scholarly journals Reduced chromatin accessibility underlies gene expression differences in homologous chromosome arms of hexaploid wheat and diploidAegilops tauschii

2019 ◽  
Author(s):  
Fu-Hao Lu ◽  
Neil McKenzie ◽  
Laura-Jayne Gardiner ◽  
Ming-Cheng Luo ◽  
Anthony Hall ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hexaploid Wheat ◽  
Large Scale ◽  
Aegilops Tauschii ◽  
Wheat Chromosome ◽  
Chromatin Accessibility ◽  
Chromosome Arm ◽  
Elevated Expression ◽  
Differential Gene ◽  
Altered Gene

AbstractPolyploidy has been centrally important in driving the evolution of plants, and leads to alterations in gene expression that are thought to underlie the emergence of new traits. Despite the common occurrence of these global patterns of altered gene expression in polyploids, the mechanisms involved are not well understood. Using a precise framework of highly conserved syntenic genes on hexaploid wheat chromosome 3DL and its progenitor 3L chromosome arm of diploidAegilops tauschii, we show that 70% of these genes exhibited proportionally reduced gene expression, in which expression in the hexaploid context of the 3DL genes was approximately 40% of the levels observed in diploidAe. tauschii.Many genes showing elevated expression during later stages of grain development in wheat compared toAe. tauschii.Gene sequence and methylation differences accounted for only a few cases of differences in gene expression. In contrast, large scale patterns of reduced chromatin accessibility of genes in the hexaploid chromosome arm compared to its diploid progenitor were correlated with observed overall reduction in gene expression and differential gene expression. Therefore, that an overall reduction in accessible chromatin underlies the major differences in gene expression that results from polyploidization.

scholarly journals Reduced chromatin accessibility underlies gene expression differences in homologous chromosome arms of diploid Aegilops tauschii and hexaploid wheat

GigaScience ◽  
2020 ◽  
Vol 9 (6) ◽  
Author(s):  
Fu-Hao Lu ◽  
Neil McKenzie ◽  
Laura-Jayne Gardiner ◽  
Ming-Cheng Luo ◽  
Anthony Hall ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hexaploid Wheat ◽  
Aegilops Tauschii ◽  
Wheat Chromosome ◽  
Pilot Scale ◽  
Chromatin Accessibility ◽  
Diploid Progenitor ◽  
Genomic Changes ◽  
Chromosome Arm ◽  
Elevated Expression

Abstract Background Polyploidy is centrally important in the evolution and domestication of plants because it leads to major genomic changes, such as altered patterns of gene expression, which are thought to underlie the emergence of new traits. Despite the common occurrence of these globally altered patterns of gene expression in polyploids, the mechanisms involved are not well understood. Results Using a precisely defined framework of highly conserved syntenic genes on hexaploid wheat chromosome 3DL and its progenitor 3 L chromosome arm of diploid Aegilops tauschii, we show that 70% of these gene pairs exhibited proportionately reduced gene expression, in which expression in the hexaploid context of the 3DL genes was ∼40% of the levels observed in diploid Ae tauschii. Several genes showed elevated expression during the later stages of grain development in wheat compared with Ae tauschii. Gene sequence and methylation differences probably accounted for only a few cases of differences in gene expression. In contrast, chromosome-wide patterns of reduced chromatin accessibility of genes in the hexaploid chromosome arm compared with its diploid progenitor were correlated with both reduced gene expression and the imposition of new patterns of gene expression. Conclusions Our pilot-scale analyses show that chromatin compaction may orchestrate reduced gene expression levels in the hexaploid chromosome arm of wheat compared to its diploid progenitor chromosome arm.

scholarly journals M3S: a comprehensive model selection for multi-modal single-cell RNA sequencing data

2019 ◽  
Vol 20 (S24) ◽  
Author(s):  
Yu Zhang ◽  
Changlin Wan ◽  
Pengcheng Wang ◽  
Wennan Chang ◽  
Yan Huo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Model Selection ◽  
Statistical Model ◽  
Single Cell ◽  
Differential Gene Expression ◽  
Large Scale ◽  
Expression Profiles ◽  
Sequencing Data ◽  
Differential Gene ◽  
Selection Of

Abstract Background Various statistical models have been developed to model the single cell RNA-seq expression profiles, capture its multimodality, and conduct differential gene expression test. However, for expression data generated by different experimental design and platforms, there is currently lack of capability to determine the most proper statistical model. Results We developed an R package, namely Multi-Modal Model Selection (M3S), for gene-wise selection of the most proper multi-modality statistical model and downstream analysis, useful in a single-cell or large scale bulk tissue transcriptomic data. M3S is featured with (1) gene-wise selection of the most parsimonious model among 11 most commonly utilized ones, that can best fit the expression distribution of the gene, (2) parameter estimation of a selected model, and (3) differential gene expression test based on the selected model. Conclusion A comprehensive evaluation suggested that M3S can accurately capture the multimodality on simulated and real single cell data. An open source package and is available through GitHub at https://github.com/zy26/M3S.

