scholarly journals Single Nuclei Chromatin Profiles of Ventral Midbrain Reveal Cell Identity Transcription Factors and Cell Type-Specific Gene Regulatory Variation

2020 ◽  
Author(s):  
Yujuan Gui ◽  
Kamil Grzyb ◽  
Mélanie H. Thomas ◽  
Jochen Ohnmacht ◽  
Pierre Garcia ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Specific Gene ◽  
Cell Type ◽  
Cell Identity ◽  
Regulatory Variation ◽  
Ventral Midbrain ◽  
Cell Type Specific ◽  
Gene Regulatory

scholarly journals Single nuclei chromatin profiles of ventral midbrain reveal cell identity transcription factors and cell type-specific gene regulatory variation

2020 ◽  
Author(s):  
Yujuan Gui ◽  
Kamil Grzyb ◽  
Mélanie H. Thomas ◽  
Jochen Ohnmacht ◽  
Pierre Garcia ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Cell Types ◽  
Mouse Strains ◽  
Specific Gene ◽  
Cell Type ◽  
Cell Identity ◽  
Regulatory Variation ◽  
Ventral Midbrain ◽  
Cell Type Specific

SUMMARYCell types in ventral midbrain are involved in diseases with variable genetic susceptibility such as Parkinson’s disease and schizophrenia. Many genetic variants affect regulatory regions and alter gene expression. We report 20 658 single nuclei chromatin accessibility profiles of ventral midbrain from two genetically and phenotypically distinct mouse strains. We distinguish ten cell types based on chromatin profiles and analysis of accessible regions controlling cell identity genes highlights cell type-specific key transcription factors. Regulatory variation segregating the mouse strains manifests more on transcriptome than chromatin level. However, cell type-level data reveals changes not captured at tissue level. To discover the scope and cell-type specificity of cis-acting variation in midbrain gene expression, we identify putative regulatory variants and show them to be enriched at differentially expressed loci. Finally, we find TCF7L2 to mediate trans-acting variation selectively in midbrain neurons. Our dataset provides an extensive resource to study gene regulation in mesencephalon.

scholarly journals Single-nuclei chromatin profiling of ventral midbrain reveals cell identity transcription factors and cell-type-specific gene regulatory variation

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Yujuan Gui ◽  
Kamil Grzyb ◽  
Mélanie H. Thomas ◽  
Jochen Ohnmacht ◽  
Pierre Garcia ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Cell Types ◽  
Mouse Strains ◽  
Cell Type ◽  
Data Set ◽  
Cell Identity ◽  
Regulatory Variation ◽  
Ventral Midbrain ◽  
Cell Type Specific

Abstract Background Cell types in ventral midbrain are involved in diseases with variable genetic susceptibility, such as Parkinson’s disease and schizophrenia. Many genetic variants affect regulatory regions and alter gene expression in a cell-type-specific manner depending on the chromatin structure and accessibility. Results We report 20,658 single-nuclei chromatin accessibility profiles of ventral midbrain from two genetically and phenotypically distinct mouse strains. We distinguish ten cell types based on chromatin profiles and analysis of accessible regions controlling cell identity genes highlights cell-type-specific key transcription factors. Regulatory variation segregating the mouse strains manifests more on transcriptome than chromatin level. However, cell-type-level data reveals changes not captured at tissue level. To discover the scope and cell-type specificity of cis-acting variation in midbrain gene expression, we identify putative regulatory variants and show them to be enriched at differentially expressed loci. Finally, we find TCF7L2 to mediate trans-acting variation selectively in midbrain neurons. Conclusions Our data set provides an extensive resource to study gene regulation in mesencephalon and provides insights into control of cell identity in the midbrain and identifies cell-type-specific regulatory variation possibly underlying phenotypic and behavioural differences between mouse strains.

scholarly journals Myeloid lncRNA LOUP Mediates Opposing Regulatory Effects of RUNX1 and RUNX1-ETO in t(8;21) AML

Blood ◽  
2021 ◽  
Author(s):  
Bon Q Trinh ◽  
Simone Ummarino ◽  
Yanzhou Zhang ◽  
Alexander K Ebralidze ◽  
Mahmoud A Bassal ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Noncoding Rna ◽  
Gene Activation ◽  
Chromatin Accessibility ◽  
Specific Gene ◽  
Cell Type ◽  
Genome Wide ◽  
Therapeutic Development ◽  
Cell Type Specific ◽  
Regulatory Effects

