contribution of regulatory and structural mutations depends on the cellular context of the metabolic function. In addition, we find that regulatory mutations affect genes that act in pathways relevant to the novel function, whereas structural mutations affect genes that act in unrelated pathways. Finally, we use population genetic modeling to show that the relative contributions of regulatory and structural mutations during functional innovation may be affected by population size. These results provide a predictive framework for the molecular basis of evolutionary innovation, which is essential for anticipating future evolutionary trajectories in the face of rapid environmental change.

GABA(B) receptors assemble from principle and auxiliary subunits. The principle subunits GABA(B1) and GABA(B2) form functional heteromeric GABA(B(1,2)) receptors that associate with homotetramers of auxiliary KCTD8, -12, -12b, or -16 (named after their K(+) channel tetramerization domain) subunits. These auxiliary subunits constitute receptor subtypes with distinct functional properties. KCTD12 and -12b generate desensitizing receptor responses while KCTD8 and -16 generate largely non-desensitizing receptor responses. The structural elements of the KCTDs underlying these differences in desensitization are unknown. KCTDs are modular proteins comprising a T1 tetramerization domain, which binds to GABA(B2), and a H1 homology domain. KCTD8 and -16 contain an additional C-terminal H2 homology domain that is not sequence-related to the H1 domains. No functions are known for the H1 and H2 domains. Here we addressed which domains and sequence motifs in KCTD proteins regulate desensitization of the receptor response. We found that the H1 domains in KCTD12 and -12b mediate desensitization through a particular sequence motif, T/NFLEQ, which is not present in the H1 domains of KCTD8 and -16. In addition, the H2 domains in KCTD8 and -16 inhibit desensitization when expressed C-terminal to the H1 domains but not when expressed as a separate protein in trans. Intriguingly, the inhibitory effect of the H2 domain is sequence-independent, suggesting that the H2 domain sterically hinders desensitization by the H1 domain. Evolutionary analysis supports that KCTD12 and -12b evolved desensitizing properties by liberating their H1 domains from antagonistic H2 domains and acquisition of the T/NFLEQ motif.

Although it is often tacitly assumed that gene regulatory interactions are finely tuned, how accurate gene regulation could evolve from a state without regulation is unclear. Moreover, gene expression noise would seem to impede the evolution of accurate gene regulation, and previous investigations have provided circumstantial evidence that natural selection has acted to lower noise levels. By evolving synthetic Escherichia coli promoters de novo, we here show that, contrary to expectations, promoters exhibit low noise by default. Instead, selection must have acted to increase the noise levels of highly regulated E. coli promoters. We present a general theory of the interplay between gene expression noise and gene regulation that explains these observations. The theory shows that propagation of expression noise from regulators to their targets is not an unwanted side-effect of regulation, but rather acts as a rudimentary form of regulation that facilitates the evolution of more accurate regulation. DOI: http://dx.doi.org/10.7554/eLife.05856.001, eLife digest Genes are stretches of DNA that contain the instructions needed to make proteins and other molecules. By changing how much protein is produced from each gene (i.e., its expression), many organisms—including humans—can produce a wide variety of cell types with very different behaviors. Similarly, single-celled organisms, such as bacteria, can adapt to survive and grow in different environments by changing gene expression levels. It is thus thought that gene expression must be precisely controlled. However, the molecular processes involved in gene expression are subject to random fluctuations, and so gene expression is inherently ‘noisy’. This means that even groups of identical cells in identical environments will show variation in their gene expression patterns. Furthermore, different genes show different levels of noise. The DNA sequence of a part of each gene, called the promoter, has a big effect on these noise levels. Consequently, gene expression noise is a genetically encoded trait, and can therefore be shaped by natural selection. But it remains largely unclear how natural selection has affected gene expression noise. Now, Wolf et al. have carefully measured the gene expression noise of hundreds of synthetic promoters that were evolved in the laboratory from random DNA sequences, and a similar number of natural promoters in a bacterium called E. coli. These experiments revealed that, contrary to expectation, most lab-evolved promoters had low levels of noise. On the other hand, many natural promoters had high levels of noise. Wolf et al. also found that noisy promoters tend to be highly regulated by transcription factors: the proteins that control gene expression by binding to promoter regions. Together, these results imply that unregulated promoters start by having low noise as a default state. Selection pressures must then have caused some E. coli promoters to become regulated by transcription factors and raise their noise levels. But, what might these selection pressures have been? Many genes need to be expressed at different levels in different conditions, and it is generally accepted that regulation by transcription factors evolves to ‘satisfy’ these requirements. However, transcription factors are themselves noisy, and this noise necessarily propagates to their target genes. Wolf et al. have now developed a general theory showing that this noise-propagation can often benefit an organism. This explains why natural selection can favor an increase in noise levels for regulated genes. Importantly, by showing that the main role of a transcription factor can be to increase the noise of its targets, it suddenly becomes very easy to see how new gene regulatory interactions can evolve from scratch. The next steps in understanding of how gene expression noise evolves will involve manipulating the expression noise of a gene, and measuring how selection acts on such changes. DOI: http://dx.doi.org/10.7554/eLife.05856.002

