Pathological mechanisms and novel drug targets in fibrotic interstitial lung disease.

BACKGROUND: Interstitial lung diseases (ILDs) are a diverse group of conditions characterized by inflammation and fibrosis in the lung. In some patients with ILD, a progressive fibrotic phenotype develops, which is associated with an irreversible decline in lung function and a poor prognosis. MAIN BODY: The pathological mechanisms that underlie this process culminate in fibroblast activation, proliferation, and differentiation into myofibroblasts, which deposit extracellular matrix proteins and result in fibrosis. Upstream of fibroblast activation, epithelial cell injury and immune activation are known initiators of fibrosis progression, with multiple diverse cell types involved. Recent years have seen an increase in our understanding of the complex and interrelated processes that drive fibrosis progression in ILD, in part due to the advent of single-cell RNA sequencing technology and integrative multiomics analyses. Novel pathological mechanisms have been identified, which represent new targets for drugs currently in clinical development. These include phosphodiesterase 4 inhibitors and other molecules that act on intracellular cyclic adenosine monophosphate signaling, as well as inhibitors of the autotaxin-lysophosphatidic acid axis and  α v  integrins. Here, we review current knowledge and recent developments regarding the pathological mechanisms that underlie progressive fibrotic ILD, including potential therapeutic targets. CONCLUSION: Knowledge of the pathological mechanisms that drive progressive fibrosis in patients with ILD has expanded, with the role of alveolar endothelial cells, the immune system, and fibroblasts better elucidated. Drugs that target novel mechanisms hold promise for expanding the future therapeutic armamentarium for progressive fibrotic ILD.

Autophagy modulation in cancer therapy: Challenges coexist with opportunities.

Autophagy, a crucial intracellular degradation process facilitated by lysosomes, plays a pivotal role in maintaining cellular homeostasis. The elucidation of autophagy key genes and signaling pathways has significantly advanced our understanding of this process and has led to the exploration of autophagy as a promising therapeutic approach. This review comprehensively assesses the latest developments in small molecule modulators targeting autophagy. Moreover, the review delves into the most recent strategies for drug discovery, specifically focusing on selective agents that exploit autophagosomes and lysosomes for targeted protein degradation. Additionally, this article highlights the prevailing challenges and outlines potential future advancements in the field. By amalgamating the cutting-edge knowledge in the field, we aim to offer valuable insights and references for the anti-cancer drug development of autophagy-targeted therapies, thus contributing to the advancement of novel therapeutic interventions.

Plausible mechanism of drug resistance and side-effects of COVID-19 therapeutics: a bottleneck for its eradication.

BACKGROUND: COVID-19 pandemic has turned our world upside down by meddling with our normal lives. While there is no definitive drug against SARS-CoV-2, antiviral drugs that are already in the market, are being repurposed against it, could now complete long-term as well as all age-specific investigations, and they are successful in saving millions of lives. Nevertheless, side-effects are emergingly seen in the patients undergoing treatment, and ineffectiveness is increasingly found due to the emerging notorious variants of the virus. Many of them are also facing serious co-infections including black fungus, Zika, and H1N1 virus to name a few. OBJECTIVES: Therefore, this review highlights both drug resistance, their side-effects, and the significance for proper and long-term clinical trials of all age groups including children. METHODS: We have explored and proposed the mechanisms of drug resistance that may arise due to the misuse or overuse of drugs based on available experimental reports. RESULTS: The review provides solutions to the aforesaid issues of drug-resistance and side-effects by providing combination therapies, ancillary treatments, and other preventive strategies that can be useful in preventing drawbacks thereby curbing COVID-19 or similar future infections to maintain our normal lives. CONCLUSION: COVID-19 and its long-term effects, if any, can be eradicated with strategic and mindful use of related therapeutics in a controlled manner.

TIN-X version 3: update with expanded dataset and modernized architecture for enhanced illumination of understudied targets.

