Conventionally used reference genes are not outstanding for normalization of gene expression in human cancer research

Shiftless Is a Novel Member of the Ribosome Stress Surveillance Machinery That Has Evolved to Play a Role in Innate Immunity and Cancer Surveillance

A longstanding paradox in molecular biology has centered on the question of how very long proteins are synthesized, despite numerous measurements indicating that ribosomes spontaneously shift reading frame at rates that should preclude their ability completely translate their mRNAs. Shiftless (SFL; C19orf66) was originally identified as an interferon responsive gene encoding an antiviral protein, indicating that it is part of the innate immune response. This activity is due to its ability to bind ribosomes that have been programmed by viral sequence elements to shift reading frame. Curiously, Shiftless is constitutively expressed at low levels in mammalian cells. This study examines the effects of altering Shiftless homeostasis, revealing how it may be used by higher eukaryotes to identify and remove spontaneously frameshifted ribosomes, resolving the apparent limitation on protein length. Data also indicate that Shiftless plays a novel role in the ribosome-associated quality control program. A model is proposed wherein SFL recognizes and arrests frameshifted ribosomes, and depending on SFL protein concentrations, either leads to removal of frameshifted ribosomes while leaving mRNAs intact, or to mRNA degradation. We propose that SFL be added to the growing pantheon of proteins involved in surveilling translational fidelity and controlling gene expression in higher eukaryotes.

LRP10, PGK1 and RPLP0: Best Reference Genes in Periprostatic Adipose Tissue under Obesity and Prostate Cancer Conditions

Obesity (OB) is a metabolic disorder characterized by adipose tissue dysfunction that has emerged as a health problem of epidemic proportions in recent decades. OB is associated with multiple comorbidities, including some types of cancers. Specifically, prostate cancer (PCa) has been postulated as one of the tumors that could have a causal relationship with OB. Particularly, a specialized adipose tissue (AT) depot known as periprostatic adipose tissue (PPAT) has gained increasing attention over the last few years as it could be a key player in the pathophysiological interaction between PCa and OB. However, to date, no studies have defined the most appropriate internal reference genes (IRGs) to be used in gene expression studies in this AT depot. In this work, two independent cohorts of PPAT samples (n = 20/n = 48) were used to assess the validity of a battery of 15 literature-selected IRGs using two widely used techniques (reverse transcription quantitative PCR [RT-qPCR] and microfluidic-based qPCR array). For this purpose, ΔCt method, GeNorm (v3.5), BestKeeper (v1.0), NormFinder (v.20.0), and RefFinder software were employed to assess the overall trends of our analyses. LRP10, PGK1, and RPLP0 were identified as the best IRGs to be used for gene expression studies in human PPATs, specifically when considering PCa and OB conditions.

Viral load: We need a new look at an old problem?

The concept of viral load was introduced in the 1980s to measure the amount of viral genetic material in a person's blood, primarily for human immunodeficiency virus (HIV). It has since become crucial for monitoring HIV infection progression and assessing the efficacy of antiretroviral therapy. However, during the coronavirus disease 2019 pandemic, the term "viral load" became widely popularized, not only for the scientific community but for the general population. Viral load plays a critical role in both clinical patient management and research, providing valuable insights for antiviral treatment strategies, vaccination efforts, and epidemiological control measures. As measuring viral load is so important, why don't researchers discuss the best way to do it? Is it simply acceptable to use raw Ct values? Relying solely on Ct values for viral load estimation can be problematic due to several reasons. First, Ct values can vary between different quantitative polymerase chain reaction assays, platforms, and laboratories, making it difficult to compare data across studies. Second, Ct values do not directly measure the quantity of viral particles in a sample and they can be influenced by various factors such as initial viral load, sample quality, and assay sensitivity. Moreover, variations in viral RNA extraction and reverse-transcription steps can further impact the accuracy of viral load estimation, emphasizing the need for careful interpretation of Ct values in viral load assessment. Interestingly, we did not observe scientific articles addressing different strategies to quantify viral load. The absence of standardized and validated methods impedes the implementation of viral load monitoring in clinical management. The variability in cell quantities within samples and the variation in viral particle numbers within infected cells further challenge accurate viral load measurement and interpretation. To advance the field and improve patient outcomes, there is an urgent need for the development and validation of tailored, standardized methods for precise viral load quantification.

Discovery of two new isoforms of the human DUT gene

In human cells two dUTPase isoforms have been described: one nuclear (DUT-N) and one mitochondrial (DUT-M), with cognate localization signals. In contrast, here we identified two additional isoforms; DUT-3 without any localization signal and DUT-4 with the same nuclear localization signal as DUT-N. Based on an RT-qPCR method for simultaneous isoform-specific quantification we analysed the relative expression patterns in 20 human cell lines of highly different origins. We found that the DUT-N isoform is expressed by far at the highest level, followed by the DUT-M and the DUT-3 isoform. A strong correlation between expression levels of DUT-M and DUT-3 suggests that these two isoforms may share the same promoter. We analysed the effect of serum starvation on the expression of dUTPase isoforms compared to non-treated cells and found that the mRNA levels of DUT-N decreased in A-549 and MDA-MB-231 cells, but not in HeLa cells. Surprisingly, upon serum starvation DUT-M and DUT-3 showed a significant increase in the expression, while the expression level of the DUT-4 isoform did not show any changes. Taken together our results indicate that the cellular dUTPase supply may also be provided in the cytoplasm and starvation stress induced expression changes are cell line dependent.

