DSpace Collection:

PRODUCTION OF BIOETHANOL USING LIGNOCELLULOSIC BIOMASS

2026-02-24T09:03:30Z

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

2026-01-13T06:31:02Z

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.

ASSESSMENT OF ALTERATIONS IN NEUROMETABOLITES AND BRAIN MICROSTRUCTURE AFTER REPETITIVE MILD TRAUMATIC BRAIN INJURY AND ITS ASSOCIATION WITH THE BEHAVIORAL OUTCOME

2025-12-29T08:37:14Z

Title: ASSESSMENT OF ALTERATIONS IN NEUROMETABOLITES AND BRAIN MICROSTRUCTURE AFTER REPETITIVE MILD TRAUMATIC BRAIN INJURY AND ITS ASSOCIATION WITH THE BEHAVIORAL OUTCOME Authors: KUMARI, MEGHA Abstract: Traumatic brain injury (TBI) is a leading cause of mortality and morbidity worldwide, affecting both civilian populations and military personnel. Survivors of TBI often experience long-term, disabling changes in cognition, motor function, and personality. The Glasgow Coma Scale (GCS) is a commonly used tool for assessing the level of consciousness and severity of TBI, evaluating three key components: eye-opening response, motor response, and verbal response. Based on the score, TBI is classified into three categories: mild, moderate, or severe. In addition to the GCS, imaging techniques such as CT and MRI scans are often used to assess the extent of injury, particularly in cases of mild TBI. TBI is inherently heterogeneous, meaning it can manifest in various ways depending on the individual. To model this complexity in humans, preclinical studies have used experimental models that replicate the key pathophysiological features of different types of human TBI. Despite the identification of numerous promising neuroprotective agents in experimental studies, none have led to significant improvements in long-term clinical outcomes. Several factors contribute to these translational failures, including differences in the types of data collected (such as histopathological, behavioral, and imaging data) and the timing of data collection (ranging from hours to weeks post-TBI) between clinical and experimental studies. Additionally, preclinical testing should involve multiple experimental TBI models, preferably across different species, to better simulate human conditions. Repetitive mild traumatic brain injury (rmTBI) refers to a condition where a person sustains multiple mild TBIs over time. Athletes in contact sports like football, hockey, and soccer, military personnel exposed to blasts, and individuals in high-risk activities for falls or head impacts are all vulnerable to rmTBI. The cumulative effects of repeated mild TBIs mean that each successive injury may have a more significant impact on brain function and recovery. Symptoms of mild TBI, such as headaches, dizziness, memory issues, concentration problems, mood changes, or fatigue, can be subtle and temporary, making detection difficult. While each mild TBI may heal relatively quickly, when injuries occur consecutively, recovery can become delayed or incomplete. Some individuals may experience persistent post- concussive symptoms or develop long-term issues. Repeated mild TBIs also increase the likelihood of subsequent injuries and lower the threshold for more severe symptoms. This cumulative effect raises v concerns about long-term consequences, such as chronic traumatic encephalopathy (CTE), a degenerative brain disease linked to repeated brain trauma. Metabolomics, the study of metabolites, offers a way to identify biomarkers associated with TBI. These biomarkers can be used to predict the severity, progression, and recovery of the injury. Nuclear magnetic resonance (NMR) metabolomics plays a vital role in TBI research, helping to understand the metabolic changes that occur following injury. In this study, NMR metabolomics was used to standardize the classification of diffuse TBI in rats and assess its impact on both the brain and serum. Two types of injury models were used: (1) blunt trauma using the modified Marmarou’s weight drop model, and (2) blast trauma using a compression- driven shock wave tube. Chapter 1 provides an overview of TBI, the history of NMR spectroscopy, its basic principles, and its application in metabolomics and TBI research. It also discusses various injury models, including those for repetitive TBI. Chapter 2 explores graded traumatic brain injury (TBI) in animal models, which simulate varying levels of injury severity, from mild to moderate to severe. This chapter offers valuable insights into the pathophysiological mechanisms of diffuse graded TBI and its effect on the metabolic homogeneity of the hippocampus at acute time points, using 1H-NMR metabolomics. Chapter 3 focuses on the differential effects of diffuse brain injury on regions distant from the impact site, such as the hippocampus, thalamus, and striatum, at acute, early sub-acute, and sub-acute time points. Using 1H-NMR metabolomics, this chapter aims to deepen our understanding of the metabolic changes and pathophysiology of TBI in these brain regions. Chapter 4 examines the metabolic changes associated with repetitive blast concussions, specifically in female rats. This study investigates how repetitive blast injury reprograms metabolism and explores the interplay between metabolic changes and epigenetic alterations following such injuries. Chapter 5 investigates TBI-induced metabolic alterations over time, from acute to chronic stages, following both mild and repetitive mild TBI. Using 1H-NMR metabolomics to analyze serum metabolic vi changes, the study also employs fecal 16S rRNA sequencing to explore changes in the gut microbiome following injury. Chapter 6 summarizes the behavioral changes observed in animals subjected to two types of TBI: diffuse blunt injury and blast-induced injury. This chapter highlights significant behavioral differences between the two injury groups, particularly at chronic time points following the initial injury. Chapter 7 provides a comprehensive summary of the thesis findings, offering key insights into the pathophysiological and behavioral changes induced by blast and blunt TBI over time.

