Identification and characterization of herbicides and plant growth regulators

Abstract

The present invention provides a high throughput method capable of screening compounds for herbicidal or plant growth regulating activity, and further allows the prediction of the mode of action of herbicidal compounds or plant growth regulators.SOLUTION: The methods provided herein also facilitate identification of mutations involved in tolerance to herbicides in plants and identification of herbicide targets. The methods further provide for the identification of mutations involved in plant growth regulation and the identification of plant growth regulator targets.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates generally to the field of herbicides and plant growth regulators. More specifically, the present invention relates to a method that facilitates any one of the following: the discovery of numerous herbicides and plant growth regulators; the prediction of the mode of action of herbicides or / and plant growth regulators; the identification of targets of herbicides or plant growth regulators; and / or the identification of mutations that confer resistance to herbicides or plant growth regulators. [Background technology]

[0002] The following discussion of the background of the invention is provided merely to aid the reader in understanding the invention and is not admitted to describe or constitute prior art to the invention.

[0003] Before the introduction of herbicides, many farmers relied on manual and mechanical weed control measures.

[0004] Manual weeding methods are highly labour intensive and have unfavorable social costs, while mechanical weed control measures such as tillage cause environmental problems due to soil structure being damaged and exposed to erosion, often encouraging further weed invasion. Slash-and-burn cultivation before planting can prevent further weed spread and can be carried out over large areas with minimal human intervention, but like tillage, it also exposes the soil surface to erosion.

[0005] Crop protection chemicals such as herbicides have been important tools for farmers for nearly 70 years, beginning with the introduction of selective herbicides. Non-selective herbicides (e.g., paraquat from the 1960s and glyphosate from the 1970s) have enabled the adoption of no-till (direct drilling, zero-till) and other reduced cultivation systems to establish crop production. When weeds are removed with herbicides before planting, plowing to bury the weeds is not required. No-till systems can increase crop yields once the crop is properly established and can provide many environmental and economic benefits.

[0006] The advantages of no-till systems, which allow for the use of herbicides alone, include reduced labor requirements, reduced soil erosion, water savings, less fuel used, reduced greenhouse gas emissions, and increased biodiversity, particularly in the fight against climate change. Plowing over-aerates the soil and causes oxidation of organic matter, which not only destroys good soil structure (e.g., piled up after rotational grazing), but also releases large amounts of carbon dioxide. Planting in a no-till system after spraying herbicides and burning off weeds can reduce CO2 emissions by more than 80%.

[0007] Reflecting their global importance, in 2018, herbicides accounted for more than 40% of the $64 billion global market for crop and non-crop pesticides. However, since the late 1990s, new herbicides have been reaching the market at a much slower rate. Many of the best-selling and most widely used active ingredients (e.g., glyphosate) have been used by farmers for decades. No major herbicides with novel modes of action have been launched in the past 30 years. This poses a very serious problem for global crop protection, as new herbicides that become available to farmers offer the same limited mode of action, leading to the development of resistant weeds.

[0008] Early herbicide discovery was primarily achieved by spraying whole plants with chemicals in greenhouse conditions and observing phenotypic changes after several days or weeks. One more recent approach is to simulate the binding of screening compounds to known or predicted herbicide targets, an approach known as in silico screening. Another approach is to empirically test the inhibitory effect of screening compounds on the activity of known or predicted herbicide targets isolated from their biological context, an approach known as in vitro screening. Yet another approach is to empirically test the inhibitory effect of screening compounds on the growth or viability of relevant biological systems, an approach known as in vivo screening. In the case of herbicide discovery, the target organism for in vivo screening is typically a weedy whole plant. However, the quality of the output data generated by in vivo herbicide screening platforms, in which whole weedy plants are exposed to screening compounds and scored for growth / viability, comes at the expense of lower throughput than in vitro or in silico approaches. Summary of the Invention [Problem to be solved by the invention]

[0009] There is an unmet need for new herbicides, particularly those with novel modes of action. Besides providing much-needed alternatives to existing herbicides with modes of action to which weeds have developed resistance, knowledge of the mode of action of a herbicide can also be useful in predicting safety for humans, wildlife, and the environment in general. [Means for solving the problem]

[0010] The present invention satisfies at least one unmet need in the art by providing a method for screening candidate compounds for herbicidal or plant growth regulating activity.

[0011] Additionally or alternatively, the methods may be used to identify the mode of action of known or newly identified herbicidal compounds or plant growth regulators, and may be used to identify mutations / causes of particular phenotypes in plants, including, for example, herbicide resistance or plant growth regulation.

[0012] The quality of output data and the speed of throughput are two important parameters for a herbicide screening platform, and striking a balance between them presents a major challenge. The present inventors have developed a highly effective in vivo herbicide screening method with high throughput capabilities. The method utilizes a screening platform based on spores and / or spore spores, such as those from non-vascular plants (e.g., mosses), to screen candidate compounds for herbicidal activity. In addition to identifying new compounds with herbicidal or plant growth regulator activity, the method can also be used to predict or determine the mode of action of newly identified and / or known herbicidal compounds or plant growth regulators. Currently, the herbicide industry uses the dicotyledonous plant Arabidopsis thaliana as a representative screening system.

[0013] Non-vascular plants such as Marchantia polymorpha have already been considered as possible tools for genetic screening, as described in detail in Ishizaki et al., 2016 (Ishizaki, K., et al. "Molecular genetic tools and techniques for Marchantia polymorpha research", Plant and Cell Physiology. 2016, v. 57, n. 2, pp. 262-270), as well as genetic models (see, for example, Sugano et al., 2014 (Sugano, SS, et al. "CRISPR / Cas9-mediated targeted mutagenesis in the liverwort Marchantia polymorpha L.", Plant and Cell Physiology. 2014, v. 55, n. 3, pp. 475-481)). However, the use of non-vascular whole plants, spores, spore germlings, explants, protoplasts, or vegetative propagules in the context of screening for herbicides or plant growth regulators is unclear. While Marchantia polymorpha cells have been studied in a limited way for the investigation of photosynthetic electron transport inhibitors (see Sato, F., et al. "Photoautotrophic cultured plant cells: a novel system to survey new photosynthetic electron transport inhibitors," Zeitschrift fur Naturforschung C. 1991, v. 46, no. 7-8, pp. 563-568), clear challenges and fundamental biological differences exist that prevent poor non-vascular whole plants, spores, spore germlings, explants, protoplasts, and / or vegetative propagules from being considered for use as screening platforms for herbicidal compounds or plant growth regulators.For example, Marchantia polymorpha is physically much smaller and represents a simpler plant system than undesirable weed plants, and indeed than the current representative screening system, Arabidopsis thaliana.In addition, non-vascular plants are structurally different from higher plants in many respects; for example, the cuticle formed in spore germinants, propagules, explants, and non-vascular whole plants is chemically very different from that of higher plants.Furthermore, Marchantia polymorpha is known to be insensitive to major herbicides such as glycophosphates, so non-vascular plants such as Marchantia polymorpha are not a logical choice for screening herbicides or plant growth regulators.

[0014] However, the present inventors have surprisingly discovered that spore germlings of non-vascular plants, including bryophytes, can be used to screen for herbicides or plant growth regulators. Furthermore, the use of non-vascular plants overcomes some of the limitations associated with the use of Arabidopsis thaliana, providing a significantly improved screening method. Given the large size and complex nature of Arabidopsis thaliana spore germlings or whole plants, screening throughput is limited by the duration required to grow the plants and the associated additional space, resources, and personnel required to cultivate them. For example, non-vascular plants typically require only four days of growth before they can be used for screening. This compares to seven days, which is typical for Arabidopsis thaliana. Thus, the present invention provides a method that typically has a throughput at least approximately ten times higher than Arabidopsis-based methods. In addition to throughput limitations, the use of Arabidopsis thaliana spore germlings or whole plants hampers the use of fluorescence imaging-based approaches for high-content screening of herbicides or plant growth regulators. Fluorescence imaging-based approaches benefit from imaged objects that fit into the smallest possible horizontal space, and the size and complexity of Arabidopsis thaliana precludes their use in such approaches. Non-vascular plants are advantageous for use in the methods of the present invention given their small size, simple body plan, sensitivity to herbicides with different modes of action, and / or amenability to genetic manipulation. This allows for a higher throughput, less expensive, and more efficient screening system for candidate compounds (e.g., herbicides and plant growth regulators) compared to currently existing more complex screening systems, such as Arabidopsis thaliana.

[0015] In a first aspect, the present invention provides a method of screening candidate compounds for herbicidal or plant growth regulating activity, the method comprising: (i) contacting a series of different candidate compounds with a plurality of test samples from a non-vascular plant; (ii) determining whether the test sample provides a phenotypic response to said series of different candidate compounds by comparing the phenotype with that of a control sample from a non-vascular plant that has not been contacted with the candidate compounds; Methods are provided wherein the test and control samples comprise whole plants, spores, spore germlings, explants, protoplasts, or vegetative propagules, and wherein the phenotypic response is indicative of herbicidal or plant growth regulating activity.

[0016] In one embodiment, the candidate compound is a candidate compound for herbicidal activity.

[0017] In another embodiment, the non-vascular plant is a moss, a hornblende, or a liverwort.

[0018] In a further embodiment, the test sample and the control sample are spore germlings.

[0019] In additional embodiments, the test spore germlings and the control spore germlings are derived from spores of the same species of non-vascular plant.

[0020] In one embodiment, the test spore germlings are moss spore germlings, liverwort spore germlings, hornwort spore germlings, or any combination thereof.

[0021] In another embodiment, each member of a series of different candidate compounds is contacted with a different test sample.

[0022] In another embodiment, multiple members of a set of different candidate compounds are contacted with a single test sample.

[0023] In yet another embodiment, the test and control samples are thalloid bryophyte spore germlings, simple thalloid bryophyte spore germlings, complex thalloid bryophyte spore germlings, or any combination thereof.

[0024] In one embodiment, the test sample and the control sample are selected from the group consisting of Marchantia alpestris spore germinants, Marchantia aquatica spore germinants, Marchantia berteroana spore germinants, Marchantia carrii spore germinants, Marchantia chenopoda spore germinants, Marchantia debilis spore germinants, Marchantia domingenis spore germinants, Marchantia emarginata spore germinants, Marchantia foliacia spore germinants, Marchantia grossibarba spore germinants, Marchantia inflexa spore germinants, Marchantia linearis spore germinants, Marchantia macropora spore germinants, Marchantia novoguineensis spore germinants, Marchantia paleacea spore germinants, Marchantia palmata spore germinants, Marchantia papillate spore germinants, Marchantia pappeana spore germplasm, Marchantia polymorpha spore germplasm, Marchantia rubribarba spore germplasm, Marchantia solomonensis spore germplasm, Marchantia streimannii spore germplasm, Marchantia subgeminata spore germplasm, Marchantia vitiensis spore germplasm, Marchantia wallisii, Marchantia nepalensis, and any combination thereof.

[0025] In one embodiment, the plurality of test spore germinants are provided in a series of different wells, each well containing 400-800 spore germinants / mL, 300-900 spore germinants / mL, or 200-1000 spore germinants / mL.

[0026] In a further embodiment, the test sample and / or the control sample are engineered to express a fluorescent molecule.

[0027] In one embodiment, the control sample is a positive control.

[0028] In a further embodiment, the positive control is contacted with a known herbicide or plant growth regulator.

[0029] In one embodiment, the control sample is a negative control.

[0030] In another embodiment, the negative control sample is not contacted with any known herbicide or plant growth regulator.

[0031] In additional embodiments, step (ii) of the method comprises comparing the phenotype of the test sample to the phenotype of a positive control sample contacted with a known herbicidal or plant growth regulating compound, and further comprises comparing the phenotype of the test sample to the phenotype of a negative control sample not contacted with the known herbicidal or plant growth regulating compound.

[0032] In additional embodiments, the known herbicidal compounds have a known mode of action, and comparison of the above-described test sample phenotype with the positive control sample phenotype is used to predict the mode of action of candidate compounds identified as having herbicidal or plant growth regulating activity.

[0033] In a further embodiment, the test sample, negative control sample, and positive control sample are spore germlings derived from spores of the same species of non-vascular plant.

[0034] In a further embodiment, the test sample, negative control sample, and positive control sample are engineered to express a fluorescent molecule.

[0035] In one embodiment, step (ii) of the method comprises measuring the phenotypic response of the test sample after growing the test sample in a suitable medium under suitable conditions with the candidate compound described above for a period of 1 to 3 days, 1 to 5 days, 3 to 6 days, 3 to 5 days, 2 to 3 days, 1 to 10 days, less than 5 days, less than 4 days, or less than 3 days after the contacting, and the phenotype of the control spore germinants described above is determined after an equivalent period of growth in the suitable medium described above under the suitable conditions described above.

[0036] In one embodiment, step (ii) of the method comprises obtaining measurements of any one or more of sample length, sample width, sample shape, sample pigmentation, sample circularity, sample chlorophyll concentration, and / or cell number per sample.

[0037] In a further embodiment, the measurements are recorded digitally.

[0038] In another embodiment, comparing the phenotypic response of the test sample to any of the above-mentioned control sample phenotypes comprises any one or more of distributed stochastic neighbor embedding, principal component analysis (generalized weighted least squares), principal component analysis (minimized weighted chi-square), principal component analysis (minimum residual), common factor analysis (principal axis), common factor analysis (maximum likelihood), or common factor analysis (weighted least squares).

