Plant Microrna Webinar Recent Work & Current Methods in Plant Microrna Research

Plant microRNA Webinar Recent Work & Current Methods In Plant microRNA Research

2012‐06‐21

Christoph Eicken, PhD Head of Technical Services – Microarrays

Julie Lang, PhD Technical Director, LC‐Bio Agenda

miRNA Intro

Recent Studies

Current methods

Case Studies

Q & A Company Overview

• 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 3 Diverse Customer Base > 1400 Institutions > 40 Countries Europe Ireland Slovenia • Academic Austria Italy Spain • Biotechnology Belgium Luxemburg Scotland, UK Bulgaria Northern Ireland Sweden • Pharmaceutical Denmark UK Switzerland • Research Hospitals France Norway The Netherlands • Government Germany Poland England, UK Hungary

Far East China Japan Singapore North America Malaysia Canada Korea United States Taiwan Mexico India Hong Kong Middle East Turkey Israel South America Georgia Brazil Australia Argentina New Zealand Columbia 4 µParaflo® Microfluidic Technology • High Density –On Chip Parallel Synthesis • Customizable –DNA, RNA, Peptides, and Analogs • Quality Data – Microfluidics / Synthesis Chemistry

Transcriptomics • microRNA Profiling Microarray Services • microRNA Discovery Sequencing Services • Seq‐ArraySM Method

Proteomics • Phosph opep tide Bindi ng Microarray SiService • Protein‐ Protein Interaction Microarray Service • Kinase Profiling Microarray Service

5 microRNAs ‐ What We Know

All miRNAs are small non‐coding RNAs, usually consisting of ׽20–22 .1 .nucleotides for animals and ׽20–24 nt for plants

2. All miRNA precursors have a well‐predicted stem loop hairpin structure, and this fold‐back hairpin structure has a low free energy

3. Many miRNAs are evolutionarily conserved, some from worm to human, or from ferns to core eudicots or monocots in plants

4. Bind to complementary mRNA molecules and act as negative regulators of translation

5. High copy number

6. Expression is tissue (and developmental stage) specific microRNAs - What We Know

1. Currently ‐ 21643 mature miRNAs across 168 plant, animal, and virus species (miRBase 18, Nov. 2011).

2. 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.

3. 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

“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] Milestones 1993 ‐ Lin‐4was 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‐7was 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

2002 – microRNA biogenesis pathway in plants (Arabidopsis) is revealed

Reinhart BJ et al. 2002 MicroRNA in Plants. Genes Dev 16(13):1616‐26. [article] PkPark W et al. 2002 CARPEL FACTORY, a Dicer hlhomolog, and HEN1, a novel protein, act in miRNAicroRNA metablibolism in AbidiArabidopsis thlihaliana. Curr Biol 12(17):1484‐95. [article]

2003 – Reinhart, Bartel, Zamore describe microRNA silencing mechanism in plants Tang G et al. 2003 A bioc hem ica l fkframework for RNA silenc ing in pltlants. Genes Dev 17(1): 49‐63. [artic le]

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

2005 –Three studies link microRNAs with cancer 2006 – microRNA lklinked with Heart Disease 2006 –miRBasegoes 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] miRNA Processing

pri‐miRNA = primary microRNA transcript

pre‐miRNA = precursor microRNA

miRNA* = antisense microRNA (now ‐3p or ‐5p)

miRISC = microRNA‐induced silencing complex

For latest iifnformati on regarding miRNA nomenclature see the miRBase.org blog. miRNABiogenesis 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] miRNA in Plants – Unique Features

1. Near‐perfect complementarity ‐ The computational identification of miRNA targets in plants is relatively more straightforward than in animals,

2. In plants, most miRNAs have perfect or near perfect complementarity to their mRNA targets. Upon binding to their mRNA targets, the miRNA‐ contiitaining RISCs ftifunction as endldonucleases, clileaving the mRNA

3. 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). 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 4500 Plant miRNAs in miRBase 4000

3500 V6. 1 ‐ 9 species  V18 ‐ 51 species 3000

ntries 2500 E E

miRBase: microRNA sequences, targets and gene nomenclature. Database 2000 Griffiths‐Jones S et al. NAR 2006 34(Database Issue):D140‐D144 [article] Plant

1500

1000

500

0 V6.1 V7.1 V8.0 V8.1 V8.2 V9.0 V9.1 V9.2 V10.0 V10.1 V11.0 V12.0 V13.0 V14.0 V15.0 V16 V17 V18 Apr‐05 Oct‐05 Feb‐06 May‐06 Jul‐06 Oct‐06 Feb‐07 May‐07 Aug‐07 Dec‐07 Apr‐08 Sep‐08 Mar‐09 Sep‐09 Apr‐10 Sep‐10 Apr‐11 Nov‐11 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 ‐ tttarget 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] CttilComputational TlTools for Plant microRNA Data Analysis

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

Too many to list – Downldload it here: http://www.lcsciences.com/documents/plant‐computational‐tools.pdf 2012 Review: Functions of microRNAs in plant stress responses MicroRNAs responsive to biotic and abiotic stresses in diverse plant species 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.

