Identification of UDP-glucuronosyltransferase (UGT) isoforms involved in the metabolism of Chlorophenols (CPs).

Chlorophenols (CPs) are a group of pollutants that pose a great threat to the environment, they are widely used in industrial and agricultural wastes, pesticides, herbicides, textiles, pharmaceuticals and plastics. Among CPs, pentachlorophenol was listed as one of the persistent organic pollutants (POPs) by the Stockholm convention. This study aims to identify the UDP-glucosyltransferase (UGT) isoforms involved in the metabolic elimination of CPs. CPs' mono-glucuronide was detected in the human liver microsomes (HLMs) incubation mixture with co-factor uridine-diphosphate glucuronic acid (UDPGA). HLMs-catalyzed glucuronidation metabolism reaction equations followed Michaelis-Menten or substrate inhibition type. Recombinant enzymes and chemical reagents inhibition experiments were utilized to phenotype the main UGT isoforms involved in the glucuronidation of CPs. UGT1A6 might be the major enzyme in the glucuronidation of mono-chlorophenol isomer. UGT1A1, UGT1A6, UGT1A9, UGT2B4 and UGT2B7 were the most important five UGT isoforms for metabolizing the di-chlorophenol and tri-chlorophenol isomers. UGT1A1 and UGT1A3 were the most important UGT isoforms in the catalysis of tetra-chlorophenol and pentachlorophenol isomers. Species differences were investigated using rat liver microsomes (RLMs), pig liver microsomes (PLMs), dog liver microsomes (DLMs), and monkey liver microsomes (MyLMs). All these results were helpful for elucidating the metabolic elimination and toxicity of CPs.

Evaluating the influence of sarcopenia and myosteatosis on clinical outcomes in gastric cancer patients undergoing immune checkpoint inhibitor.

BACKGROUND: The development and progression of gastric cancer (GC) are closely linked to the nutritional status of patients. Although immunotherapy has been demonstrated to be clinically effective, the relationships of sarcopenia and myosteatosis with the use of immune checkpoint inhibitors (ICIs) in patients with gastric cancer remain to be characterized. AIM: To assess the effects of sarcopenia and myosteatosis on the clinical outcomes of patients with GC undergoing treatment with an ICI. METHODS: We performed a retrospective study of patients who were undergoing immunotherapy for GC. For the evaluation of sarcopenia, the optimal cut-off value for the skeletal muscle index was established using receiver operating characteristic analysis of data obtained from pre-treatment computed tomography images at the L3 vertebral level. Myosteatosis was defined using the mean skeletal muscle density (SMD), with a threshold value of < 41 Hounsfield units (HU) for patients with a body mass index (BMI) < 25 kg/m² and < 33 HU for those with a BMI ≥ 25 kg/m². The log-rank test was used to compare progression-free survival (PFS) and overall survival (OS), and a Cox proportional hazard model was used to identify prognostic factors. Nomograms were developed to predict the PFS and OS of patients on the basis of the results of multivariate analyses. RESULTS: We studied 115 patients who were undergoing ICI therapy for GC, of whom 27.4% had sarcopenia and 29.8% had myosteatosis. Patients with sarcopenia or myosteatosis had significantly shorter PFS and OS than those without these conditions. Furthermore, both sarcopenia and myosteatosis were found to be independent predictors of PFS and OS in patients with GC administering an ICI. The prediction models created for PFS and OS were associated with C-indexes of 0.758 and 0.781, respectively. CONCLUSION: The presence of sarcopenia or myosteatosis is a reliable predictor of the clinical outcomes of patients with GC who are undergoing treatment with an ICI.

Explainable Machine Learning-Based Prediction Model for Diabetic Nephropathy.