[0039] In a further embodiment, candidate compounds are selected as potential herbicides using artificial intelligence algorithms such as random forest or neural network algorithms.

[0040] In one embodiment, step (ii) of the method comprises: obtaining phenotypic measurements from the test sample and any of the above-mentioned control samples, thereby generating a dataset; and using at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, at least 95%, or 99% of the dataset as at least a training set for an artificial intelligence algorithm.

[0041] In one embodiment, the control sample comprises a positive control sample and uses an artificial intelligence algorithm to predict the mode of action of any of the above-mentioned candidate compounds.

[0042] In another embodiment, the method comprises: (a) contacting candidate compounds identified in steps (i) and (ii) as having herbicidal or plant growth regulating activity with a series of mutagenized samples comprising whole plants, spores, spore germlings, explants, protoplasts, or vegetative propagules, wherein the test sample and the mutagenized sample are from the same species of non-vascular plant; (b) extracting DNA from a resistant mutagenized sample that survives the contacting described above in (a) or that does not exhibit growth abnormalities after the contacting described above in (a); (c) sequencing the genome or portion of the genome of the resistant mutagenized sample, thereby obtaining the mutagenized sample DNA sequence; (d) aligning the mutagenized DNA sequences obtained in (c) to a reference DNA sequence to identify a first set of sequence mismatches between the mutagenized sample DNA sequences and the reference DNA sequence; (e) aligning the DNA sequence from the first comparison sample to the reference DNA sequence and identifying a second set of mismatches between the first comparison DNA sequence and the reference DNA sequence; and (f) filtering the first set of mismatches with respect to the second set of mismatches to identify a first subset of mismatches that are unique to the first set of mismatches, wherein the first subset of mismatches are candidate mutations that may confer resistance to a herbicide or plant growth regulator; The first comparison sample is from an independent sample that does not survive contact with the candidate compound or shows growth abnormalities after contact with the candidate compound, is of the same genus as the resistant mutagenized sample, and the reference DNA sequence is a known reference sequence of a plant of the aforementioned genus.

[0043] In another embodiment, the method comprises: (ei) aligning the DNA sequence of the second comparison sample to the reference DNA sequence and identifying a third set of mismatches between the second comparison sample and the reference DNA sequence; (f) filtering the first set of mismatches with respect to a third set of mismatches to facilitate identification of a second subset of mismatches that are unique to the first set of mismatches; and generating a third subset of mismatches by filtering the first subset of mismatches with respect to the second subset of mismatches, wherein the first and second subsets of mismatches are candidate mutations that may confer resistance to a herbicide or a plant growth regulator; A second comparison sample is from an independent sample and is of the same genus as the mutagenized sample that does not survive contact with the candidate compound or exhibits growth abnormalities after contact with the candidate compound.

[0044] In another embodiment, the mutagenized sample is an M1 sample.

[0045] In another embodiment, the mutagenized sample contains non-naturally occurring mutations.

[0046] In a further embodiment, the method does not include a step of segregation ratio analysis, complex segregation ratio analysis, or bulk segregation ratio analysis.

[0047] In one embodiment, the aligning in (e) includes aligning the DNA sequences of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or more comparison samples to the reference DNA sequence and identifying a second set of sequence mismatches between the two sequences.

[0048] In another embodiment, the method further comprises filtering the candidate mutations with a biological filter.

[0049] In one embodiment, the mutagenized sample is singular.

[0050] In one embodiment, the candidate mutation is in a gene encoding a protein targeted by a candidate compound identified as having herbicidal or plant growth-regulating activity.

[0051] In another embodiment, step (iii) is implemented using a computer.

[0052] In another embodiment, the method further comprises identifying a plant molecule or biological pathway targeted by a candidate compound identified by the method as having herbicidal activity using any one or more of an enzymatic assay, a chlorophyll fluorescence kinetics assay, a photosynthetic oxygen evolution assay, an electrolyte leakage assay, a radiometric assay, a spectrophotometric assay, a fluorometric assay, an absorbance assay, a colorimetric assay, mass spectrometry, a mitotic index analysis, a quantitative PCR analysis, a transcriptome profiling, a proteomics profiling, a genome-wide analysis, and / or a quantitative trait locus analysis, an in silico docking study, or a chemical structure analysis.

[0053] In one embodiment, the plurality of test samples does not contain the whole plant.

[0054] definition Certain terms are used herein that have the meanings indicated below.

[0055] As used in this application, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. For example, as used herein, "a compound" also encompasses plural compounds unless specifically stated otherwise.

[0056] As used herein, the term "comprising" means "including." Variations of the word "comprising," such as "comprise" and "comprises," have correspondingly varied meanings. For example, a composition "comprising" material A may consist exclusively of material A, or may include material A and any other number of additional components (e.g., material B, and / or material C).

[0057] As used herein, the term "plurality" means more than one. In certain specific aspects or embodiments, a plurality may include 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100, 250, 300, 350, 400, 450, 500, 600, 700, 800, 900, 1000, 1250, 1500, 1750, 2000, 2500, 3000, 3500, 4500, 5000, 6000, 7000, 8000, 9000, 10 "000," "15000," "20000," "30000," "40000," "50000," "60000," "70000," "80000," "90000," "100000," "200000," "300000," "400000," "500000," "600000," "700000," "800000," "900000," "1000000," or more, and any numerical value derivable therein, and any range derivable therein.

[0058] As used herein, when used in reference to a numerical range, the term "to" includes the numerical values ​​at each of the endpoints of the range.

[0059] As used herein, "high-throughput screening" (HTS) refers to a method in which multiple synthetic compounds or natural products are screened for activity against one or more biological targets. HTS may be used to identify compounds with various biological activities, such as, for example, drugs, pesticides, herbicides, etc.

[0060] As used herein, the term "non-vascular plant" refers to a plant that lacks a vascular system (i.e., xylem and phloem). "Non-vascular plant" will be understood herein to include non-vascular whole plants, components of non-vascular whole plants, spores of non-vascular whole plants, and spore germlings of non-vascular whole plants. Non-limiting examples of non-vascular plants include bryophytes, such as mosses, liverworts, and hornworts.

[0061] As used herein, "whole plant" refers to a complete plant, particularly a complete non-vascular plant. Whole plant can also be used to refer to a dwarf plant or a young plant (i.e., a plant that has not yet reached its adult form and therefore may not be fully mature or sexually mature). In the case of Marchantia polymorpha, a whole plant can refer to a gametophyte having a root system, one or more meristems, photosynthetic tissue, epidermis, cuticle, one or more cups, and one or more reproductive receptacles (i.e., reproductive organs). In the case of a young plant or a dwarf plant of Marchantia polymorpha, the plant has a subset of the tissues, organs, and characteristics of a complete plant, including at least a root structure, photosynthetic tissue, and one or more meristems.

[0062] As used herein, "weeds" are understood to refer to plants that grow in harmful competition with cultivated plants for any one or more of water, light, nutrients, and / or space. Non-limiting characteristics of weeds include having low or no economic value compared to cultivated plants, causing ecological and / or economic losses, vigorous growth characteristics, and / or producing large numbers of seeds.

[0063] As used herein, the terms "herbicide" and "herbicidal compound" refer to a synthetic compound or natural product that is capable of killing or inhibiting the growth of plants, plant cells, plant seeds or plant tissues, including but not limited to weeds and their seeds.

[0064] As used herein, the term "plant growth regulator" refers to a compound (natural or organic) that regulates plant growth, e.g., accelerates, promotes, speeds up, retards, inhibits, delays, or otherwise alters plant growth, development, or plant maturity.

[0065] As used herein, the term "phenotype" shall be taken to mean a set of observable characteristics of an individual, e.g., an individual plant, or a portion of a plant as described above. The term "phenotypic response" shall be taken to mean an observable response of an individual, e.g., an individual plant, or a portion of a plant as described above, in response to its unique environment, e.g., in response to exposure to a candidate compound, such as a candidate herbicide or candidate plant growth regulator.

[0066] As used herein, "reference DNA sequence" refers to the reference genome sequence of a given plant. Reference DNA sequences are publicly available, for example, in a database.

[0067] As used herein, "mismatch" refers to a difference in the sequence of a read (e.g., a portion of the DNA sequence of a plant being tested to identify a causal mutation) compared to the portion of the reference DNA sequence to which the read best aligns.

[0068] As used herein, the term "causative mutation" will be taken to mean a mutation that causes or contributes to a phenotype of interest, for example, a mutation that causes or contributes to herbicide resistance.

[0069] As used herein, "M0" refers to the plant population in a mutagenesis experiment before exposure to a mutagen (i.e., the parent population). As used herein, "M1" is a designation that refers to the same plant population after exposure to a mutagen (i.e., the M0 population). As used herein, "M2" refers to the M1 progeny after self-pollination (i.e., the process of crossing a mutant with itself).

[0070] As used herein, "segregation analysis" refers to a statistical technique for fitting a formal genetic model to data regarding the trait or disease phenotype expressed in biological family members to determine the most likely mode of inheritance for the trait or disease under test. Segregation analysis requires multiple generations of family members to determine the inheritance pattern of the phenotype being analyzed.

[0071] Any description in this specification of a prior art document, or of a statement in this specification that is derived from or based on such a document, is not an admission that the document or statement from which it is derived is part of the general knowledge in the relevant art.

[0072] For purposes of description, all documents mentioned herein are incorporated by reference in their entirety unless otherwise stated.

[0073] Embodiments of the present invention will now be described, by way of example only, with reference to the accompanying drawings, which are described below. [Brief explanation of the drawings]

[0074] [Figure 1] This is a far-red fluorescence micrograph showing 50 Marchantia polymorpha germlings grown in one well of a 96-well assay plate. Germling density was adjusted to maximize the number of germlings and minimize the number of germlings growing in contact with each other. The former increased the statistical significance of the assay, while the latter was necessary to generate accurate data describing the response of the whole plant to the herbicide treatment.

[0075] [Figure 2]Provided are 10x photomicrographs of 5-day-old Marchantia polymorpha germlings. In the left image, transmitted light was recorded (bright-field image). In the middle image, only far-red photons were recorded by the camera chip (far-red image). In the right image, only cyan photons were recorded (cyan image). Images have been cropped to show a single germling. The same Marchantia polymorpha germling is shown in all three images. Images were generated using third-party software FIJI.

[0076] [Figure 3] Linked 10x photomicrographs of 5-day-old spore germlings are provided. The far-red photomicrograph on the left is overlaid with the outline of the object segmented using the bright-field image (purple line), far-red image (red line), and cyan image (blue line). Center, cyan image. Right, bright-field image. Images were generated using third-party software, GE Developer.

[0077] [Figure 4] A 10x photomicrograph of a 1-day-old Marchantia polymorpha proMpEF1a::YFP-NLS (3X) germling spore is provided. The transmitted light photomicrograph on the left shows an ungerminated spore (arrow) and a two-cell germling spore. The fluorescent photomicrograph in the middle shows two nuclei of a two-cell germling spore. Images were generated using third-party software FIJI. The image on the right provides segmented nuclear contours using third-party software GE Developer.

[0078] [Figure 5] The distribution of 10 variables from the low-resolution dataset (2x) is shown before scaling (upper panel) and after scaling (lower panel).

[0079] [Figure 6]Screen plot of factors created from a set of 10 variables in a low-resolution dataset (2x). Horizontal lines represent the recommended number of factors to form according to either the Kaiser criterion, Elbow method, or Jolliffe criterion. Images were generated using third-party software, Stratomine®.

[0080] [Figure 7]

[0023] Figure 1 is a hit selection graph showing the average phenotypic distance of plants in wells containing either 0.1% DMSO (negative control, red), the known herbicide isoxaben at low concentrations (positive control, green), or a chemical with unknown activity (screening compound, blue). The wavy red line indicates the threshold above which a compound elicited a statistically significant response in treated plants and was selected as a hit. Images were generated using third-party software, Stratomine®.

[0081] [Figure 8]

[0023] Figure 1 is a hit selection graph showing the average phenotypic distance of plants in wells containing either 0.1% DMSO (negative control, red), the known herbicide isoxaben at low concentrations (positive control, green), or a chemical with unknown activity (screening compound, blue). The wavy red line indicates the threshold above which a compound elicited a statistically significant response in treated plants and was selected as a hit. Images were generated using third-party software, Stratomine®.

[0082] [Figure 9] A contour plot showing predicted coordinates of the phenotypic response of plants treated with 0.1% DMSO (red surface) or three different herbicides with different modes of action is provided. Circles represent 20% of the dataset used to test the phenotypic model. If a circle falls on the same color surface, the corresponding model was accurate. Images were generated using third-party software, Stratomine®.

[0083] [Figure 10] A clustering graph is provided, showing hits that belong to the same cluster in different colors.

[0084] [Figure 11] Photographs of Petri dishes containing 14-day-old germlings grown on 1 μM, 100 μM, or 1000 μM of the known herbicide norflurazon are provided. On 1000 μM norflurazon, all germlings are dead. Three replicates are shown.

[0085] [Figure 12] Photographs of Petri dishes containing 14-day-old germlings grown on 1 μM, 100 μM, or 1000 μM of the known herbicide chlorsulfuron are provided. On 1000 μM chlorsulfuron, the germlings, although not all, are dead, but show a significant reduction in growth. Three replicates are shown.

[0086] [Figure 13] Dose-response curves of Marchantia spores mutagenized by UV-B irradiation for different amounts of time. Figure 13 provides two replicates. 50% kill was achieved by irradiating the spores with UV-B for 20 seconds.

