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Duhan N, Kaundal R. HuCoPIA: An Atlas of Human vs. SARS-CoV-2 Interactome and the Comparative Analysis with Other Coronaviridae Family Viruses. Viruses 2023; 15:492. [PMID: 36851706 PMCID: PMC9962590 DOI: 10.3390/v15020492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 02/01/2023] [Accepted: 02/04/2023] [Indexed: 02/12/2023] Open
Abstract
SARS-CoV-2, a novel betacoronavirus strain, has caused a pandemic that has claimed the lives of nearly 6.7M people worldwide. Vaccines and medicines are being developed around the world to reduce the disease spread, fatality rates, and control the new variants. Understanding the protein-protein interaction mechanism of SARS-CoV-2 in humans, and their comparison with the previous SARS-CoV and MERS strains, is crucial for these efforts. These interactions might be used to assess vaccination effectiveness, diagnose exposure, and produce effective biotherapeutics. Here, we present the HuCoPIA database, which contains approximately 100,000 protein-protein interactions between humans and three strains (SARS-CoV-2, SARS-CoV, and MERS) of betacoronavirus. The interactions in the database are divided into common interactions between all three strains and those unique to each strain. It also contains relevant functional annotation information of human proteins. The HuCoPIA database contains SARS-CoV-2 (41,173), SARS-CoV (31,997), and MERS (26,862) interactions, with functional annotation of human proteins like subcellular localization, tissue-expression, KEGG pathways, and Gene ontology information. We believe HuCoPIA will serve as an invaluable resource to diverse experimental biologists, and will help to advance the research in better understanding the mechanism of betacoronaviruses.
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Affiliation(s)
- Naveen Duhan
- Department of Plants, Soils, and Climate/Center for Integrated BioSystems, College of Agriculture and Applied Sciences, Utah State University, Logan, UT 84322, USA
- Bioinformatics Facility, Center for Integrated BioSystems, College of Agriculture and Applied Sciences, Utah State University, Logan, UT 84322, USA
| | - Rakesh Kaundal
- Department of Plants, Soils, and Climate/Center for Integrated BioSystems, College of Agriculture and Applied Sciences, Utah State University, Logan, UT 84322, USA
- Bioinformatics Facility, Center for Integrated BioSystems, College of Agriculture and Applied Sciences, Utah State University, Logan, UT 84322, USA
- Department of Computer Science, College of Science, Utah State University, Logan, UT 84322, USA
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2
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Sensing Host Health: Insights from Sensory Protein Signature of the Metagenome. Appl Environ Microbiol 2022; 88:e0059622. [PMID: 35862686 PMCID: PMC9361814 DOI: 10.1128/aem.00596-22] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
The human microbiota, which comprises an ensemble of taxonomically and functionally diverse but often mutually cooperating microorganisms, benefits its host by shaping the host immunity, energy harvesting, and digestion of complex carbohydrates as well as production of essential nutrients. Dysbiosis in the human microbiota, especially the gut microbiota, has been reported to be linked to several diseases and metabolic disorders. Recent studies have further indicated that tracking these dysbiotic variations could potentially be exploited as biomarkers of disease states. However, the human microbiota is not geography agnostic, and hence a taxonomy-based (microbiome) biomarker for disease diagnostics has certain limitations. In comparison, (microbiome) function-based biomarkers are expected to have a wider applicability. Given that (i) the host physiology undergoes certain changes in the course of a disease and (ii) host-associated microbial communities need to adapt to this changing microenvironment of their host, we hypothesized that signatures emanating from the abundance of bacterial proteins associated with the signal transduction system (herein referred to as sensory proteins [SPs]) might be able to distinguish between healthy and diseased states. To test this hypothesis, publicly available metagenomic data sets corresponding to three diverse health conditions, namely, colorectal cancer, type 2 diabetes mellitus, and schizophrenia, were analyzed. Results demonstrated that SP signatures (derived from host-associated metagenomic samples) indeed differentiated among healthy individual and patients suffering from diseases of various severities. Our finding was suggestive of the prospect of using SP signatures as early biomarkers for diagnosing the onset and progression of multiple diseases and metabolic disorders. IMPORTANCE The composition of the human microbiota, a collection of host-associated microbes, has been shown to differ among healthy and diseased individuals. Recent studies have investigated whether tracking these variations could be exploited for disease diagnostics. It has been noted that compared to microbial taxonomies, the ensemble of functional proteins encoded by microbial genes are less likely to be affected by changes in ethnicity and dietary preferences. These functions are expected to help the microbe adapt to changing environmental conditions. Thus, healthy individuals might harbor a different set of genes than diseased individuals. To test this hypothesis, we analyzed metagenomes from healthy and diseased individuals for signatures of a particular group of proteins called sensory proteins (SP), which enable the bacteria to sense and react to changes in their microenvironment. Results demonstrated that SP signatures indeed differentiate among healthy individuals and those suffering from diseases of various severities.
