jeongwoon kim and c. robin buelljul 29, 2015 · access to next generation sequencing 8...

1

A revolution in plant metabolism: Genome-enabled pathway discovery 1 2 Jeongwoon Kim and C. Robin Buell 3 Department of Plant Biology, Michigan State University, East Lansing, MI 48824 USA4

Plant Physiology Preview. Published on July 29, 2015, as DOI:10.1104/pp.15.00976

Copyright 2015 by the American Society of Plant Biologists

https://plantphysiol.orgDownloaded on June 2, 2021. - Published by Copyright (c) 2020 American Society of Plant Biologists. All rights reserved.

https://plantphysiol.org

2

Abstract 5 Genome-enabled discoveries are the hallmark of 21st century biology including major discoveries in the 6 biosynthesis and regulation of plant metabolic pathways. Access to next generation sequencing 7 technologies has enabled research on the biosynthesis of diverse plant metabolites, especially 8 secondary metabolites, resulting in a broader understanding of not only the structural and regulatory 9 genes involved in metabolite biosynthesis but also in the evolution of chemical diversity in the Plant 10 Kingdom. Several paradigms that govern secondary metabolism have emerged including i) gene family 11 expansion and diversification contribute to the chemical diversity found in the Plant Kingdom, ii) genes 12 encoding biochemical pathway components are frequently transcriptionally co-regulated, and iii) 13 physical clustering of non-homologous genes that encode components of secondary metabolic 14 pathways can occur. With an increasing knowledgebase that is coupled with user-friendly and 15 inexpensive technologies, biochemists are poised to accelerate annotation of biochemical pathways 16 relevant to human health, agriculture, and the environment. 17



3

Arabidopsis thaliana has been in a post-genomics era for 15 years and as a consequence, a 18 substantial portion of our knowledge of plant metabolism is derived from this model species including 19 both primary and secondary metabolites. Primary metabolites are considered to be essential for plant 20 growth and development, whereas secondary metabolites (also known as specialized metabolites) were 21 traditionally believed to improve plant function such as defense against abiotic and biotic stress. 22 However, a more recent definition to distinguish between these “groups” of metabolites and their 23 pathways is based on their taxonomic distribution with primary metabolism referring to pathways 24 universally distributed throughout the Plant Kingdom and secondary metabolism referring to pathways 25 and resulting metabolites that are taxa- or species-specific (Pichersky and Gang, 2000). 26 27 Genome sequencing as a major catalyst in understanding plant metabolism 28 The first 15 years of the 21st century has seen a revolution in genomic technologies with advances 29 continuing to impact all areas of plant biology including plant metabolism, as access to the genome 30 sequence along with associated large-scale datasets such as expression and metabolite profiles can 31 empower the discovery of the biosynthetic and regulatory pathways of not only discrete but also large 32 classes of metabolites. Indeed, generation of the genome sequence of Arabidopsis thaliana in 2000 33 (Arabidopsis Genome Initiative, 2000) was seminal in plant metabolism research as not only was the full 34 genome sequence of a plant determined, but the development of accessible and robust functional 35 genomics resources such as full-length cDNA clone sets, tagged mutant lines, and a rapid and facile 36 transformation method collectively enabled improvements in our understanding of plant metabolism 37 (Buell and Last, 2010). Another key attribute of completing the A. thaliana genome was the high quality 38 of the underlying sequence and the investment in genome annotation with publicly accessible and 39 curated databases including a dedicated biochemical pathway database (Mueller et al., 2003), thereby 40 enabling broad access to the wealth of data generated by the community on this focal species. 41

42 The A. thaliana genome was sequenced using dideoxy chain termination chemistry with a 43 bacterial artificial chromosome (BAC)-by-BAC approach that is now outdated (Arabidopsis Genome 44 Initiative, 2000). Current sequencing technologies utilize a sequencing-by-synthesis approach that is 45 ultra-high throughput, extremely inexpensive, and available to any researcher in the world. Collectively, 46 these technologies are termed “next generation sequencing” (NGS) methods with the Illumina platform 47 dominating the market (http://www.illumina.com), and the Pacific Biosciences and Ion Torrent 48 platforms employed primarily for long read generation and amplicon sequencing, respectively 49



4

(http://www.pacificbiosciences.com; https://www.lifetechnologies.com/us/en/home/brands/ion-50 torrent.html). Due to the rapid evolution of sequencing technologies, the technical aspects of these 51 platforms are not discussed in this article and the reader is referred to a review article available on these 52 platforms (Mardis, 2013). One obvious application of NGS is the generation of de novo genome and 53 transcriptome sequences, both of which have been highly informative across many species for 54 understanding plant metabolism. However, NGS technologies have numerous applications outside de 55 novo assemblies and some emerging examples of their use in revealing complexities in plant metabolism 56 are highlighted in this Update article. 57 58 While A. thaliana has and will continue to serve as a model species for plant metabolism, it fails 59 to capture the full diversity of chemistry present within the Plant Kingdom. In particular, given that 60 secondary metabolites are taxa- or even species-specific, accessibility to genomic resources of each 61 target species is essential for genome-enabled pathway discovery. Recent improvements in assembly 62 algorithms and annotation software, coupled with access to increased computational power and NGS 63 technology, has enabled generation of genome sequences of a wide number of plant species that can be 64 used as platform for secondary metabolite pathway discovery (Figure 1). While challenges still exist in 65 the assembly of large and/or repetitive genomes, polyploid species, and heterozygosity, these barriers 66 can be addressed, in part, using approaches that bypass or minimize these complexities in the genome 67 assembly process (Hirsch and Buell, 2013). Indeed, genome sequences are available for most crop 68 species albeit in varying degrees of quality, including a wide set of species with relevant secondary 69 metabolism. The discovery potential of NGS in plant metabolism is unlimited and below, we highlight 70 how access to genome sequence(s) enabled metabolite pathway discovery in species across the Plant 71 Kingdom and some interesting features in plant metabolism discovered along the way. Due to the broad 72 application of genomics in secondary metabolism, this Update article focuses on genome-enabled 73 discoveries in secondary rather than primary metabolism. 74 75 Basic genomic principles of genes involved in secondary metabolism: Duplication, Co-expression, and 76 Physical Clustering 77 One complication in plant metabolism research, especially that of secondary metabolism, is that all 78 angiosperms sequenced to date show evidence of whole genome duplication which has resulted in 79 expansions of gene families leading to metabolic diversity over evolutionary time. Additional partial 80 genome duplications, known as segmental duplications, and tandem duplications further contribute to 81



5

the large gene family size observed in plant genomes. Once duplicated, genes can evolve new functions 82 (neofunctionalization), partition function into the duplicates (sub-functionalization), or become 83 pseudogenes (Conant et al., 2014). Examination of genome sequences for 16 species that span evolution 84 from algae to higher plants from Chae et al. (2014) enabled identification of genomic signatures that 85 distinguish plant secondary metabolism genes from primary metabolism genes. Features of genes 86 involved in secondary metabolism included a tendency to be retained after gene duplication, lineage-87 specificity, and physical clustering within the genome compared to primary metabolism genes. 88 89 A correlative approach to annotating gene function is association of gene expression patterns 90 and profiles with phenotype, including metabolites. Access to NGS permits generation of rapid and 91 inexpensive expression abundance data through RNA-sequencing. From an expression atlas, in which a 92 wide range of tissues and treatments are examined, coordinated expression analyses (co-expression) 93 can reveal genes that are within the same regulatory network (Figure 2). This “guilt-by-association” 94 approach, when coupled with examination of functional annotation of genes or transcripts, has been 95 highly successful in identifying a subset of candidate biosynthetic pathway genes for functional 96 validation (see Update article by Weber). 97

98 In bacterial genomes, genes involved in metabolic and regulatory pathways are frequently 99

arranged in operons which are absent in plant genomes. However, one theme that has emerged from 100 genome analyses of secondary metabolism is that genes within specialized metabolic pathways can be 101 physically clustered (for review (DellaPenna and O'Connor, 2012)). This non-homologous gene clustering 102 is hypothesized to be a consequence of evolutionary pressure to retain all components of the pathway 103 in a discrete interval thereby ensuring that the multigenic trait is inherited as a single locus and avoiding 104 buildup of toxic intermediates or an inability to synthesize the final metabolite which may have a role in 105 adaptation or survival. We highlight below three classes of secondary metabolites that demonstrate the 106 extent to which gene/genome duplication and diversification, co-expression, and/or physical clustering 107 can facilitate discovery and an improved understanding of secondary metabolic pathways. 108 109 Terpenes. Terpenes, diverse compounds derived from five-carbon isoprene units, are the largest class of 110 plant secondary metabolites and are important in plant defense and health benefits for human (Tholl 111 and Lee, 2011). Chemical diversity of terpene metabolites is mainly governed by terpene synthases (TSs) 112 that generate the scaffold with structural diversity containing various numbers of carbons and a 113



6

multitude of cytochrome P450-dependent monooxygenases (CYPs) that modify the scaffold. From a 114 comparative genome analysis, 113 terpene systhases were identified in Eucalyptus grandis compared 115 with two in Physcomitrella patens, some of which, were lineage specific to Eucalyptus, and contributed 116 to the diversity of terpene-derived molecules such as 1,8-cineole in Eucalyptus (Myburg et al., 2014). 117

A recent study revealed terpene diversification and evolution of terpene biosynthetic genes in 118 multiple plant genomes (Boutanaev et al., 2015). This study analyzed 17 sequenced plant genomes 119 including 12 eudicot and five monocot species to determine the organization and evolution of terpene 120 pathway genes. By identifying TS and CYP gene pairs and their physical location within the genome, they 121 observed frequencies of TS/CYP gene pairs higher than the expected by chance, suggesting non-random 122 association of TS and CYP gene pairs in the surveyed genomes. In addition, TSs were predominately 123 paired with CYP71 family genes in both eudicots and monocots, confirming previously identified TS-CYP 124 gene clusters in Ricinus communis known to be involved in diterpene synthesis. Novel TS/CYP pairs were 125 also observed such as three functional TS/CYP pairs in R. communis, A. thaliana and Cucumis sativus. 126 This study also revealed different assembly patterns of TS/CYP pairs in eudicots and monocots, 127 suggesting diverged evolutionary mechanisms to assemble terpene pathways in the two major divisions 128 of angiosperms. Based on the frequency of TS/CYP pairs and their correlations, it was hypothesized that 129 eudicot TS/CYP gene pairs arose from a common ancestral gene pair by duplication, providing building 130 blocks to diversify terpene biosynthetic genes. On the contrary, no correlation was observed for TS/CYP 131 gene pairs in monocot, suggesting they may have arisen independently as a consequence of genome 132 rearrangements. 133

