Cancer, the main cause of human deaths in the modern world is a group of diseases. Anticancer drug discovery is a challenge for scientists because of involvement of multiple survival pathways of cancer cells. An extensive study on the regulation of each step of these pathways may help find a potential cancer target. Up-regulated HIF-1 expression and altered metabolic pathways are two classical characteristics of cancer. Oxygen-dependent (through pVHL, PHDs, calcium-mediated) and independent (through growth factor signaling pathway, mdm2 pathway, HSP90) regulation of HIF-1α leads to angiogenesis, metastasis, and cell survival. The two subunits of HIF-1 regulates in the same fashion through different mechanisms. HIF-1α translation upregulates via mammalian target of rapamycin and mitogen-activated protein kinase signaling pathways, whereas HIF-1β through calmodulin kinase. Further, the stabilized interactions of these two subunits are important for proper functioning. Also, metabolic pathways crucial for the formation of building blocks (pentose phosphate pathway) and energy generation (glycolysis, TCA cycle and catabolism of glutamine) are altered in cancer cells to protect them from oxidative stress and to meet the reduced oxygen and nutrient supply. Up-regulated anaerobic metabolism occurs through enhanced expression of hexokinase, phosphofructokinase, triosephosphate isomerase, glucose 6-phosphate dehydrogenase and down-regulation of aerobic metabolism via pyruvate dehydrogenase kinase and lactate dehydrogenase which compensate energy requirements along with high glucose intake. Controlled expression of these two pathways through their common intermediate may serve as potent cancer target in future.

FRET is a nonradiative process of energy transfer that is based on the dipole–dipole interactions between molecules that are fluorescent.

The pathogenesis of Type 2 diabetes mellitus (T2DM) is complex owing to molecular heterogeneity in the afflicted population. Current diagnostic methods rely on blood glucose measurements, which are noninformative with respect to progression of the disease to other associated pathologies. Thus, predicting the risk and development of T2DM-related complications, such as cardiovascular disease, remains a major challenge. We have used a combination of quantitative methods for characterization of circulating serum biomarkers of T2DM using a cohort of nondiabetic control subjects (n = 76) and patients diagnosed with T2DM (n = 106). In this case-control study, the samples were randomly divided as training and validation data sets. In the first step, iTRAQ (isobaric tagging for relative and absolute quantification) based protein expression profiling was performed for identification of proteins displaying a significant differential expression in the two study groups. Five of these protein markers were selected for validation using multiple reaction-monitoring mass spectrometry (MRM-MS) and further confirmed with Western blot and QPCR analysis. Functional pathway analysis identified perturbations in lipid and small molecule metabolism as well as pathways that lead to disruption of glucose homeostasis and blood coagulation. These putative biomarkers may be clinically useful for subset stratification of T2DM patients as well as for the development of novel therapeutics targeting the specific pathology.

Type 2 diabetes (T2DM) is a multi-factorial disease with a complex pathogenic mechanism; however a complete understanding of precise biochemical alterations accompanying the onset and progression of T2DM is lacking. Using a combination of untargeted and targeted metabolomic profiling approach we were able to delineate significantly altered metabolites in the diabetic (T2DM) group. Our results indicate significant perturbations in amino acid metabolism, TCA cycle and glycerol-phospholipid metabolism possibly impacting the overall glucose homeostasis in T2DM. A systems approach offers promise towards identification of clinically relevant markers of T2DM and novel molecular targets to foster drug discovery for effective therapeutic development for diabetes.

Tissue consequences of radiation exposure are dependent on radiation quality and high linear energy transfer (high-LET) radiation, such as heavy ions in space is known to deposit higher energy in tissues and cause greater damage than low-LET γ radiation. While radiation exposure has been linked to intestinal pathologies, there are very few studies on long-term effects of radiation, fewer involved a therapeutically relevant γ radiation dose, and none explored persistent tissue metabolomic alterations after heavy ion space radiation exposure. Using a metabolomics approach, we report long-term metabolomic markers of radiation injury and perturbation of signaling pathways linked to metabolic alterations in mice after heavy ion or γ radiation exposure. Intestinal tissues (C57BL/6J, female, 6 to 8 wks) were analyzed using ultra performance liquid chromatography coupled with electrospray quadrupole time-of-flight mass spectrometry (UPLC-QToF-MS) two months after 2 Gy γ radiation and results were compared to an equitoxic 56Fe (1.6 Gy) radiation dose. The biological relevance of the metabolites was determined using Ingenuity Pathway Analysis, immunoblots, and immunohistochemistry. Metabolic profile analysis showed radiation-type-dependent spatial separation of the groups. Decreased adenine and guanosine and increased inosine and uridine suggested perturbed nucleotide metabolism. While both the radiation types affected amino acid metabolism, the 56Fe radiation preferentially altered dipeptide metabolism. Furthermore, 56Fe radiation caused upregulation of ‘prostanoid biosynthesis’ and ‘eicosanoid signaling’, which are interlinked events related to cellular inflammation and have implications for nutrient absorption and inflammatory bowel disease during space missions and after radiotherapy. In conclusion, our data showed for the first time that metabolomics can not only be used to distinguish between heavy ion and γ radiation exposures, but also as a radiation-risk assessment tool for intestinal pathologies through identification of biomarkers persisting long after exposure.

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The availability of robust classification algorithms for the identification of high risk individuals with resectable disease is critical to improving early detection strategies and ultimately increasing survival rates in PC. We leveraged high quality biospecimens with extensive clinical annotations from patients that received treatment at the Medstar-Georgetown University hospital. We used a high resolution mass spectrometry based global tissue profiling approach in conjunction with multivariate analysis for developing a classification algorithm that would predict early stage PC with high accuracy. The candidate biomarkers were annotated using tandem mass spectrometry. We delineated a six metabolite panel that could discriminate early stage PDAC from benign pancreatic disease with >95% accuracy of classification (Specificity = 0.85, Sensitivity = 0.9). Subsequently, we used multiple reaction monitoring mass spectrometry for evaluation of this panel in plasma samples obtained from the same patients. The pattern of expression of these metabolites in plasma was found to be discordant as compared to that in tissue. Taken together, our results show the value of using a metabolomics approach for developing highly predictive panels for classification of early stage PDAC. Future investigations will likely lead to the development of validated biomarker panels with potential for clinical translation in conjunction with CA-19-9 and/or other biomarkers.

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