scholarly journals Mechanisms of enhancer action: the known and the unknown

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Anil Panigrahi ◽  
Bert W. O’Malley
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Transcriptional Activation ◽  
Target Genes ◽  
Cell Types ◽  
Regulatory Sequences ◽  
Quantitative Expression ◽  
Full Complement ◽  
Differential Gene ◽  
Target Promoters

AbstractDifferential gene expression mechanisms ensure cellular differentiation and plasticity to shape ontogenetic and phylogenetic diversity of cell types. A key regulator of differential gene expression programs are the enhancers, the gene-distal cis-regulatory sequences that govern spatiotemporal and quantitative expression dynamics of target genes. Enhancers are widely believed to physically contact the target promoters to effect transcriptional activation. However, our understanding of the full complement of regulatory proteins and the definitive mechanics of enhancer action is incomplete. Here, we review recent findings to present some emerging concepts on enhancer action and also outline a set of outstanding questions.

scholarly journals Defining cell identity beyond the premise of differential gene expression

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Hani Jieun Kim ◽  
Patrick P. L. Tam ◽  
Pengyi Yang
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Cell Fate ◽  
Differential Gene Expression ◽  
Cell Types ◽  
Cell Identity ◽  
Gene Transcripts ◽  
Differential Gene ◽  
Biological Attributes

AbstractIdentifying genes that define cell identity is a requisite step for characterising cell types and cell states and predicting cell fate choices. By far, the most widely used approach for this task is based on differential expression (DE) of genes, whereby the shift of mean expression are used as the primary statistics for identifying gene transcripts that are specific to cell types and states. While DE-based methods are useful for pinpointing genes that discriminate cell types, their reliance on measuring difference in mean expression may not reflect the biological attributes of cell identity genes. Here, we highlight the quest for non-DE methods and provide an overview of these methods and their applications to identify genes that define cell identity and functionality.

scholarly journals Single cell analysis of the effects of developmental lead (Pb) exposure on the hippocampus

2019 ◽  
Author(s):  
Kelly M. Bakulski ◽  
John F. Dou ◽  
Robert C. Thompson ◽  
Christopher Lee ◽  
Lauren Y. Middleton ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Differential Gene Expression ◽  
Cell Types ◽  
Marker Genes ◽  
Cell Type ◽  
Cell Clusters ◽  
Differential Gene ◽  
Pb Exposure ◽  
And Behavior

AbstractBackgroundLead (Pb) exposure is ubiquitous and has permanent developmental effects on childhood intelligence and behavior and adulthood risk of dementia. The hippocampus is a key brain region involved in learning and memory, and its cellular composition is highly heterogeneous. Pb acts on the hippocampus by altering gene expression, but the cell type-specific responses are unknown.ObjectiveExamine the effects of perinatal Pb treatment on adult hippocampus gene expression, at the level of individual cells, in mice.MethodsIn mice perinatally exposed to control water (n=4) or a human physiologically-relevant level (32 ppm in maternal drinking water) of Pb (n=4), two weeks prior to mating through weaning, we tested for gene expression and cellular differences in the hippocampus at 5-months of age. Analysis was performed using single cell RNA-sequencing of 5,258 cells from the hippocampus by 10x Genomics Chromium to 1) test for gene expression differences averaged across all cells by treatment; 2) compare cell cluster composition by treatment; and 3) test for gene expression and pathway differences within cell clusters by treatment.ResultsGene expression patterns revealed 12 cell clusters in the hippocampus, mapping to major expected cell types (e.g. microglia, astrocytes, neurons, oligodendrocytes). Perinatal Pb treatment was associated with 12.4% more oligodendrocytes (P=4.4×10−21) in adult mice. Across all cells, differential gene expression analysis by Pb treatment revealed cluster marker genes. Within cell clusters, differential gene expression with Pb treatment (q<0.05) was observed in endothelial, microglial, pericyte, and astrocyte cells. Pathways up-regulated with Pb treatment were protein folding in microglia (P=3.4×10−9) and stress response in oligodendrocytes (P=3.2×10−5).ConclusionBulk tissue analysis may be confounded by changes in cell type composition and may obscure effects within vulnerable cell types. This study serves as a biological reference for future single cell studies of toxicant or neuronal complications, to ultimately characterize the molecular basis by which Pb influences cognition and behavior.