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Jain A, Mittal S, Tripathi LP, Nussinov R, Ahmad S. Host-pathogen protein-nucleic acid interactions: A comprehensive review. Comput Struct Biotechnol J 2022; 20:4415-4436. [PMID: 36051878 PMCID: PMC9420432 DOI: 10.1016/j.csbj.2022.08.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 08/01/2022] [Accepted: 08/01/2022] [Indexed: 12/02/2022] Open
Abstract
Recognition of pathogen-derived nucleic acids by host cells is an effective host strategy to detect pathogenic invasion and trigger immune responses. In the context of pathogen-specific pharmacology, there is a growing interest in mapping the interactions between pathogen-derived nucleic acids and host proteins. Insight into the principles of the structural and immunological mechanisms underlying such interactions and their roles in host defense is necessary to guide therapeutic intervention. Here, we discuss the newest advances in studies of molecular interactions involving pathogen nucleic acids and host factors, including their drug design, molecular structure and specific patterns. We observed that two groups of nucleic acid recognizing molecules, Toll-like receptors (TLRs) and the cytoplasmic retinoic acid-inducible gene (RIG)-I-like receptors (RLRs) form the backbone of host responses to pathogen nucleic acids, with additional support provided by absent in melanoma 2 (AIM2) and DNA-dependent activator of Interferons (IFNs)-regulatory factors (DAI) like cytosolic activity. We review the structural, immunological, and other biological aspects of these representative groups of molecules, especially in terms of their target specificity and affinity and challenges in leveraging host-pathogen protein-nucleic acid interactions (HP-PNI) in drug discovery.
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Affiliation(s)
- Anuja Jain
- School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi 110067, India
| | - Shikha Mittal
- School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi 110067, India
- Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology, Waknaghat, Solan, Himachal Pradesh, 173234, India
| | - Lokesh P. Tripathi
- National Institutes of Biomedical Innovation, Health and Nutrition, Ibaraki, Osaka, Japan
- Riken Center for Integrative Medical Sciences, Tsurumi, Yokohama, Kanagawa, Japan
| | - Ruth Nussinov
- Computational Structural Biology Section, Basic Science Program, Frederick National, Laboratory for Cancer Research, Frederick, MD 21702, USA
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Israel
| | - Shandar Ahmad
- School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi 110067, India
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Le TD, Nguyen PD, Korkin D, Thieu T. PHILM2Web: A high-throughput database of macromolecular host–pathogen interactions on the Web. Database (Oxford) 2022; 2022:6625823. [PMID: 35776535 PMCID: PMC9248916 DOI: 10.1093/database/baac042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 04/27/2022] [Accepted: 05/31/2022] [Indexed: 12/02/2022]
Abstract
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
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Affiliation(s)
- Tuan-Dung Le
- Department of Computer Science, Oklahoma State University , Stillwater, OK, USA
| | - Phuong D Nguyen
- Department of Biochemistry and Molecular Biology, Oklahoma State University , Stillwater, OK, USA
| | - Dmitry Korkin
- Department of Computer Science and Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute , Worcester, MA, USA
| | - Thanh Thieu
- Machine Learning Department, Moffitt Cancer Center and Research Institute , Tampa, FL, USA
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5
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Kataria R, Kaundal R. Deciphering the Crosstalk Mechanisms of Wheat-Stem Rust Pathosystem: Genome-Scale Prediction Unravels Novel Host Targets. FRONTIERS IN PLANT SCIENCE 2022; 13:895480. [PMID: 35800602 PMCID: PMC9253690 DOI: 10.3389/fpls.2022.895480] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Accepted: 05/31/2022] [Indexed: 05/04/2023]
Abstract
