Interpretation Of qPCR Data Research Articles

Determination of novel reference genes for improving gene expression data normalization in selected canine reproductive tissues \u2013 a multistudy analysis

BackgroundReal time RT-PCR (qPCR) is a useful and powerful tool for quantitative measurement of gene expression. The proper choice of internal standards such as reference genes is crucial for correct data evaluation. In female dogs, as in other species, the reproductive tract is continuously undergoing hormonal and cycle stage-dependent morphological changes, which are associated with altered gene expression. However, there have been few attempts published so far targeted to the dog aimed at determining optimal reference genes for the reproductive organs. Most of these approaches relied on genes previously described in other species. Large-scale transcriptome-based experiments are promising tools for defining potential candidate reference genes, but were never considered in this context in canine research.ResultsHere, using available microarray and RNA-seq datasets derived from reproductive organs (corpus luteum, placenta, healthy and diseased uteri) of dogs, we have performed multistudy analysis to identify the most stably expressed genes for expression studies, in each tissue separately and collectively for different tissues. The stability of newly identified reference genes (EIF4H, KDELR2, KDM4A and PTK2) has been determined and ranked relative to previously used reference genes, i.e., GAPDH, β-actin and cyclophillin A/PPIA, using RefFinder and NormFinder algorithms. Finally, expression of selected target genes (luteal IL-1b and MHCII, placental COX2 and VEGFA, and uterine IGF2 and LHR) was re-evaluated and normalized. All proposed candidate reference genes were more stable, ranked higher and introduced less variation than previously used genes.ConclusionsBased on our analyses, we recommend applying KDM4A and PTK2 for normalization of gene expression in the canine CL and placenta. The inclusion of a third reference gene, EIF4H, is suggested for healthy uteri. With this, the interpretation of qPCR data will be more reliable, allowing better understanding of canine reproductive physiology.

Time to act–assessing variations in qPCR analyses in biological nitrogen removal with examples from partial nitritation/anammox systems

Quantitative PCR (qPCR) is broadly used as the gold standard to quantify microbial community fractions in environmental microbiology and biotechnology. Benchmarking efforts to ensure the comparability of qPCR data for environmental bioprocesses are still scarce. Also, for partial nitritation/anammox (PN/A) systems systematic investigations are still missing, rendering meta-analysis of reported trends and generic insights potentially precarious. We report a baseline investigation of the variability of qPCR-based analyses for microbial communities applied to PN/A systems. Round-robin testing was performed for three PN/A biomass samples in six laboratories, using the respective in-house DNA extraction and qPCR protocols. The concentration of extracted DNA was significantly different between labs, ranged between 2.7 and 328 ng mg−1 wet biomass. The variability among the qPCR abundance data of different labs was very high (1−7 log fold) but differed for different target microbial guilds. DNA extraction caused maximum variation (3–7 log fold), followed by the primers (1–3 log fold). These insights will guide environmental scientists and engineers as well as treatment plant operators in the interpretation of qPCR data.

Viral, bacterial, and protozoan pathogens and fecal markers in wells supplying groundwater to public water systems in Minnesota, USA

Drinking water supply wells can be contaminated by a broad range of waterborne pathogens. However, groundwater assessments frequently measure microbial indicators or a single pathogen type, which provides a limited characterization of potential health risk. This study assessed contamination of wells by testing for viral, bacterial, and protozoan pathogens and fecal markers. Wells supplying groundwater to community and noncommunity public water systems in Minnesota, USA (n = 145) were sampled every other month over one or two years and tested using 23 qPCR assays. Eighteen genetic targets were detected at least once, and microbiological contamination was widespread (96% of 145 wells, 58% of 964 samples). The sewage-associated microbial indicators HF183 and pepper mild mottle virus were detected frequently. Human or zoonotic pathogens were detected in 70% of wells and 21% of samples by qPCR, with Salmonella and Cryptosporidium detected more often than viruses. Samples positive by qPCR for adenovirus (HAdV), enterovirus, or Salmonella were analyzed by culture and for genotype or serotype. qPCR-positive Giardia and Cryptosporidium samples were analyzed by immunofluorescent assay (IFA), and IFA and qPCR concentrations were correlated. Comparisons of indicator and pathogen occurrence at the time of sampling showed that total coliforms, HF183, and Bacteroidales-like HumM2 had high specificity and negative predictive values but generally low sensitivity and positive predictive values. Pathogen-HF183 ratios in sewage have been used to estimate health risks from HF183 concentrations in surface water, but in our groundwater samples Cryptosporidium oocyst:HF183 and HAdV:HF183 ratios were approximately 10,000 times higher than ratios reported for sewage. qPCR measurements provided a robust characterization of microbiological water quality, but interpretation of qPCR data in a regulatory context is challenging because few studies link qPCR measurements to health risk.

