Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction

Using methylation data to improve transcription factor binding prediction

Modelling the regulatory mechanisms that determine cell fate, response to external perturbation, and disease state depends on measuring many factors, a task made more difficult by the plasticity of the epigenome. Scanning the genome for the sequence patterns defined by Position Weight Matrices (PWM) can be used to estimate transcription factor (TF) binding locations. However, this approach does not incorporate information regarding the epigenetic context necessary for TF binding. CpG methylation is an epigenetic mark influenced by environmental factors that is commonly assayed in human cohort studies. We developed a framework to score inferred TF binding locations using methylation data. We intersected motif locations identified using PWMs with methylation information captured in both whole-genome bisulfite sequencing and Illumina EPIC array data for six cell lines, scored motif locations based on these data, and compared with experimental data characterizing TF binding (ChIP-seq). We found that for most TFs, binding prediction improves using methylation-based scoring compared to standard PWM-scores. We also illustrate that our approach can be generalized to infer TF binding when methylation information is only proximally available, i.e. measured for nearby CpGs that do not directly overlap with a motif location. Overall, our approach provides a framework for inferring context-specific TF binding using methylation data. Importantly, the availability of DNA methylation data in existing patient populations provides an opportunity to use our approach to understand the impact of methylation on gene regulatory processes in the context of human disease.

Single-cell sequencing to multi-omics: technologies and applications

Cells, as the fundamental units of life, contain multidimensional spatiotemporal information. Single-cell RNA sequencing (scRNA-seq) is revolutionizing biomedical science by analyzing cellular state and intercellular heterogeneity. Undoubtedly, single-cell transcriptomics has emerged as one of the most vibrant research fields today. With the optimization and innovation of single-cell sequencing technologies, the intricate multidimensional details concealed within cells are gradually unveiled. The combination of scRNA-seq and other multi-omics is at the forefront of the single-cell field. This involves simultaneously measuring various omics data within individual cells, expanding our understanding across a broader spectrum of dimensions. Single-cell multi-omics precisely captures the multidimensional aspects of single-cell transcriptomes, immune repertoire, spatial information, temporal information, epitopes, and other omics in diverse spatiotemporal contexts. In addition to depicting the cell atlas of normal or diseased tissues, it also provides a cornerstone for studying cell differentiation and development patterns, disease heterogeneity, drug resistance mechanisms, and treatment strategies. Herein, we review traditional single-cell sequencing technologies and outline the latest advancements in single-cell multi-omics. We summarize the current status and challenges of applying single-cell multi-omics technologies to biological research and clinical applications. Finally, we discuss the limitations and challenges of single-cell multi-omics and potential strategies to address them.

DeepCBA: A deep learning framework for gene expression prediction in maize based on DNA sequences and chromatin interactions

Chromatin interactions create spatial proximity between distal regulatory elements and target genes in the genome, which has an important impact on gene expression, transcriptional regulation, and phenotypic traits. To date, several methods have been developed for predicting gene expression. However, existing methods do not take into consideration the effect of chromatin interactions on target gene expression, thus potentially reducing the accuracy of gene expression prediction and mining of important regulatory elements. In this study, we developed a highly accurate deep learning-based gene expression prediction model (DeepCBA) based on maize chromatin interaction data. Compared with existing models, DeepCBA exhibits higher accuracy in expression classification and expression value prediction. The average Pearson correlation coefficients (PCCs) for predicting gene expression using gene promoter proximal interactions, proximal-distal interactions, and both proximal and distal interactions were 0.818, 0.625, and 0.929, respectively, representing an increase of 0.357, 0.16, and 0.469 over the PCCs obtained with traditional methods that use only gene proximal sequences. Some important motifs were identified through DeepCBA; they were enriched in open chromatin regions and expression quantitative trait loci and showed clear tissue specificity. Importantly, experimental results for the maize flowering-related gene ZmRap2.7 and the tillering-related gene ZmTb1 demonstrated the feasibility of DeepCBA for exploration of regulatory elements that affect gene expression. Moreover, promoter editing and verification of two reported genes (ZmCLE7 and ZmVTE4) demonstrated the utility of DeepCBA for the precise design of gene expression and even for future intelligent breeding. DeepCBA is available at http://www.deepcba.com/ or http://124.220.197.196/.

Current and future directions in network biology

Summary

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology.

Availability and implementation

Not applicable.

Epigenome-wide impact of MAT2A sustains the androgen-indifferent state and confers synthetic vulnerability in ERG fusion-positive prostate cancer

Castration-resistant prostate cancer (CRPC) is a frequently occurring disease with adverse clinical outcomes and limited therapeutic options. Here, we identify methionine adenosyltransferase 2a (MAT2A) as a critical driver of the androgen-indifferent state in ERG fusion-positive CRPC. MAT2A is upregulated in CRPC and cooperates with ERG in promoting cell plasticity, stemness and tumorigenesis. RNA, ATAC and ChIP-sequencing coupled with histone post-translational modification analysis by mass spectrometry show that MAT2A broadly impacts the transcriptional and epigenetic landscape. MAT2A enhances H3K4me2 at multiple genomic sites, promoting the expression of pro-tumorigenic non-canonical AR target genes. Genetic and pharmacological inhibition of MAT2A reverses the transcriptional and epigenetic remodeling in CRPC models and improves the response to AR and EZH2 inhibitors. These data reveal a role of MAT2A in epigenetic reprogramming and provide a proof of concept for testing MAT2A inhibitors in CRPC patients to improve clinical responses and prevent treatment resistance.

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

GENet: A Graph-Based Model Leveraging Histone Marks and Transcription Factors for Enhanced Gene Expression Prediction

Understanding the regulatory mechanisms of gene expression is a crucial objective in genomics. Although the DNA sequence near the transcription start site (TSS) offers valuable insights, recent methods suggest that analyzing only the surrounding DNA may not suffice to accurately predict gene expression levels. We developed GENet (Gene Expression Network from Histone and Transcription Factor Integration), a novel approach that integrates essential regulatory signals from transcription factors and histone modifications into a graph-based model. GENet extends beyond simple DNA sequence analysis by incorporating additional layers of genetic control, which are vital for determining gene expression. Our method markedly enhances the prediction of mRNA levels compared to previous models that depend solely on DNA sequence data. The results underscore the significance of including comprehensive regulatory information in gene expression studies. GENet emerges as a promising tool for researchers, with potential applications extending from fundamental biological research to the development of medical therapies.

Methylome-wide analysis in systemic microbial-induced experimental periodontal disease in mice with different susceptibility

Objective

The study delved into the epigenetic factors associated with periodontal disease in two lineages of mice, namely C57bl/6 and Balb/c. Its primary objective was to elucidate alterations in the methylome of mice with distinct genetic backgrounds following systemic microbial challenge, employing high-throughput DNA methylation analysis as the investigative tool.

Methods

Porphyromonas gingivalis (Pg)was orally administered to induce periodontitis in both Balb/c and C57bl/6 lineage. After euthanasia, genomic DNA from both maxilla and blood were subjected to bisulfite conversion, PCR amplification and genome-wide DNA methylation analysis using the Ovation RRBS Methyl-Seq System coupled with the Illumina Infinium Mouse Methylation BeadChip.

Results

Of particular significance was the distinct methylation profile observed within the Pg-induced group of the Balb/c lineage, contrasting with both the control and Pg-induced groups of the C57bl/6 lineage. Utilizing rigorous filtering criteria, we successfully identified a substantial number of differentially methylated regions (DMRs) across various tissues and comparison groups, shedding light on the prevailing hypermethylation in non-induced cohorts and hypomethylation in induced groups. The comparison between blood and maxilla samples underscored the unique methylation patterns specific to the jaw tissue. Our comprehensive methylome analysis further unveiled statistically significant disparities, particularly within promoter regions, in several comparison groups.

