HOPEXGB: A Consensual Model for Predicting miRNA/lncRNA-Disease Associations Using a Heterogeneous Disease-miRNA-lncRNA Information Network.

Predicting disease-related microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) is crucial to find new biomarkers for the prevention, diagnosis, and treatment of complex human diseases. Computational predictions for miRNA/lncRNA-disease associations are of great practical significance, since traditional experimental detection is expensive and time-consuming. In this paper, we proposed a consensual machine-learning technique-based prediction approach to identify disease-related miRNAs and lncRNAs by high-order proximity preserved embedding (HOPE) and eXtreme Gradient Boosting (XGB), named HOPEXGB. By connecting lncRNA, miRNA, and disease nodes based on their correlations and relationships, we first created a heterogeneous disease-miRNA-lncRNA (DML) information network to achieve an effective fusion of information on similarities, correlations, and interactions among miRNAs, lncRNAs, and diseases. In addition, a more rational negative data set was generated based on the similarities of unknown associations with the known ones, so as to effectively reduce the false negative rate in the data set for model construction. By 10-fold cross-validation, HOPE shows better performance than other graph embedding methods. The final consensual HOPEXGB model yields robust performance with a mean prediction accuracy of 0.9569 and also demonstrates high sensitivity and specificity advantages compared to lncRNA/miRNA-specific predictions. Moreover, it is superior to other existing methods and gives promising performance on the external testing data, indicating that integrating the information on lncRNA-miRNA interactions and the similarities of lncRNAs/miRNAs is beneficial for improving the prediction performance of the model. Finally, case studies on lung, stomach, and breast cancers indicate that HOPEXGB could be a powerful tool for preclinical biomarker detection and bioexperiment preliminary screening for the diagnosis and prognosis of cancers. HOPEXGB is publicly available at https://github.com/airpamper/HOPEXGB.

Diagnostic ability of the combination of retinal microvasculature evaluation and static automated perimetry for early primary open-angle glaucoma.

PURPOSE: To investigate the diagnostic ability of retinal superficial vasculature evaluation by optic coherence tomography angiography (OCTA) combined with visual field (VF) testing for early primary open-angle glaucoma (POAG). PATIENTS AND METHODS: In this cross-sectional study, 84 participants were included, including 11 in the ocular hypertension (OHT) group, 11 in the preperimetric POAG (pre-POAG) group, 29 in the early POAG group and 33 in the control group. All participants underwent 6 × 6 mm2 scans of macula and optic nerved head by optic coherence tomography (OCT) and OCTA, along with white-on-white and blue-on-yellow VF testing by static automated perimetry. The ability of diagnosing early glaucoma by either various examinations separately or combination of examinations in both terms of function and structure was studied using the receiver operating characteristic (ROC) curve and the area under the curve (AUC). RESULTS: The superficial retinal vessel densities (VD) in peri-nasal, para-temporal, peri-temporal and peri-inferior regions around the macula, as well as vessel area densities (VAD) in all peripapillary regions, were significantly different among the four groups, with lower VD or VAD in the early POAG patients compared to the normal individuals. The diagnostic ability of peripapillary superficial retinal VAD alone or VF testing alone was limited for early POAG only. However, the combination of these two was more effective in distinguishing normal individuals from OHT subjects or pre-POAG patients without VF defects, with better performance than the combination of peripapillary retinal nerve fiber layer (RNFL) thickness and VF indicators. CONCLUSIONS: Peripapillary retinal vessel densities were generally lower in early POAG patients compared to normal individuals. The combination of peripapillary superficial retinal VAD by OCTA with white-on-white VF testing improved the ability to distinguish POAG patients at early stage without function impairment, which may help in providing reference and guidance for the following-up and treatment of suspected POAG patients.

Influence of labor migration on rural household food waste in China: Application of propensity score matching (PSM).

