US Patent Application for COMPOSITIONS AND METHODS FOR EXPRESSING GENES OF INTEREST IN HOST CELLS Patent Application (Application #20240177797 issued May 30, 2024) (2024)

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation of International Application No. PCT/US2022/031424 filed May 27, 2022, which claims priority to U.S. Provisional Patent Application No. 63/194,424 filed May 28, 2021, the contents of each of which are incorporated herein by reference in their entireties.

REFERENCE TO AN ELECTRONIC SEQUENCE LISTING

The contents of the electronic sequence listing (ALRO_009_01US_SeqList_ST26.xml; Size: 1,019,910 bytes; and Date of Creation: Nov. 7, 2023) are herein incorporated by reference in its entirety.

TECHNICAL FIELD

The present disclosure is related to compositions and methods for expressing exogenous proteins in host cells.

BACKGROUND

The global population is estimated to reach 9.1-9.7 billion by 2050, with an approximately 1.8-fold increase in income per capita. Food consumption is predicted to become increasingly based on animal derived products—mainly due to increased wealth in developing countries. Thus, population growth and increased demand for high-protein diets will require dramatic changes in the food industry.

Proteins used as ingredients in the food industry have traditionally been based on isolates from natural sources, such bovine milk. However, it is rapidly becoming unsustainable to produce animal-derived food products, given their environmental impact and limited resources for production thereof. Recombinant protein production could provide an alternative source of protein for use in food applications. However, in order to make the use of recombinant proteins in food economically feasible, high levels of protein expression must be achieved in one or more host cells. Achieving such high expression levels poses numerous technical challenges.

Ovalbumin (OVAL) and β-Lactoglobulin (LG) are two proteins that play an important role in the food industry. OVAL is the main protein found in egg white that comprises 54% of the total protein. It is a globular monomeric protein that comprises a single polypeptide chain of 385 amino acids with a 42.7 kDa molecular weight which exist as a tetramer. OVAL is widely used in the food industry for its ability to foam and to form gels. On the other hand, LG is the major whey protein of milk in most mammals, making up approximately 52% of the whey protein. It is an 18.2 kDa highly structured globular protein that can exist as monomers, dimers and multimers depending on the pH of the medium. LG is a valuable ingredient in formulating food products because it can improve properties such as emulsification, gelling, biding and can provide increased nutritional value for health and wellbeing.

There is an urgent need to develop compositions and methods that allow for production of recombinant proteins at high levels in one or more host cells, so that the proteins may be used as ingredients in food compositions. Specifically, there is a need in the art to develop compositions and methods that increase expression of OVAL and LG in various transgenic organisms, cells, and the like.

BRIEF SUMMARY

Provided is a method for selecting a nucleic acid sequence, said method comprising the steps of a) providing data on a plurality of nucleic acid sequences; b) predicting secondary structure of the plurality of nucleic acid sequences, with a plurality of RNA folding models, such that each nucleic acid sequence in the plurality of nucleic acid sequences is associated with at least two predicted secondary structures; c) determining a structural similarity score for the at least two predicted secondary structures associated with each nucleic acid sequence; and d) selecting a nucleic acid sequence with a higher structural similarity score than at least one other nucleic acid sequence in the plurality of nucleic acid sequences; wherein the selected nucleic acid sequence is predicted to accumulate at higher levels when expressed in a host cell.

Provided is a method for selecting a nucleic acid sequence, said method comprising the steps of: a) providing data on a plurality of nucleic acid sequences, each nucleic acid sequence in the plurality of nucleic acid sequences being associated with at least two predicted secondary structures from different RNA folding models; b) determining a structural similarity score for the at least two predicted secondary structures associated with each nucleic acid sequence; c) selecting a nucleic acid sequence with a higher structural similarity score than at least one other nucleic acid sequence in the plurality of nucleic acid sequences; wherein the selected nucleic acid sequence is predicted to accumulate at higher levels when expressed in a host cell. In some embodiments, at least one of the RNA folding models employs machine learning. In some embodiments, the plurality of nucleic acid sequences encode the same amino acid sequence. In some embodiments, the plurality of nucleic acid sequences encode amino acids sharing at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% sequence identity. In some embodiments, the method comprises manufacturing the selected nucleic acid sequence into a nucleic acid. In some embodiments, the method comprises expressing the selected nucleic acid sequence in a host cell. In some embodiments, the method comprises expressing the manufactured nucleic acid in a host cell. In some embodiments, the nucleic acid sequence encodes for a messenger RNA. In some embodiments, the RNA folding models comprise a model selected from the group consisting of co*cke-Younger Kasami model, inside and outside models, loop-based energy model, minimum free energy, suboptimal folding, centroid, and any combination thereof. In some embodiments, the at least two predicted secondary structures are a minimum free energy structure and a centroid structure. In some embodiments, the structural similarity score is determined via tool selected from the group consisting of Consan, Dynalign, PMcomp, Stemloc, Foldalign, locARNA, SPARSE, MARNA, FoldAlignM, Murlet, CARNA, RAF, RNAforester, RNAdistance, RNAStrAt, RNApdist, and any combination thereof. In some embodiments, the structure similarity score is based on visual inspection of the predicted secondary structures. In some embodiments, the structure similarity score is a ranking of the plurality of nucleic acid sequences based on the relative similarity of each nucleic acid sequences' predicted secondary structures. In some embodiments, the similarity score is based on degree of curve overlap in a graph depicting number of base pairs at each position of the predicted secondary structures. In some embodiments, the similarity score is based on the degree of curve overlap of the predicted secondary structures plotted in a mountain plot. In some embodiments, the similarity score is based on the correlation of curves representing the predicted secondary structures in a graph depicting number of base pairs at each position. In some embodiments, the degree of curve overlap is calculated by methodology selected from the group consisting of least squares, curve length measure, and any combination thereof.

Provided is a method of manufacturing a nucleic acid, said method comprising: a) manufacturing a selected nucleic acid sequence to produce a nucleic acid, wherein the selection of the nucleic acid sequence was based on the selected nucleic acid sequence having a higher structural similarity score than at least one other nucleic acid sequence in a plurality of nucleic acid sequences; wherein the structural similarity score is based on the structural similarity between at least two predicted secondary structures for each nucleic acid sequence, the predicted secondary structures produced by different RNA folding models. In some embodiments, at least one of the RNA folding models employs machine learning. In some embodiments, the plurality of nucleic acid sequences encode the same amino acid sequence. In some embodiments, the plurality of nucleic acid sequences encode amino acids sharing at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% sequence identity. In some embodiments, the method comprises expressing the manufactured nucleic acid in a host cell. In some embodiments, the manufactured nucleic acid expresses at a higher level than other nucleic acids containing other nucleic acid sequences from the plurality of nucleic acid sequences. In some embodiments, the RNA folding models comprise a model selected from the group consisting of co*cke-Younger Kasami model, inside and outside models, loop-based energy model, minimum free energy, suboptimal folding, centroid, and any combination thereof. In some embodiments, the at least two predicted secondary structures are a minimum free energy structure and a centroid structure. In some embodiments, the structural similarity score is determined via tool selected from the group consisting of Consan, Dynalign, PMcomp, Stemloc, Foldalign, locARNA, SPARSE, MARNA, FoldAlignM, Murlet, CARNA, RAF, RNAforester, RNAdistance, RNAStrAt, RNApdist, and any combination thereof. In some embodiments, the structure similarity score is based on visual inspection of the predicted secondary structures. In some embodiments, the structure similarity score is a ranking of the plurality of nucleic acid sequences based on the relative similarity of each nucleic acid sequences' predicted secondary structures. In some embodiments, the similarity score is based on degree of curve overlap in a graph depicting number of base pairs at each position of the predicted secondary structures. In some embodiments, the similarity score is based on the degree of curve overlap of the predicted secondary structures plotted in a mountain plot.

Provided is a nucleic acid comprising a nucleic acid sequence selected in a method of the disclosure.

Provided is a host cell comprising a nucleic acid comprising a sequence of Table 11, Table 12, or Table 15. In some embodiments, the nucleic acid comprises a sequence selected from the group consisting of: SEQ ID NO: 757, 760, 762, 763, 765, 772, 773, 778, and 780.

Provided is a host cell comprising a nucleic acid encoding any one of SEQ ID NO: 685, 687, and 695.

Provided herein is a host cell that comprises an exogenous RNA sequence that encodes a chordate protein, wherein the exogenous RNA sequence is stabilized as determined by increased expression of the chordate protein as compared to an otherwise comparable host cell lacking the exogenous RNA sequence that is stabilized, and wherein the chordate protein is expressed in the amount of at least 1% or higher per total protein weight of soluble protein extractable from the host cell.

In some embodiments, the chordate is a vertebrate. In some embodiments, the vertebrate is a mammal. In some embodiments, the mammal is a bovine. In some embodiments, the vertebrate is a bird. In some embodiments, the bird is a chicken.

In some embodiments, the chordate protein is an egg protein or a milk protein. In some embodiments, the chordate protein is a milk protein. In some embodiments, the milk protein is β-lactoglobulin. In some embodiments, the chordate protein is an egg protein. In some embodiments, the egg protein is ovalbumin. In some embodiments, the chordate protein is expressed in the amount of at least 2%, at least 3%, at least 4%, or at least 5% per total protein weight of soluble protein extractable from the host cell. In some embodiments, the chordate protein is expressed in the amount of about 1 to about 2%, about 2 to about 3%, or about 2 to about 5% per total protein weight of soluble protein extractable from the host cell.

In some embodiments, provided herein is a plant that comprises a host cell. In some embodiments, the plant is a soybean plant.

In some embodiments, provided is a DNA construct for expression of a transgene in a host cell, wherein the DNA construct comprises: (a) a codon-optimized transgene sequence that comprises at least 90% identity to a sequence selected from the group consisting of SEQ ID NO: 1, 3, 5, 7, 9-13, 83, 617-621, 683-690, and 693-700; and (b) an exogenous intron sequence, wherein the exogenous intron sequence comprises at least 90% identity to a sequence selected from the group consisting of: SEQ ID NO: 679-682. In some embodiments, the codon-optimized transgene sequence comprises a sequence selected from SEQ ID NO: 1, 3, 5, 7, 9-13, 83, 617-621, 683-690, and 693-700. In some embodiments, the exogenous intron sequence comprises a sequence selected from SEQ ID NO: 679-682.

In some embodiments, provided is a DNA construct for expression of a transgene in a host cell, wherein the DNA construct comprises: (a) a codon-optimized transgene sequence that comprises at least 90% identity to a sequence selected from the group consisting of SEQ ID NO: 1, 3, 5, 7, 9-13, 83, 617-621, 683-690, and 693-700; and (b) an exogenous intron sequence, wherein the exogenous intron sequence comprises at least 90% identity to a sequence selected from the group consisting of: SEQ ID NO: 679-682. In some embodiments, the codon-optimized transgene sequence comprises a sequence selected from SEQ ID NO: 1, 3, 5, 7, 9-13, 83, 617-621, 683-690, and 693-700. In some embodiments, the exogenous intron sequence comprises a sequence selected from SEQ ID NO: 679-682. In some embodiments, the DNA construct further comprises a signal peptide sequence. In some embodiments, the signal peptide sequence is selected from the group consisting of: SEQ ID NO: 616, 707-717. In some embodiments, the DNA construct further comprises a sequence encoding a KDEL sequence. In some embodiments, the DNA construct further comprises a sequence encoding at least one of a 5′ UTR and a 3′ UTR. In some embodiments, the DNA construct further comprises a sequence encoding a ubiquitin monomer. In some embodiments, the DNA construct further comprises an exogenous promoter sequence. In some embodiments, the exogenous promoter sequence is isolated or derived from a plant promoter sequence. In some embodiments, the exogenous promoter sequence is isolated or derived from a seed promoter sequence. In some embodiments, the DNA construct further comprises an exogenous terminator sequence.

Provided herein is also a composition that comprises a DNA construct.

Provided herein is also a method of transforming a host cell, the method comprising contacting a host cell with a composition provided herein, thereby transforming the host cell. In some embodiments, the host cell is a plant cell. In some embodiments, the method comprises bombardment or agrobacterium-mediated transformation. In some embodiments, the method further comprises cultivating the plant cell after the transforming.

Provided herein is an RNA generated from a DNA construct provided herein.

Provided herein is also a method of expressing ovalbumin or β-lactoglobulin in a plant, the method comprising: contacting at least a portion of a plant with a DNA construct of the disclosure, wherein the method is effective in increasing expression of the ovalbumin or β-lactoglobulin as compared to an otherwise comparable method lacking the contacting. In some embodiments, the method is effective in increasing expression of the ovalbumin or β-lactoglobulin by at least about 1-fold as compared to an otherwise comparable method lacking the contacting.

Provided herein is also a method of stably expressing a chordate protein in a plant cell, the method comprising: (a) contacting a plant cell with a DNA construct that comprises at least 90% identity to a sequence selected from the group consisting of SEQ ID NO: 752-766, thereby generating a transformed plant cell; and (b) cultivating a plant that comprises the transformed plant cell, thereby generating a transformed plant, wherein the chordate protein is expressed in the amount of 1% or higher per total protein weight of soluble protein extractable from the transformed plant cell. In some embodiments, the DNA construct comprises at least 95%, at least 97%, or at least 99% identity to a sequence selected from the group consisting of SEQ ID NO: 752-766.

Provided herein is also a method of stably expressing a chordate protein in a plant cell, the method comprising: (a) contacting a plant cell with a DNA construct that comprises at least 90% identity to a sequence selected from the group consisting of SEQ ID NO: 767-781, thereby generating a transformed plant cell; and (b) cultivating a plant that comprises the transformed plant cell, thereby generating a transformed plant, wherein the chordate protein is expressed in the amount of 1% or higher per total protein weight of soluble protein extractable from the transformed plant cell. In some embodiments, the DNA construct comprises at least 95%, at least 97%, or at least 99% identity to a sequence selected from the group consisting of SEQ ID NO: 767-781. In some embodiments, the chordate protein is expressed in the amount of at least 1%, at least 2%, at least 3%, at least 4%, or at least 5% per total protein weight of soluble protein extractable from the transformed plant cell. In some embodiments, the plant cell is from a soybean plant. In some embodiments, the contacting comprises bombardment or agrobacterium-mediated transformation. In some embodiments, a level of a transcript of a transgene encoded by the DNA construct is increased by at least 1-fold as compared to an otherwise comparable method lacking the contacting. In some embodiments, a level of the chordate protein encoded by the DNA construct is increased by at least 1-fold as measured by ELISA and as compared to an otherwise comparable method lacking the contacting. In some embodiments, the level is increased by at least 3-fold, at least 5-fold, at least 10-fold, at least 30-fold, or at least 50-fold. In some embodiments, the method further comprises isolating a seed from the transformed plant.

Provided herein is also a nutraceutical that comprises a chordate protein isolated from a transformed plant cell generated by a method of the disclosure.

DETAILED DESCRIPTION

The following description includes information that may be useful in understanding the present disclosure. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed disclosures, or that any publication specifically or implicitly referenced is prior art.

Provided herein are compositions and methods for increasing expression levels in a host cell of one or more proteins encoded by transgenes by way of RNA stabilization. These compositions and methods may be used to express one or more proteins, such as ovalbumin (OVAL) and β-Lactoglobulin (LG), at high levels in a host cell. Also provided are various transgenic organisms, animals, crops, and cells that comprise stabilized RNA and/or enhanced levels of proteins encoded by the stabilized RNA. The compositions and methods may be used to generate transgenic cells, organisms, crops, animals, and the like, and to produce recombinant protein therein.

