Proteopathogen, a protein database for studyingCandida albicans- host interaction

PHILM2Web: A high-throughput database of macromolecular host–pathogen interactions on the Web

During infection, the pathogen’s entry into the host organism, breaching the host immune defense, spread and multiplication are frequently mediated by multiple interactions between the host and pathogen proteins. Systematic studying of host–pathogen interactions (HPIs) is a challenging task for both experimental and computational approaches and is critically dependent on the previously obtained knowledge about these interactions found in the biomedical literature. While several HPI databases exist that manually filter HPI protein–protein interactions from the generic databases and curated experimental interactomic studies, no comprehensive database on HPIs obtained from the biomedical literature is currently available. Here, we introduce a high-throughput literature-mining platform for extracting HPI data that includes the most comprehensive to date collection of HPIs obtained from the PubMed abstracts. Our HPI data portal, PHILM2Web (Pathogen–Host Interactions by Literature Mining on the Web), integrates an automatically generated database of interactions extracted by PHILM, our high-precision HPI literature-mining algorithm. Currently, the database contains 23 581 generic HPIs between 157 host and 403 pathogen organisms from 11 609 abstracts. The interactions were obtained from processing 608 972 PubMed abstracts, each containing mentions of at least one host and one pathogen organisms. In response to the coronavirus disease 2019 (COVID-19) pandemic, we also utilized PHILM to process 25 796 PubMed abstracts obtained by the same query as the COVID-19 Open Research Dataset. This COVID-19 processing batch resulted in 257 HPIs between 19 host and 31 pathogen organisms from 167 abstracts. The access to the entire HPI dataset is available via a searchable PHILM2Web interface; scientists can also download the entire database in bulk for offline processing.

Database URL: http://philm2web.live

Interface-Based Structural Prediction of Novel Host-Pathogen Interactions

About 20% of the cancer incidences worldwide have been estimated to be associated with infections. However, the molecular mechanisms of exactly how they contribute to host tumorigenesis are still unknown. To evade host defense, pathogens hijack host proteins at different levels: sequence, structure, motif, and binding surface, i.e., interface. Interface similarity allows pathogen proteins to compete with host counterparts to bind to a target protein, rewire physiological signaling, and result in persistent infections, as well as cancer. Identification of host-pathogen interactions (HPIs)-along with their structural details at atomic resolution-may provide mechanistic insight into pathogen-driven cancers and innovate therapeutic intervention. HPI data including structural details is scarce and large-scale experimental detection is challenging. Therefore, there is an urgent and mounting need for efficient and robust computational approaches to predict HPIs and their complex (bound) structures. In this chapter, we review the first and currently only interface-based computational approach to identify novel HPIs. The concept of interface mimicry promises to identify more HPIs than complete sequence or structural similarity. We illustrate this concept with a case study on Kaposi's sarcoma herpesvirus (KSHV) to elucidate how it subverts host immunity and helps contribute to malignant transformation of the host cells.

Systems Biology Modeling to Study Pathogen-Host Interactions

Pathogen-host interactions (PHIs) underlie the process of infection. The systems biology view of the whole PHI system is superior to the investigation of the pathogen or host separately in understanding the infection mechanisms. Especially, the identification of host-oriented drug targets for the next-generation anti-infection therapeutics requires the properties of the host factors targeted by pathogens. Here, we provide an outline of computational analysis of PHI networks, focusing on the properties of the pathogen-targeted host proteins. We also provide information about the available PHI data and the related Web-based resources.

