ATTED-II in 2016: A Plant Coexpression Database Towards Lineage-Specific Coexpression

Aoki, Yosuke; Okamura, Yasunobu; Tadaka, Shu; Kinoshita, Kengo; Obayashi, Takeshi

doi:10.1093/pcp/pcv165

Cited by 156 publications

(138 citation statements)

References 32 publications

Supporting

Mentioning

137

Contrasting

Order By: Relevance

“…Thus, STIC1/ALB4 and STIC2 may cooperate in thylakoid membrane biogenesis as part of a cpSRP43-independent cpSRP pathway for a specific subset of clients or a putative alternative LHCP transport pathway (TzvetkovaChevolleau et al, 2007). Interestingly, STIC2 has a similar expression profile to some nucleus-encoded chloroplast ribosomal protein genes (e.g., PSRP4 and PRPL35) (Aoki et al, 2016), suggesting another possible role in the cotranslational targeting via ALB4 or ALB3. Accordingly, cpSRP54 and cpFtsY are both implicated in cotranslational insertion via ALB3, whereas cpSRP43 is not (Amin et al, 1999;Nilsson et al, 1999;Walter et al, 2015).…”

Section: Stic Proteins Cooperate In a Common Cpsrp-related Thylakoidmentioning

confidence: 96%

Suppressors of the Chloroplast Protein Import Mutant tic40 Reveal a Genetic Link between Protein Import and Thylakoid Biogenesis

Bédard

Trösch

et al. 2017

Plant Cell

View full text Add to dashboard Cite

To extend our understanding of chloroplast protein import and the role played by the import machinery component Tic40, we performed a genetic screen for suppressors of chlorotic tic40 knockout mutant Arabidopsis thaliana plants. As a result, two suppressor of tic40 loci, stic1 and stic2, were identified and characterized. The stic1 locus corresponds to the gene ALBINO4 (ALB4), which encodes a paralog of the well-known thylakoid protein targeting factor ALB3. The stic2 locus identified a previously unknown stromal protein that interacts physically with both ALB4 and ALB3. Genetic studies showed that ALB4 and STIC2 act together in a common pathway that also involves cpSRP54 and cpFtsY. Thus, we conclude that ALB4 and STIC2 both participate in thylakoid protein targeting, potentially for a specific subset of thylakoidal proteins, and that this targeting pathway becomes disadvantageous to the plant in the absence of Tic40. As the stic1 and stic2 mutants both suppressed tic40 specifically (other TIC-related mutants were not suppressed), we hypothesize that Tic40 is a multifunctional protein that, in addition to its originally described role in protein import, is able to influence downstream processes leading to thylakoid biogenesis.

show abstract

Section: Stic Proteins Cooperate In a Common Cpsrp-related Thylakoidmentioning

confidence: 96%

Suppressors of the Chloroplast Protein Import Mutant tic40 Reveal a Genetic Link between Protein Import and Thylakoid Biogenesis

Bédard

Trösch

et al. 2017

Plant Cell

View full text Add to dashboard Cite

show abstract

“…All of the databases except ATTED-II used PCC to build GCN from 128 to 379 microarray datasets. ATTED-II recently updated their database to provide both GCNs from microarray and RNA-Seq using PCCbased mutual rank (Aoki et al, 2016). Although PCC is widely used, there is very limited evidence to indicate that it is the optimal approach for GCN analyses.…”

mentioning

confidence: 99%

Construction and Optimization of a Large Gene Coexpression Network in Maize Using RNA-Seq Data

