Related Articles

Background

Hairy root cultures produced via Agrobacterium rhizogenes-mediated transformation have emerged as practical biological models to elucidate the biosynthesis of specialized metabolites. To effectively understand the expression patterns of the genes involved in the metabolic pathways of these compounds, reference genes need to be systematically validated under specific experimental conditions as established by the MIQE (Minimum Information for Publication of Quantitative Real-Time PCR Experiments) guidelines. In the present report we describe the first validation of reference genes for RT-qPCR in hairy root cultures of peanut which produce stilbenoids upon elicitor treatments.

Results

A total of 21 candidate reference genes were evaluated. Nineteen genes were selected based on previous qPCR studies in plants and two were from the T-DNAs transferred from A. rhizogenes. Nucleotide sequences of peanut candidate genes were obtained using their homologous sequences in Arabidopsis. To identify the suitable primers, calibration curves were obtained for each candidate reference gene. After data analysis, 12 candidate genes meeting standard efficiency criteria were selected. The expression stability of these genes was analyzed using geNorm and NormFinder algorithms and a ranking was established based on expression stability of the genes. Candidate reference gene expression was shown to have less variation in methyl jasmonate (MeJA) treated root cultures than those treated with sodium acetate (NaOAc).

Conclusions

This work constitutes the first effort to validate reference genes for RT-qPCR in hairy roots. While these genes were selected under conditions of NaOAc and MeJA treatment, we anticipate these genes to provide good targets for reference genes for hairy roots under a variety of stress conditions. The lead reference genes were a gene encoding for a TATA box binding protein (TBP2) and a gene encoding a ribosomal protein (RPL8C). A commonly used reference gene GAPDH showed low stability of expression suggesting that its use may lead to inaccurate gene expression profiles when used for data normalization in stress-stimulated hairy roots. Likewise the A. rhizogenes transgene rolC showed less expression stability than GAPDH. This study proposes that a minimum of two reference genes should be used for a normalization procedure in gene expression profiling using elicited hairy roots.

Background

Quantitative PCR (qPCR) is a widely used technique for gene expression analysis. A common normalization method for accurate qPCR data analysis involves stable reference genes to determine relative gene expression. Despite extensive research in the forest tree species Populus, there is not a resource for reference genes that meet the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) standards for qPCR techniques and analysis. Since Populus is a woody perennial species, studies of seasonal changes in gene expression are important towards advancing knowledge of this important developmental and physiological trait. The objective of this study was to evaluate reference gene expression stability in various tissues and growth conditions in two important Populus genotypes (P. trichocarpa “Nisqually 1” and P. tremula x P. alba 717 1-B4) following MIQE guidelines.

Results

We evaluated gene expression stability in shoot tips, young leaves, mature leaves and bark tissues from P. trichocarpa and P. tremula. x P. alba grown under long-day (LD), short-day (SD) or SD plus low-temperatures conditions. Gene expression data were analyzed for stable reference genes among 18S rRNA, ACT2, CDC2, CYC063, TIP4-like, UBQ7, PT1 and ANT using two software packages, geNormPLUS and BestKeeper. GeNormPLUS ranked TIP4-like and PT1 among the most stable genes in most genotype/tissue combinations while BestKeeper ranked CDC2 and ACT2 among the most stable genes.

Conclusions

This is the first comprehensive evaluation of reference genes in two important Populus genotypes and the only study in Populus that meets MIQE standards. Both analysis programs identified stable reference genes in both genotypes and all tissues grown under different photoperiods. This set of reference genes was found to be suitable for either genotype considered here and may potentially be suitable for other Populus species and genotypes. These results provide a valuable resource for the Populus research community.

Background

Fungal load quantification is a critical component of fungal community analyses. Limitation of current approaches for quantifying the fungal component in the human microbiome suggests the need for new broad-coverage techniques.

Methods

We analyzed 2,085 18S rRNA gene sequences from the SILVA database for assay design. We generated and quantified plasmid standards using a qPCR-based approach. We evaluated assay coverage against 4,968 sequences and performed assay validation following the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines.

