Splicing regulation through biomolecular condensates and membraneless organelles.

Biomolecular condensates, sometimes also known as membraneless organelles (MLOs), can form through weak multivalent intermolecular interactions of proteins and nucleic acids, a process often associated with liquid-liquid phase separation. Biomolecular condensates are emerging as sites and regulatory platforms of vital cellular functions, including transcription and RNA processing. In the first part of this Review, we comprehensively discuss how alternative splicing regulates the formation and properties of condensates, and conversely the roles of biomolecular condensates in splicing regulation. In the second part, we focus on the spatial connection between splicing regulation and nuclear MLOs such as transcriptional condensates, splicing condensates and nuclear speckles. We then discuss key studies showing how splicing regulation through biomolecular condensates is implicated in human pathologies such as neurodegenerative diseases, different types of cancer, developmental disorders and cardiomyopathies, and conclude with a discussion of outstanding questions pertaining to the roles of condensates and MLOs in splicing regulation and how to experimentally study them.

Phase transition of spindle-associated protein regulate spindle apparatus assembly.

Spindle assembly required during mitosis depends on microtubule polymerization. We demonstrate that the evolutionarily conserved low-complexity protein, BuGZ, undergoes phase transition or coacervation to promote assembly of both spindles and their associated components. BuGZ forms temperature-dependent liquid droplets alone or on microtubules in physiological buffers. Coacervation in vitro or in spindle and spindle matrix depends on hydrophobic residues in BuGZ. BuGZ coacervation and its binding to microtubules and tubulin are required to promote assembly of spindle and spindle matrix in Xenopus egg extract and in mammalian cells. Since several previously identified spindle-associated components also contain low-complexity regions, we propose that coacervating proteins may be a hallmark of proteins that comprise a spindle matrix that functions to promote assembly of spindles by concentrating its building blocks.

Superconductivity under pressure in a chromium-based kagome metal.

Superconductivity in a highly correlated kagome system has been theoretically proposed for years (refs. 1-5), yet the experimental realization is hard to achieve6,7. The recently discovered vanadium-based kagome materials8, which exhibit both superconductivity9-11 and charge-density-wave orders12-14, are nonmagnetic8,9 and weakly correlated15,16. Thus these materials are unlikely to host the exotic superconductivity theoretically proposed. Here we report the discovery of a chromium-based kagome metal, CsCr3Sb5, which is contrastingly featured with strong electron correlations, frustrated magnetism and characteristic flat bands close to the Fermi level. Under ambient pressure, this kagome metal undergoes a concurrent structural and magnetic phase transition at 55 K, with a stripe-like 4a0 structural modulation. At high pressure, the phase transition evolves into two transitions, possibly associated with charge-density-wave and antiferromagnetic spin-density-wave orderings. These density-wave-like orders are gradually suppressed with pressure and, remarkably, a superconducting dome emerges at 3.65-8.0 GPa. The maximum of the superconducting transition temperature, Tcmax = 6.4 K, appears when the density-wave-like orders are completely suppressed at 4.2 GPa, and the normal state exhibits a non-Fermi-liquid behaviour, reminiscent of unconventional superconductivity and quantum criticality in iron-based superconductors17,18. Our work offers an unprecedented platform for investigating superconductivity in correlated kagome systems.

Thousands of conductance levels in memristors integrated on CMOS.

