Methods of modulating gene expression in plants

Abstract

The present invention provides a method for computer analysis of a genome of a plant subject, comprising identifying a plurality of sequences that collectively define a promoter set of the plant subject, where each sequence within the promoter set consists of an expression control region, the expression control region extends from an initiation codon of an open reading frame to about 100 kilobases away from the 5'and / or 3 'end of the initiation codon. A transcriptome in the form of mRNA expression data of a plant subject is obtained and analyzed using a sequence-based modeling algorithm to provide a predicted value of the expression level of each protein coding gene within the transcriptome and associate this value with the sequence of the corresponding expression control region within the promoter group. The method can be used to generate new designs for expression control sequences, which are most likely to provide the desired expression profile for operably linked coding sequences. Nucleic acid libraries, plant cells, plant tissues and plants comprising the novel expression control sequences are also provided.

Description

Technical Field

[0001] The present invention relates to the field of computer design and optionally in vivo validation of novel plant genetic regulatory sequences. Background Art

[0002] Plants exhibit a remarkable amount of phenotypic plasticity, resulting from changes in the genome caused by spontaneous mutations, DNA translocations, polyploidization, or hybridization with distant relatives. Since the beginning of agriculture, farmers have been developing new plant varieties by exploiting this plasticity to improve traits such as crop yield and disease resistance. Through iterative selection for desirable traits, such as increased seed or fruit size, species have been phenotypically transformed over time, providing the wide range of varieties and cultivars used in modern agriculture. Since the second half of the last century, knowledge of the plant genome has increasingly guided efforts to create new crop varieties. However, modern breeding still relies heavily on genetic diversity that occurs spontaneously in nature.

[0003] Over the past few decades, recombinant genetic manipulation methods have come to the fore, allowing the creation of targeted genetic variation that no longer relies on natural variation that occurs by chance. One such method is transgenic technology, which involves adding new genes to a plant’s genome to confer desirable traits such as herbicide resistance. However, with a few exceptions, transgenic technology has been used in only a very small number of high-value crops and is considered controversial in some countries. Genetic variation can also be created by altering the genome of a given crop using mutagenic chemicals such as ethyl methanesulfonate or high-energy radiation. A disadvantage of mutation breeding is that it offers little control and requires the generation and screening of large populations of mutants in the hope of identifying a very small number of valuable genetic variations. Newer, more targeted mutagenesis methods such as Cas / Cas gene editing offer greater precision but do not allow for clear insight into the valuable consequences of a mutation.

[0004] Prior art methods attempt to perform so-called rational design of promoter elements in plant cells using intuitive, researcher-led approaches (see Yang et al. (2021) Plant Biotech. J. 19: 1364-1369). This approach requires much trial and error and relies heavily on a small set of existing solutions known to researchers from the literature and existing natural variation. These methods also often rely on alignment methods for de novo motif discovery, such as motif-based sequence analysis tools such as the MemeKit ( https: / / mem-suite.org ) thus rationally constructing a narrow range of solutions. As a result, these techniques significantly reduce the degrees of freedom to identify more subtle or non-intuitive positive or negative regulatory changes.

[0005] Therefore, we hope to provide new methods for developing methods to improve specific traits in plants. In particular, we hope to improve these traits by developing new plant genetic regulatory sequences, beyond natural or mutagenic variation, that control plant gene expression to provide these desirable traits.

[0006] These and other uses, features and advantages of the present invention should be apparent to those skilled in the art based on the teachings provided herein. Summary of the invention

[0007] The inventors provide a novel method for driving the evolution of new expression control sequences in plants using a novel informatics driven approach. These new sequences can be further validated using an in vivo screening method.

[0008] Therefore, a first aspect of the present invention provides a method for producing a nucleic acid library, the library consisting of a plurality of expression control sequences configured to regulate the expression of coding sequences operably linked thereto in plant cells, the method comprising:

[0009] performing an in silico analysis of the genome of the plant subject, wherein the computer analysis comprises identifying a plurality of sequences that collectively define a promoterome of the plant subject, wherein each sequence within the promoterome consists of an expression control region extending from a start codon of an open reading frame to about 100 kilobases 5' and / or 3' from the start codon;

[0010] Obtaining transcriptome of the plant subject in the form of mRNA expression data;

[0011] performing analysis using a sequence-based modeling algorithm to provide a predicted value for the expression level of each protein-coding gene within the transcriptome and linking the predicted value to the sequence of the corresponding expression control region within the promoter group;

[0012] generating a plurality of non-wild-type sequence designs of an expression control sequence that is most likely to provide a desired expression profile for an operably linked coding sequence, wherein the plurality of non-wild-type sequence designs provide information with a predictive value;

[0013] synthesizing a plurality of non-wild-type expression control sequences corresponding to the plurality of non-wild-type sequence designs; and

[0014] A nucleic acid sequence library comprising the plurality of non-wild-type expression control sequences is generated.

[0015] A second aspect of the present invention provides a nucleic acid library comprising a plurality of non-wild-type expression control sequences configured to regulate the expression of coding sequences operably linked thereto in plant cells. Suitably, the plurality of non-wild-type expression control sequences are produced by the methods defined herein.

[0016] Various aspects and embodiments of the present invention also provide plant cells, protoplasts, plant tissues, callus tissue, seedlings, seeds and whole plants and other biological materials comprising nucleic acid libraries or component sequences thereof.

[0017] A third aspect of the present invention provides a method, which can be implemented in whole or in part on a computer, for analyzing the genome of a plant subject, wherein the analysis comprises:

[0018] identifying a plurality of sequences that collectively define a plant subject promoter group data set, wherein each sequence in the promoter group consists of sequence data corresponding to an expression control region extending from a start codon of an open reading frame within a genome to about 100 kilobases 5' and / or 3' from the start codon;

[0019] Obtaining a transcriptome dataset in the form of mRNA expression data of a plant subject;

[0020] performing analysis using a sequence-based machine learning modeling algorithm trained with all or a portion of the promoterome dataset and all or a portion of the transcriptome dataset to provide predictions of the expression level of a gene placed under the effective control of any given expression control sequence contained within the promoter; and

[0021] The sequence-based machine learning modeling algorithm is queried with one or more query sequence designs, wherein if the one or more query sequence designs are introduced into an expression control region of the gene, the sequence-based machine learning modeling algorithm applies the prediction value to the one or more query sequence designs to provide a prediction of gene expression in the plant subject.

[0022] A fourth aspect of the present invention provides a method for producing a modified plant cell, comprising analyzing the genome of the plant cell as described herein, identifying at least one sequence design having a desired prediction for gene expression in the plant cell, and modifying the expression control region to conform to at least one sequence design in the plant cell to obtain a modified plant cell having the desired gene expression.

[0023] A fifth aspect of the present invention provides a modified plant cell obtained or obtainable by a method as described herein.

[0024] A sixth aspect of the present invention provides a modified plant derived from the plant cell described herein.

[0025] Within the scope of the present application, it is expressly intended that the various aspects, embodiments, examples and alternatives listed in the preceding paragraphs, claims and / or the following description and drawings, in particular their individual features, may be adopted independently or in any combination. That is, all embodiments and / or features of any embodiment may be combined in any way and / or combination, unless these features are incompatible. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] One or more embodiments of the present invention will now be described, by way of example only, with reference to the accompanying drawings, in which:

[0027] Figure 1 is a graph showing the results of a potato (vr. Georgina) tuber input differential expression analysis (DESeq2) experiment consisting of two bulk transcriptome (bulk RNA-seq) datasets collected at 0 and 36 hours after tuber bruise induction. Wild-type PPO2 expression was observed to increase by 7.5 log2 fold after 36 hours compared to 0 hours after tuber exposure to bruise injury. PPO2 has been found to be in the top 3% of the most upregulated genes.

[0028] Figure 2 A phylogenetic tree depicting the sequence similarity relationships between 74,000 promoters predicted to downregulate PPO2 expression is shown. The predicted differential expression of the evolved PPO2 sequences compared to wild-type PPO2 expression in unbruised potato was log2-fold -11. The overall predicted differential expression change compared to wild-type PPO2 expression in potato induced with bruise injury for 36 hours was log2-fold -18.5.

[0029] Figure 3 Is a map of a 6034 bp plasmid (PHY023) used for transient transfection of Arabidopsis protoplasts plant cells. A plasmid vector backbone with a variable nMYB113 promoter region was used to assemble a plasmid library for in vivo library validation.

[0030] Fig. 4 shows the results of fluorescence activated cell sorting (FACS) analysis of Arabidopsis protoplast leaf cells, showing higher green fluorescence in a cell population transiently transfected with a synthetically designed nMYB113 promoter library set (b) according to one embodiment of the present invention compared to the wild-type MYB113 promoter (a). The transfection positive control is shown in (c). The axis is: the x-axis is the green fluorescence level, and the y-axis is the red fluorescence level. The scale of the axis is log10. Red fluorescence was observed after successful transient transfection of Arabidopsis protoplasts.

[0031] Figure 5 Figure 1 is a graph of the results of the transcriptional activity assay of a subset of the nMYB113 promoter library tested by transient transfection in Arabidopsis protoplast leaf cells and SuRE-Seq. The solid line points represent the threshold activity of the wild-type MYB113 promoter, and the points represent the transcriptional profiles of the 304 variant subsets of the nMYB113 library. The x-axis shows the median log2 fold differential expression change compared to the wild-type MYB113 promoter, and the y-axis shows the statistical measure p-value in -log10. All variants above the threshold line have a statistically different expression pattern from the wild-type MYB113, p<0.05.

[0032] Figure 6 Shown are histograms of 23 nMYB113 promoter variants identified from the nMYB113 library that have significantly (p<0.05) higher gene expression than the wild-type promoter as measured by SuRE-seq. The y-axis shows the median log2 fold differential expression change compared to the wild-type MYB113 promoter. Unique molecular identifiers (UMIs) were used to identify individual elements in the library.

[0033] Figure 7 A graph is shown showing the expression distribution of one of the selected nMYB113 elements, called "OP625_short_225" after SuRE-seq with UMI barcodes. The distribution is performed using multiple different UMIs. The x-axis represents the log2 fold difference in expression and the y-axis is normalized based on the wild-type MYB113 promoter distribution. The y-axis shows the density of different UMIs for each element.

[0034] Figure 8 shows bright field and fluorescence micrographs of transiently transfected Arabidopsis protoplast leaf cells. Images show (a) expression of the GFP reporter gene from the wild-type MYB113 promoter and the 35S mCherry control; (b) reporter gene expression with a single nMYB113 promoter variant A2.1DEL; (c) shows the positive control. The fluorescence output of the nMYB113 promoter variant A2.1DEL can be qualitatively compared to the wild-type MYB113 promoter output as well as the transgenic promoter 35S.

[0035] Fig. 9 Shown is a CLUSTALW sequence alignment of the novel PPO2 promoter variants compared to the wild-type sequence.

[0036] Fig.10 Shown is a CLUSTALW sequence alignment of the novel promoter variant (OP625_225) compared to the wild-type MYB113 promoter sequence.

[0037] Figure 11 shows a graph of the predictive power of a machine learning (ML) model according to an embodiment of the present invention in a dataset taken from wheat. (a) shows the natural variation of RNA-Seq FPKM reads, and (b) shows the ML prediction.

[0038] Figure 12 shows model performance graphs for wheat root tissue, with (a) providing experimental RNA-seq data and (b) providing predictive model data.

