Pedro Pablo Gallego

A role for glutamate decarboxylase during tomato ripening: the characterisation of a cDNA encoding a putative glutamate decarboxylase with a calmodulin-binding site

A tomato fruit cDNA library was differentially screened to identify mRNAs present at higher levels in fruit of the tomato ripening mutant rin (ripening inhibitor). Complete sequencing of a unique clone ERT D1 revealed an open reading frame with homology to several glutamate decarboxylases. The deduced polypeptide sequence has 80% overall amino acid sequence similarity to a Petunia hybrida glutamate decarboxylase (petGAD) which carries a calmodulin-binding site at its carboxyl terminus and ERT D1 appears to have a similar domain. ERT D1 mRNA levels peaked at the first visible sign of fruit colour change during normal tomato ripening and then declined, whereas in fruit of the ripening impaired mutant, rin, accumulation of this mRNA continued until at least 14 days after the onset of ripening. This mRNA was present at much lower levels in other tissues, such as leaves, roots and stem, and was not increased by wounding. Possible roles for GAD, and its product γ-aminobutyric acid (GABA) in fruit, are discussed.

Artificial neural networks as an alternative to the traditional statistical methodology in plant research.

In this work, we compared the unique artificial neural networks (ANNs) technology with the usual statistical analysis to establish its utility as an alternative methodology in plant research. For this purpose, we selected a simple in vitro proliferation experiment with the aim of evaluating the effects of light intensity and sucrose concentration on the success of the explant proliferation and finally, of optimizing the process taking into account any influencing factors. After data analysis, the traditional statistical procedure and ANNs technology both indicated that low light treatments and high sucrose concentrations are required for the highest kiwifruit microshoot proliferation under experimental conditions. However, this particular ANNs software is able to model and optimize the process to estimate the best conditions and does not need an extremely specialized background. The potential of the ANNs approach for analyzing plant biology processes, in this case, plant tissue culture data, is discussed.

Improving knowledge of plant tissue culture and media formulation by neurofuzzy logic: a practical case of data mining using apricot databases.

Plant tissue growth can be regulated and controlled via culture media composition. A number of different laborious and time-consuming approaches have been used to attempt development of optimized media for a wide range of species and applications. However, plant tissue culture is a very complex task, and the identification of the influences of process factors such as mineral nutrients or plant growth regulators on a wide spectrum of growth responses cannot always well comprehended. This study employs a new approach, data mining, to uncover and integrate knowledge hidden in multiple data from plant tissue culture media formulations using apricot micropropagation databases as an example. Neurofuzzy logic technology made it possible to identify relationships among several factors (cultivars, mineral nutrients and plant growth regulators) and growth parameters (shoots number, shoots length and productivity), extracting biologically useful information from each database and combining them to create a model. The IF-THEN rule sets generated by neurofuzzy logic were completely in agreement with previous findings based on statistical analysis, but advantageously generated understandable and reusable knowledge that can be applied in future plant tissue culture media optimization.

Vascular-specific expression of GUS and GFP reporter genes in transgenic grapevine (Vitis vinifera L. cv. Albariño) conferred by the EgCCR promoter of Eucalyptus gunnii

In the view of the economic importance of grapevine and the increasing threaten represented by vascular diseases, transgenic grapevine with enhanced tolerance could represent an attractive opportunity. Hitherto, constitutive promoters have been used generally to study the effects of transgene expression in grapevine. Given the fact that constitutive gene expression may be harmful to the host plant, affecting plant growth and development, the use of tissue -specific promoters restricting gene expression to tissues of interest and at given developmental stages could be more appropriate. For this purpose, we decided to study in grapevine the activity of the Eucalyptus gunnii CCR promoter that was previously reported to be vascular-preferential. We transformed grapevine with the Sonication assisted Agrobacterium-mediated transformation (SAAT) method and a construct where both GUS and GFP (green fluorescent protein) marker genes were under control of the EgCCR promoter. High GUS and GFP activities were found to be associated with the newly formed vascular tissues in stems, leaves and petioles of transformed grapevine, suggesting a preferential activity of the EgCCR promoter in the vascular tissues of grapevine. These results suggest the tissue-specificity of this promoter from eucalyptus is conserved in grapevine and that it could be used to drive expression of defense genes in order to enhance resistance against vascular pathogens.

