Drosophila cells, identifying 50 small molecule inhibitors of cytokinesis and 214 genes important for cytokinesis, including a new protein in the Aurora B pathway (Borr). By comparing small molecule and RNAi phenotypes, we identified a small molecule that inhibits the Aurora B kinase pathway. Our protein list provides a starting point for systematic dissection of cytokinesis, a direction that will be greatly facilitated by also having diverse small molecule inhibitors, which we have identified. Dissection of the Aurora B pathway, where we found a new gene and a specific small molecule inhibitor, should benefit particularly. Our study shows that parallel RNA interference and small molecule screening is a generally useful approach to identifying active small molecules and their target pathways.Cytokinesis involves temporally and spatially coordinated action of the cell cycle and cytoskeletal and membrane systems to achieve separation of daughter cells. To dissect cytokinesis mechanisms it would be useful to have a complete catalog of the proteins involved, and small molecule tools for specifically inhibiting them with tight temporal control. Finding active small molecules by cell-based screening entails the difficult step of identifying their targets. We performed parallel chemical genetic and genome-wide RNA interference screens in  Parallel genetic and chemical screens are used to find small molecules that interfere with cell division and to identify the specific targets for these molecules Small molecule inhibitors are useful tools for studying dynamic biological processes. Compared to mutations and RNA interference (RNAi), cell-permeable small molecules allow inhibition of protein function with precise temporal control, and may also spur development of new therapeutics. One approach to finding useful small molecules is phenotypic screening, in which cells are treated with small molecules from a library and scored for inhibition of the process of interest. The rate-limiting step in this approach is identifying the cellular targets of active small molecules. Traditionally, the targets of small molecules have been identified by methods based on physical affinity, for example, affinity chromatography . These rCytokinesis is the final step of cell division, when the daughter cells are physically separated by constriction of a cleavage furrow. Complex spatiotemporal coordination of several cell systems, including the microtubule and actin cytoskeletons, the cell cycle engine, and vesicle trafficking, is required for furrow positioning, assembly, ingression, and eventual cell separation. Some important components of the cleavage furrow, for example, actin , Myosin Saccharomyces cerevisiae  from the culture medium and process them into small interfering RNAs without triggering an interferon response, in contrast to mammalian cells  and over 51,000 small molecules at the Institute of Chemistry and Cell Biology (http://iccb.med.harvard.edu). The cells were incubated for 4 d in the presence of dsRNAs to allow for depletion and turnover of targeted gene products, or 2 d for small molecules to permit each cell to complete at least one cell cycle. After fixation, cells were stained with amine-reactive tetramethylrhodamine-NHS ester to visualize total cytoplasm and Hoechst dye to visualize DNA. In the RNAi screen, microtubules were visualized by immunofluorescence. Cells were imaged by automated fluorescence microscopy, and assay wells containing a high frequency of binucleate cells were identified by a combination of automated image analysis and visual inspection.Cells that undergo mitosis normally, but fail cytokinesis, acquire two nuclei. This phenotype is a specific and irreversible consequence of cytokinesis failure, and can be scored by automated fluorescence microscopy. http://iccb.med.harvard.edu, we identified 50 small molecule inhibitors of cytokinesis, and selected 25 of the most potent and readily available for further analysis   see .Ircinia ramosa, which contains swinholide A, a known actin binder, as its active ingredient . To learn more about the cellular targets of the remaining compounds, we proceeded with our plan to systematically compare small molecule and RNAi phenotypes.Since most compounds currently known to inhibit cytokinesis are natural product actin binders, we tested if the small molecule inhibitors affected actin polymerization. Binucleines 4, 6, 24, and 25 inhibited pyrene–actin polymerization in a pure protein assay (data not shown). Binucleines 24 and 25 are the actin binders cytochalasin D and jasplakinolide, which were included in our small molecule collection as control compounds. Binucleine 4 is a natural product extract from (Act57B and Act5C), Myosin heavy chain (zipper), Anillin (scraps), a formin (diaphanous), Rho GTPase (Rho1) and its known guanine nucleotide exchange factor (pebble) and GTPase-activating protein (RacGAP50C), a kinesin (pavarotti), Citron kinase (CG10522), Aurora B kinase , and a PRC1 homolog (fascetto). We discovered one new gene essential for cytokinesis , increasing the number of specific essential proteins confirmed by RNAi to thirteen  resulted in a weak phenotype, whereas depletion of its complex partner encoded by zipper, the Myosin II heavy chain, resulted in a much higher frequency of binucleates. This illustrates the importance of identifying medium and weak phenotypes. Genes with weaker binucleate phenotypes may also be significant because depletion of cytokinesis proteins with multiple functions during the cell cycle can cause arrest prior to cytokinesis, diminishing the likelihood of detecting the phenotype in unsynchronized cells. Overlap between our screen and a recently published proteomic analysis of the midbody . The dsS2 cells , which m midbody  highlighFollowing our strategy to systematically compare chemical genetic and functional genomic data, we classified the data from both screens into four phenotypic groups  caused clusters of filamentous actin to accumulate in interphase cells, in addition to completely blocking cytokinesis. Since no dsRNA caused this phenotype, we suspect it is a gain-of-function effect of the small molecule, whose mechanism we will pursue.We identified phenotypes common to both datasets, but the detailed phenotypic analyses did not match exactly, with more phenotypic subclasses distinguished with the small molecules. Two considerations may account for the existence of additional phenotypic categories for small molecules. One is timing. During our detailed secondary analysis, we added small molecules to cells for variable amounts of time (3 h to 48 h). When cells were exposed to a drug for a short time, we were able to analyze localization of furrow components immediately after cytokinesis failure. These phenotypes became less apparent upon longer exposure because the long delay gave cells the opportunity to disassemble residual furrow structures. When cells were exposed to dsRNAs for days, long, variable delays between cytokinesis failure and fixation may have obscured interesting phenotypes, which could be revealed by subsequent real-time imaging experiments . The othIrcinia ramosa extract]). This sub-phenotype represents a portion of the genes identified as “binucleate with microtubule extensions” shown in Systematic comparison between the phenotypic categories based on detailed immunofluorescence analysis of both screens did, however, allow us to connect small molecules to two specific pathways involved in cytokinesis, namely actin cortex integrity and the Aurora B pathway. Both dsRNAs and small molecules that weakened the actin cortex caused microtubule-rich extensions to protrude from interphase cells as well as failure of cytokinesis . These iaurora B, INCENP, and CG4454, or addition of one small molecule N′-[1-(3-chloro-4-fluorophenyl)-4-cyano-1H-pyrazol-5-yl]-N,N-dimethyliminoformamide  fusion proteins to both C- and N-termini of CG4454. CG4454-GFP exhibited the signature localization of a passenger protein and co-localized with Aurora B throughout mitosis and cytokinesis . Altogether, phenotypic similarities between loss-of-function for Aurora B and binucleine 2 strongly suggest that binucleine 2 targets a protein involved in the Aurora B pathway. Several small molecule inhibitors of Aurora kinases have been reported, although their chemical scaffolds are different from binucleine 2. These small molecules are not active in fly cells, while binucleine 2 is inactive in mammalian systems (data not shown). Aurora kinase levels are elevated in some tumors, making these proteins a potential target for cancer therapy. Interestingly, lower binucleine 2 concentration or shorter Aurora B RNAi treatment favors binucleate formation, while higher drug concentration and longer incubation for RNAi results in a relative increase in large cells with diffuse DNA. Thus, the cytokinesis function of the Aurora pathway may be more sensitive to inhibition than its mitosis function. This observation may be important for understanding the response of cancer cells to Aurora inhibitors now entering clinical trials  were added to each well using a MultiDrop 384  liquid dispenser and incubated at 24 °C overnight. Then, 100 nl of compound stocks dissolved in DMSO at approximately 10 mg/ml was added using the pin transfer robot at the Institute of Chemistry and Cell Biology at Harvard Medical School (http://iccb.med.harvard.edu). Cells were incubated at 24 °C for 48 h. All fixation, staining, and washing steps were carried out using a MultiDrop liquid dispenser and 24-channel wand  for liquid removal. Cells were fixed and permeabilized in 40 μl of 100 mM Pipes/KOH (pH 6.8), 10 mM EGTA, 1 mM MgCl2, 3.7% formaldehyde, and 0.2% TritonX-100 for 15 min and washed in 50 μl of PBS. The cytoplasm was stained with 40 μl of 0.5 μg/ml NHS-tetramethylrhodamine  in PBS for 15 min. Subsequently, 40 μl of 5 μg/ml Hoechst 33342  in TBST (TBS with 1% TritonX-100) was added for 30 min. This step stains the DNA and quenches excess NHS ester to ensure uniform TMR staining. Cells were washed twice with 40 μl of TBST and sealed with aluminum seals  for image acquisition.20,000 2, 100 μM CaCl2, 100 mM KCl, 5 mM EGTA, 200 μM ATP, and 10 μM small molecule. Pyrene–actin polymerization was followed by fluorescence spectroscopy over 45 min. An increase in fluorescence indicates actin polymerization. Adapted from Pyrene-labeled actin  was added to 10 mM HEPES (pH 7.7), 2 mM MgClhttp://www.flyrnai.org). Each well contained 5 μl of approximately 0.05 μg/μl dsRNA in water. 