Effect of ovarian hormones on the healthy equine uterus: a global gene expression analysis

2016 ◽  
Vol 28 (11) ◽  
pp. 1810 ◽  
Author(s):  
Christina D. Marth ◽  
Neil D. Young ◽  
Lisa Y. Glenton ◽  
Drew M. Noden ◽  
Glenn F. Browning ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Cell Communication ◽  
Gene Expression Profiles ◽  
Global Gene Expression ◽  
Rna Seq ◽  
Complete Understanding ◽  
Pathway Enrichment ◽  
Uterine Inflammation ◽  
Global Gene Expression Analysis

The physiological changes associated with the varying hormonal environment throughout the oestrous cycle are linked to the different functions the uterus needs to fulfil. The aim of the present study was to generate global gene expression profiles for the equine uterus during oestrus and Day 5 of dioestrus. To achieve this, samples were collected from five horses during oestrus (follicle >35 mm in diameter) and dioestrus (5 days after ovulation) and analysed using high-throughput RNA sequencing techniques (RNA-Seq). Differentially expressed genes between the two cycle stages were further investigated using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The expression of 1577 genes was found to be significantly upregulated during oestrus, whereas 1864 genes were expressed at significantly higher levels in dioestrus. Most genes upregulated during oestrus were associated with the extracellular matrix, signal interaction and transduction, cell communication or immune function, whereas genes expressed at higher levels in early dioestrus were most commonly associated with metabolic or transport functions, correlating well with the physiological functions of the uterus. These results allow for a more complete understanding of the hormonal influence on gene expression in the equine uterus by functional analysis of up- and downregulated genes in oestrus and dioestrus, respectively. In addition, a valuable baseline is provided for further research, including analyses of changes associated with uterine inflammation.

scholarly journals Global Gene Expression Analysis of Yeast Cells during Sake Brewing

2006 ◽  
Vol 72 (11) ◽  
pp. 7353-7358 ◽  
Author(s):  
Hong Wu ◽  
Xiaohong Zheng ◽  
Yoshio Araki ◽  
Hiroshi Sahara ◽  
Hiroshi Takagi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Analysis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Global Gene Expression ◽  
Yeast Cells ◽  
Yeast Gene ◽  
High Concentration ◽  
Brewing Process ◽  
Global Gene Expression Analysis

ABSTRACT During the brewing of Japanese sake, Saccharomyces cerevisiae cells produce a high concentration of ethanol compared with other ethanol fermentation methods. We analyzed the gene expression profiles of yeast cells during sake brewing using DNA microarray analysis. This analysis revealed some characteristics of yeast gene expression during sake brewing and provided a scaffold for a molecular level understanding of the sake brewing process.

scholarly journals Granulosa cells from human primordial and primary follicles show differential global gene expression profiles

2018 ◽  
Vol 33 (4) ◽  
pp. 666-679 ◽  
Author(s):  
E H Ernst ◽  
S Franks ◽  
K Hardy ◽  
P Villesen ◽  
K Lykke-Hartmann
Keyword(s):  
Gene Expression ◽  
Granulosa Cells ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Global Gene Expression

scholarly journals Dynamical consequences of regional heterogeneity in the brain’s transcriptional landscape

2020 ◽  
Author(s):  
Gustavo Deco ◽  
Kevin Aquino ◽  
Aurina Arnatkevičiūtė ◽  
Stuart Oldham ◽  
Kristina Sabaroedin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Global Gene Expression ◽  
Brain Regions ◽  
Biophysical Model ◽  
Neuronal Dynamics ◽  
Regional Heterogeneity ◽  
Magnetic Resonance Imaging Mri

AbstractBrain regions vary in their molecular and cellular composition, but how this heterogeneity shapes neuronal dynamics is unclear. Here, we investigate the dynamical consequences of regional heterogeneity using a biophysical model of whole-brain functional magnetic resonance imaging (MRI) dynamics in humans. We show that models in which transcriptional variations in excitatory and inhibitory receptor (E:I) gene expression constrain regional heterogeneity more accurately reproduce the spatiotemporal structure of empirical functional connectivity estimates than do models constrained by global gene expression profiles and MRI-derived estimates of myeloarchitecture. We further show that regional heterogeneity is essential for yielding both ignition-like dynamics, which are thought to support conscious processing, and a wide variance of regional activity timescales, which supports a broad dynamical range. We thus identify a key role for E:I heterogeneity in generating complex neuronal dynamics and demonstrate the viability of using transcriptional data to constrain models of large-scale brain function.

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.

The characteristics of mesenteric adipose tissue attached to different intestinal segments and their roles in immune regulation

2022 ◽  
Author(s):  
Haowei Zhang ◽  
Yujin Ding ◽  
Qin Zeng ◽  
Dandan Wang ◽  
Ganglei Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Adipose Tissue ◽  
Immune Regulation ◽  
Expression Profiles ◽  
Critical Role ◽  
Gene Expression Profiles ◽  
Rna Seq ◽  
Mesenteric Adipose Tissue ◽  
Cell Components ◽  
Subsequent Effect

Background: Mesenteric adipose tissue (MAT) plays a critical role in the intestinal physiological ecosystems. Small and large intestines have evidently intrinsic and distinct characteristics. However, whether there exist any mesenteric differences adjacent to the small and large intestines (SMAT and LMAT) has not been properly characterized. We studied the important facets of these differences, such as morphology, gene expression, cell components and immune regulation of MATs, to characterize the mesenteric differences. Methods: The SMAT and LMAT of mice were utilized for comparison of tissue morphology. Paired mesenteric samples were analyzed by RNA-seq to clarify gene expression profiles. MAT partial excision models were constructed to illustrate the immune regulation roles of MATs, and 16S-seq was applied to detect the subsequent effect on microbiota. Results: Our data show that different segments of mesenteries have different morphological structures. SMAT not only has smaller adipocytes but also contains more fat-associated lymphoid clusters than LMAT. The gene expression profile is also discrepant between these two MATs in mice. B-cell markers were abundantly expressed in SMAT, while development-related genes were highly expressed in LMAT. Adipose-derived stem cells of LMAT exhibited higher adipogenic potential and lower proliferation rates than those of SMAT. In addition, SMAT and LMAT play different roles in immune regulation and subsequently affect microbiota components. Finally, our data clarified the described differences between SMAT and LMAT in humans. Conclusions: There were significant differences in cell morphology, gene expression profiles, cell components, biological characteristics, and immune and microbiota regulation roles between regional MATs.

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