scholarly journals 213 Transcriptome profiling of olfactory epithelium in normal cycling and acyclic gilts

2019 ◽  
Vol 97 (Supplement_2) ◽  
pp. 125-125
Author(s):  
Dan J Nonneman ◽  
Aaron M Dickey ◽  
Clay A Lents
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Olfactory Epithelium ◽  
Luteal Phase ◽  
Follicular Phase ◽  
Hormonal Control ◽  
Differentially Expressed ◽  
Receptor Protein ◽  
Genome Wide Association Studies ◽  
Differential Gene

Abstract A significant proportion of gilts that enter the herd never farrow a litter and are culled because of anestrus or failure to conceive. Genome-wide association studies for pubertal traits have identified loci involved in neuronal and olfactory pathways, and olfaction is critical for expression of reproductive behavior in mammalian females. We evaluated major olfactory epithelium (MOE) for differential gene expression in nonpubertal, behavioral anestrus and normal cycling early follicular and luteal phase gilts (n = 8/group; average age of 259 days). An average of 50 million paired-end RNA-seq reads were collected from each of the 32 RNA libraries and mapped to Sscrofa 11.1. Differential gene expression was determined using DESeq2. A total of 18,484 genes were expressed with a mean normalized expression value greater than 5. Only four genes were differentially expressed between nonpubertal or behavioral anestrus MOE and their cycling controls (early follicular and luteal, respectively). Comparing cycling follicular and luteal phase gilts showed that 1146 genes were more highly expressed in MOE from follicular phase gilts, whereas 1351 genes were more highly expressed in MOE from luteal phase gilts. Pathways for transmembrane receptor protein tyrosine kinase/growth factor signaling, cell junction and epithelium development were overrepresented in follicular phase MOE and cell cycle, chromatin remodeling, inflammatory response and sensory perception were overrepresented in luteal phase MOE. While 1348 locus IDs were identified for olfactory receptors in MOE, only 160 were expressed at an appreciable level (base mean > 5) in MOE and 16 were more highly expressed in MOE from luteal phase than follicular phase gilts. While few genes were differentially expressed in MOE between prepubertal and anestrus gilts and cycling gilts at the same ovarian stage, the comparison between ovarian stages indicates that MOE gene expression is under hormonal control. USDA is an equal opportunity provider and employer.

scholarly journals Delegating Sex: Differential Gene Expression in Stolonizing Syllids Uncovers the Hormonal Control of Reproduction

2018 ◽  
Vol 11 (1) ◽  
pp. 295-318 ◽  
Author(s):  
Patricia Álvarez-Campos ◽  
Nathan J Kenny ◽  
Aida Verdes ◽  
Rosa Fernández ◽  
Marta Novo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Hormonal Control ◽  
Differential Gene

PADGE: analysis of heterogeneous patterns of differential gene expression

2007 ◽  
Vol 32 (1) ◽  
pp. 154-159 ◽  
Author(s):  
Li Li ◽  
Amitabha Chaudhuri ◽  
John Chant ◽  
Zhijun Tang
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Web Application ◽  
Statistical Power ◽  
Profile Analysis ◽  
Expression Profiles ◽  
Differentially Expressed ◽  
Cancer Genes ◽  
Data Set ◽  
Differential Gene

We have devised a novel analysis approach, percentile analysis for differential gene expression (PADGE), for identifying genes differentially expressed between two groups of heterogeneous samples. PADGE was designed to compare expression profiles of sample subgroups at a series of percentile cutoffs and to examine the trend of relative expression between sample groups as expression level increases. Simulation studies showed that PADGE has more statistical power than t-statistics, cancer outlier profile analysis (COPA) (Tomlins SA, Rhodes DR, Perner S, Dhanasekaran SM, Mehra R, Sun XW, Varambally S, Cao X, Tchinda J, Kuefer R, Lee C, Montie JE, Shah RB, Pienta KJ, Rubin MA, Chinnaiyan AM. Science 310: 644–648, 2005), and kurtosis (Teschendorff AE, Naderi A, Barbosa-Morais NL, Caldas C. Bioinformatics 22: 2269–2275, 2006). Application of PADGE to microarray data sets in tumor tissues demonstrated its utility in prioritizing cancer genes encoding potential therapeutic targets or diagnostic markers. A web application was developed for researchers to analyze a large gene expression data set from heterogeneous biological samples and identify differentially expressed genes between subsets of sample classes using PADGE and other available approaches. Availability: http://www.cgl.ucsf.edu/Research/genentech/padge/ .

