Biochar immobilized hydrolase degrades PET microplastics and alleviates the disturbance of soil microbial function via modulating nitrogen and phosphorus cycles.

Microplastics (MPs) pose an emerging threat to soil ecological function, yet effective solutions remain limited. This study introduces a novel approach using magnetic biochar immobilized PET hydrolase (MB-LCC-FDS) to degrade soil polyethylene terephthalate microplastics (PET-MPs). MB-LCC-FDS exhibited a 1.68-fold increase in relative activity in aquatic solutions and maintained 58.5 % residual activity after five consecutive cycles. Soil microcosm experiment amended with MB-LCC-FDS observed a 29.6 % weight loss of PET-MPs, converting PET into mono(2-hydroxyethyl) terephthalate (MHET). The generated MHET can subsequently be metabolized by soil microbiota to release terephthalic acid. The introduction of MB-LCC-FDS shifted the functional composition of soil microbiota, increasing the relative abundances of Microbacteriaceae and Skermanella while reducing Arthobacter and Vicinamibacteraceae. Metagenomic analysis revealed that MB-LCC-FDS enhanced nitrogen fixation, P-uptake and transport, and organic-P mineralization in PET-MPs contaminated soil, while weakening the denitrification and nitrification. Structural equation model indicated that changes in soil total carbon and Simpson index, induced by MB-LCC-FDS, were the driving factors for soil carbon and nitrogen transformation. Overall, this study highlights the synergistic role of magnetic biochar-immobilized PET hydrolase and soil microbiota in degrading soil PET-MPs, and enhances our understanding of the microbiome and functional gene responses to PET-MPs and MB-LCC-FDS in soil systems.

Identification of Parkinson's disease subtypes with distinct brain atrophy progression and its association with clinical progression.

Background: Parkinson's disease (PD) patients suffer from progressive gray matter volume (GMV) loss, but whether distinct patterns of atrophy progression exist within PD are still unclear. Objective: This study aims to identify PD subtypes with different rates of GMV loss and assess their association with clinical progression. Methods: This study included 107 PD patients (mean age: 60.06 ± 9.98 years, 70.09% male) with baseline and ≥ 3-year follow-up structural MRI scans. A linear mixed-effects model was employed to assess the rates of regional GMV loss. Hierarchical cluster analysis was conducted to explore potential subtypes based on individual rates of GMV loss. Clinical score changes were then compared across these subtypes. Results: Two PD subtypes were identified based on brain atrophy rates. Subtype 1 (n = 63) showed moderate atrophy, notably in the prefrontal and lateral temporal lobes, while Subtype 2 (n = 44) had faster atrophy across the brain, particularly in the lateral temporal region. Furthermore, subtype 2 exhibited faster deterioration in non-motor (MDS-UPDRS-Part Ⅰ, ß = 1.26 ± 0.18, P = 0.016) and motor (MDS-UPDRS-Part Ⅱ, ß = 1.34 ± 0.20, P = 0.017) symptoms, autonomic dysfunction (SCOPA-AUT, ß = 1.15 ± 0.22, P = 0.043), memory (HVLT-Retention, ß = -0.02 ± 0.01, P = 0.016) and depression (GDS, ß = 0.26 ± 0.083, P = 0.019) compared to subtype 1. Conclusion: The study has identified two PD subtypes with distinct patterns of atrophy progression and clinical progression, which may have implications for developing personalized treatment strategies.

Canopy2: tumor phylogeny inference by bulk DNA and single-cell RNA sequencing.

