Sensory neurons promote immune homeostasis in the lung.

Cytokines employ downstream Janus kinases (JAKs) to promote chronic inflammatory diseases. JAK1-dependent type 2 cytokines drive allergic inflammation, and patients with JAK1 gain-of-function (GoF) variants develop atopic dermatitis (AD) and asthma. To explore tissue-specific functions, we inserted a human JAK1 GoF variant (JAK1GoF) into mice and observed the development of spontaneous AD-like skin disease but unexpected resistance to lung inflammation when JAK1GoF expression was restricted to the stroma. We identified a previously unrecognized role for JAK1 in vagal sensory neurons in suppressing airway inflammation. Additionally, expression of Calcb/CGRPß was dependent on JAK1 in the vagus nerve, and CGRPß suppressed group 2 innate lymphoid cell function and allergic airway inflammation. Our findings reveal evolutionarily conserved but distinct functions of JAK1 in sensory neurons across tissues. This biology raises the possibility that therapeutic JAK inhibitors may be further optimized for tissue-specific efficacy to enhance precision medicine in the future.

The RNA Exosome Syncs IAV-RNAPII Transcription to Promote Viral Ribogenesis and Infectivity.

The nuclear RNA exosome is an essential multi-subunit complex that controls RNA homeostasis. Congenital mutations in RNA exosome genes are associated with neurodegenerative diseases. Little is known about the role of the RNA exosome in the cellular response to pathogens. Here, using NGS and human and mouse genetics, we show that influenza A virus (IAV) ribogenesis and growth are suppressed by impaired RNA exosome activity. Mechanistically, the nuclear RNA exosome coordinates the initial steps of viral transcription with RNAPII at host promoters. The viral polymerase complex co-opts the nuclear RNA exosome complex and cellular RNAs en route to 3' end degradation. Exosome deficiency uncouples chromatin targeting of the viral polymerase complex and the formation of cellular:viral RNA hybrids, which are essential RNA intermediates that license transcription of antisense genomic viral RNAs. Our results suggest that evolutionary arms races have shaped the cellular RNA quality control machinery.

The exon junction complex as a node of post-transcriptional networks.

The exon junction complex (EJC) is deposited onto mRNAs following splicing and adopts a unique structure, which can both stably bind to mRNAs and function as an anchor for diverse processing factors. Recent findings revealed that in addition to its established roles in nonsense-mediated mRNA decay, the EJC is involved in mRNA splicing, transport and translation. While structural studies have shed light on EJC assembly, transcriptome-wide analyses revealed differential EJC loading at spliced junctions. Thus, the EJC functions as a node of post-transcriptional gene expression networks, the importance of which is being revealed by the discovery of increasing numbers of EJC-related disorders.

Digital quantum simulation of Floquet symmetry-protected topological phases.

Quantum many-body systems away from equilibrium host a rich variety of exotic phenomena that are forbidden by equilibrium thermodynamics. A prominent example is that of discrete time crystals1-8, in which time-translational symmetry is spontaneously broken in periodically driven systems. Pioneering experiments have observed signatures of time crystalline phases with trapped ions9,10, solid-state spin systems11-15, ultracold atoms16,17 and superconducting qubits18-20. Here we report the observation of a distinct type of non-equilibrium state of matter, Floquet symmetry-protected topological phases, which are implemented through digital quantum simulation with an array of programmable superconducting qubits. We observe robust long-lived temporal correlations and subharmonic temporal response for the edge spins over up to 40 driving cycles using a circuit of depth exceeding 240 and acting on 26 qubits. We demonstrate that the subharmonic response is independent of the initial state, and experimentally map out a phase boundary between the Floquet symmetry-protected topological and thermal phases. Our results establish a versatile digital simulation approach to exploring exotic non-equilibrium phases of matter with current noisy intermediate-scale quantum processors21.

Histone Deacetylase 3 Couples Mitochondria to Drive IL-1ß-Dependent Inflammation by Configuring Fatty Acid Oxidation.

