Science Bulletin

Cardiovascular disease is one of the leading causes of death worldwide, with myocardial infarction (MI) being a major cause of both morbidity and mortality among cardiovascular patients. MI Patients face a higher risk of cardiovascular disease recurrence afterwards. Therefore, accurately predicting the risk of recurrence and identifying key risk factors are crucial for clinical decision-making. In this paper, we consider the interrelationships among cardiovascular factors from a systemic perspective. We first construct a differential network for each patient to capture individual-specific deviations in factor relationships and propose a novel method, termed Causal Factor-aware Graph Neural Network (CFGNN), which integrates factor interactions to predict the recurrence risk of MI patients while uncovering key risk factors from a causal perspective. Experimental results demonstrate that CFGNN performs well on hospital-derived datasets in real world, effectively identifying several key risk factors. This method not only deepens our understanding of cardiovascular disease, but also paves the way for more targeted and effective interventions.

Network analysis of human brain connectivity provides a fundamental framework for identifying the neurobiological mechanisms that cause cognitive variations and neurological disorders. However, existing diagnostic models often treat structural connectivity (SC) as a fixed or optimal topological scaffold for functional connectivity (FC). This consequently overlooks the higher-order dependencies between brain regions that are critical for characterizing pathological alterations. Moreover, the distinct spatial organizations of SC and FC complicate their direct integration, as naive alignment methods may distort the inherent nonlinear patterns of brain connectivity. To address these limitations, we propose the Graph Diffusion Optimal Transport Network (GDOT-Net), which models disease-related topological evolution and achieves precise alignment between SC and FC. Unlike existing diffusion studies, the proposed model introduces an evolvable brain connectome modeling approach to infer the complex topological structure of brain networks, unveiling higher-order connectivity patterns linked to specific neuropsychiatric disorders. Furthermore, GDOT-Net incorporates a Pattern-Specific Alignment mechanism, leveraging optimal transport to align structural and functional topological representations in a geometry-aware manner. To capture nonlinear topological relationships between brain regions, a Neural Graph Aggregator Module was developed, which adaptively learns complex node interaction patterns in brain networks. By leveraging this module, GDOT-Net generates highly discriminative representations that form a robust basis for the precision diagnosis of brain disorders. Experiments on REST-meta-MDD and ADNI demonstrate that GDOT-Net surpasses SOTA methods in uncovering structural-functional misalignments and disorder-specific subnetworks. The source code is publicly available at this Link.

The cerebellum is one of the most complex structures of the brain composed of a high diversity of GABAergic and glutamatergic neurons. Whereas cerebellar biogenesis has been extensively studied in the mouse, an in-depth characterization of genes and pathways involved in cerebellar specification and maturation in the humans remains overlooked. Here, we used human pluripotent stem cells (hPSC)-derived cerebellar organoids (CRBOs) to study the temporal biogenesis of neuronal subtypes. Our results show that CRBOs acquire caudal neural tube identity at an early stage followed by a time-dependent expression of mature cerebellar neuronal markers in vitro, mimicking human neurodevelopment. CRBOs show the generation of both cerebellar excitatory and inhibitory neurons and the expression of glial cell markers, suggesting the generation of a high variety of cerebellar cell types in vitro. Further, in vitro CRBOs show expression of cerebellar disease associated genes, such as those related to ataxia. Our results establish CRBOs as a valuable platform to explore the mechanisms of human cerebellar development and related disorders.

The axon initial segment (AIS) undergoes structural plasticity and refines neuronal excitability, yet the underlying mechanisms remain unclear. We here developed an in vivo CRISPR/Cas9 knockout platform using an all-in-one triple-guide RNA vector introduced via electroporation and employed this approach to seek molecules that regulate the developmental shortening of AIS in the chicken nucleus magnocellularis. We have targeted fourteen molecules associated with microtubules and found that knockouts of glycogen synthase kinase 3{beta} (GSK3{beta}) and Tau disabled the AIS shortening. Conversely, overexpression of constitutively active form of GSK3{beta} facilitated the AIS shortening in vivo. This extensive shortening was replicated in slice cultures, which was occluded by stabilization of microtubules. These results suggested that microtubule remodeling by GSK3{beta} activity contributed to the AIS shortening. This study thus provides a genetic approach suitable for genetic screening that allows identifying regulators of the AIS plasticity in the chicken brain.

