An official website of the United States government
Abstract The Soybean Gene Atlas project provides a comprehensive map for understanding gene expression patterns in major soybean tissues from flower, root, leaf, nodule, seed, and shoot and stem. The RNA‐Seq data generated in the project serve as a valuable resource for discovering tissue‐specific transcriptome behavior of soybean genes in different tissues. We developed a computational pipeline for Soybean context‐specific network (SoyCSN) inference with a suite of prediction tools to analyze, annotate, retrieve, and visualize soybean context‐specific networks at both transcriptome and interactome levels. BicMix and Cross‐Conditions Cluster Detection algorithms were applied to detect modules based on co‐expression relationships across all the tissues. Soybean context‐specific interactomes were predicted by combining soybean tissue gene expression and protein–protein interaction data. Functional analyses of these predicted networks provide insights into soybean tissue specificities. For example, under symbiotic, nitrogen‐fixing conditions, the constructed soybean leaf network highlights the connection between the photosynthesis function and rhizobium–legume symbiosis. SoyCSN data and all its results are publicly available via an interactive web service within the Soybean Knowledge Base (SoyKB) athttp://soykb.org/SoyCSN. SoyCSN provides a useful web‐based access for exploring context specificities systematically in gene regulatory mechanisms and gene relationships for soybean researchers and molecular breeders.
more »
« less
Transcriptome‐wide expression landscape and starch synthesis pathway co‐expression network in sorghum
Abstract The gene expression landscape across different tissues and developmental stages reflects their biological functions and evolutionary patterns. Integrative and comprehensive analyses of all transcriptomic data in an organism are instrumental to obtaining a comprehensive picture of gene expression landscape. Such studies are still very limited in sorghum, which limits the discovery of the genetic basis underlying complex agricultural traits in sorghum. We characterized the genome‐wide expression landscape for sorghum using 873 RNA‐sequencing (RNA‐seq) datasets representing 19 tissues. Our integrative analysis of these RNA‐seq data provides the most comprehensive transcriptomic atlas for sorghum, which will be valuable for the sorghum research community for functional characterizations of sorghum genes. Based on the transcriptome atlas, we identified 595 housekeeping genes (HKGs) and 2080 tissue‐specific expression genes (TEGs) for the 19 tissues. We identified different gene features between HKGs and TEGs, and we found that HKGs have experienced stronger selective constraints than TEGs. Furthermore, we built a transcriptome‐wide co‐expression network (TW‐CEN) comprising 35 modules with each module enriched in specific Gene Ontology terms. High‐connectivity genes in TW‐CEN tend to express at high levels while undergoing intensive selective pressure. We also built global and seed‐preferential co‐expression networks of starch synthesis pathways, which indicated that photosynthesis and microtubule‐based movement play important roles in starch synthesis. The global transcriptome atlas of sorghum generated by this study provides an important functional genomics resource for trait discovery and insight into starch synthesis regulation in sorghum.
more »
« less
- Award ID(s):
- 1951332
- PAR ID:
- 10596982
- Publisher / Repository:
- Wiley Periodicals LLC
- Date Published:
- Journal Name:
- The Plant Genome
- Volume:
- 17
- Issue:
- 2
- ISSN:
- 1940-3372
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Extremophytes are naturally selected to survive environmental stresses, but scarcity of genetic resources for them developed with spatiotemporal resolution limit their use in stress biology. Schrenkiella parvula is one of the leading extremophyte models with initial molecular genomic resources developed to study its tolerance mechanisms to high salinity. Here we present a transcriptome atlas for S. parvula with subsequent analyses to highlight its diverse gene expression networks associated with salt responses. We included spatiotemporal expression profiles, expression specificity of each gene, and co-expression and functional gene networks representing 115 transcriptomes sequenced from 35 tissue and developmental stages examining their responses before and after 27 salt treatments in our current study. The highest number of tissue-preferentially expressed genes were found in seeds and siliques while genes in seedlings showed the broadest expression profiles among developmental stages. Seedlings had the highest magnitude of overall transcriptomic responses to salinity compared to mature tissues and developmental stages. Differentially expressed genes in response to salt were largely mutually exclusive but shared common stress response pathways spanning across tissues and developmental stages. Our foundational dataset created for S. parvula representing a stress-adapted wild plant lays the groundwork for future functional, comparative, and evolutionary studies using extremophytes aiming to uncover novel stress tolerant mechanisms.more » « less