[0087] [Figure 14] Dark-field micrographs of 14-day-old germlings growing without UV-B treatment (left) or on 0.1 ppm chlorsulfuron with UV-B treatment (left and center) are provided. The larger plants were chlorsulfuron-resistant mutants and had a phenotype similar to the UV-B-treated 14-day-old germlings growing on 0.1% DMSO shown in the right panel.

[0088] [Figure 15] FIG. 1 is a process flow diagram of a method for identifying causative mutations that give rise to a phenotype of interest in a test sample, according to an embodiment of the present invention.

[0089] [Figure 16] Rhizoid phenotypes of 2-day-old Marchantia polymorpha plants. Wild-type rhizoid phenotype (A), wavy rhizoid phenotype (B). Rhizoids are straight-growing cells in the wild type (A), but wavy cells in some mutants (B).

[0090] [Figure 17] Dorsal epidermis phenotypes of 2-month-old Marchantia polymorpha plants. Wild-type epidermis phenotype (A), elongated epidermis phenotype (B). The dorsal epidermis shows pneumothorax (A, arrows) and is elongated in some mutants (B).

[0091] [Figure 18] Performance of the non-allelic mutation discovery pipeline in UV4.32. A: Effect of increasing numbers of non-allelic mutant backgrounds on filtering efficiency. B: Number of UV4.32 mismatches remaining after the filtering step when using eight non-allelic mutant strains.

[0092] [Figure 19] Performance of the non-allelic mutation discovery pipeline in chlorsulfuron-resistant mutants. Filtering efficiency improves as the number of allelic mutant backgrounds increases. The leftmost scattered box represents the total number of mismatches in the chlorsulfuron-resistant mutant strain before filtering out mismatches also observed in the resequenced wild-type genome.

[0093] [Figure 20]Linked 4x photomicrographs of three 5-day-old spore germlings are shown. The upper panel shows a plant exposed to 0.1% DMSO, the middle panel shows a plant exposed to 10 μM isoxaben, and the lower panel shows a plant exposed to 10 μM of a test compound selected as a hit. From left to right, the far-red photomicrograph is overlaid with the outline of the object segmented using the bright-field image (outer red line) and the outline of the plant body segmented using the far-red image (nested red line). Next, the far-red photomicrograph is overlaid with the outline of the object segmented using the bright-field image (outer red line) and the outline of the plant meristem segmented using the far-red image (nested purple line). Next, the far-red photomicrograph is overlaid with the outline of the object segmented using the bright-field image (outer red line) and the outline of the plant rhizoid segmented using the far-red image (nested green line). Next, the cyan micrograph is overlaid with the outlines of the objects segmented using the bright-field image (outer red lines) and the outlines of the cyan fluorescent masses segmented using the cyan image (nested yellow lines). Finally, the far-red micrograph is overlaid with the outlines of the objects segmented using the bright-field image (outer red lines) and the outlines of the chloroplasts segmented using the far-red image (nested blue lines). DETAILED DESCRIPTION OF THE INVENTION

[0094] Herbicide resistance is a major problem affecting crop and pasture production worldwide. The level of herbicide-resistant weeds is reasonably expected to increase until herbicides exhibiting different modes of action are identified and brought to market. Methods capable of identifying new compounds with herbicidal or plant growth-regulating activity, and identifying the modes of action of such compounds, are of great importance to combat the increasing levels of herbicide resistance in weeds and other similar plants.

[0095] The present invention provides a high-throughput method capable of screening compounds for herbicidal or plant growth regulator activity, thus providing the potential for the discovery of new herbicides or plant growth regulators. The method further allows for the prediction of the mode of action of herbicidal compounds or plant growth regulators, including compounds identified by the methods described herein, and any known herbicides or plant growth regulators whose mode of action may not be characterized. Thus, the methods provided herein can be used to identify compounds (e.g., herbicidal compounds or plant growth regulators) with novel modes of action. The methods provided herein also provide for the identification of mutations involved in herbicide resistance in plants (e.g., weeds) and the identification of herbicide targets. The methods provided herein also provide for the identification of mutations involved in plant growth regulation and the identification of plant growth regulator targets.

[0096] Currently, using whole plants to screen compounds for herbicidal activity is relatively low throughput, because the size and complexity of whole plants are not suitable for high-throughput phenotypic characterization.Described herein is a high-throughput screening method using non-vascular plants, which can simultaneously screen a large number of compounds for herbicidal activity or plant growth regulation activity, predict its mode of action, and identify its target.The method described herein can also be used to identify the mutation that confers resistance to herbicidal compounds.

[0097] High-throughput screening The present invention provides an in vivo high-throughput method for screening compounds for herbicidal or plant growth-regulating activity. High-throughput screening (HTS) is a technology used to quickly and efficiently select useful compounds from a vast number of candidates, such as new drugs, pesticides, and herbicides, and can be used to identify compounds with various biological activities. Although not limited thereto, the HTS contemplated herein generally includes three elements: a suitable library of compounds for screening, an assay method, and a system for handling and / or analyzing the data generated by the assay.

[0098] Compound libraries for use in the methods of the present invention can be generated from combinatorial chemistry or from natural products, such as secondary metabolites from plants, animals, and / or microorganisms. In some embodiments of the present invention, natural compounds can be processed before being included in the compound library. A non-limiting example of a suitable processing technique known to those skilled in the art is solid-phase extraction. In further embodiments of the present invention, the combinatorial library to be screened can be synthesized in the compartment where the assay is performed, thereby providing a reference address for the candidate compound. A range of concentrations of any given compound can be tested. Various solvents can be used to solubilize solid compounds. Any suitable compound can be screened using the methods of the present invention, including, but not limited to, candidate natural compounds, synthetic compounds, and chemical compounds.

[0099] According to the methods of the present invention, non-vascular plants can be contacted with candidate compounds (either candidate herbicidal compounds or candidate plant growth regulator compounds) and their response to the candidate compounds can be evaluated. As indicated in the definitions section, the term "non-vascular plants" includes non-vascular whole plants, their components, their spores, and / or their spore germinants. The plants can also be non-vascular plants, such as liverworts, mosses, and / or hornworts.

[0100] As a non-limiting example, the non-vascular plant may be a bryophyte. The bryophyte may be a phyllophyte, a simple phyllophyte, or a complex phyllophyte. Non-limiting examples of bryophytes that may be used in the screening methods described herein are as follows: Marchantia alpestris, Marchantia aquatica, Marchantia berteroana, Marchantia carrii, Marchantia chenopoda, Marchantia debilis, Marchantia domingenis, Marchantia emarginata, Marchantia foliacia, Marchantia grossibarba, Marchantia inflexa, Marchantia linearis, Marchantia macropora, Marchantia novoguineensis, Marchantia paleacea, Marchantia palmata, Marchantia papillate, Marchantia pappeana, Marchantia polymorpha, Marchantia rubribarba, Marchantia solomonensis, Marchantia streimannii, Marchantia subgeminata, Marchantia vitiensis, Marchantia wallisii, and Marchantia nepalensis. Further non-limiting examples of liverworts that can be used in the screening methods described herein are plants of the Jungermanniopsida (e.g., plants of the subclass Jungermanniidae or Metzgeriidae), Marchantiopsida (e.g., plants of the subclass Marchantiidae or Sphaerocarpidae) or Haplomitriopsida class of liverworts.

[0101] As a non-limiting example, the non-vascular plant may be a moss. Non-limiting examples of mosses that may be used in the screening methods described herein are: Physcomitrella patens or Physcomitrella readeri moss.

[0102] As a non-limiting example, the non-vascular plant may be a horny bryophyte. Non-limiting examples of horny bryophytes that may be used in the screening methods described herein are: Anthoceros, Dendroceros, Folioceros, Megaceros, Notothylas, and Phaeoceros genera.

[0103] In some embodiments of the present invention, the non-vascular plant may be insensitive to glyphosate and / or glufosinate.

[0104] The method of the present invention uses plant material from non-vascular plants. The method may include (i) contacting a series of different candidate compounds with a plurality of test samples, and (ii) determining whether the test samples provide a phenotypic response to the series of different candidate compounds by comparing the phenotype with that of a control sample not contacted with the candidate compounds. The test and control samples may include non-vascular plant material. In one embodiment, the test and control samples include whole plants, spores, spore germinants, explants, protoplasts, or vegetative propagules from non-vascular plants. In one embodiment, the test and control samples include spores, spore germinants, explants, protoplasts, or vegetative propagules from non-vascular plants. In one embodiment, the test and control samples include spores or spore germinants from non-vascular plants. In one embodiment, the test and control samples include spores from non-vascular plants. In a preferred embodiment, the test and control samples include spores from liverwort plants. Suitable moss spores or spore germlings for use in the methods of the present invention include, for example, Marchantia spores or spore germlings.

[0105] The spore germlings used in the method of the present invention may have rhizoids, plant photosynthetic cells and / or nascent meristems.Plants can be used for high-throughput screening at less than 1 day old, less than 2 days old, less than 3 days old, less than 4 days old, less than 5 days old, less than 6 days old, less than 7 days old, less than 8 days old, less than 9 days old, less than 10 days old, less than 11 days old, less than 12 days old, less than 13 days old, or less than 14 days old.Alternatively, older plants can be used.

[0106] Non-vascular plants used in the methods described herein may be autofluorescent (i.e., contain endogenous fluorescent molecules). The nature of autofluorescence can indicate whether the non-vascular plant contains photosynthetic pigments, photoprotective pigments, stress-induced primary metabolites, or stress-induced secondary metabolites. For example, chlorophyll is a photosynthetic pigment located in the chloroplasts of non-vascular plants, and fluoresces in the "far-red" spectrum. For example, NADH and NADPH accumulate in stressed non-vascular plants and fluoresce in the "cyan" spectrum. The magnitude of autofluorescence can indicate the extent to which fluorescent molecules accumulate in non-vascular plants. By extension, the nature and magnitude of autofluorescence can be indicators of various physiological responses of non-vascular plants exposed to test compounds. For example, chlorophyll content can be an indicator of plant growth, and NAD(P)H content can be an indicator of chemical-induced cellular stress. As another example, the localization of chlorophyll and NAD(P)H in cells or plants can be indicative of light and metabolic processes that are impaired by contact with a test compound.

[0107] Non-vascular plants used in the methods described herein may be engineered to express fluorescent or luminescent cell markers. The expression of these fluorescent or luminescent markers may be used to generate digital images for use in obtaining measurements of phenotypic responses. The use of fluorescent cell marker expression to generate images of biological structures has been common in the art for some time. Various techniques exist for expressing fluorescent proteins in plant cells. Exogenous nucleic acids encoding fluorescent proteins can be introduced into plant cells using standard plant transformation methods known to those skilled in the art. One commonly used method that can be used in some embodiments of the present invention is Agrobacterium tumefaciens transfer DNA (T-DNA)-induced insertional mutagenesis. Simple and highly efficient T-DNA transformation protocols have been available to those skilled in the art for many years, including, for example, the "floral dip" method (Clough and Bent, The Plant Journal, 1998;16(6):735-743). Those skilled in the art will know that T-DNA transformation protocols are available for non-vascular plants (reviewed in Genetic transformation of moss plants, Jing et al., 2013, African Journal of Biotechnology; 12(3):227-232). Although T-DNA-mediated insertion is random, the inserted DNA fragment is flanked by a 25-bp border sequence (T-DNA). Therefore, primers designed from the left border of the T-DNA can be used to isolate the genome / T-DNA sequence junction, which can then be mapped to the genome to precisely identify the chromosomal insertion location. Transposon-mediated mutagenesis, with or without T-DNA, is commonly used in the art and can be used to express fluorescent proteins in plants used in the present invention.

[0108] Other commonly used methods for introducing exogenous nucleic acids into plant cells that can be used in the present invention include, but are not limited to, cation or polyethylene glycol treatment of protoplasts (O'Neill et al., The Plant Journal, 1993;3(5):729-738), calcium phosphate precipitation, electroporation, microinjection, viral infection, protoplast fusion, particle bombardment, stirring a cell suspension in solution with microbeads or microparticles coated with transforming DNA, direct DNA uptake, and liposome-mediated DNA uptake. Such methods are well described in a wide range of documents commonly used by those skilled in the art, such as Glick, Methods in Plant Molecular Biology and Biotechnology, 2018; CRC Press; Sambrook et al., Molecular Cloning: a laboratory manual, 1998, Cold Spring Harbor Laboratory. CRISPR / Cas9 genome editing technology can also be used to fluorescently tag endogenous plant proteins.

[0109] A wide variety of fluorescent proteins are commercially available (see, e.g., Shaner et al., Nature Methods, 2015;2(12):905-909), and one skilled in the art may select markers for use in the present invention based on the image required. Many publications are available describing the use of fluorescent proteins in imaging various plant types, plant organs, and using various microscopy techniques (see, e.g., Berg and Beachy, Methods in Cell Biology, 2005;85:153-177).

[0110] In some embodiments of the present invention, green fluorescent protein (GFP) or modified versions of GFP may be used as a fluorescent marker. The original GFP isolated from the jellyfish Aequorea victoria, as well as many modifications to wild-type GFP that enable its expression in plants, are known in the art. GFP spectral variants, such as cyan-enhanced fluorescent protein and yellow-enhanced fluorescent protein (ECFP and EYFP), are generally divided into seven types based on the type of chromophore (see Zacharias and Tsien, Green Fluorescent Protein: Properties, Applications, and Protocols, 2006, John Wiley and Sons; pp. 83-120). The choice of fluorophore depends on whether more than one fluorescent marker is used, requiring the selection of spectrally separable pairs. In some embodiments of the present invention, red fluorescent protein (RFP) or modified versions of RFP may be used as a fluorescent marker. Plants can be engineered to express fluorescent proteins (e.g., RFP and derivatives) in specific plant structures.

