Global Protein−Protein Interaction Network in the Human Pathogen Mycobacterium tuberculosis H37Rv

The protein interactome of the citrus Huanglongbing pathogen Candidatus Liberibacter asiaticus

The bacterium Candidatus Liberibacter asiaticus (CLas) causes citrus Huanglongbing disease. Our understanding of the pathogenicity and biology of this microorganism remains limited because CLas has not yet been cultivated in artificial media. Its genome is relatively small and encodes approximately 1136 proteins, of which 415 have unknown functions. Here, we use a high-throughput yeast-two-hybrid (Y2H) screen to identify interactions between CLas proteins, thus providing insights into their potential functions. We identify 4245 interactions between 542 proteins, after screening 916 bait and 936 prey proteins. The false positive rate of the Y2H assay is estimated to be 2.9%. Pull-down assays for nine protein-protein interactions (PPIs) likely involved in flagellar function support the robustness of the Y2H results. The average number of PPIs per node in the CLas interactome is 15.6, which is higher than the numbers previously reported for interactomes of free-living bacteria, suggesting that CLas genome reduction has been accompanied by increased protein multi-functionality. We propose potential functions for 171 uncharacterized proteins, based on the PPI results, guilt-by-association analyses, and comparison with data from other bacterial species. We identify 40 hub-node proteins, including quinone oxidoreductase and LysR, which are known to protect other bacteria against oxidative stress and might be important for CLas survival in the phloem. We expect our PPI database to facilitate research on CLas biology and pathogenicity mechanisms.

Phosphorylation of CFP10 modulates Mycobacterium tuberculosis virulence

IMPORTANCE

Secreted virulence factors play a critical role in bacterial pathogenesis. Virulence effectors not only help bacteria to overcome the host immune system but also aid in establishing infection. Mtb, which causes tuberculosis in humans, encodes various virulence effectors. Triggers that modulate the secretion of virulence effectors in Mtb are yet to be fully understood. To gain mechanistic insight into the secretion of virulence effectors, we performed high-throughput proteomic studies. With the help of system-level protein-protein interaction network analysis and empirical validations, we unravelled a link between phosphorylation and secretion. Taking the example of the well-known virulence factor of CFP10, we show that the dynamics of CFP10 phosphorylation strongly influenced bacterial virulence and survival ex vivo and in vivo. This study presents the role of phosphorylation in modulating the secretion of virulence factors.

In silico approaches uncovering the systematic function of N-phosphorylated proteins in human cells

Phosphorylation plays a key role in the regulation of protein function. In addition to the extensively studied O-phosphorylation of serine, threonine, and tyrosine, emerging evidence suggests that the non-canonical phosphorylation of histidine, lysine, and arginine termed N-phosphorylation, exists widely in eukaryotes. At present, the study of N-phosphorylation is still in its infancy, and its regulatory role and specific biological functions in mammalian cells are still unknown. Here, we report the in silico analysis of the systematic biological significance of N-phosphorylated proteins in human cells. The protein structural and functional domain enrichment analysis revealed that N-phosphorylated proteins are rich in RNA recognition motif, nucleotide-binding and alpha-beta plait domains. The most commonly enriched biological pathway is the metabolism of RNA. Besides, arginine phosphorylated (pArg) proteins are highly related to DNA repair, while histidine phosphorylated (pHis) proteins may play a role in the regulation of the cell cycle, and lysine phosphorylated (pLys) proteins are linked to cellular stress response, intracellular signal transduction, and intracellular transport, which are of great significance for maintaining cell homeostasis. Protein-protein interaction (PPI) network analysis revealed important hub proteins (i.e., SRSF1, HNRNPA1, HNRNPC, SRSF7, HNRNPH1, SRSF2, SRSF11, HNRNPD, SRRM2 and YBX1) which are closely related to neoplasms, nervous system diseases, and virus infection and have potential as therapeutic targets. Those proteins with clinical significance are worthy of attention, and the rational considerations of N-phosphorylation in occurrence and progression of diseases might be beneficial for further translational applications.

Functional analysis of Escherichia coli K12 toxin-antitoxin systems as novel drug targets using a network biology approach

Bacterial resistance to various drugs and antibiotics has become a significant issue in the fight against infectious diseases. Due to the presence of diverse toxin-antitoxin (TA) systems, bacteria undergo adaptive metabolic alterations and can tolerate the effects of drugs and antibiotics. Bacterial TA systems are unique and can be therapeutic targets for developing new antimicrobial agents, owing to their ability to influence bacterial fate. With this background, our study aims to identify novel drug targets against Escherichia coli K12 MG1655 antitoxin using homology modelling approach. In this study, the protein-protein interaction network of 87 E. coli K12 MG1655 TA systems identified through literature mining was screened for the identification of hub proteins. The model evaluation, assessment, and homology modelling of the hub proteins were evaluated. Furthermore, computer-aided mathematical models of selected phytochemicals have been tested against the identified hub proteins. The TA system was functionally enriched in regulation of cell growth, negative regulation of cell growth, regulation of mRNA stability, mRNA catabolic process and RNA phosphodiester bond hydrolysis. RelE, RelB, MazE, MazF, MqsR, MqsA, and YoeB were identified as hub proteins. The robustness and superior quality of the RelB and MazE modelled structure were discovered by model evaluation, quality assessment criteria, and homology modelling of hub proteins. Clorobiocin was found to be a strong inhibitor by docking these modelled structures. Clorobiocin could be utilized as an antibacterial agent against multidrug resistant E. coli which may inactivate antitoxins and cause programmed cell death.

Computational Network Inference for Bacterial Interactomics

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.

Protein-protein interaction network and potential drug target candidates of Streptococcus suis

AIMS

This study aimed to explore potential drug targets of Streptococcus suis at the system level.

METHODS AND RESULTS

A homologous protein mapping method was used in the construction of a protein-protein interaction (PPI) network of S. suis, which presented 1147 non-redundant interaction pairs among 286 proteins. The parameters of PPI networks were calculated and showed scale-free network properties. In all, 41 possibly essential proteins identified from 47 highly connected proteins were selected as potential drug target candidates. Of these proteins, 30 were already regarded as drug targets in other bacterial species. Six transporters with high connections to other functional proteins were identified as probably not essential but important functional proteins. Afterward, the subnetwork centred with cell division protein FtsZ was used in confirming the PPI network through bacterial two-hybrid analysis.

CONCLUSIONS

The predicted PPI network covers 13·04% of the proteome in S. suis. The selected 41 potential drug target candidates are conserved between S. suis and several model bacteria.

SIGNIFICANCE AND IMPACT OF THE STUDY

The predictions included proteins known to be drug targets, and a verifying experiment confirmed the reliability of predicted interactions. This work is the first to present systematic computational PPI data for S. suis and provides potential drug targets, which are valuable in exploring novel anti-streptococcus drugs.

