Identification of the best housekeeping gene for RT-qPCR analysis of human pancreatic organoids

In the last few years, there has been a considerable increase in the use of organoids, which is a new three-dimensional culture technology applied in scientific research. The main reasons for their extensive use are their plasticity and multiple applications, including in regenerative medicine and the screening of new drugs. The aim of this study was to better understand these structures by focusing on the choice of the best housekeeping gene (HKG) to perform accurate molecular analysis on such a heterogeneous system. This feature should not be underestimated because the inappropriate use of a HKG can lead to misleading data and incorrect results, especially when the subject of the study is innovative and not totally explored like organoids. We focused our attention on the newly described human pancreatic organoids (hPOs) and compared 12 well-known HKGs (ACTB, B2M, EF1α, GAPDH, GUSB, HPRT, PPIA, RNA18S, RPL13A TBP, UBC and YWHAZ). Four different statistical algorithms (NormFinder, geNorm, BestKeeper and ΔCt) were applied to estimate the expression stability of each HKG, and RefFinder was used to identify the most suitable genes for RT-qPCR data normalization. Our results showed that the intragroup and intergroup comparisons could influence the best choice of the HKG, making clear that the identification of a stable reference gene for accurate and reproducible RT-qPCR data normalization remains a critical issue. In summary, this is the first report on HKGs in human organoids, and this work provides a strong basis to pave the way for further gene analysis in hPOs.

The overexpression of GDNF in nucleus accumbens suppresses alcohol-seeking behavior in group-housed C57Bl/6J female mice

Background Craving for alcohol, in other words powerful desire to drink after withdrawal, is an important contributor to the development and maintenance of alcoholism. Here, we studied the role of GDNF (glial cell line-derived neurotrophic factor) and BDNF (brain-derived neurotrophic factor) on alcohol-seeking behavior in group-housed female mice. Methods We modeled alcohol-seeking behavior in C57Bl/6J female mice. The behavioral experiments in group-housed female mice were performed in an automated IntelliCage system. We conducted RT-qPCR analysis of Gdnf, Bdnf, Manf and Cdnf expression in different areas of the female mouse brain after alcohol drinking conditioning. We injected an adeno-associated virus (AAV) vector expressing human GDNF or BDNF in mouse nucleus accumbens (NAc) after ten days of alcohol drinking conditioning and assessed alcohol-seeking behavior. Behavioral data were analyzed by two-way repeated-measures ANOVA, and statistically significant effects were followed by Bonferroni’s post hoc test. The student’s t-test was used to analyze qPCR data. Results The RT-qPCR data showed that Gdnf mRNA level in NAc was more than four times higher (p < 0.0001) in the mice from the sweetened alcohol group compared to the water group. Our data showed a more than a two-fold decrease in Manf mRNA (p = 0.04) and Cdnf mRNA (p = 0.02) levels in the hippocampus and Manf mRNA in the VTA (p = 0.04) after alcohol consumption. Two-fold endogenous overexpression of Gdnf mRNA and lack of CDNF did not affect alcohol-seeking behavior. The AVV-GDNF overexpression in nucleus accumbens suppressed alcohol-seeking behavior while overexpression of BDNF did not. Conclusions The effect of increased endogenous Gdnf mRNA level in female mice upon alcohol drinking has remained unknown. Our data suggest that an increase in endogenous GDNF expression upon alcohol drinking occurs in response to the activation of another mesolimbic reward pathway participant.

Auto-qPCR; a python-based web app for automated and reproducible analysis of qPCR data

Quantifying changes in DNA and RNA levels is essential in numerous molecular biology protocols. Quantitative real time PCR (qPCR) techniques have evolved to become commonplace, however, data analysis includes many time-consuming and cumbersome steps, which can lead to mistakes and misinterpretation of data. To address these bottlenecks, we have developed an open-source Python software to automate processing of result spreadsheets from qPCR machines, employing calculations usually performed manually. Auto-qPCR is a tool that saves time when computing qPCR data, helping to ensure reproducibility of qPCR experiment analyses. Our web-based app (https://auto-q-pcr.com/) is easy to use and does not require programming knowledge or software installation. Using Auto-qPCR, we provide examples of data treatment, display and statistical analyses for four different data processing modes within one program: (1) DNA quantification to identify genomic deletion or duplication events; (2) assessment of gene expression levels using an absolute model, and relative quantification (3) with or (4) without a reference sample. Our open access Auto-qPCR software saves the time of manual data analysis and provides a more systematic workflow, minimizing the risk of errors. Our program constitutes a new tool that can be incorporated into bioinformatic and molecular biology pipelines in clinical and research labs.

