A multi-omic systems approach to elucidating Yersinia virulence mechanisms

Yersiniomics, a Multi-Omics Interactive Database for Yersinia Species

The genus Yersinia includes a large variety of nonpathogenic and life-threatening pathogenic bacteria, which cause a broad spectrum of diseases in humans and animals, such as plague, enteritis, Far East scarlet-like fever (FESLF), and enteric redmouth disease. Like most clinically relevant microorganisms, Yersinia spp. are currently subjected to intense multi-omics investigations whose numbers have increased extensively in recent years, generating massive amounts of data useful for diagnostic and therapeutic developments. The lack of a simple and centralized way to exploit these data led us to design Yersiniomics, a web-based platform allowing straightforward analysis of Yersinia omics data. Yersiniomics contains a curated multi-omics database at its core, gathering 200 genomic, 317 transcriptomic, and 62 proteomic data sets for Yersinia species. It integrates genomic, transcriptomic, and proteomic browsers, a genome viewer, and a heatmap viewer to navigate within genomes and experimental conditions. For streamlined access to structural and functional properties, it directly links each gene to GenBank, the Kyoto Encyclopedia of Genes and Genomes (KEGG), UniProt, InterPro, IntAct, and the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and each experiment to Gene Expression Omnibus (GEO), the European Nucleotide Archive (ENA), or the Proteomics Identifications Database (PRIDE). Yersiniomics provides a powerful tool for microbiologists to assist with investigations ranging from specific gene studies to systems biology studies. IMPORTANCE The expanding genus Yersinia is composed of multiple nonpathogenic species and a few pathogenic species, including the deadly etiologic agent of plague, Yersinia pestis. In 2 decades, the number of genomic, transcriptomic, and proteomic studies on Yersinia grew massively, delivering a wealth of data. We developed Yersiniomics, an interactive web-based platform, to centralize and analyze omics data sets on Yersinia species. The platform allows user-friendly navigation between genomic data, expression data, and experimental conditions. Yersiniomics will be a valuable tool to microbiologists.

Media matters! Alterations in the loading and release of Histoplasma capsulatum extracellular vesicles in response to different nutritional milieus

Histoplasma capsulatum is a dimorphic fungus that most frequently causes pneumonia, but can also disseminate and proliferate in diverse tissues. Histoplasma capsulatum has a complex secretion system that mediates the release of macromolecule-degrading enzymes and virulence factors. The formation and release of extracellular vesicles (EVs) are an important mechanism for non-conventional secretion in both ascomycetes and basidiomycetes. Histoplasma capsulatum EVs contain diverse proteins associated with virulence and are immunologically active. Despite the growing knowledge of EVs from H. capsulatum and other pathogenic fungi, the extent that changes in the environment impact the sorting of organic molecules in EVs has not been investigated. In this study, we cultivated H. capsulatum with distinct culture media to investigate the potential plasticity in EV loading in response to differences in nutrition. Our findings reveal that nutrition plays an important role in EV loading and formation, which may translate into differences in biological activities of these fungi in various fluids and tissues.

Unified feature association networks through integration of transcriptomic and proteomic data

High-throughput multi-omics studies and corresponding network analyses of multi-omic data have rapidly expanded their impact over the last 10 years. As biological features of different types (e.g. transcripts, proteins, metabolites) interact within cellular systems, the greatest amount of knowledge can be gained from networks that incorporate multiple types of -omic data. However, biological and technical sources of variation diminish the ability to detect cross-type associations, yielding networks dominated by communities comprised of nodes of the same type. We describe here network building methods that can maximize edges between nodes of different data types leading to integrated networks, networks that have a large number of edges that link nodes of different-omic types (transcripts, proteins, lipids etc). We systematically rank several network inference methods and demonstrate that, in many cases, using a random forest method, GENIE3, produces the most integrated networks. This increase in integration does not come at the cost of accuracy as GENIE3 produces networks of approximately the same quality as the other network inference methods tested here. Using GENIE3, we also infer networks representing antibody-mediated Dengue virus cell invasion and receptor-mediated Dengue virus invasion. A number of functional pathways showed centrality differences between the two networks including genes responding to both GM-CSF and IL-4, which had a higher centrality value in an antibody-mediated vs. receptor-mediated Dengue network. Because a biological system involves the interplay of many different types of molecules, incorporating multiple data types into networks will improve their use as models of biological systems. The methods explored here are some of the first to specifically highlight and address the challenges associated with how such multi-omic networks can be assembled and how the greatest number of interactions can be inferred from different data types. The resulting networks can lead to the discovery of new host response patterns and interactions during viral infection, generate new hypotheses of pathogenic mechanisms and confirm mechanisms of disease.

