Identification Of Drug Targets Research Articles

Recent Advances and Techniques for Identifying Novel Antibacterial Targets.

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

Prioritization of Trypanosoma brucei editosome protein interactions interfaces at residue resolution through proteome-scale network analysis

BackgroundTrypanosoma brucei is the causative agent for trypanosomiasis in humans and livestock, which presents a growing challenge due to drug resistance. While identifying novel drug targets is vital, the process is delayed due to a lack of functional information on many of the pathogen’s proteins. Accordingly, this paper presents a computational framework for prioritizing drug targets within the editosome, a vital molecular machinery responsible for mitochondrial RNA processing in T. brucei. Importantly, this framework may eliminate the need for prior gene or protein characterization, potentially accelerating drug discovery efforts.ResultsBy integrating protein-protein interaction (PPI) network analysis, PPI structural modeling, and residue interaction network (RIN) analysis, we quantitatively ranked and identified top hub editosome proteins, their key interaction interfaces, and hotspot residues. Our findings were cross-validated and further prioritized by incorporating them into gene set analysis and differential expression analysis of existing quantitative proteomics data across various life stages of T. brucei. In doing so, we highlighted PPIs such as KREL2-KREPA1, RESC2-RESC1, RESC12A-RESC13, and RESC10-RESC6 as top candidates for further investigation. This includes examining their interfaces and hotspot residues, which could guide drug candidate selection and functional studies.ConclusionRNA editing offers promise for target-based drug discovery, particularly with proteins and interfaces that play central roles in the pathogen’s life cycle. This study introduces an integrative drug target identification workflow combining information from the PPI network, PPI 3D structure, and reside-level information of their interface which can be applicable to diverse pathogens. In the case of T. brucei, via this pipeline, the present study suggested potential drug targets with residue-resolution from RNA editing machinery. However, experimental validation is needed to fully realize its potential in advancing urgently needed antiparasitic drug development.

Metabolic Signature of Warburg Effect in Cancer: An Effective and Obligatory Interplay between Nutrient Transporters and Catabolic/Anabolic Pathways to Promote Tumor Growth.

Aerobic glycolysis in cancer cells, originally observed by Warburg 100 years ago, which involves the production of lactate as the end product of glucose breakdown even in the presence of adequate oxygen, is the foundation for the current interest in the cancer-cell-specific reprograming of metabolic pathways. The renewed interest in cancer cell metabolism has now gone well beyond the original Warburg effect related to glycolysis to other metabolic pathways that include amino acid metabolism, one-carbon metabolism, the pentose phosphate pathway, nucleotide synthesis, antioxidant machinery, etc. Since glucose and amino acids constitute the primary nutrients that fuel the altered metabolic pathways in cancer cells, the transporters that mediate the transfer of these nutrients and their metabolites not only across the plasma membrane but also across the mitochondrial and lysosomal membranes have become an integral component of the expansion of the Warburg effect. In this review, we focus on the interplay between these transporters and metabolic pathways that facilitates metabolic reprogramming, which has become a hallmark of cancer cells. The beneficial outcome of this recent understanding of the unique metabolic signature surrounding the Warburg effect is the identification of novel drug targets for the development of a new generation of therapeutics to treat cancer.

How to translate genetic findings into clinical applications in spondyloarthritis?

Spondyloarthritis (SpA) is characterized by a strong genetic predisposition evidenced by the identification of up to 50 susceptibility loci, in addition to HLA-B27, the major genetic factor associated with the disease. These loci have not only deepened our understanding of disease pathogenesis but also offer the potential to improve disease management. Diagnostic delay is a major issue in SpA. HLA-B27 testing is widely used as diagnostic biomarker in SpA but its predictive value is limited. Several attempts have been made to develop more sophisticated polygenic risk score (PRS). However, these scores currently offer very little improvement as compared to HLA-B27 and are still difficult to implement in clinical routine. Genetics might also help to predict disease outcome including treatment response. Several genetic variants have been reported to be associated with radiographic damage or with poor response to TNF blockers, unfortunately with lack of coherence across studies. Large-scale studies should be conducted to obtain more robust findings. Genetic and genomic evidence in complex diseases can be further used to support the identification of new drug targets and to repurpose existing drugs. Although not fully driven by genetics, development of IL-17 blockers has been facilitated by the discovery of the association between IL23R variants and SpA. Development of recent approaches combining GWAS findings with functional genomics will help to prioritize new drug targets in the future. Although very promising, translational genetics in SpA remains challenging and will require a multidisciplinary approach that integrates genetics, genomics, immunology, and clinical research.

