Identification of potential biomarkers for bone metastasis using human cancer metastasis database.

Objective: Information theory has been successfully employed to identify optimal pathway networks, mutual information (MI), and entropy as a dynamic response in statistical methods and estimate input and output information in systems biology. This research aims to investigate potentially integrated gene signatures for bone metastasis using graph-based information theory from the dynamic interaction interphase. Methods: The expression dataset with the series ID GSE26964 for bone metastasis from prostate cancer was retrieved. The dataset was segregated for differentially expressed genes (DEGs) using the Human Cancer Metastasis Database. MI was considered to capture non-linear connections to classify the key DEGs from the collected dataset using gene-gene statistical analysis and then a protein-protein interaction network (PPIN). The PPIN was used to calculate centrality metrics, bottlenecks, and functional annotations. Results: A total of 531 DEGs were identified. Thirteen genes were classified as highly correlated based on their gene expression data matrix. The extended PPIN of the 13 genes comprised 53 nodes and 372 edges. A total of four DEGs were identified as hubs. One novel gene was identified with strong network connectivity. Conclusion: The novel biomarkers for metastasis may provide information on cancer metastasis to the bone by implying MI and information theory.

Secretory molecules from secretion systems fine-tune the host-beneficial bacteria (PGPRs) interaction.

Numerous bacterial species associate with plants through commensal, mutualistic, or parasitic association, affecting host physiology and health. The mechanism for such association is intricate and involves the secretion of multiple biochemical substances through dedicated protein systems called secretion systems SS. Eleven SS pathways deliver protein factors and enzymes in their immediate environment or host cells, as well as in competing microbial cells in a contact-dependent or independent fashion. These SS are instrumental in competition, initiation of infection, colonization, and establishment of association (positive or negative) with host organisms. The role of SS in infection and pathogenesis has been demonstrated for several phytopathogens, including Agrobacterium, Xanthomonas, Ralstonia, and Pseudomonas. Since there is overlap in mechanisms of establishing association with host plants, several studies have investigated the role of SSs in the interaction of plant and beneficial bacteria, including symbiotic rhizobia and plant growth bacteria (PGPB). Therefore, the present review updates the role of different SSs required for the colonization of beneficial bacteria such as rhizobia, Burkholderia, Pseudomonas, Herbaspirillum, etc., on or inside plants, which can lead to a long-term association. Most SS like T3SS, T4SS, T5SS, and T6SS are required for the antagonistic activity needed to prevent competing microbes, including phytopathogens, ameliorate biotic stress in plants, and produce substances for successful colonization. Others are required for chemotaxis, adherence, niche formation, and suppression of immune response to establish mutualistic association with host plants.

Nontargeted Metabolite Profiling of the Most Prominent Indian Mango (Mangifera indica L.) Cultivars Using Different Extraction Methods.

Aroma has a crucial role in assessing the quality of fresh fruit and its processed versions, which serve as reliable indications for advancing local cultivars in the mango industry. The aroma of mango is attributed to a complex of hundreds of volatile, polar, and nonpolar metabolites belonging to different chemical classes like monoterpenes, sesquiterpenes, nonterpene hydrocarbons (alkanes), alcohols, esters, fatty acids, aldehydes, lactones, amides, amines, ethers, and many more. This study looked at the volatile, nonpolar, and polar metabolites from 16 mango cultivars to determine their relative quantities and intervarietal changes using hexane, ethanol, and solid-phase microextraction (SPME), followed by gas chromatography-mass spectrometry (GC-MS) analysis. In total, 58 volatile compounds through SPME, 50 nonpolar metabolites from hexane extract, and 52 polar metabolites from ethanol extract were detected from all of the cultivars, belonging to various chemical classes. Through the SPME method, all 16 mango cultivars except Dashehari and Neelum exhibited abundant monoterpenes with maximum concentration in Kesar (91.00%) and minimum in Amrapali (60.66%). However, the abundance of fatty acids and sesquiterpenes was detected in Dashehari (37.91%) and Neelum (74.80%), respectively. In the hexane extract, 23 nonterpene hydrocarbons exhibited abundance in all 16 mango cultivars except Baneshan, with a higher concentration in Dashehari (95.45%) and lower in Ratna (77.63%). The ethanol extraction of 16 mango cultivars showed a higher concentration of esters, aldehydes, alcohols, and amides in Jamadar (52.16%), Dadamio (74.30%), Langra (64.38%), and Kesar (37.10%), respectively. There have been a lot of metabolite variations observed and analyzed using hierarchical cluster analysis (HCA) and principal component analysis (PCA) based on the similarity of various chemical compounds. Cluster analysis revealed the true similarity and pedigree of different mango cultivars, viz., Neeleswari, Dashehari, Neelum, Alphonso, Baneshan, Sonpari, and Neeleshan. They occupied the same cluster during analysis.

