Recent Work & Current Methods in Plant microRNA Research

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• Brief review of microRNA basics: history, biogenesis, function • Recent developments of microRNA research in the field of plant genomics • Current methods for microRNA discovery and profiling • Case studies and application examples

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Plant microRNA Webinar Recent Work & Current Methods In Plant microRNA Research Christoph Eicken , PhD Head of Technical Services – Microarrays Julie Lang , PhD Technical Director, LC-Bio 2012-06-21

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Agenda Recent Studies Current methods Q & A Case Studies miRNA Intro

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Global - Genomics & Proteomics Services Provider Headquarters in Houston, Texas - USA Offices in USA & China Representatives in Japan , Korea, & India Extensive Experience Providing services since 2005 Processed > 12,000 samples Primary Technological Advantage μParaflo ® Microfluidics Technology - customizable Results Diverse customer base (> 1400 institutions, > 40 countries) >300 customer publications Excellent reputation in marketplace Worldwide sales Company Overview 3

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Transcriptomics microRNA Profiling Microarray Services microRNA Discovery Sequencing Services Seq- Array SM Method Proteomics Phosphopeptide Binding Microarray Service Protein- Protein Interaction Microarray Service Kinase Profiling Microarray Service 5 µ Paraflo ® Microfluidic Technology High Density – On Chip Parallel Synthesis Customizable – DNA, RNA, Peptides, and Analogs Quality Data – Microfluidics / Synthesis Chemistry

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A ll miRNAs are small non-coding RNAs, usually consisting of ∼20–22 nucleotides for animals and ∼20–24 nt for plants. All miRNA precursors have a well-predicted stem loop hairpin structure, and this fold-back hairpin structure has a low free energy Many miRNAs are evolutionarily conserved, some from worm to human, or from ferns to core eudicots or monocots in plants Bind to complementary mRNA molecules and act as negative regulators of translation High copy number Expression is tissue (and developmental stage) specific microRNAs - What W e Know

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microRNAs - What W e Know “Based on the sheer abundance and diversity of plant miRNAs, it is likely that most, if not all, biological processes in plants involve at some point the action of one or more miRNAs.” Voinnet O. 2009 Origin, biogenesis, and activity of plant microRNAs. Cell 136(4):669-87.[ article ] Currently - 21643 mature miRNAs across 168 plant, animal, and virus species ( miRBase 18, Nov. 2011 ). Mechanism is far reaching and complex – each miRNA may control many genes and it is estimated that miRNAs regulate expression of up to 1/3 of all human genes. Operate by one of two hypothesized mechanisms : – Complete pairing mRNA is degraded - predominant in plants – Imperfect pairing translation is repressed but mRNA remains intact - predominant in animals

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1993 - Lin-4 was shown to encode two small RNA molecules (not protein) and control developmental timing in C. elegans through negative regulation of lin-14 gene. Lee RC et al. 1993 The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell 75(5):843-54. [ article ] Challenge to the Central Dogma of Biology DNA > transcription > RNA > translation > protein 2000 – Let-7 was identified in humans and Drosophila . (Reinhart et al., Slack et al.) 2001 – Bartel , Tuschl , Ambros - Discover large class of small regulatory RNAs , name them microRNA ( miRNA ) Lau NC et al. 2001 An abundant class of tiny RNAs with probable regulatory roles in Caenorhabditis elegans . Science 294(5543):858-62 . [ abstract ] Lagos-Quintana M et al. 2001 Identification of novel genes coding for small expressed RNAs . Science 294(5543):853-8 . [ abstract ] Lee RC et al. 2001 An extensive class of small RNAs in Caenorhabditis elegans . Science 294(5543):862-4. [ abstract ] 2001 – Bartel Lab - Discovers microRNA in plants Reinhart BJ et al. 2002 MicroRNA in Plants. Genes Dev 16(13):1616-26. [ article ] Rhoades MW et al. 2002 Prediction of plant microRNA targets. Cell 110(4):513-20. [ article ] Described 16 Arabidopsis miRNAs . 8 are conserved in rice genome . Milestones

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2002 – microRNA biogenesis pathway in plants (Arabidopsis) is revealed Reinhart BJ et al. 2002 MicroRNA in Plants. Genes Dev 16(13):1616-26. [ article ] Park W et al. 2002 CARPEL FACTORY, a Dicer homolog, and HEN1, a novel protein, act in microRNA metabolism in Arabidopsis thaliana. Curr Biol 12(17):1484-95. [ article ] 2003 – Reinhart, Bartel , Zamore describe microRNA silencing mechanism in plants Tang G et al. 2003 A biochemical framework for RNA silencing in plants. Genes Dev 17(1):49-63. [ article ] 2004 – miRNA link to Neuroscience 2005 – The Arabidopsis Small RNA Project begins Gustafson AM et al. 2005 ASRP: the Arabidopsis Small RNA Project Database. NAR 33(Database issue):D637-40 . [ article ] The ASRP database provides a repository for sequences of small RNAs cloned from various Arabidopsis genotypes and tissues. http://asrp.cgrb.oregonstate.edu/ 2005 – Next-Gen Sequencing is used for small RNA discovery and analysis Green Lab - uses Solexa (now Illumina ) sequencing to identify novel small RNAs in Arabidopsis plants Lu C et al. 2005 Elucidation of the small RNA component of the transcriptome . Science, 309: 1567–1569 . [ abstract ] Milestones

