scholarly journals Exon Shuffling Played a Decisive Role in the Evolution of the Genetic Toolkit for the Multicellular Body Plan of Metazoa

Genes ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 382
Author(s):  
Laszlo Patthy
Keyword(s):  
Transcription Factor ◽  
Decisive Role ◽  
Cell Types ◽  
Body Plan ◽  
Exon Shuffling ◽  
Body Parts ◽  
Multidomain Proteins ◽  
Cellular Phenotypes ◽  
Cell Matrix Interactions ◽  
Genetic Toolkit

Division of labor and establishment of the spatial pattern of different cell types of multicellular organisms require cell type-specific transcription factor modules that control cellular phenotypes and proteins that mediate the interactions of cells with other cells. Recent studies indicate that, although constituent protein domains of numerous components of the genetic toolkit of the multicellular body plan of Metazoa were present in the unicellular ancestor of animals, the repertoire of multidomain proteins that are indispensable for the arrangement of distinct body parts in a reproducible manner evolved only in Metazoa. We have shown that the majority of the multidomain proteins involved in cell–cell and cell–matrix interactions of Metazoa have been assembled by exon shuffling, but there is no evidence for a similar role of exon shuffling in the evolution of proteins of metazoan transcription factor modules. A possible explanation for this difference in the intracellular and intercellular toolkits is that evolution of the transcription factor modules preceded the burst of exon shuffling that led to the creation of the proteins controlling spatial patterning in Metazoa. This explanation is in harmony with the temporal-to-spatial transition hypothesis of multicellularity that proposes that cell differentiation may have predated spatial segregation of cell types in animal ancestors.

Use of F9 teratocarcinoma STEM cells to study cell-matrix interactions in the early mouse embryo

1992 ◽  
Vol 50 (1) ◽  
pp. 602-603
Author(s):  
Marc Lenburg ◽  
Rulang Jiang ◽  
Lengya Cheng ◽  
Laura Grabel
Keyword(s):  
Mouse Embryo ◽  
Inner Cell Mass ◽  
Cell Mass ◽  
Cell Types ◽  
Embryoid Bodies ◽  
F9 Cells ◽  
Cell Matrix ◽  
Matrix Interactions ◽  
Cell Matrix Interactions ◽  
F9 Teratocarcinoma

We are interested in defining the cell-cell and cell-matrix interactions that help direct the differentiation of extraembryonic endoderm in the peri-implantation mouse embryo. At the blastocyst stage the mouse embryo consists of an outer layer of trophectoderm surrounding the fluid-filled blastocoel cavity and an eccentrically located inner cell mass. On the free surface of the inner cell mass, facing the blastocoel cavity, a layer of primitive endoderm forms. Primitive endoderm then generates two distinct cell types; parietal endoderm (PE) which migrates along the inner surface of the trophectoderm and secretes large amounts of basement membrane components as well as tissue-type plasminogen activator (tPA), and visceral endoderm (VE), a columnar epithelial layer characterized by tight junctions, microvilli, and the synthesis and secretion of α-fetoprotein. As these events occur after implantation, we have turned to the F9 teratocarcinoma system as an in vitro model for examining the differentiation of these cell types. When F9 cells are treated in monolayer with retinoic acid plus cyclic-AMP, they differentiate into PE. In contrast, when F9 cells are treated in suspension with retinoic acid, they form embryoid bodies (EBs) which consist of an outer layer of VE and an inner core of undifferentiated stem cells. In addition, we have established that when VE containing embryoid bodies are plated on a fibronectin coated substrate, PE migrates onto the matrix and this interaction is inhibited by RGDS as well as antibodies directed against the β1 integrin subunit. This transition is accompanied by a significant increase in the level of tPA in the PE cells. Thus, the outgrowth system provides a spatially appropriate model for studying the differentiation and migration of PE from a VE precursor.

scholarly journals Transcription factor TFCP2L1 patterns cells in the mouse kidney collecting ducts

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Max Werth ◽  
Kai M Schmidt-Ott ◽  
Thomas Leete ◽  
Andong Qiu ◽  
Christian Hinze ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Notch Signaling ◽  
Salt Water ◽  
Collecting Duct ◽  
Cell Types ◽  
Nephron Segments ◽  
Collecting Ducts ◽  
Atpase Subunits ◽  
Central Characteristic ◽  
Salt And Pepper

