Search

Background The gold standard treatment for locally advanced rectal cancer is total mesorectal excision after preoperative chemoradiotherapy. Response to chemoradiotherapy varies, with some patients completely responding to the treatment and some failing to respond at all. Identifying biomarkers of response to chemoradiotherapy could allow patients to avoid unnecessary treatment-associated morbidity rate. While previous studies have attempted to identify such biomarkers, none have reached clinical utility, which may be due to heterogeneity of the cancer. In this study, potential human gene and microbial biomarkers were explored in a cohort of rectal cancer patients who underwent chemoradiotherapy. Methods RNA sequencing was carried out on matched tumour and adjacent normal rectum biopsies from patients with rectal cancer with varying chemoradiotherapy responses treated between 2016 and 2019 at two institutions. Enriched genes and microbes from tumours of complete responders were compared with those from tumours of others with lesser response. Results In 39 patients analysed, enriched gene sets in complete responders indicate involvement of immune responses, including immunoglobulin production, B cell activation and response to bacteria (adjusted P values Conclusion These results identify potential genetic and microbial biomarkers of response to chemoradiotherapy in rectal cancer, as well as suggesting a potential mechanism of complete response to chemoradiotherapy that may benefit further testing in the laboratory.

Quantifying SARS-like coronavirus (SL-CoV) evolution is critical to understanding the origins of SARS-CoV-2 and the molecular processes that could underlie future epidemic viruses. While genomic evidence implicates recombination as a factor in the emergence of SARS-CoV-2, few studies have quantified recombination rates among SL-CoVs. Here, we infer recombination rates of SL-CoVs from correlated substitutions in sequencing data using a coalescent model with recombination. Our computationally-efficient, non-phylogenetic method infers recombination parameters of both sampled sequences and the unsampled gene pools with which they recombine. We apply this approach to infer recombination parameters for a range of positive-sense RNA viruses. We then analyze a set of 191 SL-CoV sequences (including SARS-CoV-2) and find that ORF1ab and S genes frequently undergo recombination. We identify which SL-CoV sequence clusters have recombined with shared gene pools, and show that these pools have distinct structures and high recombination rates, with multiple recombination events occurring per synonymous substitution. We find that individual genes have recombined with different viral reservoirs. By decoupling contributions from mutation and recombination, we recover the phylogeny of non-recombined portions for many of these SL-CoVs, including the position of SARS-CoV-2 in this clonal phylogeny. Lastly, by analyzing 444,145 SARS-CoV-2 whole genome sequences, we show current diversity levels are insufficient to infer the within-population recombination rate of the virus since the pandemic began. Our work offers new methods for inferring recombination rates in RNA viruses with implications for understanding recombination in SARS-CoV-2 evolution and the structure of clonal relationships and gene pools shaping its origins.Significance StatementQuantifying the population genetics of SARS-like coronavirus (SL-CoV) evolution is vital to deciphering the origins of SARS-CoV-2 and pinpointing viruses with epidemic potential. While some Bayesian approaches can quantify recombination for these pathogens, the required simulations of recombination networks do not scale well with the massive amounts of sequences available in the genomics era. Our approach circumvents this by measuring correlated substitutions in sequences and fitting these data to a coalescent model with recombination. This allows us to analyze hundreds of thousands of sample sequences, and infer recombination rates for unsampled viral reservoirs. Our results provide insights into both the clonal relationships of sampled SL-CoV sequence clusters and the evolutionary dynamics of the gene pools with which they recombine.