Immune cell function depends on specific metabolic programs dictated by mitochondria, including nutrient oxidation, macromolecule synthesis, and post-translational modifications. Mitochondrial adaptations have been linked to acute and chronic inflammation, but the metabolic cues and precise mechanisms remain unclear. Here we reveal that histone deacetylase 3 (HDAC3) is essential for shaping mitochondrial adaptations for IL-1ß production in macrophages through non-histone deacetylation. In vivo, HDAC3 promoted lipopolysaccharide-induced acute inflammation and high-fat diet-induced chronic inflammation by enhancing NLRP3-dependent caspase-1 activation. HDAC3 configured the lipid profile in stimulated macrophages and restricted fatty acid oxidation (FAO) supported by exogenous fatty acids for mitochondria to acquire their adaptations and depolarization. Rather than affecting nuclear gene expression, HDAC3 translocated to mitochondria to deacetylate and inactivate an FAO enzyme, mitochondrial trifunctional enzyme subunit α. HDAC3 may serve as a controlling node that balances between acquiring mitochondrial adaptations and sustaining their fitness for IL-1ß-dependent inflammation.

Loss of UBE2S causes meiosis I arrest with normal spindle assembly checkpoint dynamics in mouse oocytes.

The timely degradation of proteins that regulate the cell cycle is essential for oocyte maturation. Oocytes are equipped to degrade proteins via the ubiquitin-proteasome system. In meiosis, anaphase promoting complex/cyclosome (APC/C), an E3 ubiquitin-ligase, is responsible for the degradation of proteins. Ubiquitin-conjugating enzyme E2 S (UBE2S), an E2 ubiquitin-conjugating enzyme, delivers ubiquitin to APC/C. APC/C has been extensively studied, but the functions of UBE2S in oocyte maturation and mouse fertility are not clear. In this study, we used Ube2s knockout mice to explore the role of UBE2S in mouse oocytes. Ube2s-deleted oocytes were characterized by meiosis I arrest with normal spindle assembly and spindle assembly checkpoint dynamics. However, the absence of UBE2S affected the activity of APC/C. Cyclin B1 and securin are two substrates of APC/C, and their levels were consistently high, resulting in the failure of homologous chromosome separation. Unexpectedly, the oocytes arrested in meiosis I could be fertilized and the embryos could become implanted normally, but died before embryonic day 10.5. In conclusion, our findings reveal an indispensable regulatory role of UBE2S in mouse oocyte meiosis and female fertility.

Cholesterol Homeostatic Regulator SCAP-SREBP2 Integrates NLRP3 Inflammasome Activation and Cholesterol Biosynthetic Signaling in Macrophages.

Cholesterol metabolism has been linked to immune functions, but the mechanisms by which cholesterol biosynthetic signaling orchestrates inflammasome activation remain unclear. Here, we have shown that NLRP3 inflammasome activation is integrated with the maturation of cholesterol master transcription factor SREBP2. Importantly, SCAP-SREBP2 complex endoplasmic reticulum-to-Golgi translocation was required for optimal activation of the NLRP3 inflammasome both in vitro and in vivo. Enforced cholesterol biosynthetic signaling by sterol depletion or statins promoted NLPR3 inflammasome activation. However, this regulation did not predominantly depend on changes in cholesterol homeostasis controlled by the transcriptional activity of SREBP2, but relied on the escort activity of SCAP. Mechanistically, NLRP3 associated with SCAP-SREBP2 to form a ternary complex which translocated to the Golgi apparatus adjacent to a mitochondrial cluster for optimal inflammasome assembly. Our study reveals that, in addition to controlling cholesterol biosynthesis, SCAP-SREBP2 also serves as a signaling hub integrating cholesterol metabolism with inflammation in macrophages.

One-Carbon Metabolism Supports S-Adenosylmethionine and Histone Methylation to Drive Inflammatory Macrophages.