Multiple mechanisms drive phage infection efficiency in nearly identical hosts

Phage-host interactions are critical to ecology, evolution, and biotechnology. Central to those is infection efficiency, which remains poorly understood, particularly in nature. Here we apply genome-wide transcriptomics and proteomics to investigate infection efficiency in nature's own experiment: two nearly identical (genetically and physiologically) Bacteroidetes bacterial strains (host18 and host38) that are genetically intractable, but environmentally important, where phage infection efficiency varies. On host18, specialist phage phi18:3 infects efficiently, whereas generalist phi38:1 infects inefficiently. On host38, only phi38:1 infects, and efficiently. Overall, phi18:3 globally repressed host18's transcriptome and proteome, expressed genes that likely evaded host restriction/modification (R/M) defenses and controlled its metabolism, and synchronized phage transcription with translation. In contrast, phi38:1 failed to repress host18's transcriptome and proteome, did not evade host R/M defenses or express genes for metabolism control, did not synchronize transcripts with proteins and its protein abundances were likely targeted by host proteases. However, on host38, phi38:1 globally repressed host transcriptome and proteome, synchronized phage transcription with translation, and infected host38 efficiently. Together these findings reveal multiple infection inefficiencies. While this contrasts the single mechanisms often revealed in laboratory mutant studies, it likely better reflects the phage-host interaction dynamics that occur in nature.

The MPLEx Protocol for Multi-omic Analyses of Soil Samples

Mass spectrometry (MS)-based integrated metaproteomic, metabolomic, and lipidomic (multi-omic) studies are transforming our ability to understand and characterize microbial communities in environmental and biological systems. These measurements are even enabling enhanced analyses of complex soil microbial communities, which are the most complex microbial systems known to date. Multi-omic analyses, however, do have sample preparation challenges, since separate extractions are typically needed for each omic study, thereby greatly amplifying the preparation time and amount of sample required. To address this limitation, a 3-in-1 method for the simultaneous extraction of metabolites, proteins, and lipids (MPLEx) from the same soil sample was created by adapting a solvent-based approach. This MPLEx protocol has proven to be both simple and robust for many sample types, even when utilized for limited quantities of complex soil samples. The MPLEx method also greatly enabled the rapid multi-omic measurements needed to gain a better understanding of the members of each microbial community, while evaluating the changes taking place upon biological and environmental perturbations.

Protein abundances can distinguish between naturally-occurring and laboratory strains of Yersinia pestis, the causative agent of plague

The rapid pace of bacterial evolution enables organisms to adapt to the laboratory environment with repeated passage and thus diverge from naturally-occurring environmental ("wild") strains. Distinguishing wild and laboratory strains is clearly important for biodefense and bioforensics; however, DNA sequence data alone has thus far not provided a clear signature, perhaps due to lack of understanding of how diverse genome changes lead to convergent phenotypes, difficulty in detecting certain types of mutations, or perhaps because some adaptive modifications are epigenetic. Monitoring protein abundance, a molecular measure of phenotype, can overcome some of these difficulties. We have assembled a collection of Yersinia pestis proteomics datasets from our own published and unpublished work, and from a proteomics data archive, and demonstrated that protein abundance data can clearly distinguish laboratory-adapted from wild. We developed a lasso logistic regression classifier that uses binary (presence/absence) or quantitative protein abundance measures to predict whether a sample is laboratory-adapted or wild that proved to be ~98% accurate, as judged by replicated 10-fold cross-validation. Protein features selected by the classifier accord well with our previous study of laboratory adaptation in Y. pestis. The input data was derived from a variety of unrelated experiments and contained significant confounding variables. We show that the classifier is robust with respect to these variables. The methodology is able to discover signatures for laboratory facility and culture medium that are largely independent of the signature of laboratory adaptation. Going beyond our previous laboratory evolution study, this work suggests that proteomic differences between laboratory-adapted and wild Y. pestis are general, potentially pointing to a process that could apply to other species as well. Additionally, we show that proteomics datasets (even archived data collected for different purposes) contain the information necessary to distinguish wild and laboratory samples. This work has clear applications in biomarker detection as well as biodefense.