The aim of this study is to analyze the effect of serum metabolites on diabetic nephropathy (DN) and predict the prevalence of DN through a machine learning approach. The dataset consists of 548 patients from April 2018 to April 2019 in the Second Affiliated Hospital of Dalian Medical University (SAHDMU). We select the optimal 38 features through a least absolute shrinkage and selection operator (LASSO) regression model and a 10-fold cross-validation. We compare four machine learning algorithms, including extreme gradient boosting (XGB), random forest, decision tree, and logistic regression, by AUC-ROC curves, decision curves, and calibration curves. We quantify feature importance and interaction effects in the optimal predictive model by Shapley additive explanation (SHAP) method. The XGB model has the best performance to screen for DN with the highest AUC value of 0.966. The XGB model also gains more clinical net benefits than others, and the fitting degree is better. In addition, there are significant interactions between serum metabolites and duration of diabetes. We develop a predictive model by XGB algorithm to screen for DN. C2, C5DC, Tyr, Ser, Met, C24, C4DC, and Cys have great contribution in the model and can possibly be biomarkers for DN.

The association between plasma free amino acids and type 2 diabetes mellitus complicated with infection in Chinese patients.

BACKGROUND: Type 2 diabetes mellitus (T2DM), one of the most common public diseases threatening human health, is always accompanied by infection. Though there are still a variety of flaws in the treatment of some infectious diseases, metabolomics provides a fresh perspective to explore the relationship between T2DM and infection. Our research aimed to investigate the association between plasma free amino acids (PFAAs) and T2DM complicated with infection in Chinese patients. METHODS: A cross-sectional study was conducted from May 2015 to August 2016. We retrieved the medical records of 1032 inpatients with T2DM from Liaoning Medical University First Affiliated Hospital and we used mass spectrometry to quantify 23 PFAAs. Infections contained 15 individual categories that could be retrieved from the database. Principal component analysis was used to extract factors of PFAAs. Multi-variable binary logistic regression was used to obtain odds ratios (OR) and their 95% confidence intervals (CI). RESULTS: Among 1032 inpatients,109 (10.6%) had infectious diseases. Six factors, accounting for 68.6% of the total variance, were extracted. Factor 4 consisted of Glu, Asp and Orn. Factor 5 consisted of Hcy and Pip. After adjusting for potential confounders, factor 4 was positively correlated with T2DM complicated with infection in Chinese T2DM patients (OR: 1.27, 95%CI: 1.06-1.52). Individual Hcy in factor 5 was positively associated with T2DM complicated with infection (OR: 1.33, 95%CI: 1.08-1.64). Furthermore, factor 4 (OR: 1.44, 95%CI: 1.11-1.87), Orn (OR: 1.01, 95%CI: 1.00-1.02) and Hcy (OR: 1.56, 95%CI: 1.14-3.14) were positively associated with bacterial infection in Chinese T2DM patients, while factor 5 (OR: 0.71, 95%CI: 0.50-1.00) was negatively associated with bacterial infection. CONCLUSIONS: Urea cycle-related metabolites (Orn, Asp, Glu) and Hcy were positively associated with T2DM complicated with infection in China. Orn and Hcy were positively associated with bacterial infection in T2DM patients in China.

Ultrahigh-throughput screening-assisted in vivo directed evolution for enzyme engineering.

BACKGROUND: Classical directed evolution is a powerful approach for engineering biomolecules with improved or novel functions. However, it traditionally relies on labour- and time-intensive iterative cycles, due in part to the need for multiple molecular biology steps, including DNA transformation, and limited screening throughput. RESULTS: In this study, we present an ultrahigh throughput in vivo continuous directed evolution system with thermosensitive inducible tunability, which is based on error-prone DNA polymerase expression modulated by engineered thermal-responsive repressor cI857, and genomic MutS mutant with temperature-sensitive defect for fixation of mutations in Escherichia coli. We demonstrated the success of the in vivo evolution platform with ß-lactamase as a model, with an approximately 600-fold increase in the targeted mutation rate. Furthermore, the platform was combined with ultrahigh-throughput screening methods and employed to evolve α-amylase and the resveratrol biosynthetic pathway. After iterative rounds of enrichment, a mutant with a 48.3% improvement in α-amylase activity was identified via microfluidic droplet screening. In addition, when coupled with an in vivo biosensor in the resveratrol biosynthetic pathway, a variant with 1.7-fold higher resveratrol production was selected by fluorescence-activated cell sorting. CONCLUSIONS: In this study, thermal-responsive targeted mutagenesis coupled with ultrahigh-throughput screening was developed for the rapid evolution of enzymes and biosynthetic pathways.

