Integration of omic networks in a developmental atlas of maize

Walley, Justin W.; Sartor, Ryan C.; Shen, Zhouxin; Schmitz, Robert J.; Wu, Kevin J.; Urich, Mark A.; Nery, Joseph R.; Smith, Laurie G.; Schnable, James C.; Ecker, Joseph R.; Briggs, Steven P.

doi:10.1126/science.aag1125

Cited by 412 publications

(547 citation statements)

References 45 publications

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“…Moderately positive correlations between transcriptome and proteome data are usual and have been reported previously (Maier et al, 2009). In a recent study in maize (Zea mays), similar correlations values were observed, but enrichment categories were found to be highly similar between transcriptome and proteome data (Walley et al, 2016). The enrichment analysis (see Supplemental Data Set 6) points to a similar trend.…”

Section: Other -Omics Data Processing and Statistical Analysissupporting

confidence: 83%

Multi-Omics of Tomato Glandular Trichomes Reveals Distinct Features of Central Carbon Metabolism Supporting High Productivity of Specialized Metabolites

et al. 2017

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Glandular trichomes are metabolic cell factories with the capacity to produce large quantities of secondary metabolites. Little is known about the connection between central carbon metabolism and metabolic productivity for secondary metabolites in glandular trichomes. To address this gap in our knowledge, we performed comparative metabolomics, transcriptomics, proteomics, and 13 C-labeling of type VI glandular trichomes and leaves from a cultivated (Solanum lycopersicum LA4024) and a wild (Solanum habrochaites LA1777) tomato accession. Specific features of glandular trichomes that drive the formation of secondary metabolites could be identified. Tomato type VI trichomes are photosynthetic but acquire their carbon essentially from leaf sucrose. The energy and reducing power from photosynthesis are used to support the biosynthesis of secondary metabolites, while the comparatively reduced Calvin-Benson-Bassham cycle activity may be involved in recycling metabolic CO 2 . Glandular trichomes cope with oxidative stress by producing high levels of polyunsaturated fatty acids, oxylipins, and glutathione. Finally, distinct mechanisms are present in glandular trichomes to increase the supply of precursors for the isoprenoid pathways. Particularly, the citrate-malate shuttle supplies cytosolic acetyl-CoA and plastidic glycolysis and malic enzyme support the formation of plastidic pyruvate. A model is proposed on how glandular trichomes achieve high metabolic productivity.

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Section: Other -Omics Data Processing and Statistical Analysissupporting

confidence: 83%

Multi-Omics of Tomato Glandular Trichomes Reveals Distinct Features of Central Carbon Metabolism Supporting High Productivity of Specialized Metabolites

et al. 2017

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“…and function of a protein of interest. However, many researchers had found inconsistencies between mRNA and protein level (Baerenfaller et al, 2008;Schwanhäusser et al, 2011;Ponnala et al, 2014;Walley et al, 2016). Although relatively fewer protein expression data are available, these data are amenable to GCN construction and could represent a more direct reflection of interacting proteins.…”

Section: The Performance Of Protein Network Did Not Exceed Aggregatimentioning

confidence: 99%

“…S2). Several network studies from plant RNA-Seq data used log2 transformation (Davidson et al, 2011;Ma and Wang, 2012;Giorgi et al, 2013;Stelpflug et al, 2016;Walley et al, 2016). In our analysis, genes with CPM greater than 2 in more than 1000 samples were included.…”

Section: Manually Curated Maize Mrna Expression Profiling From Publicmentioning

confidence: 99%

“…Although relatively fewer protein expression data are available, these data are amenable to GCN construction and could represent a more direct reflection of interacting proteins. Using a nonmodified protein expression atlas from 23 maize tissues based upon mass spectrometry data (Walley et al, 2016), four protein networks were built with PCC, SCC, MRNET, and CLR separately and then evaluated using the same PPPTY and GO dataset as previously mentioned.…”

Section: The Performance Of Protein Network Did Not Exceed Aggregatimentioning

confidence: 99%

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

et al. 2017

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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...

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“…The length of the phylogenetic tree is not proportional to the phylogenetic distance. those in nonsyntenic regions (Walley et al 2016). We tested the hypothesis that AS and conserved AS is enriched in syntenic vs. nonsyntenic regions across the grass species.…”

Section: As Conserved Between Monocot Grass and Nongrass Speciesmentioning

confidence: 99%

Evolutionarily Conserved Alternative Splicing Across Monocots

et al. 2017

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One difficulty when identifying alternative splicing (AS) events in plants is distinguishing functional AS from splicing noise. One way to add confidence to the validity of a splice isoform is to observe that it is conserved across evolutionarily related species. We use a high throughput method to identify junction-based conserved AS events from RNA-Seq data across nine plant species, including five grass monocots (maize, sorghum, rice, Brachpodium, and foxtail millet), plus two nongrass monocots (banana and African oil palm), the eudicot Arabidopsis, and the basal angiosperm Amborella. In total, 9804 AS events were found to be conserved between two or more species studied. In grasses containing large regions of conserved synteny, the frequency of conserved AS events is twice that observed for genes outside of conserved synteny blocks. In plant-specific RS and RS2Z subfamilies of the serine/arginine (SR) splicefactor proteins, we observe both conservation and divergence of AS events after the whole genome duplication in maize. In addition, plant-specific RS and RS2Z splice-factor subfamilies are highly connected with R2R3-MYB in STRING functional protein association networks built using genes exhibiting conserved AS. Furthermore, we discovered that functional protein association networks constructed around genes harboring conserved AS events are enriched for phosphatases, kinases, and ubiquitylation genes, which suggests that AS may participate in regulating signaling pathways. These data lay the foundation for identifying and studying conserved AS events in the monocots, particularly across grass species, and this conserved AS resource identifies an additional layer between genotype to phenotype that may impact future crop improvement efforts.

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Integration of omic networks in a developmental atlas of maize

Cited by 412 publications

References 45 publications

Multi-Omics of Tomato Glandular Trichomes Reveals Distinct Features of Central Carbon Metabolism Supporting High Productivity of Specialized Metabolites

Multi-Omics of Tomato Glandular Trichomes Reveals Distinct Features of Central Carbon Metabolism Supporting High Productivity of Specialized Metabolites

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

Evolutionarily Conserved Alternative Splicing Across Monocots

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