We thank Prof. Kazuki Saito in RIKEN PSC and Dr. Bjoern Usadel in MPIMP for useful discussions. TT is supported by a fellowship from the Alexander von Humboldt foundation.

1. Sample Preparation
<ol>
	<li>Plant materials are harvested and frozen immediately.</li>
	<li>Frozen plant materials are powdered by mixer mill (or mortar) and stored in Falcon tube or Eppendorf tube at -80 &deg;C.</li>
</ol>
2. Extraction for Metabolite Profiling
<ol>
	<li>Aliquot frozen plant material in a 2 ml Eppendorf tube.</li>
	<li>Add 5 &mu;l of extraction buffer per milligram of fresh weight of frozen plant material.</li>
	<li>Add one metal (or gilconia) ball and homogenize of frozen powder with the Mixer Mill for 2 min at 25 l s-1.</li>
	<li>Centrifuge 10 min at 12,000 rpm.</li>
	<li>Transfer the supernatant to NANOSEP centrifugal filter.</li>
	<li>Centrifuge 2 min at 4,000 rpm.</li>
	<li>Transfer the supernatant to new Eppendorf tube, and store at -20 &deg;C until use.</li>
</ol>
3. Metabolite Profiling by LC-MS
<ol>
	<li>Set up HPLC and check temperature of column oven and sample tray.</li>
	<li>Set up MS condition and check the state of vacuum and heating capillary.</li>
	<li>Perform m/z calibration of MS detector.</li>
	<li>Transfer of 50 &mu;l of extracts to glass vial for LC-MS.</li>
	<li>Inject 5 &mu;l of extracts to LC-MS.</li>
</ol>
4. Data Analysis
<ol>
	<li>Configure Xcalibur or Metalign 4 and select the data analysis to be processed.</li>
	<li>Prepare a table of detected peaks of your interest in accordance with compound class in Table I.</li>
	<li>Identify peaks by co-elution of standard compounds.</li>
	<li>Annotate detected peaks using MS2 analysis, literature survey, metabolite database search (Figure 2, 12,13).</li>
</ol>
5. Prediction of Metabolic Pathway
<ol>
	<li>Construct a possible pathway with detected compounds. Prediction of pathway using peak annotations should be based on the chemical structure of detected compounds by predicting linking enzymatic functions on the metabolic pathway 13. Structuring biosynthetic steps should be conducted with precise peak annotation. But determination of detailed chemical structure, for example sugar moiety, is not indispensable in this step, because prediction of sugar moiety and adducted position is very difficult to identify by MS analysis. Determination of sugar type such as hexoside and pentoside will be identified by enzymatic assay of sugar donor at the last step of project. Mostly constructing of prediction of pathway should be performed as small molecule is intermediate of larger molecule except in the some cases such as dehydration reaction. List of atomic molecular weight, for example 16 m/z for difference between -H and -OH moiety (oxidation), 14 m/z (Carbon atom) for difference between -OH and -OMe (methylation) and 162 m/z (MW-H2O) for hexose (glycosylation), is useful for prediction. Determination of modification type with correlation analysis of tissues specificities helps prediction of metabolic pathway. Database of general metabolic pathway such as KEGG database (<a href="http://www.genome.jp/kegg/" target="_blank">http://www.genome.jp/kegg/</a>) and PlantCyc (<a href="http://plantcyc.org/" target="_blank">http://plantcyc.org/</a>), is very effective for prediction of metabolic pathway of your interest.</li>
</ol>
6. Preparation of Gene List with Arabidopsis Orthologous Gene ID
<ol>
	<li>Download gene ID list from genomic database.</li>
	<li>Add Arabidopsis gene ID of orthologous gene, in case of your target plant is not Arabidopsis.</li>
	<li>Prepare a list of genes in your pathway-of-interest. Annotation of Arabidopsis pathway data and gene family data are available in TAIR website (<a href="http://www.arabidopsis.org/" target="_blank">http://www.arabidopsis.org/</a>). If you prepared a list of Arabidopsis orthologous genes, you can subsequently combine them.</li>
</ol>
7. Co-expressed Gene Analysis
<ol>
	<li>Test using the prepared gene ID list to search best database for your pathway by checking the correlation using well known gene pairs in your pathway-of-interest (Table II). If co-expression database or gene expression database are not available in the plant of your interest, Arabidopsis co-expression database should be used with a list of Arabidopsis orthologous genes. In case of barley, rice, poplar, wheat, medicago and soybean, co-expression analysis of plant species can be used (Table II).</li>
	<li>Construct the framework for your target co-expression network based on the connections of the well-known genes in your pathway-of-interest.</li>
	<li>Add correlated candidate genes (r&lt;0.4~0.90, within approximate value of coefficient value between the connections of the well-known genes in your pathway-of-interest) and check their gene annotation in your predicted families to the connections of this network for finding best candidate genes (Figure 3). Threshold of coefficient value should be coordinated according to network structure and density of correlated genes.</li>
	<li>Make list of genes which you are able to narrow down as being specialized to your target pathway.</li>
	<li>Check gene expression of the organ specificities and stress responses of your candidate genes.</li>
</ol>
8. Integration of All Information to Predict New Pathways
<ol>
	<li>Add well-characterised genes which have been used for query of co-expression analysis to predicted metabolic pathway.</li>
