scholarly journals RNA-seq Analysis of the Functional Compartments within the Rat Placentation Site

Endocrinology ◽  
2012 ◽  
Vol 153 (4) ◽  
pp. 1999-2011 ◽  
Author(s):  
Kartik Shankar ◽  
Ying Zhong ◽  
Ping Kang ◽  
Michael L. Blackburn ◽  
Michael J. Soares ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Massively Parallel Sequencing ◽  
Expression Profiles ◽  
Hormone Secretion ◽  
Direct Interaction ◽  
Late Gestation ◽  
Rna Seq ◽  
Specific Expression ◽  
Metrial Gland

The rat placentation site is distinctly organized into interacting zones, the so-called labyrinth, junctional, and metrial gland compartments. These zones house unique cell populations equipped to undertake myriad prescribed functions including transport, hormonal responses, and immune interactions. Although much is known about the genesis of these cell types and specific markers that characterize each zone, a detailed global overview of gene expression in the three zones is absent. In this report, we used massively parallel sequencing (RNA-seq) to assess mRNA expression profiles and generated transcriptomic maps for each zone of the late-gestation rat placentation site (18.5 d postcoitum). Analysis of expression profiles revealed that each compartment expressed a unique signature, characterized by biological processes specific to the zone. Transport and vasculature-related processes predominated in the labyrinth, hormone secretion in the junctional, and immune interactions in the metrial gland. Furthermore, our analysis identified approximately 4000 differentially expressed genes within the zones. Using k-means clustering, we identified transcription factors with highest expression in either labyrinth, junctional, or metrial gland. Direct interaction (pathway) analysis revealed unique transcription factor networks operating in each compartment. The site-specific expression of 27 transcription factors in the three zones was ascertained via quantitative PCR and protein expression of six transcription factors was confirmed by immunohistochemistry. Finally, we elucidated the expression of key developmentally important families (Sox, GATA, Fox, Wnt, Tead, and IGF/IGFBP) in the placentation site to reveal novel expression of these several factors. The present dataset provides a novel resource to understand zonal gene expression and function in the placenta.

scholarly journals A computational method for direct imputation of cell type-specific expression profiles and cellular compositions from bulk-tissue RNA-Seq in brain disorders

2020 ◽  
Author(s):  
Abolfazl Doostparast Torshizi ◽  
Jubao Duan ◽  
Kai Wang
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Complex Diseases ◽  
Specific Gene ◽  
Cellular Composition ◽  
Rna Seq ◽  
Cell Type ◽  
Specific Expression ◽  
Cell Type Specific Expression ◽  
Cell Type Specific

AbstractThe importance of cell type-specific gene expression in disease-relevant tissues is increasingly recognized in genetic studies of complex diseases. However, the vast majority of gene expression studies are conducted on bulk tissues, necessitating computational approaches to infer biological insights on cell type-specific contribution to diseases. Several computational methods are available for cell type deconvolution (that is, inference of cellular composition) from bulk RNA-Seq data, but cannot impute cell type-specific expression profiles. We hypothesize that with external prior information such as single cell RNA-seq (scRNA-seq) and population-wide expression profiles, it can be a computationally tractable and identifiable to estimate both cellular composition and cell type-specific expression from bulk RNA-Seq data. Here we introduce CellR, which addresses cross-individual gene expression variations by employing genome-wide tissue-wise expression signatures from GTEx to adjust the weights of cell-specific gene markers. It then transforms the deconvolution problem into a linear programming model while taking into account inter/intra cellular correlations, and uses a multi-variate stochastic search algorithm to estimate the expression level of each gene in each cell type. Extensive analyses on several complex diseases such as schizophrenia, Alzheimer’s disease, Huntington’s disease, and type 2 diabetes validated efficiency of CellR, while revealing how specific cell types contribute to different diseases. We conducted numerical simulations on human cerebellum to generate pseudo-bulk RNA-seq data and demonstrated its efficiency in inferring cell-specific expression profiles. Moreover, we inferred cell-specific expression levels from bulk RNA-seq data on schizophrenia and computed differentially expressed genes within certain cell types. Using predicted gene expression profile on excitatory neurons, we were able to reproduce our recently published findings on TCF4 being a master regulator in schizophrenia and showed how this gene and its targets are enriched in excitatory neurons. In summary, CellR compares favorably (both accuracy and stability of inference) against competing approaches on inferring cellular composition from bulk RNA-seq data, but also allows direct imputation of cell type-specific gene expression, opening new doors to re-analyze gene expression data on bulk tissues in complex diseases.

