Epigenomic Data Research Articles

Core promoterome of barley embryo

Precise localization and dissection of gene promoters are key to understanding transcriptional gene regulation and to successful bioengineering applications. The core RNA polymerase II initiation machinery is highly conserved among eukaryotes, leading to a general expectation of equivalent underlying mechanisms. Still, less is known about promoters in the plant kingdom. In this study, we employed cap analysis of gene expression (CAGE) at three embryonic developmental stages in barley to accurately map, annotate, and quantify transcription initiation events. Unsupervised discovery of de novo sequence clusters grouped promoters based on characteristic initiator and position-specific core-promoter motifs. This grouping was complemented by the annotation of transcription factor binding site (TFBS) motifs. Integration with genome-wide epigenomic data sets and gene ontology (GO) enrichment analysis further delineated the chromatin environments and functional roles of genes associated with distinct promoter categories. The TATA-box presence governs all features explored, supporting the general model of two separate genomic regulatory environments. We describe the extent and implications of alternative transcription initiation events, including those that are specific to developmental stages, which can affect the protein sequence or the presence of regions that regulate translation. The generated promoterome dataset provides a valuable genomic resource for enhancing the functional annotation of the barley genome. It also offers insights into the transcriptional regulation of individual genes and presents opportunities for the informed manipulation of promoter architecture, with the aim of enhancing traits of agronomic importance.

Efforts toward discovering inhibitors of the SYK SH2–FCER1γ interaction as potential Alzheimer’s disease chemical probes and therapeutics

AbstractBackgroundSpleen associated tyrosine kinase (SYK) plays a potential role in several neurodegenerative conditions. To date, a few pharmacological inhibitors have been developed, targeting the ATP‐binding kinase domain, and some of these inhibitors have been reported to show beneficial effects on the pathophysiology of Alzheimer’s (PMC: 9179326).In 2015, the Accelerating Medicines Partnership: Alzheimer’s Disease (AMP‐AD) Knowledge Portal analyzed and identified SYK and the FC gamma receptor (FCεR1γ) as targets for Alzheimer’s Disease (AD), through multidimensional human “omic” (genomic, epigenomic, RNAseq, and proteomic) data (PMID: 26853544). Thus, the interaction between FCER1G p‐ITAM and SYK tandem SH2 domain was selected for further target validation and small molecule hit discovery efforts through the portfolio of novel targets to treat the Alzheimer’s disease via the Target Enablement to Accelerate Therapy Development for Alzheimer’s Disease (TREAT‐AD) program were initiated. As part of the TREAT‐AD effort, we aim to provide experimental validation of a SYK–FCεR1γ interaction inhibitor for AD and begin drug discovery campaigns to identify novel inhibitors of this interaction, with the goal of developing an in vivo chemical probe.MethodUsing purified SYK tandem‐SH2 (tSH2) domain protein, a time‐resolved fluorescence energy transfer (TR‐FRET) based assay platform was developed with a fluoroprobe‐labeled phospho‐ITAM peptide of FCεR1γ. This assay was miniaturized for ultra‐high‐throughput screening (uHTS) of 100K compounds in parallel with a DNA encoded library (DEL) screening approach (Ref: X‐Chem). The hits from these screening methods were evaluated in secondary assays in an attempt to validate the initial hits.ResultWe have previous crystallography data of the SYK tSH2 domains bound to phospho‐ITAM peptides and have established a TR‐FRET based assay to discover and characterize inhibitors of the interaction between the FCεR1γ peptide and SYK tSH2 domains. Several thio‐uric acids hit compounds have been identified from uHTS. Most of them demonstrate potency in the TR‐FRET based assay of <10 µM. Although, orthogonal experimental validation is still under process.ConclusionThrough the TREAT‐AD program, we have explored the SYK–FCεR1γ interaction and demonstrated novel screening strategies, which can be further exploited to generate novel inhibitors of this interaction.

