Effect of plant growth regulators on yield parameters, yield and quality of black pepper (Piper nigram L.) variety Panniyur-1

Experiments conducted at Horticultural College and Research Institute, Periyakulam and Horticultural Research Station, Thadiankudisai (TNAU) to study the effect of different plant growth regulators (NAA, GA3, BA and 2,4-D) indicated that spraying NAA (50 ppm) has improved many commercially desirable parameters like number of berries per spike, volume and weight of berries and yield in black pepper (Piper nigrum L.). Benefit cost ratio, however, was the highest in the treatment 2,4-D (10 ppm). The high cost of chemicals outweighed the yield except in the case of 2,4-D. &nbsp

Effect of plant growth regulators on yield parameters, yield and quality of black pepper (Piper nigram L.) variety Panniyur-1

Experiments conducted at Horticultural College and Research Institute, Periyakulam and Horticultural Research Station, Thadiankudisai (TNAU) to study the effect of different plant growth regulators (NAA, GA3, BA and 2,4-D) indicated that spraying NAA (50 ppm) has improved many commercially desirable parameters like number of berries per spike, volume and weight of berries and yield in black pepper (Piper nigrum L.). Benefit cost ratio, however, was the highest in the treatment 2,4-D (10 ppm). The high cost of chemicals outweighed the yield except in the case of 2,4-D. &nbsp

Implementing cloud computing in drug discovery and telemedicine for quantitative structure-activity relationship analysis

This work aims to use cutting-edge machine learning methods to improve quantitative structure-activity relationship (QSAR) analysis, which is used in drug development and telemedicine. The major goal is to examine the performance of several predictive modeling approaches, including random forest, deep learning-based QSAR models, and support vector machines (SVM). It investigates the potential of feature selection techniques developed in chemoinformatics for enhancing model accuracy. The innovative aspect is using cloud computing resources to strengthen computational skills, allowing for managing massive amounts of chemical information. This strategy produces accurate and generalizable QSAR models. By using the cloud's scalability and constant availability, remote healthcare apps have a workable answer. The goal is to show how these methods may improve telemedicine and the drug development process. Utilizing cloud computing equips researchers with a flexible set of tools for precise and timely QSAR analysis, speeding up the discovery of bioactive chemicals for therapeutic use. This new method fits well with the dynamic nature of pharmaceutical study and has the potential to transform the way drugs are developed and delivered to patients via telemedicine

Red Cabbage Juice-Mediated Gut Microbiota Modulation Improves Intestinal Epithelial Homeostasis and Ameliorates Colitis

Gut microbiota plays a crucial role in inflammatory bowel diseases (IBD) and can potentially prevent IBD through microbial-derived metabolites, making it a promising therapeutic avenue. Recent evidence suggests that despite an unclear underlying mechanism, red cabbage juice (RCJ) alleviates Dextran Sodium Sulfate (DSS)-induced colitis in mice. Thus, the study aims to unravel the molecular mechanism by which RCJ modulates the gut microbiota to alleviate DSS-induced colitis in mice. Using C57BL/6J mice, we evaluated RCJ’s protective role in DSS-induced colitis through two cycles of 3% DSS. Mice were daily gavaged with PBS or RCJ until the endpoint, and gut microbiota composition was analyzed via shotgun metagenomics. RCJ treatment significantly improved body weight (p ≤ 0.001), survival in mice (p \u3c 0.001) and reduced disease activity index (DAI) scores. Further, RCJ improved colonic barrier integrity by enhancing the expression of protective colonic mucins (p \u3c 0.001) and tight junction proteins (p ≤ 0.01) in RCJ + DSS-treated mice compared to the DSS group. Shotgun metagenomic analysis revealed an enrichment of short-chain fatty acids (SCFAs)-producing bacteria (p \u3c 0.05), leading to increased Peroxisome Proliferator-Activated Receptor Gamma (PPAR-ү ) activation (p ≤ 0.001). This, in turn, resulted in repression of the nuclear factor кB (NFкB) signaling pathway, causing decreased production of inflammatory cytokines and chemokines. Our study demonstrates colitis remission in a DSS-induced mouse model, showcasing RCJ as a potential modulator for gut microbiota and metabolites, with promising implications for IBD prevention and treatment

In silico targeting of red complex bacteria virulence factors of periodontitis with β-defensin 1

