Effects Of RNA Degradation Research Articles

Pervasive effects of RNA degradation on Nanopore direct RNA sequencing.

Oxford Nanopore direct RNA sequencing (DRS) is capable of sequencing complete RNA molecules and accurately measuring gene and isoform expression. However, as DRS is designed to profile intact RNA, expression quantification may be more heavily dependent upon RNA integrity than alternative RNA sequencing methodologies. It is currently unclear how RNA degradation impacts DRS or whether it can be corrected for. To assess the impact of RNA integrity on DRS, we performed a degradation time series using SH-SY5Y neuroblastoma cells. Our results demonstrate that degradation is a significant and pervasive factor that can bias DRS measurements, including a reduction in library complexity resulting in an overrepresentation of short genes and isoforms. Degradation also biases differential expression analyses; however, we find that explicit correction can almost fully recover meaningful biological signal. In addition, DRS provided less biased profiling of partially degraded samples than Nanopore PCR-cDNA sequencing. Overall, we find that samples with RNA integrity number (RIN) >9.5 can be treated as undegraded and samples with RIN >7 can be utilized for DRS with appropriate correction. These results establish the suitability of DRS for a wide range of samples, including partially degraded in vivo clinical and post-mortem samples, while limiting the confounding effect of degradation on expression quantification.

Small ncRNA-Seq Results of Human Tissues: Variations Depending on Sample Integrity.

Although mature miRNAs are relatively stable in vivo, RNA degradation can have a substantial influence on small noncoding RNA (sncRNA) profiles. Using different tissue storage conditions and RNA isolation procedures, we analyzed the integrity and quality of RNA isolates from human lung and heart tissues. We sequenced a total of 64 RNA samples and quantified the effect of RNA degradation, DNA contamination, and other confounding factors on the sncRNA-seq data. Besides microRNAs, other noncoding RNA species (piRNAs, tRNAs, snoRNAs, rRNAs) were investigated. Consistent with previous results, we found that the tissue specificity of microRNAs is generally well preserved. The distribution of microRNA isoforms was similar to the distribution of canonical forms. New miRNAs were more frequently discovered in degraded samples. sncRNA Reads generated from degraded samples mapped frequently to piRNAs, tRNAs, snoRNAs, or scaRNAs. Sequencing reads that were depleted of sncRNAs showed an increased mapping frequency to bacterial species. Our data emphasize the importance of sample integrity, especially for next-generation sequencing (NGS)-based high-throughput sncRNA profiles. For the prediction of novel miRNAs in particular, only samples with the highest RNA integrity should be used in order to avoid identification of false "miRNAs."

QSVA framework for RNA quality correction in differential expression analysis

RNA sequencing (RNA-seq) is a powerful approach for measuring gene expression levels in cells and tissues, but it relies on high-quality RNA. We demonstrate here that statistical adjustment using existing quality measures largely fails to remove the effects of RNA degradation when RNA quality associates with the outcome of interest. Using RNA-seq data from molecular degradation experiments of human primary tissues, we introduce a method-quality surrogate variable analysis (qSVA)-as a framework for estimating and removing the confounding effect of RNA quality in differential expression analysis. We show that this approach results in greatly improved replication rates (>3×) across two large independent postmortem human brain studies of schizophrenia and also removes potential RNA quality biases in earlier published work that compared expression levels of different brain regions and other diagnostic groups. Our approach can therefore improve the interpretation of differential expression analysis of transcriptomic data from human tissue.

Urinary mRNA for the Diagnosis of Renal Allograft Rejection: The Issue of Normalization.

