1
|
Flores-Garza E, Hernández-Pando R, García-Zárate I, Aguirre P, Domínguez-Hüttinger E. Bifurcation analysis of a tuberculosis progression model for drug target identification. Sci Rep 2023; 13:17567. [PMID: 37845271 PMCID: PMC10579266 DOI: 10.1038/s41598-023-44569-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 10/10/2023] [Indexed: 10/18/2023] Open
Abstract
Tuberculosis (TB) is a major cause of morbidity and mortality worldwide. The emergence and rapid spread of drug-resistant M. tuberculosis strains urge us to develop novel treatments. Experimental trials are constrained by laboratory capacity, insufficient funds, low number of laboratory animals and obsolete technology. Systems-level approaches to quantitatively study TB can overcome these limitations. Previously, we proposed a mathematical model describing the key regulatory mechanisms underlying the pathological progression of TB. Here, we systematically explore the effect of parameter variations on disease outcome. We find five bifurcation parameters that steer the clinical outcome of TB: number of bacteria phagocytosed per macrophage, macrophages death, macrophage killing by bacteria, macrophage recruitment, and phagocytosis of bacteria. The corresponding bifurcation diagrams show all-or-nothing dose-response curves with parameter regions mapping onto bacterial clearance, persistent infection, or history-dependent clearance or infection. Importantly, the pathogenic stage strongly affects the sensitivity of the host to these parameter variations. We identify parameter values corresponding to a latent-infection model of TB, where disease progression occurs significantly slower than in progressive TB. Two-dimensional bifurcation analyses uncovered synergistic parameter pairs that could act as efficient compound therapeutic approaches. Through bifurcation analysis, we reveal how modulation of specific regulatory mechanisms could steer the clinical outcome of TB.
Collapse
Affiliation(s)
- Eliezer Flores-Garza
- Departamento de Biología Molecular y Biotecnología, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Ciudad Universitaria, 04510, Mexico, Mexico
| | - Rogelio Hernández-Pando
- Sección de Patología Experimental, Departamento de Patología, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Vasco de Quiroga 15, Belisario Domínguez Secc. 16, Tlalpan, 14080, Mexico City, Mexico
| | - Ibrahim García-Zárate
- Facultad de Ciencias, Universidad Nacional Autónoma de México, Ciudad Universitaria, Coyoacán, 04510, Mexico City, Mexico
| | - Pablo Aguirre
- Departamento de Matemática, Universidad Técnica Federico Santa María, Casilla 110-V, Valparaíso, Chile
| | - Elisa Domínguez-Hüttinger
- Departamento de Biología Molecular y Biotecnología, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Ciudad Universitaria, 04510, Mexico, Mexico.
| |
Collapse
|
2
|
Ponnusamy N, Arumugam M. Meta-analysis of active tuberculosis gene expression ascertains host directed drug targets. Front Cell Infect Microbiol 2022; 12:1010771. [PMID: 36275035 PMCID: PMC9581169 DOI: 10.3389/fcimb.2022.1010771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 09/21/2022] [Indexed: 12/01/2022] Open
Abstract
Multi-drug resistant tuberculosis still remains a major public health crisis globally. With the emergence of newer active tuberculosis disease, the requirement of prolonged treatment time and adherence to therapy till its completion necessitates the search of newer therapeutics, targeting human host factors. The current work utilized statistical meta-analysis of human gene transcriptomes of active pulmonary tuberculosis disease obtained from six public datasets. The meta-analysis resulted in the identification of 2038 significantly differentially expressed genes (DEGs) in the active tuberculosis disease. The gene ontology (GO) analysis revealed that these genes were major contributors in immune responses. The pathway enrichment analyses identified from various human canonical pathways are related to other infectious diseases. In addition, the comparison of the DEGs with the tuberculosis genome wide association study (GWAS) datasets revealed the presence of few genetic variants in their proximity. The analysis of protein interaction networks (human and Mycobacterium tuberculosis) and host directed drug-target interaction network led to new candidate drug targets for drug repurposing studies. The current work sheds light on host genes and pathways enriched in active tuberculosis disease and suggest potential drug repurposing targets for host-directed therapies.
Collapse
|
3
|
Abstract
Since the large-scale experimental characterization of protein–protein interactions (PPIs) is not possible for all species, several computational PPI prediction methods have been developed that harness existing data from other species. While PPI network prediction has been extensively used in eukaryotes, microbial network inference has lagged behind. However, bacterial interactomes can be built using the same principles and techniques; in fact, several methods are better suited to bacterial genomes. These predicted networks allow systems-level analyses in species that lack experimental interaction data. This review describes the current network inference and analysis techniques and summarizes the use of computationally-predicted microbial interactomes to date.
Collapse
|
4
|
Dysregulated expression of microRNAs in aqueous humor from intraocular tuberculosis patients. Mol Biol Rep 2021; 49:97-107. [PMID: 34677715 DOI: 10.1007/s11033-021-06846-4] [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: 07/20/2021] [Accepted: 10/15/2021] [Indexed: 10/20/2022]
Abstract
BACKGROUND Systemic Mycobacterium tuberculosis (Mtb) infection alters microRNA's expression that controls cellular processes and modulates host defense mechanisms. However, the role of miRNAs in intraocular tuberculosis (IOTB) remains unknown. Therefore, this study aims to identify dysregulated miRNAs in the aqueous humor (AH) of patients with IOTB. METHODS AH from intraocular tuberculosis patients (n = 2) and cataract controls (n = 2) were used for small RNA deep sequencing using HiSeq Illumina sequencing platform. Differentially expressed miRNAs and their targets were identified by the bioinformatics approach, and their regulatory functions were predicted by pathway enrichment analysis. The expression of selected miRNAs and their binding targets were further validated by real-time quantitative PCR (RT-qPCR). RESULTS In total, we identified 56 differentially expressed miRNAs in the AH of intraocular tuberculosis (IOTB) patients compared to controls. We selected four significantly dysregulated miRNAs (miR-423-5p, miR-328-3p, miR-21-5p, and miR-16-5p) based on the RT-qPCR validation and predicted their gene targets. We developed a miRNA-targets regulatory network by combining pathways of interest and genes associated with TB. We identified that these four miRNAs might play an important role in IOTB pathogenesis via tuberculosis-associated pathways; PI3K-Akt signaling, autophagy and MAPK pathway. CONCLUSIONS For the first time, this study identifies the dysregulation of four miRNAs in the AH of IOTB patients using the ultra-low input small-RNA sequencing approach. Further target prediction and validation identify the role of these miRNAs in tuberculosis pathogenesis via tuberculosis-related pathways. This study identifies miRNAs as potentially ideal biomarkers in the aqueous humor of IOTB patients.
