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EXPLORING INFLAMMATORY BIOMARKER, DRUG SCREENING, AND MOLECULAR MECHANISM FOR TREATING NEURODEGENERATIVE DISEASES

2026-01-01T00:00:00Z

Title: EXPLORING INFLAMMATORY BIOMARKER, DRUG SCREENING, AND MOLECULAR MECHANISM FOR TREATING NEURODEGENERATIVE DISEASES Authors: KARDAM, SHEFALI; KUMAR, PRAVIR (SUPERVISOR) Abstract: Neurodegenerative diseases such as Parkinson’s and Huntington’s disease arise from a web of pathological events, including protein aggregation, mitochondrial impairment, chronic oxidative stress, and sustained inflammatory activation within the nervous system. Although these mechanisms are increasingly well documented, there are still few therapies that meaningfully alter the disease course. In this thesis, a layered computational workflow was used that combined ligand‑based virtual screening with structure‑guided docking, molecular dynamics simulations, and MM/PBSA binding‑free‑energy calculations. Within this framework, candidate regulatory compounds were identified that target Sirtuin‑1 (SIRT1) in HD and DJ‑1 (PARK7) in PD, and parallel transcriptomic analyses were used to characterize immune‑related changes that may drive or modulate neurodegenerative progression. For HD, Selisistat-guided similarity searching across the LOTUS natural-products repository (276,518 compounds; ≥68% Tanimoto) yielded 1,401 structural analogues that were refined through stringent Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET), Blood-brain barrier (BBB)- permeability, and drug-likeness filters prior to docking into the high-resolution SIRT1 catalytic domain (PDB 4I5I), prepared using CHARMM force-field optimization and validated via re-docking (RMSD < 2 Å). Subsequent 100-ns all- atom MD simulations (GROMACS/CHARMM36/TIP3P) demonstrated that top candidates, particularly LTS0217483, exhibit exceptional conformational stability, low-fluctuation RMSD/RMSF profiles, persistent H-bonding, and favorable compactness (Rg), while MM/PBSA decomposition revealed pronounced van der Waals and electrostatic contributions, confirming high-affinity NAD⁺-competitive ix inhibition driven by interactions with catalytic residues His363, Phe414, Val445, and Arg446. Parallel repurposing of FDA-approved anti-inflammatory drugs against the PD-associated mutant DJ-1 (2R1T) employed SwissADME pre- screening followed by CB-Dock and Webina docking, identifying Oxatomide and Levocabastine as high-binding, BBB-permeable modulators that form stable interactions with residues essential for DJ-1’s redox-chaperone and glyoxalase functions (e.g., Glu163, Glu170, Arg27, Ala56), thereby potentially mitigating oxidative and inflammatory perturbations characteristic of PD. An overview of the key molecular pathways involved in neuroinflammatory signaling is presented in this thesis to provide a biological context for the computational findings. Toll-like receptors, STAT3, p38 MAPK, and the NLRP3 inflammasome are discussed alongside protective regulators, such as SIRT1, SOCS protein, YY1, and MEF2. It has been shown that disruption of the balance between these pathways contributes to progressive neuronal damage, highlighting potential therapeutic targets. A robust in silico framework for CNS-targeted drug discovery is shown in this multidisciplinary study, and mechanistically validated lead scaffolds are identified for HD (LTS0217483) and PD, as well as a systems-level neuroinflammatory model that informs future translation strategies and precision therapeutic development for NDDs.