Results

We designed FungiQuant, a TaqMan® qPCR assay targeting a 351 bp region in the fungal 18S rRNA gene. Our in silico analysis showed that FungiQuant is a perfect sequence match to 90.0% of the 2,617 fungal species analyzed. We showed that FungiQuant’s is 100% sensitive and its amplification efficiencies ranged from 76.3% to 114.5%, with r2-values of >0.99 against the 69 fungal species tested. Additionally, FungiQuant inter- and intra-run coefficients of variance ranged from <10% and <20%, respectively. We further showed that FungiQuant has a limit of quantification 25 copies and a limit of detection at 5 copies. Lastly, by comparing results from human-only background DNA with low-level fungal DNA, we showed that amplification in two or three of a FungiQuant performed in triplicate is statistically significant for true positive fungal detection.

Conclusions

FungiQuant has comprehensive coverage against diverse fungi and is a robust quantification and detection tool for delineating between true fungal detection and non-target human DNA.

The conclusions of thousands of peer-reviewed publications rely on data obtained using fluorescence-based quantitative real-time PCR technology. However, the inadequate reporting of experimental detail, combined with the frequent use of flawed protocols is leading to the publication of papers that may not be technically appropriate. We take the view that this problem requires the delineation of a more transparent and comprehensive reporting policy from scientific journals. This editorial aims to provide practical guidance for the incorporation of absolute minimum standards encompassing the key assay parameters for accurate design, documentation and reporting of qPCR experiments (MIQE précis) and guidance on the publication of pure 'reference gene' articles.

Motivation: Quantitative real-time polymerase chain reaction (qPCR) is routinely used for RNA expression profiling, validation of microarray hybridization data and clinical diagnostic assays. Although numerous statistical tools are available in the public domain for the analysis of microarray experiments, this is not the case for qPCR. Proprietary software is typically provided by instrument manufacturers, but these solutions are not amenable to the tandem analysis of multiple assays. This is problematic when an experiment involves more than a simple comparison between a control and treatment sample, or when many qPCR datasets are to be analyzed in a high-throughput facility.

Results: We have developed HTqPCR, a package for the R statistical computing environment, to enable the processing and analysis of qPCR data across multiple conditions and replicates.

Availability: HTqPCR and user documentation can be obtained through Bioconductor or at http://www.ebi.ac.uk/bertone/software.

Contact: bertone@ebi.ac.uk

Background

Many proteomics initiatives require integration of all information with uniformcriteria from collection of samples and data display to publication of experimental results. The integration and exchanging of these data of different formats and structure imposes a great challenge to us. The XML technology presents a promise in handling this task due to its simplicity and flexibility. Nasopharyngeal carcinoma (NPC) is one of the most common cancers in southern China and Southeast Asia, which has marked geographic and racial differences in incidence. Although there are some cancer proteome databases now, there is still no NPC proteome database.

Results

The raw NPC proteome experiment data were captured into one XML document with Human Proteome Markup Language (HUP-ML) editor and imported into native XML database Xindice. The 2D/MS repository of NPC proteome was constructed with Apache, PHP and Xindice to provide access to the database via Internet. On our website, two methods, keyword query and click query, were provided at the same time to access the entries of the NPC proteome database.

Conclusion

Our 2D/MS repository can be used to share the raw NPC proteomics data that are generated from gel-based proteomics experiments. The database, as well as the PHP source codes for constructing users' own proteome repository, can be accessed at .

Background

The vaginal microbiome plays an important role in urogenital health. Quantitative real time Polymerase Chain Reaction (qPCR) assays for the most prevalent vaginal Lactobacillus species and bacterial vaginosis species G. vaginalis and A. vaginae exist, but qPCR information regarding variation over time is still very limited. We set up qPCR assays for a selection of seven species and defined the temporal variation over three menstrual cycles in a healthy Caucasian population with a normal Nugent score. We also explored differences in qPCR data between these healthy women and an ‘at risk’ clinic population of Caucasian, African and Asian women with and without bacterial vaginosis (BV), as defined by the Nugent score.

