IRE1α silences dsRNA to prevent taxane-induced pyroptosis in triple-negative breast cancer.

Chemotherapy is often combined with immune checkpoint inhibitor (ICIs) to enhance immunotherapy responses. Despite the approval of chemo-immunotherapy in multiple human cancers, many immunologically cold tumors remain unresponsive. The mechanisms determining the immunogenicity of chemotherapy are elusive. Here, we identify the ER stress sensor IRE1α as a critical checkpoint that restricts the immunostimulatory effects of taxane chemotherapy and prevents the innate immune recognition of immunologically cold triple-negative breast cancer (TNBC). IRE1α RNase silences taxane-induced double-stranded RNA (dsRNA) through regulated IRE1-dependent decay (RIDD) to prevent NLRP3 inflammasome-dependent pyroptosis. Inhibition of IRE1α in Trp53-/- TNBC allows taxane to induce extensive dsRNAs that are sensed by ZBP1, which in turn activates NLRP3-GSDMD-mediated pyroptosis. Consequently, IRE1α RNase inhibitor plus taxane converts PD-L1-negative, ICI-unresponsive TNBC tumors into PD-L1high immunogenic tumors that are hyper-sensitive to ICI. We reveal IRE1α as a cancer cell defense mechanism that prevents taxane-induced danger signal accumulation and pyroptotic cell death.

R-loop-dependent promoter-proximal termination ensures genome stability.

The proper regulation of transcription is essential for maintaining genome integrity and executing other downstream cellular functions1,2. Here we identify a stable association between the genome-stability regulator sensor of single-stranded DNA (SOSS)3 and the transcription regulator Integrator-PP2A (INTAC)4-6. Through SSB1-mediated recognition of single-stranded DNA, SOSS-INTAC stimulates promoter-proximal termination of transcription and attenuates R-loops associated with paused RNA polymerase II to prevent R-loop-induced genome instability. SOSS-INTAC-dependent attenuation of R-loops is enhanced by the ability of SSB1 to form liquid-like condensates. Deletion of NABP2 (encoding SSB1) or introduction of cancer-associated mutations into its intrinsically disordered region leads to a pervasive accumulation of R-loops, highlighting a genome surveillance function of SOSS-INTAC that enables timely termination of transcription at promoters to constrain R-loop accumulation and ensure genome stability.

An extra-erythrocyte role of haemoglobin body in chondrocyte hypoxia adaption.

Although haemoglobin is a known carrier of oxygen in erythrocytes that functions to transport oxygen over a long range, its physiological roles outside erythrocytes are largely elusive1,2. Here we found that chondrocytes produced massive amounts of haemoglobin to form eosin-positive bodies in their cytoplasm. The haemoglobin body (Hedy) is a membraneless condensate characterized by phase separation. Production of haemoglobin in chondrocytes is controlled by hypoxia and is dependent on KLF1 rather than the HIF1/2α pathway. Deletion of haemoglobin in chondrocytes leads to Hedy loss along with severe hypoxia, enhanced glycolysis and extensive cell death in the centre of cartilaginous tissue, which is attributed to the loss of the Hedy-controlled oxygen supply under hypoxic conditions. These results demonstrate an extra-erythrocyte role of haemoglobin in chondrocytes, and uncover a heretofore unrecognized mechanism in which chondrocytes survive a hypoxic environment through Hedy.

Targeting UBE4A Revives Viperin Protein in Epithelium to Enhance Host Antiviral Defense.

Mutation and prevalence of pathogenic viruses prompt the development of broad-spectrum antiviral strategies. Viperin is a potent antiviral protein that inhibits a broad range of viruses. Unexpectedly, we found that Viperin protein production in epithelium is defective in response to both viruses and interferons (IFNs). We further revealed that viruses and IFNs stimulate expression of the acetyltransferase HAT1, which induces Lys197-acetylation on Viperin. Viperin acetylation in turn recruits UBE4A that stimulates K6-linked polyubiquitination at Lys206 of Viperin, leading to Viperin protein degradation. Importantly, UBE4A deficiency restores Viperin protein production in epithelium. We then designed interfering peptides (IPs) to inhibit UBE4A binding with Viperin. We found that VIP-IP3 rescues Viperin protein production in epithelium and therefore enhances cellular antiviral activity. VIP-IP3 renders mice more resistant to viral infection. These findings could provide strategies for both enhancing host broad-spectrum antiviral response and improving the efficacy of IFN-based antiviral therapy.

Evidence for an atomic chiral superfluid with topological excitations.