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2005 – Three studies link microRNAs with cancer 2006 – microRNA linked with Heart Disease 2006 – miRBase goes online at Sanger Institute Depository for experimentally verified microRNAs: http ://www.mirbase.org / Griffiths-Jones S et al. 2006 miRBase: microRNA sequences, targets and gene nomenclature. NAR 34(Database Issue):D140-D144 . [ article ] 2008 – Plant microRNA annotation standardized Meyers BC et al. 2008 Criteria for Annotation of Plant microRNAs . Plant Cell 20(12): 3186-90. [ article ] 2010 - Plant microRNA database goes online at China Agricultural University integrates available plant miRNA data deposited in public databases, gleaned from the recent literature, and data generated in-house includes predicted sequences http ://bioinformatics.cau.edu.cn/PMRD / Zhang Z et al. 2010 PMRD: plant microRNA database. NAR 38 (Database issue): D806-D813. [ article ] Milestones

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pri-miRNA = primary microRNA transcript pre- miRNA = precursor microRNA miRNA * = antisense microRNA (now -3p or -5p) miRISC = microRNA-induced silencing complex For latest information regarding miRNA nomenclature see the miRBase.org blog . miRNA Processing

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miRNA Biogenesis Pathway (A) Animal and (B) plant miRNA biogenesis. Mature miRNAs are indicated in red and miRNA* strands are in blue. Du T, Zamore . PD 2005 microPrimer : the Biogenesis and Function of microRNA. Development 132: 4645-4652. [ article ]

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miRNA in P lants – Unique F eatures Near-perfect complementarity - The computational identification of miRNA targets in plants is relatively more straightforward than in animals, In plants, most miRNAs have perfect or near perfect complementarity to their mRNA targets. Upon binding to their mRNA targets, the miRNA-containing RISCs function as endonucleases , cleaving the mRNA some specific characteristics of plant miRNAs, such as the variability in length of miRNA precursors (invalidating a fixed-window approach), differences in G+C content and lower sequence conservation of precursors (Based on an initial analysis).

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miRNA function in plants miRNAs regulate plant development including: leaf development, floral development, vegetative phase change, shoot and root development and vascular development miRNAs are involved in signal transduction miRNAs are involved in plant disease and resistance miRNAs are involved in environmental stress responses : a variety of biotic and abiotic environmental stresses miRNAs regulate miRNA and siRNA biogenesis and function

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Plant miRNAs in miRBase V6.1 - 9 species  V18 - 51 species miRBase: microRNA sequences, targets and gene nomenclature. Griffiths-Jones S et al. NAR 2006 34(Database Issue): D140-D144 [ article ]

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PMRD Plant microRNA Database PMRD integrates available plant miRNA data deposited in public databases ( miRBase ), gleaned from the recent literature, and data generated in-house. PMRD contains: sequence information secondary structure target genes expression profiles genome browser. Currently 127 species – 10,122 entries Latest Update: June 11, 2012 http://bioinformatics.cau.edu.cn/PMRD / Zhang Z et al. 2009 PMRD: plant microRNA database NAR 38(Database issue):D806-D813 . [ article ]

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Computational Tools for Plant microRNA Data Analysis Semirna a tool for predicting miRNAs in plant genomes Muñoz-Mérida et al., 2012 http://www.bioinfocabd.upo.es/semirna/ miRDeepFinder a miRNA analysis tool for deep sequencing of plant small RNAs Xie et al., 2012 http://www.leonxie.com/DeepFinder.php p-TAREF (plant-Target Refiner) a Support Vector Regression (SVR) approach for plant miRNA target identification Jha et al., 2011 http://sourceforge.net/projects/ptaref/ psRNATarget a plant small RNA target analysis server Dai et al., 2011 http://plantgrn.noble.org/psRNATarget/ miRDeep-P a computational tool for analyzing the microRNA transcriptome in plants Yang et al., 2011 http://faculty.virginia.edu/lilab/miRDP/ Too many to list – Download it here: http://www.lcsciences.com/documents/plant-computational-tools.pdf

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18 MicroRNAs responsive to biotic and abiotic stresses in diverse plant species Sunkar R, Li YF, Jagadeeswaran G. 2012 Functions of microRNAs in plant stress responses. Trends Plant Sci 17(4):196-203. [ abstract ] 2012 Review: Functions of microRNAs in plant stress responses The discovery of microRNAs ( miRNAs ) as gene regulators has led to a paradigm shift in the understanding of post-transcriptional gene regulation in plants and animals. miRNAs have emerged as master regulators of plant growth and development. Evidence suggesting that miRNAs play a role in plant stress responses arises from the discovery that miR398 targets genes with known roles in stress tolerance. In addition, the expression profiles of most miRNAs that are implicated in plant growth and development are significantly altered during stress. These later findings imply that attenuated plant growth and development under stress may be under the control of stress-responsive miRNAs .