In Silico Identification and Characterization of Drug Targets in Streptococcus pneumoniae ATCC 700669 (Serotype 23F) by Subtractive Genomics.

Streptococcus pneumoniae (S. pneumoniae) is an important pathogen worldwide that causes pneumococcal infections which are related to high rates of morbidity and mortality especially in young children, older adults, and immune-compromised persons. Antibiotic resistance in S. pneumoniae is a serious problem across the world from time to time, resulting in treatment failure and diminished value of older medicines. Therefore, the objective of this study was to identify new putative drug targets against S. pneumoniae serotype 23F by using subtractive genomics. By using bioinformatics tools such as NCBI, UniProt KB, PDB, KEGG, DEG, PSORTb, CD hit, DrugBank database, and other softwares, proteins involved in unique metabolic pathways of S. pneumoniae serotype 23F were studied. The result indicates that this serotype consists of 97 metabolic pathways of which 74 are common with that of human, and 23 pathways are unique to the serotype 23F. After investigation and analysis of essentiality, nonhomology, subcellular localization, having drug targets, and enzymatic activity, four proteins were prioritized as druggable targets. These druggable proteins include UDP-N-acetylglucosamine 1-carboxyvinyltransferase, UDP-N-acetyl muramate dehydrogenase, D-alanine-D-alanine ligase, and alanine racemase that are found in S. pneumoniae serotype 23F. All these four proteins are essential, are nonhomologous with human proteins, have drug targets, and are located in cell cytoplasm. Therefore, the authors recommend these proteins to be used for efficient drug design against S. pneumoniae serotype 23F after experimental validation for essentiality and druggability.

Optimization and biological evaluation of l-DOPA derivatives as potent influenza PAN endonuclease inhibitors with multi-site binding characteristics

Emerging and potential influenza pandemics still are an enormous worldwide public health challenge. The PAN endonuclease has been proved to be a promising target for anti-influenza drug design. Here, we report the discovery and optimization of potent Y-shaped PAN inhibitors featuring multi-site binding characteristics with l-DOPA as a starting point. We systematically modified the hit 1 bearing two-binding characteristics based on structure-based rational design combined with multisite binding and conformational constraint strategies, generating four families of l-DOPA derivatives for SARs analysis. Among these substances, N, 3-di-substituted 1, 2, 3, 4-tetrahydroisoquinoline derivative T-31 displayed superior properties as a lead PAN endonuclease inhibitor and antiviral agent. The lead T-31 inhibited PAN endonuclease activity with an IC50 value of 0.15 μM and showed broad and submicromolar anti-influenza potency in cell-based assays. More importantly, T-31 could simultaneously target both influenza HA and the RdRp complex, thus interfering with virus entry into host cells and viral replication. This study offers a set of novel PAN endonuclease inhibitors with multi-site binding characteristics starting from the l-DOPA skeleton.

Identification of CD8+ T cell-related biomarkers and immune infiltration characteristic of rheumatoid arthritis.

