The effect of recombination on the evolution of a population of Neisseria meningitidis.

Neisseria meningitidis (the meningococcus) is a major human pathogen with a history of high invasive disease burden, particularly in sub-Saharan Africa. Our current understanding of the evolution of meningococcal genomes is limited by the rarity of large-scale genomic population studies and lack of in-depth investigation of the genomic events associated with routine pathogen transmission. Here, we fill this knowledge gap by a detailed analysis of 2839 meningococcal genomes obtained through a carriage study of over 50,000 samples collected systematically in Burkina Faso, West Africa, before, during, and after the serogroup A vaccine rollout, 2009-2012. Our findings indicate that the meningococcal genome is highly dynamic, with highly recombinant loci and frequent gene sharing across deeply separated lineages in a structured population. Furthermore, our findings illustrate how population structure can correlate with genome flexibility, as some lineages in Burkina Faso are orders of magnitude more recombinant than others. We also examine the effect of selection on the population, in particular how it is correlated with recombination. We find that recombination principally acts to prevent the accumulation of deleterious mutations, although we do also find an example of recombination acting to speed the adaptation of a gene. In general, we show the importance of recombination in the evolution of a geographically expansive population with deep population structure in a short timescale. This has important consequences for our ability to both foresee the outcomes of vaccination programs and, using surveillance data, predict when lineages of the meningococcus are likely to become a public health concern.

Genome-wide epistasis and co-selection study using mutual information.

Covariance-based discovery of polymorphisms under co-selective pressure or epistasis has received considerable recent attention in population genomics. Both statistical modeling of the population level covariation of alleles across the chromosome and model-free testing of dependencies between pairs of polymorphisms have been shown to successfully uncover patterns of selection in bacterial populations. Here we introduce a model-free method, SpydrPick, whose computational efficiency enables analysis at the scale of pan-genomes of many bacteria. SpydrPick incorporates an efficient correction for population structure, which adjusts for the phylogenetic signal in the data without requiring an explicit phylogenetic tree. We also introduce a new type of visualization of the results similar to the Manhattan plots used in genome-wide association studies, which enables rapid exploration of the identified signals of co-evolution. Simulations demonstrate the usefulness of our method and give some insight to when this type of analysis is most likely to be successful. Application of the method to large population genomic datasets of two major human pathogens, Streptococcus pneumoniae and Neisseria meningitidis, revealed both previously identified and novel putative targets of co-selection related to virulence and antibiotic resistance, highlighting the potential of this approach to drive molecular discoveries, even in the absence of phenotypic data.

Detecting co-selection through excess linkage disequilibrium in bacterial genomes.

Population genomics has revolutionized our ability to study bacterial evolution by enabling data-driven discovery of the genetic architecture of trait variation. Genome-wide association studies (GWAS) have more recently become accompanied by genome-wide epistasis and co-selection (GWES) analysis, which offers a phenotype-free approach to generating hypotheses about selective processes that simultaneously impact multiple loci across the genome. However, existing GWES methods only consider associations between distant pairs of loci within the genome due to the strong impact of linkage-disequilibrium (LD) over short distances. Based on the general functional organisation of genomes it is nevertheless expected that majority of co-selection and epistasis will act within relatively short genomic proximity, on co-variation occurring within genes and their promoter regions, and within operons. Here, we introduce LDWeaver, which enables an exhaustive GWES across both short- and long-range LD, to disentangle likely neutral co-variation from selection. We demonstrate the ability of LDWeaver to efficiently generate hypotheses about co-selection using large genomic surveys of multiple major human bacterial pathogen species and validate several findings using functional annotation and phenotypic measurements. Our approach will facilitate the study of bacterial evolution in the light of rapidly expanding population genomic data.

Pan-pathogen deep sequencing of nosocomial bacterial pathogens in Italy in spring 2020: a prospective cohort study.