Activated macrophages adapt their metabolic pathways to drive the pro-inflammatory phenotype, but little is known about the biochemical underpinnings of this process. Here, we find that lipopolysaccharide (LPS) activates the pentose phosphate pathway, the serine synthesis pathway, and one-carbon metabolism, the synergism of which drives epigenetic reprogramming for interleukin-1ß (IL-1ß) expression. Glucose-derived ribose and one-carbon units fed by both glucose and serine metabolism are synergistically integrated into the methionine cycle through de novo ATP synthesis and fuel the generation of S-adenosylmethionine (SAM) during LPS-induced inflammation. Impairment of these metabolic pathways that feed SAM generation lead to anti-inflammatory outcomes, implicating SAM as an essential metabolite for inflammatory macrophages. Mechanistically, SAM generation maintains a relatively high SAM:S-adenosylhomocysteine ratio to support histone H3 lysine 36 trimethylation for IL-1ß production. We therefore identify a synergistic effect of glucose and amino acid metabolism on orchestrating SAM availability that is intimately linked to the chromatin state for inflammation.

KCTD10 regulates brain development by destabilizing brain disorder-associated protein KCTD13.

KCTD10 belongs to the KCTD (potassiumchannel tetramerization domain) family, many members of which are associated with neuropsychiatric disorders. However, the biological function underlying the association with brain disorders remains to be explored. Here, we reveal that Kctd10 is highly expressed in neuronal progenitors and layer V neurons throughout brain development. Kctd10 deficiency triggers abnormal proliferation and differentiation of neuronal progenitors, reduced deep-layer (especially layer V) neurons, increased upper-layer neurons, and lowered brain size. Mechanistically, we screened and identified a unique KCTD10-interacting protein, KCTD13, associated with neurodevelopmental disorders. KCTD10 mediated the ubiquitination-dependent degradation of KCTD13 and KCTD10 ablation resulted in a considerable increase of KCTD13 expression in the developing cortex. KCTD13 overexpression in neuronal progenitors led to reduced proliferation and abnormal cell distribution, mirroring KCTD10 deficiency. Notably, mice with brain-specific Kctd10 knockout exhibited obvious motor deficits. This study uncovers the physiological function of KCTD10 and provides unique insights into the pathogenesis of neurodevelopmental disorders.

Mad2 is dispensable for accurate chromosome segregation but becomes essential when oocytes are subjected to environmental stress.

Accurate chromosome segregation, monitored by the spindle assembly checkpoint (SAC), is crucial for the production of euploid cells. Previous in vitro studies by us and others showed that Mad2, a core member of the SAC, performs a checkpoint function in oocyte meiosis. Here, through an oocyte-specific knockout approach in mouse, we reconfirmed that Mad2-deficient oocytes exhibit an accelerated metaphase-to-anaphase transition caused by premature degradation of securin and cyclin B1 and subsequent activation of separase in meiosis I. However, it was surprising that the knockout mice were completely fertile and the resulting oocytes were euploid. In the absence of Mad2, other SAC proteins, including BubR1, Bub3 and Mad1, were normally recruited to the kinetochores, which likely explains the balanced chromosome separation. Further studies showed that the chromosome separation in Mad2-null oocytes was particularly sensitive to environmental changes and, when matured in vitro, showed chromosome misalignment, lagging chromosomes, and aneuploidy with premature separation of sister chromatids, which was exacerbated at a lower temperature. We reveal for the first time that Mad2 is dispensable for proper chromosome segregation but acts to mitigate environmental stress in meiotic oocytes.

DrugMetric: quantitative drug-likeness scoring based on chemical space distance.

The process of drug discovery is widely known to be lengthy and resource-intensive. Artificial Intelligence approaches bring hope for accelerating the identification of molecules with the necessary properties for drug development. Drug-likeness assessment is crucial for the virtual screening of candidate drugs. However, traditional methods like Quantitative Estimation of Drug-likeness (QED) struggle to distinguish between drug and non-drug molecules accurately. Additionally, some deep learning-based binary classification models heavily rely on selecting training negative sets. To address these challenges, we introduce a novel unsupervised learning framework called DrugMetric, an innovative framework for quantitatively assessing drug-likeness based on the chemical space distance. DrugMetric blends the powerful learning ability of variational autoencoders with the discriminative ability of the Gaussian Mixture Model. This synergy enables DrugMetric to identify significant differences in drug-likeness across different datasets effectively. Moreover, DrugMetric incorporates principles of ensemble learning to enhance its predictive capabilities. Upon testing over a variety of tasks and datasets, DrugMetric consistently showcases superior scoring and classification performance. It excels in quantifying drug-likeness and accurately distinguishing candidate drugs from non-drugs, surpassing traditional methods including QED. This work highlights DrugMetric as a practical tool for drug-likeness scoring, facilitating the acceleration of virtual drug screening, and has potential applications in other biochemical fields.