Conclusion

The differential DNA methylation patterns observed between C57bl/6 and Balb/c mouse lines suggest that epigenetic factors contribute to the variations in disease susceptibility. The identified differentially methylated regions associated with immune regulation and inflammatory response provide potential targets for further investigation. These findings emphasize the importance of considering epigenetic mechanisms in the development and progression of periodontitis.

Emerging Approaches to Profile Accessible Chromatin from Formalin-Fixed Paraffin-Embedded Sections

Nucleosomes are non-uniformly distributed across eukaryotic genomes, with stretches of 'open' chromatin strongly associated with transcriptionally active promoters and enhancers. Understanding chromatin accessibility patterns in normal tissue and how they are altered in pathologies can provide critical insights to development and disease. With the advent of high-throughput sequencing, a variety of strategies have been devised to identify open regions across the genome, including DNase-seq, MNase-seq, FAIRE-seq, ATAC-seq, and NicE-seq. However, the broad application of such methods to FFPE (formalin-fixed paraffin-embedded) tissues has been curtailed by the major technical challenges imposed by highly fixed and often damaged genomic material. Here, we review the most common approaches for mapping open chromatin regions, recent optimizations to overcome the challenges of working with FFPE tissue, and a brief overview of a typical data pipeline with analysis considerations.

Analysis of REST binding sites with canonical and non-canonical motifs in human cell lines

BACKGROUND

Repressor element 1 (RE1) silencing transcription factor (REST) is a transcriptional repressor abundantly expressed in aging human brains. It is known to regulate genes associated with oxidative stress, inflammation, and neurological disorders by binding to a canonical form of sequence motif and its non-canonical variations. Although analysis of genomic sequence motifs is crucial to understand transcriptional regulation by transcription factors (TFs), a comprehensive characterization of various forms of RE1 motifs in human cell lines has not been performed.

RESULTS

Here, we analyzed 23 ENCODE REST ChIP-seq datasets from diverse human cell lines and identified a non-redundant set of 68,975 loci with ChIP-seq peaks. Our systematic characterization of these binding sites revealed that the canonical form of REST binding motif was found primarily in ChIP-seq peaks shared across multiple cell lines, while non-canonical forms of motifs were identified in both cell-line-specific binding sites and those shared across cell lines. Remarkably, we observed a notable prevalence of non-canonical motifs that corresponded to half segments of the canonical motif. Furthermore, our analysis unveiled the presence of cell-line-specific REST binding patterns, as evidenced by the clustering of ChIP-seq experiments according to their respective cell lines. This observation underscores the cell-line specificity of REST binding at certain genomic loci, implying intricate cell-line-specific regulatory mechanisms.

CONCLUSIONS

Overall, our study provides a comprehensive characterization of REST binding motifs in human cell lines and genome-wide RE1 motif profiles. These findings contribute to a deeper understanding of REST-mediated transcriptional regulation and highlight the importance of considering cell-line-specific effects in future investigations.

NCoR1 limits angiogenic capacity by altering Notch signaling

Corepressors negatively regulate gene expression by chromatin compaction. Targeted regulation of gene expression could provide a means to control endothelial cell phenotype. We hypothesize that by targeting corepressor proteins, endothelial angiogenic function can be improved. To study this, the expression and function of nuclear corepressors in human umbilical vein endothelial cells (HUVEC) and in murine organ culture was studied. RNA-seq revealed that nuclear receptor corepressor 1 (NCoR1), silencing mediator of retinoid and thyroid hormone receptors (SMRT) and repressor element-1 silencing transcription factor (REST) are the highest expressed corepressors in HUVECs. Knockout and knockdown strategies demonstrated that the depletion of NCoR1 increased the angiogenic capacity of endothelial cells, whereas depletion of SMRT or REST did not. Interestingly, the effect was VEGF signaling independent. NCoR1 depletion significantly upregulated angiogenesis-associated genes, especially tip cell genes, including ESM1, DLL4 and NOTCH4, as observed by RNA- and ATAC-seq. Confrontation assays comparing cells with and without NCoR1-deficiency revealed that loss of NCoR1 promotes a tip-cell position during spheroid sprouting. Moreover, a proximity ligation assay identified NCoR1 as a direct binding partner of the Notch-signaling-related transcription factor RBPJk. Luciferase assays showed that siRNA-mediated knockdown of NCOR1 promotes RBPJk activity. Furthermore, NCoR1 depletion prompts upregulation of several elements in the Notch signaling cascade. Downregulation of NOTCH4, but not NOTCH1, prevented the positive effect of NCOR1 knockdown on spheroid outgrowth. Collectively, these data indicate that decreasing NCOR1 expression is an attractive approach to promote angiogenic function.

Multiomics analysis identifies novel facilitators of human dopaminergic neuron differentiation

Midbrain dopaminergic neurons (mDANs) control voluntary movement, cognition, and reward behavior under physiological conditions and are implicated in human diseases such as Parkinson's disease (PD). Many transcription factors (TFs) controlling human mDAN differentiation during development have been described, but much of the regulatory landscape remains undefined. Using a tyrosine hydroxylase (TH) human iPSC reporter line, we here generate time series transcriptomic and epigenomic profiles of purified mDANs during differentiation. Integrative analysis predicts novel regulators of mDAN differentiation and super-enhancers are used to identify key TFs. We find LBX1, NHLH1 and NR2F1/2 to promote mDAN differentiation and show that overexpression of either LBX1 or NHLH1 can also improve mDAN specification. A more detailed investigation of TF targets reveals that NHLH1 promotes the induction of neuronal miR-124, LBX1 regulates cholesterol biosynthesis, and NR2F1/2 controls neuronal activity.

Computational tools for inferring transcription factor activity

Transcription factors (TFs) are essential players in orchestrating the regulatory landscape in cells. Still, their exact modes of action and dependencies on other regulatory aspects remain elusive. Since TFs act cell type-specific and each TF has its own characteristics, untangling their regulatory interactions from an experimental point of view is laborious and convoluted. Thus, there is an ongoing development of computational tools that estimate transcription factor activity (TFA) from a variety of data modalities, either based on a mapping of TFs to their putative target genes or in a genome-wide, gene-unspecific fashion. These tools can help to gain insights into TF regulation and to prioritize candidates for experimental validation. We want to give an overview of available computational tools that estimate TFA, illustrate examples of their application, debate common result validation strategies, and discuss assumptions and concomitant limitations.

MiREx: mRNA levels prediction from gene sequence and miRNA target knowledge

Messenger RNA (mRNA) has an essential role in the protein production process. Predicting mRNA expression levels accurately is crucial for understanding gene regulation, and various models (statistical and neural network-based) have been developed for this purpose. A few models predict mRNA expression levels from the DNA sequence, exploiting the DNA sequence and gene features (e.g., number of exons/introns, gene length). Other models include information about long-range interaction molecules (i.e., enhancers/silencers) and transcriptional regulators as predictive features, such as transcription factors (TFs) and small RNAs (e.g., microRNAs - miRNAs). Recently, a convolutional neural network (CNN) model, called Xpresso, has been proposed for mRNA expression level prediction leveraging the promoter sequence and mRNAs' half-life features (gene features). To push forward the mRNA level prediction, we present miREx, a CNN-based tool that includes information about miRNA targets and expression levels in the model. Indeed, each miRNA can target specific genes, and the model exploits this information to guide the learning process. In detail, not all miRNAs are included, only a selected subset with the highest impact on the model. MiREx has been evaluated on four cancer primary sites from the genomics data commons (GDC) database: lung, kidney, breast, and corpus uteri. Results show that mRNA level prediction benefits from selected miRNA targets and expression information. Future model developments could include other transcriptional regulators or be trained with proteomics data to infer protein levels.

