WO2016057991A1

WO2016057991A1 - Directed selection of plant microbiomes

Info

Publication number: WO2016057991A1
Application number: PCT/US2015/055106
Authority: WO
Inventors: Jenny KAO-KNIFFIN
Original assignee: Cornell University
Priority date: 2014-10-10
Filing date: 2015-10-12
Publication date: 2016-04-14

Abstract

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. Other aspects of the present invention include a method of producing plants with enhanced traits and plant whole soil microbiomes per se.

Description

DIRECTED SELECTION OF PLANT MICROBIOMES

[0001] This application claims the benefit of U.S. Provisional Patent Application Serial

No. 62/062,488, filed October 10, 2014, which is hereby incorporated by reference in its entirety.

FIELD OF THE INVENTION

[0002] The present invention relates to the directed selection of plant microbiomes.

BACKGROUND OF THE INVENTION

[0003] Recent studies have highlighted the ability of plant-associated microbiomes to influence plant traits including disease resistance, growth, and abiotic stress tolerance (Swenson et. al. "Artificial Ecosystem Selection" Proc. Natl. Acad. Sci. 97:3110-9114 (2000); Mendes et.al. "Deciphering the Rhizosphere Microbiome for Disease-Suppressive Bacteria," Science. 332: 1097-1100 (2011); Lau et. al. "Rapid Responses of Soil Microorganisms Improve Plant Fitness in Novel Environments," Proc. Natl. Acad. Sci. 09: 14058-14062 (2012); Bainard et. al. "Growth Response of Crops to Soil Microbial Communities from Conventional Monocropping and Tree Based Intercropping Systems. Plant Soil 363:345-356 (2013); and Sugiyama et. al. "Relationships Between Arabidopsis Genotype Specific Biomass Accumulation and Associated Soil Microbial Communities," Botany-Botanique 91 : 123-126 (2013)). When a fast-growing plant is studied in conjunction with its microbiome across multiple generations, new forms of interactions can be observed between plants and microorganisms shaping plant development. Similar experimental designs using a multi-generational approach have been used to document rapid evolution in plant-insect interactions (Ziist et. al. "Natural Enemies Drive Geographic Variation in Plant Defenses," Science 338: 116-119 (2012) and Agrawal et. al. "A Field

Experiment Demonstrating Plant Life History Evolution and its Eco-Evolutionary Feedback to Seed Predator Populations," Am. Nat. 181 :S35-S45 (2013)). In the rhizosphere specifically, two publications have focused on soil microbiomes to address drought tolerance and disease resistance (Mendes et.al. "Deciphering the Rhizosphere Microbiome for Disease-Suppressive Bacteria," Science. 332: 1097-1100 (2011) and Lau et. al. "Rapid Responsees of Soil

Microorganisms Improve Plant Fitness in Novel Environments," Proc. Natl. Acad. Sci. 09:

14058-14062 (2012)). An earlier study (Swenson et. Al. "Artificial Ecosystem Selection" Proc. Natl. Acad. Sci. 97:3110-9114 (2000)) indicates that the microbially-mediated mechanisms of plant growth can be passed on through multiple generations of experimental evolution to modulate plant biomass levels.

[0004] Whole-microbiome investigations are not without their constraints, however. The complexity of whole microbiomes makes identification of the actual players driving the host responses difficult.

[0005] 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.

SUMMARY OF THE INVENTION

[0006] 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.

[0007] 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.

[0008] 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. These steps are repeated to produce 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.

[0009] 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.

[0010] It is shown here that 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. Furthermore, 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.

BRIEF DESCRIPTION OF THE DRAWINGS

[0011] 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.

[0012] 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.

[0013] 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.

[0014] Figure 4 shows unweighted UniFrac distances show separation of the early/late- flowering-associated microbiome treatments and controls by microbial taxa. Principle coordinates analysis (PCoA) of unweighted UniFrac distances generated from 16S rRNA sequence data obtained from the rhizosphere soils of the plant hosts. 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.

[0015] Figure 5 illustrates that a core microbiome is share across flowering time and control treatments. Specifically, 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.

[0016] 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

microbiomes. 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. Values reported are from a standard least squares regression model including control values as a covariate (ANCOVA). Plant host abbreviations correspond to B. rapa (BR) and the four A. thaliana genotypes Rid (RLD), Ler (LER), Col-0 (COL), and Be (BE). Asterisks denote statistical significance at p < 0.05. Error bars represent SEM.

[0017] Figures 7A-B show inoculant effects on flowering time and leaf biomass.

Comparison of whole microbiome and cultivated microbiomes' effects on flowering time (days to flower bolting of > 90% of individuals), as shown in Figure 7 A, and leaf biomass (dry weight), as shown in Figure 7B. The whole and cultivated microbiome phase each have distinct controls in which plants were inoculated with sterile soil (whole microbiome) or sterile water (cultivated microbiome). Whole microbiome phase bars are unshaded and cultivated

microbiome phase bars are shaded. Media abbreviations - LB: 25% Luria Broth; TSA: 10% Tryptic Soy; PSA: Pseudomonad Semi-selective; RM: Rhizosphere

[0018] 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. Media abbreviations - LB: 25% Luria Broth; TSA: 10% Tryptic Soy; PSA: Pseudomonad Semi-selective; RM:

Rhizosphere.

[0019] 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

microbiome. Relativized log (Log2-fold change) bars are grouped by phylum to assist in delineations between taxa groups.

[0020] 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.

[0021] Figure 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

"f " are families.

[0022] 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.

DETAILED DESCRIPTION OF THE INVENTION

[0023] 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.

[0024] As used herein, 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. In one embodiment of the present invention, the plants are in the form of seeds, seedlings, cutting, propagules, or whole plants. In addition, "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. In addition, "plants" include natural or wildtype plants, and plants that have been genetically modified.

[0025] "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

(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), poplar (Populus spec), pome fruit, potato, rape, raspberry, rice, rubber tree, rye, sorghum, soybean, spinach, spruce, squash, strawberry, sugar beet, sugar cane, sunflower, tea, teak, tobacco, tomato, triticale, turf, walnut, watermelon, wheat, and willow (Salix spec).

[0026] "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.

[0027] In one embodiment of the present invention, 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), poplar (Populus spec), pome fruit, potato, rape, raspberry, rice, rubber tree, rye, sorghum, soybean, spinach, spruce, squash, strawberry, sugar beet, sugar cane, sunflower, tea, teak, tobacco, tomato, triticale, turf, walnut, watermelon, wheat, and willow (Salix spec).

[0028] 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. In one embodiment of the present invention, 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.

[0029] 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.

[0030] For example, 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 by the plant; and any combination thereof.

[0031] 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.

[0032] The present invention can involve improving a plant's tolerance or resistance to biotic and/or abiotic stress factors. Biotic and abiotic stress, especially over longer terms, can have harmful effects on plants. Biotic stress is caused by living organisms while abiotic stress is caused, for example, by environmental extremes.