TIN-X (Target Importance and Novelty eXplorer) is an interactive visualization tool for illuminating associations between diseases and potential drug targets and is publicly available at newdrugtargets.org. TIN-X uses natural language processing to identify disease and protein mentions within PubMed content using previously published tools for named entity recognition (NER) of gene/protein and disease names. Target data is obtained from the Target Central Resource Database (TCRD). Two important metrics, novelty and importance, are computed from this data and when plotted as log(importance) vs. log(novelty), aid the user in visually exploring the novelty of drug targets and their associated importance to diseases. TIN-X Version 3.0 has been significantly improved with an expanded dataset, modernized architecture including a REST API, and an improved user interface (UI). The dataset has been expanded to include not only PubMed publication titles and abstracts, but also full-text articles when available. This results in approximately 9-fold more target/disease associations compared to previous versions of TIN-X. Additionally, the TIN-X database containing this expanded dataset is now hosted in the cloud via Amazon RDS. Recent enhancements to the UI focuses on making it more intuitive for users to find diseases or drug targets of interest while providing a new, sortable table-view mode to accompany the existing plot-view mode. UI improvements also help the user browse the associated PubMed publications to explore and understand the basis of TIN-X's predicted association between a specific disease and a target of interest. While implementing these upgrades, computational resources are balanced between the webserver and the user's web browser to achieve adequate performance while accommodating the expanded dataset. Together, these advances aim to extend the duration that users can benefit from TIN-X while providing both an expanded dataset and new features that researchers can use to better illuminate understudied proteins.

Exploiting gender-based biomarkers and drug targets: advancing personalized therapeutic strategies in hepatocellular carcinoma.

This review systematically examines gender differences in hepatocellular carcinoma (HCC), identifying the influence of sex hormones, genetic variance, and environmental factors on the disease's epidemiology and treatment outcomes. Recognizing the liver as a sexually dimorphic organ, we highlight how gender-specific risk factors, such as alcohol consumption and obesity, contribute differently to hepatocarcinogenesis in men and women. We explore molecular mechanisms, including the differential expression of androgen and estrogen receptors, which mediate diverse pathways in tumor biology such as cell proliferation, apoptosis, and DNA repair. Our analysis underscores the critical need for gender-specific research in liver cancer, from molecular studies to clinical trials, to improve diagnostic accuracy and therapeutic effectiveness. By incorporating a gender perspective into all facets of liver cancer research, we advocate for a more precise and personalized approach to cancer treatment that acknowledges gender as a significant factor in both the progression of HCC and its response to treatment. This review aims to foster a deeper understanding of the biological and molecular bases of gender differences in HCC and to promote the development of tailored interventions that enhance outcomes for all patients.

Exploring the mechanism of Si-Miao-Yong-An decoction on heart failure based on molecular docking and network pharmacology.

The SwissTargetPrediction was employed to predict the potential drug targets of the active component of Si-Miao-Yong-An decoction (SMYAD). The therapeutic targets for HF were searched in the Genecard database, and Cytoscape3.9.1 software was used to construct the "drug-component-target-disease network" diagram. In addition, the String platform was used to construct Protein-Protein Interaction (PPI) network, and the DAVID database was used for GO and KEGG analysis. AutoDockTools-1.5.6 software was used for molecular docking verification. Network pharmacology studies have shown that AKT 1, ALB, and CASP 3 are the key targets of action of SMYAD against heart failure. The active compounds are quercetin and kaempferol.

PTGES2 and RNASET2 identified as novel potential biomarkers and therapeutic targets for basal cell carcinoma: insights from proteome-wide mendelian randomization, colocalization, and MR-PheWAS analyses.

