Histone Acetylome-wide Association Study of Autism Spectrum Disorder.

The association of histone modification changes with autism spectrum disorder (ASD) has not been systematically examined. We conducted a histone acetylome-wide association study (HAWAS) by performing H3K27ac chromatin immunoprecipitation sequencing (ChIP-seq) on 257 postmortem samples from ASD and matched control brains. Despite etiological heterogeneity, ≥68% of syndromic and idiopathic ASD cases shared a common acetylome signature at >5,000 cis-regulatory elements in prefrontal and temporal cortex. Similarly, multiple genes associated with rare genetic mutations in ASD showed common "epimutations." Acetylome aberrations in ASD were not attributable to genetic differentiation at cis-SNPs and highlighted genes involved in synaptic transmission, ion transport, epilepsy, behavioral abnormality, chemokinesis, histone deacetylation, and immunity. By correlating histone acetylation with genotype, we discovered >2,000 histone acetylation quantitative trait loci (haQTLs) in human brain regions, including four candidate causal variants for psychiatric diseases. Due to the relative stability of histone modifications postmortem, we anticipate that the HAWAS approach will be applicable to multiple diseases.

Matching queried single-cell open-chromatin profiles to large pools of single-cell transcriptomes and epigenomes for reference supported analysis.

The true benefits of large single-cell transcriptome and epigenome data sets can be realized only with the development of new approaches and search tools for annotating individual cells. Matching a single-cell epigenome profile to a large pool of reference cells remains a major challenge. Here, we present scEpiSearch, which enables searching, comparison, and independent classification of single-cell open-chromatin profiles against a large reference of single-cell expression and open-chromatin data sets. Across performance benchmarks, scEpiSearch outperformed multiple methods in accuracy of search and low-dimensional coembedding of single-cell profiles, irrespective of platforms and species. Here we also demonstrate the unconventional utilities of scEpiSearch by applying it on single-cell epigenome profiles of K562 cells and samples from patients with acute leukaemia to reveal different aspects of their heterogeneity, multipotent behavior, and dedifferentiated states. Applying scEpiSearch on our single-cell open-chromatin profiles from embryonic stem cells (ESCs), we identified ESC subpopulations with more activity and poising for endoplasmic reticulum stress and unfolded protein response. Thus, scEpiSearch solves the nontrivial problem of amalgamating information from a large pool of single cells to identify and study the regulatory states of cells using their single-cell epigenomes.

Associating pathways with diseases using single-cell expression profiles and making inferences about potential drugs.

Finding direct dependencies between genetic pathways and diseases has been the target of multiple studies as it has many applications. However, due to cellular heterogeneity and limitations of the number of samples for bulk expression profiles, such studies have faced hurdles in the past. Here, we propose a method to perform single-cell expression-based inference of association between pathway, disease and cell-type (sci-PDC), which can help to understand their cause and effect and guide precision therapy. Our approach highlighted reliable relationships between a few diseases and pathways. Using the example of diabetes, we have demonstrated how sci-PDC helps in tracking variation of association between pathways and diseases with changes in age and species. The variation in pathways-disease associations in mice and humans revealed critical facts about the suitability of the mouse model for a few pathways in the context of diabetes. The coherence between results from our method and previous reports, including information about the drug target pathways, highlights its reliability for multidimensional utility.

Improving gene network inference with graph wavelets and making insights about ageing-associated regulatory changes in lungs.

Using gene-regulatory-networks-based approach for single-cell expression profiles can reveal unprecedented details about the effects of external and internal factors. However, noise and batch effect in sparse single-cell expression profiles can hamper correct estimation of dependencies among genes and regulatory changes. Here, we devise a conceptually different method using graphwavelet filters for improving gene network (GWNet)-based analysis of the transcriptome. Our approach improved the performance of several gene network-inference methods. Most Importantly, GWNet improved consistency in the prediction of gene regulatory network using single-cell transcriptome even in the presence of batch effect. The consistency of predicted gene network enabled reliable estimates of changes in the influence of genes not highlighted by differential-expression analysis. Applying GWNet on the single-cell transcriptome profile of lung cells, revealed biologically relevant changes in the influence of pathways and master regulators due to ageing. Surprisingly, the regulatory influence of ageing on pneumocytes type II cells showed noticeable similarity with patterns due to the effect of novel coronavirus infection in human lung.

Integrative multi-omics landscape of fluoxetine action across 27 brain regions reveals global increase in energy metabolism and region-specific chromatin remodelling.