Comparative analysis of models in predicting the effects of SNPs on TF-DNA binding using large-scale in vitro and in vivo data.

Non-coding variants associated with complex traits can alter the motifs of transcription factor (TF)-deoxyribonucleic acid binding. Although many computational models have been developed to predict the effects of non-coding variants on TF binding, their predictive power lacks systematic evaluation. Here we have evaluated 14 different models built on position weight matrices (PWMs), support vector machines, ordinary least squares and deep neural networks (DNNs), using large-scale in vitro (i.e. SNP-SELEX) and in vivo (i.e. allele-specific binding, ASB) TF binding data. Our results show that the accuracy of each model in predicting SNP effects in vitro significantly exceeds that achieved in vivo. For in vitro variant impact prediction, kmer/gkm-based machine learning methods (deltaSVM_HT-SELEX, QBiC-Pred) trained on in vitro datasets exhibit the best performance. For in vivo ASB variant prediction, DNN-based multitask models (DeepSEA, Sei, Enformer) trained on the ChIP-seq dataset exhibit relatively superior performance. Among the PWM-based methods, tRap demonstrates better performance in both in vitro and in vivo evaluations. In addition, we find that TF classes such as basic leucine zipper factors could be predicted more accurately, whereas those such as C2H2 zinc finger factors are predicted less accurately, aligning with the evolutionary conservation of these TF classes. We also underscore the significance of non-sequence factors such as cis-regulatory element type, TF expression, interactions and post-translational modifications in influencing the in vivo predictive performance of TFs. Our research provides valuable insights into selecting prioritization methods for non-coding variants and further optimizing such models.

LncRNA DANA1 promotes drought tolerance and histone deacetylation of drought responsive genes in Arabidopsis.

Although many long noncoding RNAs have been discovered in plants, little is known about their biological function and mode of action. Here we show that the drought-induced long intergenic noncoding RNA DANA1 interacts with the L1p/L10e family member protein DANA1-INTERACTING PROTEIN 1 (DIP1) in the cell nucleus of Arabidopsis, and both DANA1 and DIP1 promote plant drought resistance. DANA1 and DIP1 increase histone deacetylase HDA9 binding to the CYP707A1 and CYP707A2 loci. DIP1 further interacts with PWWP3, a member of the PEAT complex that associates with HDA9 and has histone deacetylase activity. Mutation of DANA1 enhances CYP707A1 and CYP707A2 acetylation and expression resulting in impaired drought tolerance, in agreement with dip1 and pwwp3 mutant phenotypes. Our results demonstrate that DANA1 is a positive regulator of drought response and that DANA1 works jointly with the novel chromatin-related factor DIP1 on epigenetic reprogramming of the plant transcriptome during the response to drought.

Entanglement of two quantum memories via fibres over dozens of kilometres.

A quantum internet that connects remote quantum processors1,2 should enable a number of revolutionary applications such as distributed quantum computing. Its realization will rely on entanglement of remote quantum memories over long distances. Despite enormous progress3-12, at present the maximal physical separation achieved between two nodes is 1.3 kilometres10, and challenges for longer distances remain. Here we demonstrate entanglement of two atomic ensembles in one laboratory via photon transmission through city-scale optical fibres. The atomic ensembles function as quantum memories that store quantum states. We use cavity enhancement to efficiently create atom-photon entanglement13-15 and we use quantum frequency conversion16 to shift the atomic wavelength to telecommunications wavelengths. We realize entanglement over 22 kilometres of field-deployed fibres via two-photon interference17,18 and entanglement over 50 kilometres of coiled fibres via single-photon interference19. Our experiment could be extended to nodes physically separated by similar distances, which would thus form a functional segment of the atomic quantum network, paving the way towards establishing atomic entanglement over many nodes and over much longer distances.

