emPDBA: protein-DNA binding affinity prediction by combining features from binding partners and interface learned with ensemble regression model.

Protein-deoxyribonucleic acid (DNA) interactions are important in a variety of biological processes. Accurately predicting protein-DNA binding affinity has been one of the most attractive and challenging issues in computational biology. However, the existing approaches still have much room for improvement. In this work, we propose an ensemble model for Protein-DNA Binding Affinity prediction (emPDBA), which combines six base models with one meta-model. The complexes are classified into four types based on the DNA structure (double-stranded or other forms) and the percentage of interface residues. For each type, emPDBA is trained with the sequence-based, structure-based and energy features from binding partners and complex structures. Through feature selection by the sequential forward selection method, it is found that there do exist considerable differences in the key factors contributing to intermolecular binding affinity. The complex classification is beneficial for the important feature extraction for binding affinity prediction. The performance comparison of our method with other peer ones on the independent testing dataset shows that emPDBA outperforms the state-of-the-art methods with the Pearson correlation coefficient of 0.53 and the mean absolute error of 1.11 kcal/mol. The comprehensive results demonstrate that our method has a good performance for protein-DNA binding affinity prediction. Availability and implementation: The source code is available at https://github.com/ChunhuaLiLab/emPDBA/.

Identification of metal ion-binding sites in RNA structures using deep learning method.

Metal ion is an indispensable factor for the proper folding, structural stability and functioning of RNA molecules. However, it is very difficult for experimental methods to detect them in RNAs. With the increase of experimentally resolved RNA structures, it becomes possible to identify the metal ion-binding sites in RNA structures through in-silico methods. Here, we propose an approach called Metal3DRNA to identify the binding sites of the most common metal ions (Mg2+, Na+ and K+) in RNA structures by using a three-dimensional convolutional neural network model. The negative samples, screened out based on the analysis for binding surroundings of metal ions, are more like positive ones than the randomly selected ones, which are beneficial to a powerful predictor construction. The microenvironments of the spatial distributions of C, O, N and P atoms around a sample are extracted as features. Metal3DRNA shows a promising prediction power, generally surpassing the state-of-the-art methods FEATURE and MetalionRNA. Finally, utilizing the visualization method, we inspect the contributions of nucleotide atoms to the classification in several cases, which provides a visualization that helps to comprehend the model. The method will be helpful for RNA structure prediction and dynamics simulation study. Availability and implementation: The source code is available at https://github.com/ChunhuaLiLab/Metal3DRNA.

Persistent spectral simplicial complex-based machine learning for chromosomal structural analysis in cellular differentiation.

The three-dimensional (3D) chromosomal structure plays an essential role in all DNA-templated processes, including gene transcription, DNA replication and other cellular processes. Although developing chromosome conformation capture (3C) methods, such as Hi-C, which can generate chromosomal contact data characterized genome-wide chromosomal structural properties, understanding 3D genomic nature-based on Hi-C data remains lacking. Here, we propose a persistent spectral simplicial complex (PerSpectSC) model to describe Hi-C data for the first time. Specifically, a filtration process is introduced to generate a series of nested simplicial complexes at different scales. For each of these simplicial complexes, its spectral information can be calculated from the corresponding Hodge Laplacian matrix. PerSpectSC model describes the persistence and variation of the spectral information of the nested simplicial complexes during the filtration process. Different from all previous models, our PerSpectSC-based features provide a quantitative global-scale characterization of chromosome structures and topology. Our descriptors can successfully classify cell types and also cellular differentiation stages for all the 24 types of chromosomes simultaneously. In particular, persistent minimum best characterizes cell types and Dim (1) persistent multiplicity best characterizes cellular differentiation. These results demonstrate the great potential of our PerSpectSC-based models in polymeric data analysis.

IgT-mediated mucosal immunity and microbiota dynamics in snakehead (Channa argus) post Aeromonas veronii TH0426 and Aeromonas hydrophila TPS infection: implications for aquaculture disease management.