-
In maize, starch mutants have facilitated characterization of key genes involved in endosperm starch biosynthesis such as large subunit of AGPase Shrunken2 ( Sh2 ) and isoamylase type DBE Sugary1 ( Su1 ). While many starch biosynthesis enzymes have been characterized, the mechanisms of certain genes (including Sugary enhancer1 ) are yet undefined, and very little is understood about the regulation of starch biosynthesis. As a model, we utilize commercially important sweet corn mutations, sh2 and su1 , to genetically perturb starch production in the endosperm. To characterize the transcriptomic response to starch mutations and identify potential regulators of this pathway, differential expression and coexpression network analysis was performed on near-isogenic lines (NILs) (wildtype, sh2 , and su1 ) in six genetic backgrounds. Lines were grown in field conditions and kernels were sampled in consecutive developmental stages (blister stage at 14 days after pollination (DAP), milk stage at 21 DAP, and dent stage at 28 DAP). Kernels were dissected to separate embryo and pericarp from the endosperm tissue and 3′ RNA-seq libraries were prepared. Mutation of the Su1 gene led to minimal changes in the endosperm transcriptome. Responses to loss of sh2 function include increased expression of sugar (SWEET) transporters and of genes for ABA signaling. Key regulators of starch biosynthesis and grain filling were identified. Notably, this includes Class II trehalose 6-phosphate synthases, Hexokinase1 , and Apetala2 transcription factor-like (AP2/ERF) transcription factors. Additionally, our results provide insight into the mechanism of Sugary enhancer1 , suggesting a potential role in regulating GA signaling via GRAS transcription factor Scarecrow-like1 .more » « less
-
The starch metabolic network was investigated in relation to other metabolic processes by examining a mutant with altered single-gene expression of ATP citrate lyase (ACL), an enzyme responsible for generating cytosolic acetyl-CoA pool from citrate. Previous research has shown that transgenic antisense plants with reduced ACL activity accumulate abnormally enlarged starch granules. In this study, we explored the underlying molecular mechanisms linking cytosolic acetyl-CoA generation and starch metabolism under short-day photoperiods. We performed transcriptome and quantification of starch accumulation in the leaves of wild-type and antisense seedlings with reduced ACL activity. The antisense-ACLA mutant accumulated more starch than the wild type under short-day conditions. Zymogram analyses were conducted to compare the activities of starch-metabolizing enzymes with transcriptomic changes in the seedling. Differential expression between wild-type and antisense-ACLA plants was detected in genes implicated in starch and acetyl-CoA metabolism, and cell wall metabolism. These analyses revealed a strong correlation between the transcript levels of genes responsible for starch synthesis and degradation, reflecting coordinated regulation at the transcriptomic level. Furthermore, our data provide novel insights into the regulatory links between cytosolic acetyl-CoA metabolism and starch metabolic pathways.more » « less
-
Abstract To understand phenotypic variations and key factors which affect disease susceptibility of complex traits, it is important to decipher cell‐type tissue compositions. To study cellular compositions of bulk tissue samples, one can evaluate cellular abundances and cell‐type‐specific gene expression patterns from the tissue transcriptome profiles. We develop both fixed and mixed models to reconstruct cellular expression fractions for bulk‐profiled samples by using reference single‐cell (sc) RNA‐sequencing (RNA‐seq) reference data. In benchmark evaluations of estimating cellular expression fractions, the mixed‐effect models provide similar results as an elegant machine learning algorithm named cell‐type identification by estimating relative subsets of RNA transcripts (CIBERSORTx), which is a well‐known and reliable procedure to reconstruct cell‐type abundances and cell‐type‐specific gene expression profiles. In real data analysis, the mixed‐effect models outperform or perform similarly as CIBERSORTx. The mixed models perform better than the fixed models in both benchmark evaluations and data analysis. In simulation studies, we show that if the heterogeneity exists in scRNA‐seq data, it is better to use mixed models with heterogeneous mean and variance–covariance. As a byproduct, the mixed models provide fractions of covariance between subject‐specific gene expression and cell types to measure their correlations. The proposed mixed models provide a complementary tool to dissect bulk tissues using scRNA‐seq data.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1002/tpg2.20448