[0111] Non-vascular plants used in the methods described herein may be stained with fluorescent cell dyes or probes. The fluorescent cell dyes or probes may be applied at any time during the assay to label any specific cellular compartment, any specific cell type, or any specific part of the plant. In some embodiments of the present invention, such fluorescent dyes include Calcofluor White, S4B, propidium iodide, FM1-43, FM4-64, Mitotracker dyes, or Hoechst dyes. Fluorescent cell dyes or probes may also be used as ion content indicators, including [Ca2+] or pH indicators, or redox indicators. In some embodiments of the present invention, such fluorescent dyes or probes are OxiORANGE, HySOx, HYDROP, or hydroxyphenyl fluorescin.

[0112] The screening methods of the present invention may use any suitable arrangement of compartments (eg, wells, tubes, etc.) suitable for HTS assays.

[0113] For example, 96-well microtiter plates can be used for both automated and non-automated forms of HTS. Assays can be set up manually or by robotic systems, such as liquid handling robots.

[0114] Different candidate compounds are screened for herbicidal or plant growth-regulating activity separately in individual compartments. A given candidate compound can be screened in a single compartment or in multiple compartments. Alternatively, multiple different candidate compounds can be screened for herbicidal or plant growth-regulating activity in a single compartment (e.g., a natural extract library or a synthetic molecule mixture).

[0115] In some embodiments of the present invention, a solvent can be mixed with the non-vascular plant and the candidate compound in preparation for screening. Non-limiting examples of suitable solvents include dimethyl sulfoxide (DMSO), acetone, water, methanol, and ethanol. DMSO is a carrier / universal solvent that can dissolve many small molecules and transport them through membranes. Without wishing to be bound by theory, DMSO or another suitable solvent, surfactant, and any other suitable additives can also enhance the penetration of test compounds into plant cells / tissues and help preserve plant cells / tissues during the assay.

[0116] Additionally or alternatively, a liquid or gelatinous nutrient medium can be mixed with the non-vascular plant and the candidate compound in preparation for screening. Non-limiting examples of suitable nutrient media include Johnson's medium, M51C, Gamborg B5, and MS medium. In some embodiments where Johnson medium is utilized, the Johnson medium consists of inositol (100 mg / L), sucrose (10 g / L), KNO (6000 μM), MgSO (1000 μM), Ca(NO)*4H0 (4000 μM), KCl (25 μM), HBO (10 μM), MnSO*4H0 (1 μM), ZnSO*7H0 (1 μM), CuSO*5H0 (0.25 μM), (NH)MoO*4H0 (0.25 μM), FeSO*7H0 (25 μM), NaEDTA (25 μM), NHHPO (600 μM), and (NH)SO (400 μM).

[0117] Those skilled in the art will recognize that the density of nonvascular plants per well of a given assay plate can be varied to maximize the statistical significance of the assay while avoiding overlap of material within each well, which can affect the accuracy of measurements. One exemplary embodiment of the present invention uses 50-70 spores or spore germlings per well of a standard 96-well microtiter plate. Alternatively, the density can be 40-80, 30-90, or 20-100 plants, spores, or spore germlings per well. In some embodiments, the density is 100-225 spores or spore germlings / cm. 2 , 85-260 spores or spore germlings / cm 2 , 55-285 spores or spore germlings / cm 2 Those skilled in the art will recognize that the density of non-vascular plants per well of a given assay plate can be increased to saturation, when material can overlap without affecting the accuracy of other measurements. For example, a saturation density can be between 11,400 and 17,100 spores or spore germlings / cm. 2 , 8550-19950 spores or spore germinants / cm2 , 5700-22800 spores or spore germlings / cm 2 , or 285-28,500 spores or spore germlings / cm 2 Such other measurements may include, but are not limited to, spectrophotometric measurements of spore or spore germling suspensions, or fluorometric measurements of autofluorescence, fluorescent protein fluorescence, or fluorescent dye or probe fluorescence in spore or spore germling suspensions. The exposure of non-vascular plants to light may be varied during the assay. In some embodiments, non-vascular plants may be grown under continuous illumination. Alternatively, light exposure may be interrupted during the assay. Illumination may be provided at wavelengths between 300 nm and 900 nm (e.g., 400 nm and 700 nm). Illumination may be, for example, ultraviolet (UV) light, visible light, or infrared (IR) light.

[0118] The temperature at which non-vascular plants are grown in the assay can be, for example, below 15° C., below 16° C., below 17° C., below 18° C., below 19° C., below 20° C., below 21° C., below 22° C., below 23° C., below 24° C., below 25° C., below 26° C., below 27° C., below 28° C., below 29° C., or below 30° C. In some embodiments, the temperature is between 21° C. and 24° C.

[0119] The humidity at which non-vascular plants are grown in the assay may range, for example, from 40% to 80%, from 45 to 75%, or from 50% to 60%.

[0120] The duration of the assay in which non-vascular plants are grown can be, for example, less than 1 day, less than 2 days, less than 3 days, less than 4 days, less than 5 days, less than 6 days, less than 7 days, less than 8 days, less than 9 days, less than 10 days, less than 11 days, less than 12 days, less than 13 days, less than 14 days, less than 15 days, less than 16 days, less than 17 days, less than 18 days, less than 19 days, less than 20 days, less than 21 days, less than 22 days, less than 23 days, less than 24 days, less than 25 days, less than 26 days, less than 27 days, or less than 28 days. Alternatively, older plants can be used in the assay.

[0121] In an exemplary embodiment of the invention, plant spore germlings (eg, Marchantia spore germlings) are grown under continuous light at about 23° C. for about 5 days before measurements are taken.

[0122] During and / or upon completion of the assay, suitable comparisons can be made between test samples in which non-vascular plants are treated with various candidate compounds and control samples in which non-vascular plants are not treated with various candidate compounds. The control sample(s) can be negative control samples in which non-vascular plants are not mixed with a herbicide or plant growth regulator, and / or the control sample(s) can be positive control samples in which non-vascular plants are mixed with a herbicide or plant growth regulator (e.g., a herbicide or plant growth regulator with a known mode of action). These comparisons can be used to determine factors including, but not limited to, whether a given candidate compound or mixture of candidate compounds has herbicidal activity, the observed potency of any herbicidal activity, the phenotypic response of non-vascular plants in response to a given candidate compound found to exhibit herbicidal activity, and / or the predicted mode of action of a given candidate compound found to exhibit herbicidal activity. Similarly, these factors can be determined in relation to plant growth regulators. Any suitable means of making a comparison between a test sample, a negative control sample, and / or a positive control sample known in the art can be used. For example, the comparison can be made via visual comparison, microscopic imaging (e.g., fluorescent microscopy), etc. In one embodiment, the method screens for candidates with herbicidal activity, and the phenotypic response is the death of non-vascular plants after exposure to a candidate compound (i.e., the plant material dies after exposure to the candidate compound). In one embodiment, the death is determined as the death of the plant one week after exposure. In one embodiment, the death is determined as the death of the plant two weeks after exposure. In one embodiment, the death is determined as the death of the plant three weeks after exposure.

[0123] In one embodiment, the method screens for candidates with plant growth-regulating activity, and the phenotypic response is non-vascular plant growth following exposure to the candidate compound (i.e., the plant material grows following exposure to the candidate compound). In one embodiment, growth is determined as plant growth one week after exposure. In one embodiment, growth is determined as plant growth two weeks after exposure. In one embodiment, growth is determined as plant growth three weeks after exposure. Those skilled in the art will appreciate that there are numerous methods for determining plant growth, including, but not limited to, measurements of plant size (diameter), density, width, diameter, or height. More complex analyses of phenotypic responses can be performed using high-content screening, as described in more detail below.

[0124] In one embodiment, there is provided a method of screening candidate compounds for herbicidal or plant growth regulating activity, the method comprising: (i) contacting a series of different candidate compounds with a plurality of test samples; (ii) determining whether the test sample provides a phenotypic response to said series of different candidate compounds by comparing the phenotype with that of a control sample not contacted with the candidate compounds; The method is provided in which the test sample and the control sample comprise whole plants, spores, spore germlings, explants, protoplasts, or vegetative propagules from a non-vascular plant, and the phenotypic response is indicative of herbicidal or plant growth regulating activity. In a preferred embodiment, the non-vascular plant is a liverwort, most preferably a Marchantia.

[0125] In one embodiment, there is provided a method of screening candidate compounds for herbicidal or plant growth regulating activity, the method comprising: (i) contacting a series of different candidate compounds with a plurality of test samples; (ii) determining whether the test sample provides a phenotypic response to said series of different candidate compounds by comparing the phenotype with that of a control sample not contacted with the candidate compounds; Methods are provided in which the test and control samples comprise whole plants, spores, spore germlings, explants, protoplasts, or vegetative propagules from a bryophyte, and the phenotypic response is indicative of herbicidal or plant growth regulating activity.

[0126] In one embodiment, there is provided a method of screening candidate compounds for herbicidal or plant growth regulating activity, the method comprising: (i) contacting a series of different candidate compounds with a plurality of test samples; (ii) determining whether the test sample provides a phenotypic response to said series of different candidate compounds by comparing the phenotype with that of a control sample not contacted with the candidate compounds; Methods are provided wherein the test and control samples comprise whole plants, spores, spore germlings, explants, protoplasts, or vegetative propagules from Marchantia, and the phenotypic response is indicative of herbicidal or plant growth regulating activity.

[0127] Non-limiting methods are discussed below and also provided in the examples of this application.

[0128] High Content Screening In some embodiments, the methods of the invention use high content screening (HCS), which generally uses fluorescence or luminescence measurements of samples in a high-throughput format to quantitatively analyze various parameters.

[0129] Non-limiting examples of parameters that may be used in HCS according to the methods described herein include the length, width, shape, pigmentation, circularity, chlorophyll content, and number of cells per non-vascular plant (note that, as described above, "non-vascular plant" as used herein encompasses the entire non-vascular plant, its components, their spores, and their spore germlings). Any one or more of these parameters (optionally including other parameters) comprise the phenotypic response of the plant to the compound.

[0130] Imaging can be performed by a variety of techniques. Those skilled in the art will recognize that HCS is often performed using fully automated fluorescent imaging systems. In some embodiments of the present invention, a liquid handling robot is incorporated into the fully automated fluorescent imaging system. In other embodiments, assays can be manually set up prior to imaging using a fully automated fluorescent imaging system for high-throughput imaging. The fully automated fluorescent imaging system can include a high-throughput fluorescent microscope. Some embodiments of the present invention do not require the use of confocal microscopy.

[0131] For each non-vascular plant sample utilized in a given assay, several images may be generated. In some embodiments of the present invention, images may be generated by recording transmitted light. Additionally or alternatively, images may be generated by recording transmitted light. Images may be generated using only far-red photons and / or only cyan photons. A 2x objective may be used to generate a far-red or yellow image with a field of view covering an entire well of a 96-well microtiter plate. This image may be a far-red light fluorescence micrograph. This image may be a yellow fluorescence micrograph. A 4x objective may generate a far-red, cyan, yellow, or bright-field image with a field of view that precisely fits within the boundaries of a single well of a 96-well microtiter plate. A 10x objective may be used to generate a set of 1-9 far-red, 1-9 cyan, 1-9 yellow, and 1-9 bright-field images covering a small portion of the well (e.g., 1 / 32-1 / 3 of the bottom surface of the well). In some embodiments of the invention, an image is overlaid with an outline of an object created by another image. An image may be overlaid with an outline created by an image using the same or different photons. An image may have a field of view greater than, equal to, or smaller than the diameter of the well by using an objective lens with optical magnifications of 2x, 4x, 10x, 20x, or 40x. In some embodiments of the invention, an image analysis protocol is used to distinguish plants and subcellular objects, such as nuclei, from the background.

[0132] In some embodiments of the present invention, several images are generated at different points throughout the structure of a non-vascular plant. These images are called slices. Slices may be less than 5 μm, less than 10 μm, less than 20 μm, less than 30 μm, less than 40 μm, less than 50 μm, less than 60 μm, less than 70 μm, less than 80 μm, less than 90 μm, or less than 100 μm deep. One skilled in the art can determine the number of slices required to image the entire structure based on the thickness of the sample. Manual imaging may be used to enable one skilled in the art to develop automated protocols. Spatially and / or temporally related images may be combined to form a "stack" for display and / or analysis purposes. For example, in some embodiments of the present invention, a far-red image may be created that is a maximum intensity projection of a stack composed of five slices, each 20 μm deep. A stack may be created with 2, 3, 4, 5, 6, 7, 8, 9, 10, or more images. Stacks may also be created with fewer than 20, 30, 40, 50, 60, 70, 80, 90, or 100 images. The images may be stored digitally. Any or all of the images created may be used in subsequent analysis. Some embodiments of the present invention use a computer script to add metadata to the images. The metadata may include a barcode for the assay plate, date, image acquisition protocol, and / or image analysis protocol. The computer script may record whether the image is linked to other image(s).

