MuToN Quantifies Binding Affinity Changes upon Protein Mutations by Geometric Deep Learning.

Assessing changes in protein-protein binding affinity due to mutations helps understanding a wide range of crucial biological processes within cells. Despite significant efforts to create accurate computational models, predicting how mutations affect affinity remains challenging due to the complexity of the biological mechanisms involved. In the present work, a geometric deep learning framework called MuToN is introduced for quantifying protein binding affinity change upon residue mutations. The method, designed with geometric attention networks, is mechanism-aware. It captures changes in the protein binding interfaces of mutated complexes and assesses the allosteric effects of amino acids. Experimental results highlight MuToN's superiority compared to existing methods. Additionally, MuToN's flexibility and effectiveness are illustrated by its precise predictions of binding affinity changes between SARS-CoV-2 variants and the ACE2 complex.

Emerging biologic frontiers for Sjogren's syndrome: Unveiling novel approaches with emphasis on extra glandular pathology.

Primary Sjögren's Syndrome (pSS) is a complex autoimmune disorder characterized by exocrine gland dysfunction, leading to dry eyes and mouth. Despite growing interest in biologic therapies for pSS, FDA approval has proven challenging due to trial complications. This review addresses the absence of a molecular-target-based approach to biologic therapy development and highlights novel research on drug targets and clinical trials. A literature search identified potential pSS treatment targets and recent advances in molecular understanding. Overlooking extraglandular symptoms like fatigue and depression is a notable gap in trials. Emerging biologic agents targeting cytokines, signal pathways, and immune responses have proven efficacy. These novel therapies could complement existing methods for symptom alleviation. Improved grading systems accounting for extraglandular symptoms are needed. The future of pSS treatment may involve gene, stem-cell, and tissue-engineering therapies. This narrative review offers insights into advancing pSS management through innovative biologic interventions.

LogicGep: Boolean networks inference using symbolic regression from time-series transcriptomic profiling data.

Reconstructing the topology of gene regulatory network from gene expression data has been extensively studied. With the abundance functional transcriptomic data available, it is now feasible to systematically decipher regulatory interaction dynamics in a logic form such as a Boolean network (BN) framework, which qualitatively indicates how multiple regulators aggregated to affect a common target gene. However, inferring both the network topology and gene interaction dynamics simultaneously is still a challenging problem since gene expression data are typically noisy and data discretization is prone to information loss. We propose a new method for BN inference from time-series transcriptional profiles, called LogicGep. LogicGep formulates the identification of Boolean functions as a symbolic regression problem that learns the Boolean function expression and solve it efficiently through multi-objective optimization using an improved gene expression programming algorithm. To avoid overly emphasizing dynamic characteristics at the expense of topology structure ones, as traditional methods often do, a set of promising Boolean formulas for each target gene is evolved firstly, and a feed-forward neural network trained with continuous expression data is subsequently employed to pick out the final solution. We validated the efficacy of LogicGep using multiple datasets including both synthetic and real-world experimental data. The results elucidate that LogicGep adeptly infers accurate BN models, outperforming other representative BN inference algorithms in both network topology reconstruction and the identification of Boolean functions. Moreover, the execution of LogicGep is hundreds of times faster than other methods, especially in the case of large network inference.

Critically ill children with SARS-COV-2 Omicron infection at a national children medical center, Guangdong, China.