Rheumatoid arthritis (RA) is an autoimmune rheumatic disease, which do not respond well to current treatment partially. Therefore, further in-depth elucidation of the molecular mechanism and pathogenesis of RA is urgently needed for the diagnosis, personalized therapy and drug development. Herein, we collected 111 RA samples from Gene Expression Omnibus (GEO) database, and conducted differentially expressed genes and GESA analysis. Abnormal activation and imbalance of immune cells in RA were observed. WGCNA was utilized to explore the gene modules and CD8+ T cell-related genes (CRGs) were chosen for KEGG and GO analysis. Besides, to explore biomarkers of RA in depth, machine learning algorithms and bioinformatics analysis were used, and we identified GDF15, IGLC1, and IGHM as diagnostic markers of RA, which was confirmed by clinical samples. Next, ssGSEA algorithms were adopted to investigate the differences in immune infiltration of 23 immune cell subsets between RA and healthy control group. Finally, optimal classification analysis based on consensus clustering combined with ssGSEA algorithms were conducted. GDF15 was revealed that to be positively correlated with mast cells and type 2 T helper cells, but negatively correlated with most other immune cells. On the other hand, IGHM and IGLC1 were negatively correlated with CD56dim natural killer cells, while positively associated with other immune cells. Finally, RA samples in subtype A exhibited a higher immune infiltration status. This study could provide guidance for individualized treatment of RA patients and provide new targets for drug design.

Experimental animal models for gastric ulcer / peptic ulcer: An overview

In the present study we have discussed around sixteen different animal models used worldwide for the scientific research and new drug discovery. The main aim of the using experimental animal models in drug discovery is to establish and provide evidence for non-clinical 'proof-of-concept' for the safety, efficacy, and target of interest for specific drug molecules. Experimental preparations developed in one species for the purpose of studying phenomena occurring in another species. The use of experimental animal models serves to better understand the origins, pathology, and the overall nature of comparable diseases of humans being. Similarly, animal models perform duties for in the development of safe and effective treatments and cures of such diseases and/or associated symptoms. Experimental animal models for drug discovery and development have played a major role in the characterization of the pathophysiology of diseases and associated mechanisms of injury, drug target identification, and evaluation of novel therapeutic agents for toxicity, pharmacokinetics and pharmacodynamics activity. Through animal model researchers can perform experiments that would be impractical or ethically prohibited with humans. There are various animal models used for screening of uncountable therapeutic activities, in this review our main focus is animal models used for peptic ulcer. Peptic ulcer is one of the worldwide diseases where 10% of adults are affected by peptic ulcer once in their lifetime. The antiulcer models for drug development against gastric and duodenal ulcer studies are limited in number that has hindered the progress of targeted therapy in this field. Therefore, it is necessary to review the literature on experimental animal models that are used to screen agents with potential anti-gastric ulcer activity and describe their biochemical basis in order to facilitate their use in the development of new preventive and curative antiulcer drugs. There are many models used to induce ulcer such as pylorus ligation or it can be chemically induced by ethanol, NSAIDs (e.g. indomethacin) or many more. In this review paper, current in-vivo animal models of ulcers and the pathophysiological mechanisms underlying their induction, their drawbacks, as well as the challenges associated with their use have been discussed.

Structural and Functional Characterization of Biofilm-Related Proteins of Mycobacterium spp: An in-silico Approach

Biofilm-associated infections are characterized by the chronicity, recurrence, and the requirement of a prolonged administration of multiple drugs. Several non-pathogenic and pathogenic species of microorganism including Mycobacteria spp form biofilm. Mycobacterial biofilms present a unique composition. Instead of exopolysaccharides in other bacteria, proteins are essential compounds of the biofilm matrix in mycobacteria. To tackle mycobacterial infections, a detailed understanding of the biofilm-forming mechanisms is crucial. In this present study, all available Mycobacterial proteins involved in the biofilm were selected. Their sequences were retrieved and characterized through the determination of their physicochemical properties, secondary structure, 3D structure, subcellular localization, conserved domain, ubiquitination sites, and virulence potentiality. Furthermore, druggability testing was undertaken after excluding proteins with homology to human proteins to identify possible drug targets. The results showed that they possess functionally important domains and families. All of the selected hypothetical proteins were stable. Six of them were classified as soluble and the remaining as transmembrane proteins. A sole protein was found to lack ubiquitination sites. Additionally, three of these were discovered to be virulent. Moreover, host non-homology results indicated eight pathogen-specific proteins that might be potential therapeutic targets. Among them, D-alanyl-D-alanine carboxypeptidase is a druggable target that is inhibited by beta-lactam antibiotics. The remainder of the proteins were categorized as new targets. 
 In conclusion, this study may increase our knowledge of pathogenesis and host adaptation, drug resistance, and identification of drug and vaccine targets against infections caused by Mycobacteria. It can also guide new research.

