It has been difficult to study how often RNA polymerase makes mistakes and what effect these mistakes have on organisms because the techniques available for research are labour-intensive and technically challenging. Here, Lucas Carey demonstrates that it is possible to use a technique called RNA sequencing to study the accuracy of RNA polymerase in human and yeast cells. The experiments show that altering the levels of the different subunits of RNA polymerase in cells can change how many mistakes are made during transcription.
This suggests that cells may be able regulate number of mistakes by controlling the production of specific subunits. For example, errors in specific parts of the mRNA can alter how the whole instruction is edited later, while others might make only a tiny change to the protein encoded by the gene. Carey also found evidence that the instructions encoded by genes may have evolved in such a way to minimise the effect of any errors on their roles in cells.
RNA sequencing is less labour-intensive than other methods used to study the accuracy of RNA polymerase and is already used to address other research questions on a wide variety of different organisms.
A major challenge for the future is to find out if the mistakes made by RNA polymerase can lead to cancer and other human diseases. The information that determines protein sequence is stored in the genome, but that information must be transcribed by RNA polymerase and translated by the ribosome before reaching its final form. DNA polymerase error rates have been well characterized in a variety of species and environmental conditions, and are low — on the order of one mutation per 10 8 —10 10 bases per generation Lynch, ; Lang and Murray, ; Zhu et al.
In contrast, RNA polymerase errors are uniquely positioned to generate phenotypic diversity. Error rates are high 10 -6 —10 -5 Gout et al. Likewise, because many RNAs are present at an average of less than one molecule per cell in microbes Pelechano et al.
Despite the fact that transient errors can result in altered phenotypes Gordon et al. This is because current methods for measuring polymerase fidelity are technically challenging Gout et al. The input is a set of RNA-seq fastq files and a reference genome, and the output is the error rate at each position in the genome. High quality full-length RNA-seq reads are mapped to the reference genome or transcriptome using bwa, and only reads that map completely with two or fewer mismatches are kept.
Unique errors in the middle of reads cyan box are kept and counted. Technical errors from reverse transcription and sequencing, and biological errors from RNA polymerase look identical single-nucleotide differences from the reference genome. Therefore, a major challenge in identifying single-nucleotide polymorphisms SNPs and in measuring changes in polymerase fidelity is the reduction of technical errors Kleinman and Majewski, ; Pickrell et al.
First, I map full-length untrimmed reads to the genome and discard reads with indels, with more than two mismatches, that map to multiple locations in the genome, and that do not map end to end along the full length of the read.
Next, I trim the ends of the mapped reads, as alignments are of lower quality along the ends, and the mismatch rate is higher, especially at splice junctions. I also discard any cycles within the run with abnormally high error rates, and bases with low Illumina quality scores Figure 1—figure supplement 1. Finally, using the remaining bases, I count the number of matches and mismatches to the reference genome at each position in the genome.
I discard positions with identical mismatches that are present more than once, as these are likely due to subclonal DNA polymorphisms or sequences that Illumina miscalls in a systematic manner Meacham et al. The result is a set of mismatches, many of which are technical errors and some of which are RNA polymerase errors. In order to determine if RNA-seq mismatches are due to RNA polymerase errors, it is necessary to identify sequence locations in which RNA polymerase errors are expected to have a measurable effect, or situations in which RNA polymerase fidelity is expected to vary.
I reasoned that RNA polymerase errors that alter positions necessary for splicing should result in intron retention, while sequencing errors should not affect the final structure of the mRNA Figure 2a. However, mutations in the donor and acceptor splice sites also result in decreased expression Jung et al.
The ability of RNA polymerase errors to significantly affect splicing has been proposed Fox-Walsh and Hertel, but never previously measured. This suggests that low RPB9 expression may cause decreased polymerase fidelity in vivo. I find that the nuclear fraction has a higher RNA polymerase error rate than does the cytoplasmic fraction Figure 2c,d , suggesting that either that nuclear RNA-seq has a higher technical error rate or that the cell has mechanisms for reducing the effective polymerase error rate by preventing the export of mRNAs that contain errors.
Rpb9 and Dst1 are known to be involved in RNA polymerase fidelity in vitro, yet there is conflicting evidence as to the role of Dst1 in vivo Shaw et al.