[0033] Negative factors caused by biotic stress, such as pathogens and pests, are widely known and range from dotted leaves to total destruction of the plant. 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.

[0034] 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.

[0035] Biotic and/or abiotic stress factors decrease the quantity and the quality of the stressed plants, their crops, and fruits. As far as quality is concerned, reproductive development can be affected with consequences on the crops which are important for fruits or seeds.

Synthesis, accumulation, and storage of proteins are mostly affected by temperature; growth is slowed by almost all types of stress; polysaccharide synthesis, both structural and storage, is reduced or modified. These effects result in a decrease in biomass (yield) and in changes in the nutritional value of the plant product.

[0036] 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.

[0037] As used herein, the term "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.

[0038] As used herein, the term "microbiome" includes the constituent microorganisms and their collective genetic material present in a given environment. The terms "microorganism" or "microbe" include, but are not limited to the two prokaryotic domains, Bacteria and Archaea, as well as eukaryotic fungi and protists.

[0039] A "plant whole soil microbiome," therefore, consists of the microbiome that is associated with plant soils, where plant, soil, and microbiome are defined above,

[0040] 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. For example, 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.

[0041] 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. For example, 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] In one embodiment of the present invention, the plant whole soil microbiome is applied to a different plant variety than that used to produce the plant whole soil microbiome. In this embodiment, it is contemplated that 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.

[0046] As used herein, the terms "culturing" or "cultured" refer to any method of maintaining or multiplying a microbe or microbes in a prepared growth medium. Growth medium for culturing may be solid or liquid, natural or synthetic, enriched or unenriched, selective or non-selective, differential or non-differential. Non-limiting examples of growth medium for culturing may include nutrient media, minimal media, selective media, differential media, transport media, or enriched media.

[0047] In one embodiment of the present invention, the recovered plant whole soil microbiome is cultured. In this embodiment, it is contemplated that 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.

[0048] In one embodiment of the present invention, 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. [0049] In one embodiment of the present invention, 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,

Bacteroidetes, Proteobacteria, and Verrucomicrobia, compared to soil initially used in carrying out said method.

[0050] In one embodiment of the present invention, the particular desired plant trait is biomass production, and 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.

[0051] In one embodiment of the present invention, the particular desired plant trait is biomass production, and the microbiome comprises a decreased amount of Actinobacteria compared to soil initially used in carrying out said method.

[0052] In one embodiment of the present invention, 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.

[0053] In one embodiment of the present invention, 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.

[0054] One embodiment of the present invention is the plant whole soil microbiome produced by the recited method. In this embodiment, it is contemplated in the current embodiment that 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. In this embodiment, it is also contemplated that the microbiome comprises a decreased amount of one or more microorganisms selected from the group consisting of

[0055] Identification of microbes may be achieved through phenotyping,

chemotaxonomy, protein analysis or genetic identification methods. Non-limiting examples of phenotypic identification include microscopy and staining, growth characteristics, biochemical assays, antibiograms, salt tolerance etc. Non-limiting examples of 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. 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. For example, 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. Alternatively, microorganisms may be cultured from the soil prior to DNA extraction and genotyping.

[0056] 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.

[0057] 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.

[0058] This embodiment of the present invention is carried out using the materials and procedures described above.

[0059] 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. These steps are repeated to produce 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.

[0060] This embodiment of the present invention is carried out using the materials and procedures as described above.

EXAMPLES

Methods for Examples 1-3:

[0061] 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.

Growth Chamber Conditions

[0062] All plants were grown at 22°C on a 16/8 hr day/night cycle. Relative humidity was set to 75%, and light level was set at 250μΕ. (Percival-Cornell University Weill Hall Life Sciences Growth Chamber Facility, Ithaca, NY).

Multi-Generation Selection of Microbiome Inoculants [0063] 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

incorporated by reference in its entirety. Approximately 100 _^4. 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). In each generation, 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.

[0064] 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. As the genetic pool of the plants was held constant, 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.

Transfer of Microbiomes to Novel Plant Hosts

[0065] Approximately 100 seeds of A. thaliana Col, Ler, RLD, and Be (Lehle Seed Co.) and six seeds of rapid cycling B. rapa (Carolina Biological Supply Co., Burlington, NC) were placed in separate units with 14 replicates measuring 7.6 cm diam. x 8 cm ht. Each unit contained the same potting mix used in the multi-generation microbiome selection. The soil mix was autoclaved prior to adding seeds. The early and late flowering microbiomes were inoculated separately into their corresponding replicated units. Each flowering time treatment was paired with a control with seven replicate units. Microorganisms were excluded in the control group inoculants. Each plant host was arranged in the growth chamber in a randomized block design. 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

[0066] 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.

Soil Collection and Storage

[0067] To maximize rhizosphere soil yield, loose soil was removed and soil adhering to the roots was collected. The soil was homogenized and a portion was immediately frozen at - 80°C for DNA analysis. The other half was analyzed for extracellular enzyme potential activity.

Soil Extracellular Enzyme Activity

[0068] 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. 4-methylumbelliferone (MUB) and 7-amino-4- methylcoumarin (AMC) labeled substrates (200 μΜ), and L-3,4-dihydroxyphenyl alanine (DOPA, 25 mM) substrate was used to provide quantifiable fluorescence and color for quantification of oxidation (Saiya-Cork et. al. "The Effects of Long Term Nitrogen Deposition on Extracellular Enzyme Activity in an Acer saccharum Forest Soil," Soil Biol. Biochem.

34: 1309-1315 (2002) and German et. al. "Optimization of Hydrolytic and Oxidative Enzyme Methods for Ecosystem Studies," Soil Biol. Biochem. 43: 1387-1397 (2011), which are hereby incorporated by reference in their entirety). 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, 50μΜ). A 200 volume of soil slurry and 50 μΐ, of MUB or AMC standards were added into wells of standard plate, and 200 μΐ, of soil slurry and 50 μΐ, of the labeled substrate into wells of substrate plate. Plates were incubated in the dark at 25°C for 3 hours and fluorescence was measured

immediately after removal from the incubator with a BioTek microplate reader (ex: 365nm, em: 450nm). The oxidative enzyme plate contained a buffer blank (250μί buffer), a L-DOPA blank (200ul buffer + 50μΙ, DOPA), sample blank (200μί slurry + 50μί buffer), and the sample wells (200μί slurry + 50μΙ_^ 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. "The Effects of Long Term Nitrogen Deposition on Extracellular Enzyme Activity in an Acer saccharum Forest Soil," Soil Biol. Biochem. 34: 1309-1315 (2002) and German et. al. "Optimization of Hydrolytic and Oxidative Enzyme Methods for Ecosystem Studies," Soil Biol. Biochem. 43: 1387-1397 (2011), which are hereby incorporated by reference in their entirety). Microbiome 16S rRNA gene sequencing

[0069] Soil DNA was extracted from frozen samples using the PowerSoil DNA Isolation

Kit (MO BIO Laboratories, Inc., Carlsbad, CA) according to the recommended protocol for highly organic soil. 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.