Sunkar R, Li YF, Jagadeeswaran G. 2012 Functions of microRNAs in plant stress responses. Trends Plant Sci 17(4):196‐203. [abstract] 18 MicroRNAs responsive to biotic and abiotic stresses in diverse plant species miRNA Biotic Drought Salt Cold Heat ABA Oxidative Hypoxia UV B Ath↑, Ttu↑, Hvu↑, leaf, Ath↑, Vun↑, miR156 nd Ptc↓, Mesa Tae↑ nd nd Ath↑ Ath↑, Pte↑ Osa↓ Peu↑ Zma↓ 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 Hvu↑ llfeaf, Ttu↓, Hvu↓ miR165/miR16 Ath↑ root, Mesa, Gmab, nd Ath↑, Mesa Tae↑ nd nd Zma^ Ath↑, Pte↑ 6 Peu↑&↓ miR167 Ath↑ Ath↑, Mesa, Peu↑ Ath↑, Zma↓ Osa↓ nd Osa↓ nd Zma^ Ath↑, Pte↑

Ath↑, Zma↑, miR168 nd Ath↑, Osa↓, Peu↓ Ptc↑, Ath↑ Tae↑ nd nd Zma^ Ath↑ Pte↑ Vun↑ Ath↓, Osa↑, Mtr↓, Ath↑, Zma↑, miR169 nd Ath↑, Bdi↑ Tae↑ Ath↓, Osa↓ Osa↑ Ath↑&↓ Ath↑, Pte↓ Peu↑ Vun↑, Osa↑ miR170/miR17 Ath↑, Hvu↑ leaf, Ttu↓, Mesa, Ptc↓, nd Ath↑, Ptc↓ Ptc↓ nd nd Zma↑ Ath↑ 1 Osa↑&↓, Peu↑&↓ Ath↑, Osa↓ 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 00Peu↑&↓ 0000000 Ath↑, Osa↑, Mtr↑, Ath↑, Pvu↑, miR393 Ath↑ Ath↑ Tae↑ Ath↑ Pvu↑ nd nd Ath↑, Pte↓ Pvu↑, Peu↑&↓ Osa↓ miR395 nd Peu↑&↓, Osa↑ Zma↑ Mesa nd nd nd Zmac nd Ath↑, Osa↓, Ttu↓, Ath↑, Osa↓, miR396 nd Ath↑, Mesa nd nd nd Zma↑ nd Peu↑&↓ Zma↓ Ath↑, Osa↓, Gmab, Ath↑, Bdi↑, miR397 nd Ath↑ nd Ath↑ Osa↑ nd nd Peu↑ Mesa miR398 Ath↓ Mtr↑, Ttu↑, Peu↑ Ath↓, Ptec Ath↓ nd Ath↓c, Ptec Ath↓ nd Ath↑, Pte↑ Ath↑, Mtr↑, Hvu↑, miR408 Ath↓ Vun↑ Ath↑ nd nd nd nd nd Osa↓ Abbreviations: Ath, Arabidopsis thaliana; Bdi, Brachypodium distachyon; Gma, Glycine max; Hvu, Hordeum vulgare; Mtr, Medicago truncatula; Mes, Manihot esculenta; nd, not Sunkar R, Li YF, Jagadeeswaran G. 2012 Functions determined or no change, Pvu, Phaseolus vulgaris; Peu, Populus euphratica; Ptc, Populus of microRNAs in plant stress responses. Trends trichocarpa; Pte: Populus tremula; Ttu, Triticum turgidum; Osa, Oryza sativa; Vun, Vigna Plant Sci 17(4):196‐203. [abstract] unguiculata; Zma, Zea mays; ↑, upregulated; ^, initially upregulated then returned to basal level; ↓, downregulated; ↑&↓, some members were upregulated, some were 19 downregulated. Recent Work – 2011/12:

Cross Kingdom Regulation (?)

