scholarly journals NKL Homeobox Gene VENTX Is Part of a Regulatory Network in Human Conventional Dendritic Cells

2021 ◽  
Vol 22 (11) ◽  
pp. 5902
Author(s):  
Stefan Nagel ◽  
Claudia Pommerenke ◽  
Corinna Meyer ◽  
Hans G. Drexler
Keyword(s):  
Dendritic Cells ◽  
Transcription Factors ◽  
Cell Lines ◽  
Expression Profiling ◽  
Regulatory Network ◽  
Homeobox Gene ◽  
Leukemia Cell Line ◽  
Specific Gene ◽  
Rna Seq ◽  
And Function

Recently, we documented a hematopoietic NKL-code mapping physiological expression patterns of NKL homeobox genes in human myelopoiesis including monocytes and their derived dendritic cells (DCs). Here, we enlarge this map to include normal NKL homeobox gene expressions in progenitor-derived DCs. Analysis of public gene expression profiling and RNA-seq datasets containing plasmacytoid and conventional dendritic cells (pDC and cDC) demonstrated HHEX activity in both entities while cDCs additionally expressed VENTX. The consequent aim of our study was to examine regulation and function of VENTX in DCs. We compared profiling data of VENTX-positive cDC and monocytes with VENTX-negative pDC and common myeloid progenitor entities and revealed several differentially expressed genes encoding transcription factors and pathway components, representing potential VENTX regulators. Screening of RNA-seq data for 100 leukemia/lymphoma cell lines identified prominent VENTX expression in an acute myelomonocytic leukemia cell line, MUTZ-3 containing inv(3)(q21q26) and t(12;22)(p13;q11) and representing a model for DC differentiation studies. Furthermore, extended gene analyses indicated that MUTZ-3 is associated with the subtype cDC2. In addition to analysis of public chromatin immune-precipitation data, subsequent knockdown experiments and modulations of signaling pathways in MUTZ-3 and control cell lines confirmed identified candidate transcription factors CEBPB, ETV6, EVI1, GATA2, IRF2, MN1, SPIB, and SPI1 and the CSF-, NOTCH-, and TNFa-pathways as VENTX regulators. Live-cell imaging analyses of MUTZ-3 cells treated for VENTX knockdown excluded impacts on apoptosis or induced alteration of differentiation-associated cell morphology. In contrast, target gene analysis performed by expression profiling of knockdown-treated MUTZ-3 cells revealed VENTX-mediated activation of several cDC-specific genes including CSFR1, EGR2, and MIR10A and inhibition of pDC-specific genes like RUNX2. Taken together, we added NKL homeobox gene activities for progenitor-derived DCs to the NKL-code, showing that VENTX is expressed in cDCs but not in pDCs and forms part of a cDC-specific gene regulatory network operating in DC differentiation and function.

scholarly journals Promoter G-quadruplexes and transcription factors cooperate to shape the cell type-specific transcriptome

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Sara Lago ◽  
Matteo Nadai ◽  
Filippo M. Cernilogar ◽  
Maryam Kazerani ◽  
Helena Domíniguez Moreno ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Binding Sites ◽  
Specific Gene ◽  
Open Chromatin ◽  
Rna Seq ◽  
Transcript Levels ◽  
Promoter Sequences ◽  
G Quadruplex ◽  
Cell Type Specific ◽  
Folding State

AbstractCell identity is maintained by activation of cell-specific gene programs, regulated by epigenetic marks, transcription factors and chromatin organization. DNA G-quadruplex (G4)-folded regions in cells were reported to be associated with either increased or decreased transcriptional activity. By G4-ChIP-seq/RNA-seq analysis on liposarcoma cells we confirmed that G4s in promoters are invariably associated with high transcription levels in open chromatin. Comparing G4 presence, location and transcript levels in liposarcoma cells to available data on keratinocytes, we showed that the same promoter sequences of the same genes in the two cell lines had different G4-folding state: high transcript levels consistently associated with G4-folding. Transcription factors AP-1 and SP1, whose binding sites were the most significantly represented in G4-folded sequences, coimmunoprecipitated with their G4-folded promoters. Thus, G4s and their associated transcription factors cooperate to determine cell-specific transcriptional programs, making G4s to strongly emerge as new epigenetic regulators of the transcription machinery.

