The impressive increase of novel RNA structures, during the past few years, demands automated methods for structure comparison. While many algorithms handle only small motifs, few techniques, developed in recent years,  are available for the structural comparison of large and intact RNA molecules.The FRASS web-server represents a RNA chain with its Gauss integrals and allows one to compare structures of RNA chains and to find similar entries in a database derived from the Protein Data Bank. We observed that FRASS scores correlate well with the ARTS and LaJolla similarity scores. Moreover, the-web server can also reproduce satisfactorily the DARTS classification of RNA 3D structures and the classification of the SCOR functions that was obtained by the SARA method.The FRASS web-server can be easily used to detect relationships among RNA molecules and to scan efficiently the rapidly enlarging structural databases. Three-dimensional (3D) structures of RNA have been extensively studied because of the importance of these molecules that are involved into several biological processes. Structural analysis is also essential for molecular evolution investigations and the development of structure prediction tools.The flexibility of the RNA backbone, limited to six torsion angles per base, is restricted. It was shown -3 that RPRIMOS  uses theThe structural analysis of large and intact RNA molecules, and not only of small fragments, became increasingly more common during the last few years ,2. Thereα atoms and, therefore, can be applied also to RNA molecules, with minor modifications.In the past years the number and size of novel RNA structures has dramatically increased. However, comparison methods based on structural alignments are not sufficiently rapid for interactive scanning of large databases. Quite a few alternative algorithms, which do not produce structural alignments, were designed for rapid comparison of protein structures -27. TheyIn this article, we describe a web-server developed for global comparison of RNA structures using Gauss-integrals . The FRAμ that is a series of connected line segments. The segments are specified by a sequence of phosphorous atoms . The first segment is the section between P1 and P2 atoms, the second segment is the section between P2 and P3, ect. The shape of the polygonal space curve is described by a 30-dimensional vector containing the length of the backbone and the 29 Gauss integrals of the first, second and third order.In the Gauss-integrals approach -23, the N phosphorous atoms isThe Gauss-integral of the first order for the RNA molecule containing W is the probability of seeing the i1th and i2th line segments to cross when averaged over all directions in space and the sign of this crossing is determined by right-hand rule [where and rule . The GauThe Gauss-integral of the third order isThe Gauss Integrals are defined by mutual spatial superposition of the segments and represent the mathematical description of the backbone shape of a RNA molecule. The method can not be used for small molecules because the third-order Gauss-integral defined by the spatial superposition of six segments and can be computed only for molecules containing more than 7 nucleotides. 2,703 RNA structures out of the 3,353 deposited into the PDB contain more than 7 nucleotides. The distance between two RNA structures was measured by the Euclidean distance between the two 30-dimensional vectors, one for each RNA molecule.x, simply by integrating the probability density curve from Dx to Dmax:The statistical significance of the Gauss-integral based distance (D) was determined empirically, by using the distribution of the distances d(x) computed for all pairs of RNA chains of the database. With these data, it is possible to estimate the probability pD to have D values higher than a given value DThe RNA structures were downloaded from the Protein Data Bank. Each monomeric RNA molecule was stored in a single PDB-formatted file and its Gauss integrals were computed. Given that the Protein Data Bank is frequently updated, a program for rapidly updating the RNA structure database was developed. The GI program for Gauss-integral calculations is freely available at . In our The input form for the web server, referred to as FRASS, is shown in Figure The Gauss-integrals based algorithm was benchmarked against the ARTS method by using all RNA structures that were suitable for ARTS, which does not process single chain molecules that lack base pairs. The correlation between the Gauss-integrals based distances and the ARTS similarity scores was analyzed. Figure The FRASS algorithm was benchmarked also against the LaJolla method, using the dataset of 101 tRNA chains reported in  that impPearson correlation coefficients are equal to -0.98 in both cases. The correlations are negative since the Gauss-integrals based scores are distances while the ARTS and LaJolla scores are similarities.An effective way to test the classification ability of a method is to compute a ROC curve. In the present study, the DARTS and SCOR classifications of RNA structures was used as an external benchmark. The database DARTS contains 1,333 RNA structures determined experimentally. They are classified into 94 clusters. Only the 789 single chain entries of DARTS were retained, since the web server described in the present manuscript handles only monomeric molecules. Gauss-integrals based distances were thus computed for 310,866 pairs of RNA structures. The NR95-SCOR dataset available at  containsSensitivity against (1-Specificity) defined asThe ROC curve is obtained by plotting where true positive (TP) and false positive (FP) that are the number of correctly and incorrectly predicted pairs of the same DARTS/SCOR cluster, while true negative (TN) and false negative (FN) are the number of correctly and incorrectly predicted pairs of different clusters. Different points in the ROC curve are obtained by varying the Gauss-integrals based distance value under which two structures are considered to be similar and to belong to the same DARTS/SCOR cluster.Figure 3) in time and it was observed that for long molecules  it takes about half an hour on a standard PC. Therefore, dealing with a large database, the Gauss integrals must be pre-computed and stored on a hard disk. The computing of Euclidean distances between pre-calculated Gauss integrals is on the contrary extremely fast and the database scanning takes very few seconds on a standard PC.The computation of the Gauss integrals is O(n3) in time, DIAL is O(n2) in time, and LaJolla and SARSA are O(1) in time, nothing can be pre-computed and stored on a hard disk for further elaboration. The SARSA and LaJolla methods, which transform 3D structures into 1D strings, are faster than other techniques. In particular, SARSA was shown to be faster than DIAL, though no quantitative information was published [Although the fact that FRASS does not produce structural alignments, it must be observed that, in general, methods that generate structural alignments are not suitable to work with large databases containing long RNA chains: although ARTS is O [The large 23S ribosomal RNA from solution , was choeotides) . The Gaueotides) ,29.The FRASS web-server is an effective tool for global comparison and classification of RNA structures. The similarity measure, based on the Gauss-integrals, is related to the backbone shape of a single RNA chain, represented by the positions of the phosphorous atoms. It is alternative and complementary to other similarity scores that considers base-pairs. Given the simplification of the backbone representation, computations are extremely fast. The web-server allows thus database scanning that can be used to detect relationships among RNA molecules, and to assign function to a new experimentally determined structure on the base of the structural similarity.Project name: FRASS• Project home page: http://sourceforge.net/projects/frass/• Operating systems: Platform independent for web-server, Linux for downloaded software• Programming languages: C and Perl• License: GNU GPL• SK developed the tool and drafted the manuscript. SCET and SK elaborated the tools into the web-server. OC initiated and supervised the project. All the authors read and approved the manuscript.


Epithelial ovarian cancer survivors (EOCSs) frequently report multiple complaints after their treatment. The objective was to study somatic and mental morbidity in EOCSs associated with their Self- Rated Health (SRH) assessed by a single item.Findings were compared to age-matched controls from the general population.In a cross -sectional follow-up design 189/287 (66%) EOCSs treated at The Norwegian Radiumhospital 1979–2003 responded to a mailed questionnaire on demographic data, and somatic and mental morbidity. SRH last week was rated on item #29 of the European Organization and Treatment of Cancer Quality of Life Questionnaire in 84/189 (97%) of responding EOCSs. For comparisons "good" and "poor" SRH groups were defined by the median score on the SRH item.EOCSs with "poor SRH" had higher level of somatic symptoms, anxiety, depression and fatigue than those with "good SRH" (p < .001). In multivariate analyses somatic symptoms, age and fatigue, were significantly associated with the SRH score in EOCSs, but not the cancer-related variables . The model explained 70% of the variance in SRH in linear and 77% in logistic regression analyses. The distribution of the SRH scores in EOCSs did not differ significantly from that of normative controls; however a higher proportion of controls recorded a high SRH score.SRH is strongly related to common somatic complaints, impairment and fatigue but not to cancer-related variables. A single question concerning SRH last week might be a quick screening method for collecting important information on symptoms in EOCSs, in addition to cancer – related questions. Due to successful multimodal treatment the number of women who survive epithelial ovarian cancer has increased over the last decades. The 5-year survival rate is 45% in Norway, ranging between > 80% in stage I of the International Federation of Gynecology and Obstetrics (FIGO) classification to < 20% in FIGO stage IV ,2. The aSeveral studies have shown that epithelial ovarian cancer survivors (EOCSs) have significantly higher levels of somatic and mental symptoms as well as fatigue compared to women of the general population -6. StewaButt et al.  found siTo our knowledge there are no studies of SRH and its associations to somatic and mental morbidity in EOCSs. We therefore wanted to: 1) Explore the somatic, mental and lifestyle variables associated with a single question concerning SRH (EORTC) (QLQ C-30) item #29 "How would you evaluate your overall health during the past week?" for therapeutic use in a clinical setting. 