Identification of gene specific cis-regulatory elements during differentiation of mouse embryonic stem cells: An integrative approach using high-throughput datasets

Forward programming human pluripotent stem cells into microglia

Microglia play vital roles in embryonic and post-natal development, homeostasis, and pathogen defence in the central nervous system. Human induced pluripotent stem cell (hiPSC)-based methods have emerged as an important source for the study of human microglia in vitro. Classical approaches to differentiate hiPSCs into microglia suffer from limitations including extended culture periods, consistency, and efficiency. More recently, forward programming has arisen as a promising alternative for the manufacture of bulk quantities of human microglia. This review provides a comprehensive assessment of published forward programming protocols that are based on forced expression of key lineage transcription factors (TFs). We focus on the choice of reprogramming factors, transgene delivery methods, and medium composition, which impact induction kinetics and the resulting microglia phenotype.

Endothelial gene regulatory elements associated with cardiopharyngeal lineage differentiation

Endothelial cells (EC) differentiate from multiple sources, including the cardiopharyngeal mesoderm, which gives rise also to cardiac and branchiomeric muscles. The enhancers activated during endothelial differentiation within the cardiopharyngeal mesoderm are not completely known. Here, we use a cardiogenic mesoderm differentiation model that activates an endothelial transcription program to identify endothelial regulatory elements activated in early cardiogenic mesoderm. Integrating chromatin remodeling and gene expression data with available single-cell RNA-seq data from mouse embryos, we identify 101 putative regulatory elements of EC genes. We then apply a machine-learning strategy, trained on validated enhancers, to predict enhancers. Using this computational assay, we determine that 50% of these sequences are likely enhancers, some of which are already reported. We also identify a smaller set of regulatory elements of well-known EC genes and validate them using genetic and epigenetic perturbation. Finally, we integrate multiple data sources and computational tools to search for transcriptional factor binding motifs. In conclusion, we show EC regulatory sequences with a high likelihood to be enhancers, and we validate a subset of them using computational and cell culture models. Motif analyses show that the core EC transcription factors GATA/ETS/FOS is a likely driver of EC regulation in cardiopharyngeal mesoderm.

Prediction and analysis of cis-regulatory elements in Dorsal and Ventral patterning genes of Tribolium castaneum and its comparison with Drosophila melanogaster

Insect embryonic development and morphology are characterized by their anterior-posterior and dorsal-ventral (DV) patterning. In Drosophila embryos, DV patterning is mediated by a dorsal protein gradient which activates twist and snail proteins, the important regulators of DV patterning. To activate or repress gene expression, some regulatory proteins bind in clusters to their target gene at sites known as cis-regulatory elements or enhancers. To understand how variations in gene expression in different lineages might lead to different phenotypes, it is necessary to understand enhancers and their evolution. Drosophila melanogaster has been widely studied to understand the interactions between transcription factors and the transcription factor binding sites. Tribolium castaneum is an upcoming model animal which is catching the interest of biologists and the research on the enhancer mechanisms in the insect's axes patterning is still in infancy. Therefore, the current study was designed to compare the enhancers of DV patterning in the two insect species. The sequences of ten proteins involved in DV patterning of D. melanogaster were obtained from Flybase. The protein sequences of T. castaneum orthologous to those obtained from D. melanogaster were acquired from NCBI BLAST, and these were then converted to DNA sequences which were modified by adding 20 kb sequences both upstream and downstream to the gene. These modified sequences were used for further analysis. Bioinformatics tools (Cluster-Buster and MCAST) were used to search for clusters of binding sites (enhancers) in the modified DV genes. The results obtained showed that the transcription factors in Drosophila melanogaster and Tribolium castaneum are nearly identical; however, the number of binding sites varies between the two species, indicating transcription factor binding site evolution, as predicted by two different computational tools. It was observed that dorsal, twist, snail, zelda, and Supressor of Hairless are the transcription factors responsible for the regulation of DV patterning in the two insect species.

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.

A Novel Repetition Frequency-Based DNA Encoding Scheme to Predict Human and Mouse DNA Enhancers with Deep Learning

