Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays.

Spatially resolved transcriptomic technologies are promising tools to study complex biological processes such as mammalian embryogenesis. However, the imbalance between resolution, gene capture, and field of view of current methodologies precludes their systematic application to analyze relatively large and three-dimensional mid- and late-gestation embryos. Here, we combined DNA nanoball (DNB)-patterned arrays and in situ RNA capture to create spatial enhanced resolution omics-sequencing (Stereo-seq). We applied Stereo-seq to generate the mouse organogenesis spatiotemporal transcriptomic atlas (MOSTA), which maps with single-cell resolution and high sensitivity the kinetics and directionality of transcriptional variation during mouse organogenesis. We used this information to gain insight into the molecular basis of spatial cell heterogeneity and cell fate specification in developing tissues such as the dorsal midbrain. Our panoramic atlas will facilitate in-depth investigation of longstanding questions concerning normal and abnormal mammalian development.

A Glial K/Cl Transporter Controls Neuronal Receptive Ending Shape by Chloride Inhibition of an rGC.

Neurons receive input from the outside world or from other neurons through neuronal receptive endings (NREs). Glia envelop NREs to create specialized microenvironments; however, glial functions at these sites are poorly understood. Here, we report a molecular mechanism by which glia control NRE shape and associated animal behavior. The C. elegans AMsh glial cell ensheathes the NREs of 12 neurons, including the thermosensory neuron AFD. KCC-3, a K/Cl transporter, localizes specifically to a glial microdomain surrounding AFD receptive ending microvilli, where it regulates K(+) and Cl(-) levels. We find that Cl(-) ions function as direct inhibitors of an NRE-localized receptor-guanylyl-cyclase, GCY-8, which synthesizes cyclic guanosine monophosphate (cGMP). High cGMP mediates the effects of glial KCC-3 on AFD shape by antagonizing the actin regulator WSP-1/NWASP. Components of this pathway are broadly expressed throughout the nervous system, suggesting that ionic regulation of the NRE microenvironment may be a conserved mechanism by which glia control neuron shape and function.

Disruption of the endopeptidase ADAM10-Notch signaling axis leads to skin dysbiosis and innate lymphoid cell-mediated hair follicle destruction.

Hair follicles (HFs) function as hubs for stem cells, immune cells, and commensal microbes, which must be tightly regulated during homeostasis and transient inflammation. Here we found that transmembrane endopeptidase ADAM10 expression in upper HFs was crucial for regulating the skin microbiota and protecting HFs and their stem cell niche from inflammatory destruction. Ablation of the ADAM10-Notch signaling axis impaired the innate epithelial barrier and enabled Corynebacterium species to predominate the microbiome. Dysbiosis triggered group 2 innate lymphoid cell-mediated inflammation in an interleukin-7 (IL-7) receptor-, S1P receptor 1-, and CCR6-dependent manner, leading to pyroptotic cell death of HFs and irreversible alopecia. Double-stranded RNA-induced ablation models indicated that the ADAM10-Notch signaling axis bolsters epithelial innate immunity by promoting ß-defensin-6 expression downstream of type I interferon responses. Thus, ADAM10-Notch signaling axis-mediated regulation of host-microbial symbiosis crucially protects HFs from inflammatory destruction, which has implications for strategies to sustain tissue integrity during chronic inflammation.

Harnessing deep learning for population genetic inference.

In population genetics, the emergence of large-scale genomic data for various species and populations has provided new opportunities to understand the evolutionary forces that drive genetic diversity using statistical inference. However, the era of population genomics presents new challenges in analysing the massive amounts of genomes and variants. Deep learning has demonstrated state-of-the-art performance for numerous applications involving large-scale data. Recently, deep learning approaches have gained popularity in population genetics; facilitated by the advent of massive genomic data sets, powerful computational hardware and complex deep learning architectures, they have been used to identify population structure, infer demographic history and investigate natural selection. Here, we introduce common deep learning architectures and provide comprehensive guidelines for implementing deep learning models for population genetic inference. We also discuss current challenges and future directions for applying deep learning in population genetics, focusing on efficiency, robustness and interpretability.

