Breeding for disease resistance in soybean: a global perspective.

KEY MESSAGE: This review provides a comprehensive atlas of QTLs, genes, and alleles conferring resistance to 28 important diseases in all major soybean production regions in the world. Breeding disease-resistant soybean [Glycine max (L.) Merr.] varieties is a common goal for soybean breeding programs to ensure the sustainability and growth of soybean production worldwide. However, due to global climate change, soybean breeders are facing strong challenges to defeat diseases. Marker-assisted selection and genomic selection have been demonstrated to be successful methods in quickly integrating vertical resistance or horizontal resistance into improved soybean varieties, where vertical resistance refers to R genes and major effect QTLs, and horizontal resistance is a combination of major and minor effect genes or QTLs. This review summarized more than 800 resistant loci/alleles and their tightly linked markers for 28 soybean diseases worldwide, caused by nematodes, oomycetes, fungi, bacteria, and viruses. The major breakthroughs in the discovery of disease resistance gene atlas of soybean were also emphasized which include: (1) identification and characterization of vertical resistance genes reside rhg1 and Rhg4 for soybean cyst nematode, and exploration of the underlying regulation mechanisms through copy number variation and (2) map-based cloning and characterization of Rps11 conferring resistance to 80% isolates of Phytophthora sojae across the USA. In this review, we also highlight the validated QTLs in overlapping genomic regions from at least two studies and applied a consistent naming nomenclature for these QTLs. Our review provides a comprehensive summary of important resistant genes/QTLs and can be used as a toolbox for soybean improvement. Finally, the summarized genetic knowledge sheds light on future directions of accelerated soybean breeding and translational genomics studies.

Soybean-Macrophomina phaseolina-Specific Interactions and Identification of a Novel Source of Resistance.

Charcoal rot, caused by the fungus Macrophomina phaseolina, is an economically important disease of soybean (Glycine max) worldwide. Objectives of the present research were to (i) study the genetic and pathogenic diversity in a collection of M. phaseolina isolates from Argentina and Paraguay and (ii) develop an improved in vitro phenotyping method to evaluate disease response of soybean genotypes to M. phaseolina isolates. Cluster analysis showed no clear association among simple sequence repeat profiles, year of collection, pathogenicity, and geographical origin of the isolates from Argentina and Paraguay. Subsequently, the response of four soybean genotypes against seven M. phaseolina isolates was evaluated in the field and the results were confirmed using the in vitro assay developed. This assay, which is based on root disease development on soybean seedlings, allowed the detection of a differential level of aggressiveness among the isolates on four soybean genotypes. The results suggest the existence of specific interactions among soybean genotypes and M. phaseolina isolates. In addition, cultivar Munasqa RR showed a superior response against M. phaseolina compared with DT 97-4290 (moderately resistant), thus becoming a novel source of resistance to charcoal rot.

Semantic segmentation of mFISH images using convolutional networks.

Multicolor in situ hybridization (mFISH) is a karyotyping technique used to detect major chromosomal alterations using fluorescent probes and imaging techniques. Manual interpretation of mFISH images is a time consuming step that can be automated using machine learning; in previous works, pixel or patch wise classification was employed, overlooking spatial information which can help identify chromosomes. In this work, we propose a fully convolutional semantic segmentation network for the interpretation of mFISH images, which uses both spatial and spectral information to classify each pixel in an end-to-end fashion. The semantic segmentation network developed was tested on samples extracted from a public dataset using cross validation. Despite having no labeling information of the image it was tested on, our algorithm yielded an average correct classification ratio (CCR) of 87.41%. Previously, this level of accuracy was only achieved with state of the art algorithms when classifying pixels from the same image in which the classifier has been trained. These results provide evidence that fully convolutional semantic segmentation networks may be employed in the computer aided diagnosis of genetic diseases with improved performance over the current image analysis methods. © 2018 International Society for Advancement of Cytometry.

Genome-wide association mapping of quantitative traits in a breeding population of sugarcane.