[0039] Figure 13 shows a graph of the predictive power of a machine learning (ML) model according to an embodiment of the present invention in a dataset taken from corn. (a) shows the natural variation of RNA-Seq FPKM reads, and (b) shows the ML prediction.

[0040] Figure 14 shows a graph of the predictive power of a machine learning (ML) model in a dataset taken from soybean according to an embodiment of the present invention. (a) shows the natural variation of RNA-Seq FPKM reads, and (b) shows the ML predictions. DETAILED DESCRIPTION

[0041] Before describing the present invention, a number of definitions are provided to aid in understanding the present invention.

[0042] The practice of the present invention employs, unless otherwise indicated, conventional techniques of chemistry, molecular biology, microbiology, recombinant DNA techniques and chemical methods, which are within the capabilities of ordinary skill in the art. These techniques are also explained in the literature, such as MR Green, J. Sambrook, 2012, Molecular Cloning: A Laboratory Manual, Fourth Edition, Books 1-3, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY; Ausubel, FM et al., (Current Protocols in Molecular Biology, John Wiley & Sons, Online ISSN: 1934-3647); B. Roe, J. Crabtree and A. Kahn, 1996, DNA Isolation and Sequencing: Essential Techniques, John Wiley & Sons; JM Polac and James O'D. McGee, 1990, In Situ Hybridisation: Principles and Practice, Oxford University Press; MJ Gait (Editor), 1984, Oligonucleotide Synthesis: A Practical Approach, IRL Press; DMJ Lilley and JEDahlberg, 1992, Methods of Enzymology: DNA Structure Part A: Synthesis and Physical Analysis of DNA Methods in Enzymology, Academic Press; Synthetic Biology, Part A, Methods in Enzymology, Edited by Chris Voigt, Volume 497, pages 2-662 (2011); Synthetic Biology, Part B, Computer Aided Design and DNA Assembly, Methods in Enzymology, Edited by Christopher Voigt, Volume 498, Pages 2-500 (2011); RNA Interference, Methods in Enzymology, David R. Engelke, and John J. Rossi, Volume 392, Pages 1-454 (2005). All references cited herein are incorporated herein by reference in their entirety. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0043] As used herein, the term "comprising" means that any one of the cited elements must be included, and other elements may be optionally included. "Substantially consisting of" means that any cited elements must be included, elements that would significantly affect the basic and novel characteristics of the listed elements are excluded, and other elements may be optionally included. "Consisting of" means that all elements except the listed elements are excluded. Embodiments defined by each of these terms are within the scope of the present invention.

[0044] The term "expression vector" is used to refer to a linear or circular DNA molecule, into which another DNA sequence fragment of appropriate size can be integrated. Such a DNA fragment may include additional fragments that provide transcription of the gene encoded by the DNA sequence fragment. The additional fragments may include, but are not limited to, promoters, transcription terminators, enhancers, internal ribosome entry sites, untranslated regions, polyadenylation signals, selective markers, replication origins, etc. In an embodiment of the present invention, the DNA sequence fragment may contain plant expression control sequences, including new non-wild-type plant promoters, enhancers or silencer elements. Expression vectors are generally derived from plasmids, cosmids, viral vectors and yeast artificial chromosomes; vectors are suitable recombinant molecules containing DNA sequences from several sources. Nucleic acid expression and reporter libraries of the type described herein may be contained in expression vectors.

[0045] When the term "operably linked" is applied to DNA sequences, such as in an expression vector or a recombinant modified gene construct, they function together to achieve their intended purpose, for example, an expression control sequence such as a promoter sequence allows initiation of transcription, which proceeds through the linked coding sequence to the termination sequence.

[0046] A "polynucleotide" is a single-stranded or double-stranded sequence of covalently linked nucleotides in which the 3' and 5' ends of each nucleotide are linked by a phosphodiester bond. Polynucleotides can be composed of deoxyribonucleotide bases or ribonucleotide bases. Polynucleotides include DNA and RNA, which can be synthesized in vitro or isolated from natural sources. The size of a polynucleotide is usually expressed as the number of base pairs (bp) for double-stranded polynucleotides, or as the number of nucleotides (nt) in the case of single-stranded polynucleotides. One thousand bp or nt is equal to a kilobase (kb). Polynucleotides less than about 40 nucleotides in length are often referred to as "oligonucleotides."

[0047] A "polypeptide" is a polymer of amino acid residues linked by peptide bonds, whether produced naturally or in vitro by synthetic means. Polypeptides less than about 12 amino acid residues in length are generally referred to as "peptides". As used herein, the term "polypeptide" refers to a naturally occurring polypeptide, a precursor form, or a product of a preprotein. Polypeptides also undergo maturation or post-translational modification processes, which may include, but are not limited to, glycosylation, proteolytic cleavage, lipidation, signal peptide cleavage, propeptide cleavage, phosphorylation, and the like. A "protein" is a macromolecule comprising one or more polypeptide chains.

[0048] The term "promoter" as used herein means a genetic regulatory element in a DNA sequence to which RNA polymerase will bind and initiate transcription of DNA. Promoters play a vital role in gene expression by providing a binding site for RNA polymerase. When RNA polymerase binds to the promoter region, it initiates the transcription process. Promoters are usually located in the 5' non-coding region of a gene, but not always. The 5' region refers to the upstream region of a gene, which means that it is located before the actual coding sequence of the gene, which is usually represented by the ATG start codon (for example, before the first exon). Non-coding regions are DNA fragments that are not directly involved in the formation of polypeptides or other gene products. These regions can contain various regulatory elements, including promoters. The main function of the promoter sequence is to provide a recognition site for RNA polymerase and other transcription regulatory proteins, enabling them to interact with DNA and initiate the transcription process. The binding of RNA polymerase to the promoter region marks the starting point for the assembly of the transcription machinery, ultimately leading to the synthesis of RNA molecules known as primary transcripts or pre-mRNA. Therefore, the sequence and structure of promoters are highly diverse. They contain specific DNA motifs and sequences that can be recognized by transcription factors that further regulate gene expression. Transcription factors can enhance or inhibit the binding of RNA polymerase to promoters, thereby affecting the level of gene transcription, often in a cell type or tissue-specific manner.

[0049] The term "enhancer" as used herein refers to a genetic regulatory element in a DNA sequence that, when bound to one or more transcription factors, enhances the transcription of an associated gene. Enhancers play a key role in gene expression by regulating the transcription of an associated gene or a group of genes within a locus. When an enhancer binds to one or more transcription factors, it increases the rate of transcription. Enhancers are usually located at different distances from the genes they regulate. They can be found upstream (upstream enhancers) or downstream (downstream enhancers) of a gene, sometimes even within the introns of the gene itself. Unlike promoters, enhancers do not necessarily have directional specificity and can function regardless of their orientation relative to the gene. A key function of enhancers is to provide binding sites for transcription factors and regulatory complexes. When specific transcription factors recognize and bind to enhancers, they can promote the assembly of the transcription machinery in the promoter region of the associated gene. The recruitment and interaction of transcription factors in enhancer and promoter regions can effectively initiate and regulate gene transcription. Enhancers show extraordinary flexibility and can function over long distances. They can interact with the promoter region of the target gene through a three-dimensional loop of DNA, bringing regulatory elements into close proximity. This spatial arrangement allows enhancer-bound transcription factors to interact directly with the transcription machinery at the promoter, thereby enhancing transcriptional activity. Enhancers can also have cell-type-specific or developmental stage-specific activities. This means that enhancers may only be active in certain cell types or at specific developmental stages, contributing to the precise regulation of gene expression. The specificity and activity of enhancers are controlled by the combination of transcription factors that bind to them, forming a complex regulatory network that determines the timing, level, and specificity of gene expression. In addition, enhancers can cooperate with other enhancers or regulatory elements in a combinatorial manner. This cooperation between multiple enhancers allows fine-tuning of gene expression patterns and enables cells to respond to a variety of environmental cues and signaling pathways. The combinatorial effects of enhancers provide a powerful and dynamic mechanism for gene regulation, ensuring the normal function and adaptation of cells in different environments, especially when conferring tissue specificity in the form of phenotypic gene expression.

[0050] The term "silencer" as used herein refers to a genetic regulatory element in a DNA sequence that reduces transcription of an associated promoter; typically, they are the inhibitory counterparts of enhancers. Silencers play a vital role in reducing or inhibiting the transcriptional activity of associated or adjacent promoters and contribute to the fine-tuning of gene expression. Silencers are usually located near the promoter region of the genes they regulate. They can be found upstream (upstream silencers), downstream (downstream silencers) or even introns of genes. Like enhancers, silencers are not necessarily directional specific and can function regardless of their orientation relative to genes. The main function of silencers is to provide binding sites for transcription factors that have an inhibitory effect on gene transcription. When specific transcription factors recognize and bind to silencers, they recruit co-repressors or inhibit the binding of activator proteins to the promoter region. This interference results in inhibition of the transcriptional activity of the associated promoter. Silencers can exert their inhibitory effects in a variety of ways. They can interact directly with the transcriptional machinery in the promoter region to prevent the assembly of components required for transcription initiation. Silencers can also induce chromatin modifications, such as adding methyl groups to DNA or removing acetyl groups from histones. These modifications alter chromatin structure, making the DNA less accessible to the transcriptional machinery and repressing gene expression. Similar to enhancers, silencers can exhibit cell-type-specific or developmental-stage-specific activity. This means that silencers may only function in certain cell types or at specific developmental stages, adding another layer of complexity to gene regulation. The specific combination of transcription factors that bind to a silencer determines its activity and its inhibitory effect on gene transcription. Silencers can also function in a cooperative manner, interacting with other regulatory elements, such as other silencers or enhancers, to regulate gene expression. By working in concert, these elements can fine-tune transcriptional activity and establish precise patterns of gene expression in response to a variety of signals and environmental cues. Thus, by recruiting repressive transcription factors and chromatin modifications, silencers function as repressors of transcriptional activity, allowing cells to precisely regulate gene expression levels. Their cell-type specificity and cooperative nature increase the complexity of gene regulatory networks and ensure appropriate gene expression patterns during development and in different cellular contexts. In some cases, a silencer may also be a bifunctional regulatory element that can also act as an enhancer, again depending on the cellular context.

[0051] As used herein, the terms "3'" ("3prime") and "5'" ("5prime") have their usual meanings in the art, i.e., distinguishing ends or directionality in a polynucleotide sequence. Polynucleotides have 5' and 3' ends, and polynucleotide sequences are usually written in a 5' to 3' direction. The 5' end is appropriately considered to be upstream of the 3' end of the polynucleotide sequence. Thus, a sequence referred to as upstream of a given reference point in a gene is a sequence at the 5' end of the reference point, such as the transcription start codon of an open reading frame (ORF). Similarly, a sequence indicated as downstream is a sequence at the 3' end of the reference point.

[0052] According to the present invention, the homology with any nucleic acid sequence (such as expression control sequence as described herein) is not limited to 100%, 99%, 98%, 97%, 95% or even 90% sequence identity. Although the sequence identity is obviously low, many nucleic acid sequences can prove the biochemical equivalence of each other. In the present invention, homologous nucleic acid sequences are considered to be sequences that hybridize with each other under low stringency conditions (Sambrook J. et al., Molecular Cloning: a Laboratory Manual, Cold Spring Harbor Press, Cold Spring Harbor, NY). However, in some cases, it may be necessary to distinguish between two sequences that hybridize with each other but contain some mismatches - "imprecise matching", "incomplete matching" or "imprecise complementation" - and two sequences that hybridize with each other and have no mismatches - "exact matching", "complete matching", or "exact complementation". In addition, possible mismatching degrees are also considered.

