Breast cancer predisposition genes identified to date  are responsible for less than 5% of all breast cancer cases. Many studies have shown that the cancer risks associated with individual commonly occurring single nucleotide polymorphisms (SNPs) are incremental. However, polygenic models suggest that multiple commonly occurring low to modestly penetrant SNPs of cancer related genes might have a greater effect on a disease when considered in combination.In an attempt to identify the breast cancer risk conferred by SNP interactions, we have studied 19 SNPs from genes involved in major cancer related pathways. All SNPs were genotyped by TaqMan 5'nuclease assay. The association between the case-control status and each individual SNP, measured by the odds ratio and its corresponding 95% confidence interval, was estimated using unconditional logistic regression models. At the second stage, two-way interactions were investigated using multivariate logistic models. The robustness of the interactions, which were observed among SNPs with stronger functional evidence, was assessed using a bootstrap approach, and correction for multiple testing based on the false discovery rate (FDR) principle.None of these SNPs contributed to breast cancer risk individually. However, we have demonstrated evidence for gene-gene (SNP-SNP) interaction among these SNPs, which were associated with increased breast cancer risk. Our study suggests cross talk between the SNPs of the DNA repair and immune system (XPD-[Lys751Gln] and IL10-[G(-1082)A]), cell cycle and estrogen metabolism , cell cycle and DNA repair (BARD1-[Pro24Ser] and XPD-[Lys751Gln]), and within carcinogen metabolism  pathways.The importance of these pathways and their communication in breast cancer predisposition has been emphasized previously, but their biological interactions through SNPs have not been described. The strategy used here has the potential to identify complex biological links among breast cancer genes and processes. This will provide novel biological information, which will ultimately improve breast cancer risk management. The existence of dominant predisposition alleles/mutations, conferring a high breast cancer risk, has been confirmed with the discovery of BRCA1 and BRCA2 . BesidesSNPs have been historically classified as commonly occurring (>1%) genetic variation in the general population, whereas the rare variants with obvious functional consequences on the protein have been classified as mutations. Compared to mutations, SNPs have been perceived as functionally insignificant, however, current evidence emphasizes that a considerable fraction affects the intrinsic properties and the function of the proteins to a variable degree -9. AlthoTo estimate breast cancer risk conferred by individual SNPs, as well SNP-SNP interactions, we have studied 19 SNPs from 18 key cancer genes, involved in DNA repair , cell cycle CCND1, p27, BARD1), carcinogen/estrogen metabolism , immune system  and others (MMP1)  a participating site in the NIH-funded Breast Cancer Family Registry . WrittenCases of invasive breast cancer, pathologically confirmed and diagnosed between 1996 and 1998 in the province of Ontario were identified from the population-based Ontario Cancer Registry. All female cases under 55, a random sample (35%) of female cases aged 55 to 69, and all male cases under age 80 were identified. Physician permission to contact patients was granted for 91% of cases (7668 of 8453). Patients were then mailed a cancer family history questionnaire and 65% (4957) completed it. All respondents who met a defined set of genetic risk criteria  were included in the study  and a raControls were identified by calling randomly selected residential telephone numbers from across the province of Ontario and were frequency-matched to all female OFBCR cases by 5-year age group. The number of telephone numbers was 14,653, but 1101 (8%) were invalid and no contact could be made for 841 (6%). Of the 12,711 households contacted, 7829 (62%) did not have an eligible individual. No information on eligibility was provided for 2194 (17%) households. Of the 2688 eligible individuals identified on the telephone, 1726 (64%) completed the mailed risk factor questionnaire and 75% of these agreed to be contacted about providing a blood sample. The 676 women under age 55 who had agreed to be approached about blood sampling were asked to provide a blood sample and 419 (62%) did so. Individuals who were not Caucasian were excluded from the analysis, as were those with insufficient DNA or those subsequently found to be ineligible because of age. The remaining 372 population controls were successfully genotyped in this study.All SNPs were analyzed by TaqMan 5'nuclease assay  using th2 concentrations and annealing temperatures for each SNP are given in A panel of DNA samples were sequenced for each SNP region initially, in order to identify control genotypes to be used in each experiment. PCRs were performed in 96 well plates (AXYGEN) with each plate containing four control samples for each possible genotype. Genomic DNA (10 ng) was amplified in a total volume of 10 ul in the presence of 100 uM of each of the dNTPs, 3 pmoles of each of the appropriate primers, 2 pmoles of each of the corresponding dual labelled probes, and 0.025 units of Platinum Taq DNA Polymerase (InVitrogen). PCR cycling conditions consisted of 40 cycles of 94°C for 15 sec, 55–60°C for 15 sec and 72°C for 15 sec. The optimal MgClWe sought evidence of association between each of the 19 SNPs and breast cancer risk in a multi-step process. At the first stage, we calculated crude allele and genotype frequencies for each individual polymorphism and evaluated Hardy-Weinberg equilibrium using a one-degree of freedom goodness-of-fit test among controls . The assAll analyses were performed assuming a dominant, recessive and co-dominant effect for each polymorphism. In the dominant model, both the heterozygous variant and the rare homozygous variant were combined. In the recessive model, the variant was defined as only the rare homozygous genotype and in the co-dominant model both rare homozygous and heterozygous variant effects were estimated using two dummy variables. In all analyses, the common homozygote genotype in the control population was defined as the reference category. Age was considered as a continuous variable. The likelihood ratio test was used to test the effect of each SNP at the nominal 5% significance level. Akaïke's information criterion  was alsoAt the second stage, two-way interactions were investigated using multivariate logistic models. More specifically, we tested all SNP-SNP interactions. We assumed a multiplicative interaction effect on the logit scale. Statistically significant interactions were selected using a forward stepwise selection procedure to evaluate evidence that specific interactions were independently associated with breast cancer. The initial model included all SNPs and age as main effects, and then searched for the most significant candidate interactions to enter into the model based on the score statistics at the 5% level. Backward elimination of variables was then performed using the likelihood ratio test (LRT) also at the level of 5%. Forward stepwise selection procedure has proven to be efficient in assessing interaction effects as compared to backward elimination when testing multiple interactions. First, it is more time efficient and second, when using backward elimination, a relatively large number of predictor variables may increase the risk of complete separation of the two outcome groups, which would result in numerical problems in estimating the model parameters . Since tWe have also estimated the amount of linkage disequilibrium (LD) between the two ESR1 SNPs separated by about 140 kb on chromosome 6 and investigated their haplotype effect on breast cancer using the software "Unphased" from Dudbridge .n (n1 cases + n2 controls) with replacement from the original data  was the only one showing a significant main effect in our sample based on the crude After correction for multiple testing using FDR principle, four interactions remained significant at the 5% level; XPD-[Lys751Gln] and IL10-[G(-1082)A] (p = 0.007), GSTP1-[Ile105Val] and COMT-[Met108/158Val] (p = 0.007), COMT-[Met108/158Val] and CCND1-Pro241Pro] (p = 0.014), and BARD1-[Pro24Ser] and XPD-[Lys751Gln] (p = 0.014). Based on the False Positive Report Probability (FPRP) approach, computed using the functional importance of each SNP, we found that three interactions were noteworthy at the 0.2 FPRP level A], and GSTP1-[Ile105Val] and COMT-[Met108/158Val] interactions. The P-values for the COMT-[Met108/158Val] and CCND1-[Pro241Pro], and BARD1-[Pro24Ser] and XPD-[Lys751Gln] interactions were 0.38.None of these interactions were significant after the more conservative Bonferroni adjustment. The Bonferroni adjusted P-values associated with XPD-[Lys751Gln] and IL10-[G(-1082)A], COMT-[Met108/158Val] and CCND1-[Pro241Pro], GSTP1-[Ile105Val] and COMT-[Met108/158Val], and BARD1-[Pro24Ser] and XPD-[Lys751Gln] were all significant , however the significance of the tests decreased due to the high proportion of individuals missing BMI information. Therefore, only the analyses adjusted for age are presented in Table The results of our multivariate analyses adjusted for age, BMI and family history confirmed the role of the most important interactions. The bootstrap CND1-Pro21Pro, GSTThe amount of LD between the two ESR1 SNPs was relatively small, with a D'  of 0.07 In this study, we have analysed the contribution of 19 SNPs from 18 cancer-related genes, to breast cancer risk in a case-control study of 398 breast cancer cases and 372 population controls, sampled from the population-based OFBCR. All cases and controls were Caucasian women under age 55. We found that among 19 SNPs, XPD-[Lys751Gln] substitution was the only one showing a significant association with breast cancer risk. However, after correction for multiple testing, the effect became insignificant, suggesting that this finding might be due to chance. Overall we found little evidence of breast cancer risk conferred by individual commonly occurring SNPs in this dataset.Our main focus was to understand the contribution to breast cancer risk of functionally relevant SNP-SNP interactions within and between different cancer pathways. Recently, there has been increasing evidence regarding the joint effect of commonly occurring SNPs on cancer risk, supported by polygenic breast cancer models -14. AlthP-values, caution should be used when interpreting these results. First, the statistical modeling of interactions through a product term might not correspond to or reflect any biological interaction such as synergy or antagonism  and IL10-[G(-1082)A]), cell cycle and estrogen metabolism , cell cycle and DNA repair (BARD1-[Pro24Ser] and XPD-[Lys751Gln]) as well as within a single pathway such as estrogen metabolism . Among the four interactions identified, COMT and XPD seem to play a central role since they interacted with different proteins in our set. XPD interaction was identified with both IL-10 and BARD1; whereas COMT interaction was identified with CCND1 and GSTP1. Estimation of odds ratios for particular genotype combinations can show considerable increase in the risk associated with breast cancer. For example the combination of rare genotypes of BARD1-[Ser24] (TT genotype) and XPD-[Gln751] (CC genotype) resulted in a relative risk of 7.4 (95% CI: 1.3-12.4). This is an excellent example where rare genotype combinations are associated with increased cancer risk. Such findings would have been missed in the absence of interaction analysis. These results emphasize the need for larger studies where the risk associated with such rare genotype combinations can be validated.We have further investigated the biological relationships between XPD and COMT interactions with other