scholarly journals Relationship of differential gene expression profiles in CD34+ myelodysplastic syndrome marrow cells to disease subtype and progression

Blood ◽  
2009 ◽  
Vol 114 (23) ◽  
pp. 4847-4858 ◽  
Author(s):  
Kunju Sridhar ◽  
Douglas T. Ross ◽  
Robert Tibshirani ◽  
Atul J. Butte ◽  
Peter L. Greenberg
Keyword(s):  
Gene Expression ◽  
Acute Myeloid Leukemia ◽  
Myelodysplastic Syndrome ◽  
Differential Gene Expression ◽  
Myeloid Leukemia ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Differentially Expressed ◽  
Differential Gene ◽  
Acute Myeloid

AbstractMicroarray analysis with 40 000 cDNA gene chip arrays determined differential gene expression profiles (GEPs) in CD34+ marrow cells from myelodysplastic syndrome (MDS) patients compared with healthy persons. Using focused bioinformatics analyses, we found 1175 genes significantly differentially expressed by MDS versus normal, requiring a minimum of 39 genes to separately classify these patients. Major GEP differences were demonstrated between healthy and MDS patients and between several MDS subgroups: (1) those whose disease remained stable and those who subsequently transformed (tMDS) to acute myeloid leukemia; (2) between del(5q) and other MDS patients. A 6-gene “poor risk” signature was defined, which was associated with acute myeloid leukemia transformation and provided additive prognostic information for International Prognostic Scoring System Intermediate-1 patients. Overexpression of genes generating ribosomal proteins and for other signaling pathways was demonstrated in the tMDS patients. Comparison of del(5q) with the remaining MDS patients showed 1924 differentially expressed genes, with underexpression of 1014 genes, 11 of which were within the 5q31-32 commonly deleted region. These data demonstrated (1) GEPs distinguishing MDS patients from healthy and between those with differing clinical outcomes (tMDS vs those whose disease remained stable) and cytogenetics [eg, del(5q)]; and (2) molecular criteria refining prognostic categorization and associated biologic processes in MDS.

scholarly journals СONSECUTIVE INTEGRATION OF AVAILABLE MICROARRAY DATA FOR ANALYSIS OF DIFFERENTIAL GENE EXPRESSION IN HUMAN PLACENTA

2021 ◽  
Vol 14 (1) ◽  
pp. 38-45
Author(s):  
O. Lykhenko ◽  
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Differentially Expressed Genes ◽  
Human Placenta ◽  
Normal Pregnancy ◽  
Integrative Analysis ◽  
Differentially Expressed ◽  
Second Trimester ◽  
Placental Development ◽  
Differential Gene

The purpose of the study was to provide the pipeline for processing of publicly available unprocessed data on gene expression via integration and differential gene expression analysis. Data collection from open gene expression databases, normalization and integration into a single expression matrix in accordance with metadata and determination of differentially expressed genes were fulfilled. To demonstrate all stages of data processing and integrative analysis, there were used the data from gene expression in the human placenta from the first and second trimesters of normal pregnancy. The source code for the integrative analysis was written in the R programming language and publicly available as a repository on GitHub. Four clusters of functionally enriched differentially expressed genes were identified for the human placenta in the interval between the first and second trimester of pregnancy. Immune processes, developmental processes, vasculogenesis and angiogenesis, signaling and the processes associated with zinc ions varied in the considered interval between the first and second trimester of placental development. The proposed sequence of actions for integrative analysis could be applied to any data obtained by microarray technology.

scholarly journals Reprogramming of the Wheat Transcriptome in Response to Infection with Claviceps Purpurea, the Causal Agent of Ergot

