Progress 10/15/18 to 08/14/21
Outputs
Target Audience:Students, researchers, biotechnology firms, agricultural managers. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?Postdoc Catherine Aimone was trained on how to use the NCSU Henry2 High Performance Computing Cluster, took a five-part training and certification in Linux command line, and expanded her experience significantly by serving as a Software Liason for the NCSU Bioinformatics User Group where she coordinated and established a systematic environment for script sharing among Henry2 users in GitHub and Gdrive. For professional development, she took 60 hours of project management training through the Project Management Podcast. Research Associate Elizabeth Thomas received training in Illumina library preparation for amplicon metagenomics. Graduate student Xavious Allen received training in RNAseq analysis of plant samples. How have the results been disseminated to communities of interest?Due to COVID19, we cancelled all planned presentations at universities and at scientific conferences. Manuscripts reporting results have been submitted and several are in preparation.We continue to share our data with Indigo Ag, Inc. based on our fungal licensing agreement through the University of Texas at Austin. What do you plan to do during the next reporting period to accomplish the goals?
Nothing Reported
Impacts
What was accomplished under these goals?
Objective 1: Develop a mechanistic framework for predicting fungal effects on plants Objective 1a: Understand how a range of fungi affect plant drought responses to develop a predictive framework of fungal benefits based on multi-omics measurements To address fungal effects on host drought responses, switchgrass was inoculated with individual fungi in controlled greenhouse microcosms and grown under well-watered or drought conditions. Plant response measurements included growth, physiology, gene expression, and metabolomics profiles. We discovered that fungi that resulted in a "water saver" physiology in the plant host, i.e., that minimized water loss, were consistently associated with significant downregulation of stress- and growth-related genes. In contrast, fungi that generated a "water spender" physiology in the plant, i.e., that maximized water use, had variable effects on plant gene expression. For example, Sordaria was associated with down-regulation of stress genes and up-regulation of growth genes, whereas Cercospora and Cochliobolus had the opposite effects. To our knowledge, this is the first attempt to understand the mechanisms by which fungi mediate specific plant physiological strategies. The manuscript from this work is at the editing stage and will be submitted in January 2022. We also found that metabolomics profiles in inoculated vs. control switchgrass shifted with plant growth and physiology. Smaller plants with lower rates of photosynthesis and conductance were associated with more phenolics, whereas metabolomes of larger plants that were more physiologically active were associated with more terpenoids. Production of phenolics may limit protein synthesis because these often share precursors, whereas terpenoids do not, which can affect plant growth. Beyond broad differences in metabolomics fingerprints across plants, we identified specific secondary metabolites that predicted plant response traits. Specifically, fungal derived long chain fatty acids were associated with photosynthesis, conductance, and shoot and root biomass. These fatty acids were primarily oxylipins that are important for plant-fungal communication and fungal pathogen establishment in plants, including induction of plant defense responses and stress signaling networks. Production of oxylipins such as arachidonic acid may be one method by which endophyte colonization increases plant disease resistance. Regulation of plant unsaturated fatty acid biosynthesis is also emerging as a promising agricultural tool to increase plant tolerance to stress and resistance to disease. Our work highlights the need for developing such tools in the context of fungal endophytes rather than focusing exclusively on pathogens. The manuscript reporting the metabolomics results is currently in review. Objective 1b: Develop fungal genomic resources All 8 fungi used in the Objective 3 field experiment were sequenced on Illumina MiSeq and assembled using the Maryland Super-Read Celera Assembler (MaSuRCA). Based on Benchmarking Universal Single-Copy Orthologs (BUSCO), 7 of the 8 fungal genome assemblies are more than 98% complete and single copy. One genome assembly was 90% complete and single copy, with 10% fragmented or missing. These