scholarly journals Integrative Modeling of Gene Expression and Metabolic Networks of Arabidopsis Embryos for Identification of Seed Oil Causal Genes

2021 ◽  
Vol 12 ◽  
Author(s):  
Mathieu Cloutier ◽  
Daoquan Xiang ◽  
Peng Gao ◽  
Leon V. Kochian ◽  
Jitao Zou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Time Course ◽  
Metabolic Networks ◽  
Seed Oil ◽  
Industrial Applications ◽  
Supporting Evidence ◽  
Oil Composition ◽  
Seed Embryo ◽  
Causal Genes ◽  
Future Demands

Fatty acids in crop seeds are a major source for both vegetable oils and industrial applications. Genetic improvement of fatty acid composition and oil content is critical to meet the current and future demands of plant-based renewable seed oils. Addressing this challenge can be approached by network modeling to capture key contributors of seed metabolism and to identify underpinning genetic targets for engineering the traits associated with seed oil composition and content. Here, we present a dynamic model, using an Ordinary Differential Equations model and integrated time-course gene expression data, to describe metabolic networks during Arabidopsis thaliana seed development. Through in silico perturbation of genes, targets were predicted in seed oil traits. Validation and supporting evidence were obtained for several of these predictions using published reports in the scientific literature. Furthermore, we investigated two predicted targets using omics datasets for both gene expression and metabolites from the seed embryo, and demonstrated the applicability of this network-based model. This work highlights that integration of dynamic gene expression atlases generates informative models which can be explored to dissect metabolic pathways and lead to the identification of causal genes associated with seed oil traits.

scholarly journals Engineering Trienoic Fatty Acids into Cottonseed Oil Improves Low-Temperature Seed Germination, Plant Photosynthesis and Cotton Fiber Quality

2020 ◽  
Vol 61 (7) ◽  
pp. 1335-1347 ◽  
Author(s):  
Lihong Gao ◽  
Wei Chen ◽  
Xiaoyu Xu ◽  
Jing Zhang ◽  
Tanoj K Singh ◽  
...  
Keyword(s):  
Fatty Acids ◽  
Low Temperature ◽  
Linolenic Acid ◽  
Seed Oil ◽  
Fiber Quality ◽  
Fatty Acid Desaturase ◽  
Industrial Applications ◽  
Oil Composition ◽  
Alpha Linolenic Acid ◽  
Total Oil

Abstract Alpha-linolenic acid (ALA, 18:3Δ9,12,15) and γ-linolenic acid \ (GLA, 18:3Δ6,9,12) are important trienoic fatty acids, which are beneficial for human health in their own right, or as precursors for the biosynthesis of long-chain polyunsaturated fatty acids. ALA and GLA in seed oil are synthesized from linoleic acid (LA, 18:2Δ9,12) by the microsomal ω-3 fatty acid desaturase (FAD3) and Δ6 desaturase (D6D), respectively. Cotton (Gossypium hirsutum L.) seed oil composition was modified by transforming with an FAD3 gene from Brassica napus and a D6D gene from Echium plantagineum, resulting in approximately 30% ALA and 20% GLA, respectively. The total oil content in transgenic seeds remained unaltered relative to parental seeds. Despite the use of a seed-specific promoter for transgene expression, low levels of GLA and increased levels of ALA were found in non-seed cotton tissues. At low temperature, the germinating cottonseeds containing the linolenic acid isomers elongated faster than the untransformed controls. ALA-producing lines also showed higher photosynthetic rates at cooler temperature and better fiber quality compared to both untransformed controls and GLA-producing lines. The oxidative stability of the novel cottonseed oils was assessed, providing guidance for potential food, pharmaceutical and industrial applications of these oils.

scholarly journals Unravelling the Complex Interplay of Transcription Factors Orchestrating Seed Oil Content in Brassica napus L.