Results

Temporal stability of the Lactobacillus species counts was high with L. crispatus counts of 108 copies/mL and L. vaginalis counts of 106 copies/mL. We identified 2 types of ‘normal flora’ and one ‘BV type flora’ with latent class analysis on the combined data of all women. The first group was particularly common in women with a normal Nugent score and was characterized by a high frequency of L. crispatus, L. iners, L. jensenii, and L. vaginalis and a correspondingly low frequency of L. gasseri and A. vaginae. The second group was characterized by the predominance of L. gasseri and L. vaginalis and was found most commonly in healthy Caucasian women. The third group was commonest in women with a high Nugent score but was also seen in a subset of African and Asian women with a low Nugent score and was characterized by the absence of Lactobacillus species (except for L. iners) but the presence of G. vaginalis and A. vaginae.

Conclusions

We have shown that the quantification of specific bacteria by qPCR contributes to a better description of the non-BV vaginal microbiome, but we also demonstrated that differences in populations such as risk and ethnicity also have to be taken into account. We believe that our selection of indicator organisms represents a feasible strategy for the assessment of the vaginal microbiome and could be useful for monitoring the microbiome in safety trials of vaginal products.

Reverse transcription quantitative real-time PCR (RT-qPCR) is a key method for measurement of relative gene expression. Analysis of RT-qPCR data requires many iterative computations for data normalization and analytical optimization. Currently no computer program for RT-qPCR data analysis is suitable for analytical optimization and user-controllable customization based on data quality, experimental design as well as specific research aims. Here I introduce an all-in-one computer program, SASqPCR, for robust and rapid analysis of RT-qPCR data in SAS. This program has multiple macros for assessment of PCR efficiencies, validation of reference genes, optimization of data normalizers, normalization of confounding variations across samples, and statistical comparison of target gene expression in parallel samples. Users can simply change the macro variables to test various analytical strategies, optimize results and customize the analytical processes. In addition, it is highly automatic and functionally extendable. Thus users are the actual decision-makers controlling RT-qPCR data analyses. SASqPCR and its tutorial are freely available at http://code.google.com/p/sasqpcr/downloads/list.

In quantitative single-cell studies, the critical part is the low amount of nucleic acids present and the resulting experimental variations. In addition biological data obtained from heterogeneous tissue are not reflecting the expression behaviour of every single-cell. These variations can be derived from natural biological variance or can be introduced externally. Both have negative effects on the quantification result. The aim of this study is to make quantitative single-cell studies more transparent and reliable in order to fulfil the MIQE guidelines at the single-cell level. The technical variability introduced by RT, pre-amplification, evaporation, biological material and qPCR itself was evaluated by using RNA or DNA standards. Secondly, the biological expression variances of GAPDH, TNFα, IL-1β, TLR4 were measured by mRNA profiling experiment in single lymphocytes. The used quantification setup was sensitive enough to detect single standard copies and transcripts out of one solitary cell. Most variability was introduced by RT, followed by evaporation, and pre-amplification. The qPCR analysis and the biological matrix introduced only minor variability. Both conducted studies impressively demonstrate the heterogeneity of expression patterns in individual cells and showed clearly today's limitation in quantitative single-cell expression analysis.

Background

Telomeres, the protective cap of chromosomes, have emerged as powerful markers of biological age and life history in model and non-model species. The qPCR method for telomere length estimation is one of the most common methods for telomere length estimation, but has received recent critique for being too error-prone and yielding unreliable results. This critique coincides with an increasing awareness of the potentials and limitations of the qPCR technique in general and the proposal of a general set of guidelines (MIQE) for standardization of experimental, analytical, and reporting steps of qPCR. In order to evaluate the utility of the qPCR method for telomere length estimation in non-model species, we carried out four different qPCR assays directed at humpback whale telomeres, and subsequently performed a rigorous quality control to evaluate the performance of each assay.