Introduction: Basal cell carcinoma (BCC) is the most common skin cancer, lacking reliable biomarkers or therapeutic targets for effective treatment. Genome-wide association studies (GWAS) can aid in identifying drug targets, repurposing existing drugs, predicting clinical trial side effects, and reclassifying patients in clinical utility. Hence, the present study investigates the association between plasma proteins and skin cancer to identify effective biomarkers and therapeutic targets for BCC. Methods: Proteome-wide mendelian randomization was performed using inverse-variance-weight and Wald Ratio methods, leveraging 1 Mb cis protein quantitative trait loci (cis-pQTLs) in the UK Biobank Pharma Proteomics Project (UKB-PPP) and the deCODE Health Study, to determine the causal relationship between plasma proteins and skin cancer and its subtypes in the FinnGen R10 study and the SAIGE database of Lee lab. Significant association with skin cancer and its subtypes was defined as a false discovery rate (FDR) < 0.05. pQTL to GWAS colocalization analysis was executed using a Bayesian model to evaluate five exclusive hypotheses. Strong colocalization evidence was defined as a posterior probability for shared causal variants (PP.H4) of ≥0.85. Mendelian randomization-Phenome-wide association studies (MR-PheWAS) were used to evaluate potential biomarkers and therapeutic targets for skin cancer and its subtypes within a phenome-wide human disease category. Results: PTGES2, RNASET2, SF3B4, STX8, ENO2, and HS3ST3B1 (besides RNASET2, five other plasma proteins were previously unknown in expression quantitative trait loci (eQTL) and methylation quantitative trait loci (mQTL)) were significantly associated with BCC after FDR correction in the UKB-PPP and deCODE studies. Reverse MR showed no association between BCC and these proteins. PTGES2 and RNASET2 exhibited strong evidence of colocalization with BCC based on a posterior probability PP.H4 >0.92. Furthermore, MR-PheWAS analysis showed that BCC was the most significant phenotype associated with PTGES2 and RNASET2 among 2,408 phenotypes in the FinnGen R10 study. Therefore, PTGES2 and RNASET2 are highlighted as effective biomarkers and therapeutic targets for BCC within the phenome-wide human disease category. Conclusion: The study identifies PTGES2 and RNASET2 plasma proteins as novel, reliable biomarkers and therapeutic targets for BCC, suggesting more effective clinical application strategies for patients.

Hypersensitivity to BKCa channel opening in persistent post-traumatic headache.

BACKGROUND: Large conductance  calcium-activated potassium (BKCa) channels have been implicated in the neurobiological underpinnings of migraine. Considering the clinical similarities between migraine and persistent post-traumatic headache (PPTH), we aimed to examine whether MaxiPost (a BKCa channel opener) could induce migraine-like headache in persons with PPTH. METHODS: This is a randomized double-blind, placebo-controlled, two-way crossover study from September 2023 to December 2023. Eligible participants were adults with PPTH after mild traumatic brain injury who reported having no personal history of migraine. The randomized participants received a single dose of either MaxiPost (0.05 mg/min) or placebo (isotonic saline) that was infused intravenously over 20 minutes. The two experiment sessions were scheduled at least one week apart to avoid potential carryover effects. The primary endpoint was the induction of migraine-like headache after MaxiPost as compared to placebo within 12 hours of drug administration. The secondary endpoint was the area under the curve (AUC) values for headache intensity scores between MaxiPost and placebo over the same 12-hour observation period. RESULTS: Twenty-one adult participants (comprising 14 females and 7 males) with PPTH were enrolled and completed both experiment sessions. The proportion of participants who developed migraine-like headache was 11 (52%) of 21 participants after MaxiPost infusion, in contrast to four (19%) participants following placebo (P = .02). Furthermore, the median headache intensity scores, represented by AUC values, were higher following MaxiPost than after placebo (P < .001). CONCLUSIONS: Our results indicate that BKCa channel opening can elicit migraine-like headache in persons with PPTH. Thus, pharmacologic blockade of BKCa channels might present a novel avenue for drug discovery. Additional investigations are nonetheless needed to confirm these insights and explore the therapeutic prospects of BKCa channel blockers in managing PPTH. GOV IDENTIFIER: NCT05378074.

Based on systematic druggable genome-wide Mendelian randomization identifies therapeutic targets for diabetes.

