WO2016057991A1 - Sélection orientée de microbiomes végétaux - Google Patents

Sélection orientée de microbiomes végétaux Download PDF

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Publication number
WO2016057991A1
WO2016057991A1 PCT/US2015/055106 US2015055106W WO2016057991A1 WO 2016057991 A1 WO2016057991 A1 WO 2016057991A1 US 2015055106 W US2015055106 W US 2015055106W WO 2016057991 A1 WO2016057991 A1 WO 2016057991A1
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plant
soil
plants
microbiome
group
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PCT/US2015/055106
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English (en)
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Jenny KAO-KNIFFIN
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Cornell University
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    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01H—NEW PLANTS OR NON-TRANSGENIC PROCESSES FOR OBTAINING THEM; PLANT REPRODUCTION BY TISSUE CULTURE TECHNIQUES
    • A01H3/00—Processes for modifying phenotypes, e.g. symbiosis with bacteria
    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01N—PRESERVATION OF BODIES OF HUMANS OR ANIMALS OR PLANTS OR PARTS THEREOF; BIOCIDES, e.g. AS DISINFECTANTS, AS PESTICIDES OR AS HERBICIDES; PEST REPELLANTS OR ATTRACTANTS; PLANT GROWTH REGULATORS
    • A01N63/00—Biocides, pest repellants or attractants, or plant growth regulators containing microorganisms, viruses, microbial fungi, animals or substances produced by, or obtained from, microorganisms, viruses, microbial fungi or animals, e.g. enzymes or fermentates
    • A01N63/20—Bacteria; Substances produced thereby or obtained therefrom
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12N—MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
    • C12N1/00—Microorganisms, e.g. protozoa; Compositions thereof; Processes of propagating, maintaining or preserving microorganisms or compositions thereof; Processes of preparing or isolating a composition containing a microorganism; Culture media therefor
    • C12N1/20—Bacteria; Culture media therefor

Definitions

  • the present invention relates to the directed selection of plant microbiomes.
  • the present invention is directed to overcoming the deficiencies in the art by reducing the complexity of whole microbiome communities while retaining the key microbial players.
  • One aspect of the present invention relates to a method of producing a plant whole soil microbiome useful in enhancing a particular desired plant trait.
  • the method comprises growing a group of plants in soil and identifying a subgroup of plants within the group of plants best displaying a particular desired plant trait.
  • the subgroup of plants' whole soil microbiomes are recovered and applied to another group of the plants. These steps are repeated to produce a plant whole soil plant microbiome useful in enhancing the particular desired plant trait.
  • Another aspect of the present invention relates to a method of producing plants, in which a particular desired plant trait is enhanced.
  • the method comprises growing a group of plants in soil and identifying a subgroup of plants within the group of plants best displaying a particular desired plant trait.
  • the subgroup of plants' whole soil microbiomes are recovered and applied to another group of the plants. These steps are repeated to produce a whole soil plant microbiome useful in enhancing the particular desired plant trait.
  • the plants in which the particular desired plant trait is enhanced are recovered.
  • Another aspect of the present invention relates to a method of producing a plant microbiome useful in enhancing a particular desired plant trait.
  • the method comprises growing a group of plants in soil and identifying a subgroup of plants within the group of plants best displaying a particular desired plant trait.
  • the subgroup of plants' whole microbiomes are recovered and applied to another group of the plants.
  • a plant microbiome that is useful in enhancing the particular desired plant trait and comprises at least one microorganism selected from the group consisting of Spirochaetes, Firmicutes, Archaea, Crenarchaeota, Actinobacteria, Acidobacteria, Bacteroidetes, Proteobacteria, Verrucomibrobia, and Spirochaetes.
  • the present invention provides a method for directly harnessing the power of microbes to modulate key agricultural plant traits.
  • Plant whole soil microbiomes can be engineered without the need for extensive isolation and characterization of the microbial community.
  • Removing the need for directly manipulating community structure provides a method for exploiting un-culturable soil microbes that could not be recovered via direct culturing and manual assembly of microbial products for plant application.
  • the utility of the recovered whole soil microbiomes may be transferred to a different plant variety or varieties and retain the ability to modulate key agricultural plant traits.
  • This transference between varieties thereby significantly increases the utility of an isolated plant whole soil microbiome well past the immediate plant and environment used to produce it.
  • the use of relatively simple culturing techniques, applied to the whole plant soil microbiome provides an easy and inexpensive method for reducing the complexity of the plant whole soil microbiome yet retaining key microbes involved in the modulation of plant traits.
  • Figure 1 shows flowering time diverges with selection for early- vs. late- flowering-associated microbiomes across ten successive plantings. The difference in days to uniform flower bolting from the control is shown across 16 generations of microbiome selection for progressively earlier flowering (EF, transparent triangles) and later flowering (LF, opaque triangles). Values reported are from a standard least squares regression model including control values as a covariate (ANCOVA). Generations 6-16 have statistically significant differences in means of EF and LF at p ⁇ 0.05. Error bars indicate SEM.
  • Figure 2 shows soil microbiota group together primarily by flowering time treatment and controls and a heatmap of log absolute abundance of all taxa. Classification, dendrograms, and order of samples and taxa were determined by the Prediction Analysis for Microarrays in the R statistical package. The key at the top left includes a frequency histogram of number of operational taxonomic units (OTUs) at each expression level. Vertical columns represent samples mapping primarily into 'Control', early flowering (EF), and late flowering (LF) treatment groups. The 'control' serves as a profile of the surviving and residual microbiota endemic in the soils after steam-sterilization and without inoculation of additional microbiota. While the heatmap showed strong clustering by treatment, eight samples were misclassified representing an error rate of 0.075.
  • OTUs operational taxonomic units
  • Figures 3A-B show family-level taxa uniquely associated with early/late flowering time groups and controls.
  • Figure 3 A is a ternary plot of OTUs showing the percent of each OTU's observations present in each group (EF, LF, and Control) across different plant hosts. For example, a point's position within the "0.8" triangle at the "EF" corner of the ternary plot indicates that 80% of all observations of that OTU occur within the EF group. Diameter of plotted points corresponds to relative abundance of the OTU. Compartments of the dotted grid correspond to 20% increments.
  • Figure 3B is a list of taxonomy at the family-level corresponding to OTUs of points falling within the 80% compartment of each group.
  • Figure 4 shows unweighted UniFrac distances show separation of the early/late- flowering-associated microbiome treatments and controls by microbial taxa.
  • Unweighted UniFrac distances are insensitive to relative abundance of observed OTUs and instead reveal patterns and differences in presence/absence of taxa. Samples were rarefied to an even sampling depth of 12,000 seqs/sample. The orange points refer to early flowering microbiomes, the green points are the late flowering microbiomes, and the blue points are the control microbiomes.