Collapse
|
5
|
Saha D, Kundu S. A Molecular Interaction Map of Klebsiella pneumoniae and Its Human Host Reveals Potential Mechanisms of Host Cell Subversion. Front Microbiol 2021; 12:613067. [PMID: 33679637 PMCID: PMC7930833 DOI: 10.3389/fmicb.2021.613067] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 01/11/2021] [Indexed: 12/13/2022] Open
Abstract
Klebsiella pneumoniae is a leading cause of pneumonia and septicemia across the world. The rapid emergence of multidrug-resistant K. pneumoniae strains necessitates the discovery of effective drugs against this notorious pathogen. However, there is a dearth of knowledge on the mechanisms by which this deadly pathogen subverts host cellular machinery. To fill this knowledge gap, our study attempts to identify the potential mechanisms of host cell subversion by building a K. pneumoniae-human interactome based on rigorous computational methodology. The putative host targets inferred from the predicted interactome were found to be functionally enriched in the host's immune surveillance system and allied functions like apoptosis, hypoxia, etc. A multifunctionality-based scoring system revealed P53 as the most multifunctional protein among host targets accompanied by HIF1A and STAT1. Moreover, mining of host protein-protein interaction (PPI) network revealed that host targets interact among themselves to form a network (TTPPI), where P53 and CDC5L occupy a central position. The TTPPI is composed of several inter complex interactions which indicate that K. pneumoniae might disrupt functional coordination between these protein complexes through targeting of P53 and CDC5L. Furthermore, we identified four pivotal K. pneumoniae-targeted transcription factors (TTFs) that are part of TTPPI and are involved in generating host's transcriptional response to K. pneumoniae-mediated sepsis. In a nutshell, our study identifies some of the pivotal molecular targets of K. pneumoniae which primarily correlate to the physiological response of host during K. pneumoniae-mediated sepsis.
Collapse
Affiliation(s)
- Deeya Saha
- Department of Biophysics, Molecular Biology and Bioinformatics, Faculty of Science, University of Calcutta, Kolkata, India
| | - Sudip Kundu
- Department of Biophysics, Molecular Biology and Bioinformatics, Faculty of Science, University of Calcutta, Kolkata, India
| |
Collapse
|
6
|
Balkenhol J, Kaltdorf KV, Mammadova-Bach E, Braun A, Nieswandt B, Dittrich M, Dandekar T. Comparison of the central human and mouse platelet signaling cascade by systems biological analysis. BMC Genomics 2020; 21:897. [PMID: 33353544 PMCID: PMC7756956 DOI: 10.1186/s12864-020-07215-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 11/08/2020] [Indexed: 12/12/2022] Open
Abstract
Background Understanding the molecular mechanisms of platelet activation and aggregation is of high interest for basic and clinical hemostasis and thrombosis research. The central platelet protein interaction network is involved in major responses to exogenous factors. This is defined by systemsbiological pathway analysis as the central regulating signaling cascade of platelets (CC). Results The CC is systematically compared here between mouse and human and major differences were found. Genetic differences were analysed comparing orthologous human and mouse genes. We next analyzed different expression levels of mRNAs. Considering 4 mouse and 7 human high-quality proteome data sets, we identified then those major mRNA expression differences (81%) which were supported by proteome data. CC is conserved regarding genetic completeness, but we observed major differences in mRNA and protein levels between both species. Looking at central interactors, human PLCB2, MMP9, BDNF, ITPR3 and SLC25A6 (always Entrez notation) show absence in all murine datasets. CC interactors GNG12, PRKCE and ADCY9 occur only in mice. Looking at the common proteins, TLN1, CALM3, PRKCB, APP, SOD2 and TIMP1 are higher abundant in human, whereas RASGRP2, ITGB2, MYL9, EIF4EBP1, ADAM17, ARRB2, CD9 and ZYX are higher abundant in mouse. Pivotal kinase SRC shows different regulation on mRNA and protein level as well as ADP receptor P2RY12. Conclusions Our results highlight species-specific differences in platelet signaling and points of specific fine-tuning in human platelets as well as murine-specific signaling differences. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-020-07215-4.
Collapse
Affiliation(s)
- Johannes Balkenhol
- Functional Genomics and Systems Biology Group, Department of Bioinformatics, Biocenter, Am Hubland, University of Würzburg, D-97074, Würzburg, Germany
| | - Kristin V Kaltdorf
- Functional Genomics and Systems Biology Group, Department of Bioinformatics, Biocenter, Am Hubland, University of Würzburg, D-97074, Würzburg, Germany
| | - Elmina Mammadova-Bach
- Institute of Experimental Biomedicine, University Hospital and Rudolf Virchow Centre, University of Würzburg, Würzburg, Germany.,Present address: Division of Nephrology, Department of Medicine IV, Hospital of the Ludwig, Maximilian University of Munich, D-80336, Munich, Germany
| | - Attila Braun
- Member of the German Center for Lung Research (DZL), Walther-Straub-Institute for Pharmacology and Toxicology, Ludwig-Maximilians University Munich, Munich, Germany
| | - Bernhard Nieswandt
- Institute of Experimental Biomedicine, University Hospital and Rudolf Virchow Centre, University of Würzburg, Würzburg, Germany
| | - Marcus Dittrich
- Functional Genomics and Systems Biology Group, Department of Bioinformatics, Biocenter, Am Hubland, University of Würzburg, D-97074, Würzburg, Germany.,Dept of Genetics, Biocenter, Am Hubland, University of Würzburg, Am Hubland, D 97074, Würzburg, Germany
| | - Thomas Dandekar
- Functional Genomics and Systems Biology Group, Department of Bioinformatics, Biocenter, Am Hubland, University of Würzburg, D-97074, Würzburg, Germany.
| |
Collapse
|
7
|
Mazandu GK, Hooper C, Opap K, Makinde F, Nembaware V, Thomford NE, Chimusa ER, Wonkam A, Mulder NJ. IHP-PING-generating integrated human protein-protein interaction networks on-the-fly. Brief Bioinform 2020; 22:5943797. [PMID: 33129201 DOI: 10.1093/bib/bbaa277] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 09/12/2020] [Accepted: 09/21/2020] [Indexed: 01/04/2023] Open
Abstract
Advances in high-throughput sequencing technologies have resulted in an exponential growth of publicly accessible biological datasets. In the 'big data' driven 'post-genomic' context, much work is being done to explore human protein-protein interactions (PPIs) for a systems level based analysis to uncover useful signals and gain more insights to advance current knowledge and answer specific biological and health questions. These PPIs are experimentally or computationally predicted, stored in different online databases and some of PPI resources are updated regularly. As with many biological datasets, such regular updates continuously render older PPI datasets potentially outdated. Moreover, while many of these interactions are shared between these online resources, each resource includes its own identified PPIs and none of these databases exhaustively contains all existing human PPI maps. In this context, it is essential to enable the integration of or combining interaction datasets from different resources, to generate a PPI map with increased coverage and confidence. To allow researchers to produce an integrated human PPI datasets in real-time, we introduce the integrated human protein-protein interaction network generator (IHP-PING) tool. IHP-PING is a flexible python package which generates a human PPI network from freely available online resources. This tool extracts and integrates heterogeneous PPI datasets to generate a unified PPI network, which is stored locally for further applications.