Identification of reference genes for gene expression studies among different developmental stages of murine hearts

Background Real-time quantitative polymerase chain reaction (RT-qPCR) is a widely-used standard assay for assessing gene expression. RT-qPCR data requires reference genes for normalization to make the results comparable. Therefore, the selected reference gene should be highly stable in its expression throughout the experimental datasets. So far, reports about the optimal set of reference genes in murine left ventricle (LV) across embryonic and postnatal stages are few. The objective of our research was to identify the appropriate reference genes in murine LV among different developmental stages. Methods We investigated the gene expression profiles of 21 widely used housekeeping genes in murine LV from 7 different developmental stages (almost throughout the whole period of the mouse lifespan). The stabilities of the potential reference genes were evaluated by five methods: GeNorm, NormFinder, BestKeeper, Delta-Ct and RefFinder. Results We proposed a set of reliable reference genes for normalization of RT-qPCR experimental data in different conditions. Furthermore, our results showed that 6 genes (18S, Hmbs, Ubc, Psmb4, Tfrc and Actb) are not recommended to be used as reference genes in murine LV development studies. The data also suggested that the Rplp0 gene might serve as an optimal reference gene in gene expression analysis. Conclusions Our study investigated the expression stability of the commonly used reference genes in process of LV development and maturation. We proposed a set of optimal reference genes that are suitable for accurate normalization of RT-qPCR data in specific conditions. Our findings may be helpful in future studies for investigating the gene expression patterns and mechanism of mammalian heart development.

Web-based LinRegPCR: application for the visualization and analysis of (RT)-qPCR amplification and melting data

Background The analyses of amplification and melting curves have been shown to provide valuable information on the quality of the individual reactions in quantitative PCR (qPCR) experiments and to result in more reliable and reproducible quantitative results. Implementation The main steps in the amplification curve analysis are (1) a unique baseline subtraction, not using the ground phase cycles, (2) PCR efficiency determination from the exponential phase of the individual reactions, (3) setting a common quantification threshold and (4) calculation of the efficiency-corrected target quantity with the common threshold, efficiency per assay and Cq per reaction. The melting curve analysis encompasses smoothing of the observed fluorescence data, normalization to remove product-independent fluorescence loss, peak calling and assessment of the correct peak by comparing its melting temperature with the known melting temperature of the intended amplification product. Results The LinRegPCR web application provides visualization and analysis of a single qPCR run. The user interface displays the analysis results on the amplification curve analysis and melting curve analysis in tables and graphs in which deviant reactions are highlighted. The annotated results in the tables can be exported for calculation of gene-expression ratios, fold-change between experimental conditions and further statistical analysis. Web-based LinRegPCR addresses two types of users, wet-lab scientists analyzing the amplification and melting curves of their own qPCR experiments and bioinformaticians creating pipelines for analysis of series of qPCR experiments by splitting its functionality into a stand-alone back-end RDML (Real-time PCR Data Markup Language) Python library and several companion applications for data visualization, analysis and interactive access. The use of the RDML data standard enables machine independent storage and exchange of qPCR data and the RDML-Tools assist with the import of qPCR data from the files exported by the qPCR instrument. Conclusions The combined implementation of these analyses in the newly developed web-based LinRegPCR (https://www.gear-genomics.com/rdml-tools/) is platform independent and much faster than the original Windows-based versions of the LinRegPCR program. Moreover, web-based LinRegPCR includes a novel statistical outlier detection and the combination of amplification and melting curve analyses allows direct validation of the amplification product and reporting of reactions that amplify artefacts.