Tumors are comprised of a mixture of distinct cell populations that differ in terms of genetic makeup and function. Such heterogeneity plays a role in the development of drug resistance and the ineffectiveness of targeted cancer therapies. Insight into this complexity can be obtained through the construction of a phylogenetic tree, which illustrates the evolutionary lineage of tumor cells as they acquire mutations over time. We propose Canopy2, a Bayesian framework that uses single nucleotide variants derived from bulk DNA and single-cell RNA sequencing to infer tumor phylogeny and conduct mutational profiling of tumor subpopulations. Canopy2 uses Markov chain Monte Carlo methods to sample from a joint probability distribution involving a mixture of binomial and beta-binomial distributions, specifically chosen to account for the sparsity and stochasticity of the single-cell data. Canopy2 demystifies the sources of zeros in the single-cell data and separates zeros categorized as non-cancerous (cells without mutations), stochastic (mutations not expressed due to bursting), and technical (expressed mutations not picked up by sequencing). Simulations demonstrate that Canopy2 consistently outperforms competing methods and reconstructs the clonal tree with high fidelity, even in situations involving low sequencing depth, poor single-cell yield, and highly-advanced and polyclonal tumors. We further assess the performance of Canopy2 through application to breast cancer and glioblastoma data, benchmarking against existing methods. Canopy2 is an open-source R package available at https://github.com/annweideman/canopy2.

Identification of four biotypes in temporal lobe epilepsy via machine learning on brain images.

Artificial intelligence provides an opportunity to try to redefine disease subtypes based on similar pathobiology. Using a machine-learning algorithm (Subtype and Stage Inference) with cross-sectional MRI from 296 individuals with focal epilepsy originating from the temporal lobe (TLE) and 91 healthy controls, we show phenotypic heterogeneity in the pathophysiological progression of TLE. This study was registered in the Chinese Clinical Trials Registry (number: ChiCTR2200062562). We identify two hippocampus-predominant phenotypes, characterized by atrophy beginning in the left or right hippocampus; a third cortex-predominant phenotype, characterized by hippocampus atrophy after the neocortex; and a fourth phenotype without atrophy but amygdala enlargement. These four subtypes are replicated in the independent validation cohort (109 individuals). These subtypes show differences in neuroanatomical signature, disease progression and epilepsy characteristics. Five-year follow-up observations of these individuals reveal differential seizure outcomes among subtypes, indicating that specific subtypes may benefit from temporal surgery or pharmacological treatment. These findings suggest a diverse pathobiological basis underlying focal epilepsy that potentially yields to stratification and prognostication - a necessary step for precise medicine.

Early-initiated childhood reading for pleasure: associations with better cognitive performance, mental well-being and brain structure in young adolescence.

BACKGROUND: Childhood is a crucial neurodevelopmental period. We investigated whether childhood reading for pleasure (RfP) was related to young adolescent assessments of cognition, mental health, and brain structure. METHODS: We conducted a cross-sectional and longitudinal study in a large-scale US national cohort (10 000 + young adolescents), using the well-established linear mixed model and structural equation methods for twin study, longitudinal and mediation analyses. A 2-sample Mendelian randomization (MR) analysis for potential causal inference was also performed. Important factors including socio-economic status were controlled. RESULTS: Early-initiated long-standing childhood RfP (early RfP) was highly positively correlated with performance on cognitive tests and significantly negatively correlated with mental health problem scores of young adolescents. These participants with higher early RfP scores exhibited moderately larger total brain cortical areas and volumes, with increased regions including the temporal, frontal, insula, supramarginal; left angular, para-hippocampal; right middle-occipital, anterior-cingulate, orbital areas; and subcortical ventral-diencephalon and thalamus. These brain structures were significantly related to their cognitive and mental health scores, and displayed significant mediation effects. Early RfP was longitudinally associated with higher crystallized cognition and lower attention symptoms at follow-up. Approximately 12 h/week of youth regular RfP was cognitively optimal. We further observed a moderately significant heritability of early RfP, with considerable contribution from environments. MR analysis revealed beneficial causal associations of early RfP with adult cognitive performance and left superior temporal structure. CONCLUSIONS: These findings, for the first time, revealed the important relationships of early RfP with subsequent brain and cognitive development and mental well-being.

Genome-wide analysis of transcription-coupled repair reveals novel transcription events in Caenorhabditis elegans.

