m6A-modified cenRNA stabilizes CENPA to ensure centromere integrity in cancer cells.

m6A modification is best known for its critical role in controlling multiple post-transcriptional processes of the mRNAs. Here, we discovered elevated levels of m6A modification on centromeric RNA (cenRNA) in cancerous cells compared with non-cancerous cells. We then identified CENPA, an H3 variant, as an m6A reader of cenRNA. CENPA is localized at centromeres and is essential in preserving centromere integrity and function during mitosis. The m6A-modified cenRNA stabilizes centromeric localization of CENPA in cancer cells during the S phase of the cell cycle. Mutations of CENPA at the Leu61 and the Arg63 or removal of cenRNA m6A modification lead to loss of centromere-bound CENPA during S phase. This in turn results in compromised centromere integrity and abnormal chromosome separation and hinders cancer cell proliferation and tumor growth. Our findings unveil an m6A reading mechanism by CENPA that epigenetically governs centromere integrity in cancer cells, providing potential targets for cancer therapy.

Targeting placental growth factor/neuropilin 1 pathway inhibits growth and spread of medulloblastoma.

Medulloblastoma is the most common pediatric malignant brain tumor. Although current therapies improve survival, these regimens are highly toxic and are associated with significant morbidity. Here, we report that placental growth factor (PlGF) is expressed in the majority of medulloblastomas, independent of their subtype. Moreover, high expression of PlGF receptor neuropilin 1 (Nrp1) correlates with poor overall survival in patients. We demonstrate that PlGF and Nrp1 are required for the growth and spread of medulloblastoma: PlGF/Nrp1 blockade results in direct antitumor effects in vivo, resulting in medulloblastoma regression, decreased metastasis, and increased mouse survival. We reveal that PlGF is produced in the cerebellar stroma via tumor-derived Sonic hedgehog (Shh) and show that PlGF acts through Nrp1-and not vascular endothelial growth factor receptor 1-to promote tumor cell survival. This critical tumor-stroma interaction-mediated by Shh, PlGF, and Nrp1 across medulloblastoma subtypes-supports the development of therapies targeting PlGF/Nrp1 pathway.

The microRNA408-plantacyanin module balances plant growth and drought resistance by regulating reactive oxygen species homeostasis in guard cells.

The conserved microRNA (miRNA) miR408 enhances photosynthesis and compromises stress tolerance in multiple plants, but the cellular mechanism underlying its function remains largely unclear. Here, we show that in Arabidopsis (Arabidopsis thaliana), the transcript encoding the blue copper protein PLANTACYANIN (PCY) is the primary target for miR408 in vegetative tissues. PCY is preferentially expressed in the guard cells, and PCY is associated with the endomembrane surrounding individual chloroplasts. We found that the MIR408 promoter is suppressed by multiple abscisic acid (ABA)-responsive transcription factors, thus allowing PCY to accumulate under stress conditions. Genetic analysis revealed that PCY elevates reactive oxygen species (ROS) levels in the guard cells, promotes stomatal closure, reduces photosynthetic gas exchange, and enhances drought resistance. Moreover, the miR408-PCY module is sufficient to rescue the growth and drought tolerance phenotypes caused by gain- and loss-of-function of MYB44, an established positive regulator of ABA responses, indicating that the miR408-PCY module relays ABA signaling for regulating ROS homeostasis and drought resistance. These results demonstrate that miR408 regulates stomatal movement to balance growth and drought resistance, providing a mechanistic understanding of why miR408 is selected during land plant evolution and insights into the long-pursued quest of breeding drought-tolerant and high-yielding crops.

Non-KREEP origin for Chang'e-5 basalts in the Procellarum KREEP Terrane.

