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bioRxiv2025

metaboprep v2: Broadening the application of the metaboprep beyond metabolomics

High-throughput multiplex assays for metabolomics and proteomics offer opportunities for biomarker discovery and disease stratification in epidemiological research. The complexity of these datasets requires robust, standardized and transparent preprocessing workflows to ensure reproducibility and comparability across studies. We present an updated and enhanced version of the metaboprep R package. Originally designed for metabolomics data, it has now been extended to support proteomics datasets from platforms such as Olink(R) and SomaScan(R). This release introduces a user-friendly, modular, object-oriented architecture using Rs S7 system, enabling improved input format flexibility, streamlined report generation and increased compatibility with other third-party tools. The updated pipeline is structured in three parts: data import, filtering and summary, and output generation. This structure provides a reproducible yet customizable framework for pre-analysis data preparation with utility across multiple omics platforms and particular value in supporting multi-cohort epidemiological research. Availability and implementationThe metaboprep package is implemented in R and freely available at: https://github.com/MRCIEU/metaboprep.

bioinformatics↗

bioRxiv2025

Genome-wide analysis exploring mechanisms used by Shigella sonnei to survive long-term nutrient starvation

Shigella is a major cause of severe diarrhoea with S. flexneri and S. sonnei accounting for over 90% of infections. As economies grow, S. sonnei replaces S. flexneri as the dominant cause of shigellosis, however the basis of this epidemiological shift remains unclear. Here we investigated whether S. sonnei is better equipped to survive nutrient starvation, a crucial condition for persistence both outside the host and within the colonic lumen. S. sonnei exhibited greater survival under long-term nutrient starvation (LTNS) than S. flexneri, rapidly activating survival mechanisms. We interrogated the genome of S. sonnei using Transposon Directed Insertion-site Sequencing (TraDIS) revealing that metabolic pathways (ATP, nucleotide, and amino acid synthesis), and envelope homeostasis complexes (e.g., Tol-Pal, Bam) are conditionally essential for LTNS. TraDIS findings were validated by non-competitive and competitive survival of wild-type and deletion mutant strains. We compared the homology of conditionally essential genes between S. sonnei and S. flexneri to identify putative genetic markers of differential interspecies LTNS survival. Analysis of ldcA (a peptidoglycan carboxypeptidase) and rseA (the anti-sigma factor regulator) indicated a major role in sustaining survival in LTNS in S. sonnei; however, allele-swap with S. flexneri alleles restored wild-type survival in S. sonnei suggesting that monogenic changes may not explain the divergent survival of these two species. Together, these data define the molecular adaptations of starvation resistance in S. sonnei and provide insights into its epidemiological dominance in high-income countries. Author summaryUnderstanding why S. sonnei has a higher prevalence over S. flexneri as a country undergoes economic growth is one of the most important challenges in Shigella research. The investigation of their biological and genetic differences is key to tackle the impact of the disease. We discovered that S. sonnei resists nutrient deprivation better than S. flexneri, suggesting a better adaptation to an extracellular lifestyle and a greater preservation of metabolic capabilities. Using a genome-wide transposon sequencing approach we uncovered the key pathways behind the survival of S. sonnei facing nutrient starvation, which include ATP, nucleotide, and amino acid synthesis as well as maintenance of cell envelope integrity. Comparative analysis between S. sonnei and S. flexneri did not identify a single gene responsible for the differing survival and we suggest that the differing survival may stem from the coordination of multigenic differences. Our data provides a genome-wide basis for understanding how S. sonnei adapted to nutrient-deprived settings, which may be advantageous in the gut lumen and also in hostile environments, potentially contributing to its dominance in high-income countries.

microbiology↗

bioRxiv2025

Cumulative cgMLST provides increased discrimination of nested phylogenetic groups