Triticum aestivum (wheat), a major staple food grain, is affected by various biotic stresses. Among these, fungal diseases cause about 15-20% of yield loss, worldwide. In this study, we performed a comparative analysis of protein-protein interactions between two Puccinia graminis races (Pgt 21-0 and Pgt Ug99) that cause stem (black) rust in wheat. The available molecular techniques to study the host-pathogen interaction mechanisms are expensive and labor-intensive. We implemented two computational approaches (interolog and domain-based) for the prediction of PPIs and performed various functional analysis to determine the significant differences between the two pathogen races. The analysis revealed that T. aestivum-Pgt 21-0 and T. aestivum-Pgt Ug99 interactomes consisted of ∼90M and ∼56M putative PPIs, respectively. In the predicted PPIs, we identified 115 Pgt 21-0 and 34 Pgt Ug99 potential effectors that were highly involved in pathogen virulence and development. Functional enrichment analysis of the host proteins revealed significant GO terms and KEGG pathways such as O-methyltransferase activity (GO:0008171), regulation of signal transduction (GO:0009966), lignin metabolic process (GO:0009808), plastid envelope (GO:0009526), plant-pathogen interaction pathway (ko04626), and MAPK pathway (ko04016) that are actively involved in plant defense and immune signaling against the biotic stresses. Subcellular localization analysis anticipated the host plastid as a primary target for pathogen attack. The highly connected host hubs in the protein interaction network belonged to protein kinase domain including Ser/Thr protein kinase, MAPK, and cyclin-dependent kinase. We also identified 5,577 transcription factors in the interactions, associated with plant defense during biotic stress conditions. Additionally, novel host targets that are resistant to stem rust disease were also identified. The present study elucidates the functional differences between Pgt 21-0 and Pgt Ug99, thus providing the researchers with strain-specific information for further experimental validation of the interactions, and the development of durable, disease-resistant crop lines.
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Affiliation(s)
- Raghav Kataria
- Department of Plants, Soils, and Climate, College of Agriculture and Applied Sciences, Utah State University, Logan, UT, United States
| | - Rakesh Kaundal
- Department of Plants, Soils, and Climate, College of Agriculture and Applied Sciences, Utah State University, Logan, UT, United States
- Bioinformatics Facility, Center for Integrated BioSystems, Utah State University, Logan, UT, United States
- Department of Computer Science, College of Science, Utah State University, Logan, UT, United States
- *Correspondence: Rakesh Kaundal,
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6
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Prasasty VD, Hutagalung RA, Gunadi R, Sofia DY, Rosmalena R, Yazid F, Sinaga E. Prediction of human-Streptococcus pneumoniae protein-protein interactions using logistic regression. Comput Biol Chem 2021; 92:107492. [PMID: 33964803 DOI: 10.1016/j.compbiolchem.2021.107492] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 04/21/2021] [Indexed: 02/07/2023]
Abstract
Streptococcus pneumoniae is a major cause of mortality in children under five years old. In recent years, the emergence of antibiotic-resistant strains of S. pneumoniae increases the threat level of this pathogen. For that reason, the exploration of S. pneumoniae protein virulence factors should be considered in developing new drugs or vaccines, for instance by the analysis of host-pathogen protein-protein interactions (HP-PPIs). In this research, prediction of protein-protein interactions was performed with a logistic regression model with the number of protein domain occurrences as features. By utilizing HP-PPIs of three different pathogens as training data, the model achieved 57-77 % precision, 64-75 % recall, and 96-98 % specificity. Prediction of human-S. pneumoniae protein-protein interactions using the model yielded 5823 interactions involving thirty S. pneumoniae proteins and 324 human proteins. Pathway enrichment analysis showed that most of the pathways involved in the predicted interactions are immune system pathways. Network topology analysis revealed β-galactosidase (BgaA) as the most central among the S. pneumoniae proteins in the predicted HP-PPI networks, with a degree centrality of 1.0 and a betweenness centrality of 0.451853. Further experimental studies are required to validate the predicted interactions and examine their roles in S. pneumoniae infection.