BACKGROUND: SARS-CoV-2 infection is described as asymptomatic, mild, or moderate disease in most children. SARS-CoV-2 infection related death in children and adolescents is rare according to the current reports. COVID-19 cases increased significantly in China during the omicron surge, clinical data regarding pediatric critical patients infected with the omicron variant is limited. In this study, we aim to provide an overview of the clinical characteristics and outcomes of critically ill children admitted to a national children's medical center in Guangdong Province, China, during the outbreak of the omicron variant infection. METHODS: We conducted a retrospective study from November 25, 2022, to February 8, 2023, which included 63 critically ill children, under the age of 18, diagnosed with SARS-CoV-2 infection. The patients were referred from medical institutions of Guangdong province. The medical records of these patients were analyzed and summarized. RESULTS: The median age of patients was 2 years (Interquartile Range, IQR: 1.0-8.0), sex-ratio (male/female) was 1.52. 12 (19%) patients (age ≥ 3 years) were vaccinated. The median length of hospital stay was 14 days (IQR: 6.5-23) in 63 cases, and duration of fever was 5 days (IQR: 3-8.5), pediatric intensive care unit (PICU) stay was 8 days (IQR 4.0-14.0) in 57 cases. 30 (48%) cases had clear contact history with family members who were infected with SARS-CoV-2. Three children who tested positive for SARS-CoV-2 infection did not show any abnormalities on chest imaging examination. Out of the total patients, 33 (52%) had a bacterial co-infection, with Staphylococcus aureus being the most commonly detected bacterial pathogen. Our cohort exhibited respiratory and nervous system involvement as the primary features. Furthermore, fifty (79%) patients required mechanical ventilation, with a median duration of 7 days (IQR 3.75-13.0). Among these patients, 35 (56%) developed respiratory failure, 16 (25%) patients experienced a deteriorating progression of symptoms and ultimately succumbed to the illness, septic shock was the most common condition among these patients (15 cases), followed by multiple organ failure in 12 cases, and encephalopathy identified in 7 cases. CONCLUSION: We present a case series of critically ill children infected with the SARS-CoV-2 omicron variant. While there is evidence suggesting that Omicron may cause less severe symptoms, it is important to continue striving for measures that can minimize the pathogenic impact of SARS-CoV-2 infection in children.

Cytosolic DNA initiates a vicious circle of aging-related endothelial inflammation and mitochondrial dysfunction via STING: the inhibitory effect of Cilostazol.

Endothelial senescence, aging-related inflammation, and mitochondrial dysfunction are prominent features of vascular aging and contribute to the development of aging-associated vascular disease. Accumulating evidence indicates that DNA damage occurs in aging vascular cells, especially in endothelial cells (ECs). However, the mechanism of EC senescence has not been completely elucidated, and so far, there is no specific drug in the clinic to treat EC senescence and vascular aging. Here we show that various aging stimuli induce nuclear DNA and mitochondrial damage in ECs, thus facilitating the release of cytoplasmic free DNA (cfDNA), which activates the DNA-sensing adapter protein STING. STING activation led to a senescence-associated secretory phenotype (SASP), thereby releasing pro-aging cytokines and cfDNA to further exacerbate mitochondrial damage and EC senescence, thus forming a vicious circle, all of which can be suppressed by STING knockdown or inhibition. Using next-generation RNA sequencing, we demonstrate that STING activation stimulates, whereas STING inhibition disrupts pathways associated with cell senescence and SASP. In vivo studies unravel that endothelial-specific Sting deficiency alleviates aging-related endothelial inflammation and mitochondrial dysfunction and prevents the development of atherosclerosis in mice. By screening FDA-approved vasoprotective drugs, we identified Cilostazol as a new STING inhibitor that attenuates aging-related endothelial inflammation both in vitro and in vivo. We demonstrated that Cilostazol significantly inhibited STING translocation from the ER to the Golgi apparatus during STING activation by targeting S162 and S243 residues of STING. These results disclose the deleterious effects of a cfDNA-STING-SASP-cfDNA vicious circle on EC senescence and atherogenesis and suggest that the STING pathway is a promising therapeutic target for vascular aging-related diseases. A proposed model illustrates the central role of STING in mediating a vicious circle of cfDNA-STING-SASP-cfDNA to aggravate age-related endothelial inflammation and mitochondrial damage.

Drug-target Affinity Prediction by Molecule Secondary Structure Representation Network.

