Whole genome methylation sequencing in blood from persons with mild cognitive impairment and dementia due to Alzheimer's disease identifies cognitive status.

INTRODUCTION: Whole genome methylation sequencing (WGMS) in blood identifies differential DNA methylation in persons with late-onset dementia due to Alzheimer's disease (AD) but has not been tested in persons with mild cognitive impairment (MCI). METHODS: We used WGMS to compare DNA methylation levels at 25,244,219 CpG loci in 382 blood samples from 99 persons with MCI, 109 with AD, and 174 who are cognitively unimpaired (CU). RESULTS: WGMS identified 9,756 differentially methylated positions (DMPs) in persons with MCI, including 1,743 differentially methylated genes encoding proteins in biological pathways related to synapse organization, dendrite development, and ion transport. 447 DMPs exhibit progressively increasing or decreasing DNA methylation levels between CU, MCI, and AD that correspond to cognitive status. DISCUSSION: WGMS identifies DMPs in known and newly detected genes in blood from persons with MCI and AD that support blood DNA methylation levels as candidate biomarkers of cognitive status.

GEEES: Inferring Cell-specific Gene-Enhancer Interactions from Multi-modal Single Cell Data.

MOTIVATION: Gene-enhancer interactions are central to transcriptional regulation. Current multi-modal single cell datasets that profile transcriptome and chromatin accessibility simultaneously in a single cell are yielding opportunities to infer gene-enhancer associations in a cell type specific manner. Computational efforts for such multi-modal single cell datasets thus far focused on methods for identification and refinement of cell types and trajectory construction. While initial attempts for inferring gene-enhancer interactions have emerged, these have not been evaluated against benchmark datasets that materialized from bulk genomic experiments. Furthermore, existing approaches are limited to inferring gene-enhancer associations at the level of grouped cells as opposed to individual cells, thereby ignoring regulatory heterogeneity among the cells. RESULTS: We present a new approach, GEEES for "Gene EnhancEr IntEractions from Multi-modal Single Cell Data", for inferring gene-enhancer associations at the single cell level using multi-modal single cell transcriptome and chromatin accessibility data. We evaluated GEEES alongside several multivariate regression-based alternatives we devised and state-of-the-art methods using a large number of benchmark datasets, providing a comprehensive assessment of current approaches. This analysis revealed significant discrepancies between gold-standard interactions and gene-enhancer associations derived from multi-modal single-cell data. Notably, incorporating gene-enhancer distance into the analysis markedly improved performance across all methods, positioning GEEES as a leading approach in this domain. While the overall improvement in performance metrics by GEEES is modest, it provides enhanced cell representation learning which can be leveraged for more effective downstream analysis. Furthermore, our review of existing experimentally driven benchmark datasets uncovers their limited concordance, underscoring the necessity for new high-throughput experiments to validate gene-enhancer interactions inferred from single-cell data. AVAILABILITY: https://github.com/keleslab/GEEES. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Integrative annotation scores of variants for impact on RNA binding protein activities.

MOTIVATION: The ENCODE project generated a large collection of eCLIP-seq RNA binding protein (RBP) profiling data with accompanying RNA-seq transcriptomes of shRNA knockdown of RBPs. These data could have utility in understanding the functional impact of genetic variants, however their potential has not been fully exploited. We implement INCA (Integrative annotation scores of variants for impact on RBP activities) as a multi-step genetic variant scoring approach that leverages the ENCODE RBP data together with ClinVar and integrates multiple computational approaches to aggregate evidence. RESULTS: INCA evaluates variant impacts on RBP activities by leveraging genotypic differences in cell lines used for eCLIP-seq. We show that INCA provides critical specificity, beyond generic scoring for RBP binding disruption, for candidate variants and their linkage-disequilibrium partners. As a result, it can, on average, augment scoring of 46.2% of the candidate variants beyond generic scoring for RBP binding disruption and aid in variant prioritization for follow-up analysis. AVAILABILITY AND IMPLEMENTATION: INCA is implemented in R and is available at https://github.com/keleslab/INCA.

Harnessing the regenerative potential of interleukin11 to enhance heart repair.

