Inhibition of CK2 Diminishes Fibrotic Scar Formation and Improves Outcomes After Ischemic Stroke via Reducing BRD4 Phosphorylation.

Fibrotic scars play important roles in tissue reconstruction and functional recovery in the late stage of nervous system injury. However, the mechanisms underlying fibrotic scar formation and regulation remain unclear. Casein kinase II (CK2) is a protein kinase that regulates a variety of cellular functions through the phosphorylation of proteins, including bromodomain-containing protein 4 (BRD4). CK2 and BRD4 participate in fibrosis formation in a variety of tissues. However, whether CK2 affects fibrotic scar formation remains unclear, as do the mechanisms of signal regulation after cerebral ischemic injury. In this study, we assessed whether CK2 could modulate fibrotic scar formation after cerebral ischemic injury through BRD4. Primary meningeal fibroblasts were isolated from neonatal rats and treated with transforming growth factor-ß1 (TGF-ß1), SB431542 (a TGF-ß1 receptor kinase inhibitor) or TBB (a highly potent CK2 inhibitor). Adult SD rats were intraperitoneally injected with TBB to inhibit CK2 after MCAO/R. We found that CK2 expression was increased in vitro in the TGF-ß1-induced fibrosis model and in vivo in the MCAO/R injury model. The TGF-ß1 receptor kinase inhibitor SB431542 decreased CK2 expression in fibroblasts. The CK2 inhibitor TBB reduced the increases in proliferation, migration and activation of fibroblasts caused by TGF-ß1 in vitro, and it inhibited fibrotic scar formation, ameliorated histopathological damage, protected Nissl bodies, decreased infarct volume and alleviated neurological deficits after MCAO/R injury in vivo. Furthermore, CK2 inhibition decreased BRD4 phosphorylation both in vitro and in vivo. The findings of the present study suggested that CK2 may control BRD4 phosphorylation to regulate fibrotic scar formation, to affecting outcomes after ischemic stroke.

Downregulation of peripheral luteinizing hormone rescues ovariectomy-associated cognitive deficits in APP/PS1 mice.

Alzheimer's disease (AD) is more prevalent in women than men, supposing due to the decline of estrogens in menopause, accompanied by increased gonadotropins such as luteinizing hormone (LH). We and others found that the transcription factor early growth response-1 (EGR1) regulates cholinergic function including the expression of acetylcholinesterase (AChE) and plays a significant role in cognitive decline of AD. Here we investigated in APP/PS1 mice by ovariectomy (OVX) and estradiol (E2) supplementation or inhibition of LH the effect on hippocampus-related cognition and related molecular changes. We found that OVX-associated cognitive impairment was accompanied by increased dorsal hippocampal EGR1 expression, which was rescued by downregulating peripheral LH rather than by supplementing E2. We also found in postmortem AD brains a higher expression of pituitary LH-mRNA and higher EGR1 expression in the posterior hippocampus. Both, in human and mice, there was a significant positive correlation between respectively posterior/dorsal hippocampal EGR1 and peripheral LH expression. We conclude that peripheral increased LH and increased posterior hippocampal EGR1 plays a significant role in AD pathology.

TRPM2 as a conserved gatekeeper determines the vulnerability of DA neurons by mediating ROS sensing and calcium dyshomeostasis.

Different dopaminergic (DA) neuronal subgroups exhibit distinct vulnerability to stress, while the underlying mechanisms are elusive. Here we report that the transient receptor potential melastatin 2 (TRPM2) channel is preferentially expressed in vulnerable DA neuronal subgroups, which correlates positively with aging in Parkinson's Disease (PD) patients. Overexpression of human TRPM2 in the DA neurons of C. elegans resulted in selective death of ADE but not CEP neurons in aged worms. Mechanistically, TRPM2 activation mediates FZO-1/CED-9-dependent mitochondrial hyperfusion and mitochondrial permeability transition (MPT), leading to ADE death. In mice, TRPM2 knockout reduced vulnerable substantia nigra pars compacta (SNc) DA neuronal death induced by stress. Moreover, the TRPM2-mediated vulnerable DA neuronal death pathway is conserved from C. elegans to toxin-treated mice model and PD patient iPSC-derived DA neurons. The vulnerable SNc DA neuronal loss is the major symptom and cause of PD, and therefore the TRPM2-mediated pathway serves as a promising therapeutic target against PD.