[0070] 16S rRNA gene sequences were amplified in duplicate from the extracted DNA.

PCR primers used are described in Caporaso et. al. "Ultra-High-Throughput Microbial

Community Analysis on the Illumina HiSeq and MiSeq Platforms," ISME J. 6: 161-1624 (2012), which is hereby incorporated by reference in its entirety, that target the bacterial/archaeal 16S rRNA gene variable region 4 (515F/806R) for downstream paired-end Illumina barcoded sequencing (Caporaso et. al. "Ultra-High-Throughput Microbial Community Analysis on the

Illumina HiSeq and MiSeq Platforms," ISME J. 6: 161-1624 (2012), which is hereby incorporated by reference in its entirety). Duplicate amplified samples were pooled and purified with the desalting protocol of the Qiagen QiaQuick spin filter purification kit. An epMotion 5075 was used to combine equal concentrations of all barcoded samples and then to dilute the pooled, barcoded amplicons for submission to the Cornell Life Sciences Sequencing Core with the custom sequencing primers as detailed in Caporaso et. al. "Ultra-High-Throughput Microbial Community Analysis on the Illumina HiSeq and MiSeq Platforms," ISME J. 6: 161-1624 (2012), which is hereby incorporated by reference in its entirety, for multiplexed paired-end sequencing on the Illumina MiSeq platform.

Statistics

[0071] 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 (ANCOVA) 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.

[0072] 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

permutations of data. (QIIME Documentation) Sequence Analysis

[0073] 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. 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). Assignment of taxonomy to representative OTUs was done with the RDP classifier (Lan et. al. "Using the RDP Classifier to Predict Taxonomic Novelty and Reduce the Search Space for Finding Novel Organisms," PLoS One 7(3):e32491 (2012), which is hereby incorporated by reference in its entirety), at the default 97% sequence identity and 80%> confidence level with the bundled RDP assigned taxonomies. Sequences matching plant chloroplast or mitochondrial 16S rRNA were filtered from the dataset.

[0074] 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.

[0075] 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.

Example 1 - Soil Microbiome Composition

[0076] Soil microbiotas obtained from the root zone of B. rapa and four A. thaliana genotypes grouped together primarily by flowering time treatment and controls. 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.

[0077] Distribution of operational taxonomic units (OTUs) across samples revealed a core microbiome with 60% of all OTUs shared across flowering time and control treatment groups (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.

Table 1

C-associated

p_Actinobacteria;c_Actinobacteria;o_Actinomycetales;f_Frarikiaceae;g_

p_Chloroflexi;c_Thermomicrobia;o_;f_;g_

p_Proteobacteria;c_Alphaproteobacteria;o_Rhizobiales;f_ hodobiaceae;g_Afifella

p_Actinobacteria;c_Acidimicrobiia;o_Acidimicrobiales;f_C 111 ;g_

p_Proteobacteria;c_Gammaproteobacteria;o_Xanthomonadales;f_Xanthomonadaceae;g_Lysobacter

p_Actinobacteria;c_Actinobacteria;o_Actinomycetales;f_Nocardioidaceae;Other

p_Proteobacteria;c_Betaproteobacteria;o_Burkholderiales;f_Alcaligenaceae;Other

p_Acidobacteria;c_Acidobacteria;o_Acidobacteriales;f_Acidobacteriaceae;g_Edaphobacter

p_Planctomycetes;c_Planctomycetia;o_Gemmatales;f_Isosphaeraceae;g_

EF-associated

p_Acidobacteria;c_Sva0725;o_Sva0725;f_;g_

p_Proteobacteria;c_Alphaproteobacteria;o_Rhizobiales;f_Brucellaceae;g_Ochrobactrum

p_Proteobacteria;c_Alphaproteobacteria;o_Rhodospirillales;f_Rhodospmllaceae;g_Phaeospirillum

p_Proteobacteria;c_Betaproteobacteria;o_Biu-kholderiales;f_Burkholderiaceae;g_Salinispora

p_Verrucomicrobia;c_[Pedosphaerae] ;o_[Pedosphaerales] ;f_R4-41 B;g_

p_Actinobacteria;c_Actinobacteria;o_Actinomycetales;f_Micrococcaceae;g_

p_Actinobacteria;c_Thermoleophilia;o_Solirubrobacterales;Other;Other

p_Bacteroidetes;c_Sphingobacteriia;o_Sphingobacteriales;f_Saprospiraceae;g_

p_Bacteroidetes;c_Sphingobacteriia;o_Sphingobacteriales;Other;Other

p_Chlorobi;Other;Other;Other;Other

p_OD 1 ;Other;Other;Other;Other

p_Proteobacteria;c_Alphaproteobacteria;o_Caulobacterales;f_Caulobacteraceae;g_Brevundimonas

p_Proteobacteria;c_Alphaproteobacteria;o_Rhizobiales;f_Bradyrhizobiaceae;g_Bosea

p_Proteobacteria;c_Alphaproteobacteria;o_Rhizobiales;f_Rhizobiaceae;g_Kaistia

p_Proteobacteria;c_Alphaproteobacteria;o_Sphingomonadales;f_Sphingomonadaceae;g_

p_Proteobacteria;c_Gammaproteobacteria;o_Pseudomonadales;f_Moraxellaceae;Other LF-associated

p_Bacteroidetes;Other;Other;Other;Other

p_Planctomycetes;c_vadinHA49;o_p04_C01 ;f_;g_

p_Proteobacteria;c_Gammaproteobacteria;o_Xanthomonadales;f_Xanthomonadaceae;g_Luteimonas

p_Acidobacteria;c_Acidobacteria;o_Acidobacteriales;f_Koribacteraceae;Other

p_Bacteroidetes;c_Sphingobacteriia;o_Sphingobacteriales;f_Flexibacteraceae;g_

p_Chlamydiae;c_Chlamydiia;o_Chlamydiales;f_Parachlamydiaceae;Other

p_Chloroflexi;c_Anaerolineae;o_Caldilineales;f_Caldilineaceae;g_

p_Proteobacteria;c_Gammaproteobacteria;o_Legionellales;f_Coxiellaceae;g_Aquicella

p_Verrucomicrobia;c_[Spartobacteria] ;o_[Chthoniobacterales] ;f_[Chthoniobacteraceae] ;g_Candidatus

Xiphinematobacter

p_Vermcomicrobia;c_Vermcomicrobiae;o_Vermcomicrobiales;f_Vermcomicrob

k_Archaea;p_Euryarchaeota;c_[Parvarchaea];o_WCHD3-30;f_;g_

k_Archaea;p_Euryarchaeota;Other;Other;Other;Other

p_Actinobacteria;c_Actinobacteria;o_Actinomycetales;f_Thermomonosporaceae;g_Actinoallom

p_Actinobacteria;c_Thermoleophilia;o_Gaiellales;f_;g_

p_Bacteroidetes;c_VC2_l_Bac22;o_;f_;g_

p_Cyanobacteria;c_4C0d-2;o_MLEl-12;f_;g_

p_Cyanobacteria;c_4C0d-2;o_SMlDl 1 ;f_;g_

p_Fibrobacteres;c_Fibrobacteria;o_258dslO;f_;g_

p_Planctomycetes;c_vadinHA49;o_DH61 ;f_;g_

p_Proteobacteria;c_Alphaproteobacteria;o_BD7-3;f_;g_

p_Proteobacteria;c_Alphaproteobacteria;o_Rickettsiales;Other;Other

p_Proteobacteria;c_Gammaproteobacteria;o_Legionellales;f_Coxiellaceae;g_

p_WS2;c_SHA-109;o_;f_;g_

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.

