The yeast work in the Pan lab was funded by a grant from Ajinomoto, Inc. Japan and National Institutes of Health Training Grant T32GM007183-33. We thank Dr. Ronald Wek at the Indiana University School of Medicine for long-term collaboration on the yeast projects. We also thank Dr. Kimberly Dittmar for developing the tRNA charging microarray method.

Part 1: Isolation of total charged tRNA
<ol>
<li>Pellet cells in a tabletop centrifuge at 2000 RFC for 5 minutes. The equivalent of 1 OD600 of cells should produce at least 1&mu;g of total RNA and a minimum of 30 &mu;g is recommended for the procedure.</li>
<li>Resuspend cells in lysis buffer solution consisting of 0.3 M buffered Na+-acetate (pH 4.5) and 10 mM EDTA. Vortex with an equal volume of Na+-acetate-saturated phenol/chloroform (pH 4.5) three times each for 30 s. Be sure to always keep samples on ice between vortexing. Buffered acetate can be prepared from NaOAC and HOAc while acetate saturated phenol/chloroform can be prepared by mixing 1 part 5 M buffered acetate (pH 4.5) with 9 parts phenol/chloroform. 
 Note: total RNA isolation is carried out under mildly acidic conditions and on ice to avoid deacylation of charged tRNA.</li>
<li>Centrifuge at 18,600 RFC for 15 minutes at 4&deg;C. Remove the aqueous layer and perform another acetate-saturated phenol/chloroform extraction.</li>
<li>After the second extraction precipitate the RNA by adding 2.7x volumes of ethanol and centrifuging at 18,600 RFC for 30 minutes at 4&deg;C. </li>
<li>Resuspend RNA pellets in lysis buffer solution and then precipitate with ethanol a second time. </li>
<li>Finally, resuspend the RNA in a solution containing 10 mM buffered acetate (pH 4.5) and 1 mM EDTA for subsequent treatment. The sample can be stably stored at -80&deg;C for about two weeks. Longer storage is not recommended as some charged tRNA will start to deacylate.</li>
</ol>
Part 2: Preparation of tRNA Standards
<ol>
 <li>Heat E. coli tRNA standards at 10.5 &mu;M to 85&deg;C in 45 mM Tris-Cl pH 7.5 for 2 minutes. Take the tube out and leave at room temperature for 3 minutes.</li>
 <li>Add MgCl2 to a final concentration of 20 mM and incubate at 37&deg;C for 5 minutes.</li>
 <li>Add the synthetase reaction mix so the final reaction contains 5 &mu;M tRNA, 60 mM Tris-HCl (pH 7.5), 12.5 mM MgCl2, 10 mM KCl, 3 mM dithiothreitol, 1.5 mM ATP, 1 mM spermine, 1 mM of the respective amino acid, and 4.2 units/&mu;l E. coli aminoacyl-tRNA synthetase mix. Incubate at 37&deg;C for 15 minutes.</li>
 <li>Add an equal volume of 0.5 M buffered acetate (pH 4.5) and extract with acetate-saturated phenol/CHCl3 at pH 4.5.</li>
 <li>Precipitate the tRNA standards with 2.7x volumes of ethanol and centrifuging at 18,600 RFC for 30 minutes at 4&deg;C. </li>
 <li>Resuspend the standards in 50 mM buffered acetate (pH 4.5) with 1 mM EDTA and store at &#8722;80&deg;C for up to one month.</li>
</ol>
Part 3: Cy3/Cy5 labeling of tRNA
<ol>
 <li>For the charged tRNA sample incubate total RNA at a concentration of 0.1 &mu;g/&mu;l with 0.066 &mu;M each E. coli tRNA standards (i.e. 0.67 pmole each standard per &mu;g total RNA) and 100 mM buffered acetate (pH 4.5) in the presence of 50 mM NaIO4 for 30 minutes at room temperature. For the control total tRNA sample use 50 mM NaCl in place of NaIO4. Remember to dilute the RNA only with buffered solutions to preserve charging. </li>
 <li>To quench the reaction add glucose to 100 mM and incubate at room temperature for 5 minutes.</li>
 <li>In order to remove any remaining NaIO4 from the sample perform a buffer exchange using a G25 spin column. For best results equilibrate the column first by running 200 mM buffered acetate buffer (pH 4.5) through it prior to applying your sample.</li>
 <li>Precipitate the sample by adding buffered acetate (pH 4.5) to a final concentration of 133 mM and NaCl to a final concentration of 66 mM and 2.7x volumes of ethanol. Occasionally a second precipitation may be needed for the oxidized samples. This is necessary only if the pellet looks significantly different than the control pellet of the same sample. For example a significantly bigger or more diffuse pellet. If a second ethanol precipitation is required resuspend pellet in 50 mM acetate buffer pH 4.5 and 200 mM NaCl before the addition of ethanol. </li>
