Metabolomics:Open Access

At the turn of the millennium, genomics was in full swing as scientists world-wide worked to sequence the human genome. During this time period, we were already aware that the sequenced genome did not tell the entire story. Scientists had begun to discuss functional genomics and metabolomics [1,2]. Of course we have long been aware of metabolic pathways. We have researched and taught pathways such as the Krebs Cycle for generations. In 1996 the Kyoto Encyclopedia of Genes [3] and Genomics (KEGG) published version 1.0 of their online compendium of pathways. KEGG has grown to approximately 165 metabolic pathways (Figure 1) out of a total of 425 metabolic, regulatory and signaling pathways [4]. Despite this progress, many researchers suspect that known pathways may not be completely understood and that additional metabolic pathways have yet to be discovered.

Background, cancer significance and question: BioProspecting is a novel approach that enabled our team to mine genetic marker related data from the New England Journal of Medicine (NEJM) utilizing Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) and the Human Gene Ontology (HUGO). Genes associated with disorders using the Multi-threaded Clinical Vocabulary Server (MCVS) Natural Language Processing (NLP)engine, whose output was represented as an ontology-network incorporating the semantic encodings of the literature. Metabolic functions were used to identify potentially novel relationships between (genes or proteins) and (diseases or drugs). In an effort to identify genes important to transformation of normal tissue into a malignancy, we went on to identify the genes linked to multiple cancers and then mapped those genes to metabolic and signaling pathways. Findings: Ten Genes were related to 30 or more cancers, 72 genes were related to 20 or more cancers and 191 genes were related to 10 or more cancers. The three pathways most often associated with the top 200 novel cancer markers were the Acute Phase Response Signaling, the Glucocorticoid Receptor Signaling and the Hepatic Fibrosis/ Hepatic Stellate Cell Activation pathway. Meaning and implications of the advance: This association highlights the role of inflammation in the induction and perhaps transformation of mortal cells into cancers. Major findings: BioProspecting can speed our identification and understanding of synergies between articles in the biomedical literature. In this case we found considerable synergy between the Oncology literature and the Sepsis literature. By mapping these associations to known metabolic, regulatory and signaling pathways we were able to identify further evidence for the inflammatory basis of cancer.

Schistosoma japonicum are blood flukes of humans that cause chronic, highly debilitating diseases involving extensive liver damage. In the present study, fatty-acid-binding protein of the human blood fluke Schistosoma japonicum is being used to find out highly suitable MHC binding peptides and epitopes. MHC molecules are cell surface proteins, which take active part in host immune reactions and involvement of MHC class in response to almost all antigens and it give effects on specific sites. Predicted MHC binding regions acts like red flags for antigen specific and generate immune response against the parent antigen. So a small fragment of antigen can induce immune response against whole antigen. This theme is implemented in designing subunit and synthetic peptide vaccines. Fragments identified through this approach tend to be high efficiency binders, in which larger percentage of their atoms are directly involved in binding as compared to larger molecules. Binding ability prediction of peptides to major histocompatibility complex (MHC) class I & II molecules is important in vaccine development from fatty-acid-binding protein of the human blood fluke Schistosoma japonicum.

Previous studies have reported association of migraine and metabolic syndrome (MS) and between MS and
leptin (a protein product of the obesity gene). This study aimed to determine whether there is a link between MS and its components, leptin and migraine and its covariates (frequency and duration), as data regarding this relationship are still sparse or even controversial. This study included 60 patients with MS and comorbid migraine with mean age of 47.83±7.31 years. Demographic, anthropometric, clinical and lab characteristics were identified. Serum leptin concentrations were also measured. Nearly 58.33% had episodic migraine (MoA=44.64%, MA=16.6%), 35% had chronic migraine and 6.67% had tension type headache (TTH). Obesity, type 2 diabetes mellitus and hypertension and were reported in all patients, of them 80% had hypertriglyceridemia and/or dyslipidemia, 81.67% had insulin resistance (IR) and 58.33% hyperleptinemia. Compared to patients with TTH, patients with migraine had higher measurements for BMI (39.01 ± 6.05), WC (P = 0.058), poor glycemic control (8.11 ± 1.22), SBP (P = 0.052), DBP (P = 0.050) and serum levels of LDL-c (P = 0.0001), fasting insulin (P = 0.0001) and leptin (P = 0.0001). Leptin concentrations were found to
be positively correlated with BMI (r = 0.547, P = 0.008), WC (r = 0.445, P = 0.002), HbA1c (r = 0.656, P = 0.001) and fasting insulin (r = 0.613, P = 0.008). The logistic regression to model leptin and headache parameters (frequency and duration) after adjusting age and sex and leptin levels, were found to correlate with BMI, WC and fasting insulin) but this relationship disappeared after adjustment of these covariates. We conclude that comorbid migraine with MS is related to obesity (total body obesity and abdominal adiposity) and insulin abnormalities after adjustment of other covariates.