Identification of Key Metabolic Pathways and Biomarkers Underlying Flowering Time of Guar (Cyamopsis tetragonoloba (L.) Taub.) via Integrated Transcriptome-Metabolome Analysis
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Design and Sample Collection
2.2. Metabolites Extraction, Derivatization, Identification and Statistical Analysis of Differentially Expressed Metabolites between Groups of Lines with Contrasted Flowering Time under Long Day Conditions
2.3. RNA Extraction, Library Construction, Sequencing
2.4. Reads Quality Control
2.5. RNA-Seq de novo Assembly
2.6. Differential Expression Analysis
2.7. Enrichment of Differential Expressed Genes (DEGs)
2.8. Relationship between Metabolites and Transcripts: Shiny GAM Network Construction
3. Results
3.1. Gas Chromatography–Mass Spectrometry Metabolomic Analysis
3.2. RNA Sequencing and Quality Control
3.3. RNA-Seq de novo Assembly
3.4. Differential Expression Analysis
3.5. Gene Ontology (GO) Enrichment Analysis
3.6. Integrative Approach for Metabolites and Transcripts Analysis Using Shiny GAM Network Application
3.6.1. 4-Coumarate (Cinnamic acid)
3.6.2. D-Glycerate
3.6.3. Citrate
3.6.4. S-Malate
3.6.5. Myo-Inositol
3.7. Myo-Inositol as a Biomarker of Flowering Time
4. Discussion
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Gresta, F.; Cristaudo, A.; Trostle, C.; Anastasi, U.; Guarnaccia, P.; Catara, S.; Onofri, A. Germination of Guar (Cyamopsis tetragonoloba (L.) Taub.) Genotypes with Reduced Temperature Requirements. Aust. J. Crop Sci. 2018, 12, 954. [Google Scholar] [CrossRef]
- Bernard, R.L. Two Major Genes for Time of Flowering and Maturity in Soybeans 1. Crop Sci. 1971, 11, 242–244. [Google Scholar] [CrossRef]
- Cober, E.R.; Morrison, M.J. Regulation of Seed Yield and Agronomic Characters by Photoperiod Sensitivity and Growth Habit Genes in Soybean. Theor. Appl. Genet. 2010, 120, 1005–1012. [Google Scholar] [CrossRef] [PubMed]
- Kong, F.; Nan, H.; Cao, D.; Li, Y.; Wu, F.; Wang, J.; Lu, S.; Yuan, X.; Cober, E.R.; Abe, J. A New Dominant Gene E9 Conditions Early Flowering and Maturity in Soybean. Crop Sci. 2014, 54, 2529–2535. [Google Scholar] [CrossRef]
- Kim, K.H.; Kim, J.-Y.; Lim, W.-J.; Jeong, S.; Lee, H.-Y.; Cho, Y.; Moon, J.-K.; Kim, N. Genome-Wide Association and Epistatic Interactions of Flowering Time in Soybean Cultivar. PLoS ONE 2020, 15, e0228114. [Google Scholar] [CrossRef] [PubMed]
- Teplyakova, S.; Volkov, V.; Dzyubenko, E.; Potokina, E.K. Variability of Photoperiod Response in Guar (Cyamopsis tetragonoloba (L.) Taub.) Genotypes of Different Geographic Origin. Vavilov J. Genet. Breed. 2019, 23, 730–737. [Google Scholar] [CrossRef]
- Arkhimandritova, S.; Shavarda, A.; Potokina, E. Key Metabolites Associated with the Onset of Flowering of Guar Genotypes (Cyamopsis tetragonoloba (L.) Taub). BMC Plant Biol. 2020, 20, 1–10. [Google Scholar] [CrossRef] [PubMed]
