Mini tomato genotypes resistant to the silverleaf whitefly and to two-spotted spider mites

Maciel, Gabriel Mascarenhas; Almeida, Rafaela Santos de; Rocha, Jaíne Priscila R. da; Andaló, Vanessa; Marquez, Guilherme Repeza; Santos, Nádya Carrilho; Finzi, Rafael Resende

doi:10.4238/gmr16019539

Cited by 10 publications

(8 citation statements)

References 19 publications

(46 reference statements)

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“…Tomato is a model crop species that has emerged as a system for investigating SM pathways. For example, the production of acylsugars, a specialized metabolite, in tomato and its wild relatives is important for repelling herbivores (19)(20)(21). Some specialized metabolites found in the tomato fruit also confer health benefits by, for example, reducing risk of cancers and coronary heart diseases (4,22,23).…”

Section: /Bodymentioning

confidence: 99%

Within and cross species predictions of plant specialized metabolism genes using transfer learning

Moore

Wang

Fan

et al. 2020

Preprint

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Plant specialized metabolites mediate interactions between plants and the environment and have significant agronomical/pharmaceutical value. Most genes involved in specialized metabolism (SM) are unknown because of the large number of specialized metabolites and the challenge in differentiating SM genes from general metabolism (GM) genes. We employed transfer learning, a machine learning strategy in which information from one species with substantially more experimentally derived function data (Arabidopsis thaliana) is used to build a model to predict gene functions in another species (Solanum lycopersicum). Using machine learning to integrate heterogenous gene features, we built models distinguishing tomato SM and GM genes. Although SM/GM genes can be predicted based on tomato data alone (F-measure=0.74, compared with 0.5 for random and 1.0 for perfect predictions), using information from Arabidopsis to filter likely misannotated genes significantly improves prediction (F-measure= 0.92). This study demonstrates that SM/GM genes can be better predicted by leveraging cross-species information. SignificanceWith the increase of sequenced non-model species, a major challenge in plant biology is to ascertain gene function. Model species such as Arabidopsis thaliana have large amounts of experimentally-backed annotations that non-model species lack. We show how to use a model species to better annotate the function of genes in a non-model species using a technique called transfer learning. In particular, we focus on genes involved in specialized metabolism (SM), or metabolism specific to a certain plant lineage, which are not well known because of the sizeable diversity of specialized metabolites (SMs) among plant species. We use Arabidopsis to predict SM genes in tomato, a species with many SMs of interest but with a poorer annotation than Arabidopsis.

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Section: /Bodymentioning

confidence: 99%

Within and cross species predictions of plant specialized metabolism genes using transfer learning

Moore

Wang

Fan

et al. 2020

Preprint

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“…Although this technology is rarely exploited in tomato (Sun et al, 2019), satisfactory results have been achieved by Finzi et al (2017), who obtained hybrids originating from a cross between two mini-tomato lines (dwarf size vs. normal size) to agronomic advantage.…”

Section: Introductionmentioning

confidence: 99%

Selection of F2RC1 saladette-type dwarf tomato plant populations for fruit quality and whitefly resistance

Oliveira

Maciel

Gomes

et al. 2022

Rev. bras. eng. agríc. ambient.

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This study aimed to select promising F2RC1 populations of saladette-type dwarf tomato plants for the development of breeding lines based on agronomic characteristics, fruit quality, and whitefly resistance. The experimental design was randomized blocks containing 13 treatments (10 F2RC1 populations of dwarf tomato plants, both parents, and a commercial hybrid) with four replicates. The evaluations were performed included weight, length, diameter, shape, pulp thickness, number of locules, soluble solids, β-carotene, and lycopene concentration of the fruit; plant internode length; acylsugars concentration; and number of whitefly eggs, nymphs, and adults on the leaflets. The data were analyzed using ANOVA, selection indices, and multivariate analysis. The first backcross increased the agronomic characteristics of the populations in relation to the donor parent, especially for fruit weight (169.1%), fruit length (26.1%), and fruit diameter (16.6%). The UFU SDi 7, UFU SDi 9, and UFU-SDi 17 populations were selected using two selection indices and were therefore considered promising.

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“…Tomato is a model crop that has emerged as a system for investigating SM pathways. For example, the production of acylsugars, a specialized metabolite, in tomato and its wild relatives is important for repelling herbivores ( Lucini et al 2016 ; Maciel et al 2017 ; Fan et al 2019 ). Some specialized metabolites found in the tomato fruit also confer health benefits by, for example, reducing risk of cancers and coronary heart diseases ( Giovannucci 2002 ; Blum et al 2005 ; Clifford and Brown 2006 ).…”

Section: Introductionmentioning

confidence: 99%

Within- and cross-species predictions of plant specialized metabolism genes using transfer learning

et al. 2020

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Plant specialized metabolites mediate interactions between plants and the environment and have significant agronomical/pharmaceutical value. Most genes involved in specialized metabolism (SM) are unknown because of the large number of metabolites and the challenge in differentiating SM genes from general metabolism (GM) genes. Plant models like Arabidopsis thaliana have extensive, experimentally derived annotations, whereas many non-model species do not. Here we employed a machine learning strategy, transfer learning, where knowledge from A. thaliana is transferred to predict gene functions in cultivated tomato with fewer experimentally annotated genes. The first tomato SM/GM prediction model using only tomato data performs well (F-measure=0.74, compared with 0.5 for random and 1.0 for perfect predictions), but from manually curating 88 SM/GM genes, we found many mis-predicted entries were likely mis-annotated. When the SM/GM prediction models built with A. thaliana data were used to filter out genes where the A. thaliana-based model predictions disagreed with tomato annotations, the new tomato model trained with filtered data improved significantly (F-measure=0.92). Our study demonstrates that SM/GM genes can be better predicted by leveraging cross-species information. Additionally, our findings provide an example for transfer learning in genomics where knowledge can be transferred from an information-rich species to an information-poor one.

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Mini tomato genotypes resistant to the silverleaf whitefly and to two-spotted spider mites

Cited by 10 publications

References 19 publications

Within and cross species predictions of plant specialized metabolism genes using transfer learning

Within and cross species predictions of plant specialized metabolism genes using transfer learning

Selection of F2RC1 saladette-type dwarf tomato plant populations for fruit quality and whitefly resistance

Within- and cross-species predictions of plant specialized metabolism genes using transfer learning

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