Food waste has emerged as a critical global concern, with households identified as major contributors to this pressing issue. As the world grapples with sustainability challenges, addressing food waste in the context of rural labor migration is crucial for achieving broader sustainable development goals. However, there is still limited research regarding the relationship between labor migration and food waste. We utilized propensity score matching to analyze cross-sectional data collected from 1270 rural households in China. Labor migration led to significant increases of 37% in overall food waste and 35% in plant-based food waste, respectively. Furthermore, households with labor migration exhibited 29%, 31%, and 30 % higher energy, protein, and carbohydrate waste, respectively, compared to non-migration households. Regarding micronutrients, migration led to a 39% increase in iron waste, a 42% increase in zinc waste, and a 47% increase in selenium waste. The results of the categorical analysis indicate variations in the impact of labor migration on food wastage within rural households. Food wastage in rural households with chronic illness patients responds differently to labor migration. Moreover, labor migration predominantly affects households without courier services in villages, where dietary diversity plays a significant role. Understanding these variations is essential for crafting targeted interventions and policies to address food waste in different rural contexts. The policy implications of our study are crucial for addressing food waste and advancing sustainable development in rural China, where labor migration plays a significant role.

A new inhibitor of human peptide deformylase suppresses cell proliferation and induces cell apoptosis and autophagy in cancers.

Human peptide deformylase (hsPDF) has been found overexpressed in many cancer cells and its inhibitors exhibit antitumor activity. Studies were performed to validate that hsPDF is a good antitumor target. The inhibitory effect of PDF64 on hsPDF enzymatic activity was measured and confirmed by computation analysis. Antiproliferation activity was determined and in-vivo antitumor activity were analyzed in HCT116 and HL60 nude mice xenografts. Mitochondrial membrane potential (MMP), cell apoptosis, and autophagic cell death were analyzed by flow cytometry. ATP level was quantified using an ATP assay kit. Protein expression and kinase phosphorylation were determined by western blotting. A new hsPDF inhibitor PDF64 was identified. It showed evident antiproliferation activity in 10 cancer cells and significantly suppressed tumor growth in HCT116 and HL60 xenografts. It induced an obvious decrease in MMP and caused apparent cell apoptosis and autophagy in HCT116 and Jurkat cells. PDF64 treatment also led to an evident decrease in cellular ATP levels in these cells. Moreover, PDF64 downregulated c-Myc expression and had some effects on extracellular regulated protein kinases (ERK) and protein kinase B (Akt)/ mammalian target of rapamycin (mTOR) pathways. PDF64 exhibited good antitumor effects both in vivo and in vitro . It caused cell apoptosis and autophagic death in HCT116 and Jurkat cells. The effects may be mediated by inhibiting c-Myc expression and ERK or PI3K-Akt-mTOR pathway. Therefore, PDF64 may be a promising reagent for antitumor drug development, which further supports that hsPDF is a good antitumor drug target.

Attention Mechanism-Based Graph Neural Network Model for Effective Activity Prediction of SARS-CoV-2 Main Protease Inhibitors: Application to Drug Repurposing as Potential COVID-19 Therapy.

Compared to de novo drug discovery, drug repurposing provides a time-efficient way to treat coronavirus disease 19 (COVID-19) that is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). SARS-CoV-2 main protease (Mpro) has been proved to be an attractive drug target due to its pivotal involvement in viral replication and transcription. Here, we present a graph neural network-based deep-learning (DL) strategy to prioritize the existing drugs for their potential therapeutic effects against SARS-CoV-2 Mpro. Mpro inhibitors were represented as molecular graphs ready for graph attention network (GAT) and graph isomorphism network (GIN) modeling for predicting the inhibitory activities. The result shows that the GAT model outperforms the GIN and other competitive models and yields satisfactory predictions for unseen Mpro inhibitors, confirming its robustness and generalization. The attention mechanism of GAT enables to capture the dominant substructures and thus to realize the interpretability of the model. Finally, we applied the optimal GAT model in conjunction with molecular docking simulations to screen the Drug Repurposing Hub (DRH) database. As a result, 18 drug hits with best consensus prediction scores and binding affinity values were identified as the potential therapeutics against COVID-19. Both the extensive literature searching and evaluations on adsorption, distribution, metabolism, excretion, and toxicity (ADMET) illustrate the premium drug-likeness and pharmacokinetic properties of the drug candidates. Overall, our work not only provides an effective GAT-based DL prediction tool for inhibitory activity of SARS-CoV-2 Mpro inhibitors but also provides theoretical guidelines for drug discovery in the COVID-19 treatment.