134 Monoterpene Indole Alkaloids. Catharanthus roseus (Madagascar periwinkle) synthesizes an array of 135 monoterpene indole alkaloids with high structural diversity including the anti-cancer compounds, 136 vincristine and vinblastine (O'Connor and Maresh, 2006). Historical efforts to elucidate the pathway 137 yielded some of the genes in the pathway using an array of slow and laborious processes. In 2010, large 138 expression and metabolite profiling datasets were generated that enabled discovery of a large number 139 of genes in the vinblastine/vincristine pathway (for example see (Miettinen et al., 2014)). To further 140 accelerate pathway discovery in C. roseus, a draft genome sequence was generated (Kellner et al., 2015) 141 that was used to refine the expression atlas and co-expression networks and guide discovery of 142 additional and novel candidate genes involved in monoterpene indole alkaloid regulation, biosynthesis, 143 and transport. The C. roseus genome sequence revealed evidence of gene expansion coupled with neo-144 functionalization of genes that encode not only biosynthetic pathway but also regulatory components of 145



7

monoterpene indole alkaloids. Physical clustering of a subset of genes involved in monoterpene indole 146 alkaloid synthesis was also observed, showing that even with a draft genome sequence that has a 147 limited scaffold length, new discoveries in metabolism can be readily made. 148 149 Steroidal glycoalkaloids: Solanum species such as potato and tomato produce steroidal glycoalkaloids 150 (SGAs) including α-solanine, α-chaconine and α-tomatine. Steroidal glycoalkaloids are derived from 151 alkaloids linked to a sugar moiety, and are known to play crucial roles in plant defense yet have toxicity 152 in humans (for example, see (Friedman, 2006)). Potato and tomato are agriculturally valuable crop 153 species and have served as model systems for tuber- and fruit-development studies, respectively. De 154 novo genome sequences are available for both species with substantial synteny and conserved gene 155 content between the two genomes (Potato Genome Sequencing Consortium, 2011; The Tomato 156 Genome Consortium, 2012). It was previously known that the SGA pathway entails conversion of 157 cholesterol to steroidal alkaloids through a series of hydroxylation, oxidation, and transamination 158 reactions followed by decoration with various sugar moieties using uridine 5’-diphosphate (UDP)-159 glycosyltransferases such as SOLANIDINE GALACTOSYLTRANSFERASE (SGT1) and GLYCOALKALOID 160 METABOLISM 1 (GAME1) in potato and tomato, respectively. However, the pathway still remained 161 incomplete. To discover novel genes in the SGA pathway, (Itkin et al., 2013) investigated genes that 162 were co-expressed with GAME1/SGT1 and identified 16 genes in potato and tomato including GAME4 163 which encodes a cytochrome P450. They also discovered that the SGA pathway genes were physically 164 clustered and co-expressed with GAME1 on chromosome 7 and with GAME 4 on chromosome 12 with 165 conserved synteny between tomato and potato. With functional validation of the newly identified 166 genes, they proposed a SGA pathway starting from cholesterol and decoration of up to four sugar 167 moieties linked to steroidal alkaloids. 168 169 Structural variation as a driver of metabolite diversity 170 Early sequencing efforts in bacteria revealed substantial genome diversity among isolates leading to the 171 concept of the “core” and “dispensable” genome with the collective genome sequence of a species 172 termed the “pan-genome” (Tettelin et al., 2005). This structural variation is in the form of copy number 173 variation and presence/absence variation in which genes are variable among accessions of a single 174 species (Figure 2). In plants, copy number variation and presence/absence variation have been reported 175 in several species, some which are associated with phenotypic diversity including abiotic and biotic 176 stress, development, and secondary metabolites (Cook et al., 2012; Maron et al., 2013); below are two 177



8

examples in which structural variation is associated with production of secondary metabolites of 178 importance to human health. 179 Polyphenols. Grapes are known for production of resveratrol, a natural polyphenol with health benefits 180 on obesity and aging-related diseases (Baur et al., 2006), while terpene-derived metabolites in grapes 181 contribute to the aromatic features of wine. The grapevine (Vitis vinifera) genome was sequenced in 182 2007 using the inbred Pinot Noir variety PN40024 (Jaillon et al., 2007) and access to the grapevine 183 genome enabled annotation of stilbene synthases and TSs that lead to the synthesis of resveratrol and 184 terpenoids, respectively. Both gene families were highly expanded relative to other sequenced plant 185 genomes consistent with the hypothesis that gene duplication and diversification lead to metabolic 186 diversity. With access to NGS technologies, Da Silva et al. (2013) generated sequence data for the 187 Uruguayan Tannat grapevine clone UY11 which accumulates high level of polyphenols in the berry skin 188 and seed. Comparison of the UY11 genome with the PN40024 reference genome revealed 1,873 genes 189 that were not present in the PN40024 genome. With respect to polyphenol biosynthesis, 141 novel 190 UY11 genes that encode 19 different enzymes involved in polyphenol biosynthesis and the expression of 191 cultivar-specific genes associated with altered polyphenol accumulation in UY11 were identified. 192 193 Phthalide Isoquinoline Alkaloids. Papaver somniferum (poppy) is well known for its production of opiates 194 that are used as pain suppressants. Noscapine, a phthalide isoquinoline alkaloid, has cough suppressant 195 and anti-tumor activities. Poppy varieties exhibit differential metabolite profiles with respect to the 196 isoquinoline alkaloids including high and null noscapine producing varieties. Through an integrated 197 approach using an F2 mapping population, sequencing of large insert BAC clones, and viral-induced gene 198 silencing, a 10-gene cluster on a 221 kb segment novel to the high noscapine producing variety was 199 identified for noscapine biosynthesis (Winzer et al., 2012). Based on the occurrence of small gene 200 families (carboxylesterase, CYP82 cytochrome P450, and O-methyltransferase) and single copy genes 201 (short-chain dehydrogenase/reductase, acetyltransferase and CYP719A21 cytochrome P450) in the 202 cluster of 10 biosynthetic genes, the authors hypothesized that genome re-organization occurred before 203 and after duplication, shaping the physical cluster of noscapine biosynthetic genes. They speculated that 204 this cluster evolution might benefit co-inheritance of a favorable combination of alleles leading to the 205 production of desired secondary metabolites. 206 207 Genomics-facilitated genetic approaches for metabolite pathway discovery 208



9

Classical genetic approaches to identify causal genes within a biosynthetic pathway have included 209 forward and reverse genetic screens, positional cloning, and quantitative trait locus (QTL) mapping. 210 These classical approaches can be accelerated and performed with increased efficiency through NGS 211 methodologies. Not only can NGS be used to readily identify sequence variants [single nucleotide 212 polymorphisms (SNPs), insertions/deletions] between accessions (Figure 2), it can be further applied in 213 approaches such as whole-genome resequencing in bulk segregant analyses (James et al., 2013) and k-214 mer based approaches to find causal mutations (Nordstrom et al., 2013). Indeed, due to low cost, high 215 coverage and accuracy, as well as user-friendly bioinformatics pipelines, a single laboratory can now 216 utilize NGS methods combined with genetics approaches for gene discovery. For example, access to 217 genome sequences for multiple accessions permits association studies to link phenotype (e.g., 218 metabolite) with genotype using either linkage mapping or a genome-wide association approach. With 219 access to large transcriptome datasets, transcript levels can be linked with sequence variants to identify 220 expression QTL (eQTL) associated with synthesis, regulation or transport of metabolites. 221 222 Genome-wide association studies. Next generation sequencing methods can enable discovery of large 223 numbers of SNPs for use in a genome-wide association analysis of metabolism. In rice, Chen et al. (2014) 224 re-sequenced 529 diverse accessions of Oryza sativa generating 6.4M SNPs. When combined with 225 metabolite profiling of 840 metabolites including amino acids, flavonoids and terpenoids, they revealed 226 the genetic and biochemical basis of metabolic diversity in rice by associating casual SNPs and genes 227 with metabolite profiles. For example, a SNP located in a methyltransferase gene was associated with 228 the levels of trigonelline, the N-methyl conjugate of nicotinic acid. In vitro assays revealed its enzymatic 229 activity as a nicotinic acid:N-methyltransferase and overexpression of the methyltransferase increased 230 trigonelline accumulation in transgenic lines, showing that it is a functional enzyme in trigonelline 231 biosynthesis. For flavonoid biosynthesis, a SNP located in a putative UDP-glucosyl transferase gene was 232 associated with the production of a number of flavonoids and chlorogenic acid. In this study, the 233 candidate gene was functionally annotated as a flavone 5-O-glucosyltransferase and based on 234 metabolite profiles of transgenic lines that overexpressed this gene, it was shown to regulate the level 235 of flavone 5-O-glucosyltransferase. 236 A. thaliana and other Brassicaceae species produce glucosinolates which are amino acid-derived 237 secondary metabolites known to have roles in plant defense against insects and pathogens. 238 Glucosinolates can be categorized into three groups, aliphatic, aromatic and indole glucosinolates, 239 depending on the types of amino acids from which they are derived. Chan et al. (2010) performed a 240



