scholarly journals Transcriptome Analysis in Male Strobilus Induction by Gibberellin Treatment in Cryptomeria japonica D. Don

Forests ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 633 ◽  
Author(s):  
Manabu Kurita ◽  
Kentaro Mishima ◽  
Miyoko Tsubomura ◽  
Yuya Takashima ◽  
Mine Nose ◽  
...  
Keyword(s):  
Gene Expression ◽  
Plant Hormones ◽  
Cryptomeria Japonica ◽  
Time Course ◽  
Sequence Similarity ◽  
Expression Patterns ◽  
Principal Component ◽  
Treated Group ◽  
Flower Bud ◽  
Male Strobilus

The plant hormone gibberellin (GA) is known to regulate elongating growth, seed germination, and the initiation of flower bud formation, and it has been postulated that GAs originally had functions in reproductive processes. Studies on the mechanism of induction of flowering by GA have been performed in Arabidopsis and other model plants. In coniferous trees, reproductive organ induction by GAs is known to occur, but there are few reports on the molecular mechanism in this system. To clarify the gene expression dynamics of the GA induction of the male strobilus in Cryptomeria japonica, we performed comprehensive gene expression analysis using a microarray. A GA-treated group and a nontreated group were allowed to set, and individual trees were sampled over a 6-week time course. A total of 881 genes exhibiting changed expression was identified. In the GA-treated group, genes related to ‘stress response’ and to ‘cell wall’ were initially enriched, and genes related to ‘transcription’ and ‘transcription factor activity’ were enriched at later stages. This analysis also clarified the dynamics of the expression of genes related to GA signaling transduction following GA treatment, permitting us to compare and contrast with the expression dynamics of genes implicated in signal transduction responses to other plant hormones. These results suggested that various plant hormones have complex influences on the male strobilus induction. Additionally, principal component analysis (PCA) using expression patterns of the genes that exhibited sequence similarity with flower bud or floral organ formation-related genes of Arabidopsis was performed. PCA suggested that gene expression leading to male strobilus formation in C. japonica became conspicuous within one week of GA treatment. Together, these findings help to clarify the evolution of the mechanism of induction of reproductive organs by GA.

Follistatin alters myostatin gene expression in C2C12 muscle cells

2004 ◽  
Vol 52 (2) ◽  
pp. 135-141 ◽  
Author(s):  
H. Kocams¸ ◽  
N. Gulmez ◽  
S. Aslan ◽  
M. Nazlı
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
Time Course ◽  
Expression Patterns ◽  
Mrna Level ◽  
Gene Expression Pattern ◽  
Muscle Cells ◽  
Treated Group ◽  
Myostatin Gene ◽  
Significant Difference

The objective of the present study was to determine the effects of follistatin addition on myostatin and follistatin gene expression patterns in C2C12 muscle cells. C2C12 cells were administered with 100 ng/ml recombinant human (rh) follistatin in Dulbecco's modified Eagle medium (DMEM) containing 10% fetal bovine serum (FBS), 4 mM glutamine and antibiotics daily for three days. Rh follistatin was not added in the control wells. Follistatin and myostatin gene cDNAs were synthesised by reverse transcriptase polymerase chain reaction (RT-PCR).The time course of follistatin gene expression pattern was similar in both the control and the follistatin-treated group. Myostatin mRNA level significantly increased in the follistatin-treated group after 24 h of culture (Fig. 3, P < 0.01). Amounts then sharply decreased (Fig. 3, P < 0.01) at 48 h of culture, whereas there was no significant difference between the control and the follistatin-treated group at 72 h of culture. Our results demonstrated that myostatin and follistatin mRNA were expressed in C2C12 cells and rh follistatin changed the myostatin expression pattern.