Although most nephron segments contain one type of epithelial cell, the collecting ducts consists of at least two: intercalated (IC) and principal (PC) cells, which regulate acid-base and salt-water homeostasis, respectively. In adult kidneys, these cells are organized in rosettes suggesting functional interactions. Genetic studies in mouse revealed that transcription factor Tfcp2l1 coordinates IC and PC development. Tfcp2l1 induces the expression of IC specific genes, including specific H+-ATPase subunits and Jag1. Jag1 in turn, initiates Notch signaling in PCs but inhibits Notch signaling in ICs. Tfcp2l1 inactivation deletes ICs, whereas Jag1 inactivation results in the forfeiture of discrete IC and PC identities. Thus, Tfcp2l1 is a critical regulator of IC-PC patterning, acting cell-autonomously in ICs, and non-cell-autonomously in PCs. As a result, Tfcp2l1 regulates the diversification of cell types which is the central characteristic of 'salt and pepper' epithelia and distinguishes the collecting duct from all other nephron segments.

scholarly journals HOT or not: examining the basis of high-occupancy target regions

2017 ◽  
Author(s):  
Katarzyna Wreczycka ◽  
Vedran Franke ◽  
Bora Uyar ◽  
Ricardo Wurmus ◽  
Altuna Akalin
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Binding Sites ◽  
Cell Types ◽  
Data Sets ◽  
Gene Promoters ◽  
Quadruplex Dna ◽  
Golden Standard ◽  
Multiple Cell ◽  
Multiple Species

AbstractHigh-occupancy target (HOT) regions are the segments of the genome with unusually high number of transcription factor binding sites. These regions are observed in multiple species and thought to have biological importance due to high transcription factor occupancy. Furthermore, they coincide with house-keeping gene promoters and the associated genes are stably expressed across multiple cell types. Despite these features, HOT regions are solemnly defined using ChIP-seq experiments and shown to lack canonical motifs for transcription factors that are thought to be bound there. Although, ChIP-seq experiments are the golden standard for finding genome-wide binding sites of a protein, they are not noise free. Here, we show that HOT regions are likely to be ChIP-seq artifacts and they are similar to previously proposed “hyper-ChIPable” regions. Using ChIP-seq data sets for knocked-out transcription factors, we demonstrate presence of false positive signals on HOT regions. We observe sequence characteristics and genomic features that are discriminatory of HOT regions, such as GC/CpG-rich k-mers and enrichment of RNA-DNA hybrids (R-loops) and DNA tertiary structures (G-quadruplex DNA). The artificial ChIP-seq enrichment on HOT regions could be associated to these discriminatory features. Furthermore, we propose strategies to deal with such artifacts for the future ChIP-seq studies.

scholarly journals Virtual ChIP-seq: predicting transcription factor binding by learning from the transcriptome

2018 ◽  
Author(s):  
Mehran Karimzadeh ◽  
Michael M. Hoffman
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Binding Sites ◽  
Cell Types ◽  
Transcription Factor Binding ◽  
Regulatory Function ◽  
Factor Binding ◽  
Link Type ◽  
Genomic Regions ◽  
Factor Sequence

AbstractMotivationIdentifying transcription factor binding sites is the first step in pinpointing non-coding mutations that disrupt the regulatory function of transcription factors and promote disease. ChIP-seq is the most common method for identifying binding sites, but performing it on patient samples is hampered by the amount of available biological material and the cost of the experiment. Existing methods for computational prediction of regulatory elements primarily predict binding in genomic regions with sequence similarity to known transcription factor sequence preferences. This has limited efficacy since most binding sites do not resemble known transcription factor sequence motifs, and many transcription factors are not even sequence-specific.ResultsWe developed Virtual ChIP-seq, which predicts binding of individual transcription factors in new cell types using an artificial neural network that integrates ChIP-seq results from other cell types and chromatin accessibility data in the new cell type. Virtual ChIP-seq also uses learned associations between gene expression and transcription factor binding at specific genomic regions. This approach outperforms methods that predict TF binding solely based on sequence preference, pre-dicting binding for 36 transcription factors (Matthews correlation coefficient > 0.3).AvailabilityThe datasets we used for training and validation are available at https://virchip.hoffmanlab.org. We have deposited in Zenodo the current version of our software (http://doi.org/10.5281/zenodo.1066928), datasets (http://doi.org/10.5281/zenodo.823297), predictions for 36 transcription factors on Roadmap Epigenomics cell types (http://doi.org/10.5281/zenodo.1455759), and predictions in Cistrome as well as ENCODE-DREAM in vivo TF Binding Site Prediction Challenge (http://doi.org/10.5281/zenodo.1209308).