10,000 Drosophila Kc167 cells in 10 μl of serum-free Schneiders's Drosophila Medium were added to each well containing dsRNA using a MultiDrop liquid dispenser. After 1 h of incubation at room temperature, 30 μl of medium  was added. The plates were sealed or placed in a humidified chamber and incubated for 4 d at 24 °C. Fixation, TMR, and Hoechst staining were carried out as described above for the small molecule screen. Cells were then blocked in 40 μl of AbDil (TBST with 2% BSA) for 30 min and stained overnight at 4 °C with 20 μl of 1:250 monoclonal anti-tubulin  and 2 μg/ml Alexa 488 goat anti-mouse antibody (Molecular Probes) in AbDil. Cells were washed twice with 40 μl of TBST and sealed with aluminum seals (Costar 6570) for image acquisition. In order to identify weak hits reliably, the RNAi screen was carried out in triplicate on three separate occasions.dsRNAs were aliquoted into black, clear-bottom 384-well plates  at the Drosophila RNAi Screening Center at Harvard Medical School  AutoScope or a Universal Imaging Discovery-1. The AutoScope is a Nikon  TE300 inverted fluorescence microscope with filter wheel , x-y stage (Prior H107N300), piezoelectric-motorized objective holder , and a CCD camera . MetaMorph software  running the Screen Acquisition drop-in allowed coordination of software-based autofocusing, movement between wells, imaging, and image evaluation. Two images per well were acquired in each of two  or three (RNAi screen) channels using a 20x objective with 2 × 2 binning.Given the need for greater accuracy in an annotation screen, the RNAi images were initially scored by visual inspection. We looked at two images per well from two independent datasets  and noted wells with elevated binucleate levels. To determine the percentage of binucleate cells per image, we used the Integrated Morphometry feature in the MetaMorph software to count the number of nuclei per image and then manually counted the number of binucleate cells. We collected two images per assay well and report the level of binucleates per well as an average of both images. Because the level of binucleates can vary depending on the location of the well in the assay plate or the position of the assay plate in a stack of plates, we decided to compare each proposed hit well to its two neighboring wells to prevent false positives or negatives due to local variations. If a neighboring well also exhibited a phenotype, we chose the next neighbor for our analysis. Although we performed the screen in triplicate, we were only able to apply this analysis to two datasets because the cells in the third screen were too clustered to use automated cell counting. It was possible, however, to estimate the binucleate level in the third dataset by visual inspection. We only scored a phenotype if it repeated in at least two experiments, and the vast majority of phenotypes repeated in all three datasets. In a weakly penetrant phenotype the binucleate level was increased by more than 1.25-fold relative to the average of both neighboring wells. In a medium penetrance phenotype the binucleate level was above 4%, or four times as high as the neighboring wells. In a strongly penetrant phenotype the binucleate level was above 15%.167 cells were transfected with these constructs using Insect GeneJuice transfection reagent  according to the manufacturer's instructions and were used for live cell imaging and immunofluorescence 6–7 d after transfection.For the C-terminal fusion protein, Borr (CG4454) cDNA (LD36125) was cloned into the EcoRI and KpnI sites of pEGFP-C1 , cut with NheI and KpnI, and ligated into pPacPL . For theCells were exposed to dsRNA or small molecules, fixed, and stained as described in the screening protocols. Cells were stained with TRITC-labeled phalloidin (Sigma) to visualize actin or antibodies to the following proteins (data not shown): Anillin, Aurora B (a gift from D. Glover), Diaphanous, INCENP (a gift from W. Earnshaw), Lamin (a gift from H. Saumweber), Lava-lamp, Myosin II, Pavarotti, Peanut, Pebble (a gift from H. Bellen), phospho-Histone H3 , Polo (a gift from D. Glover), Rho1  and tubulin .N,N-dimethylformamide dimethyl acetal  were refluxed in ethanol for 1 h. The product (binucleine 2) 2SO) δ 8.29 , 8.06 , 8.03 , 7.84–7.81 , 7.55 , 3.14 , 3.00 . ESI-MS calculated for C13H11ClFN5 291, [M + H]+ found 292.Since binucleine 2 is no longer available commercially, we resynthesized it see : 3-chlor167 cells were treated with 100 nM, 300 nM, 1μM, 5μM, 25μM, or 100 μM of binucleine 2 and fixed after 15 min, 30 min, 45 min, 1 h, 1.5 h, 2 h, 2.5 h, 3 h, 3.5 h, or 4h. After staining with phospho-Histone H3 antibody (Upstate), tubulin , and DNA and then imaging, cells with phospho-Histone H3 staining were counted. The total number of cells was counted using the Integrated Morphometry feature in the MetaMorph software, and the percentage of cells with H3 staining was calculated and is plotted in KcFigure S1aurora B or borr (CG4454). Cells were stained with TRITC-labeled phalloidin to visualize actin, or antibodies against Anillin, tubulin, Lava-lamp, or Lamin. Lamin-stained cells were treated with binucleine 2 (100 μM) for 24 h.Cells were untreated or treated with binucleine 2  or dsRNA corresponding to (4.5 MB TIF).Click here for additional data file.Figure S2(49KB DOC).Click here for additional data file.Figure S3(32 KB DOC).Click here for additional data file.Table S1167 cells were exposed to small molecules at 100 μM, 30 μM, or 10 μM for 48 h. In a weakly penetrant phenotype (w), the binucleate level was increased by at least 1.25-fold above background. In a medium penetrance phenotype (m), the binucleate level was above 4%, and in a strongly penetrant phenotype (s), the binucleate level was above 15%, while the average binucleate level was approximately 1%. In the binucleate phenotype column, “binucleate” indicates binucleate cells only, “diffuse DNA,” binucleate cells with large, diffuse DNA, “lc,” binucleate cells with low cell count, and “MT ext,” binucleate cells with microtubule extensions. HeLa and BSC-1 cells were exposed to small molecules at 30 μM for 24 h. Growth inhibition in drug-sensitive S. cerevisiae RDY98  was measured at a small molecule concentration of 250 μM after an overnight exposure.Kc(159 KB DOC).Click here for additional data file.Table S2CG10522, cycA, Pp4-19C, RpL32, Tra1, and Ubi-63E—or three times—crn.The “DRSC dsRNA ID” is an internal dsRNA ID number. In the potency column, “s” represents strong, “m,” medium and “w,” weak penetrance of the binucleate cell phenotype. In the phenotypic classification column, “binucleate” indicates binucleate cells only, “diffuse DNA,” binucleate cells with large, diffuse DNA, “lc,” binucleate cells with low cell count, and “MT ext,” binucleate cells with microtubule extensions. Six genes were independently identified in multiple wells, either scored twice—(141 KB DOC).Click here for additional data file.Table S3Gene names, FlyBase IDs, Gene Ontology annotations, forward and reverse primers, and amplicon lengths are shown. “HFA amplicon” and “DRSC dsRNA ID” are internal dsRNA identifiers. The number of potential secondary targets based on 21 nucleotide fragments is the number of genes that have at least one length of 21 bp or more with matching sequence of 21 bp or more of this amplicon. The criteria used in this analysis are such that it may be prone to false positives for secondary targets. The binucleate percentage per well, the relative increase in binucleates relative to the neighboring wells (1.25 = 25% increase), cell number per well, and relative increase or decrease in cell number relative to the neighboring wells are shown for datasets 1 and 2. Annotations for dataset 3 are only shown when they help to define a particular phenotype.(97KB XLS).Click here for additional data file.Table S4Functional groups are based on the predicted function as reported by FlyBase (new annotation excluded).(90 KB DOC).Click here for additional data file.Table S5(181 KB DOC).Click here for additional data file.


