Protein-protein interaction networks suggest different targets have different propensities for triggering drug resistance

The Gene and Regulatory Network Involved in Ethambutol Resistance in Mycobacterium tuberculosis

Ethambutol (EMB) is used in combination with isoniazid and rifampicin for the treatment of tuberculosis caused by Mycobacterium tuberculosis. However, the incidence of EMB resistance is alarming. The EMB targets the cell wall arabinan biosynthesis. It is important to comprehensively understand the molecular basis of EMB to slow down the drug resistance rate of EMB. This study summarized the genes implicated in EMB resistance, regulatory network and the pharmacoproteomic effect of EMB in M. tuberculosis. Many of the genes related to EMB are implicated in membrane components, drug efflux, lipid metabolism, ribosome, and detoxification. The differential response model may help to design a novel anti-tuberculosis antibiotic.

Label-Free Comparative Proteomics of Differentially Expressed Mycobacterium tuberculosis Protein in Rifampicin-Related Drug-Resistant Strains

Rifampicin (RIF) is one of the most important first-line anti-tuberculosis (TB) drugs, and more than 90% of RIF-resistant (RR) Mycobacterium tuberculosis clinical isolates belong to multidrug-resistant (MDR) and extensively drug-resistant (XDR) TB. In order to identify specific candidate target proteins as diagnostic markers or drug targets, differential protein expression between drug-sensitive (DS) and drug-resistant (DR) strains remains to be investigated. In the present study, a label-free, quantitative proteomics technique was performed to compare the proteome of DS, RR, MDR, and XDR clinical strains. We found iniC, Rv2141c, folB, and Rv2561 were up-regulated in both RR and MDR strains, while fadE9, espB, espL, esxK, and Rv3175 were down-regulated in the three DR strains when compared to the DS strain. In addition, lprF, mce2R, mce2B, and Rv2627c were specifically expressed in the three DR strains, and 41 proteins were not detected in the DS strain. Functional category showed that these differentially expressed proteins were mainly involved in the cell wall and cell processes. When compared to the RR strain, Rv2272, smtB, lpqB, icd1, and folK were up-regulated, while esxK, PPE19, Rv1534, rpmI, ureA, tpx, mpt64, frr, Rv3678c, esxB, esxA, and espL were down-regulated in both MDR and XDR strains. Additionally, nrp, PPE3, mntH, Rv1188, Rv1473, nadB, PPE36, and sseA were specifically expressed in both MDR and XDR strains, whereas 292 proteins were not identified when compared to the RR strain. When compared between MDR and XDR strains, 52 proteins were up-regulated, while 45 proteins were down-regulated in the XDR strain. 316 proteins were especially expressed in the XDR strain, while 92 proteins were especially detected in the MDR strain. Protein interaction networks further revealed the mechanism of their involvement in virulence and drug resistance. Therefore, these differentially expressed proteins are of great significance for exploring effective control strategies of DR-TB.

Identification of CTL Epitopes on Efflux Pumps of the ATP-Binding Cassette and the Major Facilitator Superfamily of Mycobacterium tuberculosis

Tuberculosis is the world's most deadly infectious disease, with 10 million people falling ill and 1.5 million people dying from the disease every year. With the increasing number of drug-resistant Mycobacterium tuberculosis (MTB) strains and prevalence of coinfection of MTB with human immunodeficiency virus, many challenges remain in the prevention and treatment of tuberculosis. Therefore, the development of safe and effective tuberculosis vaccines is an urgent issue. In this study, we identified cytotoxic T lymphocyte epitopes on drug resistance-associated membrane protein efflux pumps of MTB, the ATP-binding cassette and the major facilitator superfamilies. First, three online software were used to predict HLA-A2-restricted epitopes. Then, the candidate epitopes were confirmed with the T2A2 cell binding affinity and peptide/MHC (pMHC) complex stability assays and in vitro immune activity experiments. Two drug-resistant T lymphocyte epitopes, designated Rv1218c-p24 and Rv2477c-p182, were selected, and their immunogenic activities studied in vivo in genetically engineered mice. The immune activities of these two epitopes were improved with the help of complete Freund's adjuvant (CFA). The epitopes identified here provide a foundation for the diagnosis and treatment of patients infected with drug resistant and the future development of a multiepitope vaccine.