Urinary messenger RNA (mRNA) quantification is a promising method for noninvasive diagnosis of renal allograft rejection (AR), but the quantification of mRNAs in urine remains challenging due to degradation. RNA normalization may be warranted to overcome these issues, but the strategies of gene normalization have been poorly evaluated. Herein, we address this issue in a case-control study of 108 urine samples collected at time of allograft biopsy in kidney recipients with (n = 52) or without (n = 56) AR by comparing the diagnostic value of IP-10 and CD3ε mRNAs-two biomarkers of AR-after normalization by the total amount of RNA, normalization by one of the three widely used reference RNAs-18S, glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and Hypoxanthine-guanine phosphoribosyltransferase (HPRT)-or normalization using uroplakin 1A (UPK) mRNA as a possible urine-specific reference mRNA. Our results show that normalization based on the total quantity of RNA is not substantially improved by additional normalization and may even be worsened with some classical reference genes that are overexpressed during rejection. However, considering that normalization by a reference gene is necessary to ensure polymerase chain reaction (PCR) quality and reproducibility and to suppress the effect of RNA degradation, we suggest that GAPDH and UPK1A are preferable to 18S or HPRT RNA.

RNA degradation differentially affects quantitative mRNA measurements of endogenous reference genes in human placenta

It has been highlighted that RNA quality and appropriate reference gene selection is crucial for the interpretation of RT-qPCR results in human placental samples. In this context we investigated the effect of RNA degradation on the mRNA abundance of seven frequently used reference genes in 119 human placental samples. Combining RNA integrity measurements, RT-qPCR analysis and mathematical modeling we found major differences regarding the effect of RNA degradation on the measured expression levels between the different reference genes. Furthermore, we demonstrated that a modified RNA extraction method significantly improved RNA quality and consequently increased transcript levels of all reference genes.

A 1-year time course study of human RNA degradation in body fluids under dry and humid environmental conditions

Blood, saliva and semen are some of the forensically most relevant biological stains found at crime scenes. mRNA profiling is a reliable approach for the identification of the origin of an evidentiary trace. A stable set of markers and the knowledge about the effects of RNA degradation under different environmental conditions is necessary for the determination of an unknown biological stain. The aim of this work was to compare RNA degradation for human blood, semen and saliva at three different concentrations during a 1-year time period and exposed to dry and humid conditions. Also, this study addressed the question whether there are relevant differences in the efficiency of two RNA extraction methods.

Quality assessment and data handling methods for Affymetrix Gene 1.0 ST arrays with variable RNA integrity

BackgroundRNA and microarray quality assessment form an integral part of gene expression analysis and, although methods such as the RNA integrity number (RIN) algorithm reliably asses RNA integrity, the relevance of RNA integrity in gene expression analysis as well as analysis methods to accommodate the possible effects of degradation requires further investigation. We investigated the relationship between RNA integrity and array quality on the commonly used Affymetrix Gene 1.0 ST array platform using reliable within-array and between-array quality assessment measures. The possibility of a transcript specific bias in the apparent effect of RNA degradation on the measured gene expression signal was evaluated after either excluding quality-flagged arrays or compensation for RNA degradation at different steps in the analysis.ResultsUsing probe-level and inter-array quality metrics to assess 34 Gene 1.0 ST array datasets derived from historical, paired tumour and normal primary colorectal cancer samples, 7 arrays (20.6%), with a mean sample RIN of 3.2 (SD = 0.42), were flagged during array quality assessment while 10 arrays from samples with RINs < 7 passed quality assessment, including one sample with a RIN < 3. We detected a transcript length bias in RNA degradation in only 5.8% of annotated transcript clusters (p-value 0.05, FC ≥ |2|), with longer and shorter than average transcripts under- and overrepresented in quality-flagged samples respectively. Applying compensatory measures for RNA degradation performed at least as well as excluding quality-flagged arrays, as judged by hierarchical clustering, gene expression analysis and Ingenuity Pathway Analysis; importantly, use of these compensatory measures had the significant benefit of enabling lower quality array data from irreplaceable clinical samples to be retained in downstream analyses.ConclusionsHere, we demonstrate an effective array-quality assessment strategy, which will allow the user to recognize lower quality arrays that can be included in the analysis once appropriate measures are applied to account for known or unknown sources of variation, such as array quality- and batch- effects, by implementing ComBat or Surrogate Variable Analysis. This approach of quality control and analysis will be especially useful for clinical samples with variable and low RNA qualities, with RIN scores ≥ 2.