Purpose: Diabetes and its complications cause a heavy burden of disease worldwide. In recent years, Mendelian randomization (MR) has been widely used to discover the pathogenesis and epidemiology of diseases, as well as to discover new therapeutic targets. Therefore, based on systematic "druggable" genomics, we aim to identify new therapeutic targets for diabetes and analyze its pathophysiological mechanisms to promote its new therapeutic strategies. Material and method: We used double sample MR to integrate the identified druggable genomics to evaluate the causal effect of quantitative trait loci (eQTLs) expressed by druggable genes in blood on type 1 and 2 diabetes (T1DM and T2DM). Repeat the study using different data sources on diabetes and its complications to verify the identified genes. Not only that, we also use Bayesian co-localization analysis to evaluate the posterior probabilities of different causal variations, shared causal variations, and co-localization probabilities to examine the possibility of genetic confounding. Finally, using diabetes markers with available genome-wide association studies data, we evaluated the causal relationship between established diabetes markers to explore possible mechanisms. Result: Overall, a total of 4,477 unique druggable genes have been gathered. After filtering using methods such as Bonferroni significance (P<1.90e-05), the MR Steiger directionality test, Bayesian co-localization analysis, and validation with different datasets, Finally, 7 potential druggable genes that may affect the results of T1DM and 7 potential druggable genes that may affect the results of T2DM were identified. Reverse MR suggests that C4B may play a bidirectional role in the pathogenesis of T1DM, and none of the other 13 target genes have a reverse causal relationship. And the 7 target genes in T2DM may each affect the biomarkers of T2DM to mediate the pathogenesis of T2DM. Conclusion: This study provides genetic evidence supporting the potential therapeutic benefits of targeting seven druggable genes, namely MAP3K13, KCNJ11, REG4, KIF11, CCNE2, PEAK1, and NRBP1, for T2DM treatment. Similarly, targeting seven druggable genes, namely ERBB3, C4B, CD69, PTPN22, IL27, ATP2A1, and LT-ß, has The potential therapeutic benefits of T1DM treatment. This will provide new ideas for the treatment of diabetes and also help to determine the priority of drug development for diabetes.

Roles of alcohol dehydrogenase 1 in the biological activities of Candida albicans.

Candida albicans stands as the foremost prevalent human commensal pathogen and a significant contributor to nosocomial fungal infections. In the metabolism of C. albicans, alcohol dehydrogenase 1 (Adh1) is one of the important enzymes that converts acetaldehyde produced by pyruvate decarboxylation into ethanol at the end of glycolysis. Leveraging the foundational processes of alcoholic fermentation, Adh1 plays an active role in multiple biological phenomena, including biofilm formation, interactions between different species, the development of drug resistance, and the potential initiation of gastrointestinal cancer. Additionally, Adh1 within C. albicans has demonstrated associations with regulating the cell cycle, stress responses, and various intracellular states. Furthermore, Adh1 is extracellularly localized on the cell wall surface, where it plays roles in processes such as tissue invasion and host immune responses. Drawing from an analysis of ADH1 gene structure, expression patterns, and fundamental functions, this review elucidates the intricate connections between Adh1 and various biological processes within C. albicans, underscoring its potential implications for the prevention, diagnosis, and treatment of candidiasis.

GSEA analysis identifies potential drug targets and their interaction networks in coronary microcirculation disorders.

Coronary microcirculation dysfunction (CMD) is one of the main causes of cardiovascular disease. Traditional treatment methods lack specificity, making it difficult to fully consider the differences in patient conditions and achieve effective treatment and intervention. The complexity and diversity of CMD require more standardized diagnosis and treatment plans to clarify the best treatment strategy and long-term outcomes. The existing treatment measures mainly focus on symptom management, including medication treatment, lifestyle intervention, and psychological therapy. However, the efficacy of these methods is not consistent for all patients, and the long-term efficacy is not yet clear. GSEA is a bioinformatics method used to interpret gene expression data, particularly for identifying the enrichment of predefined gene sets in gene expression data. In order to achieve personalized treatment and improve the quality and effectiveness of interventions, this article combined GSEA (Gene Set Enrichment Analysis) technology to conduct in-depth research on potential drug targets and their interaction networks in coronary microcirculation dysfunctions. This article first utilized the Coremine medical database, GeneCards, and DrugBank public databases to collect gene data. Then, filtering methods were used to preprocess the data, and GSEA was used to analyze the preprocessed gene expression data to identify and calculate pathways and enrichment scores related to CMD. Finally, protein sequence features were extracted through the calculation of autocorrelation features. To verify the effectiveness of GSEA, this article conducted experimental analysis from four aspects: precision, receiver operating characteristic (ROC) curve, correlation, and potential drug targets, and compared them with Gene Regulatory Networks (GRN) and Random Forest (RF) methods. The results showed that compared to the GRN and RF methods, the average precision of GSEA improved by 0.11. The conclusion indicated that GSEA helped identify and explore potential drug targets and their interaction networks, providing new ideas for personalized quality of CMD.

