Plasma proteomics identifies proteins and pathways associated with incident depression in 46,165 adults.

Proteomic alterations preceding the onset of depression offer valuable insights into its development and potential interventions. Leveraging data from 46,165 UK Biobank participants and 2920 plasma proteins profiled at baseline, we conducted a longitudinal analysis with a median follow-up of 14.5 years to explore the relationship between plasma proteins and incident depression. Linear regression was then used to assess associations between depression-related proteins and brain structures, genetic factors, and stress-related events. Our analysis identified 157 proteins associated with incident depression (P <1.71 × 10-5), including novel associations with proteins such as GAST, PLAUR, LRRN1, BCAN, and ITGA11. Notably, higher expression levels of GDF15 (P = 6.18 × 10-26) and PLAUR (P = 2.88 × 10-14) were linked to an increased risk of depression, whereas higher levels of LRRN1 (P = 4.28 × 10-11) and ITGA11 (P = 3.68 × 10-9) were associated with a decreased risk. Dysregulation of the 157 proteins is correlated with brain regions implicated in depression, including the hippocampus and middle temporal gyrus. Additionally, these protein alterations were strongly correlated with stress-related events, including self-harm events, adult, and childhood trauma. Biological pathway enrichment analysis highlighted the critical roles of the immune response. EGFR and TNF emerged as key proteins in the protein-protein interaction network. BTN3A2, newly linked to incident depression (P = 4.35 × 10-10), was confirmed as a causal factor through Mendelian randomization analysis. In summary, our research identified the proteomic signatures associated with the onset of depression, highlighting its potential for early intervention and tailored therapeutic avenues.

MicroHDF: predicting host phenotypes with metagenomic data using a deep forest-based framework.

The gut microbiota plays a vital role in human health, and significant effort has been made to predict human phenotypes, especially diseases, with the microbiota as a promising indicator or predictor with machine learning (ML) methods. However, the accuracy is impacted by a lot of factors when predicting host phenotypes with the metagenomic data, e.g. small sample size, class imbalance, high-dimensional features, etc. To address these challenges, we propose MicroHDF, an interpretable deep learning framework to predict host phenotypes, where a cascade layers of deep forest units is designed for handling sample class imbalance and high dimensional features. The experimental results show that the performance of MicroHDF is competitive with that of existing state-of-the-art methods on 13 publicly available datasets of six different diseases. In particular, it performs best with the area under the receiver operating characteristic curve of 0.9182 ± 0.0098 and 0.9469 ± 0.0076 for inflammatory bowel disease (IBD) and liver cirrhosis, respectively. Our MicroHDF also shows better performance and robustness in cross-study validation. Furthermore, MicroHDF is applied to two high-risk diseases, IBD and autism spectrum disorder, as case studies to identify potential biomarkers. In conclusion, our method provides an effective and reliable prediction of the host phenotype and discovers informative features with biological insights.

Association of birth weight with neuropsychological functioning in early adolescence: A retrospective cohort study.

OBJECTIVE: This study aimed to compare the neuropsychological function in early adolescence between children born small for gestational age (SGA) or large for gestational age (LGA) and those born appropriate for gestational age (AGA). METHODS: This retrospective cohort study utilized data from the Adolescent Brain Cognitive Development study in 2016-18. Children born of singleton pregnancy with complete information of birth weight and delivery week were enrolled. Their neuropsychological functioning were assessed by the brain structural magnetic resonance imaging (MRI), combined with cognitive and behavioral measurements. Linear mixed-effects models and subgroup analyses were performed. RESULTS: Among 5,922 children aged 9-11, children born SGA and LGA demonstrated similar cognitive and behavioral performances as children born AGA (P > 0.05). In the MRI measurement, brain area and volume were lower among SGA children compared to AGA children (t=-5.626, Cohen's d = 0.448, P < 0.001; t=-6.071, Cohen's d = 0.427, P < 0.001); brain area and volume were higher among LGA children compared to AGA children (t = 8.562, Cohen's d = 0.470, P < 0.001; t = 8.562, Cohen's d = 0.470, P < 0.001). Cortical thickness was of no statistical difference (P > 0.05). These associations were confirmed by sensitivity analyses and propensity score matching. CONCLUSION: Children born of SGA and LGA status were associated with altered brain area and volume structure in early adolescence.