Effect of passive smoking exposure on risk of type 2 diabetes: a systematic review and meta-analysis of prospective cohort studies.

Objective: The effect of passive smoking exposure on the risk of type 2 diabetes has not been systematically studied. A meta-analysis was conducted to assess the association between passive smoking exposure and the risk of diabetes. Methods: We searched three major databases up to 31 October 2022 to identify relevant prospective cohort studies on the association between passive smoking and the risk of type 2 diabetes. The pooled relative risk (RR) and 95% confidence interval (CI) for the association between passive smoking exposure and the risk of type 2 diabetes were analyzed using a fixed-effect model. Results: Ten prospective cohort studies were included in this meta-analysis, with a total of 251,620 participants involved. The pooled RR showed a significantly positive association between nonsmokers exposed to passive smoking and type 2 diabetes as compared to non-smokers who were not exposed to passive smoking [RR = 1.27; 95% CI (1.19, 1.36); p < 0.001]. Sensitivity analysis indicated that the pooled RR was not substantially affected by any of the individual studies. Conclusion: Exposure to passive smoking increases the risk of type 2 diabetes. This study may have a positive effect on the prevention of type 2 diabetes. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42023372532.

[Association Analysis between Genotype and Phenotype of α,ß-Thalassaemia Carriers in Huizhou Area of Guangdong Province].

OBJECTIVE: To analyze the prevalence, genotype distribution and hematological characteristics of α,ß-thalassaemia carriers in Huizhou area of Guangdong Province. METHODS: 10 809 carriers of simple ß-thalassaemia and 1 757 carriers of α,ß-thalassaemia were enrolled as our study cohort. The hematological parameters were detected by automated blood cell counters and automatic capillary electrophoresis. Suspension array technology, gap-polymerase chain reaction (gap-PCR) and PCR-reverse dot blot were used for the genotyping of thalassaemia carriers. RESULTS: The prevalence of α,ß-thalassaemia in Huizhou area of Guangdong Province was 1.99%. A total of 62 genotypes were detected, and the most prevalent genotype was --SEA/ αα, ßCD41-42/ ßN (19.29%), the next was --SEA/ αα, ßIVS-II-654/ ßN (16.73%). Significant differences in mean corpuscular volume (MCV) and mean corpuscular hemoglobin (MCH) were found between different genotype groups for simple ß-thalassaemia and α,ß-thalassaemia. Violin plots showed that carriers with co-inheritance of ß-thalassaemia and mild α-thalassaemia expressed the lightest anemia, and carriers with co-inheritance of ß-thalassaemia and hemoglobin H (Hb H) disease expressed the most severe anemia. CONCLUSION: There is a high prevalence of α,ß-thalassaemia in Huizhou area of Guangdong Province. Because of the lack of specific hematological makers for diagnosis of α,ß-thalassaemia, it is necessary to distinguish it from simple ß-thalassaemia by genotyping of α- and ß-thalassaemia in order to correctly guide genetic counseling and prenatal disgnosis.

A Skin-Inspired Multifunctional Conductive Hydrogel with High Stretchable, Adhesive, Healable, and Decomposable Properties for Highly Sensitive Dual-Sensing of Temperature and Strain.

Developing smart hydrogels with excellent physicochemical properties and multi-sensing capabilities for various simulation of human skin's functions still remains a great challenge. Here, based on simple and convenient one-step covalent cross-linking method enhanced by dynamic RS-Ag interactions, a skin-inspired multifunctional conductive hydrogel with desirable physicochemical properties (including high stretchability, self-adhesion, self-healing, decomposition and removability) is developed for highly sensitive dual-sensing of temperature and strain. Benefiting from the synergistic action of multiple hydrogen bonds, RS-Ag bonds and S-S bonds, the gel exhibited a novel thermosensitive mechanism. The prepared hydrogels exhibited extremely high mechanical properties (maximum tensile strength of 0.35 MPa, elongation at break nearly 1800%, compressive stress over 4.43 MPa), excellent self-healing (96.82% (stress), 88.45% (temperature), 73.89% (mechanical property)), decomposition (the molecular weight after decomposition is below 700) and self-adhesion (enhanced contact with the material interface). In addition, this conductive hydrogel could also simultaneously achieve highly sensitive temperature-sensing (TCR: 10.89) and stress-sensing (GF: 1.469). As a proof-to-concept, the hydrogel displayed superior capability for simulation of human skin to perception of touch, pressure and ambient temperature simultaneously, indicating promising applications in the fields of wearable devices, personal health care, and human-machine interfaces.

