Remote sensing for estimating genetic parameters of biomass accumulation and modeling stability of growth curves in alfalfa.

Multi-spectral imaging by unoccupied aerial vehicles provides a non-destructive, high throughput approach to measuring biomass accumulation over successive alfalfa (Medicago sativa L. subsp. sativa) harvests. Information from estimated growth curves can be used to infer harvest biomass and to gain insights into the relationship between growth dynamics and forage biomass stability across cuttings and years. In this study, multi-spectral imaging and several common vegetation indices were used to estimate genetic parameters and model growth of alfalfa cultivars to determine the longitudinal relationship between vegetation indices and forage biomass. Results showed moderate heritability for vegetation indices, with median plot level heritability ranging from 0.11-0.64, across multiple cuttings in three trials planted in Ithaca, NY, and Las Cruces, NM. Genetic correlations between the normalized difference vegetation index and forage biomass were moderate to high across trials, cuttings, and the timing of multi-spectral image capture. To evaluate the relationship between growth parameters and forage biomass stability across cuttings and environmental conditions, random regression modeling approaches were used to estimate the growth parameters of cultivars for each cutting and the variance in growth was compared to the variance in genetic estimates of forage biomass yield across cuttings. These analyses revealed high correspondence between stability in growth parameters and stability of forage yield. The results of this study indicate that vegetation indices are effective at modeling genetic components of biomass accumulation, presenting opportunities for more efficient screening of cultivars and new longitudinal modeling approaches that can provide insights into temporal factors influencing cultivar stability.

Comparative transcriptomic analysis of germinating rice seedlings to individual and combined anaerobic and cold stress.

BACKGROUND: Rice is one of the most important cereals consumed worldwide. Two major abiotic factors affecting rice plants in different growth stages are flooding stress and cold stress. These abiotic stresses can take place independently or simultaneously and significantly affect rice plants during germination and seedling growth. Fortunately, a wide array of phenotypic responses conferring flooding stress and chilling stress tolerance exist within the rice germplasm, indicating the presence of different molecular mechanisms underlying tolerance to these stresses. Understanding these differences may assist in developing improved rice cultivars having higher tolerance to both stresses. In this study, we conducted a comparative global gene expression analysis of two rice genotypes with contrasting phenotypes under cold stress, anaerobic stress, and combined cold and anaerobic stress during germination. RESULTS: The differential gene expression analysis revealed that 5571 differentially expressed genes (DEGs), 7206 DEGs, and 13279 DEGs were identified under anaerobic stress, cold stress, and combined stress, respectively. Genes involved in the carbohydrate metabolic process, glucosyltransferase activity, regulation of nitrogen compound metabolic process, protein metabolic process, lipid metabolic process, cellular nitrogen compound biosynthetic process, lipid biosynthetic process, and a microtubule-based process were enriched across all stresses. Notably, the common Gene Ontology (GO) analysis identified three hub genes, namely Os08g0176800 (similar to mRNA-associated protein mrnp 41), Os11g0454200 (dehydrin), and OS10g0505900 (expressed protein). CONCLUSION: A large number of differentially expressed genes were identified under anaerobic, cold conditions during germination and the combination of the two stress conditions in rice. These results will assist in the identification of promising candidate genes for possible manipulation toward rice crops that are more tolerant under flooding and cold during germination, both independently and concurrently.

Examination of Large Chromosomal Inversions in the Genome of Erwinia amylovora Strains Reveals Worldwide Distribution and North America-Specific Types.

Erwinia amylovora is a relatively homogeneous species with low genetic diversity at the nucleotide level. However, phenotypic differences and genomic structural variations among E. amylovora strains have been documented. In this study, we identified 10 large chromosomal inversion (LCI) types in the Spiraeoideae-infecting (SI) E. amylovora strains by combining whole genome sequencing and PCR-based molecular markers. It was found that LCIs were mainly caused by homologous recombination events among seven rRNA operons (rrns) in SI E. amylovora strains. Although ribotyping results identified inter- and intra-variations in the internal transcribed spacer (ITS1 and ITS2) regions among rrns, LCIs tend to occur between rrns transcribed in the opposite directions and with the same tRNA content (tRNA-Glu or tRNA-Ile/Ala) in ITS1. Based on the LCI types, physical/estimated replichore imbalance (PRI/ERI) was examined and calculated. Among the 117 SI strains evaluated, the LCI types of Ea1189, CFBP1430, and Ea273 were the most common, with ERI values at 1.31, 7.87, and 4.47°, respectively. These three LCI types had worldwide distribution, whereas the remaining seven LCI types were restricted to North America (or certain regions of the United States). Our results indicated ongoing chromosomal recombination events in the SI E. amylovora population and showed that LCI events are mostly symmetrical, keeping the ERI less than 15°. These findings provide initial evidence about the prevalence of certain LCI types in E. amylovora strains, how LCI occurs, and its potential evolutionary advantage and history, which might help track the movement of the pathogen.

