TomatoLab@UMD - Research

Genetic compensation and redundancy are conserved mechanisms “buffering” meristem homeostasis across different plants. In tomato, this is dependent upon the “active” transcriptional reprogramming of the closest paralog of the tomato CLV3 (SlCLV3), known as SlCLE9. Multiple genetic networks converge at the CLV signaling pathway, yet it is still not known whether the same pathways control SlCLE9 buffering.

We are interested in uncovering the transcriptional regulators of this compensation mechanisms in tomato meristem homeostasis, as a proxy to understand the genetic networks controlling reduncancy, buffering and compensation in plants.

The tomato genome encodes at least 15 CLE genes (SlCLE) that are expressed in multiple -sometimes overlapping- tissues, suggesting they are involved in several developmental processes and that they might also exhibit some level of functional redundancy or active compensation.

We are interested in expanding the study of genetic interactions to the whole CLE family, and uncovering the role of these genes in agriculturally-relevant traits (plant architecture, flowering time, fruit quality traits). To achieve that, we will combine the characterization of the breadth of natural variation in tomato with the implementation of CRISPR/Cas mutagenesis screens.

Significant obstacles in plant breeding come from the long process of reshuffling standing genetic variation for qualitative and quantitative traits and the costly and laborious phenotypic and molecular evaluation of breeding germplasm. Improvement in vegetable crops like tomato has been supported by introducing ‘‘exotic’’ allelic diversity from intercrossing with wild relatives, creating novel alleles by random mutagenesis, or using genetic engineering. However, these approaches may be inefficient or time-consuming, often fail at providing variants for qualitative or quantitative traits, bring linkage drag, or do not perform equally well in multiple genetic backgrounds and environments, making its outcomes less predictable.

A long-term goal with near-term applications that my research group will pursue is to combine genome editing with breeding strategies such as genomic prediction to optimize the selection of engineered variation from candidate physiology and disease traits, allowing to better-predict phenotypes from genotypes for tomato -and in the future for other vegetables- breeding programs.

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