Sense And Antisense Strands Of Dna PdfBy Archie E. In and pdf 14.05.2021 at 13:02 7 min read
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Although genome-wide transcriptional analysis has been used for many years to study bacterial gene expression, many aspects of the bacterial transcriptome remain undefined. One example is antisense transcription, which has been observed in a number of bacteria, though the function of antisense transcripts, and their distribution across the bacterial genome, is still unclear.
Single-stranded RNA-seq results revealed a widespread and non-random pattern of antisense transcription covering more than two thirds of the B. Our analysis revealed a variety of antisense structural patterns, suggesting multiple mechanisms of antisense transcription.
The data revealed several instances of sense and antisense expression changes in different growth conditions, suggesting that antisense transcription may play a role in the ways in which B. Significantly, genome-wide antisense expression occurred at consistently higher levels on the lagging strand, while the leading strand showed very little antisense activity. Intrasample gene expression comparisons revealed a gene dosage effect in all growth conditions, where genes farthest from the origin showed the lowest overall range of expression for both sense and antisense directed transcription.
Additionally, transcription from both strands was verified using a novel strand-specific assay. The variety of structural patterns we observed in antisense transcription suggests multiple mechanisms for this phenomenon, suggesting that some antisense transcription may play a role in regulating the expression of key genes, while some may be due to chromosome replication dynamics and transcriptional noise.
Although the variety of structural patterns we observed in antisense transcription suggest multiple mechanisms for antisense expression, our data also clearly indicate that antisense transcription may play a genome-wide role in regulating the expression of key genes in Bacillus species.
This study illustrates the surprising complexity of prokaryotic RNA abundance for both strands of a bacterial chromosome. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by internal funding from the Georgia Institute of Technology. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. The RNA-seq approach is an unbiased sequencing-based method for characterizing RNA that has greatly enhanced our ability to view the transcriptomes of both eukaryotes  ,  ,  and prokaryotes  ,  , .
Previous hybridization-based methods for exploring gene expression, such as those based on microarray technology, were limited to intersample comparisons. However, the unbiased and quantitative nature of RNA-seq allows a more absolute measure of RNA abundance, and because it captures the sequence as well as abundance of each RNA, it can reveal aspects of transcriptome structure on a genome-wide scale, such as operons for prokaryotes, splice variants in eukaryotes, and transcriptional activity within non-coding regions such as riboswitches, small non-coding RNAs, and intergenic and untranslated regions.
In recent years, transcription and RNA function have been shown to be more complex and varied than expected. One surprising finding has been the observation of widespread antisense transcription in both eukaryotes and prokaryotes  ,  , . However, the precise function and mechanism s of this seemingly ubiquitous phenomenon are still unknown, particularly in bacteria, though it seems likely that antisense RNA often plays a role in regulating gene expression .
It is generally believed that within the bacterial cell, the presence of a complementary antisense copy of RNA will hybridize to the normal, sense copy of mRNA, causing it to be degraded or translated less efficiently . However, regulating gene expression in this way could be metabolically more costly to the cell due to the use of energy and metabolites.
Although several studies have shown that antisense transcription may be widespread in bacteria  ,  ,  , and several high-resolution bacterial transcriptomes have been reported  ,  , a global strand-specific quantification focusing specifically on the frequency distribution of antisense transcripts has not been reported.
Here, we describe a detailed genome-wide analysis of transcription in the Sterne 34F 2 strain of the bacterium Bacillus anthracis , the causative agent of anthrax  , .
Sterne is an attenuated strain of B. Gene expression in B. We observed that transcription is heavily dependent on genome architecture, such that antisense activity was overrepresented on the lagging strand, where RNA transcription and DNA replication occur in opposing directions, while sense-directed transcription was only slightly more prevalent on the leading strand. Additionally, the highest levels of sense transcription were mainly on the high-copy regions of the leading strand, closer to the origin of replication, indicating possible gene dosage effects.
Briefly, gene dosage effects are those caused by the presence of more copies of chromosomal DNA closer to the origin being present during DNA replication, thus providing more template copies of those genes for transcription. Lastly, our data showed specific examples of unique forms of antisense transcription that both remained constant and changed between bacterial growth conditions.
For instance, we observed: i abundant antisense within an important sigma factor sigA in all conditions; ii several genes where antisense transcription was much higher than sense directed transcription; iii a significant reversal in antisense transcript abundance for two spore-related genes between exponential and stationary phase growth; and iv a sharp rise in antisense transcription within several metabolic genes during osmotic and cold stress.
Significantly, we verified a subset of our findings using a novel strand-specific assay. Taken as a whole, our data suggest that genome architecture and perhaps species-specific gene content may be important factors in determining the distribution and abundance of antisense transcription in this Firmucute bacterium, where the genomes for this phylum have a high coding strand bias .