scholarly journals DNA Methylation May Restrict but Does Not Determine Differential Gene Expression at the Sgy/Tead2 Locus during Mouse Development

2004 ◽  
Vol 24 (5) ◽  
pp. 1968-1982 ◽  
Author(s):  
Kotaro J. Kaneko ◽  
Theo Rein ◽  
Zong-Sheng Guo ◽  
Keith Latham ◽  
Melvin L. DePamphilis
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Differential Gene Expression ◽  
Cell Lines ◽  
Regulatory Sequences ◽  
Mouse Development ◽  
Normal Cells ◽  
Established Cell Lines ◽  
Established Cell ◽  
Differential Gene

ABSTRACT Soggy (Sgy) and Tead2, two closely linked genes with CpG islands, were coordinately expressed in mouse preimplantation embryos and embryonic stem (ES) cells but were differentially expressed in differentiated cells. Analysis of established cell lines revealed that Sgy gene expression could be fully repressed by methylation of the Sgy promoter and that DNA methylation acted synergistically with chromatin deacetylation. Differential gene expression correlated with differential DNA methylation, resulting in sharp transitions from methylated to unmethylated DNA at the open promoter in both normal cells and tissues, as well as in established cell lines. However, neither promoter was methylated in normal cells and tissues even when its transcripts were undetectable. Moreover, the Sgy promoter remained unmethylated as Sgy expression was repressed during ES cell differentiation. Therefore, DNA methylation was not the primary determinant of Sgy/Tead2 expression. Nevertheless, Sgy expression was consistently restricted to basal levels whenever downstream regulatory sequences were methylated, suggesting that DNA methylation restricts but does not regulate differential gene expression during mouse development.

scholarly journals Genomic meta-analysis of the interplay between 3D chromatin organization and gene expression programs under basal and stress conditions

2018 ◽  
Author(s):  
Idan Nurick ◽  
Ron Shamir ◽  
Ran Elkon
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Cell Lines ◽  
Meta Analysis ◽  
Cell Types ◽  
Chromatin Organization ◽  
Cell Type ◽  
Differential Gene ◽  
Different Cell Types

AbstractBackgroundOur appreciation of the critical role of the 3D organization of the genome in gene regulation is steadily increasing. Recent 3C-based deep sequencing techniques elucidated a hierarchy of structures that underlie the spatial organization of the genome in the nucleus. At the top of this hierarchical organization are chromosomal territories and the megabase-scale A/B compartments that correlate with transcriptional activity within cells. Below them are the relatively cell-type invariant topologically associated domains (TADs), characterized by high frequency of physical contacts between loci within the same TAD and are assumed to function as regulatory units. Within TADs, chromatin loops bring enhancers and target promoters to close spatial proximity. Yet, we still have only rudimentary understanding how differences in chromatin organization between different cell types affect cell-type specific gene expression programs that are executed under basal and challenged conditions.ResultsHere, we carried out a large-scale meta-analysis that integrated Hi-C data from thirteen different cell lines and dozens of ChIP-seq and RNA-seq datasets measured on these cells, either under basal conditions or after treatment. Pairwise comparisons between cell lines demonstrated the strong association between modulation of A/B compartmentalization, differential gene expression and transcription factor (TF) binding events. Furthermore, integrating the analysis of transcriptomes of different cell lines in response to various challenges, we show that 3D organization of cells under basal conditions constrains not only gene expression programs and TF binding profiles that are active under the basal condition but also those induced in response to treatment.ConclusionsOur results further elucidate the role of dynamic genome organization in regulation of differential gene expression between different cell types, and indicate the impact of intra-TAD enhancer-promoter interactions that are established under basal conditions on both the basal and treatment-induced gene expression programs.

