GABRIELA VENTURINI DA SILVA
Projetos de Pesquisa
Unidades Organizacionais
LIM/13 - Laboratório de Genética e Cardiologia Molecular, Hospital das Clínicas, Faculdade de Medicina
5 resultados
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- Serum metabolomics profile of type 2 diabetes mellitus in a Brazilian rural population(2016) PADILHA, Kallyandra; VENTURINI, Gabriela; PIRES, Thiago de Farias; HORIMOTO, Andrea R. V. R.; MALAGRINO, Pamella Araujo; GOIS, Tamiris Carneiro; KIERS, Bianca; OLIVEIRA, Camila Maciel; ALVIM, Rafael de Oliveira; BLATT, Celso; KRIEGER, Jose Eduardo; PEREIRA, Alexandre CostaIntroduction The development of common forms of diabetes comes from the interaction between environmental and genetic factors, and the consequences of poor glycemic control in these patients could result in several complications. Metabolomic studies for type 2 diabetes mellitus in serum/plasma have reported changes in numerous metabolites, which might be considered possible targets for future mechanistic research. However, the specific role of a particular metabolite as cause or consequence of diabetes derangements is difficult to establish. Objectives As type 2 diabetes is a disease that changes the metabolic profile in several levels, this work aimed to compare the metabolomic profiles of type 2 diabetes mellitus and non-diabetic participants. In addition, we exploited our family-based study design to bring a better understanding of the causal relationship of identified metabolites and diabetes. Methods In the current study, population based metabolomics was applied in 939 subjects from the Baependi Heart Study. Participants were separated into two groups: diabetic (77 individuals) and non-diabetic (862 individuals), and the metabolic profile was performed by GC/MS technique. Results We have identified differentially concentrated metabolites in serum of diabetic and non-diabetic individuals. We identified 72 metabolites up-regulated in type 2 diabetes mellitus compared to non-diabetics. It was possible to recapitulate the main pathways that the literature shows as changed in diabetes. Also, based on metabolomic profile, we separated pre-diabetic individuals (with glucose concentration between 100-125 mg/dL) from non-diabetics and diabetics. Finally, using heritability analysis, we were able to suggest metabolites in which altered levels may precede diabetic development. Conclusion Our data can be used to derive a better understanding of the causal relationship of the observed associations and help to prioritize diabetes-associated metabolites for further work.
- Baitmet, a computational approach for GC-MS library-driven metabolite profiling(2017) DOMINGO-ALMENARA, Xavier; BREZMES, Jesus; VENTURINI, Gabriela; VIVO-TRUYOLS, Gabriel; PERERA, Alexandre; VINAIXA, MariaIntroduction Current computational tools for gas chromatography-mass spectrometry (GC-MS) metabolomics profiling do not focus on metabolite identification, that still remains as the entire workflow bottleneck and it relies on manual data reviewing. Metabolomics advent has fostered the development of public metabolite repositories containing mass spectra and retention indices, two orthogonal properties needed for metabolite identification. Such libraries can be used for library-driven compound profiling of large datasets produced in metabolomics, a complementary approach to current GC-MS non-targeted data analysis solutions that can eventually help to assess metabolite identities more efficiently. Results This paper introduces Baitmet, an integrated open-source computational tool written in R enclosing a complete workflow to perform high-throughput library-driven GC-MS profiling in complex samples. Baitmet capabilities were assayed in a metabolomics study involving 182 human serum samples where a set of 61 metabolites were profiled given a reference library. Conclusions Baitmet allows high-throughput and wide scope interrogation on the metabolic composition of complex samples analyzed using GC-MS via freely available spectral data. Baitmet is freely available at http://CRAN.Rproject.org/package=baitmet.
- Metabolomic characterization of renal ischemia and reperfusion in a swine model(2016) MALAGRINO, Pamella Araujo; VENTURINI, Gabriela; YOGI, Patricia Schneider; DARIOLLI, Rafael; PADILHA, Kallyandra; KIERS, Bianca; GOIS, Tamiris Carneiro; MOTTA-LEAL-FILHO, Joaquim Mauricio; TAKIMURA, Celso Kiyochi; GIRARDI, Adriana Castello Costa; CARNEVALE, Francisco Cesar; CANEVAROLO, Rafael; MALHEIROS, Denise Maria Avancini Costa; ZERI, Ana Carolina de Mattos; KRIEGER, Jose Eduardo; PEREIRA, Alexandre CostaAcute kidney injury (AKI) is a serious complication in hospitalized and transplanted patients, and is mainly caused by ischemia/reperfusion (I/R). However, the current diagnosis of AKI based on acute alterations in serum creatinine or urine output is late and unspecific. To identify new systemic biomarkers for AKI, we performed serum and urine metabolomic profile analyses during percutaneous unilateral renal I/R in a well-controlled swine model. For this, serial serum and urine samples obtained during the pre-ischemia, ischemia and reperfusion periods were analyzed by H-1 nuclear magnetic resonance at 600 MHz. Through the metabolic profiles over I/R, we identified eight serum metabolites that increased with ischemia and recovered to basal values after reperfusion, delineating the ischemic period. In addition, we identified 13 urinary metabolites that changed during the early reperfusion reflecting the ischemic kidney, being able to differentiate between pre-ischemia and post I/R periods. All selected metabolites are described in terms of disease pathophysiology (change of energetic pathway and oxidative stress), which suggest that these serum and urinary metabolites are candidate AKI biomarkers. Interestingly, the selected metabolites allowed us to identify, well described NF kappa B, leptin, INF-gamma and insulin pathways, and a new pathway (Huntingtin) that had not been previously implicated in renal I/R. Huntingtin showed different fragment patterns in ischemic versus non-ischemic kidneys. Therefore, the metabolomic profile found in renal I/R led to the identification of candidate disease biomarkers and a new pathway associated with renal injury.