Structural and thermodynamic properties of conserved water molecules in Mpro native: A combined approach by MD simulation and Grid Inhomogeneous Solvation Theory.

The new viral strains of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) are continuously rising, becoming more virulent, and transmissible. Therefore, the development of new antiviral drugs is essential. Due to its significant role in the viral life cycle of SARS-CoV-2, the main protease (Mpro) enzyme is a leading target for antiviral drug design. The Mpro monomer consists of domain DI, DII, and DI-DII interface. Twenty-one conserved water molecules (W4-W24) are occupied at these domains according to multiple crystal structure analyses. The crystal and MD structures reveal the presence of eight conserved water sites in domain DI, DII and remaining in the DI-DII interface. Grid-based inhomogeneous fluid solvation theory (GIST) was employed on MD structures of Mpro native to predict structural and thermodynamic properties of each conserved water site for focusing to identify the specific conserved water molecules that can easily be displaced by proposed ligands. Finally, MD water W13 is emerged as a promising candidate for water mimic drug design due to its low mean interaction energy, loose binding character with the protein, and its involvement in a water-mediated H-bond with catalytic His41 via the interaction Thr25(OG)---W13---W---His41(NE2). In this context, water occupancy, relative interaction energy, entropy, and topologies of W13 are thermodynamically acceptable for the water displacement method. Therefore, the strategic use of W13's geometrical position in the DI domain may be implemented for drug discovery against COVID disease by designing new ligands with appropriately oriented chemical groups to mimic its structural, electronic, and thermodynamic properties.

Post-translational modifications of Keap1: the state of the art.

The Keap1-Nrf2 signaling pathway plays a crucial role in cellular defense against oxidative stress-induced damage. Its activation entails the expression and transcriptional regulation of several proteins involved in detoxification and antioxidation processes within the organism. Keap1, serving as a pivotal transcriptional regulator within this pathway, exerts control over the activity of Nrf2. Various post-translational modifications (PTMs) of Keap1, such as alkylation, glycosylation, glutathiylation, S-sulfhydration, and other modifications, impact the binding affinity between Keap1 and Nrf2. Consequently, this leads to the accumulation of Nrf2 and its translocation to the nucleus, and subsequent activation of downstream antioxidant genes. Given the association between the Keap1-Nrf2 signaling pathway and various diseases such as cancer, neurodegenerative disorders, and diabetes, comprehending the post-translational modification of Keap1 not only deepens our understanding of Nrf2 signaling regulation but also contributes to the identification of novel drug targets and biomarkers. Consequently, this knowledge holds immense importance in the prevention and treatment of diseases induced by oxidative stress.

Predicting hotspots for disease-causing single nucleotide variants using sequences-based coevolution, network analysis, and machine learning.

To enable personalized medicine, it is important yet highly challenging to accurately predict disease-causing mutations in target proteins at high throughput. Previous computational methods have been developed using evolutionary information in combination with various biochemical and structural features of protein residues to discriminate neutral vs. deleterious mutations. However, the power of these methods is often limited because they either assume known protein structures or treat residues independently without fully considering their interactions. To address the above limitations, we build upon recent progress in machine learning, network analysis, and protein language models, and develop a sequences-based variant site prediction workflow based on the protein residue contact networks: 1. We employ and integrate various methods of building protein residue networks using state-of-the-art coevolution analysis tools (RaptorX, DeepMetaPSICOV, and SPOT-Contact) powered by deep learning. 2. We use machine learning algorithms (Random Forest, Gradient Boosting, and Extreme Gradient Boosting) to optimally combine 20 network centrality scores to jointly predict key residues as hot spots for disease mutations. 3. Using a dataset of 107 proteins rich in disease mutations, we rigorously evaluate the network scores individually and collectively (via machine learning). This work supports a promising strategy of combining an ensemble of network scores based on different coevolution analysis methods (and optionally predictive scores from other methods) via machine learning to predict hotspot sites of disease mutations, which will inform downstream applications of disease diagnosis and targeted drug design.

