Standard of care treatment for colorectal cancer liver metastasis consists of a cytotoxic chemotherapy in combination with a targeted agent. Clinical trials have guided the use of these combinatory therapies, but it remains unclear what the optimal combinations of cytotoxic chemotherapy with a targeted agent are.Using a genomic based approach, gene expression profiling was obtained from tumor samples of patient with colorectal cancer liver metastasis who received an oxaliplatin based therapy. Early passaged colorectal cancer liver metastasis cell lines and patient derived xenografts of colorectal cancer liver metastasis were then treated with oxaliplatin and a mTOR inhibitor.in vitro and in vivo.Gene set enrichment analysis revealed that the mTOR pathway was activated in patients receiving oxaliplatin based therapy. Treatment of early passaged colorectal cancer lines and patient derived xenografts with oxaliplatin resulted in activation of the mTOR pathway. Combination therapy with oxaliplatin and a mTOR inhibitor resulted in a synergistic effect both in vitro and in vivo using patient derived colorectal cancer cell lines and patient derived xenografts prior to clinical use.Our findings suggest a genomic based approach can be used to identify optimal combinations of cytotoxic chemotherapy with a targeted agent and that these observations can be validated both  Chemotherapy has been used as the backbone for the treatment of cancer for the past few decades. Currently, the choice of treatment for an individual patient with metastatic colorectal cancer is based on patient and physician preference and empiric clinical trial data. Standard of care treatment of metastatic colorectal cancer consists of cytotoxic chemotherapy in combination with a targeted agent. Specifically, 5- fluorouracil (5-FU) is combined with either oxaliplatin (FOLFOX) or irinotecan (FOLFIRI) and then with either a VEGF  inhibitor such as bevacizumab or an EGFR  inhibitor such as cetuximab or panitumumab. However, response rates when either of these two targeted agents is combined with cytotoxic chemotherapy are still only 60–65% –3 suggesTherapy based upon the biology of an individual’s tumor rather than established histopathological and anatomical classification can optimize the use of existing therapy and identify new and novel targets. As genetic and epigenetic events that results in cancer are discovered, treatment based on the biology of the cancer are being developed –6. SpeciHowever, prior to the incorporation of new therapeutic agents in the clinical setting, these drugs must be assessed for their therapeutic potential in predictive preclinical models, and therefore, mouse xenografts have been developed to screen new cancer drugs. Initiallin vitro. Finally, these findings were then validated in vivo in a patient derived xenograft (PDX) model of colorectal cancer. Everolimus was used in PDX model because it is a FDA proved orally administered rapamycin analog and it has superior pharmaceutical characteristics to rapamycin [In our present work, we use a combination of a genomic based approach with preclinical models of colorectal cancer to identify synergistic drugs that can be used for the treatment of colorectal liver metastasis. First, a gene expression profiling was performed to identify signaling pathways in colorectal cancer liver metastasis that become activated upon treatment with oxaliplatin. Based on these methods, we identified the mTOR pathway as an oxaliplatin induced signaling pathway. Next, we showed that a combination of oxaliplatin and mTOR inhibition (rapamycin) has a synergistic effect apamycin . Our reshttp://www.ncbi.nlm.nih.gov/geo/) (GSE41568).Colorectal cancer liver metastasis specimens were collected under a Duke IRB approved protocol (Pro00002435). Tumor specimens collected were frozen in OCT . An initial section of each OCT block was stained using H&E (Hematoxylin and Eosin) to determine tumor percentage and necrosis. Macrodissection of the tumor was then performed to ensure >70% tumor content. RNA was isolated from tumor specimen using a Qiagen RNA/DNA Allprep kit, converted to cDNA and labeled by one cycle IVT. IVT labeled cDNAs were prepared according to the manufacturer’s instructions, and targets hybridized to the Human U133A 2.0 GeneChip and read on an Affymetrix array scanner. Raw data was converted to CEL files and RMA normalized. CEL files used in this manuscript are available at the Gene Expression Omnibus (GEO) (http://www.broad.mit/edu/cancer/software/genepattern) was used to perform both unsupervised and supervised analysis. To check for sample outliers and batch effects, 3D principal components analysis of the global gene expression was performed. Data was first preprocessed to 8263 , 6714 genes  and 3307 genes  Hierarchical clustering was performed using complete linkage with a Pearson correlation metric method on the preprocessed data; samples were clustered into a single tree.[GenePattern . All participants provided written informed consent to participate in the study. Patient derived xenografts (PDXs) of colorectal cancer liver metastasis were then developed as described previously , 16 and 2. Clonal population of cell line was then obtained by isolating a single clone using an O ring. The following CRC PDX and cell lines were generated and used in this study; CRC057, CRC119 and CRC240. Cell lines were authenticated using the Duke University DNA Analysis Facility Human cell line authentication (CLA) service by analyzing DNA samples from each individual cell lines for polymorphic short tandem repeat (STR) markers using the GenePrint 10 kit from Promega .Early passage colorectal cancer liver metastasis cell lines were developed from the PDXs as followed. PDXs were harvested, homogenized and grown in cell culture media  at 5% COHCT15, DLD-1, LoVo, HT29 and Colo205 colorectal cancer cell lines were obtained from ATCC (American Type Culture Collection) and were not authenticated as they were used < 6 months from purchase date.CRC057, CRC119, CRC240, HCT15, DLD-1, LoVo, HT29 and Colo205 were grown in RPMI 1640 media + 10% FBS and plated in drug-free medium at a concentration of 5000 cell/well in tissue culture treated 96 well plate. Five replicate wells were used for each planned drug concentration. Control cells included cells plated in growth media without drugs and wells with growth medium but without cells. After 24 hrs of incubation at 37°C, each cell line was exposed to a series of increasing drug concentration  and subsequently incubated at 37°C for 4d. Cell cytotoxicity was assayed with CellTiter-Glo® Luminescent Cell Viability Assay kit  at day 4. Corresponding EC50 for oxaliplatin and rapamycin was calculated for each cell line using the GraphPad Prism software .Synergy for the combination of targeted pathway inhibition with conventional cytotoxic therapy was assessed using the methods of Chou and Talalay within the Calcusyn software . Briefly3, mice were randomized and treated either with oxaliplatin (10 mg/kg or 5 mg/kg) and/or everolimus . Oxaliplatin was administered weekly via intraperitoneal injection for 3 weeks, and saline was used as a control. Everolimus was administered by oral gavage in 100 μL of Ora-Plus suspending vehicle  every other day for 3 weeks, and vehicle alone was used as a control. Each group consisted of 6 or 7 mice. Tumor size was measured at least twice a week with a vernier caliper, and tumor volume was calculated as follows until humane endpoint (> 2000 mm3). Tumor volume = (width)2 x length/2. All mouse experiments were carried out in accordance with the animal guidelines and with the approval of the Institutional Animal Care and Use Committee (IACUC) at the Duke University Medical Center.To test the sensitivity of CRC057, CRC119 and CRC240 PDX to oxaliplatin and everolimus, CRC057, CRC119 and CRC240 PDX tissue were minced in PBS at 150 mg/ml. 200ul of each PDX tissue suspension were subcutaneously injected into the flanks of JAX NOD.CB17-PrkdcSCID-J mice . When tumor volume reached approximately 200 mmThe cells and homogenized PDX tissue were lysed in RIPA buffer supplied with Protease inhibitor and Phosphatase inhibitor . Western blotting was carried out as the standard protocol and as followed. Total protein concentrations were quantified using DC protein Assay . Proteins were separated by SDS-PAGE Gel Electrophoresis and subsequently transferred onto polyvinylidene fluoride (PVDF) membranes. PVDF membranes were blocked with nonfat milk for 1 hour at room temperature. Primary antibodies (p70 S6 Kinase (49D7), Phospho- p70 S6 Kinase (Thr389), and β-actin)  were added according to the specifications of the manufacturer's protocols. Corresponding Horse Radish Peroxidase (HRP) conjugated secondary antibodies were added at 1:10000 dilution for 1 hour at room temperature. Membranes were washed with Tris Buffered Saline + Tween-20 (TBST). Membranes were exposed upon addition of chemiluminescent reagent using X-ray film.PDX tumor sizes were recorded in GraphPad Prism 6 software  and One-way ANOVA analysis was used to compare the tumor size between groups .Table 1). All patients received at least 3 months of therapy and underwent resection of their cancer within 4–6 weeks after their last oxaliplatin dose. We first performed an unsupervised hierarchical clustering on the data set using a preprocessed data set of approximately 8000 genes and Fig 1 showed that treatment with an oxaliplatin based therapy did not influence the clustering of the samples. Similar results were observed using 3000 and 6000 genes .In order to identify cancer related signaling pathways that were upregulated in colorectal cancer patients treated with oxaliplatin based therapy, gene expression data using the Affymetrix U133 array was obtained on 39 patients who underwent resection of their colorectal cancer liver metastasis at Duke University between 2007 and 2011. There were 21 males and 18 females in this cohort and the majority of these patients were Caucasian (31/38). 11 of these patients received an oxaliplatin based therapy prior to their liver resection while 28 received no treatment prior to surgery (S1 Table). As we were interested in identifying targets that could be synergistic with oxaliplatin, we looked in the signaling pathway group for pathways that could be targeted and identified the WNT/β-catenin, RAS, PI3Kinase, MAP Kinase and the mTOR pathway. Based on our interest in the mTOR pathway, we decided to further study the interaction between oxaliplatin and the mTOR pathway.We next performed a supervised analysis using GSEA  algorithTable 3). Each cell line was then treated with oxaliplatin at the IC50 dose for 1–4 days. At baseline (Day 0), there was no activation of the mTOR pathway based on levels of phosphorylated p70 S6. However, by Day 3, all six cell lines displayed activation of the mTOR pathway . To further verify this observation, we performed the same experiment on 3 early passaged colorectal cancer cell lines that were developed from tumors of patients undergoing resection of the colorectal cancer liver metastasis . Similar to the ATCC CRC cell lines, at the IC50 dose, activation of the mTOR pathway was observed by Day 3 in our early passaged CRC cell lines .We first wanted to determine if treatment of CRC cell lines with oxaliplatin activates the mTOR pathway. Five colorectal cancer cell lines obtained from ATCC  were treated with oxaliplatin and rapamycin and the IC50s to oxaliplatin and rapamycin for these cell lines were determined (Table 3) for 4 days The combination index (CI) of these two drugs in each cell lines was calculated using the Calcusyn software at the ED25, ED50 and ED75 and ED90 to determine synergy based on the Chou and Talalay method.