Multi-omic regulatory networks capture downstream effects of kinase inhibition in Mycobacterium tuberculosis

The ability of Mycobacterium tuberculosis (Mtb) to adapt to diverse stresses in its host environment is crucial for pathogenesis. Two essential Mtb serine/threonine protein kinases, PknA and PknB, regulate cell growth in response to environmental stimuli, but little is known about their downstream effects. By combining RNA-Seq data, following treatment with either an inhibitor of both PknA and PknB or an inactive control, with publicly available ChIP-Seq and protein–protein interaction data for transcription factors, we show that the Mtb transcription factor (TF) regulatory network propagates the effects of kinase inhibition and leads to widespread changes in regulatory programs involved in cell wall integrity, stress response, and energy production, among others. We also observe that changes in TF regulatory activity correlate with kinase-specific phosphorylation of those TFs. In addition to characterizing the downstream regulatory effects of PknA/PknB inhibition, this demonstrates the need for regulatory network approaches that can incorporate signal-driven transcription factor modifications.

Internetwork connectivity of molecular networks across species of life

Molecular interactions are studied as independent networks in systems biology. However, molecular networks do not exist independently of each other. In a network of networks approach (called multiplex), we study the joint organization of transcriptional regulatory network (TRN) and protein-protein interaction (PPI) network. We find that TRN and PPI are non-randomly coupled across five different eukaryotic species. Gene degrees in TRN (number of downstream genes) are positively correlated with protein degrees in PPI (number of interacting protein partners). Gene-gene and protein-protein interactions in TRN and PPI, respectively, also non-randomly overlap. These design principles are conserved across the five eukaryotic species. Robustness of the TRN-PPI multiplex is dependent on this coupling. Functionally important genes and proteins, such as essential, disease-related and those interacting with pathogen proteins, are preferentially situated in important parts of the human multiplex with highly overlapping interactions. We unveil the multiplex architecture of TRN and PPI. Multiplex architecture may thus define a general framework for studying molecular networks. This approach may uncover the building blocks of the hierarchical organization of molecular interactions.

Analysis of the RNA and Protein Complexome by Grad-seq

The complexome of a cell is the entirety of its complexes. Complexome capture studies have mostly focused on protein-protein interactions, which has left a gap in our knowledge of the global interactions of RNAs. To overcome these limitations, we recently introduced gradient profiling by sequencing (Grad-seq), which analyzes in a high-throughput fashion soluble cellular complexes after their separation in a glycerol gradient by fraction-wise RNA-seq and mass spectrometry. Here, we describe a detailed Grad-seq protocol for Streptococcus pneumoniae, which should also be applicable to other bacterial species.

A Multi-Pronged Computational Pipeline for Prioritizing Drug Target Strategies for Latent Tuberculosis

Tuberculosis is one of the deadliest infectious diseases worldwide and the prevalence of latent tuberculosis acts as a huge roadblock in the global effort to eradicate tuberculosis. Most of the currently available anti-tubercular drugs act against the actively replicating form of Mycobacterium tuberculosis (Mtb), and are not effective against the non-replicating dormant form present in latent tuberculosis. With about 30% of the global population harboring latent tuberculosis and the requirement for prolonged treatment duration with the available drugs in such cases, the rate of adherence and successful completion of therapy is low. This necessitates the discovery of new drugs effective against latent tuberculosis. In this work, we have employed a combination of bioinformatics and chemoinformatics approaches to identify potential targets and lead candidates against latent tuberculosis. Our pipeline adopts transcriptome-integrated metabolic flux analysis combined with an analysis of a transcriptome-integrated protein-protein interaction network to identify perturbations in dormant Mtb which leads to a shortlist of 6 potential drug targets. We perform a further selection of the candidate targets and identify potential leads for 3 targets using a range of bioinformatics methods including structural modeling, binding site association and ligand fingerprint similarities. Put together, we identify potential new strategies for targeting latent tuberculosis, new candidate drug targets as well as important lead clues for drug design.

Shotgun EM of mycobacterial protein complexes during stationary phase stress

There is little structural information about the protein complexes conferring resistance in Mycobacterium tuberculosis (Mtb) to anti-microbial oxygen and nitrogen radicals in the phagolysosome. Here, we expose the model Mycobacterium, Mycobacterium smegmatis, to simulated oxidative-stress conditions and apply a shotgun EM method for the structural detection of the resulting protein assemblies. We identified: glutamine synthetase I, essential for Mtb virulence; bacterioferritin A, critical for Mtb iron regulation; aspartyl aminopeptidase M18, a protease; and encapsulin, which produces a cage-like structure to enclose cargo proteins. After further investigation, we found that encapsulin carries dye-decolourising peroxidase, a protein antioxidant, as its primary cargo under the conditions tested.

Conserved Conformational Changes in the Regulation of Mycobacterium tuberculosis MazEF-mt1

Toxin-antitoxin (TA) systems, which regulate many important cellular processes, are abundantly present in prokaryotic organisms. MazEF is a common type of TA system implicated in the formation of "persisters cells" of the pathogen Mycobacterium tuberculosis, which contains 10 such systems. However, the exact function and inhibition mode of each MazF protein are not quite understood. Here, we report four high-resolution crystal structures of MazF-mt1 in various forms, including one in complex with MazE-mt1. The toxin displayed two unique interlocked loops that allow the formation of a tight dimer. These loops would open upon interacting with the MazE-mt1 antitoxin mediated by the last two helices of MazE-mt1. With our structure-based design, a mutant that could bind to the antitoxin with an enhanced affinity was produced. Combined crystallographic and biochemical studies further revealed that the binding affinity of MazE-mt1 to MazF-mt1 was mainly attributed to its α3 helical region, while the terminal helix η1 contributes very little or even negatively to the association of the pair, in stark contrast to the MazEF-mt9 system. This study provides structural insight into the binding mode and the inhibition mechanism of the MazE/F-mt1 TA pair, which may reflect the functional differences between different TA systems.

Computational Identification of the Proteins Associated With Quorum Sensing and Biofilm Formation in Mycobacterium tuberculosis

With prolonged therapy and increased instances of drug resistance, tuberculosis is viewed as a serious infectious disease causing high mortality. Emerging concepts in Mycobacterium tuberculosis pathogenicity include biofilm formation, which endows bacterial survival in the host for a long time. To tackle chronic tuberculosis infection, a detailed understanding of the bacterial survival mechanisms is crucial. Using comparative genomics and literature mining, 115 M. tuberculosis proteins were shortlisted for their likely association with biofilm formation or quorum sensing. These include essential genes such as secA2, lpqY-sugABC, Rv1176c, and Rv0195, many of which are also known virulence factors. Furthermore, the functional relationship among these proteins was established by considering known protein-protein interactions, regulatory interactions, and gene expression correlation data/information. Graph centrality and motif analyses predicted the importance of proteins, such as Rv0081, DevR, RegX3, Rv0097, and Rv1996 in M. tuberculosis biofilm formation. Analysis of conservation across other biofilm-forming bacteria suggests that most of these genes are conserved in mycobacteria. As the processes, such as quorum sensing, leading to biofilm formation involve diverse pathways and interactions between proteins, these system-wide studies provide a novel perspective toward understanding mycobacterial persistence.