[0078] 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. Genet 11 :539-548 (2010); and Rokhbakhsh-Zamin et. al. "Characterization of Plant Growth Promoting Traits of Acinetobacter Species Isolated From Rhizosphere of Pennisetum glaucum," J. Microbiol. Biotechnol. 21 :556-566 (2011), which are hereby incorporated by reference in their entirety). In contrast, the late flowering treatments were associated with families that include plant growth promoting bacteria (Iamiaceae,

Alcaligenaceae, and Corynebactreiaceae) and a family of bacteria (Verrucomicrobiaceae) that are ubiquitous in soil but are poorly represented through culturing methods (Altman et. al. "The Effects of Some Chlorinated Hydrocarbons on Certain Soil Bacteria," J. Appl. Microbiol.

29:260-265 (1966); Kurahashi et. al. "Lamia majanohamensis gen. nov., sp nov., an

Actinobacterium isolated from Sea Cucumber Holothuria edulis, and Proposal of Lamiaceae fam. nov.," Int. J. Syst. Evol. Microbiol. 59:859-873 (2009) and da Rocha et. al. "The

Rhizosphere Selects for Particular Groups of Acidobacteria and Verrucomicrobia," PLOS One 8(12): e82443 (2013), which are hereby incorporated by reference in their entirety).

[0079] Principal coordinates analysis (PCoA) of the unweighted UniFrac distances showed separation of the trait-associated microbiome treatments (early vs. late flowering) and the control by microbial taxa (Figure 4). In contrast, the weighted UniFrac analysis indicated no separation of flowering time and control microbiomes in this study (Figure 5). While the majority of soil studies place emphasis on the relative abundance of taxa, to which weighted UniFrac is sensitive, the multiple generations of selection in this study may have led to enrichment of trait-associated rare taxa— meaning abundant taxa alone may not drive the observed differences in flowering time. Furthermore, patterns of presence/absence can be obscured by the high relative abundances of core microbiome taxa, making unweighted UniFrac (insensitive to relative abundance) better for elucidating these patterns (Lozupone et. al.

"UniFrac: an Effective Distance Metric for Microbial Community Comparison," ISME J. 5: 169 (2011), which is hereby incorporated by reference in its entirety).

Example 2 - Effect of Selected Microbiomes on Plant Host Traits

[0080] When the early- and late-flowering-associated microbiomes were inoculated into soils containing novel plant hosts, consistent responses in differences between flowering times were found. All A. thaliana hosts grown with late-flowering-associated microbiomes flowered 15-17% later than plants containing the early- flowering-associated microbiomes. The related crucifer, B. rapa, flowered 56% later in the late-flowering treatments than in early flowering treatments. The significant delays in flowering were associated with increases in inflorescence biomass of three of the four A. thaliana genotypes Col, RLD, and Be. Similarly, B. rapa, also showed delayed reproduction and an increase in total aboveground biomass in the late-flowering- associated microbiome treatment (Figure 6 A and 6B). Example 3 - Potential Microbiome-Mediated Shifts in Soil Environment

[0081] 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

microorganisms and plants. While above ground plant tissue N did not differ across flowering treatment groups, it is plausible that root tissue N differed (but root tissue N was not measured in this study). The potential soil extracellular enzyme activities associated with nitrogen

mineralization increased two- to five-fold in the late-flowering microbiome treatment over the early- flowering microbiome treatment (Figure 6C). Enhanced extracellular enzyme activity can indicate increased microbial coordination in the depolymerization of complex substrates and release of bioavailable nitrogen or phosphorus (Schimel et. al. "Nitrogen Mineralization:

Challenges of a Changing Paradigm," Ecology 85 :591-602 (2004) 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).

[0082] While the mechanisms underlying the apparent microbiome-driven shifts in flowering time are unknown in this study, it is speculated that microbial modification of the soil altered a suite of environmental cues controlling flowering time. Regulation of flowering time is primarily driven by abiotic factors, such as vernalization and photoperiod, but it is well known that stress and nutrient availability also influence flowering time (Simpson et. al. "Flowering Arabidopsis, the Rosetta Stone of Flowering Time?," Science 296:285-289 (2002) and Amasino "Seasonal and Developmental Timing of Flowering," Plant J. 61 : 1001-1013 (2010), which are hereby incorporated by reference in their entirety). The increase in A. 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.

[0083] The production of extracellular enzymes provides a major mechanism by which microorganisms gain access to limiting nutrients bound in soil organic matter. Under nitrogen- or phosphorus- limiting conditions, groups of microorganisms capable of producing extracellular enzymes are able to capture nitrogen or phosphorus that would otherwise be inaccessible for biological uptake (Burns "Enzyme Activity in Soil - Location and a Possible Role in Microbial Ecology," Soil Biol. Biochem. 14:423-427 (1982); Sinsabaugh "Enzymatic Analysis of Microbial Pattern and Process," Biol. Fertil. Soils 17:69-74 (1994); and Sinsabaugh "Phenol Oxidase, Peroxidase and Organic Matter Dynamics of Soil," Soil Biol. Biochem. 42:391-404 (2010), which are hereby incorporated by reference in their entirety). 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. 2: 1221-1230 (2008), which is hereby incorporated by reference in its entirety). Given this beneficial association, the production of extracellular enzymes and their value to the many organisms inhabiting the rhizosphere represent a unique situation in which selective pressures may encourage higher level coordination between plants and their microbiome (Wilson, "Theory of Group Selection," Proc. Natl. Acad. Sci. 97:9110-9114 (2000); Kerr et. al "Individualist and Multi-Level Perspectives on Selection in Structured Populations," Biol. Philos. 17:477-517 (2002); and Okasha "Individuals, Groups, Fitness and Utility: Multi-Level Selection Meets Social Choice Theory," Biol. Philos. 24:61-584 (2009), which are hereby incorporated by reference in their entirety).