[0133] Phenotypic "fingerprinting" of biological systems' responses to chemicals is widely used in the field of drug discovery (see, e.g., Reisen et al., Assay and Drug Development Technologies, 2015;13(7):415-427), and software tools to assist those skilled in the art are widely available (see, e.g., Omta et al., Assay and Drug Development Technologies, 2016;14(8):439-452).

[0134] A phenotypic fingerprint can be consistent with a known effect of a compound. For example, a compound that inhibits pigment synthesis can cause a contacted plant to produce less pigment and grow smaller. Thus, a fingerprint can be a quantitative representation of this expected plant response, as described by several phenotypic variables. Phenotypic fingerprints can also be unexpected. For example, a compound that inhibits photosynthesis can cause cell elongation and a shift in the cellular localization of chloroplasts. Thus, a fingerprint can also be a quantitative representation of a surprising plant response, as described by several phenotypic variables.

[0135] In some embodiments of the present invention, data are normalized by the median value of the negative control at the plate level to minimize plate-to-plate noise. In further embodiments, the distribution of variables is checked for normality and transformed as necessary. Data transformation may be recommended by the software used for analysis, and the software may also recommend a data transformation method and / or perform the transformation automatically. Non-limiting examples of data transformation methods that may be suitable for use in the present invention include square root, power of 2, power of 3, log, log2, log10, and reciprocal. Variables may then be scaled to equalize the weight of variables with different means in downstream analysis steps. In some embodiments of the present invention, scaling is performed at the plate level using the Z-score method. Screen-level scaling is also possible in this step.

[0136] There are no particular limitations regarding the statistical method used to analyze the data. Many software packages are suitable for use with the methods of the present invention, providing a data analysis pipeline in which each step can be customized by skilled artisans by changing the statistical methods and parameters used. In some embodiments of the present invention, correlated variables in the data of the negative control and screening compounds are reduced to factors by common factor analysis (e.g., distributed stochastic neighbor embedding, principal component analysis (generalized weighted least squares), principal component analysis (minimized weighted chi-square), principal component analysis (minimum residual), common factor analysis (principal axis), common factor analysis (maximum likelihood), or common factor analysis (weighted least squares)). In further embodiments, oblique (non-orthogonal) rotation may be used to reduce the number of factors. The number of factors to be retained may be automatically determined according to the Kaiser criterion, Elbow method, or Jolliffe criterion. Additionally or alternatively, the factors to be retained may be manually selected upon visual inspection of the scree plot.

[0137] Alternatively, one may select all or a subset of the raw variables that describe the phenotypic differences between plants treated with the candidate herbicide and the negative or positive control.

[0138] Using the retained factors, in some embodiments of the invention, compounds may be selected as "hits" according to their phenotypic Euclidean distance to the median of the negative control in multifactor space. Alternatively, all or a subset of the raw variables may be selected that describe the phenotypic difference between plants treated with the candidate herbicide and the negative or positive control. In another embodiment of the invention, a combination of raw variables and factors is retained for hit selection.

[0139] Those skilled in the art may select the level of significance at which a compound will be considered a "hit." In some embodiments, clustering of the "hits" with a positive control may be used to predict the mode of action of a potential herbicide. In some embodiments, clustering of the "hits" with a positive control may be used to predict the mode of action of a potential plant growth regulator. Clusters of "hits" and positive controls may be generated using Ward's agglomerative method, in which K-means are automatically selected and the distance between cluster centroids is calculated as Euclidean distance in a multifactor space. Alternative agglomerative methods include the McQuitty / Weighted Pair Group Method with Arithmetic Mean, Single Agglomeration, Complete Agglomeration, Centroid Agglomeration, or Median Agglomeration. Alternative distance calculations include Maximum Distance, Manhattan Distance, Canberra Distance, Minkowski Distance, or Cosine Distance. In some embodiments, if a "hit" falls outside the cluster associated with a positive control of a known mode of action, the hit may be visually inspected for the presence of atypical symptoms, and if atypical symptoms are observed, the hit may be predicted to have a novel mode of action. In some embodiments of the present invention, the "hit" cannot be predicted to have either a known or novel mode of action. In this scenario, the hit may be manually advanced to a dose-response experiment in which plants are treated with a range of concentrations of the "hit" ranging from 1 nM to 50,000 nM, and all resulting data points are processed according to the methods described herein. Exemplary concentrations for such dose-response experiments may range from 1 nM to 50,000 nM.

[0140] Artificial intelligence can be used for "hit" selection. In some embodiments of the present invention, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, or at least 80% of the data obtained by measuring the phenotypic response of plants to a negative control compound is used as a training set for an artificial intelligence algorithm. The algorithm can be a random forest algorithm. In some embodiments of the present invention, an artificial intelligence algorithm, such as a random forest algorithm, can be used to predict the mode of action. Alternatively, a neural network algorithm can be used to predict the mode of action. At least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, or at least 80% of the data obtained by measuring the phenotypic response of plants to a positive control compound can be used as a training set for the artificial intelligence algorithm used to predict the mode of action. Statistical tests can be used to determine the probability that a "hit" matches the phenotypic model generated by the artificial intelligence. In some embodiments of the present invention, this decision-making step can be automated. Additionally or alternatively, this decision-making step can be performed manually.

[0141] The present invention encompasses measuring morphological and / or physiological traits of non-vascular plants (e.g., test samples exposed to a candidate compound, a negative control, and / or a positive control) to generate a phenotype. The phenotypic response can be used to predict the mode of action. In some embodiments of the present invention, measurements may be recorded for fewer than 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 250, 500, or 1000 morphological and / or physiological traits per individual plant. Non-limiting examples of morphological and / or physiological traits that may be measured include plant length, plant width, plant shape, plant pigmentation, plant roundness, chlorophyll concentration, and cell number per plant.

[0142] In further embodiments of the present invention, measurements may be recorded for fewer than 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 250, 500, or 1000 morphological and / or physiological characteristics of the control sample. Non-limiting examples of morphological and / or physiological characteristics that may be measured for the control sample include length, width, shape, pigmentation, circularity, chlorophyll concentration, and cell number of non-vascular plants. The control sample may be a sample of the same non-vascular plant subjected to the same assay conditions as the test sample but without the addition of a test compound. In some embodiments of the present invention, DMSO may be added to the control sample instead of the test compound. In other embodiments, a different solvent may be added to the control sample. The added solvent may be the solvent added to the assay well containing the test compound.

[0143] Compounds selected using the methods of the present invention as potentially having herbicidal or plant growth regulating activity may be referred to herein as "hits." The difference between the phenotype of the non-vascular plant after the assay and the phenotype of the negative control non-vascular plant is referred to herein as the phenotypic response to the compound. Compounds may be selected as "hits" based on the magnitude of the phenotypic response of the non-vascular plant material or spore germinants to the compound. Additionally or alternatively, compounds may be selected as "hits" based on the nature of the phenotypic response of the non-vascular plant material or spore germinants to the compound.

[0144] In some embodiments of the present invention, the mode of action of a "hit" can be predicted. This can be achieved by generating a phenotype for a non-vascular plant when assayed with a compound having known herbicidal or plant growth-regulating activity. The phenotype of the plant or spore germinant assayed with the test compound can then be compared to the phenotype of the non-vascular plant assayed with a compound having known herbicidal activity (referred to herein as a "positive control"). Compounds having known herbicidal activity that can be used in the methods of the present invention as positive controls include commercially available herbicides used at concentrations known to cause specific symptoms of the mode of action. Examples of such compounds include clodinafop-propargyl, cyhalofop-butyl, diclofop-methyl, fenoxaprop-P-ethyl, fluazifop-P-butyl, haloxyfop-R-methyl, propaquizafop, quizalofop-P-ethyl, alloxydim, butroxydim, clethodim, cycloxydim, profoxydim, sethoxydim, tepraloxydin, tralkoxydim, pinoxaden, amidosulfuron, azimsulfuron, bensulfuron-methyl, chlorimuron-ethyl, chlorsulfuron, cinosulfuron, cyclosulfamuron, ethametsulfuron-methyl, ethoxysulfuron, flazasulfuron, flupyrsulfuron-methyl-Na, foramsulfuron, halosulfuron, ron-methyl, imazosulfuron, iodosulfuron, mesosulfuron, metsulfuron-methyl, nicosulfuron, oxasulfuron, primisulfuron-methyl, prosulfuron, pyrazosulfuron-ethyl, rimsulfuron, sulfometuron-methyl, sulfosulfuron, thifensulfuron-methyl, triasulfuron, tribenuron-methyl, trifloxysulfuron, triflusulfuron-methyl, tritosulfuron, imazapic, imazamethabenz-methyl, imazamox, imazapyr, imazaquin, imazethapyr, cloransulam-methyl, diclosulam, florasulam, flumetsulam, metosulam, penoxsulam, bispyribac-Na, pyribenzoxim, pyriftalid, pyrithiobac-Na, pyriminobac-methyl, flucarbazone-Na,Propoxycarbazone-Na, Benfluralin, Butralin, Dinitramine, Ethalfluralin, Oryzalin, Pendimethalin, Trifluralin, Amiprophos-methyl, Butamifos, Dithiopyr, Thiazopyr, Propyzamide = Pronamide, Tebutam, Chlorthal-dimethyl, Clomeprop, 2,4-D, 2,4-DB, 2,4-DP, MCPA, MCPB, Mecoprop, Chloramben, Dicamba, TBA, Clopyralid, Fluroxypyr, Picloram, Triclopyr, Quinclorac Carbonate, Quinmerac, Benazolin-ethyl, Amethy Atrazine, Cyanazine, Desmetrin, Dimethametryn, Prometon, Prometryn, Propazine, Simazine, Simetryn, Terbumeton, Terbuthylazine, Terbutryn, Trietazine, Hexazinone, Metamitron, Metribuzin, Amicarbazone, Bromacil, Lenacil, Terbacil, Chloridazone, Desmedipham, Phenmedipham, Bromofenoxime, Bromoxynil, Ioxynil, Bentazon, Pyridafol, Chlorbromuron, Chlorotoluron, Chloroxuron, Dimefron, Diuro , etidimuron, fenuron, fluometuron, isoproturon, isouron, linuron, methabenzthiazuron, metobromuron, metoxuron, monolinuron, nebron, siduron, tebuthiuron, propanil, pentanochlor, butyrate, cycloate, dimepiperate, EPTC, esprocarb, molinate, orbencarb, pebulate, prosulfocarb, benthiocarb, thiocarbazil, triallate, vernolate, bensulfide, benfuresate, ethofumesate, glyphosate, sulfosate, glufosinate-ammo nium, bilanaphos, amitrole, norflurazon, diflufenican, picolinafen, beflubutamid, fluridone, flurochloridone, flurtamone, clomazone, acifluorfen-Na, bifenox, clomethoxyfen, fluoroglycofen-ethyl, fomesafen, halosafen, lactofen, oxyfluorfen, fluazolate, pyraflufen-ethyl, cinidon-ethyl, flumioxazin, flumiclorac-pentyl, fluthiacet-methyl, thidiazimine,Oxadiargyl, azafenidin, carfentrazone-ethyl, sulfentrazone, pentoxazone, benzphendizone, butafenacil, pyraclonil, profluazole, flufenpyr-ethyl, acetochlor, alachlor, butachlor, dimethachlor, dimethenamid, metazachlor, metolachlor, petoxamide, pretilachlor, propachlor, propisochlor, thenylchlor, diphenamide, napropamide, naproanilide, flufenacet, mefenacet, fentrazamide, anilofos, cafenstrole, piperophos, DSMA, MSMA, asuram, naptalam, diflufenzopyr-Na, dichlobenil, chlorthiamid, isopropyl Examples of herbicides include oxaben, flupoxam, diquat, paraquat, chlorpropham, propham, carbetamide, dinitrophenol DNOC, dinoseb, dinoterb, flamprop-M-methyl / -isopropyl, quinclorac, TCA, dalapon, flupropanate, difenzoquat, mesotrione, sulcotrione, isoxachlorthor, isoxaflutole, benzofenap, pyrazolinate, pyrazoxyfen, benzobicyclon, bromobutide, (chloro)-flurenol, cinmethylin, cumyluron, dazomet, dymron, etobenzanide, fosamine, indanofan, metam, oxaziclomefone, oleic acid, pelargonic acid, and pyributicarb. Those skilled in the art may select any herbicide with a known mode of action for use as a positive control in this method. Similarly, compounds with known plant growth-regulating activity that can be used in the methods of the present invention as positive controls include commercially available plant growth regulators used at concentrations known to produce specific symptoms of their mode of action. One skilled in the art may select any plant growth regulator with a known mode of action for use as a positive control in the methods.

[0145] Target identification The methods of the invention may include analysis to identify targets (e.g., protein targets) of herbicides, e.g., targets of candidate compounds that have been screened and identified as having herbicidal activity, and / or targets of known herbicides whose mode of action is unknown.

[0146] In some embodiments, labeling may involve contacting a "hit" identified by the method of the present invention with a mutagenized non-vascular plant. DNA may then be extracted from the plant that survives the contact. Mutagenesis methods are standard in the art. The mutagen may be, for example, radiation. In some embodiments, the mutagen is selected from the group consisting of ultraviolet (UV) light, X-rays, gamma rays, and neutrons. In further embodiments, the mutagen may be UV light, which may be UV-A, UV-B, or UV-C light. Additionally or alternatively, mutagenesis may be performed using a chemical agent. Non-limiting examples include alkylating agents such as ethyl methanesulfonate (EMS). In some embodiments, dimethyl sulfate, sodium azide, or methylnitronitrosoguanidine (MNNG) may be used to introduce mutations into non-vascular plants. The chemical agent may also be a deaminating or intercalating agent. In further embodiments of the present invention, the mutagen is a transposable element.

