Progress 01/01/24 to 12/31/24
Outputs
Target Audience:During this reporting period, we engaged with a targeted audience that includedsoil microbiome and disease research, students, and extension professionals through conference presentations and a peer-reviewed publication. Changes/Problems:The project experienced significant changes that included problems and delays that may significantly impact the rate of expenditure. One of the major problems encountered was the departure of the person recruited to perform DNA and RNA isolations after being trained on the tasks. This person's departure created a setback for the project as we had to find a replacement. To mitigate this issue, we successfully recruited two graduate students, one responsible for laboratory tasks and the other for data analysis. Another major challenge we faced was the initial delay in starting the project due to the pathogen isolate used in the study not producing zoospores. This delayed the experimentation process and impacted the project timeline. Lastly, one of the collaborators passed away, prompting us to find a new collaborator and delaying the progress. What opportunities for training and professional development has the project provided?Two technicians and three graduate students are being trained on soil microbiome-related techniques, including experimental planning, data acquisition, and sample preparation. How have the results been disseminated to communities of interest?We have presented four abstracts (two for local conferences and two for Plant Health 2024, the annual meeting of the American Phytopathological Society (APS)). One protocol for RNA extraction from soil has been submitted and is now available at protocol.io. In addition, one manuscript has been accepted to be published at BMC Methods. What do you plan to do during the next reporting period to accomplish the goals?To elucidate functional changes in the rhizosphere microbiome during a compatible and an incompatible interaction of soybean andP. sojae,we will do the following: We will sequence the metatranscriptome using Zymo-Seq RiboFree® Total RNA Library Kit to capture bacterial and eukaryotic mRNA. The prepared samples will be sequenced on an Illumina HiSeq sequencer. The resulting short reads will be assembled into transcripts using our optimized bioinformatics protocol. The assembled transcripts will be annotated using the Trinotate pipeline to predict coding sequences, identify conserved protein domains, and retrieve pathway information related to KEGG and GO annotations. Lastly, we will identify differentially expressed genes using Trimmomatic and RSEM software and perform GO and KEGG enrichment analyses to find overrepresented and underrepresented pathways, functions, or biological processes.
Impacts
What was accomplished under these goals?
Protocol optimization: Oxford Nanopore sequencing We have used two different (Zymo Quick-DNA™ Fecal/Soil Microbe Miniprep Kit and Qiagen DNeasy PowerSoil Pro Kits) kits to check if the extracted DNA is suitable for downstream analyses. After DNA extractions, DNA samples were used to amplify the 16sRNA gene. The results indicated that only DNA extracted from the Qiagen DNeasy PowerSoil Pro Kit could be amplified. Therefore, this kit will be used for all subsequent DNA extractions. In addition, we have also used the extracted DNA for the bacterial 16S rRNA gene. Libraries were prepared using Rapid 16S barcoding kit 1-24 (SQK-16S024) and then sequenced on the MinION Mk1B portable sequencing device (Oxford Nanopore Technologies [ONT], Oxford, UK). This sequencing run was successful with the expected amplicon size. The resulting data were processed using EPI2ME bioinformatic pipeline and were also compared against ZymoBIOMICS microbial community DNA standard. Our data indicated that isolated soil DNA samples could be used for long-read amplicon sequencing. Optimized protocol for soil RNA extractions: Extracting RNA from soil presents a challenge due to the complex composition of soil matrices. Furthermore, commercial kits are expensive to use with many samples. Therefore, we have developed an optimized RNA extraction method using a modified CTAB protocol tailored for soybean rhizospheric microbes. Our optimized RNA extraction protocol yielded good-quality RNA with a high yield with several different soil types. We have tested this method using four different soil types from Iowa and Arkansas, and this method gave comparable or even superior results to the two commercial kits we tested. As discussed below, we have successfully used these RNA samples and RNAseq sample preparation. Established protocol for RNAseq library preparation using soil RNA