  • Percentages on each axis represent the percent variation explained by the PCs.
  • Figure 5 illustrates that a core microbiome is share across flowering time and control treatments.
  • the unweighted UniFrac analysis indicated no separation of flowering time-associated and control microbiomes.
  • PCoA of weighted UniFrac distances colored by treatment (Early-flowering, Late-flowering, and Control).
  • the square points refer to late-flowering-associated microbiomes, the triangle points are the early-f owering-associated microbiomes, and the circle points are the control microbiomes. Percentages on each axis represent the percent variation explained. No clear patterns in relative abundance of taxa between treatments.
  • Figures 6A-C indicate flowering time, reproductive biomass, and potential extracellular enzyme activity show consistent changes across plant hosts.
  • Figure 6 A shows the days to flowering of each plant host after inoculation with early- and late-flowering
  • Figure 6B shows the reproductive biomass for the A. thaliana genotypes and total biomass for B. rapa.
  • Figure 6C shows the potential extracellular enzyme activity in soils across plant hosts. Enzyme activity associated with nitrogen mineralization is represented by the sum of leucine aminopeptidase (LAP), N-acetylglucosaminidase (NAG), and phenol oxidase (PO) (Sinsabaugh, "Enzymatic analysis of microbial pattern and process," Biol. Fertil. Soils 17:69-74 (2010), which is hereby incorporated by reference in its entirety). Enzyme activity is measured in nmol/g soil/hour.
  • LAP leucine aminopeptidase
  • NAG N-acetylglucosaminidase
  • PO phenol oxidase
  • ANCOVA covariate covariate
  • Plant host abbreviations correspond to B. rapa (BR) and the four A. thaliana genotypes Rid (RLD), Ler (LER), Col-0 (COL), and Be (BE).
  • RLD B. rapa
  • LLD Rid
  • Ler Ler
  • Col-0 Col-0
  • BE Be
  • Asterisks denote statistical significance at p ⁇ 0.05. Error bars represent SEM.
  • Figures 7A-B show inoculant effects on flowering time and leaf biomass.
  • Figure 8 shows a histogram of relative abundances summarized by group and a bar chart of relative abundance of phyla summarized within groups. Each band represents a phylum and its size corresponds to the relative abundance of that phylum within the treatment group. Distinct visual patterns are present between treatment groups.
  • Figure 9 shows log2-fold change in abundance of flowering-associated taxa. Key taxa were identified by analysis with DESeq2 differential abundance analysis. Only taxa present in >80% of the samples that showed an early-flowering effect and those present in >80% that showed no flowering effect were used as inputs for DESeq in order to assess the core
  • Relativized log (Log2-fold change) bars are grouped by phylum to assist in delineations between taxa groups.
  • Figure 10 shows weighted UniFrac principal coordinates plot. Principal coordinates plot of weighted UniFrac distance matrix illustrates the similarities and differences within and between sample groups. Weighted UniFrac distances show separation of the microbiome treatments by microbial community composition. Weighted UniFrac distances are sensitive to relative abundance of observed OTUs and reveal patterns and differences in the abundance of taxa. Samples were rarefied to an even sampling depth of 9799 seqs per sample based on the sample with the smallest number of sequences. Percentages on each axis represent the percent variation explained by each of the PCs. Close proximity of points obscures individual classifications. Circles have been added around clusters, and sample points within each cluster are listed adjacent to each cluster.
  • FIG 11 shows log2-fold change in abundance of biomass-associated Taxa.
  • Key taxa were identified by analysis with DESeq2 differential abundance analysis. Only taxa present in >80% of the samples that showed a low biomass effect and those present in >80% that showed a high biomass effect were used as inputs for DESeq in order to assess the core microbiome.
  • Relativized log (Log2-fold change) bars are grouped by closest shared taxonomic level to assist in delineations between taxa groups. Taxa preceded by: "c " are classes, "o " are orders, and
  • Figure 12 shows PAMR heatmap of key taxa. Heatmap of log relative abundance of key OTUs associated with observed phenotype effects identified by DESeq2. Columns represent individual samples and cluster primarily by treatment group. The rows represent OTUs at the order level. Dendrograms on each axis illustrate the relationship between the columns and rows. The key at the top left includes a frequency histogram of number of OTUs at each log expression level. OTUs with zero expression were changed to 0.001 to allow the use of a log transformation. The whole microbiome and LB groups are the only two groups from which samples do not cluster correctly. 2 LB samples and 1 whole microbiome sample do not group with their corresponding treatments.
  • One aspect of the present invention relates to a method of producing a plant whole soil microbiome useful in enhancing a particular desired plant trait.
  • the method comprises growing a group of plants in soil and identifying a subgroup of plants within the group of plants best displaying a particular desired plant trait.
  • the subgroup of plants' whole soil microbiomes are recovered and applied to another group of the plants. These steps are repeated to produce a plant whole soil plant microbiome useful in enhancing the particular desired plant trait.
  • the term "plant” includes all parts of a plant, including seeds, seedlings, cutting, propagules, whole plants, herbaceous vegetation, leaves, roots, stems, floral structures, pollen, etc.
  • the plants are in the form of seeds, seedlings, cutting, propagules, or whole plants.
  • plant means all plants and, particularly, plants of economic importance. Plants may be categorized as agriculturally relevant or model plants, based on their human use and/or consumption.
  • plants include natural or wildtype plants, and plants that have been genetically modified.
  • Agriculturally relevant plants are plants of which a part or all is harvested or cultivated on a commercial scale or which serve as an important source of feed, food, fibers (e.g., cotton and linen), combustibles (e.g., wood, bioethanol, biodiesel, and biomass) or other chemical compounds. Agriculturally relevant plants also include vegetables, ornamental, horticultural, and silvacultural plants. Thus, agriculturally relevant plants include, but are not limited to: alfalfa, almond, apple, apricot, asparagus, avocados, bananas, barley, beans, beech
  • Model plants are extensively studied plant species chosen for the ease of investigating particular biological phenomena or for their value in biotechnology or agronomy.
  • Non-limiting examples of model plants include Arabidopsis thaliana, Boevhera spp., Selaginella moellendorfii, Brachypodium distachyon, Setaria viridis., Lotus japonicus, Lemna gibba, Zea mays, Medicago truncatula, Mimulus guttatus, Nicotiana benthamiana, Nicotiana tabacum, Oryza sativa, Physcomitrella patens, Marchantia polymorpha, and Populus spp.