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Affiliation(s)
- Vivitri Dewi Prasasty
- Faculty of Biotechnology, Atma Jaya Catholic University of Indonesia, Jakarta, 12930, Indonesia.
| | - Rory Anthony Hutagalung
- Faculty of Biotechnology, Atma Jaya Catholic University of Indonesia, Jakarta, 12930, Indonesia
| | - Reinhart Gunadi
- Department of Biology, Faculty of Life Sciences, Universitas Surya, Tangerang, Banten, 15143, Indonesia
| | - Dewi Yustika Sofia
- Department of Biology, Faculty of Life Sciences, Universitas Surya, Tangerang, Banten, 15143, Indonesia
| | - Rosmalena Rosmalena
- Department of Medical Chemistry, Faculty of Medicine, Universitas Indonesia, Jakarta, 10430, Indonesia
| | - Fatmawaty Yazid
- Department of Medical Chemistry, Faculty of Medicine, Universitas Indonesia, Jakarta, 10430, Indonesia
| | - Ernawati Sinaga
- Faculty of Biology, Universitas Nasional, Jakarta, 12520, Indonesia.
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7
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Loaiza CD, Duhan N, Lister M, Kaundal R. In silico prediction of host-pathogen protein interactions in melioidosis pathogen Burkholderia pseudomallei and human reveals novel virulence factors and their targets. Brief Bioinform 2020; 22:5842243. [PMID: 32444871 DOI: 10.1093/bib/bbz162] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Revised: 11/13/2019] [Accepted: 11/20/2019] [Indexed: 12/13/2022] Open
Abstract
The aerobic, Gram-negative motile bacillus, Burkholderia pseudomallei is a facultative intracellular bacterium causing melioidosis, a critical disease of public health importance, which is widely endemic in the tropics and subtropical regions of the world. Melioidosis is associated with high case fatality rates in animals and humans; even with treatment, its mortality is 20-50%. It also infects plants and is designated as a biothreat agent. B. pseudomallei is pathogenic due to its ability to invade, resist factors in serum and survive intracellularly. Despite its importance, to date only a few effector proteins have been functionally characterized, and there is not much information regarding the host-pathogen protein-protein interactions (PPI) of this system, which are important to studying infection mechanisms and thereby develop prevention measures. We explored two computational approaches, the homology-based interolog and the domain-based method, to predict genome-scale host-pathogen interactions (HPIs) between two different strains of B. pseudomallei (prototypical, and highly virulent) and human. In total, 76 335 common HPIs (between the two strains) were predicted involving 8264 human and 1753 B. pseudomallei proteins. Among the unique PPIs, 14 131 non-redundant HPIs were found to be unique between the prototypical strain and human, compared to 3043 non-redundant HPIs between the highly virulent strain and human. The protein hubs analysis showed that most B. pseudomallei proteins formed a hub with human dnaK complex proteins associated with tuberculosis, a disease similar in symptoms to melioidosis. In addition, drug-binding and carbohydrate-binding mechanisms were found overrepresented within the host-pathogen network, and metabolic pathways were frequently activated according to the pathway enrichment. Subcellular localization analysis showed that most of the pathogen proteins are targeting human proteins inside cytoplasm and nucleus. We also discovered the host targets of the drug-related pathogen proteins and proteins that form T3SS and T6SS in B. pseudomallei. Additionally, a comparison between the unique PPI patterns present in the prototypical and highly virulent strains was performed. The current study is the first report on developing a genome-scale host-pathogen protein interaction networks between the human and B. pseudomallei, a critical biothreat agent. We have identified novel virulence factors and their interacting partners in the human proteome. These PPIs can be further validated by high-throughput experiments and may give new insights on how B. pseudomallei interacts with its host, which will help medical researchers in developing better prevention measures.