AN INTEGRATED APPROACH TOWARDS THE IDENTIFICATION OF NOVEL BIOMARKERS IN RESPIRATORY DISORDERS

2025-11-01T00:00:00Z

Title: AN INTEGRATED APPROACH TOWARDS THE IDENTIFICATION OF NOVEL BIOMARKERS IN RESPIRATORY DISORDERS Authors: TANWAR, NAKUL; Hasija, Yasha (SUPERVISOR) Abstract: Respiratory disorders such as COPD, ILD, CPFE, and lung cancer are primarily lung diseases, yet they do not operate within isolated physiological boundaries. These conditions share a deeply interconnected inflammatory landscape, where chronic immune activation, oxidative stress, epithelial injury, and aberrant tissue repair collectively drive both disease progression and coexistence. This interconnectedness is evident in clinical practice, where patients frequently present with overlapping respiratory conditions such as COPD coexisting with ILD or lung cancer because they are shaped by the same underlying molecular and inflammatory pathways. The presence of such overlap points to a broader biological principle that chronic inflammation exists along a continuum across the body rather than remaining confined to a single organ. As a result, it emerges as a systemic process capable of linking diseases that traditionally appear unrelated. This becomes clearer when considering how circulating inflammatory mediators, dysregulated immune cells, and miRNA-driven signaling can influence tissues beyond the lungs. Within this continuum, some immune-mediated conditions for example, Multiple Sclerosis (MS) further demonstrate how shared inflammatory and immune-regulatory disturbances can bridge organ systems, reinforcing the idea that complex diseases are often unified by common immunological mechanisms rather than separated by anatomical boundaries. Traditional diagnostic tools including imaging, pulmonary function tests, and histopathology frequently detect disease only at advanced stages. In parallel, although omics technologies have generated large-scale genomic and transcriptomic datasets, their clinical translation is hindered by the complexity of multi-omics signals and by the “black-box’’ nature of most machine learning approaches. The present research addresses these gaps by integrating multi-omics analysis, machine learning, explainable artificial intelligence (XAI), miRNA–mRNA regulatory network exploration, and large language model (LLM)-based interpretability to uncover shared biomarkers, elucidate mechanistic relationships across diseases, and develop an accessible, clinically interpretable decision-support system for lung disease indication. The first component of the study investigates coexistence among COPD, ILD, and CPFE through integrative transcriptomic and regulatory network analyses. Using the GSE47460 microarray dataset (582 lung tissue samples), rigorous preprocessing, quantile normalization, and classbalancing with SMOTE were applied, followed by a Random Forest classifier to distinguish COPD, ILD, and control samples. Explainable AI using SHAP revealed 20 key genes including OCIAD2, IRS2, TRIM2, MUC20, and CCDC109B—that consistently contributed to model performance across all classes. Functional enrichment analysis demonstrated that these genes participate in oxidative stress, immune activation, epithelial repair, extracellular matrix remodeling, and calcium signaling pathways, all of which underpin the shared pathogenesis of COPD, ILD, and CPFE. Subsequent validation via heatmaps, gene co-expression networks, singlecell expression analysis, and miRNA–mRNA regulatory mapping confirmed the biological relevance of these markers and identified their involvement in fibroblast activation, inflammatory fibroblast signatures, and altered epithelial homeostasis. Collectively, these findings provide strong evidence of convergent mechanisms underlying respiratory disease coexistence and highlight candidate biomarkers with diagnostic and therapeutic utility. The second component explores systemic inflammatory connections among COPD, lung cancer, and MS using the GSE61741 peripheral blood miRNA dataset (237 samples) along with an independent