Collapse
Affiliation(s)
- Gaston K Mazandu
- Computational Biology Division, Department of Integrative Biomedical Sciences, IDM, CIDRI-Africa WT Centre, University of Cape Town, Health Sciences Campus. Anzio Rd, Observatory, 7925, South Africa.,African Institute for Mathematical Sciences, 5-7 Melrose Road, Muizenberg, 7945, Cape Town, South Africa.,Division of Human Genetics, Department of Pathology, University of Cape Town, Health Sciences Campus, Anzio Rd, Observatory, 7925, South Africa
| | - Christopher Hooper
- Computational Biology Division, Department of Integrative Biomedical Sciences, IDM, CIDRI-Africa WT Centre, University of Cape Town, Health Sciences Campus. Anzio Rd, Observatory, 7925, South Africa
| | - Kenneth Opap
- Computational Biology Division, Department of Integrative Biomedical Sciences, IDM, CIDRI-Africa WT Centre, University of Cape Town, Health Sciences Campus. Anzio Rd, Observatory, 7925, South Africa
| | - Funmilayo Makinde
- Computational Biology Division, Department of Integrative Biomedical Sciences, IDM, CIDRI-Africa WT Centre, University of Cape Town, Health Sciences Campus. Anzio Rd, Observatory, 7925, South Africa.,African Institute for Mathematical Sciences, 5-7 Melrose Road, Muizenberg, 7945, Cape Town, South Africa
| | - Victoria Nembaware
- Division of Human Genetics, Department of Pathology, University of Cape Town, Health Sciences Campus, Anzio Rd, Observatory, 7925, South Africa
| | - Nicholas E Thomford
- Division of Human Genetics, Department of Pathology, University of Cape Town, Health Sciences Campus, Anzio Rd, Observatory, 7925, South Africa.,School of Medical Sciences, University of Cape Coast, PMB, Cape Coast, Ghana
| | - Emile R Chimusa
- Division of Human Genetics, Department of Pathology, University of Cape Town, Health Sciences Campus, Anzio Rd, Observatory, 7925, South Africa
| | - Ambroise Wonkam
- Division of Human Genetics, Department of Pathology, University of Cape Town, Health Sciences Campus, Anzio Rd, Observatory, 7925, South Africa
| | - Nicola J Mulder
- Computational Biology Division, Department of Integrative Biomedical Sciences, IDM, CIDRI-Africa WT Centre, University of Cape Town, Health Sciences Campus. Anzio Rd, Observatory, 7925, South Africa
| |
Collapse
|
8
|
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.
Collapse
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.
| |
Collapse
|
9
|
Mazandu GK, Chimusa ER, Rutherford K, Zekeng EG, Gebremariam ZZ, Onifade MY, Mulder NJ. Large-scale data-driven integrative framework for extracting essential targets and processes from disease-associated gene data sets. Brief Bioinform 2019; 19:1141-1152. [PMID: 28520909 DOI: 10.1093/bib/bbx052] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2017] [Indexed: 12/20/2022] Open
Abstract
Populations worldwide currently face several public health challenges, including growing prevalence of infections and the emergence of new pathogenic organisms. The cost and risk associated with drug development make the development of new drugs for several diseases, especially orphan or rare diseases, unappealing to the pharmaceutical industry. Proof of drug safety and efficacy is required before market approval, and rigorous testing makes the drug development process slow, expensive and frequently result in failure. This failure is often because of the use of irrelevant targets identified in the early steps of the drug discovery process, suggesting that target identification and validation are cornerstones for the success of drug discovery and development. Here, we present a large-scale data-driven integrative computational framework to extract essential targets and processes from an existing disease-associated data set and enhance target selection by leveraging drug-target-disease association at the systems level. We applied this framework to tuberculosis and Ebola virus diseases combining heterogeneous data from multiple sources, including protein-protein functional interaction, functional annotation and pharmaceutical data sets. Results obtained demonstrate the effectiveness of the pipeline, leading to the extraction of essential drug targets and to the rational use of existing approved drugs. This provides an opportunity to move toward optimal target-based strategies for screening available drugs and for drug discovery. There is potential for this model to bridge the gap in the production of orphan disease therapies, offering a systematic approach to predict new uses for existing drugs, thereby harnessing their full therapeutic potential.
Collapse
Affiliation(s)
- Gaston K Mazandu
- Institute of Infectious Disease and Molecular Medicine at UCT and a Researcher at AIMS
| | | | | | | | - Zoe Z Gebremariam
- Institute of Infection and Global Health, University of Liverpool, UK
| | - Maryam Y Onifade
- African Institute for Mathematical Sciences jointly with University of Cape Coast, Ghana
| | - Nicola J Mulder
- Department of Integrative Biomedical Sciences and the Head of the Computational Biology Division, UCT
| |
Collapse
|
10
|
Lian X, Yang S, Li H, Fu C, Zhang Z. Machine-Learning-Based Predictor of Human–Bacteria Protein–Protein Interactions by Incorporating Comprehensive Host-Network Properties. J Proteome Res 2019; 18:2195-2205. [DOI: 10.1021/acs.jproteome.9b00074] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Xianyi Lian
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Shiping Yang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Hong Li
- Key Laboratory of Tropical Biological Resources of Ministry of Education, Hainan University, Haikou, 570228, China
| | - Chen Fu
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Ziding Zhang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| |
Collapse
|
11
|
Cao T, Lyu L, Jia H, Wang J, Du F, Pan L, Li Z, Xing A, Xiao J, Ma Y, Zhang Z. A Two-Way Proteome Microarray Strategy to Identify Novel Mycobacterium tuberculosis-Human Interactors. Front Cell Infect Microbiol 2019; 9:65. [PMID: 30984625 PMCID: PMC6448480 DOI: 10.3389/fcimb.2019.00065] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Accepted: 03/01/2019] [Indexed: 12/13/2022] Open
Abstract
Tuberculosis (TB) is still a serious threat to human health which is caused by mycobacterium tuberculosis (Mtb). The main reason for failure to eliminate TB is lack of clearly understanding the molecular mechanism of Mtb pathogenesis. Determining human Mtb-interacting proteins enables us to characterize the mechanism and identify potential molecular targets for TB diagnosis and treatment. However, experimentally systematic Mtb interactors are not readily available. In this study, we performed an unbiased, comprehensive two-way proteome microarray based approach to systematically screen global human Mtb interactors and determine the binding partners of Mtb effectors. Our results, for the first time, screened 84 potential human Mtb interactors. Bioinformatic analysis further highlighted these protein candidates might engage in a wide range of cellular functions such as activation of DNA endogenous promoters, transcription of DNA/RNA and necrosis, as well as immune-related signaling pathways. Then, using Mtb proteome microarray followed His tagged pull-down assay and Co-IP, we identified one interacting partner (Rv0577) for the protein candidate NRF1 and three binding partners (Rv0577, Rv2117, Rv2423) for SMAD2, respectively. This study gives new insights into the profile of global Mtb interactors potentially involved in Mtb pathogenesis and demonstrates a powerful strategy in the discovery of Mtb effectors.