The aquaculture sector, vital to global food security, grapples with bacterial pathogens compromising fish health and industry sustainability. This investigation probes mucosal immune responses and gut microbiota dynamics in snakehead (Channa argus) post-Aeromonas infection, a prevalent aquaculture challenge. Employing infection models, we delineated the integral role of immunoglobulin T (IgT) in mucosal immunity and its interaction with gut microbiota. Fish from a local farm, maintained under controlled conditions, were infected with Aeromonas veronii TH0426 and Aeromonas hydrophila TPS. Post-infection, daily monitoring and sample collection at specified intervals were conducted for comprehensive analysis. Histopathology, quantitative PCR, immunofluorescence, and microbiota profiling revealed significant immune and microbial changes, particularly at day 7. Intestinal IgT, IgM, and pIgR gene expression surged, indicative of a robust response. Immunofluorescence microscopy confirmed increased IgT+ and pIgR+ cell infiltration in the epithelium. Post-infection dysbiosis, with altered bacterial composition, was partially offset by elevated IgT levels. These insights underscore IgT's crucial function in mucosal defense and suggest potential for probiotic and vaccine strategies to enhance aquaculture disease resilience.

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.

RNAfcg: RNA Flexibility Prediction Based on Topological Centrality and Global Features.

The dynamics of RNAs are related intimately to their functions. Molecular flexibility, as a starting point for understanding their dynamics, has been utilized to predict many characteristics associated with their functions. Since the experimental measurement methods are time-consuming and labor-intensive, it is urgently needed to develop reliable theoretical methods to predict RNA flexibility. In this work, we develop an effective machine learning method, RNAfcg, to predict RNA flexibility, where the Random Forest (RF) is trained by features including the topological centralities, flexibility-rigidity index, and global characteristics first introduced by us, as well as some traditional sequence and structural features. The analyses show that the three types of features introduced first have significant contributions to RNA flexibility prediction, among which the topological type contributes the most, which indicates the importance of structural topology in determining RNA flexibility. The performance comparison indicates that RNAfcg outperforms the state-of-the-art machine learning methods and the commonly used Gaussian Network Model (GNM) models, achieving a much higher Pearson correlation coefficient (PCC) of 0.6619 on the test data set. This work is helpful for understanding RNA dynamics and can be used to predict RNA function information. The source code is available at https://github.com/ChunhuaLab/RNAfcg/.

Prediction of Protein Allosteric Sites with Transfer Entropy and Spatial Neighbor-Based Evolutionary Information Learned by an Ensemble Model.

Allostery is one of the most direct and efficient ways to regulate protein functions. The diverse allosteric sites make it possible to design allosteric modulators of differential selectivity and improved safety compared with those of orthosteric drugs targeting conserved orthosteric sites. Here, we develop an ensemble machine learning method AllosES to predict protein allosteric sites in which the new and effective features are utilized, including the entropy transfer-based dynamic property, secondary structure features, and our previously proposed spatial neighbor-based evolutionary information besides the traditional physicochemical properties. To overcome the class imbalance problem, the multiple grouping strategy is proposed, which is applied to feature selection and model construction. The ensemble model is constructed where multiple submodels are trained on multiple training subsets, respectively, and their results are then integrated to be the final output. AllosES achieves a prediction performance of 0.556 MCC on the independent test set D24, and additionally, AllosES can rank the real allosteric sites in the top three for 83.3/89.3% of allosteric proteins from the test set D24/D28, outperforming the state-of-the-art peer methods. The comprehensive results demonstrate that AllosES is a promising method for protein allosteric site prediction. The source code is available at https://github.com/ChunhuaLab/AllosES.

Factors Influencing Iatrogenic Skin Injury in Neonates and Nursing Strategies.