genomes will be used in the transcriptome and metabolome analyses in Objective 3, including identifying biosynthetic gene clusters. Objective 2: Scale predictions from individual to multiple fungi in a single host We completed the experiment to test how diversity in the fungal endophyte community altered drought responses in the host plant. In the diversity experiment, plants were inoculated with 0, 1, 2, 4, or 8 fungal taxa, with 4 replicated communities for diversity levels >2. Plants were grown for 4 weeks under well-watered conditions and then half the replicates were harvested before the other half were subjected to 4 weeks fo drought. The final harvest was in Jan 2021. We had two surprising results based on measurements of plant growth, physiology, and pathogen incidence at the harvest. First, in previous experiments, plant physiological processes such as photosynthesis, conductance, and water use efficiency often responded non-additively when plants were inoculated with pairs of fungi compared to individual fungi. In the diversity experiment, the range of plant physiological rates was maximized when plants were inoculated with 2 fungi, but those synergies disappeared when plants were inoculated with 4 or 8 fungi. Second, we found that the proportion of leaves experiencing disease symptoms increased significantly under drought and with increasing fungal diversity. This suggests that more diverse mixtures of fungi can result in a greater degree of antagonism among the taxa. In addition, consortia development for field inoculations will require an understanding of those interactions to maximize function. We are continuing to investigate the actual fungal communities that resulted from the inoculated treatments. To measure the abundance of fungal taxa in the plants resulting from each diversity, we developed quantitative mock communities for each treatment to run alongside the samples in Illumina library preparation and sequencing. To do this, we first designed strain-specific qPCR primers for single-copy MCM7 genes and used qPCR to generate known copy numbers of the fungi, which were then mixed into equal and unequal mixtures representing each treatment. Using these mocks to create standard curves of sequence reads vs. true copy numbers, we can calculate the abundances of each fungus in each treatment. These samples have been submitted to our core facility for sequencing. Objective 3: Determine host generality and feasibility in field conditions. To understand how fungal treatments work in real-world conditions, we used a field experiment in which switchgrass plants were inoculated with individual fungi (n=8) or no-inoculum controls and subjected to drought or well-watered conditions. As reported previously, all plant size and physiology response data have been analyzed, and the resulting fungal community composition in the plants was characterized with amplicon metagenomics of ITS rDNA using Illumina MiSeq. We have since completed the metabolomics and optimized the transcriptomic methods. For gene expression, we tested both RNAseq and TaqRNAseq approaches and found that the former worked best to characterize the plant and fungal transcriptomes, with 50-80% of reads mapping to Panicum virgatum, 1-2% mapping to the inoculated fungi, and 1-15% mapping to other fungi in the DOE Joint Genome Institute (JGI) Mycocosm fungal genome database. All samples were submitted to our core facility for sequencing; they remain in the queue as the facility still has a large backlog of samples from the recent COVID shutdown combined with staffing issues. In the meantime, we used the test data to get our bioinformatics pipeline running on the NCSU high performance computing cluster. The pipeline includes (1) FastQC to determine read number and quality from the RNAseq libraries, (2) TuxNet to map reads of each sample were mapped to Panicum virgatum reference genome available through the DOE JGI, with ea-utils fastq-mcf for Illumina read processing and hisat2 for alignment, and (3) Cufflinks for differential expression analysis.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2021
Citation:
Zhalnina K, Hawkes CV, Shade A, Firestone M, Pett-Ridge J (2021) Managing plant microbiomes for sustainable biofuel production. Phytobiomes J 5: 3-13. doi: 10.1094/PBIOMES-12-20-0090-E
- Type:
Journal Articles
Status:
Under Review
Year Published:
2022
Citation:
Sandy M, Bui TI, Segura Aba K, Ruiz N, Connor EW, Paszalek J, Hawkes CV. Plant host traits mediated by foliar fungal symbionts and secondary metabolites. Microbial Ecology in review
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Progress 10/15/19 to 10/14/20
