145 OVIDUCTAL EXPRESSION OF GENES INVOLVED IN GROWTH FACTOR, CYTOKINE, HORMONE, AND WNT SIGNALING DURING THE EARLY ESTROUS CYCLE OF THE COW

2016 ◽  
Vol 28 (2) ◽  
pp. 202
Author(s):  
P. Tribulo ◽  
L. G. Siqueira ◽  
P. J. Hansen
Keyword(s):  
Gene Expression ◽  
Embryonic Development ◽  
Corpus Luteum ◽  
Fixed Effects ◽  
Reproductive Tract ◽  
Expression Patterns ◽  
Random Effect ◽  
Housekeeping Genes ◽  
Cell Types ◽  
Endocrine Control

Knowledge of the molecules used by the maternal reproductive tract to regulate development of the pre-implantation embryo, called embryokines, is largely incomplete. To identify possible candidates for this function, an experiment was conducted to assess expression patterns during the first 7 days after ovulation for 92 genes that could be involved in control of development. Included were genes for 27 growth factors, 11 cytokines, 22 interleukins, 3 hormones, 19 WNT ligands, and 9 WNT regulatory molecules. Cows were slaughtered at Days 0, 3, 5, and 7 relative to predicted ovulation. Reproductive tracts were obtained and transversal sections from the isthmus of oviducts ipsi- and contralateral to the corpus luteum were harvested for gene expression analysis. Abundance of specific mRNA molecules was determined using the NanoString nCounter analysis system (NanoString Technologies, Seattle, WA, USA). Data were normalized against 6 housekeeping genes (ACTB, ERK1, GAPDH, RPL19, SLC30A6, SUZ12) and internal positive controls. Genes were considered expressed if the number of reads was greater than 2 standard deviations above the mean of negative controls. Data were analysed by ANOVA using the GLM procedure of SAS (SAS Institute Inc., Cary, NC, USA) with day, side, and day × side as fixed effects, and cow as random effect. Side did not have any significant effect so data were analysed without side and day × side in the model. In contrast to what was observed earlier for endometrium (P. Tribulo and P. J. Hansen, unpublished data), we found no difference in gene expression between oviducts ipsi- and contralateral to the corpus luteum. Overall, there was wide variation in the magnitude of gene expression. Among the 20 most expressed genes, average reads varied from 164 to over 10 726. All genes were detected at Days 0, 3, and 7 but only 67 of the 92 genes were expressed at Day 5. The 10 highest-expressed genes were CTGF, CXCL3, CXCL10, CXCL12, GRO1, IGF2, IK, SFRP1, WNT5A, and WNT6. Of these, CTGF, CXCL12, IGF2, IK, HDGF, WNT5A, and CXCL3 were within the 10 highest expressed at all days (P. Tribulo and P. J. Hansen, unpublished data). There were only 6 genes whose expression was significantly affected by day. Expression was highest at oestrus (VEGFA), Day 5 (GRO1, SFRP1) or Day 7 (BMP4, IK, WNT16). This experiment identifies some potential maternal regulators of embryonic development. Expression of most of these putative embryokine genes did not vary with stage of the oestrous cycle, suggesting that expression is either not under endocrine control or varies between cell types within the oviduct. Further studies are needed to determine the effect of these maternally secreted molecules on embryonic development. Study was supported by the National Institutes of Health (HD080855).

scholarly journals Identifying cell types from single-cell data based on similarities and dissimilarities between cells

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Yuanyuan Li ◽  
Ping Luo ◽  
Yi Lu ◽  
Fang-Xiang Wu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Spectral Clustering ◽  
Incidence Matrix ◽  
Expression Patterns ◽  
Cell Types ◽  
Clustering Method ◽  
Different Types ◽  
Cell Data ◽  
Spectral Clustering Method