Definitions

While the following terms are believed to be well understood by one of ordinary skill in the art, the following definitions are set forth to facilitate explanation of the presently disclosed subject matter.

All technical and scientific terms used herein, unless otherwise defined below, are intended to have the same meaning as commonly understood by one of ordinary skill in the art. References to techniques employed herein are intended to refer to the techniques as commonly understood in the art, including variations on those techniques and/or substitutions of equivalent techniques that would be apparent to one of skill in the art.

As used herein, the singular forms “a,” “an,” and “the: include plural referents unless the content clearly dictates otherwise.

The term “about” or “approximately” when immediately preceding a numerical value means a range (e.g., plus or minus 10% of that value). For example, “about 50” can mean 45 to 55, “about 25,000” can mean 22,500 to 27,500, etc., unless the context of the disclosure indicates otherwise, or is inconsistent with such an interpretation. When used in conjunction with a range or series of values, the term “about” applies to the endpoints of the range or each of the values enumerated in the series, unless otherwise indicated. Similarly, the term “about” when preceding a series of numerical values or a range of values (e.g., “about 10, 20, 30” or “about 10-30”) refers, respectively to all values in the series, or the endpoints of the range.

As used herein, “mammalian milk” can refer to milk derived from any mammal, such as bovine, human, goat, sheep, camel, buffalo, water buffalo, dromedary, llama and any combination thereof. In some embodiments, a mammalian milk is a bovine milk.

As used herein, “rennet” refers to a set of enzymes typically produced in the stomachs of ruminant mammals. Chymosin, its key component, is a protease enzyme that cleaves x-casein (to produce para-κ-casein and a macropeptide). In addition to chymosin, rennet contains other enzymes, such as pepsin and lipase. Rennet is used to separate milk into solid curds (for cheesemaking) and liquid whey. Rennet or rennet substitutes are used in the production of many cheeses.

The term “plant” includes reference to whole plants, plant organs, plant tissues, and plant cells and progeny of same, but is not limited to angiosperms and gymnosperms such as Arabidopsis, potato, tomato, tobacco, alfalfa, lettuce, carrot, strawberry, sugar beet, cassava, sweet potato, soybean, lima bean, pea, chickpea, maize (corn), turf grass, wheat, rice, barley, sorghum, oat, oak, eucalyptus, walnut, palm and duckweed as well as fern and moss. Thus, a plant may be a monocot, a dicot, a vascular plant reproduced from spores such as fem or a nonvascular plant such as moss, liverwort, hornwort and algae. The word “plant,” as used herein, also encompasses plant cells, seeds, plant progeny, propagule whether generated sexually or asexually, and descendants of any of these, such as cuttings or seed. Plant cells include suspension cultures, callus, embryos, meristematic regions, callus tissue, leaves, roots, shoots, gametophytes, sporophytes, pollen, seeds and microspores. Plants may be at various stages of maturity and may be grown in liquid or solid culture, or in soil or suitable media in pots, greenhouses or fields. Expression of an introduced leader, trailer or gene sequences in plants may be transient or permanent.

The term “vascular plant” refers to a large group of plants that are defined as those land plants that have lignified tissues (the xylem) for conducting water and minerals throughout the plant and a specialized non-lignified tissue (the phloem) to conduct products of photosynthesis. Vascular plants include the clubmosses, horsetails, ferns, gymnosperms (including conifers) and angiosperms (flowering plants). Scientific names for the group include Tracheophyta and Tracheobionta. Vascular plants are distinguished by two primary characteristics. First, vascular plants have vascular tissues which distribute resources through the plant. This feature allows vascular plants to evolve to a larger size than non-vascular plants, which lack these specialized conducting tissues and are therefore restricted to relatively small sizes. Second, in vascular plants, the principal generation phase is the sporophyte, which is usually diploid with two sets of chromosomes per cell. Only the germ cells and gametophytes are haploid. By contrast, the principal generation phase in non-vascular plants is the gametophyte, which is haploid with one set of chromosomes per cell. In these plants, only the spore stalk and capsule are diploid.

The term “non-vascular plant” refers to a plant without a vascular system consisting of xylem and phloem. Many non-vascular plants have simpler tissues that are specialized for internal transport of water. For example, mosses and leafy liverworts have structures that look like leaves but are not true leaves because they are single sheets of cells with no stomata, no internal air spaces and have no xylem or phloem. Non-vascular plants include two distantly related groups. The first group are the bryophytes, which is further categorized as three separate land plant Divisions, namely Bryophyta (mosses), Marchantiophyta (liverworts), and Anthocerotophyta (hornworts). In all bryophytes, the primary plants are the haploid gametophytes, with the only diploid portion being the attached sporophyte, consisting of a stalk and sporangium. Because these plants lack lignified water-conducting tissues, they can't become as tall as most vascular plants. The second group is the algae, especially the green algae, which consists of several unrelated groups. Only those groups of algae included in the Viridiplantae are still considered relatives of land plants.

The term “plant part” refers to any part of a plant including but not limited to the embryo, shoot, root, stem, seed, stipule, leaf, petal, flower bud, flower, ovule, bract, trichome, branch, petiole, internode, bark, pubescence, tiller, rhizome, frond, blade, ovule, pollen, stamen, and the like. The two main parts of plants grown in some sort of media, such as soil or vermiculite, are often referred to as the “above-ground” part, also often referred to as the “shoots”, and the “below-ground” part, also often referred to as the “roots”.

The term “plant tissue” refers to any part of a plant, such as a plant organ. Examples of plant organs include, but are not limited to the leaf, stem, root, tuber, seed, branch, pubescence, nodule, leaf axil, flower, pollen, stamen, pistil, petal, peduncle, stalk, stigma, style, bract, fruit, trunk, carpel, sepal, anther, ovule, pedicel, needle, cone, rhizome, stolon, shoot, pericarp, endosperm, placenta, berry, stamen, and leaf sheath.

The term “seed” is meant to encompass the whole seed and/or all seed components, including, for example, the coleoptile and leaves, radicle and coleorhiza, scutellum, starchy endosperm, aleurone layer, pericarp and/or testa, either during seed maturation and seed germination.

The term “transgenic plant” means a plant that has been transformed with one or more exogenous nucleic acids. “Transformation” refers to a process by which a nucleic acid is integrated into the genome of a plant cell. “Stably integrated” refers to the permanent, or non-transient retention and/or expression of a polynucleotide in and by a cell genome. Thus, a stably integrated polynucleotide is one that is a fixture within a transformed cell genome and can be replicated and propagated through successive progeny of the cell or resultant transformed plant. Transformation may occur under natural or artificial conditions using various methods well known in the art. Transformation may rely on any known method for the insertion of nucleic acid sequences into a prokaryotic or eukaryotic host cell, including Agrobacterium-mediated transformation protocols, viral infection, whiskers, electroporation, heat shock, lipofection, polyethylene glycol treatment, micro-injection, and particle bombardment.

As used herein, the terms “stably expressed” or “stable expression” when used in reference to a protein refer to expression and accumulation of a protein in a host cell, such as a plant cell. In some embodiments, a protein may accumulate in a cell because it is not degraded by endogenous host cell proteases. In some embodiments, a protein is considered to be stably expressed in a plant if it is present in the plant in an amount of 1% or higher per total protein weight of soluble protein extractable from the plant.

The term “recombinant” refers to nucleic acids or proteins formed by laboratory methods of genetic recombination (e.g., molecular cloning) to bring together genetic material from multiple sources, creating sequences that would not otherwise be found in the genome. A recombinant fusion protein is a protein created by combining sequences encoding two or more constituent proteins, such that they are expressed as a single polypeptide. Recombinant fusion proteins may be expressed in vivo in various types of host cells, including plant cells, bacterial cells, fungal cells, mammalian cells, etc. Recombinant fusion proteins may also be generated in vitro.

The term “promoter” or a “transcription regulatory region” refers to nucleic acid sequences that influence and/or promote initiation of transcription. Promoters are typically considered to include regulatory regions, such as enhancer or inducer elements. The promoter will generally be appropriate to the host cell in which the target gene is being expressed. The promoter, together with other transcriptional and translational regulatory nucleic acid sequences (also termed “control sequences”), is necessary to express any given gene. In general, the transcriptional and translational regulatory sequences include, but are not limited to, promoter sequences, ribosomal binding sites, transcriptional start and stop sequences, translational start and stop sequences, and enhancer or activator sequences.

The term signal peptide—also known as “signal sequence”, “targeting signal”, “localization signal”, “localization sequence”, “transit peptide”, “leader sequence”, or “leader peptide”, is used herein to refer to an N-terminal peptide which directs a newly synthesized protein to a specific cellular location or pathway. Signal peptides are often cleaved from a protein during translation or transport and are therefore not typically present in a mature protein.

The term “proteolysis” or “proteolytic” or “proteolyze” means the breakdown of proteins into smaller polypeptides or amino acids. Uncatalyzed hydrolysis of peptide bonds is extremely slow. Proteolysis is typically catalyzed by cellular enzymes called proteases but may also occur by intra-molecular digestion. Low pH or high temperatures can also cause proteolysis non-enzymatically. Limited proteolysis of a polypeptide during or after translation in protein synthesis often occurs for many proteins. This may involve removal of the N-terminal methionine, signal peptide, and/or the conversion of an inactive or non-functional protein to an active one.

The term “purifying” is used interchangeably with the term “isolating” and generally refers to the separation of a particular component from other components of the environment in which it was found or produced. For example, purifying a recombinant protein from plant cells in which it was produced typically means subjecting transgenic protein containing plant material to biochemical purification and/or column chromatography.

When referring to expression of a protein in a specific amount per the total protein weight of the soluble protein extractable from the plant (“TSP”), it is meant an amount of a protein of interest relative to the total amount of protein that may reasonably be extracted from a plant using standard methods. Methods for extracting total protein from a plant are known in the art. For example, total protein may be extracted from seeds by bead beating seeds at about 15000 rpm for about 1 min. The resulting powder may then be resuspended in an appropriate buffer (e.g., 50 mM Carbonate-Bicarbonate pH 10.8, 1 mM DTT, 1× Protease Inhibitor co*cktail). After the resuspended powder is incubated at about 4° C. for about 15 minutes, the supernatant may be collected after centrifuging (e.g., at 4000 g, 20 min, 4° C.). Total protein may be measured using standard assays, such as a Bradford assay. The amount of protein of interest may be measured using methods known in the art, such as an ELISA or a Western Blot.

When referring to a nucleic acid sequence or protein sequence, the term “identity” is used to denote similarity between two sequences. Unless otherwise indicated, percent identities described herein are determined using the BLAST algorithm available at the world wide web address: blast.ncbi.nlm.nih.gov/Blast.cgi using default parameters.

As used herein, the terms “dicot” or “dicotyledon” or “dicotyledonous” refer to a flowering plant whose embryos have two seed leaves or cotyledons. Examples of dicots include, but are not limited to, Arabidopsis, tobacco, tomato, potato, sweet potato, cassava, alfalfa, lima bean, pea, chickpea, soybean, carrot, strawberry, lettuce, oak, maple, walnut, rose, mint, squash, daisy, Quinoa, buckwheat, mung bean, cow pea, lentil, lupin, peanut, fava bean, French beans (i.e., common beans), mustard, or cactus.

The terms “monocot” or “monocotyledon” or “monocotyledonous” refer to a flowering plant whose embryos have one cotyledon or seed leaf. Examples of monocots include, but are not limited to turf grass, maize (corn), rice, oat, wheat, barley, sorghum, orchid, iris, lily, onion, palm, and duckweed.

As used herein, a “low lactose product” is any food composition considered by the FDA to be “lactose reduced”, “low lactose”, or “lactose free”.

As used herein, a “milk protein” is any protein, or fragment or variant thereof, that is typically found in one or more mammalian milks.

As used herein, a “non-milk” protein is any protein that is not typically found in any mammalian milk. One non-limiting example of a non-milk protein is green fluorescent protein (GFP).

As used herein, an “exogenous intron” refers to an intronic sequence, or portion thereof, derived from a first cell type that is introduced into a second cell type. Thus, exogenous introns are not native to a host cell and/or host plant. Exogenous introns may, in some embodiments, comprise synthetic sequences and chimeric sequences. Exogenous introns do not typically code for amino acids, and are removed (i.e., spliced) by the host cell during translation of a protein from the transgene.

As used herein, a “nucleic acid” refers to a physical nucleic acid chemical structure. A nucleic acid is a polymeric form of nucleotides of any length, either deoxyribonucleotides or ribonucleotides or analogs thereof. The term also refers to both double and single stranded nucleic acid molecules. The following are non-limiting examples of a nucleic acid: a gene or gene fragment (for example, a probe, primer), an exon, an intron, intergenic DNA (including, without limitation, heterochromatic DNA), messenger RNA (mRNA), transfer RNA (tRNA), ribosomal RNA (rRNA), a ribozyme, cDNA, a recombinant polynucleotide, a branched polynucleotide, a plasmid, a vector, isolated DNA of a sequence, isolated RNA of a sequence, sgRNA, guide RNA, a nucleic acid probe, a primer, an snRNA, a long non-coding RNA, a snoRNA, a siRNA, a miRNA, a tRNA-derived small RNA (tsRNA), an antisense RNA, an shRNA, or a small rDNA-derived RNA (srRNA). A nucleic acid can comprise modified nucleotides, such as methylated nucleotides and nucleotide analogs. If present, modifications to the nucleotide structure can be imparted before or after assembly of the nucleic acid. The sequence of nucleotides can be interrupted by non-nucleotide components. A nucleic acid can be further modified after polymerization, such as by conjugation with a labeling component. A nucleic acid can also have secondary or tertiary structure. A nucleic acid can base pair, such as by way of Watson Crick base pairing.

As used herein, a “nucleic acid sequence” refers to a non-physical succession of bases indicating the order of nucleotides of a nucleic acid. A nucleic acid sequence is observable in written form such as on a machine (in silico) or handwritten. A nucleic acid sequence can be input or obtained from databases in a computer having a central processing unit

As used herein, the term “machine learning” refers to use of mathematical algorithms/models and related software that leverage data to improve performance of a task (e.g., predictions). Machine learning encompasses learning models capable of supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. In some embodiments, machine learning utilizes alignment data and/or empirical data regarding RNA secondary or tertiary structure to improve RNA folding predictions or RNA structural comparisons. In a non-limiting example, the predictions made by machine learning algorithms and models of the present disclosure can inform selection of nucleic acid sequences for manufacture of a nucleic acid.

Nucleic Acid Sequence Selection Based on Predicted Structures

In some embodiments, the present disclosure teaches methods for selecting a nucleic acid sequence based on differences in the predicted or empirically-determined structures of those sequences. Specifically, the present disclosure teaches a method of selecting nucleic acid sequences that result in similar predicted secondary structures from different RNA folding models. This invention is based in part, on the inventor's discovery that nucleic acid sequences that produce more similar predicted secondary structures across different RNA folding models, result in higher or more stable expression/accumulation of nucleic acids in vivo compared to nucleic acid sequences that produce more dissimilar predicted secondary structures.

