Corrigendum: Exploring the Causality Between Hypothyroidism and Non-alcoholic Fatty Liver: A Mendelian Randomization Study.

[This corrects the article DOI: 10.3389/fcell.2021.643582.].

Dysbiosis of Gut Microbiota in Patients With Acute Myocardial Infarction.

Acute myocardial infarction (AMI) continues as the main cause of morbidity and mortality worldwide. Interestingly, emerging evidence highlights the role of gut microbiota in regulating the pathogenesis of coronary heart disease, but few studies have systematically assessed the alterations and influence of gut microbiota in AMI patients. As one approach to address this deficiency, in this study the composition of fecal microflora was determined from Chinese AMI patients and links between gut microflora and clinical features and functional pathways of AMI were assessed. Fecal samples from 30 AMI patients and 30 healthy controls were collected to identify the gut microbiota composition and the alterations using bacterial 16S rRNA gene sequencing. We found that gut microflora in AMI patients contained a lower abundance of the phylum Firmicutes and a slightly higher abundance of the phylum Bacteroidetes compared to the healthy controls. Chao1 (P = 0.0472) and PD-whole-tree (P = 0.0426) indices were significantly lower in the AMI versus control group. The AMI group was characterized by higher levels of the genera Megasphaera, Butyricimonas, Acidaminococcus, and Desulfovibrio, and lower levels of Tyzzerella 3, Dialister, [Eubacterium] ventriosum group, Pseudobutyrivibrio, and Lachnospiraceae ND3007 group as compared to that in the healthy controls (P < 0.05). The common metabolites of these genera are mostly short-chain fatty acids, which reveals that the gut flora is most likely to affect the occurrence and development of AMI through the short-chain fatty acid pathway. In addition, our results provide the first evidence revealing remarkable differences in fecal microflora among subgroups of AMI patients, including the STEMI vs. NSTEMI, IRA-LAD vs. IRA-Non-LAD and Multiple (≥2 coronary stenosis) vs. Single coronary stenosis groups. Several gut microflora were also correlated with clinically significant characteristics of AMI patients, including LVEDD, LVEF, serum TnI and NT-proBNP, Syntax score, counts of leukocytes, neutrophils and monocytes, and fasting serum glucose levels. Taken together, the data generated enables the prediction of several functional pathways as based on the fecal microfloral composition of AMI patients. Such information may enhance our comprehension of AMI pathogenesis.

Exploring the Causality Between Hypothyroidism and Non-alcoholic Fatty Liver: A Mendelian Randomization Study.

The etiology of non-alcoholic fatty liver disease (NAFLD) involves complex interaction of genetic and environmental factors. A large number of observational studies have shown that hypothyroidism contributes to a high risk of NAFLD. However, the exact causality is still unknown. Due to the progress of genome-wide association study (GWAS) and the discovery of Mendelian randomization (MR), it is possible to explore the causality between the two diseases. In this study, in order to research into the influence of intermediate phenotypes on outcome, nine independent genetic variants of hypothyroidism obtained from the GWAS were used as instrumental variables (IVs) to perform MR analysis on NAFLD. Since there was no heterogeneity between IVs (P = 0.70), a fixed-effects model was used. The correlation between hypothyroidism and NAFLD was evaluated by using inverse-variance weighted (IVW) method and weighted median method. Then the sensitivity test was analyzed. The results showed that there was a high OR (1.7578; 95%CI 1.1897-2.5970; P = 0.0046) and a low intercept (-0.095; P = 0.431). None of the genetic variants drove the overall result (P < 0.01). Simply, we proved for the first time that the risk of NAFLD increases significantly on patients with hypothyroidism. Furthermore, we explained possible causes of NAFLD caused by hypothyroidism.

Metformin relieves H/R-induced cardiomyocyte injury through miR-19a/ACSL axis - possible therapeutic target for myocardial I/R injury.