Transcriptome profiling of muscle by RNA-Seq reveals significant differences in digital gene expression profiling between Angus and Luxi cattle

2015 ◽  
Vol 55 (9) ◽  
pp. 1172 ◽  
Author(s):  
G. F. Liu ◽  
H. J. Cheng ◽  
W. You ◽  
E. L. Song ◽  
X. M. Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Beef Cattle ◽  
Growth And Development ◽  
Massively Parallel Sequencing ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Digital Gene Expression ◽  
Transcriptome Profiling ◽  
Rna Seq ◽  
Angus Cattle

The development of massively parallel sequencing technologies enables the sequencing of total cDNA to identify unigene expression and to discover novel regions of transcription. Here, we report the first use of RNA sequencing (RNA-Seq) to find the digital gene expression profiles (DGEs) associated with the growth and development of muscle in Chinese Luxi and Angus beef cattle. More than 9 243 921 clean reads were found in samples of muscle tissue. We found 232 DGEs between Luxi cattle and Angus cattle (false discovery ratio ≤0.001 and |log2 ratio| ≥1). Among the DGEs, we determined that 147 genes were downregulated and 85 genes were upregulated. Gene Ontology and KEGG Pathway analyses were performed to analyse the biological role of the DGEs and determine their contribution to the differences seen in muscle growth and development between local Chinese Luxi cattle and the introduced Angus cattle. The results suggest that RNA-Seq is a useful tool for predicting differences in gene expression between Luxi and Angus beef cattle; moreover, our results provides unprecedented resolution of mRNAs that are expressed across the two breeds.

scholarly journals A computational method for direct imputation of cell type-specific expression profiles and cellular compositions from bulk-tissue RNA-Seq in brain disorders

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Abolfazl Doostparast Torshizi ◽  
Jubao Duan ◽  
Kai Wang
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Complex Diseases ◽  
Specific Gene ◽  
Cellular Composition ◽  
Rna Seq ◽  
Cell Type ◽  
Specific Expression ◽  
Cell Type Specific Expression ◽  
Cell Type Specific

Abstract The importance of cell type-specific gene expression in disease-relevant tissues is increasingly recognized in genetic studies of complex diseases. However, most gene expression studies are conducted on bulk tissues, without examining cell type-specific expression profiles. Several computational methods are available for cell type deconvolution (i.e. inference of cellular composition) from bulk RNA-Seq data, but few of them impute cell type-specific expression profiles. We hypothesize that with external prior information such as single cell RNA-seq and population-wide expression profiles, it can be computationally tractable to estimate both cellular composition and cell type-specific expression from bulk RNA-Seq data. Here we introduce CellR, which addresses cross-individual gene expression variations to adjust the weights of cell-specific gene markers. It then transforms the deconvolution problem into a linear programming model while taking into account inter/intra cellular correlations and uses a multi-variate stochastic search algorithm to estimate the cell type-specific expression profiles. Analyses on several complex diseases such as schizophrenia, Alzheimer’s disease, Huntington’s disease and type 2 diabetes validated the efficiency of CellR, while revealing how specific cell types contribute to different diseases. In summary, CellR compares favorably against competing approaches, enabling cell type-specific re-analysis of gene expression data on bulk tissues in complex diseases.

scholarly journals Transcriptomal dissection of soybean circadian rhythmicity in two geographically, phenotypically and genetically distinct cultivars

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yanlei Yue ◽  
Ze Jiang ◽  
Enoch Sapey ◽  
Tingting Wu ◽  
Shi Sun ◽  
...  
Keyword(s):  
Gene Expression ◽  
Circadian Clock ◽  
Chromosome Structure ◽  
Clock Genes ◽  
Expression Profiles ◽  
Circadian Rhythmicity ◽  
Rna Seq ◽  
Night Time ◽  
Time Points ◽  
Circadian Clock Genes

