Normalization strategies for mRNA expression data in cartilage research

A comparative assessment of reference genes in mouse brown adipocyte differentiation and thermogenesis in vitro

Adipogenic differentiation and thermogenesis in brown adipose tissue (BAT) undergo dynamic processes, altering phenotypes and gene expressions. Proper reference genes in gene expression analysis are crucial to mitigate experimental variances and ensure PCR efficacy. Unreliable reference genes can lead to erroneous gene expression quantification, resulting in data misinterpretation. This study focused on identifying suitable reference genes for mouse brown adipocyte research, utilizing brown adipocytes from the Ucp1-luciferase ThermoMouse model. Comparative analysis of gene expression data under adipogenesis and thermogenesis conditions was conducted, validating 13 housekeeping genes through various algorithms, including DeltaCq, BestKeeper, geNorm, Normfinder, and RefFinder. Tbp and Rer1 emerged as optimal references for Ucp1 and Pparg expression in brown adipogenesis, while Tbp and Ubc were ideal for the expression analysis of these target genes in thermogenesis. Conversely, certain conventional references, including Actb, Tubb5, and Gapdh, proved unstable as reference genes under both conditions. These findings stress the critical consideration of reference gene selection in gene expression analysis within specific biological systems to ensure accurate conclusions.

Urinary Microbiome Dysbiosis and Immune Dysregulations as Potential Diagnostic Indicators of Bladder Cancer

There are a total of 82,290 new cases and 16,710 deaths estimated for bladder cancer in the United States in 2023. Currently, urine cytology tests are widely used for bladder cancer diagnosis, though they suffer from variable sensitivity, ranging from 45 to 97%. More recently, the microbiome has become increasingly recognized for its role in human diseases, including cancers. This study attempts to characterize urinary microbiome bladder cancer-specific dysbiosis to explore its diagnostic potential. RNA-sequencing data of urine samples from patients with bladder cancer (n = 18) and matched controls (n = 12) were mapped to bacterial sequences to yield species-level abundance approximations. Urine samples were analyzed at both the population and species level to reveal dysbiosis associated with bladder cancer. A panel of 35 differentially abundant species was discovered, which may be useful as urinary biomarkers for this disease. We further assessed whether these species were of similar significance in a validation dataset (n = 81), revealing that the genera Escherichia, Acinetobacter, and Enterobacter were consistently differentially abundant. We discovered distinct patterns of microbial-associated immune modulation in these samples. Several immune pathways were found to be significantly enriched with respect to the abundance of these species, including antigen processing and presentation, cytosolic DNA sensing, and leukocyte transendothelial migration. Differential cytokine activity was similarly observed, suggesting the urinary microbiome's correlation to immune modulation. The adherens junction and WNT signaling pathways, both implicated in the development and progression of bladder cancer, were also enriched with these species. Our findings indicate that the urinary microbiome may reflect both microbial and immune dysregulations of the tumor microenvironment in bladder cancer. Given the potential biomarker species identified, the urinary microbiome may provide a non-invasive, more sensitive, and more specific diagnostic tool, allowing for the earlier diagnosis of patients with bladder cancer.

Identification of Culprit Genes for Different Diseases by Analyzing Microarray Data

The identification of disease-causing genes is the first and most important step toward understanding the biological mechanisms underlying a disease. Microarray analysis is one such powerful method that is widely used to identify genes that are expressed differently in two or more conditions (disease vs. normal). Because of its large library of statistical R packages and user-friendly interface, the R programming language provides a platform for microarray analysis. In this chapter, we will go over how to identify disease-causing culprit genes from the raw microarray data, using various packages of R programming. The pipeline overviews the steps in microarray analysis, such as data pre-processing, normalization, and statistical analysis using visualization techniques such as heatmaps, box plots, and so on. To better understand the function of the altered genes, gene ontology and pathway analysis are performed.

