- Research article
- Open Access
Prediction of host - pathogen protein interactions between Mycobacterium tuberculosis and Homo sapiens using sequence motifs
© Huo et al. 2015
- Received: 14 October 2014
- Accepted: 13 March 2015
- Published: 26 March 2015
Emergence of multiple drug resistant strains of M. tuberculosis (MDR-TB) threatens to derail global efforts aimed at reigning in the pathogen. Co-infections of M. tuberculosis with HIV are difficult to treat. To counter these new challenges, it is essential to study the interactions between M. tuberculosis and the host to learn how these bacteria cause disease.
We report a systematic flow to predict the host pathogen interactions (HPIs) between M. tuberculosis and Homo sapiens based on sequence motifs. First, protein sequences were used as initial input for identifying the HPIs by ‘interolog’ method. HPIs were further filtered by prediction of domain-domain interactions (DDIs). Functional annotations of protein and publicly available experimental results were applied to filter the remaining HPIs. Using such a strategy, 118 pairs of HPIs were identified, which involve 43 proteins from M. tuberculosis and 48 proteins from Homo sapiens. A biological interaction network between M. tuberculosis and Homo sapiens was then constructed using the predicted inter- and intra-species interactions based on the 118 pairs of HPIs. Finally, a web accessible database named PATH (Protein interactions of M. tuberculosis and Human) was constructed to store these predicted interactions and proteins.
This interaction network will facilitate the research on host-pathogen protein-protein interactions, and may throw light on how M. tuberculosis interacts with its host.
- Mycobacterium Tuberculosis
- Functional Annotation
- Intraspecific Interaction
- Query Protein Sequence
Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), is a major global health concern . According to the World Health Organization (WHO) report , there were an estimated 8.7 million new cases of TB (13% co-infected with HIV) and 1.4 million TB-related deaths in 2011. Clearly, the number of TB-related deaths in single year is alarmingly higher than the roughly 300,000 deaths reported for the bird flu pandemic in 2009 . Further, the regimens recommended for the treatment of TB are complex, often very long and include highly toxic drugs that have side effects. An antibiotic course consisting of four first-line drugs like isoniazid, rifampicin, ethambutol and pyrazinamide for six months is recommended for treatment of TB. These first-line drugs were discovered more than 50 years ago [2,4]. Drug discovery for TB continues to lag behind. Co-infection with retroviruses like HIV further complicates TB treatment. Emergence of multi-drug resistant and extensively-drug resistant strains of Mycobacterium has threatened to derail global efforts for reigning in this pathogen . Therefore, there is an urgent need to develop new anti-mycobacterial drugs  through an understanding of the genetics and physiology of M. tuberculosis.
M. tuberculosis primarily infects the respiratory system where it encounters alveolar macrophages and dendritic cells patrolling the lungs. However, the bacterium has an uncanny ability to survive the onslaught and in fact it uses the host macrophages for replication . Virulence factors like an unusual cell wall made up of mycolic acid, UreC gene that prevents acidification of phagosomes, and the ability of the pathogen to neutralize reactive nitrogen and oxygen intermediates using reductases helps the bacterium evade the host immune system. In addition to macrophages, T-cells have been shown to participate in host cell response against mycobacterium [6,7]. However, mycobacterium evades elimination by the host immune response and causes disease. Therefore, it is essential to study the interactions between M. tuberculosis and the host to learn how these bacteria cause disease . The availability of the complete genome sequence of the pathogen M. tuberculosis  and the host Homo sapiens  provides an essential tool for prediction of these host-pathogen protein interactions.
Host-pathogen protein interactions (HPIs) are often involved in the pathogen’s strategy to invade the host organism, breach the host’s immune defenses, as well as replicate and persist within the organism [10,11]. Experimentally, there are two main approaches for detecting interacting proteins: binary approaches such as the yeast two-hybrid (Y2H) system and luminescence-based mammalian interactome mapping and co-complex methods such as co-immunoprecipitation (coIP) coupled with mass spectrometry (MS) . However, these methods are time-consuming and expensive, especially when adopted in high-throughput mode . Therefore, many computational methods have been developed to improve the coverage, accuracy, and efficiency in identifying protein pairs. These methods for predicting protein-protein interaction (PPI) take advantages of high-throughput data  and are based on protein sequence, structural and genomic features that are related to interactions and functional relationships [15,16], including phylogenetic profile [17,18], gene neighbor and gene cluster methods [19,20] and interologs [21,22]. Interologs, also referred to as homologous PPI method, is based on the assumption that homologous proteins preserve their ability to interact . Recently, it has been applied for not only recognizing PPIs within an individual organism [24,25], but has also been used to detect host-pathogen protein interactions [26,27].
In this work, we developed a systematic flow to predict the HPIs between M. tuberculosis and Homo sapiens based on sequence motifs. First, protein sequences were used as initial input for identifying the HPIs between M. tuberculosis and Homo sapiens by ‘interolog’ method. The HPIs were further filtered by domain-domain interactions (DDIs) prediction. Then, protein functional annotations and existing experiments results were applied to remaining HPIs. As a result, 118 pairs of HPI were identified, which involve 43 proteins from M. tuberculosis and 48 proteins from Homo sapiens. Intra-species PPIs were further predicted for the proteins from M. tuberculosis and proteins from Homo sapiens using VisANT , Reactome , InteroPorc , IntAct , DIP , MPIDB , MINT , and HPRD . A biological interaction network between M. tuberculosis and Homo sapiens was then constructed by the predicted inter- and intra-species interactions. Finally, a database named PATH (Protein interactions of M.tuberculosis and Human) was constructed to store these predicted interactions and proteins.