2020 ◽  
Author(s):  
Lesley A. Boyd ◽  
Eleni Tente ◽  
Nelzo Ereful ◽  
Anyela Camargo Rodriguez ◽  
Paul Grant ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Early Stage ◽  
Receptor Protein ◽  
Claviceps Purpurea ◽  
Long Distance ◽  
Transmitting Tissue ◽  
Significant Differential Expression ◽  
Differential Gene ◽  
Wheat Transcriptome

Abstract Background: Ergot, caused by the fungal pathogen Claviceps purpurea, infects the female flowers of a range of cereal crops, including wheat. To understand the interaction between C. purpurea and hexaploid wheat we undertook an extensive examination of the reprogramming of the wheat transcriptome in response to C. purpurea infection through floral tissues (i.e. the stigma, transmitting and base ovule tissues of the ovary) and over time. Results: C. purpurea hyphae were observed to have grown into and down the stigma at 24 hours (H) after inoculation. By 48H hyphae had grown through the transmitting tissue into the base, while by 72H hyphae had surrounded the ovule. By 5 days (D) the ovule had been replaced by fungal tissue. Significant differential gene expression was first observed at 1H in the stigma tissue. Many of the wheat genes differentially transcribed in response to C. purpurea infection were associated with plant hormones and included the ethylene (ET), auxin, cytokinin, gibberellic acid (GA), salicylic acid and jasmonic acid (JA) biosynthetic and signaling pathways. Hormone-associated genes were first detected in the stigma and base tissues at 24H, but not in the transmitting tissue. Genes associated with GA and JA pathways were seen in the stigma at 24H, while JA and ET-associated genes were identified in the base at 24H. In addition, several defence-associated genes were differential expressed in response to C. purpurea infection, including antifungal proteins, endocytosis/exocytosis-related proteins, NBS-LRR class proteins, genes involved in programmed cell death, receptor protein kinases and transcription factors. Of particular interest was the identification of significant differential expression of wheat genes in the base tissue well before the appearance of fungal hyphae, suggesting that a mobile signal, either pathogen or plant-derived, is delivered to the base prior to colonisation.Conclusions: Multiple host hormonal biosynthesis and signalling pathways were significantly perturbed from an early stage in the wheat – C. purpurea interaction. Significant differential gene expression at the base of the ovary, ahead of arrival of the pathogen, indicated the potential presence of a long-distance signal modifying host gene expression.

scholarly journals Identification of gene expression alterations in C. elegans adr-2 mutants using high- throughput sequencing

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Jackson Townsend ◽  
Heather A. Hundley
Keyword(s):  
Gene Expression ◽  
Young Adult ◽  
Rna Editing ◽  
Differential Gene Expression ◽  
Differentially Expressed Genes ◽  
High Throughput Sequencing ◽  
Life Stage ◽  
Differentially Expressed ◽  
C Elegans ◽  
Differential Gene

Background and Hypothesis: RNA editing is one of several mechanisms regulating gene expression. One type of RNA editing, the deamination of adenosine to inosine, is carried out by ADAR enzymes. ADAR enzymes are essential for neural function and aberrant editing is implicated in various forms of neuropathology. C. elegans lacking the RNA editing enzyme, ADR-2, are viable allowing us to ascertain how loss of RNA editing affects neural gene expression. The effects of loss of adr-2 on neural gene expression will be analyzed in both the first larval (L1) and young adult stages. We hypothesize that the transcriptome will change depending on life stage and the presence of ADR-2. Methods: Three replicates of neural cells isolated from wild type and adr-2(-) L1 and young adult stage animals were obtained. Total RNA was extracted from each population and mRNA was isolated using an oligo-dT bead. The mRNA was fragmented, and reverse transcribed to generate a complentary DNA (cDNA) library. The cDNA was sequenced by a facility at Indiana University. Quality of the library was evaluated using FASTqc. DE-seq2 software evaluated the differential gene expression. Results: I examined differential gene expression in two life stages of the WT and adr-2 neural samples. After obtaining the differentially expressed genes, the portions of the transcriptome that require ADR-2 was determined. WT young adults showed increased (3715) and decreased (2504) expression of neural genes when compared to the L1 stage. Many differentially expressed genes required adr-2 (~40% of the upregulated and 78% of the downregulated genes.) In addition, some genes were uniquely altered (631 upregulated, 196 downregulated) in the absence of adr-2. Conclusion and Potential Impact: The life stage and presence of ADR-2 alter the neural transcriptome and this function changes throughout development. Future studies will determine whether these genes are altered due to the lack of RNA editing or binding by ADR-2.