Predicting the effect of CRISPR-Cas9-based epigenome editing

Epigenetic regulation orchestrates mammalian transcription, but functional links between them remain elusive. To tackle this problem, we here use epigenomic and transcriptomic data from 13 ENCODE cell types to train machine learning models to predict gene expression from histone post-translational modifications (PTMs), achieving transcriptome-wide correlations of ~ 0.70 - 0.79 for most samples. In addition to recapitulating known associations between histone PTMs and expression patterns, our models predict that acetylation of histone subunit H3 lysine residue 27 (H3K27ac) near the transcription start site (TSS) significantly increases expression levels. To validate this prediction experimentally and investigate how engineered vs. natural deposition of H3K27ac might differentially affect expression, we apply the synthetic dCas9-p300 histone acetyltransferase system to 8 genes in the HEK293T cell line. Further, to facilitate model building, we perform MNase-seq to map genome-wide nucleosome occupancy levels in HEK293T. We observe that our models perform well in accurately ranking relative fold changes among genes in response to the dCas9-p300 system; however, their ability to rank fold changes within individual genes is noticeably diminished compared to predicting expression across cell types from their native epigenetic signatures. Our findings highlight the need for more comprehensive genome-scale epigenome editing datasets, better understanding of the actual modifications made by epigenome editing tools, and improved causal models that transfer better from endogenous cellular measurements to perturbation experiments. Together these improvements would facilitate the ability to understand and predictably control the dynamic human epigenome with consequences for human health.

Alterations in the hepatocyte epigenetic landscape in steatosis

Fatty liver disease or the accumulation of fat in the liver, has been reported to affect the global population. This comes with an increased risk for the development of fibrosis, cirrhosis, and hepatocellular carcinoma. Yet, little is known about the effects of a diet containing high fat and alcohol towards epigenetic aging, with respect to changes in transcriptional and epigenomic profiles. In this study, we took up a multi-omics approach and integrated gene expression, methylation signals, and chromatin signals to study the epigenomic effects of a high-fat and alcohol-containing diet on mouse hepatocytes. We identified four relevant gene network clusters that were associated with relevant pathways that promote steatosis. Using a machine learning approach, we predict specific transcription factors that might be responsible to modulate the functionally relevant clusters. Finally, we discover four additional CpG loci and validate aging-related differential CpG methylation. Differential CpG methylation linked to aging showed minimal overlap with altered methylation in steatosis.

The adapted Activity-By-Contact model for enhancer-gene assignment and its application to single-cell data

MOTIVATION

Identifying regulatory regions in the genome is of great interest for understanding the epigenomic landscape in cells. One fundamental challenge in this context is to find the target genes whose expression is affected by the regulatory regions. A recent successful method is the Activity-By-Contact (ABC) model which scores enhancer-gene interactions based on enhancer activity and the contact frequency of an enhancer to its target gene. However, it describes regulatory interactions entirely from a gene's perspective, and does not account for all the candidate target genes of an enhancer. In addition, the ABC model requires two types of assays to measure enhancer activity, which limits the applicability. Moreover, there is neither implementation available that could allow for an integration with transcription factor (TF) binding information nor an efficient analysis of single-cell data.

RESULTS

We demonstrate that the ABC score can yield a higher accuracy by adapting the enhancer activity according to the number of contacts the enhancer has to its candidate target genes and also by considering all annotated transcription start sites of a gene. Further, we show that the model is comparably accurate with only one assay to measure enhancer activity. We combined our generalized ABC model with TF binding information and illustrated an analysis of a single-cell ATAC-seq dataset of the human heart, where we were able to characterize cell type-specific regulatory interactions and predict gene expression based on TF affinities. All executed processing steps are incorporated into our new computational pipeline STARE.

AVAILABILITY AND IMPLEMENTATION

The software is available at https://github.com/schulzlab/STARE.

CONTACT

marcel.schulz@em.uni-frankfurt.de.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The Single-Cell Landscape of Intratumoral Heterogeneity and The Immunosuppressive Microenvironment in Liver and Brain Metastases of Breast Cancer

Distant metastasis remains the major cause of morbidity for breast cancer. Individuals with liver or brain metastasis have an extremely poor prognosis and low response rates to anti-PD-1/L1 immune checkpoint therapy compared to those with metastasis at other sites. Therefore, it is urgent to investigate the underlying mechanism of anti-PD-1/L1 resistance and develop more effective immunotherapy strategies for these patients. Using single-cell RNA sequencing, a high-resolution map of the entire tumor ecosystem based on 44 473 cells from breast cancer liver and brain metastases is depicted. Identified by canonical markers and confirmed by multiplex immunofluorescent staining, the metastatic ecosystem features remarkable reprogramming of immunosuppressive cells such as FOXP3+ regulatory T cells, LAMP3+ tolerogenic dendritic cells, CCL18+ M2-like macrophages, RGS5+ cancer-associated fibroblasts, and LGALS1+ microglial cells. In addition, PD-1 and PD-L1/2 are barely expressed in CD8+ T cells and cancer/immune/stromal cells, respectively. Interactions of the immune checkpoint molecules LAG3-LGALS3 and TIGIT-NECTIN2 between CD8+ T cells and cancer/immune/stromal cells are found to play dominant roles in the immune escape. In summary, this study dissects the intratumoral heterogeneity and immunosuppressive microenvironment in liver and brain metastases of breast cancer for the first time, providing insights into the most appropriate immunotherapy strategies for these patients.

xcore: an R package for inference of gene expression regulators

BACKGROUND

Elucidating the Transcription Factors (TFs) that drive the gene expression changes in a given experiment is a common question asked by researchers. The existing methods rely on the predicted Transcription Factor Binding Site (TFBS) to model the changes in the motif activity. Such methods only work for TFs that have a motif and assume the TF binding profile is the same in all cell types.

RESULTS

Given the wealth of the ChIP-seq data available for a wide range of the TFs in various cell types, we propose that gene expression modeling can be done using ChIP-seq "signatures" directly, effectively skipping the motif finding and TFBS prediction steps. We present xcore, an R package that allows TF activity modeling based on ChIP-seq signatures and the user's gene expression data. We also provide xcoredata a companion data package that provides a collection of preprocessed ChIP-seq signatures. We demonstrate that xcore leads to biologically relevant predictions using transforming growth factor beta induced epithelial-mesenchymal transition time-courses, rinderpest infection time-courses, and embryonic stem cells differentiated to cardiomyocytes time-course profiled with Cap Analysis Gene Expression.

CONCLUSIONS

xcore provides a simple analytical framework for gene expression modeling using linear models that can be easily incorporated into differential expression analysis pipelines. Taking advantage of public ChIP-seq databases, xcore can identify meaningful molecular signatures and relevant ChIP-seq experiments.

TF-Prioritizer: a Java pipeline to prioritize condition-specific transcription factors

BACKGROUND

Eukaryotic gene expression is controlled by cis-regulatory elements (CREs), including promoters and enhancers, which are bound by transcription factors (TFs). Differential expression of TFs and their binding affinity at putative CREs determine tissue- and developmental-specific transcriptional activity. Consolidating genomic datasets can offer further insights into the accessibility of CREs, TF activity, and, thus, gene regulation. However, the integration and analysis of multimodal datasets are hampered by considerable technical challenges. While methods for highlighting differential TF activity from combined chromatin state data (e.g., chromatin immunoprecipitation [ChIP], ATAC, or DNase sequencing) and RNA sequencing data exist, they do not offer convenient usability, have limited support for large-scale data processing, and provide only minimal functionality for visually interpreting results.