SNPs using protein-protein interaction databases and a literature search. As seen in Figure The biological pathways investigated in this study have been previously implicated in breast cancer development; however, their genetic interactions, detected through variant alleles (SNPs), have not been previously described. These data and the statistical approaches applied to them have the potential to assist in the identification of complex biological relationships among cancer processes during the development of breast cancer. When moving into the era of "genetic dissection of complex traits ", we wilOur focus in this study has been to uncover SNP-SNP interactions, which additively or synergistically contribute to breast cancer risk. From our small pool of SNPs, we have shown significant statistical interactions suggesting biological cross talk among genes/SNPs from DNA repair, cell cycle, immune system and carcinogen metabolism pathways. Our immediate task is to apply this strategy to a larger sample, with the aim of replicating our findings and investigating more complex interactions (involving three or more SNPs). This line of research has the potential to identify important cross talk between members of the cancer pathways in the disease state. This study not only provides insight into the analysis of the multi-genic nature of breast cancer, but also provides important information regarding how cell function relates to breast cancer development. We believe that these and other interactions in breast cancer will one day be identified and used in clinics to identify individuals at increased risk of breast cancer and develop preventive strategies.SNP – single nucleotide polymorphismsFDR – false discovery rateFPRP – false positive report probabilityOFBCR – Ontario familial breast cancer registryOR – odds ratioLRT – likelihood ratio testASL – achieved significance levelLD – linkage disequilibriumThe author(s) declare that they have no competing interests.VUO participated in design of the study, in data production and management, and manuscript preparation.LB carried out the statistical analyses and wrote of the statistical sections of the manuscript.JAK participated in formation of epidemiological study design and manuscript preparation.ES participated in statistical analysis.YW participated in statistical analysis.SW participated in production of genotype data.HL participated in production of genotype data.IR participated in statistical analysis.ILA provided information about study samples and OFBCR, and participated in manuscript preparation.HO participated in the design and coordination of the study, and manuscript preparation.All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:Primer_and_probe_information_for_19_SNPs. Positions of primers and probes used in the study, in their appropriate accession numbers, are given in this table.Click here for file2 concentrations and annealing temperatures for each SNP PCR are given in this table.PCR_Conditions_for_19_SNPs. The optimal MgClClick here for file


The positions of 100 synteny breakpoints relative to the assembled human genome were determined at a resolution of about 200 kb. Interestingly, 46% of the gibbon–human synteny breakpoints occur in regions that correspond to segmental duplications in the human lineage, indicating a common source of plasticity leading to a different outcome in the two species. Additionally, the full sequences of 11 gibbon BACs spanning evolutionary breakpoints reveal either segmental duplications or interspersed repeats at the exact breakpoint locations. No specific sequence element appears to be common among independent rearrangements. We speculate that the extraordinarily high level of rearrangements seen in gibbons may be due to factors that increase the incidence of chromosome breakage or fixation of the derivative chromosomes in a homozygous state.Gibbons are part of the same superfamily (Hominoidea) as humans and great apes, but their karyotype has diverged faster from the common hominoid ancestor. At least 24 major chromosome rearrangements are required to convert the presumed ancestral karyotype of gibbons into that of the hominoid ancestor. Up to 28 additional rearrangements distinguish the various living species from the common gibbon ancestor. Using the northern white-cheeked gibbon (2n = 52)  It is commonly accepted that mammalian chromosomes have undergone a limited number of rearrangements during the course of more than 100 million years of evolution. Surprisingly, some species have experienced a large increase in the incidence of rearrangements, including translocations (exchange between two non-homologous chromosomes), inversions , fissions, and fusions. Within the primate order, gibbons exhibit the most strikingly unstable chromosome pattern. Gibbon chromosomal structure greatly differs from that of their most recent common ancestor with humans from which they diverged over 15 million years ago. The authors are interested in the mechanisms causing this extraordinary instability. In this study, they employed modern techniques to compare the human and white-cheeked gibbon chromosomes and to localize all the regions of disrupted homology between the two species. Their findings indicate that the molecular mechanism of gibbon chromosomal reshuffling is based on the same principles as in other mammalian species. To explain the 10-fold higher incidence of gibbon chromosomal rearrangements, it will be necessary to pursue future studies into other biological factors such as inbreeding and population dynamics. Symphalangus (siamang) 2n = 50, Nomascus (crested gibbon) 2n = 52, Hylobates (Hylobates group) 2n = 44, and Hoolock (hoolock gibbon) 2n = 38 [During recent years, great progress has been made in understanding the evolutionary processes governing mammalian chromosomal organization. It is now commonly accepted that the mammalian karyotype has undergone a limited number of major rearrangements over the course of more than 100 million years –5 The sm 2n = 38 –8. The eDrosophila [Recent studies describing the dynamics of mammalian genome evolution indicate a “reuse” of genomic regions for independent evolutionary breakpoints in different lineages as well as the presence of hotspots and fragile sites more prone to rearrangements. These fragile loci frequently coincide with regions enriched for segmental duplications (SDs) in primates and involved in human genomic disorders –17. Moreosophila ,19 and eosophila ,20,21.Gibbon karyotypic changes have previously been investigated by cytogenetic banding analysis ,22–24 anNomascus leucogenys leucogenys (NLE) (northern white-cheeked gibbon) and human using a combination of high-resolution genomic technologies: array comparative genome hybridization painting [Here, we compared the karyotypes of painting ,33, bacthttp://bacpac.chori.org/library.php?id=228, and 3) the latest genome assemblies of rhesus macaque (UCSC build rheMac2) and chimpanzee (UCSC build panTro1) used as outgroups to resolve rearrangements to the great ape or small ape lineage.The three main resources used in this study were 1) high-resolution microarray slides containing about 32,000 BAC clones spanning the entire human genome (“32K set”), 2) a genomic BAC library for the northern white-cheeked gibbon (CHORI-271) described in more detail at To better understand karyotype instability, gibbon-specific sequences at the break of synteny regions (BOSRs) need to be analyzed. To determine if it is possible to attribute breakpoints to specific sequence elements or to the genomic architecture of these regions, we sequenced a preliminary set of 11 gibbon BAC clones spanning BOSRs.We employed array painting  in a preliminary array-painting experiment. Array-painting experiments were then economized by pooling the sorted gibbon chromosomes so that the gibbon chromosomes in each pool detected distinct human chromosomes. This “smart pooling” was possible because the gibbon–human synteny regions have previously been identified through chromosome painting ,28,29,31Single test and reference hybridization results revealed that the signal-to-noise ratio was too low for accurate detection of BOSR coordinates. After determining that the hybridization noise was systematic, we developed a noise-reduction method  to obtaiAltogether, 64 BOSRs have been mapped to the human genome  . Among tIn order to confirm the accuracy of array-painting results, ten BOSRs were validated by FISH experiments where BAC clones from the “32K set” were hybridized to NLE metaphase preparations. In most of the cases, a single BAC hybridized to two disparate locations as expected. Only BOSR-32 and BOSR-11 demonstrated a single hybridization signal, suggesting that the breakpoint was located between two BACs or in a small region of overlap between them. A few examples of FISH experiments are shown in Hylobates lar (HLA) (2n = 44) using the same human FISH probes  or duplicated regions in the gibbon and were removed from further analysis. We also screened the gibbon BAC CHORI-271 library  in order to identify at least two additional clones spanning identified breakpoints. Based on our pairing criteria, most of the clones identified as spanning intrachromosomal rearrangements resulted from insertion/deletions (indels) in either the gibbon or the human genomes (74 out of 97). Indels cause discrepancies between paired BES mapping distances on the human genome compared to the average gibbon BAC insert size. We formally defined this as BES mappings at a distance less than or greater than three standard deviations from the 172-kb-clone insert size. We verified the insert size of the 74 clones spanning putative indels using NotI digestion and pulsed-field electrophoresis. Based on the pulsed-field electrophoresis, the 60 gibbon BACs with BES mapping distances of 40–60 kb relative to the human genome were found to not be indels, as they had actual insert sizes in the 40–60 kb range. We presumed that the remaining 14 clones with BES distances exceeding 300 kb represent actual insertions in the human genome or deletions in the gibbon genome. Results are summarized in Clones putatively spanning inversion breakpoints were validated by comparisons with BOSRs previously defined by array painting . Large-sThe goals of our study were 1) to obtain a map of the BOSRs between human and gibbon at high resolution and 2) to identify species-specific clones spanning chromosomal rearrangements for use in further molecular analysis. In pursuit of our second goal, we selected for further analysis 38 gibbon BAC clones corresponding to BOSRs identified by array painting human BACs. We constructed probes across these BOSRs at 75-kb intervals based on the human genome sequence and used these probes to screen the gibbon library. Using this approach, we identified an additional 26 gibbon clones containing breakpoint loci (15 inversions and 11 translocations) .To ensure that we identified rearrangements that occurred in the gibbon lineage, we mapped the BES of gibbon clones identified as spanning rearrangement breakpoints onto the latest genome assemblies of rhesus macaque (UCSC Build rheMac2) and chimpanzee (UCSC Build panTro1) using BLAT. We removed ambiguous mappings and classified the remaining mappings using the same pairing criteria applied to the human genome mappings. We classified putative rearrangements as 1) gibbon specific if gibbon was rearranged relative to human, chimpanzee, and macaque; 2) great ape specific if gibbon was not rearranged relative to macaque, but was rearranged relative to human and chimpanzee; and 3) human specific if gibbon was not rearranged relative to macaque and chimpanzee but was rearranged relative to human . Based oOne of the events classified as great ape–specific is the inversion of human Chromosome 3, with breakpoints at 3p25 and 3q21, which are regions already known as rearrangement hotspots in primates . BOSR-10In total, we