General Graph Neural Network-Based Model To Accurately Predict Cocrystal Density and Insight from Data Quality and Feature Representation.

Cocrystal engineering as an effective way to modify solid-state properties has inspired great interest from diverse material fields while cocrystal density is an important property closely correlated with the material function. In order to accurately predict the cocrystal density, we develop a graph neural network (GNN)-based deep learning framework by considering three key factors of machine learning (data quality, feature presentation, and model architecture). The result shows that different stoichiometric ratios of molecules in cocrystals can significantly influence the prediction performances, highlighting the importance of data quality. In addition, the feature complementary is not suitable for augmenting the molecular graph representation in the cocrystal density prediction, suggesting that the complementary strategy needs to consider whether extra features can sufficiently supplement the lacked information in the original representation. Based on these results, 4144 cocrystals with 1:1 stoichiometry ratio are selected as the dataset, supplemented by the data augmentation of exchanging a pair of coformers. The molecular graph is determined to learn feature representation to train the GNN-based model. Global attention is introduced to further optimize the feature space and identify important atoms to realize the interpretability of the model. Benefited from the advantages, our model significantly outperforms three competitive models and exhibits high prediction accuracy for unseen cocrystals, showcasing its robustness and generality. Overall, our work not only provides a general cocrystal density prediction tool for experimental investigations but also provides useful guidelines for the machine learning application. All source codes are freely available at https://github.com/Xiao-Gua00/CCPGraph.

A consensual machine-learning-assisted QSAR model for effective bioactivity prediction of xanthine oxidase inhibitors using molecular fingerprints.

Xanthine oxidase inhibitors (XOIs) have been widely studied due to the promising potential as safe and effective therapeutics in hyperuricemia and gout. Currently, available XOI molecules have been developed from different experiments but they are with the wide structure diversity and significant varying bioactivities. So it is of great practical significance to present a consensual QSAR model for effective bioactivity prediction of XOIs based on a systematic compiling of these XOIs across different experiments. In this work, 249 XOIs belonging to 16 scaffolds were collected and were integrated into a consensual dataset by introducing the concept of IC50 values relative to allopurinol (RIC50). Here, extended connectivity fingerprints (ECFPs) were employed to represent XOI molecules. By performing effective feature selection by machine-learning method, 54 crucial fingerprints were indicated to be valuable for predicting the inhibitory potency (IP) of XOIs. The optimal predictor yields the promising performance by different cross-validation tests. Besides, an external validation of 43 XOIs and a case study on febuxostat also provide satisfactory results, indicating the powerful generalization of our predictor. Here, the predictor was interpreted by shapely additive explanation (SHAP) method which revealed several important substructures by mapping the featured fingerprints to molecular structures. Then, 15 new molecules were designed and predicted by our predictor to show superior IP than febuxostat. Finally, molecular docking simulation was performed to gain a deep insight into molecular binding mode with xanthine oxidase (XO) enzyme, showing that molecules with selenazole moiety, cyano group and isopropyl group tended to yield higher IP. The absorption, distribution, metabolism, excretion and toxicity (ADMET) prediction results further enhanced the potential of these novel XOIs as drug candidates. Overall, this work presents a QSAR model for accurate prediction of IP of XOIs, and is expected to provide new insights for further structure-guided design of novel XOIs.

The status of the global food waste mitigation policies: experience and inspiration for China.

Food waste has become a significant challenge faced by the community with a shared future for mankind, and it has also caused a considerable impact on China's food security. Scholars across disciplines, international organizations, and especially policymakers are increasingly interested in food waste. Policies are seen as a powerful factor in reducing food waste, but current research on related policies is more scattered. This paper summarizes and analyzes the experiences of food waste policy development and implementation by systematically reviewing the studies on food waste reduction policies. The results of this paper's analysis show that current global food waste policies are focused at the national strategic level, with approaches such as legislation, food donation, waste recycling, awareness and education, and data collection. At the same time, we find that the current experience of developed countries in policy formulation and implementation is beneficial for policy formulation in developing countries. And taking China as an example, we believe that developing countries can improve food waste policies in the future by improving legislation, guiding the development of food banks, promoting social governance, and strengthening scientific research projects. These policies will all contribute strongly to global environmental friendliness. In addition, we discuss some of the factors that influence the development of food waste policies and argue that in the future, more consideration needs to be given to the effects of policy implementation and that case studies should focus more on developing countries. This will contribute to the global sustainable development process. Supplementary Information: The online version contains supplementary material available at 10.1007/s10668-023-03132-0.