RESULTS

We developed TF-Prioritizer, an automated pipeline that prioritizes condition-specific TFs from multimodal data and generates an interactive web report. We demonstrated its potential by identifying known TFs along with their target genes, as well as previously unreported TFs active in lactating mouse mammary glands. Additionally, we studied a variety of ENCODE datasets for cell lines K562 and MCF-7, including 12 histone modification ChIP sequencing as well as ATAC and DNase sequencing datasets, where we observe and discuss assay-specific differences.

CONCLUSION

TF-Prioritizer accepts ATAC, DNase, or ChIP sequencing and RNA sequencing data as input and identifies TFs with differential activity, thus offering an understanding of genome-wide gene regulation, potential pathogenesis, and therapeutic targets in biomedical research.

Exploiting Genomic Features to Improve the Prediction of Transcription Factor-Binding Sites in Plants

The identification of transcription factor (TF) target genes is central in biology. A popular approach is based on the location by pattern matching of potential cis-regulatory elements (CREs). During the last few years, tools integrating next-generation sequencing data have been developed to improve the performance of pattern matching. However, such tools have not yet been comprehensively evaluated in plants. Hence, we developed a new streamlined method aiming at predicting CREs and target genes of plant TFs in specific organs or conditions. Our approach implements a supervised machine learning strategy, which allows decision rule models to be learnt using TF ChIP-chip/seq experimental data. Different layers of genomic features were integrated in predictive models: the position on the gene, the DNA sequence conservation, the chromatin state and various CRE footprints. Among the tested features, the chromatin features were crucial for improving the accuracy of the method. Furthermore, we evaluated the transferability of predictive models across TFs, organs and species. Finally, we validated our method by correctly inferring the target genes of key TFs controlling metabolite biosynthesis at the organ level in Arabidopsis. We developed a tool-Wimtrap-to reproduce our approach in plant species and conditions/organs for which ChIP-chip/seq data are available. Wimtrap is a user-friendly R package that supports an R Shiny web interface and is provided with pre-built models that can be used to quickly get predictions of CREs and TF gene targets in different organs or conditions in Arabidopsis thaliana, Solanum lycopersicum, Oryza sativa and Zea mays.

Quantitative trait locus (xQTL) approaches identify risk genes and drug targets from human non-coding genomes

Advances and reduction of costs in various sequencing technologies allow for a closer look at variations present in the non-coding regions of the human genome. Correlating non-coding variants with large-scale multi-omic data holds the promise not only of a better understanding of likely causal connections between non-coding DNA and expression of traits but also identifying potential disease-modifying medicines. Genome-phenome association studies have created large datasets of DNA variants that are associated with multiple traits or diseases, such as Alzheimer's disease; yet, the functional consequences of variants, in particular of non-coding variants, remain largely unknown. Recent advances in functional genomics and computational approaches have led to the identification of potential roles of DNA variants, such as various quantitative trait locus (xQTL) techniques. Multi-omic assays and analytic approaches toward xQTL have identified links between genetic loci and human transcriptomic, epigenomic, proteomic and metabolomic data. In this review, we first discuss the recent development of xQTL from multi-omic findings. We then highlight multimodal analysis of xQTL and genetic data for identification of risk genes and drug targets using Alzheimer's disease as an example. We finally discuss challenges and future research directions (e.g. artificial intelligence) for annotation of non-coding variants in complex diseases.

Predicting which genes will respond to transcription factor perturbations

The ability to predict which genes will respond to the perturbation of a transcription factor serves as a benchmark for our systems-level understanding of transcriptional regulatory networks. In previous work, machine learning models have been trained to predict static gene expression levels in a biological sample by using data from the same or similar samples, including data on their transcription factor binding locations, histone marks, or DNA sequence. We report on a different challenge-training machine learning models to predict which genes will respond to the perturbation of a transcription factor without using any data from the perturbed cells. We find that existing transcription factor location data (ChIP-seq) from human cells have very little detectable utility for predicting which genes will respond to perturbation of a transcription factor. Features of genes, including their preperturbation expression level and expression variation, are very useful for predicting responses to perturbation of any transcription factor. This shows that some genes are poised to respond to transcription factor perturbations and others are resistant, shedding light on why it has been so difficult to predict responses from binding locations. Certain histone marks, including H3K4me1 and H3K4me3, have some predictive power when located downstream of the transcription start site. However, the predictive power of histone marks is much less than that of gene expression level and expression variation. Sequence-based or epigenetic properties of genes strongly influence their tendency to respond to direct transcription factor perturbations, partially explaining the oft-noted difficulty of predicting responsiveness from transcription factor binding location data. These molecular features are largely reflected in and summarized by the gene's expression level and expression variation. Code is available at https://github.com/BrentLab/TFPertRespExplainer.

Epigenetic reactivation of transcriptional programs orchestrating fetal lung development in human pulmonary hypertension

Phenotypic alterations in resident vascular cells contribute to the vascular remodeling process in diseases such as pulmonary (arterial) hypertension [P(A)H]. How the molecular interplay between transcriptional coactivators, transcription factors (TFs), and chromatin state alterations facilitate the maintenance of persistently activated cellular phenotypes that consequently aggravate vascular remodeling processes in PAH remains poorly explored. RNA sequencing (RNA-seq) in pulmonary artery fibroblasts (FBs) from adult human PAH and control lungs revealed 2460 differentially transcribed genes. Chromatin immunoprecipitation sequencing (ChIP-seq) revealed extensive differential distribution of transcriptionally accessible chromatin signatures, with 4152 active enhancers altered in PAH-FBs. Integrative analysis of RNA-seq and ChIP-seq data revealed that the transcriptional signatures for lung morphogenesis were epigenetically derepressed in PAH-FBs, including coexpression of T-box TF 4 (TBX4), TBX5, and SRY-box TF 9 (SOX9), which are involved in the early stages of lung development. These TFs were expressed in mouse fetuses and then repressed postnatally but were maintained in persistent PH of the newborn and reexpressed in adult PAH. Silencing of TBX4, TBX5, SOX9, or E1A-associated protein P300 (EP300) by RNA interference or small-molecule compounds regressed PAH phenotypes and mesenchymal signatures in arterial FBs and smooth muscle cells. Pharmacological inhibition of the P300/CREB-binding protein complex reduced the remodeling of distal pulmonary vessels, improved hemodynamics, and reversed established PAH in three rodent models in vivo, as well as reduced vascular remodeling in precision-cut tissue slices from human PAH lungs ex vivo. Epigenetic reactivation of TFs associated with lung development therefore underlies PAH pathogenesis, offering therapeutic opportunities.