Selection and validation of novel stable reference genes for qPCR analysis in EMT and MET

Quantitative real-time polymerase chain reaction is a powerful tool for quantifying gene expression. The relative quantification relies on normalizing the data to reference genes or internal controls not modulated by the experimental conditions. The most widely used internal controls occasionally show changed expression patterns in different experimental settings, such as the mesenchymal to epithelial transition. Thus, identifying appropriate internal controls is of utmost importance. We analyzed multiple RNA-Seq datasets using a combination of statistical approaches such as percent relative range and coefficient of variance to define a list of candidate internal control genes, which was then validated experimentally and by using in silico analyses as well. We identified a group of genes as strong internal control candidates with high stability compared to the classical ones. We also presented evidence for the superiority of the percent relative range method for calculating expression stability in data sets with larger sample sizes. We used multiple methods to analyze data collected from several RNA-Seq datasets; we identified Rbm17 and Katna1 as the most stable reference genes in EMT/MET studies. The percent relative range approach surpasses other methods when analyzing datasets of larger sample sizes.

Regulation of 3' splice site selection after step 1 of splicing by spliceosomal C* proteins

Alternative precursor messenger RNA splicing is instrumental in expanding the proteome of higher eukaryotes, and changes in 3' splice site (3'ss) usage contribute to human disease. We demonstrate by small interfering RNA-mediated knockdowns, followed by RNA sequencing, that many proteins first recruited to human C* spliceosomes, which catalyze step 2 of splicing, regulate alternative splicing, including the selection of alternatively spliced NAGNAG 3'ss. Cryo-electron microscopy and protein cross-linking reveal the molecular architecture of these proteins in C* spliceosomes, providing mechanistic and structural insights into how they influence 3'ss usage. They further elucidate the path of the 3' region of the intron, allowing a structure-based model for how the C* spliceosome potentially scans for the proximal 3'ss. By combining biochemical and structural approaches with genome-wide functional analyses, our studies reveal widespread regulation of alternative 3'ss usage after step 1 of splicing and the likely mechanisms whereby C* proteins influence NAGNAG 3'ss choices.

H19 and TUG1 lncRNAs as Novel Biomarkers for Irritable Bowel Syndrome in Diabetic Patients

Introduction: Irritable bowel syndrome (IBS) is a gastrointestinal disorder due to enteric nervous system impairment that produces different patterns of digestion. IBS is a common finding in diabetic patients. The functions of lncRNAs in IBS are still not clear and need to be further investigated. The aim of this study was to assess the diagnostic roles of lncRNA H19 and TUG1 for IBS associated with diabetes and to evaluate their association with clinical and laboratory findings. Subjects and Methods: Samples from 42 diabetic patients, 42 diabetic patients with IBS, and 42 healthy controls were obtained. The LncRNA H19 and TUG1 expressions were measured by quantitative real-time PCR. Results: The patients with IBS had significantly lower levels of lncRNA H19 and TUG1 expression than the healthy controls and diabetic-only patients (p < 0.001). LncRNA H19 and TUG1 can discriminate between diabetic-only patients and those with IBS (areas under the ROC curves of 0.95 and 0.722, respectively). The TUG1 expression levels were significantly different among types of IBS (IBS-D lower than IBS-M and IBS-C lower than IBS-M; p = 0.0165 and p = 0.043, respectively). H19 and TUG1 were downregulated in patients with poor glycemic control. lncRNA H19 and TUG1 expression in diabetic patients with IBS significantly negatively correlated with the IBS severity scoring system. Both lncRNAs’ expression significantly predicted the disease severity. LncRNA H19 expression can be an independent predictor for disease severity (adjusted odds ratio = 0.00001, 95% CI = 0−0.5, p = 0.045). Conclusions: Diabetic patients with IBS had significantly lower levels of lncRNA H19 and TUG1 expression than healthy controls and diabetic-only patients. LncRNA H19 had better diagnostic performance criteria for IBS. LncRNA H19 expression can be an independent predictor for IBS severity.

Cosbin: cosine score-based iterative normalization of biologically diverse samples

Motivation

Data normalization is essential to ensure accurate inference and comparability of gene expression measures across samples or conditions. Ideally, gene expression data should be rescaled based on consistently expressed reference genes. However, to normalize biologically diverse samples, the most commonly used reference genes exhibit striking expression variability and size-factor or distribution-based normalization methods can be problematic when the amount of asymmetry in differential expression is significant.