Abstract Background With the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research. However, the clustering of cell types becomes more complex with the mutual penetration between different types of cells and the instability of gene expression. One way of overcoming this problem is to group similar, related single cells together by the means of various clustering analysis methods. Although some methods such as spectral clustering can do well in the identification of cell types, they only consider the similarities between cells and ignore the influence of dissimilarities on clustering results. This methodology may limit the performance of most of the conventional clustering algorithms for the identification of clusters, it needs to develop special methods for high-dimensional sparse categorical data. Results Inspired by the phenomenon that same type cells have similar gene expression patterns, but different types of cells evoke dissimilar gene expression patterns, we improve the existing spectral clustering method for clustering single-cell data that is based on both similarities and dissimilarities between cells. The method first measures the similarity/dissimilarity among cells, then constructs the incidence matrix by fusing similarity matrix with dissimilarity matrix, and, finally, uses the eigenvalues of the incidence matrix to perform dimensionality reduction and employs the K-means algorithm in the low dimensional space to achieve clustering. The proposed improved spectral clustering method is compared with the conventional spectral clustering method in recognizing cell types on several real single-cell RNA-seq datasets. Conclusions In summary, we show that adding intercellular dissimilarity can effectively improve accuracy and achieve robustness and that improved spectral clustering method outperforms the traditional spectral clustering method in grouping cells.

scholarly journals Histone modifications form a cell-type-specific chromosomal bar code that persists through the cell cycle

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
John A. Halsall ◽  
Simon Andrews ◽  
Felix Krueger ◽  
Charlotte E. Rutledge ◽  
Gabriella Ficz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Histone Modifications ◽  
Expression Patterns ◽  
Cell Types ◽  
Cell Type ◽  
Bar Code ◽  
Genes Encoding ◽  
Cell Type Specific ◽  
Rolling Windows

AbstractChromatin configuration influences gene expression in eukaryotes at multiple levels, from individual nucleosomes to chromatin domains several Mb long. Post-translational modifications (PTM) of core histones seem to be involved in chromatin structural transitions, but how remains unclear. To explore this, we used ChIP-seq and two cell types, HeLa and lymphoblastoid (LCL), to define how changes in chromatin packaging through the cell cycle influence the distributions of three transcription-associated histone modifications, H3K9ac, H3K4me3 and H3K27me3. We show that chromosome regions (bands) of 10–50 Mb, detectable by immunofluorescence microscopy of metaphase (M) chromosomes, are also present in G1 and G2. They comprise 1–5 Mb sub-bands that differ between HeLa and LCL but remain consistent through the cell cycle. The same sub-bands are defined by H3K9ac and H3K4me3, while H3K27me3 spreads more widely. We found little change between cell cycle phases, whether compared by 5 Kb rolling windows or when analysis was restricted to functional elements such as transcription start sites and topologically associating domains. Only a small number of genes showed cell-cycle related changes: at genes encoding proteins involved in mitosis, H3K9 became highly acetylated in G2M, possibly because of ongoing transcription. In conclusion, modified histone isoforms H3K9ac, H3K4me3 and H3K27me3 exhibit a characteristic genomic distribution at resolutions of 1 Mb and below that differs between HeLa and lymphoblastoid cells but remains remarkably consistent through the cell cycle. We suggest that this cell-type-specific chromosomal bar-code is part of a homeostatic mechanism by which cells retain their characteristic gene expression patterns, and hence their identity, through multiple mitoses.

scholarly journals Cell-type Specific Expression Quantitative Trait Loci Associated with Alzheimer Disease in Blood and Brain Tissue

2020 ◽  
Author(s):  
Devanshi Patel ◽  
Xiaoling Zhang ◽  
John J. Farrell ◽  
Jaeyoon Chung ◽  
Thor D. Stein ◽  
...  
Keyword(s):  
Gene Expression ◽  
Quantitative Trait ◽  
Expression Patterns ◽  
Regulation Of Gene Expression ◽  
Cell Types ◽  
Eqtl Analysis ◽  
Cell Type ◽  
Specific Expression ◽  
Cell Type Specific Expression ◽  
Cell Type Specific