Topological superfluidity is an important concept in electronic materials as well as ultracold atomic gases1. However, although progress has been made by hybridizing superconductors with topological substrates, the search for a material-natural or artificial-that intrinsically exhibits topological superfluidity has been ongoing since the discovery of the superfluid 3He-A phase2. Here we report evidence for a globally chiral atomic superfluid, induced by interaction-driven time-reversal symmetry breaking in the second Bloch band of an optical lattice with hexagonal boron nitride geometry. This realizes a long-lived Bose-Einstein condensate of 87Rb atoms beyond present limits to orbitally featureless scenarios in the lowest Bloch band. Time-of-flight and band mapping measurements reveal that the local phases and orbital rotations of atoms are spontaneously ordered into a vortex array, showing evidence of the emergence of global angular momentum across the entire lattice. A phenomenological effective model is used to capture the dynamics of Bogoliubov quasi-particle excitations above the ground state, which are shown to exhibit a topological band structure. The observed bosonic phase is expected to exhibit phenomena that are conceptually distinct from, but related to, the quantum anomalous Hall effect3-7 in electronic condensed matter.

Reciprocal communication between FAPs and muscle cells via distinct extracellular vesicle miRNAs in muscle regeneration.

Muscle regeneration is a complex process relying on precise teamwork between multiple cell types, including muscle stem cells (MuSCs) and fibroadipogenic progenitors (FAPs). FAPs are also the main source of intramuscular adipose tissue (IMAT). Muscles without FAPs exhibit decreased IMAT infiltration but also deficient muscle regeneration, indicating the importance of FAPs in the repair process. Here, we demonstrate the presence of bidirectional crosstalk between FAPs and MuSCs via their secretion of extracellular vesicles (EVs) containing distinct clusters of miRNAs that is crucial for normal muscle regeneration. Thus, after acute muscle injury, there is activation of FAPs leading to a transient rise in IMAT. These FAPs also release EVs enriched with a selected group of miRNAs, a number of which come from an imprinted region on chromosome 12. The most abundant of these is miR-127-3p, which targets the sphingosine-1-phosphate receptor S1pr3 and activates myogenesis. Indeed, intramuscular injection of EVs from immortalized FAPs speeds regeneration of injured muscle. In late stages of muscle repair, in a feedback loop, MuSCs and their derived myoblasts/myotubes secrete EVs enriched in miR-206-3p and miR-27a/b-3p. The miRNAs repress FAP adipogenesis, allowing full muscle regeneration. Together, the reciprocal communication between FAPs and muscle cells via miRNAs in their secreted EVs plays a critical role in limiting IMAT infiltration while stimulating muscle regeneration, hence providing an important mechanism for skeletal muscle repair and homeostasis.

scMLC: an accurate and robust multiplex community detection method for single-cell multi-omics data.

Clustering cells based on single-cell multi-modal sequencing technologies provides an unprecedented opportunity to create high-resolution cell atlas, reveal cellular critical states and study health and diseases. However, effectively integrating different sequencing data for cell clustering remains a challenging task. Motivated by the successful application of Louvain in scRNA-seq data, we propose a single-cell multi-modal Louvain clustering framework, called scMLC, to tackle this problem. scMLC builds multiplex single- and cross-modal cell-to-cell networks to capture modal-specific and consistent information between modalities and then adopts a robust multiplex community detection method to obtain the reliable cell clusters. In comparison with 15 state-of-the-art clustering methods on seven real datasets simultaneously measuring gene expression and chromatin accessibility, scMLC achieves better accuracy and stability in most datasets. Synthetic results also indicate that the cell-network-based integration strategy of multi-omics data is superior to other strategies in terms of generalization. Moreover, scMLC is flexible and can be extended to single-cell sequencing data with more than two modalities.

Crystal-Phase Engineering in Heterogeneous Catalysis.

The performance of a chemical reaction is critically dependent on the electronic and/or geometric structures of a material in heterogeneous catalysis. Over the past century, the Sabatier principle has already provided a conceptual framework for optimal catalyst design by adjusting the electronic structure of the catalytic material via a change in composition. Beyond composition, it is essential to recognize that the geometric atomic structures of a catalyst, encompassing terraces, edges, steps, kinks, and corners, have a substantial impact on the activity and selectivity of a chemical reaction. Crystal-phase engineering has the capacity to bring about substantial alterations in the electronic and geometric configurations of a catalyst, enabling control over coordination numbers, morphological features, and the arrangement of surface atoms. Modulating the crystallographic phase is therefore an important strategy for improving the stability, activity, and selectivity of catalytic materials. Nonetheless, a complete understanding of how the performance depends on the crystal phase of a catalyst remains elusive, primarily due to the absence of a molecular-level view of active sites across various crystal phases. In this review, we primarily focus on assessing the dependence of catalytic performance on crystal phases to elucidate the challenges and complexities inherent in heterogeneous catalysis, ultimately aiming for improved catalyst design.