The landscape of miRNA-related ceRNA networks for marking different renal cell carcinoma subtypes.

We know that different types of cancers usually have different responses to the same treatment. Therefore, it is important to understand the similarities and differences across subtypes of cancers, so as to provide a basis for the individualized treatments. Until now, no comprehensive investigation on competing endogenous RNAs (ceRNAs) has been reported for the three main subtypes of renal cell carcinoma (RCC), so the regulation characteristics of ceRNAs in three subtypes are not well revealed. This paper firstly describes a comparative analysis of ceRNA-ceRNA interaction networks for all the three subtypes of RCC based on differential microRNAs (miRNAs). We comprehensively summarized all miRNA and messenger RNAdata of RCC from 126 matched tumor-normal tissues in The Cancer Genome Atlas, systematically analyzed a total of more than 80 000 ceRNA interactions and highlighted the common and specific properties among them, aiming to identify critical genes to classify them for providing supplementary help in the precise diagnosis of RCC. From three aspects, including common or specific ceRNAs, upregulated or downregulated and classifications across the three subtypes, we highlighted the common and specific properties for the three subtypes and also explored the classification of RCC by combining the specific ceRNAs with differential regulations. Moreover, for the most major subtype of clear cell renal cell carcinoma (KIRC), three critical genes were screened out from KIRC ceRNA network and further demonstrated to be the potential biomarkers of KIRC by performing biological experiments at the transcriptional level.

An Interpretable Convolutional Neural Network Framework for Analyzing Molecular Dynamics Trajectories: a Case Study on Functional States for G-Protein-Coupled Receptors.

Molecular dynamics (MD) simulations have made great contribution to revealing structural and functional mechanisms for many biomolecular systems. However, how to identify functional states and important residues from vast conformation space generated by MD remains challenging; thus an intelligent navigation is highly desired. Despite intelligent advantages of deep learning exhibited in analyzing MD trajectory, its black-box nature limits its application. To address this problem, we explore an interpretable convolutional neural network (CNN)-based deep learning framework to automatically identify diverse active states from the MD trajectory for G-protein-coupled receptors (GPCRs), named the ICNNMD model. To avoid the information loss in representing the conformation structure, the pixel representation is introduced, and then the CNN module is constructed to efficiently extract features followed by a fully connected neural network to realize the classification task. More importantly, we design a local interpretable model-agnostic explanation interpreter for the classification result by local approximation with a linear model, through which important residues underlying distinct active states can be quickly identified. Our model showcases higher than 99% classification accuracy for three important GPCR systems with diverse active states. Notably, some important residues in regulating different biased activities are successfully identified, which are beneficial to elucidating diverse activation mechanisms for GPCRs. Our model can also serve as a general tool to analyze MD trajectory for other biomolecular systems. All source codes are freely available at https://github.com/Jane-Liu97/ICNNMD for aiding MD studies.

Biased Activation Mechanism Induced by GPCR Heterodimerization: Observations from µOR/δOR Dimers.