Without wishing to be bound by any one theory, it is hypothesized that differences in predicted secondary structure produced from different RNA folding models are associated with decreased structural stability of the manufactured nucleic acid in vivo. That is, it is hypothesized that the differences in predicted structures from different models is indicative or suggestive that the sequence may take on different- or multiple-structures, when expressed in vivo, which may have deleterious effects on nucleic acid expression or accumulation in vivo.

In some embodiments, the present disclosure teaches a method comprising the steps of a) providing a plurality of nucleic acid sequences for evaluation; b) predicting secondary structure of the plurality of nucleic acid sequences, with a plurality of RNA folding models, such that each nucleic acid sequence in the plurality of nucleic acid sequences is associated with at least two predicted secondary structures; c) assessing structural similarity for the at least two predicted secondary structures associated with each nucleic acid sequence (e.g., via assignment of a structural similarity score); and d) selecting a nucleic acid sequence with higher structural similarity between predicted secondary structures than at least one other nucleic acid sequence in the plurality of nucleic acid sequences. In some embodiments, the selected nucleic acid sequence with higher similarity in its predicted secondary structures is predicted to express or accumulate at higher levels when expressed in vivo. In some embodiments, the predicted secondary structures are provided, and need not be predicted. The various aspects of the presently-disclosed invention are discussed in more detail, below.

Plurality of Nucleic Acid Sequences

The present disclosure provides techniques for selecting a nucleic acid sequence from amongst a plurality of nucleic acid sequences. In some embodiments, the methods of the present disclosures are most effective when the selection is made within a group of related nucleic acid sequences. In some embodiments, it is hypothesized that selection of related nucleic acid sequences permits for selection based on predicted structure, while reducing potential confounding effects related to non-structural issues, such as the presence of RNAi targets, binding, or shuttling of nucleic acids in vivo, or other potential expression regulatory controls that may vary between highly disparate sequences. Thus, in some embodiments, the techniques of the present disclosure are more effective (i.e. are expected to produce the most accurate expression predictions), when applied to related sequences, including but not limited to: nucleic acid variants encoding the same or similar amino acid sequence (e.g., codon variants, or other sequence variations that in non-coding regions) or comprising other nucleic acid sequence variations that encode for amino acid chains that are at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% sequence identity.

In some embodiments, the plurality of nucleic acid sequences are codon variants encoding the same or similar amino acid sequence. In some embodiments, the plurality of sequences encode for nucleic acids comprising RNAi hairpins, wherein the sequences vary in nucleic acids, but continue to be processed into small RNAs capable of interacting with the same target nucleotide. In some embodiments, the plurality of nucleic acid sequences are sequence other sequence variants that do not exhibit a biological function. For example, in some embodiments, the presently disclosed techniques can be applied to nucleic acid sequences encoding nucleic acids with laboratory applications, including probes, primers, linkers, bar codes, etc. In some embodiments the plurality of nucleic acid sequences are variants of riboswitches, aptamers, rRNAs or other non-coding RNAs.

In some embodiments, the plurality of nucleic acid sequences can be from any source, including, without limitation, randomly generated sequences, sequences derived from natural diversity (e.g., related sequences from related species), sequence rearrangements, artificial sequences, mutational library sequences, etc. Disclosure related to various types of plurality of nucleic acids is provided in this document, and is also known to those with skill in the art.

Secondary Structures

Provided are nucleic acids with secondary structure. RNA secondary structure is represented by a sequence of bases, paired by hydrogen bonding, within its nucleotide sequence. Stacking these base pairs forms the scaffold driving the folding of RNA three-dimensional structures. As a result, the knowledge of the RNA secondary structure is useful for modeling RNA structures and understanding any functional mechanisms. The present disclosure provides in silico and wet lab approaches to determining secondary structures.

RNA Folding Models

The present disclosure provides methods for predicting nucleic acid expression based on the similarity of secondary (or tertiary structures) developed from different RNA folding models. In some embodiments, the methods of the present disclosure are compatible with any RNA folding model. Persons having skill in the art are familiar with a variety of RNA folding models.

In some embodiments the RNA folding model utilizes comparative sequence analysis. Comparative sequence analysis is considered by some to be the most accurate computational method for determining the RNA secondary structure. This method assumes that the RNA secondary structure is evolutionarily conserved to a greater extent than the RNA sequence. This method usually finds the base pairs that covary to maintain Watson-Crick and wobble base pairs (compensatory mutations) of a given sequence using a set of hom*ologous sequences. In some embodiments, comparative sequence analysis can be combined with score-based methods (See e.g., RNAalifold-Hofacker I L, Fekete M, Flamm C, Huynen M A, Rauscher S, Stolorz P E, et al. Automatic detection of conserved RNA structure elements in complete RNA virus genomes. Nucleic Acids Res. 1998; 26 (16):3825-36; KnetFold-Bindewald E, Shapiro B A. RNA secondary structure prediction from sequence alignments using a network of k-nearest neighbor classifiers. RNA. 2006; 12(3):342-52; and IL-Ruan J, Stormo G D, Zhang W. An iterated loop matching approach to the prediction of RNA secondary structures with pseudoknots. Bioinformatics. 2004; 20(1):58-66).

Other RNA folding models are based on score assignment, where only a single RNA sequence is required as the input. These methods assume that the native RNA structure is a structure with a minimum/maximum total score, depending on the hypothesis of RNA folding mechanism or its simplification. Hence, the problem of RNA secondary structure prediction is transformed into an optimization problem. Since the RNA secondary structure can be recursively broken down into smaller elements with independent score contributions, the dynamic programming (DP) algorithm is often employed to identify the optimal structure. Evaluation of the score for structure elements requires a score scheme of many parameter.

In some embodiments, any RNA folding model can be utilized in a method of the disclosure. Exemplary, non-limiting methods are provided in Table 1.

In some embodiments, an RNA folding model is selected from (and/or is contained within the software identified in) Table 1. In some embodiments, an RNA folding model comprises a model selected from the group consisting of co*cke-Younger Kasami model, inside and outside model, loop-based energy model, minimum free energy, centroid, CONTRAfold, CentroidFold, ContextFold, and combinations thereof. In some embodiments, an RNA structure is determined by a model selected from the group consisting of minimum free energy, centroid (e.g., centroidFold), suboptimal folding, and any combination thereof.

In some embodiments, a nearest-neighbor (NN) model, and variants derived therefrom, is utilized to predict RNA structure. The NN model can be used for the calculation of energy changes of any structure of a given RNA molecule, and the DP algorithm can be also employed to efficiently find the MFE structure. For predicting a structure with noncanonical base pairs, some other score schemes can be employed as scoring functions, such as nucleotide cyclic motifs score system or equilibrium partition function. In an exemplary approach for RNA secondary-structure prediction, a single RNA sequence is folded according to an appropriate scoring function. In this approach, RNA structure can be divided into substructures such as loops and stems according to the nearest-neighbor model. Dynamic programming algorithms can then be employed for locating the global minimum or probabilistic structures from these substructures. The scoring parameters of each substructure can be obtained experimentally (e.g., RNAfold, RNAstructure, and RNAshapes) or by machine learning (e.g., CONTRAfold, CentroidFold, ContextFold, and the like). In some embodiments, RNAfold is utilized. In some embodiments, CentroidFold is utilized.

In some embodiments, RNA expression can be associated with predicted secondary or tertiary structures across multiple RNA folding models. In some embodiments, methods disclosed herein comprise use of one or more models. In some embodiments, methods disclosed herein comprise use of two or more models. In some embodiments, from about 0, 1, 2, 3, 4, or 5 models are employed.

In some embodiments, nucleic acid sequences having increased RNA and/or protein expression comprise similar or identical predicted secondary or tertiary structure across two or more models. In some embodiments, nucleic acid sequences comprising a codon variation comprise increased RNA and/or protein expression. The nucleic acid sequences comprising the codon variation may also have similar or identical predicted secondary or tertiary structure across two or more models. Exemplary codon variations are provided herein and any of which can be employed to increase expression.

ML-based methods for RNA secondary structure prediction can generally be divided into 3 categories according to the subprocess that ML participates in, i.e., score scheme based on ML, preprocessing and postprocessing based on ML, and prediction process based on ML. In some embodiments, the ML-based models learn functions that map inputs (features) to outputs by adjusting model parameters based on the known input-output pairs. Many of them employ free energy parameters, encoded RNA sequences, sequence patterns, or evolutionary information as key features, and their outputs can be classification labels (such as paired or unpaired) or continuous values (such as free energy). When a new input is fed to the trained model, the model can classify a corresponding label or predict a corresponding value. A non-limiting list of ML RNA folding models is provided in Table 2, below. In some embodiments, an RNA folding model of Table 1 also employs machine learning.

Wet-Lab Nucleic Acid Structures

In some embodiments, a wet lab method is utilized to predict or determine RNA structure. X-ray crystallography and nuclear magnetic resonance (NMR) are exemplary approaches for determining RNA structures, both of which can offer structural information at a single base pair resolution.

In some embodiments, a wet lab method comprises X-ray crystallography, see Westhof E. Twenty years of RNA crystallography. RNA. 2015; 21(4):486-7, included by reference herein in its entirety. In some embodiments, a wet lab method comprises NMR, see Westhof E. Twenty years of RNA crystallography. RNA. 2015; 21(4):486-7, herein incorporated by reference in its entirety.

In some embodiments, a method employing a wet-lab approach can precede a method comprising machine learning. In other words, structural predictions obtained from a wet-lab method can be input into a method employing machine learning. In some embodiments, images of a structure from a wet-lab analysis can be loaded onto a software of the disclose for nucleic acid structural comparison to inform a selection.

In some embodiments, the present disclosure teaches a modified method for nucleic acid structures determined via wet lab methods. Rather than reciting at least two predicted secondary structures from different RNA folding models, wet lab approaches seek to identify possible folding variants actually identified/observed using the wet lab technique. Thus in some embodiments, the step of predicting secondary structure is replaced with determining structure, and the step of determining structure similarity between predicted structures is replaced with determining structural similarity between actually observed structures.

Assessing Similarity of Nucleic Acid Sequence Structures (Structural Similarity Scores)

The presently disclosed methods can employ any method for assessing similarity between secondary structures of a nucleic acid sequence. In some embodiments, the secondary structure comprises RNA structure. In some embodiments the secondary structure comprises single stranded DNA. In some embodiments, secondary structures of nucleic acids can be compared visually, or can be assessed entirely in silico. In some embodiments, the secondary structures are assessed via hybrid approaches. In some embodiments, similarity scores are saved/recorded/written down to permit further review/analysis. In some embodiments, similarity scores are assessed, but never recorded.

In-Silico Similarity

In some embodiments, a comparison between predicted secondary or tertiary structures is determined. A comparison can employ in silico methods. In some embodiments, an in-silico method utilizes software that is configured to accept information (e.g., an image or other data file conveying information about a chemical structure of a nucleic acid or a nucleic acid sequence) and provide an output related to secondary structure and optionally a comparison of secondary structures of a plurality of nucleic acid sequences. Table 3 provides a summary of exemplary software that quantifies differences between nucleic acid sequences. Any of the software of Table 1 can also be utilized to analyze similarity of structures of nucleic acids.

In some embodiments, structural similarity is determined by any other algorithmic/computational method known to persons having skill in the art, including those disclosed in Nikolova, N. and Jaworska, J. (2003), Approaches to Measure Chemical Similarity—a Review. QSAR Comb. Sci., 22: 1006-1026.

In some embodiments, structural similarity of the NP is evaluated by calculating the pairwise nucleic acid sequence secondary structure based on the Tanimoto coefficient and using the python library RDKit (www.rdkit.org). Briefly, morgan fingerprints are prepared for the at least two secondary structures. These fingerprints are then compared to assess similarity.

In some embodiments, the Tanimoto coefficient is calculated with the formula for dichotomous variables.

$S_{AB}=\frac{C}{A+B-C}$

In some embodiments, the Tanimoto Coefficient is calculated using formula 1 for continuous variables.

$\begin{array}{cc}{S}_{A,B}=\frac{\left[\sum \mathrm{nj}=1\mathrm{XjAXjB}\right]}{\left[\sum \mathrm{nj}=1\left(\mathrm{xjA}\right)2+\sum \mathrm{nj}=1\left(\mathrm{xjB}\right)2-\sum \mathrm{nj}=1\mathrm{XjAXjB}\right]}& \mathrm{Formula}1\end{array}$

Wherein the SAB similarity score between molecules A and B is calculated by dividing the “C” features in common between two structures, by the “A” the features of a first structure plus the “B” features of a second structure, minus C. That is, A is the number of on bits in molecule A, B is number of on bits in molecule B, while C is the number of bits that are on in both molecules. x_jA means the j-th feature of molecule A. x_jB means the j-th feature of molecule B. For more information on how to calculate the Tanimoto coefficient, see Bajusz, D., Rácz, A. & Héberger, K. Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations?. J Cheminform 7, 20 (2015).

In some embodiments Tanimoto coefficients range from 0 to 1 with 0 being no similarity and 1 being an identical molecule. In some embodiments. In some embodiments, two natural product structures are considered similar if they have a Tanimoto similarity coefficient of at least 0.6, 0.7, 0.8, 0.9, or 0.95, including all ranges and subranges therebetween.

Visual (or Observed/Perceived) Similarity

In some embodiments, secondary structures of nucleic acid sequences are assessed visually or observationally. In some embodiments, the assessment results are quantitative. In some embodiments, the assessment is qualitative. The examples of the present disclosure identify a strong correlation between differences in secondary structures of the same nucleic acid sequence, and the resulting nucleic acid's expression/stability in vivo. Thus, in some embodiments, selecting a nucleic acid sequence with superior expression/stability can be done by picking out a sequence with secondary structures that are less different compared to the secondary structures of at least one other nucleic acid sequence. In some embodiments, this type of distinction can be achieved visually/observationally. For example, at least two secondary structures (e.g., a predicted minimum free energy structure and a predicted centroid structure) can be visually compared and optionally ranked according to perceived similarity. This visual analysis can be completed, for example, by stacking the predicted structure figures with about 50% translucency in a suitable computer program (e.g., Microsoft Word or the like), and visually assessing the amount of overlap. In some embodiments, the predicted structure figures are stacked with about 20%, 30%, 40%, 50%, 60%, 70%, 80%, or up to about 90% translucency, including all ranges and subranges therebetween. In some embodiments, the visual analysis can be conducted by comparing the structures side-by-side, such as on different columns of a table, or sequentially, such as in a flip book.

The visual assessment can be conducted purely on qualitative perception of differences, or can be done by counting number and size of different structures, such as number of loops, steps, helices, and the number of nucleic acids within them. In some embodiments the analysis can be done by assigning a score to each set of structures, or by instead ranking sets based on their similarity. Regardless of whether structure sets are assigned individual scores, or relative scores (e.g., ranking), these are considered a sequence similarity score within the context of this disclosure.