PROSCA: an online platform for humanized scaffold mining facilitating rational protein engineering.

Protein scaffolds with small size, high stability and low immunogenicity show important applications in the field of protein engineering and design. However, no relevant computational platform has been reported yet to mining such scaffolds with the desired properties from massive protein structures in human body. Here, we developed PROSCA, a structure-based online platform dedicated to explore the space of the entire human proteome, and to discovery new privileged protein scaffolds with potential engineering value that have never been noticed. PROSCA accepts structure of protein as an input, which can be subsequently aligned with a certain class of protein structures (e.g. the human proteome either from experientially resolved or AlphaFold2 predicted structures, and the human proteins belonging to specific families or domains), and outputs humanized protein scaffolds which are structurally similar with the input protein as well as other related important information such as families, sequences, structures and expression level in human tissues. Through PROSCA, the user can also get excellent experience in visualizations of protein structures and expression overviews, and download the figures and tables of results which can be customized according to the user's needs. Along with the advanced protein engineering and selection technologies, PROSCA will facilitate the rational design of new functional proteins with privileged scaffolds. PROSCA is freely available at https://idrblab.org/prosca/.

A small-molecule drug inhibits autophagy gene expression through the central regulator TFEB.

Autophagy supports the fast growth of established tumors and promotes tumor resistance to multiple treatments. Inhibition of autophagy is a promising strategy for tumor therapy. However, effective autophagy inhibitors suitable for clinical use are currently lacking. There is a high demand for identifying novel autophagy drug targets and potent inhibitors with drug-like properties. The transcription factor EB (TFEB) is the central transcriptional regulator of autophagy, which promotes lysosomal biogenesis and functions and systematically up-regulates autophagy. Despite extensive evidence that TFEB is a promising target for autophagy inhibition, no small molecular TFEB inhibitors were reported. Here, we show that an United States Food and Drug Administration (FDA)-approved drug Eltrombopag (EO) binds to the basic helix-loop-helix-leucine zipper domain of TFEB, specifically the bottom surface of helix-loop-helix to clash with DNA recognition, and disrupts TFEB-DNA interaction in vitro and in cellular context. EO selectively inhibits TFEB's transcriptional activity at the genomic scale according to RNA sequencing analyses, blocks autophagy in a dose-dependent manner, and increases the sensitivity of glioblastoma to temozolomide in vivo. Together, this work reveals that TFEB is targetable and presents the first direct TFEB inhibitor EO, a drug compound with great potential to benefit a wide range of cancer therapies by inhibiting autophagy.

Decoding the potential role of regulatory T cells in sepsis-induced immunosuppression.

Sepsis, a multiorgan dysfunction with high incidence and mortality, is caused by an imbalanced host-to-infection immune response. Organ-support therapy improves the early survival rate of sepsis patients. In the long term, those who survive the "cytokine storm" and its secondary damage usually show higher susceptibility to secondary infections and sepsis-induced immunosuppression, in which regulatory T cells (Tregs) are evidenced to play an essential role. However, the potential role and mechanism of Tregs in sepsis-induced immunosuppression remains elusive. In this review, we elucidate the role of different functional subpopulations of Tregs during sepsis and then review the mechanism of sepsis-induced immunosuppression from the aspects of regulatory characteristics, epigenetic modification, and immunometabolism of Tregs. Thoroughly understanding how Tregs impact the immune system during sepsis may shed light on preclinical research and help improve the translational value of sepsis immunotherapy.

PPP4C facilitates homologous recombination DNA repair by dephosphorylating PLK1 during early embryo development.

