Statistical models in assessing fold change of gene expression in real-time RT-PCR experiments

Fu, Wenjiang; Hu, Jianbo; Spencer, Thomas E.; Carroll, Raymond J.; Wu, Guoyao

doi:10.1016/j.compbiolchem.2005.10.005

Cited by 64 publications

(54 citation statements)

References 4 publications

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“…79,2011 HOST, PATHOGEN, AND MEASURES OF SUSCEPTIBILITY 2373 logarithmus dualis (log 2 ) scale, which assumes an optimum PCR efficiency (E) of 2. When applicable, 95% confidence intervals (CI) were computed with a lower limit of 2 Ϫ⌬⌬CT Ϫ Z1.960SEM and an upper limit of 2 Ϫ⌬⌬CT ϩ Z1.960SEM , where Ϫ⌬⌬CT Ϯ Z 1.960 SEM denotes the upper and lower confidence limits of the change in Ϫ⌬⌬CT, SEM is the standard error of the mean change Ϫ⌬⌬CT, and Z 1.960 is the 95th percentile of the standard normal distribution (16). Differences in Ifng, Il12b, and Il4 expression levels were analyzed by a three-way ANOVA model with unequal variances.…”

Section: Animalsmentioning

confidence: 99%

Joint Effects of Host Genetic Background and Mycobacterial Pathogen on Susceptibility to Infection

et al. 2011

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The present study examined the differential contribution of host genetic background and mycobacterial pathogen variability to biological and mechanistic phenotypes of infection. For this purpose, A/J and C57BL/6J mice were infected intravenously with a low dose of Mycobacterium tuberculosis H37Rv or the Russia, Japan, and Pasteur substrains of Mycobacterium bovis bacille Calmette-Guérin (BCG). The pulmonary bacterial counts (number of CFU) and transcript levels of select cytokines (e.g., Ifng, Il12b, and Il4) at 1, 3, and 6 weeks postinfection were measured as biological and mechanistic phenotypes, respectively. The individual and combined impact of the host and mycobacteria on these phenotypes was assessed using three-way analysis of variance (ANOVA), which partitions phenotypic variation into host, pathogen, time, and interaction effects. All phenotypes, except pulmonary Il4 transcript levels, displayed evidence for host-mycobacterium specificity by means of significant interaction terms. Pulmonary expression profiles of 34 chemokines and chemokine-related genes were compared across the hosts and mycobacteria. The differences in induction of these immune messenger genes between A/J and C57BL/6J mice were modest and generally failed to reach significance. In contrast, the mycobacteria induced significant variance in a subset of the immune messenger genes, which was more evident in A/J mice relative to that in C57BL/6J mice. Overall, the results demonstrated the importance of considering the joint effects of the mycobacterial and host genetic backgrounds on susceptibility to mycobacterial infections.Exposure of humans to Mycobacterium tuberculosis, the cause of tuberculosis, induces a highly variable response. Among persons exposed to M. tuberculosis, only 30 to 50% become infected during an outbreak, and of those infected, only approximately 10% develop clinical disease. There is now clear evidence for the important impact of host genetic factors on this variable response (3,11,12). Evidence for an equally important role of M. tuberculosis strain variability is emerging (reviewed in references 18 and 35). A univariate view would suggest that those who develop disease either display low resistance to M. tuberculosis or are infected with high-virulence strains of M. tuberculosis. An alternate multivariate view is that M. tuberculosis and its human hosts have coevolved such that host susceptibility and pathogen virulence might better be considered in combination. A specific M. tuberculosis isolate may cause disease in certain hosts but not in others, while a specific host may be susceptible to certain M. tuberculosis isolates but resistant to others. The extent to which multivariate interactions are decisive for tuberculosis susceptibility has far-reaching consequences for tuberculosis control efforts, including vaccine development. Although studies have examined the impact of host background and mycobacterial type separately, strategies that focus on the simultaneous analysis of host and mycobacteria in disease e...