identified 110 breakpoints between human and gibbon chromosomes due to intra- and interchromosomal rearrangements. Of those, 100 occurred during the evolution of the gibbon.It is widely accepted that regions of chromosomal instability and SDs colocalize more frequently than expected by random chance. SDs are blocks of DNA 1–400 kb in length, repeated in the genome with a high level of sequence identity (>90%) . The assWe first analyzed the overlap between the human regions orthologous to gibbon-specific BOSRs and the human SDs reported in the UCSC browser . We founTo statistically validate the significance of these data, a simulation was run in which the 100 breakpoint regions were randomly relocated 1,000 times in their original chromosome, emulating a random-breakage model . The resMeasuring overlap alone does not sufficiently express the proximity of these regions to SDs; thus, the breakpoint regions were expanded in 100 kb increments and monitored for variation in the number of regions overlapping with SDs B. Out ofSymphalangus syndactylus and HLA) belonging to different genera. These data suggest that the SDs most likely appeared within a common ancestor . These duplications contain a second gene from the growth hormone cluster (CHS2). It has been shown [Five BOSRs, including the two BOSRs mentioned above, were within 5 kb of SDs. One example is the clone CH271-262E11 mapping to NLE Chromosome 19 and spanning a breakpoint between human Chromosomes 2 and 17. The BOSR in this clone was identified at the base-pair level and was found to be adjacent to the growth-hormone gene cluster. The breakpoint is 20 bp away from a duplicated segment containing the ortholog of gene en shown  that theen shown  recentlyHylobates agilis, Bunopithecus hoolock, and S. syndactylus). We could therefore exclude the possibility that the repeat was a consequence of the rearrangement.Six of the BOSRs coincided with interspersed repeats . In clone CH271-236L11, an alpha-satellite was identified due to the proximity of the BOSR to a centromere . Clones Nomascus genus. However, the cooccurrence of an evolutionary breakpoint and an AT-rich repeat region is intriguing.The presence of the AT-rich repeat in relation to these BOSRs may indicate a different breakpoint-inducing mechanism. Recently, Gotter and colleagues showed that the propensity to form secondary structures such as stem-loops can confer fragility to DNA . Using tThis study describes the mapping and validation of a large number of syntenic breakpoints between homologous chromosomes of human and NLE. All the translocation breakpoints previously identified by chromosome-painting studies were mapped to the human genome at a greatly increased resolution. About 20 additional rearrangements were discovered as a result of the higher sensitivity of our approaches. Overall, our research identified about 100 breakpoints occurring in the gibbon lineage. The study also yielded gibbon BACs containing breakpoint sites. In 11 sequenced gibbon BACs, we found elements near the breakpoints previously shown to play a key role in primate chromosome plasticity and evolution. Within the sequenced BACs three different patterns were evident. First, two BACs contained additional breakpoints that may have resulted from a complex, nonreciprocal translocation event or from subsequent chromosomal rearrangements. Recent high-resolution breakpoint analyses on human translocations thought to be balanced showed various “microtranslocations” . Thus, tNo generalized pattern unique to gibbon breakpoints is evident from the present molecular data. It remains to be determined if the greater number of chromosomal rearrangements in the small apes is due to an enhanced frequency of chromosomal breakages or an increased ability to rescue derivative chromosomes in comparison to other mammals, possibly due to mating behavior or inbreeding. We believe that these questions may be answered by examining additional aspects of small ape biology such as behavioral factors and population dynamics.www.corning.com). Gibbon chromosomes were sorted on a FACS Vantage flow cytometry system  as described for previous experiments [http://www.moleculardevices.com). The images were first processed using GenePix Pro 5.1 (Axon Instruments). The primary experimental data (GenePix Results files) were subjected to fully standardized data analysis  by uploading them to the BioArray Software Environment microarray analysis software installation [2 ratio values versus chromosome location as well as a whole-genome view.BACs  spanning 95% of the human euchromatic genome were assembled and rearrayed into 384-well microtiter dishes ,47. DNA eriments . The NLEeriments  and Schreriments . Karyotyeriments  from floeriments . Hybridiallation , which pS. syndactylus were the ones described by Jauch et al. [Homo sapiens (HSA) metaphase preparations were prepared from peripheral blood culture.FISH was used as a validation method for BOSRs identified uniquely by array painting or for gibbon BACs spanning inversions. Metaphase preparations of NLE were obtained from the same cell line used for chromosome sorting and previously described by Müller et al.  and Schrh et al.  and Koehh et al. , respectwww.vectorlabs.com) and anti-digoxigenin-rhodamine antibodies . When confirming translocation breakpoints on gibbon chromosomes, BACs were hybridized together with chromosome-specific painting probes obtained from sorting lymphoblastoid and somatic hybrid cell line chromosomes followed by DOP PCR [www.zeiss.com) microscope equipped with a CCD-1300DS  or a SenSys  cooled CCD camera. Pseudocoloring and merging of images was performed using SmartCapture  or Adobe Photoshop software .BAC DNA extraction was performed as reported by Ventura et al. . FISH ex DOP PCR . DigitalTo identify BACs spanning putative breakpoint regions, overgo probes of 40 bp  were deswww.imagingresearch.com).The images were analyzed with the software ArrayVision Ver6.0  we identified all 64 BOSRs at a resolution of 300 kb (average) with the employment of a limited number of experiments. The figure shows the results obtained for all human chromosomes.(240 MB PPT)Click here for additional data file.Figure S2This tool was developed in order to easily localize gibbon clones spanning a translocation or inversion breakpoint in human. (493 KB PPT)Click here for additional data file.Figure S3S. syndactylus.A sample of gibbon BAC clones overlapping human SDs was hybridized on NLE nuclei. The presence of duplications was revealed by the presence of either multiple signals or a single but broadened signal. The figure shows the results obtained with four clones also tested on HLA and (122 KB JPG)Click here for additional data file.Protocol S1Click here for additional data file.(41 KB DOC)Table S1The gibbon chromosomes were divided into four pools in order to minimize the number of array-painting experiments. A smart-pooling strategy was used, taking advantage of the data available in the literature. Through this approach the repetition of one human chromosome in the same pool was avoided. Additionally, three gibbon chromosomes were hybridized in individual experiments.(45 KB DOC)Click here for additional data file.Table S2The table reports the outcome of the mapping of gibbon clones spanning BOSRs on the latest genome assembly of Rhesus macaque (reMach2) and chimpanzee (panTro1). Depending on the result, clones were classified into three evolutionary groups: 1) gibbon specific, 2) great ape specific, and 3) human specific.Mapping results not consistent with human are in italics.(134 KB DOC)Click here for additional data file.http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=gene) accession numbers for the genes discussed in this paper are CASP1 (NM_033294), CASP4 (837), CASP5 (838), GH2 (2689), KRTAP5–10 (387273), KRTAP5–11 (440051), KRTAP5–7 (440050), KRTAP5–8 (57830), KRTAP5–9 (3846), MRC1 (4360), OR11L1 (391189),OR13G1 (441933), OR1C1 (26188), OR2G2 (81470), OR2G3 (81469), OR5AT1 (284532), and OR6F1 (343169).The National Center for Biotechnology Information (NCBI) Entrez Gene (


Microarray technology has become highly valuable for identifying complex global changes in gene expression patterns. The effective correlation of observed changes in gene expression with shared transcription regulatory elements remains difficult to demonstrate convincingly. One reason for this difficulty may result from the intricate convergence of both transcriptional and mRNA turnover events which, together, directly influence steady-state mRNA levels.In order to investigate the relative contribution of gene transcription and changes in mRNA stability regulation to standard analyses of gene expression, we used two distinct microarray methods which individually measure nuclear gene transcription and changes in polyA mRNA gene expression. Gene expression profiles were obtained from both polyA mRNA (whole-cell) and nuclear run-on (newly transcribed) RNA across a time course of one hour following the activation of human Jurkat T cells with PMA plus ionomycin. Comparative analysis revealed that regulation of mRNA stability may account for as much as 50% of all measurements of changes in polyA mRNA in this system, as inferred by the absence of any corresponding regulation of nuclear gene transcription activity for these groups of genes. Genes which displayed dramatic elevations in both mRNA and nuclear run-on RNA were shown to be inhibited by Actinomycin D (ActD) pre-treatment of cells while large numbers of genes regulated only through altered mRNA turnover (both up and down) were ActD-resistant. Consistent patterns across the time course were observed for both transcribed and stability-regulated genes.We propose that regulation of mRNA stability contributes significantly to the observed changes in gene expression in response to external stimuli, as measured by high throughput systems. This early work provided evidence that transcriptional activation of genes does not necessarily lead to a corresponding increase of their steady-state mRNA levels.Virtually all microarray studies to-date have measured changes in steady-state mRNA levels by harvesting total cellular RNA and using it to generate probes through a variety of strategies including end-labeling of purified mRNA , incorpoMore recently, our laboratory has examined the relationship between newly transcribed (NRO) RNA and polyA mRNA in a stress model using human non-small lung carcinoma H1299 cells. In response to a variety of stresses , we found that approximately half of the observed changes in mRNA levels of stress-regulated genes were accompanied by a corresponding increase or decrease in gene transcription as measured by NRO. The remaining half of stress-altered changes in gene expression was largely attributable to changes in mRNA turnover, thus suggesting that, on a global level, changes in mRNA turnover profoundly influence gene expression patterns .myc induction and stress experiments, as mentioned above, measurements of both newly transcribed and polyA mRNAs were made at a single time point, there existed a reasonable possibility of temporal disjunctions between the timing of mRNA new gene synthesis and the rates of accumulation of mRNA in the cell. The second question remaining unanswered is whether or not highly significant levels of mRNA stability regulation  is common to different biological model systems. In order to begin to address these questions we investigated changes at the levels of transcription and total cellular mRNA abundance simultaneously across a time course of activation using Jurkat T cells.Several questions, however, remained to be answered from these earlier studies. Since in both the 2+ from intracellular stores. Subsequent activation of signal transduction cascades culminates in the implementation of gene expression patterns characteristic of the immune response. Initial microarray studies using T cells have