10

genome-wide association study with 96 A. thaliana accessions, assessing the natural diversity of 43 241 glucosinolate phenotypes using approximately 230,000 SNPs. They identified 172 genes containing SNPs 242 associated with glucosinolate profiles and discovered two 2-oxoglutarate-dependent dioxygenase genes 243 (AOP2, AOP3) and methylthioalkylmalate synthase 1 (MAM1) that control two previously known 244 glucosinolate-related QTLs. They also detected extended linkage disequilibrium surrounding AOP2, 245 AOP3, and MAM1 genes as a haplotype that was associated with glucosinolate chemotypes in the 96 A. 246 thaliana accessions, suggesting selection during evolution of the pathway. In addition to the genes 247 directly affecting glucosinolate biosynthesis, transcriptional variation of a glutamate cysteine ligase 248 involved in glutathione metabolism, which had been hypothesized to indirectly control glucosinolate 249 accumulation via a regulatory and/or biosynthetic linkage, was observed to alter glucosinolate 250 production in this study. 251 252 Expression QTLs. In maize, Fu et al. (2013) integrated transcriptome and metabolite profiling to identify 253 genes involved in kernel development including carotenoid levels. RNA-sequencing was used to 254 generate transcript abundances and SNPs from developing kernels of 368 maize inbred lines which 255 yielded 16,408 eQTL for 14,375 genes. Of the 20 genes in carotenoid metabolic pathway whose 256 expression was associated with carotenoid levels, six genes were associated with an eQTL including 257 lycopene epsilon cyclase1 and β-carotene hydroxylase 1. In addition, 55 genes were correlated with 258 carotenoid production, of which, 19 genes were associated with an eQTL. Co-expression analyses of 259 genes associated with eQTLs revealed three clusters of genes that were co-expressed with known 260 carotenoid pathway genes, providing a list of candidate genes in the pathway. This study demonstrates 261 how an eQTL study, when combined with metabolite profiling, can discover novel genes even in well-262 studied biosynthetic pathways. 263 264 Comparative and phylogenetic approaches leverage evolutionary events across taxa to reveal genes 265 involved in secondary metabolism 266 With the increasing number of genome sequences available, the power of comparative and 267 phylogenetic approaches to gene discovery can be harnessed. Traditionally, phylogenetic relationships 268 of key metabolic enzymes were performed at the single gene level with limited sequence information 269 from a small number of species. However, with advances in NGS technology, whole genome sequences 270 and annotation are available not only for multiple species within a taxonomic family but also for basal 271 angiosperm species such as Amborella, gymnosperms, lycopods, and moss that permit more robust 272



11

queries into the mechanisms by which metabolic pathways evolved. With the availability of multiple 273 genomes from a single or related taxonomic group, synteny (shared gene order), which reflects shared 274 evolution and enables prediction of the emergence of a trait and orthologous genes in a pathway 275 between species and taxa, can be identified. With the abundance of genome sequences within some 276 clades as well as breadth of genome sequences across the Plant Kingdom, comparative analyses not only 277 between species but also within species can be a powerful approach to reveal the evolutionary origins of 278 biochemical pathways. 279 280 Purine Alkaloids. Caffeine belongs to a class of purine alkaloid metabolites, whose synthesis is derived 281 from purine nucleotides followed by multiple steps of N-methylation (Ziegler and Facchini, 2008). The 282 coffee genome, an important beverage crop with stimulating effects due to production of caffeine, was 283 sequenced in 2014 using a doubled haploid accession of Coffea canephora (Denoeud et al., 2014). 284 Analyses of the genome revealed that N-methyltransferases (NMTs) including xanthosine 285 methyltrasferase, theobromine synthase, and caffeine synthase that catalyze later steps in caffeine 286 biosynthesis were enriched in a Coffea lineage-specific ortholog group. Phylogenetic analysis with genes 287 from the ortholog group containing known caffeine biosynthetic genes and NMTs from tea and cacao 288 revealed a cluster of Coffea NMTs distinct from tea and cacao NMTs. The species-specific expansion of 289 NMT gene families in the Coffea genome, coupled with the convergent evolution of caffeine 290 biosynthesis genes in coffee, cacao and tea, highlight the power of comparative genomics in 291 understanding the evolution of secondary metabolite pathways in plants. 292 293 Cannabinoids: The Cannabis sativa (marijuana) genome is an example of successful application of NGS 294 for metabolism study in non-model system (van Bakel et al., 2011). C. sativa produces cannabinoids, a 295 class of prenylated polyketides that are synthesized from the short chain fatty acid hexanoate and 296 geranyl diphosphate. To understand cannabinoid biosynthesis, the authors generated a draft genome of 297 the marijuana-producing strain (Purple Kush) and identified cannabinoid biosynthetic genes such as 298 tetrahydrocannabinol acid synthase. By re-sequencing a non-marijuana strain (Finola) and comparing it 299 with the Purple Kush sequence, they revealed a large expansion of an Acyl activating enzyme3 (AAE) 300 gene encoding an enzyme that may be involved in synthesis of hexanoate, a precursor of 301 tetrahydrocannabinol acid, in the marijuana-producing strain but not in the non-marijuana-producing 302 strain. They extended their comparative analysis by re-sequencing two additional strains, USO-31 (non-303 marijuana strain) and Chemdawg (marijuana strain), and generating a phylogenic tree that revealed 304



12

closer relationships within marijuana strains and non-marijuana strains than between the two 305 chemotypes. This approach expanded understanding of metabolic pathway for the production of 306 alkaloids and revealed genetic variation associated with chemical variation within the species. 307 308 It is not just the DNA sequence per se: Epigenetic modification of DNA leads to new phenotypes 309 Application of NGS technologies extend past that of determining the primary sequence of DNA and RNA 310 molecules and a wide range of applications permit assessment of other features of the genome and 311 transcriptome including that of the epigenome. In tomato, Quadrana et al. (2014) identified epialleles 312 that regulate vitamin E accumulation. Through promoter sequence analyses, differential methylation of 313 a retrotransposon located in the promoter of 2-methyl-6-phytylquinol methyltransferase (VTE3), which 314 encodes the final step in the biosynthesis of γ- and α-tocopherols, was discovered. Using a combination 315 of genomic, transcriptomic and metabolomic approaches in different tomato genetic backgrounds, it 316 was shown that different epialleles lead to differential expression of VTE3 gene, causing diverse 317 accumulation of vitamin E content in tomato fruit. This study shows the example of how advances in 318 NGS technologies (i.e., epigenome profiling) when coupled with genetics and metabolite profiling, can 319 enable new discoveries on regulation of metabolism. 320 321 Conclusions and Future Prospects 322 In less than a decade since the emergence of the first NGS platform, the incorporation of this technology 323 into plant metabolism has been astounding. Certainly, improvements in the read length, quality, 324 throughput, and cost will further integrate NGS methods into metabolic pathway discovery and 325 reconstruction. Having complete and high quality draft genomes with limited expenditure of effort will 326 facilitate discovery of physically clustered pathway components and dissection of gene duplications 327 associated with biochemical diversity within or between species. However, one challenge in employing a 328 genomics approach to understand plant metabolism is that the majority of functional annotation is 329 automated and derived from transitive annotation from other genomes or via sequence similarity. The 330 circular nature of functional annotation leads to, at best, vague and at worst, incorrect annotation. 331 Coupled with the high frequency of whole genome duplication and the resulting large gene family 332 content in most plant genomes, the lack of high quality functional annotation presents a challenge in 333 accelerating plant metabolism research. Thus, the need for paradigm-changing methods to sift through 334 a set of candidate genes with improved efficiency and effort is great and development of methods for 335 high throughput functional screening will enable more robust data-mining of biochemical pathways 336



13

across the Plant Kingdom. Another challenge is how to select optimal computational pipeline and 337 validate its performance and how to interpret the results properly. Application of different algorithms 338 with arbitrary assumptions or cutoffs may lead to substantial differences in the output and affect 339 biological interpretations; thus, knowledge of the underlying principles of employed algorithms and 340 associated software as well as the biology is imperative (Omranian et al., 2015). Lastly, higher spatial 341 resolution transcriptomic and metabolomics methods need to be incorporated into genome-guided 342 pathway discovery approaches to enable annotation of tissue- and organelle-specific enzymatic 343 reactions and /or inter- or intracellular transportation of intermediates and final products, thereby 344 minimizing errors generated from automated genome prediction, and whole tissue expression and 345 metabolite data (Ziegler and Facchini, 2008; Dixon and Pasinetti, 2010). Regardless of these challenges, 346 the era of genome-enabled pathway discovery is upon us now and the eventual rewards of this 347 knowledge to agriculture, human health, and the environment will be many. 348 349 Figure Legends 350 Figure 1. Secondary metabolites for which genome sequence access facilitated gene discovery and 351 understanding of evolution of secondary metabolites. A, Catharanthus roseus with the monoterpene 352 indole alkaloid vinblastine (Kellner et al., 2015), B, Arabidopsis thaliana with amino acid-derived 353 secondary metabolite glucosinolate (Chan et al., 2010), C, Eucalyptus grandis with the terpene, 1-8-354 cineole (Myburg et al., 2014), D, Coffea canephora with the alkaloid caffeine (Denoeud et al., 2014), E, 355 Zea mays with the carotenoid β-carotene (Fu et al., 2013), F, Cannabis sativa with the cannabinoid 356 tetrahydrocannabinol (van Bakel et al., 2011), G, Papaver somminiferum with the phthalide isoquinoline 357 alkaloid noscapine (Winzer et al., 2012), H, Vitis vinifera with the polyphenol resveratrol (Jaillon et al., 358 2007), I, Solanum lycopersicum with vitamin E (Quadrana et al., 2014) and J, Solanum tuberosum with 359 the glycoalklaloid α-tomatine (Itkin et al., 2013). 360 361 Figure 2. Schematic workflow of next-generation sequencing guided de no genome assembly and 362 integrative genome analysis to understand plant metabolism. High accuracy and coverage of next-363 generation sequencing-derived reads allow de novo genome assembly and gene annotation. Integrative 364 genome analyses coupled with transcriptomic and metabolic datasets enable identification of 365 biosynthetic and regulatory pathway controlling plant metabolism. Commonly used approaches are 366 shown with an example of a study where these approaches were applied; A, Single nucleotide 367 polymorphism (SNP) and insertion/deletion (InDel) discovery (Chen et al., 2014), B, Structural variant 368



14

discovery (Da Silva et al., 2013), C, Phylogenic analysis (Denoeud et al., 2014), D, Network analysis (Itkin 369 et al., 2013), E, Transcriptome analysis (Fu et al., 2013) and F, Physical cluster identification (Winzer et 370 al., 2012). 371 372 References 373 374 Arabidopsis Genome Initiative (2000) Analysis of the genome sequence of the flowering plant 375

Arabidopsis thaliana. Nature 408: 796-815 376 Baur JA, Pearson KJ, Price NL, Jamieson HA, Lerin C, Kalra A, Prabhu VV, Allard JS, Lopez-Lluch G, Lewis 377

K, Pistell PJ, Poosala S, Becker KG, Boss O, Gwinn D, Wang M, Ramaswamy S, Fishbein KW, 378 Spencer RG, Lakatta EG, Le Couteur D, Shaw RJ, Navas P, Puigserver P, Ingram DK, de Cabo R, 379 Sinclair DA (2006) Resveratrol improves health and survival of mice on a high-calorie diet. 380 Nature 444: 337-342 381

Boutanaev AM, Moses T, Zi J, Nelson DR, Mugford ST, Peters RJ, Osbourn A (2015) Investigation of 382 terpene diversification across multiple sequenced plant genomes. Proc Natl Acad Sci U S A 112: 383 E81-88 384

