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

Unsupervised Machine Learning Organization of the Functional Dark Proteome of Gram-Negative "Superbugs": Six Protein Clusters Amenable for Distinct Scientific Applications

Uncharacterized proteins have been underutilized as targets for the development of novel therapeutics for difficult-to-treat bacterial infections. To facilitate the exploration of these proteins, 2819 predicted, uncharacterized proteins (19.1% of the total) from reference strains of multidrug Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa species were organized using an unsupervised k-means machine learning algorithm. Classification using normalized values for protein length, pI, hydrophobicity, degree of conservation, structural disorder, and %AT of the coding gene rendered six natural clusters. Cluster proteins showed different trends regarding operon membership, expression, presence of unknown function domains, and interactomic relevance. Clusters 2, 4, and 5 were enriched with highly disordered proteins, nonworkable membrane proteins, and likely spurious proteins, respectively. Clusters 1, 3, and 6 showed closer distances to known antigens, antibiotic targets, and virulence factors. Up to 21.8% of proteins in these clusters were structurally covered by modeling, which allowed assessment of druggability and discontinuous B-cell epitopes. Five proteins (4 in Cluster 1) were potential druggable targets for antibiotherapy. Eighteen proteins (11 in Cluster 6) were strong B-cell and T-cell immunogen candidates for vaccine development. Conclusively, we provide a feature-based schema to fractionate the functional dark proteome of critical pathogens for fundamental and biomedical purposes.

In silico Methods for Identification of Potential Therapeutic Targets

At the initial stage of drug discovery, identifying novel targets with maximal efficacy and minimal side effects can improve the success rate and portfolio value of drug discovery projects while simultaneously reducing cycle time and cost. However, harnessing the full potential of big data to narrow the range of plausible targets through existing computational methods remains a key issue in this field. This paper reviews two categories of in silico methods—comparative genomics and network-based methods—for finding potential therapeutic targets among cellular functions based on understanding their related biological processes. In addition to describing the principles, databases, software, and applications, we discuss some recent studies and prospects of the methods. While comparative genomics is mostly applied to infectious diseases, network-based methods can be applied to infectious and non-infectious diseases. Nonetheless, the methods often complement each other in their advantages and disadvantages. The information reported here guides toward improving the application of big data-driven computational methods for therapeutic target discovery.

Graphical abstract

Synthesis and in vitro studies for structure-based design of novel chalcones as antitubercular agents targeting InhA

Background: The authors aimed to estimate the therapeutic potential of novel chalcones against tuberculosis. Methods: 11 synthesized compounds were tested for in vitro antimycobacterial activity against Mycobacterium tuberculosis (H37RV; American Type Culture Collection number: 27294) using the microplate alamarBlue assay. Molecular docking and pharmacokinetic parameter analyses were then performed. Results: The most potent compounds, (2E)-1-(4-bromophenyl) (2E)-1-(2-nitrophenyl) prop-2-en-1-one, -3-(2-nitrophenyl) prop-2-en-1-one (4-bromophenyl) (2E)-1-(3-phenoxyphenyl)prop-2-en-1-one, 3-(phenoxyphenyl)prop-2-en-1-one (4-bromophenyl) prop-2-en-1-one and (2E)-1-(4-bromophenyl)-3-(5-chloro-2-hydroxyphenyl)-prop-2-en-1-one, showed in vitro activity, with a minimum inhibitory concentration (MIC) of 6.25 μg/ml. Conclusion: Compounds LSD2, LSD12, LSD13 and LSD15 showed strong in vitro antimycobacterial activity at a concentration of 6.25 μg/ml.