	<li>Check uncharacterized parts in this pathway, for example uncharacterized enzymatic steps, transport proteins and transcription factors.</li>
	<li>Predict most suitable gene annotation for these missing uncharacterized steps.</li>
	<li>Combine the results of metabolite profiling and candidate genes of in silico gene expression based on the predicted pathway.</li>
	<li>Arrange your candidate genes on the predicted pathway according to gene function, for example, acetyltransferase for acetylated metabolite, glycosyltransferase for glycoside, P450 for oxidised compound. Phylogenetic tree analysis of amino acid sequences is useful for some wide gene family such as P450 and glycosyltransferase.</li>
	<li>Check the consistency of tissue specificities or stress responses between metabolite accumulation and gene expression level of candidate genes.</li>
	<li>Check the connections to other metabolism for providing substrate and stress responsive genes.</li>
</ol>
9. Experiments for Gene Identification Using Bio-resources
<ol>
	<li>Check the availability of bioresources for facilitation of experiment for candidate gene identification.</li>
	<li>Perform an experiment for identification of gene function using bio-resources, such as KO mutant library and full-length cDNA library. The experiments for functional identification of genes with preparation of overexpression plants and knockout mutants, enzymatic assay and promoter binding assay, have to be performed for the best candidate genes in your prediction. Recombinant protein assay for characterisation of protein properties and preparation of overexpression plants better to be carried out after confirmation of metabolite profile using KO mutant since it takes considerably longer to prepare recombinant protein and gene cloning for transformation.</li>
</ol>
10. Representative Results
The procedure of integrated analysis described in this protocol has many possibilities depending on specified experimental purpose and choice of biological and analytical combinations. Choice of procedures and experimental design should be carried out properly on the basis of your target pathway, compounds and plant species. The integration strategy described in this protocol is focused on annotation of plant gene function and the discovery of novel gene functions with an efficient usage of several bio- and data-resource. Expected outcome is promised to provide with the only case of conclusive prediction. This fact indicates that if enough evidences can not be given by combination profiles, experiment should not be started. For this reason, in any cases, additional preliminary experiments such as targeted gene expression profiling by RT-PCR, can support your prediction of gene function. Accuracy and correctness of prediction correlates higher depending on qualitative difference and number of variation of combination. In addition, good candidates and valid outcomes can only come from accurate prediction of pathways. Peak annotation should be conducted by combination of several approaches, for example literature survey, reference plant extract, MSn analysis, organ specificity and mutant analysis 13.
<img alt="Figure 1" src="/files/ftp_upload/3487/3487fig1.jpg" /> 
Figure 1. Overview of the experimental flow of gene annotation via combined approach. In some cases, projects start with the discovery of a novel peak which is detected in special conditions or tissues, and the desire to understand its role within its metabolism. In other instances the purpose of the project is gene identification or discovery of key regulatory factors such as transcription factors. Design of experiment should be planed with an data set which shows clear differences of metabolite levels in your target pathway, using a wide range of tissue samples from different organs, and for differentially grown plants or plants exposed to stress conditions, and subjecting the material to metabolite profiling. Mutant and transgenic plants as well as QTL harbouring breeding material also represent suitable genetic material for these studies. Prediction of novel pathway should be performed carefully with accurate peak annotation and combination approach with different type of metabolotype such as organ securities and stress responses according to gene expression data of your pathway-of-interest. In the last step, metabolite and transcript profiling should be performed which will eventually, when combined with in silico analysis of web-resources and in vitro characterisation of gene expression via heterologous expression, lead to the confirmation of the gene candidate and elucidation of its function and position within a metabolic pathway. Abbreviations: QTL, Quantitative Trait Loci.
<img alt="Figure 2" src="/files/ftp_upload/3487/3487fig2.jpg" /> 
Figure 2. Work flow of combinational approach for peak annotation. An procedure for peak identification and annotation by the standard compound, comparison of wild type and knock out mutants, multi-dimensional mass spectrometry of the target peak referring to mass spectra of pure compounds from the databases 12. Abbreviations: DB, database; KO, knock-out; 1-D, one- dimensional; 2-D, two-dimensional; NMR, nuclear magnetic resonance; IR, infra-red; MSn, mass-mass spectrometries.
<img alt="Figure 3" src="/files/ftp_upload/3487/3487fig3.jpg" /> 
