Lineage structure of Streptococcus pneumoniae may be driven by immune selection on the groEL heat-shock protein

Darwin review: the evolution of virulence in human pathogens

By definition, all pathogens cause some level of harm to their hosts. If this harm occurs while the pathogen is transmitting, it can negatively affect the pathogen's fitness by shortening the duration over which transmission can occur. However, many of the factors that increase virulence (i.e. harm to host) also promote transmission, driving the pathogen population towards an optimal state of intermediate virulence. A wider spectrum of virulence may be maintained among pathogen populations which are structured into multiple discrete strains though direct resource and immune-mediated competition. These various evolutionary outcomes, and the effects of medical and public health interventions, are best understood within a framework that recognizes the complex relationship between transmission and virulence in the context of the antigenic diversity of the pathogen population.

Pneumococcal population genomics changes during the early time period of conjugate vaccine uptake in southern India

Streptococcus pneumoniae is a major cause of invasive disease of young children in low- and middle-income countries. In southern India, pneumococcal conjugate vaccines (PCVs) that can prevent invasive pneumococcal disease began to be used more frequently after 2015. To characterize pneumococcal evolution during the early time period of PCV uptake in southern India, genomes were sequenced and selected characteristics were determined for 402 invasive isolates collected from children <5 years of age during routine surveillance from 1991 to 2020. Overall, the prevalence and diversity of vaccine type (VT) and non-vaccine type (NVT) isolates did not significantly change post-uptake of PCV. Individually, serotype 1 and global pneumococcal sequence cluster (GPSC or strain lineage) 2 significantly decreased, whereas serotypes 6B, 9V and 19A and GPSCs 1, 6, 10 and 23 significantly increased in proportion post-uptake of PCV. Resistance determinants to penicillin, erythromycin, co-trimoxazole, fluoroquinolones and tetracycline, and multidrug resistance significantly increased in proportion post-uptake of PCV and especially among VT isolates. Co-trimoxazole resistance determinants were common pre- and post-uptake of PCV (85 and 93 %, respectively) and experienced the highest rates of recombination in the genome. Accessory gene frequencies were seen to be changing by small amounts across the frequency spectrum specifically among VT isolates, with the largest changes linked to antimicrobial resistance determinants. In summary, these results indicate that as of 2020 this pneumococcal population was not yet approaching a PCV-induced equilibrium and they highlight changes related to antimicrobial resistance. Augmenting PCV coverage and prudent use of antimicrobials are needed to counter invasive pneumococcal disease in this region.

Polymorphisms predicting phylogeny in hepatitis B virus

Hepatitis B viruses (HBVs) are compact viruses with circular genomes of ∼3.2 kb in length. Four genes (HBx, Core, Surface, and Polymerase) generating seven products are encoded on overlapping reading frames. Ten HBV genotypes have been characterised (A-J), which may account for differences in transmission, outcomes of infection, and treatment response. However, HBV genotyping is rarely undertaken, and sequencing remains inaccessible in many settings. We set out to assess which amino acid (aa) sites in the HBV genome are most informative for determining genotype, using a machine learning approach based on random forest algorithms (RFA). We downloaded 5,496 genome-length HBV sequences from a public database, excluding recombinant sequences, regions with conserved indels, and genotypes I and J. Each gene was separately translated into aa, and the proteins concatenated into a single sequence (length 1,614 aa). Using RFA, we searched for aa sites predictive of genotype and assessed covariation among the sites with a mutual information-based method. We were able to discriminate confidently between genotypes A-H using ten aa sites. Half of these sites (5/10) sites were identified in Polymerase (Pol), of which 4/5 were in the spacer domain and one in reverse transcriptase. A further 4/10 sites were located in Surface protein and a single site in HBx. There were no informative sites in Core. Properties of the aa were generally not conserved between genotypes at informative sites. Among the highest co-varying pairs of sites, there were fifty-five pairs that included one of these 'top ten' sites. Overall, we have shown that RFA analysis is a powerful tool for identifying aa sites that predict the HBV lineage, with an unexpectedly high number of such sites in the spacer domain, which has conventionally been viewed as unimportant for structure or function. Our results improve ease of genotype prediction from limited regions of HBV sequences and may have future applications in understanding HBV evolution.

