The ensembl regulatory build

Network medicine informed multiomics integration identifies drug targets and repurposable medicines for Amyotrophic Lateral Sclerosis

Amyotrophic Lateral Sclerosis (ALS) is a devastating, immensely complex neurodegenerative disease by lack of effective treatments. We developed a network medicine methodology via integrating human brain multi-omics data to prioritize drug targets and repurposable treatments for ALS. We leveraged non-coding ALS loci effects from genome-wide associated studies (GWAS) on human brain expression quantitative trait loci (QTL) (eQTL), protein QTL (pQTL), splicing QTL (sQTL), methylation QTL (meQTL), and histone acetylation QTL (haQTL). Using a network-based deep learning framework, we identified 105 putative ALS-associated genes enriched in known ALS pathobiological pathways. Applying network proximity analysis of predicted ALS-associated genes and drug-target networks under the human protein-protein interactome (PPI) model, we identified potential repurposable drugs (i.e., Diazoxide and Gefitinib) for ALS. Subsequent validation established preclinical evidence for top-prioritized drugs. In summary, we presented a network-based multi-omics framework to identify drug targets and repurposable treatments for ALS and other neurodegenerative disease if broadly applied.

Astrocyte allocation during brain development is controlled by Tcf4-mediated fate restriction

Astrocytes in the brain exhibit regional heterogeneity contributing to regional circuits involved in higher-order brain functions, yet the mechanisms controlling their distribution remain unclear. Here, we show that the precise allocation of astrocytes to specific brain regions during development is achieved through transcription factor 4 (Tcf4)-mediated fate restriction based on their embryonic origin. Loss of Tcf4 in ventral telencephalic neural progenitor cells alters the fate of oligodendrocyte precursor cells to transient intermediate astrocyte precursor cells, resulting in mislocalized astrocytes in the dorsal neocortex. These ectopic astrocytes engage with neocortical neurons and acquire features reminiscent of dorsal neocortical astrocytes. Furthermore, Tcf4 functions as a suppressor of astrocyte fate during the differentiation of oligodendrocyte precursor cells derived from the ventral telencephalon, thereby restricting the fate to the oligodendrocyte lineage in the dorsal neocortex. Together, our findings highlight a previously unappreciated role for Tcf4 in regulating astrocyte allocation, offering additional insights into the mechanisms underlying neurodevelopmental disorders linked to Tcf4 mutations.

Systematic perturbations of SETD2, NSD1, NSD2, NSD3, and ASH1L reveal their distinct contributions to H3K36 methylation

BACKGROUND

Methylation of histone 3 lysine 36 (H3K36me) has emerged as an essential epigenetic component for the faithful regulation of gene expression. Despite its importance in development and disease, how the molecular agents collectively shape the H3K36me landscape is unclear.

RESULTS

We use mouse mesenchymal stem cells to perturb the H3K36me methyltransferases (K36MTs) and infer the activities of the five most prominent enzymes: SETD2, NSD1, NSD2, NSD3, and ASH1L. We find that H3K36me2 is the most abundant of the three methylation states and is predominantly deposited at intergenic regions by NSD1, and partly by NSD2. In contrast, H3K36me1/3 are most abundant within exons and are positively correlated with gene expression. We demonstrate that while SETD2 deposits most H3K36me3, it may also deposit H3K36me2 within transcribed genes. Additionally, loss of SETD2 results in an increase of exonic H3K36me1, suggesting other (K36MTs) prime gene bodies with lower methylation states ahead of transcription. While NSD1/2 establish broad intergenic H3K36me2 domains, NSD3 deposits H3K36me2 peaks on active promoters and enhancers. Meanwhile, the activity of ASH1L is restricted to the regulatory elements of developmentally relevant genes, and our analyses implicate PBX2 as a potential recruitment factor.

CONCLUSIONS

Within genes, SETD2 primarily deposits H3K36me3, while the other K36MTs deposit H3K36me1/2 independently of SETD2 activity. For the deposition of H3K36me1/2, we find a hierarchy of K36MT activities where NSD1 > NSD2 > NSD3 > ASH1L. While NSD1 and NSD2 are responsible for most genome-wide propagation of H3K36me2, the activities of NSD3 and ASH1L are confined to active regulatory elements.

Methods for constructing and evaluating consensus genomic interval sets

The amount of genomic region data continues to increase. Integrating across diverse genomic region sets requires consensus regions, which enable comparing regions across experiments, but also by necessity lose precision in region definitions. We require methods to assess this loss of precision and build optimal consensus region sets. Here, we introduce the concept of flexible intervals and propose three novel methods for building consensus region sets, or universes: a coverage cutoff method, a likelihood method, and a Hidden Markov Model. We then propose three novel measures for evaluating how well a proposed universe fits a collection of region sets: a base-level overlap score, a region boundary distance score, and a likelihood score. We apply our methods and evaluation approaches to several collections of region sets and show how these methods can be used to evaluate fit of universes and build optimal universes. We describe scenarios where the common approach of merging regions to create consensus leads to undesirable outcomes and provide principled alternatives that provide interoperability of interval data while minimizing loss of resolution.

TULIPs decorate the three-dimensional genome of PFA ependymoma

Posterior fossa group A (PFA) ependymoma is a lethal brain cancer diagnosed in infants and young children. The lack of driver events in the PFA linear genome led us to search its 3D genome for characteristic features. Here, we reconstructed 3D genomes from diverse childhood tumor types and uncovered a global topology in PFA that is highly reminiscent of stem and progenitor cells in a variety of human tissues. A remarkable feature exclusively present in PFA are type B ultra long-range interactions in PFAs (TULIPs), regions separated by great distances along the linear genome that interact with each other in the 3D nuclear space with surprising strength. TULIPs occur in all PFA samples and recur at predictable genomic coordinates, and their formation is induced by expression of EZHIP. The universality of TULIPs across PFA samples suggests a conservation of molecular principles that could be exploited therapeutically.

Integration of chromosome locations and functional aspects of enhancers and topologically associating domains in knowledge graphs enables versatile queries about gene regulation

Knowledge about transcription factor binding and regulation, target genes, cis-regulatory modules and topologically associating domains is not only defined by functional associations like biological processes or diseases but also has a determinative genome location aspect. Here, we exploit these location and functional aspects together to develop new strategies to enable advanced data querying. Many databases have been developed to provide information about enhancers, but a schema that allows the standardized representation of data, securing interoperability between resources, has been lacking. In this work, we use knowledge graphs for the standardized representation of enhancers and topologically associating domains, together with data about their target genes, transcription factors, location on the human genome, and functional data about diseases and gene ontology annotations. We used this schema to integrate twenty-five enhancer datasets and two domain datasets, creating the most powerful integrative resource in this field to date. The knowledge graphs have been implemented using the Resource Description Framework and integrated within the open-access BioGateway knowledge network, generating a resource that contains an interoperable set of knowledge graphs (enhancers, TADs, genes, proteins, diseases, GO terms, and interactions between domains). We show how advanced queries, which combine functional and location restrictions, can be used to develop new hypotheses about functional aspects of gene expression regulation.

Impact of 5' Near Gene Variants of Mannose Binding Lectin (MBL2) on Breast Cancer Risk

The immune system plays a bifaceted role in tumour development through modulation of inflammation. MBL binds to damage-associated molecular patterns and induces inflammation through the activation of complement pathway. Dysregulated inflammation plays a major role in breast cancer pathogenesis, thereby suggesting its contribution towards breast cancer risk. Literature asserts single-nucleotide polymorphisms (SNPs) modulating serum MBL levels. Therefore, studying MBL2 SNPs in breast cancer might provide valuable insight in the disease pathogenesis. The present case-control association study aimed to elucidate the association between MBL2 5' near gene SNPs and breast cancer risk. Breast cancer patients were recruited from Government Medical College, G.N.D. Hospital, Amritsar. The age- and gender-matched genetically unrelated healthy individuals, from adjoining regions, with no history of malignancy up to three generations were recruited as controls. The SNPs of MBL2 from the 5' near gene region with putative functional significance were selected based upon the in silico analysis and literature review. The genotypic, allelic and haplotype frequencies for the studied variants were assessed and compared in the study participants by ARMS-PCR and PCR-RFLP. No difference in allelic, genotypic and haplotype frequencies was reported for rs7096206, rs7084554 and rs11003125 in both the participant groups. rs7084554 (CC) was found to confer risk towards hormone receptor-positive breast cancer. An intermediate LD was observed between rs7084554 and rs11003125. The study reports association between MBL2 variant (rs7084554) and hormone receptor-positive breast cancer risk. Further research in this direction might validate the findings.