Evaluation of Reference Genes and Normalization Strategy for Quantitative Real-Time PCR in Human Pancreatic Carcinoma

Histologically verified pairs (n= 10) of pancreatic tumors and non-neoplastic tissues were used for quantitative real-time PCR and the stability of 24 reference genes was analyzed with geNorm and NormFinder software. Raw Cqvalues correlated with the degree of RNA degradation. This correlation was abolished by normalization to Cqof 18S endogenous control gene. Both geNorm and NormFinder programs suggested EIF2B1, ELF1, MRPL19, and POP4 as the same most stable genes. We have thus identified suitable reference genes for future expression studies in pancreatic carcinoma. Normalization method reducing the effects of RNA degradation on the quality of results was also developed.

Estimating RNA-quality using GeneChip microarrays.

BackgroundMicroarrays are a powerful tool for transcriptome analysis. Best results are obtained using high-quality RNA samples for preparation and hybridization. Issues with RNA integrity can lead to low data quality and failure of the microarray experiment.ResultsMicroarray intensity data contains information to estimate the RNA quality of the sample. We here study the interplay of the characteristics of RNA surface hybridization with the effects of partly truncated transcripts on probe intensity. The 3′/5′ intensity gradient, the basis of microarray RNA quality measures, is shown to depend on the degree of competitive binding of specific and of non-specific targets to a particular probe, on the degree of saturation of the probes with bound transcripts and on the distance of the probe from the 3′-end of the transcript. Increasing degrees of non-specific hybridization or of saturation reduce the 3′/5′ intensity gradient and if not taken into account, this leads to biased results in common quality measures for GeneChip arrays such as affyslope or the control probe intensity ratio. We also found that short probe sets near the 3′-end of the transcripts are prone to non-specific hybridization presumable because of inaccurate positional assignment and the existence of transcript isoforms with variable 3′ UTRs. Poor RNA quality is associated with a decreased amount of RNA material hybridized on the array paralleled by a decreased total signal level. Additionally, it causes a gene-specific loss of signal due to the positional bias of transcript abundance which requires an individual, gene-specific correction. We propose a new RNA quality measure that considers the hybridization mode. Graphical characteristics are introduced allowing assessment of RNA quality of each single array (‘tongs plot’ and ‘degradation hook’). Furthermore, we suggest a method to correct for effects of RNA degradation on microarray intensities.ConclusionsThe presented RNA degradation measure has best correlation with the independent RNA integrity measure RIN, and therefore presents itself as a valuable tool for quality control and even for the study of RNA degradation. When RNA degradation effects are detected in microarray experiments, a correction of the induced bias in probe intensities is advised.

Evaluation of reference genes and normalization strategy for quantitative real-time PCR in human pancreatic carcinoma.

Histologically verified pairs (n = 10) of pancreatic tumors and non-neoplastic tissues were used for quantitative real-time PCR and the stability of 24 reference genes was analyzed with geNorm and NormFinder software. Raw Cq values correlated with the degree of RNA degradation. This correlation was abolished by normalization to Cq of 18S endogenous control gene. Both geNorm and NormFinder programs suggested EIF2B1, ELF1, MRPL19, and POP4 as the same most stable genes. We have thus identified suitable reference genes for future expression studies in pancreatic carcinoma. Normalization method reducing the effects of RNA degradation on the quality of results was also developed.

Combined expression of miR‐122a, miR‐1, and miR‐200b can differentiate degraded RNA samples from liver, pancreas, and stomach

The effect of RNA degradation on the diagnostic utility of microRNA has not been systematically evaluated in clinical samples. We asked if the microRNA profile is preserved in degraded RNA samples derived from mouse and human tissue. We selected tissue-specific microRNA candidates from published human microarray data, and validated them using quantitative reverse transcription polymerase chain reaction (QRTPCR) analyses on flash-frozen, normal mouse liver, pancreas, and stomach tissue samples. MiR-122a, miR-1, and miR-200b were identified as tissue-specific, and the 3-microRNA-based QRTPCR could predict the tissue origin for mouse tissue samples that were left at room temperature for 2 h with an accuracy of 91.7%. When we applied this 3-microRNA predictor to clinical specimens with various degree of RNA degradation, the predictor differentiated degraded RNA samples from liver, pancreas, and stomach with an accuracy of 90% (26/29). Expression levels of miR-122a, miR-1, and miR-200b were modestly changed after the extended (2-4 h) storage at room temperature, but the magnitudes of expression changes were small compared to the expression differences between various tissues of origin. This proof-of-principle study demonstrates that RNA degradation due to extended storage at room temperature does not affect the predictive power of tissue-specific microRNA QRTPCR predictor.