Screening and identification of potential biomarkers for pancreatic cancer: An integrated bioinformatics analysis

Pancreatic cancer is one of the highly invasive and the seventh most common cause of death among cancers worldwide. To identify essential genes and the involved mechanisms in pancreatic cancer, we used bioinformatics analysis to identify potential biomarkers for pancreatic cancer management. Gene expression profiles of pancreatic cancer patients and normal tissues were screened and downloaded from The Cancer Genome Atlas (TCGA) bioinformatics database. The Differentially expressed genes (DEGs) were identified among gene expression signatures of normal and pancreatic cancer, using R software. Then, enrichment analysis of the DEGs, including Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, was performed by an interactive and collaborative HTML5 gene list enrichment analysis tool (enrichr) and ToppGene. The protein-protein interaction (PPI) network was also constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) database and ToppGenet web based tool followed by identifying hub genes of the top 100 DEGs in pancreatic cancer using Cytoscape software. Over 2000 DEGs with variable log2 fold (LFC) were identified among 34,706 genes. Principal component analysis showed that the top 20 DEGs, including H1-4, H1-5, H4C3, H4C2, RN7SL2, RN7SL3, RN7SL4P, RN7SKP80, SCARNA12, SCARNA10, SCARNA5, SCARNA7, SCARNA6, SCARNA21, SCARNA9, SCARNA13, SNORA73B, SNORA53, SNORA54 might distinguish pancreatic cancer from normal tissue. GO analysis showed that the top DEGs have more enriched in the negative regulation of gene silencing, negative regulation of chromatin organization, negative regulation of chromatin silencing, nucleosome positioning, regulation of chromatin silencing, and nucleosomal DNA binding. KEGG analysis identified an association between pancreatic cancer and systemic lupus erythematosus, alcoholism, neutrophil extracellular trap formation, and viral carcinogenesis. In PPI network analysis, we found that the different types of histone-encoding genes are involved as hub genes in the carcinogenesis of pancreatic cancer. In conclusion, our bioinformatics analysis identified genes that were significantly related to the prognosis of pancreatic cancer patients. These genes and pathways could serve as new potential prognostic markers and be used to develop treatments for pancreatic cancer patients.

Habitual nappers and non-nappers differ in circadian rhythms of LIPE expression in abdominal adipose tissue explants

Background and purpose

Napping is a widespread practice worldwide and has in recent years been linked to increased abdominal adiposity. Lipase E or LIPE encodes the protein hormone-sensitive lipase (HSL), an enzyme that plays an important role in lipid mobilization and exhibits a circadian expression rhythm in human adipose tissue. We hypothesized that habitual napping may impact the circadian expression pattern of LIPE, which in turn may attenuate lipid mobilization and induce abdominal fat accumulation.

Methods

Abdominal adipose tissue explants from participants with obesity (n = 17) were cultured for a 24-h duration and analyzed every 4 h. Habitual nappers (n = 8) were selected to match non-nappers (n = 9) in age, sex, BMI, adiposity, and metabolic syndrome traits. Circadian LIPE expression rhythmicity was analyzed using the cosinor method.

Results

Adipose tissue explants exhibited robust circadian rhythms in LIPE expression in non-nappers. In contrast, nappers had a flattened rhythm. LIPE amplitude was decreased in nappers as compared with non-nappers (71% lower). The decrease in amplitude among nappers was related to the frequency of napping (times per week) where a lower rhythm amplitude was associated with a higher napping frequency (r = -0.80; P = 0.018). Confirmatory analyses in the activity of LIPE's protein (i.e., HSL) also showed a significant rhythm in non-nappers, whereas significance in the activity of HSL was lost among nappers.

Conclusion

Our results suggest that nappers display dysregulated circadian LIPE expression as well as dysregulated circadian HSL activity, which may alter lipid mobilization and contribute to increased abdominal obesity in habitual nappers.

Comparison of Data Normalization Strategies for Array-Based MicroRNA Profiling Experiments and Identification and Validation of Circulating MicroRNAs as Endogenous Controls in Hypertension

Introduction: MicroRNAs are small noncoding RNAs with potential regulatory roles in hypertension and drug response. The presence of many of these RNAs in biofluids has spurred investigation into their role as possible biomarkers for use in precision approaches to healthcare. One of the major challenges in clinical translation of circulating miRNA biomarkers is the limited replication across studies due to lack of standards for data normalization techniques for array-based approaches and a lack of consensus on an endogenous control normalizer for qPCR-based candidate miRNA profiling studies.