Results

We report an efficient and accurate data-driven method—Cosine score-based iterative normalization (Cosbin)—to normalize biologically diverse samples. Based on the Cosine scores of cross-condition expression patterns, the Cosbin pipeline iteratively eliminates asymmetric differentially expressed genes, identifies consistently expressed genes, and calculates sample-wise normalization factors. We demonstrate the superior performance and enhanced utility of Cosbin compared with six representative peer methods using both simulation and real multi-omics expression datasets. Implemented in open-source R scripts and specifically designed to address normalization bias due to significant asymmetry in differential expression across multiple conditions, the Cosbin tool complements rather than replaces the existing methods and will allow biologists to more accurately detect true molecular signals among diverse phenotypic groups.

Availability and implementation

The R scripts of Cosbin pipeline are freely available at https://github.com/MinjieSh/Cosbin.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Reference gene identification for normalisation of RT-qPCR analysis in plasma samples of the rat middle cerebral artery occlusion model

OBJECTIVE

In quantitative reverse transcription-polymerase chain reaction (RT-qPCR) studies, the selection and validation of reference genes are crucial for the accurate analysis of MicroRNAs (miRNAs) expression. In this work, the optimal reference genes for RT-qPCR normalisation in plasma samples of rat middle cerebral artery occlusion (MCAO) models were identified.

METHODS

Six rat MCAO models were established. Blood samples were collected before modelling and approximately 16-24 h after modelling. Two commonly used reference genes (U6 and 5S) and three miRNAs (miR-24, miR-122 and miR-9a) were selected as candidate reference genes, and the expression of these genes was detected with RT-qPCR. The acquired data were analysed using geNorm, Normfinder, BestKeeper, RefFinder and comparative delta threshold cycle statistical models.

RESULTS

The analysed results consistently showed that miR-24 was the most stably expressed reference gene. The 'optimal combination' calculated by geNorm was miR-24, U6 and5S. The expression level of the target gene miR124 was similar when the most stable reference gene miR-24 or the 'optimal combination' was used as a reference gene. However, compared with miR24 or the 'optimal combination', the less stable reference genes influenced the fold change and the data accuracy with a large standard deviation.

CONCLUSION

These results confirmed the importance of selecting suitable reference genes for normalisation to obtain reliable results in RT-qPCR studies and demonstrated that the identified reference gene miR-24 or the 'optimal combination' could be used as an internal control for gene expression analysis in the rat MCAO model.

Amelioration for an ignored pitfall in reference gene selection by considering the mean expression and standard deviation of target genes

Routine tissue-specific reference genes are often used in expression studies, but target genes are not taken into account. Using the relative RT-qPCR approach, we evaluated the expression of three target genes. At the same time, meta-analyses were conducted in various ethnic groups, genders, and thyroid cancer subtypes. When eight common reference genes were examined, it was discovered that some of them not only lacked consistent expression but also had considerable expression variance. It is worth noting that while choosing a reference gene, the mean gene expression and its standard deviation should be carefully addressed. An equation was developed based on this, and it was used to perform statistical analysis on over 25,000 genes. According to the subtype of thyroid cancer and, of course, the target genes in this investigation, appropriate reference genes were proposed. The intuitive choice of GAPDH as a common reference gene caused a major shift in the quantitative expression data of target genes, inverting the relative expression values. As a result, choosing the appropriate reference gene(s) for quantification of transcription data, and especially for relative studies of the expression of target gene(s), is critical and should be carefully considered during the study design.

Obtaining Reliable RT-qPCR Results in Molecular Diagnostics—MIQE Goals and Pitfalls for Transcriptional Biomarker Discovery

In this review, we discuss the development pipeline for transcriptional biomarkers in molecular diagnostics and stress the importance of a reliable gene transcript quantification strategy. Hence, a further focus is put on the MIQE guidelines and how to adapt them for biomarker discovery, from signature validation up to routine diagnostic applications. First, the advantages and pitfalls of the holistic RNA sequencing for biomarker development will be described to establish a candidate biomarker signature. Sequentially, the RT-qPCR confirmation process will be discussed to validate the discovered biomarker signature. Examples for the successful application of RT-qPCR as a fast and reproducible quantification method in routinemolecular diagnostics are provided. Based on the MIQE guidelines, the importance of “key steps” in RT-qPCR is accurately described, e.g., reverse transcription, proper reference gene selection and, finally, the application of automated RT-qPCR data analysis software. In conclusion, RT-qPCR proves to be a valuable tool in the establishment of a disease-specific transcriptional biomarker signature and will have a great future in molecular diagnostics or personalized medicine.

swCAM: estimation of subtype-specific expressions in individual samples with unsupervised sample-wise deconvolution

MOTIVATION

Complex biological tissues are often a heterogeneous mixture of several molecularly distinct cell subtypes. Both subtype compositions and subtype-specific (STS) expressions can vary across biological conditions. Computational deconvolution aims to dissect patterns of bulk tissue data into subtype compositions and STS expressions. Existing deconvolution methods can only estimate averaged STS expressions in a population, while many downstream analyses such as inferring co-expression networks in particular subtypes require subtype expression estimates in individual samples. However, individual-level deconvolution is a mathematically underdetermined problem because there are more variables than observations.