INTRODUCTION: Identification of drug-target interactions (DTI) is a crucial step in drug development with high specificity and low toxicity. To accelerate the process, computer-aided DTI prediction algorithms have been used to screen compounds or targets rapidly. Furthermore, DTI prediction can be used to identify potential targets for existing drugs, thus uncovering new indications and repositioning them. Therefore, it is of great importance to develop efficient and accurate DTI prediction algorithms. METHOD: Current algorithms usually represent drugs as extracted features, which are learned by convolutional neural networks (CNNs) from its linear representation, or utilize graph neural networks (GNNs) to learn its graph representation. However, these methods either lose information or fail to capture the structural information of the drug. To address this issue, a novel molecule secondary structure representation network (MSSRN) is proposed to learn drug characterization more accurately. Firstly, the network performs relational graph convolutional networks (R-GCNs) on the drug's molecular graph and integrates drug sequence convolutions to learn the sequential information. Secondly, inspired by the attention mechanism, spatial importance weights of the drug sequence are calculated to guide R-GCNs to learn the topological information of the drug. RESULT: A drug-target affinity model, called MSSRN-DTA, was then constructed by using MSSRN to learn drug structure and CNN to learn protein sequence. CONCLUSION: The effectiveness of the proposed method is verified by comparing it with other alternative methods and baseline models on two benchmark datasets.

Network Activity Evaluation Reveals Significant Gene Regulatory Architectures during SARS-CoV-2 Viral Infection from Dynamic scRNA-seq Data.

The key to understand COVID-19 caused by SARS-CoV-2, which has caused massive deaths worldwide, is to reveal the gene activities at molecular level. Single-cell RNA-sequencing (scRNA-seq) technology allows us to capture gene expression at high resolution, thereby delineating cell-specific gene regulatory network (GRN). Network activity refers to the degree of consistency between GRN architectures and gene expression profiles in a specific condition or cellular microenvironment. Currently, numerous experimentally determined molecular interactions, including regulatory relationships closely related to SARS-CoV-2 infection, are documented in knowledge-bases. However, GRN activity is closely related to the cell dynamic environment and the heterogeneity of cell clusters. Therefore, to evaluate the consistency of GRN with gene expression profiles, we propose a single-cell Network Activity Evaluation framework, called scNAE. First, scNAE performs ODE modeling of time-course gene expression data. Then, the loss function with regularization penalty terms is constructed for formulating GRN inference rules from transcriptomic data. Furthermore, we have devised a rapid-convergence alternating direction method of multipliers to solve the regularized and constrained programs. Finally, an empirical P-value is derived based on a permutation statistical testing procedure to quantify the likelihood significance of the network matching with the data. The efficiency and advantage of scNAE have also been demonstrated by extensive numerical experiments, which can clearly depict the dynamic responses underlying GRN architectures triggered by the infection of SARS-CoV-2 in cells. The code and data of scNAE are available at https://github.com/zpliulab/scNAE.

ActivePPI: quantifying protein-protein interaction network activity with Markov random fields.

MOTIVATION: Protein-protein interactions (PPI) are crucial components of the biomolecular networks that enable cells to function. Biological experiments have identified a large number of PPI, and these interactions are stored in knowledge bases. However, these interactions are often restricted to specific cellular environments and conditions. Network activity can be characterized as the extent of agreement between a PPI network (PPIN) and a distinct cellular environment measured by protein mass spectrometry, and it can also be quantified as a statistical significance score. Without knowing the activity of these PPI in the cellular environments or specific phenotypes, it is impossible to reveal how these PPI perform and affect cellular functioning. RESULTS: To calculate the activity of PPIN in different cellular conditions, we proposed a PPIN activity evaluation framework named ActivePPI to measure the consistency between network architecture and protein measurement data. ActivePPI estimates the probability density of protein mass spectrometry abundance and models PPIN using a Markov-random-field-based method. Furthermore, empirical P-value is derived based on a nonparametric permutation test to quantify the likelihood significance of the match between PPIN structure and protein abundance data. Extensive numerical experiments demonstrate the superior performance of ActivePPI and result in network activity evaluation, pathway activity assessment, and optimal network architecture tuning tasks. To summarize it succinctly, ActivePPI is a versatile tool for evaluating PPI network that can uncover the functional significance of protein interactions in crucial cellular biological processes and offer further insights into physiological phenomena. AVAILABILITY AND IMPLEMENTATION: All source code and data are freely available at https://github.com/zpliulab/ActivePPI.

Block-based teaching method based on cybernetics: a trial with 115 Chinese undergraduate medical students.