Pneumococcal Competition Modulates Antibiotic Resistance in the Pre-Vaccination Era: A Modelling Study

The ongoing emergence of antibiotic resistant strains and high frequencies of antibiotic resistance of Streptococcus pneumoniae poses a major public health challenge. How and which ecological and evolutionary mechanisms maintain the coexistence of antibiotic resistant and susceptible strains remains largely an open question. We developed an individual-based, stochastic model expanding on a previous pneumococci modelling framework. We explore how between- and within-host mechanisms of competition can sustain observed levels of resistance to antibiotics in the pre-vaccination era. Our framework considers that within-host competition for co-colonization between resistant and susceptible strains can arise via pre-existing immunity (immunological competition) or intrinsic fitness differences due to resistance costs (ecological competition). We find that beyond stochasticity, population structure or movement, competition at the within-host level can explain observed resistance frequencies. We compare our simulation results to pneumococcal antibiotic resistance data in the European region using approximate Bayesian computation. Our results demonstrate that ecological competition for co-colonization can explain the variation in co-existence of resistant and susceptible pneumococci observed in the pre-vaccination era. Furthermore, we show that within-host pneumococcal competition can facilitate the maintenance of resistance in the pre-vaccination era. Accounting for these competition-related components of pneumococcal dynamics can improve our understanding of drivers for the emergence and maintenance of antibiotic resistance in pneumococci.

Frequency-dependent selection can forecast evolution in Streptococcus pneumoniae

Predicting how pathogen populations will change over time is challenging. Such has been the case with Streptococcus pneumoniae, an important human pathogen, and the pneumococcal conjugate vaccines (PCVs), which target only a fraction of the strains in the population. Here, we use the frequencies of accessory genes to predict changes in the pneumococcal population after vaccination, hypothesizing that these frequencies reflect negative frequency-dependent selection (NFDS) on the gene products. We find that the standardized predicted fitness of a strain, estimated by an NFDS-based model at the time the vaccine is introduced, enables us to predict whether the strain increases or decreases in prevalence following vaccination. Further, we are able to forecast the equilibrium post-vaccine population composition and assess the invasion capacity of emerging lineages. Overall, we provide a method for predicting the impact of an intervention on pneumococcal populations with potential application to other bacterial pathogens in which NFDS is a driving force.

Hospital discharges in urban sanitation systems: Long-term monitoring of wastewater resistome and microbiota in relationship to their eco-exposome

Wastewaters (WW) are important sources for the dissemination of antimicrobial resistance (AMR) into the environment. Hospital WW (HWW) contain higher loads of micro-pollutants and AMR markers than urban WW (UWW). Little is known about the long-term dynamics of H and U WW and the impact of their joined treatment on the general burden of AMR. Here, we characterized the resistome, microbiota and eco-exposome signature of 126 H and U WW samples treated separately for three years, and then mixed, over one year. Multi-variate analysis and machine learning revealed a robust signature for each WW with no significant variation over time before mixing, and once mixed, both WW closely resembled Urban signatures. We demonstrated a significant impact of pharmaceuticals and surfactants on the resistome and microbiota of H and U WW. Our results present considerable targets for AMR related risk assessment of WW.

Quantification of Human Oral and Fecal Streptococcus parasanguinis by Use of Quantitative Real-Time PCR Targeting the groEL Gene

Two pairs of species-specific PCR primers targeting the housekeeping groEL gene, Spa146f-Spa525r and Spa93f-Spa525r, were designed to quantify human oral and fecal Streptococcus parasanguinis. Blast analysis against reference sequences of NCBI nucleotide collection database and the Chaperonin Sequence Database showed the forward primers Spa146f and Spa93f 100% matched only with S. parasanguinis, and the in silico Simulated PCR algorithm showed both primer pairs hit only S. parasanguinis groEL gene in Chaperonin Sequence Database. The two primer pairs were respectively used to perform PCR with saliva DNA of each of 6 human subjects, and the amplicons of individual PCR reactions were cloned. The phylogenetic analysis showed cloned sequences were all affiliated to S. parasanguinis, which further validates the specificity of two primer pairs, and that individual subjects harbored multiple genotypes of S. parasanguinis in saliva. By spiking S. parasanguinis into human fecal samples, we found the quantification limit of quantitative real-time PCR (qPCR) assays for both primer pairs was 5-6 log₁₀ groEL copies/g feces. Human fecal S. parasanguinis amounts quantified with qPCR using each of the two primer pairs correlated well with those determined with metagenomic sequencing. qPCR with either primer pair showed periodontitis patients had significantly lower level of saliva S. parasanguinis than healthy people. In both feces and saliva, the S. parasanguinis abundances quantified with two primer pairs exhibited strong and significant correlation. Our results show that the two S. parasanguinis-specific primer pairs can be used to quantify and profile human saliva and fecal S. parasanguinis.