GPCRs regulate multiple intracellular signaling cascades. Biasedly activating one signaling pathway over the others provides additional clinical utility to optimize GPCR-based therapies. GPCR heterodimers possess different functions from their monomeric states, including their selectivity to different transducers. However, the biased signaling mechanism induced by the heterodimerization remains unclear. Motivated by the issue, we select an important GPCR heterodimer (µOR/δOR heterodimer) as a case and use microsecond Gaussian accelerated molecular dynamics simulation coupled with potential of mean force and protein structure network (PSN) to probe mechanisms regarding the heterodimerization-induced constitutive ß-arrestin activity and efficacy change of the agonist DAMGO. The results show that only the lowest energy state of the µOR/δOR heterodimer, which adopts a slightly outward shift of TM6 and an ICL2 conformation close to the receptor core, can selectively accommodate ß-arrestins. PSN further reveals important roles of H8, ICL1, and ICL2 in regulating the constitutive ß-arrestin-biased activity for the apo µOR/δOR heterodimer. In addition, the heterodimerization can allosterically alter the binding mode of DAMGO mainly by means of W7.35. Consequently, DAMGO transmits the structural signal mainly through TM6 and TM7 in the dimer, rather than TM3 similar to the µOR monomer, thus changing the efficacy of DAMGO from a balanced agonist to the ß-arrestin-biased one. On the other side, the binding of DAMGO to the heterodimer can stabilize µOR/δOR heterodimers through a stronger interaction of TM1/TM1 and H8/H8, accordingly enhancing the interaction of µOR with δOR and the binding affinity of the dimer to the ß-arrestin. The agonist DAMGO does not change main compositions of the regulation network from the dimer interface to the transducer binding pocket of the µOR protomer, but induces an increase in the structural communication of the network, which should contribute to the enhanced ß-arrestin coupling. Our observations, for the first time, reveal the molecular mechanism of the biased signaling induced by the heterodimerization for GPCRs, which should be beneficial to more comprehensively understand the GPCR bias signaling.

Probing Allosteric Regulation Mechanism of W7.35 on Agonist-Induced Activity for µOR by Mutation Simulation.

The residue located at 15 positions before the most conserved residue in TM7 (7.35 of Ballesteros-Weinstein number) plays important roles in ligand binding and the receptor activity for class A GPCRs. Nevertheless, its regulation mechanism has not been clearly clarified in experiments, and some controversies also exist for its impact on µ-opioid receptors (µOR) bound by agonists. Thus, we chose the µ-opioid receptor (µOR) of class A GPCRs as a representative and utilized a microsecond accelerated molecular dynamics simulation (aMD) coupled with a protein structure network (PSN) to explore the effect of W3187.35 on its functional activity induced by the agonist endomorphin2 mainly by a comparison of the wild system and its W7.35A mutant. When endomorphin2 binds to the wild-type µOR, TM6 in µOR moves outward to form an open intracellular conformation that is beneficial to accommodating the ß-arrestin transducer, rather than the G-protein transducer due to the clash with the α5 helix of G-protein, thus acting as a ß-arrestin biased agonist. However, the W318A mutation induces the intracellular part of µOR to form a closed state, which disfavors coupling with either G-protein or ß-arrestin. The allosteric pathway analysis further reveals that the binding of endomorphin2 to the wild-type µOR transmits more activation signals to the ß-arrestin binding site while the W318A mutation induces more structural signals to transmit to common binding residues of the G protein and ß-arrestin. More interestingly, the residue at the 7.35 position regulates the shortest allosteric pathway in indirect ways by influencing the interactions between other ligand-binding residues and endomorphin2. W2936.48 and F2896.44 are important for regulating the different activities of µOR induced either by the agonist or by the mutation. Y3367.53, F3438.50, and D3408.47 play crucial roles in activating the ß-arrestin biased signal induced by the agonist endomorphin2, while L1583.43 and V2866.41 devote important contributions to the change in the activity of endomorphin2 from the ß-arrestin biased agonist to the antagonist upon the W318A mutation.

Correlated RNN Framework to Quickly Generate Molecules with Desired Properties for Energetic Materials in the Low Data Regime.

Motivated by the challenging of deep learning on the low data regime and the urgent demand for intelligent design on highly energetic materials, we explore a correlated deep learning framework, which consists of three recurrent neural networks (RNNs) correlated by the transfer learning strategy, to efficiently generate new energetic molecules with a high detonation velocity in the case of very limited data available. To avoid the dependence on the external big data set, data augmentation by fragment shuffling of 303 energetic compounds is utilized to produce 500,000 molecules to pretrain RNN, through which the model can learn sufficient structure knowledge. Then the pretrained RNN is fine-tuned by focusing on the 303 energetic compounds to generate 7153 molecules similar to the energetic compounds. In order to more reliably screen the molecules with a high detonation velocity, the SMILE enumeration augmentation coupled with the pretrained knowledge is utilized to build an RNN-based prediction model, through which R2 is boosted from 0.4446 to 0.9572. The comparable performance with the transfer learning strategy based on an existing big database (ChEMBL) to produce the energetic molecules and drug-like ones further supports the effectiveness and generality of our strategy in the low data regime. High-precision quantum mechanics calculations further confirm that 35 new molecules present a higher detonation velocity and lower synthetic accessibility than the classic explosive RDX, along with good thermal stability. In particular, three new molecules are comparable to caged CL-20 in the detonation velocity. All the source codes and the data set are freely available at https://github.com/wangchenghuidream/RNNMGM.

