3D Genome of macaque fetal brain reveals evolutionary innovations during primate corticogenesis.

Elucidating the regulatory mechanisms of human brain evolution is essential to understanding human cognition and mental disorders. We generated multi-omics profiles and constructed a high-resolution map of 3D genome architecture of rhesus macaque during corticogenesis. By comparing the 3D genomes of human, macaque, and mouse brains, we identified many human-specific chromatin structure changes, including 499 topologically associating domains (TADs) and 1,266 chromatin loops. The human-specific loops are significantly enriched in enhancer-enhancer interactions, and the regulated genes show human-specific expression changes in the subplate, a transient zone of the developing brain critical for neural circuit formation and plasticity. Notably, many human-specific sequence changes are located in the human-specific TAD boundaries and loop anchors, which may generate new transcription factor binding sites and chromatin structures in human. Collectively, the presented data highlight the value of comparative 3D genome analyses in dissecting the regulatory mechanisms of brain development and evolution.

Cross-species cell-type assignment from single-cell RNA-seq data by a heterogeneous graph neural network.

Cross-species comparative analyses of single-cell RNA sequencing (scRNA-seq) data allow us to explore, at single-cell resolution, the origins of the cellular diversity and evolutionary mechanisms that shape cellular form and function. Cell-type assignment is a crucial step to achieve that. However, the poorly annotated genome and limited known biomarkers hinder us from assigning cell identities for nonmodel species. Here, we design a heterogeneous graph neural network model, CAME, to learn aligned and interpretable cell and gene embeddings for cross-species cell-type assignment and gene module extraction from scRNA-seq data. CAME achieves significant improvements in cell-type characterization across distant species owing to the utilization of non-one-to-one homologous gene mapping ignored by early methods. Our large-scale benchmarking study shows that CAME significantly outperforms five classical methods in terms of cell-type assignment and model robustness to insufficiency and inconsistency of sequencing depths. CAME can transfer the major cell types and interneuron subtypes of human brains to mouse and discover shared cell-type-specific functions in homologous gene modules. CAME can align the trajectories of human and macaque spermatogenesis and reveal their conservative expression dynamics. In short, CAME can make accurate cross-species cell-type assignments even for nonmodel species and uncover shared and divergent characteristics between two species from scRNA-seq data.

Defining the separation landscape of topological domains for decoding consensus domain organization of the 3D genome.

Topologically associating domains (TADs) have emerged as basic structural and functional units of genome organization and have been determined by many computational methods from Hi-C contact maps. However, the TADs obtained by different methods vary greatly, which makes the accurate determination of TADs a challenging issue and hinders subsequent biological analyses about their organization and functions. Obvious inconsistencies among the TADs identified by different methods indeed make the statistical and biological properties of TADs overly depend on the chosen method rather than on the data. To this end, we use the consensus structural information captured by these methods to define the TAD separation landscape for decoding the consensus domain organization of the 3D genome. We show that the TAD separation landscape could be used to compare domain boundaries across multiple cell types for discovering conserved and divergent topological structures, decipher three types of boundary regions with diverse biological features, and identify consensus TADs (ConsTADs). We illustrate that these analyses could deepen our understanding of the relationships between the topological domains and chromatin states, gene expression, and DNA replication timing.

High-density generation of spatial transcriptomics with STAGE.

Spatial transcriptome technologies have enabled the measurement of gene expression while maintaining spatial location information for deciphering the spatial heterogeneity of biological tissues. However, they were heavily limited by the sparse spatial resolution and low data quality. To this end, we develop a spatial location-supervised auto-encoder generator STAGE for generating high-density spatial transcriptomics (ST). STAGE takes advantage of the customized supervised auto-encoder to learn continuous patterns of gene expression in space and generate high-resolution expressions for given spatial coordinates. STAGE can improve the low quality of spatial transcriptome data and smooth the generated manifold of gene expression through the de-noising function on the latent codes of the auto-encoder. Applications to four ST datasets, STAGE has shown better recovery performance for down-sampled data than existing methods, revealed significant tissue structure specificity, and enabled robust identification of spatially informative genes and patterns. In addition, STAGE can be extended to three-dimensional (3D) stacked ST data for generating gene expression at any position between consecutive sections for shaping high-density 3D ST configuration.

