Recipient Organization
UNIVERSITY OF CALIFORNIA, RIVERSIDE
(N/A)
RIVERSIDE,CA 92521
Performing Department
Evolution, Ecology & Organismal Biology
Non Technical Summary
Rhizobia are a group of beneficial soil bacteria that provide fixed nitrogen to host legumes, promoting plant performance, rapid growth, and seed production in compatible hosts. Application of rhizobia, as an alternative to chemical fertilization, has great potential for providing improved sustainability in agriculture. But the deployment of rhizobial inocula is challenged by the so-called rhizobial competition problem, where introduced elite strains often cannot gain access to hosts or persist in soils because they are outcompeted by indigenous strains that provide less benefit to plants. The objective of the proposed work is to uncover competitive rhizobia from a large library of strains collected from nodules of various field-grown legumes in California. A large body of foundational work has already been accomplished. Using genomic and ecological methods, the research group has studied rhizobia in native plants growing across the landscape and is ideally positioned to translate findings to managed agricultural settings. By completing the objectives of this proposal, the research group will advance towards meeting the goal of being able to design strains that are elite -- not only in symbiosis -- but in competitiveness for accessing key crop legumes in California.
Animal Health Component
40%
Research Effort Categories
Basic
20%
Applied
40%
Developmental
40%
Goals / Objectives
The goals are to characterize the genetic and ecological mechanisms that drive rhizobial competitiveness to nodulate California crop legumes and to apply this knowledge to improve crop sustainability via biological nitrogen fixation.1. Method Development: A novel method to discover elite crop inoculum strains will be developed. The protocol, termed biogeographic genetic screening, is a method that could ultimately be patented.2. Biogeographic genetic screening will be conducted on crop-field rhizobial populations to develop a California legume microbiome strain library. Agronomic soil sources will be genetically surveyed to quantify relative abundance of rhizobial genotypes and their capacity to compete for nodulation on a panel of crop genotypes. Frequency distributions of rhizobia genotypes from the field samples will be quantified in a set of cultured nodules from a factorial soil-inoculation experiment to uncover competitive rhizobia compatible with multiple soils and host genotypes.3. Elite inoculum rhizobia will be developed that are superior in their capacity to establish on target legume hosts. These novel biofertilizer strains will be developed from the biogeographic genetic screen, and will provide a cheap and sustainable alternative to chemical nitrogen fertilization in California. Rhizobial populations are often characterized by a subset of genotypes that dominate the host plants at a local site and spread geographically to new host populations. Yet, little work has studied the drivers of these epidemic rhizobial genotypes, as has been the intense research focus for infectious diseases. To uncover epidemic drivers, it is critical to gather a geographically widespread sample of a microbial population, to genotype the isolates at multiple loci, and to investigate genotype-frequency distributions to uncover common strains and the conditions in which they flourish. Only by examining rhizobia genotype frequencies at a large spatial scale and with multiple host and soil types can we uncover the rhizobia that are best suited to compete for nodulation under diverse agronomic settings. A common toolbox that was developed to tackle the mechanistic bases of bacterial epidemics is applicable to our goals of biogeographic genetic screening. A typical protocol includes i) widespread screening and genotyping of the bacterial population to assess genotype frequencies in different ecological scenarios, ii) testing representative isolates in hosts to investigate infectiousness and host effects, and iii) comparing genome sequences and testing gene knockouts to identify molecular mechanisms of infectiousness. This three step approach has been successful in uncovering the ecological and molecular mechanisms that drive the epidemic spread of Escherichia coli (O157:H7), Pseudomonas aeruginosa (PAPI1), and Yersinia pestis (HPI). The genomic mechanisms that drive their epidemics - attributed to the acquisition of fitness-associated mobile elements - is of particular relevance here. These pathogens are all proteobacteria (like rhizobia), and share similarities in their modular genome architecture and lifestyle, in which they must thrive in the environment and must also colonize and infect specific hosts. Because rhizobial populations are most often characterized by an epidemic population structure, a similar approach will be initiated here to study drivers of rhizobial success under the agronomic conditions.