Objective: Iatrogenic skin injury is a common neonatal skin problem that can have a severe impact on the health and life of newborns. The purpose of this study was to explore the factors influencing iatrogenic skin injury in neonates, identify and correct nursing behaviors that may lead to skin damage, thereby reduce the occurrence of skin damage and protect the health of newborns. Methods: The clinical data of 87 neonates with iatrogenic skin injury admitted to the Department of Neonatology of Shangrao People's Hospital, China, between January and June 2022, were retrospectively collected as a research group. The causes of iatrogenic skin injury were statistically analyzed. 50 neonates without iatrogenic skin injury in the same department during the same period were selected as the control group. The general data of the two groups were contracted, and the independent risk factors affecting iatrogenic skin injury in neonates were explored using multivariate Logistic regression. The corresponding nursing strategies were analyzed. Result: (1) Among the 87 neonates with iatrogenic skin injury, the causes included adhesive dressing stripping (41.38%, 36/87), skin scratch during blue light phototherapy (25.29%, 22/87), diaper dermatitis (20.69%, 18/87), and skin pressure redness related to ventilator and continuous positive airway pressure (CPAP) (12.64%, 11/87). (2) The gestational age, birth weight, length of stay, use of noninvasive mechanical ventilation, orotracheal intubation, gastric tube, PICC catheterization, and skin allergy history of the two groups had statistically significant differences (P < .05). (3) The results of multivariate Logistic regression analysis indicated that the length of stay (OR=2.994, 95% CI=1.341~6.686), orotracheal intubation use (OR=0.015, 95% CI=0.004~0.060), and gastric tube use (OR=17.132, 95% CI=5.231~56.108) were independent risk factors of iatrogenic skin injury in neonates (P < .05). Conclusion: Length of stay, orotracheal intubation use, and gastric tube use are independent risk factors for iatrogenic skin injury in neonates. Hospital stays and unnecessary use of orotracheal intubation and gastric tube should be reduced in future clinical management. Attention should be paid to strengthening skin observation and care, keeping skin dry and clean, and preventing iatrogenic skin injury.

The Role of Microglia with Mitochondrial Dysfunction and Its Therapeutic Prospects in Alzheimer's Disease.

Alzheimer's disease (AD), a primary cause of dementia, is rapidly emerging as one of the most financially taxing, lethal, and burdensome diseases of the 21st century. Increasing evidence suggests that microglia-mediated neuroinflammation plays a key role in both the initiation and progression of AD. Recently, emerging evidence has demonstrated mitochondrial dysfunction, particular in microglia where precedes neuroinflammation in AD. Multiple signaling pathways are implicated in this process and pharmaceutical interventions are potentially involved in AD treatment. In this review, advance over the last five years in the signaling pathways and pharmaceutical interventions are summarized and it is proposed that targeting the signaling pathways in microglia with mitochondrial dysfunction could represent a novel direction for AD treatment.

A united framework modeling of spatial-temporal characteristics for county-level agricultural carbon emission with an application to Hunan in China.

Effectively tackling extreme climate change requires sound knowledge about carbon emissions and their driving forces. Currently, agricultural carbon emission assessment often deals with its inventory, efficiency, determinants, and response independently, which will leave out the complex interactions among its various components, thus there is a lack of comprehensive, scalable, comparable explanations for agricultural carbon emissions. Herein, we introduce an integrated agricultural carbon emission assessment framework (IEDR): Inventory (I) × Efficiency (E) × Determinants (D) × Response (R), which was then applied to an illustration for the county-level agricultural carbon emissions in Hunan Province, China. Results show that: (1) Agricultural carbon emission inventory (ACEI) increased from 20.06 × 106 tC in 2006 to 21.99 × 106 tC in 2014 and decreased to 19.07 × 106 tC by 2020, depicting a fluctuating trend. Meanwhile, there was remarkable spatial heterogeneity, with higher ACEI in the North and South than in the East and West. (2) Agricultural carbon emission efficiency (ACEE) increased from 0.8520 in 2006 to 0.8992 in 2020, depicting a growing trend driven by technological progress. Spatially distributed in contrast to ACEI, regions with higher ACEE were located in the eastern and western areas. (3) ACEI was negatively correlated with ACEE (-0.657), indicating that increasing ACEE is a key strategy for reducing emissions. (4) The natural environment, rural development level, and policy support had critical impacts on ACEE and ACEI. In particular, the cultivated area and rural water affairs development were significant influences on ACEE and ACEI. Given the externalities of carbon emissions and its important public goods characteristics of the atmosphere, local carbon issues are also global concerns. Therefore, the case study of the IEDR model not only validates this theoretical paradigm and realizes regional responsibility for global carbon reduction but also supports and expands the theoretical and empirical corpus in the field of agricultural carbon emissions and efficiency, providing insights and references for other global regions facing similar challenges.