DECIPHERING THE MECHANISMS OF ALZHEIMER'S AND PARKINSON'S DISEASES USING NETWORK BIOLOGY AND A FUNCTIONAL GENOMICS APPROACH

2025-09-02T06:33:41Z

Title: DECIPHERING THE MECHANISMS OF ALZHEIMER'S AND PARKINSON'S DISEASES USING NETWORK BIOLOGY AND A FUNCTIONAL GENOMICS APPROACH Authors: TRIPATHI, RAHUL Abstract: Neurodegenerative disorders are known to exhibit genetic overlap and shared pathophysiology. This study aims to find the shared genetic architecture of Alzheimer's disease (AD) and Parkinson's disease (PD), two major age-related progressive neurodegenerative disorders. The gene expression profiles of GSE67333 (containing samples from AD patients) and GSE114517 (containing samples from PD patients) were retrieved from the Gene Expression Omnibus (GEO) functional genomics database managed by the National Center for Biotechnology Information (NCBI). The web application GREIN (GEO RNA-seq Experiments Interactive Navigator) was used to identify differentially expressed genes (DEGs). 617 DEGs (239 upregulated and 379 downregulated) were identified from the GSE67333 dataset. Likewise, 723 DEGs (378 upregulated and 344 downregulated) were identified from the GSE114517 dataset. The protein-protein interaction (PPI) networks of the differentially expressed genes (DEGs) were constructed, and the top 50 hub genes were identified from the network of the respective dataset. Of the 4 common hub genes between the two datasets, CXCR4 was selected due to its gene expression signature profile and the same direction of differential expression between the two datasets. Mavorixafor was chosen as the reference drug due to its known inhibitory activity against CXCR4 and its ability to cross the blood-brain barrier. Molecular docking and molecular dynamics simulation of 51 molecules having structural similarity with Mavorixafor were performed to find two novel molecules, ZINC49067615 and ZINC103242147. Natural compounds are gaining prominence in the therapy of neurodegenerative disorders due to their biocompatibility and potential neuroprotective properties, including their ability to modulate CXCR4 expression. Recent advancements in artificial intelligence (AI) and machine learning (ML) algorithms have opened new avenues for drug discovery research across various therapeutic areas, including neurodegenerative disorders. We produced an ML model using cheminformatics-guided machine learning algorithms using data of compounds with known CXCR4 activity, retrieved from the Binding Database, to analyse diverse physicochemical attributes of natural compounds obtained from the COCONUT Database and predict their inhibitory activity against CXCR4.