[0084] It is conceivable that the multi-generational approach to microbiome assembly may have led to the development of microbiomes in the late-flowering treatment that enhance nitrogen mineralization via extracellular enzyme production. The resulting increase in mineralized nitrogen could modulate nutrient stress responses thereby favoring delays in bolting. Reproductive delay in A. thaliana grown in low phosphorus soils has been shown to increase biomass by 30% presumably by allowing greater time for soil phosphorus mineralization and root exploration (Nord et. al. "Delayed Reproduction in Arabidopsis thaliana Improves Fitness in Soil with Suboptimal Phosphorus Availability," Plant Cell Environ. 31 : 1432- 1441 (2008), which is hereby incorporated by reference in its entirety). Similarly, in Swenson et. al. "Artificial Ecosystem Selection," Proc. Natl. Acad. Sci. 97:9110-9114 (2000), which is hereby incorporated by reference in its entirety, continuous selection for high vs. low biomass A. thaliana plants showed changes in soil chemistry. By generations 13 and 14, phosphorus was one of the major factors explaining the separation of soil nutrients by low vs. high host biomass selection lines.

[0085] The ability to reproduce microbiome function in novel plant hosts suggests that microbiome composition is also reproducible. However, 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. For example, 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 Defences Against Ineffective Rhizobia," Proc. R. Soc. B. Biologic. Sci. 274:3119- 3126 (2007), which is hereby incorporated by reference in its entirety). This study shows that the plant trait-associated microbiomes developed over multiple generations were able to assemble into distinct community profiles by flowering time treatment across novel plant hosts. Although the soils were steam-sterilized to reduce viable microorganisms, a small fraction of the community is still able to persist as found in other studies (Lau et. al. "Evolutionary Ecology of Plant Microbe Interactions: Soil Microbial Structure Alters Selection on Plant Traits," New Phytol. 192:215-224 (2011), which is hereby incorporated by reference in its entirety). In spite of the persistence, the inoculated microbiomes were able to populate the soils of novel hosts and induce plastic responses in flowering phenology and soil function. While bacterial sequencing was emphasized in this study as in other plant microbiome-focused papers (Lundberg et. al. "Defining the Core Arabidopsis thaliana Root Microbiome," Nature 488:86 (2012); Bulgarelli et. al. "Revealing Structure and Assembly Cues for Arabidopsis Root-Inhabiting Bacterial Microbiota," Nature 488:91-95 (2012); and Peiffer et. al. "Diversity and Heritability of the Maize Rhizosphere under Field Conditions," Proc. Natl. Acad. Sci. 110:6548-6553 (2013), which are hereby incorporated by reference in their entirety), fungi could have played a significant role in modulating flowering time, altering extracellular enzyme activities, and enhancing reproductive biomass. 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 Root- Colonizing Endophyte Pirifomospora indica Confers Drought Tolerance in Arabidopsis by Stimulating the Expression of Drought Stress Related Genes in Leaves," Molec. Plant Microbe Interact. 21 :799-807 (2008), which are hereby incorporated by reference in their entirety).

While the multi-generation approach to enriching microbiomes is likely to favor bacterial populations, it is conceivable that certain fungi are enriched across plantings assuming that fungal hyphae were able to persist through the inoculation procedure we used and establish in host tissues or rhizospheres within the short lifecycle of the rapidly cycling Brassicas.

Irreproducibility of Microbiome Function in Landsberg Erecta

[0086] 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.

"Defining the Core Arabidopsis thaliana Root Microbiome," Nature 488:86 (2012), which is hereby incorporated by reference in its entirety). Similarly, a study conducted on maize genotypes showed that a similar fraction of variation in rhizosphere microbial diversity was explained by plant host genetics (Peiffer et. al. "Diversity and Heritability of the Maize

Rhizosphere under Field Conditions,'" Proc. Natl. Acad. Sci. 110:6548-6553 (2013), which is hereby incorporated by reference in its entirety). In this study, the lack of a significant response found in Ler to the inoculated microbiomes could be related to variation in host genetics. In particular, Ler shows unique genetic traits relevant to flowering regulation that could contribute to a reduced ability to delay flowering. For example, late flowering associated with the

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).

[0087] It has been shown that experimental selection on soil microbial communities can alter major plant traits, including flowering time. These trait-associated microbiomes can then populate the soils of novel hosts and reproduce their intended functions. The ability of microbiomes to reproduce their effects on soil processes and host plant traits is critical to advancing the use of soil microbiomes in plant production systems. Findings from the sequencing analysis indicate that rare taxa may play important roles in plant trait development. Accordingly, these results suggest that selection based on diverse microbial communities holds strong potential for using microbiomes to address key agronomic and environmental concerns.

Methods for Examples 4-8

[0088] In this study, early- flowering microbiome enriched over 15 generations of selection was examined, in comparison to subsets of strains cultivated from fresh soil and cryo- preserved cultivates for their ability to reproduce effects on plant traits in A. thaliana. Cultivated fractions were derived from soil and water mixtures incubated on four solid media. Revived microbiomes were derived from cultivated microbiomes preserved in glycerol at -80°C.

Flowering was considered the primary trait modulated by the microbiome and cultivated microbiomes, while other attributes such as rhizosphere pH and plant biomass were considered secondary traits. Comparison of the primary and secondary trait effects of whole microbiomes, cultivated microbiomes, and revived microbiomes can demonstrate the potential of using sub- populations of microbiomes to modify plant traits. It was hypothesized that inoculation of the early-flowering cultivable subsets into A. thaliana soils would reproduce the flowering response, but that variation in microbiota across the different growing media and cultivation methods would lead to variations in the secondary plant trait (biomass) indirectly selected on through the multi-generation study.

Growth Chamber Conditions

[0089] 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

Facility, Ithaca, NY) Light intensity at plant height was 250 UE m-2 s-1. Relative humidity was set to 70% for the duration of the study. Plant Growth Conditions

[0090] 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.

Assembly of Early Flowering Microbiomes

[0091] Inoculants for early-flowering microbiomes were generated through an iterative selection process detailed previously (7, 13). Microcosms (n=14, 7.6 cm diam. pots) of -100 A. thaliana individuals grown in a 1 : 1 mixture of field soil: potting mix soil (Lambert General Purpose Mix) were prepared. 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. The potting mix was autoclaved prior to each generation, and after the first generation, it became the sole growing medium for the remainder of the study. In each generation, a subset of soil from the four pots displaying the earliest flowering was set aside as the inoculant for the next generation. Biomass and soils were harvested immediately following flower bolting of >90% of the individuals within all pots.

Control pots consisted of the plants and steam-sterilized soils but the units were not inoculated with early- or late-flowering microbiomes.

[0092] In this study, the subsets of soil for inoculation were pooled and "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. Peters, Inc., Allentown, PA, USA). The 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. As the genetic pool of the plants was held constant using a highly in-bred genotype, the only adaptive traits to advance over generations were limited to those derived from the soil inoculation (of trait-associated microbiota). This selection process continued for 15 successive plantings to develop distinct, trait-associated soil microbiomes associated with early flowering time.

Cultivation

[0093] 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.

[0094] 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.

Table 2.

