KEGG for representation and analysis of molecular networks involving diseases and drugs

Most human diseases are complex multi-factorial diseases resulting from the combination of various genetic and environmental factors. In the KEGG database resource (http://www.genome.jp/kegg/), diseases are viewed as perturbed states of the molecular system, and drugs as perturbants to the molecular system. Disease information is computerized in two forms: pathway maps and gene/molecule lists. The KEGG PATHWAY database contains pathway maps for the molecular systems in both normal and perturbed states. In the KEGG DISEASE database, each disease is represented by a list of known disease genes, any known environmental factors at the molecular level, diagnostic markers and therapeutic drugs, which may reflect the underlying molecular system. The KEGG DRUG database contains chemical structures and/or chemical components of all drugs in Japan, including crude drugs and TCM (Traditional Chinese Medicine) formulas, and drugs in the USA and Europe. This database also captures knowledge about two types of molecular networks: the interaction network with target molecules, metabolizing enzymes, other drugs, etc. and the chemical structure transformation network in the history of drug development. The new disease/drug information resource named KEGG MEDICUS can be used as a reference knowledge base for computational analysis of molecular networks, especially, by integrating large-scale experimental datasets

Identification of "pathologs" (disease-related genes) from the RIKEN mouse cDNA dataset using human curation plus FACTS, a new biological information extraction system

BACKGROUND: A major goal in the post-genomic era is to identify and characterise disease susceptibility genes and to apply this knowledge to disease prevention and treatment. Rodents and humans have remarkably similar genomes and share closely related biochemical, physiological and pathological pathways. In this work we utilised the latest information on the mouse transcriptome as revealed by the RIKEN FANTOM2 project to identify novel human disease-related candidate genes. We define a new term "patholog" to mean a homolog of a human disease-related gene encoding a product (transcript, anti-sense or protein) potentially relevant to disease. Rather than just focus on Mendelian inheritance, we applied the analysis to all potential pathologs regardless of their inheritance pattern. RESULTS: Bioinformatic analysis and human curation of 60,770 RIKEN full-length mouse cDNA clones produced 2,578 sequences that showed similarity (70–85% identity) to known human-disease genes. Using a newly developed biological information extraction and annotation tool (FACTS) in parallel with human expert analysis of 17,051 MEDLINE scientific abstracts we identified 182 novel potential pathologs. Of these, 36 were identified by computational tools only, 49 by human expert analysis only and 97 by both methods. These pathologs were related to neoplastic (53%), hereditary (24%), immunological (5%), cardio-vascular (4%), or other (14%), disorders. CONCLUSIONS: Large scale genome projects continue to produce a vast amount of data with potential application to the study of human disease. For this potential to be realised we need intelligent strategies for data categorisation and the ability to link sequence data with relevant literature. This paper demonstrates the power of combining human expert annotation with FACTS, a newly developed bioinformatics tool, to identify novel pathologs from within large-scale mouse transcript datasets

Identification of a Novel Drug Target Protein Against Haemophilus Influenzae Rd KW20: An Insilico Approach

Haemophilus Influenzae (H. Influenzae) is the gram negative bacteria causes infection at respiratory tract in human. Rd KW20 strain is mostly responsible for this disease. According to WHO statistics it kills 386,000 child per year in all over the world. In this approach we have identified some drug target protein which can be used as novel drug against this deadly pathogen. The metabolic pathways which are absent in the human but present in H. Influenza are taken as unique metabolic pathways. Here there are four such unique pathways are present only in case of bacteria, but not available in human. The genes present in these unique pathways were analyzed and listed on the basis of essentiality. These genes are crucial for survival of the pathogen and shortlisted from the Database of Essential Genes (DEG). The essential genes are blasted against the human genome through using BLASTP tool to shortlist the non-homologous genes. The gene named ponA, known as penicillin-binding protein is the best gene used for target against pathogen. The three-dimensional structure of this protein is predicted using Modeler 9.14, DeepView, RasWin and PyMol software. The active site for this gene is identified using CastP and the energically stabilized structure is chosen using Ramachandran plot

Identification of Drug Target Against Bacteroides Fragilis 638R: Through Insilico Genome Analysis