- Metabolites related to eGFR: Evaluation of candidate molecules for GFR estimation using untargeted metabolomics(2019) TITAN, S. M.; VENTURINI, G.; PADILHA, K.; TAVARES, G.; ZATZ, R.; BENSENOR, I; LOTUFO, P. A.; RHEE, E. P.; I, R. Thadhani; PEREIRA, A. C.Background: Metabolomics can be used to identify novel metabolites related to renal function and that could therefore be used for estimating GFR. We evaluated metabolites replicated and related to eGFR in 3 studies (CKD) and general population). Methods: Metabolomics was performed by GC-MS. The Progredir Cohort (n = 454, class 3 and 4 CKD) was used as the derivation study and adjusted linear regression models on eGFR-CKDEPI were built. Bonferroni correction was applied for selecting metabolites to be independently validated in the Diabetic Nephropathy Study (n = 56 macroalbuminuric DN) and in the Baependi Heart Study (BHS, n = 1145, general population). Results: In the Progredir Cohort, 72 metabolites where associated with eGFR. Of those, 11 were also significantly associated to eGFR in the DN Study and 8 in the BHS. Four metabolites were replicated and significantly associated to eGFR in all 3 studies: D-threitol, myo-inositol, 4-deoxierythronic acid and galacturonic acid. In addition, pseudouridine was strongly correlated to eGFR only in the 2 CKD populations. Conclusions: Our results demonstrate metabolites that are potential biomarkers of renal function: D-threitol, myo-inositol, 4-deoxierythronic acid, galacturonic acid and pseudouridine. Further investigation is needed to determine their performance against otherwise gold-standard methods, most notably among those with normal eGFR.
- 1,5-Anhydroglucitol predicts CKD progression in macroalbuminuric diabetic kidney disease: results from non-targeted metabolomics(2018) TAVARES, Gesiane; VENTURINI, Gabriela; PADILHA, Kallyandra; ZATZ, Roberto; PEREIRA, Alexandre C.; THADHANI, Ravi I.; RHEE, Eugene P.; TITAN, Silvia M. O.Introduction Metabolomics allows exploration of novel biomarkers and provides insights on metabolic pathways associated with disease. To date, metabolomics studies on CKD have been largely limited to Caucasian populations and have mostly examined surrogate end points. Objective In this study, we evaluated the role of metabolites in predicting a primary outcome defined as dialysis need, doubling of serum creatinine or death in Brazilian macroalbuminuric DKD patients. Methods Non-targeted metabolomics was performed on plasma from 56 DKD patients. Technical triplicates were done. Metabolites were identified using Agilent Fiehn GC/MS Metabolomics and NIST libraries (Agilent MassHunter Work-station Quantitative Analysis, version B. 06.00). After data cleaning, 186 metabolites were left for analyses. Results During a median follow-up time of 2.5 years, the PO occurred in 17 patients (30.3%). In non-parametric testing, 13 metabolites were associated with the PO. In univariate Cox regression, only 1,5-anhydroglucitol (HR 0.10; 95% CI 0.01-0.63, p =.01), norvaline and l-aspartic acid were associated with the PO. After adjustment for baseline renal function, 1,5-anhydroglucitol (HR 0.10; 95% CI 0.02-0.63, p =.01), norvaline (HR 0.01; 95% CI 0.001-0.4, p =.01) and aspartic acid (HR 0.12; 95% CI 0.02-0.64, p =.01) remained significantly and inversely associated with the PO. Conclusion Our results show that lower levels of 1,5-anhydroglucitol, norvaline and l-aspartic acid are associated with progression of macroalbuminuric DKD. While norvaline and l-aspartic acid point to interesting metabolic pathways, 1,5-anhydroglucitol is of particular interest since it has been previously shown to be associated with incident CKD. This inverse biomarker of hyperglycemia should be further explored as a new tool in DKD.