- Abid, G.; Sassi, K.; Muhovski, Y.; Jacquemin, J.-M.; Mingeot, D.; Tarchoun, N.; Baudoin, J.-P. Comparative Expression and Cellular Localization of Myo-Inositol Phosphate Synthase (MIPS) in the Wild Type and in an EMS Mutant during Common Bean (Phaseolus Vulgaris L.) Seed Development. Plant Mol. Biol. Report. 2012, 30, 780–793. [Google Scholar] [CrossRef]
- Teplyakova, S.B.; Shavarda, A.L.; Shelenga, T.V.; Dzyubenko, E.A.; Potokina, E.K. A Simple and Efficient Method to Extract Polar Metabolites from Guar Leaves (Cyamopsis tetragonoloba (L.) Taub.) for GC-MS Metabolome Analysis. Vavilov J. Genet. Breed. 2019, 23, 49–54. [Google Scholar] [CrossRef]
- Fiehn, O.; Kopka, J.; Dörmann, P.; Altmann, T.; Trethewey, R.N.; Willmitzer, L. Metabolite Profiling for Plant Functional Genomics. Nat. Biotechnol. 2000, 18, 1157–1161. [Google Scholar] [CrossRef] [PubMed]
- Jorge, T.F.; Mata, A.T.; António, C. Mass Spectrometry as a Quantitative Tool in Plant Metabolomics. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 2016, 374, 20150370. [Google Scholar] [CrossRef] [PubMed]
- Chong, J.; Soufan, O.; Li, C.; Caraus, I.; Li, S.; Bourque, G.; Wishart, D.S.; Xia, J. MetaboAnalyst 4.0: Towards More Transparent and Integrative Metabolomics Analysis. Nucleic Acids Res. 2018, 46, W486–W494. [Google Scholar] [CrossRef] [Green Version]
- Ewels, P.; Magnusson, M.; Lundin, S.; Käller, M. MultiQC: Summarize Analysis Results for Multiple Tools and Samples in a Single Report. Bioinformatics 2016, 32, 3047–3048. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Bolger, A.M.; Lohse, M.; Usadel, B. Trimmomatic: A Flexible Trimmer for Illumina Sequence Data. Bioinformatics 2014, 30, 2114–2120. [Google Scholar] [CrossRef] [Green Version]
- Song, L.; Florea, L. Rcorrector: Efficient and Accurate Error Correction for Illumina RNA-Seq Reads. GigaScience 2015, 4. [Google Scholar] [CrossRef] [Green Version]
- Bushnell, B. BBMap: A Fast, Accurate, Splice-Aware Aligner; Lawrence Berkeley National Lab. (LBNL): Berkeley, CA, USA, 2014. [Google Scholar]
- Griffiths-Jones, S.; Bateman, A.; Marshall, M.; Khanna, A.; Eddy, S.R. Rfam: An RNA Family Database. Nucleic Acids Res. 2003, 31, 439–441. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Quast, C.; Pruesse, E.; Yilmaz, P.; Gerken, J.; Schweer, T.; Yarza, P.; Peplies, J.; Glöckner, F.O. The SILVA Ribosomal RNA Gene Database Project: Improved Data Processing and Web-Based Tools. Nucleic Acids Res. 2012, 41, D590–D596. [Google Scholar] [CrossRef] [PubMed]
- Bushmanova, E.; Antipov, D.; Lapidus, A.; Prjibelski, A.D. RnaSPAdes: A de Novo Transcriptome Assembler and Its Application to RNA-Seq Data. GigaScience 2019, 8, giz100. [Google Scholar] [CrossRef] [Green Version]