Human coronavirus OC43 (HCoV-OC43) constitutes one of the most common causes of the seasonal cold but can also cause severe disease among elderly and immuno-compromised. Currently, there are no approved antiviral drugs to combat HCoV-OC43 infection. Coronaviruses are positive single-stranded RNA (+ssRNA) viruses and utilize -1 programmed ribosomal frameshifting (-1 PRF) to obtain the correct stoichiometry of viral protein components. As such, the ribosomal frameshifting stimulation element (FSE) is a promising target for antiviral drug discovery, due to its high conservation. By repurposing available drugs, we identified a group of anthracycline compounds that can reduce -1 PRF of HCoV-OC43 and reduce viral infection of cells. Furthermore, we show that anthracyclines that reduce infection also bind the FSE and reduce frameshift frequency. We also show that the selected anthracyclines reduce SARS-CoV-2 infection, but without affecting -1 PRF frequency. All together, we demonstrate that a subset of anthracyclines selectively binds and inhibit the HCoV-OC43 FSE and could thus serve as a robust framework when developing new antivirals targeting coronaviruses.

The recurrent outbreaks and geographical expansion of mosquito-borne arboviruses pose a significant challenge to public health worldwide. The disease outcome for arboviral infections ranges from acute febrile illness to severe conditions such as encephalitis, hemorrhagic shock, and mortality. Current treatment options for these viruses are limited to supportive care, necessitating an urgent need for a safe and effective broad-spectrum antiviral. In this study, we have identified Trifluoperazine (TFP), an FDA-approved antipsychotic, as a potent broad-spectrum antiviral against Japanese encephalitis Virus (JEV), Dengue virus (DENV) and Chikungunya virus (CHIKV) infections. The antiviral effect of TFP was also seen in the animal models of JEV and CHIKV with significantly reduced disease severity. Mechanistically, TFP treatment increased the phosphorylation of eIF2a and induced an adaptive ER stress response in diverse cell types. Alleviation of TFP-induced ER stress by chemical chaperone 4PBA abolished the antiviral activity of the drug and rescued virus replication in cells. The robust in vitro and in vivo efficacy of the drug against arboviruses highlights the potential for repurposing TFP as a broad-spectrum antiviral candidate.

Despite of the highly potent antiretroviral therapies, HIV-1 establishes persistent infection and causes chronic inflammation in AIDS patients. Beyond CD4+ T cells, HIV-1 infects myeloid cells, including circulating monocytes and tissue-resident macrophages, and integrates with host genomes to form stable viral reservoirs. To achieve a functional HIV cure, latency-promoting agents (LPAs) have been developed for the "block-and-lock" strategy to reinforce deep HIV-1 latency and permanently silence proviruses. However, most LPAs have been tested mainly in CD4+ T cells, and their efficacy in myeloid cells remains unclear. In this study, we reported that levosimendan (LSM), a drug approved for clinic use to treat heart failures, is able to inhibit HIV lytic infection and reactivation in myeloid cells. LSM blocked viral lytic reactivation in HIV-1 latently infected monocytic cells (TH89GFP, U1) and microglial cells (HC69). LSM also inhibited HIV infection in human induced pluripotent stem cell (iPSC) derived microglia (iMG), primary human resident liver macrophages (Kupffer cells) as well as human monocyte-derived macrophages (MDMs). Furthermore, we demonstrated that overexpression of a predicted drug target of LSM, the conserved serine/threonine kinase RIOK1 (RIO kinase 1), overcomes LSMs anti-HIV effect. Overall, our studies concluded that LSM is a promising LPA to inhibit HIV-1 infection in myeloid cells in the RIOK1-dependent manner.