Bacteroides fragilis is a Gram-negative, rod shaped bacterium and the most standard anaerobic bacterium creating bacteremia in people. It is a part of the normal endogenous flora in human body and is normally commensal, but can cause infection if displaced into the bloodstream or surrounding tissue following trauma, surgery or disease. In this approach we have found certain target protein which can give rise to novel drug for the B.fragilis disease 638R. All the metabolic pathways which are present in the pathogen but not present in the human are taken as unique metabolic pathways. Here there are five pathways which unique and present only in bacteria. Whole genome sequence of the human pathogen Bacteroides fragilis 638R was explored to identify drugs targets. 526 Total number of protein coding genes were studied from B.fragilis, and 74 gene were having greater than 100 AA( amino acids) in there coding sequence were identified because of less than 100 amino acids in length were most unlikely to represent essential protein, we found 30 genes were identified human non-homologs. These human nonhomologs genes and there encoding protein were categorized on basis of the metabolic pathways involved in the basic survival mechanisms of the bacterium. After that we found 15 human non-homologous essential genes. Among all the human non-homologous essential genes BF638R_1443 , having EC no: 5.1.3.20 is showing best Blast P result. This gene is present in the Cytoplasm and involves in the biological process like Carbohydrate metabolism process , Lipopolysaccharide biosynthesis. This in-silico genome analysis provides rapid and potential approach for identification of drug target and designing of dru

The Effect Of Oil Palm Phenolics (opp) On Pancreatic Ductal Adenocarcinoma (pdac) In Transgenic Mouse Model

Pancreatic ductal adenocarcinoma (PDAC) is an extremely aggressive form of pancreatic cancer with low survival rates partly due to late diagnosis and poor treatment outcomes. The use of chemotherapy drug, gemcitabine alone often provides minimal benefits. This study explored the in vivo effect of oil palm phenolics (OPP), a water-soluble extract from oil palm in transgenic mouse model of PDAC and its combination with gemcitabine. Administration of 5% dietary OPP was found to be non-toxic in non-PDAC controls. Compared to single agent therapy with either OPP or gemcitabine, OPP-gemcitabine combination showed a superior benefit with profound synergistic effect both as chemotherapeutic and chemopreventive agent evident in halted tumor and cyst growth, as well as lowered high grade precursor lesion count. Favorable regulation of several tumorigenesis markers through immunohistochemistry (S100P and SMAD4) and real-time PCR (Notch1, MMP9 and CCND1) that was partially displayed by OPP but was more significant with the combinatorial therapy has provided an insight on the molecular targets responsible for the anticancer effect of OPP and OPP-gamcitabine combination . Using multivariate analysis software, SIMCA-P+, discrimination in urinary 1H NMR metabolomic profiles between groups was revealed. Metabolite profiling has identified decreased levels of alanine, creatinine, succinate and taurine following intervention with OPP and its combination with gemcitabine. Metabolomic profiles were shown to be strongly correlated with Notch1 and MMP9 expression, also with total precursor lesion count following regression analyses. Finally, pathway analyses by MetaboAnalyst software, based on the information from regression analyses revealed that taurine, which involved in taurine and hypotaurine metabolism plays a major role in the anticancer effect exhibited by both interventions of OPP alone and OPP-gemitabine combination. Collectively, OPP as a single agent exhibited a milder therapeutic effect than the use of OPP in combination with gemcitabine which displayed superior advantage. This demonstrates the potential benefit of dietary OPP as part of combinatorial therapy against progression of PDAC

Identification of "pathologs" (disease-related genes) from the RIKEN mouse cDNA dataset using human curation plus FACTS, a new biological information extraction system

BACKGROUND: A major goal in the post-genomic era is to identify and characterise disease susceptibility genes and to apply this knowledge to disease prevention and treatment. Rodents and humans have remarkably similar genomes and share closely related biochemical, physiological and pathological pathways. In this work we utilised the latest information on the mouse transcriptome as revealed by the RIKEN FANTOM2 project to identify novel human disease-related candidate genes. We define a new term "patholog" to mean a homolog of a human disease-related gene encoding a product (transcript, anti-sense or protein) potentially relevant to disease. Rather than just focus on Mendelian inheritance, we applied the analysis to all potential pathologs regardless of their inheritance pattern. RESULTS: Bioinformatic analysis and human curation of 60,770 RIKEN full-length mouse cDNA clones produced 2,578 sequences that showed similarity (70–85% identity) to known human-disease genes. Using a newly developed biological information extraction and annotation tool (FACTS) in parallel with human expert analysis of 17,051 MEDLINE scientific abstracts we identified 182 novel potential pathologs. Of these, 36 were identified by computational tools only, 49 by human expert analysis only and 97 by both methods. These pathologs were related to neoplastic (53%), hereditary (24%), immunological (5%), cardio-vascular (4%), or other (14%), disorders. CONCLUSIONS: Large scale genome projects continue to produce a vast amount of data with potential application to the study of human disease. For this potential to be realised we need intelligent strategies for data categorisation and the ability to link sequence data with relevant literature. This paper demonstrates the power of combining human expert annotation with FACTS, a newly developed bioinformatics tool, to identify novel pathologs from within large-scale mouse transcript datasets