Recipes for Solid Media. The preparation instructions for the four solid media used in this study. [0095] The "rhizosphere medium" was prepared by blending rhizosphere soil with agar

(50% soil by volume) and autoclaving. Selective agents (antibiotics and cycloheximide) were filter sterilized and added after autoclaving media, immediately prior to pouring. Reproduction of Function in Cultivated Fractions of Early-Flowering Microbiomes (From Fresh Soil)

[0096] Cultures were inoculated into a plug flat containing Lambert General Purpose

Mix. A streak from each plate was taken and suspended in 2 mL of phosphate-buffered saline (PBS). Plugs were inoculated with 60 uL of the mixture. The surfaces of the plug flats were sprayed with sterile water to saturate the potting soil. After 48 hours, five A. thaliana Col seeds were sown into each plug. Control plugs were inoculated with either sterile water or PBS. The different treatment and control plugs were situated within the flats at random. Domes were added to create high humidity conditions until germination and establishment, after which they were removed. Flowering times were recorded following the complete flower bolting of a microcosm. Both leaf and reproductive tissue biomass were harvested, and dried at 50°C until constant weight.

Reproduction of Function in Revived Microbiomes of Cryo-Preserved Cultivates

[0097] The frozen glycerol stocks were revived for both liquid and solid cultivation. For the liquid cultivation method, 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. For the plate method, 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.

[0098] 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.

16S rRNA Gene Sequencing

[0099] Soil DNA was extracted from frozen samples using the PowerSoil DNA Isolation

Kit (MO BIO Laboratories, Inc., Carlsbad, CA, USA) according to the recommended protocol for highly organic soil. Approximately 0.15 g of soil from each sample was used for isolation of soil DNA. Quantification was performed with the standard dsDNA quantification protocol for Picogreen (Thermo Fisher Scientific, Inc., Waltham, MA, USA). All pipetting for DNA extraction was conducted with an Eppendorf epMotion 5075 pipetting robot (Eppendorf AG,

Hamburg, Germany). 16 S rRNA gene sequences were amplified in duplicate from the extracted DNA. The PCR primers used are those described in Caporaso et. al. "Ultra-High-Throughput Microbial Community Analysis on the Illumina HiSeq and MiSeq Platforms," ISME J. 6 : 1621 - 1624 (2012), which is hereby incorporated by reference in its entirety, that target the

bacterial/archaeal 16 S rRNA gene variable region 4 (515 F/806 R) for downstream paired-end Illumina (Illumina, Inc., San Diego, CA, USA) barcoded sequencing (Caporaso et. al. "Ultra- High-Throughput Microbial Community Analysis on the Illumina HiSeq and MiSeq Platforms," ISME J. 6: 1621-1624 (2012), which is hereby incorporated by reference in its entirety).

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.,

Valencia, CA, USA). The amplicon pool was submitted to the Cornell Life Sciences Sequencing Core with the custom sequencing primers detailed in Caporaso et. al. "Ultra-High-Throughput Microbial Community Analysis on the Illumina HiSeq and MiSeq Platforms," ISME J. 6 : 1621 - 1624 (2012), which is hereby incorporated by reference in its entirety.

Statistics and Sequence Analysis

[0100] Plant trait data were analyzed by Analysis of Variance (ANOVA) using JMP 10

(SAS Inc.). Significance between groups was determined at an a-level of 0.05. Contrasts between means were found using post-hoc analysis (Fisher LSD and Tukey's HSD).

[0101] For 16S rRNA gene sequence analysis, 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 (OTUs) 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. Alpha diversity measures (within-sample diversity) were calculated with Strong's dominance (Strong, "Assessing Species Abundance Unevenness Within and Between Plant Communities," Community Ecol. 3:237-246 (2002), which is hereby incorporated by reference in its entirety). Beta diversity measures (between-sample diversity) 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

Comparison," ISME J. 5: 169-172 (2011), which is hereby incorporated by reference in its entirety). The heatmap of key taxa was created from the log abundance of all orders that differ significantly between samples exhibiting early- flowering/biomass shifts and those that did not. These were then classified by the Prediction Analysis for Microarrays 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.99:656 '-6572 (2002), which is hereby incorporated by reference in its entirety). Log2-fold-change and significance of taxa shifts were computed using the DESeq2 method (Love et. al. "Moderated Estimation of Fold Change and Dispersion for RNA-Seq Data with DESeq2," bioRXiv doi: 10.1186/sl3059-014- 0550-8 (2014), which is hereby incorporated by reference in its entirety). The inputs used for DESeq2 were only taxa present in >80% of the samples of a given phenological group (e.g. early-flowering vs. no flowering effect); in other words, the core microbiomes associated with the plant phenotype effects. Example 4 - Whole Microbiome Phase

[0102] Inoculation of whole early-flowering microbiomes into soils of A. thaliana genotype Col led to decreases in leaf biomass and a fewer number of days to flowering, when compared to a sterile inoculant control. Reproductive biomass was unaffected by treatment in all three phases of the experiment. Individual plant leaf biomass in the treatment group was decreased by 61.2% from the control group: 0.0047 + 0.0014 and 0.0124 + 0.0022 respectively (Figure 7B). Similarly, flowering time decreased in the treatment group by 9.1% compared to the control group: 30.15 + 0.38 and 33.17 + 0.63 days respectively (Figure 7D).

Example 5 - Cultivable Microbiome Phase (Cultured From Soil)

[0103] 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).

Example 6 - Revived Microbiome Phase (From Cryo-Preserved Cultivated Microbiomes)

[0104] Revived microbiome inoculation yielded no significant differences in plant biomass from control microcosms (Figure 8 A-B). In addition, no flowering effect was observed for any of the treatment groups, indicating a complete loss of treatment effect (Figure 8 C-D). PBS had no effect on plant growth.

Example 7 - Control Comparisons

[0105] 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).

Example 8 - 16S rRNA Gene Sequencing Analysis

[0106] Bacterial community patterns show visible shifts in taxa abundance between the whole and cultivated groups (Figure 8). The whole microbiome and LB cultivated groups show significant variation within-group as well. Principle coordinates analysis (PCoA) illustrates the within-group variance in the whole microbiome and LB groups, the uniformity of the TSA, PSA, and RM groups, and the relationship between all of the treatment groups (Figure 10). The results of the DESeq2 analysis produced a list of only 197 OTUs that differ significantly between samples exhibiting an early flowering effect and those that did not (Figure 9). In addition, the shift from low biomass to high biomass was characterized by significant shifts in only 31 OTUs (Figure 11). These 228 "key taxa" were used as the input for the PAMR heatmap to visualize the shifts between groups (Figure 12).

[0107] The cultivation of mixed strains or taxa from rhizosphere soil presents potential benefits over the use of single isolate methods to modify a plant trait. However, approaches to preserve and revive these cultivable microbiomes resulted in a loss of the trait effect.

