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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.

Much is still not understood about how gene regulatory interactions control cell fate decisions in single cells, in part due to the difficulty of directly observing gene regulatory processes in vivo. We introduce here a novel integrated setup consisting of a microfluidic chip and accompanying analysis software that enable long-term quantitative tracking of growth and gene expression in single cells. The dual-input Mother Machine (DIMM) chip enables controlled and continuous variation of external conditions, allowing direct observation of gene regulatory responses to changing conditions in single cells. The Mother Machine Analyzer (MoMA) software achieves unprecedented accuracy in segmenting and tracking cells, and streamlines high-throughput curation with a novel leveraged editing procedure. We demonstrate the power of the method by uncovering several novel features of an iconic gene regulatory program: the induction of Escherichia coli’s lac operon in response to a switch from glucose to lactose., How gene regulatory pathways control cell fate decisions in single cells is not fully understood. Here the authors present an integrated dual-input microfluidic chip and a linked analysis software, enabling tracking of gene regulatory responses of single bacterial cells to changing conditions.

Bacteria adapt to changes in their environment by regulating gene expression, often at the level of transcription. However, since the molecular processes underlying gene regulation are subject to thermodynamic and other stochastic fluctuations, gene expression is inherently noisy, and identical cells in a homogeneous environment can display highly heterogeneous expression levels. To study how stochasticity affects gene regulation at the single-cell level, it is crucial to be able to directly follow gene expression dynamics in single cells under changing environmental conditions. Recently developed microfluidic devices, used in combination with quantitative fluorescence time-lapse microscopy, represent a highly promising experimental approach, allowing tracking of lineages of single cells over long time-scales while simultaneously measuring their growth and gene expression. However, current devices do not allow controlled dynamical changes to the environmental conditions which are needed to study gene regulation. In addition, automated analysis of the imaging data from such devices is still highly challenging and no standard software is currently available. To address these challenges, we here present an integrated experimental and computational setup featuring, on the one hand, a new dual-input microfluidic chip which allows mixing and switching between two growth media and, on the other hand, a novel image analysis software which jointly optimizes segmentation and tracking of the cells and allows interactive user-guided fine-tuning of its results. To demonstrate the power of our approach, we study the lac operon regulation in E. coli cells grown in an environment that switches between glucose and lactose, and quantify stochastic lag times and memory at the single cell level.

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

X-linked lymphoproliferative disease (XLP) is a primary immunodeficiency characterized by extreme susceptibility to Epstein-Barr virus. The XLP disease gene product SH2D1A (SAP) interacts via its SH2 domain with a motif (TIYXXV) present in the cytoplasmic tail of the cell-surface receptors CD150/SLAM, CD84, CD229/Ly-9, and CD244/2B4. Characteristically, the SH2D1A three-pronged interaction with Tyr(281) of CD150 can occur in absence of phosphorylation. Here we analyze the effect of SH2D1A protein missense mutations identified in 10 XLP families. Two sets of mutants were found: (i) mutants with a marked decreased protein half-life (e.g. Y7C, S28R, Q99P, P101L, V102G, and X129R) and (ii) mutants with structural changes that differently affect the interaction with the four receptors. In the second group, mutations that disrupt the interaction between the SH2D1A hydrophobic cleft and Val +3 of its binding motif (e.g. T68I) and mutations that interfere with the SH2D1A phosphotyrosine-binding pocket (e.g. C42W) abrogated SH2D1A binding to all four receptors. Surprisingly, a mutation in SH2D1A able to interfere with Thr -2 of the CD150 binding motif (mutant T53I) severely impaired non-phosphotyrosine interactions while preserving unaffected the binding of SH2D1A to phosphorylated CD150. Mutant T53I, however, did not bind to CD229 and CD224, suggesting that SH2D1A controls several critical signaling pathways in T and natural killer cells. Because no correlation is present between identified types of mutations and XLP patient clinical presentation, additional unidentified genetic or environmental factors must play a strong role in XLP disease manifestations.

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Growth cones are guided to their final destination by intermediate targets. Here, we identify intermediate targets and signaling components acting on zebrafish habenula commissural axons. Live imaging establishes that axons pause at the medial habenula before and after crossing the roof plate. esrom mutants axons fail to advance beyond the ipsilateral medial habenula. Tsc2 function is reduced in mutant axons, indicating cell autonomous defects in signaling. Consistent with signaling properties changing outside the roof plate, EphB is surface localized on axon segments within a zone demarcated by the medial habenula. wnt4a is expressed in the medial habenula and morpholino knockdown causes loss of the commissure. Electroporation of truncated Ryk causes axons to reenter the midline after reaching the contralateral habenula. These data identify Esrom as a mediator of growth cone navigation at an intermediate target and underscore the importance of midline boundaries as signaling centers for commissure formation.

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.