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Affiliation(s)
- Cristian D Loaiza
- Center for Integrated BioSystems/Department of Plants, Soils, and Climate, College of Agriculture and Applied Sciences, Utah State University, USA
| | - Naveen Duhan
- Center for Integrated BioSystems/Department of Plants, Soils, and Climate, College of Agriculture and Applied Sciences, Utah State University, USA
| | - Matthew Lister
- Bioinformatics Facility, Center for Integrated BioSystems, Utah State University, USA
| | - Rakesh Kaundal
- Department of Plants, Soils, and Climate/Center for Integrated BioSystems, College of Agriculture and Applied Sciences, Utah State University, Logan, UT 84322 USA
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8
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Bose T, Venkatesh KV, Mande SS. Investigating host-bacterial interactions among enteric pathogens. BMC Genomics 2019; 20:1022. [PMID: 31881845 PMCID: PMC6935094 DOI: 10.1186/s12864-019-6398-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Accepted: 12/15/2019] [Indexed: 01/07/2023] Open
Abstract
Background In 2017, World Health Organization (WHO) published a catalogue of 12 families of antibiotic-resistant “priority pathogens” that are posing the greatest threats to human health. Six of these dreaded pathogens are known to infect the human gastrointestinal system. In addition to causing gastrointestinal and systemic infections, these pathogens can also affect the composition of other microbes constituting the healthy gut microbiome. Such aberrations in gut microbiome can significantly affect human physiology and immunity. Identifying the virulence mechanisms of these enteric pathogens are likely to help in developing newer therapeutic strategies to counter them. Results Using our previously published in silico approach, we have evaluated (and compared) Host-Pathogen Protein-Protein Interaction (HPI) profiles of four groups of enteric pathogens, namely, different species of Escherichia, Shigella, Salmonella and Vibrio. Results indicate that in spite of genus/ species specific variations, most enteric pathogens possess a common repertoire of HPIs. This core set of HPIs are probably responsible for the survival of these pathogen in the harsh nutrient-limiting environment within the gut. Certain genus/ species specific HPIs were also observed. Conslusions The identified bacterial proteins involved in the core set of HPIs are expected to be helpful in understanding the pathogenesis of these dreaded gut pathogens in greater detail. Possible role of genus/ species specific variations in the HPI profiles in the virulence of these pathogens are also discussed. The obtained results are likely to provide an opportunity for development of novel therapeutic strategies against the most dreaded gut pathogens.
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Affiliation(s)
- Tungadri Bose
- Bio-Sciences R&D Division, TCS Innovation Labs, Tata Consultancy Services Limited, Pune, India.,Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - K V Venkatesh
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Sharmila S Mande
- Bio-Sciences R&D Division, TCS Innovation Labs, Tata Consultancy Services Limited, Pune, India.
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9
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Sen R, Tagore S, De RK. Cluster Quality based Non-Reductional (CQNR) oversampling technique and effector protein predictor based on 3D structure (EPP3D) of proteins. Comput Biol Med 2019; 112:103374. [DOI: 10.1016/j.compbiomed.2019.103374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Revised: 07/26/2019] [Accepted: 07/26/2019] [Indexed: 11/28/2022]
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10
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Da Silva WM, Bei J, Amigo N, Valacco MP, Amadio A, Zhang Q, Wu X, Yu T, Larzabal M, Chen Z, Cataldi A. Quantification of enterohemorrhagic Escherichia coli O157:H7 protein abundance by high-throughput proteome. PLoS One 2018; 13:e0208520. [PMID: 30596662 PMCID: PMC6312284 DOI: 10.1371/journal.pone.0208520] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Accepted: 11/19/2018] [Indexed: 12/22/2022] Open
Abstract
Enterohemorrhagic Escherichia coli (EHEC) O157:H7 is a human pathogen responsible for diarrhea, hemorrhagic colitis and hemolytic uremic syndrome (HUS). To promote a comprehensive insight into the molecular basis of EHEC O157:H7 physiology and pathogenesis, the combined proteome of EHEC O157:H7 strains, Clade 8 and Clade 6 isolated from cattle in Argentina, and the standard EDL933 (clade 3) strain has been analyzed. From shotgun proteomic analysis a total of 2,644 non-redundant proteins of EHEC O157:H7 were identified, which correspond approximately 47% of the predicted proteome of this pathogen. Normalized spectrum abundance factor analysis was performed to estimate the protein abundance. According this analysis, 50 proteins were detected as the most abundant of EHEC O157:H7 proteome. COG analysis showed that the majority of the most abundant proteins are associated with translation processes. A KEGG enrichment analysis revealed that Glycolysis / Gluconeogenesis was the most significant pathway. On the other hand, the less abundant detected proteins are those related to DNA processes, cell respiration and prophage. Among the proteins that composed the Type III Secretion System, the most abundant protein was EspA. Altogether, the results show a subset of important proteins that contribute to physiology and pathogenicity of EHEC O157:H7.