SmartGate is a spatial metabolomics tool for resolving tissue structures.

Imaging mass spectrometry (IMS) is one of the powerful tools in spatial metabolomics for obtaining metabolite data and probing the internal microenvironment of organisms. It has dramatically advanced the understanding of the structure of biological tissues and the drug treatment of diseases. However, the complexity of IMS data hinders the further acquisition of biomarkers and the study of certain specific activities of organisms. To this end, we introduce an artificial intelligence tool, SmartGate, to enable automatic peak selection and spatial structure identification in an iterative manner. SmartGate selects discriminative m/z features from the previous iteration by differential analysis and employs a graph attention autoencoder model to perform spatial clustering for tissue segmentation using the selected features. We applied SmartGate to diverse IMS data at multicellular or subcellular spatial resolutions and compared it with four competing methods to demonstrate its effectiveness. SmartGate can significantly improve the accuracy of spatial segmentation and identify biomarker metabolites based on tissue structure-guided differential analysis. For multiple consecutive IMS data, SmartGate can effectively identify structures with spatial heterogeneity by introducing three-dimensional spatial neighbor information.

Single-cell RNA-sequencing data clustering using variational graph attention auto-encoder with self-supervised leaning.

The emergence of single-cell RNA-seq (scRNA-seq) technology makes it possible to capture their differences at the cellular level, which contributes to studying cell heterogeneity. By extracting, amplifying and sequencing the genome at the individual cell level, scRNA-seq can be used to identify unknown or rare cell types as well as genes differentially expressed in specific cell types under different conditions using clustering for downstream analysis of scRNA-seq. Many clustering algorithms have been developed with much progress. However, scRNA-seq often appears with characteristics of high dimensions, sparsity and even the case of dropout events', which make the performance of scRNA-seq data clustering unsatisfactory. To circumvent the problem, a new deep learning framework, termed variational graph attention auto-encoder (VGAAE), is constructed for scRNA-seq data clustering. In the proposed VGAAE, a multi-head attention mechanism is introduced to learn more robust low-dimensional representations for the original scRNA-seq data and then self-supervised learning is also recommended to refine the clusters, whose number can be automatically determined using Jaccard index. Experiments have been conducted on different datasets and results show that VGAAE outperforms some other state-of-the-art clustering methods.

STAMarker: determining spatial domain-specific variable genes with saliency maps in deep learning.

Spatial transcriptomics characterizes gene expression profiles while retaining the information of the spatial context, providing an unprecedented opportunity to understand cellular systems. One of the essential tasks in such data analysis is to determine spatially variable genes (SVGs), which demonstrate spatial expression patterns. Existing methods only consider genes individually and fail to model the inter-dependence of genes. To this end, we present an analytic tool STAMarker for robustly determining spatial domain-specific SVGs with saliency maps in deep learning. STAMarker is a three-stage ensemble framework consisting of graph-attention autoencoders, multilayer perceptron (MLP) classifiers, and saliency map computation by the backpropagated gradient. We illustrate the effectiveness of STAMarker and compare it with serveral commonly used competing methods on various spatial transcriptomic data generated by different platforms. STAMarker considers all genes at once and is more robust when the dataset is very sparse. STAMarker could identify spatial domain-specific SVGs for characterizing spatial domains and enable in-depth analysis of the region of interest in the tissue section.

CdSe Quantum Dots Enable High Thermoelectric Performance in Solution-Processed Polycrystalline SnSe.

Here, a high peak ZT of ≈2.0 is reported in solution-processed polycrystalline Ge and Cd codoped SnSe. Microstructural characterization reveals that CdSe quantum dots are successfully introduced by solution process method. Ultraviolet photoelectron spectroscopy evinces that CdSe quantum dots enhance the density of states in the electronic structure of SnSe, which leads to a large Seebeck coefficient. It is found that Ge and Cd codoping simultaneously optimizes carrier concentration and improves electrical conductivity. The enhanced Seebeck coefficient and optimization of carrier concentration lead to marked increase in power factor. CdSe quantum dots combined with strong lattice strain give rise to strong phonon scattering, leading to an ultralow lattice thermal conductivity. Consequently, high thermoelectric performance is realized in solution-processed polycrystalline SnSe by designing quantum dot structures and introducing lattice strain. This work provides a new route for designing prospective thermoelectric materials by microstructural manipulation in solution chemistry.