The use of large-scale cDNA analysis to profile differential gene expression in KYSE 410 human esophageal cancer cells after irradiation

2000 ◽  
Vol 130 (3) ◽  
pp. 882-885 ◽  
Author(s):  
J. L. Sebastian ◽  
D. A. Rigberg ◽  
E. Shrivatsan ◽  
E. Revasova ◽  
D. M. McFadden ◽  
...  
Keyword(s):  
Gene Expression ◽  
Esophageal Cancer ◽  
Cancer Cells ◽  
Differential Gene Expression ◽  
Large Scale ◽  
Cdna Analysis ◽  
Differential Gene ◽  
Human Esophageal Cancer ◽  
Esophageal Cancer Cells

Protein Interaction Networks

2006 ◽  
Author(s):  
Soumya Raychaudhuri
Keyword(s):  
Gene Expression ◽  
Protein Interactions ◽  
Large Scale ◽  
Scientific Literature ◽  
Genetic Interaction ◽  
Genetic Interactions ◽  
Protein Protein Interactions ◽  
Expression Array ◽  
Biological Responses ◽  
Altered Gene

Genes and proteins interact with each other in many complicated ways. For example, proteins can interact directly with each other to form complexes or to modify each other so that their function is altered. Gene expression can be repressed or induced by transcription factor proteins. In addition there are countless other types of interactions. They constitute the key physiological steps in regulating or initiating biological responses. For example the binding of transcription factors to DNA triggers the assembly of the RNA assembly machinery that transcribes the mRNA that then is used as the template for protein production. Interactions such as these have been carefully elucidated and have been described in great detail in the scientific literature. Modern assays such as yeast-2-hybrid screens offer rapid means to ascertain many of the potential protein–protein interactions in an organism in a large-scale approach. In addition, other experimental modalities such as gene-expression array assays offer indirect clues about possible genetic interactions. One area that has been greatly explored in the bioinformatics literature is the possibility of learning genetic or protein networks, both from the scientific literature and from large-scale experimental data. Indeed, as we get to know more and more genes, it will become increasingly important to appreciate their interactions with each other. An understanding of the interactions between genes and proteins in a network allows for a meaningful global view of the organism and its physiology and is necessary to better understand biology. In this chapter we will explore methods to either (1) mine the scientific literature to identify documented genetic interactions and build networks of genes or (2) to confirm protein interactions that have been proposed experimentally. Our focus here is on direct physical protein–protein interactions, though the techniques described could be extended to any type of biological interaction between genes or proteins. There are multiple steps that must be addressed in identifying genetic interaction information contained within the text. After compiling the necessary documents and text, the first step is to identify gene and protein names in the text.

scholarly journals Gene expression profiling of pre-eclamptic placentae by RNA sequencing

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Tea Kaartokallio ◽  
◽  
Alejandra Cervera ◽  
Anjuska Kyllönen ◽  
Krista Laivuori ◽  
...  
Keyword(s):  
Gene Expression ◽  
Vascular Function ◽  
Placental Development ◽  
Phenotypic Characteristics ◽  
Altered Gene Expression ◽  
Differential Gene ◽  
Altered Gene ◽  
Microarray Studies ◽  
Functional Analyses ◽  
Differently Expressed Genes

Abstract Pre-eclampsia is a common and complex pregnancy disorder that often involves impaired placental development. In order to identify altered gene expression in pre-eclamptic placenta, we sequenced placental transcriptomes of nine pre-eclamptic and nine healthy pregnant women in pools of three. The differential gene expression was tested both by including all the pools in the analysis and by excluding some of the pools based on phenotypic characteristics. From these analyses, we identified altogether 53 differently expressed genes, a subset of which was validated by qPCR in 20 cases and 19 controls. Furthermore, we conducted pathway and functional analyses which revealed disturbed vascular function and immunological balance in pre-eclamptic placenta. Some of the genes identified in our study have been reported by numerous microarray studies (BHLHE40, FSTL3, HK2, HTRA4, LEP, PVRL4, SASH1, SIGLEC6), but many have been implicated in only few studies or have not previously been linked to pre-eclampsia (ARMS2, BTNL9, CCSAP, DIO2, FER1L4, HPSE, LOC100129345, LYN, MYO7B, NCMAP, NDRG1, NRIP1, PLIN2, SBSPON, SERPINB9, SH3BP5, TET3, TPBG, ZNF175). Several of the molecules produced by these genes may have a role in the pathogenesis of pre-eclampsia and some could qualify as biomarkers for prediction or detection of this pregnancy complication.

scholarly journals 281 Differential gene expression and chromatin accessibility reveal Th17 polarization in skin-homing T cells

2020 ◽  
Vol 140 (7) ◽  
pp. S34
Author(s):  
Z. Zhang ◽  
L.C. Tsoi ◽  
R. Nair ◽  
H. Zhang ◽  
P. Stuart ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Differential Gene Expression ◽  
Chromatin Accessibility ◽  
Skin Homing ◽  
Differential Gene

scholarly journals Large-scale analysis of differential gene expression in the hindlimb muscles and diaphragm of mdx mouse

