Integrative analysis to select cancer candidate biomarkers to targeted validation

FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOTargeted proteomics has flourished as the method of choice for prospecting for and validating potential candidate biomarkers in many diseases. However, challenges still remain due to the lack of standardized routines that can prioritize a limited number of proteins to be further validated in human samples. To help researchers identify candidate biomarkers that best characterize their samples under study, a well-designed integrative analysis pipeline, comprising MS-based discovery, feature selection methods, clustering techniques, bioinformatic analyses and targeted approaches was performed using discovery-based proteomic data from the secretomes of three classes of human cell lines (carcinoma, melanoma and non-cancerous). Three feature selection algorithms, namely, Beta-binomial, Nearest Shrunken Centroids (NSC), and Support Vector Machine-Recursive Features Elimination (SVM-RFE), indicated a panel of 137 candidate biomarkers for carcinoma and 271 for melanoma, which were differentially abundant between the tumor classes. We further tested the strength of the pipeline in selecting candidate biomarkers by immunoblotting, human tissue microarrays, label-free targeted MS and functional experiments. In conclusion, the proposed integrative analysis was able to pre-qualify and prioritize candidate biomarkers from discovery-based proteomics to targeted MS.Targeted proteomics has flourished as the method of choice for prospecting for and validating potential candidate biomarkers in many diseases. However, challenges still remain due to the lack of standardized routines that can prioritize a limited number of proteins to be further validated in human samples. To help researchers identify candidate biomarkers that best characterize their samples under study, a well-designed integrative analysis pipeline, comprising MS-based discovery, feature selection methods, clustering techniques, bioinformatic analyses and targeted approaches was performed using discovery-based proteomic data from the secretomes of three classes of human cell lines (carcinoma, melanoma and non-cancerous). Three feature selection algorithms, namely, Beta-binomial, Nearest Shrunken Centroids (NSC), and Support Vector Machine-Recursive Features Elimination (SVM-RFE), indicated a panel of 137 candidate biomarkers for carcinoma and 271 for melanoma, which were differentially abundant between the tumor classes. We further tested the strength of the pipeline in selecting candidate biomarkers by immunoblotting, human tissue microarrays, label-free targeted MS and functional experiments. In conclusion, the proposed integrative analysis was able to pre-qualify and prioritize candidate biomarkers from discovery-based proteomics to targeted MS6414363543652FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOFAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO2009/54067-3; 2010/19278-0; 2011/22421-2; 2009/53839-2470567/2009-0; 470549/2011-4; 301702/2011-0; 470268/2013-

Xanthan Gum Removal for 1H-NMR Analysis of the Intracellular Metabolome of the Bacteria Xanthomonas axonopodis pv. citri 306

Xanthomonas is a genus of phytopathogenic bacteria, which produces a slimy, polysaccharide matrix known as xanthan gum, which involves, protects and helps the bacteria during host colonization. Although broadly used as a stabilizer and thickener in the cosmetic and food industries, xanthan gum can be a troubling artifact in molecular investigations due to its rheological properties. In particular, a cross-reaction between reference compounds and the xanthan gum could compromise metabolic quantification by NMR spectroscopy. Aiming at an efficient gum extraction protocol, for a 1H-NMR-based metabolic profiling study of Xanthomonas, we tested four different interventions on the broadly used methanol-chloroform extraction protocol for the intracellular metabolic contents observation. Lower limits for bacterial pellet volumes for extraction were also probed, and a strategy is illustrated with an initial analysis of X. citri’s metabolism by 1H-NMR spectroscopy

Early metabolic response after resistance exercise with blood flow restriction in well-trained men: a metabolomics approach

The present study aimed to compare the early metabolic response between high-load resistance exercise (HL-RE) and low-load resistance exercise with blood flow restriction (LL-BFR). Nine young well-trained men participated in a randomized crossover design in which each subject completed LL-BFR, HL-RE or condition control (no exercise) with a one-week interval between them. Blood samples were taken immediately before and five minutes after the exercise sessions. Nuclear magnetic resonance (NMR) spectroscopy identified and quantified 48 metabolites, six of which presented significant changes among the exercise protocols. The HL-RE promoted a higher increase in pyruvate, lactate and alanine compared to the LL-BFR and the control. HL-RE and LL-BFR promoted a higher increase in succinate compared to the control, however, there was no difference between HL-RE and LL-BFR. Also, while there was no difference in acetoacetate between HL-RE and LL-BFR, a greater decrease was observed in both compared to the control. Finally, LL-BFR promoted a greater decrease in choline compared to the control. In conclusion, this study provides by metabolomics a new insight in metabolic response between LL-BFR and HL-RE by demonstrating a distinct response to some metabolites that are not commonly analyzed.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Analysis and characterisation of bovine oocyte and embryo biomarkers by matrix-assisted desorption ionisation mass spectrometry imaging