An intronic LINE-1 regulates IFNAR1 expression in human immune cells

BackgroundDespite their origins as selfish parasitic sequences, some transposons in the human genome have been co-opted to serve as regulatory elements, contributing to the evolution of transcriptional networks. Most well-characterized examples of transposon-derived regulatory elements derive from endogenous retroviruses (ERVs), due to the intrinsic regulatory activity of proviral long terminal repeat regions. However, one subclass of transposable elements, the Long Interspersed Nuclear Elements (LINEs), have been largely overlooked in the search for functional regulatory transposons, and considered to be broadly epigenetically repressed.ResultsWe examined the chromatin state of LINEs by analyzing epigenomic data from human immune cells. Many LINEs are marked by the repressive H3K9me3 modification, but a subset exhibits evidence of enhancer activity in human immune cells despite also showing evidence of epigenetic repression. We hypothesized that these competing forces of repressive and activating epigenetic marks might lead to inducible enhancer activity. We investigated a specific L1M2a element located within the first intron of Interferon Alpha/Beta Receptor 1 (IFNAR1). This element shows epigenetic signatures of B cell-specific enhancer activity, despite being repressed by the Human Silencing Hub (HUSH) complex. CRISPR deletion of the element in B lymphoblastoid cells revealed that the element acts as an enhancer that regulates both steady state and interferon-inducible expression of IFNAR1.ConclusionsOur study experimentally demonstrates that an L1M2a element was co-opted to function as an interferon-inducible enhancer of IFNAR1, creating a feedback loop wherein IFNAR1 is transcriptionally upregulated by interferon signaling. This finding suggests that other LINEs may exhibit cryptic cell type-specific or context-dependent enhancer activity. LINEs have received less attention than ERVs in the effort to understand the contribution of transposons to the regulatory landscape of cellular genomes, but these are likely important, lineage-specific players in the rapid evolution of immune system regulatory networks and deserve further study.

Unlocking the Potential of Artificial Intelligence in Acute Myeloid Leukemia and Myelodysplastic Syndromes.

This review aims to elucidate the transformative impact and potential of machine learning (ML) in the diagnosis, prognosis, and clinical management of myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML). It further aims to bridge the gap between current advances of ML and their practical application in these diseases. Recent advances in ML have revolutionized prognostication, diagnosis, and treatment of MDS and AML. ML algorithms have proven effective in predicting disease progression, optimizing treatment responses, and in the stratification of patient groups. Particularly, the use of ML in genomic and epigenomic data analysis has unveiled novel insights into the molecular heterogeneity of MDS and AML, leading to better-informed therapeutic strategies. Furthermore, deep learning techniques have shown promise in analyzing complex patterns in bone marrow biopsy images, providing a potential pathway towards early and accurate diagnosis. While still in the nascent stages, ML applications in MDS and AML signify a paradigm shift towards precision medicine. The integration of ML with traditional clinical practices could potentially enhance diagnostic accuracy, refine risk stratification, and improve therapeutic approaches. However, challenges related to data privacy, standardization, and algorithm interpretability must be addressed to realize the full potential of ML in this field. Future research should focus on the development of robust, transparent ML models and their ethical implementation in clinical settings.

Statistical learning quantifies transposable element-mediated cis-regulation

BackgroundTransposable elements (TEs) have colonized the genomes of most metazoans, and many TE-embedded sequences function as cis-regulatory elements (CREs) for genes involved in a wide range of biological processes from early embryogenesis to innate immune responses. Because of their repetitive nature, TEs have the potential to form CRE platforms enabling the coordinated and genome-wide regulation of protein-coding genes by only a handful of trans-acting transcription factors (TFs).ResultsHere, we directly test this hypothesis through mathematical modeling and demonstrate that differences in expression at protein-coding genes alone are sufficient to estimate the magnitude and significance of TE-contributed cis-regulatory activities, even in contexts where TE-derived transcription fails to do so. We leverage hundreds of overexpression experiments and estimate that, overall, gene expression is influenced by TE-embedded CREs situated within approximately 500 kb of promoters. Focusing on the cis-regulatory potential of TEs within the gene regulatory network of human embryonic stem cells, we find that pluripotency-specific and evolutionarily young TE subfamilies can be reactivated by TFs involved in post-implantation embryogenesis. Finally, we show that TE subfamilies can be split into truly regulatorily active versus inactive fractions based on additional information such as matched epigenomic data, observing that TF binding may better predict TE cis-regulatory activity than differences in histone marks.ConclusionOur results suggest that TE-embedded CREs contribute to gene regulation during and beyond gastrulation. On a methodological level, we provide a statistical tool that infers TE-dependent cis-regulation from RNA-seq data alone, thus facilitating the study of TEs in the next-generation sequencing era.