focused on gene expression changes occurring several hours after activation UTP , and incubated for 30 min at 30°C with shaking. Samples were then incubated with DNase I  for 20 min at 37°C, and with proteinase K (1 μg/μl) for 1 h at 37°C. Finally, nascent RNA was purified by Sephadex G-50 column filtration. For the preparation of polyA mRNA cellular RNA, resting or stimulated Jurkat cells were lysed in STAT60™  as described dCTP-labeled cDNA or [α-33P]UTP RNA probes overnight at 50°C as previously described [. Hybridized arrays were rinsed in 2 X SSC and 0.1% SDS twice at 55°C followed by washes in 2 X SSC and 0.1% SDS at 55°C. Microarrays were exposed for 1–3 d and scanned using a PhosphorImager  at a 50-μm pixel resolution. ArrayPro software  was used to convert the hybridization signals into raw intensity values; data generated were transferred into Microsoft Excel for further analysis.Microarray construction and hybridization were previously described . Brieflyescribed , protocoSingle endpoint PCR was carried out on polyA mRNA for gene detection and relative quantitative comparison between baseline (0 time) and I hour activation (with PMA +I). In brief, GS320 libraries for both control and activated polyA mRNA were prepared as described , normali8 cell-equivalents) were used in each hybridization. Raw intensity data for each experiment was transformed to log10, then used for the calculation of Z scores as described [RNA samples  were prepared from multiple experiments, each of which consisted of a consecutive series of time points. Equal amounts of total RNA (5 μg) or NRO RNA (10escribed  [see Addescribed . Signifiescribed ), using Hierarchical clustering was performed using the Cluster and TreeView software programs, developed at Stanford University . The clu. See Mapping of transcription factor binding sites in selected genes was performed using software from Genomatix Software Gmbh, Munich, Germany NRO, nuclear run-on; P+I, PMA plus Ionomycin; ActD, Actinomycin DCC and JF performed the microarray assays . CC carried out statistical analysis, and drafted the manuscript (with MG & KGB). JR and LD performed the PCR validation assays. TW assisted with promoter analysis and YSC-C participated in the design of the study. All authors read and approved the final manuscript.Cheadle et al – BMC Genomics – basic data 05-27-05.xls, contains all original data , significance testing, and barcode assignments.Click here for fileCheadle et al – BMC Genomics TF binding sites frequency analysis 05-27-05.xls, contains all calculations for TF binding site analysis.Click here for file


Fundulus heteroclitus tolerate a broad range of environmental conditions including temperature, salinity, hypoxia and chemical pollutants. Strikingly, populations of Fundulus inhabit and have adapted to highly polluted Superfund sites that are contaminated with persistent toxic chemicals. These natural populations provide a foundation to discover critical gene pathways that have evolved in a complex natural environment in response to environmental stressors.Natural populations of the teleost fish Fundulus cDNA arrays to compare metabolic gene expression patterns in the brains of individuals among nine populations: three independent, polluted Superfund populations and two genetically similar, reference populations for each Superfund population. We found that up to 17% of metabolic genes have evolved adaptive changes in gene expression in these Superfund populations. Among these genes, two (1.2%) show a conserved response among three polluted populations, suggesting common, independently evolved mechanisms for adaptation to environmental pollution in these natural populations.We used Fundulus populations. Among three independent, Superfund populations, two genes have a common response indicating that high selective pressures may favor specific responses.Significant differences among individuals between polluted and reference populations, statistical analyses indicating shared adaptive changes among the Superfund populations, and lack of reduction in gene expression variation suggest that common mechanisms of adaptive resistance to anthropogenic pollutants have evolved independently in multiple  Fundulus heteroclitus that inhabit and have adapted to highly polluted Superfund sites [Many natural populations are continuously exposed to chemical stressors. One indication of this is that in 2004, over 4.24 billion pounds of industrial chemicals were disposed or released to the environment by facilities required to report to the EPA, including 576 million pounds of air emissions and 210 million pounds of surface water releases [Fundulus that inhabit and have adapted to highly polluted Superfund sites. The Superfund sites are highly contaminated with aromatic hydrocarbons, including polychlorinated biphenyls (PCBs), dibenzo-p-dioxins, and polycyclic aromatic hydrocarbons (PAHs) among other contaminants [Fundulus is <60 hours [To address this question we examined brain gene expression in individuals from three independent populations of aminants -9. Gene aminants . Althougaminants , dioxin 60 hours ). We assWe used a metabolic array since exposure to stress is likely to affect metabolic gene expression due to the additional energy needed to protect against harmful effects. Metabolic gene expression was examined in brains since brain tissue is metabolically active and may be highly susceptible to lipophilic pollutants: high energy demands, high lipid content, low antioxidant levels, reactive microglial cells, and inability to replicate neurons make it vulnerable to pollution and resulting oxidative stress ,14.Fundulus populations that have independently evolved adaptive resistance to complex suites of pollutants provide independent contrasts for identifying genes involved in adaptation. We compared brain gene expression levels in common gardened Fundulus heteroclitus from three independent, resistant Superfund sites to individuals from six nearby reference populations. We report significant differences in gene expression that were unique to each polluted population or shared among two polluted populations as well as conserved responses in gene expression among all three Superfund populations.Multiple Fundulus metabolic gene expression [Fundulus [2: 0.63–0.83; see Additional file We found large amounts of significant variation in gene expression among individuals within each of nine populations . These results are similar to previous studies of pression -17. UsinFundulus ,19 exhibversus two reference populations will be significant if it is greater than the average variation within the polluted population and the combined reference populations. If the variation is significant, it exceeds the expectation of neutral evolutionary models [With the large number of differences in gene expression among individuals within a population, one should expect differences between genetically distinct populations. These differences are not necessarily due to evolution by natural selection since the variation in expression does not necessarily affect a biologically important phenotype. To distinguish biologically important differences, those evolving by natural selection, from among the many genes exhibiting significant differences within and among populations, we specifically tested whether gene expression in a Superfund population is statistically different from the two reference populations Fig. . These ty models  and thusversus reference), the variation in gene expression due to random drift is accounted for in the ANOVA. Thus, similar to the separate comparisons with the three Superfund populations, the genetic differences in gene expression most likely represent evolution by natural selection and in these comparisons, adaptation to chronic exposure to pollution.We also asked which gene expression levels were significantly different in all three polluted populations compared to all six reference populations. Since there is as much genetic distance among populations within these two groups (polluted or reference) as there is between groups  for each comparison of a Superfund population to two reference populations Fig. . The NewWe statistically compared brain gene expression patterns among individuals from all three polluted populations  to individuals from all six reference populations to determine if there were gene expression changes due to common pollution stress. Analyses indicate that 17% (27/170) of the genes were more similar among polluted populations and significantly different from reference populations at p = 0.01 Fig. . Sixteenversus all reference populations contrasted with p-values from separate population comparisons of a polluted population versus two reference populations. NDUB2 is consistently differentially expressed at p < 0.01 in the four separate comparisons: the three separate comparisons of each Superfund site versus surrounding reference sites and the comparison of all polluted versus all reference populations. At p < 0.05, CYP1B1 also is consistently differentially expressed in all four comparisons.Table Fundulus that inhabit and have adapted to highly polluted Superfund sites to identify gene expression changes involved in adaptation to pollution. Expression of 17% of the metabolic genes examined differed between the New Bedford Harbor, MA Superfund population compared to two reference populations, 12% differed in the Newark Bay, NJ comparison, and 5% differed in the Elizabeth River comparison. Genes contributing to phase I and phase II biotransformation of xenobiotics are likely candidates for adaptation at the physiological or genetic level in pollution-impacted populations. Transcripts for the phase I enzymes CYP2N2 and CYP1B1 were less expressed in the New Bedford Harbor population and CYP2N2 expression also was lower in the Newark Bay population. CYP1A induction is important in many toxic responses to dioxin-like compounds although it is refractory to induction in individuals from all of these polluted populations [We used natural populations of ulations ,21. Not via phase I and II metabolism, there is an increase in glutathione S-transferase A and glutathione peroxidase expression, as seen in other studies [Several gene expression changes are consistent with toxic effects from exposure to dioxin-like contaminants. For instance, expression of many genes involved in oxidative phosphorylation differed in the three polluted populations, as might be expected since dioxin affects the respiratory mitochondrial electron chain . Additio studies ,24. The  studies .Drosophila, the number of evolved responses depends on selection strength: altered expression of different cytochrome P450s (CYP) develops in response to DDT in laboratory Drosophila, yet in resistant field isolates, over-expression of CYP6g1 predominates [An important question for adaptation in general is whether common solutions to environmental stress evolve. Recent transcriptional studies suggest multiple mechanisms to a biological end ,27. In Dominates  (but seeominates ). One apversus surrounding reference sites and also in the comparison of all polluted versus all reference populations. Thus its altered expression is unlikely to be due to a type I error. If we are interested in defining the limited number of genes with a common response among all polluted populations, we are concerned with type II error  rather than type I error. To better control for type II error we use a less stringent significance level of p < 0.05 and stipulate that direction of expression is consistent . At this significance level, CYP1B1 also is consistently differentially expressed in all four comparisons. Thus, in the comparisons of each superfund site versus its two reference sites and among the three polluted versus six reference populations comparison, two of the 170 genes (1.2%) appear to have shared derived changes in gene expression in response to anthropogenic