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  • 2020-2024  (925)
  • Biodiversity Research  (925)
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  • 2020-2024  (925)
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  • 1
    In: Nucleic Acids Research, Oxford University Press (OUP), Vol. 50, No. D1 ( 2022-01-07), p. D27-D38
    Abstract: The National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB), provides a family of database resources to support global research in both academia and industry. With the explosively accumulated multi-omics data at ever-faster rates, CNCB-NGDC is constantly scaling up and updating its core database resources through big data archive, curation, integration and analysis. In the past year, efforts have been made to synthesize the growing data and knowledge, particularly in single-cell omics and precision medicine research, and a series of resources have been newly developed, updated and enhanced. Moreover, CNCB-NGDC has continued to daily update SARS-CoV-2 genome sequences, variants, haplotypes and literature. Particularly, OpenLB, an open library of bioscience, has been established by providing easy and open access to a substantial number of abstract texts from PubMed, bioRxiv and medRxiv. In addition, Database Commons is significantly updated by cataloguing a full list of global databases, and BLAST tools are newly deployed to provide online sequence search services. All these resources along with their services are publicly accessible at https://ngdc.cncb.ac.cn.
    Type of Medium: Online Resource
    ISSN: 0305-1048 , 1362-4962
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    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
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  • 2
    In: Nucleic Acids Research, Oxford University Press (OUP), Vol. 52, No. D1 ( 2024-01-05), p. D18-D32
    Abstract: The National Genomics Data Center (NGDC), which is a part of the China National Center for Bioinformation (CNCB), provides a family of database resources to support the global academic and industrial communities. With the rapid accumulation of multi-omics data at an unprecedented pace, CNCB-NGDC continuously expands and updates core database resources through big data archiving, integrative analysis and value-added curation. Importantly, NGDC collaborates closely with major international databases and initiatives to ensure seamless data exchange and interoperability. Over the past year, significant efforts have been dedicated to integrating diverse omics data, synthesizing expanding knowledge, developing new resources, and upgrading major existing resources. Particularly, several database resources are newly developed for the biodiversity of protists (P10K), bacteria (NTM-DB, MPA) as well as plant (PPGR, SoyOmics, PlantPan) and disease/trait association (CROST, HervD Atlas, HALL, MACdb, BioKA, BioKA, RePoS, PGG.SV, NAFLDkb). All the resources and services are publicly accessible at https://ngdc.cncb.ac.cn.
    Type of Medium: Online Resource
    ISSN: 0305-1048 , 1362-4962
    RVK:
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2024
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  • 3
    In: BioScience, Oxford University Press (OUP), ( 2024-06-19)
    Abstract: The fundamental value of universal nomenclatural systems in biology is that they enable unambiguous scientific communication. However, the stability of these systems is threatened by recent discussions asking for a fairer nomenclature, raising the possibility of bulk revision processes for “inappropriate” names. It is evident that such proposals come from very deep feelings, but we show how they can irreparably damage the foundation of biological communication and, in turn, the sciences that depend on it. There are four essential consequences of objective codes of nomenclature: universality, stability, neutrality, and transculturality. These codes provide fair and impartial guides to the principles governing biological nomenclature and allow unambiguous universal communication in biology. Accordingly, no subjective proposals should be allowed to undermine them.