2) Explore to what extent the SRH score of EOCS differ from those of age-matched women in the general population (NORM).The EOCSs were 20–70 years at survey with > 18 months survival since diagnosis. They had been treated according to protocols for FIGO stage I-III epithelial ovarian cancer (EOC) at the The Norwegian Radium Hospital [All EOCSs had primary surgery with BO, and the majority also had hysterectomy, omentectomy and maximum debulking. Primary treatment was done according to established protocols, as either surgery only, or combined with subsequent chemotherapy depending on FIGO stage, histology and ploidy .Platinum-based chemotherapy represented the most frequent systemic treatment. Paclitaxel was incorporated into the combination chemotherapy in the 1990-ies . The majThe EORTC QLQ-C30  consistsItem # 29 had seven score alternatives and was considered to reflect SRH. We transformed the 1 to 7 response score to 0 to 100 and used the median score (66.67) to separate the EOCSs into a "poor SRH" and a"good SRH" group. The correlation between item #29 and #30 was r = 0.83 with 69% explained variance in our EOCSs sample.As EORTC QLQ-C30 has been psychometrically validated cross-culturally, all scales and single items have been tested according to standards for reliability . The intThe HADS  consistsThe Fatigue Questionnaire (FQ) consists of a total of 13 items, where 11 assess the presence and intensity of fatigue symptoms [symptoms . Seven iThe Body Image Scale (BIS) is a 10-items self-rating-scale developed to assess changes of the body image in cancer patients [patients . The BISThe Intimate Bond Measure (IBM) is a 24-item self-rating scale measuring experienced care and control from her partner in the patient's current relationship [tionship . Both caThe Menopause-Specific Quality of Life Questionnaire(M-QOL) is a 29 items questionnaire covering the vasomotor, physical, psychosocial and sexual menopause-related domains [ domains . The M-Q domains . Each itThe Sexual Activity Questionnaire (SAQ) consists of three sections covering: 1) Relation status, 2) Reasons for sexual inactivity; and 3) Sexual functioning (SAQ-F) [ (SAQ-F) ,26. The Education was categorized into three levels based on the number of completed school years . Paired relation described those married or cohabiting. Having paid work was defined as income from employment or independent business. Level of physical activity was dichotomized into "minimal", or "moderate or more" according to Thorsen et al. [Physical impairment and mental impairment were defined as significant impairment in daily life for more than one year due to disease, injury or symptoms.n et al. . Physicaco-morbid conditions were self-reported by responses to the general formulation: "Have your doctor ever said that you suffer from...?" The self-reported diagnoses were not validated by their regular GPs or hospital records. Treated hypertension was defined by current use of an antihypertensive drug, while current hypertension was defined by a blood pressure ≥ 140/90 mm Hg. Hormone replacement therapy (HRT) was reported as current use of sex hormones, while analgesic and psychotropic medication was reported as regular use last year. Musculo-skeletal diseases  were defined as a diagnosis given by their GPs. Daily smoking concerned current consumption of any number of cigarettes.All A clinical Prognostic Index in EOCSs had been constructed by our group ,28 basedNormative data for the EORTC QLQ-C30 was obtained by the Cancer Clinic in 2004 . Using p2 test. 95% confidence intervals (95%CI) were included as appropriate. Non-parametric tests were applied for skewed distributions. Internal consistency of a domain/subscale was examined with Cronbach's coefficient alpha. Statistically significant findings on continuous variables and on 2 × 2 contingency tables were also calculated as effect sizes (ESs) and values ≥ .40 were considered as clinically significant [2) and by change in explained variance (R2-change) for each step. The same procedure was done with the dichotomized SRH score using logistic regression analyses, however the strength of association were expressed as odds ratios (ORs) with 95% confidence intervals (95%CI).Continuous data were analyzed by independent sample t-tests and categorical data with Pearson's χnificant ,31. ReleThe level of significance was set at p < .05 and all tests were two-tailed.The study was approved by the Regional Ethics Review Board of South-Norway, and the Norwegian Data Inspectorate. All EOCSs provided written informed consent.An attrition analysis of the total number of EOCSs respondents (N = 189) and non-respondents (N = 98) showed that except for longer time since primary treatment among the respondent , no significant differences in other clinical variables were observed. (Data not shown).SRH last week was rated by 184 of 189 responding EOCSs (97%). Among these 184 EOCSs the median age at diagnosis was 52 years (range 23–68 years), median age at survey 59 years (range 31–71 years) and median follow-up time 4 years (range 2–27 years). The FIGO stage distribution was: stage I 42%, stage II 18% and stage III 40%. Thirty-three percent of EOCSs had surgery only as primary treatment, while 67% also got chemotherapy. Using a five years limit since primary treatment, 44% were considered as long-term and 56% as short-term survivors. Relapse ever was observed in 31% of EOCSs. Sixty-five per cent had < 13 years of basic education, 57% had paid work, and 42% were pensioned, unemployed or on social support, and 75% were in a paired relation.  95%CI 41–57%) EOCSs had"good SRH" and 95 (52%) (95% CI 44–59%) had"poor SRH". Mean age at survey was significantly lower in the patients with"poor SRH" (ES = .37), but no significant differences were observed for other demographic or for cancer-related variables such as FIGO stage, recurrence in < 6 months, chemotherapy ever, follow-up time or prognostic index. . The demographic variables explained 8% of the variance in SRH (p < .05), physical or mental impairment contributed 7% (p = .001) and anxiety and fatigue contributed 3% (p = .001). Cancer-related variables , use of medication, life style and menopausal variables did not make any significant contributions to the explained variance of the categorical SRH score. In total the eight step model explained 77% of the variance in SRH.No significant differences were found in level of education, paired relation, use of analgesics, psychotropics, HRT or antihypertensive drugs between EOCSs and NORM (data not shown). No significant differences in the overall distribution of the SRH scores between the EOCSs and NORM were observed, however a higher proportion of controls recorded a SRH score of 100 (p < .02) , mental distress, fatigue, body image and menopause-related QoL, were significantly more common among EOCSs with "poor" rather than "good" SRH when using the median score to separate the two groups. Item #29 of the EORTC QLQ C-30 was thus strongly associated with common somatic complaints, which explained 39% of the variance using dimensional SRH and 46% using categorical SRH, but also with age, physical impairment and mental distress which explained a small but significant association. The 7% difference in explained variance using dimensional or categorical SRH and the different significance of pain, physical symptoms and prior use of psychotropic medicaments is important and interesting, but difficult to explain. The main interpretation of both models show a significant and important contribution for symptoms which appears to have clinical implications. The relevance of this single item in EOCSs may be supported by the fact that these symptoms are known to be frequently present among EOCSs, and are all conditions which may be accessible for therapeutic interventions. Interestingly the cancer-related variables , use of medication, life style and menopausal variables did not make any significant contributions to the explained variance of the SRH score in EOCSs used both as dimensional and categorical dependent variable. We did not observe any significant differences in the overall distribution of the SRH scores between the EOCSs and NORM, however, it was a difference in the highest score level, as a significantly higher proportion of controls recorded a SRH score of 100 , however treatment and follow-up protocols were considered to be quite similar during that time period. Furthermore, the phenomenon of "response shift", explained as how individuals integrate changes in their health state so that their internal standards, values, or concepts of quality of life are nearly unchanged from before, may be considered as a limitation , as studThe cross- sectional design has its limitation by the present knowledge about previous health status. The use of item #29 of the EORTC QLQ-C30 and the median score as the cut off of a clinical classification for "poor" and "good" SRH may be open for discussion, compared to the use of the variable as a continuous measure. However, we found that a cut-off for "poor" and "good" SRH was useful to identify those at highest risk of morbidities and complaints and decreased SRH in EOCSs.SRH was strongly associated to common somatic complaints, but also to impairment and fatigue, which are well known symptoms in EOCSs, but not to cancer-related variables as FIGO stage, recurrence < 6 months or chemotherapy ever. A single question concerning SRH last week might be a quick method to screen for symptoms in EOCSs, some of which may be accessible for therapeutic interventions and in addition to cancer – related questions.2-change: explained variance.EOCSs: Epithelial ovarian cancer survivors; SRH: Self- Rated Health; FIGO: The International Federation of Gynecology and Obstetrics; BO: Bilateral- oophorectomy; GPs: General practitioners; EORTC: European Organization and Treatment of Cancer; QLQ C-30: Quality of Life Questionnaire; NORM: Women in the general population; HADS: The Hospital Anxiety and Depression Scale; FQ: The Fatigue Questionnaire; BIS: The Body Image Scale; IBM: The Intimate Bond Measure; M-QOL: The Menopause-Specific Quality of Life Questionnaire; SAQ: The Sexual Activity Questionnaire; HRT: Hormone replacement therapy; ESs: effect sizes; RThe authors declare that they have no competing interests.AHL has contributed to conception, design, provision of study material and patients, collection and assembly of data, data analysis and interpretation and manuscript writing.AD has contributed to conception, design, provision of study materials and patients, collection and assembly of data, data analysis and interpretation, financial and administrative support and manuscript writing. SDF has contributed to collection and assembly of data, data analysis and interpretation and manuscript writing. CT has contributed to provision of study materials and patients, administrative support and manuscript writing. AAD has contributed to conception, design, data analysis and interpretation and manuscript writing. All authors have contributed, read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2407/9/2/prepub