Results

Performance differed substantially among assays and only one assay was found useful for telomere length estimation in humpback whales. The most notable factors causing these inter-assay differences were primer design and choice of using singleplex or multiplex assays. Inferred amplification efficiencies differed by up to 40% depending on assay and quantification method, however this variation only affected telomere length estimates in the worst performing assays.

Conclusion

Our results suggest that seemingly well performing qPCR assays may contain biases that will only be detected by extensive quality control. Moreover, we show that the qPCR method for telomere length estimation can be highly precise and accurate, and thus suitable for telomere measurement in non-model species, if effort is devoted to optimization at all experimental and analytical steps. We conclude by highlighting a set of quality controls which may serve for further standardization of the qPCR method for telomere length estimation, and discuss some of the factors that may cause variation in qPCR experiments.

The Guideline Elements Model (GEM) was developed in 2000 to organize the information contained in clinical practice guidelines using XML and to represent guideline content in a form that can be understood by human readers and processed by computers. In this work, we systematically reviewed the literature to better understand how GEM was being used, potential barriers to its use, and suggestions for improvement. Fifty external and twelve internally produced publications were identified and analyzed. GEM was used most commonly for modeling and ontology creation. Other investigators applied GEM for knowledge extraction and data mining, for clinical decision support for guideline generation. The GEM Cutter software—used to markup guidelines for translation into XML— has been downloaded 563 times since 2000. Although many investigators found GEM to be valuable, others critiqued its failure to clarify guideline semantics, difficulties in markup, and the fact that GEM files are not usually executable.

Quantitative real-time PCR (qPCR) is a commonly used validation tool for confirming gene expression results obtained from microarray analysis; however, microarray and qPCR data often result in disagreement. The current study assesses factors contributing to the correlation between these methods in five separate experiments employing two-color 60-mer oligonucleotide microarrays and qPCR using SYBR green. Overall, significant correlation was observed between microarray and qPCR results (ρ=0.708, p<0.0001, n=277) using these platforms. The contribution of factors including up- vs. down-regulation, spot intensity, ρ-value, fold-change, cycle threshold (Ct), array averaging, tissue type, and tissue preparation was assessed. Filtering of microarray data for measures of quality (fold-change and ρ-value) proves to be the most critical factor, with significant correlations of ρ>0.80 consistently observed when quality scores are applied.

Background

Real-time quantitative PCR (qPCR) is a method for rapid and reliable quantification of mRNA transcription. Internal standards such as reference genes are used to normalise mRNA levels between different samples for an exact comparison of mRNA transcription level. Selection of high quality reference genes is of crucial importance for the interpretation of data generated by real-time qPCR.

Results

In this study nine commonly used reference genes were investigated in 17 different pig tissues using real-time qPCR with SYBR green. The genes included beta-actin (ACTB), beta-2-microglobulin (B2M), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), hydroxymethylbilane synthase (HMBS), hypoxanthine phosphoribosyltransferase 1 (HPRT1), ribosomal protein L4 (RPL4), succinate dehydrogenase complex subunit A (SDHA), TATA box binding protein (TPB)and tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein zeta polypeptide (YWHAZ). The stability of these reference genes in different pig tissues was investigated using the geNorm application. The range of expression stability in the genes analysed was (from the most stable to the least stable): ACTB/RPL4, TBP, HPRT, HMBS, YWHAZ, SDHA, B2M and GAPDH.

Conclusion

Expression stability varies greatly between genes. ACTB, RPL4, TPB and HPRT1 were found to have the highest stability across tissues. Based on both expression stability and expression level, our data suggest that ACTB and RPL4 are good reference genes for high abundant transcripts while TPB and HPRT1 are good reference genes for low abundant transcripts in expression studies across different pig tissues.

The ImmunoDeficiency Resource (IDR), freely available at http://www.uta.fi/imt/bioinfo/idr/, is a comprehensive knowledge base on immunodeficiencies. It is designed for different user groups such as researchers, physicians and nurses as well as patients and their families and the general public. Information on immunodeficiencies is stored as fact files, which are disease- and gene-based information resources. We have developed an inherited disease markup language (IDML) data model, which is designed for storing disease- and gene-specific data in extensible markup language (XML) format. The fact files written by the IDML can be used to present data in different contexts and platforms. All the information in the IDR is validated by expert curators.

