Patent Application
20070111227
The instant application contains a “lengthy” Sequence Listing of SEQ ID NOs: 1-185,413 which has been submitted via CD-Rs in lieu of a printed paper copy, and is hereby incorporated by reference in its entirety. Said CD-R, recorded on Jul. 28, 2006, is labeled “CRF”, “Copy 1” and “Copy 2”, respectively, and each contains only one identical 27.5 MB file (99689009. APP).
The present invention relates generally to the isolation and identification of small ribonucleic acids (RNAs) from an organism and methods for their use. In particular the invention relates to novel small inhibitory RNAs (siRNAs), microRNAs (miRNAs), tiny RNAs or combinations thereof from an organism, for example, Arabidopsis thaliana. In a related aspect the invention relates to methods of using the small RNAs disclosed herein.
Small ribonucleic acid (RNA) molecules are short RNA sequences (e.g., 15 to 30 nucleotides in size, but generally 21-24 nucleotides in size) that are produced by nearly all eukaryotes (e.g., fungi, plants, and animals). However, rather than encoding a protein, small RNAs function to reduce the mRNA abundance or protein abundance of the gene which is the “target.” In certain instances small RNAs can also result in target gene regulation by affecting chromatin structure. The two major types of small RNAs are known as small interfering RNAs (siRNAs) and microRNAs (miRNAs). Both types of molecules are processed from double-stranded RNA by RNase III enzymes called DICERs. Although relatively short in length, 15 to 30 nucleotides, small RNAs typically correspond to a single location in the host genome.
Small RNAs do not necessarily demonstrate perfect base pair complementarity with their target RNA. This phenomena allows for a single small RNA to interact with multiple targets such as those encoded by members of a gene family that share short regions of similarity. Therefore, although small RNAs may not match perfectly to their targets (i.e., they contain one or more base-pair mismatches) they retain the ability to direct cleavage or inhibit translation of the target mRNAs.
While similar in size, the biogenesis and function of siRNAs and miRNAs can be substantially different. For instance, siRNAs are processed from longer double-stranded RNA molecules and represent both strands of the RNA. In addition, siRNAs are incorporated into a multi-protein complex known as the RNA-induced silencing complex (RISC), where they can act as guides to target and degrade complementary mRNA molecules. In some systems, siRNAs can also trigger transcriptional silencing by guiding nuclear complexes that target either histone modifications or DNA methylation or both.
MicroRNA molecules, on the other hand, originate from distinct genomic loci predicted to encode transcripts that form ‘hairpin’ structures. These small RNAs, which are derived from one strand of the hairpin, guide the RISC (or a similar RNA-protein complex) to specific RNAs, such as mRNAs by forming base-pairing interactions. Like siRNA, miRNAs can induce cleavage and accelerate degradation of the mRNA targets. A second mechanism by which miRNAs affect gene function is to reduce or prevent mRNA translation and thereby limit protein production.
However, not all small RNAs fit precisely into these two categories. For example, trans-acting siRNAs (ta-siRNAs), recently found in plants, are technically siRNAs because they require the action of an RNA-dependent RNA polymerase to generate their double-stranded RNA precursors. After the ta-siRNAs are formed by cleavage of the double-stranded RNA by a DICER enzyme, they act like miRNAs to silence genes in trans that usually have little resemblance to the genes from which they derive (Vasquez et al, 2004; Peragine et al., 2004). Work in plants also led to a new model for the evolution of miRNA genes from inverted duplication of target genes. Founder genes formed by these initial inversions are thought to produce siRNAs that are replaced by miRNA as the sequence of the founder genes diverges (Allen et al., 2004).
As indicated above, small RNAs have many roles in organisms. For example, miRNAs are critical for development in both plants and animals. The first miRNAs were discovered for their role in the development of the nematode Caenorhabditis elegans (Lee at al., 1993). Numerous diverse examples have emerged subsequently including important roles of miRNAs in brain development in vertebrates and flower development in plants. Other studies have associated miRNA metabolism with cancer, and other human diseases. Small RNAs have also been associated with stress responses, hormonal responses, reproductive development, and small RNA metabolism. Endogenous siRNAs are also thought to function in part to protect the genome against damage or invasion by mobile genetic elements such as retro-transposons and viruses, which produce aberrant RNA or dsRNA in the host cell when they become active. It is well known however, that small RNA function can have profound effects on cellular physiology as well as the overall phenotype. Yet, these and other numerous examples likely represent only a subset of the roles of these molecules in eukaryotes. In theory they could regulate any gene so they could contribute to any biological function in an organism. Conversely, inhibiting elevating, or otherwise modulating the level of a given small RNA is a means of creating new advantageous traits. For example, modulating the expression of certain genes in a plant could affect its tolerance to pesticides, temperature, or soil conditions.
Currently, the typical method for the isolation and identification of small RNAs involves cloning, either as single molecules or “concatamers,” and subsequent sequencing by standard methods. Using this approach, a modest number of small RNA sequences have been identified from, for example, human, Drosophila melanogaster, mouse, Caenorhabditis elegans, and Arabidopsis thaliana. Obviously, these methods do not sequence deeply enough to sample the full complexity of small RNAs in plant and animal systems. While modern microarray-based methods for the quantification of small RNA abundance offer advantages of scale, they are relatively new, and their sensitivity and specificity have yet to be fully characterized. Therefore, most current analyses rely on RNA gel blots or assays with oligonucleotide probes that only detect individual or closely related small RNA sequences.
Recently, we demonstrated a method of performing massively parallel signature sequencing™ (“MPSS”) to sequence more than two million small RNAs from seedlings and the inflorescence stage of the model plant Arabidopsis thaliana. This method is the subject of U.S. patent application Ser. Nos. 11/204,903, which is incorporated herein by reference in its entirety. This technique allows for the efficient identification and isolation of many hundreds of thousands of individual sequences, the generation of a “library” of small RNAs. The abundance or frequency of occurrence of each distinct sequence from a small RNA “library” is indicative of the quantity in the original tissue from which the RNA was obtained. Moreover, by comparison of the signature sequences, which are typically 17-20 nucleotides in length, to a genomic DNA database it is possible to determine the locations on the DNA that serve as sources for the small RNAs. Comparisons to genome annotations, cDNA databases, and other data can often be used to identify the larger RNA precursors of the small RNAs. Most significantly, MPSS provides the ability to address small RNA biology on a genome-wide scale.
While, MPSS provides extraordinary depth, sequencing a half million or more molecules per library, utilizing another parallel sequencing approach, the 454 technology Margulies, M., et al., 2005. Genome sequencing in microfabricated high-density picolitre reactors. Nature 437: 376-380, provides longer reads and thereby provides information about length. Both methods provide quantitative data based on the frequency of the molecules that are sequenced. However, without identification, it is impossible to discover the functional significance of a given small RNA.
Interestingly, the small RNA population in plants may be among the most complex because, in addition to producing microRNAs (miRNAs) that play critical role in various developmental, stress, and signaling responses Chen, X., et al., 2005. MicroRNA Biogenesis and Function In Plants. FEBS Lett 579: 5923-5931; Zhang, B., et al., 2006, Conservation and Divergence of Plant MicroRNA Genes. Plant J 46: 243-259, plants also produce a complex set of small interfering RNAs (siRNAs); Vaucheret, H., et al., 2006, AGO1 Homeostasis Entails Coexpression of MIR168 and AGO1 and Preferential Stabilization of miR168 by AGO1. Mol Cell 22: 129-136. Among the approximately 77,000 different small RNAs that have been sequenced from Arabidopsis, it is likely that miRNAs account for less than 10%, so the non-redundant set of siRNAs must number more than 70,000 Lu, C., et al., 2005, Elucidation of the Small RNA Component of the Transcriptome. Science 309: 1567-1569. Most of these siRNAs match to repeated sequences such as transposons and retrotransposons. Thus, in cereals and other plant species with larger genomes and correspondingly higher contents of repeated DNA, the complexity of siRNAs is expected to be far greater.
While the ‘upstream’ biochemical steps that produce small RNAs have been relatively well characterized much remains to be understood about the complexity, abundance, targeting, and regulatory function of small RNAs. Because the search for these small RNAs has only occurred in the last 5 to 7 years, and because no methods prior to our invention permitted the large-scale characterization of these molecules (see U.S. Ser. No. 11/204,903), their ‘downstream’ role in many aspects of biology, and commercial utility has been poorly explored.
In addition to the transcriptional or post-transcriptional gene regulatory mechanisms that are mediated by small RNAs made within an organism (endogenous small RNAs), small RNAs can also be useful for purposes of RNA interference (RNAi). RNAi refers to the specific silencing of genes which bear substantial homology in nucleic acid sequence to small RNAs that are introduced or engineered to be produced within an organism, cell, or cell-free experimental system. RNAi is a process that appears to be conserved in eukaryotic cells across evolutionary lines, and involves some of the same cellular components and mechanisms involved in the small RNA mediated gene regulation mechanisms. For example, U.S. Pat. No. 7,022,828 to McSwiggen, which is incorporated herein by reference in its entirety, is one of the first patents to describe a small RNA molecule useful as an RNAi therapeutic for modulating immune responses in an animal.
In addition to therapeutic uses, there exists an overwhelming need for agents having agricultural applications, for example, to modify disease and pesticide resistance, and/or enhance plant growth, nutritional value, abundance, etc . . . . As such, the present invention relates to small RNA compositions and methods for the preparation and use thereof, for example, for agricultural use.