Protein isoelectric point distribution in the interactomes across the domains of life

The distribution of the protein isoelectric point (pI) in the protein-protein interaction (PPI) networks across the domains of life has not been investigated yet. This work attempts to correlate the pI with the number of direct interacting partners in the experimentally supported networks involving 226.085 PPIs from 14 various organisms including human, mouse, yeast, bacteria, viruses and 53.606 virus-host interactions. The results showed that the acidic proteins (pI<3) have the highest average number of interactions in eukaryotes, while in bacteria more neutral proteins. On the contrary, the basic proteins (pI>11) have the lowest average number of interactions in human, mouse, yeast, bacteria and human-viral interactomes and the highest average in intraviral interactomes. We examined the correlation of the pI of the interacting partners by calculating the assortativity index of various PPI networks. We found that the interactions between the acidic, neutral and basic proteins have a fairly random mix, implying weak if any association between the acidic and basic proteins. Furthermore, protein features such as biological function, structurally order and disorder, subcellular localization, and homodimerization were classified according to pI in prokaryote and eukaryote proteomes.

Protein-protein complexes as targets for drug discovery against infectious diseases

Antibiotics are therapeutic agents against bacterial infections, however, the emergence of multiple and extremely drug-resistant microbes (Multi-Drug Resistant and Extremely Drug-Resistant) are compromising the effectiveness of the currently available treatment options. The drug resistance is not a novel crisis, the current pace of drug discovery has failed to compete with the growth of MDR and XDR pathogenic strains and therefore, it is highly central to find out novel antimicrobial drugs with unique mechanisms of action which may reduce the burden of MDR and XDR pathogenic strains. Protein-protein interactions (PPIs) are involved in a countless of the physiological and cellular phenomena and have become an attractive target to treat the diseases. Therefore, targeting PPIs in infectious agents may offer a completely novel strategy of intervention to develop anti-infective drugs that may combat the ever-increasing rate of drug resistant strains. This chapter describes how small molecule candidate inhibitors that are capable of disrupting the PPIs in pathogenic microbes and it could be an alternative lead discovery strategy to obtain novel antibiotics. Over the last three decades, there has been increasing efforts focused on the manipulation of PPIs in order to develop novel therapeutic interventions. The diversity and complexity of such a complex and highly dynamic systems pose many challenges in targeting PPIs by drug-like molecules with necessary selectivity and potency. Traditional and novel drug discovery strategies have provided tools for designing and assessing PPI inhibitors against infectious diseases.

In Vivo Methods to Study Protein-Protein Interactions as Key Players in Mycobacterium Tuberculosis Virulence

Studies on protein–protein interactions (PPI) can be helpful for the annotation of unknown protein functions and for the understanding of cellular processes, such as specific virulence mechanisms developed by bacterial pathogens. In that context, several methods have been extensively used in recent years for the characterization of Mycobacterium tuberculosis PPI to further decipher tuberculosis (TB) pathogenesis. This review aims at compiling the most striking results based on in vivo methods (yeast and bacterial two-hybrid systems, protein complementation assays) for the specific study of PPI in mycobacteria. Moreover, newly developed methods, such as in-cell native mass resonance and proximity-dependent biotinylation identification, will have a deep impact on future mycobacterial research, as they are able to perform dynamic (transient interactions) and integrative (multiprotein complexes) analyses.

Structural and Biochemical Characterization of the Cognate and Heterologous Interactions of the MazEF-mt9 TA System

Toxin-antitoxin (TA) modules widely exist in bacteria, and their activities are associated with the persister phenotype of the pathogen Mycobacterium tuberculosis (M. tb). M. tb causes tuberculosis, a contagious and severe airborne disease. There are 10 MazEF TA systems in M. tb that play important roles in stress adaptation. How the antitoxins antagonize toxins in M. tb or how the 10 TA systems crosstalk to each other are of interest, but the detailed molecular mechanisms are largely unclear. MazEF-mt9 is a unique member among the MazEF family due to its tRNase activity, which is usually carried out by the VapC toxins. Here, we present the cocrystal structure of the MazEF-mt9 complex at 2.7 Å. By characterizing the association mode between the TA pairs through various techniques, we found that MazF-mt9 bound not only its cognate antitoxin but also the noncognate antitoxin MazE-mt1, a phenomenon that could be also observed in vivo. Based on our structural and biochemical work, we propose that the cognate and heterologous interactions among different TA systems work together in vivo to relieve the toxicity of MazF-mt9 toward M. tb cells.

Protein-Protein Interactions in Candida albicans

Despite being one of the most important human fungal pathogens, Candida albicans has not been studied extensively at the level of protein-protein interactions (PPIs) and data on PPIs are not readily available in online databases. In January 2018, the database called "Biological General Repository for Interaction Datasets (BioGRID)" that contains the most PPIs for C. albicans, only documented 188 physical or direct PPIs (release 3.4.156) while several more can be found in the literature. Other databases such as the String database, the Molecular INTeraction Database (MINT), and the Database for Interacting Proteins (DIP) database contain even fewer interactions or do not even include C. albicans as a searchable term. Because of the non-canonical codon usage of C. albicans where CUG is translated as serine rather than leucine, it is often problematic to use the yeast two-hybrid system in Saccharomyces cerevisiae to study C. albicans PPIs. However, studying PPIs is crucial to gain a thorough understanding of the function of proteins, biological processes and pathways. PPIs can also be potential drug targets. To aid in creating PPI networks and updating the BioGRID, we performed an exhaustive literature search in order to provide, in an accessible format, a more extensive list of known PPIs in C. albicans.

Protein interaction networks revealed by proteome coevolution

Residue-residue coevolution has been observed across a number of protein-protein interfaces, but the extent of residue coevolution between protein families on the whole-proteome scale has not been systematically studied. We investigate coevolution between 5.4 million pairs of proteins in Escherichia coli and between 3.9 millions pairs in Mycobacterium tuberculosis We find strong coevolution for binary complexes involved in metabolism and weaker coevolution for larger complexes playing roles in genetic information processing. We take advantage of this coevolution, in combination with structure modeling, to predict protein-protein interactions (PPIs) with an accuracy that benchmark studies suggest is considerably higher than that of proteome-wide two-hybrid and mass spectrometry screens. We identify hundreds of previously uncharacterized PPIs in E. coli and M. tuberculosis that both add components to known protein complexes and networks and establish the existence of new ones.