BACKGROUND: Molecular markers associated with relevant agronomic traits could significantly reduce the time and cost involved in developing new sugarcane varieties. Previous sugarcane genome-wide association analyses (GWAS) have found few molecular markers associated with relevant traits at plant-cane stage. The aim of this study was to establish an appropriate GWAS to find molecular markers associated with yield related traits consistent across harvesting seasons in a breeding population. Sugarcane clones were genotyped with DArT (Diversity Array Technology) and TRAP (Target Region Amplified Polymorphism) markers, and evaluated for cane yield (CY) and sugar content (SC) at two locations during three successive crop cycles. GWAS mapping was applied within a novel mixed-model framework accounting for population structure with Principal Component Analysis scores as random component. RESULTS: A total of 43 markers significantly associated with CY in plant-cane, 42 in first ratoon, and 41 in second ratoon were detected. Out of these markers, 20 were associated with CY in 2 years. Additionally, 38 significant associations for SC were detected in plant-cane, 34 in first ratoon, and 47 in second ratoon. For SC, one marker-trait association was found significant for the 3 years of the study, while twelve markers presented association for 2 years. In the multi-QTL model several markers with large allelic substitution effect were found. Sequences of four DArT markers showed high similitude and e-value with coding sequences of Sorghum bicolor, confirming the high gene microlinearity between sorghum and sugarcane. CONCLUSIONS: In contrast with other sugarcane GWAS studies reported earlier, the novel methodology to analyze multi-QTLs through successive crop cycles used in the present study allowed us to find several markers associated with relevant traits. Combining existing phenotypic trial data and genotypic DArT and TRAP marker characterizations within a GWAS approach including population structure as random covariates may prove to be highly successful. Moreover, sequences of DArT marker associated with the traits of interest were aligned in chromosomal regions where sorghum QTLs has previously been reported. This approach could be a valuable tool to assist the improvement of sugarcane and better supply sugarcane demand that has been projected for the upcoming decades.

Selecting putative drought-tolerance markers in two contrasting soybeans.

Identifying high-yield genotypes under low water availability is essential for soybean climate-smart breeding. However, a major bottleneck lies in phenotyping, particularly in selecting cost-efficient markers associated with stress tolerance and yield stabilization. Here, we conducted in-depth phenotyping experiments in two soybean genotypes with contrasting drought tolerance, MUNASQA (tolerant) and TJ2049 (susceptible), to better understand soybean stress physiology and identify/statistically validate drought-tolerance and yield-stabilization traits as potential breeding markers. Firstly, at the critical reproductive stage (R5), the molecular differences between the genotype's responses to mild water deficit were explored through massive analysis of cDNA ends (MACE)-transcriptomic and gene ontology. MUNASQA transcriptional profile, compared to TJ2049, revealed significant differences when responding to drought. Next, both genotypes were phenotyped under mild water deficit, imposed in vegetative (V3) and R5 stages, by evaluating 22 stress-response, growth, and water-use markers, which were subsequently correlated between phenological stages and with yield. Several markers showed high consistency, independent of the phenological stage, demonstrating the effectiveness of the phenotyping methodology and its possible use for early selection. Finally, these markers were classified and selected according to their cost-feasibility, statistical weight, and correlation with yield. Here, pubescence, stomatal density, and canopy temperature depression emerged as promising breeding markers for the early selection of drought-tolerant soybeans.

A brassinosteroid functional analogue increases soybean drought resilience.

Drought severely affects soybean productivity, challenging breeding/management strategies to increase crop resilience. Hormone-based biostimulants like brassinosteroids (BRs) modulate growth/defence trade-off, mitigating yield losses; yet, natural molecule's low stability challenges the development of cost-effective and long-lasting analogues. Here, we investigated for the first time the effects of BR functional analogue DI-31 in soybean physiology under drought by assessing changes in growth, photosynthesis, water relations, antioxidant metabolism, nodulation, and nitrogen homeostasis. Moreover, DI-31 application frequencies' effects on crop cycle and commercial cultivar yield stabilisation under drought were assessed. A single foliar application of DI-31 favoured plant drought tolerance, preventing reductions in canopy development and enhancing plant performance and water use since the early stages of stress. The analogue also increased the antioxidant response, favouring nitrogen homeostasis maintenance and attenuating the nodular senescence. Moreover, foliar applications of DI-31 every 21 days enhanced the absolute yield by ~ 9% and reduced drought-induced yield losses by ~ 7% in four commercial cultivars, increasing their drought tolerance efficiency by ~ 12%. These findings demonstrated the practical value of DI-31 as an environmentally friendly alternative for integrative soybean resilience management under drought.

