SWI/SNF Complex in Vascular Smooth Muscle Cells and Its Implications in Cardiovascular Pathologies.

Mature vascular smooth muscle cells (VSMC) exhibit a remarkable degree of plasticity, a characteristic that has intrigued cardiovascular researchers for decades. Recently, it has become increasingly evident that the chromatin remodeler SWItch/Sucrose Non-Fermentable (SWI/SNF) complex plays a pivotal role in orchestrating chromatin conformation, which is critical for gene regulation. In this review, we provide a summary of research related to the involvement of the SWI/SNF complexes in VSMC and cardiovascular diseases (CVD), integrating these discoveries into the current landscape of epigenetic and transcriptional regulation in VSMC. These novel discoveries shed light on our understanding of VSMC biology and pave the way for developing innovative therapeutic strategies in CVD treatment.

Myeloid BAF60a deficiency alters metabolic homeostasis and exacerbates atherosclerosis.

Atherosclerosis, a leading health concern, stems from the dynamic involvement of immune cells in vascular plaques. Despite its significance, the interplay between chromatin remodeling and transcriptional regulation in plaque macrophages is understudied. We discovered the reduced expression of Baf60a, a component of the switch/sucrose non-fermentable (SWI/SNF) chromatin remodeling complex, in macrophages from advanced plaques. Myeloid-specific Baf60a deletion compromised mitochondrial integrity and heightened adhesion, apoptosis, and plaque development. BAF60a preserves mitochondrial energy homeostasis under pro-atherogenic stimuli by retaining nuclear respiratory factor 1 (NRF1) accessibility at critical genes. Overexpression of BAF60a rescued mitochondrial dysfunction in an NRF1-dependent manner. This study illuminates the BAF60a-NRF1 axis as a mitochondrial function modulator in atherosclerosis, proposing the rejuvenation of perturbed chromatin remodeling machinery as a potential therapeutic target.

ncRNAVar: A Manually Curated Database for Identification of Noncoding RNA Variants Associated with Human Diseases.

While variants of noncoding RNAs (ncRNAs) have been experimentally validated as a new class of biomarkers and drug targets, the discovery and interpretation of relationships between ncRNA variants and human diseases become important and challenging. Here we present ncRNAVar (http://www.liwzlab.cn/ncrnavar/), the first database that provides association data between validated ncRNA variants and human diseases through manual curation on 2650 publications and computational annotation. ncRNAVar contains 4565 associations between 711 human disease phenotypes and 3112 variants from 2597 ncRNAs. Each association was reviewed by professional curators, incorporated with valuable annotation and cross references, and designated with an association score by our refined score model. ncRNAVar offers web applications including association prioritization, network visualization, and relationship mapping. ncRNAVar, presenting a landscape of ncRNA variants in human diseases and a useful resource for subsequent software development, will improve our insight of relationships between ncRNA variants and human health.

Novel visualized quantitative epigenetic imprinted gene biomarkers diagnose the malignancy of ten cancer types.

BACKGROUND: Epigenetic alterations are involved in most cancers, but its application in cancer diagnosis is still limited. More practical and intuitive methods to detect the aberrant expressions from clinical samples using highly sensitive biomarkers are needed. In this study, we developed a novel approach in identifying, visualizing, and quantifying the biallelic and multiallelic expressions of an imprinted gene panel associated with cancer status. We evaluated the normal and aberrant expressions measured using the imprinted gene panel to formulate diagnostic models which could accurately distinguish the imprinting differences of normal and benign cases from cancerous tissues for each of the ten cancer types. RESULTS: The Quantitative Chromogenic Imprinted Gene In Situ Hybridization (QCIGISH) method developed from a 1013-case study which provides a visual and quantitative analysis of non-coding RNA allelic expressions identified the guanine nucleotide-binding protein, alpha-stimulating complex locus (GNAS), growth factor receptor-bound protein (GRB10), and small nuclear ribonucleoprotein polypeptide N (SNRPN) out of five tested imprinted genes as efficient epigenetic biomarkers for the early-stage detection of ten cancer types. A binary algorithm developed for cancer diagnosis showed that elevated biallelic expression (BAE), multiallelic expression (MAE), and total expression (TE) measurements for the imprinted gene panel were associated with cell carcinogenesis, with the formulated diagnostic models achieving consistently high sensitivities (91-98%) and specificities (86-98%) across the different cancer types. CONCLUSIONS: The QCIGISH method provides an innovative way to visually assess and quantitatively analyze individual cells for cancer potential extending from hyperplasia and dysplasia until carcinoma in situ and invasion, which effectively supplements standard clinical cytologic and histopathologic diagnosis for early cancer detection. In addition, the diagnostic models developed from the BAE, MAE, and TE measurements of the imprinted gene panel GNAS, GRB10, and SNRPN could provide important predictive information which are useful in early-stage cancer detection and personalized cancer management.