Retinitis Pigmentosa (RP) is a group of inherited retinal degenerations for which most subtypes lack effective drug treatments. This challenge is particularly critical for autosomal dominant (ad) RP, which is often unsuitable for gene replacement therapy. To address this challenge, we screened an FDA-approved compound library using a zebrafish adRP model expressing a human RHODOPSIN transgene with the Q344X mutation. The screen evaluated drug effects on larval visual behavior by assessing the visual-motor response (VMR). Four compounds significantly improved VMR in Q344X zebrafish: amitriptyline, difluprednate, maprotiline, and prednisolone. Further characterization revealed that these hits act through distinct mechanisms, including reducing rod death, promoting rod neogenesis, and enhancing the function of extraocular photoreceptors. Together, these findings demonstrate the potential to repurpose these drugs for adRP caused by the RHO Q344X mutation, providing preclinical candidates and revealing potential targets for future drug development.

Heterogeneous nuclear ribonucleoprotein U (HNRNPU) deficiency is a rare genetic cause of neurodevelopmental disorders (NDDs) lacking targeted therapies. Here, we developed a transcriptomic-guided compound prioritization pipeline using Connectivity Map (CMap) analysis on multi-model transcriptomic signatures from HNRNPU-deficient human cells and mouse models. Ten compounds were selected through manual curation and functionally screened in patient-derived HNRNPU-deficient neuroepithelial stem (NES) cells with earlier observed cellular phenotypes. Two of the compounds, AS601245 and Lenalidomide, significantly reduced the elevated neural progenitor population during differentiation, and their combination further decreased primary cilia incidence, indicating partial rescue of the patient-specific cellular phenotypes. To understand the mechanisms underlying the partial rescue, we employed proteome integral solubility alteration (PISA) and expression proteomics. PISA assay identified TMEM150C and GSK3A as proximal targets of combined treatment. Additionally, we observed reversal of multiple biological pathways including downregulation of Wnt signalling and upregulation of mitochondrial pathways and transmembrane proteins. Altogether, we established a computational-experimental pipeline for transcriptomic-guided drug repurposing for a monogenic NDD, and demonstrated that the network-level modulation partially rescues the delayed neural differentiation in HNRNPU-deficient neural cells.

Temporal lobe epilepsy (TLE) is a complex neurological disorder characterized by spontaneous recurrent seizures and its underlying mechanism remains elusive. This study aimed to investigate the role of cystine-knot AMPAR modulating protein 44 (CKAMP44) in the pathological process of TLE and its potential as a therapeutic target using kainic acid (KA)-induced epilepsy mouse model of TLE. Our results showed that CKAMP44 protein and mRNA expression was significantly increased and primarily localized to neurons during the chronic phase of TLE. Nkx2-1 regulated the transcription of CKAMP44 in the hippocampus brain tissues of KA-induced TLE mice. Inhibition of CKAMP44 suppressed seizure susceptibility and severity in the KA-induced epilepsy mice via behavioral and local field potential monitoring. Furthermore, inhibition of CKAMP44 decreased frequency and amplitudes of spontaneous excitatory postsynaptic currents indicating that the excitatory synaptic transmission was reduced in an in vitro epilepsy model. Mechanistically, inhibition of CKAMP44 specifically upregulated the membrane surface expression of GluA1 and the phosphorylation level of GluA1-ser831 by downregulating protein phosphatase 3 regulatory subunit B(PPP3r2) expression. Overexpression of PPP3r2 downregulated the phosphorylation level and surface expression of GluA1, which ultimately exacerbated the seizure activity suppressed by CKAMP44 knockdown. Collectively, our results indicate that CKAMP44 may be a potential therapeutic target for the treatment of TLE.

Astrocytes play a key role in neuronal homeostasis and in various neural disorders. The generation of astrocytes from neural progenitor cells (NPCs) and its functions are under a complex control of several signaling networks and transcription factors. In this study, we demonstrate that the transcription factor, GLIS similar 3 (GLIS3), which has been implicated in several neurodegenerative diseases, is highly expressed in astrocytes, and is required for the efficient differentiation of human NPCs into astrocytes. Loss of GLIS3 function greatly impairs astrocytes differentiation, resulting in reduced expression of astrocyte markers, whereas expression of exogenous GLIS3 restores the induction of astrocyte specific genes indicating a critical role for GLIS3 in astrocyte differentiation. Integrated transcriptomic and cistromic analyses revealed that GLIS3 directly regulates the transcription of several astrocyte-associated genes, including GFAP, SLC1A2, NFIA, and ATF3, in coordination with lineage-determining factors, such as STAT3, NFIA, and SOX9. We hypothesize that GLIS3 dysfunction disrupts this transcriptional network thereby contributing to astrocyte-associated neurological disorders. Identification of GLIS3 as a key regulator of astrocyte differentiation and gene expression will advance our understanding of its role in neurodegenerative diseases and may provide a new therapeutic target.

