Systems and Computational Screening identifies SRC and NKIRAS2 as Baseline Correlates of Risk (CoR) for Live Attenuated Oral Typhoid Vaccine (TY21a) associated Protection.

AIM: We investigated the molecular underpinnings of variation in immune responses to the live attenuated typhoid vaccine (Ty21a) by analyzing the baseline immunological profile. We utilized gene expression datasets obtained from the Gene Expression Omnibus (GEO) database (accession number: GSE100665) before and after immunization. We then employed two distinct computational approaches to identify potential baseline biomarkers associated with responsiveness to the Ty21a vaccine. MAIN METHODS: The first pipeline (knowledge-based) involved the retrieval of differentially expressed genes (DEGs), functional enrichment analysis, protein-protein interaction network construction, and topological network analysis of post-immunization datasets before gauging their pre-vaccination expression levels. The second pipeline utilized an unsupervised machine learning algorithm for data-driven feature selection on pre-immunization datasets. Supervised machine-learning classifiers were employed to computationally validate the identified biomarkers. KEY FINDINGS: Baseline activation of NKIRAS2 (a negative regulator of NF-kB signalling) and SRC (an adaptor for immune receptor activation) was negatively associated with Ty21a vaccine responsiveness, whereas LOC100134365 exhibited a positive association. The Stochastic Gradient Descent (SGD) algorithm accurately distinguished vaccine responders and non-responders, with 88.8%, 70.3%, and 85.1% accuracy for the three identified genes, respectively. SIGNIFICANCE: This dual-pronged novel analytical approach provides a comprehensive comparison between knowledge-based and data-driven methods for the prediction of baseline biomarkers associated with Ty21a vaccine responsiveness. The identified genes shed light on the intricate molecular mechanisms that influence vaccine efficacy from the host perspective while pushing the needle further towards the need for development of precise enteric vaccines and on the importance of pre-immunization screening.

Systems and computational analysis of gene expression datasets reveals GRB-2 suppression as an acute immunomodulatory response against enteric infections in endemic settings.

Introduction: Enteric infections are a major cause of under-5 (age) mortality in low/middle-income countries. Although vaccines against these infections have already been licensed, unwavering efforts are required to boost suboptimalefficacy and effectiveness in regions that are highly endemic to enteric pathogens. The role of baseline immunological profiles in influencing vaccine-induced immune responses is increasingly becoming clearer for several vaccines. Hence, for the development of advanced and region-specific enteric vaccines, insights into differences in immune responses to perturbations in endemic and non-endemic settings become crucial. Materials and methods: For this reason, we employed a two-tiered system and computational pipeline (i) to study the variations in differentially expressed genes (DEGs) associated with immune responses to enteric infections in endemic and non-endemic study groups, and (ii) to derive features (genes) of importance that keenly distinguish between these two groups using unsupervised machine learning algorithms on an aggregated gene expression dataset. The derived genes were further curated using topological analysis of the constructed STRING networks. The findings from these two tiers are validated using multilayer perceptron classifier and were further explored using correlation and regression analysis for the retrieval of associated gene regulatory modules. Results: Our analysis reveals aggressive suppression of GRB-2, an adaptor molecule integral for TCR signaling, as a primary immunomodulatory response against S. typhi infection in endemic settings. Moreover, using retrieved correlation modules and multivariant regression models, we found a positive association between regulators of activated T cells and mediators of Hedgehog signaling in the endemic population, which indicates the initiation of an effector (involving differentiation and homing) rather than an inductive response upon infection. On further exploration, we found STAT3 to be instrumental in designating T-cell functions upon early responses to enteric infections in endemic settings. Conclusion: Overall, through a systems and computational biology approach, we characterized distinct molecular players involved in immune responses to enteric infections in endemic settings in the process, contributing to the mounting evidence of endemicity being a major determiner of pathogen/vaccine-induced immune responses. The gained insights will have important implications in the design and development of region/endemicity-specific vaccines.

Targeting NF-κB pathway for the anti-inflammatory potential of Bhadradarvadi kashayam on stimulated RAW 264.7 macrophages.