For RNAseq sample preparation, removing highly abundant ribosomal RNA is often required to analyze regulatory and messenger RNA. The latter two are important for the functional analysis of the microbiome. The difficulty lies in conventional library preparation methods' inability to simultaneously remove rRNA from both prokaryotes and eukaryotes in a mixed organism pool like soil RNA samples. Therefore, we used Zymo-Seq RiboFree® Total RNA Library Kit (Zymo Research, Cat# R3000) with RNA samples obtained from soil samples using the modified CTAB method. During this protocol, RNA is subjected to cDNA Synthesis. Then, cDNA is denatured and renatured to form RNA-cDNA hybrids, which are treated with a series of depletion reagents inside a thermal cycler for the removal of rRNA-cDNA hybrids. Subsequent steps prepared the cDNA molecules for high throughput sequencing using an Illumina sequencer. This kit has never been tested with environment RNA samples from a pool of organisms. Our data demonstrate successful rRNA removal and the generation of superior-quality microbial transcript data, enabling the assessment of microbial activity. Development of Bioinformatics pipeline: There is a lack of agreement in the literature regarding the most effective informatics approaches for metatranscriptome data analysis. Moreover, the extensive variability among soil organisms can complicate the use of tools for transcriptome analysis and assemblers, with many soil microbes remaining underexplored. Additionally, many metatranscriptomics studies prioritize the identification of taxa over differential expression. Therefore, we wanted to develop a versatile bioinformatics pipeline specifically tailored for diverse soil types, enabling the generation of metatranscriptomic profiles and facilitating differential expression and pathway analysis. Following a comprehensive literature review, we identified potential bioinformatics steps and compared various tools for each step. We established criteria for evaluating pre-assembly steps and assemblers and found that SPAdes produced the longest contigs with the highest mapping percentage among the three aligners tested. To define changes in the composition of the rhizosphere microbiome during a compatible and an incompatible interaction of soybean andP. sojae,we are planning to do the following: We will complete long-read sequencing for marker genes (16S rRNA genes for bacteria or 18S ribosomal RNA genes for fungi and oomycetes) using Native Barcoding Kit V14 (Cat# SQK-NBD114). The resulting reads will be processed using the dada2 R package to perform the taxonomic assignment and analyze α-diversity, β-diversity, and differences in the abundance of taxa among treatments. Microbial diversity, as measured by the Shannon Index, significantly increased in pathogen-inoculated tolerant soybean cultivars (Williams82) compared to mock-inoculated controls at 9 days after inoculation (DAI), whereas the susceptible cultivar exhibited minimal change. To investigate the relationship between microbial diversity and pathogen presence, we quantified P. sojae abundance in rhizosphere soils using qPCR. As expected, pathogen levels were negligible at 0 hours but rose sharply by 9 DAI in the susceptible cultivar. In contrast, no detectable pathogen levels were observed in the rhizosphere of the tolerant cultivar, indicating effective suppression. This inverse relationship--greater microbial diversity coupled with lower pathogen abundance in the tolerant cultivar--suggests that resistance to P. sojae may influence rhizosphere microbiome composition to enhance disease suppression. This is consistent with findings by Mendes et al (2018) who reported elevated bacterial diversity and abundance in the rhizosphere of Fusarium-resistant common beans compared to both bulk soil and more susceptible cultivars. CLR-transformed abundance data revealed statistically significant increases in the bacterial genera Bacillus, Lysinibacillus, Paenibacillus, and Pseudomonas at 9 DAI in pathogen-inoculated treatments compared to mock controls (DESeq2; p.adj ≤ 0.05). These changes were particularly evident in the tolerant soybean cultivar. The enrichment of these taxa, many of which are associated with biocontrol and plant growth-promotion, suggests an active microbial response potentially contributing to pathogen suppression. These observations provide additional evidence that resistance to P. sojae may be partially mediated through rhizosphere microbiome modulation. Mendes, L. W., Mendes, R., Raaijmakers, J. M. & Tsai, S. M. Breeding for soil-borne pathogen resistance impacts active rhizosphere microbiome of common bean. ISME J. 12, 3038-3042 (2018).