Abstract Background In soybean, some circadian clock genes have been identified as loci for maturity traits. However, the effects of these genes on soybean circadian rhythmicity and their impacts on maturity are unclear. Results We used two geographically, phenotypically and genetically distinct cultivars, conventional juvenile Zhonghuang 24 (with functional J/GmELF3a, a homolog of the circadian clock indispensable component EARLY FLOWERING 3) and long juvenile Huaxia 3 (with dysfunctional j/Gmelf3a) to dissect the soybean circadian clock with time-series transcriptomal RNA-Seq analysis of unifoliate leaves on a day scale. The results showed that several known circadian clock components, including RVE1, GI, LUX and TOC1, phase differently in soybean than in Arabidopsis, demonstrating that the soybean circadian clock is obviously different from the canonical model in Arabidopsis. In contrast to the observation that ELF3 dysfunction results in clock arrhythmia in Arabidopsis, the circadian clock is conserved in soybean regardless of the functional status of J/GmELF3a. Soybean exhibits a circadian rhythmicity in both gene expression and alternative splicing. Genes can be grouped into six clusters, C1-C6, with different expression profiles. Many more genes are grouped into the night clusters (C4-C6) than in the day cluster (C2), showing that night is essential for gene expression and regulation. Moreover, soybean chromosomes are activated with a circadian rhythmicity, indicating that high-order chromosome structure might impact circadian rhythmicity. Interestingly, night time points were clustered in one group, while day time points were separated into two groups, morning and afternoon, demonstrating that morning and afternoon are representative of different environments for soybean growth and development. However, no genes were consistently differentially expressed over different time-points, indicating that it is necessary to perform a circadian rhythmicity analysis to more thoroughly dissect the function of a gene. Moreover, the analysis of the circadian rhythmicity of the GmFT family showed that GmELF3a might phase- and amplitude-modulate the GmFT family to regulate the juvenility and maturity traits of soybean. Conclusions These results and the resultant RNA-seq data should be helpful in understanding the soybean circadian clock and elucidating the connection between the circadian clock and soybean maturity.

scholarly journals Single-nucleus RNA-seq and FISH identify coordinated transcriptional activity in mammalian myofibers

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Matthieu Dos Santos ◽  
Stéphanie Backer ◽  
Benjamin Saintpierre ◽  
Brigitte Izac ◽  
Muriel Andrieu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Skeletal Muscle ◽  
Transcription Factors ◽  
Neuromuscular Junction ◽  
Heavy Chain ◽  
Transcriptional Activity ◽  
Rna Seq ◽  
Hybrid Fibers ◽  
Adult Mice ◽  
Single Nucleus

Abstract Skeletal muscle fibers are large syncytia but it is currently unknown whether gene expression is coordinately regulated in their numerous nuclei. Here we show by snRNA-seq and snATAC-seq that slow, fast, myotendinous and neuromuscular junction myonuclei each have different transcriptional programs, associated with distinct chromatin states and combinations of transcription factors. In adult mice, identified myofiber types predominantly express either a slow or one of the three fast isoforms of Myosin heavy chain (MYH) proteins, while a small number of hybrid fibers can express more than one MYH. By snRNA-seq and FISH, we show that the majority of myonuclei within a myofiber are synchronized, coordinately expressing only one fast Myh isoform with a preferential panel of muscle-specific genes. Importantly, this coordination of expression occurs early during post-natal development and depends on innervation. These findings highlight a previously undefined mechanism of coordination of gene expression in a syncytium.

scholarly journals HIV-1 Infection Transcriptomics: Meta-Analysis of CD4+ T Cells Gene Expression Profiles

Viruses ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 244 ◽  
Author(s):  
Antonio Victor Campos Coelho ◽  
Rossella Gratton ◽  
João Paulo Britto de Melo ◽  
José Leandro Andrade-Santos ◽  
Rafael Lima Guimarães ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Differentially Expressed Genes ◽  
Cd4 T Cells ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Infected Cells ◽  
Hiv 1

HIV-1 infection elicits a complex dynamic of the expression various host genes. High throughput sequencing added an expressive amount of information regarding HIV-1 infections and pathogenesis. RNA sequencing (RNA-Seq) is currently the tool of choice to investigate gene expression in a several range of experimental setting. This study aims at performing a meta-analysis of RNA-Seq expression profiles in samples of HIV-1 infected CD4+ T cells compared to uninfected cells to assess consistently differentially expressed genes in the context of HIV-1 infection. We selected two studies (22 samples: 15 experimentally infected and 7 mock-infected). We found 208 differentially expressed genes in infected cells when compared to uninfected/mock-infected cells. This result had moderate overlap when compared to previous studies of HIV-1 infection transcriptomics, but we identified 64 genes already known to interact with HIV-1 according to the HIV-1 Human Interaction Database. A gene ontology (GO) analysis revealed enrichment of several pathways involved in immune response, cell adhesion, cell migration, inflammation, apoptosis, Wnt, Notch and ERK/MAPK signaling.

scholarly journals Comparative RNA-Seq Analyses of Solenopsis japonica (Hymenoptera: Formicidae) Reveal Gene in Response to Cold Stress

Genes ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1610
Author(s):  
Mohammad Vatanparast ◽  
Youngjin Park
Keyword(s):  
Gene Expression ◽  
Cold Stress ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Predatory Behavior ◽  
P Value ◽  
Molecular Response ◽  
Rna Seq ◽  
Protein Database ◽  
Brood Chamber