Background

Real-time quantitative RT-PCR (qPCR) is a powerful technique capable of accurately quantitating mRNA expression levels over a large dynamic range. This makes qPCR the most widely used method for studying quantitative gene expression. An important aspect of qPCR is selecting appropriate controls or normalization factors to account for any differences in starting cDNA quantities between samples during expression studies. Here, we report on the selection of a concise set of housekeeper genes for the accurate normalization of quantitative gene expression data in differentiating osteoblasts, osteoclasts and macrophages. We implemented the use of geNorm, an algorithm that determines the suitability of genes to function as housekeepers by assessing expression stabilities. We evaluated the expression stabilities of 18S, ACTB, B2M, GAPDH, HMBS and HPRT1 genes.

Findings

Our analyses revealed that 18S and GAPDH were regulated during osteoblast differentiation and are not suitable for use as reference genes. The most stably expressed genes in osteoblasts were ACTB, HMBS and HPRT1 and their geometric average constitutes a suitable normalization factor upon which gene expression data can be normalized. In macrophages, 18S and GAPDH were the most variable genes while HMBS and B2M were the most stably expressed genes. The geometric average of HMBS and B2M expression levels forms a suitable normalization factor to account for potential differences in starting cDNA quantities during gene expression analysis in macrophages. The expression stabilities of the six candidate reference genes in osteoclasts were, on average, more variable than that observed in macrophages but slightly less variable than those seen in osteoblasts. The two most stably expressed genes in osteoclasts were HMBS and B2M and the genes displaying the greatest levels of variability were 18S and GAPDH. Notably, 18S and GAPDH were the two most variably expressed control genes in all three cell types. The geometric average of HMBS, B2M and ACTB creates an appropriate normalization factor for gene expression studies in osteoclasts.

Conclusion

We have identified concise sets of genes suitable to use as normalization factors for quantitative real-time RT-PCR gene expression studies in osteoblasts, osteoclasts and macrophages.

Background

Normalization of target gene expression, measured by real-time quantitative PCR (qPCR), is a requirement for reducing experimental bias and thereby improving data quality. The currently used normalization approach is based on using one or more reference genes. Yet, this approach extends the experimental work load and suffers from assumptions that may be difficult to meet and to validate.

Results

We developed a data driven normalization algorithm (NORMA-Gene). An analysis of the performance of NORMA-Gene compared to reference gene normalization on artificially generated data-sets showed that the NORMA-Gene normalization yielded more precise results under a large range of parameters tested. Furthermore, when tested on three very different real qPCR data-sets NORMA-Gene was shown to be best at reducing variance due to experimental bias in all three data-sets compared to normalization based on the use of reference gene(s).

Conclusions

Here we present the NORMA-Gene algorithm that is applicable to all biological and biomedical qPCR studies, especially those that are based on a limited number of assayed genes. The method is based on a data-driven normalization and is useful for as little as five target genes comprising the data-set. NORMA-Gene does not require the identification and validation of reference genes allowing researchers to focus their efforts on studying target genes of biological relevance.

Background

As real-time quantitative PCR (RT-QPCR) is increasingly being relied upon for the enforcement of legislation and regulations dependent upon the trace detection of DNA, focus has increased on the quality issues related to the technique. Recent work has focused on the identification of factors that contribute towards significant measurement uncertainty in the real-time quantitative PCR technique, through investigation of the experimental design and operating procedure. However, measurement uncertainty contributions made during the data analysis procedure have not been studied in detail. This paper presents two additional approaches for standardising data analysis through the novel application of statistical methods to RT-QPCR, in order to minimise potential uncertainty in results.