CREdb: A comprehensive database of Cis-Regulatory Elements and their activity in human cells and tissues

BACKGROUND

Cis-regulatory elements (CREs) play a pivotal role in gene expression regulation, allowing cells to serve diverse functions and respond to external stimuli. Understanding CREs is essential for personalized medicine and disease research, as an increasing number of genetic variants associated with phenotypes and diseases overlap with CREs. However, existing databases often focus on subsets of regulatory elements and present each identified instance of element individually, confounding the effort to obtain a comprehensive view. To address this gap, we have created CREdb, a comprehensive database with over 10 million human regulatory elements across 1,058 cell types and 315 tissues harmonized from different data sources. We curated and aligned the cell types and tissues to standard ontologies for efficient data query.

RESULTS

Data from 11 sources were curated and mapped to standard ontological terms. 11,223,434 combined elements are present in the final database, and these were merged into 5,666,240 consensus elements representing the combined ranges of the individual elements informed by their overlap. Each consensus element contains curated metadata including the number of elements supporting it and a hash linking to the source databases. The inferred activity of each consensus element in various cell-type and tissue context is also provided. Examples presented here show the potential utility of CREdb in annotating non-coding genetic variants and informing chromatin accessibility profiling analysis.

CONCLUSIONS

We developed CREdb, a comprehensive database of CREs, to simplify the analysis of CREs by providing a unified framework for researchers. CREdb compiles consensus ranges for each element by integrating the information from all instances identified across various source databases. This unified database facilitates the functional annotation of non-coding genetic variants and complements chromatin accessibility profiling analysis. CREdb will serve as an important resource in expanding our knowledge of the epigenome and its role in human diseases.

RNA editing regulates glutamatergic synapses in the frontal cortex of a molecular subtype of Amyotrophic Lateral Sclerosis

BACKGROUND

Amyotrophic Lateral Sclerosis (ALS) is a highly heterogenous neurodegenerative disorder that primarily affects upper and lower motor neurons, affecting additional cell types and brain regions. Underlying molecular mechanisms are still elusive, in part due to disease heterogeneity. Molecular disease subtyping through integrative analyses including RNA editing profiling is a novel approach for identification of molecular networks involved in pathogenesis.

METHODS

We aimed to highlight the role of RNA editing in ALS, focusing on the frontal cortex and the prevalent molecular disease subtype (ALS-Ox), previously determined by transcriptomic profile stratification. We established global RNA editing (editome) and gene expression (transcriptome) profiles in control and ALS-Ox cases, utilizing publicly available RNA-seq data (GSE153960) and an in-house analysis pipeline. Functional annotation and pathway analyses identified molecular processes affected by RNA editing alterations. Pearson correlation analyses assessed RNA editing effects on expression. Similar analyses on additional ALS-Ox and control samples (GSE124439) were performed for verification. Targeted re-sequencing and qRT-PCR analysis targeting CACNA1C, were performed using frontal cortex tissue from ALS and control samples (n = 3 samples/group).

RESULTS

We identified reduced global RNA editing in the frontal cortex of ALS-Ox cases. Differentially edited transcripts are enriched in synapses, particularly in the glutamatergic synapse pathway. Bioinformatic analyses on additional ALS-Ox and control RNA-seq data verified these findings. We identified increased recoding at the Q621R site in the GRIK2 transcript and determined positive correlations between RNA editing and gene expression alterations in ionotropic receptor subunits GRIA2, GRIA3 and the CACNA1C transcript, which encodes the pore forming subunit of a post-synaptic L-type calcium channel. Experimental data verified RNA editing alterations and editing-expression correlation in CACNA1C, highlighting CACNA1C as a target for further study.

CONCLUSIONS

We provide evidence on the involvement of RNA editing in the frontal cortex of an ALS molecular subtype, highlighting a modulatory role mediated though recoding and gene expression regulation on glutamatergic synapse related transcripts. We report RNA editing effects in disease-related transcripts and validated editing alterations in CACNA1C. Our study provides targets for further functional studies that could shed light in underlying disease mechanisms enabling novel therapeutic approaches.

Cross-ancestry atlas of gene, isoform, and splicing regulation in the developing human brain

Neuropsychiatric genome-wide association studies (GWASs), including those for autism spectrum disorder and schizophrenia, show strong enrichment for regulatory elements in the developing brain. However, prioritizing risk genes and mechanisms is challenging without a unified regulatory atlas. Across 672 diverse developing human brains, we identified 15,752 genes harboring gene, isoform, and/or splicing quantitative trait loci, mapping 3739 to cellular contexts. Gene expression heritability drops during development, likely reflecting both increasing cellular heterogeneity and the intrinsic properties of neuronal maturation. Isoform-level regulation, particularly in the second trimester, mediated the largest proportion of GWAS heritability. Through colocalization, we prioritized mechanisms for about 60% of GWAS loci across five disorders, exceeding adult brain findings. Finally, we contextualized results within gene and isoform coexpression networks, revealing the comprehensive landscape of transcriptome regulation in development and disease.

MultiSuSiE improves multi-ancestry fine-mapping in All of Us whole-genome sequencing data

Leveraging data from multiple ancestries can greatly improve fine-mapping power due to differences in linkage disequilibrium and allele frequencies. We propose MultiSuSiE, an extension of the sum of single effects model (SuSiE) to multiple ancestries that allows causal effect sizes to vary across ancestries based on a multivariate normal prior informed by empirical data. We evaluated MultiSuSiE via simulations and analyses of 14 quantitative traits leveraging whole-genome sequencing data in 47k African-ancestry and 94k European-ancestry individuals from All of Us. In simulations, MultiSuSiE applied to Afr47k+Eur47k was well-calibrated and attained higher power than SuSiE applied to Eur94k; interestingly, higher causal variant PIPs in Afr47k compared to Eur47k were entirely explained by differences in the extent of LD quantified by LD 4th moments. Compared to very recently proposed multi-ancestry fine-mapping methods, MultiSuSiE attained higher power and/or much lower computational costs, making the analysis of large-scale All of Us data feasible. In real trait analyses, MultiSuSiE applied to Afr47k+Eur94k identified 579 fine-mapped variants with PIP > 0.5, and MultiSuSiE applied to Afr47k+Eur47k identified 44% more fine-mapped variants with PIP > 0.5 than SuSiE applied to Eur94k. We validated MultiSuSiE results for real traits via functional enrichment of fine-mapped variants. We highlight several examples where MultiSuSiE implicates well-studied or biologically plausible fine-mapped variants that were not implicated by other methods.

Mouse Genome Informatics: an integrated knowledgebase system for the laboratory mouse

Mouse Genome Informatics (MGI) is a federation of expertly curated information resources designed to support experimental and computational investigations into genetic and genomic aspects of human biology and disease using the laboratory mouse as a model system. The Mouse Genome Database (MGD) and the Gene Expression Database (GXD) are core MGI databases that share data and system architecture. MGI serves as the central community resource of integrated information about mouse genome features, variation, expression, gene function, phenotype, and human disease models acquired from peer-reviewed publications, author submissions, and major bioinformatics resources. To facilitate integration and standardization of data, biocuration scientists annotate using terms from controlled metadata vocabularies and biological ontologies (e.g. Mammalian Phenotype Ontology, Mouse Developmental Anatomy, Disease Ontology, Gene Ontology, etc.), and by applying international community standards for gene, allele, and mouse strain nomenclature. MGI serves basic scientists, translational researchers, and data scientists by providing access to FAIR-compliant data in both human-readable and compute-ready formats. The MGI resource is accessible at https://informatics.jax.org. Here, we present an overview of the core data types represented in MGI and highlight recent enhancements to the resource with a focus on new data and functionality for MGD and GXD.