Outputs
Target Audience:Students, researchers, biotechnology firms, agricultural managers. Changes/Problems:Schedule changes NCSU has been fully or partially shut down since March 2020 - we are still working at reduced capacity in lab spaces. Although we are close to completing our objectives, we will plan to continue working on the project beyond the Feb 2021 grant end date. To do this, I will support postdoc Gina Chaput for an additional 6 months on my NCSU startup funds, which will facilitate manuscript submissions. Other changes During late summer 2020, our collaborator Dr. Sandy moved from UT Austin to a new position as the Director of the Quantitative Metabolite Analysis Center at University of California San Francisco. She continues to fully collaborate on the project. What opportunities for training and professional development has the project provided?Postdoc Gina Chaput has been expanded her analytical skills in R via DataCamp online training. Graduate student Xavious Allen received training in analysis of fungal genomes, plant microbiomes, and plant metabolomics through his work on the project, and also attended formal training in Data Security offered by NCSU (Sep 2020). Graduate student Rachel Hammer received training in image analysis and basic statistics through her work on this project, and took a workshop offered through the NCSU Libraries, "MATLAB Hands-On Virtual Lab: Machine Learning" (Sep 2020). In addition, both students were trained in plant-fungal microcosm set up, experimental design, and plant microcosm measurements. Through our collaborator, Dr. Moriah Sandy at UT Austin, two undergraduates continued to be mentored on this project. Kenia Segura Aba, a Biochemistry major, and Amanda Pittman, a Biology major. Both are Hispanic women and first-generation college students. In Spring and Fall 2020, the students continued to assist with plant tissue extractions, LCMS analysis, and statistical analysis. Kenia Segura Aba completed her senior research project (Spring 2020) on examining the effects of leaf age, fungal treatment, and water treatment on plant metabolomes, and is now enrolled in a PhD program at Michigan State University. Amanda Pittman is now a 5th year biology major and is expected to graduate in spring 2021. How have the results been disseminated to communities of interest?Due to COVID19, we were forced to cancel most planned presentations at universities and at scientific conferences. However, we continue to share our data with Indigo Ag, Inc. based on our fungal licensing agreement through the University of Texas at Austin. What do you plan to do during the next reporting period to accomplish the goals?In 2020-2021, we plan to submit the manuscript based on Objective 1 greenhouse experiments testing mechanisms for individual fungal effects on plant drought physiology. We will also complete the Objective 1b comparative genomics assembly and analysis, harvest and analyze the Objective 2 microcosm experiments to test priority and diversity effects, and finalize the full set of analysis and draft manuscript in Objective 3.
Impacts
What was accomplished under these goals?
Objective 1a: Understand how a range of fungi affect plant drought responses to develop a predictive framework of fungal benefits based on multi-omics measurements In the greenhouse experiment where switchgrass was inoculated with individual fungi (n = 20 plus fungus-free controls) in controlled microcosms to test individual fungal effects, we ground all (n=257) leaf samples in liquid N2 in preparation for metabolomics and transcriptomics. RNAseq is currently being optimized on Objective 3 field samples for which there is more biomass, but for the metabolomics leaf extractions were completed. Lipid and secondary metabolite profiles were made from high resolution liquid chromatography electrospray ionization mass spectrometry (HR-LCESIMS) analysis conducted in both positive and negative ionization mode, primary metabolite profiles were created from GCMS, and chlorophyll pigment profiles were created from UV-Vis data. We also developed and finalized our metabolomics data analysis pipeline. Mass Hunter software was first used to subtract technical blanks from samples and convert to mzdata format. XCMS was then used for metabolite compound identification based on m/z and retention time (with least-squares correction method). A metabolite abundance matrix for all detected compounds was created based on peak intensities. Analysis of the metabolite by treatment matrix is being analyzed in the R package, MetaboShiny. Objective 1b: Develop genomic resources for 20 fungi. Fungi that were used in Objectives 1a