DeepPhagy: a deep learning framework for quantitatively measuring autophagy activity in Saccharomyces cerevisiae.

Seeing is believing. The direct observation of GFP-Atg8 vacuolar delivery under confocal microscopy is one of the most useful end-point measurements for monitoring yeast macroautophagy/autophagy. However, manually labelling individual cells from large-scale sets of images is time-consuming and labor-intensive, which has greatly hampered its extensive use in functional screens. Herein, we conducted a time-course analysis of nitrogen starvation-induced autophagy in wild-type and knockout mutants of 35 AuTophaGy-related (ATG) genes in Saccharomyces cerevisiae and obtained 1,944 confocal images containing > 200,000 cells. We manually labelled 8,078 autophagic and 18,493 non-autophagic cells as a benchmark dataset and developed a new deep learning tool for autophagy (DeepPhagy), which exhibited superior accuracy in recognizing autophagic cells compared to other existing methods, with an area under the curve (AUC) value of 0.9710 from 10-fold cross-validations. We further used DeepPhagy to automatically analyze all the images and quantitatively classified the autophagic phenotypes of the 35 atg knockout mutants into 3 classes. The high consistency in our computational and biochemical results indicated the reliability of DeepPhagy for measuring autophagic activity. Moreover, we used DeepPhagy to analyze 3 additional types of autophagic phenotypes, including the targeting of Atg1-GFP to the vacuole, the vacuolar delivery of GFP-Atg19, and the disintegration of autophagic bodies indicated by GFP-Atg8, all with satisfying accuracies. Taken together, our study not only enables the GFP-Atg8 fluorescence assay to become a quantitative measurement for analyzing autophagic phenotypes in S. cerevisiae but also demonstrates that deep learning-based methods could potentially be applied to different types of autophagy.Abbreviations:Ac: accuracy; ALP: alkaline phosphatase; ALR: autophagic lysosomal reformation; ATG: AuTophaGy-related; AUC: area under the curve; CNN: convolutional neural network; Cvt: cytoplasm-to-vacuole targeting; DeepPhagy: deep learning for autophagy; fc_2: second fully connected; GFP: green fluorescent protein; MAP1LC3/LC3: microtubule-associated protein 1 light chain 3 beta; HAT: histone acetyltransferase; HemI: Heat map Illustrator; JRE: Java Runtime Environment; KO: knockout; LRN: local response normalization; MCC: Mathew Correlation Coefficient; OS: operating system; PAS: phagophore assembly site; PC: principal component; PCA: principal component analysis; PPI: protein-protein interaction; Pr: precision; QPSO: Quantum-behaved Particle Swarm Optimization; ReLU: rectified linear unit; RF: random forest; ROC: receiver operating characteristic; ROI: region of interest; SD: systematic derivation; SGD: stochastic gradient descent; Sn: sensitivity; Sp: specificity; SRG: seeded region growing; t-SNE: t-distributed stochastic neighbor embedding; 2D: 2-dimensional; WT: wild-type.

Identification of Chinese Herbal Compounds with Potential as JAK3 Inhibitors.

The Janus kinases (JAKs) consist of four similar tyrosine kinases and function as key hubs in the signaling pathways that are implicated in both innate and adaptive immunity. Among the four members, JAK3 is probably the more attractive target for treatment of inflammatory diseases because its inhibition demonstrates the greatest immunosuppression and most profound effect in the treatment of such disorders. Although many JAK3 inhibitors are already available, certain shortcomings have been identified, mostly acquired drug resistance or unwanted side effects. To discover and identify new promising lead candidates, in this study, the structure of JAK3 (3LXK) was obtained from the Protein Data Bank and used for simulation modeling and protein-ligand interaction analysis. The ~36,000 Chinese herbal compounds obtained from TCM Database@Taiwan were virtually screened by AutoDock Vina docking program and filtered with Lipinski's Rules and ADME/T virtual predictions. Because of high occurrence of fake hits during docking, we selected 12 phytochemicals which have demonstrated modulating JAKs expressions among the top 50 chemicals from docking results. To validate whether these compounds are able to directly mediate JAK3 kinase, we have investigated the inhibitory activity using enzymatic activity assays, western blot, and HEK 293 cell STAT5 transactivity assays. The molecular analysis included docking and molecular dynamics (MD) simulations in order to investigate structural conformations and to explore the key amino acids in the interaction between JAK3 kinase and its putative ligands. The results demonstrated that Cryptotanshinone, Icaritin, and Indirubin exhibited substantial inhibitory activity against JAK3 kinase in vitro. The results also provide binding models of the protein-ligand interaction, detailing the interacting amino acid residues at the active ATP-binding domains of JAK3 kinase. In conclusion, our work discovered 3 potential natural inhibitors of JAK3 kinase and could provide new possibilities and stimulate new insights for the treatment of JAK3-targeted diseases.

