Dissecting embryonic and extraembryonic lineage crosstalk with stem cell co-culture.

Embryogenesis necessitates harmonious coordination between embryonic and extraembryonic tissues. Although stem cells of both embryonic and extraembryonic origins have been generated, they are grown in different culture conditions. In this study, utilizing a unified culture condition that activates the FGF, TGF-ß, and WNT pathways, we have successfully derived embryonic stem cells (FTW-ESCs), extraembryonic endoderm stem cells (FTW-XENs), and trophoblast stem cells (FTW-TSCs) from the three foundational tissues of mouse and cynomolgus monkey (Macaca fascicularis) blastocysts. This approach facilitates the co-culture of embryonic and extraembryonic stem cells, revealing a growth inhibition effect exerted by extraembryonic endoderm cells on pluripotent cells, partially through extracellular matrix signaling. Additionally, our cross-species analysis identified both shared and unique transcription factors and pathways regulating FTW-XENs. The embryonic and extraembryonic stem cell co-culture strategy offers promising avenues for developing more faithful embryo models and devising more developmentally pertinent differentiation protocols.

Bursting translation on single mRNAs in live cells.

Stochasticity has emerged as a mechanism of gene regulation. Much of this so-called "noise" has been attributed to bursting transcription. Although bursting transcription has been studied extensively, the role of stochasticity in translation has not been fully investigated due to the lack of enabling imaging technology. In this study, we developed techniques to track single mRNAs and their translation in live cells for hours, allowing the measurement of previously uncharacterized translation dynamics. We applied genetic and pharmacological perturbations to control translation kinetics and found that, like transcription, translation is not a constitutive process but instead cycles between inactive and active states, or "bursts." However, unlike transcription, which is largely frequency-modulated, complex structures in the 5'-untranslated region alter burst amplitudes. Bursting frequency can be controlled through cap-proximal sequences and trans-acting factors such as eIF4F. We coupled single-molecule imaging with stochastic modeling to quantitatively determine the kinetic parameters of translational bursting.

Creation of memory-memory entanglement in a metropolitan quantum network.

Towards realizing the future quantum internet1,2, a pivotal milestone entails the transition from two-node proof-of-principle experiments conducted in laboratories to comprehensive multi-node set-ups on large scales. Here we report the creation of memory-memory entanglement in a multi-node quantum network over a metropolitan area. We use three independent memory nodes, each of which is equipped with an atomic ensemble quantum memory3 that has telecom conversion, together with a photonic server where detection of a single photon heralds the success of entanglement generation. The memory nodes are maximally separated apart for 12.5 kilometres. We actively stabilize the phase variance owing to fibre links and control lasers. We demonstrate concurrent entanglement generation between any two memory nodes. The memory lifetime is longer than the round-trip communication time. Our work provides a metropolitan-scale testbed for the evaluation and exploration of multi-node quantum network protocols and starts a stage of quantum internet research.

Niche Tet maintains germline stem cells independently of dioxygenase activity.

Ten-eleven translocation (TET) proteins are dioxygenases that convert 5-methylcytosine (5mC) into 5-hydroxylmethylcytosine (5hmC) in DNA and RNA. However, their involvement in adult stem cell regulation remains unclear. Here, we identify a novel enzymatic activity-independent function of Tet in the Drosophila germline stem cell (GSC) niche. Tet activates the expression of Dpp, the fly homologue of BMP, in the ovary stem cell niche, thereby controlling GSC self-renewal. Depletion of Tet disrupts Dpp production, leading to premature GSC loss. Strikingly, both wild-type and enzyme-dead mutant Tet proteins rescue defective BMP signaling and GSC loss when expressed in the niche. Mechanistically, Tet interacts directly with Bap55 and Stat92E, facilitating recruitment of the Polybromo Brahma associated protein (PBAP) complex to the dpp enhancer and activating Dpp expression. Furthermore, human TET3 can effectively substitute for Drosophila Tet in the niche to support BMP signaling and GSC self-renewal. Our findings highlight a conserved novel catalytic activity-independent role of Tet as a scaffold protein in supporting niche signaling for adult stem cell self-renewal.

Ectosomal PKM2 Promotes HCC by Inducing Macrophage Differentiation and Remodeling the Tumor Microenvironment.