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]

Discussion: http://biologyfiles.fieldofscience.com/2012/ 01/why‐did‐atlantic‐publish‐this‐piece.html

20 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 modifie d miRNA precursors results in the production of artificial miRNAs (amiRNAs) targeting viral RNA sequences. The amiRNA‐mediated virus Jelly NS, Schellenbaum P, Walter B, Maillot P. 2012 Transient expression of artificial microRNAs targeting Grapevine fanleaf resistance is efficient and superior to the long viral virus and evidence for RNA silencing in grapevine somatic RNA‐bdbased antiv ira l approaches in tha t properly embryos. Transgenic Res [Epub ahead of print]. [abstract] selected amiRNA sequences would have little chance Kung YJ, Lin SS, Huang YL, Chen TC, Harish SS, Chua NH, Yeh SD. 2012 Multiple artificial microRNAs targeting conserved motifs to target the host plant genes or to complement or of the replicase gene confer robust transgenic resistance to recombine with other invading viruses. 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 Hihlighly effic ient 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]

21 microRNA Research Tools

miRNA Identification SM Seq‐Array • Genetic screening • Direct cloning, sequencing • Computational strategy – MIRCheck, findMiRNA, MIRscan, MiRAlign • Tiling Microarrays • Next-gen Sequencing ACGT101‐miR

Detection & Profiling

• Northern Blotting • In situ hybridization Pathway Analysis Stem‐Loop Specific • Real-time PCR • Microarray analysis • Bioinformatics • Next-gen Sequencing

Target Determination Functional Analysis Degradsome Seq • Bioinformatics • Lucifierase Assays • Gene / proteome expression analysis • Gene knockout/overexpression models • Pull-down assays • miRNA inhibition - antagomirs •5′ RACE analyses • miRNA mimicry Digital Gene Expression • DdDegradome Sequenc ing

22 Pathway Network 3 MjMajor Steps & ThTechno log ies

Discovery Profiling Quantitation Validation

custom Next Gen miRNA miRNA qRT-PCR Sequencing microarray microarray

Seq‐Array SM SamplePreparation

FFPE Block Norgen Biotek Cell Line Fatty Tissue (Viscous Samples)

Tissue Total RNA Plant Total RNA MtMater ilial Extraction Kit (1‐4 µg) Qiagen Blood Serum Algea Plasma Urine miRNeasy Kit

Ambion • Select a kit designed to retain small RNA • Select kit based on your sample type

miRVana Kit • Use the same kit for all

24 Customer Sample Quality Control

You can check the UV spectrum of your sample with a spectrophotometer. 260nm 260nm ↑ 101.0 ↑ 181.8 230nm 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.

25 microRNA Microarray Expression Profiling Customer Sample Quality Control Good Poor Good Poor Good Poor

15%1.5% FFldhdormaldehyde Agarose GlGel Urea‐PAGE Gel Agilent BioAnalyzer Gel Image

Failure in recovery of RNA <200 nt (including microRNA) 26 Reg. Experimental Design Sample Replicates for Expression Studies

Biooogcalogical Replicates – Still Veyery Importa nt • 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] 27 Microarray vs RNA Sequencing

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

28 ® μParaflo Microfluidics Chip

Microfl u idic A rray Pl atf orm

10 µl total volume 270 pl / reaction chamber 4000 features uniform flow rate Flexibility Allows miRBase Synchronicity Version 18 # of sequences Nov 2011 (all species)

2003 2011 miRBase Version microRNA Microarray Expression Profiling

Customer Customer Comprehensive Microarray Services Sequences Total RNA • Sample QC miRBase Sample QC • Sample preparation Chip Design Small RNA Isolation • Hybridization reactions Chip Synthesis Labeling • Advanced data analysis Chip QC Hybridization • High level customer support

Signal Amplification

Image Acquisition

Data Extraction

Data Analysis

Customer Analysis Report

31 microRNA Microarray Expression Profiling Differentiated miRNAs of Biological & Statistical Significance ‐ Multiple Chips

Biological repeats Control / Treated Control Treated

p < 0.05 Array assay

t‐Test

p < 0.01

Multi‐array normalization and clustering analysis

32 microRNA Microarray Expression Profiling

Repeats microRNA Microarray Expression Profiling

Advanced Data Analysis Package Includes: • The original and processed chip images • An array ltlayout 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 representtitative 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.

