Identifying genes associated with invasive disease in S. pneumoniae by applying a machine learning approach to whole genome sequence typing data

Disease-Associated Streptococcus pneumoniae Genetic Variation

Streptococcus pneumoniae is an opportunistic pathogen that causes substantial illness and death among children worldwide. The genetic backgrounds of pneumococci that cause infection versus asymptomatic carriage vary substantially. To determine the evolutionary mechanisms of opportunistic pathogenicity, we conducted a genomic surveillance study in China. We collected 783 S. pneumoniae isolates from infected and asymptomatic children. By using a 2-stage genomewide association study process, we compared genomic differences between infection and carriage isolates to address genomic variation associated with pathogenicity. We identified 8 consensus k-mers associated with adherence, antimicrobial resistance, and immune modulation, which were unevenly distributed in the infection isolates. Classification accuracy of the best k-mer predictor for S. pneumoniae infection was good, giving a simple target for predicting pathogenic isolates. Our findings suggest that S. pneumoniae pathogenicity is complex and multifactorial, and we provide genetic evidence for precise targeted interventions.

The metabolic, virulence and antimicrobial resistance profiles of colonising Streptococcus pneumoniae shift after PCV13 introduction in urban Malawi

Streptococcus pneumoniae causes substantial mortality among children under 5-years-old worldwide. Polysaccharide conjugate vaccines (PCVs) are highly effective at reducing vaccine serotype disease, but emergence of non-vaccine serotypes and persistent nasopharyngeal carriage threaten this success. We investigated the hypothesis that following vaccine, adapted pneumococcal genotypes emerge with the potential for vaccine escape. We genome sequenced 2804 penumococcal isolates, collected 4-8 years after introduction of PCV13 in Blantyre, Malawi. We developed a pipeline to cluster the pneumococcal population based on metabolic core genes into "Metabolic genotypes" (MTs). We show that S. pneumoniae population genetics are characterised by emergence of MTs with distinct virulence and antimicrobial resistance (AMR) profiles. Preliminary in vitro and murine experiments revealed that representative isolates from emerging MTs differed in growth, haemolytic, epithelial infection, and murine colonisation characteristics. Our results suggest that in the context of PCV13 introduction, pneumococcal population dynamics had shifted, a phenomenon that could further undermine vaccine control and promote spread of AMR.

Pan-Genome-Wide Association Study of Serotype 19A Pneumococci Identifies Disease-Associated Genes

Despite the widespread implementation of pneumococcal vaccines, hypervirulent Streptococcus pneumoniae serotype 19A is endemic worldwide. It is still unclear whether specific genetic elements contribute to complex pathogenicity of serotype 19A isolates. We performed a large-scale pan-genome-wide association study (pan-GWAS) of 1,292 serotype 19A isolates sampled from patients with invasive disease and asymptomatic carriers. To address the underlying disease-associated genotypes, a comprehensive analysis using three methods (Scoary, a linear mixed model, and random forest) was performed to compare disease and carriage isolates to identify genes consistently associated with disease phenotype. By using three pan-GWAS methods, we found consensus on statistically significant associations between genotypes and disease phenotypes (disease or carriage), with a subset of 30 consistently significant disease-associated genes. The results of functional annotation revealed that these disease-associated genes had diverse predicted functions, including those that participated in mobile genetic elements, antibiotic resistance, virulence, and cellular metabolism. Our findings suggest the multifactorial pathogenicity nature of this hypervirulent serotype and provide important evidence for the design of novel protein-based vaccines to prevent and control pneumococcal disease. IMPORTANCE It is important to understand the genetic and pathogenic characteristics of S. pneumoniae serotype 19A, which may provide important information for the prevention and treatment of pneumococcal disease. This global large-sample pan-GWAS study has identified a subset of 30 consistently significant disease-associated genes that are involved in mobile genetic elements, antibiotic resistance, virulence, and cellular metabolism. These findings suggest the multifactorial pathogenicity nature of hypervirulent S. pneumoniae serotype 19A isolates and provide implications for the design of novel protein-based vaccines.