A novel targeted/untargeted GC-Orbitrap metabolomics methodology applied to Candida albicans and Staphylococcus aureus biofilms

INTRODUCTION

Combined infections from Candida albicans and Staphylococcus aureus are a leading cause of death in the developed world. Evidence suggests that Candida enhances the virulence of Staphylococcus-hyphae penetrate through tissue barriers, while S. aureus tightly associates with the hyphae to obtain entry to the host organism. Indeed, in a biofilm state, C. albicans enhances the antimicrobial resistance characteristics of S. aureus. The association of these microorganisms is also associated with significantly increased morbidity and mortality. Due to this tight association we hypothesised that metabolic effects were also in evidence.

OBJECTIVES

To explore the interaction, we used a novel GC-Orbitrap-based mass spectrometer, the Q Exactive GC, which combines the high peak capacity and chromatographic resolution of gas chromatography with the sub-ppm mass accuracy of an Orbitrap system. This allows the capability to leverage the widely available electron ionisation libraries for untargeted applications, along with expanding accurate mass libraries and targeted matches based around authentic standards.

METHODS

Optimised C. albicans and S. aureus mono- and co-cultured biofilms were analysed using the new instrument in addition to the fresh and spent bacterial growth media.

RESULTS

The targeted analysis experiment was based around 36 sugars and sugar phosphates, 22 amino acids and five organic acids. Untargeted analysis resulted in the detection of 465 features from fresh and spent medium and 405 from biofilm samples. Three significantly changing compounds that matched to high scoring library fragment patterns were chosen for validation.

CONCLUSION

Evaluation of the results demonstrates that the Q Exactive GC is suitable for metabolomics analysis using a targeted/untargeted methodology. Many of the results were as expected: e.g. rapid consumption of glucose and fructose from the medium regardless of the cell type. Modulation of sugar-phosphate levels also suggest that the pentose phosphate pathway could be enhanced in the cells from co-cultured biofilms. Untargeted metabolomics results suggested significant production of cell-wall biosynthesis components and the consumption of non-proteinaceous amino-acids.

Yersinia adhesins: An arsenal for infection

The Yersiniae are a group of Gram-negative coccobacilli inhabiting a wide range of habitats. The genus harbors three recognized human pathogens: Y. enterocolitica and Y. pseudotuberculosis, which both cause gastrointestinal disease, and Y. pestis, the causative agent of plague. These three organisms have served as models for a number of aspects of infection biology, including adhesion, immune evasion, evolution of pathogenic traits, and retracing the course of ancient pandemics. The virulence of the pathogenic Yersiniae is heavily dependent on a number of adhesin molecules. Some of these, such as the Yersinia adhesin A and invasin of the enteropathogenic species, and the pH 6 antigen of Y. pestis, have been extensively studied. However, genomic sequencing has uncovered a host of other adhesins present in these organisms, the functions of which are only starting to be investigated. Here, we review the current state of knowledge on the adhesin molecules present in the Yersiniae, and their functions and putative roles in the infection process.

Proteomics As a Tool for Studying Bacterial Virulence and Antimicrobial Resistance

Proteomic studies have improved our understanding of the microbial world. The most recent advances in this field have helped us to explore aspects beyond genomics. For example, by studying proteins and their regulation, researchers now understand how some pathogenic bacteria have adapted to the lethal actions of antibiotics. Proteomics has also advanced our knowledge of mechanisms of bacterial virulence and some important aspects of how bacteria interact with human cells and, thus, of the pathogenesis of infectious diseases. This review article addresses these issues in some of the most important human pathogens. It also reports some applications of Matrix-Assisted Laser Desorption/Ionization-Time-Of-Flight (MALDI-TOF) mass spectrometry that may be important for the diagnosis of bacterial resistance in clinical laboratories in the future. The reported advances will enable new diagnostic and therapeutic strategies to be developed in the fight against some of the most lethal bacteria affecting humans.