and 3 have been prepared and submitted for sequencing to the NSCU Genome Sequencing Lab (GSL) core facility. The GSL is still working through their backlog from the COVID19 shutdown, but we hope to have the sequences back by end of the semester. In the meantime, we are moving our analysis pipeline onto the NCSU High Performance Computing Cluster. Objective 2: Scale predictions from individual to multiple fungi in a single host Two experiments are in progress to test how diversity and priority effects in the fungal endophyte community alter drought responses in the host plant. In the diversity experiment, plants are inoculated with from 0 to 8 fungal taxa. In the priority experiment, plants are inoculated with either (a) 1 fungus first, followed by a fungal consortia 1 week later to test priority, or (b) a consortia first followed by 1 fungus 1 week later to test invasion ability. We set up 500 microcosms for these experiments with switchgrass, which are in a growth chamber with summer light and temperature conditions. The harvest is planned for Jan 2021. To prepare for this harvest, we have also begun development of qPCR protocols based on single genes to assess the ending abundances of the fungi in each treatment. We are focusing on two single-copy genes that have been successfully used for diverse endophytic fungi in the Ascomycota: TEF-1a and MCM7 genes. In preparation for these experiments, we analyzed a preliminary data set for interactions of two fungi at a time, which helped us to develop the expected analysis pipeline for the diversity and priority experiments. In the preliminary dataset, pairs of fungi were grown on switchgrass under drought or well-watered conditions. There were significant interaction effects of fungus and water treatments on plant growth and water use, which was mediated by shifts in the plant metabolome. Notably, the abundance of fungi as assessed by Illumina sequencing had no relationship to the treatments. Objective 3: Determine host generality and feasibility in field conditions. To understand how fungal treatments work in real-world conditions, we used a field experiment in which switchgrass plants were inoculated with individual fungi (n=8, plus no-inoculum controls) and subjected to drought or well-watered conditions. All plant size and physiology response data have been analyzed as reported previously. Plant responses are also the dependent variable in an analysis to determine if metabolites or genes can predict observed outcomes. However, 7 samples exploded in the mass spec during metabolite analysis and need to be re-run (in progress) and the RNAseq analysis is still being optimized (see below). Once these data are available, we will finalize the analyses. We completed tissue grinding in liquid N2 and RNA extractions from all leaf samples in the field experiment (n=72). Three extractions of 50 mg leaf tissue per sample were used to produce sufficient RNA. We are using the TaqRNAseq protocol developed by Meyer et al. (2011) and modified by Lohman et al. (2016), but this has required several optimizations for our samples. We optimized the total RNA input and reduced fragmentation time to maximize RNA integrity and quantity. To increase RNA yields, we also modified the RNA XP bead-based clean up step to elute in smaller volumes. To increase cDNA yield, we further increased amplification cycles from 13 to 16. Troubleshooting of the first strand synthesis and amplification for cDNA is currently in progress, which includes testing custom oligos, temperatures, reaction times, and product concentrations in each step. Once this is complete, barcoding, size selection, and sequencing will be completed prior to submission for sequencing. In the meantime, we optimized our RNAseq bioinformatics pipeline with a smaller data set. We used SortMeRNA to remove rRNA reads, Trimmomatic to remove low quality reads, TruSeq Mpx to remove adapter sequences, and FastQC to detect any other quality issues on processed reads. Reads were aligned to appropriate genomes using Salmon. Transcript abundances were then imported into R and differential expression was analyzed with DeSeq2.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2020
Citation:
Hawkes CV, Bull JJ, Lau JA (2020) Symbiosis and stress: how plant microbiomes affect host evolution. Philosophical Transactions of the Royal Society B 375: 20190590. doi: 10.1098/rstb.2019.0590
- Type:
Theses/Dissertations
Status:
Published
Year Published:
2020
Citation:
Segura Aba, Kenia (2020) Effect of leaf age, water treatment, and fungal treatment on the Panicum virgatum metabolome. Undergraduate Senior Thesis, University of Texas at Austin.