Mycobacterium tuberculosis nucleoside diphosphate kinase shows interaction with putative ATP binding cassette (ABC) transporter, Rv1273c

Protein-protein interactions are crucial for all biological processes. Compiling this network provides many new insights into protein function and gives directions for the development of new drugs targeted to the pathogen. Mycobacterium tuberculosis Nucleoside diphosphate kinase (Mtb Ndk) has been reported to promote survival of mycobacterium within the macrophage and contribute significantly to mycobacterium virulence. Hence, the present study was aimed to identify and characterize the interacting partner for Ndk. The in vitro experiments, pull down and far western blotting have demonstrated that Mtb Ndk interacts with Rv1273c, a probable drug ABC transporter ATP-binding protein annotated to export drugs across the membrane. This observation was further confirmed by molecular docking and dynamic simulations studies. The homology model of Rv1273c was constructed and docked with Mtb Ndk for protein-protein interaction analysis. The critical residues involved at interface of Rv1273c-Ndk interaction were identified. MDS and Principal Component analysis carried out for conformational feasibility and stability concluded that the complex between the two proteins is more stable as compared to apo proteins. Our findings would be expected to improve the dissection of protein-protein interaction network and significantly advance our understanding of tuberculosis infection.Communicated by Ramaswamy H. Sarma.

Redox-Sensing Iron-Sulfur Cluster Regulators

SIGNIFICANCE

Iron-sulfur cluster proteins carry out multiple functions, including as regulators of gene transcription/translation in response to environmental stimuli. In all known cases, the cluster acts as the sensory module, where the inherent reactivity/fragility of iron-sulfur clusters with small/redox-active molecules is exploited to effect conformational changes that modulate binding to DNA regulatory sequences. This promotes an often substantial reprogramming of the cellular proteome that enables the organism or cell to adapt to, or counteract, its changing circumstances. Recent Advances: Significant progress has been made recently in the structural and mechanistic characterization of iron-sulfur cluster regulators and, in particular, the O₂ and NO sensor FNR, the NO sensor NsrR, and WhiB-like proteins of Actinobacteria. These are the main focus of this review.

CRITICAL ISSUES

Striking examples of how the local environment controls the cluster sensitivity and reactivity are now emerging, but the basis for this is not yet fully understood for any regulatory family.

FUTURE DIRECTIONS

Characterization of iron-sulfur cluster regulators has long been hampered by a lack of high-resolution structural data. Although this still presents a major future challenge, recent advances now provide a firm foundation for detailed understanding of how a signal is transduced to effect gene regulation. This requires the identification of often unstable intermediate species, which are difficult to detect and may be hard to distinguish using traditional techniques. Novel approaches will be required to solve these problems.

Protein-protein interactions in bacteria: a promising and challenging avenue towards the discovery of new antibiotics

Antibiotics are potent pharmacological weapons against bacterial infections; however, the growing antibiotic resistance of microorganisms is compromising the efficacy of the currently available pharmacotherapies. Even though antimicrobial resistance is not a new problem, antibiotic development has failed to match the growth of resistant pathogens and hence, it is highly critical to discover new anti-infective drugs with novel mechanisms of action which will help reducing the burden of multidrug-resistant microorganisms. Protein-protein interactions (PPIs) are involved in a myriad of vital cellular processes and have become an attractive target to treat diseases. Therefore, targeting PPI networks in bacteria may offer a new and unconventional point of intervention to develop novel anti-infective drugs which can combat the ever-increasing rate of multidrug-resistant bacteria. This review describes the progress achieved towards the discovery of molecules that disrupt PPI systems in bacteria for which inhibitors have been identified and whose targets could represent an alternative lead discovery strategy to obtain new anti-infective molecules.

Integrating Multifaceted Information to Predict Mycobacterium tuberculosis-Human Protein-Protein Interactions

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.

Identification of drug target candidates of the swine pathogen Actinobacillus pleuropneumoniae by construction of protein-protein interaction network

Porcine pleuropneumonia caused by Actinobacillus pleuropneumoniae has led to severe economic losses in the pig industry worldwide. A. pleuropneumoniae displays various levels of antimicrobial resistance, leading to the dire need to identify new drug targets. Protein-protein interaction (PPI) network can aid the identification of drug targets by discovering essential proteins during the life of bacteria. The aim of this study is to identify drug target candidates of A. pleuropneumoniae from essential proteins in PPI network. The homologous protein mapping method (HPM) was utilized to construct A. pleuropneumoniae PPI network. Afterwards, the subnetwork centered with H-NS was selected to verify the PPI network using bacterial two-hybrid assays. Drug target candidates were identified from the hub proteins by analyzing the topology of the network using interaction degree and homologous comparison with the pig proteome. An A. pleuropneumoniae PPI network containing 2737 non-redundant interaction pairs among 533 proteins was constructed. These proteins were distributed in 21 COG functional categories and 28 KEGG metabolic pathways. The A. pleuropneumoniae PPI network was scale free and the similar topological tendencies were found when compared with other bacteria PPI network. Furthermore, 56.3% of the H-NS subnetwork interactions were validated. 57 highly connected proteins (hub proteins) were identified from the A. pleuropneumoniae PPI network. Finally, 9 potential drug targets were identified from the hub proteins, with no homologs in swine. This study provides drug target candidates, which are promising for further investigations to explore lead compounds against A. pleuropneumoniae.

In Silico Enhancing M. tuberculosis Protein Interaction Networks in STRING To Predict Drug-Resistance Pathways and Pharmacological Risks

Bacterial protein-protein interaction (PPI) networks are significant to reveal the machinery of signal transduction and drug resistance within bacterial cells. The database STRING has collected a large number of bacterial pathogen PPI networks, but most of the data are of low quality without being experimentally or computationally validated, thus restricting its further biomedical applications. We exploit the experimental data via four solutions to enhance the quality of M. tuberculosis H37Rv (MTB) PPI networks in STRING. Computational results show that the experimental data derived jointly by two-hybrid and copurification approaches are the most reliable to train an L₂-regularized logistic regression model for MTB PPI network validation. On the basis of the validated MTB PPI networks, we further study the three problems via breadth-first graph search algorithm: (1) discovery of MTB drug-resistance pathways through searching for the paths between known drug-target genes and drug-resistance genes, (2) choosing potential cotarget genes via searching for the critical genes located on multiple pathways, and (3) choosing essential drug-target genes via analysis of network degree distribution. In addition, we further combine the validated MTB PPI networks with human PPI networks to analyze the potential pharmacological risks of known and candidate drug-target genes from the point of view of system pharmacology. The evidence from protein structure alignment demonstrates that the drugs that act on MTB target genes could also adversely act on human signaling pathways.

Proteome Data Improves Protein Function Prediction in the Interactome of Helicobacter pylori

Helicobacter pylori is a common pathogen that is estimated to infect half of the human population, causing several diseases such as duodenal ulcer. Despite one of the first pathogens to be sequenced, its proteome remains poorly characterized as about one-third of its proteins have no functional annotation. Here, we integrate and analyze known protein interactions with proteomic and genomic data from different sources. We find that proteins with similar abundances tend to interact. Such an observation is accompanied by a trend of interactions to appear between proteins of similar functions, although some show marked cross-talk to others. Protein function prediction with protein interactions is significantly improved when interactions from other bacteria are included in our network, allowing us to obtain putative functions of more than 300 poorly or previously uncharacterized proteins. Proteins that are critical for the topological controllability of the underlying network are significantly enriched with genes that are up-regulated in the spiral compared with the coccoid form of H. pylori Determining their evolutionary conservation, we present evidence that 80 protein complexes are identical in composition with their counterparts in Escherichia coli, while 85 are partially conserved and 120 complexes are completely absent. Furthermore, we determine network clusters that coincide with related functions, gene essentiality, genetic context, cellular localization, and gene expression in different cellular states.

