https://ir.vidyasagar.ac.in/jspui/handle/123456789/1322 2026-04-26T22:12:26Z https://ir.vidyasagar.ac.in/jspui/handle/123456789/6105 Title: Implementation of machine learning technique to predict essential genes of Saccharomyces cerevisiae Authors: Das, Partha Sarathi Abstract: INTRODUCTION: Bioinformatics is an emerging and rapidly growing field of a cross-disciplinary science. As a consequence of the large amount of data produced in the field of molecular biology, most of the current bioinformatics projects deal with structural and functional aspects of genes and proteins. The data produced by thousands of research teams all over the world are collected and organized in databases specialized for particular subjects. The existence of public databases with billions of data entries requires a robust analytical approach to cataloguing and representing this with respect to its biological significance. Therefore, computational tools are needed to analyse the collected data in the most efficient manner. Essential genes The genome of an organism characterizes the complete set of genes that it is capable of encoding. However, not all of the genes are transcribed and translated under any defined condition. The robustness that an organism exhibits to environmental perturbations is partly conferred by the genes that are constitutively expressed under all the conditions, and partly by a subset of genes that are induced under the defined conditions. An essential gene is defined here as a gene necessary for growth to a fertile adult. (Kemphues). Essential genes of an organism constitute its minimal gene set, which is the smallest possible group of genes that would be sufficient to sustain a functioning cellular life form under the most favourable conditions (Kunin et al, Glass et al). The deletion of only one of these genes is sufficient to confer a lethal phenotype on an organism regardless the presence of remaining genes. Therefore, the functions encoded by essential genes are crucial for survival and could be considered as a foundation of life itself. The identification of essential genes is important not only for the understanding of the minimal requirements for cellular life, but also for practical purposes. For example, since most antibiotics target essential cellular processes, essential gene products of microbial cells are promising new targets for such drugs (Sarangi A N, et al). In the era of complete genomes, the total number of genes in a sequenced organism can now be predicted (Claverie), but the function and selective importance of a substantial fraction of genes remains unknown (Hollon). The conditional importance of genes in conferring robustness can be understood in the context of the functional attributes of these genes and their correlations to the defined environmental conditions. However, a priori prediction of such genes for a given condition is yet not possible. The prediction and discovery of essential genes have been performed by experimental procedures such as single gene knockouts, RNA interference and conditional knockouts (Gustafson), but these techniques require a large investment of time and resources and they are not always feasible. Considering these experimental constraints, a computational approach capable of accurately predicting essential genes would be of great value. For prediction of essential genes, some investigators have implemented computational approaches in which most are based on sequence features of genes and proteins with or without homology comparison (Gabriel del Rio et al). With the accumulation of data derived from experimental small-scale studies and high-throughput techniques, however, it is now possible to construct networks of gene and proteins interaction (De la Rivas J) and then investigate whether the topological properties of these networks would be useful for predicting essential genes. Implementation of machine learning techniques to predict microbial essential genes Attempts have been made to identify essential genes of prokaryotes through wet lab and insilico techniques. In most cases the experimental basis of identifying essential genes of the organisms in the wet lab has been gene knock-out experiments where a mutant was raised with a single gene “knocked out” and observation was recorded whether the mutation was lethal or if the organism was able to grow as a fertile being or not (Karp,Palsson). This is a very cumbersome task and needs huge sampling to validate the test cases. This has been successful in case of organisms like E. coli (Baba et al), S. cerevisae , Mus musculus (house mouse) etc . All these works have met varying degrees of success. In-silico techniques, machine learning methods have been attempted to predict the essential genes of the organisms mentioned above (Plaimas et al, Chen et al, Heber et al). The availability of the protein-protein interaction networks has made this possible (Gong et al). There have been attempts also to predict essential genes of Saccharomyces. In few cases this system has been used to predict disease causing genes of prokaryotes. Application of neural network technique in identification of essential genes of S. cerevisiae Wet lab experiments have been performed to identify the essential genes of S. cerevisiae and a database has been created under DEG (Database of Essential Genes) (Zhang,2009). In the database it is interesting to note that for the yeast (Saccharomyces cerevisiae) 1110 essential genes have been identified. It is very difficult and costly to knockout single genes from higher organisms and see their expression. Sometimes for ethical reasons it is inhibitory to conduct