Buell CR, Last RL (2010) Twenty-first century plant biology: impacts of the Arabidopsis genome on plant 385 biology and agriculture. Plant Physiol 154: 497-500 386

Chae L, Kim T, Nilo-Poyanco R, Rhee SY (2014) Genomic signatures of specialized metabolism in plants. 387 Science 344: 510-513 388

Chan EKF, Rowe HC, Kliebenstein DJ (2010) Understanding the Evolution of Defense Metabolites in 389 Arabidopsis thaliana Using Genome-wide Association Mapping. Genetics 185: 991-1007 390

Chen W, Gao Y, Xie W, Gong L, Lu K, Wang W, Li Y, Liu X, Zhang H, Dong H, Zhang W, Zhang L, Yu S, 391 Wang G, Lian X, Luo J (2014) Genome-wide association analyses provide genetic and 392 biochemical insights into natural variation in rice metabolism. Nat Genet 46: 714-721 393

Conant GC, Birchler JA, Pires JC (2014) Dosage, duplication, and diploidization: clarifying the interplay of 394 multiple models for duplicate gene evolution over time. Curr Opin Plant Biol 19: 91-98 395

Cook DE, Lee TG, Guo X, Melito S, Wang K, Bayless AM, Wang J, Hughes TJ, Willis DK, Clemente TE, 396 Diers BW, Jiang J, Hudson ME, Bent AF (2012) Copy number variation of multiple genes at Rhg1 397 mediates nematode resistance in soybean. Science 338: 1206-1209 398

Da Silva C, Zamperin G, Ferrarini A, Minio A, Dal Molin A, Venturini L, Buson G, Tononi P, Avanzato C, 399 Zago E, Boido E, Dellacassa E, Gaggero C, Pezzotti M, Carrau F, Delledonne M (2013) The high 400 polyphenol content of grapevine cultivar tannat berries is conferred primarily by genes that are 401 not shared with the reference genome. Plant Cell 25: 4777-4788 402

DellaPenna D, O'Connor SE (2012) Plant science. Plant gene clusters and opiates. Science 336: 1648-403 1649 404

Denoeud F, Carretero-Paulet L, Dereeper A, Droc G, Guyot R, Pietrella M, Zheng C, Alberti A, Anthony 405 F, Aprea G, Aury JM, Bento P, Bernard M, Bocs S, Campa C, Cenci A, Combes MC, Crouzillat D, 406 Da Silva C, Daddiego L, De Bellis F, Dussert S, Garsmeur O, Gayraud T, Guignon V, Jahn K, 407 Jamilloux V, Joet T, Labadie K, Lan T, Leclercq J, Lepelley M, Leroy T, Li LT, Librado P, Lopez L, 408 Munoz A, Noel B, Pallavicini A, Perrotta G, Poncet V, Pot D, Priyono, Rigoreau M, Rouard M, 409 Rozas J, Tranchant-Dubreuil C, VanBuren R, Zhang Q, Andrade AC, Argout X, Bertrand B, de 410 Kochko A, Graziosi G, Henry RJ, Jayarama, Ming R, Nagai C, Rounsley S, Sankoff D, Giuliano G, 411 Albert VA, Wincker P, Lashermes P (2014) The coffee genome provides insight into the 412 convergent evolution of caffeine biosynthesis. Science 345: 1181-1184 413



15

Dixon RA, Pasinetti GM (2010) Flavonoids and isoflavonoids: from plant biology to agriculture and 414 neuroscience. Plant Physiol 154: 453-457 415

Friedman M (2006) Potato glycoalkaloids and metabolites: roles in the plant and in the diet. J Agric Food 416 Chem 54: 8655-8681 417

Fu J, Cheng Y, Linghu J, Yang X, Kang L, Zhang Z, Zhang J, He C, Du X, Peng Z, Wang B, Zhai L, Dai C, Xu J, 418 Wang W, Li X, Zheng J, Chen L, Luo L, Liu J, Qian X, Yan J, Wang J, Wang G (2013) RNA 419 sequencing reveals the complex regulatory network in the maize kernel. Nat Commun 4: 2832 420

Hirsch CN, Buell CR (2013) Tapping the promise of genomics in species with complex, nonmodel 421 genomes. Annu Rev Plant Biol 64: 89-110 422

Itkin M, Heinig U, Tzfadia O, Bhide AJ, Shinde B, Cardenas PD, Bocobza SE, Unger T, Malitsky S, Finkers 423 R, Tikunov Y, Bovy A, Chikate Y, Singh P, Rogachev I, Beekwilder J, Giri AP, Aharoni A (2013) 424 Biosynthesis of antinutritional alkaloids in solanaceous crops is mediated by clustered genes. 425 Science 341: 175-179 426

Jaillon O, Aury JM, Noel B, Policriti A, Clepet C, Casagrande A, Choisne N, Aubourg S, Vitulo N, Jubin C, 427 Vezzi A, Legeai F, Hugueney P, Dasilva C, Horner D, Mica E, Jublot D, Poulain J, Bruyere C, 428 Billault A, Segurens B, Gouyvenoux M, Ugarte E, Cattonaro F, Anthouard V, Vico V, Del Fabbro 429 C, Alaux M, Di Gaspero G, Dumas V, Felice N, Paillard S, Juman I, Moroldo M, Scalabrin S, 430 Canaguier A, Le Clainche I, Malacrida G, Durand E, Pesole G, Laucou V, Chatelet P, Merdinoglu 431 D, Delledonne M, Pezzotti M, Lecharny A, Scarpelli C, Artiguenave F, Pe ME, Valle G, Morgante 432 M, Caboche M, Adam-Blondon AF, Weissenbach J, Quetier F, Wincker P, French-Italian Public 433 Consortium for Grapevine Genome C (2007) The grapevine genome sequence suggests 434 ancestral hexaploidization in major angiosperm phyla. Nature 449: 463-467 435

James GV, Patel V, Nordstrom KJ, Klasen JR, Salome PA, Weigel D, Schneeberger K (2013) User guide 436 for mapping-by-sequencing in Arabidopsis. Genome Biol 14: R61 437

Kellner F, Kim J, Clavijo BJ, Hamilton JP, Childs KL, Vaillancourt B, Cepela J, Habermann M, Steuernagel 438 B, Clissold L, McLay K, Buell CR, O'Connor SE (2015) Genome-guided investigation of plant 439 natural product biosynthesis. Plant J 82: 680-692 440

Mardis ER (2013) Next-generation sequencing platforms. Annu Rev Anal Chem (Palo Alto Calif) 6: 287-441 303 442

Maron LG, Guimaraes CT, Kirst M, Albert PS, Birchler JA, Bradbury PJ, Buckler ES, Coluccio AE, 443 Danilova TV, Kudrna D, Magalhaes JV, Pineros MA, Schatz MC, Wing RA, Kochian LV (2013) 444 Aluminum tolerance in maize is associated with higher MATE1 gene copy number. Proc Natl 445 Acad Sci U S A 110: 5241-5246 446

Miettinen K, Dong L, Navrot N, Schneider T, Burlat V, Pollier J, Woittiez L, van der Krol S, Lugan R, Ilc T, 447 Verpoorte R, Oksman-Caldentey KM, Martinoia E, Bouwmeester H, Goossens A, Memelink J, 448 Werck-Reichhart D (2014) The seco-iridoid pathway from Catharanthus roseus. Nat Commun 5: 449 3606 450

Mueller LA, Zhang P, Rhee SY (2003) AraCyc: a biochemical pathway database for Arabidopsis. Plant 451 Physiol 132: 453-460 452

Myburg AA, Grattapaglia D, Tuskan GA, Hellsten U, Hayes RD, Grimwood J, Jenkins J, Lindquist E, Tice 453 H, Bauer D, Goodstein DM, Dubchak I, Poliakov A, Mizrachi E, Kullan AR, Hussey SG, Pinard D, 454 van der Merwe K, Singh P, van Jaarsveld I, Silva-Junior OB, Togawa RC, Pappas MR, Faria DA, 455 Sansaloni CP, Petroli CD, Yang X, Ranjan P, Tschaplinski TJ, Ye CY, Li T, Sterck L, Vanneste K, 456 Murat F, Soler M, Clemente HS, Saidi N, Cassan-Wang H, Dunand C, Hefer CA, Bornberg-Bauer 457 E, Kersting AR, Vining K, Amarasinghe V, Ranik M, Naithani S, Elser J, Boyd AE, Liston A, 458 Spatafora JW, Dharmwardhana P, Raja R, Sullivan C, Romanel E, Alves-Ferreira M, Kulheim C, 459 Foley W, Carocha V, Paiva J, Kudrna D, Brommonschenkel SH, Pasquali G, Byrne M, Rigault P, 460 Tibbits J, Spokevicius A, Jones RC, Steane DA, Vaillancourt RE, Potts BM, Joubert F, Barry K, 461



16

Pappas GJ, Strauss SH, Jaiswal P, Grima-Pettenati J, Salse J, Van de Peer Y, Rokhsar DS, 462 Schmutz J (2014) The genome of Eucalyptus grandis. Nature 510: 356-362 463

Nordstrom KJ, Albani MC, James GV, Gutjahr C, Hartwig B, Turck F, Paszkowski U, Coupland G, 464 Schneeberger K (2013) Mutation identification by direct comparison of whole-genome 465 sequencing data from mutant and wild-type individuals using k-mers. Nat Biotechnol 31: 325-466 330 467

O'Connor SE, Maresh JJ (2006) Chemistry and biology of monoterpene indole alkaloid biosynthesis. Nat 468 Prod Rep 23: 532-547 469

Omranian N, Kleessen S, Tohge T, Klie S, Basler G, Mueller-Roeber B, Fernie AR, Nikoloski Z (2015) 470 Differential metabolic and coexpression networks of plant metabolism. Trends Plant Sci. 20: 471 266-268 472

Pichersky E, Gang DR (2000) Genetics and biochemistry of secondary metabolites in plants: an 473 evolutionary perspective. Trends Plant Sci 5: 439-445 474

Potato Genome Sequencing Consortium (2011) Genome sequence and analysis of the tuber crop 475 potato. Nature 475: 189-195 476

Quadrana L, Almeida J, Asis R, Duffy T, Dominguez PG, Bermudez L, Conti G, Correa da Silva JV, Peralta 477 IE, Colot V, Asurmendi S, Fernie AR, Rossi M, Carrari F (2014) Natural occurring epialleles 478 determine vitamin E accumulation in tomato fruits. Nat Commun 5: 3027 479

