Complex transcriptional regulation and independent evolution of fungal-like traits in a relative of animals

Cell-type specification through differential genome regulation is a hallmark of complex multicellularity. However, it remains unclear how this process evolved during the transition from unicellular to multicellular organisms. To address this question, we investigated transcriptional dynamics in the ichthyosporean Creolimax fragrantissima, a relative of animals that undergoes coenocytic development. We find that Creolimax utilizes dynamic regulation of alternative splicing, long inter-genic non-coding RNAs and co-regulated gene modules associated with animal multicellularity in a cell-type specific manner. Moreover, our study suggests that the different cell types of the three closest animal relatives (ichthyosporeans, filastereans and choanoflagellates) are the product of lineage-specific innovations. Additionally, a proteomic survey of the secretome reveals adaptations to a fungal-like lifestyle. In summary, the diversity of cell types among protistan relatives of animals and their complex genome regulation demonstrates that the last unicellular ancestor of animals was already capable of elaborate specification of cell types.

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The complement of desmosomal plaque proteins in different cell types.

The Journal of Cell Biology ◽

10.1083/jcb.101.4.1442 ◽

1985 ◽

Vol 101 (4) ◽

pp. 1442-1454 ◽

Cited By ~ 122

Author(s):

P Cowin ◽

H P Kapprell ◽

W W Franke

Keyword(s):

Guinea Pig ◽

Cell Types ◽

Cardiac Cells ◽

Myocardial Tissue ◽

Cell Type ◽

Wide Range ◽

Specific Manner ◽

A Cell ◽

Cell Type Specific ◽

Different Cell Types

Desmosomal plaque proteins have been identified in immunoblotting and immunolocalization experiments on a wide range of cell types from several species, using a panel of monoclonal murine antibodies to desmoplakins I and II and a guinea pig antiserum to desmosomal band 5 protein. Specifically, we have taken advantage of the fact that certain antibodies react with both desmoplakins I and II, whereas others react only with desmoplakin I, indicating that desmoplakin I contains unique regions not present on the closely related desmoplakin II. While some of these antibodies recognize epitopes conserved between chick and man, others display a narrow species specificity. The results show that proteins whose size, charge, and biochemical behavior are very similar to those of desmoplakin I and band 5 protein of cow snout epidermis are present in all desmosomes examined. These include examples of simple and pseudostratified epithelia and myocardial tissue, in addition to those of stratified epithelia. In contrast, in immunoblotting experiments, we have detected desmoplakin II only among cells of stratified and pseudostratified epithelial tissues. This suggests that the desmosomal plaque structure varies in its complement of polypeptides in a cell-type specific manner. We conclude that the obligatory desmosomal plaque proteins, desmoplakin I and band 5 protein, are expressed in a coordinate fashion but independently from other differentiation programs of expression such as those specific for either epithelial or cardiac cells.

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Heterogeneity of Transcription Factor binding specificity models within and across cell lines

10.1101/028787 ◽

2015 ◽

Cited By ~ 1

Author(s):

Mahfuza Sharmin ◽

Hector Corrada Bravo ◽

Sridhar S. Hannenhalli

Keyword(s):

Expression Patterns ◽

Specific Interaction ◽

Cell Types ◽

Cell Type ◽

Binding Interaction ◽

Interaction Partners ◽

A Cell ◽

Cell Type Specific ◽

Different Cell Types

Complex gene expression patterns are mediated by binding of transcription factors (TF) to specific genomic loci. The in vivo occupancy of a TF is, in large part, determined by the TFs DNA binding interaction partners, motivating genomic context based models of TF occupancy. However, the approaches thus far have assumed a uniform binding model to explain genome wide bound sites for a TF in a cell-type and as such heterogeneity of TF occupancy models, and the extent to which binding rules underlying a TFs occupancy are shared across cell types, has not been investigated. Here, we develop an ensemble based approach (TRISECT) to identify heterogeneous binding rules of cell-type specific TF occupancy and analyze the inter-cell-type sharing of such rules. Comprehensive analysis of 23 TFs, each with ChIP-Seq data in 4-12 cell-types, shows that by explicitly capturing the heterogeneity of binding rules, TRISECT accurately identifies in vivo TF occupancy (93%) substantially improving upon previous methods. Importantly, many of the binding rules derived from individual cell-types are shared across cell-types and reveal distinct yet functionally coherent putative target genes in different cell-types. Closer inspection of the predicted cell-type-specific interaction partners provides insights into context-specific functional landscape of a TF. Together, our novel ensemble-based approach reveals, for the first time, a widespread heterogeneity of binding rules, comprising interaction partners within a cell-type, many of which nevertheless transcend cell-types. Notably, the putative targets of shared binding rules in different cell-types, while distinct, exhibit significant functional coherence.

