Transcriptomic buffering of cryptic genetic variation contributes to meningococcal virulence

Classification of Neisseria meningitidis genomes with a bag-of-words approach and machine learning

Whole genome sequencing of bacteria is important to enable strain classification. Using entire genomes as an input to machine learning (ML) models would allow rapid classification of strains while using information from multiple genetic elements. We developed a "bag-of-words" approach to encode, using SentencePiece or k-mer tokenization, entire bacterial genomes and analyze these with ML. Initial model selection identified SentencePiece with 8,000 and 32,000 words as the best approach for genome tokenization. We then classified in Neisseria meningitidis genomes the capsule B group genotype with 99.6% accuracy and the multifactor invasive phenotype with 90.2% accuracy, in an independent test set. Subsequently, in silico knockouts of 2,808 genes confirmed that the ML model predictions aligned with our current understanding of the underlying biology. To our knowledge, this is the first ML method using entire bacterial genomes to classify strains and identify genes considered relevant by the classifier.

The Host-Pathogen Interactions and Epicellular Lifestyle of Neisseria meningitidis

Neisseria meningitidis is a gram-negative diplococcus and a transient commensal of the human nasopharynx. It shares and competes for this niche with a number of other Neisseria species including N. lactamica, N. cinerea and N. mucosa. Unlike these other members of the genus, N. meningitidis may become invasive, crossing the epithelium of the nasopharynx and entering the bloodstream, where it rapidly proliferates causing a syndrome known as Invasive Meningococcal Disease (IMD). IMD progresses rapidly to cause septic shock and meningitis and is often fatal despite aggressive antibiotic therapy. While many of the ways in which meningococci survive in the host environment have been well studied, recent insights into the interactions between N. meningitidis and the epithelial, serum, and endothelial environments have expanded our understanding of how IMD develops. This review seeks to incorporate recent work into the established model of pathogenesis. In particular, we focus on the competition that N. meningitidis faces in the nasopharynx from other Neisseria species, and how the genetic diversity of the meningococcus contributes to the wide range of inflammatory and pathogenic potentials observed among different lineages.

Phenotype-Based Threat Assessment

Assessing the threat posed by bacterial samples is fundamentally important to safeguarding human health. Whole-genome sequence analysis of bacteria provides a route to achieving this goal. However, this approach is fundamentally constrained by the scope, the diversity, and our understanding of the bacterial genome sequences that are available for devising threat assessment schemes. For example, genome-based strategies offer limited utility for assessing the threat associated with pathogens that exploit novel virulence mechanisms or are recently emergent. To address these limitations, we developed PathEngine, a machine learning strategy that features the use of phenotypic hallmarks of pathogenesis to assess pathogenic threat. PathEngine successfully classified potential pathogenic threats with high accuracy and thereby establishes a phenotype-based, sequence-independent pipeline for threat assessment.

Bacterial pathogen identification, which is critical for human health, has historically relied on culturing organisms from clinical specimens. More recently, the application of machine learning (ML) to whole-genome sequences (WGSs) has facilitated pathogen identification. However, relying solely on genetic information to identify emerging or new pathogens is fundamentally constrained, especially if novel virulence factors exist. In addition, even WGSs with ML pipelines are unable to discern phenotypes associated with cryptic genetic loci linked to virulence. Here, we set out to determine if ML using phenotypic hallmarks of pathogenesis could assess potential pathogenic threat without using any sequence-based analysis. This approach successfully classified potential pathogenetic threat associated with previously machine-observed and unobserved bacteria with 99% and 85% accuracy, respectively. This work establishes a phenotype-based pipeline for potential pathogenic threat assessment, which we term PathEngine, and offers strategies for the identification of bacterial pathogens.

Staphylococcusaureus Transcriptome Data and Metabolic Modelling Investigate the Interplay of Ser/Thr Kinase PknB, Its Phosphatase Stp, the glmR/yvcK Regulon and the cdaA Operon for Metabolic Adaptation

