Table of Contents    
ORIGINAL ARTICLE
Year : 2020  |  Volume : 11  |  Issue : 2  |  Page : 194-197  

Uncovering physical interactions among human and Mycobacterium tuberculosis proteins


Department of Biotechnology and Bioinformatics, School of Life Sciences, University of Hyderabad, Hyderabad, Telangana, India

Date of Submission01-Jan-2020
Date of Decision12-Mar-2020
Date of Acceptance25-May-2020
Date of Web Publication22-Jul-2020

Correspondence Address:
Vaibhav Vindal
Department of Biotechnology and Bioinformatics, School of Life Sciences, University of Hyderabad, Hyderabad - 500 046, Telangana
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jnsbm.JNSBM_3_20

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   Abstract 


Background: Pathogens usually evade and manipulate host immune pathways through host-pathogen protein interactions. Uncovering these interactions is crucial for determining the mechanisms underlying pathogen infection and the defense system. The growing prevalence of tuberculosis (TB) infection in the world necessitated advances in TB research. With the rising information from several divisions of biosciences, computational approaches are promising to analyze and interpret the data at the system level. Methods: In the present study, in silico two-hybrid systems is employed on model organisms to predict physical interactions among proteins of Human and Mycobacterium tuberculosis (Mtb). Consistent protein interactions are identified by the Interlog method. Co-expression analysis and functional annotations are performed to infer significant Human and Mtb protein physical interactions (HMIs). Results: The interactions identified in this study support the current TB research through an improved understanding of the pathogen infection and survival mechanism. A network of HMIs highlighted dnaK as the most highly interacting protein. Further, dnaK, eno, tuf, and gap proteins are found to trigger toll-like receptor signaling pathways and initiate pathogenesis. Conclusion: The interactions proteins identified in this study may incline the researchers to explore for novel therapeutic intervention strategies.

Keywords: Co-expression analysis, functional annotations, in silico two hybrid, Interlog


How to cite this article:
Bharne D, Tawar B, Vindal V. Uncovering physical interactions among human and Mycobacterium tuberculosis proteins. J Nat Sc Biol Med 2020;11:194-7

How to cite this URL:
Bharne D, Tawar B, Vindal V. Uncovering physical interactions among human and Mycobacterium tuberculosis proteins. J Nat Sc Biol Med [serial online] 2020 [cited 2020 Nov 25];11:194-7. Available from: http://www.jnsbm.org/text.asp?2020/11/2/194/290491




   Introduction Top


Recent technological advances have paved the way for substantial progress in tuberculosis (TB) research. However, achievements do not have any noticeable impact on the current global trends of TB disease.[1] TB is a chronic disease that could develop over many years. During this period, the causative agent, Mycobacterium tuberculosis (Mtb) could not be isolated.[2] Further, a long generation time of the pathogen makes it difficult to grow in vitro. All these factors prolong the invention of new therapeutic interventions and slow down the advances in TB research. Nevertheless, the improved interest in research and funding from various public sectors and organizations is giving optimism. Recently, the Stop TB Partnership led by the WHO has defined a global plan to eradicate TB by 2050. This is possible to be accomplished only with the enlarged interdisciplinary research and development. Computational biology is one of the interdisciplinary research fields that can elucidate some of the critical aspects of TB through various studies such as host-pathogen protein interactions and drug discovery and development. The study of host-pathogen protein interactions is one of the fundamental approaches in understanding the virulence and infection mechanism of the pathogen. The past attempts for the prediction of Human and Mtb protein interactions have generated only a few results.[3] Therefore, the investigation of novel physical interactions of Mtb proteins with the proteins of the Human host is crucial for a systematic understanding of the pathogen. In the present study, in silico two hybrids (I2H) system[4] is employed to predict Human and Mtb protein physical interactions (HMIs) through the correlated mutations. The consistent HMIs are identified by mapping the interacting proteins with the protein interactions from Database of Interacting Proteins (DIP),[5] STRING[6] and HitPredict[7] databases through the Interlog method. These HMIs are enriched with functional annotations and expression data to derive significant interactions that are essential for infection and progression of the disease. Further, an interaction network is built from the significant HMIs to expose critical proteins for novel drug targets.