Plant Stomata: An Unrealized Possibility in Plant Defense against Invading Pathogens and Stress Tolerance.

Stomata are crucial structures in plants that play a primary role in the infection process during a pathogen's attack, as they act as points of access for invading pathogens to enter host tissues. Recent evidence has revealed that stomata are integral to the plant defense system and can actively impede invading pathogens by triggering plant defense responses. Stomata interact with diverse pathogen virulence factors, granting them the capacity to influence plant susceptibility and resistance. Moreover, recent studies focusing on the environmental and microbial regulation of stomatal closure and opening have shed light on the epidemiology of bacterial diseases in plants. Bacteria and fungi can induce stomatal closure using pathogen-associated molecular patterns (PAMPs), effectively preventing entry through these openings and positioning stomata as a critical component of the plant's innate immune system; however, despite this defense mechanism, some microorganisms have evolved strategies to overcome stomatal protection. Interestingly, recent research supports the hypothesis that stomatal closure caused by PAMPs may function as a more robust barrier against pathogen infection than previously believed. On the other hand, plant stomatal closure is also regulated by factors such as abscisic acid and Ca2+-permeable channels, which will also be discussed in this review. Therefore, this review aims to discuss various roles of stomata during biotic and abiotic stress, such as insects and water stress, and with specific context to pathogens and their strategies for evading stomatal defense, subverting plant resistance, and overcoming challenges faced by infectious propagules. These pathogens must navigate specific plant tissues and counteract various constitutive and inducible resistance mechanisms, making the role of stomata in plant defense an essential area of study.

RSAD2: An exclusive target protein for Zika virus comparative modeling, characterization, energy minimization and stabilization.

Objective: The major purpose of the present study was to predict the structure of Radical s-adenosyl-L-methionine Domain 2 (RSAD2), the most targeted protein of the Zika virus using comparative modeling, to validate the models that were generated and molecular dynamics (MD) simulations were performed. Methods: The secondary structure of RSAD2 was estimated using the Garnier-Osguthorpe-Robson, Self-Optimized Prediction method with Alignment, and Position-Specific Iterative-Blast based secondary structure prediction algorithms. The best of them were preferred based on their DOPE score, then three-dimensional structure identification using SWISS-MODEL and the Protein Homology/Analogy Recognition Engine (Phyre2) server. SAVES 6.0 was used to validate the models, and the preferred model was then energetically stabilized. The model with least energy minimization was used for MD simulations using iMODS. Results: The model predicted using SWISS-MODEL was determined as the best among the predicted models. In the Ramachandran plot, there were 238 residues (90.8%) in favored regions, 23 residues (8.8%) in allowed regions, and 1 residue (0.4%) in generously allowed regions. Energy minimization was calculated using Swiss PDB viewer, reporting the SWISS-MODEL with the lowest energy (E = -18439.475 KJ/mol) and it represented a stable structure conformation at three-dimensional level when analyzed by MD simulations. Conclusion: A large amount of sequence and structural data is now available, for tertiary protein structure prediction, hence implying a computational approach in all the aspects becomes an opportunistic strategy. The best three-dimensional structure of RSAD2 was built and was confirmed with energy minimization, secondary structure validation and torsional angles stabilization. This modeled protein is predicted to play a role in the development of drugs against Zika virus infection.

An Integrated Approach to Identify Intrinsically Disordered Regions in Osteopontin with its Interacting Network in Rheumatoid Arthritis.