 <li>For deacylation the tRNA samples are resuspended in 50 mM Tris-HCl (pH 9) and incubated at 37 &deg;C for 30 min. The reaction is neutralized by the addition of an equal volume of 50 mM buffered acetate (pH 4.5) and 100 mM NaCl. Precipitate with 2.7x volumes of ethanol. After precipitation, RNA is resuspended in water at ~1 &mu;g/&mu;l. Both the control and oxidized samples should be run on agarose gels to check RNA quality.</li>
 <li>To attach fluorescent oligo tags onto the tRNA 0.1 &mu;g/&mu;l deacylated RNA is incubated in 1x ligase buffer, 15% DMSO, 4 &mu;M Cy3- or Cy5-containing oligonucleotides, 0.5 units/&mu;l T4 DNA ligase, and yeast exo-phosphatase (5,000 units/&mu;l) at 16&deg;C overnight (over 16 h). The exo-phosphatase is necessary only for samples obtained from yeast and can be omitted if this protocol is used for E. coli or human samples. </li>
 <li>After ligation samples are mixed with 4 volumes of 50 mM KOAc (pH 7), 200 mM KCl, and then extracted with an equal volume of phenol/chloroform. Following extraction of the aqueous phase, RNA preparations are precipitated with ethanol and resuspended in water to approximately 0.1 &mu;g/&mu;l.</li>
 <li>Ligation efficiency can be assessed by running 5-10% of the samples on 12% polyacrylamide gels containing 7M urea and visualized with a fluorescent gel scanner. This PAGE analysis is also useful to determine the amount of oxidized and control samples needed for microarray hybridization. A good ligation result should show ~10% or more of the Cy3/Cy5 containing oligonucleotide being ligated to the tRNA. For samples with good labeling efficiency, 0.1-0.5 &mu;g total RNA per sample is used for array hybridization.</li>
</ol>
Part 4: Hybridization and Analysis of the Microarray
<ol>
 <li>Prior to hybridization microarray slides are boiled in distilled water for 1-2 minutes to remove unbound oligonucleotides.</li>
 <li>Cy3/Cy5 labeled samples are combined with 140 &mu;l of hybridization buffer and salmon sperm DNA to a final concentration of 140 &mu;g/ml and poly(A) to a final concentration of 70 &mu;g/ml.</li>
 <li>A hybridization program, (table 1), is run on an automatic array hybridizer. For tRNA work, it is highly recommended that an automatic hybridization machine be used since manual hybridization produces high background.</li>
 <li>After the program is complete manually wash the slides with Wash 3 solution at least twice by gently shaking in a 50 ml conical tube for 3-5 minutes.</li>
 <li>Slides can be dried by centrifuging at a low speed, less than 200 RFC, for 5-10 minutes. It is important to keep the slides out of the light since exposure to light will bleach the fluorophores.</li>
 <li>After the slides are dried they should be scanned immediately for optimal results; if necessary they can be saved for several days in a dry dark place at room temperature. Slides can be scanned 2-3 times without noticeable degradation in image quality.</li>
</ol>
Part 5: Representative Results
When isolated properly the total RNA sample should produce a very clean spectrum with an OD260:OD280 ratio near 2. There should be no detectable protein or phenol contamination. When run on 1% agarose gels, a strong tRNA band should be observed with other possible nucleic acid bands varying between organisms. After oxidation a clean tRNA band should be observed. If a sample is noticeably weaker than other samples or smearing is observed, samples should not be used for microarrays. Microarrays should have very low background which is an order of magnitude lower than the weakest tRNA probe. High background is usually due to problems during the washing steps. This can usually be fixed by preparing fresh wash solutions and thoroughly washing all equipment and tubing to remove residual and precipitated SDS.
<table border="1">
<tbody>
 <tr>
 <td>Step</td>
 <td>Details</td>
 </tr>
 <tr>
 <td>O-ring conditioning</td>
 <td>75&deg;C, 2 min</td>
 </tr>
 <tr>
 <td>Introduce sample</td>
 <td>60&deg;C</td>
 </tr>
 <tr>
 <td>Denature Sample</td>
 <td>90&deg;C, 5 min</td>
 </tr>
 <tr>
 <td>Hybridization</td>
 <td>60&deg;C, 16h</td>
 </tr>
 <tr>
 <td>Wash 1</td>
 <td>50&deg;C, flow 10s hold 20s</td>
 </tr>
 <tr>
 <td>Wash 2</td>
 <td>42&deg;C, flow 10s hold 20s</td>
 </tr>
 <tr>
 <td>Wash 3</td>
 <td>42&deg;C, flow 10s hold 20s</td>
 </tr>
 </tbody>
</table>
Table 1: Hybridization protocol