Collapse
Affiliation(s)
- Tingming Cao
- Beijing Key Laboratory for Drug Resistant Tuberculosis Research, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing Chest Hospital, Capital Medical University, Beijing, China
| | - Lingna Lyu
- Beijing Key Laboratory for Drug Resistant Tuberculosis Research, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing Chest Hospital, Capital Medical University, Beijing, China
| | - Hongyan Jia
- Beijing Key Laboratory for Drug Resistant Tuberculosis Research, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing Chest Hospital, Capital Medical University, Beijing, China
| | - Jinghui Wang
- Beijing Key Laboratory for Drug Resistant Tuberculosis Research, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing Chest Hospital, Capital Medical University, Beijing, China
| | - Fengjiao Du
- Beijing Key Laboratory for Drug Resistant Tuberculosis Research, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing Chest Hospital, Capital Medical University, Beijing, China
| | - Liping Pan
- Beijing Key Laboratory for Drug Resistant Tuberculosis Research, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing Chest Hospital, Capital Medical University, Beijing, China
| | - Zihui Li
- Beijing Key Laboratory for Drug Resistant Tuberculosis Research, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing Chest Hospital, Capital Medical University, Beijing, China
| | - Aiying Xing
- Beijing Key Laboratory for Drug Resistant Tuberculosis Research, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing Chest Hospital, Capital Medical University, Beijing, China
| | - Jing Xiao
- Beijing Key Laboratory for Drug Resistant Tuberculosis Research, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing Chest Hospital, Capital Medical University, Beijing, China
| | - Yu Ma
- Beijing Key Laboratory for Drug Resistant Tuberculosis Research, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing Chest Hospital, Capital Medical University, Beijing, China
| | - Zongde Zhang
- Beijing Key Laboratory for Drug Resistant Tuberculosis Research, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing Chest Hospital, Capital Medical University, Beijing, China
| |
Collapse
|
12
|
Kumar S, Lata KS, Sharma P, Bhairappanavar SB, Soni S, Das J. Inferring pathogen-host interactions between Leptospira interrogans and Homo sapiens using network theory. Sci Rep 2019; 9:1434. [PMID: 30723266 PMCID: PMC6363727 DOI: 10.1038/s41598-018-38329-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2017] [Accepted: 12/20/2018] [Indexed: 12/19/2022] Open
Abstract
Leptospirosis is the most emerging zoonotic disease of epidemic potential caused by pathogenic species of Leptospira. The bacterium invades the host system and causes the disease by interacting with the host proteins. Analyzing these pathogen-host protein interactions (PHPIs) may provide deeper insight into the disease pathogenesis. For this analysis, inter-species as well as intra-species protein interactions networks of Leptospira interrogans and human were constructed and investigated. The topological analyses of these networks showed lesser connectivity in inter-species network than intra-species, indicating the perturbed nature of the inter-species network. Hence, it can be one of the reasons behind the disease development. A total of 35 out of 586 PHPIs were identified as key interactions based on their sub-cellular localization. Two outer membrane proteins (GpsA and MetXA) and two periplasmic proteins (Flab and GlyA) participating in PHPIs were found conserved in all pathogenic, intermediate and saprophytic spp. of Leptospira. Furthermore, the bacterial membrane proteins involved in PHPIs were found playing major roles in disruption of the immune systems and metabolic processes within host and thereby causing infectious disease. Thus, the present results signify that the membrane proteins participating in such interactions hold potential to serve as effective immunotherapeutic candidates for vaccine development.
Collapse
Affiliation(s)
- Swapnil Kumar
- Gujarat Biotechnology Research Centre, Department of Science & Technology, Government of Gujarat, Gandhinagar, 382011, India
| | - Kumari Snehkant Lata
- Gujarat Biotechnology Research Centre, Department of Science & Technology, Government of Gujarat, Gandhinagar, 382011, India
| | - Priyanka Sharma
- Gujarat Biotechnology Research Centre, Department of Science & Technology, Government of Gujarat, Gandhinagar, 382011, India
| | - Shivarudrappa B Bhairappanavar
- Gujarat Biotechnology Research Centre, Department of Science & Technology, Government of Gujarat, Gandhinagar, 382011, India
| | - Subhash Soni
- Gujarat Biotechnology Research Centre, Department of Science & Technology, Government of Gujarat, Gandhinagar, 382011, India
| | - Jayashankar Das
- Gujarat Biotechnology Research Centre, Department of Science & Technology, Government of Gujarat, Gandhinagar, 382011, India.
| |
Collapse
|
13
|
Sun J, Yang LL, Chen X, Kong DX, Liu R. Integrating Multifaceted Information to Predict Mycobacterium tuberculosis-Human Protein-Protein Interactions. J Proteome Res 2018; 17:3810-3823. [PMID: 30269499 DOI: 10.1021/acs.jproteome.8b00497] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Tuberculosis (TB) is one of the biggest infectious disease killers caused by Mycobacterium tuberculosis (MTB). Studying the protein-protein interactions (PPIs) between MTB and human can deepen our understanding of the pathogenesis of TB and offer new clues to the treatment against MTB infection, but the experimentally validated interactions are especially scarce in this regard. Herein we proposed an integrated framework that combined template-, domain-domain interaction-, and machine learning-based methods to predict MTB-human PPIs. As a result, we established a network composed of 13 758 PPIs including 451 MTB proteins and 3167 human proteins ( http://liulab.hzau.edu.cn/MTB/ ). Compared to known human targets of various pathogens, our predicted human targets show a similar tendency in terms of the network topological properties and enrichment in important functional genes. Additionally, these human targets largely have longer sequence lengths, more protein domains, more disordered residues, lower evolutionary rates, and older protein ages. Functional analysis demonstrates that these proteins show strong preferences toward the phosphorylation, kinase activity, and signaling transduction processes and the disease and immune related pathways. Dissecting the cross-talk among top-ranked pathways suggests that the cancer pathway may serve as a bridge in MTB infection. Triplet analysis illustrates that the paired targets interacting with the same partner are adjacent to each other in the intraspecies network and tend to share similar expression patterns. Finally, we identified 36 potential anti-MTB human targets by integrating known drug target information and molecular properties of proteins.