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19 MicroRNAs responsive to biotic and abiotic stresses in diverse plant species Sunkar R, Li YF, Jagadeeswaran G. 2012 Functions of microRNAs in plant stress responses. Trends Plant Sci 17(4):196-203. [ abstract ] Abbreviations: Ath , Arabidopsis thaliana; Bdi , Brachypodium distachyon ; Gma , Glycine max; Hvu , Hordeum vulgare ; Mtr , Medicago truncatula ; Mes , Manihot esculenta ; nd , not determined or no change, Pvu , Phaseolus vulgaris ; Peu , Populus euphratica ; Ptc , Populus trichocarpa; Pte : Populus tremula ; Ttu , Triticum turgidum ; Osa , Oryza sativa; Vun , Vigna unguiculata ; Zma , Zea mays; ↑, upregulated; ^, initially upregulated then returned to basal level; ↓, downregulated; ↑&↓, some members were upregulated, some were downregulated. miRNA Biotic Drought Salt Cold Heat ABA Oxidative Hypoxia UV B miR156 nd Ath ↑, Ttu ↑, Hvu ↑, leaf, Osa ↓ Peu ↑ Ath ↑, Vun ↑, Zma ↓ Ptc↓, Mesa Tae↑ nd nd Ath↑ Ath↑, Pte↑ miR159 Ath ↑ Ath ↑, Peu ↑ Ath ↑ Mesa Tae↑ Ath↑ nd Ath↑, Zmac↑ Ath↑, Pte↓ miR160 Ath ↑ Mesa, Peu ↑ Vun ↑ Mesa Tae↑ Ath↑ nd Zmac Ath↑, Pte↑ miR162 nd Peu ↑ Zma ↑, Vun ↑ Mesa nd nd nd Zma^ nd miR165/miR166 Ath ↑ Hvu ↑ leaf, Ttu ↓, Hvu ↓ root, Mes a , Gma b , Peu ↑ & ↓ nd Ath ↑, Mesa Tae↑ nd nd Zma^ Ath↑, Pte↑ miR167 Ath↑ Ath ↑, Mesa, Peu ↑ Ath ↑, Zma ↓ Osa ↓ nd Osa↓ nd Zma^ Ath↑, Pte↑ miR168 nd Ath↑, Osa↓, Peu↓ Ath ↑, Zma ↑, Vun ↑ Ptc ↑, Ath ↑ Tae↑ nd nd Zma ^ Ath ↑ Pte ↑ miR169 nd Ath↓, Osa↑, Mtr↓, Peu↑ Ath ↑, Zma ↑, Vun ↑, Osa ↑ Ath↑, Bdi↑ Tae↑ Ath↓, Osa↓ Osa↑ Ath↑ & ↓ Ath↑, Pte↓ miR170/miR171 nd Ath↑, Hvu↑ leaf, Ttu↓, Osa↑ & ↓, Peu↑ & ↓ Ath ↑, Ptc ↓ Mesa, Ptc↓, Ath↑, Osa↓ Ptc ↓ nd nd Zma↑ Ath↑ miR172 nd Osa↓, Peu↑ & ↓ nd Ath↑, Bdi↑ Tae↓ nd nd Ath↑ Ath↑ miR390 Ath↓ Peu↓ nd nd nd nd nd Ath↑ nd miR319 Ath↑ Ath↑, Osa↑ & ↓ Ath↑ Ath ↑, Osa ↓ nd Ath ↑ Osa ↑ nd nd nd 0 0 Peu↑&↓ 0 0 0 0 0 0 0 miR393 Ath↑ Ath↑, Osa↑, Mtr↑, Pvu↑, Peu↑ & ↓ Ath↑, Pvu↑, Osa↓ Ath ↑ Tae↑ Ath↑ Pvu↑ nd nd Ath↑, Pte↓ miR395 nd Peu↑ & ↓, Osa↑ Zma↑ Mesa nd nd nd Zmac nd miR396 nd Ath↑, Osa↓, Ttu↓, Peu↑ & ↓ Ath↑, Osa↓, Zma↓ Ath↑, Mesa nd nd nd Zma ↑ nd miR397 nd Ath↑, Osa↓, Gmab, Peu↑ Ath↑ Ath↑, Bdi↑, Mesa nd Ath↑ Osa ↑ nd nd miR398 Ath↓ Mtr↑, Ttu↑, Peu↑ Ath↓, Ptec Ath↓ nd Ath↓ c , Pte c Ath↓ nd Ath ↑, Pte ↑ miR408 Ath↓ Ath↑, Mtr↑, Hvu↑, Osa↓ Vun↑ Ath↑ nd nd nd nd nd

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20 Group from Nanjing University demonstrate that that exogenous plant miRNAs in food can regulate the expression of target genes in mammals Zhang L et al. 2012 Exogenous plant MIR168a specifically targets mammalian LDLRAP1: evidence of cross-kingdom regulation by microRNA. Cell Res 22(1):107-26. [ article ] Recent Work – 2011/12: Discussion: http:// biologyfiles.fieldofscience.com/2012/01/why-did-atlantic-publish-this-piece.html Cross Kingdom Regulation (?)