Neural networks based on memristive devices1-3 have the ability to improve throughput and energy efficiency for machine learning4,5 and artificial intelligence6, especially in edge applications7-21. Because training a neural network model from scratch is costly in terms of hardware resources, time and energy, it is impractical to do it individually on billions of memristive neural networks distributed at the edge. A practical approach would be to download the synaptic weights obtained from the cloud training and program them directly into memristors for the commercialization of edge applications. Some post-tuning in memristor conductance could be done afterwards or during applications to adapt to specific situations. Therefore, in neural network applications, memristors require high-precision programmability to guarantee uniform and accurate performance across a large number of memristive networks22-28. This requires many distinguishable conductance levels on each memristive device, not only laboratory-made devices but also devices fabricated in factories. Analog memristors with many conductance states also benefit other applications, such as neural network training, scientific computing and even 'mortal computing'25,29,30. Here we report 2,048 conductance levels achieved with memristors in fully integrated chips with 256 × 256 memristor arrays monolithically integrated on complementary metal-oxide-semiconductor (CMOS) circuits in a commercial foundry. We have identified the underlying physics that previously limited the number of conductance levels that could be achieved in memristors and developed electrical operation protocols to avoid such limitations. These results provide insights into the fundamental understanding of the microscopic picture of memristive switching as well as approaches to enable high-precision memristors for various applications. Fig. 1 HIGH-PRECISION MEMRISTOR FOR NEUROMORPHIC COMPUTING.: a, Proposed scheme of the large-scale application of memristive neural networks for edge computing. Neural network training is performed in the cloud. The obtained weights are downloaded and accurately programmed into a massive number of memristor arrays distributed at the edge, which imposes high-precision requirements on memristive devices. b, An eight-inch wafer with memristors fabricated by a commercial semiconductor manufacturer. c, High-resolution transmission electron microscopy image of the cross-section view of a memristor. Pt and Ta serve as the bottom electrode (BE) and top electrode (TE), respectively. Scale bars, 1 µm and 100 nm (inset). d, Magnification of the memristor material stack. Scale bar, 5 nm. e, As-programmed (blue) and after-denoising (red) currents of a memristor are read by a constant voltage (0.2 V). The denoising process eliminated the large-amplitude RTN observed in the as-programmed state (see Methods). f, Magnification of three nearest-neighbour states after denoising. The current of each state was read by a constant voltage (0.2 V). No large-amplitude RTN was observed, and all of the states can be clearly distinguished. g, An individual memristor on the chip was tuned into 2,048 resistance levels by high-resolution off-chip driving circuitry, and each resistance level was read by a d.c. voltage sweeping from 0 to 0.2 V. The target resistance was set from 50 µS to 4,144 µS with a 2-µS interval between neighbouring levels. All readings at 0.2 V are less than 1 µS from the target conductance. Bottom inset, magnification of the resistance levels. Top inset, experimental results of an entire 256 × 256 array programmed by its 6-bit on-chip circuitry into 64 32 × 32 blocks, and each block is programmed into one of the 64 conductance levels. Each of the 256 × 256 memristors has been previously switched over one million cycles, demonstrating the high endurance and robustness of the devices.

Dissecting the biophysics and biology of intrinsically disordered proteins.

Intrinsically disordered regions (IDRs) within human proteins play critical roles in cellular information processing, including signaling, transcription, stress response, DNA repair, genome organization, and RNA processing. Here, we summarize current challenges in the field and propose cutting-edge approaches to address them in physiology and disease processes, with a focus on cancer.

Asymmetric pore windows in MOF membranes for natural gas valorization.

To use natural gas as a feedstock alternative to coal and oil, its main constituent, methane, needs to be isolated with high purity1. In particular, nitrogen dilutes the heating value of natural gas and is, therefore, of prime importance for removal2. However, the inertness of nitrogen and its similarities to methane in terms of kinetic size, polarizability and boiling point pose particular challenges for the development of energy-efficient nitrogen-removing processes3. Here we report a mixed-linker metal-organic framework (MOF) membrane based on fumarate (fum) and mesaconate (mes) linkers, Zr-fum67-mes33-fcu-MOF, with a pore aperture shape specific for effective nitrogen removal from natural gas. The deliberate introduction of asymmetry in the parent trefoil-shaped pore aperture induces a shape irregularity, blocking the transport of tetrahedral methane while allowing linear nitrogen to permeate. Zr-fum67-mes33-fcu-MOF membranes exhibit record-high nitrogen/methane selectivity and nitrogen permeance under practical pressures up to 50 bar, removing both carbon dioxide and nitrogen from natural gas. Techno-economic analysis shows that our membranes offer the potential to reduce methane purification costs by about 66% for nitrogen rejection and about 73% for simultaneous removal of carbon dioxide and nitrogen, relative to cryogenic distillation and amine-based carbon dioxide capture.