Collapse
|
14
|
Artigas-Jerónimo S, De La Fuente J, Villar M. Interactomics and tick vaccine development: new directions for the control of tick-borne diseases. Expert Rev Proteomics 2018; 15:627-635. [PMID: 30067120 DOI: 10.1080/14789450.2018.1506701] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
INTRODUCTION Ticks are obligate hematophagous arthropod ectoparasites that transmit pathogens responsible for a growing number of tick-borne diseases (TBDs) throughout the world. Vaccines have been shown to be the most efficient, cost-effective, and environmentally friendly approach for the control of ticks and the prevention of TBDs. Although at its infancy, interactomics has shown the possibilities that the knowledge of the interactome offers in understanding tick biology and the molecular mechanisms involved in pathogen infection and transmission. Furthermore, interactomics has provided information for the identification of candidate vaccine protective antigens. Areas covered: In this special report, we review the different approaches used for the study of protein-protein physical and functional interactions, and summarize the application of interactomics to the characterization of tick biology and tick-host-pathogen interactions, and the possibilities that offers to vaccine development for the control of ticks and TBDs. Expert commentary: The combination of interacting proteins in antigen formulations may increase vaccine efficacy. In the near future, the combination of interactomics with other omics approaches such as transcriptomics, proteomics, metabolomics, and regulomics together with intelligent Big Data analytic techniques will improve the high throughput discovery and characterization of vaccine protective antigens for the prevention and control of TBDs.
Collapse
Affiliation(s)
- Sara Artigas-Jerónimo
- a SaBio. Instituto de Investigación en Recursos Cinegéticos IREC-CSIC-UCLM-JCCM , Ciudad Real , Spain
| | - José De La Fuente
- a SaBio. Instituto de Investigación en Recursos Cinegéticos IREC-CSIC-UCLM-JCCM , Ciudad Real , Spain.,b Department of Veterinary Pathobiology , Center for Veterinary Health Sciences, Oklahoma State University , Stillwater OK , USA
| | - Margarita Villar
- a SaBio. Instituto de Investigación en Recursos Cinegéticos IREC-CSIC-UCLM-JCCM , Ciudad Real , Spain
| |
Collapse
|
15
|
Koch AS, Brites D, Stucki D, Evans JC, Seldon R, Heekes A, Mulder N, Nicol M, Oni T, Mizrahi V, Warner DF, Parkhill J, Gagneux S, Martin DP, Wilkinson RJ. The Influence of HIV on the Evolution of Mycobacterium tuberculosis. Mol Biol Evol 2017; 34:1654-1668. [PMID: 28369607 PMCID: PMC5455964 DOI: 10.1093/molbev/msx107] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
HIV significantly affects the immunological environment during tuberculosis coinfection, and therefore may influence the selective landscape upon which M. tuberculosis evolves. To test this hypothesis whole genome sequences were determined for 169 South African M. tuberculosis strains from HIV-1 coinfected and uninfected individuals and analyzed using two Bayesian codon-model based selection analysis approaches: FUBAR which was used to detect persistent positive and negative selection (selection respectively favoring and disfavoring nonsynonymous substitutions); and MEDS which was used to detect episodic directional selection specifically favoring nonsynonymous substitutions within HIV-1 infected individuals. Among the 25,251 polymorphic codon sites analyzed, FUBAR revealed that 189-fold more were detectably evolving under persistent negative selection than were evolving under persistent positive selection. Three specific codon sites within the genes celA2b, katG, and cyp138 were identified by MEDS as displaying significant evidence of evolving under directional selection influenced by HIV-1 coinfection. All three genes encode proteins that may indirectly interact with human proteins that, in turn, interact functionally with HIV proteins. Unexpectedly, epitope encoding regions were enriched for sites displaying weak evidence of directional selection influenced by HIV-1. Although the low degree of genetic diversity observed in our M. tuberculosis data set means that these results should be interpreted carefully, the effects of HIV-1 on epitope evolution in M. tuberculosis may have implications for the design of M. tuberculosis vaccines that are intended for use in populations with high HIV-1 infection rates.
Collapse
Affiliation(s)
- Anastasia S Koch
- Wellcome Centre for Infectious Disease Research in Africa, Institute of Infectious Disease and Molecular Medicine, and Department of Medicine, University of Cape Town, Cape Town, South Africa
| | - Daniela Brites
- Department of Medical Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, Basel, Switzerland.,University of Basel, Basel, Switzerland
| | - David Stucki
- Department of Medical Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, Basel, Switzerland.,University of Basel, Basel, Switzerland
| | - Joanna C Evans
- Molecular Mycobacteriology Research Unit, Institute of Infectious Disease and Molecular Medicine and Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Ronnett Seldon
- Molecular Mycobacteriology Research Unit, Institute of Infectious Disease and Molecular Medicine and Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Alexa Heekes
- Department of Integrative Biomedical Sciences, Institute of Infectious Diseases and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Nicola Mulder
- Department of Integrative Biomedical Sciences, Institute of Infectious Diseases and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Mark Nicol
- University of Cape Town, and National Health Laboratory Service, Cape Town, South Africa
| | - Tolu Oni
- Division of Public Health Medicine, School of Public Health and Family Medicine, University of Cape Town, Cape Town, South Africa.,The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Valerie Mizrahi
- Molecular Mycobacteriology Research Unit, Institute of Infectious Disease and Molecular Medicine and Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Digby F Warner
- Molecular Mycobacteriology Research Unit, Institute of Infectious Disease and Molecular Medicine and Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Julian Parkhill
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Sebastien Gagneux
- Department of Medical Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, Basel, Switzerland.,University of Basel, Basel, Switzerland
| | - Darren P Martin
- Division of Computational Biology, Department of Integrated Biology Sciences and Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Robert J Wilkinson
- Wellcome Centre for Infectious Disease Research in Africa, Institute of Infectious Disease and Molecular Medicine, and Department of Medicine, University of Cape Town, Cape Town, South Africa.,Department of Medicine, Imperial College, London, United Kingdom.,Francis Crick Institute, London, United Kingdom
| |
Collapse
|
16
|
Kirschner D, Pienaar E, Marino S, Linderman JJ. A review of computational and mathematical modeling contributions to our understanding of Mycobacterium tuberculosis within-host infection and treatment. ACTA ACUST UNITED AC 2017; 3:170-185. [PMID: 30714019 DOI: 10.1016/j.coisb.2017.05.014] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Tuberculosis (TB) is an ancient and deadly disease characterized by complex host-pathogen dynamics playing out over multiple time and length scales and physiological compartments. Computational modeling can be used to integrate various types of experimental data and suggest new hypotheses, mechanisms, and therapeutic approaches to TB. Here, we offer a first-time comprehensive review of work on within-host TB models that describe the immune response of the host to infection, including the formation of lung granulomas. The models include systems of ordinary and partial differential equations and agent-based models as well as hybrid and multi-scale models that are combinations of these. Many aspects of M. tuberculosis infection, including host dynamics in the lung (typical site of infection for TB), granuloma formation, roles of cytokine and chemokine dynamics, and bacterial nutrient availability have been explored. Finally, we survey applications of these within-host models to TB therapy and prevention and suggest future directions to impact this global disease.