2021 ◽  
Vol 22 (3) ◽  
pp. 1033
Author(s):  
Abirami Rajavel ◽  
Selina Klees ◽  
Johanna-Sophie Schlüter ◽  
Hendrik Bertram ◽  
Kun Lu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Brassica Napus ◽  
Oil Content ◽  
Seed Oil ◽  
Seed Oil Content ◽  
Complex Interplay ◽  
Brassica Napus L ◽  
Data Set ◽  
Developmental Switches

Transcription factors (TFs) and their complex interplay are essential for directing specific genetic programs, such as responses to environmental stresses, tissue development, or cell differentiation by regulating gene expression. Knowledge regarding TF–TF cooperations could be promising in gaining insight into the developmental switches between the cultivars of Brassica napus L., namely Zhongshuang11 (ZS11), a double-low accession with high-oil- content, and Zhongyou821 (ZY821), a double-high accession with low-oil-content. In this regard, we analysed a time series RNA-seq data set of seed tissue from both of the cultivars by mainly focusing on the monotonically expressed genes (MEGs). The consideration of the MEGs enables the capturing of multi-stage progression processes that are orchestrated by the cooperative TFs and, thus, facilitates the understanding of the molecular mechanisms determining seed oil content. Our findings show that TF families, such as NAC, MYB, DOF, GATA, and HD-ZIP are highly involved in the seed developmental process. Particularly, their preferential partner choices as well as changes in their gene expression profiles seem to be strongly associated with the differentiation of the oil content between the two cultivars. These findings are essential in enhancing our understanding of the genetic programs in both cultivars and developing novel hypotheses for further experimental studies.

scholarly journals Bayesian approach for predicting responses to therapy from high-dimensional time-course gene expression profiles

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Arika Fukushima ◽  
Masahiro Sugimoto ◽  
Satoru Hiwa ◽  
Tomoyuki Hiroyasu
Keyword(s):  
Gene Expression ◽  
Time Course ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Response To Therapy ◽  
Hcv Infection ◽  
Entire Treatment Period ◽  
Time Points ◽  
Time Course Data ◽  
Conventional Methods

Abstract Background Historical and updated information provided by time-course data collected during an entire treatment period proves to be more useful than information provided by single-point data. Accurate predictions made using time-course data on multiple biomarkers that indicate a patient’s response to therapy contribute positively to the decision-making process associated with designing effective treatment programs for various diseases. Therefore, the development of prediction methods incorporating time-course data on multiple markers is necessary. Results We proposed new methods that may be used for prediction and gene selection via time-course gene expression profiles. Our prediction method consolidated multiple probabilities calculated using gene expression profiles collected over a series of time points to predict therapy response. Using two data sets collected from patients with hepatitis C virus (HCV) infection and multiple sclerosis (MS), we performed numerical experiments that predicted response to therapy and evaluated their accuracies. Our methods were more accurate than conventional methods and successfully selected genes, the functions of which were associated with the pathology of HCV infection and MS. Conclusions The proposed method accurately predicted response to therapy using data at multiple time points. It showed higher accuracies at early time points compared to those of conventional methods. Furthermore, this method successfully selected genes that were directly associated with diseases.

scholarly journals SPLICE-q: a Python tool for genome-wide quantification of splicing efficiency

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Verônica R. de Melo Costa ◽  
Julianus Pfeuffer ◽  
Annita Louloupi ◽  
Ulf A. V. Ørom ◽  
Rosario M. Piro
Keyword(s):  
Gene Expression ◽  
Cancer Progression ◽  
Time Course ◽  
Progressive Increase ◽  
Rna Seq ◽  
Transcript Processing ◽  
Aberrant Transcript ◽  
Genome Wide ◽  
Splicing Efficiency ◽  
Nascent Rna

Abstract Background Introns are generally removed from primary transcripts to form mature RNA molecules in a post-transcriptional process called splicing. An efficient splicing of primary transcripts is an essential step in gene expression and its misregulation is related to numerous human diseases. Thus, to better understand the dynamics of this process and the perturbations that might be caused by aberrant transcript processing it is important to quantify splicing efficiency. Results Here, we introduce SPLICE-q, a fast and user-friendly Python tool for genome-wide SPLICing Efficiency quantification. It supports studies focusing on the implications of splicing efficiency in transcript processing dynamics. SPLICE-q uses aligned reads from strand-specific RNA-seq to quantify splicing efficiency for each intron individually and allows the user to select different levels of restrictiveness concerning the introns’ overlap with other genomic elements such as exons of other genes. We applied SPLICE-q to globally assess the dynamics of intron excision in yeast and human nascent RNA-seq. We also show its application using total RNA-seq from a patient-matched prostate cancer sample. Conclusions Our analyses illustrate that SPLICE-q is suitable to detect a progressive increase of splicing efficiency throughout a time course of nascent RNA-seq and it might be useful when it comes to understanding cancer progression beyond mere gene expression levels. SPLICE-q is available at: https://github.com/vrmelo/SPLICE-q

scholarly journals IL-8 and MCP Gene Expression and Production by LPS-Stimulated Human Corneal Stromal Cells