validation dataset. Machine learning models, supported by SMOTE-based class 6 | P a g e balancing and 5-fold cross-validation, achieved high predictive accuracy for all four classes. SHAP interpretability revealed 20 core miRNAs including hsa-let-7c, hsa-miR-223, hsa-miR-92a, and hsa-miR-454 that serve as central regulators across these diseases. These miRNAs converged on six shared inflammatory genes (IL6, IL10, CCL2, CCL5, MYC, and ITGB3), forming a crossdisease regulatory axis linking neuroinflammation, chronic respiratory inflammation, fibrosis, and oncogenesis. Downstream enrichment analyses identified common signaling pathways such as NF-κB, JAK-STAT, PI3K-Akt, cytokine–cytokine receptor interactions, and immune cell activation cascades. Single-cell expression mapping further demonstrated that these genes and miRNAs are enriched in inflammatory fibroblasts, macrophages, T cells, and epithelial populations, suggesting a shared pathological microenvironment across lung and neurological diseases. This objective provides a unified molecular explanation for the epidemiologically observed association between MS and COPD and for the heightened risk of lung cancer in COPD patients. It also identifies cross-disease miRNA signatures that hold promise as non-invasive biomarkers for early detection, risk stratification, and therapeutic targeting. The third component translates these findings into a practical, interactive clinical tool through the development of a SHAP–LLM powered chatbot for lung disease indication. Using a structured dataset of 5,000 individuals with 17 clinical and behavioral features, an XGBoost classifier with monotonic constraints was trained to ensure biologically consistent predictions. The model achieved high accuracy, cross-validation stability, and strong performance on independent validation sets. SHAP-based interpretations were integrated into a conversational interface powered by an LLM, enabling users to query risk predictions, feature contributions, and disease mechanisms in natural language. The system automatically contextualizes SHAP explanations, interprets biomarker relevance, and supports free-text clinical queries, thereby bridging the gap between computational prediction and clinician/patient comprehension. This represents a novel fusion of clinical feature–based risk prediction, XAI-driven transparency, and LLM-powered interpretability, enabling real-time, user-friendly insights from questionnaire and physiological data. By integrating SHAP explanations with a conversational interface, the system transforms conventional tabular risk scores into intuitive, clinically meaningful guidance, with potential applications in telemedicine, early screening, patient counseling, and front-line clinical decision support. Taken together, this thesis advances three major contributions: (i) the identification of shared multi-omics biomarkers and regulatory programs underlying the coexistence of COPD, ILD, CPFE, and related conditions; (ii) the discovery of cross-disease miRNA signatures and inflammatory axes connecting respiratory and neuroinflammatory disorders; and (iii) the development of an interpretable, LLM-augmented clinical decision-support system based on questionnaire-derived features rather than molecular biomarkers.. The findings offer a foundation for integrated biomarker panels for early diagnosis, unified therapeutic strategies targeting shared pathways, and AI-driven decision-support tools capable of enhancing clinical workflows. Future research can expand these models to include proteomics, metabolomics, longitudinal patient monitoring, and real-time wearable sensor integration. Further refinement of the chatbot into a clinically validated decision-support system may facilitate adoption in primary care and personalized respiratory healthcare. Ultimately, the study demonstrates how multi-omics analytics, explainable machine learning, and advanced language models can be combined to address longstanding challenges in understanding and managing complex respiratory disorders.