In some embodiments, a visual structure similarity analysis can be supplemented with an in silico analysis. For example, in some embodiments, the assessment of differences between two or more secondary structures can be conducted on the structure itself. In some embodiments, the assessment of differences can be conducted on a representation of structures. For example, in some embodiments, the secondary structures are assessed with the nucleotides represented in the structure. In some embodiments the secondary structures are assessed against wire models of the structures that do not identify individual nucleic acids. In some embodiments, the structures are assessed via shadow (e.g., silhouette) cutout representations of the space occupied by a structure. In some embodiments, structure similarity assessments can also be conducted on more abstract representations of structure. For example, in some embodiments, the structural similarity comparison can be conducted on mountain plot curves, representing the number of nucleic acid residues per position in a predicted structure (see discussion on mountain plots in the disclosure and also in Andreas R. Gruber, Ronny Lorenz, Stephan H. Bernhart, Richard Neuböck, Ivo L. Hofacker, The Vienna RNA Websuite, Nucleic Acids Research, Volume 36, Issue suppl_2, 1 Jul. 2008, Pages W70-W74).

Similarity Variation of Wet-Lab Nucleic Acid Structures

In some embodiments, a wet-lab method is used to predict a structure of a nucleic acid sequence. Wet-lab methods such as X-ray crystallography and nuclear magnetic resonance (NMR) can offer structural information at a single base pair resolution. Although many methods have been developed to infer the state of nucleotides (paired or unpaired) in an RNA molecule using enzymatic or chemical probes coupled with next-generation sequencing most of them can only be used to capture the RNA secondary structure in vitro. The obtained structure may differ markedly from the in vivo conformation. Accordingly, wet-lab methods can be combined with at least one other structural prediction method disclosed herein. In some embodiments, a method comprises a wet-lab method (X-ray crystallography or NMR) and at least one other RNA structure prediction method (e.g., any from Table 1).

Structures obtained from wet-lab techniques can be evaluated in the same way as those developed from RNA folding models. In some embodiments differences in structures observed via wet lab techniques can be assessed visually (observationally) as described herein. In some embodiments differences in structures observed via wet lab techniques can be assessed in silico, using any known structure comparison strategy, including those described herein.

Selection of Nucleic Acid Sequence

In some embodiments, the methods of the present disclosure recite selecting a nucleic acid sequence from amongst the plurality of nucleic acid sequences based on the similarity of the predicted secondary structure. In some embodiments, a nucleic acid sequence is selected if it has a structural similarity score that is higher (i.e., more similar) than at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, or more, other nucleic acid sequence(s) in the plurality of nucleic acid sequences. In some embodiments, a nucleic acid sequence is selected if it has the highest structural similarity score (i.e., most similar) than at least one other nucleic acid sequence in the plurality of nucleic acid sequences. In some embodiments, the selection is supervised or unsupervised. In some embodiments the selection is done in silico based on a set of pre-existing rules. In some embodiments, the selection is made by a user. As noted above, selection may utilize any appropriate scale for evaluating similarity. Exemplary means comprise, scoring, ranking, percent ranking, and combinations thereof.

In some embodiments, a selection can consider further elements beyond the similarity score. For example, in some embodiments, selection of the nucleic acid sequence can take into account presence or absence of a desired sequence, presence or absence of base pairing or base pairing potential, distance of predicted structural features (e.g., bulge, hairpin, internal loop), relaxed base-pair score, and combinations thereof. For example, in some embodiments, a nucleic acid sequence with a similarity score higher than at least one other nucleic acid sequence may further be evaluated/selected based on the presence of a desired loop length or the presence of other structures. In some embodiments, a nucleic acid sequence is selected due to the presence of an loops with optimal loop length of about 4-8 bp, and/or containing a tetraloop UUCG. In some embodiments a nucleic acid would be less likely to be selected if it exhibited unstable structures that would be expected to present pseudo-knots such as large loops with no secondary structure of their own and loops of less than 4 and more than 8 bp. Selection can also take into amount the frequency of any of the aforementioned aspects.

Scoring can comprise assigning a number from 0-1000. In some embodiments, a selection of a nucleic acid sequence is based on a lower number. In some embodiments a selection is based on a nucleic acid sequence having a higher number. In some embodiments, a sequence is selected having a score of about 0-5, 1-5, 1-10, 0-10, 5-15, 5-20, 15-30, 15-45, 0-50, 5-10, 1-20, 10-30, 15-75, and any subrange in between. In some embodiments, a sequence is selected having a score of about 50-200, 50-150, 100-200, 250-500, 300-500, or 500-1000, and any subrange in between.

In some embodiments, a selection comprises ranking a plurality of nucleic acid sequences according to their score. A selection can comprise selecting lower ranked nucleic acid sequences or higher ranked nucleic acid sequences. Percent ranking can also be employed. Percentile rank of a given score is the percentage of scores in its frequency distribution that are less than that score.

In some embodiments, selection of a nucleic acid sequence can be based on a nucleic acid sequence having a higher structural similarity score than at least one other nucleic acid sequence in a plurality of nucleic acid sequences. In some embodiments, methods comprised herein comprise an analysis comprising a mountain plot. Krouwer and Monti (1995) devised the mountain plot (also known as a folded empirical cumulative distribution plot) as a complementary representation of the difference plot. It shows the distribution of the differences with an emphasis on the center and the tails of the distribution. A mountain plot can be used to estimate the median of the differences, the central 95% interval, the range, and the percentage of observations outside the total allowable error bands. The mountain plot is a useful complementary plot to the Bland & Altman plot which can also be employed in any of the methods provided herein.

A structural similarity score can be increased by any amount. In some embodiments, a structural similarity score is increased by at least about or at most about 1-fold, 5-fold, 10-fold, 15-fold, 25-fold, 50-fold, 75-fold, 100-fold, 125-fold, 150-fold, 175-fold, 200-fold, 225-fold, 250-fold, 275-fold, 300-fold, 325-fold, 350-fold, 375-fold, 400-fold, 425-fold, 450-fold, 475-fold, or up to about 500-fold, including all ranges and subranges therebetween. In some embodiments, a structural similarity score is increased by at least about or at most about 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%, 110%, 120%, 130%, 140%, 150%, or up to about 200%, including all ranges and subranges therebetween.

In some embodiments, a method provided herein comprises determining a structural similarity score. A structural similarity score can be determined by way of any of the RNA structure prediction models provided herein.

In some embodiments, an in silico structural similarity analysis can inform selection of a nucleic acid sequence, from a plurality of nucleic acid sequences, to be manufactured into a nucleic acid. In some embodiments, provided methods comprise selecting a nucleic acid sequence with a higher structural similarity score than at least one other nucleic acid sequence in a plurality of nucleic acid sequences. In some embodiments, a selected nucleic acid sequence comprises an optimized codon sequence.

In some embodiments, manufactured nucleic acids can be evaluated for expression in host cells. In some embodiments, a structural similarity score obtained from an in silico analysis is plotted against empirical expression data obtained from manufacture. In some embodiments, nucleic acids showing high expression, comprise a logarithmic correlation between an in silico structural similarity analysis and a related empirical analysis. In some embodiments, a logarithmic correlation comprises an R2 correlation coefficient of at least about or at most about 0.65, 0.66, 0.67, 0.68, 0.69, 0.7, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, or up to about 1, including all ranges and subranges therebetween.

Manufacturing

Provided herein are methods of manufacturing a nucleic acid. Persons having skill in the art will be familiar with the multiple strategies for manufacturing nucleic acids. In some embodiments, a selected nucleic acid sequence is manufactured into a nucleic acid. In some embodiments, the manufactured nucleic acid exhibits greater expression in a host cell as compared to a non-selected nucleic acid, if the non-selected nucleic acid were manufactured. In some embodiments, the increased expression is at least about or at most about: 0.5-fold, 1-fold, 3-fold, 5-fold, 7-fold, 10-fold, 12-fold, 15-fold, 20-fold, 30-fold, 40-fold, 50-fold, 60-fold, 70-fold, 80-fold, 90-fold, 100-fold, 110-fold, 120-fold, 130-fold, 140-fold, 150-fold, or up to about 200-fold, including all ranges and subranges therebetween.

In some embodiments, a nucleic acid comprises a sequence with at least about or at most about 85%, 90%, 95%, 96%, 97%, 98%, 99%, or 100% identity to any one of SEQ ID NO: 757, 760, 762, 763, 765, 772, 773, 778, and 780. In some embodiments, a nucleic acid comprises a sequence selected from the group consisting of: SEQ ID NO: 757, 760, 762, 763, 765, 772, 773, 778, and 780. In some embodiments, a nucleic acid comprises SEQ ID NO: 757. In some embodiments, a nucleic acid comprises SEQ ID NO: 760. In some embodiments, a nucleic acid comprises SEQ ID NO: 762. In some embodiments, a nucleic acid comprises SEQ ID NO: 763. In some embodiments, a nucleic acid comprises SEQ ID NO: 765. In some embodiments, a nucleic acid comprises SEQ ID NO: 772. In some embodiments, a nucleic acid comprises SEQ ID NO: 773. In some embodiments, a nucleic acid comprises SEQ ID NO: 778. In some embodiments, a nucleic acid comprises SEQ ID NO: 780. In some embodiments, a host cell comprises a nucleic acid encoding any one of SEQ ID NO: 685, 687, and 695. In some embodiments, a host cell comprises a nucleic acid encoding a sequence comprising at least about 85%, 90%, 95%, 96%, 97%, 98%, 99%, or 100% identity to any one of SEQ ID NO: 685, 687, and 695.

In some embodiments, a plurality of nucleic acid sequences that undergo a method to inform a selection encode for the same amino acid sequence (e.g., a protein with comparable identity and/or function). In some embodiments, the amino acid sequences comprise at least about or at most about 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, or 100% sequence identity, including all ranges and subranges therebetween.

Computer and Robotic System

In some embodiments, a method of the disclosure comprises a computer system and optionally a robotic system.

In addition, any or call of the methods of the disclosure can comprise automation, for example the systems may be at least partially automated or fully automated. In some embodiments, a system of the disclosure can comprise one or more work modules (e.g., a DNA/RNA synthesis module, a vector cloning module, a selection module, a sequencing module, and combinations thereof).

As will be appreciated by those in the art, an automated system can include a wide variety of components, including, but not limited to: liquid handlers; one or more robotic arms; plate handlers for the positioning of microplates; plate sealers, plate piercers, automated lid handlers to remove and replace lids for wells on non-cross contamination plates; disposable tip assemblies for sample distribution with disposable tips; washable tip assemblies for sample distribution; 96 well loading blocks; integrated thermal cyclers; cooled reagent racks; microtiter plate pipette positions (optionally cooled); stacking towers for plates and tips; magnetic bead processing stations; filtrations systems; plate shakers; barcode readers and applicators; and one or more computer systems. Provided systems can comprise use of a microtiter plate (e.g., a 96- or 384-well plate), optionally configured for automated systems.

In some embodiments, the robotic systems of the present disclosure comprise automated handling (e.g., a robotic arm) enabling high-throughput pipetting to perform any or all of the steps in a method described herein. Exemplary methods can comprise aspiration, dispensing, mixing, diluting, washing, accurate volumetric transfers; retrieving and discarding of pipette tips; and repetitive pipetting of identical volumes for multiple deliveries from a single sample aspiration. These manipulations can be cross-contamination free.

In some embodiments, the automated systems of the present disclosure are compatible with platforms for multi-well plates, deep-well plates, square well plates, reagent troughs, test tubes, mini tubes, microfuge tubes, cryovials, filters, micro array chips, optic fibers, beads, agarose and acrylamide gels, and other solid-phase matrices or platforms are accommodated on an upgradeable modular deck. In some embodiments, the automated systems of the present disclosure contain at least one modular deck for multi-position work surfaces for placing source and output samples, reagents, sample and reagent dilution, assay plates, sample and reagent reservoirs, pipette tips, and an active tip-washing station.

In some embodiments, an integrated thermal cycler and/or thermal regulators are used for stabilizing the temperature of heat exchangers such as controlled blocks or platforms to provide accurate temperature control of incubating samples from 0° C. to 100° C.

In some embodiments, an automated system of the present disclosure is designed to be flexible and adaptable with multiple hardware add-ons to allow the system to carry out multiple applications. In some embodiments, a software program module can allow creation, modification, and running of methods. A system's diagnostic modules can allow setup, instrument alignment, and motor operations. The customized tools, labware, and liquid and particle transfer patterns allow different applications to be programmed and performed. A database can allow method and parameter storage. Robotic and computer interfaces allow communication between instruments.

Persons having skill in the art will recognize the various robotic platforms capable of carrying out any of the methods of the present disclosure. Table 3.5 below provides a non-exclusive list of scientific equipment capable of carrying out steps of the disclosure.

Computer System Hardware

Provided herein is hardware that can be used with any of the computer systems described herein. A computer system may be used to execute program code stored in a non-transitory computer readable medium (e.g., memory) in accordance with any of the embodiments of the disclosure. A computer system can comprise an input/output subsystem, which may be used to interface with human users and/or other computer systems depending upon the application. The system may include, e.g., a keyboard, mouse, graphical user interface, touchscreen, or other interfaces for input, and, e.g., an LED or other flat screen display, or other interfaces for output, including application program interfaces (APIs). Other elements of embodiments of the disclosure, such as the components of the LIMS system, may be implemented with a computer system.

Program code may be stored in non-transitory media such as persistent storage in secondary memory or main memory or both. Main memory may include volatile memory such as random access memory (RAM) or non-volatile memory such as read only memory (ROM), as well as different levels of cache memory for faster access to instructions and data. Secondary memory may include persistent storage such as solid state drives, hard disk drives or optical disks. One or more processors can read program code from one or more non-transitory media and execute the code to enable the computer system to accomplish a method herein. Those skilled in the art will understand that the processor(s) may ingest source code, and interpret or compile the source code into machine code that is understandable at the hardware gate level of the processor(s). The processor(s) may include graphics processing units (GPUs) for handling computationally intensive tasks. Particularly in machine learning, one or more CPUs may offload the processing of large quantities of data to one or more GPUs.

In some embodiments, a processor(s) may communicate with external networks via one or more communications interfaces, such as a network interface card, WiFi transceiver, etc. A bus communicatively couples the I/O subsystem, the processor(s), peripheral devices, communications interfaces, memory, and persistent storage. Embodiments of the disclosure are not limited to this representative architecture.

As used herein, the term component in this context refers broadly to software, hardware, or firmware (or any combination thereof) component. Components are typically functional components that can generate useful data or other output using specified input(s). A component may or may not be self-contained. An application program (also called an “application”) may include one or more components, or a component can include one or more application programs.

The term “memory” can be any device or mechanism used for storing information. In accordance with some embodiments of the present disclosure, memory is intended to encompass any type of, but is not limited to: volatile memory, nonvolatile memory, and dynamic memory. For example, memory can be random access memory, memory storage devices, optical memory devices, magnetic media, floppy disks, magnetic tapes, hard drives, SIMMs, SDRAM, DIMMs, RDRAM, DDR RAM, SODIMMS, erasable programmable read-only memories (EPROMs), electrically erasable programmable read-only memories (EEPROMs), compact disks, DVDs, and/or the like. In accordance with some embodiments, memory may include one or more disk drives, flash drives, databases, local cache memories, processor cache memories, relational databases, flat databases, servers, cloud-based platforms, and/or the like. In addition, those of ordinary skill in the art will appreciate many additional devices and techniques for storing information can be used as memory.

In some embodiments, memory may be used to store instructions for running one or more applications or modules on a processor. For example, memory could be used in some embodiments to house all or some of the instructions needed to execute the functionality of one or more of the modules and/or applications disclosed in this application.