Results

Experimental data was generated in order to develop the two aspects of data handling and analysis that can contribute towards measurement uncertainty in results. This paper describes preliminary aspects in standardising data through the application of statistical techniques to the area of RT-QPCR. The first aspect concerns the statistical identification and subsequent handling of outlying values arising from RT-QPCR, and discusses the implementation of ISO guidelines in relation to acceptance or rejection of outlying values. The second aspect relates to the development of an objective statistical test for the comparison of calibration curves.

Conclusion

The preliminary statistical tests for outlying values and comparisons between calibration curves can be applied using basic functions found in standard spreadsheet software. These two aspects emphasise that the comparability of results arising from RT-QPCR needs further refinement and development at the data-handling phase. The implementation of standardised approaches to data analysis should further help minimise variation due to subjective judgements. The aspects described in this paper will help contribute towards the development of a set of best practice guidelines regarding standardising handling and interpretation of data arising from RT-QPCR experiments.

Quantitative real-time reverse-transcription PCR (RT-qPCR) is presently the method of choice for validating gene expression results from high-density microarrays. However, the low throughput of traditional gene-by-gene RT-qPCR makes this process labor intensive and time consuming. To accelerate this laborious task, the SuperArray RT²Profiler PCR Array combines SYBR Green–based real-time RT-qPCR technology with a multi-gene array plate format to simultaneously analyze a panel of genes related to a specific disease or biological pathway.

Each assay on the PCR array plate has been experimentally validated to insure gene-specific amplification. The reliability and reproducibility of the RT²Profiler PCR Array have been demonstrated by DNA sequencing and intra/inter-laboratory reproducibility comparisons. The DNA sequencing demonstrated 100% of the PCR products amplified from the correct target genes. In one laboratory, individual PCR assays produced a standard deviation of 0.24 cycles and a coefficient of variance of 0.92% in technical replicates. The correlation coefficient for Ct values between replicate runs was 0.997 and for fold changes (ΔΔCt) across thermocyclers was 0.976. Comparisons between two different laboratories using different thermocyclers showed correlation coefficients of 0.972 and 0.976 for ΔCt and ΔΔCt, respectively. Each PCR array also includes stringent controls to monitor RNA quality by assessing reverse transcription efficiency and genomic DNA contamination to ensure the reliability of the PCR array data.

A practical application for the PCR array was demonstrated by identifying human pancreatic tumor–associated genes using the Cancer PathwayFinder RT²Profiler PCR Array. Results showed 23 genes exhibiting a statistically significant threefold or greater change in expression between a human pancreatic tumor and normal pancreas, including many genes previously linked to pancreatic cancer. Hence, the RT2 Profiler PCR Array system offers a simple, reliable, and convenient tool for multi-gene profiling and microarray data validation.

In order to confirm a microscopic diagnosis of ‘eperythrozoonosis’ made over 40 years ago in a captive owl monkey (Aotus trivirgatus), DNA was extracted from archived fixed and stained blood smears and subjected to generic haemotropic mycoplasma (haemoplasma) quantitative real-time PCR (qPCR) and a human glyceraldehyde-3-phosphate dehydrogenase qPCR as an amplification control. The qPCRs confirmed the extraction of host DNA from the samples and the presence of a haemoplasma species. Partial 16S rRNA and ribonuclease P ribosomal gene fragments were amplified by PCR, cloned and sequenced. Sequence data and phylogeny showed the owl monkey haemoplasma to lie in the haemominutum clade of haemoplasmas, most closely related to ‘Candidatus Mycoplasma kahaneii’. This study confirms the use of generic haemoplasma qPCRs to successfully amplify haemoplasma DNA from fixed, stained and archived blood smears from the early 1970s and provides molecular confirmation of the existence of a novel haemoplasma species in an owl monkey, for which the name ‘Candidatus Mycoplasma aoti’ sp. nov. is proposed.

Background

In real-time RT quantitative PCR (qPCR) the accuracy of normalized data is highly dependent on the reliability of the reference genes (RGs). Failure to use an appropriate control gene for normalization of qPCR data may result in biased gene expression profiles, as well as low precision, so that only gross changes in expression level are declared statistically significant or patterns of expression are erroneously characterized. Therefore, it is essential to determine whether potential RGs are appropriate for specific experimental purposes. Aim of this study was to identify and validate RGs for use in the differentiation of normal and tumor lung expression profiles.