PRODUCTION OF BIOETHANOL USING LIGNOCELLULOSIC BIOMASS

2026-02-01T00:00:00Z

Title: PRODUCTION OF BIOETHANOL USING LIGNOCELLULOSIC BIOMASS Authors: VYAS, MANJARY Abstract: Fast declining fossil fuels, acute energy crisis energy, a spike in greenhouse gas emissions, and various environmental problems had led to a search for sustainable biofuels production. Extensive research is done in the field of biofuels such as bioethanol due to energy security, low environmental pollution effects, and cost benefits. The bioethanol production process has some necessary steps: pretreatment process, enzymatic hydrolysis, fermentation, distillation, and drying. The conventional ethanol-producing substances are consumable food-based material. Nowadays, attention has been searched for a new alternative to raw material (i.e., nonfood based material) due to the world food crisis. Microorganisms have received much attention for biofuel production through lignocellulosic feedstocks. They secrete collaborative enzymes for digesting the lignocellulosic biomass to simple sugars and afterward fermenting them to alcohol. Other fuels are produced from the same feedstocks as bioethanol. Still, it faces challenges compared to its production processes, such as the price of raw material, pretreatment methods, enzymatic hydrolysis, and low tolerance of the fermenting strain leading to its fewer yields, downstream processing, production of undesired solvents, and fermentation inhibitors. So a new promising biofuel as bioethanol has taken a great attraction of the researchers due to its competitive properties compared to gasoline. The research and development on bioethanol as a gasoline substitute indicates that it is a representative renewable energy source, able to elevate engine performance, combustion and also reduce greenhouse gas emissions. In present research, the duckweed (Lemna minor, Family-Lemnaceae) has been highlighted as a feasible feedstock due to its rapid growth rate and adaptability in various aquatic environments. Its biochemical makeup, characterized by high starch content and low lignin content, allows for effective fermentation and saccharification. Duckweed offers numerous advantages, including minimal land use, considerable biomass output, and wastewater treatment. The paper highlights the potential of duckweed, specifically Lemna minor, as a sustainable source of bioethanol. Its advantageous characteristics like non-food feedstock, fastest growing angiosperm, v global adaptability across the world climates, assimilation of high starch with some nutrient modification, and less or no lignin content, make it a suitable candidate for bioethanol production. This research includes the production of starch-enhanced Lemna minor in nutrition starvation conditions for bioethanol production from it. The starch enhancement technique was standardized by performing experiments sequentially with organic manure, nitrogen-free Hoagland media and full-strength Hoagland media. Initially, the starch quantity was found to be 7% but it has been observed that during nitrogen stress, high starch accumulation was observed in Lemna minor, and on the 9th day it was found to be maximum, which was 26 % with standard deviation± 0.3. It was also observed that protein, glucose, and fructose composition were dropped during this experiment. One-way ANOVA analysis was performed for statistical analysis. It has been found that during the starch enhancement experiment, the starch level was significantly (P < 0.05) higher in nitrogen-free Hoagland media compared to the organic manure and full-strength Hoagland media. So Lemna minor During ethanol production amylase is commonly used enzyme to saccharify the raw plant biomass. This research mainly entailed the collection and preparation of raw materials, maintenance of microorganisms, and optimization of physical and chemical variables for the production of amylolytic cocktail and ethanol. OFAT determined the significant components that influenced the yield of enzymatic activity and ethanol. RSM with CCD was used to determine the influences between factors to obtain the highest yields of enzymes and ethanol. ANOVA was done to examine the significance of factor interactions while response surface plots showed how different factors affected enzyme reaction. Aspergillus niger MTCC-12987, Saccharomyces cereviceaeMTCC-171, and Candida shehatae MTCC-12913 were used for amylolytic cocktail and ethanol production respectively. Minitab 22 software was employed and response surface regression with Central composite design was used to examined an optimized process for maximum yield of enzyme activity and ethanol production. The highest enzyme activity value predicted by the software is 1063.81U/ml, which was closer to the experimental value of 1072.4 U/ml. Again the maximum ethanol production predicted by software is 11.83 % was closer to the experimental value of 10.77 %. Thus, the experimental performance under optimal conditions fitted the model's predictions fairly close. For the purpose to evaluate the combined effects of all independent variables in a fermentation process that may have developed from their interaction with one another, the RSM methodology has been employed as an alternative tool for statistical analysis.