Artificial Neural Networks Technology to Model and Predict Plant Biology Process

The recent and significant technological advances applied to biology places the researchers in front of an unprecedented new influx of large data set from different levels as genomics, transcriptomics, proteomics, metabolomics and ionomics (Hirai et al., 2004; Belostotsky & Rose, 2005; Schauer & Fernie, 2006; Kliebenstein, 2010). Thousands of data sets including millions of measurements have been generated, and moreover, most are freely available for plant researchers worldwide from plant specific databases, as for example the whole sequencing of different plant genomes like rice, Arabidopsis, poplar, papaya, grapevine and others... (Jaillon et al., 2007; Ming et al., 2008; Brady & Provart, 2009). There is a wide concern of integrating molecular, cellular, histological, biochemical, genetic and physiological information in plant biology (Katagiri, 2003; Thum et al., 2003; Trewavas 2006; Boone et al., 2007; Alvarez-Buylla et al., 2007) and also in other related fields such as crop improvement (Hammer et al., 2002), ecology (Hilbert & Ostendorf, 2001; Jimenez et al., 2008) and biological engineering (Huang, 2009). Biological processes are both time variant and nonlinear in nature, and their complexity can be understood as the composition of many different and interacting elements governed by non-deterministic rules and influenced by external factors (Coruzzi et al., 2009, Gago et al., 2009). Commonly, most of biological interactions cannot be elucidated by a simple stepwise algorithm or a precise formula, particularly when the data set are complex, noisy, vague, uncompleted or formed by different kind of data (Prasad & Dutta Gupta, 2008; Gago et al., 2010a). It is important to point out that many times the behaviour of a biological system over a time period is difficult to understand and interpret and additionally, genetic and environmental factors show a very high degree of intraand inter-individual variability, yielding a wide spectrum of biological responses (Karim et al., 1997; Guegan et al., 1998). The Scientific community agrees with the idea that plant biology requires more efforts in developing platforms to integrate multidimensional data and to derive models for describing biological interactions in plants (Kitano, 2002; Hammer et al., 2004; Struik et al., 2005; Tardieu, 2003; Yuan et al., 2008; Brady & Provart, 2009). In this sense, more efforts are recommended to shift our view from a reductionist way to a systems-level view. This concept can be illustrated by the Coruzzi & co-workers (2009) example of the painting “La Grande Jatte” by the

Strengths of artificial neural networks in modeling complex plant processes.

Commonly, simple mathematical models can not be used to describe exactly the biological processes due to their higher complexity. In fact, most biological interactions cannot be elucidated by a simple stepwise algorithm or a precise formula, particularly when the data are complex or noisy. ANNs allows an accurate description of those kind of biological processes in plant science, offering new advantages over traditional treatments as the possibility of a model, prediction and optimize results. Different kind of data can be analyzed using a unique and “easy to use” technology. Researchers with a high specialized mathematical background are not required and ANNs offer the possibility of achieving the whole view of the experimental study with a limited number of experiments and costs. Additionally, it is possible to add new inputs and outputs to the database to reach a new understanding.

Design of tissue culture media for efficient Prunus rootstock micropropagation using artificial intelligence models