Prioritization of potential drug targets and antigenic vaccine candidates against Klebsiella aerogenes using the computational subtractive proteome-driven approach

Klebsiella aerogenes is a multidrug-resistant Gram-negative bacterium that causes nosocomial infections. The organism showed resistance to most of the conventional antibiotics available. Because of the high resistance of the species, the treatment of K. aerogenes is difficult. These species are resistant to third-generation cephalosporins due to the production of chromosomal beta-lactams with cephalosporin activity. The lack of better treatment and the development of therapeutic resistance in hospitals hinders better/new broad-spectrum-based treatment against this pathogen. This study identifies potential drug targets/vaccine candidates through a computational subtractive proteome-driven approach. This method is used to predict proteins that are not homologous to humans and human symbiotic intestinal flora. The resultant proteome of K. aerogenes was further searched for proteins, which are essential, virulent, and determinants of antibiotic/drug resistance. Subsequently, their druggability properties were also studied. The data set was reduced based on its presence in the pathogen-specific metabolic pathways. The subtractive proteome analysis predicted 13 proteins as potential drug targets for K. aerogenes. Furthermore, these target proteins were annotated based on their spectrum of activity, cellular localization, and antigenicity properties, which ensured that they are potent candidates for broad-spectrum antibiotic and vaccine design. The results open up new opportunities for designing and manufacturing powerful antigenic vaccines against K. aerogenes and the detection and release of new and active drugs against K. aerogenes without altering the gut microbiome.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42485-021-00068-9.

Identifying and Tackling Emergent Vulnerability in Drug-Resistant Mycobacteria

The global mechanisms and associated molecular alterations that occur in drug-resistant mycobacteria are poorly understood. To address this, we obtain genomics data and then construct a genome-scale response network in isoniazid-resistant Mycobacterium smegmatis and apply a network-mining algorithm. Through this, we decipher global alterations in an unbiased manner and identify emergent vulnerabilities in resistant bacilli, of which redox response was prominent. Using phenotypic profiling, we find that resistant bacilli exhibit collateral sensitivity to several compounds that block antioxidant responses. We find that nanogram/milliliter concentrations of ebselen, vancomycin, and phenylarsine oxide, in combination with isoniazid, are highly effective against Mycobacterium tuberculosis H37Rv and three clinical drug-resistant strains. Dynamic measurements of cytoplasmic redox potential revealed a surprisingly diminished capacity of clinical drug-resistant strains to counteract oxidative stress, providing a mechanistic basis for efficient and synergistic mycobactericidal activity of the drug combinations. Ebselen and vancomycin appear to be promising repurposable drugs.

EpiTracer - an algorithm for identifying epicenters in condition-specific biological networks

BACKGROUND

In biological systems, diseases are caused by small perturbations in a complex network of interactions between proteins. Perturbations typically affect only a small number of proteins, which go on to disturb a larger part of the network. To counteract this, a stress-response is launched, resulting in a complex pattern of variations in the cell. Identifying the key players involved in either spreading the perturbation or responding to it can give us important insights.

RESULTS

We develop an algorithm, EpiTracer, which identifies the key proteins, or epicenters, from which a large number of changes in the protein-protein interaction (PPI) network ripple out. We propose a new centrality measure, ripple centrality, which measures how effectively a change at a particular node can ripple across the network by identifying highest activity paths specific to the condition of interest, obtained by mapping gene expression profiles to the PPI network. We demonstrate the algorithm using an overexpression study and a knockdown study. In the overexpression study, the gene that was overexpressed (PARK2) was highlighted as the most important epicenter specific to the perturbation. The other top-ranked epicenters were involved in either supporting the activity of PARK2, or counteracting it. Also, 5 of the identified epicenters showed no significant differential expression, showing that our method can find information which simple differential expression analysis cannot. In the second dataset (SP1 knockdown), alternative regulators of SP1 targets were highlighted as epicenters. Also, the gene that was knocked down (SP1) was picked up as an epicenter specific to the control condition. Sensitivity analysis showed that the genes identified as epicenters remain largely unaffected by small changes.