   Materials and Methods Top


Identification of HMIs using in silico two hybrid system

Complete proteome sets of Mtb and Homo sapiens (Human) along with seven evolutionary diverse model organisms, viz., Escherichia coli, Dictyostelium discoideum, Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, Danio rerio, and Mus musculus, were retrieved from the NCBI genome database. Reciprocal best BLAST hit[8] was performed on the model organisms to obtain orthologs of Mtb proteins and the orthologs of Human proteins. Multiple sequence alignments (MSAs)[9] from orthologs of an Mtb protein was reduced to at least five coincident species in orthologs of a Human protein. I2H was performed on such MSAs of Mtb and Human proteins to predict HMIs. To extract consistent HMIs, the predicted HMIs are filtered with the Mtb and Human interlogs[10] derived using complete protein-protein interaction (PPIs) from DIP and HitPredict databases and binding, experimental and co-expressing PPIs from STRING database. These HMIs were processed through functional annotations[11] using common ontology terms for Mtb and Human proteins.[12] Further, co-expressing Mtb proteins and Human proteins were integrated[13],[14] to infer significant HMIs. The significant HMIs were used to construct a protein interactions network. A core HMIs network was generated using Cytoscape software.[15] Further, the topological properties of the core interaction network were analyzed. Gene Ids of Human proteins in the core interactions network were mapped in Kyoto Encyclopedia of Genes and Genomes (KEGG)[16] pathways to identify critical proteins involved in TB pathways.


   Results Top


Predicted Human - Mycobacterium tuberculosis protein interactions

Using I2H, as discussed in the methods section, 20,891 HMIs were predicted. 73 Mtb and Human interlogs were identified from DIP database PPIs data, 140 from HitPredict database and 7,711 Mtb and Human interlogs from String database PPIs data. Filtering the predicted HMIs with Mtb and Human interlogs, as represented in [Figure 1], generated 485 consistent HMIs. It was observed from ontology terms of Mtb proteins and Human proteins in the consistent HMIs that there were four common biological processes, three common molecular functions, and two common cellular components. The extraction of these proteins from consistent HMIs produced 283 HMIs as significant interactions. In the consistent HMIs, there were 290 Human proteins with at least one common interacting Mtb protein partner and 22 Mtb proteins with at least one common interacting Human protein partner. Co-expression of these Human proteins in multiple microarray experiments substantiated 230 consistent HMIs as significant interactions, while the co-expression of Mtb proteins substantiated 177 consistent HMIs as significant interactions. Overall, the total number of unique and significant HMIs derived through the analysis was 419.
Figure 1: The consistent HMIs. HMIs predicted by both in silico two hybrid and Interlog methods are represented in intersection (at column width)

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Human - Mycobacterium tuberculosis protein interactions network

A protein interaction network generated for the significant HMIs is shown in the following [Figure 2]. It was observed from the interaction network that there were 251 Human proteins and 20 Mtb proteins. 11 of the Mtb proteins were previously revealed to play crucial roles during infection;[3],[17] the other nine Mtb proteins, viz., accD1, dnaJ2, echA8, gmdA, Mdh, Nuo, pckA, Rv2970A, and sahH are the new and significant proteins from the present study. The top highest degree nodes of Mtb were observed to be dnaK with a value of 147 followed by rpoC with 59 and atpD with a value of 47. The number of interactions for these nodes was measured to be >50% of the total number of interactions in the HMIs network. Therefore, the removal of such nodes causes fatality.[18] The top highest degree nodes of Human were observed to be DNJA4 with a value of seven, followed by ENO, TUFM, and MRPS12 with a value of six and ATP5A1 and HSPA9 with a value of five. These nodes were measured to contribute more than 8% of the total number of interactions in the HMIs network. Therefore, the study of these nodes would help in understanding the regulatory mechanisms involved in TB disease.
Figure 2: An interactions network of significant HMIs. Green colored circular balls represent Human proteins while pink colored circular balls represent Mtb proteins. Black colored lines represent the interactions between the Human and Mtb proteins (at full page width)