Prevalently carried HLA allele associates with inflammation and poor prognosis in HF patients via co-stimulatory, adhesion and MHC-II enhanceosome regulation

Abstract Background Heart failure with reduced ejection fraction (HFrEF) is associated with impaired inflammation resolution, adverse remodeling, restricted function, and poor prognosis. Furthermore, immune cells play crucial roles in pathological remodeling in cardiovascular diseases. Thus identifying subsets of patients with unique inflammatory profiles may aid in better risk stratification and precision therapies for chronic HFrEF patients with altered immune profiles. Methods and Results A pilot study using scRNA-seq of circulating immune cells (n=16) showed strong T cell activation in HFrEF patients, which was validated via flow cytometry in HFrEF patients and healthy controls (n=180 patients) with a shift into the memory/effector phenotype (p<0.0001). Thus, HFrEF patients may have unique MHC signatures promoting aberrant T cell activation, leading to chronic inflammation and pathology. We investigated antigen-presenting cells (APCs) that activate CD4+ T cells via MHC-II and costimulatory molecules. Indeed, we found MHC-II and costimulatory molecule signatures associated with HFrEF and T cell activation, such as HLA-DRB5 (DRB5) and ICAM-1, indicating both T cell activation potential and a pro-inflammatory status. To study whether HLA-DRB5 selectively promotes immune dysregulation, circulating monocytes from healthy DRB5 carriers and non-carriers were investigated using scRNA-seq showing enhanced antigen presentation, co-stimulatory and inflammatory molecule expression (e.g. HLA-DRA, ICAM-2, JAK2). ScRNA-seq confirmed HLA-DRB5 regulation of MHC-II, inflammatory and adhesion (e.g. ITGAM and ITGB1) properties by silencing HLA-DRB5 in THP1 cells. In silico analysis of monocyte epigenome data predicted unique transcription factors (e.g. ERG) controlling of HLA-DRB5 via regulatory elements suggesting a role in selective MHC-II molecule activation. To gain insights as to whether HLA carrier status might associate with prognosis, we performed HLA sequencing on age and gender matched HFrEF patients (n=93). During follow up of this cohort we identified HLA-DRB5*0101, which belongs to the DR2-supertype, as a predictor for death (p<0.05). We hypothesized whether HLA-DRB5 expression could impact T cell infiltration into the heart. Thus, we stimulated naïve T cells with pre-treated THP1-derived DCs (stress v. no stress cardiac organoid medium) and found HLA-DRB5 silencing reduced the infiltration of T cells into unstressed cardiac organoids compared to control group. Conclusions This is the first study to provide mechanistic insights for how HLA-DRB5 regulates innate and adaptive immunity in the context of heart failure, while also showing corresponding changes in T cell activation, infiltration capacity and prognosis in HFrEF patients. These data provide novel insights for risk stratification of heart failure patients and novel immunological interventions which may improve prognosis in HFrEF patients.

Bone Marrow Microenvironment and Hematopoietic Cell Types Are Differentially Affected By Transcriptomic Quality Control Filtering Metrics in Single Cell Data

A crucial step prior to analysis of single cell transcriptomic data includes filtering out low-quality (e.g., dying) cells and artifacts (e.g., empty droplets). Choosing appropriate quality control parameters leads to correct cell assignment, so that the data for downstream analysis is high-quality and is representative of all processed cell types. These metrics include high and low cut-offs for mitochondrial content, total number of genes, number of different genes, and ribosomal content per cell. They have been found to vary based on cell and tissue type, as well biological variables such as age and disease, and technical variables such as sequencing technology. The aim of this study was to investigate the variation of these quality control metrics in a data set that included stem, progenitor, and differentiated cell types of the hematopoietic, mesenchymal, and endothelial lineages. Furthermore, we set out to determine whether applying standard cut-offs may differentially affect different cell types. We calculated individual filtering parameters for each cell type using the R package ddqc (Subramanian et al. Genome Biol. 2022). Thus, any biological variability based on mitochondrial, ribosomal, and gene counts was accounted for. Notably, distribution of measurements for each of the above quality control metrics varied by cluster, and certain cell-type means were adjacent to commonly used cut-off values. For instance, we observed higher content of mitochondrial genes in osteoblasts, sinusoidal endothelial cells, a subset of adipocyte-primed mesenchymal cells (MSPC), as well as hematopoietic progenitor cells, pre-B, and B cells. On the other hand, subsets of erythroblasts, neutrophils, as well as arteriolar endothelial cells had the lowest mitochondrial content. In addition, lower total gene counts and lower gene complexity were found in adipocyte-primed MSPCs, neutrophils, and megakaryocyte-erythrocyte progenitors, while higher counts were found in granulocyte-monocyte progenitor cells, megakaryocyte/erythroid-primed multipotent progenitor cells and subsets of erythroblasts. Applying universal cutoff values to this data set would greatly reduce, and some cases, eliminate cell types that have high mitochondrial content or lower gene counts and complexity, while leaving those cell types on the opposite spectrum unaffected. This would also skew proportions of cells in downstream analyses, thus affecting biological interpretation of the results. Overall, our results demonstrate biological variability of the quality control metric distributions in a single cell transcriptomic data set that includes diverse bone marrow niche and hematopoietic cell types. This reveals novel biological information regarding distinct cell types as well as cautions that applying standard filtering paraments on such a data set would eliminate some cell types and bias downstream analysis for others. Optimization of filtering criteria will be important when broadening single cell analysis to multiplexed approaches incorporating transcriptome, genome, epigenome, and proteome-level data.

