A F F Y M E T R I X

MICROARRAYBULLETIN

Scientists in the United States andJapan have discovered that ~10% of thegenome is under control of the circadianclock-the biological time keeper that tellsyour body when it’s time for bed andwhen to wake up. The findings mayimprove the ability to diagnose disordersin body rhythms and ultimately helpphysicians administer drugs at the time ofday when they would be most effective.

John Hogenesch, Garret FitzGeraldand coworkers at the University ofPennsylvania School of Medicine recent-ly used mouse expression arrays touncover that about 10% of the genes inthe mouse aorta have a circadian rhythmin their mRNA expression, which is sim-ilar to other tissue that was examined.Further study of these genes may helpscientists understand why blood pres-sure, coagulation, and contractile func-tion of the heart vary based on the timeof day according to the body, and whyheart attacks and strokes occur moreoften at certain times of day. His studywas published in the October 25, 2005issue of Circulation.

Circadian rhythms, which are con-served along the evolutionary tree fromcyanobacteria to humans, result fromcertain aspects of physiology that eachspecies regulates to stay in tune with theenvironment. Research such asHogenesch’s will help reveal the exactmechanisms involved in these processes.

“It’s very exciting because we’re start-ing to be able to make predictions aboutwhat types of mutations and genes will

cause what types of deficits in the clock,”said Hogenesch, who did much of thiswork as head of the Genomics depart-ment at the Genomics Institute of theNovartis Research Foundation.

At the RIKEN Center forDevelopmental Biology in Japan,

researcher Hiroki Ueda used AffymetrixU74 mouse expression microarrays anddiscovered a cohort of genes that deter-mine body time. He found that a single-time-point expression profile couldaccurately detect body time and providea diagnostic for body rhythm disorders.

Scientists Find the Genes that Make Our InternalClocks Tick RIKEN CDB’s Hiroki Ueda and John Hogenesch of the University of Pennsylvania School of Medicine discusstheir methods for discovering temporally expressed genes that regulate the timing of gene expression

By Stacey Ryder

Summer 2006

E X P R E S S I O N ■ R E G U L A T I O N

V O L U M E 2 ■ I S S U E 3

A M B R E P R I N T

www.microarraybulletin.com

1AMB INTERVIEW REPRINT ■ SUMMER 2006

is head of the Laboratory

for Systems Biology and manager of the

Functional Genomics subunit at the Center for

Developmental Biology, RIKEN in Kobe,

Japan. He is also a visiting professor at Tohoku

and Tokushima Universities. He received his M.

D. and his Ph.D. in pharmacology from the

University of Tokyo. His laboratory is focused

on investigating biological mechanisms, includ-

ing circadian clocks, at the systems level. His

team used the Affymetrix U74Av1 microarray

to develop methods for detection of body time

and rhythm disorders as reported in the

August 2004 edition of the

Proceedings of the National

Academy of Sciences,

U.S.A.

Hiroki Ueda

2 SUMMER 2006 ■ AMB INTERVIEW REPRINT

His research was published in the August2004 edition of PNAS. Ueda has startedusing tiling arrays to study the

Drosophila clock system and is uncover-ing new transcripts that vary with circadi-an rhythms.

“Because we’re using tiling arrays, wefound that some specific gene isoformsare clock controlled and also found somenon-coding genes and unannotatedgenes with cyclic expression in a flyhead,” said Ueda. “We’re developingalgorithms to detect specific isoformswith specific expression patterns and todetect functional non-coding RNAsexpressed in some tissues.”

Hogenesch and Ueda recently spoketo AMB Managing Editor, TommyBroudy about the ways that microarraysare changing the way they look at circadi-an biology. They discussed:

■ Improvements in microarray tech-nology and computation methods

■ Experimental design of studieslooking for circadian patterns

■ Challenges of analyzing vastamounts of data

Detecting more cycling genes usingnew microarrays and new computa-tional methods

Broudy: How have increases inmicroarray density and new computa-tional methods influenced your findings?And have your methods changed overthe last several years?

Ueda: I’m impressed by the develop-ment in microarray density. In 2001, Istarted a mammalian circadian clockproject using the U74 array-the mousearray with 12,000 transcripts and 7,000protein-encoding genes. We found 100clock-controled genes in the suprachias-

matic nucleus, a deep brain region thatcontrols circadian rhythms in mammals.We later used 430A and B chips, cover-

ing 39,000 transcripts and found threetimes the number of transcripts.

We are also working on a pilot studyusing Drosophila tiling chips. We foundthat specific isoforms of known genes areclock controlled and we also found non-coding genes and even unannotated geneswith cyclic expression in the fly head.

Now we have developed a method todetect the strandness of clock-controlledtranscripts and we are trying to investi-

gate sense and anti-sense information ofsuch clock-controlled regions. I believesuch technology can be applied to themammalian circadian clock, as well.