Orthohantaviruses cause severe human diseases including hemorrhagic fever with renal syndrome (HFRS) and hantavirus cardiopulmonary syndrome (HCPS), with case fatality rates up to 40%. No FDA-approved therapeutics are currently available, highlighting urgent need for drug development following recent outbreak events. We systematically examined host protease dependencies in hantavirus replication, focusing on Signal Peptidase (SP) and Signal Peptide Peptidase (SPP) essential for viral glycoprotein maturation. Through comprehensive database mining and molecular docking analysis, we identified six potential protease inhibitors, with Compound E achieving the highest binding confidence score (-0.28) against SPP. Our analysis reveals that targeting host ER proteases represents a viable antiviral strategy, providing a systematic framework for protease-targeted antihantavirus drug development and identifying specific lead compounds for experimental validation.

Neuromodulators influence critical functions of the developing human brain and regulate behavioral states. Dysfunction of neuromodulatory systems is often involved in neuropsychiatric disease and many drugs for these conditions act on these signaling pathways. Recent advances in stem cell biology have made it possible to derive a wide range of cells across the developing human nervous system in regionalized organoids and to functionally integrate them into assembloids, however they currently do not systematically incorporate neuromodulation. Here, we generated human midbrain-hindbrain organoids (hMHO) from human induced pluripotent stem (hiPS) cells and fused them with human cortical organoids (hCO) to form neuromodulatory assembloids (hNMA). We focus on serotonin (5-hydroxytryptamine, 5-HT) as one key neuromodulator and found characteristic gene expression patterns and electrophysiological properties of serotonergic neurons (5-HT neurons) in the hMHO. In hNMA, 5-HT neurons projected into hCO, released 5-HT and modulated cortical network activity. To explore the applicability of this system in human disease, we studied 22q11.2 deletion syndrome (22q11.2DS), a common microdeletion associated with high risk for neuropsychiatric disease and defects in 5-HT signaling. We found aberrant 5-HT dynamics in hNMA from patient hiPS cell lines that were rescued by administration of a selective serotonin reuptake inhibitor (SSRI). Taken together, hNMA can be used to study human 5-HT dynamics and uncover disease phenotypes which could facilitate therapeutic development.

African swine fever (ASF) is a highly pathogenic disease caused by the African swine fever virus (ASFV) infection, which can affect pigs of all ages and breeds, posing significant threat to the global pig farming industry. The ASFV p30 protein is an early-expressed viral structural protein; however, its function is not fully understood. In this study, the interaction of viral p30 with host TRIM21 was identified. The ectopic TRIM21 inhibited ASFV replication, while knockdown or knockout of TRIM21 promoted ASFV replication. Further, p30 was found to interact with RIG-I-like receptor (RLR) signaling adaptor MAVS, and during ASFV infection, p30-TRIM21-MAVS interacted with each other. Mechanistically, TRIM21 activated the K27 polyubiquitination of MAVS to induce IRF3 mediated type I interferon (IFN) production, whereas p30 counteracted TRIM21 activated MAVS K27 polyubiquitination to evade RLR signaling mediated antiviral IFN induction. In summary, our study revealed a novel function of ASFV p30, and provided new insights into the immune evasion of ASFV.