Part of these conflicts may result from the fact that the only available assays for RNA polymerase fidelity are special reporter strains that rely on DNA sequences known to increase the frequency of RNA polymerase errors.
Furthermore, differences in RNA levels do not necessitate differences in stoichiometry among the subunits in active Pol II complexes. Likewise, cells with low DST1 have high error rates Figure 3a. The increase in errors rate is not a property of cells defective for transcription elongation Figure 3—figure supplement 1.
The increase in error rates due to mutations in Rpb9 and Dst1 have not been robustly measured, however, there are some rough numbers. In addition, we observe more single-nucleotide insertions in the RNA-seq data from the high error rate samples, suggesting that depletion of RPB9 and DST1 results in increased insertions in transcripts, but not increased deletions Figure 3—figure supplement 2.
Finally, genetic reduction in RNA polymerase fidelity results in increased intron retention, consistent with RNA polymerase errors causing reduced splicing efficiency Figure 3b. A unique advantage of MORPhEUS is that it measures thousands of RNA polymerase errors across the entire transcriptome in a single experiment, and thus enables he complete characterization of the mutation spectrum and biases of RNA polymerase. This result, along with other sequencing based results Gout et al.
Interestingly, I find that coding sequences have evolved so that errors are less likely to produce in-frame stop codons than out-of-frame stop codons, suggesting that natural selection may act to minimize the effect of polymerase errors Figure 4.
For all genes in yeast, I calculated the number of codons which are one polymerase error from a stop codon. Much existing RNA-seq data is available as bam files aligned to the human genome.
Each frame was, on average, taken about 60 minutes apart some frames were lost due to loss of focus. The bright field frames are on the left, the GFP fluorescence frames are on the right and begin after the microcolony has become well established.
The cell outlined in yellow is the cell that will change phenotype from OFF to ON, and the descendants of that cell will also manifest the ON phenotype.
The cell outlined in red is the direct relative of the cell that switches ON, but that cell, and all the descendants of that cell remain OFF.
All the GFP fluorescence frames, until the last frame, have ms exposure times and the resulting images were over-exposed using the ColorSync Utility to observe the faint fluorescence signal. The last GFP fluorescence frame had a ms exposure time. We thank S. Rosenberg, H. Bellen, G. Ira and H. Dierick for critically reviewing the manuscript. We thank the anonymous reviewers for stimulating discussion. Abstract Transmission of cellular identity relies on the faithful transfer of information from the mother to the daughter cell.
Author Summary The transfer of information from cell to cell is crucial for preserving cellular identity. Introduction Stable phenotypic change is mostly associated with DNA alteration [1] , the hardware of the cell, but rarely as the consequence of errors in the transmission of cellular genetic programs, the software of the cell [2] , [3]. Download: PPT. Figure 1. Novel system to study the consequences of error-prone transcription sequences.
Figure 2. Bistability, hysteresis and stochastic switching in the lac system. Figure 3. Transcriptional Slippage in the lacI Gene Increases Epigenetic Stochastic Switching To determine the proportion of cells that are ON, we used the green fluorescent protein gene integrated within the lac operon Figure 1A [18]. Figure 4. Figure 5. Transcription errors, not translational frameshifting, at the lacI A 9 sequence influences stochastic switching.
Stochastic Errors in Information Transfer Have Heritable Phenotypic Consequences DNA makes RNA makes protein; until now, errors in making two of the three elements in information transfer, DNA replication and protein folding, have been shown to modify cellular inheritance through mutation or prion conformational change [43] Figure 6. Figure 6. Phenotypic consequences from errors in information transfer in a cellular lineage. Materials and Methods Bacterial Strains All strains used in this study are derived from the wild-type sequenced E.
Growth Conditions and Media To demonstrate hysteresis and bistability in lac operon expression in single cells, a bacterial culture grown in minimal A salts [33] plus MgSO 4 1 mM with succinate 0. Supporting Information. Figure S1. Figure S2. Figure S3. Figure S4. Figure S5. Figure S6. Movie S1. Table S1.