Bulky DNA adducts such as those induced by ultraviolet light are removed from the genomes of multicellular organisms by nucleotide excision repair, which occurs through two distinct mechanisms, global repair, requiring the DNA damage recognition-factor XPC (xeroderma pigmentosum complementation group C), and transcription-coupled repair (TCR), which does not. TCR is initiated when elongating RNA polymerase II encounters DNA damage, and thus analysis of genome-wide excision repair in XPC-mutants only repairing by TCR provides a unique opportunity to map transcription events missed by methods dependent on capturing RNA transcription products and thus limited by their stability and/or modifications (5'-capping or 3'-polyadenylation). Here, we have performed the eXcision Repair-sequencing (XR-seq) in the model organism Caenorhabditis elegans to generate genome-wide repair maps from a wild-type strain with normal excision repair, a strain lacking TCR (csb-1), or one that only repairs by TCR (xpc-1). Analysis of the intersections between the xpc-1 XR-seq repair maps with RNA-mapping datasets (RNA-seq, long- and short-capped RNA-seq) reveal previously unrecognized sites of transcription and further enhance our understanding of the genome of this important model organism.

Two neurostructural subtypes: results of machine learning on brain images from 4,291 individuals with schizophrenia.

Machine learning can be used to define subtypes of psychiatric conditions based on shared clinical and biological foundations, presenting a crucial step toward establishing biologically based subtypes of mental disorders. With the goal of identifying subtypes of disease progression in schizophrenia, here we analyzed cross-sectional brain structural magnetic resonance imaging (MRI) data from 4,291 individuals with schizophrenia (1,709 females, age=32.5 years±11.9) and 7,078 healthy controls (3,461 females, age=33.0 years±12.7) pooled across 41 international cohorts from the ENIGMA Schizophrenia Working Group, non-ENIGMA cohorts and public datasets. Using a machine learning approach known as Subtype and Stage Inference (SuStaIn), we implemented a brain imaging-driven classification that identifies two distinct neurostructural subgroups by mapping the spatial and temporal trajectory of gray matter (GM) loss in schizophrenia. Subgroup 1 (n=2,622) was characterized by an early cortical-predominant loss (ECL) with enlarged striatum, whereas subgroup 2 (n=1,600) displayed an early subcortical-predominant loss (ESL) in the hippocampus, amygdala, thalamus, brain stem and striatum. These reconstructed trajectories suggest that the GM volume reduction originates in the Broca's area/adjacent fronto-insular cortex for ECL and in the hippocampus/adjacent medial temporal structures for ESL. With longer disease duration, the ECL subtype exhibited a gradual worsening of negative symptoms and depression/anxiety, and less of a decline in positive symptoms. We confirmed the reproducibility of these imaging-based subtypes across various sample sites, independent of macroeconomic and ethnic factors that differed across these geographic locations, which include Europe, North America and East Asia. These findings underscore the presence of distinct pathobiological foundations underlying schizophrenia. This new imaging-based taxonomy holds the potential to identify a more homogeneous sub-population of individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.

More extensive structural damage in temporal lobe epilepsy with hippocampal sclerosis type 1.

OBJECTIVE: To explore clinical and structural differences between mesial temporal lobe epilepsy (mTLE) patients with different hippocampal sclerosis (HS) subtypes. METHODS: High-resolution T1-weighted MRI and diffusion tensor imaging data were obtained in 41 refractory mTLE patients and 52 age- and sex-matched healthy controls. Postoperative histopathological examination confirmed HS type 1 in 30 patients and HS type 2 in eleven patients. Clinical features, postoperative seizure outcomes, hippocampal subfields volumes, fractional anisotropy (FA) values of white matter regions and graph theory parameters were explored and compared between the HS type 1 and HS type 2 groups. RESULTS: No significant differences in clinical features and postsurgical seizure outcomes were found between the HS type 1 and type 2 groups. However, the HS type 1 group showed extra atrophy in ipsilateral parasubiculum than healthy controls and more severe atrophy in contralateral hippocampal fissure than the HS type 2 group. More extensive FA decrease were also observed in the HS type 1 group, involving ipsilateral optic radiation, superior fronto-occipital fasciculus, contralateral uncinate fasciculus, tapetum, bilateral hippocampal cingulum, corona radiata, etc. Furthermore, in spite of similar impairments in characteristic path length, global efficiency and local efficiency in two HS groups, the HS type 1 group showed additional decrease of clustering coefficient than healthy controls. CONCLUSIONS: HS type 1 and 2 groups had similar clinical characteristics and postoperative seizure outcomes. More widespread neuronal cell loss in the HS type 1 group contributed to more extensive structural damage and connectivity abnormality. These results shed new light on the imaging correlates of different HS pathology.