RESULTS

We report a sample-wise Convex Analysis of Mixtures (swCAM) method that can estimate subtype proportions and STS expressions in individual samples from bulk tissue transcriptomes. We extend our previous CAM framework to include a new term accounting for between-sample variations and formulate swCAM as a nuclear-norm and ℓ2,1-norm regularized matrix factorization problem. We determine hyperparameter values using cross-validation with random entry exclusion and obtain a swCAM solution using an efficient alternating direction method of multipliers. Experimental results on realistic simulation data show that swCAM can accurately estimate STS expressions in individual samples and successfully extract co-expression networks in particular subtypes that are otherwise unobtainable using bulk data. In two real-world applications, swCAM analysis of bulk RNASeq data from brain tissue of cases and controls with bipolar disorder or Alzheimer's disease identified significant changes in cell proportion, expression pattern and co-expression module in patient neurons. Comparative evaluation of swCAM versus peer methods is also provided.

AVAILABILITY AND IMPLEMENTATION

The R Scripts of swCAM are freely available at https://github.com/Lululuella/swCAM. A user's guide and a vignette are provided.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Variable expression of eighteen common housekeeping genes in human non-cancerous kidney biopsies

Housekeeping, or reference genes (RGs) are, by definition, loci with stable expression profiles that are widely used as internal controls to normalize mRNA levels. However, due to specific events, such as pathological changes, or technical procedures, their expression might be altered, failing to fulfil critical normalization pre-requisites. To identify RG genes suitable as internal controls in human non-cancerous kidney tissue, we selected 18 RG candidates based on previous data and screen them in 30 expression datasets (>800 patients), including our own, publicly available or provided by independent groups. Datasets included specimens from patients with hypertensive and diabetic nephropathy, Fabry disease, focal segmental glomerulosclerosis, IgA nephropathy, membranous nephropathy, and minimal change disease. We examined both microdissected and whole section-based datasets. Expression variability of 4 candidate genes (YWHAZ, SLC4A1AP, RPS13 and ACTB) was further examined by qPCR in biopsies from patients with hypertensive nephropathy (n = 11) and healthy controls (n = 5). Only YWHAZ gene expression remained stable in all datasets whereas SLC4A1AP was stable in all but one Fabry dataset. All other RGs were differentially expressed in at least 2 datasets, and in 4.5 datasets on average. No differences in YWHAZ, SLC4A1AP, RPS13 and ACTB gene expression between hypertensive and control biopsies were detected by qPCR. Although RGs suitable to all techniques and tissues are unlikely to exist, our data suggest that in non-cancerous kidney biopsies expression of YWHAZ and SLC4AIAP genes is stable and suitable for normalization purposes.

RPS13, a potential universal reference gene for normalisation of gene expression in multiple human normal and cancer tissue samples

BACKGROUND

Reference genes are considered stable genes and are used for normalizing the gene expression profile across different cell types; as well as, in normal and diseased samples. However, these gene associates with different biological processes, and hence expression vary in different pathological conditions. Therefore, in the present study, eight different reference genes were used and compared to identify common reference gene usable for an array of different cell types and human cancers.

METHODS AND RESULTS

The expression stability of the eight reference genes across eleven normal and cancerous tissues was confirmed through real time-qPCR. Ribosomal protein S13 (RPS13) was found to be a common and stable reference gene across intra- and inter-comparison between various normal and tumor tissue types. Further, TCGA data analysis across and between normal and tumor tissue types also showed minimum deviation in expression of RPS13 gene out of eight routinely used reference genes.

CONCLUSION

RPS13 is the common stable reference gene in normalization for gene expression based analysis in cancer research.

Identification of Novel Endogenous Controls for qPCR Normalization in SK-BR-3 Breast Cancer Cell Line

Normalization of gene expression using internal controls or reference genes (RGs) has been the method of choice for standardizing the technical variations in reverse transcription quantitative polymerase chain reactions (RT-qPCR). Conventionally, ACTB and GAPDH have been used as reference genes despite evidence from literature discouraging their use. Hence, in the present study we identified and investigated novel reference genes in SK-BR-3, an HER2-enriched breast cancer cell line. Transcriptomic data of 82 HER2-E breast cancer samples from TCGA database were analyzed to identify twelve novel genes with stable expression. Additionally, thirteen RGs from the literature were analyzed. The expression variations of the candidate genes were studied over five successive passages (p) in two parallel cultures S1 and S2 and in acute and chronic hypoxia using various algorithms. Finally, the most stable RGs were selected and validated for normalization of the expression of three genes of interest (GOIs) in normoxia and hypoxia. Our results indicate that HSP90AB1, DAD1, PFN1 and PUM1 can be used in any combination of three (triplets) for optimizing intra- and inter-assay gene expression differences in the SK-BR-3 cell line. Additionally, we discourage the use of conventional RGs (ACTB, GAPDH, RPL13A, RNA18S and RNA28S) as internal controls for RT-qPCR in SK-BR-3 cell line.