[Fig 3A show that no synergy was observed between oxaliplatin and rapamycin in HCT15 (CI > 1). In DLD-1 and LoVo, synergy was observed between oxaliplatin and rapamycin (CI < 1); in Colo205, synergy was observed at ED50, ED75 and ED90; and in HT29, synergy was observed at ED75 and ED90. To further verify this observation, this experiment was performed in our 3 early passaged colorectal cancer cell lines . Similar to the ATCC cell lines, synergy was also observed between oxaliplatin and rapamycin in all three of our early passaged colorectal cancer cell lines (CI < 1) .As oxaliplatin activated the mTOR pathway, we next wanted to determine if the combination of oxaliplatin and a mTOR inhibitor was synergistic. The five ATCC cell lines  were treated with either oxaliplatin (IC50 dose), rapamycin (IC50 dose) or in combination at a fixed ratio  model of colorectal cancer. CRC119 was established subcutaneously in the flanks of JAX NOD.CB17-PrkdcSCID-J mice as previously described.[3, mice were treated with oxaliplatin  and tumors were harvested on Days 2 and 4. Western blots showed that similar to the in vitro experiments with the cell lines, the level of phospho-p70 S6 kinase was increased during the oxaliplatin treatment period from the second day to the fourth day in our CRC119 PDX model .To determine if the effects observed escribed., 16 Whenin vitro was also observed in vivo, synergy experiments were performed. CRC119 was established subcutaneously in the flanks of JAX NOD.CB17-PrkdcSCID-J mice and when the tumor volume reached approximately 200 mm3, mice were treated with oxaliplatin (10 mg/kg IP weekly), everolimus  or in combination . Fig 5 shows that CRC119 was resistant to both everolimus and oxaliplatin (p > 0.05). However, the combination of everolimus and oxaliplatin resulted in significance decrease in tumor growth inhibition (p < 0.05) suggesting a synergistic effect between the two drugs.Finally, to determine if the synergistic effect observed Currently, the treatment of colorectal cancer in the first line setting using targeted therapy is limited to anti-VEGF therapy  or anti-EGFR therapy  in combination with an oxaliplatin based therapy. –3 Howeveserine/threonine protein kinase that regulates cell growth, cell proliferation, cell motility, cell survival, protein synthesis, and transcription and has been studied in colorectal cancer a potential therapeutic target. One potential agent to target mTOR in colorectal cancer is everolimus (RAD001) and there have been several early studies of the mTOR inhibitors suggesting some benefit in these patients. In one phase I trial of RAD-001, one partial response (PR) was seen in a patient with colorectal cancer lasting 5.3 months with a disease free survival (DFS) of 9 months.[mTOR is a 9 months. In the s9 months.Thus, our work is the first study to determine the effectiveness of combining everolimus and oxaliplatin using early-passaged cell lines derived from colorectal cancer liver metastasis tumors and colorectal cancer liver metastatic patient-derived xenograft (PDX) model. Previous studies tested the combination of everolimus and cisplatin in non-small cell lung cancer (NSLC) in mice xenografts models and observed synergy with the combination in NSCLC.  As cisplin vitro and in vivo validation using a preclinical model of colorectal cancer to show that the combination of oxaliplatin and a mTOR inhibitor is synergistic can be considered in patients with CRC liver metastasis. Although our initial study focused on the mTOR pathway, further analysis of the gene set enrichment analysis (Table 3) offer other opportunities to look at combinatorial therapies such as oxaliplatin and a WNT inhibitor in the treatment of colorectal cancer liver metastasis.Thus, the power of our current study is that combination of a genomic based approach with in vitro and in vivo preclinical models to identify optimal combinations of cytotoxic chemotherapy and a biological agent. Specifically, the combination of oxaliplatin and a mTOR inhibitor (everolimus) can potentially be used in the treatment of colorectal cancer liver metastasis.Our current studies have provided a blueprint for the use of a genomic based approach coupled with both S1 FigUnsupervised hierarchical clustering showed that oxaliplatin treatment did not influence the clustering of the 39 samples.(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.


These studies rely on direct observational data, however, and tools for assessing decadal-scale changes in African ecology without observation are lacking. We use isotopic records of historical common hippopotamus (Hippopotamus amphibius) canines to quantify herbaceous vegetation change in Queen Elizabeth National Park, Uganda following a period of civil unrest and poaching. This poaching event led to population collapse of two threatened African megaherbivore species: hippopotamus and African elephants (Loxodonta africana). Serial carbon isotope ratios (δ13C) in canine enamel from individuals that lived between 1960–2000 indicated substantial increases in C3 herbaceous plants in their diet (<20% C3 in the 1960s to 30–45% C3 in the 80s and 90s), supported by other observational and ecological data. These data indicate megaherbivore loss results in succession of both woody and herbaceous C3 vegetation and further reaching effects, such as decreased grazing capacity and herbivore biodiversity in the area. Given multiple lines of evidence, these individuals appear to accurately capture herbaceous vegetation change in Mweya.Megaherbivores (>1000 kg) are critical for ecosystem health and function, but face population collapse and extinction globally. The future of these megaherbivore-impoverished ecosystems is difficult to predict, though many studies have demonstrated increasing representation of C Megaherbivores exert a profound influence on ecosystem function and structure across ecosystems globally. They provide critical ecosystem services, such as nutrient cycling, soil transformation, increasing net primary productivity, maintenance of open environments and modification of trophic guild structure1Hippopotamus amphibius) and African elephants (Loxodonta africana). Hippopotamus significantly engineer vegetation and environments near their aquatic habitats by feeding on ground-layer vegetation, transforming grassy areas into short-grass grazing lawns, and building water channels through areas near their habitat56In African ecosystems, important threatened megaherbivores include the common hippopotamus (3 shrubs and trees) in the grassy layer (C4 grasses) of savannas is an issue of major concern for ecologists, as increased shrub presence alters soil conditions and suppress grass productivity, affecting organisms at many trophic levels3 woody plants, and little attention has been given to the role of herbaceous non-grassy plants (C3 forbs and herbs). Replacement of grasses by herbaceous plants within savannas can be just as deleterious for ecosystem health and biodiversity as succession of woody vegetation, though the effects of herbaceous plant expansion within grassy biomes have understudied consequencesThese megaherbivores are critical to the health and maintenance of African savanna ecosystems. Savannas can be described as “mixed tree-grass systems characterized by a discontinuous tree canopy in a conspicuous grass layer”9101112δ13C) in hippopotamus canine enamel. This paper illuminates long-term ecological effects following population collapse of hippopotamus and elephants in a savanna ecosystem in Queen Elizabeth National Park (QENP), Uganda. A period of civil unrest during the regime of Idi Amin resulted in widespread poaching and near extirpation of elephants from the park during the 1970s. Historical diet records obtained from sequentially-sampled hippopotamus canine enamel for stable carbon isotopes indicate that herbaceous vegetation shifted such that these hippopotamus switched their diets from >80% C4 grass in the 1960s to as low as 55% C4 grass in the 80s and 90s on the Mweya Peninsula, previously one of the most diverse parts of the park. Data that indicates changes in the diets of these individuals accurately reflect both environmental changes and dietary shifts of the population at large, and are supported by other lines of evidence, including gut content studies, vegetation surveys and isotope studies of individuals from discreet time periods1617181920δ13C from hippopotamus canines could serve as a useful measure of ecological change in other savanna parks where hippopotamus are present, and such skeletal collections exist.Evaluating the potential short and long-term effects on plant succession within ecosystems after megaherbivore collapse requires historical records of ecology from ecosystems where previous such population collapses have occurred. Comparisons are often drawn between the current rates of extinction and Pleistocene megafaunal extinctions, and though useful, those extinctions resulted in modern ecosystems for which there are no fossil analogs2, surrounding Lakes Edward and George. Rainfall varies between 600 and 1400 mm/yr during March-May and in September-November1622−2) on EarthQueen Elizabeth National Park  is locat4 grasses that grow in heavily grazed areas; trees and shrubs occurred in small thickets, clustered around emergent Euphorbia candelabrum25The effects of three major ecosystem drivers are elephant browsing, hippo grazing, and fire56From 1972 to 1980, during the regime of Idi Amin, management of all national parks essentially ceased, and widespread poaching decimated herbivore populations across the country2628Euphorbia candelabrum and Turraea robusta62530Heavy wildlife poaching continued into the mid 1980sδ13C) of herbivore tooth enamel can reveal the relative proportions of C3 vs. C4 in the diets of African herbivores, and the relationship between δ13Cdiet and δ13Cenamel values is well-understood in African ungulates35Stable isotope analysis is a powerful tool for understanding aspects of mammalian herbivore ecology in Africa, and is particularly useful for generating ecological records on temporal and spatial scales that are difficult or impossible to observe193233A common issue in modern ecological systems is generating continuous, decadal-scale historical records of environmental change. Serial samples of biological materials present a time-series of ecological information that can be useful as archives of environment, as each isotope sample represents an independent measure of diet and ecology for that individual213437363924 grasses1613C/12C) in hippopotamus biological tissues have revealed considerable variability in the consumption of C3 plants (herbs and forbs) and C4 plants , indicating a range in diets from purely C3-based to purely C4-based1945P < 0.0001The common hippopotamus is a large bodied herbivore found across Africa that lives in varied environments, ranging from arid savanna to forest441Lower hippopotamus canines were sampled from individuals who died on the Mweya Peninsula within Queen Elizabeth National Park. Date of death was assigned using recorded death dates (if known) or through radiocarbon dating of tusk enamel2O2 for 30 minutes to remove organics, then washed 3 times with distilled water. Enamel samples were reacted with >100% phosphoric acid in a common acid bath in a dual-inlet Carboflo carbonate device. Stable isotope ratios (13C/12C and 18O/16O) of resulting CO2 were analyzed on an MAT 252 isotope ratio mass spectrometer, and stable isotope ratios are reported as delta (δ) values relative to the international carbon isotope standard, Vienna Pee Dee Belemnite (VPDB), following the standard permil (‰) notation, where δ13C = (Rsample/Rstandard − 1) × 1000. Enamel isotope values were corrected relative to an internal carbonate standard (Carrara Marble) calibrated to V-PBD and two in-house enamel standards (“MHS” and “MRS”). Dietary designations for hippos are given based on estimated dietary intake of C4 plants  between diet and herbivore tooth enamel (14.1‰)3 and C4 plants in eastern and central Africawhere ε4 grass intake from ca. 1960 to 1970  from 1982 to 1991, following collapse of the elephant population in Queen  are smaller than changes in carbon isotopes from this dietary shift. In non-quantitative vegetation counts conducted in 1992 and 2009, Plumptre and