Regulation of pluripotency and self- renewal of ESCs through epigenetic-threshold modulation and mRNA pruning.

Embryonic stem cell (ESC) pluripotency requires bivalent epigenetic modifications of key developmental genes regulated by various transcription factors and chromatin-modifying enzymes. How these factors coordinate with one another to maintain the bivalent chromatin state so that ESCs can undergo rapid self-renewal while retaining pluripotency is poorly understood. We report that Utf1, a target of Oct4 and Sox2, is a bivalent chromatin component that buffers poised states of bivalent genes. By limiting PRC2 loading and histone 3 lysine-27 trimethylation, Utf1 sets proper activation thresholds for bivalent genes. It also promotes nuclear tagging of messenger RNAs (mRNAs) transcribed from insufficiently silenced bivalent genes for cytoplasmic degradation through mRNA decapping. These opposing functions of Utf1 promote coordinated differentiation. The mRNA degradation function also ensures rapid cell proliferation by blocking the Myc-Arf feedback control. Thus, Utf1 couples the core pluripotency factors with Myc and PRC2 networks to promote the pluripotency and proliferation of ESCs.

UTX condensation underlies its tumour-suppressive activity.

UTX (also known as KDM6A) encodes a histone H3K27 demethylase and is an important tumour suppressor that is frequently mutated in human cancers1. However, as the demethylase activity of UTX is often dispensable for mediating tumour suppression and developmental regulation2-8, the underlying molecular activity of UTX remains unknown. Here we show that phase separation of UTX underlies its chromatin-regulatory activity in tumour suppression. A core intrinsically disordered region (cIDR) of UTX forms phase-separated liquid condensates, and cIDR loss caused by the most frequent cancer mutation of UTX is mainly responsible for abolishing tumour suppression. Deletion, mutagenesis and replacement assays of the intrinsically disordered region demonstrate a critical role of UTX condensation in tumour suppression and embryonic stem cell differentiation. As shown by reconstitution in vitro and engineered systems in cells, UTX recruits the histone methyltransferase MLL4 (also known as KMT2D) to the same condensates and enriches the H3K4 methylation activity of MLL4. Moreover, UTX regulates genome-wide histone modifications and high-order chromatin interactions in a condensation-dependent manner. We also found that UTY, the Y chromosome homologue of UTX with weaker tumour-suppressive activity, forms condensates with reduced molecular dynamics. These studies demonstrate a crucial biological function of liquid condensates with proper material states in enabling the tumour-suppressive activity of a chromatin regulator.

Unique insights into the design of low-strain single-crystalline Ni-rich cathodes with superior cycling stability.

Micro-sized single-crystalline Ni-rich cathodes are emerging as prominent candidates owing to their larger compact density and higher safety compared with poly-crystalline counterparts, yet the uneven stress distribution and lattice oxygen loss result in the intragranular crack generation and planar gliding. Herein, taking LiNi0.83Co0.12Mn0.05O2 as an example, an optimal particle size of 3.7 µm is predicted by simulating the stress distributions at various states of charge and their relationship with fracture free-energy, and then, the fitted curves of particle size with calcination temperature and time are further built, which guides the successful synthesis of target-sized particles (m-NCM83) with highly ordered layered structure by a unique high-temperature short-duration pulse lithiation strategy. The m-NCM83 significantly reduces strain energy, Li/O loss, and cationic mixing, thereby inhibiting crack formation, planar gliding, and surface degradation. Accordingly, the m-NCM83 exhibits superior cycling stability with highly structural integrity and dual-doped m-NCM83 further shows excellent 88.1% capacity retention.

scEWE: high-order element-wise weighted ensemble clustering for heterogeneity analysis of single-cell RNA-sequencing data.