Microarray is a high-throughput technology to study expression of thousands of genes in parallel. A critical aspect of microarray production is the design aimed at space optimization while maximizing the number of gene probes and their replicates to be spotted.We have developed a software called 'ArrayD' that offers various alternative design solutions for an array given a set of user requirements. The user feeds the following inputs: type of source plates to be used, number of gene probes to be printed, number of replicates and number of pins to be used for printing. The solutions are stored in a text file. The choice of a design solution to be used will be governed by the spotting chemistry to be used and the accuracy of the robot..ArrayD is a software for standard cartesian robots. The software aids users in preparing a judicious and elegant design. ArrayD is universally applicable and is available at  Microarray is a popularly used high-throughput technology to investigate gene expression of thousands of genes simultaneously at the level of mRNA. Ever since the development of this technology -3, transA critical aspect of microarray production is the design considering space optimization to produce high-density arrays for a given set of samples and replicates. The softwares generally supplied with robotic spotters translate user input parameters into a set of instructions in robot language for printing arrays. These softwares do not offer design capabilities in which spotting parameters and grid configurations can be chosen for a given set of samples and replicates. Presently various solutions have to be derived manually in most academic laboratories. We have developed a user-friendly software 'ArrayD' that can be used by experts and novice alike to fill this gap to simplify and aid in rapid design. ArrayD offers a variety of design solutions given a set of requirements: Number of gene probes, number of replicates, and the source plate (384 well or 96 well). Because the algorithm implemented in ArrayD is inherently simple and uses fundamental principles of robot operation, the design solutions offered by ArrayD are universally applicable to any system. The choice of a design solution would be governed by the spotting chemistry and the humidity used in addition to elegant appearance. The hallmark of ArrayD is its overall simplicity and the variety of alternative designs it offers for users to decide on choosing the appropriate spotting parameters. The multiple design solutions offered by ArrayD provides a wide range of arrays from compact to loosely spaced spots as well as convenient grid patterning, which can be user selected for printing.ArrayD program is developed in C and can be compiled and operated on UNIX V5.1, IRIX 5.1 and Red Hat Linux 7.0 (or higher) operating systems. A companion computer program ArraySolution was developed in Perl  version 5.6.1 and can be implemented on any UNIX or Linux operating system.(a) Type of source plate to be usedThe input parameter toggles between either 96 well or 384 well plate.(b) Number of gene probes to be printedThe number of gene probes including positive and negative controls and blanks.(c) Number of replicatesThe number of replicates of each gene probe. Although the most common pattern chosen is duplicates, users can choose any number of replicates. Replicates are assumed to be printed in the Y axis.(d) Number of pins to be used for printingThis parameter relates to time taken for printing the slides and the number of spots arrayed per slide. The number of pins in X-axis and Y-axis need to be specified. The type of pins used is assumed to be stealth pins, which are widely used. It is not necessary to specify pin type for ArrayD. Instead, this aspect is considered in the printing software according to the pin type used for implementing a particular design.A general microarray design layout is displayed in Figure The reference direction of the robot for picking probes from source plate is left → right followed by top → down; the printing direction is top → down followed by left → right. Replicates are considered to be spotted in Y-axis Figure . After tThe program first validates the input given by the user for appropriate number of pins in each direction and the plate type to be used. For a valid input, ArrayD calculates maximum possible number of super grids in X (or Y) direction based on the coverslip dimensions, pin number in X (or Y) direction and pin-to-pin distance Figure . The covArrayD uses a predefined inter-spot distance database. Design solutions of ArrayD encompass various inter-spot distances that would be compatible with different spotting chemistries and conditions of humidity. We have used inter-spot distances of 170 μm, 180 μm, 190 μm, 200 μm, 220 μm, 250 μm and 300 μm. This database can be expanded to incorporate even lower inter-spot distances for use with other spotting chemistries by simple modification. We chose 170 μm as least distance based on several trial experiments in our laboratory using 50% DMSO as spotting solution and SMP3 pin type. In our experience, a minimum inter-spot distance of 200 microns works best with 50% DMSO at 40% – 50% humidity at 25°C.The options for the inter-spot distance currently offered by the software can work successfully for SMP2, SMP2B, SMP2XB, SMP2.5, SMP2.5B, SMP2.5XB, SMP3, SMP3B, SMP3XB, SMP4, SMP4B, SMP4XB, SMP5, SMP5B and SMP5XB stealth pins ', or 90° rotated 'Rectangle ' based on the geometry of a given design solution. If the number of grids are equal in both the direction we have a 'Square' design. In all other cases we obtain a 'Rectangle' design, which can be either of two types: the long side of the array is parallel to the length  or the width  of the slide. The output of ArraySolution is a tab-delimited text file called 'filename.solution' where filename corresponds to the input name of the file carrying design solutions. The tabular report consist of Number of super grids in X – direction, Number of super grids in Y – direction, Number of spots per grid in X – direction, Number of spots per grid in Y – direction, Distance between two spots (in microns), Distance Area ratio and geometry of design (Square or Rectangle). This can aid users to decide on a particular design solution based on space optimization and elegant appearance.An example of a sample run is provided in Figure We have developed a simple and rapid software ArrayD that offers various design solutions of designing microarrays for a specific set of user-defined requirements.ramu@igib.res.in or ramucbt@yahoo.com.The source code and the executable file for ArrayD and ArraySolution programs are freely available and can be downloaded from our website . The souAS explained the operational details to software expert, did the experimentation, maintenance and testing of the software. GPS did the basic software writing and implementation of 'ArrayD'. VKS prepared the code for the companion program 'ArraySolution' to classify the solutions on the basis of design geometry. SR is the Group Leader generating demand, sourcing and linking people, explaining the concepts, testing, critical examination, presentation of data, providing salary through grants.Report generated by ArraySolution. A total of 67 different design solutions were classified into three categories of Square, Rectangle  and Rectangle  as mentioned in text. The detailed design parameters including number of supergrids in X and Y direction, number of spots per grid in X and Y direction, distance between the spots, distance area ratio and geometry of design are provided for preparing elegant microarrays.Click here for file


MBD2/3 is the only candidate gene in the Drosophila genome with extended homologies to mammalian MBD2 and MBD3 proteins, which represent a co-repressor and an integral component of the Nucleosome Remodelling and Deacetylase (NuRD) complex, respectively. An association of Drosophila MBD2/3 with the Drosophila NuRD complex has been suggested previously. We have now analyzed the molecular interactions between MBD2/3 and the NuRD complex in greater detail.Methyl-DNA binding proteins help to translate epigenetic information encoded by DNA methylation into covalent histone modifications. Drosophila NuRD complex.The two MBD2/3 isoforms precisely cofractionated with NuRD proteins during gel filtration of extracts derived from early and late embryos. In addition, we demonstrate that MBD2/3 forms multimers, and engages in specific interactions with the p55 and MI-2 subunits of the Drosophila MBD2/3 and NuRD proteins. Additionally, this work provides a first analysis of the architecture of the Drosophila NuRD complex.Our data provide novel insights into the association between  Nucleosome Remodelling and Deacetylase complex (NuRD) to methylated CpG dinucleotides [Methyl-DNA binding proteins are connecting DNA methylation to transcriptional silencing -4. Up toleotides ,3, whereleotides  is an inleotides . Kaiso ileotides .Drosophila gene MBD2/3 encodes a protein, which shares high homology to mammalian MBD2 and MBD3 [Drosophila MBD2/3 is expressed in two isoforms, the smaller one is lacking part of the putative methyl-DNA binding domain [Drosophila NuRD complex [Drosophila cells [The and MBD3 ,4. Due tg domain -15. The g domain ,15. In i complex -14. Morela cells ,14.The NuRD complexes of vertebrates are a heterogeneous group of complexes containing both histone deacetylase and nucleosome remodelling activities . NuRD coDrosophila genome contains clear homologues for all verterbrate NuRD proteins. Recombinant Drosophila MI-2 was shown to have ATPase and nucleosome mobilization properties [Drosophila the HDAC gene Rpd3 is important for segmentation of the embryo [Drosophila p55, a WD-40 protein, is homologous to the histone deacetylase-associated proteins RbAp46 and RbAp48 [Drosophila MTA-like displays extensive homology to the vertebrate MTA proteins [Strikingly, the operties . In Drose embryo . Drosophd RbAp48 . Finallyproteins .Drosophila NuRD complex implies a conserved function during the animal development. In previous studies cell lines were analysed that express only the small isoform of MBD2/3, lacking part of the putative methyl-DNA binding domain and a Drosophila specific domain. In order to analyse isoform-specific differences of MBD2/3 and their ability to bind NuRD proteins, we now extend our analysis to both isoforms in Drosophila embryos.The strong conservation between vertebrate NuRD complexes and the Drosophila MBD2/3 with the NuRD complex has been suggested previously [Drosophila embryos we size-fractionated nuclear protein extracts from 2–4 h or 0–12 h wild type embryos, respectively, by Superose 6 gel filtration chromatography. Proteins from individual fractions were then separated by standard SDS-PAGE and analysed by Western blotting using antibodies directed against known homologous subunits of the NuRD complex. MI-2 and MTA-like, which are homologues of subunits that are specific for vertebrate NuRD complexes [Drosophila NuRD complex. The RPD3 and p55 proteins also cofractionated with MBD2/3 isoforms, but they were also found in fractions of lower molecular weight  of MBD2/3 we first used a GST-pulldown assay. In order to eliminate unspecific interactions with NuRD proteins, we established highly stringent assay conditions. This was achieved by assessing the interactions between radioactively labeled SV40 large T antigen Fig. , large TDrosophila embryos we immunoprecipitated nuclear extracts with MBD2/3-specific antibodies and with myc-specific control antibodies. Precipitates were analysed by Western blot for the presence of p55 and GAGA factor. This revealed a specific association of MBD2/3 with the Drosophila NuRD homologue p55, but not with the unrelated GAGA factor  antibodies were added to 75 μl of nuclear extracts prepared as mentioned below and incubated for 12 h at 4°C. Protein G beads (Amersham) were blocked in 3 mg/ml BSA for 20 min at room temperature, washed three times with 300 mM KCl, 0.2 % NP-40 and added to the samples. Incubation was carried out for an additional 1 h at 4°C. The beads were collected by centrifugation and washed four times in 1 ml 300 mM KCl, 0.2 % NP-40. The beads were resuspended in loading buffer for SDS-PAGE and vigorously vortexed for 15 sec. The immunoprecipitates were separated by SDS-PAGE, without futher boiling, transferred to a PVDF membrane and probed with antibodies against RPD3, p55 and GAGA factor, respectively.Drosophila embryos were prepared as described previously [For baculovirus expression, the MBD2/3 cDNA  was subceviously . Extract35S-methionine-labelled proteins were generated by in vitro trancription/translation of pGADT7_T, pGADT7_Mi-2, pGADT7_MTA-like, pGADT7_Rpd3, pGADT7_p55, pGADT7_MBD2/3 long isoform and pGADT7_MBD2/3 small isoform using the TNT coupled reticulocyte lysate system (Promega) according to the manufacturer's protocol. GST-MBD2/3 long isoform and GST-MBD2/3 small isoform fusionproteins were obtained by cloning the coding region of the two isoforms in pGEX4T1 (Amersham) and subsequent expressing of the constructs in BL-21 bacteria according to the manufacturer's protocol. GST-MBD2a was obtained from Hidetoshi Fujita [i Fujita . GST-pulThe Matchmaker Two-Hybrid system 3 (Clontech) was used for all experiments, according to the manufacturer's instructions. The AH109 strain was used as host and transformed with various constructs (see above) using standard procedures. Transformants were selected on SD/-Ade/-His/-Leu/-Trp plates.KK and JM carried out the molecular interaction studies. AB carried out the gel filtration experiments. JM conceived of the study, and participated in its design and coordination. All authors read and approved the final manuscript.