Evaluation of Malus Germplasm Identifies Genetic Sources of Powdery Mildew and Frogeye Leaf Spot Resistance for Apple Breeding.

Apple is an important fruit crop of temperate regions. The narrow genetic base of commercially cultivated apples has resulted in its vulnerability to a large number of fungal, bacterial, and viral pathogens. Apple breeders are always seeking new sources of resistance within the cross-compatible Malus species that can be deployed into elite genetic backgrounds. We have evaluated resistance to two major fungal diseases of apples: powdery mildew and frogeye leaf spot, using a germplasm collection of 174 Malus accessions to identify novel sources of genetic resistance. In 2020 and 2021, we evaluated these accessions for the incidence and severity of powdery mildew and frogeye leaf spot diseases at Cornell AgriTech, Geneva, New York, in a partially managed orchard. The severity and incidence of powdery mildew and frogeye leaf spot, as well as weather parameters were recorded in June, July, and August. Total incidence of powdery mildew and frogeye leaf spot infections increased from 33 to 38%, and 56 to 97% in 2020 and 2021, respectively. Our analysis showed that relative humidity and precipitation correlate with powdery mildew and frogeye leaf spot susceptibility. The predictor variables with highest impact to the variability of powdery mildew were accessions and relative humidity in May. A total of 65 Malus accessions were found to be resistant to powdery mildew, and only one accession showed moderate resistance to frogeye leaf spot. Several of these accessions belong to Malus hybrid species and domesticated apples and can therefore be potential sources of novel resistance alleles for apple breeding.

Genetic factors underlying anaerobic germination in rice: Genome-wide association study and transcriptomic analysis.

The success of rice (Oryza sativa L.) germination and survival under submerged conditions is mainly determined by the rapid growth of the coleoptile to reach the water surface. Previous reports have shown the presence of genetic variability within rice accessions in the levels of flooding tolerance during germination or anaerobic germination (AG). Although many studies have focused on the physiological mechanisms of oxygen stress, few studies have explored the breadth of natural variation in AG tolerance-related traits in rice. In this study, we evaluated the coleoptile lengths of a geographically diverse rice panel of 241 accessions, including global accessions along with elite breeding lines and released cultivars from the United States, under the normal and flooded conditions in laboratory and greenhouse environments. A genome-wide association study (GWAS) was performed using a 7K single-nucleotide polymorphism (SNP) array and the phenotypic data of normal coleoptile length, flooded coleoptile length, flooding tolerance index, and survival at 14 d after seeding (DAS). Out of the 30 significant GWAS quantitative trait loci (QTL) regions identified, 14 colocalized with previously identified candidate genes of AG tolerance, whereas 16 were potentially novel. Two rice accessions showing contrasting phenotypic responses to AG stress were selected for the transcriptomics study. The combined approach of GWAS and transcriptomics analysis identified 77 potential candidate genes related to AG tolerance. The findings of our study may assist rice improvement programs in developing rice cultivars with robust tolerance under flooding stress during germination and the early seedling stage.

Potential Therapeutic Applications of Plant-Derived Alkaloids against Inflammatory and Neurodegenerative Diseases.

Alkaloids are a type of natural compound possessing different pharmacological activities. Natural products, including alkaloids, which originate from plants, have emerged as potential protective agents against neurodegenerative disorders (NDDs) and chronic inflammations. A wide array of prescription drugs are used against these conditions, however, not free of limitations of potency, side effects, and intolerability. In the context of personalized medicine, further research on alkaloids to unravel novel therapeutic approaches in reducing complications is critical. In this review, a systematic survey was executed to collect the literature on alkaloids and their health complications, from which we found that majority of alkaloids exhibit anti-inflammatory action via nuclear factor-κB and cyclooxygenase-2 (COX-2), and neuroprotective interaction through acetylcholinesterase (AChE), COX, and ß-site amyloid precursor protein activity. In silico ADMET and ProTox-II-related descriptors were calculated to predict the pharmacological properties of 280 alkaloids isolated from traditional medicinal plants towards drug development. Out of which, eight alkaloids such as tetrahydropalmatine, berberine, tetrandrine, aloperine, sinomenine, oxymatrine, harmine, and galantamine are found to be optimal within the categorical range when compared to nicotine. These alkaloids could be exploited as starting materials for novel drug synthesis or, to a lesser extent, manage inflammation and neurodegenerative-related complications.