ABSTRACTBecause regulation of gene expression is heritable and context-dependent, we investigated AD-related gene expression patterns in cell-types in blood and brain. Cis-expression quantitative trait locus (eQTL) mapping was performed genome-wide in blood from 5,257 Framingham Heart Study (FHS) participants and in brain donated by 475 Religious Orders Study/Memory & Aging Project (ROSMAP) participants. The association of gene expression with genotypes for all cis SNPs within 1Mb of genes was evaluated using linear regression models for unrelated subjects and linear mixed models for related subjects. Cell type-specific eQTL (ct-eQTL) models included an interaction term for expression of “proxy” genes that discriminate particular cell type. Ct-eQTL analysis identified 11,649 and 2,533 additional significant gene-SNP eQTL pairs in brain and blood, respectively, that were not detected in generic eQTL analysis. Of note, 386 unique target eGenes of significant eQTLs shared between blood and brain were enriched in apoptosis and Wnt signaling pathways. Five of these shared genes are established AD loci. The potential importance and relevance to AD of significant results in myeloid cell-types is supported by the observation that a large portion of GWS ct-eQTLs map within 1Mb of established AD loci and 58% (23/40) of the most significant eGenes in these eQTLs have previously been implicated in AD. This study identified cell-type specific expression patterns for established and potentially novel AD genes, found additional evidence for the role of myeloid cells in AD risk, and discovered potential novel blood and brain AD biomarkers that highlight the importance of cell-type specific analysis.

scholarly journals Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms

2021 ◽  
Author(s):  
Milton Pividori ◽  
Sumei Lu ◽  
Binglan Li ◽  
Chun Su ◽  
Matthew E. Johnson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Association Studies ◽  
Expression Patterns ◽  
Cell Types ◽  
Joint Analysis ◽  
Genetic Associations ◽  
Disease Etiology ◽  
Mediating Role ◽  
Gene Modules ◽  
Trait Associations

Understanding how dysregulated transcriptional processes result in tissue-specific pathology requires a mechanistic interpretation of expression regulation across different cell types. It has been shown that this insight is key for the development of new therapies. These mechanisms can be identified with transcriptome-wide association studies (TWAS), which have represented an important step forward to test the mediating role of gene expression in GWAS associations. However, due to pervasive eQTL sharing across tissues, TWAS has not been successful in identifying causal tissues, and other methods generally do not take advantage of the large amounts of RNA-seq data publicly available. Here we introduce a polygenic approach that leverages gene modules (genes with similar co-expression patterns) to project both gene-trait associations and pharmacological perturbation data into a common latent representation for a joint analysis. We observed that diseases were significantly associated with gene modules expressed in relevant cell types, such as hypothyroidism with T cells and thyroid, hypertension and lipids with adipose tissue, and coronary artery disease with cardiomyocytes. Our approach was more accurate in predicting known drug-disease pairs and revealed stable trait clusters, including a complex branch involving lipids with cardiovascular, autoimmune, and neuropsychiatric disorders. Furthermore, using a CRISPR-screen, we show that genes involved in lipid regulation exhibit more consistent trait associations through gene modules than individual genes. Our results suggest that a gene module perspective can contextualize genetic associations and prioritize alternative treatment targets when GWAS hits are not druggable.

scholarly journals Development of cDNA normalization system and preliminary transcription analysis of KCS genes in apple tissues

2011 ◽  
Vol 59 (3) ◽  
pp. 9-12 ◽  
Author(s):  
Zsolt Albert ◽  
Cs. Deák ◽  
A. Miskó ◽  
M. Tóth ◽  
I. Papp
Keyword(s):  
Gene Expression ◽  
Expression System ◽  
Expression Patterns ◽  
Housekeeping Genes ◽  
Apple Fruit ◽  
Rt Pcr ◽  
Specific Primers ◽  
Specific Expression ◽  
Transcription Analysis ◽  
Tissue Specific