BACKGROUND: Credentials of molecular diagnostic approaches are an important goal. Since protein-protein interaction (PPI) network analysis is an apposite method for molecular valuation, a PPI grid related to Intrinsically Disordered Proteins (IDPs) of RA was targeted in the present research. AIM: The aim of the study is to analyse the role of highly disordered proteins and their functional parameters in causing Rheumatoid Arthritis (RA). METHODS: Cytoscape software helped in identifying molecular interaction networks. Intrinsically disordered proteins lack higher order structure and have functional advantages, but their dysregulation can cause several diseases. All the significant proteins responsible for RA were identified. On the basis of the data obtained, highly disordered proteins were selected. Further, MSA was done to find the similarity among the highly disordered proteins and their functional partners. To determine the most relevant functional partner( s)/interacting protein(s) out of large network, three filters were introduced in the methodology. RESULTS: The two filtered proteins, IBSP and FGF2, have common functions and also play a vital role in the pathways of RA. Thus, gives an in-depth knowledge of molecular mechanisms involved in Rheumatoid Arthritis and targeted therapeutics. CONCLUSION: The network analysis of these proteins has been explored using Cytoscape, and the proteins with favourable values of graph centrality parameters such as IBSP and FGF2 are identified. Interesting functional cross talk such as bio mineralization, boneremodelling, angiogenesis, cell differentiation, etc., of SPP1 with IBSP and FGF2 is found, which throws light into the fact that these two proteins play a vital role in the pathways of RA.

Postbiotic production: harnessing the power of microbial metabolites for health applications.

Postbiotics, which are bioactive substances derived from the metabolic processes of beneficial microbes, have received considerable attention in the field of microbiome science in recent years, presenting a promising path for exploration and innovation. This comprehensive analysis looks into the multidimensional terrain of postbiotic production, including an extensive examination of diverse postbiotic classes, revealing their sophisticated mechanisms of action and highlighting future applications that might significantly affect human health. The authors thoroughly investigate the various mechanisms that support postbiotic production, ranging from conventional fermentation procedures to cutting-edge enzyme conversion and synthetic biology approaches. The review, as an acknowledgment of the field's developing nature, not only highlights current achievements but also navigates through the problems inherent in postbiotic production. In order to successfully include postbiotics in therapeutic interventions and the production of functional food ingredients, emphasis is given to critical elements, including improving yields, bolstering stability, and assuring safety. The knowledge presented herein sheds light on the expanding field of postbiotics and their potential to revolutionize the development of novel therapeutics and functional food ingredients.

Dual Inoculation of Plant Growth-Promoting Bacillus endophyticus and Funneliformis mosseae Improves Plant Growth and Soil Properties in Ginger.

Co-inoculation with beneficial microbes has been suggested as a useful practice for the enhancement of plant growth, nutrient uptake, and soil nutrients. For the first time in Uzbekistan the role of plant-growth-promoting Bacillus endophyticus IGPEB 33 and arbuscular mycorrhizal fungi (AMF) on plant growth, the physiological properties of ginger (Zingiber officinale), and soil enzymatic activities was studied. Moreover, the coinoculation of B. endophyticus IGPEB 33 and AMF treatment significantly increased the plant height by 81%, leaf number by 70%, leaf length by 82%, and leaf width by 40% compared to the control. B. endophyticus IGPEB 33 individually increased plant height significantly by 51%, leaf number by 56%, leaf length by 67%, and leaf width by 27% as compared to the control treatment. Compared to the control, B. endophyticus IGPEB 33 and AMF individually significantly increased chlorophyll a by 81-58%, chlorophyll b by 68-37%, total chlorophyll by 74-53%, and carotenoid content by 67-55%. However, combination of B. endophyticus IGPEB 33 and AMF significantly increased chlorophyll a by 86%, chlorophyll b by 72%, total chlorophyll by 82%, and carotenoid content by 83% compared to the control. Additionally, plant-growth-promoting B. endophyticus IGPEB 33 and AMF inoculation improved soil nutrients and soil enzyme activities compared to the all treatments. Co-inoculation with plant-growth-promoting B. endophyticus and AMF could be an alternative for the production of ginger that is more beneficial to soil nutrient deficiencies. We suggest that a combination of plant-growth-promoting B. endophyticus and AMF inoculation could be a more sustainable and eco-friendly approach in a nutrient-deficient soil.