Joint sequencing of human and pathogen genomes reveals the genetics of pneumococcal meningitis

Streptococcus pneumoniae is a common nasopharyngeal colonizer, but can also cause life-threatening invasive diseases such as empyema, bacteremia and meningitis. Genetic variation of host and pathogen is known to play a role in invasive pneumococcal disease, though to what extent is unknown. In a genome-wide association study of human and pathogen we show that human variation explains almost half of variation in susceptibility to pneumococcal meningitis and one-third of variation in severity, identifying variants in CCDC33 associated with susceptibility. Pneumococcal genetic variation explains a large amount of invasive potential (70%), but has no effect on severity. Serotype alone is insufficient to explain invasiveness, suggesting other pneumococcal factors are involved in progression to invasive disease. We identify pneumococcal genes involved in invasiveness including pspC and zmpD, and perform a human-bacteria interaction analysis. These genes are potential candidates for the development of more broadly-acting pneumococcal vaccines.

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

Streptococcus pneumoniae, a normal commensal of the upper respiratory tract, is a major public health concern, responsible for substantial global morbidity and mortality due to pneumonia, meningitis and sepsis. Why some pneumococci invade the bloodstream or CSF (so-called invasive pneumococcal disease; IPD) is uncertain. In this study we identify genes associated with IPD. We transform whole genome sequence (WGS) data into a sequence typing scheme, while avoiding the caveat of using an arbitrary genome as a reference by substituting it with a constructed pangenome. We then employ a random forest machine-learning algorithm on the transformed data, and find 43 genes consistently associated with IPD across three geographically distinct WGS data sets of pneumococcal carriage isolates. Of the genes we identified as associated with IPD, we find 23 genes previously shown to be directly relevant to IPD, as well as 18 uncharacterized genes. We suggest that these uncharacterized genes identified by us are also likely to be relevant for IPD.

Reverse immunodynamics: a new method for identifying targets of protective immunity

Despite a dramatic increase in our ability to catalogue variation among pathogen genomes, we have made far fewer advances in using this information to identify targets of protective immunity. Epidemiological models predict that strong immune selection can cause antigenic variants to structure into genetically discordant sets of antigenic types (e.g. serotypes). A corollary of this theory is that targets of immunity may be identified by searching for non-overlapping associations of amino acids among co-circulating antigenic variants. We propose a novel population genetics methodology that combines such predictions with phylogenetic analyses to identify genetic loci (epitopes) under strong immune selection. We apply this concept to the AMA-1 protein of the malaria parasite Plasmodium falciparum and find evidence of epitopes among certain regions of low variability which could render them ideal vaccine candidates. The proposed method can be applied to a myriad of multi-strain pathogens for which vast amounts of genetic data has been collected in recent years.

Puzzling Over the Pneumococcal Pangenome

The Gram positive bacterium Streptococcus pneumoniae (pneumococcus) is a major human pathogen. It is a common colonizer of the human host, and in the nasopharynx, sinus, and middle ear it survives as a biofilm. This mode of growth is optimal for multi-strain colonization and genetic exchange. Over the last decades, the far-reaching use of antibiotics and the widespread implementation of pneumococcal multivalent conjugate vaccines have posed considerable selective pressure on pneumococci. This scenario provides an exceptional opportunity to study the evolution of the pangenome of a clinically important bacterium, and has the potential to serve as a case study for other species. The goal of this review is to highlight key findings in the studies of pneumococcal genomic diversity and plasticity.

Open-access bacterial population genomics: BIGSdb software, the PubMLST.org website and their applications

The PubMLST.org website hosts a collection of open-access, curated databases that integrate population sequence data with provenance and phenotype information for over 100 different microbial species and genera.  Although the PubMLST website was conceived as part of the development of the first multi-locus sequence typing (MLST) scheme in 1998 the software it uses, the Bacterial Isolate Genome Sequence database (BIGSdb, published in 2010), enables PubMLST to include all levels of sequence data, from single gene sequences up to and including complete, finished genomes.  Here we describe developments in the BIGSdb software made from publication to June 2018 and show how the platform realises microbial population genomics for a wide range of applications.  The system is based on the gene-by-gene analysis of microbial genomes, with each deposited sequence annotated and curated to identify the genes present and systematically catalogue their variation.  Originally intended as a means of characterising isolates with typing schemes, the synthesis of sequences and records of genetic variation with provenance and phenotype data permits highly scalable (whole genome sequence data for tens of thousands of isolates) means of addressing a wide range of functional questions, including: the prediction of antimicrobial resistance; likely cross-reactivity with vaccine antigens; and the functional activities of different variants that lead to key phenotypes.  There are no limitations to the number of sequences, genetic loci, allelic variants or schemes (combinations of loci) that can be included, enabling each database to represent an expanding catalogue of the genetic variation of the population in question.  In addition to providing web-accessible analyses and links to third-party analysis and visualisation tools, the BIGSdb software includes a RESTful application programming interface (API) that enables access to all the underlying data for third-party applications and data analysis pipelines.