[0053] Therefore, the present invention provides a new discovery platform for plant expression control sequences, including new plant promoters, enhancers and / or silencer elements obtained outside of natural diversity. The present invention utilizes a method that combines artificial intelligence (AI) technology in an embodiment, and the further option of in vivo high-throughput library screening, for precise control of plant gene expression. In a specific embodiment of the present invention, a new gene expression control element nucleic acid sequence library is provided, which can be screened for desired gene expression elements. Plant cells, plant tissues and plants (species, varieties or cultivars) containing one or more novel gene expression elements can be generated to enhance desired traits.

[0054] Embodiments of the present invention utilize artificial intelligence bioinformatics methods to guide the evolution of new gene expression control sequences. Exemplary embodiments of the present invention are described in more detail below.

[0055] Unless already assembled, a new DNA genome assembly is performed for the plant subject (e.g., the target plant species or variety and any related varieties). The working data for the genome assembly may include short Illumina reads and long reads, or a mixture of long and short reads (e.g., using PacBio or Oxford Nanopore sequencing), and include bioinformatic assembly methods that take into account plant polyploidy and allelic variation. For example, a Bayesian statistical framework is used in haplotype-based variant detectors such as FreeBayes, see Garrison E, Marth G. Haplotype-based variant detection from short-read sequencing. arXiv preprint arXiv:1207.3907[g-bio.GN]2012.

[0056] The regional identification sequence around / on each annotation coding gene identified in the assembled genome. Coding genes can be identified using reference genome data, published literature supplements or homology analysis of assembled genomes. These genomic DNA sequences define single data chains, which are anchored at theoretical translation start positions (such as start codons) or transcription start positions (such as TATA boxes) upstream 5', and extend to about 100,000 base pairs. The promoter group can also or alternatively include a similar distance of downstream 3', and / or also include coding genes. This captures multiple DNA sequences, which can jointly define the "promoter group" of plant subjects. In an alternative embodiment of the present invention, the promoter group can only include sequences located at 5' or upstream of the open reading frame. In an alternative embodiment, the promoter group can only include sequences located at 3' or downstream of the open reading frame. The promoter group can include one or more exons, one or more introns, one or more transposable elements and / or one or more heterologous insertion factors, which may have been introduced by previous gene editing or recombination techniques.

[0057] In a specific embodiment of the invention, a promoter group may comprise a plurality of nucleic acid sequences extending up to 100,000 base pairs (bps), 90,000 bps, 80,000 bps, 70,000 bps, 60,000 bps, 50,000 bps, 40,000 bps, 30,000 bps, 20,000 bps, 10,000 bps and 5000 bps upstream and / or downstream of the first translation start position. In one embodiment of the invention, a promoter group is defined as comprising a plurality of sequences extending upstream a greater distance than the distance extending downstream from a reference point in a gene, suitably at least 1.5 times, at least 2 times, at least 3 times and at most at least 4 times. In another alternative embodiment of the invention, a promoter group is defined as comprising a plurality of sequences extending upstream a less distance than the distance extending downstream from a reference point in a gene, suitably at least 1.5 times, at least 2 times, at least 3 times and at most at least 4 times. As mentioned previously, the reference point can be a transcription or translation start site, and in the case where alternative variants or transcripts may exist for a given gene, it can include any possible transcription start site.

[0058] Transcriptome information can be provided in the form of expression data of plant subjects. Transcriptomes of specific plant cells, plant cell types, plant tissue types (e.g., leaves or fruits) can be obtained. Transcriptomes can be obtained from bulk RNA sequencing technology (RNA-seq) and single-cell RNA-seq based on UMI (unique molecular identifier or "barcode"), such as but not limited to drop-RNAseq, and other RNA-seq technologies using transient transfection, such as STARR-seq and SuRE-seq methods. In addition, in certain embodiments of the present invention, data input can be supplemented with additional data sets to evaluate chromatin accessibility and transcription factor binding information.

[0059] Suitable chromatin-based sequencing methods may include:

[0060] - ATAC-seq (Assay for Transposase Accessible Chromatin by High-Throughput Sequencing): ATAC-seq is a method to study chromatin accessibility in a sample. In this assay, the genome is treated with a transposase (enzyme) called Tn5. Tn5 marks open chromatin regions by cleaving and inserting adapter sequences, which can then be detected by subsequent sequencing. ATAC-seq has shown utility in assessing genome-wide changes in chromatin accessibility following editing events (Buenrostro et al., Curr Protoc Mol Biol. (2015); 2015: 21.29.1-21.29.9).

[0061] - Chromosome conformation capture (3C) methods, such as Hi-C analysis, can be used to assess chromatin accessibility, 3D organization and interconnectivity of the genome to identify any changes in chromatin accessibility and 3D structure of the genome, including local chromosome neighborhoods and / or transcription factories (Lieberman Aiden et al., Science. 2009 Oct 9; 326(5950): 289-293)

[0062] - Chromatin immunoprecipitation followed by sequencing (ChIP-seq): ChIP-seq is a core method for epigenetic research. Genome-wide analysis of histone modifications, such as enhancer analysis and genome-wide chromatin state annotation, can systematically analyze how the epigenomic landscape contributes to cell identity, development, lineage specification, and disease. In particular, the effects of editing on histone modifications, including but not limited to regulatory elements (H3K27Ac, ​​H3K4Me1), promoter accessibility (H3K4Me3), heterochromatin formation (H3K9Me3), and gene bodies (H3K36Me3, H3K27Me3) should be evaluated - see Furey (2012) Nat. Rev. Genet., 13(12), pp.840-852.

[0063] - Methyl sequencing (Methyl-seq): This approach assesses the impact of editing on the DNA methylation profile within the genome, thereby estimating changes in chromatin accessibility. Methyl sequencing can be performed chemically (bisulfite sequencing) or enzymatically (EM-seq) - see Vaisvila et al. (2021) Genome Res. Jul;31(7):1280-1289.

[0064] - DNase accessibility data: Deoxyribonuclease (DNase for short) is an enzyme that catalyzes the hydrolytic cleavage of phosphodiester bonds in the DNA backbone, thereby degrading DNA. DNase is a nuclease, a general term for enzymes that are able to hydrolyze the phosphodiester bonds that link nucleotides. DNase activity is a method to assess chromatin accessibility and determine the importance of cell type-specific regions within target plant tissues / cells.

[0065] Analysis of data obtained from one or more of the above sequencing technologies allows the evaluation of promoter groups at the epigenetic and transcriptional levels. This evaluation includes the determination of transcription factor binding sites, transcription factor coding regions, genetic regulatory elements (such as enhancers, silencers and repressors), DNA methylation, histone modifications and the activity and chromosomal accessibility of transcription factories.

[0066] Analysis of promoterome and transcriptome input data is accomplished by using a sequence-based modeling algorithm configured to provide a predicted value for the expression level of each protein-coding gene contained within the transcriptome and to relate the predicted value to the corresponding sequence of the expression control region contained within the promoterome.

[0067] Bioinformatics processing of expression data can include, but is not limited to, the following techniques:

[0068] Map next generation sequencing reads to create read counts, such as HISAT2

[0069] Normalization and expression analysis - e.g. DESeq2

[0070] Batch effect removal - e.g. Combat

[0071] Sequence-based modeling algorithms can utilize artificial intelligence (AI) and machine learning (ML) methods. The model can be trained using input data related to previously described transcriptomics and promoteromics to provide predictions of gene expression contained in the genome assembly, called predicted values. Predicted values ​​can be assigned to genes, so that each identified gene in the gene database can be queried. The database can be queried based on multiple criteria and metadata assigned to each entry, such as gene sequence, coding sequence, protein sequence, expression level, related UMI, and predicted value. Gene sequences may include regulatory regions (promoters and non-coding parts), as well as other parts of genes, such as 5' and 3' untranslated regions (UTRs), coding sequences (CDS), introns, exons, and other regions. In addition, the model can be used to query the expression values ​​of new sequences outside the gene database, followed by further transcriptional validation and in vivo validation if necessary.

[0072] As used herein, the term "prediction value" can represent a number or score that characterizes gene expression. Prediction value can be a relative value, such as relative to the expression level (positive or negative) of a theoretical benchmark (notionalbenchmark), such as the expression of a housekeeper or other appropriate reference genes. Therefore, prediction value can be a number, a logarithmic value or other dimensionless values, such as color or alphanumeric character encoding, wherein the prediction value is between a specific threshold value. In an alternative embodiment, prediction value can be an absolute value corresponding to the prediction of gene expression, usually determined by one or more techniques, such as but not limited to RNA sequencing (RNA-seq), microarray analysis, quantitative RT-PCR (qPCR), Northern or Western blot, in situ hybridization and immunohistochemistry. Therefore, according to a specific embodiment of the present invention, prediction value is expressed as the RNA-seq counts of a given sequence design. RNA-seq counts can be provided in the form of unit counts, such as FPKM (fragments mapped per million reads of transcription per kilobase) or TPM (per million transcripts).

[0073] Sequence-based modeling architectures and sequence prediction algorithms, including those common in natural language processing, can be used in embodiments of the present invention, which provide predicted values ​​for the expression level of each protein-coding gene included in the transcriptome, and link the predicted value to the corresponding sequence of the expression control region included in the promoter group. Suitable sequence-based modeling algorithms can include artificial neural networks (ANNs), such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), including bidirectional RNNs, transformers, and masked language models. These algorithms are a series of specific implementations of machine learning models called neural networks, in which network "neurons" are arranged in an architecture suitable for modeling sequence data (such as genomic sequences). They have been used in various biological applications known to those skilled in the art. Descriptions of algorithms and their successful applications in biology are provided by, for example, Greener et al. Nature Reviews Molecular Cell Biology, volume 23: 40-55 (2022).

[0074] According to one embodiment of the present invention, multiple non-wild-type sequence designs are generated for the novel expression control sequence that is most likely to provide the required expression profile for the operably connected encoding sequence. The predicted value generated by the sequence-based modeling step described herein provides information for multiple non-wild-type sequence designs. Therefore, in the case of wishing to increase the expression of the operably connected encoding sequence, the sequence of the high expression level value that shows the prediction is sought. On the contrary, in the case of wishing to reduce the expression of the operably connected encoding sequence, the sequence of the low level expression value that shows the prediction is sought.

[0075] A variety of sequence designs of novel expression control sequences are generated by computer mutagenesis methods. This method can include rule-based sequence and / or generative sequence design. In a specific embodiment of the present invention, a "minimal change" specification is adopted, which favors a minimal amount of base pair changes, insertions or deletions compared to the template / starting wild-type (WT) promoter. The crop-specific requirement of "minimal change" can address the use of new breeding technologies (NBT), such as CRISPR-CAS-guided nuclease-based genome editing to make non-transgenic, targeted changes to crop genomes. In some jurisdictions, this approach is classified as non-GMO.

[0076] The minimal change approach is also a major advantage over other known AI-based synthetic design methods used in industrial biotechnology or medicine, which tend to optimize properties (such as "fitness") to local or global maxima without considering the number of changes compared to a given starting sequence. In contrast, the minimal change approach allows for the optimization of properties, such as fitness, that pass quality control (QC) thresholds for in-planta performance and that take into account (a) the native functions of promoters / enhancers / silencers and (b) realistic legislative and regulatory aspects of DNA changes in plants used as commercial crops.