Missense variants are a common type of genetic mutation that can alter the structure and function of proteins, thereby affecting the normal physiological processes of organisms. Accurately distinguishing damaging missense variants from benign ones is of great significance for clinical genetic diagnosis, treatment strategy development, and protein engineering. Here, we propose the VarDCL method, which ingeniously integrates multimodal protein language model embeddings and self-distilled contrastive learning to identify subtle sequence and structural differences before and after protein mutations, thereby accurately predicting pathogenic missense variants. First, leveraging sequence and structural information before and after mutations, VarDCL generates sequence-structural multimodal features via different language models. It incorporates both global and local perspectives of feature embeddings to provide the model with dynamic, multimodal, and multi-view input data. Additionally, a Self-distilled Contrastive Learning (SDCL) module was proposed to enable more effective information integration and feature learning, enhancing the models ability to detect sequence and structural changes induced by mutations. Within this module, the multi-level contrastive learning framework excels at capturing information differences before and after mutations within the same modality; meanwhile, the feature self-distillation mechanism effectively utilizes high-level fused features to guide the learning of low-level differential features, facilitating information interaction across different modalities. The VarDCL framework not only ensures the models capacity to learn dynamic changes pre- and post-mutation but also significantly improves cross-modal information interaction between sequence and structure, thereby remarkably boosting the models performance in distinguishing pathogenic mutations from benign ones. To validate the effectiveness of VarDCL, extensive experiments were conducted. The ablation study demonstrates that all key components of VarDCL contribute significantly. On an independent test set containing 18,731 clinical variants, VarDCL achieved an AUC of 0.917, an AUPR of 0.876, an MCC of 0.690, and an F1-score of 0.789, outperforming 21 state-of-the-art existing methods. Benchmark analysis shows that VarDCL can be utilized as an accurate and potent tool for predicting missense variant effects.

The impact of cellular activities on large-scale brain dynamics is thought to determine brain functioning and disease, yet the causal relationships of neural mechanisms across scales remain unclear. Recently, the cerebellum has been reported to affect whole-brain dynamics during sensorimotor integration. To disclose the underlying mechanisms, we have developed a multiscale digital brain co-simulator, in which a spiking neural network of the olivo-cerebellar microcircuit is embedded in a mouse virtual brain and wired with other nodes using an atlas-based long-range connectome. Parameters and bi-directional interfaces between the spiking olivo-cerebellar network and other rate-coded modules were tuned to match experimental data of primary sensory and motor cortex (M1 and S1) power spectral densities and neuronal spiking rates. Then, the role of the cerebellar circuitry on sensorimotor integration was analyzed by lesioning critical circuit connections in silico. Simulations showed that spike processing within the cerebellar circuit is key to explaining the gamma-band coherence between M1 and S1 during sensorimotor integration. These results provide a mechanistic explanation of how the cerebellum promotes the formation of sensorimotor contingencies in relevant cortical modules as the basis of its critical role in sensorimotor prediction. On a broader perspective, this modelling approach opens new perspectives for the multiscale investigation of brain physiological and pathological states in relation to specific cellular and microcircuit properties.

Accurate prediction of drug synergy is paramount for developing effective combination therapies and advancing personalized medicine. Although methods based on graph neural networks (GNNs) have become a prevalent approach, they often treat molecules as flat graphs of connected atoms, thus overlooking their inherent hierarchical structure (i.e., atoms forming functional groups) and the critical topological information that governs molecular interactions. To address this limitation, we introduce TopoFuseNet, a novel hierarchical graph representation learning framework that integrates multi-scale topological features. The core innovations of TopoFuseNet include: 1) The first-ever application of "Group Centrality" from network science to cheminformatics, enabling the identification and quantification of functional groups crucial to drug activity; 2) A systematic, multi- path strategy to seamlessly integrate node-level (atom) and group-level (functional group) topological features into a Graph Attention Network (GAT) via feature augmentation, attention biasing, and hierarchical pooling; 3) A Differential Transformer module to deeply fuse multi-modal features learned from sequences, fingerprints, and our proposed hierarchical graph representations. Extensive experiments on two large-scale benchmark datasets, DrugComb and DrugCombDB, demonstrate that TopoFuseNet significantly outperforms state-of-the-art methods across multiple key metrics, including AUC, AUPRC, and F1-score, while exhibiting exceptional generalization robustness under various stringent cold-start scenarios. In-depth ablation studies further confirm the effectiveness and necessity of each proposed innovative module. Furthermore, multi-scale interpretability analysis and zero-shot cross-domain transfer experiments reveal that the model successfully captures molecular interaction rules with clear pharmacological significance, demonstrating immense practical potential for discovering novel combination therapies through large-scale virtual screening. Our work not only delivers a superior model for drug synergy prediction, but more importantly, it establishes a novel and scalable paradigm for effectively integrating hierarchical molecular structures and topological information into GNNs.

