Recipient Organization
MICHIGAN STATE UNIV
(N/A)
EAST LANSING,MI 48824
Performing Department
Plant, Soil and Microbial Science
Non Technical Summary
Fungi and bacteria are ubiquitous in soils and plant microbiomes, but are often studied separately or in parallel. The research proposed here will characterize the distribution of fungi and bacteria in plant holobionts and soils, and will focus on associations that are beneficial to plant health, nutrition, and stress tolerance. Consortia of microbes appear to be central to organismal health and functioning, and animals and plants with maladapted microbiomes have been shown to be more susceptible to disease and stress. The microbiome concept has led to a paradigm shift and new understanding of host-microbial interactions and interdependencies, and raises many questions regarding the role of keystone microbial taxa and indicator microbial species in plant health and resilience. Thus, the acquisition, assembly and function of fungal, plant and soil microbiomes is of broad biological interest, and important and relevant to agricultural and bioenergy cropping systems.This umbrella project is specifically focused on fungal and bacterial consortia that occur in soils and on plants having healthy and resilient phenotypes. Fungi and bacterial consortia are not only important in plant health through metabolite feedback cycles, but also impact soil carbon and nutrient cycling. Soils contain the largest stocks of organic C in the terrestrial biosphere (Luo, Wang, and Wang 2019), and recent studies find that bacterial and fungal activity, cell turnover, and necromass, contribute significantly to the stabilization of organic matter pools in forest soils (Adamczyk et al. 2019; Ma et al. 2018; Liang et al. 2019; Sun et al. 2018).This umbrella research will view interactions between fungi, bacteria and plants through a holistic lens, and is relevant to knowledge areas of agriculture, forestry as well as soil, plant, water and nutrient relationships.?
Animal Health Component
50%
Research Effort Categories
Basic
50%
Applied
50%
Developmental
(N/A)
Goals / Objectives
?Objective 1 - Improve understanding of plant holobionts and the microbiome of fungal isolates and fungal fruiting structures. This aim is focused on plant crops that are important to human nutrition (soy, wheat, corn) and bioenergy (switchgrass, poplar), bacterial associates of these isolates and fungal fruiting structures.Objective 2: Determine impacts of root metabolite fractions on bacterial and fungal cell activity, turnover and metabolism. This aim will focus on sampling, purification, and microbial baiting through cross-feeding experiments with root metabolite fractions from focal plants (e.g. switchgrass).
Project Methods
Objective 1. Plant microbiomes (bacteria and fungi) will be characterized via high throughput amplicon sequencing with Illumina MiSeq. We will determine functional guilds of microbes and how they vary with condition of host. DNA will be extracted from field soils, leaves, stems and roots for microbiome analysis, a common procedure used in the labs of the PIs. Genomic DNA will be extracted from soils using the MO BIO PowerSoil® kit (MO BIO Laboratories Inc., Carlsbad, CA). PCR will be performed using the FastStart High Fidelity PCR System (Roche Diagnostics, Basel, Switzerland). Genomic DNA will be extracted from leaves, stems and roots using the Mag-Bind® Plant DNA Plus Kit (Omega Bio-tek, Norcross, Georgia) following the manufacturer's protocols with tissue ground on a Retsch Oscillating Mill M400 (Verder Scientific, Newtown, PA) and extractions performed on a KingFisher™ Flex Purification System (ThermoFisher Scientific, Waltham, Massachusetts). Fungal ITS1 will be amplified with ITS1f and ITS4 and sequenced (Benucci et al. 2019). Bacterial 16S rDNA (V4 region) will be amplified using the universal primers 533F and 906R, a standard 16S rDNA primer set for the Earth Microbiome Project (Gilbert et al. 2010). Reference soils and mock communities of bacteria and fungi, mixed in a range of concentrations, will be used as internal standards and controls. Multiplexed amplicons of fungi and bacteria will be sequenced with the Illumina MiSeq 300bp pair-end sequencing platform at the Research Technology Support Facility at MSU.To access bacterial communities in epigeous and hypogeous Pezizales and other macro-fungi, we will obtain 0.5 cm2 samples of tissue from fresh, healthy-looking fruiting bodies (i.e. having no signs of larval galleries, freeze or dry damage or injuries made by animal or harvesting tools) using a flame-sterilized scalpel. Samples will be placed directly into CTAB 2x DNA buffer for DNA extraction following standard protocols (Benucci and Bonito 2016). Soils directly beneath fruiting bodies will also be sampled for comparison, and extracted with the MoBio Powersoil kit. Negative control samples based on CTAB tubes that will be opened in the field and processed alongside treatment samples will be included in the study as a way to monitor potential background contamination. A synthetic mock community with 12 bacterial taxa and 4 negative controls (no DNA) will be included to assess sequencing quality and barcode