Wax production is an important aspect of apple (Malus domestica Borkh.) fruit development from both theoretical and practical point of views. The complex molecular mechanism that controls wax biosynthesis is still widely unknown but many studies focused on this topic. We aimed to develop further the experimental framework of these efforts with a description of an improved reference genes expression system. Results in the literature show that similarities exist among the expression of some housekeeping genes of different plant species. Based on these considerations and on gene expression data from Arabidopsis thaliana, some genes in apple were assigned for analysis. EST sequences of apple were used to design specific primers for RT-PCR experiments. Isolation of intact RNA from different apple tissues and performing RT-PCR reaction were also key point in obtaining expression patterns. To monitor DNA contamination of the RNA samples, specific primers were used that amplify intron-containing sequences from the cDNA. We found that actin primers can be used for the detection of intron containing genomic DNA, and tubulin primers are good internal controls in RT-PCR experiments. We were able to make a difference between tissue-specific and tissue-independent gene-expression, furthermore we found tissue specific differences between the expression patterns of candidate genes, that are potentially involved in wax-biosynthesis. Our results show that KCS1 and KCS4 are overexpressed in the skin tissue, this could mean that these genes have skin-specific expression in apple fruit.

scholarly journals Identification of Split-GAL4 Drivers and Enhancers That Allow Regional Cell Type Manipulations of the Drosophila melanogaster Intestine

Genetics ◽  
2020 ◽  
Vol 216 (4) ◽  
pp. 891-903
Author(s):  
Ishara S. Ariyapala ◽  
Jessica M. Holsopple ◽  
Ellen M. Popodi ◽  
Dalton G. Hartwick ◽  
Lily Kahsai ◽  
...  
Keyword(s):  
Gene Expression ◽  
Progenitor Cells ◽  
Expression Patterns ◽  
Cell Types ◽  
Enteroendocrine Cells ◽  
Epithelial Tissue ◽  
Cell Type ◽  
Cell Functions ◽  
Distinct Subset ◽  
Targeted Gene Expression

The Drosophila adult midgut is a model epithelial tissue composed of a few major cell types with distinct regional identities. One of the limitations to its analysis is the lack of tools to manipulate gene expression based on these regional identities. To overcome this obstacle, we applied the intersectional split-GAL4 system to the adult midgut and report 653 driver combinations that label cells by region and cell type. We first identified 424 split-GAL4 drivers with midgut expression from ∼7300 drivers screened, and then evaluated the expression patterns of each of these 424 when paired with three reference drivers that report activity specifically in progenitor cells, enteroendocrine cells, or enterocytes. We also evaluated a subset of the drivers expressed in progenitor cells for expression in enteroblasts using another reference driver. We show that driver combinations can define novel cell populations by identifying a driver that marks a distinct subset of enteroendocrine cells expressing genes usually associated with progenitor cells. The regional cell type patterns associated with the entire set of driver combinations are documented in a freely available website, providing information for the design of thousands of additional driver combinations to experimentally manipulate small subsets of intestinal cells. In addition, we show that intestinal enhancers identified with the split-GAL4 system can confer equivalent expression patterns on other transgenic reporters. Altogether, the resource reported here will enable more precisely targeted gene expression for studying intestinal processes, epithelial cell functions, and diseases affecting self-renewing tissues.

scholarly journals Impact of AHR Ligand TCDD on Human Embryonic Stem Cells and Early Differentiation

2020 ◽  
Vol 21 (23) ◽  
pp. 9052
Author(s):  
Indrek Teino ◽  
Antti Matvere ◽  
Martin Pook ◽  
Inge Varik ◽  
Laura Pajusaar ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Embryonic Stem Cells ◽  
Human Embryonic Stem Cells ◽  
Target Genes ◽  
Expression Patterns ◽  
Embryonic Stem ◽  
Stem Cell Differentiation ◽  
Cell Types ◽  
Early Differentiation