Assessment of differential gene expression in vestibular epithelial cell types using microarray analysis

2005 ◽  
Vol 133 (1) ◽  
pp. 19-36 ◽  
Author(s):  
Ricardo Cristobal ◽  
P. Ashley Wackym ◽  
Joseph A. Cioffi ◽  
Christy B. Erbe ◽  
Joseph P. Roche ◽  
...  
Keyword(s):  
Gene Expression ◽  
Epithelial Cell ◽  
Differential Gene Expression ◽  
Microarray Analysis ◽  
Cell Types ◽  
Differential Gene

scholarly journals Differential gene expression, GATA1 target genes, and the chemotherapy sensitivity of Down syndrome megakaryocytic leukemia

Blood ◽  
2006 ◽  
Vol 107 (4) ◽  
pp. 1570-1581 ◽  
Author(s):  
Yubin Ge ◽  
Alan A. Dombkowski ◽  
Katherine M. LaFiura ◽  
Dana Tatman ◽  
Ravikiran S. Yedidi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bone Marrow ◽  
Down Syndrome ◽  
Differential Gene Expression ◽  
Stromal Cells ◽  
Bone Marrow Stromal Cells ◽  
Target Genes ◽  
Marrow Stromal Cells ◽  
Full Length ◽  
Differential Gene

Children with Down syndrome (DS) with acute megakaryocytic leukemia (AMkL) have very high survival rates compared with non-DS AMkL patients. Somatic mutations identified in the X-linked transcription factor gene, GATA1, in essentially all DS AMkL cases result in the synthesis of a shorter (40 kDa) protein (GATA1s) with altered transactivation activity and may lead to altered expression of GATA1 target genes. Using the Affymetrix U133A microarray chip, we identified 551 differentially expressed genes between DS and non-DS AMkL samples. Transcripts for the bone marrow stromal-cell antigen 2 (BST2) gene, encoding a transmembrane glycoprotein potentially involved in interactions between leukemia cells and bone marrow stromal cells, were 7.3-fold higher (validated by real-time polymerase chain reaction) in the non-DS compared with the DS group. Additional studies confirmed GATA1 protein binding and transactivation of the BST2 promoter; however, stimulation of BST2 promoter activity by GATA1s was substantially reduced compared with the full-length GATA1. CMK sublines, transfected with the BST2 cDNA and incubated with HS-5 bone marrow stromal cells, exhibited up to 1.7-fold reduced cytosine arabinoside (ara-C)-induced apoptosis, compared with mock-transfected cells. Our results demonstrate that genes that account for differences in survival between DS and non-DS AMkL cases may be identified by microarray analysis and that differential gene expression may reflect relative transactivation capacities of the GATA1s and full-length GATA1 proteins.

scholarly journals Cell type-specific differential expression for spatial transcriptomics

2021 ◽  
Author(s):  
Dylan M Cable ◽  
Evan Murray ◽  
Vignesh Shanmugam ◽  
Simon Zhang ◽  
Michael Z Diao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Differential Gene Expression ◽  
Cell Types ◽  
Specific Cell ◽  
Cell Type ◽  
Unified Framework ◽  
Wide Range ◽  
Cell Type Specific ◽  
Differential Gene

Spatial transcriptomics enables spatially resolved gene expression measurements at near single-cell resolution. There is a pressing need for computational tools to enable the detection of genes that are differentially expressed across tissue context for cell types of interest. However, changes in cell type composition across space and the fact that measurement units often detect transcripts from more than one cell type introduce complex statistical challenges. Here, we introduce a statistical method, Robust Cell Type Differential Expression (RCTDE), that estimates cell type-specific patterns of differential gene expression while accounting for localization of other cell types. By using general log-linear models, we provide a unified framework for defining and identifying gene expression changes for a wide-range of relevant contexts: changes due to pathology, anatomical regions, physical proximity to specific cell types, and cellular microenvironment. Furthermore, our approach enables statistical inference across multiple samples and replicates when such data is available. We demonstrate, through simulations and validation experiments on Slide-seq and MERFISH datasets, that our approach accurately identifies cell type-specific differential gene expression and provides valid uncertainty quantification. Lastly, we apply our method to characterize spatially-localized tissue changes in the context of disease. In an Alzheimer's mouse model Slide-seq dataset, we identify plaque-dependent patterns of cellular immune activity. We also find a putative interaction between tumor cells and myeloid immune cells in a Slide-seq tumor dataset. We make our RCTDE method publicly available as part of the open source R package https://github.com/dmcable/spacexr.

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