Operando studies reveal active Cu nanograins for CO2 electroreduction.

Carbon dioxide electroreduction facilitates the sustainable synthesis of fuels and chemicals1. Although Cu enables CO2-to-multicarbon product (C2+) conversion, the nature of the active sites under operating conditions remains elusive2. Importantly, identifying active sites of high-performance Cu nanocatalysts necessitates nanoscale, time-resolved operando techniques3-5. Here, we present a comprehensive investigation of the structural dynamics during the life cycle of Cu nanocatalysts. A 7 nm Cu nanoparticle ensemble evolves into metallic Cu nanograins during electrolysis before complete oxidation to single-crystal Cu2O nanocubes following post-electrolysis air exposure. Operando analytical and four-dimensional electrochemical liquid-cell scanning transmission electron microscopy shows the presence of metallic Cu nanograins under CO2 reduction conditions. Correlated high-energy-resolution time-resolved X-ray spectroscopy suggests that metallic Cu, rich in nanograin boundaries, supports undercoordinated active sites for C-C coupling. Quantitative structure-activity correlation shows that a higher fraction of metallic Cu nanograins leads to higher C2+ selectivity. A 7 nm Cu nanoparticle ensemble, with a unity fraction of active Cu nanograins, exhibits sixfold higher C2+ selectivity than the 18 nm counterpart with one-third of active Cu nanograins. The correlation of multimodal operando techniques serves as a powerful platform to advance our fundamental understanding of the complex structural evolution of nanocatalysts under electrochemical conditions.

Structural Basis of the Activation of Heterotrimeric Gs-Protein by Isoproterenol-Bound ß1-Adrenergic Receptor.

Cardiac disease remains the leading cause of morbidity and mortality worldwide. The ß1-adrenergic receptor (ß1-AR) is a major regulator of cardiac functions and is downregulated in the majority of heart failure cases. A key physiological process is the activation of heterotrimeric G-protein Gs by ß1-ARs, leading to increased heart rate and contractility. Here, we use cryo-electron microscopy and functional studies to investigate the molecular mechanism by which ß1-AR activates Gs. We find that the tilting of α5-helix breaks a hydrogen bond between the sidechain of His373 in the C-terminal α5-helix and the backbone carbonyl of Arg38 in the N-terminal αN-helix of Gαs. Together with the disruption of another interacting network involving Gln59 in the α1-helix, Ala352 in the ß6-α5 loop, and Thr355 in the α5-helix, these conformational changes might lead to the deformation of the GDP-binding pocket. Our data provide molecular insights into the activation of G-proteins by G-protein-coupled receptors.

Embedding electronic perpetual motion into single-atom catalysts for persistent Fenton-like reactions.

In our quest to leverage the capabilities of the emerging single-atom catalysts (SACs) for wastewater purification, we confronted fundamental challenges related to electron scarcity and instability. Through meticulous theoretical calculations, we identified optimal placements for nitrogen vacancies (Nv) and iron (Fe) single-atom sites, uncovering a dual-site approach that significantly amplified visible-light absorption and charge transfer dynamics. Informed by these computational insights, we cleverly integrated Nv into the catalyst design to boost electron density around iron atoms, yielding a potent and flexible photoactivator for benign peracetic acid. This exceptional catalyst exhibited remarkable stability and effectively degraded various organic contaminants over 20 cycles with self-cleaning properties. Specifically, the Nv sites captured electrons, enabling their swift transfer to adjacent Fe sites under visible light irradiation. This mechanism accelerated the reduction of the formed "peracetic acid-catalyst" intermediate. Theoretical calculations were used to elucidate the synergistic interplay of dual mechanisms, illuminating increased adsorption and activation of reactive molecules. Furthermore, electron reduction pathways on the conduction band were elaborately explored, unveiling the production of reactive species that enhanced photocatalytic processes. A six-flux model and associated parameters were also applied to precisely optimize the photocatalytic process, providing invaluable insights for future photocatalyst design. Overall, this study offers a molecule-level insight into the rational design of robust SACs in a photo-Fenton-like system, with promising implications for wastewater treatment and other high-value applications.

