{
"METABOLOMICS WORKBENCH":{"STUDY_ID":"ST002198","ANALYSIS_ID":"AN003598","VERSION":"1","CREATED_ON":"June 16, 2022, 6:45 am"},

"PROJECT":{"PROJECT_TITLE":"Untargeted metabolomics revealed essential biochemical rearrangements towards heat x drought stress acclimatization in Pinus pinaster","PROJECT_TYPE":"LC-MS analysis","PROJECT_SUMMARY":"Current projections for global climate change predict an increase in the intensity and frequency of heat waves and droughts. The improvement in our understanding of the mechanisms of how trees precisely can predict environmental threats and cope with these stresses benefits our natural selection or genetic improvement to the maintenance of forest sustainability. In this work, we investigate the metabolic changes in heat and drought combined stress in Pinus pinaster plantlets. Maritime pine is a coniferous tree with native populations distributed across the European Atlantic and Mediterranean basins and the north of Africa ranging from cool moist to warm dry climates. This species shows high plasticity and a contrasting adaptive capacity and resilience. This plasticity in the response to stress exposure may be associated with a differential ability to modulate their secondary metabolism. For this reason, the current study aims to investigate the gradual and synergetic metabolomic response using liquid chromatography coupled to mass spectrometry (LC-MS) based on untargeted metabolomic profiling of four stress levels. These metabolic profiles were supported by physiological and biochemical determinations. Our results showed that the metabolic profiles induced by low-stress exposition represent an adaptive conditioning mode with metabolome changes that help seedlings to cope with upcoming stress. The metabolism pathways involved in this response were mainly included in amino acid metabolism and carbohydrate metabolism leading to an enhanced accumulation of phenolics, flavonoids, and terpenoids. However, when the plantlets were exposed to higher-stress exposition, the secondary metabolites that starred the response are more complex and decorated, such as alkaloids, lignans, and glycosyloxyflavones. Those changes could help to maintain homeostasis and control the response magnitude on establishing and facilitating the plantlets’ survival. Overall, our findings provide new insights into the responsive mechanisms of the maritime pine under heat and drought stress in terms of metabolic profiles.","INSTITUTE":"Universidad de Oviedo","DEPARTMENT":"Department of Organisms and Systems Biology","LABORATORY":"Plant Physiology","LAST_NAME":"López Hidalgo","FIRST_NAME":"Cristina","ADDRESS":"C/ Catedrático Rodrigo Uría s/n Oviedo 33071","EMAIL":"lopezhcristina@uniovi.es","PHONE":"985104774","FUNDING_SOURCE":"This work is an output of the projects financed by the Spanish Ministry of Economy, Industry, and Competitiveness (AGL2017-83988-R)"},

"STUDY":{"STUDY_TITLE":"Untargeted metabolomics of Pinus pinaster needles under heat and drought stress","STUDY_TYPE":"Untargeted MS-based metabolomics","STUDY_SUMMARY":"Current projections for global climate change predict an increase in the intensity and frequency of heat waves and droughts. The improvement in our understanding of the mechanisms of how trees precisely can predict environmental threats and cope with these stresses benefits our natural selection or genetic improvement to the maintenance of forest sustainability. In this work, we investigate the metabolic changes in heat and drought combined stress in Pinus pinaster plantlets. Maritime pine is a coniferous tree with native populations distributed across the European Atlantic and Mediterranean basins and the north of Africa ranging from cool moist to warm dry climates. This species shows high plasticity and a contrasting adaptive capacity and resilience. This plasticity in the response to stress exposure may be associated with a differential ability to modulate their secondary metabolism. For this reason, the current study aims to investigate the gradual and synergetic metabolomic response using liquid chromatography coupled to mass spectrometry (LC-MS) based on untargeted metabolomic profiling of four stress levels. These metabolic profiles were supported by physiological and biochemical determinations. Our results showed that the metabolic profiles induced by low-stress exposition represent an adaptive conditioning mode with metabolome changes that help seedlings to cope with upcoming stress. The metabolism pathways involved in this response were mainly included in amino acid metabolism and carbohydrate metabolism leading to an enhanced accumulation of phenolics, flavonoids, and terpenoids. However, when the plantlets were exposed to higher-stress exposition, the secondary metabolites that starred the response are more complex and decorated, such as alkaloids, lignans, and glycosyloxyflavones. Those changes could help to maintain homeostasis and control the response magnitude on establishing and facilitating the plantlets’ survival. Overall, our findings provide new insights into the responsive mechanisms of the maritime pine under heat and drought stress in terms of metabolic profiles.","INSTITUTE":"Universidad de Oviedo","DEPARTMENT":"Department of Organisms and Systems Biology","LABORATORY":"Plant Physiology","LAST_NAME":"López Hidalgo","FIRST_NAME":"Cristina","ADDRESS":"C/ Catedrático Rodrigo Uría s/n Oviedo 33071","EMAIL":"lopezhcristina@uniovi.es","PHONE":"985104774"},