Relationship of Cultivated Grain Amaranth Species and Wild Relative Accessions.

Amaranthus is a genus of C4 dicotyledonous herbaceous plants, and three New World species have been domesticated to produce grain crops with light colored seed which are classified as pseudo-cereals rich in protein and minerals. A core collection of grain amaranths and immediate precursor species has been established, representing the closest related species. The goal of this study was to evaluate the genetic diversity in that collection of cultivated and wild species, using competitive allele single nucleotide polymorphism markers. A secondary objective was to determine the relationships among the three cultivated species and non-domesticated Amaranthus, while a third objective was to evaluate the utility of the markers in detecting diversity in the 276 genotypes. The markers were found to be highly variable with an average polymorphism information content of 0.365. All markers were bi-allelic; and the major allele frequency ranged from 0.388 to 0.871. Population structure analysis of the cultigens revealed the presence of two sub populations. Phylogeny confirmed that the two Mesoamerican species, Amaranthus cruentus and Amaranthus hypochondriacus, were related and distant from the South American species Amaranthus caudatus, which in turn was very closely clustered with Amaranthus quitensis, even though this is considered a weedy relative. The first pair of species were likely to have inter-crossed, while the latter two likely exist in a wild-cultivated hybrid state. In conclusion, the results of this SNP study provided insights on amaranth cultivars and their relationship to wild species, the probable domestication events leading to the cultivars, and possible crop breeding or germplasm conservation strategies.

Enriched-GWAS and Transcriptome Analysis to Refine and Characterize a Major QTL for Anaerobic Germination Tolerance in Rice.

Tolerance of anaerobic germination (AG) is a key trait in the development of direct seeded rice. Through rapid and sustained coleoptile elongation, AG tolerance enables robust seedling establishment under flooded conditions. Previous attempts to fine map and characterize AG2 (qAG7.1), a major centromere-spanning AG tolerance QTL, derived from the indica variety Ma-Zhan Red, have failed. Here, a novel approach of "enriched haplotype" genome-wide association study based on the Ma-Zhan Red haplotype in the AG2 region was successfully used to narrow down AG2 from more than 7 Mb to less than 0.7 Mb. The AG2 peak region contained 27 genes, including the Rc gene, responsible for red pericarp development in pigmented rice. Through comparative variant and transcriptome analysis between AG tolerant donors and susceptible accessions several candidate genes potentially controlling AG2 were identified, among them several regulatory genes. Genome-wide comparative transcriptome analysis suggested differential regulation of sugar metabolism, particularly trehalose metabolism, as well as differential regulation of cell wall modification and chloroplast development to be implicated in AG tolerance mechanisms.

Genome-wide association mapping identifies novel loci underlying fire blight resistance in apple.

Fire blight, caused by epiphytotic gram-negative bacteria Erwinia amylovora, is the most destructive bacterial disease of apple (Malus spp.). Genetic mechanisms of fire blight resistance have mainly been studied using traditional biparental quantitative trait loci (QTL) mapping approaches. Here, we use large-scale historic shoot and blossom fire blight data collected over multiple years and genotyping-by-sequencing (GBS) markers to identify significant marker-trait associations in a diverse set of 566 apple [Malus domestica (Suckow) Borkh.] accessions. There was large variation in fire blight resistance and susceptibility in these accessions. We identified 23 and 38 QTL significantly (p < .001) associated with shoot and blossom blight resistance, respectively. The QTL are distributed across all 17 chromosomes of apple. Four shoot blight and 19 blossom blight QTL identified in this study colocalized with previously identified QTL associated with resistance to fire blight or apple scab. Using transcriptomics data of two apple cultivars with contrasting fire blight responses, we also identified candidate genes for fire blight resistance that are differentially expressed between resistant and susceptible cultivars and located within QTL intervals for fire blight resistance. However, further experiments are needed to confirm and validate these marker-trait associations and develop diagnostic markers before use in marker-assisted breeding to develop apple cultivars with decreased fire blight susceptibility.

The Plant Pathology Challenge 2020 data set to classify foliar disease of apples.