Taking a color photo: A homozygous 25-bp deletion in Bace2 may cause brown-and-white coat color in giant pandas.

Brown-and-white giant pandas (hereafter brown pandas) are distinct coat color mutants found exclusively in the Qinling Mountains, Shaanxi, China. However, its genetic mechanism has remained unclear since their discovery in 1985. Here, we identified the genetic basis for this coat color variation using a combination of field ecological data, population genomic data, and a CRISPR-Cas9 knockout mouse model. We de novo assembled a long-read-based giant panda genome and resequenced the genomes of 35 giant pandas, including two brown pandas and two family trios associated with a brown panda. We identified a homozygous 25-bp deletion in the first exon of Bace2, a gene encoding amyloid precursor protein cleaving enzyme, as the most likely genetic basis for brown-and-white coat color. This deletion was further validated using PCR and Sanger sequencing of another 192 black giant pandas and CRISPR-Cas9 edited knockout mice. Our investigation revealed that this mutation reduced the number and size of melanosomes of the hairs in knockout mice and possibly in the brown panda, further leading to the hypopigmentation. These findings provide unique insights into the genetic basis of coat color variation in wild animals.

A dominant autoinflammatory disease caused by non-cleavable variants of RIPK1.

Activation of RIPK1 controls TNF-mediated apoptosis, necroptosis and inflammatory pathways1. Cleavage of human and mouse RIPK1 after residues D324 and D325, respectively, by caspase-8 separates the RIPK1 kinase domain from the intermediate and death domains. The D325A mutation in mouse RIPK1 leads to embryonic lethality during mouse development2,3. However, the functional importance of blocking caspase-8-mediated cleavage of RIPK1 on RIPK1 activation in humans is unknown. Here we identify two families with variants in RIPK1 (D324V and D324H) that lead to distinct symptoms of recurrent fevers and lymphadenopathy in an autosomal-dominant manner. Impaired cleavage of RIPK1 D324 variants by caspase-8 sensitized patients' peripheral blood mononuclear cells to RIPK1 activation, apoptosis and necroptosis induced by TNF. The patients showed strong RIPK1-dependent activation of inflammatory signalling pathways and overproduction of inflammatory cytokines and chemokines compared with unaffected controls. Furthermore, we show that expression of the RIPK1 mutants D325V or D325H in mouse embryonic fibroblasts confers not only increased sensitivity to RIPK1 activation-mediated apoptosis and necroptosis, but also induction of pro-inflammatory cytokines such as IL-6 and TNF. By contrast, patient-derived fibroblasts showed reduced expression of RIPK1 and downregulated production of reactive oxygen species, resulting in resistance to necroptosis and ferroptosis. Together, these data suggest that human non-cleavable RIPK1 variants promote activation of RIPK1, and lead to an autoinflammatory disease characterized by hypersensitivity to apoptosis and necroptosis and increased inflammatory response in peripheral blood mononuclear cells, as well as a compensatory mechanism to protect against several pro-death stimuli in fibroblasts.

TGF-ß1 impairs IgA class switch recombination and production in porcine Peyer's patches B cells.