New insights into Sirt1: potential therapeutic targets for the treatment of cerebral ischemic stroke.

Ischemic stroke is one of the main causes of mortality and disability worldwide. However, the majority of patients are currently unable to benefit from intravenous thrombolysis or intravascular mechanical thrombectomy due to the limited treatment windows and serious complications. Silent mating type information regulation 2 homolog 1 (Sirt1), a nicotine adenine dinucleotide-dependent enzyme, has emerged as a potential therapeutic target for ischemic stroke due to its ability to maintain brain homeostasis and possess neuroprotective properties in a variety of pathological conditions for the central nervous system. Animal and clinical studies have shown that activation of Sirt1 can lessen neurological deficits and reduce the infarcted volume, offering promise for the treatment of ischemic stroke. In this review, we summarized the direct evidence and related mechanisms of Sirt1 providing neuroprotection against cerebral ischemic stroke. Firstly, we introduced the protein structure, catalytic mechanism and specific location of Sirt1 in the central nervous system. Secondly, we list the activators and inhibitors of Sirt1, which are primarily divided into three categories: natural, synthetic and physiological. Finally, we reviewed the neuroprotective effects of Sirt1 in ischemic stroke and discussed the specific mechanisms, including reducing neurological deficits by inhibiting various programmed cell death such as pyroptosis, necroptosis, ferroptosis, and cuproptosis in the acute phase, as well as enhancing neurological repair by promoting angiogenesis and neurogenesis in the later stage. Our review aims to contribute to a deeper understanding of the critical role of Sirt1 in cerebral ischemic stroke and to offer novel therapeutic strategies for this condition.

M2 macrophages mediate fibrotic scar formation in the early stages after cerebral ischemia in rats.

In the central nervous system, the formation of fibrotic scar after injury inhibits axon regeneration and promotes repair. However, the mechanism underlying fibrotic scar formation and regulation remains poorly understood. M2 macrophages regulate fibrotic scar formation after injury to the heart, lung, kidney, and central nervous system. However, it remains to be clarified whether and how M2 macrophages regulate fibrotic scar formation after cerebral ischemia injury. In this study, we found that, in a rat model of cerebral ischemia induced by middle cerebral artery occlusion/reperfusion, fibrosis and macrophage infiltration were apparent in the ischemic core in the early stage of injury (within 14 days of injury). The number of infiltrated macrophages was positively correlated with fibronectin expression. Depletion of circulating monocyte-derived macrophages attenuated fibrotic scar formation. Interleukin 4 (IL4) expression was strongly enhanced in the ischemic cerebral tissues, and IL4-induced M2 macrophage polarization promoted fibrotic scar formation in the ischemic core. In addition, macrophage-conditioned medium directly promoted fibroblast proliferation and the production of extracellular matrix proteins in vitro. Further pharmacological and genetic analyses showed that sonic hedgehog secreted by M2 macrophages promoted fibrogenesis in vitro and in vivo, and that this process was mediated by secretion of the key fibrosis-associated regulatory proteins transforming growth factor beta 1 and matrix metalloproteinase 9. Furthermore, IL4-afforded functional restoration on angiogenesis, cell apoptosis, and infarct volume in the ischemic core of cerebral ischemia rats were markedly impaired by treatment with an sonic hedgehog signaling inhibitor, paralleling the extent of fibrosis. Taken together, our findings show that IL4/sonic hedgehog/transforming growth factor beta 1 signaling targeting macrophages regulates the formation of fibrotic scar and is a potential therapeutic target for ischemic stroke.

A hybrid positive unlabeled learning framework for uncovering scaffolds across human proteome by measuring the propensity to drive phase separation.

Scaffold proteins drive liquid-liquid phase separation (LLPS) to form biomolecular condensates and organize various biochemical reactions in cells. Dysregulation of scaffolds can lead to aberrant condensate assembly and various complex diseases. However, bioinformatics predictors dedicated to scaffolds are still lacking and their development suffers from an extreme imbalance between limited experimentally identified scaffolds and unlabeled candidates. Here, using the joint distribution of hybrid multimodal features, we implemented a positive unlabeled (PU) learning-based framework named PULPS that combined ProbTagging and penalty logistic regression (PLR) to profile the propensity of scaffolds. PULPS achieved the best AUC of 0.8353 and showed an area under the lift curve (AUL) of 0.8339 as an estimation of true performance. Upon reviewing recent experimentally verified scaffolds, we performed a partial recovery with 2.85% increase in AUL from 0.8339 to 0.8577. In comparison, PULPS showed a 45.7% improvement in AUL compared with PLR, whereas 8.2% superiority over other existing tools. Our study first proved that PU learning is more suitable for scaffold prediction and demonstrated the widespread existence of phase separation states. This profile also uncovered potential scaffolds that co-drive LLPS in the human proteome and generated candidates for further experiments. PULPS is free for academic research at http://pulps.zbiolab.cn.