Energy Landscape Reveals the Underlying Mechanism of Cancer-Adipose Conversion in Gene Network Models.

Cancer is a systemic heterogeneous disease involving complex molecular networks. Tumor formation involves an epithelial-mesenchymal transition (EMT), which promotes both metastasis and plasticity of cancer cells. Recent experiments have proposed that cancer cells can be transformed into adipocytes via a combination of drugs. However, the underlying mechanisms for how these drugs work, from a molecular network perspective, remain elusive. To reveal the mechanism of cancer-adipose conversion (CAC), this study adopts a systems biology approach by combing mathematical modeling and molecular experiments, based on underlying molecular regulatory networks. Four types of attractors are identified, corresponding to epithelial (E), mesenchymal (M), adipose (A) and partial/intermediate EMT (P) cell states on the CAC landscape. Landscape and transition path results illustrate that intermediate states play critical roles in the cancer to adipose transition. Through a landscape control approach, two new therapeutic strategies for drug combinations are identified, that promote CAC. These predictions are verified by molecular experiments in different cell lines. The combined computational and experimental approach provides a powerful tool to explore molecular mechanisms for cell fate transitions in cancer networks. The results reveal underlying mechanisms of intermediate cell states that govern the CAC, and identified new potential drug combinations to induce cancer adipogenesis.

A catalog of small proteins from the global microbiome.

Small open reading frames (smORFs) shorter than 100 codons are widespread and perform essential roles in microorganisms, where they encode proteins active in several cell functions, including signal pathways, stress response, and antibacterial activities. However, the ecology, distribution and role of small proteins in the global microbiome remain unknown. Here, we construct a global microbial smORFs catalog (GMSC) derived from 63,410 publicly available metagenomes across 75 distinct habitats and 87,920 high-quality isolate genomes. GMSC contains 965 million non-redundant smORFs with comprehensive annotations. We find that archaea harbor more smORFs proportionally than bacteria. We moreover provide a tool called GMSC-mapper to identify and annotate small proteins from microbial (meta)genomes. Overall, this publicly-available resource demonstrates the immense and underexplored diversity of small proteins.

Individual cell modification with cell surface specific atom transfer radical polymerization for enhanced Cr(VI) removal.

Modifying cells with polymers on the surface can enable them to gain or enhance function with various applications, wherein the atom transfer radical polymerization (ATRP) has garnered significant potential due to its biocompatibility. However, specifically initiating ATRP from the cell surface for in-situ modification remains challenging. This study established a bacterial surface-initiated ATRP method and further applied it for enhanced Cr(VI) removal. The cell surface specificity was facilely achieved by cell surface labelling with azide substrates, following alkynyl ATRP initiator specifically anchoring with azide-alkyne click chemistry. Then, the ATRP polymerization was initiated from the cell surface, and different polymers were successfully applied to in-situ modification. Further analysis revealed that the modification of Shewanella oneidensis with poly (4-vinyl pyridine) and sodium polymethacrylate improved the heavy metal tolerance and enhanced the Cr(VI) removal rate of 2.6 times from 0.088 h-1 to 0.314 h-1. This work provided a novel idea for bacterial surface modification and would extend the application of ATRP in bioremediation.

Long-read sequencing reveals extensive gut phageome structural variations driven by genetic exchange with bacterial hosts.

Genetic variations are instrumental for unraveling phage evolution and deciphering their functional implications. Here, we explore the underlying fine-scale genetic variations in the gut phageome, especially structural variations (SVs). By using virome-enriched long-read metagenomic sequencing across 91 individuals, we identified a total of 14,438 nonredundant phage SVs and revealed their prevalence within the human gut phageome. These SVs are mainly enriched in genes involved in recombination, DNA methylation, and antibiotic resistance. Notably, a substantial fraction of phage SV sequences share close homology with bacterial fragments, with most SVs enriched for horizontal gene transfer (HGT) mechanism. Further investigations showed that these SV sequences were genetic exchanged between specific phage-bacteria pairs, particularly between phages and their respective bacterial hosts. Temperate phages exhibit a higher frequency of genetic exchange with bacterial chromosomes and then virulent phages. Collectively, our findings provide insights into the genetic landscape of the human gut phageome.

VirRep: a hybrid language representation learning framework for identifying viruses from human gut metagenomes.