Tumor-derived extracellular vesicles are important mediators of cell-to-cell communication during tumorigenesis. Here, we demonstrated that hepatocellular carcinoma (HCC)-derived ectosomes remodel the tumor microenvironment to facilitate HCC progression in an ectosomal PKM2-dependent manner. HCC-derived ectosomal PKM2 induced not only metabolic reprogramming in monocytes but also STAT3 phosphorylation in the nucleus to upregulate differentiation-associated transcription factors, leading to monocyte-to-macrophage differentiation and tumor microenvironment remodeling. In HCC cells, sumoylation of PKM2 induced its plasma membrane targeting and subsequent ectosomal excretion via interactions with ARRDC1. The PKM2-ARRDC1 association in HCC was reinforced by macrophage-secreted cytokines/chemokines in a CCL1-CCR8 axis-dependent manner, further facilitating PKM2 excretion from HCC cells to form a feedforward regulatory loop for tumorigenesis. In the clinic, ectosomal PKM2 was clearly detected in the plasma of HCC patients. This study highlights a mechanism by which ectosomal PKM2 remodels the tumor microenvironment and reveals ectosomal PKM2 as a potential diagnostic marker for HCC.

The Endotoxin Delivery Protein HMGB1 Mediates Caspase-11-Dependent Lethality in Sepsis.

Caspase-11, a cytosolic endotoxin (lipopolysaccharide: LPS) receptor, mediates pyroptosis, a lytic form of cell death. Caspase-11-dependent pyroptosis mediates lethality in endotoxemia, but it is unclear how LPS is delivered into the cytosol for the activation of caspase-11. Here we discovered that hepatocyte-released high mobility group box 1 (HMGB1) was required for caspase-11-dependent pyroptosis and lethality in endotoxemia and bacterial sepsis. Mechanistically, hepatocyte-released HMGB1 bound LPS and targeted its internalization into the lysosomes of macrophages and endothelial cells via the receptor for advanced glycation end-products (RAGE). Subsequently, HMGB1 permeabilized the phospholipid bilayer in the acidic environment of lysosomes. This resulted in LPS leakage into the cytosol and caspase-11 activation. Depletion of hepatocyte HMGB1, inhibition of hepatocyte HMGB1 release, neutralizing extracellular HMGB1, or RAGE deficiency prevented caspase-11-dependent pyroptosis and death in endotoxemia and bacterial sepsis. These findings indicate that HMGB1 interacts with LPS to mediate caspase-11-dependent pyroptosis in lethal sepsis.

The LIKE SEX FOUR 1-malate dehydrogenase complex functions as a scaffold to recruit ß-amylase to promote starch degradation.

In plant leaves, starch is composed of glucan polymers that accumulate in chloroplasts as the products of photosynthesis during the day; starch is mobilized at night to continuously provide sugars to sustain plant growth and development. Efficient starch degradation requires the involvement of several enzymes, including ß-amylase and glucan phosphatase. However, how these enzymes cooperate remains largely unclear. Here, we show that the glucan phosphatase LIKE SEX FOUR 1 (LSF1) interacts with plastid NAD-dependent malate dehydrogenase (MDH) to recruit ß-amylase (BAM1), thus reconstituting the BAM1-LSF1-MDH complex. The starch hydrolysis activity of BAM1 drastically increased in the presence of LSF1-MDH in vitro. We determined the structure of the BAM1-LSF1-MDH complex by a combination of cryo-electron microscopy, crosslinking mass spectrometry, and molecular docking. The starch-binding domain of the dual-specificity phosphatase and carbohydrate-binding module of LSF1 was docked in proximity to BAM1, thus facilitating BAM1 access to and hydrolysis of the polyglucans of starch, thus revealing the molecular mechanism by which the LSF1-MDH complex improves the starch degradation activity of BAM1. Moreover, LSF1 is phosphatase inactive, and the enzymatic activity of MDH was dispensable for starch degradation, suggesting nonenzymatic scaffold functions for LSF1-MDH in starch degradation. These findings provide important insights into the precise regulation of starch degradation.

Using heparan sulfate octadecasaccharide (18-mer) as a multi-target agent to protect against sepsis.