Figure 3. Example co-regulation network analysis of the anthocyanin pathway. Coexpression analyses were performed using the PRIMe (<a href="http://prime.psc.riken.jp/?action=coexpression_index" target="_blank">http://prime.psc.riken.jp/?action=coexpression_index</a>) based on the data set of ATTEDII version 3 8,2 with the Pajek program (<a href="http://vlado.fmf.uni-lj.si/pub/networks/pajek/" target="_blank">http://vlado.fmf.uni-lj.si/pub/networks/pajek/</a>). Positive correlations (r&lt;0.5) are used to make network connections. Red node: twelve anthocyanin enzymatic genes (At5g13930, CHS, TT4, chalcone synthase; At3g55120, CHI, TT5, chalcone isomerise; At3g51240, F3H, TT6, flavanone 3-hydroxylase; At5g07990, F3'H, TT7, flavonoid 3'-hydroxylase; At5g17050, Fd3GT, UGT78D2, flavonoid 3-O-glucosyltransferase; At5g17220, AtGSTF12, TT19; At5g42800, DFR, TT3, dihydroflavonol reductase; At4g22880, ANS/LDOX, TT18, anthocyanidin synthese; At4g14090, A5GT, anthocyanin 5-O-glucosyltransferase; At5g54060, A3G2"XT, putative anthocyanin 3-O-glucoside 2"-O-xylosyltransferase; At3g29590, A5GMaT, anthocyanin 5-O-glucoside 6'''-O-malonyltransferase; At1g03940, A3GCouT, anthocyanin 3-O-glucoside 6"-O-p-coumaroyltransferase) and two transcription factors for anthocyanin production (At1g56650, PAP1; At1g66390, PAP2) was used for searching candidate genes. Candidate genes were found by an "intersection of sets" search with a threshold value with a coefficient of r&gt;0.50 queried by intersection of sets by all genes queried (Fourteen anthocyanin biosynthetic genes). A co-expression network, including correlated candidate genes (68 genes) and queried genes (14 genes), was re-constructed by an "interconnection of sets" search with r&gt;0.50 using the PRIMe database. The output files that were formatted with a '.net' file from the PRIMe database and networks were drawn using Pajek software. Blue node indicates candidate genes which correlated with anthocyanin genes.
<table border="1">
	<tbody>
		<tr>
			<td>species</td>
			<td>Major secondary metabolite</td>
		</tr>
		<tr>
			<td>Arabidopsis thaliana</td>
			<td>Glucosinolate, flavonol, anthocyanin, sinapoyl derivative</td>
		</tr>
		<tr>
			<td>Populus trichocarpa</td>
			<td>Flavonol, anthocyanin, salicylate derivative</td>
		</tr>
		<tr>
			<td>Vitis vinifera</td>
			<td>Flavonol, anthocyanin, tannin, stilbene</td>
		</tr>
		<tr>
			<td>Solanum lycopersicum</td>
			<td>Flavonol, anthocyanin, glycoalkaloid, chrologenate related,</td>
		</tr>
		<tr>
			<td>Nicotiana tabacum</td>
			<td>Flavonol, anthocyanin, nicotianamide, chrologenate related, acylsugar</td>
		</tr>
		<tr>
			<td>Oryza sativa</td>
			<td>Glycoflavone, anthocyanin, sterol derivatives</td>
		</tr>
		<tr>
			<td>Zea may</td>
			<td>Glycoflavone, anthocyanin, benzoxazinone, sterol derivatives</td>
		</tr>
		<tr>
			<td>Medicago truncatula</td>
			<td>Isoflavone, anthocyanin, saponin,</td>
		</tr>
		<tr>
			<td>Lotus japonica</td>
			<td>Isoflavone, flavonol, anthocyanin, saponin,</td>
		</tr>
	</tbody>
</table>
Table I. Major secondary metabolites in model plant species.
<table border="1">
	<tbody>
		<tr>
			<td>Co-expression database</td>
			<td>Address</td>
		</tr>
		<tr>
			<td>Plant cross species</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>COP</td>
			<td><a href="http://webs2.kazusa.or.jp/kagiana/cop0911/" target="_blank">http://webs2.kazusa.or.jp/kagiana/cop0911/</a></td>
		</tr>
		<tr>
			<td>PlaNet</td>
			<td><a href="http://aranet.mpimp-golm.mpg.de/" target="_blank">http://aranet.mpimp-golm.mpg.de/</a></td>
		</tr>
		<tr>
			<td>Plant species</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>ATEED-II</td>
			<td><a href="http://atted.jp/" target="_blank">http://atted.jp/</a></td>
		</tr>
		<tr>
			<td>BAR</td>
			<td><a href="http://142.150.214.117/welcome.htm" target="_blank">http://142.150.214.117/welcome.htm</a></td>
		</tr>
		<tr>
			<td>COP</td>
			<td><a href="http://webs2.kazusa.or.jp/kagiana/cop" target="_blank">http://webs2.kazusa.or.jp/kagiana/cop</a></td>
		</tr>
		<tr>
			<td>GeneCAT</td>
			<td><a href="http://genecat.mpg.de/" target="_blank">http://genecat.mpg.de/</a></td>
		</tr>
		<tr>
			<td>Arabidopsis</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>ACT</td>
			<td><a href="http://www.arabidopsis.leeds.ac.uk/act/coexpanalyser" target="_blank">http://www.arabidopsis.leeds.ac.uk/act/coexpanalyser</a></td>
		</tr>
		<tr>
			<td>AthCoR@CSB.DB</td>
			<td><a href="http://csbdb.mpimp-golm.mpg.de/csbdb/dbcor/ath.html" target="_blank">http://csbdb.mpimp-golm.mpg.de/csbdb/dbcor/ath.html</a></td>
		</tr>
		<tr>
			<td>CressExpress</td>
			<td><a href="http://cressexpress.org/" target="_blank">http://cressexpress.org/</a></td>
		</tr>
		<tr>
			<td>PRIMe</td>
			<td><a href="http://prime.psc.riken.jp/?action=coexpression_index" target="_blank">http://prime.psc.riken.jp/?action=coexpression_index</a></td>
		</tr>
		<tr>
			<td>Oryza sativa </td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>RiceArrayNet</td>
			<td><a href="http://arraynet.mju.ac.kr/arraynet/" target="_blank">http://arraynet.mju.ac.kr/arraynet/</a></td>
		</tr>
		<tr>
			<td>Rice Array Database</td>
			<td><a href="http://www.ricearray.org/coexpression/coexpression.shtml" target="_blank">http://www.ricearray.org/coexpression/coexpression.shtml</a></td>
		</tr>
	</tbody>
</table>
Table II. Available gene expression database for in silico co-expression analysis.