Specifically, 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)

Spermatophores," Aquaculture 261 :944-951 (2006), which are hereby incorporated by reference in their entirety). The cultivable microbiome, while less complex than the whole microbiome, appears to retain a sufficient portion of the microbial community responsible for the early- flowering effect. The loss of this effect in the revived cultivable microbiome indicates the need to study the role of complex communities in plant-microbiome interactions. In addition, inoculation of the cultivated sub-populations into the soils containing A. thaliana led to unexpected changes in plant flowering and leaf biomass responses.

[0108] Previous speculation on the driving processes behind microbiome-mediated shifts in flowering time include alteration of environmental cues tied to flowering (photoperiod and vernalization), pathogen pressures, and nutrient availability stresses (Wagner et. al. "Natural Soil Microbes Alter Flowering Phenology and the Intensity of Selection on Flowering Time in a Wild Arabidopsis Relative," Ecol. Lett. 17:717-726 (2014) and Amasino "Seasonal and

Developmental Timing of Flowering," Plant J. 61 : 1001-1013 (2010), which are hereby incorporated by reference in their entirety). Results of this study, however, suggest that the factors driving flowering time modulation may not be so straightforward. The whole

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). However, 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. These observations suggest that the plant-microbe-environment interactions that induce the primary early- flowering response are not necessarily linked to the secondary leaf biomass response. The increase in biomass observed in the cultivated microbiome phase is consistent with previous studies on initiation of flowering in A. thaliana (Bernier et. al. "Physiological Signals that Induce Flowering," Plant Cell 5: 1147-1155 (1993) and El-Lithy et. al. "Relation Among Plant Growth, Carbohydrates and Flowering Time in the Arabidopsis Landsberg erecta Kondara Recombinant Inbred Line Population," Plant Cell Environ. 33: 1369-1382 (2010), which are hereby incorporated by reference in their entirety). This may indicate that cultivation of the early-flowering microbiome can eliminate deleterious members of the microbiome responsible for the decrease in leaf biomass in the whole microbiome. Furthermore, decoupling of the early-flowering and low-biomass traits could potentially suggest a more direct role of the microbiome in flowering time modulation.

[0109] The differences in biomass and flowering responses between cultivation media present cultivation as a strategy for eliminating undesirable taxa from microbiomes. Cultivation and inoculation into a sterilized rhizosphere can disrupt existing associations between microorganisms of the whole microbiome and potentially change interactions with the new plant host. Similar disruption of established plant-microbe associations has been observed to change overall community function (Qui et. al. "De-coupling of Root-Microbiome Associations

Followed by Antagonist Inoculation Improves Rhizosphere Soil Suppressiveness," Biol. Fertil. Soils 50:217-224 (2013), which is hereby incorporated by reference in its entirety). In this regard, choice of cultivation medium and plant host appear to play a crucial role in determining microbial succession dynamics within the new host rhizosphere and, in turn, overall function in the plant host. This is supported by the observation of larger, more dominant community members in the whole microbiome treatment and smaller, more numerous members in the cultivated treatments, as determined by alpha diversity indices (Strong "Assessing Species Abundance Unevenness Within and Between Plant Communities," Community Ecol. 3:237-246 (2002), which is hereby incorporated by reference in its entirety).

[0110] Community analysis revealed that the taxa shifts apparently driving these effects come down to a very small percentage of the overall community. The early-flowering effect is characterized by shifts in only 197 key taxa, including overall decreases in abundance of certain Actinobacteria, Acidobacteria, Bacteroidetes, Proteobacteria, and Verrucomicrobia, and increases in Spirochaetes, Firmicutes, and the Archaea Crenarchaeota (Class MBGA) (Figure 9). The biomass effect was characterized by shifts in just 31 key taxa between the low and high biomass effects (Figure 10). 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).

[0111] Taken together, these results highlight cultivation as a method for simplifying microbiome communities while retaining, enhancing, or modifying microbiome function. A lack of mechanistic understanding currently limits studies of bio-inoculant efficacy for commercial production (Owen et. al. "Use of Commercial Bio-Inoculants to Increase Agricultural Production Through Improved Phosphorous Acquisition," Appl. Soil Ecol. 86:41-54 (2015), which is hereby incorporated by reference in its entirety). The associated reduction in complexity and changes in cultivated microbiome effects between cultivation media supports the utility of using sub- populations of the microbiome in pursuing mechanism-level understanding of plant-microbe interactions. Cultivation may also contribute to the development of novel technologies and processes for plant production systems. Current methods of commercial bio-inoculation can be readily adapted for use with more complex cultivated microbiomes over single-strain inoculants (Bashan et. al. "Advances in Plant Growth-Promoting Bacterial Inoculant Technology:

Formulations and Practical Perspectives," Plant Soil 378: 1-33 (2014), which is hereby incorporated by reference in its entirety). The biomass differences observed between plants grown with the whole and cultivated microbiomes illustrate the potential of cultivation for maintaining primary plant traits of a microbiome and modulating secondary traits. For example, a microbiome that accelerates time to flowering and increases plant biomass is well-suited for agriculture and could reduce production time and costs. Finally, based on microbial community profile data, cultivation and subsequent reintroduction to the rhizosphere appears to allow for the transfer and enrichment of taxa that cannot be isolated in culture.

[0112] The cultivation of functional microbiomes is presented here as a potential tool for further study of the microbe-microbe and plant-microbe interactions involved in the elicitation of desired plant responses offering unique advantages over whole-microbiome and isolation/culture-based approaches. The ability of cultivated microbiomes, derived from the early- flowering microbiome, to both reproduce the primary plant response (flowering time) and modulate the secondary response (leaf biomass) contributes strongly to evidence in support of microbiome-use in plant production systems. Specifically, it presents an experimental platform for refining functional microbiomes to facilitate mechanism-level understanding of their interaction with plants.

[0113] Although preferred embodiments have been depicted and described in detail herein, it will be apparent to those skilled in the relevant art that various modifications, additions, substitutions, and the like can be made without departing from the spirit of the invention and these are therefore considered to be within the scope of the invention as defined in the claims which follow.

Claims

WHAT IS CLAIMED:

1. A method of producing a plant whole soil microbiome useful in enhancing a particular desired plant trait, said method comprising:

(a) growing a group of plants in soil;

(b) identifying a subgroup of plants within the group of plants best displaying a particular desired plant trait;

(c) recovering the subgroup of plants' whole soil microbiomes;

(d) applying the recovered whole soil microbiomes to another group of the plants; and

(e) repeating steps (a) to (d) to produce a plant whole soil microbiome useful in enhancing the particular desired plant trait.

2. The method of claim 1 further comprising:

applying the plant whole soil microbiome obtained in step (e) to a different plant variety than that used to carry out steps (a) - (e).

3. The method of claim 2, wherein the different plant variety is agriculturally relevant.

4. The method of claim 2, wherein the plant used to carry out steps (a) through (e) is a model plant.

5. The method of claim 1 further comprising:

culturing the recovered plant whole soil microbiomes after step (e).

