To do so, we collected expression data from tissue samples that were allowed to decay for varying amounts of time prior to RNA extraction. We observed widespread effects of RNA quality on measurements of gene expression levels, as well as a slight but significant loss of library complexity in more degraded samples.
While standard normalizations failed to account for the effects of degradation, we found that by explicitly controlling for the effects of RIN using a linear model framework we can correct for the majority of these effects.
We conclude that in instances in which RIN and the effect of interest are not associated, this approach can help recover biologically meaningful signals in data from degraded RNA samples. Degradation of RNA transcripts by the cellular machinery is a complex and highly regulated process. In live cells and tissues, the abundance of mRNA is tightly regulated, and transcripts are degraded at different rates by various mechanisms [ 1 ], partially in relation to their biological function [ 2 — 5 ].
In contrast, the fates of RNA transcripts in dying tissue, and the decay of isolated RNA are not part of normal cellular physiology and, therefore, are less likely to be tightly regulated.
It remains largely unclear whether most transcript types decay at similar rates under such conditions or whether rates of RNA decay in dying tissues are associated with transcript-specific properties. These questions are of great importance for studies that rely on sample collection in the field or in clinical settings both from human populations as well as from other species , in which tissue samples often cannot immediately be stored in conditions that prevent RNA degradation.
In these settings, extracted RNA is often partly degraded and may not faithfully represent in vivo gene expression levels. Sample storage in stabilizers like RNALater lessens this problem [ 6 ] but is not always feasible. The degree to which this confounder affects estimates of gene expression levels is not well understood. There is also no consensus on the level of RNA decay that renders a sample unusable or on approaches to control for the effect of ex vivo processes in the analysis of gene expression data.
Thus, while standardized RNA quality metrics such as the Degradometer [ 7 ] or the RNA Integrity Number RIN; [ 8 ] , provide well-defined empirical methods to assess and compare sample quality, there is no widely accepted criterion for sample inclusion.
For example, proposed thresholds for sample inclusion have varied between RIN values as high as 8 [ 9 ] and as low as 3. The recent Genotype-Tissue Expression GTEx project [ 11 ], for instance, reports both the number of total RNA samples they collected as well as the number of RNA samples with RIN scores higher than 6, presumably as a measure of the number of high quality samples in the study.
Broadly speaking, three approaches can be adopted to deal with RNA samples of variable quality. It also could exclude the possibility of utilizing unique and difficult to collect samples from remote locations or historical collections. Second, if investigators are willing to assume that all transcript types decay at a similar rate, variation in gene expression estimates due to differences in RNA integrity could be accounted for by applying standard normalization procedures.
Third, if different transcripts decay at different rates, and if these rates are consistent across samples for a given level of RNA degradation — for example, a given RIN value — a model that explicitly incorporates measured, sample-specific, degradation levels could be applied to gene expression data to correct for the confounding effects of degradation. To date, most studies apply a combination of the first two approaches: an application of an arbitrary RNA quality cutoff typically based on RIN score , followed by standard normalization of the data, which assumes that RNA samples at any RIN value higher than the chosen cutoff are not subjected to transcript-specific decay rates.
However, current work on the effects of RNA decay has not yet provided clear guidelines with respect to these approaches. These studies broadly suggest that both the quantity and quality of recovered RNA from tissues can be affected by acute pre-mortem stressors, such as pyrexia or prolonged hypoxia [ 12 — 14 ], and by the time to sample preservation and RNA extraction. The quantity and quality of recovered RNA are strongly dependent on the type of tissue studied [ 15 ], even when sampling from the same individual [ 16 , 17 ].
These differences in yield across tissues have resulted in a wide range of recommendations for an acceptable post-mortem interval for extracting usable, high-quality RNA, ranging from as little as 10 minutes [ 18 ] to upwards of 48 hours [ 19 ], depending on tissue source and preservation conditions. Similarly, studies examining changes in the relative abundance of specific transcripts as a result of ex vivo RNA decay have reached somewhat contradictory recommendations.
Some of this conflict may be attributable to methodological differences. Studies that focused on small numbers of genes assayed through quantitative PCR consistently report little to no effect of variation in RNA quality on gene expression estimates [ 6 , 19 — 22 ]. Conversely, microarray-based studies have repeatedly reported significant effects of variation of RNA quality on gene expression estimates, even after applying standard normalization approaches.