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Affiliation(s)
- Wanderson Marques Da Silva
- Institute of Biotechnology, CICVyA, National Institute of Agricultural Technology, Hurlingham, Buenos Aires, Argentina
| | - Jinlong Bei
- AGRO-Biological Gene Research Center, Guangdong Academy of Agricultural Sciences (GDAAS), Guangzhou, China
| | - Natalia Amigo
- Institute of Biotechnology, CICVyA, National Institute of Agricultural Technology, Hurlingham, Buenos Aires, Argentina
| | - María Pía Valacco
- CEQUIBIEM (Mass Spectrometry Facility), Faculty of Exact and Natural Sciences, University of Buenos Aires and CONICET (National Research Council), Buenos Aires, Argentina
| | - Ariel Amadio
- Rafaela Experimental Station, National Institute of Agricultural Technology, Rafaela, Santa Fe, Argentina
| | - Qi Zhang
- AGRO-Biological Gene Research Center, Guangdong Academy of Agricultural Sciences (GDAAS), Guangzhou, China
| | - Xiuju Wu
- AGRO-Biological Gene Research Center, Guangdong Academy of Agricultural Sciences (GDAAS), Guangzhou, China
| | - Ting Yu
- AGRO-Biological Gene Research Center, Guangdong Academy of Agricultural Sciences (GDAAS), Guangzhou, China
| | - Mariano Larzabal
- Institute of Biotechnology, CICVyA, National Institute of Agricultural Technology, Hurlingham, Buenos Aires, Argentina
| | - Zhuang Chen
- AGRO-Biological Gene Research Center, Guangdong Academy of Agricultural Sciences (GDAAS), Guangzhou, China
| | - Angel Cataldi
- Institute of Biotechnology, CICVyA, National Institute of Agricultural Technology, Hurlingham, Buenos Aires, Argentina
- * E-mail:
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11
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Bose T, Das C, Dutta A, Mahamkali V, Sadhu S, Mande SS. Understanding the role of interactions between host and Mycobacterium tuberculosis under hypoxic condition: an in silico approach. BMC Genomics 2018; 19:555. [PMID: 30053801 PMCID: PMC6064076 DOI: 10.1186/s12864-018-4947-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2018] [Accepted: 07/19/2018] [Indexed: 01/17/2023] Open
Abstract
Background Mycobacterium tuberculosis infection in humans is often associated with extended period of latency. To adapt to the hostile hypoxic environment inside a macrophage, M. tuberculosis cells undergo several physiological and metabolic changes. Previous studies have mostly focused on inspecting individual facets of this complex process. In order to gain deeper insights into the infection process and to understand the coordination among different regulatory/ metabolic pathways in the pathogen, the current in silico study investigates three aspects, namely, (i) host-pathogen interactions (HPIs) between human and M. tuberculosis proteins, (ii) gene regulatory network pertaining to adaptation of M. tuberculosis to hypoxia and (iii) alterations in M. tuberculosis metabolism under hypoxic condition. Subsequently, cross-talks between these components have been probed to evaluate possible gene-regulatory events as well as HPIs which are likely to drive metabolic changes during pathogen’s adaptation to the intra-host hypoxic environment. Results The newly identified HPIs suggest the pathogen’s ability to subvert host mediated reactive oxygen intermediates/ reactive nitrogen intermediates (ROI/ RNI) stress as well as their potential role in modulating host cell cycle and cytoskeleton structure. The results also indicate a significantly pronounced effect of HPIs on hypoxic metabolism of M. tuberculosis. Findings from the current study underscore the necessity of investigating the infection process from a systems-level perspective incorporating different facets of intra-cellular survival of the pathogen. Conclusions The comprehensive host-pathogen interaction network, a Boolean model of M. tuberculosis H37Rv (Mtb) hypoxic gene-regulation, as well as a genome scale metabolic model of Mtb, built for this study are expected to be useful resources for future studies on tuberculosis infection. Electronic supplementary material The online version of this article (10.1186/s12864-018-4947-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Tungadri Bose
- Bio-Sciences R&D Division, TCS Research, Tata Consultancy Services Limited, Pune, India.,Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Chandrani Das
- Bio-Sciences R&D Division, TCS Research, Tata Consultancy Services Limited, Pune, India.,Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Anirban Dutta
- Bio-Sciences R&D Division, TCS Research, Tata Consultancy Services Limited, Pune, India.
