Implication of high variance in germplasm characteristics.

The beauty of conserving germplasm is the securement of genetic resources with numerous important traits, which could be utilized whenever they need to be incorporated into current cultivars. However, it would not be as useful as expected if the proper information was not given to breeders and researchers. In this study, we demonstrated that there is a large variation, both among and within germplasm, using a low-cost image-based phenotyping method; this could be valuable for improving gene banks' screening systems and for crop breeding. Using the image analyses of 507 accessions of buckwheat, we identified a wide range of variations per trait between germplasm accessions and within an accession. Since this implies a similarity with other important agronomic traits, we suggest that the variance of the presented traits should be checked and provided for better germplasm enhancement.

Time-Series Growth Prediction Model Based on U-Net and Machine Learning in Arabidopsis.

Yield prediction for crops is essential information for food security. A high-throughput phenotyping platform (HTPP) generates the data of the complete life cycle of a plant. However, the data are rarely used for yield prediction because of the lack of quality image analysis methods, yield data associated with HTPP, and the time-series analysis method for yield prediction. To overcome limitations, this study employed multiple deep learning (DL) networks to extract high-quality HTTP data, establish an association between HTTP data and the yield performance of crops, and select essential time intervals using machine learning (ML). The images of Arabidopsis were taken 12 times under environmentally controlled HTPP over 23 days after sowing (DAS). First, the features from images were extracted using DL network U-Net with SE-ResXt101 encoder and divided into early (15-21 DAS) and late (∼21-23 DAS) pre-flowering developmental stages using the physiological characteristics of the Arabidopsis plant. Second, the late pre-flowering stage at 23 DAS can be predicted using the ML algorithm XGBoost, based only on a portion of the early pre-flowering stage (17-21 DAS). This was confirmed using an additional biological experiment (P < 0.01). Finally, the projected area (PA) was estimated into fresh weight (FW), and the correlation coefficient between FW and predicted FW was calculated as 0.85. This was the first study that analyzed time-series data to predict the FW of related but different developmental stages and predict the PA. The results of this study were informative and enabled the understanding of the FW of Arabidopsis or yield of leafy plants and total biomass consumed in vertical farming. Moreover, this study highlighted the reduction of time-series data for examining interesting traits and future application of time-series analysis in various HTPPs.

High-Throughput Phenotyping (HTP) Data Reveal Dosage Effect at Growth Stages in Arabidopsis thaliana Irradiated by Gamma Rays.

The effects of radiation dosages on plant species are quantitatively presented as the lethal dose or the dose required for growth reduction in mutation breeding. However, lethal dose and growth reduction fail to provide dynamic growth behavior information such as growth rate after irradiation. Irradiated seeds of Arabidopsis were grown in an environmentally controlled high-throughput phenotyping (HTP) platform to capture growth images that were analyzed with machine learning algorithms. Analysis of digital phenotyping data revealed unique growth patterns following treatments below LD50 value at 641 Gy. Plants treated with 100-Gy gamma irradiation showed almost identical growth pattern compared with wild type; the hormesis effect was observed >21 days after sowing. In 200 Gy-treated plants, a uniform growth pattern but smaller rosette areas than the wild type were seen (p < 0.05). The shift between vegetative and reproductive stages was not retarded by irradiation at 200 and 300 Gy although growth inhibition was detected under the same irradiation dose. Results were validated using 200 and 300 Gy doses with HTP in a separate study. To our knowledge, this is the first study to apply a HTP platform to measure and analyze the dosage effect of radiation in plants. The method enabled an in-depth analysis of growth patterns, which could not be detected previously due to a lack of time-series data. This information will improve our knowledge about the effects of radiation in model plant species and crops.

Inhibitory Effects of Pterocarpus santalinus Extract against IgE/Antigen-Sensitized Mast Cells and Atopic Dermatitis-Like Skin Lesions.