Sepsis is a lethal syndrome manifested by an unregulated, overwhelming inflammation from the host in response to infection. Here, we exploit the use of a synthetic heparan sulfate octadecasaccharide (18-mer) to protect against sepsis. The 18-mer not only inhibits the pro-inflammatory activity of extracellular histone H3 and high mobility group box 1 (HMGB1), but also elicits the anti-inflammatory effect from apolipoprotein A-I (ApoA-I). We demonstrate that the 18-mer protects against sepsis-related injury and improves survival in cecal ligation and puncture mice and reduces inflammation in an endotoxemia mouse model. The 18-mer neutralizes the cytotoxic histone-3 (H3) through direct interaction with the protein. Furthermore, the 18-mer enlists the actions of ApoA-I to dissociate the complex of HMGB1 and lipopolysaccharide, a toxic complex contributing to cell death and tissue damage in sepsis. Our study provides strong evidence that the 18-mer mitigates inflammatory damage in sepsis by targeting numerous mediators, setting it apart from other potential therapies with a single target.

Single-nucleus transcriptomic survey of cell diversity and functional maturation in postnatal mammalian hearts.

A fundamental challenge in understanding cardiac biology and disease is that the remarkable heterogeneity in cell type composition and functional states have not been well characterized at single-cell resolution in maturing and diseased mammalian hearts. Massively parallel single-nucleus RNA sequencing (snRNA-seq) has emerged as a powerful tool to address these questions by interrogating the transcriptome of tens of thousands of nuclei isolated from fresh or frozen tissues. snRNA-seq overcomes the technical challenge of isolating intact single cells from complex tissues, including the maturing mammalian hearts; reduces biased recovery of easily dissociated cell types; and minimizes aberrant gene expression during the whole-cell dissociation. Here we applied sNucDrop-seq, a droplet microfluidics-based massively parallel snRNA-seq method, to investigate the transcriptional landscape of postnatal maturing mouse hearts in both healthy and disease states. By profiling the transcriptome of nearly 20,000 nuclei, we identified major and rare cardiac cell types and revealed significant heterogeneity of cardiomyocytes, fibroblasts, and endothelial cells in postnatal developing hearts. When applied to a mouse model of pediatric mitochondrial cardiomyopathy, we uncovered profound cell type-specific modifications of the cardiac transcriptional landscape at single-nucleus resolution, including changes of subtype composition, maturation states, and functional remodeling of each cell type. Furthermore, we employed sNucDrop-seq to decipher the cardiac cell type-specific gene regulatory network (GRN) of GDF15, a heart-derived hormone and clinically important diagnostic biomarker of heart disease. Together, our results present a rich resource for studying cardiac biology and provide new insights into heart disease using an approach broadly applicable to many fields of biomedicine.

Low molecular weight heparin promotes the PPAR pathway by protecting the glycocalyx of cells to delay the progression of diabetic nephropathy.

Diabetic nephropathy (DN) is one of the most important comorbidities for diabetic patients, which is the main factor leading to end-stage renal disease. Heparin analogs can delay the progression of DN, but the mechanism is not fully understood. In this study, we found that low molecular weight heparin therapy significantly upregulated some downstream proteins of the peroxisome proliferator-activated receptor (PPAR) signaling pathway by label-free quantification of the mouse kidney proteome. Through cell model verification, low molecular weight heparin can protect the heparan sulfate of renal tubular epithelial cells from being degraded by heparanase that is highly expressed in a high-glucose environment, enhance the endocytic recruitment of fatty acid-binding protein 1, a coactivator of the PPAR pathway, and then regulate the activation level of intracellular PPAR. In addition, we have elucidated for the first time the molecular mechanism of heparan sulfate and fatty acid-binding protein 1 interaction. These findings provide new insights into understanding the role of heparin in the pathogenesis of DN and developing corresponding treatments.

LPAD: using network construction and label propagation to detect topologically associating domains from Hi-C data.

With the development of chromosome conformation capture technique, the study of spatial conformation of a genome based on Hi-C technique has made a quantum leap. Previous studies reveal that genomes are folded into hierarchy of three-dimensional (3D) structures associated with topologically associating domains (TADs), and detecting TAD boundaries is of great significance in the chromosome-level analysis of 3D genome architecture. In this paper, we propose a novel TAD identification method, LPAD, which first extracts node correlations from global interactions of chromosomes based on the random walk with restart and then builds an undirected graph from Hi-C contact matrix. Next, LPAD designs a label propagation-based approach to discover communities and generates TADs. Experimental results verify the effectiveness and quality of TAD detections compared with existing methods. Furthermore, experimental evaluation of chromatin immunoprecipitation sequencing data shows that LPAD performs high enrichment of histone modifications remarkably nearby the TAD boundaries, and these results demonstrate LPAD's advantages on TAD identification accuracy.