[0077] Therefore, in embodiments of the present invention, a minimum change threshold associated with the number and / or type of changes allowed to be made to a wild-type expression control sequence is established for non-wild-type sequence design. This minimum change threshold can be incorporated into rule-based sequence and / or generative sequence design algorithms that use sequence perturbations to design mutant sequences with desired gene expression profiles. In embodiments of the present invention, concepts from the field of explainable artificial intelligence, such as by assigning feature importance or interaction strength, can be used to infer the position and structure of changes in a wild-type sequence that are most likely to achieve the desired changes in its expression value prediction (see Molnar, C. "Interpretable Machine Learning: A Guide For Making Black Box Models Explainable" 2 nd Edition(2022)ISBN:979-8411463330).

[0078] Novel non-wild-type sequence designs can be scored by high-throughput computational techniques, such as using one or more AI-based training models, to determine the top-ranked sequence designs that are most likely to provide the best desired expression profile.

[0079] In vivo validation of new non-wild-type sequence designs can be performed by designing and constructing a library of new gene expression control sequences created by de novo DNA synthesis. Comparable wild-type gene expression control sequences can be synthesized de novo or amplified from plant subject source material by standard recombinant techniques such as polymerase chain reaction (PCR).

[0080] The methods of the invention can be used to generate various genetic control elements that can be introduced into plant cells and / or tissues. Suitably, the plant cells may be selected from gametophytes, reproductive cells, vegetative cells and / or meristematic cells.

[0081] In certain embodiments, plant cells are in the form of protoplasts. As used herein, the term "plant protoplasts" (also referred to as "protoplasts" in this disclosure) refers to plant cells whose cell walls are completely or partially removed. The removal of cell walls can be achieved by mechanical, chemical or enzymatic methods. In embodiments, cell wall digestion enzymes are used to obtain protoplasts from suitable plant materials. For example, enzymes such as cellulase, macerozyme, pectinase, hemicellulase, pectin lyase, driselase, xylanase and combinations thereof may be suitable for background of the present invention. In embodiments, cellulase can be used at a concentration of 1w%-1.5w%. In embodiments, macerozyme can be used at a concentration of 0.2w%-0.4w%. In embodiments, hemicellulase can be used at a concentration of 2w%-5w%. In embodiments, pectinase can be used at a concentration of 0.01w%-0.5w%. In embodiments, driselase can be used at a concentration of 0.5w%-2w%. Protocols for obtaining protoplasts from plant tissues are known in the art and will not be discussed further here.

[0082] In embodiments, suitable plant tissue is selected from: leaf, stem, root, tuber, seed, branch, pubescence, node, leaf axil, flower, pollen, stamen, pistil, petal, peduncle, rod, stigma, style, bract, fruit, trunk, carpel, sepal, anther, ovule, pedicel, needle, cone, rhizome, runner, bud, pericarp, endosperm, placenta, berry, stamen or leaf sheath. In specific embodiments, plant cell material may include root tissue, mesophyll and / or cultured callus. In embodiments, the protoplast is obtained using the protocol described in Yoo, Cho and Sheen (2007) Nature Protocols volume 2, pages 1565-1572, which is incorporated herein by reference.

[0083] In an embodiment, in vivo verification may include a method for detecting and optionally selecting encapsulated protoplasts or plant cells based on one or more desired features. The system of the present invention includes an optical detection system, such as a fluorescence detection system. In combination with a sorting mechanism, this enables the selection of protoplasts or cells with fluorescent markers indicating target features or as simple expression reporters. In an embodiment, the method of the present invention includes introducing the coding sequence of a fluorescent protein into a protoplast or plant cell culture. In these embodiments, the method of the present invention may include using a fluorescence detection system, such as a fluorescence activated cell sorting (FACS) system, to detect the expression of fluorescent proteins in the tested protoplasts or plant cells. For example, suitable screening and propagation methods are described and illustrated in WO-A-2020 / 212713.

[0084] Some examples of intracellular reporter proteins that can be used as fluorescent markers for gene expression include green fluorescent protein (GFP) and its homologs or derivatives, such as enhanced GFP (eGFP), blue fluorescent protein (BFP, Azurite, mKalama1), cyan fluorescent protein (CFP, CyPet), yellow fluorescent protein (TFP, Citrine), and mCherry. This allows for easy identification of transfected protoplasts using conventional cell sorting techniques.

[0085] In embodiments, the fluorescence detection system can be configured to detect a signal indicative of the presence of chlorophyll and a signal indicative of the presence of a fluorescent marker of a target feature. In these embodiments, the detection system can be connected to a sorting mechanism, and the system can be configured such that the sorting mechanism separates the microcapsules between two different channels based on the combined presence of the signal indicative of the presence of chlorophyll and the signal indicative of the presence of the fluorescent marker.

[0086] In embodiments, chemiluminescence detection system can also be used. Combined with sorting mechanism, this makes it possible to select the protoplasts with the chemiluminescence labeling of indicating target feature. In embodiments, method of the present invention comprises the coding sequence of one or more chemiluminescent proteins (such as fluorescein, aequorin etc.) is introduced in the protoplast culture. In these embodiments, method of the present invention can comprise using luminescence detection system to detect the expression of chemiluminescent protein in the encapsulated protoplasts. Protoplasts, as mentioned above, single encapsulated protoplasts can be manually or automatically put into tissue culture system, for example, on the agar gel plate containing plant growth medium, or put into the microplate containing gel and the liquid containing plant growth promoting medium.

[0087] In embodiments, callus induction medium may be used. For example, GamborgB5, Murashige Skoog or other culture medium may be used. It will be appreciated by those skilled in the art that various salts, vitamins and auxins, cytokinins and / or other hormones that promote the growth of single protoplasts into callus may be included in the culture medium or tissue culture plate.

[0088] In embodiments, callus reaching a predetermined size can be moved into a plant growth medium having different ratios of auxin and cytokinin to induce the formation of buds. In embodiments, callus that has undergone bud formation can be moved into another plant growth medium to induce root formation. Preferably, any manipulation of callus can be performed under aseptic conditions.

[0089] The plantlets produced by the above method can be used in conventional micropropagation techniques.

[0090] According to embodiments of the present invention, methods and devices for effectively engineering and recovering plants or fertile plant materials (e.g., seeds) comprising target non-wild-type gene expression patterns or features can be provided. The ability of single-cell rapid phenotyping and efficient recovery of whole plants using novel libraries provided by the present invention is particularly advantageous in the context of plant genetic engineering.

[0091] The target plant indicated or desired for analysis using the methods of the present invention is referred to as a "plant" or "plant subject". Particularly useful plant subject species and genera of the present invention include, but are not limited to, Solanum spp. (e.g., tomato (S. lycopersicum), potato (S. tuberosum), melon (S. melongena), ginseng fruit (S. muricatum), beet (S. betaceum); Brassica spp. (e.g., cabbage (B. oleracea), rutabaga (B. napobrassica), Brassica napus (B. napus), B. cretica, B. rupestris and Brassica rapa (B. rapa)); Capsicum spp. spp.) (e.g., C. annuum, C. baccatum, C. chinense, C. frutescens, C. pubescens); Lupinus spp. (e.g., L. angustifolius, L. albus, L. mutabilis, and L. luteus); Phaseolus spp. (e.g., P. phaseolus, P. coccineus, P. lunatus, P. vulgaris, and P. dumosus); Vigna spp. spp.) (e.g., V. aconitifolia, V. angularis, V. mungo, V. radiata, V. subterranea, and V. unguiculata); Vicia faba; Cicer arietinum; Pisum sativum; Lathyrus spp. (e.g., L. sativus and L. tuberosus); Lens spp. (e.g., L. culinaris and L. esculenta); Glycine max; Psophocarpus; Cajanus cajan; Arachis hypogaea; Lactuca spp.) (such as L. sativa, L. serriola, L. saligna, L. virosa, and L.taterica; Asparagus (Asparagus officinalis); Celery (Apium graveolens); Allium spp. (such as common onion (A.cepa), shallot (A.oschaninii), European leek (A.ampeloprasum), A.wakegi, leek (A.porrum), garlic (A.sativum) and chives (A.schoenoprasum); Beet (Beta vulgaris); Chicory (Cichorium intybus); Medicinal dandelion (Taraxacum officinale); Rocket (Eruca spp.) (such as E.vesicaria and E.sativa); Cucurbita spp. spp.) (e.g., C.argyosperma, C.digitata, C.pepo, C.moschata, C.ecuadorensis, C.ficifolia, C.foetidissima, C.galeottii, C.lundelliana, C.maxima, C.moshata, C.pedatifolia, C.radicans); Spinacia oleracea; Nasturtium officinale; Cucumbers (Cucumis spp.) (e.g., C.sativus, C.melo, C.hystrix, C.picrocarpus, and C.anguria); Olives (Oleaeuropaea); Carrots (Daucus carota); Sweet potatoes (Ipomoea batatas; Ipomoea eriocarpa; Manihotesculenta; Ginger (Zingiber officinale); Horseradish (Armoracia rusticana); Helianthus spp. (such as sunflower (H. annuus) and Jerusalem artichoke (H. tuberosus); Cannabis spp. (such as common hemp (C. sativa) and Indian hemp (C. indica); Pastinacasativa; Radish (Raphanus sativus); Turmeric (Curcuma longa); Dioscorea spp. (such as white yam (D.rotundata), D.alata, D.polystachya, D.bulbifera, D.esculenta, D.dumetorum, D.trifid and D.cayennensis; Piper spp. (such as P.aduncum, P.guineense and P.nigrum); Zea spp. (such as Z.mays and Z.diploperennis); Hordeum spp. (such as H.vulgare, H.pusillum, H.murinum, H.murinum, H.jubatum and H.intercedens); Gossypium spp.) (such as G. hirsutum, G. barbadense, G. arboreum and G. herbaceum); Triticum spp. (such as T. aestivum and T. timopheevii); Vitis vinifera; Prunus sp. (such as P. avium, P. armeniaca, P. cerasifera, P. cerasus, P. domestica, P. persica and P. dulcis); Malus domestica; Pyrus spp. (such as P. communis, P. cordata and P. pyrifolia); Fragaria spp. (such as P. communis, P. cordata and P. pyrifolia); vesca and strawberry (Fragaria × ananassa); raspberry (Rubus idaeus); sugarcane (Saccharum officinarum); cultivated sorghum (Sorghum saccharatum); wild banana (Musa balbisiana) and banana (Musax paradisiaca); rice (Oryza sativa); tobacco (Nicotiana tabacum); Arabidopsis thaliana; citrus spp. (such as lime (C. × aurantifolia), orange (C. × aurantium), broad-leaved lime (C. × latifolia), lemon (C.×limon), C. ×limonia, C. ×paradisi, C. ×sinensis, and C. ×tangerina; Populus spp. (e.g. P. tremula, P. balsamifera, and P. tomentosa); Tulip (Tulipagesneriana); Alfalfa (Medicago sativa); Balsam fir (Abies balsamea); Avena orientalis; Bromus mango; Calendula officinalis; Chrysanthemum balsamita; Dianthus caryophyllus; Eucalyptus spp. (such as E.leucoxylon, E.maculata, E.polybractea, E.sargentii); Impatiens biflora; Linum usitatissimum; Lycopersiconesculentum; Mangifera indica; Nelumbo spp. (such as N.nucifera and N.pentapatala); Poacea spp.; Secale cereale; Tagetes erecta and Tagetes minuta. Plants and plant cells of any of the above species, whose genomes, in particular, contain modified sequences in one or more expression control sequences, can be produced by the methods described herein.

