Growth and nutrient absorption of Cape Gooseberry (Physalis Peruviana L.) in soilless culture

"This is an Author's Accepted Manuscript of an article published in [include the complete citation information for the final version of the article as published in the Journal of Plant Nutrition 2015 March, available online at: http://www.tandfonline.com/10.1080/01904167.2014.934474."Cape gooseberry (Physalis peruviana L.) is a solanaceous plant. The growth and time-course of nutrient accumulation of the plant and its partitioning between roots, stems, leaves, and fruits were examined. The study was conducted analyzing two nutrient solutions in soilless culture under greenhouse conditions during two consecutive seasons. The macronutrient contents were analyzed. On average, the yield was 8.9 t.ha(-1). Growth of the plant until 90 d after transplanting obeys an exponential function of time and the relative growth rate for this period was determined. Nitrogen (N) was the element that showed the highest concentration, corresponding to leaves (4.67%), followed by potassium (K) in stems (4.46%). The highest accumulations of N, phosphorous (P), calcium (Ca), and magnesium (Mg) were found in leaves and of K in the stems. Potassium showed the highest nutrient accumulation (29 g.plant(-1)) and the highest specific uptake rate.Torres Rubio, JF.; Pascual Seva, N.; San Bautista Primo, A.; Pascual España, B.; López Galarza, SV.; Alagarda Pardo, J.; Maroto Borrego, JV. (2015). Growth and nutrient absorption of Cape Gooseberry (Physalis Peruviana L.) in soilless culture. Journal of Plant Nutrition. 38(4):485-496. doi:10.1080/01904167.2014.934474S485496384Bellaloui, N., & Brown, P. H. (1998). Plant and Soil, 198(2), 153-158. doi:10.1023/a:1004343031242Bennett, J. P., Oshima, R. J., & Lippert, L. F. (1979). Effects of ozone on injury and dry matter partitioning in pepper plants. 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Nutrient uptake of pepino plants in soilless cultivation. The Journal of Horticultural Science and Biotechnology, 76(3), 338-343. doi:10.1080/14620316.2001.11511373Heuvelink, E., Bakker, M. J., Elings, A., Kaarsemaker, R. C., & Marcelis, L. F. M. (2005). EFFECT OF LEAF AREA ON TOMATO YIELD. Acta Horticulturae, (691), 43-50. doi:10.17660/actahortic.2005.691.2Leskovar, D. I., & Cantliffe, D. J. (1993). Comparison of Plant Establishment Method, Transplant, or Direct Seeding on Growth and Yield of Bell Pepper. Journal of the American Society for Horticultural Science, 118(1), 17-22. doi:10.21273/jashs.118.1.17Marcelis, L. F. M. (1993). Fruit growth and biomass allocation to the fruits in cucumber. 1. Effect of fruit load and temperature. Scientia Horticulturae, 54(2), 107-121. doi:10.1016/0304-4238(93)90059-yPuente, L. A., Pinto-Muñoz, C. A., Castro, E. S., & Cortés, M. (2011). Physalis peruviana Linnaeus, the multiple properties of a highly functional fruit: A review. Food Research International, 44(7), 1733-1740. doi:10.1016/j.foodres.2010.09.034Radford, P. J. (1967). Growth Analysis Formulae - Their Use and Abuse1. Crop Science, 7(3), 171. doi:10.2135/cropsci1967.0011183x000700030001xRamadan, M. F., & Moersel, J. T. (2007). Impact of enzymatic treatment on chemical composition, physicochemical properties and radical scavenging activity of goldenberry (Physalis peruviana L.) juice. Journal of the Science of Food and Agriculture, 87(3), 452-460. doi:10.1002/jsfa.2728Ramadan, M. F., & Moersel, J.-T. (2009). Oil extractability from enzymatically treated goldenberry (Physalis peruvianaL.) pomace: range of operational variables. International Journal of Food Science & Technology, 44(3), 435-444. doi:10.1111/j.1365-2621.2006.01511.xSalazar, M. R., Jones, J. W., Chaves, B., & Cooman, A. (2008). A model for the potential production and dry matter distribution of Cape gooseberry (Physalis peruviana L.). Scientia Horticulturae, 115(2), 142-148. doi:10.1016/j.scienta.2007.08.015Scholberg, J., McNeal, B. L., Jones, J. W., Boote, K. J., Stanley, C. D., & Obreza, T. A. (2000). Growth and Canopy Characteristics of Field-Grown Tomato. Agronomy Journal, 92(1), 152. doi:10.2134/agronj2000.921152xTrinchero, G. D., Sozzi, G. O., Cerri, A. M., Vilella, F., & Fraschina, A. A. (1999). Ripening-related changes in ethylene production, respiration rate and cell-wall enzyme activity in goldenberry (Physalis peruviana L.), a solanaceous species. Postharvest Biology and Technology, 16(2), 139-145. doi:10.1016/s0925-5214(99)00011-3Turner, A. (1994). Dry Matter Assimilation and Partitioning in Pepper Cultivars Differing in Susceptibility to Stress-induced Bud and Flower Abscission. Annals of Botany, 73(6), 617-622. doi:10.1006/anbo.1994.1077WILLIAMS, R. F. (1946). The Physiology of Plant Growth with Special Reference to the Concept of Net Assimilation Rate. Annals of Botany, 10(1), 41-72. doi:10.1093/oxfordjournals.aob.a083119Zapata, J.L., A. Saldarriaga, M. Londoño, and C. Díaz. 2002. Cape gooseberry Management in Colombia. Antioquia, Colombia: Rionegro, Programa Nacional de Transferencia de Tecnología Agropecuaria - Corpoica Regional Cuatro (in Spanish).Zerihun, A. (2000). Compensatory Roles of Nitrogen Uptake and Photosynthetic N-use Efficiency in Determining Plant Growth Response to Elevated CO2: Evaluation Using a Functional Balance Model. Annals of Botany, 86(4), 723-730. doi:10.1006/anbo.2000.123