Differences in genetic flux in invasive Streptococcus pneumoniae associated with bacteraemia and meningitis

Background

Genetic flux, a crucial process of pneumococcal evolution, is an essential aspect of bacterial physiology during human pathogenesis. However, the role of these genetic changes and the selective forces that drive them is not fully understood. Elucidating the underlying selective forces that determine the magnitude and direction (gene gain or loss) of gene transfer is important for better understanding the pathogenesis process, and may also highlight potential therapeutic and diagnostic targets.

Methods

Here, we leveraged data from high throughput genome sequencing and robust probabilistic models to discover the magnitude and likely direction of genetic flux events, but not the source, in 209 multi-lineage invasive pneumococcal genomes generated from blood (n = 147) and CSF (n = 62) isolates, associated with bacteremia and meningitis respectively. The Gain and Loss Mapping Engine (GLOOME) was used to infer gene gain and loss more accurately by taking into account differences in rates of gene gain and loss among gene families, as well as independent evolution within and across lineages.

Results

Our results show the likely extent and direction of gene fluctuations at different niche, during pneumococcal pathogenesis, highlighting that evolutionary dynamics are important for tissue-specific host invasion and survival.

Conclusion

These findings improve insights on evolutionary dynamics during invasive pneumococcal disease, and highlight potential diagnostic and therapeutic targets.

Forest and Trees: Exploring Bacterial Virulence with Genome-wide Association Studies and Machine Learning

The advent of inexpensive and rapid sequencing technologies has allowed bacterial whole-genome sequences to be generated at an unprecedented pace. This wealth of information has revealed an unanticipated degree of strain-to-strain genetic diversity within many bacterial species. Awareness of this genetic heterogeneity has corresponded with a greater appreciation of intraspecies variation in virulence. A number of comparative genomic strategies have been developed to link these genotypic and pathogenic differences with the aim of discovering novel virulence factors. Here, we review recent advances in comparative genomic approaches to identify bacterial virulence determinants, with a focus on genome-wide association studies and machine learning.

A Narrative Review of the Molecular Epidemiology and Laboratory Surveillance of Vaccine Preventable Bacterial Meningitis Agents: Streptococcus pneumoniae, Neisseria meningitidis, Haemophilus influenzae and Streptococcus agalactiae

This narrative review describes the public health importance of four most common bacterial meningitis agents, Streptococcus pneumoniae, Neisseria meningitidis, Haemophilus influenzae, and S. agalactiae (group B Streptococcus). Three of them are strict human pathogens that normally colonize the nasopharynx and may invade the blood stream to cause systemic infections and meningitis. S. agalactiae colonizes the genito-gastrointestinal tract and is an important meningitis agent in newborns, but also causes invasive infections in infants or adults. These four bacteria have polysaccharide capsules that protect them against the host complement defense. Currently licensed conjugate vaccines (against S. pneumoniae, H. influenza, and N. meningitidis only but not S. agalactiae) can induce protective serum antibodies in infants as young as two months old offering protection to the most vulnerable groups, and the ability to eliminate carriage of homologous serotype strains in vaccinated subjects lending further protection to those not vaccinated through herd immunity. However, the serotype-specific nature of these vaccines have driven the bacteria to adapt by mechanisms that affect the capsule antigens through either capsule switching or capsule replacement in addition to the possibility of unmasking of strains or serotypes not covered by the vaccines. The post-vaccine molecular epidemiology of vaccine-preventable bacterial meningitis is discussed based on findings obtained with newer genomic laboratory surveillance methods.

Identification of Nitrogen Fixation Genes in Lactococcus Isolated from Maize Using Population Genomics and Machine Learning

Sierra Mixe maize is a landrace variety from Oaxaca, Mexico, that utilizes nitrogen derived from the atmosphere via an undefined nitrogen fixation mechanism. The diazotrophic microbiota associated with the plant’s mucilaginous aerial root exudate composed of complex carbohydrates was previously identified and characterized by our group where we found 23 lactococci capable of biological nitrogen fixation (BNF) without containing any of the proposed essential genes for this trait (nifHDKENB). To determine the genes in Lactococcus associated with this phenotype, we selected 70 lactococci from the dairy industry that are not known to be diazotrophic to conduct a comparative population genomic analysis. This showed that the diazotrophic lactococcal genomes were distinctly different from the dairy isolates. Examining the pangenome followed by genome-wide association study and machine learning identified genes with the functions needed for BNF in the maize isolates that were absent from the dairy isolates. Many of the putative genes received an ‘unknown’ annotation, which led to the domain analysis of the 135 homologs. This revealed genes with molecular functions needed for BNF, including mucilage carbohydrate catabolism, glycan-mediated host adhesion, iron/siderophore utilization, and oxidation/reduction control. This is the first report of this pathway in this organism to underpin BNF. Consequently, we proposed a model needed for BNF in lactococci that plausibly accounts for BNF in the absence of the nif operon in this organism.