The impact of serotype-specific vaccination on phylodynamic parameters of Streptococcus pneumoniae and the pneumococcal pan-genome

In the United States, the introduction of the heptavalent pneumococcal conjugate vaccine (PCV) largely eliminated vaccine serotypes (VT); non-vaccine serotypes (NVT) subsequently increased in carriage and disease. Vaccination also disrupts the composition of the pneumococcal pangenome, which includes mobile genetic elements and polymorphic non-capsular antigens important for virulence, transmission, and pneumococcal ecology. Antigenic proteins are of interest for future vaccines; yet, little is known about how the they are affected by PCV use. To investigate the evolutionary impact of vaccination, we assessed recombination, evolution, and pathogen demographic history of 937 pneumococci collected from 1998-2012 among Navajo and White Mountain Apache Native American communities. We analyzed changes in the pneumococcal pangenome, focusing on metabolic loci and 19 polymorphic protein antigens. We found the impact of PCV on the pneumococcal population could be observed in reduced diversity, a smaller pangenome, and changing frequencies of accessory clusters of orthologous groups (COGs). Post-PCV7, diversity rebounded through clonal expansion of NVT lineages and inferred in-migration of two previously unobserved lineages. Accessory COGs frequencies trended toward pre-PCV7 values with increasing time since vaccine introduction. Contemporary frequencies of protein antigen variants are better predicted by pre-PCV7 values (1998-2000) than the preceding period (2006-2008), suggesting balancing selection may have acted in maintaining variant frequencies in this population. Overall, we present the largest genomic analysis of pneumococcal carriage in the United States to date, which includes a snapshot of a true vaccine-naïve community prior to the introduction of PCV7. These data improve our understanding of pneumococcal evolution and emphasize the need to consider pangenome composition when inferring the impact of vaccination and developing future protein-based pneumococcal vaccines.

Vaccination can drive an increase in frequencies of antibiotic resistance among nonvaccine serotypes of Streptococcus pneumoniae

The bacterial pathogen Streptococcus pneumoniae is a major public health concern, being responsible for more than 1.5 million deaths annually through pneumonia, meningitis, and septicemia. Available vaccines target only a subset of serotypes, so vaccination is often accompanied by a rise in the frequency of nonvaccine serotypes. Epidemiological studies suggest that such a change in serotype frequencies is often coupled with an increase of antibiotic resistance among nonvaccine serotypes. Building on previous multilocus models for bacterial pathogen population structure, we have developed a theoretical framework incorporating variation of serotype and antibiotic resistance to examine how their associations may be affected by vaccination. Using this framework, we find that vaccination can result in a rapid increase in the frequency of preexisting resistant variants of nonvaccine serotypes due to the removal of competition from vaccine serotypes.

Competing species leave many potential niches unfilled

A cornerstone of biology is that coexisting species evolve to occupy separate ecological niches. Classical theory predicts that interspecific competition should lead to all potential niches being occupied, yet observational data suggest that many niches are unfilled. Here we show that theory can be reconciled with observational data by reconceptualising competition in the Hutchinsonian niche space to distinguish between substitutable and non-substitutable resources. When resources are substitutable (e.g. seeds of different size), the components of competition along the niche axes combine multiplicatively, leading to a densely packed niche space. However, when resources are non-substitutable (e.g. seeds and nest sites), we show that the components of competition combine additively. Disruptive selection therefore limits niche overlap between non-substitutable niche axes, leaving most potential niches unfilled. A key corollary is that increasing the number of niche axes may greatly increase the number of potential niches but does not necessarily increase diversity. We discuss observational data that are consistent with our model and consider implications for systems with invasive species. Our work reinforces the power of competition to drive major ecological patterns: while niche space informs on species that might exist, only a small and potentially arbitrary subset will coexist in sympatry.