The present invention relates to unique small ribonucleic acid molecules, for example siRNAs and miRNAs, identified and isolated using MPSS. Specifically, the invention is directed to the identification of approximately 185,409 unique small RNA sequences from Arabidopsis thaliana (SEQ ID NOS. 1-185,409). In one aspect the invention includes nucleic acids, for example, small RNAs, of from about 15 to about 30 nucleotides in length. In certain preferred embodiments the nucleic acids identified using MPSS are about 17 nucleotides in length. These nucleic acids can be extended with genomic sequence to 21-24 nucleotides in length in order to, for example, determine the entire biologically active or full sequence.
The present invention further relates to a method for genome-scale identification of small RNAs in an organism. Related is the development of a genome-wide library of small RNA sequences of an organism.
Another object of this invention includes the identification of a nucleic acid signature sequence using MPSS that corresponds to at least 15 nucleotides of a small RNA followed by a method for extending such signature sequence to the full length small RNA sequence and/or its mRNA precursor by comparing the signature sequence to a genomic sequence database.
It is a further aspect of the invention to determine, by performing the signature sequence-genomic comparison, one or more discrete locations within the genome where sequence identity is 100%.
Another aspect of the present invention relates to the generation of a library of small RNA molecules identified and/or isolated from an organism. In certain aspects the invention relates to signature sequences and full length small RNA molecules identified and/or isolated from Arabidopsis thaliana. While in other aspects, it is related to a library of signature sequences relating to the small RNAs identified, and/or isolated from an organism.
A specific alternative embodiment of the invention includes a library comprising a plurality of sequences selected from the group consisting of SEQ ID NOs: 1-185,413.
Another embodiment of the present invention includes a small RNA comprising a sequence complementary to a sequence selected from the group consisting of SEQ ID NOs: 1-185,413.
Another embodiment of the present invention includes includes a library comprising a plurality of signature sequences selected from the group consisting of SEQ ID NOs: 1-185,396.
A further aspect of the invention relates to the creation of a database containing, in silico, the sequences of the small RNA molecules identified and/or isolated according to the method of the invention.
Yet another aspect of the present invention relates to the creation of genome-wide small RNA libraries for at least two species, and identifying small RNAs with sequence homology conserved across the species.
It is an additional object of the invention to provide small RNA sequences useful for creating a microarray platform for the identification of differentially regulated small RNAs under any number of conditions.
It is still another object of the invention to provide small RNA sequences useful for “teaching” or training a computer program or algorithm to predict and design small RNA molecules for study or therapeutic applications.
In yet a further object, the invention relates to a vector comprising an RNA sequence and/or transgene that contains at least one recombinant small RNA molecule of the invention. In yet a further object, the invention relates to a vector comprising a DNA sequence and/or transgene that contains recombinant DNA corresponding to a small RNA molecule of the invention. In a related aspect the invention relates to a cell, cell line, or recombinant organism that contains at least one small RNA of the invention, either alone, from its natural precursor and/or in a suitable vector.
In another aspect, the small RNA sequences themselves are useful for performing biological functions, such as for example, RNA interference, gene knockdown or knockout, generating expression mutants, modulating cell growth, differentiation, signaling or a combination thereof for purposes of, for example, experimentation, generating a therapeutic, therapeutic discovery, or generating a novel biological strain. As such, in certain embodiments the invention comprises an isolated small RNA molecule that down-regulates a plant gene, for example, an Arabidopsis thaliana gene, comprising a nucleic acid having at least 75% homology to a member selected from the group consisting of SEQ ID NO. 185,396-185,409 [See Table 13 miR771-miR183], and wherein the nucleic acid is sufficiently complementary to the plant gene to down-regulate the plant gene by RNA interference.
In one embodiment, the invention comprises a small RNA molecule that down-regulates expression of an NBS-LRR disease resistance gene via RNA interference (RNAi). In a preferred embodiment, the small RNA molecule comprises a nucleic acid having at least 75% homology to SEQ ID NO. 185,398.
In another embodiment, the invention comprises a small RNA molecule that down-regulates expression of a DNA (cytosine-5)-methyltransferase gene via RNAi. In a preferred embodiment, the small RNA molecule comprises a nucleic acid having at least 75% homology to SEQ ID NO. 185,399.
In another embodiment, the invention comprises a small RNA molecule that down-regulates expression of an F-box family gene via RNAi. In a preferred embodiment, the small RNA molecule comprises a nucleic acid having at least 75% homology to SEQ ID NO. 185,400.
In another embodiment, the invention comprises a small RNA molecule that down-regulates expression of a galactosidyltransferase gene via RNAi. In a preferred embodiment, the small RNA molecule comprises a nucleic acid having at least 75% homology to SEQ ID NO. 185,401.
In another embodiment, the invention comprises a small RNA molecule that down-regulates expression of a SET domain-containing gene via RNAi. In a preferred embodiment, the small RNA molecule comprises a nucleic acid having at least 75% homology to SEQ ID NO. 185,404.
In another embodiment, the invention comprises a small RNA molecule that down-regulates expression of an S-locus protein kinase gene via RNAi. In a preferred embodiment, the small RNA molecule comprises a nucleic acid having at least 75% homology to SEQ ID NO. 185,405.
In another embodiment, the invention comprises a small RNA molecule that down-regulates expression of an Extra-large G-Protein-related gene via RNAi. In a preferred embodiment, the small RNA molecule comprises a nucleic acid having at least 75% homology to SEQ ID NO. 185,409.
In still another aspect the invention relates to an expression vector comprising a nucleic acid sequence encoding a nucleic acid having at least 75% homology to a member selected from the group consisting of SEQ ID NO. 1-185,409, wherein the expression vector comprises a transcription initiation region; a transcription termination region; and wherein said nucleic acid sequence is operably linked to said initiation region and said termination region. In a preferred embodiment, the expression vector comprises a nucleic acid selected from the group consisting of SEQ ID NO. 185,397-185,409.
These potential uses are given by way of non-limiting example, and are not intended in any way to narrow or limit the scope of the present invention. Other uses will be apparent to those of ordinary skill in the art and are considered as being within the general scope of the present invention.
As used herein, the term “small RNA” refers to those RNA molecules that are larger than about 10 nucleic acids in length but less than about 50 nucleotides, and is used generally to refer to siRNAs, miRNAs, and other small or tiny RNAs. Small RNAs may be produced in an intact form or following processing from a larger molecule. Small RNA molecules are generally “noncoding” and exert their function as RNAs.
As used herein, the term “nucleic acid” is used in a general sense to refer at least one of ribonucleic acid (RNA), ribonucleotide, deoxyribonucleic acid (DNA), deoxyribonucleotide, nucleic acid analog, synthetic nucleotide analogs, nucleic acid conjugates, for example peptide nucleic acids or locked nucleic acids, nucleic acid derivatives, polymeric forms thereof, and includes either single- or double-stranded forms. Also, unless expressly limited, the term “nucleic acid” includes known analogues of natural nucleotides that have similar binding properties as the reference nucleic acid. In addition, a particular nucleotide or nucleic acid sequence includes conservative variations based on the nucleotides adenine (“A”), guanine (“G”), cytosine (“C”), thymine (“T”), uracil (“U”), and inosine (“I”).
Previously we presented a method for the isolation of small RNA from Arabidopsis. (U.S. application Ser. No. 11/204,903) This method allowed for an increase in the number of distinct small RNA sequences known by more than an order of magnitude. The present invention relates generally to the isolation and identification of small ribonucleic acids (RNAs), for example, small inhibitory RNAs (siRNAs), microRNAs (miRNAs), tiny RNAs or combinations thereof from an organism using the process disclosed in the above patent applications. The present invention is directed to identification of small RNAs from the flowering plant Arabidopsis thaliana. We have identified approximately 185,396 unique nucleic acid signature sequences (SEQ ID NOS. 1-185,396) from Arabidopsis thaliana.
In a preferred embodiment, SEQ ID NOS 1-185,396 are referred to as signature sequences. Generally, these signature sequences do not always correspond to the full length, endogenously or biologically functional small RNA sequence. In a preferred embodiment, the present invention relates to a method for determining the full length small RNA sequence and/or its mRNA precursor by comparing the signature sequence, for example a 17-mer, to a high quality genomic sequence database, for example by BLAST or other sequence comparing algorithm. By performing the signature sequence-genomic comparison, one or more discrete locations within the genome can be identified where sequence identity is 100%. The full length small RNA can therefore be determined by extending the 17-mer signature sequence in either the 5′ or 3′ direction upon which direction the molecule is sequenced from. In certain aspects of this embodiment, the signature sequence is extended in the 3′ direction for a suitable number of nucleotides. More particularly, the signature sequence is extended in the 3′ direction by from about 1 to about 13 bases. It is generally accepted that the major type of siRNAs (chromatin siRNAs) in plants are about 24 nucleotides, and miRNAs are typically about 21 nucleotides in length. Therefore, in a particularly preferred embodiment the 17 nucleotide signature sequence would be extended about 7 bases in the case of a siRNA, or about 4 bases in the case of a miRNA. However, one of ordinary skill in the art will recognize that the precise number of nucleotides selected to extend the signature sequence to a full length small RNA will depend on a number of considerations, such as for example, whether the small RNA appears to be a siRNA or a miRNA, whether the small RNA appears to be located within a cluster, and the like.