Comparison of gene expression profiling by reverse transcription quantitative PCR between fresh frozen and formalin-fixed, paraffin-embedded breast cancer tissues

Recent reports demonstrate the feasibility of quantifying gene expression by using RNA isolated from blocks of formalin-fixed, paraffin-embedded (FFPE) tumor tissue. The development of molecular tests for clinical use based on archival materials would be of great utility in the search for and validation of important genes or gene expression profiles. In this study, we compared the performance of different normalization strategies in the correlation of quantitative data between fresh frozen (FF) and FFPE samples and analyzed the parameters that characterize such correlation for each gene. Total RNA extracted from FFPE samples presented a shift in raw cycle threshold (Cq) values that can be explained by its extensive degradation. Proper normalization can compensate for the effects of RNA degradation in gene expression measurements. We show that correlation between normalized expression values is better for moderately to highly expressed genes whose expression varies significantly between samples. Nevertheless, some genes had no correlation. These genes should not be included in molecular tests for clinical use based on FFPE samples. Our results could serve as a guide when developing clinical diagnostic tests based on RT-qPCR analyses of FFPE tissues in the coming era of treatment decision-making based on gene expression profiling.

Selection of REFERENCE Genes for Normalization of qRT-PCR Data Derived From FFPE Breast Tumors

Gene expression analysis of formalin-fixed paraffin-embedded (FFPE) material by quantitative real-time polymerase chain reaction is a valuable tool for the retrospective analysis of clinical samples. The degraded nature of RNA from FFPE tissue can lead to variation in detection of gene expression between samples, hence, it is necessary to select reference genes that can reflect this variation. To identify suitable references for the normalization of target genes in FFPE breast tumors, expression of 10 potential references were measured in 10 endocrine therapy-naive, primary invasive breast tumors. The most stably expressed reference genes were identified with the use of the freely available algorithm geNorm. Using the geometric mean of MRPL19, TBP, and TFRC as a reference factor for FFPE tissue, there was a good correlation of ERalpha and HER2 measurements between FFPE and matched frozen tumors, indicating that this combination of reference genes successfully removed the effect of RNA degradation on quantitative real-time polymerase chain reaction analysis.

Reverse transcription-quantitative polymerase chain reaction: description of a RIN-based algorithm for accurate data normalization

BackgroundReverse transcription-quantitative polymerase chain reaction (RT-qPCR) is the gold standard technique for mRNA quantification, but appropriate normalization is required to obtain reliable data. Normalization to accurately quantitated RNA has been proposed as the most reliable method for in vivo biopsies. However, this approach does not correct differences in RNA integrity.ResultsIn this study, we evaluated the effect of RNA degradation on the quantification of the relative expression of nine genes (18S, ACTB, ATUB, B2M, GAPDH, HPRT, POLR2L, PSMB6 and RPLP0) that cover a wide expression spectrum. Our results show that RNA degradation could introduce up to 100% error in gene expression measurements when RT-qPCR data were normalized to total RNA. To achieve greater resolution of small differences in transcript levels in degraded samples, we improved this normalization method by developing a corrective algorithm that compensates for the loss of RNA integrity. This approach allowed us to achieve higher accuracy, since the average error for quantitative measurements was reduced to 8%. Finally, we applied our normalization strategy to the quantification of EGFR, HER2 and HER3 in 104 rectal cancer biopsies. Taken together, our data show that normalization of gene expression measurements by taking into account also RNA degradation allows much more reliable sample comparison.ConclusionWe developed a new normalization method of RT-qPCR data that compensates for loss of RNA integrity and therefore allows accurate gene expression quantification in human biopsies.