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21 Jelly NS, Schellenbaum P, Walter B, Maillot P. 2012 Transient expression of artificial microRNAs targeting Grapevine fanleaf virus and evidence for RNA silencing in grapevine somatic embryos. Transgenic Res [Epub ahead of print]. [ abstract ] Kung YJ, Lin SS, Huang YL, Chen TC, Harish SS, Chua NH, Yeh SD. 2012 Multiple artificial microRNAs targeting conserved motifs of the replicase gene confer robust transgenic resistance to negative-sense single-stranded RNA plant virus. Mol Plant Pathol 13(3):303-17. [ abstract ] Fahim M, Millar AA, Wood CC, Larkin PJ. 2102 Resistance to Wheat streak mosaic virus generated by expression of an artificial polycistronic microRNA in wheat. Plant Biotechnol J 10(2):150-63. [ abstract ] Ai T, Zhang L, Gao Z, Zhu CX, Guo X. 2011 Highly efficient virus resistance mediated by artificial microRNAs that target the suppressor of PVX and PVY in plants. Plant Biol ( Stuttg ) 13(2):304-16. [ abstract ] Zhang X, Li H, Zhang J, Zhang C, Gong P, Ziaf K, Xiao F, Ye Z. 2011 Expression of artificial microRNAs in tomato confers efficient and stable virus resistance in a cell-autonomous manner. Transgenic Res 20(3):569-81. [ abstract ] Recent Work – 2011/12 Use of Artificial MicroRNAs for Plant Virus Resistance Recently, microRNAs (miRNAs), have been exploited to engineer virus resistance in plants. Expression of modified miRNA precursors results in the production of artificial miRNAs ( amiRNAs ) targeting viral RNA sequences. The amiRNA -mediated virus resistance is efficient and superior to the long viral RNA-based antiviral approaches in that properly selected amiRNA sequences would have little chance to target the host plant genes or to complement or recombine with other invading viruses.

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Seq-Array SM ACGT101-miR Stem-Loop Specific 22 Target Determination Detection & Profiling Functional Analysis Northern Blotting In situ hybridization Real-time PCR Microarray analysis Next-gen Sequencing Bioinformatics Gene / proteome expression analysis Pull-down assays 5′ RACE analyses Degradome Sequencing Lucifierase Assays Gene knockout/overexpression models miRNA inhibition - antagomirs miRNA mimicry Degradsome Seq Digital Gene Expression Pathway Network miRNA Identification Genetic screening Direct cloning, sequencing Computational strategy – MIRCheck , findMiRNA , MIRscan , MiRAlign Tiling Microarrays Next-gen Sequencing Pathway Analysis Bioinformatics microRNA Research Tools

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miRNA microarray qRT -PCR custom miRNA microarray Next Gen Sequencing Discovery Profiling Quantitation Validation 3 Major Steps & Technologies } Seq-Array SM

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24 Sample Preparation Cell Line Tissue Blood Serum Plasma FFPE Block Algea Plant Material Fatty Tissue (Viscous Samples) Total RNA (1-4 µg) Norgen Biotek Ambion Qiagen S elect a kit designed to retain small RNA Select kit based on your sample type Use the same kit for all miRVana Kit miRNeasy Kit Total RNA Extraction Kit Urine

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You can check the UV spectrum of your sample with a spectrophotometer. 25 ↑ 1.0 ↑ 1.8 260nm 230nm 260nm 280nm Bioanalyzer or 1-1.5% agarose gel 28S rRNA band at 4.5kb - ~2X intensity 18S rRNA band at 1.9kb. For Average Cell Line or Tissue sample – RIN number must be ↑ 7 For other sample types such as Blood or Plant – RIN number does not apply Excessive smearing on the gel indicates degraded RNA. Customer Sample Quality Control

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26 Customer Sample Quality Control microRNA Microarray Expression Profiling Good Poor Good Poor Good Poor Failure in recovery of RNA <200 nt (including microRNA) 1.5% Formaldehyde Agarose Gel Agilent BioAnalyzer Gel Image Urea-PAGE Gel

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27 Biological Replicates – Still Very Important For experiments performed with a small number of biological replicates, significant results may be due to biological diversity between individuals and may not be reproducible - it is impossible to know whether expression patterns are specific to the individuals in the study or are a characteristic of the test condition. There is no statistical significance for a difference observed between 2 samples. There is no magic to RNA-Seq. These ideas are widely accepted for DNA microarray experiments, where a large number of biological replicates are now required to justify scientific conclusions . Hansen KD, Wu Z, Irizarry RA, Leek JT.  2011 Sequencing technology does not eliminate biological variability. Nat Biotechnol 29:572–573. [ abstract ] Reg. Experimental Design Sample Replicates for Expression Studies

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Key Advantages of RNA-Seq Provides a comprehensive view of the transcriptome. All transcripts can be analyzed (mRNA, ncRNA, snoRNA, lncRNA , miRNA, ...). Not necessarily dependent on any prior sequence knowledge. Increased dynamic range and tunable sensitivity . Can detect structural variations such as gene fusions and alternative splicing events. A truly digital solution (absolute abundance vs relative abundance). Microarray vs RNA Sequencing 28 Key Advantages of Microarray Robust, reliable method, proven over decades of use High through-put method – 100s of samples analyzed per month Streamlined handling – can be easily automated Straightforward data analysis Short turn-around time – 5 days Lower cost

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Microfluidic Array Platform μ Paraflo ® Microfluidics Chip 10 µl total volume 4000 features 270 pl / reaction chamber uniform flow rate

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miRBase Version # of sequences (all species) Flexibility Allows miRBase Synchronicity Version 18 Nov 2011 2003 2011