In Silico Drug Discovery Strategies Identified ADMET Properties of Decoquinate RMB041 and Its Potential Drug Targets against Mycobacterium tuberculosis

The highly adaptive cellular response of Mycobacterium tuberculosis to various antibiotics and the high costs for clinical trials, hampers the development of novel antimicrobial agents with improved efficacy and safety. Subsequently, in silico drug screening methods are more commonly being used for the discovery and development of drugs, and have been proven useful for predicting the pharmacokinetics, toxicities, and targets, of prospective new antimicrobial agents. In this investigation we used a reversed target fishing approach to determine potential hit targets and their possible interactions between M. tuberculosis and decoquinate RMB041, a propitious new antituberculosis compound. Two of the 13 identified targets, Cyp130 and BlaI, were strongly proposed as optimal drug-targets for dormant M. tuberculosis, of which the first showed the highest comparative binding affinity to decoquinate RMB041. The metabolic pathways associated with the selected target proteins were compared to previously published molecular mechanisms of decoquinate RMB041 against M. tuberculosis, whereby we confirmed disrupted metabolism of proteins, cell wall components, and DNA. We also described the steps within these pathways that are inhibited and elaborated on decoquinate RMB041’s activity against dormant M. tuberculosis. This compound has previously showed promising in vitro safety and good oral bioavailability, which were both supported by this in silico study. The pharmacokinetic properties and toxicity of this compound were predicted and investigated using the online tools pkCSM and SwissADME, and Discovery Studio software, which furthermore supports previous safety and bioavailability characteristics of decoquinate RMB041 for use as an antimycobacterial medication.

IMPORTANCE This article elaborates on the mechanism of action of a novel antibiotic compound against both, active and dormant Mycobacterium tuberculosis and describes its pharmacokinetics (including oral bioavailability and toxicity). Information provided in this article serves useful during the search for drugs that shorten the treatment regimen for Tuberculosis and cause minimal adverse effects.

Machine Learning for Prediction of Drug Targets in Microbe Associated Cardiovascular Diseases by Incorporating Host-pathogen Interaction Network Parameters

Host-pathogen interactions play a crucial role in invasion, infection, and induction of immune response in humans. In this work, four machine learning algorithms, namely Logistic regression, K-nearest neighbor, Support Vector Machine, and Random Forest were implemented for the classification of drug targets. The algorithms were trained using 3400 hosts and 3800 pathogen drug and non-drug target proteins as learning instances. For each protein, 68 pathogen and 73 host features were computed that included sequence, structure, biological and host-pathogen network centrality characteristics. The Random Forest classifier model achieved the best accuracy after 10-fold cross-validation. 99 % accuracy was achieved with a ROC-AUC score of 0.99±0.01 for both pathogen and host training sets. The Eigenvector Centrality of host-pathogen interactions and host-host interactions was the top feature in performing classification of pathogen and host targets respectively. Other features important for classification were the presence of catalytic and binding sites, low instability/aliphatic index, and cellular location. The Random Forest classifier was then used for prediction of drug targets involved in Microbe Associated Cardiovascular Diseases. 331 host and 743 pathogen proteins were predicted as drug targets by the random forest model and can be validated experimentally for therapeutic intervention in Microbe Associated Cardiovascular Diseases.

A SARS-CoV-2 (COVID-19) biological network to find targets for drug repurposing

The Coronavirus disease 2019 (COVID-19) caused by the SARS-CoV-2 virus needs a fast recognition of effective drugs to save lives. In the COVID-19 situation, finding targets for drug repurposing can be an effective way to present new fast treatments. We have designed a two-step solution to address this approach. In the first step, we identify essential proteins from virus targets or their associated modules in human cells as possible drug target candidates. For this purpose, we apply two different algorithms to detect some candidate sets of proteins with a minimum size that drive a significant disruption in the COVID-19 related biological networks. We evaluate the resulted candidate proteins sets with three groups of drugs namely Covid-Drug, Clinical-Drug, and All-Drug. The obtained candidate proteins sets approve 16 drugs out of 18 in the Covid-Drug, 273 drugs out of 328 in the Clinical-Drug, and a large number of drugs in the All-Drug. In the second step, we study COVID-19 associated proteins sets and recognize proteins that are essential to disease pathology. This analysis is performed using DAVID to show and compare essential proteins that are contributed between the COVID-19 comorbidities. Our results for shared proteins show significant enrichment for cardiovascular-related, hypertension, diabetes type 2, kidney-related and lung-related diseases.