Large-scale gene expression analysis of post-mortem brain tissue offers unique opportunities for investigating genetic mechanisms of psychiatric and neurodegenerative disorders. On the other hand microarray data analysis associated with these studies is a challenging task. In this publication we address the issue of low RNA quality data and corresponding data analysis strategies.A detailed analysis of effects of post chip RNA quality on the measured abundance of transcripts is presented. Overall Affymetrix GeneChip data (HG-U133_AB and HG-U133_Plus_2.0) derived from ten different brain regions was investigated. Post chip RNA quality being assessed by 5'/3' ratio of housekeeping genes was found to introduce a well pronounced systematic noise into the measured transcript expression levels. According to this study RNA quality effects have: 1) a "random" component which is introduced by the technology and 2) a systematic component which depends on the features of the transcripts and probes. Random components mainly account for numerous negative correlations of low-abundant transcripts. These negative correlations are not reproducible and are mainly introduced by an increased relative level of noise. Three major contributors to the systematic noise component were identified: the first is the probe set distribution, the second is the length of mRNA species, and the third is the stability of mRNA species. Positive correlations reflect the 5'-end to 3'-end direction of mRNA degradation whereas negative correlations result from the compensatory increase in stable and 3'-end probed transcripts. Systematic components affect the expressed transcripts by introducing irrelevant gene correlations and can strongly influence the results of the main experiment. A linear model correcting the effect of RNA quality on measured intensities was introduced.In addition the contribution of a number of pre-mortem and post-mortem attributes to the overall detected RNA quality effect was investigated. Brain pH, duration of agonal stage, post-mortem interval before sampling and donor's age of death within considered limits were found to have no significant contribution.Basic conclusions for data analysis in expression profiling study are as follows: 1) testing for RNA quality dependency should be included in the preprocessing of the data; 2) investigating inter-gene correlation without regard to RNA quality effects could be misleading; 3) data normalization procedures relying on housekeeping genes either do not influence the correlation structure (if 3'-end intensities are used) or increase it for negatively correlated transcripts ; 4) sample sets should be matched with regard to RNA quality; 5) RMA preprocessing is more sensitive to RNA quality effect, than MAS 5.0. Analysis of microarray gene expression profiles of post-mortem brain tissues have become an important tool in studying neurodegenerative and psychiatric disorders -5. In adThe limited availability of donor material may be one reason for difficulties in establishing proper experimental designs with respect to possible confounding factors. In order to address this issue a number of potential confounding factors and their effect on transcript abundances in the post-mortem brain samples have been intensively studied -22. It iIn this paper we present detailed analyses of the effects of RNA quality on measured transcript intensities and corresponding data analysis strategies in the context of post-mortem brain tissue microarray studies.ACTB) and GAPDH, was found to introduce well-pronounced systematic noise into transcript expression levels. Some components of the noise have a clear biological basis. An algorithm to correct for the effect of RNA quality on the detected transcript intensities was investigated. As a measure of the quality of the correction procedure the correlation between the HG-U133_AB and HG-U133_Plus_2 derived expression profiles was used.The main aim of our study was the analysis of genes deregulated in Parkinson's disease. On the basis of data derived from the GeneLogic database  gene expIn addition the contribution of a number of pre-mortem and post-mortem attributes to the overall detected RNA quality was investigated. In particular, the brain pH, duration of agonal stage, post-mortem interval before sampling and donor's age of death within considered limits were found to have no well pronounced and significant contribution.post chip (after hybridization) RNA quality parameter commonly used in Affymetrix U133 chips is 3'/5' ratio of the housekeeping genes GAPDH  and ACTB (beta actin) which actually is RNA degradation index. This index reflects not only the original level of RNA integrity but also the accuracy of sample processing, namely RNA purification, reverse transcription, in-vitro amplification and labelling, fragmentation and hybridization. A low post chip RNA degradation index (3'/5' ratio < 3) corresponds to high quality material while high post chip RNA degradation index (3'/5' ratio > 3) could indicate both low quality material and/or problems encountered during sample processing. Correlation between 28s/18s rRNA ratio and 3'/5' ratio reported in recent publication [GAPDH and -0.48 for ACTB.In general the quality of mRNA material can be defined by the 28s/18s rRNA ratio, RIN or by some other less generally used indices ,25. Theolication  for postdegradation. However, 3'/5' ratio is not good for computational purposes. First, it has singularity at zero in denominator which means irrelevant growth of values when 5'-end intensity goes down. Second, it is insensitive to changes in the low to moderate range of degradation especially if sample set contains both high and extremely low quality samples. And thirdly it is an RNA degradation measure while we would like to consider RNA quality measure . For these reasons we used the inverse ratio which is less popular but can also be used [Historically 3'-end and 5'-end located probeset intensities of housekeeping genes were arranged into 3'/5' ratio and evaluated as a measure of transcript  be used ,10.GAPDH genes. The mean of two 5'/3' ratios is designated as post chip RNA quality (denoted further as RNA QC) of Affymetrix GeneChip sample in GeneLogic database. 5'/3' ratio of housekeeping genes and RNA quality vary from 0 to 1 with 0 stands for lower bound of RNA quality and 1 stands for upper bound of RNA quality. Figure Thus, to characterise post chip RNA quality we use 5'/3' ratio of beta actin and Overall, the RNA quality of the investigated data sets is rather poor and varies considerably. For this reason the effect of the RNA quality and methods of how to deal with it were investigated. Since Affymetrix microarrays tolerate RNA degradation due to the 3'-end biased probe set positions ,27 it isCorrelation between the intensities of all transcripts and the corresponding beta actin ratios was investigated (Spearman rank correlation coefficient was used to avoid outlier effects). Figure Figure Two observations are striking: 1) number of RNA quality dependent transcripts differ significantly within tissue sets and chip platforms; 2) all distributions of correlation coefficient  ones for subthalamic nucleus sample set is shown in Figure Another interesting feature of the distributions of correlation coefficients identified in this study is the significant predominance and strength of negative correlations between transcript intensity and post chip RNA quality. In order to investigate the reason for this finding the overall intensity of transcripts with pronounced positive or negative correlation was examined. As an example, a barchart containing the top 2000 negatively (-0.97 < r < -0.66) and top 2000 positively (0.6 < r < 0.97) correlating transcripts out of 7035 significant  are either not expressed or low expressed ones. Negative correlation to RNA quality means that the worse the quality the stronger the signal becomes. One possible reason for this effect in the case of unexpressed transcripts is the enrichment of low RNA quality material with fragments of degraded mRNA. Fragments of degraded RNA could subsequently increase low intensity signals of unexpressed transcripts on low quality chips by nonspecific hybridization. Another possible reason is related to the commonly used normalization strategies. In the case of a decrease in the abundance of actual expressed transcripts due to the decrease of RNA quality the relative level of low signals increases. This also holds true for MAS 5.0 preprocessing due to the scaling step as RMA (or refRMA) due to the quantile normalization.Taking advantage of the availability of the two chip platforms and numerous sample sets we could check the stability of the observed correlations i.e. their consistency and reproducibility in different experiments. The number of consistently (the same positive or negative sign) and inconsistently (opposite sign) correlated to RNA quality transcripts among ones found to be significant in two hybridizations are shown on Figure Septal area (not involved into Parkinson's disease) seemed to show strong deregulation related to Parkinson's disease which appeared to result from high levels of noise introduced by RNA quality. In contrast Caudate nucleus (involved into the disease) showed moderate dependency on RNA quality and strong deregulation related to Parkinson's disease.Basic conclusions to be made from this data are 1) a correlation structure in microarray data exist which is determined by post chip RNA quality; 2) within a sample set significantly different levels of dependency to RNA quality may occur; 3) the majority of transcripts show negative correlation to RNA quality. Since from 10% to 30% of all transcripts present on the chip may correlate to RNA quality the second point is explicitly stressed here. RNA quality can strongly affect the results of the experiment as described previously  but thisIt is important to note that the third point concerns data normalization procedures relying on housekeeping genes. The 3'-end intensities of housekeeping genes show poor correlation to the RNA quality in considered datasets. Hence, this normalization does not influence the overall correlation structure introduced by RNA quality. It might be a reason why normalization on housekeeping genes did not eliminate the correlation structure mentioned previously in  as an opSince the majority of expressed transcripts are brain specific it is reasonable to investigate between tissue consistency of the RNA quality effect. Consider all 10 brain sample sets and their correlation profiles to RNA quality. Let each transcript be characterized by a 10-dimensional vector of correlation coefficients between its intensity and the 5'/3' beta actin ratio. Summary of principal component analysis of these vectors is presented in Table Consistent dependencies of transcripts on RNA quality among a number of experiments imply the presence of transcript specific components of the effect. More detailed investigations revealed that the sign of correlation to RNA quality is partially determined by distribution of the probes in the mRNA (median of relative probe positions and their standard deviation), the length of the mRNA, and probably overall stability of mRNA species Figure , 9. FiguThe mixture of positively and negatively correlated transcripts in the range of 0.1–0.8 of the median of relative probe positions .Since expression profiles are available for two chip platforms and probe sets detected by these platforms largely overlap we used the inter-platform correlation as a quality measure for the applied correction procedures. Expression profiles generated by using material stemming from the same set of donors should correlate if they are related to actually present transcripts and should show no correlation in the case of actually absent transcripts. On the other hand systematic noise is introduced by the RNA quality. Between 5000 and 16000 transcripts were found to be RNA quality dependent on HG-U133_Plus_2.0 chip and up to 20% of them showed the same behavior on HG-U133_AB chip while others did not. Thus RNA quality could inflate irrelevant correlations and suppress relevant ones.Post chip RNA quality profiles themselves should display inter-chip set correlation if the input of original sample quality is constitutive and sample processing introduces modest random fluctuations. Therefore an appropriate correction procedure should reconstitute the perturbed real inter-chip set correlations of expression profiles and eliminate the irrelevant ones.A summary of correcting procedures applied to all 10 considered sample sets is presented in Table One of the major tasks when analyzing brain tissue using microarrays is detecting changes in low-abundant genes . Inter-cDonor age of death, pre-mortem hypoxia, other agonal events, duration of agonal stage, brain pH, post-mortem interval before sampling, and RNA integrity were previously described