IDENTIFICATION AND EXPRESSION ANALYSIS OF GENES INVOLVED IN VITAMINS BIOSYNTHESIS AND FRUIT RIPENING IN CAPSICUM SPECIES

2025-12-01T00:00:00Z

Title: IDENTIFICATION AND EXPRESSION ANALYSIS OF GENES INVOLVED IN VITAMINS BIOSYNTHESIS AND FRUIT RIPENING IN CAPSICUM SPECIES Authors: DUBEY, MEENAKSHI Abstract: Chili peppers (Capsicum spp., family Solanaceae) are among the world‘s most economically important vegetable crops, valued for their rich nutritional and medicinal properties. These diploid plants (2n = 24; ~3.5 Gb genome) are believed to have originated in tropical Central and South America around 7500 BCE. Of the more than 38 reported species, five are widely cultivated: C. annuum, C. chinense, C. frutescens, C. baccatum, and C. pubescens. India, the world‘s leading producer (1.7 million tons annually; Ministry of Agriculture & Farmers' Welfare, 2022; FAO, 2022; USDA FAS, 2023), harbors remarkable genetic diversity, particularly in the Northeast, which is home to unique landraces such as the fiery hot Bhut jolokia (C. chinense). The genus is a biochemical powerhouse, rich in vitamins (both water- and fat-soluble), antioxidants (carotenoids, flavonoids, polyphenols), and capsaicinoids, which collectively contribute to numerous health benefits including anti-cancer, anti-microbial, and anti-inflammatory effects, as well as industrial applications such as food coloring and pest control. Although closely related to tomato- a well-established model for fruit crops the molecular mechanisms underlying important traits in Capsicum, such as fruit development, ripening, and the biosynthesis of essential vitamins (e.g., ascorbate and tocopherol), remain poorly understood. This knowledge gap has hindered targeted breeding efforts to improve desired fruit traits, quality and nutritional value in Capsicum. This study employed an integrative genomics approach to address these gaps. Orthology- based mining identified key genes involved in fruit development, ripening, and vitamin biosynthesis (Vitamin C and E) pathways in C. annuum, C. baccatum, and C. chinense genomes. Their expression profiles were analyzed using RNA-Seq data and quantitative real- time PCR (qRT-PCR), and correlated with phenotypic data, including vitamin quantification by High-Performance Liquid Chromatography HPLC at three distinct fruit developmental stages in contrasting genotypes. Furthermore, a set of functional molecular markers- Insertions/Deletions (InDels) and Simple Sequence Repeats (SSRs) were developed from the iv candidate genes associated with fruit development, ripening, and Vitamin C and E biosynthesis. Our investigation yielded several important findings. We identified 32 orthologs associated with fruit development and ripening, of which 12 showed significant differential expressions, further validated by qRT-PCR, six out 12 genes e.g., MADS- RIN, SGR1, ETR4, LeSPL, XTH5, MADS-protein1) shows almost similar expression in both qRT and transcriptome-MADS-RIN gene showed consistent, extreme upregulation (>500-fold) in ripening fruit across all species. SGR1 displayed consistently high expression in fruit, with the highest levels in C. frutescens in both datasets. ETR4, LeSPL, XTH5, MADS-protein1- Both methods captured the same pattern of higher expression in early stages (flower, Early fruit, breaker) and a decline upon maturity. Out of above some genes found with opposite/discordant expression, 2 genes GLK2 and hydroquinone glucosyltransferase, and NSGT1 significant differences in both qRT and transcriptome- GLK2 qRT-PCR showed upregulation in C. annuum but downregulation in others, while the transcriptome suggested downregulation in all three species. Hydroquinone glucosyltransferase showed opposing regulatory trends between the two methods. NSGT1 expression patterns differed significantly between the platforms. While in TAGL1, FUL1, and FUL2, both methods agreed on high expression, they showed minor differences in the precise level and tissue-specificity of expression. Significantly, the MADS-RIN ortholog (LOC107847473) exhibited a striking >500-fold upregulation in Mature fruit, underscoring its central regulatory role. Expression analyses of ethylene receptors revealed species-specific ripening behaviors, with C. frutescens displaying climacteric-like characteristics. These results demonstrate strong concordance between transcriptome profiling and qRT-PCR validation across developmental stages. Analysis of vitamins