Browsing by Subject "metabolomics"

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Now showing items 21-27 of 27
  • Soderholm, Sandra; Fu, Yu; Gaelings, Lana; Belanov, Sergey; Yetukuri, Laxma; Berlinkov, Mikhail; Cheltsov, Anton V.; Anders, Simon; Aittokallio, Tero; Nyman, Tuula A.; Matikainen, Sampsa; Kainov, Denis E. (2016)
    Human influenza A viruses (IAVs) cause global pandemics and epidemics. These viruses evolve rapidly, making current treatment options ineffective. To identify novel modulators of IAV-host interactions, we re-analyzed our recent transcriptomics, metabolomics, proteomics, phosphoproteomics, and genomics/virtual ligand screening data. We identified 713 potential modulators targeting 199 cellular and two viral proteins. Anti-influenza activity for 48 of them has been reported previously, whereas the antiviral efficacy of the 665 remains unknown. Studying anti-influenza efficacy and immuno/neuro-modulating properties of these compounds and their combinations as well as potential viral and host resistance to them may lead to the discovery of novel modulators of IAV-host interactions, which might be more effective than the currently available anti-influenza therapeutics.
  • Muktadir, Md Abdul; Adhikari, Kedar Nath; Merchant, Andrew; Belachew, Kiflemariam; Vandenberg, Albert; Stoddard, Fred; Khazaei, Hamid (2020)
    Grain legumes are commonly used for food and feed all over the world and are the main source of protein for over a billion people worldwide, but their production is at risk from climate change. Water deficit and heat stress both significantly reduce the yield of grain legumes, and the faba bean is considered particularly susceptible. The genetic improvement of faba bean for drought adaptation (water deficit tolerance) by conventional methods and molecular breeding is time-consuming and laborious, since it depends mainly on selection and adaptation in multiple sites. The lack of high-throughput screening methodology and low heritability of advantageous traits under environmental stress challenge breeding progress. Alternatively, selection based on secondary characters in a controlled environment followed by field trials is successful in some crops, including faba beans. In general, measured features related to drought adaptation are shoot and root morphology, stomatal characteristics, osmotic adjustment and the efficiency of water use. Here, we focus on the current knowledge of biochemical and physiological markers for legume improvement that can be incorporated into faba bean breeding programs for drought adaptation.
  • Kwon, Hyuk Nam; Lee, Hyuk; Park, Ji Won; Kim, Young-Ho; Park, Sunghyouk; Kim, Jae J. (2020)
    The early detection of gastric cancer (GC) could decrease its incidence and mortality. However, there are currently no accurate noninvasive markers for GC screening. Therefore, we developed a noninvasive diagnostic approach, employing urine nuclear magnetic resonance (NMR) metabolomics, to discover putative metabolic markers associated with GC. Changes in urine metabolite levels during oncogenesis were evaluated using samples from 103 patients with GC and 100 age- and sex-matched healthy controls. Approximately 70% of the patients with GC (n = 69) had stage I GC, with the majority (n = 56) having intramucosal cancer. A multivariate statistical analysis of the urine NMR data well discriminated between the patient and control groups and revealed nine metabolites, including alanine, citrate, creatine, creatinine, glycerol, hippurate, phenylalanine, taurine, and 3-hydroxybutyrate, that contributed to the difference. A diagnostic performance test with a separate validation set exhibited a sensitivity and specificity of more than 90%, even with the intramucosal cancer samples only. In conclusion, the NMR-based urine metabolomics approach may have potential as a convenient screening method for the early detection of GC and may facilitate consequent endoscopic examination through risk stratification.
  • Ahonen, Linda; Jäntti, Sirkku; Suvitaival, Tommi; Theilade, Simone; Risz, Claudia; Kostiainen, Risto; Rossing, Peter; Oresic, Matej; Hyötyläinen, Tuulia (2019)
    Several small molecule biomarkers have been reported in the literature for prediction and diagnosis of (pre)diabetes, its co-morbidities, and complications. Here, we report the development and validation of a novel, quantitative method for the determination of a selected panel of 34 metabolite biomarkers from human plasma. We selected a panel of metabolites indicative of various clinically-relevant pathogenic stages of diabetes. We combined these candidate biomarkers into a single ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method and optimized it, prioritizing simplicity of sample preparation and time needed for analysis, enabling high-throughput analysis in clinical laboratory settings. We validated the method in terms of limits of detection (LOD) and quantitation (LOQ), linearity (R-2), and intra- and inter-day repeatability of each metabolite. The method's performance was demonstrated in the analysis of selected samples from a diabetes cohort study. Metabolite levels were associated with clinical measurements and kidney complications in type 1 diabetes (T1D) patients. Specifically, both amino acids and amino acid-related analytes, as well as specific bile acids, were associated with macro-albuminuria. Additionally, specific bile acids were associated with glycemic control, anti-hypertensive medication, statin medication, and clinical lipid measurements. The developed analytical method is suitable for robust determination of selected plasma metabolites in the diabetes clinic.
  • Ján, Labuda; Richard P., Bowater; Miroslav, Fojta; Günter, Gauglitz; Zdeněk, Glatz; Ivan, Hapala; Jan, Havliš; Ferenc, Kilar; Aniko, Kilar; Lenka, Malinovská; Siren, Heli Marja Marita; Petr, Skládal; Federico, Torta; Martin, Valachovič; Michaela, Wimmerová; Zbyněk, Zdráhal; David Brynn, Hibbert (2018)
    Recommendations are given concerning the terminology of methods of bioanalytical chemistry. With respect to dynamic development particularly in the analysis and investigation of biomacromolecules, terms related to bioanalytical samples, enzymatic methods, immunoanalytical methods, methods used in genomics and nucleic acid analysis, proteomics, metabolomics, glycomics, lipidomics, and biomolecules interaction studies are introduced.
  • Velagapudi, Vidya R.; Hezaveh, Rahil; Reigstad, Christopher S.; Gopalacharyulu, Peddinti; Yetukuri, Laxman; Islam, Sama; Felin, Jenny; Perkins, Rosie; Boren, Jan; Oresic, Matej; Backhed, Fredrik (2010)
  • Nandania, Jatin; Peddinti, Gopal; Pessia, Alberto; Kokkonen, Meri; Velagapudi, Vidya (2018)
    The use of metabolomics profiling to understand the metabolism under different physiological states has increased in recent years, which created the need for robust analytical platforms. Here, we present a validated method for targeted and semiquantitative analysis of 102 polar metabolites that cover major metabolic pathways from 24 classes in a single 17.5-min assay. The method has been optimized for a wide range of biological matrices from various organisms, and involves automated sample preparation and data processing using an inhouse developed R-package. To ensure reliability, the method was validated for accuracy, precision, selectivity, specificity, linearity, recovery, and stability according to European Medicines Agency guidelines. We demonstrated an excellent repeatability of retention times (CV <4%), calibration curves (R-2 > 0.980) in their respective wide dynamic concentration ranges (CV <3%), and concentrations (CV <25%) of quality control samples interspersed within 25 batches analyzed over a period of one year. The robustness was demonstrated through a high correlation between metabolite concentrations measured using our method and the NIST reference values (R-2 = 0.967), including cross-platform comparability against the BIOCRATES AbsoluteIDQp180 kit (R-2 = 0.975) and NMR analyses (R-2 = 0.884). We have shown that our method can be successfully applied in many biomedical research fields and clinical trials, including epidemiological studies for biomarker discovery. In summary, a thorough validation demonstrated that our method is reproducible, robust, reliable, and suitable for metabolomics studies.