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Progress 10/15/18 to 10/14/19
Outputs
Target Audience:Students, researchers, biotechnology firms, agricultural managers. Changes/Problems:The 6-month delay in funding transfer (expected in October 2018, arrived in March 2019; partly due to the government shut down) set back all work on the project. While waiting, I used my NCSU startup funding to pay one postdoc part-time to work on the Objective 3 fungal community analysis. We searched for and hired a postdoc to work on the RNAseq analysis and genomics in Objectives 1 and 3 after the grant funding was transferred; she was able to start in September 2019. We thus requested and received 1-year no cost extension. What opportunities for training and professional development has the project provided?Postdoc Gina Chaput received training at two workshops. In September 2019, she attended the Ensembl Genome Browser Workshop, which introduced the Ensembl database and methods for identifying gene function, constructing gene phylogeny trees, and predicting and annotating functions associated with genetic variants. We expect to use components of Ensembl for Objective 1b. In October 2019, she attended the NCSU High Performance Computing (HPC) Beginner Workshop. This training familiarized participants with file transfer, storage options, and running basic applications with simple job scripts. We plan to implement our pipeline for RNAseq analysis via the HPC to facilitate rapid analysis for both Objective 1 and 3. Through our collaborator, Dr. Moriah Sandy at UT Austin, two undergraduates were mentored on this project. Kenia Segura Aba, a senior Biochemistry major, and Amanda Pittman, a senior Biology major. Both are Hispanic women and first-generation college students. In Spring and Fall 2019, students assisted with plant tissue extractions, LCMS analysis, and statistical analysis. Notably, one student, Kenia Segura Aba, chose to focus her senior research project (Fall 2019 - Spring 2020) on examining the effects of leaf age, fungal treatment, and water treatment on plant metabolomes. Amanda Pittman built on her experience developing plant tissue extractions for Panicum virgatum and trained ten students in the Supramolecular Sensors class on field experimental design, plant leaf collection, tissue extraction and LCMS metabolomic analysis for grape leaves (Summer 2019). How have the results been disseminated to communities of interest?We gave seven presentations at universities and scientific conferences, as well as one undergraduate poster. We provided our most recent data on fungal performance from the field and greenhouse experiments to IndigoAg, Inc. based on our licensing agreement through University of Texas at Austin. Funding from this agreement was designated to support the metabolomics research carried by our collaborator, Dr. Moriah Sandy, at UT Austin. What do you plan to do during the next reporting period to accomplish the goals?In winter 2020, we plan to complete the Objective 1a gene expression and metabolomics analyses, carry out the Objective 1b comparative genomics, and finalize the gene expression analysis in Objective 3. In spring and summer 2020, we will run the microcosm experiments to test priority and diversity effects in Objective 2. In fall 2020, we will use greenhouse experiments to test whether fungi that best rescue switchgrass from drought stress have similar effects on other crops. We are currently hiring a part-time technician to assist with the greenhouse work.
Impacts
What was accomplished under these goals?