Exerkines: Benign adaptation for exercise and benefits for non-alcoholic fatty liver disease.

Exercise has multiple beneficial effects on human metabolic health and is regarded as a "polypill" for various diseases. At present, the lack of physical activity usually causes an epidemic of chronic metabolic syndromes, including obesity, cardiovascular diseases, and non-alcoholic fatty liver disease (NAFLD). Remarkably, NAFLD is emerging as a serious public health issue and is associated with the development of cirrhosis and hepatocellular carcinoma. Unfortunately, specific drug therapies for NAFLD and its more severe form, non-alcoholic steatohepatitis (NASH), are currently unavailable. Lifestyle modification is the foundation of treatment recommendations for NAFLD and NASH, especially for exercise. There are under-appreciated organs that crosstalk to the liver during exercise such as muscle-liver crosstalk. Previous studies have reported that certain exerkines, such as FGF21, GDF15, irisin, and adiponectin, are beneficial for liver metabolism and have the potential to be targeted for NAFLD treatment. In addition, some of exerkines can be modified for the new proteins and get enhanced functions, like IL-6/IC7Fc. Another importance of exercise is the physiological adaptation that combats metabolic diseases. Thus, this review aims to summarize the known exerkines and utilize a multi-omics mining tool to identify more exerkines for the future research. Overall, understanding the mechanisms by which exercise-induced exerkines exert their beneficial effects on metabolic health holds promise for the development of novel therapeutic strategies for NAFLD and related diseases.

Identifying therapeutic targets for breast cancer: insights from systematic Mendelian randomization analysis.

Background: Breast cancer (BC) exhibits a high incidence rate, imposing a substantial burden on healthcare systems. Novel drug targets are urgently needed for BC. Mendelian randomization (MR) has gained widespread application for identifying fresh therapeutic targets. Our endeavor was to pinpoint circulatory proteins causally linked to BC risk and proffer potential treatment targets for BC. Methods: Through amalgamating protein quantitative trait loci from 2,004 circulating proteins and comprehensive genome-wide association study data from the Breast Cancer Association Consortium, we conducted MR analyses. Employing Steiger filtering, bidirectional MR, Bayesian colocalization, phenotype scanning, and replication analyses, we further solidified MR study outcomes. Additionally, protein-protein interaction (PPI) network was harnessed to unveil latent associations between proteins and prevailing breast cancer medications. The phenome-wide MR (Phe-MR) was employed to assess potential side effects and indications for the druggable proteins of BC. Finally, we further affirmed the drugability of potential drug targets through mRNA expression analysis and molecular docking. Results: Through comprehensive analysis, we identified five potential drug targets, comprising four (TLR1, A4GALT, SNUPN, and CTSF) for BC and one (TLR1) for BC_estrogen receptor positive. None of these five potential drug targets displayed reverse causation. Bayesian colocalization suggested that these five latent drug targets shared variability with breast cancer. All drug targets were replicated within the deCODE cohort. TLR1 exhibited PPI with current breast cancer therapeutic targets. Furthermore, Phe-MR unveiled certain adverse effects solely for TLR1 and SNUPN. Conclusion: Our study uncovers five prospective drug targets for BC and its subtypes, warranting further clinical exploration.

Pharmacophore modelling based virtual screening and molecular dynamics identified the novel inhibitors and drug targets against Waddlia chondrophila.