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No conflicts of interest declared.

Given that transcriptomics and metabolomics technologies have been used for several years, the process of data integration for metabolomics assisted gene annotation generally begins with the identification of a novel peak representing an unknown metabolite. This fact leads to the next stage which is to evaluate quantitative variance in metabolite peaks or the novel candidate genes thought to be responsible for their biosynthesis. The strategy described in this protocol, however, has three major problems i) difficulty of peak annotation, ii) complexity of pathway prediction, iii) resolution of gene information and quality of gene expression data. To counter the first problem, peak annotation should be carried out with co-elution of standard compounds or combinatorial approach utilizing information from MSn analysis, reference extract, mutant analysis, metabolite database search and literature survey (Figure 2, 12). For the second problem, pathway prediction can only be obtained by correct peak annotation. However, metabolite profiling of tissue specificity also can be support peak annotation, because metabolite accumulation should be correlated with the gene expressions of related genes. Therefore combination profiles of different tissues and growth conditions can be helpful for this second problem. The third problem concerning the resolution of gene information depends on the progress of sequence data. In case of the model plant without completion of genome sequence, co-expression analysis using orthologous genes in other model plants is useful. Detailed alignment comparison and phylogenetic tree analysis of amino acid sequence can support to connect model organisms to other species.
This protocol is suitable for all metabolisms. It is most efficient in the analysis of intermediate and secondary metabolisms which are well characterised to be subject to strong transcriptional control 1,5,11,16. In some examples, co-expression analysis succeeded to be performed in sulfur assimilation, genes for &beta;-oxidation, branched-chain amino acid degradation, chlorophyll breakdown, and the lysine catabolism 3, cell wall metabolism 10,7 and light signalling cascade 14. Annotation of gene function via combined genomics, metabolomics and informatics is not only for biosynthetic gene and direct regulator of transcription factor but also for understanding physiological process and response (see example Figure 3. 14).
To develop this approach from model plants to crop species, metabolic comparison of across plant species is powerful approach in some general metabolisms. For example, if same compound is detected in different plant species, and some orthologous genes are found in these plant species, cross species co-expression analysis using orthologous genes can provide strong support for your prediction. This approach can be performed in Arabidopsis, poplar, medicago, in addition important crops such as barley, rice, wheat and soybean, by co-expression analysis of plant species (6, PlaNet: <a href="http://aranet.mpimp-golm.mpg.de/" target="_blank">http://aranet.mpimp-golm.mpg.de/</a>,; 9, COP: <a href="http://webs2.kazusa.or.jp/kagiana/cop0911/" target="_blank">http://webs2.kazusa.or.jp/kagiana/cop0911/</a>; see an example, 15).

<table border="1">
	<tbody>
		<tr>
			<td>Name of the reagent</td>
			<td>Company</td>
			<td>Catalogue number</td>
		</tr>
		<tr>
			<td>Distilled water ULC/MS grad</td>
			<td>BIOSOLVE</td>
			<td>23214102</td>
		</tr>
		<tr>
			<td>Acetonitrile (ACN) ULC/MS grade</td>
			<td>BIOSOLVE</td>
			<td>01204102</td>
		</tr>
		<tr>
			<td>Methanol (MeOH) ULC/MS grade</td>
			<td>BIOSOLVE</td>
			<td>13684102</td>
		</tr>
		<tr>
			<td>Formic acid (HCOOH) ULC/MS grade for liquid chromatography</td>
			<td>BIOSOLVE</td>
			<td>06914131</td>
		</tr>
		<tr>
			<td>Standard compounds</td>
			<td>EXTRASYNTHESE</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>Linear ion trap (IT) ESI-MS system FINNIGAN-LTQ</td>
			<td>Thermo Finnigan</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>HPLC system Surveyor</td>
			<td>Thermo Finnigan</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>Analytical column Luna C18(2), 2.0 mm diameter, 150 mm length, 100 &Aring; pore size and spherical particles of 3 mm</td>
			<td>Phenomenex</td>
			<td>00F-4251-B0</td>
		</tr>
		<tr>
			<td>Xcalibur software</td>
			<td>Thermo Finnigan</td>
			<td>&nbsp;</td>
		</tr>
	</tbody>
</table>

annotation of plant gene function via combined genomics, metabolomics and informatics