Molecular persistent spectral image (Mol-PSI) representation for machine learning models in drug design.

Artificial intelligence (AI)-based drug design has great promise to fundamentally change the landscape of the pharmaceutical industry. Even though there are great progress from handcrafted feature-based machine learning models, 3D convolutional neural networks (CNNs) and graph neural networks, effective and efficient representations that characterize the structural, physical, chemical and biological properties of molecular structures and interactions remain to be a great challenge. Here, we propose an equal-sized molecular 2D image representation, known as the molecular persistent spectral image (Mol-PSI), and combine it with CNN model for AI-based drug design. Mol-PSI provides a unique one-to-one image representation for molecular structures and interactions. In general, deep models are empowered to achieve better performance with systematically organized representations in image format. A well-designed parallel CNN architecture for adapting Mol-PSIs is developed for protein-ligand binding affinity prediction. Our results, for the three most commonly used databases, including PDBbind-v2007, PDBbind-v2013 and PDBbind-v2016, are better than all traditional machine learning models, as far as we know. Our Mol-PSI model provides a powerful molecular representation that can be widely used in AI-based drug design and molecular data analysis.

FSL-Kla: A few-shot learning-based multi-feature hybrid system for lactylation site prediction.

As a novel lactate-derived post-translational modification (PTM), lysine lactylation (Kla) is involved in diverse biological processes, and participates in human tumorigenesis. Identification of Kla substrates with their exact sites is crucial for revealing the molecular mechanisms of lactylation. In contrast with labor-intensive and time-consuming experimental approaches, computational prediction of Kla could provide convenience and increased speed, but is still lacking. In this work, although current identified Kla sites are limited, we constructed the first Kla benchmark dataset and developed a few-shot learning-based architecture approach to leverage the power of small datasets and reduce the impact of imbalance and overfitting. A maximum 11.7% (0.745 versus 0.667) increase of area under the curve (AUC) value was achieved in contrast to conventional machine learning methods. We conducted a comprehensive survey of the performance by combining 8 sequence-based features and 3 structure-based features and tailored a multi-feature hybrid system for synergistic combination. This system achieved >16.2% improvement of the AUC value (0.889 versus 0.765) compared with single feature-based models for the prediction of Kla sites in silico. Taken few-shot learning and hybrid system together, we present our newly designed predictor named FSL-Kla, which is not only a cutting-edge tool for Kla site profile but also could generate candidates for further experimental approaches. The webserver of FSL-Kla is freely accessible for academic research at http://kla.zbiolab.cn/.

GPS-Palm: a deep learning-based graphic presentation system for the prediction of S-palmitoylation sites in proteins.

As an important reversible lipid modification, S-palmitoylation mainly occurs at specific cysteine residues in proteins, participates in regulating various biological processes and is associated with human diseases. Besides experimental assays, computational prediction of S-palmitoylation sites can efficiently generate helpful candidates for further experimental consideration. Here, we reviewed the current progress in the development of S-palmitoylation site predictors, as well as training data sets, informative features and algorithms used in these tools. Then, we compiled a benchmark data set containing 3098 known S-palmitoylation sites identified from small- or large-scale experiments, and developed a new method named data quality discrimination (DQD) to distinguish data quality weights (DQWs) between the two types of the sites. Besides DQD and our previous methods, we encoded sequence similarity values into images, constructed a deep learning framework of convolutional neural networks (CNNs) and developed a novel algorithm of graphic presentation system (GPS) 6.0. We further integrated nine additional types of sequence-based and structural features, implemented parallel CNNs (pCNNs) and designed a new predictor called GPS-Palm. Compared with other existing tools, GPS-Palm showed a >31.3% improvement of the area under the curve (AUC) value (0.855 versus 0.651) for general prediction of S-palmitoylation sites. We also produced two species-specific predictors, with corresponding AUC values of 0.900 and 0.897 for predicting human- and mouse-specific sites, respectively. GPS-Palm is free for academic research at http://gpspalm.biocuckoo.cn/.