Classifying Integrated Signature Molecules in Macrophages of Rheumatoid Arthritis, Osteoarthritis, and Periodontal Disease: An Omics-Based Study.

Rheumatoid arthritis (RA), osteoarthritis (OA), and periodontal disease (PD) are chronic inflammatory diseases that are globally prevalent, and pose a public health concern. The search for a potential mechanism linking PD to RA and OA continues, as it could play a significant role in disease prevention and treatment. Recent studies have linked RA, OA, and PD to Porphyromonas gingivalis (PG), a periodontal bacterium, through a similar dysregulation in an inflammatory mechanism. This study aimed to identify potential gene signatures that could assist in early diagnosis as well as gain insight into the molecular mechanisms of these diseases. The expression data sets with the series IDs GSE97779, GSE123492, and GSE24897 for macrophages of RA, OA synovium, and PG stimulated macrophages (PG-SM), respectively, were retrieved and screened for differentially expressed genes (DEGs). The 72 common DEGs among RA, OA, and PG-SM were further subjected to gene-gene correlation analysis. A GeneMANIA interaction network of the 47 highly correlated DEGs comprises 53 nodes and 271 edges. Network centrality analysis identified 15 hub genes, 6 of which are DEGs (API5, ATE1, CCNG1, EHD1, RIN2, and STK39). Additionally, two significantly up-regulated non-hub genes (IER3 and RGS16) showed interactions with hub genes. Functional enrichment analysis of the genes showed that "apoptotic regulation" and "inflammasomes" were among the major pathways. These eight genes can serve as important signatures/targets, and provide new insights into the molecular mechanism of PG-induced RA, OA, and PD.

Toxicity Mitigation of Textile Dye Reactive Blue 4 by Hairy Roots of Helianthus annuus and Testing Its Effect in In Vivo Model Systems.

An anthraquinone textile dye, Reactive Blue 4 (RB4), poses environmental health hazards. In this study, remediation of RB4 (30-110 ppm) was carried out by hairy roots (HRs). UV-visible spectroscopy and FTIR analysis showed that the dye undergoes decolourization followed by degradation. In addition, toxicity and safety analyses of the bioremediated dye were performed on Allium cepa and zebrafish embryos, which revealed lesser toxicity of the bioremediated dye as compared to untreated dye. For Allium cepa, the highest concentration, i.e., 110 ppm of the treated dye, showed less chromosomal aberrations with a mitotic index of 8.5 ± 0.5, closer to control. Two-fold decrease in mortality of zebrafish embryos was observed at the highest treated dye concentration indicating toxicity mitigation. A higher level of lipid peroxidation (LPO) was recorded in the zebrafish embryo when exposed to untreated dye, suggesting a possible role of oxidative stress-inducing mortality of embryos. Further, the level of LPO was significantly normalized along with the other antioxidant enzymes in embryos after dye bioremediation. At lower concentrations, mitigated samples displayed similar antioxidant activity comparable to control underlining the fact that the dye at lesser concentration can be more easily degraded than the dye at higher concentration.

Porphyromonas gingivalis resistance and virulence: An integrated functional network analysis.

