Biology subjects

Messina, C.

Publications and source records attributed to Messina, C..

10 recordsLinked to original sources

bioRxiv2026

An indirect organogenesis-based citrus transformation system monitored using visible reporter markers

Genetic transformation of citrus is essential for functional genomics and trait improvement, yet remains limited by prolonged regeneration cycles, genotype-dependent responses, and inefficient screening of transformed tissues. Here, we established an Agrobacterium-mediated indirect organogenesis-based transformation system for Carrizo citrange and evaluated two visible reporter systems, the anthocyanin regulator ROSEA1 and the betalain biosynthetic cassette RUBY, for monitoring transformed tissues throughout regeneration. An optimized regeneration protocol enabled visible callus ini-tiation within 7 days following a 5-day pre-culture treatment and recovery of PCR-confirmed transgenic plantlets within 5 months. Both visible reporters were compatible with callus development and shoot regeneration throughout the indirect organogenesis workflow. During callus proliferation, visible pigmentation closely corresponded with GFP fluorescence, enabling rapid, non-destructive identification of transformed tissues. However, reporter performance diverged during shoot regeneration: RUBY maintained stable pigmentation throughout regeneration, whereas ROSEA1-associated pigmentation progressively declined despite continued GFP fluorescence. Consequently, RUBY exhibited a 4.7-fold higher pigmenta-tion-based detection rate than ROSEA1 at the shoot stage and showed closer agreement with GFP-based detection. Putative transgenic events were confirmed by PCR in independent lines of both constructs, together with GFP fluorescence in leaves and root tips. Together, these findings establish an efficient indirect organogenesis-based transformation platform for citrus and demonstrate that both ROSEA1 and RUBY are effective visual reporters during callus proliferation, whereas RUBY provides more reliable visual identification during shoot regeneration and plant recovery.

plant biology↗

bioRxiv2023

Crop improvement can accelerate agriculture adaptation to societal demands and climate change.

A key question today is how to harmonize future crop improvement efforts for regenerative cropping systems that can mitigate further environmental degeneration and improve societal adaptation to climate change. Here we show that the US corn-belt based maize improvement system has been adapting to the changing climate. Analyses of the longest running field experiment (1990-2021) designed to quantify yield gains (37 hybrids sold from 1930 to 1990) demonstrate that rates of genetic gain were always positive and have increased over time (from 70 to 150 kg ha-1 y-1 in 1990 and 2021, respectively). Between 1930 and 2021 for the May through October period total rainfall for the U.S. corn belt increased 113 mm, and daily temperature amplitude decreased 1.3{degrees}C. At the same time, farmers modified their farming practices, helping modern hybrids to out-perform their older counterparts by a larger degree. In contrast to the conclusions reached by other observational studies, genetic gain estimates demonstrate that maize breeding and the production system are adapting to modern scenarios. Climate change is commonly linked to fears of food insecurity, but new cropping systems capable of providing food while regenerating resources such as water, the circularization of nutrients, and reduction of greenhouse gas emissions are possible.

genetics↗

bioRxiv2023

Root system growth and function response to soil temperature in maize (Zea mays L.)

Crop adaptation to the mixture of environments that defines the target population of environments is the result from a balanced resource allocation between roots, shoots and reproductive organs. Root growth places a critical role in the determination of this balance. Root growth and function responses to temperature can determine the strength of roots as sinks but also influence the crops ability to uptake water and nutrients. Surprisingly, this behavior has not been studied in maize since the middle of the last century, and the genetic determinants are unknown. Low temperatures often recorded in deep soil layers limit root growth and soil exploration and may constitute a bottleneck towards increasing drought tolerance, nitrogen recovery, sequestration of carbon and productivity in maize. High throughput phenotyping (HTP) systems were developed to investigate these responses and to examine genetic variability therein across diverse maize germplasm. Here we show that there is: 1) genetic variation of root growth under low temperature and below 10{degrees}C, and 2) genotypic variation in water transport under low temperature. Using simulation, we demonstrate that the measured variation for both traits contribute to drought tolerance and explain important components of yield variation in the US corn-belt. The trait set examined herein and HTP platform developed for its characterization reveal a unique opportunity to remove a major bottleneck for crop improvement, and adaptation to climate change.

