Choline-magnesium trisalicylate modulates acute myelogenous leukemia gene expression during induction chemotherapy

Predicting metabolite-disease associations based on auto-encoder and non-negative matrix factorization

Metabolism refers to a series of orderly chemical reactions used to maintain life activities in organisms. In healthy individuals, metabolism remains within a normal range. However, specific diseases can lead to abnormalities in the levels of certain metabolites, causing them to either increase or decrease. Detecting these deviations in metabolite levels can aid in diagnosing a disease. Traditional biological experiments often rely on a lot of manpower to do repeated experiments, which is time consuming and labor intensive. To address this issue, we develop a deep learning model based on the auto-encoder and non-negative matrix factorization named as MDA-AENMF to predict the potential associations between metabolites and diseases. We integrate a variety of similarity networks and then acquire the characteristics of both metabolites and diseases through three specific modules. First, we get the disease characteristics from the five-layer auto-encoder module. Later, in the non-negative matrix factorization module, we extract both the metabolite and disease characteristics. Furthermore, the graph attention auto-encoder module helps us obtain metabolite characteristics. After obtaining the features from three modules, these characteristics are merged into a single, comprehensive feature vector for each metabolite-disease pair. Finally, we send the corresponding feature vector and label to the multi-layer perceptron for training. The experiment demonstrates our area under the receiver operating characteristic curve of 0.975 and area under the precision-recall curve of 0.973 in 5-fold cross-validation, which are superior to those of existing state-of-the-art predictive methods. Through case studies, most of the new associations obtained by MDA-AENMF have been verified, further highlighting the reliability of MDA-AENMF in predicting the potential relationships between metabolites and diseases.

Therapeutic Vulnerabilities of Transcription Factors in AML

Acute myeloid leukemia (AML) is characterized by impaired myeloid lineage differentiation, uncontrolled proliferation, and inhibition of proapoptotic pathways. In spite of a relatively homogeneous clinical disease presentation, risk of long-term survival in AML varies from 20% to 80% depending on molecular disease characteristics. In recognition of the molecular heterogeneity of AML, the European Leukemia Net (ELN) and WHO classification systems now incorporate cytogenetics and increasing numbers of gene mutations into AML prognostication. Several of the genomic AML subsets are characterized by unique transcription factor alterations that are highlighted in this review. There are many mechanisms of transcriptional deregulation in leukemia. We broadly classify transcription factors based on mechanisms of transcriptional deregulation including direct involvement of transcription factors in recurrent translocations, loss-of-function mutations, and intracellular relocalization. Transcription factors, due to their pleiotropic effects, have been attractive but elusive targets. Indirect targeting approaches include inhibition of upstream kinases such as TAK1 for suppression of NFκB signaling and downstream effectors such as FGF signaling in HOXA-upregulated leukemia. Other strategies include targeting scaffolding proteins like BrD4 in the case of MYC or coactivators such as menin to suppress HOX expression; disrupting critical protein interactions in the case of β-catenin:TCF/LEF, and preventing transcription factor binding to DNA as in the case of PU.1 or FOXM1. We comprehensively describe the mechanism of deregulation of transcription factors in genomic subsets of AML, consequent pathway addictions, and potential therapeutic strategies.

Effects of Choline and Magnesium Concurrent Supplementation on Coagulation and Lipid Profile in Patients with Type 2 Diabetes Mellitus: a Pilot Clinical Trial

Metabolic failure is associated with dyslipidemia and coagulation which can result in a higher risk of cardiovascular disease (CVD) in type 2 diabetes mellitus (T2DM). The aim of this study was to assess the effects of choline and magnesium co-supplementation on lipid profile and coagulation parameters in patients with T2DM. In a randomized, double-blind, placebo-controlled trial, supplements of choline bitartrate (1000 mg), magnesium oxide (500 mg), choline plus magnesium, or placebo were administered for 2 months to 96 diabetic participants of both sexes aged 30-60 years. Anthropometric characteristics, dietary intake, physical activity, serum lipids, and coagulation markers were measured in all subjects. Significant differences were observed in plasminogen activator inhibitor-1 (PAI-1) levels in the magnesium and choline-magnesium groups (p < 0.05). Moreover, tissue plasminogen activator (tPA) levels increased in choline-magnesium groups (p < 0.001). When adjusted for potential confounders, a significant decrease in PAI-1 (p = 0.03) and a marginally significant increase in tPA (p = 0.054) were found in the choline-magnesium group compared with the other groups. Compared with baseline values, there were significant differences in serum magnesium, HDL, and triglycerides (TG) following choline-magnesium co-supplementation (p < 0.05); however, there were no significant differences in serum magnesium, HDL, and TG among the groups (p > 0.05). Overall, concurrent supplementation of magnesium and choline is more effective than either magnesium or choline alone to improve coagulation in subjects with T2DM.

The Effects of Choline and Magnesium Co-Supplementation on Metabolic Parameters, Inflammation, and Endothelial Dysfunction in Patients With Type 2 Diabetes Mellitus: A Randomized, Double-Blind, Placebo-Controlled Trial

Objective: To our knowledge, no study has investigated the effects of choline and magnesium co-supplementation on metabolic parameters, inflammation, and endothelial dysfunction in patients with type 2 diabetes mellitus (T2DM). The aim of this study was investigation of the effects of the choline and magnesium co-supplementation on metabolic parameters, inflammation, and endothelial dysfunction in patients with T2DM.Methods: A randomized double-blind placebo-controlled parallel clinical trial was carried out among 96 diabetic patients. Ninety-six patients were randomly assigned to either choline, magnesium, choline-magnesium, or placebo for 2 months. Anthropometric measurement; metabolic, inflammatory, and endothelial markers; dietary intake; and physical activity were assessed at baseline and after treatment.Results: There was a significant change in serum magnesium in both magnesium and choline-magnesium groups (p < 0.05). Also, significant changes were observed in interleukin (IL)-6 levels in magnesium and choline-magnesium groups (p < 0.05). Moreover, vascular cell adhesion molecule-1 (VCAM-1) levels decreased in choline and choline-magnesium groups (p < 0.05). When adjusted for potential confounders, inflammation and endothelial factors (IL-6 and VCAM-1) decreased significantly in the choline-magnesium group as compared to other groups (p < 0.05). Compared to baseline values there were no significant differences in all anthropometric measurements and metabolic factors among four groups (p > 0.05).Conclusions: Choline and magnesium co-supplementation was more effective in improving inflammation and endothelial dysfunction than supplementation with choline or magnesium alone.