    Type of Medium: Online Resource
    ISSN: 0006-3568 , 1525-3244
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    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2024
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  • 4
    In: Biomaterials, Elsevier BV, Vol. 309 ( 2024-09), p. 122613-
    Type of Medium: Online Resource
    ISSN: 0142-9612
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2024
    detail.hit.zdb_id: 2004010-6
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  • 5
    In: Science, American Association for the Advancement of Science (AAAS), Vol. 384, No. 6698 ( 2024-05-24)
    Abstract: Schizophrenia population genomics has identified strong germline genetic associations for this highly heritable disorder, and molecular investigation of postmortem brain samples has yielded evidence of transcriptomic and epigenomic alterations associated with this disease. However, identifying molecular and cellular pathophysiological processes linking etiological risk factors and clinical presentation remains a challenge, due in part to the complex cellular architecture of the brain. RATIONALE Past work has implicated specific populations of excitatory and inhibitory neurons in the pathophysiology of schizophrenia, but existing large transcriptomic datasets of bulk tissue samples cannot directly assess cell type–specific contributions to disease. Single-cell RNA sequencing technologies allow measurement of genome-wide gene expression in individual cells with high-throughput, moving beyond bulk tissue measures to map disease-associated transcriptional changes in discrete cellular populations without bias toward preselected cell types. Investigating disease-associated phenotypic changes across the myriad cellular populations of the human brain can produce new insights into neuropsychiatric disease biology. RESULTS Using multiplexed single-nucleus RNA sequencing, we developed a single-cell resolution transcriptomic atlas of the prefrontal cortex across subjects with and without schizophrenia and present data from 468,727 nuclei isolated from 140 individuals across two well-defined and independently assayed cohorts. We identified expression profiles of brain cell types and neuronal subpopulations and systematically characterized the transcriptional changes associated with schizophrenia in each. For completeness, we report independent, cohort-specific analyses and joint meta-analysis of differential expression across 25 cell types. Using these data, we identified highly cell type–specific and reproducible expression changes, with 6634 differential expression events affecting 2455 genes and favoring down-regulated gene expression within excitatory neuronal populations. We found significant overlap with previously reported bulk cortex expression changes, primarily for excitatory neuronal populations, whereas changes in lower-abundance cell types were less efficiently captured in tissue-level profiling. Differentially expressed genes enrich neurodevelopmental and synapse-related molecular pathways and point to a regulatory core of coexpressed transcription factors linked to genetic risk variants for schizophrenia and developmental delay. Transcription factor targeting of schizophrenia differentially expressed genes in neuronal populations was validated with CUT & Tag in neuronal nuclei isolated from human prefrontal cortex. Furthermore, both transcriptional changes and putative upstream regulatory factors were enriched with genes harboring common and rare risk variants for schizophrenia, presenting evidence that genetic risk variants across the population frequency spectrum tend to target genes with measurable expression alterations in the excitatory neurons of patients with schizophrenia. Finally, the magnitude of schizophrenia-associated transcriptomic change segregated two populations of schizophrenia subjects. Transcriptomic heterogeneity within the cohorts was associated with specific cellular states shared across multiple neuronal populations, marked by genes related to synaptic function and one-carbon metabolism, suggesting genes characterizing distinct molecular phenotypes of schizophrenia. CONCLUSION Our results provide a valuable resource to investigate the molecular pathophysiology of schizophrenia at single-cell resolution, offering insights into preferential dysregulation of specific neuronal populations and their potential role in mediating genetic risk. Together, they suggest convergence of etiological genetic risk factors, neuronal transcriptional dysregulation, and symptomatic manifestation in schizophrenia. Single-cell schizophrenia transcriptomics. Single-nucleus RNA sequencing (snRNA-seq) identified cell type–specific differentially expressed genes (DEGs) in 25 cell types. DEG sets enrich disease-relevant biological pathways, implicate a coherently expressed transcription factors module, and are associated with schizophrenia genetic risk variants. Magnitude of transcriptional change identified neuronal cell state–associated subgroups. SZ, schizophrenia; CON, control; McL, McLean; MSSM, Mount Sinai School of Medicine.