2012 ◽  
Vol 2012 ◽  
pp. 1-4 ◽  
Author(s):  
Roni M. Shtein ◽  
Susan G. Elner ◽  
Zong-Mei Bian ◽  
Victor M. Elner
Keyword(s):  
Gene Expression ◽  
Northern Blot ◽  
Stromal Cells ◽  
Blot Analysis ◽  
Time Course ◽  
Northern Blot Analysis ◽  
Statistical Significance ◽  
Dependent Manner ◽  
Corneal Inflammation ◽  
Chemotactic Protein

Purpose. To determine time course of effect of lipopolysaccharide (LPS) on production of interleukin-8 (IL-8) and monocyte chemotactic protein (MCP) by cultured human corneal stromal cells.Methods. Human corneal stromal cells were harvested from donor corneal specimens, and fourth to sixth passaged cells were used. Cell cultures were stimulated with LPS for 2, 4, 8, and 24 hours. Northern blot analysis of IL-8 and MCP gene expression and ELISA for IL-8 and MCP secretion were performed. ELISA results were analyzed for statistical significance using two-tailed Student'st-test.Results. Northern blot analysis demonstrated significantly increased IL-8 and MCP gene expression after 4 and 8 hours of exposure to LPS. ELISA for secreted IL-8 and MCP demonstrated statistically significant increases (P<0.05) after corneal stromal cell stimulation with LPS.Conclusions. This paper suggests that human corneal stromal cells may participate in corneal inflammation by secreting potent leukocyte chemotactic and activating proteins in a time-dependent manner when exposed to LPS.

scholarly journals Robust regression for periodicity detection in non-uniformly sampled time-course gene expression data

2007 ◽  
Vol 8 (1) ◽  
Author(s):  
Miika Ahdesmäki ◽  
Harri Lähdesmäki ◽  
Andrew Gracey ◽  
llya Shmulevich ◽  
Olli Yli-Harja
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Time Course ◽  
Robust Regression ◽  
Expression Data ◽  
Periodicity Detection

scholarly journals Distinct Biphasic mRNA Changes in Response to Asian Soybean Rust Infection

2007 ◽  
Vol 20 (8) ◽  
pp. 887-899 ◽  
Author(s):  
Martijn van de Mortel ◽  
Justin C. Recknor ◽  
Michelle A. Graham ◽  
Dan Nettleton ◽  
Jaime D. Dittman ◽  
...  
Keyword(s):  
Gene Expression ◽  
Time Course ◽  
Expression Profiles ◽  
Fungal Growth ◽  
Initial Response ◽  
Soybean Rust ◽  
Specific Response ◽  
Susceptible Genotype ◽  
Rust Infection ◽  
Asian Soybean Rust

Asian soybean rust (ASR), caused by Phakopsora pachyrhizi, is now established in all major soybean-producing countries. Currently, there is little information about the molecular basis of ASR–soybean interactions, which will be needed to assist future efforts to develop effective resistance. Toward this end, abundance changes of soybean mRNAs were measured over a 7-day ASR infection time course in mock-inoculated and infected leaves of a soybean accession (PI230970) carrying the Rpp2 resistance gene and a susceptible genotype (Embrapa-48). The expression profiles of differentially expressed genes (ASR-infected compared with the mock-inoculated control) revealed a biphasic response to ASR in each genotype. Within the first 12 h after inoculation (hai), which corresponds to fungal germination and penetration of the epidermal cells, differential gene expression changes were evident in both genotypes. mRNA expression of these genes mostly returned to levels found in mock-inoculated plants by 24 hai. In the susceptible genotype, gene expression remained unaffected by rust infection until 96 hai, a time period when rapid fungal growth began. In contrast, gene expression in the resistant genotype diverged from the mock-inoculated control earlier, at 72 h, demonstrating that Rpp2-mediated defenses were initiated prior to this time. These data suggest that ASR initially induces a non-specific response that is transient or is suppressed when early steps in colonization are completed in both soybean genotypes. The race-specific resistance phenotype of Rpp2 is manifested in massive gene expression changes after the initial response prior to the onset of rapid fungal growth that occurs in the susceptible genotype.

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