The mechano-chemical circuit drives skin organoid self-organization.

Stem cells in organoids self-organize into tissue patterns with unknown mechanisms. Here, we use skin organoids to analyze this process. Cell behavior videos show that the morphological transformation from multiple spheroidal units with morphogenesis competence (CMU) to planar skin is characterized by two abrupt cell motility-increasing events before calming down. The self-organizing processes are controlled by a morphogenetic module composed of molecular sensors, modulators, and executers. Increasing dermal stiffness provides the initial driving force (driver) which activates Yap1 (sensor) in epidermal cysts. Notch signaling (modulator 1) in epidermal cyst tunes the threshold of Yap1 activation. Activated Yap1 induces Wnts and MMPs (epidermal executers) in basal cells to facilitate cellular flows, allowing epidermal cells to protrude out from the CMU. Dermal cell-expressed Rock (dermal executer) generates a stiff force bridge between two CMU and accelerates tissue mixing via activating Laminin and ß1-integrin. Thus, this self-organizing coalescence process is controlled by a mechano-chemical circuit. Beyond skin, self-organization in organoids may use similar mechano-chemical circuit structures.

Green-Solvent-Processed High-Performance Broadband Organic Photodetectors.

Solution-processed organic photodetectors with broadband activity have been demonstrated with an environmentally benign solvent, ortho-xylene (o-xylene), as the processing solvent. The organic photodetectors employ a wide band gap polymer donor PBDB-T and a narrow band gap small-molecule non-fullerene acceptor CO1-4F, both dissolvable in o-xylene at a controlled temperature. The o-xylene-processed devices have shown external quantum efficiency of up to 70%, surpassing the counterpart processed with chlorobenzene. With a well-suppressed dark current, the device can also present a high specific detectivity of over 1012 Jones at -2 V within practical operation frequencies and is applicable for photoplethysmography with its fast response. These results further highlight the potential of green-solvent-processed organic photodetectors as a high-performing alternative to their counterparts processed in toxic chlorinated solvents without compromising the excellent photosensing performance.

A Metabolism-Based Interpretable Machine Learning Prediction Model for Diabetic Retinopathy Risk: A Cross-Sectional Study in Chinese Patients with Type 2 Diabetes.

The burden of diabetic retinopathy (DR) is increasing, and the sensitive biomarkers of the disease were not enough. Studies have found that the metabolic profile, such as amino acid (AA) and acylcarnitine (AcylCN), in the early stages of DR patients might have changed, indicating the potential of metabolites to become new biomarkers. We are amid to construct a metabolite-based prediction model for DR risk. This study was conducted on type 2 diabetes (T2D) patients with or without DR. Logistic regression and extreme gradient boosting (XGBoost) prediction models were constructed using the traditional clinical features and the screening features, respectively. Assessing the predictive power of the models in terms of both discrimination and calibration, the optimal model was interpreted using the Shapley Additive exPlanations (SHAP) to quantify the effect of features on prediction. Finally, the XGBoost model incorporating AA and AcylCN variables had the best comprehensive evaluation (ROCAUC = 0.82, PRAUC = 0.44, Brier score = 0.09). C18 : 1OH lower than 0.04 µmol/L, C18 : 1 lower than 0.70 µmol/L, threonine higher than 27.0 µmol/L, and tyrosine lower than 36.0 µmol/L were associated with an increased risk of developing DR. Phenylalanine higher than 52.0 µmol/L was associated with a decreased risk of developing DR. In conclusion, our study mainly used AAs and AcylCNs to construct an interpretable XGBoost model to predict the risk of developing DR in T2D patients which is beneficial in identifying high-risk groups and preventing or delaying the onset of DR. In addition, our study proposed possible risk cut-off values for DR of C18 : 1OH, C18 : 1, threonine, tyrosine, and phenylalanine.