A method of extending the signature sequences identified using MPSS to their full functional length through the use of a high quality genomic database for the organism of interest is preferably used. Generally stated, the method comprises the steps of: (a) providing a high quality genomic DNA database; (b) providing identification of small RNA signature sequences of from about 15 to about 20 nucleotides in length; (c) comparing the small RNA signature sequences to the genomic database, for example, by using a string (text)-searching program or a sequence identity algorithm such as BLAST; (d) identifying the genomic regions that indicate identity with the signature sequence; and (e) extending the signature sequence in the 3′ direction by from 1 to about 13 nucleotides to obtain the full sequence of the biologically active molecule. This method allows for the identification of the full length small RNA or the small RNA source or precursor without performing tedious cloning steps that are not sensitive enough to clone the majority of low abundance small RNAs.
In a preferred embodiment the present invention encompasses nucleic acid molecules, for example, single or double stranded small RNAs, siRNAs, miRNAs, tiny RNAs, analogs, precursor molecules of DNA or RNA, and combinations thereof, isolated from the plant, Arabidopsis thaliana, that are associated with physiological regulatory mechanisms. In yet another of the preferred embodiments, the small RNAs of the present invention preferably have a length of from about 15 to about 30 nucleotides, but may be provided as a precursor with a length of from about 16-100 nucleotides.
In a particular preferred embodiment, the present invention relates to the small RNAs SEQ ID NOS 1-185,413, and sequences containing at least about 75% homology to those sequences. The present invention also relates to any sequence having the same biological activity as any of SEQ ID NOS 1-185,413, and, alternatively, covers any sequence that is adjacent to or overlaps the target site by at least about 75% homology. In another of the preferred embodiments the present invention encompasses nucleic acid sequences which hybridize under stringent conditions with the nucleic acid sequences listed in SEQ ID NOS 1-185,413.
In another of the preferred embodiments the invention encompasses a nucleic acid molecule that contains at least one modified nucleic acid or non-naturally occurring nucleotide analog. It is contemplated that the modified or non-naturally occurring nucleic acid or nucleotide analog may be placed anywhere along the length of the sequence, for example, at the 5′-end, or the 3′end.
In still another preferred embodiment the present invention encompasses a recombinant expression or cloning vector, for example a bacterial plasmid-derived vector, or viral vector, comprising a small RNA molecule of the invention, SEQ. ID: 1-185,413. The vector may be an RNA or DNA vector adapted for use in a suitable system or organism, or a combination thereof under suitable conditions. The vector preferably results in the transcription of the small RNA molecule or cluster of small RNA molecules as such, a precursor or primary transcript thereof, which is further processed to the desired small RNA molecule. A “cluster” refers to more than one small RNA that match to nearby genomic sequences. In an aspect of this embodiment, the small RNAs of the invention may be delivered by any suitable means known to those in the art, including for example, T-DNA mediated transformation, particle bombardment, electroporation, receptor-mediated gene therapy, recombinant virus gene therapy, liposome mediated gene transfer, calcium phosphate mediated gene transfer, polyamine conjugated nucleic acid gene transfer, and the like.
In still another aspect the invention relates to an expression vector comprising a nucleic acid sequence encoding a nucleic acid having at least 75% homology to a member selected from the group consisting of SEQ ID NO. 1-185,413, wherein the expression vector comprises a transcription initiation region; a transcription termination region; and wherein said nucleic acid sequence is operably linked to said initiation region and said termination region. In a preferred embodiment, the expression vector comprises a nucleic acid selected from the group consisting of SEQ ID NO. 185,397-185,413.
The invention is further directed to the development of a library of small RNAs from a particular organism comprising a plurality of sequences identified using the method of the invention. In a preferred embodiment, the library consists of virtually all small RNA sequences of a particular organism, or at least all of those small RNA sequences that are consistently expressed throughout all tissues of said organism. It is contemplated herein that SEQ ID NOs: 1-185,396 are the signature sequences for the small RNA sequences of the organism Arabidopsis thaliana that are most consistently expressed throughout the tissues of this plant. In a preferred embodiment, therefore, the invention relates to a library consisting of a plurality of small RNA sequences selected from SEQ ID NOs: 1-185,396. The invention is further directed to a library consisting of the full length sequences identified from SEQ ID NOs: 1-185,396. Alternatively stated, the invention is directed to the creation of a database containing, in silico, the sequences of the small RNA molecules identified and isolated according to the method of the invention.
The invention is also directed to the isolation and identification of individual full length small RNA molecules from Arabidopsis thaliana. Upon such identification, biological function of the small RNA molecule can be tested using a variety of methods known in the art. Once biological activity of a small RNA has been identified, specific functional aspects of the organism can be purposefully addressed. For example, contemplated herein is a method of changing or introducing a phenotypic trait of an organism by increasing or decreasing the function or level of one or more small RNAs, which impact their ability to silence target genes or regions of the genome they target. In a related embodiment the invention includes a method for performing RNA interference (RNAi) comprising the delivery of an effective amount of at least one small RNA sequence of the invention, in a suitable form that results in gene knockdown, knock-up, or knockout. In other related embodiments, multiple small RNAs of the invention may be delivered, for example a siRNA cluster, to affect a gene, family of genes, or signaling pathway that results in an altered trait. Some specific aspects of this embodiment include, for example overproduction of a small RNA to make plants more resistant to salt stress comprising the steps of (a) selecting a small RNA randomly or based on a characteristic, for example, being induced when plants are treated with the plant hormone ABA that controls responses to salt and other stresses; (b) overproducing the small RNA resulting in plants to create salt-resistant traits. Another example would include modulation of the expression of certain genes in a plant that would affect its tolerance to pesticides, temperatures or soil condition.
More detailed examples of this embodiment include use of a small RNA of the invention that could identify a small RNA source gene that could in turn be inactivated to accomplish the control of a process such as the control of nutrient uptake or content. The term “nutrient uptake” is intended to describe nutrient uptake that helps the plant grow more efficiently or in difficult growing conditions, for example. The term “nutrient content” is intended to describe the nutrients produced in the plant, such as, for example, lysine, vitamin A, vitamin C, etc. This method comprises, (a) predicting targets of the small RNA that may silence nutrient genes involved in the uptake of nutrients or production of genes that would affect nutrient content; (b) choosing such a small RNA and identify insertion mutants from public collections that have insertions in the source gene or near the DNA (genomic match) for the small RNA; and (c) testing if these mutants have altered or improved nutrient uptake or content.
In yet another example of this embodiment, a small RNA of the invention can be used to create a therapeutic or viral resistance trait using knowledge from natural small RNAs. This method comprises, (a) using small RNA sequence characteristics (e.g. siRNA sequences) to refine computer programs currently used to design dsRNA sequences to be used for RNAi against the RNA from for example, a harmful virus or other plant pathogen such as bacterial, fungal, nematode, or parasitic plant; (b) building a dsRNA gene that in the plant will make small RNA with optimized design that will be complementary to the virus or other pathogen RNA; and (c) introducing this gene into the plant to test if it works better to control viral or pathogen infection than others designed without using the natural small RNAs to train the computer program.
In certain embodiments, the invention relates to the use of the full length small RNA sequences of the invention themselves are useful for performing biological functions, such as for example, RNA interference, gene knockdown or knockout, generating expression mutants, modulating cell growth, differentiation, signaling or a combination thereof for purposes of, for example, experimentation, generating a therapeutic, therapeutic discovery, or generating a novel biological strain. As such, in certain embodiments the invention comprises an isolated small RNA molecule that down-regulates a plant gene, for example, an Arabidopsis thaliana gene, comprising a nucleic acid having at least 75% homology to a member selected from the group consisting of SEQ ID NO. 185,397-185,409 [See Table 13], and wherein the nucleic acid is sufficiently complementary to the plant gene to down-regulate the plant gene by RNA interference.
In one embodiment, the invention comprises a small RNA molecule that down-regulates expression of an NBS-LRR disease resistance gene via RNA interference (RNAi). In a preferred embodiment, the small RNA molecule comprises a nucleic acid having at least 75% homology to SEQ ID NO. 185,398.
In another embodiment, the invention comprises a small RNA molecule that down-regulates expression of a DNA (cytosine-5)-methyltransferase gene via RNAi. In a preferred embodiment, the small RNA molecule comprises a nucleic acid having at least 75% homology to SEQ ID NO. 185,399.
In another embodiment, the invention comprises a small RNA molecule that down-regulates expression of an F-box family gene via RNAi. In a preferred embodiment, the small RNA molecule comprises a nucleic acid having at least 75% homology to SEQ ID NO. 185,400.
In another embodiment, the invention comprises a small RNA molecule that down-regulates expression of a galactosidyltransferase gene via RNAi. In a preferred embodiment, the small RNA molecule comprises a nucleic acid having at least 75% homology to SEQ ID NO. 185,401.
In another embodiment, the invention comprises a small RNA molecule that down-regulates expression of a SET domain-containing gene via RNAi. In a preferred embodiment, the small RNA molecule comprises a nucleic acid having at least 75% homology to SEQ ID NO. 185,404.
In another embodiment, the invention comprises a small RNA molecule that down-regulates expression of an S-locus protein kinase gene via RNAi. In a preferred embodiment, the small RNA molecule comprises a nucleic acid having at least 75% homology to SEQ ID NO. 185,405.
In another embodiment, the invention comprises a small RNA molecule that down-regulates expression of an Extra-large G-Protein-related gene via RNAi. In a preferred embodiment, the small RNA molecule comprises a nucleic acid having at least 75% homology to SEQ ID NO. 185,409.