Germline cis variant determines epigenetic regulation of the anti-cancer drug metabolism gene dihydropyrimidine dehydrogenase (DPYD)

Enhancers are critical for regulating tissue-specific gene expression, and genetic variants within enhancer regions have been suggested to contribute to various cancer-related processes, including therapeutic resistance. However, the precise mechanisms remain elusive. Using a well-defined drug-gene pair, we identified an enhancer region for dihydropyrimidine dehydrogenase (DPD, DPYD gene) expression that is relevant to the metabolism of the anti-cancer drug 5-fluorouracil (5-FU). Using reporter systems, CRISPR genome-edited cell models, and human liver specimens, we demonstrated in vitro and vivo that genotype status for the common germline variant (rs4294451; 27% global minor allele frequency) located within this novel enhancer controls DPYD transcription and alters resistance to 5-FU. The variant genotype increases recruitment of the transcription factor CEBPB to the enhancer and alters the level of direct interactions between the enhancer and DPYD promoter. Our data provide insight into the regulatory mechanisms controlling sensitivity and resistance to 5-FU.

Increased enhancer-promoter interactions during developmental enhancer activation in mammals

Remote enhancers are thought to interact with their target promoters via physical proximity, yet the importance of this proximity for enhancer function remains unclear. Here we investigate the three-dimensional (3D) conformation of enhancers during mammalian development by generating high-resolution tissue-resolved contact maps for nearly a thousand enhancers with characterized in vivo activities in ten murine embryonic tissues. Sixty-one percent of developmental enhancers bypass their neighboring genes, which are often marked by promoter CpG methylation. The majority of enhancers display tissue-specific 3D conformations, and both enhancer-promoter and enhancer-enhancer interactions are moderately but consistently increased upon enhancer activation in vivo. Less than 14% of enhancer-promoter interactions form stably across tissues; however, these invariant interactions form in the absence of the enhancer and are likely mediated by adjacent CTCF binding. Our results highlight the general importance of enhancer-promoter physical proximity for developmental gene activation in mammals.

Germline cis variant determines epigenetic regulation of the anti-cancer drug metabolism gene dihydropyrimidine dehydrogenase (DPYD)

Enhancers are critical for regulating tissue-specific gene expression, and genetic variants within enhancer regions have been suggested to contribute to various cancer-related processes, including therapeutic resistance. However, the precise mechanisms remain elusive. Using a well-defined drug-gene pair, we identified an enhancer region for dihydropyrimidine dehydrogenase (DPD, DPYD gene) expression that is relevant to the metabolism of the anti-cancer drug 5-fluorouracil (5-FU). Using reporter systems, CRISPR genome edited cell models, and human liver specimens, we demonstrated in vitro and vivo that genotype status for the common germline variant (rs4294451; 27% global minor allele frequency) located within this novel enhancer controls DPYD transcription and alters resistance to 5-FU. The variant genotype increases recruitment of the transcription factor CEBPB to the enhancer and alters the level of direct interactions between the enhancer and DPYD promoter. Our data provide insight into the regulatory mechanisms controlling sensitivity and resistance to 5-FU.

Local patterns of genetic sharing challenge the boundaries between neuropsychiatric and insulin resistance-related conditions

The co-occurrence of insulin resistance (IR)-related metabolic conditions with neuropsychiatric disorders is a complex public health challenge. Evidence of the genetic links between these phenotypes is emerging, but little is currently known about the genomic regions and biological functions that are involved. To address this, we performed Local Analysis of [co]Variant Association (LAVA) using large-scale (N=9,725-933,970) genome-wide association studies (GWASs) results for three IR-related conditions (type 2 diabetes mellitus, obesity, and metabolic syndrome) and nine neuropsychiatric disorders. Subsequently, positional and expression quantitative trait locus (eQTL)-based gene mapping and downstream functional genomic analyses were performed on the significant loci. Patterns of negative and positive local genetic correlations (|rg|=0.21-1, pFDR<0.05) were identified at 109 unique genomic regions across all phenotype pairs. Local correlations emerged even in the absence of global genetic correlations between IR-related conditions and Alzheimer's disease, bipolar disorder, and Tourette's syndrome. Genes mapped to the correlated regions showed enrichment in biological pathways integral to immune-inflammatory function, vesicle trafficking, insulin signalling, oxygen transport, and lipid metabolism. Colocalisation analyses further prioritised 10 genetically correlated regions for likely harbouring shared causal variants, displaying high deleterious or regulatory potential. These variants were found within or in close proximity to genes, such as SLC39A8 and HLA-DRB1, that can be targeted by supplements and already known drugs, including omega-3/6 fatty acids, immunomodulatory, antihypertensive, and cholesterol-lowering drugs. Overall, our findings underscore the complex genetic landscape of IR-neuropsychiatric multimorbidity, advocating for an integrated disease model and offering novel insights for research and treatment strategies in this domain.

Genetic Susceptibility to Astrovirus Diarrhea in Bangladeshi Infants

Background

Astroviral infections commonly cause acute nonbacterial gastroenteritis in children globally. However, these infections often go undiagnosed outside of research settings. There is no treatment available for astrovirus, and Astroviridae strain diversity presents a challenge to potential vaccine development.

Methods

To address our hypothesis that host genetic risk factors are associated with astrovirus disease susceptibility, we performed a genome-wide association study of astrovirus infection in the first year of life from children enrolled in 2 Bangladeshi birth cohorts.

Results

We identified a novel region on chromosome 1 near the loricrin gene (LOR) associated with astrovirus diarrheal infection (rs75437404; meta-analysis P = 8.82 × 10-9; A allele odds ratio, 2.71) and on chromosome 10 near the prolactin releasing hormone receptor gene (PRLHR) (rs75935441; meta-analysis P = 1.33 × 10-8; C allele odds ratio, 4.17). The prolactin-releasing peptide has been shown to influence feeding patterns and energy balance in mice. In addition, several single-nucleotide polymorphisms in the chromosome 1 locus have previously been associated with expression of innate immune system genes PGLYRP4, S100A9, and S100A12.

Conclusions

This study identified 2 significant host genetic regions that may influence astrovirus diarrhea susceptibility and should be considered in further studies.

Integrative Epigenetic and Molecular Analysis Reveals a Novel Promoter for a New Isoform of the Transcription Factor TEAD4

TEAD4 is a transcription factor that plays a crucial role in the Hippo pathway by regulating the expression of genes related to proliferation and apoptosis. It is also involved in the maintenance and differentiation of the trophectoderm during pre- and post-implantation embryonic development. An alternative promoter for the TEAD4 gene was identified through epigenetic profile analysis, and a new transcript from the intronic region of TEAD4 was discovered using the 5'RACE method. The transcript of the novel promoter encodes a TEAD4 isoform (TEAD4-ΔN) that lacks the DNA-binding domain but retains the C-terminal protein-protein interaction domain. Gene expression studies, including end-point PCR and Western blotting, showed that full-length TEAD4 was present in all investigated tissues. However, TEAD4-ΔN was only detectable in certain cell types. The TEAD4-ΔN promoter is conserved throughout evolution and demonstrates transcriptional activity in transient-expression experiments. Our study reveals that TEAD4 interacts with the alternative promoter and increases the expression of the truncated isoform. DNA methylation plays a crucial function in the restricted expression of the TEAD4-ΔN isoform in specific tissues, including the umbilical cord and the placenta. The data presented indicate that the DNA-methylation status of the TEAD4-ΔN promoter plays a critical role in regulating organ size, cancer development, and placenta differentiation.