Relationship between plasma glutamate and cardiovascular disease risk in Chinese patients with type 2 diabetes mellitus by gender.

Objectives: This study aimed to assess the association between plasma glutamate (Glu) and the risk of cardiovascular disease (CVD) in patients with type 2 diabetes mellitus (T2DM) and whether this association differs by gender. Material and methods: We retrieved clinical information on 1032 consecutive patients with T2DM from a same tertiary care center from May 2015 to August 2016. Glu was quantified by liquid chromatography-tandem mass spectrometry analysis. Glu was converted into a categorical variable based on the median concentration in the whole population, while logistic regression was used to obtain the odds ratio (OR) and 95% confidence interval (CI), and the correlation between Glu and various biochemical indices was analyzed. Results: We found that Glu was positively associated with the risk of CVD in patients with T2DM. This correlation was more significant in women. In T2DM patients, the higher the age, body mass index (BMI), weight and systolic blood pressure (SBP), the lower the glycosylated hemoglobin (HbA1C) concentration and the higher the Glu. In female patients, the correlation between age, weight, BMI, SBP, and plasma Triglycerides (TG), and Glu was also statistically significant. Conclusion: In conclusion, female T2DM patients with high levels of Glu have a higher risk of developing CVD.

Evaluation of Serum NLRC4 as a Potential Prognostic Biochemical Marker in Humans with Severe Traumatic Brain Injury: A Prospective Cohort Study.

Objective: Involvement of NLR CARD domain containing 4 (NLRC4) in neuroinflammation has been demonstrated. The aim of this study was to ascertain the prognostic role of serum NLRC4 in severe traumatic brain injury (sTBI). Methods: In this prospective cohort study including 140 sTBI patients and 140 controls, serum NLRC4 levels were quantified. Follow-up time was 180 days after trauma and poor prognosis was designated as extended Glasgow outcome scale (GOSE) scores of 1-4. Severity correlations and prognosis associations were determined under multivariate models. Results: Enhanced serum NLRC4 levels after sTBI, in comparison to controls (median, 0.8 ng/mL versus 0.1 ng/mL; P < 0.001), were independently correlated with Glasgow coma scale (GCS) scores (ß, -0.091; 95% confidence interval (CI), -0.161-0.021; P = 0.011), Rotterdam computed tomography (CT) scores (ß, 0.136; 95% CI, 0.024-0.248; P = 0.018), serum C-reactive protein levels (ß, 0.016; 95% CI, 0.002-0.030; P = 0.025) and 180-day GOSE scores (ß, -0.906; 95% CI, -1.632-0.180; P = 0.015); and were independently predictive of 180-day death (odds ratio, 4.307; 95% CI, 1.706-10.879; P = 0.014)), overall survival (hazard ratio, 2.360; 95% CI, 1.118-4.981; P = 0.040) and poor prognosis (odds ratio, 6.705; 95% CI, 2.889-15.561; P = 0.016). Under receiver operating characteristic curve, combination of serum NLRC4 levels, GCS scores and Rotterdam CT scores had significantly higher death predictive ability than Rotterdam CT scores (P = 0.040), but not than GCS scores (P = 0.070); and exhibited substantially higher predictive capability for poor prognosis than Rotterdam CT scores (P < 0.001) and GCS scores alone (P = 0.023). Conclusion: There is a dramatical elevation of serum NLRC4 levels after sTBI, which has strong correlation with severity and inflammation, and is significantly associated with long-term death and poor outcome, substantializing serum NLRC4 as an inflammatory, prognostic biomarker in sTBI.

Associations of serum amino acids related to urea cycle with risk of chronic kidney disease in Chinese with type 2 diabetes.