The mechanism underlying cell type-specific gene induction conferred by ubiquitous transcription factors as well as disruptions caused by their chimeric derivatives in leukemia is not well understood. Here we investigate whether RNAs coordinate with transcription factors to drive myeloid gene transcription. In an integrated genome-wide approach surveying for gene loci exhibiting concurrent RNA- and DNA-interactions with the broadly expressed transcription factor RUNX1, we identified the long noncoding RNA LOUP. This myeloid-specific and polyadenylated lncRNA induces myeloid differentiation and inhibits cell growth, acting as a transcriptional inducer of the myeloid master regulator PU.1. Mechanistically, LOUP recruits RUNX1 to both the PU.1 enhancer and the promoter, leading to the formation of an active chromatin loop. In t(8;21) acute myeloid leukemia, wherein RUNX1 is fused to ETO, the resulting oncogenic fusion protein RUNX1-ETO limits chromatin accessibility at the LOUP locus, causing inhibition of LOUP and PU.1 expression. These findings highlight the important role of the interplay between cell type-specific RNAs and transcription factors as well as their oncogenic derivatives in modulating lineage-gene activation and raise the possibility that RNA regulators of transcription factors represent alternative targets for therapeutic development.

scholarly journals Myeloid lncRNA LOUP Mediates Opposing Regulatory Effects of RUNX1 and RUNX1-ETO in t(8;21) AML

2020 ◽  
Author(s):  
Bon Q. Trinh ◽  
Simone Ummarino ◽  
Alexander K. Ebralidze ◽  
Emiel van der Kouwe ◽  
Mahmoud A. Bassal ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Cell Growth ◽  
Noncoding Rna ◽  
Gene Activation ◽  
Chromatin Accessibility ◽  
Myeloid Differentiation ◽  
Specific Gene ◽  
List Type ◽  
Cell Type ◽  
Cell Type Specific

ABSTRACTThe mechanism underlying cell type-specific gene induction conferred by ubiquitous transcription factors as well as disruptions caused by their chimeric derivatives in leukemia is not well understood. Here we investigate whether RNAs coordinate with transcription factors to drive myeloid gene transcription. In an integrated genome-wide approach surveying for gene loci exhibiting concurrent RNA- and DNA-interactions with the broadly expressed transcription factor RUNX1, we identified the long noncoding RNA LOUP. This myeloid-specific and polyadenylated lncRNA induces myeloid differentiation and inhibits cell growth, acting as a transcriptional inducer of the myeloid master regulator PU.1. Mechanistically, LOUP recruits RUNX1 to both the PU.1 enhancer and the promoter, leading to the formation of an active chromatin loop. In t(8;21) acute myeloid leukemia, wherein RUNX1 is fused to ETO, the resulting oncogenic fusion protein RUNX1-ETO limits chromatin accessibility at the LOUP locus, causing inhibition of LOUP and PU.1 expression. These findings highlight the important role of the interplay between cell type-specific RNAs and transcription factors as well as their oncogenic derivatives in modulating lineage-gene activation and raise the possibility that RNA regulators of transcription factors represent alternative targets for therapeutic development.KEY POINTSlncRNA LOUP coordinates with RUNX1 to induces PU.1 long-range transcription, conferring myeloid differentiation and inhibiting cell growth.RUNX1-ETO limits chromatin accessibility at the LOUP locus, causing inhibition of LOUP and PU.1 expression in t(8;21) AML.

scholarly journals scTenifoldKnk: a machine learning workflow performing virtual knockout experiments on single-cell gene regulatory networks

2021 ◽  
Author(s):  
Daniel Osorio ◽  
Yan Zhong ◽  
Guanxun Li ◽  
Qian Xu ◽  
Andrew E. Hillhouse ◽  
...  
Keyword(s):  
Machine Learning ◽  
Single Cell ◽  
Gene Function ◽  
Specific Gene ◽  
Cell Type ◽  
Cell Technology ◽  
Cell Type Specific ◽  
Gene Regulatory ◽  
The Impact ◽  
Cell Gene

