ESPDHot: An Effective Machine Learning-Based Approach for Predicting Protein-DNA Interaction Hotspots.

Protein-DNA interactions are pivotal to various cellular processes. Precise identification of the hotspot residues for protein-DNA interactions holds great significance for revealing the intricate mechanisms in protein-DNA recognition and for providing essential guidance for protein engineering. Aiming at protein-DNA interaction hotspots, this work introduces an effective prediction method, ESPDHot based on a stacked ensemble machine learning framework. Here, the interface residue whose mutation leads to a binding free energy change (ΔΔG) exceeding 2 kcal/mol is defined as a hotspot. To tackle the imbalanced data set issue, the adaptive synthetic sampling (ADASYN), an oversampling technique, is adopted to synthetically generate new minority samples, thereby rectifying data imbalance. As for molecular characteristics, besides traditional features, we introduce three new characteristic types including residue interface preference proposed by us, residue fluctuation dynamics characteristics, and coevolutionary features. Combining the Boruta method with our previously developed Random Grouping strategy, we obtained an optimal set of features. Finally, a stacking classifier is constructed to output prediction results, which integrates three classical predictors, Support Vector Machine (SVM), XGBoost, and Artificial Neural Network (ANN) as the first layer, and Logistic Regression (LR) algorithm as the second one. Notably, ESPDHot outperforms the current state-of-the-art predictors, achieving superior performance on the independent test data set, with F1, MCC, and AUC reaching 0.571, 0.516, and 0.870, respectively.

PMSPcnn: Predicting protein stability changes upon single point mutations with convolutional neural network.

Protein missense mutations and resulting protein stability changes are important causes for many human genetic diseases. However, the accurate prediction of stability changes due to mutations remains a challenging problem. To address this problem, we have developed an unbiased effective model: PMSPcnn that is based on a convolutional neural network. We have included an anti-symmetry property to build a balanced training dataset, which improves the prediction, in particular for stabilizing mutations. Persistent homology, which is an effective approach for characterizing protein structures, is used to obtain topological features. Additionally, a regression stratification cross-validation scheme has been proposed to improve the prediction for mutations with extreme ΔΔG. For three test datasets: Ssym, p53, and myoglobin, PMSPcnn achieves a better performance than currently existing predictors. PMSPcnn also outperforms currently available methods for membrane proteins. Overall, PMSPcnn is a promising method for the prediction of protein stability changes caused by single point mutations.

Longitudinal skeletal growth and growth plate morphological characteristics of chondro-tissue specific CUL7 knockout mice.

BACKGROUND: 3 M syndrome is first reported in 1975，which characterized by severe pre- and postnatal growth retardation, skeletal malformation and facial dysmorphism. These three genes (CUL7, OBSL1 and CCDC8) have been identified to be respond for 3 M syndrome, of which CUL7 is accounting for approximately 70%. To date, the molecular mechanism underlying the pathogenesis of 3 M syndrome remains poorly understood. Previous studies showed that no Cul7-/- mice could survive after birth, because of growth retardation at late gestational stage and respiratory distress after birth. The establishment of the animal model of cartilage specific Cul7 knockout mice (Cul7fl/fl;Col2a1-CreERT2 mice) has confirmed that Cul7fl/fl;Col2a1-CreERT2 mice can be selective in a time- and tissue-dependent manner, which can provide an experimental basis for further research on severe genetic diseases related to growth plates. OBJECTIVE: To establish a model of Cul7fl/fl;Col2a1-CreERT2 mice based on Cre/LoxP system, and to further observe its phenotype and morphological changes in growth plate. METHODS: The Cul7fl/fl;Col2a1-CreERT2 mice were taken as the experimental group, while the genotype of Cul7fl/+;Col2a1-CreERT2 mice were used as the control group. The gross morphological features and X-ray films of limbs in the two groups were observed every week for 3-6 consecutive weeks, and the length of the mice from nose to the tail, the length of femur and tibia were recorded. In the meantime, The histological morphology of tibial growth plates was compared between the two groups. RESULTS: A preliminary model of Cul7fl/fl;Col2a1-CreERT2 mice was established. The Cul7fl/fl;Col2a1-CreERT2 mice had abnormally short and deformed limbs (P<0.05), increased thickness of growth plate, the disorderly arranged chondrocyte columns, decreased number of cells in the proliferation zone, changes in the shape from flat to round, obviously expanded extracellular matrix, and disordered arrangement, thickening and loosening of bone trabecula at the proximal metaphysis of the femur. CONCLUSIONS: The knockout of Cul7 gene may affect both the proliferation of chondrocytes and the endochondral osteogenesis, confirming that Cul7 is essential for the normal development of bone in the body.

Insights into Activation Dynamics and Functional Sites of Inwardly Rectifying Potassium Channel Kir3.2 by an Elastic Network Model Combined with Perturbation Methods.

The inwardly rectifying potassium channel Kir3.2, a member of the inward rectifier potassium (Kir) channel family, exerts important biological functions through transporting potassium ions outside of the cell, during which a large-scale synergistic movement occurs among its different domains. Currently, it is not fully understood how the binding of the ligand to the Kir3.2 channel leads to the structural changes and which key residues are responsible for the channel gating and allosteric dynamics. Here, we construct the Gaussian network model (GNM) of the Kir3.2 channel with the secondary structure and covalent interaction information considered (sscGNM), which shows a better performance in reproducing the channel's flexibility compared with the traditional GNM. In addition, the sscANM-based perturbation method is used to simulate the channel's conformational transition caused by the activator PIP2's binding. By applying certain forces to the PIP2 binding pocket, the coarse-grained calculations generate the similar conformational changes to the experimental observation, suggesting that the topology structure as well as PIP2 binding are crucial to the allosteric activation of the Kir3.2 channel. We also utilize the sscGNM-based thermodynamic cycle method developed by us to identify the key residues whose mutations significantly alter the channel's binding free energy with PIP2. We identify not only the residues important for the specific binding but also the ones critical for the allosteric transition coupled with PIP2 binding. This study is helpful for understanding the working mechanism of Kir3.2 channels and can provide important information for related drug design.