Objective 1: Develop a mechanistic framework for predicting fungal effects on plants Objective 1a: Understand how a range of fungi affect plant drought responses to develop a predictive framework of fungal benefits based on multi-omics measurements We used a greenhouse experiment with switchgrass that were inoculated with individual fungi (n=20 plus fungus-free controls) in controlled microcosms that were maintained under drought or well-watered conditions. All frozen leaf tissues from the greenhouse experiment were ground in liquid N in preparation for extraction. Because of the limited biomass available, we processed these after we optimized methods using samples from the field experiment in Objective 3. Extensive testing was done to determine the lower limit of biomass needed for both metabolites and RNAseq (10 mg per assay). Because of this limitation, we reduced to n=4 instead of n=6 per treatment in order to be able to obtain both metabolomics and gene expression profiles on the same samples (20 mg minimum). We are on track to extract all leaf tissues for metabolite profiling in December and expect mass spec results in January. RNAseq extractions are planned for January. A subset of six fungi were tested on plants in a small pilot experiment to establish the full RNAseq workflow. There were more than 1000 differentially expressed plant genes overall, but these varied across fungal and water treatments (range 3-693). In analyzing these results with residual randomized permutation tests, we also found a significant interaction of fungus treatment by water treatment on the composition of differentially expressed genes. We are currently characterizing the nature of those gene expression differences. Frozen root and soil samples sent to Lawrence Livermore National Lab have been tested for the ability to obtain metabolite and DNA profiles. We tested the extractions because of the low root biomass and sand soils, which meant that metabolite detection might be challenging. We tested metabolite extractions on a subset of root and soil samples and analyzed those extracts with LC-MS/MS. For the greenhouse samples, we were able to detect metabolites from both the root and sand samples, despite the low organic carbon content of the sand (3 ppm). Of the detected LC-MS/MS features (metabolites), 65% were unique to the roots while only 15% were unique to the sand and 20% were shared by both. ? Objective 2: Scale predictions from individual to multiple fungi in a single host Both of objectives 2a and 2b will be carried out in 2020-21. Objective 3: Determine host generality and feasibility in field conditions. To understand how fungal treatments work in real-world conditions, we used a field experiment in which switchgrass plants were inoculated with individual fungi (n=8, plus no-inoculum controls) and subjected to drought or well-watered conditions. All plant data were analyzed. We found a strong interaction of fungal treatment and water treatment on photosynthesis and conductance; however, aboveground plant biomass and LAI were unaffected by the treatments. We also compared physiological responses to water treatment for the 8 fungal inocula that overlapped between the field experiment in Objective 3 and the greenhouse experiment in Objective 1. We found strong qualitative agreement between the two experiments, but the greenhouse results were not a quantitative predictor of field effects. All frozen leaf tissues were extracted for RNA, DNA, and metabolites. Before extracting the RNA samples we tested 7 kits to determine the best method for maximizing RNA quantity and quality from switchgrass leaves. The tested kits were: EZNA Plant RNA Kit, Norgen Plant/Fungi Total RNA Purification Kit, Qiagen RNeasy Power Plant Kit, Qiagen RNeasy Plant Mini Kit, Qiagen RNeasy UCP Micro Kit, Trizol Plus RNA Purification Kit, and Zymo Quick RNA MiniPrep Kit. Each kit was assayed across a range of leaf tissue biomass resulting in 0.07 to 1.1 ug RNA per mg biomass and RNA Integrity Numbers of 1.7-8.6. We selected the Qiagen RNeasy Plant Mini Kit, which performed overall. RNA is currently being extracted from all samples. Once RNA quantity and quality are confirmed, we will proceed with mRNA enrichment and construction of cDNA libraries. We anticipate completing the RNAseq preparation for sequencing submission in January 2020. The DNA was used for ITS amplicon metagenomics to identify if fungal treatments applied in the field persisted. Initial results from Illumina MiSeq showed that the ITS1 primers (ITS1F and ITS2) amplified primarily host DNA rather than fungal DNA: approximately 80% of reads were identified as Panicum virgatum. To address this, we are developing a peptide nucleic acid (PNA) blocker to reduce host amplification. PNA candidates are being designed using an in silico approach following guidelines from Lundberg et al. (2013 Nature Methods 10: 999-1002). The top two candidate PNAs will be tested on a small number of samples with an Illumina NanoSeq run before a final PNA is selected. We recently did this successfully for the ITS2 region, reducing