DataSheet_2_Is foliar tissue drying and grinding required for reliable and reproducible extraction of total inorganic nutrients? A comparative study of three tissue preparation methods.pdf

In response to abiotic and biotic stress or experimental treatment(s), foliar concentrations of inorganic nutrients and metabolites often change in concert to maintain a homeostatic balance within the cell’s environment thus allowing normal functions to carry on. Therefore, whenever possible, changes in cellular chemistry, metabolism, and gene expressions should be simultaneously evaluated using a common pool of tissue. This will help advance the knowledge needed to fill the gaps in our understanding of how these variables function together to maintain cellular homeostasis. Currently, foliar samples of trees for total inorganic nutrients and metabolic analyses are often collected at different times and are stored and processed in different ways before analyses. The objective of the present study was to evaluate whether a pool of wet (previously frozen) intact tissue that is used for metabolic and molecular work would also be suitable for analyses of foliar total inorganic nutrients. We compared quantities of nutrients extracted from wet-intact, dried-intact, and dried-ground tissues taken from a common pool of previously frozen foliage of black oak (Quercus velutina L.), sugar maple (Acer saccharum Marshall), red spruce (Picea rubens Sarg.), and white pine (Pinus strobus L.). With a few exceptions in the case of hardwoods where concentrations of total Ca, Mg, K, and P extracted from wet-intact tissue were significantly higher than dry tissue, data pooled across all collection times suggest that the extracted nutrient concentrations were comparable among the three tissue preparation methods and all for species. Based on the data presented here, it may be concluded that drying and grinding of foliage may not be necessary for nutrient analyses thus making it possible to use the same pool of tissue for total inorganic nutrients and metabolic and/or genomic analyses. To our knowledge, this is the first report on such a comparison.</p

DataSheet_1_Is foliar tissue drying and grinding required for reliable and reproducible extraction of total inorganic nutrients? A comparative study of three tissue preparation methods.pdf

In response to abiotic and biotic stress or experimental treatment(s), foliar concentrations of inorganic nutrients and metabolites often change in concert to maintain a homeostatic balance within the cell’s environment thus allowing normal functions to carry on. Therefore, whenever possible, changes in cellular chemistry, metabolism, and gene expressions should be simultaneously evaluated using a common pool of tissue. This will help advance the knowledge needed to fill the gaps in our understanding of how these variables function together to maintain cellular homeostasis. Currently, foliar samples of trees for total inorganic nutrients and metabolic analyses are often collected at different times and are stored and processed in different ways before analyses. The objective of the present study was to evaluate whether a pool of wet (previously frozen) intact tissue that is used for metabolic and molecular work would also be suitable for analyses of foliar total inorganic nutrients. We compared quantities of nutrients extracted from wet-intact, dried-intact, and dried-ground tissues taken from a common pool of previously frozen foliage of black oak (Quercus velutina L.), sugar maple (Acer saccharum Marshall), red spruce (Picea rubens Sarg.), and white pine (Pinus strobus L.). With a few exceptions in the case of hardwoods where concentrations of total Ca, Mg, K, and P extracted from wet-intact tissue were significantly higher than dry tissue, data pooled across all collection times suggest that the extracted nutrient concentrations were comparable among the three tissue preparation methods and all for species. Based on the data presented here, it may be concluded that drying and grinding of foliage may not be necessary for nutrient analyses thus making it possible to use the same pool of tissue for total inorganic nutrients and metabolic and/or genomic analyses. To our knowledge, this is the first report on such a comparison.</p

Table_1_Seasonal changes in foliar calcium oxalate concentrations in conifer and hardwood trees: a potentially bioavailable source of cellular calcium and/or oxalate under stress.DOCX