Identifying viruses from metagenomes is a common step to explore the virus composition in the human gut. Here, we introduce VirRep, a hybrid language representation learning framework, for identifying viruses from human gut metagenomes. VirRep combines a context-aware encoder and an evolution-aware encoder to improve sequence representation by incorporating k-mer patterns and sequence homologies. Benchmarking on both simulated and real datasets with varying viral proportions demonstrates that VirRep outperforms state-of-the-art methods. When applied to fecal metagenomes from a colorectal cancer cohort, VirRep identifies 39 high-quality viral species associated with the disease, many of which cannot be detected by existing methods.

Association between twin status with cognitive, behavioral development and brain structure in early adolescence: a retrospective cohort analysis based on the Adolescent Brain Cognitive Development Study.

Twin births are related with maternal and fetal adverse outcomes. Little was known about the comparability of the cognitive, behavioral development and brain structure between twins and singletons in early adolescence. This retrospective cohort study was based on data from the United States population-based, prospective, longitudinal observational Adolescent Brain Cognitive Development study. Children with complete twin status information were enrolled, and the exposure variable was twin status. Primary outcomes were cognitive, behavioral development and brain structure in early adolescence. Cognitive and behavioral outcomes were assessed by using the NIH Toolbox and Child Behavioral Checklist, respectively. Brain structure was evaluated by the cortical thickness, area, and volume extracted from the magnetic resonance imaging (MRI) data. Subgroup analyses were conducted by prematurity, birth weight, with sibling, genetic profiles, and twin types (zygosity). From 1st September 2016 to 15th November 2018, 11545 children (9477 singletons and 2068 twins) aged 9-10 years were enrolled. Twins showed mildly lower cognitive performance (|t|> 5.104, P-values < 0.001, False Discovery Rate [FDR] < 0.001), better behavioral outcome (|t|> 2.441, P-values < 0.015, FDR < 0.042), such as lower scores for multiple psychiatric disorders and behavioral issues, and smaller cortical volume (t = - 3.854, P-values < 0.001, FDR < 0.001) and cortical area (t = - 3.872, P-values < 0.001, FDR < 0.001). The observed differences still held when stratified for prematurity, birth weight, presence of siblings, genetic profiles, and twin types (zygosity). Furthermore, analyses on the two-year follow-up data showed consistent results with baseline data. Twin status is associated with lower cognitive and better behavioral development in early adolescence accompanied by altered brain structure. Clinicians should be aware of the possible difference when generalizing results from adolescent twin samples to singletons.

Discovery of antimicrobial peptides in the global microbiome with machine learning.

Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine-learning-based approach to predict antimicrobial peptides (AMPs) within the global microbiome and leverage a vast dataset of 63,410 metagenomes and 87,920 prokaryotic genomes from environmental and host-associated habitats to create the AMPSphere, a comprehensive catalog comprising 863,498 non-redundant peptides, few of which match existing databases. AMPSphere provides insights into the evolutionary origins of peptides, including by duplication or gene truncation of longer sequences, and we observed that AMP production varies by habitat. To validate our predictions, we synthesized and tested 100 AMPs against clinically relevant drug-resistant pathogens and human gut commensals both in vitro and in vivo. A total of 79 peptides were active, with 63 targeting pathogens. These active AMPs exhibited antibacterial activity by disrupting bacterial membranes. In conclusion, our approach identified nearly one million prokaryotic AMP sequences, an open-access resource for antibiotic discovery.

Unnatural Direct Interspecies Electron Transfer Enabled by Living Cell-Cell Click Chemistry.

Direct interspecies electron transfer (DIET) is essential for maintaining the function and stability of anaerobic microbial consortia. However, only limited natural DIET modes have been identified and DIET engineering remains highly challenging. In this study, an unnatural DIET between Shewanella oneidensis MR-1 (SO, electron donating partner) and Rhodopseudomonas palustris (RP, electron accepting partner) was artificially established by a facile living cell-cell click chemistry strategy. By introducing alkyne- or azide-modified monosaccharides onto the cell outer surface of the target species, precise covalent connections between different species in high proximity were realized through a fast click chemistry reaction. Remarkably, upon covalent connection, outer cell surface C-type cytochromes mediated DIET between SO and RP was achieved and identified, although this was never realized naturally. Moreover, this connection directly shifted the natural H2 mediated interspecies electron transfer (MIET) to DIET between SO and RP, which delivered superior interspecies electron exchange efficiency. Therefore, this work demonstrated a naturally unachievable DIET and an unprecedented MIET shift to DIET accomplished by cell-cell distance engineering, offering an efficient and versatile solution for DIET engineering, which extends our understanding of DIET and opens up new avenues for DIET exploration and applications.