Strategies for Stabilization of RNA

Provided herein are nucleic acids that may be used to for stable expression of RNAs in one or more host cells. As described herein, stable expression of RNAs may refer to mechanisms that increase one or more of (i) transcription levels of RNA, (ii) half-life of RNA in a cell, or (iii) efficiency of RNA translation.

In some embodiments, a DNA construct comprises a transgene that encodes one or more proteins. Accordingly, also provided are methods of expressing one or more proteins in a host cell, using the DNA constructs and/or stabilized RNAs disclosed herein. In some embodiments, use of the DNA constructs and/or stabilized RNAs may lead to (i) increased levels of protein expression in a host cell, (ii) increased half-life of the protein in a host cell, and/or (iii) increased accumulation of the protein in a host cell.

Stabilization of RNA can be achieved using a variety of approaches, such as any of those previously described. Exemplary methodologies comprise the use of elements in the construct which modulate transcriptional regulation and/or translational regulation. Illustrative methods for modulation of transcriptional regulation include codon optimization. Illustrative methods for modulation of translational regulation include modification of a promoter and/or terminator, codon optimization, modification of an intron sequence, insertion of exogenous intron sequences, insertion or modification of a 5′ and/or 3′ untranslated region (UTR), the use of a ubiquitin monomer, and any combination thereof.

In some embodiments, the DNA constructs of the disclosure can comprise one or more elements, and the elements may be provided in any order. For example, a DNA construct may comprise the following elements, in order from 5′ to 3′: promoter-signal sequence-transgene-KDEL-terminator. In some embodiments, a DNA construct may comprise an intron. In some embodiments, the intron is located within the transgene sequence, or 5′ or 3′ thereto. In some embodiments, the intron is located between the promoter and the signal sequence, between the signal sequence and the transgene, between the transgene and the KDEL, or between the KDEL and the terminator.

Promoters

In some embodiments, stabilization of RNA in a host cell can be achieved via transcriptional regulation. For example, in some embodiments, transcriptional regulation of an RNA may be achieved by modulation of a promoter sequence in a DNA construct encoding the RNA. Modulation of a promoter can refer to modulation of an endogenous promoter, such as making one or more nucleotide substitutions relative to an endogenous promoter. In some embodiments, modulation of a promoter may refer to the addition of one or more exogenous promoters to a DNA construct.

In some embodiments, a DNA construct comprises a promoter that is capable of stably expressing an RNA in a cell. In some embodiments, a DNA construct comprises a promoter that is capable of increasing the level of RNA in a cell. In some embodiments, a DNA construct comprises a promoter that leads to increased half-life of an RNA in a cell. The promoters described herein may be derived in their entirety from a native gene or be composed of different elements derived from different promoters found in nature, or even comprise synthetic DNA segments.

In some embodiments, the promoter may be a plant promoter. A “plant promoter” is a promoter capable of initiating transcription in plant cells and can drive or facilitate transcription of a nucleotide sequence or fragment thereof of the instant invention. Such promoters need not be of plant origin. For example, promoters derived from plant viruses, such as the CaMV35S promoter or from Agrobacterium tumefaciens, such as the T-DNA promoters, can be plant promoters. A typical example of a plant promoter of plant origin is the maize ubiquitin-1 (ubi-1) promoter known to those of skill. Plant promoters can be from a monocot, dicot, Arabidopsis, rice, modified versions thereof, or combinations thereof.

Promoters can be selected based on the desired outcome, and may include constitutive, tissue-specific, inducible, or other promoters for expression in the host organism. In some embodiments, a promoter is a constitutive promoter. Promoters referred to herein as “constitutive promoters” actively promote transcription under most, but not necessarily all, environmental conditions and states of development or cell differentiation. Promoters from viral (Verdaguer et al., 1998; Schenk et al., 1999; Bohorova et al., 2001; Samac et al., 2004; Davies et al., 2014) and plant polyubiquitin (UBQ) genes (Lu et al., 2008; Mann et al., 2011) can be used to obtain enhanced constitutive transgene expression. Exemplary constitutive plant promoters comprise: Cauliflower Mosaic Virus 35S (35S), 1′ or 2′ promoter derived from T-DNA of Agrobacterium tumefaciens, maize ubiquitin-1, or modified versions of any of these.

In choosing a promoter to use in the methods of the disclosure, it may be desirable to use a tissue-specific or developmentally regulated promoter. In some cases, a promoter is a specific promoter. A specific promoter refers to a promoter that has a high preference for being active in a specific tissue or cell and/or at a specific time during development of a plant. By “high preference” is meant at least a 3-fold, preferably 5-fold, more preferably at least 10-fold still more preferably at least a 20-fold, 50-fold or 100-fold increase in transcription in the desired tissue over the transcription in any other tissue. Typical examples of temporal and/or tissue specific promoters of plant origin that can be used with the polynucleotides of the present invention, are: SH-EP from Vigna mungo and EP-C1 from Phaseolus vulgaris (Yamauchi et al. (1996) Plant Mol Biol. 30:321-9.); RCc2 and RCc3, promoters that direct root-specific gene transcription in rice (Xu et al. (1995) Plant Mol. Biol. 27:237) and TobRB27, a root-specific promoter from tobacco (Yamamoto et al. (1991) Plant Cell 3:371).

Promoters which are seed or embryo-specific and may be useful in disclosure include soybean Kunitz trypsin inhibitor (Kti3, Jof*cku and Goldberg, Plant Cell 1:1079-1093 (1989)), patatin (potato tubers) (Rocha-Sosa, M., et al. (1989) EMBO J. 8:23-29), convicilin, vicilin, and legumin (pea cotyledons) (Rerie, W. G., et al. (1991) Mol. Gen. Genet. 259:149-157; Newbigin, E. J., et al. (1990) Planta 180:461-470; Higgins, T. J. V., et al. (1988) Plant. Mol. Biol. 11:683-695), zein (maize endosperm) (Schemthaner, J. P., et al. (1988) EMBO J. 7:1249-1255), phaseolin (bean cotyledon) (Segupta-Gopalan, C., et al. (1985) Proc. Natl. Acad. Sci. U.S.A. 82:3320-3324), phytohemagglutinin (bean cotyledon) (Voelker, T. et al. (1987) EMBO J. 6:3571-3577), β-conglycinin and glycinin (soybean cotyledon) (Chen, Z-L, et al. (1988) EMBO J. 7:297-302), glutelin (rice endosperm), hordein (barley endosperm) (Marris, C., et al. (1988) Plant Mol. Biol. 10:359-366), glutenin and gliadin (wheat endosperm) (Colot, V., et al. (1987) EMBO J. 6:3559-3564), and sporamin (sweet potato tuberous root) (Hattori, T., et al. (1990) Plant Mol. Biol. 14:595-604).

In some embodiments, a promoter can be a soybean promoter. In some cases, a promoter can be a soybean seed specific promoter. Exemplary suitable soybean promoters comprise: AtOle1, GmBg7S1, Gm2S-1, GmBBId-II, GmCons4, GmCons6, GmCons10, GmRoot1, GmRoot2, GmRoot3, GmRoot5, GmRoot6, GmRoot7, GmRoot8, GmSeed2, GmSeed3, GmSeed5, GmSeed6, GmSeed7, GmSeed8, GmSeed10, GmSeed11, GmSeed12, GmCEP1-L, GmGRD, GmFAB1, GmFAB2, GmFAB3, GmFAB5, GmFAB8, GmFAB9, GmFAB10, GmFAB11, GmFAB17, GmTHIC, GmOLEA, GmOLEB, GmWRKY13, GmWRKY17, GmWRKY21, GmWRKY27, GmWRKY43, GmWRKY54, GmWRKY67, GmWRKY79, GmWRKY80, GmWRKY82, GmWRKY85, GmWRKY162, PvDlec2, PvPhas, pBCON, LfKCS3, FAE1, BoACP, BnNap, BnaNapinC, SSPRO2745.1, SSPRO2743.1, modifications thereof, and any combination thereof.

In some embodiments, a promoter is selected from those provided in Table 4 or Table 11. In some embodiments, a promoter comprises a sequence having at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, at least 97%, at least 98%, at least 99%, or 100% identity, including all ranges and subranges therebetween, with a sequence selected from Table 4 or Table 11. In some embodiments, a promoter is selected from the group that consists of: gmSeed2, gmSeed12, and pvPhas. In some embodiments, a DNA construct for stably expressing RNA in a host cell comprises a gmSeed2 promoter operably linked to a transgene. In some embodiments, a DNA construct for stably expressing RNA in a host cell comprises a gmSeed12 promoter operably linked to a transgene. In some embodiments, a DNA construct for stably expressing RNA in a host cell comprises a pvPhas promoter operably linked to a transgene.

In some embodiments, the promoter is an inducible promoter. Inducible promoters selectively express an operably linked DNA sequence in response to the presence of an endogenous or exogenous stimulus, for example by chemical compounds (chemical inducers) or in response to environmental, hormonal, chemical, and/or developmental signals. Inducible or regulated promoters include, for example, promoters regulated by light, heat, stress, flooding or drought, phytohormones, wounding, or chemicals such as ethanol, jasmonate, salicylic acid, or safeners.

Additional promoters for regulating the expression of the transgenes of the present disclosure in plants are stalk-specific promoters. Such stalk-specific promoters include the alfalfa S2A promoter (GenBank Accession No. EF030816; Abrahams et al., Plant Mol. Biol. 27:513-528 (1995)) and S2B promoter (GenBank Accession No. EF030817) and the like, herein incorporated by reference.

The location of promoters used in the DNA constructs designed herein may be selected to increase RNA expression, and/or downstream protein expression. For example, in some embodiments, the promoter may be located proximal to the transcriptional start site. In some embodiments, the promoter may be located distal to the transcriptional start site (i.e., it may be located thousands and more nucleotides adjacent, typically upstream, of the transcriptional start site.) In some embodiments, the promoter may be a minimal promoter. A “minimal promoter” may be, for example, a truncated or modified version of a wildtype promoter that includes substantially only those sequences required to properly initiate transcription.

In some cases, a promoter can be paired with a transcription terminator to achieve improved RNA stability and/or transgene expression in plants. Transcriptional termination is the process by which RNA synthesis by RNA polymerase is substantially stopped, and both the processed messenger RNA and the enzyme are released from the DNA template. In some cases, improper termination of an RNA transcript can affect the stability of the RNA, and hence can affect protein expression. Variability of transgene expression is sometimes attributed to variability of termination efficiency.

Terminator

A “transcriptional terminator” or “terminator” is a nucleic acid sequence that can halt transcription. It comprises a DNA sequence involved in specific termination of RNA transcription by RNA polymerase. Transcriptional terminator sequences can prevent transcriptional activation of downstream nucleic acid sequences by upstream promoters. A transcriptional terminator may be required in vivo to achieve the desired level of expression or to avoid transcription of a particular sequence. A transcription terminator is considered operably linked to a nucleotide sequence if it can reduce or eliminate transcription of the sequence to which it is linked. In some embodiments, the terminator is a forward terminator. Normally, a forward terminator interrupts transcription when placed upstream of a nucleic acid sequence to be transcribed. In some embodiments, the terminator is a bi-directional terminator. A bi-directional terminator may stop transcription for both the forward and reverse strands and may have the capability of terminating transcription in both 5′ to 3′, and 3′ to 5′ orientations. A single sequence element that acts as a bidirectional terminator can terminate transcription initiated from two convergent promoters.

In some embodiments, the terminator is a reverse transcription terminator, which typically terminates transcription upon reverse strand swallowing.

Terminator sequences can contain polyadenylation (poly(A)) signals, which control the steps involved in 3′ end formation: recognition, endonucleolytic cleavage, and polyadenylation of primary RNA (pre-mRNA). These steps can impact gene expression by influencing mRNA termination, stability, localization, export to cytoplasm, and/or translation efficiency. In some embodiments, a terminator is selected from those provided in Table 4 or Table 11. In some embodiments, a terminator comprises a sequence having from 70%, 80%, 85%, 90%, 95%, 97%, 98%, 99%, or 100% identity, including all ranges and subranges therebetween, with a sequence selected from Table 4 or Table 11.

In eukaryotic systems, such as in plants, a terminator may contain special DNA sequences that allow site-specific cleavage of new transcripts to expose polyadenylation sites. This signals a specialized endogenous polymerase, adding a stretch of about 200 A residues (polyA) to the 3′ end of the transcript. RNA molecules modified with this poly A tail are believed to be more stable and more efficiently translated. Thus, in some embodiments, the terminator may include a signal for RNA cleavage. In some embodiments, the terminator signal promotes polyadenylation of the message. Terminators and/or elements of the polyadenylation site can serve to enhance the output nucleic acid levels and/or to minimize readthrough between nucleic acids.

In some embodiments, a DNA construct comprises a terminator that promotes stable expression of an RNA in a cell. In some embodiments, a DNA construct comprises a terminator that is capable of increasing the level of RNA in a cell. In some embodiments, a DNA construct comprises a terminator that leads to increased half-life of an RNA in a cell. In some embodiments, a DNA construct comprises a combination of a promoter and a terminator that is capable of increasing the level of RNA in a cell. In some embodiments, a DNA construct comprises a combination of a promoter and a terminator that leads to increased half-life of an RNA in a cell.

In some embodiments, terminators for use in accordance with the present disclosure include any terminators described herein or known to those of skill in the art. Examples of terminators include, but are not limited to, termination sequences of genes such as, for example, bovine growth hormone terminator, and, for example, NOS, ARC, EU, Rb7, HSP, ATHSP, AtUbi10, Stubi3, TM6, Octopine Synthase (OCS), SV40 terminator, spy, yejM, secG-leuU, thrLABC, rmB T1, hisLGDCBHAFI, metZWV, rmC, xapR, aspA, EU:Rb7, AtHSP:AtUbi10, EU:StUbi3, EU:TM6. In some embodiments, the terminator comprises a virus termination sequences such as an arcA terminator. In some embodiments, the terminator may be a sequence that cannot be transcribed or translated, such as that resulting from sequence truncation. In some embodiments, a terminator is a dual terminator and is selected from the group consisting of: EU:Rb7, AtHSP:AtUbi10, EU:StUbi3, and EU:TM6. In some embodiments, a terminator is selected from the group consisting of NOS, ARC, EU, Rb7, HSP, AtHSP, AtUbi10, Stubi3, and TM6.

Any heterologous polynucleotide of interest can be operably linked to a terminator sequence provided in the disclosure. Examples of polynucleotides of interest that can be operably linked to the terminator sequences described herein include, but are not limited to, polynucleotides comprising regulatory elements such as introns, enhancers, promoters, translation leader sequences, protein-coding regions from disease and insect resistance genes, genes conferring nutritional value, genes conferring yield and heterosis increase, genes that confer male and/or female sterility, antifungal, antibacterial or antiviral genes, selectable marker genes, herbicide resistance genes and the like.

In embodiments, RNA stabilization can be achieved via combination of a terminator and promoter provided herein. In some embodiments, a synergistic effect on RNA stabilization is observed when a terminator and a promoter are present in a DNA construct of the disclosure. In some embodiments, RNA stabilization is increased by at least about 1-fold, about 2-fold, about 3-fold, about 5-fold, about 10-fold, about 20-fold, about 50-fold, about 100-fold, about 300-fold, about 500-fold, or about 1000-fold, including all ranges and subranges therebetween, when a promoter and terminator are present in a DNA construct as compared to an otherwise comparable construct lacking at least one of the promoter or terminator.