Macrophage-arbitrated inflammation is associated with the regulation of rheumatoid arthritis (RA). Low risk and better efficiency are steered herbal drugs more credible than conventional medicines in RA management. Bhadradarvadi (BDK) concoction has been traditionally used for rheumatism in Ayurveda. However, the mechanisms at the molecular level are still elusive. This study was designed to inspect the process of immunomodulation and anti-inflammatory properties of BDK in lipopolysaccharide (LPS)-stimulated RAW 264.7 macrophages for the first time. BDK concoction was prepared and evaluated with the stimulated murine macrophage-like RAW 264.7 cell lines. TNF-α, IL6, and PGE2 were quantified by ELISA. The normalization of the fold change in the expression of the target gene mRNA was done by comparing the values of the ß-actin housekeeping gene using the 2-ΔΔCt comparative cycle threshold. The expression of TNF-α, IL6, iNOS, and COX-2 in the RAW 264.7 macrophage cells was analyzed using flow cytometry. Our results showed that BDK (150-350 µl/ml) treatment significantly decreased the inflammatory cytokines (TNF-α, and IL-6) and inflammatory mediators (PGE2) in LPS-stimulated RAW 264.7 macrophage cells. The pro-inflammatory cytokines (TNF-α, IL-1ß, and IL-6) expression, inflammatory enzymes (iNOS and COX-2), and NF-κBp65 were significantly downregulated at transcriptome level in LPS-stimulated RAW 264.7 macrophage cells. The flow cytometry analysis revealed that BDK treatment diminished the TNF-α, IL-6, iNOS, and COX-2 expression at the proteome level, as well as obstruction of NF-κB-p65 nuclear translocation was observed by immunofluorescence analysis in LPS-stimulated RAW 264.7 macrophage cells. Collectively, BDK can intensely augment the anti-inflammatory activities via inhibiting the NF-κB signaling pathway trigger for treating autoimmune disorders including RA.

Gene expression analysis reveals GRIN1, SYT1, and SYN2 as significant therapeutic targets and drug repurposing reveals lorazepam and lorediplon as potent inhibitors to manage Alzheimer's disease.

Alzheimer's disease (AD) is a slowly progressive neurodegenerative disease and a leading cause of dementia. We aim to identify key genes for the development of therapeutic targets and biomarkers for potential treatments for AD. Meta-analysis was performed on six microarray datasets and identified the differentially expressed genes between healthy and Alzheimer's disease samples. Thereafter, we filtered out the common genes which were present in at least four microarray datasets for downstream analysis. We have constructed a gene-gene network for the common genes and identified six hub genes. Furthermore, we investigated the regulatory mechanisms of these hub genes by analysing their interaction with miRNAs and transcription factors. The gene ontology analysis results highlighted the enriched terms significantly associated with hub genes. Through an extensive literature survey, we found that three of the hub genes including GRIN1, SYN2, and SYT1 were critically involved in disease development. To leverage existing drugs for potential repurposing, we predicted drug-gene interaction using the drug-gene interaction database, and performed molecular docking studies. The docking results revealed that the drug compounds had strong interactions and favorable binding with selected hub genes. Lorazepam exhibits a binding energy of -7.3 kcal/mol with GRIN1, Lorediplon exhibits binding energies of -7.7 kcal/mol and -6.3 kcal/mol with the SYT1, and SYN2 respectively. In addition, 100 ns molecular dynamics simulations were carried out for the top complexes and apo protein as well. Furthermore, the MM-PBSA free energy calculations also revealed that these complexes are stable and had favorable energies. According to our study, the identified hub gene could serve as a biomarker as well as a therapeutic target for AD, and the proposed repurposed drug molecules appear to have promising efficacy in treating the disease.Communicated by Ramaswamy H. Sarma.

Advances in computational frameworks in the fight against TB: The way forward.

