EnteroBase: hierarchical clustering of 100 000s of bacterial genomes into species/subspecies and populations.

The definition of bacterial species is traditionally a taxonomic issue while bacterial populations are identified by population genetics. These assignments are species specific, and depend on the practitioner. Legacy multilocus sequence typing is commonly used to identify sequence types (STs) and clusters (ST Complexes). However, these approaches are not adequate for the millions of genomic sequences from bacterial pathogens that have been generated since 2012. EnteroBase (http://enterobase.warwick.ac.uk) automatically clusters core genome MLST allelic profiles into hierarchical clusters (HierCC) after assembling annotated draft genomes from short-read sequences. HierCC clusters span core sequence diversity from the species level down to individual transmission chains. Here we evaluate HierCC's ability to correctly assign 100 000s of genomes to the species/subspecies and population levels for Salmonella, Escherichia, Clostridoides, Yersinia, Vibrio and Streptococcus. HierCC assignments were more consistent with maximum-likelihood super-trees of core SNPs or presence/absence of accessory genes than classical taxonomic assignments or 95% ANI. However, neither HierCC nor ANI were uniformly consistent with classical taxonomy of Streptococcus. HierCC was also consistent with legacy eBGs/ST Complexes in Salmonella or Escherichia and with O serogroups in Salmonella. Thus, EnteroBase HierCC supports the automated identification of and assignment to species/subspecies and populations for multiple genera. This article is part of a discussion meeting issue 'Genomic population structures of microbial pathogens'.

Antimicrobial resistance determinants are associated with Staphylococcus aureus bacteraemia and adaptation to the healthcare environment: a bacterial genome-wide association study.

Staphylococcus aureus is a major bacterial pathogen in humans, and a dominant cause of severe bloodstream infections. Globally, antimicrobial resistance (AMR) in S. aureus remains challenging. While human risk factors for infection have been defined, contradictory evidence exists for the role of bacterial genomic variation in S. aureus disease. To investigate the contribution of bacterial lineage and genomic variation to the development of bloodstream infection, we undertook a genome-wide association study comparing bacteria from 1017 individuals with bacteraemia to 984 adults with asymptomatic S. aureus nasal carriage. Within 984 carriage isolates, we also compared healthcare-associated (HA) carriage with community-associated (CA) carriage. All major global lineages were represented in both bacteraemia and carriage, with no evidence for different infection rates. However, kmers tagging trimethoprim resistance-conferring mutation F99Y in dfrB were significantly associated with bacteraemia-vs-carriage (P=10-8.9-10-9.3). Pooling variation within genes, bacteraemia-vs-carriage was associated with the presence of mecA (HMP=10-5.3) as well as the presence of SCCmec (HMP=10-4.4). Among S. aureus carriers, no lineages were associated with HA-vs-CA carriage. However, we found a novel signal of HA-vs-CA carriage in the foldase protein prsA, where kmers representing conserved sequence allele were associated with CA carriage (P=10-7.1-10-19.4), while in gyrA, a ciprofloxacin resistance-conferring mutation, L84S, was associated with HA carriage (P=10-7.2). In an extensive study of S. aureus bacteraemia and nasal carriage in the UK, we found strong evidence that all S. aureus lineages are equally capable of causing bloodstream infection, and of being carried in the healthcare environment. Genomic variation in the foldase protein prsA is a novel genomic marker of healthcare origin in S. aureus but was not associated with bacteraemia. AMR determinants were associated with both bacteraemia and healthcare-associated carriage, suggesting that AMR increases the propensity not only to survive in healthcare environments, but also to cause invasive disease.

HierCC: a multi-level clustering scheme for population assignments based on core genome MLST.

MOTIVATION: Routine infectious disease surveillance is increasingly based on large-scale whole-genome sequencing databases. Real-time surveillance would benefit from immediate assignments of each genome assembly to hierarchical population structures. Here we present pHierCC, a pipeline that defines a scalable clustering scheme, HierCC, based on core genome multi-locus typing that allows incremental, static, multi-level cluster assignments of genomes. We also present HCCeval, which identifies optimal thresholds for assigning genomes to cohesive HierCC clusters. HierCC was implemented in EnteroBase in 2018 and has since genotyped >530 000 genomes from Salmonella, Escherichia/Shigella, Streptococcus, Clostridioides, Vibrio and Yersinia. AVAILABILITY AND IMPLEMENTATION: https://enterobase.warwick.ac.uk/ and Source code and instructions: https://github.com/zheminzhou/pHierCC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genome Reduction Is Associated with Bacterial Pathogenicity across Different Scales of Temporal and Ecological Divergence.