| | - Vishnuvardhan Mahamkali
- Bio-Sciences R&D Division, TCS Research, Tata Consultancy Services Limited, Pune, India.,Present Address: Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, Australia
| | - Sudipta Sadhu
- Bio-Sciences R&D Division, TCS Research, Tata Consultancy Services Limited, Pune, India
| | - Sharmila S Mande
- Bio-Sciences R&D Division, TCS Research, Tata Consultancy Services Limited, Pune, India.
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12
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Miskiewicz A, Kowalczyk P, Oraibi SM, Cybulska K, Misiewicz A. Bird feathers as potential sources of pathogenic microorganisms: a new look at old diseases. Antonie van Leeuwenhoek 2018; 111:1493-1507. [PMID: 29460207 PMCID: PMC6097735 DOI: 10.1007/s10482-018-1048-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 12/14/2017] [Accepted: 02/09/2018] [Indexed: 12/22/2022]
Abstract
This article describes methods of treatment for avian zoonoses, modern antibiotic therapy and drug resistance of selected pathogens, which pose a threat to the population’s health. A tabular form has been used to present the current data from the European Union from 2011 to 2017 regarding human morbidity and mortality and the costs incurred by national health systems for the treatment of zoonoses occurring in humans and animals. Moreover, the paper includes descriptions of selected diseases, which indirectly affect birds. Scientists can obtain information regarding the occurrence of particular diseases, their aetiology, epidemiology, incubation period and symptoms caused by dangerous microorganisms and parasites. This information should be of particular interest for people who have frequent contact with birds, such as ornithologists, as well as veterinarians, farm staff, owners of accompanying animals and zoological workers. This paper presents a review used for identification and genetic characterization of bacterial strains isolated from a variety of environmental sources, e.g., bird feathers along with their practical application. We describe the bacterial, viral and fungal serotypes present on avian feathers after the slaughter process. This review also enables us to effectively identify several of the early stages of infectious diseases from heterogeneous avian research material.
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Affiliation(s)
- Andrzej Miskiewicz
- Department of Periodontology and Oral Diseases, Medical University of Warsaw, 18 Miodowa St., 00-246, Warsaw, Poland
| | - Paweł Kowalczyk
- Department of Animal Nutrition, The Kielanowski Institute of Animal Physiology and Nutrition, Polish Academy of Sciences, Jabłonna, Poland.
| | - Sanaa Mahdi Oraibi
- Department of Chemistry, Microbiology and Environmental Biotechnology, Faculty of Environmental Management and Agriculture, West Pomeranian University of Technology, Słowackiego 17 Str., 71-434, Szczecin, Poland
| | - Krystyna Cybulska
- Department of Chemistry, Microbiology and Environmental Biotechnology, Faculty of Environmental Management and Agriculture, West Pomeranian University of Technology, Słowackiego 17 Str., 71-434, Szczecin, Poland
| | - Anna Misiewicz
- Institute of Agricultural and Food Biotechnology, Rakowiecka 36, 02-532, Warsaw, Poland
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