This study proposed to investigate the function of miR-19a/ACSL axis in hypoxia/reoxygenation (H/R)-induced myocardial injury and determine whether metformin exerts its protective effect via miR-19a/ACSL axis. Firstly, bioinformatics analysis of data from Gene Expression Omnibus (GEO) database indicated that miR-19a was downregulated in patients with myocardial infarction (MI) compared to that in control group. H/R model was constructed with AC16 cells in vitro. qRT-PCR assay revealed that miR-19a was downregulated in H/R-treated AC16 cells. Then, CCK-8 assay demonstrated that upregulation of miR-19a significantly alleviated H/R-induced decline of cell viability. Moreover, bioinformatics prediction, western blotting and dual-luciferase reporter assays were performed to check the target genes of miR-19a, and ACSL1 was determined as a downstream target gene of miR-19a. Besides, the analysis based on Comparative Toxicogenomics Database (CTD) suggested that metformin targeting ACSL1 can be used as a potential drug for further research. Biological function experiments in vitro revealed that H/R markedly declined the viability and elevated the apoptosis of AC16 cells, while metformin can significantly mitigate these effects. Furthermore, overexpression of miR-19a significantly strengthened the beneficial effect of metformin on H/R-induced AC16 cells injury, which can be reversed by upregulation of ACSL1. In conclusion, metformin can alleviate H/R-induced cells injury via regulating miR-19a/ACSL axis, which lays a foundation for identifying novel targets for myocardial I/R injury therapy.

A Review of Approaches for Predicting Drug-Drug Interactions Based on Machine Learning.

Drug-drug interactions play a vital role in drug research. However, they may also cause adverse reactions in patients, with serious consequences. Manual detection of drug-drug interactions is time-consuming and expensive, so it is urgent to use computer methods to solve the problem. There are two ways for computers to identify drug interactions: one is to identify known drug interactions, and the other is to predict unknown drug interactions. In this paper, we review the research progress of machine learning in predicting unknown drug interactions. Among these methods, the literature-based method is special because it combines the extraction method of DDI and the prediction method of DDI. We first introduce the common databases, then briefly describe each method, and summarize the advantages and disadvantages of some prediction models. Finally, we discuss the challenges and prospects of machine learning methods in predicting drug interactions. This review aims to provide useful guidance for interested researchers to further promote bioinformatics algorithms to predict DDI.

Selecting Essential MicroRNAs Using a Novel Voting Method.

Among the large number of known microRNAs (miRNAs), some miRNAs play negligible roles in cell regulation. Therefore, selecting essential miRNAs is an important initial step for a deeper understanding of miRNAs and their functions. In this study, we generated 60 classification models by combining 12 representative feature extraction methods and 5 commonly used classification algorithms. The optimal model for essential miRNA classification that we obtained is based on the Mismatch feature extraction method combined with the random forest algorithm. The F-Measure, area under the curve, and accuracy values of this model were 93.2%, 96.7%, and 93.0%, respectively. We also found that the distribution of the positive and negative examples of the first few features greatly influenced the classification results. The feature extraction methods performed best when the differences between the positive and negative examples were obvious, and this led to better classification of essential miRNAs. Because each classifier's predictions for the same sample may be different, we employed a novel voting method to improve the accuracy of the classification of essential miRNAs. The performance results showed that the best classification results were obtained when five classification models were used in the voting. The five classification models were constructed based on the Mismatch, pseudo-distance structure status pair composition, Subsequence, Kmer, and Triplet feature extraction methods. The voting result was 95.3%. Our results suggest that the voting method can be an important tool for selecting essential miRNAs.

MicroRNA-148a-3p promotes survival and migration of endothelial cells isolated from Apoe deficient mice through restricting circular RNA 0003575.