P. virgatum host reads to less than 10% of total; thus we anticipate the same success for ITS1. For leaf metabolites, we first tested our methyl tertiary butyl ether, methanol, water (MTBE:MeOH:H2O) extraction methods on 1 mg, 2 mg, 5 mg, 10 mg, 15, mg, 20 mg and 25 mg of plant tissue. We determined that 10 mg plant tissue is the lower limit required for consistent metabolomic profiling. We also tested different solvent systems and solvent gradients for secondary metabolite and lipid LCMS analysis. We determined that optimal peak resolution for secondary metabolite analysis was achieved using a reverse-phase C18 column (stationary phase) with a linear gradient of 5% - 100% methanol in water with 0.1% formic acid (mobile phase) over twelve minutes followed by an eight-minute hold of 100% methanol. For lipid analysis, the best peak resolution occurred with a reverse-phase C3 column (stationary phase) with a gradient of 5-95% acetonitrile in water with 0.1% formic acid (mobile phase). We completed leaf extractions and LCMS, GCMS, UV-Vis analysis of lipids, secondary metabolites, primary metabolites, and chlorophylls. Secondary metabolite and lipid profiles were made from high resolution liquid chromatography electrospray ionization mass spectrometry (HR-LCESIMS) analysis conducted in both positive and negative ionization mode, primary metabolite profiles were created from GCMS data, and chlorophyll pigment profiles were created from UV-Vis data. Plant secondary metabolite profiles were significantly affected by the interaction of water and fungal treatments, but lipids were not. Based on linear modeling, secondary metabolite profiles predicted ~40% of the variation in leaf transpiration, but had no relationship to other measures of plant size or physiology. For all lipid and secondary metabolite mass spec data, we are now in the process of matching metabolites (identified by METLIN and NIST databases) to biochemical pathways (KEGG and PMN databases). However, of the 2874 secondary metabolites detected, approximately 63% are unknown. In order to get more detailed structural information on compounds, we are developing tandem mass spectrometry (ESI-MSMS) methods to reveal mass fragment functionality. Molecular networking analysis of MSMS fragments coupled to compound retention time analysis can provide information on structural relationships and improve classification of metabolites. For frozen root and soil samples sent to LLNL, we developed a protocol to extract metabolites and DNA for both metabolomics and sequencing. In those samples, we identified a range of metabolites, including a variety of sugars, organic acids, and vitamins. DNA extraction from the same samples recovered approximately 6.5 µg DNA per gram of soil. We are currently proceeding with extractions on the remaining samples.
Publications
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2019
Citation:
Whitaker BK, Hawkes CV (2019) Direct and indirect effects of fungal inoculants on switchgrass physiology in drought and well-watered conditions. ASA-CSSA-SSSA International Annual Meeting. San Antonio, TX
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2019
Citation:
Whitaker BK, Hawkes CV (2019) Getting outside the lab: endophytes as moderators of plant stress. Society for In Vitro Biology Tampa, FL
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2019
Citation:
Segura Aba, K (2019) Effect of leaf age, water treatment, and fungal treatment on metabolite production in Panicum virgatum. Poster Presentation, University of Texas Undergraduate Research Forum, Austin TX
- Type:
Journal Articles
Status:
Published
Year Published:
2019
Citation:
Giauque H, Connor EW, Hawkes CV (2019) Endophyte traits relevant to stress tolerance, resource use, and habitat of origin predict effects on host plants. New Phytologist 221: 2239-2249
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2019
Citation:
Hawkes CV (2019) Microbial mediation of switchgrass drought physiology. Keynote, Switchgrass Microbiome Symposium, UC Berkeley, LLNL, & JGI, Berkeley, CA
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2019
Citation:
Hawkes CV (2019) How can we predict outcomes of stress-dependent, non-additive plant-symbiont interactions? Seminar, Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, TN
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2019
Citation:
Hawkes CV (2019) Using ecological theory to understand fungal mediation of switchgrass drought physiology. Seminar, Department of Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, MN
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2019
Citation:
Sandy M (2019) Discovery, biosynthesis, and engineering of natural products. Invited Seminar, School for Engineering of Matter, Transport and Energy, Arizona State University, Phoenix, AZ
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2019
Citation:
Sandy M (2019) Natural product discovery from microbe and microbe-host interactions. Invited Seminar, Benioff Center for Microbiome Medicine, University of California, San Francisco
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