Construction of an lncRNA–mRNA Co-Expression Regulatory Network Mediating Inflammation and Regeneration Following Acute Pancreatitis Injury

2021 ◽  
Author(s):  
Jing Wang ◽  
Tianjie Chen ◽  
Xiaohua Zhang ◽  
Shulei Zhao
Keyword(s):  
Acute Pancreatitis ◽  
Mrna Expression ◽  
Regulatory Network ◽  
Noncoding Rnas ◽  
Pancreatic Injury ◽  
Control Group ◽  
Rna Seq ◽  
Damage Repair ◽  
Injury And Repair ◽  
And Function

Abstract Long noncoding RNAs (lncRNAs) play important roles in the occurrence and development of many diseases and can be used as targets for diagnosis and treatment. However, the expression and function of lncRNAs in the injury and repair of acute pancreatitis (AP) are unclear. To decipher lncRNAs’ regulatory roles in AP, we reanalyzed an RNA-seq dataset of 24 pancreatic tissues, including those of normal control mice (BL), those 7 days after mild AP (D7), and those 14 days after mild AP (D14). The results showed significant differences in lncRNA and mRNA expression of D7/D14 groups compared with the control group. Co-expression analysis showed that differentially expressed (DE) lncRNAs were closely related to immunity- and inflammation-related pathways by trans-regulating mRNA expression. The lncRNA–mRNA network showed that the lncRNAs Dancer, Gmm20488, Terc, Snhg3, and Snhg20 were significantly correlated with AP pathogenesis. WGCNA and cis regulation analysis also showed that AP repair-associated lncRNAs were correlated with extracellular and inflammation-related genes, which affect the repair and regeneration of pancreatic injury after AP. In conclusion, the systemic dysregulation of lncRNAs is strongly involved in remodeling AP’s gene expression regulatory network, and the lncRNA–mRNA expression network could identify targets for AP treatment and damage repair.

scholarly journals The effect of neurogenin3 deficiency on pancreatic gene expression in embryonic mice

2006 ◽  
Vol 37 (2) ◽  
pp. 301-316 ◽  
Author(s):  
Andreas Petri ◽  
Jonas Ahnfelt-Rønne ◽  
Klaus Stensgaard Frederiksen ◽  
David George Edwards ◽  
Dennis Madsen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Microarray Analysis ◽  
Molecular Mechanisms ◽  
Homeobox Gene ◽  
Specific Gene ◽  
Wild Type ◽  
Specific Gene Expression ◽  
And Function ◽  
Deficient Mice

To understand the molecular mechanisms regulating pancreatic endocrine development and function, pancreatic gene expression was compared between Ngn3-deficient mice and littermate controls on embryonic days 13 and 15. Microarray analysis identified 504 genes with significant differences in expression. Fifty-two of these showed at least twofold reduction in Ngn3 knockouts compared to controls. Many of them were previously described to be involved in endocrine development and function. Among the genes not previously characterized were Rhomboid veinlet-like 4, genes involved in tetrahydrobiopterin biosynthesis and the Iroquois-type homeobox gene Irx1, the latter was selected for further investigation. In situ hybridisation demonstrated that two Iroquois genes, Irx1 and Irx2, were expressed in pancreatic endoderm of wild-type, but not Ngn3 mutant embryos. Furthermore, ectopic Ngn3 induced prominent Irx2 expression in chicken endoderm. Co-labelling established that Irx1 and Irx2 mRNA is located to glucagon-, but not insulin- or somatostatin-producing cells in mice and chicken. These data suggest that Irx1 and Irx2 serve an evolutionary conserved role in the regulation of α-cell-specific gene expression.

Gene expression profiling of microdissected Hodgkin Reed-Sternberg cells correlates with treatment outcome in classical Hodgkin lymphoma

Blood ◽  
2012 ◽  
Vol 120 (17) ◽  
pp. 3530-3540 ◽  
Author(s):  
Christian Steidl ◽  
Arjan Diepstra ◽  
Tang Lee ◽  
Fong Chun Chan ◽  
Pedro Farinha ◽  
...  
Keyword(s):  
Gene Expression ◽  
In Situ Hybridization ◽  
Treatment Failure ◽  
Gene Expression Profiling ◽  
Hodgkin Lymphoma ◽  
Cell Lines ◽  
Expression Profiling ◽  
Specific Gene ◽  
Classical Hodgkin Lymphoma