  • the plant is selected from the group consisting of alfalfa, almond, apple, apricot, asparagus, avocados, bananas, barley, beans, beech (Fagus spec), begonia, birch, blackberry, blueberry, cabbage, camphor, canola, carrot, castor oil plant, cherry, cinnamon, citrus, cocoa bean, coffee, corn, cotton, cucumber, cucurbit, eucalyptus, fir, flax, fodder beet, fuchsia, garlic, geranium, grapes, ground nut, hemp, hop, juneberry, juncea (Brassica juncea), jute, lentil, lettuce, linseed, melon, mustard, oak, oats, oil palm, oil-seed rape, olive, onion, paprika, pea, peanut, peach, pear, pelargonium, peppers, petunia, pine (Pinus spec),
  • Desired plant traits may be related to plant physiology, including but not limited to phytochemistry, cellular interactions, molecular and cell biology, plant morphology and environmental interactions encompassing both biotic and abiotic stresses.
  • the particular desired plant trait is selected from the group consisting of early flowering, late flowering, biomass production, grain yield, seed yield, fruit yield, delayed senescence, plant nutrient capture or utilization, nutrient use efficiency, photosynthetic use efficiency, disease resistance, abiotic stress tolerance or biotic stress tolerance.
  • Identifying enhancements to a particular desired plant trait may be achieved through measurement of one or more of the observable characteristics of an individual, relating in part or in whole to said desirable plant trait.
  • the present invention can involve improving plant vigor.
  • Plant vigor becomes manifest in several aspects, including the general visual appearance of the plant.
  • Improved plant vigor can be characterized by, inter alia, the following: improved vitality of the plant; improved plant growth; improved plant development; improved visual appearance;
  • improved plant stand (less plant verse/lodging); improved emergence; enhanced root growth and/or more developed root system; enhanced nodulation, in particular rhizobial nodulation; bigger leaf blade; bigger size; increased plant height; increased tiller number; increased number of side shoots; increased number of flowers per plant; increased shoot growth; increased root growth (extensive root system); enhanced photosynthetic activity; enhanced pigment content; earlier or later flowering; earlier or later fruiting; earlier or later and improved germination; earlier or later grain maturity; fewer non-productive tillers; fewer dead basal leaves; less input needed (such as fertilizers or water); greener leaves; complete maturation under shortened vegetation periods; less fertilizer needed; fewer sowing of seeds needed; easier harvesting; faster and more uniform ripening; longer shelf-life; longer panicles; delay of senescence; stronger and/or more productive tillers; better extractability of ingredients; improved quality of seeds (for being seeded in the following seasons for seed production); reduced production of ethylene and/or the inhibition of its reception
  • the present invention can involve improving the quality of a plant and/or its products. Improvements in plant quality may include, without limitation, improving certain plant characteristics, such as increasing the content and/or composition of certain ingredients by a measurable or noticeable amount over the same factor of the plant produced under the same conditions, but without application of the composition of the present invention.
  • Enhanced quality can be characterized by, inter alia, the following: increased nutrient content; increased protein content; increased content of fatty acids; increased metabolite content; increased carotenoid content; increased sugar content; increased amount of essential amino acids;
  • improved nutrient composition improved protein composition; improved composition of fatty acids; improved metabolite composition; improved carotenoid composition; improved sugar composition; improved amino acids composition; improved or optimal fruit color; improved leaf color; higher storage capacity; higher processability of the harvested products; or any combination thereof
  • the present invention can involve improving a plant's tolerance or resistance to biotic and/or abiotic stress factors.
  • Biotic and abiotic stress can have harmful effects on plants.
  • Biotic stress is caused by living organisms while abiotic stress is caused, for example, by environmental extremes.
  • Biotic stress can be caused by living organisms, such as pests (e.g., insects, arachnids, and nematodes), competing plants (e.g., weeds), microorganisms (e.g., phytopathogenic fungi and/or bacteria), and/or viruses.
  • pests e.g., insects, arachnids, and nematodes
  • competing plants e.g., weeds
  • microorganisms e.g., phytopathogenic fungi and/or bacteria
  • Negative factors caused by abiotic stress are also well-known and can often be observed either as reduced plant vigor (as described above) or by the following symptoms: dotted leaves, "burned” leaves, reduced growth, fewer flowers, less biomass, less crop yield, reduced nutritional value of the crop, and later crop maturity, to give just a few examples.
  • Abiotic stress can be caused by, inter alia: extremes in temperature such as heat or cold (heat stress/cold stress), strong variations in temperature, temperatures unusual for the specific season, drought (drought stress), extreme wetness, high salinity (salt stress), radiation (e.g., by increased UV radiation due to the decreasing ozone layer), increased ozone levels (ozone stress), organic pollution (e.g., by phytotoxic amounts of pesticides), inorganic pollution (e.g., by heavy metal contaminants), and any combination thereof.
  • extremes in temperature such as heat or cold (heat stress/cold stress), strong variations in temperature, temperatures unusual for the specific season, drought (drought stress), extreme wetness, high salinity (salt stress), radiation (e.g., by increased UV radiation due to the decreasing ozone layer), increased ozone levels (ozone stress), organic pollution (e.g., by phytotoxic amounts of pesticides), inorganic pollution (e.g., by heavy metal contaminants), and any combination thereof.
  • the above identified indicators for the health condition of a plant may be interdependent and may result from each other. For example, an increased resistance to biotic and/or abiotic stress may lead to a better plant vigor, e.g., to better and bigger crops, and thus to an increased yield. Inversely, a more developed root system may result in an increased resistance to biotic and/or abiotic stress.
  • soil refers to a growth medium for plants, which may include but is not limited to, field soils or other natural soil derived from the upper layer of earth, or "soilless" growth medium comprised of one or more of the following: peat moss, hypnaceous moss, reed and sedge, humus or muck, sphagnum moss, wood residues, leaf mold, sawdust, barks, bagasse, rice hulls, sand, perlite, vermiculite, calcined clays, expanded polystyrene, urea formaldehydes, hydroponic solutions or tissue culture gels.
  • microbiome includes the constituent microorganisms and their collective genetic material present in a given environment.
  • microorganism or “microbe” include, but are not limited to the two prokaryotic domains, Bacteria and Archaea, as well as eukaryotic fungi and protists.
  • the plant whole soil microbiome for the initial generation of plant growth may be obtained from naturally occurring soils or other materials, or through the direct inoculation of soil or other growth media with a known or unknown complement of microbes.
  • field soil or soils may be obtained from agricultural, forest, or grassland soils and mixed with potting soil to provide a diversity of soil microorganisms for the initial generation of plant growth.
  • Soil may also be inoculated with a known or unknown complement of microbes to provide soil microorganisms for the initial generation of plant growth. Said soil may also be sterilized prior to inoculation with said microbes.
  • the plant whole soil microbiome may be recovered through direct harvesting of the soil or other growth medium in which the selected plants have been growing, and applied through direct transfer, or through the application of soil slurries to the soil or growth medium.