[0092] Novel gene expression control sequences, such as sequences identified by the methods described herein, can be used for technologies involving targeted gene editing of plant cells or plant subjects. Various methods of targeted gene editing are available to those skilled in the art, including techniques that rely on sequence-guided endonucleases, such as genome editing systems based on CRISPR / Cas. Therefore, embodiments of the present invention can provide methods for producing plants or plant propagation materials (e.g., seeds) for genome editing. Genome editing systems based on CRISPR / Cas can target specific nucleic acid sequences. For example, the guide RNA of CRISPR / Cas is designed to bind to nucleic acid molecules so that the Cas endonuclease can recognize the protospacer adjacent motif (PAM) in the nucleic acid molecule and cut (or nick) the nucleic acid molecule. The guide RNA (gRNA) in the CRISPR / Cas genome editing system targets a specific position adjacent to or close to the appropriate nucleotide protospacer adjacent motif (PAM) in the genome of the plant subject, as known to those skilled in the art. gRNA can be used to target a specific position in the plant genome that is contained in or near a wild-type expression control element. Cutting sequences in or near a gene expression control element can destroy endogenous sequences, or insert new genetic control sequences into genes. Suitable novel genetic control sequences will include one or more validated non-wild-type sequence designs determined by the methods of the present invention. Highly specific targeting enables CRISPR / Cas-based genome editing technology to make minimal genomic changes but achieve maximum phenotypic effects. Therefore, it is believed that this gene editing technology has great prospects for plant genome engineering due to its simplicity and efficiency.

[0093] In a specific embodiment of the present invention, a method is provided, wherein a new sequence design determined by the method of the present invention as described herein is used to modify plant cells. The new sequence design is used to guide the selection of target genes or genomic regions, thereby designing a guide RNA (gRNA) that guides Cas proteins (such as Cas9 or Cas12a) to the target site, constructing a CRISPR / Cas system comprising Cas proteins and gRNA, and delivering the CRISPR / Cass system to plant cells by established methods (such as Agrobacterium-mediated transformation). Cas proteins undergo DNA cleavage at the desired genomic position, and then activate the DNA repair mechanism of plant cells, such as by non-homologous end joining (NHEJ) and homology-mediated repair (HDR), and then screen and select the edited plant cells or tissues. Therefore, the method allows for precise modification, and the new sequence design is introduced into the plant genome, such as by HDR or by NHEJ to destroy the target gene. The edited plant cells or tissues can be regenerated into complete plants, so that the genome modification and the resulting phenotypic effects can be further characterized and evaluated. The disclosed CRISPR / Cas genome editing method has great application potential in plant research, crop improvement, disease resistance enhancement and development of new plant varieties.

[0094] In a specific embodiment of the present invention, a system is provided, the system comprising at least one processor configured to operate a DNA sequence model based on a convolutional neural network and a transformer-based architecture. The model is trained to provide a predicted value in the form of a digital output, and the predicted value represents the gene expression level of the DNA sequence information only. In one embodiment, the input gene is defined as a sequence of about 3000bp centered on the TSS (transcription start site) of the gene. The predicted value is expressed as a predicted RNA abundance count, which is a standard method for expressing gene expression, determined by measuring the amount of RNA molecules corresponding to a single gene in a given time point and a given tissue. RNA abundance is usually obtained by experimental RNA-seq determination. In this way, RNA is extracted from plant tissues and a sequencing method is performed to count the number of RNA transcripts associated with the genes present. The number is usually expressed in unit counts, such as FPKM (fragments per million mapped reads per kilobase of transcription) or TPM (transcripts per million). Using 80% of the genes of a given species as input genes for model training, the RNA-seq counts in FPKM or TPM under given conditions can be determined. For example, RNA-seq counts can be determined for a given plant in a particular tissue type (e.g., leaves) or under an environmental stress (e.g., drought). According to the methods of the present invention, models can be trained to predict RNA-seq counts based solely on the input gene sequence, i.e., without experimentally measuring RNA-seq counts. This amazingly powerful ability to predict gene expression outputs from query DNA sequences alone enables the generation of new expression control sequences, for example, through iterative modification, to change the predicted value in a positive (upregulating gene expression) or negative (downregulating gene expression).

[0095] Embodiments of the present invention enable in silico simulation of DNA sequence generation / promoter mutations coupled with model predictions (setting predicted values), e.g., by assigning RNA-seq counts to natural or synthetic query sequences. This approach allows for rapid regulation and / or improvement of gene expression in plants through iterative cycles of predicted gene expression with promoter and gene sequences without the need to validate each round of sequence changes in the laboratory. In practice, the sequence designs generated and sequence designs that are evaluated through multiple steps of model predicted values ​​(e.g., RNA-seq counts) can be further validated in a wet lab to further characterize behavior in plant cells and intact plants. Data generated through wet lab validation can also be used to further inform the model.

[0096] The invention will now be further illustrated by the following non-limiting examples.

[0097] Example

[0098] Example 1 - Altered PPO2 (PGSC0003DMG400018916) promoter in potato (Georgina variety) Computer-aided design

[0099] Potato bruising is a common agricultural problem that causes significant losses every year. One of the main genes affecting potato bruising is polyphenol oxidase 2 (PPO2). Controlling PPO2 expression in potatoes could result in potatoes without bruising.

[0100] The objective of this example was to evolve the regulatory region of the PPO2 gene in potato (Georgina variety) to downregulate the expression of PPO2 in potatoes and minimize discoloration associated with potato bruising.

[0101] Start data entry:

[0102] Whole potato genome sequence (Georgina variety, HiFi PacBio+IlluminaPE150 reads).

[0103] A 5000 base pair (bp) sequence region was selected upstream of the 5'UTR of all annotated coding sequence genes known in the potato (Georgina cv.) genome.

[0104] Differential expression analysis of potato transcriptome (Georgina variety) after 36 h exposure to bruise injury compared with the transcriptome without bruise (0 h).

[0105] result:

[0106] Analysis of the potato transcriptome showed that these genes are upregulated upon tuber bruising (see Figure 1 Among these genes, the polyphenol oxidase 2 (PPO2) gene was found to be in the top 3% of up-regulated genes. PPO2 was selected as a candidate for further analysis.

[0107] 19,000,000 promoter variants of 5000 bp from the 5'UTR PPO2 promoter region were screened for calculated differences at 917 DNA sites within the potato genome. The maximum number of base pairs altered within the selected 5000 base pairs was 150 bp, or a maximum of 3%.

[0108] Among 19,000,000 promoter variants, a model trained to classify the potato promoter set for expression in tubers predicted that 74,000 in silico evolved promoter variants significantly down-regulated PPO2, with a class differential expression of -11 log2 fold difference in potatoes (Georgina variety) exposed to bruise injury for 36 h compared to unexposed potatoes (see Figure 2 ). Fig. 9An alignment of an exemplary variant promoter sequence, designated variant_50981 [SEQ ID NO: 1], with wild-type PPO2 [SEQ ID ID: 1] is shown in FIG.

[0109] Example 2 - Computer-assisted expression of the MYB113 (AT1G66370) promoter in Arabidopsis thaliana (ecotypecol-0) evolution

[0110] Arabidopsis thaliana is a well-characterized model organism for plant science and is used here to demonstrate the present method.

[0111] Anthocyanin-based purple change is a change that occurs in Arabidopsis thaliana when it is stressed. It is controlled by the transcription factor gene MYB113, which is very tightly regulated. It is not expressed at any physiologically relevant level under non-stress conditions. By evolving the regulatory region of MYB113, it is possible to drive MYB113 expression in the absence of stress, which can produce plants that grow normally but have purple leaves.

[0112] The aim of this experiment was to evolve the regulatory region of the MYB113 gene in Arabidopsis thaliana (ecotypeCol-0) using existing computer-assisted evolution algorithms to upregulate MYB113 expression in leaves in the absence of stress, thereby producing plants with purple leaves.

[0113] Start data entry:

[0114] The Arabidopsis genome (ecotype Col-0, TAIR10 assembly) was obtained from Ensembl Plants (see Yates et al., Nucleic Acids Research, Volume 50, Issue D1, 7 January 2022, Pages D996-D1003).

[0115] The variable base pair sequence region upstream of the 5'UTR of all annotated coding sequence genes in the Arabidopsis thaliana (ecotype Col-0) genome.

[0116] Transcriptome expression dataset of RNA levels in different plant organs of Arabidopsis thaliana (ecotype Col-0) at different developmental ages obtained from the University of Toronto (Klepikova Atlas) (Klepikowa et al. (2016), The Plant J., Volume 88, Issue 6; 1058-1070).

[0117] result:

[0118] 9,000,000 promoter variants of the 754 bp 5'UTR MYB113 promoter region were computationally screened. The largest base pair (bp) changed was 35 bp, or a maximum of 4.6% of the 754 base pairs selected.

[0119] Out of 9,000,000 promoter variants, the model evaluated 16,000 promoter variants that were predicted to be significantly upregulated in Arabidopsis leaves compared with the wild-type MYB113 sequence.

[0120] To validate the model predictions, 1250 sequences (nMYB113) were randomly selected for the convenience of DNA library synthesis.

[0121] A plasmid library of 1250 variants containing the 754 bp 5' regulatory region of the MYB113 gene immediately upstream of the 5' MYB113 UTR was assembled using conventional techniques known to those skilled in the art. Other components of the plasmid included:

[0122] 1. 5'MYB113UTR from Arabidopsis thaliana TAIR10 genome,

[0123] 2. The gene encoding green fluorescent protein (EGFP),

[0124] 3. The 4-codon variable region at the beginning of the GFP gene serves as a unique molecular identifier (UMI),

[0125] 4. Derived from Arabidopsis thaliana TAIR10 genome 3'MYB113UTR,

[0126] 5.35S terminator

[0127] 6.35S expression cassette, including 35S promoter, 5'UTR, mCherry gene encoding fluorescent reporter gene, 3'UTR and terminator.

[0128] The vector map of the plasmid is as follows Figure 3 shown.

[0129] Population observation of transiently transfected Arabidopsis protoplast leaf cells showed that there was a promoter driving GFP expression in the nMYB113 library pool that triggered an increase in green fluorescence compared to the wild type, and there were variants of the MYB113 promoter sequence that had minor changes and were able to drive protein expression (see Figure 4 ).

[0130] Transcriptional activity assays with unique molecular identifiers (UMIs) were used to reveal individual promoter variants that resulted in increased gene expression. Specifically, for transcriptional activity assays, a library pool of 1250 variants of the 754 bp 5' regulatory region of the MYB113 gene immediately upstream of the 5' MYB113 UTR was cloned into a SuRE plasmid vector derived from a proprietary partner (Arensbergen et al. (2017) Nat Biotechnol. Feb; 35(2): 145-153).