[0147] DNA extraction methods are standard in the art. DNA may be extracted using phenol, chloroform, and isoamyl alcohol. Other well-known DNA extraction techniques include enzymatic methods, silica (spin) column-based methods, anionic resins, magnetic bead methods, and CaCl density gradient DNA extraction. Cetyltrimethylammonium bromide (CTAB) and 2-β-mercaptoethanol are commonly used for plant DNA extraction, where plant tissues contain high levels of polysaccharides, polyphenols, and / or other secondary metabolites (see, e.g., Clark, Plant molecular biology—a laboratory manual, 1997; Springer: 305-328). DNA may be extracted from mutant non-vascular plant whole plants, or parts of mutant non-vascular plant whole plants, or mutant spores, spore germlings, explants, protoplasts, or vegetative propagules of mutant non-vascular plants.

[0148] In some embodiments of the present invention, a genomic DNA library is prepared. Then, in a further embodiment, the library is sequenced. Any high-throughput sequencing technology capable of sequencing the entire plant genome can be used, including clonal amplification-based technology, semiconductor-based technology, and single molecule real-time (SMRT) sequencing (for a recent review of potentially suitable commercial platforms, see Reuter et al., Molecular Cell, 2015;58:586-597). The raw sequencing reads can then be "trimmed" to remove poor-quality sequences and / or artifacts from the sequencing process, such as primers and sequencing adapters. For example, any suitable known software program, such as Trimmomatic, can be used. Trimmomatic trims Illumina sequencing adapters and portions of the reads associated with poor sequencing quality. Other known processes for performing quality trimming can also be used.

[0149] In some embodiments of the present invention, the read file may be interleaved. Interleaving may be performed using any suitable parsing script. For example, when paired reads are obtained by a sequencing system, a parsing script may be used to recombine two mate pairs of all paired reads into a single file.

[0150] Some embodiments may include a normalization step. The normalization process may be performed by normalizing by 31-mer using a script that calls any suitable known software program, such as Khmer. In this example, the normalization program uses a predefined value of k to examine the distribution of k-mers in all reads, and discards a corresponding amount of reads that contain the most frequent k-mer because they only provide redundant information. This step may be performed to make the alignment process more memory-efficient.

[0151] The normalized read files can then be deinterleaved or decoupled using any suitable parsing script that separates the two mate pairs of all paired reads in the two files. This step is the opposite of the interleaving step. For each paired read, there are two mates identified as belonging to the same paired read. These can be written to the same file (i.e., interleaved) or written to separate files (deinterleaved). The process from one to the other simply involves parsing according to the tagging string that identifies the mates as belonging to the same paired read. This tagging comes from the file generated by the sequencing platform and may look like, for example, XYZ / 1 for mate 1 and XYZ / 2 for mate 2. The software identifies them by text matching and writes the corresponding DNA sequences either to the same file or to two separate files.

[0152] The sequenced genome can then be aligned with the reference genome of the plant.The reference DNA sequence can be the known reference sequence of the plant of the genus.The reference DNA sequence is published in a publicly available database.The whole genome sequence is publicly available for many non-vascular plants, including, for example, liverworts such as Marchantia (reference sequences for nuclear genome and organelle genome are publicly available for Marchantia).

[0153] In some embodiments of the present invention, the reference DNA sequence may be aligned to an additional comparison sequence. The comparison sequence may be from an independent plant of the same genus that does not survive contact with the compound. In some embodiments, the method of the present invention may include obtaining a set of mismatches between the DNA sequence of the mutant plant and the reference DNA sequence. A second set of mismatches may then be obtained between the reference DNA sequence and the comparison sequence. Further embodiments of the present invention may then include filtering the first set of mismatches with respect to the second set of mismatches to identify a subset of mismatches that are unique to the first set of mismatches. The subset of mismatches may be candidate mutations that cause herbicide resistance or plant growth regulation. This may help identify targets for novel herbicides or plant growth regulators identified by the method of the present invention.

[0154] A method for identifying causative mutations giving rise to a phenotype of interest in a test sample according to an embodiment of the present invention is provided in FIG.

[0155] In one embodiment, targeting to identify mutations associated with a phenotype of interest in non-vascular plants is carried out according to the following method. (a) aligning a DNA sequence of a test sample to a reference DNA sequence and identifying a first set of sequence mismatches between the two sequences; (b) aligning the DNA sequence of at least one comparison sample to the reference DNA sequence and identifying a second set of sequence mismatches between the two sequences; (c) filtering the first set of mismatches with respect to the second set of mismatches to identify a subset of mismatches that are common to the first and second sets of mismatches, where the subset of mismatches are candidate mutations for the causal mutation; the test sample and comparison sample(s) are from independent non-vascular plants that exhibit the phenotype of interest, and the independent non-vascular plants are of the same genus; The reference DNA sequence is a known reference sequence for a non-vascular plant of that genus.

[0156] In one embodiment, targeting to identify mutations associated with a phenotype of interest in non-vascular plants is carried out according to the following method. (a) aligning a DNA sequence of a test sample to a reference DNA sequence and identifying a first set of sequence mismatches between the two sequences; (b) aligning the DNA sequence of at least one comparison sample to the reference DNA sequence and identifying a second set of sequence mismatches between the two sequences; (c) filtering the first set of mismatches with respect to the second set of mismatches to identify a subset of mismatches that are unique to the first set of mismatches, where the subset of mismatches are candidate mutations for the causative mutation; the test sample is from a non-vascular plant that exhibits the phenotype of interest, and the comparison sample is from an independent non-vascular plant of the same genus that does not exhibit the phenotype of interest; The reference DNA sequence is a known reference sequence for a non-vascular plant of that genus.

[0157] In one embodiment, the test sample and / or at least one comparative sample is biological material from a non-vascular land plant, and the non-vascular plant is a bryophyte. In one embodiment, the test sample and / or at least one comparative sample is biological material from a bryophyte selected from the group consisting of mosses, liverworts, and hornworts. In one embodiment, the test sample and / or at least one comparative sample is biological material from a phyllophyte, a simple phyllophyte, or a complex phyllophyte. In one embodiment, the test sample and / or at least one comparative sample is biological material from a Marchantia plant.

[0158] In some embodiments of the present invention, the DNA sequence of an additional comparison sample may be aligned with the reference DNA sequence to identify a third set of sequence mismatches between the two sequences. The first set of mismatches may then be filtered with respect to the third set of mismatches to identify a subset of mismatches that are common to the first and third sets of mismatches. The two subsets of mismatches may then be candidate mutations for herbicide resistance or plant growth regulation. The additional comparison sample may be from an independent plant of the same genus that does not survive contact with the compound. Some embodiments of the present invention include aligning the DNA sequences of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or more comparison samples with the reference DNA sequence to identify a set of mismatches between the two sequences, which may then be used to compare the set of mismatches between the DNA sequence of the mutant plant and the reference DNA sequence to identify specific mismatches for candidate mutations for herbicide resistance or plant growth regulation. Many software packages are available to assist one skilled in the art in aligning DNA sequences and filtering out sets of mismatches.

[0159] In some embodiments of the present invention, regions of the genome where more reads than expected align can be excluded. That is, sequencing depth is defined by the number of sequencing reads from a sample that align to a region of a reference DNA sequence. When sequencing a sample's DNA sequence, a user may select how many times to sequence the same portion of the DNA sequence. This selection defines the expected sequencing depth. For example, aiming for a sequencing depth of 1 requires a sampling system that sequences the entire DNA sequence of the sample at once. If the expected sequencing depth is 20, the sampling system will sequence the sample's DNA 20 times.

[0160] Thus, for example, if the observed sequencing depth at a defined position is 10, 10 sequencing reads will be aligned to the region of the reference DNA sequence that includes this position. If the expected sequencing depth is 1, this would suggest that 9 out of 10 reads are incorrectly aligned to this region of the DNA sequence. Therefore, the software will consider any mismatch in the region of the reference DNA sequence where the observed sequencing depth is greater than the expected sequencing depth as a possible result of having an incorrectly aligned read, and therefore remove it from the set of mismatch data. In other words, the mismatch is considered an alignment artifact, not a candidate mutation, and is therefore discarded or removed from the data set. In other words, to determine the first set or additional set of mismatched DNA sequence data, the described method and software can reject at least one region of the sample DNA sequence that aligns with the reference DNA sequence based on the actual reading depth exceeding the expected reading depth. Many suitable software programs can be used to implement this function.

[0161] Furthermore, the frequency of mismatch occurrence in a group of reads that align at a certain position in genome can be used to filter out alignment artifacts.For example, if a mutant is a diploid species, the expected frequency of mismatch in the mutant genome is 50%, while in a monoploid species it is 100%.If the observed mismatch frequency does not match the expected mismatch frequency for the defined species, the related read will be discarded from the data set.Once again, this applies to both the data set of sample and comparison DNA sequence.

[0162] In some embodiments of the present invention, the mutagenized non-vascular plant is an M1 mutant. "M1" refers to the first generation of the mutant, meaning that the M1 mutant is not a descendant of a mutagenized non-vascular plant. The mutagenized non-vascular plant may contain mutations that do not occur in nature. Some embodiments of the present invention do not include the steps of segregation analysis, complex segregation analysis, or bulk segregation analysis.

[0163] Additionally or alternatively, target identification can be achieved by any one of enzymatic assays, chlorophyll fluorescence kinetic assays, photosynthetic oxygen evolution assays, electrolyte leakage assays, radiometric assays, spectrophotometric assays, fluorometric assays, absorbance assays, colorimetric assays, mass spectrometry, mitotic index analysis, quantitative PCR analysis, transcriptome profiling, proteomics profiling, genome-wide analysis, quantitative trait locus analysis, in silico docking studies, and chemical structure analysis (partially reviewed in Dayan 2015, "Biochemical markers and enzyme assays for herbicide mode of action and resistance studies"). Those skilled in the art will be familiar with all of the aforementioned techniques for downstream analysis.

[0164] Inclusion by cross-reference This application claims priority to Australian Provisional Patent Application No. 2019 / 904145, filed on 4 November 2019, the entire contents of which are incorporated herein by cross-reference. [Example]

[0165] The present invention will now be described with reference to the following specific examples, which should not be construed as limiting in any way.

[0166] Example 1: Marchantia polymorpha spore germlings as a screening system for herbicidal activity Preparation of assay plates Front-end assays were set up manually or by liquid handling robots. Manual assay plate preparation began with the addition of 1 μL of a 10% DMSO solution to the bottom of a well in a 96-well microtiter plate, followed by the addition of 99 μL of cell suspension in liquid nutrient medium. Automated assay plate preparation began with the addition of 7 μL of a 1.4% DMSO solution to the bottom of a well in a 96-well microtiter plate, followed by the addition of 93 μL of cell suspension in liquid nutrient medium. Typically, only one screening compound was tested per well, and only one well was used to test a screening compound.

[0167] To maximize the statistical significance of the assay while avoiding overlap of spore germinants, 50–70 Marchantia polymorpha spores were placed in each well of a 96-well plate (Figure 1).

[0168] growth conditions The assay plates were placed in a light and temperature controlled cabinet. Plants were grown under continuous light for 5 days. The temperature was set at 23°C. Plants were grown in Johnson medium.

[0169] Imaging Microtiter plates containing 1- to 5-day-old Marchantia polymorpha germlings grown in the presence of either a negative control (DMSO), a positive control (a commercial herbicide), or a screening compound were placed in a high-throughput fluorescence microscope, the InCell Analyzer 2500 (GE Healthcare). Several images were generated (Figures 2 and 5). These images included images recorded by the camera chip using only far-red photons (far-red image), only cyan photons (cyan image), only yellow photons (yellow image), and transmitted light (bright-field image). A 2x objective lens was used to generate far-red images with a field of view covering the entire well. Here, the far-red images were 2D fluorescence micrographs. A 4x objective lens was used to generate far-red images with a field of view that precisely fit within the boundaries of the well. Again, the far-red images were 2D fluorescence micrographs. A 10x objective was used to generate a set of four far-red, four cyan, four yellow, and four bright-field images covering a small portion (one-eighth) of the well. The far-red images were maximum intensity projections of a five-slice stack, each 20 µm deep. The cyan and yellow images were maximum intensity projections of a four-slice stack, each 20 µm deep. The bright-field image was a single 20 µm deep slice. Any or all four channel images may be used for subsequent image analysis. Images may have fields of view larger, equal to, or smaller than the diameter of the well by using objectives with optical magnifications of 2x, 4x, 10x, 20x, or 40x.

[0170] Consideration Marchantia spores, or any germinant-forming plant spores, have not been used as model organisms for herbicide discovery to date. The choice of Marchantia polymorpha as a model organism for herbicide discovery was not obvious because Marchantia polymorpha, like common liverworts, is known to be insensitive to the major herbicides glyphosate and glufosinate.

[0171] In order to grow large numbers of plants suitable for high-content phenotypic characterization, the developmental stage of Marchantia spore germlings had to be fine-tuned.

[0172] -Influence of developmental stage Marchantia spores were grown for 5 days until they reached a developmental stage in which a diversity of cell types and tissues representative of the whole plant was formed. Five-day-old spore germlings possessed rhizoids, plant photosynthetic cells, and nascent meristems. Furthermore, 5-day-old spore germlings exhibited a size and shape more convenient for microscopic examination than later developmental stages.