BackgroundCore genome multilocus sequence typing (cgMLST) is a powerful method for bacterial strain genotyping. However, the size of the core genome decreases as the phylogenetic breadth of the target group increases, reducing discriminatory power. To overcome this discrimination/applicability tradeoff, here we developed a cumulative cgMLST approach, where sets of core loci conserved within nested phylogenetic entities are added. We illustrate this approach using the Klebsiella pneumoniae species complex (KpSC), for which a widely used cgMLST scheme (KpSC-cgMLST) comprises only 629 genes. MethodsWe created non-redundant cgMLST schemes for the individual species K. pneumoniae sensu stricto (Kpn-cgMLST scheme), and its multidrug resistant sublineages (SLs) SL147 and SL307. To extract core genes, we used 37,874 genome assemblies originating from over 80 countries worldwide. A methodology was set to filter redundant loci before importing them into the genotyping tool BIGSdb, where they were combined into schemes together with preexisting loci conserved at higher phylogenetic levels. The performance of the cumulative cgMLST schemes was evaluated on previously published datasets and on novel data from an inter-hospital outbreak of SL307. ResultsThe Kpn-cgMLST, SL147 and SL307 schemes comprise 2752, 852, and 947 additional loci, respectively. The mean allele call rate of the novel loci was >99% in the validation datasets. Compared to the KpSC scheme used alone, pairwise allelic distances among isolates increased on average 5.6-fold using the Kpn scheme, and further by 20% and 30% using the SL147 and SL307 schemes, respectively. We demonstrate the added value of this increased discriminatory power for epidemiological analyses and show nearly equal discrimination when compared to whole-genome single nucleotide polymorphisms analysis. ConclusionsThe cumulative cgMLST strategy combines broad phylogenetic applicability and nearly complete genotyping resolution, expanding the utility of this harmonized approach for genomic epidemiology.

microbiology↗

bioRxiv2017

Polygenic risk scores applied to a single cohort reveal pleiotropy among hundreds of human phenotypes

BackgroundThere is now convincing evidence that pleiotropy across the genome contributes to the correlation between human traits and comorbidity of diseases. The recent availability of genome-wide association study (GWAS) results have made the polygenic risk score (PRS) approach a powerful way to perform genetic prediction and identify genetic overlap among phenotypes.\n\nMethods and findingsHere we use the PRS method to assess evidence for shared genetic aetiology across hundreds of traits within a single epidemiological study - the Northern Finland Birth Cohort 1966 (NFBC1966). We replicate numerous recent findings, such as a genetic association between Alzheimers disease and lipid levels, while the depth of phenotyping in the NFBC1966 highlights a range of novel significant genetic associations between traits.\n\nConclusionsThis study illustrates the power in taking a hypothesis-free approach to the study of shared genetic aetiology between human traits and diseases. It also demonstrates the potential of the PRS method to provide important biological insights using only a single well-phenotyped epidemiological study of moderate sample size (~5k), with important advantages over evaluating genetic correlations from GWAS summary statistics only.

genetics↗

bioRxiv2017

From Cohorts to Molecules: Adverse Impacts of Endocrine Disrupting Mixtures

Convergent evidence associates endocrine disrupting chemicals (EDCs) with major, increasingly-prevalent human disorders. Regulation requires elucidation of EDC-triggered molecular events causally linked to adverse health outcomes, but two factors limit their identification. First, experiments frequently use individual chemicals, whereas real life entails simultaneous exposure to multiple EDCs. Second, population-based and experimental studies are seldom integrated. This drawback was exacerbated until recently by lack of physiopathologically meaningful human experimental systems that link epidemiological data with results from model organisms.\n\nWe developed a novel approach, integrating epidemiological with experimental evidence. Starting from 1,874 mother-child pairs we identified mixtures of chemicals, measured during early pregnancy, associated with language delay or low-birth weight in offspring. These mixtures were then tested on multiple complementary in vitro and in vivo models. We demonstrate that each EDC mixture, at levels found in pregnant women, disrupts hormone-regulated and disease-relevant gene regulatory networks at both the cellular and organismal scale.