Balancing between regenerative processes and fibrosis is crucial for heart repair, yet strategies regulating this balance remain a barrier to developing therapies. While Interleukin11 (IL11) is known as a fibrotic factor, its contribution to heart regeneration is poorly understood. We uncovered that il11a, an Il11 homolog in zebrafish, can trigger robust regenerative programs in zebrafish hearts, including cardiomyocytes proliferation and coronary expansion, even in the absence of injury. However, prolonged il11a induction in uninjured hearts causes persistent fibroblast emergence, resulting in fibrosis. While deciphering the regenerative and fibrotic effects of il11a, we found that il11-dependent fibrosis, but not regeneration, is mediated through ERK activity, suggesting to potentially uncouple il11a dual effects on regeneration and fibrosis. To harness the il11a's regenerative ability, we devised a combinatorial treatment through il11a induction with ERK inhibition. This approach enhances cardiomyocyte proliferation with mitigated fibrosis, achieving a balance between regenerative processes and fibrosis. Thus, we unveil the mechanistic insights into regenerative il11 roles, offering therapeutic avenues to foster cardiac repair without exacerbating fibrosis.

PUF partner interactions at a conserved interface shape the RNA-binding landscape and cell fate in Caenorhabditis elegans.

Protein-RNA regulatory networks underpin much of biology. C. elegans FBF-2, a PUF-RNA-binding protein, binds over 1,000 RNAs to govern stem cells and differentiation. FBF-2 interacts with multiple protein partners via a key tyrosine, Y479. Here, we investigate the in vivo significance of partnerships using a Y479A mutant. Occupancy of the Y479A mutant protein increases or decreases at specific sites across the transcriptome, varying with RNAs. Germline development also changes in a specific fashion: Y479A abolishes one FBF-2 function-the sperm-to-oocyte cell fate switch. Y479A's effects on the regulation of one mRNA, gld-1, are critical to this fate change, though other network changes are also important. FBF-2 switches from repression to activation of gld-1 RNA, likely by distinct FBF-2 partnerships. The role of RNA-binding protein partnerships in governing RNA regulatory networks will likely extend broadly, as such partnerships pervade RNA controls in virtually all metazoan tissues and species.

Whole genome methylation sequencing in blood identifies extensive differential DNA methylation in late-onset dementia due to Alzheimer's disease.

INTRODUCTION: DNA microarray-based studies report differentially methylated positions (DMPs) in blood between late-onset dementia due to Alzheimer's disease (AD) and cognitively unimpaired individuals, but interrogate < 4% of the genome. METHODS: We used whole genome methylation sequencing (WGMS) to quantify DNA methylation levels at 25,409,826 CpG loci in 281 blood samples from 108 AD and 173 cognitively unimpaired individuals. RESULTS: WGMS identified 28,038 DMPs throughout the human methylome, including 2707 differentially methylated genes (e.g., SORCS3, GABA, and PICALM) encoding proteins in biological pathways relevant to AD such as synaptic membrane, cation channel complex, and glutamatergic synapse. One hundred seventy-three differentially methylated blood-specific enhancers interact with the promoters of 95 genes that are differentially expressed in blood from persons with and without AD. DISCUSSION: WGMS identifies differentially methylated CpGs in known and newly detected genes and enhancers in blood from persons with and without AD. HIGHLIGHTS: Whole genome DNA methylation levels were quantified in blood from persons with and without Alzheimer's disease (AD). Twenty-eight thousand thirty-eight differentially methylated positions (DMPs) were identified. Two thousand seven hundred seven genes comprise DMPs. Forty-eight of 75 independent genetic risk loci for AD have DMPs. One thousand five hundred sixty-eight blood-specific enhancers comprise DMPs, 173 of which interact with the promoters of 95 genes that are differentially expressed in blood from persons with and without AD.

Deep Phenotyping of Sleep in Drosophila.