Recent studies have shown that DNA enhancers have an important role in the regulation of gene expression. They are responsible for different important biological elements and processes such as development, homeostasis, and embryogenesis. However, experimental prediction of these DNA enhancers is time-consuming and costly as it requires laboratory work. Therefore, researchers started to look for alternative ways and started to apply computation-based deep learning algorithms to this field. Yet, the inconsistency and unsuccessful prediction performance of computational-based approaches among various cell lines led to the investigation of these approaches as well. Therefore, in this study, a novel DNA encoding scheme was proposed, and solutions were sought to the problems mentioned and DNA enhancers were predicted with BiLSTM. The study consisted of four different stages for two scenarios. In the first stage, DNA enhancer data were obtained. In the second stage, DNA sequences were converted to numerical representations by both the proposed encoding scheme and various DNA encoding schemes including EIIP, integer number, and atomic number. In the third stage, the BiLSTM model was designed, and the data were classified. In the final stage, the performance of DNA encoding schemes was determined by accuracy, precision, recall, F1-score, CSI, MCC, G-mean, Kappa coefficient, and AUC scores. In the first scenario, it was determined whether the DNA enhancers belonged to humans or mice. As a result of the prediction process, the highest performance was achieved with the proposed DNA encoding scheme, and an accuracy of 92.16% and an AUC score of 0.85 were calculated, respectively. The closest accuracy score to the proposed scheme was obtained with the EIIP DNA encoding scheme and the result was observed as 89.14%. The AUC score of this scheme was measured as 0.87. Among the remaining DNA encoding schemes, the atomic number showed an accuracy score of 86.61%, while this rate decreased to 76.96% with the integer scheme. The AUC values of these schemes were 0.84 and 0.82, respectively. In the second scenario, it was determined whether there was a DNA enhancer and, if so, it was decided to which species this enhancer belonged. In this scenario, the highest accuracy score was obtained with the proposed DNA encoding scheme and the result was 84.59%. Moreover, the AUC score of the proposed scheme was determined as 0.92. EIIP and integer DNA encoding schemes showed accuracy scores of 77.80% and 73.68%, respectively, while their AUC scores were close to 0.90. The most ineffective prediction was performed with the atomic number and the accuracy score of this scheme was calculated as 68.27%. Finally, the AUC score of this scheme was 0.81. At the end of the study, it was observed that the proposed DNA encoding scheme was successful and effective in predicting DNA enhancers.

Computational approaches to understand transcription regulation in development

Gene regulatory networks (GRNs) serve as useful abstractions to understand transcriptional dynamics in developmental systems. Computational prediction of GRNs has been successfully applied to genome-wide gene expression measurements with the advent of microarrays and RNA-sequencing. However, these inferred networks are inaccurate and mostly based on correlative rather than causative interactions. In this review, we highlight three approaches that significantly impact GRN inference: (1) moving from one genome-wide functional modality, gene expression, to multi-omics, (2) single cell sequencing, to measure cell type-specific signals and predict context-specific GRNs, and (3) neural networks as flexible models. Together, these experimental and computational developments have the potential to significantly impact the quality of inferred GRNs. Ultimately, accurately modeling the regulatory interactions between transcription factors and their target genes will be essential to understand the role of transcription factors in driving developmental gene expression programs and to derive testable hypotheses for validation.

A genome-wide relay of signalling-responsive enhancers drives hematopoietic specification

Developmental control of gene expression critically depends on distal cis-regulatory elements including enhancers which interact with promoters to activate gene expression. To date no global experiments have been conducted that identify their cell type and cell stage-specific activity within one developmental pathway and in a chromatin context. Here, we describe a high-throughput method that identifies thousands of differentially active cis-elements able to stimulate a minimal promoter at five stages of hematopoietic progenitor development from embryonic stem (ES) cells, which can be adapted to any ES cell derived cell type. We show that blood cell-specific gene expression is controlled by the concerted action of thousands of differentiation stage-specific sets of cis-elements which respond to cytokine signals terminating at signalling responsive transcription factors. Our work provides an important resource for studies of hematopoietic specification and highlights the mechanisms of how and where extrinsic signals program a cell type-specific chromatin landscape driving hematopoietic differentiation.

Using epigenomics to understand cellular responses to environmental influences in diseases

It is a generally accepted model that environmental influences can exert their effects, at least in part, by changing the molecular regulators of transcription that are described as epigenetic. As there is biochemical evidence that some epigenetic regulators of transcription can maintain their states long term and through cell division, an epigenetic model encompasses the idea of maintenance of the effect of an exposure long after it is no longer present. The evidence supporting this model is mostly from the observation of alterations of molecular regulators of transcription following exposures. With the understanding that the interpretation of these associations is more complex than originally recognised, this model may be oversimplistic; therefore, adopting novel perspectives and experimental approaches when examining how environmental exposures are linked to phenotypes may prove worthwhile. In this review, we have chosen to use the example of nonalcoholic fatty liver disease (NAFLD), a common, complex human disease with strong environmental and genetic influences. We describe how epigenomic approaches combined with emerging functional genetic and single-cell genomic techniques are poised to generate new insights into the pathogenesis of environmentally influenced human disease phenotypes exemplified by NAFLD.