  • Soil slurries are prepared by combining sterile, deionized water and harvested soil comprising the plant whole soil microbiome and shaking vigorously.
  • the soil or growth medium may also be further processed via dilution, filtration, centrifugation, or culturing. Specific fractions of plant associated soil may be harvested for the recovery of the plant whole soil microbiome.
  • the rhizosphere soil may be harvested independently of surrounding soil. Rhizosphere soil may be isolated by removing loose soil and harvesting soil adhering to plant roots.
  • the iterative process of growing plants, recovering the plant whole microbiome, and applying said whole plant microbiome to another group of plants may be repeated one or more times. Repetitions of at least four times or eight to ten times are suitable.
  • Plants may be grown to any level of maturity and can be grown until the time of manifestation of the trait or traits of interest. Plants may be grown to varying maturity levels, both within and between iterations, and selection for the trait of interest may be performed on all, none, or a subset of the plants in a given iteration.
  • Plant growth conditions may include controlled conditions, including but not limited to temperature, light, humidity, atmosphere, and nutrient conditions, or may occur under partially controlled or uncontrolled conditions. For example, water and nutrients may be limited, thereby providing a strong filter to impose microbiome effects on soil nutrient mineralization.
  • the plant whole soil microbiome is applied to a different plant variety than that used to produce the plant whole soil microbiome.
  • the different plant variety is agriculturally relevant, and it is also contemplated that the plant used to produce the plant whole soil microbiome is a model plant.
  • growth medium for culturing may be solid or liquid, natural or synthetic, enriched or unenriched, selective or non-selective, differential or non-differential.
  • growth medium for culturing may include nutrient media, minimal media, selective media, differential media, transport media, or enriched media.
  • the recovered plant whole soil microbiome is cultured.
  • the culturing of the plant whole soil microbiome is carried out using a culture medium selected from the group consisting of 25% Luria broth, 10%> tryptic soy agar, pseudomonad selective agar, and rhizosphere medium.
  • the particular desired plant trait is early flowering, and the microbiome comprises an increased amount of one or more
  • microorganisms selected from the group consisting of Spirochaetes, Firmicutes, Archaea and Crenarchaeota, compared to soil initially used in carrying out said method.
  • the particular desired plant trait is early flowering, and the microbiome comprises a decreased amount of one or more
  • microorganisms selected from the group consisting of Actinobacteria, Acidobacteria, and
  • Bacteroidetes, Proteobacteria, and Verrucomicrobia compared to soil initially used in carrying out said method.
  • the particular desired plant trait is biomass production
  • the microbiome comprises an increased amount of one or more microorganisms selected from the group consisting of Firmicutes, Bacteroidetes, Spirochaetes, Proteobacteria, and Actinobacteria, compared to soil initially used in carrying out said method.
  • the particular desired plant trait is biomass production
  • the microbiome comprises a decreased amount of Actinobacteria compared to soil initially used in carrying out said method.
  • the microbiome comprises an increased amount of one or more microorganisms selected from the group consisting of
  • Actinobaceria compared to soil initially used in carrying out said method.
  • the microbiome comprises a decreased amount of one or more microorganisms selected from the group consisting of
  • Actinobacteria Acidobacteria, Bacteroidetes, Proteobacteria, and Verrucomibrobia, compared to soil initially used in carrying out said method.
  • One embodiment of the present invention is the plant whole soil microbiome produced by the recited method.
  • the microbiome comprises an increased amount of one or more microorganisms selected from the group consisting of Spirochaetes, Firmicutes, Archaea, Crenarchaeota, Bacteroidetes, Proteobacteria, and Actinobaceria, compared to soil initially used in carrying out said method.
  • the microbiome comprises a decreased amount of one or more microorganisms selected from the group consisting of
  • Actinobacteria Acidobacteria, Bacteroidetes, Proteobacteria, and Verrucomibrobia, compared to soil initially used in carrying out said method.
  • chemotaxonomy protein analysis or genetic identification methods.
  • phenotypic identification include microscopy and staining, growth characteristics, biochemical assays, antibiograms, salt tolerance etc.
  • chemotaxonomy identification methods include fatty acid methyl ester (FAME) analysis, pyrolysis mass spectrometry (PyMS) analysis, polyamine analysis, and polar lipids analysis.
  • Non-limiting examples of protein analysis include sequencing, sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) of whole cell proteins, Fourier Transform Infrared Spectroscopy (FTIRS), Raman spectroscopy, Matrix Assisted Laser Desorption/Ionization Mass Spectrometry (MALDI-TOF MS), electrospray ionization mass spectrometry (ESI MS), serology, zymograms, etc.
  • FIRS Fourier Transform Infrared Spectroscopy
  • MALDI-TOF MS Matrix Assisted Laser Desorption/Ionization Mass Spectrometry
  • ESI MS electrospray ionization mass spectrometry
  • Non-limiting examples of genetic identification include DNA or rRNA sequencing, hybridization, and genotyping through methods such as polymerase chain reaction (PCR), nested PCR, multiplex PCR, real-time PCR, random amplified polymorphic DNA (RAPD), amplified fragment length polymorphism (AFLP), restriction fragment length polymorphism (RFLP), BOX-PCR, repetitive element palindromic PCR (REP-PCR), multi locus sequencing typing (MLST), genotyping by sequencing (GBS), micro-arrays and oligonucleotide probes.
  • PCR polymerase chain reaction
  • nested PCR multiplex PCR
  • real-time PCR random amplified polymorphic DNA
  • AFLP amplified fragment length polymorphism
  • RFLP restriction fragment length polymorphism
  • BOX-PCR repetitive element palindromic PCR
  • REP-PCR repetitive element palindromic PCR
  • MLST multi locus sequencing typing
  • GSS genotyping by sequencing
  • micro-arrays and
  • DNA may be extracted directly from soil samples, and a diagnostic sequence, such as a portion of the bacterial/archaeal 16S rRNA gene may be genetically typed or directly sequenced and used for taxonomic classification.
  • a diagnostic sequence such as a portion of the bacterial/archaeal 16S rRNA gene may be genetically typed or directly sequenced and used for taxonomic classification.
  • microorganisms may be cultured from the soil prior to DNA extraction and genotyping.
  • Microbial abundance may be measured in a variety of ways, using culture- dependent or culture-independent techniques including, but not limited to: dilution plating and culturing methods, community-level physiological profiles, phospholipid fatty acid analysis, nucleic acid techniques, phylogenetic analysis, and fluorescent in situ hybridization.
  • Another aspect of the present invention relates to a method of producing plants, in which a particular desired plant trait is enhanced.