Depression and anxiety are major global health burdens. Although SSRIs targeting the serotonergic system are prescribed over 200 million times annually, they have variable therapeutic efficacy and side effects, and mechanisms of action remain incompletely understood. Here, we comprehensively characterise the molecular landscape of gene regulatory changes associated with fluoxetine, a widely-used SSRI. We performed multimodal analysis of SSRI response in 27 mammalian brain regions using 310 bulk RNA-seq and H3K27ac ChIP-seq datasets, followed by in-depth characterisation of two hippocampal regions using single-cell RNA-seq (20 datasets). Remarkably, fluoxetine induced profound region-specific shifts in gene expression and chromatin state, including in the nucleus accumbens shell, locus coeruleus and septal areas, as well as in more well-studied regions such as the raphe and hippocampal dentate gyrus. Expression changes were strongly enriched at GWAS loci for depression and antidepressant drug response, stressing the relevance to human phenotypes. We observed differential expression at dozens of signalling receptors and pathways, many of which are previously unknown. Single-cell analysis revealed stark differences in fluoxetine response between the dorsal and ventral hippocampal dentate gyri, particularly in oligodendrocytes, mossy cells and inhibitory neurons. Across diverse brain regions, integrative omics analysis consistently suggested increased energy metabolism via oxidative phosphorylation and mitochondrial changes, which we corroborated in vitro; this may thus constitute a shared mechanism of action of fluoxetine. Similarly, we observed pervasive chromatin remodelling signatures across the brain. Our study reveals unexpected regional and cell type-specific heterogeneity in SSRI action, highlights under-studied brain regions that may play a major role in antidepressant response, and provides a rich resource of candidate cell types, genes, gene regulatory elements and pathways for mechanistic analysis and identifying new therapeutic targets for depression and anxiety.

UniPath: a uniform approach for pathway and gene-set based analysis of heterogeneity in single-cell epigenome and transcriptome profiles.

Recent advances in single-cell open-chromatin and transcriptome profiling have created a challenge of exploring novel applications with a meaningful transformation of read-counts, which often have high variability in noise and drop-out among cells. Here, we introduce UniPath, for representing single-cells using pathway and gene-set enrichment scores by a transformation of their open-chromatin or gene-expression profiles. The robust statistical approach of UniPath provides high accuracy, consistency and scalability in estimating gene-set enrichment scores for every cell. Its framework provides an easy solution for handling variability in drop-out rate, which can sometimes create artefact due to systematic patterns. UniPath provides an alternative approach of dimension reduction of single-cell open-chromatin profiles. UniPath's approach of predicting temporal-order of single-cells using their pathway enrichment scores enables suppression of covariates to achieve correct order of cells. Analysis of mouse cell atlas using our approach yielded surprising, albeit biologically-meaningful co-clustering of cell-types from distant organs. By enabling an unconventional method of exploiting pathway co-occurrence to compare two groups of cells, our approach also proves to be useful in inferring context-specific regulations in cancer cells. Available at https://reggenlab.github.io/UniPathWeb/.

Emerging Two-Dimensional Materials for Electromagnetic Interference Shielding Application.

Graphene is the first two-dimensional material that becomes the center material in various research areas of material science, chemistry, condensed matter, and engineering due to its advantageous properties, including larger specific area, lower density, outstanding electrical conductivity, and ease of processability. These properties attracted the attention of material researchers that resulted in a large number of publications on EMI shielding in a short time and play a central role in addressing the problems and challenges faced in this modern era of electronics by electromagnetic interference. After the popularity of graphene, the community of material researchers investigated other two-dimensional materials like MXenes, hexagonal boron nitride, black phosphorous, transition metal dichalcogenides, and layered double hydroxides, to additionally enhance the EMI shielding response of materials. The present article conscientiously reviews the current progress in EMI shielding materials in reference to two-dimensional materials and addresses the future challenges and research directions to achieve the goals.

CellAtlasSearch: a scalable search engine for single cells.

Owing to the advent of high throughput single cell transcriptomics, past few years have seen exponential growth in production of gene expression data. Recently efforts have been made by various research groups to homogenize and store single cell expression from a large number of studies. The true value of this ever increasing data deluge can be unlocked by making it searchable. To this end, we propose CellAtlasSearch, a novel search architecture for high dimensional expression data, which is massively parallel as well as light-weight, thus infinitely scalable. In CellAtlasSearch, we use a Graphical Processing Unit (GPU) friendly version of Locality Sensitive Hashing (LSH) for unmatched speedup in data processing and query. Currently, CellAtlasSearch features over 300 000 reference expression profiles including both bulk and single-cell data. It enables the user query individual single cell transcriptomes and finds matching samples from the database along with necessary meta information. CellAtlasSearch aims to assist researchers and clinicians in characterizing unannotated single cells. It also facilitates noise free, low dimensional representation of single-cell expression profiles by projecting them on a wide variety of reference samples. The web-server is accessible at: http://www.cellatlassearch.com.