BACKGROUND: Class attendance is important for academic performance. Personal interactions between teachers and students are difficult in large classes; the number of medical undergraduate students in China ranges from dozens to over 100. It is important for teachers to control the teaching process to improve student attendance and participation. METHODS: Two classes of fourth-year undergraduate medical students, with each class comprising 115 students, participated in the study. One class, the trial group, was taught by the block-based teaching method based on cybernetics. This study was conducted with three of the courses in the Introduction to Oncology subject, and the trial group's courses included several blocks. Each block had a test paper that the students responded to immediately in class using the Internet. The teacher obtained feedback from the students when the rate of correct responses to block-test questions was less than 90%. The teacher adjusted the teaching in the following blocks according to the feedback information. The other class, the control group, was taught using the traditional lecture-based teaching method. RESULTS: The average attendance in the trial group was 104/115 (90.43%), and that in the control group was 83/115 (72.17%) (p = 0.0003). The teacher adjusted the teaching three times in the radiotherapy course owing to the complex ideas. After feedback, information on chemotherapy for the upper body was adjusted once, as was that on chemotherapy for the lower body, owing to students' attitudes. The average total score of the trial group was 86.06 ± 17.46 and that of the control group was 80.38 ± 6.97 (p = 0.041). Questionnaire I showed that the trial group students' attendance and participation were better than in the control group. Questionnaire II showed that the block-based teaching method based on cybernetics was approved by the students. CONCLUSIONS: The block-based teaching method based on cybernetics used in medical classes with large numbers of Chinese undergraduate students had positive effects.

Regulation of toll-like receptor (TLR) signaling pathways in atherosclerosis: from mechanisms to targeted therapeutics.

Atherosclerosis, one of the life-threatening cardiovascular diseases (CVDs), has been demonstrated to be a chronic inflammatory disease, and inflammatory and immune processes are involved in the origin and development of the disease. Toll-like receptors (TLRs), a class of pattern recognition receptors that trigger innate immune responses by identifying pathogen-associated molecular patterns (PAMPs) and danger-associated molecular patterns (DAMPs), regulate numerous acute and chronic inflammatory diseases. Recent studies reveal that TLRs have a vital role in the occurrence and development of atherosclerosis, including the initiation of endothelial dysfunction, interaction of various immune cells, and activation of a number of other inflammatory pathways. We herein summarize some other inflammatory signaling pathways, protein molecules, and cellular responses associated with TLRs, such as NLRP3, Nrf2, PCSK9, autophagy, pyroptosis and necroptosis, which are also involved in the development of AS. Targeting TLRs and their regulated inflammatory events could be a promising new strategy for the treatment of atherosclerotic CVDs. Novel drugs that exert therapeutic effects on AS through TLRs and their related pathways are increasingly being developed. In this article, we comprehensively review the current knowledge of TLR signaling pathways in atherosclerosis and actively seek potential therapeutic strategies using TLRs as a breakthrough point in the prevention and therapy of atherosclerosis.

Multimodal recurrence scoring system for prediction of clear cell renal cell carcinoma outcome: a discovery and validation study.

BACKGROUND: Improved markers for predicting recurrence are needed to stratify patients with localised (stage I-III) renal cell carcinoma after surgery for selection of adjuvant therapy. We developed a novel assay integrating three modalities-clinical, genomic, and histopathological-to improve the predictive accuracy for localised renal cell carcinoma recurrence. METHODS: In this retrospective analysis and validation study, we developed a histopathological whole-slide image (WSI)-based score using deep learning allied to digital scanning of conventional haematoxylin and eosin-stained tumour tissue sections, to predict tumour recurrence in a development dataset of 651 patients with distinctly good or poor disease outcome. The six single nucleotide polymorphism-based score, which was detected in paraffin-embedded tumour tissue samples, and the Leibovich score, which was established using clinicopathological risk factors, were combined with the WSI-based score to construct a multimodal recurrence score in the training dataset of 1125 patients. The multimodal recurrence score was validated in 1625 patients from the independent validation dataset and 418 patients from The Cancer Genome Atlas set. The primary outcome measured was the recurrence-free interval (RFI). FINDINGS: The multimodal recurrence score had significantly higher predictive accuracy than the three single-modal scores and clinicopathological risk factors, and it precisely predicted the RFI of patients in the training and two validation datasets (areas under the curve at 5 years: 0·825-0·876 vs 0·608-0·793; p<0·05). The RFI of patients with low stage or grade is usually better than that of patients with high stage or grade; however, the RFI in the multimodal recurrence score-defined high-risk stage I and II group was shorter than in the low-risk stage III group (hazard ratio [HR] 4·57, 95% CI 2·49-8·40; p<0·0001), and the RFI of the high-risk grade 1 and 2 group was shorter than in the low-risk grade 3 and 4 group (HR 4·58, 3·19-6·59; p<0·0001). INTERPRETATION: Our multimodal recurrence score is a practical and reliable predictor that can add value to the current staging system for predicting localised renal cell carcinoma recurrence after surgery, and this combined approach more precisely informs treatment decisions about adjuvant therapy. FUNDING: National Natural Science Foundation of China, and National Key Research and Development Program of China.