molecular biology↗

bioRxiv2017

ITHANET: Information and database community portal for haemoglobinopathies

Haemoglobinopathies are the commonest monogenic diseases, with millions of carriers and patients worldwide. Online resources for haemoglobinopathies are largely divided into specialised sites catering for patients, researchers and clinicians separately. However, the severity, ubiquity and surprising genetic complexity of the haemoglobinopathies call for an integrated website to serve as a free and comprehensive repository and tool for patients, scientists and health professionals alike. This paper presents the ITHANET community portal, an expanding resource for clinicians and researchers dealing with haemoglobinopathies. It integrates information on news, events, publications, clinical trials and haemoglobinopathy-related organisations and experts and, most importantly, databases of variations, epidemiology and diagnostic and clinical data. Specifically, ITHANET provides annotation for 2690 haemoglobinopathy-related variations, epidemiological data for more than 180 countries and information for more than 600 HPLC diagnostic reports. The ITHANET portal accepts and incorporates contributions to its content by local experts from any country in the world and is freely available for the public at http://www.ithanet.eu.

bioinformatics↗

bioRxiv2017

De Novo Mutations Resolve Disease Transmission Pathways in Clonal Malaria

Detecting de novo mutations in viral and bacterial pathogens enables researchers to reconstruct detailed networks of disease transmission and is a key technique in genomic epidemiology. However these techniques have not yet been applied to the malaria parasite, Plasmodium falciparum, in which a larger genome, slower generation times, and a complex life cycle make them difficult to implement. Here we demonstrate the viability of de novo mutation studies in P. falciparum for the first time. Using a set of clinical samples and novel methods of sequencing, library preparation, and genotyping, we have genotyped low-complexity regions of the genome with a high degree of accuracy. Despite its slower evolutionary rate compared to bacterial or viral species, de novo mutation can be detected in P. falciparum across timescales of just 1-2 years and evolutionary rates in low-complexity regions of the genome can be up to twice that detected in the rest of the genome. The increased mutation rate allows the identification of separate clade expansions that cannot be found using previous genomic epidemiology approaches and could be a crucial tool for mapping residual transmission patterns in disease elimination campaigns and reintroduction scenarios.

genomics↗

bioRxiv2017

Resolving outbreak dynamics using Approximate Bayesian Computation for stochastic birth-death models

Earlier research has suggested that Approximate Bayesian Computation (ABC) makes it possible to fit simulator-based intractable birth-death models to investigate communicable disease outbreak dynamics with accuracy comparable to that of exact Bayesian methods. However, recent findings have indicated that key parameters such as the reproductive number R may remain poorly identifiable. Here we show that the identifiability issue can be resolved by taking into account disease-specific characteristics of the transmission process in closer detail. Using tuberculosis (TB) in the San Francisco Bay area as a case-study, we consider the situation where the genotype data are generated as a mixture of three stochastic processes, each with their distinct dynamics and clear epidemiological interpretation.\n\nThe ABC inference yields stable and accurate posterior inferences about outbreak dynamics from aggregated annual case data with genotype information. We also show that under the proposed model, the infectious population size can be reliably inferred from the data. The estimate is approximately two orders of magnitude smaller compared to assumptions made in the earlier ABC studies, and is much better aligned with epidemiological knowledge about active TB prevalence. Similarly, the reproductive number R related to the primary underlying transmission process is estimated to be nearly three-fold compared with the previous estimates, which has a substantial impact on the interpretation of the fitted outbreak model.

bioinformatics↗

bioRxiv2017

Outbreak of invasive wound mucormycosis in a burn unit due to multiple strains of Mucor circinelloides f. circinelloides resolved by whole genome sequencing