Methods

A meta-analysis of lung cancer transcription profiles generated with the GeneChip technology was used to identify five putative RGs. Their consistency and that of seven commonly used RGs was tested by using Taqman probes on 18 paired normal-tumor lung snap-frozen specimens obtained from non-small-cell lung cancer (NSCLC) patients during primary curative resection.

Results

The 12 RGs displayed showed a wide range of Ct values: except for rRNA18S (mean 9.8), the mean values of all the commercial RGs and ESD ranged from 19 to 26, whereas those of the microarray-selected RGs (BTF-3, YAP1, HIST1H2BC, RPL30) exceeded 26. RG expression stability within sample populations and under the experimental conditions (tumour versus normal lung specimens) was evaluated by: (1) descriptive statistic; (2) equivalence test; (3) GeNorm applet. All these approaches indicated that the most stable RGs were POLR2A, rRNA18S, YAP1 and ESD.

Conclusion

These data suggest that POLR2A, rRNA18S, YAP1 and ESD are the most suitable RGs for gene expression profile studies in NSCLC. Furthermore, they highlight the limitations of commercial RGs and indicate that meta-data analysis of genome-wide transcription profiling studies may identify new RGs.

Objective

The authors present an Electronic Healthcare Record (EHR) server, designed and developed as a proof of concept of the revised prEN13606:2005 European standard concerning EHR communications.

Methods

The development of the server includes five modules: the libraries for the management of the standard reference model, for the demographic package and for the data types; the permanent storage module, built on a relational database; two communication interfaces through which the clients can send information or make queries; the XML (eXtensible Markup Language) process module; and the tools for the validation of the extracts managed, implemented on a defined XML-Schema.

Results

The server was subjected to four phases of trials, the first three with ad hoc test data and processes to ensure that each of the modules complied with its specifications and that the interaction between them provided the expected functionalities. The fourth used real extracts generated by other research groups for the additional purpose of testing the validity of the standard in real-world scenarios.

Conclusion

The acceptable performance of the server has made it possible to include it as a middleware service in a platform for the out-of-hospital follow-up and monitoring of patients with chronic heart disease which, at the present time, supports pilot projects and clinical trials for the evaluation of eHealth services.

In radiology departments with multiple geographically separated reporting areas, locating radiologists can be challenging. We have developed an in-house solution to minimise the time spent looking for radiologists utilising near real-time data stored with our radiology information system (RIS). An auto updating Extensible Markup Language (XML) data feed from our RIS provider provides information about users logged into the RIS. It includes user names, their contact details and specialty interests, their location within the department, and a time stamp of last recorded dictation or report verification. The information is then displayed on our internal intranet and on a self-refreshing screen in our main department corridor. In order to estimate time savings made through the tools creation, usage statistics were calculated and combined with assessments of time taken to find a named radiologist prior to the tools implementation. Over the month of April 2009, there were 2,798 hits on the locator page. Radiologists were responsible for 1,248 hits and radiology administration staff for 1,550 hits. The average time for using the tool was calculated at 5 s. Reviewing a roster and calling/paging a radiologist took on average 30 s, and a full walk around of the department took 195 s. Through utilisation of near real-time data available within our RIS system and display of these data in an accessible form, we have developed a tool that has realised savings of up to 16 h of radiologist reporting time per week.

Background

Quantitative real-time RT-PCR (RT-qPCR) has become a valuable molecular technique in basic and translational biomedical research, and is emerging as an equally valuable clinical tool. Correlation of inter-sample values requires data normalization, which can be accomplished by various means, the most common of which is normalization to internal, stably expressed, reference genes. Recently, such traditionally utilized reference genes as GAPDH and B2M have been found to be regulated in various circumstances in different tissues, emphasizing the need to identify genes independent of factors influencing the tissue, and that are stably expressed within the experimental milieu. In this study, we identified genes for normalization of RT-qPCR data for invasive breast cancer (IBC), with special emphasis on estrogen receptor positive (ER+) IBC, but also examined their applicability to ER- IBC, normal breast tissue and breast cancer cell lines.