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Hypothetical protein

In the interactions network, Rv2970A, a conserved hypothetical protein of Mtb, was found to interact with four Human proteins, viz., ADH1B, ADH1C, ADH1A, and ADH4. These Human proteins were different isoforms of alcohol dehydrogenase enzyme. Recently, it was demonstrated that the alcohol metabolizing enzyme is genetically associated with the risk of causing TB.[19] Therefore, Rv2970A would have properties similar to the alcohol dehydrogenase enzyme and would play crucial functions during TB infection.

Pathway analysis

Gene Ids of 251 Human proteins in the significant HMIs were extracted from the protein table of the NCBI genome and were mapped for pathways with pink and blue color-coding at the KEGG database. It indicated that the six Human proteins, namely, HSPA9 (HSP70 gene family), HSPD1 (HSP65 gene family) and ATP6VOB, ATP6VOC, ATP6VOD1 and ATP6VOD2 (V-ATPase gene family) of the HMIs network are involved in TB pathway. It was reported that HSPA9 plays a role in the proliferation and maintenance of the cells[20] while HSPD1 plays a role in the survival and inflammatory responses.[21] ATP6V0C, ATP6V0B, ATP6V0D1, and ATP6V0D2 are the V-type proton ATPase subunit isoforms[22] that involve in phagosome maturation arrest[23] which cause the inhibition of antigen presentation. Thus, these Human proteins of the HMIs network provide insights into the TB infection and successive pathways.


   Discussion Top


The TB pathway indicated that dnaK chaperone of Mtb interacts with HSPD1/HSPA9 of Human proteins, which triggers the TLR4 and TLR2 and TLR1/6 protein to initiate toll-like receptor signaling pathway. The eno (enolase), tuf (elongation factor Tu), gap (glyceraldehyde 3-phosphate dehydrogenase) and atpA (ATP synthase subunit alpha) genes of Mtb trigger toll-like receptor pathway through interaction with HSPA9 that interacts with TLR2 and TLR1/6. Both atpA and aptD (ATP synthase subunit beta) of Mtb interacts with V-ATPase proteins such as ATP6V0D1, ATP6V0D2, and ATP6V0B for phagosome maturation arrest while atpD also interact with ATP6V0C. From the DEG database,[24] all these Mtb proteins were observed to be essential gene products. It was observed from the Tuberculist[25] database that dnaK is involved in virulence, detoxification and adaptation processes, eno, gap, atpA and atpD are involved in intermediary metabolism and respiration processes while tuf is involved in information pathways. The significance of Mtb proteins in TB pathway is represented in [Table 1]. The atpD, atpA, dnaK and eno were the already known drug targets.[3] Therefore, tuf and gap would guide the researchers to investigate these as the novel drug targets.
Table 1: Significance of Mycobacterium tuberculosis proteins in tuberculosis pathways

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   Conclusion Top


Using I2H system, the present study predicted 20,891 HMIs. Integration of HMIs with the Human and Mtb interlogs identified 485 consistent HMIs. These HMIs were further processed through functional annotations and co-expression analysis to transform into 419 significant HMIs. During this process, nine novel and significant Mtb proteins involved in TB infection were identified. An interaction network of the significant HMIs revealed few critical and hypothetical Mtb proteins. Further, the present study uncovered tuf and gap as novel Mtb proteins to investigate and develop therapeutic approaches against TB.

Acknowledgments

We acknowledge Bioinformatics Infrastructure Facility, University of Hyderabad, India.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
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    Figures

  [Figure 1], [Figure 2]
 
 
    Tables

  [Table 1]



 

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