Mucorales are ubiquitous environmental molds responsible for mucormycosis in diabetic, immunocompromised, and severely burned patients. Small outbreaks of invasive wound mucormycosis (IWM) have already been reported in burn units without extensive microbiological investigations. We faced an outbreak of IWM in our center and investigated the clinical isolates with whole genome sequencing (WGS) analysis.\n\nWe analyzed M. circinelloides isolates from patients in our burn unit (BU1) together with non-outbreak isolates from burn unit 2 (BU2, Paris area) and from France over a two-year period (2013-2015). For each isolate, WGS and a de novo genome assembly was performed from read data extracted from the aligned contig sequences of the reference genome (1006PhL).\n\nA total of 21 isolates were sequenced including 14 isolates from six BU1 patients. Phylogenetic classification showed that the clinical isolates clustered in four highly divergent clades. Clade1 contained at least one of the strains from the six epidemiologically-linked BU1 patients. The clinical isolates seemed specific to each patient. Two patients were infected with more than two strains from different clades suggesting that an environmental reservoir of clonally unrelated isolates was the source of contamination. Only two patients shared one strain in BU1, suggesting direct transmission or contamination with the same environmental source.\n\nWGS coupled with precise epidemiological data and analysis of several isolates per patients revealed in our study a complex situation with both potential cross-transmission and multiple contaminations with a heterogeneous pool of strains from a cryptic environmental reservoir.\n\nImportanceInvasive wound mucormycosis (IWM) is a severe infection due to the environmental molds belonging to the order Mucorales. Severely burned patients are particularly at risk for IWM. Here, we used Whole Genome Sequencing (WGS) analysis to resolve an outbreak of IWM due to Mucor circinelloides that occurred in our hospital (BU1). We sequenced 21 clinical isolates, including 14 from BU1 and 7 unrelated isolates, and compared them to the reference genome (1006PhL). This analysis revealed that the outbreak was mainly due to multiple strains that seemed patient-specific, suggesting that the patients were more likely infected from a pool of diverse strains from the environment rather than from direct transmission between the patients. This study revealed the complexity of a Mucorales outbreak in the settings of IWM in burn patients, which has been highlighted based on whole genome sequencing and careful sampling.

microbiology↗

bioRxiv2018

Gene composition as a potential barrier to large recombinations in the bacterial pathogen Klebsiella pneumoniae

Klebsiella pneumoniae (Kp) is one of the most important nosocomial pathogens world-wide, being responsible for frequent hospital outbreaks and causing sepsis and multi-organ infections with a high mortality rate and frequent hospital outbreaks. The most prevalent and widely disseminated lineage of K. pneumoniae is clonal group 258 (CG258), which includes the highly resistant \"high-risk\" genotypes ST258 and ST11. Recent studies revealed that very large recombination events have occurred during the recent emergence of Kp lineages. A striking example is provided by ST258, which has undergone a recombination event that replaced over 1 Mb of the genome with DNA from an unrelated Kp donor. Although several examples of this phenomenon have been documented in Kp and other bacterial species, the significance of these very large recombination events for the emergence of either hyper-virulent or resistant clones remains unclear. Here we present an analysis of 834 Kp genomes that provides data on the frequency of these very large recombination events (defined as those involving >100Kb), their distribution within the genome, and the dynamics of gene flow within the Kp population. We note that very large recombination events occur frequently, and in multiple lineages, and that the majority of recombinational exchanges are clustered within two overlapping genomic regions, which result to be involved by recombination events with different frequencies. Our results also indicate that certain non-CG258 lineages are more likely to act as donors to CG258 recipients than others. Furthermore, comparison of gene content in CG258 and non-CG258 strains agrees with this pattern, suggesting that the success of a large recombination depends on gene composition in the exchanged genomic portion.\n\nAuthor SummaryKlebsiella pneumoniae (Kp) is an opportunistic bacterial pathogen, a major cause of deadly infections and outbreaks in hospitals worldwide. This bacterium is able to exchange large genomic portions (up to a fourth of the entire genome) within a single recombination event. Indeed, the most epidemiologically important Kp clone, is actually a hybrid which emerged after a > 1Mb recombination event. In this work, we investigated how recombinations affected the evolution of the most studied Kp Clonal Group, CG258. We found that large recombinations occurred frequently during Kp evolution, and occurred preferentially in a well-delimited genomic region. Furthermore, we found that four epidemiologically important clones emerged after large recombinations. We identified the donors of several large recombinations: despite many Kp lineages acted as donors during CG258 evolution, two of them have been involved more frequently. We hypothesize that the observed pattern of donors-recipients in recombinations, and the presence of a large recombinogenic region in Kp genome, could be related to gene composition. Indeed, genomic analyses showed a pattern compatible with this hypothesis, suggesting that gene content can represent a main factor in the success of a large recombination.

evolutionary biology↗

bioRxiv2018

Kaptive Web: user-friendly capsule and lipopolysaccharide serotype prediction for Klebsiella genomes