Environmental adaptation in fish induced changes in the regulatory region of fatty acid elongase gene, elovl5, involved in long-chain polyunsaturated fatty acid biosynthesis

Fish are the main source of long-chain polyunsaturated fatty acids (LC-PUFA) for human consumption. In the process of evolution via natural selection, adaptation to distinct environments has likely driven changes in the endogenous capacity for LC-PUFA biosynthesis between marine and freshwater fishes. However, the molecular mechanisms underlying adaptive changes in this metabolic pathway are poorly understood. Here, we compared the transcriptional regulation of elongation of very long chain fatty acids protein 5 (Elovl5), which is one of the critical enzymes in LC-PUFA biosynthesis pathway, in marine large yellow croaker (Larimichthys crocea) and freshwater rainbow trout (Oncorhynchus mykiss). Comparative transcriptomic and absolute mRNA quantification analyses revealed that the expression of elovl5 in rainbow trout was markedly higher than that in large yellow croaker. Correspondingly, the number of chromatin accessible areas in the regulatory region of elovl5 in rainbow trout was higher than in large yellow croaker, which revealed that chromatin accessibility in the regulatory region of elovl5 in rainbow trout was higher. Furthermore, the differences in sequence and activity of the elovl5 promoter were observed between rainbow trout and large yellow croaker, and transcription factors including CCAAT/enhancer-binding protein β (CEBPβ), GATA binding protein 3 (GATA3) and upstream stimulatory factor 2 (USF2) displayed different regulatory roles on elovl5 expression between the two species. We propose that changes in the gene regulatory region driven by natural selection likely play a key role in differences in elovl5 expression and the activity of Elovl5, which may influence the LC-PUFA biosynthesis capacities of rainbow trout and large yellow croaker. These findings may also provide opportunities to improve the quality of aquatic products and, consequently, human health.

Temporal modelling using single-cell transcriptomics

Methods for profiling genes at the single-cell level have revolutionized our ability to study several biological processes and systems including development, differentiation, response programmes and disease progression. In many of these studies, cells are profiled over time in order to infer dynamic changes in cell states and types, sets of expressed genes, active pathways and key regulators. However, time-series single-cell RNA sequencing (scRNA-seq) also raises several new analysis and modelling issues. These issues range from determining when and how deep to profile cells, linking cells within and between time points, learning continuous trajectories, and integrating bulk and single-cell data for reconstructing models of dynamic networks. In this Review, we discuss several approaches for the analysis and modelling of time-series scRNA-seq, highlighting their steps, key assumptions, and the types of data and biological questions they are most appropriate for.

Constructing gene regulatory networks using epigenetic data

The biological processes that drive cellular function can be represented by a complex network of interactions between regulators (transcription factors) and their targets (genes). A cell's epigenetic state plays an important role in mediating these interactions, primarily by influencing chromatin accessibility. However, how to effectively use epigenetic data when constructing a gene regulatory network remains an open question. Almost all existing network reconstruction approaches focus on estimating transcription factor to gene connections using transcriptomic data. In contrast, computational approaches for analyzing epigenetic data generally focus on improving transcription factor binding site predictions rather than deducing regulatory network relationships. We bridged this gap by developing SPIDER, a network reconstruction approach that incorporates epigenetic data into a message-passing framework to estimate gene regulatory networks. We validated SPIDER's predictions using ChIP-seq data from ENCODE and found that SPIDER networks are both highly accurate and include cell-line-specific regulatory interactions. Notably, SPIDER can recover ChIP-seq verified transcription factor binding events in the regulatory regions of genes that do not have a corresponding sequence motif. The networks estimated by SPIDER have the potential to identify novel hypotheses that will allow us to better characterize cell-type and phenotype specific regulatory mechanisms.

A deep learning model to identify gene expression level using cobinding transcription factor signals

Gene expression is directly controlled by transcription factors (TFs) in a complex combination manner. It remains a challenging task to systematically infer how the cooperative binding of TFs drives gene activity. Here, we quantitatively analyzed the correlation between TFs and surveyed the TF interaction networks associated with gene expression in GM12878 and K562 cell lines. We identified six TF modules associated with gene expression in each cell line. Furthermore, according to the enrichment characteristics of TFs in these TF modules around a target gene, a convolutional neural network model, called TFCNN, was constructed to identify gene expression level. Results showed that the TFCNN model achieved a good prediction performance for gene expression. The average of the area under receiver operating characteristics curve (AUC) can reach up to 0.975 and 0.976, respectively in GM12878 and K562 cell lines. By comparison, we found that the TFCNN model outperformed the prediction models based on SVM and LDA. This is due to the TFCNN model could better extract the combinatorial interaction among TFs. Further analysis indicated that the abundant binding of regulatory TFs dominates expression of target genes, while the cooperative interaction between TFs has a subtle regulatory effects. And gene expression could be regulated by different TF combinations in a nonlinear way. These results are helpful for deciphering the mechanism of TF combination regulating gene expression.

Integrative analysis of epigenetics data identifies gene-specific regulatory elements

Understanding how epigenetic variation in non-coding regions is involved in distal gene-expression regulation is an important problem. Regulatory regions can be associated to genes using large-scale datasets of epigenetic and expression data. However, for regions of complex epigenomic signals and enhancers that regulate many genes, it is difficult to understand these associations. We present StitchIt, an approach to dissect epigenetic variation in a gene-specific manner for the detection of regulatory elements (REMs) without relying on peak calls in individual samples. StitchIt segments epigenetic signal tracks over many samples to generate the location and the target genes of a REM simultaneously. We show that this approach leads to a more accurate and refined REM detection compared to standard methods even on heterogeneous datasets, which are challenging to model. Also, StitchIt REMs are highly enriched in experimentally determined chromatin interactions and expression quantitative trait loci. We validated several newly predicted REMs using CRISPR-Cas9 experiments, thereby demonstrating the reliability of StitchIt. StitchIt is able to dissect regulation in superenhancers and predicts thousands of putative REMs that go unnoticed using peak-based approaches suggesting that a large part of the regulome might be uncharted water.

A randomized Phase II trial evaluating efficacy, safety, and tolerability of oral BI 409306 in attenuated psychosis syndrome: Design and rationale

AIM

Attenuated psychosis syndrome (APS), a condition for further study in the Diagnostic and Statistical Manual of Mental Disorders-5, comprises psychotic symptoms that are qualitatively similar to those observed in schizophrenia but are less severe. Patients with APS are at high risk of converting to first-episode psychosis (FEP). As evidence for effective pharmacological interventions in APS is limited, novel treatments may provide symptomatic relief and delay/prevent psychotic conversion. This trial aims to investigate the efficacy, safety, and tolerability of BI 409306, a potent and selective phosphodiesterase-9 inhibitor, versus placebo in APS. Novel biomarkers of psychosis are being investigated.

METHODS

In this Phase II, multinational, double-blind, parallel-group trial, randomized (1:1) patients will receive BI 409306 50 mg or placebo twice daily for 52 weeks. Patients (n = 300) will be enrolled to determine time to remission of APS, time to FEP, change in everyday functional capacity (Schizophrenia Cognition Rating Scale), and change from baseline in Brief Assessment of Cognition composite score and Positive and Negative Syndrome Scale scores. Potential biomarkers of psychosis under investigation include functional measures of brain activity and automated speech analyses. Safety is being assessed throughout.

CONCLUSIONS

This trial will determine whether BI 409306 is superior to placebo in achieving sustainable remission of APS and improvements in cognition and functional capacity. These advances may provide evidence-based treatment options for symptomatic relief in APS. Furthermore, the study will assess the effect of BI 409306 on psychotic conversion and explore the identification of patients at risk for conversion using novel biomarkers.