<ol>
	<li>Zaborske, J. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genome-wide+analysis+of+tRNA+charging+and+activation+of+the+eIF2+kinase+Gcn2p.">Genome-wide analysis of tRNA charging and activation of the eIF2 kinase Gcn2p.</a> J Biol Chem. 284 (37), 25254-25267 (2009).</li><li>Wek, S. A., Zhu, S., Wek, R. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+histidyl-tRNA+synthetase-related+sequence+in+the+eIF-2+alpha+protein+kinase+GCN2+interacts+with+tRNA+and+is+required+for+activation+in+response+to+starvation+for+different+amino+acids.">The histidyl-tRNA synthetase-related sequence in the eIF-2 alpha protein kinase GCN2 interacts with tRNA and is required for activation in response to starvation for different amino acids.</a> Mol Cell Biol. , 4497-4506 (1995).</li><li>Hinnebusch, A. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Translational+Regulation+of+GCN4+and+the+General+Amino+Acid+Control+of+Yeast.">Translational Regulation of GCN4 and the General Amino Acid Control of Yeast.</a> Annual Review of Microbiology. 59 (1), 407-450 (2005).</li><li>Braeken, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=New+horizons+for+(p)ppGpp+in+bacterial+and+plant+physiology.">New horizons for (p)ppGpp in bacterial and plant physiology.</a> Trends in Microbiology. 14 (1), 45-54 (2006).</li><li>Dong, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Uncharged+tRNA+Activates+GCN2+by+Displacing+the+Protein+Kinase+Moiety+from+a+Bipartite+tRNA-Binding+Domain.">Uncharged tRNA Activates GCN2 by Displacing the Protein Kinase Moiety from a Bipartite tRNA-Binding Domain.</a> Molecular Cell. 6 (2), 269-279 (2000).</li><li>Cashel, M., Neidhardt, F. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Stringent+Response.">The Stringent Response.</a> Escherichia coli and Salmonella: cellular and molecular biology. , 1458-1496 (1996).</li><li>Dittmar, K. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Selective+charging+of+tRNA+isoacceptors+induced+by+amino-acid+starvation.">Selective charging of tRNA isoacceptors induced by amino-acid starvation.</a> EMBO Rep. 6 (2), 151-157 (2005).</li><li>Zhou, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High+levels+of+tRNA+abundance+and+alteration+of+tRNA+charging+by+bortezomib+in+multiple+myeloma.">High levels of tRNA abundance and alteration of tRNA charging by bortezomib in multiple myeloma.</a> Biochem Biophys Res Commun. 385 (2), 160-164 (2009).</li></ol>

No conflicts of interest declared.

We present a method for simultaneously determining the relative charging levels of all tRNAs at the genomic scale. The protocol presented here has been optimized for S. cerevisiae, and it has also been successfully used to measure tRNA charging profiles in E. coli and human cell cultures. It can be modified to accommodate any organism with known genome sequences (allowing for annotation of all tRNA genes) as long as total charged tRNA can be obtained.