Drosophila, there are seven ligands that function through combinations of three type I receptors and two type II receptors. These signals can be roughly grouped into two major TGFβ pathways, the dpp/BMP and activin pathways, which signal primarily through thick veins (tkv) and baboon (babo). Few downstream targets are known for either pathway, especially targets expressed in the Drosophila brain.Members of TGFβ superfamily are found to play important roles in many cellular processes, such as proliferation, differentiation, development, apoptosis, and cancer. In tkv and babo both affect the growth of tissues, but have varying effects on patterning. We have identified targets for the tkv and babo pathways by employing microarray techniques using activated forms of the receptors expressed in the brain. In these experiments, we compare the similarities of target genes of these two pathways in the brain. About 500 of 13,500 examined genes changed expression at 95% confidence level (P < 0.05). Twenty-seven genes are co-regulated 1.5 fold by both the tkv and babo pathways. These regulated genes cluster into various functional groups such as DNA/RNA binding, signal transducers, enzymes, transcription regulators, and neuronal regulators. RNAi knockdown experiments of homologs of several of these genes show abnormal growth regulation, suggesting these genes may execute the growth properties of TGFβ.tkv and babo pathways and provided new insights into downstream effectors of two distinct TGFβ like pathways. Many of these genes are novel and several genes are implicated in growth control. Among the genes regulated by both pathways is ultraspiracle, which further connects TGFβ with neuronal remodeling.Our genomic-wide microarray analysis has revealed common targets for the  TGFβ pathways are conserved between primitive animals, such as sponges and sea anemone ,2 and veIn recent years, the core signaling components of the TGFβ pathways have been elucidated by a combination of genetics and biochemical approaches. Unique to these signaling pathways are transmembrane receptor serine-threonine kinases that are novel in animals. Signaling is initiated when dimeric ligands bind to the type I receptor or a complex of the type I and type II receptors. The type II receptor phosphorylates the type I receptor, which renders it active. R-Smads are phosphorylated by the type I receptor, the complex with a co-Smad, and translocate to the nucleus. Smads bind DNA promoter elements weakly and require co-factors for efficient regulation of target genes.Drosophila, seven ligands have been identified from the genomic sequence [thick veins (tkv), saxophone, baboon (babo) and two type II receptors, punt and wishful thinking (wit), interact with either of two R-Smads, mothers against dpp (mad) or dSmad2 [tkv transmits a dpp/BMP signal through mad, and babo transmits an activin signal through dSmad2.In sequence -6. Theser dSmad2 -12. Althdpp and activin pathways have known functions in the brain, although our understanding of it role is rudimentary. dpp is expressed in two areas adjacent to the outer proliferation center (OPC), where it modulates wingless expression [babo and dSmad2 (activin pathway components) are involved in neuronal remodeling, which occurs in the larval-pupal transition [The pression . To acqupression . Proper pression ,16. baboansition . One tartkv and babo), we have used microarray technology to identify common targets of the BMP and activin pathways in the Drosophila brain.In spite of intense study using classical genetic approaches and biochemical methods, very few targets of the pathway have been identified. A better understanding of the growth and patterning properties of the pathway require a more complete list of target genes. Using activated receptors  and activin signals. These pathways and receptors are conserved through evolution, but few downstream targets are known for these pathways in any organism. To learn more about the growth regulatory and pattering properties of these signals in the fly brain, we used microarray technology to identify downstream targets. In these experiments, Affymetrix™ chips containing the entire protein coding capacity of the Drosophila genome  were screened. Genomic-wide microarray analysis allows us to examine similarities and differences between two signaling pathways in a tissue where both are known to function.Few targets of heat shock GAL4 driver (hs-GAL4) [gfp reporter. Since additional time is required for the induction of downstream signaling targets versus the time required for appearance of the GFP reporter, we collected RNA samples from third instar larvae in a broad time period roughly 30 minutes after the peak of GFP expression. Data resulting from induced ectopic expression of tkv, and babo were compared with each other and to the control .Constitutively active forms of the receptors were made by single amino acid substitutions , renderihs-GAL4) ,20. To atkv and babo pathways or by one of the pathways only.Three independent replicates for each treatment were generated. Hierarchical clustering Fig  and Printkv and babo on microarrays, semi-quantitative real-time RT-PCR was performed on selected genes. Six among the 27 genes were picked for validation. Real time RT-PCR showed similar results similar to the microarray results for four of the six genes  than increased (n = 90)  Fig . This coted Fig. .tkv and babo. Among these 27 co-regulated transcripts, there are transcription factors, enzymes, transporters, signal transducers, miscellaneous proteins and four unknown genes  gene has the highest expression level increase , which is a subunit of a nuclear receptor [The fact that both DPP and activin signaling pathways share some common features in differentiation and growth control in various tissues suggests that both pathways might share some downstream target genes. Microarray experiments identified 27 genes Table  co-regulenes Fig . The trareceptor . USP forreceptor ,23.Drosophila activin signaling pathway partially mediates neuronal remodeling through regulating EcR-B1 expression [babo and dSmad2 [Drosophila activin signaling results in induction of the EcR-B1 isoform. Upon binding of ecdysone to the EcR-B1/USP heterodimeric receptors, neuronal remodeling is initiated via transcriptional activation of downstream target genes [usp is also induced by ectopic expression of tkv and babo. In addition, we find that EcR-A expression is repressed by the induction of babo. Using real-time PCR, we confirmed that EcR-B1 is induced by ectopic expression of babo (1.5 fold), a more modest change than the increases on usp by tkv and babo. These finding suggest that Drosophila activin signaling mediates neuronal remodeling by regulation of both EcR-B1 and usp expression, while inhibiting EcR-A induction.Previous studies have shown that the pression . Two indd dSmad2 , both ofd dSmad2 ,9. Furthet genes . Our mictkv and babo both induce EcR-B1, although it is not known which receptor combinations or ligands are responsible for these effects.BMP-like pathways, as well as activin pathways, have been implicated in neuronal remodeling ,17. PUNTdpp (and presumably tkv) has other known roles in organizing the visual center of the brain [wingless, acting through dpp, is an important participant in organizing the optical centers of the brain [wingless is expressed at the tips of the crescent shaped OPC. Fourteen hours later, wingless induces dpp expression in adjacent cells, in two spots in each brain hemisphere. These dpp expressing cells also express fasciclin II. BrdU staining shows that wingless, dpp, and fasciclin II expressing cells proliferate throughout larval development. However, a reduction of wingless or dpp results in a reduction in the rate of proliferation in the OPC, resulting in smaller optic lobes of the brain. Loss of wingless also results in a severe reduction of the medulla, where the photoreceptor axons R7 and R8 migrate. Another defect noted in wingless mutant animals is that the OPC derived precursor cells had failed to assume their proper neuronal fate.he brain . It has he brain . winglesusp, two other transcription factor genes, CG7839 and TfIIFβ, are up regulated by tkv and babo. Both are implicated in growth processes. CG7839 has 30% homology over 1016 residues to C. elegans F23B12.7, which shows a slow growing phenotype in RNAi experiments [tkv and babo TGFβ pathways operate through these transcription factors.Besides eriments . TfIIFβ antp), are down regulated by both pathways during brain development. antp is a well-studied Hox gene in Drosophila, which controls many developmental decisions, most notably, the differentiation of the antennae and legs from homologous structures [Hox genes, and each Hox gene controls the development of a specific region of the body plan [Drosophila, differences between segments, such as the presence or absence of appendages, are often controlled by Hox transcription factors. The role of antp in brain development is not known, but it is tempting to speculate that both dpp and activin might regulate brain development, at least partially, through interaction with the Hox gene antp. Determining the mechanisms by which Hox proteins regulate gene expression will be important for understanding animal development and pattern formation.Two transcription factors, CG14422 and Antennapedia , a G-protein coupled receptor protein involved in neuroblast cell division and cell size control [Many of the other genes that are significantly regulated by sphatase , has thesphatase -30. Ninae kinase  with cale kinase . CG9236  control ,34, and  control .C. elegans and in vertebrates. While their molecular functions are not currently known, the C. elegans CG7986 homolog F41E6.13 is involved in positive regulation of growth. RNAi experiments with the C. elegans homolog of CG3857, Y54E2A.2, revealed no mutant phenotype. Four transporters  are transporters regulated by the tkv and babo pathways. Fatp is a long-chain fatty acid transporter. Atpa is a sodium/potassium-exchanging ATPase, while CG8533 is a glutamate-gated ion channel and CG6293 is a L-ascorbate:sodium symporter.CG3857 and CG7986 are two novel proteins that have homologs in tkv and babo signaling pathways in the developing Drosophila brain. One of the most striking developmental events in the fly brain is neuronal remodeling. These results indicate usp is positively regulated by tkv and babo, and thus adds another important link to their roles in brain remodeling. Many of the 27 genes are strongly conserved in other species. If their biological functions are also conserved, then the RNAi experiments in their C. elegans counterparts show that several of them are involved in growth regulation. This is particularly useful since few downstream targets of BMP or activin signaling pathways are known, particularly the targets that execute their growth regulatory properties. Not surprisingly, mutational analysis of several of these genes has not been done, but the genetic tools in Drosophila make this relatively straightforward. Further characterization of these downstream genes may provide insights into the integration of tkv and babo signaling pathways in Drosophila brain development, and provide hints into their functions in other organisms.Microarray experiments revealed that 27 genes are co-regulated in both tkv, virgin females from UAS-CA-tkv were crossed to hs-Gal4 males. For over-expression of constitutively activated babo and the control, UAS-CA-babo and UAS-gfp were crossed to hs-Gal4 flies. The larvae were raised in standard medium at 25°C.For over-expression of constitutively activated tkv, babo, and the gfp control . Animals were heat shocked at 37°C for 1 hour, followed by cooling to room temperature for 30 minutes, and then kept at 25°C for one hour to allow expression before dissection. Approximately 150-200 larvae were dissected and the brains were collected in a drop of PBT  on Sylgard (Dow Corning). Total RNA was extracted from the tissue using the Trizol™ reagent  according to the manufacturer's protocol.Wandering third-instar larvae were heat-shocked to induce ectopic expression of tkv, three babo and three gfp) was prepared for hybridization according to the Affymetrix GeneChip® Expression Analysis Technical Manual . The Superscript Choice System kit  was used to make complementary DNA (cDNA) from 5 μg. First strand synthesis was primed with a T7-(dT)24oligonucelotide primer containing a T7 RNA polymerase promoter sequence on the 5' end . Second strand products were cleaned with the GeneChip® Sample Cleanup Module  and used as a template for in vitro transcription (IVT) with biotin-labeled nucleotides . The copy RNA (cRNA) product was cleaned with