pollution.A statistically more powerful test of a shared adaptive response in gene expression is to compare all three polluted populations to all six reference populations. In these analyses, expression levels of 17% of the genes examined were statistically significantly different in the polluted populations compared to reference populations. These differentially expressed genes suggest a shared response to pollution stress in the independent polluted populations. However, by comparing significant expression among polluted populations, many of these differences in gene expression appear to be driven by only one or two of the Superfund populations  have a consistent difference in expression among all polluted sites. Consistent significant differences in gene expression between polluted and reference populations, statistical analyses that indicate evolved adaptive changes, and lack of reduction in variation in gene expression suggest that multiple Fundulus populations have independently evolved common mechanisms of adaptive resistance to complex suites of pollutants. Integrating an evolutionary population genetics approach with an environmental toxicology study allowed us to differentiate genes that vary due to chronic pollutant exposure from those that vary due to random genetic drift. The shared response of two genes among these three independent populations supports the idea that high selective pressures may favor similar responses.Independent Fundulus heteroclitus were collected by minnow trap from three Superfund Sites , and six reference populations where pairs of these reference sites were located north and south of each polluted site . Prior to sampling, fish were subjected to pseudo-winter  for four to six weeks, then maintained for a minimum of 6 weeks with a light cycle of 16:8, light:dark. After the pseudo-winter, Fundulus came into reproductive condition and spawned, and reproductive tissues regressed. The reproductive tissues were in regression when fish were assayed.Populations and individuals within a population were chosen randomly for sacrifice and sampled in the morning and early afternoon for two consecutive days in order to minimize physiological changes due to diurnal cycles. Five fish from each of 9 populations were sacrificed by cervical dislocation. Fish were mixed sex and ranged in weight, with an average weight of 7.28 ± 0.33 g. Neither sex nor weight were significant variables in the mixed models discussed below. Dissected organs, including brain, for subsequent RNA extraction were immediately frozen in a dry ice/ethanol bath and stored at -80°C. This experiment was performed according to an approved Institutional Animal Care and Use Committee at North Carolina State University.Total RNA was isolated from each brain using a guanidinium thiocyanate buffer  followed®  spotfinding software.Labeled aRNA samples (5 pmol/ul) were hybridized to slides in 12 ul of hybridization buffer , and 1 mg/ml RNAse free poly(A) RNA (Sigma) for 42.5 hours at 42°C. Slides were prepared by blocking according to the manufacturer's recommendations with an additional treatment of 66 mM sodium borohydride to minimize background autofluorescence . After hFundulus heteroclitus heart and liver libraries were spotted onto CodeLink activated slides  at the University of Miami core microarray facility (slides were the kind gift of D. Crawford). Each slide contained 4 spatially separated arrays, and each array contained 4 replicates of 384 spots (genes) including controls. Printed cDNAs encode essential proteins for cellular metabolism based on KEGG (Kyoto Encyclopedia of Genes and Genomes). Sequence information, annotation and gene ontology are available for Fundulus on the FunnyBase website [Amplified cDNA sequences for 384 metabolic genes from  website .Ctenophore specific cDNAs) and genes that either saturated the photomultiplier tube or had signals less than the negative controls. The number of genes examined were 214, 216, and 248 for the New Bedford Harbor, Newark Bay, and Elizabeth River comparisons, respectively, and the analysis combining all locations (9 populations) used 170 common genes.Of these 384 genes, not all genes were analyzed. Unanalyzed spots included negative controls  to remove the effects of dye (fixed effect) and array (random effect) following a joint regional and spatial Lowess transformation in MAANOVA Version 0.98.8 for R to account for both intensity and spatial bias [ij = μ + Ai + Dj + (AxD)ij + εij, where, yij is the signal from the ith array with dye j, μ is the sample mean, Ai and Dj are the overall variation in arrays (1–15) and dyes (Cy3 and Cy5), (AxD)ij is the array × dye interaction and εij is the stochastic error [Logial bias . The modic error ,45. Resiijkgm = μ + Ai + Dj + Tk + P(T)gk + S(A)mi + εijkgm, where Tk is the kth treatment (polluted or reference), P(T)gk is the gth population nested in treatment, and S(A)mi is the mth spot nested in array. To compare all nine sites (3 polluted and 6 reference populations hybridized across 3 separate loops), loop (Ll), dye by loop interactions, and array nested in loop terms were added in the mixed model normalization according to the model yijl = μ + Ai + Dj + (AxD)ij + Ll + (DxL)jl + A(L)il + εijl. To compare all nine populations, we used population as a fixed effect in the gene-by-gene model according to the model rijgm = μ + Ai + Dj + Pg+ S(A)mi + εijgm. To test for a treatment effect using all nine populations, loop, treatment × loop interactions, and population nested in loop × treatment interactions were incorporated as fixed effects into the gene-by-gene model according to the model rijklgm = μ + Ai + Dj + Tk + Ll + (LxT)lk+ P(TxL)gkl + S(A)mi + εijklgm. To examine individual variation among locations, the mixed model gene-by-gene analysis used individual nested in population, population and dye as fixed effects according to the model rijghm = μ + Ai + Dj + Pg + I(P)gh + S(A)mi + εijghm.Normalized data were modeled by residual maximum likelihood on a gene-by-gene basis using a linear mixed model in SAS using PROC MIXED. To test for a treatment effect (effect of chronic exposure to pollution), normalized data  were modeled using treatment, population nested in treatment and dye as fixed effects and array and spot nested in array as random effects according to the model rversus reference populations using analysis of variance (ANOVA) and Brown-Forsythe's test. We also used Brown-Forsythe's test for homogeneity of variance among all populations. The heat map of differentially expressed genes using all nine populations was visualized using the Macintosh version [Mixed model analyses were performed for each loop or combined loop analysis, and we used a nominal p-value cut-off for significant genes of p < 0.01. Using this p-value reveals more genes that may differentially expressed but risks identifying genes that may be false positives. Genes identified as also being significant after false positives are reduced by a more stringent multiple comparison correction were analyzed using a Bonferroni correction (p = 0.05). Homogeneity of variance was assessed among all polluted  version  of TreeV version . The corpolychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons (PAHs), NADH-ubiquinone oxidoreductase AGGG subunit precursor (NDUB2), cytochrome P450 1B1 (CYP1B1), cytochrome P450 2N2 (CYP2N2), cytochrome P450 1A (CYP1A)MAF and MFO designed research; MAF performed research; MAF and MFO analyzed data; and MAF and MFO wrote the paper.Variation among and within populations. Additional file Click here for fileSites, latitude and longitude in this study. Additional file Click here for fileversus respective reference populations.Significant Differences in Expression. Additional file Click here for file


Microarray gene expression (MAGE) signatures allow insights into the transcriptional processes of leukemias and may evolve as a molecular diagnostic test. Introduction of MAGE into clinical practice of leukemia diagnosis will require comprehensive assessment of variation due to the methodologies. Here we systematically assessed the impact of three different total RNA isolation procedures on variation in expression data: method A: lysis of mononuclear cells, followed by lysate homogenization and RNA extraction; method B: organic solvent based RNA isolation, and method C: organic solvent based RNA isolation followed by purification.We analyzed 27 pediatric acute leukemias representing nine distinct subtypes and show that method A yields better RNA quality, was associated with more differentially expressed genes between leukemia subtypes, demonstrated the lowest degree of variation between experiments, was more reproducible, and was characterized with a higher precision in technical replicates. Unsupervised and supervised analyses grouped leukemias according to lineage and clinical features in all three methods, thus underlining the robustness of MAGE to identify leukemia specific signatures.The signatures in the different subtypes of leukemias, regardless of the different extraction methods used, account for the biggest source of variation in the data. Lysis of mononuclear cells, followed by lysate homogenization and RNA extraction represents the optimum method for robust gene expression data and is thus recommended for obtaining robust classification results in microarray studies in acute leukemias. Microarrays have been demonstrated to be a powerful technology capable of successfully identifying novel taxonomies for various types of cancers -5 and geHere we present a comparative study of the microarray data using three different RNA isolation and purification techniques . We have performed standardized experiments with total RNA extracted from pediatric acute leukemia patients to investigate whether different extraction protocols (see methods) result in comparable gene expression data from the same sample source Figure . MoreoveBacillus subtilis control transcripts from the Poly-A control kit  are greater or equal to 1, and the intensity ratio of the 3' probe set to the 5' probe set for the housekeeping gene GAPD is less than 3.0. Four samples showed a higher 3'/5' GAPD ratio  but had otherwise acceptable quality parameters.In this study we first monitored data quality parameters. All gene expression profiles passed the quality filter and met our criteria for inclusion into further data analyses [see Additional File GAPD ratios. Preparations of total RNA by TRIzol (method B) yield slightly higher amount of cRNA, generate a lower image background as measured by Q value, but have a higher 3'/5' GAPD ratio. When the total RNA was prepared by TRIzol followed by RNeasy purification (method C) the cRNA yield was high, the background low, with the 3'/5' GAPD ratio being a little bit higher than for preparations of total RNA by QIAshredder homogenization followed by RNeasy purification. All three preparation methods generated an acceptable range of present calls on the whole genome microarray.As illustrated in Figure Simpleaffy" Bioconductor analysis package [Total RNA quality can also be indirectly assessed by a so-called RNA degradation plot analysis as implemented in the " package . The samTo assess the comparability of global gene expression data between samples isolated with different preparation methods it is useful to examine the overall signal distribution of all probe sets as density curve for each microarray experiment. Outlier experiments would be detected by their different behavior of the density curves. As shown in Figure We next investigated the consistency of gene expression measurements of leukemia samples when using different total RNA extraction methods by performing an unsupervised hierarchical clustering analysis. Expression data have been normalized using the PQN algorithm . 