Structural host-microbiota interaction networks

Hundreds of different species colonize multicellular organisms making them "metaorganisms". A growing body of data supports the role of microbiota in health and in disease. Grasping the principles of host-microbiota interactions (HMIs) at the molecular level is important since it may provide insights into the mechanisms of infections. The crosstalk between the host and the microbiota may help resolve puzzling questions such as how a microorganism can contribute to both health and disease. Integrated superorganism networks that consider host and microbiota as a whole-may uncover their code, clarifying perhaps the most fundamental question: how they modulate immune surveillance. Within this framework, structural HMI networks can uniquely identify potential microbial effectors that target distinct host nodes or interfere with endogenous host interactions, as well as how mutations on either host or microbial proteins affect the interaction. Furthermore, structural HMIs can help identify master host cell regulator nodes and modules whose tweaking by the microbes promote aberrant activity. Collectively, these data can delineate pathogenic mechanisms and thereby help maximize beneficial therapeutics. To date, challenges in experimental techniques limit large-scale characterization of HMIs. Here we highlight an area in its infancy which we believe will increasingly engage the computational community: predicting interactions across kingdoms, and mapping these on the host cellular networks to figure out how commensal and pathogenic microbiota modulate the host signaling and broadly cross-species consequences.

Dissecting Candida albicans Infection from the Perspective of C. albicans Virulence and Omics Approaches on Host-Pathogen Interaction: A Review

Candida bloodstream infections remain the most frequent life-threatening fungal disease, with Candida albicans accounting for 70% to 80% of the Candida isolates recovered from infected patients. In nature, Candida species are part of the normal commensal flora in mammalian hosts. However, they can transform into pathogens once the host immune system is weakened or breached. More recently, mortality attributed to Candida infections has continued to increase due to both inherent and acquired drug resistance in Candida, the inefficacy of the available antifungal drugs, tedious diagnostic procedures, and a rising number of immunocompromised patients. Adoption of animal models, viz. minihosts, mice, and zebrafish, has brought us closer to unraveling the pathogenesis and complexity of Candida infection in human hosts, leading towards the discovery of biomarkers and identification of potential therapeutic agents. In addition, the advancement of omics technologies offers a holistic view of the Candida-host interaction in a non-targeted and non-biased manner. Hence, in this review, we seek to summarize past and present milestone findings on C. albicans virulence, adoption of animal models in the study of C. albicans infection, and the application of omics technologies in the study of Candida–host interaction. A profound understanding of the interaction between host defense and pathogenesis is imperative for better design of novel immunotherapeutic strategies in future.

HPIDB 2.0: a curated database for host-pathogen interactions

Identification and analysis of host–pathogen interactions (HPI) is essential to study infectious diseases. However, HPI data are sparse in existing molecular interaction databases, especially for agricultural host–pathogen systems. Therefore, resources that annotate, predict and display the HPI that underpin infectious diseases are critical for developing novel intervention strategies. HPIDB 2.0 (http://www.agbase.msstate.edu/hpi/main.html) is a resource for HPI data, and contains 45, 238 manually curated entries in the current release. Since the first description of the database in 2010, multiple enhancements to HPIDB data and interface services were made that are described here. Notably, HPIDB 2.0 now provides targeted biocuration of molecular interaction data. As a member of the International Molecular Exchange consortium, annotations provided by HPIDB 2.0 curators meet community standards to provide detailed contextual experimental information and facilitate data sharing. Moreover, HPIDB 2.0 provides access to rapidly available community annotations that capture minimum molecular interaction information to address immediate researcher needs for HPI network analysis. In addition to curation, HPIDB 2.0 integrates HPI from existing external sources and contains tools to infer additional HPI where annotated data are scarce. Compared to other interaction databases, our data collection approach ensures HPIDB 2.0 users access the most comprehensive HPI data from a wide range of pathogens and their hosts (594 pathogen and 70 host species, as of February 2016). Improvements also include enhanced search capacity, addition of Gene Ontology functional information, and implementation of network visualization. The changes made to HPIDB 2.0 content and interface ensure that users, especially agricultural researchers, are able to easily access and analyse high quality, comprehensive HPI data. All HPIDB 2.0 data are updated regularly, are publically available for direct download, and are disseminated to other molecular interaction resources.