Secretory IgA is crucial for preventing the invasion of entero-pathogens via intestinal mucosa. While it is well-established that Transforming growth factor ß1 (TGF-ß1) regulates IgA production in human and mouse B cells, our previous investigation revealed different functions of TGF-ß1 in IgA generation in pigs compared with humans and mice, with the underlying mechanism remaining elusive. In this study, IgM+ B cells from porcine Peyer's patches (PPs) were isolated and stimulated with recombinant porcine TGF-ß1 to evaluate the effect of TGF-ß1 on pigs. The results showed that antibody production from B cells of PPs was impaired by TGF-ß1 ex vivo. Furthermore, TGF-ß1 treatment led to a decrease in the expression of germ-line transcript αand postswitch transcript α. Moreover, we observed that TGF-ß1 predominantly inhibited the phosphorylation of p38-mitogen-activated protein kinases (MAPK), confirming the involvement of the p38-MAPK pathway in porcine IgA generation and IgA class switch recombination. The application of p38-MAPK inhibitor resulted in decreased B-cell differentiation levels. Collectively, this study demonstrates that exogenous TGF-ß1 restrains the production and class switch recombination of IgA antibodies by inhibiting p38-MAPK signaling in porcine PPs B cells, which may constitute a component of TGF-ß1-mediated inhibition of B-cell activation.

dbEmbryo multi-omics database for analyses of synergistic regulation in early mammalian embryo development.

During early mammalian embryo development, different epigenetic marks undergo reprogramming and play crucial roles in the mediation of gene expression. Currently, several databases provide multi-omics information on early embryos. However, how interconnected epigenetic markers function together to coordinate the expression of the genetic code in a spatiotemporal manner remains difficult to analyze, markedly limiting scientific and clinical research. Here, we present dbEmbryo, an integrated and interactive multi-omics database for human and mouse early embryos. dbEmbryo integrates data on gene expression, DNA methylation, histone modifications, chromatin accessibility, and higher-order chromatin structure profiles for human and mouse early embryos. It incorporates customized analysis tools, such as "multi-omics visualization," "Gene&Peak annotation," "ZGA gene cluster," "cis-regulation," "synergistic regulation," "promoter signal enrichment," and "3D genome." Users can retrieve gene expression and epigenetic profile patterns to analyze synergistic changes across different early embryo developmental stages. We showed the uniqueness of dbEmbryo among extant databases containing data on early embryo development and provided an overview. Using dbEmbryo, we obtained a phase-separated model of transcriptional control during early embryo development. dbEmbryo offers web-based analytical tools and a comprehensive resource for biologists and clinicians to decipher molecular regulatory mechanisms of human and mouse early embryo development.

Identifying the potential miRNA biomarkers based on multi-view networks and reinforcement learning for diseases.

MicroRNAs (miRNAs) play important roles in the occurrence and development of diseases. However, it is still challenging to identify the effective miRNA biomarkers for improving the disease diagnosis and prognosis. In this study, we proposed the miRNA data analysis method based on multi-view miRNA networks and reinforcement learning, miRMarker, to define the potential miRNA disease biomarkers. miRMarker constructs the cooperative regulation network and functional similarity network based on the expression data and known miRNA-disease relations, respectively. The cooperative regulation of miRNAs was evaluated by measuring the changes of relative expression. Natural language processing was introduced for calculating the miRNA functional similarity. Then, miRMarker integrates the multi-view miRNA networks and defines the informative miRNA modules through a reinforcement learning strategy. We compared miRMarker with eight efficient data analysis methods on nine transcriptomics datasets to show its superiority in disease sample discrimination. The comparison results suggested that miRMarker outperformed other data analysis methods in receiver operating characteristic analysis. Furthermore, the defined miRNA modules of miRMarker on colorectal cancer data not only show the excellent performance of cancer sample discrimination but also play significant roles in the cancer-related pathway disturbances. The experimental results indicate that miRMarker can build the robust miRNA interaction network by integrating the multi-view networks. Besides, exploring the miRNA interaction network using reinforcement learning favors defining the important miRNA modules. In summary, miRMarker can be a hopeful tool in biomarker identification for human diseases.

Organic Electrode Materials for Energy Storage and Conversion: Mechanism, Characteristics, and Applications.