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Single-cell transcriptomics and cell-specific proteomics reveals molecular signatures of sleep

10.1101/2020.12.18.423331 ◽

2020 ◽

Author(s):

Pawan K. Jha ◽

Utham K. Valekunja ◽

Sandipan Ray ◽

Mathieu Nollet ◽

Akhilesh B. Reddy

Keyword(s):

Single Cell ◽

Molecular Mechanisms ◽

Cell Types ◽

Brain Regions ◽

Specific Protein ◽

Cell Type ◽

Transcriptional Responses ◽

A Cell ◽

Cell Type Specific ◽

Different Cell Types

Every day, we sleep for a third of the day. Sleep is important for cognition, brain waste clearance, metabolism, and immune responses. The molecular mechanisms governing sleep are largely unknown. Here, we used a combination of single cell RNA sequencing and cell-type specific proteomics to interrogate the molecular underpinnings of sleep. Different cell types in three important brain regions for sleep (brainstem, cortex, and hypothalamus) exhibited diverse transcriptional responses to sleep need. Sleep restriction modulates astrocyte-neuron crosstalk and sleep need enhances expression of specific sets of transcription factors in different brain regions. In cortex, we also interrogated the proteome of two major cell types: astrocytes and neurons. Sleep deprivation differentially alters the expression of proteins in astrocytes and neurons. Similarly, phosphoproteomics revealed large shifts in cell-type specific protein phosphorylation. Our results indicate that sleep need regulates transcriptional, translational, and post-translational responses in a cell-specific manner.

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An analytical method for the identification of cell type-specific disease gene modules

Journal of Translational Medicine ◽

10.1186/s12967-020-02690-5 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Jinting Guan ◽

Yiping Lin ◽

Yang Wang ◽

Junchao Gao ◽

Guoli Ji

Keyword(s):

Disease Gene ◽

Gene Interaction ◽

Cell Types ◽

Autism Spectrum ◽

Specific Gene ◽

Cell Type ◽

Specific Disease ◽

Cell Type Specific ◽

Gene Modules ◽

Disease Associated Genes

Abstract Background Genome-wide association studies have identified genetic variants associated with the risk of brain-related diseases, such as neurological and psychiatric disorders, while the causal variants and the specific vulnerable cell types are often needed to be studied. Many disease-associated genes are expressed in multiple cell types of human brains, while the pathologic variants affect primarily specific cell types. We hypothesize a model in which what determines the manifestation of a disease in a cell type is the presence of disease module comprised of disease-associated genes, instead of individual genes. Therefore, it is essential to identify the presence/absence of disease gene modules in cells. Methods To characterize the cell type-specificity of brain-related diseases, we construct human brain cell type-specific gene interaction networks integrating human brain nucleus gene expression data with a referenced tissue-specific gene interaction network. Then from the cell type-specific gene interaction networks, we identify significant cell type-specific disease gene modules by performing statistical tests. Results Between neurons and glia cells, the constructed cell type-specific gene networks and their gene functions are distinct. Then we identify cell type-specific disease gene modules associated with autism spectrum disorder and find that different gene modules are formed and distinct gene functions may be dysregulated in different cells. We also study the similarity and dissimilarity in cell type-specific disease gene modules among autism spectrum disorder, schizophrenia and bipolar disorder. The functions of neurons-specific disease gene modules are associated with synapse for all three diseases, while those in glia cells are different. To facilitate the use of our method, we develop an R package, CtsDGM, for the identification of cell type-specific disease gene modules. Conclusions The results support our hypothesis that a disease manifests itself in a cell type through forming a statistically significant disease gene module. The identification of cell type-specific disease gene modules can promote the development of more targeted biomarkers and treatments for the disease. Our method can be applied for depicting the cell type heterogeneity of a given disease, and also for studying the similarity and dissimilarity between different disorders, providing new insights into the molecular mechanisms underlying the pathogenesis and progression of diseases.

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SeqEnhDL: sequence-based classification of cell type-specific enhancers using deep learning models

10.1101/2020.05.13.093997 ◽

2020 ◽

Author(s):