others (2010) found that Cynodon dactylon (a C4 grass), Commelina diffusa, Commelina africana (C3 herbs), Asystasia gangetica (C3 forb), Cyanotis foecunda (a C3 flowering herb), Achyranthes aspera (C3 herb), Ocimum suaveolens (C3 herb), Oplismenus hirtellus (C4 grass) and two Cyperus species have become dominant, which suggests that hippopotamus have been increasingly relying on these C3 forbs and herbs. Though useful, it should be noted that these vegetation counts are not quantitative, and we present these data here as a validation for using hippopotamus diet change to infer ecological change.Hippopotamus canine carbon isotopes reveal a diet shift associated with increased consumption of herbaceous vegetation, which indicates a significant encroachment of C3 herbaceous groundcover when preferred grasses are no longer present. Hippopotamus have traditionally been characterized as selective C4-grazers that feed on short grasses and sedges (which comprise 95–99% of their diet), supplemented by forbs4 grasses25Our findings reveal that hippopotamus have more flexible diets than previously thought, and can shift their diets to accommodate increased C3 encroachment in the herbaceous ground layer of savannas. The expansion of non-grassy ground vegetation restricts grazing capacity and decreases herbivore biodiversity and biomass, and these effects are intensified by the loss of hippopotamus-maintained grazing lawns27The effects of poaching in the park have resulted in ecological restructuring of QENP. Aerial photographs and photomosaic analysis of vegetation types within the park between 1950 and 2006 indicate an increase in woody cover of ~30% across QENP4 grass growth, a critical resource for numerous mesoherbivores in savanna ecosystems. The combined effects of bush encroachment, which has already taken place across Africa1451The long-term and wide-reaching effects of bush encroachment, such as changes in nutrient composition of soil, and %C and %N depletion in heavily encroached areasCivil war in eastern Democratic Republic of the Congo (DRC) in 1998 led to heavy poaching in Virunga National Park and other areas near QENP, resulting in herbivore migration to QENP and a partial recovery of herbivore communities, which may explain the delayed later succession of woody plants3-vegetation, not only in woody layers as a result of elephant culling, but also in herbaceous ground layers. These isotopic data are supported by other lines of evidence for increasing C3 plant representation in the area. These data are critical for wildlife management within African savanna parks, as increasing C3 vegetation in both woody and herbaceous layers of savanna parks decreases grazing capacity for grass-feeding herbivores. The outcome of this ecological change is that grazers have been pushed to other parts of the national park, and even outside the park itself, posing serious threats for conservation in Africa. Our data suggest that hippopotamus canine isotopes could be used in savanna parks to build decadal records of ecological change in places where direct observational data are lacking.Megaherbivores are key ecological engineers that affect change at nearly every trophic level in African savannas, consuming and spreading nutrients on land and transforming soil properties and landscapes that increase environmental heterogeneity. Dietary stable isotope data from serially sampled hippopotamus canines from the Mweya Peninsula of QENP provide long-term ecological records that reflect increases in CHow to cite this article: Chritz, K. L. et al. Hippopotamus (H. amphibius) diet change indicates herbaceous plant encroachment following megaherbivore population collapse. Sci. Rep.6, 32807; doi: 10.1038/srep32807 (2016).


Citrus reticulata×C. reticulata×C. paradisi)×Temple (C. reticulata×C. sinensis)), grapefruit (C. paradisi), Pineapple sweet orange (C. sinensis), and their seedless mutants. Seed abortion in seedless mutants was observed at 26 days post anthesis (Time point 2). Affymetrix transcriptomic analysis revealed 359 to 1077 probe sets with differential transcript abundance in the comparison of seedless versus seedy fruits for each citrus genotypes and time points. The GDTA identified by 18 microarray probe sets were validated by qPCR. Hierarchical clustering analysis revealed a range of GDTA associated with development, hormone and protein metabolism, all of which may reflect genes associated with seedless fruit development. There were 14, 9 and 12 genes found exhibiting similar abundance ratios in all three seedless versus seedy genotype comparisons at time point 1, 2 and 3, respectively. Among those genes were genes coding for an aspartic protease and a cysteine protease, which may play important roles in seedless fruit development. New insights into seedless citrus fruit development may contribute to biotech approaches to create seedless cultivars.Identification of genes with differential transcript abundance (GDTA) in seedless mutants may enhance understanding of seedless citrus development. Transcriptome analysis was conducted at three time points during early fruit development (Phase 1) of three seedy citrus genotypes: Fallglo (Bower citrus hybrid ( Seedless citrus fruits can be obtained through parthenocarpy, stenospermocarpy, male or female sterility, self-incompatibility, abnormal embryo sacs and unfertilized ovules, and various factors that result in meiotic irregularities.3–6 Parthenocarpy indicates production of fruit without fertilization or embryo abortion. In the absence of pollination, parthenocarpic plants will set seedless fruit.7 Parthenocarpy has a genetic basis and hence can be targeted for genetic engineering of seedlessness.8 For example, RNA interference (RNAi)-mediated suppression of chalcone synthase, the first enzymatic step in the flavonoid pathway, resulted in parthenocarpic tomato (Solanum lycopersicum) fruits.9 Parthenocarpic fruit development was also seen in tobacco (Nicotiana tabacum) and eggplant (Solanum melongena) expressing the coding region of the iaaM gene  from Pseudomonas syringae pv. savastanoi, under control of the promoter of DefH9 .10 The DefH9::iaaM expression promotes the synthesis of auxin (IAA) specifically in the placenta, ovules and tissues derived therefrom.11 The agronomical advantages of DefH9::iaaM genetically modified plants have been assessed in greenhouse and field trials using transgenic eggplant,12 strawberry and raspberry 13 and cucumber (Cucumis sativus).14 Goetz et al.15 demonstrated that AUXIN RESPONSE FACTOR 8 (ARF8) is a negative regulator of fruit initiation in the absence of fertilization in Arabidopsis. Parthenocarpy was also induced in Arabidopsis and tomato through constitutive expression of the mutant genomic (g) Atarf8-4 sequence 16 or ovary specific expression of a tyrosine phosphatase RolB gene (proposed as an activator of an array of secondary metabolic processes).17 Accordingly, parthenocarpy can be induced in a variety of agricultural species by the exogenous application of auxins, cytokinins, or gibberellin,19 indicating that a number of independent and possibly redundant hormone pathways can direct parthenocarpy. In citrus, parthenocarpy occurs naturally in Navel orange (C. sinensis) and Satsuma mandarin (C. unshiu Marc.). Only in ‘Wilking’ mandarin hybrid has a gene been reported to cause parthenocarpy-related seed abortion, and it was a recessive gene responsible for asynapsis in meiosis.20Citrus is the most widely cultivated fruit in the world.ch-Cpn21) gene has higher expression during flowering in seedy compared to seedless Thompson grapes and silencing of ch-Cpn21 in tobacco and tomato resulted in seed abortion.21 The ubiquitin extension protein S27a is differentially expressed in developing flower organs of Thompson seedless versus seedy grape isogenic clones. Overexpression of ubiquitin extension protein S27a gene in carpels and integuments led to embryo abortion and seedlessness.22 A male sterility-like gene was identified in ‘Ponkan’ mandarin and its transcript is more abundant in seedless mutants than in the seedy progenitor.23 This gene codes for a fatty acyl-CoA reductase involved in lipid metabolism. A comparative analysis of miRNAs between a fertile line and male sterile cybrid line of pummelo (Citrus grandis) revealed that miR167a was involved in floral bud development and cytoplasmic male sterility in citrus.24 The genes associated with male sterility or parthenocarpy have been exploited in efforts for seedlessness development in citrus. For example, the chimeric barnase gene, a ribonuclease derived from Bacillus amyloliquefaciens, was introduced into embryogenic callus of ‘Ponkan’ mandarin although there were no further reports about the outcomes.25 The Arabidopsis thaliana MAC12.2 gene, associated with male sterility and parthenocarpy, was introduced into precocious trifoliate orange (Poncirus trifoliata) and resulted in fruits with reduced number of seeds.26Comparing GDTA between seedless and seedy variants during development of fruitlets can provide insight into molecular mechanism of seed formation. These genes would then be potential targets for inducing seedlessness using gene knockout or overexpression. The chloroplast chaperonin 21 (Citrus reticulata) is an early-maturing, seedy mandarin-type derived from ‘Bower’ × ‘Temple’.27 ‘Duncan’ grapefruit (C. paradisi) and ‘Pineapple’ sweet orange (C. sinensis) are seedy citrus varieties grown in Florida. Numerous seedless grapefruit varieties grown are ultimately derived from seedy grapefruit similar to ‘Duncan’ through selection of natural or induced mutants, and all true grapefruit are near isogenic.28 ‘US Early Pride’ and ‘US Seedless Pineapple’ are released cultivars from irradiated ‘Fallglo’ and ‘Pineapple’ sweet orange, respectively.30 In this study, we compared the transcriptome profiles of three seedy citrus genotypes: ‘Fallglo’ mandarin hybrid, ‘Pineapple’ sweet orange and ‘Duncan’-like seedy grapefruit and associated seedless mutants during early fruit development (Phase 1).31 The objective of this work was to identify GDTA in seedless versus seedy fruits  at three time points across all three citrus genotypes. Ultimately, it is hoped the genes identified will be useful in development of seedless variants of desirable but seedy citrus varieties.