Serine/threonine kinase PknB and its corresponding phosphatase Stp are important regulators of many cell functions in the pathogen S. aureus. Genome-scale gene expression data of S. aureus strain NewHG (sigB⁺) elucidated their effect on physiological functions. Moreover, metabolic modelling from these data inferred metabolic adaptations. We compared wild-type to deletion strains lacking pknB, stp or both. Ser/Thr phosphorylation of target proteins by PknB switched amino acid catabolism off and gluconeogenesis on to provide the cell with sufficient components. We revealed a significant impact of PknB and Stp on peptidoglycan, nucleotide and aromatic amino acid synthesis, as well as catabolism involving aspartate transaminase. Moreover, pyrimidine synthesis was dramatically impaired by stp deletion but only slightly by functional loss of PknB. In double knockouts, higher activity concerned genes involved in peptidoglycan, purine and aromatic amino acid synthesis from glucose but lower activity of pyrimidine synthesis from glucose compared to the wild type. A second transcriptome dataset from S. aureus NCTC 8325 (sigB⁻) validated the predictions. For this metabolic adaptation, PknB was found to interact with CdaA and the yvcK/glmR regulon. The involved GlmR structure and the GlmS riboswitch were modelled. Furthermore, PknB phosphorylation lowered the expression of many virulence factors, and the study shed light on S. aureus infection processes.

LsrB, the hub of ABC transporters involved in the membrane damage mechanisms of heavy ion irradiation in Escherichia coli

BACKGROUND

Ionizing radiation, especially heavy ion (HI) beams, has been widely used in biology and medicine. However, the mechanism of membrane damage by such radiation remains primarily uncharacterized.

PURPOSE

Transcriptomic profiles of Escherichia coli (E. coli) treated with HI illustrated the response mechanisms of the membrane, mainly ABC transporters, related genes regulated by antibiotics treatment through enrichment analyses of GO and KEGG. The networks of protein-protein interactions indicated that LsrB was the crucial one among the ABC transporters specially regulated by HI through the calculation of plugins MCODE and cytoHubba of Cytoscape. Finally, the expression pattern, GO/KEGG enrichment terms, and the interaction between nine LuxS/AI-2 quorum sensing system members were investigated.

CONCLUSIONS

Above all, results suggested that HI might perform membrane damage through regulated material transport, inhibited LuxS/AI-2 system, finally impeded biofilm formation. This work provides further evidence for the role of ABC transporters, especially LsrB, in membrane damage of E. coli to HI. It will provide new strategies for improving the precise application of HI.

Modeling of stringent-response reflects nutrient stress induced growth impairment and essential amino acids in different Staphylococcus aureus mutants

Stapylococcus aureus colonises the nose of healthy individuals but can also cause a wide range of infections. Amino acid (AA) synthesis and their availability is crucial to adapt to conditions encountered in vivo. Most S. aureus genomes comprise all genes required for AA biosynthesis. Nevertheless, different strains require specific sets of AAs for growth. In this study we show that regulation inactivates pathways under certain conditions which result in these observed auxotrophies. We analyzed in vitro and modeled in silico in a Boolean semiquantitative model (195 nodes, 320 edges) the regulatory impact of stringent response (SR) on AA requirement in S. aureus HG001 (wild-type) and in mutant strains lacking the metabolic regulators RSH, CodY and CcpA, respectively. Growth in medium lacking single AAs was analyzed. Results correlated qualitatively to the in silico predictions of the final model in 92% and quantitatively in 81%. Remaining gaps in our knowledge are evaluated and discussed. This in silico model is made fully available and explains how integration of different inputs is achieved in SR and AA metabolism of S. aureus. The in vitro data and in silico modeling stress the role of SR and central regulators such as CodY for AA metabolisms in S. aureus.

The Meningococcal Cysteine Transport System Plays a Crucial Role in Neisseria meningitidis Survival in Human Brain Microvascular Endothelial Cells

Neisseria meningitidis colonizes at a nasopharynx of human as a unique host and has many strains that are auxotrophs for amino acids for their growth. To cause invasive meningococcal diseases (IMD) such as sepsis and meningitis, N. meningitidis passes through epithelial and endothelial barriers and infiltrates into blood and cerebrospinal fluid as well as epithelial and endothelial cells. However, meningococcal nutrients, including cysteine, become less abundant when it more deeply infiltrates the human body even during inflammation, such that N. meningitidis has to acquire nutrients in order to survive/persist, disseminate, and proliferate in humans. This was the first study to examine the relationship between meningococcal cysteine acquisition and the pathogenesis of meningococcal infections. The results of the present study provide insights into the mechanisms by which pathogens with auxotrophs acquire nutrients in hosts and may also contribute to the development of treatments and prevention strategies for IMD.