Identifying ICU admission decision patterns in a '20-questions game' approach using network analysis

Background

The complex intensive care unit (ICU) admission decision process has numerous non-linear relationships involving multiple factors. To better describe and analyse this process, exploration of novel techniques to clearly delineate the importance and interrelationships of factors is warranted. Network analysis (NA), based on graph theory, attempts to identify patterns of connections within a network and may be useful in this regard.

Objectives

To identify patterns of ICU decision-making pertaining to patients referred for admission to ICU and to identify key factors, their distribution, connection and relative importance. The secondary aim was to compare subgroups as per decision outcomes and case labels.

Methods

NA was performed using Gephi software package as a secondary analysis on a dataset generated from a previous study on ICU admission decision-making process using a 20-questions game approach. The data were standardised and coded up to a quaternary level for this analysis.

Results

The coding process generated 31 nodes and 964 edges. Regardless of the measure used (centrality, prestige, authority and hubs), properties of the acute illness, progress of the acute illness and properties of comorbidities emerged consistently as among the most important factors and their relative rankings differed. Using different measures allowed important factors to emerge differentially. The six subgroups that emerged from the modularity measure bore little resemblance to traditional factor subgroups. Differences were noted in the subgroup comparisons of decision outcomes and case prognoses.

Conclusion

The use of NA with its various measures has facilitated a more comprehensive exploration of the ICU admission decision, allowing us to reflect on the process. Further studies with larger datasets are needed to elucidate the exact role of NA in decision-making processes.

Contributions of the study

We performed a novel analysis of a complex decision-making process that allowed for comparison with traditional analytic methods. It allowed for identification of key factors, their distribution, connection and relative importance. This may subsequently allow for reflection on difficult decision-making processes, thereby leading to more appropriate outcomes. Moreover, this may lead to new considerations in developing decision support systems such as the formulation of pro-forma data-capture tools (e.g. referral forms). Further, the way factors have been traditionally subgrouped may need to be reconsidered, with different subgroups being partitioned to better reflect their connection. This study offers a good basis for more advanced future studies in this area to use a new variety of analytical tools.

Identification and Validation of Key Genes in Hepatocellular Carcinoma by Bioinformatics Analysis

Hepatocellular carcinoma (HCC) is the most frequent primary liver cancer and has poor outcomes. However, the potential molecular biological process underpinning the occurrence and development of HCC is still largely unknown. The purpose of this study was to identify the core genes related to HCC and explore their potential molecular events using bioinformatics methods. HCC-related expression profiles GSE25097 and GSE84005 were selected from the Gene Expression Omnibus (GEO) database, and the differentially expressed genes (DEGs) between 306 HCC tissues and 281 corresponding noncancerous tissues were identified using GEO2R online tools. The protein-protein interaction network (PPIN) was constructed and visualized using the STRING database. Gene Ontology (GO) and KEGG pathway enrichment analyses of the DEGs were carried out using DAVID 6.8 and KOBAS 3.0. Additionally, module analysis and centrality parameter analysis were performed by Cytoscape. The expression differences of key genes in normal hepatocyte cells and HCC cells were verified by quantitative real-time fluorescence polymerase chain reaction (qRT-PCR). Additionally, survival analysis of key genes was performed by GEPIA. Our results showed that a total of 291 DEGs were identified including 99 upregulated genes and 192 downregulated genes. Our results showed that the PPIN of HCC was made up of 287 nodes and 2527 edges. GO analysis showed that these genes were mainly enriched in the molecular function of protein binding. Additionally, KEGG pathway analysis also revealed that DEGs were mainly involved in the metabolic, cell cycle, and chemical carcinogenesis pathways. Interestingly, a significant module with high centrality features including 10 key genes was found. Among these, CDK1, NDC80, HMMR, CDKN3, and PTTG1, which were only upregulated in HCC patients, have attracted much attention. Furthermore, qRT-PCR also confirmed the upregulation of these five key genes in the normal human hepatocyte cell line (HL-7702) and HCC cell lines (SMMC-7721, MHCC-97L, and MHCC-97H); patients with upregulated expression of these five key genes had significantly poorer survival and prognosis. CDK1, NDC80, HMMR, CDKN3, and PTTG1 can be used as molecular markers for HCC. This finding provides potential strategies for clinical diagnosis, accurate treatment, and prognosis analysis of liver cancer.