In the field of ‘single cell analysis’, many classical strategies like immunofluorescence and electron microscopy are the primary techniques of choice. However, these methodologies are time consuming and do not permit direct identification of specific molecular classes, such as lipids. In the present study, a novel mass spectrometry-based analytical approach was applied to bovine oocytes and embryos. This new metabolomics-based application uses mass spectrometry imaging (MSI), efficient data processing and multivariate data analysis. Metabolic fingerprinting (MF) was applied to the analysis of unfertilised oocytes, 2-, 4- and 8-cell embryos and blastocysts. A semiquantitative strategy for sphingomyelin [SM (16 : 0) + Na]+ (m/z 725) and phosphatidylcholine [PC (32 : 0) + Na]+ (m/z 756) was developed, showing that lipid concentration was useful for selecting the best metabolic biomarkers. This study demonstrates that a combination of MF, MSI features and chemometric analysis can be applied to discriminate cell stages, characterising specific biomarkers and relating them to developmental pathways. This information furthers our understanding of fertilisation and preimplantation events during bovine embryo development283293301COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPSem informação2010/01077-9; 2011/18085-

Metabolomics by NMR Combined with Machine Learning to Predict Neoadjuvant Chemotherapy Response for Breast Cancer

Neoadjuvant chemotherapy (NACT) is offered to patients with operable or inoperable breast cancer (BC) to downstage the disease. Clinical responses to NACT may vary depending on a few known clinical and biological features, but the diversity of responses to NACT is not fully understood. In this study, 80 women had their metabolite profiles of pre-treatment sera analyzed for potential NACT response biomarker candidates in combination with immunohistochemical parameters using Nuclear Magnetic Resonance (NMR). Sixty-four percent of the patients were resistant to chemotherapy. NMR, hormonal receptors (HR), human epidermal growth factor receptor 2 (HER2), and the nuclear protein Ki67 were combined through machine learning (ML) to predict the response to NACT. Metabolites such as leucine, formate, valine, and proline, along with hormone receptor status, were discriminants of response to NACT. The glyoxylate and dicarboxylate metabolism was found to be involved in the resistance to NACT. We obtained an accuracy in excess of 80% for the prediction of response to NACT combining metabolomic and tumor profile data. Our results suggest that NMR data can substantially enhance the prediction of response to NACT when used in combination with already known response prediction factors

Cytotoxic 3,4,5-trimethoxychalcones as mitotic arresters and cell migration inhibitors

Based on classical colchicine site ligands and a computational model of the colchicine binding site on beta tubulin, two classes of chalcone derivatives were designed, synthesized and evaluated for inhibition of tubulin assembly and toxicity in human cancer cell lines. Docking studies suggested that the chalcone scaffold could fit the colchicine site on tubulin in an orientation similar to that of the natural product. In particular, a 3,4,5-trimethoxyphenyl ring adjacent to the carbonyl group appeared to benefit the ligand-tubulin interaction, occupying the same subcavity as the corresponding moiety in colchicine. Consistent with modeling predictions, several 3,4,5-trimethoxychalcones showed improved cytotoxicity to murine acute lymphoblastic leukemia cells compared with a previously described parent compound, and inhibited tubulin assembly in vitro as potently as colchicine. The most potent chalcones inhibited the growth of human leukemia cell lines at nanomolar concentrations, caused microtubule destabilization and mitotic arrest in human cervical cancer cells, and inhibited human breast cancer cell migration in scratch wound and Boyden chamber assays.CNPqCAPESFAPESPNIH (CA78039)University of Pittsburgh Cancer Institute (UPCI) Chemical Biology Facility (ChBF) (P30CA047904