Emerging bacterial pathogens threaten global health and food security, and so it is important to ask whether these transitions to pathogenicity have any common features. We present a systematic study of the claim that pathogenicity is associated with genome reduction and gene loss. We compare broad-scale patterns across all bacteria, with detailed analyses of Streptococcus suis, an emerging zoonotic pathogen of pigs, which has undergone multiple transitions between disease and carriage forms. We find that pathogenicity is consistently associated with reduced genome size across three scales of divergence (between species within genera, and between and within genetic clusters of S. suis). Although genome reduction is also found in mutualist and commensal bacterial endosymbionts, genome reduction in pathogens cannot be solely attributed to the features of their ecology that they share with these species, that is, host restriction or intracellularity. Moreover, other typical correlates of genome reduction in endosymbionts (reduced metabolic capacity, reduced GC content, and the transient expansion of nonfunctional elements) are not consistently observed in pathogens. Together, our results indicate that genome reduction is a consistent correlate of pathogenicity in bacteria.

Accurate reconstruction of bacterial pan- and core genomes with PEPPAN.

Bacterial genomes can contain traces of a complex evolutionary history, including extensive homologous recombination, gene loss, gene duplications, and horizontal gene transfer. To reconstruct the phylogenetic and population history of a set of multiple bacteria, it is necessary to examine their pangenome, the composite of all the genes in the set. Here we introduce PEPPAN, a novel pipeline that can reliably construct pangenomes from thousands of genetically diverse bacterial genomes that represent the diversity of an entire genus. PEPPAN outperforms existing pangenome methods by providing consistent gene and pseudogene annotations extended by similarity-based gene predictions, and identifying and excluding paralogs by combining tree- and synteny-based approaches. The PEPPAN package additionally includes PEPPAN_parser, which implements additional downstream analyses, including the calculation of trees based on accessory gene content or allelic differences between core genes. To test the accuracy of PEPPAN, we implemented SimPan, a novel pipeline for simulating the evolution of bacterial pangenomes. We compared the accuracy and speed of PEPPAN with four state-of-the-art pangenome pipelines using both empirical and simulated data sets. PEPPAN was more accurate and more specific than any of the other pipelines and was almost as fast as any of them. As a case study, we used PEPPAN to construct a pangenome of approximately 40,000 genes from 3052 representative genomes spanning at least 80 species of Streptococcus The resulting gene and allelic trees provide an unprecedented overview of the genomic diversity of the entire Streptococcus genus.

Severe infections emerge from commensal bacteria by adaptive evolution.

Bacteria responsible for the greatest global mortality colonize the human microbiota far more frequently than they cause severe infections. Whether mutation and selection among commensal bacteria are associated with infection is unknown. We investigated de novo mutation in 1163 Staphylococcus aureus genomes from 105 infected patients with nose colonization. We report that 72% of infections emerged from the nose, with infecting and nose-colonizing bacteria showing parallel adaptive differences. We found 2.8-to-3.6-fold adaptive enrichments of protein-altering variants in genes responding to rsp, which regulates surface antigens and toxin production; agr, which regulates quorum-sensing, toxin production and abscess formation; and host-derived antimicrobial peptides. Adaptive mutations in pathogenesis-associated genes were 3.1-fold enriched in infecting but not nose-colonizing bacteria. None of these signatures were observed in healthy carriers nor at the species-level, suggesting infection-associated, short-term, within-host selection pressures. Our results show that signatures of spontaneous adaptive evolution are specifically associated with infection, raising new possibilities for diagnosis and treatment.

Identifying lineage effects when controlling for population structure improves power in bacterial association studies.

Bacteria pose unique challenges for genome-wide association studies because of strong structuring into distinct strains and substantial linkage disequilibrium across the genome(1,2). Although methods developed for human studies can correct for strain structure(3,4), this risks considerable loss-of-power because genetic differences between strains often contribute substantial phenotypic variability(5). Here, we propose a new method that captures lineage-level associations even when locus-specific associations cannot be fine-mapped. We demonstrate its ability to detect genes and genetic variants underlying resistance to 17 antimicrobials in 3,144 isolates from four taxonomically diverse clonal and recombining bacteria: Mycobacterium tuberculosis, Staphylococcus aureus, Escherichia coli and Klebsiella pneumoniae. Strong selection, recombination and penetrance confer high power to recover known antimicrobial resistance mechanisms and reveal a candidate association between the outer membrane porin nmpC and cefazolin resistance in E. coli. Hence, our method pinpoints locus-specific effects where possible and boosts power by detecting lineage-level differences when fine-mapping is intractable.

Where next for the reproducibility agenda in computational biology?

BACKGROUND: The concept of reproducibility is a foundation of the scientific method. With the arrival of fast and powerful computers over the last few decades, there has been an explosion of results based on complex computational analyses and simulations. The reproducibility of these results has been addressed mainly in terms of exact replicability or numerical equivalence, ignoring the wider issue of the reproducibility of conclusions through equivalent, extended or alternative methods. RESULTS: We use case studies from our own research experience to illustrate how concepts of reproducibility might be applied in computational biology. Several fields have developed 'minimum information' checklists to support the full reporting of computational simulations, analyses and results, and standardised data formats and model description languages can facilitate the use of multiple systems to address the same research question. We note the importance of defining the key features of a result to be reproduced, and the expected agreement between original and subsequent results. Dynamic, updatable tools for publishing methods and results are becoming increasingly common, but sometimes come at the cost of clear communication. In general, the reproducibility of computational research is improving but would benefit from additional resources and incentives. CONCLUSIONS: We conclude with a series of linked recommendations for improving reproducibility in computational biology through communication, policy, education and research practice. More reproducible research will lead to higher quality conclusions, deeper understanding and more valuable knowledge.