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31 Comprehensive Microarray Services Sample QC Sample preparation Hybridization reactions Advanced data analysis High level customer support Customer Total RNA Small RNA Isolation Labeling Customer Sequences Chip Design Chip Synthesis Chip QC Hybridization Signal Amplification Image Acquisition Customer Analysis Report Data Extraction Data Analysis Sample QC miRBase microRNA Microarray Expression Profiling

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Control / Treated Biological repeats t-Test p < 0.05 p < 0.01 Control Treated Multi-array normalization and clustering analysis Array assay Differentiated miRNAs of Biological & Statistical Significance - Multiple Chips 32 microRNA Microarray Expression Profiling

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Repeats microRNA Microarray Expression Profiling

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34 Advanced Data Analysis Package Includes: The original and processed chip images An array layout file A raw intensity data file in Excel A fully analyzed data file in Excel A Data Summary containing a catalog of data files, images Images of representative regions of corresponding arrays Descriptions of specific features of the arrays A list of up and down regulated transcripts that are called based on a statistical analysis. microRNA Microarray Expression Profiling

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Key Advantages of RNA-Seq Provides a comprehensive view of the transcriptome. All transcripts can be analyzed (mRNA, ncRNA, snoRNA, lncRNA , miRNA, ...). Not necessarily dependent on any prior sequence knowledge. Increased dynamic range and tunable sensitivity . Can detect structural variations such as gene fusions and alternative splicing events. A truly digital solution (absolute abundance vs relative abundance). Microarray vs RNA Sequencing 35 Key Advantages of Microarray Robust, reliable method, proven over decades of use High through-put method – 100s of samples analyzed per month Streamlined handling – can be easily automated Straightforward data analysis Short turn-around time – 5 days Lower cost

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36 Small RNA Sequencing and Data Analysis LC Sciences - Comprehensive RNA Sequencing Services Sample QC Sample preparation Library preparation High-throughput sequencing Advanced bioinformatics analysis High level customer support

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37 From Illumina® TruSeq ™ Small RNA Sample Preparation Guide, Rev. C, March 2011 Sample Preparation Cluster Generation From Illumina® Sequencing Technology Guide, Oct 2010 Small RNA Sequencing and Data Analysis

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38 Sequencing Run Instrument: Illumina Genome Analyzer GAIIx Length of Reads: 35 bases Number of Reads: ~20-30 Million Data Output: ~0.7-1.0 Gb Bar-coding (Indexing) Samples: We recommend 3 per lane, Max is 6 per lane The total number of reads does not change with bar-coding Sacrifice sequencing depth for lower cost Total Reads / Lane Number of Samples / Lane Reads/ Sample 30 M 1 30 M 30 M 2 15 M 30 M 3 10 M 30 M 4 7.5 M 30 M 5 6 M 30 M 6 5 M Small RNA Sequencing and Data Analysis

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39 Basic Data Package Includes: Illumina base-calling and analysis LC Sciences analysis and quality filtering - processed data is reduced to mappable reads Customer data report - includes a list of unique sequences and their copy numbers Advanced Bioinformatics Package Includes: Custom construction of reference database(s) - miRBase, genome, etc and mapping of all quality reads Alignment, classification, & functional annotation of all mapped reads Prediction of possible novel miRs Biostatistical analysis – expression analysis, multi-parameter data analysis, length distribution, transcript copy number comparisons, etc Small RNA Sequencing and Data Analysis

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Data Flow 40 Mappable reads Raw reads Reads mapped to plant mirs in miRbase Reads un-mapped to plant mirs in miRbase mirs mapped to species genome mirs un-mapped to species genome Reads mapped to species genome Reads un-mapped to species genome Reads un-mapped to mRNA, Rfam, and repbase Reads mapped to mRNA, Rfam, and repbase Reads mapped to species genome Reads un-mapped to species genome Group 4 no hit others Group 1 Group 2 Group 3 Known species miRNAs Known miRNAs candidate species miRNAs Candidate species miRNAs genome inconsistent with miRBase Potentially novel miRNAs ACGT101-miR v3.5 Software Small RNA Sequencing and Data Analysis

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41 19,842,938 reads are mappable ADT, Junk, & Seq Filter 11,426,638 reads are mapped to miRBase or are miRNA candidates 2,400,521 reads mapped to mRNA, Rfam and repbase 6,015,779 reads - no hit 10,713,874 reads are filtered out 30,556,812 raw reads from sequencer Grp 1 - 8,007,998 Grp 2 - 14,067 Grp 3 - 64,086 Grp 4 - 3,340,487 64.9% 19.7% 15.1% 0.3% 57.6% 30.3% 12.1% 70.1% 29.2% 0.1% 0.6% Data Flow Small RNA Sequencing and Data Analysis

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42 Length Distribution of Mappable Reads Small RNA Sequencing and Data Analysis

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Can explain discrepancies array data vs qRT-PCR validation 43 IsomiRs Small RNA Sequencing and Data Analysis