Publications
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Progress 01/01/23 to 12/31/23
Outputs
Target Audience:
Nothing Reported
Changes/Problems:The project experienced significant changes that included problems and delays that may significantly impact the rate of expenditure. One of the major problems encountered was the departure of the person recruited to perform DNA and RNA isolations after being trained on the tasks. This person's departure created a setback for the project as we had to find a replacement. To mitigate this issue, we successfully recruited two graduate students, one responsible for laboratory tasks and the other for data analysis. Another major challenge we faced was the initial delay in starting the project due to the pathogen isolate used in the study not producing zoospores. This delayed the experimentation process and impacted the project timeline. What opportunities for training and professional development has the project provided?Two technicians and three graduate students have been trained on soil microbiome-related techniques, including experimental planning, data acquisition, and sample preparation. How have the results been disseminated to communities of interest?We submitted four abstracts (two for local conferences and two for Plant Health 2024, the annual meeting of the American Phytopathological Society (APS)). In addition, we are preparing a manuscript, which will be submitted to BMC Methods. What do you plan to do during the next reporting period to accomplish the goals?To define changes in the composition of the rhizosphere microbiome during a compatible and an incompatible interaction of soybean andP. sojae,we are planning to do the following: We will complete long-read sequencing for marker genes (16S rRNA genes for bacteria or 18S ribosomal RNA genes for fungi and oomycetes) using Native Barcoding Kit V14 (Cat# SQK-NBD114). The resulting reads will be processed using the dada2 R package to perform the taxonomic assignment and analyze α-diversity, β-diversity, and differences in the abundance of taxa among treatments. To elucidate functional changes in the rhizosphere microbiome during a compatible and an incompatible interaction of soybean andP. sojae,we will do the following: We will sequence the metatranscriptome using Zymo-Seq RiboFree® Total RNA Library Kit to capture bacterial and eukaryotic mRNA. The prepared samples will be sequenced on an Illumina HiSeq sequencer. The resulting short reads will be assembled into transcripts using our optimized bioinformatics protocol.The assembled transcripts will be annotated using the Trinotate pipeline to predict coding sequences, identify conserved protein domains, and retrieve pathway information related to KEGG and GO annotations. Lastly, we will identify differentially expressed genes using Trimmomatic and RSEM software and perform GO and KEGG enrichment analyses to find overrepresented and underrepresented pathways, functions, or biological processes.
Impacts
What was accomplished under these goals?
Completed sample collection: Seedling roots and rhizosphere soil samples were collected from soybean inoculated with zoospores ofP. sojae6497.Two varieties of soybean were inoculated,Williams (rps) and Williams82 (Rps1k) varieties. Briefly, soybean seeds (N=10) were planted in a circular patttern in 12 oz foam cups filled with a soil/Perlite mix.A glass tube was placed in the middle of the ring of seeds. The cups were placed under normal growth conditions. When the seedlings were approximately 7 days old, the glass tube was removed, and a zoospore suspension (~103zoospores ml-1)was gently poured into the hole left by the tube. Samples were collected (24 hours after inoculation (hai), 48hai, 72hai, and 9 dai) by gently massaging the cups to release soil/Perlite mix, separating seedlings, and shaking off bulk soil from roots. The roots were cut from each seedling, placed in orange capped 50ml tubes and flash frozen. A sample of roots from seedlings 9dai was tested by PCR usingP. sojae-specific primers and the presence of a 300 bp amplicon indicated the Williams seedlings were infected, while no amplicon was observed for Williams82 seedlings. In addition, approximately 10 seedlings were kept for up to 4 weeks after inoculation and monitored weekly for root rot development. No root rot was observed.