TRIF-dependent signaling and its role in liver diseases.

TIR domain-containing adaptor inducing IFN-β (TRIF) is a crucial adaptor molecule downstream of toll-like receptors 3 (TLR3) and 4 (TLR4). TRIF directly binds to TLR3 through its TIR domain, while it associates with TLR4 indirectly through the bridge adaptor molecule TRIF-related adaptor molecule (TRAM). TRIF plays a pivotal role in regulating interferon beta 1 (IFN-β) response, nuclear factor kappa B (NF-κB) signaling, apoptosis, and necroptosis signaling mediated by TLR3 and TLR4. It accomplishes these by recruiting and activating various kinases or transcription factors via its distinct domains. In this review, we comprehensively summarize the TRIF-dependent signaling pathways mediated by TLR3 and TLR4, elucidating key target molecules and downstream pathways. Furthermore, we provide an overview of TRIF's impact on several liver disorders, including drug-induced liver injury, ischemia-reperfusion liver injury, autoimmune hepatitis, viral hepatitis, alcohol-associated liver disease (ALD), metabolic dysfunction-associated steatotic liver disease (MASLD) and metabolic dysfunction-associated steatohepatitis (MASH). We also explore its effects on liver steatosis, inflammation, fibrosis, and carcinogenesis. A comprehensive understanding of the TRIF-dependent signaling pathways, as well as the intricate relationship between TRIF and liver diseases, can facilitate the identification of potential drug targets and the development of novel and effective therapeutics against hepatic disorders.

TaqMan-quantitative PCR assays applied in Neospora caninum knock-outs generated through CRISPR-Cas9 allow to determine the copy numbers of integrated dihydrofolate reductase-thymidylate synthase drug selectable markers.

As for many other organisms, CRISPR-Cas9 mediated genetic modification has gained increasing importance for the identification of vaccine candidates and drug targets in Neospora caninum, an apicomplexan parasite causing abortion in cattle and neuromuscular disease in dogs. A widely used approach for generating knock-out (KO) strains devoid of virulence factors is the integration of a drug selectable marker such as mutated dihydrofolate reductase-thymidylate synthase (mdhfr-ts) into the target gene, thus preventing the synthesis of respective protein and mediating resistance to pyrimethamine. However, CRISPR-Cas9 mutagenesis is not free of off-target effects, which can lead to integration of multiple mdhfr-ts copies into other sites of the genome. To determine the number of integrated mdhfr-ts in N. caninum, a duplex quantitative TaqMan PCR was developed. For this purpose, primers were designed that amplifies a 106 bp fragment from wild-type (WT) parasites corresponding to the single copy wtdhfrs-ts gene, as well as the mutated mdhfrs-ts present in KO parasites that confers resistance and were used simultaneously with primers amplifying the diagnostic NC5 gene. Thus, the dhfr-ts to NC5 ratio should be approximately 1 in WT parasites, while in KO parasites with a single integrated mdhrf-ts gene this ratio is doubled, and in case of multiple integration events even higher. This approach was applied to the Neospora KO strains NcΔGRA7 and NcΔROP40. For NcΔGRA7, the number of tachyzoites determined by dhfr-ts quantification was twice the number of tachyzoites determined by NC5 quantification, thus indicating that only one mdhfr-ts copy was integrated. The results obtained with the NcΔROP40 strain, however, showed that the number of dhfr-ts copies per genome was substantially higher, indicating that at least three copies of the selectable mdhfr-ts marker were integrated into the genomic DNA during gene editing by CRISPR-Cas9. This duplex TaqMan-qPCR provides a reliable and easy-to-use tool for assessing CRISPR-Cas9 mediated mutagenesis in WT N. caninum strains.