Intron

In some embodiments, RNA stabilization can be achieved or improved via the addition or removal of endogenous or exogenous intronic sequences in a DNA construct. Introns are non-coding sections of an RNA transcript that can be removed by RNA splicing during maturation of the final RNA product.

Intron sequences may be incorporated at any location within a DNA construct, including but not limited to a 5′ end, 3′ end, within or adjacent to a transgene sequence, and any combination thereof. In some cases, an intron sequence is added within a transgene sequence. In some cases, an intron is located within about 0-5, 1-10, 5-25, or 10-30 bases from a start of a transgene sequence. In some cases, an intron is located up to about 0, 5, 10, 15, 20, 30, 40, 50, 70, 90, or 100 bases from a start of a transgene sequence. In some embodiments, an intron is placed adjacent to a 5′UTR. In some embodiments, an intron is placed within a coding sequence of a transgene. In some embodiments, an intron is placed after a promoter sequence. In some embodiments, an intron is placed between a promoter sequence and a coding sequence. In some cases, an endogenous or native intron is replaced with an exogenous intron. Replacement may be full replacement or partial replacement. In cases comprising partial intron replacement, a portion of an endogenous intron remains and can be adjacent to an exogenous intron sequence.

In some embodiments, an intron sequence used in a DNA construct is isolated or derived from a eukaryote. For example, the intron may be isolated or derived from an intronic sequence of a eukaryote selected from animals, plants, and fungi. In some cases, an intron sequence may be isolated or derived from a plant. The plant intronic sequence may be from the same plant species as a host cell, a different plant species as compared to a host cell, or a hybrid species. In some embodiments, an intron sequence isolated or derived from glycine max, Arabidopsis thaliana, or both. In some embodiments, an intron sequence is isolated or derived from a soybean (Glycine max). In some embodiments, an intron sequence is isolated or derived from elongation factor TA.

The DNA constructs described herein may comprise any number of introns. For example, a DNA construct may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more introns. In some embodiments, a DNA construct comprises a transgene, wherein the transgene comprises 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more introns. In some embodiments, a transgene may comprise a reduced number of introns relative to the wildtype gene. For example, in some embodiments, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more introns may be removed from a wildtype gene to produce a transgene as described herein. In some embodiments, a transgene sequence comprises about 1 to about 3 intronic sequences. In some cases, a transgene comprises one intron, two introns, or three introns.

In some embodiments, an intron comprises a sequence having at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, at least 97%, at least 98%, at least 99%, or 100% identity, including all ranges and subranges therebetween, with a sequence selected from Table 11 or Table 14.

Untranslated Regions (UTRs)

In some embodiments, RNA stabilization can be achieved or improved via addition or removal of 5′ or 3′ untranslated regions (5′UTR or 3′UTR) to a DNA construct. UTRs are known to control gene expression and protein function via a wide range of mechanisms. Exemplary mechanisms include: (A) Alternative polyadenylation: either more than one polyA site is utilized to produce mRNA variants that differ in 3′UTR length or, if the polyA is located upstream of the stop codon, truncated transcripts are produced; (B) Riboswitching: 3′ or 5′UTRs form folding structures that sense a metabolite and modulate transcript stability; (C) Adenosine methylation (m6A): The presence of m6A in 5′UTR promotes CAP-independent translation; (D) Short-peptide translation: A short open reading frame in 5′UTR (uORF) can either repress the translation of the main ORF or induce mRNA decay via the NMD pathway; (E) Nonsense-mediated decay (NMD): A pre-mature termination codon (PTC) upstream of the regular termination codon (TC) recruits NMD factors that mediate mRNA decay; (F) Alternative splicing: Retention of intronic elements in 5′UTR can either promote or repress translation, while retention of intronic elements in 3′UTR can modulate miRNA-mediated cleavage. In some embodiments, a 5′UTR and/or 3′ UTR is modulated to regulate (increase or decrease) the transport of an mRNA out of a plant nucleus. Any one of the aforementioned mechanisms can be employed in disclosed strategies to stabilize RNA in a plant.

5′ untranslated regions (UTRs) play an important role in optimizing gene expression. 5′UTR are short sequences (˜65 bp) upstream of the start codon (AUG) that can affect translation initiation by its secondary structure and the existence of the AUG. The κ′ UTR can have a positive or negative effect on translation since they are the target for the binding of microRNAs. Additionally, 5′ UTRs can have a role in mRNA stabilization. In some embodiments, a DNA construct described herein comprise a 5′ UTR. In some embodiments, a DNA construct described herein comprises a 3′ UTR. In some embodiments, a DNA construct described herein comprises a 5′ UTR and a 3′UTR.

In some embodiments, a UTR (e.g., a 5′ UTR or a 3′ UTR) comprises a sequence that is isolated or derived from a plant. For example, in some embodiments, a UTR can comprise a sequence that is isolated or derived from a soybean plant. In some embodiments, a UTR comprises a sequence that is isolated or derived from a mammal, such as any of the mammals described herein. In some embodiments, a UTR comprises a sequence that is isolated or derived from a gene encoding a milk protein, such as β-Lactoglobulin. In some embodiments, a UTR comprises a sequence that is isolated or derived from a gene encoding an egg protein, such as ovalbumin. In some embodiments, a DNA construct described herein comprise a 5′UTR selected from: Arc5′UTR, glnB1UTR, native UTRs of ovalbumin, native UTR of β lactoglobulin, and combinations thereof. In some embodiments, a UTR is selected from those provided in Table 4, or Table 11. In some embodiments, a UTR comprises a sequence having at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, at least 97%, at least 98%, at least 99%, or 1000 identity, including all ranges and subranges therebetween, with a sequence selected from Table 4 or Table 11.

Ubiquitin Monomer

In some embodiments, RNA can be stabilized via use of a ubiquitin monomer. Ubiquitin is a small protein that can be covalently linked to lysine residues of proteins targeted for intracellular degradation by proteasomes. Expression of recombinant/transgenic proteins fused with a ubiquitin monomer can be an advantageous strategy to enhance protein accumulation. The ubiquitin monomer may act as a chaperone for the incorporation of the ribosomal protein into the ribosome. During translation, ubiquitin can be accurately cleaved from the protein by endogenous ubiquitin-specific proteases, leaving the protein of interest free of unnecessary sequences.

In some embodiments, ubiquitin monomers from plants can be utilized in DNA constructs in order to enhance protein expression. The ubiquitin monomer is cleaved either immediately after or during translation improving translational regulation.

In some embodiments, a ubiquitin monomer is from a plant, a mammal, or a fungus. Any of the disclosed plants provided herein can be the source of a ubiquitin monomer, including but not limited to soybean, potato, wheat, corn, and the like. In some cases, the ubiquitin monomer is isolated or derived from a potato.

A ubiquitin monomer sequence can be located at any position of a transgene. In some cases, a monomer is at a 5′ end, 3′ end, in or adjacent to a coding sequence or promoter sequence, and combinations thereof. In some embodiments, a monomer is adjacent to a promoter sequence. In some embodiments, a monomer is located 3′ to a promoter sequence. In some embodiments, a promoter is located 5′ to any sequence provided herein. In some embodiments, a monomer is located within about 0-5, 1-10, 5-25, or 10-30 bases 5′ or 3′ of any sequence provided herein. In some embodiments, a monomer is located between a promoter and a signal peptide.

In some embodiments, a ubiquitin monomer comprises a sequence having at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, or 100% identity, including all ranges and subranges therebetween, with a sequence selected from Table 11.

Codon Optimization

In some embodiments, the present disclosure teaches that RNA stabilization can also be achieved through codon optimization of the DNA sequence encoding the RNA. In some embodiments, codon optimized variants of genes produce pluralities of nucleic acid sequences that can be evaluated for expression potential and stability via the methods of the present disclosure.

The genetic code consists of three-nucleotide units called codons. There are 64 possible codons, each specifying one of twenty amino acids or an end to translation (“STOP codons”). Therefore, at least some codons are redundant. In the coding system used by the vast majority of organisms, two amino acids are each encoded by a single codon, whereas all other amino acids are separately encoded by two, three, four, or six codons, with three STOP codons. For amino acids represented by two, three, or four codons, the codons typically differ from each other at the third nucleotide position. For amino acids represented by two codons, the third position is either a purine (A, G) or pyrimidine (C, T) in both cases. For the three amino acids that are represented by six codons (Arg, Leu, and Ser), each has one block of four codons that follows this pattern by differing in the third position, plus one additional set of two codons. Arg and Leu are each represented by a two-codon block different from each other by a change in the first and second nucleotide positions. The two-codon representation of serine (Ser) is different from that of the Arg two-codon block only in the third nucleotide position.

For a particular amino acid, a given organism does not use the possible codons equally. Organisms each have a bias in codon usage. The pattern of bias in codon usage is distinct for an organism and its close relatives throughout the genome. For example, in Streptomyces spp., frequent codons generally include G or C in the third nucleotide position. Rare codons generally include A or T in the third position. In other organisms, A or T is preferred in the third position. Within a particular species, there can be distinct categories of genes with their own codon bias. In E. coli, for example, there are roughly three classes of genes, each with a distinctive codon usage signature. One class is rich in important proteins that are abundantly expressed; the second class includes proteins that are expressed at relatively low levels; and the third class includes proteins likely to have been recently acquired from other species.

In most synthetic gene design strategies, the process attempts to match the codon composition of a synthetic gene to the codon compositions of genes of a host in which the synthetic gene will be expressed. See, e.g., U.S. Patent Publication No. US2007/0292918. Such strategies may in some situations lead to increased expression of the synthetic gene in the host. For example, codon optimization in yeast may significantly improve the translation of heterologous gene transcripts due to minimizing the effects of, e.g., limiting aminoacyl-tRNAs and transcription termination at AT-rich sequences. See, e.g., Daly and Hearn (2004) J. Mol. Recognition 18:119-38.

Codon optimization comprises a variety of approaches that involve synonymous substitutions to increase protein expression. One strategy that is preferred by some is to maximize the use of frequent codons in the expression host species during the design of heterologous genes. A second strategy preferred by others is to place maximum value on the context of particular codons, and therefore to maximize the use of codon pairs that occur frequently in the expression host. For example, in some embodiments, codon optimization pursues a codon harmonization approach, which seeks to maintain regions of slow translation that are thought to be important for protein folding.

A third strategy is to make the codon usage of the new coding sequence in the new species resemble the codon usage of the reference coding sequence in the species of origin. This third strategy places high value on the recognition of possible requirements for rare codons to ensure proper secondary structure of transcript RNA molecules. A further strategy is to make the codon composition of the heterologous gene resemble the overall codon composition of expressed genes of the new host. Sequence changes resulting in synonymous codons can also be used to alter numerous features of mRNA coding sequences that can inhibit expression, including putative splice donor and acceptor sites. Additionally, simply using the same frequently-occurring codon repeatedly in a heterologous sequence is expected to eventually have the same effect as selecting a rare codon, e.g., overuse of the corresponding tRNA will limit the availability of the tRNA. Thus, in some embodiments codon optimization should also seek to balance these strategies and their underlying concerns in order to produce best results.

Persons having skill in the art will be familiar with how to deploy codon-optimization techniques. Codon usage tables for almost all characterized organisms can be found online, including, the Kazusa database (world wide web at.kazusa.or.jp/codon/) and hive database (hive.biochemistry.gwu.edu/review/codon). In addition, a non-limiting list of software tools capable of generating codon optimized sequence variants is provided in Table 5, below.

Nucleic Acid Secondary Structure

Codon changes also have the potential to affect structure of nucleic acids in vivo. Specifically changes in primary sequence can affect folding, and therefore RNA stability. Therefore, in some embodiments, the present disclosure teaches methods for selecting primary transcript sequences based on their predicted secondary structures. Codon usage bias can be analyzed and optimized using various techniques. In some embodiments, a plurality of different codon optimized transgenes can be generated and evaluated for their RNA structure in silico, using publicly available programs such as RNAfold. Different parameters, such as thermodynamic parameters, can be analyzed including but not limited to minimum free energy structures (MFE), base pair probabilities, and energy mountain plot. In addition, locations of 5′ regions and/or start codons within different structures, such as MFE, can be determined to further analyze RNA structure within those regions to optimize as necessary. In some embodiments, using information gathered from thermodynamic parameter analysis, codon optimized sequences can be selected that yield RNA sequences that comprise a stable structure. Exemplary stable structures can comprise secondary structures, such as loops, bulges, base pair mismatches, hairpin loops, internal loops, helices, multibranch loops, terminal mismatches, dangling ends, and combinations thereof.

In some embodiments, a stable RNA structure comprises a loop. A loop of the disclosure can be of any length. In some cases, a loop of the disclosure that confers increased stability comprises at least about 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 29, 30, or more base pairs. In some embodiments, a stable structure comprises a loop with a length of about 4 to about 8 base pairs. Additional exemplary stable structures can contain tetraloops, such as UUCG (SEQ ID NO: 615). In some embodiments, a stable structure comprises two or more hairpin loops, wherein the hairpin loops are each less than 8 base pairs.

In some embodiments, codon usage bias can be utilized to reduce or prevent unstable structures. An unstable structure can comprise a loop of less than about 4 base pairs or over 8 base pairs. In some embodiments, an unstable structure may lack a secondary structure. In some embodiments, an unstable structure comprises at least one of a pseudo-knot (e.g. a loop with no secondary structure), a loop of less than 4 base pairs or over 8 base pairs, or a large hairpin loop (e.g. over than about 8 base pairs).

In some embodiments, a sequence can be codon optimized for expression in a host cell, such as a plant cell. Other host cells are also contemplated. For example, a sequence can be codon optimized for expression in any of the plants of the disclosure. In some embodiments, a sequence is codon optimized for expression in a soybean plant (Glycine max).