Objective: Serum levels of amino acids related to urea cycle are associated with risk of type 2 diabetes mellitus (T2DM). Our study aimed to explore whether serum levels of amino acids related to urea cycle, i.e., arginine, citrulline, and ornithine, are also associated with increased risk of chronic kidney disease (CKD) in T2DM. Methods: We extracted medical records of 1032 consecutive patients with T2DM from the Electronic Administrative System of Liaoning Medical University First Affiliated Hospital (LMUFAH) system from May 2015 to August 2016. Of them, 855 patients with completed data available were used in the analysis. CKD was defined as estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2. Serum amino acids were measured by mass spectrometry (MS) technology. Binary logistic regression was performed to obtain odds ratios (ORs) and their 95% confidence intervals (CIs). Results: 52.3% of the 855 T2DM patients were male, and 143 had CKD. In univariable analysis, high serum citrulline, high ratio of arginine to ornithine, and low ratio of ornithine to citrulline were associated with markedly increased risk of CKD (OR of top vs. bottom tertile: 2.87, 95%CI, 1.79-4.62 & 1.98, 95%CI,1.25-3.14 & 2.56, 95%CI, 1.61-4.07, respectively). In multivariable analysis, the ORs of citrulline and ornithine/citrulline ratio for CKD remained significant (OR of top vs. bottom tertile: 2.22, 95%CI, 1.29-3.82 & 2.24, 1.29-3.87, respectively). Conclusions: In Chinese patients with T2DM, high citrulline and low ornithine/citrulline ratio were associated with increased risk of CKD.

Prevalence of Diabetic Retinopathy and Use of Common Oral Hypoglycemic Agents Increase the Risk of Diabetic Nephropathy-A Cross-Sectional Study in Patients with Type 2 Diabetes.

OBJECTIVE: This study investigated the effect of amino acid metabolism on the risk of diabetic nephropathy under different conditions of the diabetic retinopathy, and the use of different oral hypoglycemic agents. METHODS: This study retrieved 1031 patients with type 2 diabetes from the First Affiliated Hospital of Liaoning Medical University in Jinzhou, which is located in Liaoning Province, China. We conducted a spearman correlation study between diabetic retinopathy and amino acids that have an impact on the prevalence of diabetic nephropathy. Logistic regression was used to analyze the changes of amino acid metabolism in different diabetic retinopathy conditions. Finally, the additive interaction between different drugs and diabetic retinopathy was explored. RESULTS: It is showed that the protective effect of some amino acids on the risk of developing diabetic nephropathy is masked in diabetic retinopathy. Additionally, the additive effect of the combination of different drugs on the risk of diabetic nephropathy was greater than that of any one drug alone. CONCLUSIONS: We found that diabetic retinopathy patients have a higher risk of developing diabetic nephropathy than the general type 2 diabetes population. Additionally, the use of oral hypoglycemic agents can also increase the risk of diabetic nephropathy.

Inhibition of human sulfotransferases (SULTs) by per- and polyfluoroalkyl substances (PFASs) and structure-activity relationship.

Per- and polyfluoroalkyl substances (PFASs) are a family of highly fluorinated aliphatic substances widely used in industrial and commercial applications. This study aims to determine the inhibition of PFASs towards sulfotransferases (SULTs) activity, and trying to explain the toxicity mechanism of PFASs. In vitro recombinant SULTs-catalyzed sulfation of p-nitrophenol (PNP) was utilized as a probe reaction. The incubation system was consisted of PFASs, SULTs, PNP, 3'-phosphoadenosine-5'-phosphosulfate, MgCl2 and Tris-HCl buffer. Ultra-performance liquid chromatography was employed for analysis of the metabolites. All tested PFASs showed inhibition towards SULTs. The longer the carbon chain length of the PFASs terminated with -COOH, the higher is its capability of inhibiting SULT1A3. PFASs with -SO3H had a relatively higher ability to inhibit SULT1A3 activity than those with -COOH, -I and -OH. The inhibition kinetic parameter was 2.16 and 1.42 µM for PFOS-SULT1A1, PFTA-SULT1B1. In vitro in vivo extrapolation showed that the concentration of PFOS and PFTA in human matrices might be higher than the threshold for inducing inhibition of SULTs. Therefore, PFASs could interfere with the metabolic pathways catalyzed by SULTs in vivo. All these results will help to understand the toxicity of PFASs from the perspective of metabolism.

MRTF-A alleviates myocardial ischemia reperfusion injury by inhibiting the inflammatory response and inducing autophagy.