In yet another embodiment, the small RNAs of the invention can be used in a method of performing cross-species analysis of small RNAs. This method includes taking one or more of the small RNA SEQ ID NOS 1-185,413, from Arabidopsis thaliana, and performing a sequence identity comparison, for example, using BLAST analysis, with a genomic-wide library of small RNA isolated from another species, for example, another eukaryote such as another plant species, fungi, yeast or a mammal, and isolating those small RNAs that display conservation over at least part of the small RNA sequence. In a related embodiment, the invention comprises taking one or more of the small RNA SEQ ID NOS 1-185,413, from Arabidopsis thaliana, and performing a sequence identity comparison, for example using BLAST analysis, with a genomic library from another species, for example, another eukaryote such as another plant species, fungi, yeast, or mammal, and identifying those small RNAs that display conservation over at least part of the small RNA sequence. Generally, a nucleotide sequence demonstrating at least 30% homology is considered homologous. This can provide useful information about target genes, small RNA precursors, as well as small RNA regulation and control over phenotypic traits. Several algorithms have been proposed for performing this analysis, such as by Rhoades, M., et al., 2002, Prediction of Plant MicroRNA Targets. Cell 110: 513-520; Lewis B P, et al. Prediction of Mammalian MicroRNA Targets. Cell 2003, 115:787-798; and Wang, X, et al., 2004, Prediction and Identification of Arabidopsis thaliana MicroRNAs and their mRNA Targets. Genome Biol 5: R65, which are incorporated herein by reference in their entirety. In a related aspect, one or more small RNA sequences of SEQ ID NOS. 1-185,413 can be used to generate a database useful for comparison with small RNA from other plant species isolated under varying conditions, other developmental states, other organisms or the like. In still another related aspect, one or more small RNA sequences, of SEQ ID NOS. 1-185,413 comprise a microarray, for example a DNA chip, to allow for high-throughput analysis of differential regulation of the small RNAs in the library.
In certain embodiments the small RNAs of the invention can be useful for experimental or therapeutic applications. For example, quantitative measurements of small RNA sequences identified according to this method would be useful for understanding processes such as cell differentiation, gene expression, cell signaling responses and pathways, and disease state cell processes.
Alternatively, identified small RNAs can be useful for determining genes and RNA molecules that are critical for development, growth, and maintenance of an organism by identifying small RNA molecules that have been evolutionarily conserved across species. For instance, genome-wide small RNA libraries could be created for at least two species, and small RNAs with sequence homology conserved across the species can be identified. In certain instances, the small RNAs can be used to identify those molecules unique to a species. In other instances the small RNAs of the invention can be used to predict the endogenous mRNA or noncoding RNA targets of miRNAs or other trans-acting small RNAs such as siRNAs. Basic strategies and algorithms for performing these predictions have been published by Rhoades, M., et al., 2002, Prediction of Plant MicroRNA Targets. Cell 110: 513-520; Lewis B P, et al. Prediction of Mammalian MicroRNA Targets. Cell 2003, 115:787-798; and Wang, X, et al., 2004, Prediction and Identification of Arabidopsis thaliana MicroRNAs and their mRNA Targets. Genome Biol 5: R65, which are incorporated herein by reference in their entirety.
In certain aspects of the preferred embodiments miRNA targets can be found with the assistance of computer algorithms designed for that, or by looking at the RNA levels for all genes of an organism, for example Arabidopsis, with DNA microarrays, and sequence comparisons for regions complementary to the small RNAs. In other aspects of this embodiment, siRNA targets are determined by identifying the siRNA source, because often times the siRNAs cause the corresponding DNA to be silenced at the chromatin level by methylation. Targets can be identified with sequences having as low as 75% homology to SEQ ID NOS. 1-185,413 in accordance with the rules for mismatch analysis, etc. as described in the references above. In some aspects, the small RNAs identified can be used to identify genomic sequences with perfect or near perfect matches that are targeted for chromatin modification or other forms of regulation by the small RNAs. Alternatively, the creation of an in silico series of variants of the natural small RNAs could be used to create variant small RNA genes with different target specificity, whilst preserving the flanking sequences such as hairpin-like structures.
Other embodiments include small RNA sequences that can be used to create a microarray platform, for example, nucleic acid “chips,” polymeric microspheres or beads, and the like for the identification of differentially regulated small RNAs under any number of conditions, for example, treatment with a chemical compound, developmental stage, disease condition, and the like. In related embodiments, small RNA sequences can be used for “teaching” or training a computer program or algorithm to predict and design small RNA molecules for study or therapeutic applications. The small RNA sequences can also provide information that can be used to design better double-stranded RNA for RNAi strategies.
In alternate embodiments, a small RNA sequence and/or transgene that contains at least one recombinant small RNA molecule can be incorporated into a vector. The vector may be, for example, a plasmid vector or a bacterial vector or a viral vector, as an RNA or DNA molecule or modified RNA molecule suitable for expression or function in a particular cell, for example, a prokaryotic cell, a eukaryotic cell, a primary cell, or a cell line. Relatedly, the invention relates to a cell, cell line, or recombinant organism that contains at least one small RNA of the invention, either alone, from its natural precursor and/or in a suitable vector.
The small RNA sequences themselves can also be useful for performing biological functions, such as for example, RNA interference, gene knockdown or knockout, generating expression mutants, modulating cell growth, differentiation, signaling or a combination thereof for purposes of, for example, experimentation, generating a therapeutic, therapeutic discovery, or generating a novel biological strain. As described earlier, the small RNAs can be used to change or introduce phenotypic traits by increasing or decreasing the function or level of one or more small RNAs, which impact their ability to silence target genes or regions of the genome they target. In some cases, multiple small RNAs, for example, a cluster of siRNAs, might be used at one time to regulate one or more targets to create a desired or advantageous trait. As such, the present invention also relates to a transgene or vector comprising, encoding, or facilitating the production of multiple small RNAs or a small RNA cluster.
In another of the preferred embodiments, the small RNAs of the invention, SEQ ID NOS 1-185,413 comprise a “teaching” set of sequences for a computer algorithm to improve and enhance in silico design and prediction confidences of small RNAs, their genes, or precursors. In addition, a library of the small RNAs of the invention can be used to design algorithms that are better able to predict and design sequences for use in RNAi.
In yet another embodiment, the invention includes a kit comprising one or more small RNAs of the invention. In a preferred embodiment, the kit includes a library of small RNAs. The invention also relates to the diagnostic, trait improvement, such as crop improvement, therapeutic, or prophylactic use of the small RNA sequences. For example, detection of any one of the small RNAs of SEQ ID: 1-185,413 may be used to determine or classify a particular condition, classify a cell or tissue type, or developmental stage.
In another embodiment of the present invention the small RNA of the invention may be used as starting materials for the manufacture of sequence-modified small RNA molecules, which may contain nucleic acid modifications in order to modify the target-specificity of the small RNA.
It will be understood by those of ordinary skill that the compositions of the present invention may be used in any suitable form, for example, a solution, a spray, a powder, an injectable solution, an ointment, tablet, suspension, emulsion, and the like; combined with any suitable carrier that increases the stability, facilitates uptake or both, for example, a liposome, a cation, and the like; or administered in any suitable way, for example, by transfection, infection, injection, or topical delivery.
It is understood that the examples and embodiments described herein are for illustrative purposes only and that various modifications or changes in light thereof will be suggested to persons skilled in the art and are included within the spirit and purview of this application and are considered within the scope of the appended claims. All publications, patents, and patent applications cited herein are hereby incorporated by reference in their entirety for all purposes.
As will be understood by one of ordinary skill in the art, the techniques described and hereby incorporated into the present invention are generally applicable and may be varied in any number of ways without departing from the general scope of the invention. Also, additional advantages and features of the present invention will be recognized by those of skill in the art in view of the description and the following examples. The examples provided herein are provided for illustrative purposes only and are in no way considered to be limiting to the present invention. For example, the relative quantities of the ingredients may be varied to achieve different desired effects, additional ingredients may be added, and/or similar ingredients may be substituted for one or more of the ingredients described.
Adaptation of MPSS for small RNA analysis. To investigate the full complexity of small RNAs, we modified and customized the MPSS vectors and procedures to adapt the MPSS methodology for the sequencing of these molecules. We sought to take advantage of the power of MPSS to sequence hundreds of thousands of molecules per sequencing run. Prior applications of MPSS made use of the poly(A) tail of mRNAs to facilitate cDNA synthesis and sequenced only molecules with a 5′ terminal sequence of ‘GATC’ or ‘CATG’, generated by a restriction enzyme like DpnII or NlaIII. Because most small RNAs are unlikely to begin with these restriction sites or contain a poly(A), the MPSS cloning vectors were adapted to initiate sequencing from the first nucleotide, regardless of the sequence. An overview of the method is shown in Supplementary
Genome-wide analysis of small RNAs in Arabidopsis. Pericentromeric heterochromatin is known to be a rich source of small RNAs due to a high concentration of transposable elements. We examined the distribution of small RNAs on the five Arabidopsis chromosomes and we compared this distribution to that of repeats and mRNA abundance data (
Table 1 (See
The relative number of distinct small RNAs per megabase of sequence was lower for genes than for any other genomic sequence (
The number of distinct small RNAs that matched to intergenic regions exceeded the numbers that matched to genes, pseudogenes, transposons, or retrotransposons (
Previous studies have demonstrated that miRNAs monitored with “sensor” transgenes can lead to the production of secondary siRNAs that match the sensor mRNA outside the sequence originally targeted. This production of secondary siRNAs is known as transitivity. We examined 61 known or predicted targets of Arabidopsis miRNAs for evidence of transitivity. Only four targets (At1 g62670, At1 g63080, At1 g63150, and At1 g63400), all of which encode pentatricopeptide (PPR) repeat-containing proteins, matched substantial numbers of small RNAs, and these were primarily in repeated regions within each gene. Most targets had no matching small RNAs other than miRNAs, or the only matching small RNAs were few, of very low abundance, or corresponded to repeats. This indicates that transitivity by miRNAs is of little biological significance in seedlings or inflorescence and is likely a transgene phenomenon as hypothesized previously.