Sleep is an evolutionarily conserved behavior, whose function is unknown. Here, we present a method for deep phenotyping of sleep in Drosophila, consisting of a high-resolution video imaging system, coupled with closed-loop laser perturbation to measure arousal threshold. To quantify sleep-associated microbehaviors, we trained a deep-learning network to annotate body parts in freely moving flies and developed a semi-supervised computational pipeline to classify behaviors. Quiescent flies exhibit a rich repertoire of microbehaviors, including proboscis pumping (PP) and haltere switches, which vary dynamically across the night. Using this system, we characterized the effects of optogenetically activating two putative sleep circuits. These data reveal that activating dFB neurons produces micromovements, inconsistent with sleep, while activating R5 neurons triggers PP followed by behavioral quiescence. Our findings suggest that sleep in Drosophila is polyphasic with different stages and set the stage for a rigorous analysis of sleep and other behaviors in this species.

MuDCoD: multi-subject community detection in personalized dynamic gene networks from single-cell RNA sequencing.

MOTIVATION: With the wide availability of single-cell RNA-seq (scRNA-seq) technology, population-scale scRNA-seq datasets across multiple individuals and time points are emerging. While the initial investigations of these datasets tend to focus on standard analysis of clustering and differential expression, leveraging the power of scRNA-seq data at the personalized dynamic gene co-expression network level has the potential to unlock subject and/or time-specific network-level variation, which is critical for understanding phenotypic differences. Community detection from co-expression networks of multiple time points or conditions has been well-studied; however, none of the existing settings included networks from multiple subjects and multiple time points simultaneously. To address this, we develop Multi-subject Dynamic Community Detection (MuDCoD) for multi-subject community detection in personalized dynamic gene networks from scRNA-seq. MuDCoD builds on the spectral clustering framework and promotes information sharing among the networks of the subjects as well as networks at different time points. It clusters genes in the personalized dynamic gene networks and reveals gene communities that are variable or shared not only across time but also among subjects. RESULTS: Evaluation and benchmarking of MuDCoD against existing approaches reveal that MuDCoD effectively leverages apparent shared signals among networks of the subjects at individual time points, and performs robustly when there is no or little information sharing among the networks. Applications to population-scale scRNA-seq datasets of human-induced pluripotent stem cells during dopaminergic neuron differentiation and CD4+ T cell activation indicate that MuDCoD enables robust inference for identifying time-varying personalized gene modules. Our results illustrate how personalized dynamic community detection can aid in the exploration of subject-specific biological processes that vary across time. AVAILABILITY AND IMPLEMENTATION: MuDCoD is publicly available at https://github.com/bo1929/MuDCoD as a Python package. Implementation includes simulation and real-data experiments together with extensive documentation.

Debiased personalized gene coexpression networks for population-scale scRNA-seq data.

Population-scale single-cell RNA-seq (scRNA-seq) data sets create unique opportunities for quantifying expression variation across individuals at the gene coexpression network level. Estimation of coexpression networks is well established for bulk RNA-seq; however, single-cell measurements pose novel challenges owing to technical limitations and noise levels of this technology. Gene-gene correlation estimates from scRNA-seq tend to be severely biased toward zero for genes with low and sparse expression. Here, we present Dozer to debias gene-gene correlation estimates from scRNA-seq data sets and accurately quantify network-level variation across individuals. Dozer corrects correlation estimates in the general Poisson measurement model and provides a metric to quantify genes measured with high noise. Computational experiments establish that Dozer estimates are robust to mean expression levels of the genes and the sequencing depths of the data sets. Compared with alternatives, Dozer results in fewer false-positive edges in the coexpression networks, yields more accurate estimates of network centrality measures and modules, and improves the faithfulness of networks estimated from separate batches of the data sets. We showcase unique analyses enabled by Dozer in two population-scale scRNA-seq applications. Coexpression network-based centrality analysis of multiple differentiating human induced pluripotent stem cell (iPSC) lines yields biologically coherent gene groups that are associated with iPSC differentiation efficiency. Application with population-scale scRNA-seq of oligodendrocytes from postmortem human tissues of Alzheimer's disease and controls uniquely reveals coexpression modules of innate immune response with distinct coexpression levels between the diagnoses. Dozer represents an important advance in estimating personalized coexpression networks from scRNA-seq data.