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44 Seq-Array SM is a combination of technologies that maximizes the effectiveness of each while overcoming the limitations of the other. Seq-Array SM provides an efficient pathway from initial broad miRNA search to focused biological insights Seq-Array SM is particularly suited for focused studies consisting of large sample numbers. Seq-Array SM is also useful for discovery (Seq) and validation (Array) of biomarkers of clinical significance. Seq-Array SM microRNA Discovery & Profiling Services Sequencing Microarray Comprehensive Genome-wide Sensitive Digital solution High-throughput Cost efficient Flexible Expression profiling Seq-Array SM

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45 Seq-Array SM Deep Sequencing Advanced Bioinformatics Custom Microarray Send a total RNA sample Deep sequencing is performed to generate a comprehensive atlas of all possible miRNAs for your species/system. In-house developed bioinformatics tools filter the raw sequencing data, map the quality reads to reference genomes or sequence databases if they exist, classify all mapped reads as known miRNAs, novel miRNAs, or other types of small RNAs, and predict possible novel miRNAs from unmapped reads. A custom microarray is designed based on the bioinformatics analysis of the above results and your specific research goals. Microarray expression profiling is performed on small or large sample groups by custom synthesized SeqArrays™ based on your unique design.

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46 Seq -Array SM Zhou et al. 2011 [ article ]- Barrel Medic ( Medicago truncatula ) 52 new miRNA candidates identified by sequencing 70 known miRNA mature sequences from miRBase (Release 14) 12 miRNAs were specifically induced by Hg(II ) exposure Li et al. 2011 [ article ] - Poplar Tree ( Populus euphratica ) 58 new miRNAs identified by sequencing 114 known mature P. trichocarpa miRNAs from miRBase (Release 13) 104 miRNA sequences were up-regulated 27 were down-regulated under drought stress Wong et al. 2011 [ article ] - Soybean ( Glycine max ) 8 putative novel miRNAs identified with sequencing known mature Soybean miRNAs from miRBase novel expression pattern of miR-390 in soybean Chen et al. 2010 [ abstract ] - Rice subspecies and their reciprocal hybrids. ( Oryza sativa – subspecies japonica cv. Nipponbare and indica cv. 93-11) 999 sequences ranging at 16-26nt chosen from small RNAs Illumina sequencing dataset 142 unique annotated miRNA from Rice miRBase 11.0 12% - 13% of miRNAs were identified as being significantly differentially expressed in two reciprocal hybrids

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Why study miRNAs in Plants? 47 microRNA Microarray Expression Profiling Basic Research / Discovery Studies Stress Response Studies Disease Specific Biomarkers

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Basic Research / Discovery Studies 48 microRNA Microarray Expression Profiling Soybean ( Glycene max ) – Shoot Apical Meristem – Identify miRNAs that may have regulatory roles in various developmental processes including in SAM during shoot development. Coconut ( Cocos nucifera ) – Endosperm – Identify miRNAs potentially involved in tissue development and compound anabolism. Rapeseed ( Brassica napus ), Pumpkin ( Cucurbita maxima) – Phloem, Phloem Sap – Determine if small RNAs involved in long-distance information transfer via the vasculature of the plant. Tomato ( Solanum lycopersicum ) – Fruit, Leaf – Identify miRNAs that may be associated with vegetative growth, generative growth and flower development. Rockcress ( Boechera sp. ) – Flower – Determine if miRNAs are involved in the switch from sexual to apomictic reproduction, a potentially important agronomic trait.

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Basic Research / Discovery Studies 49 By searching known miRNAs identified from plant species against tomato nucleotide sequences, 13 pre-miRNAs were predicted. To confirm the prediction, a miRNA-detecting microarray was designed with probes complementary to all non-redundant mature plant miRNAs documented to date. After hybridizing with small RNAs extracted from tomato leaf tissue, 78 highly expressed mature miRNAs were detected, including all the miRNAs predicted above. Identification of conserved microRNAs and their targets from Solanum lycopersicum Zhang J, Zeng R, Chen J, Liu X, Liao Q. 2008 Identification of conserved microRNAs and their targets from Solanum lycopersicum Mill. Gene 423(1):1-7. [ abstract ] microRNA Microarray Expression Profiling

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Bioinformatic analysis used to identify potential conserved miRNAs. Validation with a custom synthesized microarray containing all known plant miRNAs that were available in the miRBase Release 14 (total 1117 unique mature miRNAs) and the Plant miRNA Database, PMRD (total 5690 unique mature miRNAs) This study constitutes the first extensive insight into the conservation and expression of microRNAs in Boechera sexual and apomictic species. Basic Research / Discovery Studies 50 Analysis of conserved microRNAs in floral tissues of Boechera species Percentage of conserved miRNAs between Boechera and other plant species Ath, Arabidopsis thaliana; Ptc, Populus trichocarpa; Osa, Oryza sativa; Zma, Zea mays; Mtr, Medicago truncatula; Gma, Glycine max; Sbi, Sorghum bicolor; Vvi, Vitis vinifera; Ppt, Physcomitrella patens; Rco, Ricinus communis; Tae, Triticum aestivum; Ghr, Gossypium hirsutum; Sly, Solanum lycopersicum; Ahy, Arachis hypogaea; Sof, Saccharum officinarum . Amiteye S, Corral JM, Vogel H, Sharbel TF. 2011 Analysis of conserved microRNAs in floral tissues of sexual and apomictic Boechera species. BMC Genomics 12(1):500. [ article ] microRNA Microarray Expression Profiling