Abstract In classical Hodgkin lymphoma (CHL), 20%-30% of patients experience relapse or progressive disease after initial treatment. The pathogenesis and biology of treatment failure are still poorly understood, in part because the molecular phenotype of the rare malignant Hodgkin Reed-Sternberg (HRS) cells is difficult to study. Here we examined microdissected HRS cells from 29 CHL patients and 5 CHL-derived cell lines by gene expression profiling. We found significant overlap of HL-specific gene expression in primary HRS cells and HL cell lines, but also differences, including surface receptor signaling pathways. Using integrative analysis tools, we identified target genes with expression levels that significantly correlated with genomic copy-number changes in primary HRS cells. Furthermore, we found a macrophage-like signature in HRS cells that significantly correlated with treatment failure. CSF1R is a representative of this signature, and its expression was significantly associated with progression-free and overall survival in an independent set of 132 patients assessed by mRNA in situ hybridization. A combined score of CSF1R in situ hybridization and CD68 immunohistochemistry was an independent predictor for progression-free survival in multivariate analysis. In summary, our data reveal novel insights into the pathobiology of treatment failure and suggest CSF1R as a drug target of at-risk CHL.

scholarly journals TLR4 and C5aR crosstalk in dendritic cells induces a core regulatory network of RSK2, PI3Kβ, SGK1, and FOXO transcription factors

2017 ◽  
Vol 102 (4) ◽  
pp. 1035-1054 ◽  
Author(s):  
Anouk Zaal ◽  
Benjamin Nota ◽  
Kat S. Moore ◽  
Miranda Dieker ◽  
S. Marieke van Ham ◽  
...  
Keyword(s):  
Dendritic Cells ◽  
Transcription Factors ◽  
Regulatory Network ◽  
Foxo Transcription Factors

scholarly journals SCENIC: Single-cell regulatory network inference and clustering

2017 ◽  
Author(s):  
Sara Aibar ◽  
Carmen Bravo González-Blas ◽  
Thomas Moerman ◽  
Jasper Wouters ◽  
Vân Anh Huynh-Thu ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Single Cell ◽  
Regulatory Network ◽  
Network Inference ◽  
Target Genes ◽  
Single Cells ◽  
State Transitions ◽  
Rna Seq ◽  
Cell State ◽  
Gene Regulatory

AbstractSingle-cell RNA-seq allows building cell atlases of any given tissue and infer the dynamics of cellular state transitions during developmental or disease trajectories. Both the maintenance and transitions of cell states are encoded by regulatory programs in the genome sequence. However, this regulatory code has not yet been exploited to guide the identification of cellular states from single-cell RNA-seq data. Here we describe a computational resource, called SCENIC (Single Cell rEgulatory Network Inference and Clustering), for the simultaneous reconstruction of gene regulatory networks (GRNs) and the identification of stable cell states, using single-cell RNA-seq data. SCENIC outperforms existing approaches at the level of cell clustering and transcription factor identification. Importantly, we show that cell state identification based on GRNs is robust towards batch-effects and technical-biases. We applied SCENIC to a compendium of single-cell data from the mouse and human brain and demonstrate that the proper combinations of transcription factors, target genes, enhancers, and cell types can be identified. Moreover, we used SCENIC to map the cell state landscape in melanoma and identified a gene regulatory network underlying a proliferative melanoma state driven by MITF and STAT and a contrasting network controlling an invasive state governed by NFATC2 and NFIB. We further validated these predictions by showing that two transcription factors are predominantly expressed in early metastatic sentinel lymph nodes. In summary, SCENIC is the first method to analyze scRNA-seq data using a network-centric, rather than cell-centric approach. SCENIC is generic, easy to use, and flexible, and allows for the simultaneous tracing of genomic regulatory programs and the mapping of cellular identities emerging from these programs. Availability: SCENIC is available as an R workflow based on three new R/Bioconductor packages: GENIE3, RcisTarget and AUCell. As scalable alternative to GENIE3, we also provide GRNboost, paving the way towards the network analysis across millions of single cells.

scholarly journals Intracellular and Intercellular Gene Regulatory Network Inference From Time-Course Individual RNA-Seq