Myocardin-related transcription factor A (MRTF-A) has an inhibitory effect on myocardial infarction; however, the mechanism is not clear. This study reveals the mechanism by which MRTF-A regulates autophagy to alleviate myocardial infarct-mediated inflammation, and the effect of silent information regulator 1 (SIRT1) on the myocardial protective effect of MRTF-A was also verified. MRTF-A significantly decreased cardiac damage induced by myocardial ischemia. In addition, MRTF-A decreased NLRP3 inflammasome activity, and significantly increased the expression of autophagy protein in myocardial ischemia tissue. Lipopolysaccharide (LPS) and 3-methyladenine (3-MA) eliminated the protective effects of MRTF-A. Furthermore, simultaneous overexpression of MRTF-A and SIRT1 effectively reduced the injury caused by myocardial ischemia; this was associated with downregulation of inflammatory factor proteins and when upregulation of autophagy-related proteins. Inhibition of SIRT1 activity partially suppressed these MRTF-A-induced cardioprotective effects. SIRT1 has a synergistic effect with MRTF-A to inhibit myocardial ischemia injury through reducing the inflammation response and inducing autophagy.

DomBpred: Protein Domain Boundary Prediction Based on Domain-Residue Clustering Using Inter-Residue Distance.

Domain boundary prediction is one of the most important problems in the study of protein structure and function, especially for large proteins. At present, most domain boundary prediction methods have low accuracy and limitations in dealing with multi-domain proteins. In this study, we develop a sequence-based protein domain boundary prediction, named DomBpred. In DomBpred, the input sequence is first classified as either a single-domain protein or a multi-domain protein through a designed effective sequence metric based on a constructed single-domain sequence library. For the multi-domain protein, a domain-residue clustering algorithm inspired by Ising model is proposed to cluster the spatially close residues according inter-residue distance. The unclassified residues and the residues at the edge of the cluster are then tuned by the secondary structure to form potential cut points. Finally, a domain boundary scoring function is proposed to recursively evaluate the potential cut points to generate the domain boundary. DomBpred is tested on a large-scale test set of FUpred comprising 2549 proteins. Experimental results show that DomBpred better performs than the state-of-the-art methods in classifying whether protein sequences are composed by single or multiple domains, and the Matthew's correlation coefficient is 0.882. Moreover, on 849 multi-domain proteins, the domain boundary distance and normalised domain overlap scores of DomBpred are 0.523 and 0.824, respectively, which are 5.0% and 4.2% higher than those of the best comparison method, respectively. Comparison with other methods on the given test set shows that DomBpred outperforms most state-of-the-art sequence-based methods and even achieves better results than the top-level template-based method. The executable program is freely available at https://github.com/iobio-zjut/DomBpred and the online server at http://zhanglab-bioinf.com/DomBpred/.

KW-2449 and VPA exert therapeutic effects on human neurons and cerebral organoids derived from MECP2-null hESCs.

BACKGROUND: Rett syndrome (RTT), mainly caused by mutations in methyl-CpG binding protein 2 (MECP2), is one of the most prevalent neurodevelopmental disorders in girls. However, the underlying mechanism of MECP2 remains largely unknown and currently there is no effective treatment available for RTT. METHODS: We generated MECP2-KO human embryonic stem cells (hESCs), and differentiated them into neurons and cerebral organoids to investigate phenotypes of MECP2 loss-of-function, potential therapeutic agents, and the underlying mechanism by transcriptome sequencing. RESULTS: We found that MECP2 deletion caused reduced number of hESCs-derived neurons and simplified dendritic morphology. Moreover, MECP2-KO cortical organoids exhibited fewer neural progenitor cells and neurons at day 60. Electrophysiological recordings showed that MECP2 deletion altered synaptic activity in organoids. Transcriptome analysis of organoids identified many genes in the PI3K-AKT pathway downregulated following MECP2 deletion. Treatment with either KW-2449 or VPA, small molecules for the activation of PI3K-AKT signaling pathway, alleviated neuronal deficits and transcriptome changes in MECP2-KO human neuronal models. CONCLUSIONS: These findings suggest that KW-2449 and VPA might be promising drugs for RTT treatment.