Environmental Regulation of Yersinia Pathophysiology

Hallmarks of Yersinia pathogenesis include the ability to form biofilms on surfaces, the ability to establish close contact with eukaryotic target cells and the ability to hijack eukaryotic cell signaling and take over control of strategic cellular processes. Many of these virulence traits are already well-described. However, of equal importance is knowledge of both confined and global regulatory networks that collaborate together to dictate spatial and temporal control of virulence gene expression. This review has the purpose to incorporate historical observations with new discoveries to provide molecular insight into how some of these regulatory mechanisms respond rapidly to environmental flux to govern tight control of virulence gene expression by pathogenic Yersinia.

Analysis of differentially expressed proteins in Yersinia enterocolitica-infected HeLa cells

Yersinia enterocolitica is a facultative intracellular pathogen and a causative agent of yersiniosis, which can be contracted by ingestion of contaminated food. Yersinia secretes virulence factors to subvert critical pathways in the host cell. In this study we utilized shotgun label-free proteomics to study differential protein expression in epithelial cells infected with Y.enterocolitica. We identified a total of 551 proteins, amongst which 42 were downregulated (including Prostaglandin E Synthase 3, POH-1 and Karyopherin alpha) and 22 were upregulated (including Rab1 and RhoA) in infected cells. We validated some of these results by western blot analysis of proteins extracted from Caco-2 and HeLa cells. The proteomic dataset was used to identify host canonical pathways and molecular functions modulated by this infection in the host cells. This study constitutes a proteome of Yersinia-infected cells and can support new discoveries in the area of host-pathogen interactions.

STATEMENT OF SIGNIFICANCE OF THE STUDY

We describe a proteome of Yersinia enterocolitica-infected HeLa cells, including a description of specific proteins differentially expressed upon infection, molecular functions as well as pathways altered during infection. This proteomic study can lead to a better understanding of Y. enterocolitica pathogenesis in human epithelial cells.

Yersinia pestis in the Age of Big Data

As omics-driven technologies developed rapidly, genomics, transcriptomics, proteomics, metabolomics and other omics-based data have been accumulated in unprecedented speed. Omics-driven big data in biology have changed our way of research. "Big science" has promoted our understanding of biology in a holistic overview that is impossibly achieved by traditional hypothesis-driven research. In this chapter, we gave an overview of omics-driven research on Y. pestis, provided a way of thinking on Yersinia pestis research in the age of big data, and made some suggestions to integrate omics-based data for systems understanding of Y. pestis.

Investigation of Yersinia pestis Laboratory Adaptation through a Combined Genomics and Proteomics Approach

The bacterial pathogen Yersinia pestis, the cause of plague in humans and animals, normally has a sylvatic lifestyle, cycling between fleas and mammals. In contrast, laboratory-grown Y. pestis experiences a more constant environment and conditions that it would not normally encounter. The transition from the natural environment to the laboratory results in a vastly different set of selective pressures, and represents what could be considered domestication. Understanding the kinds of adaptations Y. pestis undergoes as it becomes domesticated will contribute to understanding the basic biology of this important pathogen.

In this study, we performed a parallel serial passage experiment (PSPE) to explore the mechanisms by which Y. pestis adapts to laboratory conditions, hypothesizing that cells would undergo significant changes in virulence and nutrient acquisition systems. Two wild strains were serially passaged in 12 independent populations each for ~750 generations, after which each population was analyzed using whole-genome sequencing, LC-MS/MS proteomic analysis, and GC/MS metabolomics. We observed considerable parallel evolution in the endpoint populations, detecting multiple independent mutations in ail, pepA, and zwf, suggesting that specific selective pressures are shaping evolutionary responses. Complementary LC-MS/MS proteomic data provide physiological context to the observed mutations, and reveal regulatory changes not necessarily associated with specific mutations, including changes in amino acid metabolism and cell envelope biogenesis. Proteomic data support hypotheses generated by genomic data in addition to suggesting future mechanistic studies, indicating that future whole-genome sequencing studies be designed to leverage proteomics as a critical complement.

