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`Meeemelee
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`Genome & Co. v. Univ. of Chicago
`PGR2019-00002
`UNIV. CHICAGO EX. 2076
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`PROPERTY OF THE
`NATIONAL
`LIBRARY OF
`MEDICINE
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`ORIVINGSTONMIZAL CELL-SAZE TRANSITIONS
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`T AE
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`PLANT
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`Cc
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`Volume 20 Number7—July 2008
`
`The electronic form of this issue, available at www.plantcell.org, is the journal of record.
`
`ON THE COVER
`
`EDITORIAL
`
`
`
`Stomata are formed through a
`series of differentiation events
`mediated by a trio of basic-helix-
`loop-helix (BHLH) proteins: SPEECH-
`LESS (SPCH), MUTE, and FAMA.
`Through characterization of a domi-
`nant mutant, scream-D (serm-D),
`which produces an epidermis con-
`sisting entirely of stomata, Kanaoka
`et al. (pages 1775-1785)identified
`two paralogous Arabidopsis bHLH
`proteins, SCRM and SCRMe2, that
`partner with SPCH, MUTE, and
`FAMA, to drive initiation, prolifer-
`ation, and terminal differentiation
`of stomata. The cover shows the
`rosette leaf epidermis of a mute
`serm-D double mutant, which is
`
`Refining Our Standards
`Cathie Martin
`
`IN THIS ISSUE
`
`Effector Trafficking: RXLR-dEER as Extra Gearfor Delivery into Plant Cells
`Francine Govers and Klaas Bouwmeester
`
`IN BRIEF
`
`Effector XopD Suppresses Tissue Degeneration in Xanthomonas-Infected
`Tomato Leaves
`Jennifer Mach
`
`They All Scream for ICE1/SCRM2: Core Regulatory Units in Stomatal
`Development
`Nancy R. Hofmann
`
`Auxin Regulation of Late Stamen Development
`Nancy A. Eckardt
`
`LETTERS TO THE EDITOR
`
`Towards a Systematic Validation of References in Real-Time RT-PCR
`Laurent Gutierrez, Mélanie Mauriat, Jérome Pelloux, Catherine Bellini,
`and Olivier Van Wuytswinkel
`
`Eleven Golden Rules of Quantitative RT-PCR
`Michael K. Udvardi, Tomasz Czechowski, and Wolf-Riidiger Scheible
`
`REVIEW
`
`The Evolving Complexity of the Auxin Pathway
`Steffen Lau, Gerd Jurgens, and Ive De Smet
`
`1727
`
`1728
`
`1731
`
`1732
`
`1733
`
`1734
`
`1736
`
`1738
`
`composedof triangular stomatal
`
`precursor called=meri-cells
`
`RESEARCH ARTICLES
`stemoids and their sister cells.
`
`ICE1, a
`Surprisingly, SCRM is
`key upstream regulator of cold-
`induced gene expression, there-
`fore suggesting a link between the
`transcriptional regulation of envi-
`ronmental adaptation and devel-
`opment.
`
`Calmodulin? Plays an Important Role as Transcriptional Regulatorin
`Arabidopsis Seedling Development
`Ritu Kushwaha, Aparna Singh, and Sudip Chattopadhyay
`
`Auxin Regulates Arabidopsis Anther Dehiscence, Pollen Maturation,
`and Filament Elongation|
`Valentina Cecchetti, Maria Maddalena Altamura, Giuseppina Falasca,
`Paolo Costantino, and Maura Cardarelli
`
`1747
`
`1760
`
`
`
`EDITORIAL BOARD
`Editor in Chief
`Cathie Martin
`Coeditors
`Sarah M. Assmann
`Jody Banks
`Alice Barkan
`Kathy Barton
`David Baum
`Sebastian Bednarek
`James Birchler
`Ulla Bonas
`Christopher Bowler
`Nigel Crawford
`Xing Wang Deng