BACKGROUND: The gram-negative bacteria Porphyromonas gingivalis (PG) is the most prevalent cause of periodontal diseases and multidrug-resistant (MDR) infections. Periodontitis and MDR infections are severe due to PG's ability to efflux antimicrobial and virulence factors. This gives rise to colonisation, biofilm development, evasion, and modulation of the host defence system. Despite extensive studies on the MDR efflux pump in other pathogens, little is known about the efflux pump and its association with the virulence factor in PG. Prolonged infection of PG leads to complete loss of teeth and other systemic diseases. This necessitates the development of new therapeutic interventions to prevent and control MDR. OBJECTIVE: The study aims to identify the most indispensable proteins that regulate both resistance and virulence in PG, which could therefore be used as a target to fight against the MDR threat to antibiotics. METHODS: We have adopted a hierarchical network-based approach to construct a protein interaction network. Firstly, individual networks of four major efflux pump proteins and two virulence regulatory proteins were constructed, followed by integrating them into one. The relationship between proteins was investigated using a combination of centrality scores, k-core network decomposition, and functional annotation, to computationally identify the indispensable proteins. RESULTS: Our study identified four topologically significant genes, PG_0538, PG_0539, PG_0285, and PG_1797, as potential pharmacological targets. PG_0539 and PG_1797 were identified to have significant associations between the efflux pump and virulence genes. This type of underpinning research may help in narrowing the drug spectrum used for treating periodontal diseases, and may also be exploited to look into antibiotic resistance and pathogenicity in bacteria other than PG.

Prioritization of potential vaccine candidates and designing a multiepitope-based subunit vaccine against multidrug-resistant Salmonella Typhi str. CT18: A subtractive proteomics and immunoinformatics approach.

Salmonella enterica serovar Typhi (S. Typhi), a causative agent of typhoid fever, is a Gram-negative, human-restricted pathogen that causes significant morbidity and mortality, particularly in developing countries. The currently available typhoid vaccines are not recommended to children below six years of age and have poor long-term efficacy. Due to these limitations and the emerging threat of multidrug-resistance (MDR) strains, the development of a new vaccine is urgently needed. The present study aims to design a multiepitope-based subunit vaccine (MESV) against MDR S. Typhi str. CT18 using a computational-based approach comprising subtractive proteomics and immunoinformatics. Firstly, we investigated the proteome of S. Typhi str. CT18 using subtractive proteomics and identified twelve essential, virulent, host non-homologous, and antigenic outer membrane proteins (OMPs) as potential vaccine candidates with low transmembrane helices (≤1) and molecular weight (≤110 kDa). The OMPs were mapped for cytotoxic T lymphocyte(CTL) epitopes, helper T lymphocyte (HTL) epitopes, and linear B lymphocyte (LBL) epitopes using various immunoinformatics tools and servers. A total of 6, 12, and 11 CTL, HTL, and LBL epitopes were shortlisted, respectively, based on their immunogenicity, antigenicity, allergenicity, toxicity, and hydropathicity potential. Four MESV constructs (MESVCs), MESVC-1, MESVC-2, MESVC-3, and MESVC-4, were designed by linking the CTL, HTL, and LBL epitopes with immune-modulating adjuvants, linkers, and PADRE (Pan HLA DR-binding epitope) sequences. The MESVCs were evaluated for their physicochemical properties, allergenicity, antigenicity, toxicity, and solubility potential to ensure their safety and immunogenic behavior. Secondary and tertiary structures of shortlisted MESVCs (MESVC-1, MESVC-3, and MESVC-4) were predicted, modeled, refined, validated, and then docked with various MHC I, MHC II, and TLR4/MD2 complex. Molecular dynamics (MD) simulation of the final selected MESVC-4 with TLR4/MD2 complex confirms its binding affinity and stability. Codon optimization and in silico cloning verified the translation efficiency and successful expression of MESVC-4 in E. coli str. K12. Finally, the efficiency of MESVC-4 to trigger an effective immune response was assessed by an in silico immune simulation. In conclusion, our findings show that the designed MESVC-4 can elicit humoral and cellular immune responses, implying that it may be used for prophylactic or therapeutic purposes. Therefore, it should be subjected to further experimental validations.

Gene co-expression network analysis for identifying genetic markers in Parkinson's disease - a three-way comparative approach.

Parkinson's disease (PD) is a neurodegenerative disorder involving progressive deterioration of dopaminergic neurons. Although few genetic markers for familial PD are known, the etiology of sporadic PD remains poorly understood. Microarray data was analysed for induced pluripotent stem cells (iPSCs) derived from PD patients and mature neuronal cells (mDA) differentiated from these iPSCs. Combining expression and semantic similarity, a highly-correlated PD interactome was constructed that included interactions of established Parkinson's disease marker genes. A novel three-way comparative approach was employed, delineating topologically and functionally important genes. These genes showed involvement in pathways like Parkin-ubiquitin proteosomal system (UPS), immune associated biological processes and apoptosis. Of interest are three genes, eEF1A1, CASK, and PSMD6 that are linked to PARK2 activity in the cell and thereby form attractive candidate genes for understanding PD. Network biology approach delineated in this study can be applied to other neurodegenerative disorders for identification of important genetic regulators.