Tettelin H, Masignani V, Cieslewicz MJ, Donati C, Medini D, Ward NL, Angiuoli SV, Crabtree J, Jones AL, 480 Durkin AS, Deboy RT, Davidsen TM, Mora M, Scarselli M, Margarit y Ros I, Peterson JD, Hauser 481 CR, Sundaram JP, Nelson WC, Madupu R, Brinkac LM, Dodson RJ, Rosovitz MJ, Sullivan SA, 482 Daugherty SC, Haft DH, Selengut J, Gwinn ML, Zhou L, Zafar N, Khouri H, Radune D, Dimitrov 483 G, Watkins K, O'Connor KJ, Smith S, Utterback TR, White O, Rubens CE, Grandi G, Madoff LC, 484 Kasper DL, Telford JL, Wessels MR, Rappuoli R, Fraser CM (2005) Genome analysis of multiple 485 pathogenic isolates of Streptococcus agalactiae: implications for the microbial "pan-genome". 486 Proc Natl Acad Sci U S A 102: 13950-13955 487

The Tomato Genome Consortium (2012) The tomato genome sequence provides insights into fleshy 488 fruit evolution. Nature 485: 635-641 489

Tholl D, Lee S (2011) Terpene Specialized Metabolism in Arabidopsis thaliana. Arabidopsis Book 9: 490 e0143 491

van Bakel H, Stout JM, Cote AG, Tallon CM, Sharpe AG, Hughes TR, Page JE (2011) The draft genome 492 and transcriptome of Cannabis sativa. Genome Biol 12: R102 493

Winzer T, Gazda V, He Z, Kaminski F, Kern M, Larson TR, Li Y, Meade F, Teodor R, Vaistij FE, Walker C, 494 Bowser TA, Graham IA (2012) A Papaver somniferum 10-gene cluster for synthesis of the 495 anticancer alkaloid noscapine. Science 336: 1704-1708 496

Ziegler J, Facchini PJ (2008) Alkaloid biosynthesis: metabolism and trafficking. Annu Rev Plant Biol 59: 497 735-769 498

499 500



Parsed CitationsArabidopsis Genome Initiative (2000) Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature 408:796-815

Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title

Baur JA, Pearson KJ, Price NL, Jamieson HA, Lerin C, Kalra A, Prabhu VV, Allard JS, Lopez-Lluch G, Lewis K, Pistell PJ, Poosala S,Becker KG, Boss O, Gwinn D, Wang M, Ramaswamy S, Fishbein KW, Spencer RG, Lakatta EG, Le Couteur D, Shaw RJ, Navas P,Puigserver P, Ingram DK, de Cabo R, Sinclair DA (2006) Resveratrol improves health and survival of mice on a high-calorie diet.Nature 444: 337-342


Boutanaev AM, Moses T, Zi J, Nelson DR, Mugford ST, Peters RJ, Osbourn A (2015) Investigation of terpene diversification acrossmultiple sequenced plant genomes. Proc Natl Acad Sci U S A 112: E81-88


Buell CR, Last RL (2010) Twenty-first century plant biology: impacts of the Arabidopsis genome on plant biology and agriculture.Plant Physiol 154: 497-500


Chae L, Kim T, Nilo-Poyanco R, Rhee SY (2014) Genomic signatures of specialized metabolism in plants. Science 344: 510-513Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title

Chan EKF, Rowe HC, Kliebenstein DJ (2010) Understanding the Evolution of Defense Metabolites in Arabidopsis thaliana UsingGenome-wide Association Mapping. Genetics 185: 991-1007


Chen W, Gao Y, Xie W, Gong L, Lu K, Wang W, Li Y, Liu X, Zhang H, Dong H, Zhang W, Zhang L, Yu S, Wang G, Lian X, Luo J (2014)Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism. Nat Genet46: 714-721


Conant GC, Birchler JA, Pires JC (2014) Dosage, duplication, and diploidization: clarifying the interplay of multiple models forduplicate gene evolution over time. Curr Opin Plant Biol 19: 91-98


Cook DE, Lee TG, Guo X, Melito S, Wang K, Bayless AM, Wang J, Hughes TJ, Willis DK, Clemente TE, Diers BW, Jiang J, HudsonME, Bent AF (2012) Copy number variation of multiple genes at Rhg1 mediates nematode resistance in soybean. Science 338:1206-1209


Da Silva C, Zamperin G, Ferrarini A, Minio A, Dal Molin A, Venturini L, Buson G, Tononi P, Avanzato C, Zago E, Boido E, DellacassaE, Gaggero C, Pezzotti M, Carrau F, Delledonne M (2013) The high polyphenol content of grapevine cultivar tannat berries isconferred primarily by genes that are not shared with the reference genome. Plant Cell 25: 4777-4788


DellaPenna D, O'Connor SE (2012) Plant science. Plant gene clusters and opiates. Science 336: 1648-1649Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title

Denoeud F, Carretero-Paulet L, Dereeper A, Droc G, Guyot R, Pietrella M, Zheng C, Alberti A, Anthony F, Aprea G, Aury JM, BentoP, Bernard M, Bocs S, Campa C, Cenci A, Combes MC, Crouzillat D, Da Silva C, Daddiego L, De Bellis F, Dussert S, Garsmeur O,Gayraud T, Guignon V, Jahn K, Jamilloux V, Joet T, Labadie K, Lan T, Leclercq J, Lepelley M, Leroy T, Li LT, Librado P, Lopez L,Munoz A, Noel B, Pallavicini A, Perrotta G, Poncet V, Pot D, Priyono, Rigoreau M, Rouard M, Rozas J, Tranchant-Dubreuil C,VanBuren R, Zhang Q, Andrade AC, Argout X, Bertrand B, de Kochko A, Graziosi G, Henry RJ, Jayarama, Ming R, Nagai C,Rounsley S, Sankoff D, Giuliano G, Albert VA, Wincker P, Lashermes P (2014) The coffee genome provides insight into theconvergent evolution of caffeine biosynthesis. Science 345: 1181-1184

Pubmed: Author and Title https://plantphysiol.orgDownloaded on June 2, 2021. - Published by Copyright (c) 2020 American Society of Plant Biologists. All rights reserved.