    Type of Medium: Online Resource
    ISSN: 0036-8075 , 1095-9203
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    Language: English
    Publisher: American Association for the Advancement of Science (AAAS)
    Publication Date: 2024
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  • 6
    In: Science, American Association for the Advancement of Science (AAAS), Vol. 384, No. 6698 ( 2024-05-24)
    Abstract: Gene regulatory elements play a major role in human brain development and disease etiology. Numerous potential gene regulatory elements and disease-related genetic variants in the developing brain have been identified through experiments and computational predictions. However, functionally characterizing these elements and studying how DNA nucleotide variants within them lead to disease are challenging as a result of their cell type–specific activity, our limited understanding of how nucleotide changes impact gene regulation, and the limitations of high-throughput functional assays. Lentivirus-based massively parallel reporter assays (lentiMPRAs) can overcome these limitations, providing the ability to test thousands of sequences and variants for their regulatory activity in hard-to-transfect cells, such as neurons and cerebral organoids. With this much quantitative activity data it is possible to train machine learning models to predict functional and cell type–specific regulatory elements and to perform massive in silico experiments that pinpoint nucleotide variants that alter enhancer activity. RATIONALE We combined lentiMPRA and deep learning to evaluate over 100,000 candidate regulatory elements and variants in mid-gestation human cortical cells and cerebral organoids. These include sequences with accessible chromatin in specific cell types of the developing brain and psychiatric disorder–associated variants. Comparing results in primary cells and cerebral organoids enabled us to evaluate whether organoids can be effectively utilized as an in vitro model for MPRA studies. Training a sequence-to-activity neural network model on lentiMPRA data enabled it to learn the regulatory grammar encoded in our experimental results, allowing us to predict the effects of nucleotide changes on enhancer function. RESULTS Using lentiMPRA, we identified 46,802 sequences that exhibited enhancer activity. In addition, we found 164 variants associated with psychiatric disorders showing differential enhancer activity between alleles in human cortical cells. Moreover, lentiMPRA experiments testing the same sequences in cerebral organoids showed highly consistent activity between both contexts, with some differences attributable to distinct cellular environments. We trained a deep learning model that predicts lentiMPRA activity with state-of-the-art accuracy. Applying an explainable artificial intelligence technique called in silico mutagenesis to the model allowed us to learn sequence determinants of regulatory activity in human brain development, categorize transcription factors as repressors versus activators in this context, and predict nucleotide changes with large effects on regulatory activity. CONCLUSION We generated a large-scale catalog of sequences that are active gene regulatory elements in mid-gestation human cortical cells and cerebral organoids that could have important roles in human brain development. Characterization of regulatory variants in regions associated with psychiatric disorders identified 164 variants that alter gene regulatory activity, providing insights into how gene regulatory variants could lead to phenotypic effects. In addition, we demonstrated the potential of brain organoids as a viable model to study gene regulation during early brain development. The high accuracy of our sequence-to-activity model allowed us to predict the regulatory effects of numerous additional variants not tested in our assays, including sites that do not commonly vary across healthy individuals. In summary, this work increases our understanding of the regulatory code during human brain development and generates tools that can predict how regulatory elements are perturbed by nucleotide changes. Massively parallel characterization and prediction of gene regulatory activity in the developing brain. We performed lentiMPRA to test the regulatory potential of 102,767 sequences in primary cortical cells and cerebral organoids. This dataset allowed the development of computational models that predict regulatory activity from sequence.