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"Factors":{"Factor":"T0.40.WWC"},
"Additional sample data":{"RAW_FILE_NAME":"T0.40.WWC4_neg.mzXML"}
},
{
"Subject ID":"-",
"Sample ID":"T0.40.WWC5_neg",
"Factors":{"Factor":"T0.40.WWC"},
"Additional sample data":{"RAW_FILE_NAME":"T0.40.WWC5_neg.mzXML"}
},
{
"Subject ID":"-",
"Sample ID":"T0.40.WWC6_neg",
"Factors":{"Factor":"T0.40.WWC"},
"Additional sample data":{"RAW_FILE_NAME":"T0.40.WWC6_neg.mzXML"}
},
{
"Subject ID":"-",
"Sample ID":"T1.40.HWS1_neg",
"Factors":{"Factor":"T1.40.HWS"},
"Additional sample data":{"RAW_FILE_NAME":"T1.40.HWS1_neg.mzXML"}
},
{
"Subject ID":"-",
"Sample ID":"T1.40.LWS1_neg",
"Factors":{"Factor":"T1.40.LWS"},
"Additional sample data":{"RAW_FILE_NAME":"T1.40.LWS1_neg.mzXML"}
},
{
"Subject ID":"-",
"Sample ID":"T1.40.HWS2_neg",
"Factors":{"Factor":"T1.40.HWS"},
"Additional sample data":{"RAW_FILE_NAME":"T1.40.HWS2_neg.mzXML"}
},
{
"Subject ID":"-",
"Sample ID":"T1.40.LWS2_neg",
"Factors":{"Factor":"T1.40.LWS"},
"Additional sample data":{"RAW_FILE_NAME":"T1.40.LWS2_neg.mzXML"}
},
{
"Subject ID":"-",
"Sample ID":"T1.40.HWS3_neg",
"Factors":{"Factor":"T1.40.HWS"},
"Additional sample data":{"RAW_FILE_NAME":"T1.40.HWS3_neg.mzXML"}
},
{
"Subject ID":"-",
"Sample ID":"T1.40.LWS3_neg",
"Factors":{"Factor":"T1.40.LWS"},
"Additional sample data":{"RAW_FILE_NAME":"T1.40.LWS3_neg.mzXML"}
},
{
"Subject ID":"-",
"Sample ID":"T3.40.HWS1_neg",
"Factors":{"Factor":"T3.40.HWS"},
"Additional sample data":{"RAW_FILE_NAME":"T3.40.HWS1_neg.mzXML"}
},
{
"Subject ID":"-",
"Sample ID":"T3.40.LWS1_neg",
"Factors":{"Factor":"T3.40.LWS"},
"Additional sample data":{"RAW_FILE_NAME":"T3.40.LWS1_neg.mzXML"}
},
{
"Subject ID":"-",
"Sample ID":"T3.40.HWS2_neg",
"Factors":{"Factor":"T3.40.HWS"},
"Additional sample data":{"RAW_FILE_NAME":"T3.40.HWS2_neg.mzXML"}
},
{
"Subject ID":"-",
"Sample ID":"T3.40.LWS2_neg",
"Factors":{"Factor":"T3.40.LWS"},
"Additional sample data":{"RAW_FILE_NAME":"T3.40.LWS2_neg.mzXML"}
},
{
"Subject ID":"-",
"Sample ID":"T3.40.HWS3_neg",
"Factors":{"Factor":"T3.40.HWS"},
"Additional sample data":{"RAW_FILE_NAME":"T3.40.HWS3_neg.mzXML"}
},
{
"Subject ID":"-",
"Sample ID":"T3.40.LWS3_neg",
"Factors":{"Factor":"T3.40.LWS"},
"Additional sample data":{"RAW_FILE_NAME":"T3.40.LWS3_neg.mzXML"}
},
{
"Subject ID":"-",
"Sample ID":"T5.40.HWS1_neg",
"Factors":{"Factor":"T5.40.HWS"},
"Additional sample data":{"RAW_FILE_NAME":"T5.40.HWS1_neg.mzXML"}
},
{
"Subject ID":"-",
"Sample ID":"T5.40.LWS1_neg",
"Factors":{"Factor":"T5.40.LWS"},
"Additional sample data":{"RAW_FILE_NAME":"T5.40.LWS1_neg.mzXML"}
},
{
"Subject ID":"-",
"Sample ID":"T5.40.HWS2_neg",
"Factors":{"Factor":"T5.40.HWS"},
"Additional sample data":{"RAW_FILE_NAME":"T5.40.HWS2_neg.mzXML"}
},
{
"Subject ID":"-",
"Sample ID":"T5.40.LWS2_neg",
"Factors":{"Factor":"T5.40.LWS"},
"Additional sample data":{"RAW_FILE_NAME":"T5.40.LWS2_neg.mzXML"}
},
{
"Subject ID":"-",
"Sample ID":"T5.40.HWS3_neg",
"Factors":{"Factor":"T5.40.HWS"},
"Additional sample data":{"RAW_FILE_NAME":"T5.40.HWS3_neg.mzXML"}
},
{
"Subject ID":"-",
"Sample ID":"T5.40.LWS3_neg",
"Factors":{"Factor":"T5.40.LWS"},
"Additional sample data":{"RAW_FILE_NAME":"T5.40.LWS3_neg.mzXML"}
},
{
"Subject ID":"-",
"Sample ID":"T7.40.HWS1_neg",
"Factors":{"Factor":"T7.40.HWS"},
"Additional sample data":{"RAW_FILE_NAME":"T7.40.HWS1_neg.mzXML"}
},
{
"Subject ID":"-",
"Sample ID":"T7.40.LWS1_neg",
"Factors":{"Factor":"T7.40.LWS"},
"Additional sample data":{"RAW_FILE_NAME":"T7.40.LWS1_neg.mzXML"}
},
{
"Subject ID":"-",
"Sample ID":"T7.40.HWS2_neg",
"Factors":{"Factor":"T7.40.HWS"},
"Additional sample data":{"RAW_FILE_NAME":"T7.40.HWS2_neg.mzXML"}
},
{
"Subject ID":"-",
"Sample ID":"T7.40.LWS2_neg",
"Factors":{"Factor":"T7.40.LWS"},
"Additional sample data":{"RAW_FILE_NAME":"T7.40.LWS2_neg.mzXML"}
},
{
"Subject ID":"-",
"Sample ID":"T7.40.HWS3_neg",
"Factors":{"Factor":"T7.40.HWS"},
"Additional sample data":{"RAW_FILE_NAME":"T7.40.HWS3_neg.mzXML"}
},
{
"Subject ID":"-",
"Sample ID":"T7.40.LWS3_neg",
"Factors":{"Factor":"T7.40.LWS"},
"Additional sample data":{"RAW_FILE_NAME":"T7.40.LWS3_neg.mzXML"}
}
],
"COLLECTION":{"COLLECTION_SUMMARY":"Plantlets were sampled on the first-day assay (T0) under well-watered conditions before the temperature change in both chambers. The following day to T0, the exposure to combined stress began. HWS plantlets were watering with 25 % of the weight loss each day, while LWS plantlets with 50 %. Afterward, heat-stressed and water-stressed plantlets were sampled at the end of the 6-h heat exposure on day 1 (T1), day 3 (T3), day 5 (T5), and day 7 (T7). The water deficit more or less severe was imposed for seven days by progressively depleting soil water content. Immediately after sampling, cell membrane damage and leaf water status were measured in fresh needles by quantifying relative EL and RWC (see below). Other needles were frozen in liquid nitrogen, lyophilized, and stored in the dark and cold (-20 ºC) until use.","SAMPLE_TYPE":"Plant","STORAGE_CONDITIONS":"-20℃"},