CONCLUSIONS

We develop an algorithm, EpiTracer, to find epicenters in condition-specific biological networks, given the PPI network and gene expression levels. EpiTracer includes programs which can extract the immediate influence zone of epicenters and provide a summary of dysregulated genes, facilitating quick biological analysis. We demonstrate its efficacy on two datasets with differing characteristics, highlighting its general applicability. We also show that EpiTracer is not sensitive to minor changes in the network. The source code for EpiTracer is provided at Github ( https://github.com/narmada26/EpiTracer ).

Target identification in Fusobacterium nucleatum by subtractive genomics approach and enrichment analysis of host-pathogen protein-protein interactions

Background

Fusobacterium nucleatum, a well studied bacterium in periodontal diseases, appendicitis, gingivitis, osteomyelitis and pregnancy complications has recently gained attention due to its association with colorectal cancer (CRC) progression. Treatment with berberine was shown to reverse F. nucleatum-induced CRC progression in mice by balancing the growth of opportunistic pathogens in tumor microenvironment. Intestinal microbiota imbalance and the infections caused by F. nucleatum might be regulated by therapeutic intervention. Hence, we aimed to predict drug target proteins in F. nucleatum, through subtractive genomics approach and host-pathogen protein-protein interactions (HP-PPIs). We also carried out enrichment analysis of host interacting partners to hypothesize the possible mechanisms involved in CRC progression due to F. nucleatum.

Results

In subtractive genomics approach, the essential, virulence and resistance related proteins were retrieved from RefSeq proteome of F. nucleatum by searching against Database of Essential Genes (DEG), Virulence Factor Database (VFDB) and Antibiotic Resistance Gene-ANNOTation (ARG-ANNOT) tool respectively. A subsequent hierarchical screening to identify non-human homologous, metabolic pathway-independent/pathway-specific and druggable proteins resulted in eight pathway-independent and 27 pathway-specific druggable targets. Co-aggregation of F. nucleatum with host induces proinflammatory gene expression thereby potentiates tumorigenesis. Hence, proteins from IBDsite, a database for inflammatory bowel disease (IBD) research and those involved in colorectal adenocarcinoma as interpreted from The Cancer Genome Atlas (TCGA) were retrieved to predict drug targets based on HP-PPIs with F. nucleatum proteome. Prediction of HP-PPIs exhibited 186 interactions contributed by 103 host and 76 bacterial proteins. Bacterial interacting partners were accounted as putative targets. And enrichment analysis of host interacting partners showed statistically enriched terms that were in positive correlation with CRC, atherosclerosis, cardiovascular, osteoporosis, Alzheimer’s and other diseases.

Conclusion

Subtractive genomics analysis provided a set of target proteins suggested to be indispensable for survival and pathogenicity of F. nucleatum. These target proteins might be considered for designing potent inhibitors to abrogate F. nucleatum infections. From enrichment analysis, it was hypothesized that F. nucleatum infection might enhance CRC progression by simultaneously regulating multiple signaling cascades which could lead to up-regulation of proinflammatory responses, oncogenes, modulation of host immune defense mechanism and suppression of DNA repair system.

Electronic supplementary material

The online version of this article (doi:10.1186/s12866-016-0700-0) contains supplementary material, which is available to authorized users.

Comparative Genome and Network Centrality Analysis to Identify Drug Targets of Mycobacterium tuberculosis H37Rv

Potential drug targets of Mycobacterium tuberculosis H37Rv were identified through systematically integrated comparative genome and network centrality analysis. The comparative analysis of the complete genome of Mycobacterium tuberculosis H37Rv against Database of Essential Genes (DEG) yields a list of proteins which are essential for the growth and survival of the pathogen. Those proteins which are nonhomologous with human were selected. The resulting proteins were then prioritized by using the four network centrality measures: degree, closeness, betweenness, and eigenvector. Proteins whose centrality value is close to the centre of gravity of the interactome network were proposed as a final list of potential drug targets for the pathogen. The use of an integrated approach is believed to increase the success of the drug target identification process. For the purpose of validation, selective comparisons have been made among the proposed targets and previously identified drug targets by various other methods. About half of these proteins have been already reported as potential drug targets. We believe that the identified proteins will be an important input to experimental study which in the way could save considerable amount of time and cost of drug target discovery.