Project Methods
Field soils from five geographically separated agronomic research centers in California will be sampled, selecting sites where planting, fertilization, and inoculation histories are documented, and where legumes that are compatible with Bradyrhizobium have been planted in the previous growth season. Soil cores will be collected from research test fields at UC Riverside Agricultural Experiment Station (Riverside, CA), Kearney Agricultural Research and Extension Center (Kearney, CA), Coachella Valley Agricultural Research Station (Martinez, CA), UC Davis Agricultural Experiment Station (Davis, CA), and Jordan Agricultural Research Center (Fresno, CA). Fields at each of the sites have been planted with Bradyrhizobium-compatible legume species over multiple seasons in the last five years. At each field site 50 soil cores will be collected (13 cm deep, 5.5 cm wide) spread across ten hectare-sized sampling fields, with each core spaced 10-50m apart. Collected cores will be stored in large sterile bags and thoroughly mixed upon return to the lab to create a bulked sample for each site. The soil corer is sterilized between field sites. Bulked soil samples from each site will be separated into portions used for i) DNA extraction and sequencing of rhizobia-specific loci, ii) analysis of biochemical soil characteristics, and iii) preparation of soil slurries for inoculation of trap plants. The slurry from each soil source will be separately inoculated into trap plants. Seeds of trap plants will be surface-sterilized, planted in sterilized pots with sterile soils, inoculated with soil slurries, grown to early maturity, and de-potted to isolate root-surface rhizobia and nodule rhizobia for genotyping. After roots are washed of visible soil, ten 1cm root sections will be dissected from root tips per plant, placed in sterile water with mild detergent, and the rinse water will be concentrated and used for DNA extraction and amplicon sequencing. Nodule isolates will be cultured, archived at -80°C, and genotyped in parallel. Genotype frequency distributions will be determined to test for epidemic Bradyrhizobium genotypes (i.e., in the soil samples, on the root surfaces, and in the nodules of each host genotype) to characterize epidemic genotypes and test how their competitiveness and ecological distribution varies dependent on soil characteristics and the host genotypes. A high-throughput short-amplicon sequencing approach will be used that employs unique molecular identifiers (UMIs) and is ideal for investigation of intraspecies differences in large geographic samples. We will use extract DNA from the soil (MoBio Powerlyzer Powersoil kit) and sequence the dnaK and nodD genes located in the chromosomal and SI genome regions, respectively, which we have successfully used in a previous study to uncover Bradyrhizobium epidemics. We have developed a pipeline for identifying and categorizing genotypes by rhizobial species and abundance. The novel method, called MAUI-seq, uses a nested PCR with primers that assign random UMIs to each template DNA during the initial elongation cycle of the PCR, thus eliminating problems with chimeric reads and allowing the researcher to distinguish alleles that differ at only a single position. The MAUI-seq method allows for amplification of multiple loci per sample and avoids inaccurate base-calling on Illumina platforms, which arises when assessing single amplicons. Soil biochemical characteristics will be analyzed using portions of each bulk soil collection, including pH, cation exchange capacity, electrical conductivity, sodium absorption ratio, and major and minor nutrients. These soil variables can directly impact the abundance and genotypic makeup of soil microbiota. For instance, soil acidity and low cation exchange capacity can depress the abundance and diversity of many soil bacteria, whereas Bradyrhizobium can achieve greater abundances in acidic than neutral soils. The abiotic soil data will be analyzed to associate patterns of genotypic abundance and diversity of the rhizobia in each sample. These multivariate associations will be calculated using a partial redundancy analysis, a computational method that removes the effect of one or more explanatory variables in a complex set of response variables. Slurries will be generated from each soil sample by sieving out larger particles (>2mm), saturating the remainder with sterile water, incubating 10 hours, and filtering the liquid portion through stacked layers of sterile cheesecloth to remove solids. The liquid extract from each field soil will be divided into two portions, one that will be used as a live inoculum and one that will be autoclave-sterilized to use as a dead control. The live inoculum and control will be separately inoculated onto the panel of 9 legume lines in the greenhouse (i.e., 3 lines x 3 species). For each soil and plant line combination we will have 10 live-inoculum plant replicates, and three control plants (i.e., dead inoculum; 585 total plants). Plants will be depotted after 8 weeks of growth. Control plants will be checked and confirmed for the lack of nodules, and their roots and shoots will be separated and dried. Nodules will be selected for dissection from the live-inoculated plants based on size and color, the combination of which are highly reliable markers of efficient BNF within those nodules. Selected nodules will be in the top quartile in diameter among the nodules on each plant i.e., because nodule size is strongly correlated with its effectiveness and will have a red to pink color, an indicator of leghemoglobin and an actively fixing nodule From each live-inoculated host plant eight nodules will be cultured (3600 isolates total). Each culture will be archived at -80°C and will be genotyped using MAUI-seq of dnaK and nodD, as described above. Roots and shoot portions will be separated and dried in an oven. Root plus shoot biomass will be measured for all plants. We quantify host growth response (i.e., net marginal effects of live inoculum on hosts), calculated as the mean percentage difference in total plant biomass between inoculated plants and matched uninoculated control plants. After host growth response is calculated, a subset of plant dried shoot tissues will be used to quantify nitrogen fixation, via atom percent difference (d15N) between live-inoculum and control plants. Leaves of plants incorporating symbiotically fixed nitrogen have lowered d15N relative to uninfected plants. Leaf tissues will be analyzed at the Facility for Isotope Ratio Mass Spectrometry at UC Riverside. A predictive framework will be developed to uncover rhizobia genotypes with optimal characteristics for field inoculation. Rhizobia that are ideal for field inoculation will achieve the highest frequencies within soil, thrive in diverse soil types, and be competitive to colonize root surfaces and nodulate the most host genotypes -- based on characteristics of genotype frequencies in nodules from different soil sources and on different host genotypes. GLMMs will be used to test for statistical associations between the epidemiological categories and factors of soil source, host genotype, and interaction effects. Partial redundancy analysis (RDA) will be used to associate genotype frequencies in nodules and the soil extracts with abiotic soil features. Finally, RDA and other randomization approaches will be used to compare the rhizobial genotype communities between the soil extracts and the nodule genotypes.