  • the method comprises growing a group of plants in soil and identifying a subgroup of plants within the group of plants that best display a particular desired plant trait. Following plant selection, the subgroup of plants' whole soil microbiomes are recovered and applied to another group of the plants. These steps are repeated to produce a whole soil plant microbiome useful in enhancing the particular desired plant trait. Finally, the plants in which the particular desired plant trait is enhanced are recovered.
  • Another aspect of the present invention relates to a method of producing a plant microbiome useful in enhancing a particular desired plant trait.
  • the method comprises growing a group of plants in soil and identifying a subgroup of plants within the group of plants best displaying a particular desired plant trait.
  • the subgroup of plants' whole microbiomes are recovered and applied to another group of the plants.
  • a plant microbiome that is useful in enhancing the particular desired plant trait and comprises at least one microorganism selected from the group consisting of Spirochaetes, Firmicutes, Archaea, Crenarchaeota, Actinobacteria, Acidobacteria, Bacteroidetes, Proteobacteria, Verrucomibrobia, and Spirochaetes.
  • This study uses a multi-generation approach to generate enriched microbiomes that induce flowering time as the targeted plant trait.
  • Applying community selection on microbiomes through observable changes on a plant trait can demonstrate the potential for complex communities of microorganisms to shape rapid change in plant population traits.
  • the ability of soil microbiomes selected over multiple iterations of plantings ( Figure 1) for progressively earlier or later flowering in Arabidopsis thaliana genotype Col to induce the same early- and late-flowering times in four novel plant hosts was examined. The soils received low fertilizer inputs to maintain nutrient limitation throughout the study and the soils were steam- sterilized to facilitate establishment of the inoculating microbiome into new soils.
  • the central focus of this study features microbiomes from the tenth generation of plantings inoculated into the soils of novel plant hosts that included Brassica rapa (BR) and three A. thaliana genotypes: Rid, Landsberg erecta (Ler), and Bensheim (Be). It was hypothesized that the community selection of microbiomes across ten generations of earlier or later flowering times in A. thaliana Col would result in early vs. late flowering plastic responses across all A. thaliana hosts and the related B. rapa upon inoculation into these novel host soils, and that these microbiomes would differ in phylogenetic composition by flowering time responses.
  • BR Brassica rapa
  • Be Bensheim
  • Inoculants for early- and late-flowering-associated (EF and LF) microbiomes were generated through an iterative selection process adapted from Swenson et. al. "Artificial Ecosystem Selection,” Proc. Natl. Acad. Sci. 97:9110-9114 (2000), which is hereby
  • thaliana seeds were placed in each of 14 replicate microcosms (7.6 cm diam. x 8 cm ht. pots) containing 1 : 1 mixture of field soil: potting mix soil (Lambert General Purpose Mix).
  • the field soil was obtained from a collection of sites across Ithaca, NY (42.456583, -76.368822; 42.452265, -76.369477; and 42.414913, -76.442272) representative of agricultural, forest, and grassland ecosystems.
  • the intention was to include a diversity of soil microorganisms for the initial generation.
  • the potting mix was autoclaved for each generation, and became the growing media for the experimental selection.
  • the early- and late-flowering-associated treatments were established with 14 replicate units each per planting and a control group included seven units paired with each flowering treatment (14 control units).
  • 14 control units included seven units paired with each flowering treatment (14 control units).
  • four microcosms were selected based on the highest degree of the plant trait desired. This corresponded with progressively later flowering or earlier flowering as determined by uniform flower bolting in 90% of the individuals in a unit.
  • Controls were paired with each flowering time microbiome treatment to examine plant traits and soil extracellular enzyme activity results relative to plant phenology.
  • the controls consisted of the plants and steam-sterilized soils, but the units were not inoculated with early- or late- flowering-associated microbiomes.
  • Biomass and soils were harvested immediately following flowering of all pots within a group. Loose soil was separated from roots of the four earliest versus four latest flowering replicate units of each treatment group, pooled, and mixed with sterile water to form the EF and LF inoculants. Soil slurry inoculants were prepared with 180 mL of sterile deionized water and 30 g of fresh rhizosphere soil, and then shaken vigorously for 60 seconds upon preparation and periodically during inoculation to make a soil suspension. Each unit for the subsequent generation received 12 mL of the corresponding treatment inoculant. The control group did not receive inoculants of the microbiomes.
  • All seeds across the multi-generation planting were derived from a static seed pool of a highly inbred line, A. thaliana Col (Lehle Seed Co., Round Rock, TX). Seeds were derived from this common seed pool to maintain consistent allelic frequencies across all generations and to ensure that any changes in plant traits are the result of microbiome selection. For example, the same pool of seeds was used across generations 1 through 10 and in the early flowering, late flowering, and control treatments. All microcosms were watered through capillary action using individual reservoirs for each unit. A low level of available nutrients in the potting medium, as well as in the watering regime ensured that the plants were under nutrient limitation, providing a strong filter to impose microbiome effects on soil nutrient mineralization.
  • Fertilizer requirements for A. thaliana are high (200 ppm N every other day) to achieve optimal growing conditions, but a fraction of the amount was used comprising applications of lOppm N for generations 1 through 5 for each watering event and three applications of lOppm N per generation for generations 6 through 10.
  • the only adaptive traits to evolve over the iterative generations were derived from the soil inoculation (soil microbial community). This selection process continued for 10 successive generations (plantings) to develop distinct, trait-associated soil microbiomes associated with early/late flowering time.
  • Plant Biomass All units of a plant host received the same amount of fertilizer consisting of a 10% solution (lOppm N, 10.5% nitrate/89.5% urea) of 20-10-20 Jack's Professional General Purpose Fertilizer (J.R. Peters, Inc., Allentown, PA). Plant hosts Be, Col, and Ler received three equal doses of fertilizer during growth for a total of 0.9 mg added nitrogen, while RLD received two doses for a total of 0.65mg added nitrogen, and B. rapa received no fertilizer. The difference in fertilizing regimes was due to the rapid flowering, and completion of life cycle, in the early flowering group for RLD and B. rapa in advance of the fertilization schedule and the need to keep nutrient addition constant between treatments. Plant Biomass
  • Plant aboveground biomass was harvested after flower bolting had begun in 90%> of the individuals of each replicate microcosm. Biomass was harvested in two separate portions, reproductive structure and leaf tissue, for the A. thaliana genotypes, and whole for B. rapa. Harvested tissue was dried at 50°C until constant weight.
  • Microbiome influence on soil processes was assessed by measuring potential activities of soil extracellular enzymes involved in nitrogen mineralization.
