Selection of reference genes for quantitative PCR: identifying reference genes for airway epithelial cells exposed to Pseudomonas aeruginosa

Identification of stable reference genes for relative quantification of long RNA expression in urinary extracellular vesicles

Urinary extracellular vesicles (uEVs) are rich in valuable biomolecule information which are increasingly recognized as potential biomarkers for various diseases. uEV long RNAs are among the critical cargos capable of providing unique transcriptome information of the source cells. However, consensus regarding ideal reference genes for relative long RNAs quantification in uEVs is not available as of date. Here we explored stable reference genes through profiling the long RNA expression by RNA-seq following unsupervised analysis and validation studies. Candidate reference genes were identified using four algorithms: NormFinder, GeNorm, BestKeeper and the Delta Ct method, followed by validation. RNA profile showed uEVs contained abundant long RNAs information and the core transcriptome was related to cellular structures, especially ribosome which functions mainly as translation, protein and RNA binding molecules. Analysis of RNA-seq data identified RPL18A, RPL11, RPL27, RACK1, RPSA, RPL41, H1-2, RPL4, GAPDH, RPS27A as candidate reference genes. RT-qPCR validation revealed that RPL41, RPSA and RPL18A were reliable reference genes for long RNA quantification in uEVs from patients with diabetes mellitus (DM), diabetic nephropathy (DN), IgA nephropathy (IgAN) and prostate cancer (PCA). Interestingly, RPL41 also outperformed traditional reference genes in renal tissues of DN and IgAN, as well as in plasma EVs of several types of cancers. The stable reference genes identified in this study may facilitate development of uEVs as novel biomarkers and increase the accuracy and comparability of biomarker studies.

A Strategy for the Selection of RT-qPCR Reference Genes Based on Publicly Available Transcriptomic Datasets

In the next-generation sequencing era, RT-qPCR is still widely employed to quantify levels of nucleic acids of interest due to its popularity, versatility, and limited costs. The measurement of transcriptional levels through RT-qPCR critically depends on reference genes used for normalization. Here, we devised a strategy to select appropriate reference genes for a specific clinical/experimental setting based on publicly available transcriptomic datasets and a pipeline for RT-qPCR assay design and validation. As a proof-of-principle, we applied this strategy to identify and validate reference genes for transcriptional studies of bone-marrow plasma cells from patients with AL amyloidosis. We performed a systematic review of published literature to compile a list of 163 candidate reference genes for RT-qPCR experiments employing human samples. Next, we interrogated the Gene Expression Omnibus to assess expression levels of these genes in published transcriptomic studies on bone-marrow plasma cells from patients with different plasma cell dyscrasias and identified the most stably expressed genes as candidate normalizing genes. Experimental validation on bone-marrow plasma cells showed the superiority of candidate reference genes identified through this strategy over commonly employed “housekeeping” genes. The strategy presented here may apply to other clinical and experimental settings for which publicly available transcriptomic datasets are available.

Reference Gene Selection for Gene Expression Analyses in Mouse Models of Acute Lung Injury

qRT-PCR still remains the most widely used method for quantifying gene expression levels, although newer technologies such as next generation sequencing are becoming increasingly popular. A critical, yet often underappreciated, problem when analysing qRT-PCR data is the selection of suitable reference genes. This problem is compounded in situations where up to 25% of all genes may change (e.g., due to leukocyte invasion), as is typically the case in ARDS. Here, we examined 11 widely used reference genes for their suitability in commonly used models of acute lung injury (ALI): ventilator-induced lung injury (VILI), in vivo and ex vivo, lipopolysaccharide plus mechanical ventilation (MV), and hydrochloric acid plus MV. The stability of reference gene expression was determined using the NormFinder, BestKeeper, and geNorm algorithms. We then proceeded with the geNorm results because this is the only algorithm that provides the number of reference genes required to achieve normalisation. We chose interleukin-6 (Il-6) and C-X-C motif ligand 1 (Cxcl-1) as the genes of interest to analyse and demonstrate the impact of inappropriate normalisation. Reference gene stability differed between the ALI models and even within the subgroup of VILI models, no common reference gene index (RGI) could be determined. NormFinder, BestKeeper, and geNorm produced slightly different, but comparable results. Inappropriate normalisation of Il-6 and Cxcl1 gene expression resulted in significant misinterpretation in all four ALI settings. In conclusion, choosing an inappropriate normalisation strategy can introduce different kinds of bias such as gain or loss as well as under- or overestimation of effects, affecting the interpretation of gene expression data.

Let-7b-5p in vesicles secreted by human airway cells reduces biofilm formation and increases antibiotic sensitivity of P. aeruginosa

Pseudomonas aeruginosa is an opportunistic pathogen that forms antibiotic-resistant biofilms, which facilitate chronic infections in immunocompromised hosts. We have previously shown that P. aeruginosa secretes outer-membrane vesicles that deliver a small RNA to human airway epithelial cells (AECs), in which it suppresses the innate immune response. Here, we demonstrate that interdomain communication through small RNA-containing membrane vesicles is bidirectional and that microRNAs (miRNAs) in extracellular vesicles (EVs) secreted by human AECs regulate protein expression, antibiotic sensitivity, and biofilm formation by P. aeruginosa Specifically, human EVs deliver miRNA let-7b-5p to P. aeruginosa, which systematically decreases the abundance of proteins essential for biofilm formation, including PpkA and ClpV1-3, and increases the ability of beta-lactam antibiotics to reduce biofilm formation by targeting the beta-lactamase AmpC. Let-7b-5p is bioinformatically predicted to target not only PpkA, ClpV1, and AmpC in P. aeruginosa but also the corresponding orthologs in Burkholderia cenocepacia, another notorious opportunistic lung pathogen, suggesting that the ability of let-7b-5p to reduce biofilm formation and increase beta-lactam sensitivity is not limited to P. aeruginosa Here, we provide direct evidence for transfer of miRNAs in EVs secreted by eukaryotic cells to a prokaryote, resulting in subsequent phenotypic alterations in the prokaryote as a result of this interdomain communication. Since let-7-family miRNAs are in clinical trials to reduce inflammation and because chronic P. aeruginosa lung infections are associated with a hyperinflammatory state, treatment with let-7b-5p and a beta-lactam antibiotic in nanoparticles or EVs may benefit patients with antibiotic-resistant P. aeruginosa infections.