Collapse
Affiliation(s)
- Denise Kirschner
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI
| | - Elsje Pienaar
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI
| | - Simeone Marino
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI
| | | |
Collapse
|
17
|
Mahajan G, Mande SC. Using structural knowledge in the protein data bank to inform the search for potential host-microbe protein interactions in sequence space: application to Mycobacterium tuberculosis. BMC Bioinformatics 2017; 18:201. [PMID: 28376709 PMCID: PMC5379762 DOI: 10.1186/s12859-017-1550-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2016] [Accepted: 02/16/2017] [Indexed: 12/31/2022] Open
Abstract
Background A comprehensive map of the human-M. tuberculosis (MTB) protein interactome would help fill the gaps in our understanding of the disease, and computational prediction can aid and complement experimental studies towards this end. Several sequence-based in silico approaches tap the existing data on experimentally validated protein-protein interactions (PPIs); these PPIs serve as templates from which novel interactions between pathogen and host are inferred. Such comparative approaches typically make use of local sequence alignment, which, in the absence of structural details about the interfaces mediating the template interactions, could lead to incorrect inferences, particularly when multi-domain proteins are involved. Results We propose leveraging the domain-domain interaction (DDI) information in PDB complexes to score and prioritize candidate PPIs between host and pathogen proteomes based on targeted sequence-level comparisons. Our method picks out a small set of human-MTB protein pairs as candidates for physical interactions, and the use of functional meta-data suggests that some of them could contribute to the in vivo molecular cross-talk between pathogen and host that regulates the course of the infection. Further, we present numerical data for Pfam domain families that highlights interaction specificity on the domain level. Not every instance of a pair of domains, for which interaction evidence has been found in a few instances (i.e. structures), is likely to functionally interact. Our sorting approach scores candidates according to how “distant” they are in sequence space from known examples of DDIs (templates). Thus, it provides a natural way to deal with the heterogeneity in domain-level interactions. Conclusions Our method represents a more informed application of local alignment to the sequence-based search for potential human-microbial interactions that uses available PPI data as a prior. Our approach is somewhat limited in its sensitivity by the restricted size and diversity of the template dataset, but, given the rapid accumulation of solved protein complex structures, its scope and utility are expected to keep steadily improving. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1550-y) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Gaurang Mahajan
- National Centre for Cell Science, Ganeshkhind, Pune, 411 007, India. .,Indian Institute of Science Education and Research, Pashan, Pune, 411 008, India.
| | - Shekhar C Mande
- National Centre for Cell Science, Ganeshkhind, Pune, 411 007, India
| |
Collapse
|
18
|
Ammari MG, Gresham CR, McCarthy FM, Nanduri B. HPIDB 2.0: a curated database for host-pathogen interactions. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2016; 2016:baw103. [PMID: 27374121 PMCID: PMC4930832 DOI: 10.1093/database/baw103] [Citation(s) in RCA: 164] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2016] [Accepted: 06/08/2016] [Indexed: 11/13/2022]
Abstract
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
Collapse
Affiliation(s)
- Mais G Ammari
- School of Animal and Comparative Biomedical Sciences, University of Arizona, Tucson, AZ 85721, USA
| | - Cathy R Gresham
- Institute for Genomics, Biocomputing and Biotechnology, College of Veterinary Medicine, Institute for Genomics, Mississippi State University, Mississippi State, MS 39762, USA
| | - Fiona M McCarthy
- School of Animal and Comparative Biomedical Sciences, University of Arizona, Tucson, AZ 85721, USA
| | - Bindu Nanduri
- Institute for Genomics, Biocomputing and Biotechnology, College of Veterinary Medicine, Institute for Genomics, Mississippi State University, Mississippi State, MS 39762, USA
| |
Collapse
|
19
|
de la Fuente J, Kopáček P, Lew-Tabor A, Maritz-Olivier C. Strategies for new and improved vaccines against ticks and tick-borne diseases. Parasite Immunol 2016; 38:754-769. [PMID: 27203187 DOI: 10.1111/pim.12339] [Citation(s) in RCA: 89] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2016] [Accepted: 05/13/2016] [Indexed: 01/12/2023]
Abstract
Ticks infest a variety of animal species and transmit pathogens causing disease in both humans and animals worldwide. Tick-host-pathogen interactions have evolved through dynamic processes that accommodated the genetic traits of the hosts, pathogens transmitted and the vector tick species that mediate their development and survival. New approaches for tick control are dependent on defining molecular interactions between hosts, ticks and pathogens to allow for discovery of key molecules that could be tested in vaccines or new generation therapeutics for intervention of tick-pathogen cycles. Currently, tick vaccines constitute an effective and environmentally sound approach for the control of ticks and the transmission of the associated tick-borne diseases. New candidate protective antigens will most likely be identified by focusing on proteins with relevant biological function in the feeding, reproduction, development, immune response, subversion of host immunity of the tick vector and/or molecules vital for pathogen infection and transmission. This review addresses different approaches and strategies used for the discovery of protective antigens, including focusing on relevant tick biological functions and proteins, reverse genetics, vaccinomics and tick protein evolution and interactomics. New and improved tick vaccines will most likely contain multiple antigens to control tick infestations and pathogen infection and transmission.
Collapse
Affiliation(s)
- J de la Fuente
- SaBio, Instituto de Investigación en Recursos Cinegéticos IREC (CSIC-UCLM-JCCM), Ciudad Real, Spain.,Department of Veterinary Pathobiology, Center for Veterinary Health Sciences, Oklahoma State University, Stillwater, OK, USA
| | - P Kopáček
- Institute of Parasitology, Biology Centre Czech Academy of Sciences, Ceske Budejovice, Czech Republic
| | - A Lew-Tabor
- Queensland Alliance for Agriculture & Food Innovation, The University of Queensland, St. Lucia, Qld, Australia.,Centre for Comparative Genomics, Murdoch University, Perth, WA, Australia
| | - C Maritz-Olivier
- Department of Genetics, Faculty of Natural and Agricultural Sciences, University of Pretoria, Pretoria, South Africa
| |
Collapse
|
20
|
Folk WR, Smith A, Song H, Chuang D, Cheng J, Gu Z, Sun G. Does Concurrent Use of Some Botanicals Interfere with Treatment of Tuberculosis? Neuromolecular Med 2016; 18:483-6. [PMID: 27155670 DOI: 10.1007/s12017-016-8402-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2016] [Accepted: 05/02/2016] [Indexed: 01/20/2023]
Abstract
Millions of individuals with active TB do not receive recommended treatments, and instead may use botanicals, or use botanicals concurrently with established treatments. Many botanicals protect against oxidative stress, but this can interfere with redox-dependent activation of isoniazid and other prodrugs used for prophylaxis and treatment of TB, as suggested by results of a recent clinical trial of the South African botanical Sutherlandia frutescens (L.) R. Br. (Sutherlandia). Here we provide a brief summary of Sutherlandia's effects upon rodent microglia and neurons relevant to tuberculosis of the central nervous system (CNS-TB). We have observed that ethanolic extracts of Sutherlandia suppress production of reactive oxygen species (ROS) in rat primary cortical neurons stimulated by NMDA and also suppress LPS- and interferon γ (IFNγ)-induced ROS and nitric oxide (NO) production by microglial cells. Sutherlandia consumption mitigates microglial activation in the hippocampus and striatum of ischemic brains of mice. RNAseq analysis indicates that Sutherlandia suppresses gene expression of oxidative stress, inflammatory signaling and toll-like receptor pathways that can reduce the host's immune response to infection and reactivation of latent Mycobacterium tuberculosis. As a precautionary measure, we recommend that individuals receiving isoniazid for pulmonary or cerebral TB, be advised not to concurrently use botanicals or dietary supplements having antioxidant activity.