ConspectusLithium ion batteries (LIBs) with inorganic intercalation compounds as electrode active materials have become an indispensable part of human life. However, the rapid increase in their annual production raises concerns about limited mineral reserves and related environmental issues. Therefore, organic electrode materials (OEMs) for rechargeable batteries have once again come into the focus of researchers because of their design flexibility, sustainability, and environmental compatibility. Compared with conventional inorganic cathode materials for Li ion batteries, OEMs possess some unique characteristics including flexible molecular structure, weak intermolecular interaction, being highly soluble in electrolytes, and moderate electrochemical potentials. These unique characteristics make OEMs suitable for applications in multivalent ion batteries, low-temperature batteries, redox flow batteries, and decoupled water electrolysis. Specifically, the flexible molecular structure and weak intermolecular interaction of OEMs make multivalent ions easily accessible to the redox sites of OEMs and facilitate the desolvation process on the redox site, thus improving the low-temperature performance, while the highly soluble nature enables OEMs as redox couples for aqueous redox flow batteries. Finally, the moderate electrochemical potential and reversible proton storage and release of OEMs make them suitable as redox mediators for water electrolysis. Over the past ten years, although various new OEMs have been developed for Li-organic batteries, Na-organic batteries, Zn-organic batteries, and other battery systems, batteries with OEMs still face many challenges, such as poor cycle stability, inferior energy density, and limited rate capability. Therefore, previous reviews of OEMs mainly focused on organic molecular design for organic batteries or strategies to improve the electrochemical performance of OEMs. A comprehensive review to explore the characteristics of OEMs and establish the correlation between these characteristics and their specific application in energy storage and conversion is still lacking.In this Account, we initially provide an overview of the sustainability and environmental friendliness of OEMs for energy storage and conversion. Subsequently, we summarize the charge storage mechanisms of the different types of OEMs. Thereafter, we explore the characteristics of OEMs in comparison with conventional inorganic intercalation compounds including their structural flexibility, high solubility in the electrolyte, and appropriate electrochemical potential in order to establish the correlations between their characteristics and potential applications. Unlike previous reviews that mainly introduce the electrochemical performance progress of different organic batteries, this Account specifically focuses on some exceptional applications of OEMs corresponding to the characteristics of organic electrode materials in energy storage and conversion, as previously published by our groups. These applications include monovalent ion batteries, multivalent ion batteries, low-temperature batteries, redox flow batteries with soluble OEMs, and decoupled water electrolysis employing organic electrodes as redox mediators. We hope that this Account will make an invaluable contribution to the development of organic electrode materials for next-generation batteries and help to unlock a world of potential energy storage applications.

Genomic landscape of Down syndrome-associated acute lymphoblastic leukemia.

Trisomy 21, the genetic cause of Down syndrome (DS), is the most common congenital chromosomal anomaly. It is associated with a 20-fold increased risk of acute lymphoblastic leukemia (ALL) during childhood and results in distinctive leukemia biology. To comprehensively define the genomic landscape of DS-ALL, we performed whole-genome sequencing and whole-transcriptome sequencing (RNA-Seq) on 295 cases. Our integrated genomic analyses identified 15 molecular subtypes of DS-ALL, with marked enrichment of CRLF2-r, IGH::IGF2BP1, and C/EBP altered (C/EBPalt) subtypes compared with 2257 non-DS-ALL cases. We observed abnormal activation of the CEBPD, CEBPA, and CEBPE genes in 10.5% of DS-ALL cases via a variety of genomic mechanisms, including chromosomal rearrangements and noncoding mutations leading to enhancer hijacking. A total of 42.3% of C/EBP-activated DS-ALL also have concomitant FLT3 point mutations or insertions/deletions, compared with 4.1% in other subtypes. CEBPD overexpression enhanced the differentiation of mouse hematopoietic progenitor cells into pro-B cells in vitro, particularly in a DS genetic background. Notably, recombination-activating gene-mediated somatic genomic abnormalities were common in DS-ALL, accounting for a median of 27.5% of structural alterations, compared with 7.7% in non-DS-ALL. Unsupervised hierarchical clustering analyses of CRLF2-rearranged DS-ALL identified substantial heterogeneity within this group, with the BCR::ABL1-like subset linked to an inferior event-free survival, even after adjusting for known clinical risk factors. These results provide important insights into the biology of DS-ALL and point to opportunities for targeted therapy and treatment individualization.