As whole genome sequencing becomes an established component of the microbiologists toolbox, it is imperative that researchers, clinical microbiologists and public health professionals have access to genomic analysis tools for rapid extraction of epidemiologically and clinically relevant information. For the gram-negative hospital pathogens such as Klebsiella pneumoniae, initial efforts have focused on detection and surveillance of antimicrobial resistance genes and clones. However, with the resurgence of interest in alternative infection control strategies targeting Klebsiella surface polysaccharides, the ability to extract information about these antigens is increasingly important.\n\nHere we present Kaptive Web, an online tool for rapid typing of Klebsiella K and O loci, which encode the polysaccharide capsule and lipopolysaccharide O antigen, respectively. Kaptive Web enables users to upload and analyse genome assemblies in a web browser. Results can be downloaded in tabular format or explored in detail via the graphical interface, making it accessible for users at all levels of computational expertise.\n\nWe demonstrate Kaptive Webs utility by analysis of >500 K. pneumoniae genomes. We identify extensive K and O locus diversity among 201 genomes belonging to the carbapenemase- associated clonal group 258 (25 K and six O loci). Characterisation of a further 309 genomes indicates that such diversity is common among the multi-drug resistant clones and that these loci represent useful epidemiological markers for strain subtyping. These findings reinforce the need for rapid, reliable and accessible typing methods such as Kaptive Web.\n\nKaptive Web is available for use at kaptive.holtlab.net and source code is available at github.com/kelwyres/Kaptive-Web.

microbiology↗

bioRxiv2018

Inferring causal pathways among three or more variables from steady-state correlations in a homeostatic system

Cross-sectional correlations between two variables have limited implications for causality. We show here that in a homeostatic system with three or more inter-correlated variables, it is possible to make causal inferences from steady-state data. Every putative pathway between three variables makes a set of differential predictions that can be tested with steady state data. For example, among 3 variables, A, B and C, the coefficient of determination, [Formula] is predicted by the product of [Formula] and [Formula] for some pathways, but not for others. Residuals from a regression line are independent of residuals from another regression for some pathways, but positively or negatively correlated for certain other pathways. Different pathways therefore have different prediction signatures, which can be used to accept or reject plausible pathways. We apply these principles to test the classical pathway leading to a hyperinsulinemic normoglycemic insulin-resistant, or pre-diabetic state using four different sets of epidemiological data. Currently, a set of indices called HOMA-IR and HOMA-{beta} are used to represent insulin resistance and glucose-stimulated insulin response by {beta} cells respectively. Our analysis shows that if we assume the HOMA indices to be faithful indicators, the classical pathway must in turn, be rejected. Among the populations sampled, the classical pathway and faithfulness of the HOMA indices cannot be simultaneously true. The principles and tools described here can find wide application in inferring plausible regulatory mechanisms in homeostatic systems based on epidemiological data.

physiology↗

bioRxiv2018

Beyond the SNP threshold: identifying outbreak clusters using inferred transmissions

Whole genome sequencing (WGS) is increasingly used to aid in understanding pathogen transmission [1]. Very often the number of single nucleotide polymorphisms (SNPs) separating isolates collected during an epidemiological study are used to identify sets of cases that are potentially linked by direct transmission. However, there is little agreement in the literature as to what an appropriate SNP cut-off threshold should be, or indeed whether a simple SNP threshold is appropriate for identifying sets of isolates to be treated as \"transmission clusters\". The SNP thresholds that have been adopted for inferring transmission vary widely even for one pathogen. As an alternative to reliance on a strict SNP threshold, we suggest that the key inferential target when studying the spread of an infectious disease is the number of transmission events separating cases. Here we describe a new framework for deciding whether two pathogen genomes should be considered as part of the same transmission cluster, based jointly on the number of SNP differences and the length of time over which those differences have accumulated. Our approach allows us to probabilistically characterize the number of inferred transmission events that separate cases. We show how this framework can be modified to consider variable mutation rates across the genome (e.g. SNPs associated with drug resistance) and we indicate how the methodology can be extended to incorporate epidemiological data such as spatial proximity. We use recent data collected from tuberculosis studies from British Columbia, Canada and the Republic of Moldova to apply and compare our clustering method to the SNP threshold approach. In the British Columbia data, different cases break off from the main clusters as cut-off thresholds are lowered; the transmission-based method obtains slightly different clusters than the SNP cut-offs. For the Moldova data, straightforward application of the methods shows no appreciable difference, but when we take into account the fact that resistance conferring sites likely do not follow the same mutation clock as most sites due to selection, the transmission-based approach differs from the SNP cut-off method. Outbreak simulations confirm that our transmission based method is at least as good at identifying direct transmissions as a SNP cut-off. We conclude that the new method is a promising step towards establishing a more robust identification of outbreaks.