Gene knockout (KO) experiments are a proven approach for studying gene function. A typical KO experiment usually involves the phenotypic characterization of KO organisms. The recent advent of single-cell technology has greatly boosted the resolution of cellular phenotyping, providing unprecedented insights into cell-type-specific gene function. However, the use of single-cell technology in large-scale, systematic KO experiments is prohibitive due to the vast resources required. Here we present scTenifoldKnk, a machine learning workflow that performs virtual KO experiments using single-cell RNA sequencing (scRNA-seq) data. scTenifoldKnk first uses data from wild-type (WT) samples to construct a single-cell gene regulatory network (scGRN). Then, a gene is knocked out from the constructed scGRN by setting weights of the gene's outward edges to zeros. ScTenifoldKnk then compares this "pseudo-KO" scGRN with the original scGRN to identify differentially regulated (DR) genes. These DR genes, also called virtual-KO perturbed genes, are used to assess the impact of the gene KO and reveal the gene's function in analyzed cells. Using existing data sets, we demonstrate that the scTenifoldKnk analysis recapitulates the main findings of three real-animal KO experiments and confirms the functions of genes underlying three Mendelian diseases. We show the power of scTenifoldKnk as a predictive method to successfully predict the outcomes of two KO experiments that involve intestinal enterocytes in Ahr-/- mice and pancreatic islet cells in Malat1-/- mice, respectively. Finally, we demonstrate the use of scTenifoldKnk to perform systematic KO analyses, in which a large number of genes are virtually deleted, allowing gene functions to be revealed in a cell type-specific manner.

scholarly journals Pituitary Tumor Transforming Gene 1 orchestrates gene regulatory variation in mouse ventral midbrain during aging

2020 ◽  
Author(s):  
Yujuan Gui ◽  
Mélanie H. Thomas ◽  
Pierre Garcia ◽  
Mona Karout ◽  
Rashi Halder ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Dopaminergic Neurons ◽  
Inbred Mouse ◽  
Differentially Expressed ◽  
Molecular Networks ◽  
Mouse Strains ◽  
Rna Seq ◽  
Regulatory Variation ◽  
Ventral Midbrain ◽  
Gene Regulatory

AbstractBackgroundDopaminergic neurons in the midbrain are of particular interest due to their role in diseases such as Parkinson’s disease and schizophrenia. Genetic variation between individuals can affect the integrity and function of dopaminergic neurons but the DNA variants and molecular cascades modulating dopaminergic neurons and other cells types of ventral midbrain remain poorly defined. Three genetically diverse inbred mouse strains — C57BL/6J, A/J, and DBA/2J — differ significantly in their genomes (~7 million variants), motor and cognitive behavior, and susceptibility to neurotoxins.ResultsTo further dissect the underlying molecular networks responsible for these variable phenotypes, we generated RNA-seq and ChIP-seq data from ventral midbrains of the 3 mouse strains. We defined 1000–1200 transcripts that are differentially expressed among them. These widespread differences may be due to altered activity or expression of upstream transcription factors. Interestingly, transcription factors were significantly underrepresented among the differentially expressed genes, and only one TF, Pttg1, showed significant differences among all strains. The changes in Pttg1 expression were accompanied by consistent alterations in histone H3 lysine 4 trimethylation at Pttg1 transcription start site. The ventral midbrain transcriptome of three-month-old C57BL/6J congenic Pttg1-/- mutants was only modestly altered, but shifted towards that of A/J and DBA/2J in nine-month-old mice. Principle component analysis identified the genes underlying the transcriptome shift and deconvolution of these bulk RNA-seq changes using midbrain single cell RNA-seq data suggested that the changes were occurring in several different cell types, including neurons, oligodendrocytes, and astrocytes.ConclusionTaken together, our results show that Pttg1 contributes to gene regulatory variation between mouse strains and influences mouse midbrain transcriptome during aging.

scholarly journals Structural Changes in Transcriptional Regulatory Networks for Cell-type-specific Gene Expression During Hematopoiesis

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Regulatory Networks ◽  
Structural Changes ◽  
Specific Gene ◽  
Cell Type ◽  
Hematopoietic Stem ◽  
Specific Gene Expression ◽  
Transcriptional Regulatory ◽  
Cell Type Specific