ATG5 and ATG7 Expression Levels Are Reduced in Cutaneous Melanoma and Regulated by NRF1

Autophagy is a highly conserved cellular process in which intracellular proteins and organelles are sequestered and degraded after the fusion of double-membrane vesicles known as autophagosomes with lysosomes. The process of autophagy is dependent on autophagy-related (ATG) proteins. The role of autophagy in cancer is very complex and still elusive. We investigated the expression of ATG proteins in benign nevi, primary and metastatic melanoma tissues using customized tissue microarrays (TMA). Results from immunohistochemistry show that the expression of ATG5 and ATG7 is significantly reduced in melanoma tissues compared to benign nevi. This reduction correlated with changes in the expression of autophagic activity markers, suggesting decreased basal levels of autophagy in primary and metastatic melanomas. Furthermore, the analysis of survival data of melanoma patients revealed an association between reduced ATG5 and ATG7 levels with an unfavourable clinical outcome. Currently, the mechanisms regulating ATG expression levels in human melanoma remains unknown. Using bioinformatic predictions of transcription factor (TF) binding motifs in accessible chromatin of primary melanocytes, we identified new TFs involved in the regulation of core ATGs. We then show that nuclear respiratory factor 1 (NRF1) stimulates the production of mRNA and protein as well as the promoter activity of ATG5 and ATG7. Moreover, NRF1 deficiency increased in vitro migration of melanoma cells. Our results support the concept that reduced autophagic activity contributes to melanoma development and progression, and identifies NRF1 as a novel TF involved in the regulation of both ATG5 and ATG7 genes.

Epigenomic and transcriptional profiling identifies impaired glyoxylate detoxification in NAFLD as a risk factor for hyperoxaluria

Epigenetic modifications (e.g. DNA methylation) in NAFLD and their contribution to disease progression and extrahepatic complications are poorly explored. Here, we use an integrated epigenome and transcriptome analysis of mouse NAFLD hepatocytes and identify alterations in glyoxylate metabolism, a pathway relevant in kidney damage via oxalate release-a harmful waste product and kidney stone-promoting factor. Downregulation and hypermethylation of alanine-glyoxylate aminotransferase (Agxt), which detoxifies glyoxylate, preventing excessive oxalate accumulation, is accompanied by increased oxalate formation after metabolism of the precursor hydroxyproline. Viral-mediated Agxt transfer or inhibiting hydroxyproline catabolism rescues excessive oxalate release. In human steatotic hepatocytes, AGXT is also downregulated and hypermethylated, and in NAFLD adolescents, steatosis severity correlates with urinary oxalate excretion. Thus, this work identifies a reduced capacity of the steatotic liver to detoxify glyoxylate, triggering elevated oxalate, and provides a mechanistic explanation for the increased risk of kidney stones and chronic kidney disease in NAFLD patients.

Chromatin loop anchors predict transcript and exon usage

Epigenomics and transcriptomics data from high-throughput sequencing techniques such as RNA-seq and ChIP-seq have been successfully applied in predicting gene transcript expression. However, the locations of chromatin loops in the genome identified by techniques such as Chromatin Interaction Analysis with Paired End Tag sequencing (ChIA-PET) have never been used for prediction tasks. Here, we developed machine learning models to investigate if ChIA-PET could contribute to transcript and exon usage prediction. In doing so, we used a large set of transcription factors as well as ChIA-PET data. We developed different Gradient Boosting Trees models according to the different tasks with the integrated datasets from three cell lines, including GM12878, HeLaS3 and K562. We validated the models via 10-fold cross validation, chromosome-split validation and cross-cell validation. Our results show that both transcript and splicing-derived exon usage can be effectively predicted with at least 0.7512 and 0.7459 of accuracy, respectively, on all cell lines from all kinds of validations. Examining the predictive features, we found that RNA Polymerase II ChIA-PET was one of the most important features in both transcript and exon usage prediction, suggesting that chromatin loop anchors are predictive of both transcript and exon usage.

Lasp1 regulates adherens junction dynamics and fibroblast transformation in destructive arthritis

The LIM and SH3 domain protein 1 (Lasp1) was originally cloned from metastatic breast cancer and characterised as an adaptor molecule associated with tumourigenesis and cancer cell invasion. However, the regulation of Lasp1 and its function in the aggressive transformation of cells is unclear. Here we use integrative epigenomic profiling of invasive fibroblast-like synoviocytes (FLS) from patients with rheumatoid arthritis (RA) and from mouse models of the disease, to identify Lasp1 as an epigenomically co-modified region in chronic inflammatory arthritis and a functionally important binding partner of the Cadherin-11/β-Catenin complex in zipper-like cell-to-cell contacts. In vitro, loss or blocking of Lasp1 alters pathological tissue formation, migratory behaviour and platelet-derived growth factor response of arthritic FLS. In arthritic human TNF transgenic mice, deletion of Lasp1 reduces arthritic joint destruction. Therefore, we show a function of Lasp1 in cellular junction formation and inflammatory tissue remodelling and identify Lasp1 as a potential target for treating inflammatory joint disorders associated with aggressive cellular transformation.

Inferring transcriptomic cell states and transitions only from time series transcriptome data

Cellular stages of biological processes have been characterized using fluorescence-activated cell sorting and genetic perturbations, charting a limited landscape of cellular states. Time series transcriptome data can help define new cellular states at the molecular level since the analysis of transcriptional changes can provide information on cell states and transitions. However, existing methods for inferring cell states from transcriptome data use additional information such as prior knowledge on cell types or cell-type-specific markers to reduce the complexity of data. In this study, we present a novel time series clustering framework to infer TRAnscriptomic Cellular States (TRACS) only from time series transcriptome data by integrating Gaussian process regression, shape-based distance, and ranked pairs algorithm in a single computational framework. TRACS determines patterns that correspond to hidden cellular states by clustering gene expression data. TRACS was used to analyse single-cell and bulk RNA sequencing data and successfully generated cluster networks that reflected the characteristics of key stages of biological processes. Thus, TRACS has a potential to help reveal unknown cellular states and transitions at the molecular level using only time series transcriptome data. TRACS is implemented in Python and available at http://github.com/BML-cbnu/TRACS/ .

Automated generation of context-specific gene regulatory networks with a weighted approach in Drosophila melanogaster

The regulation of gene expression is a key factor in the development and maintenance of life in all organisms. Even so, little is known at whole genome scale for most genes and contexts. We propose a method, Tool for Weighted Epigenomic Networks in Drosophila melanogaster (Fly T-WEoN), to generate context-specific gene regulatory networks starting from a reference network that contains all known gene regulations in the fly. Unlikely regulations are removed by applying a series of knowledge-based filters. Each of these filters is implemented as an independent module that considers a type of experimental evidence, including DNA methylation, chromatin accessibility, histone modifications and gene expression. Fly T-WEoN is based on heuristic rules that reflect current knowledge on gene regulation in D. melanogaster obtained from the literature. Experimental data files can be generated with several standard procedures and used solely when and if available. Fly T-WEoN is available as a Cytoscape application that permits integration with other tools and facilitates downstream network analysis. In this work, we first demonstrate the reliability of our method to then provide a relevant application case of our tool: early development of D. melanogaster. Fly T-WEoN together with its step-by-step guide is available at https://weon.readthedocs.io.

Quantification, Dynamic Visualization, and Validation of Bias in ATAC-Seq Data with ataqv

The assay for transposase-accessible chromatin using sequencing (ATAC-seq) has become the preferred method for mapping chromatin accessibility due to its time and input material efficiency. However, it can be difficult to evaluate data quality and identify sources of technical bias across samples. Here, we present ataqv, a computational toolkit for efficiently measuring, visualizing, and comparing quality control (QC) results across samples and experiments. We use ataqv to analyze 2,009 public ATAC-seq datasets; their QC metrics display a 10-fold range. Tn5 dosage experiments and statistical modeling show that technical variation in the ratio of Tn5 transposase to nuclei and sequencing flowcell density induces systematic bias in ATAC-seq data by changing the enrichment of reads across functional genomic annotations including promoters, enhancers, and transcription-factor-bound regions, with the notable exception of CTCF. ataqv can be integrated into existing computational pipelines and is freely available at https://github.com/ParkerLab/ataqv/.