[0173] Example 2: Identifying herbicide hits and predicting mode of action using MoA Galaxy background Plants respond in a variety of ways to herbicide treatments, ranging from barely detectable physiological changes, minor lesions, to plant death. The magnitude of the response correlates with the efficacy of the chemical applied to the plant. The nature of the response varies for chemicals that act via different modes of action.

[0174] Quantifying the magnitude of the phenotypic response of plants treated with a screening compound can aid in the selection of hits based on herbicide efficacy. The nature of symptoms exhibited by plants treated with a screening compound, independent of the intensity of the response, can be used to select hits. Collectively, the magnitude and nature of a plant's response to a screening compound are indicative not only of herbicide efficacy but may also be indicative of the herbicide's mode of action. Phenotypic fingerprinting of biological system responses to chemicals has been used in drug discovery (linking phenotype and mode of action via high-content fingerprint screening), and software tools are widely available to assist experimenters. This approach to chemical screening, called high-content screening, relies on simultaneously recording multiple phenotypic descriptors, such as length, area, color, and shape.

[0175] However, translating this approach to herbicide discovery presents challenges because currently used in vivo models are hindered by their morphological complexity. 3D seedlings are even more complex to image than 2D cell cultures, especially with sufficient throughput to screen large chemical libraries. We developed a high-content screening platform that takes advantage of the adaptability of Marchantia spores to be grown in near-2D conditions (and transformed with fluorescent cell markers).

[0176] method -Image analysis Images were processed individually using software developed by the software developer. Image analysis protocols were designed to distinguish plant and subcellular objects, such as nuclei, from the background (Figures 3 and 20).

[0177] When multiple images were generated, segmented objects could be linked so that any object segmented using one image could be the parent of numerous objects segmented using another image. Next, 10–50 measurements describing the plant's morphological and physiological phenotype were extracted. A non-exhaustive list of measurements included, for example, "plant length," "plant width," "plant circularity," "chlorophyll fluorescence intensity," and "cell number per plant." Measurements were finally recorded in a .csv or .txt file for each plant in each well imaged.

[0178] -Data formatting The output files were reformatted for downstream analysis suitability using a custom parsing script. The script automatically added relevant metadata, such as the barcode of the assay plate being analyzed and / or the date and protocol of image acquisition and analysis. Furthermore, the script for the high-resolution analysis output files (4x and 10x datasets) introduced default values ​​± error factors for variables that take on values ​​only when a cyan object, a far-red object, or both a cyan object and a far-red object are defined, in cases where the parent brightfield object was not linked to a cyan object, a far-red object, or both a cyan object and a far-red object, respectively.

[0179] -Data creation Data analysis was performed using HC StratoMineR software developed by CoreLifeAnalytics. This software provided a data analysis pipeline where each step could be customized by changing the statistical methods and parameters used. The steps, methods, and parameters used in this example are provided below.

[0180] Data were normalized at the plate level by the median value of the negative (DMSO) control to minimize interplate noise. The distribution of variables was checked for normality and transformed, if necessary, according to the software's automated recommendations. Data transformation methods could also be manually selected from a list of square root, power of 2, power of 3, log, log2, log10, and reciprocal. Variables were then scaled to equalize the weight of variables with different means in downstream analysis steps. Scaling was performed at the plate level using the Z-score method (Figure 5). If the number of data points per plate was too low to accurately perform downstream analysis steps, screen-level scaling was also possible in this step.

[0181] Correlated variables in the negative control and screening compound data were reduced to factors by common factor analysis using 200 t-SNE iterations, t-SNE perplexity set to 30, oblique rotation, and factor scoring methods such as ten Berge. The number of factors retained can be determined automatically according to the Kaiser, Elbow, or Jolliffe criteria, or manually selected upon visual inspection of the scree plot (Figure 6).

[0182] -Hit selection -Unsupervised hit selection Screening compounds were selected as hits according to their phenotypic Euclidean distance to the median of the negative control in the multifactor space defined by the previously retained factors. A p-value of 0.0001 was chosen as the significance threshold above which the screening compounds were considered significantly different from the median of the negative control (Figure 7).

[0183] - Artificial Intelligence Supervised Hit Selection The screening compounds were selected as hits according to their differences from the negative control phenotype model (Figure 8). The negative and positive control phenotype models were generated by a random forest algorithm with 128 trees using 80% of the corresponding dataset for training and the remaining 20% ​​for testing the generated models (Figure 9).

[0184] -Prediction of known or unknown modes of action -Unsupervised mode of action prediction The mode of action of unsupervised selected hits was predicted by clustering the hits with positive controls. The positive controls were commercial herbicides used at concentrations known to cause specific mode-of-action symptoms. The concentrations of commercial herbicides known to cause specific mode-of-action symptoms were experimentally determined by visual inspection of dose-response experiments in which plants were exposed to a range of herbicide concentrations.

[0185] Clusters of hits and positive controls were generated using the Ward agglomerative method, where K-means was automatically selected and distances between cluster centroids were calculated as Euclidean distances in multifactor space with a significance threshold of p-value = 0.0001 applied (Figure 10).

[0186] On average, automated classification of hits into clusters was 86% concordant with manual classification of hits into phenotypic similarity groups. If a hit fell outside of a cluster associated with a positive control of a known mode of action, the hit was visually inspected for the presence of unique symptoms, and if unique symptoms were observed, the hit was predicted to have a novel mode of action.

[0187] If a hit could not be predicted to have either a known or novel mode of action, the hit was manually advanced to a dose-response experiment in which plants were treated with a range of concentrations of the hit, ranging from 1 nM to 50,000 nM, and all resulting data points were processed according to the methods already described under "Image analysis."

[0188] -AI supervised mode of action prediction A statistical test was performed to determine the probability that the hit matched a phenotype model (one of the positive controls). If the probability was higher than an arbitrarily defined threshold depending on the number of positive control phenotype models, the hit was predicted to have a known mode of action, and this mode of action was predicted to be the mode of action of the herbicide of the phenotype model with which the hit was most strongly associated. This decision-making step was currently performed manually, but could be automated by a custom parsing script.

[0189] If a hit fell below an arbitrarily defined significance threshold for all positive control phenotype models, the hit was visually inspected for the presence of unique symptoms, and if unique symptoms were observed, the hit was predicted to have a novel mode of action.

[0190] If a hit could not be predicted to have either a known or novel mode of action, the hit was carried forward into a dose-response experiment in which plants were treated with a range of hit concentrations from 1 to 50,000 nM, and all resulting data points were processed according to the methods described under "Image analysis."

[0191] Consideration High-content screening has not currently been applied to herbicide discovery screening. The current state-of-the-art techniques for high-content analysis applied to herbicide discovery and simultaneous mode-of-action prediction are limited to a few modes of action (automated quantitative image analysis tools for identifying microtubule patterns in plants). Furthermore, these methods require higher resolution (confocal microscopy) and consequently rely on plant explants or parts rather than dwarf plants or whole-plant screening systems.

[0192] In contrast, the present method uses dwarf plants because it is applicable to more modes of action and does not rely on confocal microscopy. As a result, the method of the present invention has a higher throughput and a broader scope.

[0193] Due to the lack of availability of screening systems using dwarf plants or whole plants, it was not obvious that high-content screening approaches could be applied to herbicide discovery efforts.

[0194] Example 3: Identification of mutants resistant to herbicide hits background Knowledge of the target provides information on the mode of action, toxicity, resistance breaking, and further screening or lead optimization efforts. Mutations in genes encoding protein targets can confer resistance to herbicides. Therefore, reverse identification of target genes was attempted by the inventors of the present invention.

[0195] Mutagenized microspores were a convenient system for screening mutations that confer herbicide resistance because of their small size and suitability for simple irradiation mutagenesis methods. Screening for herbicide resistance after spore mutagenesis has not been previously reported in Marchantia.

[0196] method The lethal concentration of the herbicide hit was determined in terms of the mutagenic concentration. 20,000 Marchantia spores were placed in a 90 mm Petri dish containing 25 mL of Johnson medium containing 1.4% agar and supplemented with a range of herbicide hit concentrations, ranging from 1 to 50,000 nM. The lethal concentration was defined as the minimum concentration of herbicide hit sufficient to kill 100% of wild-type Marchantia plants (Figure 11).

[0197] If a lethal concentration is not observed, the highest concentration is used instead of the lethal concentration, provided that the concentration causes the plant to exhibit a phenotype different from that of an untreated plant. For example, this alternative phenotype could be a strong reduction in growth (Figure 12), a significant change in plant shape, or a change in plant pigmentation.

[0198] Mutagenized populations of Marchantia spores were generated using either physical or chemical mutagens at doses that killed an arbitrarily defined proportion of the mutagenized population of spores (Figure 13). The arbitrarily defined proportion of the mutagenized population of spores that defined the experimental mutagen dose may be 50%, as is standard in the art, or may be higher or lower, depending on the phenotype of wild-type plants treated with the herbicide hit at the mutagen screening concentration.

[0199] A collection of over 400,000 mutagenized spores was spread onto twenty 90 mm Petri dishes containing 25 mL of Johnson medium with 1.4% agar and supplemented with a 10-fold higher than lethal concentration of the herbicide hit. Alternatively, over 400,000 wild-type spores were spread onto twenty 90 mm Petri dishes containing 25 mL of Johnson medium with 1.4% agar and supplemented with a 10-fold higher than lethal concentration of the herbicide hit; the wild-type spores were then mutagenized.

[0200] One example of a mutagenesis method is UV-B mutagenesis. 400,000 spores were spread onto 20 90 mm Petri dishes containing 25 mL of Johnson's medium containing 1.4% agar and supplemented with a 10-fold higher than lethal concentration of herbicide. The Petri dishes were then inserted upside down into a UV-B transilluminator with the lid removed, with the spores directly facing the UV-B light source. UV-B light was applied to the spores for the duration required to achieve the desired mutagenic dose. The Petri dishes were then closed and wrapped in aluminum foil to exclude light, and the Petri dishes were placed in an incubator at 23°C overnight.

[0201] Mutagenized spores in Petri dishes containing the desired concentration of herbicide hit were placed in an incubator with constant light, lux, and 23°C, and plants were grown for 14 days. Surviving (Figure 14) or otherwise untreated-appearing plants were transferred to new Petri dishes in the absence of the herbicide hit and grown for an additional 14 days. Herbicide resistance was verified by transferring pieces of grown plants to fresh Johnson medium containing 1.4% agar and supplemented with a 10-fold higher than lethal concentration.

[0202] If the resistance phenotype was verified, genomic DNA was extracted from any part of the mutant plant or from the whole mutant plant using any DNA extraction method (including but not limited to phenol-chloroform-IAA extraction). Genomic DNA libraries were prepared and sequenced using any Illumina Next-Generation Sequencing system more recent than the HiSeq 2000.

[0203] Example 4: Identification of a causative mutation in a plant enhancer protein gene RHO GTPase that impairs fertility (Case B) In this example, the methodologies of the present invention are used to identify causal mutations associated with rhizoid / epidermal phenotypes in Marchantia polymorpha, as described in detail below. These methods are equally applicable to determining phenotypes associated with herbicide tolerance or plant growth regulation.

[0204] Several independent mutant strains were generated by irradiating Marchantia polymorpha spores with ultraviolet B. The mutant strains fell into two phenotypic groups: some had straight rhizoids (Fig. 16A) and an intact cuticle (Fig. 17A), and some had wavy rhizoids (Fig. 16B) and an elongated cuticle (Fig. 17B).

[0205] We aimed to identify the causative mutation in the UV4.32 mutant, which has wavy rhizoids and an elongated epidermis. Using whole plants as samples, we extracted DNA from the UV4.32 mutant, which has wavy rhizoids and an elongated epidermis, using standard DNA phenolchlorophorm-IAA extraction. The genome of UV4.32 and seven independent mutants with straight rhizoids and an intact epidermis were sequenced using Illumina's HiSeq-2000 platform technology.

[0206] Raw data were quality trimmed using Trimmomatic-0.32 and normalized using Khmer0.7.1 with a k-mer size of 31. The resulting reads were aligned to a reference genome using bowtie2-2.1.0 set to --very-sensitive-local mode. The reference genome used is the manuscript Marchantia polymorpha genome assembly publicly available in the NCBI Whole Genome Shotgun (WGS) database.

[0207] Alignments were sorted by position, and mismatches within reads with a q quality greater than 35 were extracted using the functions sort and mpileup from bio-samtools-2.0.5. Mismatches in regions with greater than 100x coverage were filtered out using the varFilter function from bcftools in the samtools-0.1.9 package, as they were likely caused by incorrect alignments. Mismatches were then retained only if they were supported by more than seven reads and if they appeared sufficiently homozygous based on a negative FQ value or an AF1 value greater than 0.5001.

[0208] In total, 143,292 mismatches were identified in UV4.32 before filtering, and the number of mismatches specific to UV4.32 decreased as the number of UV mutant lines with straight rhizoids and intact epidermis used for filtering decreased (Figure 18A).

[0209] Ultimately, with all filtered strains sequenced, the number of candidate mismatches was reduced to 12,000 mismatches, or a reduction of more than 90% (Figure 18B), demonstrating that the filtering step, which subtracts the set of mismatches in the test sample by the set of mismatches in the comparison sample predicted not to harbor the causative mutation, increased the stringency of candidate mismatch identification prior to the standard filtering step.