The genetic architecture of fornix white matter microstructure and their involvement in neuropsychiatric disorders.

The fornix is a white matter bundle located in the center of the hippocampaldiencephalic limbic circuit that controls memory and executive functions, yet its genetic architectures and involvement in brain disorders remain largely unknown. We carried out a genome-wide association analysis of 30,832 UK Biobank individuals of the six fornix diffusion magnetic resonance imaging (dMRI) traits. The post-GWAS analysis allowed us to identify causal genetic variants in phenotypes at the single nucleotide polymorphisms (SNP), locus, and gene levels, as well as genetic overlap with brain health-related traits. We further generalized our GWAS in adolescent brain cognitive development (ABCD) cohort. The GWAS identified 63 independent significant variants within 20 genomic loci associated (P < 8.33 × 10-9) with the six fornix dMRI traits. Geminin coiled-coil domain containing (GMNC) and NUAK family SNF1-like kinase 1 (NUAK1) gene were highlighted, which were found in UKB and replicated in ABCD. The heritability of the six traits ranged from 10% to 27%. Gene mapping strategies identified 213 genes, where 11 were supported by all of four methods. Gene-based analyses revealed pathways relating to cell development and differentiation, with astrocytes found to be significantly enriched. Pleiotropy analyses with eight neurological and psychiatric disorders revealed shared variants, especially with schizophrenia under the conjFDR threshold of 0.05. These findings advance our understanding of the complex genetic architectures of fornix and their relevance in neurological and psychiatric disorders.

A living ex vivo platform for functional, personalized brain cancer diagnosis.

Functional precision medicine platforms are emerging as promising strategies to improve pre-clinical drug testing and guide clinical decisions. We have developed an organotypic brain slice culture (OBSC)-based platform and multi-parametric algorithm that enable rapid engraftment, treatment, and analysis of uncultured patient brain tumor tissue and patient-derived cell lines. The platform has supported engraftment of every patient tumor tested to this point: high- and low-grade adult and pediatric tumor tissue rapidly establishes on OBSCs among endogenous astrocytes and microglia while maintaining the tumor's original DNA profile. Our algorithm calculates dose-response relationships of both tumor kill and OBSC toxicity, generating summarized drug sensitivity scores on the basis of therapeutic window and allowing us to normalize response profiles across a panel of U.S. Food and Drug Administration (FDA)-approved and exploratory agents. Summarized patient tumor scores after OBSC treatment show positive associations to clinical outcomes, suggesting that the OBSC platform can provide rapid, accurate, functional testing to ultimately guide patient care.

Destin2: Integrative and cross-modality analysis of single-cell chromatin accessibility data.

We propose Destin2, a novel statistical and computational method for cross-modality dimension reduction, clustering, and trajectory reconstruction for single-cell ATAC-seq data. The framework integrates cellular-level epigenomic profiles from peak accessibility, motif deviation score, and pseudo-gene activity and learns a shared manifold using the multimodal input, followed by clustering and/or trajectory inference. We apply Destin2 to real scATAC-seq datasets with both discretized cell types and transient cell states and carry out benchmarking studies against existing methods based on unimodal analyses. Using cell-type labels transferred with high confidence from unmatched single-cell RNA sequencing data, we adopt four performance assessment metrics and demonstrate how Destin2 corroborates and improves upon existing methods. Using single-cell RNA and ATAC multiomic data, we further exemplify how Destin2's cross-modality integrative analyses preserve true cell-cell similarities using the matched cell pairs as ground truths. Destin2 is compiled as a freely available R package available at https://github.com/yuchaojiang/Destin2.