Efficient Recovery of Complete Gut Viral Genomes by Combined Short- and Long-Read Sequencing.

Current metagenome assembled human gut phage catalogs contained mostly fragmented genomes. Here, comprehensive gut virome detection procedure is developed involving virus-like particle (VLP) enrichment from ≈500 g feces and combined sequencing of short- and long-read. Applied to 135 samples, a Chinese Gut Virome Catalog (CHGV) is assembled consisting of 21,499 non-redundant viral operational taxonomic units (vOTUs) that are significantly longer than those obtained by short-read sequencing and contained ≈35% (7675) complete genomes, which is ≈nine times more than those in the Gut Virome Database (GVD, ≈4%, 1,443). Interestingly, the majority (≈60%, 13,356) of the CHGV vOTUs are obtained by either long-read or hybrid assemblies, with little overlap with those assembled from only the short-read data. With this dataset, vast diversity of the gut virome is elucidated, including the identification of 32% (6,962) novel vOTUs compare to public gut virome databases, dozens of phages that are more prevalent than the crAssphages and/or Gubaphages, and several viral clades that are more diverse than the two. Finally, the functional capacities are also characterized of the CHGV encoded proteins and constructed a viral-host interaction network to facilitate future research and applications.

STAB2: an updated spatio-temporal cell atlas of the human and mouse brain.

The brain is constituted of heterogeneous types of neuronal and non-neuronal cells, which are organized into distinct anatomical regions, and show precise regulation of gene expression during development, aging and function. In the current database release, STAB2 provides a systematic cellular map of the human and mouse brain by integrating recently published large-scale single-cell and single-nucleus RNA-sequencing datasets from diverse regions and across lifespan. We applied a hierarchical strategy of unsupervised clustering on the integrated single-cell transcriptomic datasets to precisely annotate the cell types and subtypes in the human and mouse brain. Currently, STAB2 includes 71 and 61 different cell subtypes defined in the human and mouse brain, respectively. It covers 63 subregions and 15 developmental stages of human brain, and 38 subregions and 30 developmental stages of mouse brain, generating a comprehensive atlas for exploring spatiotemporal transcriptomic dynamics in the mammalian brain. We also augmented web interfaces for querying and visualizing the gene expression in specific cell types. STAB2 is freely available at https://mai.fudan.edu.cn/stab2.

Compared to histamine-2 receptor antagonist, proton pump inhibitor induces stronger oral-to-gut microbial transmission and gut microbiome alterations: a randomised controlled trial.

OBJECTIVE: We aim to compare the effects of proton pump inhibitors (PPIs) and histamine-2 receptor antagonists (H2RAs) on the gut microbiota through longitudinal analysis. DESIGN: Healthy volunteers were randomly assigned to receive either PPI (n=23) or H2RA (n=26) daily for seven consecutive days. We collected oral (saliva) and faecal samples before and after the intervention for metagenomic next-generation sequencing. We analysed intervention-induced alterations in the oral and gut microbiome including microbial abundance and growth rates, oral-to-gut transmissions, and compared differences between the PPI and H2RA groups. RESULTS: Both interventions disrupted the gut microbiota, with PPIs demonstrating more pronounced effects. PPI usage led to a significantly higher extent of oral-to-gut transmission and promoted the growth of specific oral microbes in the gut. This led to a significant increase in both the number and total abundance of oral species present in the gut, including the identification of known disease-associated species like Fusobacterium nucleatum and Streptococcus anginosus. Overall, gut microbiome-based machine learning classifiers could accurately distinguish PPI from non-PPI users, achieving an area under the receiver operating characteristic curve (AUROC) of 0.924, in contrast to an AUROC of 0.509 for H2RA versus non-H2RA users. CONCLUSION: Our study provides evidence that PPIs have a greater impact on the gut microbiome and oral-to-gut transmission than H2RAs, shedding light on the mechanism underlying the higher risk of certain diseases associated with prolonged PPI use. TRIAL REGISTRATION NUMBER: ChiCTR2300072310.