[0131] Quantification of transcriptional activity assays based on the use of SuRE plasmids with a pool of nMYB113 promoter elements showed that a portion of the computer-designed library was able to upregulate transcription. Combined with UMI barcoding, 23 non-wild-type promoter variants were identified that had higher transcriptional activity than wild-type MYB113 in protoplast cells. This is a critical validation prior to whole-plant physiological testing. This also demonstrates the advantages of powerful high-throughput biological validation of AI-designed libraries, as surprisingly only a small portion of the computer-designed library behaved as predicted in plant cells (see Figure 5 and Figure 6 ).

[0132] Figure 7 Shown is the expression distribution of one of the nMYB113 design elements, called OP625_short_225. The distribution is done using multiple different UMIs. The X-axis represents the log2 fold difference in expression, normalized to the WT MYB113 promoter distribution. The Y-axis shows the density of different UMIs for each element.

[0133] Individually designed promoter elements can be further validated when removed from the library pool environment and tested at low throughput by fluorescence microscopy. In this experiment, an element named A2.1DEL was observed to drive protein expression in the absence of stress and produce a strong fluorescence signal in transiently transfected protoplast cells (see Figure 8(b)). For comparison, the wild-type MYB113 promoter drove negligible fluorescence in GFP expression (Figure 8(a)). Fig.10 An alignment of an exemplary variant promoter sequence named OP625_225 [SEQ ID NO: 3] with wild-type MYB113 [SEQ ID ID: 4] is shown in FIG.

[0134] Therefore, following high- and low-throughput confirmation of increased gene expression in Arabidopsis protoplasts, a small subset of the nMYB113 promoter library can be further selected to validate changes in whole-plant physiology achieved by stable transformation or genome editing.

[0135] Materials and methods:

[0136] Production of protoplasts. Arabidopsis protoplasts were obtained using the protocol described in Yoo, Cho and Sheen (2007), supra. They were washed twice in buffer to remove any free calcium ions. The washed protoplasts were then resuspended in a first solution to a cell concentration of 200 cells / μl. The first solution consisted of 100 mM CaCl2, 100 mM EDTA, 2% w / v sodium alginate and 0.5 M mannitol and was sterilized by high pressure steam.

[0137] Transfection: Isolated Arabidopsis protoplasts were transfected with plasmids containing the nMYB113 promoter library or control plasmids using the PEG-mediated transfection method described by Yoo, Cho, and Sheen (2007). The control plasmids had either GFP or mCHERRY fluorescent genes under the 35s expression cassette. The nMYB113 promoter library was characterized by the presence of MYB113 promoter variants upstream of the GFP gene and the presence of the 35s promoter upstream of the mCHERRY gene, which served as an internal control. The transfected protoplasts were incubated overnight at room temperature in a buffer containing sodium chloride (154 mM), calcium chloride (135 mM), potassium chloride (5 mM), glucose (5 mmol), and MES (1.5 mM) to ensure expression of fluorescent proteins downstream of the promoter.

[0138] FACS: Fluorescence activated cell sorting (FACS) was used to sort the transfected cells based on their fluorescent properties and measurements, using the method described by Gronlund et al., 2012. Control protoplasts were fluorescent due to the expression of GFP (green) or mCHERRY (red) downstream of the 35s promoter. On the other hand, protoplasts containing the nMYB113 library were sorted based on the expression of GFP driven by the activity of these novel promoters as well as the expression of mCHERRY driven by the 35s promoter used as an internal control. Sorted cells containing active MYB113 promoters were collected for DNA / RNA extraction.

[0139] DNA extraction and PCR sequencing: DNA was extracted from sorted protoplasts using the Zymo Quick DNA Plant / Sed Miniprep Kit (https: / / files.zymoresearch.com / protocols / _d6020_quick-dna_plant-seed_miniprep_kit.pdf). This DNA was then used as a template to amplify MYB113 promoter variants present in the sorted cells, which were sent for next generation sequencing (NGS) for identification.

[0140] RNA extraction, cDNA synthesis and PCR sequencing: Alternatively, total RNA was extracted from sorted protoplasts using the Sigma Spectrum Plant Total RNA Extraction Kit (https: / / www.sigmaaldrich.com / deepweb / assets / sigmaaldrich / product / documents / 885 / 605 / strn10bul.pdf). The extracted RNA was used as a template for cDNA synthesis using NEB cDNA synthesis was performed using the First Strand cDNA Synthesis Kit (https: / / international.neb.com / protocols / 0001 / 01 / 01 / first-strand-cdna-synthesis-e6300). MYB113 promoter variants present in sorted cells were amplified from purified cDNA and sent to NGS for identification.

[0141] Transcriptional activity assay: For transcriptional activity assay, the nMYB113 library was cloned into SuRE plasmid with a unique molecular identifier (UMI) for each promoter variant. This plasmid library was then transfected into Arabidopsis protoplasts using the method described above (Yoo et al., 2007). Total RNA was then extracted from the transfected protoplasts using the Sigma Spectrum Plant Total RNA Kit and sent to the supplier for further transcriptional analysis.

[0142] Example 3 - A computer model based on a convolutional neural network and a Transformer architecture can predict a given RNA-seq counts in FPKM or TPM for multiple genes across plant tissues and environmental conditions

[0143] Machine Learning (ML) Model (CRE.AI.TIVE TM v 3.5) has been trained to predict RNA-seq count values ​​for genes under different conditions based on a dataset consisting of genomes from multiple different plant species. The accuracy metric takes biological replicate variation into account. E.g. for gene A sampled in tissue A (such as seeds), individual seeds will have slightly different RNA-seq counts. When considering the accuracy of a model's predictions, it is best to look at how many predictions the model creates that are indistinguishable from natural biological replicate variation. The gene inputs used to create the model's predictions are saved as a test set away from model training, and the model weights are not affected by the test set.

[0144] wheat

[0145] Genome input: DNA input from the annotated reference genome wheat (Triticum aestivum, IWSC.v51) - Ensembl Plants

[0146] Transcriptome input: RNA-seq dataset (FPKM) from the Plant Public RNA-seq Database (https: / / doi.org / 10.1111 / pbi.13798)

[0147] 25 RNA-seq datasets

[0148] 11311 genes in the natural variation dataset

[0149] 2,273,511 data points in the Natural Variation Dataset

[0150] 90,487 genes in the training set

[0151] 11311 genes in the test set

[0152] 2544950 data points in total (training and validation)

[0153] Results: Natural variation in gene expression and model-predicted gene expression are shown in Figure 11. The natural variability and model prediction ability of 11,311 genes tested in 25 RNA-seq experiments are shown in Figure 11 (a). Figure 11 (a) shows the natural variation of biological replicates, and 94.71% of the genes have biological replicate RNA-seq count (FPKM) measurements within ±5 FPKM of the mean. Figure 11 (b) shows the model-predicted gene expression of genes in the test set, and 83.38% of the genes have predicted RNA-seq count values ​​within ±5 FPKM of the mean experimental value.

[0154] Natural variation in gene expression and model-predicted gene expression are shown in Figure 12. Natural variation and pattern prediction capabilities were tested for 11,311 genes in a single RNA-seq experiment in root tissue. Figure 12(a) shows the average gene expression in root tissue for 11,311 genes (three replicates per gene) plotted against the experimental RNA-seq data. The x-axis shows the individual gene expression values ​​and their minimum / maximum replicate values ​​for the genes measured, in order of gene expression intensity, from left to right from lowest expression to highest expression. The y-axis shows the intensity of gene expression in FPKM. Figure 12(b) shows the predictions created by the trained model, which are plotted for each individual gene. The dark grey points are within 5 FPKM of the experimentally observed mean. The light grey points are outside the 5 FPKM boundary of the mean experimentally observed value. Therefore, it is clear that the model predictions are in very good agreement with the in vivo expression data.

[0155] corn

[0156] Genome input: DNA input from annotated reference genome B73-B73.4 - EnsemblPlants

[0157] Transcriptomics input: RNA-seq dataset (FPKM) from the Plant Public RNA-seq Database (https: / / doi.org / 10.1111 / pbi.13798)

[0158] 116 RNA-seq datasets

[0159] 4554 genes in the natural variation dataset

[0160] 1,962,774 data points in the Natural Variation Dataset

[0161] 36435 genes in the training set

[0162] 4554 genes in the test set

[0163] 4754724 data points in total (training and validation)

[0164] Results: Natural variation in gene expression and model-predicted gene expression are shown in Figure 13. The natural variation and model prediction ability of 4554 genes tested in 116 RNA-seq experiments are shown in Figure 13 (a). Figure 13 (a) shows the natural variation of biological replicates, where 91.9% of the genes have biological replicate RNA-seq count (FPKM) measurements within ±5 FPKM of the mean. Figure 13 (b) shows the model-predicted gene expression of genes in the test set, and 64.95% of the genes have predicted RNA-seq count values ​​within ±5 FPKM of the mean experimental value.

[0165] Soybean

[0166] Genome input: DNA input from the annotated reference genome Williams 82 (Wm82.a2.v1) – genome assembly from Soybase, annotation from Ensembl Plants

[0167] Transcriptomics input: RNA-seq dataset (FPKM) from the Plant Public RNA-seq Database (https: / / doi.org / 10.1111 / pbi.13798)

[0168] 152 RNA-seq datasets

[0169] 5559 genes in the natural variation dataset

[0170] 2,295,867 data points in the Natural Variation Dataset

[0171] 44,835 genes in the training set

[0172] 5559 genes in the test set

[0173] Total 7659888 data points (training and validation)

[0174] Results: The natural variation of gene expression and the gene expression predicted by the model are shown in Figure 14. The natural variation and model prediction ability of 5559 genes were tested in 152 RNA-seq experiments. Figure 14 (a) shows the natural variation of biological replicates, and the biological replicate RNA-seq count (FPKM) measurements of 96.48% of the genes are within ±5FPKM of the mean. Figure 14 (b) The model predicted the gene expression of the genes in the test set, and the predicted RNA-seq count values ​​of 68.64% of the genes are within ±5FPKM of the mean experimental value.

[0175] in conclusion

[0176] The results show that a robust gene expression prediction model can create highly predictive RNA-seq count data purely from gene sequence data. The model has demonstrated this predictive ability across a range of plant species and different tissues and developmental stages.

[0177] Although specific embodiments of the present invention are disclosed in detail herein, this is only by way of example and is for illustrative purposes only. The above embodiments are not intended to limit the scope of the appended claims. The selection of nucleic acid starting materials, cloning of targets, or the type of library used is considered to be a routine matter for those skilled in the art after understanding the embodiments currently described. The inventors anticipate that various substitutions, changes, and modifications may be made to the present invention without departing from the spirit and scope of the present invention as defined in the claims.

Claims

1. A method for producing a nucleic acid library, the nucleic acid library consisting of a plurality of expression control sequences, the expression control sequences being configured to regulate the expression of coding sequences operably linked thereto in plant cells, the method comprising: performing a computer analysis of the genome of the plant subject, wherein the computer analysis comprises determining a plurality of sequences that collectively define a promoter group of the plant subject, wherein each sequence within the promoter group consists of an expression control region extending from a start codon of an open reading frame to about 100 kilobases 5' and / or 3' from the start codon; Obtaining transcriptome of the plant subject in the form of mRNA expression data; performing analysis using a sequence-based modeling algorithm to provide a predicted value for the expression level of each protein-coding gene within the transcriptome and linking the predicted value to the sequence of the corresponding expression control region within the promoter group; generating a plurality of non-wild-type sequence designs of an expression control sequence that is most likely to provide a desired expression profile for an operably linked coding sequence, wherein the plurality of non-wild-type sequence designs provide information with a predictive value; synthesizing a plurality of non-wild-type expression control sequences corresponding to the plurality of non-wild-type sequence designs; as well as A nucleic acid sequence library comprising the plurality of non-wild-type expression control sequences is generated.