Quantum Neural Network for Quantum Neural Computing.

Neural networks have achieved impressive breakthroughs in both industry and academia. How to effectively develop neural networks on quantum computing devices is a challenging open problem. Here, we propose a new quantum neural network model for quantum neural computing using (classically controlled) single-qubit operations and measurements on real-world quantum systems with naturally occurring environment-induced decoherence, which greatly reduces the difficulties of physical implementations. Our model circumvents the problem that the state-space size grows exponentially with the number of neurons, thereby greatly reducing memory requirements and allowing for fast optimization with traditional optimization algorithms. We benchmark our model for handwritten digit recognition and other nonlinear classification tasks. The results show that our model has an amazing nonlinear classification ability and robustness to noise. Furthermore, our model allows quantum computing to be applied in a wider context and inspires the earlier development of a quantum neural computer than standard quantum computers.

Discovering pathway biomarkers of hepatocellular carcinoma occurrence and development by dynamic network entropy analysis.

OBJECTIVE: Gene expression profiling techniques measure the transcription of thousands of genes in a parallel manner. With more and more hepatocellular carcinoma (HCC) transcriptomic data becoming available, the high-throughput data provides an unprecedented opportunity to discover HCC diagnostic biomarkers. In this work, we propose a bioinformatics method based on dynamic network entropy analysis, called DNEA, to identify potential pathway biomarkers for HCC occurrence and development by integrating transcriptome and interactome. METHODS: We firstly collect the pathways documented in different knowledge-bases and then impose the genome-wide human transcriptomic data of multistage cancerous tissues during the development and progression of HCC. After linking the gene sets of pathways into individual connected networks, we map the corresponding gene expression information onto these pathways. The dynamic network entropy of individual pathways is calculated to evaluate its activities and dysfunctionalities during the disease occurrence and development. We use the overall significant difference in the entropic dynamics during the time course to prioritize distinctive pathways during disease progression. Then machine learning classification methods are employed to screen out pathway biomarkers with the classification ability to distinguish different-stage samples of HCC progression. RESULTS: Pathway biomarkers discovered based on DNEA demonstrate good classification performance in measuring HCC progression. The classification accuracy is as follows: DNA replication pathway (mean AUC = 0.82, 20 genes) from KEGG, FMLP pathway (mean AUC = 0.84, 14 genes) from BioCarta, and downstream signaling of activated FGFR pathway (mean AUC = 0.80, 15 genes) from Reactome. At the same time, previous studies have shown that these genes and pathways screened are closely related to the occurrence and development of HCC in terms of oncogenesis dysfunctions. CONCLUSIONS: Our method for cancer biomarker discovery based on dynamic network entropy analysis is effective and efficient in identifying pathway biomarkers related to the progression of complex diseases.

LogBTF: gene regulatory network inference using Boolean threshold network model from single-cell gene expression data.