Long read sequencing technologies now allow routine highly contiguous assembly of bacterial genomes. However, because of the lower accuracy of some long read data, it is often combined with short read data (e.g. Illumina), to improve assembly quality. There are a number of methods available for producing such hybrid assemblies. Here we use Illumina and Oxford Nanopore (ONT) data from 49 natural isolates of Escherichia coli to characterise differences in assembly accuracy for five assembly methods (Canu, Unicycler, Raven, Flye, and Redbean). We evaluate assembly accuracy using five metrics designed to measure structural accuracy and sequence accuracy (indel and substitution frequency). We assess structural accuracy by quantifying (1) the contiguity of chromosomes and plasmids; (2) the fraction of concordantly mapped Illumina reads withheld from the assembly; and (3) whether rRNA operons are correctly oriented. We assess indel and substitution frequency by quantifying (1) the fraction of open reading frames that appear truncated and (2) the number of variants that are called using Illumina reads only. Applying these assembly metrics to a large number of E. coli strains, we find that different assembly methods offer different advantages. In particular, we find that Unicycler assemblies have the highest sequence accuracy in non-repetitive regions, while Flye and Raven tend to be the most structurally accurate. In addition, we find that there are unidentified strain-specific characteristics that affect ONT consensus accuracy, despite individual reads having similar levels of accuracy. The differences in consensus accuracy of the ONT reads can preclude accurate assembly regardless of assembly method. These results provide quantitative insight into the best approaches for hybrid assembly of bacterial genomes and the expected levels of structural and sequence accuracy. They also show that there are intrinsic idiosyncratic strain-level differences that inhibit accurate long read bacterial genome assembly. However, we also show it is possible to diagnose problematic assemblies, even in the absence of ground truth, by comparing long-read first and short-read first assemblies.Author NotesAll supporting data, code and protocols have been provided within the article or through supplementary data files. The supporting code is available from the GitHub repository https://github.com/GeorgiaBreckell/assembly_pipeline. nine supplementary figures and three supplementary tables are available with the online version of this article.Data summarySequence data and genome assemblies for the natural isolates are available at https://www.ebi.ac.uk/ena/browser/view/PRJEB36951. Genome assemblies for additional E. coli strains used here are available from NCBI: (MG1655, SE11, REL606, CFT073, W, IA136, O157:H7-EDL933)

Most animal mitochondrial genomes are small, circular, and structurally conserved. However, recent work indicates that diverse taxa possess unusual mitochondrial genomes. In Isopoda, species in multiple lineages have atypical and rearranged mitochondrial genomes. However, more species of this speciose taxon need to be evaluated to understand the evolutionary origins of atypical mitochondrial genomes in this group. In this study, we report the presence of an atypical mitochondrial structure in the New Zealand endemic marine isopod, Isocladus armatus. Data from long and short read DNA sequencing, suggests that I. armatus has two mitochondrial chromosomes. The first chromosome consists of two mitochondrial genomes that have been inverted and fused together in a circular form, and the second chromosome consists of a single mitochondrial genome in a linearized form. This atypical mitochondrial structure has been detected in other isopod lineages, and our data from an additional divergent isopod lineage (Sphaeromatidae) lends support to the hypothesis that atypical structure evolved early in the evolution of Isopoda. Additionally, we find that a heteroplasmic site previously observed across many species within Isopoda is absent in I. armatus, but confirm the presence of two heteroplasmic sites recently reported in two other isopod species.

Most animal mitochondrial genomes are small, circular and structurally conserved. However, recent work indicates that diverse taxa possess unusual mitochondrial genomes. In Isopoda , species in multiple lineages have atypical and rearranged mitochondrial genomes. However, more species of this speciose taxon need to be evaluated to understand the evolutionary origins of atypical mitochondrial genomes in this group. In this study, we report the presence of an atypical mitochondrial structure in the New Zealand endemic marine isopod, Isocladus armatus. Data from long- and short-read DNA sequencing suggest that I. armatus has two mitochondrial chromosomes. The first chromosome consists of two mitochondrial genomes that have been inverted and fused together in a circular form, and the second chromosome consists of a single mitochondrial genome in a linearized form. This atypical mitochondrial structure has been detected in other isopod lineages, and our data from an additional divergent isopod lineage (Sphaeromatidae) lends support to the hypothesis that atypical structure evolved early in the evolution of Isopoda . Additionally, we find that an asymmetrical site previously observed across many species within Isopoda is absent in I. armatus , but confirm the presence of two asymmetrical sites recently reported in two other isopod species.