One of the characteristics of siRNAs is that multiple siRNAs are cleaved from the same dsRNA precursor, and these can derive from either strand. Thus, the population of precursors from a given region leads to the production of numerous siRNAs that will be particularly abundant for repetitive sequences if the repeats are all sources of siRNAs. Despite the 21-24 nucleotide size of these small RNAs, the presumably stochastic nature of this process is unlikely to lead to regular pattern or periodicity in most genomic regions; we saw no evidence of a regular 21 to 24 nucleotide pattern of small RNAs when measured across the genome (data not shown). However, repetitive sources of siRNAs should produce dense clusters of small RNAs. In contrast, miRNAs are produced from cleavage at specific sites of a precursor, usually resulting in one prominent miRNA and sometimes a low abundance miRNA* from a specific region. As a consequence, comparing the absence or total abundance of individual small RNA sequences across libraries is less informative for siRNAs than it is for miRNAs. In order to compare siRNA abundances, we developed a proximity-based algorithm to build clusters of small RNAs, with the goal of comparing across libraries the presence, absence or total abundance of small RNAs in the clusters with overlapping genomic locations (see Methods). The characteristics of these clusters may help differentiate novel miRNAs from siRNAs, as sparse clusters may characterize miRNAs and dense clusters may characterize siRNAs.
Genes matched by small RNAs contained an average of one sparse cluster (Table 1C). In contrast, many transposons contained more than one cluster, and typically these were dense clusters. In the intergenic, unannotated regions of the Arabidopsis genome, more than 4,300 clusters of small RNAs were identified in the inflorescence library alone, suggesting a previously unrecognized transcriptional activity for a large proportion of the intergenic space. We also found that a high proportion of dense clusters overlapped the 5′ end of annotated genes and transposable elements, possibly representing siRNA-silenced promoters (Table 1C). The edges of these and other dense clusters likely represent the boundary of biologically-defined silenced sequences and may help refine genomic annotations.
Our analysis may underestimate the functional impact of small RNAs because we utilized perfectly matching signatures, and it is known that small RNAs are active against imperfectly-matched targets. Table 3 (See
Differential accumulation of small RNAs. We next examined the differences in the small RNA populations isolated from the inflorescence and seedling libraries. Of particular interest were small RNAs that showed differences in accumulation indicative of tissue-specific regulation. A set of small RNAs matching to approximately 17% of 4,063 genes was found in only one of the two libraries (Table 4), and of these genes, four times as many were specific to inflorescence as to seedling. Comparison of clusters across the libraries demonstrated that the proportion of sparse clusters that are tissue-specific (11%) is lower than that of genes, and only 7% of dense clusters were tissue specific (Table 4). Most of the dense clusters varied only 1- to 10-fold between libraries, suggesting that these dense clusters may not be developmentally regulated, at least in these two diverse tissues. Interestingly, the genes with the most abundant seedling-specific small RNAs were PAIL and PAI2 (At1g07780 and At5g05590) which are known to be strongly regulated by epigenetic events in other Arabidopsis ecotypes. Some repetitive sequences also demonstrated tissue-specific regulation; for example, both At1 g77095, a copia-like retrotransposon, and TR2558, the tandem repeat downstream of At4g04990, specifically matched small RNAs that were found only in the inflorescence library. It was a general pattern that the inflorescence library contained more diverse small RNAs and these small RNAs matched more genes in a tissue-specific manner than the seedling library. This could reflect a greater variety of specialized cell types in the inflorescence tissue, or an increased use of small RNAs in all cell types within the inflorescence.
Clusters containing signatures matching to tRNAs, rRNAs, snRNAs or snoRNAs were not considered. For each library, the number of clusters or genes was calculated by the fold difference of the sum of abundances for all signatures comparing inflorescence and seedling.
aThe total number of genes or clusters matched by the two libraries. This includes values in columns to the right, plus all of the genes or clusters that were specific to only one of the two libraries; fold differences could not be calculated for tissue-specific genes or clusters.
bThis category includes small RNAs with 1X to 10X difference between the two libraries, or <10 TPQ in both libraries.
cThis category includes only genes or clusters that had no small RNAs in one library and small RNAs totaling ≧10 TPQ in the other library.
dThe complete list of genes and abundance values used in this calculation is provided in Supplemental File 2. Signatures were grouped by genes independent of the clusters. Therefore, each column contains a unique set of gene IDs.
The small RNA MPSS data clearly represent a mixture of both miRNAs and siRNAs. One source of siRNAs may be antisense transcripts that could form dsRNA with sense transcripts. Several groups have reported an abundance of antisense transcripts in Arabidopsis. If this dsRNA is formed, it could be degraded to form siRNAs that could decrease sense RNA abundance. Alternatively, interference by RNA polymerase II transcription activity on the antisense strand could restrict sense-strand transcription. Among the genes with mRNA MPSS data, about 10% also had matching small RNA signatures in libraries made from similar developmental stages (Table 5). However, we found a similarly low proportion of genes with both antisense mRNAs and small RNAs. This suggests that antisense transcripts may regulate gene activity predominantly by transcriptional interference, rather than through the production of dsRNA and small RNAs. Consistent with this, the mRNA level of genes with antisense transcripts was approximately the same whether or not they matched to small RNAs (data not shown).
Values were calculated using the 25,835 genes and pseudogenes (removing genes classified as t/sn/sno/rRNAs, retrotransposons and transposons) and 23,435 IGRs in the TIGR version 5.0 annotation. For small RNA data, signatures were clustered by gene ID and intergenic region.
aThe “+” for mRNA MPSS indicates the presence of a signature uniquely matching to a gene and expressed at levels considered “significant” and “reliable” (Meyers et al., 2004, Gen. Research 14: 1641). This publication also describes the classification system used for mRNA MPSS signatures (Class 1 to 7), which indicate whether the signatures match in an intron, exon or intergenic region and specify the strand that is matched. For genes with antisense
bSmall RNA presence in genes was based on the presence of any number of signatures at any abundance level, and included matches within the gene or UTRs. Signatures from both strands were summed. Because many pseudogenes are expressed, this set was included with genes in this analysis, and therefore the total numbers for genes in this table are higher than those of Supplemental Table 3A, which considers genes and pseudogenes separately.
We combined several computational and experimental approaches to separate siRNAs from miRNAs. Initially we compared our data with a previous study that predicted miRNAs by filtering whole genome data for sequences that form hairpin-like secondary structures, exhibited conservation with rice, and had other characteristics (AtSet1, AtSet2, and AtSet3 to AtSet6, respectively, described in ref. 48). Most of the matches between our experimental data and their predictions were found with only folding and conservation as filters, and their additional filters removed relatively few small RNAs (Table 6). The results of this comparison were consistent with Arabidopsis miRNAs numbering in the hundreds, but this approach was rudimentary.
aNumbers under each AtSet# indicate the number of sequences in each dataset defined by Jones-Rhoades and Bartel (2004, Mol. Cell 14: 787). Each set is a subset of the previous group of sequences. Briefly, AtSet1 sequences folded into hairpins, AtSet2 is conserved in rice, and the additional AtSet#s indicate miRNA-specific filters as described (Jones-Rhoades and Bartel, 2004).
bTissue indicates signatures that were found in only one of the two libraries or were found in both libraries.
cIndicates the number of Arabidopsis sequences that were overlapping in both the small RNA MPSS data (17-base signatures) and the Jones-Rhoades and Bartel (2004) computational predictions (20-base sequences). The first number in each cell indicates the number of distinct small RNA signatures that matched, while the second number indicates the number of distinct AtSet# 20-mers that were matched. “Exact match” indicates the
We developed a less exclusionary approach to enrich for miRNAs present in the small RNA MPSS data based on an overlapping set of filters. This method allowed us to implement and use multiple data filters in parallel and showed the numbers of small RNAs passing a subset of the filters (
The small RNAs and groups are as described in
aAtSet6 is a set of candidate miRNAs defined by Jones and Bartel (2004, Mol. Cell 14: 787).
bIncludes all perfect matches of small RNA signatures to miRNAs including matches with annotated 5′ ends. Some signatures match to multiple genomic locations, so the same known miRNAs may be matched by multiple groups; therefore, the total number of known miRNAs and miRNA families is less than the sum of these columns.
The large number of small RNA sequences obtained by MPSS identified more than 10-fold more small RNAs than previously described. However, this data did not reveal if we had achieved saturation of the small RNAs. Therefore, we carried out a second sequencing run on the seedling library that yielded 802,978 signatures matching to 20,379 genomic locations. Of these, 7,549 genomic matches were not identified in the first run (Table 8B) and they corresponded to 838 genes and 3,287 clusters not previously identified. Therefore, our analysis was not saturating and numerous Arabidopsis small RNAs remain to be identified. In maize and other large genomes, small RNAs are likely to be even more diverse due to the generation of diverse siRNAs from repetitive sequences that comprise the bulk of the genome. This may require even deeper sequencing of small RNAs in order to achieve saturation, although the siRNAs matching to the large families of repetitive sequences may be less interesting than small RNAs matching genes.
aThe signatures sequenced for each library reflects the sum of two sequencing reactions.
b“Distinct” refers to the number of different sequences found within the set. “Total” refers to the union of the different libraries.
cDistinct signatures that perfectly match to at least one location in the genome, and includes signatures matching to tRNAs, rRNAs, snRNAs or snoRNAs.