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<td>GE Healthcare</td>
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genome-wide analysis of aminoacylation (charging) levels of trna using microarrays

tRNA aminoacylation, or charging, levels can rapidly change within a cell in response to the environment[1]. Changes in tRNA charging levels in both prokaryotic and eukaryotic cells lead to translational regulation which is a major cellular mechanism of stress response. Familiar examples are the stringent response in E. coli and the Gcn2 stress response pathway in yeast ([2-6]). Recent work in E. coli and S. cerevisiae have shown that tRNA charging patterns are highly dynamic and depends on the type of stress experienced by cells [1, 6, 7]. The highly dynamic, variable nature of tRNA charging makes it essential to determine changes in tRNA charging levels at the genomic scale, in order to fully elucidate cellular response to environmental variations. In this review we present a method for simultaneously measuring the relative charging levels of all tRNAs in S. cerevisiae . While the protocol presented here is for yeast, this protocol has been successfully applied for determining relative charging levels in a wide variety of organisms including E. coli and human cell cultures[7, 8].

Watch this Scientific Journal Video about Genome-wide Analysis of Aminoacylation Charging Levels of tRNA Using Microarrays at JoVE.com

Genome-wide Analysis of Aminoacylation Charging Levels of tRNA Using Microarrays

We describe a method for microarray analysis to determine relative aminoacylation levels of all tRNAs from S. cerivisiae.

Genome-wide Analysis of Aminoacylation (Charging) Levels of tRNA Using Microarrays

tRNA aminoacylation, or charging, levels can rapidly change within a cell in response to the environment[1]. Changes in tRNA charging levels in both ...

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12.3K Views.  University of Chicago. We describe a method for microarray analysis to determine relative aminoacylation levels of all tRNAs from S. cerivisiae.

Video: Genome-wide Analysis of Aminoacylation Charging Levels of tRNA Using Microarrays

Bacterial Gene Expression Analysis Using Microarrays

Genome-wide Analysis using ChIP to Identify Isoform-specific Gene Targets

Recruitment of transcriptional and epigenetic factors to their targets is a key step in their regulation. Prominently featured in recruitment are the protein domains that bind to specific histone modifications. One such domain is the plant homeodomain (PHD), found in several chromatin-binding proteins. The epigenetic factor RBP2 has multiple PHD domains, however, they have different functions (Figure 4). In particular, the C-terminal PHD domain, found in a RBP2 oncogenic fusion in human leukemia, binds to trimethylated lysine 4 in histone H3 (H3K4me3)1. The transcript corresponding to the RBP2 isoform containing the C-terminal PHD accumulates during differentiation of promonocytic, lymphoma-derived, U937 cells into monocytes2. Consistent with both sets of data, genome-wide analysis showed that in differentiated U937 cells, the RBP2 protein gets localized to genomic regions highly enriched for H3K4me33. Localization of RBP2 to its targets correlates with a decrease in H3K4me3 due to RBP2 histone demethylase activity and a decrease in transcriptional activity. In contrast, two other PHDs of RBP2 are unable to bind H3K4me3. Notably, the C-terminal domain PHD of RBP2 is absent in the smaller RBP2 isoform4. It is conceivable that the small isoform of RBP2, which lacks interaction with H3K4me3, differs from the larger isoform in genomic location. The difference in genomic location of RBP2 isoforms may account for the observed diversity in RBP2 function. Specifically, RBP2 is a critical player in cellular differentiation mediated by the retinoblastoma protein (pRB). Consistent with these data, previous genome-wide analysis, without distinction between isoforms, identified two distinct groups of RBP2 target genes: 1) genes bound by RBP2 in a manner that is independent of differentiation; 2) genes bound by RBP2 in a differentiation-dependent manner.
To identify differences in localization between the isoforms we performed genome-wide location analysis by ChIP-Seq. Using antibodies that detect both RBP2 isoforms we have located all RBP2 targets. Additionally we have antibodies that only bind large, and not small RBP2 isoform (Figure 4). After identifying the large isoform targets, one can then subtract them from all RBP2 targets to reveal the targets of small isoform. These data show the contribution of chromatin-interacting domain in protein recruitment to its binding sites in the genome.

Here we are presenting a chromatin immunoprecipitation (ChIP) procedure for genome-wide location analysis of protein isoforms that differ in a histone-binding domain. We are applying it to ChIP-Seq analysis to identify the targets of the KDM5A/JARID1A/RBP2 histone demethylase.

Recruitment of transcriptional and epigenetic factors to their targets is a key step in their regulation. Prominently featured in recruitment are the ...