the GeneChip® Sample Cleanup Module  and a 20 μg aliquot was heated at 94°C for 35 min in fragmentation buffer provided with the Cleanup Module .Total RNA from each of nine independent samples , washed and visualized with a Genearray™ Scanner . Images were inspected visually for hybridization artifacts. In addition, quality assessment metrics were generated for each scanned image and evaluated based on empirical data from pervious hybridizations and on the signal intensity of internal standards that were present in the hybridization cocktail. Samples that did not pass quality assessment were eliminated from further analyses.Fifteen μg of adjusted cRNA from each sample was hybridized for 16 hr at 45°C to an Affymetrix  Microarray Suite version 5  was used to generate *.cel files. Probe Profiler™ version 1.3.11 software  was used to convert cel file intensity data into quantitative estimates of gene expression for each probe set. For each probe set, a probability statistic is generated. Genes not significantly expressed above background in any of the samples (P > 0.05) were considered absent. Absent genes were removed from the data set and not included in further analyses.Gene expression levels were subjected to a 1-way analysis of variance (Anova) for 3 treatments  and 3 replications using AnalyzeIt Tools, a custom software program developed by the Interdisciplinary Center for Biotechnology Research , for the analysis of microarray data. In this software, the statistical package, R, serves as the backend for Anova. Genes were considered to have a significant treatment effect if P-level was less than 0.05.The expression values of those genes that were considered to have a significant treatment effect were normalized by performing a Z- transformation , therebytkv (or babo) average/Control average or Control average/tkv (or babo) average) and statistically significant ) were considered as differentially expressed. AnalyzeIt Tools and notations in Flybase were used for classification of genes by gene ontology in molecular function and biological process categories.To eliminate noise from low-level expression, spots quantified less than 5 were replaced by value 5. The following criteria were used to filter the data. Only transcripts with the fold change difference over 1.5 . Each of the samples were analyzed three independent times, resulting in six repeats. These six repeats were averaged and the tkv and babo samples were compared with the gfp controls. Approximately 1 μg of the each total RNA was used for first strand cDNA reaction using Superscript First Strand Synthesis kit  according to the manufacturer's protocol. For real-time PCR, the reaction consisted of cDNA first strand template, primer mix, Rox  and SYBR Green PCR Master Mix  in a total volume of 25 μl. Three reactions per template were performed in parallel. Actin 42F was used as an internal standard to generate a standard curve and to normalize the amount of cDNA samples. The fold change  average/Control average or Control average/(tkv or babo) average. The experiments were performed using a Rotor Gene 3000 . To validate the specificity of PCR reaction, a melting curve was produced by denaturation of PCR end products from 60 to 99°C at 0.5°C/min steep and the end products were also assayed with 1.5% agarose gel electrophoresis after cycling.Two independent total RNA samples were generated for each of the three experimental conditions (two MY and DN carried out experiments in the project and YF assisted in the experimental design. RWP implemented and supervised the project. RWP and MY prepared the manuscript.


Microarray experiments are often performed with a small number of biological replicates, resulting in low statistical power for detecting differentially expressed genes and concomitant high false positive rates. While increasing sample size can increase statistical power and decrease error rates, with too many samples, valuable resources are not used efficiently. The issue of how many replicates are required in a typical experimental system needs to be addressed. Of particular interest is the difference in required sample sizes for similar experiments in inbred vs. outbred populations (e.g. mouse and rat vs. human).We hypothesize that if all other factors  were equal, fewer individuals would be needed for the same statistical power using inbred animals as opposed to unrelated human subjects, as genetic effects on gene expression will be removed in the inbred populations. We apply the same normalization algorithm and estimate the variance of gene expression for a variety of cDNA data sets  comparing two conditions. Using one sample, paired sample or two independent sample t-tests, we calculate the sample sizes required to detect a 1.5-, 2-, and 4-fold changes in expression level as a function of false positive rate, power and percentage of genes that have a standard deviation below a given percentile.Factors that affect power and sample size calculations include variability of the population, the desired detectable differences, the power to detect the differences, and an acceptable error rate. In addition, experimental design, technical variability and data pre-processing play a role in the power of the statistical tests in microarrays. We show that the number of samples required for detecting a 2-fold change with 90% probability and a p-value of 0.01 in humans is much larger than the number of samples commonly used in present day studies, and that far fewer individuals are needed for the same statistical power when using inbred animals rather than unrelated human subjects. Microarray technology has become an important tool for studying gene expression levels on the whole genome scale . One impIn general, the required sample size depends on the magnitude of the variability of the population, the magnitude of the expression change that is biologically meaningful (or desirable to detect), the power to detect the expression change, and the P-value/significance level/false positive rate. However, power and sample size have been viewed as complicated and difficult issues for microarray studies due to the large number of genes being investigated and little knowledge of the degree of natural expression variation within a population. To date, very few studies have assessed power and sample size requirements in microarray experiments. Pan et al.  proposedPair-wise comparisons between conditions/groups/treatments are frequently used in microarray studies. Parametric and nonparametric statistical methods have been proposed to identify differentially expressed genes, among which t-tests are most commonly used. This paper is intended to provide some guidelines for sample size planning for pair-wise comparisons. Normalization is an essential and important pre-processing step in microarray data analysis. To our knowledge, no previous studies using multiple data sets have pre-processed the data sets in a comparable way. In addition, previous studies did not look at the effect of inbred vs outbred populations on the variation of gene expression. In order to make the results more comparable, we make use of 7 cDNA microarray data sets and apply the same normalization method .th, 75th, 50th and 25th percentile of genes ranked by variability at fixed settings for false positive and false negative rates. The sample size calculation provides the approximate but not exact number of replicates required for a given set of criteria.We estimate the variance by one sample t-test, paired t-tests or two sample t-tests on a gene-by-gene basis using several large expression data sets from both human, rats and mice. We then calculate the sample size required to detect a 1.5-, 2-, and 4-fold change in expression levels for the 90. Removing the gene IDs does not affect the analysis in this paper in any way. The same website also provides the links for the published data sets. In addition, all the control genes were removed from the data analysis for the cDNA data sets.We estimate the standard deviation and required sample size from 1 unpublished and 6 published cDNA data sets (Table Data set A comprises data generated from 40 liver RNA samples isolated from paired liver hepatocellular carcinoma (HCC) tumor and adjacent cirrhotic non-tumor tissue from 20 HCV infected Caucasian patients . The objData set B was generated from 41 matched pairs of prostate tumor and non-tumor tissue hybridized to arrays spotted with 38627 cDNAs. All samples were labeled with Cy5 and co-hybridized with a common reference labeled in Cy3 [Data set C was generated from RNA isolated from paired HCC tumor and adjacent non tumor liver from 41 HBV infected patients ,9 hybridData set D was generated from RNA isolated from paired liver and kidney tissue from 6 male C57BL6 mice  hybridizData set E was generated from RNA isolated from 36 breast ductal tumor and 16 lobular tumor tissues  hybridizData set F consists of data generated from 24 liver tissue samples from 12 inbred mice (unpublished data). One third or two thirds of the liver was removed from each mouse and used as the baseline samples. At 12 hours post operation, the mice were sacrificed and the remaining liver tissue was used as the experimental sample. The aim of this study was to screen for genes potentially related to liver regeneration after hepatectomy. RNA samples from the 12 hour post-operation livers were co-hybridized with their own baseline liver samples. A total of four DNA arrays were used for each sample comparison. Two sets of arrays (MOD1 and MOD2), each containing 6528 different cDNAs spotted in duplicate (A and B) on each array were used. In addition, each comparison was done with a dye flip pair of slides. This data set made use of arrays generated at the University of Washington Center for Expression Arrays.The goal of data set G was to identify genes with altered expression in the liver tissues of two mouse models with very low HDL cholesterol levels (treatment groups) as compared to inbred control mice. The mouse model considered in this study is the Apolipoprotein AI (ApoAI) knock-out, where ApoAI is a gene known to play a pivotal roles in HDL metabolism ,12. EachIn summary, three of the data sets  are from inbred mouse and rat strains respectively, and the other four data sets  are from large scale studies of gene expression in humans. If all other factors  were equal, one might anticipate that fewer individuals would be needed for the same statistical power using inbred animals as opposed to unrelated human subjects..Background adjustment and normalization is necessary to remove systematic biases of non-biological origin in microarray studies. A number of methods of background correction and normalization have been proposed ,14. We u2 transformed prior to data analysis. Figure 2 ratio (sample/reference) of two independent groups.The distribution of the standard deviations estimated from these 7 data sets are presented in Figure σ), the desired detectable fold change (δ), the power to detect this change , and a chosen type I error rate (α). For microarrays, a combination of fold-change and test p-value is commonly used for selecting differentially expressed genes between two groups or conditions. In this study, sample sizes were calculated in R using the function of power.t.test. The required input parameters are the log scale fold change of interest δ , significance level, power, and the standard deviation , the type of t-tests , and the type of test (two sided or one sided).The required sample size of an experiment depends on the variance component (δ = 1) in expression level between tumor and non-tumor tissue in the 75% least variable genes (σ <= 0.5884) with a two sided 0.001 significance level test with 90% power, we could use the following R functionFor example, in the case of data set A (one sample t-test), if we wish to find out the approximate sample size to detect a 2 fold change th percentile of the standard deviation of log ratio.Where sd = 0.5584 is the 75δ = 1) in expression level between knock-out and control mice in the 75% least variable genes (σ <= 0.3102) with a two sided 0.001 significance level test with 90% power, we could use the following R functionIn the case of data set G (two sample t-test), if we wish to find the approximate sample size to detect a 2 fold change th percentile of the common standard deviation of log (sample/reference).Where sd = 0.3102 is the 75β to reject for a two sided testing and strict interpretation of tail probability with significance level α for detecting a difference of δ, the minimum number of samples or pairs is obtained by solving the following equation iterativelyIn R, for a one sample t-test or a paired t-test to have power 1-v, ncp < tα/2v, ) + PrPower = Pr, then the sample size (n) for each group is obtained by solving the following equation iterativelyFor a two sample t-test with equal sample sizes, if we wish to have a large enough sample to detect a difference v, ncp < tα/2v, ) + PrPower = Pr of 0.05 is often employed in hypothesis testing. Thousands of genes are usually studied in microarray experiments. When more than 10,000 genes are tested independently, we would expect more than 500 genes to appear as false positives when the 0.05 significance level is applied. Hence, a smaller cut-off p-value should be used in order to reduce the number of false positives. Many multiple testing correction methods have been proposed. The simplest one is the Bonferroni correction (family wise error control) where the nominal significance level is divided by the number of tests. The Bonferroni correction is very stringent. False discovery rate (FDR) , the proFor reference, Table Factors that affect sample size calculation include the magnitude of the variability of the population, the magnitude of the desired detectable expression change, the chosen power to detect the expression change, and the cut-off P-value/significance level/false positive rate. For a given study, the variability of the population being studied is fixed, and once researchers have identified the desired detectable expression change, the required sample size depends on the chosen false positive and false negative rates. The variability of human subject data is typically larger than that seen with laboratory animals and cell lines due to genetic influences on gene expression. Hence, more replicates are needed for studies that involve human subjects (or any other outbred population) than for studies with samples from an inbred population. This is readily apparent in the cDNA data in Multiple levels of replicates are common in two color microarray experiments. Multiple arrays probed with RNA samples isolated from multiple individuals of a population/treatment/group are referred to as biological replicates. Multiple arrays hybridized using the same RNA or multiple replicates of the same gene within an array are referred to as technical replicates. Although technical replicates can improve the precision and the reliability of the measurement and provide information for quality control, biological replicates are most effective in reducing the variance of the estimate of mean difference. Biological replicates therefore increase the power to detect biologically significant gene expression differences. More importantly, when trying to identify differences between a treatment and a control group, accurate estimates of the biological variability within the groups is essential to determine if the between group differences are meaningful  or other method).Careful experimental design is necessary to maximize the statistical power of the test -18 whileThis paper is intended to give some guidance to those planning microarray experiments. The sample size calculations we performed provide an approximate number of replicates for a given set of criteria. Our studies were limited to a small number of published microarray studies for which the following criteria were true:1) A reasonably large number of biological replicates were analyzed.2) Raw data was readily available so that we could reprocess all data with the same algorithms.3) Other potentially large sources of variability such as flow sorting, laser micro-dissection and/or multiple rounds of amplification were not present.We have only analyzed date from a limited number of tissue types – liver, prostate, breast and blood in human, liver and kidney in mouse, and mammary gland in rat. It is entirely possible that different tissue types will have larger or smaller degrees of biological variation and hence will require more or fewer samples to reach a given conclusion. In addition, lab or experiment specific methods of obtaining and processing samples may induce greater degrees of expression variation than seen in our sample data. As more large data sets become available, it will be useful to extend these studies to better define the magnitude of gene expression variation in purebred animals and in outbred humans across a variety of tissues.However, the data shown in Background adjustment and normalization is needed in microarray data analysis in order to remove non-biological variation. Intensity based normalization methods such as locally weighted least square polynomial regression (lowess) is commonly used in cDNA microarray experiments. The background subtracted intensities were normalized by the spatial lowess method using the R add on package MAANOVA written by the Jackson Lab. For the two cDNA experiments with replicate panels within each array, we normalized the two panels separately. All control genes were excluded from data analysis for data sets A-G.Pair-wise comparisons among conditions/groups/treatments of gene expression levels are common goals of microarray studies. Simultaneous comparison of more than two treatments/conditions using one way ANOVA can be advantageous. However, a significant F for a comparison of several treatments does not provide information about which particular groups differ from each other. In addition, one way ANOVA is not sensitive to treatment effects when only one or two samples out of many are quite different. T-tests are commonly used to compare individual treatments in pairs.2 ratio of tumor/non-tumor as the response variable. Data sets B-D were generated from paired samples using a reference design on cDNA arrays; Paired t-tests are appropriate for these three data sets. We performed two tailed, two sample (or independent) t-tests on data sets E-G with normalized log ratio as the response variable. The two sample t-tests are based on unequal variances for the two groups of samples.In order to calculate power and plan sample size, one must first estimate the variance. We applied paired or two sample t-tests in this study based on the correlation between the two groups. For data set A, as the pairs of tumor and adjacent non-tumor tissue are highly correlated, we used two tailed one sample t-tests with the normalized logThe variances of the data sets with paired samples are the variance of the difference. The common variance of the datasets with independent samples was estimated by the following formula:1, n2 are the number of observations for group 1, and group 2, respectively; and S1 and S2 are the standard deviation for group 1, and group 2, respectively.Where nbiological variance, the log ratios of the 4 technical replicates of data sets A, E, and G were averaged for each RNA pair (data set A) or sample (data sets E and G). The standard deviations across biological replicates were estimated on a gene-by-gene basis. Sample sizes were calculated using R for detecting a 1.5-, 2- or 4-fold change for the 90%, 75%, 50%, and 25% least variable genes with a range of power  and confidence level , assuming equal sample size for the two groups. The numbers for sample size are rounded to integers.To simplify power and sample size calculation, and to focus our calculation on JL provided the mouse liver microarray data set F prior to publication. CW performed all the analysis in this paper. RB supervised JL and CW and contributed to the design, coordination and writing. All authors read and approved of the final manuscript.α) and power (1-β). This additional file shows the estimated sample size to a 1.5-, 2, and 4-fold changes of expression level for the 90%, 75%, 50%, and 25% least variable genes for a given settings of false positive rates (α) and power (1-β) for all of the data sets referred in Table 1.Sample size required to detect a 1.5-, 2-, and 4-fold changes of expression level for the 90%, 75%, 50%, and 25% least variable genes for a given settings of false positive rates (Click here for file


Ancient DNA enables researchers to study the genetics of populations in the past; despite difficulties in its extraction, aDNA reveals that evolution is even more complex than we had imagined The study of genetic material from ancient specimens was, in its early years, dominated by a race to sequence DNA from extinct species like the dodo and the woolly mammoth. Now that the supply of these crowd-pleasing curiosities has run dry, scientists are starting to ask new questions of ancient DNA (aDNA) that are revealing how the genetic make-up of prehistoric populations changed through time. These findings look set to trounce assumptions about how evolution really unfolded. However, there is still concern that many studies are not paying enough attention to the exacting protocols needed to overcome the technical challenges of the discipline and to defend it from the ridicule that has plagued it in the past.Jurassic Park was still taking in millions of dollars at the box office, scientists claimed to have extracted and sequenced DNA from an 80-million-year-old dinosaur  can both hinder the publication of good studies that do not adhere to all the criteria, and also enable the publication of erroneous results that adhere strictly to them,” he says. Part of the problem is that many referees of aDNA papers do not have a background working with aDNA, so are inclined to use the authenticity criteria as a checklist rather than critically evaluating each bit of research on a case-by-case basis. For example, he says, a recent high-profile study that followed all the criteria found that aDNA from two Cro-Magnon-type humans was very similar to DNA from modern humans . But thiMost tissues under the scrutiny of the aDNA researcher will contain not only DNA from the organism of interest, but also DNA from bacteria, fungi, and all sorts of other organisms. Of course, most of this confusion can be cleared up using a speciesspecific primer in the PCR to amplify DNA from just one genome. “You're after a needle in a haystack,” says Alan Cooper, “but fortunately that's what the PCR technique is so good at doing— sorting through that haystack for you.”A lot of focus has been placed on contamination in the laboratory. This “external contamination” is a particular problem because low levels of aDNA can easily be outnumbered by just a tiny amount of high-quality modern DNA floating around the lab. One of the criteria of authenticity—separating the site of DNA extraction from the site of PCR amplification—is designed to minimise this.In addition, there is “innate contamination” that has occurred to the sample before it even reaches the laboratory. This is much harder to control and is a real problem for those working on human origins because it's difficult to tell what is ancient human DNA and what is a modern contaminant. Bone is like sponge. “In life, bone is about 8% air. In death,…40% or 50% of it is air,” says Gilbert. So if an archaeologist digs up and washes a bone from some human ancestor, it's almost certain that modern DNA will find its way into the centre of the bone . Some arThis explains why many aDNA researchers focus on unusual species like the bison or the cave bear: these animals are extinct, so the chances of contamination are nil. It's easier to come up with real results that the scientific community will accept, says Gilbert. There will always be a temptation to work on humans, however, because it's these studies that grab the headlines, he says.But allowing authors the freedom to use the criteria as they see fit could come at a cost, says Cooper. “The trouble with a case-by-case basis is that it basically equates to no standards, because then people will do what they feel like doing and we're back to the 1990s again,” he says.This ongoing disagreement over how aDNA studies should be judged does, however, stem from a common concern. As more and more biologists come to appreciate the unique ability of aDNA to probe the evolutionary process, it is more important than ever to stress the immense challenges of working with just a few fragments of degraded DNA that might have come from several different sources. It is obvious why an archaeology lab might want to set up its own aDNA facility. But this is like creating molecular biologists without a license, says Pääbo. “You wouldn't buy an accelerator and say 'I will now start doing my own carbon dating,'” he says. “You have to really have experience working with low copy number.”However, in spite of the continual problem of eager but inexperienced biologists trying to extract DNA from specimens in the university museum, there is a sense that aDNA is starting to fill in the gaps in our understanding of key moments in evolutionary history. So at the start of 2005, as aDNA research enters its 21st year, the discipline is, perhaps, coming of age.