such experiments on primates and humans. Establishment of such a predictive model which may be later extended to higher organisms will translate into various benefits. The machine learning techniques have been applied in different fields of bioinformatics (Brown et al, Furey et al, Hua et al) but little work has been done to identify yeast essential genes with a holistic approach. This study aims at optimizing the features to identify the essential genes of yeasts by machine learning technique. Availability of Saccharomyces genome database (YGD) (Engel S R et al.) will help to explore all the genes of yeast. Feature selection Identification of key parameters or features A machine learning framework ideally depends on two key components training and testing. On the basis of training received the framework may predict outcomes of novel cases or inputs. Machine learning can be viewed as the acquisition of structural descriptions from examples. The kind of descriptions found can be used for prediction, explanation, and understanding. Various kinds of features were considered to be tested under the machine learning framework. The features were selected based on their reference in published literature as important feature for essential genes or were proposed during the current research work. i.Location of the gene in DNA strand ii. ORF Length: iii. Codon usage bias: iv. Use of rare codons: v. Expression Level: vi. Disorderness of proteins vi. Protein abundance: vii. protein complex number : viii. Protein-protein interaction ( PPI) network Machine Learning Here in this work Rapidminer version 5.3.015 community edition was used. Classifier selection Neural network has been used as a classifier. Machine learning framework For machine learning framework, Rapidminer version 5.3.015 (community edition ) which is a widely accepted open source software environment for predictive analytics was used. The dataset employed here included 2564 S. cerevisiae proteins , out of which 577 were essential and the rest i.e 1987 were non-essential ones. Results and discussion: The machine learning framework employed here could effectively segregate between essential and non-essential genes with 72.5% accuracy. 2021-07-27T00:00:00Z https://ir.vidyasagar.ac.in/jspui/handle/123456789/6020 Title: Impact of commercial probiotics on experimental animal health in hypobaric environment Authors: Samanta, Animesh Abstract: In nature oxygen availability is essential for existence of aerobic organism on the earth. At high altitude (HA) a decrease in partial pressure of O2 in air known as hypobaric hypoxia. This environmental conditionis very fatal and causes of major risk factor for developing acute mountain sickness (AMS). Millions of people like pilgrims, trekkers, scientist and military personnel visit high altitude for different purpose and suffer from AMS including high- altitude cerebral edema (HACE) and high-altitude pulmonary edema (HAPE). Simultaneously, changes in haematological parameters, electrolytes imbalance and oxidative stress collectively create a selective pressure on gut microbial ecology that indirectly increase the burden of AMS. However less medical options are available to HA sufferers to reduce the burden during acclimatization at HA. In the present study male albino rats were exposed to different high altitude condition respectively in 11.8 psi (at 6000 feet altitude, group HA-I), 9.3 psi (at 12000 feet altitude, group HA-II), and 7.3 psi air pressure (at 18000 feet altitude, group HA-III), whereas the control group was kept at normobaric condition (14.3 psi, group NA/C). At the same time, different commercial probiotics (VSL#3, TruBiotics, Yogut and Propolis Plus) were ingested during hypoxic stress and different physiological and haematological parameters, uremic profiles, oxidative stress markers, microbial diversity and microbial associated enzymes were studied. Besides, histological studies were performed of kidney and liver tissues. From the experiment it was found that lower atmospheric pressure at high altitude reduce the body weight and decreased organ weight including kidney & liver at HA-II and HA-III during acclimatization of 28 days. Increased in concentration of RBC, Hb, WBC and imbalanced of electrolytes like sodium, potassium and chloride was found in HA-II and HA- III groups. Apart from imbalance in electrolytes, uremic toxins like urea and creatinine level increased in plasma of hypobaric hypoxic exposed animals. Accumulation of uremic toxins in blood lowered the antioxidant enzymes like SOD and catalase and increased in MDA formation. The changes in physio-chemicals parameters encouraged the growth of facultative anaerobes (E. coli) in gut that encouraged the growth of another total anaerobes and other anaerobes like Bifidoacterium sp, Lactic acid bacteria and Bacteroidetes sp. The increase in population density of facultative anaerobes encouraged in more acids and gas formation in the gut. The increase in microbial associated enzymes like -amylase, proteinase, - glucuronidase and alkaline phosphatase indicated the alteration of metabolic activities in gut microenvironment and increasedthe immunoglobulins (IgG & IgA). The histological structures of kidney and liver of group HA-II and HA-III animals showed a severe disorganization of glomerulus and dilation of renal tubules which indicate nephrotoxicity or acute renal failure at hypobaric hypoxia.The changes of these parameters were observed above 12000 ft. of HA and major risks were associated at 18000 ft. and the adverse effects were more intense upto seven days of acclimatization. Co-administration of probiotics in