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Background RNA sequencing allows the measuring of gene expression at a resolution unmet by expression arrays or RT-qPCR. It is however necessary to normalize sequencing data by library size, transcript size and composition, among other factors, before comparing expression levels. The use of internal control genes or spike-ins is advocated in the literature for scaling read counts, but the methods for choosing reference genes are mostly targeted at RT-qPCR studies and require a set of pre-selected candidate controls or pre-selected target genes. Results Here, we report an R-based pipeline to select internal control genes based solely on read counts and gene sizes. This novel method first normalizes the read counts to Transcripts per Million (TPM) and then excludes weakly expressed genes using the DAFS script to calculate the cut-off. It then selects as references the genes with lowest TPM coefficient of variation. We used this method to pick custom reference genes for the differential expression analysis of three transcriptome sets from transgenic Arabidopsis plants expressing heterologous fungal effector proteins tagged with GFP (using GFP alone as the control). The custom reference genes showed lower coefficient of variation and fold change as well as a broader range of expression levels than commonly used reference genes. When analyzed with NormFinder, both typical and custom reference genes were considered suitable internal controls, but the custom selected genes were more stably expressed. geNorm produced a similar result in which most custom selected genes ranked higher (i.e. were more stably expressed) than commonly used reference genes. Conclusions The proposed method is innovative, rapid and simple. Since it does not depend on genome annotation, it can be used with any organism, and does not require pre-selected reference candidates or target genes that are not always available.

Similar to other types of cancer, acidification of tumor microenvironment is an important feature of osteosarcoma, and a major source of cellular stress that triggers cancer aggressiveness, drug resistance, and progression. Among the different effects of low extracellular pH on tumor cells, we have recently found that short-term exposure to acidosis strongly affects gene expression. This alteration might also occur for the most commonly used housekeeping genes (HKG), thereby causing erroneous interpretation of RT-qPCR data. On this basis, by using osteosarcoma cells cultured at different pH values, we aimed to identify the ideal HKG to be considered in studies on tumor-associated acidosis. We verified the stability of 15 commonly used HKG through five algorithms (NormFinder, geNorm, BestKeeper, ΔCT, coefficient of variation) and found that no universal HKG is suitable, since at least four HKG are necessary for proper normalization. Furthermore, according to the acceptable range of values, YWHAZ, GAPDH, GUSB, and 18S rRNA were the most stable reference genes at different pH. Our results will be helpful for future investigations focusing on the effect of altered microenvironment on cancer behavior, particularly on the effectiveness of anticancer therapies in acid conditions.

Housekeeping genes are commonly used as reference genes for analyzing expression data. We introduce an algorithm that scores and ranks genes based on their expression ubiquity using three criteria: the proportion of samples with high expression, overall expression levels, and expression invariance. The resulting continuous and configurable measure of gene ubiquity overcomes the limitations of current categorical classifications. Using this measure, we systematically evaluate how genes traditionally assumed to be ubiquitously expressed (so-called stable housekeeping genes) respond to drugs. By integrating expression data from the Genotype-Tissue Expression portal (physiological conditions) and the Connectivity Map (drug effects), we identify subsets of genes that maintain strong invariance under chemical stimuli, rendering them particularly suitable as reference genes. At the same time, we show that many seemingly ubiquitous genes are strongly influenced by commonly used drugs. Our findings open up new conceptual perspectives on drug mechanisms and cellular regulatory responses and they call into question the reliability of several classic housekeeping genes. To facilitate broader application of these results, we present an interactive web interface ( https://ubigen.uni-rostock.de ) that allows researchers to explore gene rankings, adjust scoring criteria and analyze specific gene sets. This platform facilitates the selection of robust, ubiquitously expressed reference genes for experimental applications and deepens our understanding of transcriptional regulation under physiological and chemically perturbed conditions.