In-silico-Systemanalyse von Biopathways

Chen M. In silico systems analysis of biopathways. Bielefeld (Germany): Bielefeld University; 2004.In the past decade with the advent of high-throughput technologies, biology has migrated from a descriptive science to a predictive one. A vast amount of information on the metabolism have been produced; a number of specific genetic/metabolic databases and computational systems have been developed, which makes it possible for biologists to perform in silico analysis of metabolism. With experimental data from laboratory, biologists wish to systematically conduct their analysis with an easy-to-use computational system. One major task is to implement molecular information systems that will allow to integrate different molecular database systems, and to design analysis tools (e.g. simulators of complex metabolic reactions). Three key problems are involved: 1) Modeling and simulation of biological processes; 2) Reconstruction of metabolic pathways, leading to predictions about the integrated function of the network; and 3) Comparison of metabolism, providing an important way to reveal the functional relationship between a set of metabolic pathways. This dissertation addresses these problems of in silico systems analysis of biopathways. We developed a software system to integrate the access to different databases, and exploited the Petri net methodology to model and simulate metabolic networks in cells. It develops a computer modeling and simulation technique based on Petri net methodology; investigates metabolic networks at a system level; proposes a markup language for biological data interchange among diverse biological simulators and Petri net tools; establishes a web-based information retrieval system for metabolic pathway prediction; presents an algorithm for metabolic pathway alignment; recommends a nomenclature of cellular signal transduction; and attempts to standardize the representation of biological pathways. Hybrid Petri net methodology is exploited to model metabolic networks. Kinetic modeling strategy and Petri net modeling algorithm are applied to perform the processes of elements functioning and model analysis. The proposed methodology can be used for all other metabolic networks or the virtual cell metabolism. Moreover, perspectives of Petri net modeling and simulation of metabolic networks are outlined. A proposal for the Biology Petri Net Markup Language (BioPNML) is presented. The concepts and terminology of the interchange format, as well as its syntax (which is based on XML) are introduced. BioPNML is designed to provide a starting point for the development of a standard interchange format for Bioinformatics and Petri nets. The language makes it possible to exchange biology Petri net diagrams between all supported hardware platforms and versions. It is also designed to associate Petri net models and other known metabolic simulators. A web-based metabolic information retrieval system, PathAligner, is developed in order to predict metabolic pathways from rudimentary elements of pathways. It extracts metabolic information from biological databases via the Internet, and builds metabolic pathways with data sources of genes, sequences, enzymes, metabolites, etc. The system also provides a navigation platform to investigate metabolic related information, and transforms the output data into XML files for further modeling and simulation of the reconstructed pathway. An alignment algorithm to compare the similarity between metabolic pathways is presented. A new definition of the metabolic pathway is proposed. The pathway defined as a linear event sequence is practical for our alignment algorithm. The algorithm is based on strip scoring the similarity of 4-hierarchical EC numbers involved in the pathways. The algorithm described has been implemented and is in current use in the context of the PathAligner system. Furthermore, new methods for the classification and nomenclature of cellular signal transductions are recommended. For each type of characterized signal transduction, a unique ST number is provided. The Signal Transduction Classification Database (STCDB), based on the proposed classification and nomenclature, has been established. By merging the ST numbers with EC numbers, alignments of biopathways are possible. Finally, a detailed model of urea cycle that includes gene regulatory networks, metabolic pathways and signal transduction is demonstrated by using our approaches. A system biological interpretation of the observed behavior of the urea cycle and its related transcriptomics information is proposed to provide new insights for metabolic engineering and medical care

NFS1 undergoes positive selection in lung tumours and protects cells from ferroptosis

Environmental nutrient levels impact cancer cell metabolism, resulting in context-dependent gene essentiality. Here, using loss-of-function screening based on RNA interference, we show that environmental oxygen levels are a major driver of differential essentiality between in vitro model systems and in vivo tumours. Above the 3-8% oxygen concentration typical of most tissues, we find that cancer cells depend on high levels of the iron-sulfur cluster biosynthetic enzyme NFS1. Mammary or subcutaneous tumours grow despite suppression of NFS1, whereas metastatic or primary lung tumours do not. Consistent with a role in surviving the high oxygen environment of incipient lung tumours, NFS1 lies in a region of genomic amplification present in lung adenocarcinoma and is most highly expressed in well-differentiated adenocarcinomas. NFS1 activity is particularly important for maintaining the iron-sulfur co-factors present in multiple cell-essential proteins upon exposure to oxygen compared to other forms of oxidative damage. Furthermore, insufficient iron-sulfur cluster maintenance robustly activates the iron-starvation response and, in combination with inhibition of glutathione biosynthesis, triggers ferroptosis, a non-apoptotic form of cell death. Suppression of NFS1 cooperates with inhibition of cysteine transport to trigger ferroptosis in vitro and slow tumour growth. Therefore, lung adenocarcinomas select for expression of a pathway that confers resistance to high oxygen tension and protects cells from undergoing ferroptosis in response to oxidative damage