Disease category-specific annotation of variants using an ensemble learning framework.

Understanding the impact of non-coding sequence variants on complex diseases is an essential problem. We present a novel ensemble learning framework-CASAVA, to predict genomic loci in terms of disease category-specific risk. Using disease-associated variants identified by GWAS as training data, and diverse sequencing-based genomics and epigenomics profiles as features, CASAVA provides risk prediction of 24 major categories of diseases throughout the human genome. Our studies showed that CASAVA scores at a genomic locus provide a reasonable prediction of the disease-specific and disease category-specific risk prediction for non-coding variants located within the locus. Taking MHC2TA and immune system diseases as an example, we demonstrate the potential of CASAVA in revealing variant-disease associations. A website (http://zhanglabtools.org/CASAVA) has been built to facilitate easily access to CASAVA scores.

Predicting latent lncRNA and cancer metastatic event associations via variational graph auto-encoder.

Long non-coding RNA (lncRNA) are shown to be closely associated with cancer metastatic events (CME, e.g., cancer cell invasion, intravasation, extravasation, proliferation) that collaboratively accelerate malignant cancer spread and cause high mortality rate in patients. Clinical trials may accurately uncover the relationships between lncRNAs and CMEs; however, it is time-consuming and expensive. With the accumulation of data, there is an urgent need to find efficient ways to identify these relationships. Herein, a graph embedding representation-based predictor (VGEA-LCME) for exploring latent lncRNA-CME associations is introduced. In VGEA-LCME, a heterogeneous combined network is constructed by integrating similarity and linkage matrix that can maintain internal and external characteristics of networks, and a variational graph auto-encoder serves as a feature generator to represent arbitrary lncRNA and CME pair. The final robustness predicted result is obtained by ensemble classifier strategy via cross-validation. Experimental comparisons and literature verification show better remarkable performance of VGEA-LCME, although the similarities between CMEs are challenging to calculate. In addition, VGEA-LCME can further identify organ-specific CMEs. To the best of our knowledge, this is the first computational attempt to discover the potential relationships between lncRNAs and CMEs. It may provide support and new insight for guiding experimental research of metastatic cancers. The source code and data are available at https://github.com/zhuyuan-cug/VGAE-LCME.

Characteristics of humification, functional enzymes and bacterial community metabolism during manganese dioxide-added composting of municipal sludge.

The aim of this study was to assess effects of MnO2 addition (CK-0%, T1-2% and T2-5%) on humification and bacterial community during municipal sludge (MS) composting. The results suggested that MnO2 addition inhibited the growth of Nitrospira but stimulated Nonomuraea, Actinomadura, Streptomyces and Thermopolyspora, facilitating the lignocellulose degradation and humification with the increase in organic matter degradation by 13.8%-19.2% and humic acid content by 10.9%-20.6%. Compared to CK, the abundances of exoglucanase (EC:3.2.1.91), endo-1,4-beta-xylanase (EC:3.2.1.136) and endomannanase (EC:3.2.1.78) increased by 88-99, 52-66 and 4-15 folds, respectively. However, 5%-MnO2 induced the enrichment of Mizugakiibacter that harms the environment of agricultural production. The addition of 2%-MnO2 was recommended for MS composting. Furthermore, metabolic function analysis indicated that MnO2 addition altered amino acid and carbohydrate metabolism, especially enhancing propanoate metabolism and butanoate metabolism but inhibiting citrate cycle. Structural equation modeling revealed that Nonomuraea and Actinomadura were the main drivers for lignocellulose degradation. This study provided theoretical guidance in regulating humification via MnO2 for MS composting.

Relationship between Vitamin D Concentration and Lipid Concentration in Patients with NAFLD in the Hulunbuir Region of China.