[0210] Subsequent filtering steps were performed to filter mismatches that did not match the UV signature, to filter mismatches that were outside the gene coding sequence, and to filter nonsynonymous mismatches. These three filtering steps further reduced the number of candidate mismatches to 10 mutations that matched the expected UV mutation signature (Figure 18), were predicted to be in the coding sequence of the gene (Figure 18), and were predicted to alter the amino acid sequence of the corresponding protein (Table 1). [Table 1]

[0211] Among the 10 mutations, the most severe mutation is a two-base pair deletion that results in a premature stop codon in MpREN (Table 1). Ren mutants are known to exhibit the same phenotype as UV4.32 (Honkanen et al., 2016), suggesting that the subsequent filtering step was sufficiently conservative.

[0212] Taken together, this demonstrates that aspects of our pipeline, which are based on subtracting the set of mismatches in a test sample by the set of mismatches in a comparison sample that are predicted not to harbor the causative mutation, enable the identification of a small number of mutations, including the causative mutation, without the need to outcross mutant strains.

[0213] Example 5: Discovery of a mutation in the acetolactate synthase gene causing chlorsulfuron resistance (Case A) We irradiated Marchantia polymorpha spores with ultraviolet B radiation and identified seven independent mutant strains resistant to the herbicide chlorsulfuron. Chlorsulfuron resistance was determined by surviving Marchantia polymorpha plants two weeks after exposure to a lethal dose of chlorsulfuron (0.1 ppm, i.e., a dose sufficient to kill 100% of wild-type plants).

[0214] Because all mutant plants shared the same phenotype, chlorsulfuron resistance, we hypothesized that each had the same causative mutation. We compared the chlorsulfuron-resistant mutants to the reference genome and individually identified over 100,000 mismatches, first filtering out mismatches that were also present in the M0 wild-type genome (Figure 19, two left-most scattered boxes).

[0215] To test the efficiency of the non-allelic aspect of our pipeline, we applied it to combinations of 4, 5, 6, and all 7 chlorsulfuron mutants. The more allele subtraction lines used, the more efficient the pipeline becomes. Indeed, using all 7 chlorsulfuron-resistant strains, we reduced the number of mismatches from nearly 100,000 to 11 candidate mutations that matched the expected mutational signature and were located in the coding sequence of the gene (Figure 19).

[0216] Of the 11 candidate mutations common to all seven chlorsulfuron-resistant mutants but absent in the wild type, five cause changes in the amino acid sequence of the encoded protein (Table 2). Of these five candidate mutations, only one is in a gene with a predicted function. Indeed, this very mutation in the acetolactate synthase gene is known to cause chlorsulfuron resistance in other plant models. [Table 2]

[0217] Example 6: Discovery of mutations in the acetolactate synthase gene causing chlorsulfuron resistance (cases AB) Both approaches were combined to improve the power of the pipeline illustrated in Examples 1 and 2. In this embodiment of the pipeline, causal mutations are sought in the set of mismatches that are common to allelic variants and absent from the wild type and non-allelic variants.

[0218] Using the three chlorsulfuron-susceptible mutagenized strains, we filtered out four of the 11 chlorsulfuron-resistant specific mismatches previously identified as matching the expected mutational signature and located in the coding sequence of the gene, ultimately leaving only four candidate mutations predicted to cause changes in the amino acid sequence of the protein (Table 3 ).

[0219] This represents a 20-30% increase in pipeline force compared to the pipeline exemplified solely in Example 2. Because the pipeline force of Examples 1 and 2 increases with the number of allelic and non-allelic subtraction lines, respectively, we predict that the pipeline force exemplified in this example will increase further when more allelic and non-allelic subtraction lines are used. [Table 3]

Claims

1. 1. A method of screening candidate compounds for herbicidal or plant growth regulating activity, said method comprising: (i) contacting a series of different candidate compounds with a plurality of test samples from a non-vascular plant; (ii) determining whether the test sample provides a phenotypic response to the series of different candidate compounds by comparing the phenotype with that of a control sample from a non-vascular plant that has not been contacted with a candidate compound; The method, wherein the test sample and the control sample comprise whole plants, spores, sporelings, explants, protoplasts, or vegetative propagules, and the phenotypic response is indicative of the herbicidal activity or the plant growth regulating activity.

2. 10. The method of claim 1, wherein the candidate compound is a candidate compound for herbicidal activity.

3. 3. The method of claim 1 or 2, wherein the non-vascular plant is a moss, a hornblende, or a liverwort.

4. The method of any one of claims 1 to 3, wherein the test sample and the control sample are spore germlings.

5. 5. The method of claim 4, wherein the test spore germlings and the control spore germlings are derived from spores of the same species of non-vascular plant.

6. 6. The method of claim 4 or 5, wherein the test spore germlings are moss spore germlings, liverwort spore germlings, hornwort spore germlings, or any combination thereof.

7. The method of any one of claims 1 to 6, wherein each member of the series of different candidate compounds is contacted with a different test sample.

8. The method of any one of claims 1 to 7, wherein multiple members of the set of different candidate compounds are contacted with a single test sample.

9. The method of any one of claims 1 to 8, wherein the test sample and control sample are thalloid bryophyte spore germlings, simple thalloid bryophyte spore germlings, complex thalloid bryophyte spore germlings, or any combination thereof.

10. The test sample and the control sample are selected from the group consisting of Marchantia alpestris spore germinants, Marchantia aquatica spore germinants, Marchantia berteroana spore germinants, Marchantia carrii spore germinants, Marchantia chenopoda spore germinants, Marchantia devilis spore germinants, Marchantia domingenis spore germinants, Marchantia emarginata spore germinants, Marchantia foliacia spore germinants, Marchantia grossibarba spore germinants, Marchantia inflexa spore germinants, Marchantia linearis spore germinated bodies, Marchantia macropora spore germinated bodies, Marchantia novoguineensis spore germinated bodies, Marchantia paleacea spore germinated bodies, Marchantia palmata spore germinated bodies, Marchantia papillate spore germinated bodies, Marchantia pappena spore germinated bodies, Marchantia polymorpha spore germinated bodies, Marchantia rubribarba spore germinated bodies, Marchantia solomonensis spore germinated bodies, Marchantia streimannii spore germinated bodies, Marchantia 10. The method of any one of claims 1 to 9, wherein the selected fungus is selected from the group consisting of Marchantia subgeminata spore germlings, Marchantia vitiensis spore germlings, Marchantia wallisii, Marchantia nepalensis, and any combination thereof.

11. 11. The method of any one of claims 1 to 10, wherein a plurality of test spore germlings are provided in a series of different wells, each well containing 400-800 spore germlings / mL, 300-900 spore germlings / mL, or 200-1000 spore germlings / mL.

12. The method of any one of claims 1 to 11, wherein the test sample and / or the control sample are engineered to express a fluorescent molecule.

13. The method of any one of claims 1 to 10, wherein the control sample is a positive control.

14. 14. The method of claim 13, wherein the positive control is contacted with a known herbicide or plant growth regulator.

15. The method of any one of claims 1 to 10, wherein the control sample is a negative control.

16. 15. The method of claim 14, wherein the negative control sample has not been contacted with any known herbicide or plant growth regulator.

17. 17. The method of any one of claims 1 to 16, wherein step (ii) comprises comparing the phenotype of the test sample to the phenotype of a positive control sample that has been contacted with a known herbicidal or plant growth regulator compound, and further comprises comparing the phenotype of the test sample to the phenotype of a negative control sample that has not been contacted with the known herbicidal or plant growth regulator compound.

18. 18. The method of claim 17, wherein the known herbicidal compounds have a known mode of action, and comparing the test sample phenotype with a positive control sample phenotype is used to predict the mode of action of a candidate compound identified as having herbicidal or plant growth regulating activity.

19. 19. The method of claim 17 or 18, wherein the test sample, the negative control sample, and the positive control sample are spore germlings derived from spores of the same species of non-vascular plant.

20. 20. The method of any one of claims 17 to 19, wherein the test sample, the negative control sample, and the positive control sample are engineered to express a fluorescent molecule.

21. 21. The method of any one of claims 1 to 20, wherein step (ii) comprises measuring the phenotypic response of the test sample after growing the test sample in a suitable medium with the candidate compound under suitable conditions for a period of 1 to 3 days, 1 to 5 days, 3 to 6 days, 3 to 5 days, 2 to 3 days, 1 to 10 days, less than 5 days, less than 4 days, or less than 3 days after said contacting, and wherein the phenotype of the control spore germinants is determined after an equivalent period of growth in the suitable medium under the suitable conditions.

22. 22. The method of any one of claims 1 to 21, wherein step (ii) comprises obtaining measurements of any one or more of sample length, sample width, sample shape, sample pigmentation, sample circularity, sample chlorophyll concentration, and / or number of cells per sample.

23. 23. The method of claim 22, wherein the measurements are recorded digitally.

24. Comparing the phenotypic response of the test sample to any of the control sample phenotypes can be performed using methods such as distributed stochastic neighbor embedding, principal component analysis (generalized weighted least squares), principal component analysis (minimized weighted chi-square), principal component analysis (minimum residuals), common factor analysis (principal axis), common factor analysis (maximum likelihood), or the like. or common factor analysis (weighted least squares).

25. 25. The method of any one of claims 1 to 24, wherein the candidate compounds are selected as potential herbicides using an artificial intelligence algorithm, such as a random forest algorithm or a neural network algorithm.

26. Step (ii) is obtaining phenotypic measurements from said test sample and any said control sample, thereby generating a dataset; and using at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, at least 95%, or 99% of the data set as a training set for at least the artificial intelligence algorithm.

27. 27. The method of claim 26, wherein the control sample comprises a positive control sample and the artificial intelligence algorithm is used to predict the mode of action of any of the candidate compounds.

28. The method comprises: (a) contacting a candidate compound identified in steps (i) and (ii) as having herbicidal or plant growth regulating activity with a series of mutagenized samples comprising whole plants, spores, spore germlings, explants, protoplasts, or vegetative propagules, wherein the test sample and the mutagenized sample are from the same species of non-vascular plant; (b) extracting DNA from a resistant mutagenized sample that survives said contacting in (a) or does not exhibit growth abnormalities after said contacting in (a); (c) sequencing the genome or portion of the genome of said resistant mutagenized sample, thereby obtaining a mutagenized sample DNA sequence; (d) aligning the mutagenized DNA sequences obtained in (c) to a reference DNA sequence and identifying a first set of sequence mismatches between the mutagenized sample DNA sequences and the reference DNA sequence; (e) aligning DNA sequences from a first comparison sample to the reference DNA sequences and identifying a second set of mismatches between the first comparison DNA sequences and the reference DNA sequences; and (f) filtering the first set of mismatches with respect to the second set of mismatches to identify a first subset of mismatches that are unique to the first set of mismatches, wherein the first subset of mismatches are candidate mutations that may confer resistance to a herbicide or plant growth regulator; 28. The method of any one of claims 1 to 27, wherein the first comparison sample is from an independent sample that does not survive contact with the candidate compound or shows growth abnormalities after contact with the candidate compound, and is of the same genus as the resistance mutagenized sample, and the reference DNA sequence is a known reference sequence for a plant of the genus.

29. The method comprises: (ei) aligning the DNA sequence of a second comparison sample to the reference DNA sequence and identifying a third set of mismatches between the second comparison sample and the reference DNA sequence; (f) filtering the first set of mismatches with respect to the third set of mismatches to facilitate identification of a second subset of mismatches that are unique to the first set of mismatches; and generating a third subset of mismatches by filtering the first subset of mismatches with respect to the second subset of mismatches, wherein the first and second subsets of mismatches are candidate mutations that may confer tolerance to a herbicide or a plant growth regulator; 29. The method of claim 28, wherein the second comparison sample is from an independent sample that does not survive contact with the candidate compound or exhibits growth abnormalities after contact with the candidate compound and is of the same genus as the mutagenized sample.

30. 30. The method of claim 28 or 29, wherein the mutagenized sample is an M1 sample.

31. The method of any one of claims 28 to 30, wherein the mutagenized sample comprises non-naturally occurring mutations.

32. The method of any one of claims 28 to 31, wherein the method does not include the steps of segregation ratio analysis, complex segregation ratio analysis, or bulk segregation ratio analysis.

33. 33. The method of any one of claims 28-32, wherein the aligning in (e) comprises aligning the DNA sequences of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or more comparison samples to the reference DNA sequence and identifying the second set of sequence mismatches between the two sequences.

34. 34. The method of any one of claims 28 to 33, wherein the method further comprises filtering the candidate mutations with a biological filter.

35. The method of any one of claims 28 to 34, wherein the mutagenized sample is singular.

36. 36. The method of any one of claims 28 to 35, wherein the candidate mutation is in a gene encoding a protein targeted by the candidate compound identified as having herbicidal or plant growth regulating activity.

37. The method of any one of claims 28 to 36, wherein step (iii) is implemented using a computer.

38. 38. The method of any one of claims 1 to 37, wherein the method further comprises identifying a plant molecule or biological pathway targeted by a candidate compound identified by the method as having herbicidal activity using any one or more of an enzymatic assay, a chlorophyll fluorescence kinetics assay, a photosynthetic oxygen evolution assay, an electrolyte leakage assay, a radiometric assay, a spectrophotometric assay, a fluorometric assay, an absorbance assay, a colorimetric assay, mass spectrometry, a mitotic index analysis, a quantitative PCR analysis, a transcriptome profiling, a proteomics profiling, a genome-wide analysis, and / or a quantitative trait locus analysis, an in silico docking study, or a chemical structure analysis.

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