KDEL (Lys-Asp-Glu-Leu, SEQ ID NO: 616) and Related Sequences

Additionally, stabilization of RNA can be achieved through the use of a KDEL sequence. KDEL is a target peptide sequence in mammals and plants located on the C-terminal end of an amino acid structure of a protein. The KDEL sequence reduces or eliminates a protein from being secreted from the endoplasmic reticulum (ER) and can facilitate its return if it is exported. A protein with a functional KDEL motif will be retrieved from the Golgi apparatus by retrograde transport to the ER lumen. It also targets proteins from other locations (such as the cytoplasm) to the ER. Proteins can leave the ER after this sequence has been cleaved off. The instant inventors have surprisingly discovered that the presence of a KDEL sequence in an RNA may increase the stability thereof. Accordingly, provided herein are stably expressed RNA sequences comprising a sequence encoding a KDEL sequence. Also provided herein are DNAs comprising a KDEL sequence, that are capable of stably expressing an RNA in a host cell.

hom*ologues of KDEL are also contemplated in the present disclosure. A hom*ologue may be a similar sequence employed in other organisms. For example, the sequence HDEL (His-Asp-Glu-Leu) performs the same function in yeasts as KDEL. In some embodiments, a DNA sequence described herein may comprise a sequence encoding any one of a KDEL, HDEL, and the like. In some embodiments, a DNA sequence described herein may comprise a sequence selected from the group consisting of: KDEL (SEQ ID NO: 616), HDEF (SEQ ID NO: 632), HDEL (SEQ ID NO: 633), RDEF (SEQ ID NO: 634), RDEL (SEQ ID NO: 635), WDEL (SEQ ID NO: 636), YDEL (SEQ ID NO: 637), HEEF (SEQ ID NO: 638), HEEL (SEQ ID NO: 639), KEEL (SEQ ID NO: 640), REEL (SEQ ID NO: 641), KAEL (SEQ ID NO: 642), KCEL (SEQ ID NO: 643), KFEL (SEQ ID NO: 644), KGEL (SEQ ID NO: 645), KHEL (SEQ ID NO: 646), KLEL (SEQ ID NO: 647), KNEL (SEQ ID NO: 648), KQEL (SEQ ID NO: 649), KREL (SEQ ID NO: 650), KSEL (SEQ ID NO: 651), KVEL (SEQ ID NO: 652), KWEL (SEQ ID NO: 653), KYEL (SEQ ID NO: 654), KEDL (SEQ ID NO: 655), KIEL (SEQ ID NO: 656), DKEL (SEQ ID NO: 657), FDEL (SEQ ID NO: 658), KDEF (SEQ ID NO: 659), KKEL (SEQ ID NO: 660), HADL (SEQ ID NO: 661), HAEL (SEQ ID NO: 662), HIEL (SEQ ID NO: 663), HNEL (SEQ ID NO: 664), HTEL (SEQ ID NO: 665), KTEL (SEQ ID NO: 666), HVEL (SEQ ID NO: 667), NDEL (SEQ ID NO: 668), QDEL (SEQ ID NO: 669), REDL (SEQ ID NO: 670), RNEL (SEQ ID NO: 671), RTDL (SEQ ID NO: 672), RTEL (SEQ ID NO: 673), SDEL (SEQ ID NO: 674), TDEL (SEQ ID NO: 675), SKEL (SEQ ID NO: 676), STEL (SEQ ID NO: 677), and EDEL (SEQ ID NO: 678).

In some embodiments, a sequence of the disclosure can be modified to add a KDEL sequence or a hom*ologue thereof. In some embodiments, a sequence is modified to remove a KDEL sequence or hom*ologue thereof. In some embodiments, a sequence can be modulated to potentiation, reduce, or otherwise strengthen or dampen an existing KDEL sequence or hom*ologue thereof.

Transgenes, Including Chordate Proteins

The DNA constructs described herein may comprise one or more transgenes. The transgenes may encode one or more of a protein or RNA of interest. In some embodiments, the transgene encodes a protein. In some embodiments, the transgene encodes a chordate protein. The chordate proteins provided herein may comprise proteins of a variety of chordates. Chordates are divided into three subphyla: Vertebrata (fish, amphibians, reptiles, birds, and mammals); Tunicata or Urochordata (sea squirts, salps); and Cephalochordata (which includes lancelets). Proteins from any of the aforementioned chordates can be utilized in compositions and methods of the disclosure.

In some embodiments, the chordate is a mammal. Accordingly, in some embodiments, the transgene is a mammalian protein. In some embodiments, the mammalian protein can comprise one or more milk proteins. As used herein the term “milk protein” refers to any protein, or fragment or variant thereof, that is typically found in one or more mammalian milks. Caseins and whey proteins are the major proteins of milk. Casein constitutes approximately 80% (29.5 g/L) of the total protein in bovine milk, and whey protein accounts for about 20% (6.3 g/L). Casein is chiefly phosphate-conjugated and mainly consists of calcium phosphate-micelle complexes. It is a heterogeneous family of 4 major components including alpha- (αs1- and αs2-casein), beta-, gamma-, para-κ-casein, and kappa-casein.

Illustrative milk proteins that may be used in a transgene of the disclosure include members of the casein family of proteins, such as α-S1 casein, α-S2 casein, β-casein, and κ-casein. The caseins are phosphoproteins and make up approximately 80% of the protein content in bovine milk and about 20-45% of the protein in human milk. Caseins form a multi-molecular, granular structure called a casein micelle in which some enzymes, water, and salts, such as calcium and phosphorous, are present. The micellar structure of casein in milk is significant in terms of a mode of digestion of milk in the stomach and intestine and a basis for separating some proteins and other components from cow milk. In practice, casein proteins in bovine milk can be separated from whey proteins by acid precipitation of caseins, by breaking the micellar structure by partial hydrolysis of the protein molecules with proteolytic enzymes, or microfiltration to separate the smaller soluble whey proteins from the larger casein micelle. Caseins are relatively hydrophobic, making them poorly soluble in water.

In some embodiments, the casein proteins described herein (e.g., α-S1 casein, α-S2 casein, β-casein, and/or κ-casein) are isolated or derived from cow (Bos taurus), goat (Capra hircus), sheep (Ovis aries), water buffalo (Bubalus bubalis), dromedary camel (Camelus dromedaries), bactrian camel (Camelus bactrianus), wild yak (Bos mutus), horse (Equus caballus), donkey (Equus asinus), reindeer (Rangifer tarandus), eurasian elk (Alces alces), alpaca (Vicugna pacos), zebu (Bos indicus), llama (Lama glama), or human (hom*o sapiens). In some embodiments, a casein protein (e.g., α-S1 casein, α-S2 casein, β-casein, or κ-casein) has at least 80%, at least 85%, at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% identity with a casein protein from one or more of cow (Bos taurus), goat (Capra hircus), sheep (Ovis aries), water buffalo (Bubalus bubalis), dromedary camel (Camelus dromedaries), bactrian camel (Camelus bactrianus), wild yak (Bos mutus), horse (Equus caballus), donkey (Equus asinus), reindeer (Rangifer tarandus), eurasian elk (Alces alces), alpaca (Vicugna pacos), zebu (Bos indicus), llama (Lama glama), or human (hom*o sapiens).

As used herein, the term “α-S1 casein” refers to not only the α-S1 casein protein, but also fragments or variants thereof α-S1 casein is found in the milk of numerous different mammalian species, including cow, goat, and sheep. The sequence, structure and physical/chemical properties of α-S1 casein derived from various species is highly variable. An illustrative sequence for bovine α-S1 casein can be found at Uniprot Accession No. P02662, and an illustrative sequence for goat α-S1 casein can be found at GenBank Accession No. X59836.1. The terms “α-S1 casein” and “alpha-S1-casein” (and similar terms) are used interchangeably herein.

As used herein, the term “α-S2 casein” refers to not only the α-S2 casein protein, but also fragments or variants thereof α-S2 is known as epsilon-casein in mouse, Gamma-casein in rat, and casein-A in guinea pig. The sequence, structure and physical/chemical properties of α-S2 casein derived from various species is highly variable. An illustrative sequence for bovine α-S2 casein can be found at Uniprot Accession No. P02663, and an illustrative sequence for goat α-S2 casein can be found at Uniprot Accession No. P33049. The terms “α-S2 casein” and “alpha-S2-casein” (and similar terms) are used interchangeably herein.

As used herein, the term “β-casein” refers to not only the β-casein protein, but also fragments or variants thereof. For example, A1 and A2 β-casein are genetic variants of the β-casein milk protein that differ by one amino acid (at amino acid 67, A2 β-casein has a proline, whereas A1 has a histidine). Other genetic variants of β-casein include the A3, B, C, D, E, F, H1, H2, I and G genetic variants. The sequence, structure and physical/chemical properties of β-casein derived from various species is highly variable. Exemplary sequences for bovine β-casein can be found at Uniprot Accession No. P02666 and GenBank Accession No. MI5132.1. The terms “β-casein”, “beta-casein” and “B-casein” (and similar terms) are used interchangeably herein.

As used herein, the term “κ-casein” refers to not only the κ-casein protein, but also fragments or variants thereof. κ-casein is cleaved by rennet, which releases a macropeptide from the C-terminal region. The remaining product with the N-terminus and approximately two-thirds of the original peptide chain is referred to as para-κ-casein. The sequence, structure and physical/chemical properties of κ-casein derived from various species is highly variable. Illustrative sequences for bovine κ-casein can be found at Uniprot Accession No. P02668 and GenBank Accession No. CAA25231. The terms “κ-casein”, “κ-casein” and “kappa-casein” (and similar terms) are used interchangeably herein.

In some embodiments, the milk protein comprises from about: 75-85%, 80%-85%, 80%-90%, 85%-95%, 90%-95%, or 95%-100%, including all ranges and subranges therebetween, identity to a sequence selected from SEQ ID NO: 1-SEQ ID NO: 614. Provided milk proteins of the disclosure can have from about: 50%, 51%, 52%, 53%, 54%, 55%, 56%, 57%, 58%, 59%, 60%, 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100% sequence identity, including all ranges and subranges therebetween to sequence provided and/or referenced in Table 17.

In some embodiments, the milk protein is a casein protein, for example, α-S1 casein, α-S2 casein, β-casein, and or κ-casein. In some embodiments, the milk protein is κ-casein and comprises the sequence of SEQ ID NO: 4, or a sequence at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% identical thereto. In some embodiments, the milk protein is para-κ-casein and comprises the sequence of SEQ ID NO: 2, or a sequence at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% identical thereto. In some embodiments, the milk protein is β-casein and comprises the sequence of SEQ ID NO: 6, or a sequence at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% identical thereto. In some embodiments, the milk protein is α-S1 casein and comprises the sequence SEQ ID NO: 8, or a sequence at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% identical thereto. In some embodiments, milk protein is α-S2 casein and comprises the sequence SEQ ID NO: 84, or a sequence at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% identical thereto.

In some embodiments, the milk protein comprises a sequence that is at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% identical to SEQ ID NO: 4. In some embodiments, the milk protein comprises a sequence that is at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% identical to SEQ ID NO: 2. In some embodiments, the milk protein comprises a sequence that is at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% identical to SEQ ID NO: 6. In some embodiments, the milk protein comprises a sequence that is at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% identical to SEQ ID NO: 8. In some embodiments, the milk protein comprises a sequence that is at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% identical to SEQ ID NO: 84.

In some embodiments, α-S1 casein is encoded by the sequence of SEQ ID NO: 7, or a sequence at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% identical thereto. In some embodiments, α-S2 casein is encoded by the sequence of SEQ ID NO: 83, or a sequence at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% identical thereto. In some embodiments, β-casein is encoded by the sequence of SEQ ID NO: 5, or a sequence at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% identical thereto. In some embodiments, κ-casein is encoded by the sequence of SEQ ID NO: 3, or a sequence at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% identical thereto. In some embodiments, para-κ-casein is encoded by the sequence of SEQ ID NO: 1, or a sequence at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% identical thereto.

In some embodiments, the milk protein is encoded by a sequence that is at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% identical to SEQ ID NO: 7. In some embodiments, the milk protein is encoded by a sequence that is at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% identical to SEQ ID NO: 83. In some embodiments, the milk protein is encoded by a sequence that is at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% identical to SEQ ID NO: 3. In some embodiments, the milk protein is encoded by a sequence that is at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% identical to SEQ ID NO: 1. In some embodiments, the milk protein is encoded by a sequence that is at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% identical to SEQ ID NO: 5.

In some embodiments, the milk protein is a casein protein, and comprises a sequence that is at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% identical to any one of SEQ ID NO: 85-133, or 148-563. In some embodiments, the milk protein is a casein protein and comprises the sequence of any one of SEQ ID NO: 85-133 or 148-563.

In some embodiments, the milk protein comprises a sequence that is at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% identical to any one of SEQ ID NO: 85-98 or 148-340. In some embodiments, the milk protein comprises the sequence of any one of SEQ ID NO: 85-98 or 148-340.

In some embodiments, the milk protein comprises a sequence that is at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% identical to any one of SEQ ID NO: 99-109 or 341-440. In some embodiments, the milk protein comprises the sequence of any one of SEQ ID NO: 99-109 or 341-440.

In some embodiments, the milk protein comprises a sequence that is at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% identical to any one of SEQ ID NO: 110-120 or 441-494. In some embodiments, the milk protein comprises the sequence of any one of SEQ ID NO: 110-120 or 441-494.

In some embodiments, the milk protein comprises a sequence that is at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% identical to any one of SEQ ID NO: 121-133 or 495-563. In some embodiments, the milk protein comprises the sequence of any one of SEQ ID NO: 121-133 or 495-563 or 495-563.

In some embodiments, the milk protein is not a casein protein. Examples of non-casein milk proteins include, for example, β-lactoglobulin, α-lactalbumin, lysozyme, lactoferrin, lactoperoxidase, serum albumin, or an immunoglobulin.

In some embodiments, a chordate protein comprises whey. As used herein “whey” refers to the liquid remaining after milk has been curdled and strained, for example during cheesemaking. Whey comprises a collection of globular proteins, typically a mixture of β-lactoglobulin, α-lactalbumin, bovine serum albumin, and immunoglobulins. The term “whey protein” may be used herein to refer to a milk protein which heat labile and is soluble in milk at about pH 4.6 in its undenatured state. Alpha-Lactalbumin (α-LA) and beta-lactoglobulin (β-LG) are the predominant whey proteins and comprise about 70-80% of the total whey proteins. Other types of whey proteins include, immunoglobulins (Igs) (e.g., IgA, IgG, IgM, IgE), serum albumin, lysozyme, lactoferrin (LF), lactoperoxidase (LP), and protease-peptones.

In some embodiments, the milk protein is a protein typically found in whey. In some embodiments, the milk protein is β-lactoglobulin or a functional fragment thereof. In some embodiments, the milk protein is β-lactoglobulin and comprises the sequence of SEQ ID NO: 10, or a sequence at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% identical thereto. In some embodiments, the milk protein is β-lactoglobulin and is encoded by the sequence of any one of SEQ ID NO: 9, 11, 12, or 13, or a sequence at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% identical to any one of SEQ ID NO: 9, 11, 12, or 13. In some embodiments, the milk protein comprises a sequence that is at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% identical to any one of SEQ ID NO: 9-13 or 564-614. In some embodiments, the milk protein comprises the sequence of any one of SEQ ID NO: 10 or 564-614.

In an aspect, a chordate protein is an egg protein. In some embodiments, an egg protein used in the compositions and methods described herein is an egg white protein. Egg white is made up of at least 40 different kinds of proteins. Ovalbumin is the major egg white protein, along with ovotransferrin and ovomucoid. Other proteins of interest include flavoprotein, which binds riboflavin; avidin, which can bind and inactivate biotin; and lysozyme, which has lytic action against bacteria.

In some embodiments, an egg protein used in the compositions and methods described herein is an egg yolk protein. Exemplary egg yolk proteins comprise: Phosvitins, vitellin, lipophorin, and combinations thereof.

In some embodiments, an egg protein is any one of: ovalbumin, ovotransferrin, ovomucoid, ovoglobulin G2, ovoglobulin G3, alpha-ovomucin, beta-ovomucin, lysozyme, ovoinhibitor, ovoglycoprotein, flavoprotein, ovomacroglobulin, avidin, cystatin, ovostatin, ovalbumin related protein X, ovalbumin related protein Y, vitellogenin, alpha-lipovitellin, beta-lipovitellin, alpha-livetin, beta-livetin, gamma-livetin, phosvitin, apovitellenin I, apovitellenin II, apovitellenin III, apovitellenin IV, apovitellenin V, apovitellenin VI, VLDL-II, apo-B, and any combination thereof. In other embodiments, an egg protein is selected from the group consisting of ovalbumin, ovotransferrin, ovomucoid, lysozyme, ovoglobulin G2, ovoglobulin G3, alpha-ovomucin, beta-ovomucin, apovitellenin-1, alpha-lipovitellin, beta-lipovitellin and any combination thereof. In some embodiments, an egg protein used in the DNA constructs described herein may include apolipoproteins, egg yolk globulin, or riboflavin binding protein. In some embodiments, a transgene construct described herein comprises a transgene encoding an ovalbumin protein. In some embodiments, an egg protein comprises a sequence or is encoded by a sequence selected from Table 6-Table 8. In some embodiments, an egg protein comprises a sequence or is encoded by a sequence that is at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, or at least 100% identical to, including all ranges and subranges therebetween, a sequence selected from Table 6-Table 8.