BACKGROUND: We aimed to characterize the relationship between the serum 25-hydroxyvitamin D concentration and the circulating lipid concentrations of patients with NAFLD in the Hulunbuir region of China. METHODS: One hundred fifty-six patients, who were diagnosed with NAFLD in the Physical Examination Department of the Second Clinical College of Inner Mongolia University for the Nationalities between January 2021 and March 2023, were recruited as NAFLD group, and 160 healthy people were recruited as a control group during the same period. The serum 25(OH)VitD, TBIL, TG, TC, LDL-C, HDL-C, AST, ALT, GGT, and FPG activities of the participants were measured, and hepatic ultrasonography was performed. RESULTS: The BMI of the NAFLD group was higher than of the control group (p < 0.05). The serum 25(OH)VitD3 (p < 0.05) and the HDL-C concentrations of the NAFLD group were lower than those of the normal control group. However, the AST (p < 0.05), ALT (p < 0.05), and GGT (p < 0.05) activities, and the serum TG (p < 0.05), TC (p < 0.05), LDL-C (p < 0.05), and the fasting glucose (p < 0.05) concentrations of the NAFLD group were higher than those of the normal control group. The serum 25(OH)VitD3 concentrations of the NAFLD group significantly cor-related negatively with BMI (r = -0.302, p < 0.01), TG (r = -0.221, p < 0.05), and fasting glucose (r = -0.236, p < 0.05). The BMI, TG, and fasting glucose of vitamin D-deficient participants were higher than of the participants with adequate or insufficient levels of vitamin D (p < 0.05). Finally, the BMI of vitamin D-deficient participants was higher than of those with an adequate vitamin D status (p < 0.05). CONCLUSIONS: A deficiency of 25(OH)VitD is more common in people from the Hulunbuir region of China than elsewhere. In addition, the vitamin D status is significantly associated with NAFLD; as the serum vitamin D concentration decreases, patients with NAFLD show greater dyslipidemia and hyperglycemia and a higher BMI.

A competitive-type photoelectrochemical aptasensor for 17 beta-estradiol detection in microfluidic devices based on a novel Au@Cd:SnO2/SnS2 nanocomposite.

A competitive-type photoelectrochemical (PEC) aptasensor coupled with a novel Au@Cd:SnO2/SnS2 nanocomposite was designed for the detection of 17ß-estradiol (E2) in microfluidic devices. The designed Au@Cd:SnO2/SnS2 nanocomposites exhibit high photoelectrochemical activity owing to the good matching of cascade band-edge and the efficient separation of photo-generated e-/h+ pairs derived from the Cd-doped defects in the energy level. The Au@Cd:SnO2/SnS2 nanocomposites were loaded into carbon paste electrodes (CPEs) to immobilize complementary DNA (cDNA) and estradiol aptamer probe DNA (E2-Apt), forming a double-strand DNA structure on the CPE surface. As the target E2 interacts with the double-strand DNA, E2-Apt is sensitively released from the CPE, subsequently increasing the photocurrent intensity due to the reduced steric hindrance of the electrode surface. The competitive-type sensing mechanism, combined with high PEC activity of the Au@Cd:SnO2/SnS2 nanocomposites, contributed to the rapid and sensitive detection of E2 in a "signal on" manner. Under the optimized conditions, the PEC aptasensor exhibited a linear range from 1.0 × 10-13 mol L-1 to 3.2 × 10-6 mol L-1 and a detection limit of 1.2 × 10-14 mol L-1 (S/N = 3). Moreover, the integration of microfluidic device with smartphone controlled portable electrochemical workstation enables the on-site detection of E2. The small sample volume (10 µL) and short analysis time (40 min) demonstrated the great potential of this strategy for E2 detection in rat serum and river water. With these advantages, the PEC aptasensor can be utilized for point-of-care testing (POCT) in both clinical and environmental applications.

ncR2Met (lncR2metasta v2.0): An updated database for experimentally supported ncRNAs during cancer metastatic events.