Consensus RT-nested PCR detection of yellow head complex genotypes in penaeid shrimp

A consensus RT-nested (n)PCR is described that detects the six distinct genotypic variants in the yellow head virus (YHV) complex. The PCR primers targeted ORF1b gene regions more highly conserved amongst the reference strains of YHV (genotype 1) and gill-associated virus (GAV, genotype 2) and a set of 57 field isolates containing multiple representatives of each genotype. The test employed short PCR (359 bp) and nPCR (147 bp) amplicons to minimise the effects of RNA degradation. To ensure ≤8-primer degeneracy, two primers were designed to each site, one accommodating sequence variations amongst genotype 1 isolates and the other variations amongst isolates of the other genotypes. The analytical sensitivity limits of the PCR and nPCR were estimated to be ∼1250 and ∼1.25 RNA copies, respectively. The superior group-specificity of the consensus RT-nPCR compared to other OIE-recommended PCR tests for YHV/GAV was demonstrated using RNA from 17 Penaeus monodon shrimp infected with representatives of each of the six genotypes. Phylogenetic analysis using the 94 nt ORF1b gene sequence spanned by the nPCR primers generated genotype assignments that were consistent with those obtained using the extended 671 nt sequence used for the initial identification of genotypes.

Application of transcript profiling in formalin‐fixed paraffin‐embedded diagnostic prostate cancer needle biopsies

To investigate the feasibility of transcript profiling in diagnostic formalin-fixed and paraffin-embedded (FFPE) biopsies for prostate cancer. Laser-capture microdissection (LCM) was used to microdissect glandular epithelium as well as stromal tissue in archival prostate needle biopsies. Optimized RNA extraction, reverse transcription and real-time PCR (QPCR) protocols were used to detect transcript expression. RNA degradation effects were assessed using hydrolysed cell line RNA and matched xenograft FFPE and frozen tumours. LCM and RNA extraction was achieved in all biopsies from a pilot cohort of five patients. cDNA produced was successfully used to detect expression of glyceraldehyde-3-phosphate dehydrogenase, RPL13, prostate-specific antigen, vimentin, inhibitor of differentiation/DNA binding 1 (Id-1) and polycomb group protein enhancer of zeste homolog 2 (EZH2) transcripts. In the cell line and xenograft models, we investigated the effect of RNA degradation on transcript quantification by QPCR. In both models normalization of transcript quantity with a housekeeping gene resulted in restored expression in all degraded samples to within a 50% difference of control samples. Using an extended cohort of 29 biopsies, we tested application in detecting differences in EZH2 and Id-1 expression between malignant and benign epithelium. The results confirmed that our technique was capable of quantifying significant differences in expression between malignant and benign epithelium consistent with the reported trends. This study reports the use of standard FFPE needle biopsies for transcript profiling and supports the concept of molecular prognostic studies in tissue acquired at diagnosis in prostate cancer.

Effects of RNA degradation on gene expression analysis of human postmortem tissues

The Affymetrix GeneChip platform was used to build a gene expression database of the normal human body. Postmortem human tissues represent a valuable source of biological materials for this type of study, but their use entails some delays before harvesting such tissues. We first evaluated the RNA quality obtained from tissues obtained 3-5 h postmortem and found variations that were both tissue and donor-dependent. RNAs extracted from brain regions were of higher quality than those obtained from the gut, while the cause of death was a significant factor in donor-dependent differences. To avoid these variables, we used rat duodenum to determine the effects of RNA degradation on the analysis of gene expression. Surprisingly, even samples exhibiting significant RNA degradation yielded robust gene expression results, comparable to those obtained using intact samples at a certain signal intensity cutoff. We extended these findings to our human expression database and obtained similar results, indicating that the Affymetrix platform, which is biased to the 3' end of transcripts for detection, can tolerate significant RNA degradation, while still yielding high quality expression data. Our resulting body index expression database is a valuable research tool. As examples of potential uses, we report novel expression sites for four potential therapeutic targets--CCL27, GPR22, GPR113 and GPR128--and as well as a set of thymus-specific genes, including three not previously associated with the thymus.