scholarly journals An Algorithm for Cellular Reprogramming

2017 ◽  
Author(s):  
Scott Ronquist ◽  
Geoff Patterson ◽  
Markus Brown ◽  
Stephen Lindsly ◽  
Haiming Chen ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Transcription Factor ◽  
Transcription Factors ◽  
Cell Biology ◽  
Human Fibroblasts ◽  
Cell Types ◽  
Approximate Model ◽  
Cellular Reprogramming ◽  
Biological Processes ◽  
Moderate Complexity

AbstractThe day we understand the time evolution of subcellular elements at a level of detail comparable to physical systems governed by Newton’s laws of motion seems far away. Even so, quantitative approaches to cellular dynamics add to our understanding of cell biology, providing data-guided frameworks that allow us to develop better predictions about, and methods for, control over specific biological processes and system-wide cell behavior. In this paper, we describe an approach to optimizing the use of transcription factors (TFs) in the context of cellular reprogramming. We construct an approximate model for the natural evolution of a cell cycle synchronized population of human fibroblasts, based on data obtained by sampling the expression of 22,083 genes at several time points along the cell cycle. In order to arrive at a model of moderate complexity, we cluster gene expression based on the division of the genome into topologically associating domains (TADs) and then model the dynamics of the TAD expression levels. Based on this dynamical model and known bioinformatics, such as transcription factor binding sites (TFBS) and functions, we develop a methodology for identifying the top transcription factor candidates for a specific cellular reprogramming task. The approach used is based on a device commonly used in optimal control. Our data-guided methodology identifies a number of transcription factors previously validated for reprogramming and/or natural differentiation. Our findings highlight the immense potential of dynamical models, mathematics, and data-guided methodologies for improving strategies for control over biological processes.Significance StatementReprogramming the human genome toward any desirable state is within reach; application of select transcription factors drives cell types toward different lineages in many settings. We introduce the concept of data-guided control in building a universal algorithm for directly reprogramming any human cell type into any other type. Our algorithm is based on time series genome transcription and architecture data and known regulatory activities of transcription factors, with natural dimension reduction using genome architectural features. Our algorithm predicts known reprogramming factors, top candidates for new settings, and ideal timing for application of transcription factors. This framework can be used to develop strategies for tissue regeneration, cancer cell reprogramming, and control of dynamical systems beyond cell biology.

scholarly journals Balancing dynamic tradeoffs drives cellular reprogramming

2018 ◽  
Author(s):  
Kimberley N. Babos ◽  
Kate E. Galloway ◽  
Kassandra Kisler ◽  
Madison Zitting ◽  
Yichen Li ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Cell Division ◽  
Motor Neuron ◽  
Molecular Mechanisms ◽  
Cell Types ◽  
Cellular Reprogramming ◽  
Cellular Event ◽  
Chemical Factors ◽  
Number Of Cells ◽  
Transcription And Replication

AbstractAlthough cellular reprogramming continues to generate new cell types, reprogramming remains a rare cellular event. The molecular mechanisms that limit reprogramming, particularly to somatic lineages, remain unclear. By examining fibroblast-to-motor neuron conversion, we identify a previously unappreciated dynamic between transcription and replication that determines reprogramming competency. Transcription factor overexpression forces most cells into states that are refractory to reprogramming and are characterized by either hypertranscription with little cell division, or hyperproliferation with low transcription. We identify genetic and chemical factors that dramatically increase the number of cells capable of both hypertranscription and hyperproliferation. Hypertranscribing, hyperproliferating cells reprogram at 100-fold higher, near-deterministic rates. We demonstrate that elevated topoisomerase expression endows cells with privileged reprogramming capacity, suggesting that biophysical constraints limit cellular reprogramming to rare events.

scholarly journals Analysis of human bone marrow with monoclonal antibodies.