Hogenesch: Scientists went fromlooking at a relatively small number ofprotein-encoding genes-around 7,000-inlate 2000 to 35,000 in 2004 and 2005. Interms of density, it’s following Moore’sLaw, with a doubling every 18 months.

Surprisingly, one thing that hasremained almost unchanged is ourexperimental design. It’s basically thesame. It speaks to how effective thedesign is for finding a large number ofoutput genes. We’ve discovered manymore than we previously knew about.

On the other hand, computationalalgorithms have also changed quite a bit-from using Mas 4 to Mas 5 to RMA andD Chip. My current favorite is GCRMA.We play around with many differentcomputational algorithms now ratherthan using those included with the soft-ware. As a result, we are finding algo-

“We found that specific isoforms of known

genes are clock controlled and we also found

non-coding genes and even unannotated genes

with cyclic expression in the fly head. “

is an Associate Professor in Pharmacology at the University of

Pennsylvania School of Medicine. His laboratory is interested in functional genomics strategies and

circadian regulation of physiology. The team has used a variety of Affymetrix microarrays. Most

recently, they used the U74Av2 microarray in a study of circadian gene expression in the mouse aorta,

as published in the October 2005 edition of Circulation.

John Hogenesch

rithms with better signal to noise proper-ties and consequently are better at detec-tion of cycling genes.

Both of our groups have worked veryhard on the visualization of data. That’sstill progressing. But the biggest changebetween then and now is our ability tohypothesis test. Our goal in 2000 was tofind cycling genes-now our goal is toscreen those cycling genes for new clockcomponents.

Hiro’s [Ueda’s] group has really beena pioneer in developing high-throughputmethods to test using siRNAs andcDNAs in cyclic cell assays. We nowhave some valuable new tools where wecan pan our lists of genes for causal fac-tors. Several years ago, Dr. Ueli Schiblershowed that peripheral cells and tissueshave independent circadian oscillators inmammals.

Ueli and his colleagues at theUniversity of Geneva showed that evenperipheral cells, like fibroblasts, seemedto have their own endogenous,autonomous circadian clocks. When hestuck them in culture, he could use eitherTaqman® or microarrays to detectrhythmic transcription.

Hiro has moved that system overusing reporter arrays to study cyclicluciferase activity in peripheral cells likeNIH3T3 cells. He can then knock genesdown or add genes using cDNA overex-pression or siRNA knockdowns andstudy the effect.

Ueda: We developed two machinesin collaboration with Dr. Takao Kondofrom Nagoya University: PMT-tron andCCD-tron. The PMT-tron uses photo-multiplier tubes to accurately detectluminescence from cultured cells. We use24 PMT, photomultiplier tubes, to detectbioluminescence from a 24-well plateand a turntable (“tron”) that contains 12plates. Using this machine we can moni-tor 288 wells simultaneously per experi-ment. Now we’re developing anothertype of machine called the CCD-tron,which uses a CCD camera to monitorbioluminescence in a high-throughputmanner. The CCD-tron can monitor 96-well plates and also 384-well plates. Withthe CCD-tron, we can simultaneously

monitor 4,608 samples per machine, perexperiment to screen or verify the func-tion of a clock gene candidate. We use 3CCD-tron in high-throughput firstscreening of 13,824 samples and 6 PMT-tron in more accurate second screeningof 1,728 samples per one experiment.

Hogenesch: If you think about it interms of a filter, the RNA dynamicswould be the first step, the cell-basedscreens would be the second step, andour ultimate step is showing geneticallythat these observations make a differ-ence in mouse behavior. That’s really thebottleneck for us right now.

Broudy: What are some of the toolsyou’ve developed from the microarraywork?

Hogenesch: We developed severalgene expression databases. Everyday

biologists can go to these databases andask simple questions: Does the gene I’minterested in cycle? What’s its temporalexpression pattern? The overreachinggoal was to take sophisticated packagesof software, circa 1999-2000, and make apackage that the institute head or evenmy mother could use. I think we’velargely succeeded.

Another interesting development isthat over the course of the last six or

seven years, Affymetrix has gone from aproprietary software platform to encour-aging outside researchers to code soft-ware and algorithms. It’s a major stepforward. Our project and many otherpackages have been contributed. Verybright people from all over the world arecontributing packages and making themavailable as part of Bioconductor andthe R-project.

Ueda: John’s database is very easy touse. We’re still developing tiling arrayvisualization tools right now. We hopeour algorithm can eventually be used inAffymetrix’s IGB and distributed all overthe world.

Broudy: What are some of theadded features you’re trying to build intoyour algorithm that may be implementedin IGB?

