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Medina-Urrutia A.,National Institute of Cardiology Ignacio Chavez | Posadas-Romero C.,National Institute of Cardiology Ignacio Chavez | Posadas-Sanchez R.,National Institute of Cardiology Ignacio Chavez | Jorge-Galarza E.,National Institute of Cardiology Ignacio Chavez | And 6 more authors.
Cardiovascular Diabetology | Year: 2015

Background: Experimental studies have shown that high free fatty acid (FFA) and low adiponectin (ADIPO) levels are involved in the mechanisms by which adiposity promotes insulin resistance (IR). However, no previous clinical studies have simultaneously analysed the relative contribution of FFA and ADIPO levels on the relation of abdominal visceral fat (AVF) with insulin resistance. Objective: To analyse the contribution of low ADIPO (adiponectin<=p25th: 8.67μg/mL in women and 5.30μg/mL in men), and high FFAs (FFAs>=p75th: 0.745mEq/L in women and 0.60mEq/L in men) to the association of high AVF (AVF>=p75th: 127cm2 in women; 152.7cm2 in men) with insulin resistance (HOMA-IR>=75th: 3.58 in women and 3.12 in men), in non-diabetic subjects. Material and methods: A cross-sectional analysis was performed including 1217 control participants of the Genetics of Atherosclerotic Disease study (GEA). Clinical, tomographic and biochemical parameters were measured in all participants. Logistic regression models were used to assess the association of high AVF with IR stratifying according to gender, and to normal or low ADIPO and normal or high FFA serum levels. Results: In comparison to referent group, in men low ADIPO unlike high FFA increased the risk of IR. Females with normal AVF and low ADIPO, or high AVF and normal ADIPO had aprox 3 folds risk of IR (OR [IC95%]: 3.7 [2.1-6.6], p<0.001, and 3.4 [2.0-5.7], p<0.001; respectively). The risk increased to 7.6 [4.2-13.8], p<0.001 when high AVF and low ADIPO were present. Irrespective of AVF, the effect of low ADIPO on IR was higher than that seen for high FFA. Besides, our results suggest an additive effect of high AVF, high FFA and low ADIPO on the IR prevalence. Conclusions: The present study provides novel and important information about the combined effect of high AVF and low ADIPO on the risk of IR. Furthermore, our data suggest that the effect of low adiponectin levels on the high AVF-IR association is stronger than that observed for high FFA, suggesting that adiponectin could be used as biomarker to identify subjects at high risk for T2DM and CAD. © 2015 Medina-Urrutia et al. Source


Rivera-Chavez J.,National Autonomous University of Mexico | Gonzalez-Andrade M.,Instituto Nacional Of Medicina Genomica Inmegen | Del Carmen Gonzalez M.,National Autonomous University of Mexico | Glenn A.E.,U.S. Department of Agriculture | Mata R.,National Autonomous University of Mexico
Phytochemistry | Year: 2013

Bioassay-guided fractionation of the bio-active organic extract obtained from solid-media culture of MEXU 27095, an endophytic fungus isolated from the Mexican medicinal plant Hintonia latiflora (Rubia-ceae), led to separation of three tridepsides which were identified as thielavins A, J and K. All three compounds inhibited Saccharomyces cerevisieae α-glucosidase (αGHY) in a concentration-dependent manner with IC50 values of 23.8, 15.8, and 22.1 μM, respectively. Their inhibitory action was higher than that of acarbose (IC50 = 545 μM), used as a positive control. Kinetic analysis established that the three compounds acted as non-competitive inhibitors with ki values of 27.8, 66.2 and 55.4 μM, respectively (α= 1.0, 1.2, 0.7, respectively); acarbose behaved as competitive inhibitor with a ki value of 156.1 μM. Thielavin J inhibited the activity of a-glucosidase from Bacillus stearothermophilus (aGHBs) with an IC50 of 30.5 μM, being less active than acarbose (IC50 = 0. 015 μM); in this case, compound (2) (ki = 20.0 μM and α = 2.9) and acarbose (ki = 0.008 μM and α = 1.9) behaved as non-competitive inhibitors. Docking analysis predicted that all three thielavins and acarbose bind to homologated αGHBs and to αGHY (PDB: 3A4A) in a pocket close to the catalytic site for maltose and isomaltose, respectively. The α-gluco-sidase inhibitory properties of thielavin K (3) were corroborated in vivo since it induced a noted antihy-perglycemic action during an oral sucrose tolerance test (3.1, 10.0 and 31.6mg/kg) in normal and nicotinamide-streptozotocin diabetic mice. In addition, at a dose of 10 mg/kg, it provoked a moderate hypoglycemic activity in diabetic mice. © 2013 Elsevier Ltd. All rights reserved. Source