switching (Palmer et al. 2018). PCRs will amplify the V4 region of 16S rDNA with primers 515F and 806R (Benucci and Bonito 2016). Amplicons will be normalized, cleaned and sequenced on Illumina MiSeq with the v3 600 cycle kit. Raw sequence reads will be submitted to NCBI's SRA archive. Sequence quality will be evaluated with FastQC and sequences will be demultiplexed in QIIME (Caporaso et al. 2010). Adapters and sequencing primers will be removed using Cutadapt (Martin 2011). Reads will then be quality filtered, trimmed to equal length (Edgar and Flyvbjerg 2015; Edgar 2016) and de-replicated. After singletons are removed, remaining sequences will be clustered into OTUs based on 97% sequence similarity with UPARSE (Edgar 2013). Taxonomy assignments will be performed in QIIME with the RDP Naïve Bayesian Classifier (Wang et al. 2007) and Greengenes database (DeSantis et al. 2006) (16S rDNA vers. gg_13_8) and CONSTAX (Gdanetz et al. 2017) based on the most recent UNITE fungal ITS rDNA database (for fungal samples, Aim 2) (Kõljalg et al. 2005).An otu_table.biom (McDonald et al. 2012) with embedded taxonomy classifications and metadata.txt files for each marker gene will be imported into the R statistical environment (R Core Team 2017) for analysis after removing OTUs with <10 sequences (Oliver, Callaham, and Jumpponen 2015; Lindahl et al. 2013). Contaminant OTUs found in the negative controls will be removed across all samples when ≥10 reads are present in any single control. Observed OTU richness (S)(Simpson 1949), Shannon's diversity index (Hill 1973), and evenness (Kindt and Coe 2005) will be used as α-diversity metrics. Diversity indexes will be calculated with the "specnumber" and "diversity" functions in the R package vegan (Oksanen et al. 2019) and with the function "diversityresult" in the package BiodiversityR (Kindt and Coe 2005). After assessing for data normality and homogeneity of variances, significant differences between mean alpha-diversity measures will be assessed with ANOVA and Tukey's tests. Rarefaction curves will be plotted to assess OTU richness. To avoid biases and data loss in some groups of samples due to inherent variations in alpha-diversity in soils compared to fruiting bodies, OTUs will be normalized using the R package metagenomeSeq before calculating β-diversity (Paulson et al. 2013). Principal coordinate decomposition (PCoA) will be used to investigate community β-diversity with the "ordinate" function with phyloseq (McMurdie and Holmes 2013). Diversity patterns will then be tested for statistical differences across sites, species, substrate, lineage and ecological habit (epigeous vs. hypogeous) in the vegan R package with the PERMANOVA function "adonis" and tested for homogeneity of variances with the function "betadisper". OTUs showing high and significant correlation with sample groups will be identified through the function "multipatt" in the indicspecies package (De Cáceres and Legendre 2009).Objective 2. Research focused on assessing how microbes and abiotic variable impact plant rhizodeposition and health will be carried out by inoculating with experimental microbiomes, which are referred to as 'constructed communities' from now on. Constructed communities are being made to include different functional guilds of fungi and bacteria (Table 1). Experimental designs in all plant microbiome communities need to include negative controls consisting of no microbiome and positive controls consisting of alien microbiome microbiota (non-native/non-soil). Impact of constructed communities on plant health and growth traits will be recorded and microbiomes will be sampled at the end of the experiment for 16S and ITS rDNA amplicon sequencing following methods published by the Bonito lab to determine the persistence of the inocula (Benucci et al. 2019).Extracted metabolites from roots of established switchgrass, poplar, soybean, wheat or corn hosts will be collected after 6 months establishment will be fractionated by solubility and charge with solid-phase extraction (SPE), quantified and characterized (Figures 2 and 3). It is envisioned that at least four different fractions will be analyzed by liquid chromatography time-of-flight mass spectrometry (LC/TOF MS) (e.g. HILIC/positive-ion and HILIC/negative-ion for polar and water-soluble compounds; reversed-phase/positive-ion and reversed-phase/negative-ion for more lipophilic substances) for each extract to ensure extensive coverage of metabolomes. These four plant-microbiome fractions will be added to living isolates of the microbiome consortia and 96-well plate assays to measure direct impacts of metabolite fractions on fungal and bacterial growth, fungal network architecture (e.g. hyphal branching, density, etc.) and cell turnover as measured by respiration, biomass, cell quantification, fatty acid biomarker, nucleus quantification via qPCR, and live-cell (SYTO 9): dead cell (propidium iodide) staining and quantification over time. High-throughput growth assays of fungi and bacteria will be quantified based on absorbance in a 96-well plate format using bioassays established for filamentous and non-filamentous microbes(Noel et al. 2019; Li et al. 2017; Bala et al. 2011).