Transcriptomic and metabolomic analyses reveals keys genes and metabolic pathways in tea (Camellia sinensis) against six-spotted spider mite (Eotetranychus Sexmaculatus).

BACKGROUND: Six-spotted spider mite (Eotetranychus sexmaculatus) is one of the most damaging pests of tea (Camellia sinensis). E. sexmaculatus causes great economic loss and affects tea quality adversely. In response to pests, such as spider mites, tea plants have evolved resistance mechanisms, such as expression of defense-related genes and defense-related metabolites. RESULTS: To evaluate the biochemical and molecular mechanisms of resistance in C. sinensis against spider mites, "Tianfu-5" (resistant to E. sexmaculatus) and "Fuding Dabai" (susceptible to E. sexmaculatus) were inoculated with spider mites. Transcriptomics and metabolomics based on RNA-Seq and liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) technology were used to analyze changes in gene expression and metabolite content, respectively. RNA-Seq data analysis revealed that 246 to 3,986 differentially expressed genes (DEGs) were identified in multiple compared groups, and these DEGs were significantly enriched in various pathways, such as phenylpropanoid and flavonoid biosynthesis, plant-pathogen interactions, MAPK signaling, and plant hormone signaling. Additionally, the metabolome data detected 2,220 metabolites, with 194 to 260 differentially abundant metabolites (DAMs) identified in multiple compared groups, including phenylalanine, lignin, salicylic acid, and jasmonic acid. The combined analysis of RNA-Seq and metabolomic data indicated that phenylpropanoid and flavonoid biosynthesis, MAPK signaling, and Ca2+-mediated PR-1 signaling pathways may contribute to spider mite resistance. CONCLUSIONS: Our findings provide insights for identifying insect-induced genes and metabolites and form a basis for studies on mechanisms of host defense against spider mites in C. sinensis. The candidate genes and metabolites identified will be a valuable resource for tea breeding in response to biotic stress.

An ensemble approach to predict binding hotspots in protein-RNA interactions based on SMOTE data balancing and Random Grouping feature selection strategies.

MOTIVATION: The identification of binding hotspots in protein-RNA interactions is crucial for understanding their potential recognition mechanisms and drug design. The experimental methods have many limitations, since they are usually time-consuming and labor-intensive. Thus, developing an effective and efficient theoretical method is urgently needed. RESULTS: Here, we present SREPRHot, a method to predict hotspots, defined as the residues whose mutation to alanine generate a binding free energy change ≥2.0 kcal/mol, while others use a cutoff of 1.0 kcal/mol to obtain balanced datasets. To deal with the dataset imbalance, Synthetic Minority Over-sampling Technique (SMOTE) is utilized to generate minority samples to achieve a dataset balance. Additionally, besides conventional features, we use two types of new features, residue interface propensity previously developed by us, and topological features obtained using node-weighted networks, and propose an effective Random Grouping feature selection strategy combined with a two-step method to determine an optimal feature set. Finally, a stacking ensemble classifier is adopted to build our model. The results show SREPRHot achieves a good performance with SEN, MCC and AUC of 0.900, 0.557 and 0.829 on the independent testing dataset. The comparison study indicates SREPRHot shows a promising performance. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/ChunhuaLiLab/SREPRHot. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The application of HER2 and CD47 CAR-macrophage in ovarian cancer.