as important factors related to data consistency in the context of microarray analysis of post-mortem brain samples ,19,21. BIn these analyses dataset 2 was introduced which contained donors with known age of death, duration of agonal stage, brain pH, and post-mortem interval before sampling. Since pre-mortem hypoxia was difficult to determine it was not considered here. In any case, all samples were taken post-mortem and thus tissues are assumed to have suffered from hypoxia. Due to the lack of variety in agonal events in the given sample set this feature was also not considered.r = 0.53) can be seen. This is an indication of the existence of a systematic component of RNA quality that is independent of the actual chip type.A scatter plot of paired beta actin ratios for HG-U133_AB and HG-U133_Plus_2 data sets is shown in Figure Dataset 2 contains from 3 to 19 brain tissue samples related to each of 63 donors with known pre- and post-mortem history. The medians of beta actin ratios related to the same donor were calculated and considered as a function of known pre-mortem and post-mortem details. Results are shown in Figure r = 0.26) this cannot be accept as a strong evidence. As displayed in the same figure the HG-U133_AB chip data does not follow the same trend.Brain pH is mentioned as a factor of importance for RNA quality in some publications . Even thSome bias of trend lines for post-mortem intervals more than 9 hours can also be observed. In general one should expect expression levels of at least some transcripts to depend on post-mortem interval before sampling. For example, some mRNA species in brain tissues were reported to have short half lives, degrading within the first 2–3 hours after death ,18. Howeσ is 8 ± 6 hours). Probably all mRNA species with a short half-life period are already degraded at the moment of sampling whereas stable mRNAs stay constant within this time interval. This may be the reason why no systematic component in the dependency of transcript intensity on post-mortem interval before sampling was found.Direct influences of post-mortem intervals on the signal values can be examined by the same strategy as in section 3. One would expect predisposed mRNA species to be affected in the same way during the time before sampling through all brain tissues. However, the first principal components of the correlation coefficients between signal values and post-mortem intervals exhibited only marginal inter-tissue consistencies (data not shown). An important fact in this regard maybe a bias in the sample sets. Only very few samples in Dataset 1 show a post-mortem interval of 2–3 hours or less while others were sampled more than 5 hours after death  to integral Affymetrix chip characteristics as percent of present calls and scaling factor (MAS 5.0). On the other hand pre- and post-mortem parameters, namely, agony factor score, brain pH and post-mortem interval, do correlate to percent of present calls and scaling factor but do not correlate to RNA quality measures.In a recent publication  a numberThese findings suggest that the input of pre- and post-mortem parameters in the post chip RNA quality signal is rather weak. On the other hand post chip RNA quality input in expression profile is very strong because the 3'/5' ratio is directly related to measured intensities. Therefore our data suggests that in microarray analysis of post-mortem brains one should 1) collect all pre- and post-mortem parameters together with microarray sample; 2) check pre- and post-mortem parameters influence directly by considering expression profiles; 3) use this information in analysis of relevance of found deregulated gene set.In current study we revealed the detailed profile of post chip RNA quality effect and investigated its technological and biological origin. RNA quality was found to introduce well pronounced systematic noise into signals obtained from microarray analysis of post-mortem brains. Between 10% and 30% of all transcripts in a chip correlate to RNA quality. According to this study RNA quality effects have: 1) a "random" component which is introduced by the technology and 2) a systematic component which depends on the features of the transcripts and probes. Random components mainly account for numerous negative correlations of low-abundant transcripts. These negative correlations emerge from non-specific hybridization and the biased data-normalization technology. They are not reproducible and are mainly introduced by an increased relative level of noise. RMA (or refRMA) preprocessing seems to be more sensitive to the effect than MAS 5.0 preprocessing. This finding can be explained by the quantile normalization step.Three major contributors to the systematic noise component were identified: the first is the probe set distribution, the second is the length of mRNA species, and the third is the stability of mRNA species. Positive correlations reflect the 5'-end to 3'-end direction of mRNA degradation whereas negative correlations result from the compensatory increase in stable and 3'-end probed transcripts. Systematic components affect the expressed transcripts by introducing irrelevant gene correlations and can strongly influence the results of the main experiment.A linear model correcting the effect of RNA quality on measured intensities was investigated. If the set of consistently dependent on RNA quality transcripts is corrected, subtraction of linear RNA quality effects results in an average increase of inter-platform correlation. This is the evidence of real elimination of noise by the procedure. Application of the correcting procedure to two chip platforms independently shows an average decrease of between platform correlations. So, one should cautiously apply the data correction procedure. Nevertheless, taking into consideration RNA quality dependency of transcripts can help in reducing the number of false positive hits in analysing gene deregulation.Using the 5'/3' beta actin ratio, we revealed a strong correlation structure within the Affymetrix GeneChip gene expression data. As a consequence more reliable data analysis strategies could be applied. The following points are worth mentioning in cases with a wide range of post chip RNA quality in considered sample set: 1) testing for RNA quality dependency should be included in the preprocessing of the data; 2) investigating inter-gene correlation without regard to RNA quality effects could be misleading; 3) data normalization procedures relying on housekeeping genes either do not influence the correlation structure (if 3'-end intensities are used) or increase it for negatively correlated transcripts ; 4) sample sets should be balanced with regard to RNA quality.GeneLogic has collected gene expression profiles stemming from various different tissues and cells accompanied by an exhaustive number of clinical parameters. These data cover key therapeutic areas such as oncology, inflammation, cardiovascular disease, disorders of the central nervous system and metabolic disorders .Since Parkinson's disease was the main subject of the current study sample sets representing ten different brain regions were included. Four of them are primarily involved in Parkinson's disease whereas six regions are located spatially close to the former ones and act as a control to detect non specific deregulation (see Table The main criteria for donor selection were: 1) presence of Parkinson's disease and absence of other neurodegenerative disorders for experimental groups; 2) absence of neurodegenerative and psychiatric disorders and an age of death greater than 50 years for the control groups.GAPDH 5'/3' ratio should be greater than 0.1 . Due to low between chip correlation less than 2 outlying chips were filtered out from each group. Filtered chips are real outliers as they have low correlation with all other chips. Due to the second criterion 3–4 chips with extremely low RNA quality were excluded from the whole dataset.The main criteria for sample selection concerning data quality were: 1) minimum value of between chip correlation within a single tissue sample set should be greater than 0.8; 2) mean beta actin and Expression profiles obtained with two different Affymetrix GeneChips platforms, HG-U133_AB and HG-U133_Plus_2.0, were available for analysis. Comparison of the measured expression intensities of the two chip platforms was performed on the basis of 44792 common probesets. Due to intersecting sets of donors  and beta actin ratio, calculated as ratio of _5_at and _3_at probe sets intensities,GAPDH levels  and GAPDH ratio calculated as ratio of _5_at and _3_at probe sets intensities,3. GAPDH,4. RNA quality index (RNA QC) that is mean 5'/3' ratio of beta actin and 5. post-mortem interval before sampling,6. brain pH,7. duration of agony stage.Since information about brain pH and duration of agony stage is not available for the majority of donors in Dataset 1, an additional dataset (Dataset 2) was compiled.Dataset 2 consists of all brain tissue samples with known donor brain pH and duration of agony stage contained in the database. These samples belong to various brain regions . Donor's age of death was restricted to be in the same range as in Dataset 1 (from 51 to 90 years). Overall, Dataset 2 includes data derived from 63 donors with 3–19 Affymetrix GeneChip expression profiles each.2 intensity Yijk of ith sample and jth probe-set corresponding to chip platform k is modelled by the following linear equation:The observed normalised (MAS5 or RMA) logαjk represents an interception, βjk is a slope, Rijk stands for corresponding RNA quality measure namely, 5'/3' beta actin ratio, εijk is an error assumed to be normally distributed with zero mean, Nk is a number of transcripts on GeneChip (N1 = 44792 corresponds to HG-U133_AB platform and N2 = 54613 corresponds to HG-U133_Plus_2.0), and Mk is number of chip samples. The model is fitted independently for two chip platforms based on observations related to Control group.where Having estimated interception Here subtraction of RNA quality effect is equivalent to consideration of model errors instead of initial values.5'/3' beta actin ratio close to 1 correspond to ideal quality chip data. Using linear model one can calculate estimation of baseline transcript intensity obtained on ideal quality chip:YIjk can be obtained:Thus corrected data of baseline intensity level Linear model correction is performed for those transcripts that are considered to be RNA quality dependent  either for one chip platform or for both.YCij1}i = 1,..., M and {YCij2}i = 1,..., M (where M is the number of paired samples) was compared to correlation between initial expression profiles {Yij1}i = 1,..., M and {Yij2}i = 1,..., M on the basis of sample sets completed by the same donors and for those transcripts j which are common for two chip platforms. The shift in correlation was taken into account if either correlation after correction or correlation before correction were not less than 0.3 (in order to ignore not expressed and noisy profiles). Correcting HG-U133_AB and HG-U133_Plus2.0 data independently means that each profile correlated to RNA quality with <0.05 significance level was corrected. Correcting consistent transcripts means correcting only those profiles that show RNA quality dependency of the same sign with significance <0.1 on both chips.Correlation between corrected expression profiles {TP carried out the main computational study and drafted manuscript. DM participated in analysis of computational results. AW supervised the project. KQ contributed to design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.