With the emergence of large amount of single-cell RNA sequencing (scRNA-seq) data, the exploration of computational methods has become critical in revealing biological mechanisms. Clustering is a representative for deciphering cellular heterogeneity embedded in scRNA-seq data. However, due to the diversity of datasets, none of the existing single-cell clustering methods shows overwhelming performance on all datasets. Weighted ensemble methods are proposed to integrate multiple results to improve heterogeneity analysis performance. These methods are usually weighted by considering the reliability of the base clustering results, ignoring the performance difference of the same base clustering on different cells. In this paper, we propose a high-order element-wise weighting strategy based self-representative ensemble learning framework: scEWE. By assigning different base clustering weights to individual cells, we construct and optimize the consensus matrix in a careful and exquisite way. In addition, we extracted the high-order information between cells, which enhanced the ability to represent the similarity relationship between cells. scEWE is experimentally shown to significantly outperform the state-of-the-art methods, which strongly demonstrates the effectiveness of the method and supports the potential applications in complex single-cell data analytical problems.

RNA polymerase II promotes the organization of chromatin following DNA replication.

Understanding how chromatin organisation is duplicated on the two daughter strands is a central question in epigenetics. In mammals, following the passage of the replisome, nucleosomes lose their defined positioning and transcription contributes to their re-organisation. However, whether transcription plays a greater role in the organization of chromatin following DNA replication remains unclear. Here we analysed protein re-association with newly replicated DNA upon inhibition of transcription using iPOND coupled to quantitative mass spectrometry. We show that nucleosome assembly and the re-establishment of most histone modifications are uncoupled from transcription. However, RNAPII acts to promote the re-association of hundreds of proteins with newly replicated chromatin via pathways that are not observed in steady-state chromatin. These include ATP-dependent remodellers, transcription factors and histone methyltransferases. We also identify a set of DNA repair factors that may handle transcription-replication conflicts during normal transcription in human non-transformed cells. Our study reveals that transcription plays a greater role in the organization of chromatin post-replication than previously anticipated.

Role for Dpy-30 in ES cell-fate specification by regulation of H3K4 methylation within bivalent domains.

Histone H3K4 methylation is associated with active genes and, along with H3K27 methylation, is part of a bivalent chromatin mark that typifies poised developmental genes in embryonic stem cells (ESCs). However, its functional roles in ESC maintenance and differentiation are not established. Here we show that mammalian Dpy-30, a core subunit of the SET1/MLL histone methyltransferase complexes, modulates H3K4 methylation in vitro, and directly regulates chromosomal H3K4 trimethylation (H3K4me3) throughout the mammalian genome. Depletion of Dpy-30 does not affect ESC self-renewal, but significantly alters the differentiation potential of ESCs, particularly along the neural lineage. The differentiation defect is accompanied by defects in gene induction and in H3K4 methylation at key developmental loci. Our results strongly indicate an essential functional role for Dpy-30 and SET1/MLL complex-mediated H3K4 methylation, as a component of the bivalent mark, at developmental genes during the ESC fate transitions.

Activation of P53 pathway contributes to Xenopus hybrid inviability.

Hybrid incompatibility as a kind of reproductive isolation contributes to speciation. The nucleocytoplasmic incompatibility between Xenopus tropicalis eggs and Xenopus laevis sperm (te×ls) leads to specific loss of paternal chromosomes 3L and 4L. The hybrids die before gastrulation, of which the lethal causes remain largely unclear. Here, we show that the activation of the tumor suppressor protein P53 at late blastula stage contributes to this early lethality. We find that in stage 9 embryos, P53-binding motif is the most enriched one in the up-regulated Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-seq) peaks between te×ls and wild-type X. tropicalis controls, which correlates with an abrupt stabilization of P53 protein in te×ls hybrids at stage 9. Inhibition of P53 activity via either tp53 knockout or overexpression of a dominant-negative P53 mutant or Murine double minute 2 proto-oncogene (Mdm2), a negative regulator of P53, by mRNA injection can rescue the te×ls early lethality. Our results suggest a causal function of P53 on hybrid lethality prior to gastrulation.

Myosin 9 and N-glycans jointly regulate human papillomavirus entry.