content across different fruit developmental stages in contrasting genotypes revealed the highest accumulation of Vitamin C in Capsicum species, in C. chinense, as in Cc-9 (38.41 mg/g) in C. frutescens as in Cf-7 (29.19 mg/g), and in C. annuum in Ca-8 (41.77 mg/g). while highest Vitamin E levels were recorded in C. v chinense in Cc-10 (109.39 mg/g), in C. frutescens in Cf-9 (112.44 mg/g), and in C. annuum in Ca-5 (111.43 mg/g). On average, C. chinense exhibited the highest Vitamin C content across stages, (25.33 mg/g), followed by C. annuum (24.25 mg/g) and C. frutescens (22.09 mg/g). Genotypes such as from C. chinense (Cc-1,Cc-2, Cc-3, Cc-5, Cc-6 and Cc-9), from C. annuum (Ca-3, Ca-4,Ca-7 and Ca-8) and from C. frutescens (Cf-4, Cf-5, Cf-7 and Cf-8) for Vitamin C, and genotypes from C. chinense (Cc-2, Cc-3, Cc-9, and Cc-10), from C. annuum (Ca-1, Ca-2 ,Ca-f and Ca-6) and from C. frutescens (Cf-2, Cf-4, Cf-7, Cf-9 and Cf-10) for Vitamin E, represent valuable genetic resources for breeding programs aimed at developing Capsicum varieties enriched in Vitamins C and E content. A comprehensive genome-wide analysis identified 29 and 81, 44 and 85, and 36 and 70 putative genes involved in Vitamin C and Vitamin E biosynthesis/degradation in the genomes of C. annuum, C. baccatum, and C. chinense, respectively. Among these, several genes associated with Vitamins C and E were found to be differentially expressed in RNA-Seq data generated from three fruit developmental stages of contrasting vitamins-rich species (C. annuum, C. chinense, and C. frutescens), including key regulators for Vitamin C such as GMP downregulated during fruit development in all species, GME shows same expression pattern in both qRT and transcriptome, AKR2 & GPI: both downregulated in all three species, GGP expression higher in C. annuum (qRT) vs higher in Early fruit (transcriptome), MIOX shows high in C. chinense (qRT) vs low in all species (transcriptome), AKR38 high in C. annuum (qRT) vs no expression in transcriptome, GPP upregulated in maturity in qRT and downregulated transcriptome, GalDH higher expression in qRT and very low expression in transcriptome and PMI gene expression higher in C. frutescens qRT and in transcriptome higher in C. annuum. Similarly for Vitamin E key regulators genes HPPD & IPI upregulated during fruit development in both qRT and transcriptome, VTE3 & VTE4: Nearly identical expression patterns in both methods, TAT shows species-specific expression in qRT and opposite pattern in transcriptome, TYRA expression high in C. frutescens qRT and low/no detection in transcriptome, VTE1 found in qRT and low/no expression in transcriptome, vi VTE2 expressed in all samples in qRT vs minimal expression transcriptome and VTE5 gene expressed across tissues in qRT vs no expression in transcriptome. qRT-PCR validation confirmed the transcriptome-based expression patterns in Vitamin C with Similar expression 4 genes were found (GMP, GME, AKR2, GPI, AKR1) in both qRT and transcriptome and Opposite/Discordant expression in 6 genes (GGP, MIOX, AKR38, GPP, GalDH, PMI) and in Vitamin E with similar expression 5 genes (GGDR, HPPD, IPI, VTE3, VTE4) and in opposite/discordant expression 5 genes (TAT, TYRA, VTE1, VTE2, VTE5) was found in both qRT and transcriptome. Correlation analysis between gene expression and fruit vitamin content revealed significant associations, suggesting that these genes represent strong candidates for functional validation through approaches such as overexpression or genome editing. To facilitate future genetic and breeding studies, we developed 49 gene-based SSR markers associated with fruit development and ripening traits, along with 24 SSR and 37 InDel markers linked to vitamin traits. These markers, after validation, could be deployed in breeding programs targeting improved fruit quality and nutritional content. This is the first comprehensive study to report genome-wide identification and expression profiling of genes involved in fruit development, ripening, and vitamin biosynthesis in Capsicum. The identified candidate genes, together with the novel SSR and InDel markers, represent valuable genomic resources that can accelerate molecular breeding of biofortified Capsicum varieties with enhanced vitamin content. These findings provide a foundational framework for researchers working on fruit crop improvement, particularly in the molecular dissection and manipulation of key nutritional and fruit traits.