Hernandez Patino C.E.,National Autonomous University of Mexico | Jaime-Munoz G.,National Autonomous University of Mexico | Resendis-Antonio O.,Instituto Nacional Of Medicina Genomica Inmegen
Frontiers in Physiology | Year: 2013

One of the main objectives in systems biology is to understand the biological mechanisms that give rise to the phenotype of a microorganism by using high-throughput technologies (HTs) and genome-scale mathematical modeling. The computational modeling of genome-scale metabolic reconstructions is one systemic and quantitative strategy for characterizing the metabolic phenotype associated with human diseases and potentially for designing drugs with optimal clinical effects. The purpose of this short review is to describe how computational modeling, including the specific case of constraint-based modeling, can be used to explore, characterize, and predict the metabolic capacities that distinguish the metabolic phenotype of cancer cell lines. As we show herein, this computational framework is far from a pure theoretical description, and to ensure proper biological interpretation, it is necessary to integrate high-throughput data and generate predictions for later experimental assessment. Hence, genome-scale modeling serves as a platform for the following: (1) the integration of data from HTs, (2) the assessment of how metabolic activity is related to phenotype in cancer cell lines, and (3) the design of new experiments to evaluate the outcomes of the in silico analysis. By combining the functions described above, we show that computational modeling is a useful methodology to construct an integrative, systemic, and quantitative scheme for understanding the metabolic profiles of cancer cell lines, a first step to determine the metabolic mechanism by which cancer cells maintain and support their malignant phenotype in human tissues. © 2013 Hernández Patiño, Jaime-Muñoz and Resendis-Antonio. Source


Flores-Tellez T.N.J.,CINVESTAV | Lopez T.V.,Instituto Nacional Of Medicina Genomica Inmegen | Vasquez Garzon V.R.,Benito Juarez University | Villa-Trevino S.,CINVESTAV
PLoS ONE | Year: 2015

Background and Aim: Prognostic markers are important for predicting the progression and staging of hepatocellular carcinoma (HCC). Ezrin (EZR) and Podocalyxin (PODXL) are proteins associated with invasion, migration and poor prognosis in various types of cancer. Recently, it has been observed that chloride intracellular channel 5 (CLIC5) forms a complex with EZR and PODXL and that it is required for podocyte structure and function. In this study, we evaluated the overexpression of EZR, PODXL and CLIC5 in HCC. Methods: The modified resistant hepatocyte model (MRHR), human biopsies and HCC cell lines (HepG2, Huh7 and SNU387) were used in this study. Gene and protein expression levels were evaluated in the MRHR by qRT-PCR, Western blot and immunohistochemistry analyses, and protein expression in the human biopsies was evaluated by immunohistochemistry. Protein expression in the HCC cell lines was evaluated by immunofluorescence and Western blot, also the migration and invasive abilities of Huh7 cells were evaluated using shRNA-mediated inhibition. Results: Our results indicated that these genes and proteins were overexpressed in HCC. Moreover, when the expression of CLIC5 and PODXL was inhibited in Huh7 cells, we observed decreased migration and invasion. Conclusion: This study suggested that EZR, CLIC5 and PODXL could be biological markers to predict the prognosis of HCC and that these proteins participate in migration and invasion processes. © 2015 Flores-Téllez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Source


Gonzalez-Covarrubias V.,Leiden University | Gonzalez-Covarrubias V.,Instituto Nacional Of Medicina Genomica Inmegen
Biogerontology | Year: 2013

The role of classical lipids in aging diseases and human longevity has been widely acknowledged. Triglyceride and cholesterol concentrations are clinically assessed to infer the risk of cardiovascular disease while larger lipoprotein particle size and low triglyceride levels have been identified as markers of human longevity. The rise of lipidomics as a branch of metabolomics has provided an additional layer of accuracy to pinpoint specific lipids and its association with aging diseases and longevity. The molecular composition and concentration of lipid species determine their cellular localization, metabolism, and consequently, their impact in disease and health. For example, low density lipoproteins are the main carriers of sphingomyelins and ceramides, while high density lipoproteins are mostly loaded with ether phosphocholines, partly explaining their opposing roles in atherogenesis. Moreover, the identification of specific lipid species in aging diseases and longevity would aid to clarify how these lipids alter health and influence longevity. For instance, ether phosphocholines PC (O-34:1) and PC (O-34:3) have been positively associated with longevity and negatively with diabetes, and hypertension, but other species of phosphocholines show no effect or an opposite association with these traits confirming the relevance of the identification of molecular lipid species to tackle our understanding of healthy aging and disease. Up-to-date, a minor fraction of the human plasma lipidome has been associated to healthy aging and longevity, further research would pinpoint toward specific lipidomic profiles as potential markers of healthy aging and metabolic diseases. © 2013 Springer Science+Business Media Dordrecht. Source

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