34 Microarray vs RNA Sequencing

35 Small RNA Sequencing and Data Analysis LC Sciences ‐ Comprehensive RNA Sequencing Services • Sample QC • Sample pppreparation • Library preparation • High‐throughput sequencing • Advanced bioinformatics analysis • High level customer support

36 Small RNA Sequencing and Data Analysis

SlSample PPireparation Cluster GiGeneration

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

Number of Total Reads / Lane Reads/ Sample Samples / Lane 30 M130 M 30 M215 M 30 M310 M 30 M 4 757.5 M 30 M5 6 M 30 M6 5 M 38 Small RNA Sequencing and Data Analysis

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 dis tr ibu tion, tittranscript copy number comparisons, etc 39 Small RNA Sequencing and Data Analysis Data Flow Raw reads Mappable reads

Reads mapped to Reads un-mapped plant mirs in to plant mirs in miRbase ACGT101‐miR miRbase v3.5 Software

mirs mapped to mirs un-mapped to Reads un-mapped Reads mapped species genome species genome to mRNA, Rfam, to mRNA, Rfam, and repbase and repbase Group 1 others Known species miRNAs

Reads mapped to Reads un-mapped Reads mapped to Reads un-mapped species genome to species genome species genome to species genome

Group 2 Group 3 Group 4noot hit

Known miRNAs Candidate species Potentially novel candidate species miRNAs genome miRNAs miRNAs inconsistent with miRBase 40 Small RNA Sequencing and Data Analysis

30,556 ,812 raw reads DtData Flow from sequencer

0.3% 15.1% 10,713,874 reads 19.7% 19,842,938 reads are filtere d out 64.9% are mappable ADT, Junk, & Seq Filter

6,015,779 reads ‐ no hit 30.3% 11,426,638 reads are mapped to 57.6% miRBase or are miRNA candidates 2,400,521 reads mapped to 12.1% mRNA, Rfam and repbase

Grp 1 ‐ 8,007,998 29.2% Grp 2 ‐ 14,067 0.1% 70.1% Grp 3 ‐ 64,086 0.6% Grp 4 ‐ 3,340,487

41 Small RNA Sequencing and Data Analysis

LthLength Dist rib uti on of MblMappable RdReads 00 00 23% 4000 5

) 18% 00

14% 3000 Reads (x Reads 100 ff

8% 2000 Number o Number

5% 5% 4% 4% 3% 1000 3% 2% 2% 2% 2% 2% 1% 1% 1% 0 15 17 19 21 23 25 27 29 31

Length (nt) 42 Small RNA Sequencing and Data Analysis

IsomiRs

Can explain discrepancies array data vs qRT‐PCR validation

43 Seq‐ArraySM microRNA Discovery & Profiling Services

• Seq‐ArraySM is a combination of technologies that maximizes the effectiveness of each while overcoming the limitations of the other. • Seq‐ArraySM provides an efficient pathway from initial broad miRNA search to focused biological insights • Seq‐ArraySM is particularly suited for focused studies consisting of large sample numbers. • Seq‐ArraySM is also useful for discovery (Seq) and validation (Array) of biomarkers of clinical significance.

Sequencing Microarray

Comprehensive High‐throughput Genome‐wide Cost efficient Sensitive Flexible Digital solution Expression profiling

Seq‐ArraySM 44 SM Deep Advanced Custom Seq‐Array Sequencing Bioinformatics Microarray 1. SdSend a ttltotal RNA sample

2. Deep sequencing is performed to generate a comprehensive atlas of all possible miRNAs for your species/system. 3. 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. 4. A custom microarray is designed based on the bioinformatics analysis of the above results and your specific research goals.

5. Microarray expression profiling is performed on small or large sample groups by custom synthesized SeqyqArrays™ based on your unique design.

45 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 46 microRNA Microarray Expression Profiling

Why study miRNAs in Plants?

Basic Research / Discovery Studies Stress Response Studies Disease SSifipecific Biomar kers

47 microRNA Microarray Expression Profiling Basic Research / Discovery Studies

• 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. 48 microRNA Microarray Expression Profiling Basic Research / Discovery Studies Identification of conserved microRNAs and their targets from Solanum lycopersicum

By searching known miRNAs identified from plant species against tomato nucleotide seqq,uences, 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.

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] 49 microRNA Microarray Expression Profiling Basic Research / Discovery Studies Analysis of conserved microRNAs in floral tissues of Boechera species

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 Ath,Arabidopsis thaliana; Ptc, Populus trichocarpa; expression of microRNAs in Boechera Osa, Oryza sativa; Zma,Zea mays; sexual and apomictic species. Mtr, Medicago truncatula; Gma, Glycine max; Sbi, Sorghum bicolor; Vvi, Vitis vinifera; Ppt, Physcomitrella patens; Rco, Ricinus communis; Tae, Triticum aestivum; Amiteye S, Corral JM, Vogel H, Sharbel TF. 2011 Ghr, GihitGossypium hirsutum; Analysis of conserved microRNAs in floral tissues Sly,Solanum lycopersicum; of sexual and apomictic Boechera species. BMC Ahy, Arachis hypogaea; Genomics 12(1):500. [article] Sof, Saccharum officinarum. 50 Percentage of conserved miRNAs between Boechera and other plant species microRNA Sequencing Basic Research / Discovery Studies Discovery of novel microRNAs & their targets in cucumber leaves and roots 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 Expression levels of cucumber miRNA families miRNAs for the first time in cucumber. assessed using Illum ina sequencing These targets were associated with development, reactive oxygen species scavenging, signaling transduction and transcriptional regulation.