BACKGROUND: Nosocomial infections pose a considerable risk to patients who are susceptible, and this is particularly acute in intensive care units when hospital-associated bacteria are endemic. During the first wave of the COVID-19 pandemic, the surge of patients presented a significant obstacle to the effectiveness of infection control measures. We aimed to assess the risks and extent of nosocomial pathogen transmission under a high patient burden by designing a novel bacterial pan-pathogen deep-sequencing approach that could be integrated with standard clinical surveillance and diagnostics workflows. METHODS: We did a prospective cohort study in a region of northern Italy that was severely affected by the first wave of the COVID-19 pandemic. Inpatients on both ordinary and intensive care unit (ICU) wards at the San Matteo hospital, Pavia were sampled on multiple occasions to identify bacterial pathogens from respiratory, nasal, and rectal samples. Diagnostic samples collected between April 7 and May 10, 2020 were cultured on six different selective media designed to enrich for Acinetobacter baumannii, Escherichia coli, Enterococcus faecium, Enterococcus faecalis, Klebsiella pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus, and Streptococcus pneumoniae, and DNA from each plate with positive growth was deep sequenced en masse. We used mSWEEP and mGEMS to bin sequencing reads by sequence cluster for each species, followed by mapping with snippy to generate high quality alignments. Antimicrobial resistance genes were detected by use of ARIBA and CARD. Estimates of hospital transmission were obtained from pairwise bacterial single nucleotide polymorphism distances, partitioned by within-patient and between-patient samples. Finally, we compared the accuracy of our binned Acinetobacter baumannii genomes with those obtained by single colony whole-genome sequencing of isolates from the same hospital. FINDINGS: We recruited patients from March 1 to May 7, 2020. The pathogen population among the patients was large and diverse, with 2148 species detections overall among the 2418 sequenced samples from the 256 patients. In total, 55 sequence clusters from key pathogen species were detected at least five times. The antimicrobial resistance gene prevalence was correspondingly high, with key carbapenemase and extended spectrum ß-lactamase genes detected in at least 50 (40%) of 125 patients in ICUs. Using high-resolution mapping to infer transmission, we established that hospital transmission was likely to be a significant mode of acquisition for each of the pathogen species. Finally, comparison with single colony Acinetobacter baumannii genomes showed that the resolution offered by deep sequencing was equivalent to single-colony sequencing, with the additional benefit of detection of co-colonisation of highly similar strains. INTERPRETATION: Our study shows that a culture-based deep-sequencing approach is a possible route towards improving future pathogen surveillance and infection control at hospitals. Future studies should be designed to directly compare the accuracy, cost, and feasibility of culture-based deep sequencing with single colony whole-genome sequencing on a range of bacterial species. FUNDING: Wellcome Trust, European Research Council, Academy of Finland Flagship program, Trond Mohn Foundation, and Research Council of Norway.

Diversification in immunogenicity genes caused by selective pressures in invasive meningococci.

We studied population genomics of 486 Neisseria meningitidis isolates causing meningitis in the Netherlands during the period 1979-2003 and 2006-2013 using whole-genome sequencing to evaluate the impact of a hyperendemic period of serogroup B invasive disease. The majority of serogroup B isolates belonged to ST-41/44 (41 %) and ST-32 complex (16 %). Comparing the time periods, before and after the decline of serogroup B invasive disease, there was a decrease of ST-41/44 complex sequences (P=0.002). We observed the expansion of a sub-lineage within ST-41/44 complex sequences being associated with isolation from the 1979-2003 time period (P=0.014). Isolates belonging to this sub-lineage expansion within ST-41/44 complex were marked by four antigen allele variants. Presence of these allele variants was associated with isolation from the 1979-2003 time period after correction for multiple testing (Wald test, P=0.0043 for FetA 1-5; P=0.0035 for FHbp 14; P=0.012 for PorA 7-2.4 and P=0.0031 for NHBA two peptide allele). These sequences were associated with 4CMenB vaccine coverage (Fisher's exact test, P<0.001). Outside of the sub-lineage expansion, isolates with markedly lower levels of predicted vaccine coverage clustered in phylogenetic groups showing a trend towards isolation in the 2006-2013 time period (P=0.08). In conclusion, we show the emergence and decline of a sub-lineage expansion within ST-41/44 complex isolates concurrent with a hyperendemic period in meningococcal meningitis. The expansion was marked by specific antigen peptide allele combinations. We observed preliminary evidence for decreasing 4CMenB vaccine coverage in the post-hyperendemic period.

Producing polished prokaryotic pangenomes with the Panaroo pipeline.

Population-level comparisons of prokaryotic genomes must take into account the substantial differences in gene content resulting from horizontal gene transfer, gene duplication and gene loss. However, the automated annotation of prokaryotic genomes is imperfect, and errors due to fragmented assemblies, contamination, diverse gene families and mis-assemblies accumulate over the population, leading to profound consequences when analysing the set of all genes found in a species. Here, we introduce Panaroo, a graph-based pangenome clustering tool that is able to account for many of the sources of error introduced during the annotation of prokaryotic genome assemblies. Panaroo is available at https://github.com/gtonkinhill/panaroo .

The global meningitis genome partnership.

Genomic surveillance of bacterial meningitis pathogens is essential for effective disease control globally, enabling identification of emerging and expanding strains and consequent public health interventions. While there has been a rise in the use of whole genome sequencing, this has been driven predominately by a subset of countries with adequate capacity and resources. Global capacity to participate in surveillance needs to be expanded, particularly in low and middle-income countries with high disease burdens. In light of this, the WHO-led collaboration, Defeating Meningitis by 2030 Global Roadmap, has called for the establishment of a Global Meningitis Genome Partnership that links resources for: N. meningitidis (Nm), S. pneumoniae (Sp), H. influenzae (Hi) and S. agalactiae (Sa) to improve worldwide co-ordination of strain identification and tracking. Existing platforms containing relevant genomes include: PubMLST: Nm (31,622), Sp (15,132), Hi (1935), Sa (9026); The Wellcome Sanger Institute: Nm (13,711), Sp (> 24,000), Sa (6200), Hi (1738); and BMGAP: Nm (8785), Hi (2030). A steering group is being established to coordinate the initiative and encourage high-quality data curation. Next steps include: developing guidelines on open-access sharing of genomic data; defining a core set of metadata; and facilitating development of user-friendly interfaces that represent publicly available data.