http://www.ncbi.nlm.nih.gov/pubmed?term=%28Nature%5BTitle%5D%29 AND 408%5BVolume%5D AND 796%5BPagination%5D AND Arabidopsis Genome Initiative%5BAuthor%5D&dopt=abstracthttp://search.crossref.org/?page=1&rows=2&q=Arabidopsis Genome Initiative (2000) Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature 408: 796-815http://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Arabidopsis&hl=en&c2coff=1http://scholar.google.com/scholar?as_q=Analysis of the genome sequence of the flowering plant Arabidopsis thaliana&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1http://scholar.google.com/scholar?as_q=Analysis of the genome sequence of the flowering plant Arabidopsis thaliana&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Arabidopsis&as_ylo=2000&as_allsubj=all&hl=en&c2coff=1http://www.ncbi.nlm.nih.gov/pubmed?term=%28Nature%5BTitle%5D%29 AND 444%5BVolume%5D AND 337%5BPagination%5D AND Baur JA%2C Pearson KJ%2C Price NL%2C Jamieson HA%2C Lerin C%2C Kalra A%2C Prabhu VV%2C Allard JS%2C Lopez%2DLluch G%2C Lewis K%2C Pistell PJ%2C Poosala S%2C Becker KG%2C Boss O%2C Gwinn D%2C Wang M%2C Ramaswamy S%2C Fishbein KW%2C Spencer RG%2C Lakatta EG%2C Le Couteur D%2C Shaw RJ%2C Navas P%2C Puigserver P%2C Ingram DK%2C de Cabo R%2C Sinclair DA%5BAuthor%5D&dopt=abstracthttp://search.crossref.org/?page=1&rows=2&q=Baur JA, Pearson KJ, Price NL, Jamieson HA, Lerin C, Kalra A, Prabhu VV, Allard JS, Lopez-Lluch G, Lewis K, Pistell PJ, Poosala S, Becker KG, Boss O, Gwinn D, Wang M, Ramaswamy S, Fishbein KW, Spencer RG, Lakatta EG, Le Couteur D, Shaw RJ, Navas P, Puigserver P, Ingram DK, de Cabo R, Sinclair DA (2006) Resveratrol improves health and survival of mice on a high-calorie diet. Nature 444: 337-342http://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Baur&hl=en&c2coff=1http://scholar.google.com/scholar?as_q=Resveratrol improves health and survival of mice on a high-calorie diet&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1http://scholar.google.com/scholar?as_q=Resveratrol improves health and survival of mice on a high-calorie diet&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Baur&as_ylo=2006&as_allsubj=all&hl=en&c2coff=1http://www.ncbi.nlm.nih.gov/pubmed?term=%28Proc Natl Acad Sci U S A%5BTitle%5D%29 AND 112%5BVolume%5D AND E81%5BPagination%5D AND Boutanaev AM%2C Moses T%2C Zi J%2C Nelson DR%2C Mugford ST%2C Peters RJ%2C Osbourn A%5BAuthor%5D&dopt=abstracthttp://search.crossref.org/?page=1&rows=2&q=Boutanaev AM, Moses T, Zi J, Nelson DR, Mugford ST, Peters RJ, Osbourn A (2015) Investigation of terpene diversification across multiple sequenced plant genomes. Proc Natl Acad Sci U S A 112: E81-88http://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Boutanaev&hl=en&c2coff=1http://scholar.google.com/scholar?as_q=Investigation of terpene diversification across multiple sequenced plant genomes&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1http://scholar.google.com/scholar?as_q=Investigation of terpene diversification across multiple sequenced plant genomes&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Boutanaev&as_ylo=2015&as_allsubj=all&hl=en&c2coff=1http://www.ncbi.nlm.nih.gov/pubmed?term=%28Plant Physiol%5BTitle%5D%29 AND 154%5BVolume%5D AND 497%5BPagination%5D AND Buell CR%2C Last RL%5BAuthor%5D&dopt=abstracthttp://search.crossref.org/?page=1&rows=2&q=Buell CR, Last RL (2010) Twenty-first century plant biology: impacts of the Arabidopsis genome on plant biology and agriculture. Plant Physiol 154: 497-500http://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Buell&hl=en&c2coff=1http://scholar.google.com/scholar?as_q=Twenty-first century plant biology: impacts of the Arabidopsis genome on plant biology and agriculture&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1http://scholar.google.com/scholar?as_q=Twenty-first century plant biology: impacts of the Arabidopsis genome on plant biology and agriculture&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Buell&as_ylo=2010&as_allsubj=all&hl=en&c2coff=1http://www.ncbi.nlm.nih.gov/pubmed?term=%28Science%5BTitle%5D%29 AND 344%5BVolume%5D AND 510%5BPagination%5D AND Chae L%2C Kim T%2C Nilo%2DPoyanco R%2C Rhee SY%5BAuthor%5D&dopt=abstracthttp://search.crossref.org/?page=1&rows=2&q=Chae L, Kim T, Nilo-Poyanco R, Rhee SY (2014) Genomic signatures of specialized metabolism in plants. Science 344: 510-513http://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Chae&hl=en&c2coff=1http://scholar.google.com/scholar?as_q=Genomic signatures of specialized metabolism in plants&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1http://scholar.google.com/scholar?as_q=Genomic signatures of specialized metabolism in plants&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Chae&as_ylo=2014&as_allsubj=all&hl=en&c2coff=1http://www.ncbi.nlm.nih.gov/pubmed?term=%28Genetics%5BTitle%5D%29 AND 185%5BVolume%5D AND 991%5BPagination%5D AND Chan EKF%2C Rowe HC%2C Kliebenstein DJ%5BAuthor%5D&dopt=abstracthttp://search.crossref.org/?page=1&rows=2&q=Chan EKF, Rowe HC, Kliebenstein DJ (2010) Understanding the Evolution of Defense Metabolites in Arabidopsis thaliana Using Genome-wide Association Mapping. Genetics 185: 991-1007http://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Chan&hl=en&c2coff=1http://scholar.google.com/scholar?as_q=Understanding the Evolution of Defense Metabolites in Arabidopsis thaliana Using Genome-wide Association Mapping&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1http://scholar.google.com/scholar?as_q=Understanding the Evolution of Defense Metabolites in Arabidopsis thaliana Using Genome-wide Association Mapping&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Chan&as_ylo=2010&as_allsubj=all&hl=en&c2coff=1http://www.ncbi.nlm.nih.gov/pubmed?term=%28Nat Genet%5BTitle%5D%29 AND 46%5BVolume%5D AND 714%5BPagination%5D AND Chen W%2C Gao Y%2C Xie W%2C Gong L%2C Lu K%2C Wang W%2C Li Y%2C Liu X%2C Zhang H%2C Dong H%2C Zhang W%2C Zhang L%2C Yu S%2C Wang G%2C Lian X%2C Luo J%5BAuthor%5D&dopt=abstracthttp://search.crossref.org/?page=1&rows=2&q=Chen W, Gao Y, Xie W, Gong L, Lu K, Wang W, Li Y, Liu X, Zhang H, Dong H, Zhang W, Zhang L, Yu S, Wang G, Lian X, Luo J (2014) Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism. Nat Genet 46: 714-721http://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Chen&hl=en&c2coff=1http://scholar.google.com/scholar?as_q=Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1http://scholar.google.com/scholar?as_q=Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Chen&as_ylo=2014&as_allsubj=all&hl=en&c2coff=1http://www.ncbi.nlm.nih.gov/pubmed?term=%28Curr Opin Plant Biol%5BTitle%5D%29 AND 19%5BVolume%5D AND 91%5BPagination%5D AND Conant GC%2C Birchler JA%2C Pires JC%5BAuthor%5D&dopt=abstracthttp://search.crossref.org/?page=1&rows=2&q=Conant GC, Birchler JA, Pires JC (2014) Dosage, duplication, and diploidization: clarifying the interplay of multiple models for duplicate gene evolution over time. Curr Opin Plant Biol 19: 91-98http://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Conant&hl=en&c2coff=1http://scholar.google.com/scholar?as_q=Dosage, duplication, and diploidization: clarifying the interplay of multiple models for duplicate gene evolution over time&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1http://scholar.google.com/scholar?as_q=Dosage, duplication, and diploidization: clarifying the interplay of multiple models for duplicate gene evolution over time&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Conant&as_ylo=2014&as_allsubj=all&hl=en&c2coff=1http://www.ncbi.nlm.nih.gov/pubmed?term=%28Science%5BTitle%5D%29 AND 338%5BVolume%5D AND 1206%5BPagination%5D AND Cook DE%2C Lee TG%2C Guo X%2C Melito S%2C Wang K%2C Bayless AM%2C Wang J%2C Hughes TJ%2C Willis DK%2C Clemente TE%2C Diers BW%2C Jiang J%2C Hudson ME%2C Bent AF%5BAuthor%5D&dopt=abstracthttp://search.crossref.org/?page=1&rows=2&q=Cook DE, Lee TG, Guo X, Melito S, Wang K, Bayless AM, Wang J, Hughes TJ, Willis DK, Clemente TE, Diers BW, Jiang J, Hudson ME, Bent AF (2012) Copy number variation of multiple genes at Rhg1 mediates nematode resistance in soybean. Science 338: 1206-1209http://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Cook&hl=en&c2coff=1http://scholar.google.com/scholar?as_q=Copy number variation of multiple genes at Rhg1 mediates nematode resistance in soybean&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1http://scholar.google.com/scholar?as_q=Copy number variation of multiple genes at Rhg1 mediates nematode resistance in soybean&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Cook&as_ylo=2012&as_allsubj=all&hl=en&c2coff=1http://www.ncbi.nlm.nih.gov/pubmed?term=%28Plant Cell%5BTitle%5D%29 AND 25%5BVolume%5D AND 4777%5BPagination%5D AND Da Silva C%2C Zamperin G%2C Ferrarini A%2C Minio A%2C Dal Molin A%2C Venturini L%2C Buson G%2C Tononi P%2C Avanzato C%2C Zago E%2C Boido E%2C Dellacassa E%2C Gaggero C%2C Pezzotti M%2C Carrau F%2C Delledonne M%5BAuthor%5D&dopt=abstracthttp://search.crossref.org/?page=1&rows=2&q=Da Silva C, Zamperin G, Ferrarini A, Minio A, Dal Molin A, Venturini L, Buson G, Tononi P, Avanzato C, Zago E, Boido E, Dellacassa E, Gaggero C, Pezzotti M, Carrau F, Delledonne M (2013) The high polyphenol content of grapevine cultivar tannat berries is conferred primarily by genes that are not shared with the reference genome. Plant Cell 25: 4777-4788http://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Da&hl=en&c2coff=1http://scholar.google.com/scholar?as_q=The high polyphenol content of grapevine cultivar tannat berries is conferred primarily by genes that are not shared with the reference genome&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1http://scholar.google.com/scholar?as_q=The high polyphenol content of grapevine cultivar tannat berries is conferred primarily by genes that are not shared with the reference genome&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Da&as_ylo=2013&as_allsubj=all&hl=en&c2coff=1http://www.ncbi.nlm.nih.gov/pubmed?term=%28Plant gene clusters and opiates%2E Science%5BTitle%5D%29 AND 336%5BVolume%5D AND 1648%5BPagination%5D AND DellaPenna D%2C O%27Connor SE%5BAuthor%5D&dopt=abstracthttp://search.crossref.org/?page=1&rows=2&q=DellaPenna D, O'Connor SE (2012) Plant science. Plant gene clusters and opiates. Science 336: 1648-1649http://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=DellaPenna&hl=en&c2coff=1http://scholar.google.com/scholar?as_q=Plant science&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1http://scholar.google.com/scholar?as_q=Plant science&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=DellaPenna&as_ylo=2012&as_allsubj=all&hl=en&c2coff=1http://www.ncbi.nlm.nih.gov/pubmed?term=%28Science%5BTitle%5D%29 AND 345%5BVolume%5D AND 1181%5BPagination%5D AND Denoeud F%2C Carretero%2DPaulet L%2C Dereeper A%2C Droc G%2C Guyot R%2C Pietrella M%2C Zheng C%2C Alberti A%2C Anthony F%2C Aprea G%2C Aury JM%2C Bento P%2C Bernard M%2C Bocs S%2C Campa C%2C Cenci A%2C Combes MC%2C Crouzillat D%2C Da Silva C%2C Daddiego L%2C De Bellis F%2C Dussert S%2C Garsmeur O%2C Gayraud T%2C Guignon V%2C Jahn K%2C Jamilloux V%2C Joet T%2C Labadie K%2C Lan T%2C Leclercq J%2C Lepelley M%2C Leroy T%2C Li LT%2C Librado P%2C Lopez L%2C Munoz A%2C Noel B%2C Pallavicini A%2C Perrotta G%2C Poncet V%2C Pot D%2C Priyono%2C Rigoreau M%2C Rouard M%2C Rozas J%2C Tranchant%2DDubreuil C%2C VanBuren R%2C Zhang Q%2C Andrade AC%2C Argout X%2C Bertrand B%2C de Kochko A%2C Graziosi G%2C Henry RJ%2C Jayarama%2C Ming R%2C Nagai C%2C Rounsley S%2C Sankoff D%2C Giuliano G%2C Albert VA%2C Wincker P%2C Lashermes P%5BAuthor%5D&dopt=abstracthttps://plantphysiol.org

CrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title

Dixon RA, Pasinetti GM (2010) Flavonoids and isoflavonoids: from plant biology to agriculture and neuroscience. Plant Physiol 154:453-457


Friedman M (2006) Potato glycoalkaloids and metabolites: roles in the plant and in the diet. J Agric Food Chem 54: 8655-8681Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title

Fu J, Cheng Y, Linghu J, Yang X, Kang L, Zhang Z, Zhang J, He C, Du X, Peng Z, Wang B, Zhai L, Dai C, Xu J, Wang W, Li X, Zheng J,Chen L, Luo L, Liu J, Qian X, Yan J, Wang J, Wang G (2013) RNA sequencing reveals the complex regulatory network in the maizekernel. Nat Commun 4: 2832


Hirsch CN, Buell CR (2013) Tapping the promise of genomics in species with complex, nonmodel genomes. Annu Rev Plant Biol64: 89-110


Itkin M, Heinig U, Tzfadia O, Bhide AJ, Shinde B, Cardenas PD, Bocobza SE, Unger T, Malitsky S, Finkers R, Tikunov Y, Bovy A,Chikate Y, Singh P, Rogachev I, Beekwilder J, Giri AP, Aharoni A (2013) Biosynthesis of antinutritional alkaloids in solanaceouscrops is mediated by clustered genes. Science 341: 175-179