Identification of new reference genes with stable expression patterns for gene expression studies using human cancer and normal cell lines

Reverse transcription—quantitative real-time PCR (RT-qPCR) is a ubiquitously used method in biological research, however, finding appropriate reference genes for normalization is challenging. We aimed to identify genes characterized with low expression variability among human cancer and normal cell lines. For this purpose, we investigated the expression of 12 candidate reference genes in 13 widely used human cancer cell lines (HeLa, MCF-7, A-549, K-562, HL-60(TB), HT-29, MDA-MB-231, HCT 116, U-937, SH-SY5Y, U-251MG, MOLT-4 and RPMI-8226) and, in addition, 7 normal cell lines (HEK293, MRC-5, HUVEC/TERT2, HMEC, HFF-1, HUES 9, XCL-1). In our set of genes, we included SNW1 and CNOT4 as novel candidate reference genes based on the RNA HPA cell line gene data from The Human Protein Atlas. HNRNPL and PCBP1 were also included along with the „classical” reference genes ACTB, GAPDH, IPO8, PPIA, PUM1, RPL30, TBP and UBC. Results were evaluated using GeNorm, NormFiner, BestKeeper and the Comparative ΔCt methods. In conclusion, we propose IPO8, PUM1, HNRNPL, SNW1 and CNOT4 as stable reference genes for comparing gene expression between different cell lines. CNOT4 was also the most stable gene upon serum starvation.

iRGvalid: A Robust in silico Method for Optimal Reference Gene Validation

Background

Appropriate reference genes are critical to accurately quantifying relative gene expression in research and clinical applications. Numerous efforts have been made to select the most stable reference gene(s), but a consensus has yet to be achieved. In this report, we propose an in silico reference gene validation method, iRGvalid, that can be used as a universal tool to validate the reference genes recommended from different resources so as to identify the best ones without a need for any wet lab validation tests.

Methods

iRGvalid takes advantage of high throughput gene expression data and is built on a double-normalization strategy. First, the expression level of each individual gene is normalized against the total gene expression level of each sample, followed by a target gene normalization to the candidate reference gene(s). Linear regression analysis is then performed between the pre- and post- normalized target gene across the whole sample set to evaluate the stability of the reference gene(s), which is positively associated with the Pearson correlation coefficient, Rt. The higher the Rt value, the more stable the reference gene. We applied iRGvalid to 14 candidate reference genes to validate and identify the most stable reference genes in four cancer types: lung adenocarcinoma, breast cancer, colon adenocarcinoma, and nasopharyngeal cancer. The stability of the reference gene is evaluated both individually and in groups of all possible combinations.

Results

Highly stable reference genes resulted in high Rt values regardless of the target gene used. The highest stability was achieved with a specific combination of 3 to 6 reference genes. A few genes were among the best reference genes across the cancer types studied here.

Conclusion

iRGvalid provides an easy and robust method to validate and identify the most stable reference gene or genes from a pool of candidate reference genes. The inclusivity of large expression data sets as well as the direct comparison of candidate reference genes makes it possible to identify reference genes with universal quality. This method can be used in any other gene expression studies when large cohorts of expression data are available.

A functional genomic approach to identify reference genes for human pancreatic beta cell real-time quantitative RT-PCR analysis

Exposure of human pancreatic beta cells to pro-inflammatory cytokines or metabolic stressors is used to model events related to type 1 and type 2 diabetes, respectively. Quantitative real-time PCR is commonly used to quantify changes in gene expression. The selection of the most adequate reference gene(s) for gene expression normalization is an important pre-requisite to obtain accurate and reliable results. There are no universally applicable reference genes, and the human beta cell expression of commonly used reference genes can be altered by different stressors. Here we aimed to identify the most stably expressed genes in human beta cells to normalize quantitative real-time PCR gene expression.We used comprehensive RNA-sequencing data from the human pancreatic beta cell line EndoC-βH1, human islets exposed to cytokines or the free fatty acid palmitate in order to identify the most stably expressed genes. Genes were filtered based on their level of significance (adjusted P-value >0.05), fold-change (|fold-change| <1.5) and a coefficient of variation <10%. Candidate reference genes were validated by quantitative real-time PCR in independent samples.We identified a total of 264 genes stably expressed in EndoC-βH1 cells and human islets following cytokines - or palmitate-induced stress, displaying a low coefficient of variation. Validation by quantitative real-time PCR of the top five genes ARF1, CWC15, RAB7A, SIAH1 and VAPA corroborated their expression stability under most of the tested conditions. Further validation in independent samples indicated that the geometric mean of ACTB and VAPA expression can be used as a reliable normalizing factor in human beta cells.