`Rebecca Doerge
`Mark Estelle
`Pascal Genschik
`
`Jean T. Greenberg
`Thomas Guilfoyle
`Peter Hepler
`Ann Hirsch
`
`Richard A. Jorgensen
`Patricia Leon
`William Lucas
`Marjori Matzke
`Blake Meyers
`Krishna K. Niyogi
`Joseph Noel
`Magnus Nordborg
`Michael Palmgren
`Markus Pauly
`Scott C. Peck
`David Smyth
`Uwe Sonnewald
`Chris J. Staiger
`Nicholas J. Talbot
`Masamitsu Wada
`
`Managing Editor
`John Long
`Newsand ReviewsEditor
`Nancy A. Eckardt
`Science Editors
`Greg Bertoni
`Kathleen L. Farquharson
`Nancy R. Hofmann
`Jennifer M. Mach
`Production Manager
`Susan L. Entwistle
`Manuscript Manager
`Annette Kessler
`Publications Director
`Nancy A. Winchester
`Publisher
`American Society of
`Plant Biologists
`Executive Director,
`Crispin Taylor
`Editorial Office
`15501 Monona Drive
`Rockville, Maryland 20855-2768
`Telephone: 301/251 -0560, ext. 119
`Fax: 301/279-2996
`http://www.aspb.org
`Online at www.plantcell.org
`
`SCREAM/ICE1 and SCREAM2 Specity Three Cell-State Transitional Steps
`Leading to Arabidopsis StomatalDifferentiation W) 0A
`Masahiro M. Kanaoka, Lynn Jo Pillitteri, Hiroaki Fujii, Yuki Yoshida,
`Naomi L. Bogenschutz, Junji Takabayashi, Jian-Kang Zhu, and Keiko U. Torii
`
`Mutations in SUPPRESSOR OF VARIEGATION(1, a Factor Required for
`Normal Chloroplast Translation, Suppress var2-Mediated Leaf Variegation
`in Arabidopsis
`Fei Yu, Xiayan Liu, Muath Alsheikh, Sungsoon Park, and Steve Rodermel
`
`The EPIP Peptide of INFLORESCENCE DEFICIENTIN ABSCISSION Is
`Sufficient to Induce Abscission in Arabidopsis through the Receptor-Like
`Kinases HAESA and HAESA-LIKE2 W[oa
`Grethe-Elisabeth Stenvik, Nora M. Tandstad, Yongfeng Guo, Chun-Lin Shi,
`Wenche Kristiansen, Asbjorn Holmgren, Steven E. Clark, Reidunn B. Aalen,
`and Melinka A. Butenko
`
`Arabidopsis 10-Formyl Tetrahydrofolate Deformylases Are Essential
`for Photorespiration |w 0a
`Eva Collakova, Aymeric Goyer, Valeria Naponelli, Inga Krassovskaya,
`Jesse F. Gregory Ill, Andrew D. Hanson, and Yair Shachar-Hill
`
`Mutation of the Plastidial «-Glucan Phosphorylase Genein Rice Affects
`the Synthesis and Structure of Starch in the Endosperm Ww)
`Hikaru Satoh, Kensuke Shibahara, Takashi Tokunaga, Aiko Nishi,
`Mikako Tasaki, Seon-Kap Hwang, Thomas W. Okita, Nanae Kaneko,
`Naoko Fujita, Mayumi Yoshida, Yuko Hosaka, Aya Sato, Yoshinori Utsumi,
`Takashi Ohdan, and Yasunori Nakamura
`
`Badh2, Encoding Betaine Aldehyde Dehydrogenase,Inhibits the Biosynthesis
`of 2-Acetyl-1-Pyrroline, a Major Component in Rice Fragrance W/
`Saihua Chen, Yi Yang, Weiwei Shi, Qing Ji, Fei He, Ziding Zhang,
`Zhukuan Cheng, Xiangnong Liu, and Mingliang Xu
`
`Sphingolipid Long-Chain Base Hydroxylation Is Important for Growth and
`Regulation of Sphingolipid Content and Composition in Arabidopsis W
`Ming Chen, Jonathan E. Markham, Charles R. Dietrich, Jan G. Jaworski,
`and Edgar B. Cahoon
`
`Dolichol Biosynthesis andIts Effects on the Unfolded Protein Response and
`Abiotic Stress Resistance in Arabidopsis W 0
`Hairong Zhang, Kiyoshi Ohyama, Julie Boudet, Zhizhong Chen, Jilai Yang,
`Min Zhang, Toshiya Muranaka, Christophe Maurel, Jian-Kang Zhu, and
`Zhizhong Gong
`
`Arabidopsis PUB22 and PUB23 Are Homologous U-Box E3 Ubiquitin Ligases
`That Play Combinatory Roles in Response to Drought Stress |W
