Perspective for genomic-enabled prediction against black sigatoka disease and drought stress in polyploid species

Phenotyping of Drought-Stressed Poplar Saplings Using Exemplar-Based Data Generation and Leaf-Level Structural Analysis

Drought stress is one of the main threats to poplar plant growth and has a negative impact on plant yield. Currently, high-throughput plant phenotyping has been widely studied as a rapid and nondestructive tool for analyzing the growth status of plants, such as water and nutrient content. In this study, a combination of computer vision and deep learning was used for drought-stressed poplar sapling phenotyping. Four varieties of poplar saplings were cultivated, and 5 different irrigation treatments were applied. Color images of the plant samples were captured for analysis. Two tasks, including leaf posture calculation and drought stress identification, were conducted. First, instance segmentation was used to extract the regions of the leaf, petiole, and midvein. A dataset augmentation method was created for reducing manual annotation costs. The horizontal angles of the fitted lines of the petiole and midvein were calculated for leaf posture digitization. Second, multitask learning models were proposed for simultaneously determining the stress level and poplar variety. The mean absolute errors of the angle calculations were 10.7° and 8.2° for the petiole and midvein, respectively. Drought stress increased the horizontal angle of leaves. Moreover, using raw images as the input, the multitask MobileNet achieved the highest accuracy (99% for variety identification and 76% for stress level classification), outperforming widely used single-task deep learning models (stress level classification accuracies of <70% on the prediction dataset). The plant phenotyping methods presented in this study could be further used for drought-stress-resistant poplar plant screening and precise irrigation decision-making.

Screening of Triploid Banana Population Under Natural and Controlled Black Sigatoka Disease for Genomic Selection

Black sigatoka disease (BSD) is the most important foliar threat in banana production, and breeding efforts against it should take advantage of genomic selection (GS), which has become one of the most explored tools to increase genetic gain, save time, and reduce selection costs. To evaluate the potential of GS in banana for BSD, 210 triploid accessions were obtained from the African Banana and Plantain Research Center to constitute a training population. The variability in the population was assessed at the phenotypic level using BSD- and agronomic-related traits and at the molecular level using single-nucleotide polymorphisms (SNPs). The analysis of variance showed a significant difference between accessions for almost all traits measured, although at the genomic group level, there was no significant difference for BSD-related traits. The index of non-spotted leaves among accessions ranged from 0.11 to 0.8. The accessions screening in controlled conditions confirmed the susceptibility of all genomic groups to BSD. The principal components analysis with phenotypic data revealed no clear diversity partition of the population. However, the structure analysis and the hierarchical clustering analysis with SNPs grouped the population into four clusters and two subpopulations, respectively. The field and laboratory screening of the banana GS training population confirmed that all genomic groups are susceptible to BSD but did not reveal any genetic structure, whereas SNP markers exhibited clear genetic structure and provided useful information in the perspective of applying GS.

Stomata variation in the process of polyploidization in Chinese chive (Allium tuberosum)

BACKGROUND

Stomatal variation, including guard cell (GC) density, size and chloroplast number, is often used to differentiate polyploids from diploids. However, few works have focused on stomatal variation with respect to polyploidization, especially for consecutively different ploidy levels within a plant species. For example, Allium tuberosum, which is mainly a tetraploid (2n = 4x = 32), is also found at other ploidy levels which have not been widely studied yet.

RESULTS

We recently found cultivars with different ploidy levels, including those that are diploid (2n = 2x = 16), triploid (2n = 3x = 24), pseudopentaploid (2n = 34-42, mostly 40) and pseudohexaploid (2n = 44-50, mostly 48). GCs were evaluated for their density, size (length and width) and chloroplast number. There was no correspondence between ploidy level and stomatal density, in which anisopolyploids (approximately 57 and 53 stomata/mm2 in triploid and pseudopentaploid, respectively) had a higher stomatal density than isopolyploids (approximately 36, 43, and 44 stomata/mm2 in diploid, tetraploid and pseudohexaploid, respectively). There was a positive relationship between ploidy level and GC chloroplast number (approximately 44, 45, 51, 72 and 90 in diploid to pseudohexaploid, respectively). GC length and width also increased with ploidy level. However, the length increased approximately 1.22 times faster than the width during polyploidization.

CONCLUSIONS

This study shows that GC size increased with increasing DNA content, but the rate of increase differed between length and width. In the process of polyploidization, plants evolved longer and narrower stomata with more chloroplasts in the GCs.

Narrow genetic diversity in germplasm from the Guinean and Sudano-Guinean zones in Benin indicates the need to broaden the genetic base of sweet fig banana (Musa acuminata cv Sotoumon)

Sweet fig (M. acuminata cv. Sotoumon) is an economically important dessert banana in Benin, with high nutritional, medicinal, and cultural values. Nevertheless, its productivity and yield are threatened by biotic and abiotic stresses. Relevant knowledge of the genetic diversity of this economically important crop is essential for germplasm conservation and the development of breeding programs. However, very little is known about the genetic makeup of this cultivar in Benin. To advance the understanding of genetic diversity in sweet fig banana germplasm, a Genotype-By-Sequencing (GBS) was performed on a panel of 273 accessions collected in different phytogeographical zones of Benin. GBS generated 8,457 quality SNPs, of which 1992 were used for analysis after filtering. The results revealed a low diversity in the studied germplasm (He = 0.0162). Genetic differentiation was overall very low in the collection as suggested by the negative differentiation index (Fstg = -0.003). The Analysis of Molecular Variance (AMOVA) indicated that the variation between accessions within populations accounted for 83.8% of the total variation observed (P < 0.001). The analysis of population structure and neighbor-joining tree partitioned the germplasm into three clusters out of which a predominant major one contained 98.1% of all accessions. These findings demonstrate that current sweet fig banana genotypes shared a common genetic background, which made them vulnerable to biotic and abiotic stress. Therefore, broadening the genetic base of the crop while maintaining its quality attributes and improving yield performance is of paramount importance. Moreover, the large genetic group constitutes an asset for future genomic selection studies in the crop and can guide the profiling of its conservation strategies.

Analysis of genetic diversity and agronomic variation in banana sub-populations for genomic selection under drought stress in southern Benin

In the perspective of investigating genomic selection (GS) among Musa genotypes in West and Central Africa, banana accessions were phenotyped under natural drought stress in Benin and genotyped using genotyping by sequencing. Sixty-one (61) accessions grouped into three major genomic groups AAA, AAB and ABB and those without genomic affiliation information were used. Variation within the population was determined by phenotypic variables while population structure and clustering analysis were carried out to understand the genetic diversity at the molecular level. Among the genomic groups evaluated, the group AAB showed the best performance for fruit weight at maturity, (3.41 ± 1.99 kg) and for plant height (198.46 ± 12.66 cm). At the accession level, HD 117 S1 and NIA 27 showed the best plant height (263.16 ± 20.98 cm) and the best fruit weight at maturity (9.43 ± 0.0 kg) respectively. Phenotypic data did not reveal clear genetic diversity among accessions; however, the genetic diversity was conspicuous at the molecular level using 5000 markers. The affiliations of local accessions in genomic groups were determined for the first time based on the phenotypic and molecular data obtained in this study. The knowledge generated allows the possibility to apply GS in banana.