Likewise, a substantial fraction of genes in peripheral blood mononuclear cells PBMCs appears to be sensitive to ex vivo incubation [ 21 ].
Other microarray-based studies have reached similar conclusions, both in samples from humans [ 15 , 16 , 22 , 23 ] and other organisms [ 24 ], and have urged caution when analyzing RNA samples with medium or low RIN scores, although the definition of an acceptable RNA quality threshold remains elusive.
To examine the effects of RNA degradation in a setting relevant to field study sample collection, we sequenced RNA extracted from PBMC samples that were stored unprocessed at room temperature for different time periods, up to 84 hours.
Due to the high sensitivity and resolution of high-throughput RNA sequencing, our data provide an unprecedentedly detailed picture of the dynamics of RNA degradation in stressed, ex vivo cells.
Based on our results, we develop specific recommendations for accounting for these effects in gene expression studies.
The PBMC samples were stored at room temperature for 0 hours, 12 hours, 24 hours, 36 hours, 48 hours, 60 hours, 72 hours and 84 hours prior to RNA extraction. Based on the RIN values we chose to focus on 20 samples from five time points 0 hours, 12 hours, 24 hours, 48 hours and 84 hours that spanned the entire scale of RNA quality.
We generated poly-A-enriched RNA sequencing libraries from the 20 samples using a standard RNA sequencing library preparation protocol see [ 25 ]. We used BWA 0. Sequence reads from individual 2 were poorly mapped, especially in the later time-points see Methods and Additional file 2 : Figure S1 ; we thus excluded the data from all samples from this individual in subsequent analysis. Principal component analysis of our data demonstrates that much of the variation A correlation matrix based on the gene expression data Figure 1 B indicates that while samples of relatively high quality RNA cluster by individual, data from RNA samples that experienced high yet similar degradation levels are more correlated than data from samples from the same individual across the time-points.
Similarly, these effects are robust to the choice of mapping algorithm. Because BWA does not map reads across exon splice junctions, we also remapped our data excluding individual 2 using TopHat 2. Finally, we found that the global effects of RNA degradation on estimated gene expression levels could not be eliminated by globally regressing out RIN scores [see Additional file 8 : Figure S6].
Broad effects of RNA degradation. A PCA plot of the 15 samples included in the study based on data from 29, genes with at least one mapped read in a single individual. Different colors identify different time-points, while each shape indicates a particular individual in the data set.
B Spearman correlation plot of the 15 samples in the study. PCA, principal component analysis. The possibility of reduced sequencing library complexity is often cited as a reason to exclude RNA samples of low quality. This concern is primarily based on the observation that sequencing RNA samples of lower RNA quality results in relatively decreased proportions of mappable reads, an observation corroborated in our study [see Additional file 2 : Figure S1].
Yet, it is unclear to what extent this property affects the ability to estimate gene expression levels in RNA samples of low quality. To assess the effects of RIN on sample complexity, we plotted the distribution of RPKM values within individuals at different time points.
This seems counterintuitive, but can be explained by the presence of a few highly expressed genes in the samples of low RNA quality. This suggests that a non-uniform effect of RNA degradation on gene expression levels results in somewhat lower complexity of the sequencing library Figure 2 , Additional file 10 : Figure S8.
On the other hand, both within a single individual and across the whole dataset, we found that nearly all genes whose expression could be measured at 0 hours are also detected as expressed throughout the entire time-course experiment. Only a few genes Table 1 present in all individuals up until a given time point were completely absent from the data at later time points. Changes in library complexity over time. Dashed lines indicate median RPKM at each time-point.
B as A, but 0 hours and 24 hours. C as A, but 0 hours and 48 hours. D as A, but 0 hours and 84 hours. RPKM, reads per kilobase transcript per million. We sought to understand better the nature of transcript degradation in the RNA samples of lower quality.
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Advanced Search. Search Menu. Article Navigation. Close mobile search navigation Article Navigation. Volume Article Contents Abstract. Complexes of RNases. The RNases in action. RNA degradation in eukaryotes. Concluding remarks. The critical role of RNA processing and degradation in the control of gene expression. Oxford Academic. Susana Domingues. Michal Malecki. Rute G. Ricardo N. Filipa P. Margarida Saramago.