The present study compared seasonal changes in the concentrations of calcium oxalate (CaOx) crystals and total calcium (Ca) in the foliage of red spruce (Picea rubens Sarg.), white pine (Pinus strobus L.), black oak (Quercus velutina L.), and sugar maple (Acer saccharum Marshall) trees. Samples were collected from the same four replicate trees of each species starting in June 2014 through September 2015 for a total of six times for conifers and four times for hardwoods. Calcium oxalate was extracted from tissues using a method developed in our laboratory in 2015. The purity of the extracted CaOx was indicated by an r2 of 0.98 between Ca and oxalate (Ox) for the data pooled across all species and all sampling times. As expected, the concentrations of CaOx varied between species. We hypothesized that the only role of CaOx crystals is to bind excess Ca, so based on this hypothesis the concentrations of CaOx would increase over the growing season both in conifer and hardwood trees, and in conifers, its quantities would be higher in the older relative to the younger needles. However, we found, that for most species, CaOx concentrations were not significantly different from each other for all collection times. In addition, relative to total Ca, the percent of Ca that existed in the form of CaOx varied widely with species, time of collection within a species, and needle age. Thus, no specific trend was observed for CaOx accumulations with changes in seasons. Concentrations of CaOx were indeed higher in older spruce and pine needles. Based on the available literature on this topic and our data, this could mean that CaOx amounts are dynamic and are continuously being adjusted according to the metabolic needs of cells for either Ca or Ox while still performing the function of shedding off excess Ca.</p

Table_1_Regulatory Roles of Small Non-coding RNAs in Sugar Beet Resistance Against Beet curly top virus.XLSX

Beet curly top virus (BCTV) mediated yield loss in sugar beets is a major problem worldwide. The circular single-stranded DNA virus is transmitted by the beet leafhopper. Genetic sources of BCTV resistance in sugar beet are limited and commercial cultivars rely on chemical treatments versus durable genetic resistance. Phenotypic selection and double haploid production have resulted in sugar beet germplasm (KDH13; 13 and KDH4-9; 4) that are highly resistant to BCTV. The molecular mechanism of resistance to the virus is unknown, especially the role of small non-coding RNAs (sncRNAs) during early plant–viral interaction. Using the resistant lines along with a susceptible line (KDH19-17; 19), we demonstrate the role of sugar beet microRNAs (miRNAs) in BCTV resistance during early infection stages when symptoms are not yet visible. The differentially expressed miRNAs altered the expression of their corresponding target genes such as pyruvate dehydrogenase (EL10Ac1g02046), carboxylesterase (EL10Ac1g01087), serine/threonine protein phosphatase (EL10Ac1g01374), and leucine-rich repeats (LRR) receptor-like (EL10Ac7g17778), that were highly expressed in the resistant lines versus susceptible lines. Pathway enrichment analysis of the miRNA target genes showed an enrichment of genes involved in glycolysis/gluconeogenesis, galactose metabolism, starch, and sucrose metabolism to name a few. Carbohydrate analysis revealed altered glucose, galactose, fructose, and sucrose concentrations in the infected leaves of resistant versus susceptible lines. We also demonstrate differential regulation of BCTV derived sncRNAs in the resistant versus susceptible lines that target sugar beet genes such as LRR (EL10Ac1g01206), 7-deoxyloganetic acid glucosyltransferase (EL10Ac5g12605), and transmembrane emp24 domain containing (EL10Ac6g14074) and altered their expression. In response to viral infection, we found that plant derived miRNAs targeted BCTV capsid protein/replication related genes and showed differences in expression among resistant and susceptible lines. The data presented here demonstrate the contribution of miRNA mediated regulation of metabolic pathways and cross-kingdom RNA interference (RNAi) in sugar beet BCTV resistance.</p

Additional file 1: Figure S1. of Genetic manipulation of putrescine biosynthesis reprograms the cellular transcriptome and the metabolome

The pathway for the biosynthesis of polyamines and related metabolites starting from the assimilation of nitrogen (Adapted from Majumdar et al. 2016). Figure S2. (A, B) - Quality control scatter plots showing expression level for data that passed CV and dye-swap tests. Red spots indicate data that passed statistical analysis for differential expression between the control and the HP cells. Figure S3. The loading plots (S-plot) of the OPLS-DA results for the control and HP cell extracts on days 2 (A), 4 (B), and 6 (C). In the S-plot, each point represents a single metabolite (marker). The x-axis shows the variable contributions. The farther away a data point is from the 0 value, the more it contributes to sample variance. The y-axis shows the sample correlations within the same sample group. The farther away a metabolite is from the 0 value, the better is its correlation from injection to injection. As a result, the metabolites on both ends of the S-shaped curve represent the leading contributing ions from each sample group. The OPLS-DA is a multivariate analysis model which separates the systematic variation in X into two parts, one that is linearly related (and therefore predictive) to Y and one that is orthogonal to Y (unrelated); the Y-predictive/related part represents the between-class variation, the Y-orthogonal (ToPo) part constitutes the within-class variation. (DOCX 1653 kb