    Type of Medium: Online Resource
    ISSN: 0036-8075 , 1095-9203
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    Language: English
    Publisher: American Association for the Advancement of Science (AAAS)
    Publication Date: 2024
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  • 7
    In: Science, American Association for the Advancement of Science (AAAS), Vol. 384, No. 6698 ( 2024-05-24)
    Abstract: Genome-wide association studies (GWASs) have identified thousands of loci associated with neurodevelopmental and psychiatric disorders, yet our lack of understanding of the target genes and biological mechanisms underlying these associations remains a major challenge. GWAS signals for many neuropsychiatric disorders, including autism spectrum disorder, schizophrenia, and bipolar disorder, are particularly enriched for gene-regulatory elements active during human brain development. However, the lack of a unified population-scale, ancestrally diverse gene-regulatory atlas of human brain development has been a major obstacle for the functional assessment of top loci and post-GWAS integrative analyses. RATIONALE To address this critical gap in knowledge, we have uniformly processed and systematically characterized gene, isoform, and splicing quantitative trait loci (cumulatively referred to as xQTLs) in the developing human brain across 672 unique samples from 4 to 39 postconception weeks spanning European, African-American, and Latino/admixed American ancestries). With this expanded atlas, we sought to specifically localize the timing and molecular features mediating the greatest proportion of neuropsychiatric GWAS heritability, to prioritize candidate risk genes and mechanisms for top loci, and to compare with analogous results using larger adult brain functional genomic reference panels. RESULTS In total, we identified 15,752 genes harboring a gene, isoform and/or splicing cis -xQTL, including 49 genes associated with four large, recurrent inversions. Highly concordant effect sizes were observed across populations, and our diverse reference panel improved resolution to fine-map underlying candidate causal regulatory variants. Substantially more genes were found to harbor QTLs in the first versus second trimester of brain development, with a notable drop in gene expression and splicing heritability observed from 10 to 18 weeks coinciding with a period of rapidly increasing cellular heterogeneity in the developing brain. Isoform-level regulation, particularly in the second trimester, mediated a greater proportion of heritability across multiple psychiatric GWASs compared with gene expression regulation. Through colocalization and transcriptome-wide association studies, we prioritized biological mechanisms for ~60% of GWAS loci across five neuropsychiatric disorders, with 〉 2-fold more colocalizations observed compared with larger adult brain functional genomic reference panels. We observed convergence between common and rare-variant associations, including a cryptic splicing event in the high-confidence schizophrenia risk gene SP4 . Finally, we constructed a comprehensive set of developmentally regulated gene and isoform coexpression networks harboring unique cell-type specificity and genetic enrichments. Leveraging this cell-type specificity, we identified 〉 8000 module interaction QTLs, many of which exhibited additional GWAS colocalizations. Overall, neuropsychiatric GWASs and rare variant signals localized more strongly within maturing excitatory- and interneuron-associated modules compared with those enriched for neural progenitor cell types. Results can be visualized at devbrainhub.gandallab.org . CONCLUSION We have generated a large-scale, cross-population resource of gene, isoform, and splicing regulation in the developing human brain, providing comprehensive developmental and cell-type-informed mechanistic insights into the genetic underpinnings of complex neurodevelopmental and psychiatric disorders. A comprehensive transcriptome regulatory atlas of the developing human neocortex. RNA-sequencing and single-nucleotide polymorphism genotypes were uniformly integrated within a diverse set of 672 samples of the developing human neocortex. Gene regulation was systematically assessed across the gene, isoform expression, and local splicing levels, yielding 15,752 genes harboring a significant xQTL. Gene regulation was highly dynamic, with a substantial drop observed in gene expression heritability over development. Integrative analyses with neuropsychiatric GWASs uncovered hundreds of candidate risk genes and mechanisms, providing insights into the cellular, molecular, and developmental specificity underlying disease-associated genetic variation.