- Haas, B.J.; Papanicolaou, A.; Yassour, M.; Grabherr, M.; Blood, P.D.; Bowden, J.; Couger, M.B.; Eccles, D.; Li, B.; Lieber, M. De Novo Transcript Sequence Reconstruction from RNA-Seq Using the Trinity Platform for Reference Generation and Analysis. Nat. Protoc. 2013, 8, 1494–1512. [Google Scholar] [CrossRef]
- Tanwar, U.K.; Pruthi, V.; Randhawa, G.S. RNA-Seq of Guar (Cyamopsis tetragonoloba, L. Taub.) Leaves: De Novo Transcriptome Assembly, Functional Annotation and Development of Genomic Resources. Front. Plant Sci. 2017, 8, 91. [Google Scholar] [CrossRef] [Green Version]
- Al-Qurainy, F.; Alshameri, A.; Gaafar, A.-R.; Khan, S.; Nadeem, M.; Alameri, A.A.; Tarroum, M.; Ashraf, M. Comprehensive Stress-Based de Novo Transcriptome Assembly and Annotation of Guar (Cyamopsis tetragonoloba (L.) Taub.): An Important Industrial and Forage Crop. Int. J. Genom. 2019, 2019. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Smith-Unna, R.; Boursnell, C.; Patro, R.; Hibberd, J.M.; Kelly, S. TransRate: Reference-Free Quality Assessment of de Novo Transcriptome Assemblies. Genome Res. 2016, 26, 1134–1144. [Google Scholar] [CrossRef] [Green Version]
- Zimin, A.V.; Marçais, G.; Puiu, D.; Roberts, M.; Salzberg, S.L.; Yorke, J.A. The MaSuRCA Genome Assembler. Bioinformatics 2013, 29, 2669–2677. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Dobin, A.; Davis, C.A.; Schlesinger, F.; Drenkow, J.; Zaleski, C.; Jha, S.; Batut, P.; Chaisson, M.; Gingeras, T.R. STAR: Ultrafast Universal RNA-Seq Aligner. Bioinformatics 2013, 29, 15–21. [Google Scholar] [CrossRef] [PubMed]
- Langmead, B.; Salzberg, S.L. Fast Gapped-Read Alignment with Bowtie 2. Nat. Methods 2012, 9, 357. [Google Scholar] [CrossRef] [Green Version]
- Simão, F.A.; Waterhouse, R.M.; Ioannidis, P.; Kriventseva, E.V.; Zdobnov, E.M. BUSCO: Assessing Genome Assembly and Annotation Completeness with Single-Copy Orthologs. Bioinformatics 2015, 31, 3210–3212. [Google Scholar] [CrossRef] [Green Version]
- Li, W.; Godzik, A. Cd-Hit: A Fast Program for Clustering and Comparing Large Sets of Protein or Nucleotide Sequences. Bioinformatics 2006, 22, 1658–1659. [Google Scholar] [CrossRef] [Green Version]
- Altschul, S.F.; Gish, W.; Miller, W.; Myers, E.W.; Lipman, D.J. Basic Local Alignment Search Tool. J. Mol. Biol. 1990, 215, 403–410. [Google Scholar] [CrossRef]
- Goodstein, D.M.; Shu, S.; Howson, R.; Neupane, R.; Hayes, R.D.; Fazo, J.; Mitros, T.; Dirks, W.; Hellsten, U.; Putnam, N. Phytozome: A Comparative Platform for Green Plant Genomics. Nucleic Acids Res. 2012, 40, D1178–D1186. [Google Scholar] [CrossRef]
- Li, B.; Dewey, C.N. RSEM: Accurate Transcript Quantification from RNA-Seq Data with or without a Reference Genome. BMC Bioinform. 2011, 12, 1–16. [Google Scholar] [CrossRef] [Green Version]