Secondary bile acids function through the vitamin D receptor in myeloid progenitors to promote myelopoiesis.

Metabolic products of the microbiota can alter hematopoiesis. However, the contribution and site of action of bile acids is poorly understood. Here, we demonstrate that the secondary bile acids, deoxycholic acid (DCA) and lithocholic acid (LCA), increase bone marrow myelopoiesis. Treatment of bone marrow cells with DCA and LCA preferentially expanded immunophenotypic and functional colony-forming unit-granulocyte and macrophage (CFU-GM) granulocyte-monocyte progenitors (GMPs). DCA treatment of sorted hematopoietic stem and progenitor cells (HSPCs) increased CFU-GMs, indicating that direct exposure of HSPCs to DCA sufficed to increase GMPs. The vitamin D receptor (VDR) was required for the DCA-induced increase in CFU-GMs and GMPs. Single-cell RNA sequencing revealed that DCA significantly upregulated genes associated with myeloid differentiation and proliferation in GMPs. The action of DCA on HSPCs to expand GMPs in a VDR-dependent manner suggests microbiome-host interactions could directly affect bone marrow hematopoiesis and potentially the severity of infectious and inflammatory disease.

Dozer: Debiased personalized gene co-expression networks for population-scale scRNA-seq data.

Population-scale single cell RNA-seq (scRNA-seq) datasets create unique opportunities for quantifying expression variation across individuals at the gene co-expression network level. Estimation of co-expression networks is well-established for bulk RNA-seq; however, single-cell measurements pose novel challenges due to technical limitations and noise levels of this technology. Gene-gene correlation estimates from scRNA-seq tend to be severely biased towards zero for genes with low and sparse expression. Here, we present Dozer to debias gene-gene correlation estimates from scRNA-seq datasets and accurately quantify network level variation across individuals. Dozer corrects correlation estimates in the general Poisson measurement model and provides a metric to quantify genes measured with high noise. Computational experiments establish that Dozer estimates are robust to mean expression levels of the genes and the sequencing depths of the datasets. Compared to alternatives, Dozer results in fewer false positive edges in the co-expression networks, yields more accurate estimates of network centrality measures and modules, and improves the faithfulness of networks estimated from separate batches of the datasets. We showcase unique analyses enabled by Dozer in two population-scale scRNA-seq applications. Co-expression network-based centrality analysis of multiple differentiating human induced pluripotent stem cell (iPSC) lines yields biologically coherent gene groups that are associated with iPSC differentiation efficiency. Application with population-scale scRNA-seq of oligodendrocytes from postmortem human tissues of Alzheimer disease and controls uniquely reveals co-expression modules of innate immune response with markedly different co-expression levels between the diagnoses. Dozer represents an important advance in estimating personalized co-expression networks from scRNA-seq data.

AdaLiftOver: high-resolution identification of orthologous regulatory elements with Adaptive liftOver.

MOTIVATION: Elucidating functionally similar orthologous regulatory regions for human and model organism genomes is critical for exploiting model organism research and advancing our understanding of results from genome-wide association studies (GWAS). Sequence conservation is the de facto approach for finding orthologous non-coding regions between human and model organism genomes. However, existing methods for mapping non-coding genomic regions across species are challenged by the multi-mapping, low precision, and low mapping rate issues. RESULTS: We develop Adaptive liftOver (AdaLiftOver), a large-scale computational tool for identifying functionally similar orthologous non-coding regions across species. AdaLiftOver builds on the UCSC liftOver framework to extend the query regions and prioritizes the resulting candidate target regions based on the conservation of the epigenomic and the sequence grammar features. Evaluations of AdaLiftOver with multiple case studies, spanning both genomic intervals from epigenome datasets across a wide range of model organisms and GWAS SNPs, yield AdaLiftOver as a versatile method for deriving hard-to-obtain human epigenome datasets as well as reliably identifying orthologous loci for GWAS SNPs. AVAILABILITY AND IMPLEMENTATION: The R package and the data for AdaLiftOver is available from https://github.com/keleslab/AdaLiftOver.

Joint tensor modeling of single cell 3D genome and epigenetic data with Muscle.