CDKN2B (p15) cell cycle progression inhibitor gene which is inactivated by DNA methylation in haematological malignancies of the myeloid lineage. Its promoter region usually shows heterogeneous methylation and is only rarely fully methylated. The methylation status of CDKN2B can be used as a biomarker of response to treatment. Therefore the accurate characterisation of its methylation is desirable.Methylation-sensitive high resolution melting (MS-HRM) methodology is able to recognise heterogeneously methylated sequences by their characteristic melting profiles. To further analyse heterogeneously methylated sequences, we adopted a digital approach to MS-HRM (dMS-HRM) that involves the amplification of single templates after limiting dilution to quantify and to determine the degree of methylation. We used this approach to study methylation of the CDKN2B methylation in acute myeloid leukaemia (AML) samples. All the AML samples that were methylated at the CDKN2B promoter (40/93) showed varying degrees of heterogeneous methylation. Six representative samples were selected for further study. dMS-HRM was used to simultaneously count the methylated alleles and assess the degree of methylation. Direct sequencing of selected dMS-HRM products was used to determine the exact DNA methylation pattern and confirmed the degree of methylation estimated by dMS-HRM.MS-HRM was used to assess CDKN2B and other heterogeneously methylated genes. It eliminates both PCR and cloning bias towards either methylated or unmethylated DNA. Potentially complex information is simplified into a digital output, allowing counting of methylated and unmethylated alleles and providing an overall picture of methylation at the given locus. Downstream sequencing is minimised as dMS-HRM acts as a screen to select only methylated clones for further analysis.dMS-HRM is a powerful technique for the analysis of methylation in  Epigenetic mechanisms, in particular DNA methylation, play a major role in the modulation of gene activity in cancer. Methylation has been shown to silence a large number of genes in nearly every type of cancer ,2. WhilsCDKN2B (p15), which is silenced in a variety of haematological malignancies  nucleotides 99845 to 99958, and encompasses nine CpG dinucleotides. PCR was performed in 100 μl PCR tubes (Corbett Research) with a final volume of 20 μl, containing 200 nmol/l of each primer, 200 μmol/l of each dNTP, 0.5 U of HotStarTaq DNA polymerase (Qiagen) in the supplied PCR buffer containing 2.5 mmol/l MgCl2, 5 μmol/l SYTO9 , and 10 ng of bisulphite-treated DNA. The initial denaturation  was followed by 50 cycles for MS-HRM of 10 seconds at 95°C, 30 seconds at 59°C, 30 seconds at 72°C; one cycle of 1 minute at 95°C, 72°C for 1.5 minutes and a HRM step from 65°C to 90°C rising at 0.2°C per second, and holding for 1 second after each stepwise increment. The annealing temperature of 59°C was chosen as it gave a near-proportional amplification of methylated and unmethylated templates.The primers used to amplify bisulphite-treated DNA were CDKN2B-F, 5'-GTTAGGCGTTTTTTTTTAGAAGTAATTTAGG-3' and CDKN2B-R, 5'-TACGACTTAAAACCCCGTACAATAACC-3' and do not amplify unmodified genomic DNA (data not shown). The amplified region corresponds to [GenBank: The basis of dMS-HRM is the PCR amplification of single molecules following limiting dilution ,16. To pThe Poisson distribution is used as a guideline to determine the expected distribution of templates. For example, if the sample is diluted such that one amplifiable template is found on average per reaction, 36.8% of reactions will have no templates, 36.8% will have one template, and 26.4% will have more than one template. Amplification will occur when one or more template molecules are present in the PCR reaction.Amplifications from single templates can be readily identified when the melting curves are analysed. They show a smooth and sharp single signal. Melting curves from two (or more) templates generally result in signals showing two peaks if heteroduplexes are not formed, or more complex patterns when heteroduplexes are formed. Digital MS-HRM was performed using 60 cycles of amplification. Sixty replicates of diluted template were analysed per sample.dMS-HRM products were cleaned up with the PCR-M clean-up kit , according to the manufacturer's instructions, further processed with ExoSapIT , followed by the sequencing reaction using Big Dye Terminator v3.1 chemistry  according to the manufacturer's instructions. Sequencing was performed in both directions using the PCR primers given above as sequencing primers. The initial denaturation  was followed by 30 cycles of 10 seconds at 95°C, 30 seconds at 59°C and 3 minutes at 72°C. The sequencing products were purified by ethanol precipitation and separated on a 3100 Genetic Analyser (Applied Biosystems). The sequencing data for the dMS-HRM products were analysed using BiQ Analyzer software  .HRM differentiates between DNA molecules based on their sequence-dependent thermostability Figure , panel aCDKN2B promoter region in AML samples. Figure Forty of the 93 acute myeloid leukaemia (AML) samples were methylated. All showed a heterogeneous melting profile. Figure Six AML samples that represented the range of variation observed were analysed digitally. Figure In dMS-HRM, an increase in the melting temperature of the products relative to the controls can be seen Figure . HoweverIncomplete conversion was detected on one occasion as the peak had a higher melting temperature than fully methylated 'clones' Figure . This waIn our work on developing MS-HRM , we founQuantification of the degree of heterogeneous methylation by MS-HRM remains problematic. MS-HRM shows great sensitivity when the samples contains a mixture of fully methylated and fully unmethylated templates  as it haTo use a PCR-based technique, PCR bias needs to be eliminated or at least minimised. Given that the DNA methylation pattern in the entire interrogated sequence, and not just the primer binding sites, influences PCR bias , eliminaDigital PCR from single templates eliminates these problems . When siBRCA1 promoter [CDKN2B promoter. Cameron et al determined that about 40% of the CpG sites within the CDKN2B CpG island need to be methylated to achieve complete silencing of the CDKN2B gene, regardless of the exact CpG methylation pattern [CDKN2B promoter has been problematic as many methodologies are not well suited to the study of heterogeneously methylated regions.We previously introduced dMS-HRM as a tool to count the number of methylated and unmethylated alleles for the promoter . In thisA cost- and time-effective quantitative method is desirable for analysis of both research and clinical specimens where it is useful to know both the number of methylated templates and the degree of their methylation. Bisulphite sequencing of individual clones is an effective research approach but is too laborious and expensive for routine clinical use ,5. Our rCDKN2B [MGMT [MS-HRM and dMS-HRM can be compared with two other readily performed approaches that can recognise heterogeneous methylation. Denaturing gradient gel electrophoresis ,24 is anCDKN2B ,28 and M2B [MGMT . HeterogCDKN2B (p15) gene as an example. Although MS-HRM will often be sufficient to assess the methylation status of the CDKN2B gene, dMS-HRM will provide a more readily interpretable visualisation which may be quantified by scoring the individual peaks. dMS-HRM can also rapidly generate clonal templates for sequencing or pyrosequencing, eliminating the need for conventional cloning. Moreover, dMS-HRM will significantly reduce the amount of sequencing required as only clearly methylated amplicons will need to be sequenced.MS-HRM can readily identify heterogeneously methylated templates by their characteristic melting patterns. This paper introduces dMS-HRM as an analytical method for the rapid study of heterogeneous methylation using the clinically important AML: acute myeloid leukaemia; dMS-HRM: digital methylation-sensitive high resolution melting; MS-HRM: methylation-sensitive high resolution melting; WGA: whole genome amplification.The authors declare that no competing interests exist. The Peter MacCallum Cancer Centre and the University of Aarhus have a provisional patent on the MS-HRM methodology.ILMC participated in experimental design, performed the experiments and data analysis and co-wrote the manuscript. TM participated in experimental design, performed the data analysis and co-wrote the manuscript. PH provided the acute myeloid leukaemia samples. AD conceptualised the study, participated in experimental design, supervised the work and co-wrote the manuscript. All authors contributed to the writing of the manuscript and have read and approved the manuscript.