MOTIVATION: From a systematic perspective, it is crucial to infer and analyze gene regulatory network (GRN) from high-throughput single-cell RNA sequencing data. However, most existing GRN inference methods mainly focus on the network topology, only few of them consider how to explicitly describe the updated logic rules of regulation in GRNs to obtain their dynamics. Moreover, some inference methods also fail to deal with the over-fitting problem caused by the noise in time series data. RESULTS: In this article, we propose a novel embedded Boolean threshold network method called LogBTF, which effectively infers GRN by integrating regularized logistic regression and Boolean threshold function. First, the continuous gene expression values are converted into Boolean values and the elastic net regression model is adopted to fit the binarized time series data. Then, the estimated regression coefficients are applied to represent the unknown Boolean threshold function of the candidate Boolean threshold network as the dynamical equations. To overcome the multi-collinearity and over-fitting problems, a new and effective approach is designed to optimize the network topology by adding a perturbation design matrix to the input data and thereafter setting sufficiently small elements of the output coefficient vector to zeros. In addition, the cross-validation procedure is implemented into the Boolean threshold network model framework to strengthen the inference capability. Finally, extensive experiments on one simulated Boolean value dataset, dozens of simulation datasets, and three real single-cell RNA sequencing datasets demonstrate that the LogBTF method can infer GRNs from time series data more accurately than some other alternative methods for GRN inference. AVAILABILITY AND IMPLEMENTATION: The source data and code are available at https://github.com/zpliulab/LogBTF.

GeoBind: segmentation of nucleic acid binding interface on protein surface with geometric deep learning.

Unveiling the nucleic acid binding sites of a protein helps reveal its regulatory functions in vivo. Current methods encode protein sites from the handcrafted features of their local neighbors and recognize them via a classification, which are limited in expressive ability. Here, we present GeoBind, a geometric deep learning method for predicting nucleic binding sites on protein surface in a segmentation manner. GeoBind takes the whole point clouds of protein surface as input and learns the high-level representation based on the aggregation of their neighbors in local reference frames. Testing GeoBind on benchmark datasets, we demonstrate GeoBind is superior to state-of-the-art predictors. Specific case studies are performed to show the powerful ability of GeoBind to explore molecular surfaces when deciphering proteins with multimer formation. To show the versatility of GeoBind, we further extend GeoBind to five other types of ligand binding sites prediction tasks and achieve competitive performances.

LZTFL1 inhibits kidney tumor cell growth by destabilizing AKT through ZNRF1-mediated ubiquitin proteosome pathway.

LZTFL1 is a tumor suppressor located in chromosomal region 3p21.3 that is deleted frequently and early in various cancer types including the kidney cancer. However, its role in kidney tumorigenesis remains unknown. Here we hypothesized a tumor suppressive function of LZTFL1 in clear cell renal cell carcinoma (ccRCC) and its mechanism of action based on extensive bioinformatics analysis of patients' tumor data and validated it using both gain- and loss-functional studies in kidney tumor cell lines and patient-derive xenograft (PDX) model systems. Our studies indicated that LZTFL1 inhibits kidney tumor cell proliferation by destabilizing AKT through ZNRF1-mediated ubiquitin proteosome pathway and inducing cell cycle arrest at G1. Clinically, we found that LZTFL1 is frequently deleted in ccRCC. Downregulation of LZTFL1 is associated with a poor ccRCC outcome and may be used as prognostic maker. Furthermore, we show that overexpression of LZTFL1 in PDX via lentiviral delivery suppressed PDX growth, suggesting that re-expression of LZTFL1 may be a therapeutic strategy against ccRCC.

PKI: A bioinformatics method of quantifying the importance of nodes in gene regulatory network via a pseudo knockout index.