Around 1.6 million people lost their life to Tuberculosis in 2021 according to WHO estimates. Although an intensive treatment plan exists against the causal agent, Mycobacterium Tuberculosis, evolution of multi-drug resistant strains of the pathogen puts a large number of global populations at risk. Vaccine which can induce long-term protection is still in the making with many candidates currently in different phases of clinical trials. The COVID-19 pandemic has further aggravated the adversities by affecting early TB diagnosis and treatment. Yet, WHO remains adamant on its "End TB" strategy and aims to substantially reduce TB incidence and deaths by the year 2035. Such an ambitious goal would require a multi-sectoral approach which would greatly benefit from the latest computational advancements. To highlight the progress of these tools against TB, through this review, we summarize recent studies which have used advanced computational tools and algorithms for-early TB diagnosis, anti-mycobacterium drug discovery and in the designing of the next-generation of TB vaccines. At the end, we give an insight on other computational tools and Machine Learning approaches which have successfully been applied in biomedical research and discuss their prospects and applications against TB.

Mucosal and systemic immune responses to Vibrio cholerae infection and oral cholera vaccines (OCVs) in humans: a systematic review.

INTRODUCTION: Cholera is an enteric disease caused by Vibrio cholerae, a water-borne pathogen, and characterized by severe diarrhea. Vaccines have been recommended for use by the WHO in resource-limited settings. Efficacies of the currently licensed cholera vaccines are not optimal in endemic settings and low in children below the age of five, a section of the population most susceptible to the disease. Development of next generation of cholera vaccines would require a detailed understanding of the required protective immune responses. AREA COVERED: In this review, we revisit clinical trials which are focused on the early transcriptional mucosal responses elicited during Vibrio cholerae infection and upon vaccination along with summarizing various components of the effector immune response against Vibrio cholerae. EXPERT OPINION: The inability of currently licensed killed/inactivated vaccines to elicit key inflammatory pathways locally may explain their restricted efficacy in endemic settings. More studies are required to understand the immunogenicity of the live attenuated cholera vaccine in these regions. Various extrinsic and intrinsic factors influence anti-cholera immunity and need to be considered to develop region-specific next generation vaccines.

Computational strategies towards developing novel antimelanogenic agents.

AIMS: Extrinsic ageing or photoageing relates to the onset of age-linked phenotypes such as skin hyperpigmentation due to UV exposure. UV induced upregulated production of tyrosinase enzyme, which catalyses the vital biochemical reactions of melanin synthesis is responsible for the inception of skin hyperpigmentation. We aimed to generate a validated QSAR model with a dataset consisting of 69 thio-semicarbazone derivatives to elucidate the physicochemical properties of compounds essential for tyrosinase inhibition and to identify novel lead molecules with enhanced tyrosinase inhibitory activity and bioavailability. MAIN METHODS: Lead optimization and insilico approaches were employed in this research work. QSAR model was generated and validated by exploiting Multiple Linear Regression method. Prioritization of lead-like compounds was accomplished by performing multi parameter optimization depleting molecular docking, bioavailability assessments and toxicity prediction for 69 compounds Derivatives of best lead compound were retrieved from chemical spaces. KEY FINDINGS: Molecular descriptors explicated the significance of chemical properties essential for chelation of copper ions present in the active site of tyrosinase protein target. Further, derivatives which comprise of electron donating groups in their chemical structure were predicted and analysed for tyrosinase inhibitory activity by employing insilico methodologies including chemical space exploration. SIGNIFICANCE: Our research work resulted in the generation of a validated QSAR model with higher degree of external predictive ability and significance to tyrosinase inhibitory activity. We propose 11 novel derivative compounds with enhanced tyrosinase inhibitory activity and bioavailability.

Designing of an epitope-based peptide vaccine against walking pneumonia: an immunoinformatics approach.

Mycoplasma pneumoniae is a substantial respiratory pathogen that develops not only pneumonia but also other respiratory diseases, which mimic viral respiratory syndromes. Nevertheless, vaccine development for this pathogen delays behind as immunity correlated with protection is now predominantly unknown. In the present study, an immunoinformatics pipeline is utilized for epitope-based peptide vaccine design, which can trigger a critical immune response against M. pneumoniae. A total of 105 T-cell epitopes from 12 membrane associated proteins and 7 T-cell epitopes from 5 cytadherence proteins of M. pneumoniae were obtained and validated. Thus, 18 peptides with 9-mer core sequence were identified as best T-cell epitopes by considering the number of residues with > 75% in favored region. Further, the crucial screening studies predicted three peptides with good binding affinity towards HLA molecules as best T-cell and B-cell epitopes. Based on this result, visualization, and dynamic simulation for the three epitopes (WIHGLILLF, VILLFLLLF, and LLAWMLVLF) were assessed. The predicted epitopes needs to be further validated for their adept use as vaccine. Collectively, the study opens up a new horizon with extensive therapeutic application against M. pneumoniae and its associated diseases.