Early Drug Development and Evaluation of Putative Antitubercular Compounds in the -Omics Era

Tuberculosis (TB) is an infectious disease caused by the bacterium Mycobacterium tuberculosis. According to the WHO, the disease is one of the top 10 causes of death of people worldwide. Mycobacterium tuberculosis is an intracellular pathogen with an unusually thick, waxy cell wall and a complex life cycle. These factors, combined with M. tuberculosis ability to enter prolonged periods of latency, make the bacterium very difficult to eradicate. The standard treatment of TB requires 6-20months, depending on the drug susceptibility of the infecting strain. The need to take cocktails of antibiotics to treat tuberculosis effectively and the emergence of drug-resistant strains prompts the need to search for new antitubercular compounds. This review provides a perspective on how modern -omic technologies facilitate the drug discovery process for tuberculosis treatment. We discuss how methods of DNA and RNA sequencing, proteomics, and genetic manipulation of organisms increase our understanding of mechanisms of action of antibiotics and allow the evaluation of drugs. We explore the utility of mathematical modeling and modern computational analysis for the drug discovery process. Finally, we summarize how -omic technologies contribute to our understanding of the emergence of drug resistance.

A bioinformatics investigation into the pharmacological mechanisms of the effect of the Yinchenhao decoction on hepatitis C based on network pharmacology

Background

Globally, more than 170 million people are infected with hepatitis C virus, a major cause of cirrhosis and hepatocellular carcinoma. The Yinchenhao Decoction (YCHD) is a classic formula comprising three herbal medicines. This decoction have long been used in China for clinically treating acute and chronic infectious hepatitis and other liver and gallbladder damp heat-accumulation disorders.

Methods

In this study, we identified 32 active ingredients and 200 hepatitis C proteins and established a compound-predicted target network and a hepatitis C protein–protein interaction network by using Cytoscape 3.6.1. Then, we systematically analyzed the potential targets of the YCHD for the treatment of hepatitis C. Finally, molecular docking was applied to verify the key targets. In addition, we analyzed the mechanism of action of the predicted targets by the Kyoto Encyclopedia of Genes and Genomes and gene ontology analyses.

Results

This study adopted a network pharmacology approach, mainly comprising target prediction, network construction, module detection, functional enrichment analysis, and molecular docking to systematically investigate the mechanisms of action of the YCHD in hepatitis C. The targets of the YCHD in the treatment of hepatitis C mainly involved PIK3CG, CASP3, BCL2, CASP8, and MMP1. The module and pathway enrichment analyses showed that the YCHD had the potential to influence varieties of biological pathways, including the TNF signaling pathway, Ras signaling pathway, PI3K-Akt signaling pathway, FoxO signaling pathway, and pathways in cancer, that play an important role in the pathogenesis of hepatitis C.

Conclusion

The results of this study preliminarily verified the basic pharmacological effects and related mechanisms of the YCHD in the treatment of hepatitis C.

Disruption of Protein Complexes from Weighted Complex Networks

Essential proteins are indispensable units for living organisms. Removing those leads to disruption of protein complexes and causing lethality. Recently, theoretical methods have been presented to detect essential proteins in protein interaction network. In these methods, an essential protein is predicted as a high-degree vertex of protein interaction network. However, interaction data are usually incomplete and an essential protein cannot have high-connection due to data deficiency. Then, it is critical to design informative networks from other biological data sources. In this paper, we defined a minimal set of proteins to disrupt the maximum number of protein complexes. We constructed a weighted graph using a set of given complexes. We proposed a more appropriate method based on betweenness values to diagnose a minimal set of proteins whose removal would generate the disruption of protein complexes. The effectiveness of the proposed method was benchmarked using given dataset of complexes. The results of our method were compared to the results of other methods in terms of the number of disrupted complexes. Also, results indicated significant superiority of the minimal set of proteins in the massive disruption of complexes. Finally, we investigated the performance of our method for yeast and human datasets and analyzed biological properties of the selected proteins. Our algorithm and some example are freely available from http://bs.ipm.ac.ir/softwares/DPC/DPC.zip.