ZNF146/OZF and ZNF507 target LINE-1 sequences

Repetitive sequences including transposable elements and transposon-derived fragments account for nearly half of the human genome. While transposition-competent transposable elements must be repressed to maintain genomic stability, mutated and fragmented transposable elements comprising the bulk of repetitive sequences can also contribute to regulation of host gene expression and broader genome organization. Here, we analyzed published ChIP-seq data sets to identify proteins broadly enriched on transposable elements in the human genome. We show 2 of the proteins identified, C2H2 zinc finger-containing proteins ZNF146 (also known as OZF) and ZNF507, are targeted to distinct sites within LINE-1 ORF2 at thousands of locations in the genome. ZNF146 binding sites are found at old and young LINE-1 elements. In contrast, ZNF507 preferentially binds at young LINE-1 sequences correlated to sequence changes in LINE-1 elements at ZNF507's binding site. To gain further insight into ZNF146 and ZNF507 function, we disrupt their expression in HEK293 cells using CRISPR/Cas9 and perform RNA sequencing, finding modest gene expression changes in cells where ZNF507 has been disrupted. We further identify a physical interaction between ZNF507 and PRMT5, suggesting ZNF507 may target arginine methylation activity to LINE-1 sequences.

Loss of epigenetic polarity is a hallmark of hematopoietic stem cell aging

Changes of polarity in somatic stem cells upon aging or disease lead to a functional deterioration of stem cells and consequently loss of tissue homeostasis, likely due to changes in the mode (symmetry versus asymmetry) of stem cell divisions. Changes in polarity of epigenetic markers (or 'epi-polarity') in stem cells, which are linked to alterations in chromatin architecture, might explain how a decline in the frequency of epipolar stem cells can have a long-lasting impact on the function of especially aging stem cells. The drift in epipolarity might represent a novel therapeutic target to improve stem cell function upon aging or disease. Here we review basic biological principles of epigenetic polarity, with a special focus on epipolarity and aging of hematopoietic stem cells.

Interactions between lineage-associated transcription factors govern haematopoietic progenitor states

Recent advances in molecular profiling provide descriptive datasets of complex differentiation landscapes including the haematopoietic system, but the molecular mechanisms defining progenitor states and lineage choice remain ill-defined. Here, we employed a cellular model of murine multipotent haematopoietic progenitors (Hoxb8-FL) to knock out 39 transcription factors (TFs) followed by RNA-Seq analysis, to functionally define a regulatory network of 16,992 regulator/target gene links. Focussed analysis of the subnetworks regulated by the B-lymphoid TF Ebf1 and T-lymphoid TF Gata3 revealed a surprising role in common activation of an early myeloid programme. Moreover, Gata3-mediated repression of Pax5 emerges as a mechanism to prevent precocious B-lymphoid differentiation, while Hox-mediated activation of Meis1 suppresses myeloid differentiation. To aid interpretation of large transcriptomics datasets, we also report a new method that visualises likely transitions that a progenitor will undergo following regulatory network perturbations. Taken together, this study reveals how molecular network wiring helps to establish a multipotent progenitor state, with experimental approaches and analysis tools applicable to dissecting a broad range of both normal and perturbed cellular differentiation landscapes.

Interactions between lineage-associated transcription factors govern haematopoietic progenitor states

Recent advances in molecular profiling provide descriptive datasets of complex differentiation landscapes including the haematopoietic system, but the molecular mechanisms defining progenitor states and lineage choice remain ill-defined. Here, we employed a cellular model of murine multipotent haematopoietic progenitors (Hoxb8-FL) to knock out 39 transcription factors (TFs) followed by RNA-Seq analysis, to functionally define a regulatory network of 16,992 regulator/target gene links. Focussed analysis of the subnetworks regulated by the B-lymphoid TF Ebf1 and T-lymphoid TF Gata3 revealed a surprising role in common activation of an early myeloid programme. Moreover, Gata3-mediated repression of Pax5 emerges as a mechanism to prevent precocious B-lymphoid differentiation, while Hox-mediated activation of Meis1 suppresses myeloid differentiation. To aid interpretation of large transcriptomics datasets, we also report a new method that visualises likely transitions that a progenitor will undergo following regulatory network perturbations. Taken together, this study reveals how molecular network wiring helps to establish a multipotent progenitor state, with experimental approaches and analysis tools applicable to dissecting a broad range of both normal and perturbed cellular differentiation landscapes.

Data integration for inferring context-specific gene regulatory networks

Transcriptional regulatory networks control context-specific gene expression patterns and play important roles in normal and disease processes. Advances in genomics are rapidly increasing our ability to measure different components of the regulation machinery at the single-cell and bulk population level. An important challenge is to combine different types of regulatory genomic measurements to construct a more complete picture of gene regulatory networks across different disease, environmental, and developmental contexts. In this review, we focus on recent computational methods that integrate regulatory genomic data sets to infer context specificity and dynamics in regulatory networks.