Pterocarpus santalinus has been traditionally used as a medicinal plant owing to its anti-inflammatory, anthelmintic, tonic, antihyperglycemic, and diaphoretic properties. We hypothesized that P. santalinus might have therapeutic potential in alleviating allergy and atopic dermatitis. Thus, we investigated the inhibitory effects of P. santalinus extract against allergic responses and 2,4-dinitrochlorobenzene-induced atopic dermatitis-like dorsal skin lesions using immunoglobulin E-sensitized rat basophilic leukemia-2H3 mast cells and NC/Nga mice. Degranulation and enzyme-linked immunosorbent assays were conducted to measure degranulation, proinflammatory cytokine levels, and prostaglandin E2 concentrations in immunoglobulin E/antigen-sensitized RBL-2H3 mast cells. The therapeutic efficacy of P. santalinus extract in 2,4-dinitrochlorobenzene-induced atopic dermatitis was evaluated through morphological, physiological, and immunological analysis. P. santalinus extract inhibited ß-hexosaminidase and histamine release and reduced tumor necrosis factor-α, interleukin-4, and prostaglandin E2 secretion. Furthermore, P. santalinus extract suppressed atopic dermatitis-like skin lesions by regulating the serum levels of immunoglobulin E and immunoglobulin G2a, and messenger ribonucleic acid expression of T helper cell 1- and T helper cell 2-related mediators in the skin lesions. Histopathological analyses showed a decrease in epidermal thickness and intradermal inflammatory cell infiltration. These results suggested that P. santalinus extract might have beneficial effects in treating allergic and atopic dermatitis-like skin disorders.

An automated, high-throughput plant phenotyping system using machine learning-based plant segmentation and image analysis.

A high-throughput plant phenotyping system automatically observes and grows many plant samples. Many plant sample images are acquired by the system to determine the characteristics of the plants (populations). Stable image acquisition and processing is very important to accurately determine the characteristics. However, hardware for acquiring plant images rapidly and stably, while minimizing plant stress, is lacking. Moreover, most software cannot adequately handle large-scale plant imaging. To address these problems, we developed a new, automated, high-throughput plant phenotyping system using simple and robust hardware, and an automated plant-imaging-analysis pipeline consisting of machine-learning-based plant segmentation. Our hardware acquires images reliably and quickly and minimizes plant stress. Furthermore, the images are processed automatically. In particular, large-scale plant-image datasets can be segmented precisely using a classifier developed using a superpixel-based machine-learning algorithm (Random Forest), and variations in plant parameters (such as area) over time can be assessed using the segmented images. We performed comparative evaluations to identify an appropriate learning algorithm for our proposed system, and tested three robust learning algorithms. We developed not only an automatic analysis pipeline but also a convenient means of plant-growth analysis that provides a learning data interface and visualization of plant growth trends. Thus, our system allows end-users such as plant biologists to analyze plant growth via large-scale plant image data easily.

Assembly and annotation of a draft genome sequence for Glycine latifolia, a perennial wild relative of soybean.

Glycine latifolia (Benth.) Newell & Hymowitz (2n = 40), one of the 27 wild perennial relatives of soybean, possesses genetic diversity and agronomically favorable traits that are lacking in soybean. Here, we report the 939-Mb draft genome assembly of G. latifolia (PI 559298) using exclusively linked-reads sequenced from a single Chromium library. We organized scaffolds into 20 chromosome-scale pseudomolecules utilizing two genetic maps and the Glycine max (L.) Merr. genome sequence. High copy numbers of putative 91-bp centromere-specific tandem repeats were observed in consecutive blocks within predicted pericentromeric regions on several pseudomolecules. No 92-bp putative centromeric repeats, which are abundant in G. max, were detected in G. latifolia or Glycine tomentella. Annotation of the assembled genome and subsequent filtering yielded a high confidence gene set of 54 475 protein-coding loci. In comparative analysis with five legume species, genes related to defense responses were significantly overrepresented in Glycine-specific orthologous gene families. A total of 304 putative nucleotide-binding site (NBS)-leucine-rich-repeat (LRR) genes were identified in this genome assembly. Different from other legume species, we observed a scarcity of TIR-NBS-LRR genes in G. latifolia. The G. latifolia genome was also predicted to contain genes encoding 367 LRR-receptor-like kinases, a family of proteins involved in basal defense responses and responses to abiotic stress. The genome sequence and annotation of G. latifolia provides a valuable source of alternative alleles and novel genes to facilitate soybean improvement. This study also highlights the efficacy and cost-effectiveness of the application of Chromium linked-reads in diploid plant genome de novo assembly.

Distribution and Diversity of Ocular Microbial Communities in Diabetic Patients Compared with Healthy Subjects.