2. The method of claim 1, wherein the expression control region comprises one or more sequences selected from the group consisting of: a promoter, an enhancer, a silencer, a transcription factor binding site, an intron, a transgenic sequence, and a transposon.

3. The method according to claim 1 or 2, wherein the promoter set comprises all or part of the protein coding region. The method according to claim 1 or 2 , wherein the promoter set does not contain a protein coding region.

5. The method according to any one of claims 1 to 4, wherein each sequence within the promoter group consists of an expression control region extending from the start codon of the open reading frame to less than about 100 kilobases 5' and / or 3' from the start codon, suitably less than 50 kilobases 5' and / or 3' from the start codon.

6. The method according to any one of claims 1 to 5, wherein: Analysis using sequence-based modeling algorithms involves training artificial intelligence (AI) or machine learning (ML) models.

7. The method of claim 6, wherein the AI ​​or ML model comprises a sequence prediction algorithm.

8. According to the method of claim 7, the sequence prediction algorithm is selected from: an artificial neural network (ANN) algorithm, for example, selected from a convolutional neural network (CNN) and a recurrent neural network (RNN), including a bidirectional RNN, a masked language model and a transformer network.

9. The method of any one of claims 1 to 8, wherein the desired expression profile preferentially provides expression control sequences that provide a higher expression level than a predicted value.

10. The method of any one of claims 1 to 8, wherein the desired expression profile preferentially provides expression control sequences that provide expression levels lower than a predicted value.

11. The method according to any one of claims 1 to 10, wherein the plurality of non-wild-type sequence designs are subject to constraints of minimal change requirements.

12. The method according to any one of claims 1 to 11, wherein the method further comprises validating one or more of the plurality of non-wild-type expression control sequences in vivo.

13. The method of claim 12, wherein the in vivo validation comprises cloning one or more of the plurality of non-wild-type expression control sequences into one or more plasmids, the plasmids comprising a reporter gene, the expression of the reporter gene being operably linked to the corresponding non-wild-type expression control sequence.

14. The method of claim 13, wherein the in vivo validation comprises performing a massively parallel reporter assay (MPRA).

15. The method of claim 14, wherein the MPRA comprises fluorescence activated cell sorting (FACS) analysis.

16. The method of claim 13, wherein each of the one or more plasmids comprising a reporter gene further comprises a unique molecular identifier (UMI) sequence.

17. The method of claim 16, wherein the in vivo validation comprises performing a transcriptional activity assay that measures the amount of reporter mRNA produced and associates the amount with a corresponding non-wild-type expression control sequence via a UMI.

18. The method according to any one of claims 12 to 17, wherein the in vivo validation is performed in plant protoplasts.

19. The method according to any one of claims 12 to 17, wherein the in vivo validation is performed in a plant cell.

20. The method according to any one of claims 12 to 17, wherein the in vivo validation is performed in a plant or a part of a plant.

21. The method of any one of claims 1 to 21, wherein the plant subject comprises a plant species or plant variety.

22. The method of claim 21, wherein the plant species or plant variety is selected from the group consisting of: Solanum spp. (e.g., tomato (S. lycopersicum), potato (S. tuberosum), melon (S. melongena), ginseng fruit (S. muricatum), sugar beet (S. betaceum); Brassica spp. (e.g., cabbage (B. oleracea), turnip (B. napobrassica), Brassica napus (B. napus), B. cretica, B. rupestris and Brassica rapa); Capsicum spp. (e.g., annual pepper (C. annuum), pendulous pepper (C. baccatum), Chinese pepper (C. chinense), bush pepper (C. frutescens), hairy pepper (C. pubescens); Lupinus spp. (e.g., spp.) (e.g., L. angustifolius, L. albus, L. mutabilis, and L. luteus); Phaseolus spp. (e.g., P. phaseolus, P. coccineus, P. lunatus, P. vulgaris, and P. dumosus); Vigna spp. (e.g., V. aconitifolia, V. angularis, V. mungo, V. radiata, V. subterranea, and V. unguiculata); Vicia faba; Chickpea (Cicer arietinum; pea (Pisumsativum); Lathyrus spp. (such as L.sativus and L.tuberosus); Lens spp. (such as L.culinaris and L.esculenta); soybean (Glycinemax); winged bean (Psophocarpus); pigeon pea (Cajanus cajan); peanut (Arachis hypogaea); Lactuca spp. (such as L.sativa, L.serriola, L.saligna, L.virosa and L.taterica; Asparagus (Asparagus officinalis); Celery (Apium graveolens); Allium spp. (such as common onion (A.cepa), shallot (A.oschaninii), European leek (A.ampeloprasum), A.wakegi, leek (A.porrum), garlic (A.sativum) and chives (A.schoenoprasum); Beet (Beta vulgaris); Chicory (Cichoriumintybus); Medicinal dandelion (Taraxacum officinale); Rocket (Eruca spp.) (such as E.vesicaria and E.sativa); Cucurbita spp. spp.) (e.g., C.argyosperma, C.digitata, C.pepo, C.moschata, C.ecuadorensis, C.ficifolia, C.foetidissima, C.galeottii, C.lundelliana, C.maxima, C.moshata, C.pedatifolia, C.radicans); Spinacia oleracea; Nasturtium officinale; Cucumbers (Cucumis spp.) (e.g., C.sativus, C.melo, C.hystrix, C.picrocarpus, and C.anguria); Olives (Oleaeuropaea); Carrots (Daucus carota); Sweet potatoes (Ipomoea batatas; Ipomoea eriocarpa; Manihotesculenta; Ginger (Zingiber officinale); Horseradish (Armoracia rusticana); Helianthus spp. (such as sunflower (H. annuus) and Jerusalem artichoke (H. tuberosus); Cannabis spp. (such as common hemp (C. sativa) and Indian hemp (C. indica); Parsnip (Pastina casativa); Radish (Raphanus sativus); Turmeric (Curcuma longa); Dioscorea spp. (such as white yam (D.rotundata), D.alata, D.polystachya, D.bulbifera, D.esculenta, D.dumetorum, D.trifid and D.cayennensis; Piper spp. (e.g., P.aduncum, P.guineense and P.nigrum); Zea spp. (e.g., Z.mays and Z.diploperennis); Hordeum spp. (e.g., H.vulgare, H.pusillum, H.murinum, H.murinum, H.jubatum and H.intercedens); Gossypium spp.) (such as G. hirsutum, G. barbadense, G. arboreum and G. herbaceum); Triticum spp. (such as T. aestivum and T. timopheevii); Vitis vinifera; Prunus sp. (such as P. avium, P. armeniaca, P. cerasifera, P. cerasus, P. domestica, P. persica and P. dulcis); Malus domestica; Pyrus spp. (such as P. communis, P. cordata and P. pyrifolia); Fragaria spp. (such as P. communis, P. cordata and P. pyrifolia); vesca and strawberry (Fragaria × ananassa); raspberry (Rubus idaeus); sugarcane (Saccharum officinarum); cultivated sorghum (Sorghum saccharatum); wild banana (Musa balbisiana) and banana (Musax paradisiaca); rice (Oryza sativa); tobacco (Nicotiana tabacum); Arabidopsis thaliana; citrus spp. (such as lime (C. × aurantifolia), orange (C. × aurantium), broad-leaved lime (C. × latifolia), lemon (C.×limon), C. ×limonia, C. ×paradisi, C. ×sinensis, and C. ×tangerina; Populus spp. (e.g. P. tremula, P. balsamifera, and P. tomentosa); Tulip (Tulipagesneriana); Alfalfa (Medicago sativa); Balsam fir (Abies balsamea); Avena orientalis; Bromus mango; Calendula officinalis; Chrysanthemum balsamita; Dianthus caryophyllus; Eucalyptus spp. (such as E.leucoxylon, E.maculata, E.polybractea, E.sargentii); Impatiens biflora; Linum usitatissimum; Lycopersicon esculentum; Mangifera indica; Nelumbo spp. (such as N.nucifera and N.pentapatala); Poacea spp.; Secale cereale; Tagetes erecta and Tagetes minuta.

23. A nucleic acid library comprising a plurality of non-wild-type expression control sequences configured to regulate the expression of coding sequences operably linked thereto in a plant cell.

24. A nucleic acid library comprising a plurality of non-wild-type expression control sequences configured to regulate the expression of coding sequences operably linked thereto in plant cells, wherein the plurality of non-wild-type expression control sequences are produced by the method of any one of claims 1 to 22.

25. A computer-implemented method for genome analysis of a plant subject, wherein the analysis comprises: determining a plurality of sequences that collectively define a plant subject promoter group data set, wherein each sequence in the promoter group consists of sequence data corresponding to an expression control region extending from a start codon of an open reading frame within a genome to about 100 kilobases 5' and / or 3' from the start codon; Obtaining a transcriptome dataset in the form of mRNA expression data of a plant subject; performing analysis using a sequence-based machine learning modeling algorithm trained with all or a portion of the promoter set data set and all or a portion of the transcriptome data set to provide a predictive value for the expression level of a gene placed under the operable control of any given expression control sequence contained within the promoter set; as well as The sequence-based machine learning modeling algorithm is queried with one or more query sequence designs, wherein if the one or more query sequence designs are introduced into an expression control region of the gene, the sequence-based computer learning modeling algorithm applies the prediction value to the one or more query sequence designs to provide a prediction of gene expression in the plant subject.

26. The method of claim 25, wherein the prediction of the gene expression is provided in the form of RNA-Seq unit count values ​​for the gene.

27. The method of claim 26, wherein the RNA-Seq unit counts are provided in fragments per kilobase of transcript per million mapped reads (FPKM).

28. The method of claim 26, wherein the RNA-Seq unit counts are provided as transcripts per million (TPM).

29. The method of any one of claims 25 to 28, wherein one or more query sequence designs result in a predicted increase in expression of the gene.

30. The method of any one of claims 25 to 28, wherein one or more query sequence designs result in a predicted decrease in expression of the gene.

31. The method according to any one of claims 25 to 30, wherein the transcriptome dataset is obtained from a plant under environmental stress conditions, optionally wherein the plant is under water stress conditions or salt stress conditions.

32. The method of any one of claims 25 to 31, wherein the transcriptome dataset is obtained from plant tissue.

33. The method of claim 32, wherein the plant tissue is selected from the group consisting of: leaf, stem, root, tuber, seed, branch, pubescence, node, axil, flower, pollen, stamen, pistil, petal, peduncle, stem, stigma, style, bract, fruit, trunk, carpel, sepal, anther, ovule, pedicel, needle, cone, rhizome, runner, bud, pericarp, endosperm, placenta, berry, stamen, or sheath.