Methods: We conducted genome-wide profiling of 754 miRNAs in baseline plasma of 36 European American individuals with uncomplicated hypertension selected from the PEAR clinical trial, who had been untreated for hypertension for at least one month prior to sample collection. After appropriate quality control with amplification score and missingness filters, we tested different normalization strategies such as normalization with global mean of imputed and unimputed data, mean of restricted set of miRNAs, quantile normalization, and endogenous control miRNA normalization to identify the method that best reduces the technical/experimental variability in the data. We identified best endogenous control candidates with expression pattern closest to the mean miRNA expression in the sample, as well as by assessing their stability using a combination of NormFinder, geNorm, Best Keeper and Delta Ct algorithms under the Reffinder software. The suitability of the four best endogenous controls was validated in 50 hypertensive African Americans from the same trial with reverse-transcription–qPCR and by evaluating their stability ranking in that cohort.

Results: Among the compared normalization strategies, quantile normalization and global mean normalization performed better than others in terms of reducing the standard deviation of miRNAs across samples in the array-based data. Among the four strongest candidate miRNAs from our selection process (miR-223-3p, 19b, 106a, and 126-5p), miR-223-3p and miR-126-5p were consistently expressed with the best stability ranking in the validation cohort. Furthermore, the combination of miR-223-3p and 126-5p showed better stability ranking when compared to single miRNAs.

Conclusion: We identified quantile normalization followed by global mean normalization to be the best methods in reducing the variance in the data. We identified the combination of miR-223-3p and 126-5p as potential endogenous control in studies of hypertension.

Circulating miRNA expression over the course of colorectal cancer treatment

Colorectal cancer (CRC) is the third-most common cancer type in males and the second-most common cancer type in females, and has the second-highest overall mortality rate worldwide. Approximately 50% of patients in stage I–III develop metastases, mostly localized to the liver. All physiological conditions occurring in the organism are also reflected in the levels of circulating microRNAs (miRNAs/miRs) in patients. miRNAs are a class of small, non-coding, single-stranded RNAs consisting of 18–25 nucleotides, which have important roles in various cellular processes. The aim of the present study was to evaluate a panel of seven circulating miRNAs (miR-106a-5p, miR-210-5p, miR-155-5p, miR-21-5p, miR-103a-3p, miR-191-5p and miR-16-5p) as biomarkers for monitoring patients undergoing adjuvant treatment of CRC. Total RNA was extracted from the plasma of patients with CRC prior to surgery, in the early post-operative period (n=60) and 3 months after surgery (n=14). The levels of the selected circulating miRNAs were measured with the miRCURY LNA miRNA PCR system and fold changes were calculated using the standard ∆∆Cq method. DIANA-miRPath analysis was used to evaluate the role of significantly deregulated miRNAs. The results indicated significant upregulation of miR-155-5p, miR-21-5p and miR-191-5p, and downregulation of miR-16-5p directly after the surgery. In paired follow-up samples, the most significant upregulation was detected for miR-106a-5p and miR-16-5p, and the most significant downregulation was for miR-21-5p. Pathway analysis outlined the role of the differentially expressed miRNAs in cancer development, but the same pathways are also involved in wound healing and regeneration of intestinal epithelium. It may be suggested that these processes should also be considered in studies investigating sensitive and easily detectable circulating biomarkers for recurrence in patients.