Database URL:http://www.agbase.msstate.edu/hpi/main.html

Apoptosis of Candida albicans during the Interaction with Murine Macrophages: Proteomics and Cell-Death Marker Monitoring

Macrophages may induce fungal apoptosis to fight against C. albicans, as previously hypothesized by our group. To confirm this hypothesis, we analyzed proteins from C. albicans cells after 3 h of interaction with macrophages using two quantitative proteomic approaches. A total of 51 and 97 proteins were identified as differentially expressed by DIGE and iTRAQ, respectively. The proteins identified and quantified were different, with only seven in common, but classified in the same functional categories. The analyses of their functions indicated that an increase in the metabolism of amino acids and purine nucleotides were taking place, while the glycolysis and translation levels dropped after 3 h of interaction. Also, the response to oxidative stress and protein translation were reduced. In addition, seven substrates of metacaspase (Mca1) were identified (Cdc48, Fba1, Gpm1, Pmm1, Rct1, Ssb1, and Tal1) as decreased in abundance, plus 12 proteins previously described as related to apoptosis. Besides, the monitoring of apoptotic markers along 24 h of interaction (caspase-like activity, TUNEL assay, and the measurement of ROS and cell examination by transmission electron microscopy) revealed that apoptotic processes took place for 30% of the fungal cells, thus supporting the proteomic results and the hypothesis of macrophages killing C. albicans by apoptosis.

Challenges and Strategies for Proteome Analysis of the Interaction of Human Pathogenic Fungi with Host Immune Cells

Opportunistic human pathogenic fungi including the saprotrophic mold Aspergillus fumigatus and the human commensal Candida albicans can cause severe fungal infections in immunocompromised or critically ill patients. The first line of defense against opportunistic fungal pathogens is the innate immune system. Phagocytes such as macrophages, neutrophils and dendritic cells are an important pillar of the innate immune response and have evolved versatile defense strategies against microbial pathogens. On the other hand, human-pathogenic fungi have sophisticated virulence strategies to counteract the innate immune defense. In this context, proteomic approaches can provide deeper insights into the molecular mechanisms of the interaction of host immune cells with fungal pathogens. This is crucial for the identification of both diagnostic biomarkers for fungal infections and therapeutic targets. Studying host-fungal interactions at the protein level is a challenging endeavor, yet there are few studies that have been undertaken. This review draws attention to proteomic techniques and their application to fungal pathogens and to challenges, difficulties, and limitations that may arise in the course of simultaneous dual proteome analysis of host immune cells interacting with diverse morphotypes of fungal pathogens. On this basis, we discuss strategies to overcome these multifaceted experimental and analytical challenges including the viability of immune cells during co-cultivation, the increased and heterogeneous protein complexity of the host proteome dynamically interacting with the fungal proteome, and the demands on normalization strategies in terms of relative quantitative proteome analysis.

A review on computational systems biology of pathogen-host interactions

Pathogens manipulate the cellular mechanisms of host organisms via pathogen-host interactions (PHIs) in order to take advantage of the capabilities of host cells, leading to infections. The crucial role of these interspecies molecular interactions in initiating and sustaining infections necessitates a thorough understanding of the corresponding mechanisms. Unlike the traditional approach of considering the host or pathogen separately, a systems-level approach, considering the PHI system as a whole is indispensable to elucidate the mechanisms of infection. Following the technological advances in the post-genomic era, PHI data have been produced in large-scale within the last decade. Systems biology-based methods for the inference and analysis of PHI regulatory, metabolic, and protein-protein networks to shed light on infection mechanisms are gaining increasing demand thanks to the availability of omics data. The knowledge derived from the PHIs may largely contribute to the identification of new and more efficient therapeutics to prevent or cure infections. There are recent efforts for the detailed documentation of these experimentally verified PHI data through Web-based databases. Despite these advances in data archiving, there are still large amounts of PHI data in the biomedical literature yet to be discovered, and novel text mining methods are in development to unearth such hidden data. Here, we review a collection of recent studies on computational systems biology of PHIs with a special focus on the methods for the inference and analysis of PHI networks, covering also the Web-based databases and text-mining efforts to unravel the data hidden in the literature.