Mao W, Li Z, Xia X, Li Y, Yu J. 2012 A Combined Approach of High‐Throughput Sequencing and Degradome Analysis Reveals Tissue Specific 51 Expression of MicroRNAs and Their Targets in Novel cucumber miRNAs identified by high‐throughput sequencing Cucumber. PLoS One 7(3), e33040. [article] microRNA Microarray Expression Profiling Stress Response Studies Biotic Stress • Viral Infection • Tomato response to Cucumber mosaic virus infection • Squash response to Zucchini yellow mosaic virus infection • Fungal Infection ‐ Soybean resistance to Phyyptophthora sojae • Bacterial Infection

Abiotic Stress • Salt –Maize • Cold – Rice • Submergence – Maize • Drought –Wheat, Rice

52 microRNA Microarray Expression Profiling Stress Response Studies 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

53 microRNA Microarray Expression Profiling Stress Response Studies 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 ‐uni que matur e miRNAs adand 365 sequence‐unique miRNA*s.

These results also show that, in accordance with the phenotype of the developing leaves, the tomato miRNAs are differenti all y expressed at differen t stages of pltlant development and that CMV infection can induce or Differentially regulated suppress the expression of miRNAs as well as up‐ miRNAs in CMV infected regulate some star miRNAs (miRNA*s) which are plants vs mock infection 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] microRNA Microarray Expression Profiling 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.

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] 55 microRNA Sequencing Stress Response Studies

Waterlogging responsive miRNAs Abiotic Stress: Salt tolerance in displayed the similar expression profile in Hz32 and Mo17. The cluster was done maize roots on the basis of log2 (expression level in treatment/expression level in control). Red shows up‐regulation. Green shows miRNA sequencing detected miRNAs and down‐regulation. 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 Zhai L, Liu Z, Zou X, Jiang Y, Qiu F, Zhengand Y, Zhang Z. (2012) between inbred lines. X‐axis shows the inbred lines Genome‐wide identification and analysis of microRNA responding and miRNAs. Y‐axis shows the log2 (expression level in to long‐term waterlogging in crown roots of maize seedlings. treatment/expression level in control). Physiologia Plantarum [Epub ahead of print]. [abstract] 56 microRNA Sequencing 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 Zhai L, Liu Z, Zou X, Jiang Y, Qiu F, Zhengand Y, Zhang Z. (2012) filters in the ACGT101‐miR program. Genome‐wide identification and analysis of microRNA responding RNA class: RNAs originating from known types of RNA (mRNA, rRNA, tRNA, snRNA, to long‐term waterlogging in crown roots of maize seedlings. snoRNA and repeats). Physiologia Plantarum [Epub ahead of print]. [abstract] M = million base pairs; K = thousand base pairs. 57 microRNA Sequencing 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 Differential expression of conserved miRNAs responses and metabolic processes. in response to N deficiency in shoots (A) and roots (()B).

Zhao M, Tai H, Sun S, Zhang F, Xu Y, et al. (2012) Cloning and Only miRNA genes with > 2‐fold relative Characterization of Maize miRNAs Involved in Responses to change are shown. Selected miRNAs from Nitrogen Deficiency. PLoS ONE 7(1), e29669. roots were validated by Real time RT‐PCR (C) or small RNA northern blot (D). 58 microRNA Microarray Expression Profiling Plant Breeding Applications

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. 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 Chen F, He G, He H, Chen W, Zhu X, Liang M, Chen L, Deng XW. reciprocal hybrids. (D) Differential expression of small RNAs between F1 hybrids and 2010 Expression analysis of miRNAs and highly‐expressed small their parents. (E) (F) Non‐additive expression patterns of small RNAs in Nip/93‐11 and RNAs in two rice subspecies and their reciprocal hybrids. J 93‐11/Nip. Integr Plant Biol 52(11):971‐80. [abstract] 59 microRNA Microarray Expression Profiling Plant Breeding Applications

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. AliAnalysis 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 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 roots. The signal interactions can occur both pairs in the root of a novel auxin‐resistant mutant. Planta 230(5):883‐ upstream and downstream of the miRNA s 98. [abstract] (e.g., miR169 and miR395) 60 Global Reach – Distribution Channels

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