Sandra C. Revision received:. Select Format Select format. Permissions Icon Permissions. Abstract The continuous degradation and synthesis of prokaryotic mRNAs not only give rise to the metabolic changes that are required as cells grow and divide but also rapid adaptation to new environmental conditions.
RNases , RNA , post-transcriptional control of gene expression. Open in new tab Download slide. Google Scholar Crossref. Search ADS. The role of the S1 domain in exoribonucleolytic activity: substrate specificity and multimerization. PNPase is a key player in the regulation of small RNAs that control the expression of outer membrane proteins. A conditional lethal mutant of Escherichia coli which affects the processing of ribosomal RNA.
Google Scholar PubMed. Stabilization of discrete mRNA breakdown products in ams pnp rnb multiple mutants of Escherichia coli K Analysis of the in vivo decay of the Escherichia coli dicistronic pyr F- orf F transcript: evidence for multiple degradation pathways. Characterizing ribonucleases in vitro examples of synergies between biochemical and structural analysis. RNase activity of polynucleotide phosphorylase is critical at low temperature in Escherichia coli and is complemented by RNase II.
The Ams altered mRNA stability protein and ribonuclease E are encoded by the same structural gene of Escherichia coli. Characterization in vitro of the defect in a temperature-sensitive mutant of the protein subunit of RNase P from Escherichia coli.
An lrp-like gene of Bacillus subtilis involved in branched-chain amino acid transport. Cold-temperature induction of Escherichia coli polynucleotide phosphorylase occurs by reversal of its autoregulation. The yeast exosome functions as a macromolecular cage to channel RNA substrates for degradation.
Nucleotide sequence and in vitro processing of a precursor molecule to Escherichia coli 4. Overexpression of the polynucleotide phosphorylase gene pnp of Streptomyces antibioticus affects mRNA stability and poly A tail length but not ppGpp levels. Polynucleotide phosphorylase hinders mRNA degradation upon ribosomal protein S1 overexpression in Escherichia coli.
Polyadenylated RNA isolated from the archaebacterium Halobacterium halobium. Messenger ribonucleic acid content of Bacillus amyloliquefaciens throughout its growth cycle compared with Bacillus subtilis The ribonucleases J1 and J2 are essential for growth and have independent roles in mRNA decay in Streptococcus pyogenes. Interaction network containing conserved and essential protein complexes in Escherichia coli. Structural framework for the mechanism of archaeal exosomes in RNA processing.
RNase II levels change according to the growth conditions: characterization of gmr, a new Escherichia coli gene involved in the modulation of RNase II. The reaction mechanism of ribonuclease II and its interaction with nucleic acid secondary structures. Autogenous regulation of Escherichia coli polynucleotide phosphorylase expression revisited. Loss of RNase R induces competence development in Legionella pneumophila. Purification and characterization of the Escherichia coli exoribonuclease RNase R.
Probing the functional importance of the hexameric ring structure of RNase PH. Ribosomal protein L20 controls expression of the Bacillus subtilis infC operon via a transcription attenuation mechanism. Polynucleotide phosphorylase is a global regulator of virulence and persistency in Salmonella enterica. Overexpression, purification, and properties of Escherichia coli ribonuclease II. Reconstitution of the degradation of the mRNA for ribosomal protein S20 with purified enzymes.
Novel activities of glycolytic enzymes in Bacillus subtilis : interactions with essential proteins involved in mRNA processing. Escherichia coli RNase D. Dam Mikkelsen. A novel oligoribonuclease of Escherichia coli. Crystal structure of the ribonuclease H domain of HIV-1 reverse transcriptase. Recycling of a regulatory protein by degradation of the RNA to which it binds. De La Sierra-Gallay. Del Favero. Regulation of Escherichia coli polynucleotide phosphorylase by ATP.
Enzymatic basis for hydrolytic versus phosphorolytic mRNA degradation in Escherichia coli and Bacillus subtilis. RNase PH: an Escherichia coli phosphate-dependent nuclease distinct from polynucleotide phosphorylase. An evolutionarily conserved RNA stem-loop functions as a sensor that directs feedback regulation of RNase E gene expression.