PURPOSE: The aim of this study was to identify differences in the major (core vs. variable) microbial genera of human subjects with and without diabetes. METHODS: Bacterial 16S rRNA genes obtained from conjunctival swabs of 19 healthy subjects and 30 diabetic patients were sequenced using the Illumina MiSeq platform, and the sequencing data were analyzed using QIIME 1.9.1. To elucidate the microbial diversity in the ocular surface (OS), test programs from various bioinformatics domains were used. RESULTS: Diversity index and rarefaction analysis showed that the microbial community of the diabetic patients was more diverse than that of the healthy subjects. Proteobacteria, Firmicutes, Actinobacteria, Cyanobacteria and Bacteroidetes were the dominant taxa present in the OS, and there was a significant difference in the relative abundance of the bacterial phyla between the diabetic patients and control subjects. Proteobacteria were more abundant in the diabetic group, whereas Firmicutes was more abundant in the control group. Analysis of bacterial taxa at the genus level showed that the core microbiome of diabetic patients comprised Acinetobacter, Burkholderia, Sphingomonas, and Ralstonia, whereas that of the controls comprised Bradyrhizobiaceae, Staphylococcus, Corynebacterium, Pseudomonas, Novosphingobium, Neisseriaceae, and Acinetobacter. CONCLUSIONS: There was a significant difference in the microbial community composition between diabetic patients and healthy subjects. A high abundance of Acinetobacter in the OS of diabetic patients may arise from the unique characteristics of the OS compared with those of other organ surfaces.

Comparative mapping of the wild perennial Glycine latifolia and soybean (G. max) reveals extensive chromosome rearrangements in the genus Glycine.

Soybean (Glycine max L. Mer.), like many cultivated crops, has a relatively narrow genetic base and lacks diversity for some economically important traits. Glycine latifolia (Benth.) Newell & Hymowitz, one of the 26 perennial wild Glycine species related to soybean in the subgenus Glycine Willd., shows high levels of resistance to multiple soybean pathogens and pests including Alfalfa mosaic virus, Heterodera glycines Ichinohe and Sclerotinia sclerotiorum (Lib.) de Bary. However, limited information is available on the genomes of these perennial Glycine species. To generate molecular resources for gene mapping and identification, high-density linkage maps were constructed for G. latifolia using single nucleotide polymorphism (SNP) markers generated by genotyping by sequencing and evaluated in an F2 population and confirmed in an F5 population. In each population, greater than 2,300 SNP markers were selected for analysis and segregated to form 20 large linkage groups. Marker orders were similar in the F2 and F5 populations. The relationships between G. latifolia linkage groups and G. max and common bean (Phaseolus vulgaris L.) chromosomes were examined by aligning SNP-containing sequences from G. latifolia to the genome sequences of G. max and P. vulgaris. Twelve of the 20 G. latifolia linkage groups were nearly collinear with G. max chromosomes. The remaining eight G. latifolia linkage groups appeared to be products of multiple interchromosomal translocations relative to G. max. Large syntenic blocks also were observed between G. latifolia and P. vulgaris. These experiments are the first to compare genome organizations among annual and perennial Glycine species and common bean. The development of molecular resources for species closely related to G. max provides information into the evolution of genomes within the genus Glycine and tools to identify genes within perennial wild relatives of cultivated soybean that could be beneficial to soybean production.

Identification of high-quality single-nucleotide polymorphisms in Glycine latifolia using a heterologous reference genome sequence.

Like many widely cultivated crops, soybean [Glycine max (L.) Merr.] has a relatively narrow genetic base, while its perennial distant relatives in the subgenus Glycine Willd. are more genetically diverse and display desirable traits not present in cultivated soybean. To identify single-nucleotide polymorphisms (SNPs) between a pair of G. latifolia accessions that were resistant or susceptible to Sclerotinia sclerotiorum (Lib.) de Bary, reduced-representations of DNAs from each accession were sequenced. Approximately 30 % of the 36 million 100-nt reads produced from each of the two G. latifolia accessions aligned primarily to gene-rich euchromatic regions on the distal arms of G. max chromosomes. Because a genome sequence was not available for G. latifolia, the G. max genome sequence was used as a reference to identify 9,303 G. latifolia SNPs that aligned to unique positions in the G. max genome with at least 98 % identity and no insertions and deletions. To validate a subset of the SNPs, nine TaqMan and 384 GoldenGate allele-specific G. latifolia SNP assays were designed and analyzed in F2 G. latifolia populations derived from G. latifolia plant introductions (PI) 559298 and 559300. All nine TaqMan markers and 91 % of the 291 polymorphic GoldenGate markers segregated in a 1:2:1 ratio. Genetic linkage maps were assembled for G. latifolia, nine of which were uninterrupted and nearly collinear with the homoeologous G. max chromosomes. These results made use of a heterologous reference genome sequence to identify more than 9,000 informative high-quality SNPs for G. latifolia, a subset of which was used to generate the first genetic maps for any perennial Glycine species.