H-EM: An algorithm for simultaneous cell diameter and intensity quantification in low-resolution imaging cytometry.

Fluorescent cytometry refers to the quantification of cell physical properties and surface biomarkers using fluorescently-tagged antibodies. The generally preferred techniques to perform such measurements are flow cytometry, which performs rapid single cell analysis by flowing cells one-by-one through a channel, and microscopy, which eliminates the complexity of the flow channel, offering multi-cell analysis at a lesser throughput. Low-magnification image-based cytometers, also called "cell astronomy" systems, hold promise of simultaneously achieving both instrumental simplicity and high throughput. In this magnification regime, a single cell is mapped to a handful of pixels in the image. While very attractive, this idea has, so far, not been proven to yield quantitative results of cell-labeling, mainly due to the poor signal-to-noise ratio present in those images and to partial volume effects. In this work we present a cell astronomy system that, when coupled with custom-developed algorithms, is able to quantify cell intensities and diameters reliably. We showcase the system using calibrated MESF beads and fluorescently stained leukocytes, achieving good population identification in both cases. The main contribution of the proposed system is in the development of a novel algorithm, H-EM, that enables inter-cluster separation at a very low magnification regime (2x). Such algorithm provides more accurate brightness estimates than DAOSTORM when compared to manual analysis, while fitting cell location, brightness, diameter, and background level concurrently. The algorithm first performs Fisher discriminant analysis to detect bright spots. From each spot an expectation-maximization algorithm is initialized over a heterogeneous mixture model (H-EM), this algorithm recovers both the cell fluorescence and diameter with sub-pixel accuracy while discriminating the background noise. Finally, a recursive splitting procedure is applied to discern individual cells in cell clusters.

Use of AFLP markers to estimate molecular diversity of Phakopsora pachyrhizi

Background Asian soybean rust (SBR) caused by Phakopsora pachyrhizi Syd. & Syd., is one of the main diseases affecting soybean and has been reported as one of the most economically important fungal pathogens worldwide. Knowledge of the genetic diversity of this fungus should be considered when developing resistance breeding strategies. We aimed to analyze the genetic diversity of P. pachyrhizi combining simple sampling with a powerful and reproducible molecular technique. Results We employed Amplified Fragment Length Polymorphism (AFLP) technique for the amplification of P. pachyrhizi DNA extracted from naturally SBR-infected plants from 23 production fields. From a total of 1919 markers obtained, 77% were polymorphic. The high percentage of polymorphism and the Nei's genetic diversity coefficient (0.22) indicated high pathogen diversity. Analysis of molecular variance showed higher genetic variation within countries than among them. Temporal analysis showed a higher genetic variation within a year than between years. Cluster, phylogenetic and principal co-ordinate analysis showed that samples group by year of collection and then by country sampled. Conclusions The study proposed combining a simple collection of urediniospore with a subsequent analysis by AFLP was useful to examine the molecular polymorphism of samples of P. pachyrhizi collected and might have a significant contribution to the knowledge of its genetic diversity. Also, AFLP analysis is an important and potent molecular tool for the study of genetic diversity and could be useful to carry out wider genetic diversity studies.

Osteomielitis crónica multifocal recurrente / Chronic recurrent multifocal osteomyelitis

La osteomielitis crónica multifocal recurrente se considera actualmente una variante de un trastorno autoinflamatorio infrecuente, que requiere un alto índice de sospecha clínica para efectuar un adecuado abordaje diagnóstico y terapéutico. Presentamos un caso clínico típico, aportando datos sobre las hipótesis etiopatogénicas que se manejan actualmente en esta entidad (AU)

Chronic recurrent multifocal osteomyelitis is currently considered a variant of a rare autoinflammatory disorder, which requires a high index of clinic suspicion for suitable diagnostic and therapeutic approach. We present a typical case, providing data on etiopathogenetic hypothesis currently handled in this entity. The early detection of these malformations can prevent the appearance of chronic lung diseases and can allow for the best therapeutic approach. Although most of the cases do not require treatment, some of them require specific and more aggressive handling (AU)