Final report for GW18-034
The goal of this project is to determine the physiological and genetic basis underlying the potential production of more nutrient dense crops in farming systems. This will enable farmers of crops produced in amended soils to integrate systems that favor more sustainable production. Previously, we demonstrated that tomato fruit grown under an organic fertilizer regime had elevated phytonutrient content compared to tomato fruit grown under a conventional fertilizer regime. Using a comprehensive transcriptome analysis, we tested the following hypotheses: 1.) Growth under organic fertilizer regime will result in differential expression of the tomato genome and 2.) Genes and pathways associated with phytonutrients that were observed to be significantly higher under organic fertilizer regime will demonstrate higher expression. Both hypotheses tested true, indicating an adjustment of the plants’ genomic activity in response to a different nitrogen regime. We identified genes and associated pathways –among them, lycopene, ascorbate, soluble solids, and salvage pathways –which are expressed at higher levels under organic conditions (Sharpe et al. 2020).
Building upon previous work, we aimed to examine the effects an organic fertilizer regime on soil microbial communities. In addition to using organic fertilizer, we used organic-certified biochar as a soil amendment as it is known to foster microbial growth. Utilization of biochar has arisen as a promising strategy for both enhancement of soil fertility and long-term sequestration of carbon. During the course of this work, we tested the hypothesis that the genomic responses of tomato roots, and microbial community profiles, will change over time following plant growth in control and soil amended with biochar under an organic fertilizer regime. We utilized a metatranscriptomic approach to evaluate this question. Taxonomic classification of microbial transcripts allowed us to characterize the spectrum of microbes present in the soil at each stage of development and under each biochar regime. Metatranscriptomic analysis is ongoing and is expected to reveal genes that are differentially expressed within the tomato roots, as well as within the various bacteria and fungi present in the soil, over time in each biochar treatment. Together, the results lend further insight into the precise impacts of biochar-based soil amendment for organic tomato growth and will inform new strategies for enhancement of soil fertility.
Short-term: We aimed to increase knowledge of the relationship between phytonutrient content and underlying gene expression changes in plants when grown under different soil fertility management systems in a model crop plant (tomato). We hope to provide a basis for understanding of rhizosphere gene expression changes and metabolic profile shifts, which may in turn result in nutritional differences, following organic biochar amendment of soil. We aim to develop a model for comprehensive analysis of crop production in the field. We seek to use our study results to apply for grant funding in the USDA’s AFRI program in order to conduct a more comprehensive study, which will include field research.
Intermediate-term: Knowledge generated from the project, particularly regarding effects on plant growth, alteration of the root/soil microbiome, and carbon sequestration, as well as corresponding phytonutrient profiles in the different regimes, will assist sustainable producers to develop management plans for soil fertility. Understanding of gene function and of markers associated with genes expressed differentially under different soil amendment regimes will help plant breeders in the crossing and selection of more efficient crop cultivars that optimize both nutritional quality and yields of crops grown using more sustainable farming practices.
Long-term: Increase consumption of foods with improved nutritional quality, thereby contributing to the health of American children and adults and reducing health care costs. Implement more sustainable farming practices, particularly related to soil fertility and pest management, will enhance the quality of U.S. agro-ecosystems. Contribute to the long-term economic viability of American farmers.
Experiments were conducted using organic-certified biochar provided by Ag Energy Solutions (Spokane, WA). Biochar was augmented with certified organic BioLink 3-3-3 fertilizer (Westbridge Agricultural Products Inc., CA) (1:10 by volume) and zinc sulfate and copper sulfate (Old Bridge Chemicals, Inc., NJ) (1:100 by volume).
Field Plot and Experimental Design:
A 4 x 30-meter study plot at the Eggert Organic Farm (WSU) was utilized for this study. Two days before planting, four, evenly-spaced rows were dug in the plot to a depth of 20cm. To two of the rows, biochar applied at rates of 1 ton (1T) (1206 grams total) and 2 tons (2T) (2413 grams total) per row. The soil was mixed homogenously to incorporate the biochar into the soil for the treatment rows while non-amended farm soil was re-incorporated into the control rows.