- Love, M.I.; Huber, W.; Anders, S. Moderated Estimation of Fold Change and Dispersion for RNA-Seq Data with DESeq2. Genome Biol. 2014, 15, 1–21. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Yu, G.; Wang, L.-G.; Han, Y.; He, Q.-Y. ClusterProfiler: An R Package for Comparing Biological Themes among Gene Clusters. Omics A J. Integr. Biol. 2012, 16, 284–287. [Google Scholar] [CrossRef] [PubMed]
- Sergushichev, A.A.; Loboda, A.A.; Jha, A.K.; Vincent, E.E.; Driggers, E.M.; Jones, R.G.; Pearce, E.J.; Artyomov, M.N. GAM: A Web-Service for Integrated Transcriptional and Metabolic Network Analysis. Nucleic Acids Res. 2016, 44, W194–W200. [Google Scholar] [CrossRef] [PubMed]
- Thakur, O.; Randhawa, G.S. Identification and Characterization of SSR, SNP and InDel Molecular Markers from RNA-Seq Data of Guar (Cyamopsis tetragonoloba, L. Taub.) Roots. BMC Genom. 2018, 19, 951. [Google Scholar] [CrossRef] [Green Version]
- Li, S.-C.; Han, J.-W.; Chen, K.-C.; Chen, C.-S. Purification and Characterization of Isoforms of β-Galactosidases in Mung Bean Seedlings. Phytochemistry 2001, 57, 349–359. [Google Scholar] [CrossRef]
- Ahn, Y.O.; Zheng, M.; Bevan, D.R.; Esen, A.; Shiu, S.-H.; Benson, J.; Peng, H.-P.; Miller, J.T.; Cheng, C.-L.; Poulton, J.E. Functional Genomic Analysis of Arabidopsis thaliana Glycoside Hydrolase Family 35. Phytochemistry 2007, 68, 1510–1520. [Google Scholar] [CrossRef]
- Forkmann, G.; Heller, W. Biosynthesis of Flavonoids. In Comprehensive Natural Products Chemistry; Elsevier: Amsterdam, The Netherlands, 1999; pp. 713–748. ISBN 978-0-08-091283-7. [Google Scholar]
- Liu, T.; Yao, R.; Zhao, Y.; Xu, S.; Huang, C.; Luo, J.; Kong, L. Cloning, Functional Characterization and Site-Directed Mutagenesis of 4-Coumarate: Coenzyme A Ligase (4CL) Involved in Coumarin Biosynthesis in Peucedanum praeruptorum Dunn. Front. Plant Sci. 2017, 8. [Google Scholar] [CrossRef] [Green Version]
- Soltani, B.M.; Ehlting, J.; Hamberger, B.; Douglas, C.J. Multiple Cis-Regulatory Elements Regulate Distinct and Complex Patterns of Developmental and Wound-Induced Expression of Arabidopsis thaliana 4CL Gene Family Members. Planta 2006, 224, 1226–1238. [Google Scholar] [CrossRef]
- Peterhansel, C.; Horst, I.; Niessen, M.; Blume, C.; Kebeish, R.; Kürkcüoglu, S.; Kreuzaler, F. Photorespiration. Arab. Book 2010, 8, e0130. [Google Scholar] [CrossRef]
- Zhong, R.; Cui, D.; Richardson, E.A.; Phillips, D.R.; Azadi, P.; Lu, G.; Ye, Z.-H. Cytosolic Acetyl-CoA Generated by ATP-Citrate Lyase Is Essential for Acetylation of Cell Wall Polysaccharides. Plant Cell Physiol. 2020, 61, 64–75. [Google Scholar] [CrossRef]