Persistent high-risk HPV infection is closely associated with cervical cancer development, and there is no drug targeting HPV on the market at present, so it is particularly important to understand the interaction mechanism between HPV and the host which may provide the novel strategies for treating HPV diseases. HPV can hijack cell surface heparan sulfate proteoglycans (HSPGs) as primary receptors. However, the secondary entry receptors for HPV remain elusive. We identify myosin-9 (NMHC-IIA) as a host factor that interacts with HPV L1 protein and mediates HPV internalization. Efficient HPV entry required myosin-9 redistribution to the cell surface regulated by HPV-hijacked MEK-MLCK signaling. Myosin-9 maldistribution by ML-7 or ML-9 significantly inhibited HPV pseudoviruses infection in vitro and in vivo. Meanwhile, N-glycans, especially the galactose chains, may act as the decoy receptors for HPV, which can block the interaction of HPV to myosin-9 and influence the way of HPV infection. Taken together, we identify myosin-9 as a novel functional entry receptor for high-risk HPV both in vitro and in vivo, and unravel the new roles of myosin-9 and N-glycans in HPV entry, which provides the possibilities for host targets of antiviral drugs.

scTPC: a novel semisupervised deep clustering model for scRNA-seq data.

MOTIVATION: Continuous advancements in single-cell RNA sequencing (scRNA-seq) technology have enabled researchers to further explore the study of cell heterogeneity, trajectory inference, identification of rare cell types, and neurology. Accurate scRNA-seq data clustering is crucial in single-cell sequencing data analysis. However, the high dimensionality, sparsity, and presence of "false" zero values in the data can pose challenges to clustering. Furthermore, current unsupervised clustering algorithms have not effectively leveraged prior biological knowledge, making cell clustering even more challenging. RESULTS: This study investigates a semisupervised clustering model called scTPC, which integrates the triplet constraint, pairwise constraint, and cross-entropy constraint based on deep learning. Specifically, the model begins by pretraining a denoising autoencoder based on a zero-inflated negative binomial distribution. Deep clustering is then performed in the learned latent feature space using triplet constraints and pairwise constraints generated from partial labeled cells. Finally, to address imbalanced cell-type datasets, a weighted cross-entropy loss is introduced to optimize the model. A series of experimental results on 10 real scRNA-seq datasets and five simulated datasets demonstrate that scTPC achieves accurate clustering with a well-designed framework. AVAILABILITY AND IMPLEMENTATION: scTPC is a Python-based algorithm, and the code is available from https://github.com/LF-Yang/Code or https://zenodo.org/records/10951780.

EGF-driven EGFR/miR-27b-3p/FOXO1 promotes rat FSH synthesis and secretion.

The adenopituitary secretes follicle-stimulating hormone (FSH), which plays a crucial role in regulating the growth, development, and reproductive functions of organisms. Investigating the process of FSH synthesis and secretion can offer valuable insights into potential areas of focus for reproductive research. Epidermal growth factor (EGF) is a significant paracrine/autocrine factor within the body, and studies have demonstrated its ability to stimulate FSH secretion in animals. However, the precise mechanisms that regulate this action are still poorly understood. In this research, in vivo and in vitro experiments showed that the activation of epidermal growth factor receptor (EGFR) by EGF induces the upregulation of miR-27b-3p and that miR-27b-3p targets and inhibits Foxo1 mRNA expression, resulting in increased FSH synthesis and secretion. In summary, this study elucidates the precise molecular mechanism through which EGF governs the synthesis and secretion of FSH via the EGFR/miR-27b-3p/FOXO1 pathway.

Downregulation of miR-503-5p Promotes the Development of Pancreatic Cancer by Targeting Cyclin E2.

