scholarly journals Meta-Analysis of RNA-Seq Studies Reveals Genes With Dominant Functions During Flower Bud Endo- To Eco-Dormancy Transition in Prunus Species

2021 ◽  
Author(s):  
Monica Canton ◽  
Cristian Forestan ◽  
Claudio Bonghi ◽  
Serena Varotto
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Fruit Trees ◽  
Day Length ◽  
Specific Gene ◽  
Flower Buds ◽  
Flower Bud

Abstract In deciduous fruit trees, entrance into dormancy occurs in later summer/fall, concomitantly with the shortening of day length and decrease in temperature. Dormancy can be divided into endodormancy, ecodormancy and paradormancy. In Prunus species flower buds, entrance into the dormant stage occurs when the apical meristem is partially differentiated; during dormancy, flower verticils continue their growth and differentiation. Each species and/or cultivar requires exposure to low winter temperature followed by warm temperatures, quantified as chilling and heat requirements, to remove the physiological blocks that inhibit budburst. A comprehensive meta-analysis of transcriptomic studies on flower buds of sweet cherry, apricot and peach was conducted, by investigating the gene expression profiles during bud endo- to ecodormancy transition in genotypes differing in chilling requirements. Conserved and distinctive expression patterns were observed, allowing the identification of gene specifically associated with endodormancy or ecodormancy. In addition to the MADS-box transcription factor family, hormone-related genes, chromatin modifiers, macro- and micro-gametogenesis related genes and environmental integrators, were identified as novel biomarker candidates for flower bud development during winter in stone fruits. In parallel, flower bud differentiation processes were associated to dormancy progression and termination and to environmental factors triggering dormancy phase-specific gene expression.

scholarly journals Meta-analysis of RNA-Seq studies reveals genes with dominant functions during flower bud endo- to eco-dormancy transition in Prunus species

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Monica Canton ◽  
Cristian Forestan ◽  
Claudio Bonghi ◽  
Serena Varotto
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Fruit Trees ◽  
Day Length ◽  
Specific Gene ◽  
Flower Buds ◽  
Flower Bud

AbstractIn deciduous fruit trees, entrance into dormancy occurs in later summer/fall, concomitantly with the shortening of day length and decrease in temperature. Dormancy can be divided into endodormancy, ecodormancy and paradormancy. In Prunus species flower buds, entrance into the dormant stage occurs when the apical meristem is partially differentiated; during dormancy, flower verticils continue their growth and differentiation. Each species and/or cultivar requires exposure to low winter temperature followed by warm temperatures, quantified as chilling and heat requirements, to remove the physiological blocks that inhibit budburst. A comprehensive meta-analysis of transcriptomic studies on flower buds of sweet cherry, apricot and peach was conducted, by investigating the gene expression profiles during bud endo- to ecodormancy transition in genotypes differing in chilling requirements. Conserved and distinctive expression patterns were observed, allowing the identification of gene specifically associated with endodormancy or ecodormancy. In addition to the MADS-box transcription factor family, hormone-related genes, chromatin modifiers, macro- and micro-gametogenesis related genes and environmental integrators, were identified as novel biomarker candidates for flower bud development during winter in stone fruits. In parallel, flower bud differentiation processes were associated to dormancy progression and termination and to environmental factors triggering dormancy phase-specific gene expression.

scholarly journals Identification of Molecular Subgroups in Liver Cirrhosis by Gene Expression Profiles

2021 ◽  
Author(s):  
Ying-xue Zhang ◽  
Feng-xia Sun ◽  
Xiao-ling Li ◽  
Qing-hua Liu ◽  
Zi-meng Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Liver Cirrhosis ◽  
Expression Analysis ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Receptor Interaction ◽  
Molecular Characteristics ◽  
Specific Gene ◽  
Subgroup Ii

Abstract Background: Cirrhosis is a common clinical chronic progressive liver disease and has become one of the main causes of death worldwide. The condition of liver cirrhosis is complex and there is also clinical heterogeneity. Identifying liver cirrhosis based on molecular characteristics has become a challenge.Methods: To reveal the potential molecular characteristics of different types of cirrhosis, we divided 79 patients with cirrhosis into 4 subgroups based on gene expression profiles. These gene expression profiles were retrieved from the mprehensive gene expression database. In addition, these subgroups showed different expression patterns. To reveal the differences between subgroups, we used weighted gene co-expression analysis and identified six subgroup-specific gene co-expression analysis modules.Results: The characteristics ofWCGNAmodules indicate that TGF - β signaling pathway,viral protein interaction with cytokines and cytokine receptors, including a variety of chemokines and inflammatory factors, are upregulated in subgroup I, indicating that subjects in subgroup I may show inflammatory characteristics; fatty acid metabolism, biosynthesis of cofactors, carbon metabolism and protein processing pathway in endoplasmic reticulum were significantly enriched in subgroup II, which indicated that the subjects in subgroup II might have the characteristics of active metabolism; arrhythmogenic right ventricular cardiomyopathy and Neuroactive ligand−receptor interaction are significantly enriched in subgroup IV; we did not find a significant upregulation pathway in the third subgroup.Conclusion: The subgroups classification of liver cirrhosis cases shows that patients from different subgroups may have unique gene expression patterns, which indicates that patients in each subgroup should receive more personalized treatment.