Waddlia chondrophila is a possible cause of fetal death in humans. This Chlamydia-related bacterium is an emergent pathogen that causes human miscarriages and ruminant abortions, which results in financial losses. Despite the years of efforts, the underlying mechanism behind the pathogenesis of W. chondrophila is little known which hindered the development of novel treatment options. In the framework of current study, computational approaches were used to identify novel inhibitors (phytocompounds) and drug targets against W. chondrophila. At first, RNA polymerase sigma factor SigA and 3-deoxy-D-manno-octulosonic acid transferase were identified through subtractive proteomics pipeline. Afterwards, extensive docking and simulation analyses were conducted to optimize potentially novel phytocompounds by assessing their binding affinity to target proteins. A 100ns molecular dynamics simulation well complimented the compound's binding affinity and indicated strong stability of predicted compounds at the docked site. The calculation of binding free energies with MMGBSA corroborated the significant binding affinity between phytocompounds and target protein binding sites. The proposed phytocompounds may be a viable treatment option for patients infected with W. chondrophila; however, further research is required to ensure their safety.

The causal relationship between anti-diabetic drugs and gastrointestinal disorders: a drug-targeted mendelian randomization study.

BACKGROUND: The incidence of diabetic gastrointestinal diseases is increasing year by year. This study aimed to investigate the causal relationship between antidiabetic medications and gastrointestinal disorders, with the goal of reducing the incidence of diabetes-related gastrointestinal diseases and exploring the potential repurposing of antidiabetic drugs. METHODS: We employed a two-sample Mendelian randomization (TSMR) design to investigate the causal association between antidiabetic medications and gastrointestinal disorders, including gastroesophageal reflux disease (GERD), gastric ulcer (GU), chronic gastritis, acute gastritis, Helicobacter pylori infection, gastric cancer (GC), functional dyspepsia (FD), irritable bowel syndrome (IBS), ulcerative colitis (UC), Crohn's disease (CD), diverticulosis, and colorectal cancer (CRC). To identify potential inhibitors of antidiabetic drug targets, we collected single-nucleotide polymorphisms (SNPs) associated with metformin, GLP-1 receptor agonists, SGLT2 inhibitors, DPP-4 inhibitors, insulin, and its analogs, thiazolidinediones, sulfonylureas, and alpha-glucosidase inhibitors from published genome-wide association study statistics. We then conducted a drug-target Mendelian randomization (MR) analysis using inverse variance weighting (IVW) as the primary analytical method to assess the impact of these inhibitors on gastrointestinal disorders. Additionally, diabetes was selected as a positive control. RESULTS: Sulfonylureas were found to significantly reduce the risk of CD (IVW: OR [95% CI] = 0.986 [0.978, 0.995], p = 1.99 × 10- 3), GERD (IVW: OR [95% CI] = 0.649 [0.452, 0.932], p = 1.90 × 10- 2), and chronic gastritis (IVW: OR [95% CI] = 0.991 [0.982, 0.999], p = 4.50 × 10- 2). However, they were associated with an increased risk of GU development (IVW: OR [95%CI] = 2 0.761 [1.259, 6.057], p = 1 0.12 × 10- 2). CONCLUSIONS: The results indicated that sulfonylureas had a positive effect on the prevention of CD, GERD, and chronic gastritis but a negative effect on the development of gastric ulcers. However, our research found no causal evidence for the impact of metformin, GLP-1 agonists, SGLT2 inhibitors, DPP 4 inhibitors, insulin and its analogs, thiazolidinediones, or alpha-glucosidase inhibitors on gastrointestinal diseases.

Pan-cancer proteogenomics expands the landscape of therapeutic targets.

Fewer than 200 proteins are targeted by cancer drugs approved by the Food and Drug Administration (FDA). We integrate Clinical Proteomic Tumor Analysis Consortium (CPTAC) proteogenomics data from 1,043 patients across 10 cancer types with additional public datasets to identify potential therapeutic targets. Pan-cancer analysis of 2,863 druggable proteins reveals a wide abundance range and identifies biological factors that affect mRNA-protein correlation. Integration of proteomic data from tumors and genetic screen data from cell lines identifies protein overexpression- or hyperactivation-driven druggable dependencies, enabling accurate predictions of effective drug targets. Proteogenomic identification of synthetic lethality provides a strategy to target tumor suppressor gene loss. Combining proteogenomic analysis and MHC binding prediction prioritizes mutant KRAS peptides as promising public neoantigens. Computational identification of shared tumor-associated antigens followed by experimental confirmation nominates peptides as immunotherapy targets. These analyses, summarized at https://targets.linkedomics.org, form a comprehensive landscape of protein and peptide targets for companion diagnostics, drug repurposing, and therapy development.