The expanding knowledge of the variety of synthetic genetic elements has enabled the construction of new and more efficient genetic circuits and yielded novel insights into molecular mechanisms. However, context dependence, in which interactions between cis‐ or trans‐genetic elements affect the behavior of these elements, can reduce their general applicability or predictability. Genetic insulators, which mitigate unintended context‐dependent cis‐interactions, have been used to address this issue. One of the most commonly used genetic insulators is a self‐splicing ribozyme called RiboJ, which can be used to decouple upstream 5′ UTR in mRNA from downstream sequences (e.g., open reading frames). Despite its general use as an insulator, there has been no systematic study quantifying the efficiency of RiboJ splicing or whether this autocatalytic activity is robust to trans‐ and cis‐genetic context. Here, we determine the robustness of RiboJ splicing in the genetic context of six widely divergent E. coli strains. We also check for possible cis‐effects by assessing two SNP versions close to the catalytic site of RiboJ. We show that mRNA molecules containing RiboJ are rapidly spliced even during rapid exponential growth and high levels of gene expression, with a mean efficiency of 98%. We also show that neither the cis‐ nor trans‐genetic context has a significant impact on RiboJ activity, suggesting this element is robust to both cis‐ and trans‐genetic changes., Genetic insulators, such as RiboJ self‐splicing ribozyme, have been used to mitigate unintended context‐dependent cis‐interactions when designing genetic circuits. Despite the general use of RiboJ as an insulator, there has been no study quantifying the RiboJ splicing efficiency or whether this autocatalytic activity is robust to trans‐ and cis‐genetic context. Here, we show that mRNA molecules containing RiboJ are spliced with a mean efficiency of 98% and neither the cis‐ nor trans‐genetic context has a significant impact on RiboJ activity.

Bacteria often respond to dynamically changing environments by regulating gene expression. Despite this regulation being critically important for growth and survival, little is known about how selection shapes gene regulation in natural populations. To better understand the role natural selection plays in shaping bacterial gene regulation, here we compare differences in the regulatory behaviour of naturally segregating promoter variants from Escherichia coli (which have been subject to natural selection) to randomly mutated promoter variants (which have never been exposed to natural selection). We quantify gene expression phenotypes (expression level, plasticity, and noise) for hundreds of promoter variants across multiple environments, and show that segregating promoter variants are enriched for mutations with minimal effects on expression level. In many promoters, we infer that there is strong selection to maintain high levels of plasticity, and direct selection to decrease or increase cell-to-cell variability in expression. Finally, taking an integrated view, we show that across all phenotypes combined, segregating promoter variants are far more phenotypically similar than would be expected given their genetic divergence. This is the consequence of both stabilizing and directional selection acting on individual phenotypes to minimize differences among segregating variants. Taken together, these results expand our knowledge of how gene regulation is affected by natural selection and highlight the power of comparing naturally segregating polymorphisms to de novo random mutations to quantify the action of selection.

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

SummaryBackgroundReal-time genomic sequencing has played a major role in tracking the global spread and local transmission of SARS-CoV-2, contributing greatly to disease mitigation strategies. After effectively eliminating the virus, New Zealand experienced a second outbreak of SARS-CoV-2 in August 2020. During this August outbreak, New Zealand utilised genomic sequencing in a primary role to support its track and trace efforts for the first time, leading to a second successful elimination of the virus.MethodsWe generated the genomes of 80% of the laboratory-confirmed samples of SARS-CoV-2 from New Zealand’s August 2020 outbreak and compared these genomes to the available global genomic data.FindingsGenomic sequencing was able to rapidly identify that the new COVID-19 cases in New Zealand belonged to a single cluster and hence resulted from a single introduction. However, successful identification of the origin of this outbreak was impeded by substantial biases and gaps in global sequencing data.InterpretationAccess to a broader and more heterogenous sample of global genomic data would strengthen efforts to locate the source of any new outbreaks.FundingThis work was funded by the Ministry of Health of New Zealand, New Zealand Ministry of Business, Innovation and Employment COVID-19 Innovation Acceleration Fund (CIAF-0470), ESR Strategic Innovation Fund and the New Zealand Health Research Council (20/1018 and 20/1041).

Rapid identification of bacterial pathogens and their antimicrobial resistance (AMR) profiles is critical for minimising patient morbidity and mortality. While many sequencing methods allow deep genomic and metagenomic profiling of samples, widespread use (for example atpoint-of-care settings) is impeded because substantial sequencing and computational infrastructure is required for sequencing and analysis. Here we present Bac-PULCE (Bacterial strain and antimicrobial resistance Profiling Using Long reads via CRISPR Enrichment), which combines CRISPR-cas9 based targeted sequence enrichment with long-read sequencing. We show that this method allows simultaneous bacterial strain-level identification and antimicrobial resistance profiling of single isolates or metagenomic samples with minimal sequencing throughput. In contrast to short read sequencing, long read sequencing used in Bac-PULCE enables strain-level resolution even when targeting and sequencing highly conserved genomic regions, such as 16S rRNA. We show that these long reads allow sequencing of additional AMR genes linked to the targeted region. Additionally, long reads can be used to identify which species in a metagenomic sample harbour specific AMR loci. The ability to massively multiplex crRNAs suggests that this method has the potential to substantially increase the speed and specificity of pathogen strain identification and AMR profiling, while ensuring low computational overhead.ImportanceThere is a critical need for rapid and identification of bacterial strains and antibiotic resistance profiles in clinical settings. However, most current methods require both substantial laboratory infrastructure (e.g. for DNA sequencing), substantial compute infrastructure (e.g. for bioinformatic analyses), or both. Here we present a new method, Bac-PULCE, (Bacterial strain and antimicrobial resistance Profiling Using Long reads via CRISPR Enrichment), which combines CRISPR-cas9 based targeted sequence enrichment with long-read sequencing on the Oxford Nanopore platform. This allows rapid profiling of bacterial strains and antibiotic resistance genes in a sample while requiring very little laboratory or computational infrastructure.