Yupeng Wang ◽

Rosario B. Jaime-Lara ◽

Abhrarup Roy ◽

Ying Sun ◽

Xinyue Liu ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Dna Sequences ◽

Cell Types ◽

Learning Models ◽

Cell Type ◽

Coding Sequences ◽

Sequence Features ◽

Cell Type Specific ◽

Different Cell Types

AbstractWe propose SeqEnhDL, a deep learning framework for classifying cell type-specific enhancers based on sequence features. DNA sequences of “strong enhancer” chromatin states in nine cell types from the ENCODE project were retrieved to build and test enhancer classifiers. For any DNA sequence, sequential k-mer (k=5, 7, 9 and 11) fold changes relative to randomly selected non-coding sequences were used as features for deep learning models. Three deep learning models were implemented, including multi-layer perceptron (MLP), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). All models in SeqEnhDL outperform state-of-the-art enhancer classifiers including gkm-SVM and DanQ, with regard to distinguishing cell type-specific enhancers from randomly selected non-coding sequences. Moreover, SeqEnhDL is able to directly discriminate enhancers from different cell types, which has not been achieved by other enhancer classifiers. Our analysis suggests that both enhancers and their tissue-specificity can be accurately identified according to their sequence features. SeqEnhDL is publicly available at https://github.com/wyp1125/SeqEnhDL.

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Epigenetic mechanisms mediating cell state transitions in chondrocytes

10.1101/2020.10.26.319822 ◽

2020 ◽

Author(s):

Manuela Wuelling ◽

Christoph Neu ◽

Andrea M. Thiesen ◽

Simo Kitanovski ◽

Yingying Cao ◽

...

Keyword(s):

Metabolic Pathways ◽

Cell Lineage ◽

Cell Types ◽

State Transitions ◽

Epigenetic Mechanisms ◽

Cell Type ◽

Transition Analysis ◽

Lineage Differentiation ◽

A Cell ◽

Cell Type Specific

AbstractEpigenetic modifications play critical roles in regulating cell lineage differentiation, but the epigenetic mechanisms guiding specific differentiation steps within a cell lineage have rarely been investigated. To decipher such mechanisms, we used the defined transition from proliferating (PC) into hypertrophic chondrocytes (HC) during endochondral ossification as a model. We established a map of activating and repressive histone modifications for each cell type. ChromHMM state transition analysis and Pareto-based integration of differential levels of mRNA and epigenetic marks revealed that differentiation associated gene repression is initiated by the addition of H3K27me3 to promoters still carrying substantial levels of activating marks. Moreover, the integrative analysis identified genes specifically expressed in cells undergoing the transition into hypertrophy.Investigation of enhancer profiles detected surprising differences in enhancer number, location, and transcription factor binding sites between the two closely related cell types. Furthermore, cell type-specific upregulation of gene expression was associated with a shift from low to high H3K27ac decoration. Pathway analysis identified PC-specific enhancers associated with chondrogenic genes, while HC-specific enhancers mainly control metabolic pathways linking epigenetic signature to biological functions.

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Inducible cell-specific mouse models for paired epigenetic and transcriptomic studies of microglia and astroglia

Communications Biology ◽

10.1038/s42003-020-01418-x ◽

2020 ◽

Vol 3 (1) ◽

Cited By ~ 1

Author(s):

Ana J. Chucair-Elliott ◽

Sarah R. Ocañas ◽

David R. Stanford ◽

Victor A. Ansere ◽

Kyla B. Buettner ◽

...

Keyword(s):

Gene Expression ◽

Affinity Purification ◽

Regulation Of Gene Expression ◽

Cell Types ◽

Tissue Level ◽

Cell Phenotype ◽

Specific Gene ◽

Cell Type ◽

A Cell ◽

Cell Type Specific

AbstractEpigenetic regulation of gene expression occurs in a cell type-specific manner. Current cell-type specific neuroepigenetic studies rely on cell sorting methods that can alter cell phenotype and introduce potential confounds. Here we demonstrate and validate a Nuclear Tagging and Translating Ribosome Affinity Purification (NuTRAP) approach for temporally controlled labeling and isolation of ribosomes and nuclei, and thus RNA and DNA, from specific central nervous system cell types. Analysis of gene expression and DNA modifications in astrocytes or microglia from the same animal demonstrates differential usage of DNA methylation and hydroxymethylation in CpG and non-CpG contexts that corresponds to cell type-specific gene expression. Application of this approach in LPS treated mice uncovers microglia-specific transcriptome and epigenome changes in inflammatory pathways that cannot be detected with tissue-level analysis. The NuTRAP model and the validation approaches presented can be applied to any brain cell type for which a cell type-specific cre is available.

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Single-molecule imaging of the transcription factor SRF reveals prolonged chromatin-binding kinetics upon cell stimulation

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1812734116 ◽

2018 ◽

Vol 116 (3) ◽

pp. 880-889 ◽

Cited By ~ 22

Author(s):

Lisa Hipp ◽

Judith Beer ◽

Oliver Kuchler ◽

Matthias Reisser ◽

Daniela Sinske ◽

...