Making the Right Choice: Critical Parameters of the Y2H Systems

Two-hybrid methods remain among the most preferred choices for detecting protein-protein interactions (PPIs) and much of the PPI data in databases have been produced using yeast two-hybrid (Y2H) screens. The Y2H methods are extensively used to detect PPIs because of their scalability and accessibility. Several variants of Y2H methods have been developed and used by different research groups, increasing the accessibility of these methods and their applications in detecting different types of PPIs. However, the availability of variations on the same core methodology emphasizes the need to have a systematic comparison of available Y2H methods in the context of their applicability, coverage and efficiency. In this chapter, we discuss the key parameters of Y2H methods, namely proteins of interest, vectors, libraries, screening strategies, data analysis, and provide a flowchart that should help to decide which Y2H strategy is most appropriate for a protein interaction screen.

Redox-sensing iron-sulfur cluster regulators

SIGNIFICANCE

Iron-sulfur cluster proteins carry out a wide range of functions, including as regulators of gene transcription/translation in response to environmental stimuli. In all known cases, the cluster acts as the sensory module, where the inherent reactivity/fragility of iron-sulfur clusters towards small/redox active molecules is exploited to effect conformational changes that modulate binding to DNA regulatory sequences. This promotes an often substantial re-programming of the cellular proteome that enables the organism or cell to adapt to, or counteract, its changing circumstances. Recent Advances. Significant progress has been made recently in the structural and mechanistic characterization of iron-sulfur cluster regulators and, in particular, the O2 and NO sensor FNR, the NO sensor NsrR, and WhiB-like proteins of Actinobacteria. These are the main focus of this review.

CRITICAL ISSUES

Striking examples of how the local environment controls the cluster sensitivity and reactivity are now emerging, but the basis for this is not yet fully understood for any regulatory family.

FUTURE DIRECTIONS

Characterization of iron-sulfur cluster regulators has long been hampered by a lack of high resolution structural data. Though this still presents a major future challenge, recent advances now provide a firm foundation for detailed understanding of how a signal is transduced to effect gene regulation. This requires the identification of often unstable intermediate species, which are difficult to detect and may be hard to distinguish using traditional techniques. Novel approaches will be required to solve these problems.

Insights from the protein-protein interaction network analysis of Mycobacterium tuberculosis toxin-antitoxin systems

Protein-protein interaction (PPI) network analysis is a powerful strategy to understand M. tuberculosis (Mtb) system level physiology in the identification of hub proteins. In the present study, the PPI network of 79 Mtb toxin-antitoxin (TA) systems comprising of 167 nodes and 234 edges was investigated. The topological properties of PPI network were examined by 'Network analyzer' a cytoscape plugin app and STRING database. The key enriched biological processes and the molecular functions of Mtb TA systems were analyzed by STRING. Manual curation of the PPI data identified four proteins (i.e. Rv2762c, VapB14, VapB42 and VapC42) to possess the highest number of interacting partners. The top 15% hub proteins were identified in the PPI network by employing two statistical measures, i.e. betweenness and radiality by employing cytohubba. Insights gained from the molecular protein models of VapC9 and VapC10 are also documented.

Efficacy of β-lactam/β-lactamase inhibitor combination is linked to WhiB4-mediated changes in redox physiology of Mycobacterium tuberculosis

Mycobacterium tuberculosis (Mtb) expresses a broad-spectrum β-lactamase (BlaC) that mediates resistance to one of the highly effective antibacterials, β-lactams. Nonetheless, β-lactams showed mycobactericidal activity in combination with β-lactamase inhibitor, clavulanate (Clav). However, the mechanistic aspects of how Mtb responds to β-lactams such as Amoxicillin in combination with Clav (referred as Augmentin [AG]) are not clear. Here, we identified cytoplasmic redox potential and intracellular redox sensor, WhiB4, as key determinants of mycobacterial resistance against AG. Using computer-based, biochemical, redox-biosensor, and genetic strategies, we uncovered a functional linkage between specific determinants of β-lactam resistance (e.g. β-lactamase) and redox potential in Mtb. We also describe the role of WhiB4 in coordinating the activity of β-lactamase in a redox-dependent manner to tolerate AG. Disruption of WhiB4 enhances AG tolerance, whereas overexpression potentiates AG activity against drug-resistant Mtb. Our findings suggest that AG can be exploited to diminish drug-resistance in Mtb through redox-based interventions.

The Protein Interactome of Streptococcus pneumoniae and Bacterial Meta-interactomes Improve Function Predictions

The functions of roughly a third of all proteins in Streptococcus pneumoniae, a significant human-pathogenic bacterium, are unknown. Using a yeast two-hybrid approach, we have determined more than 2,000 novel protein interactions in this organism. We augmented this network with meta-interactome data that we defined as the pool of all interactions between evolutionarily conserved proteins in other bacteria. We found that such interactions significantly improved our ability to predict a protein's function, allowing us to provide functional predictions for 299 S. pneumoniae proteins with previously unknown functions. IMPORTANCE Identification of protein interactions in bacterial species can help define the individual roles that proteins play in cellular pathways and pathogenesis. Very few protein interactions have been identified for the important human pathogen S. pneumoniae. We used an experimental approach to identify over 2,000 new protein interactions for S. pneumoniae, the most extensive interactome data for this bacterium to date. To predict protein function, we used our interactome data augmented with interactions from other closely related bacteria. The combination of the experimental data and meta-interactome data significantly improved the prediction results, allowing us to assign possible functions to a large number of poorly characterized proteins.

Bacterial protein meta-interactomes predict cross-species interactions and protein function

Background

Protein-protein interactions (PPIs) can offer compelling evidence for protein function, especially when viewed in the context of proteome-wide interactomes. Bacteria have been popular subjects of interactome studies: more than six different bacterial species have been the subjects of comprehensive interactome studies while several more have had substantial segments of their proteomes screened for interactions. The protein interactomes of several bacterial species have been completed, including several from prominent human pathogens. The availability of interactome data has brought challenges, as these large data sets are difficult to compare across species, limiting their usefulness for broad studies of microbial genetics and evolution.

Results

In this study, we use more than 52,000 unique protein-protein interactions (PPIs) across 349 different bacterial species and strains to determine their conservation across data sets and taxonomic groups. When proteins are collapsed into orthologous groups (OGs) the resulting meta-interactome still includes more than 43,000 interactions, about 14,000 of which involve proteins of unknown function. While conserved interactions provide support for protein function in their respective species data, we found only 429 PPIs (~1% of the available data) conserved in two or more species, rendering any cross-species interactome comparison immediately useful. The meta-interactome serves as a model for predicting interactions, protein functions, and even full interactome sizes for species with limited to no experimentally observed PPI, including Bacillus subtilis and Salmonella enterica which are predicted to have up to 18,000 and 31,000 PPIs, respectively.