An augmented Mendelian randomization approach provides causality of brain imaging features on complex traits in a single biobank-scale dataset.

Mendelian randomization (MR) is an effective approach for revealing causal risk factors that underpin complex traits and diseases. While MR has been more widely applied under two-sample settings, it is more promising to be used in one single large cohort given the rise of biobank-scale datasets that simultaneously contain genotype data, brain imaging data, and matched complex traits from the same individual. However, most existing multivariable MR methods have been developed for two-sample setting or a small number of exposures. In this study, we introduce a one-sample multivariable MR method based on partial least squares and Lasso regression (MR-PL). MR-PL is capable of considering the correlation among exposures (e.g., brain imaging features) when the number of exposures is extremely upscaled, while also correcting for winner's curse bias. We performed extensive and systematic simulations, and demonstrated the robustness and reliability of our method. Comprehensive simulations confirmed that MR-PL can generate more precise causal estimates with lower false positive rates than alternative approaches. Finally, we applied MR-PL to the datasets from UK Biobank to reveal the causal effects of 36 white matter tracts on 180 complex traits, and showed putative white matter tracts that are implicated in smoking, blood vascular function-related traits, and eating behaviors.

Engineering of cytosine base editors with DNA damage minimization and editing scope diversification.

Cytosine base editors (CBEs), which enable precise C-to-T substitutions, have been restricted by potential safety risks, including DNA off-target edits, RNA off-target edits and additional genotoxicity such as DNA damages induced by double-strand breaks (DSBs). Though DNA and RNA off-target edits have been ameliorated via various strategies, evaluation and minimization of DSB-associated DNA damage risks for most CBEs remain to be resolved. Here we demonstrate that YE1, an engineered CBE variant with minimized DNA and RNA off-target edits, could induce prominent DSB-associated DNA damage risks, manifested as γH2AX accumulation in human cells. We then perform deaminase engineering for two deaminases lamprey LjCDA1 and human APOBEC3A, and generate divergent CBE variants with eliminated DSB-associated DNA damage risks, in addition to minimized DNA/RNA off-target edits. Furthermore, the editing scopes and sequence preferences of APOBEC3A-derived CBEs could be further diversified by internal fusion strategy. Taken together, this study provides updated evaluation platform for DSB-associated DNA damage risks of CBEs and further generates a series of safer toolkits with diversified editing signatures to expand their applications.

Common and disorder-specific cortical thickness alterations in internalizing, externalizing and thought disorders during early adolescence: an Adolescent Brain and Cognitive Development study.

BACKGROUND: A growing body of neuroimaging studies has reported common neural abnormalities among mental disorders in adults. However, it is unclear whether the distinct disorder-specific mechanisms operate during adolescence despite the overlap among disorders. METHODS: We studied a large cohort of more than 11 000 preadolescent (age 9-10 yr) children from the Adolescent Brain and Cognitive Development cohort. We adopted a regrouping approach to compare cortical thickness (CT) alterations and longitudinal changes between healthy controls (n = 4041) and externalizing (n = 1182), internalizing (n = 1959) and thought disorder (n = 347) groups. Genome-wide association study (GWAS) was performed on regional CT across 4468 unrelated European youth. RESULTS: Youth with externalizing or internalizing disorders exhibited increased regional CT compared with controls. Externalizing (p = 8 × 10-4, Cohen d = 0.10) and internalizing disorders (p = 2 × 10-3, Cohen d = 0.08) shared thicker CT in the left pars opercularis. The somatosensory and the primary auditory cortex were uniquely affected in externalizing disorders, whereas the primary motor cortex and higher-order visual association areas were uniquely affected in internalizing disorders. Only youth with externalizing disorders showed decelerated cortical thinning from age 10-12 years. The GWAS found 59 genome-wide significant associated genetic variants across these regions. Cortical thickness in common regions was associated with glutamatergic neurons, while internalizing-specific regional CT was associated with astrocytes, oligodendrocyte progenitor cells and GABAergic neurons. LIMITATIONS: The sample size of the GWAS was relatively small. CONCLUSION: Our study provides strong evidence for the presence of specificity in CT, developmental trajectories and underlying genetic underpinnings among externalizing and internalizing disorders during early adolescence. Our results support the neurobiological validity of the regrouping approach that could supplement the use of a dimensional approach in future clinical practice.