Genome-wide Screen for miRNA Targets Using the MISSION Target ID Library

The Target ID Library is designed to assist in discovery and identification of microRNA (miRNA) targets. The Target ID Library is a plasmid-based, genome-wide cDNA library cloned into the 3'UTR downstream from the dual-selection fusion protein, thymidine kinase-zeocin (TKzeo). The first round of selection is for stable transformants, followed with introduction of a miRNA of interest, and finally, selecting for cDNAs containing the miRNA's target. Selected cDNAs are identified by sequencing (see Figure 1-3 for Target ID Library Workflow and details).
To ensure broad coverage of the human transcriptome, Target ID Library cDNAs were generated via oligo-dT priming using a pool of total RNA prepared from multiple human tissues and cell lines. Resulting cDNA range from 0.5 to 4 kb, with an average size of 1.2 kb, and were cloned into the p3&#900;TKzeo dual-selection plasmid (see Figure 4 for plasmid map). The gene targets represented in the library can be found on the Sigma-Aldrich webpage. Results from Illumina sequencing (Table 3), show that the library includes 16,922 of the 21,518 unique genes in UCSC RefGene (79%), or 14,000 genes with 10 or more reads (66%).

The Target ID Library is a plasmid-based, genome-wide collection of cloned cDNA used to identify miRNA targets. Here we demonstrate its use and application.

The Target ID Library is designed to assist in discovery and identification of microRNA (miRNA) targets. The Target ID Library is a plasmid-based, ...

Quantitative and Automated High-throughput Genome-wide RNAi Screens in C. elegans

RNA interference is a powerful method to understand gene function, especially when conducted at a whole-genome scale and in a quantitative context. In C. elegans, gene function can be knocked down simply and efficiently by feeding worms with bacteria expressing a dsRNA corresponding to a specific gene 1. While the creation of libraries of RNAi clones covering most of the C. elegans genome 2,3 opened the way for true functional genomic studies (see for example 4-7), most established methods are laborious. Moy and colleagues have developed semi-automated protocols that facilitate genome-wide screens 8. The approach relies on microscopic imaging and image analysis. 
	Here we describe an alternative protocol for a high-throughput genome-wide screen, based on robotic handling of bacterial RNAi clones, quantitative analysis using the COPAS Biosort (Union Biometrica (UBI)), and an integrated software: the MBioLIMS (Laboratory Information Management System from Modul-Bio) a technology that provides increased throughput for data management and sample tracking. The method allows screens to be conducted on solid medium plates. This is particularly important for some studies, such as those addressing host-pathogen interactions in C. elegans, since certain microbes do not efficiently infect worms in liquid culture.
	We show how the method can be used to quantify the importance of genes in anti-fungal innate immunity in C. elegans. In this case, the approach relies on the use of a transgenic strain carrying an epidermal infection-inducible fluorescent reporter gene, with GFP under the control of the promoter of the antimicrobial peptide gene nlp 29 and a red fluorescent reporter that is expressed constitutively in the epidermis. The latter provides an internal control for the functional integrity of the epidermis and nonspecific transgene silencing9. When control worms are infected by the fungus they fluoresce green. Knocking down by RNAi a gene required for nlp 29 expression results in diminished fluorescence after infection. Currently, this protocol allows more than 3,000 RNAi clones to be tested and analyzed per week, opening the possibility of screening the entire genome in less than 2 months.

Quantitative and Automated High-throughput Genome-wide RNAi Screens in C. elegans

We describe a protocol using C. elegans and RNAi feeding libraries that allows automated measurement of multiple parameters such as fluorescence, size and opacity of individual worms in a population. We give one example of a screen to identify genes involved in anti-fungal innate immunity in C. elegans.

RNA interference is a powerful method to understand gene function, especially when conducted at a whole-genome scale and in a quantitative context. In C. ...

Fabrication and Use of MicroEnvironment microArrays (MEArrays)