Open resource of clinical data from patients with pneumonia for the prediction of COVID-19 outcomes via deep learning.

Data from patients with coronavirus disease 2019 (COVID-19) are essential for guiding clinical decision making, for furthering the understanding of this viral disease, and for diagnostic modelling. Here, we describe an open resource containing data from 1,521 patients with pneumonia (including COVID-19 pneumonia) consisting of chest computed tomography (CT) images, 130 clinical features (from a range of biochemical and cellular analyses of blood and urine samples) and laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) clinical status. We show the utility of the database for prediction of COVID-19 morbidity and mortality outcomes using a deep learning algorithm trained with data from 1,170 patients and 19,685 manually labelled CT slices. In an independent validation cohort of 351 patients, the algorithm discriminated between negative, mild and severe cases with areas under the receiver operating characteristic curve of 0.944, 0.860 and 0.884, respectively. The open database may have further uses in the diagnosis and management of patients with COVID-19.

[Progress on axon regeneration in model organisms].

Different from neurons in the peripheral nervous system, mature neurons in the mammalian central nervous system often fail to regenerate after injury. Recent studies have found that calcium transduction, injury signaling, mitochondrial transportation, cytoskeletal remodeling and protein synthesis play essential roles in axon regeneration. Firstly, axon injury increases the intracellular concentration of calcium, and initiates the injury signaling pathways including cyclic adenosine monophosphate (cAMP)-protein kinase A (PKA) and dual leucine kinase (DLK), which are found to promote axon regeneration in multiple animal injury models. The second step for axonal regrowth is to rebuild growth cones. Overexpressing proteins that promote dynamics of microtubules and actin filaments is beneficial for the reassembly of cytoskeletons and initiation of new growth cones. Thirdly, mitochondria, the power factory for cells, also play important roles in growth cone formation and axonal extension. The last but not the least important step is the regulation of gene transcription and protein translation to sustain the regrowth of axons. This review summarizes important findings revealing the functions and mechanisms of these biological progresses.

HybridSucc: A Hybrid-learning Architecture for General and Species-specific Succinylation Site Prediction.

As an important protein acylation modification, lysine succinylation (Ksucc) is involved in diverse biological processes, and participates in human tumorigenesis. Here, we collected 26,243 non-redundant known Ksucc sites from 13 species as the benchmark data set, combined 10 types of informative features, and implemented a hybrid-learning architecture by integrating deep-learning and conventional machine-learning algorithms into a single framework. We constructed a new tool named HybridSucc, which achieved area under curve (AUC) values of 0.885 and 0.952 for general and human-specific prediction of Ksucc sites, respectively. In comparison, the accuracy of HybridSucc was 17.84%-50.62% better than that of other existing tools. Using HybridSucc, we conducted a proteome-wide prediction and prioritized 370 cancer mutations that change Ksucc states of 218 important proteins, including PKM2, SHMT2, and IDH2. We not only developed a high-profile tool for predicting Ksucc sites, but also generated useful candidates for further experimental consideration. The online service of HybridSucc can be freely accessed for academic research at http://hybridsucc.biocuckoo.org/.

GPS-PBS: A Deep Learning Framework to Predict Phosphorylation Sites that Specifically Interact with Phosphoprotein-Binding Domains.

Protein phosphorylation is essential for regulating cellular activities by modifying substrates at specific residues, which frequently interact with proteins containing phosphoprotein-binding domains (PPBDs) to propagate the phosphorylation signaling into downstream pathways. Although massive phosphorylation sites (p-sites) have been reported, most of their interacting PPBDs are unknown. Here, we collected 4458 known PPBD-specific binding p-sites (PBSs), considerably improved our previously developed group-based prediction system (GPS) algorithm, and implemented a deep learning plus transfer learning strategy for model training. Then, we developed a new online service named GPS-PBS, which can hierarchically predict PBSs of 122 single PPBD clusters belonging to two groups and 16 families. By comparison, GPS-PBS achieved a highly competitive accuracy against other existing tools. Using GPS-PBS, we predicted 371,018 mammalian p-sites that potentially interact with at least one PPBD, and revealed that various PPBD-containing proteins (PPCPs) and protein kinases (PKs) can simultaneously regulate the same p-sites to orchestrate important pathways, such as the PI3K-Akt signaling pathway. Taken together, we anticipate GPS-PBS can be a great help for further dissecting phosphorylation signaling networks.