Integrated Analysis of Chromatin and Transcriptomic Profiling Identifies PU.1 as a Core Regulatory Factor in Microglial Activation Induced by Chronic Cerebral Hypoperfusion

In addition to causing white matter lesions, chronic cerebral hypoperfusion (CCH) can also cause damage to gray matter, but the underlying molecular mechanisms remain largely unknown. In order to obtain a better understanding of the relationship between gene expression and transcriptional regulation alterations, novel upstream regulators could be identified using integration analysis of the transcriptome and epigenetic approaches. Here, a bilateral common carotid artery stenosis (BCAS) model was established for inducing CCH in mice. The spatial cognitive function of mice was evaluated, and changes in cortical microglia morphology were observed. RNA-sequencing (RNA-seq) and the assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) were performed on isolated mouse cortical brain tissue. Then, a systematic joint analysis of BCAS hypoperfusion-induced cortex-specific RNA-seq and ATAC-seq was conducted in order to assess the extent of the correlation between the two, and PU.1 was found to be greatly enriched through motif analysis and transcription factor annotation. Also, the core regulatory factor PU.1 induced by BCAS hypoperfusion was shown to be colocalized with microglia. Based on the above analysis, PU.1 plays a key regulatory role in microglial activation induced by CCH. And the transcriptome and epigenomic data presented in this study can help identify potential targets for future research exploring chronic hypoperfusion-induced brain injury.

Childhood Treatment with Adeno-Associated Viral Gene Therapy Results in Stable FVIII Expression and Improved Bleeding Phenotype in Adult Severe Hemophilia A Dogs

