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features are reported using their Genedata cluster identifiers. Accurate m/z and retention time were transferred from the feature-cluster metadata. Because the submitted acquisition contains full-scan data without confirmatory MS/MS or authentic-standard matching, the MAF does not claim confirmed chemical structures; features are assigned Metabolomics Standards Initiative reliability level 4 (unknown molecular feature)."],"repository":["MetaboLights"],"study_status":["Public"],"ptm_modification":[""],"instrument_platform":["Liquid Chromatography MS - alternating - reverse-phase"],"chromatography_protocol":["Chromatography used a Thermo Scientific Vanquish UHPLC system with a C18 reversed-phase column (2.1 x 100 mm, 1.7 um) at 30 degrees C. Mobile phase A was water with 0.1% formic acid and mobile phase B was acetonitrile with 0.1% formic acid. At 0.3 mL/min, the gradient increased from 2% to 100% B over 11 min, was held for 4 min, and was followed by 5 min re-equilibration. Injection volume was 5 uL."],"publication":["Conserved leaf–root metabolomic network asymmetry underpins divergent drought strategies. 10.1093/plcell/koag217. PMID:42455049"],"submitter_name":["Mirza Shoaib"],"submitter_affiliation":["Agriculture Victoria Research and La Trobe University"],"organism_part":["root","leaf"],"technology_type":["mass spectrometry assay"],"disease":[""],"extraction_protocol":["Frozen leaf and root samples were freeze-dried, ground at 1200 rpm for 4 min with 30 s breaks each minute, and 20 mg dry material was extracted with 1 mL 80% methanol. Samples were shaken for 5 min, sonicated for 10 min, shaken for 10 min, incubated at room temperature for 1 h, centrifuged at 12,300 rpm for 10 min, and 100 uL supernatant was transferred to an HPLC vial."],"organism":["Triticum aestivum"],"full_dataset_link":["https://www.ebi.ac.uk/metabolights/MTBLS15125"],"author":["Mirza Shoaib. Agriculture Victoria; School of Applied Systems Biology, La Trobe University. Grains Innovation Park, 110 Natimuk Road, Horsham, Victoria 3400, Australia. shoaib.mirza@agriculture.vic.gov.au."],"data_transformation_protocol":["Raw LC-MS data were processed in Genedata Expressionist Refiner MS 18.0.1 using data import, chromatogram extraction, moving-average chemical-noise subtraction, pairwise alignment-tree retention-time alignment, curvature-based peak detection with refinement and consistency filtering, and isotope clustering. Positive and negative scan events were processed with mode-specific workflows. Features with insufficient replicate support were removed, missing values were imputed by Random Forest, outliers were assessed by Isolation Forest, and abundances were asinh-transformed. The final combined dataset contains 2,471 molecular features."],"study_factor":["Stress protocol","Treatment","Time point","Sampling time","Organism part","Genotype","Biological replicate"],"submitter_email":["shoaibmirza2200@gmail.com"],"sample_collection_protocol":["Two bread wheat genotypes, Gladius (G1, drought-tolerant) and DAS5_003811 (G2, drought-susceptible), were grown as independent plants in aerated hydroponics under a 22 h light/2 h dark cycle (22 degrees C light, 17 degrees C dark), 60-70% relative humidity and pH 6.5. Seven biological replicates were sampled per genotype, treatment and time point. Batch 1 received acute osmotic stress with 0.3 M sorbitol and was sampled 1, 2 and 3 days after stress onset; Batch 2 received prolonged stress with 0.15 M sorbitol and was sampled 3, 6 and 9 days after stress onset. The youngest fully expanded leaf and root tissue were harvested separately, quenched in liquid nitrogen and stored at -80 degrees C. No material was pooled across plants."],"omics_type":["Metabolomics"],"study_design":["biological sample","Metabolomics","compound treatment design","untargeted analysis","biological replicate design","genotype comparison design","leaf","organism part comparison design","Thermo Scientific Vanquish UHPLC System","root","time series design","Thermo Scientific Q Exactive Plus Orbitrap mass spectrometer","Triticum aestivum"],"curator_keywords":["biological