Jaillon O, Aury JM, Noel B, Policriti A, Clepet C, Casagrande A, Choisne N, Aubourg S, Vitulo N, Jubin C, Vezzi A, Legeai F,Hugueney P, Dasilva C, Horner D, Mica E, Jublot D, Poulain J, Bruyere C, Billault A, Segurens B, Gouyvenoux M, Ugarte E,Cattonaro F, Anthouard V, Vico V, Del Fabbro C, Alaux M, Di Gaspero G, Dumas V, Felice N, Paillard S, Juman I, Moroldo M,Scalabrin S, Canaguier A, Le Clainche I, Malacrida G, Durand E, Pesole G, Laucou V, Chatelet P, Merdinoglu D, Delledonne M,Pezzotti M, Lecharny A, Scarpelli C, Artiguenave F, Pe ME, Valle G, Morgante M, Caboche M, Adam-Blondon AF, Weissenbach J,Quetier F, Wincker P, French-Italian Public Consortium for Grapevine Genome C (2007) The grapevine genome sequencesuggests ancestral hexaploidization in major angiosperm phyla. Nature 449: 463-467


James GV, Patel V, Nordstrom KJ, Klasen JR, Salome PA, Weigel D, Schneeberger K (2013) User guide for mapping-by-sequencingin Arabidopsis. Genome Biol 14: R61


Kellner F, Kim J, Clavijo BJ, Hamilton JP, Childs KL, Vaillancourt B, Cepela J, Habermann M, Steuernagel B, Clissold L, McLay K,Buell CR, O'Connor SE (2015) Genome-guided investigation of plant natural product biosynthesis. Plant J 82: 680-692


Mardis ER (2013) Next-generation sequencing platforms. Annu Rev Anal Chem (Palo Alto Calif) 6: 287-303Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title

Maron LG, Guimaraes CT, Kirst M, Albert PS, Birchler JA, Bradbury PJ, Buckler ES, Coluccio AE, Danilova TV, Kudrna D,Magalhaes JV, Pineros MA, Schatz MC, Wing RA, Kochian LV (2013) Aluminum tolerance in maize is associated with higher MATE1gene copy number. Proc Natl Acad Sci U S A 110: 5241-5246


Miettinen K, Dong L, Navrot N, Schneider T, Burlat V, Pollier J, Woittiez L, van der Krol S, Lugan R, Ilc T, Verpoorte R, Oksman-Caldentey KM, Martinoia E, Bouwmeester H, Goossens A, Memelink J, Werck-Reichhart D (2014) The seco-iridoid pathway fromCatharanthus roseus. Nat Commun 5: 3606


Mueller LA, Zhang P, Rhee SY (2003) AraCyc: a biochemical pathway database for Arabidopsis. Plant Physiol 132: 453-460Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title https://plantphysiol.orgDownloaded on June 2, 2021. - Published by

Copyright (c) 2020 American Society of Plant Biologists. All rights reserved.