    Type of Medium: Online Resource
    ISSN: 0036-8075 , 1095-9203
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    Language: English
    Publisher: American Association for the Advancement of Science (AAAS)
    Publication Date: 2024
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  • 8
    In: Science, American Association for the Advancement of Science (AAAS), Vol. 384, No. 6698 ( 2024-05-24)
    Abstract: The cortical layers of the human neocortex were classically defined by histological distinction of cell types according to size, shape, and density. However, emerging single-cell and spatially resolved transcriptomic technologies have facilitated the identification of molecularly defined cell populations and spatial domains that move beyond classic cell type definitions and cytoarchitectural boundaries. RATIONALE Given the close relationship between brain structure and function, assigning gene expression to distinct anatomical subdivisions and cell populations within the human brain improves our understanding of these highly specialized regions and how they contribute to brain disorders. We sought to create a data-driven molecular neuroanatomical map of the human dorsolateral prefrontal cortex (DLPFC) at cellular resolution using unsupervised transcriptomic approaches to identify spatial domains associated with neuropsychiatric and neurodevelopmental disorders. RESULTS We generated complementary single-cell and spatial transcriptomic data from 10 adult, neurotypical control donors across the anterior-posterior axis of the DLPFC. Unsupervised spatial clustering revealed fine-resolution data-driven spatial domains with distinct molecular signatures, including deep cortical sublayers and a vasculature-enriched meninges layer. Cell type clustering of single-nucleus RNA-sequencing (snRNA-seq) data revealed 29 distinct populations across seven broad neuronal and glial cell types, including 15 excitatory subpopulations. To add cellular resolution to our data-driven molecular atlas, we took two complementary approaches to integrate single-cell and spatial transcriptomics data. First, we used our previously developed spatial registration framework to map the paired snRNA-seq data to specific unsupervised spatial domains, providing anatomy-based laminar identities to excitatory neuron subpopulations. Second, we used three existing spot-level deconvolution tools to computationally predict the cell type composition of spatial domains on the basis of the paired snRNA-seq reference data. These tools were rigorously benchmarked against a newly generated gold-standard reference dataset acquired with the Visium Spatial Proteogenomics assay, which enabled us to label and quantify four broad cell types across the DLPFC on the basis of protein marker expression, including neurons, oligodendrocytes, astrocytes, and microglia. Using these approaches, we identified the proportion of cell types in each spatial domain and showed that these proportions were consistent across individuals and the DLPFC anterior-posterior axis. We demonstrated the clinical relevance of our highly integrated molecular atlas using cell-cell communication analyses to spatially map cell type–specific ligand-receptor interactions associated with genetic risk for schizophrenia (SCZ). For example, we mapped the interaction between ephrin ligand EFNA5 and ephrin receptor EPHA5 to deep-layer excitatory neuron subtypes and spatial domains. To leverage the rich single-cell data generated by PsychENCODE Consortium companion studies, we spatially registered eight DLPFC snRNA-seq datasets collected across the consortium in the context of different neuropsychiatric disorders and demonstrated a convergence of excitatory, inhibitory, and non-neuronal cell types in relevant spatial domains. Using PsychENCODE Consortium and other publicly available gene sets, we further demonstrated the clinical relevance of our data-driven molecular atlas by mapping the enrichment of cell types and genes associated with neuropsychiatric disorders—including autism spectrum disorder, posttraumatic stress disorder, and major depressive disorder—to discrete spatial domains. CONCLUSION Our study identified high-resolution, data-driven spatial domains across the human DLPFC, providing anatomical context for cell type–specific gene expression changes associated with neurodevelopmental disorders and psychiatric illness. We provide a roadmap for the implementation and biological validation of unsupervised spatial clustering approaches in other regions of the human brain. We share interactive data resources for the scientific community to further interrogate molecular mechanisms associated with complex brain disorders. Data-driven molecular anatomy of the human DLPFC. Integrated single-nucleus and spatial transcriptomics data were generated across the anterior-posterior axis of the human DLPFC from 10 neurotypical control donors to create a data-driven molecular neuroanatomical atlas of the neocortex identifying spatial domains. Integrative analyses revealed distinct cell type compositions, cell-cell interactions, and colocalization of ligand-receptor pairs linked to schizophrenia genetic risk. t-SNE, t -distributed stochastic neighbor embedding. [Created with BioRender.com .]