Mare volcanics on the Moon are the key record of thermo-chemical evolution throughout most of lunar history1-3. Young mare basalts-mainly distributed in a region rich in potassium, rare-earth elements and phosphorus (KREEP) in Oceanus Procellarum, called the Procellarum KREEP Terrane (PKT)4-were thought to be formed from KREEP-rich sources at depth5-7. However, this hypothesis has not been tested with young basalts from the PKT. Here we present a petrological and geochemical study of the basalt clasts from the PKT returned by the Chang'e-5 mission8. These two-billion-year-old basalts are the youngest lunar samples reported so far9. Bulk rock compositions have moderate titanium and high iron contents  with KREEP-like rare-earth-element and high thorium concentrations. However, strontium-neodymium isotopes indicate that these basalts were derived from a non-KREEP mantle source. To produce the high abundances of rare-earth elements and thorium, low-degree partial melting and extensive fractional crystallization are required. Our results indicate that the KREEP association may not be a prerequisite for young mare volcanism. Absolving the need to invoke heat-producing elements in their source implies a more sustained cooling history of the lunar interior to generate the Moon's youngest melts.

Normalizing granuloma vasculature and matrix improves drug delivery and reduces bacterial burden in tuberculosis-infected rabbits.

Host-directed therapies (HDTs) represent an emerging approach for bacterial clearance during tuberculosis (TB) infection. While most HDTs are designed and implemented for immuno-modulation, other host targets-such as nonimmune stromal components found in pulmonary granulomas-may prove equally viable. Building on our previous work characterizing and normalizing the aberrant granuloma-associated vasculature, here we demonstrate that FDA-approved therapies (bevacizumab and losartan, respectively) can be repurposed as HDTs to normalize blood vessels and extracellular matrix (ECM), improve drug delivery, and reduce bacterial loads in TB granulomas. Granulomas feature an overabundance of ECM and compressed blood vessels, both of which are effectively reduced by losartan treatment in the rabbit model of TB. Combining both HDTs promotes secretion of proinflammatory cytokines and improves anti-TB drug delivery. Finally, alone and in combination with second-line antitubercular agents (moxifloxacin or bedaquiline), these HDTs significantly reduce bacterial burden. RNA sequencing analysis of HDT-treated lung and granuloma tissues implicates up-regulated antimicrobial peptide and proinflammatory gene expression by ciliated epithelial airway cells as a putative mechanism of the observed antitubercular benefits in the absence of chemotherapy. These findings demonstrate that bevacizumab and losartan are well-tolerated stroma-targeting HDTs, normalize the granuloma microenvironment, and improve TB outcomes, providing the rationale to clinically test this combination in TB patients.

Spatiotemporal trends of hemorrhagic fever with renal syndrome (HFRS) in China under climate variation.

Hemorrhagic fever with renal syndrome (HFRS) is a zoonotic disease caused by the rodent-transmitted orthohantaviruses (HVs), with China possessing the most cases globally. The virus hosts in China are Apodemus agrarius and Rattus norvegicus, and the disease spread is strongly influenced by global climate dynamics. To assess and predict the spatiotemporal trends of HFRS from 2005 to 2098, we collected historical HFRS data in mainland China (2005-2020), historical and projected climate and population data (2005-2098), and spatial variables including biotic, environmental, topographical, and socioeconomic. Spatiotemporal predictions and mapping were conducted under 27 scenarios incorporating multiple integrated representative concentration pathway models and population scenarios. We identify the type of magistral HVs host species as the best spatial division, including four region categories. Seven extreme climate indices associated with temperature and precipitation have been pinpointed as key factors affecting the trends of HFRS. Our predictions indicate that annual HFRS cases will increase significantly in 62 of 356 cities in mainland China. Rattus regions are predicted to be the most active, surpassing Apodemus and Mixed regions. Eighty cities are identified as at severe risk level for HFRS, each with over 50 reported cases annually, including 22 new cities primarily located in East China and Rattus regions after 2020, while 6 others develop new risk. Our results suggest that the risk of HFRS will remain high through the end of this century, with Rattus norvegicus being the most active host, and that extreme climate indices are significant risk factors. Our findings can inform evidence-based policymaking regarding future risk of HFRS.

Separation of transcriptional repressor and activator functions in Drosophila HDAC3.