Gene expression is subject to random perturbations that lead to fluctuations in the rate of protein production. As a consequence, for any given protein, genetically identical organisms living in a constant environment will contain different amounts of that particular protein, resulting in different phenotypes. This phenomenon is known as “phenotypic noise.” In bacterial systems, previous studies have shown that, for specific genes, both transcriptional and translational processes affect phenotypic noise. Here, we focus on how the promoter regions of genes affect noise and ask whether levels of promoter-mediated noise are correlated with genes' functional attributes, using data for over 60% of all promoters in Escherichia coli. We find that essential genes and genes with a high degree of evolutionary conservation have promoters that confer low levels of noise. We also find that the level of noise cannot be attributed to the evolutionary time that different genes have spent in the genome of E. coli. In contrast to previous results in eukaryotes, we find no association between promoter-mediated noise and gene expression plasticity. These results are consistent with the hypothesis that, in bacteria, natural selection can act to reduce gene expression noise and that some of this noise is controlled through the sequence of the promoter region alone., PLoS Genetics, 8 (1), ISSN:1553-7390, ISSN:1553-7404

The most consistent result in more than two decades of experimental evolution is that the fitness of populations adapting to a constant environment does not increase indefinitely, but reaches a plateau. Using experimental evolution with bacteriophage, we show here that the converse is also true. In populations small enough such that drift overwhelms selection and causes fitness to decrease, fitness declines down to a plateau. We demonstrate theoretically that both of these phenomena must be due either to changes in the ratio of beneficial to deleterious mutations, the size of mutational effects, or both. We use mutation accumulation experiments and molecular data from experimental evolution to show that the most significant change in mutational effects is a drastic increase in the rate of beneficial mutation as fitness decreases. In contrast, the size of mutational effects changes little even as organismal fitness changes over several orders of magnitude. These findings have significant implications for the dynamics of adaptation., PLoS Biology, 5 (4), ISSN:1544-9173, ISSN:1545-7885

Background Various definitions of biological complexity have been proposed: the number of genes, cell types, or metabolic processes within an organism. As knowledge of biological systems has increased, it has become apparent that these metrics are often incongruent. Methodology Here we propose an alternative complexity metric based on the number of genetically uncorrelated phenotypic traits contributing to an organism's fitness. This metric, phenotypic complexity, is more objective than previous suggestions, as complexity is measured from a fundamental biological perspective, that of natural selection. We utilize a model linking the equilibrium fitness (drift load) of a population to phenotypic complexity. We then use results from viral evolution experiments to compare the phenotypic complexities of two viruses, the bacteriophage X174 and vesicular stomatitis virus, and to illustrate the consistency of our approach and its applicability. Conclusions/Significance Because Darwinian evolution through natural selection is the fundamental element unifying all biological organisms, we propose that our metric of complexity is potentially a more relevant metric than others, based on the count of artificially defined set of objects., PLoS ONE, 2 (2), ISSN:1932-6203

Background Bacterial persistence describes a phenomenon wherein a small subpopulation of cells is able to survive a challenge with high doses of an antibiotic (or other stressor) better than the majority of the population. Previous work has shown that cells that are in a dormant or slow-growing state are persistent to antibiotic treatment and that populations with higher fractions of dormant cells exhibit higher levels of persistence. These data suggest that a major determinant of the fraction of persisters within a population is the rate at which cells enter and exit from dormancy. However, it is not known whether there are physiological changes in addition to dormancy that influence persistence. Here, we use quantitative measurements of persister fractions in a set of environmental isolates of E. coli together with a mathematical model of persister formation to test whether a single general physiological change, such as cell dormancy, can explain the differences in persister phenotypes observed in different strains. Results If a single physiological change (e.g. cell dormancy) underlies most persister phenotypes, then strains should exhibit characteristic fractions of persister cells: some strains will consistently have high fractions of persisters (dormant cells), whereas others will have low fractions. Although we found substantial variation in the fraction of persisters between different environmental isolates of E. coli, these fractions were not correlated across antibiotics. Some strains exhibited high persister fractions in one antibiotic, but low persister fractions in a second antibiotic. Surprisingly, no correlation in persister fractions was observed between any two drugs, even for antibiotics with nearly identical modes of action (ciprofloxacin and nalidixic acid). Conclusions These data support the hypothesis that there is no single physiological change that determines the persistence level in a population of cells. Instead, the fraction of cells that survive antibiotic treatment (persist) depends critically on the specific antibiotic that is used, suggesting that physiological changes in addition to dormancy can underlie persister phenotypes.