Identification and Validation of Reference Genes in Clostridium beijerinckii NRRL B-598 for RT-qPCR Using RNA-Seq Data

Gene expression analysis through reverse transcription-quantitative real-time polymerase chain reaction (RT-qPCR) depends on correct data normalization by reference genes with stable expression. Although Clostridium beijerinckii NRRL B-598 is a promising Gram-positive bacterium for the industrial production of biobutanol, validated reference genes have not yet been reported. In this study, we selected 160 genes with stable expression based on an RNA sequencing (RNA-Seq) data analysis, and among them, seven genes (zmp, rpoB1, rsmB, greA, rpoB2, topB2, and rimO) were selected for experimental validation by RT-qPCR and gene ontology (GO) enrichment analysis. According to statistical analyses, zmp and greA were the most stable and suitable reference genes for RT-qPCR normalization. Furthermore, our methodology can be useful for selection of the reference genes in other strains of C. beijerinckii and it also suggests that the RNA-Seq data can be used for the initial selection of novel reference genes, however, their validation is required.

Approaches to Identify and Characterise the Post-Transcriptional Roles of lncRNAs in Cancer

It is becoming increasingly evident that the non-coding genome and transcriptome exert great influence over their coding counterparts through complex molecular interactions. Among non-coding RNAs (ncRNA), long non-coding RNAs (lncRNAs) in particular present increased potential to participate in dysregulation of post-transcriptional processes through both RNA and protein interactions. Since such processes can play key roles in contributing to cancer progression, it is desirable to continue expanding the search for lncRNAs impacting cancer through post-transcriptional mechanisms. The sheer diversity of mechanisms requires diverse resources and methods that have been developed and refined over the past decade. We provide an overview of computational resources as well as proven low-to-high throughput techniques to enable identification and characterisation of lncRNAs in their complex interactive contexts. As more cancer research strategies evolve to explore the non-coding genome and transcriptome, we anticipate this will provide a valuable primer and perspective of how these technologies have matured and will continue to evolve to assist researchers in elucidating post-transcriptional roles of lncRNAs in cancer.

Replication stress and FOXM1 drive radiation induced genomic instability and cell transformation

In contrast to the vast majority of research that has focused on the immediate effects of ionizing radiation, this work concentrates on the molecular mechanism driving delayed effects that emerge in the progeny of the exposed cells. We employed functional protein arrays to identify molecular changes induced in a human bronchial epithelial cell line (HBEC3-KT) and osteosarcoma cell line (U2OS) and evaluated their impact on outcomes associated with radiation induced genomic instability (RIGI) at day 5 and 7 post-exposure to a 2Gy X-ray dose, which revealed replication stress in the context of increased FOXM1b expression. Irradiated cells had reduced DNA replication rate detected by the DNA fiber assay and increased DNA resection detected by RPA foci and phosphorylation. Irradiated cells increased utilization of homologous recombination-dependent repair detected by a gene conversion assay and DNA damage at mitosis reflected by RPA positive chromosomal bridges, micronuclei formation and 53BP1 positive bodies in G1, all known outcomes of replication stress. Interference with the function of FOXM1, a transcription factor widely expressed in cancer, employing an aptamer, decreased radiation-induced micronuclei formation and cell transformation while plasmid-driven overexpression of FOXM1b was sufficient to induce replication stress, micronuclei formation and cell transformation.

The pseudogene problem and RT-qPCR data normalization; SYMPK: a suitable reference gene for papillary thyroid carcinoma

In RT-qPCR, accuracy requires multiple levels of standardization, but results could be obfuscated by human errors and technical limitations. Data normalization against suitable reference genes is critical, yet their observed expression can be confounded by pseudogenes. Eight reference genes were selected based on literature review and analysis of papillary thyroid carcinoma (PTC) microarray data. RNA extraction and cDNA synthesis were followed by RT-qPCR amplification in triplicate with exon-junction or intron-spanning primers. Several statistical analyses were applied using Microsoft Excel, NormFinder, and BestKeeper. In normal tissues, the least correlation of variation (CqCV%) and the lowest maximum fold change (MFC) were respectively recorded for PYCR1 and SYMPK. In PTC tissues, SYMPK had the lowest CqCV% (5.16%) and MFC (1.17). According to NormFinder, the best reference combination was SYMPK and ACTB (stability value = 0.209). BestKeeper suggested SYMPK as the best reference in both normal (r = 0.969) and PTC tissues (r = 0.958). SYMPK is suggested as the best reference gene for overcoming the pseudogene problem in RT-qPCR data normalization, with a stability value of 0.319.

Selecting suitable reference genes for qPCR normalization: a comprehensive analysis in MCF-7 breast cancer cell line

Background

MCF-7 breast cancer cell line is undoubtedly amongst the most extensively studied patient-derived research models, providing pivotal results that have over the decades translated to constantly improving patient care. Many research groups, have previously identified suitable reference genes for qPCR normalization in MCF-7 cell line. However, over the course of identification of suitable reference genes, a comparative analysis comprising these genes together in a single study has not been reported. Furthermore, the expression dynamics of these reference genes within sub-clones cultured over multiple passages (p) has attracted limited attention from research groups. Therefore, we investigated the expression dynamics of 12 previously suggested reference genes within two sub-clones (culture A1 and A2) cultured identically over multiple passages. Additionally, the effect of nutrient stress on reference gene expression was examined to postulate an evidence-based recommendation of the least variable reference genes that could be employed in future gene expression studies.