The histone deacetylase HDAC3 is associated with the NCoR/SMRT co-repressor complex, and its canonical function is in transcriptional repression, but it can also activate transcription. Here, we show that the repressor and activator functions of HDAC3 can be genetically separated in Drosophila. A lysine substitution in the N terminus (K26A) disrupts its catalytic activity and activator function, whereas a combination of substitutions (HEBI) abrogating the interaction with SMRTER enhances repressor activity beyond wild type in the early embryo. We conclude that the crucial functions of HDAC3 in embryo development involve catalytic-dependent gene activation and non-enzymatic repression by several mechanisms, including tethering of loci to the nuclear periphery.

Identification, characterization and expression analysis of circRNA encoded by SARS-CoV-1 and SARS-CoV-2.

Virus-encoded circular RNA (circRNA) participates in the immune response to viral infection, affects the human immune system, and can be used as a target for precision therapy and tumor biomarker. The coronaviruses SARS-CoV-1 and SARS-CoV-2 (SARS-CoV-1/2) that have emerged in recent years are highly contagious and have high mortality rates. In coronaviruses, little is known about the circRNA encoded by the SARS-CoV-1/2. Therefore, this study explores whether SARS-CoV-1/2 encodes circRNA and characteristics and functions of circRNA. Based on RNA-seq data of SARS-CoV-1 and SARS-CoV-2 infections, we used circRNA identification tools (circRNA_finder, find_circ and CIRI2) to identify circRNAs. The number of circRNAs encoded by SARS-CoV-1 and SARS-CoV-2 was identified as 151 and 470, respectively. It can be found that SARS-CoV-2 shows more prominent circRNA encoding ability than SARS-CoV-1. Expression analysis showed that only a few circRNAs encoded by SARS-CoV-1/2 showed high expression levels, and the positive strand produced more abundant circRNAs. Then, based on the identified SARS-CoV-1/2-encoded circRNAs, we performed circRNA identification and characterization using the previously developed CirRNAPL. Finally, target gene prediction and functional enrichment analysis were performed. It was found that viral circRNA is closely related to cancer and has a potential role in regulating host cell functions. This study studied the characteristics and functions of viral circRNA encoded by coronavirus SARS-CoV-1/2, providing a valuable resource for further research on the function and molecular mechanism of coronavirus circRNA.

MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation.

We introduce MetaboAnalyst version 6.0 as a unified platform for processing, analyzing, and interpreting data from targeted as well as untargeted metabolomics studies using liquid chromatography - mass spectrometry (LC-MS). The two main objectives in developing version 6.0 are to support tandem MS (MS2) data processing and annotation, as well as to support the analysis of data from exposomics studies and related experiments. Key features of MetaboAnalyst 6.0 include: (i) a significantly enhanced Spectra Processing module with support for MS2 data and the asari algorithm; (ii) a MS2 Peak Annotation module based on comprehensive MS2 reference databases with fragment-level annotation; (iii) a new Statistical Analysis module dedicated for handling complex study design with multiple factors or phenotypic descriptors; (iv) a Causal Analysis module for estimating metabolite - phenotype causal relations based on two-sample Mendelian randomization, and (v) a Dose-Response Analysis module for benchmark dose calculations. In addition, we have also improved MetaboAnalyst's visualization functions, updated its compound database and metabolite sets, and significantly expanded its pathway analysis support to around 130 species. MetaboAnalyst 6.0 is freely available at https://www.metaboanalyst.ca.

Losartan controls immune checkpoint blocker-induced edema and improves survival in glioblastoma mouse models.

Immune checkpoint blockers (ICBs) have failed in all phase III glioblastoma trials. Here, we found that ICBs induce cerebral edema in some patients and mice with glioblastoma. Through single-cell RNA sequencing, intravital imaging, and CD8+ T cell blocking studies in mice, we demonstrated that this edema results from an inflammatory response following antiprogrammed death 1 (PD1) antibody treatment that disrupts the blood-tumor barrier. Used in lieu of immunosuppressive corticosteroids, the angiotensin receptor blocker losartan prevented this ICB-induced edema and reprogrammed the tumor microenvironment, curing 20% of mice which increased to 40% in combination with standard of care treatment. Using a bihemispheric tumor model, we identified a "hot" tumor immune signature prior to losartan+anti-PD1 therapy that predicted long-term survival. Our findings provide the rationale and associated biomarkers to test losartan with ICBs in glioblastoma patients.