    Type of Medium: Online Resource
    ISSN: 0036-8075 , 1095-9203
    RVK:
    RVK:
    Language: English
    Publisher: American Association for the Advancement of Science (AAAS)
    Publication Date: 2024
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    detail.hit.zdb_id: 2066996-3
    SSG: 11
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  • 9
    In: Science, American Association for the Advancement of Science (AAAS), Vol. 384, No. 6698 ( 2024-05-24)
    Abstract: Single-cell genomics offers a powerful method to understand how variants influence gene expression, especially across the numerous cell types in the human brain. Moreover, it can potentially refine our understanding of the regulatory mechanisms underlying brain-related traits. However, population-scale cohorts with a wide range of brain phenotypes are needed to infer key associations among variants and to develop models of regulation at the single-cell scale. RATIONALE To address this, the PsychENCODE Consortium performed many single-cell experiments [single-nucleus RNA sequencing (snRNA-seq), snATAC-seq (ATAC, assay for transposase-accessible chromatin), and snMultiome plus genotyping] and computational analyses on prefrontal-cortex samples of adults with a range of brain-related disorders such as schizophrenia, autism spectrum disorder, bipolar disorder, and Alzheimer’s disease, as well as controls. RESULTS We developed a uniformly processed resource comprising 〉 2.8 million nuclei from 388 individuals ( brainscope.psychencode.org ). The resource is based on harmonized cell typing, with 28 neuronal and non-neuronal cell types (registered against BICCN). Partitioning the expression variation within these types revealed higher cell-type variability than interindividual variability; this pattern was amplified in neurotransmitter and neurorelated drug-target genes such as CNR1 . Integration of expression and genotype data revealed 〉 1.4 million single-cell expression quantitative trait loci (eQTLs), many of which were not seen in bulk gene-expression datasets and a subset of which involved variants related to brain disorders. Moreover, we found that expression patterns across cell types recapitulated the spatial relationships of excitatory neurons across cortical layers and enabled the identification of “dynamic eQTLs,” with smooth changes in regulatory effect across cortical layers. The chromatin datasets in the resource allowed for identification of 〉 550,000 single-cell cis-regulatory elements, which were enriched at loci linked to brain-related traits. Combining expression, chromatin, and eQTL datasets, we built cell type–specific gene regulatory networks. In these, information-flow bottleneck genes tended to be specific to particular cell types, in contrast to hubs. We also developed cell-to-cell communication networks, which highlighted differences in signaling pathways in disorders, including altered Wnt signaling in schizophrenia and bipolar disorder. We developed an integrative deep-learning model with embedded layers for genotypes, eQTLs, and regulatory and cell-to-cell communications networks. The model allowed for accurate imputation of cell type–specific expression and phenotype from genotype. It prioritized 〉 250 risk genes and drug targets for brain-related disorders along with associated cell types. Simulated perturbation of individual genes led to predicted expression changes mirroring those for disease cases, suggesting drug targets. Lastly, we constructed predictive models for aging and Alzheimer’s disease, showing, for instance, that expression and chromatin in specific neurons were highly predictive of an individual’s age. CONCLUSION Our population-scale single-cell resource for the human brain can help facilitate precision-medicine approaches for neuropsychiatric disorders, especially by prioritizing follow-up genes and drug targets linked to cell types. brainSCOPE resource. snRNA-seq and snATAC-seq from 388 individuals allowed assessment of regulatory elements (scCREs), single-cell eQTLs (scQTLs), and gene regulatory networks across cell types. These were integrated into a model (LNCTP, Linear Network of Cell Type Phenotypes) to predict phenotypes and prioritize genes and cell types.
    Type of Medium: Online Resource
    ISSN: 0036-8075 , 1095-9203
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    Language: English
    Publisher: American Association for the Advancement of Science (AAAS)
    Publication Date: 2024
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    detail.hit.zdb_id: 2066996-3
    SSG: 11
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  • 10
    In: Journal of Genetics and Genomics, Elsevier BV, Vol. 49, No. 3 ( 2022-03), p. 249-253
    Type of Medium: Online Resource
    ISSN: 1673-8527
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2022
    detail.hit.zdb_id: 2299030-6
    SSG: 12
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