Keyword(s):

Residence Time ◽

Single Molecule ◽

Map Kinases ◽

Serum Response Factor ◽

Cell Types ◽

Early Gene ◽

Cell Type ◽

Quiescent Cells ◽

Transcriptional Dynamics ◽

A Cell

Serum response factor (SRF) mediates immediate early gene (IEG) and cytoskeletal gene expression programs in almost any cell type. So far, SRF transcriptional dynamics have not been investigated at single-molecule resolution. We provide a study of single Halo-tagged SRF molecules in fibroblasts and primary neurons. In both cell types, individual binding events of SRF molecules segregated into three chromatin residence time regimes, short, intermediate, and long binding, indicating a cell type-independent SRF property. The chromatin residence time of the long bound fraction was up to 1 min in quiescent cells and significantly increased upon stimulation. Stimulation also enhanced the long bound SRF fraction at specific timepoints (20 and 60 min) in both cell types. These peaks correlated with activation of the SRF cofactors MRTF-A and MRTF-B (myocardin-related transcription factors). Interference with signaling pathways and cofactors demonstrated modulation of SRF chromatin occupancy by actin signaling, MAP kinases, and MRTFs.

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Profiling of Primary and Mature miRNA Expression in Atherosclerosis Associated Cell Types

Arteriosclerosis Thrombosis and Vascular Biology ◽

10.1161/atvbaha.121.315579 ◽

2021 ◽

Author(s):

Pierre R. Moreau ◽

Vanesa Tomas Bosch ◽

Maria Bouvy-Liivrand ◽

Kadri Õunap ◽

Tiit Örd ◽

...

Keyword(s):

Endothelial Cells ◽

Smooth Muscle ◽

Molecular Mechanisms ◽

Umbilical Vein ◽

Cell Types ◽

Integrative Approach ◽

Dependent Manner ◽

Cell Type ◽

A Cell ◽

Cell Type Specific

Objective: Atherosclerosis is the underlying cause of most cardiovascular diseases. The main cell types associated with disease progression in the vascular wall are endothelial cells, smooth muscle cells, and macrophages. Although their role in atherogenesis has been extensively described, molecular mechanisms underlying gene expression changes remain unknown. The objective of this study was to characterize microRNA (miRNA)-related regulatory mechanisms taking place in the aorta during atherosclerosis: Approach and Results: We analyzed the changes in primary human aortic endothelial cells and human umbilical vein endothelial cell, human aortic smooth muscle cell, and macrophages (CD14+) under various proatherogenic stimuli by integrating GRO-seq, miRNA-seq, and RNA-seq data. Despite the highly cell-type-specific expression of multi-variant pri-miRNAs, the majority of mature miRNAs were found to be common to all cell types and dominated by 2 to 5 abundant miRNA species. We demonstrate that transcription contributes significantly to the mature miRNA levels although this is dependent on miRNA stability. An analysis of miRNA effects in relation to target mRNA pools highlighted pathways and targets through which miRNAs could affect atherogenesis in a cell-type-dependent manner. Finally, we validate miR-100-5p as a cell-type specific regulator of inflammatory and HIPPO-YAP/TAZ-pathways. Conclusions: This integrative approach allowed us to characterize miRNA dynamics in response to a proatherogenic stimulus and identify potential mechanisms by which miRNAs affect atherogenesis in a cell-type-specific manner.

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Dynamic regulatory module networks for inference of cell type specific transcriptional networks

10.1101/2020.07.18.210328 ◽

2020 ◽

Author(s):

Alireza Fotuhi Siahpirani ◽

Deborah Chasman ◽

Morten Seirup ◽

Sara Knaack ◽

Rupa Sridharan ◽

...

Keyword(s):

Regulatory Network ◽

Regulatory Networks ◽

Network Dynamics ◽

Cell Types ◽

Small Sample ◽

Cell Type ◽

Regulatory Module ◽

A Cell ◽

Cell Type Specific ◽

Time Point

AbstractChanges in transcriptional regulatory networks can significantly alter cell fate. To gain insight into transcriptional dynamics, several studies have profiled transcriptomes and epigenomes at different stages of a developmental process. However, integrating these data across multiple cell types to infer cell type specific regulatory networks is a major challenge because of the small sample size for each time point. We present a novel approach, Dynamic Regulatory Module Networks (DRMNs), to model regulatory network dynamics on a cell lineage. DRMNs represent a cell type specific network by a set of expression modules and associated regulatory programs, and probabilistically model the transitions between cell types. DRMNs learn a cell type’s regulatory network from input expression and epigenomic profiles using multi-task learning to exploit cell type relatedness. We applied DRMNs to study regulatory network dynamics in two different developmental dynamic processes including cellular reprogramming and liver dedifferentiation. For both systems, DRMN predicted relevant regulators driving the major patterns of expression in each time point as well as regulators for transitioning gene sets that change their expression over time.

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