Predicting mRNA Abundance Directly from Genomic Sequence Using Deep Convolutional Neural Networks

Algorithms that accurately predict gene structure from primary sequence alone were transformative for annotating the human genome. Can we also predict the expression levels of genes based solely on genome sequence? Here, we sought to apply deep convolutional neural networks toward that goal. Surprisingly, a model that includes only promoter sequences and features associated with mRNA stability explains 59% and 71% of variation in steady-state mRNA levels in human and mouse, respectively. This model, termed Xpresso, more than doubles the accuracy of alternative sequence-based models and isolates rules as predictive as models relying on chromatic immunoprecipitation sequencing (ChIP-seq) data. Xpresso recapitulates genome-wide patterns of transcriptional activity, and its residuals can be used to quantify the influence of enhancers, heterochromatic domains, and microRNAs. Model interpretation reveals that promoter-proximal CpG dinucleotides strongly predict transcriptional activity. Looking forward, we propose cell-type-specific gene-expression predictions based solely on primary sequences as a grand challenge for the field.

Functional analysis of low-grade glioma genetic variants predicts key target genes and transcription factors

BACKGROUND

Large-scale genome-wide association studies (GWAS) have implicated thousands of germline genetic variants in modulating individuals' risk to various diseases, including cancer. At least 25 risk loci have been identified for low-grade gliomas (LGGs), but their molecular functions remain largely unknown.

METHODS

We hypothesized that GWAS loci contain causal single nucleotide polymorphisms (SNPs) that reside in accessible open chromatin regions and modulate the expression of target genes by perturbing the binding affinity of transcription factors (TFs). We performed an integrative analysis of genomic and epigenomic data from The Cancer Genome Atlas and other public repositories to identify candidate causal SNPs within linkage disequilibrium blocks of LGG GWAS loci. We assessed their potential regulatory role via in silico TF binding sequence perturbations, convolutional neural network trained on TF binding data, and simulated annealing-based interpretation methods.

RESULTS

We built an interactive website (http://education.knoweng.org/alg3/) summarizing the functional footprinting of 280 variants in 25 LGG GWAS regions, providing rich information for further computational and experimental scrutiny. We identified as case studies PHLDB1 and SLC25A26 as candidate target genes of rs12803321 and rs11706832, respectively, and predicted the GWAS variant rs648044 to be the causal SNP modulating ZBTB16, a known tumor suppressor in multiple cancers. We showed that rs648044 likely perturbed the binding affinity of the TF MAFF, as supported by RNA interference and in vitro MAFF binding experiments.

CONCLUSIONS

The identified candidate (causal SNP, target gene, TF) triplets and the accompanying resource will help accelerate our understanding of the molecular mechanisms underlying genetic risk factors for gliomas.

Modeling transcriptional regulation using gene regulatory networks based on multi-omics data sources

Background

Transcriptional regulation is complex, requiring multiple cis (local) and trans acting mechanisms working in concert to drive gene expression, with disruption of these processes linked to multiple diseases. Previous computational attempts to understand the influence of regulatory mechanisms on gene expression have used prediction models containing input features derived from cis regulatory factors. However, local chromatin looping and trans-acting mechanisms are known to also influence transcriptional regulation, and their inclusion may improve model accuracy and interpretation. In this study, we create a general model of transcription factor influence on gene expression by incorporating both cis and trans gene regulatory features.

Results

We describe a computational framework to model gene expression for GM12878 and K562 cell lines. This framework weights the impact of transcription factor-based regulatory data using multi-omics gene regulatory networks to account for both cis and trans acting mechanisms, and measures of the local chromatin context. These prediction models perform significantly better compared to models containing cis-regulatory features alone. Models that additionally integrate long distance chromatin interactions (or chromatin looping) between distal transcription factor binding regions and gene promoters also show improved accuracy. As a demonstration of their utility, effect estimates from these models were used to weight cis-regulatory rare variants for sequence kernel association test analyses of gene expression.

Conclusions

Our models generate refined effect estimates for the influence of individual transcription factors on gene expression, allowing characterization of their roles across the genome. This work also provides a framework for integrating multiple data types into a single model of transcriptional regulation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04126-3.

Advanced single-cell technologies to guide the development of bioengineered lungs

Lung transplantation remains the only viable option for individuals suffering from end-stage lung failure. However, a number of current limitations exist including a continuing shortage of suitable donor lungs and immune rejection following transplantation. To address these concerns, engineering a decellularized biocompatible lung scaffold from cadavers reseeded with autologous lung cells to promote tissue regeneration is being explored. Proof-of-concept transplantation of these bioengineered lungs into animal models has been accomplished. However, these lungs were incompletely recellularized with resulting epithelial and endothelial leakage and insufficient basement membrane integrity. Failure to repopulate lung scaffolds with all of the distinct cell populations necessary for proper function remains a significant hurdle for the progression of current engineering approaches and precludes clinical translation. Advancements in 3D bioprinting, lung organoid models, and microfluidic device and bioreactor development have enhanced our knowledge of pulmonary lung development, as well as important cell-cell and cell-matrix interactions, all of which will help in the path to a bioengineered transplantable lung. However, a significant gap in knowledge of the spatiotemporal interactions between cell populations as well as relative quantities and localization within each compartment of the lung necessary for its proper growth and function remains. This review will provide an update on cells currently used for reseeding decellularized scaffolds with outcomes of recent lung engineering attempts. Focus will then be on how data obtained from advanced single-cell analyses, coupled with multiomics approaches and high-resolution 3D imaging, can guide current lung bioengineering efforts for the development of fully functional, transplantable lungs.

Machine learning for deciphering cell heterogeneity and gene regulation

Epigenetics studies inheritable and reversible modifications of DNA that allow cells to control gene expression throughout their development and in response to environmental conditions. In computational epigenomics, machine learning is applied to study various epigenetic mechanisms genome wide. Its aim is to expand our understanding of cell differentiation, that is their specialization, in health and disease. Thus far, most efforts focus on understanding the functional encoding of the genome and on unraveling cell-type heterogeneity. Here, we provide an overview of state-of-the-art computational methods and their underlying statistical concepts, which range from matrix factorization and regularized linear regression to deep learning methods. We further show how the rise of single-cell technology leads to new computational challenges and creates opportunities to further our understanding of epigenetic regulation.

Evidence of widespread, independent sequence signature for transcription factor cobinding

Transcription factors (TFs) are the vocabulary that genomes use to regulate gene expression and phenotypes. The interactions among TFs enrich this vocabulary and orchestrate diverse biological processes. Although simple models identify open chromatin and the presence of TF motifs as the two major contributors to TF binding patterns, it remains elusive what contributes to the in vivo TF cobinding landscape. In this study, we developed a machine learning algorithm to explore the contributors of the cobinding patterns. The algorithm substantially outperforms the state-of-the-field models for TF cobinding prediction. Game theory-based feature importance analysis reveals that, for most of the TF pairs we studied, independent motif sequences contribute one or more of the two TFs under investigation to their cobinding patterns. Such independent motif sequences include, but are not limited to, transcription initiation-related proteins and known TF complexes. We found the motif sequence signatures and the TFs are rarely mutual, corroborating a hierarchical and directional organization of the regulatory network and refuting the possibility of artifacts caused by shared sequence similarity with the TFs under investigation. We modeled such regulatory language with directed graphs, which reveal shared, global factors that are related to many binding and cobinding patterns.