The interactions between cells and their surrounding microenvironment have functional consequences for cellular behaviour. On the single cell level, distinct microenvironments can impose differentiation, migration, and proliferation phenotypes, and on the tissue level the microenvironment processes as complex as morphogenesis and tumorigenesis1. Not only do the cell and molecular contents of microenvironments impact the cells within, but so do the elasticity2 and geometry3 of the tissue. Defined as the sum total of cell-cell, -ECM, and -soluble factor interactions, in addition to physical characteristics, the microenvironment is complex. The phenotypes of cells within a tissue are partially due to their genomic content and partially due to the combinatorial interactions with the microenviroment. A major challenge is to link specific combinations of microenvironmental components with distinctive behaviours.
Here, we present the microenvironment microarray (MEArray) platform for cell-based functional screening of interactions with combinatorial microenvironments4. The method allows for simultaneous control of the molecular composition and the elastic modulus, and combines the use of widely available microarray and micropatterning technologies. MEArray screens require as few as 10,000 cells per array, which facilitates functional studies of rare cell types such as adult progenitor cells. A limitation of the technology is that entire tissue microenvironments cannot be completely recapitulated on MEArrays. However, comparison of responses in the same cell type to numerous related microenvironments, for instance pairwise combinations of ECM proteins that characterize a given tissue, will provide insights into how microenvironmental components elicit tissue-specific functional phenotypes.
MEArrays can be printed using a wide variety of recombinant growth factors, cytokines, and purified ECM proteins, and combinations thereof. The platform is limited only by the availability of specific reagents. MEArrays are amenable to time-lapsed analysis, but most often are used for end point analyses of cellular functions that are measureable with fluorescent probes. For instance, DNA synthesis, apoptosis, acquisition of differentiated states, or production of specific gene products are commonly measured. Briefly, the basic flow of an MEArray experiment is to prepare slides coated with printing substrata and to prepare the master plate of proteins that are to be printed. Then the arrays are printed with a microarray robot, cells are allowed to attach, grow in culture, and then are chemically fixed upon reaching the experimental endpoint. Fluorescent or colorimetric assays, imaged with traditional microscopes or microarray scanners, are used to reveal relevant molecular and cellular phenotypes (Figure 1).

Fabrication and Use of MicroEnvironment microArrays MEArrays

A combinatorial functional screening method for gaining insights into the impacts of the molecular composition of microenvironments on cellular functions is described. The method takes advantage of existing microarray-based technologies to generate arrays of defined combinatorial microenvironments that support cell adhesion and functional analysis.

The interactions between cells and their surrounding microenvironment have functional consequences for cellular behaviour. On the single cell level, ...

Glycan Profiling of Plant Cell Wall Polymers using Microarrays

Plant cell walls are complex matrixes of heterogeneous glycans which play an important role in the physiology and development of plants and provide the raw materials for human societies (e.g. wood, paper, textile and biofuel industries)1,2. However, understanding the biosynthesis and function of these components remains challenging.
Cell wall glycans are chemically and conformationally diverse due to the complexity of their building blocks, the glycosyl residues. These form linkages at multiple positions and differ in ring structure, isomeric or anomeric configuration, and in addition, are substituted with an array of non-sugar residues. Glycan composition varies in different cell and/or tissue types or even sub-domains of a single cell wall3. Furthermore, their composition is also modified during development1, or in response to environmental cues4.
In excess of 2,000 genes have Plant cell walls are complex matrixes of heterogeneous glycans been predicted to be involved in cell wall glycan biosynthesis and modification in Arabidopsis5. However, relatively few of the biosynthetic genes have been functionally characterized 4,5. Reverse genetics approaches are difficult because the genes are often differentially expressed, often at low levels, between cell types6. Also, mutant studies are often hindered by gene redundancy or compensatory mechanisms to ensure appropriate cell wall function is maintained7. Thus novel approaches are needed to rapidly characterise the diverse range of glycan structures and to facilitate functional genomics approaches to understanding cell wall biosynthesis and modification.
Monoclonal antibodies (mAbs)8,9 have emerged as an important tool for determining glycan structure and distribution in plants. These recognise distinct epitopes present within major classes of plant cell wall glycans, including pectins, xyloglucans, xylans, mannans, glucans and arabinogalactans. Recently their use has been extended to large-scale screening experiments to determine the relative abundance of glycans in a broad range of plant and tissue types simultaneously9,10,11.
Here we present a microarray-based glycan screening method called Comprehensive Microarray Polymer Profiling (CoMPP) (Figures 1 &amp; 2)10,11 that enables multiple samples (100 sec) to be screened using a miniaturised microarray platform with reduced reagent and sample volumes. The spot signals on the microarray can be formally quantified to give semi-quantitative data about glycan epitope occurrence. This approach is well suited to tracking glycan changes in complex biological systems12 and providing a global overview of cell wall composition particularly when prior knowledge of this is unavailable.