Multiple loci condition seed transmission of soybean mosaic virus (SMV) and SMV-induced seed coat mottling in soybean.

Infection of soybean plants with Soybean mosaic virus (SMV), which is transmitted by aphids and through seed, can cause significant reductions in seed production and quality. Because seedborne infections are the primary sources of inoculum for SMV infections in North America, host-plant resistance to seed transmission can limit the pool of plants that can serve as sources of inoculum. To examine the inheritance of SMV seed transmission in soybean, crosses were made between plant introductions (PIs) with high (PI88799), moderate (PI60279), and low (PI548391) rates of transmission of SMV through seed. In four F(2) populations, SMV seed transmission segregated as if conditioned by two or more genes. Consequently, a recombinant inbred line population was derived from a cross between PIs 88799 and 548391 and evaluated for segregation of SMV seed transmission, seed coat mottling, and simple sequence repeat markers. Chromosomal regions on linkage groups C1 and C2 were significantly associated with both transmission of isolate SMV 413 through seed and SMV-induced seed coat mottling, and explained ≈42.8 and 46.4% of the variability in these two traits, respectively. Chromosomal regions associated with seed transmission and seed coat mottling contained homologues of Arabidopsis genes DCL3 and RDR6, which encode enzymes involved in RNA-mediated transcriptional and posttranscriptional gene silencing.

Identifying differentially expressed genes in leaves of Glycine tomentella in the presence of the fungal pathogen Phakopsora pachyrhizi.

To compare transcription profiles in genotypes of Glycine tomentella that are differentially sensitive to soybean rust, caused by the fungal pathogen Phakopsora pachyrhizi, four cDNA libraries were constructed using the suppression subtractive hybridization method. Libraries were constructed from rust-infected and non-infected leaves of resistant (PI509501) and susceptible (PI441101) genotypes of G. tomentella, and subjected to subtractive hybridization. A total of 1,536 sequences were obtained from these cDNA libraries from which 195 contigs and 865 singletons were identified. Of these sequenced cDNA clones, functions of 646 clones (61%) were determined. In addition, 160 clones (15%) had significant homology to hypothetical proteins; while the remaining 254 clones (24%) did not reveal any hits. Of those 646 clones with known functions, different genes encoding protein products involved in metabolism, cell defense, energy, protein synthesis, transcription, and cellular transport were identified. These findings were subsequently confirmed by real time RT-PCR and dot blot hybridization.

Transcriptome analysis of resistant and susceptible genotypes of Glycine tomentella during Phakopsora pachyrhizi infection reveals novel rust resistance genes.

Soybean rust, caused by Phakopsora pachyrhizi, is a destructive foliar disease in nearly all soybean-producing countries. To identify genes controlling resistance to soybean rust, transcriptome profiling was conducted in resistant and susceptible Glycine tomentella genotypes triggered by P. pachyrhizi infection. Among 38,400 genes monitored using a soybean microarray, at 5% false discovery rate, 1,342 genes were identified exhibiting significant differential expression between uninfected and P. pachyrhizi-infected leaves at 12, 24, 48, and 72 h post-inoculation (hpi) in both rust-susceptible and rust-resistant genotypes. Differentially expressed genes were grouped into 12 functional categories, and among those, large numbers relate to basic plant metabolism. Transcripts for genes involved in the phenylpropanoid pathway were up-regulated early during rust infection. Similarly, genes coding for proteins related to stress and defense responses such as glutathione-S-transferases, peroxidases, heat shock proteins, and lipoxygenases were consistently up-regulated following infection at all four time points. Whereas, subsets of genes involved in cellular transport, cellular communication, cell cycle, and DNA processing were down-regulated. Quantitative real-time reverse-transcription polymerase chain reaction (qRT-PCR) on randomly selected genes from the different categories confirmed these findings. Of differentially expressed genes, those associated with the flavonoid biosynthesis pathway as well as those coding for peroxidases and lipoxygenases were likely to be involved in rust resistance in soybean, and would serve as good candidates for functional studies. These findings provided insights into mechanisms underlying resistance and general activation of plant defense pathways in response to rust infection.