Transposable elements mediate genetic effects altering the expression of nearby genes in colorectal cancer

Transposable elements (TEs) are prevalent repeats in the human genome, play a significant role in the regulome, and their disruption can contribute to tumorigenesis. However, TE influence on gene expression in cancer remains unclear. Here, we analyze 275 normal colon and 276 colorectal cancer samples from the SYSCOL cohort, discovering 10,231 and 5,199 TE-expression quantitative trait loci (eQTLs) in normal and tumor tissues, respectively, of which 376 are colorectal cancer specific eQTLs, likely due to methylation changes. Tumor-specific TE-eQTLs show greater enrichment of transcription factors, compared to shared TE-eQTLs suggesting specific regulation of their expression in tumor. Bayesian networks reveal 1,766 TEs as mediators of genetic effects, altering the expression of 1,558 genes, including 55 known cancer driver genes and show that tumor-specific TE-eQTLs trigger the driver capability of TEs. These insights expand our knowledge of cancer drivers, deepening our understanding of tumorigenesis and presenting potential avenues for therapeutic interventions.

Spatial chromatin accessibility sequencing resolves high-order spatial interactions of epigenomic markers

As the genome is organized into a three-dimensional structure in intracellular space, epigenomic information also has a complex spatial arrangement. However, most epigenetic studies describe locations of methylation marks, chromatin accessibility regions, and histone modifications in the horizontal dimension. Proper spatial epigenomic information has rarely been obtained. In this study, we designed spatial chromatin accessibility sequencing (SCA-seq) to resolve the genome conformation by capturing the epigenetic information in single-molecular resolution while simultaneously resolving the genome conformation. Using SCA-seq, we are able to examine the spatial interaction of chromatin accessibility (e.g. enhancer-promoter contacts), CpG island methylation, and spatial insulating functions of the CCCTC-binding factor. We demonstrate that SCA-seq paves the way to explore the mechanism of epigenetic interactions and extends our knowledge in 3D packaging of DNA in the nucleus.

Multimodal epigenetic sequencing analysis (MESA) of cell-free DNA for non-invasive colorectal cancer detection

BACKGROUND

Detecting human cancers through cell-free DNA (cfDNA) in blood is a sensitive and non-invasive option. However, capturing multiple forms of epigenetic information remains a technical and financial challenge.

METHODS

To address this, we developed multimodal epigenetic sequencing analysis (MESA), a flexible and sensitive approach to capturing and integrating a diverse range of epigenetic features in cfDNA using a single experimental assay, i.e., non-disruptive bisulfite-free methylation sequencing, such as Enzymatic Methyl-seq. MESA enables simultaneous inference of four epigenetic modalities: cfDNA methylation, nucleosome occupancy, nucleosome fuzziness, and windowed protection score for regions surrounding gene promoters and polyadenylation sites.

RESULTS

When applied to 690 cfDNA samples from 3 colorectal cancer clinical cohorts, MESA's novel modalities, which include nucleosome fuzziness, and genomic features, including polyadenylation sites, improve cancer detection beyond the traditional epigenetic markers of promoter DNA methylation.

CONCLUSIONS

Together, MESA stands as a major advancement in the field by utilizing comprehensive and complementary epigenetic profiles of cfDNA for effective non-invasive cancer detection.

Multi-omics analysis of innate and adaptive responses to BCG vaccination reveals epigenetic cell states that predict trained immunity

Immune responses are tightly regulated yet highly variable between individuals. To investigate human population variation of trained immunity, we immunized healthy individuals with Bacillus Calmette-Guérin (BCG). This live-attenuated vaccine induces not only an adaptive immune response against tuberculosis but also triggers innate immune activation and memory that are indicative of trained immunity. We established personal immune profiles and chromatin accessibility maps over a 90-day time course of BCG vaccination in 323 individuals. Our analysis uncovered genetic and epigenetic predictors of baseline immunity and immune response. BCG vaccination enhanced the innate immune response specifically in individuals with a dormant immune state at baseline, rather than providing a general boost of innate immunity. This study advances our understanding of BCG's heterologous immune-stimulatory effects and trained immunity in humans. Furthermore, it highlights the value of epigenetic cell states for connecting immune function with genotype and the environment.

Combining a prioritization strategy and functional studies nominates 5'UTR variants underlying inherited retinal disease

BACKGROUND

5' untranslated regions (5'UTRs) are essential modulators of protein translation. Predicting the impact of 5'UTR variants is challenging and rarely performed in routine diagnostics. Here, we present a combined approach of a comprehensive prioritization strategy and functional assays to evaluate 5'UTR variation in two large cohorts of patients with inherited retinal diseases (IRDs).

METHODS

We performed an isoform-level re-analysis of retinal RNA-seq data to identify the protein-coding transcripts of 378 IRD genes with highest expression in retina. We evaluated the coverage of their 5'UTRs by different whole exome sequencing (WES) kits. The selected 5'UTRs were analyzed in whole genome sequencing (WGS) and WES data from IRD sub-cohorts from the 100,000 Genomes Project (n = 2397 WGS) and an in-house database (n = 1682 WES), respectively. Identified variants were annotated for 5'UTR-relevant features and classified into seven categories based on their predicted functional consequence. We developed a variant prioritization strategy by integrating population frequency, specific criteria for each category, and family and phenotypic data. A selection of candidate variants underwent functional validation using diverse approaches.

RESULTS

Isoform-level re-quantification of retinal gene expression revealed 76 IRD genes with a non-canonical retina-enriched isoform, of which 20 display a fully distinct 5'UTR compared to that of their canonical isoform. Depending on the probe design, 3-20% of IRD genes have 5'UTRs fully captured by WES. After analyzing these regions in both cohorts, we prioritized 11 (likely) pathogenic variants in 10 genes (ARL3, MERTK, NDP, NMNAT1, NPHP4, PAX6, PRPF31, PRPF4, RDH12, RD3), of which 7 were novel. Functional analyses further supported the pathogenicity of three variants. Mis-splicing was demonstrated for the PRPF31:c.-9+1G>T variant. The MERTK:c.-125G>A variant, overlapping a transcriptional start site, was shown to significantly reduce both luciferase mRNA levels and activity. The RDH12:c.-123C>T variant was found in cis with the hypomorphic RDH12:c.701G>A (p.Arg234His) variant in 11 patients. This 5'UTR variant, predicted to introduce an upstream open reading frame, was shown to result in reduced RDH12 protein but unaltered mRNA levels.

CONCLUSIONS

This study demonstrates the importance of 5'UTR variants implicated in IRDs and provides a systematic approach for 5'UTR annotation and validation that is applicable to other inherited diseases.

Rutin is a potent senomorphic agent to target senescent cells and can improve chemotherapeutic efficacy

Aging is a major risk factor for most chronic disorders, for which cellular senescence is one of the central hallmarks. Senescent cells develop the pro-inflammatory senescence-associated secretory phenotype (SASP), which significantly contributes to organismal aging and age-related disorders. Development of senotherapeutics, an emerging class of therapeutic agents to target senescent cells, allows to effectively delay aging and alleviate chronic pathologies. Here we report preliminary outputs from screening of a natural medicinal agent (NMA) library for senotherapeutic candidates and validated several agents with prominent potential as senomorphics. Rutin, a phytochemical constituent found in a number of plants, showed remarkable capacity in targeting senescent cells by dampening expression of the full spectrum SASP. Further analysis indicated that rutin restrains the acute stress-associated phenotype (ASAP) by specifically interfering with the interactions of ATM with HIF1α, a master regulator of cellular and systemic homeostasis activated during senescence, and of ATM with TRAF6, part of a key signaling axis supporting the ASAP development toward the SASP. Conditioned media produced by senescent stromal cells enhanced the malignant phenotypes of prostate cancer cells, including in vitro proliferation, migration, invasion, and more importantly, chemoresistance, while rutin remarkably downregulated these gain-of-functions. Although classic chemotherapy reduced tumor progression, the treatment outcome was substantially improved upon combination of a chemotherapeutic agent with rutin. Our study provides a proof of concept for rutin as an emerging natural senomorphic agent, and presents an effective therapeutic avenue for alleviating age-related pathologies including cancer.