`Seok Keun Cho, Moon Young Ryu, Charlotte Song, June M. Kwak, and
`Woo Taek Kim
`
`XopD SUMOProtease Affects Host Transcription, Promotes Pathogen
`Growth, and Delays Symptom Development in Xanthomonas-Infected
`Tomato Leaves W/0A
`Jung-Gun Kim, Kyle W. Taylor, Andrew Hotson, Mark Keegan,
`Eric A. Schmelz, and Mary Beth Mudgett
`
`RXLR-Mediated Entry of Phytophthora sojae Effector Avrib into Soybean
`Cells Does Not Require Pathogen-Encoded Machinery W
`Daolong Dou, Shiv D. Kale, Xia Wang, Rays H.Y. Jiang, Nathan A. Bruce,
`Felipe D. Arredondo, Xuemin Zhang, and Brett M. Tyler
`
`1775
`
`1786
`
`1805
`
`1818
`
`1833
`
`1850
`
`1862
`
`1879
`
`1899
`
`1915
`
`1930
`
`
`
`The Cladosporium fulvum Virulence Protein Avr2 Inhibits Host Proteases
`Required for Basal Defense W)/04\
`H. Peter van Esse, John W. van't Klooster, Melvin D. Bolton,
`Koste A. Yadeta, Peter van Baarlen, Sjef Boeren, Jacques Vervoort,
`Pierre J.G.M. de Wit, and Bart P.H.J. Thomma
`
`Tomato Protein Kinase 1b Mediates Signaling of Plant Responsesto
`Necrotrophic Fungi and Insect Herbivory |W
`Synan AbuQamar, Mao-Feng Chai, Hongli Luo, Fengming Song, and
`Tesfaye Mengiste
`
`Induced Plant Defensesin the Natural Environment: Nicotiana attenuata
`WRKY3 and WRKY6Coordinate Responsesto Herbivory |W’
`Melanie Skibbe, Nan Qu, Ivan Galis, and lan T. Baldwin
`
`CORRECTION
`
`Hong Li, Zengyong He, Guihua Lu, Sung Chul Lee, Jose Alonso,
`Joseph R. Ecker, and Sheng Luan (2007). A WD40 Domain Cyclophilin
`Interacts with Histone H3 and Functions in Gene Repression and
`Organogenesis in Arabidopsis. Plant Gell 19: 2403-2416.
`
`‘wiOnline version contains Web-only data.
`(0A|Open Accessarticles can be viewed online without a subscription.
`
`1964
`
`1984
`
`2001
`
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`
`The Plant Cell, Vol. 20: 1736-1737, July 2008, www.plantcell.org © 2008 American Society of Plant Biologists
`
`LETTER TO THE EDITOR
`
`Eleven Golden Rules of Quantitative RT-PCR
`
`Reverse transcription followed by quanti-
`ploy sequence-specific fluorescent probes,
`primers for a reference gene that are—1 kb
`tative polymerase chain reaction analysis,
`such as TaqMan probes, and to absolute
`apart. Typically, the Ct value for the primer
`or qRT-PCR,
`is an extremely sensitive,
`quantification methods (http://www.gene-
`pair at the 5'-end of a cDNA will be higher
`cost-effective method for quantifying gene
`quantification.info/).
`than the Ct value of the primer pair at the
`transcripts from plant cells. The availabil-
`(1) Harvest material from at least three
`3’-end, as reverse transcription begins at
`ity of nonspecific double-stranded DNA
`biological replicates to facilitate statistical
`the 3’
`[poly(A)] end of the template mRNA
`(dsDNA) binding fluorophors,
`such as
`analysis of data,
`freeze immediately in
`and doesnot always extend to the 5'-end of
`SYBR Green, and 384-well-plate real-time
`liquid nitrogen, and store at
`—80°C to
`the template.
`Ideally, the Ct value of the
`PCR machines that can measure fluores-
`preservefull-length RNA.
`5'-end primer pair should not exceedthatof
`cence at the end of each PCR cycle makeit
`(2) Use an RNAisolation procedure that
`the 3'-end pair by more than one cycle
`possible to perform qRT-PCR on hundreds
`number.