Biochemical characterization of the RNase II family of exoribonucleases from the human pathogens Salmonella typhimurium and Streptococcus pneumoniae. RNA processing is involved in the post-transcriptional control of the citQRP operon from Lactococcus lactis biovar diacetylactis. A comparative analysis of the citrate permease P mRNA stability in Lactococcus lactis biovar diacetylactis and Escherichia coli. The yeast mitochondrial degradosome.
A single subunit, Dis3, is essentially responsible for yeast exosome core activity. Cold shock exoribonuclease R VacB is involved in Aeromonas hydrophila pathogenesis. Ribonucleases J1 and J2: two novel endoribonucleases in B. Rrp4 and Csl4 are needed for efficient degradation but not for polyadenylation of synthetic and natural RNA by the archaeal exosome.
Factors influencing RNA degradation by Thermus thermophilus polynucleotide phosphorylase. Murray P. In recent years, our knowledge of the mechanisms of RNA degradation has increased considerably with discovery of the participating RNases and analysis of mutants affected in the various degradative pathways.
In this review, each of these processes is described, as it is currently understood in bacteria. The picture that emerges is that decay of mRNA and degradation of stable RNA share many common features, and that their initial steps also overlap with those of RNA maturation. Thus, bacterial cells do not contain dedicated machinery for degradation of different classes of RNA or for different processes.
Rather, only the specificity of the RNase and the accessibility of the substrate determine whether or not a particular RNA will be acted upon. For mRNAs, rapid decay serves to continuously adjust the message population to the needs of the cell for specific proteins 1 — 4. Traditionally, these two processes have been regarded as separate areas of investigation, and while considerable effort has gone into understanding mRNA decay, studies of stable RNA degradation generally have languished.
Consequently, new information obtained in one of these areas often has not transferred easily to studies in other areas. Nevertheless, each of the aforementioned processes requires the action of ribonucleases RNases.
As more of these enzymes have been identified, and as we have learned more details about their functional roles, it has become increasingly clear that many of them participate in multiple RNA metabolic pathways, and that there is considerable overlap among the diverse processes mentioned above.
Thus, while this article will focus on RNA degradation as it is currently understood in bacteria, particular emphasis will be placed on discussion of the many similarities between the turnover of mRNA and the removal of stable RNAs during stress or quality control, as well as on how the degradative machinery may overlap with that of RNA maturation.
The rapid turnover of bacterial mRNAs has been known since the time of their discovery, and over the years much effort has been devoted to understanding the mechanisms responsible for this dramatic instability [recent reviews are in Refs 1 — 3 ]. Such studies have identified multiple cis -acting structural features within the message itself as well as the participation of specific RNases that together contribute to the relative stabilities of different mRNAs. In addition, the translatability of a particular message, as determined by the strength of its Shine—Dalgarno sequence and other factors that influence the extent of ribosome loading also affect how rapidly an mRNA is degraded 6.
In considering mRNA decay, it is useful to distinguish between factors that influence initiation of the process from those that ultimately lead to complete breakdown of the message to mononucleotides, although since intermediates rarely accumulate, it is likely that the two phases of degradation are tightly coupled. In Escherichia coli , initiation of mRNA degradation is primarily due to endonucleolytic attack, generally mediated by the essential enzyme, RNase E 1 , 2.
An important role for RNase E in mRNA decay was first suggested from studies of total mRNA turnover, and subsequently confirmed by many studies examining breakdown of individual messages 1 , 2. More recent genomic analyses using microarrays have established that RNase E is a major participant in the turnover process 7. However, a number of other endoribonucleases also participate in mRNA decay to a limited degree.
In addition, under certain circumstances, which are not well understood, a number of bacterial toxins such as RelE, MazF and Kid may also initiate mRNA degradation 8.
An interesting feature of RNase E action is that it appears to function as part of a multiprotein complex, the RNA degradosome, that contains, in addition, the exoribonuclease, polynucleotide phosphorylase PNPase , an RNA helicase, RhlB, and the glycolytic pathway enzyme, enolase 1 , 2.