Quantification of absolute transcription factor binding affinities in the native chromatin context using BANC-seq

Transcription factor binding across the genome is regulated by DNA sequence and chromatin features. However, it is not yet possible to quantify the impact of chromatin context on transcription factor binding affinities. Here, we report a method called binding affinities to native chromatin by sequencing (BANC-seq) to determine absolute apparent binding affinities of transcription factors to native DNA across the genome. In BANC-seq, a concentration range of a tagged transcription factor is added to isolated nuclei. Concentration-dependent binding is then measured per sample to quantify apparent binding affinities across the genome. BANC-seq adds a quantitative dimension to transcription factor biology, which enables stratification of genomic targets based on transcription factor concentration and prediction of transcription factor binding sites under non-physiological conditions, such as disease-associated overexpression of (onco)genes. Notably, whereas consensus DNA binding motifs for transcription factors are important to establish high-affinity binding sites, these motifs are not always strictly required to generate nanomolar-affinity interactions in the genome.

Whole-genome sequencing uncovers two loci for coronary artery calcification and identifies ARSE as a regulator of vascular calcification

Coronary artery calcification (CAC) is a measure of atherosclerosis and a well-established predictor of coronary artery disease (CAD) events. Here we describe a genome-wide association study (GWAS) of CAC in 22,400 participants from multiple ancestral groups. We confirmed associations with four known loci and identified two additional loci associated with CAC (ARSE and MMP16), with evidence of significant associations in replication analyses for both novel loci. Functional assays of ARSE and MMP16 in human vascular smooth muscle cells (VSMCs) demonstrate that ARSE is a promoter of VSMC calcification and VSMC phenotype switching from a contractile to a calcifying or osteogenic phenotype. Furthermore, we show that the association of variants near ARSE with reduced CAC is likely explained by reduced ARSE expression with the G allele of enhancer variant rs5982944. Our study highlights ARSE as an important contributor to atherosclerotic vascular calcification, and a potential drug target for vascular calcific disease.

Recent advances in the genomic resources for sheep

Sheep (Ovis aries) provide a vital source of protein and fibre to human populations. In coming decades, as the pressures associated with rapidly changing climates increase, breeding sheep sustainably as well as producing enough protein to feed a growing human population will pose a considerable challenge for sheep production across the globe. High quality reference genomes and other genomic resources can help to meet these challenges by: (1) informing breeding programmes by adding a priori information about the genome, (2) providing tools such as pangenomes for characterising and conserving global genetic diversity, and (3) improving our understanding of fundamental biology using the power of genomic information to link cell, tissue and whole animal scale knowledge. In this review we describe recent advances in the genomic resources available for sheep, discuss how these might help to meet future challenges for sheep production, and provide some insight into what the future might hold.

Genetic Factors Contribute to the Phenotypic Variability in GJB2-Related Hearing Impairment

Recessive variants in GJB2 are the most important genetic cause of sensorineural hearing impairment (SNHI) worldwide. Phenotypes vary significantly in GJB2-related SNHI, even in patients with identical variants. For instance, patients homozygous for the GJB2 p.V37I variant, which is highly prevalent in the Asian populations, usually present with mild-to-moderate SNHI; yet severe-to-profound SNHI is occasionally observed in approximately 10% of p.V37I homozygotes. To investigate the genomic underpinnings of the phenotypic variability, we performed next-generation sequencing of GJB2 and other deafness genes in 63 p.V37I homozygotes with extreme phenotypic severities. Additional pathogenic variants of other deafness genes were identified in five of the 35 patients with severe-to-profound SNHI. Furthermore, case-control association analyses were conducted for 30 unrelated p.V37I homozygotes with severe-to-profound SNHI against 28 p.V37I homozygotes with mild-to-moderate SNHI, and 120 population controls from the Taiwan Biobank. The severe-to-profound group exhibited a higher frequency of the crystallin lambda 1 (CRYL1) variant (rs14236), located upstream of GJB2, than the mild-to-moderate and Taiwan Biobank groups. Our results demonstrated that pathogenic variants in other deafness genes and a possible modifier, the CRYL1 rs14236 variant, may contribute to phenotypic variability in GJB2-realted SNHI, highlighting the importance of comprehensive genomic surveys to delineate the genotype-phenotype correlations.

Mosaic chromosomal alterations in blood across ancestries using whole-genome sequencing

Megabase-scale mosaic chromosomal alterations (mCAs) in blood are prognostic markers for a host of human diseases. Here, to gain a better understanding of mCA rates in genetically diverse populations, we analyzed whole-genome sequencing data from 67,390 individuals from the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine program. We observed higher sensitivity with whole-genome sequencing data, compared with array-based data, in uncovering mCAs at low mutant cell fractions and found that individuals of European ancestry have the highest rates of autosomal mCAs and the lowest rates of chromosome X mCAs, compared with individuals of African or Hispanic ancestry. Although further studies in diverse populations will be needed to replicate our findings, we report three loci associated with loss of chromosome X, associations between autosomal mCAs and rare variants in DCPS, ADM17, PPP1R16B and TET2 and ancestry-specific variants in ATM and MPL with mCAs in cis.

Cross-ancestry analyses identify new genetic loci associated with 25-hydroxyvitamin D

Vitamin D status-a complex trait influenced by environmental and genetic factors-is tightly associated with skin colour and ancestry. Yet very few studies have investigated the genetic underpinnings of vitamin D levels across diverse ancestries, and the ones that have, relied on small sample sizes, resulting in inconclusive results. Here, we conduct genome-wide association studies (GWAS) of 25 hydroxyvitamin D (25OHD)-the main circulating form of vitamin D-in 442,435 individuals from four broad genetically-determined ancestry groups represented in the UK Biobank: European (N = 421,867), South Asian (N = 9,983), African (N = 8,306) and East Asian (N = 2,279). We identify a new genetic determinant of 25OHD (rs146759773) in individuals of African ancestry, which was not detected in previous analysis of much larger European cohorts due to low minor allele frequency. We show genome-wide significant evidence of dominance effects in 25OHD that protect against vitamin D deficiency. Given that key events in the synthesis of 25OHD occur in the skin and are affected by pigmentation levels, we conduct GWAS of 25OHD stratified by skin colour and identify new associations. Lastly, we test the interaction between skin colour and variants associated with variance in 25OHD levels and identify two loci (rs10832254 and rs1352846) whose association with 25OHD differs in individuals of distinct complexions. Collectively, our results provide new insights into the complex relationship between 25OHD and skin colour and highlight the importance of diversity in genomic studies. Despite the much larger rates of vitamin D deficiency that we and others report for ancestry groups with dark skin (e.g., South Asian), our study highlights the importance of considering ancestral background and/or skin colour when assessing the implications of low vitamin D.

Systematic perturbations of SETD2, NSD1, NSD2, NSD3 and ASH1L reveals their distinct contributions to H3K36 methylation

Background

Methylation of histone 3 lysine 36 (H3K36me) has emerged as an essential epigenetic component for the faithful regulation of gene expression. Despite its importance in development, disease, and cancer, how the molecular agents collectively shape the H3K36me landscape is unclear.

Results

We use a mouse mesenchymal stem cell model to perturb the H3K36me deposition machinery and infer the activities of the five most prominent players: SETD2, NSD1, NSD2, NSD3, and ASH1L. We find that H3K36me2 is the most abundant of the three methylation states and is predominantly deposited at intergenic regions by NSD1, and partly by NSD2. In contrast, H3K36me1/3 are most abundant within exons and are positively correlated with gene expression. We demonstrate that while SETD2 deposits most H3K36me3, it also deposits H3K36me2 within transcribed genes. Additionally, loss of SETD2 results in an increase of exonic H3K36me1, suggesting other H3K36 methyltransferases (K36MTs) prime gene bodies with lower methylation states ahead of transcription. Through a reductive approach, we uncover the distribution patterns of NSD3- and ASH1L-catalyzed H3K36me2. While NSD1/2 establish broad intergenic H3K36me2 domains, NSD3 deposits H3K36me2 peaks on active promoters and enhancers. Meanwhile, the activity of ASH1L is restricted to the regulatory elements of developmentally relevant genes, and our analyses implicate PBX2 as a potential recruitment factor.