Solenopsis japonica, as a fire ant species, shows some predatory behavior towards earthworms and woodlice, and preys on the larvae of other ant species by tunneling into a neighboring colony’s brood chamber. This study focused on the molecular response process and gene expression profiles of S. japonica to low (9 °C)-temperature stress in comparison with normal temperature (25 °C) conditions. A total of 89,657 unigenes (the clustered non-redundant transcripts that are filtered from the longest assembled contigs) were obtained, of which 32,782 were annotated in the NR (nonredundant protein) database with gene ontology (GO) terms, gene descriptions, and metabolic pathways. The results were 81 GO subgroups and 18 EggNOG (evolutionary genealogy of genes: Non-supervised Orthologous Groups) keywords. Differentially expressed genes (DEGs) with log2fold change (FC) > 1 and log2FC < −1 with p-value ≤ 0.05 were screened for cold stress temperature. We found 215 unigenes up-regulated and 115 unigenes down-regulated. Comparing transcriptome profiles for differential gene expression resulted in various DE proteins and genes, including fatty acid synthases and lipid metabolism, which have previously been reported to be involved in cold resistance. We verified the RNA-seq data by qPCR on 20 up- and down-regulated DEGs. These findings facilitate the basis for the future understanding of the adaptation mechanisms of S. japonica and the molecular mechanisms underlying the response to low temperatures.

scholarly journals A tensor decomposition-based integrated analysis applicable to multiple gene expression profiles without sample matching

2021 ◽  
Author(s):  
Taguchi Y-h. ◽  
Turki Turki
Keyword(s):  
Gene Expression ◽  
Learning Strategies ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Tensor Decomposition ◽  
Integrated Analysis ◽  
Rna Seq ◽  
Prior Learning ◽  
Multiple Gene ◽  
Machine Learning Applications

Abstract The integrated analysis of multiple gene expression profiles measured in distinct studies is always problematic. Especially, missing sample matching and missing common labeling between distinct studies prevent the integration of multiple studies in fully data-driven and unsupervised manner. In this study, we propose a strategy enabling the integration of multiple gene expression profiles among multiple independent studies without either labeling or sample matching, using tensor decomposition-based unsupervised feature extraction. As an example, we applied this strategy to Alzheimer’s disease (AD)-related gene expression profiles that lack exact correspondence among samples as well as AD single-cell RNA-seq (scRNA-seq) data. We found that we could select biologically reasonable genes with integrated analysis. Overall, integrated gene expression profiles can function analogously to prior learning and/or transfer learning strategies in other machine learning applications. For scRNA-seq, the proposed approach was able to drastically reduce the required computational memory.

scholarly journals Transcriptomic profiles of non-embryogenic and embryogenic callus cells in a highly regenerative Upland Cotton line (Gossypium hirsutum L.)

2020 ◽  
Author(s):  
Li Wen ◽  
Wei Li ◽  
Stephen Parris ◽  
Matthew West ◽  
John Lawson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Somatic Embryogenesis ◽  
Transcription Factors ◽  
Gossypium Hirsutum ◽  
Embryogenic Callus ◽  
Developmental Stages ◽  
Baby Boom ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Study Gene Expression

Abstract • Background • Genotype independent transformation and whole plant regeneration through somatic embryogenesis relies heavily on the intrinsic ability of a genotype to regenerate. • Results • In this study, gene expression profiles of a highly regenerable Gossypium hirsutum L. cultivar, Jin668, were analyzed at two critical developmental stages during somatic embryogenesis, non-embryogenic callus (NEC) cells and embryogenic callus (EC) cells. The rate of EC formation in Jin668 is 96%. Differential gene expression analysis revealed a total of 5,333 differentially expressed genes (DEG) with 2,534 upregulated and 2,799 downregulated in EC. A total of 144 genes were unique to NEC cells and 174 genes unique to EC. Clustering and enrichment analysis identified genes upregulated in EC that function as transcription factors/DNA binding, phytohormone response, oxidative reduction, and regulators of transcription; while genes categorized in methylation pathways were downregulated. Four key transcription factors were identified based on their sharp upregulation in EC tissue; LEAFY COTYLEDON 1 (LEC1), BABY BOOM (BBM), FUSCA (FUS3) and AGAMOUS-LIKE15 with distinguishable subgenome expression bias. • Conclusions • This comparative analysis of NEC and EC transcriptomes gives new insights into the genetic underpinnings of somatic embryogenesis in cotton.

Sign in / Sign up

Export Citation Format

Share Document