http://search.crossref.org/?page=1&rows=2&q=Denoeud F, Carretero-Paulet L, Dereeper A, Droc G, Guyot R, Pietrella M, Zheng C, Alberti A, Anthony F, Aprea G, Aury JM, Bento P, Bernard M, Bocs S, Campa C, Cenci A, Combes MC, Crouzillat D, Da Silva C, Daddiego L, De Bellis F, Dussert S, Garsmeur O, Gayraud T, Guignon V, Jahn K, Jamilloux V, Joet T, Labadie K, Lan T, Leclercq J, Lepelley M, Leroy T, Li LT, Librado P, Lopez L, Munoz A, Noel B, Pallavicini A, Perrotta G, Poncet V, Pot D, Priyono, Rigoreau M, Rouard M, Rozas J, Tranchant-Dubreuil C, VanBuren R, Zhang Q, Andrade AC, Argout X, Bertrand B, de Kochko A, Graziosi G, Henry RJ, Jayarama, Ming R, Nagai C, Rounsley S, Sankoff D, Giuliano G, Albert VA, Wincker P, Lashermes P (2014) The coffee genome provides insight into the convergent evolution of caffeine biosynthesis. Science 345: 1181-1184http://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Denoeud&hl=en&c2coff=1http://scholar.google.com/scholar?as_q=The coffee genome provides insight into the convergent evolution of caffeine biosynthesis&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1http://scholar.google.com/scholar?as_q=The coffee genome provides insight into the convergent evolution of caffeine biosynthesis&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Denoeud&as_ylo=2014&as_allsubj=all&hl=en&c2coff=1http://www.ncbi.nlm.nih.gov/pubmed?term=%28Plant Physiol%5BTitle%5D%29 AND 154%5BVolume%5D AND 453%5BPagination%5D AND Dixon RA%2C Pasinetti GM%5BAuthor%5D&dopt=abstracthttp://search.crossref.org/?page=1&rows=2&q=Dixon RA, Pasinetti GM (2010) Flavonoids and isoflavonoids: from plant biology to agriculture and neuroscience. Plant Physiol 154: 453-457http://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Dixon&hl=en&c2coff=1http://scholar.google.com/scholar?as_q=Flavonoids and isoflavonoids: from plant biology to agriculture and neuroscience&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1http://scholar.google.com/scholar?as_q=Flavonoids and isoflavonoids: from plant biology to agriculture and neuroscience&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Dixon&as_ylo=2010&as_allsubj=all&hl=en&c2coff=1http://www.ncbi.nlm.nih.gov/pubmed?term=%28J Agric Food Chem%5BTitle%5D%29 AND 54%5BVolume%5D AND 8655%5BPagination%5D AND Friedman M%5BAuthor%5D&dopt=abstracthttp://search.crossref.org/?page=1&rows=2&q=Friedman M (2006) Potato glycoalkaloids and metabolites: roles in the plant and in the diet. J Agric Food Chem 54: 8655-8681http://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Friedman&hl=en&c2coff=1http://scholar.google.com/scholar?as_q=Potato glycoalkaloids and metabolites: roles in the plant and in the diet&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1http://scholar.google.com/scholar?as_q=Potato glycoalkaloids and metabolites: roles in the plant and in the diet&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Friedman&as_ylo=2006&as_allsubj=all&hl=en&c2coff=1http://www.ncbi.nlm.nih.gov/pubmed?term=%28RNA sequencing reveals the complex regulatory network in the maize kernel%2E%5BTitle%5D%29 AND Fu J%2C Cheng Y%2C Linghu J%2C Yang X%2C Kang L%2C Zhang Z%2C Zhang J%2C He C%2C Du X%2C Peng Z%2C Wang B%2C Zhai L%2C Dai C%2C Xu J%2C Wang W%2C Li X%2C Zheng J%2C Chen L%2C Luo L%2C Liu J%2C Qian X%2C Yan J%2C Wang J%2C Wang G%5BAuthor%5D&dopt=abstracthttp://search.crossref.org/?page=1&rows=2&q=Fu J, Cheng Y, Linghu J, Yang X, Kang L, Zhang Z, Zhang J, He C, Du X, Peng Z, Wang B, Zhai L, Dai C, Xu J, Wang W, Li X, Zheng J, Chen L, Luo L, Liu J, Qian X, Yan J, Wang J, Wang G (2013) RNA sequencing reveals the complex regulatory network in the maize kernel. Nat Commun 4: 2832http://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Fu&hl=en&c2coff=1http://scholar.google.com/scholar?as_q=RNA sequencing reveals the complex regulatory network in the maize kernel.&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1http://scholar.google.com/scholar?as_q=RNA sequencing reveals the complex regulatory network in the maize kernel.&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Fu&as_ylo=2013&as_allsubj=all&hl=en&c2coff=1http://www.ncbi.nlm.nih.gov/pubmed?term=%28Annu Rev Plant Biol%5BTitle%5D%29 AND 64%5BVolume%5D AND 89%5BPagination%5D AND Hirsch CN%2C Buell CR%5BAuthor%5D&dopt=abstracthttp://search.crossref.org/?page=1&rows=2&q=Hirsch CN, Buell CR (2013) Tapping the promise of genomics in species with complex, nonmodel genomes. Annu Rev Plant Biol 64: 89-110http://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Hirsch&hl=en&c2coff=1http://scholar.google.com/scholar?as_q=Tapping the promise of genomics in species with complex, nonmodel genomes&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1http://scholar.google.com/scholar?as_q=Tapping the promise of genomics in species with complex, nonmodel genomes&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Hirsch&as_ylo=2013&as_allsubj=all&hl=en&c2coff=1http://www.ncbi.nlm.nih.gov/pubmed?term=%28Science%5BTitle%5D%29 AND 341%5BVolume%5D AND 175%5BPagination%5D AND Itkin M%2C Heinig U%2C Tzfadia O%2C Bhide AJ%2C Shinde B%2C Cardenas PD%2C Bocobza SE%2C Unger T%2C Malitsky S%2C Finkers R%2C Tikunov Y%2C Bovy A%2C Chikate Y%2C Singh P%2C Rogachev I%2C Beekwilder J%2C Giri AP%2C Aharoni A%5BAuthor%5D&dopt=abstracthttp://search.crossref.org/?page=1&rows=2&q=Itkin M, Heinig U, Tzfadia O, Bhide AJ, Shinde B, Cardenas PD, Bocobza SE, Unger T, Malitsky S, Finkers R, Tikunov Y, Bovy A, Chikate Y, Singh P, Rogachev I, Beekwilder J, Giri AP, Aharoni A (2013) Biosynthesis of antinutritional alkaloids in solanaceous crops is mediated by clustered genes. Science 341: 175-179http://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Itkin&hl=en&c2coff=1http://scholar.google.com/scholar?as_q=Biosynthesis of antinutritional alkaloids in solanaceous crops is mediated by clustered genes&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1http://scholar.google.com/scholar?as_q=Biosynthesis of antinutritional alkaloids in solanaceous crops is mediated by clustered genes&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Itkin&as_ylo=2013&as_allsubj=all&hl=en&c2coff=1http://www.ncbi.nlm.nih.gov/pubmed?term=%28Nature%5BTitle%5D%29 AND 449%5BVolume%5D AND 463%5BPagination%5D AND Jaillon O%2C Aury JM%2C Noel B%2C Policriti A%2C Clepet C%2C Casagrande A%2C Choisne N%2C Aubourg S%2C Vitulo N%2C Jubin C%2C Vezzi A%2C Legeai F%2C Hugueney P%2C Dasilva C%2C Horner D%2C Mica E%2C Jublot D%2C Poulain J%2C Bruyere C%2C Billault A%2C Segurens B%2C Gouyvenoux M%2C Ugarte E%2C Cattonaro F%2C Anthouard V%2C Vico V%2C Del Fabbro C%2C Alaux M%2C Di Gaspero G%2C Dumas V%2C Felice N%2C Paillard S%2C Juman I%2C Moroldo M%2C Scalabrin S%2C Canaguier A%2C Le Clainche I%2C Malacrida G%2C Durand E%2C Pesole G%2C Laucou V%2C Chatelet P%2C Merdinoglu D%2C Delledonne M%2C Pezzotti M%2C Lecharny A%2C Scarpelli C%2C Artiguenave F%2C Pe ME%2C Valle G%2C Morgante M%2C Caboche M%2C Adam%2DBlondon AF%2C Weissenbach J%2C Quetier F%2C Wincker P%2C French%2DItalian Public Consortium for Grapevine Genome C%5BAuthor%5D&dopt=abstracthttp://search.crossref.org/?page=1&rows=2&q=Jaillon O, Aury JM, Noel B, Policriti A, Clepet C, Casagrande A, Choisne N, Aubourg S, Vitulo N, Jubin C, Vezzi A, Legeai F, Hugueney P, Dasilva C, Horner D, Mica E, Jublot D, Poulain J, Bruyere C, Billault A, Segurens B, Gouyvenoux M, Ugarte E, Cattonaro F, Anthouard V, Vico V, Del Fabbro C, Alaux M, Di Gaspero G, Dumas V, Felice N, Paillard S, Juman I, Moroldo M, Scalabrin S, Canaguier A, Le Clainche I, Malacrida G, Durand E, Pesole G, Laucou V, Chatelet P, Merdinoglu D, Delledonne M, Pezzotti M, Lecharny A, Scarpelli C, Artiguenave F, Pe ME, Valle G, Morgante M, Caboche M, Adam-Blondon AF, Weissenbach J, Quetier F, Wincker P, French-Italian Public Consortium for Grapevine Genome C (2007) The grapevine genome sequence suggests ancestral hexaploidization in major angiosperm phyla. Nature 449: 463-467http://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Jaillon&hl=en&c2coff=1http://scholar.google.com/scholar?as_q=The grapevine genome sequence suggests ancestral hexaploidization in major angiosperm phyla&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1http://scholar.google.com/scholar?as_q=The grapevine genome sequence suggests ancestral hexaploidization in major angiosperm phyla&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Jaillon&as_ylo=2007&as_allsubj=all&hl=en&c2coff=1http://www.ncbi.nlm.nih.gov/pubmed?term=%28User guide for mapping%2Dby%2Dsequencing in Arabidopsis%2E%5BTitle%5D%29 AND James GV%2C Patel V%2C Nordstrom KJ%2C Klasen JR%2C Salome PA%2C Weigel D%2C Schneeberger K%5BAuthor%5D&dopt=abstracthttp://search.crossref.org/?page=1&rows=2&q=James GV, Patel V, Nordstrom KJ, Klasen JR, Salome PA, Weigel D, Schneeberger K (2013) User guide for mapping-by-sequencing in Arabidopsis. Genome Biol 14: R61http://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=James&hl=en&c2coff=1http://scholar.google.com/scholar?as_q=User guide for mapping-by-sequencing in Arabidopsis.&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1http://scholar.google.com/scholar?as_q=User guide for mapping-by-sequencing in Arabidopsis.&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=James&as_ylo=2013&as_allsubj=all&hl=en&c2coff=1http://www.ncbi.nlm.nih.gov/pubmed?term=%28Plant J%5BTitle%5D%29 AND 82%5BVolume%5D AND 680%5BPagination%5D AND Kellner F%2C Kim J%2C Clavijo BJ%2C Hamilton JP%2C Childs KL%2C Vaillancourt B%2C Cepela J%2C Habermann M%2C Steuernagel B%2C Clissold L%2C McLay K%2C Buell CR%2C O%27Connor SE%5BAuthor%5D&dopt=abstracthttp://search.crossref.org/?page=1&rows=2&q=Kellner F, Kim J, Clavijo BJ, Hamilton JP, Childs KL, Vaillancourt B, Cepela J, Habermann M, Steuernagel B, Clissold L, McLay K, Buell CR, O'Connor SE (2015) Genome-guided investigation of plant natural product biosynthesis. Plant J 82: 680-692http://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Kellner&hl=en&c2coff=1http://scholar.google.com/scholar?as_q=Genome-guided investigation of plant natural product biosynthesis&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1http://scholar.google.com/scholar?as_q=Genome-guided investigation of plant natural product biosynthesis&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Kellner&as_ylo=2015&as_allsubj=all&hl=en&c2coff=1http://www.ncbi.nlm.nih.gov/pubmed?term=%28Annu Rev Anal Chem Palo Alto Calif%5BTitle%5D%29 AND 6%5BVolume%5D AND 287%5BPagination%5D AND Mardis ER%5BAuthor%5D&dopt=abstracthttp://search.crossref.org/?page=1&rows=2&q=Mardis ER (2013) Next-generation sequencing platforms. Annu Rev Anal Chem (Palo Alto Calif) 6: 287-303http://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Mardis&hl=en&c2coff=1http://scholar.google.com/scholar?as_q=Next-generation sequencing platforms&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1http://scholar.google.com/scholar?as_q=Next-generation sequencing platforms&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Mardis&as_ylo=2013&as_allsubj=all&hl=en&c2coff=1http://www.ncbi.nlm.nih.gov/pubmed?term=%28Proc Natl Acad Sci U S A%5BTitle%5D%29 AND 110%5BVolume%5D AND 5241%5BPagination%5D AND Maron LG%2C Guimaraes CT%2C Kirst M%2C Albert PS%2C Birchler JA%2C Bradbury PJ%2C Buckler ES%2C Coluccio AE%2C Danilova TV%2C Kudrna D%2C Magalhaes JV%2C Pineros MA%2C Schatz MC%2C Wing RA%2C Kochian LV%5BAuthor%5D&dopt=abstracthttp://search.crossref.org/?page=1&rows=2&q=Maron LG, Guimaraes CT, Kirst M, Albert PS, Birchler JA, Bradbury PJ, Buckler ES, Coluccio AE, Danilova TV, Kudrna D, Magalhaes JV, Pineros MA, Schatz MC, Wing RA, Kochian LV (2013) Aluminum tolerance in maize is associated with higher MATE1 gene copy number. Proc Natl Acad Sci U S A 110: 5241-5246http://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Maron&hl=en&c2coff=1http://scholar.google.com/scholar?as_q=Aluminum tolerance in maize is associated with higher MATE1 gene copy number&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1http://scholar.google.com/scholar?as_q=Aluminum tolerance in maize is associated with higher MATE1 gene copy number&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Maron&as_ylo=2013&as_allsubj=all&hl=en&c2coff=1http://www.ncbi.nlm.nih.gov/pubmed?term=%28The seco%2Diridoid pathway from Catharanthus roseus%2E%5BTitle%5D%29 AND Miettinen K%2C Dong L%2C Navrot N%2C Schneider T%2C Burlat V%2C Pollier J%2C Woittiez L%2C van der Krol S%2C Lugan R%2C Ilc T%2C Verpoorte R%2C Oksman%2DCaldentey KM%2C Martinoia E%2C Bouwmeester H%2C Goossens A%2C Memelink J%2C Werck%2DReichhart D%5BAuthor%5D&dopt=abstracthttp://search.crossref.org/?page=1&rows=2&q=Miettinen K, Dong L, Navrot N, Schneider T, Burlat V, Pollier J, Woittiez L, van der Krol S, Lugan R, Ilc T, Verpoorte R, Oksman-Caldentey KM, Martinoia E, Bouwmeester H, Goossens A, Memelink J, Werck-Reichhart D (2014) The seco-iridoid pathway from Catharanthus roseus. Nat Commun 5: 3606http://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Miettinen&hl=en&c2coff=1http://scholar.google.com/scholar?as_q=The seco-iridoid pathway from Catharanthus roseus.&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1http://scholar.google.com/scholar?as_q=The seco-iridoid pathway from Catharanthus roseus.&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Miettinen&as_ylo=2014&as_allsubj=all&hl=en&c2coff=1http://www.ncbi.nlm.nih.gov/pubmed?term=%28Plant Physiol%5BTitle%5D%29 AND 132%5BVolume%5D AND 453%5BPagination%5D AND Mueller LA%2C Zhang P%2C Rhee SY%5BAuthor%5D&dopt=abstracthttp://search.crossref.org/?page=1&rows=2&q=Mueller LA, Zhang P, Rhee SY (2003) AraCyc: a biochemical pathway database for Arabidopsis. Plant Physiol 132: 453-460http://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Mueller&hl=en&c2coff=1http://scholar.google.com/scholar?as_q=AraCyc: a biochemical pathway database for Arabidopsis&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1http://scholar.google.com/scholar?as_q=AraCyc: a biochemical pathway database for Arabidopsis&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Mueller&as_ylo=2003&as_allsubj=all&hl=en&c2coff=1https://plantphysiol.org

Myburg AA, Grattapaglia D, Tuskan GA, Hellsten U, Hayes RD, Grimwood J, Jenkins J, Lindquist E, Tice H, Bauer D, Goodstein DM,Dubchak I, Poliakov A, Mizrachi E, Kullan AR, Hussey SG, Pinard D, van der Merwe K, Singh P, van Jaarsveld I, Silva-Junior OB,Togawa RC, Pappas MR, Faria DA, Sansaloni CP, Petroli CD, Yang X, Ranjan P, Tschaplinski TJ, Ye CY, Li T, Sterck L, Vanneste K,Murat F, Soler M, Clemente HS, Saidi N, Cassan-Wang H, Dunand C, Hefer CA, Bornberg-Bauer E, Kersting AR, Vining K,Amarasinghe V, Ranik M, Naithani S, Elser J, Boyd AE, Liston A, Spatafora JW, Dharmwardhana P, Raja R, Sullivan C, Romanel E,Alves-Ferreira M, Kulheim C, Foley W, Carocha V, Paiva J, Kudrna D, Brommonschenkel SH, Pasquali G, Byrne M, Rigault P,Tibbits J, Spokevicius A, Jones RC, Steane DA, Vaillancourt RE, Potts BM, Joubert F, Barry K, Pappas GJ, Strauss SH, Jaiswal P,Grima-Pettenati J, Salse J, Van de Peer Y, Rokhsar DS, Schmutz J (2014) The genome of Eucalyptus grandis. Nature 510: 356-362

Pubmed: Author and Ti

jeongwoon kim and c. robin buelljul 29, 2015 · access to next generation sequencing 8...

Documents