Ncoa3 functions as an essential Esrrb coactivator to sustain embryonic stem cell self-renewal and reprogramming.

Embryonic stem cell (ESC) pluripotency depends on a well-characterized gene regulatory network centered on Oct4, Sox2, and Nanog. In contrast, little is known about the identity of the key coregulators and the mechanisms by which they may potentiate transcription in ESCs. Alongside core transcription factors, the orphan nuclear receptor Esrrb (estrogen-related receptor ß) is vital for the maintenance of ESC identity and furthermore is uniquely associated with the basal transcription machinery. Here, we show that Ncoa3, an essential coactivator, is required to mediate Esrrb function in ESCs. Ncoa3 interacts with Esrrb via its ligand-binding domain and bridges Esrrb to RNA polymerase II complexes. Functionally, Ncoa3 is critical for both the induction and maintenance of pluripotency. Through chromatin immunoprecipitation (ChIP) sequencing and microarray experiments, we further demonstrate that Ncoa3 shares overlapping gene regulatory functions with Esrrb and cooperates genome-wide with the Oct4-Sox2-Nanog circuitry at active enhancers to up-regulate genes involved in self-renewal and pluripotency. We propose an integrated model of transcriptional and coactivator control, mediated by Ncoa3, for the maintenance of ESC self-renewal and somatic cell reprogramming.

Comprehensive benchmarking reveals H2BK20 acetylation as a distinctive signature of cell-state-specific enhancers and promoters.

Although over 35 different histone acetylation marks have been described, the overwhelming majority of regulatory genomics studies focus exclusively on H3K27ac and H3K9ac. In order to identify novel epigenomic traits of regulatory elements, we constructed a benchmark set of validated enhancers by performing 140 enhancer assays in human T cells. We tested 40 chromatin signatures on this unbiased enhancer set and identified H2BK20ac, a little-studied histone modification, as the most predictive mark of active enhancers. Notably, we detected a novel class of functionally distinct enhancers enriched in H2BK20ac but lacking H3K27ac, which was present in all examined cell lines and also in embryonic forebrain tissue. H2BK20ac was also unique in highlighting cell-type-specific promoters. In contrast, other acetylation marks were present in all active promoters, regardless of cell-type specificity. In stimulated microglial cells, H2BK20ac was more correlated with cell-state-specific expression changes than H3K27ac, with TGF-beta signaling decoupling the two acetylation marks at a subset of regulatory elements. In summary, our study reveals a previously unknown connection between histone acetylation and cell-type-specific gene regulation and indicates that H2BK20ac profiling can be used to uncover new dimensions of gene regulation.

A Role for RE-1-Silencing Transcription Factor in Embryonic Stem Cells Cardiac Lineage Specification.

During development, lineage specification is controlled by several signaling pathways involving various transcription factors (TFs). Here, we studied the RE-1-silencing transcription factor (REST) and identified an important role of this TF in cardiac differentiation. Using mouse embryonic stem cells (ESC) to model development, we found that REST knockout cells lost the ability to differentiate into the cardiac lineage. Detailed analysis of specific lineage markers expression showed selective downregulation of endoderm markers in REST-null cells, thus contributing to a loss of cardiogenic signals. REST regulates cardiac differentiation of ESCs by negatively regulating the Wnt/ß-catenin signaling pathway and positively regulating the cardiogenic TF Gata4. We propose here a new role for REST in cell fate specification besides its well-known repressive role of neuronal differentiation.

Genome wide analysis reveals Zic3 interaction with distal regulatory elements of stage specific developmental genes in zebrafish.

Zic3 regulates early embryonic patterning in vertebrates. Loss of Zic3 function is known to disrupt gastrulation, left-right patterning, and neurogenesis. However, molecular events downstream of this transcription factor are poorly characterized. Here we use the zebrafish as a model to study the developmental role of Zic3 in vivo, by applying a combination of two powerful genomics approaches--ChIP-seq and microarray. Besides confirming direct regulation of previously implicated Zic3 targets of the Nodal and canonical Wnt pathways, analysis of gastrula stage embryos uncovered a number of novel candidate target genes, among which were members of the non-canonical Wnt pathway and the neural pre-pattern genes. A similar analysis in zic3-expressing cells obtained by FACS at segmentation stage revealed a dramatic shift in Zic3 binding site locations and identified an entirely distinct set of target genes associated with later developmental functions such as neural development. We demonstrate cis-regulation of several of these target genes by Zic3 using in vivo enhancer assay. Analysis of Zic3 binding sites revealed a distribution biased towards distal intergenic regions, indicative of a long distance regulatory mechanism; some of these binding sites are highly conserved during evolution and act as functional enhancers. This demonstrated that Zic3 regulation of developmental genes is achieved predominantly through long distance regulatory mechanism and revealed that developmental transitions could be accompanied by dramatic changes in regulatory landscape.