For more than a decade, microarrays have been a powerful and widely used tool to explore the transcriptome of biological systems. However, the amount of biological material from cell sorting or laser capture microdissection is much too small to perform microarray studies. To address this issue, RNA amplification methods have been developed to generate sufficient targets from picogram amounts of total RNA to perform microarray hybridisation.In this study, four commercial protocols for amplification of picograms amounts of input RNA for microarray expression profiling were evaluated and compared. The quantitative and qualitative performances of the methods were assessed. Microarrays were hybridised with the amplified targets and the amplification protocols were compared with respect to the quality of expression profiles, reproducibility within a concentration range of input RNA, and sensitivity. The results demonstrate significant differences between these four methods.In our hands, the WT-Ovation pico system proposed by Nugen appears to be the most suitable for RNA amplification. This comparative study will be useful to scientists needing to choose an amplification method to carry out microarray experiments involving samples comprising only a few cells and generating picogram amounts of RNA. Gene expression profiling using microarray technology is a powerful method to investigate the phenotype of complex biological systems . Over thin vitro transcription (IVT) of a cDNA template into complementary RNA (cRNA), using T7 RNA polymerase [The most routinely used amplification method is based on linear amplification by lymerase -8. Severlymerase ,9-20. Relymerase . This telymerase -29.We assume that the purification of total RNA from cultured cells or tissue after isolation using these methods can be largely method-, cell- or tissue-dependent. In this study, to minimise this source of error and to focus on the amplification method, a commercial source of total RNA, i.e. human universal RNA, was used. We compared four commercial RNA amplification protocols to the standard target labelling procedure proposed by Affymetrix in side-by-side experiments to evaluate the most suitable method to perform gene expression profiling from picogram amounts of input RNA on Affymetrix GeneChip microarrays. The following amplification protocols were compared: i) Arcturus RiboAmp™ system, ii) Ambion MessageAmp™, iii) Epicentre TargetAmp™, and iv) Nugen WT-Amplification™ pico system. Protocols proposed by Arcturus, Ambion and Epicentre are adapted from the IVT method first described by Eberwine et al. -8. The sQualitative and quantitative analysis of the input total RNA confirmed its high quality Figure .For each protocol, one RNA amplification was performed from 250 pg and one from 500 pg of human universal RNA by two operators in two independent laboratories. A negative control  and a positive control (if available) were included in each experimental batch. The quality of each amplified product (cRNA or cDNA) was assessed using microfluidic electrophoresis Figure  and compThe size of the main population of aRNAs synthesised according the protocol proposed by Ambion Figure  ranged bThe quantities of aRNA and cDNA obtained after amplification are presented in Table After amplification, each labelled cRNA or cDNA target was biotinylated, fragmented and hybridised on Affymetrix HG U133 plus 2.0 to assess the impact of each protocol on expression profiles. Raw .CEL files were normalised using the MAS5 algorithm from Affymetrix, and metrics for all hybridisations were analysed and compared . The quality of each amplified cDNA was assessed Figure . Not surTo assess the sensitivity of Nugen system and to determine whether it allowed the measurement of differential gene expression, the expression profiles of two cell lines (SkBr3 and HCC38) were compared using the Nugen WT-Amplification™ pico system and the Affymetrix one-cycle IVT amplification. 500 pg and 2 μg of RNA of each cell line were amplified according to the protocols proposed by Nugen and Affymetrix, respectively, and hybridised on Affymetrix HG U133 plus 2.0 GeneChip. As expected, Affymetrix and Nugen protocols showed similar yields of reproducibility with correlation coefficients up to 0.90 between replicates (data not shown). Differential gene expression analysis was performed for each system using the present, absent, marginal increase, marginal decrease, increase and decrease criteria proposed by Affymetrix and implemented in GCOS software Table . AlthougGene expression profiling on several hundred cells isolated by cell-sorting technologies, or by LCM prompts the development of new amplification procedures. Over recent years, academic, private sector and commercial researchers have been developing tools and methods to generate expression profiles from small amounts of biological material. Improvements have been made in all steps of the process of expression profiling experiments such as sample collection, RNA purification, or acquisition and analysis of expression data. Over the last few years, improvements in RNA amplification methods now allow the synthesis of sufficient targets to perform microarray hybridisation on as little as picogram amounts of input RNA. In this study, we evaluated and compared four commercial RNA amplification protocols using picogram amounts of input RNA. This comparative study will be useful to researchers when planning new experiments involving samples derived from a few cells.This comparative study was based on several assessments of amplification products. First of all, electrophoresis was performed and examined in combination with cRNA or cDNA yields. This analysis is performed routinely prior to hybridisation, as it provides an estimate of the overall amplification performance. Unusual traces or insufficient yield correspond to either abnormal input RNA, or amplification failure. The poor yield obtained with the protocol proposed by Epicentre is due to amplification failures since the quality of the commercial RNA has been checked. Experimental errors might be the cause of these failures. However, as amplifications were carried out in duplicate by two experienced operators in two laboratories, the quality of the batch and its reproducibility may be responsible for these errors.The overall size of aRNA, estimated by the electrophoretic profile, has been previously described as a good metric when looking at within-sample fidelity . As expeThe percentage of present call has been previously described as a good metric when evaluating the sensitivity of a method ,34. GeneWith respect to hybridisation specificities, the Nugen technology shows a fundamental difference as it generates a single-stranded cDNA target whereas others protocols yields cRNA targets. As RNA/DNA interactions are stronger than DNA/DNA interactions, cDNA hybridisations should theoretically be more specific but also less sensitive. However, the protocol proposed by Nugen was not associated with decreased sensitivity and the lower average background value of the chips hybridised with cDNA targets (Nugen) suggests reduced non-specific target/background interactions but additional studies should be conducted to evaluate the potential increased specificity of the probe/target interaction and its biological impact on expression profiling.Practical criteria should be considered to choose an amplification approach to carry out expression profiling experiments. These include completion time, handling difficulties and labour intensiveness. Major differences were observed between the various protocols evaluated in terms of these criteria. IVT-based amplifications  involve numerous steps and are therefore time-consuming and labour-intensive. However, the high quality of technical support and optimisation of chemistries (most reagents are conveniently pre-dispensed and pre-mixed) simplify the handling of the Ambion and Arcturus systems. The Nugen technology is fundamentally different from the IVT-based amplifications. Fewer steps are needed to achieve amplification, and the process does not include delicate RNA handling during the amplification process. The Nugen WT-Amplification™ protocol is therefore shorter and easier to complete, and consequently less error-prone. Ultimately, investigators will need to compare experimental results obtained by different laboratories. Experimental variables (such as operator) on expression data could be limited if fewer steps are needed to achieve amplification.When expression profiling experiments are performed on just a few cells, it is currently impossible to strictly assess the quality and quantity of the purified RNA. Even the most modern and accurate spectrophotometers or microfluidic-based electrophoresis chips require at least 100 pg of RNA to characterise nucleic acids extracted from such minute samples. The results presented here show that a two-fold variation of the amount of input RNA had almost no impact on the transcriptomes analysed when starting with picogram amounts of RNA. Moreover, high reproducibility within a given protocol (correlation coefficient of about 0.95) was observed for amplifications that were carried out in two different laboratories by two different operators. This shows that trained laboratories could conduct amplifications with the technologies proposed by Ambion, Arcturus, or Nugen and produce data that could effectively be compared across laboratories using the same amplification protocols. However, the results obtained in this study indicate large variations between different protocols. This suggests, as previously shown, that i) when conducting expression profiling experiments the same amplification protocol should be used in order to maximize the comparability of the results ,33,35,36Amplifications were performed by two operators in two different laboratories in order to mimic "real experimental conditions" of expression profiling. In our hands and under these experimental conditions, Nugen WT-Amplification™ pico protocol appeared the most suitable. Additional experiments were performed in order to further assess this system. Amplifications were performed on amounts of input RNA ranging from 50 pg to 1 ng to test the system at the upper and lower limits of input amounts corresponding to a few cells isolated by FACS or LCM. The results obtained show that the overall quality, comparability and reproducibility of expression measurements were very good for RNA input amounts ranging from 250 pg to 1000 pg. The system was less efficient with 100 pg and 50 pg of RNA inputs with respect to sensitivity and reproducibility. It is important to analyse this result in the light of manufacturer's specifications indicating that the minimal RNA input of the system is 500 pg.The results of a differential gene expression experiment using Nugen WT-Amplification™ chemistry or Affymetrix one-round IVT amplification were also compared. If we strictly count the number of probesets with the same status  in both chimistries, discrepancies were observed only for 25% of probesets. However the direction of differential expression diverged in few measurements. These results show that most genes have similar differentially expressed patterns and that one amplification protocol does not create an artificial variability of the measurements compared to the other. The observed discrepancies do not appear to be due to the decreased