Third coronary artery in monkey heart

2004 ◽  
Vol 52 (3) ◽  
pp. 253-257 ◽  
Author(s):  
Valentina Nikolić ◽  
Gordana Teofilovski-Parapid ◽  
Gordana Stanković ◽  
Biljana Parapid ◽  
S. Malobabić ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
Time Course ◽  
Expression Patterns ◽  
Mrna Level ◽  
Gene Expression Pattern ◽  
Treated Group ◽  
Myostatin Gene ◽  
Significant Difference ◽  
Fetal Bovine

The objective of the present study was to determine the effects of follistatin addition on myostatin and follistatin gene expression patterns in C2C12 muscle cells. C2C12 cells were administered with 100 ng/ml recombinant human (rh) follistatin in Dulbecco's modified Eagle medium (DMEM) containing 10% fetal bovine serum (FBS), 4 mM glutamine and antibiotics daily for three days. Rh follistatin was not added in the control wells. Follistatin and myostatin gene cDNAs were synthesised by reverse transcriptase polymerase chain reaction (RT-PCR).The time course of follistatin gene expression pattern was similar in both the control and the follistatin-treated group. Myostatin mRNA level significantly increased in the follistatin-treated group after 24 h of culture (Fig. 3, P < 0.01). Amounts then sharply decreased (Fig. 3, P < 0.01) at 48 h of culture, whereas there was no significant difference between the control and the follistatin-treated group at 72 h of culture. Our results demonstrated that myostatin and follistatin mRNA were expressed in C2C12 cells and rh follistatin changed the myostatin expression pattern.

scholarly journals Comprehensive Comparison of Amnion Stromal Cells and Chorion Stromal Cells by RNA-Seq

2021 ◽  
Vol 22 (4) ◽  
pp. 1901
Author(s):  
Brielle Jones ◽  
Chaoyang Li ◽  
Min Sung Park ◽  
Anne Lerch ◽  
Vimal Jacob ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stromal Cells ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Principal Component ◽  
Differentially Expressed ◽  
Inflammatory Factor ◽  
Next Generation Sequencing Technology ◽  
Angiogenic Potential ◽  
Anti Inflammatory

Mesenchymal stromal cells derived from the fetal placenta, composed of an amnion membrane, chorion membrane, and umbilical cord, have emerged as promising sources for regenerative medicine. Here, we used next-generation sequencing technology to comprehensively compare amniotic stromal cells (ASCs) with chorionic stromal cells (CSCs) at the molecular and signaling levels. Principal component analysis showed a clear dichotomy of gene expression profiles between ASCs and CSCs. Unsupervised hierarchical clustering confirmed that the biological repeats of ASCs and CSCs were able to respectively group together. Supervised analysis identified differentially expressed genes, such as LMO3, HOXA11, and HOXA13, and differentially expressed isoforms, such as CXCL6 and HGF. Gene Ontology (GO) analysis showed that the GO terms of the extracellular matrix, angiogenesis, and cell adhesion were significantly enriched in CSCs. We further explored the factors associated with inflammation and angiogenesis using a multiplex assay. In comparison with ASCs, CSCs secreted higher levels of angiogenic factors, including angiogenin, VEGFA, HGF, and bFGF. The results of a tube formation assay proved that CSCs exhibited a strong angiogenic function. However, ASCs secreted two-fold more of an anti-inflammatory factor, TSG-6, than CSCs. In conclusion, our study demonstrated the differential gene expression patterns between ASCs and CSCs. CSCs have superior angiogenic potential, whereas ASCs exhibit increased anti-inflammatory properties.

scholarly journals Jonckheere–Terpstra–Kendall-based non-parametric analysis of temporal differential gene expression

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Hitoshi Iuchi ◽  
Michiaki Hamada
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Time Course ◽  
Time Series Data ◽  
Expression Patterns ◽  
Detection Methods ◽  
Series Data ◽  
Expression Levels ◽  
Over Time ◽  
Non Parametric

Abstract Time-course experiments using parallel sequencers have the potential to uncover gradual changes in cells over time that cannot be observed in a two-point comparison. An essential step in time-series data analysis is the identification of temporal differentially expressed genes (TEGs) under two conditions (e.g. control versus case). Model-based approaches, which are typical TEG detection methods, often set one parameter (e.g. degree or degree of freedom) for one dataset. This approach risks modeling of linearly increasing genes with higher-order functions, or fitting of cyclic gene expression with linear functions, thereby leading to false positives/negatives. Here, we present a Jonckheere–Terpstra–Kendall (JTK)-based non-parametric algorithm for TEG detection. Benchmarks, using simulation data, show that the JTK-based approach outperforms existing methods, especially in long time-series experiments. Additionally, application of JTK in the analysis of time-series RNA-seq data from seven tissue types, across developmental stages in mouse and rat, suggested that the wave pattern contributes to the TEG identification of JTK, not the difference in expression levels. This result suggests that JTK is a suitable algorithm when focusing on expression patterns over time rather than expression levels, such as comparisons between different species. These results show that JTK is an excellent candidate for TEG detection.