KGDiff: towards explainable target-aware molecule generation with knowledge guidance.

Designing 3D molecules with high binding affinity for specific protein targets is crucial in drug design. One challenge is that the atomic interaction between molecules and proteins in 3D space has to be taken into account. However, the existing target-aware methods solely model the joint distribution between the molecules and proteins, disregarding the binding affinities between them, which leads to limited performance. In this paper, we propose an explainable diffusion model to generate molecules that can be bound to a given protein target with high affinity. Our method explicitly incorporates the chemical knowledge of protein-ligand binding affinity into the diffusion model, and uses the knowledge to guide the denoising process towards the direction of high binding affinity. Specifically, an SE(3)-invariant expert network is developed to fit the Vina scoring functions and jointly trained with the denoising network, while the domain knowledge is distilled and conveyed from Vina functions to the expert network. An effective guidance is proposed on both continuous atom coordinates and discrete atom types by taking advantages of the gradient of the expert network. Experiments on the benchmark CrossDocked2020 demonstrate the superiority of our method. Additionally, an atom-level explanation of the generated molecules is provided, and the connections with the domain knowledge are established.

Cooperation of structural motifs controls drug selectivity in cyclin-dependent kinases: an advanced theoretical analysis.

Understanding drug selectivity mechanism is a long-standing issue for helping design drugs with high specificity. Designing drugs targeting cyclin-dependent kinases (CDKs) with high selectivity is challenging because of their highly conserved binding pockets. To reveal the underlying general selectivity mechanism, we carried out comprehensive analyses from both the thermodynamics and kinetics points of view on a representative CDK12 inhibitor. To fully capture the binding features of the drug-target recognition process, we proposed to use kinetic residue energy analysis (KREA) in conjunction with the community network analysis (CNA) to reveal the underlying cooperation effect between individual residues/protein motifs to the binding/dissociating process of the ligand. The general mechanism of drug selectivity in CDKs can be summarized as that the difference of structural cooperation between the ligand and the protein motifs leads to the difference of the energetic contribution of the key residues to the ligand. The proposed mechanisms may be prevalent in drug selectivity issues, and the insights may help design new strategies to overcome/attenuate the drug selectivity associated problems.

ClusterX: a novel representation learning-based deep clustering framework for accurate visual inspection in virtual screening.

Molecular clustering analysis has been developed to facilitate visual inspection in the process of structure-based virtual screening. However, traditional methods based on molecular fingerprints or molecular descriptors limit the accuracy of selecting active hit compounds, which may be attributed to the lack of representations of receptor structural and protein-ligand interaction during the clustering. Here, a novel deep clustering framework named ClusterX is proposed to learn molecular representations of protein-ligand complexes and cluster the ligands. In ClusterX, the graph was used to represent the protein-ligand complex, and the joint optimisation can be used efficiently for learning the cluster-friendly features. Experiments on the KLIFs database show that the model can distinguish well between the binding modes of different kinase inhibitors. To validate the effectiveness of the model, the clustering results on the virtual screening dataset further demonstrated that ClusterX achieved better or more competitive performance against traditional methods, such as SIFt and extended connectivity fingerprints. This framework may provide a unique tool for clustering analysis and prove to assist computational medicinal chemists in visual decision-making.

Can molecular dynamics simulations improve predictions of protein-ligand binding affinity with machine learning?

Binding affinity prediction largely determines the discovery efficiency of lead compounds in drug discovery. Recently, machine learning (ML)-based approaches have attracted much attention in hopes of enhancing the predictive performance of traditional physics-based approaches. In this study, we evaluated the impact of structural dynamic information on the binding affinity prediction by comparing the models trained on different dimensional descriptors, using three targets (i.e. JAK1, TAF1-BD2 and DDR1) and their corresponding ligands as the examples. Here, 2D descriptors are traditional ECFP4 fingerprints, 3D descriptors are the energy terms of the Smina and NNscore scoring functions and 4D descriptors contain the structural dynamic information derived from the trajectories based on molecular dynamics (MD) simulations. We systematically investigate the MD-refined binding affinity prediction performance of three classical ML algorithms (i.e. RF, SVR and XGB) as well as two common virtual screening methods, namely Glide docking and MM/PBSA. The outcomes of the ML models built using various dimensional descriptors and their combinations reveal that the MD refinement with the optimized protocol can improve the predictive performance on the TAF1-BD2 target with considerable structural flexibility, but not for the less flexible JAK1 and DDR1 targets, when taking docking poses as the initial structure instead of the crystal structures. The results highlight the importance of the initial structures to the final performance of the model through conformational analysis on the three targets with different flexibility.