Collapse
Affiliation(s)
- William R Folk
- Department of Biochemistry, University of Missouri, Columbia, MO, 65211, USA.
| | - Aaron Smith
- Department of Biochemistry, University of Missouri, Columbia, MO, 65211, USA
| | - Hailong Song
- Department of Pathology and Anatomical Sciences, University of Missouri, Columbia, MO, 65211, USA
- Center for Translational Neurosciences, University of Missouri, Columbia, MO, 65211, USA
| | - Dennis Chuang
- Department of Pathology and Anatomical Sciences, University of Missouri, Columbia, MO, 65211, USA
- Center for Translational Neurosciences, University of Missouri, Columbia, MO, 65211, USA
| | - Jianlin Cheng
- Department of Computer Science, University of Missouri, Columbia, MO, 65211, USA
| | - Zezong Gu
- Department of Pathology and Anatomical Sciences, University of Missouri, Columbia, MO, 65211, USA
- Center for Translational Neurosciences, University of Missouri, Columbia, MO, 65211, USA
| | - Grace Sun
- Department of Biochemistry, University of Missouri, Columbia, MO, 65211, USA
- Department of Pathology and Anatomical Sciences, University of Missouri, Columbia, MO, 65211, USA
- Center for Translational Neurosciences, University of Missouri, Columbia, MO, 65211, USA
| |
Collapse
|
21
|
Akinola RO, Mazandu GK, Mulder NJ. A Quantitative Approach to Analyzing Genome Reductive Evolution Using Protein-Protein Interaction Networks: A Case Study of Mycobacterium leprae. Front Genet 2016; 7:39. [PMID: 27066064 PMCID: PMC4809885 DOI: 10.3389/fgene.2016.00039] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2015] [Accepted: 03/08/2016] [Indexed: 01/18/2023] Open
Abstract
The advance in high-throughput sequencing technologies has yielded complete genome sequences of several organisms, including complete bacterial genomes. The growing number of these available sequenced genomes has enabled analyses of their dynamics, as well as the molecular and evolutionary processes which these organisms are under. Comparative genomics of different bacterial genomes have highlighted their genome size and gene content in association with lifestyles and adaptation to various environments and have contributed to enhancing our understanding of the mechanisms of their evolution. Protein–protein functional interactions mediate many essential processes for maintaining the stability of the biological systems under changing environmental conditions. Thus, these interactions play crucial roles in the evolutionary processes of different organisms, especially for obligate intracellular bacteria, proven to generally have reduced genome sizes compared to their nearest free-living relatives. In this study, we used the approach based on the Renormalization Group (RG) analysis technique and the Maximum-Excluded-Mass-Burning (MEMB) model to investigate the evolutionary process of genome reduction in relation to the organization of functional networks of two organisms. Using a Mycobacterium leprae (MLP) network in comparison with a Mycobacterium tuberculosis (MTB) network as a case study, we show that reductive evolution in MLP was as a result of removal of important proteins from neighbors of corresponding orthologous MTB proteins. While each orthologous MTB protein had an increase in number of interacting partners in most instances, the corresponding MLP protein had lost some of them. This work provides a quantitative model for mapping reductive evolution and protein–protein functional interaction network organization in terms of roles played by different proteins in the network structure.
Collapse
Affiliation(s)
- Richard O Akinola
- Computational Biology Group, Department of Integrative Biomedical Sciences, Medical School, Institute of Infectious Disease and Molecular Medicine, University of Cape TownCape Town, South Africa; Department of Mathematics, Faculty of Natural Sciences, University of JosJos, Nigeria
| | - Gaston K Mazandu
- Computational Biology Group, Department of Integrative Biomedical Sciences, Medical School, Institute of Infectious Disease and Molecular Medicine, University of Cape TownCape Town, South Africa; African Institute for Mathematical SciencesCape Town, South Africa; African Institute for Mathematical SciencesCape Coast, Ghana
| | - Nicola J Mulder
- Computational Biology Group, Department of Integrative Biomedical Sciences, Medical School, Institute of Infectious Disease and Molecular Medicine, University of Cape Town Cape Town, South Africa
| |
Collapse
|
22
|
Uncovering New Pathogen-Host Protein-Protein Interactions by Pairwise Structure Similarity. PLoS One 2016; 11:e0147612. [PMID: 26799490 PMCID: PMC4723085 DOI: 10.1371/journal.pone.0147612] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2015] [Accepted: 01/06/2016] [Indexed: 01/31/2023] Open
Abstract
Pathogens usually evade and manipulate host-immune pathways through pathogen-host protein-protein interactions (PPIs) to avoid being killed by the host immune system. Therefore, uncovering pathogen-host PPIs is critical for determining the mechanisms underlying pathogen infection and survival. In this study, we developed a computational method, which we named pairwise structure similarity (PSS)-PPI, to predict pathogen-host PPIs. First, a high-quality and non-redundant structure-structure interaction (SSI) template library was constructed by exhaustively exploring heteromeric protein complex structures in the PDB database. New interactions were then predicted by searching for PSS with complex structures in the SSI template library. A quantitative score named the PSS score, which integrated structure similarity and residue-residue contact-coverage information, was used to describe the overall similarity of each predicted interaction with the corresponding SSI template. Notably, PSS-PPI yielded experimentally confirmed pathogen-host PPIs of human immunodeficiency virus type 1 (HIV-1) with performance close to that of in vitro high-throughput screening approaches. Finally, a pathogen-host PPI network of human pathogen Mycobacterium tuberculosis, the causative agent of tuberculosis, was constructed using PSS-PPI and refined using filtration steps based on cellular localization information. Analysis of the resulting network indicated that secreted proteins of the STPK, ESX-1, and PE/PPE family in M. tuberculosis targeted human proteins involved in immune response and phagocytosis. M. tuberculosis also targeted host factors known to regulate HIV replication. Taken together, our findings provide insights into the survival mechanisms of M. tuberculosis in human hosts, as well as co-infection of tuberculosis and HIV. With the rapid pace of three-dimensional protein structure discovery, the SSI template library we constructed and the PSS-PPI method we devised can be used to uncover new pathogen-host PPIs in the future.