Methods

The reference genes investigated by qRT-PCR were RPLP0, TBP, PUM1, ACTB, GUS-B, ABL1, GAPDH and B2M. Biopsies of 18 surgically-excised tissue specimens (11 ER+ IBCs, 4 ER- IBCs, 3 normal breast tissues) and 3 ER+ cell lines were examined and the data analyzed by descriptive statistics, geNorm and NormFinder. In addition, the expression of selected reference genes in laser capture microdissected ER+ IBC cells were compared with that of whole-tissue.

Results

A group of 3 genes, TBP, RPLP0 and PUM1, were identified for both the combined group of human tissue samples (ER+ and ER- IBC and normal breast tissue) and for the invasive cancer samples (ER+ and ER- IBC) by GeNorm, where NormFinder consistently identified PUM1 at the single best gene for all sample combinations.

Conclusion

The reference genes of choice when performing RT-qPCR on normal and malignant breast specimens should be either the collected group of 3 genes (TBP, RPLP0 and PUM1) employed as an average, or PUM1 as a single gene.

Background

Quantitative real-time PCR (qPCR) is a commonly used technique to quantify gene expression levels. Validated normalization is essential to obtain reliable qPCR data. In that context, normalizing to multiple reference genes has become the most popular method. However, expression of reference genes may vary per tissue type, developmental stage and in response to experimental treatment. It is therefore imperative to determine stable reference genes for a specific sample set and experimental model. The present study was designed to validate potential reference genes in hippocampal tissue from rats that had experienced early-life febrile seizures (FS). To this end, we applied an established model in which FS were evoked by exposing 10-day old rat pups to heated air. One week later, we determined the expression stability of seven frequently used reference genes in the hippocampal dentate gyrus.

Results

Gene expression stability of 18S rRNA, ActB, GusB, Arbp, Tbp, CycA and Rpl13A was tested using geNorm and Normfinder software. The ranking order of reference genes proposed by geNorm was not identical to that suggested by Normfinder. However, both algorithms indicated CycA, Rpl13A and Tbp as the most stable genes, whereas 18S rRNA and ActB were found to be the least stably expressed genes.

Conclusions

Our data demonstrate that the geometric averaging of at least CycA, Rpl13A and Tbp allows reliable interpretation of gene expression data in this experimental set-up. The results also show that ActB and 18S rRNA are not suited as reference genes in this model.

Background

Quantitative real-time PCR (qPCR) has been the method of choice for the quantification of mRNA. Due to the various artifactual factors that may affect the accuracy of qPCR, internal reference genes are most often used to normalize qPCR data. Recently, many studies have employed computer programs such as GeNorm, BestKeeper and NormFinder in selecting reference genes, but very few statistically validate the outcomes of these programs. Thus, in this study, we selected reference genes for qPCR of liver and ovary samples of yellow (juvenile), migratory (silver) and 11-KT treated juveniles of New Zealand shortfinned eels (Anguilla australis) using the three computer programs and validate the selected genes statistically using REST 2009 software and the Mann-Whitney test. We also tested for the repeatability of use for the best reference genes by applying them to a data set obtained in a similar experiment conducted the previous year.

Results

Out of six candidate genes, the combination of 18 s and eef1 was found to be the best statistically validated reference for liver, while in ovary it was l36. However, discrepancies in gene rankings were found between the different programs. Also, statistical validation procedures showed that several genes put forward as being the best by the programs were in fact, regulated, making them unsuitable as reference genes. Additionally, eef1 which was found to be a suitable - though not the top ranked - reference gene for liver tissues in one year, was regulated in another.

Conclusions

Our study highlights the need for external validations of reference gene selections made by computer programs. Researchers need to be vigilant in validating and reporting the rationale for the use of reference gene in published studies.