sensitivity of one of the two protocols because the same proportion of genes was modulated when using the Affymetrix and Nugen protocols. Although the effects of RNA amplification on differential gene expression measurements have been previously reported ,35,37-39The preservation of the relative abundance levels of gene transcripts is an important issue when performing RNA amplification prior to genome-wide expression measurements. This issue has been widely studied in comparing expression measurements i) from the amplification of different amounts of RNA ,21,40, iTherefore, a) we do not recommend to perform amplification from less than 250 pg of input RNA, and b) when it is possible, researchers should try to increase the RNA input quantity to at least 500 pg to be more confident in the biological interpretation of the results, particularly concerning low express transcripts.In this study, we evaluated and compared four commercial RNA amplification protocols using picogram amounts of input RNA. An operational flow chart was designed to evaluate the effects of laboratory and operator intervariability on expression results. In our hands, the WT-Ovation™ pico system proposed by Nugen appears the most suitable for amplification and gene expression analysis. The amplification method proposed by Nugen is fast, easy to perform, and does not require several rounds of amplification or exponential PCR cycles. It provides a high quality expression profile that appears to allow identification of differentially expressed genes. Finally, the reproducibility of the results of amplification across laboratories and across amounts of RNA input indicates that this system is an efficient tool to conduct gene expression profiling experiments from LCM or cell-sorted isolated samples.RNA derived from SkBr3 and HCC38 cells was purified using the RNeasy Mini Kit (Qiagen) including on-column DNase (Qiagen) digestion as described by the manufacturer's protocol. The Universal Human Reference RNA (Stratagene) representing a pool of 10 different human cell lines was used as control RNA.After nucleic acid quantification using a ND-1000 spectrophotometer (Nanodrop Technologies), all RNAs were serially diluted in RNAse-free water to obtain a 250 pg/μL stock solution. RNA quality was ensured by analysing separation trace of RNA using the RNA6000 PicoAssay for the Bioanalyzer 2100 (Agilent). Aliquots were prepared and stored at -80°C. The same RNA was used for all experiments as starting RNA for amplification. Each aliquot was used once.When provided within the amplification kits, internal RNA controls were also used as starting material (MessageAmp™ II aRNA Amplification (Ambion), RiboAmp™ HS RNA Amplification Kit (Arcturus)).All amplification experiments were carried out in duplicate by two trained operators in two different laboratories in order to assess the consistency and robustness of amplification and labelling protocols. Microarray hybridisations, quantitative and qualitative measurements were performed by the same experienced technician on the same instruments to ensure that expression level discrepancies were only due to amplification and labelling procedures.RNA amplifications were performed using the following kits: One- and two-round amplification kit (Affymetrix), RiboAmp™ HS RNA Amplification Kit (Arcturus), MessageAmp™ II aRNA Amplification (Ambion), TargetAmp™ 2-Round Aminoallyl-aRNA Amplification (Epicentre), and WT-Amplification™ Pico (NuGEN). For all experiments, the manufacturers' protocols were strictly followed.in-vitro transcription. When input RNA quantity is below standard requirements, a two-round amplification protocol is performed with 10–100 ng of total RNA. In this case, the first IVT does not use any labelled nucleotide to generate unlabelled cRNA. This cRNA is used for reverse transcription (RT) with random primers, dsDNA synthesis and finally labelled cRNA synthesis.Briefly, the standard protocol described by Affymetrix amplifies and produces after a single round T7 amplification, more than 40 μg of labelled cRNA from 1–4 micrograms of total RNA. Total RNA is used for cDNA synthesis using an oligo-dT-T7 primer. The cDNA is used as template for double-strand DNA (dsDNA) synthesis and purification. Linear amplification and labelling of complementary RNA (cRNA) is performed by in vitro transcription (IVT) using labelled nucleotides.The MessageAmp™ II aRNA Amplification Kit (Ambion) is based on the RNA amplification protocol described by Eberwine et al. , and desRiboAmp HS version C (Arcturus RiboAmp HS protocol) enables target synthesis from 100 pg to 500 ng. Version C of RiboAmp HS is composed of two-round amplification similar to the protocols proposed by Affymetrix or Ambion. Reactions were performed according to the manufacturer's protocol.The TargetAmp 2-Round Aminoallyl-aRNA Amplification Kit 1.0 (Epicentre) is designed to be an improvement of the other linear T7 RNA amplification process described above to produce microgram amounts of aminoallyl-aRNA from as little as 10 pg of total RNA. Two rounds of amplification combine two generations of SuperScript Reverse Transcriptases from Invitrogen.In contrast with other manufacturer protocols, the WT-Ovation™ Pico RNA Amplification System (Nugen) is not based on T7 polymerase cRNA synthesis. Nugen has designed a technique called Ribo-SPIA™, which is a three-step process that generates amplified cDNA from as little as 500 picograms of total RNA. First sThe labeled cRNA targets are synthesised using the genechip IVT labelling kit (Affymetrix) from dsDNA obtained with Affymetrix and Arcturus protocols. TargetAmp amplification products (Epicentre protocol) are labelled using the Biotin-X-X-NHS kit (Epicentre). WT-Ovation™ Pico products (NuGEN) are labelled using the FL-Ovation™ cDNA Biotin Module V2 (NuGEN). Each labeled cRNA targets are synthesised according to manufacturer's protocols.The quantity and quality of the amplified cRNA or cDNA were assessed by a ND-1000 spectrophotometer (Nanodrop Technologies), Agilent Bioanalyzer and RNA 6000 nanoChips , respectively.Hybridisation mixtures were prepared according to Affymetrix procedures to accommodate 10–20 μg of labelled cRNA targets from Affymetrix, Arcturus, Ambion and Epicentre amplifications, or 5 μg of cDNA targets from NuGEN amplification. If less than 10 μg of cRNA or 5 μg of cDNA targets were generated, the entire quantity of material was used as input. Human Genome U133 Plus 2.0 GeneChips (Affymetrix) were hybridised, revealed and washed according to the Affymetrix protocol. GeneChips were scanned using a 7 G scanner (Affymetrix) and images (DAT files) were converted to CEL files using GCOS software (Affymetrix). All of the microarray raw data have been deposited in the Gene Expression Omnibus under the accession number GSE15398 Data analysis was performed using expression console V 1.1 (Affymetrix) and genome analysis tools (Partek). Differential gene expression analysis was performed using GCOS software (Affymetrix).MCZ, CB and CD designed the study. MCZ and DG performed the experiments. MCZ, DG and CD performed the data analysis. MCZ, DG and CD drafted the manuscript. CB, JPT, SL and CD supervised the study. All authors read and approved the final manuscript.


Brucella. The characterization of brucelae transcriptome's during host-pathogen interaction has been limited due to the difficulty of obtaining an adequate quantity of good quality eukaryotic RNA-free pathogen RNA for downstream applications.Brucellosis is a worldwide anthropozoonotic disease caused by an in vivo intracellular pathogen belonging to genus B. melitensis in a quantity and quality suitable for pathogen gene expression analysis. Initially, B. melitensis total RNA was enriched from a host:pathogen mixed RNA sample by reducing the eukaryotic RNA..Then, to increase the Brucella RNA concentration and simultaneously minimize the contaminated host RNA in the mixed sample, a specific primer set designed to anneal to all B. melitensis ORF allows the selective linear amplification of sense-strand prokaryotic transcripts in a previously enriched RNA sample.Here, we describe a combined protocol to prepare RNA from intracellular B. melitensis when limited amounts of pathogen RNA are present, and is potentially applicable to both in vivo and in vitro models of infection, even at early infection time points.The novelty of the method we present here allows analysis of the gene expression profile of  Brucella. In spite of the massively underreported cases, brucellosis is considered the world's most widespread zoonotic infection  and then lysed in 1 ml of J-buffer containing freshly made 10% lysozyme solution  at 37°C for 10 min. Later, 0.1 ml of RNAse solution (1 mg/ml) was added and incubated at 37°C for another 10 min. Finally, the solution was heated to 70°C for 3 min. DNA was released from the cells by adding 0.08 ml of 30% sodium salt of N-lauroyl sarcosine (Sigma) and incubated at 37°C for 1 h, followed by digestion of proteins by 4 mg of proteinase K. The resulting solution was dialyzed against TE  overnight at 37°C. Next day, the preparation was transfered back to a plastic tube and DNA was subsequently twice extracted with ten minute of gentle inversion in neutral, water-saturated phenol, followed by two-time ether extract. Finally, DNA was again dialyzed for 8 h against TE. DNA concentration was quantified by NanoDrop® ND-1000 (NanoDrop) and stored at 4°C until used.For isolation of ®  according to manufacturer's instructions. The resultant RNA pellet was re-suspended in DEPC-treated water (Ambion) with 2% DTT and 1% RNase inhibitor . Contaminant genomic DNA was removed by RNase-free DNase I treatment (Ambion) according to the manufacturer's instructions, and samples were stored at -80°C until used. RNA concentration was quantified by NanoDrop® ND-1000 , and the RNA quality was determined using the Agilent 2100 Bioanalyzer .Total RNA from HeLa S3 and Madin-Darby bovine kidney (MDBK) (ATCC) cell cultures was extracted by TRI-ReagentB. melitensis ORFs  according to the manufacturer's instructions. After enrichment, the remaining RNA was precipitated in 100% ethanol at -20°C for at least 1 h, centrifuged for 30 min at 10,000 × g at 4°C, and washed twice in ice-cold 70% ethanol. After 5 min centrifugation at 10,000 × g at 4°C, the RNA was re-suspended in 25 μl of DEPC-treated water (Ambion) and immediately amplified in a 3 step-protocol, previously described in detail  (Invitrogen) was used instead of the mix from the kit along with 3 μl of Cy5dCTP . In both cases, uncoupled dye was removed using PCR purification kit (Qiagen) and dye incorporation calculated by NanoDrop® ND-1000 (NanoDrop).The labeling and hybridization procedures was taken from the protocol developed by The Institute for Genomic Research  and adapB. melitensis gDNA to the final volume of 35 μl. Samples were heated at 95°C for 5 min and then kept at 45°C until hybridization. Thirty five μl of 2× formamide-based hybridization buffer  was added to each pre-annealed samples, well mixed and applied to a pre-treated custom 3.2 K B. melitensis oligo-array. Slides were hybridized at 45°C for ~20 h in a dark, humid chamber (Corning) and washed for 10 min at 45°C with low stringency buffer  followed by two 5-min washes in a higher stringency buffer  at room temperature with agitation. Slides were dried by centrifugation at 800 × g for 2 min and immediately