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.

Rapid and cost-efficient whole-genome sequencing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes coronavirus disease 2019, is critical for understanding viral transmission dynamics. Here we show that using a new multiplexed set of primers in conjunction with the Oxford Nanopore Rapid Barcode library kit allows for faster, simpler, and less expensive SARS-CoV-2 genome sequencing. This primer set results in amplicons that exhibit lower levels of variation in coverage compared to other commonly used primer sets. Using five SARS-CoV-2 patient samples with Cq values between 20 and 31, we show that high-quality genomes can be generated with as few as 10 000 reads (∼5 Mbp of sequence data). We also show that mis-classification of barcodes, which may be more likely when using the Oxford Nanopore Rapid Barcode library prep, is unlikely to cause problems in variant calling. This method reduces the time from RNA to genome sequence by more than half compared to the more standard ligation-based Oxford Nanopore library preparation method at considerably lower costs.

Rapid and cost-efficient whole-genome sequencing of SARS-CoV-2, the virus that causes COVID-19, is critical for understanding viral transmission dynamics. Here we show that using a new multiplexed set of primers in conjunction with the Oxford Nanopore Rapid Barcode library kit allows for faster, simpler, and less expensive SARS-CoV-2 genome sequencing. This primer set results in amplicons that exhibit lower levels of variation in coverage compared to other commonly used primer sets. Using five SARS-CoV-2 patient samples with Cq values between 20 and 31, we show that high-quality genomes can be generated with as few as 10,000 reads (approximately 5 Mbp of sequence data). We also show that mis-classification of barcodes, which may be more likely when using the Oxford Nanopore Rapid Barcode library prep, is unlikely to cause problems in variant calling. This method reduces the time from RNA to genome sequence by more than half compared to the more standard ligation-based Oxford Nanopore library preparation method at considerably lower costs.

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.

Marine organisms generally exhibit one of two developmental modes: biphasic, with distinct adult and larval morphology, and direct development, in which larvae resemble adults. Developmental mode is thought to significantly influence dispersal, with direct developers expected to have much lower dispersal potential. However, in contrast to our relatively good understanding of dispersal and population connectivity for biphasic species, comparatively little is known about direct developers. In this study, we use a panel of 8,020 SNPs to investigate population structure and gene flow for a direct developing species, the New Zealand endemic marine isopodIsocladus armatus. On a small spatial scale (20 kms), gene flow between locations is extremely high and suggests an island model of migration. However, over larger spatial scales (600km), populations exhibit a clear pattern of isolation-by-distance. 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. Our results indicate thatI. armatusexhibits significant migration across these barriers, and suggests that ocean currents associated with these locations do not present a barrier to dispersal. Interestingly, we do find evidence of a north-south population genetic break occurring between Māhia and Wellington, two locations where there are no obvious biogeographic barriers between them. We conclude that developmental life history largely predicts dispersal in intertidal marine isopods. However, localised biogeographic processes can disrupt this expectation.

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 use simulated error prone Oxford Nanopore and 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). Conclusions We then show that for two popular taxonomic classifiers, long error-prone reads can significantly increase classification accuracy, and this is most pronounced for non-microbial communities. 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.

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.