The present study evaluated the stability of candidate reference genes during adipogenic differentiation of 3T3-L1 cells cultured on different extracellular matrices. The aim was to investigate the effects of matrix composition and differentiation stage on the expression of candidate housekeeping genes and to compare validation strategies in dynamic in vitro models. Eleven candidate reference genes ( , , , , , , , , , , and ) were analyzed by RT-qPCR in 3T3-L1 cells cultured on TC, collagen, gelatin, and Matrigel at Days 7 and 14 of differentiation. Gene stability was assessed using geNorm, NormFinder, RefFinder, comparative ΔCt, BestKeeper, generalized linear model (GLM), linear mixed model (LMM), and correlation analyses with the adipogenic markers and . The results demonstrated that the expression of most housekeeping genes was influenced by matrix composition, differentiation stage, or their interaction. and exhibited the strongest condition-dependent variability and pronounced matrix sensitivity. and showed significant correlations with both and , while correlated with , suggesting that these reference genes may not be fully independent of adipogenic status. demonstrated markedly contrasting rankings across analytical approaches, highlighting limitations of single-method stability assessment. The findings confirm that universal housekeeping genes are unlikely to exist across different matrix conditions and differentiation stages. The results highlight the need for multi-level validation strategies and experimentally validated normalization panels to minimize normalization bias and avoid misleading RT-qPCR expression profiles. Functional validation identified and as the most suitable two-gene normalization panel for the experimental model evaluated, whereas remained a strong complementary reference gene candidate.

Lactiplantibacillus plantarum is a lactic acid bacterium often isolated from a wide variety of niches. Its ubiquity can be explained by a large, flexible genome that helps it adapt to different habitats. The consequence of this is great strain diversity, which may make their identification difficult. Accordingly, this review provides an overview of molecular techniques, both culture-dependent, and culture-independent, currently used to detect and identify L. plantarum. Some of the techniques described can also be applied to the analysis of other lactic acid bacteria.

Synovial tissue plays a key role in osteoarthritis (OA) pathogenesis. Gene expression analysis is widely used to investigate underlying pathomechanisms; however, accurate normalization of target mRNA expression depends on the use of stable reference genes. This study evaluated the suitability of common reference genes for determining synovial tissue mRNA expression, considering various factors. In the synovial tissue of 20 patients with end-stage OA, the stability of the expression of 10 reference genes ( GAPDH , RPLP0 , YWHAZ , TBP , PPIA , EEF1A1 , ACTB , HRPT1 , SDHA , and RPL13A ) was evaluated using four different analysis methods (NormFinder, geNorm, the ΔCt method, and Pearson correlation analysis). The most stable genes were identified by means of a subsequent ranking analysis. Both the entire sample pool and various subgroups (sex, age, BMI, and synovitis score) were considered. RPL13A , PPIA , and EEF1A1 were identified as the most stable reference genes overall, with minor variability across subgroups. In contrast, GAPDH proved to be the least suitable reference gene, showing the most variable expression. The stability of the reference gene might be affected by sex, age, and BMI and this should be taken into account.

Background Real-time quantitative PCR (RT-qPCR) is a widely used method to investigate gene expression in neuroscience studies. Accurate relative quantification of RT-qPCR requires the selection of reference genes that are stably expressed across the experimental conditions and tissues of interest. While RT-qPCR is often performed to investigate gene expression changes during neurodevelopment, few studies have examined the expression stability of commonly used reference genes in the developing mouse cortex. Results Here, we evaluated the stability of five housekeeping genes, Actb , Gapdh , B2m , Rpl13a , and Hprt , in cortical tissue from mice at embryonic day 15 to postnatal day 0 to identify optimal reference genes with stable expression during late corticogenesis. The expression stability was assessed using five computational algorithms: BestKeeper, geNorm, NormFinder, DeltaCt, and RefFinder. Our results showed that B2m , Gapdh , and Hprt , or a combination of B2m/Gapdh and B2m/Hprt , were the most stably expressed genes or gene pairs. In contrast, Actb and Rpl13a were the least stably expressed. Conclusion This study identifies B2m , Gapdh , and Hprt as suitable reference genes for relative quantification in RT-qPCR-based cortical development studies spanning the period of embryonic day 15 to postnatal day 0.