Genome programs changed our view of bacteria as cell factories, by making them amenable to systematic rational improvement. As a first step, isolated genes (including those of the metagenome), or small gene clusters are improved and expressed in a variety of hosts. New techniques derived from functional genomics  now allow users to shift from this single-gene approach to a more integrated view of the cell, where it is more and more considered as a factory. One can expect in the near future that bacteria will be entirely reprogrammed, and perhaps even created de novo from bits and pieces, to constitute man-made cell factories. This will require exploration of the landscape made of neighbourhoods of all the genes in the cell. Present work is already paving the way for that futuristic view of bacteria in industry. More than 40,000 pages are indexed in the WWW Browser Engine Google for the keyword "cell factory". Early in 1999 the European Union launched a research programme on the cell factory , stating that « The concept of the "bio-product" is as old as the knowledge involved in the making of bread, beer, wine or cheese. However, recent techniques and knowledge in molecular biology and genetics mean that living cells – from bacteria to man – are now becoming real "factories". In vast fermentation vats, engineers can direct and control natural metabolism in order to produce all sorts of substances with a high added value: proteins, amino acids, alcohols, citric acid, solvents and even bio-plastics. This industrial mastery of the mechanisms of life opens up revolutionary perspectives in the development of new kinds of medicines, foodstuffs with specific nutritional properties, and biodegradable biochemical products » [At the date of October 30Taken together these pieces of information show that exploration of the potential of microbes as industrial tools is shifting from its former status of traditional biotechnology assets to new high technology devices, meant to perform highly specific tasks, with the highest possible yields and security, and genomics as the background support. We shall not here review the use of microbes in traditional production  but, ra<name of species>genome sequencing project resulted in the discovery of a number of new genes with potential interest for biotechnological applications ».Genomes are most often viewed as bags of genes. While this is not our personal view – we see genomes as highly organized set of genes, certainly not randomly distributed along the chromosomes , this isEscherichia coli, for example [Photorhabus luminescens [Streptomyces species in particular [Indeed, many biotechnological procedures still rest (and will rest) on the isolation of individual genes, or series of genes involved in the biosynthesis of a specific compound (this is illustrated in the complex case of coenzyme B12 biosynthesis in  example ). Remark example -11. As ainescens , when itrticular -16, and rticular .The genome concept for identifying new genes of biotechnological interest has now been expanded to that of a « metagenome », formed of communities of organisms (often non-cultivatable) in a given environment ,19. ThisHowever interesting, these « single-gene » approaches remain conceptually very limited, they only explore the surface of what could be provided by the knowledge of genomes. Furthermore, they often aim at the preparation of a single enzyme, that is meant to be used in a process that does not make use of the adaptation and maintenance potential of living organisms. In short, the cell is not used for what it is in reality, a factory. This is however dramatically changed with the advent of genomics as we shall now see.Progresses in genome sequencing were followed by attempts to better understand how a cell behaves as a whole. The knowledge of complete genome sequences permitted scientists to set up expression profiling techniques that play an ever increasing role in biotechnology . Indeed Many metabolic engineering strategies now use genome-wide methodologies such as DNA sequencing, transcription profiling and global analysis of metabolites. These techniques allow the identification of genetic differences and provide insight into their cellular effects. Inverse metabolic engineering endeavours to map differences between strains with different degree of a certain desired phenotype and subsequent identification of factors conferring that phenotype. Briefly reviewed, expression profiling can be divided into three major branches that each have a particular outcome, and gives a specific knowledge on the organism.With all genes known from a cell it has been possible to create DNA arrays sampling a subfamily or all genes on a variety of physical supports. These arrays can subsequently been used to monitor the level of expression of each gene in a particular condition. While this transcriptome approach is widely used, its interpretation is still a matter of research ,26 but iThe second level of expression profiling is, of course, that of the direct access to the gene products, the proteins. Two-dimensional gel electrophoresis has been developed for thirty years, with considerable success, but it is still extremely limited by the lack of reproducibility of 2-D gel patterns . Other mFermentation processes often aim at producing a given metabolite. The major problem facing industry in this domain is to improve the production yield, often for products that do not have a very high added-value , in a background that has already been improved by generations of mutational improvement. Furthermore, many metabolites have to be as pure as possible, trying to prevent contamination by side-products that may be toxic . It is tCorynebacterium glutamicum [The traditional way for biotechnology to improve its processes was to select mutants having interesting properties . This required long and tedious procedures where relevant features were usually gradually improving . Howeverutamicum .Further progress is certainly possible. It is important to try to understand whether genomes are simply random collection of genes, or whether they show rules, that might be exploited for using cells as factories. Remarkably, at least in bacteria, the organisation of the genome reflects some kind of optimisation of gene expression . Genes dWhile this has not yet, to our knowledge, be taken into consideration for improvement of production by industrial strains, it is more than likely that this will be performed in the near future . Regulation of gene expression is also of major interest. It must be understood however that this feature of life is evolving much more rapidly than catalytic or structural components of the cell. One should therefore be cautious in extrapolating knowledge from an organism to another one. Theoretical studies, associated to validation experiments have now begun to decipher the rules that govern regulation of gene expression, and it is certainly already possible to construct subtle regulation systems , that arAmong the recent discoveries that will play a considerable role in genome-mediated control of gene expression is that of riboswitches . This moIn the same way quorum-sensing has much to say for the control of gene expression at high cell density . Until nStreptomyces lividans [Corynebacterium glutamicum [Bacillus subtilis [Escherichia coli [Zymomonas mobilis [3) are now growing Xanthomonas campestris, used as a supplement not only in food, but also in dentrifrice, housekeeping products and even in painting, to prevent it from making drops [Many bacteria have been used as cell factories. In most cases this was to produce small molecules . These bacteria were usually the result of continuous improvement using standard mutagenesis/screening techniques of bacteria isolated in the wild. Streptomyces species, for example, account for a large number of antibiotics production. lividans , Corynebutamicum , Bacillusubtilis , Escherihia coli , Zymomon mobilis , to giveng drops . In the E. coli as a ubiquitous cell factory. This trend was particularly emphasized as soon as genetic engineering techniques were developed, as early as in the early eighties, with the construction of new vectors for expressing foreign proteins at will . Mid-eiabolites . EscheriB. subtilis, are much more difficult to use, except for biomass production, or secreted proteins, because heterologous protein expression is difficult there. This has been understood after the genome was deciphered, as a consequence of the poorly versatile control of translation initiation , as welrticular .Taken together these observations suggest the rational choice of a new organism that would play the role of a ubiquitous cell factory. This organism should have several properties. It should be non pathogenic, and its envelope should not trigger inflammation reactions in animals (Man included). It should be easily transformable and allow recombination with linear DNA, with as little matches needed for recombination as possible. It should grow fast at temperature compatible with the size of fermenters (metabolic activity heats up the medium), and it should reach high cell density. More specialized views, adapted to specific productions will also be considered at some point, but it will be interesting to witness the choice of new model bacteria in the new era of the cell-factory.E. coli, producing heterologous proteins. As we now sequence a new genome every third day or so, it is clear that we will be able soon to understand the core of bacterial life, and probably be able to choose new models, better suited to the goals of industry. However we must always remember that life is full of surprises, even in the best explored domains: who would have thought that E. coli communicates with its kins using the boron atom? Discovery cannot be planned, and the most surprising observations, that have the most considerable consequences in terms of applications of research, come from studies that are totally academic in nature (who would have thought that the discovery of RNAi would have come from the study of variagation in petunia flowers?). One should not mix up domains: discovery first, and this needs a considerable degree of freedom of choice in the topics explored, and then, naturally, one can think of applications of research. Constructing the best of bacterial cell factory would be such a goal.Bacteria have been used as factories for a long time. A first step to rationalize this approach has been met with the first genetic engineering of GRAS: Generally Recognized As SafeRNAi: RNA interferenceThe author declares that he has no competing interests.