RNA-Seq is not required to determine stable reference genes for qPCR normalization

Assessment of differential gene expression by qPCR is heavily influenced by the choice of reference genes. Although numerous statistical approaches have been proposed to determine the best reference genes, they can give rise to conflicting results depending on experimental conditions. Hence, recent studies propose the use of RNA-Seq to identify stable genes followed by the application of different statistical approaches to determine the best set of reference genes for qPCR data normalization. In this study, we demonstrate that the statistical approach to determine the best reference genes from randomly selected candidates is more important than the preselection of ‘stable’ candidates from RNA-Seq data. Using a qPCR data normalization workflow that we have previously established; we show that qPCR data normalization using randomly chosen conventional reference genes renders the same results as stable reference genes selected from RNA-Seq data. We validated these observations in two distinct cross-sectional experimental conditions involving human iPSC derived microglial cells and mouse sciatic nerves. These results taken together show that given a robust statistical approach for reference gene selection, stable genes selected from RNA-Seq data do not offer any significant advantage over commonly used reference genes for normalizing qPCR assays.

Catechol, a Natural Allelochemical Thwarts Virulent Dimorphism in Candida Albicans and Potentiates the Antifungal Efficacy of Azoles and Polyenes.

The present study was deliberately focused to explore the antivirulence efficacy of a plant allelochemical –catechol against Candida albicans, and attempts were made to elucidate the underlying mechanisms as well. Catechol at its sub-MIC concentrations (2 to 256 μg/mL) exhibited a dose dependent biofilm as well as hyphal inhibitory efficacies, which were ascertained through both light and fluorescence microscopic analyses. Further, sub-MICs of catechol displayed remarkable antivirulence efficacy, as it substantially inhibited C. albicans’ virulence enzymes i.e. secreted hydrolases. Notably, FTIR analysis divulged the potency of catechol in effective loosening of C. albicans’ exopolymeric matrix, which was further reinforced using EPS quantification assay. Although, catechol at BIC (256 μg/mL) did not disrupt the mature biofilms of C. albicans, their initial adherence was significantly impeded by reducing their hydrophobic nature. Besides, FTIR analysis also unveiled the ability of catechol in enhancing the production of farnesol -a metabolite of C. albicans, whose accumulation naturally blocks yeast-hyphal transition. The qPCR data showed significant down-regulation of candidate genes viz., RAS1, HWP1 and ALS3 which are responsible for the regulation of Ras-cAMP-PKA pathway -the pathway that contribute for C. albicans’ pathogenesis. Interestingly, the up-regulation of TUP1 (a gene responsible for farnesol-mediated hyphal inhibition) during catechol exposure strengthen the speculation of catechol triggered farnesol-mediated hyphal inhibition. Furthermore, catechol profusely enhanced the fungicidal efficacy of certain known antifungal agent’s viz., azoles (ketoconazole and miconazole) and polyenes (amphotericin-B and nystatin).

Use and Misuse of Cq in qPCR Data Analysis and Reporting

In the analysis of quantitative PCR (qPCR) data, the quantification cycle (Cq) indicates the position of the amplification curve with respect to the cycle axis. Because Cq is directly related to the starting concentration of the target, and the difference in Cq values is related to the starting concentration ratio, the only results of qPCR analysis reported are often Cq, ΔCq or ΔΔCq values. However, reporting of Cq values ignores the fact that Cq values may differ between runs and machines, and, therefore, cannot be compared between laboratories. Moreover, Cq values are highly dependent on the PCR efficiency, which differs between assays and may differ between samples. Interpreting reported Cq values, assuming a 100% efficient PCR, may lead to assumed gene expression ratios that are 100-fold off. This review describes how differences in quantification threshold setting, PCR efficiency, starting material, PCR artefacts, pipetting errors and sampling variation are at the origin of differences and variability in Cq values and discusses the limits to the interpretation of observed Cq values. These issues can be avoided by calculating efficiency-corrected starting concentrations per reaction. The reporting of gene expression ratios and fold difference between treatments can then easily be based on these starting concentrations.