Proteomics unravels extracellular vesicles as carriers of classical cytoplasmic proteins in Candida albicans

The commensal fungus Candida albicans secretes a considerable number of proteins and, as in different fungal pathogens, extracellular vesicles (EVs) have also been observed. Our report contains the first proteomic analysis of EVs in C. albicans and a comparative proteomic study of the soluble secreted proteins. With this purpose, cell-free culture supernatants from C. albicans were separated into EVs and EV-free supernatant and analyzed by LC-MS/MS. A total of 96 proteins were identified including 75 and 61 proteins in EVs and EV-free supernatant, respectively. Out of these, 40 proteins were found in secretome by proteomic analysis for the first time. The soluble proteins were enriched in cell wall and secreted pathogenesis related proteins. Interestingly, more than 90% of these EV-free supernatant proteins were classical secretory proteins with predicted N-terminal signal peptide, whereas all the leaderless proteins involved in metabolism, including some moonlighting proteins, or in the exocytosis and endocytosis process were exclusively cargo of the EVs. We propose a model of the different mechanisms used by C. albicans secreted proteins to reach the extracellular medium. Furthermore, we tested the potential of the Bgl2 protein, identified in vesicles and EV-free supernatant, to protect against a systemic candidiasis in a murine model.

HoPaCI-DB: host-Pseudomonas and Coxiella interaction database

Bacterial infectious diseases are the result of multifactorial processes affected by the interplay between virulence factors and host targets. The host-Pseudomonas and Coxiella interaction database (HoPaCI-DB) is a publicly available manually curated integrative database (http://mips.helmholtz-muenchen.de/HoPaCI/) of host–pathogen interaction data from Pseudomonas aeruginosa and Coxiella burnetii. The resource provides structured information on 3585 experimentally validated interactions between molecules, bioprocesses and cellular structures extracted from the scientific literature. Systematic annotation and interactive graphical representation of disease networks make HoPaCI-DB a versatile knowledge base for biologists and network biology approaches.

Pathogenicity phenomena in three model systems: from network mining to emerging system-level properties

Understanding the interconnections of microbial pathogenicity phenomena, such as biofilm formation, quorum sensing and antimicrobial resistance, is a tremendous open challenge for biomedical research. Progress made by wet-lab researchers and bioinformaticians in understanding the underlying regulatory phenomena has been significant, with converging evidence from multiple high-throughput technologies. Notably, network reconstructions are already of considerable size and quality, tackling both intracellular regulation and signal mediation in microbial infection. Therefore, it stands to reason that in silico investigations would play a more active part in this research. Drug target identification and drug repurposing could take much advantage of the ability to simulate pathogen regulatory systems, host-pathogen interactions and pathogen cross-talking. Here, we review the bioinformatics resources and tools available for the study of the gram-negative bacterium Pseudomonas aeruginosa, the gram-positive bacterium Staphylococcus aureus and the fungal species Candida albicans. The choice of these three microorganisms fits the rationale of the review converging into pathogens of great clinical importance, which thrive in biofilm consortia and manifest growing antimicrobial resistance.

A Candida albicans PeptideAtlas

Candida albicans public proteomic datasets, though growing steadily in the last few years, still have a very limited presence in online repositories. We report here the creation of a C. albicans PeptideAtlas comprising near 22,000 distinct peptides at a 0.24% False Discovery Rate (FDR) that account for over 2500 canonical proteins at a 1.2% FDR. Based on data from 16 experiments, we attained coverage of 41% of the C. albicans open reading frame sequences (ORFs) in the database used for the searches. This PeptideAtlas provides several useful features, including comprehensive protein and peptide-centered search capabilities and visualization tools that establish a solid basis for the study of basic biological mechanisms key to virulence and pathogenesis such as dimorphism, adherence, and apoptosis. Further, it is a valuable resource for the selection of candidate proteotypic peptides for targeted proteomic experiments via Selected Reaction Monitoring (SRM) or SWATH-MS.

BIOLOGICAL SIGNIFICANCE

This C. albicans PeptideAtlas resolves the previous absence of fungal pathogens in the PeptideAtlas project. It represents the most extensive characterization of the proteome of this fungus that exists up to the current date, including evidence for uncharacterized ORFs. Through its web interface, PeptideAtlas supports the study of interesting proteins related to basic biological mechanisms key to virulence such as apoptosis, dimorphism and adherence. It also provides a valuable resource to select candidate proteotypic peptides for future (SRM) targeted proteomic experiments. This article is part of a Special Issue entitled: Trends in Microbial Proteomics.