genomics↗

bioRxiv2018

Deconvoluting Virome-Wide Antiviral Antibody Profiling Data

The ability to comprehensively characterize exposures and immune responses to viral infections will be critical to better understanding human health and disease. We previously described the VirScan system, a phage-display based technology for profiling antibody binding to a comprehensive library of peptides designed to represent the human virome. The previous VirScan analytical approach did not fully account for disproportionate representation of viruses in the library or for antibody cross-reactivity among sequences shared by related viruses. Here we present the AntiViral Antibody Response Deconvolution Algorithm ( AVARDA), a multi-module software package for analyzing VirScan datasets. AVARDA provides a probabilistic assessment of infection at species-level resolution by considering alignment of all library peptides to each other and to all human viruses. We employed AVARDA to analyze VirScan data from a cohort of encephalitis patients with either known viral infections or undiagnosed etiologies. By comparing acute and convalescent sera, AVARDA successfully confirmed or detected antibody responses to human herpesviruses 1, 3, 4, 5, and 6, thereby improving the rate of diagnosing viral encephalitis in this cohort by 62.5%. We further assessed AVARDAs utility in the setting of an epidemiological study, demonstrating its ability to determine infections acquired in a child followed prospectively from infancy. We consider ways in which AVARDAs conceptual framework may be further developed in the future and describe how its analyses may be extended beyond investigations of viral infection. AVARDA, in combination with VirScan and other pan-pathogen serological techniques, is likely to find broad utility in the epidemiology and diagnosis of infectious diseases.

bioinformatics↗

bioRxiv2018

Co-circulating mumps lineages at multiple geographic scales

Despite widespread vaccination, eleven thousand mumps cases were reported in the United States (US) in 2016-17, including hundreds in Massachusetts, primarily in college settings. We generated 203 whole genome mumps virus (MuV) sequences from Massachusetts and 15 other states to understand the dynamics of mumps spread locally and nationally, as well as to search for variants potentially related to vaccination. We observed multiple MuV lineages circulating within Massachusetts during 2016-17, evidence for multiple introductions of the virus to the state, and extensive geographic movement of MuV within the US on short time scales. We found no evidence that variants arising during this outbreak contributed to vaccine escape. Combining epidemiological and genomic data, we observed multiple co-circulating clades within individual universities as well as spillover into the local community. Detailed data from one well-sampled university allowed us to estimate an effective reproductive number within that university significantly greater than one. We also used publicly available small hydrophobic (SH) gene sequences to estimate migration between world regions and to place this outbreak in a global context, but demonstrate that these short sequences, historically used for MuV genotyping, are inadequate for tracing detailed transmission. Our findings suggest continuous, often undetected, circulation of mumps both locally and nationally, and highlight the value of combining genomic and epidemiological data to track viral disease transmission at high resolution.

genomics↗

bioRxiv2018

TreeN93: a non-parametric distance-based method for inferring viral transmission clusters

SummaryHighly-used methods for identifying transmission clusters of rapidly-evolving pathogens from molecular data require a user-determined distance threshold. The choice of threshold is often motivated by epidemiological information known a priori, which may be unfeasible for epidemics without rich epidemiological information. TreeN93 is a fully non-parametric distance-based method for transmission cluster identification that scales polynomially.\n\nAvailability and implementationTreeN93 is implemented in Python 3 and is freely available at https://github.com/niemasd/TreeN93/.\n\nContactniemamoshiri@gmail.com

bioinformatics↗

bioRxiv2018

Transcriptomic metaanalyses of autistic brains reveals shared gene expression and biological pathway abnormalities with cancer.