Maximum flow approach to prioritize potential drug targets of Mycobacterium tuberculosis H37Rv from protein-protein interaction network

Background

In spite of the implementations of several strategies, tuberculosis (TB) is overwhelmingly a serious global public health problem causing millions of infections and deaths every year. This is mainly due to the emergence of drug-resistance varieties of TB. The current treatment strategies for the drug-resistance TB are of longer duration, more expensive and have side effects. This highlights the importance of identification and prioritization of targets for new drugs. This study has been carried out to prioritize potential drug targets of Mycobacteriumtuberculosis H37Rv based on their flow to resistance genes.

Methods

The weighted proteome interaction network of the pathogen was constructed using a dataset from STRING database. Only a subset of the dataset with interactions that have a combined score value ≥770 was considered. Maximum flow approach has been used to prioritize potential drug targets. The potential drug targets were obtained through comparative genome and network centrality analysis. The curated set of resistance genes was retrieved from literatures. Detail literature review and additional assessment of the method were also carried out for validation.

Results

A list of 537 proteins which are essential to the pathogen and non-homologous with human was obtained from the comparative genome analysis. Through network centrality measures, 131 of them were found within the close neighborhood of the centre of gravity of the proteome network. These proteins were further prioritized based on their maximum flow value to resistance genes and they are proposed as reliable drug targets of the pathogen. Proteins which interact with the host were also identified in order to understand the infection mechanism.

Conclusion

Potential drug targets of Mycobacteriumtuberculosis H37Rv were successfully prioritized based on their flow to resistance genes of existing drugs which is believed to increase the druggability of the targets since inhibition of a protein that has a maximum flow to resistance genes is more likely to disrupt the communication to these genes. Purposely selected literature review of the top 14 proteins showed that many of them in this list were proposed as drug targets of the pathogen.

Electronic supplementary material

The online version of this article (doi:10.1186/s40169-015-0061-6) contains supplementary material, which is available to authorized users.

Deficiency of the novel exopolyphosphatase Rv1026/PPX2 leads to metabolic downshift and altered cell wall permeability in Mycobacterium tuberculosis

Mycobacterium tuberculosis can persist for decades in the human host. Stringent response pathways involving inorganic polyphosphate [poly(P)], which is synthesized and hydrolyzed by polyphosphate kinase (PPK) and exopolyphosphatase (PPX), respectively, are believed to play a key regulatory role in bacterial persistence. We show here that M. tuberculosis poly(P) accumulation is temporally linked to bacillary growth restriction. We also identify M. tuberculosis Rv1026 as a novel exopolyphosphatase with hydrolytic activity against long-chain poly(P). Using a tetracycline-inducible expression system to knock down expression of Rv1026 (ppx2), we found that M. tuberculosis poly(P) accumulation leads to slowed growth and reduced susceptibility to isoniazid, increased resistance to heat and acid pH, and enhanced intracellular survival during macrophage infection. By transmission electron microscopy, the ppx2 knockdown strain exhibited increased cell wall thickness, which was associated with reduced cell wall permeability to hydrophilic drugs rather than induction of drug efflux pumps or altered biofilm formation relative to the empty vector control. Transcriptomic and metabolomic analysis revealed a metabolic downshift of the ppx2 knockdown characterized by reduced transcription and translation and a downshift of glycerol-3-phosphate levels. In summary, poly(P) plays an important role in M. tuberculosis growth restriction and metabolic downshift and contributes to antibiotic tolerance through altered cell wall permeability.

The stringent response, involving the regulatory molecules inorganic polyphosphate [poly(P)] and (p)ppGpp, is believed to mediate Mycobacterium tuberculosis persistence. In this study, we identified a novel enzyme (Rv1026, PPX2) responsible for hydrolyzing long-chain poly(P). A genetically engineered M. tuberculosis strain deficient in the ppx2 gene showed increased poly(P) levels, which were associated with early bacterial growth arrest and reduced susceptibility to the first-line drug isoniazid, as well as increased bacterial survival during exposure to stress conditions and within macrophages. Relative to the control strain, the mutant showed increased thickness of the cell wall and reduced drug permeability. Global gene expression and metabolite analysis revealed reduced expression of the transcriptional and translational machinery and a shift in carbon source utilization. In summary, regulation of the poly(P) balance is critical for persister formation in M. tuberculosis.