Ueda: Because we’re using tilingarrays, we found that some specific iso-forms are clock-controlled and foundunannotated genes and non-codinggenes with cyclic expression in a fly head.We’re developing algorithms to detectthese specific isoforms with specificexpression patterns and to detect func-tional RNAs expressed in some tissues.Incorporating replicate experi-ments and multiple circadian cyclesinto study design

Broudy: What research design stepsare most valuable for your microarraywork?

Hogenesch: We still basically useour original design; studying multiplecycles and watching the pattern repeatitself. When you have a non-randomrepeating pattern, you have a unique wayto go into a data set. You can calculatefalse positive and false negative rates byrandomization and set thresholds appro-priately. Multiple biological replicates ateach time point, and multiple cycles havebeen a powerful method for us to dis-cover new clock-control genes. Themore replicates, the better.

Ueda: We use the same number ofchips as John, 24, and the same replicateconcept, but we expose the mouse to lightfor 12 hours and to darkness for 12 hours.We sample the mouse every four hoursover two days in light-dark conditions and

3AMB INTERVIEW REPRINT ■ SUMMER 2006

Hiroki Ueda

then in constant darkness. John samplesonly in the constant darkness condition.

Hogenesch: You and I are exposedto a light-dark cycle. If you lived inSweden, it might be different for certaintimes of the year. We use an artificial wayto get at just clock-control genes by sam-pling only in constant darkness. Both ofour groups have been studying the prob-lem of “phase control.” If you have 24hours during the day, and 100 or 300cycling genes in the suprachiasmaticnucleus, some of those genes are timedto the morning, some to the evening, andthe rest to all other times of the day.

We’ve been trying to figure out howresponse elements in the genome dictatephase. Hiro’s Nature Genetics paper in

2005 showed that when you combinegenes in particular contexts, you can dic-tate expression to whatever time of dayyou want, and that’s how these networksseem to originate.

Ueda: That’s still a working hypothe-sis that we have to verify. We implement-ed artificial transcriptional circuits inmammalian cells to verify this hypothe-sis. By changing the expression timing oftranscriptional activator and repressor,we try to control transcriptional outputs.

Broudy: What types of controls doyou use?

Hogenesch: There are statisticalmethods you can use. You can take the12 time points and randomly shufflethem and fit those random patterns tocosign waves. The repeating pattern of24 hours is a powerful tool. Those pat-terns don’t originate from noise veryeasily. Of course, you’d like to check asmany transcripts as possible using othermethods. We have validated predictions

by northern blots. Hiro’s group set upTaqman to confirm most of them.

Broudy: When you’re pulling somany genes off a microarray, is it feasibleto take 10 genes out of a 500-gene signa-ture to validate? What are your thoughtson picking all vs. hand-picking genes?

Hogenesch: At some point youreach a level of comfort where youdon’t have to pick. For the Gene Atlaspaper we published in 2002 in PNAS,we did 1800 rtPCR reactions and hadan 82% confirmation rate. We validateeverything that would go into a publi-cation for sure. I’m pretty comfortablewith being 82% right as a biologist.With statistical methods, we know ourfalse positive rate.

Our real goal is not to confirmcycling patterns, but to develop high-throughput methods to validate thefact/hyphothesis that those cycling pat-terns have an influence and are causal inclock function. It’s a much trickier job.

Analyzing biological replicates and shar-ing data

Broudy: What are some of the chal-lenges in pulling samples and analyzingwhat may be an average across biologicalreplicates?

Hogenesch: There are several chal-lenges. Papers have been written on themerits of pooling strategies. Individualmice vary and if you can sample multiplemice of the same strain and same sex ata particular time, you get much cleanerdata. We came upon this without formaltesting in ‘99 and 2000, but it’s now beenformally tested by statisticians.

Ueda: One challenge would be aquality control of more than 1,000 sam-

plings in dynamic phenomena like circa-dian clock. Our lab performs some qual-ity control before we perform DNA chipanalysis. We use a morning gene, anevening gene and a night gene as a posi-tive control and then quantify the expres-sion of these three genes throughquantitative PCR, which controls thequality of the samples.

Hogenesch: Challenges still remain.Obviously at the informatics level, bothof us would say we’re certainly missingreally important protein-encoding andnoncoding genes in the genome. If thegenes haven’t yet been identified, itshard to tell whether they cycle.

One challenge is how we define theentire transcriptome. Tom Gingeras atAffymetrix has shown that a goodnumber of the transcriptional units inthe genome, maybe half, don’t appar-ently code for proteins, and we haven’tyet focused on those.

A significant challenge for all of biolo-gy is to figure out what these things do. Sothere’s the content of the arrays. I thinkwe’re pretty proficient, after five years ofbanging away on it, at finding the cyclinggenes. A major challenge that both of ourlaboratories and many other laboratories,not just in circadian biology, face is how totake this information and synthesizetestable hypotheses? How do we developmethods that allow us to test hypothesesfaster?