Other components in sub-stoichiometric amounts may also be present 9. The association of an endoribonuclease, an exoribonuclease and an RNA helicase would seem to make the degradosome ideally suited for the breakdown of RNA molecules. Nevertheless, it has been difficult to prove this point or even to demonstrate that the degradosome actually exists in vivo.
In fact, cells containing truncated forms of RNase E, which precludes degradosome assembly, grow relatively normally and display normal half-lives for several mRNAs On the other hand, a more extensive analysis of degradosome function using DNA microarrays revealed that the assembled multiprotein complex was necessary for decay of some mRNAs in vivo 7.
In addition, the degradosome was shown to be important for removal of mRNA fragments containing highly structured repeated extragenic palindrome REP elements Thus, the most recent evidence suggests that the degradosome does function in vivo , but that it may play only a limited role.
Following an initial endonucleolytic cleavage which likely inactivates the message for translation, additional cleavages result in breakdown of the mRNA into fragments. Details of these secondary cleavages regarding the enzymes involved, the number of cuts and the sites of cutting remain somewhat murky as intermediates in the process rarely accumulate due to subsequent degradation to the mononucleotide level by exoribonucleases.
Interestingly, although each of these three exoribonucleases has distinct catalytic properties in vitro , they display significant functional overlap in vivo. Thus, mutant cells lacking just one of the three nucleases grow essentially normally, indicating that the remaining two enzymes can rescue the missing function.
On the other hand, since cells lacking RNase II and RNase R are relatively unaffected, the remaining enzyme in this situation, PNPase, appears to have sufficiently broad specificity to carry out all essential functions at rates that do not have a major impact on cell growth Table 2. What might these essential functions be? RNase R, by itself 15 , and PNPase, as part of the degradosome 16 or in association with RhlB 17 , can degrade structured RNA fragments in vitro , and are required for such degradation in vivo.
One possibility is that there is simply insufficient RNase R to carry out all the essential functions normally carried out by the two missing nucleases.
If this explanation is correct, overexpression of RNase R should rescue the double mutant strain. This latter point raises the question of what are the relative contributions of hydrolytic and phosphorolytic degradation to overall mRNA decay when all RNases are present. Early work, using 18 O analysis to determine the mode of phosphodiester bond breakage, indicated that in E. The enzymatic basis for this difference was demonstrated by the finding that crude extracts of E. These data indicate that while the initial endonucleolytic cleavages can continue in the absence of PNPase, the rate of removal of the resulting fragments is greatly slowed.
Nevertheless, PNPase is not an essential enzyme 24 presumably because in its absence, other RNases assume a more important role. The situation in E. Inasmuch as mRNA decay is primarily hydrolytic 20 , the role of PNPase, and by inference the degradosome, must be limited, at least under usual laboratory growth conditions. Perhaps, there are certain conditions in which phosphorolytic decay assumes a greater role.
For example, it is already known that PNPase levels increase during cold shock Moreover, in the wild, where famine conditions may be more prevalent, phosphorolytic degradation could help to conserve energy during the constant synthesis and decay of mRNAs.
However, under conditions in which hydrolytic degradation is the norm, then the relative contributions of RNase II and RNase R need to be considered. However, this idea was thrown into question based on a genome-wide analysis of message levels in cells lacking RNase II In addition, an important role for RNase R in mRNA decay was subsequently discovered, especially for those molecules with considerable secondary structure RNase R was also found to increase dramatically in response to a variety of stress conditions 27 , These residual products are digested to mononucleotides by oligoribonuclease, an exoribonuclease specific for very short chains 8.
In the absence of oligoribonuclease small fragments derived from mRNA, 2—5 nt in length, accumulate to high levels Since oligoribonuclease is essential for cell viability, it is presumed that the presence of these RNA fragments is deleterious to the cell, but this remains to be proven.
However, under certain physiological conditions or treatment of bacteria with certain agents, extensive degradation of these molecules may occur. Since a detailed review of this area was published relatively recently 5 , only a few relevant highlights will be discussed here. In fact, it has been known for many years that stable RNA, particularly rRNA, can be extensively degraded under starvation conditions 5. Other slow growth conditions such as stationary phase or following a nutritional downshift also lead to rRNA degradation 5.
The mechanisms that protect ribosomes from degradation during exponential phase, but allow extensive degradation under various slow- or no-growth conditions, are not understood.
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