34. The method of any one of claims 25 to 30, wherein the plant subject is selected from: Solanum spp. (e.g., tomato (S. lycopersicum), potato (S. tuberosum), melon (S. melongena), ginseng fruit (S. muricatum), beet (S. betaceum); Brassica spp. (e.g., cabbage (B. oleracea), turnip (B. napobrassica), Brassica napus (B. napus), B. cretica, B. rupestris and Brassica rapa (B. rapa)); Capsicum spp. (e.g., annual pepper (C. annuum), pendulous pepper (C. baccatum), Chinese pepper (C. chinense), shrub pepper (C. frutescens), hairy pepper (C. pubescens)); Lupinus spp. (e.g., spp.) (e.g., L. angustifolius, L. albus, L. mutabilis, and L. luteus); Phaseolus spp. (e.g., P. phaseolus, P. coccineus, P. lunatus, P. vulgaris, and P. dumosus); Vigna spp. (e.g., V. aconitifolia, V. angularis, V. mungo, V. radiata, V. subterranea, and V. unguiculata); Vicia faba; Chickpea (Cicera rietinum); Pea (Pisum sativum; Lathyrus spp. (such as L. sativus and L. tuberosus); Lens spp. (such as L. culinaris and L. esculenta); soybean (Glycine max); winged bean (Psophocarpus); pigeon pea (Cajanus cajan); peanut (Arachis hypogaea); Lactuca spp. (such as L. sativa, L. serriola, L. saligna, L. virosa and L.taterica; Asparagus (Asparagus officinalis); Celery (Apium graveolens); Allium spp. (such as common onion (A.cepa), shallot (A.oschaninii), European leek (A.ampeloprasum), A.wakegi, leek (A.porrum), garlic (A.sativum) and chives (A.schoenoprasum); Beet (Beta vulgaris); Chicory (Cichorium intybus); Medicinal dandelion (Taraxacum officinale); Rocket (Eruca spp.) (such as E.vesicaria and E.sativa); Cucurbita spp. spp.) (e.g., C.argyosperma, C.digitata, C.pepo, C.moschata, C.ecuadorensis, C.ficifolia, C.foetidissima, C.galeottii, C.lundelliana, C.maxima, C.moshata, C.pedatifolia, C.radicans); Spinacia oleracea; Nasturtium officinale; Cucumber spp. (e.g., C.sativus, C.melo, C.hystrix, C.picrocarpus, and C.anguria); Olive (Olea europaea); Carrot (Daucus carota); Sweet potato (Ipomoea batatas; Ipomoea eriocarpa; Manihotesculenta; Ginger (Zingiber officinale); Horseradish (Armoracia rusticana); Helianthus spp. (such as sunflower (H. annuus) and Jerusalem artichoke (H. tuberosus); Cannabis spp. (such as common hemp (C. sativa) and Indian hemp (C. indica); Pastinaca sativa; Radish (Raphanus sativus); Turmeric (Curcuma longa); Dioscorea spp. (such as white yam (D.rotundata), D.alata, D.polystachya, D.bulbifera, D.esculenta, D.dumetorum, D.trifid and D.cayennensis; Piper spp. (e.g., P.aduncum, P.guineense and P.nigrum); Zea spp. (e.g., Z.mays and Z.diploperennis); Hordeum spp. (e.g., H.vulgare, H.pusillum, H.murinum, H.murinum, H.jubatum and H.intercedens); Gossypium spp.) (such as G. hirsutum, G. barbadense, G. arboreum and G. herbaceum); Triticum spp. (such as T. aestivum and T. timopheevii); Vitis vinifera; Prunus sp. (such as P. avium, P. armeniaca, P. cerasifera, P. cerasus, P. domestica, P. persica and P. dulcis); Malus domestica; Pyrus spp. (such as P. communis, P. cordata and P. pyrifolia); Fragaria spp. (such as P. communis, P. cordata and P. pyrifolia); vesca and strawberry (Fragaria × ananassa); raspberry (Rubus idaeus); sugarcane (Saccharum officinarum); cultivated sorghum (Sorghum saccharatum); wild banana (Musa balbisiana) and banana (Musax paradisiaca); rice (Oryza sativa); tobacco (Nicotiana tabacum); Arabidopsis thaliana; citrus spp. (such as lime (C. × aurantifolia), orange (C. × aurantium), broad-leaved lime (C. × latifolia), lemon (C.×limon), C. ×limonia, C. ×paradisi, C. ×sinensis, and C. ×tangerina; Populus spp. (e.g. P. tremula, P. balsamifera, and P. tomentosa); Tulip (Tulipagesneriana); Alfalfa (Medicago sativa); Balsam fir (Abies balsamea); Avena orientalis; Bromus mango; Calendula officinalis; Chrysanthemum balsamita; Dianthus caryophyllus; Eucalyptus spp. (such as E.leucoxylon, E.maculata, E.polybractea, E.sargentii); Impatiens biflora; Linum usitatissimum; Lycopersicon esculentum; Mangifera indica; Nelumbo spp. (such as N.nucifera and N.pentapatala); Poacea spp.; Secale cereale; Tagetes erecta and Tagetes minuta.

35. The method according to any one of claims 25 to 28, wherein the method further comprises the following steps: One or more query sequence designs having desired gene expression predictions are identified and the one or more query sequence designs are validated in an in vivo validation assay.

36. The method of claim 35, wherein the in vivo validation assay is performed in plant protoplasts.

37. The method of claim 35, wherein the in vivo validation assay is performed in a plant cell.

38. The method of claim 35, wherein the in vivo validation assay is performed in a plant or a part of a plant.

39. The method of any one of claims 35 to 38, wherein the in vivo validation assay comprises cloning the one or more query sequence designs into expression control sequences within one or more expression plasmids, the one or more expression plasmids comprising a reporter gene, the expression of the reporter gene being operably linked to the expression control sequences.

40. The method of claim 39, wherein the in vivo validation comprises performing a massively parallel reporter assay (MPRA).

41. The method of claim 40, wherein the MPRA comprises fluorescence activated cell sorting (FACS) analysis.

42. The method of any one of claims 39 to 41, wherein each of the one or more expression plasmids comprising a reporter gene further comprises a unique molecular identifier (UMI) sequence.

43. The method of claim 42, wherein the in vivo validation assay comprises performing a transcriptional activity assay that measures the amount of reporter mRNA produced and relates the amount to a corresponding expression control sequence via a UMI.

44. A method for producing a modified plant cell, comprising analyzing the genome of a plant cell according to any one of claims 25 to 43, determining at least one sequence design that has a desired prediction for gene expression in the plant cell, and modifying an expression control region to conform to at least one sequence design in the plant cell to obtain a modified plant cell with desired gene expression.

45. The method of claim 44, wherein the expression control region is modified by using CRISPR / Cas genome editing technology.

46. ​​A modified plant cell obtained or obtainable according to the method of claim 44 or 45.

47. The modified plant cell of claim 46, wherein the plant cell is selected from the group consisting of: Solanum spp. (e.g., tomato (S. lycopersicum), potato (S. tuberosum), melon (S. melongena), ginseng fruit (S. muricatum), sugar beet (S. betaceum); Brassica spp. (e.g., cabbage (B. oleracea), turnip (B. napobrassica), Brassica napus (B. napus), B. cretica, B. rupestris and Brassica rapa); Capsicum spp. (e.g., annual pepper (C. annuum), pendulous pepper (C. baccatum), Chinese pepper (C. chinense), bush pepper (C. frutescens), hairy pepper (C. pubescens); Lupinus spp. (e.g., spp.) (e.g., L. angustifolius, L. albus, L. mutabilis, and L. luteus); Phaseolus spp. (e.g., P. phaseolus, P. coccineus, P. lunatus, P. vulgaris, and P. dumosus); Vigna spp. (e.g., V. aconitifolia, V. angularis, V. mungo, V. radiata, V. subterranea, and V. unguiculata); Vicia faba; Chickpea (Cicer arietinum; pea (Pisumsativum); Lathyrus spp. (such as L.sativus and L.tuberosus); Lens spp. (such as L.culinaris and L.esculenta); soybean (Glycinemax); winged bean (Psophocarpus); pigeon pea (Cajanus cajan); peanut (Arachis hypogaea); Lactuca spp. (such as L.sativa, L.serriola, L.saligna, L.virosa and L.taterica; Asparagus (Asparagus officinalis); Celery (Apium graveolens); Allium spp. (such as common onion (A.cepa), shallot (A.oschaninii), European leek (A.ampeloprasum), A.wakegi, leek (A.porrum), garlic (A.sativum) and chives (A.schoenoprasum); Beet (Beta vulgaris); Chicory (Cichoriumintybus); Medicinal dandelion (Taraxacum officinale); Rocket (Eruca spp.) (such as E.vesicaria and E.sativa); Cucurbita spp. spp.) (e.g., C.argyosperma, C.digitata, C.pepo, C.moschata, C.ecuadorensis, C.ficifolia, C.foetidissima, C.galeottii, C.lundelliana, C.maxima, C.moshata, C.pedatifolia, C.radicans); Spinacia oleracea; Nasturtium officinale; Cucumbers (Cucumis spp.) (e.g., C.sativus, C.melo, C.hystrix, C.picrocarpus, and C.anguria); Olives (Oleaeuropaea); Carrots (Daucus carota); Sweet potatoes (Ipomoea batatas; Ipomoea eriocarpa; Manihotesculenta; Ginger (Zingiber officinale); Horseradish (Armoracia rusticana); Helianthus spp. (such as sunflower (H. annuus) and Jerusalem artichoke (H. tuberosus); Cannabis spp. (such as common hemp (C. sativa) and Indian hemp (C. indica); Parsnip (Pastina casativa); Radish (Raphanus sativus); Turmeric (Curcuma longa); Dioscorea spp. (such as white yam (D.rotundata), D.alata, D.polystachya, D.bulbifera, D.esculenta, D.dumetorum, D.trifid and D.cayennensis; Piper spp. (e.g., P.aduncum, P.guineense and P.nigrum); Zea spp. (e.g., Z.mays and Z.diploperennis); Hordeum spp. (e.g., H.vulgare, H.pusillum, H.murinum, H.murinum, H.jubatum and H.intercedens); Gossypium spp.) (such as G. hirsutum, G. barbadense, G. arboreum and G. herbaceum); Triticum spp. (such as T. aestivum and T. timopheevii); Vitis vinifera; Prunus sp. (such as P. avium, P. armeniaca, P. cerasifera, P. cerasus, P. domestica, P. persica and P. dulcis); Malus domestica; Pyrus spp. (such as P. communis, P. cordata and P. pyrifolia); Fragaria spp. (such as P. communis, P. cordata and P. pyrifolia); vesca and strawberry (Fragaria × ananassa); raspberry (Rubus idaeus); sugarcane (Saccharum officinarum); cultivated sorghum (Sorghum saccharatum); wild banana (Musa balbisiana) and banana (Musax paradisiaca); rice (Oryza sativa); tobacco (Nicotiana tabacum); Arabidopsis thaliana; citrus spp. (such as lime (C. × aurantifolia), orange (C. × aurantium), broad-leaved lime (C. × latifolia), lemon (C.×limon), C. ×limonia, C. ×paradisi, C. ×sinensis, and C. ×tangerina; Populus spp. (e.g. P. tremula, P. balsamifera, and P. tomentosa); Tulip (Tulipagesneriana); Alfalfa (Medicago sativa); Balsam fir (Abies balsamea); Avena orientalis; Bromus mango; Calendula officinalis; Chrysanthemum balsamita; Dianthus caryophyllus; Eucalyptus spp. (such as E.leucoxylon, E.maculata, E.polybractea, E.sargentii); Impatiens biflora; Linum usitatissimum; Lycopersicon esculentum; Mangifera indica; Nelumbo spp. (such as N.nucifera and N.pentapatala); Poacea spp.; Secale cereale; Tagetes erecta and Tagetes minuta.

48. A modified plant derived from the plant cell of any one of claims 46 or 47.

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