group HA-II and HA-III animals showed normal arrangement of this tissue. The scanning electronic microscopic analysis revealed that the hypoxic state diminished the proliferation of small intestinal epithelia, whereas probiotics supplemented groups showed normal intestinal villi as found in control group. The oral administration of commercial probiotics decreased plasma uremic toxic level as compared to the group HA. This study also revealed that the commercial probiotics decreased the urinary KIM-1 level in the experimental animals whereas urinary KIM-1 level was found to be increased in hypobaric hypoxia conditions as compared to control. Ingestion of probiotics during hypoxic stress prevent the alteration of microbial population and try to maintain the microbial population like normobaric condition. As a result, decreased acid and gas formation and regulates the microbial associated enzymes. It was notable that, among the four commercial probiotics, VSL#3 showed the better activity against hypobaric hypoxic stress. From the study it can be concluded that the hypobaric hypoxia directly or indirectly hampered the haematological and physiological parameters along with increase the uremic toxins. Ingestion of probiotics during hypoxic stress reduce the stress by inducing antioxidant defence, nephrotoxicity and established the beneficial functions of gut microbial community as well as improves the overall health. In future, supplementation of probiotics during acclimatization at hypobaric hypoxia can be used as a therapeutics to reduce the hypoxic induced stress at HA. 2021-04-22T00:00:00Z https://ir.vidyasagar.ac.in/jspui/handle/123456789/5743 Title: Studies on acidophilus amylase from newly isolated soil fungus and its potential application in starch saccharification Authors: Mukherjee, Riddha Abstract: The fungi Aspergillus niger RBP7 (KX100578.1) was newly isolated from municipal dumping area of Midnapore town, West Bengal, India for the production of amylase. The isolate was identified through phenotypic and microscopic observation. Then the optimization of enzyme production was studied under solid state fermentation (SSF) (using potato peel as substrate) and submerged fermentation (SmF) through one-variable-at-atime (OVAT) and followed by response surface methodology (RSM) which enhanced enzyme titer. After fermentation acidophilic α-amylase from Aspergillus niger RBP7 was purified from the fermented mass. The purified α– amylase (37.5kDa) exhibited the Km and Vmax of 1.4 mg/ml and 0.992 µ/ mol/ min respectively. The enzyme was found stable in range of pH (2.0-6.0), high NaCl concentration (3M) and at (40-70 °C) temperature. The enzyme showed its optimum activity at pH 3.0 and 45 °C temperature. The stability of enzyme was also tested in presence of different surfactant, inhibitory agent etc and found to be inhibited by Hg2+. After fermentation the hydrolysis of raw starchy food stuff (taro, yam, malanga and sweet potato) by crude enzyme, obtained from SSF was studied. The crude enzyme produced mono-meric and dimeric sugars like glucose and maltose, determined through paper chromatography. These characteristics make the crude enzyme suitable for use as digestive dysfunction and for the improvement of digestibility of animal feed ingredients. The purified acidophilic amylase enzyme can also digest different heterogeneous food materials and its activity almost similar to the commercially available diastase. The enzyme was also applicable in different waste management processes. There was no cytotoxic effect shown by purified acidophilic amylase in cell viability test. The In silico approach has been taken to understand the molecular adaptation mechanisms of α-amylase in low pH medium. Initially 36 α-amylase including 7 acid α-amylase sequences were retrieved from four different biological databases. Protein sequence based comparative study and evolutionary analysis indicated that, though all the selected sequences were functionally similar, they have remarkable sequence diversity. Although having sequence diversity, all acid α-amylase were found in a separate cluster of phylogenetic tree. The secondary and topology comparison among acid α-amylase and neutral α-amylase showed conserved beta-sheet regions containing the catalytic amino acid residues of 117-D, 204-R, 206-D, 230-E, 296-H and 297-D within all. But changes observed in Ca2+ binding site. In all the acid α-amylase, acidic amino acid Glu 210 was observed instead of a basic His210. As Ca2+ binding directly proportionate the α-amylase stability at different pH. Change in Ca2+ binding amino acid directly indicated their adaptation in changed pH. In future, directed mutation study to alter His to Glu at 210 position may produce more potent genetically engineered acid α-amylase to solve different industrial purpose. 