Hematopoiesis is an extensively studied model system for cell differentiation. Cell-type-specific gene expression patterns are observed during hematopoiesis. Gene expression is governed by regulatory networks composed of cell-type-specific transcription factors. Resolving the transcriptional regulatory network for cell-type-specific gene expression provides a promising means of understanding the mechanisms underlying cell fate decisions. In this study, transcriptional regulatory networks in hematopoietic stem and progenitor cells were predicted based on gene expression profiles and distributions of transcription factor binding motifs in the promoter regions of cell-type-specific transcription factors. In particular, structural changes that occur when pluripotent stem cells progress to lineage-committed progenitors were evaluated. Marked changes in the regulatory circuit of transcription throughout the differentiation process could be elucidated by network analysis. Modular structures were a frequently described feature of biological networks observed in estimated networks. Within a module, most transcription factors were found to be regulated by a small number of regulators acting as downstream targets. Certain regulators within these modules coincide with known key regulators of hematopoietic cell differentiation. In addition to the modular structure, a twolayered structure was clearly observed in progenitor regulatory networks. Transcription factors could be distinctly divided into regulators within the regulatory layer and into targets in the output layer according to their degree of distribution. The restriction of mutual regulation between transcription factors was remarkable in that it allowed for alterations in network structures between hematopoietic stem cells and progenitors. Thus, using this approach, the relationships among transcription factors could be revealed by a reduction in mutual regulation to form a modular structure within the regulatory network

scholarly journals ANANSE: An enhancer network-based computational approach for predicting key transcription factors in cell fate determination

2020 ◽  
Author(s):  
Quan Xu ◽  
Georgios Georgiou ◽  
Gert Jan C. Veenstra ◽  
Huiqing Zhou ◽  
Simon J. van Heeringen
Keyword(s):  
Transcription Factors ◽  
Cell Fate ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Cell Types ◽  
Cell Fate Determination ◽  
Cell Type ◽  
Fate Determination ◽  
Cell Type Specific ◽  
Gene Regulatory

AbstractProper cell fate determination is largely orchestrated by complex gene regulatory networks centered around transcription factors. However, experimental elucidation of key transcription factors that drive cellular identity is currently often intractable. Here, we present ANANSE (ANalysis Algorithm for Networks Specified by Enhancers), a network-based method that exploits enhancer-encoded regulatory information to identify the key transcription factors in cell fate determination. As cell type-specific transcription factors predominantly bind to enhancers, we use regulatory networks based on enhancer properties to prioritize transcription factors. First, we predict genome-wide binding profiles of transcription factors in various cell types using enhancer activity and transcription factor binding motifs. Subsequently, applying these inferred binding profiles, we construct cell type-specific gene regulatory networks, and then predict key transcription factors controlling cell fate conversions using differential gene networks between cell types. This method outperforms existing approaches in correctly predicting major transcription factors previously identified to be sufficient for trans-differentiation. Finally, we apply ANANSE to define an atlas of key transcription factors in 18 normal human tissues. In conclusion, we present a ready-to-implement computational tool for efficient prediction of transcription factors in cell fate determination and to study transcription factor-mediated regulatory mechanisms. ANANSE is freely available at https://github.com/vanheeringen-lab/ANANSE.

scholarly journals The Interplay Between Chromatin Architecture and Lineage-Specific Transcription Factors and the Regulation of Rag Gene Expression

2021 ◽  
Vol 12 ◽  
Author(s):  
Kazuko Miyazaki ◽  
Masaki Miyazaki
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Cell Fate ◽  
Molecular Mechanisms ◽  
Current Knowledge ◽  
Specific Gene ◽  
Cell Type ◽  
Cell Fate Decisions ◽  
Chromatin Architecture ◽  
Cell Type Specific

Cell type-specific gene expression is driven through the interplay between lineage-specific transcription factors (TFs) and the chromatin architecture, such as topologically associating domains (TADs), and enhancer-promoter interactions. To elucidate the molecular mechanisms of the cell fate decisions and cell type-specific functions, it is important to understand the interplay between chromatin architectures and TFs. Among enhancers, super-enhancers (SEs) play key roles in establishing cell identity. Adaptive immunity depends on the RAG-mediated assembly of antigen recognition receptors. Hence, regulation of the Rag1 and Rag2 (Rag1/2) genes is a hallmark of adaptive lymphoid lineage commitment. Here, we review the current knowledge of 3D genome organization, SE formation, and Rag1/2 gene regulation during B cell and T cell differentiation.

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