2821 geA supervised analysis was performed to assess the potential impact of the use of different total RNA extraction methods on a leukemia classification approach. An all-pairwise t-test analysis identified differentially expressed genes that would distinguish between the 9 classes of pediatric leukemias that are represented in our dataset. A gene set of 1089 differentially expressed probe sets was then examined by three-dimensional PCA. As shown in Figure As shown in Figure We next investigated the percentage of overlapping genes that are found to be differentially expressed between the three methods used when analyzing the various leukemia subclasses in a supervised way. The percentage of overlapping genes is another suitable parameter to address the impact of the use of different total RNA extraction methods on a leukemia classification approach. Figure Additionally, to further illustrate the assay performance, a statistical power analysis for the RNA preparation methods A, B, and C is performed based on the Bioconductor package "ssize". The power analysis is used, for statistical comparison of identical leukemia samples, to assess the precision of technical replicates obtained from different RNA preparation methods. The data sets generated based on the preparations of total RNA following the methods A and B have greater average statistical power than the microarray data set based on method C [see Additional File In summary, these analyses indicate that preparation of total RNA by QIAshredder homogenization followed by RNeasy purification is a robust sample preparation method for microarray experiments that outperforms other procedures for isolation of total RNA.2), box plots, scatter plots, and coefficient of variation (CV) assessments. These analyses included all 54675 probe sets represented on the HG-U133 Plus 2.0 microarray.As three patients had been analyzed with three technical replicates Figure  we there2 range from 0.985 to 0.989 for preparations of total RNA by QIAshredder homogenization followed by RNeasy purification (method A), 0.976 to 0.987 for TRIzol isolation (method B), and 0.967 to 0.988 for TRIzol followed by RNeasy purification (method C). Between the three different sample preparation methods the mean value of R2 is 0.952 and standard deviation is 0.005 for method A versus method B, 0.976 mean value and 0.005 standard deviation for method A versus method C, and 0.965 mean value and 0.011 standard deviation for method B versus method C, respectively.As shown in Figure Analysis of coefficient of variation is a useful way for assessment of reproducibility and precision of the gene expression profiles generated from three different total RNA sources. The box plots demonstrate the variability in gene expression measurements within the three technical replicates using different sample preparation methods [see Additional File Recent investigations successfully applied gene expression microarrays to classify known tumor types and also various hematological malignancies ,25,28-34P = 5,308e-12), the isolation of total RNA using QIAshredder homogenization followed by RNeasy purification (method A) resulted in a better quality of starting material as demonstrated by the A260/280 ratio of cRNA , by very reproducible low 3'/5' GAPD ratios, and by consistently lower scaling factors . This was then further examined by a so-called RNA degradation plot analysis as implemented in the Simpleaffy Bioconductor analysis package [After a first analysis of the quality of our microarray data, we could assert that since in all cases the quality parameters met our criteria, each of the three preparation methods is able to generate acceptable gene expression profiles of pediatric leukemias. We found that samples representing different leukemia subclasses and extracted using different RNA preparation methods are characterized by a high comparability of gene expression data thus demonstrating that sample preparation procedures do not impair the overall probe set signal intensity distribution. Importantly, even though yielding lower amounts of cRNA if compared to TRIzol (method B) and TRIzol followed by RNeasy (method C) protocols  differentially expressed genes between the nine distinct leukemia categories that we studied here, all samples are clearly separated by leukemia lineages and without being influenced by the total RNA isolation method. Furthermore, AML with normal karyotype is separated from the two patient samples with AML with t(11q23)/MLL demonstrating an intra-lineage distinction within the AML group. The same separation can be observed in the B lineage ALL group where samples with the chromosomal aberrations t, t, t, or t are split into distinct groups. As such, this is also an independent confirmation of the clustering organizations as presented in recent gene expression profiling studies of acute lymphoblastic leukemias ,30-33,36The first conclusion we draw from this study is that underlying biological characteristics of the pediatric acute leukemia classes are quite significant and largely exceed the variations between different total RNA sample preparation protocols. Having shown that at a chosen false discovery rate of 0.01% method A is producing a higher number of differentially expressed genes as compared to method B and method C, we would propose that lysis of the mononuclear cells, followed by lysate homogenization (QIAshredder) and total RNA purification (Qiagen) is the more robust total RNA isolation procedure for gene expression experiments using microarray technology. The importance of this new data is further strengthened by the analysis of the technical replicates. In fact, the gene expression data obtained with method A show the lowest degree of variation and are more reproducible, as compared to the alternative methods we tested for the isolation of total RNA. Finally, all these evidences, combined with the standardized microarray analysis protocol that we followed for this study let us conclude that the initial homogenization of the leukemia cell lysate followed by total RNA purification using spin columns is currently the optimal protocol available with respect to the robustness of gene expression data and that this method is practical for a routine laboratory use. Here we limited our microarray study to pediatric leukemia, but certainly these statements could also be applied to similar cohorts of adult leukemias.E2A-PBX1), t(MLL-AF4), t(BCR-ABL) t(TEL-AML1), t(AML1-ETO), t(PML-RARA), inv(16)(CBFB-MYH11), and t were screened following the BIOMED-1 concert action protocol [MLL rearrangement  and #26 is t) and AML patients with normal karyotype or other abnormalities (n = 2). The ALL group included Pro-B-ALL t (n = 1), Pro-B-ALL/c-ALL with t (n = 2), T-ALL (n = 5), c-ALL with t (n = 3), Pre-B-ALL with t (n = 1), B lineage ALL with hyperdiploid karyotype (n = 3), and B lineage ALL negative for the screened recurrent translocations and with a DNA index value equal to 1.0 (n = 8). The percentage of blast cells ranged between 70% and 98%.Between December 2005 and March 2006 samples from twenty-seven acute pediatric leukemia patients were analyzed at the time of diagnosis. All patients received a laboratory diagnosis based on white blood cell count, cytomorphology, cytochemistry, multiparameter immunophenotyping, cytogenetics, fluorescence in situ hybridization (FISH), and molecular genetics (PCR). Chromosome aberrations t1;19). Subsequently, total RNA was extracted from aliquots of 5 × 106 cells and 10 × 106 cells following two distinct total RNA purification method A and method B, respectively . Total RNA obtained from method B was either used for the subsequent microarray analysis without further purification (method B), or was additionally purified following method C . Microarray analysis was performed on each sample and each preparation method (Affymetrix HG-U133 Plus 2.0). Thus, for 24 patient samples a total of 72 microarrays were analyzed , resulting in additional 27 gene expression profiles on Affymetrix HG-U133 Plus 2.0 microarrays  and electropherogram .Mononuclear cells were processed immediately after or within 24 hours after the biopsy was obtained. Appearance and fluidity of the samples were monitored before starting with RNA isolation. Total RNA was isolated using three different methods. Method A: lysis of the mononuclear cells, followed by lysate homogenization using a biopolymer shredding system in a microcentrifuge spin-column format , followed by total RNA purification using selective binding columns . The cell lysate homogenization phase reduces viscosity caused by high-molecular-weight cellular components and cell debris. Method B: TRIzol RNA isolation . Method C: TRIzol RNA isolation (Invitrogen) followed by a purification step . The RNA purification step previously mentioned combines the selective binding properties of a silica-based membrane with the speed of microspin technology. This system allows only RNA longer than 200 bases to bind to the silica membrane, providing an enriching for mRNA since nucleotides shorter than 200 nucleotides are selectively excluded. In all three methods we followed the protocols provided by the manufacturers. After extraction, total RNA was stored at -80°C until used for microarray analyses. RNA quality was assessed on the Agilent Bioanalyzer 2100 using the Agilent RNA 6000 Nano Assay kit . RNA concentration was determined using the NanoDrop ND-1000 spectrophotometer . The overall total RNA quality was assessed by A24 – T7 primer  and the Poly-A control transcripts . The generated cDNA was purified using the GeneChip Sample Cleanup Module (Affymetrix). Then, labeled cRNA was generated using the Microarray RNA target synthesis kit (Roche Applied Science) and an in vitro transcription labeling nucleotide mixture (Affymetrix). The generated cRNA was purified using the GeneChip Sample Cleanup Module (Affymetrix) and quantified using the NanoDrop ND-1000 spectrophotometer. In each preparation an amount of 11.0 μg cRNA were fragmented with 5× Fragmentation Buffer (Affymetrix) in a final reaction volume of 25 μl. The incubation steps during cDNA synthesis, in vitro transcription reaction, and target fragmentation were performed using the Hybex Microarray Incubation System  and Eppendorf ThermoStat plus instruments . Hybridization, washing, staining and scanning protocols, respectively, were performed on Affymetrix GeneChip instruments  as recommended by the manufacturer.From each RNA preparation 2.0 μg of total RNA were converted into double-stranded cDNA by reverse transcription using a cDNA Synthesis System kit including an oligo(dT)260/A280 ratio included: (i) background noise , (ii) percentage of present called probe sets, (iii) scaling factor, (iv) information about exogenous Bacillus subtilis control transcripts from the Affymetrix Poly-A control kit , and (v) the ratio of intensities of 3' probes to 5' probes for a housekeeping gene (GAPD).Microarray image files (.cel data) were generated using default Affymetrix microarray analysis parameters GCOS 1.2 software). Subsequently, intensity signals were calculated based on the non-central trimmed mean of Perfect Match intensities with Quantile Normalization . For eac softwareThe data pre-processing included the summarization to generate probe set level signals for each microarray experiment and was performed using the PS or PQN algorithms as described elsewhere . To analThis study is part of the MILE Study (Microarray Innovations In LEukemia) program, an ongoing collaborative effort headed by the European Leukemia Network (ELN) and sponsored by Roche Molecular Systems, Inc., addressing gene expression signatures in acute and chronic leukemias. This study further supports the AmpliChip Leukemia Test program, a gene expression microarray test for the subclassification of leukemia. Roche Molecular Systems, Inc. has business relationships with Qiagen and is currently validating Qiagen products for the AmpliChip Leukemia Test.MCDO performed the microarray experiments and wrote the paper, LT contributed to perform the experiments, AZ, RL, and WML analyzed the microarray data, GB recorded clinical data, GK supervised the study and writing of the manuscript, and AK provided the original concept of the study, and contributed to writing the paper.Supplementary Data. This file contains supplementary figures with additional comments explaining details of analysis, results, and interpretation.Click here for fileThis Excel file contains further details about each total RNA isolation method, including cRNA quality and quantity values as well as microarray quality and quantity values for each experiment.Click here for fileThis Excel file contains details about each total RNA isolation method and leukemia classification details for each CEL file. All microarray raw data (*.cel files) are available online through the Gene Expression Omnibus database with the series accession number GSE7757.Click here for file


