Homocysteine causes neuronal leptin resistance and endoplasmic reticulum stress

A new framework for exploratory network mediator analysis in omics data

Omics methods are widely used in basic biology and translational medicine research. More and more omics data are collected to explain the impact of certain risk factors on clinical outcomes. To explain the mechanism of the risk factors, a core question is how to find the genes/proteins/metabolites that mediate their effects on the clinical outcome. Mediation analysis is a modeling framework to study the relationship between risk factors and pathological outcomes, via mediator variables. However, high-dimensional omics data are far more challenging than traditional data: (1) From tens of thousands of genes, can we overcome the curse of dimensionality to reliably select a set of mediators? (2) How do we ensure that the selected mediators are functionally consistent? (3) Many biological mechanisms contain nonlinear effects. How do we include nonlinear effects in the high-dimensional mediation analysis? (4) How do we consider multiple risk factors at the same time? To meet these challenges, we propose a new exploratory mediation analysis framework, medNet, which focuses on finding mediators through predictive modeling. We propose new definitions for predictive exposure, predictive mediator, and predictive network mediator, using a statistical hypothesis testing framework to identify predictive exposures and mediators. Additionally, two heuristic search algorithms are proposed to identify network mediators, essentially subnetworks in the genome-scale biological network that mediate the effects of single or multiple exposures. We applied medNet on a breast cancer data set and a metabolomics data set combined with food intake questionnaire data. It identified functionally consistent network mediators for the exposures' impact on the outcome, facilitating data interpretation.

Gene expression of cardiovascular risk markers in mononuclear cells of pregnant woman in relation to plasma leptin and homocysteine levels: A cross sectional study

OBJECTIVE

To investigate the relationship between anthropometric, biochemical, and hematologic parameters and serum leptin and homocysteine (Hcy) levels. Also, to determine the effect of leptin and Hcy on expression of genes associated with cardiovascular disease susceptibility (APOA1, LRP1, COX-1, and COX-2) in mononuclear cells of healthy pregnant women.

METHODS

Between August 2018 and January 2020, a cross-sectional study was conducted on 161 healthy pregnant women in Tabasco, southeastern Mexico. The study population was classified by trimester, according to gestational pregnancy. Anthropometric, biochemical (leptin and homocysteine), and hematologic data were obtained under fasting conditions. APOA1, LRP1, COX-1, and COX-2 expression in mononuclear cells was evaluated using RT-qPCR.

RESULTS

Red cell indices (hemoglobin, hematocrit, and erythrocytes) were negatively and positively correlated with leptin and Hcy levels, respectively, in the first- and second-trimester groups. Increased leptin levels and low red cell indices were significantly associated with BMI <25.0 in the second-trimester group; however, no significant differences were observed in Hcy levels. Increased leptin and Hcy levels were significantly associated with high lipid indicators in the first- and third-trimester groups, respectively. High APOA1 and COX-2 expression was significantly associated with reduced leptin and increased Hcy levels in the second- and third-trimester groups.

CONCLUSION

Increased leptin and Hcy levels during pregnancy, mainly associated with modifications in erythrocytes and lipid indices, may lead to early modification of genes related to lipid metabolism (APOA1) and proinflammatory response (COX-2) and, thereby, increase cardiovascular disease risk.