‘Fallglo’ mandarin hybrid ; Pineapple and US Seedless Pineapple; and individual trees of three seedy true grapefruit  and individual trees of three seedless mutants . Three trees of any single named grapefruit cultivar were not available at the same location as the other genotypes. However, grapefruit are near-isogenic and therefore three seedy and seedless cultivars represent reasonable biological replicates. Controlled crosses were made on 10 flowers per tree and monitored for fruit growth, to provide reference fruit sizes for approximate days from pollination in material collected for RNA extraction. Three trees of each seedy and seedless genotype were sampled on 17 April 2009 (time point 1), 4 May 2009 (time point 2) and 28 May 2009 (time point 3). On each date from each tree 12–25 fruits were collected. Several fruits were assessed for seed abortion, while the remainder were subjected to RNA extraction from entire fruits. At harvest, fruits were wrapped in aluminum foil, immediately frozen in liquid nitrogen and stored at −80 °C for RNA extraction. Fruits at each time point from each tree were assessed for ovule development by hand-sectioning and Toluidine Blue staining.32 and pellets were resuspended in 500 μl RNase-free H2O. RNA was purified using MinElute purification and was eluted in 30 μl RNase-free H2O, followed by DNase treatment . RNA samples were pooled for the three trees of each seedy or seedless variant of each citrus genotype for each sampling date. The concentration of RNA was measured in a NanoDrop ND-1000 spectrophotometer . RNA quality was evaluated using an Agilent Bioanalyzer Model 2100 .RNA extractions were performed using the Guanidinium thiocyanate procedureHybridization were performed using the Affymetrix Citrus GeneChip  at the Interdisciplinary Center for Biotechnology Research Microarray Core, University of Florida, Gainesville, according to the manufacturer’s instructions. The GeneChip Citrus Genome Array contains 30,171 probe sets representing up to 33,879 citrus transcripts, and 5023 SNP probe sets.P⩽0.05 and false discovery rate (FDR)⩽0.05, in combination with a cutoff value of abundance ratio >2 or<−2.The signal intensities were determined with a GeneChip scanner controlled by GCOS  Affymetrix software. Then we used the GCOS software to analyze fluorescence readings with Robust multiarray analysis (RMA). Criteria for inclusion in further analysis were that the probes display fluorescence significantly above background and that they had an absolute reading of 20 or greater. The signal ratio between seedless and seedy was calculated to represent the abundance ratio for each probe set. The differentially expressed probes between seedless versus seedy citrus were chosen with the criterion of Arabidopsis orthologs of differentially expressed probe set IDs on the Citrus GeneChip by searching the Arabidopsis thaliana genome via the HarvEST database.33 In some cases, several probe set IDs were annotated to the same Arabidopsis gene. The average fold change of probe IDs representing the same gene was calculated and used for Venn diagram and MapMan pathway analysis. We used MapMan to locate the genes with differential transcript abundance (GDTA) in the metabolic network.34 Hierarchical Cluster Analysis of GDTA transcription profiles was achieved with Permutmatrix software  with Euclidian distance and McQuitty's linkage WPGMA method.35Further analysis was conducted using the corresponding 36 RT-qPCR was performed using an ABI7500 thermal cycler  with Bright Green PCR Master Mix . Melting cures and electrophoresis were performed to verify the specificity and identify of the PCR products. Citrus 5.8s rRNA gene was used for internal control as described71 and 23 genes were selected for validation. The relative quantification of gene expression level was determined by the comparative Ct method 2−ΔΔCt;,37 where ΔΔCt=(Ct (target gene)—Ct )seedless—[Ct (target gene)—Ct ]seedy.The same RNA samples used for microarray experiments were also used for qPCR. The cDNA synthesis was performed following the protocol as described.38 Seed abortion was apparent in the seedless mutants of all three genotypes by the second time point, which was about 4 weeks after anthesis . PDTA at time point 1, 2 and 3, respectively were: 994, 593 and 408 probe sets in Fallglo; 638, 784, and 1077 probes sets in grapefruit; and 792, 359 and 886 probe sets in Pineapple sweet orange . The detR2=0.84) was obtained  validation and are listed in obtained . In addiArabidopsis thaliana genomic data. In the MapMan analysis, we used the corresponding Arabidopsis thaliana Genome Initiative IDs (AtGID) of Arabidopsis orthologs instead of the citrus probe set IDs. It is noteworthy that not all the probes set IDs have AtGID annotations and one AtGID could cover multiple probe set IDs. In our analysis, we chose probes with matching AtGIDs and when multiple probes matched the same AtGID, we calculated the average abundance ratio. A summary of the number of matched GDTA in seedless versus seedy fruits at each time point of the three citrus genotypes are presented in The probe sets in the Affymetrix Citrus Genechip were annotated by searching the The GDTA detected at each time point for each of the three citrus genotypes were imported into MapMan software for metabolic pathway analysis. The MapMan metabolism overview of each set of GDTA are shown in Arabidopsis aspartic protease gene (AT2G03200) exhibited lower transcript abundance at all time points and in all seedless genotypes compared with seedy varieties, strongly suggesting its involvement in seed development. A gene coding for a group 4 late embryogenesis abundant (LEA) domain-containing protein exhibited higher transcript abundance in all of the seedless genotypes at all tested time points and its expression peaked at time point 2, except for grapefruit where it showed lower transcript abundance at time point 1. Three genes have different expression patterns among different citrus, suggesting genetic variations between the genotypes may differentially regulate the timing of the gene induction related to seedless fruit development. These genes included a gene coding for GASA1 protein homolog 1, a gibberellic acid (GA)-responsive protein, which exhibited higher transcript abundance at all three time points in Fallglo, at time point 2 in Grapefruit and at time point 1 and 2 in Pineapple, respectively. A gene coding for a protein homologous to pathogenesis-related protein 4 (PR4) was found to have a regulation pattern identical to the GASA1 gene. Last, a gene coding for a hydrolase with unknown specific biological function showed differential transcript abundance at all time points.However, 5 GDTAs were common across all three time points and across all three citrus genotypes , and theThe genes associated with seedless fruit development might be strictly regulated at certain times during early fruit development. At other time points, they may not be differentially expressed in seedless versus seedy fruits. As a result, the GDTA at each time point shared by three citrus genotypes, especially those with the same expression trend, indicate a high probability of being associated with seedless fruit development. As shown in 40 Three other identified genes encoded proteins of unknown function. The five lower transcript abundance genes encoded: 1) GA2OX8  a UDP-glucosyl transferase family protein involved in indole-3-acetic acid (IAA) homeostasis; 3) an aspartic protease; 4) PGY2 (PIGGYBACK2) coding for a ribosomal structural constituent involved in development/pattern regulation, and 5) a GDSL-motif lipase family protein from a large family of proteins involved in plant development and morphogenesis.41 The common differential transcript levels of these genes at time point 1 in all three citrus genotypes suggested an involvement of nutrients, secondary metabolites, development regulatory processes and hormones at the early stage of seed abortion.Among the 68 GDTA shared across all three genotypes at time point 1, 9 genes exhibited higher transcript abundance and 5 genes exhibited lower transcript abundance across all three seedless genotypes . Of the Arabidopsis protein 100 (ANAC100) and protein 72 (ANAC72) and one gene encodes late embryogenesis abundant (LEA) group 4 protein. A gene that codes for GA-responsive (GASA1) protein exhibited higher transcript abundance in all three seedless citrus genotypes. Only two genes exhibited lower transcript abundance across three citrus genotypes at time point 2. One encodes for a homolog to GLYOXALASE I (ATGLX1) and the other for an aspartyl protease involved in proteolysis. These genes with similar transcript patterns across three citrus genotypes at this time point might be associated with the development of seedless fruit, but are unlikely to be the cause of seedlessness.At time point 2, 7 genes exhibited higher transcript abundance and 2 exhibited lower abundance in seedless versus seedy fruits across three citrus genotypes . Those gArabidopsis.42 Three proteins encoding genes involved in protein metabolism all exhibited lower abundance in all three seedless citrus genotypes at this time point. The citrus gene homologous to Arabidopsis cysteine protease1 (CEP1) that exhibited higher transcript abundance at time point 1  are considered commercially seedless.44 Seedless versus seedy fruit development in each genotype may exploit some different genes however it seems likely that some key differences in gene expression are shared by different low-seeded genotypes although possibly at somewhat different time points and with different magnitude. By collecting fruits at the same time points, we may have captured different developmental stages from each genotypes. Regardless, seed abortion was observed at time point 2 for all genotypes  involved in gibberellin degradation, exhibited lower abundance in seedless versus seedy variants in all citrus types. Overexpression of AtGA2ox8 reduced gibberellin levels in tobacco plants.46 The suppression of this gene homolog in citrus at this time point suggests that these seedless citrus genotypes may have elevated endogenous GA levels associated with seedless fruit development. Increase in endogenous GA levels have been observed in parthenocarpic Fino clementine47 and exogenous GA applications during early fruit development have been used routinely to enhance set of seedless citrus.43 It is possible that silencing citrus GA2ox8 would further enhance seedless citrus fruit development. The transcript abundance of a gene homologous to lipoxygenases (LOX2) increased at time point 1. Several roles have been suggested for lipoxygenases in seeds: fatty acid peroxidation in membranes, seed-storage lipids and production of growth regulators including jasmonates and abscisic acid (ABA).48 The balance between GA and ABA appears to play an important role in the regulation of fruit development in seedless mandarins.47 Plants regulate auxin levels through complex interactions including the formation and hydrolysis of amide-linked conjugates that act as storage or inactivation forms of the hormone.49 The transcripts of a gene homologous to UDP glucosyltransferase decreased at time point 1 in all citrus genotypes. The UDP-glucosyltransferase catalytic enzyme has been reported to act on indole-3-butyric acid (IBA) as a substrate to perturb indole-3-acetic acid (IAA) homeostasis in Arabidopsis.50 Moreover, the transcript of a gene homolog to PGP13 (P-GLYCOPROTEIN 13), an ATPase involved in auxin transport, increased at time point 1 in all three citrus genotypes. Taken together, reduced expression of GA2ox8 and UDP- glucosyltransferase, and increased expression of LOX2 and PGP13 at early stage of fruit development may permit a high degree of parthenocarpy via effects on hormone metabolism in seedless mutants.It has been shown that auxins, cytokinins or gibberellin, can induce parthenocarpy in plants and that parthenocarpic tomatoes have higher endogenous levels of auxin and gibberellin than seedy types.Arabidopsis cysteine protease1 (CEP1) which has been reported to be upregulated in developing seeds and involved in programmed cell death (PCD).51 CEP1 knock-out mutants in Arabidopsis exhibited disrupted tapetal PCD and decreased pollen fertility with abnormal pollen exine, indicating that normal CEP1 expression is necessary for timely degeneration of tapetal cells and development of functional pollen.52 Notably, the citrus cysteine protease gene exhibited higher transcript abundance at time point 1, no abundance difference was found at time point 2 and lower transcript abundance was observed at time point 3 . A network illustration showing the multiple pathways involved in citrus seedlessness was hypothesized (By comparing transcriptome profiles of seedy Fallglo tangerine, grapefruit and Pineapple sweet oranges with their seedless mutants, we have identified a list of GDTA between seedy and near isogenic seedless citrus cultivars. The study of gene transcriptional abundance at three time points in early fruit development identified seedless versus seedy GDTA that were common in all three seedless citrus genotypes. It must be noted that a global assessment of transcriptomes will provide numerous potential relationships, with many having unclear function in the biological differences in the material studied. However, some of the GDTA revealed in this study are reasonable candidate genes where transcript level differences may be important for seedless fruit development, especially the genes shared by three citrus genotypes at time point 1 such as thesized .The seed