Beatrix, Carthage, Daegal, Dulcie, Fancypants, Fenn, Inca, Naira, and Robyn are newly isolated bacteriophages capable of infecting Mycolicibacterium smegmatis mc2 155. We discovered, sequenced, and annotated these New Zealand bacteriophages., Beatrix, Carthage, Daegal, Dulcie, Fancypants, Fenn, Inca, Naira, and Robyn are newly isolated bacteriophages capable of infecting Mycolicibacterium smegmatis mc2 155. We discovered, sequenced, and annotated these New Zealand bacteriophages. These phages illustrate that New Zealand harbors a selection of the highly diverse and distributed mycobacteriophage clusters found globally.

BackgroundUsing metagenomics to determine animal diet offers a new and promising alternative to current methods. Here we show that rapid and inexpensive diet quantification is possible through metagenomic sequencing with the portable Oxford Nanopore Technologies (ONT) MinION. Using an amplification-free approach, we profiled the stomach contents from wild-caught rats.ResultsWe conservatively identified diet items from over 50 taxonomic orders, ranging across nine phyla that include plants, vertebrates, invertebrates, and fungi. This highlights the wide range of taxa that can be identified using this simple approach. We calibrate the accuracy of this method by comparing the characteristics of reads matching the ground-truth host genome (rat) to those matching diet items, and show that at the family-level, false positive taxon assignments are approximately 97.5% accurate. We also suggest a way to mitigate for database biases in metagenomic approaches. Finally, we implement a constrained ordination analysis and show that we can identify the sampling location of an individual rat within tens of kilometres based on diet content alone.ConclusionsThis work establishes proof-of-principle for long-read metagenomic methods in quantitative diet analysis. We show that diet content can be quantified even with limited expertise, using a simple, amplification free workflow and a relatively inexpensive and accessible next generation sequencing method. Continued increases in the accuracy and throughput of ONT sequencing, along with improved genomic databases, suggests that a metagenomic approach to quantification of animal diets will become an important method in the future.

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.

Seven mycobacteriophages from distinct geographical locations were isolated, using Mycobacterium smegmatis mc 2 155 as the host, and then purified and sequenced. All of the genomes are related to cluster A mycobacteriophages, BobSwaget and Lokk in subcluster A2; Fred313, KADY, Stagni, and StepMih in subcluster A3; and MyraDee in subcluster A18, the first phage to be assigned to that subcluster.

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.

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.

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.

Bacteriophages are the most numerous entities in the biosphere. Despite this numerical dominance, the genetic structure of bacteriophage populations is poorly understood. Here, we present a biogeography study involving 25 previously undescribed bacteriophages from the Cystoviridae clade, a group characterized by a dsRNA genome divided into three segments. Previous laboratory manipulation has shown that, when multiple Cystoviruses infect a single host cell, they undergo ( i ) rare intrasegment recombination events and ( ii ) frequent genetic reassortment between segments. Analyzing linkage disequilibrium (LD) within segments, we find no significant evidence of intrasegment recombination in wild populations, consistent with ( i ). An extensive analysis of LD between segments supports frequent reassortment, on a time scale similar to the genomic mutation rate. The absence of LD within this group of phages is consistent with expectations for a completely sexual population, despite the fact that some segments have >50% nucleotide divergence at 4-fold degenerate sites. This extraordinary rate of genetic exchange between highly unrelated individuals is unprecedented in any taxa. We discuss our results in light of the biological species concept applied to viruses.

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

Studies of microbial evolutionary dynamics are being transformed by the availability of affordable high-throughput sequencing technologies, which allow whole-genome sequencing of hundreds of related taxa in a single study. Reconstructing a phylogenetic tree of these taxa is generally a crucial step in any evolutionary analysis. Instead of constructing genome assemblies for all taxa, annotating these assemblies, and aligning orthologous genes, many recent studies 1) directly map raw sequencing reads to a single reference sequence, 2) extract single nucleotide polymorphisms (SNPs), and 3) infer the phylogenetic tree using maximum likelihood methods from the aligned SNP positions. However, here we show that, when using such methods to reconstruct phylogenies from sets of simulated sequences, both the exclusion of nonpolymorphic positions and the alignment to a single reference genome, introduce systematic biases and errors in phylogeny reconstruction. To address these problems, we developed a new method that combines alignments from mappings to multiple reference sequences and show that this successfully removes biases from the reconstructed phylogenies. We implemented this method as a web server named REALPHY (Reference sequence Alignment-based Phylogeny builder), which fully automates phylogenetic reconstruction from raw sequencing reads., Molecular Biology and Evolution, 31 (5), ISSN:0737-4038, ISSN:1537-1719

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.