Mutations in TSPAN12 gene causing familial exudative vitreoretinopathy.

BACKGROUND: To report newly found TSPAN12 mutations with a unique form of familial exudative vitreoretinopathy (FEVR) and find out the possible mechanism of a repeated novel intronic variant in TSPAN12 led to FEVR. RESULTS: Nine TSPAN12 mutations with a unique form of FEVR were detected by panel-based NGS. MINI-Gene assay showed two splicing modes of mRNA that process two different bands A and B, and mutant-type shows replacement with the splicing mode of Exon11 hopping. Construction of wild-type and mutant TSPAN12 vector showed the appearance of premature termination codons (PTC). In vitro expression detection showed significant down-regulated expression level of TSPAN12 mRNAs and proteins in cells transfected with mutant vectors compared with in wild-type group. On the contrary, translation inhibitor CHX and small interfering RNA of UPF1 (si-UPF1) significantly increased mRNA or protein expression of TSPAN12 in cells transfected with the mutant vectors. CONCLUSIONS: Nine mutations in TSPAN12 gene are reported in 9 FEVR patients with a unique series of ocular abnormalities. The three novel TSPAN12 mutations trigger NMD would cause the decrease of TSPAN12 proteins that participate in biosynthesis and assembly of microfibers, which might lead to FEVR, and suggest that intronic sequence analysis might be a vital tool for genetic counseling and prenatal diagnoses.

Hepatocyte-specific Selenoi deficiency predisposes mice to hepatic steatosis and obesity.

Selenoprotein I (Selenoi) is highly expressed in liver and plays a key role in lipid metabolism as a phosphatidylethanolamine (PE) synthase. However, the precise function of Selenoi in the liver remains elusive. In the study, we generated hepatocyte-specific Selenoi conditional knockout (cKO) mice on a high-fat diet to identify the physiological function of Selenoi. The cKO group exhibited a significant increase in body weight, with a 15.6% and 13.7% increase in fat accumulation in white adipose tissue (WAT) and the liver, respectively. Downregulation of the lipolysis-related protein (p-Hsl) and upregulation of the adipogenesis-related protein (Fasn) were observed in the liver of cKO mice. The cKO group also showed decreased oxygen consumption (VO2), carbon dioxide production (VCO2), and energy expenditure (p < .05). Moreover, various metabolites of the steroid hormone synthesis pathway were affected in the liver of cKO mice. A potential cascade of Selenoi-phosphatidylethanolamine-steroid hormone synthesis might serve as a core mechanism that links hepatocyte-specific Selenoi cKO to biochemical and molecular reactions. In conclusion, we revealed that Selenoi inhibits body fat accumulation and hepatic steatosis and elevates energy consumption; this protein could also be considered a therapeutic target for such related diseases.

Transformations of sensory information in the brain suggest changing criteria for optimality.