IL-27 Gene Therapy Ameliorates IPEX Syndrome Caused by Germline Mutation of Foxp3 Gene: A Major Role for Induction of IL-10.

Inactivating mutations of Foxp3, the master regulator of regulatory T cell development and function, lead to immune dysregulation, polyendocrinopathy, enteropathy, X-linked (IPEX) syndrome in mice and humans. IPEX is a fatal autoimmune disease, with allogeneic stem cell transplant being the only available therapy. In this study, we report that a single dose of adeno-associated virus (AAV)-IL-27 to young mice with naturally occurring Foxp3 mutation (Scurfy mice) substantially ameliorates clinical symptoms, including growth retardation and early fatality. Correspondingly, AAV-IL-27 gene therapy significantly prevented naive T cell activation, as manifested by downregulation of CD62L and upregulation of CD44, and immunopathology typical of IPEX. Because IL-27 is known to induce IL-10, a key effector molecule of regulatory T cells, we evaluated the contribution of IL-10 induction by crossing IL-10-null allele to Scurfy mice. Although IL-10 deficiency does not affect the survival of Scurfy mice, it largely abrogated the therapeutic effect of AAV-IL-27. Our study revealed a major role for IL-10 in AAV-IL-27 gene therapy and demonstrated that IPEX is amenable to gene therapy.

Selection-free precise gene repair using high-capacity adenovector delivery of advanced prime editing systems rescues dystrophin synthesis in DMD muscle cells.

Prime editors have high potential for disease modelling and regenerative medicine efforts including those directed at the muscle-wasting disorder Duchenne muscular dystrophy (DMD). However, the large size and multicomponent nature of prime editing systems pose substantial production and delivery issues. Here, we report that packaging optimized full-length prime editing constructs in adenovector particles (AdVPs) permits installing precise DMD edits in human myogenic cells, namely, myoblasts and mesenchymal stem cells (up to 80% and 64%, respectively). AdVP transductions identified optimized prime-editing reagents capable of correcting DMD reading frames of ∼14% of patient genotypes and restoring dystrophin synthesis and dystrophin-ß-dystroglycan linkages in unselected DMD muscle cell populations. AdVPs were equally suitable for correcting DMD iPSC-derived cardiomyocytes and delivering dual prime editors tailored for DMD repair through targeted exon 51 deletion. Moreover, by exploiting the cell cycle-independent AdVP transduction process, we report that 2- and 3-component prime-editing modalities are both most active in cycling than in post-mitotic cells. Finally, we establish that combining AdVP transduction with seamless prime editing allows for stacking chromosomal edits through successive delivery rounds. In conclusion, AdVPs permit versatile investigation of advanced prime editing systems independently of their size and component numbers, which should facilitate their screening and application.

KinomeMETA: a web platform for kinome-wide polypharmacology profiling with meta-learning.

Kinase-targeted inhibitors hold promise for new therapeutic options, with multi-target inhibitors offering the potential for broader efficacy while minimizing polypharmacology risks. However, comprehensive experimental profiling of kinome-wide activity is expensive, and existing computational approaches often lack scalability or accuracy for understudied kinases. We introduce KinomeMETA, an artificial intelligence (AI)-powered web platform that significantly expands the predictive range with scalability for predicting the polypharmacological effects of small molecules across the kinome. By leveraging a novel meta-learning algorithm, KinomeMETA efficiently utilizes sparse activity data, enabling rapid generalization to new kinase tasks even with limited information. This significantly expands the repertoire of accurately predictable kinases to 661 wild-type and clinically-relevant mutant kinases, far exceeding existing methods. Additionally, KinomeMETA empowers users to customize models with their proprietary data for specific research needs. Case studies demonstrate its ability to discover new active compounds by quickly adapting to small dataset. Overall, KinomeMETA offers enhanced kinome virtual profiling capabilities and is positioned as a powerful tool for developing new kinase inhibitors and advancing kinase research. The KinomeMETA server is freely accessible without registration at https://kinomemeta.alphama.com.cn/.

Astrocytes Modulate a Specific Paraventricular Thalamus→Prefrontal Cortex Projection to Enhance Consciousness Recovery from Anesthesia.