BACKGROUND: The chimeric antigen receptor (CAR)-T therapy has a limited therapeutic effect on solid tumors owing to the limited CAR-T cell infiltration into solid tumors and the inactivation of CAR-T cells by the immunosuppressive tumor microenvironment. Macrophage is an important component of the innate and adaptive immunity, and its unique phagocytic function has been explored to construct CAR macrophages (CAR-Ms) against solid tumors. This study aimed to investigate the therapeutic application of CAR-Ms in ovarian cancer. METHODS: In this study, we constructed novel CAR structures, which consisted of humanized anti-HER2 or CD47 scFv, CD8 hinge region and transmembrane domains, as well as the 4-1BB and CD3ζ intracellular domains. We examined the phagocytosis of HER2 CAR-M and CD47 CAR-M on ovarian cancer cells and the promotion of adaptive immunity. Two syngeneic tumor models were used to estimate the in vivo antitumor activity of HER2 CAR-M and CD47 CAR-M. RESULTS: We constructed CAR-Ms targeting HER2 and CD47 and verified their phagocytic ability to ovarian cancer cells in vivo and in vitro. The constructed CAR-Ms showed antigen-specific phagocytosis of ovarian cancer cells in vitro and could activate CD8+ cytotoxic T lymphocyte (CTL) to secrete various anti-tumor factors. For the in vivo model, mice with human-like immune systems were used. We found that CAR-Ms enhanced CD8+ T cell activation, affected tumor-associated macrophage (TAM) phenotype, and led to tumor regression. CONCLUSIONS: We demonstrated the inhibition effect of our constructed novel HER2 CAR-M and CD47 CAR-M on target antigen-positive ovarian cancer in vitro and in vivo, and preliminarily verified that this inhibitory effect is due to phagocytosis, promotion of adaptive immunity and effect on tumor microenvironment.

Dynamic Insights into the Self-Activation Pathway and Allosteric Regulation of the Orphan G-Protein-Coupled Receptor GPR52.

Within over 800 members of G-protein-coupled receptors, there are numerous orphan receptors whose endogenous ligands are largely unknown, providing many opportunities for novel drug discovery. However, the lack of an in-depth understanding of the intrinsic working mechanism for orphan receptors severely limits the related rational drug design. The G-protein-coupled receptor 52 (GPR52) is a unique orphan receptor that constitutively increases cellular 5'-cyclic adenosine monophosphate (cAMP) levels without binding any exogenous agonists and has been identified as a promising therapeutic target for central nervous system disorders. Although recent structural biology studies have provided snapshots of both active and inactive states of GPR52, the mechanism of the conformational transition between these states remains unclear. Here, an acceptable self-activation pathway for GPR52 was proposed through 6 µs Gaussian accelerated molecular dynamics (GaMD) simulations, in which the receptor spontaneously transitions from the active state to that matching the inactive crystal structure. According to the three intermediate states of the receptor obtained by constructing a reweighted potential of mean force, how the allosteric regulation occurs between the extracellular orthosteric binding pocket and the intracellular G-protein-binding site is revealed. Combined with the independent gradient model, several important microswitch residues and the allosteric communication pathway that directly links the two regions are both identified. Transfer entropy calculations not only reveal the complex allosteric signaling within GPR52 but also confirm the unique role of ECL2 in allosteric regulation, which is mutually validated with the results of GaMD simulations. Overall, this work elucidates the allosteric mechanism of GPR52 at the atomic level, providing the most detailed information to date on the self-activation of the orphan receptor.

Cytotoxicity of alkaline serine protease (ASPNJ) on Jurkat cells and its correlation with changes in the expression of membrane-associated proteins.