Introduction: In patients with severe hemophilia A, adeno-associated virus (AAV)-mediated factor VIII (FVIII) gene therapy is associated with reduced bleeding compared with FVIII prophylaxis. AAV5-FVIII gene therapy is approved in the U.S. and conditionally approved in Europe for the treatment of adults with severe hemophilia A. Gene therapy during childhood could potentially prevent the development of arthropathy and improve quality of life. However, there are unknowns regarding long-term safety and whether the predominant episomal nature of AAV persistence would lead to loss of transgene expression with hepatocyte divisions and liver expansion during childhood. The inflammatory response to AAV gene therapy in children is also largely uncharacterized. We have previously reported the treatment of five neonatal or infant dogs with a single vector infusion of a codon-optimized AAV5-B-domain deleted canine FVIII (cFVIII) construct with a hybrid liver promoter (AAV5-cFVIII). Dogs treated at 2 weeks of age demonstrated improved whole blood clot time (WBCT) over a 6-month period despite minimal cFVIII expression (<3%). Dogs treated at 2 months of age demonstrated stable cFVIII expression measured over a 6-month period and decreased WBCT. Aims: To describe the early inflammatory response to AAV5-cFVIII in infant and neonatal dogs, and to provide an update on the safety and efficacy of AAV5-cFVIII in these animals 12-16 months post-treatment. Methods: Hemophilia A dogs were treated with a single vector infusion (dose=2.0e14 vg/kg) of AAV5-cFVIII at 2 weeks (n=2) or 2 months (n=3) of age. Epigenomic data suggests that 2 months of age in dogs is equivalent to 9 months in humans. Liver volume was measured by magnetic resonance imaging at baseline, 3, 6, and 12 months post-AAV5-cFVIII treatment. Plasma cytokine levels were measured using a cytokine array pre-treatment (day 0) and post-treatment (days 2-28). Percutaneous liver biopsy samples were obtained at 2 weeks, 6 months, and 12 months post-treatment. Results: Cytokine array analyses of samples demonstrated that one dog who received AAV5-cFVIII at 2 weeks experienced transient post-treatment plasma elevation of IL-6, KC-like, MCP-1, TGFβ-1, and TGFβ-2 that returned to baseline levels by day 18. Two dogs treated at 2 months experienced a transient increase in GMC-SF, IL-2, IL-6, IL-7, IL-15, and IL-18 that returned to baseline levels by day 21. No changes in levels of IFNγ, IL-8, IL-10, IP-10, TNFα, or TGFβ-3 were observed. Changes in plasma cytokine levels were not associated with an elevation in plasma alanine transaminase levels. Hemophilia A dogs treated with AAV5-cFVIII at 2 weeks of age maintained an improved WBCT 12 to 16 months post-treatment, although cFVIII expression as measured by one-stage (OSA) and chromogenic substrate assays (CSA) remained minimal (<3%). Body weight and liver volume increased by 10.6-fold (measured at 16 months) and 12-fold (measured at 12 months) respectively. Dogs treated with AAV5-cFVIII at 2 months of age demonstrated sustained cFVIII expression by OSA and CSA after 12 to 16 months (8.9-13.39% measured at 16 months by OSA), despite a 3.2-fold increase in body weight (measured at 16 months), and a 3.9-fold increase in liver volume (measured at 12 months). This was associated with improved WBCT observed over the 12 to 16-month period for all three dogs. Overall, a reduction in bleeding events (BEs) was observed in all dogs. No BEs were recorded prior to AAV5-cFVIII treatment for dogs treated at 2 weeks of age. During the 16 months post-treatment, one dog experienced two spontaneous BEs, while no BEs were observed for the second dog. The dogs treated at 2 months experienced 4 (3 spontaneous and 1 traumatic) BEs prior to AAV5-cFVIII treatment, and 1 traumatic BE 48 hours post-treatment prior to the detection of FVIII expression. For the remaining 16 months post-treatment, no BEs were observed in this group. Conclusions: Treatment using AAV5-cFVIII at 2 months of age in a hemophilia A dog model resulted in stable FVIII expression into adult life despite significant liver expansion. For some dogs, AAV-cFVIII treatment resulted in an early transient increase in plasma proinflammatory cytokine levels with no evidence of transaminitis that resolved within three weeks.Further studies on liver biopsy samples are ongoing to evaluate cellular implications, vector genome distribution, and mechanisms of AAV persistence.

NCBI GEO: archive for gene expression and epigenomics data sets: 23-year update.

The Gene Expression Omnibus (GEO) is an international public repository that archives gene expression and epigenomics data sets generated by next-generation sequencing and microarray technologies. Data are typically submitted to GEO by researchers in compliance with widespread journal and funder mandates to make generated data publicly accessible. The resource handles raw data files, processed data filesand descriptive metadata for over 200000 studies and 6.5 million samples, all of which are indexed, searchable and downloadable. Additionally, GEO offers web-based tools that facilitate analysis and visualization of differential gene expression. This article presents the current status and recent advancements in GEO, including the generation of consistently computed gene expression count matrices for thousands of RNA-seq studies, and new interactive graphical plots in GEO2R that help users identify differentially expressed genes and assess data set quality. The GEO repository is built and maintained by the National Center for Biotechnology Information (NCBI), a division of the National Library of Medicine (NLM), and is publicly accessible at https://www.ncbi.nlm.nih.gov/geo/.

CisDynet: An integrated platform for modeling gene-regulatory dynamics and networks.

Chromatin accessibility sequencing has been widely used for uncovering genetic regulatory mechanisms and inferring gene regulatory networks. However, effectively integrating large-scale chromatin accessibility datasets has posed a significant challenge. This is due to the lack of a comprehensive end-to-end solution, as many existing tools primarily emphasize data preprocessing and overlook downstream analyses. To bridge this gap, we have introduced cisDynet, a holistic solution that combines streamlined data preprocessing using Snakemake and R functions with advanced downstream analysis capabilities. cisDynet excels in conventional data analyses, encompassing peak statistics, peak annotation, differential analysis, motif enrichment analysis, and more. Additionally, it allows to perform sophisticated data exploration, such as tissue-specific peak identification, time course data modeling, integration of RNA-seq data to establish peak-to-gene associations, constructing regulatory networks, and conducting enrichment analysis of genome-wide association study (GWAS) variants. As a proof of concept, we applied cisDynet to reanalyze comprehensive ATAC-seq datasets across various tissues from the Encyclopedia of DNA Elements (ENCODE) project. The analysis successfully delineated tissue-specific open chromatin regions (OCRs), established connections between OCRs and target genes, and effectively linked these discoveries with 1861 GWAS variants. Furthermore, cisDynet was instrumental in dissecting the time course open chromatin data of mouse embryonic development, revealing the dynamic behavior of OCRs over developmental stages and identifying key transcription factors governing differentiation trajectories. In summary, cisDynet offers researchers a user-friendly solution that minimizes the need for extensive coding, ensures the reproducibility of results, and greatly simplifies the exploration of epigenomic data.