hsa_circ_0007919 promotes pancreatic cancer metastasis by modulating Sp1-mediated THBS1 transcription.

CircRNAs are abnormally expressed in various cancers and play an important role in the occurrence and development of cancers. However, their biological functions and the underlying molecular mechanisms in pancreatic cancer (PC) metastasis are incompletely understood. Differentially expressed circRNAs were identified by second-generation transcriptome sequencing in three pairs of PC tissues and adjacent tissues. The expression and prognostic significance of hsa_circ_0007919 were evaluated by qRT-PCR and Kaplan-Meier survival curves. Gain- and loss-of-function assays were conducted to detect the role of hsa_circ_0007919 in PC metastasis in vitro. A lung metastasis model and IHC experiments were conducted to confirm the effects of hsa_circ_0007919 on tumor metastasis in vivo. Mechanistically, RNA immunoprecipitation and chromatin immunoprecipitation assays were conducted to explore the interplay among hsa_circ_0007919, Sp1, and the THBS1 promoter. hsa_circ_0007919 was significantly upregulated in PC tissues and cells and was correlated with lymph node metastasis, TNM stage, and poor prognosis. Knockdown of hsa_circ_0007919 significantly suppressed the migration and invasion of PC cells in vitro and inhibited tumor metastasis in vivo. However, overexpression of hsa_circ_0007919 exerted the opposite effects. Mechanistically, hsa_circ_0007919 could recruit the transcription factor Sp1 to inhibit THBS1 transcription, thereby facilitating PC metastasis. hsa_circ_0007919 can promote the metastasis of PC by inhibiting THBS1 expression. hsa_circ_0007919 may be a potential therapeutic target in PC.

Oxytocin, but not vasopressin, decreases willingness to harm others by promoting moral emotions of guilt and shame.

Prosocial and moral behaviors have overlapping neural systems and can both be affected in a number of psychiatric disorders, although whether they involve similar neurochemical systems is unclear. In the current registered randomized placebo-controlled trial on 180 adult male and female subjects, we investigated the effects of intranasal administration of oxytocin and vasopressin, which play key roles in influencing social behavior, on moral emotion ratings for situations involving harming others and on judgments of moral dilemmas where others are harmed for a greater good. Oxytocin, but not vasopressin, enhanced feelings of guilt and shame for intentional but not accidental harm and reduced endorsement of intentionally harming others to achieve a greater good. Neither peptide influenced arousal ratings for the scenarios. Effects of oxytocin on guilt and shame were strongest in individuals scoring lower on the personal distress subscale of trait empathy. Overall, findings demonstrate for the first time that oxytocin, but not vasopressin, promotes enhanced feelings of guilt and shame and unwillingness to harm others irrespective of the consequences. This may reflect associations between oxytocin and empathy and vasopressin with aggression and suggests that oxytocin may have greater therapeutic potential for disorders with atypical social and moral behavior.

A dual computational and experimental strategy to enhance TSLP antibody affinity for improved asthma treatment.

Thymic stromal lymphopoietin is a key cytokine involved in the pathogenesis of asthma and other allergic diseases. Targeting TSLP and its signaling pathways is increasingly recognized as an effective strategy for asthma treatment. This study focused on enhancing the affinity of the T6 antibody, which specifically targets TSLP, by integrating computational and experimental methods. The initial affinity of the T6 antibody for TSLP was lower than the benchmark antibody AMG157. To improve this, we utilized alanine scanning, molecular docking, and computational tools including mCSM-PPI2 and GEO-PPI to identify critical amino acid residues for site-directed mutagenesis. Subsequent mutations and experimental validations resulted in an antibody with significantly enhanced blocking capacity against TSLP. Our findings demonstrate the potential of computer-assisted techniques in expediting antibody affinity maturation, thereby reducing both the time and cost of experiments. The integration of computational methods with experimental approaches holds great promise for the development of targeted therapeutic antibodies for TSLP-related diseases.