Given the ever expanding number of model plant species for which complete genome sequences are available and the abundance of bio-resources such as knockout mutants, wild accessions and advanced breeding populations, there is a rising burden for gene functional annotation. In this protocol, annotation of plant gene function using combined co-expression gene analysis, metabolomics and informatics is provided (Figure 1). This approach is based on the theory of using target genes of known function to allow the identification of non-annotated genes likely to be involved in a certain metabolic process, with the identification of target compounds via metabolomics. Strategies are put forward for applying this information on populations generated by both forward and reverse genetics approaches in spite of none of these are effortless. By corollary this approach can also be used as an approach to characterise unknown peaks representing new or specific secondary metabolites in the limited tissues, plant species or stress treatment, which is currently the important trial to understanding plant metabolism.

Watch this Scientific Journal Video about Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics at JoVE.com

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Combination of genomics, co-expression gene analysis and the identification of target compounds via metabolism give gene functional annotation.

Given the ever expanding number of model plant species for which complete genome sequences are available and the abundance of bio-resources such as ...

annotation-plant-gene-function-via-combined-genomics-metabolomics

Nanyang Technological University

Research

JoVE Journal

Biology

19.6K Views.  Max-Planck-Institut. Combination of genomics, co-expression gene analysis and the identification of target compounds via metabolism give gene functional annotation.

Video: Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Virus-induced Gene Silencing (VIGS) in Nicotiana benthamiana and Tomato

RNA interference (RNAi) is a highly specific gene-silencing phenomenon triggered by dsRNA1. This silencing mechanism uses two major classes of RNA regulators: microRNAs, which are produced from non-protein coding genes and short interfering RNAs (siRNAs). Plants use RNAi to control transposons and to exert tight control over developmental processes such as flower organ formation and leaf development2,3,4. Plants also use RNAi to defend themselves against infection by viruses. Consequently, many viruses have evolved suppressors of gene silencing to allow their successful colonization of their host5.
Virus-induced gene silencing (VIGS) is a method that takes advantage of the plant RNAi-mediated antiviral defense mechanism. In plants infected with unmodified viruses the mechanism is specifically targeted against the viral genome. However, with virus vectors carrying sequences derived from host genes, the process can be additionally targeted against the corresponding host mRNAs. VIGS has been adapted for high-throughput functional genomics in plants by using the plant pathogen Agrobacterium tumefaciens to deliver, via its Ti plasmid, a recombinant virus carrying the entire or part of the gene sequence targeted for silencing. Systemic virus spread and the endogenous plant RNAi machinery take care of the rest. dsRNAs corresponding to the target gene are produced and then cleaved by the ribonuclease Dicer into siRNAs of 21 to 24 nucleotides in length. These siRNAs ultimately guide the RNA-induced silencing complex (RISC) to degrade the target transcript2.
Different vectors have been employed in VIGS and one of the most frequently used is based on tobacco rattle virus (TRV). TRV is a bipartite virus and, as such, two different A. tumefaciens strains are used for VIGS. One carries pTRV1, which encodes the replication and movement viral functions while the other, pTRV2, harbors the coat protein and the sequence used for VIGS6,7. Inoculation of Nicotiana benthamiana and tomato seedlings with a mixture of both strains results in gene silencing. Silencing of the endogenous phytoene desaturase (PDS) gene, which causes photobleaching, is used as a control for VIGS efficiency. It should be noted, however, that silencing in tomato is usually less efficient than in N. benthamiana. RNA transcript abundance of the gene of interest should always be measured to ensure that the target gene has efficiently been down-regulated. Nevertheless, heterologous gene sequences from N. benthamiana can be used to silence their respective orthologs in tomato and vice versa8.

Virus-induced Gene Silencing VIGS in Nicotiana benthamiana and Tomato

Description of a virus-induced gene silencing (VIGS) method for knock-down of gene expression in Nicotiana benthamiana and tomato.

RNA interference (RNAi) is a highly specific gene-silencing phenomenon triggered by dsRNA1. This silencing mechanism uses two major classes of RNA ...