An intronic enhancer of Cebpa regulates adipocyte differentiation and adipose tissue development via long-range loop formation.

Cebpa is a master transcription factor gene for adipogenesis. However, the mechanisms of enhancer-promoter chromatin interactions controlling Cebpa transcriptional regulation during adipogenic differentiation remain largely unknown. To reveal how the three-dimensional structure of Cebpa changes during adipogenesis, we generated high-resolution chromatin interactions of Cebpa in 3T3-L1 preadipocytes and 3T3-L1 adipocytes using circularized chromosome conformation capture sequencing (4C-seq). We revealed dramatic changes in chromatin interactions and chromatin status at interaction sites during adipogenic differentiation. Based on this, we identified five active enhancers of Cebpa in 3T3-L1 adipocytes through epigenomic data and luciferase reporter assays. Next, epigenetic repression of Cebpa-L1-AD-En2 or -En3 by the dCas9-KRAB system significantly down-regulated Cebpa expression and inhibited adipocyte differentiation. Furthermore, experimental depletion of cohesin decreased the interaction intensity between Cebpa-L1-AD-En2 and the Cebpa promoter and down-regulated Cebpa expression, indicating that long-range chromatin loop formation was mediated by cohesin. Two transcription factors, RXRA and PPARG, synergistically regulate the activity of Cebpa-L1-AD-En2. To test whether Cebpa-L1-AD-En2 plays a role in adipose tissue development, we injected dCas9-KRAB-En2 lentivirus into the inguinal white adipose tissue (iWAT) of mice to suppress the activity of Cebpa-L1-AD-En2. Repression of Cebpa-L1-AD-En2 significantly decreased Cebpa expression and adipocyte size, altered iWAT transcriptome, and affected iWAT development. We identified functional enhancers regulating Cebpa expression and clarified the crucial roles of Cebpa-L1-AD-En2 and Cebpa promoter interaction in adipocyte differentiation and adipose tissue development.

ScGRN: a comprehensive single-cell gene regulatory network platform of human and mouse.

Gene regulatory networks (GRNs) are interpretable graph models encompassing the regulatory interactions between transcription factors (TFs) and their downstream target genes. Making sense of the topology and dynamics of GRNs is fundamental to interpreting the mechanisms of disease etiology and translating corresponding findings into novel therapies. Recent advances in single-cell multi-omics techniques have prompted the computational inference of GRNs from single-cell transcriptomic and epigenomic data at an unprecedented resolution. Here, we present scGRN (https://bio.liclab.net/scGRN/), a comprehensive single-cell multi-omics gene regulatory network platform of human and mouse. The current version of scGRN catalogs 237051 cell type-specific GRNs (62 999692 TF-target gene pairs), covering 160 tissues/cell lines and 1324 single-cell samples. scGRN is the first resource documenting large-scale cell type-specific GRN information of diverse human and mouse conditions inferred from single-cell multi-omics data. We have implemented multiple online tools for effective GRN analysis, including differential TF-target network analysis, TF enrichment analysis, and pathway downstream analysis. We also provided details about TF binding to promoters, super-enhancers and typical enhancers of target genes in GRNs. Taken together, scGRN is an integrative and useful platform for searching, browsing, analyzing, visualizingand downloading GRNs of interest, enabling insight into the differences in regulatory mechanisms across diverse conditions.

Integrative analyses highlight functional regulatory variants associated with neuropsychiatric diseases.

Noncoding variants of presumed regulatory function contribute to the heritability of neuropsychiatric disease. A total of 2,221 noncoding variants connected to risk for ten neuropsychiatric disorders, including autism spectrum disorder, attention deficit hyperactivity disorder, bipolar disorder, borderline personality disorder, major depression, generalized anxiety disorder, panic disorder, post-traumatic stress disorder, obsessive-compulsive disorder and schizophrenia, were studied in developing human neural cells. Integrating epigenomic and transcriptomic data with massively parallel reporter assays identified differentially-active single-nucleotide variants (daSNVs) in specific neural cell types. Expression-gene mapping, network analyses and chromatin looping nominated candidate disease-relevant target genes modulated by these daSNVs. Follow-up integration of daSNV gene editing with clinical cohort analyses suggested that magnesium transport dysfunction may increase neuropsychiatric disease risk and indicated that common genetic pathomechanisms may mediate specific symptoms that are shared across multiple neuropsychiatric diseases.