In vivo genome-wide analysis of multiple tissues identifies gene regulatory networks, novel functions and downstream regulatory genes for Bapx1 and its co-regulation with Sox9 in the mammalian vertebral column.

BACKGROUND: Vertebrate organogenesis is a highly complex process involving sequential cascades of transcription factor activation or repression. Interestingly a single developmental control gene can occasionally be essential for the morphogenesis and differentiation of tissues and organs arising from vastly disparate embryological lineages. RESULTS: Here we elucidated the role of the mammalian homeobox gene Bapx1 during the embryogenesis of five distinct organs at E12.5 - vertebral column, spleen, gut, forelimb and hindlimb - using expression profiling of sorted wildtype and mutant cells combined with genome wide binding site analysis. Furthermore we analyzed the development of the vertebral column at the molecular level by combining transcriptional profiling and genome wide binding data for Bapx1 with similarly generated data sets for Sox9 to assemble a detailed gene regulatory network revealing genes previously not reported to be controlled by either of these two transcription factors. CONCLUSIONS: The gene regulatory network appears to control cell fate decisions and morphogenesis in the vertebral column along with the prevention of premature chondrocyte differentiation thus providing a detailed molecular view of vertebral column development.

Design Guidelines for Thin Diaphragm-Based Microsystems through Comprehensive Numerical and Analytical Studies.

This paper presents comprehensive guidelines for the design and analysis of a thin diaphragm that is used in a variety of microsystems, including microphones and pressure sensors. It highlights the empirical relations that can be utilized for the design of thin diaphragm-based microsystems (TDMS). Design guidelines developed through a Finite Element Analysis (FEA) limit the iterative efforts to fabricate TDMS. These design guidelines are validated analytically, with the assumption that the material properties are isotropic, and the deviation from anisotropic material is calculated. In the FEA simulations, a large deflection theory is taken into account to incorporate nonlinearity, such that a critical dimensional ratio of a/h or 2r/h can be decided to have the linear response of a thin diaphragm. The observed differences of 12% in the deflection and 13% in the induced stresses from the analytical calculations are attributed to the anisotropic material consideration in the FEA model. It suggests that, up to a critical ratio (a/h or 2r/h), the thin diaphragm shows a linear relationship with a high sensitivity. The study also presents a few empirical relations to finalize the geometrical parameters of the thin diaphragm in terms of its edge length or radius and thickness. Utilizing the critical ratio calculated in the static FEA analysis, the basic conventional geometries are considered for harmonic analyses to understand the frequency response of the thin diaphragms, which is a primary sensing element for microphone applications and many more. This work provides a solution to microelectromechanical system (MEMS) developers for reducing cost and time while conceptualizing TDMS designs.

Patterns of transcription factor binding and epigenome at promoters allow interpretable predictability of multiple functions of non-coding and coding genes.

Understanding the biological roles of all genes only through experimental methods is challenging. A computational approach with reliable interpretability is needed to infer the function of genes, particularly for non-coding RNAs. We have analyzed genomic features that are present across both coding and non-coding genes like transcription factor (TF) and cofactor ChIP-seq (823), histone modifications ChIP-seq (n = 621), cap analysis gene expression (CAGE) tags (n = 255), and DNase hypersensitivity profiles (n = 255) to predict ontology-based functions of genes. Our approach for gene function prediction was reliable (>90% balanced accuracy) for 486 gene-sets. PubMed abstract mining and CRISPR screens supported the inferred association of genes with biological functions, for which our method had high accuracy. Further analysis revealed that TF-binding patterns at promoters have high predictive strength for multiple functions. TF-binding patterns at the promoter add an unexplored dimension of explainable regulatory aspects of genes and their functions. Therefore, we performed a comprehensive analysis for the functional-specificity of TF-binding patterns at promoters and used them for clustering functions to reveal many latent groups of gene-sets involved in common major cellular processes. We also showed how our approach could be used to infer the functions of non-coding genes using the CRISPR screens of coding genes, which were validated using a long non-coding RNA CRISPR screen. Thus our results demonstrated the generality of our approach by using gene-sets from CRISPR screens. Overall, our approach opens an avenue for predicting the involvement of non-coding genes in various functions.