HiBrowser: an interactive and dynamic browser for synchronous Hi-C data visualization.

With the development of chromosome conformation capture technology, the genome-wide investigation of higher-order chromatin structure by using high-throughput chromatin conformation capture (Hi-C) technology is emerging as an important component for understanding the mechanism of gene regulation. Considering genetic and epigenetic differences are typically used to explore the pathological reasons on the chromosome and gene level, visualizing multi-omics data and performing an intuitive analysis by using an interactive browser become a powerful and welcomed way. In this paper, we develop an effective sequence and chromatin interaction data display browser called HiBrowser for visualizing and analyzing Hi-C data and their associated genetic and epigenetic annotations. The advantages of HiBrowser are flexible multi-omics navigation, novel multidimensional synchronization comparisons and dynamic interaction system. In particular, HiBrowser first provides an out of the box web service and allows flexible and dynamic reconstruction of custom annotation tracks on demand during running. In order to conveniently and intuitively analyze the similarities and differences among multiple samples, such as visual comparisons of normal and tumor tissue samples, and pan genomes of multiple (consanguineous) species, HiBrowser develops a clone mode to synchronously display the genome coordinate positions or the same regions of multiple samples on the same page of visualization. HiBrowser also supports a pluralistic and precise search on correlation data of distal cis-regulatory elements and navigation to any region on Hi-C heatmap of interest according to the searching results. HiBrowser is a no-build tool, and could be easily deployed in local server. The source code is available at https://github.com/lyotvincent/HiBrowser.

Multi-view spectral clustering with latent representation learning for applications on multi-omics cancer subtyping.

Driven by multi-omics data, some multi-view clustering algorithms have been successfully applied to cancer subtypes prediction, aiming to identify subtypes with biometric differences in the same cancer, thereby improving the clinical prognosis of patients and designing personalized treatment plan. Due to the fact that the number of patients in omics data is much smaller than the number of genes, multi-view spectral clustering based on similarity learning has been widely developed. However, these algorithms still suffer some problems, such as over-reliance on the quality of pre-defined similarity matrices for clustering results, inability to reasonably handle noise and redundant information in high-dimensional omics data, ignoring complementary information between omics data, etc. This paper proposes multi-view spectral clustering with latent representation learning (MSCLRL) method to alleviate the above problems. First, MSCLRL generates a corresponding low-dimensional latent representation for each omics data, which can effectively retain the unique information of each omics and improve the robustness and accuracy of the similarity matrix. Second, the obtained latent representations are assigned appropriate weights by MSCLRL, and global similarity learning is performed to generate an integrated similarity matrix. Third, the integrated similarity matrix is used to feed back and update the low-dimensional representation of each omics. Finally, the final integrated similarity matrix is used for clustering. In 10 benchmark multi-omics datasets and 2 separate cancer case studies, the experiments confirmed that the proposed method obtained statistically and biologically meaningful cancer subtypes.

PhiHER2: phenotype-informed weakly supervised model for HER2 status prediction from pathological images.

MOTIVATION: Human epidermal growth factor receptor 2 (HER2) status identification enables physicians to assess the prognosis risk and determine the treatment schedule for patients. In clinical practice, pathological slides serve as the gold standard, offering morphological information on cellular structure and tumoral regions. Computational analysis of pathological images has the potential to discover morphological patterns associated with HER2 molecular targets and achieve precise status prediction. However, pathological images are typically equipped with high-resolution attributes, and HER2 expression in breast cancer (BC) images often manifests the intratumoral heterogeneity. RESULTS: We present a phenotype-informed weakly supervised multiple instance learning architecture (PhiHER2) for the prediction of the HER2 status from pathological images of BC. Specifically, a hierarchical prototype clustering module is designed to identify representative phenotypes across whole slide images. These phenotype embeddings are then integrated into a cross-attention module, enhancing feature interaction and aggregation on instances. This yields a phenotype-based feature space that leverages the intratumoral morphological heterogeneity for HER2 status prediction. Extensive results demonstrate that PhiHER2 captures a better WSI-level representation by the typical phenotype guidance and significantly outperforms existing methods on real-world datasets. Additionally, interpretability analyses of both phenotypes and WSIs provide explicit insights into the heterogeneity of morphological patterns associated with molecular HER2 status. AVAILABILITY AND IMPLEMENTATION: Our model is available at https://github.com/lyotvincent/PhiHER2.