Molecular insights into the allosteric coupling mechanism between an agonist and two different transducers for µ-opioid receptors.

G protein-coupled receptors (GPCRs) as the most important class of pharmacological targets regulate G-protein and ß-arrestin-mediated signaling through allosteric interplay, which are responsible for different biochemical and physiological actions like therapeutic efficacy and side effects. However, the allosteric mechanism underlying preferentially recruiting one transducer versus the other has been poorly understood, limiting drug design. Motivated by this issue, we utilize accelerated molecular dynamics simulation coupled with potential of mean force (PMF), molecular mechanics Poisson Boltzmann surface area (MM/PBSA) and protein structure network (PSN) to study two ternary complex systems of a representative class A GPCR (µ-opioid receptor (µOR)) bound by an agonist and one specific transducer (G-protein and ß-arrestin). The results show that no significant difference exists in the whole structure of µOR between two transducer couplings, but displays transducer-dependent changes in the intracellular binding region of µOR, where the ß-arrestin coupling results in a narrower crevice with TM7 inward movement compared with the G-protein. In addition, both the G-protein and ß-arrestin coupling can increase the binding affinity of the agonist to the receptor. However, the interactions between the agonist and µOR also exhibit transducer-specific changes, in particular for the interaction with ECL2 that plays an important role in recruiting ß-arrestin. The allosteric network analysis further indicates that Y1483.33, F1523.37, F1563.41, N1914.49, T1603.45, Y1062.42, W2936.48, F2896.44, I2485.54 and Y2525.58 play important roles in equally activating G-protein and ß-arrestin. In contrast, M1613.46 and R1653.50 devote important contributions to preferentially recruit G-protein while D1643.49 and R179ICL2 are revealed to be important for selectively activating ß-arrestin. The observations provide useful information for understanding the biased activation mechanism.

A Transfer-Learning-Based Deep Convolutional Neural Network for Predicting Leukemia-Related Phosphorylation Sites from Protein Primary Sequences.

As one of the most important post-translational modifications (PTMs), phosphorylation refers to the binding of a phosphate group with amino acid residues like Ser (S), Thr (T) and Tyr (Y) thus resulting in diverse functions at the molecular level. Abnormal phosphorylation has been proved to be closely related with human diseases. To our knowledge, no research has been reported describing specific disease-associated phosphorylation sites prediction which is of great significance for comprehensive understanding of disease mechanism. In this work, focusing on three types of leukemia, we aim to develop a reliable leukemia-related phosphorylation site prediction models by combing deep convolutional neural network (CNN) with transfer-learning. CNN could automatically discover complex representations of phosphorylation patterns from the raw sequences, and hence it provides a powerful tool for improvement of leukemia-related phosphorylation site prediction. With the largest dataset of myelogenous leukemia, the optimal models for S/T/Y phosphorylation sites give the AUC values of 0.8784, 0.8328 and 0.7716 respectively. When transferred learning on the small size datasets, the models for T-cell and lymphoid leukemia also give the promising performance by common sharing the optimal parameters. Compared with other five machine-learning methods, our CNN models reveal the superior performance. Finally, the leukemia-related pathogenesis analysis and distribution analysis on phosphorylated proteins along with K-means clustering analysis and position-specific conversation profiles on the phosphorylation site all indicate the strong practical feasibility of our easy-to-use CNN models.

Comprehensive characterization of pathological stage-related genes of papillary thyroid cancer along with survival prediction.