Conclusions

In the course of this work, we have assembled cross-species interactome comparisons that will allow interactomics researchers to anticipate the structures of yet-unexplored microbial interactomes and to focus on well-conserved yet uncharacterized interactors for further study. Such conserved interactions should provide evidence for important but yet-uncharacterized aspects of bacterial physiology and may provide targets for anti-microbial therapies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1585-0) contains supplementary material, which is available to authorized users.

A Comparison of Two-Hybrid Approaches for Detecting Protein-Protein Interactions

Two-hybrid systems are one of the most popular, preferred, cost effective, and scalable in vivo genetic approaches for screening protein-protein interactions. A number of variants of yeast and bacterial two-hybrid systems exist, rendering them ideal for modern, flexible proteomics-driven studies. For mapping protein interactions at genome scales (that is, constructing an interactome), the yeast two-hybrid system has been extensively tested and is preferred over bacterial two-hybrid systems, given that users have created more resources such as a variety of vectors and other modifications. Each system has its own advantages and limitations and thus needs to be compared directly. For instance, the bacterial two-hybrid method seems a better fit than the yeast two-hybrid system to screen membrane-associated proteins. In this chapter, we provide detailed protocols for yeast and bacterial two-hybrid systems as well as a comparison of outcomes for each approach using our own and published data.

Genetic-and-Epigenetic Interspecies Networks for Cross-Talk Mechanisms in Human Macrophages and Dendritic Cells during MTB Infection

Tuberculosis is caused by Mycobacterium tuberculosis (Mtb) infection. Mtb is one of the oldest human pathogens, and evolves mechanisms implied in human evolution. The lungs are the first organ exposed to aerosol-transmitted Mtb during gaseous exchange. Therefore, the guards of the immune system in the lungs, such as macrophages (Mϕs) and dendritic cells (DCs), are the most important defense against Mtb infection. There have been several studies discussing the functions of Mϕs and DCs during Mtb infection, but the genome-wide pathways and networks are still incomplete. Furthermore, the immune response induced by Mϕs and DCs varies. Therefore, we analyzed the cross-talk genome-wide genetic-and-epigenetic interspecies networks (GWGEINs) between Mϕs vs. Mtb and DCs vs. Mtb to determine the varying mechanisms of both the host and pathogen as it relates to Mϕs and DCs during early Mtb infection. First, we performed database mining to construct candidate cross-talk GWGEIN between human cells and Mtb. Then we constructed dynamic models to characterize the molecular mechanisms, including intraspecies gene/microRNA (miRNA) regulation networks (GRNs), intraspecies protein-protein interaction networks (PPINs), and the interspecies PPIN of the cross-talk GWGEIN. We applied a system identification method and a system order detection scheme to dynamic models to identify the real cross-talk GWGEINs using the microarray data of Mϕs, DCs and Mtb. After identifying the real cross-talk GWGEINs, the principal network projection (PNP) method was employed to construct host-pathogen core networks (HPCNs) between Mϕs vs. Mtb and DCs vs. Mtb during infection process. Thus, we investigated the underlying cross-talk mechanisms between the host and the pathogen to determine how the pathogen counteracts host defense mechanisms in Mϕs and DCs during Mtb H37Rv early infection. Based on our findings, we propose Rv1675c as a potential drug target because of its important defensive role in Mϕs. Furthermore, the membrane essential proteins v1098c, and Rv1696 (or cytoplasm for Rv0667), in Mtb could also be potential drug targets because of their important roles in Mtb survival in both cell types. We also propose the drugs Lopinavir, TMC207, ATSM, and GTSM as potential therapeutic treatments for Mtb infection since they target the above potential drug targets.

Nucleotide Metabolism and DNA Replication

The development and application of a highly versatile suite of tools for mycobacterial genetics, coupled with widespread use of "omics" approaches to elucidate the structure, function, and regulation of mycobacterial proteins, has led to spectacular advances in our understanding of the metabolism and physiology of mycobacteria. In this article, we provide an update on nucleotide metabolism and DNA replication in mycobacteria, highlighting key findings from the past 10 to 15 years. In the first section, we focus on nucleotide metabolism, ranging from the biosynthesis, salvage, and interconversion of purine and pyrimidine ribonucleotides to the formation of deoxyribonucleotides. The second part of the article is devoted to DNA replication, with a focus on replication initiation and elongation, as well as DNA unwinding. We provide an overview of replication fidelity and mutation rates in mycobacteria and summarize evidence suggesting that DNA replication occurs during states of low metabolic activity, and conclude by suggesting directions for future research to address key outstanding questions. Although this article focuses primarily on observations from Mycobacterium tuberculosis, it is interspersed, where appropriate, with insights from, and comparisons with, other mycobacterial species as well as better characterized bacterial models such as Escherichia coli. Finally, a common theme underlying almost all studies of mycobacterial metabolism is the potential to identify and validate functions or pathways that can be exploited for tuberculosis drug discovery. In this context, we have specifically highlighted those processes in mycobacterial DNA replication that might satisfy this critical requirement.

Prediction and characterization of protein-protein interaction network in Bacillus licheniformis WX-02

In this study, we constructed a protein-protein interaction (PPI) network of B. licheniformis strain WX-02 with interolog method and domain-based method, which contained 15,864 edges and 2,448 nodes. Although computationally predicted networks have relatively low coverage and high false-positive rate, our prediction was confirmed from three perspectives: local structural features, functional similarities and transcriptional correlations. Further analysis of the COG heat map showed that protein interactions in B. licheniformis WX-02 mainly occurred in the same functional categories. By incorporating the transcriptome data, we found that the topological properties of the PPI network were robust under normal and high salt conditions. In addition, 267 different protein complexes were identified and 117 poorly characterized proteins were annotated with certain functions based on the PPI network. Furthermore, the sub-network showed that a hub protein CcpA jointed directly or indirectly many proteins related to γ-PGA synthesis and regulation, such as PgsB, GltA, GltB, ProB, ProJ, YcgM and two signal transduction systems ComP-ComA and DegS-DegU. Thus, CcpA might play an important role in the regulation of γ-PGA synthesis. This study therefore will facilitate the understanding of the complex cellular behaviors and mechanisms of γ-PGA synthesis in B. licheniformis WX-02.