A technique called Comprehensive Microarray Polymer Profiling (CoMPP) for the characterisation of plant cell wall glycans is described. This method combines the specificity of monoclonal antibodies directed to defined glycan-epitopes with a miniature microarray analytical platform allowing screening of glycan occurrence in a broad range of biological contexts.

Plant cell walls are complex matrixes of heterogeneous glycans which play an important role in the physiology and development of plants and provide the ...

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

ChIPseq is a widely used technique for investigating protein-DNA interactions. Read density profiles are generated by using next-sequencing of protein-bound DNA and aligning the short reads to a reference genome. Enriched regions are revealed as peaks, which often differ dramatically in shape, depending on the target protein1. For example, transcription factors often bind in a site- and sequence-specific manner and tend to produce punctate peaks, while histone modifications are more pervasive and are characterized by broad, diffuse islands of enrichment2. Reliably identifying these regions was the focus of our work.
Algorithms for analyzing ChIPseq data have employed various methodologies, from heuristics3-5 to more rigorous statistical models, e.g. Hidden Markov Models (HMMs)6-8. We sought a solution that minimized the necessity for difficult-to-define, ad hoc parameters that often compromise resolution and lessen the intuitive usability of the tool. With respect to HMM-based methods, we aimed to curtail parameter estimation procedures and simple, finite state classifications that are often utilized.
Additionally, conventional ChIPseq data analysis involves categorization of the expected read density profiles as either punctate or diffuse followed by subsequent application of the appropriate tool. We further aimed to replace the need for these two distinct models with a single, more versatile model, which can capably address the entire spectrum of data types.
To meet these objectives, we first constructed a statistical framework that naturally modeled ChIPseq data structures using a cutting edge advance in HMMs9, which utilizes only explicit formulas-an innovation crucial to its performance advantages. More sophisticated then heuristic models, our HMM accommodates infinite hidden states through a Bayesian model. We applied it to identifying reasonable change points in read density, which further define segments of enrichment. Our analysis revealed how our Bayesian Change Point (BCP) algorithm had a reduced computational complexity-evidenced by an abridged run time and memory footprint. The BCP algorithm was successfully applied to both punctate peak and diffuse island identification with robust accuracy and limited user-defined parameters. This illustrated both its versatility and ease of use. Consequently, we believe it can be implemented readily across broad ranges of data types and end users in a manner that is easily compared and contrasted, making it a great tool for ChIPseq data analysis that can aid in collaboration and corroboration between research groups. Here, we demonstrate the application of BCP to existing transcription factor10,11 and epigenetic data12 to illustrate its usefulness.

Our Bayesian Change Point (BCP) algorithm builds on state-of-the-art advances in modeling change-points via Hidden Markov Models and applies them to chromatin immunoprecipitation sequencing (ChIPseq) data analysis. BCP performs well in both broad and punctate data types, but excels in accurately identifying robust, reproducible islands of diffuse histone enrichment.

ChIPseq is a widely used technique for investigating protein-DNA interactions. Read density profiles are generated by using next-sequencing of ...

Genome-wide Gene Deletions in Streptococcus sanguinis by High Throughput PCR

Transposon mutagenesis and single-gene deletion are two methods applied in genome-wide gene knockout in bacteria 1,2. Although transposon mutagenesis is less time consuming, less costly, and does not require completed genome information, there are two weaknesses in this method: (1) the possibility of a disparate mutants in the mixed mutant library that counter-selects mutants with decreased competition; and (2) the possibility of partial gene inactivation whereby genes do not entirely lose their function following the insertion of a transposon. Single-gene deletion analysis may compensate for the drawbacks associated with transposon mutagenesis. To improve the efficiency of genome-wide single gene deletion, we attempt to establish a high-throughput technique for genome-wide single gene deletion using Streptococcus sanguinis as a model organism. Each gene deletion construct in S. sanguinis genome is designed to comprise 1-kb upstream of the targeted gene, the aphA-3 gene, encoding kanamycin resistance protein, and 1-kb downstream of the targeted gene. Three sets of primers F1/R1, F2/R2, and F3/R3, respectively, are designed and synthesized in a 96-well plate format for PCR-amplifications of those three components of each deletion construct. Primers R1 and F3 contain 25-bp sequences that are complementary to regions of the aphA-3 gene at their 5' end. A large scale PCR amplification of the aphA-3 gene is performed once for creating all single-gene deletion constructs. The promoter of aphA-3 gene is initially excluded to minimize the potential polar effect of kanamycin cassette. To create the gene deletion constructs, high-throughput PCR amplification and purification are performed in a 96-well plate format. A linear recombinant PCR amplicon for each gene deletion will be made up through four PCR reactions using high-fidelity DNA polymerase. The initial exponential growth phase of S. sanguinis cultured in Todd Hewitt broth supplemented with 2.5% inactivated horse serum is used to increase competence for the transformation of PCR-recombinant constructs. Under this condition, up to 20% of S. sanguinis cells can be transformed using ~50 ng of DNA. Based on this approach, 2,048 mutants with single-gene deletion were ultimately obtained from the 2,270 genes in S. sanguinis excluding four gene ORFs contained entirely within other ORFs in S. sanguinis SK36 and 218 potential essential genes. The technique on creating gene deletion constructs is high throughput and could be easy to use in genome-wide single gene deletions for any transformable bacteria.