2021 ◽  
Vol 1 ◽  
Author(s):  
Makoto Kashima ◽  
Yuki Shida ◽  
Takashi Yamashiro ◽  
Hiromi Hirata ◽  
Hiroshi Kurosaka
Keyword(s):  
Transcription Factors ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Time Course ◽  
Network Inference ◽  
Molecular Mechanisms ◽  
Rna Seq ◽  
Gene Regulatory Network Inference ◽  
Gene Regulatory ◽  
Multicellular Organisms

Gene regulatory network (GRN) inference is an effective approach to understand the molecular mechanisms underlying biological events. Generally, GRN inference mainly targets intracellular regulatory relationships such as transcription factors and their associated targets. In multicellular organisms, there are both intracellular and intercellular regulatory mechanisms. Thus, we hypothesize that GRNs inferred from time-course individual (whole embryo) RNA-Seq during development can reveal intercellular regulatory relationships (signaling pathways) underlying the development. Here, we conducted time-course bulk RNA-Seq of individual mouse embryos during early development, followed by pseudo-time analysis and GRN inference. The results demonstrated that GRN inference from RNA-Seq with pseudo-time can be applied for individual bulk RNA-Seq similar to scRNA-Seq. Validation using an experimental-source-based database showed that our approach could significantly infer GRN for all transcription factors in the database. Furthermore, the inferred ligand-related and receptor-related downstream genes were significantly overlapped. Thus, the inferred GRN based on whole organism could include intercellular regulatory relationships, which cannot be inferred from scRNA-Seq based only on gene expression data. Overall, inferring GRN from time-course bulk RNA-Seq is an effective approach to understand the regulatory relationships underlying biological events in multicellular organisms.

UNCOVER CONTEXT-SPECIFIC GENE REGULATION BY TRANSCRIPTION FACTORS AND microRNAs USING BAYESIAN SPARSE NONNEGATIVE FACTOR REGRESSION

2012 ◽  
Vol 20 (04) ◽  
pp. 377-402
Author(s):  
JIA MENG ◽  
YIDONG CHEN ◽  
YUFEI HUANG
Keyword(s):  
Transcription Factors ◽  
Gene Regulatory Network ◽  
Gibbs Sampling ◽  
Regulatory Network ◽  
Messenger Rna ◽  
The Cancer Genome Atlas ◽  
Specific Gene ◽  
Data Types ◽  
Gene Regulatory ◽  
Factor Regression

In multicellular organisms, transcription factors (TFs) and microRNAs (miRNA) embody two largest families of molecules that modulate messenger RNA (mRNA) expressions through transcriptional and post-transcriptional regulations. While mRNA and microRNA expressions can be measured by microarray technique, the activities of transcription factors manifested by their protein expression are still difficult to observe, making it usually a complex problem to reconstruct a collaborative gene regulatory network (GRN) by TFs and miRNAs from expression data. In this paper, a novel Bayesian sparse non-negative factor regression (BSNFR) model is proposed for modeling the joint regulations of mRNAs by TFs and miRNAs and integration of multiple data types including gene expressions, microRNA expressions, TF targeted genes, and microRNA targets. Powered by a Gibbs sampling solution, BSNFR can infer both the TF/microRNA-mediated mRNA regulations and the unknown TF activities. Additionally, since BSNFR directly models the non-negative activities of TFs, it avoids the common problem of sign ambiguity with factor models and is capable of accurate prediction of the types (up or down) of regulations as well. BSNFR also includes a nonparametric Bayesian model for the latent factor activities, which enables the discovery of the clustering effects among samples due to (disease) subtypes. The proposed BSNFR model and the developed Gibbs sampling solution were validated on simulated systems and applied to real data of glioblastoma multiforme (GBM) patients from The Cancer Genome Atlas (TCGA). A GBM specific gene regulatory network by TFs and miRNAs was reconstructed. This GBM network includes 107 regulations recorded in the existing databases and 16 new regulations. Functional analysis suggests that the regulated genes are enriched in cell cycle and P53 pathways. In addition, BSNFR also identified 3 clusters among GBM patient samples, two of which demonstrates significant survival differences (p=0.004). Finally, the estimated TF activities imply that EGR-1 is significantly correlated with patient survivals (p=0.004) and may be used as a prognostic biomarker. The data and matlab code are available at: http://compgenomics.cbi.utsa.edu/BSNFR .

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