Current approaches in identification of a novel drug targets for drug repurposing.

Currently, millions of drugs and their licence have been expired or will be expiring in near future. Therefore, existing USFDA approved drug can be used for treating another disease. The above-mentioned approach falls under the category of drug repurposing. Drug repurposing is an alternative strategy for finding new applications of existing USFDA approved drugs. Identification of a novel drug target is one of the go to way for drug repurposing so that new therapeutic applications of USFDA approved drugs could be determined. Recent advances in computational biology and bioinformatics can help to accelerate the same. Drug repurposing can save time and resource as compared to discovery of an entirely new drug molecule. In this chapter, we explore different strategies for discovery of a novel drug target and its uses for drug repurposing to treat disease.

Insulinotropic effect of Teucrium polium; Identification of potential mechanistic drug targets

Teucrium polium (T. polium), commonly known as golden germander, is a herb that grows in the Mediterranean region and is used by the locals to treat diabetes mellitus. The glucose-lowering effect of T. polium is well established in rodents. This study aims to examine whether the glucose-lowering effect is by inducing the release of insulin from pancreatic beta cells and to identify key measurable biomolecules/sites that are modified in the lead-up to insulin release. Insulin secretion was examined in BRIN-BD11 pancreatic beta cells treated with T. polium extract in vitro and key flavonoids were identified using LCMS. The T. polium extract promoted increased insulin secretion (p<0.05), in a dose-dependent manner, and increased glucose uptake (p<0.05) within 30min. This was accompanied by an increase in GLUT2 (p<0.05) and glucokinase (p<0.01) expression. Also, mitochondrial metabolism and glycolytic rates were enhanced (p<0.05), and ATP production increased (p<0.01), which coincided with increased (p<0.01) calcium influx. Rutin and apigenin were detected, but not quercetin, in this extract. T. polium promotes insulin secretion. T. polium does this by GLUT2 and glucokinase expression, increased glucose transport, metabolism and ATP production, ultimately increasing intracellular calcium. Keywords: Diabetes mellitus, phenolics, flavonoids, hyperglycaemia, glucose lowering, insulin secretagogue, pancreatic beta cells.

Strategies for translating proteomics discoveries into drug discovery for dementia.

Tauopathies, diseases characterized by neuropathological aggregates of tau including Alzheimer's disease and subtypes of frontotemporal dementia, make up the vast majority of dementia cases. Although there have been recent developments in tauopathy biomarkers and disease-modifying treatments, ongoing progress is required to ensure these are effective, economical, and accessible for the globally ageing population. As such, continued identification of new potential drug targets and biomarkers is critical. "Big data" studies, such as proteomics, can generate information on thousands of possible new targets for dementia diagnostics and therapeutics, but currently remain underutilized due to the lack of a clear process by which targets are selected for future drug development. In this review, we discuss current tauopathy biomarkers and therapeutics, and highlight areas in need of improvement, particularly when addressing the needs of frail, comorbid and cognitively impaired populations. We highlight biomarkers which have been developed from proteomic data, and outline possible future directions in this field. We propose new criteria by which potential targets in proteomics studies can be objectively ranked as favorable for drug development, and demonstrate its application to our group's recent tau interactome dataset as an example.

Discerning Endoscopic Severity of Inflammatory Bowel Disease by Scoping the Peripheral Blood Transcriptome