Comparison of microbial source tracking efficacy for detection of cattle fecal contamination by quantitative PCR

Identification of fecal contamination sources in surface water has become heavily dependent on quantitative PCR (qPCR) because this technique allows for the rapid enumeration of fecal indicator bacteria as well as the detection and quantification of fecal source-associated genetic markers in the environment. Identification of contamination sources in impaired waters is a prerequisite for developing best management practices to reduce future pollution. Proper management decisions rely on the quality and interpretation of qPCR data. In this study, we developed a method to determine analytical and process lower limits of detection (LLOD) and quantification (LLOQ) using two cattle-associated genetic markers targeting Bacteroidales. Analytical LLOD (ALLOD) for both CowM2 and CowM3 genetic markers in the qPCR assay were five gene copies per reaction. Using composite fecal DNA, the analytical LLOQ (ALLOQ) determined for CowM2 and CowM3 were 78 and 195 gene copies/reaction, respectively. When plasmid DNA was used, the ALLOQ for CowM2 and CowM3 were 46 and 20 gene copies/reaction, respectively. The process LLOD (PLLOD) for CowM2 and CowM3 were 0.4 and 0.02 mg feces/filter (wet weight), respectively. Using the standard deviation value of 0.25 as a cut-off point for LLOQ in regression analysis, the process LLOQ (PLLOQ) for CowM2 and CowM3 were 3.2 and 0.3 mg feces/filter, respectively. These results indicate that CowM3 exhibited superior performance characteristics compared with CowM2 for fecal samples collected from our geographical region. Moreover, the method for calculating LLOD and LLOQ developed here can be applied to other microbial source tracking studies.

A human fecal contamination score for ranking recreational sites using the HF183/BacR287 quantitative real-time PCR method

Human fecal pollution of recreational waters remains a public health concern worldwide. As a result, there is a growing interest in the application of human-associated fecal source identification quantitative real-time PCR (qPCR) technologies for water quality research and management. However, there are currently no standardized approaches for field implementation and interpretation of qPCR data. In this study, a standardized HF183/BacR287 qPCR method was combined with a water sampling strategy and a novel Bayesian weighted average approach to establish a human fecal contamination score (HFS) that can be used to prioritize sampling sites for remediation based on measured human waste levels. The HFS was then used to investigate 975 study design scenarios utilizing different combinations of sites with varying sampling intensities (daily to once per week) and number of qPCR replicates per sample (2–14 replicates). Findings demonstrate that site prioritization with HFS is feasible and that both sampling intensity and number of qPCR replicates influence reliability of HFS estimates. The novel data analysis strategy presented here provides a prescribed approach for the implementation and interpretation of human-associated HF183/BacR287 qPCR data with the goal of site prioritization based on human fecal pollution levels. In addition, information is provided for future users to customize study designs for optimal HFS performance.

QuantGenius: implementation of a decision support system for qPCR-based gene quantification

BackgroundQuantitative molecular biology remains a challenge for researchers due to inconsistent approaches for control of errors in the final results. Due to several factors that can influence the final result, quantitative analysis and interpretation of qPCR data are still not trivial. Together with the development of high-throughput qPCR platforms, there is a need for a tool allowing for robust, reliable and fast nucleic acid quantification.ResultsWe have developed “quantGenius” (http://quantgenius.nib.si), an open-access web application for a reliable qPCR-based quantification of nucleic acids. The quantGenius workflow interactively guides the user through data import, quality control (QC) and calculation steps. The input is machine- and chemistry–independent. Quantification is performed using the standard curve approach, with normalization to one or several reference genes. The special feature of the application is the implementation of user-guided QC-based decision support system, based on qPCR standards, that takes into account pipetting errors, assay amplification efficiencies, limits of detection and quantification of the assays as well as the control of PCR inhibition in individual samples. The intermediate calculations and final results are exportable in a data matrix suitable for further statistical analysis or visualization. We additionally compare the most important features of quantGenius with similar advanced software tools and illustrate the importance of proper QC system in the analysis of qPCR data in two use cases.ConclusionsTo our knowledge, quantGenius is the only qPCR data analysis tool that integrates QC-based decision support and will help scientists to obtain reliable results which are the basis for biologically meaningful data interpretation.