On Brucella pathogenesis: looking for the unified challenge in systems and synthetic biology

Brucellosis is a zoonotic infection transmitted to humans from infected animals and is one of the widely spread zoonoses. Recently, six species were recognized within the genus Brucella wherein B. melitensis, B. suis and B. abortus are considered virulent for humans. While these species differ phenotypically by their pattern of metabolic activities, there has been an imperative need to understand pathogenesis of Brucella species. It has been foreseen that creating a human vaccine for Brucellosis would entail decreased dose of antibiotics. However the emerging role of Brucella pathogenesis still centers on isolation of the organism and various diagnostic tests thereby leading to varying strategies of treatment cycle. In view of disease heterogeneity, we focus systems and synthetic biology challenges that might improve our understanding the Brucella pathogenesis.

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.

Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review

Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

Resistome analysis of Mycobacterium tuberculosis: Identification of aminoglycoside 2'-Nacetyltransferase (AAC) as co-target for drug desigining

: The emergence of multidrug resistant tuberculosis (MDRTB) highlights the urgent need to understand the mechanisms of resistance to the drugs and to develop a new arena of therapeutics to treat the disease. Ethambutol, isonazid, pyrazinamide, rifampicin are first line of drugs against TB, whereas aminoglycoside, polypeptides, fluoroquinolone, ethionamide are important second line of bactericidal drugs used to treat MDRTB, and resistance to one or both of these drugs are defining characteristic of extensively drug resistant TB. We retrieved 1,221 resistant genes from Antibiotic Resistance Gene Database (ARDB), which are responsible for resistance against first and second line antibiotics used in treatment of Mycobacterium tuberculosis infection. From network analysis of these resistance genes, 53 genes were found to be common. Phylogenetic analysis shows that more than 60% of these genes code for acetyltransferase. Acetyltransferases detoxify antibiotics by acetylation, this mechanism plays central role in antibiotic resistance. Seven acetyltransferase (AT-1 to AT-7) were selected from phylogenetic analysis. Structural alignment shows that these acetyltransferases share common ancestral core, which can be used as a template for structure based drug designing. From STRING analysis it is found that acetyltransferase interact with 10 different proteins and it shows that, all these interaction were specific to M. tuberculosis. These results have important implications in designing new therapeutic strategies with acetyltransferase as lead co-target to combat against MDR as well as Extreme drug resistant (XDR) tuberculosis.

ABBREVIATIONS

AA - amino acid, AT - Acetyltransferase, AAC - Aminoglycoside 2'-N-acetyltransferase, XDR - Extreme drug-resistant, MDR - Multidrug-resistant, Mtb - Mycobacterium tuberculosis, TB - Tuberculosis.

Study of the rifampin monoresistance mechanism in Mycobacterium tuberculosis

Rifampin (RIF) susceptibility is a key factor in determining the treatment effectiveness of the standardized treatment regimens. In Mycobacterium tuberculosis, both target gene mutation and the efflux pump play major roles in the resistance to antituberculosis drugs. By eliminating RIF-resistant strains with rpoB mutation, the choice of RIF-monoresistant strains may allow us to identify the RIF-specific efflux pump genes. This study explored the RIF monoresistance mechanism in M. tuberculosis. Data from DNA sequencing and MIC measurements revealed that specific mutations, including Ser531Leu and His526Asp in RpoB, show high-level drug resistance. Three-dimensional structure modeling provided further evidence that the affinity between RIF and RpoB mutants was in accordance with the drug resistance level of the corresponding isolates. Furthermore, transcription-level analysis among the nonmutated isolates indicated that three efflux pumps (Rv0783, Rv2936, and Rv0933) might be involved in exporting RIF from the cell. Compared to 8 μg/ml for wild-type Escherichia coli, the MICs for the transgenic E. coli strains with either Rv0783 or Rv2936 were 32 and 16 μg/ml, respectively. In conclusion, our study indicated that several RpoB mutant types, including Ser531Leu and His526Asp, show high-level RIF resistance attributed to low affinity between RpoB mutant proteins and RIF. In addition, this work demonstrates that Rv2936 and Rv0783 may be responsible for low-level resistance to RIF by exporting RIF from cells. The predicted structure of RpoB and the newly identified efflux pumps in this study will provide a novel approach to design new drugs and develop novel diagnosis technologies.