Computational discovery of fast interstitial oxygen conductors.

New highly oxygen-active materials may enhance many energy-related technologies by enabling efficient oxygen-ion transport at lower temperatures, for example, below ~400 °C. Interstitial oxygen conductors have the potential to realize such performance but have received far less attention than vacancy-mediated conductors. Here we combine physically motivated structure and property descriptors, ab initio simulations and experiments to demonstrate an approach to discover new fast interstitial oxygen conductors. Multiple new families were found, which adopt completely different structures from known oxygen conductors. From these families, we synthesized and studied oxygen kinetics in La4Mn5Si4O22+δ, a representative member of the perrierite/chevkinite family. We found that La4Mn5Si4O22+δ has higher oxygen-ion conductivity than the widely used yttria-stabilized ZrO2, and among the highest surface oxygen exchange rates at the intermediate temperature of known materials. The fast oxygen kinetics is the result of simultaneously active interstitial and interstitialcy diffusion pathways. We propose that the essential features for forming an effective interstitial oxygen conductor are the availability of electrons and structural flexibility, enabling a sufficient accessible volume. This work provides a powerful approach for understanding and discovering new interstitial oxygen conductors.

AGC kinases OXI1 and AGC2-2 regulate camalexin secretion and disease resistance by phosphorylating transporter PDR6.

Plant transporters regulating the distribution of secondary metabolites play critical roles in defending against pathogens, insects, and interacting with beneficial microbes. The phosphorylation of these transporters can alter their activity, stability, and intracellular protein trafficking. However, the regulatory mechanism underlying this modification remains elusive. In this study, we discovered two orthologs of mammalian PKA, PKG, and PKC (AGC) kinases, oxidative signal-inducible 1 (OXI1) and its closest homologue, AGC subclass 2 member 2 (AGC2-2; 75% amino acid sequence identity with OXI1), associated with the extracellular secretion of camalexin and Arabidopsis (Arabidopsis thaliana) resistance to Pseudomonas syringae, and Botrytis cinerea. These kinases can undergo in vitro kinase reactions with three pleiotropic drug resistance (PDR) transporters: PDR6, PDR8, and PDR12. Moreover, our investigation confirmed PDR6 interaction with OXI1 and AGC2-2. By performing LC-MS/MS and parallel reaction monitoring, we identified the phosphorylation sites on PDR6 targeted by these kinases. Notably, chitin-induced PDR6 phosphorylation at specific residues, namely S31, S33, S827, and T832. Additional insights emerged by expressing dephosphorylated PDR6 variants in a pdr6 mutant background, revealing that the target residues S31, S33, and S827 promote PDR6 efflux activity, while T832 potentially contributes to PDR6 stability within the plasma membrane. The findings of this study elucidate partial mechanisms involved in the activity regulation of PDR-type transporters, providing valuable insights for their potential application in future plant breeding endeavors.

CRISPR-M: Predicting sgRNA off-target effect using a multi-view deep learning network.

Using the CRISPR-Cas9 system to perform base substitutions at the target site is a typical technique for genome editing with the potential for applications in gene therapy and agricultural productivity. When the CRISPR-Cas9 system uses guide RNA to direct the Cas9 endonuclease to the target site, it may misdirect it to a potential off-target site, resulting in an unintended genome editing. Although several computational methods have been proposed to predict off-target effects, there is still room for improvement in the off-target effect prediction capability. In this paper, we present an effective approach called CRISPR-M with a new encoding scheme and a novel multi-view deep learning model to predict the sgRNA off-target effects for target sites containing indels and mismatches. CRISPR-M takes advantage of convolutional neural networks and bidirectional long short-term memory recurrent neural networks to construct a three-branch network towards multi-views. Compared with existing methods, CRISPR-M demonstrates significant performance advantages running on real-world datasets. Furthermore, experimental analysis of CRISPR-M under multiple metrics reveals its capability to extract features and validates its superiority on sgRNA off-target effect predictions.