Simultaneous Improvement in the Precision, Accuracy, and Robustness of Label-free Proteome Quantification by Optimizing Data Manipulation Chains

The label-free proteome quantification (LFQ) is multistep workflow collectively defined by quantification tools and subsequent data manipulation methods that has been extensively applied in current biomedical, agricultural, and environmental studies. Despite recent advances, in-depth and high-quality quantification remains extremely challenging and requires the optimization of LFQs by comparatively evaluating their performance. However, the evaluation results using different criteria (precision, accuracy, and robustness) vary greatly, and the huge number of potential LFQs becomes one of the bottlenecks in comprehensively optimizing proteome quantification. In this study, a novel strategy, enabling the discovery of the LFQs of simultaneously enhanced performance from thousands of workflows (integrating 18 quantification tools with 3,128 manipulation chains), was therefore proposed. First, the feasibility of achieving simultaneous improvement in the precision, accuracy, and robustness of LFQ was systematically assessed by collectively optimizing its multistep manipulation chains. Second, based on a variety of benchmark datasets acquired by various quantification measurements of different modes of acquisition, this novel strategy successfully identified a number of manipulation chains that simultaneously improved the performance across multiple criteria. Finally, to further enhance proteome quantification and discover the LFQs of optimal performance, an online tool (https://idrblab.org/anpela/) enabling collective performance assessment (from multiple perspectives) of the entire LFQ workflow was developed. This study confirmed the feasibility of achieving simultaneous improvement in precision, accuracy, and robustness. The novel strategy proposed and validated in this study together with the online tool might provide useful guidance for the research field requiring the mass-spectrometry-based LFQ technique.

Systematic evaluation of repeatability of IR-MALDESI-MS and normalization strategies for correcting the analytical variation and improving image quality

Mass spectrometry imaging is a powerful tool widely used in biological, clinical, and forensic research, but its often poor repeatability limits its application for quantitative and large-scale analysis. A systematic evaluation of infrared matrix-assisted laser desorption electrospray ionization mass spectrometry (IR-MALDESI-MS) repeatability in absolute ion abundances during short- and long-term experiments was carried out on liver slices from the same rat with minimal biological variability to be expected. Results of median %RSDs ranging from 14 to 45, pooled %RMADs ranging from 11 to 33, and Pearson correlation coefficients ranging from 0.83 to 1.00 demonstrated an acceptable repeatability of IR-MALDESI-MS. Normalization is commonly applied for the purpose of accounting for analytical variability of spectra generated from different runs so as to reveal real biological differences. Nine data normalization strategies were performed on the rat liver data sets to examine their effects on reducing analytical variation, and further on a hen ovary data set containing more morphological features for the investigation of their impact on ion images. Results demonstrated that the majority of normalization approaches benefit data quality to some extent, and local normalization methods significantly outperform their global counterparts, resulting in a reduction of median %RSD up to 22. Local median normalization was found to be promisingly robust for both homogeneous and heterogeneous samples.

Identification of appropriate reference genes for RT-qPCR analysis in Juglans regia L

Reverse transcription quantitative real-time PCR (RT-qPCR) is a popular adopted technique to detect gene expression, and the selection of appropriate reference genes is crucial for data normalization. In the present study, seven candidate reference genes were screened to evaluate their expression stability in various flower buds, leaf buds, tissues and cultivars of the English walnut (Juglans regia L.) based on four algorithms (geNorm, Normfinder, Bestkeeper and RefFinder). The results demonstrated that TUA, EF1 and TUB were appropriate reference genes for flower buds at different stages of female flower buds differentiation; TUB and 18S rRNA were best for leaf buds at different stages of female flower buds differentiation; TUB and TUA were suitable for different cultivars; and ACT2, 18S rRNA and GAPDH were useful for different tissues. Moreover, the expression of ACT was not stable among different flower buds, leaf buds and cultivars. The stability of reference genes were confirmed through the analysis of the expression of SPL18 gene. These results will contribute to a reliable normalization of gene expression in J. regia.

Archaic Adaptive Introgression in TBX15/WARS2

A recent study conducted the first genome-wide scan for selection in Inuit from Greenland using single nucleotide polymorphism chip data. Here, we report that selection in the region with the second most extreme signal of positive selection in Greenlandic Inuit favored a deeply divergent haplotype that is closely related to the sequence in the Denisovan genome, and was likely introgressed from an archaic population. The region contains two genes, WARS2 and TBX15, and has previously been associated with adipose tissue differentiation and body-fat distribution in humans. We show that the adaptively introgressed allele has been under selection in a much larger geographic region than just Greenland. Furthermore, it is associated with changes in expression of WARS2 and TBX15 in multiple tissues including the adrenal gland and subcutaneous adipose tissue, and with regional DNA methylation changes in TBX15.