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Small RNA libraries from cucumber leaves and roots were constructed and sequenced with the high-throughput Illumina Solexa system. A total of 29 known miRNA families and 2 novel miRNA families containing a total of 64 miRNA were identified. QRT-PCR analysis revealed that some of the cucumber miRNAs were preferentially expressed in certain tissues. High throughput degradome sequencing identified 21 target mRNAs of known miRNAs for the first time in cucumber. These targets were associated with development, reactive oxygen species scavenging, signaling transduction and transcriptional regulation. Basic Research / Discovery Studies 51 Discovery of novel microRNAs & their targets in cucumber leaves and roots Novel cucumber miRNAs identified by high-throughput sequencing Mao W, Li Z, Xia X, Li Y, Yu J. 2012 A Combined Approach of High-Throughput Sequencing and Degradome Analysis Reveals Tissue Specific Expression of MicroRNAs and Their Targets in Cucumber. PLoS One 7(3), e33040. [ article ] Expression levels of cucumber miRNA families assessed using Illumina sequencing microRNA Sequencing

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Stress Response Studies 52 Biotic Stress Viral Infection Tomato response to Cucumber mosaic virus infection Squash response to Zucchini yellow mosaic virus infection Fungal Infection - Soybean resistance to Phytophthora sojae Bacterial Infection Abiotic Stress Salt – Maize Cold – Rice Submergence – Maize Drought – Wheat, Rice microRNA Microarray Expression Profiling

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Stress Response Studies 53 Nutrient Deprivation Phosphate Deprivation Sulfate Deprivation Copper Deprivation Nitrogen Deficiency – Maize Others Stresses Chemical Exposure – Festuca arundinacea – foliar glyphosate application Pollution Exposure – Medicago truncatula – heavy metal exposure microRNA Microarray Expression Profiling

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Biotic Stress: Tomato response to Cucumber mosaic virus infection Custom microarray of plant miRNAs was used to examine the expression of miRNAs in the leaves of tomato plants infected with Cucumber mosaic virus (CMV). The array design was based on 513 well-characterized miRNAs and 511 miRNA*s from Arabidopsis, rice, maize, sorghum, medick , sugarcane, and soybean. The design included 165 sequence-unique mature miRNAs and 365 sequence-unique miRNA*s. These results also show that, in accordance with the phenotype of the developing leaves, the tomato miRNAs are differentially expressed at different stages of plant development and that CMV infection can induce or suppress the expression of miRNAs as well as up-regulate some star miRNAs (miRNA*s) which are normally present at much lower levels. Lang QL, Zhou XC, Zhang XL, Drabek R, Zuo ZX, Ren YL, Li TB, Chen JS, Gao XL. 2011 Microarray-based identification of tomato microRNAs and time course analysis of their response to Cucumber mosaic virus infection. J Zhejiang Univ Sci B 12(2):116-125. [ abstract ] Differentially regulated miRNAs in CMV infected plants vs mock infection Stress Response Studies microRNA Microarray Expression Profiling

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55 Ding D, Zhang L, Wang H, Liu Z, Zhang Z, Zheng Y. 2009 Differential expression of miRNAs in response to salt stress in maize roots. Ann Bot ( Lond ) 103(1):29-38. [ article ] Stress Response Studies Abiotic Stress: Salt tolerance in maize roots miRNA microarray hybridization revealed that a total of 98 miRNAs, from 27 plant miRNA families, had significantly altered expression after salt treatment. 18 miRNAs were found which were only expressed in the salt-tolerant maize line, and 25 miRNAs that showed a delayed regulation pattern in the salt-sensitive line. A gene model was proposed that showed how miRNAs could regulate the abiotic stress-associated process and the gene networks coping with the stress. heterosis . microRNA Microarray Expression Profiling

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56 Zhai L, Liu Z, Zou X, Jiang Y, Qiu F, Zhengand Y, Zhang Z. (2012) Genome-wide identification and analysis of microRNA responding to long-term waterlogging in crown roots of maize seedlings. Physiologia Plantarum [Epub ahead of print]. [ abstract ] Stress Response Studies Abiotic Stress: Salt tolerance in maize roots miRNA sequencing detected miRNAs and their targets expressed in waterlogged crown roots of maize seedlings in two inbred lines (Hz32 and Mo17). A total of 61 mature miRNAs were found including 36 known maize ( zma ) miRNAs and 25 potential novel miRNA candidates. Comparison of miRNA expression in both waterlogged and control crown roots revealed 32 waterlogging -responsive miRNAs, most were consistently down-regulated under waterlogging in the two inbred lines. The miRNA targets were identified through degradome sequencing. Expression profiles of five miRNAs that differed between inbred lines. X-axis shows the inbred lines and miRNAs. Y-axis shows the log2 (expression level in treatment/expression level in control). Waterlogging responsive miRNAs displayed the similar expression profile in Hz32 and Mo17. The cluster was done on the basis of log2 (expression level in treatment/expression level in control). Red shows up-regulation. Green shows down-regulation. microRNA Sequencing