plant biology↗

bioRxiv2022

Extending the breeder's equation to take aim at the Target Population of Environments

1)A major focus for genomic prediction has been on improving trait prediction accuracy using combinations of algorithms and the training data sets available from plant breeding multi-environment trials (METs). Any improvements in prediction accuracy are viewed as pathways to improve traits in the reference population of genotypes and product performance in the target population of environments (TPE). To realise these breeding outcomes there must be a positive MET-TPE relationship that provides consistency between the trait variation expressed within the MET data sets that are used to train the genome-to-phenome (G2P) model for applications of genomic prediction and the realised trait and performance differences in the TPE for the genotypes that are the prediction targets. The strength of this MET-TPE relationship is usually assumed to be high, however it is rarely quantified. To date investigations of genomic prediction methods have not given adequate attention to quantifying the structure of the TPE and the MET-TPE relationship and its potential impact on training the G2P model for applications of genomic prediction to accelerate breeding outcomes for the on-farm TPE. We provide a perspective on the importance of the MET-TPE relationship as a key component for the design of genomic prediction methods to realize improved rates of genetic gain for the target yield, quality, stress tolerance and yield stability traits in the on-farm TPE.

genetics↗

bioRxiv2022

Transgene by Germplasm Interactions Can Impact Transgene Evaluation

Transgenes have been successfully commercialized for qualitatively inherited insect and herbicide resistance traits that show similar effects across genetic backgrounds. However, for quantitative traits like yield, genetic background may affect the measured transgene value. In this paper, we evaluated whether different genetic backgrounds impact the estimated value of a transgene for grain yield, ear height, and anthesis-silking interval for maize by developing isogenic pairs of lines with and without a transgene and testing them in hybrid combination with non-transgenic lines from a complementary heterotic group across eleven environments in the USA. Over all hybrid combinations, the transgene increased yield by 0.2 Mg ha-1. Across multiple non-transgenic lines of the opposing heterotic group, the transgene effect within a line pair ranged from an increase of 0.8 Mg ha-1 for the NSS4 and SS7 transgenic lines to a reduction of 0.3 Mg ha-1 for the NSS5 transgenic line when compared to their non-transgenic isoline. Transgenic hybrids were often taller than non-transgenic hybrids (P<0.05). Anthesis to silking interval was reduced by 4{square}C growing degree units overall, but no transgene x genotype interaction was detected among line pairs. Our results show the importance of testing transgene efficacy across a large sample of elite hybrid pairs to assess the genes value. By only testing in a specific hybrid background, as may be done for qualitative traits like insect resistance, transgenes could be erroneously advanced or eliminated.

plant biology↗

bioRxiv2022

Methods for Evaluating Effects of Transgenes for Quantitative Traits

Transgenes that improve quantitative traits have traditionally been evaluated in one or a few genetic backgrounds across multiple environments. However, testing across multiple genetic backgrounds can be equally important to accurately quantify the value of a transgene for breeding objectives. Creating near-isogenic lines across a wide germplasm space is costly and time consuming, which renders it impractical during early stages of testing. In this experiment, we evaluate three approaches to sample the genetic space while concurrently testing across environments. We created both transgenic and non-transgenic doubled haploid lines, F2:3 lines, and bulk F3 families to determine if all methods resulted in similar estimation of transgene value and to identify the number of yield trial plots from each method necessary to obtain a stable estimate of the transgene value. With one exception, the three methods consistently estimated a similar effect of the transgene. We suggest that bulked F3 lines topcrossed to a tester inbred is the most effective method to estimate the value of a transgene across both genetic space and environments.

plant biology↗

bioRxiv2022

Plant Breeding in the face of climate change

Climate change will have a net negative and inequitable impact on agriculture. Genetics for crop improvement ranks in the top set of technologies that can contribute to human adaptation to climate change. However, a framework for how to breed crops for climate change adaptation is lacking. Here we propose a framework to develop new genotype (G) x management (M) technologies (G x M) to adapt to climate change, and to transition from current to future G x M technologies in a way that future food security does not come at the expense of current food security. The framework integrate genomic, agronomic, and environmental (E) predictors to accomplish two critical goals: 1-predict emergent phenotypes that stems from the dynamic interplay between G, E and M, and thus enable the breeder to consider the behavior of new genetic and trait combinations in environments that plants have not been exposed or tested before, and 2-identify G x M technologies that could increase food and nutritional security while regenerating natural and production resources. We highlight the need to invest in artificial intelligence and information technologies for breeders to harness multiple sources of information to create G x M technologies to address the diverse cultural and geographically granular societal needs.