Determinants of correlated expression of transcription factors and their target genes

While transcription factors (TFs) are known to regulate the expression of their target genes (TGs), only a weak correlation of expression between TFs and their TGs has generally been observed. As lack of correlation could be caused by additional layers of regulation, the overall correlation distribution may hide the presence of a subset of regulatory TF-TG pairs with tight expression coupling. Using reported regulatory pairs in the plant Arabidopsis thaliana along with comprehensive gene expression information and testing a wide array of molecular features, we aimed to discern the molecular determinants of high expression correlation of TFs and their TGs. TF-family assignment, stress-response process involvement, short genomic distances of the TF-binding sites to the transcription start site of their TGs, few required protein-protein-interaction connections to establish physical interactions between the TF and polymerase-II, unambiguous TF-binding motifs, increased numbers of miRNA target-sites in TF-mRNAs, and a young evolutionary age of TGs were found particularly indicative of high TF-TG correlation. The modulating roles of post-transcriptional, post-translational processes, and epigenetic factors have been characterized as well. Our study reveals that regulatory pairs with high expression coupling are associated with specific molecular determinants.

Prediction of single-cell gene expression for transcription factor analysis

BACKGROUND

Single-cell RNA sequencing is a powerful technology to discover new cell types and study biological processes in complex biological samples. A current challenge is to predict transcription factor (TF) regulation from single-cell RNA data.

RESULTS

Here, we propose a novel approach for predicting gene expression at the single-cell level using cis-regulatory motifs, as well as epigenetic features. We designed a tree-guided multi-task learning framework that considers each cell as a task. Through this framework we were able to explain the single-cell gene expression values using either TF binding affinities or TF ChIP-seq data measured at specific genomic regions. TFs identified using these models could be validated by the literature.

CONCLUSION

Our proposed method allows us to identify distinct TFs that show cell type-specific regulation. This approach is not limited to TFs but can use any type of data that can potentially be used in explaining gene expression at the single-cell level to study factors that drive differentiation or show abnormal regulation in disease. The implementation of our workflow can be accessed under an MIT license via https://github.com/SchulzLab/Triangulate.

MTTFsite: cross-cell type TF binding site prediction by using multi-task learning

Motivation

The prediction of transcription factor binding sites (TFBSs) is crucial for gene expression analysis. Supervised learning approaches for TFBS predictions require large amounts of labeled data. However, many TFs of certain cell types either do not have sufficient labeled data or do not have any labeled data.

Results

In this paper, a multi-task learning framework (called MTTFsite) is proposed to address the lack of labeled data problem by leveraging on labeled data available in cross-cell types. The proposed MTTFsite contains a shared CNN to learn common features for all cell types and a private CNN for each cell type to learn private features. The common features are aimed to help predicting TFBSs for all cell types especially those cell types that lack labeled data. MTTFsite is evaluated on 241 cell type TF pairs and compared with a baseline method without using any multi-task learning model and a fully shared multi-task model that uses only a shared CNN and do not use private CNNs. For cell types with insufficient labeled data, results show that MTTFsite performs better than the baseline method and the fully shared model on more than 89% pairs. For cell types without any labeled data, MTTFsite outperforms the baseline method and the fully shared model by more than 80 and 93% pairs, respectively. A novel gene expression prediction method (called TFChrome) using both MTTFsite and histone modification features is also presented. Results show that TFBSs predicted by MTTFsite alone can achieve good performance. When MTTFsite is combined with histone modification features, a significant 5.7% performance improvement is obtained.

Availability and implementation

The resource and executable code are freely available at http://hlt.hitsz.edu.cn/MTTFsite/ and http://www.hitsz-hlt.com:8080/MTTFsite/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Weighted elastic net for unsupervised domain adaptation with application to age prediction from DNA methylation data

MOTIVATION

Predictive models are a powerful tool for solving complex problems in computational biology. They are typically designed to predict or classify data coming from the same unknown distribution as the training data. In many real-world settings, however, uncontrolled biological or technical factors can lead to a distribution mismatch between datasets acquired at different times, causing model performance to deteriorate on new data. A common additional obstacle in computational biology is scarce data with many more features than samples. To address these problems, we propose a method for unsupervised domain adaptation that is based on a weighted elastic net. The key idea of our approach is to compare dependencies between inputs in training and test data and to increase the cost of differently behaving features in the elastic net regularization term. In doing so, we encourage the model to assign a higher importance to features that are robust and behave similarly across domains.

RESULTS

We evaluate our method both on simulated data with varying degrees of distribution mismatch and on real data, considering the problem of age prediction based on DNA methylation data across multiple tissues. Compared with a non-adaptive standard model, our approach substantially reduces errors on samples with a mismatched distribution. On real data, we achieve far lower errors on cerebellum samples, a tissue which is not part of the training data and poorly predicted by standard models. Our results demonstrate that unsupervised domain adaptation is possible for applications in computational biology, even with many more features than samples.

AVAILABILITY AND IMPLEMENTATION

Source code is available at https://github.com/PfeiferLabTue/wenda.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Seq-ing answers: Current data integration approaches to uncover mechanisms of transcriptional regulation

Advancements in the field of next generation sequencing lead to the generation of ever-more data, with the challenge often being how to combine and reconcile results from different OMICs studies such as genome, epigenome and transcriptome. Here we provide an overview of the standard processing pipelines for ChIP-seq and RNA-seq as well as common downstream analyses. We describe popular multi-omics data integration approaches used to identify target genes and co-factors, and we discuss how machine learning techniques may predict transcriptional regulators and gene expression.

Protection from DNA re-methylation by transcription factors in primordial germ cells and pre-implantation embryos can explain trans-generational epigenetic inheritance

Background

A growing body of evidence suggests that certain epiphenotypes can be passed across generations via both the male and female germlines of mammals. These observations have been difficult to explain owing to a global loss of the majority of known epigenetic marks present in parental chromosomes during primordial germ cell development and after fertilization.

Results

By integrating previously published BS-seq, DNase-seq, ATAC-seq, and RNA-seq data collected during multiple stages of primordial germ cell and pre-implantation development, we find that the methylation status of the majority of CpGs genome-wide is restored after global de-methylation, despite the fact that global CpG methylation drops to 10% in primordial germ cells and 20% in the inner cell mass of the blastocyst. We estimate the proportion of such CpGs with preserved methylation status to be 78%. Further, we find that CpGs at sites bound by transcription factors during the global re-methylation phases of germline and embryonic development remain hypomethylated across all developmental stages observed. On the other hand, CpGs at sites not bound by transcription factors during the global re-methylation phase have high methylation levels prior to global de-methylation, become de-methylated during global de-methylation, and then become re-methylated.

Conclusions

The results suggest that transcription factors can act as carriers of epigenetic information during germ cell and pre-implantation development by ensuring that the methylation status of CpGs is maintained. These findings provide the basis for a mechanistic description of trans-generational inheritance of epigenetic information in mammals.

TEPIC 2-an extended framework for transcription factor binding prediction and integrative epigenomic analysis

Summary

Prediction of transcription factor (TF) binding from epigenetics data and integrative analysis thereof are challenging. Here, we present TEPIC 2 a framework allowing for fast, accurate and versatile prediction, and analysis of TF binding from epigenetics data: it supports 30 species with binding motifs, computes TF gene and scores up to two orders of magnitude faster than before due to improved implementation, and offers easy-to-use machine learning pipelines for integrated analysis of TF binding predictions with gene expression data allowing the identification of important TFs.

Availability and implementation

TEPIC is implemented in C++, R, and Python. It is freely available at https://github.com/SchulzLab/TEPIC and can be used on Linux based systems.

Supplementary information

Supplementary data are available at Bioinformatics online.