Our data indicate that the small RNA component of the genome and its regulatory role is more extensive and complex than previously demonstrated. For example, many regions of the genome considered inactive or featureless were found in our analyses to be sites of considerable small RNA activity. In plants or any other organism that utilizes small RNAs as an endogenous regulatory mechanism, it should be possible to develop a more complete picture of gene and small RNA regulation by combining small RNA MPSS data from diverse samples with the genomic sequence and mRNA transcript data. For example, the small RNA MPSS data can add a new level of analysis to studies of molecular systems biology. Additional experiments, such as the analysis of small RNAs metabolism mutants, should lead to a better understanding of the sources, biological activities, turnover rates, and signaling pathways for the full range of small RNAs that we have described.
Sequencing of Arabidopsis rdr2 mutants by MPSS and 454. Previous reports have indicated that rdr2 mutants show a dramatic reduction in endogenous siRNAs and a corresponding increase in miRNAs, Xie, Z., et al. 2004, Genetic and Functional Diversification of Small RNA Pathways in Plants. PLoS Biol 2: E104. It was reasoned that deep sequencing in this mutant would reveal the full complement of miRNAs in Arabidopsis. Two methods were utilized for the high-throughout sequencing of small RNAs, Meyers, B., et al., 2006, Sweating the Small Stuff: microRNA Discovery in Plants. Curr Opin Biotechnol 17: 139-146, including Massively Parallel Signature Sequencing Lu, C., et al., 2005, Elucidation of the Small RNA Component of the Transcriptome. Science 309: 1567-1569, and the 454 technology, Margulies, M. et al., 2005, Genome Sequencing in Microfabricated High-Density Picolitre Reactors. Nature 437: 376-380.
MPSS provides extraordinary depth, sequencing a half million or more molecules per library, while 454 has longer reads and thereby provides information about length. Both methods provide quantitative data based on the frequency of the molecules that were sequenced. The small RNA molecules were isolated by size fractionation, sequentially ligated to RNA adapters at the 5′ and 3′ ends, and used to make cDNA template for sequencing. Libraries were generated using mixed stage inflorescences, which are known to be a rich source of small RNAs Lu, C., et al., 2005, Elucidation of the Small RNA Component of the Transcriptome. Science 309: 1567-1569. MPSS produced 915,856 17-nucleotide signatures from rdr2 (Table 9), which is comparable to the 721,044 signatures previously obtained for wildtype Arabidopsis inflorescence. However, the rdr2 complexity was reduced by more than 80% compared to wildtype in terms of sequence diversity (9,066 different genome-matched sequences in rdr2 compared to 56,920 in wildtype). This dramatic difference was despite the larger total number of sequencing reads.
aThe signatures sequenced for each library reflects the sum of two sequencing reactions. “Total” is the sum of the different libraries. Numbers for the 454 data indicate only those sequences for which both 5′ and 3′ adapters were identified and removed, and the insert was ≧15 bp in length.
b“Distinct” refers to the number of different sequences found within the set. “Total” is the union of the libraries.
cDistinct signatures are counted that perfectly match to at least one location in the genome, and includes signatures matching to tRNAs, rRNAs, snRNAs or snoRNAs. “Total” is the union of the libraries.
Similarly, the 454 sequencing data demonstrated a reduced complexity for rdr2 small RNAs. Using 454, 11,631 small RNAs from wildtype inflorescence were sequenced (5,713 distinct, genome-matching) and 7,134 from rdr2 (686 distinct, genome-matching). The rdr2 diversity was less than 13% that of wildtype, although in the case of the 454 data, fewer small RNAs were sequenced than with MPSS. The MPSS and 454 data correlated much better for the rdr2 mutant than the wildtype, probably because the reduced complexity of rdr2 allowed a more saturating level of sampling for even low levels of sequences (
Because rdr2 is known to lack many heterochromatic siRNAs Xie, Z., et al., 2004, Genetic and Functional Diversification of Small RNA Pathways in Plants. PLoS Biol 2: E104, wildtype and rdr2 sequences were compared to determine if the small RNAs remaining in rdr2 are primarily a subset of those in wildtype. As measured by both MPSS and 454, approximately 20% of the rdr2 small RNAs were also observed in the wildtype library (
Next, the population of miRNAs in the rdr2 mutant was examined and compared to wildtype. The most obvious trend was the expected enrichment of nearly all miRNAs in rdr2 compared to the wildtype (Tables 10 and 12). The overall enrichment of miRNAs in rdr2 was 1.8-fold, based on the proportion of small RNAs represented by known miRNAs (Table 11), a level similar to the 2.2-fold enrichment reported for a low level of sequencing. Eight miRNAs were enriched more than 5-fold in rdr2, including miR158, miR163, miR171, miR172, miR173, miR393, miR399, and miR402 (Table 10). The most abundant miRNA in rdr2 was miR172. This miRNA was also the most abundant in a dcl2/3/4 triple mutant Henderson, I. R., et al., 2006. Dissecting Arabidopsis DICER function in small RNA processing, gene silencing, and DNA methylation pafterning. Nat Genet In press., which, as discussed below, has a small RNA profile similar to rdr2. Both of these mutants lack many common siRNAs, and perhaps this indirectly and positively impacts miR172 abundance. At the other extreme, miR167 had a lower abundance in rdr2 than wildtype, and this was also observed in dcl2/3/4. Across the remaining miRNAs, relatively few qualitative differences were observed in terms of miRNAs that were present or absent (Tables 10 and 12). For example, the MPSS data showed that only two known miRNA families were present in rdr2 that had not been detected in wildtype inflorescence (miR157, miR400), while only miR395 was observed in wildtype but not the rdr2 454 library (and this may be due to the low sampling depth of the 454 data). Fourteen known miRNAs were never observed in either wildtype or rdr2 libraries (Table 10 and 12); this could indicate that these miRNAs are not expressed in the tissues or conditions that we sampled, some of these are not bona fide miRNAs as previously suggested, or sequence-based biases in cloning and/or sequencing steps led to their absence.
“wt” indicates wildtype.
Values indicate TPQ (MPSS) or raw (454) abundance for perfect matches to known miRNAs with matches located within one nucleotide of the annotated 5′ end of the miRNA. Loci with the same name were combined for this analysis; sequences matching individual loci are described in Table S1.
aBecause the 454 values are raw values and not normalized, this row indicates the number of genome-matching small RNAs sequenced in each 454 library as a reference for the miRNA abundance.
aNumbers of retrotransposons and transposons include sequences annotated as genes in the TIGR annotation as well as those intergenic regions identified as retrotransposons and transposons by low stringency analysis with RepeatMasker.
bCentromeric repeats were defined based on regions matching the 180 bp centromeric repeats by BLAST analysis with an E-value <e−10.
c“Sum of abundance” is the sum of TPQ-normalized abundances for all locations of all matching signatures. Signatures with multiple matches in the genome were counted for each type of genomic region in which they matched. Values are not indicated for the type “rRNA, tRNA, snoRNA or snRNA” because the abundances for these signatures were excluded from our analysis and were not normalized.
The rdr2 small RNAs showed a much more limited distribution on the Arabidopsis chromosomes compared to wildtype, due to their reduced complexity. The small RNAs from the rdr2 mutant did not show a pericentromeric concentration, which is a noticeable contrast with wildtype small RNAs; this is consistent with a loss of heterochromatic siRNAs in rdr2. However, there were many more loci matching small RNAs in rdr2 than are represented by the 117 known miRNA loci. This could indicate that many miRNAs, ta-siRNAs or other RDR2-independent small RNAs have yet to be described. As a first step to determine the nature of these RDR2-independent small RNAs, the relationship between rdr2 small RNAs and different genomic regions was examined. Compared to wildtype, small RNAs were reduced in rdr2 in each class of genomic sequence that we investigated (Table 11 and
Experimental Validation of Novel miRNAs.