"TREATMENT":{"TREATMENT_SUMMARY":"The experimental layout was based on a factorial design with two factors: temperature and water availability. Before starting the experiment, plants were divided into two chambers for testing two temperatures (30 ºC and 40 ºC), which in turn were split again into two levels of water availability (“low-water-stress”, LWS, and “high-water-stress”, HWS). Consequently, four stress levels, 40 ºC-HWS, 40 ºC-LWS, 30 ºC-HWS, and 30 ºC-LWS were tested. In both chambers, twelve plants were divided into six pools of two plants (three for HWS and three for LWS). These pools of two plants were kept across the sampling and formed the three independent biological replicates analyzed for each stress level.","TREATMENT":"Heat and Drought","TREATMENT_DOSE":"High Water Stress and Low Water Stress in 30ºC and 40ºC.","TREATMENT_VEHICLE":"Fitoclima 1200, Aralab Ltd, Sintra, Portugal","PLANT_GROWTH_SUPPORT":"Fitoclima 1200, Aralab Ltd, Sintra, Portugal","PLANT_GROWTH_LOCATION":"Oviedo, Asturias","PLANT_PLOT_DESIGN":"Randomized design","PLANT_LIGHT_PERIOD":"During this month, the plants in the growth chamber (Fitoclima 1200, Aralab Ltd, Sintra, Portugal) were kept at a light intensity of 400 µmol m−2·s−1 under long-day conditions (16 h light/8 h dark for photoperiod).","PLANT_HUMIDITY":"Relative humidity (RH) were set to 25 ºC and 50 % RH during the day, and 15 ºC and 60 % RH during the night","PLANT_TEMP":"25ºC, 30ºC, and 40ºC","PLANT_WATERING_REGIME":"Plants were well-watered to field capacity until soil dropped every two days.","PLANT_NUTRITIONAL_REGIME":"efore trial, seedlings had been acclimated over one month inside the chamber and were watered to field capacity with nutritive solution (N:P:K; 5:8:10).","PLANT_GROWTH_STAGE":"Two-year-old seedlings","PLANT_METAB_QUENCH_METHOD":"Liquid N2","PLANT_HARVEST_METHOD":"Liquid N2","PLANT_STORAGE":"Lyophilized"},