Identification of suitable reference genes in bone marrow stromal cells from osteoarthritic donors

Bone marrow stromal cells (BMSCs) are key cellular components for musculoskeletal tissue engineering strategies. Furthermore, recent data suggest that BMSCs are involved in the development of Osteoarthritis (OA) being a frequently occurring degenerative joint disease. Reliable reference genes for the molecular evaluation of BMSCs derived from donors exhibiting OA as a primary co-morbidity have not been reported on yet. Hence, the aim of the study was to identify reference genes suitable for comparative gene expression analyses using OA-BMSCs. Passage 1 bone marrow derived BMSCs were isolated from n=13 patients with advanced stage idiopathic hip osteoarthritis and n=15 age-matched healthy donors. The expression of 31 putative reference genes was analyzed by quantitative reverse transcription polymerase chain reaction (qRT-PCR) using a commercially available TaqMan(®) assay. Calculating the coefficient of variation (CV), mRNA expression stability was determined and afterwards validated using geNorm and NormFinder algorithms. Importin 8 (IPO8), TATA box binding protein (TBP), and cancer susceptibility candidate 3 (CASC3) were identified as the most stable reference genes. Notably, commonly used reference genes, e.g. beta-actin (ACTB) and beta-2-microglobulin (B2M) were among the most unstable genes. For normalization of gene expression data of OA-BMSCs the combined use of IPO8, TBP, and CASC3 gene is recommended.

A hexadecylamide derivative of hyaluronan (HYMOVIS®) has superior beneficial effects on human osteoarthritic chondrocytes and synoviocytes than unmodified hyaluronan

Background

Intra-articular hyaluronan (HA) injection provides symptomatic benefit in the treatment of osteoarthritis (OA). Previously we found superior beneficial effects in a large animal OA model of a hexadecylamide derivative compared with unmodified HA of the same initial molecular weight. The current study sought to define possible molecular mechanisms whereby this enhanced relief of symptoms was occurring.

Methods

Chondrocytes and synovial fibroblasts were isolated from tissues of patients undergoing arthroplasty for knee OA. Monolayer cultures of cells were treated with 0, 0.5, 1.0 or 1.5 mg/mL of unmodified HA (500–730 kDa) or a hexadecylamide derivative of HA of the same initial molecular weight (HYADD4®-G; HYMOVIS®) simultaneously or 1 hour before incubation with interleukin (IL)-1beta (2 ng/mL). Cultures were terminated 15 or 30 minutes later (chondrocytes and synovial fibroblasts, respectively) for quantitation of phosphorylated-(p)-JNK, p-NFkappaB, p-p38, or at 24 hours for quantitation of gene expression (MMP1 &13, ADAMTS4 &5, TIMP1 &3, CD44, COL1A1 &2A1, ACAN, PTGS2, IL6, TNF) and matrix metalloproteinase (MMP)-13 activity.

Results

The hexadecylamide derivative of HA had significantly better amelioration of IL-1beta-induced gene expression of key matrix degrading enzymes (MMP1, MMP13, ADAMTS5), and inflammatory mediators (IL6, PTGS2) by human OA chondrocytes and synovial fibroblasts. Pre-incubation of cells with the derivatized HA for 1 hour prior to IL-1beta exposure significantly augmented the inhibition of MMP1, MMP13, ADAMTS4 and IL6 expression by chondrocytes. The reduction in MMP13 mRNA by the amide derivative of HA was mirrored in reduced MMP-13 protein and enzyme activity in IL-1beta-stimulated chondrocytes. This was associated in part with a greater inhibition of phosphorylation of the cell signalling molecules JNK, p38 and NF-kappaB.