SVMRFE based approach for prediction of most discriminatory gene target for type II diabetes.

Type II diabetes is a chronic condition that affects the way our body metabolizes sugar. The body's important source of fuel is now becoming a chronic disease all over the world. It is now very necessary to identify the new potential targets for the drugs which not only control the disease but also can treat it. Support vector machines are the classifier which has a potential to make a classification of the discriminatory genes and non-discriminatory genes. SVMRFE a modification of SVM ranks the genes based on their discriminatory power and eliminate the genes which are not involved in causing the disease. A gene regulatory network has been formed with the top ranked coding genes to identify their role in causing diabetes. To further validate the results pathway study was performed to identify the involvement of the coding genes in type II diabetes. The genes obtained from this study showed a significant involvement in causing the disease, which may be used as a potential drug target.

Unwinding the Novel Genes Involved in the Differentiation of Embryonic Stem Cells into Insulin-Producing Cells: A Network-Based Approach.

Diabetes is one of the main causes of death in the world. Diabetes is marked by high blood glucose levels and develops when the body doesn't produce enough insulin or is not able to use insulin effectively, or both. Type I diabetes is a chronic sickness caused by lack of insulin due to the autoimmune destruction of pancreatic insulin-producing beta cells. Research on permanent cure for diabetes is in progress with several remarkable findings in the past few decades among which stem cell therapy has turned out to be a promising way to cure diabetes. Stem cells have the remarkable potential to differentiate into glucose-responsive beta cells through controlled differentiation protocols. Discovering novel targets that could potentially influence the differentiation to specific cell type will help in disease therapy. The present work focuses on finding novel genes or transcription factors involved in the human embryonic stem cell differentiation into insulin-producing beta cells using network biology approach. The interactome of 321 genes and their associated molecules involved in human embryonic stem cell differentiation into beta cells was constructed, which includes 1937 nodes and 8105 edges with a scale-free topology. Pathway analysis for the hubs obtained through MCODE revealed that four highly interactive hubs were relevant to embryonic stem cell differentiation into insulin-producing cells. Their role in different pathways and stem cell differentiation was studied. Centrality parameters were applied to identify the potential controllers of the differentiation processes: BMP4, SALL4, ZIC1, NTS, RNF2, FOXO1, AKT1 and GATA4. This type of approach gives an insight to identify potential genes/transcription factors which may play influential role in many complex biological processes.

Implying Analytic Measures for Unravelling Rheumatoid Arthritis Significant Proteins Through Drug-Target Interaction.

Rheumatoid arthritis (RA) is a systemic autoimmune and inflammatory disease that mainly alters the synovial joints and ultimately leads to their destruction. The involvement of the immune system and its related cells is a basic trademark of autoimmune-associated diseases. The present work focuses on network analysis and its functional characterization to predict novel targets for RA. The interactive model called as rheumatoid arthritis drug-target-protein (RA-DTP) is built of 1727 nodes and 7954 edges followed the power-law distribution. RA-DTP comprised of 20 islands, 55 modules and 123 submodules. Good interactome coverage of target-protein was detected in island 2 (Q-Score 0.875) which includes 673 molecules with 20 modules and 68 submodules. The biological landscape of these modules was examined based on the participation molecules in specific cellular localization, molecular function and biological pathway with favourable p value. Functional characterization and pathway analysis through KEGG, Biocarta and Reactome also showed their involvement in relation to the immune system and inflammatory processes and biological processes such as cell signalling and communication, glucosamine metabolic process, renin-angiotensin system, BCR signals, galactose metabolism, MAPK signalling, complement and coagulation system and NGF signalling pathways. Traffic values and centrality parameters were applied as the selection criteria for identifying potential targets from the important hubs which resulted into FOS, KNG1, PTGDS, HSP90AA1, REN, POMC, FCER1G, IL6, ICAM1, SGK1, NOS3 and PLA2G4A. This approach provides an insight into experimental validation of these associations of potential targets for clinical value to find their effect on animal studies.