Data_Sheet_1_Regulatory Roles of Small Non-coding RNAs in Sugar Beet Resistance Against Beet curly top virus.PDF

Beet curly top virus (BCTV) mediated yield loss in sugar beets is a major problem worldwide. The circular single-stranded DNA virus is transmitted by the beet leafhopper. Genetic sources of BCTV resistance in sugar beet are limited and commercial cultivars rely on chemical treatments versus durable genetic resistance. Phenotypic selection and double haploid production have resulted in sugar beet germplasm (KDH13; 13 and KDH4-9; 4) that are highly resistant to BCTV. The molecular mechanism of resistance to the virus is unknown, especially the role of small non-coding RNAs (sncRNAs) during early plant–viral interaction. Using the resistant lines along with a susceptible line (KDH19-17; 19), we demonstrate the role of sugar beet microRNAs (miRNAs) in BCTV resistance during early infection stages when symptoms are not yet visible. The differentially expressed miRNAs altered the expression of their corresponding target genes such as pyruvate dehydrogenase (EL10Ac1g02046), carboxylesterase (EL10Ac1g01087), serine/threonine protein phosphatase (EL10Ac1g01374), and leucine-rich repeats (LRR) receptor-like (EL10Ac7g17778), that were highly expressed in the resistant lines versus susceptible lines. Pathway enrichment analysis of the miRNA target genes showed an enrichment of genes involved in glycolysis/gluconeogenesis, galactose metabolism, starch, and sucrose metabolism to name a few. Carbohydrate analysis revealed altered glucose, galactose, fructose, and sucrose concentrations in the infected leaves of resistant versus susceptible lines. We also demonstrate differential regulation of BCTV derived sncRNAs in the resistant versus susceptible lines that target sugar beet genes such as LRR (EL10Ac1g01206), 7-deoxyloganetic acid glucosyltransferase (EL10Ac5g12605), and transmembrane emp24 domain containing (EL10Ac6g14074) and altered their expression. In response to viral infection, we found that plant derived miRNAs targeted BCTV capsid protein/replication related genes and showed differences in expression among resistant and susceptible lines. The data presented here demonstrate the contribution of miRNA mediated regulation of metabolic pathways and cross-kingdom RNA interference (RNAi) in sugar beet BCTV resistance.</p

Additional file 2: of Genetic manipulation of putrescine biosynthesis reprograms the cellular transcriptome and the metabolome

Methods for RNA extraction, cDNA preparation and labeling, microarray hybridization and processing, and metabolomic analysis. Supplemental data Microarrays: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE79420 . Metabolomic Data: https://mynotebook.labarchives.com/share/ulav72/MjIuMXwxNzEzMTkvMTcvVHJlZU5vZGUvMzg1Mzg2MTkxNHw1Ni4x . (PDF 451 kb

Table_6_Regulatory Roles of Small Non-coding RNAs in Sugar Beet Resistance Against Beet curly top virus.XLSX

Beet curly top virus (BCTV) mediated yield loss in sugar beets is a major problem worldwide. The circular single-stranded DNA virus is transmitted by the beet leafhopper. Genetic sources of BCTV resistance in sugar beet are limited and commercial cultivars rely on chemical treatments versus durable genetic resistance. Phenotypic selection and double haploid production have resulted in sugar beet germplasm (KDH13; 13 and KDH4-9; 4) that are highly resistant to BCTV. The molecular mechanism of resistance to the virus is unknown, especially the role of small non-coding RNAs (sncRNAs) during early plant–viral interaction. Using the resistant lines along with a susceptible line (KDH19-17; 19), we demonstrate the role of sugar beet microRNAs (miRNAs) in BCTV resistance during early infection stages when symptoms are not yet visible. The differentially expressed miRNAs altered the expression of their corresponding target genes such as pyruvate dehydrogenase (EL10Ac1g02046), carboxylesterase (EL10Ac1g01087), serine/threonine protein phosphatase (EL10Ac1g01374), and leucine-rich repeats (LRR) receptor-like (EL10Ac7g17778), that were highly expressed in the resistant lines versus susceptible lines. Pathway enrichment analysis of the miRNA target genes showed an enrichment of genes involved in glycolysis/gluconeogenesis, galactose metabolism, starch, and sucrose metabolism to name a few. Carbohydrate analysis revealed altered glucose, galactose, fructose, and sucrose concentrations in the infected leaves of resistant versus susceptible lines. We also demonstrate differential regulation of BCTV derived sncRNAs in the resistant versus susceptible lines that target sugar beet genes such as LRR (EL10Ac1g01206), 7-deoxyloganetic acid glucosyltransferase (EL10Ac5g12605), and transmembrane emp24 domain containing (EL10Ac6g14074) and altered their expression. In response to viral infection, we found that plant derived miRNAs targeted BCTV capsid protein/replication related genes and showed differences in expression among resistant and susceptible lines. The data presented here demonstrate the contribution of miRNA mediated regulation of metabolic pathways and cross-kingdom RNA interference (RNAi) in sugar beet BCTV resistance.</p