However, signaling network models have rarely been used to predict how cell phenotypic behaviors result from the integrated operation of these networks. We recently developed a decision tree model for how EGF-induced fibroblast cell motility across two-dimensional fibronectin-coated surfaces depends on the integrated activation status of five key signaling nodes, including a proximal regulator of transcellular contractile force generation, MLC (myosin light chain) [Hautaniemi a priori prediction for the outcome of a targeted molecular intervention into the signaling network: that partially reducing activation of MLC would increase cell motility on moderately adhesive surfaces. This prediction was indeed confirmed experimentally: partial inhibition of the activating MLC kinase (MLCK) upstream using the pharmacologic agent ML-7 resulted in increased motility of NR6 fibroblasts. We further extended this exciting finding by showing that partial reduction of MLC activation similarly enhanced the transmigration of the human breast carcinoma cell line MDA-213 through a Matrigel barrier.In this new work, we construct an improved decision tree model for the combined influence of EGF and fibronectin on fibroblast cell migration based on a wider spectrum of experimental protein signaling and cell motility measurements, and directly test a significant and non-intuitive These findings specifically highlight a central regulatory role for transcellular contractility in governing cell motility, while at the same time demonstrating the value of a decision tree approach to a systems "signal-response" model in discerning non-intuitive behavior arising from integrated operation a cell signaling network. Cue combinations can generate distinct cell behavioral responses by selectively inducing signal transduction pathway activation. It is rare, however, that a particular signal actuates only a single pathway; rather, the rule is for the activation of multiple divergent pathways that together hold potential to elicit numerous, often mutually exclusive, cellular responses. The cell phenotypic outcome may be considered to derive from a governing pattern of activation across the network comprising particular pathways in interconnected fashion. While the simplest hope might be to understand and manipulate cell behavior in terms of targeting an intervention at some "most important" molecular "switch", in reality there is a greater likelihood that such an intervention will impact the network as a whole much more broadly – and quite possibly with unexpected phenotypic outcome effects. Thus, systems biology approaches are now being conceived for application to signaling network control of complex cell responses, in order to gain rationale capability for predicting the effects of targeted interventions [Phenotypic cell behaviors are strongly governed by various extracellular cues, such as binding of cell surface receptors to soluble , as key to migration speed. Further, our results predicted that maximal speed would be achieved at intermediate levels of MLC activity. We test this prediction explicitly by modulating MLC activation status directly, finding successful experimental confirmation of a non-intuitive effect that an inhibitory pharmacological agent enhances migration speed – not only for the original model-foundation fibroblasts but also for an additional test case, breast tumor cells.Computational modeling can compile and classify data sets in a quantitative manner and consequently provide testable predictions to extract vital information not readily apparent by conventional analytical techniques. In addition, mathematical models can expand data sets to proportions that can be used to make non-intuitive predictions related to biological responses . We haveet al  that eluet al . Here, wet al  to new set al [We aimed to elucidate the relative contributions of different signaling proteins in mediating biophysical migratory processes of adherent cells across different extracellular conditions. As a model system, we chose a mouse fibroblast line for which biophysical data included cell speed, membrane protrusion activity, cell spread area, surface adhesion, and membrane retraction . Our neet al ,12.Addition of EGF activated EGFR within minutes and this signal was transmitted downstream to signaling cascades measured Figure . InteresMLC activation begins within a few minutes of EGF stimulation and reaches a plateau at about 2 hours; increases were still appreciable up to 24 hours after EGF stimulus (unpublished observations). Interestingly, after 1 hour and longer exposure to EGF, MLC activity was inversely biphasic across fibronectin, with lowest levels at intermediate FN concentration (0.3 and 1 μg/ml) Figures  and 2B. a priori prediction of effects of signal inhibition, we focused on the 1-hour model because of its superior performance with the independent validation data sets.A complex and well-orchestrated cellular response such as migration can only manifest from optimal quantitative activation/involvement of tens and hundreds of signaling proteins. Accordingly, it is important to address the relative contributions of such protein clusters in order to define the most significant switches that can be altered for therapeutic purposes. We constructed decision trees using the above five key signaling proteins activated on different levels of fibronectin by EGF that predicted the quantitative contribution of signaling proteins in dictating cell speed. The utility of decision trees is to identify prediction rules from the data and then illustrate them as a binary tree where each terminal node (leaf) corresponds to a class and other nodes represent measured variables. The decision trees obtained from three different EGF treatments  yielded different classification efficiencies of observations from the training data set. The 5-minute decision tree accounted for approximately 70% of observations from the 1000 independent validation data sets correctly Figure , whereaset al [Contractile force production is enabled through the actin-myosin coupling upon activation of regulatory myosin light chains ,14. Whilet al , the bipi.e., despite high MLC activation in both conditions, there is too little substratum adhesion at 0.1 μg/ml while it is in excess at 3 μg/ml [Our model predicted that subtotal lowering of MLC activation would increase fibroblast cell speed. Our experimental data set indicated that while cell speed showed a biphasic response, MLC activation was inversely biphasic across fibronectin concentration of the surfaces. Thus, at the two extreme conditions, where surface fibronectin was either too low (0.1 μg/ml) or too high (3 μg/ml), cell migration speed was minimal. From our polynomial model, these two conditions corroborated with surface FN concentrations below 0.522 μg/ml or greater than 2.6 μg/ml. At these two conditions there is apparent dysregulation in the balance between the substratum adhesion strength versus contractility;  3 μg/ml . Thus, ain vitro wound healing assays minimize increases in cell speed. The outcomes of these experiments determining the effect of partial reduction of MLC activation are in accordance with the predictions from our decision tree model.To test the model predictions under such conditions, we employed a well-characterized MLCK inhibitor, ML-7, to measure fibroblast migration speed under the same extracellular conditions (4 FN concentrations -/+ EGF). Such a downstream inhibitor was chosen (over PKCδ inhibitor Rottlerin) because it is MLC kinase-specific and hence the resultant cellular responses can be attributed directly and specifically to MLC inhibition since PKCδ is involved in diverse cellular responses in addition to motility . In addiTo assess whether our predictions of hierarchical control could be extended to a different application of EGF-induced cell motility behavior, we utilized the MDA-MB-231 invasive human breast cancer cell lines and measured their migratory response across a range of MLC kinase (and hence MLC) inhibition. These cells overexpress EGF receptor and actively exhibit autocrine stimulatory loops that drive their migration and invasiveness . In accoi.e., they arise from alterations within multiple molecular regulatory pathways. Signaling pathways represent an especially critical domain for pathological dysregulation, as they contain forward- and reverse-feedback cascades that can act as signal amplifiers, transmitters, or distributors to a multitude of highly-connected protein nodes across numerous pathways within a network. Thus, multiple signaling proteins with interactive activity profiles govern phenotypic cell behavioral phenomena underlying normal physiology and pathology. Altering cell behaviors is difficult without a thorough understanding of how these signaling switches work in relation to each other. While enormous data sets are available for biological conditions, such data sets have not been integrated to provide information about the interlinked and branched signaling networks. Therefore, targeted therapies often fail because cells utilize parallel and alternative pathways to mediate the necessary biological functions. Identification and modulation of key signaling nexi from such complex networks can alter cell behaviors and yield favorable responses [The vast majority of diseases are now appreciated to be "complex"; esponses ,18.in vivo environments, where motility of cells is dictated by the cellular ecology, cell-substratum and cell-cell adhesion profile, extracellular matrix components along with a spectrum of soluble and matrix-embedded extracellular stimuli [etc). However, our model achieved 75% accuracy for independent validation data sets, which is more than twice expected by random association. Future experiments are aimed at incorporating other key signaling proteins within this foundational decision tree model.We utilized here decision tree analysis to identify the crucial effectors of cell motility depending upon a set of extracellular cues. Fibronectin was selected since NR6WT fibroblast express α5β1 integrin receptors that are actively involved in cell signaling during motility. Also, these being adhesion receptors provide a counter-balance against the motogenic EGF receptor that is overexpressed in these cell lines. Such adhesion versus motility balance is present  stimuli ,20. Our et al [Our decision tree model clearly identified MLC-mediated contractility as a key regulatory biophysical event during EGF induced motility. This does not mean, however, that disrupting other cellular events, such as PLCγ-based lamellipodal protrusion, will not abrogate motility. The utility of a decision tree model is to predict the switches that upon disruption can produce highly significant responses and illustrate them as a hierarchical logic. Decision trees represent non-linear depictions of contributory influencers and do not imply hierarchies or linkages between the constituent molecules or events. A decision