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Section: Animalsmentioning

confidence: 99%

Joint Effects of Host Genetic Background and Mycobacterial Pathogen on Susceptibility to Infection

et al. 2011

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“…Generalized estimating equations (GEE) were used to model the real-time qRT-PCR data. 20 For each miRNA, a GEE model was fit to estimate the effect of the covariate (obstetric condition or gestational age) on the observed expression using the gee package in R statistical software,21 specifying an exchangeable correlation structure (every pair of technical replicates has the same correlation). A P value was derived to assess the significance of the covariate into the model, and the resulting P values were adjusted using the false discovery rate (FDR) correction.…”

Section: Rna Isolationmentioning

confidence: 99%

Differential expression of microRNAs with progression of gestation and inflammation in the human chorioamniotic membranes

Montenegro

Romero

Pineles

et al. 2007

American Journal of Obstetrics and Gynecology

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Objective-The aim of this study was to identify differential expression of microRNAs (miRNAs) in chorioamniotic membranes with advancing gestation, labor, and inflammation.Study design-Expression profiles of 157 miRNAs in the chorioamniotic membranes were obtained from patients in the following groups: 1) term not in labor (n=10); 2) term in labor (n=10); 3) preterm labor with histologic chorioamnionitis (n=9); and 4) without histologic chorioamnionitis (n=10).Results-More than 95% of the miRNAs screened were expressed. Gestational age-dependent changes in expression were observed for 13 miRNAs. No differences in miRNA expression were observed between women without labor and women in labor. Membranes with chorioamnionitis displayed increased expression of miR-223 and miR-338. Gene Ontology analysis of genes targeted by differentially expressed miRNAs revealed enrichment for specific biological process categories.Conclusion-Chorioamniotic membranes with advancing gestational age and chorioamnionitis are associated with the differential expression of a subset of miRNAs.

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“…The more target there is in the starting material, the earlier the instrument can detect the fluorescence and the lower the C t . This correlation between fluorescence and amount of amplified product permits accurate quantification of target molecules over a wide dynamic range and the homogeneous format significantly reduces hands-on time and the risk of contamination [10].The consistency and reliability of RT-qPCR assays depend on the proper execution of a number of steps, in particular those relating to sample selection, template quality, assay design and data analysis [11], as well as the correct application of statistical models and methods for data analysis [12]. If correct procedure is adhered to, then results are generally robust, reproducible and accurate.…”

mentioning

confidence: 99%

“…The consistency and reliability of RT-qPCR assays depend on the proper execution of a number of steps, in particular those relating to sample selection, template quality, assay design and data analysis [11], as well as the correct application of statistical models and methods for data analysis [12]. If correct procedure is adhered to, then results are generally robust, reproducible and accurate.…”

mentioning

confidence: 99%

Real‐Time Quantitative RT‐PCR for mRNA Profiling

Bustin

Nolan²

2010

Genomics

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The real-time reverse transcription quantitative polymerase chain reaction (RT-qPCR) [1] is characterized by its specificity, sensitivity, simplicity and speed. These attributes have made it the method of choice for the detection and quantification of RNA [2, 3], and have effected its extensive use in biotechnology [4], microbiology [5], virology [6] and molecular medicine [7] applications. The assay involves a conventional reverse transcription (RT) procedure, followed by the qPCR step, which makes use of fluorescent reporter molecules to combine the amplification and detection components of the PCR in a single tube format [8,9]. An increase in fluorescent signal is proportional to the amount of DNA produced during each PCR cycle and produces a characteristic threshold cycle (C t ) or crossing point (C p ) for every reaction. The C t or C p is defined as the PCR cycle at which the signal first rises above background fluorescence. The more target there is in the starting material, the earlier the instrument can detect the fluorescence and the lower the C t . This correlation between fluorescence and amount of amplified product permits accurate quantification of target molecules over a wide dynamic range and the homogeneous format significantly reduces hands-on time and the risk of contamination [10].The consistency and reliability of RT-qPCR assays depend on the proper execution of a number of steps, in particular those relating to sample selection, template quality, assay design and data analysis [11], as well as the correct application of statistical models and methods for data analysis [12]. If correct procedure is adhered to, then results are generally robust, reproducible and accurate. Specifically, the reliability of the RT-qPCR assay is Genomics: Essential M ethodsEdite d by Mike S ta r ke y a nd R a mna th Ela swa r a pu

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Statistical models in assessing fold change of gene expression in real-time RT-PCR experiments

Cited by 64 publications

References 4 publications

Joint Effects of Host Genetic Background and Mycobacterial Pathogen on Susceptibility to Infection

Joint Effects of Host Genetic Background and Mycobacterial Pathogen on Susceptibility to Infection

Differential expression of microRNAs with progression of gestation and inflammation in the human chorioamniotic membranes

Real‐Time Quantitative RT‐PCR for mRNA Profiling

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