Genome-wide Gene Deletions in Streptococcus sanguinis by High Throughput PCR

An efficient genome-wide single gene mutation method has been established using Streptococcus sanguinis as a model organism. This method has achieved via high throughput recombinant PCRs and transformations.

Transposon  mutagenesis and single-gene deletion are two methods applied in genome-wide  gene knockout in bacteria 1,2. Although transposon mutagenesis is ...

Polysome Fractionation and Analysis of Mammalian Translatomes on a Genome-wide Scale

mRNA translation plays a central role in the regulation of gene expression and represents the most energy consuming process in mammalian cells. Accordingly, dysregulation of mRNA translation is considered to play a major role in a variety of pathological states including cancer. Ribosomes also host chaperones, which facilitate folding of nascent polypeptides, thereby modulating function and stability of newly synthesized polypeptides. In addition, emerging data indicate that ribosomes serve as a platform for a repertoire of signaling molecules, which are implicated in a variety of post-translational modifications of newly synthesized polypeptides as they emerge from the ribosome, and/or components of translational machinery. Herein, a well-established method of ribosome fractionation using sucrose density gradient centrifugation is described. In conjunction with the in-house developed &#8220;anota&#8221; algorithm this method allows direct determination of differential translation of individual mRNAs on a genome-wide scale. Moreover, this versatile protocol can be used for a variety of biochemical studies aiming to dissect the function of ribosome-associated protein complexes, including those that play a central role in folding and degradation of newly synthesized polypeptides.

Ribosomes play a central role in protein synthesis. Polyribosome (polysome) fractionation by sucrose density gradient centrifugation allows direct determination of translation efficiencies of individual mRNAs on a genome-wide scale. In addition, this method can be used for biochemical analysis of ribosome- and polysome-associated factors such as chaperones and signaling molecules.

mRNA translation plays a central role in the regulation of gene expression and represents the most energy consuming process in mammalian cells. ...

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay (PCA) in Living Cells

Proteins are the building blocks, effectors and signal mediators of cellular processes. A protein&#8217;s function, regulation and localization often depend on its interactions with other proteins. Here, we describe a protocol for the yeast protein-fragment complementation assay (PCA), a powerful method to detect direct and proximal associations between proteins in living cells. The interaction between two proteins, each fused to a dihydrofolate reductase (DHFR) protein fragment, translates into growth of yeast strains in presence of the drug methotrexate (MTX). Differential fitness, resulting from different amounts of reconstituted DHFR enzyme, can be quantified on high-density colony arrays, allowing to differentiate interacting from non-interacting bait-prey pairs. The high-throughput protocol presented here is performed using a robotic platform that parallelizes mating of bait and prey strains carrying complementary DHFR-fragment fusion proteins and the survival assay on MTX. This protocol allows to systematically test for thousands of protein-protein interactions (PPIs) involving bait proteins of interest and offers several advantages over other PPI detection assays, including the study of proteins expressed from their endogenous promoters without the need for modifying protein localization and for the assembly of complex reporter constructs.

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay PCA in Living Cells

Proteins interact with each other and these interactions determine in a large part their functions. Protein interaction partners can be identified at high-throughput in vivo using a yeast fitness assay based on the dihydrofolate reductase protein-fragment complementation assay (DHFR-PCA).

Proteins are the building blocks, effectors and signal mediators of cellular processes. A protein&#8217;s function, regulation and localization often ...