Broudy: Both of you are generatingtremendous amounts of data. What isthe best way to deliver a simple and rea-sonably accurate picture of circadianrhythm given all the data that bothgroups are outputting from microarrays?

Ueda: We are still developing ourwebsite to look at our expression dataand to disseminate our data to thepublic once it’s published. I am happyto share our data with other researchersin all over the world.

Hogenesch: That’s a very interestingquestion and it’s sort of hotly debated inthe field. Obviously, depositing the rawdata is an important aspect. We depositour published work into GEO.

I was finding back in ‘99 and 2000,and I think it’s still true today, that most

4 SUMMER 2006 ■ AMB INTERVIEW REPRINT

“A significant challenge for all of biology is to

figure out what these things do. So there’s the

content of the arrays. I think we’re pretty profi-

cient, after five years of banging away on it, at

finding the cycling genes.”

5AMB INTERVIEW ■ JULY 2005

biologists are not comfortable enoughwith bioinformatic software programs toextract data and process it themselves.On top of depositing things into publicdata sources, we’ve also developed Web-based tools that allow other people tovisualize our data sets.

I’m happy to say that we’ve had mil-lions of visitors, looking at either theGene Atlas data sets (http://symatlas.gnf.org) or circadian data sets. Nothingmakes you happier than when somebodyelse can take something that you’ve doneand really build on it, especially whenthey find human disease genes as ourcolleague Vamsi Mootha has done atHarvard and the Broad.

Editorial StaffWes Conard, Editor-in-Chiefwes_conard@affymetrix.com

Tommy Broudy, Managing Editorthomas_broudy@affymetrix.com

Rachel Shreter, Editorrachel_shreter@affymetrix.com

Kamalia Dam, Associate EditorStacey Ryder, Associate EditorDaniel Noble, Copy EditorMarva Maida, Contributing Designer

A F F Y M E T R I X

MICROARRAYBULLETIN

Contacts■ John Hogenesch, Ph.D.Associate ProfessorDept of PharmacologyUniversity of Pennsylvania School ofMedicine810/835 BRBII/III421 Curie BlvdPhiladelphia 19104-6160

■ Hiroki R. Ueda, M.D., Ph.D.RIKEN Center for Developmental Biology2-2-3 Minatojima-minamimachiChuo-kuKobe 650-0047Japanuedah-tky@umin.ac.jp

Companies■ Affymetrix Inc. - http://www.affymetrix.com

Organizations■ RIKEN Center for Developmental Biology- http://www.cdb.riken.jp/en/

People■ Ueli SchiblerUnversity of Genevahttp://www.molbio.unige.ch/schibler/index.php■ James Eberwine, Ph.D.University of Pennsylvaniahttp://www.med.upenn.edu/ins/faculty/eberwine.htm■ Russel Van Gelder, M.D., Ph.D.Washington Universityhttp://dbbs.wustl.edu/dbbs/website.nsf/RIB/ACD082780D7C6DB386256D4E005B2DE5■ Dr. Takao KondoNagoya University

Further Reading■ Ueda HR, Hayashi S, Chen W, Sano M,Machida M, Shigeyoshi Y, Iino M, HashimotoS. System-level identification of transcription-al circuits underlying mammalian circadianclocks. Nat Genet. 2005 Feb;37(2):187-92.■ Rudic RD, McNamara P, Reilly D, GrosserT, Curtis AM, Price TS, Panda S, HogeneschJB, FitzGerald GA. Bioinformatic analysis ofcircadian gene oscillation in mouse aorta.Circulation. 2005 Oct 25;112(17):2716-24.

■ Baggs JE, Hayes KR, Hogenesch JB.Comparative genomics as a tool in the under-standing of eukaryotic transcriptional regula-tion. Curr Opin Genet Dev. 2005Dec;15(6):634-9.■ Hayes KR, Baggs JE, Hogenesch JB.Circadian clocks are seeing the systems biolo-gy light. Genome Biol. 2005;6(5):219.■ Walker JR, Hogenesch JB. RNA profiling incircadian biology. Methods Enzymol.2005;393:366-76.■ Ueda HR, Chen W, Minami Y, Honma S,Honma K, Iino M, Hashimoto S. Molecular-timetable methods for detection of body timeand rhythm disorders from single-time-pointgenome-wide expression profiles. Proc NatlAcad Sci U S A. 2004 Aug 3;101(31):11227-32.■ Su AI, Cooke MP, Ching KA, Hakak Y,Walker JR, Wiltshire T, Orth AP, Vega RG,Sapinoso LM, Moqrich A, Patapoutian A,Hampton GM, Schultz PG, Hogenesch JB.Large-scale analysis of the human and mousetranscriptomes. Proc Natl Acad Sci U S A. 2002Apr 2;99(7):4465-70.

F O R M O R E I N F O R M AT I O N