Bacterial genome-wide association study of hyper-virulent pneumococcal serotype 1 identifies genetic variation associated with neurotropism

Hyper-virulent Streptococcus pneumoniae serotype 1 strains are endemic in Sub-Saharan Africa and frequently cause lethal meningitis outbreaks. It remains unknown whether genetic variation in serotype 1 strains modulates tropism into cerebrospinal fluid to cause central nervous system (CNS) infections, particularly meningitis. Here, we address this question through a large-scale linear mixed model genome-wide association study of 909 African pneumococcal serotype 1 isolates collected from CNS and non-CNS human samples. By controlling for host age, geography, and strain population structure, we identify genome-wide statistically significant genotype-phenotype associations in surface-exposed choline-binding (P = 5.00 × 10-08) and helicase proteins (P = 1.32 × 10-06) important for invasion, immune evasion and pneumococcal tropism to CNS. The small effect sizes and negligible heritability indicated that causation of CNS infection requires multiple genetic and other factors reflecting a complex and polygenic aetiology. Our findings suggest that certain pathogen genetic variation modulate pneumococcal survival and tropism to CNS tissue, and therefore, virulence for meningitis.

Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review

Human healthcare is one of the most important topics for society. It tries to find the correct effective and robust disease detection as soon as possible to patients receipt the appropriate cares. Because this detection is often a difficult task, it becomes necessary medicine field searches support from other fields such as statistics and computer science. These disciplines are facing the challenge of exploring new techniques, going beyond the traditional ones. The large number of techniques that are emerging makes it necessary to provide a comprehensive overview that avoids very particular aspects. To this end, we propose a systematic review dealing with the Machine Learning applied to the diagnosis of human diseases. This review focuses on modern techniques related to the development of Machine Learning applied to diagnosis of human diseases in the medical field, in order to discover interesting patterns, making non-trivial predictions and useful in decision-making. In this way, this work can help researchers to discover and, if necessary, determine the applicability of the machine learning techniques in their particular specialties. We provide some examples of the algorithms used in medicine, analysing some trends that are focused on the goal searched, the algorithm used, and the area of applications. We detail the advantages and disadvantages of each technique to help choose the most appropriate in each real-life situation, as several authors have reported. The authors searched Scopus, Journal Citation Reports (JCR), Google Scholar, and MedLine databases from the last decades (from 1980s approximately) up to the present, with English language restrictions, for studies according to the objectives mentioned above. Based on a protocol for data extraction defined and evaluated by all authors using PRISMA methodology, 141 papers were included in this advanced review.

Interest of bacterial pangenome analyses in clinical microbiology

Thanks to the progress and decreasing costs in genome sequencing technologies, more than 250,000 bacterial genomes are currently available in public databases, covering most, if not all, of the major human-associated phylogenetic groups of these microorganisms, pathogenic or not. In addition, for many of them, sequences from several strains of a given species are available, thus enabling to evaluate their genetic diversity and study their evolution. In addition, the significant cost reduction of bacterial whole genome sequencing as well as the rapid increase in the number of available bacterial genomes have prompted the development of pangenomic software tools. The study of bacterial pangenome has many applications in clinical microbiology. It can unveil the pathogenic potential and ability of bacteria to resist antimicrobials as well identify specific sequences and predict antigenic epitopes that allow molecular or serologic assays and vaccines to be designed. Bacterial pangenome constitutes a powerful method for understanding the history of human bacteria and relating these findings to diagnosis in clinical microbiology laboratories in order to optimize patient management.