Secretome profile analysis of hypervirulent Mycobacterium tuberculosis CPT31 reveals increased production of EsxB and proteins involved in adaptation to intracellular lifestyle

Epidemiological information and animal models have shown various Mycobacterium tuberculosis phenotypes ranging from hyper- to hypovirulent forms. Recent genomic and proteomic studies suggest that the outcome of infection depends on the M. tuberculosis fitness, which is a direct consequence of its phenotype. However, little is known about the molecular and cellular mechanisms used by mycobacteria to survive, replicate and persist during infection. The aim of this study was to perform a comprehensive proteomic analysis of culture filtrate from hypo- (CPT23) and hypervirulent (CPT31) M. tuberculosis isolates. Using two-dimensional electrophoresis we observed that 70 proteins were unique, or more abundant in culture filtrate of CPT31, and 15 of these were identified by mass spectrometry. Our analysis of protein expression showed that most of the proteins identified are involved in lipid metabolism (FadA3, FbpB and EchA3), detoxification and adaptation (GroEL2, SodB and HspX) and cell wall processes (LprA, Tig and EsxB). These results suggest that overrepresented proteins in M. tuberculosis CPT31 secretome could facilitate mycobacterial infection and persistence.

Computational analysis of protein interaction networks for infectious diseases

Infectious diseases caused by pathogens, including viruses, bacteria and parasites, pose a serious threat to human health worldwide. Frequent changes in the pattern of infection mechanisms and the emergence of multidrug-resistant strains among pathogens have weakened the current treatment regimen. This necessitates the development of new therapeutic interventions to prevent and control such diseases. To cater to the need, analysis of protein interaction networks (PINs) has gained importance as one of the promising strategies. The present review aims to discuss various computational approaches to analyse the PINs in context to infectious diseases. Topology and modularity analysis of the network with their biological relevance, and the scenario till date about host–pathogen and intra-pathogenic protein interaction studies were delineated. This would provide useful insights to the research community, thereby enabling them to design novel biomedicine against such infectious diseases.

Prediction of MicroRNA-Disease Associations Based on Social Network Analysis Methods

MicroRNAs constitute an important class of noncoding, single-stranded, ~22 nucleotide long RNA molecules encoded by endogenous genes. They play an important role in regulating gene transcription and the regulation of normal development. MicroRNAs can be associated with disease; however, only a few microRNA-disease associations have been confirmed by traditional experimental approaches. We introduce two methods to predict microRNA-disease association. The first method, KATZ, focuses on integrating the social network analysis method with machine learning and is based on networks derived from known microRNA-disease associations, disease-disease associations, and microRNA-microRNA associations. The other method, CATAPULT, is a supervised machine learning method. We applied the two methods to 242 known microRNA-disease associations and evaluated their performance using leave-one-out cross-validation and 3-fold cross-validation. Experiments proved that our methods outperformed the state-of-the-art methods.

Network representations of immune system complexity

The mammalian immune system is a dynamic multiscale system composed of a hierarchically organized set of molecular, cellular, and organismal networks that act in concert to promote effective host defense. These networks range from those involving gene regulatory and protein–protein interactions underlying intracellular signaling pathways and single‐cell responses to increasingly complex networks of in vivo cellular interaction, positioning, and migration that determine the overall immune response of an organism. Immunity is thus not the product of simple signaling events but rather nonlinear behaviors arising from dynamic, feedback‐regulated interactions among many components. One of the major goals of systems immunology is to quantitatively measure these complex multiscale spatial and temporal interactions, permitting development of computational models that can be used to predict responses to perturbation. Recent technological advances permit collection of comprehensive datasets at multiple molecular and cellular levels, while advances in network biology support representation of the relationships of components at each level as physical or functional interaction networks. The latter facilitate effective visualization of patterns and recognition of emergent properties arising from the many interactions of genes, molecules, and cells of the immune system. We illustrate the power of integrating ‘omics’ and network modeling approaches for unbiased reconstruction of signaling and transcriptional networks with a focus on applications involving the innate immune system. We further discuss future possibilities for reconstruction of increasingly complex cellular‐ and organism‐level networks and development of sophisticated computational tools for prediction of emergent immune behavior arising from the concerted action of these networks. WIREs Syst Biol Med 2015, 7:13–38. doi: 10.1002/wsbm.1288

This article is categorized under: 1

Analytical and Computational Methods > Computational Methods

2

Laboratory Methods and Technologies > Macromolecular Interactions, Methods

Succinylome analysis reveals the involvement of lysine succinylation in metabolism in pathogenic Mycobacterium tuberculosis

Mycobacterium tuberculosis (Mtb), the causative agent of human tuberculosis, remains one of the most prevalent human pathogens and a major cause of mortality worldwide. Metabolic network is a central mediator and defining feature of the pathogenicity of Mtb. Increasing evidence suggests that lysine succinylation dynamically regulates enzymes in carbon metabolism in both bacteria and human cells; however, its extent and function in Mtb remain unexplored. Here, we performed a global succinylome analysis of the virulent Mtb strain H37Rv by using high accuracy nano-LC-MS/MS in combination with the enrichment of succinylated peptides from digested cell lysates and subsequent peptide identification. In total, 1545 lysine succinylation sites on 626 proteins were identified in this pathogen. The identified succinylated proteins are involved in various biological processes and a large proportion of the succinylation sites are present on proteins in the central metabolism pathway. Site-specific mutations showed that succinylation is a negative regulatory modification on the enzymatic activity of acetyl-CoA synthetase. Molecular dynamics simulations demonstrated that succinylation affects the conformational stability of acetyl-CoA synthetase, which is critical for its enzymatic activity. Further functional studies showed that CobB, a sirtuin-like deacetylase in Mtb, functions as a desuccinylase of acetyl-CoA synthetase in in vitro assays. Together, our findings reveal widespread roles for lysine succinylation in regulating metabolism and diverse processes in Mtb. Our data provide a rich resource for functional analyses of lysine succinylation and facilitate the dissection of metabolic networks in this life-threatening pathogen.

Microbial Proteome Profiling and Systems Biology: Applications to Mycobacterium tuberculosis

Each year, 1.3 million people die from tuberculosis, an infectious disease caused by Mycobacterium tuberculosis. Systems biology-based strategies might significantly contribute to the knowledge-guided development of more effective vaccines and drugs to prevent and cure infectious diseases. To build models simulating the behaviour of a system in response to internal or external stimuli and to identify potential targets for therapeutic intervention, systems biology approaches require the acquisition of quantitative molecular profiles on many perturbed states. Here we review the current state of proteomic analyses in Mycobacterium tuberculosis and discuss the potential of recently emerging targeting mass spectrometry-based techniques which enable fast, sensitive and accurate protein measurements.

Bioluminescence resonance energy transfer system for measuring dynamic protein-protein interactions in bacteria

Protein-protein interactions are important for virtually every biological process, and a number of elegant approaches have been designed to detect and evaluate such interactions. However, few of these methods allow the detection of dynamic and real-time protein-protein interactions in bacteria. Here we describe a bioluminescence resonance energy transfer (BRET) system based on the bacterial luciferase LuxAB. We found that enhanced yellow fluorescent protein (eYFP) accepts the emission from LuxAB and emits yellow fluorescence. Importantly, BRET occurred when LuxAB and eYFP were fused, respectively, to the interacting protein pair FlgM and FliA. Furthermore, we observed sirolimus (i.e., rapamycin)-inducible interactions between FRB and FKBP12 and a dose-dependent abolishment of such interactions by FK506, the ligand of FKBP12. Using this system, we showed that osmotic stress or low pH efficiently induced multimerization of the regulatory protein OmpR and that the multimerization induced by low pH can be reversed by a neutralizing agent, further indicating the usefulness of this system in the measurement of dynamic interactions. This method can be adapted to analyze dynamic protein-protein interactions and the importance of such interactions in bacterial processes such as development and pathogenicity.