The genetic architecture of human amygdala volumes and their overlap with common brain disorders.

The amygdala is a crucial interconnecting structure in the brain that performs several regulatory functions, yet its genetic architectures and involvement in brain disorders remain largely unknown. We carried out the first multivariate genome-wide association study (GWAS) of amygdala subfield volumes in 27,866 UK Biobank individuals. The whole amygdala was segmented into nine nuclei groups using Bayesian amygdala segmentation. The post-GWAS analysis allowed us to identify causal genetic variants in phenotypes at the SNP, locus, and gene levels, as well as genetic overlap with brain health-related traits. We further generalized our GWAS in Adolescent Brain Cognitive Development (ABCD) cohort. The multivariate GWAS identified 98 independent significant variants within 32 genomic loci associated (P < 5 × 10-8) with amygdala volume and its nine nuclei. The univariate GWAS identified significant hits for eight of the ten volumes, tagging 14 independent genomic loci. Overall, 13 of the 14 loci identified in the univariate GWAS were replicated in the multivariate GWAS. The generalization in ABCD cohort supported the GWAS results with the 12q23.2 (RNA gene RP11-210L7.1) being discovered. All of these imaging phenotypes are heritable, with heritability ranging from 15% to 27%. Gene-based analyses revealed pathways relating to cell differentiation/development and ion transporter/homeostasis, with the astrocytes found to be significantly enriched. Pleiotropy analyses revealed shared variants with neurological and psychiatric disorders under the conjFDR threshold of 0.05. These findings advance our understanding of the complex genetic architectures of amygdala and their relevance in neurological and psychiatric disorders.

Genome-wide association study of cerebellar white matter microstructure and genetic overlap with common brain disorders.

BACKGROUND: The cerebellum is recognized as being involved in neurocognitive and motor functions with communication with extra-cerebellar regions relying on the white matter integrity of the cerebellar peduncles. However, the genetic determinants of cerebellar white matter integrity remain largely unknown. METHODS: We conducted a genome-wide association analysis of cerebellar white matter microstructure using diffusion tensor imaging data from 25,415 individuals from UK Biobank. The integrity of cerebellar white matter microstructure was measured as fractional anisotropy (FA) and mean diffusivity (MD). Identification of independent genomic loci, functional annotation, and tissue and cell-type analysis were conducted with FUMA. The linkage disequilibrium score regression (LDSC) was used to calculate genetic correlations between cerebellar white matter microstructure and regional brain volumes and brain-related traits. Furthermore, the conditional/conjunctional false discovery rate (condFDR/conjFDR) framework was employed to identify the shared genetic basis between cerebellar white matter microstructure and common brain disorders. RESULTS: We identified 11 genetic loci (P < 8.3 × 10-9) and 86 genes associated with cerebellar white matter microstructure. Further functional enrichment analysis implicated the involvement of GABAergic neurons and cholinergic pathways. Significant polygenetic overlap between cerebellar white matter tracts and their anatomically connected or adjacent brain regions was detected. In addition, we report the overall genetic correlation and specific loci shared between cerebellar white matter microstructural integrity and brain-related traits, including movement, cognitive, psychiatric, and cerebrovascular categories. CONCLUSIONS: Collectively, this study represents a step forward in understanding the genetics of cerebellar white matter microstructure and its shared genetic etiology with common brain disorders.

In vivo characterization of magnetic resonance imaging-based T1w/T2w ratios reveals myelin-related changes in temporal lobe epilepsy.