  • the enzymes include N-acetyl glucosaminidase (NAG), leucine aminopeptidase (LAP), and phenol oxidase (PO). They function in depolymerizing organic matter and facilitate microbial access to N sequestered within the complex structures (Sinsabaugh "Phenol oxidase, peroxidase and organic matter dynamics of soil," Soil Biol. Biochem. 42:391-404 (2010), which is hereby incorporated by reference in its entirety). NAG and LAP were measured by fluorometric quantification and PO was quantified by absorption.
  • Soil slurries were prepared from 5g fresh soil in 150 ml sodium bicarbonate buffer (50mM, pH 7) and homogenized with an immersion blender for 1 min. Hydrolytic enzyme assays were conducted in black 96-well microplates and oxidative assays were carried out in transparent-bottom 96-well microplates. Standard curves were made for each soil sample (soil slurry + MUB or AMC standard of 0, 2.5, 5, 10, 25, 50 ⁇ ).
  • the oxidative enzyme plate contained a buffer blank (250 ⁇ buffer), a L-DOPA blank (200ul buffer + 50 ⁇ , DOPA), sample blank (200 ⁇ slurry + 50 ⁇ buffer), and the sample wells (200 ⁇ slurry + 50 ⁇ ⁇ DOPA). Oxidative plates were incubated in the dark at 25°C for 3 hours absorbance was measured at 460nm with the BioTek microplate reader. Activity was calculated based on equations from previous work (Saiya-Cork et. al.
  • Soil DNA was extracted from frozen samples using the PowerSoil DNA Isolation
  • Kit MO BIO Laboratories, Inc., Carlsbad, CA
  • Approximately 0.1 g of soil from each sample was used for isolation of soil DNA. Isolated samples were normalized to a concentration of 10 ng/ul by dilution with PCR- grade water. Quantification was performed with the standard dsDNA quantification protocol for Picogreen. Samples with concentrations below 10 ng/ul were extracted again at lower elution volume and pooled until a concentration above 10 ng/ul was reached for normalization. All pipetting for DNA extraction and normalization was conducted with an Eppendorf epMotion 5075 pipetting robot.
  • 16S rRNA gene sequences were amplified in duplicate from the extracted DNA.
  • the R statistical package and JMP were used for all statistical modeling. All manipulations and calculations on 16S rRNA gene sequence data were conducted in the R statistical package. Biomass, flowering, tissue nutrient, and enzyme activity data were modeled by standard least squares linear regression with control group values for each response variable included as a covariate to control for the effect of being grown at separate times.
  • the Analysis of Covariance evaluates each dependent variable across our treatment groups while controlling for covariates. Treatment means adjusted to account for covariates are what are presented in figures to compare differences between the divergent treatment groups. Statistical significances of these comparisons are from the application of a post-hoc Fisher's test of each plant host, and dependent variable, individually.
  • Multivariate statistics included multiple linear regression, correlation, and covariance matrices to understand data structure and interactions and were conducted both on the biomass, enzyme potential activity, and flowering data, but also on the relative abundance data of the major phyla/classes. Significance of differences in abundance data were determined by ANOVA (False Discovery Rate-corrected) and significant differences between community composition across groups (Late Flowering, Early Flowering, and Control) were assessed by a nonparametric statistical method, adonis, which identifies relevant centroids, calculates squared deviations, and determines significance by F-tests on sequential sums of squares from
  • Paired-end sequences were truncated at the first low quality base and quality filtered to remove those with an average quality score below 25, fewer than 200nt, greater than 700nt, ambiguous bases, primer mismatches, erroneous barcodes, and homopolymer runs exceeding 6 bases. Paired end reads were joined and then demultiplexed within the Quantitative Insights into Microbial Ecology (QIIME) software package Caporaso et. al. "QIIME Allows Analysis of High-Throughput Community Sequence Data," Nat. Methods 7:355-336 (2010), which is hereby incorporated by reference in its entirety.
  • QIIME Quantitative Insights into Microbial Ecology
  • 16S rRNA gene sequences were analyzed in the QIIME software tool with the default parameters for each step. De novo OTU picking was performed with uclust option in QIIME (Edgar et. al. "Search and Clustering Orders of Magnitude Faster than BLAST,” Bioinformatics 26:2460-2461 (2010), which is hereby incorporated by reference in its entirety). Representative OTU sequences were aligned using the PyNAST algorithm with a minimum percent identity of 80% (Caporaso et. al. "PyNAST: A Flexible Tool for Aligning Sequences to a Template Alignment,” Bioinformatics 26:266-2637 (2010), which is hereby incorporated by reference in its entirety).
  • Optimal sampling depth was determined through examination of exploratory rarefaction curves of observed species plotted against sampling depth and the dataset was rarefied to 12000 sequences per sample. Samples with fewer reads were removed. Alpha diversity metrics were computed within QIIME. Distance matrices were generated with the unweighted and weighted UniFrac methods to compare relative abundance and presence/absence patterns between treatment groups. Beta diversity measures (between sample diversity) were computed with QIIME and jackknifed by repeatedly sampling at 3000 sequences per sample. Beta diversity was then plotted by principal coordinates analysis with confidence ellipses generated from the jackknifing procedure.
  • the heatmap was created from the log abundance of all genera and classified by the Prediction Analysis for Microarrays (PAM) for the R package, which uses the least shrunken centroid method (Tibshirani et. al. "Diagnosis of Multiple Cancer Types by Shrunken Centroids of Gene Expression,” Proc. Natl. Acad. Sci. 99:6567-6572 (2002), which is hereby incorporated by reference in its entirety).
  • the ternary plot was created with ggplot2 in R.
  • Figure 2 shows a heatmap of log absolute abundance for all taxa. The samples grouped specifically by early flowering (EF), late flowering (LF), and control (C) treatments. The 'control' serves as a profile of the surviving and residual microbiota endemic in the soils after steam-sterilization and without inoculation of additional microbiota. While the heatmap showed strong clustering by treatment, eight samples were misclassified representing an error rate of 0.075.
  • OTUs operational taxonomic units
  • Figure 3A The center of the ternary plot shows the core microbiome (high density of circles) across the early flowering (EF), late flowering (LF), and control treatments.
  • the OTUs uniquely associated with a specific treatment (where more than 80% of the total abundance of a particular OTU is uniquely associated with only one group) corresponded to the points within the corners of the ternary plot.
  • the genera assigned to these OTUs fall into a handful of key families ( Figure 3B), with more specific associations in Table 1.
  • hizosphere taxa exclusively associated with a single treatment group (EF, LF, Control) but present across all plant hosts.
  • Taxa unique to microbiome treatments in treatment-sensitive plant hosts were determined by multiple successive filtering of taxa observations. Taxa present in all samples and taxa unique to only one plant host were removed from consideration and only taxa present in 90% or greater of all samples across a treatment group (EF, LF, Control) were retained.