- Fatland, B.L.; Nikolau, B.J.; Wurtele, E.S. Reverse Genetic Characterization of Cytosolic Acetyl-CoA Generation by ATP-Citrate Lyase in Arabidopsis. Plant Cell 2005, 17, 182–203. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Francisco, M.; Kliebenstein, D.J.; Rodríguez, V.M.; Soengas, P.; Abilleira, R.; Cartea, M.E. Fine Mapping Identifies NAD-ME1 as a Candidate Underlying a Major Locus Controlling Temporal Variation in Primary and Specialized Metabolism in Arabidopsis. Plant J. 2021, 106, 454–467. [Google Scholar] [CrossRef] [PubMed]
- Jiao, Y.; Lau, O.S.; Deng, X.W. Light-Regulated Transcriptional Networks in Higher Plants. Nat. Rev. Genet. 2007, 8, 217–230. [Google Scholar] [CrossRef] [PubMed]
- Zhuo, C.; Wang, T.; Lu, S.; Zhao, Y.; Li, X.; Guo, Z. A Cold Responsive Galactinol Synthase Gene from Medicago Falcata (MfGolS1) Is Induced by Myo-Inositol and Confers Multiple Tolerances to Abiotic Stresses. Physiol Plant. 2013, 149, 67–78. [Google Scholar] [CrossRef] [PubMed]
- Jang, J.-H.; Shang, Y.; Kang, H.K.; Kim, S.Y.; Kim, B.H.; Nam, K.H. Arabidopsis Galactinol Synthases 1 (AtGOLS1) Negatively Regulates Seed Germination. Plant Sci. 2018, 267, 94–101. [Google Scholar] [CrossRef]
- Grigoreva, E.; Ulianich, P.; Ben, C.; Gentzbittel, L.; Potokina, E. First Insights into the Guar (Cyamopsis tetragonoloba (L.) Taub.) Genome of the ‘Vavilovskij 130’ Accession, Using Second and Third-Generation Sequencing Technologies. Russ. J. Genet. 2019, 55, 1406–1416. [Google Scholar] [CrossRef]
Plant ID | Early or Delayed Flowering on Long Day Conditions | Line ID | VIR Cat. Number | Origin | Accession |
---|---|---|---|---|---|
34_1 | Early | 34 | 82 | India | landrace |
34_2 | Early | 34 | 82 | India | landrace |
34_3 | Early | 34 | 82 | India | landrace |
97_1 | Early | 97 | 52,586 | USA | cv. Lewis |
97_2 | Early | 97 | 52,586 | USA | cv. Lewis |
97_3 | Early | 97 | 52,586 | USA | cv.Lewis |
97_4 | Early | 97 | 52,586 | USA | cv.Lewis |
69_1 | Early | 69 | 52,585 | USA | cv.Kinman |
69_2 | Early | 69 | 52,585 | USA | cv.Kinman |
28_1 | Delayed | 28 | 550 | India | landrace |
28_2 | Delayed | 28 | 550 | India | landrace |
28_3 | Delayed | 28 | 550 | India | landrace |
75_1 | Delayed | 75 | 52,580 | Pakistan | landrace |
75_2 | Delayed | 75 | 52,580 | Pakistan | landrace |
75_3 | Delayed | 75 | 52,580 | Pakistan | landrace |
Metrics | Results | |||
---|---|---|---|---|
rnaSPAdes | Trinity de novo 32-mer | Trinity de novo 25-mer | Trinity Genome-Guided | |
Number of contigs | 132,825 | 102,909 | 112,788 | 102,539 |
Shortest contigs (bp) | 131 | 197 | 201 | 186 |
Longest contigs (bp) | 40,377 | 59,908 | 59,908 | 14,406 |
Number of bases (bp) | 78,453,910 | 91,396,199 | 103,168,619 | 96,000,969 |
Mean length (bp) | 539 | 888 | 914 | 936 |
Number of contigs over 1000 bp | 22,143 | 307 | 32,499 | 32,819 |
Number of contigs over 10,000 bp | 95 | 29,453 | 229 | 3 |
Mean ORF % | 56 | 54 | 54 | 54 |
N90 (bp) | 317 | 324 | 343 | 357 |
N70 (bp) | 792 | 862 | 901 | 980 |