6. The method of claim 5, wherein said culturing 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.

7. The method of claim 1, wherein 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), poplar (Populus spec), pome fruit, potato, rape, raspberry, rice, rubber tree, rye, sorghum, soybean, spinach, spruce, squash, strawberry, sugar beet, sugar cane, sunflower, tea, teak, tobacco, tomato, triticale, turf, walnut, watermelon, wheat, and willow (Salix spec).

8. The method of claim 1, wherein 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 and biotic stress tolerance.

9. The method of claim 8, wherein 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.

10. The method of claim 8, wherein 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, Bacteroidetes, Proteobacteria, and Verrucomicrobia, compared to soil initially used in carrying out said method.

11. The method of claim 8, wherein the particular desired plant trait is biomass production and 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.

12. The method of claim 8, wherein the particular desired plant trait is biomass production and the microbiome comprises a decreased amount of Actinobacteria compared to soil initially used in carrying out said method.

13. The method of claim 8, wherein 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.

14. The method of claim 8, wherein 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.

15. The method of claim 1, wherein the plants are in the form of seeds, seedlings, cutting, propagules, or whole plants.

16. The plant whole soil microbiome produced by the method of claim 1.

17. The plant whole soil microbiome of claim 16, wherein the microbiome comprises an increased amount of one or more microorganisms selected from the group consisting of Spirochaetes, Firmicutes, Archaea Crenarchaeota, Bacteroidetes, Proteobacteria, and

Actinobacteria, compared to soil initially used in carrying out said method.

18. The plant whole soil microbiome of claim 16, wherein the microbiome comprises a decreased amount of one or more microorganisms selected from the group consisting of Actinobacteria, Acidobacteria, Bacteroidetes, Proteobacteria, and Verrucomicrobia, compared to soil initially used in carrying out said method.

19. The plant whole soil microbiome produced by the method of claim 5.

20. The plant whole soil microbiome of claim 19, wherein the microbiome comprises an increased amount of one or more microorganisms selected from the group consisting of Spirochaetes, Firmicutes, Archaea Crenarchaeota, Bacteroidetes, Proteobacteria, and

Actinobacteria, compared to soil initially used in carrying out said method.

21. The plant whole soil microbiome of claim 19, wherein the microbiome comprises a decreased amount of one or more microorganisms selected from the group consisting of Actinobacteria, Acidobacteria, Bacteroidetes, Proteobacteria, and Verrucomicrobia, compared to soil initially used in carrying out said method.

22. A method of producing plants in which a particular desired plant trait is enhanced, said method comprising:

(a) growing a group of plants in soil;

(c) recovering the subgroup of plants' whole soil microbiomes;

(d) applying the recovered whole soil microbiomes to another group of the plants;

(e) repeating steps (a) to (d) to produce a whole soil plant microbiome useful in enhancing the particular desired plant trait; and

(f) recovering plants, after completion of step (e), in which the particular desired plant trait is enhanced.

23. The method of claim 22 further comprising:

applying the plant whole soil microbiome obtained in step (e) to a different plant variety than that used to carry out steps (a) - (e), wherein the different plant variety is recovered in step

(f).

24. The method of claim 23, wherein the different plant variety is agriculturally relevant.

25. The method of claim 23, wherein the plant used to carry out steps (a) through (e) is a model plant.

26. The method of claim 22 further comprising:

culturing the plant whole soil microbiomes obtained in step (e) prior to step (f).

27. The method of claim 26, wherein said culturing 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.

28. The method of claim 22, wherein the plants are 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 (Brassicajuncea), 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), poplar (Populus spec), pome fruit, potato, rape, raspberry, rice, rubber tree, rye, sorghum, soybean, spinach, spruce, squash, strawberry, sugar beet, sugar cane, sunflower, tea, teak, tobacco, tomato, triticale, turf, walnut, watermelon, wheat, and willow (Salix spec).

29. The method of claim 22, wherein 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 and biotic stress tolerance.

30. The method of claim 29, wherein 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.

31. The method of claim 29, wherein 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, Bacteroidetes,

Proteobacteria, and Verrucomicrobia compared to soil initially used in carrying out said method.

32. The method of claim 29, wherein the particular desired plant trait is biomass production and 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.

33. The method of claim 29, wherein the particular desired plant trait is biomass production and the microbiome comprises a decreased amount of Actinobacteria compared to soil initially used in carrying out said method.

34. The method of claim 29 wherein 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.

35. The method of claim 29, wherein 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.

36. The plants produced by the method of claim 22.

37. The plants of claim 36, wherein the plants are 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 (Brassicajuncea), 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), poplar (Populus spec), pome fruit, potato, rape, raspberry, rice, rubber tree, rye, sorghum, soybean, spinach, spruce, squash, strawberry, sugar beet, sugar cane, sunflower, tea, teak, tobacco, tomato, triticale, turf, walnut, watermelon, wheat, and willow (Salix spec).

38. The plants of claim 36, wherein the plants are in the form of seeds, seedlings, cutting, propagules, or whole plants.

39. The plants produced by the method of claim 26.

40. The plants of claim 39, wherein the plants are 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 (Brassicajuncea), 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), poplar (Populus spec), pome fruit, potato, rape, raspberry, rice, rubber tree, rye, sorghum, soybean, spinach, spruce, squash, strawberry, sugar beet, sugar cane, sunflower, tea, teak, tobacco, tomato, triticale, turf, walnut, watermelon, wheat, and willow (Salix spec).

41. The plants of claim 39, wherein the plants are in the form of seeds, seedlings, cutting, propagules, or whole plants.

42. A method of producing a plant microbiome useful in enhancing a particular desired plant trait, said method comprising:

(a) growing a group of plants in soil;

(c) recovering the subgroup of plants' whole microbiomes;

(d) applying the recovered whole microbiomes to another group of the plants; and

(e) repeating steps (a) to (d) to produce 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, Verrucomicrobia, and Spirochaetes.

43. The method of claim 42 further comprising:

applying the plant microbiome obtained in step (e) to a different plant variety than that used to carry out steps (a) - (e).

44. The method of claim 43, wherein the different plant variety is agriculturally relevant.

45. The method of claim 43, wherein the plant used to carry out steps (a) through (e) is a model plant.

46. The method of claim 42 further comprising:

culturing the recovered plant whole soil microbiomes after step (e).

47. The method of claim 46, wherein said culturing 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.

48. The method of claim 42, wherein the plant is selected from the group consisting of alfalfa, almond, 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), poplar (Populus spec), pome fruit, potato, rape, raspberry, rice, rubber tree, rye, sorghum, soybean, spinach, spruce, squash, strawberry, sugar beet, sugar cane, sunflower, tea, teak, tobacco, tomato, triticale, turf, walnut, watermelon, wheat, and willow (Salix spec).

49. The method of claim 42, wherein 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 and biotic stress tolerance.

50. The method of claim 42, wherein the plants are in the form of seeds, seedlings, cutting, propagules, or whole plants.

51. The plant microbiome produced by the method of any of claims 42-50.