"SAMPLEPREP":{"SAMPLEPREP_SUMMARY":"Metabolites were extracted from 20 mg (lyophilized weight) of needles. Metabolite extraction was performed according to Valledor et al., (2014). Briefly, 600 µL of cold (4 ºC) metabolite extraction solution (methanol: chloroform: H2O (2.5:1:0.5) was added to each tube and strongly vortexed. Then, the tubes were incubated in a cold ultrasound bath for 10 min. Later, the tubes were centrifuged at 20.000 x g for 6 min at 4 °C. The supernatant containing metabolites from each tube was transferred to a new tube containing 300 µL of chloroform: water (1:1) to allow phase separation. Six hundred µL of cold (4 ºC) metabolite extraction solution were added to the remaining pellets, and vortexing, ultrasound bath, and centrifugation were repeated. The new supernatant was transferred to the previous tube that contained the phase separation solution and the old supernatant. These tubes were vortexed and then centrifuged at 15.000 x g for 5 min at 4 °C. After centrifugation, two layers are formed; the upper-aqueous layer (methanol: water) containing the polar metabolites was transferred to a separate microcentrifuge tube and then cleaned from non-polar metabolites adding 300 µL of cold (4 ºC) chloroform: water (1:1), vortexed, and centrifuged at 15.000 x g for 4 min at 4 °C. The new upper phase was transferred to a new tube. The polar extract was dried using a speedvac at 25 °C.","PROCESSING_METHOD":"Methanol:Chloroform:Water","PROCESSING_STORAGE_CONDITIONS":"On ice","EXTRACTION_METHOD":"Methanol:Chloroform:Water","EXTRACT_ENRICHMENT":"Polar metabolites","EXTRACT_CLEANUP":"Centrifugation","EXTRACT_STORAGE":"-80℃","SAMPLE_RESUSPENSION":"Methanol","SAMPLE_DERIVATIZATION":"NO","SAMPLE_SPIKING":"NO"},