Current anesthetic theory is mostly based on neurons and/or neuronal circuits. A role for astrocytes also has been shown in promoting recovery from volatile anesthesia, while the exact modulatory mechanism and/or the molecular target in astrocytes is still unknown. In this study by animal models in male mice and electrophysiological recordings in vivo and in vitro, we found that activating astrocytes of the paraventricular thalamus (PVT) and/or knocking down PVT astrocytic Kir4.1 promoted the consciousness recovery from sevoflurane anesthesia. Single-cell RNA sequencing of the PVT reveals two distinct cellular subtypes of glutamatergic neurons: PVT GRM and PVT ChAT neurons. Patch-clamp recording results proved astrocytic Kir4.1-mediated modulation of sevoflurane on the PVT mainly worked on PVT ChAT neurons, which projected mainly to the mPFC. In summary, our findings support the novel conception that there is a specific PVT→prefrontal cortex projection involved in consciousness recovery from sevoflurane anesthesia, which is mediated by the inhibition of sevoflurane on PVT astrocytic Kir4.1 conductance.

Defining unique structural features in the MAFA and MAFB transcription factors that control Insulin gene activity.

MAFA and MAFB are related basic-leucine-zipper domain-containing transcription factors which have important overlapping and distinct regulatory roles in a variety of cellular contexts, including hormone production in pancreatic islet cells. Here we first examined how mutating conserved MAF protein-DNA contact sites obtained from X-ray crystal structure analysis impacted their DNA-binding and Insulin enhancer-driven activity. While most of these interactions were essential and their disruption severely compromised activity, we identified that regions outside of these contact sites also contributed to transcriptional activity. AlphaFold 2, an artificial intelligence-based structural prediction program, was used to determine if there were also differences in the three-dimensional organization of the non-DNA binding/dimerization sequences of MAFA and MAFB. This analysis was conducted on the wildtype (WT) proteins as well as the pathogenic MAFASer64Phe and MAFBSer70Alatrans-activation domain mutants, with differences revealed between MAFAWT and MAFBWT as well as between MAFASer64Phe and MAFAWT, but not between MAFBSer70Ala and MAFBWT. Moreover, dissimilarities between these proteins were also observed in their ability to cooperatively stimulate Insulin enhancer-driven activity in the presence of other islet-enriched transcription factors. Analysis of MAFA and MAFB chimeras disclosed that these properties were influenced by their unique C-terminal region structural differences predicted by AlphaFold 2. Our findings have revealed key structural features of these closely related proteins that impact their ability to regulate gene expression.

Demethylation and Up-Regulation of an Oncogene after Hypomethylating Therapy.

BACKGROUND: Although hypomethylating agents are currently used to treat patients with cancer, whether they can also reactivate and up-regulate oncogenes is not well elucidated. METHODS: We examined the effect of hypomethylating agents on SALL4, a known oncogene that plays an important role in myelodysplastic syndrome and other cancers. Paired bone marrow samples that were obtained from two cohorts of patients with myelodysplastic syndrome before and after treatment with a hypomethylating agent were used to explore the relationships among changes in SALL4 expression, treatment response, and clinical outcome. Leukemic cell lines with low or undetectable SALL4 expression were used to study the relationship between SALL4 methylation and expression. A locus-specific demethylation technology, CRISPR-DNMT1-interacting RNA (CRISPR-DiR), was used to identify the CpG island that is critical for SALL4 expression. RESULTS: SALL4 up-regulation after treatment with hypomethylating agents was observed in 10 of 25 patients (40%) in cohort 1 and in 13 of 43 patients (30%) in cohort 2 and was associated with a worse outcome. Using CRISPR-DiR, we discovered that demethylation of a CpG island within the 5' untranslated region was critical for SALL4 expression. In cell lines and patients, we confirmed that treatment with a hypomethylating agent led to demethylation of the same CpG region and up-regulation of SALL4 expression. CONCLUSIONS: By combining analysis of patient samples with CRISPR-DiR technology, we found that demethylation and up-regulation of an oncogene after treatment with a hypomethylating agent can indeed occur and should be further studied. (Funded by Associazione Italiana per la Ricerca sul Cancro and others.).

FEED: a feature selection method based on gene expression decomposition for single cell clustering.