From genome-scale data to models of infectious disease: A Bayesian network-based strategy to drive model development

High-throughput, genome-scale data present a unique opportunity to link host to pathogen on a molecular level. Forging such connections will help drive the development of mathematical models to better understand and predict both pathogen behavior and the epidemiology of infectious diseases, including malaria. However, the datasets that can aid in identifying these links and models are vast and not amenable to simple, reductionist, and univariate analyses. These datasets require data mining in order to identify the truly important measurements that best describe clinical and molecular observations. Moreover, these datasets typically have relatively few samples due to experimental limitations (particularly for human studies or in vivo animal experiments), making data mining extremely difficult. Here, after first providing a brief overview of common strategies for data reduction and identification of relationships between variables for inclusion in mathematical models, we present a new generalized strategy for performing these data reduction and relationship inference tasks. Our approach emphasizes the importance of robustness when using data to drive model development, particularly when using genome-scale, small-sample in vivo data. We identify the use of appropriate feature reduction combined with data permutations and subsampling strategies as being critical to enable increasingly robust results from network inference using high-dimensional, low-observation data.

Multi -omics and metabolic modelling pipelines: challenges and tools for systems microbiology

Integrated -omics approaches are quickly spreading across microbiology research labs, leading to (i) the possibility of detecting previously hidden features of microbial cells like multi-scale spatial organization and (ii) tracing molecular components across multiple cellular functional states. This promises to reduce the knowledge gap between genotype and phenotype and poses new challenges for computational microbiologists. We underline how the capability to unravel the complexity of microbial life will strongly depend on the integration of the huge and diverse amount of information that can be derived today from -omics experiments. In this work, we present opportunities and challenges of multi -omics data integration in current systems biology pipelines. We here discuss which layers of biological information are important for biotechnological and clinical purposes, with a special focus on bacterial metabolism and modelling procedures. A general review of the most recent computational tools for performing large-scale datasets integration is also presented, together with a possible framework to guide the design of systems biology experiments by microbiologists.

Acclimatisation-induced stress influenced host metabolic and gut microbial composition change

An integrated metabonomics and metagenomics approach utilised here showed that acclimatisation-induced stress leads to host metabolic and gut microbiotal changes.

Coregulation of host-adapted metabolism and virulence by pathogenic yersiniae

Deciphering the principles how pathogenic bacteria adapt their metabolism to a specific host microenvironment is critical for understanding bacterial pathogenesis. The enteric pathogenic Yersinia species Yersinia pseudotuberculosis and Yersinia enterocolitica and the causative agent of plague, Yersinia pestis, are able to survive in a large variety of environmental reservoirs (e.g., soil, plants, insects) as well as warm-blooded animals (e.g., rodents, pigs, humans) with a particular preference for lymphatic tissues. In order to manage rapidly changing environmental conditions and interbacterial competition, Yersinia senses the nutritional composition during the course of an infection by special molecular devices, integrates this information and adapts its metabolism accordingly. In addition, nutrient availability has an impact on expression of virulence genes in response to C-sources, demonstrating a tight link between the pathogenicity of yersiniae and utilization of nutrients. Recent studies revealed that global regulatory factors such as the cAMP receptor protein (Crp) and the carbon storage regulator (Csr) system are part of a large network of transcriptional and posttranscriptional control strategies adjusting metabolic changes and virulence in response to temperature, ion and nutrient availability. Gained knowledge about the specific metabolic requirements and the correlation between metabolic and virulence gene expression that enable efficient host colonization led to the identification of new potential antimicrobial targets.

Cell physiology of the biotechnological relevant bacterium Bacillus pumilus-an omics-based approach

Members of the species Bacillus pumilus get more and more in focus of the biotechnological industry as potential new production strains. Based on exoproteome analysis, B. pumilus strain Jo2, possessing a high secretion capability, was chosen for an omics-based investigation. The proteome and metabolome of B. pumilus cells growing either in minimal or complex medium was analyzed. In total, 1542 proteins were identified in growing B. pumilus cells, among them 1182 cytosolic proteins, 297 membrane and lipoproteins and 63 secreted proteins. This accounts for about 43% of the 3616 proteins encoded in the B. pumilus Jo2 genome sequence. By using GC-MS, IP-LC/MS and H NMR methods numerous metabolites were analyzed and assigned to reconstructed metabolic pathways. In the genome sequence a functional secretion system including the components of the Sec- and Tat-secretion machinery was found. Analysis of the exoproteome revealed secretion of about 70 proteins with predicted secretion signals. In addition, selected production-relevant genome features such as restriction modification systems and NRPS clusters of B. pumilus Jo2 are discussed.