Vcmax and Jmax vary within-season due to interactions between ontogenetic processes and meteorological variables. Remote sensing-based estimation of Vcmax and Jmax using leaf reflectance spectra is promising, but temporal variation in relationships between these key determinants of photosynthetic capacity, leaf reflectance spectra, and the models that link these variables has not been evaluated. To address this issue, we studied hybrid poplar (Populus spp.) during a 7-week mid-summer period to quantify seasonally-dynamic relationships between Vcmax, Jmax, and leaf spectra. We compared in situ estimates of Vcmax and Jmax from gas exchange measurements to estimates of Vcmax and Jmax derived from partial least squares regression (PLSR) and fresh-leaf reflectance spectroscopy. PLSR models were robust despite dynamic temporal variation in Vcmax and Jmax throughout the study period. Within-population variation in plant stress modestly reduced PLSR model predictive capacity. Hyperspectral vegetation indices were well-correlated to Vcmax and Jmax, including the widely-used Normalized Difference Vegetation Index. Our results show that hyperspectral estimation of plant physiological traits using PLSR may be robust to temporal variation. Additionally, hyperspectral vegetation indices may be sufficient to detect temporal changes in photosynthetic capacity in contexts similar to those studied here. Overall, our results highlight the potential for hyperspectral remote sensing to estimate determinants of photosynthetic capacity during periods with dynamic temporal variations related to seasonality and plant stress, thereby improving estimates of plant productivity and characterization of the associated carbon budget.Earth's future carbon balance and regional carbon exchange dynamics are inextricably linked to plant photosynthesis. Spectral vegetation indices are widely used as proxies for vegetation greenness and to estimate state variables such as vegetation cover and leaf area index. However, the capacity of green leaves to take up carbon can change throughout the season. We quantify photosynthetic capacity as the maximum rate of RuBP carboxylation (V Photosynthesis by land plants plays a critical role in regional and global carbon balance –3. Globa2 into biomass varies dynamically throughout the season and with plant stress [Spatial and temporal variation in plant photosynthesis can be estimated using remote sensing-derived spectral indices. Spectral estimates of green vegetation, including vegetation indices such as the Normalized Difference Vegetation Index (NDVI), are widely used to estimate photosynthesis and vegetation productivity across spatial and temporal scales –18. Estit stress –26. Manyt stress ; howevert stress . The phot stress . PRI is t stress ; however2 under optimal light and water conditions. In this study, we estimate photosynthetic capacity through measurements of the maximum rate of carboxylation of RuBP by the enzyme rubisco (Vcmax) and the maximum rate of electron transport driving RuBP regeneration (Jmax)[cmax and Jmax as fixed parameters [2 concentrations [Photosynthetic capacity represents the potential of vegetation to fix COon (Jmax). Some terameters . Howeverrameters –35, atmotrations , plant strations , and seatrations –40. Thistrations .cmax and Jmax across space and constrain terrestrial biosphere model estimates of plant function. Predictive models of photosynthetic capacity, conditioned based on leaf reflectance metrics, have been developed using partial least squares regression (PLSR) for multiple tree species across glasshouse temperature regimes [cmax and Jmax are robust to temporal variation in these key determinants of photosynthetic capacity. Hyperspectral leaf reflectance correlates with leaf characteristics likely to cause seasonal variation in photosynthetic capacity such as chlorophyll content [cmax and Jmax are robust to temporal variation in these key determinants of photosynthetic capacity. We hypothesize that if seasonal variation in photosynthetic capacity is caused by a combination of leaf changes detectable by reflectance in bands outside of narrowband multispectral bands, then PLSR models that utilize the full reflectance spectrum will predict seasonal changes in Vcmax and Jmax.Although hyperspectral remote sensing shows promise for predicting photosynthetic capacity based on leaf optical properties, questions remain regarding temporal variability. Estimates of photosynthetic capacity from remote sensing methods are desirable because of their potential to map V regimes , in C4 c regimes , and in  regimes . Emergen regimes –43. Stil content , nitroge content , light-u content , and wat content , while scmax and Jmax and examined the influence of water stress on the robustness of these estimates of photosynthetic capacity. We compared leaf reflectance spectra from hybrid poplar (Populus spp.) to Vcmax and Jmax estimates throughout a 7-week period in the middle part of the growing season. We discuss our results in the context of emerging hyperspectral remote sensing methods and terrestrial biosphere models of global carbon dynamics.In this study, we evaluated the ability of hyperspectral data to represent and predict within-season temporal variation in VPopulus deltoides hybrid poplar trees. Trees were planted in random arrangement with 1 x 1 m spacing in January 2013. Each year during the dormant season, the trees were coppiced and destructively sampled for biomass. Study trees were randomly selected before the start of the experiment in May 2016. During the study period (5/24/2016–7/5/2016), the mean high temperature was 34.4°C and the mean low temperature was 21.3°C. We applied 38 liters of water per day per tree during the pre-dawn period for 2 weeks prior to the start of the experiment (5/1/2016–5/14/2016) using an irrigation system to begin the study in well-watered conditions. We also fertilized the trees on 5/14/2016 using tree and shrub food (Arizona’s Best) to ensure the trees were not nutrient-limited at the start of the induced dry-down. The trees were exposed to ambient climate conditions and had no irrigation except for supplemental watering from 6/2/2016 through 6/6/2016 and additional watering on 6/20/2016, which avoided senescence and caused variance in the water status of the trees. On sampling dates, we measured predawn leaf water potential (Ψpd), leaf gas exchange, and hyperspectral leaf reflectance for each tree. The full suite of measurements was conducted on all 12 trees on consecutive sampling dates (6 trees per day), except for 6/30 when we measured all 12 trees in one day.Our study was conducted using poplar trees, grown outdoors at the Biosphere 2 Research Center near Oracle, AZ, USA . Biosphere 2 is leased to the University of Arizona. Authors RKM, DJPM, and GABG were responsible for the poplar stand at Biosphere 2. No additional permission was required to carry out this study, which did not involve endangered or protected species. We studied the relationship between spectral reflectance and photosynthetic capacity in 12 individual pd) was measured using a pressure chamber . Leaves were collected before sunrise, transported to the lab in a cooler, and measured within 30 minutes of collection. One leaf per plant was measured per time point.Predawn leaf water potential  of 1800μmol m-2 s-1 until the photosynthetic rate (A) stabilized. Gas exchange curves were conducted only on leaves with an initial A ≥ 10 μmol CO2 m-2 s-1 to ensure that the leaves were active enough to yield appropriate estimates for photosynthetic capacity. Each curve consisted of 13 different intercellular CO2 concentrations (Ci) starting at the ambient CO2 concentration of 400 ppm and then decreasing to 300, 200, 100, 50 to 0 ppm before increasing to 400, 400, 600, 800, 1200, 1600, 2000 ppm. We used the Predictive Ecosystem Analyzer (PEcAn) photosynthesis package to perform quality control of CO2 response data before fitting A/Ci curves (https://github.com/PecanProject/pecan). In total, 86 CO2 response curves passed quality control. We fit A/Ci curves using the ‘fitaci’ function in ‘plantecophys’ package in R [2 and estimates Vcmax and Jmax along with their standard errors.Leaf gas exchange was measured with two LI-COR portable photosynthesis systems  equipped with a 6400-02B LED light source. Gas exchange measurements were performed on the youngest, most fully-expanded leaf on the south-facing side of each tree. Leaves were acclimated to the chamber at 25°C, a chamber-air COage in R . The ‘fiage in R  to measuReflectance was measured on the same leaves as the gas exchange measurements using a high-spectral resolution ASD FieldSpec® 3 Full-Range (350–2500 nm) spectroradiometer . Reflectance measurements were taken with a leaf-clip assembly containing an internal calibrated light source and a black background panel face. The relative leaf reflectance data were standardized prior to measurements of each leaf by measuring a standard white reference reflectance target. ViewSpec Pro® software was used to convert binary data to ASCII data. The spectral resolution is 3nm at 700 nm, 10nm at 1400 and 2100nm across the full spectrum. The hyperspectral data are sampled at every 1nm. The spectroradiometer was turned on for at least 30 minutes before reflectance measurements were taken. The white panel reference reflectance was captured every 2–5 minutes. All measurements were taken from the leaf adaxial surface, avoiding the midrib. Three reflectance measurements were obtained on three different areas of each leaf lamina, resulting in nine spectra per leaf. Each measurement required no more than 5 s. Spectral reflectance measurements were quality-checked by removing negative reflectance values. We then averaged the nine spectra to determine mean optical properties for each leaf. Measures of leaf optical properties generally occurred between 10:30am and 11:30am and followed gas exchange measurements by no more than 2 hours.https://github.com/uwadaira/plsropt). The model with the number of components that minimized the Root Mean Squared Error of Prediction (RMSEP) was selected as the most parsimonious PLSR model. Each PLSR model was generated independently for Vcmax and for Jmax. The spectrum range for all models was 450–2500 nm. Performance parameters were generated to assess the predictive ability of each model including the coefficient of determination (R2).Partial least-squares regression (PLSR) was performed to generate predictive models using the package ‘PLS’ in R . PLSR ispd). The two consecutive sampling dates with the largest individual variation in Ψpd,  were held out. The model was trained on all data except for the holdout dataset and was tested on the 12 observations with large variation in Ψpd. The purpose of this test was to assess model predictive capacity in a situation with large within-population variation in environmental stress. For each PLSR model, Selectivity Ratio (SR) scores were calculated to enable comparison of the relative significance of each wavelength in its contribution to the final model. Although the Variable Importance of the Projection (VIP) score is more widely used in the current scientific literature, SR has been found to be more reliable for model predictions and is thus presented in this study [Three different evaluations of the PLSR model were performed in increasing order of statistical rigor. The first test was a "leave-one-out" cross-validation approach, which trains the model on all but one observation, and then makes a prediction based on the single remaining observation . The secis study .cmax and Jmax estimated using standard gas exchange techniques and spectral indices were tested and R2 reported.Spectral vegetation indices for estimating chlorophyll content, water stress, and carotenoid pigments were tested using the full set of observations ; these apd) varied over the course of our study  and Vcmax and Jmax. This procedure was performed 100 times with different randomly selected 20% holdout datasets  and tested it on the two consecutive sampling days with highest individual variation in Ψpd,  was used as a method of variable selection. The SR was used to assess