Fluorescence Activated Cell Sorting of Plant Protoplasts

High-resolution, cell type-specific analysis of gene expression greatly enhances understanding of developmental regulation and responses to environmental stimuli in any multicellular organism. In situ hybridization and reporter gene visualization can to a limited extent be used to this end but for high resolution quantitative RT-PCR or high-throughput transcriptome-wide analysis the isolation of RNA from particular cell types is requisite. Cellular dissociation of tissue expressing a fluorescent protein marker in a specific cell type and subsequent Fluorescence Activated Cell Sorting (FACS) makes it possible to collect sufficient amounts of material for RNA extraction, cDNA synthesis/amplification and microarray analysis.
An extensive set of cell type-specific fluorescent reporter lines is available to the plant research community. In this case, two marker lines of the Arabidopsis thaliana root are used: PSCR::GFP (endodermis and quiescent center) and PWOX5::GFP (quiescent center). Large numbers (thousands) of seedlings are grown hydroponically or on agar plates and harvested to obtain enough root material for further analysis. Cellular dissociation of plant material is achieved by enzymatic digestion of the cell wall. This procedure makes use of high osmolarity-induced plasmolysis and commercially available cellulases, pectinases and hemicellulases to release protoplasts into solution.
FACS of GFP-positive cells makes use of the visualization of the green versus the red emission spectra of protoplasts excited by a 488 nm laser. GFP-positive protoplasts can be distinguished by their increased ratio of green to red emission. Protoplasts are typically sorted directly into RNA extraction buffer and stored for further processing at a later time.
This technique is revealed to be straightforward and practicable. Furthermore, it is shown that it can be used without difficulty to isolate sufficient numbers of cells for transcriptome analysis, even for very scarce cell types (e.g. quiescent center cells). Lastly, a growth setup for Arabidopsis seedlings is demonstrated that enables uncomplicated treatment of the plants prior to cell sorting (e.g. for the cell type-specific analysis of biotic or abiotic stress responses). Potential supplementary uses for FACS of plant protoplasts are discussed.

A method for isolating specific cell types from plant material is demonstrated. This technique employs transgenic marker lines expressing fluorescent proteins in particular cell types, cellular dissociation and Fluorescence Activated Cell Sorting. Additionally, a growth setup is established here that facilitates treatment of Arabidopsis thaliana seedlings prior to cell sorting.

High-resolution, cell type-specific analysis of gene expression greatly enhances understanding of developmental regulation and responses to environmental ...

A &#946;-glucuronidase (GUS) Based Cell Death Assay

We have developed a novel transient plant expression system that simultaneously expresses the reporter gene, &beta;-glucuronidase (GUS), with putative positive or negative regulators of cell death. In this system, N. benthamiana leaves are co-infiltrated with a 35S driven expression cassette containing the gene to be analyzed, and the GUS vector pCAMBIA 2301 using Agrobacterium strain LBA4404 as a vehicle. Because live cells are required for GUS expression to occur, loss of GUS activity is expected when this marker gene is co-expressed with positive regulators of cell death. Equally, increased GUS activity is observed when anti-apoptotic genes are used compared to the vector control. As shown below, we have successfully used this system in our lab to analyze both pro- and anti-death players. These include the plant anti-apoptotic Bcl-2 Associated athanoGene (BAG) family, as well as, known mammalian inducers of cell death, such as BAX. Additionally, we have used this system to analyze the death function of specific truncations within proteins, which could provide clues on the possible post-translational modification/activation of these proteins. Here, we present a rapid and sensitive plant based method, as an initial step in investigating the death function of specific genes.

A &#946;-glucuronidase GUS Based Cell Death Assay

Programmed cell death assays commonly used in mammalian systems such as DNA laddering or TUNEL assays, are often difficult to reproduce in plants. In combination with a GUS reporter system, we propose a rapid, plant based transient assay to analyze the potential death properties of specific genes.

We have developed a novel transient plant expression system that simultaneously expresses the reporter gene, &beta;-glucuronidase (GUS), with putative ...

Agrobacterium-Mediated Virus-Induced Gene Silencing Assay In Cotton

Cotton (Gossypium hirsutum) is one of the most important crops worldwide. Considerable efforts have been made on molecular breeding of new varieties. The large-scale gene functional analysis in cotton has been lagged behind most of the modern plant species, likely due to its large size of genome, gene duplication and polyploidy, long growth cycle and recalcitrance to genetic transformation1. To facilitate high throughput functional genetic/genomic study in cotton, we attempt to develop rapid and efficient transient assays to assess cotton gene functions.
Virus-Induced Gene Silencing (VIGS) is a powerful technique that was developed based on the host Post-Transcriptional Gene Silencing (PTGS) to repress viral proliferation2,3. Agrobacterium-mediated VIGS has been successfully applied in a wide range of dicots species such as Solanaceae, Arabidopsis and legume species, and monocots species including barley, wheat and maize, for various functional genomic studies3,4. As this rapid and efficient approach avoids plant transformation and overcomes functional redundancy, it is particularly attractive and suitable for functional genomic study in crop species like cotton not amenable for transformation.
In this study, we report the detailed protocol of Agrobacterium-mediated VIGS system in cotton. Among the several viral VIGS vectors, the tobacco rattle virus (TRV) invades a wide range of hosts and is able to spread vigorously throughout the entire plant yet produce mild symptoms on the hosts5. To monitor the silencing efficiency, GrCLA1, a homolog gene of Arabidopsis Cloroplastos alterados 1 gene (AtCLA1) in cotton, has been cloned and inserted into the VIGS binary vector pYL156. CLA1 gene is involved in chloroplast development6, and previous studies have shown that loss-of-function of AtCLA1 resulted in an albino phenotype on true leaves7, providing an excellent visual marker for silencing efficiency. At approximately two weeks post Agrobacterium infiltration, the albino phenotype started to appear on the true leaves, with 100% silencing efficiency in all replicated experiments. The silencing of endogenous gene expression was also confirmed by RT-PCR analysis. Significantly, silencing could potently occur in all the cultivars we tested, including various commercially grown varieties in Texas. This rapid and efficient Agrobacterium-mediated VIGS assay provides a very powerful tool for rapid large-scale analysis of gene functions at genome-wide level in cotton.