scholarly journals Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data

2017 ◽  
Vol 14 (2) ◽  
Author(s):  
Qihua Tan ◽  
Mads Thomassen ◽  
Mark Burton ◽  
Kristian Fredløv Mose ◽  
Klaus Ejner Andersen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Correlation Coefficient ◽  
Parametric Analysis ◽  
Time Course ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Microarray Time Course ◽  
Time Course Data ◽  
Generalized Correlation ◽  
Non Parametric

AbstractModeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health.

scholarly journals Effects of Collagen–Glycosaminoglycan Mesh on Gene Expression as Determined by Using Principal Component Analysis-Based Unsupervised Feature Extraction

Polymers ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 4117
Author(s):  
Y-h. Taguchi ◽  
Turki Turki
Keyword(s):  
Gene Expression ◽  
Principal Component Analysis ◽  
Feature Extraction ◽  
Cell Lines ◽  
Time Course ◽  
Expression Profiles ◽  
Principal Component ◽  
Component Analysis ◽  
Altered Expression ◽  
Unsupervised Feature Extraction

The development of the medical applications for substances or materials that contact cells is important. Hence, it is necessary to elucidate how substances that surround cells affect gene expression during incubation. In the current study, we compared the gene expression profiles of cell lines that were in contact with collagen–glycosaminoglycan mesh and control cells. Principal component analysis-based unsupervised feature extraction was applied to identify genes with altered expression during incubation in the treated cell lines but not in the controls. The identified genes were enriched in various biological terms. Our method also outperformed a conventional methodology, namely, gene selection based on linear regression with time course.

CURVE-BASED CLUSTERING OF TIME COURSE GENE EXPRESSION DATA USING SELF-ORGANIZING MAPS

2009 ◽  
Vol 07 (04) ◽  
pp. 645-661 ◽  
Author(s):  
XIN CHEN
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Regulatory Networks ◽  
Time Course ◽  
Clustering Algorithm ◽  
Expression Patterns ◽  
Self Organizing Map ◽  
Expression Data ◽  
Wide Range ◽  
Self Organizing

There is an increasing interest in clustering time course gene expression data to investigate a wide range of biological processes. However, developing a clustering algorithm ideal for time course gene express data is still challenging. As timing is an important factor in defining true clusters, a clustering algorithm shall explore expression correlations between time points in order to achieve a high clustering accuracy. Moreover, inter-cluster gene relationships are often desired in order to facilitate the computational inference of biological pathways and regulatory networks. In this paper, a new clustering algorithm called CurveSOM is developed to offer both features above. It first presents each gene by a cubic smoothing spline fitted to the time course expression profile, and then groups genes into clusters by applying a self-organizing map-based clustering on the resulting splines. CurveSOM has been tested on three well-studied yeast cell cycle datasets, and compared with four popular programs including Cluster 3.0, GENECLUSTER, MCLUST, and SSClust. The results show that CurveSOM is a very promising tool for the exploratory analysis of time course expression data, as it is not only able to group genes into clusters with high accuracy but also able to find true time-shifted correlations of expression patterns across clusters.

Blood-based gene expression profiling to reveal potential response biomarkers for immunotherapy in advanced lung cancer.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e15155-e15155
Author(s):  
Jie Wu ◽  
Weihua Huang ◽  
Changhong Yin ◽  
Yee Him Cheung ◽  
Debra Abrams ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Time Course ◽  
Γδ T Cells ◽  
Principal Component ◽  
Genetic Mutation ◽  
Differentially Expressed ◽  
Advanced Lung Cancer

