Cardiac fibrosis after myocardial infarction (MI) has been identified as a key factor in the development of heart failure. Although dysregulation of microRNA (miRNA) is involved in various pathophysiological processes in the heart, the role of miRNA in fibrosis regulation after MI is not clear. Previously we observed the correlation between fibrosis and the miR-24 expression in hypertrophic hearts, herein we assessed how miR-24 regulates fibrosis after MI. Using qRT-PCR, we showed that miR-24 was down-regulated in the MI heart; the change in miR-24 expression was closely related to extracellular matrix (ECM) remodelling. In vivo, miR-24 could improve heart function and attenuate fibrosis in the infarct border zone of the heart two weeks after MI through intramyocardial injection of Lentiviruses. Moreover, in vitro experiments suggested that up-regulation of miR-24 by synthetic miR-24 precursors could reduce fibrosis and also decrease the differentiation and migration of cardiac fibroblasts (CFs). TGF-β (a pathological mediator of fibrotic disease) increased miR-24 expression, overexpression of miR-24 reduced TGF-β secretion and Smad2/3 phosphorylation in CFs. By performing microarray analyses and bioinformatics analyses, we found furin to be a potential target for miR-24 in fibrosis (furin is a protease which controls latent TGF-β activation processing). Finally, we demonstrated that protein and mRNA levels of furin were regulated by miR-24 in CFs. These findings suggest that miR-24 has a critical role in CF function and cardiac fibrosis after MI through a furin–TGF-β pathway. Thus, miR-24 may be used as a target for treatment of MI and other fibrotic heart diseases.

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BACKGROUND Major depressive disorder (MDD) and posttraumatic stress disorder (PTSD) are highly comorbid and exhibit strong correlations with one another. We aimed to investigate mechanisms of underlying relationships between PTSD and 3 kinds of depressive phenotypes, namely, MDD, depressed affect (DAF), and depression (DEP, including both MDD and the broad definition of depression). METHODS Genetic correlations between PTSD and the depressive phenotypes were tested using linkage disequilibrium score regression. Polygenic overlap analysis was used to estimate shared and trait-specific causal variants across a pair of traits. Causal relationships between PTSD and the depressive phenotypes were investigated using Mendelian randomization. Shared genomic loci between PTSD and MDD were identified using cross-trait meta-analysis. RESULTS Genetic correlations of PTSD with the depressive phenotypes were in the range of 0.71–0.80. The estimated numbers of causal variants were 14,565, 12,965, 10,565, and 4,986 for MDD, DEP, DAF, and PTSD, respectively. In each case, causal variants contributing to PTSD were completely or largely covered by causal variants defining each of the depressive phenotypes. Mendelian randomization analysis indicated that the genetically determined depressive phenotypes confer a causal effect on PTSD ( b = 0.21–0.31). Notably, genetically determined PTSD confers a causal effect on DEP ( b = 0.14) and DAF ( b = 0.15), but not MDD. Cross-trait meta-analysis of MDD and PTSD identified 47 genomic loci, including 29 loci shared between PTSD and MDD. CONCLUSION Evidence from shared genetics suggests that PTSD is a subtype of MDD. This study provides support to the efforts in reducing diagnostic heterogeneity in psychiatric nosology. FUNDING The National Key Research and Development Program of China and the National Natural Science Foundation of China.

The fractional amplitude of low-frequency fluctuations (fALFF) has been widely used as potential clinical biomarkers for resting-state functional magnetic resonance imaging based schizophrenia diagnosis. However, previous studies usually measure the fALFF with specific bands from 0.01-0.08 Hz, which cannot fully delineate the complex variations of spontaneous fluctuations in the resting-state brain. As we konow, fALFF data is intrinsically constrained by the brain structure, but most of the traditional methods have not consider it in feature selection. For addressing these problems, we propose a model to classify schizophrenia in multi-frequency bands with tree-guided group sparse learning. In detail, we first acquire the fALFF data in multi-frequency bands (i.e., slow-5:0.01-0.027 Hz, slow-4:0.027-0.073 Hz, slow-3:0.073-0.198 Hz and slow-2:0.198-0.25 Hz). Then, we divide the whole brain into different candidate patches, and select those significant patches related to schizophrenia using random forest-based importancescore. Moreover, we use tree-structured sparse learning method for feature selection with above patches spatial constraint. Finally, considering biomarkers from multi-frequency bands can reflect complementary information among multiple frequency bands, we adopt the multi-kernel learning (MKL) method to combine features of multi-frequency bands for classification. Our experimental results show that these biomarkers from multi-frequency bands can achieve a classification accuracy of 91.1% on 17 schizophrenia patients and 17 healthy controls, further demonstrating the multi-frequency bands analysis can better account for classification of schizophrenia.

Background: Patients with treatment-resistant schizophrenia (TRS) and non-treatment-resistant schizophrenia (NTRS) respond to antipsychotic drugs differently. Previous studies demonstrated that patients with TRS or NTRS exhibited abnormal neural activity in different brain regions. Accordingly, in the present study, we tested the hypothesis that a regional homogeneity (ReHo) approach could be used to distinguish between patients with TRS and NTRS.Methods: A total of 17 patients with TRS, 17 patients with NTRS, and 29 healthy controls (HCs) matched in sex, age, and education levels were recruited to undergo resting-state functional magnetic resonance imaging (RS-fMRI). ReHo was used to process the data. ANCOVA followed by post-hoc t-tests, receiver operating characteristic curves (ROC), and correlation analyses were applied for the data analysis.Results: ANCOVA analysis revealed widespread differences in ReHo among the three groups in the occipital, frontal, temporal, and parietal lobes. ROC results indicated that the optimal sensitivity and specificity of the ReHo values in the left postcentral gyrus, left inferior frontal gyrus/triangular part, and right fusiform could differentiate TRS from NTRS, TRS from HCs, and NTRS from HCs were 94.12 and 82.35%, 100 and 86.21%, and 82.35 and 93.10%, respectively. No correlation was found between abnormal ReHo and clinical symptoms in patients with TRS or NTRS.Conclusions: TRS and NTRS shared most brain regions with abnormal neural activity. Abnormal ReHo values in certain brain regions might be applied to differentiate TRS from NTRS, TRS from HC, and NTRS from HC with high sensitivity and specificity.