We aim to study the inhibitory effect of alkaline serine protease (ASPNJ) on lymphocytic leukemia Jurkat cells and its related mechanism through examining the expression of membrane proteins or membrane-associated proteins. MTT assay and trypan blue staining were used to detect the inhibitory effect of ASPNJ on the proliferation and growth of Jurkat cells. Wright-Giemsa staining was used to observe the effect of ASPNJ on the morphology of Jurkat cells. The effect of ASPNJ on Jurkat cell apoptosis was detected by flow cytometry. Two-dimensional electrophoresis-mass spectrometry (2-DE-MS) was used to detect and identify the differentially expressed proteins of Jurkat cells treated with ASPNJ (4 µg/mL, 3 h), of which three were selected and verified by Western blot. ASPNJ significantly inhibited the proliferation of leukemia cells (Raji, U937, and Jurkat), caused obvious morphological changes, and induced apoptosis of Jurkat cells. ASPNJ also increased the sensitivity of Jurkat cells to vincristine (VCR). Seven differentially expressed proteins were obtained through 2DE-MS, of which Peroxiredoxin-6 (PRDX6), Calcium-binding protein (CHP1), and 40S ribosomal protein SA (RPSA) were validated. ASPNJ can cause significant toxic effects on Jurkat cells and enhance the effects of VCR. The mechanism of action of ASPNJ on Jurkat cells may be related to differentially expressed proteins such as PRDX6. This study provides a new experimental basis and direction for antileukemia research.

Study on chemical constituents and fingerprints of Phellodendron amurense Rupr. based on ultra-performance liquid chromatography-quadrupole-time-of-flight-mass spectrometry.

The chemical constituents from Phellodendron amurense Rupr. were characterized systematically by ultra-performance liquid chromatography-quadrupole-time-of-flight-mass spectrometry method for collecting mass spectrometry data, and the fingerprints method was established, providing reference for its quality control. The chromatographic column was ACQUITY UPLC BEH-C18 (100 mm×2.1 mm, 1.7 µm). The mobile phase was acetonitrile-0.1% formic acid aqueous solution and the compounds from P. amurense Rupr. were identified by Qualitative Analysis 10.0 software, reference substance, retention time, mass spectrometry fragmentation pattern and database retrieval. Meanwhile, liquid chromatography-mass spectrometry fingerprint methods of P. amurense Rupr. and Phellodendron chinense Schneid. were established by using the similarity evaluation system of chromatographic fingerprint of traditional Chinese medicine (2012 edition), and the differences were analyzed by multivariate statistical analysis methods. A total of 105 compounds were identified, including 102 alkaloids, two phenolic acids, and one lactone compound. Liquid chromatography-mass spectrometry fingerprint method was established with ideal precision, stability and repeatability, and 12 quality differential markers were recognized between the above two herbs. Liquid chromatography-mass spectrometry method can be used for qualitative analysis of the constituents of Phellodendron amurense Rupr., providing reference for clarifying the material basis and promoting the clinical precision medication and quality evaluation of P. amurense Rupr.

The Effect of Air Turbulence on Vortex Beams in Nonlinear Propagation.

Vortex beams with orthogonality can be widely used in atmospheric applications. We numerically analyzed the statistical regularities of vortex beams propagating through a lens or an axicon with different series of turbulent air phase screens. The simulative results revealed that the distortion of the transverse intensity was sensitive to the location and the structure constant of the turbulence screen. In addition, the axicon can be regarded as a very useful optical device, since it can not only suppress the turbulence but also maintain a stable beam pattern. We further confirmed that a vortex beam with a large topological charge can suppress the influence of air turbulence. Our outcomes are valuable for many applications in the atmospheric air, especially for optical communication and remote sensing.

Lactiplantibacillus plantarum LPJZ-658 Improves Non-Alcoholic Steatohepatitis by Modulating Bile Acid Metabolism and Gut Microbiota in Mice.