Mammalian early embryo cells have complex DNA repair mechanisms to maintain genomic integrity, and homologous recombination (HR) plays the main role in response to double-strand DNA breaks (DSBs) in these cells. Polo-like kinase 1 (PLK1) participates in the HR process and its overexpression has been shown to occur in a variety of human cancers. Nevertheless, the regulatory mechanism of PLK1 remains poorly understood, especially during the S and G2 phase. Here, we show that protein phosphatase 4 catalytic subunit (PPP4C) deletion causes severe female subfertility due to accumulation of DNA damage in oocytes and early embryos. PPP4C dephosphorylated PLK1 at the S137 site, negatively regulating its activity in the DSB response in early embryonic cells. Depletion of PPP4C induced sustained activity of PLK1 when cells exhibited DNA lesions that inhibited CHK2 and upregulated the activation of CDK1, resulting in inefficient loading of the essential HR factor RAD51. On the other hand, when inhibiting PLK1 in the S phase, DNA end resection was restricted. These results demonstrate that PPP4C orchestrates the switch between high-PLK1 and low-PLK1 periods, which couple the checkpoint to HR.

BatmanNet: bi-branch masked graph transformer autoencoder for molecular representation.

Although substantial efforts have been made using graph neural networks (GNNs) for artificial intelligence (AI)-driven drug discovery, effective molecular representation learning remains an open challenge, especially in the case of insufficient labeled molecules. Recent studies suggest that big GNN models pre-trained by self-supervised learning on unlabeled datasets enable better transfer performance in downstream molecular property prediction tasks. However, the approaches in these studies require multiple complex self-supervised tasks and large-scale datasets , which are time-consuming, computationally expensive and difficult to pre-train end-to-end. Here, we design a simple yet effective self-supervised strategy to simultaneously learn local and global information about molecules, and further propose a novel bi-branch masked graph transformer autoencoder (BatmanNet) to learn molecular representations. BatmanNet features two tailored complementary and asymmetric graph autoencoders to reconstruct the missing nodes and edges, respectively, from a masked molecular graph. With this design, BatmanNet can effectively capture the underlying structure and semantic information of molecules, thus improving the performance of molecular representation. BatmanNet achieves state-of-the-art results for multiple drug discovery tasks, including molecular properties prediction, drug-drug interaction and drug-target interaction, on 13 benchmark datasets, demonstrating its great potential and superiority in molecular representation learning.

Suppression of N-Glycosylation of Zinc Finger Protein 471 Affects Proliferation, Invasion, and Docetaxel Sensitivity of Tongue Squamous Cell Carcinoma via Regulation of c-Myc.

Zinc finger protein 471 (ZNF471) is a member of the Krüppel-related domain zinc finger protein family, and has recently attracted attention because of its anti-cancer effects. N-glycosylation regulates expression and functions of the protein. This study aimed to investigate the effects of ZNF471 N-glycosylation on the proliferation, invasion, and docetaxel sensitivity of tongue squamous cell carcinoma (TSCC). It analyzed the expression, function, and prognostic significance of ZNF471 in TSCC using bioinformatics techniques such as gene differential expression analysis, univariate Cox regression analysis, functional enrichment analysis, and gene set enrichment analysis. Using site-specific mutagenesis, this study generated three mutant sites for ZNF471 N-glycosylation to determine the effect of N-glycosylation on ZNF471 protein levels and function. Quantitative real-time PCR, Western blot analysis, and immunohistochemistry tests confirmed the down-regulation of ZNF471 expression in TSCC. Low expression of ZNF471 is associated with poor prognosis of patients with TSCC. Overexpression of ZNF471 in vitro retarded the proliferation of TSCC cells and suppressed cell invasion and migration ability. Asparagine 358 was identified as a N-glycosylation site of ZNF471. Suppressing N-glycosylation of ZNF471 enhanced the protein stability and promoted the translocation of protein to the cell nucleus. ZNF471 binding to c-Myc gene promoter suppressed oncogene c-Myc expression, thereby playing the anti-cancer effect and enhancing TSCC sensitivity to docetaxel. In all, N-glycosylation of ZNF471 affects the proliferation, invasion, and docetaxel sensitivity of TSCC via regulation of c-Myc.