Implying analytic measures for unraveling rheumatoid arthritis significant proteins through drug target interaction.

Rheumatoid arthritis (RA) is a systemic auto-immune and inflammatory disease that mainly alters the synovial joints and ultimately leads to their destruction. The involvement of the immune system and its related cells is a basic trademark of auto-immune associated diseases. The present work focuses on network analysis and its functional characterization to predict novel targets for RA. The interactive model called as Rheumatoid Arthritis Drug-Target-Protein (RA-DTP) is built of 1727 nodes and 7954 edges followed the power law distribution. RADTP comprised of 20 islands, 55 modules and 123 sub modules. Good interactome coverage of target-protein was detected in Island 2 (Q-Score 0.875) which includes 673 molecules with 20 modules and 68 sub modules. The biological landscape of these modules was examined based on the participation molecules in specific cellular localization, molecular function and biological pathway with favourable p value. Functional characterization and pathway analysis through KEGG, Biocarta and Reactome also showed their involvement in relation to the immune system and inflammatory processes and biological processes such as cell signalling and communication, glucosamine metabolic process, Renin Angiotensin system, BCR signals, Galactose metabolism, MAPK signalling, Complement and Coagulation system and NGF signalling pathways. Traffic values and centrality parameters were applied as the selection criteria for identifying potential targets from the important hubs which resulted into FOS, KNG1, PTGDS, HSP90AA1, REN, POMC, FCER1G, IL6, ICAM1, SGK1, NOS3 and PLA2G4A. This approach provides an insight to experimental validation of these associations of potential targets for clinical value to find their effect on animal studies.

Uncovering potential Drug Targets for Tuberculosis using Protein Networks.

The emergence of HIV-TB co-infection and multi-drug resistant strains of Mycobacterium tuberculosis (Mtb) drive the need for new therapeutics against the infectious disease tuberculosis. Among the reported putative TB targets in the literature, the identification and characterization of the most probable therapeutic targets that influence the complex infectious disease, primarily through interactions with other influenced proteins, remains a statistical and computational challenge in proteomic epidemiology. Protein interaction network analysis provides an effective way to understand the relationships between protein products of genes by interconnecting networks of essential genes and its protein-protein interactions for 5 broad functional categories in Mtb. We also investigated the substructure of the protein interaction network and focused on highly connected nodes known as cliques by giving weight to the edges using data mining algorithms. Cliques containing Sulphate assimilation and Shikimate pathway enzymes appeared continuously inspite of increasing constraints applied by the K-Core algorithm during Network Decomposition. The potential target narrowed down through Systems approaches was Prephanate Dehydratase present in the Shikimate pathway this gives an insight to develop novel potential inhibitors through Structure Based Drug Design with natural compounds.

Diagnosis of arthritis through fuzzy inference system.

Expert or knowledge-based systems are the most common type of AIM (artificial intelligence in medicine) system in routine clinical use. They contain medical knowledge, usually about a very specifically defined task, and are able to reason with data from individual patients to come up with reasoned conclusion. Although there are many variations, the knowledge within an expert system is typically represented in the form of a set of rules. Arthritis is a chronic disease and about three fourth of the patients are suffering from osteoarthritis and rheumatoid arthritis which are undiagnosed and the delay of detection may cause the severity of the disease at higher risk. Thus, earlier detection of arthritis and treatment of its type of arthritis and related locomotry abnormalities is of vital importance. Thus the work was aimed to design a system for the diagnosis of Arthitis using fuzzy logic controller (FLC) which is, a successful application of Zadeh's fuzzy set theory. It is a potential tool for dealing with uncertainty and imprecision. Thus, the knowledge of a doctor can be modelled using an FLC. The performance of an FLC depends on its knowledge base which consists of a data base and a rule base. It is observed that the performance of an FLC mainly depends on its rule base, and optimizing the membership function distributions stored in the data base is a fine tuning process.