  • the bacteria most strongly associated with the early flowering treatments include genera within two families with many known plant pathogens (Xanthomonadaceae and
  • Pseudomonadaceae and genera within three families with members associated with nutrient mineralization and substrate depolymerization (Moraxellaceae, Cellulomonadaceae, and
  • Saprospiraceae (Sarkar et. al. "Evolution of the Core Genome of Pseudomonas syringae, a Highly Clonal, Endemic Plant Pathogen," Appl. Environ. Microbiol. 70: 1999-2012 (2004); Xia etl al. “Identification and Ecophysio logical Characterization of Epiphytic Protein-Hydro lyzing Saprospiraceae ("Candidatus epiflobacte "r spp.) in activated sludge,” Appl. Environ. Microbiol. 74:2229-2238 (2008); Dodds et. al. "Plant Immunity: Towards an Integrated View of Plant Pathogen Interactions," Nat. Rev.
  • Alcaligenaceae, and Corynebactreiaceae Alcaligenaceae, and Corynebactreiaceae
  • a family of bacteria Verrucomicrobiaceae
  • Alcaligenaceae, and Corynebactreiaceae Alcaligenaceae, and Corynebactreiaceae
  • Verrucomicrobiaceae a family of bacteria that are ubiquitous in soil but are poorly represented through culturing methods
  • Rhizosphere Selects for Particular Groups of Acidobacteria and Verrucomicrobia," PLOS One 8(12): e82443 (2013), which are hereby incorporated by reference in their entirety).
  • Soil microbial communities play a strong role in biogeochemical processes that determine soil environmental parameters such as pH, mineralization, and nutrient availability (Burns et. al. "Enzyme Activity in Soil - Location and a Possible Role in Microbial Ecology,” Soil Biol. Biotech. 14:423-427 (1982) and Allison et. al. "Cheaters, Diffusion and Nutrients Constrain Decomposition by Microbial Enzymes in Spatially Structured Environments," Ecol. Lett. 8:626-635 (2005), which are hereby incorporated by reference in their entirety). No significant changes in soil pH between treatments and plant hosts were observed, which indicates that pH is not responsible for observed differences in plant growth and phenology. Soil inorganic NH4 + and N03 " concentrations did not differ across treatments, but any differences generated from mineralization could be explained by rapid immobilization in soil
  • thaliana reproductive biomass and B. rapa total biomass associated with the late flowering microbiomes points to the possibility that either changes in soil resource pools may alter flowering time or delayed reproduction alters soil resource pools.
  • the delay in flowering corresponded to a 50-100% increase in host reproductive or total biomass. Minor increases in bioavailable nitrogen or other limiting nutrients could result in the biomass gains observed in the plant hosts particularly because the plants in this experiment were grown under nutrient limitation.
  • These groups of microorganisms may include both bacteria and fungi, although fungi were not specifically examined in this study due to the lack of mycorhizal association in A. thaliana and less robust community profiling methods.
  • Plant rhizodeposition and root exudates represent a potential catalyst needed to prime the breakdown of complex polymers that release mineralized nitrogen and phosphorus (Haichar et. al. Plant Host Habitat and Root Exudates Shape Soil Bacterial Community Structure," ISME J.
  • microbiome composition is also reproducible.
  • inoculation of a plant's root-associated microbiome into the soils of novel plant hosts does not necessarily lead to a reassembly of microbial communities representative of the inoculant.
  • legumes inoculated with a mixture of rhizobial strains showed that nodule formation with the effective strain was not achieved uniformly across legume genotypes (Kiers et. al. "Human Selection and the Relaxation of Legume Defenses against Ineffective Rhizobia," Proc. R. Soc. B. Biologic. Sci. 274:3119- 3126 (2007), which is hereby incorporated by reference in its entirety).
  • Root colonizing endophytic fungi and root-associated fungi are able to modulate stress and enhance plant growth in Arabidopsis and other hosts (McLellan et. al. "A Rhizosphere Fungus Enhances Arabidopsis thermotolerance through production of an HSP90 inhibitor," Plant Physiol. 145: 174-182 (2007) and Sherameti et. al.
  • the A. thaliana genotype Ler showed microbiome profiles consistent with the other plant hosts, but was unable to show the same significant shifts in flowering time, biomass, and soil extracellular enzyme activities. Genotypic variability within a species can influence the composition of plant-associated microorganisms. For A. thaliana, a study conducted on eight genotypes in two different soil types showed that genotype explained a small but significant fraction of variation in the composition of the endophytic microbiome (Lundberg et. al.
  • FRIGIDA (FRI) gene is partially suppressed in Ler and the suppressor allele found in Ler (FLC- Ler) may constrain the expression of the late-flowering phenotype through inhibiting increases in Flowering Locus C (FLC) expression (Michaels et. al. "Loss of FLOWERING LOCUS C Activity Eliminates the Late-Flowering Phenotype of FRIGIDA and Autonomous Pathway Mutations but not Responsiveness to Vernalization," Plant Cell 13:935-941 (2011), which is hereby incorporated by reference in its entirety).
  • Plants were grown at a constant 22°C under a 16hr/8hr day/night cycle in a growth chamber. (Percival-Cornell University Weill Hall Life Sciences Growth Chamber
  • All seeds across all phases of this study came from a static seed pool of a highly inbred line, A. thaliana Col (Lehle Seed Co., Round Rock, TX). Seeds were used from this common seed pool to fix allelic frequencies across all phases of this study and to ensure that any changes in plant traits when compared to controls or other phases of the study are the result of microbiome inoculation. All microcosms were watered from bottom reservoirs.
  • Inoculants for early-flowering microbiomes were generated through an iterative selection process detailed previously (7, 13).
  • the field soil was obtained from a collection of sites across Ithaca, NY (42.456583, -76.368822; 42.452265, -76.369477; and 42.414913, -76.442272) representing agricultural, forest, and grassland soils.
  • the mixed environment soil was added to provide a diversity of soil microorganisms for the initial generation.
  • Control pots consisted of the plants and steam-sterilized soils but the units were not inoculated with early- or late-flowering microbiomes.
  • inoculant slurries were prepared by combining 180 mL of sterile, deionized water and 35g of the harvested rhizosphere soil. Slurries were shaken vigorously for 60 seconds upon preparation and periodically during inoculation. The autoclaved soil in each pot of the subsequent generation was inoculated with 12 mL of the slurry. The control group pots were treated with a sterile inoculant. Plants were watered with a 10% solution (lOppm N, 10.5% nitrate/89.5%) urea) of 20- 10-20 Jack's Professional General Purpose Fertilizer (J.R.
  • Cultivation methods were employed to test the ability of the cultivable fraction to reproduce the function of the early flowering microbiome.