Sub-proteomic study on macrophage response to Candida albicans unravels new proteins involved in the host defense against the fungus

In previous proteomic studies on the response of murine macrophages against Candida albicans, many differentially expressed proteins involved in processes like inflammation, cytoskeletal rearrangement, stress response and metabolism were identified. In order to look for proteins important for the macrophage response, but in a lower concentration in the cell, 3 sub-cellular extracts were analyzed: cytosol, organelle/membrane and nucleus enriched fractions from RAW 264.7 macrophages exposed or not to C. albicans SC5314 for 3 h. The samples were studied using DIGE technology, and 17 new differentially expressed proteins were identified. This sub-cellular fractionation permitted the identification of 2 mitochondrion proteins, a membrane receptor, Galectin-3, and some ER related proteins, that are not easily detected in total cell extracts. Besides, the study of different fractions allowed us to detect, not only total increase in Galectin-3 protein amount, but its distinct allocation along the interaction. The identified proteins are involved in the pro-inflammatory and oxidative responses, immune response, unfolded protein response and apoptosis. Some of these processes increase the host response and others could be the effect of C. albicans resistance to phagocytosis. Thus, the sub-proteomic approach has been a very useful tool to identify new proteins involved in macrophage-fungus interaction. This article is part of a Special Issue entitled: Translational Proteomics.

Identification of virulence determinants of the human pathogenic fungi Aspergillus fumigatus and Candida albicans by proteomics

Both fungi Candida albicans and Aspergillus fumigatus can cause a number of life-threatening systemic infections in humans. The commensal yeast C. albicans is one of the main causes of nosocomial fungal infectious diseases, whereas the filamentous fungus A. fumigatus has become one of the most prevalent airborne fungal pathogens. Early diagnosis of these fungal infections is challenging, only a limited number of antifungals for treatment are available, and the molecular details of pathogenicity are hardly understood. The completion of both the A. fumigatus and C. albicans genome sequence provides the opportunity to improve diagnosis, to define new drug targets, to understand the functions of many uncharacterised proteins, and to study protein regulation on a global scale. With the application of proteomic tools, particularly two-dimensional gel electrophoresis and LC/MS-based methods, a comprehensive overview about the proteins of A. fumigatus and C. albicans present or induced during environmental changes and stress conditions has been obtained in the past 5 years. However, for the discovery of further putative virulence determinants, more sensitive and targeted proteomic methods have to be applied. Here, we review the recent proteome data generated for A. fumigatus and C. albicans that are related to factors required for pathogenicity.

Quantitative proteome and acidic subproteome profiling of Candida albicans yeast-to-hypha transition

Candida albicans yeast-to-hypha morphological transition is involved in the virulence strategy of this opportunistic fungal pathogen. Changes in relative abundance of the Candida proteome related to this process were analyzed using different two-dimensional differential in-gel electrophoresis (2D-DIGE)-based approaches. First, a comparative analysis of yeast and hyphal cytoplasmic proteins allowed the detection of 106 protein spots with significant variation in abundance. Sixty-one of them, corresponding to 46 proteins, were identified. As most of the differentially abundant proteins had an acidic isoelectric point, a large-scale prefractionation approach to analyze the acidic C. albicans subproteome was carried out. Ninety acidic C. albicans proteins were identified by either gel-based or nongel-based approaches. Additionally, different workflows combining preparative isoelectric focusing, Cy labeling, and narrow pH gradient 2-DE gels were tested to analyze the differences in relative protein abundance between yeast and hyphal acidic subproteomes. It was possible to identify 21 differentially abundant acidic proteins; 10 of them were not identified in the previous 2D-DIGE gels. Functional and network interaction analyses of the 56 differentially abundant proteins identified by both approaches rendered an integrated view of metabolic and cellular process reorganization during the yeast-to-hypha transition. With these results, we propose a model of metabolic reorganization.