This study aimed to elucidate the role of microRNA-503 (miR-503) in pancreatic cancer (PC) progression and the underlying regulatory mechanisms. We acquired miR-503-3p and miR-503-5p expression data along with survival times of PC and normal samples from the UCSC Xena database. Using the t-test, we compared the expression of miR-503-3p and miR-503-5p between PC and normal samples, and evaluated their prognostic significance via Kaplan-Meier survival analysis. The expression of miR-503-5p in PC cells was detected by quantitative PCR. We subsequently overexpressed miR-503-5p in PC cells and examined cell viability, apoptosis, and migration through CCK8 assay, flow cytometry, and Transwell assay, respectively. Potential functional targets were identified using miRTarBase and validated by dual-luciferase reporter assay. Both miR-503-3p and miR-503-5p expression were found to be downregulated in PC; however, only miR-503-5p was linked to cancer prognosis based on public data. In vitro experiments demonstrated that overexpression of miR-503-5p substantially decreased cell viability, induced apoptosis, caused G0/G1 arrest, and inhibited cell migration. miR-503-5p was found to target cyclin E2 (CCNE2), and overexpression of CCNE2 could counteract the effects of miR-503-5p on PC cells. Conclusion: The downregulation of miR-503-5p enhances the progression of PC by targeting CCNE2. The detection of miR-503-5p expression may provide valuable insights for the prevention and prognostic evaluation of PC.

Soft Fiber Electronics Based on Semiconducting Polymer.

Fibers, originating from nature and mastered by human, have woven their way throughout the entire history of human civilization. Recent developments in semiconducting polymer materials have further endowed fibers and textiles with various electronic functions, which are attractive in applications such as information interfacing, personalized medicine, and clean energy. Owing to their ability to be easily integrated into daily life, soft fiber electronics based on semiconducting polymers have gained popularity recently for wearable and implantable applications. Herein, we present a review of the previous and current progress in semiconducting polymer-based fiber electronics, particularly focusing on smart-wearable and implantable areas. First, we provide a brief overview of semiconducting polymers from the viewpoint of materials based on the basic concepts and functionality requirements of different devices. Then we analyze the existing applications and associated devices such as information interfaces, healthcare and medicine, and energy conversion and storage. The working principle and performance of semiconducting polymer-based fiber devices are summarized. Furthermore, we focus on the fabrication techniques of fiber devices. Based on the continuous fabrication of one-dimensional fiber and yarn, we introduce two- and three-dimensional fabric fabricating methods. Finally, we review challenges and relevant perspectives and potential solutions to address the related problems.

Charged Gold Nanoparticles for Target Identification-Alignment and Automatic Segmentation of CT Image-Guided Adaptive Radiotherapy in Small Hepatocellular Carcinoma.

Because of the challenges posed by anatomical uncertainties and the low resolution of plain computed tomography (CT) scans, implementing adaptive radiotherapy (ART) for small hepatocellular carcinoma (sHCC) using artificial intelligence (AI) faces obstacles in tumor identification-alignment and automatic segmentation. The current study aims to improve sHCC imaging for ART using a gold nanoparticle (Au NP)-based CT contrast agent to enhance AI-driven automated image processing. The synthesized charged Au NPs demonstrated notable in vitro aggregation, low cytotoxicity, and minimal organ toxicity. Over time, an in situ sHCC mouse model was established for in vivo CT imaging at multiple time points. The enhanced CT images processed using 3D U-Net and 3D Trans U-Net AI models demonstrated high geometric and dosimetric accuracy. Therefore, charged Au NPs enable accurate and automatic sHCC segmentation in CT images using classical AI models, potentially addressing the technical challenges related to tumor identification, alignment, and automatic segmentation in CT-guided online ART.

SSF-DDI: a deep learning method utilizing drug sequence and substructure features for drug-drug interaction prediction.

BACKGROUND: Drug-drug interactions (DDI) are prevalent in combination therapy, necessitating the importance of identifying and predicting potential DDI. While various artificial intelligence methods can predict and identify potential DDI, they often overlook the sequence information of drug molecules and fail to comprehensively consider the contribution of molecular substructures to DDI. RESULTS: In this paper, we proposed a novel model for DDI prediction based on sequence and substructure features (SSF-DDI) to address these issues. Our model integrates drug sequence features and structural features from the drug molecule graph, providing enhanced information for DDI prediction and enabling a more comprehensive and accurate representation of drug molecules. CONCLUSION: The results of experiments and case studies have demonstrated that SSF-DDI significantly outperforms state-of-the-art DDI prediction models across multiple real datasets and settings. SSF-DDI performs better in predicting DDI involving unknown drugs, resulting in a 5.67% improvement in accuracy compared to state-of-the-art methods.