Table S2. Bacterial strains. Table S3. Table S4. Text S1. Text S2. References for supporting information. Acknowledgments We thank S. References 1.
Cell — View Article Google Scholar 2. Monod J, Jacob F General conclusions: teleonomic mechanisms in cellular metabolism, growth, and differentiation.
View Article Google Scholar 5. Cell — View Article Google Scholar 7. Dubnau D, Losick R Bistability in bacteria.
Novick A, Weiner M Enzyme induction as an all-or-none phenomenon. A standing challenge is to elucidate what limits the possibility to decrease the error rates in these crucial processes in the central dogma even further, say to values similar to those achieved by DNA polymerase. Is there a biophysical tradeoff in play or maybe the observed error rates have some selective advantages? What is the error rate in transcription and translation?
Reader Mode The central dogma recognizes the flow of genomic information from the DNA into functional proteins via the act of transcription, which results in synthesis of messenger RNA, and the subsequent process of translation of that RNA into the string of amino acids that make up a protein.
Purchase Draft Download About us. What is the mutation rate during genome replication? What is the rate of recombination? As with replication errors, most environmentally induced DNA damage is repaired, resulting in fewer than 1 out of every 1, chemically induced lesions actually becoming permanent mutations.
The same is true of so-called spontaneous mutations. Rather, they are usually caused by normal chemical reactions that go on in cells, such as hydrolysis. These types of errors include depurination , which occurs when the bond connecting a purine to its deoxyribose sugar is broken by a molecule of water, resulting in a purine-free nucleotide that can't act as a template during DNA replication, and deamination , which results in the loss of an amino group from a nucleotide, again by reaction with water.
Again, most of these spontaneous errors are corrected by DNA repair processes. But if this does not occur, a nucleotide that is added to the newly synthesized strand can become a permanent mutation. Mutation rates vary substantially among taxa, and even among different parts of the genome in a single organism. Scientists have reported mutation rates as low as 1 mistake per million 10 -8 to 1 billion 10 -9 nucleotides, mostly in bacteria , and as high as 1 mistake per 10 -2 to 1, 10 -3 nucleotides, the latter in a group of error-prone polymerase genes in humans Johnson et al.
Even mutation rates as low as 10 can accumulate quickly over time, particularly in rapidly reproducing organisms like bacteria. This is one reason why antibiotic resistance is such an important public health problem; after all, mutations that accumulate in a population of bacteria provide ample genetic variation with which to adapt or respond to the natural selection pressures imposed by antibacterial drugs Smolinski et al.
Take E. The genome of this common intestinal bacterium has about 4. Assuming a mutation rate of 10 -9 i.
That may not seem like much. At that point, approximately 10, of these bacteria will have accumulated at least one mutation. As the number of bacteria carrying different mutations increases, so too does the likelihood that at least one of them will develop a drug-resistant phenotype. Likewise, in eukaryotes, cells accumulate mutations as they divide. In humans, if enough somatic mutations i. Or, less frequently, some cancer mutations are inherited from one or both parents; these are often referred to as germ-line mutations.
One of the first cancer-associated somatic mutations was discovered in , when researchers found that a mutated HRAS gene was associated with bladder cancer Reddy et al. HRAS encodes for a protein that helps regulate cell division.
Since then, scientists have identified several hundred additional "cancer genes. Of course, not all mutations are "bad. However, too much of a good thing can be dangerous. If DNA repair were perfect and no mutations ever accumulated, there would be no genetic variation—and this variation serves as the raw material for evolution.
Successful organisms have thus evolved the means to repair their DNA efficiently but not too efficiently, leaving just enough genetic variability for evolution to continue. Crick, F. Codon-anticodon pairing: The wobble hypothesis. Journal of Molecular Biology 19 , — link to article.
Johnson, R. Journal of Biological Chemistry , — Reddy, E. A point mutation is responsible for the acquisition of transforming properties by the T24 human bladder carcinoma oncogene. Nature , — link to article. Smolinski, M. Streisinger, G. Frameshift mutations and the genetic code.
Watson, J. Molecular structure of nucleic acids. Wijnen, J. Nature Genetics 20 , — link to article. Restriction Enzymes. Genetic Mutation.
Functions and Utility of Alu Jumping Genes.
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