Pan-GWAS of Streptococcus agalactiae Highlights Lineage-Specific Genes Associated with Virulence and Niche Adaptation

GBS is a leading cause of mortality in newborn babies in high- and low-income countries worldwide. Different strains of GBS are characterized by different degrees of virulence, where some are harmlessly carried by humans or animals and others are much more likely to cause disease. The genome sequences of almost 2,000 GBS samples isolated from both animals and humans in high- and low- income countries were analyzed using a pan-genome-wide association study approach. This allowed us to identify 279 genes which are associated with different lineages of GBS, characterized by a different virulence and preferred host. Additionally, we propose that the GBS now carried in humans may have first evolved in animals before expanding clonally once adapted to the human host. These findings are essential to help understand what is causing GBS disease and how the bacteria have evolved and are transmitted.

Streptococcus agalactiae (group B streptococcus; GBS) is a colonizer of the gastrointestinal and urogenital tracts, and an opportunistic pathogen of infants and adults. The worldwide population of GBS is characterized by clonal complexes (CCs) with different invasive potentials. CC17, for example, is a hypervirulent lineage commonly associated with neonatal sepsis and meningitis, while CC1 is less invasive in neonates and more commonly causes invasive disease in adults with comorbidities. The genetic basis of GBS virulence and the extent to which different CCs have adapted to different host environments remain uncertain. We have therefore applied a pan-genome-wide association study (GWAS) approach to 1,988 GBS strains isolated from different hosts and countries. Our analysis identified 279 CC-specific genes associated with virulence, disease, metabolism, and regulation of cellular mechanisms that may explain the differential virulence potential of particular CCs. In CC17 and CC23, for example, we have identified genes encoding pilus, quorum-sensing proteins, and proteins for the uptake of ions and micronutrients which are absent in less invasive lineages. Moreover, in CC17, carriage and disease strains were distinguished by the allelic variants of 21 of these CC-specific genes. Together our data highlight the lineage-specific basis of GBS niche adaptation and virulence.

The genome sequences of almost 2,000 GBS samples isolated from both animals and humans in high- and low- income countries were analyzed using a pan-genome-wide association study approach. This allowed us to identify 279 genes which are associated with different lineages of GBS, characterized by a different virulence and preferred host. Additionally, we propose that the GBS now carried in humans may have first evolved in animals before expanding clonally once adapted to the human host.

These findings are essential to help understand what is causing GBS disease and how the bacteria have evolved and are transmitted.

Detection, forecasting and control of infectious disease epidemics: modelling outbreaks in humans, animals and plants

The 1918 influenza pandemic is one of the most devastating infectious disease epidemics on record, having caused approximately 50 million deaths worldwide. Control measures, including prohibiting non-essential gatherings as well as closing cinemas and music halls, were applied with varying success and limited knowledge of transmission dynamics. One hundred years later, following developments in the field of mathematical epidemiology, models are increasingly used to guide decision-making and devise appropriate interventions that mitigate the impacts of epidemics. Epidemiological models have been used as decision-making tools during outbreaks in human, animal and plant populations. However, as the subject has developed, human, animal and plant disease modelling have diverged. Approaches have been developed independently for pathogens of each host type, often despite similarities between the models used in these complementary fields. With the increased importance of a One Health approach that unifies human, animal and plant health, we argue that more inter-disciplinary collaboration would enhance each of the related disciplines. This pair of theme issues presents research articles written by human, animal and plant disease modellers. In this introductory article, we compare the questions pertinent to, and approaches used by, epidemiological modellers of human, animal and plant pathogens, and summarize the articles in these theme issues. We encourage future collaboration that transcends disciplinary boundaries and links the closely related areas of human, animal and plant disease epidemic modelling. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'. This issue is linked with the subsequent theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'.

Coinfinder: detecting significant associations and dissociations in pangenomes

The accessory genes of prokaryote and eukaryote pangenomes accumulate by horizontal gene transfer, differential gene loss, and the effects of selection and drift. We have developed Coinfinder, a software program that assesses whether sets of homologous genes (gene families) in pangenomes associate or dissociate with each other (i.e. are 'coincident') more often than would be expected by chance. Coinfinder employs a user-supplied phylogenetic tree in order to assess the lineage-dependence (i.e. the phylogenetic distribution) of each accessory gene, allowing Coinfinder to focus on coincident gene pairs whose joint presence is not simply because they happened to appear in the same clade, but rather that they tend to appear together more often than expected across the phylogeny. Coinfinder is implemented in C++, Python3 and R and is freely available under the GNU license from https://github.com/fwhelan/coinfinder.