It is crucial to understand the differences across papillary thyroid cancer (PTC) stages, so as to provide a basis for individualized treatments. Here, comprehensive function characterization of PTC stage-related genes was performed and a new prognostic signature was developed for advanced patients. Two gene modules were confirmed to be closely associated with PTC stages and further six hub genes were identified that yield excellent diagnostic efficiency between tumour and normal tissues. Genetic alteration analysis indicates that they are much conservative since mutations in the DNA of them rarely occur, but changes of DNA methylation on these six genes show that 12 DNA methylation sites are significantly associated with their corresponding genes' expression. Validation data set testing also suggests that these six stage-related hub genes would be probably potential biomarkers for marking four stages. Subsequently, a 21-mRNA-based prognostic risk model was constructed for PTC stage III/IV patients and it could effectively predict the survival of patients with strong prognostic ability. Functional analysis shows that differential expression genes between high- and low-risk patients would promote the progress of PTC to some extent. Moreover, tumour microenvironment (TME) of high-risk patients may be more conducive to tumour growth by ESTIMATE analysis.

Predicting HIV drug resistance using weighted machine learning method at target protein sequence-level.

Acquired immune deficiency syndrome (AIDS) is a fatal disease caused by human immunodeficiency virus (HIV). Although 23 different drugs have been available, the treatment of AIDS remains challenging because the virus mutates very quickly which can lead to drug resistance. Therefore, predicting drug resistance before treatment is crucial for individual treatments. Here, based on HIV target protein sequence information, we analyzed 21-drug resistance caused by mutated residues using machine learning (ML) methods. To transform target sequences into numeric vectors, seven physicochemical properties were used, which can well represent the interacting characteristics of target proteins. Then, principal component analysis (PCA) method was adopted to reduce the feature dimensionality. Random forest (RF) and support vector machine (SVM) based on three different kernel functions, including linear, polynomial and radial basis function (RBF), were all employed. By comparisons, we found that RBF-based SVM method gives a comparative performance with RF model. Further, we added the weight information to RBF-based SVM method by four different weight evaluation methods of RF, eXtreme Gradient Boosting (XGB), CfsSubsetEval and ReliefFAttributeEval, respectively. Results show that the RF-weighted RBF-based SVM yield the superior performance and 13 out of 21 drug models provide the correlation coefficients (R2) over 0.8 and 3 of them are higher than 0.9. Finally, position-specific importance analysis indicates that most of the mutation residues with high RF weight scores are proved to be closely related with drug resistance, which has been revealed in previous reports. Overall, we can expect that this method can be a supplementary tool for predicting HIV drug resistance for newly discovered mutations. Here, based on HIV target protein sequence information, we analyzed 21-drug resistance caused by mutated residues using machine learning (ML) methods by fusing the weight information of different mutation positions.

TNF-α/IFN-γ profile of HBV-specific CD4 T cells is associated with liver damage and viral clearance in chronic HBV infection.

BACKGROUND & AIMS: The role of hepatitis B virus (HBV)-specific CD4 T cells in patients with chronic HBV infection is not clear. Thus, we aimed to elucidate this in patients with chronic infection, and those with hepatitis B flares. METHODS: Through intracellular IFN-γ and TNF-α staining, HBV-specific CD4 T cells were analyzed in 68 patients with chronic HBV infection and alanine aminotransferase (ALT) <2x the upper limit of normal (ULN), and 28 patients with a hepatitis B flare. HBV-specific HLA-DRB1*0803/HLA-DRB1*1202-restricted CD4 T cell epitopes were identified. RESULTS: TNF-α producing cells were the dominant population in patients' HBV-specific CD4 T cells. In patients with ALT <2xULN, both the frequency and the dominance of HBV-specific IFN-γ producing CD4 T cells increased sequentially in patients with elevated levels of viral clearance: HBV e antigen (HBeAg) positive, HBeAg negative, and HBV surface antigen (HBsAg) negative. In patients with a hepatitis B flare, the frequency of HBV core-specific TNF-α producing CD4 T cells was positively correlated with patients' ALT and total bilirubin levels, and the frequency of those cells changed in parallel with the severity of liver damage. Patients with HBeAg/HBsAg loss after flare showed higher frequency and dominance of HBV-specific IFN-γ producing CD4 T cells, compared to patients without HBeAg/HBsAg loss. Both the frequency and the dominance of HBV S-specific IFN-γ producing CD4 T cells were positively correlated with the decrease of HBsAg during flare. A differentiation process from TNF-α producing cells to IFN-γ producing cells in HBV-specific CD4 T cells was observed during flare. Eight and 9 HBV-derived peptides/pairs were identified as HLA-DRB1*0803 restricted epitopes and HLA-DRB1*1202 restricted epitopes, respectively. CONCLUSIONS: HBV-specific TNF-α producing CD4 T cells are associated with liver damage, while HBV-specific IFN-γ producing CD4 T cells are associated with viral clearance in patients with chronic HBV infection. LAY SUMMARY: TNF-α producing cells are the dominant population of hepatitis B virus (HBV)-specific CD4 T cells in patients with chronic HBV infection. This population of cells might contribute to the aggravation of liver damage in patients with a hepatitis B flare. HBV-specific IFN-γ producing CD4 T cells are associated with HBV viral clearance. Differentiation from HBV-specific TNF-α producing CD4 T cells into HBV-specific IFN-γ producing CD4 T cells might favor HBV viral clearance.