N50 (bp) | 1394 | 1586 | 1615 | 1661 |
N30 (bp) | 2243 | 2427 | 2404 | 2359 |
N10 (bp) | 16,753 | 4939 | 4231 | 3634 |
GC% | 40 | 41 | 40 | 39 |
Mean of overall alignment rate of the transcripts against assembly (%) | 96 | 97 | 99 | 96 |
Assembly Metrics | Assembly (Genome-Guided Trinity, this Study) | Assembly Tanwar et al., 2017 [21] | Assembly Al-Qurainy et al., 2019 [22] |
---|---|---|---|
N50 | 1661 | 1035 | 2552 |
Total unigenes | 79,863 | 61,508 | 62,146 |
Average transcript length (bp) | 936 | 679 | 1045 |
Metabolite Name (Shiny GAM) | Metabolite log2fc (2019) | Metabolite p-Value (2019) | Metabolite p-Value (2018) | Connected Genes in Shiny GAM | Gene log2fc (2019) | Gene p-Value (2019) |
---|---|---|---|---|---|---|
C00243 (Lactose) | −1.726 | 0.001 | nd | AT1G72990 (BGAL17) | −0.711 | 0.001 |
C00137 (Myo-inostiol) | 0.670 | 1.55 × 10−6 | 1.12 × 10−7 | AT2G47180 (ATGOLS1) | −3.017 | 2.6 × 10−10 |
C00811 (4-Coumarate) | 2.243 | 5.62 × 10−4 | 6.53 × 10−7 | AT1G65060 (4CL3) | −1.034 | 2.36 × 10−6 |
C00258 (D-Glycerate) | 0.415 | 0.043 | 0.006 | AT1G80380 (GLYK) | −0.303 | 0.039 |
C00158 (Citrate) | 1.119 | 1.36 × 10−4 | 3.45 × 10−5 | AT1G10670 (ACLA-1) | 0.497 | 0.001 |
C00149 (S-Malate) | 0.467 | 9.87 × 10−5 | 0.006 | AT2G13560 (NAD-ME1); AT5G58330 (NADP-MDH) | −0.220; −0.630 | 0.042; 0.004 |
C01595 (Linonate) | 0.493 | 0.003 | nd | AT5G04040 (SDP1) | −0.9 | 0.055 |
C00049 (L-Aspartate) | 0.621 | 0.009 | nd | AT2G30970 (ASP1); AT5G22300 (NIT4) | −0.386; 0.533 | 0.097; 0.006 |
C00065 (L-Serine) | 0.393 | 0.008 | 0.001 | AT1G55920 (ATSERAT2;1) | −1.448 | 5.492 × 10−5 |
Enzyme Entry | Gene Name | Definition (RefSeq) | Arabidopsis thaliana ID | Gene log2fc | Gene p-Value | MetaCyc Database Reaction |
---|---|---|---|---|---|---|
3.1.3.25 | IMPL1 | myo-inositol 1-phosphate monophosphatase | AT1G31190 | 0.1375 | 0.3832 | 1D-myo-inositol 3-monophosphate + H2O → myo-inositol + phosphate |
3.1.3.25 | IMPL2 | inositol-phosphate phosphatase | AT4G39120 | 0.3604 | 0.0600 | 1D-myo-inositol 3-monophosphate + H2O → myo-inositol + phosphate |
3.1.3.25 | VTC4 | L-galactose-1-phosphate phosphatase | AT3G02870 | 0.3508 | 0.0522 | β-L-galactose 1-phosphate + H2O → L-galactopyranose + phosphate |
PTEN | PTEN2 | phosphatidylinositol-3,4,5-trisphosphate 3-phosphatase | AT3G19420 | 0.1911 | 0.1132 | 1-phosphatidyl-1D-myo-inositol 3-phosphate + H2O → a 1-phosphatidyl-1D-myo-inositol + phosphate |
3.1.3.57 | SAL1 | Inositol polyphosphate 1-phosphatase | AT5G63980 | 0.2393 | 0.0582 | D-myo-inositol (1,4)-bisphosphate + H2O → 1D-myo-inositol 4-monophosphate + phosphate |
PLC | PLC1 | Phosphoinositide-specific phospholipase C family protein | AT5G58670 | 1.9714 | 0.10466 | 1-phosphatidyl-1D-myo-inositol 4,5-bisphosphate + H2O → a 1,2-diacyl-sn-glycerol + D-myo-inositol (1,4,5)-trisphosphate + H+ |