Massively parallel functional dissection of schizophrenia-associated noncoding genetic variants

Schizophrenia (SCZ) is a highly heritable mental disorder with thousands of associated genetic variants located mostly in the noncoding space of the genome. Translating these associations into insights regarding the underlying pathomechanisms has been challenging because the causal variants, their mechanisms of action, and their target genes remain largely unknown. We implemented a massively parallel variant annotation pipeline (MVAP) to perform SCZ variant-to-function mapping at scale in disease-relevant neural cell types. This approach identified 620 functional variants (1.7%) that operate in a highly developmental context and neuronal-activity-dependent manner. Multimodal integration of epigenomic and CRISPRi screening data enabled us to link these functional variants to target genes, biological processes, and ultimately alterations of neuronal physiology. These results provide a multistage prioritization strategy to map functional single-nucleotide polymorphism (SNP)-to-gene-to-endophenotype relations and offer biological insights into the context-dependent molecular processes modulated by SCZ-associated genetic variation.

Identification of the lipodepsipeptide selethramide encoded in a giant nonribosomal peptide synthetase from a Burkholderia bacterium

Bacterial natural products have found many important industrial applications. Yet traditional discovery pipelines often prioritize individual natural product families despite the presence of multiple natural product biosynthetic gene clusters in each bacterial genome. Systematic characterization of talented strains is a means to expand the known natural product space. Here, we report genomics, epigenomics, and metabolomics studies of Burkholderia sp. FERM BP-3421, a soil isolate and known producer of antitumor spliceostatins. Its genome is composed of two chromosomes and two plasmids encoding at least 29 natural product families. Metabolomics studies showed that FERM BP-3421 also produces antifungal aminopyrrolnitrin and approved anticancer romidepsin. From the orphan metabolome features, we connected a lipopeptide of 1,928 Da to an 18-module nonribosomal peptide synthetase encoded as a single gene in chromosome 1. Isolation and structure elucidation led to the identification of selethramide which contains a repeating pattern of serine and leucine and is cyclized at the side chain oxygen of the one threonine residue at position 13. A (R)-3-hydroxybutyric acid moiety decorates the N-terminal serine. Initial attempts to obtain deletion mutants to probe the role of selethramide failed. After acquiring epigenome (methylome) data for FERM BP-3421, we employed a mimicry by methylation strategy that improved DNA transfer efficiency. Mutants defective in selethramide biosynthesis showed reduced surfactant activity and impaired swarming motility that could be chemically complemented with selethramide. This work unveils a lipopeptide that promotes surface motility, establishes improved DNA transfer efficiency, and sets the stage for continued natural product identification from a prolific strain.

SoyMD: a platform combining multi-omics data with various tools for soybean research and breeding.

Advanced multi-omics technologies offer much information that can uncover the regulatory mechanisms from genotype to phenotype. In soybean, numerous multi-omics databases have been published. Although they cover multiple omics, there are still limitations when it comes to the types and scales of omics datasets and analysis methods utilized. This study aims to address these limitations by collecting and integrating a comprehensive set of multi-omics datasets. This includes 38 genomes, transcriptomes from 435 tissue samples, 125 phenotypes from 6686 accessions, epigenome data involving histone modification, transcription factor binding, chromosomal accessibilityand chromosomal interaction, as well as genetic variation data from 24501 soybean accessions. Then, common analysis pipelines and statistical methods were applied to mine information from these multi-omics datasets, resulting in the successful establishment of a user-friendly multi-omics database called SoyMD (https://yanglab.hzau.edu.cn/SoyMD/#/). SoyMD provides researchers with efficient query options and analysis tools, allowing them to swiftly access relevant omics information and conduct comprehensive multi-omics data analyses. Another notable feature of SoyMD is its capability to facilitate the analysis of candidate genes, as demonstrated in the case study on seed oil content. This highlights the immense potential of SoyMD in soybean genetic breeding and functional genomics research.

Machine learning nominates the inositol pathway and novel genes in Parkinson's disease.