Collapse
|
23
|
In Silico Designing and Analysis of Inhibitors against Target Protein Identified through Host-Pathogen Protein Interactions in Malaria. INTERNATIONAL JOURNAL OF MEDICINAL CHEMISTRY 2016; 2016:2741038. [PMID: 27057354 PMCID: PMC4739458 DOI: 10.1155/2016/2741038] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2015] [Revised: 11/13/2015] [Accepted: 11/17/2015] [Indexed: 01/20/2023]
Abstract
Malaria, a life-threatening blood disease, has been a major concern in the field of healthcare. One of the severe forms of malaria is caused by the parasite Plasmodium falciparum which is initiated through protein interactions of pathogen with the host proteins. It is essential to analyse the protein-protein interactions among the host and pathogen for better understanding of the process and characterizing specific molecular mechanisms involved in pathogen persistence and survival. In this study, a complete protein-protein interaction network of human host and Plasmodium falciparum has been generated by integration of the experimental data and computationally predicting interactions using the interolog method. The interacting proteins were filtered according to their biological significance and functional roles. α-tubulin was identified as a potential protein target and inhibitors were designed against it by modification of amiprophos methyl. Docking and binding affinity analysis showed two modified inhibitors exhibiting better docking scores of −10.5 kcal/mol and −10.43 kcal/mol and an improved binding affinity of −83.80 kJ/mol and −98.16 kJ/mol with the target. These inhibitors can further be tested and validated in vivo for their properties as an antimalarial drug.
Collapse
|
24
|
Abbasi WA, Minhas FUAA. Issues in performance evaluation for host-pathogen protein interaction prediction. J Bioinform Comput Biol 2016; 14:1650011. [PMID: 26932275 DOI: 10.1142/s0219720016500116] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The study of interactions between host and pathogen proteins is important for understanding the underlying mechanisms of infectious diseases and for developing novel therapeutic solutions. Wet-lab techniques for detecting protein-protein interactions (PPIs) can benefit from computational predictions. Machine learning is one of the computational approaches that can assist biologists by predicting promising PPIs. A number of machine learning based methods for predicting host-pathogen interactions (HPI) have been proposed in the literature. The techniques used for assessing the accuracy of such predictors are of critical importance in this domain. In this paper, we question the effectiveness of K-fold cross-validation for estimating the generalization ability of HPI prediction for proteins with no known interactions. K-fold cross-validation does not model this scenario, and we demonstrate a sizable difference between its performance and the performance of an alternative evaluation scheme called leave one pathogen protein out (LOPO) cross-validation. LOPO is more effective in modeling the real world use of HPI predictors, specifically for cases in which no information about the interacting partners of a pathogen protein is available during training. We also point out that currently used metrics such as areas under the precision-recall or receiver operating characteristic curves are not intuitive to biologists and propose simpler and more directly interpretable metrics for this purpose.
Collapse
Affiliation(s)
- Wajid Arshad Abbasi
- 1 Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Nilore, Islamabad, Pakistan
| | - Fayyaz Ul Amir Afsar Minhas
- 1 Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Nilore, Islamabad, Pakistan
| |
Collapse
|
25
|
Zhang H, Ouyang H, Wang D, Shi J, Ouyang C, Chen H, Xiao S, Fang L. Mycobacterium tuberculosis Rv2185c contributes to nuclear factor-κB activation. Mol Immunol 2015; 66:147-53. [DOI: 10.1016/j.molimm.2015.02.020] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2014] [Revised: 02/13/2015] [Accepted: 02/19/2015] [Indexed: 12/15/2022]
|
26
|
Cui T, He ZG. Improved understanding of pathogenesis from protein interactions inMycobacteriumtuberculosis. Expert Rev Proteomics 2014; 11:745-55. [DOI: 10.1586/14789450.2014.971762] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
|
27
|
Shi J, Zhang H, Fang L, Xi Y, Zhou Y, Luo R, Wang D, Xiao S, Chen H. A novel firefly luciferase biosensor enhances the detection of apoptosis induced by ESAT-6 family proteins of Mycobacterium tuberculosis. Biochem Biophys Res Commun 2014; 452:1046-53. [DOI: 10.1016/j.bbrc.2014.09.047] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2014] [Accepted: 09/11/2014] [Indexed: 12/14/2022]
|
28
|
Mulder NJ, Akinola RO, Mazandu GK, Rapanoel H. Using biological networks to improve our understanding of infectious diseases. Comput Struct Biotechnol J 2014; 11:1-10. [PMID: 25379138 PMCID: PMC4212278 DOI: 10.1016/j.csbj.2014.08.006] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Infectious diseases are the leading cause of death, particularly in developing countries. Although many drugs are available for treating the most common infectious diseases, in many cases the mechanism of action of these drugs or even their targets in the pathogen remain unknown. In addition, the key factors or processes in pathogens that facilitate infection and disease progression are often not well understood. Since proteins do not work in isolation, understanding biological systems requires a better understanding of the interconnectivity between proteins in different pathways and processes, which includes both physical and other functional interactions. Such biological networks can be generated within organisms or between organisms sharing a common environment using experimental data and computational predictions. Though different data sources provide different levels of accuracy, confidence in interactions can be measured using interaction scores. Connections between interacting proteins in biological networks can be represented as graphs and edges, and thus studied using existing algorithms and tools from graph theory. There are many different applications of biological networks, and here we discuss three such applications, specifically applied to the infectious disease tuberculosis, with its causative agent Mycobacterium tuberculosis and host, Homo sapiens. The applications include the use of the networks for function prediction, comparison of networks for evolutionary studies, and the generation and use of host–pathogen interaction networks.
Collapse
Affiliation(s)
- Nicola J Mulder
- Computational Biology Group, Department of Clinical Laboratory Sciences, IDM, University of Cape Town Faculty of Health Sciences, Anzio Road, Observatory, Cape Town, South Africa
| | - Richard O Akinola
- Computational Biology Group, Department of Clinical Laboratory Sciences, IDM, University of Cape Town Faculty of Health Sciences, Anzio Road, Observatory, Cape Town, South Africa
| | - Gaston K Mazandu
- Computational Biology Group, Department of Clinical Laboratory Sciences, IDM, University of Cape Town Faculty of Health Sciences, Anzio Road, Observatory, Cape Town, South Africa
| | - Holifidy Rapanoel
- Computational Biology Group, Department of Clinical Laboratory Sciences, IDM, University of Cape Town Faculty of Health Sciences, Anzio Road, Observatory, Cape Town, South Africa
| |
Collapse
|