Molecular and disease association of gestational diabetes mellitus affected mother and placental datasets reveal a strong link between insulin growth factor (IGF) genes in amino acid transport pathway: A network biology approach.

Discerning the relationship between molecules involved in diseases based on their underlying biological mechanisms is one of the greatest challenges in therapeutic development today. Gestational diabetes mellitus (GDM) is one of the most common complications during pregnancy, which adversely affects both mothers and offspring during and after pregnancy. We have constructed two datasets of (GDM associated genes from affected mother and placenta to systematically analyze and evaluate their interactions like gene-gene, gene-protein, gene-microRNA (miRNA), gene-transcription factors, and gene-associated diseases to enhance our current knowledge, which may lead to further advancements in disease diagnosis, prognosis, and treatment. The results identify the key genes with respect to maternal dataset as insulin receptor, insulin (INS), leptin (LEP), glucokinase, and hepatocyte nuclear factor 1 alpha, whereas from placenta include insulin-like growth factor 1, growth hormone receptor, and breast cancer anti-estrogen resistance protein 1, which are found to be highly enriched in pancreas, ovary, adipocyte, heart, and placental tissues. The key transcription factors include Sp1 transcription factor, pancreatic and duodenal homeobox 1, and hepatocyte nuclear factor 4 alpha, whereas miRNA includes has-miR-5699-5p and has-miR-3158-3p. The study also reveals that GDM has associations with diseases like type I and II diabetes mellitus, obesity, and preeclampsia. More significantly, we could trace out a significant connection between the key molecules like LEP and placental growth hormone from mother and placental dataset, which plays a critical role in INS secretion, INS signaling, and ß-cell dysfunction pathways.

Database of transcription factors in lung cancer (DBTFLC): A novel resource for exploring transcription factors associated with lung cancer.

Lung cancer is considered as the most prevalent form of cancer and it is found to be frequent cause of cancer related death. Even though, approved molecular targeted therapies other than chemotherapy are currently unavailable, the mechanism of pathogenesis in lung cancer remains still unclear. Transcription factors (TFs) play a critical role in cancer cell processes, such as cell proliferation, apoptosis, migration, and regulate gene expression. Thus, the identification and characterization of transcription factors involved in lung cancer would provide valuable information for further elucidation of the mechanism(s) underlying pathogenesis and the identification of potential therapeutic target types, which are critical for the development of therapeutic strategies. Through an extensive literature survey, we have identified 349 transcription factors noted for their strong involvement in lung cancer. Database of Transcription Factors in Lung Cancer (DBTFLC) was constructed as a data repository and analytical platform for systematic collection, curation of TFs and their interacting partners. The database includes all pertinent information such as lung cancer related TFs, chromosomal location, family, lung cancer type, references, TF-TF interaction(s), and TF-target gene interaction(s); thus, it could serve as a valuable resource for therapeutic studies in lung cancer. The database is freely available at http://www.vit.ac.in/files/database/Home.php.

Naringenin and quercetin--potential anti-HCV agents for NS2 protease targets.

Nonstructural proteins of hepatitis C virus had drawn much attention for the scientific fraternity in drug discovery due to its important role in the disease. 3D structure of the protein was predicted using molecular modelling protocol. Docking studies of 10 medicinal plant compounds and three drugs available in the market (control) with NS2 protease were employed by using rigid docking approach of AutoDock 4.2. Among the molecules tested for docking study, naringenin and quercetin revealed minimum binding energy of - 7.97 and - 7.95 kcal/mol with NS2 protease. All the ligands were docked deeply within the binding pocket region of the protein. The docking study results showed that these compounds are potential inhibitors of the target; and also all these docked compounds have good inhibition constant, vdW+Hbond+desolv energy with best RMSD value.