The pyruvate-tricarboxylic acid cycle node: a focal point of virulence control in the enteric pathogen Yersinia pseudotuberculosis

Despite our increasing knowledge of the specific pathogenicity factors in bacteria, the contribution of metabolic processes to virulence is largely unknown. Here, we elucidate a tight connection between pathogenicity and core metabolism in the enteric pathogen Yersinia pseudotuberculosis by integrated transcriptome and [(13)C]fluxome analysis of the wild type and virulence-regulator mutants. During aerobic growth on glucose, Y. pseudotuberculosis reveals an unusual flux distribution with a high level of secreted pyruvate. The absence of the transcriptional and post-transcriptional regulators RovA, CsrA, and Crp strongly perturbs the fluxes of carbon core metabolism at the level of pyruvate metabolism and the tricarboxylic acid (TCA) cycle, and these perturbations are accompanied by transcriptional changes in the corresponding enzymes. Knock-outs of regulators of this metabolic branch point and of its central enzyme, pyruvate kinase (ΔpykF), result in mutants with significantly reduced virulence in an oral mouse infection model. In summary, our work identifies the pyruvate-TCA cycle node as a focal point for controlling the host colonization and virulence of Yersinia.

Drug repurposing: a better approach for infectious disease drug discovery?

•

Infectious disease investigators need to embrace new drug repurposing approaches.

•

Key components include public databases and predictive computational methods.

•

This approach could significantly reduce cost and time for drug development.

•

Support for large scale drug and disease phenotype screening is essential.

The advent of publicly available databases containing system-wide phenotypic data of the host response to both drugs and pathogens, in conjunction with bioinformatics and computational methods now allows for in silico predictions of FDA-approved drugs as treatments against infection diseases. This systems biology approach captures the complexity of both the pathogen and drug host response in the form of expression patterns or molecular interaction networks without having to understand the underlying mechanisms of action. These drug repurposing techniques have been successful in identifying new drug candidates for several types of cancers and were recently used to identify potential therapeutics against influenza, the newly discovered Middle Eastern Respiratory Syndrome coronavirus and several parasitic diseases. These new approaches have the potential to significantly reduce both the time and cost for infectious diseases drug discovery.

GIM3E: condition-specific models of cellular metabolism developed from metabolomics and expression data

MOTIVATION

Genome-scale metabolic models have been used extensively to investigate alterations in cellular metabolism. The accuracy of these models to represent cellular metabolism in specific conditions has been improved by constraining the model with omics data sources. However, few practical methods for integrating metabolomics data with other omics data sources into genome-scale models of metabolism have been developed.

RESULTS

GIM(3)E (Gene Inactivation Moderated by Metabolism, Metabolomics and Expression) is an algorithm that enables the development of condition-specific models based on an objective function, transcriptomics and cellular metabolomics data. GIM(3)E establishes metabolite use requirements with metabolomics data, uses model-paired transcriptomics data to find experimentally supported solutions and provides calculations of the turnover (production/consumption) flux of metabolites. GIM(3)E was used to investigate the effects of integrating additional omics datasets to create increasingly constrained solution spaces of Salmonella Typhimurium metabolism during growth in both rich and virulence media. This integration proved to be informative and resulted in a requirement of additional active reactions (12 in each case) or metabolites (26 or 29, respectively). The addition of constraints from transcriptomics also impacted the allowed solution space, and the cellular metabolites with turnover fluxes that were necessarily altered by the change in conditions increased from 118 to 271 of 1397.

AVAILABILITY

GIM(3)E has been implemented in Python and requires a COBRApy 0.2.x. The algorithm and sample data described here are freely available at: http://opencobra.sourceforge.net/

CONTACTS

brianjamesschmidt@gmail.com

Omics strategies for revealing Yersinia pestis virulence

Omics has remarkably changed the way we investigate and understand life. Omics differs from traditional hypothesis-driven research because it is a discovery-driven approach. Mass datasets produced from omics-based studies require experts from different fields to reveal the salient features behind these data. In this review, we summarize omics-driven studies to reveal the virulence features of Yersinia pestis through genomics, trascriptomics, proteomics, interactomics, etc. These studies serve as foundations for further hypothesis-driven research and help us gain insight into Y. pestis pathogenesis.