e15155 Background: Novel immunotherapies are becoming a viable option for advanced lung cancer treatment. High-throughput gene expression profiling (GEP) has enabled better understanding of patient responsiveness to targeted immunotherapy. However, non-invasive blood-based signatures are needed to monitor and predict response. Methods: To evaluate the predictive and prognostic role of blood-based GEP, we designed a longitudinal study by enrolling 15 patients with advanced lung cancers. A total of 65 samples were collected for RNA sequencing, ~4 blood specimens per patient, before and during anti-PD-1 treatment. We used multiple analyses, including time-course differential gene expression, principal component, lymphocyte compartment deconvolution, and genetic mutation, to search for and assess potential predictive and prognostic aspects. Results: Of 15 patients, 11 were classified as Responders (partial responders) and four were Non-Responders (one stable and three progressive diseases). Our analyses demonstrated: 1) By comparing baseline GEPs from Responders vs. Non-Responders before the first treatment, we identified potential markers (e.g., LY6E is significantly lower expressed in Responders, with Log2 Fold change = -3.44 and p = 1.83E-04) that can be used as predictors of responsiveness of the patients; 2) Immunoglobulin subunits- and T cell receptor complex-related genes were differentially expressed in Responders (DAVID analysis, p = 6.7E-3 and 2.1E-2, respectively), but not in Non-responders; 3) γδ T cells in the lymphocyte compartment were relatively increased in Responders; 4) Despite a different set of genes differentially expressed at different time points, the biggest GEP changes were at ~ week 6, after the second treatment. Additionally, we observed certain genes consistently up- or down-regulated through the whole course of treatment. Furthermore, after the first treatment, genes in the immune response pathway were regulated to different directions in Responders and Non-Responders. For example, interleukin receptor genes, such as IL18R1 and IL18RAP, and CD24 were down-regulated in Responders, but up-regulated in Non-Responders (p = 0.042, 0.023 and 0.044 respectively, t-test for the differential expression in these two groups). Conclusions: The utility of blood GEP to identify predictive and prognostic factors for precision immunotherapy is encouraging. Nevertheless, these results, predictive of the anti-PD-1 clinical response, are preliminary and need to be validated in a larger cohort.

scholarly journals Expansion of the circadian transcriptome in Brassica rapa and genome-wide diversification of paralog expression patterns

2020 ◽  
Author(s):  
Kathleen Greenham ◽  
Ryan C. Sartor ◽  
Stevan Zorich ◽  
Ping Lou ◽  
Todd C. Mockler ◽  
...  
Keyword(s):  
Gene Expression ◽  
Brassica Rapa ◽  
Regulatory Network ◽  
Time Course ◽  
Expression Patterns ◽  
Time Of Day ◽  
Circadian Regulation ◽  
Genome Wide ◽  
Circadian Control ◽  
Gene Regulatory

AbstractAn important challenge of crop improvement strategies is assigning function to paralogs in polyploid crops. Gene expression is one method for determining the activity of paralogs; however, the majority of transcript abundance data represents a static point that does not consider the spatial and temporal dynamics of the transcriptome. Studies in Arabidopsis have estimated up to 90% of the transcriptome to be under diel or circadian control depending on the condition. As a result, time of day effects on the transcriptome have major implications on how we characterize gene activity. In this study, we aimed to resolve the circadian transcriptome in the polyploid crop Brassica rapa and explore the fate of multicopy orthologs of Arabidopsis circadian regulated genes. We performed a high-resolution time course study with 2 h sampling density to capture the genes under circadian control. Strikingly, more than two-thirds of expressed genes exhibited rhythmicity indicative of circadian regulation. To compare the expression patterns of paralogous genes, we developed a program in R called DiPALM (Differential Pattern Analysis by Linear Models) that analyzes time course data to identify transcripts with significant pattern differences. Using DiPALM, we identified genome-wide divergence of expression patterns among retained paralogs. Cross-comparison with a previously generated diel drought experiment in B. rapa revealed evidence for differential drought response for these diverging paralog pairs. Using gene regulatory network models we compared transcription factor targets between B. rapa and Arabidopsis circadian networks to reveal additional evidence for divergence in expression between B. rapa paralogs that may be driven in part by variation in conserved non coding sequences. These findings provide new insight into the rapid expansion and divergence of the transcriptional network in a polyploid crop and offer a new method for assessing paralog activity at the transcript level.SignificanceThe circadian regulation of the transcriptome leads to time of day changes in gene expression that coordinates environmental conditions with physiological responses. Brassica rapa, a morphologically diverse crop species, has undergone whole genome triplication since diverging from Arabidopsis resulting in an expansion of gene copy number. To examine how this expansion has influenced the circadian transcriptome we developed a new method for comparing gene expression patterns. This method facilitated the discovery of genome-wide expansion of expression patterns for genes present in multiple copies and divergence in temporal abiotic stress response. We find support for conserved sequences outside the gene body contributing to these expression pattern differences and ultimately generating new connections in the gene regulatory network.

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