scholarly journals Identifying Transcriptomic Correlates of Histology using Deep Learning

2020 ◽  
Author(s):  
Liviu Badea ◽  
Emil Stănescu
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Genome Wide ◽  
Gene Perturbations ◽  
Whole Slide Images

AbstractLinking phenotypes to specific gene expression profiles is an extremely important problem in biology, which has been approached mainly by correlation methods or, more fundamentally, by studying the effects of gene perturbations. However, genome-wide perturbations involve extensive experimental efforts, which may be prohibitive for certain organisms. On the other hand, the characterization of the various phenotypes frequently requires an expert’s subjective interpretation, such as a histopathologist’s description of tissue slide images in terms of complex visual features (e.g. ‘acinar structures’). In this paper, we use Deep Learning to eliminate the inherent subjective nature of these visual histological features and link them to genomic data, thus establishing a more precisely quantifiable correlation between transcriptomes and phenotypes. Using a dataset of whole slide images with matching gene expression data from 39 normal tissue types, we first developed a Deep Learning tissue classifier with an accuracy of 94%. Then we searched for genes whose expression correlates with features inferred by the classifier and demonstrate that Deep Learning can automatically derive visual (phenotypical) features that are well correlated with the transcriptome and therefore biologically interpretable. As we are particularly concerned with interpretability and explainability of the inferred histological models, we also develop visualizations of the inferred features and compare them with gene expression patterns determined by immunohistochemistry. This can be viewed as a first step toward bridging the gap between the level of genes and the cellular organization of tissues.

scholarly journals Identifying transcriptomic correlates of histology using deep learning

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242858
Author(s):  
Liviu Badea ◽  
Emil Stănescu
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Genome Wide ◽  
Gene Perturbations ◽  
Whole Slide Images

Linking phenotypes to specific gene expression profiles is an extremely important problem in biology, which has been approached mainly by correlation methods or, more fundamentally, by studying the effects of gene perturbations. However, genome-wide perturbations involve extensive experimental efforts, which may be prohibitive for certain organisms. On the other hand, the characterization of the various phenotypes frequently requires an expert’s subjective interpretation, such as a histopathologist’s description of tissue slide images in terms of complex visual features (e.g. ‘acinar structures’). In this paper, we use Deep Learning to eliminate the inherent subjective nature of these visual histological features and link them to genomic data, thus establishing a more precisely quantifiable correlation between transcriptomes and phenotypes. Using a dataset of whole slide images with matching gene expression data from 39 normal tissue types, we first developed a Deep Learning tissue classifier with an accuracy of 94%. Then we searched for genes whose expression correlates with features inferred by the classifier and demonstrate that Deep Learning can automatically derive visual (phenotypical) features that are well correlated with the transcriptome and therefore biologically interpretable. As we are particularly concerned with interpretability and explainability of the inferred histological models, we also develop visualizations of the inferred features and compare them with gene expression patterns determined by immunohistochemistry. This can be viewed as a first step toward bridging the gap between the level of genes and the cellular organization of tissues.

Automated Sorting of Monocytes from Whole Blood for Reproducible Gene Expression Profiling.

Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 3840-3840
Author(s):  
Carsten Poggel ◽  
Timo Adams ◽  
Sabine Martin ◽  
Carola Pickel ◽  
Nicole Prahl ◽  
...  
Keyword(s):  
Gene Expression ◽  
Blood Cell ◽  
Gene Expression Profiling ◽  
Whole Blood ◽  
Expression Profiling ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Cell Types ◽  
Specific Gene

Abstract Microarray-based gene expression profiling has been used to develop clinically relevant molecular classifiers for many different diseases. Furthermore, it has been shown for various chronic diseases that specific gene expression patterns are reflected at the level of blood cells. However, blood is a complex tissue comprising numerous cell types. Therefore, the contribution of rare cell types to a whole blood expression profile might not be detected and a substantial proportion of what is usually reported as “up-regulation” or “down-regulation” might actually be the result of a shift in cell populations and not of a true regulatory process. In order to circumvent these problems, several techniques have been established to analyze purified subpopulations rather than whole blood samples. Previously, it has been shown, for example, that reproducible gene expression profiles can be generated by positive selection of blood cell subsets from PBMCs1. As the preparation of PBMCs by, for example, Ficoll is time-consuming, inconvenient, and not amenable to automation, we have set up a combined direct whole blood cell separation and gene expression profiling protocol. By using Whole Blood CD14 MicroBeads in combination with the autoMACS Pro™ Separator, the separation protocol generally allowed enrichment of monocytes from whole blood within 30 min with purities higher than 90%. In combination with the depletion of neutrophils, the major source of contaminating RNA, purities increased to over 95% for all tested blood donors. Monocytes included the CD14bright/CD16− as well as the CD14dim/CD16+ populations. To assess the reproducibility of gene expression profiles and the influence of several experimental parameters, monocytes were sorted from 5 ml whole blood. RNA was extracted and hybridized to microarrays and the Pearson correlation coefficients of pairwise comparisons were calculated. Technical repeats of monocyte analysis from blood donated at different days showed a higher correlation coefficient than whole blood RNA. Blood storage at room temperature resulted in a strong deregulation of many genes, whereas blood stored at 4°C showed minimal changes, which is in agreement with previous studies. Skipping the centrifugation step, which is used to remove unbound MicroBeads did not alter the gene expression profiles. Incubation of sorted cells in PrepProtect™ Stabilization Buffer showed no alteration of gene expression thus enabling the shipping of cells without liquid nitrogen. Monocytes play a crucial role in diseases like atherosclerosis. Our rapid and simple protocol for combined direct cell sorting from whole blood and gene expression profiling of monocytes might help to ease the discovery of new biomarkers and to screen and monitor patients. 1 Lyons et al., BMC Genomics (2007), 8:64.