Computational challenges in detection of cancer using cell-free DNA methylation.

Cell-free DNA(cfDNA) methylation profiling is considered promising and potentially reliable for liquid biopsy to study progress of diseases and develop reliable and consistent diagnostic and prognostic biomarkers. There are several different mechanisms responsible for the release of cfDNA in blood plasma, and henceforth it can provide information regarding dynamic changes in the human body. Due to the fragmented nature, low concentration of cfDNA, and high background noise, there are several challenges in its analysis for regular use in diagnosis of cancer. Such challenges in the analysis of the methylation profile of cfDNA are further aggravated due to heterogeneity, biomarker sensitivity, platform biases, and batch effects. This review delineates the origin of cfDNA methylation, its profiling, and associated computational problems in analysis for diagnosis. Here we also contemplate upon the multi-marker approach to handle the scenario of cancer heterogeneity and explore the utility of markers for 5hmC based cfDNA methylation pattern. Further, we provide a critical overview of deconvolution and machine learning methods for cfDNA methylation analysis. Our review of current methods reveals the potential for further improvement in analysis strategies for detecting early cancer using cfDNA methylation.

Covid Assossiated Invasive Fungal Sinusitis.

Acute invasive fungal sinusitis is an aggressive infection affecting immunocomprosmised patients and carries a high mortality. Patients with Covid-pneumonia are at an increased risk of developing invasive pulmonary fungal infections probably due to their reduced immunological competence. Here, we review three cases of Covid-associated invasive fungal sinusitis.

Improving Chromatin-Interaction Prediction Using Single-Cell Open-Chromatin Profiles and Making Insight Into the Cis-Regulatory Landscape of the Human Brain.

Single-cell open-chromatin profiles have the potential to reveal the pattern of chromatin-interaction in a cell type. However, currently available cis-regulatory network prediction methods using single-cell open-chromatin profiles focus more on local chromatin interactions despite the fact that long-range interactions among genomic sites play a significant role in gene regulation. Here, we propose a method that predicts both short and long-range interactions among genomic sites using single-cell open chromatin profiles. Our method, termed as single-cell epigenome based chromatin-interaction analysis (scEChIA) exploits signal imputation and refined L1 regularization. For a few single-cell open-chromatin profiles, scEChIA outperformed other tools even in terms of accuracy of prediction. Using scEChIA, we predicted almost 0.7 million interactions among genomic sites across seven cell types in the human brain. Further analysis revealed cell type for connection between genes and expression quantitative trait locus (eQTL) in the human brain and making insight about target genes of human-accelerated-elements and disease-associated mutations. Our analysis enabled by scEChIA also hints about the possible action of a few transcription factors (TFs), especially through long-range interaction in brain endothelial cells.

Learning the Mental Health Impact of COVID-19 in the United States With Explainable Artificial Intelligence: Observational Study.

BACKGROUND: The COVID-19 pandemic has affected the health, economic, and social fabric of many nations worldwide. Identification of individual-level susceptibility factors may help people in identifying and managing their emotional, psychological, and social well-being. OBJECTIVE: This study is focused on learning a ranked list of factors that could indicate a predisposition to a mental disorder during the COVID-19 pandemic. METHODS: In this study, we have used a survey of 17,764 adults in the United States from different age groups, genders, and socioeconomic statuses. Through initial statistical analysis and Bayesian network inference, we have identified key factors affecting mental health during the COVID-19 pandemic. Integrating Bayesian networks with classical machine learning approaches led to effective modeling of the level of mental health prevalence. RESULTS: Overall, females were more stressed than males, and people in the age group 18-29 years were more vulnerable to anxiety than other age groups. Using the Bayesian network model, we found that people with a chronic mental illness were more prone to mental disorders during the COVID-19 pandemic. The new realities of working from home; homeschooling; and lack of communication with family, friends, and neighbors induces mental pressure. Financial assistance from social security helps in reducing mental stress during the COVID-19-generated economic crises. Finally, using supervised machine learning models, we predicted the most mentally vulnerable people with ~80% accuracy. CONCLUSIONS: Multiple factors such as social isolation, digital communication, and working and schooling from home were identified as factors of mental illness during the COVID-19 pandemic. Regular in-person communication with friends and family, a healthy social life, and social security were key factors, and taking care of people with a history of mental disease appears to be even more important during this time.