Molecular Mechanism Regarding Allosteric Modulation of Ligand Binding and the Impact of Mutations on Dimerization for CCR5 Homodimer.

In this work, we combined accelerated molecular dynamics (aMD) and conventional molecular dynamics (cMD) simulations coupled with the potential of mean force (PMF), correlation analysis, principal component analysis (PCA), and protein structure network (PSN) to study the effects of dimerization and the mutations of I52V and V150A on the CCR5 homodimer, in order to elucidate the mechanism regarding cooperativity of the ligand binding between two protomers and to address the controversy about the mutation-induced dimer-separation. The results reveal that the dimer with interface involved in TM1, TM2, TM3, and TM4 is stable for the CCR5 homodimer. The dimerization induces an asymmetric impact on the overall structure and the ligand-binding pocket. As a result, the two protomers exhibit an asymmetric binding to the maraviroc (one anti-HIV drug). The binding of one protomer to the drug is enhanced while the other is weakened. The PSN result further reveals the allosteric pathway of the ligand-binding pocket between the two protomers. Six important residues in the pathway were identified, including two residues unreported. The results from PMF, PCA, and the correlation analysis clearly indicate that the two mutations induce strong anticorrelation motions in the interface, finally leading to its separation. The observations from the work could advance our understanding of the structure of the G protein-coupled receptor dimers and implications for their functions.

A novel mutation in the BCHE gene and phenotype identified in a child with low butyrylcholinesterase activity: a case report.

BACKGROUND: Butyrylcholinesterase (BChE), an ester hydrolase produced mainly by the liver, hydrolyzes certain short-acting neuromuscular blocking agents, like succinylcholine and mivacurium that are widely used during anesthesia. Patients with BChE deficiency are possibly in danger of postanesthetic apnea. Hereditary BChE deficiency results from the mutations of BCHE gene located on chromosome 3, 3q26.1-q26.2, between nucleotides 165,490,692-165,555,260. CASE PRESENTATION: This study describes a novel mutation in a child with BChE deficiency. In general, this child appeared healthy and well-developed with a normal appearance. However, the results of Wechsler Intelligence Scale showed that the full-scale intelligence quotient (FIQ) was 53, classified into the group with the minor defect. The BChE activity was 32.0 U/L, considerably lower than the normal lower limit (reference range: 5000-12,000 U/L). Sanger sequencing showed that there were 2 mutations in the exon 2 of BCHE gene of this child. One is a heterozygous mutation rs764588882 (NM_000055.3: c.401_402insA, p.Asn134Lysfs*23). The other one is a heterozygous mutation (NM_000055.3: c.73A > T, p.Lys25Ter) that has never been reported before. The two mutations lead to a premature stop of transcription. CONCLUSIONS: Double heterozygous recessive mutations are the cause of BChE deficiency of this boy in this study, including a novel mutation c.73A > T. Intellectual disability is a new phenotype that is probably associated with this mutation.