1985 ◽  
Vol 33 (12) ◽  
pp. 1183-1189 ◽  
Author(s):  
P J Thurlow ◽  
L Kerrigan ◽  
R A Harris ◽  
I F McKenzie
Keyword(s):  
Bone Marrow ◽  
Monoclonal Antibodies ◽  
Specific Antigen ◽  
Cell Types ◽  
Diagnostic Value ◽  
Immunofluorescence Assay ◽  
Simple Method ◽  
Antigenic Phenotype ◽  
Beta 2 Microglobulin ◽  
Cellular Phenotypes

In order to study the antigenic phenotype of different hemopoietic cells, we used a series of monoclonal antibodies to investigate normal bone marrow in a standard immunofluorescence assay. The antibodies detected the following antigens: HLA-ABC, beta 2-microglobulin (beta 2m), HLA-DR (Ia), a lymphocyte subset and specific antigen (T and B) HuLy-m2, m3, T lymphocyte antigen (HuLy-m1), lymphocyte T200 antigen (HuLy-m4), a viral-associated antigen (HuLy-m5), and platelet-specific glycoproteins IIb-IIIa (HuPl-m1). The following results were obtained: (a) normoblasts were weakly HLA-ABC+, beta 2m+ and Ia-; all other lymphocyte and platelet antigens were not detected. (b) Myeloid cells at all stages of differentiation (promyelocytes, myelocytes, metamyelocytes, and neutrophils) were HLA-ABC+; beta 2m+; HuLy-m1-, m2-, m3+/- (20%), m4+, m5+/- (20%); HuPl-m1-; in addition, promyelocytes and myelocytes were Ia+ but neutrophils and metamyelocytes were Ia-. (c) Lymphocytes were HLA-ABC+, beta 2m+, Ia+/- (20-30%), HuLy-m1+/- (40-50%), m2+/- (60-70%), m3+, m4+, m5+; Pl-m1-. (d) Platelets and megakaryocytes were HLA-ABC+; beta 2m+; Ia-; HuLy-m1+-, m2-, m3-, m4-, m5-, HuPl-m1+, and the putative "megakaryocyte precursors" were HuPl-m1+, Ia-, HuLy-m1-. The different cell types in bone marrow could readily be distinguished, particularly cells of the myeloid series (Ia and HuLy-m4, m5), lymphocytes (Ia and HuLy-m1, m2, m3), and platelets and their precursor cells (HuPl-m1). This simple method of defining cellular phenotypes in bone marrow has demonstrated the practicality of using monoclonal antibodies to identify marrow cells and should be of diagnostic value.

The cellular transcription factor CREB corresponds to activating transcription factor 47 (ATF-47) and forms complexes with a group of polypeptides related to ATF-43

1990 ◽  
Vol 10 (12) ◽  
pp. 6192-6203
Author(s):  
H C Hurst ◽  
N Masson ◽  
N C Jones ◽  
K A Lee
Keyword(s):  
Transcription Factor ◽  
Dna Binding ◽  
Multigene Family ◽  
Protein Complexes ◽  
Heat Stability ◽  
Cell Types ◽  
Transcriptional Level ◽  
Sequence Motif ◽  
Cellular Transcription Factor ◽  
Activating Transcription Factor

Promoter elements containing the sequence motif CGTCA are important for a variety of inducible responses at the transcriptional level. Multiple cellular factors specifically bind to these elements and are encoded by a multigene family. Among these factors, polypeptides termed activating transcription factor 43 (ATF-43) and ATF-47 have been purified from HeLa cells and a factor referred to as cyclic AMP response element-binding protein (CREB) has been isolated from PC12 cells and rat brain. We demonstrated that CREB and ATF-47 are identical and that CREB and ATF-43 form protein-protein complexes. We also found that the cis requirements for stable DNA binding by ATF-43 and CREB are different. Using antibodies to ATF-43 we have identified a group of polypeptides (ATF-43) in the size range from 40 to 43 kDa. ATF-43 polypeptides are related by their reactivity with anti-ATF-43, DNA-binding specificity, complex formation with CREB, heat stability, and phosphorylation by protein kinase A. Certain cell types vary in their ATF-43 complement, suggesting that CREB activity is modulated in a cell-type-specific manner through interaction with ATF-43. ATF-43 polypeptides do not appear simply to correspond to the gene products of the ATF multigene family, suggesting that the size of the ATF family at the protein level is even larger than predicted from cDNA-cloning studies.

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