NanoRNase from Aeropyrum pernix shows nuclease activity on ssDNA and ssRNA.

In cells, degrading DNA and RNA by various nucleases is very important. These processes are strictly controlled and regulated to maintain DNA integrity and to mature or recycle various RNAs. NanoRNase (Nrn) is a 3'-exonuclease that specifically degrades nanoRNAs shorter than 5 nucleotides. Several Nrns have been identified and characterized in bacteria, mainly in Firmicutes. Archaea often grow in extreme environments and might be subjected to more damage to DNA/RNA, so DNA repair and recycling of damaged RNA are very important in archaea. There is no report on the identification and characterization of Nrn in archaea. Aeropyrum pernix encodes three potential Nrns: NrnA (Ape1437), NrnB (Ape0124), and an Nrn-like protein Ape2190. Biochemical characterization showed that only Ape0124 could degrade ssDNA and ssRNA from the 3'-end in the presence of Mn2+. Interestingly, unlike bacterial Nrns, Ape0124 prefers ssDNA, including short nanoDNA, and degrades nanoRNA with lower efficiency. The 3'-DNA backbone was found to be required for efficiently hydrolyzing the phosphodiester bonds. In addition, Ape0124 also degrads the 3'-overhang of double-stranded DNA. Interestingly, Ape0124 could hydrolyze pAp into AMP, which is a feature of bacterial NrnA, not NrnB. Our results indicate that Ape0124 is a novel Nrn with a combined substrate profile of bacterial NrnA and NrnB.

Psychotic Symptoms and Attitudes toward Medication Mediate the Effect of Insight on Personal-Social Functions in Patients with Schizophrenia: One-Year Randomized Controlled Trial and Follow-Up.

AIMS: This study aimed to investigate the mediating pathway of 3 factors (psychotic symptoms, attitude toward medication, and cognitive processing speed) on the effect of insight on personal-social functioning in patients with schizophrenia. METHODS: Chinese inpatients with schizophrenia (n = 168; mean age 18 ± 50 years) diagnosed according to the DSM-IV were randomly assigned to treatment with antipsychotic medication alone or combined treatment. Positive and Negative Syndrome Scale (PANSS), Drug Attitude Inventory (DAI), Assessment of Insight (SAI), and Social-Personal Performance Scale (PSPS) scores were evaluated at baseline and at 3, 6, and 12 months. Cognitive function was assessed at baseline. Multiple mediation analyses were conducted with baseline data, end point data, and changes-in-scale scores between baseline and the end point, respectively. RESULTS: At baseline and at 12 months, only psychotic symptoms mediated the effect of insight on personal-social functioning. For changes-in-scale scores over the 12-month follow-up, in patients receiving treatment with medication alone, the effect of improved insight on improved personal-social function was mediated by psychotic symptoms only; in patients receiving a combined treatment, the effect of improved insight on improved personal-social functioning was mediated by both psychotic symptoms and attitudes toward medication, independently. CONCLUSIONS: The link between insight and personal-social functions is mainly mediated by psychotic symptoms. Psychosocial intervention improves the predicting effect of insight on personal-social function by improving both the attitude toward medication and psychotic symptoms independently.

Two-stage integrated care versus antipsychotic medication alone on outcomes of schizophrenia: One-year randomized controlled trial and follow-up.

Integrated care can reduce rate of relapse and improve personal and social functions in patients with schizophrenia. We established and evaluated a new model of "intensive-consolidation" two-stage integrated care (IC) for inpatients with schizophrenia. Data were collected between 2012 and 2015. Chinese inpatients with schizophrenia (n=170) diagnosed according to DSM-IV were randomly assigned to antipsychotic medication-alone (n=84) or two-stage IC (n=86) and followed up for 12 months. The IC model included intensive treatments (antipsychotics plus the cognitive behavior therapy and rehabilitation treatment) during hospitalization and 3-time consolidation treatments with 3-month intervals at clinics. Outcome measures included the rate of relapse, psychiatric symptoms and social functioning. Compared with medication-alone group, the rate of relapse were significantly lower in IC group (p=0.012); the Mixed-Effects Model for Repeated-Measures analyses showed that the IC group significantly improved in positive symptoms over time; greater improvement in self-care and less aggressive behaviors were observed over time in IC group (all p<0.008). The findings support the feasibility and effectiveness of the new two-stage model of integrated care as an intervention for middle-acute-phase inpatients with schizophrenia. The model is particularly informative to countries where medical resources are mainly distributed in developed regions.