Background and AimsUlcerative colitis (UC) and Crohn’s disease (CD) are chronic inflammatory bowel diseases (IBD) with an incompletely understood etiology and pathogenesis. Identification of suitable drug targets and assessment of disease severity are crucial for optimal management. MethodsUsing RNA-sequencing, we investigated differential gene expression in peripheral blood samples from IBD-patients and non-inflamed controls, analysed pathway enrichment, and identified genes whose expression correlated with endoscopic disease severity. ResultsNeutrophil degranulation emerged as the most significant pathway across all IBD sample types. Signalling by interleukins was prominent in patients with active intestinal inflammation, but also enriched in CD and UC patients without intestinal inflammation. Nevertheless, genes correlated to endoscopic disease severity implicated the primary cilium in CD patients, and translation and focal adhesion in UC patients. Moreover, several of these genes were located in genome-wide associated loci linked to IBD, cholesterol levels, blood cell counts, and levels of markers assessing liver and kidney function. These genes also suggested connections to intestinal epithelial barrier dysfunction, contemporary IBD drug treatment, and new actionable drug targets. A large number of genes associated with endoscopic disease severity corresponded to non-coding RNAs. ConclusionsThis study revealed biological pathways associated with IBD disease state and endoscopic disease severity, thus providing insights into the underlying mechanisms of IBD pathogenesis as well as identifying potential biomarkers and therapies. Peripheral blood might constitute a suitable non-invasive diagnostic sample type, in which gene expression profiles might serve as indicators of ongoing mucosal inflammation, and thus guide personalized treatment decisions.

Synergizing drug repurposing and target identification for neurodegenerative diseases.

Despite dedicated research efforts, the absence of disease-curing remedies for neurodegenerative diseases (NDDs) continues to jeopardize human society and stands as a challenge. Drug repurposing is an attempt to find new functionality of existing drugs and take it as an opportunity to discourse the clinically unmet need to treat neurodegeneration. However, despite applying this approach to rediscover a drug, it can also be used to identify the target on which a drug could work. The primary objective of target identification is to unravel all the possibilities of detecting a new drug or repurposing an existing drug. Lately, scientists and researchers have been focusing on specific genes, a particular site in DNA, a protein, or a molecule that might be involved in the pathogenesis of the disease. However, the new era discusses directing the signaling mechanism involved in the disease progression, where receptors, ion channels, enzymes, and other carrier molecules play a huge role. This review aims to highlight how target identification can expedite the whole process of drug repurposing. Here, we first spot various target-identification methods and drug-repositioning studies, including drug-target and structure-based identification studies. Moreover, we emphasize various drug repurposing approaches in NDDs, namely, experimental-based, mechanism-based, and in silico approaches. Later, we draw attention to validation techniques and stress on drugs that are currently undergoing clinical trials in NDDs. Lastly, we underscore the future perspective of synergizing drug repurposing and target identification in NDDs and present an unresolved question to address the issue.

Multicellular network-informed survival model for identification of drug targets of gliomas.

Increasing evidence suggests that communication between tumor cells (TCs) and tumor-associated macrophages (TAMs) plays a substantial role in promoting progression of low-grade gliomas (LGG). Hence, it is becoming critical to model TAM-TC interplay and interrogate how the crosstalk affects prognosis of LGG patients. This paper proposed a translational research pipeline to construct the multicellular interaction gene network (MIGN) for identification of druggable targets to develop novel therapeutic strategies. Firstly, we selected immunotherapy-related feature genes (IFGs) for TAMs and TCs using RNA-seq data of glioma mice from preclinical trials. After translating the IFGs to human genome, we constructed TAM- and TC- associated networks separately, using a training set of 524 human LGGs. Subsequently, clustering analysis was performed within each network, and the concordance measure K-index was adopted to correlate gene clusters with patient survival. The MIGN was built by combining the clusters highly associated with survival in TAM- and TC-associated networks. We then developed a MIGN-based survival model to identify prognostic signatures comprised of ligands, receptors and hub genes. An independent cohort of 172 human LGG samples was leveraged to validate predictive accuracy of the signature. The areas under time-dependent ROC curves were 0.881, 0.867, and 0.839 with respect to 1-year, 3-year, and 5-year survival rates respectively in the validation set. Furthermore, literature survey was conducted on the signature genes, and potential clinical responses to targeted drugs were evaluated for LGG patients, further highlighting potential utilities of the MIGN signature to develop novel immunotherapies to extend survival of LGG patients.