PIKFYVE | FAB1A | 1-phosphatidylinositol 3-phosphate 5-kinase | AT4G33240 | −0.6137 | 0.0185 | 1-phosphatidyl-1D-myo-inositol 3-phosphate + ATP → 1-phosphatidyl-1D-myo-inositol 3,5-bisphosphate + ADP + H+ |
P14K | ATPI4K_ALPHA | Phosphatidylinositol 4-kinase alpha 1 | AT1G49340 | −0.2559 | 0.0727 | 1-phosphatidyl-1D-myo-inositol + ATP 1-phosphatidyl-1D-myo-inositol 4-phosphate + ADP + H+ |
2.7.11.59 | ITPK3 | Inositol 1,3,4-trisphosphate 5/6-kinase family protein | AT4G08170 | −0.4706 | 0.0392 | D-myo-inositol (1,3,4)-trisphosphate + ATP→ D-myo-inositol (1,3,4,5)-tetrakisphosphate + ADP + H+ |
2.7.1.107 | DGK1 | diacylglycerol kinase 1 | AT5G07920 | −0.8194 | 0.00438 | ATP + 1,2-diacyl-sn-glycerol → a 1,2-diacyl-sn-glycerol 3-phosphate + ADP + H+ |
2.7.4.24 | ATVIP1 | diphosphoinositol-pentakisphosphate kinase; PP-IP5 kinase | AT3G01310 | −0.9682 | 0.1076 | ATP + phytate → 1D-myo-inositol 1-diphosphate 2,3,4,5,6-pentakisphosphate +ADP |
PIP5K | PIP5K1 | phosphatidylinositol-4-phosphate 5-kinase 1 | AT1G21980 | −0.5195 | 0.00026 | 1-phosphatidyl-1D-myo-inositol 4-phosphate + ATP → a 1-phosphatidyl-1D-myo-inositol 4,5-bisphosphate + ADP + H+ |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Grigoreva, E.; Tkachenko, A.; Arkhimandritova, S.; Beatovic, A.; Ulianich, P.; Volkov, V.; Karzhaev, D.; Ben, C.; Gentzbittel, L.; Potokina, E. Identification of Key Metabolic Pathways and Biomarkers Underlying Flowering Time of Guar (Cyamopsis tetragonoloba (L.) Taub.) via Integrated Transcriptome-Metabolome Analysis. Genes 2021, 12, 952. https://doi.org/10.3390/genes12070952
Grigoreva E, Tkachenko A, Arkhimandritova S, Beatovic A, Ulianich P, Volkov V, Karzhaev D, Ben C, Gentzbittel L, Potokina E. Identification of Key Metabolic Pathways and Biomarkers Underlying Flowering Time of Guar (Cyamopsis tetragonoloba (L.) Taub.) via Integrated Transcriptome-Metabolome Analysis. Genes. 2021; 12(7):952. https://doi.org/10.3390/genes12070952
Chicago/Turabian StyleGrigoreva, Elizaveta, Alexander Tkachenko, Serafima Arkhimandritova, Aleksandar Beatovic, Pavel Ulianich, Vladimir Volkov, Dmitry Karzhaev, Cécile Ben, Laurent Gentzbittel, and Elena Potokina. 2021. "Identification of Key Metabolic Pathways and Biomarkers Underlying Flowering Time of Guar (Cyamopsis tetragonoloba (L.) Taub.) via Integrated Transcriptome-Metabolome Analysis" Genes 12, no. 7: 952. https://doi.org/10.3390/genes12070952
APA StyleGrigoreva, E., Tkachenko, A., Arkhimandritova, S., Beatovic, A., Ulianich, P., Volkov, V., Karzhaev, D., Ben, C., Gentzbittel, L., & Potokina, E. (2021). Identification of Key Metabolic Pathways and Biomarkers Underlying Flowering Time of Guar (Cyamopsis tetragonoloba (L.) Taub.) via Integrated Transcriptome-Metabolome Analysis. Genes, 12(7), 952. https://doi.org/10.3390/genes12070952