Emerging single cell technologies that simultaneously capture long-range interactions of genomic loci together with their DNA methylation levels are advancing our understanding of three-dimensional genome structure and its interplay with the epigenome at the single cell level. While methods to analyze data from single cell high throughput chromatin conformation capture (scHi-C) experiments are maturing, methods that can jointly analyze multiple single cell modalities with scHi-C data are lacking. Here, we introduce Muscle, a semi-nonnegative joint decomposition of Multiple single cell tensors, to jointly analyze 3D conformation and DNA methylation data at the single cell level. Muscle takes advantage of the inherent tensor structure of the scHi-C data, and integrates this modality with DNA methylation. We developed an alternating least squares algorithm for estimating Muscle parameters and established its optimality properties. Parameters estimated by Muscle directly align with the key components of the downstream analysis of scHi-C data in a cell type specific manner. Evaluations with data-driven experiments and simulations demonstrate the advantages of the joint modeling framework of Muscle over single modality modeling or a baseline multi modality modeling for cell type delineation and elucidating associations between modalities. Muscle is publicly available at https://github.com/keleslab/muscle.

Pathogenic human variant that dislocates GATA2 zinc fingers disrupts hematopoietic gene expression and signaling networks.

Although certain human genetic variants are conspicuously loss of function, decoding the impact of many variants is challenging. Previously, we described a patient with leukemia predisposition syndrome (GATA2 deficiency) with a germline GATA2 variant that inserts 9 amino acids between the 2 zinc fingers (9aa-Ins). Here, we conducted mechanistic analyses using genomic technologies and a genetic rescue system with Gata2 enhancer-mutant hematopoietic progenitor cells to compare how GATA2 and 9aa-Ins function genome-wide. Despite nuclear localization, 9aa-Ins was severely defective in occupying and remodeling chromatin and regulating transcription. Variation of the inter-zinc finger spacer length revealed that insertions were more deleterious to activation than repression. GATA2 deficiency generated a lineage-diverting gene expression program and a hematopoiesis-disrupting signaling network in progenitors with reduced granulocyte-macrophage colony-stimulating factor (GM-CSF) and elevated IL-6 signaling. As insufficient GM-CSF signaling caused pulmonary alveolar proteinosis and excessive IL-6 signaling promoted bone marrow failure and GATA2 deficiency patient phenotypes, these results provide insight into mechanisms underlying GATA2-linked pathologies.

Normalization and de-noising of single-cell Hi-C data with BandNorm and scVI-3D.

Single-cell high-throughput chromatin conformation capture methodologies (scHi-C) enable profiling of long-range genomic interactions. However, data from these technologies are prone to technical noise and biases that hinder downstream analysis. We develop a normalization approach, BandNorm, and a deep generative modeling framework, scVI-3D, to account for scHi-C specific biases. In benchmarking experiments, BandNorm yields leading performances in a time and memory efficient manner for cell-type separation, identification of interacting loci, and recovery of cell-type relationships, while scVI-3D exhibits advantages for rare cell types and under high sparsity scenarios. Application of BandNorm coupled with gene-associating domain analysis reveals scRNA-seq validated sub-cell type identification.

Regulatory Architecture of the RCA Gene Cluster Captures an Intragenic TAD Boundary, CTCF-Mediated Chromatin Looping and a Long-Range Intergenic Enhancer.

The Regulators of Complement Activation (RCA) gene cluster comprises several tandemly arranged genes with shared functions within the immune system. RCA members, such as complement receptor 2 (CR2), are well-established susceptibility genes in complex autoimmune diseases. Altered expression of RCA genes has been demonstrated at both the functional and genetic level, but the mechanisms underlying their regulation are not fully characterised. We aimed to investigate the structural organisation of the RCA gene cluster to identify key regulatory elements that influence the expression of CR2 and other genes in this immunomodulatory region. Using 4C, we captured extensive CTCF-mediated chromatin looping across the RCA gene cluster in B cells and showed these were organised into two topologically associated domains (TADs). Interestingly, an inter-TAD boundary was located within the CR1 gene at a well-characterised segmental duplication. Additionally, we mapped numerous gene-gene and gene-enhancer interactions across the region, revealing extensive co-regulation. Importantly, we identified an intergenic enhancer and functionally demonstrated this element upregulates two RCA members (CR2 and CD55) in B cells. We have uncovered novel, long-range mechanisms whereby autoimmune disease susceptibility may be influenced by genetic variants, thus highlighting the important contribution of chromatin topology to gene regulation and complex genetic disease.

scGAD: single-cell gene associating domain scores for exploratory analysis of scHi-C data.