Real-time measurement of protein-protein interactions in prokaryotes is highly desirable for determining the roles of protein complex in the development or virulence of bacteria, but methods that allow such measurement are not available. Here we describe the development of a bioluminescence resonance energy transfer (BRET) technology that meets this need. The use of endogenous excitation light in this strategy circumvents the requirement for the sophisticated instrument demanded by standard fluorescence resonance energy transfer (FRET). Furthermore, because the LuxAB substrate decanal is membrane permeable, the assay can be performed without lysing the bacterial cells, thus allowing the detection of protein-protein interactions in live bacterial cells. This BRET system added another useful tool to address important questions in microbiological studies.

Identification of protein partners in mycobacteria using a single-step affinity purification method

Tuberculosis is a leading cause of death in developing countries. Efforts are being made to both prevent its spread and improve curability rates. Understanding the biology of the bacteria causing the disease, Mycobacterium tuberculosis (M. tuberculosis), is thus vital. We have implemented improved screening methods for protein–protein interactions based on affinity purification followed by high-resolution mass spectrometry. This method can be efficiently applied to both medium- and high-throughput studies aiming to characterize protein–protein interaction networks of tubercle bacilli. Of the 4 tested epitopes FLAG, enhanced green fluorescent protein (eGFP), protein A and haemagglutinin, the eGFP tag was found to be most useful on account of its easily monitored expression and its ability to function as a simultaneous tool for subcellular localization studies. It presents a relatively low background with cost-effective purification. RNA polymerase subunit A (RpoA) was used as a model for investigation of a large protein complex. When used as bait, it co-purified with all remaining RNA polymerase core subunits as well as many accessory proteins. The amount of RpoA strongly correlated with the amount of quantification peptide used as part of the tagging system in this study (SH), making it applicable for semi-quantification studies. Interactions between the components of the RpoA-eGFP protein complex were further confirmed using protein cross-linking. Dynamic changes in the composition of protein complexes under induction of UV damage were observed when UvrA-eGFP expressing cells treated with UV light were used to co-purify UvrA interaction partners.

A second-generation protein-protein interaction network of Helicobacter pylori

Helicobacter pylori infections cause gastric ulcers and play a major role in the development of gastric cancer. In 2001, the first protein interactome was published for this species, revealing over 1500 binary protein interactions resulting from 261 yeast two-hybrid screens. Here we roughly double the number of previously published interactions using an ORFeome-based, proteome-wide yeast two-hybrid screening strategy. We identified a total of 1515 protein-protein interactions, of which 1461 are new. The integration of all the interactions reported in H. pylori results in 3004 unique interactions that connect about 70% of its proteome. Excluding interactions of promiscuous proteins we derived from our new data a core network consisting of 908 interactions. We compared our data set to several other bacterial interactomes and experimentally benchmarked the conservation of interactions using 365 protein pairs (interologs) of E. coli of which one third turned out to be conserved in both species.

Systems biology-based identification of Mycobacterium tuberculosis persistence genes in mouse lungs

Identifying Mycobacterium tuberculosis persistence genes is important for developing novel drugs to shorten the duration of tuberculosis (TB) treatment. We developed computational algorithms that predict M. tuberculosis genes required for long-term survival in mouse lungs. As the input, we used high-throughput M. tuberculosis mutant library screen data, mycobacterial global transcriptional profiles in mice and macrophages, and functional interaction networks. We selected 57 unique, genetically defined mutants (18 previously tested and 39 untested) to assess the predictive power of this approach in the murine model of TB infection. We observed a 6-fold enrichment in the predicted set of M. tuberculosis genes required for persistence in mouse lungs relative to randomly selected mutant pools. Our results also allowed us to reclassify several genes as required for M. tuberculosis persistence in vivo. Finally, the new results implicated additional high-priority candidate genes for testing. Experimental validation of computational predictions demonstrates the power of this systems biology approach for elucidating M. tuberculosis persistence genes.

Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), has a genetic repertoire that permits it to persist in the face of host immune responses. Identification of such persistence genes could reveal novel drug targets and elucidate mechanisms by which the organism eludes the immune system and resists drugs. Genetic screens have identified a total of 31 persistence genes, but to date only 15% of the ~4,000 M. tuberculosis genes have been tested experimentally. In this paper, as an alternative to brute force experimental screens, we describe computational methods that predict new persistence genes by combining known examples with growing databases of biological networks. Experimental testing demonstrated that these predictions are highly accurate, validating the computational approach and providing new information about M. tuberculosis persistence in host tissues. Using the new experimental results as additional input highlights additional genes for testing. Our approach can be extended to other data types and target organisms to characterize host-pathogen interactions relevant to this and other infectious diseases.

A multi-level multi-scale approach to study essential genes in Mycobacterium tuberculosis

Background

The set of indispensable genes that are required by an organism to grow and sustain life are termed as essential genes. There is a strong interest in identification of the set of essential genes, particularly in pathogens, not only for a better understanding of the pathogen biology, but also for identifying drug targets and the minimal gene set for the organism. Essentiality is inherently a systems property and requires consideration of the system as a whole for their identification. The available experimental approaches capture some aspects but each method comes with its own limitations. Moreover, they do not explain the basis for essentiality in most cases. A powerful prediction method to recognize this gene pool including rationalization of the known essential genes in a given organism would be very useful. Here we describe a multi-level multi-scale approach to identify the essential gene pool in a deadly pathogen, Mycobacterium tuberculosis.

Results

The multi-level workflow analyses the bacterial cell by studying (a) genome-wide gene expression profiles to identify the set of genes which show consistent and significant levels of expression in multiple samples of the same condition, (b) indispensability for growth by using gene expression integrated flux balance analysis of a genome-scale metabolic model, (c) importance for maintaining the integrity and flow in a protein-protein interaction network and (d) evolutionary conservation in a set of genomes of the same ecological niche. In the gene pool identified, the functional basis for essentiality has been addressed by studying residue level conservation and the sub-structure at the ligand binding pockets, from which essential amino acid residues in that pocket have also been identified. 283 genes were identified as essential genes with high-confidence. An agreement of about 73.5% is observed with that obtained from the experimental transposon mutagenesis technique. A large proportion of the identified genes belong to the class of intermediary metabolism and respiration.

Conclusions

The multi-scale, multi-level approach described can be generally applied to other pathogens as well. The essential gene pool identified form a basis for designing experiments to probe their finer functional roles and also serve as a ready shortlist for identifying drug targets.