Conclusions

Within genes, SETD2 deposits both H3K36me2/3, while the other K36MTs are capable of depositing H3K36me1/2 independently of SETD2 activity. For the deposition of H3K36me1/2, we find a hierarchy of K36MT activities where NSD1>NSD2>NSD3>ASH1L. While NSD1 and NSD2 are responsible for most genome-wide propagation of H3K36me2, the activities of NSD3 and ASH1L are confined to active regulatory elements.

Genic constraint against nonsynonymous variation across the mouse genome

BACKGROUND

Selective constraint, the depletion of variation due to negative selection, provides insights into the functional impact of variants and disease mechanisms. However, its characterization in mice, the most commonly used mammalian model, remains limited. This study aims to quantify mouse gene constraint using a new metric called the nonsynonymous observed expected ratio (NOER) and investigate its relationship with gene function.

RESULTS

NOER was calculated using whole-genome sequencing data from wild mouse populations (Mus musculus sp and Mus spretus). Positive correlations were observed between mouse gene constraint and the number of associated knockout phenotypes, indicating stronger constraint on pleiotropic genes. Furthermore, mouse gene constraint showed a positive correlation with the number of pathogenic variant sites in their human orthologues, supporting the relevance of mouse models in studying human disease variants.

CONCLUSIONS

NOER provides a resource for assessing the fitness consequences of genetic variants in mouse genes and understanding the relationship between gene constraint and function. The study's findings highlight the importance of pleiotropy in selective constraint and support the utility of mouse models in investigating human disease variants. Further research with larger sample sizes can refine constraint estimates in mice and enable more comprehensive comparisons of constraint between mouse and human orthologues.

Gene signature reveals decreased SOX10-dependent transcripts in malignant cells from immune checkpoint inhibitor-resistant cutaneous melanomas

Evidence is mounting for cross-resistance between immune checkpoint and targeted kinase inhibitor therapies in cutaneous melanoma patients. Since the loss of the transcription factor, SOX10, causes tolerance to MAPK pathway inhibitors, we used bioinformatic techniques to determine if reduced SOX10 expression/activity is associated with immune checkpoint inhibitor resistance. We integrated SOX10 ChIP-seq, knockout RNA-seq, and knockdown ATAC-seq data from melanoma cell models to develop a robust SOX10 gene signature. We used computational methods to validate this signature as a measure of SOX10-dependent activity in independent single-cell and bulk RNA-seq SOX10 knockdown, cell line panel, and MAPK inhibitor drug-resistant datasets. Evaluation of patient single-cell RNA-seq data revealed lower levels of SOX10-dependent transcripts in immune checkpoint inhibitor-resistant tumors. Our results suggest that SOX10-deficient melanoma cells are associated with cross-resistance between targeted and immune checkpoint inhibitors and highlight the need to identify therapeutic strategies that target this subpopulation.

Exome Sequencing Implicates DGKZ , ESRRA , and GXYLT1 for Modulating Granuloma Formation in Crohn Disease

Non-caseating granulomas may indicate a more aggressive phenotype of Crohn disease (CD). Genetic associations of granulomatous CD (GCD) may help elucidate disease pathogenesis. Whole-exome sequencing was performed on peripheral blood-derived DNA from 17 pediatric patients with GCD and 19 with non-GCD (NGCD), and from an independent validation cohort of 44 GCD and 19 NGCD cases. PLINK (a tool set for whole-genome association and population-based linkage analyses) analysis was used to identify single nucleotide polymorphisms (SNPs) differentiating between groups, and subgroup allele frequencies were also compared to a public genomic database (gnomAD). The Combined Annotation Dependent Depletion scoring tool was used to predict deleteriousness of SNPs. Human leukocyte antigen (HLA) haplotype findings were compared to a control group (n = 8496). PLINK-based analysis between GCD and NGCD groups did not find consistently significant hits. gnomAD control comparisons, however, showed consistent subgroup associations with DGKZ , ESRRA , and GXYLT1 , genes that have been implicated in mammalian granulomatous inflammation. Our findings may guide future research and precision medicine.

DECIPHER: Improving Genetic Diagnosis Through Dynamic Integration of Genomic and Clinical Data

DECIPHER (Database of Genomic Variation and Phenotype in Humans Using Ensembl Resources) shares candidate diagnostic variants and phenotypic data from patients with genetic disorders to facilitate research and improve the diagnosis, management, and therapy of rare diseases. The platform sits at the boundary between genomic research and the clinical community. DECIPHER aims to ensure that the most up-to-date data are made rapidly available within its interpretation interfaces to improve clinical care. Newly integrated cardiac case-control data that provide evidence of gene-disease associations and inform variant interpretation exemplify this mission. New research resources are presented in a format optimized for use by a broad range of professionals supporting the delivery of genomic medicine. The interfaces within DECIPHER integrate and contextualize variant and phenotypic data, helping to determine a robust clinico-molecular diagnosis for rare-disease patients, which combines both variant classification and clinical fit. DECIPHER supports discovery research, connecting individuals within the rare-disease community to pursue hypothesis-driven research.

In Silico Gene Prioritization Highlights the Significance of Bone Morphogenetic Protein 4 (BMP4) Promoter Methylation across All Methylation Clusters in Colorectal Cancer

The cytosine-phosphate-guanine (CpG) island methylator phenotype (CIMP) represents one of the pathways involved in the development of colorectal cancer, characterized by genome-wide hypermethylation. To identify samples exhibiting hypermethylation, we used unsupervised hierarchical clustering on genome-wide methylation data. This clustering analysis revealed the presence of four distinct subtypes within the tumor samples, namely, CIMP-H, CIMP-L, cluster 3, and cluster 4. These subtypes demonstrated varying levels of methylation, categorized as high, intermediate, and very low. To gain further insights, we mapped significant probes from all clusters to Ensembl Regulatory build 89, with a specific focus on those located within promoter regions or bound regions. By intersecting the methylated promoter and bound regions across all methylation subtypes, we identified a total of 253 genes exhibiting aberrant methylation patterns in the promoter regions across all four subtypes of colorectal cancer. Among these genes, our comprehensive genome-wide analysis highlights bone morphogenic protein 4 (BMP4) as the most prominent candidate. This significant finding was derived through the utilization of various bioinformatics tools, emphasizing the potential role of BMP4 in colorectal cancer development and progression.

OGRE: calculate, visualize, and analyze overlap between genomic input regions and public annotations

BACKGROUND

Modern genome sequencing leads to an ever-growing collection of genomic annotations. Combining these elements with a set of input regions (e.g. genes) would yield new insights in genomic associations, such as those involved in gene regulation. The required data are scattered across different databases making a manual approach tiresome, unpractical, and prone to error. Semi-automatic approaches require programming skills in data parsing, processing, overlap calculation, and visualization, which most biomedical researchers lack. Our aim was to develop an automated tool providing all necessary algorithms, benefiting both bioinformaticians and researchers without bioinformatic training.

RESULTS

We developed overlapping annotated genomic regions (OGRE) as a comprehensive tool to associate and visualize input regions with genomic annotations. It does so by parsing regions of interest, mining publicly available annotations, and calculating possible overlaps between them. The user can thus identify location, type, and number of associated regulatory elements. Results are presented as easy to understand visualizations and result tables. We applied OGRE to recent studies and could show high reproducibility and potential new insights. To demonstrate OGRE's performance in terms of running time and output, we have conducted a benchmark and compared its features with similar tools.