scanned.Dried, labeled cDNA samples were resuspended in nuclease-free water (Ambion) and mixed with 0.5 μg of labeled B. melitensis genomic DNA, as previously described [t test was also performed, and genes were deemed as significant if the p value was ≤ 0.05.Microarrays were scanned using a commercial laser scanner  with independent excitation of the fluorophores Cy3 and Cy5. Fluorescent signal and local background intensities were quantified for each spot by using image analysis software . Genes with fluorescent signal values below background were disregarded in all analyses and the arrays were normalized against escribed . Data weet al. (2004) and Lawson et al. (2006) demonstrated the potential usefulness of GDPs for the detection of Mycobacterium tuberculosis and Yersinia pestis gene expression in vivo, respectively [B. melitensis mRNA and subsequent hybridization to microarrays, a primer set (BmGDPs) generated from GDP-Finder software was compared with a commercially available set of RHPs. Each method was used on two identical B. melitensis RNA samples isolated from log phase cultures, and the resulting cDNA was labeled with Cy3 and co-hybridized with Cy5-labeled genomic DNA (gDNA) on two arrays for each primer type. Initially, the amount of cDNA generated was lower using BmGDPs than RHPs , which could be due to the selective annealing of BmGDPs to B. melitensis mRNA. For inter-array comparison after labeling and hybridization, 63 of 9,681 spots with signal values flagged "bad" by GenePixPro 6.0 were removed across all four data sets to make them comparable. The consistency of the signal from samples reverse-transcribed with GDPs was slightly higher (R2 = 0.7535) compared to RHPs samples (R2 = 0.7116)  over RHPs . Likewise, correlation analysis indicated that GDPs (87%) were slightly more consistent than RHPs (84%), and the consistency was greater when considering average signal intensity values (GDPs = 1665 & 1724 vs. 1912 & 1492 of RHPs), average standard deviation (490 vs. 540) and standard deviation of average intensities (41 vs. 297). We also compared the average ratio between experimental replicates (1.3 for GDPs and 0.91 for RHPs), as well as the average signal log ratio between each experimental replicate and the co-hybridized gDNA (0.77 and 0.69 for GDPs and 0.69 and 0.83 for RHPs). While the average ratio between experimental samples was higher for GDPs than for RHPs, the standard deviation of signal log ratios, which is a more reliable measure of consistency between arrays, was lower (0.06 for GDPs and 0.10 for RHPs). These results indicate greater consistency between arrays hybridized with samples reverse-transcribed using GDPs rather than RHPs.Talaat ectively ,20. In a6) Figs.  and 1B. 2 value was much higher for GDPs (0.83) compared to RHPs (0.74), despite similar standard deviations (0.10 for GDPs and 0.09 for RHPs). These results indicated that GDP replicate spots were more similar to one another than were RHP replicate spots. Altogether, these results indicate that the reverse transcription of B. melitensis RNA using GDPs generates more specific probes than those generated with RHPs.We next examined the consistency between replicate spots on each array (intra-array comparison). To streamline the comparison, genes that were flagged as "bad" and any "matching" replicate spots were removed across all arrays. Eighty-one spots were eliminated, which left 9,600 spots representing 3,200 different genes (genes spotted in triplicate). The comparison of the average signal values , their standard deviations (42 v 298) and the average standard deviation between replicate spots  essentially confirmed a higher intra-array consistency for the GDPs over the RHPs samples. As a final measure of consistency/variability between arrays, we separated all of the replicate spots and treated them as replicate samples/arrays in order to gauge the trend of consistency across all replicate spots: two replicates (arrays) with triplicate spots for each, yields 6 "theoretical arrays" for each condition (GDPs and RHPs). The average RBmGDPs are 8-mer oligonucleotide long primers, a potential concern that they anneal and amplify contaminating RNA sequences from the host that overlap with sequences of the B. melitensis transcripts and cross-hybridized with probes on B. melitensis oligoarrays, was considered. Total RNA isolated from HeLa cells culture was labeled and hybridized on B. melitensis microarrays under the same conditions as RNA isolated from Brucella cultures. Initially, we observed a reduced amount of cDNA generated , which indicated some restriction of priming of BmGDPs to eukaryotic RNA. When the cDNA generated from host RNA using BmGDPs was hybridized on the pathogen array, only 105 of 3120 genes (3.3%) were consistently detected , vs. 34% when RNA from B. melitensis was used. These data illustrate that BmGDPs biased priming to B. melitensis transcripts, along with the oligonucleotides printed on pathogen array, highly reduce the chances of eukaryotic transcripts to hybridize on the array.As Brucella RNA from a host:pathogen mixed RNA sample before amplification. Twenty-five μg of total eukaryote RNA from HeLa or MDBK cell lines was mixed with 2 μg of B. melitensis RNA (ratio 12.5:1). The MICROB Enrich® kit (Ambion) was then used to remove the eukaryotic RNA from the mixed sample. The total RNA yield after treatment was 3.72 μg (an 86.2% reduction) and 4 μg (85% reduction) for HeLa:Brucella and MDBK:Brucella mix, respectively. The integrity and composition of the samples pre- and post-treatment were evaluated by agarose gel electrophoresis and bioanalyzed  procedures and the measure of the reproducibility of the protocol were evaluated by spiking 2 different samples of 25 μg of total RNA extracted from HeLa or MDBK cell culture with 0.2 μg of B. melitensis RNA (host:pathogen RNA = 125:1). The mixed RNA samples were initially enriched, which decreased the RNA concentration more than 88% . Then, 1:10 volume of the remaining RNA  was amplified using a 3 step protocol described in the M&M. This protocol enables amplification of sense-stranded prokaryotic transcripts. One round of amplification yielded more than 80 μg of amplified sense total RNA from each sample (>than 260-fold amplification). Two aliquots of 10 μg from every E&A RNA sample were indirectly labeled and co-hybridized against B. melitensis gDNA on B. melitensis microarrays (4 arrays). The degree of bias introduced by the enrichment and amplification was determined by evaluating the performance of the E&A RNA samples against identical labeling and hybridization protocol from non-treated B. melitensis RNA (i.e. RNA extracted from a log culture).To increase the template concentration when small amounts of pathogen mRNA are present in the sample, such as the early stage of infection, we linearly amplified the enriched 2 values (slopes) represented the level of consistency (similarity) between each slide, which was similar for biological and technical replicates , but the difference was small and indicates that amplification does not necessarily correspond to a higher signal.Prior to analysis, spots that were flagged as "unacceptable" were removed leaving a total of 3,120 acceptable spots . We employed genomic normalization to improve data quality and to compare multiple samples in a minimum of experiments . To compr) between the fluorescence intensity in E&A vs. control samples and then compared the expression profiles of hybridizations of 2 independently amplified RNA samples. Our results confirmed a good correlation (r = 0.8496) between the expression profile of the E&A samples against the control sample and a high degree of correlation between independent replicates (r = 0.9823). Altogether, these results suggest that our protocol is highly reproducible with a high level of maintenance of the original information. Considering the fact that the magnitude of the bias can be under-estimated when it is highly reproducible [in vitro or in vivo systems of infection.In order to evaluate the bias introduced by the enrichment and amplification process and the reproducibility of the protocol, we first calculated the correlation  that is insufficient for microarray studies. To increase the Brucella RNA concentration and simultaneously minimize the contaminated host RNA in the mixed sample, we applied a protocol of linear amplification of sense-stranded RNA biased to pathogen transcripts in the previously enriched RNA sample. In the first step, the protocol utilizes BmGDPs to bias the reverse transcription to bacterial transcripts and the overhang tailing activity of Moloney murine leukemia virus (MMLV) reverse transcriptase to add the T7 promoter to cDNAs during reverse transcription. Then, the second-strand cDNA is synthesized, and finally in vitro transcription is carried out using a T7 polymerase. The methodology was reproducible, as indicated by correlation analysis of the gene expression detected in an original sample (non-enriched non-amplified B. melitensis RNA) vs. an E&A host:pathogen mixed RNA sample (i.e. B. melitensis RNA spiked with HeLa RNA = 1:125). Bearing in mind that the presence of contaminant eukaryotic RNA and the extra handling of the samples could introduce some degree of bias into the population of treated RNA [r = 0.8496) between these 2 samples with high reproducibility of the technique (correlation between independent replicates = 0.9823). This result is in concordance with previous correlation study of pathogen gene expression from a mixed host:pathogen RNA sample with the original pathogen RNA sample [B. melitensis RNA), our study yielded a higher correlation level compared to other studies [r = 0.81398 vs. 0.77 [B. melitensis RNA sample (r = 0.38296). These results differ from another study that found a more complete bacteria global expression profile applying direct amplification on the host:pathogen mixed sample [Modern technology has made gene expression detection of mammalian systems straightforward and robust. However, the study of the transcriptional profile of intracellular bacteria is challenging due to the difficulty of obtaining adequate high quality pathogen RNA free of eukaryotic-RNA for downstream applications. To reduce the interference of host RNA from the heterogeneous population of RNA, we used a commercial kit to significantly reduce the eukaryotic RNA and enrich the ated RNA , the anavs. 0.77  or 0.800vs. 0.77 ) , which select against the host RNA amplification, labeling and hybridization. However, if one desires to eliminate any doubt, one could hybridize in parallel, the untreated infected sample on the pathogen array and disregard from future analysis all those spots that produced a signal in the untreated sample. This novel alternative, which has not been reported in previous studies that have evaluated the in vivo or in vitro transcriptional profile of intracellular bacteria using similar strategies [Another potential concern was that eukaryotic RNA present in the sample may interfere with pathogen gene expression detection. This possibility is remote due to the different levels of filters applied . This table describes the minimal number (89) of primers to specifically anneal to the 3,198 B. melitensis ORFs. Primers were designed using a computer-based algorithm (GDP-Finder) that predicts reverse primers of 8-mer oligonucleotides required to anneal to all ORFs in a given genome.Click here for file