Very little is known about the correlates of adoption and maintenance of vigorous exercise. The purpose of this study was to understand the sociodemographic correlates of exercise adoption and maintenance in a community sample.917 women and 229 men completed annual surveys as part of a community-based weight gain prevention trial over four years. Multivariate regressions evaluated predictive factors for maintenance of vigorous exercise over time in regular exercisers, and predictors of adoption of exercise in adults who were sedentary at baseline.Exercise maintenance at Years 2 and 3 was associated with ethnicity and exercise level at baseline, while exercise maintenance at Year 4 was associated with television watching, BMI and exercise at baseline. Exercise level at baseline was associated with exercise initiation at Year 2 and Year 3. Income level, marital status, and smoking status predicted exercise initiation at Year 4.Predictors of vigorous exercise maintenance were more consistent than predictors of vigorous exercise initiation. Results suggest that those who adopt vigorous exercise are a heterogeneous group and intervention messages could be more broadly focused. These data also suggest that exercise maintenance interventions should continue to target low-income populations with messages regarding smoking, weight and television. Clearly further research is needed to understand the factors that contribute to exercise initiation and maintenance, and to develop effective interventions to improve levels of physical activity levels. Vigorous exercise is considered one of the key-components of a healthy lifestyle and cardiovascular fitness. Higher levels of exercise are associated with lower risks of hypertension, diabetes, osteoporosis, colon cancer, coronary heart disease, depression, anxiety, and has been shown to enhance weight control -3. WhileNumerous studies have examined predictors of exercise maintenance among participants in organized exercise programs . HoweverAlthough more research is being conducted on correlates of adoption and maintenance of exercise, more longitudinal research is needed among demographically diverse community samples . ConsideThe present study sought to cross-sectionally and prospectively examine the correlates of regular vigorous exercise in a sample of community adults over a four year period. Specifically, this study attempted to expand on the current literature by understanding the relationship between regular vigorous exercise and exercise adoption and maintenance by evaluating three comparisons in a community sample: 1) cross-sectional differences in characteristics of regular vigorous exercisers compared to non-exercisers, 2) predictive factors for maintenance of vigorous exercise over time in people who are already exercising at a high level as compared to those who do not maintain their exercise level, and 3) predictors of adoption of exercise in community adults who were sedentary at baseline as compared to those who do not adopt vigorous exercise. The study sample included 917 women and 229 men who completed baseline surveys as part of a 3-year community-based weight gain prevention trial . Participants were recruited using a variety of methods, including direct mail sent to university employees, and advertisements in community newspapers, health department employee newsletters and radio public service announcements. In addition, recruitment also targeted lower-income women at commercial shopping centers and at community health department clinics. Lower income women were paid $20 to enroll in the study. All participants were informed that they would be randomly assigned to either a mail-based educational program, or a no contact control group and that they would be measured once per year for a total of 4 years. This study was approved by the University of Minnesota Institutional Review Board.Study participants completed questionnaires and were measured for height and weight at baseline and at three annual data collection visits  following baseline. Measures included in the present study are listed below.Planned vigorous exercise behavior was assessed using 5 questions from a self-administered version of the Physical Activity History questionnaire (PAH). The PAH is reliable and valid and has been used in several large epidemiologic studies . For thiTo evaluate competing activities, we evaluated participant's report of time spent watching television. Time spent watching television has been positively correlated with body weight, presumably in part due to its displacement of exercise behaviors . ParticiSocial support from family and friends has been found to be related to exercise in a number of studies . ParticiHeight was measured to the nearest centimeter using a wall-mounted ruler and weight was measured to the nearest half pound using calibrated balance beam scales. Body mass index was calculated using the formula weight (kg)/height (m2). All demographic information was self-reported and included age, educational attainment (highest level completed), gender, employment status, income, ethnicity, smoking status and marital status.2 = 14.14, p = .042) and at Year 4 . Initiation of exercise at Year 2 and Year 3 were not significantly associated with treatment status. However, initiation of exercise at Year 4 was marginally positively associated with treatment . Due to these associations, treatment assignment (intervention vs control) was controlled in multiple regression analyses.Before performing analyses evaluating the predictors of exercise maintenance and initiation, the possibility of an intervention effect was evaluated. Maintenance of high intensity exercise at Year 2 was not significantly associated with treatment, however, maintenance of high intensity exercise at Years 3 and 4 were associated with treatment status. Intervention participants were more likely to be maintaining exercise behavior at Year 3 (XParticipants were classified as regular vigorous exercisers (> 3 times per week) and non-regular exercisers (< 3 times per week) based on their responses to the five planned high-intensity exercises on the PAH at each of the 4 assessment points. Using these classifications (yes/no vigorous exercise), the participants were classified as maintainers if they exercised more than three times per week at both baseline and at a subsequent annual evaluation , and non-maintainers if they exercised three times per week at baseline and less than three times per week at a subsequent annual evaluation . Similarly, participants were classified as initiators if they reported less than three exercise sessions per week at baseline and more than three exercise sessions per week at subsequent annual evaluation , and non-initiators if they exercised less than three times per week at both baseline and a subsequent annual evaluation .Descriptive analyses using t-tests and chi-square tests were utilized to assess univariate associations between the baseline predictor variables and exercise status. Maintainers were compared with non-maintainers, and consistently sedentary participants were compared with participants who adopted vigorous activity at Years 2, 3 and 4. Logistic regression was used to further assess the statistical significance of associations between the predictor variables and exercise maintenance and adoption. These univariate logistic regression models predicted exercise maintenance (yes/no) and exercise initiation (yes/no) at Years 2, 3 and 4 using the baseline demographic factors as independent variables. Furthermore, the variables that were significant in the univariate analyses were entered into a multivariate logistic regression model to assess the relative weight and statistical significance of associations between the predictor variables and the variables representing exercise maintenance and initiation. These multivariate logistic regression models predicted exercise maintenance (yes/no) and exercise initiation (yes/no) at Years 2, 3 and 4. In these multivariate regressions, we controlled for exercise level at baseline and treatment status. All statistical analyses were conducted using the SAS Version 6.12 .Since other research studies have indicated gender differences in predictors of exercise maintenance and initiation , the genUsing the definition of vigorous exercise described above, 564 participants  vigorously exercised more than 3 times per week and 582 participants  did not vigorously exercise more than 3 times per week at baseline. Sociodemographic characteristics of these participants are described in Table Maintainers and non-maintainers were compared on baseline demographic variables, social support, hours spent watching television and smoking. Similar patterns of associations were found for annual visits at Years 2, 3 and 4. Participants who maintained their exercise level at evaluation Years 2, 3 and 4 weighed less, were employed, Caucasian, of a higher income group, more highly educated, more likely to be non-smokers and watched less television per day at baseline. At evaluation Years 3 and 4, the maintainers were older than the non-maintainers at baseline. The maintainers and non-maintainers did not report differences in marital status or social support. These results are presented in Table Multivariate logistic regression analyses were performed to examine the predictors exercise maintenance at Years 2, 3 and 4. Demographic variables at baseline  were entered into logistic regression model along with exercise level at baseline and treatment group. Exercise maintenance at Years 2, 3 and 4 were the dependent variables in the three models. At Year 2, ethnicity , BMI  and exercise at baseline  were significant predictors of exercise maintenance. Ethnicity  and exercise at baseline  were significant predictors of exercise maintenance at Year 2. Television hours per day , BMI  and exercise at baseline  were significant predictors of exercise maintenance at Year 4.Results of univariate analyses relating exercise initiation with baseline variables are shown in Table Logistic regression analyses were also performed to examine the predictors of exercise initiation and Years 2, 3 and 4. Similar to the evaluations for exercise maintenance, demographic variables at baseline which were significant in the univariate analyses  were entered into three separate models controlling for exercise level at baseline and treatment group to identify predictors exercise initiation at Years 2, 3 and 4. Four variables were significant predictors of exercise initiation at one or more time points. Exercise level at baseline was positively related to exercise initiation at Year 2  and at Year 3 . Baseline income group , marital status  and smoking status  predicted exercise initiation at Year 4.This study evaluated the demographic predictors of vigorous exercise initiation and maintenance in a community sample. To our knowledge, this is the first study to look at baseline demographic factors as predictors for exercise adoption and maintenance over a four-year period. This study found that demographic predictors were more consistent for exercise maintenance than exercise initiation over the four years evaluated in this study. Results showed that compared to those who did not maintain vigorous exercise, participants who maintained exercise over a 2, 3 and 4 year period were more likely to be employed, Caucasian, have a higher income, have more education, be a nonsmoker, watch less television and have a lower BMI. One of the interesting findings was that correlates of exercise initiation were less consistent than correlates of exercise maintenance over time. There did not appear to be a consistent pattern that described the associations between the predictors included in this study and vigorous exercise initiation. Although certain predictors were associated with exercise initiation at Years 2, 3 or 4, such as gender or BMI, none of the predictors were consistent over the years measured in the study. These results suggest that people in community populations who initiate vigorous exercise are a heterogeneous group, and there may be greater difficulties predicting who will initiate exercise. It could be much simpler to identify the participants who will be more likely to continue exercise, as compared to the participants who will adopt exercise. It is possible that changes in attitudes, life circumstances (such as sickness), new relationships, or variables that were not measured in this study may be better predictors of exercise adoption or maintenance. However, since we did not measure these variables, we can only speculate whether they may disrupt exercise patterns. The processes of vigorous exercise adoption may be better represented by theoretical understandings of exercise behavior, such as the health belief model , transthThese results may have implications for designing and implementing exercise interventions. The results support others studies that suggest targeting exercise maintenance interventions at the lower income participants. However, to achieve the Healthy People 2010 objectives by increasing participation rate in vigorous exercise, interventions need to be designed to promote exercise adoption. Unfortunately, the present study's results only suggest that we can not characterize this group well. This lack of characterization of this group may actually be a benefit for interventions. Ethnic group, gender, and socio-economic status did not predict exercise initiation in this study. This suggests that each group was as likely to begin a vigorous exercise program. This is good news as exercise initiation may not be limited to those who can afford health clubs and trainers, or those who are the member of a specific ethnic group or gender. Although previous studies have stratified participants by gender , no inteStrengths and limitations of the present study should be recognized. This is one of a few studies that evaluate predictors of exercise initiation, and the first to evaluate exercise initiation over a four-year period. In addition, the sample is large, diverse, and includes longitudinal measurements. This study did not evaluate the predictors of moderate exercise initiation or maintenance. Thus, these results can not be directly interpreted for public health interventions targeted at increasing moderate activity in the general population. In addition, this study may also be affected by a selection bias. The participants in this study volunteered for a study on weight gain prevention, for which the participants may have expected to include a message on increasing exercise. Of note, 49% of the sample reported vigorous exercise more than 3 times per week at baseline, which is much higher than the 15–25% reported in national surveillance studies ,2. AlthoConsidering these limitations, this study does add to the knowledge base about who initiates and maintains vigorous exercise. The results suggest that vigorous exercise maintenance interventions should continue to target low-income populations and that interventions could incorporate messages regarding smoking, weight control, and television. We found that those who adopt vigorous exercise are a more heterogeneous group, and that no one group is more likely to adopt exercise than the others. This suggests that vigorous exercise intervention messages could be more broadly based. This study also suggests that further research is needed to identify participants and effective interventions for those who begin exercise programs. None declared. KB conceptualized the study, planned and executed the analyses, interpreted the results, and drafted the manuscript. RJ participated in conceptualization of the study, interpretation of the results and assisted in drafting the manuscript. SF participated in conceptualization of the study, interpretation of the results and assisted in drafting the manuscript. All authors read and approved the final manuscript.