2000 ◽  
Vol 1500 (1) ◽  
pp. 17-30 ◽  
Author(s):  
Andrei V Tkatchenko ◽  
Ginette Le Cam ◽  
Jean J Léger ◽  
Claude A Dechesne
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Large Scale ◽  
Mdx Mouse ◽  
Scale Analysis ◽  
Hindlimb Muscles ◽  
Large Scale Analysis ◽  
Differential Gene

scholarly journals FAK regulates IL-33 expression by controlling chromatin accessibility at c-Jun motifs

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Billie G. C. Griffith ◽  
Rosanna Upstill-Goddard ◽  
Holly Brunton ◽  
Graeme R. Grimes ◽  
Andrew V. Biankin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Focal Adhesions ◽  
Gene Promoter ◽  
Chromatin Accessibility ◽  
Dependent Manner ◽  
Rna Seq ◽  
Interleukin 33 ◽  
Control Gene Expression ◽  
Differential Gene

AbstractFocal adhesion kinase (FAK) localizes to focal adhesions and is overexpressed in many cancers. FAK can also translocate to the nucleus, where it binds to, and regulates, several transcription factors, including MBD2, p53 and IL-33, to control gene expression by unknown mechanisms. We have used ATAC-seq to reveal that FAK controls chromatin accessibility at a subset of regulated genes. Integration of ATAC-seq and RNA-seq data showed that FAK-dependent chromatin accessibility is linked to differential gene expression, including of the FAK-regulated cytokine and transcriptional regulator interleukin-33 (Il33), which controls anti-tumor immunity. Analysis of the accessibility peaks on the Il33 gene promoter/enhancer regions revealed sequences for several transcription factors, including ETS and AP-1 motifs, and we show that c-Jun, a component of AP-1, regulates Il33 gene expression by binding to its enhancer in a FAK kinase-dependent manner. This work provides the first demonstration that FAK controls transcription via chromatin accessibility, identifying a novel mechanism by which nuclear FAK regulates biologically important gene expression.

scholarly journals An essential role for MEF2C in the cortical response to loss of sleep

2020 ◽  
Author(s):  
Theresa E. Bjorness ◽  
Ashwinikumar Kulkarni ◽  
Volodymer Rybalchenko ◽  
Ayako Suzuki ◽  
Catherine Bridges ◽  
...  
Keyword(s):  
Gene Expression ◽  
Slow Wave ◽  
Transcriptional Activity ◽  
Slow Wave Sleep ◽  
Sleep Loss ◽  
Wave Activity ◽  
Altered Gene Expression ◽  
Mepsc Frequency ◽  
Differential Gene ◽  
Altered Gene

AbstractNeuronal activity and gene expression in response to the loss of sleep can provide a window into the enigma of sleep function. Sleep loss is associated with brain differential gene expression, an increase in pyramidal cell mEPSC frequency and amplitude, and a characteristic rebound and resolution of slow wave sleep-slow wave activity (SWS-SWA). However, the molecular mechanism(s) mediating the sleep loss response are not well understood. We show that sleep-loss regulates MEF2C phosphorylation, a key mechanism regulating MEF2C transcriptional activity, and that MEF2C function in postnatal excitatory forebrain neurons is required for the biological events in response to sleep loss. These include altered gene expression, the increase and recovery of synaptic strength, and the rebound and resolution of SWS-SWA, which implicate MEF2C as an essential regulator of sleep function.One Sentence SummaryMEF2C is critical to the response to sleep loss.

scholarly journals FAK regulates IL-33 expression by controlling chromatin accessibility at c-Jun motifs

2020 ◽  
Author(s):  
Billie G. C. Griffith ◽  
Rosanna Upstill-Goddard ◽  
Holly Brunton ◽  
Graeme R. Grimes ◽  
Andrew V. Biankin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Focal Adhesions ◽  
Gene Promoter ◽  
Chromatin Accessibility ◽  
Dependent Manner ◽  
Rna Seq ◽  
Interleukin 33 ◽  
Control Gene Expression ◽  
Differential Gene

AbstractFocal adhesion kinase (FAK) localizes to focal adhesions and is overexpressed in many cancers. FAK can also translocate to the nucleus, where it binds to, and regulates, several transcription factors, including MBD2, p53 and IL-33, to control gene expression by unknown mechanisms. We have used ATAC-seq to reveal that FAK controls chromatin accessibility at a subset of regulated genes. Integration of ATAC-seq and RNA-seq data showed that FAK-dependent chromatin accessibility is linked to differential gene expression, including of the FAK-regulated cytokine and transcriptional regulator interleukin-33 (Il33), which controls anti-tumor immunity. Analysis of the accessibility peaks on the Il33 gene promoter/enhancer regions revealed sequences for several transcription factors, including ETS and AP-1 motifs, and we show that c-Jun, a component of AP-1, regulates Il33 gene expression by binding to its enhancer in a FAK kinase-dependent manner. This work provides the first demonstration that FAK controls transcription via chromatin accessibility, identifying a novel mechanism by which nuclear FAK regulates biologically important gene expression.

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