  • Inoculant slurries for cultivation were prepared by combining 30g of trait-associated rhizosphere soil from each of the four pots that displayed earliest flowering and 25 mL of sterile, deionized water in a 50 mL tube, and shaking the mixture for one hour. Soil was pelleted at 3500 x g for 30 minutes and 750 uL of supernatant was inoculated onto each of five replicate plates and spread using a flame-sterilized glass spreader. The plates were incubated at 25°C in the dark for seven days. Glycerol stocks (25%) of all plates were made from a swipe and stored at -80°C for the revival portion of the study.
  • the four solid media 25 % Luria broth (LB), 10% tryptic soy agar (TS A), pseudomonad selective agar (PSA) (14), and rhizosphere medium (RM) were prepared according to the recipes in Table 2.
  • the frozen glycerol stocks were revived for both liquid and solid cultivation.
  • glycerol stocks were inoculated into lmL of the respective medium in which they were originally cultured, but without selective agents (antibiotic and antifungal) or agar. These were then incubated for 4 hours at 25°C. Starter cultivations of 250uL were then transferred into 5mL liquid cultures containing the selective agents detailed in Table 2.
  • inoculant was retrieved from the glycerol stock and placed into 200 mL of the respective medium, incubated for one hour, and plated onto the respective solid medium, complete with selective agents. Two replicates were prepared for each glycerol stock sample (solid and liquid), and all cultivations were incubated at 25°C in the dark.
  • Cultivated microbiomes were incubated for 5 days and were inoculated randomly into plug flats of sterile potting mix. Growing conditions and sample collection were as described in the previous section. For the plate method, a streak of the plate colonies was suspended in PBS. Then, 60uL of either liquid cultivation or a PBS-slurry of the solid medium cultivation was inoculated into each plug. Duplicates of each replicate were inoculated to mitigate error from edge effects and microclimatic variation. Control plugs were inoculated with either sterile water or PBS and were also randomly placed.
  • Soil DNA was extracted from frozen samples using the PowerSoil DNA Isolation
  • Amplicon were quantified with Picogreen and 200ng of each sample were pooled and purified with the desalting protocol of the Qiagen QiaQuick spin filter purification kit (QIAGEN Inc.,
  • paired-end reads were truncated at the first low-quality base and quality filtered to remove those with an average quality score below 25, fewer than 200 nt, ambiguous bases, primer mismatches, erroneous barcodes, and homopolymer runs exceeding six bases. Paired-end reads were joined and then demultiplexed within the QIIME software package (Qiime.org) (Caporaso et. al. "QIIME Allows Analysis of High-Throughput Community Sequencing Data," Nat. Methods 7:335-336 (2010), which is hereby incorporated by reference in its entirety).
  • Operational taxonomic units were picked de novo by clustering similar sequences with uclust (Edgar, "Search and Clustering Orders of Magnitude Faster than BLAST,” Bioinformatics 26:335-336 (2010), which is hereby incorporated by reference in its entirety). Sequences with sequence identity below 60% and sequences matching plant chloroplast or mitochondrial 16 S rRNA were filtered from the dataset. The smallest number of sequences belonging to any sample was 9799. This value was used to rarify all samples to that number of input sequences for analysis requiring even samples sizes for robust results.
  • Beta diversity measures were computed with weighted UniFrac and the resulting distance matrix was used to create the principal coordinates plot (Lozupone et. al. "UniFrac: an Effective Distance Metric for Microbial Community
  • Cultivated microbiomes showed significant differences in both leaf biomass and days to flowering from control microcosms and one another.
  • PBS was used as an isotonic solution to suspend cultivated inoculants prior to inoculation.
  • the PBS-inoculated controls and sterile inoculant controls were compared to determine if the addition of PBS altered plant growth. PBS showed no effect on plant growth.
  • Flowering responses in the culturing phase were also significant: 8.7% and 10.9% earlier than the control for LB and TSA media, respectively, and 4.7% percent later for RM (Figure 7A).
  • Leaf biomass was characterized by significant increases of 49.4% and 38.5% for LB and TSA media, respectively ( Figure 7C).
  • Phases were analyzed independently of one another due to the difference in microcosm size between the whole microbiome phase and the culturing and revival phases. In order to ensure the robustness of comparing results between phases, control groups were compared across all phases. There was no significant difference between control groups across phases for either flowering time or leaf biomass (Figure 9).
  • the inability to maintain the flowering and biomass effects through cryopreservation and revival of the cultivated microbiome is likely a function of poor survival of taxa associated with these plant traits and selection for taxa that are tolerant of cryopreservation (Mazzilli et. al. "Survival of Micro-Organisms in Cryostorage of Human Sperm,” Cell Tissue Bank 7:75-79 (2006) and Nimrat et. al. "Chilled Storage of White Shrimp (Litopenaeus vannamei)
  • microbiome treatment was characterized by significant decreases in flowering time and leaf biomass, which is consistent with low-nutrient or non-lethal pathogen accumulation stress responses (Simpson et. al. "Flowering - Arabidopsis, the Rosetta Stone of Flowering Time?,” Science 296:285-289, which is hereby incorporated by reference in its entirety).
  • two of the cultivated microbiomes (grown on LB and TSA) retained roughly equivalent decreases in flowering time, but exhibited -40-50% increases in leaf biomass in comparison to controls.
  • the high biomass effect was represented by relative increases in select Firmicutes, Bacteroidetes, Spirochaetes, Proteobacteria, and the Archaea Crenarchaeota (Class MBGB), and relative decreases in Actinobacteria. Only 6 of these 228 taxa are associated with both effects. Furthermore, many of these taxa are virtually unstudied and lie outside the traditional plant growth-promoting groups that typically include Pseudomonads, Rhizobia, Azospirillum, Bacillus, Streptomycetes, Azotobacter, and Agrobacterium (Glick, "Plant Growth- Promoting Bacteria: Mechanisms and Applications,” Scientifica 2012:963401 (2012), which is hereby incorporated by reference in its entirety).

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Abstract

Un aspect de la présente invention concerne une méthode de production d'un microbiome du sol entier de plantes utilisé pour améliorer une caractéristique végétale particulière souhaitée. La méthode consiste à faire pousser un groupe de plantes dans le sol, et à identifier un sous-groupe de plantes dans le groupe de plantes présentant le mieux une caractéristique végétale particulière souhaitée. Les microbiomes du sol entier du sous-groupe de plantes sont récupérés et appliqués à un autre groupe des plantes. Ces étapes sont répétées pour produire un microbiome du sol entier de plantes utilisé pour améliorer la caractéristique végétale particulière souhaitée. Dans d'autres aspects, la présente invention concerne également une méthode de fabrication de plantes présentant des caractéristiques améliorées et des microbiomes du sol entier de plantes en soi.
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