Non-alcoholic steatohepatitis (NASH) is one of the most prevalent diseases worldwide; it is characterized by hepatic lipid accumulation, inflammation, and progressive fibrosis. Here, a Western diet combined with low-dose weekly carbon tetrachloride was fed to C57BL/6J mice for 12 weeks to build a NASH model to investigate the attenuating effects and possible mechanisms of Lactiplantibacillus plantarum LPJZ-658. Hepatic pathology, lipid profiles, and gene expression were assessed. The metabolomic profiling of the serum was performed. The composition structure of gut microbiota was profiled using 16s rRNA sequencing. The results show that LPJZ-658 treatment significantly attenuated liver injury, steatosis, fibrosis, and inflammation in NASH mice. Metabolic pathway analysis revealed that several pathways, such as purine metabolism, glycerophospholipid metabolism, linoleic acid metabolism, and primary bile acid biosynthesis, were associated with NASH. Notably, we found that treatment with LPJZ-658 regulated the levels of bile acids (BAs) in the serum. Moreover, LPJZ-658 restored NASH-induced gut microbiota dysbiosis. The correlation analysis deduced obvious interactions between BAs and gut microbiota. The current study indicates that LPJZ-658 supplementation protects against NASH progression, which is accompanied by alternating BA metabolic and modulating gut microbiota.

Dynamics of binding interactions of TDP-43 and RNA: An equally weighted multiscale elastic network model study.

Transactive response DNA binding protein 43 (TDP-43), an alternative-splicing regulator, can specifically bind long UG-rich RNAs, associated with a range of neurodegenerative diseases. Upon binding RNA, TDP-43 undergoes a large conformational change with two RNA recognition motifs (RRMs) connected by a long linker rearranged, strengthening the binding affinity of TDP-43 with RNA. We extend the equally weighted multiscale elastic network model (ewmENM), including its Gaussian network model (ewmGNM) and Anisotropic network model (ewmANM), with the multiscale effect of interactions considered, to the characterization of the dynamics of binding interactions of TDP-43 and RNA. The results reveal upon RNA binding a loss of flexibility occurs to TDP-43's loop3 segments rich in positively charged residues and C-terminal of high flexibility, suggesting their anchoring RNA, induced fit and conformational adjustment roles in recognizing RNA. Additionally, based on movement coupling analyses, it is found that RNA binding strengthens the interactions among intra-RRM ß-sheets and between RRMs partially through the linker's mediating role, which stabilizes RNA binding interface, facilitating RNA binding efficiency. In addition, utilizing our proposed thermodynamic cycle method combined with ewmGNM, we identify the key residues for RNA binding whose perturbations induce a large change in binding free energy. We identify not only the residues important for specific binding, but also the ones critical for the conformational rearrangement between RRMs. Furthermore, molecular dynamics simulations are also performed to validate and further interpret the ENM-based results. The study demonstrates a useful avenue to utilize ewmENM to investigate the protein-RNA interaction dynamics characteristics.

Specific recognition between YTHDF3 and m6 A-modified RNA: An all-atom molecular dynamics simulation study.

The YTH domain of YTHDF3 belongs to a class of protein "readers" recognizing the N6-methyladenosine (m6 A) modification in mRNA. Although static crystal structure reveals m6 A recognition by a conserved aromatic cage, the dynamic process in recognition and importance of aromatic cage residues are not completely clear. Here, molecular dynamics (MD) simulations are performed to explore the issues and negative selectivity of YTHDF3 toward unmethylated substrate. Our results reveal that there exist conformation selectivity and induced-fit in YTHDF3 binding with m6 A-modified RNA, where recognition loop and loop6 play important roles in the specific recognition. m6 A modification enhances the stability of YTHDF3 in complex with RNA. The methyl group of m6 A, like a warhead, enters into the aromatic cage of YTHDF3, where Trp492 anchors the methyl group and constraints m6 A, making m6 A further stabilized by π-π stacking interactions from Trp438 and Trp497. In addition, the methylation enhances the hydrophobicity of adenosine, facilitating water molecules excluded out of the aromatic cage, which is another reason for the specific recognition and stronger intermolecular interaction. Finally, the comparative analyses of hydrogen bonds and binding free energy between the methylated and unmethylated complexes reveal the physical basis for the preferred recognition of m6 A-modified RNA by YTHDF3. This study sheds light on the mechanism by which YTHDF3 specifically recognizes m6 A-modified RNA and can provide important information for structure-based drug design.