Identifying HPIs by sequence comparison
To predict homologs, the basic local alignment search tool BLAST (Basic local alignment search tool)  was employed to compute sequence similarities. Query protein sequences were aligned against all sequences with known interactions stored in the databases BIPS  and HPIDB . BIPS and HPIDB are integrated databases including several data sources such as DIP  and IntAct , and both of the databases allow the users to set the parameters freely. The e-value and identity parameters were set to 1e-10 and 30 respectively, and the source of target interactors was set to Homo sapiens (taxid:9606). The query protein sequences were obtained from TB database .
Detecting domain-domain interactions (DDIs)
To identify DDIs, the proteome of M. tuberculosis and Homo sapiens was aligned with Pfam families or domains with an E-value cut-off of 1e-10 using the Pfam-map program . Then, protein-domain databases including 3DID , iPfam , DOMINE , DAPID  were selected to draw the DDI map.
Filtering HPIs by biological context or functional annotation
The information of each protein in the HPI pairs (including subcellular location, tissue specificity, biological process, molecular function, and cellular component) was obtained from the Uniprot website (www.uniprot.org). If the functional annotation of the pair of interactors in the quasi-credible HPI was found to correspond with at least one of the defined terms, the quasi-credible HPI was selected and upgraded as credible HPIs. The terms were selected from previously published studies on the infection and pathology of MTB [48-51].
Identifying intraspecific PPI network in Homo sapiens and Mycobacterium tuberculosis
The protein A from Homo sapiens, and protein B from Mycobacterium tuberculosis, that were involved in a HPI, were further screened against PPI databases to identify intraspecific PPIs. The resource of the intraspecific PPI for Mycobacterium tuberculosis included VisANT, Reactome, InteroPorc, IntAct, DIP, MPIDB, MINT, whereas for Homo sapiens, IntAct, HPRD, MINT, Reactome, DIP were included. We also attempted to use more databases such as virusmint , virhostnet , and STRING , while the number of PPIs would not be increased due to the overlaps and redundancy among the databases.
The interspecific interactions between Homo sapiens and Mycobacterium tuberculosis
The human-MTB specific DDIs in different databases
Number of Host-pathogen DDIs
The “keywords filter” and its number of corresponding hits
Number of corresponding proteins
T cell (human)
Dendritic cell (human)
B cell (human)
Toll-like receptor (human)
We checked the validity of these predictions by assessing the specificity and sensitivity. Random sets or true negatives were usually used for calculating the specificity [38,61]. In our work, we used the negatome database  as a source for non-interactions. 6532 non-interacting pairs from negatome as a reference set were processed by our method including sequence comparison and DDI detection. There were 618 pairs remained after the BLAST step, and they were further narrowed down to 376 pairs after DDI filter. Specificity was calculated as the percentage of correctly predicted true negatives out of 6532 non-interacting pairs. Thus the specificity of our method was 94.2% ((6532-376)/6532). Since gold-standard datasets of experimentally verified human-MTB PPIs are not readily available, we compared our predictions with previous reports to assess the sensitivity and accuracy. Our predictions included 23 MTB proteins (53.5%) that were suggested to play a significant role in the infection and intracellular survival [50,63-65]. In addition, we also enriched our results with the KEGG pathway and identified more proteins involved in the HPI such as Rv0934, Rv1411c and Rv3875 [66-68]. The coverage of our method depended on the previous experimental observations of similar interactions (template PPI), thus the coverage and accuracy would be increased as more template PPIs were identified.
To improve the accuracy, an increasing number of approaches have been developed taking advantage of the information residing in the motifs or structures. A structure-based interaction network between MTB and human was constructed recently emphasizing the importance of physical interactions . This structure-based prediction could probably eliminate true negatives, while it was limited by the number of known protein complexes (templates). However, a simultaneous time-course microarray method was developed, which aimed at discovering the HPIs experimentally instead of solely depending on the known templates [70,71]. The experiment-based method would make biological sense, while the application of the microarray may not be easy and convenient to any species. All in all, each method would have a good performance in some aspects, and the credibility of known templates was the key point to the “interologs” predictions that mainly based on the sequence comparison.
The intraspecific interactions among Homo sapiens and Mycobacterium tuberculosis
For the 43 proteins of Mycobacterium tuberculosis and 48 proteins of Homo sapiens in the host-pathogen interactions, intraspecific interactions were further studied. The interactions of Mycobacterium tuberculosis originated from 7 databases: VisANT, Reactome, InteroPorc, IntAct, DIP, MPIDB, and MINT, whereas the Homo sapiens interactions originated from 5 databases: IntAct, HPRD, MINT, Reactome, and DIP. By removing the redundancy from various data sources, there were 587 direct intraspecific interactions in Mycobacterium tuberculosis containing 374 MTB proteins and 7157 interactions in Homo sapiens containing 3062 human proteins.
Host-pathogen interaction map and key proteins
The structure of PATH
In this work, we present a specific and integrated database (PATH), which is publicly available and incorporates the predicted interspecific and intraspecific interactions between Homo sapiens and Mycobacterium tuberculosis. To our knowledge, PATH is the first specialized database for HPIs on Mycobacterium tuberculosis. Our interactions prediction model combined in silico algorithms with biological functional annotations. In this study, 118 credible HPIs were identified and stored in the PATH database. In PATH database, users can acquire the interspecific and intraspecific interactions between MTB and human and their related protein interactors by keyword search. The PATH database might facilitate understanding of mechanisms that causes TB, hence help to develop new therapeutic intervention tools for TB.
We would like to thank staff members of TJAB for their technical support. This work was supported by grants from the State Key Development Program for Basic Research of the Ministry of Science and Technology of China (973 Project Grant Nos 2014CB542800, 2011CB915501 and 2011CB910304).
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