Results

The analysis revealed the presence of differential reference gene expression within the sub-clones of MCF-7. In culture A1, GAPDH-CCSER2 were identified as the least variable reference genes while for culture A2, GAPDH-RNA28S were identified. However, upon validation using genes of interest, both these pairs were found to be unsuitable control pairs. Normalization of AURKA and KRT19 with triplet pair GAPDH-CCSER2-PCBP1 yielded successful results. The triplet also proved its capability to handle variations arising from nutrient stress.

Conclusions

The variance in expression behavior amongst sub-clones highlights the potential need for exercising caution while selecting reference genes for MCF-7. GAPDH-CCSER2-PCBP1 triplet offers a reliable alternative to otherwise traditionally used internal controls for optimizing intra- and inter-assay gene expression differences. Furthermore, we suggest avoiding the use of ACTB, GAPDH and PGK1 as single internal controls.

Relative quantification of BCL2 mRNA for diagnostic usage needs stable uncontrolled genes as reference

Dysregulation of BCL2 is a pathophysiology observed in haematological malignancies. For implementation of available treatment-options it is preferred to know the relative quantification of BCL2 mRNA with appropriate reference genes. For the choice of reference genes-(i) Reference Genes were selected by assessing variation of >60,000 genes from 4 RNA-seq datasets of haematological malignancies followed by filtering based on their GO biological process annotations and proximity of their chromosomal locations to known disease translocations. Selected genes were experimentally validated across various haematological malignancy samples followed by stability comparison using geNorm, NormFinder, BestKeeper and RefFinder. (ii) 43 commonly used Reference Genes were obtained from literature through extensive systematic review. Levels of BCL2 mRNA was assessed by qPCR normalized either by novel reference genes from this study or GAPDH, the most cited reference gene in literature and compared. The analysis showed PTCD2, PPP1R3B and FBXW9 to be the most unregulated genes across lymph-nodes, bone marrow and PBMC samples unlike the Reference Genes used in literature. BCL2 mRNA level shows a consistent higher expression in haematological malignancy patients when normalized by these novel Reference Genes as opposed to GAPDH, the most cited Reference Gene. These reference genes should also be applicable in qPCR platforms using Taqman probes and other model systems including cell lines and rodent models. Absence of sample from healthy-normal individual in diagnostic cases call for careful selection of Reference Genes for relative quantification of a biomarker by qPCR.BCL2 can be used as molecular diagnostics only if normalized with a set of reference genes with stable yet low levels of expression across different types of haematological malignancies.

Moonlight human ribosomal protein L13a downregulation is associated with p53 and HER2/neu expression in breast cancer

Breast cancer is the most common malignancy among females worldwide. Recent studies have shown extra-ribosomal roles of the moonlight ribosomal proteins in the development of human cancers. Accurate quantification of the gene expression level is based on the selection of the reference genes whose expression is independent of cancer properties and patient's characteristics. The aim of this study was the evaluation of the expression level of a previously proposed ribosomal protein as moonlight, L13a (RPL13A), in breast cancer samples and their adjacent tissues. Its association with genes of known roles in developing cancers was also investigated. Traditionally used housekeeping genes were selected and their expression was analyzed in 80 surgically excised breast tissue specimens (40 tumors and 40 tumor-adjacent tissues) by applying three software tools including GeNorm, NormFinder, and BestKeeper to select the most stable reference genes. Then, mRNA expression levels of RPL13A and p53 were evaluated. Additionally, protein expression levels of RPL13A were measured. It was demonstrated that PUM1 and ACTB are the most reliable reference genes and RPL13A is the least stable gene. There was a positive correlation between RPL13A and p53 mRNA expression levels in all the tumor samples. Moreover, significant downregulation of RPL13A expression levels was revealed in HER2+ tumor samples compared to HER2- ones. There was also a marked decrease in p53 mRNA expression levels in HER2+ tumor subtypes. Our results suggest that there is a probable relationship between RPL13A decreased expression with p53 and HER2/neu expression in the breast cancer.