et al. 2017

View full text Add to dashboard Cite

ORCID IDs: 0000-0002-6182-800X (J.H.); 0000-0001-6612-3570 (S.V.); 0000-0002-9564-8146 (K.M.M.).With the emergence of massively parallel sequencing, genomewide expression data production has reached an unprecedented level. This abundance of data has greatly facilitated maize research, but may not be amenable to traditional analysis techniques that were optimized for other data types. Using publicly available data, a gene coexpression network (GCN) can be constructed and used for gene function prediction, candidate gene selection, and improving understanding of regulatory pathways. Several GCN studies have been done in maize (Zea mays), mostly using microarray datasets. To build an optimal GCN from plant materials RNA-Seq data, parameters for expression data normalization and network inference were evaluated. A comprehensive evaluation of these two parameters and a ranked aggregation strategy on network performance, using libraries from 1266 maize samples, were conducted. Three normalization methods and 10 inference methods, including six correlation and four mutual information methods, were tested. The three normalization methods had very similar performance. For network inference, correlation methods performed better than mutual information methods at some genes. Increasing sample size also had a positive effect on GCN. Aggregating single networks together resulted in improved performance compared to single networks.Maize (Zea mays) is the most widely produced crop in United States, and U.S. agriculture accounted for 36% of world maize production in 2015 (USDA, 2016). Maize has also been in the center of genetics research for more than 100 years, including McClintock's pioneering work with transposable elements (reviewed by McClintock, 1983;Fedoroff, 2012). Due to recent technological advances in nucleic acid sequencing and the availability of the maize genome sequence (Schnable et al., 2009), maize genomics research has been greatly expedited.RNA-sequencing (RNA-Seq) has become the favored technique for detecting genomewide expression patterns. RNA-Seq has some advantages over microarray analysis of gene expression, including single base-pair resolution, detection of novel transcripts, and the ability to analyze transcript abundance without existing genome information (reviewed by Wang et al., 2009;Han et al., 2015;Conesa et al., 2016). RNA-Seq data provides information about single nucleotide polymorphisms, which facilitates genomewide association studies (Fu et al., 2013;Li et al., 2013a;Lonsdale et al., 2013;Fadista et al., 2014). Because of its widespread adaptability, greater than 5000 Illumina platform Maize RNA-Seq libraries (Fig. 1A) are available in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) database (Leinonen et al., 2010), adding to the body of data that can be used to study the maize genome.The maize genome is large and heterogeneous, and the genome annotation is still far from complete (Cigan et al., 2005;Ficklin and Feltus, 2011). Although recent work has...

show abstract

“…The most severe phenotype of araSP mutants was characterized by very small size, red color of cotyledons, underdeveloped roots, no apical meristem and life expectancy of less than 20 days (Bötler et al 2006). The ARASP protein tightly coexpresses with S2P2 (encoded by the AT1G5140 gene; Aoki et al 2016). There is; however, no direct evidence indicating cooperation of these proteins, or any experimental research indicating the potential role of S2P2.…”

Section: Introductionmentioning

confidence: 99%

Arabidopsis thaliana intramembrane proteases

et al. 2017

View full text Add to dashboard Cite

Proteolysis is considered as a crucial factor determining the proper development of the plant and its efficient functioning in variable environmental conditions. The role of proteases in protein quality control and protein turnover processes is well documented. The results of studies performed in recent years reveal; however, that proteolytic enzymes also participate in signal transduction pathways by releasing membrane-anchored transcription factors in the process known as regulated intramembrane proteolysis (RIP). The first described intramembrane protease was identified in human cells in 1997. In turn, the first plant intramembrane protease was identified in 2005, in Arabidopsis thaliana. To date, most studies concerning the RIP process in plants have been performed on this model plant. The knowledge concerning the potential physiological role of RIP is very limited. However, continuously accumulating information concerning this issue indicates that RIP, like the other proteolytic mechanisms, has a significant effect on plant ontogenesis, acclimatization and fertility. The aim of this article is to gather and systemize the present knowledge concerning the intramembrane proteases in A. thaliana.

show abstract

ATTED-II in 2016: A Plant Coexpression Database Towards Lineage-Specific Coexpression

Cited by 156 publications

References 32 publications

Suppressors of the Chloroplast Protein Import Mutant tic40 Reveal a Genetic Link between Protein Import and Thylakoid Biogenesis

Suppressors of the Chloroplast Protein Import Mutant tic40 Reveal a Genetic Link between Protein Import and Thylakoid Biogenesis

Construction and Optimization of a Large Gene Coexpression Network in Maize Using RNA-Seq Data

Arabidopsis thaliana intramembrane proteases

Contact Info

Product

Resources

About