Abstract Background Periodontitis is a multi-factorial infection with red complex bacteria playing a crucial role in the pathogenesis. As bacteria are tending to develop resistance against conventional antibiotics, new treatment modalities need to be developed. Antimicrobial peptides (AMPs) are potential tools for drug development and are gaining widespread interest. β-defensin 1 is an important AMP and forms the first-line host defense mechanism. The present study analyzed the structure and molecular docking of β-defensin 1 with the virulence factors of red complex bacteria of periodontitis. The physico-chemical properties of β-defensin 1 were determined by various online tools such as ProtParam, ProteinPredict, ToxinPred, and BioPep web servers. The structure of β-defensin 1was predicted by the SWISS-MODEL web server and the structure was evaluated by different web tools. The structure of lipopolysaccharide of Porphyromonas gingivalis was drawn using Chem3D ultra 11.0 software. The structure of important protein virulence factors of red complex bacteria of periodontitis was determined by the SWISS-MODEL web server. The interaction study between β-defensin 1 and virulence factors was carried out by molecular docking using Auto dock version 4.0 software and pyDock WEB server. Results Using online tools, β-defensin 1 was predicted to be stable and non-toxic. SWISS-MODEL web server predicted Ramachandran score as 94.12% and clash score 0.0 for β-defensin 1. Auto dock version 4.0 software and pyDock WEB server analyzed the interaction to have low binding energies and hydrogen bonds were formed between the peptide and virulence factors. Conclusion β-defensin 1 was found to have good binding interaction with the disease-causing factors of red complex bacteria of periodontitis and in turn could play a role in reducing the severity of infection. β-defensin 1 could be a potential candidate for drug development for periodontitis. </jats:sec

In silico analysis of non-synonymous single nucleotide polymorphisms of human DEFB1 gene

Abstract Background Single nucleotide polymorphisms (SNPs) play a significant role in differences in individual’s susceptibility to diseases, and it is imperative to differentiate potentially harmful SNPs from neutral ones. Defensins are small cationic antimicrobial peptides that serve as antimicrobial and immunomodulatory molecules, and SNPs in β-defensin 1 (DEFB1 gene) have been associated with several diseases. In this study, we have determined deleterious SNPs of the DEFB1 gene that can affect the susceptibility to diseases by using different computational methods. Non-synonymous SNPs (nsSNPs) of the DEFB1 gene that have the ability to affect protein structure and functions were determined by several in silico tools—SIFT, PolyPhen v2, PROVEAN, SNAP, PhD-SNP, and SNPs&amp;GO. Then, nsSNPs identified to be potentially deleterious were further analyzed by I-Mutant and ConSurf. Post-translational modifications mediated by nsSNPs were predicted by ModPred, and gene-gene interaction was studied by GeneMANIA. Finally, nsSNPs were submitted to Project HOPE analysis. Results Ten nsSNPs of the DEFB1 gene were found to be potentially deleterious: rs1800968, rs55874920, rs56270143, rs140503947, rs145468425, rs146603349, rs199581284, rs201260899, rs371897938, rs376876621. I-Mutant server showed that nsSNPs rs140503947 and rs146603349 decreased stability of the protein, and ConSurf analysis revealed that SNPs were located in conserved regions. The physiochemical properties of the polymorphic amino acid residues and their effect on structure were determined by Project HOPE. Conclusion This study has determined high-risk deleterious nsSNPs of β-defensin 1 and could increase the knowledge of nsSNPs towards the impact of mutations on structure and functions mediated by β-defensin 1 protein. </jats:sec

Determination of deleterious single-nucleotide polymorphisms of human LYZ C gene: an in silico study

Abstract Background Single-nucleotide polymorphisms (SNPs) have a crucial function in affecting the susceptibility of individuals to diseases and also determine how an individual responds to different treatment options. The present study aimed to predict and characterize deleterious missense nonsynonymous SNPs (nsSNPs) of lysozyme C (LYZ C) gene using different computational methods. Lyz C is an important antimicrobial peptide capable of damaging the peptidoglycan layer of bacteria leading to osmotic shock and cell death. The nsSNPs were first analyzed by SIFT and PolyPhen v2 tools. The nsSNPs predicted as deleterious were then assessed by other in silico tools — SNAP, PROVEAN, PhD-SNP, and SNPs & GO. These SNPs were further examined by I-Mutant 3.0 and ConSurf. GeneMANIA and STRING tools were used to study the interaction network of the LYZ C gene. NetSurfP 2.0 was used to predict the secondary structure of Lyz C protein. The impact of variations on the structural characteristics of the protein was studied by HOPE analysis. The structures of wild type and variants were predicted by SWISS-MODEL web server, and energy minimization was carried out using XenoPlot software. TM-align tool was used to predict root-mean-square deviation (RMSD) and template modeling (TM) scores. Results Eight missense nsSNPs (T88N, I74T, F75I, D67H, W82R, D85H, R80C, and R116S) were found to be potentially deleterious. I-Mutant 3.0 determined that the variants decreased the stability of the protein. ConSurf predicted rs121913547, rs121913549, and rs387906536 nsSNPs to be conserved. Interaction network tools showed that LYZ C protein interacted with lactoferrin (LTF). HOPE tool analyzed differences in physicochemical properties between wild type and variants. TM-align tool predicted the alignment score, and the protein folding was found to be identical. PyMOL was used to visualize the superimposition of variants over wild type. Conclusion This study ascertained the deleterious missense nsSNPs of the LYZ C gene and could be used in further experimental analysis. These high-risk nsSNPs could be used as molecular targets for diagnostic and therapeutic interventions