The incidence of atherosclerosis is greatly increased, which becomes the leading cause for the death and disability worldwide. Endothelial cells dysfunction plays a substantial role in the pathogenesis of atherosclerosis. MicroRNA-148a-3p (miR-148a-3p) and circular RNA 0003575 (circ_0003575) modulated lipid metabolism and proliferative function of endothelial cells, respectively. However, the role of them in modulation of endothelial cell function and progression of atherosclerosis remains unknown. Endothelial cells were isolated from the aorta of Apoe-/- mice. miR-148a-3p in atherosclerosis patients and healthy controls were measured by qRT-PCR. Overexpression and knockdown of miR-148a-3p in endothelial cells were established. The proliferation, migration and apoptosis of endothelial cells were measured by MTT, Transwell, and fluorescence microscope, respectively. Online software (miRWalk 2.0 and RegRNA2.0) and databases (miRWalk, miRanda, RNA22, and Targetscan) were used to predict potential target genes of miR-148a-3p and circ_0003575. The expression of target genes was detected through western blotting. The expression of miR-148a-3p was significantly upregulated in patients with atherosclerosis as relative to healthy people. Overexpression of miR-148a-3p exhibited stimulatory effects on endothelial cell proliferation and migration and inhibited programmed cell death. Six intersection target genes, c-MAF, FOXO4, FOXO3, MITF, ETV7, and CRX, were predicted between miR-148a-3p and circ_0003575. The opposite effects of circ_0003575 and miR-148a-3p on the expression of FOXO4 and FOXO3, which are essential for lipid metabolism. We demonstrate that miR-148a-3p suppresses FOXO4 and FOXO3 expression via interruption of circ_0003575 function, which in turn impairs the proliferative and migratory function of endothelial cells, eventually exacerbating the atherosclerosis.

Intrahippocampal miR-342-3p inhibition reduces ß-amyloid plaques and ameliorates learning and memory in Alzheimer's disease.

Accumulation of extracellular amyloid-ß (Aß) in hippocampal subregions is a hallmark of Alzheimer's disease (AD), which promotes neuronal apoptosis, potentiates cognitive decline and play a causative role in AD pathogenesis. However, whether this process is controlled by distinct miRNAs at the posttranscriptional level remain fascinating but poorly understood. Using post mortem hippocampal samples from human AD patients and 3xTg-AD mouse, we demonstrate that miR-342-3p expression was significantly induced during the AD development. With the aid of intrahippocampal injection of miR-342-3p antagomir, we further show that in vivo miR-342-3p inhibition synergistically improved cognitive deficits in 3xTg-AD mice. The hippocampal Aß-plaque burden in 3xTg-AD mice, as revealed by immunohistochemical analysis with 4G8 antibody, was attenuated also. Mechanistically, the upregulation of neuronal miR-342-3p is linked to an increase in the activation of the stress kinase c-Jun N-terminal kinase with the subsequent death of the neurons in Aß-challenged HT22 hippocampal neuronal cells. These findings support the model that derangement of hippocampus signal transduction and subsequent neuronal apoptosis in AD arises as a consequence of increased Aß burden and chronic activation of the JNK MAPK cascade in a miR-342-3p-dependent manner. Overall, we described for the first time the regulatory activity of miR-342-3p on relevant Aß metabolism pathways in Alzheimer's disease.

BMP2/7 heterodimer enhances osteogenic differentiation of rat BMSCs via ERK signaling compared with respective homodimers.

Bone morphogenetic protein (BMP)2/7 heterodimer shows greater efficacy in enhancing bone regeneration. However, the precise mechanism and the role of mitogen-activated protein kinase (MAPK) signaling network in BMP2/7-driven osteogenesis remain ambiguous. In this study, we evaluated the effects of BMP2/7 heterodimers on osteoblastic differentiation in rat bone marrow mesenchymal stem cells (BMSCs), with the aim to elaborate how MAPKs might be involved in this cellular process by treatment of rat BMSCs with BMP2/-7 with a special signal-pathway inhibitor. We found that BMP2/7 heterodimer induced a much stronger osteogenic response in rat BMSCs compared with either homodimer. Most interestingly, extracellular signal-regulated kinase (ERK) demonstrated a highly sustained phosphorylation and activation in the BMP2/7 heterodimer treatment groups, and inhibition of ERK cascades using U0126 special inhibitor that significantly reduced the activity of ALP and calcium mineralization to a substantial degree in rat BMSCs treated with BMP2/7 heterodimers. Collectively, we demonstrate that BMP2/7 heterodimer shows a potent ability to stimulate osteogenesis in rat BMSCs. The activated ERK signaling pathway involved in this process may contribute partially to an increased osteogenic potency of heterodimeric BMP2/7 growth factors.