Mobile elements drive recombination hotspots in the core genome of Staphylococcus aureus.

Horizontal gene transfer is an important driver of bacterial evolution, but genetic exchange in the core genome of clonal species, including the major pathogen Staphylococcus aureus, is incompletely understood. Here we reveal widespread homologous recombination in S. aureus at the species level, in contrast to its near-complete absence between closely related strains. We discover a patchwork of hotspots and coldspots at fine scales falling against a backdrop of broad-scale trends in rate variation. Over megabases, homoplasy rates fluctuate 1.9-fold, peaking towards the origin-of-replication. Over kilobases, we find core recombination hotspots of up to 2.5-fold enrichment situated near fault lines in the genome associated with mobile elements. The strongest hotspots include regions flanking conjugative transposon ICE6013, the staphylococcal cassette chromosome (SCC) and genomic island νSaα. Mobile element-driven core genome transfer represents an opportunity for adaptation and challenges our understanding of the recombination landscape in predominantly clonal pathogens, with important implications for genotype-phenotype mapping.

MicroRNA expression profiles in avian haemopoietic cells.

MicroRNAs (miRNAs) are small, abundant, non-coding RNAs that modulate gene expression by interfering with translation or stability of mRNA transcripts in a sequence-specific manner. A total of 734 precursor and 996 mature miRNAs have so far been identified in the chicken genome. A number of these miRNAs are expressed in a cell type-specific manner, and understanding their function requires detailed examination of their expression in different cell types. We carried out deep sequencing of small RNA populations isolated from stimulated or transformed avian haemopoietic cell lines to determine the changes in the expression profiles of these important regulatory molecules during these biological events. There were significant changes in the expression of a number of miRNAs, including miR-155, in chicken B cells stimulated with CD40 ligand. Similarly, avian leukosis virus (ALV)-transformed DT40 cells also showed changes in miRNA expression in relation to the naïve cells. Embryonic stem cell line BP25 demonstrated a distinct cluster of upregulated miRNAs, many of which were shown previously to be involved in embryonic stem cell development. Finally, chicken macrophage cell line HD11 showed changes in miRNA profiles, some of which are thought to be related to the transformation by v-myc transduced by the virus. This work represents the first publication of a catalog of microRNA expression in a range of important avian cells and provides insights into the potential roles of miRNAs in the hematopoietic lineages of cells in a model non-mammalian species.

The McDonald-Kreitman test and slightly deleterious mutations.

It is possible to estimate the proportion of substitutions that are due to adaptive evolution using the numbers of silent and nonsilent polymorphisms and substitutions in a McDonald and Kreitman-type analysis. Unfortunately, this estimate of adaptive evolution is biased downward by the segregation of slightly deleterious mutations. It has been suggested that 1 way to cope with the effects of these slightly deleterious mutations is to remove low-frequency polymorphisms from the analysis. We investigate the performance of this method theoretically. We show that although removing low-frequency polymorphisms does indeed reduce the bias in the estimate of adaptive evolution, the estimate is always downwardly biased, often to the extent that one would not be able to detect adaptive evolution, even if it existed. The method is reasonably satisfactory, only if the rate of adaptive evolution is high and the distribution of fitness effects for slightly deleterious mutations is very leptokurtic. Our analysis suggests that adaptive evolution could be quite prevalent in humans (>8%) and still not be detectable using current methodologies. Our analysis also suggests that the level of adaptive evolution has probably been underestimated, possibly substantially, in both bacteria and Drosophila.

The other side of the nearly neutral theory, evidence of slightly advantageous back-mutations.

We argue that if there is a category of slightly deleterious mutations, then there should be a category of slightly advantageous back-mutations. We show that when there are both slightly deleterious and advantageous back-mutations, there is likely to be an increase in the rate of evolution after a population size expansion. This increase in the rate of evolution is short-lived. However, we show how its signature can be captured by comparing the rate of evolution in species that have undergone population size expansion versus contraction. We test our model by comparing the pattern of evolution in pairs of island and mainland species in which the colonization event was either island-to-mainland (population size expansion) or mainland-to-island (contraction). We show that the predicted pattern of evolution is observed.

The rate of adaptive evolution in enteric bacteria.

Here we estimate the rate of adaptive substitution in a set of 410 genes that are present in 6 Escherichia coli and 6 Salmonella enterica genomes. We estimate that more than 50% of amino acid substitutions in this set of genes have been fixed by positive selection between the E. coli and S. enterica lineages. We also show that the proportion of adaptive substitutions is uncorrelated with the rate of amino acid substitution or gene function but that it may be correlated with levels of synonymous codon usage bias.