Recycling of irrigation water increases disease risks due to spread of waterborne oomycete plant pathogens such as Phytophthora, Pythium, and Phytopythium. A comprehensive metabarcoding study was conducted to determine spatial and temporal dynamics of oomycete communities present in irrigation water collected from a creek (main water source), a pond, retention reservoirs, a chlorinated water reservoir, and runoff channels within a commercial container nursery in Oregon over the course of 1 year. Two methods, filtration and leaf baiting, were compared for the detection of oomycete communities. Oomycete communities in recycled irrigation water were less diverse but highly enriched with biologically active plant pathogens as compared with the creek water. The filtration method captured a larger portion of oomycete diversity, while leaf baiting was more selective for plant-associated oomycete species of Phytophthora and a few Pythium and Phytopythium species. Seasonality strongly influenced oomycete diversity in irrigation water and detection with leaf baiting. Phytophthora was the major colonizer of leaf baits in winter, while all three genera were equally abundant on leaf baits in summer. The metabarcoding approach was highly effective in studying oomycete ecology, however, it failed to distinguish some closely related species. We developed a custom oomycete internal transcribed spacer (ITS)1 reference database containing shorter sequences flanked by ITS6 and ITS7 primers used in metabarcoding and used it to assemble a list of indistinguishable species complexes and clusters to improve identification. The predominant bait-colonizing species detected in recycled irrigation water were the Phytophthora citricola-complex, Phytophthora syringae, Phytophthora parsiana-cluster, Phytophthora chlamydospora, Phytophthora gonapodyides, Phytophthora irrigata, Phytophthora taxon Oaksoil-cluster, Phytophthora citrophthora-cluster, Phytophthora megasperma-cluster, Pythium chondricola-complex, Pythium dissotocum-cluster, and Phytopythium litorale.
Discovery of new germplasm sources and identification of haplotypes for the durable Soybean mosaic virus resistance gene, Rsv 4, provide novel resources for map-based cloning and genetic improvement efforts in soybean. The Soybean mosaic virus (SMV) resistance locus Rsv4 is of interest because it provides a durable type of resistance in soybean [Glycine max (L.) Merr.]. To better understand its molecular basis, we used a population of 309 BC3F2 individuals to fine-map Rsv4 to a ~120 kb interval and leveraged this genetic information in a second study to identify accessions 'Haman' and 'Ilpumgeomjeong' as new sources of Rsv4. These two accessions along with three other Rsv4 and 14 rsv4 accessions were used to examine the patterns of nucleotide diversity at the Rsv4 region based on high-depth resequencing data. Through a targeted association analysis of these 19 accessions within the ~120 kb interval, a cluster of four intergenic single-nucleotide polymorphisms (SNPs) was found to perfectly associate with SMV resistance. Interestingly, this ~120 kb interval did not contain any genes similar to previously characterized dominant disease resistance genes. Therefore, a haplotype analysis was used to further resolve the association signal to a ~94 kb region, which also resulted in the identification of at least two Rsv4 haplotypes. A haplotype phylogenetic analysis of this region suggests that the Rsv4 locus in G. max is recently introgressed from G. soja. This integrated study provides a strong foundation for efforts focused on the cloning of this durable virus resistance gene and marker-assisted selection of Rsv4-mediated SMV resistance in soybean breeding programs.
BackgroundLow phytic acid (lpa) crops are potentially eco-friendly alternative to conventional normal phytic acid (PA) crops, improving mineral bioavailability in monogastric animals as well as decreasing phosphate pollution. The lpa crops developed to date carry mutations that are directly or indirectly associated with PA biosynthesis and accumulation during seed development. These lpa crops typically exhibit altered carbohydrate profiles, increased free phosphate, and lower seedling emergence, the latter of which reduces overall crop yield, hence limiting their large-scale cultivation. Improving lpa crop yield requires an understanding of the downstream effects of the lpa genotype on seed development. Towards that end, we present a comprehensive comparison of gene-expression profiles between lpa and normal PA soybean lines (Glycine max) at five stages of seed development using RNA-Seq approaches. The lpa line used in this study carries single point mutations in a myo-inositol phosphate synthase gene along with two multidrug-resistance protein ABC transporter genes.ResultsRNA sequencing data of lpa and normal PA soybean lines from five seed-developmental stages (total of 30 libraries) were used for differential expression and functional enrichment analyses. A total of 4235 differentially expressed genes, including 512-transcription factor genes were identified. Eighteen biological processes such as apoptosis, glucan metabolism, cellular transport, photosynthesis and 9 transcription factor families including WRKY, CAMTA3 and SNF2 were enriched during seed development. Genes associated with apoptosis, glucan metabolism, and cellular transport showed enhanced expression in early stages of lpa seed development, while those associated with photosynthesis showed decreased expression in late developmental stages. The results suggest that lpa-causing mutations play a role in inducing and suppressing plant defense responses during early and late stages of seed development, respectively.ConclusionsThis study provides a global perspective of transcriptomal changes during soybean seed development in an lpa mutant. The mutants are characterized by earlier expression of genes associated with cell wall biosynthesis and a decrease in photosynthetic genes in late stages. The biological processes and transcription factors identified in this study are signatures of lpa-causing mutations.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-2283-9) contains supplementary material, which is available to authorized users.
Accelerating the pace of microbiome science to enhance crop productivity and agroecosystem health will require transdisciplinary studies, comparisons among datasets, and synthetic analyses of research from diverse crop management contexts. However, despite the widespread availability of crop-associated microbiome data, variation in field sampling and laboratory processing methodologies, as well as metadata collection and reporting, significantly constrains the potential for integrative and comparative analyses. Here we discuss the need for agriculture-specific metadata standards for microbiome research, and propose a list of “required” and “desirable” metadata categories and ontologies essential to be included in a future minimum information metadata standards checklist for describing agricultural microbiome studies. We begin by briefly reviewing existing metadata standards relevant to agricultural microbiome research, and describe ongoing efforts to enhance the potential for integration of data across research studies. Our goal is not to delineate a fixed list of metadata requirements. Instead, we hope to advance the field by providing a starting point for discussion, and inspire researchers to adopt standardized procedures for collecting and reporting consistent and well-annotated metadata for agricultural microbiome research.
A dominant loss of function mutation in myo-inositol phosphate synthase (MIPS) gene and recessive loss of function mutations in two multidrug resistant protein type-ABC transporter genes not only reduce the seed phytic acid levels in soybean, but also affect the pathways associated with seed development, ultimately resulting in low emergence. To understand the regulatory mechanisms and identify key genes that intervene in the seed development process in low phytic acid crops, we performed computational inference of gene regulatory networks in low and normal phytic acid soybeans using a time course transcriptomic data and multiple network inference algorithms. We identified a set of putative candidate transcription factors and their regulatory interactions with genes that have functions in myo-inositol biosynthesis, auxin-ABA signaling, and seed dormancy. We evaluated the performance of our unsupervised network inference method by comparing the predicted regulatory network with published regulatory interactions in Arabidopsis. Some contrasting regulatory interactions were observed in low phytic acid mutants compared to non-mutant lines. These findings provide important hypotheses on expression regulation of myo-inositol metabolism and phytohormone signaling in developing low phytic acid soybeans. The computational pipeline used for unsupervised network learning in this study is provided as open source software and is freely available at https://lilabatvt.github.io/LPANetwork/.
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