scholarly journals Why Pashmina Goat Produces Long Hair-fiber and Barbari doesn’t: A Differential Gene Expression Study

2021 ◽  
Author(s):  
Rashid Saif ◽  
Tania Mahmood ◽  
Aniqa Ejaz ◽  
Saeeda Zia
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Transcriptome Assembly ◽  
Hair Shaft ◽  
Hair Follicles ◽  
Differentially Expressed ◽  
Keratinocyte Differentiation ◽  
Dermal Papilla Cells ◽  
Differential Gene ◽  
Hair Fiber

The Pashmina and Barbari are two famous goat breeds found in the wide areas of the Indo-Pak region. Pashmina is famous for its long hair-fiber (Cashmere) production while Barbari is not-selected for this trait. So, the mRNA expression profiling in the skin samples of both breeds would be an attractive and judicious approach for detecting putative genes involved in this valued trait. Here, we performed differential gene expression analysis on publicly available RNA-Seq data from both breeds. Out of 44,617,994 filtered reads of Pashmina and 55,995,999 of Barbari which are 76.48% and 73.69% mapped to the ARS1 reference transcriptome assembly respectively. A pairwise comparison of both breeds resulted in 47,159 normalized expressed transcripts while 8,414 transcripts are differentially expressed above the significant threshold. Among these, 4,788 are upregulated in Pashmina while 3,626 transcripts are upregulated in Barbari. Fifty-nine transcripts harbor 57 genes including 32 LOC genes and 24 are annotated genes which were selected on the basis of TMM counts > 500. Genes with ectopic expressions other than uncharacterized and LOC symbol genes are Keratins (KRT) and Keratin Associated Proteins (KRTAPs), CystatinA&6, TCHH, SPRR4, PPIA, SLC25A4, S100A11, DMKN, LOR, ANXA2, PRR9 and SFN. All of these genes are likely to be involved in keratinocyte differentiation, sulfur matrix proteins, dermal papilla cells, hair follicles proliferation, hair curvature, wool fiber diameter, hair transition, hair shaft differentiation and its keratinization. These differentially expressed reported genes are critically valuable for enhancing the quality and quantity of the pashmina fiber and overall breed improvement. This study will also provide important information on hair follicle differentiation for further enrichment analyses and introducing this valued trait to other goat breeds as well.

scholarly journals RNA-seq 2G: online analysis of differential gene expression with comprehensive options of statistical methods

2017 ◽  
Author(s):  
Zhe Zhang ◽  
Yuanchao Zhang ◽  
Perry Evans ◽  
Asif Chinwalla ◽  
Deanne Taylor
Keyword(s):  
Gene Expression ◽  
Statistical Methods ◽  
Differential Gene Expression ◽  
Data Distribution ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Web Portal ◽  
Shiny App ◽  
Resource Requirements ◽  
Differential Gene

ABSTRACTRNA-seq has become the most prevalent technology for measuring genome-wide gene expression, but the best practices for processing and analysing RNA-seq data are still an open question. Many statistical methods have been developed to identify genes differentially expressed between sample groups from RNA-seq data. These methods differ by their data distribution assumptions, choice of statistical test, and computational resource requirements. Over 25 methods of differential expression detection were validated and made available through a user-friendly web portal, RNA-seq 2G. All methods are suitable for analysing differential gene expression between two groups of samples. They commonly use a read count matrix derived from RNA-seq data as input and statistically compare groups for each gene. The web portal uses a Shiny app front-end and is hosted by a cloud-based server provided by Amazon Web Service. The comparison of methods showed that the data distribution assumption is the major determinant of differences between methods. Most methods are more likely to find that longer genes are differentially expressed, which substantially impacts downstream gene set-level analysis. Combining results from multiple methods can potentially diminish this bias. RNA-seq 2G makes the analysis of RNA-seq data more accessible and efficient, and is freely available at http://rnaseq2g.awsomics.org.

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