We present the detailed protocol for Agrobacterium-mediated virus-induced gene silencing (VIGS) assay in cotton. The tobacco rattle virus (TRV)-derived VIGS vectors were deployed to induce RNA silencing of cotton GrCLA1, Cloroplastos alterados 1 gene. The albino phenotype caused by silencing GrCLA1 was observed at the seedling stage within 2 weeks after inoculation.

Cotton (Gossypium hirsutum) is one of the most important crops worldwide. Considerable efforts have been made on molecular breeding of new varieties.  The ...

Isolation of Specific Genomic Regions and Identification of Associated Molecules by enChIP

The identification of molecules associated with specific genomic regions of interest is required to understand the mechanisms of regulation of the functions of these regions. To enable the non-biased identification of molecules interacting with a specific genomic region of interest, we recently developed the engineered DNA-binding molecule-mediated chromatin immunoprecipitation (enChIP) technique. Here, we describe how to use enChIP to isolate specific genomic regions and identify the associated proteins and RNAs. First, a genomic region of interest is tagged with a transcription activator-like (TAL) protein or a clustered regularly interspaced short palindromic repeats (CRISPR) complex consisting of a catalytically inactive form of Cas9 and a guide RNA. Subsequently, the chromatin is crosslinked and fragmented by sonication. The tagged locus is then immunoprecipitated and the crosslinking is reversed. Finally, the proteins or RNAs that are associated with the isolated chromatin are subjected to mass spectrometric or RNA sequencing analyses, respectively. This approach allows the successful identification of proteins and RNAs associated with a genomic region of interest.

The identification of molecules associated with specific genomic regions of interest is required to understand the mechanisms of regulation of the functions of these regions. This protocol describes procedures to perform engineered DNA-binding molecule-mediated chromatin imunoprecipitation (enChIP) for identification of proteins and RNAs associated with a specific genomic region.

The identification of molecules associated with specific genomic regions of interest is required to understand the mechanisms of regulation of the ...

Visualizing Stromule Frequency with Fluorescence Microscopy

Stromules, or "stroma-filled tubules", are narrow, tubular extensions from the surface of the chloroplast that are universally observed in plant cells but whose functions remain mysterious. Alongside growing attention on the role of chloroplasts in coordinating plant responses to stress, interest in stromules and their relationship to chloroplast signaling dynamics has increased in recent years, aided by advances in fluorescence microscopy and protein fluorophores that allow for rapid, accurate visualization of stromule dynamics. Here, we provide detailed protocols to assay stromule frequency in the epidermal chloroplasts of Nicotiana benthamiana, an excellent model system for investigating chloroplast stromule biology. We also provide methods for visualizing chloroplast stromules in vitro by extracting chloroplasts from leaves. Finally, we outline sampling strategies and statistical approaches to analyze differences in stromule frequencies in response to stimuli, such as environmental stress, chemical treatments, or gene silencing. Researchers can use these protocols as a starting point to develop new methods for innovative experiments to explore how and why chloroplasts make stromules.

Protocols to investigate the dynamics of chloroplast stromules, the stroma-filled tubules that extend from the surface of chloroplasts, are described.

Stromules, or "stroma-filled tubules", are narrow, tubular extensions from the surface of the chloroplast that are universally observed in plant cells but ...

Split Green Fluorescent Protein System to Visualize Effectors Delivered from Bacteria During Infection

Bacteria, one of the most important causative agents of various plant diseases, secrete a set of effector proteins into the host plant cell to subvert the plant immune system. During infection cytoplasmic effectors are delivered to the host cytosol via a type III secretion system (T3SS). After delivery into the plant cell, the effector(s) targets the specific compartment(s) to modulate host cell processes for survival and replication of the pathogen. Although there has been some research on the subcellular localization of effector proteins in the host cells to understand their function in pathogenicity by using fluorescent proteins, investigation of the dynamics of effectors directly injected from bacteria has been challenging due to the incompatibility between the T3SS and fluorescent proteins.
Here, we describe our recent method of an optimized split superfolder green fluorescent protein system (sfGFPOPT) to visualize the localization of effectors delivered via the bacterial T3SS in the host cell. The sfGFP11 (11th &#946;-strand of sfGFP)-tagged effector secreted through the T3SS can be assembled with a specific organelle targeted sfGFP1-10OPT (1-10th &#946;-strand of sfGFP) leading to fluorescence emission at the site. This protocol provides a procedure to visualize the reconstituted sfGFP fluorescence signal with an effector protein from Pseudomonas syringae in a particular organelle in the Arabidopsis and Nicotiana benthamiana plants.