Temporal lobe epilepsy (TLE) is the most common type of intractable epilepsy in adults. Although brain myelination alterations have been observed in TLE, it remains unclear how the myelination network changes in TLE. This study developed a novel method in characterization of myelination structural covariance network (mSCN) by T1-weighted and T2-weighted magnetic resonance imaging (MRI). The mSCNs were estimated in 42 left TLE (LTLE), 42 right TLE (RTLE) patients, and 41 healthy controls (HCs). The topology of mSCN was analyzed by graph theory. Voxel-wise comparisons of myelination laterality were also examined among the three groups. Compared to HC, both patient groups showed decreased myelination in frontotemporal regions, amygdala, and thalamus; however, the LTLE showed lower myelination in left medial temporal regions than RTLE. Moreover, the LTLE exhibited decreased global efficiency compared with HC and more increased connections than RTLE. The laterality in putamen was differently altered between the two patient groups: higher laterality at posterior putamen in LTLE and higher laterality at anterior putamen in RTLE. The putamen may play a transfer station role in damage spreading induced by epileptic seizures from the hippocampus. This study provided a novel workflow by combination of T1-weighted and T2-weighted MRI to investigate in vivo the myelin-related microstructural feature in epileptic patients first time. Disconnections of mSCN implicate that TLE is a system disorder with widespread disruptions at regional and network levels.

TIVAN-indel: a computational framework for annotating and predicting non-coding regulatory small insertions and deletions.

MOTIVATION: Small insertion and deletion (sindel) of human genome has an important implication for human disease. One important mechanism for non-coding sindel (nc-sindel) to have an impact on human diseases and phenotypes is through the regulation of gene expression. Nevertheless, current sequencing experiments may lack statistical power and resolution to pinpoint the functional sindel due to lower minor allele frequency or small effect size. As an alternative strategy, a supervised machine learning method can identify the otherwise masked functional sindels by predicting their regulatory potential directly. However, computational methods for annotating and predicting the regulatory sindels, especially in the non-coding regions, are underdeveloped. RESULTS: By leveraging labeled nc-sindels identified by cis-expression quantitative trait loci analyses across 44 tissues in Genotype-Tissue Expression (GTEx), and a compilation of both generic functional annotations and large-scale epigenomic profiles, we develop TIssue-specific Variant Annotation for Non-coding indel (TIVAN-indel), which is a supervised computational framework for predicting non-coding regulatory sindels. As a result, we demonstrate that TIVAN-indel achieves the best prediction performance in both with-tissue prediction and cross-tissue prediction. As an independent evaluation, we train TIVAN-indel from the 'Whole Blood' tissue in GTEx and test the model using 15 immune cell types from an independent study named Database of Immune Cell Expression. Lastly, we perform an enrichment analysis for both true and predicted sindels in key regulatory regions such as chromatin interactions, open chromatin regions and histone modification sites, and find biologically meaningful enrichment patterns. AVAILABILITY AND IMPLEMENTATION: https://github.com/lichen-lab/TIVAN-indel. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Joint gene network construction by single-cell RNA sequencing data.

In contrast to differential gene expression analysis at the single-gene level, gene regulatory network (GRN) analysis depicts complex transcriptomic interactions among genes for better understandings of underlying genetic architectures of human diseases and traits. Recent advances in single-cell RNA sequencing (scRNA-seq) allow constructing GRNs at a much finer resolution than bulk RNA-seq and microarray data. However, scRNA-seq data are inherently sparse, which hinders the direct application of the popular Gaussian graphical models (GGMs). Furthermore, most existing approaches for constructing GRNs with scRNA-seq data only consider gene networks under one condition. To better understand GRNs across different but related conditions at single-cell resolution, we propose to construct Joint Gene Networks with scRNA-seq data (JGNsc) under the GGMs framework. To facilitate the use of GGMs, JGNsc first proposes a hybrid imputation procedure that combines a Bayesian zero-inflated Poisson model with an iterative low-rank matrix completion step to efficiently impute zero-inflated counts resulted from technical artifacts. JGNsc then transforms the imputed data via a nonparanormal transformation, based on which joint GGMs are constructed. We demonstrate JGNsc and assess its performance using synthetic data. The application of JGNsc on two cancer clinical studies of medulloblastoma and glioblastoma gains novel insights in addition to confirming well-known biological results.