Conclusions

The present studies have demonstrated several potential key mechanisms whereby the intra-articular injection of a hexadecylamide derivative of HA may be acting in joints with OA.

Identification of stable normalization genes for quantitative real-time PCR in porcine articular cartilage

Background

Expression levels for genes of interest must be normalized with an appropriate reference, or housekeeping gene, to make accurate comparisons of quantitative real-time PCR results. The purpose of this study was to identify the most stable housekeeping genes in porcine articular cartilage subjected to a mechanical injury from a panel of 10 candidate genes.

Results

Ten candidate housekeeping genes were evaluated in three different treatment groups of mechanically impacted porcine articular cartilage. The genes evaluated were: beta actin, beta-2-microglobulin, glyceraldehyde-3-phosphate dehydrogenase, hydroxymethylbilane synthase, hypoxanthine phosphoribosyl transferase, peptidylprolyl isomerase A (cyclophilin A), ribosomal protein L4, succinate dehydrogenase flavoprotein subunit A, TATA box binding protein, and tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein—zeta polypeptide. The stability of the genes was measured using geNorm, BestKeeper, and NormFinder software. The four most stable genes measured via geNorm were (most to least stable) succinate dehydrogenase flavoprotein, subunit A, peptidylprolyl isomerase A, glyceraldehyde-3-phosphate dehydrogenase, beta actin; the four most stable genes measured via BestKeeper were glyceraldehyde-3-phosphate dehydrogenase, peptidylprolyl isomerase A, beta actin, succinate dehydrogenase flavoprotein, subunit A; and the four most stable genes measured via NormFinder were peptidylprolyl isomerase A, succinate dehydrogenase flavoprotein, subunit A, glyceraldehyde-3-phosphate dehydrogenase, beta actin.

Conclusions

BestKeeper, geNorm, and NormFinder all generated similar results for the most stable genes in porcine articular cartilage. The use of these appropriate reference genes will facilitate accurate gene expression studies of porcine articular cartilage and suggest appropriate housekeeping genes for articular cartilage studies in other species.

A systematic approach for analysis of peptide array kinome data

The central roles of kinases in cellular processes and diseases make them highly attractive as indicators of biological responses and as therapeutic targets. Peptide arrays are emerging as an important means of characterizing kinome activity. Currently, the computational tools used to perform high-throughput kinome analyses are not specifically tailored to the nature of the data, which hinders extraction of biological information and overall progress in the field. We have developed a method for kinome analysis, which is implemented as a software pipeline in the R environment. Components and parameters were chosen to address the technical and biological characteristics of kinome microarrays. We performed comparative analysis of kinome data sets that corresponded to stimulation of immune cells with ligands of well-defined signaling pathways: bovine monocytes treated with interferon-γ (IFN-γ), CpG-containing nucleotides, or lipopolysaccharide (LPS). The data sets for each of the treatments were analyzed with our methodology as well as with three other commonly used approaches. The methods were evaluated on the basis of statistical confidence of calculated values with respect to technical and biological variability, and the statistical confidence (P values) by which the known signaling pathways could be independently identified by the pathway analysis of InnateDB (a Web-based resource for innate immunity interactions and pathways). By considering the particular attributes of kinome data, we found that our approach identified more of the peptides involved in the pathways than did the other compared methods and that it did so at a much higher degree of statistical confidence.

Quantification of mRNA using real-time PCR and Western blot analysis of MAPK events in chondrocyte/agarose constructs

In vitro models of chondrocyte mechanobiology have been used to compare the intracellular signalling pathways altered in normal and osteoarthritis-affected cartilage. However, differences in the model system and type of loading configuration have led to complicated pathways. This chapter is a follow-on of previous studies from our group utilising 3D agarose as a physiological model to study mechanotransduction pathways. Experimental methods are described to assess targets at the protein and gene expression level by Western blot analysis and real-time PCR, respectively. This chapter provides a quantitative gene expression approach to explore the intracellular pathways activated by both mechanical loading and inflammatory mediators and examine upstream phosphorylation events. Ultimately, development of methods used to analyse mechano-sensitive pathways will provide important information for the identification of appropriate pharmacological and physiotherapeutic agents for the treatment of osteoarthritis.