S100A4 mRNA-protein relationship uncovered by measurement noise reduction

Intrinsic biological fluctuation and/or measurement error can obscure the association of gene expression patterns between RNA and protein levels. Appropriate normalization of reverse-transcription quantitative PCR (RT-qPCR) data can reduce technical noise in transcript measurement, thus uncovering such relationships. The accuracy of gene expression measurement is often challenged in the context of cancer due to the genetic instability and "splicing weakness" involved. Here, we sequenced the poly(A) cancer transcriptome of canine osteosarcoma using mRNA-Seq. Expressed sequences were resolved at the level of two consecutive exons to enable the design of exon-border spanning RT-qPCR assays and ranked for stability based on the coefficient of variation (CV). Using the same template type for RT-qPCR validation, i.e. poly(A) RNA, avoided skewing of stability assessment by circular RNAs (circRNAs) and/or rRNA deregulation. The strength of the relationship between mRNA expression of the tumour marker S100A4 and its proportion score of quantitative immunohistochemistry (qIHC) was introduced as an experimental readout to fine-tune the normalization choice. Together with the essential logit transformation of qIHC scores, this approach reduced the noise of measurement as demonstrated by uncovering a highly significant, strong association between mRNA and protein expressions of S100A4 (Spearman's coefficient ρ = 0.72 (p = 0.006)). KEY MESSAGES: • RNA-seq identifies stable pairs of consecutive exons in a heterogeneous tumour. • Poly(A) RNA templates for RT-qPCR avoid bias from circRNA and rRNA deregulation. • HNRNPL is stably expressed across various cancer tissues and osteosarcoma. • Logit transformed qIHC score better associates with mRNA amount. • Quantification of minor S100A4 mRNA species requires poly(A) RNA templates and dPCR.

Development of a Bioinformatics Framework for Identification and Validation of Genomic Biomarkers and Key Immunopathology Processes and Controllers in Infectious and Non-infectious Severe Inflammatory Response Syndrome

Sepsis is defined as dysregulated host response caused by systemic infection, leading to organ failure. It is a life-threatening condition, often requiring admission to an intensive care unit (ICU). The causative agents and processes involved are multifactorial but are characterized by an overarching inflammatory response, sharing elements in common with severe inflammatory response syndrome (SIRS) of non-infectious origin. Sepsis presents with a range of pathophysiological and genetic features which make clinical differentiation from SIRS very challenging. This may reflect a poor understanding of the key gene inter-activities and/or pathway associations underlying these disease processes. Improved understanding is critical for early differential recognition of sepsis and SIRS and to improve patient management and clinical outcomes. Judicious selection of gene biomarkers suitable for development of diagnostic tests/testing could make differentiation of sepsis and SIRS feasible. Here we describe a methodologic framework for the identification and validation of biomarkers in SIRS, sepsis and septic shock patients, using a 2-tier gene screening, artificial neural network (ANN) data mining technique, using previously published gene expression datasets. Eight key hub markers have been identified which may delineate distinct, core disease processes and which show potential for informing underlying immunological and pathological processes and thus patient stratification and treatment. These do not show sufficient fold change differences between the different disease states to be useful as primary diagnostic biomarkers, but are instrumental in identifying candidate pathways and other associated biomarkers for further exploration.

Galectin-1 promotes hepatocellular carcinoma and the combined therapeutic effect of OTX008 galectin-1 inhibitor and sorafenib in tumor cells

Background

Galectins are beta-galactose specific binding proteins. In human cancers, including hepatocellular carcinoma (HCC), galectin-1 (Gal-1) is often found to be overexpressed. In order to combat the dismal diagnosis and death rates of HCC, gene silencing and targeted inhibition of Gal-1 was investigated for its improved therapeutic potential.

Methods

Cellular and secretory Gal-1 levels were analyzed using HCC clinical samples. The study of Gal-1 was carried by both knockdown and overexpression approaches. The stable clones were tested by in vitro assays and in vivo experiments. Mass spectrometry was used to identify downstream targets of Gal-1. The upstream regulator of Gal-1, microRNA-22 (miR-22) was characterized by functional assays. The therapeutic effect of inhibiting Gal-1 was also analyzed.

Results

Gal-1 overexpression was observed in HCC and correlated with aggressive clinicopathological features and poorer survival. The loss of Gal-1 resulted in hindered cell migration, invasion and anchorage independent growth. This was also observed in the animal models, in that when Gal-1 was knocked down, there were fewer lung metastases. Proteomic profiling of control and Gal-1 knockdown cells identified that the level of retention in endoplasmic reticulum 1 (RER1) was suppressed when Gal-1 level was reduced. The cell motility of Gal-1 knockdown cells was enhanced upon the rescue of RER1 expression. In HCC tissues, Gal-1 and RER1 expressions displayed a significant positive correlation. The upstream regulator of Gal-1, miR-22 was observed to be underexpressed in HCC tissues and negatively correlated with Gal-1. Silencing of miR-22 resulted in the upregulation of Gal-1 and enhanced cell growth, migration and invasion. However, such enhancement was abolished in cells treated with OTX008, an inhibitor of Gal-1. Combinational treatment of OTX008 and sorafenib significantly reduced tumor growth and size.

Conclusions

Gal-1 overexpression was detected in HCC and this played a role in promoting tumorigenic processes and metastasis. The function of Gal-1 was found to be mediated through RER1. The correlations between miR-22, Gal-1 and RER1 expressions demonstrated the importance of miR-22 regulation on Gal-1/RER1 oncogenic activity. Lastly, the combinational treatment of OTX008 and sorafenib proved to be an improved therapeutic option compared to when administering sorafenib alone.

Electronic supplementary material

The online version of this article (10.1186/s13046-019-1402-x) contains supplementary material, which is available to authorized users.