relative contributions of different portions of the spectrum to the overall PLSR model. Both Vinfrared , Fig 2C.for Jmax . Contribx models .cmax and Jmax and a suite of hyperspectral vegetation indices were compared  based on the coefficient of determination: Maccioni (R2 = 0.72), Double Difference (R2 = 0.74), Vogelmann2 (R2 = 0.73), and SR1 (R2 = 0.74) (max (R2 = 0.72) outperformed all tested hyperspectral indices, the best of which was SR3  and Jmax (R2 = 0.46). The MODIS-like NDVI (“mod_NDVI”) performed better than the hyperspectral NDVI for both Vcmax (R2 = 0.60) and Jmax (R2 = 0.50)  . The PLSand SIPI . The mNDand SIPI . The hyp = 0.50) , Table 1cmax and Jmax. Predictions of Vcmax and Jmax from PLSR models derived from leaf reflectance spectra were proportionally-sensitive to observed variation in Vcmax and Jmax. These results support our hypothesis that PLSR models that utilize the full spectrum can predict photosynthetic capacity through time. These findings highlight the potential of hyperspectral remote sensing methods to accurately predict Vcmax and Jmax despite dynamic temporal variation in photosynthetic capacity related to within-season variation and plant stress.Our results show that hypers can predict photosynthetic parameters across time, suggesting that these hyperspectral remote sensing techniques have great potential to constrain model estimates of plant function. In this study, relationships between leaf reflectance spectra and photosynthetic capacity were robust throughout a 7-week period with dynamic change in photosynthetic capacity. Hyperspectral vegetation indices to estimate chlorophyll content were correlated with the key determinants of photosynthetic capacity, Vin situ within-season temporal variation on estimates of photosynthetic capacity derived from leaf reflectance spectra. As in previous studies, measured and PLSR-modeled leaf traits were significantly correlated [cmax and Jmax during a 7-week period with dynamic declines in photosynthetic capacity. This is confirmation that the spectral signals detected by leaf reflectance observations are accurately tracking seasonal metabolic adjustments made within the photosynthetic machinery of the leaf. When PLSR models were trained on only 80% of the data and tested on the remaining 20%, their mean predictive capability were similar to that of the full PLSR models. This shows that relationships between leaf reflectance spectra and Vcmax and Jmax are robust despite large variation in these values. Known hyperspectral vegetation indices had varying predictive capabilities. Hyperspectral vegetation indices developed for estimating chlorophyll content  and plant stress (SRCarter) had the highest correlations with plant photosynthetic capacity (cmax and Jmax largely fell in the visible and short wavelength end of the near-infrared (~500 to 850 nm), with minor contributions from wavelengths in the short-wave infrared. Important wavelengths in the visible region fell largely in the blue region (450–495 nm) and red regions (620–650 nm) for the Vcmax models, which is consistent with the chlorophyll absorption regions (i.e. ∼430–460 nm and 640–670 nm). The highest-performing hyperspectral vegetation indices generally leveraged diagnostic differences in the red-edge portion (680–750 nm) of the spectrum to estimate chlorophyll content. The red edge region of reflectance is known to be sensitive to differences in chlorophyll content, and chlorophyll content is generally positively correlated with photosynthetic capacity [Leaf-level hyperspectral data have previously been used to estimate photosynthesis  and photrrelated ,42,66,67capacity . The wav portion 0–750 nm cmax and Jmax [cmax and Jmax values are often parameterized based on limited or poorly represented data sets [cmax improved process-based biosphere model  representation of seasonal carbon dynamics  in a tropical evergreen forest in Brazil, however, seasonal parameter variations could not be extrapolated spatially [Accurate representation of photosynthesis in terrestrial biosphere models is essential to predicting future carbon and global change dynamics . Althougand Jmax ,70,71, mand Jmax . Furtherata sets . Monthlypatially . Remote patially ,55,72. Ocmax and Jmax were generally similar, there were differences in PLSR model performance and sensitivity between the two variables. The mean predictive capability of the 80% PLSR models were comparable for Vcmax and Jmax   and NASAral NDVI . Detecticmax and Jmax in hybrid poplar was robust to temporal variation of up to 200% in photosynthetic capacity. Applying remote sensing tools to predict photosynthetic capacity across a wider range of wildlands requires an in situ test of this method outside of agroecosystem or controlled glasshouse conditions across a time period of variable abiotic and biotic conditions. Our results show that relationships between photosynthetic capacity and leaf reflectance spectra developed from limited data can in some cases be extrapolated temporally. These results highlight the potential of hyperspectral remote sensing methods to detect dynamic temporal variations in Vcmax and Jmax related to seasonality and plant stress, thereby aiding improved estimates of plant productivity and associated carbon budget. Furthermore, our results suggest that terrestrial biosphere models could use hyperspectral remote sensing to parameterize Vcmax and Jmax within season to improve predictions of future carbon dynamics. Reliable and precise methods to estimate Vcmax and Jmax across spatial and temporal scales will improve understanding of ecosystem carbon uptake and the terrestrial carbon sink.In this study, spectral estimation of VS1 FigEach point represents the r-squared between predicted and actual Vcmax/Jmax values from PLSR using a random sample corresponding to the designated proportion of training data (each proportion was sampled 100 times).(TIF)Click here for additional data file.S1 Table2 of 100 PLSR models per training proportion are represented.The mean, median, and standard deviation in R(DOCX)Click here for additional data file.


When standing dead trees (snags) fall, they have major impacts on forest ecosystems. Snag fall can redistribute wildlife habitat and impact public safety, while governing important carbon (C) cycle consequences of tree mortality because ground contact accelerates C emissions during deadwood decay. Managing the consequences of altered snag dynamics in changing forests requires predicting when snags fall as wood decay erodes mechanical resistance to breaking forces. Previous studies have pointed to common predictors, such as stem size, degree of decay and species identity, but few have assessed the relative strength of underlying mechanisms driving snag fall across biomes. Here, we analyze nearly 100,000 repeated snag observations from boreal to subtropical forests across the eastern United States to show that wood decay controls snag fall in ways that could generate previously unrecognized forest-climate feedback. Warmer locations where wood decays quickly had much faster rates of snag fall. The effect of temperature on snag fall was so strong that in a simple forest C model, anticipated warming by mid-century reduced snag C by 22%. Furthermore, species-level differences in wood decay resistance (durability) accurately predicted the timing of snag fall. Differences in half-life for standing dead trees were similar to expected differences in the service lifetimes of wooden structures built from their timber. Strong effects of temperature and wood durability imply future forests where dying trees fall and decay faster than at present, reducing terrestrial C storage and snag-dependent wildlife habitat. These results can improve the representation of forest C cycling and assist forest managers by helping predict when a dead tree may fall. Standing dead trees, also called snags, play pivotal roles in the structure and function of changing forests . Snags aIn addition to their pivotal role in the forest C cycle, standing dead trees threaten public safety and property. Hazard trees, including snags, are a major source of litigation in the United States where falling trees kill more than 200 people each year ,14. ManyEvaluating the impacts of snag dynamics in changing forests requires improving predictions for when snags fall based on a mechanistic understanding of how they fall ,16,17. AMany factors that influence snag fall rates vary with features of the environment where snags form. Regional climate, topography, and stand-level factors may all play roles. Across broad geographic gradients, climatic variation can influence both exposure to mechanical stress and wood decay. Higher wind loads promote tree fall ,21 and iBesides factors that vary with a snag’s surroundings, snags vary individually in many key ways that may influence how quickly they fall. Existing studies consistently recover effects for tree species identity, stem size, and existing decay –32. The Despite growing interest in how snag fall affects changing forests, the relative strength of key drivers across scales is unclear because existing analyses have focused on limited areas. According to a recent review, most tree fall studies have assessed dynamics in particular regions . StudiesTo identify broad-scale drivers of variation in snag fall rates, we modelled snag persistence in a forest inventory of almost 100,000 revisited snags, representing over 200 tree species growing from boreal to subtropical forests in over 30,000 plots across the eastern United States. We focused on comparing the strength of factors that control wood decay relative to those that expose snags to breaking forces. Specifically, we predicted that warmer temperatures, mesic hydrology, lower wood durability, and narrower stem diameters would accelerate snag fall by increasing wood decay, while high wood density, calm winds, gradual slopes, and dense surrounding stands would decrease snag fall by limiting the impact of breaking forces. We tested these predictions using a hierarchical model that linked the forest inventory data to independent datasets of species’ wood traits and climate. We used the data-informed model to project changes in snag fall rates as a function of changes in key drivers that are anticipated by mid-century. Finally, we incorporated snag fall projections into a simple forest C model to quantify the effect of temperature driven change on structure and function of an intensively studied forest.