Plant material and soil samples:
Tomato seeds (Solanum lycopersicum L.) ‘Oregon Spring’ sourced from Territorial Seed Company (OR), were started in Sunshine® #1 Natural & Organic potting mix (Sun Gro Horticulture, MA) in the greenhouse. Greenhouse temperatures were maintained at 21.1/18.3°C (day/night) with a 14 h day, supplemented with high-pressure sodium lamps and 10h night photoperiod. Four-week emergent seedlings with two, fully-expanded true leaves, were transplanted into the Organic Farm soil. Throughout the experiment, all plants in each treatment group were fertilized once a week with 100mL of diluted organic Alaska Fish Fertilizer 5-1-1 (Pennington Seed Inc, GA).
Time Course Tissue Sampling:
Bulk soil samples and rhizosphere soil were collected, and flash frozen in liquid nitrogen, at four time points: TP1 - juvenile field plants, TP2 - pre-flowering plants, TP3 - flowering and fruit producing plants, and TP4 - at least 75% red-ripe tomato fruit bearing plants.
Assessment of shoot fresh/dry weight and fruit soluble solid content:
Fruit were harvested at TP3 and TP4, and total soluble solid content was measured for ‘breaker stage’ and ‘red ripe’ fruits using a handheld refractometer. At TP4, above-ground biomass was collected by cutting the plant at soil level, and fresh weight was recorded. Plants were dried in a hot oven for a week before recording their dry weight.
Root/Soil RNA Extraction and Sequencing:
RNA was isolated from bulk and rhizosphere soil using an in-house CTAB protocol. Briefly, 0.5mL of CTAB solution was added to 2mL, sterilized tubes containing 0.22mm glass beads. In each tube, approximately 0.5g sample, 0.5 μL RNase free water, and 0.5mL chloroform- isoamyl alcohol- phenol (CIP) mixture (pH ~6) were mixed thoroughly at 4°C for 3 minutes. Samples were centrifuged at 14,000 rpm for 10 minutes at 4°C. The organic phase was removed and transferred to a fresh tube with an equal amount of chloroform – phenol (1:1). The tubes were inverted continuously at room temperature for 1 minute before centrifugation 14,000 rpm for 10 minutes at 4°C. The nucleic acid fraction was transferred into a new 1.5 ml microcentrifuge tube. A DNA/RNA precipitating solution was added in a 2:1 ratio and the tubes were incubated at room temperature for two hours. Samples were centrifuged for 15 minutes at 14,000 rpm. The aqueous layer was removed carefully by low volume pipette. The DNA/RNA pellet was dried in a sterile hood by inverting the tubes and allowing them to dry on fresh paper towel for 15 minutes. The pellet was resuspended in 40μL of molecular grade water. Resuspended samples were treated with DNase using Ambion Turbo DNA-free kit (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s guidelines RNA was quality checked using agarose gel electrophoresis, and quantified using Nanodrop 2000 spectrophotometer (Thermo Scientific, Waltham, MA, USA). RNA libraries underwent library preparation and sequencing at the WSU Genomics Core on an Illumina HiSeq 2500 as single, 100-base pair reads.