2021-02-01T00:00:00Z https://ir.vidyasagar.ac.in/jspui/handle/123456789/5590 Title: Study of Diversity and Antibacterial Potential of Mushroom in Gurguripal Ecoforest Authors: Singha, Krishanu Abstract: Exploration of natural sources for novel bioactive compounds from mushrooms having considerable therapeutic potentials has been an emerging field of modern research over the past decades. Mushrooms are represented by 41,000 species across the globe; however, only 2% have been reported from India, despite the fact that one-third of the total global fungal diversity exists in the tropical Indian region. The diversity study was conducted in and around Gurguripal eco-forest (22°25" - 35°8"N and 87°13" - 42°4"E) of Paschim Medinipur district in West Bengal, India. A total number of 2031 mushroom specimens (individuals) were observed, distributed among 67 mushroom species of 44 genera belonging to 27 families. Among the mushroom species 34 were edible, 31 non edible and 2 reported to be poisonous. The mushroom flora in this area are dominated by the family Russulaceae representing 11 species. The species richness of Gurguripal eco-forest was calculated as 0.941(Simpson’s index) and the relative abundance of species was found to be 3.687 (Shannon’s index) indicating the rich diversity and abundance of mushroom flora in this area. Low value of species evenness (calculated as 0.87) referred that all the 67 mushroom species were not evenly distributed numerically in the community indicating the existence of different microhabitats and microenvironments within Gurguripal eco-forest. Ethno-mycological survey revealed that 19 mushroom species were effectively used in solving various human ailments and that precious knowledge was generally confined to traditional healers of the villages in Gurguripal. In the present investigation, nine prime wild edible mushroom species occurring in this region were evaluated for their nutritional compositions. The results showed that the protein, carbohydrate and lipid content of mushrooms ranged between 20.4 - 39.2%, 33.2 - 43.4% and 0.8 - 3.4% respectively on dry weight basis. The crude fibre and ash content varied from 2.0 - 8.6% and 2.3 - 11.5% respectively on dry weight basis. Through overall comparative analysis it has been revealed that Termitomyces heimii and Volvariella volvacea possessed higher proximate compositions and nutritional potentials. Polyphenolic fractions of T. heimii and V. volvacea were extracted and tested for the estimation of major phenolic compounds and the results indicated that T. heimii was richer in phenols, flavonoids and ascorbic acids contents than V. volvacea. This result showed positive correlation with antioxidant activity as T. heimii exhibited higher radical scavenging ability through DPPH and FRAP assay. The results of gas chromatography revealed that higher quantities of unsaturated fatty acids (MUFA and PUFA) are present in T. heimii and the most abundant fatty acid was recorded as linoleic acid. EDAX analysis showed that phosphorus (P) and potassium (K) content of T. heimii is significantly higher than V. volvacea. Altogether, the present findings suggested that due to presence of higher nutritional attributes as well as remarkable antioxidant potentials T. heimii is preferred among the studied mushroom species as a potential dietary supplement. The antibacterial potentials of different mushroom extracts from seven selected species were studied and it was found that methanolic extract of T. heimii showed the highest antibacterial activity against Staphylococcus aureus (18 mm ZOI) and Shigella flexneri (16 mm ZOI). The partially purified methanolic fraction of T. heimii namely F11 has shown highest antibacterial potential and further analysed through HPLC. The chromatogram indicated the presence of four carbohydrates and three major phenolic compounds, among which the highest peak was identified as p-coumaric acid (p-CA). Silver nanoparticles synthesized using T. heimii extract exhibited enhanced antibacterial activity showing 19 mm and 18 mm clear ZOI against S. aureus and S. flexneri respectively. Polysaccharide fractions from T. heimii namely THP-I and THP-II were purified and collected through gel permeation chromatography (GPC). Through MIC and MBC assay, THP-1 exhibited higher antibacterial efficacy against Gram positive bacteria than the ram negative one. LC-MS analysis of THP-I indicated the abundant presence of glucose molecules. The proton magnetic resonance spectrum (1H NMR) of the THP-I spotted five anomeric protons at δH 3.40, 3.42, 3.44, 3.45 and 3.94 ppm confirmed that the compound is a polysaccharide. The in vitro cytotoxicity of THP-I by MTT assay exhibited that the sample had significant cellular toxicity against Human Colorectal Carcinoma cell line (HCT) at a dose concentration 200 μg/ml and showed more destructive effects over a dose of 600 μg/ml. T. heimii possess rich quantity of p-coumaric acid and pure form of p-CA has a remarkable bactericidal effect on pathogenic bacteria (for S. aureus and E. coli the MIC values were found to be 80 μg/ml and 30 μg/ml respectively). To find out the molecular mechanism of p-CA action, a total of 642 and 1121 trans-membrane protein sequences from S. aureus and E. coli were retrieved from microbial whole genome database IMG JGI. Those selected protein sequences from both bacteria were individually aligned using Clustal X2 and PHYLIP 3.69 software for constructing phylogenetic tree and among them 72 sequences were found to share sequential similarities. Through molecular docking study p-CA showed higher affinity towards 99 trans-membrane protein structures of S. aureus, of which 62 proteins were found to be transport proteins. On the basis of ACE values the proper channel blocking by p-CA was best observed for CDP-diacylglycerol-glycerol-3-phosphate 3-phosphatidyltransferase, a bacterial membrane bound enzyme which plays an important role in conversion of 1,2- diacylglycerol (DAG) to phosphatidylglycerol (PG) which is an very important integral membrane protein of bacteria. In this regard, binding and inactivation of CDP-diacylglycerol-glycerol-3-phosphate 3-phosphatidyltransferase by p-CA will influence the accumulation of lethal DAG within bacterial cell causing membrane lysis. The present research work has explored the health benefits of mushrooms along with their bioactive potentials and can lead a new way to combat multidrug resistant bacteria. 2020-12-15T00:00:00Z