Non-coding RNAs (ncRNAs) are widely involved in cancer metastatic events (CMEs, e.g., cancer cell invasion, intravasation, extravasation, proliferation), which collaboratively accelerate tumor spread and cause high patient mortality. In early 2020, we developed a manually curated database named 'lncR2metasta' to provide a comprehensive repository for long ncRNA (lncRNA) regulation during CMEs. We updated this database by supplementing other two important ncRNA types, microRNAs (miRNAs) and circular RNAs (circRNAs), for their involvement during CMEs after a thorough manual curation from published studies. ncR2metasta documents 1565 lncRNA-associated, 882 miRNA-associated, and 628 circRNA-associated entries for ncRNA-CME associations during 50 CMEs across 63 human cancer subtypes. ncR2Met has a concise web interface for researchers to easily browse, search and download as well as to submit novel ncRNA-CME associations. We anticipated that it could be a valuable resource, which will significantly improve our understanding of ncRNA functions in metastasis. It is freely available at http://ncr2met.wchoda.com.

Transcription factor GATA4 drives RNA polymerase III-directed transcription and transformed cell proliferation through a filamin A/GATA4/SP1 pathway.

RNA polymerase III (pol III) products play fundamental roles in a variety of cellular processes, including protein synthesis and cancer cell proliferation. In addition, dysregulation of pol III-directed transcription closely correlates with tumorigenesis. It is therefore of interest to identify novel pathways or factors governing pol III-directed transcription. Here, we show that transcription factor (TF) GATA binding protein 4 (GATA4) expression in SaOS2 cells was stimulated by the silencing of filamin A (FLNA), a repressor of pol III-directed transcription, suggesting that GATA4 is potentially associated with the regulation of pol III-directed transcription. Indeed, we show that GATA4 expression positively correlates with pol III-mediated transcription and tumor cell proliferation. Mechanistically, we found that GATA4 depletion inhibits the occupancies of the pol III transcription machinery factors at the loci of pol III target genes by reducing expression of both TFIIIB subunit TFIIB-related factor 1 and TFIIIC subunit general transcription factor 3C subunit 2 (GTF3C2). GATA4 has been shown to activate specificity factor 1 (Sp1) gene transcription by binding to the Sp1 gene promoter, and Sp1 has been confirmed to activate pol III gene transcription by directly binding to both Brf1 and Gtf3c2 gene promoters. Thus, the findings from this study suggest that GATA4 links FLNA and Sp1 signaling to form an FLNA/GATA4/Sp1 axis to modulate pol III-directed transcription and transformed cell proliferation. Taken together, these results provide novel insights into the regulatory mechanism of pol III-directed transcription.

STAT3 potentiates RNA polymerase I-directed transcription and tumor growth by activating RPA34 expression.

BACKGROUND: Deregulation of either RNA polymerase I (Pol I)-directed transcription or expression of signal transducer and activator of transcription 3 (STAT3) correlates closely with tumorigenesis. However, the connection between STAT3 and Pol I-directed transcription hasn't been investigated. METHODS: The role of STAT3 in Pol I-directed transcription was determined using combined techniques. The regulation of tumor cell growth mediated by STAT3 and Pol I products was analyzed in vitro and in vivo. RNAseq, ChIP assays and rescue assays were used to uncover the mechanism of Pol I transcription mediated by STAT3. RESULTS: STAT3 expression positively correlates with Pol I product levels and cancer cell growth. The inhibition of STAT3 or Pol I products suppresses cell growth. Mechanistically, STAT3 activates Pol I-directed transcription by enhancing the recruitment of the Pol I transcription machinery to the rDNA promoter. STAT3 directly activates Rpa34 gene transcription by binding to the RPA34 promoter, which enhances the occupancies of the Pol II transcription machinery factors at this promoter. Cancer patients with RPA34 high expression lead to poor survival probability and short survival time. CONCLUSION: STAT3 potentiates Pol I-dependent transcription and tumor cell growth by activating RPA34 in vitro and in vivo.

Joint reconstruction of cis-regulatory interaction networks across multiple tissues using single-cell chromatin accessibility data.