Mycobacterial biofilms: Understanding the genetic factors playing significant role in pathogenesis, resistance and diagnosis.

Even though the genus Mycobacterium is a diverse group consisting of a majority of environmental bacteria known as non-tuberculous mycobacteria (NTM), it also contains some of the deadliest pathogens (Mycobacterium tuberculosis) in history associated with chronic disease called tuberculosis (TB). Formation of biofilm is one of the unique strategies employed by mycobacteria to enhance their ability to survive in hostile conditions. Biofilm formation by Mycobacterium species is an emerging area of research with significant implications for understanding its pathogenesis and treatment of related infections, specifically TB. This review provides an overview of the biofilm-forming abilities of different species of Mycobacterium and the genetic factors influencing biofilm formation with a detailed focus on M. tuberculosis. Biofilm-mediated resistance is a significant challenge as it can limit antibiotic penetration and promote the survival of dormant mycobacterial cells. Key genetic factors promoting biofilm formation have been explored such as the mmpL genes involved in lipid transport and cell wall integrity as well as the groEL gene essential for mature biofilm formation. Additionally, biofilm-mediated antibiotic resistance and pathogenesis highlighting the specific niches, sites of infection along with the possible mechanisms of biofilm dissemination have been discussed. Furthermore, drug targets within mycobacterial biofilm and their role as potential biomarkers in the development of rapid diagnostic tools have been highlighted. The review summarises the current understanding of the complex nature of Mycobacterium biofilm and its clinical implications, paving the way for advancements in the field of disease diagnosis, management and treatment against its multi-drug resistant species.

An integrated in silico approach for the identification of novel potential drug target and chimeric vaccine against Neisseria meningitides strain 331401 serogroup X by subtractive genomics and reverse vaccinology.

Neisseria meningitidis, commonly known as the meningococcus, leads to substantial illness and death among children and young adults globally, revealing as either epidemic or sporadic meningitis and/or septicemia. In this study, we have designed a novel peptide-based chimeric vaccine candidate against the N. meningitidis strain 331,401 serogroup X. Through rigorous analysis of subtractive genomics, two essential cytoplasmic proteins, namely UPI000012E8E0(UDP-3-O-acyl-GlcNAc deacetylase) and UPI0000ECF4A9(UDP-N-acetylglucosamine acyltransferase) emerged as potential drug targets. Additionally, using reverse vaccinology, the outer membrane protein UPI0001F4D537 (Membrane fusion protein MtrC) identified by subcellular localization and recognized for its known indispensable role in bacterial survival was identified as a novel chimeric vaccine target. Following a careful comparison of MHC-I, MHC-II, T-cell, and B-cell epitopes, three epitopes derived from UPI0001F4D537 were linked with three types of linkers-GGGS, EAAAK, and the essential PADRE-for vaccine construction. This resulted in eight distinct vaccine models (V1-V8). Among them V1 model was selected as the final vaccine construct. It exhibits exceptional immunogenicity, safety, and enhanced antigenicity, with 97.7 % of its residues in the Ramachandran plot's most favored region. Subsequently, the vaccine structure was docked with the TLR4/MD2 complex and six different HLA allele receptors using the HADDOCK server. The docking resulted in the lowest HADDOCK score of 39.3 ± 9.0 for TLR/MD2. Immune stimulation showed a strong immune response, including antibodies creation and the activation of B-cells, T Cytotoxic cells, T Helper cells, Natural Killer cells, and interleukins. Furthermore, the vaccine construct was successfully expressed in the Escherichia coli system by reverse transcription, optimization, and ligation in the pET-28a (+) vector for the expression study. The current study proposes V1 construct has the potential to elicit both cellular and humoral responses, crucial for the developing an epitope-based vaccine against N. meningitidis strain 331,401 serogroup X.