Single-cell clustering is a critical step in biological downstream analysis. The clustering performance could be effectively improved by extracting cell-type-specific genes. The state-of-the-art feature selection methods usually calculate the importance of a single gene without considering the information contained in the gene expression distribution. Moreover, these methods ignore the intrinsic expression patterns of genes and heterogeneity within groups of different mean expression levels. In this work, we present a Feature sElection method based on gene Expression Decomposition (FEED) of scRNA-seq data, which selects informative genes to enhance clustering performance. First, the expression levels of genes are decomposed into multiple Gaussian components. Then, a novel gene correlation calculation method is proposed to measure the relationship between genes from the perspective of distribution. Finally, a permutation-based approach is proposed to determine the threshold of gene importance to obtain marker gene subsets. Compared with state-of-the-art feature selection methods, applying FEED on various scRNA-seq datasets including large datasets followed by different common clustering algorithms results in significant improvements in the accuracy of cell-type identification. The source codes for FEED are freely available at https://github.com/genemine/FEED.

CAKE: a flexible self-supervised framework for enhancing cell visualization, clustering and rare cell identification.

Single cell sequencing technology has provided unprecedented opportunities for comprehensively deciphering cell heterogeneity. Nevertheless, the high dimensionality and intricate nature of cell heterogeneity have presented substantial challenges to computational methods. Numerous novel clustering methods have been proposed to address this issue. However, none of these methods achieve the consistently better performance under different biological scenarios. In this study, we developed CAKE, a novel and scalable self-supervised clustering method, which consists of a contrastive learning model with a mixture neighborhood augmentation for cell representation learning, and a self-Knowledge Distiller model for the refinement of clustering results. These designs provide more condensed and cluster-friendly cell representations and improve the clustering performance in term of accuracy and robustness. Furthermore, in addition to accurately identifying the major type cells, CAKE could also find more biologically meaningful cell subgroups and rare cell types. The comprehensive experiments on real single-cell RNA sequencing datasets demonstrated the superiority of CAKE in visualization and clustering over other comparison methods, and indicated its extensive application in the field of cell heterogeneity analysis. Contact: Ruiqing Zheng. (rqzheng@csu.edu.cn).

Benchmarking of computational methods for predicting circRNA-disease associations.

Accumulating evidences demonstrate that circular RNA (circRNA) plays an important role in human diseases. Identification of circRNA-disease associations can help for the diagnosis of human diseases, while the traditional method based on biological experiments is time-consuming. In order to address the limitation, a series of computational methods have been proposed in recent years. However, few works have summarized these methods or compared the performance of them. In this paper, we divided the existing methods into three categories: information propagation, traditional machine learning and deep learning. Then, the baseline methods in each category are introduced in detail. Further, 5 different datasets are collected, and 14 representative methods of each category are selected and compared in the 5-fold, 10-fold cross-validation and the de novo experiment. In order to further evaluate the effectiveness of these methods, six common cancers are selected to compare the number of correctly identified circRNA-disease associations in the top-10, top-20, top-50, top-100 and top-200. In addition, according to the results, the observation about the robustness and the character of these methods are concluded. Finally, the future directions and challenges are discussed.

Computational approaches for detecting disease-associated alternative splicing events.

Alternative splicing (AS) is a key transcriptional regulation pathway. Recent studies have shown that AS events are associated with the occurrence of complex diseases. Various computational approaches have been developed for the detection of disease-associated AS events. In this review, we first describe the metrics used for quantitative characterization of AS events. Second, we review and discuss the three types of methods for detecting disease-associated splicing events, which are differential splicing analysis, aberrant splicing detection and splicing-related network analysis. Third, to further exploit the genetic mechanism of disease-associated AS events, we describe the methods for detecting genetic variants that potentially regulate splicing. For each type of methods, we conducted experimental comparison to illustrate their performance. Finally, we discuss the limitations of these methods and point out potential ways to address them. We anticipate that this review provides a systematic understanding of computational approaches for the analysis of disease-associated splicing.

KinomeMETA: meta-learning enhanced kinome-wide polypharmacology profiling.

Kinase inhibitors are crucial in cancer treatment, but drug resistance and side effects hinder the development of effective drugs. To address these challenges, it is essential to analyze the polypharmacology of kinase inhibitor and identify compound with high selectivity profile. This study presents KinomeMETA, a framework for profiling the activity of small molecule kinase inhibitors across a panel of 661 kinases. By training a meta-learner based on a graph neural network and fine-tuning it to create kinase-specific learners, KinomeMETA outperforms benchmark multi-task models and other kinase profiling models. It provides higher accuracy for understudied kinases with limited known data and broader coverage of kinase types, including important mutant kinases. Case studies on the discovery of new scaffold inhibitors for membrane-associated tyrosine- and threonine-specific cdc2-inhibitory kinase and selective inhibitors for fibroblast growth factor receptors demonstrate the role of KinomeMETA in virtual screening and kinome-wide activity profiling. Overall, KinomeMETA has the potential to accelerate kinase drug discovery by more effectively exploring the kinase polypharmacology landscape.