Fluorescent protein-based approaches to monitor effectors secreted by bacteria into host cells are challenging. This is due to the incompatibility between fluorescent proteins and the type-III secretion system. Here, an optimized split superfolder GFP system is used for visualization of effectors secreted by bacteria into the host plant cell.

Bacteria, one of the most important causative agents of various plant diseases, secrete a set of effector proteins into the host plant cell to subvert the ...

High-Throughput Live Imaging of Microcolonies to Measure Heterogeneity in Growth and Gene Expression

Precise measurements of between- and within-strain heterogeneity in microbial growth rates are essential for understanding genetic and environmental inputs into stress tolerance, pathogenicity, and other key components of fitness. This manuscript describes a microscope-based assay that tracks approximately 105&#160;Saccharomyces cerevisiae microcolonies per experiment. After automated time-lapse imaging of yeast immobilized in a multiwell plate, microcolony growth rates are easily analyzed with custom image-analysis software. For each microcolony, expression and localization of fluorescent proteins and survival of acute stress can also be monitored. This assay allows precise estimation of strains&#39; average growth rates, as well as comprehensive measurement of heterogeneity in growth, gene expression, and stress tolerance within clonal populations.

Yeast growth phenotypes are precisely measured through highly parallel time-lapse imaging of immobilized cells growing into microcolonies. Simultaneously, stress tolerance, protein expression, and protein localization can be monitored, generating integrated datasets to study how environmental and genetic differences, as well as gene-expression heterogeneity among isogenic cells, modulate growth.

Precise measurements of between- and within-strain heterogeneity in microbial growth rates are essential for understanding genetic and environmental ...

mirMachine: A One-Stop Shop for Plant miRNA Annotation

Of different types of noncoding RNAs, microRNAs (miRNAs) have arguably been in the spotlight over the last decade. As post-transcriptional regulators of gene expression, miRNAs play key roles in various cellular pathways, including both development and response to a/biotic stress, such as drought and diseases. Having high-quality reference genome sequences enabled identification and annotation of miRNAs in several plant species, where miRNA sequences are highly conserved. As computational miRNA identification and annotation processes are mostly error-prone processes, homology-based predictions increase prediction accuracy. We developed and have improved the miRNA annotation pipeline, SUmir, in the last decade, which has been used for several plant genomes since then. 
This study presents a fully automated, new miRNA pipeline, mirMachine (miRNA Machine), by (i) adding an additional filtering step on the secondary structure predictions, (ii) making it fully automated, and (iii) introducing new options to predict either known miRNA based on homology or novel miRNAs based on small RNA sequencing reads using the previous pipeline. The new miRNA pipeline, mirMachine, was tested using The Arabidopsis Information Resource, TAIR10, release of the Arabidopsis genome and the International Wheat Genome Sequencing Consortium (IWGSC) wheat reference genome v2.

Herein, we present a new and fully automated miRNA pipeline, mirMachine that 1) can identify known and novel miRNAs more accurately and 2) is fully automated and freely available. Users can now execute a short submission script to run the fully automated mirMachine pipeline.

Of different types of noncoding RNAs, microRNAs (miRNAs) have arguably been in the spotlight over the last decade. As post-transcriptional regulators of ...

Determination of Tripartite Interaction between Two Monomers of a MADS-box Transcription Factor and a Calcium Sensor Protein by BiFC-FRET-FLIM Assay

Protein-protein interactions are an integral part of all biological processes in the cells as they play a crucial role in regulating, maintaining, and amending cellular functions. These interactions are involved in a wide range of phenomena such as signal transduction, pathogen response, cell-cell interactions, metabolic and developmental processes. In the case of transcription factors, these interactions may lead to oligomerization of subunits, sequestering in specific subcellular contexts such as the nucleus, cytoplasm, etc., which, in turn, might have a more profound effect on the expression of the downstream genes. Here, we demonstrate a methodology to visualize in vivo tripartite interaction using Bimolecular Fluorescence Complementation (BiFC) based F&#246;rster Resonance Energy Transfer (FRET) involving Fluorescence Lifetime Imaging (FLIM). Two of the proteins selected for this demonstration interact as BiFC partners, and their reconstituted fluorescence activity is used to assay FRET-FLIM with the third partner. Four to five-week-old growth-chamber-grown Nicotiana benthamiana plants have been used as the model plant system for this demonstration.

Here we present, a method to visualize ternary complex formation between three protein partners using fluorescent-tagged proteins by BiFC based FRET-FLIM assay. This method is valuable for studying protein-protein interaction complexes in vivo.

Protein-protein interactions are an integral part of all biological processes in the cells as they play a crucial role in regulating, maintaining, and ...