DupScan: predicting and visualizing vertebrate genome duplication database.

Duplicated genes prevail in vertebrates and are important in the acquisition of new genes and novelties. Whole genome duplication (WGD) is one of the sources of duplicated genes. It can provide raw materials for natural selection by increasing the flexibility and complexity of the genome. WGDs are the driving force for the evolution of vertebrates and contribute greatly to their species diversity, especially in fish species with complicated WGD patterns. Here, we constructed the DupScan database (https://dupscan.sysumeg.com/) by integrating 106 chromosomal-level genomes, which can analyze and visualize synteny at both the gene and genome scales, visualize the Ka, Ks, and 4DTV values, and browse genomes. DupScan was used to perform functional adaptation for the intricate WGD investigation based on synteny matching. DupScan supports the analysis of five WGD rounds (R): VGD2 (vertebrate genome duplication 2), Ars3R (Acipenser-ruthenus-specific 3R), Pss3R (Polyodon-spathula-specific 3R), Ts3R (teleost-specific duplication 3R), Ss4R (salmonid-specific 4R), and Cs4R (carp-specific 4R). DupScan serves as one-stop analysis platform for synteny and WGD research in which users can analyze and predict synteny and WGD patterns across 106 species of whole genome sequences. This further aided us in elucidating genome evolutionary patterns across over 60,000 vertebrate species with synteny and WGD events.

Circumventing drug resistance in gastric cancer: A spatial multi-omics exploration of chemo and immuno-therapeutic response dynamics.

BACKGROUND: Gastric Cancer (GC) characteristically exhibits heterogeneous responses to treatment, particularly in relation to immuno plus chemo therapy, necessitating a precision medicine approach. This study is centered around delineating the cellular and molecular underpinnings of drug resistance in this context. METHODS: We undertook a comprehensive multi-omics exploration of postoperative tissues from GC patients undergoing the chemo and immuno-treatment regimen. Concurrently, an image deep learning model was developed to predict treatment responsiveness. RESULTS: Our initial findings associate apical membrane cells with resistance to fluorouracil and oxaliplatin, critical constituents of the therapy. Further investigation into this cell population shed light on substantial interactions with resident macrophages, underscoring the role of intercellular communication in shaping treatment resistance. Subsequent ligand-receptor analysis unveiled specific molecular dialogues, most notably TGFB1-HSPB1 and LTF-S100A14, offering insights into potential signaling pathways implicated in resistance. Our SVM model, incorporating these multi-omics and spatial data, demonstrated significant predictive power, with AUC values of 0.93 and 0.84 in the exploration and validation cohorts respectively. Hence, our results underscore the utility of multi-omics and spatial data in modeling treatment response. CONCLUSION: Our integrative approach, amalgamating mIHC assays, feature extraction, and machine learning, successfully unraveled the complex cellular interplay underlying drug resistance. This robust predictive model may serve as a valuable tool for personalizing therapeutic strategies and enhancing treatment outcomes in gastric cancer.

Ectopic expression of meiotic cohesin generates chromosome instability in cancer cell line.

Many tumors express meiotic genes that could potentially drive somatic chromosome instability. While germline cohesin subunits SMC1B, STAG3, and REC8 are widely expressed in many cancers, messenger RNA and protein for RAD21L subunit are expressed at very low levels. To elucidate the potential of meiotic cohesins to contribute to genome instability, their expression was investigated in human cell lines, predominately in DLD-1. While the induction of the REC8 complex resulted in a mild mitotic phenotype, the expression of the RAD21L complex produced an arrested but viable cell pool, thus providing a source of DNA damage, mitotic chromosome missegregation, sporadic polyteny, and altered gene expression. We also found that genomic binding profiles of ectopically expressed meiotic cohesin complexes were reminiscent of their corresponding specific binding patterns in testis. Furthermore, meiotic cohesins were found to localize to the same sites as BORIS/CTCFL, rather than CTCF sites normally associated with the somatic cohesin complex. These findings highlight the existence of a germline epigenomic memory that is conserved in cells that normally do not express meiotic genes. Our results reveal a mechanism of action by unduly expressed meiotic cohesins that potentially links them to aneuploidy and chromosomal mutations in affected cells.