Epidemiological and clinical evidence points to cancer as a comorbidity in people with autism spectrum disorders (ASD). A significant overlap of genes and biological processes between both diseases has also been reported. Here, for the first time, we compared the gene expression profiles of ASD frontal cortex tissues and 22 cancer types obtained by differential expression meta-analysis. Four cancer types (brain, thyroid, kidney, and pancreatic cancers) presented a significant overlap in gene expression deregulations in the same direction as ASD whereas two cancer types (lung and prostate cancers) showed differential expression profiles significantly deregulated in the opposite direction from ASD. Functional enrichment and LINCS L1000 based drug set enrichment analyses revealed the implication of several biological processes and pathways that were affected jointly in both diseases, including impairments of the immune system, and impairments in oxidative phosphorylation and ATP synthesis among others. Our data also suggest that brain and kidney cancer have patterns of transcriptomic dysregulation in the PI3K/AKT/MTOR axis that are similar to those found in ASD. These shared transcriptomic alterations could help explain epidemiological observations suggesting direct and inverse comorbid associations between ASD and particular cancer types.

systems biology↗

bioRxiv2018

Temporal population structure of invasive Group B Streptococcus during a period of rising disease incidence shows expansion of a CC17 clone

Group B Streptococcus (GBS) is a major cause of neonatal invasive disease worldwide. In the Netherlands, the incidence of the disease increased, despite the introduction of prevention guidelines in 1999. This was accompanied by changes in pathogen genotype distribution, with a significant increase in the prevalence of isolates belonging to clonal complex (CC) 17. To better understand the mechanisms of temporal changes in the epidemiology of GBS genotypes that correlated with the rise in disease incidence, we applied whole genome sequencing (WGS) to study a national collection of invasive GBS isolates. A total of 1345 isolates from patients aged 0 - 89 days and collected between 1987 and 2016 in the Netherlands were sequenced and characterised. The GBS population contained 5 major lineages representing CC17 (39%), CC19 (25%), CC23 (18%), CC10 (9%), and CC1 (7%). There was a significant rise in the prevalence of isolates representing CC17 and CC23 among cases of early-and late-onset disease, due to expansion of discrete sub-lineages. The most prominent was shown by a CC17 sub-lineage, identified here as CC17-1A, which experienced a major clonal expansion at the end of the 1990s. The CC17-1A expansion correlated with the emergence of a novel phage carrying a gene encoding a putative adhesion protein, named here StrP. The first occurrence of this phage (designated phiStag1) within the collection in 1997, was followed by multiple, independent acquisitions by CC17 and parallel clonal expansions of CC17-1A and another cluster, CC17-1B. The CC17-1A clone was identified in external datasets, and represents a globally distributed invasive sub-lineage of CC17. Our work describes how a sudden change in the epidemiology of specific GBS sub-lineages, in particular CC17-1A, correlates with the rise in the disease incidence, and indicates a putative key role of a novel phage in driving the expansion of this CC17 clone.\n\nAuthor summaryGroup B Streptococcus (GBS) is a commensal organism of the gastrointestinal and genitourinary tracts. However, it is also an opportunistic pathogen and a major cause of neonatal invasive disease, which can be classified into early-onset (0 - 6 days of life) or late-onset (7 - 89 days of life). Current disease prevention strategy involves intrapartum antibiotic prophylaxis (IAP), which aims to prevent the transmission of GBS from mother to baby during labour. Many developed countries adapted national IAP guidelines. In the Netherlands, these were introduced in 1999. However, the incidence of GBS disease increased after IAP introduction. In this study we applied whole genome sequencing to characterise a nationwide collection of invasive GBS from cases of neonatal disease that occurred between 1987 and 2016. Analysis of GBS population structure involving phylogenetic partitioning of individual lineages revealed that the rise in disease incidence involved the expansion of specific clusters from two major GBS lineages, CC17 and CC23. Our study provides new insights into the recent evolution of the hypervirulent CC17 and describes a rapid expansion of a discrete, pre-existing sub-lineage that occurred after acquisition of a novel phage carrying a putative adhesion protein gene, underscoring the major role of CC17 in neonatal diseases.

genomics↗