Soil Metatranscriptome Assembly:
The single, 100-bp fastq files generated using the Illumina HiSeq 2500 were imported into the CLC Genomics Workbench (Version 8.0.1) (Qiagen) for pre-processing and assembly. The CLC ‘Create Sequencing QC Report’ tool was used to assess quality. The CLC ‘Trim Sequence’ process was used to trim quality scores with a limit of 0.01, corresponding to a Phred value of 20. Ambiguous nucleotides were trimmed; terminal nucleotides with low-quality sequence were removed based on the results of the quality report. Reads below length 34 were discarded. To remove host (tomato) reads from putative metatranscriptome reads, reads from individual treatments were mapped to the tomato reference genome (SL3.0, GCA_000188115.3) using a specified length fraction of 0.9 and similarity fraction of 0.6. Reads that mapped to the transcriptome were deemed to be of tomato origin and were thus consolidated for root transcriptome analysis. Unmapped reads, determined to be of soil metatranscriptomic origin, were imported into the OmicsBox metatranscriptome suite (version 1.3.11) (BioBam Bioinformatics S.L., Valencia, Spain) where they were de novo assembled using the MEGAHIT application of the metagenomics module. The de novo assembly resulted in production of 43,935 contiguous sequences (contigs). The assembly consensus was exported as a fasta, which was then imported back into CLC for subsequent read mapping steps. For each individual dataset (treatment/time point) the original, non-trimmed reads were mapped back to the master metatranscriptome assembly. Default parameters were used, except for the length and similarity fractions, which were set to 0.5 and 0.9, respectively. Mapping resulted in the generation of individual treatment sample reads per contig. The master metatranscriptome was exported as a fasta file for functional annotation and the read counts for each dataset were exported as tab delimited text files for normalization with the Reads Per Kilobase per Million reads (RPKM) method.
Tomato Transcriptome Mapping:
Reads from each treatment group that mapped to the tomato genome in the original mapping step underwent a second mapping, this time to the reference transcriptome (46,846 contigs) derived from the SL3.0 genome. Read counts for each treatment mapping were exported as tab delimited text files for normalization with the Reads Per Kilobase per Million reads (RPKM) method.
Metatranscriptome Taxonomic Classification:
Within the OmicsBox Metagenomics Module, Taxonomic Classification with Kraken 2 (version 2.0.8) (Wood et al. 2019) was utilized for taxonomic analysis of the metatranscriptome treatment groups. Briefly, the raw sequencing reads from each treatment group (which did not map to the tomato host genome) were imported into the taxonomic classification wizard as single-end reads. No contaminant index was selected, as tomato is not one of the currently available host libraries available in the application, and host reads had already been removed through mapping to the tomato genome. The analysis was run, resulting in generation of a table that included the Operational Taxonomic Unit (OTU) counts for taxa (ranging from Superkingdom to sub-species levels) across all treatment groups.
The master metatranscriptome fasta produced from the MEGAHIT assembly was functionally annotated in the OmicsBox Transcriptomics Module (Gotz et al. 2008). Briefly, contig sequences were identified by a blastx alignment against the NCBI ‘nr’ database with and e-value specification of 10.0E-3. GO annotation was assigned using the ‘Mapping’ and ‘Annotation’ features using default parameters to generate a functionally annotated master assembly. InterPro scan was conducted in conjunction with the blastx step, and results of this scan were merged with go annotations to provide additionial functional information to the contigs.
For the tomato transcriptome, annotations corresponding to RNAs derived from the tomato reference genome (SL3.0) were downloaded from GenBank and concatenated with their numeric contig IDs.
Differential Expression Analyses (NoiSeq):
For the metatranscriptome treatment mappings as well as the tomato transcriptome treatment mappings, pairwise differential expression analyses were conducted in the OmicsBox Transcriptomics Module using the NoiSeq-sim application to compare all treatments at each timepoint. NoiSeq-sim is designed to infer significant differential expression, even in the absence of replicates. In the case that replication is absent, as was the case in our study, in which biological replicates were pooled for sequencing, the application simulates technical replicates based on the assumption that read counts follow a multinomial distribution (Tarazona et al. 2015).
GO Enrichment Analyses:
Gene ontology (GO) enrichment analyses was conducted to determine over and underrepresented biological processes, molecular functions, and cellular components among the differentially expressed genes in each treatment/time using the OmicsBox Enrichment Analysis (Fisher’s Exact Test) function (Gotz et al. 2008). The annotated master transcriptome was used as the reference dataset, and the set of genes identified as differentially expressed over time in the treatment group versus the control group was used as the test dataset.