Evaluation of regression models in metabolic physiology: predicting fluxes from isotopic data without knowledge of the pathway

This study explores the ability of regression models, with no knowledge of the underlying physiology, to estimate physiological parameters relevant for metabolism and endocrinology. Four regression models were compared: multiple linear regression (MLR), principal component regression (PCR), partial least-squares regression (PLS) and regression using artificial neural networks (ANN). The pathway of mammalian gluconeogenesis was analyzed using [U−(13)C]glucose as tracer. A set of data was simulated by randomly selecting physiologically appropriate metabolic fluxes for the 9 steps of this pathway as independent variables. The isotope labeling patterns of key intermediates in the pathway were then calculated for each set of fluxes, yielding 29 dependent variables. Two thousand sets were created, allowing independent training and test data. Regression models were asked to predict the nine fluxes, given only the 29 isotopomers. For large training sets (>50) the artificial neural network model was superior, capturing 95% of the variability in the gluconeogenic flux, whereas the three linear models captured only 75%. This reflects the ability of neural networks to capture the inherent non-linearities of the metabolic system. The effect of error in the variables and the addition of random variables to the data set was considered. Model sensitivities were used to find the isotopomers that most influenced the predicted flux values. These studies provide the first test of multivariate regression models for the analysis of isotopomer flux data. They provide insight for metabolomics and the future of isotopic tracers in metabolic research where the underlying physiology is complex or unknown

Metabolomic approaches reveal that phosphatidic and phosphatidyl glycerol phospholipids are major discriminatory non-polar metabolites in responses by Brachypodium distachyon to challenge by Magnaporthe grisea.

Metabolomic approaches were used to elucidate some key metabolite changes occurring during interactions of Magnaporthe grisea– the cause of rice blast disease – with an alternate host, Brachypodium distachyon. Fourier‐transform infrared (FT‐IR) spectroscopy provided a high‐throughput metabolic fingerprint of M. grisea interacting with the B. distachyon accessions ABR1 (susceptible) and ABR5 (resistant). Principal component–discriminant function analysis (PC‐DFA) allowed the differentiation between developing disease symptoms and host resistance. Alignment of projected ‘test‐set’ on to ‘training‐set’ data indicated that our experimental approach produced highly reproducible data. Examination of PC‐DFA loading plots indicated that fatty acids were one chemical group that discriminated between responses by ABR1 and ABR5 to M. grisea. To identify these, non‐polar extracts of M. grisea‐challenged B. distachyon were directly infused into an electrospray ionization mass spectrometer (ESI‐MS). PC‐DFA indicated that M. grisea‐challenged ABR1 and ABR5 were differentially clustered away from healthy material. Subtraction spectra and PC‐DFA loadings plots revealed discriminatory analytes (m/z) between each interaction and seven metabolites were subsequently identified as phospholipids (PLs) by ESI‐MS‐MS. Phosphatidyl glycerol (PG) PLs were suppressed during both resistant and susceptible responses. By contrast, different phosphatidic acid PLs either increased or were reduced during resistance or during disease development. This suggests considerable and differential PL processing of membrane lipids during each interaction which may be associated with the elaboration/suppression of defence mechanisms or developing disease symptoms