Unveiling Active Constituents and Potential Targets Related to the Hematinic Effect of Steamed Panax notoginseng Using Network Pharmacology Coupled With Multivariate Data Analyses

Steamed Panax notoginseng (SPN) has been used as a tonic to improve the blood deficiency syndrome (BDS) in the theory of traditional Chinese medicine. Here, we aim to unveil active constituents and potential targets related to the hematinic effect of SPN, which has not been answered before. In the study a constituent-target-disease network was constructed by combining the SPN-specific and anemia-specific target proteins with protein-protein interactions. And the network pharmacology was used to screen out the underlying targets and mechanisms of SPN treating anemia. Also, the multivariate data analyses were performed for the double screening. According to the results, 11 targets related to chemical constituents of SPN were found to be closely associated with the hematinic effect of SPN. Among them, the direct target protein of mitochondrial ferrochelatase (FECH) had the major role through the metabolic pathway. Meanwhile, Rk3 and 20(S)-Rg3 were predicted to be major constituents related to the hematinic effect of SPN by both multivariate data analyses and network pharmacology. And it was been validated by the pharmacologic tests that Rk3 and 20(S)-Rg3 could significantly increase the levels of blood routine parameters, FECH and its downstream protein of heme in mice with BDS. The study provides evidences for the mechanism understanding and drug development of SPN for the treatment of anemia.

Identifying Nursing Diagnosis Patterns in Three Intensive Care Units Using Network Analysis

PURPOSE

This study aimed to analyze the patterns of nursing diagnoses used in three different types of intensive care units (ICUs) using network analysis.

METHODS

A secondary analysis was conducted using clinical datasets of 582 patients. Frequency, degree/betweenness centrality, and subgroup analysis were performed.

FINDINGS AND CONCLUSIONS

The findings illuminated core nursing diagnoses with high centrality as well as high frequency. The centrality analysis identified the differences between and unique characteristics of each ICU. The subgroup analysis revealed the nursing problem groups related to the specific nursing care delivered to ICU patients.

IMPLICATIONS FOR NURSING PRACTICE

Theses results provide a knowledge base to aid ICU nurses' prompt decision making regarding nursing diagnoses.

A side-effect free method for identifying cancer drug targets

Identifying effective drug targets, with little or no side effects, remains an ever challenging task. A potential pitfall of failing to uncover the correct drug targets, due to side effect of pleiotropic genes, might lead the potential drugs to be illicit and withdrawn. Simplifying disease complexity, for the investigation of the mechanistic aspects and identification of effective drug targets, have been done through several approaches of protein interactome analysis. Of these, centrality measures have always gained importance in identifying candidate drug targets. Here, we put forward an integrated method of analysing a complex network of cancer and depict the importance of k-core, functional connectivity and centrality (KFC) for identifying effective drug targets. Essentially, we have extracted the proteins involved in the pathways leading to cancer from the pathway databases which enlist real experimental datasets. The interactions between these proteins were mapped to build an interactome. Integrative analyses of the interactome enabled us to unearth plausible reasons for drugs being rendered withdrawn, thereby giving future scope to pharmaceutical industries to potentially avoid them (e.g. ESR1, HDAC2, F2, PLG, PPARA, RXRA, etc). Based upon our KFC criteria, we have shortlisted ten proteins (GRB2, FYN, PIK3R1, CBL, JAK2, LCK, LYN, SYK, JAK1 and SOCS3) as effective candidates for drug development.

Elevated transcriptional levels of aldolase A (ALDOA) associates with cell cycle-related genes in patients with NSCLC and several solid tumors

BACKGROUND

Aldolase A (ALDOA) is one of the glycolytic enzymes primarily found in the developing embryo and adult muscle. Recently, a new role of ALDOA in several cancers has been proposed. However, the underlying mechanism remains obscure and inconsistent. In this study, we tried to investigate ALDOA-associated (AA) genes using available microarray datasets to help elucidating the role of ALDOA in cancer.