Here we provide preliminary results of soil metatranscriptome taxonomic classification. The abundance of identified taxa for each rhizosphere sample is indicated by Operational Taxonomic Units (OTUs). Differential expression analyses tomato and soil are in progress, therefore the results of that portion of the study will not be included in this report.
Fresh Weight Biomass:
Fresh weight shoot biomass (sampled at TP4) was significantly elevated for plants derived from soil treated with 1T and 2T biochar (p<0.05). Compared to the control, increases in 1T and 2T treatments were 16% and 4%, respectively.
At TP3, 1T treatment presented a 12% increase and the 2T treatment presented a significant 21% increase in fruit yield in comparison with the control (0<0.05). However, no significant differences in fruit yield were observed at TP4.
Total soluble solid content (°Brix) was measured in ‘breaker’ and ‘red ripe’ fruits harvested at TP3 and TP4. Breaker stage fruits derived from 1T and 2T biochar-treated soils displayed higher °Brix than fruit derived from the control soil; in the case of the 2T treatment group, the increase was significant in comparison with the control (p<0.05). However, at the red ripe stage, there were no significant differences in total soluble solids.
Metatranscriptome Taxonomic Classification and Biodiversity Analysis:
Shannon diversity (H), calculated using the OmicsBox Taxonomic Classification feature, increased gradually over time in the control fruit (Figure 1). Addition of biochar appeared to influence the level of active microbial biodiversity at the onset of the experiment, with higher levels seen in both 1T and 2T treatments in comparison with the control. As time progressed, the two biochar treatments altered biodiversity inversely, with the 2T treatment corresponding to an enhancement of biodiversity and the 1T treatment corresponding to a reduction in diversity at TP3, respectively (Figure 1). Interestingly, TP3, the point at which rhizosphere biodiversity was highest for the 2T treatment group, was also the point at which the greatest differences in biomass, fruit yield, and quality were seen between the control plants and plants grown in the biochar-treated soils. This finding suggests that soil microbial diversity may be directly linked to plant health. Biochar, applied in the right amount, could potentially foster enhanced diversity.
The overall microbial diversity profile of each treatment/time are represented in Figure 2A.
The microbes with the highest Operational Taxonomic Unit (OUT) counts in all samples were of the genus Streptomyces (Figure 2B). Streptomyces pertain to a group of common soil microorganisms known as the Actinomycetes and have been described as one of the most important soil microbes due to their production of beneficial bioactive compounds, including enzymes that can enhance soil health and fertility, and have positive impacts on agriculture overall (Olanrewaju et al. 2019). Interestingly, Streptomyces spp. OTUs were higher in control soil than either of the biochar treatments, particularly at TP4; at this stage, abundance of the Streptomyces in the control group greatly increased, compared to the gradual increase observed for the biochar treated soils (Figure 2B). Other common soil microbes that were among the most abundant in the rhizosphere samples, and which displayed the highest OTUs included Sphingobium, Pseudomonas, Actinoplanes, Flavobacterium, Massilia, Microbacterium,Variovorax, and Nocardioides (Figure 2B). For a majority of these bacteria, OTUs were highest in the 2T biochar treatment at TP3 and were highest in the control at TP4. The 1T biochar treatment did not appear to elicit the same effects on rhizosphere biodiversity at any time point. Together, these results indicate that biochar, applied in the right concentration, may indeed influence the temporal abundance of important soil microbes. Through direct, and potentially symbiotic, interaction with the tomato roots, these microbial communities are expected to influence plant growth, and ultimately fruit yield and quality.
Education and Outreach
Thus far, we have engaged in discussion with visitors from various farming groups to the lab regarding the ongoing research and its potential implications.
We have graduated a student with MS thesis related to this project.