The rapid accumulation of single-cell chromatin accessibility data offers a unique opportunity to investigate common and specific regulatory mechanisms across different cell types. However, existing methods for cis-regulatory network reconstruction using single-cell chromatin accessibility data were only designed for cells belonging to one cell type, and resulting networks may be incomparable directly due to diverse cell numbers of different cell types. Here, we adopt a computational method to jointly reconstruct cis-regulatory interaction maps (JRIM) of multiple cell populations based on patterns of co-accessibility in single-cell data. We applied JRIM to explore common and specific regulatory interactions across multiple tissues from single-cell ATAC-seq dataset containing ~80 000 cells across 13 mouse tissues. Reconstructed common interactions among 13 tissues indeed relate to basic biological functions, and individual cis-regulatory networks show strong tissue specificity and functional relevance. More importantly, tissue-specific regulatory interactions are mediated by coordination of histone modifications and tissue-related TFs, and many of them may reveal novel regulatory mechanisms.

LncR2metasta: a manually curated database for experimentally supported lncRNAs during various cancer metastatic events.

Mounting evidence has shown the involvement of long non-coding RNAs (lncRNAs) during various cancer metastatic events (abbreviated as CMEs, e.g. cancer cell invasion, intravasation, extravasation, proliferation, etc.) that may cooperatively facilitate malignant tumor spread and cause massive patient deaths. The study of lncRNA-CME associations might help understand lncRNA functions in metastasis and present reliable biomarkers for early dissemination detection and optimized treatment. Therefore, we developed a database named 'lncR2metasta' by manually compiling experimentally supported lncRNAs during various CMEs from existing studies. LncR2metasta documents 1238 associations between 304 lncRNAs and 39 CMEs across 54 human cancer subtypes. Each entry of lncR2metasta contains detailed information on a lncRNA-CME association, including lncRNA symbol, a specific CME, brief description of the association, lncRNA category, lncRNA Entrez or Ensembl ID, lncRNA genomic location and strand, lncRNA experiment, lncRNA expression pattern, detection method, target gene (or pathway) of lncRNA, lncRNA regulatory role on a CME, cancer name and the literature reference. An easy-to-use web interface was deployed in lncR2metasta for its users to easily browse, search and download as well as to submit novel lncRNA-CME associations. LncR2metasta will be a useful resource in cancer research community. It is freely available at http://lncR2metasta.wchoda.com.

Prediction of the transcription factor binding sites with meta-learning.

With the accumulation of ChIP-seq data, convolution neural network (CNN)-based methods have been proposed for predicting transcription factor binding sites (TFBSs). However, biological experimental data are noisy, and are often treated as ground truth for both training and testing. Particularly, existing classification methods ignore the false positive and false negative which are caused by the error in the peak calling stage, and therefore, they can easily overfit to biased training data. It leads to inaccurate identification and inability to reveal the rules of governing protein-DNA binding. To address this issue, we proposed a meta learning-based CNN method (namely TFBS_MLCNN or MLCNN for short) for suppressing the influence of noisy labels data and accurately recognizing TFBSs from ChIP-seq data. Guided by a small amount of unbiased meta-data, MLCNN can adaptively learn an explicit weighting function from ChIP-seq data and update the parameter of classifier simultaneously. The weighting function overcomes the influence of biased training data on classifier by assigning a weight to each sample according to its training loss. The experimental results on 424 ChIP-seq datasets show that MLCNN not only outperforms other existing state-of-the-art CNN methods, but can also detect noisy samples which are given the small weights to suppress them. The suppression ability to the noisy samples can be revealed through the visualization of samples' weights. Several case studies demonstrate that MLCNN has superior performance to others.

[Exercise regulates bone metabolism via microRNAs].

It has been well documented that exercise can improve bone metabolism, promote bone growth and development, and alleviate bone loss. MicroRNAs (miRNAs) are widely involved in the proliferation and differentiation of bone marrow mesenchymal stem cells, osteoblasts, osteoclasts and other bone tissue cells, and regulation of balance between bone formation and bone resorption by targeting osteogenic factors or bone resorption factors. Thus miRNAs play an important role in the regulation of bone metabolism. Recently, regulation of miRNAs are shown to be one of the ways by which exercise or mechanical stress promotes the positive balance of bone metabolism. Exercise induces changes of miRNAs expression in bone tissue and regulates the expression of related osteogenic factors or bone resorption factors, to further strengthen the osteogenic effect of exercise. This review summarizes relevant studies on the mechanism whereby exercise regulates bone metabolism via miRNAs, providing a theoretical basis for osteoporosis prevention and treatment with exercise.