RESULTS

In the dataset of patients with non-small-cell lung cancer (NSCLC, E-GEOD-19188), 3448 differentially expressed genes (DEGs) including ALDOA were identified, in which 710 AA genes were found to be positively associated with ALDOA. Then according to correlation coefficients between each pair of AA genes, ALDOA-associated gene co-expression network (GCN) was constructed including 182 nodes and 1619 edges. 11 clusters out of GCN were detected by ClusterOne plugin in Cytoscape, and only 3 of them have more than three nodes. These three clusters were functionally enriched. A great number of genes (43/79, 54.4%) in the biggest cluster (Cluster 1) primarily involved in biological process like cell cycle process (P_a = 6.76E-26), mitotic cell cycle (P_a = 4.09E-19), DNA repair (P_a = 1.13E-04), M phase of meiotic cell cycle (P_a = 0.006), positive regulation of ubiquitin-protein ligase activity during mitotic cell cycle (P_a = 0.014). AA genes with highest degree and betweenness were considered as hub genes of GCN, namely CDC20, MELK, PTTG1, CCNB2, CDC45, CCNB1, TK1 and PSMB2, which could distinguish cancer from normal controls with ALDOA. Their positive association with ALDOA remained after removing the effect of HK2 and PKM, the two rate limiting enzymes in glycolysis. Further, knocking down ALDOA blocked breast cancer cells in the G0/G1 phase under minimized glycolysis. All suggested that ALDOA might affect cell cycle progression independent of glycolysis. RT-qPCR detection confirmed the relationship of ALDOA with CDC45 and CCNB2 in breast tumors. High expression of the hub genes indicated poor outcome in NSCLC. ALDOA could improve their predictive power.

CONCLUSIONS

ALDOA could contribute to the progress of cancer, at least partially through its association with genes relevant to cell cycle independent of glycolysis. AA genes plus ALDOA represent a potential new signature for development and prognosis in several cancers.

Bypass of Candida albicans Filamentation/Biofilm Regulators through Diminished Expression of Protein Kinase Cak1

Biofilm formation on implanted medical devices is a major source of lethal invasive infection by Candida albicans. Filamentous growth of this fungus is tied to biofilm formation because many filamentation-associated genes are required for surface adherence. Cell cycle or cell growth defects can induce filamentation, but we have limited information about the coupling between filamentation and filamentation-associated gene expression after cell cycle/cell growth inhibition. Here we identified the CDK activating protein kinase Cak1 as a determinant of filamentation and filamentation-associated gene expression through a screen of mutations that diminish expression of protein kinase-related genes implicated in cell cycle/cell growth control. A cak1diminished expression (DX) strain displays filamentous growth and expresses filamentation-associated genes in the absence of typical inducing signals. In a wild-type background, expression of filamentation-associated genes depends upon the transcription factors Bcr1, Brg1, Efg1, Tec1, and Ume6. In the cak1 DX background, the dependence of filamentation-associated gene expression on each transcription factor is substantially relieved. The unexpected bypass of filamentation-associated gene expression activators has the functional consequence of enabling biofilm formation in the absence of Bcr1, Brg1, Tec1, Ume6, or in the absence of both Brg1 and Ume6. It also enables filamentous cell morphogenesis, though not biofilm formation, in the absence of Efg1. Because these transcription factors are known to have shared target genes, we suggest that cell cycle/cell growth limitation leads to activation of several transcription factors, thus relieving dependence on any one.

The ability of the pathogen Candida albicans to grow on surfaces as biofilms is a determinant of infection ability, because biofilms on implanted medical devices seed infections. Biofilm formation by this organism requires growth in the form of filamentous cells and the expression of filamentation-associated genes. Inhibition of cell proliferation can induce filamentous cell formation, as we find here for strains that express greatly reduced levels of the cell cycle regulator Cak1. Surprisingly, biofilm formation occurs independently of many central biofilm regulatory genes when Cak1 levels are reduced. This response to proliferation inhibition may reflect the activation of numerous biofilm regulators, thus relieving the dependence on any one regulator. The stimulation of biofilm formation by proliferation inhibition, a property of many bacterial pathogens as well, may contribute to the limited effectiveness of antimicrobials against biofilms.