Altered Resting State Networks Before and After Temporal Lobe Epilepsy Surgery.

OBJECTIVES: To explore the resting state networks (RSNs) alterations in patients with unilateral mesial temporal lobe epilepsy (mTLE) before and after successful surgery. METHODS: Resting-state functional MRI and T1-weighted structural MRI were obtained in 37 mTLE patients who achieved seizure freedom after anterior temporal lobectomy. Patients were scanned before surgery and at two years after surgery. Twenty-eight age- and sex-matched healthy controls were scanned once. Functional connectivity (FC) changes within and between ten common RSNs before and after surgery, and FC changes between hippocampus and RSNs were explored. RESULTS: Before surgery, decreased FC was found within visual network and basal ganglia network, while after surgery, FC within basal ganglia network further decreased but FC within sensorimotor network and dorsal attention network increased. Before surgery, between-network FC related to basal ganglia network, visual network and dorsal attention network decreased, while between-network FC related to default mode network increased. After surgery, between-network FC related to visual network and dorsal attention network significantly increased. In addition, before surgery, ipsilateral hippocampus showed decreased FC with visual network, basal ganglia network, sensorimotor network, default mode network and frontoparietal network, while contralateral rostral hippocampus showed increased FC with salience network. After surgery, no obvious FC changes were found between contralateral hippocampus and these RSNs. CONCLUSION: MTLE patients showed significant RSNs alterations before and after surgery. Basal ganglia network showed progressive decline in functional connectivity. Successful surgery may lead to RSNs reorganization. These results provide preliminary evidence for postoperative functional remodeling at whole-brain-network level.

Nonparametric single-cell multiomic characterization of trio relationships between transcription factors, target genes, and cis-regulatory regions.

The epigenetic control of gene expression is highly cell-type and context specific. Yet, despite its complexity, gene regulatory logic can be broken down into modular components consisting of a transcription factor (TF) activating or repressing the target gene expression through its binding to a cis-regulatory region. We propose a nonparametric approach, TRIPOD, to detect and characterize the three-way relationships between a TF, its target gene, and the accessibility of the TF's binding site using single-cell RNA and ATAC multiomic data. We apply TRIPOD to interrogate the cell-type-specific regulatory logic in peripheral blood mononuclear cells and contrast our results to detections from enhancer databases, cis-eQTL studies, ChIP-seq experiments, and TF knockdown/knockout studies. We then apply TRIPOD to mouse embryonic brain data and identify regulatory relationships, validated by ChIP-seq and PLAC-seq. Finally, we demonstrate TRIPOD on the SHARE-seq data of differentiating mouse hair follicle cells and identify lineage-specific regulation supported by histone marks and super-enhancer annotations. A record of this paper's transparent peer review process is included in the supplemental information.

EPIC: Inferring relevant cell types for complex traits by integrating genome-wide association studies and single-cell RNA sequencing.

More than a decade of genome-wide association studies (GWASs) have identified genetic risk variants that are significantly associated with complex traits. Emerging evidence suggests that the function of trait-associated variants likely acts in a tissue- or cell-type-specific fashion. Yet, it remains challenging to prioritize trait-relevant tissues or cell types to elucidate disease etiology. Here, we present EPIC (cEll tyPe enrIChment), a statistical framework that relates large-scale GWAS summary statistics to cell-type-specific gene expression measurements from single-cell RNA sequencing (scRNA-seq). We derive powerful gene-level test statistics for common and rare variants, separately and jointly, and adopt generalized least squares to prioritize trait-relevant cell types while accounting for the correlation structures both within and between genes. Using enrichment of loci associated with four lipid traits in the liver and enrichment of loci associated with three neurological disorders in the brain as ground truths, we show that EPIC outperforms existing methods. We apply our framework to multiple scRNA-seq datasets from different platforms and identify cell types underlying type 2 diabetes and schizophrenia. The enrichment is replicated using independent GWAS and scRNA-seq datasets and further validated using PubMed search and existing bulk case-control testing results.