Figure S6 is incorrectly a duplicate of Figure S7. The correct Figure S6 can be viewed here: Click here for additional data file.(244K, pdf) [^]

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 genetic structure of natural bacteriophage populations is poorly understood. Recent metagenomic studies suggest that phage biogeography is characterized by frequent migration. Using virus samples mostly isolated in Southern California, we recently showed that very little population structure exists in segmented RNA phage of the Cystoviridae family due to frequent segment reassortment (sexual genetic mixis) between unrelated virus individuals. Here we use a larger genetic dataset to examine the structure of Cystoviridae phage isolated from three geographic locations in Southern New England. We document extensive natural variation in the physical sizes of RNA genome segments for these viruses. In addition, consistent with earlier findings, our phylogenetic analyses and calculations of linkage disequilibrium (LD) show no evidence of within-segment recombination in wild populations. However, in contrast to the prior study, our analysis finds that reassortment of segments between individual phage plays a lesser role among cystoviruses sampled in New England, suggesting that the evolutionary importance of genetic mixis in Cystoviridae phage may vary according to geography. We discuss possible explanations for these conflicting results across the studies, such as differing local ecology and its impact on phage growth, and geographic differences in selection against hybrid phage genotypes.

The genetic structure of natural bacteriophage populations is poorly understood. Recent metagenomic studies suggest that phage biogeography is characterized by frequent migration. Using virus samples mostly isolated in Southern California, we recently showed that very little population structure exists in segmented RNA phage of the Cystoviridae family due to frequent segment reassortment (sexual genetic mixis) between unrelated virus individuals. Here we use a larger genetic dataset to examine the structure of Cystoviridae phage isolated from three geographic locations in Southern New England. We document extensive natural variation in the physical sizes of RNA genome segments for these viruses. In addition, consistent with earlier findings, our phylogenetic analyses and calculations of linkage disequilibrium (LD) show no evidence of within-segment recombination in wild populations. However, in contrast to the prior study, our analysis finds that reassortment of segments between individual phage plays a lesser role among cystoviruses sampled in New England, suggesting that the evolutionary importance of genetic mixis in Cystoviridae phage may vary according to geography. We discuss possible explanations for these conflicting results across the studies, such as differing local ecology and its impact on phage growth, and geographic differences in selection against hybrid phage genotypes.

Genetically identical populations of unicellular organisms often show marked variation in some phenotypic traits. To investigate the molecular causes and possible biological functions of this phenotypic noise, it would be useful to have a method to identify genes whose expression varies stochastically on a certain time scale. Here, we developed such a method and used it for identifying genes with high levels of phenotypic noise in Salmonella enterica ssp. I serovar Typhimurium (S. Typhimurium). We created a genomic plasmid library fused to a green fluorescent protein (GFP) reporter and subjected replicate populations harboring this library to fluctuating selection for GFP expression using fluorescent-activated cell sorting (FACS). After seven rounds of fluctuating selection, the populations were strongly enriched for promoters that showed a high amount of noise in gene expression. Our results indicate that the activity of some promoters of S. Typhimurium varies on such a short time scale that these promoters can absorb rapid fluctuations in the direction of selection, as imposed during our experiment. The genomic fragments that conferred the highest levels of phenotypic variation were promoters controlling the synthesis of flagella, which are associated with virulence and host–pathogen interactions. This confirms earlier reports that phenotypic noise may play a role in pathogenesis and indicates that these promoters have among the highest levels of noise in the S. Typhimurium genome. This approach can be applied to many other bacterial and eukaryotic systems as a simple method for identifying genes with noisy expression., PLoS Genetics, 4, 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. Orthologous genes are frequently presumed to perform similar functions. However, outside of model organisms, this is rarely tested. One means of inferring changes in function is if there are changes in the level of gene conservation and selective constraint. Here we compare levels of gene conservation across three bacterial groups to test for changes in gene functionality. Findings. The level of gene conservation for different orthologous genes is highly correlated across clades, even for highly divergent groups of bacteria. These correlations do not arise from broad differences in gene functionality (e.g. informational genes vs. metabolic genes), but instead seem to result from very specific differences in gene function. Furthermore, these functional differences appear to be maintained over very long periods of time. Conclusion. These results suggest that even over broad time scales, most bacterial genes are under a nearly constant level of purifying selection, and that bacterial evolution is thus dominated by selective and functional stasis., BMC Research Notes, 2 (1), ISSN:1756-0500

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.