sample","Metabolomics","compound treatment design","untargeted analysis","biological replicate design","genotype comparison design","leaf","organism part comparison design","Thermo Scientific Vanquish UHPLC System","root","time series design","Thermo Scientific Q Exactive Plus Orbitrap mass spectrometer","Triticum aestivum"],"mass_spectrometry_protocol":["A Thermo Scientific Q Exactive Plus Orbitrap mass spectrometer with heated electrospray ionization acquired alternating positive- and negative-polarity full-scan events. Positive and negative spray voltages were 3600 V and 3300 V, capillary temperature was 300 degrees C, sheath gas was 28 arbitrary units and S-lens RF was 64. Embedded instrument-method metadata in the submitted raw files reports a scan range of m/z 80-1200 and resolution 35,000. No MS/MS acquisition is 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orchestrate tissue-specific metabolic responses to osmotic stress, a major determinant of drought tolerance and crop productivity. Yet how leaf and root responses are coordinated to confer tolerance remains poorly understood. Here we show that drought tolerance in wheat is associated with a reproducible architectural asymmetry between tissue-level metabolomic correlation networks under controlled osmotic stress. In a drought-tolerant genotype, leaf networks are ∼40% denser and highly integrated, consistent with rapid photosynthetic adaptation, whereas root networks are modular and fragmented, consistent with localized responses. Temporal analysis revealed a decline in cross-tissue coordination, from early synchrony (ρ ≈ 0.546) toward greater tissue-specific organization (ρ ≈ 0.350) under prolonged stress, a pattern absent in the susceptible genotype. Bayesian structure learning provided convergent support for these architectures as statistically robust, nonrandom network organizations (P < 0.001). Our findings suggest that drought tolerance is associated with contrasting tissue-level correlation-network organization and temporally structured leaf-root coordination under osmotic stress. This framework advances our understanding of stress adaptation and provides a conceptual basis for breeding climate-resilient crops by targeting key network properties."],"pubmed_title":["Conserved leaf-root metabolomic network asymmetry underpins divergent drought strategies."],"pubmed_authors":["Shoaib Mirza M, Rochfort Simone J SJ, Reddy Priyanka P, Ram Doris D, Hayden Matthew J MJ, Kant Surya S"],"additional_accession":[]},"is_claimable":false,"name":"Conserved leaf–root metabolomic network asymmetry underpins divergent drought strategies","description":"Plants orchestrate tissue-specific metabolic responses to osmotic stress, a major determinant of drought tolerance and crop productivity. Yet how leaf and root responses are coordinated to confer tolerance remains poorly understood. Here we show that drought tolerance in wheat is associated with a reproducible architectural asymmetry between tissue-level metabolomic correlation networks under controlled osmotic stress. In a drought-tolerant genotype, leaf networks are ~40% denser and highly integrated, consistent with rapid photosynthetic adaptation, whereas root networks are modular and fragmented, consistent with localised responses. Temporal analysis revealed a decline in cross-tissue coordination, from early synchrony (ρ ≈ 0.546) toward greater tissue-specific organisation (ρ ≈ 0.350) under prolonged stress, a pattern absent in the susceptible genotype. Bayesian structure learning provided convergent support for these architectures as statistically robust, non-random network organisations (P < 0.001). Our findings suggest that drought tolerance is associated with contrasting tissue-level correlation-network organisation and temporally structured leaf–root coordination under osmotic stress. This framework advances our understanding of stress adaptation and provides a conceptual basis for breeding climate-resilient crops by targeting key network properties.","dates":{"publication":"2026-07-22","submission":"2026-07-22"},"accession":"MTBLS15125","cross_references":{"pubmed":["42455049"]}}