Neurons throughout the brain modulate their firing rate lawfully in response to sensory input. Theories of neural computation posit that these modulations reflect the outcome of a constrained optimization in which neurons aim to robustly and efficiently represent sensory information. Our understanding of how this optimization varies across different areas in the brain, however, is still in its infancy. Here, we show that neural sensory responses transform along the dorsal stream of the visual system in a manner consistent with a transition from optimizing for information preservation towards optimizing for perceptual discrimination. Focusing on the representation of binocular disparities-the slight differences in the retinal images of the two eyes-we re-analyze measurements characterizing neuronal tuning curves in brain areas V1, V2, and MT (middle temporal) in the macaque monkey. We compare these to measurements of the statistics of binocular disparity typically encountered during natural behaviors using a Fisher Information framework. The differences in tuning curve characteristics across areas are consistent with a shift in optimization goals: V1 and V2 population-level responses are more consistent with maximizing the information encoded about naturally occurring binocular disparities, while MT responses shift towards maximizing the ability to support disparity discrimination. We find that a change towards tuning curves preferring larger disparities is a key driver of this shift. These results provide new insight into previously-identified differences between disparity-selective areas of cortex and suggest these differences play an important role in supporting visually-guided behavior. Our findings emphasize the need to consider not just information preservation and neural resources, but also relevance to behavior, when assessing the optimality of neural codes.

Targeting the nuclear long noncoding transcript LSP1P5 abrogates extracellular matrix deposition by trans-upregulating CEBPA in keloids.

Keloids are characterized by fibroblast hyperproliferation and excessive accumulation of extracellular matrix (ECM) and are a major global health care burden among cutaneous diseases. However, the function of long noncoding RNA (lncRNA)-mediated ECM remodeling during the pathogenesis of keloids is still unclear. Herein, we identified a long noncoding transcript, namely, lymphocyte-specific protein 1 pseudogene 5 (LSP1P5), that modulates ECM component deposition in keloids. First, high-throughput transcriptome analysis showed that LSP1P5 was selectively upregulated in keloids and correlated with more severe disease in a clinical keloid cohort. Therapeutically, the attenuation of LSP1P5 significantly decreased the expression of ECM markers (COL1, COL3, and FN1) both in vitro and in vivo. Intriguingly, an antifibrotic gene, CCAAT enhancer binding protein alpha (CEBPA), is a functional downstream candidate of LSP1P5. Mechanistically, LSP1P5 represses CEBPA expression by hijacking Suppressor of Zeste 12 to the promoter of CEBPA, thereby enhancing the polycomb repressive complex 2-mediated H3K27me3 and changing the chromosomal opening status of CEBPA. Taken together, these findings indicate that targeting LSP1P5 abrogates fibrosis in keloids through epigenetic regulation of CEBPA, revealing a novel antifibrotic therapeutic strategy that bridges our current understanding of lncRNA regulation, histone modification and ECM remodeling in keloids.

A Determined "Hesitation" on H3K27me3 Empowers Stem Cells to Differentiate.

To uncover the precise mechanisms coordinating proliferation and fate choice of stem cells, in this issue of Molecular Cell and in an accompanying paper in Cell Reports, Mazo and colleagues (Petruk et al. 2017a, 2017b) reveal that delayed accumulation of H3K27me3 on nascent DNA is essential to recruit pioneer transcription factors in stem cell differentiation.

IAnimal: a cross-species omics knowledgebase for animals.

With the exponential growth of multi-omics data, its integration and utilization have brought unprecedented opportunities for the interpretation of gene regulation mechanisms and the comprehensive analyses of biological systems. IAnimal (https://ianimal.pro/), a cross-species, multi-omics knowledgebase, was developed to improve the utilization of massive public data and simplify the integration of multi-omics information to mine the genetic mechanisms of objective traits. Currently, IAnimal provides 61 191 individual omics data of genome (WGS), transcriptome (RNA-Seq), epigenome (ChIP-Seq, ATAC-Seq) and genome annotation information for 21 species, such as mice, pigs, cattle, chickens, and macaques. The scale of its total clean data has reached 846.46 TB. To better understand the biological significance of omics information, a deep learning model for IAnimal was built based on BioBERT and AutoNER to mine 'gene' and 'trait' entities from 2 794 237 abstracts, which has practical significance for comprehending how each omics layer regulates genes to affect traits. By means of user-friendly web interfaces, flexible data application programming interfaces, and abundant functional modules, IAnimal enables users to easily query, mine, and visualize characteristics in various omics, and to infer how genes play biological roles under the influence of various omics layers.