Publications
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Progress 01/01/22 to 12/31/22
Outputs
Target Audience:
Nothing Reported
Changes/Problems:Several problems and delays occurred that may significantly impact the rate of expenditure. The person recruited to perform DNA and RNA isolations left the project after being trained. This resulted in a setback for the project as we had to find a replacement. We have successfully recruited two graduate students, one who will be responsible for laboratory tasks and the other for data analysis. Other challenges we encountered that caused a delay in the experimentation process and may impact the project timeline are: (i) the isolate of P. sojae used in the study did not consistently produce zoospores. Consequently, we are using another isolate P6497, which was the firstgenome-sequenced isolateof P. sojae; and (ii) poor germination in the first run of the inoculation experiments. We are not sure if this was due to residual herbicide or Pythium damping off. Fresh soil samples were collected, and germination was good in a repeated run of the experiment. What opportunities for training and professional development has the project provided?Two technicians and two graduate students have been trained in soil microbiome-related techniques, including experimental planning, data acquisition, and sample preparation. How have the results been disseminated to communities of interest?Since the project is still in the preliminary stages, no results have been shared. However, we plan to present X posters and talks at international research conferences this summer, for example, the 2nd International Molecular Plant Protection Congress. What do you plan to do during the next reporting period to accomplish the goals?To define changes in the composition of the rhizosphere microbiome during a compatible and an incompatible interaction of soybean andP. sojae,we will complete long-read sequencing for marker genes (16S rRNA gene for bacteria or 18S rRNA gene for fungi and oomycetes). Loop Genomics will prepare samples for sequencing using LoopSeq Microbiome 16S kit or LoopSeq Mycobiome 18S-ITS kit. The Illumina Novoseq sequencer will be used to sequence around 12,000 molecules per sample for each marker gene. The resulting short reads will be processed using the dada2 R package to perform the taxonomic assignment and analyze α-diversity, β-diversity, and differences in the abundance of taxa among treatments.We have successfully used both LoopSeq platforms for soybean rhizosphere microbiome analysis for a separate project. To elucidate functional changes in the rhizosphere microbiome during a compatible and an incompatible interaction of soybean andP. sojae,we will sequence the metatranscriptome using Cappable-Seq or ReCappable-Seq methods to capture bacterial or eukaryotic mRNA. The LoopSeq Transcriptome 3x8-plex Kit will be used for library preparation, and the prepared samples will be sequenced on an Illumina HiSeq sequencer. The resulting short reads will be assembled into transcripts using Loop Genomics cloud-based platform, followed by quality filtering, denoising, and chimera removal of the SLRs using the dada2 R package. Next, the assembled sequences will be annotated using Trinotate pipeline to predict coding sequences, identify conserved protein domains, and retrieve pathway information related to KEGG and GO annotations. Lastly, we will identify differentially expressed genes using Trimmomatic and RSEM software and perform GO and KEGG enrichment analyses to find overrepresented and underrepresented pathways, functions, or biological processes.
Impacts
What was accomplished under these goals?
Phytophthora stem and root rot (PSR) is a disease in soybean caused by Phytophthora sojae, leading to annual crop losses of up to $200 million in the US alone. With the global population projected to reach 10 billion in the following decades, it is critical to managing plant diseases to meet food demand. PSR is predominantly managed by planting resistant soybean varieties that recognize effector proteins produced by the pathogen. Thus, P.sojae evolves rapidly to overcome this resistance. Thus, there is an urgent need to find new ways to manage this devastating disease. Recent research suggests that plant pathogens may manipulate the soil microbiome to promote successful infection, potentially by using their effector proteins. Little is known about howP. sojaeinteracts with the soil microbiome. Our research aims to shed light on the mechanisms by whichP. sojaemanipulates the soil microbiome to gain an advantage and cause PSR. Ultimately, our findings may lead to new strategies for managing PSR and improving soybean production. What was accomplished under these goals? Soil selection: We selected and collected field soil from three Iowa State University (ISU) Research Farms. Nicollet soil was collected from the ISU Agricultural Engineering and Agronomy Research Farm in Boone County and is a poorly drained