Mathematical model of oxygen, nutrient, and drug transport in tuberculosis granulomas.

Physiological abnormalities in pulmonary granulomas-pathological hallmarks of tuberculosis (TB)-compromise the transport of oxygen, nutrients, and drugs. In prior studies, we demonstrated mathematically and experimentally that hypoxia and necrosis emerge in the granuloma microenvironment (GME) as a direct result of limited oxygen availability. Building on our initial model of avascular oxygen diffusion, here we explore additional aspects of oxygen transport, including the roles of granuloma vasculature, transcapillary transport, plasma dilution, and interstitial convection, followed by cellular metabolism. Approximate analytical solutions are provided for oxygen and glucose concentration, interstitial fluid velocity, interstitial fluid pressure, and the thickness of the convective zone. These predictions are in agreement with prior experimental results from rabbit TB granulomas and from rat carcinoma models, which share similar transport limitations. Additional drug delivery predictions for anti-TB-agents (rifampicin and clofazimine) strikingly match recent spatially-resolved experimental results from a mouse model of TB. Finally, an approach to improve molecular transport in granulomas by modulating interstitial hydraulic conductivity is tested in silico.

SGAE-MDA: Exploring the MiRNA-disease associations in herbal medicines based on semi-supervised graph autoencoder.

Research indicates that miRNAs present in herbal medicines are crucial for identifying disease markers, advancing gene therapy, facilitating drug delivery, and so on. These miRNAs maintain stability in the extracellular environment, making them viable tools for disease diagnosis. They can withstand the digestive processes in the gastrointestinal tract, positioning them as potential carriers for specific oral drug delivery. By engineering plants to generate effective, non-toxic miRNA interference sequences, it's possible to broaden their applicability, including the treatment of diseases such as hepatitis C. Consequently, delving into the miRNA-disease associations (MDAs) within herbal medicines holds immense promise for diagnosing and addressing miRNA-related diseases. In our research, we propose the SGAE-MDA model, which harnesses the strengths of a graph autoencoder (GAE) combined with a semi-supervised approach to uncover potential MDAs in herbal medicines more effectively. Leveraging the GAE framework, the SGAE-MDA model exactly integrates the inherent feature vectors of miRNAs and disease nodes with the regulatory data in the miRNA-disease network. Additionally, the proposed semi-supervised learning approach randomly hides the partial structure of the miRNA-disease network, subsequently reconstructing them within the GAE framework. This technique effectively minimizes network noise interference. Through comparison against other leading deep learning models, the results consistently highlighted the superior performance of the proposed SGAE-MDA model. Our code and dataset can be available at: https://github.com/22n9n23/SGAE-MDA.

Intolerance of uncertainty affects the behavioral and neural mechanisms of higher generalization.

Intolerance of uncertainty (IU) is associated with several anxiety disorders. In this study, we employed rewards and losses as unconditioned positive and negative stimuli, respectively, to explore the effects of an individual's IU level on positive and negative generalizations using magnetic resonance imaging technology. Following instrumental learning, 48 participants (24 high IU; 24 low IU) were invited to complete positive and negative generalization tasks; their behavioral responses and neural activities were recorded by functional magnetic resonance imaging. The behavior results demonstrated that participants with high IUs exhibited higher generalizations to both positive and negative cues as compared with participants having low IUs. Neuroimaging results demonstrated that they exhibited higher activation levels in the right anterior insula and the default mode network (i.e. precuneus and posterior cingulate gyrus), as well as related reward circuits (i.e. caudate and right putamen). Therefore, higher generalization scores and the related abnormal brain activation may be key markers of IU as a vulnerability factor for anxiety disorders.