Gene profiling predicts rheumatoid arthritis responsiveness to IL-1Ra (anakinra)

OBJECTIVES

The overall non-response rate to biologics remains 30-40% for patients with RA resistant to MTX. The objective of this study was to predict responsiveness to the anakinra-MTX combination by peripheral blood mononuclear cell gene profiling in order to optimize treatment choice.

METHODS

Thirty-two patients treated with anakinra (100 mg/day s.c.) and MTX were categorized as responders when their 28-joint DAS (DAS-28) had decreased by ≥1.2 at 3 months. Pre-treatment blood samples had been drawn.

RESULTS

For seven responders and seven non-responders, 52 microarray-identified mRNAs were expressed as a function of the response to treatment, and unsupervised hierarchical clustering correctly separated responders from non-responders. The levels of seven of these 52 transcripts, as assessed by real-time, quantitative RT-PCR, were able to accurately classify 15 of 18 other patients (8 responders and 10 non-responders), with 87.5% specificity and 77.8% negative-predictive value for responders. Among the 52 genes, 56% were associated with IL-1β.

CONCLUSION

This predictive gene expression profile was obtained with a non-invasive procedure. After further validation in other cohorts of patients, it could be proposed and used on a large scale to select likely RA responders to combined anakinra-MTX. Trial registration. Clinical Trials; NCT00213538 (http://www.clinicaltrials.gov).

Deciphering cellular functions of protein phosphatases by comparison of gene expression profiles in Saccharomyces cerevisiae

Expression profiles of protein phosphatase (PPase) disruptants were analyzed by use of Pearson's correlation coefficient to find profiles that correlated with those of 316 Reference Gene (RG) disruptants harboring deletions in genes with known functions. Twenty-six Deltappase disruptants exhibited either a positive or negative correlation with 94 RG disruptants when the p value for Pearson's correlation coefficient was >0.2. Some of the predictions that arose from this analysis were tested experimentally and several new Delta ppase phenotypes were found. Notably, Delta sit4 and Delta siw14 disruptants exhibited hygromycin B sensitivity, Delta sit4 and Delta ptc1 disruptants grew slowly on glycerol medium, the Delta ptc1 disruptant was found to be sensitive to calcofluor white and congo red, while the Delta ppg1 disruptant was found to be sensitive to congo red. Because on-going analysis of expression profiles of Saccharomyces cerevisiae disruptants is rapidly generating new data, we suggest that the approach used in the present study to explore PPase function is also applicable to other genes.

Reference gene selection for head and neck squamous cell carcinoma gene expression studies

BACKGROUND

It is no longer adequate to choose reference genes blindly. We present the first study that defines the suitability of 12 reference genes commonly used in cancer studies (ACT, ALAS, B2M, GAPDH, HMBS, HPRT, KALPHA, RPS18, RPL27, RPS29, SHAD and TBP) for the normalization of quantitative expression data in the field of head and neck squamous cell carcinoma (HNSCC).

RESULTS

Raw expression levels were measured by RT-qPCR in HNSCC and normal matched mucosa of 46 patients. We analyzed the expression stability using geNorm and NormFinder and compared the expression levels between subgroups. In HNSCC and/or normal mucosa, the four best normalization genes were ALAS, GAPDH, RPS18 and SHAD and the most stable combination of two genes was GAPDH-SHAD. We recommend using KALPHA-TBP for the study of T1T2 tumors, RPL27-SHAD for T3T4 tumors, KALPHA-SHAD for N0 tumors, and ALAS-TBP for N+ tumors. ACT, B2M, GAPDH, HMBS, HPRT, KALPHA, RPS18, RPS29, SHAD and TBP were slightly misregulated (<1.7-fold) between tumor and normal mucosa but can be used for normalization, depending on the resolution required for the assay.

CONCLUSION

In the field of HNSCC, this study will guide researchers in selecting the most appropriate reference genes from among 12 potentially suitable reference genes, depending on the specific setting of their experiments.