While Neisseria meningitidis typically exists in an asymptomatic nasopharyngeal carriage state, it may cause potentially lethal diseases in humans, such as septicemia or meningitis, by invading deeper sites in the body. Since the nutrient compositions of human cells are not always conducive to meningococci, N. meningitidis needs to exploit nutrients from host environments. In the present study, the utilization of cysteine by the meningococcal cysteine transport system (CTS) was analyzed for the pathogenesis of meningococcal infections. A N. meningitidis strain deficient in one of the three cts genes annotated as encoding cysteine-binding protein (cbp) exhibited approximately 100-fold less internalization into human brain microvascular endothelial cells (HBMEC) than the wild-type strain. This deficiency was restored by complementation with the three cts genes together, and the infectious phenotype of HBMEC internalization correlated with cysteine uptake activity. However, efficient accumulation of ezrin was observed beneath the cbp mutant. The intracellular survival of the cbp mutant in HBMEC was markedly reduced, whereas equivalent reductions of glutathione concentrations and of resistance to reactive oxygens species in the cbp mutant were not found. The cbp mutant grew well in complete medium but not in synthetic medium supplemented with less than 300 μM cysteine. Taking cysteine concentrations in human cells and other body fluids, including blood and cerebrospinal fluid, into consideration, the present results collectively suggest that the meningococcal CTS is crucial for the acquisition of cysteine from human cells and participates in meningococcal nutrient virulence.

Anticoagulants impact on innate immune responses and bacterial survival in whole blood models of Neisseria meningitidis infection

Neisseria meningitidis (meningococcus) causes invasive diseases such as meningitis or septicaemia. Ex vivo infection of human whole blood is a valuable tool to study meningococcal virulence factors and the host innate immune responses. In order to consider effects of cellular mediators, the coagulation cascade must be inhibited to avoid clotting. There is considerable variation in the anticoagulants used among studies of N. meningitidis whole blood infections, featuring citrate, heparin or derivatives of hirudin, a polypeptide from leech saliva. Here, we compare the influence of these three different anticoagulants, and additionally Mg/EGTA, on host innate immune responses as well as on viability of N. meningitidis strains isolated from healthy carriers and disease cases, reflecting different sequence types and capsule phenotypes. We found that the anticoagulants significantly impact on cellular responses and, strain-dependently, also on bacterial survival. Hirudin does not inhibit complement and is therefore superior over the other anticoagulants; indeed hirudin-plasma most closely reflects the characteristics of serum during N. meningitidis infection. We further demonstrate the impact of heparin on complement activation on N. meningitidis and its consequences on meningococcal survival in immune sera, which appears to be independent of the heparin binding antigens Opc and NHBA.

Neisseria genomics: current status and future perspectives

High-throughput whole genome sequencing has unlocked a multitude of possibilities enabling members of the Neisseria genus to be examined with unprecedented detail, including the human pathogens Neisseria meningitidis and Neisseria gonorrhoeae. To maximise the potential benefit of this for public health, it is becoming increasingly important to ensure that this plethora of data are adequately stored, disseminated and made readily accessible. Investigations facilitating cross-species comparisons as well as the analysis of global datasets will allow differences among and within species and across geographic locations and different times to be identified, improving our understanding of the distinct phenotypes observed. Recent advances in high-throughput platforms that measure the transcriptome, proteome and/or epigenome are also becoming increasingly employed to explore the complexities of Neisseria biology. An integrated approach to the analysis of these is essential to fully understand the impact these may have in the Neisseria genus. This article reviews the current status of some of the tools available for next generation sequence analysis at the dawn of the ‘post-genomic’ era.

Interplay Between Virulence and Variability Factors as a Potential Driver of Invasive Meningococcal Disease

Neisseria meningitidis (Nm) is frequently found in the upper respiratory tract of the human population. Despite its prevalence as a commensal organism, Nm can occasionally invade the pharyngeal mucosal epithelium causing septicemia and life-threatening disease. A number of studies have tried to identify factors that are responsible for the onset of a virulent phenotype. Despite this however, we still miss clear causative elements. Several factors have been identified to be associated to an increased susceptibility to meningococcal disease in humans. None of them, however, could unambiguously discriminate healthy carrier from infected individuals. Similarly, comparative studies of virulent and apathogenic strains failed to identify virulence factors that could explain the emergence of the pathogenic phenotype. In line with this, a recent study of within host evolution found that Nm accumulates genomic changes during the asymptomatic carriage phase and that these are likely to contribute to the shift to a pathogenic phenotype. These results suggest that the presence of virulence factors in the meningococcal genome is not a sufficient condition for developing virulent traits, but is rather the ability to promote phenotypic variation, through the stochastic assortment of the repertoire of such factors, which could explain the occasional and unpredictable onset of IMD. Here, we present a series of argumentations supporting the hypothesis that invasive meningococcal disease comes as a result of the coexistence of bacterial virulence and variability factors in a plot that can be further complicated by additional latent factors, like host pre-existing immune status and genetic predisposition.