Transcranial magnetic stimulation (TMS) shows promise in autism spectrum disorder (ASD), but variable outcomes may reflect suboptimal targeting. We developed a functional-connectivity (FC)-guided individualized TMS approach by identifying an ASD-relevant effective region and selecting superficial targets. In a multi-site mega-analysis of Autism Brain Imaging Data Exchange I data (298 ASD, 348 controls), the region with the greatest regional homogeneity (ReHo) abnormality was defined as the effective region. Individualized dorsolateral prefrontal cortex (DLPFC) and inferior parietal lobule (IPL) targets were localized as sites with strongest FC to this region. Group differences, symptom associations, and a six-patient case series were examined. The posterior cingulate cortex (PCC) showed the greatest ReHo abnormality and was implicated in theory-of-mind (ToM) circuitry. PCC-guided targets showed weaker FC in ASD in the right IPL, correlating with Autism Diagnostic Interview social scores; left DLPFC FC differences lacked symptom associations. In the case series, individualized PCC-IPL-guided TMS reduced ToM-related symptoms and Childhood Autism Rating Scale scores. PCC-IPL FC-guided TMS is a biologically informed intervention for modulating ToM circuitry in ASD.

The escalating crisis of antimicrobial resistance necessitates novel therapeutic strategies, among which drug combination therapy shows great promise by enhancing efficacy and reducing toxicity. However, identifying effective synergistic pairs from the vast combinatorial space remains experimentally challenging and resource-intensive. To address this, we introduce GCN-Mamba, a deep learning framework that integrates Graph Convolutional Networks (GCN) with the Mamba State Space Model. This architecture captures both local molecular topological structures and global implicit interactions by leveraging Extended 3-Dimensional Fingerprints (E3FP) and bacterial gene expression profiles. Evaluation on a comprehensive dataset demonstrated that GCN-Mamba significantly outperforms classical machine learning models in predictive accuracy. In a targeted case study against Methicillin-resistant Staphylococcus aureus (MRSA), the model successfully rediscovered known synergistic pairs, such as Quercetin and Curcumin, consistent with recent literature. Furthermore, prospective in vitro validation confirmed a novel synergistic combination of Shikimic acid and Oxacillin, validating the models practical utility. By efficiently prioritizing potential candidates, GCN-Mamba serves as a powerful and reliable tool for accelerating the discovery of synergistic antimicrobial combinations, effectively bridging the gap between computational prediction and experimental validation.

Neonates requiring intensive care are at increased risk for long-term neuropsychiatric disorders. However, clinical adoption of risk prediction models remains limited when their performance lacks adequate interpretability for informed clinical decision-making. Here, we investigated whether longitudinal neonatal electronic health record (EHR) data from the first 90 days of life can support clinically meaningful interpretation of long-term risk signals for major neuropsychiatric diagnoses by age seven. In a retrospective register-based cohort of 17,655 at-risk children from an academic medical center, of whom 8.0\% (1,420) received a major neuropsychiatric diagnosis during follow-up, we applied a time-aware transformer model (Self-supervised Transformer for Time-Series; STraTS) and thoroughly evaluated its predictions using three complementary interpretability approaches: perturbation-based variable importance, value-dependent effect analysis, and leave-one-out (LOO) feature attribution. STraTS achieved the highest area under the precision--recall curve (AUPRC 0.171 {+/-} 0.022), compared with Random Forest (0.166 {+/-} 0.008), logistic regression (0.151 {+/-} 0.007), and XGBoost (0.128 {+/-} 0.010). Across interpretability methods, five predictors were consistently identified: birth weight, gender, Apgar score at 1 minute, umbilical serum thyroid stimulating hormone (uS-TSH), and treatment time in hospital. Indicators of early clinical severity, including chromosomal abnormalities and neonatal cerebral-status disturbances, showed the largest risk-increasing effects. Furthermore, the model's learned vector representations of subject-specific EHR sequences formed clinically coherent latent embeddings that reflect population heterogeneity along established perinatal risk dimensions. These findings demonstrate that combining multiple complementary interpretability methods yields stable, clinically plausible risk signals while revealing limitations that would remain undetected by any single approach, highlighting the importance of careful interpretability analysis of deep learning-based risk predictions.