Computational screening of potential drug targets for pathogens causing bacterial pneumonia

Recent Advances and Techniques for Identifying Novel Antibacterial Targets

BACKGROUND

With the emergence of drug-resistant bacteria, the development of new antibiotics is urgently required. Target-based drug discovery is the most frequently employed approach for the drug development process. However, traditional drug target identification techniques are costly and time-consuming. As research continues, innovative approaches for antibacterial target identification have been developed which enabled us to discover drug targets more easily and quickly.

METHODS

In this review, methods for finding drug targets from omics databases have been discussed in detail including principles, procedures, advantages, and potential limitations. The role of phage-driven and bacterial cytological profiling approaches is also discussed. Moreover, current article demonstrates the advancements being made in the establishment of computational tools, machine learning algorithms, and databases for antibacterial target identification.

RESULTS

Bacterial drug targets successfully identified by employing these aforementioned techniques are described as well.

CONCLUSION

The goal of this review is to attract the interest of synthetic chemists, biologists, and computational researchers to discuss and improve these methods for easier and quicker development of new drugs.

Integrated bioinformatics approach to unwind key genes and pathways involved in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is the fifth leading cause of death in India. Until now, the exact pathogenesis concerning CRC signaling pathways is largely unknown; however, the diseased condition is believed to deteriorate with lifestyle, aging, and inherited genetic disorders. Hence, the identification of hub genes and therapeutic targets is of great importance for disease monitoring.

OBJECTIVE

Identification of hub genes and targets for identification of candidate hub genes for CRC diagnosis and monitoring.

MATERIALS AND METHODS

The present study applied gene expression analysis by integrating two profile datasets (GSE20916 and GSE33113) from NCBI-GEO database to elucidate the potential key candidate genes and pathways in CRC. Differentially expressed genes (DEGs) between CRC (195 CRC tissues) and healthy control (46 normal mucosal tissue) were sorted using GEO2R tool. Further, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis were performed using Cluster Profiler in Rv. 3.6.1. Moreover, protein-protein interactions (PPI), module detection, and hub gene identification were accomplished and visualized through the Search Tool for the Retrieval of Interacting Genes, Molecular Complex Detection (MCODE) plug-in of Cytoscape v3.8.0. Further hub genes were imported into ToppGene webserver for pathway analysis and prognostic expression analysis was conducted using Gene Expression Profiling Interactive Analysis webserver.

RESULTS

A total of 2221 DEGs, including 1286 up-regulated and 935down-regulated genes mainly enriched in signaling pathways of NOD-like receptor, FoxO, AMPK signalling and leishmaniasis. Three key modules were detected from PPI network using MCODE. Besides, top 20 high prioritized hub genes were selected. Further, prognostic expression analysis revealed ten of the hub genes, namely IL1B, CD44, Glyceraldehyde-3-phosphate dehydrogenase (GAPDH, MMP9, CREB1, STAT1, vascular endothelial growth factor (VEGFA), CDC5 L, Ataxia-telangiectasia mutated (ATM + and CDH1 to be differently expressed in normal and cancer patients.

CONCLUSION

The present study proposed five novel therapeutic targets, i.e., ATM, GAPDH, CREB1, VEGFA, and CDH1 genes that might provide new insights into molecular oncogenesis of CRC.

Exploration of Streptococcus core genome to reveal druggable targets and novel therapeutics against S. pneumoniae

Streptococcus pneumoniae (S. pneumoniae), the major etiological agent of community-acquired pneumonia (CAP) contributes significantly to the global burden of infectious diseases which is getting resistant day by day. Nearly 30% of the S. pneumoniae genomes encode hypothetical proteins (HPs), and better understandings of these HPs in virulence and pathogenicity plausibly decipher new treatments. Some of the HPs are present across many Streptococcus species, systematic assessment of these unexplored HPs will disclose prospective drug targets. In this study, through a stringent bioinformatics analysis of the core genome and proteome of S. pneumoniae PCS8235, we identified and analyzed 28 HPs that are common in many Streptococcus species and might have a potential role in the virulence or pathogenesis of the bacteria. Functional annotations of the proteins were conducted based on the physicochemical properties, subcellular localization, virulence prediction, protein-protein interactions, and identification of essential genes, to find potentially druggable proteins among 28 HPs. The majority of the HPs are involved in bacterial transcription and translation. Besides, some of them were homologs of enzymes, binding proteins, transporters, and regulators. Protein-protein interactions revealed HP PCS8235_RS05845 made the highest interactions with other HPs and also has TRP structural motif along with virulent and pathogenic properties indicating it has critical cellular functions and might go under unconventional protein secretions. The second highest interacting protein HP PCS8235_RS02595 interacts with the Regulator of chromosomal segregation (RocS) which participates in chromosome segregation and nucleoid protection in S. pneumoniae. In this interacting network, 54% of protein members have virulent properties and 40% contain pathogenic properties. Among them, most of these proteins circulate in the cytoplasmic area and have hydrophilic properties. Finally, molecular docking and dynamics simulation demonstrated that the antimalarial drug Artenimol can act as a drug repurposing candidate against HP PCS8235_RS 04650 of S. pneumoniae. Hence, the present study could aid in drugs against S. pneumoniae.

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

Decoding Psoriasis: Integrated Bioinformatics Approach to Understand Hub Genes and Involved Pathways

BACKGROUND

Psoriasis is a chronic immune mediated skin disorder with global prevalence of 0.2- 11.4%. Despite rare mortality, the severity of the disease could be understood by the accompanying comorbidities, that has even led to psychological problems among several patients. The cause and the disease mechanism still remain elusive.