tree model may also suggest molecules that need to be inhibited together to alter the cell phenotypic behavioral outcome. In our model that was based on 5-minute and 1-hour EGF stimulation data set Figure , contribet al  that motet al ,12. ThisOur model, non-intuitively predicted that lowering MLC activation, but not totally abrogating it, can paradoxically increase cell speed. These predictions held true in the population based 'scratch assay' that assessed cell migration distance as well as single cell tracking that assessed migration speed, under different concentrations of MLCKinase inhibitor, ML-7. An especially important consequence is that of subtotal inhibition of MLCKinase under higher fibronectin concentration of substratum increased cell speed Figure  whereas These findings have profound implications for therapy. Identifying key nodes enables quantitative manipulations using pharmacologic methods for specifically desired cellular responses. It also points to the importance of how these signaling proteins are regulated stoichiometrically. Our predictions held true even when applied to breast cancer cells, where subtotal inhibitory doses of ML-7 promoted cell migration. While a complete abrogation of MLC can be beneficial in limiting tumor cell motility and hence invasion, partial inhibition using lower pharmacological doses can paradoxically increase tumor cell motility and invasion leading to devastating consequences. This further points to the importance of applying newer modeling approaches to fully characterize the role of signaling cascades in mediating cellular behaviors. Such understanding will enable precise therapeutic targeting of key signaling nodes and open the door to individualized 'patient-tailored therapy' .NR6WT cells expressing human EGF receptor (EGFR) were maintained in modified Eagle's medium-α containing (MEMα) 7.5% fetal bovine serum (FBS) and 1% of each of the following: penicillin/streptomycin, L-Glutamine, non-essential amino acids and sodium pyruvate . The medium contained 350 μg/ml of G418 as a selection agent for human EGFR. Cells were quiesced in a medium containing 0.5% dialyzed FBS for 24 hours before addition of EGF. The MDA-MB-231 invasive human breast cancer cell line was maintained in RPMI 1640 medium (GIBCO) containing 10% FBS and 1% penicillin/streptomycin. Migration and immunoblotting assays were conducted by quiescing the cells in a medium containing 0.5% dialyzed FBS for 24 hours prior to experimentation.Antibodies used to detect activated status of EGFR (phosphorylated Tyr1173), ERK (phosphorylated Thr202/Tyr204), PKCδ (phosphorylated ser643), and myosin light chain (phosphorylated ser19) were obtained from Cell Signaling technology . Activated status of phospholipase Cγ (PLCγ) was probed using a rabbit polyclonal antibody against phosphorylated tyrosine 783 residue obtained from Santa Cruz Biotechnology . ML-7 was utilized as MLCKinase specific inhibitor and was purchased from Calbiochem, EMD Biosciences .Fibronectin coating concentrations of the surfaces were 0.1, 0.3, 1 and 3 μg/ml. Tissue culture plates were incubated with fibronectin at required concentrations diluted in PBS at room temperature for a period of 2 hours. The plates were washed once with PBS and incubated with 1% bovine serum albumin for another 1 hour to block non-specific protein binding during the course of the experiment. The plates were washed three times with PBS and cells plated directly in complete growth medium over these surfaces.NR6WT mouse fibroblasts engineered to express human EGFR were utilized for our baseline modeling studies. These cells are derived from the 3T3 lineage, are devoid of an endogenous EGF receptor and serve as an excellent model system to study EGFR mediated cell migratory events. Equal number of NR6 WT cells were plated on fibronectin coated surfaces and allowed to grow in MEMα containing 7.5% fetal bovine serum (FBS) for 24 hours, by which time cells reached about 90% confluence. Subsequently, cells were quiesced in media containing 0.5% dialyzed FBS for another 24 hours, to minimize the effect of exogenous growth factors present in the serum. Cells were either lysed in the quiescent medium without any exogenous human EGF or stimulated with 10 nM (saturating concentration) of human EGF for either five minutes, one hour or 16 hours. Such time frames were selected to capture the entire spectrum of signaling protein activation during the motility response . After sFn = 0.1 and EGF = 0) for each immunoblot. After this between-band normalization, the numbers within an immunoblot become comparable to other immunoblots since the experimental conditions in each of the experiment were kept constant. For quality control purposes, the bands were also within-band normalized: all protein conditions in a band without exogenous EGF were divided by the value with EGF = 0 and Fn = 0.1, while all protein conditions in a band with exogenous EGF were divided by the value with EGF = 1 and Fn = 0.1. The within-band normalization ensures that proteins under the same EGF condition within a band are comparable. Prior to normalization all basal values below 250 were converted to 250 in order to prevent division by a small value that is likely due to noise. After normalization, all the values were log2-transformed.Prior to polynomial modeling and decision tree analysis, the data were thoroughly preprocessed by normalization and quality-control approaches described in . First, Normalization was followed by the ANOVA based quality control approach and statistical outliers were discarded . Each vaOur goal was to create a predictive model that is able to predict migration speed as a function of signaling proteins, and provide insight on what signaling proteins could key elements governing migration. Accordingly, we chose the decision tree methodology since decision trees both show the predictive structure of the signaling proteins and are fairly accurate classifiers . As therPrediction algorithms in general require large training and validation data sets to ensure that the resulting predictor is reliable and the results reproducible. Therefore, we developed mathematical models that capture signaling protein activity and migration speed profiles as a function of fibronectin concentrations. Variables  were modeled using the polynomial function family. Polynomial functions family was chosen because it allows for modeling of a large spectrum of different trends. To choose degree for a polynomial model, we applied normalized maximum-likelihood (NML) approach, which is an implementation of the minimum description length (MDL) principle and aims at describing the data best without overfitting . Technicspooled) used in the simulations are given in Additional File The polynomial models were constructed separately for the values with or without exogenous EGF. As the first value (no exogenous EGF and fibronectin concentration of 0.1 μg/ml) in each immunoblot was used in normalization, the polynomial modeling for data without exogenous EGF was done with three data points, whereas data with exogenous EGF was modeled with four values. Accordingly, the maximum polynomial degree in the NML modeling step was set to two. The resulting polynomial estimates (β) and squared pooled standard errors , medium (1) and high (2) phosphorylation levels. We have illustrated the discrete regions for cell migration speed and MLC in Figure We used the resulting polynomial models to create 10000 simulated training sets (58002 cases in each data set) and 1000 validation data sets (5802 cases in each data set). Data for each signaling protein and migration speed were then discretized using the Lloyds algorithm , which mDecision tree predictors aim to uncover the predictive structure of a classification or prediction problem while still maintaining good prediction accuracy. Here, we used the classification and regression trees (CART) approach . A more EGFR is low" results in 20790 cases having slow migration speed of 22883 cases belonging to slow migration category (91%). Further, as in the data set split to the right there are only 8211 cases belonging to medium speed and 4 to fast speed classes, the data are not split further and the rule "EGFR is low" predicts slow migration speed. If EGFR is medium or high, however, the set of 22883 cases is split further until sufficiently good prediction accuracy is achieved. The parameters for the decision tree learning were as follows. Purity function was the Gini-index, variables having more than five cases were considered for a split and prior probability for ith class was obtained by dividing the number of the cases of ith class by the total amount of observations. The cost of a misclassification from high to low speed was 2, medium to high or low was 1, and the cost for correct classification was 0. After constructing a decision tree, we applied the cost-complexity pruning method [The CART results in a decision tree where interior nodes represent signaling proteins and leaves migration speed classes. Each interior node is actually a question that splits the data into two subsets. For example, the first question in the 1 h decision tree  to remove dead cells, and kept under serum free conditions throughout the experiment. EGF at 10 nM (and inhibitors or diluent as indicated) was added to the serum free medium. Cells were then photographed using an inverted microscope immediately following scraping (0-hour condition) and 24 hours later (24-hour condition) in exactly same three different areas. The photographs were merged and analyzed using Adobe Photoshop program to determine the average distance traveled by the cells in 24 hours. All experiments were performed in triplicate.For final validation of cell migration, individual cell speeds were measured using time-lapse videomicroscopy. 6,000 cells were plated on each fibronectin-coated DeltaT imaging dish (Bioptechs) in 2 ml of assay medium containing 0.5% dialyzed FBS and 1% BSA. 16 hours post-seeding, the medium was replaced with 3.2 ml of fresh assay medium. In migration versus fibronectin validation studies, the replacement medium contained 10 nM EGF. In MLC inhibition studies, the replacement medium contained 0, 2, 4, or 10 μM ML-7 (MLCK inhibitor), and 10 nM EGF was added 45 minutes after ML-7 exposure. The plates were then sealed with a vacuum grease-lined coverglass lid and placed in a heated stage insert for a Ludl 99S008 motorized stage on a Zeiss Axiovert 35 microscope. Three fields of cells, with five to ten cells per field, were tracked by recording an image for each field every 15 minutes for up to 20 hours. Individual cell speeds were calculated using Visible , which determines speeds by generating instantaneous velocity vectors for each pixel of the image that is associated with a cell. We found that cell speeds reach a steady-state 4–6 hours after adding EGF as previously reported [SK performed many of the final signal activation measurements and the tumor cell motility experiments; he also wrote the initial manuscript drafts. SH constructed the decision trees as well as quality controlled the data; he contributed to the initial ideations and writing. SW performed the single cell tracking experiments and contributed experimental design and writing of the manuscript. AI performed the initial signal activation measurements in the fibroblasts, and shaped the origins of the project. DAL and AW provided overall guidance, initial conception of the project, integrated the varied aspects, and finalized the communications. All authors reviewed the data and contributed interpretations. All authors agree to the submitted manuscript.Polynomial estimates and pooled error calculations for model/data fits. Each of the experimental data sets  involves interpolating polynomials; the polynomials corresponding to each set, and the associated error in model data-fitting, are listed here.Click here for file