`produces high-quality total RNA from all
`of genesor treatments in parallel. This has
`samples to be analyzed. Check RNA qual-
`facilitated the comparative analysis ofall
`ity using an Agilent 2100 Bioanalyzer (RNA
`members of large gene families, such as
`integrity number, RIN = 7 andideally > 9)
`transcription factor genes
`(Czechowski
`or by electrophoretic separation on a high-
`et al., 2004). Giventhe relatively low cost
`resolution agarose gel
`(look for sharp
`of PCR reagents, and the precision, sensi-
`ethidium bromide-stained rRNA bands)
`tivity, flexibility, and scalability of GRT-PCR,
`and spectrophotometry (Asgo/Asan = 1.8
`it is little wonder that thousandsof research
`and Azgo/Azan
`~ 2.0). Quantify RNA using
`labs around the world have embracedit as
`Asgo Values.
`the method of choice for measuring tran-
`(3) Digest purified RNA with DNase | to
`script levels. However, despite its popular-
`remove
`contaminating
`genomic DNA,
`ity, we continue to see systematic errorsin
`which can act as template during PCR
`the application of methods for qRT-PCR
`and lead to spurious results. Subsequently,
`analysis, which can compromise the inter-
`perform PCR on the treated RNA, using
`pretation ofresults. The letter to the editor
`gene-specific primers, to confirm absence
`by Gutierrez et al.
`in this issue highlights
`of genomic DNA.
`one of many common sources of error,
`(4) Perform RT reactions with a robust re-
`namely, the inappropriate choice of refer-
`verse transcriptase with no RNaseH activity
`ence genesfor normalizing transcript levels
`(like SuperScriptill fromInvitrogen or Array-
`of test genes prior to comparative analysis
`Script
`from Ambion)
`to maximize cDNA
`of different biological samples. The follow-
`length and yield. Use ultraclean oligo(dT)
`ing are 11 golden rules of qRT-PCR that,
`primer of high integrity. qRT-PCR gene
`when observed, should ensure reproduc:
`expression Measurements are comparable
`ible and accurate measurements of tran-
`only when the same priming strategy and
`script abundancein plant and other cells.
`reaction conditions are usedin all experi
`Theserulesarefor relative quantification of
`ments and reactions contain the same total
`RNA using two-step RT-PCR (where the
`amount of RNA (Stahiberget al., 2004).
`product of a single RT reaction is used as
`(5) Test cDNA yield and quality. Perform
`template in multiple PCR reactions), SYBR
`qPCR onan aliquot of cDNA from each
`Green to detect gene-specific PCR prod-
`sample, using primers to one or more ref-
`ucts, and reference genes for normalizing
`erence genes that are known to be stably
`transcriptlevels of test genes before com-
`expressed in the organ(s)/tissue(s) under
`paring samples. Further details can be
`the range of experimental conditions tested.
`found elsewhere (Czechowski et al., 2004.
`Threshold cycle (Ct) values should be within
`2005). Most of these rules also apply to
`the range mean *
`1 for each reference gene
`relative quantification methods that em-
`across all samples to ensure similar cDNA
`yield from each RT reaction, Quality of
`cDNAcan be assessed using twopairs of
`
`www. plantcell.org/cgi/doi/10.1105/tpce.108.061143
`
`(6) Design gene-specific PCR primers
`using a standard set of design criteria (e.g.,
`primer T,,
`= 60 + 1°C,
`length 18 to 25
`bases, GC content between 40 and 60%),
`which generate a unique, short PCR prod-
`uct (between 60 and 150 bp) of the ex-
`pected length and sequence from a complex
`cDNA sample in preliminary tests, to facili-
`tate multiparallel GPCR using a standard
`PCR program. The 3'-untranslated region is
`a good target for primer design becauseit
`iS generally more unique than coding se-
`quence and closerto the RTstart site.
`(7) Reduce technical errors in PCR re-
`action setup by standardizing (robotize if
`possible) and minimizing the number of
`pipetting steps. Mix cDNA with qPCR
`reagents, then aliquot a standard volume
`of this ‘master mix’’ into each reaction well
`
`containing a standard volume of specific
`primers. Set up reactions in a clean envi-
`ronment free of dust, preferably under a
`positive airflow hood. Routinely check for
`DNA contamination of primer and reagent
`stocks by performing PCR reactions on no
`template (water) controls,
`(8) For relative quantification of transcript
`levels,
`design
`and
`test gene-specific
`primers for at least four potential reference
`genes selected from the literature (e.g.,
`Czechowski et al, 2005) or from your own
`experience that are likely to be stably
`expressed throughout all organs and treat-
`ments to be compared. Validate reference
`genes in preliminary experiments on the
`range of tissues and treatments you wish
`to compare using a foreign CRNA addedto
`each RNA sample prior
`to RT-PCR to
`
`
`
`normalize data for reference gene tran-
`scripts prior to assessmentof their expres-
`sion stability (Czechowski et al., 2005).