βo, in Among 99,213 standing dead trees observed in 31,411 locations spanning the eastern United States, 48.2% had fallen before resurvey, which occurred 5.01 ± 0.002 (s.e.) years later. The model correctly predicted 66.5% of all observations, improving the prediction odds ratio by a factor of four . The annualized baseline probability of a snag remaining standing in the eastern United States from 2000–2010, which corresponds to the inverse logit of the intercept term, AT, -1 yr-1). Accelerated snag fall with 2.4°C warming reduced snag C by 1.85 Mg ha-1  or ~ 22%. The associated decrease in NEP was smaller, about 1%  or 7 kg ha-1 yr-1. Both changes only reflected modelled differences in snag fall due to temperature, not potentially faster wood decay. The strong effect of temperature contrasted with the weak effect of average wind speed, a hypothesized predictor that was associated with slightly slower rates of snag fall in a subset of the data  and individual tree diameter (DIA) had the next strongest effects  is uniquely associated with exposure to breaking forces and its effect on snag fall was much weaker  diminished with advancing initial decay stage  compared to 35.8 years  for Maclura pomifera (Osage Orange). Snag persistence also varied widely among different geographic locations  in northwestern Canada, where snag fall rates were significantly higher in locations with faster wind speeds [The strong effect of temperature on snag fall contrasted with the weak effect of average wind speed, suggesting that variation in exposure to chronic mechanical stress poorly explains snag fall rates in eastern North America. This result contrasted with an analysis of drought-killed aspen  Program . In 1999WND) at 10 m altitude during the interval from 2000–2010 based on the National Atmospheric North American Regional Reanalaysis [2. We estimated average annual temperature (AT) over the 30-year interval from 1981 to 2010 based on PRISM, which estimates temperatures at a resolution of 800 m2 [We estimated climatic conditions at the reported location for each plot, which, for privacy reasons, is within 1 km of the actual plot location. We estimated average wind speed  and physiographic class. FIA defines physiographic class using a cross classification of forest habitats by hydrology and landform (QMD) of all living trees over 12.7 cm DBH. To estimate density, we calculated the number of living trees per hectare (TPH) over 12.7 cm DBH. At the scale of each individual snag, we included both DBH (DIA) and initial decay class (DC). FIA assigns snags to one of five progressive decay classes based on external conditions [For quantifying effects of factors that vary at the landscape, stand, and individual scales, we obtained additional covariates based on measurements taken during the FIA surveys. For topography, we included subplot slope (landform . At the nditions .DEN) from Miles & Smith [DURPinus sylvestris sapwood. Sapwood stakes from Pinus sylvestris resist impact breakage tests for about 5 years when suspended above ground in temperate conditions [To quantify species-level variation in wood traits, we compiled two additional datasets. We drew our estimates of species-level intact wood density , remains standing (Sijkl = 0 or 1) at the end of a census interval of length t (years) in a plot with a specified physiographic class j , spatial location k , and that had been previously identified as species l . We assumed Sijkl represents a single Bernoulli trial:pijkl as a linear function of covariates in a logistic regression framework:tijkl is a row vector of continuous predictors and ρ is a vector of associated effects. We covariate-centered the continuous predictors (t’s) by subtracting their mean such that βo is the annualized baseline log odds of remaining standing at “average” covariate conditions. We evaluated many candidate models including effects for tree DBH, stand QMD and TPH, plot SL, WND and AT  are constrained to sum to zero relative to the baseline persistence rate, βo, and To account for variation among physiographic classes, we modelled their effects as coming from a population of potential effects:k = 1, 2, …, p = 151 cells. This grid resolution was small enough to capture coarse spatial variation while being sufficiently large to include multiple plots in most cells. We then estimated spatially structured effects as a Gaussian conditional autoregressive process [k ≠ h:δk defines the cells that are spatially adjacent to the focal cell k, akh is a binary operator that equals 1 only if cell h is adjacent to k, ak+ is the number of cells adjoining k, and vk’s) to sum to zero so that the spatial autoregressive effects represent local deviations from the baseline persistence rate, βo.To model the spatially structured location effects, we assigned each location to a 1.7° x 1.7° grid consisting of  process , such thμl is the expected species-specific deviation from the baseline persistence probability, βo, and wl’s) to sum to zero and modeled the expected species-specific deviations (μl) as a function of wood durability (DUR):βDURis the effect of wood durability on the probability of snag persistence, and DUR estimates were available.For modelling species-level effects, we incorporated known differences in species’ wood traits using a hierarchical approach:DUR estimates. Rather than exclude them, we imputed their DUR values based on DUR estimates available for other species by assuming a hierarchical model for DUR based on a coarse taxonomy [DUR variable to the real number line; f(l) denotes family f associated with species l;μf is the family-level logit-transformed DUR value; and DUR among species within families. Then we modeled the family-level means as nested in division (i.e. gymnosperm versus angiosperm):d(f) denotes division d associated with family f; μd is the logit-transformed DUR for either angiosperms or gymnosperms; and DUR among families within divisions.Several species lacked taxonomy  whereby tion see  and rescNormal, for all of the continuous effect parameters , for the standard deviation terms associated with each variance component . Finally, following [Gamma prior for the precision of the conditional Gaussian distribution for the spatially structured effects . We also plotted the expected versus observed counts at every level of the three categorical predictors .We assessed model adequacy by comparing observed to expected snag persistence given the hierarchical model and posterior mean parameter estimates. For every observation ρ in βDUR in DUR) for some species, our estimate of the standardized effect size for this variable was averaged over 5000 draws from the posterior distribution for the missing DUR estimates. For the relative influence of categorical predictors, we also report the posterior mean and 95% CI for the standard deviation of the random effects .For continuous predictors  in the response of interest (y)—which is related to different indices of rates of snag persistence—with a specified change (ξ) in a predictor variable of interest (x) as:r denotes an individual tree, as indexed by all observed unique combinations of i, j, k, and l ; and X is a matrix of all other predictors excluding the focal predictor, x  is described in We expressed the difference in expected rates of snag persistence in two different ways. First we computed the average predictive difference in the annualized probability of remaining standing such that:per se without including changes in decay rate or potential feedbacks mediated by climate Click here for additional data file.S2 Appendix(DOCX)Click here for additional data file.S3 Appendix(DOCX)Click here for additional data file.S1 FigThick central curve corresponds to the posterior mean for the effect of average wind speed at 10m on decay class 2 snag persistence and the transparent curve overlay represents uncertainty by showing 100 curves drawn from the posterior distribution of the relevant parameters. The vertical spread of the violin plots represents the distributions of the predictor values for standing (S = 1) versus fallen trees (S = 0).(TIFF)Click here for additional data file.S2 FigSymbol diameter is scaled by species abundance.(PDF)Click here for additional data file.S3 FigSymbol diameter is scaled by the abundance of snags per grid cell.(PDF)Click here for additional data file.S4 FigSymbol diameter is scaled by the abundance of snags per physiographic class.(PDF)Click here for additional data file.S1 TableThe final column indicates subsets of the data where the 95% CI for the effect excluded 0.(DOCX)Click here for additional data file.S2 TableThe set of parameters selected via backwards elimination from the fully hierarchical model is indicated in bold.(XLSX)Click here for additional data file.S1 DatasetThe least resistant category was set as 0 and the most resistant category as 4. Sources that used ambiguous designations (e.g. non-resistant or slightly resistant) were given the average numeric score of the corresponding categories (i.e. 0.5).(XLSX)Click here for additional data file.


Solanum tuberosum L. The number of non-glandular trichomes on the leaf surface was counted on images made by light microscope while glandular trichomes and pavement cells were counted on images made under scanning electronic microscope. Volatile compounds were identified and quantified using coupled gas chromatography–mass spectrometry (GC-MS). Treated plants changed their pattern of biomass distribution; they had lower stem mass fraction and higher branch and leaf mass fraction than untouched plants. Size, weight and number of tubers were not significantly affected. Touching did not cause trichome damage nor change their total number on touched terminal leaves. However, on primary leaves the number of glandular trichomes and pavement cells was significantly increased. Touching altered the volatile emission of treated plants; they released higher quantities of the sesquiterpenes (E)-β-caryophyllene, germacrene D-4-ol and (E)-nerolidol, and lower quantities of the terpenes (E)-ocimene and linalool, indicating a systemic effect of the treatment. The odour of touched plants was significantly less preferred by the aphids Macrosiphum euphorbiae and Myzus persicae compared to odour of untouched plants. The results suggest that light contact may have a potential role in the detection of neighbouring plants and may affect plant-insect interactions.In natural habitats plants can be exposed to brief and light contact with neighbouring plants. This mechanical stimulus may represent a cue that induces responses to nearby plants. However, little is known about the effect of touching on plant growth and interaction with insect herbivores. To simulate contact between plants, a soft brush was used to apply light and brief mechanical stimuli to terminal leaves of potato  Coexistence with neighbouring plants is one of the most important challenges faced by plants. The impossibility of escape from unfavourable growing conditions makes plants’ survival directly dependent on their sensitivity and ability to respond to subtle cues from their surroundings. Plants detect neighbouring plants by a range of cues including light quality , 2, sounMimosa pudica and Venus fly trap Dionaea muscipula respond immediately [Mechanical stimuli are among the many cues to which plants can respond in order to quickly adapt their growth and enhance survival in a specific environment , 11. Plaediately , 13, whiediately , 15. Comediately .Agricultural crops are particularly interesting because they are exposed to many different types of mechanical stimuli that include bending , rubbingPlants are the basis of food webs, and the effects of plant responses to their environment can impact organisms at higher trophic levels. For example, herbivorous arthropods can be highly sensitive to changes in host plant status , and plaThe aim of the current study was to investigate whether brief mechanical stimuli affect plant morphology, physiology and interaction with insect herbivores. Using potato as a model, we tested the effect of touching on biomass allocation, glandular and non-glandular trichome occurrence, volatile chemical emissions and olfactory attraction of aphids.Solanum tuberosum L. (Solanaceae), one of the most important vegetable crops for human nutrition, cultivated in more than 100 countries [As a model plant we used cultivated potato ountries . Potato Macrosiphum euphorbiae (Thomas) and green peach aphid Myzus persicae (Sulzer) were grown in cultures under the same conditions as the test-plants but in different climate chambers. Macrosiphum euphorbiae was reared on potted potato plants Solanum tuberosum L. cv. King Edward while M. persicae was reared on potted rapeseed plants Brassica napus L. These aphid species are the most important pests of potato since they vector a number of plant -viruses [The potato aphid -viruses .Potato leaves were treated with an artificial light mechanical stimulus, starting 17 days after planting. A soft squirrel hair face brush (Rouge)  was used. The terminal leaves (the first leaves to come into contact with neighbours) on the top of each potato branch were gently touched from the leaf base to the top . The trePlants used for morphological analyses were touched as described above for 17 days. The period was based on the time needed by potato plants to start to develop flowers. Potato plants were cut at ground level using scissors and then separated into stem, branches, leaves and tubers. Tubers from each plant were carefully washed of soil particles under running