DTF: Deep Tensor Factorization for predicting anticancer drug synergy.

MOTIVATION: Combination therapies have been widely used to treat cancers. However, it is cost and time consuming to experimentally screen synergistic drug pairs due to the enormous number of possible drug combinations. Thus, computational methods have become an important way to predict and prioritize synergistic drug pairs. RESULTS: We proposed a Deep Tensor Factorization (DTF) model, which integrated a tensor factorization method and a deep neural network (DNN), to predict drug synergy. The former extracts latent features from drug synergy information while the latter constructs a binary classifier to predict the drug synergy status. Compared to the tensor-based method, the DTF model performed better in predicting drug synergy. The area under precision-recall curve (PR AUC) was 0.58 for DTF and 0.24 for the tensor method. We also compared the DTF model with DeepSynergy and logistic regression models, and found that the DTF outperformed the logistic regression model and achieved similar performance as DeepSynergy using several performance metrics for classification task. Applying the DTF model to predict missing entries in our drug-cell-line tensor, we identified novel synergistic drug combinations for 10 cell lines from the 5 cancer types. A literature survey showed that some of these predicted drug synergies have been identified in vivo or in vitro. Thus, the DTF model could be a valuable in silico tool for prioritizing novel synergistic drug combinations. AVAILABILITY AND IMPLEMENTATION: Source code and data are available at https://github.com/ZexuanSun/DTF-Drug-Synergy. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Deep graph embedding for prioritizing synergistic anticancer drug combinations.

Drug combinations are frequently used for the treatment of cancer patients in order to increase efficacy, decrease adverse side effects, or overcome drug resistance. Given the enormous number of drug combinations, it is cost- and time-consuming to screen all possible drug pairs experimentally. Currently, it has not been fully explored to integrate multiple networks to predict synergistic drug combinations using recently developed deep learning technologies. In this study, we proposed a Graph Convolutional Network (GCN) model to predict synergistic drug combinations in particular cancer cell lines. Specifically, the GCN method used a convolutional neural network model to do heterogeneous graph embedding, and thus solved a link prediction task. The graph in this study was a multimodal graph, which was constructed by integrating the drug-drug combination, drug-protein interaction, and protein-protein interaction networks. We found that the GCN model was able to correctly predict cell line-specific synergistic drug combinations from a large heterogonous network. The majority (30) of the 39 cell line-specific models show an area under the receiver operational characteristic curve (AUC) larger than 0.80, resulting in a mean AUC of 0.84. Moreover, we conducted an in-depth literature survey to investigate the top predicted drug combinations in specific cancer cell lines and found that many of them have been found to show synergistic antitumor activity against the same or other cancers in vitro or in vivo. Taken together, the results indicate that our study provides a promising way to better predict and optimize synergistic drug pairs in silico.

DrLLPS: a data resource of liquid-liquid phase separation in eukaryotes.

Here, we presented an integrative database named DrLLPS (http://llps.biocuckoo.cn/) for proteins involved in liquid-liquid phase separation (LLPS), which is a ubiquitous and crucial mechanism for spatiotemporal organization of various biochemical reactions, by creating membraneless organelles (MLOs) in eukaryotic cells. From the literature, we manually collected 150 scaffold proteins that are drivers of LLPS, 987 regulators that contribute in modulating LLPS, and 8148 potential client proteins that might be dispensable for the formation of MLOs, which were then categorized into 40 biomolecular condensates. We searched potential orthologs of these known proteins, and in total DrLLPS contained 437 887 known and potential LLPS-associated proteins in 164 eukaryotes. Furthermore, we carefully annotated LLPS-associated proteins in eight model organisms, by using the knowledge integrated from 110 widely used resources that covered 16 aspects, including protein disordered regions, domain annotations, post-translational modifications (PTMs), genetic variations, cancer mutations, molecular interactions, disease-associated information, drug-target relations, physicochemical property, protein functional annotations, protein expressions/proteomics, protein 3D structures, subcellular localizations, mRNA expressions, DNA & RNA elements, and DNA methylations. We anticipate DrLLPS can serve as a helpful resource for further analysis of LLPS.