There are 78 loci associated with Parkinson's disease in the most recent genome-wide association study (GWAS), yet the specific genes driving these associations are mostly unknown. Herein, we aimed to nominate the top candidate gene from each Parkinson's disease locus and identify variants and pathways potentially involved in Parkinson's disease. We trained a machine learning model to predict Parkinson's disease-associated genes from GWAS loci using genomic, transcriptomic and epigenomic data from brain tissues and dopaminergic neurons. We nominated candidate genes in each locus and identified novel pathways potentially involved in Parkinson's disease, such as the inositol phosphate biosynthetic pathway (INPP5F, IP6K2, ITPKB and PPIP5K2). Specific common coding variants in SPNS1 and MLX may be involved in Parkinson's disease, and burden tests of rare variants further support that CNIP3, LSM7, NUCKS1 and the polyol/inositol phosphate biosynthetic pathway are associated with the disease. Functional studies are needed to further analyse the involvements of these genes and pathways in Parkinson's disease.

Transcriptional signature of islet neogenesis-associated protein peptide-treated rat pancreatic islets reveals induction of novel long non-coding RNAs.

Diabetes mellitus is characterized by chronic hyperglycemia with loss of β-cell function and mass. An attractive therapeutic approach to treat patients with diabetes in a non-invasive way is to harness the innate regenerative potential of the pancreas. The Islet Neogenesis-Associated Protein pentadecapeptide (INGAP-PP) has been shown to induce β-cell regeneration and improve their function in rodents. To investigate its possible mechanism of action, we report here the global transcriptional effects induced by the short-term INGAP-PP in vitro treatment of adult rat pancreatic islets. Rat pancreatic islets were cultured in vitro in the presence of INGAP-PP for 4 days, and RNA-seq was generated from triplicate treated and control islet samples. We performed a de novo rat gene annotation based on the alignment of RNA-seq reads. The list of INGAP-PP-regulated genes was integrated with epigenomic data. Using the new gene annotation generated in this work, we quantified RNA-seq data profiled in INS-1 cells treated with IL1β, IL1β+Calcipotriol (a vitamin D agonist) or vehicle, and single-cell RNA-seq data profiled in rat pancreatic islets. We found 1,669 differentially expressed genes by INGAP-PP treatment, including dozens of previously unannotated rat transcripts. Genes differentially expressed by the INGAP-PP treatment included a subset of upregulated transcripts that are associated with vitamin D receptor activation. Supported by epigenomic and single-cell RNA-seq data, we identified 9 previously unannotated long noncoding RNAs (lncRNAs) upregulated by INGAP-PP, some of which are also differentially regulated by IL1β and vitamin D in β-cells. These include Ri-lnc1, which is enriched in mature β-cells. Our results reveal the transcriptional program that could explain the enhancement of INGAP-PP-mediated physiological effects on β-cell mass and function. We identified novel lncRNAs that are induced by INGAP-PP in rat islets, some of which are selectively expressed in pancreatic β-cells and downregulated by IL1β treatment of INS-1 cells. Our results suggest a relevant function for Ri-lnc1 in β-cells. These findings are expected to provide the basis for a deeper understanding of islet translational results from rodents to humans, with the ultimate goal of designing new therapies for people with diabetes.

ScMHNN: a novel hypergraph neural network for integrative analysis of single-cell epigenomic, transcriptomic and proteomic data

Abstract Technological advances have now made it possible to simultaneously profile the changes of epigenomic, transcriptomic and proteomic at the single cell level, allowing a more unified view of cellular phenotypes and heterogeneities. However, current computational tools for single-cell multi-omics data integration are mainly tailored for bi-modality data, so new tools are urgently needed to integrate tri-modality data with complex associations. To this end, we develop scMHNN to integrate single-cell multi-omics data based on hypergraph neural network. After modeling the complex data associations among various modalities, scMHNN performs message passing process on the multi-omics hypergraph, which can capture the high-order data relationships and integrate the multiple heterogeneous features. Followingly, scMHNN learns discriminative cell representation via a dual-contrastive loss in self-supervised manner. Based on the pretrained hypergraph encoder, we further introduce the pre-training and fine-tuning paradigm, which allows more accurate cell-type annotation with only a small number of labeled cells as reference. Benchmarking results on real and simulated single-cell tri-modality datasets indicate that scMHNN outperforms other competing methods on both cell clustering and cell-type annotation tasks. In addition, we also demonstrate scMHNN facilitates various downstream tasks, such as cell marker detection and enrichment analysis.