Aryl hydrocarbon receptor (AHR) is a ligand-activated transcription factor, which mediates the effects of a variety of environmental stimuli in multiple tissues. Recent advances in AHR biology have underlined its importance in cells with high developmental potency, including pluripotent stem cells. Nonetheless, there is little data on AHR expression and its role during the initial stages of stem cell differentiation. The purpose of this study was to investigate the temporal pattern of AHR expression during directed differentiation of human embryonic stem cells (hESC) into neural progenitor, early mesoderm and definitive endoderm cells. Additionally, we investigated the effect of the AHR agonist 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) on the gene expression profile in hESCs and differentiated cells by RNA-seq, accompanied by identification of AHR binding sites by ChIP-seq and epigenetic landscape analysis by ATAC-seq. We showed that AHR is differentially regulated in distinct lineages. We provided evidence that TCDD alters gene expression patterns in hESCs and during early differentiation. Additionally, we identified novel potential AHR target genes, which expand our understanding on the role of this protein in different cell types.

scholarly journals Global Analysis of Cell Type-Specific Gene Expression

2003 ◽  
Vol 4 (2) ◽  
pp. 208-215 ◽  
Author(s):  
David W. Galbraith
Keyword(s):  
Gene Expression ◽  
Fluorescent Protein ◽  
Global Analysis ◽  
Expression Patterns ◽  
Three Dimensional ◽  
Global Gene Expression ◽  
Cell Types ◽  
Reasonable Assumption ◽  
Specific Gene ◽  
Different Cell Types

The tissues and organs of multicellular eukaryotes are frequently observed to comprise complex three-dimensional interspersions of different cell types. It is a reasonable assumption that different global patterns of gene expression are found within these different cell types. This review outlines general experimental strategies designed to characterize these global gene expression patterns, based on a combination of methods of transgenic fluorescent protein (FP) expression and targeting, of flow cytometry and sorting and of high-throughput gene expression analysis.

scholarly journals Similarities and differences in uterine gene expression patterns caused by treatment with physiological and non-physiological estrogens

2003 ◽  
Vol 31 (3) ◽  
pp. 487-497 ◽  
Author(s):  
H Watanabe ◽  
A Suzuki ◽  
M Kobayashi ◽  
DB Lubahn ◽  
H Handa ◽  
...  
Keyword(s):  
Gene Expression ◽  
Estrogen Receptor ◽  
Reproductive Tract ◽  
Stress Proteins ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Response Patterns ◽  
Genes Encoding ◽  
The Many ◽  
Dose Responses

Administration of physiological and non-physiological estrogens during pregnancy or after birth is known to have adverse effects on the development of the reproductive tract and other organs. Although it is believed that both estrogens have similar effects on gene expression, this view has not been tested systematically. To compare the effects of physiological (estradiol; E2) and non-physiological (diethylstilbestrol; DES) estrogens, we used DNA microarray analysis to examine the uterine gene expression patterns induced by the two estrogens. Although E2 and DES induced many genes to respond in the same way, different groups of genes showed varying levels of maximal activities to each estrogen, resulting in different dose-response patterns. Thus, each estrogen has a distinct effect on uterine gene expression. The genes were classified into clusters according to their dose-responses to the two estrogens. Of the eight clusters, only two correlated well with the uterotropic effect of different doses of E2. One of these clusters contained genes that were upregulated by E2, which included genes encoding several stress proteins and transcription factors. The other cluster contained genes that were downregulated by E2, including genes related to metabolism, transcription and detoxification processes. The expression of these genes in estrogen receptor-deficient mice was not affected by E2 treatment, indicating that these genes are affected by the E2-bound estrogen receptor. Thus, of the many genes that are affected by estrogen, it was suggested that only a small number are directly involved in the uterotropic effects of estrogen treatment.

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