Establishing optimized protocols for micropropagation of some economical plants, such as Prunus sp., is still one of the most important challenges for in vitro plant culture researchers. As an example, micropropagation of GF677 hybrid rootstocks (peachxa0×xa0almond) are extremely dependent on the medium ingredients and a large undesirable proportion of GF677 shoots need to be discarded as a result of hyperhydricity and chlorosis. In this study, an artificial intelligence technique—specifically neurofuzzy logic—has been employed, as a modeling tool, to increase knowledge on the effect of 8 ion macronutrients (NH4+, NO3−, Ca2+, K+, Mg2+, SO42−, PO42− and Na+; as inputs) on three growth parameters (outputs): total number of shoots per explant, healthy number of shoots per explant, and their bud number. The model delivered new insights, by three sets of IF–THEN rules, pinpointing the key role of NO3− and their interactions (NO3−xa0×xa0Ca2+ and NO3−xa0×xa0Ca2+xa0×xa0K+) on all growth parameters measured. All growth parameters showed a high correlation ratio between experimental and predicted values being 77.48, 91.78 and 90.78 for total shoots, healthy number and bud number, respectively. Regression coefficients higher than 77xa0% together with statistical significant ANOVA (pxa0<xa00.01) indicated good performance of neurofuzzy logic models. Moreover, The model also can be used for inferring the best combination of ion concentrations to obtain high quality GF677 micropropagated shoots. In conclusion, we assess the utility of neurofuzzy logic technology in modeling complex databases, identifying new complex interactions among macronutrients, and inferring new results and valuable knowledge, which can be applied to design new plant tissue culture media and improve plant micropropagation.

Genetic transformation of Eucalyptus globulus using the vascular-specific EgCCR as an alternative to the constitutive CaMV35S promoter

Strong constitutive promoters, such as CaMV35S, are widely used for plant transformation, but undesirable phenotypic changes have been reported when used to drive biotic stress tolerance and/or for modifying lignin content. The promoter of the eucalyptus cinnamoyl CoA reductase (CCR), a key enzyme of the lignin biosynthetic pathway, was shown to be preferentially expressed in vascular tissues both in herbaceous and woody transgenic plants but not eucalyptus. In this work, we transformed Eucalyptus globulus with the EgCCR promoter governing both β-glucuronidase (GUS) and GFP activity patterns. No statistical differences were found between the survival rate and percentage of GUS positive shoots between eucalyptus transformed with either the constitutive CaMV35Sor with the EgCCR promoter. The EgCCR transformed plantlets exhibited high GUS expression levels associated with the vascular tissues opening the possibility of targeting vascular-associated traits such as lignin content or vascular pathogen resistance in adult elite plants of eucalyptus while avoiding the undesirable pleiotropic effects caused by strong constitutive promoters.

Artificial neural networks modeling the in vitro rhizogenesis and acclimatization of Vitis vinifera L.

This study employs artificial neural networks (ANNs) to create a model to identify relationships between variables affecting the in vitro rhizogenesis and acclimatization of two cultivars of Vitis vinifera L. Albariño and Mencía. The effects of three factors (inputs), the type of cultivar, concentration and exposure time to indolebutyric acid (IBA), on the success of in vitro rhizogenesis and acclimatization were evaluated. The developed model, using ANNs software, was assessed using a separate set of validation data and was in good agreement with the observed results. Exposure time to IBA was found to have the dominant role in influencing the height of acclimatized plantlets. ANNs can be a useful tool for modeling different complex processes and data sets, in plant tissue cultures or, more generally, in plant biology.

Modeling the Effects of Light and Sucrose on In Vitro Propagated Plants: A Multiscale System Analysis Using Artificial Intelligence Technology

Background Plant acclimation is a highly complex process, which cannot be fully understood by analysis at any one specific level (i.e. subcellular, cellular or whole plant scale). Various soft-computing techniques, such as neural networks or fuzzy logic, were designed to analyze complex multivariate data sets and might be used to model large such multiscale data sets in plant biology. Methodology and Principal Findings In this study we assessed the effectiveness of applying neuro-fuzzy logic to modeling the effects of light intensities and sucrose content/concentration in the in vitro culture of kiwifruit on plant acclimation, by modeling multivariate data from 14 parameters at different biological scales of organization. The model provides insights through application of 14 sets of straightforward rules and indicates that plants with lower stomatal aperture areas and higher photoinhibition and photoprotective status score best for acclimation. The model suggests the best condition for obtaining higher quality acclimatized plantlets is the combination of 2.3% sucrose and photonflux of 122–130 µmol m−2 s−1. Conclusions Our results demonstrate that artificial intelligence models are not only successful in identifying complex non-linear interactions among variables, by integrating large-scale data sets from different levels of biological organization in a holistic plant systems-biology approach, but can also be used successfully for inferring new results without further experimental work.