In some embodiments, the chordate protein is ovalbumin or a functional fragment thereof. In some embodiments, the disclosure teaches ovalbumin protein sequence that is encoded by codon-optimized SEQ ID NO: 617. In a particular embodiment, the ovalbumin protein comprising an amino acid sequence having at least about 70%, about 71%, about 72%, about 73%, about 74%, about 75%, about 76%, about 77%, about 78%, about 79%, about 80%, about 81%, about 82%, about 83%, about 84%, about 85%, about 86%, about 87%, about 88%, about 89%, about 90%, about 91%, about 92%, about 93%, about 94%, about 95%, about 96%, about 97%, about 98%, or about 99%, sequence identity to SEQ ID NO:622 is provided. In some embodiments, the ovalbumin protein has the amino acid sequence of SEQ ID NO: 622.

In some embodiments, the disclosure teaches ovotransferrin protein sequence that is encoded by codon-optimized SEQ ID NO: 618. In a particular embodiment, the ovotransferrin protein comprising an amino acid sequence having at least about 70%, about 71%, about 72%, about 73%, about 74%, about 75%, about 76%, about 77%, about 78%, about 79%, about 80%, about 81%, about 82%, about 83%, about 84%, about 85%, about 86%, about 87%, about 88%, about 89%, about 90%, about 91%, about 92%, about 93%, about 94%, about 95%, about 96%, about 97%, about 98%, or about 99%, sequence identity to SEQ ID NO:623 is provided. In some embodiments, the ovotransferrin protein has the amino acid sequence of SEQ ID NO: 623.

In other embodiments, the disclosure teaches ovomucoid protein sequence that is encoded by codon-optimized SEQ ID NO: 619. In a particular embodiment, the ovomucoid protein comprising an amino acid sequence having at least about 70%, about 71%, about 72%, about 73%, about 74%, about 75%, about 76%, about 77%, about 78%, about 79%, about 80%, about 81%, about 82%, about 83%, about 84%, about 85%, about 86%, about 87%, about 88%, about 89%, about 90%, about 91%, about 92%, about 93%, about 94%, about 95%, about 96%, about 97%, about 98%, or about 99%, sequence identity to SEQ ID NO:624 is provided. In some embodiments, the ovomucoid protein has the amino acid sequence of SEQ ID NO: 624.

In other embodiments, the disclosure teaches lysozyme sequence that is encoded by codon-optimized SEQ ID NO: 620. In a particular embodiment, the lysozyme protein comprising an amino acid sequence having at least about 70%, about 71%, about 72%, about 73%, about 74%, about 75%, about 76%, about 77%, about 78%, about 79%, about 80%, about 81%, about 82%, about 83%, about 84%, about 85%, about 86%, about 87%, about 88%, about 89%, about 90%, about 91%, about 92%, about 93%, about 94%, about 95%, about 96%, about 97%, about 98%, or about 99%, sequence identity to SEQ ID NO:625 is provided. In some embodiments, the lysozyme protein has the amino acid sequence of SEQ ID NO: 625.

In other embodiments, the disclosure teaches apovitellenin-1 protein sequences that is encoded by codon-optimized SEQ ID NO: 621. In a particular embodiment, the apovitellenin-1 protein comprising an amino acid sequence having at least about 70%, about 71%, about 72%, about 73%, about 74%, about 75%, about 76%, about 77%, about 78%, about 79%, about 80%, about 81%, about 82%, about 83%, about 84%, about 85%, about 86%, about 87%, about 88%, about 89%, about 90%, about 91%, about 92%, about 93%, about 94%, about 95%, about 96%, about 97%, about 98%, or about 99%, sequence identity to SEQ ID NO:626 is provided. In some embodiments, the apovitellenin-1 protein has the amino acid sequence of SEQ ID NO: 626.

In some embodiments, an egg protein can be a protein that is typically found in an egg without a yolk. Yolkless eggs can comprise wind eggs, dwarf eggs, or fart eggs. Egg proteins may include proteins present in the yolk portion of an egg. In some embodiments, the egg protein is typically found in a fertilized egg.

Chordates

In some embodiments, a transgene of the disclosure encodes a chordate protein, wherein the chordae is a vertebrate. Illustrative vertebrates are described below.

In some embodiments, a vertebrate is a mammal. For example, in some embodiments, the vertebrate is a bovine. Illustrative bovine species includes, but are not limited to: Holstein, jersey, brown swiss, guernsey, Ayrshire, red and white Holstein, milking shorthorn, simmental, French brown, tux-zillertal, marnau-werdenfel, Icelandic, Danish jersey, aldemey, abigar, Chinese black, agerolese, Australian milking zebu, achham, aulie-ata, Australian Friesian, Jamaica hope, burlina, and butana and kenana. In some cases, the bovine is selected from the group consisting of: Holstein, Jersey, Brown Swiss, Guernsey, Ayrshire, Milking Shorthorn, and Red and White Holstein.

In some embodiments, the vertebrate is a placental mammal. The placental mammals belong to the sub-class Eutheria. In some embodiments, a mammal of the disclosure is a placental mammal selected from the group consisting of a: camel, goat, cow, yak, buffalo, horse, donkey, zebu, sheep, reindeer, giraffe, and co*ckroach.

In some embodiments, the vertebrate is a bird. Exemplary birds comprise any one of a: chicken, turkey, duck, goose, pheasant, quail, ostrich, guinea fowl, rhea, bantam, pigeon, emu, and dodo, penguin. In some embodiments, the vertebrate is a domesticated bird and/or a bird that are bred to produce eggs for consumption.

In some embodiments, the vertebrate is a chicken. In some embodiments, the vertebrate is a hybrid chicken. Hybrid chickens are bred to lay more eggs than their unmodified or unbred counterparts. In some embodiments, the vertebrate is a chicken selected from a golden comet, Rhode Island red, leghorn, Sussex, Plymouth rock, Ancona, bamevelder, hamburg, maran, buff orpington, easter egger, Ameraucana, Australorp, Delaware, Euskal oiloa, Faverolle, Golden laced Wyandotte, Isa brown, Jaerhon, New Hampshire red, Red sex link, or Welsummer. In some embodiments, the vertebrate is a chicken selected from: australorp, white leghom, Sussex, goldline, hybrid, Plymouth Rock, and Rhode Island Red.

In some embodiments, the vertebrate is a non-bird animal such as a turtle, iguana, alligator, snake, platypus, echidna, reptile, fish, amphibian, insect, lizard, crocodile, alligator, crab, shrimp, ant eater, and modified versions thereof.

In some embodiments, the vertebrate is a marsupial. Marsupials give birth to barely formed offspring, and the baby grows in a pouch on the mother's belly. Marsupial mammals belong to the Sub-class Metatheria.

Host Cells

Also provided herein are host cells for expressing a transgene of interest. In some embodiments, a protein encoded by a transgene of interest accumulates at a high level in the host cell. In some embodiments, an RNA of interest accumulates at a high level in the host cell. In some embodiments, an RNA of interest has an increased half-life in the host cell.

In some embodiments, the host cell may be a plant cell. For example, the host cell may be a plant cell isolated or derived from any one of the plant species described above. In some embodiments, the host cell can be isolated or derived from a species which is not a plant.

Provided herein are plants, transgenic plants, and portions thereof (for example host cells from plants) that comprise any of the transgene or modifications disclosed herein. Plants may be in any condition including but not limited to dead, alive, pre-germination, post-germination, flowering, seed stage, and combinations thereof. A plant may be edible. A plant may be inedible or poisonous. In some cases, a plant is a crop.

In some embodiments, a plant is a monocot. For example, in some embodiments, the plant may be a monocot selected from turf grass, maize (corn), rice, oat, wheat, barley, sorghum, orchid, iris, lily, onion, palm, and duckweed.

In some embodiments, a plant is a dicot. For example, in some embodiments, the plant may be a dicot selected from Arabidopsis, tobacco, tomato, potato, sweet potato, cassava, alfalfa, lima bean, pea, chickpea, soybean, carrot, strawberry, lettuce, oak, maple, walnut, rose, mint, squash, daisy, Quinoa, buckwheat, mung bean, cow pea, lentil, lupin, peanut, fava bean, French beans (i.e., common beans), mustard, or cactus. In some embodiments, the plant is a soybean (Glycine max). In some embodiments, the plant is Arabidopsis thaliana.

In some embodiments, a plant is a non-vascular plant selected from moss, liverwort, homwort or algae. In some embodiments, the plant is a vascular plant reproducing from spores (e.g., a fern).

Exemplary plants that can be used with the compositions and methods of the disclosure include but are not limited to: spermatophytes (spermatophyta), acrogymnospermae, angiosperms (magnoliophyta), ginkgoidae, pinidae, mesangiospermae, cycads, Ginkgo, conifers, gnetophytes, Ginkgo biloba, cypress, junipers, thuja, cedarwood, pines, angelica, caraway, coriander, cumin, fennel, parsley, dill, dandelion, helichrysum, marigold, mugwort, safflower, camomile, lettuce, wormwood, calendula, citronella, sages, thyme, chia seed, mustard, olive, coffee, capsicum, eggplant, paprika, cranberry, kiwi, vegetable plants (e.g., carrot, celery), tagetes, tansy, tarragon, sunflower, wintergreen, basil, hyssop, lavender, lemon verbena, marjoram, melissa, patchouli, pennyroyal, peppermint, rosemary, sesame, spearmint, primroses, samara, pepper, pimento, potato, sweet potato, tomato, blueberry, nightshades, petunia, morning glory, lilac, jasmin, honeysuckle, snapdragon, psyllium, wormseed, buckwheat, amaranth, chard, quinoa, spinach, rhubarb, jojoba, cypselea, chlorella, manila, hazelnut, canola, kale, bok choy, rutabaga, frankincense, myrrh, elemi, hemp, pumpkin, squash, curcurbit, manioc, dalbergia, legume plants (e.g., alfalfa, lentils, beans, clovers, peas, fava coceira, frijole bola roja, frijole negro, lespedeza, licorice, lupin, mesquite, carob, soybean, peanut, tamarind, wisteria, cassia, chickpea, garbanzo, fenugreek, green pea, yellow pea, snow pea, lima bean, fava bean), geranium, flax, pomegranate, cotton, okra, neem, fig, mulberry, clove, eucalyptus, tea tree, niaouli, fruiting plants (e.g., apple, apricot, peach, plum, pear, nectarine), strawberry, blackberry, raspberry, cherry, prune, rose, tangerine, citrus (e.g., grapefruit, lemon, lime, orange, bitter orange, mandarin), mango, citrus bergamot, buchu, grape, broccoli, brussels, sprout, camelina, cauliflower, rape, rapeseed (canola), turnip, cabbage, cucumber, watermelon, honeydew melon, zucchini, birch, walnut, cassava, baobab, allspice, almond, breadfruit, sandalwood, macadamia, taro, tuberose, aloe vera, garlic, onion, shallot, vanilla, yucca, vetiver, galangal, barley, corn, curcuma aromatica, ginger, lemon grass, oat, palm, pineapple, rice, rye, sorghum, triticale, turmeric, yam, bamboo, barley, cajuput, canna, cardamom, maize, oat, wheat, cinnamon, sassafras, lindera benzoin, bay laurel, avocado, ylang-ylang, mace, nutmeg, moringa, horsetail, oregano, cilantro, chervil, chive, aggregate fruits, grain plants, herbal plants, leafy vegetables, non-grain legume plants, nut plants, succulent plants, land plants, water plants, delbergia, millets, drupes, schizocarps, flowering plants, non-flowering plants, cultured plants, wild plants, trees, shrubs, flowers, grasses, herbaceous plants, brushes, lianas, cacti, green algae, tropical plants, subtropical plants, temperate plants, and derivatives and crosses thereof.

In some embodiments, the host cell comprises a non-plant cell. Exemplary non-plant host cells can be isolated or derived from a microbe, algae, fungi, yeast, and the like. Examples of microbes that may be used as host cells include but are not limited to firmicutes, cyanobacteria (blue-green algae), oscillatoriophcideae, bacillales, lactobacillales, oscillatoriales, bacillaceae, lactobacillaceae, Acetobacter suboxydans, Acetobacter xylinum, Actinoplane missouriensis, Arthrospira platensis, Arthrospira maxima, Bacillus cereus, Bacillus coagulans, Bacillus subtilus, Bacillus cerus, Bacillus licheniformis, Bacillus stearothermophilus, Bacillus subtilis, Escherichia coli, Lactobacillus acidophilus, Lactobacillus bulgaricus, Lactococcus lactis, Lactococcus lactis Lancefield Group N, Lactobacillus reuteri, Leuconostoc citrovorum, Leuconostoc dextranicum, Leuconostoc mesenteroides strain NRRL B-512(F), Micrococcus lysodeikticus, Spirulina, Streptococcus cremoris, Streptococcus lactis, Streptococcus lactis subspecies diacetylactis, Streptococcus thermophilus, Streptomyces chattanoogensis, Streptomyces griseus, Streptomyces natalensis, Streptomyces olivaceus, Streptomyces olivochromogenes, Streptomyces rubiginosus, Tetrahymena thermophile, Tetrahymena hegewischi, Tetrahymena hyperangularis, Tetrahymena malaccensis, Tetrahymena pigmentosa, Tetrahymena pyriformis, and Tetrahymena vorax, and Xanthom*onas campestris, and derivatives and crosses thereof.

Examples of algae that may be used as host cells include but are not limited to green algae (e.g., Chlorella), brown algae (e.g., Alaria marginata, Analipus japonicus, Ascophyllum nodosum, Ecklonia sp, Eisenia bicyclis, Hizikia fusiforme, Kjellmaniella gyrata, Laminaria angustata, Laminaria longirruris, Laminaria Longissima, Laminaria ochotensis, Laminaria claustonia, Laminaria saccharina, Laminaria digitata, Laminaria japonica, Macrocystis pyrifera, Petalonia fascia, Scytosiphon lome), red algae (e.g., Gigartinaceae, Soliericeae, Chondrus crispus, Chondrus ocellatus, Eucheuma cottonii, Eucheuma spinosum, Furcellaria fastigiata, Gracilaria bursa-pastoris, Gracilaria lichenoides, Gloiopeltis furcata, Gigartina acicularis, Gigartina bursa-pastoris, Gigartina pistillata, Gigartina radula, Gigartina skottsbergii, Gigartina stellata, Palmaria palmata, Porphyra columbina, Porphyra crispata, Porhyra deutata, Porhyra perforata, Porhyra suborbiculata, Porphyra tenera, Porphyridium cruentum, Porphyridium purpureum, Porphyridium aerugineum, Rhodella maculate, Rhodella reticulata, Rhodella violacea, Rhodophyceae, Rhodymenia palmata), and derivatives and crosses thereof.