plant biology↗

bioRxiv2020

Two decades of creating drought tolerant maize and underpinning prediction technologies in the US corn-belt: Review and perspectives on the future of crop design

Over the last decade, society witnessed the largest expansion of agricultural land planted with drought tolerant (DT) maize (Zea mays L.) Dedicated efforts to drought breeding led to development of DT maize. Here we show that after two decades of sustained breeding efforts the rate of crop improvement under drought is in the range 1.0-1.6% yr-1, which is higher than rates (0.7% yr-1) reported prior to drought breeding. Prediction technologies that leverage biological understanding and statistical learning to improve upon the quantitative genetics framework will further accelerate genetic gain. A review of published and unpublished analyses conducted on data including 138 breeding populations and 93 environments between 2009 and 2019 demonstrated an average prediction skill (r) improvement around 0.2. These methods applied to pre-commercial stages showed accuracies higher that current statistical approaches (0.85 vs. 0.70). Improvement in hybrid and management choice can increase water productivity. Digital gap analyses are applicable at field scale suggesting the possibility of transition from evaluating hybrids to designing genotype x management (GxM) technologies for target cropping systems in drought prone areas. Due to the biocomplexity of drought, research and development efforts should be sustained to advance knowledge and iteratively improve models. HighlightCrop improvement rate in maize increased after implementation of drought breeding efforts. Harnessing crop, quantitative genetics and gap models will enable the transition from genetic evaluation to crop design.

plant biology↗

bioRxiv2020

Modelling selection response in plant breeding programs using crop models as mechanistic gene-to-phenotype (CGM-G2P) multi-trait link functions

Plant breeding programs are designed and operated over multiple cycles to systematically change the genetic makeup of plants to achieve improved trait performance for a Target Population of Environments (TPE). Within each cycle, selection applied to the standing genetic variation within a structured reference population of genotypes (RPG) is the primary mechanism by which breeding programs make the desired genetic changes. Selection operates to change the frequencies of the alleles of the genes controlling trait variation within the RPG. The structure of the RPG and the TPE has important implications for the design of optimal breeding strategies. The breeders equation, together with the quantitative genetic theory behind the equation, informs many of the principles for design of breeding programs. The breeders equation can take many forms depending on the details of the breeding strategy. Through the genetic changes achieved by selection, the cultivated varieties of crops (cultivars) are improved for use in agriculture. From a breeding perspective, selection for specific trait combinations requires a quantitative link between the effects of the alleles of the genes impacted by selection and the trait phenotypes of plants and their breeding value. This gene-to-phenotype link function provides the G2P map for one to many traits. For complex traits controlled by many genes, the infinitesimal model for trait genetic variation is the dominant G2P model of quantitative genetics. Here we consider motivations and potential benefits of using the hierarchical structure of crop models as CGM-G2P trait link functions in combination with the infinitesimal model for the design and optimisation of selection in breeding programs.

genetics↗

bioRxiv2020

Reproductive resilience but not root architecture underpin yield improvement in maize

Plants capture soil resources to produce the grains required to feed a growing population. Because plants capture water and nutrients through roots, it was proposed that changes in root systems architecture (RSA) underpin the three-fold increase in maize grain yield over the last century1,2,3,4. Within this framework, improvements in reproductive resilience due to selection are caused by increased water capture1. Here we show that both root architecture and yield have changed with decades of maize breeding, but not the water capture. Consistent with Darwinian agriculture5 theory, improved reproductive resilience6,7 enabled farmers increase the number of plants per unit land8,9,10, capture soil resources, and produced more dry matter and grain. Throughout the last century, selection operated to adapt roots to crowding, enabling reallocation of C from large root systems to the growing ear and the small roots of plants cultivated in high plant populations in modern agriculture.

plant biology↗