Tumor-specific gene expression patterns with gene expression profiles

2006 ◽  
Vol 49 (3) ◽  
pp. 293-304 ◽  
Author(s):  
Xiaogang Ruan ◽  
Yingxin Li ◽  
Jiangeng Li ◽  
Daoxiong Gong ◽  
Jinlian Wang
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Gene Expression Patterns ◽  
Specific Gene ◽  
Specific Gene Expression

Gene Expression Profiles as Predictors of Poor Outcomes in Stage II Colorectal Cancer: A Systematic Review and Meta-analysis

2009 ◽  
Vol 8 (4) ◽  
pp. 207-214 ◽  
Author(s):  
An-Ting T. Lu ◽  
Shelley R. Salpeter ◽  
Anthony E. Reeve ◽  
Steven Eschrich ◽  
Patrick G. Johnston ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Systematic Review ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Gene Expression Profiles ◽  
Stage Ii ◽  
Stage Ii Colorectal Cancer ◽  
Poor Outcomes

scholarly journals Discovering Distinct Patterns in Gene Expression Profiles

2008 ◽  
Vol 5 (2) ◽  
Author(s):  
Li Teng ◽  
Laiwan Chan
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Clustering Methods ◽  
Gene Expressions ◽  
Real Gene ◽  
Large Scale Dataset ◽  
Coexpressed Genes

SummaryTraditional analysis of gene expression profiles use clustering to find groups of coexpressed genes which have similar expression patterns. However clustering is time consuming and could be diffcult for very large scale dataset. We proposed the idea of Discovering Distinct Patterns (DDP) in gene expression profiles. Since patterns showing by the gene expressions reveal their regulate mechanisms. It is significant to find all different patterns existing in the dataset when there is little prior knowledge. It is also a helpful start before taking on further analysis. We propose an algorithm for DDP by iteratively picking out pairs of gene expression patterns which have the largest dissimilarities. This method can also be used as preprocessing to initialize centers for clustering methods, like K-means. Experiments on both synthetic dataset and real gene expression datasets show our method is very effective in finding distinct patterns which have gene functional significance and is also effcient.

CFTR ΔF508 mutation has minimal effect on the gene expression profile of differentiated human airway epithelia

2005 ◽  
Vol 289 (4) ◽  
pp. L545-L553 ◽  
Author(s):  
Joseph Zabner ◽  
Todd E. Scheetz ◽  
Hakeem G. Almabrazi ◽  
Thomas L. Casavant ◽  
Jian Huang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cystic Fibrosis ◽  
Large Scale ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Primary Cultures ◽  
Gene Expression Profiles ◽  
Filter Method ◽  
Tissue Destruction ◽  
Airway Epithelia

Cystic fibrosis (CF) is caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR), an epithelial chloride channel regulated by phosphorylation. Most of the disease-associated morbidity is the consequence of chronic lung infection with progressive tissue destruction. As an approach to investigate the cellular effects of CFTR mutations, we used large-scale microarray hybridization to contrast the gene expression profiles of well-differentiated primary cultures of human CF and non-CF airway epithelia grown under resting culture conditions. We surveyed the expression profiles for 10 non-CF and 10 ΔF508 homozygote samples. Of the 22,283 genes represented on the Affymetrix U133A GeneChip, we found evidence of significant changes in expression in 24 genes by two-sample t-test ( P < 0.00001). A second, three-filter method of comparative analysis found no significant differences between the groups. The levels of CFTR mRNA were comparable in both groups. There were no significant differences in the gene expression patterns between male and female CF specimens. There were 18 genes with significant increases and 6 genes with decreases in CF relative to non-CF samples. Although the function of many of the differentially expressed genes is unknown, one transcript that was elevated in CF, the KCl cotransporter (KCC4), is a candidate for further study. Overall, the results indicate that CFTR dysfunction has little direct impact on airway epithelial gene expression in samples grown under these conditions.

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