`(9) Perform real-time PCR on test and
`reference genesin parallel for each sample
`to capture fluorescence data on dsDNA
`after each cycle of amplification. Also,
`perform dsDNA melting curve analysis at
`the end of the PCR run. When relying on
`nonspecific DNA binding fluorophors, such
`as SYBR Green, to quantify relative dsDNA
`amount, ensure that only a single PCR
`amplicon of the expected length and melt-
`ing temperature is produced using gel
`electrophoresis and PCR amplicon melting
`curve data, respectively. We typically use a
`commercial mixture of hot-start Taq poly-
`merase, SYBR Green, and other reagents,
`such as Power SYBR Green Master Mix
`from Applied Biosystems, and have ob-
`served significant differences in the effi-
`cacy (PCR efficiency, specificity, and/or
`yield) of such products from different sup-
`pliers.
`(10) Determine which reference gene(s)is
`best for normalization of test gene tran-
`script
`levels amongst all samples (e.g.,
`using
`geNorm [Vandesompele
`et
`al.,
`2002] or BestKeeper software [Pfaffl et al.,
`2004]), which use as input not only the Ct
`value, but also the PCR efficiency for each
`
`reaction. PCR efficiency can be derived
`conveniently from amplification plots using
`the program LinRegPCR (Ramakersetal.,
`2003). Estimation via the classical calibra-
`tion dilution curve and slope calculation is
`also possible, albeit more complicated
`(http://www.gene-quantification.info/).
`(11) Finally, calculate relative transcript
`abundance for each gene in each sample
`using a formula that
`incorporates PCR
`efficiency for the test gene and Ct values
`for both test and reference genes (http://
`www.gene-quantification.info/).
`
`Michael K. Udvardi
`The Samuel Roberts Noble Foundation
`Ardmore, OK 73401
`mudvardi@noble.org
`
`Tomasz Czechowski
`Department of Biology (Area 7)
`CNAP Research Laboratories
`University of York
`Heslington, York YO10 5YW, UK
`
`Wolf-Riidiger Scheible
`Max Planck Institute of Molecular
`Plant Physiology
`14476 Potsdam, Germany
`
`July 2008
`
`1737
`
`REFERENCES
`
`Czechowski, T., Bari, R.P., Stitt, M., Scheible,
`W.R., and Udvardi, M.K.
`(2004). Real-time
`RT-PCR profiling of over 1400 Arabidopsis
`transcription factors: Unprecedented sensitiy-
`ity reveals novel root- and shoot-specific genes.
`Plant J. 38: 366-379.
`
`Czechowski, T., Stitt, M., Altmann, T., Udvardi,
`M.K., and Scheible, W.R.
`(2005). Genome-
`wide identification and testing of superior
`reference genes for transcript normalization in
`Arabidopsis. Plant Physiol. 139: 5-17.
`Pfaffl, M.W., Tichopad, A., Prgomet, C., and
`Neuvians, T.P. (2004). Determination of stable
`housekeeping genes, differentially regulated
`target genes and sampleintegrity: BestKeeper-
`Excel-based tool using pair-wise correlations.
`Biotechnol. Lett. 26: 509-551.
`Ramakers, C., Ruijter, J.M., Deprez, R.H.,
`and Moorman, A.F.
`(2003). Assumption-
`free analysis of quantitative real-time poly-
`merase chain reaction (PCR) data. Neurosci.
`Lett. 339: 62-66.
`Stahlberg, A., Hakansson, J., Xian, X., Semb,
`H., and Kubista, M. (2004). Properties of the
`reverse transcription reaction in mRNA quan-
`tification. Clin. Chem. 50: 509-515.
`Vandesompele, J., De Preter, K., Pattyn, F.,
`Poppe, B., Van Roy, N., De Paepe, A., and
`Speleman, F. (2002). Accurate normalization
`of
`real-time quantitative RT-PCR data by
`geometric averaging of multiple internal con-
`trol genes. GenomeBiol. 3: RESEARCHO0034.
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`