CONCLUSIONS

OGRE's functions and built-in annotations can be applied as a downstream overlap association step, which is compatible with most genomic sequencing outputs, and can thus enrich pre-existing analyses pipelines. Compared to similar tools, OGRE shows competitive performance, offers additional features, and has been successfully applied to two recent studies. Overall, OGRE addresses the lack of tools for automatic analysis, local genomic overlap calculation, and visualization by providing an easy to use, end-to-end solution for both biologists and computational scientists.

A comprehensive revisit of the machine-learning tools developed for the identification of enhancers in the human genome

Enhancers are non-coding DNA elements that play a crucial role in enhancing the transcription rate of a specific gene in the genome. Experiments for identifying enhancers can be restricted by their conditions and involve complicated, time-consuming, laborious, and costly steps. To overcome these challenges, computational platforms have been developed to complement experimental methods that enable high-throughput identification of enhancers. Over the last few years, the development of various enhancer computational tools has resulted in significant progress in predicting putative enhancers. Thus, researchers are now able to use a variety of strategies to enhance and advance enhancer study. In this review, an overview of machine learning (ML)-based prediction methods for enhancer identification and related databases has been provided. The existing enhancer-prediction methods have also been reviewed regarding their algorithms, feature selection processes, validation techniques, and software utility. In addition, the advantages and drawbacks of these ML approaches and guidelines for developing bioinformatic tools have been highlighted for a more efficient enhancer prediction. This review will serve as a useful resource for experimentalists in selecting the appropriate ML tool for their study, and for bioinformaticians in developing more accurate and advanced ML-based predictors.

Major Genetic Risk Factors for Dupuytren's Disease Are Inherited From Neandertals

Dupuytren's disease is characterized by fingers becoming permanently bent in a flexed position. Whereas people of African ancestry are rarely afflicted by Dupuytren's disease, up to ∼30% of men over 60 years suffer from this condition in northern Europe. Here, we meta-analyze 3 biobanks comprising 7,871 cases and 645,880 controls and find 61 genome-wide significant variants associated with Dupuytren's disease. We show that 3 of the 61 loci harbor alleles of Neandertal origin, including the second and third most strongly associated ones (P = 6.4 × 10-132 and P = 9.2 × 10-69, respectively). For the most strongly associated Neandertal variant, we identify EPDR1 as the causal gene. Dupuytren's disease is an example of how admixture with Neandertals has shaped regional differences in disease prevalence.

Nursing Informatics and Epigenetics: Methodological Considerations for Big Data Analysis

Nursing informatics requires an understanding of patient-centered data and clinical workflow, and epigenetic research requires an understanding of data analysis. The purpose of this article is to document the methodology that nursing informatics specialists can use to conduct epigenetic research and subsequently strengthen patient-centered care. A pilot study of a secondary methylation data analysis using The Cancer Genome Atlas data from individuals with colon cancer is utilized to illustrate the methodology. The steps for conducting the study using public and free resources are discussed. These steps include finding a data source; downloading and analyzing differentially methylated regions; annotating differentially methylated region, gene ontology and function analysis; and reporting results. A model of epigenetic testing workflow is provided, as is a list of publicly available data and analysis sources that can be used to conduct epigenetic research.

Candidate SNP Markers Significantly Altering the Affinity of TATA-Binding Protein for the Promoters of Human Hub Genes for Atherogenesis, Atherosclerosis and Atheroprotection

Atherosclerosis is a systemic disease in which focal lesions in arteries promote the build-up of lipoproteins and cholesterol they are transporting. The development of atheroma (atherogenesis) narrows blood vessels, reduces the blood supply and leads to cardiovascular diseases. According to the World Health Organization (WHO), cardiovascular diseases are the leading cause of death, which has been especially boosted since the COVID-19 pandemic. There is a variety of contributors to atherosclerosis, including lifestyle factors and genetic predisposition. Antioxidant diets and recreational exercises act as atheroprotectors and can retard atherogenesis. The search for molecular markers of atherogenesis and atheroprotection for predictive, preventive and personalized medicine appears to be the most promising direction for the study of atherosclerosis. In this work, we have analyzed 1068 human genes associated with atherogenesis, atherosclerosis and atheroprotection. The hub genes regulating these processes have been found to be the most ancient. In silico analysis of all 5112 SNPs in their promoters has revealed 330 candidate SNP markers, which statistically significantly change the affinity of the TATA-binding protein (TBP) for these promoters. These molecular markers have made us confident that natural selection acts against underexpression of the hub genes for atherogenesis, atherosclerosis and atheroprotection. At the same time, upregulation of the one for atheroprotection promotes human health.

Genomic benchmarks: a collection of datasets for genomic sequence classification

BACKGROUND

Recently, deep neural networks have been successfully applied in many biological fields. In 2020, a deep learning model AlphaFold won the protein folding competition with predicted structures within the error tolerance of experimental methods. However, this solution to the most prominent bioinformatic challenge of the past 50 years has been possible only thanks to a carefully curated benchmark of experimentally predicted protein structures. In Genomics, we have similar challenges (annotation of genomes and identification of functional elements) but currently, we lack benchmarks similar to protein folding competition.

RESULTS

Here we present a collection of curated and easily accessible sequence classification datasets in the field of genomics. The proposed collection is based on a combination of novel datasets constructed from the mining of publicly available databases and existing datasets obtained from published articles. The collection currently contains nine datasets that focus on regulatory elements (promoters, enhancers, open chromatin region) from three model organisms: human, mouse, and roundworm. A simple convolution neural network is also included in a repository and can be used as a baseline model. Benchmarks and the baseline model are distributed as the Python package 'genomic-benchmarks', and the code is available at https://github.com/ML-Bioinfo-CEITEC/genomic_benchmarks .

CONCLUSIONS

Deep learning techniques revolutionized many biological fields but mainly thanks to the carefully curated benchmarks. For the field of Genomics, we propose a collection of benchmark datasets for the classification of genomic sequences with an interface for the most commonly used deep learning libraries, implementation of the simple neural network and a training framework that can be used as a starting point for future research. The main aim of this effort is to create a repository for shared datasets that will make machine learning for genomics more comparable and reproducible while reducing the overhead of researchers who want to enter the field, leading to healthy competition and new discoveries.

Whole Genome Analysis of Venous Thromboembolism: the Trans-Omics for Precision Medicine Program

BACKGROUND

Risk for venous thromboembolism has a strong genetic component. Whole genome sequencing from the TOPMed program (Trans-Omics for Precision Medicine) allowed us to look for new associations, particularly rare variants missed by standard genome-wide association studies.

METHODS

The 3793 cases and 7834 controls (11.6% of cases were individuals of African, Hispanic/Latino, or Asian ancestry) were analyzed using a single variant approach and an aggregate gene-based approach using our primary filter (included only loss-of-function and missense variants predicted to be deleterious) and our secondary filter (included all missense variants).

RESULTS

Single variant analyses identified associations at 5 known loci. Aggregate gene-based analyses identified only PROC (odds ratio, 6.2 for carriers of rare variants; P=7.4×10-14) when using our primary filter. Employing our secondary variant filter led to a smaller effect size at PROC (odds ratio, 3.8; P=1.6×10-14), while excluding variants found only in rare isoforms led to a larger one (odds ratio, 7.5). Different filtering strategies improved the signal for 2 other known genes: PROS1 became significant (minimum P=1.8×10-6 with the secondary filter), while SERPINC1 did not (minimum P=4.4×10-5 with minor allele frequency <0.0005). Results were largely the same when restricting the analyses to include only unprovoked cases; however, one novel gene, MS4A1, became significant (P=4.4×10-7 using all missense variants with minor allele frequency <0.0005).

CONCLUSIONS

Here, we have demonstrated the importance of using multiple variant filtering strategies, as we detected additional genes when filtering variants based on their predicted deleteriousness, frequency, and presence on the most expressed isoforms. Our primary analyses did not identify new candidate loci; thus larger follow-up studies are needed to replicate the novel MS4A1 locus and to identify additional rare variation associated with venous thromboembolism.