OBJECTIVE

To identify potential therapeutic targets and affecting pathways for better insight of the disease pathogenesis.

METHOD

The gene expression profile GSE13355 and GSE14905 were retrieved from NCBI, Gene Expression Omnibus database. The GEO profiles were integrated and the DEGs of lesional and non-lesional psoriasis skin were identified using the affy package in R software. The Kyoto Encyclopaedia of Genes and Genomes pathways of the DEGs were analyzed using clusterProfiler. Cytoscape, V3.7.1 was utilized to construct protein interaction network and analyze the interactome map of candidate proteins encoded in DEGs. Functionally relevant clusters were detected through Cytohubba and MCODE.

RESULTS

A total of 1013 genes were differentially expressed in lesional skin of which 557 were upregulated and 456 were downregulated. Seven dysregulated genes were extracted in non-lesional skin. The disease gene network of these DEGs revealed 75 newly identified differentially expressed gene that might have a role in development and progression of the disease. GO analysis revealed keratinocyte differentiation and positive regulation of cytokine production to be the most enriched biological process and molecular function. Cytokines -cytokine receptor was the most enriched pathways. Among 1013 identified DEGs in lesional group, 36 DEGs were found to have altered genetic signature including IL1B and STAT3 which are also reported as hub genes. CCNB1, CCNA2, CDK1, IL1B, CXCL8, MKI 67, ESR1, UBE2C, STAT1 and STAT3 were top 10 hub gene.

CONCLUSION

The hub genes, genomic altered DEGs and other newly identified differentially dysregulated genes would improve our understanding of psoriasis pathogenesis, moreover, the hub genes could be explored as potential therapeutic targets for psoriasis.

The search for novel treatment strategies for Streptococcus pneumoniae infections

This review provides an overview of the most important novel treatment strategies against Streptococcus pneumoniae infections published over the past 10 years. The pneumococcus causes the majority of community-acquired bacterial pneumonia cases, and it is one of the prime pathogens in bacterial meningitis. Over the last 10 years, extensive research has been conducted to prevent severe pneumococcal infections, with a major focus on (i) boosting the host immune system and (ii) discovering novel antibacterials. Boosting the immune system can be done in two ways, either by actively modulating host immunity, mostly through administration of selective antibodies, or by interfering with pneumococcal virulence factors, thereby supporting the host immune system to effectively overcome an infection. While several of such experimental therapies are promising, few have evolved to clinical trials. The discovery of novel antibacterials is hampered by the high research and development costs versus the relatively low revenues for the pharmaceutical industry. Nevertheless, novel enzymatic assays and target-based drug design, allow the identification of targets and the development of novel molecules to effectively treat this life-threatening pathogen.

CEG 2.0: an updated database of clusters of essential genes including eukaryotic organisms

Essential genes are key elements for organisms to maintain their living. Building databases that store essential genes in the form of homologous clusters, rather than storing them as a singleton, can provide more enlightening information such as the general essentiality of homologous genes in multiple organisms. In 2013, the first database to store prokaryotic essential genes in clusters, CEG (Clusters of Essential Genes), was constructed. Afterward, the amount of available data for essential genes increased by a factor >3 since the last revision. Herein, we updated CEG to version 2, including more prokaryotic essential genes (from 16 gene datasets to 29 gene datasets) and newly added eukaryotic essential genes (nine species), specifically the human essential genes of 12 cancer cell lines. For prokaryotes, information associated with drug targets, such as protein structure, ligand–protein interaction, virulence factor and matched drugs, is also provided. Finally, we provided the service of essential gene prediction for both prokaryotes and eukaryotes. We hope our updated database will benefit more researchers in drug targets and evolutionary genomics.

Database URL:http://cefg.uestc.cn/ceg

Genome Subtraction and Comparison for the Identification of Novel Drug Targets against Mycobacterium avium subsp. hominissuis

Mycobacterium avium complex (MAC) is a major cause of non-tuberculous pulmonary and disseminated diseases worldwide, inducing bronchiectasis, and affects HIV and immunocompromised patients. In MAC, Mycobacterium avium subsp. hominissuis is a pathogen that infects humans and mammals, and that is why it is a focus of this study. It is crucial to find essential drug targets to eradicate the infections caused by these virulent microorganisms. The application of bioinformatics and proteomics has made a significant impact on discovering unique drug targets against the deadly pathogens. One successful bioinformatics methodology is the use of in silico subtractive genomics. In this study, the aim was to identify the unique, non-host and essential protein-based drug targets of Mycobacterium avium subsp. hominissuis via in silico a subtractive genomics approach. Therefore, an in silico subtractive genomics approach was applied in which complete proteome is subtracted systematically to shortlist potential drug targets. For this, the complete dataset of proteins of Mycobacterium avium subsp. hominissuis was retrieved. The applied subtractive genomics method, which involves the homology search between the host and the pathogen to subtract the non-druggable proteins, resulted in the identification of a few prioritized potential drug targets against the three strains of M. avium subsp. Hominissuis, i.e., MAH-TH135, OCU466 and A5. In conclusion, the current study resulted in the prioritization of vital drug targets, which opens future avenues to perform structural as well as biochemical studies on predicted drug targets against M. avium subsp. hominissuis.