Table_2_Regulatory Roles of Small Non-coding RNAs in Sugar Beet Resistance Against Beet curly top virus.XLSX

Beet curly top virus (BCTV) mediated yield loss in sugar beets is a major problem worldwide. The circular single-stranded DNA virus is transmitted by the beet leafhopper. Genetic sources of BCTV resistance in sugar beet are limited and commercial cultivars rely on chemical treatments versus durable genetic resistance. Phenotypic selection and double haploid production have resulted in sugar beet germplasm (KDH13; 13 and KDH4-9; 4) that are highly resistant to BCTV. The molecular mechanism of resistance to the virus is unknown, especially the role of small non-coding RNAs (sncRNAs) during early plant–viral interaction. Using the resistant lines along with a susceptible line (KDH19-17; 19), we demonstrate the role of sugar beet microRNAs (miRNAs) in BCTV resistance during early infection stages when symptoms are not yet visible. The differentially expressed miRNAs altered the expression of their corresponding target genes such as pyruvate dehydrogenase (EL10Ac1g02046), carboxylesterase (EL10Ac1g01087), serine/threonine protein phosphatase (EL10Ac1g01374), and leucine-rich repeats (LRR) receptor-like (EL10Ac7g17778), that were highly expressed in the resistant lines versus susceptible lines. Pathway enrichment analysis of the miRNA target genes showed an enrichment of genes involved in glycolysis/gluconeogenesis, galactose metabolism, starch, and sucrose metabolism to name a few. Carbohydrate analysis revealed altered glucose, galactose, fructose, and sucrose concentrations in the infected leaves of resistant versus susceptible lines. We also demonstrate differential regulation of BCTV derived sncRNAs in the resistant versus susceptible lines that target sugar beet genes such as LRR (EL10Ac1g01206), 7-deoxyloganetic acid glucosyltransferase (EL10Ac5g12605), and transmembrane emp24 domain containing (EL10Ac6g14074) and altered their expression. In response to viral infection, we found that plant derived miRNAs targeted BCTV capsid protein/replication related genes and showed differences in expression among resistant and susceptible lines. The data presented here demonstrate the contribution of miRNA mediated regulation of metabolic pathways and cross-kingdom RNA interference (RNAi) in sugar beet BCTV resistance.</p

Additional file 3: Table S1. of Genetic manipulation of putrescine biosynthesis reprograms the cellular transcriptome and the metabolome

Summary of changes in expression of genes involved in polyamine metabolism. For abbreviations see Fig. 1. Sequences for AS, CARB, DAO, NAGK, NAGPR, NAOD, NAOGAcT, ODC, OTC and SPDS were not represented on the microarray. Table S2. Functional clustering of gene models showing significant (P ≤ 0.05) differences (≥2 fold) between the HP and the control cell lines on both day 3 and day 5. Table S3. Functional clustering of gene models showing significant (P ≤ 0.05) differences (≥2 fold) between the HP and the control cell lines on day 3 only. Table S4. Functional clustering of gene models showing significant (P ≤ 0.05) differences (≥2 fold) between the HP and the control cell lines on day 5 only. Table S5. List of metabolites that were positively identified in poplar control and HP cell lines. ND = not detectable. Values that are significantly different (P ≤ 0.05) in the HP cells from the corresponding control cells on a given day are marked in bold. (DOCX 103 kb