As a first step towards the identification of novel miRNAs, rdr2 MPSS sequences were compared with previously-identified wildtype small RNAs in a five-way Venn diagram (
As a complementary experimental approach to validate candidate miRNAs, the expression of candidate miRNAs in different genetic backgrounds was evaluated by RNA gel blot analysis of low molecular weight RNA isolated from inflorescence tissues. Canonical miRNAs generally require DCL1 (not DCL2, 3 or 4), but not RDR2 or RDR6, while 21 nt siRNAs from ta-siRNA loci require DCL1, DCL4 and RDR6 but not RDR2. Arabidopsis mutants with defects in Dicer and RdRp genes, therefore, are important tools to distinguish among different classes of small RNAs. Of the 31 candidate hairpin-forming genomic loci from the Venn diagram, we conducted RNA gel blot analysis of 13 from boxes containing small RNA signatures with an MPSS abundance of ≧40 transcripts per quarter-million (TPQ), including three small RNAs that we previously predicted to be miRNAs. Bands within the size range of 21 to 24 nt expected for mature miRNAs were observed for 12 of 13 candidates that we tested, and of these, nine small RNAs had genetic requirements similar to those of typical, known miRNAs (
Plant miRNAs function in the regulation of gene expression either by inducing cleavage of their mRNA targets or by translational repression. Therefore, to characterize the function of the new miRNAs identified, regulatory targets were predicted using an algorithm similar to the one described by Jones-Rhoades and Bartel (2004). In general, cleavage is predominant and can be experimentally assessed using a modified 5′-RACE approach to validate these mRNA targets. Targets were predicted with a penalty score of 2.5 or better for seven of the nine new miRNAs (Table 14A), using the 21 nt sequence derived from the 17 nt MPSS tag plus four adjacent nucleotides from the matching genomic location. The new Arabidopsis miRNA genes are expressed at relatively low abundances as demonstrated by the MPSS data and RNA gel blots (
Three new miRNA targets were verified among which two have a predicted role in plant defense responses. Two transcripts encoding the CC-NBS-LRR class of putative disease resistance proteins (At5g43740 and At1 g51480) were experimentally validated as in vivo targets of miR772 (
Other RDR2-independent small RNAs in Arabidopsis. A significant number of Arabidopsis endogenous siRNAs match to various kind of repeats. Xie et al. have shown the requirement of RDR2 and DCL3 for the biosynthesis of a subset of repeat-associated siRNAs. However, considering the presence of multiple RdRps in Arabidopsis and the diversity of repeats, it is unclear which populations of siRNAs generated from repeat sequences are dependent on RDR2 activity. The RDR2-dependent and RDR2-independent inverted and tandem repeats were separately characterized; these repeats are known to be sources of small RNAs. The RDR2-dependent inverted repeat set, comprising a total of 461 genomic locations, were defined as those for which: 1) the sum of abundance is ≧10 TPQ in wildtype; 2) the sum of abundance is at least 10-fold higher in wildtype than in rdr2. Similarly, a repeat was considered to be RDR2-independent only if the sum of abundance from the repeat is ≧10 TPQ and not down-regulated (rdr2/wt≧1) in rdr2. As shown in Table 15, 55 loci were found for this set (12% of the total). The repeat score of the RDR2-independent set was significantly higher than that of the RDR2-dependent set (Mann-Whitney Test: P−value=0.0048). One of the primary determinants of the score is the length of the repeat, suggesting that the RDR2-dependence of inverted repeats may be based on their length. This is consistent with a previous study suggesting that for some inverted repeats, RDR2 may contribute to the formation or stability of a complex that contains active DCL3. For genomic loci that contain long inverted duplications and can form extensive dsRNA structures (“foldbacks”), RDR2 is most likely dispensable for siRNA production (Table 15). One hypothesis is that one or more Dicers can efficiently process long dsRNA precursors even in the absence of RDR2. In agreement with this, closer examination of some RDR2-independent inverted repeats revealed that these loci usually showed complex patterns of siRNA accumulation with different size classes affected by different Dicer mutants (
A potential foldback structure in the S-receptor kinase gene (SRK) was identified as one of the most strongly expressed RDR2-independent siRNA-producing regions (
Unlike inverted repeats from which dsRNA is readily generated simply by folding of a single RNA, tandem repeats should require an RdRp to form dsRNA structures. Indeed, tandem repeats show a higher overall dependence on RDR2 than inverted repeats (Table 15). Our RDR2-dependent tandem repeat set contained 3491 genomic locations whereas the RDR2-independent tandem repeat set contained only 82 loci (2% of the total). Interestingly, the average length of the tandem repeat unit in RDR2-dependent set is significantly larger than that of the RDR2-independent set (Mann-Whitney Test: P−value=0.0001). Therefore, high quality and long tandem repeats generally appear to require RDR2 to generate dsRNAs and sustain siRNA production. Other RdRps probably facilitate dsRNA production from these short tandem repeats because the Arabidopsis genome contains six RdRp homologs. Without being limited by any particular theory, one likely hypothesis is that different RdRps could function redundantly on tandem repeats.
In each case, RDR2-dependent is defined as the sum of abundance is ≧10 TPQ in wild type and the sum of abundance is at least 10-fold higher in wildtype than in rdr2; RDR2-independent is defined as the sum of abundance from the repeat is ≧10 TPQ in rdr2 and the small RNAs are not down-regulated in rdr2 (rdr2/wt ≧1). Mean values for each category are indicated followed by standard error (±). The score was determined by the programs Einverted or Etandem, and represents
a“Gap” indicates the average gap between arms of the inverted repeat (in nucleotides).
b“Count” refers to the number of tandem repeats.
c“Size” indicates the average length of the repeats at each locus (in nucleotides).
Known ta-siRNA loci were the most enriched small RNA sources in the rdr2 background. For the four previously characterized ta-siRNA loci, the sum of small RNA abundance was at least 20-fold higher in rdr2 than in wildtype based on the MPSS data (Table 16A and
aThe number of distinct signatures was calculated as the sum of distinct signatures in the wildtype and rdr2 libraries.
The filters used to identify these loci are as follows: 1) The sum of abundance in rdr2 ≧ 100. 2) The number of distinct small RNAs in rdr2 ≧ 10. 3) The ratio of rdr2/wt ≧ 5. 4) The loci do not correspond to miRNAs, known ta-siRNAs, transposons, retrotransposons, or centromeric repeats. “Hits” indicates the number of distinct small RNAs found at each locus in both rdr2 and wildtype.
aPPR gene families are noted because they have been described as strong sources of small RNAs (Lu et al., 2005).
*** indicates that RNA gel blots were performed using a small RNA sequence selected from this locus (data not shown), which was confirmed to have the expression pattern of a canonical ts-siRNA (present in wildtype, enriched in rdr2, absent in rdr6, dcl1-7 and dcl2/3/4).
bThese loci also showed phasing similar to known ta-siRNAs, and are shown in more detail, along with the RNA gel blot, in
Small RNA size distribution in rdr2 and the small RNA populations in other mutants. The enrichment of miRNAs and loss of heterochromatic siRNAs in rdr2 should correlate with a shift in the sizes of the small RNA population. Canonical miRNAs are 21 nt while canonical heterochromatic siRNAs are 24 nt. Because the MPSS sequence data is limited to 17 nucleotides for small RNAs, we used the 454 sequence data to determine the size distribution of the small RNAs. As an additional comparison to wildtype and rdr2 inflorescences, small RNAs from the inflorescence of the Arabidopsis mutants rdr6 and dcl1-7 were also sequenced, and compared these to data we recently obtained for dcl2/3/4. All of these mutants are altered in important genes for small RNA biogenesis. The size distribution based on both distinct sequences and total abundances was assessed (
Even the modest depth of the 454 sequencing was sufficient to identify differential effects of specific mutants on the accumulation on miRNA families. Although DCL1 appears to be the only Dicer protein responsible for miRNA biogenesis in Arabidopsis, some miRNAs are affected less than others by the dcl1-7 mutant. The most extreme case was miR168 which did not decrease at all in dcl1-7 based on the 454 data (Table 10). These results are in agreement with Vaucheret et al., who reported no decrease in miR168 levels in three different dcl1 partial loss-of-function mutants. This fits well with the model that miR168 levels are not limited by DCL1 activity but are instead controlled by a feedback loop involving AGO1, the target of miR168; AGO1 is hypothesized to both stabilize miR168 and also slice its own mRNA using miR168 as a guide. The accumulation of miR159 and miR165/166 has also been reported to be somewhat less sensitive to dcl mutations than other miRNAs tested and we also observed these subtleties. Finally, members of the miR161 family, and miR408 are known to be rather insensitive to the dcl1-7 allele and the dcl1-9 allele respectively, results quite consistent with our 454 data. Based on the close recapitulation of published observations with this dcl1 data, it seems likely that other differential accumulation characteristics resulting from this data set represent regulatory characteristics of biological significance. These would include miR167, which is down-regulated in rdr2 compared to wild type, and miR172 which is of particularly high abundance in rdr2 and dcl2/3/4 (Table 10). Another miRNA with unusual characteristics is miR169. This miRNA is an outlier in the correlation of rdr2 and dcl2/3/4 (
Prior experimental and computational efforts over the last several years have resulted in the identification of 117 miRNA genes in Arabidopsis which can be grouped into 42 families. The miRNAs SEQ ID NO: 185,397-185,409 all represent new families that presumably escaped previous discovery because of their low abundance. These new miRNAs increase the total number of Arabidopsis miRNA families by 25%. Eight of the newly described miRNAs are found only in Arabidopsis. For non-conserved miRNAs, it is more difficult to confidently predict targets because the conservation of the target site cannot be used as a filter to remove false positives. Therefore, a highly stringent score (≦2.5) was applied in target prediction. Potential regulatory targets were found for 10 of the 13 miRNAs. Some of the biological roles of the newly confirmed or predicted targets resemble those of previously identified Arabidopsis miRNAs. At least three of these are bona fide because we could map the cleavage products and we predict that others were simply beneath our threshold of detection. MiR774 (SEQ ID NO. 185,400) targets the mRNA of at least one F-box protein. Combined with six previously identified F-box genes, there are at least seven F-box mRNAs targeted by miRNAs, suggesting that the protein degradation machinery is subject to considerable miRNA regulation. Our observation that miR773 (SEQ ID NO. 185,399) mediates the cleavage of at least two, and potentially more, members of the CC-NBS-LRR class of putative disease resistance proteins suggests a previously unknown role of miRNAs in plant defense. As new and more sensitive methods for verifying miRNA targets are developed, it will be exciting to see if some of the other interesting putative targets such as the methytransferases in
RDR2-independent siRNAs. Tandem repeats are prone to epigenetic silencing mediated by RNA interference. Previous studies have shown that several siRNAs corresponding to tandem repeats in the Arabidopsis genome were absent in rdr2. It has been proposed that tandem repeats can sustain RdRp activity because the first round siRNAs can randomly initiate subsequent rounds of siRNA production and perpetuate the siRNA pool. While this model has not been proven, it is substantiated by our MPSS data indicating that almost all the tandem repeats in the Arabidopsis genome required RDR2 activity to generate siRNAs. However, for some of these tandem repeats, the small RNAs were significantly higher in rdr2 than in wildtype. Something about these tandem repeats, perhaps their relatively low quality, may allow these sequences to be silenced independently of RDR2. In this case, other components of the siRNA biogenesis machinery must be involved in the recognition and generation of siRNAs from these specific loci. This suggests that the biogenesis pathway for repeat-associated siRNAs is more complex than initially believed and the production of some repeat-associated siRNAs does not require RDR2 activity.