PREMISE: Apple orchards in the United States are under constant threat from a large number of pathogens and insects. Appropriate and timely deployment of disease management depends on early disease detection. Incorrect and delayed diagnosis can result in either excessive or inadequate use of chemicals, with increased production costs and increased environmental and health impacts. METHODS AND RESULTS: We have manually captured 3651 high-quality, real-life symptom images of multiple apple foliar diseases, with variable illumination, angles, surfaces, and noise. A subset of images, expert-annotated to create a pilot data set for apple scab, cedar apple rust, and healthy leaves, was made available to the Kaggle community for the Plant Pathology Challenge as part of the Fine-Grained Visual Categorization (FGVC) workshop at the 2020 Computer Vision and Pattern Recognition conference (CVPR 2020). Participants were asked to use the image data set to train a machine learning model to classify disease categories and develop an algorithm for disease severity quantification. The top three area under the ROC curve (AUC) values submitted to the private leaderboard were 0.98445, 0.98182, and 0.98089. We also trained an off-the-shelf convolutional neural network on this data for disease classification and achieved 97% accuracy on a held-out test set. DISCUSSION: This data set will contribute toward development and deployment of machine learning-based automated plant disease classification algorithms to ultimately realize fast and accurate disease detection. We will continue to add images to the pilot data set for a larger, more comprehensive expert-annotated data set for future Kaggle competitions and to explore more advanced methods for disease classification and quantification.

An improved 7K SNP array, the C7AIR, provides a wealth of validated SNP markers for rice breeding and genetics studies.

Single nucleotide polymorphisms (SNPs) are highly abundant, amendable to high-throughput genotyping, and useful for a number of breeding and genetics applications in crops. SNP frequencies vary depending on the species and populations under study, and therefore target SNPs need to be carefully selected to be informative for each application. While multiple SNP genotyping systems are available for rice (Oryza sativa L. and its relatives), they vary in their informativeness, cost, marker density, speed, flexibility, and data quality. In this study, we report the development and performance of the Cornell-IR LD Rice Array (C7AIR), a second-generation SNP array containing 7,098 markers that improves upon the previously released C6AIR. The C7AIR is designed to detect genome-wide polymorphisms within and between subpopulations of O. sativa, as well as O. glaberrima, O. rufipogon and O. nivara. The C7AIR combines top-performing SNPs from several previous rice arrays, including 4,007 SNPs from the C6AIR, 2,056 SNPs from the High Density Rice Array (HDRA), 910 SNPs from the 384-SNP GoldenGate sets, 189 SNPs from the 44K array selected to add information content for elite U.S. tropical japonica rice varieties, and 8 trait-specific SNPs. To demonstrate its utility, we carried out a genome-wide association analysis for plant height, employing the C7AIR across a diversity panel of 189 rice accessions and identified 20 QTLs contributing to plant height. The C7AIR SNP chip has so far been used for genotyping >10,000 rice samples. It successfully differentiates the five subpopulations of Oryza sativa, identifies introgressions from wild and exotic relatives, and is useful for quantitative trait loci (QTL) and association mapping in diverse materials. Moreover, data from the C7AIR provides valuable information that can be used to select informative and reliable SNP markers for conversion to lower-cost genotyping platforms for genomic selection and other downstream applications in breeding.

Genome-Wide Association Mapping to Identify Genetic Loci for Cold Tolerance and Cold Recovery During Germination in Rice.

Low temperature significantly affects rice growth and yield. Temperatures lower than 15°C are generally detrimental for germination and uniform seedling stand. To investigate the genetic architecture underlying cold tolerance during germination in rice, we conducted a genome-wide association study using a novel diversity panel of 257 rice accessions from around the world and the 7K SNP marker array. Phenotyping was conducted in controlled growth chambers under dark conditions at 13°C. The rice accessions were measured for low-temperature germinability, germination index, coleoptile length under cold stress, plumule length at 4-day recovery, and plumule length recovery rate. A total of 51 QTLs were identified at p < 0.001 and 17 QTLs were identified using an FDR < 0.05 across the different chilling indices with the whole panel of accessions. At the threshold of p < 0.001, a total of 20 QTLs were identified in the subset of japonica accessions, while 9 QTLs were identified in the subset of indica accessions. Considering the recurring SNPs and linked SNPs across different chilling indices, we identified 31 distinct QTL regions in the whole panel, 13 QTL regions in the japonica subset, and 7 distinct QTL regions in the indica subset. Among these QTL regions, three regions were common between the whole panel and japonica, three regions were common between the whole panel and indica, and one region was common between indica and japonica. A subset of QTL regions was potentially colocalized with previously identified genes and QTLs, including 10 from the japonica subset, 4 from the indica subset, and 6 from the whole panel. On the other hand, a total of 21 potentially novel QTL regions from the whole panel, 10 from the japonica subset, and 1 from the indica subset were identified. The results of our study provide useful information on the genetic architecture underlying cold tolerance during germination in rice, which in turn can be used for further molecular study and crop improvement for low-temperature stressed environments.