water. Stem, branches and leaves from each plant were separately scanned using a dual lens scanner (Epson 4490Pro). Leaf surface, stem height, total branch length and average branch diameter were calculated using WinRHIZO (Regent Instruments), an image analysis system specifically designed for plant morphological measurements. Leaves, stems and branches from each plant were separately packed into labelled aluminium bags and dried for 48 h at 70°C to constant mass dry weights. All samples spent 24 h at room temperature before they were weighed. These data were used to calculate integral morphological indices. Specific leaf area (SLA) is calculated as proportion of leaf surface to leaf dry weight. Detailed information of the morphological indices is presented in 2) from sampled leaves. Samples were taken only on the areas between leaf veins [Trichomes on the adaxial leaf surface were examined to determine whether mechanical stimuli affected the number of trichomes both on the touched terminal leaves and on untouched primary leaves on the same branch. Leaf samples were carefully harvested from one randomly chosen branch without causing damage to the surfaces. From the selected branch we collected two leaf discs, one from the touched terminal leaf and one from the first primary leaf . The samaf veins .Light microscopy was used to determine the number of non-glandular trichomes on the leaf surface. Leaf discs obtained as described above were placed under a light microscope (Leica MZFLIII) under magnification 1.0 × to make a digital image for further analyses. The number of trichomes per leaf disc was counted using a graphical program CorelDraw  in which2. SEM characterization of leaf discs was carried out using a Hitachi TM-1000-μDeX environmental table top electron microscope, without coating of the samples in a charge-up reduction mode. Glandular trichomes were determined according to a classification previously described by Glass et al. [We used SEM to determine the frequency of glandular trichomes and pavement cells on the leaf discs, and to check whether glandular trichomes were damaged by the touching treatment. One randomly chosen branch was harvested from five control and five treated plants. Leaf discs from terminal and primary leaves (obtained by hollow cylinder as described above) were attached to double-sided adhesive carbon sticker (same size as leaf discs) previously mounted on aluminium SEM stubs. Each leaf disc spent a maximum of 15 minutes inside the SEM to avoid effects of higher temperatures on the trichomes. Images made under SEM covered an area of 1 mms et al.  (see dat2 per field). The number of whole pavement cells inside each square field was estimated in CorelDraw [Pavement cells were counted from SEM images where each recording was expressed as the mean cell number of three randomly chosen square fields per leaf  oven bags  were baked in an oven at 140°C for 2 h to remove contaminants. Glass tubes (5 mm diameter) containing Porapak Q  were rinsed with redistilled dichloromethane (DCM) and baked overnight under nitrogen flow at a temperature of 140°C and cooled to room temperature just before volatile collection started.-1 and a tube containing Porapak was inserted through a hole in the top of the bag and air drawn through via PTFE tubing connected to a pump (400 ml min-1). The difference in flow rates created a positive pressure to minimize entry of unfiltered air. A small hole cut in the top of the bag prevented build-up of pressure. Air was pumped in for 30 minutes prior to volatile collection to flush contaminating volatiles from the system. Volatile collection was carried out for 48h under controlled environmental conditions . Six replicates were carried out for each treatment, and two control treatments consisting of pots and soil without plants were included.Plants were subjected to touching treatment as described above for 8 days and control plants were untreated. Twenty four hours after this treatment, pots containing one potato plant were carefully enclosed in PET bags, taking care not to touch the leaves and shoots. Charcoal-filtered air was pumped in at 600 ml minCollected volatile compounds were extracted from PPQ traps with 750 μL of redistilled DCM into a 2 ml glass vial. An internal standard (1-nonene) was added to achieve a concentration of 50ng/μl in the sample. The samples were then reduced to 50 μl volume under a gentle nitrogen flow.–1 to 250°C. The carrier gas was helium with a flow rate of 1 mL min–1. Identifications were made by comparison of spectra with those of authentic samples in a commercial database (NIST 2008) and by comparing mass spectra and retention times with those of authentic standards where available. Only compounds appearing in the headspace of plants and not of pots with soil were quantified. Quantifications were made using the internal standard. Chemical standards were obtained as follows: (α)-pinene (98%), 6-methyl-5-hepten-2-one (99%), linalool (97%), methyl salicylate (98%), (+)-cyclosativene (99%) , (E)-β-caryophyllene , (E)-nerolidol (>85%Fluka), hexahydrofarnesyl acetone . Standards of (E)-ocimene, -4,8,12-Trimethyl-1,3,7,11-tridecatetraene (TMTT) and (E)-4,8-dimethyl-1,3,7-nonatriene (DMTT) were kindly provided by Dr Mike Birkett, Rothamsted Research, UK. Standards were not available for (α)-copaene, cadinene, germacrene D-4-ol, and two unidentified compounds assumed to be sesquiterpenes based on their retention indices and mass spectra ; unknown sesquiterpene 2 ). Germacrene D and germacrene D-4-ol have been previously reported as volatiles from potato leaves [Compounds were identified and quantified using coupled gas chromatography–mass spectrometry (GC-MS). A 2μl aliquot of each sample was injected onto a HP-5 column  housed in a 7890A gas chromatograph  coupled to an Agilent 5975C mass spectrometer. Ionization was by electron impact at 70 eV. The oven temperature was held at 30°C for 1min, then programmed at 10°C mino leaves  (see datPlants used for olfactory bioassays were exposed to touching treatment for 8 days as described above. Aphid olfactory preference was tested using a Perspex two way airflow olfactometer consisting of two stimulus zones (length 4 cm) directly opposite each other connected by a neutral central zone 2.5 × 2.5 cm) [ × 2.5 cmTested aphids were randomly collected from cultures using a fine paintbrush and placed in Petri dishes with moistened filter paper to prevent dehydration. Aphids were left in the bioassay room for 1 h to acclimatize prior to the experiments. A single aphid was introduced into the central zone of the olfactometer through a hole in the top and, after an adaptation period of 10 min, the position of the aphid in the arms, defined as a visit, was recorded at 3 min intervals over a 30-min period. The accumulated number of visits of a single aphid in a single arm after ten recordings was regarded as one replicate. Data were expressed as mean of individual aphid visits per olfactometer arm during the observation period of 30 min  was used to reduce the complexity of multivariate volatile data and to determine if there were overall differences between the peak areas of volatile compounds released from touched and control potato plants. The first two principal components PC1 and PC2 biplots of the total PC scores and loadings were used to visualize the results. T- test was used to determine significant differences between individual volatile compounds of treated and control plants. Data from olfactory bioassays were analysed with Wilcoxon’s matched pairs tests. Analyses were performed with the Dell Statistica software .The effects of brief and light touching on potato plants are presented in Leaf surface of touched plants was significantly higher than untouched plants . The num1, 36 = 3.78; P = 0.06; glandular F1, 16 = 4.37; P = 0.053). However, the interaction between treatment and the type of leaf was significant . Post hoc comparisons with Tukey’s HSD tests showed that primary untreated leaves on branches with touched terminal leaves had significantly higher numbers of both types of trichome than the equivalent leaves of control plants (non-glandular P = 0.012)   (glandul= 0.012) .1, 16 = 16.12; P = 0.00004). Post hoc comparisons with Tukey’s HSD tests showed that primary untreated leaves on branches with touched terminal leaves had significantly more pavement cells than the equivalent leaves of control plants (P = 0.005)  .Compounds identified in headspace collections from touched and control potato plants are shown in M. euphorbiae   (Both aphid species showed a significantly lower preference (measured as mean number of visits in odour field) for odour of touched potato plants compared to untreated plants: = 0.005)  and M. p = 0.02) .Our results show that one minute of light touching of potato leaves, can induce changes in biomass allocation and increase trichome production on adjacent leaves via a systemic effect. The touching also induced changes in the blend of volatile organic compounds released by plants, and this resulted in reduced olfactory attraction of insect herbivores. The results suggest that plant responses to even light contact can reprogram the growth of individual organs.Solanaceae have a well-developed capacity to distribute biomass among above ground parts [The ability of plants to modify their growth and morphology is fundamental to reproductive performance and fitness . A recennd parts . Our resnd parts , 39, 40.nd parts , 42, butnd parts  which mand parts , 44. In et al. who found no evidence for trade-offs between potato tuber yield and higher trichome production [The changes in trichome number observed on touched plants did not affect the number and average weight of fresh tubers per plant. These findings are consistent with those of Kaplan oduction . In contoduction . Plant soduction . In our oduction . Howeveroduction , 50, sugTrichomes on the leaf surface may play multiple functional roles important for plant growth. For example, increase in trichome production is negatively correlated with transpiration rate , 52, impThe brief and light touching resulted in significant changes in emission of volatile organic compounds by the plant. A similar effect has been found in maize and bean plants . Three sM. euphorbiae and M. persicae were less attracted to odour of touched plants than to odour of untouched plants. This could be due to aphid responses to individual compounds such as (E)-nerolidol, which has been shown to repel M. persicae [Plant volatiles are used by arthropod herbivores to locate hosts, discriminate hosts from non-hosts and gain information on plant quality and condition . For aphpersicae . It has persicae –63. Sincpersicae , the chapersicae , 64.Leaf trichomes may also serve as mechanoreceptors or sensors of insect movement on the leaf surface even in the absence of leaf rupture. Insect contact with trichomes on the leaf was shown to be sufficient to up-regulate defence transcripts and activate defences that provide extra protection against newly hatched larvae , 66. SucThe number of pavement cells was also increased by touching in our study, showing that the epidermal cells structure that provides mechanical strength and protection for inner tissues was also affected by the treatment. The pavement cells are a barrier for most piecing insects that puncture the leaves between epidermal cells such as aphids .Interaction between plants by light touching can regulate patterns of biomass distribution between different organs, enabling plants to establish a competitive above ground habitus. The capacity of potato plants to respond to touching may potentially be a trait that enables them to redirect growth into organs from which they may benefit most in competitive situation with neighbours. These responses appear to induce the side effects of making potato plants less attractive to aphids through the changes in released volatile blend. Our findings, together with other recent studies , 28, sugS1 Fig(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.