soil that formed in a calcareous loamy glacial till. Spillville soil was collected from the Hinds Farm, which is part of the Central Iowa Research and Demonstration Farms in Story County and is moderately well-drained or somewhat poorly-drained soil formed in dark-colored, medium-textured alluvium. Grundy soil was collected from the ISU McNay Memorial Research and Demonstration Farm in Lucas County and is a somewhat poorly drained soil formed in loess. P. sojae detection on filed soils: Given that P. sojae is present in most agricultural soils and its presence could alter the soil microbiome, we first checked for the presence of P. sojae in our soils. All three soils were baited twice with theP. sojaesusceptible cultivar Sloan according to Dorranceet al., 20081.No PSR was observed and P. sojaewas not recovered from all three soils in both experimental runs. In addition, DNA was extracted from soils using DNeasy PowerLyzer PowerSoil Kit (Qiagen) and tested forthe presence of P. sojae using standard PCR-based detection with primers PS1 and PS2 2, PSOJF1 and PSOJR13, and ITS4 and ITS64. The genomic DNA ofP. sojaewas used as a positive control.The pathogen was not detected in all three soils. Protocol optimization: To optimize various aspects of the protocol, several small-scale experiments were conducted. These experiments focused on optimizing planting density, inoculation method, zoospore production, sample collection, and DNA and RNA preparation. Experimental Run In the first experimental run, field soils were sifted through a 5mm screen and mixed with horticultural perlite in a 75% soil to 25% perlite ratio. Foam cups with four drainage holes were filled with 150ml of this soil mix, and susceptible (Williams) or resistant (Williams 82 with theRps1kgene) cultivars were planted in each cup. Coarse vermiculite was used to cover the seeds, and the cups were watered with deionized water. The cups were incubated at 25ºC with a 16h photoperiod for 15 days until the first trifoliate leaf stage. Seven days after planting, the seedlings were thinned to one per cup and watered twice weekly. Next, the cups were inoculated with 10e4P. sojaezoospores in 25ml of deionized water, and individual seedling roots with attached soil were collected and flash-frozen in liquid nitrogen. The time points for the collection were 0, 24h, 48h, and 72h after inoculation, and there were four cups per treatment per experimental run.A control treatment, which consisted of deionized water alone, was also included. DNA and RNA preparation We used two DNA extraction kits (Zymo Quick-DNA™ Fecal/Soil Microbe Miniprep Kit and Qiagen DNeasy PowerSoil Pro Kits) to extract DNA from our soil samples and determine if the extracted DNA is suitable for downstream analyses. After DNA extractions, DNA samples were used to amplify the 16S rRNA gene. Only DNA extracted from the Qiagen DNeasy PowerSoil Pro Kit could be amplified. Therefore, this kit will be used for all subsequent DNA extractions. In addition, we used the extracted DNA for bacterial 16S rRNA gene sequencing on the MinION Mk1B portable sequencing device (Oxford Nanopore Technologies [ONT], Oxford, UK), after preparing libraries using the 16S barcoding kit 1-24 (SQK-16S024) and the "rapid sequencing amplicons--16S barcoding protocol" version. The sequencing run was successful with the expected amplicon size. The resulting data were processed using NanoCLUST bioinformatic pipeline and were compared against ZymoBIOMICS microbial community DNA 2nd International Molecular Plant Protection Congress. Our data indicated that the isolated soil DNA samples could be used for long-read amplicon sequencing. Currently, we are in the process of extracting DNA from all 192 samples. We tested three protocols/kits for soil RNA extractions: Qiagen RNeasy PowerSoil Total RNA kit, Quick-RNA Fecal/Soil Microbe Microprep Kit, and a custom protocol by Buchner (2022)5. While we were able to isolate RNA from all these protocols, we still need to optimize the protocols to yield enough RNA for mRNA enrichments. References: 1. Dorrance, A. E., Berry, S. A., Anderson, T. R. & Meharg, C. Isolation, Storage, Pathotype Characterization, and Evaluation of Resistance for Phytophthora sojae in Soybean. Plant Health Prog. 9, 35 (2008). 2. Wang, Y., Zhang, W., Wang, Y. & Zheng, X. Rapid and Sensitive Detection of Phytophthora sojae in Soil and Infected Soybeans by Species-Specific Polymerase Chain Reaction Assays. Phytopathology 96, 1315-1321 (2006). 3. Bienapfl, J. C., Malvick, D. K. & Percich, J. A. Specific molecular detection of Phytophthora sojae using conventional and real-time PCR. Fungal Biol. 115, 733-740 (2011). 4. Cooke, D. E. L., Drenth, A., Duncan, J. M., Wagels, G. & Brasier, C. M. A Molecular Phylogeny of Phytophthora and Related Oomycetes. Fungal Genet. Biol. 30, 17-32 (2000). 5. Buchner, D. Co-extraction of RNA and DNA from soil and sediment samples. (2022).
Publications
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