Aberrant activation of TCL1A promotes stem cell expansion in clonal haematopoiesis

Mutations in a diverse set of driver genes increase the fitness of haematopoietic stem cells (HSCs), leading to clonal haematopoiesis1. These lesions are precursors for blood cancers2-6, but the basis of their fitness advantage remains largely unknown, partly owing to a paucity of large cohorts in which the clonal expansion rate has been assessed by longitudinal sampling. Here, to circumvent this limitation, we developed a method to infer the expansion rate from data from a single time point. We applied this method to 5,071 people with clonal haematopoiesis. A genome-wide association study revealed that a common inherited polymorphism in the TCL1A promoter was associated with a slower expansion rate in clonal haematopoiesis overall, but the effect varied by driver gene. Those carrying this protective allele exhibited markedly reduced growth rates or prevalence of clones with driver mutations in TET2, ASXL1, SF3B1 and SRSF2, but this effect was not seen in clones with driver mutations in DNMT3A. TCL1A was not expressed in normal or DNMT3A-mutated HSCs, but the introduction of mutations in TET2 or ASXL1 led to the expression of TCL1A protein and the expansion of HSCs in vitro. The protective allele restricted TCL1A expression and expansion of mutant HSCs, as did experimental knockdown of TCL1A expression. Forced expression of TCL1A promoted the expansion of human HSCs in vitro and mouse HSCs in vivo. Our results indicate that the fitness advantage of several commonly mutated driver genes in clonal haematopoiesis may be mediated by TCL1A activation.

Development of a novel prognostic signature derived from enhancer RNA-regulated genes in head neck squamous cell carcinoma

BACKGROUND

Enhancer RNAs (eRNAs) are increasingly recognized as prognostic biomarkers-across human cancers. Here, we sought to develop a novel eRNA-regulated genes (ERGs)-derived prognostic signature for head neck squamous cell carcinoma (HNSCC).

METHODS

Candidate ERGs were identified via co-expression between individual survival-related eRNAs and their putative targets by Spearman's correlation analyses. The ERG signature was developed by univariate Cox regression, Kaplan-Meier survival analysis and maximum AUC in 1000 iterations of LASSO-penalized multivariate Cox regression. An ERG nomogram incorporating ERG signature and selected clinicopathological parameters were constructed by multivariate Cox regression. Biological roles of eRNA of interest were further explored in vitro.

RESULTS

The ERG signature successfully stratified patients into subgroups with distinct survival in multiple cohorts. An ERG nomogram was developed with satisfactory performance in prognostication. Inhibition of ENSR00000165816 significantly reduced transcript level of SLC2A9 and impaired cell proliferation and invasion.

CONCLUSION

Our results establish ERG signature and nomogram as powerful prognostic predictors for HNSCC.

Cross-ancestry, cell-type-informed atlas of gene, isoform, and splicing regulation in the developing human brain

Genomic regulatory elements active in the developing human brain are notably enriched in genetic risk for neuropsychiatric disorders, including autism spectrum disorder (ASD), schizophrenia, and bipolar disorder. However, prioritizing the specific risk genes and candidate molecular mechanisms underlying these genetic enrichments has been hindered by the lack of a single unified large-scale gene regulatory atlas of human brain development. Here, we uniformly process and systematically characterize gene, isoform, and splicing quantitative trait loci (xQTLs) in 672 fetal brain samples from unique subjects across multiple ancestral populations. We identify 15,752 genes harboring a significant xQTL and map 3,739 eQTLs to a specific cellular context. We observe a striking drop in gene expression and splicing heritability as the human brain develops. Isoform-level regulation, particularly in the second trimester, mediates the greatest proportion of heritability across multiple psychiatric GWAS, compared with eQTLs. Via colocalization and TWAS, we prioritize biological mechanisms for ~60% of GWAS loci across five neuropsychiatric disorders, nearly two-fold that observed in the adult brain. Finally, we build a comprehensive set of developmentally regulated gene and isoform co-expression networks capturing unique genetic enrichments across disorders. Together, this work provides a comprehensive view of genetic regulation across human brain development as well as the stage-and cell type-informed mechanistic underpinnings of neuropsychiatric disorders.

Jasmine and Iris: population-scale structural variant comparison and analysis

The availability of long reads is revolutionizing studies of structural variants (SVs). However, because SVs vary across individuals and are discovered through imprecise read technologies and methods, they can be difficult to compare. Addressing this, we present Jasmine and Iris ( https://github.com/mkirsche/Jasmine/ ), for fast and accurate SV refinement, comparison and population analysis. Using an SV proximity graph, Jasmine outperforms six widely used comparison methods, including reducing the rate of Mendelian discordance in trio datasets by more than fivefold, and reveals a set of high-confidence de novo SVs confirmed by multiple technologies. We also present a unified callset of 122,813 SVs and 82,379 indels from 31 samples of diverse ancestry sequenced with long reads. We genotype these variants in 1,317 samples from the 1000 Genomes Project and the Genotype-Tissue Expression project with DNA and RNA-sequencing data and assess their widespread impact on gene expression, including within medically relevant genes.

Differentially Expressed Genes and Molecular Susceptibility to Human Age-Related Diseases

Mainstream transcriptome profiling of susceptibility versus resistance to age-related diseases (ARDs) is focused on differentially expressed genes (DEGs) specific to gender, age, and pathogeneses. This approach fits in well with predictive, preventive, personalized, participatory medicine and helps understand how, why, when, and what ARDs one can develop depending on their genetic background. Within this mainstream paradigm, we wanted to find out whether the known ARD-linked DEGs available in PubMed can reveal a molecular marker that will serve the purpose in anyone's any tissue at any time. We sequenced the periaqueductal gray (PAG) transcriptome of tame versus aggressive rats, identified rat-behavior-related DEGs, and compared them with their known homologous animal ARD-linked DEGs. This analysis yielded statistically significant correlations between behavior-related and ARD-susceptibility-related fold changes (log₂ values) in the expression of these DEG homologs. We found principal components, PC1 and PC2, corresponding to the half-sum and the half-difference of these log₂ values, respectively. With the DEGs linked to ARD susceptibility and ARD resistance in humans used as controls, we verified these principal components. This yielded only one statistically significant common molecular marker for ARDs: an excess of Fcγ receptor IIb suppressing immune cell hyperactivation.

Computational Methods for Single-cell DNA Methylome Analysis

Dissecting intercellular epigenetic differences is key to understanding tissue heterogeneity. Recent advances in single-cell DNA methylome profiling have presented opportunities to resolve this heterogeneity at the maximum resolution. While these advances enable us to explore frontiers of chromatin biology and better understand cell lineage relationships, they pose new challenges in data processing and interpretation. This review surveys the current state of computational tools developed for single-cell DNA methylome data analysis. We discuss critical components of single-cell DNA methylome data analysis, including data preprocessing, quality control, imputation, dimensionality reduction, cell clustering, supervised cell annotation, cell lineage reconstruction, gene activity scoring, and integration with transcriptome data. We also highlight unique aspects of single-cell DNA methylome data analysis and discuss how techniques common to other single-cell omics data analyses can be adapted to analyze DNA methylomes. Finally, we discuss existing challenges and opportunities for future development.

Ensembl 2023

Ensembl (https://www.ensembl.org) has produced high-quality genomic resources for vertebrates and model organisms for more than twenty years. During that time, our resources, services and tools have continually evolved in line with both the publicly available genome data and the downstream research and applications that utilise the Ensembl platform. In recent years we have witnessed a dramatic shift in the genomic landscape. There has been a large increase in the number of high-quality reference genomes through global biodiversity initiatives. In parallel, there have been major advances towards pangenome representations of higher species, where many alternative genome assemblies representing different breeds, cultivars, strains and haplotypes are now available. In order to support these efforts and accelerate downstream research, it is our goal at Ensembl to create high-quality annotations, tools and services for species across the tree of life. Here, we report our resources for popular reference genomes, the dramatic growth of our annotations (including haplotypes from the first human pangenome graphs), updates to the Ensembl Variant Effect Predictor (VEP), interactive protein structure predictions from AlphaFold DB, and the beta release of our new website.