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57 Zhai L, Liu Z, Zou X, Jiang Y, Qiu F, Zhengand Y, Zhang Z. (2012) Genome-wide identification and analysis of microRNA responding to long-term waterlogging in crown roots of maize seedlings. Physiologia Plantarum [Epub ahead of print]. [ abstract ] Stress Response Studies Characteristics of the high quality reads from GA-IIX (Illumina) sequencing runs of eight small RNA libraries. Reads Unique seq Mappable Percentage RNA Class Percentage > 3 Copies Percentage MCK 12.2M 6.7M 124K 1.86 162K 2.4 3.41M 50.9 M1D 16.1M 4.8M 191K 3.94 200K 4.1 3.7M 75.5 M2D 11.2M 4.0M 145K 3.65 167K 4.2 3.0M 74.3 M3D 7.2M 3.9M 77K 2.01 112K 2.9 1.6M 42.1 Mean 11.7M 4.8M 134K 2.87 160K 3.4 2.9M 60.7 HCK 11.6M 6.3M 147K 2.32 170K 2.7 3.8M 60.6 H1D 10.3M 5.9M 114K 1.92 143K 2.4 3.0M 51.1 H2D 11.7M 5.1M 103K 2.01 138K 2.7 2.9M 55.5 H3D 9.2M 3.4M 77K 2.25 110K 3.2 1.9M 54.7 Mean 10.7M 5.2M 110K 2.13 140K 2.8 2.9M 55.5 MCK, M1D, M2D, and M3D indicate Mo17 plants waterlogged for 0, 1, 2, and 3 days, respectively. HCK, H1D, H2D, and H3D indicate Hz32 plants waterlogged for 0, 1, 2, and 3 days, respectively. Mappable: "Mappable sequences" are raw sequences that were digitally filtered using Illumina's Genome Analyzer Pipeline software and the quality and purity filters in the ACGT101-miR program. RNA class: RNAs originating from known types of RNA (mRNA, rRNA, tRNA, snRNA, snoRNA and repeats). M = million base pairs; K = thousand base pairs. microRNA Sequencing

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58 Zhao M, Tai H, Sun S, Zhang F, Xu Y, et al. (2012) Cloning and Characterization of Maize miRNAs Involved in Responses to Nitrogen Deficiency. PLoS ONE 7(1), e29669. Stress Response Studies Nitrogen deficiency in maize roots & shoots Constructed four small RNA libraries and one degradome from maize seedlings exposed to N deficiency. Discovered a total of 99 absolutely new loci belonging to 47 miRNA families by small RNA deep sequencing and degradome sequencing, as well as 9 new loci were the paralogs of previously reported miR169, miR171, and miR398, significantly expanding the reported 150 high confidence genes within 26 miRNA families in maize. Predicted and degradome -validated targets of the newly identified miRNAs suggest their involvement in a broad range of cellular responses and metabolic processes. Differential expression of conserved miRNAs in response to N deficiency in shoots (A) and roots (B). Only miRNA genes with > 2-fold relative change are shown. Selected miRNAs from roots were validated by Real time RT-PCR (C) or small RNA northern blot (D). microRNA Sequencing

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Plant Breeding Applications 59 Heterosis (Hybrid Vigor) Studies Analyzed the expression of miRNAs in two rice subspecies ( japonica cv. Nipponbare and indica cv. 93-11) and their reciprocal hybrids using microarrays. Found that of all the 1141 small RNAs tested, 140 (12%, 140 of 1141) and 157 (13%, 157 of 1141) were identified being significantly differentially expressed in two reciprocal hybrids, respectively. 15 miRNAs displayed stark opposite expression trends relative to mid-parent in reciprocal hybrids. These findings reveal that small RNAs play roles in heterosis and add a new layer in the understanding and exploitation of molecular mechanisms of heterosis . Chen F, He G, He H, Chen W, Zhu X, Liang M, Chen L, Deng XW. 2010 Expression analysis of miRNAs and highly-expressed small RNAs in two rice subspecies and their reciprocal hybrids. J Integr Plant Biol 52(11):971-80. [ abstract ] Diversity of small RNAs in composition and expression between parents and hybrids. (A)(B) statistical analysis of differentially expressed small RNAs among genotypes. (C) Additive and non-additive variation of small RNAs expression in the reciprocal hybrids. (D) Differential expression of small RNAs between F1 hybrids and their parents. (E) (F) Non-additive expression patterns of small RNAs in Nip/93-11 and 93-11/Nip. microRNA Microarray Expression Profiling

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Plant Breeding Applications 60 Meng Y, Huang F, Shi Q, Cao J, Chen D, Zhang J, Ni J, Wu P, Chen M. 2009 Genome-wide survey of rice microRNAs and microRNA-target pairs in the root of a novel auxin -resistant mutant. Planta 230(5):883-98. [ abstract ] Mutant Variety Studies Used microarray analysis to investigate the miRNA expression patterns of a novel auxin -resistant rice mutant with plethoric root defects. Clustering analysis revealed some novel auxin -sensitive miRNAs in roots. Analysis of miRNA duplication and expression patterns suggested the evolutionary conservation between miRNAs and protein-coding genes. Comparative analysis of miRNA and protein-coding gene expression datasets provided information about the regulatory network between miRNAs and protein-coding genes (e.g. auxin response factor ) MicroRNA -mediated signal interactions between auxin and nutrition or stress in rice roots. The signal interactions can occur both upstream and downstream of the miRNA s (e.g., miR169 and miR395 ) microRNA Microarray Expression Profiling

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