The 4T1 model is extensively employed in murine studies to elucidate the mechanisms underlying the carcinogenesis of triple-negative breast cancer. Molecular biology serves as a cornerstone in these investigations. However, accurate gene expression analyses necessitate data normalization employing housekeeping genes (HKGs) to avert spurious results. Here, we initially delve into the characteristics of the tumor evolution induced by 4T1 in mice, underscoring the imperative for additional tools for tumor monitoring and assessment methods for tracking the animals, thereby facilitating prospective studies employing this methodology. Subsequently, leveraging various software platforms, we assessed ten distinct HKGs (GAPDH, 18 S, ACTB, HPRT1, B2M, GUSB, PGK1, CCSER2, SYMPK, ANKRD17) not hitherto evaluated in the 4T1 breast cancer model, across tumors and diverse tissues afflicted by metastasis. Our principal findings underscore GAPDH as the optimal HKG for gene expression analyses in tumors, while HPRT1 emerged as the most stable in the liver and CCSER2 in the lung. These genes demonstrated consistent expression and minimal variation among experimental groups. Furthermore, employing these HKGs for normalization, we assessed TNF-α and VEGF expression in tissues and discerned significant disparities among groups. We posit that this constitutes the inaugural delineation of an ideal HKG for experiments utilizing the 4T1 model, particularly in vivo settings.

In the wake of recent progress of high throughput transcriptome profiling technologies, extensive housekeeping gene mining has been conducted in humans. However, very few studies have been reported in maize (Zea mays L.), an important crop plant, and none were conducted on a genome -wide level. In this study, we surveyed housekeeping genes throughout the maize transcriptome using RNA-seq and microarray techniques, and validated the housekeeping profile with quantitative polymerase chain reaction (qPCR) under a series of conditions including different genotypes and nitrogen supplies. Seven microarray datasets and two RNA-seq libraries representing 40 genotypes at more than 20 developmental stages were selected to screen for commonly expressed genes. A total of 1,661 genes showed constitutive expression in both microarray and RNA-seq datasets, serving as our starting housekeeping gene candidates. To determine for stably expressed housekeeping genes, NormFinder was used to select the top 20 % invariable genes to be the more likely candidates, which resulted in 48 and 489 entries from microarray and RNA-seq data, respectively. Among them, nine genes (2OG-Fe, CDK, DPP9, DUF, NAC, RPN, SGT1, UPF1 and a hypothetical protein coding gene) were expressed in all 40 maize diverse genotypes tested covering 16 tissues at more than 20 developmental stages under normal and stress conditions, implying these as being the most reliable reference genes. qPCR analysis confirmed the stable expression of selected reference gene candidates compared to two widely used housekeeping genes. All the reference gene candidates showed higher invariability than ACT and GAPDH. The hypothetical protein coding gene exhibited the most stable expression across 26 maize lines with different nitrogen treatments with qPCR, followed by CDK encoding the cyclin-dependent kinase. As the first study to systematically screen for housekeeping genes in maize, we identified candidates by examining the transcriptome atlas generated from RNA-seq and microarray technologies. The nine top-ranked qPCR-validated novel housekeeping genes provide a valuable resource of reference genes for maize gene expression analysis.

Rodents are commonly used as animal models in studies investigating various experimental conditions, often requiring gene expression analysis. Quantitative real-time reverse transcription PCR (RT-qPCR) is the most widely used tool to quantify target gene expression levels under different experimental conditions in various biological samples. Relative normalization with reference genes is a crucial step in RT-qPCR to obtain reliable quantification results. In this work, the main reference genes used in gene expression studies among the three rodents commonly employed in scientific research—hamster, rat, and mouse—are analyzed and described. An individual literature search for each rodent was conducted using specific search terms in three databases: PubMed, Scopus, and Web of Science. A total of 157 articles were selected (rats = 73, mice = 79, and hamsters = 5), identifying various reference genes. The most commonly used reference genes were analyzed according to each rodent, sample type, and experimental condition evaluated, revealing a great variability in the stability of each gene across different samples and conditions. Classic genes, which are expected to be stably expressed in both samples and conditions analyzed, demonstrated greater variability, corroborating existing concerns about the use of these genes. Therefore, this review provides important insights for researchers seeking to identify suitable reference genes for their validation studies in rodents.