SUMMARY: Quantitative tools are needed to leverage the unprecedented resolution of single-cell high-throughput chromatin conformation (scHi-C) data and integrate it with other single-cell data modalities. We present single-cell gene associating domain (scGAD) scores as a dimension reduction and exploratory analysis tool for scHi-C data. scGAD enables summarization at the gene unit while accounting for inherent gene-level genomic biases. Low-dimensional projections with scGAD capture clustering of cells based on their 3D structures. Significant chromatin interactions within and between cell types can be identified with scGAD. We further show that scGAD facilitates the integration of scHi-C data with other single-cell data modalities by enabling its projection onto reference low-dimensional embeddings. This multi-modal data integration provides an automated and refined cell-type annotation for scHi-C data. AVAILABILITY AND IMPLEMENTATION: scGAD is part of the BandNorm R package at https://sshen82.github.io/BandNorm/articles/scGAD-tutorial.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Gene by environment interaction mouse model reveals a functional role for 5-hydroxymethylcytosine in neurodevelopmental disorders.

Mouse knockouts of Cntnap2 show altered neurodevelopmental behavior, deficits in striatal GABAergic signaling, and a genome-wide disruption of an environmentally sensitive DNA methylation modification (5-hydroxymethylcytosine [5hmC]) in the orthologs of a significant number of genes implicated in human neurodevelopmental disorders. We tested adult Cntnap2 heterozygous mice (Cntnap2 +/-; lacking behavioral or neuropathological abnormalities) subjected to a prenatal stress and found that prenatally stressed Cntnap2 +/- female mice show repetitive behaviors and altered sociability, similar to the homozygote phenotype. Genomic profiling revealed disruptions in hippocampal and striatal 5hmC levels that are correlated to altered transcript levels of genes linked to these phenotypes (e.g., Reln, Dst, Trio, and Epha5). Chromatin immunoprecipitation coupled with high-throughput sequencing and hippocampal nuclear lysate pull-down data indicated that 5hmC abundance alters the binding of the transcription factor CLOCK near the promoters of these genes (e.g., Palld, Gigyf1, and Fry), providing a mechanistic role for 5hmC in gene regulation. Together, these data support gene-by-environment hypotheses for the origins of mental illness and provide a means to identify the elusive factors contributing to complex human diseases.

PKM2-TMEM33 axis regulates lipid homeostasis in cancer cells by controlling SCAP stability.

The pyruvate kinase M2 isoform (PKM2) is preferentially expressed in cancer cells to regulate anabolic metabolism. Although PKM2 was recently reported to regulate lipid homeostasis, the molecular mechanism remains unclear. Herein, we discovered an ER transmembrane protein 33 (TMEM33) as a downstream effector of PKM2 that regulates activation of SREBPs and lipid metabolism. Loss of PKM2 leads to up-regulation of TMEM33, which recruits RNF5, an E3 ligase, to promote SREBP-cleavage activating protein (SCAP) degradation. TMEM33 is transcriptionally regulated by nuclear factor erythroid 2-like 1 (NRF1), whose cleavage and activation are controlled by PKM2 levels. Total plasma cholesterol levels are elevated by either treatment with PKM2 tetramer-promoting agent TEPP-46 or by global PKM2 knockout in mice, highlighting the essential function of PKM2 in lipid metabolism. Although depletion of PKM2 decreases cancer cell growth, global PKM2 knockout accelerates allografted tumor growth. Together, our findings reveal the cell-autonomous and systemic effects of PKM2 in lipid homeostasis and carcinogenesis, as well as TMEM33 as a bona fide regulator of lipid metabolism.