Role of gut microbiota in the pathogenesis and therapeutics of minimal hepatic encephalopathy via the gut-liver-brain axis.

Minimal hepatic encephalopathy (MHE) is a frequent neurological and psychiatric complication of liver cirrhosis. The precise pathogenesis of MHE is complicated and has yet to be fully elucidated. Studies in cirrhotic patients and experimental animals with MHE have indicated that gut microbiota dysbiosis induces systemic inflammation, hyperammonemia, and endotoxemia, subsequently leading to neuroinflammation in the brain via the gut-liver-brain axis. Related mechanisms initiated by gut microbiota dysbiosis have significant roles in MHE pathogenesis. The currently available therapeutic strategies for MHE in clinical practice, including lactulose, rifaximin, probiotics, synbiotics, and fecal microbiota transplantation, exert their effects mainly by modulating gut microbiota dysbiosis. Microbiome therapies for MHE have shown promised efficacy and safety; however, several controversies and challenges regarding their clinical use deserve to be intensively discussed. We have summarized the latest research findings concerning the roles of gut microbiota dysbiosis in the pathogenesis of MHE via the gut-liver-brain axis as well as the potential mechanisms by which microbiome therapies regulate gut microbiota dysbiosis in MHE patients.

Altered gut microbiota is associated with sleep disturbances in patients with minimal hepatic encephalopathy caused by hepatitis B-related liver cirrhosis.

BACKGROUND: Sleep disturbances are prevalent in patients with minimal hepatic encephalopathy (MHE). This study aimed to evaluate the association between sleep disturbances and altered gut microbiota in patients with MHE caused by hepatitis B-related liver cirrhosis. RESEARCH DESIGN AND METHODS: Ninety-eight and 45 patients with MHE were included in exploration and validation cohorts, respectively. Sleep disturbances were assessed using the Chinese version of the Pittsburgh Sleep Quality Index (PSQI) questionnaire. Microbiota in fecal samples were analyzed via amplicon sequencing of bacterial 16S ribosomal RNA genes. RESULTS: The gut microbiomes of MHE patients with sleep disturbances were characterized by lower bacterial diversity and distinct bacterial composition. Relative abundances of Streptococcus salivarius and Veillonella were independent predictors of sleep disturbances in MHE patients and well-distinguished MHE patients with and without sleep disturbances in both the exploration and validation cohorts. Moreover, the relative abundances of S. salivarius were positively correlated with plasma ammonia levels, and functional modules associated with protein digestion and absorption and lipopolysaccharide biosynthesis were enriched in the microbiomes of MHE patients with sleep disturbances. CONCLUSIONS: Both S. salivarius and Veillonella were associated with sleep disturbances in patients with MHE caused by hepatitis B-related liver cirrhosis.

Attenuated ZHX3 expression is predictive of poor outcome for liver cancer: Indication for personalized therapy.

The zinc-fingers and homeoboxes (ZHX) family members have been characterized as master regulators in cancer initiation and development. The present study performed in silico data-mining with publicly available datasets and immunohistochemistry to assess the expression status of ZHX factors and the corresponding prognostic implications in liver cancer. Increased ZHX3 mRNA expression was associated with favorable overall survival in patients with liver cancer. Subgroups analyses revealed a significant association between the expression of ZHX factors and outcomes in select patient cohorts. Immunohistochemical analysis supported that ZHX3 expression was an independent prognostic indicator for patient survival. These results suggested that dysregulation of ZHX factors is involved in disease progression and ZHX3 expression may serve as a prognostic biomarker for liver cancer.

EncephalApp stroop test versus electronic number connection test-A for screening minimal hepatic encephalopathy in patients with liver cirrhosis: a comparative study.

OBJECTIVES: Minimal hepatic encephalopathy (MHE) is a common neuropsychiatric complication of liver cirrhosis. Both EncephalApp Stroop test (EncephalApp) and electronic number connection test-A (eNCT-A) are novel computerised psychometric tests for MHE screening. We aimed to compare the efficiency, convenience, accessibility, and acceptability of EncephalApp with that of eNCT-A for MHE screening in cirrhotic patients. METHODS: Ninety-five patients with hepatitis B-induced liver cirrhosis were included and respectively tested by the psychometric hepatic encephalopathy score (PHES), EncephalApp, and eNCT-A. Using PHES as the gold standard for MHE diagnosis, the efficiency of EncephalApp and eNCT-A for MHE screening were respectively analysed by the receiver operating characteristic (ROC) curve, and the areas under the ROC curve (AUROC) were compared. The convenience, accessibility, and acceptability of PHES, EncephalApp and eNCT-A were respectively evaluated by the 5-point Likert scale. RESULTS: Fifty-two (55%) of included cirrhotic patients were diagnosed with MHE. The EncephalApp had a sensitivity of 84.6%, a specificity of 74.4%, and an AUROC of 0.836. Meanwhile, the eNCT-A had a sensitivity of 78.8%, a specificity of 83.7%, and an AUROC of 0.845. No significant difference in AUROC was detected between the EncephalApp and eNCT-A (p = .453). Compared with the EncephalApp, the eNCT-A presented better convenience and higher acceptability in cirrhotic patients undergoing MHE screening (p = .019 and p < .001, respectively). CONCLUSIONS: As with the EncephalApp, the eNCT-A will be a potential home monitoring and point-of-care tool for cirrhotic patients at high risk of MHE.