BACKGROUND: Gene regulatory network (GRN) is a model that characterizes the complex relationships between genes and thereby provides an informatics environment to measure the importance of nodes. The evaluation of important nodes in a GRN can effectively refer to their functional implications severing as key players in particular biological processes, such as master regulator and driver gene. Currently, it is mainly based on network topological parameters and focuses only on evaluating a single node individually. However, genes and products play their functions by interacting with each other. It is worth noting that the effects of gene combinations in GRN are not simply additive. Key combinations discovery is of significance in revealing gene sets with important functions. Recently, with the development of single-cell RNA-sequencing (scRNA-seq) technology, we can quantify gene expression profiles of individual cells that provide the potential to identify crucial nodes in gene regulations regarding specific condition, e.g., stem cell differentiation. RESULTS: In this paper, we propose a bioinformatics method, called Pseudo Knockout Importance (PKI), to quantify the importance of node and node sets in a specific GRN structure using time-course scRNA-seq data. First, we construct ordinary differential equations to approach the gene regulations during cell differentiation. Then we design gene pseudo knockout experiments and define PKI score evaluation criteria based on the coefficient of determination. The importance of nodes can be described as the influence on the ODE system of removing variables. For key gene combinations, PKI is derived as a combinatorial optimization problem of quantifying the in silico gene knockout effects. CONCLUSIONS: Here, we focus our analyses on the specific GRN of embryonic stem cells with time series gene expression profile. To verify the effectiveness and advantage of PKI method, we compare its node importance rankings with other twelve kinds of centrality-based methods, such as degree and Latora closeness. For key node combinations, we compare the results with the method based on minimum dominant set. Moreover, the famous combinations of transcription factors in induced pluripotent stem cell are also employed to verify the vital gene combinations identified by PKI. These results demonstrate the reliability and superiority of the proposed method.

Therapeutic targeting of histone lysine demethylase KDM4B blocks the growth of castration-resistant prostate cancer.

Epigenetics is an emerging mechanism for tumorigenesis. Treatment that targets epigenetic regulators is becoming an attractive strategy for cancer therapy. The role of epigenetic therapy in prostate cancer (PCa) remains elusive. Previously we demonstrated that upregulation of histone lysine demethylase KDM4B correlated with the appearance of castration resistant prostate cancer (CRPC) and identified a small molecular inhibitor of KDM4B, B3. In this study, we further investigated the role of KDM4B in promoting PCa progression and tested the efficacy of B3 using clinically relevant PCa models including PCa cell line LNCaP and 22Rv1 and xenografts derived from these cell lines. In loss and gain-functional studies of KDM4B in PCa cells, we found that overexpression of KDM4B in LNCaP cells enhanced its tumorigenicity whereas knockdown of KDM4B in 22Rv1 cells reduced tumor growth in castrated mice. B3 suppressed the growth of 22Rv1 xenografts and sensitized tumor to anti-androgen receptor (AR) antagonist enzalutamide inhibition. B3 also inhibited 22Rv1 tumor growth synergistically with rapamycin, leading to cell apoptosis. Comparative transcriptomic analysis performed on KDM4B knockdown and B3-treated 22Rv1 cells revealed that B3 inhibited both H3K9me3 and H3K27me3 demethylase activities. Our studies establish KDM4B as a target for CRPC and B3 as a potential therapeutic agent. B3 as monotherapy or in combination with other anti-PCa therapeutics offers proof of principle for the clinical translation of epigenetic therapy targeting KDMs for CRPC patients.

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To explore the effects of multiple time-scales, climatic and stand factors on tree mortality in forests, we examined the changes in annual and inventory-cycle tree mortality patterns across 264 forest inventory plots in four national forests of eastern Texas. These data were obtained from the Forest Inventory and Analysis (FIA) Program and the plots had been individually surveyed in four inventory cycles over the past 20 years. The generalized linear mixed effects model (GLMM) was used to explore the effects of climatic factors (drought severity, duration of drought, mean annual temperature, and mean annual precipitation), tree size (diameter at breast height) and stand factors (basal area, stand density, and stand age) on tree mortality. The results showed that tree mortality rates increased by 151% in the particular year with severe drought and by 123% during exceptional inventory cycle during the inventory cycle with severe drought. The major cause of death was weather (exceptional drought and large hurricanes). Both drought severity as measured by standardized precipitation evapotranspiration index (SPEI) and the duration of drought had significant negative effects, whereas annual precipitation had a significant positive effect on tree survival. Tree basal area had a significant negative effect, while tree size, stand age and stand density had significant positive effects on tree survival. Trees with larger size (DBH) were more vulnerable to drought than smaller ones. During the exceptional drought, tree mortality rate of pine species (2.1%) was lower than that of hardwood species (3.9%), while tree mortality in the natural forests (3.0%) was higher than that in the pine plantations (1.9%). Our results suggested that it was essential to consider the relative importance of both intrinsic (tree size) and extrinsic (stand factors and climatic factors) factors in analyzing tree mortality.