"CHROMATOGRAPHY":{"CHROMATOGRAPHY_SUMMARY":"A fifty-three-minute mobile phase gradient was employed. Gradient elution chromatography was performed starting with 100 % A to 98 % A in 1 min; hold for 9 min, gradient to 60 % A in 21 min, gradient to 45 % A in 5 min; hold for 2 min, gradient to 5 % A in 3 min; hold 5 min, return to initial conditions in 3 min and equilibrate for 5 min (total run time: 53 min). Solvent A was 100 % H2O containing 0.1 % formic acid, and solvent B was 100 % ACN containing 0.1 % formic acid. A flow rate of 0.1 mL/min was used.","CHROMATOGRAPHY_TYPE":"Reversed phase","INSTRUMENT_NAME":"Thermo Dionex Ultimate 3000","COLUMN_NAME":"Phenomenex Luna Omega Polar C18 column (1.7 µm, 100 x 2.1 mm)","FLOW_GRADIENT":"Gradient elution chromatography was performed starting with 100 % A to 98 % A in 1 min; hold for 9 min, gradient to 60 % A in 21 min, gradient to 45 % A in 5 min; hold for 2 min, gradient to 5 % A in 3 min; hold 5 min, return to initial conditions in 3 min and equilibrate for 5 min (total run time: 53 min)","FLOW_RATE":"0.1 mL/min","COLUMN_TEMPERATURE":"30 ºC","SOLVENT_A":"100 % H2O containing 0.1 % formic acid","SOLVENT_B":"100 % ACN containing 0.1 % formic acid","RETENTION_TIME":"53 min","SAMPLE_INJECTION":"5 uL","CAPILLARY_VOLTAGE":"4.5 kV","WASHING_BUFFER":"IPA and Methanol","RANDOMIZATION_ORDER":"True"},

"ANALYSIS":{"ANALYSIS_TYPE":"MS","LABORATORY_NAME":"Severo Ochoa","DATA_FORMAT":".D"},

"MS":{"INSTRUMENT_NAME":"Bruker Impact II HD","INSTRUMENT_TYPE":"QTOF","MS_TYPE":"ESI","ION_MODE":"NEGATIVE","MS_COMMENTS":"The column eluent was analyzed using a Bruker Impact II HD (Bruker, Karlsruhe, Germany) quadrupole time-of-flight (Q-TOF) mass spectrometer equipped with an ESI source operating in positive polarity and negative polarity. Mass spectra were acquired with the following parameters of mass spectrometer: ion capillary voltage 4.5 kV (same for positive and negative mode), dry gas flow 6 L/min, dry gas temperature 250 ºC, nebulizer pressure 2 bar, collision RF 650 V, transfer time 80 µs and prepulse storage 5 µs. Spectra data, MS1 and MS2, were acquired in a data-dependent manner at 2 Hz, fragmenting the three most abundant precursor ions per MS1 scan, acquiring MS/MS data between 50 and 1300 m/z. Repetitive MS/MS sampling was limited by exclusion after 3 spectra at a particular mass within a window of 0.2 min. MS/MS fragmentation of the 3 most intense selected ions per spectrum was performed using ramped collision-induced dissociation energy of 7–17.5 eV. Hexakis (1H, 1H, 3H-tetrafluoropropoxy) phosphazene (Agilent Technologies, Santa Clara, CA, USA) was introduced as an internal calibrant after the run ends . Each sample was analyzed twice, first using the positive ion mode and then the negative.","CAPILLARY_VOLTAGE":"4.5 kV","COLLISION_ENERGY":"ramped collision-induced dissociation energy of 7–17.5 eV","DRY_GAS_FLOW":"6 L/min","DRY_GAS_TEMP":"250 ºC","GAS_PRESSURE":"2 bar","MS_RESULTS_FILE":"ST002198_AN003598_Results.txt UNITS:peak area Has m/z:Yes Has RT:Yes RT units:Minutes"}

}