siRNA accumulation from inverted-repeat loci is dependent on RDR2 and DCL3. While DCL3 clearly functions as the ribonuclease to process dsRNA precursors, it is unclear why RDR2 is essential to this pathway. Another example is siRNA production from constructs used for inverted-repeat post-transcriptional gene silencing (IR-PTGS, typically used for RNAi). Although widely-used as a research tool, IR-PTGS remains one of the least understood plant RNA silencing processes. Until recently, no mutant defective in this pathway had been recovered, and IR-transgene induced siRNA accumulation is not affected by single gene mutations. Our analysis of rdr2 by MPSS may provide an explanation for these apparently contradictory observations. In agreement with previous studies, the majority of endogenous inverted-repeats, such as the siRNA02 locus, did not accumulate siRNAs in the absence of RDR2. However, we also identified a group of inverted-repeats which produced siRNAs independently of RDR2. One difference between RDR2-dependent and RDR2-independent inverted repeats is that the latter set tends to have a higher repeat score and larger size of repeat unit. Although it is difficult to rule out alternative hypotheses completely, the simplest interpretation of the data is that RDR2 and DCL3 are required for only a subset of inverted-repeats, generally with low scores and relatively short repeat units. In the case of longer and higher scoring inverted repeats, RDR2 activity (and probably DCL3) may not be required, similar to IR-transgenes. One likely scenario is that the high quality dsRNA structures generated from long inverted repeats are subject to the activity of different Dicers. Consistent with this model, recent analyses of combinatorial Dicer knockout mutants indicated that the functions of different Arabidopsis Dicer proteins are highly redundant.
The combined deep profiling data from MPSS and full-length sequencing of small RNAs from different genotypes by 454 demonstrate that small RNA sequence libraries are a rich and novel source of data that have yet to be fully exploited in Arabidopsis or any other organism. As sequencing costs drop with the advent of new short-read sequencing technologies, the approaches that we have implemented for the analysis of Arabidopsis mutants are likely to be more broadly applied for experimental investigation of different conditions, mutants, and organisms.
Methods
Plant growth. All plant material was from Arabidopsis ecotype Col-0. The rdr2, rdr6, dcl1-7, and dcl2/3/4 mutants have been described previously. Inflorescence tissue was harvested from plants grown in soil in a growth chamber with 16 hours of light for 5 weeks. Floral tissue included the inflorescence meristem and early stage floral buds (up to Stage 11/12). Total RNA was isolated using Trizol reagents (Invitrogen, Carlsbad, Calif.). Seedlings were grown at 23° C. under the same 16 hour long day conditions and were harvested after two weeks. Inflorescence and seedling material was harvested approximately at eight hours into the subjective day.
RNA gel blot analysis. Blot hybridization analysis was performed as described. Total RNA was extracted using Trizol (Invitrogen, Carlsbad, Calif.). High molecular weight (HMW) RNA was precipitated with 5% PEG8000 and 0.5M NaCl. The low molecular weight (LMW) RNA which remained in the supernatant was precipitated with ethanol. LMW RNA was resolved on 15% polyacrylamide gels, blotted to Zeta-Probe GT genomic blotting membrane (Bio-Rad Laboratories, Hercules, Calif.) for 2 hrs at 400 mA, and UV cross-linked. Radiolabeled probes for specific small RNAs were made by end-labeling synthetic DNA oligos (IDT, Coralville, Iowa) with γ-32P-dATP using T4 polynucleotide kinase (USB, Cleveland, Ohio). Blots were prehybridized and hybridized using ULTRAhyb-Oligo buffer (Ambion, Austin, Tex.). Blots were washed at 42° C. with 2×SSC/0.5% SDS. All blots shown are representative of at least two independent experiments. Locked nucleic acid (LNA) probes were used as indicated in the figure legends; these probes were used when the hybridization signal was not detectable using regular oligonucleotides. LNA oligos were obtained from Sigma-Proligo (St. Louis, Mo.). Hybridization conditions were as described.
MPSS and 454 data generation and analysis. All MPSS sequencing and analysis was performed essentially as described. The small RNA libraries were constructed as previously described. The raw and normalized MPSS data are available at http://mpss.udel.edu/at. 454 analysis was performed essentially as described. Adapter sequences were identified and removed using local alignments. The summary statistics of the rdr2 and wildtype 454 libraries are described in the text; the dcl1-7 and rdr6 libraries included 12,060 and 16,856 adapter-trimmed small RNA inserts, respectively, and the dcl2/3/4 triple mutant 454 library has recently been described.
MPSS signatures were compared to the TIGR annotation version 5.0 and assigned signatures to each location at which a perfect match was found. The number of matches was recorded as the “hits”. As previously described, we merged the MPSS sequencing runs and calculated a single abundance normalized to “transcripts per quarter million” (TPQ) after the removal of rRNAs, tRNAs, snoRNAs, or snRNAs signatures. Clustering of small RNAs was based on the previously described proximity-based algorithm, with the same setting of a 500 bp window for the clusters that was used in our prior analysis. Repeat analysis was also performed as described previously using a combination of programs including RepeatMasker (http://www.repeatmasker.org/), Einverted and Etandem.
A proximity-based algorithm to clusters of small RNA was developed. The clusters were dependent on only the distance between small RNAs and were independent of annotated genomic features such as genes. This facilitated the comparison of clusters across libraries while removing the bias that the annotation might introduce. The optimal cluster size was determined by comparing the results of clustering based on joining signatures within 100, 250 or 500 bp of each other for each library (Table 17A and 17B). Clusters joining small RNAs within 500 bp of each other were used because this size reduced the number of single, unclustered signatures by approximately two-thirds in each library. The exceptionally high average abundance for certain cluster sizes was due to several specific small RNAs such as miRNAs with high abundances. Based on the number of distinct small RNAs contained within each cluster and not the abundance of the signatures, the clusters were then classified in the arbitrarily assigned categories of sparse (1 to 10 signatures), moderate (11 to 25 signatures), or dense (more than 25 signatures).
Excludes clusters containing any signatures matching to annotated rRNAs, tRNAs, snoRNAs, and snRNAs.
aIncludes 239,745 distinct signatures.
bIncludes 106,088 distinct signatures.
Repeats in the Arabidopsis genome were identified using a combination of programs. For the identification of transposons and retrotransposons, we utilized a dataset comprised of those sequences annotated by TIGR (version 5.0) augmented with the results of RepeatMasker™. For tandem and inverted repeats, we used the programs Einverted and Etandem.
While most Arabidopsis miRNAs have been identified by traditional cloning and sequencing of small RNAs, it is unlikely that these screens are saturating for rare or tissue-specific miRNAs. The need for additional methods of miRNA identification led to the development of bioinformatics methods for the prediction of miRNAs. Most of these computer algorithms rely on evolutionary conservation of miRNA sequences between different species, and therefore are limited to the detection of only conserved miRNAs, although at least one analysis has relied only on intra-genomic comparisons. Even these predictions ultimately require either high-throughput or highly sensitive methods for validation. With MPSS and other high-throughput sequencing technologies, the sequencing of small RNAs is no longer a limiting factor in the discovery of novel miRNAs. However, by combining these approaches with mutants in which miRNAs are significantly enriched compared with wild type, such as rdr2 and dcl2/3/4, we can efficiently delineate the small RNAs as miRNAs, siRNAs, or other categories. Even at relatively low sampling depths, many known miRNAs were observed and their abundance was measured. Compared to wildtype, the MPSS data for rdr2 was dramatically simplified and “cleaned up” of siRNAs, making miRNA candidates much easier to identify. 454 analysis indicated that the rdr2 and dcl2/3/4 triple mutants are most similar in their small RNA profiles, consistent with the idea that these genes may be in the same pathway involved in heterochromatic siRNA production and a mutant of either type (rdr2 and dcl2/3/4) enriches for miRNAs.
The following references are incorporated herein by reference in their entirety.
107. Wortman, J. R., B. J. Haas, L. I. Hannick, R. K. Smith, Jr., R. Maiti, C. M. Ronning, A. P. Chan, C. Yu, M. Ayele, C. A. Whitelaw, O. R. White, and C. D. Town. 2003. Annotation of the Arabidopsis genome. Plant Physiol 132: 461-468.
Under 35 U.S.C. § 119(e) this application claims the benefit of U.S. Provisional Application No. 60/703,215, filed Jul. 28, 2005; and U.S. Provisional Application No. 60/772,666, filed Feb. 13, 2006, which are hereby incorporated by reference in their entirety and for all purposes.
The work described in this application was sponsored by the NSF SGER under Contract Number 0439186; with additional support from the NSF Plant Genome Program Grant Number 0321437 (B.C.M) DOE DE-FG02-04ER15541 (P.J.G.) and NIH P20RR16472-04.
| Number | Date | Country | |
|---|---|---|---|
| 60703215 | Jul 2005 | US | |
| 60772666 | Feb 2006 | US |