Computational exploration of the global microbiome for antibiotic discovery.

Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine learning-based approach to predict prokaryotic antimicrobial peptides (AMPs) by leveraging a vast dataset of 63,410 metagenomes and 87,920 microbial genomes. This led to the creation of AMPSphere, a comprehensive catalog comprising 863,498 non-redundant peptides, the majority of which were previously unknown. We observed that AMP production varies by habitat, with animal-associated samples displaying the highest proportion of AMPs compared to other habitats. Furthermore, within different human-associated microbiota, strain-level differences were evident. To validate our predictions, we synthesized and experimentally tested 50 AMPs, demonstrating their efficacy against clinically relevant drug-resistant pathogens both in vitro and in vivo. These AMPs exhibited antibacterial activity by targeting the bacterial membrane. Additionally, AMPSphere provides valuable insights into the evolutionary origins of peptides. In conclusion, our approach identified AMP sequences within prokaryotic microbiomes, opening up new avenues for the discovery of antibiotics.

Enterotypes of the human gut mycobiome.

BACKGROUND: The fungal component of the human gut microbiome, also known as the mycobiome, plays a vital role in intestinal ecology and human health. However, the overall structure of the gut mycobiome as well as the inter-individual variations in fungal composition remains largely unknown. In this study, we collected a total of 3363 fungal sequencing samples from 16 cohorts across three continents, including 572 newly profiled samples from China. RESULTS: We identify and characterize four mycobiome enterotypes using ITS profiling of 3363 samples from 16 cohorts. These enterotypes exhibit stability across populations and geographical locations and significant correlation with bacterial enterotypes. Particularly, we notice that fungal enterotypes have a strong age preference, where the enterotype dominated by Candida (i.e., Can_type enterotype) is enriched in the elderly population and confers an increased risk of multiple diseases associated with a compromised intestinal barrier. In addition, bidirectional mediation analysis reveals that the fungi-contributed aerobic respiration pathway associated with the Can_type enterotype might mediate the association between the compromised intestinal barrier and aging. CONCLUSIONS: We show that the human gut mycobiome has stable compositional patterns across individuals and significantly correlates with multiple host factors, such as diseases and host age. Video Abstract.

Prioritizing genes associated with brain disorders by leveraging enhancer-promoter interactions in diverse neural cells and tissues.

BACKGROUND: Prioritizing genes that underlie complex brain disorders poses a considerable challenge. Despite previous studies have found that they shared symptoms and heterogeneity, it remained difficult to systematically identify the risk genes associated with them. METHODS: By using the CAGE (Cap Analysis of Gene Expression) read alignment files for 439 human cell and tissue types (including primary cells, tissues and cell lines) from FANTOM5 project, we predicted enhancer-promoter interactions (EPIs) of 439 cell and tissue types in human, and examined their reliability. Then we evaluated the genetic heritability of 17 diverse brain disorders and behavioral-cognitive phenotypes in each neural cell type, brain region, and developmental stage. Furthermore, we prioritized genes associated with brain disorders and phenotypes by leveraging the EPIs in each neural cell and tissue type, and analyzed their pleiotropy and functionality for different categories of disorders and phenotypes. Finally, we characterized the spatiotemporal expression dynamics of these associated genes in cells and tissues. RESULTS: We found that identified EPIs showed activity specificity and network aggregation in cell and tissue types, and enriched TF binding in neural cells played key roles in synaptic plasticity and nerve cell development, i.e., EGR1 and SOX family. We also discovered that most neurological disorders exhibit heritability enrichment in neural stem cells and astrocytes, while psychiatric disorders and behavioral-cognitive phenotypes exhibit enrichment in neurons. Furthermore, our identified genes recapitulated well-known risk genes, which exhibited widespread pleiotropy between psychiatric disorders and behavioral-cognitive phenotypes (i.e., FOXP2), and indicated expression specificity in neural cell types, brain regions, and developmental stages associated with disorders and phenotypes. Importantly, we showed the potential associations of brain disorders with brain regions and developmental stages that have not been well studied. CONCLUSIONS: Overall, our study characterized the gene-enhancer regulatory networks and genetic mechanisms in the human neural cells and tissues, and illustrated the value of reanalysis of publicly available genomic datasets.