We used strand-specific RNA-seq RNA-seq  to define the transcriptome of Bacillus anthracis under four unique growth conditions in order to better understand the extent to which antisense transcription is present in logarithmically growing bacteria in different growth environments. We collected RNA from four biological replicates of B.
Our goal was to determine the extent to which antisense RNA is produced during bacterial growth under unique growth environments, and whether discrete patterns of antisense transcription could be revealed . The experiments described here represent an extremely high-resolution view of a bacterial transcriptome 22, Mb total sequence data generated Table S1 using the Applied Biosystems SOLiD next generation sequencing technology.
Briefly, sequence reads were mapped to the B. Each strand was considered independently, such that sense and antisense transcription were measured separately. Because gene scores are calculated using gene length as the denominator, scores are by definition corrected for differences in gene size, and so can be directly compared. After confirming correlation between biological replicates, gene scores for each replicate were normalized based on the total number of unambiguously mapped reads for each sample to correct for differing depths of sequencing coverage.
The gene scores for the four biological replicates of each experiment were then square-root transformed for subsequent expression analyses in order to include gene expression scores of 0 when comparing samples Table S3 , and also log base 2 transformed for plotting frequencies in Figures 1 and 2. B-C Frequency distributions of percentage of genes per leading and lagging strands for sense B and antisense C transcripts in Control sample. D-E Median gene expression and interquartile range per quadrant divisions outlined in A for sense D and antisense E gene expression.
As in a previous RNA-seq study, the log 2 distribution of transcript abundance followed a smooth, continuous curve . Before exploring the possible biological significance of AS transcription, we sought to verify that our findings were not a result of mismapping of sequence reads by our mapping algorithm. We generated color strings beginning at every position of the B. The NanoString assay is a very sensitive, hybridization-based assay that measures RNA abundance in a multiplex format using color-coded single-stranded probes see  ,  for an excellent description of the assay.
Thus, we selected a set of sense and AS probes for 17 genes that showed a variety of levels of sense and AS transcription in the RNA-seq measurements. Replicates were averaged and Spearman Rank correlations comparing RNA-seq and nCounter sense:AS ratios showed that the two methods were highly correlated note that sense measurements do not correlate to AS measurements — not shown. Taken together with the bioinformatics experiment discussed above, these data strongly confirm the sense and AS signals observed in our single-stranded RNA-seq experiment.
It is widely accepted that genes are distributed throughout bacterial chromosomes in a non-random way for an excellent review, see . For instance, in most bacterial replicons, the majority of genes are present on the leading strands  ,  ,  , which is putatively favorable because collisions between RNA polymerases performing transcription and DNA polymerases performing replication are less likely if both enzymes proceed in the same direction. Additionally, because chromosome replication in bacteria proceeds from a single origin, the genes near that origin are often present in the cell at a higher copy number than the rest of the chromosome, leading to a potential gene dosage effect  caused by the presence of more physical copies of the DNA synthesized nearer to the origin than farther away from it Figure 2A.
Given these facts, we were interested in determining the effects of genome architecture on both sense and AS transcript abundance by analyzing gene expression by gene locale. Specifically, we looked at i the genome-wide distributions and abundance of the sense and AS signals separately, both genome-wide and per the leading and lagging strands Figure 2B—E ; and then, ii explored the combined sense-plus-AS signals of each protein-coding gene Figures 3 — 6 next section.
B-D Expression coverage plots illustrating 3 categories of sense plus AS transcription for 6 genomic regions. Genome Coordinates from B. A Single nucleotide coverage plots for: Control left , osmotic stress center — NaCl and cold stress right — Cold for B. Traces are plotted at the single nucleotide level from the additive RNA-seq sequence data. B Illustration of gene region structure and table showing results of 1-Way ANOVA with Bonferroni multiple comparisons test analysis of gene expression 4 biological replicates for each condition for both the Sense and AS directions between the Control Sample versus NaCl top 2 rows and versus Cold bottom two rows.
Top row is locus tag number. We performed a non-parametric Mann-Whitney test of the normalized square-root transformed gene scores between leading and lagging strands 4 biological replicates for each of 4 conditions. The null hypothesis for this analysis was that all genes, regardless of chromosomal strand locale, have an equal chance of being transcribed to any level, in either the sense or AS direction, and that transcription levels in either the sense or AS direction are evenly dispersed across the entire chromosome, regardless of strandedness or proximity to the origin.
The results showed a significant difference in medians when comparing the leading versus lagging strands both for Sense and Antisense data p. For all experiments, the overall trends showed that the genome-wide expression medians were 1 decreased on the lagging strand in the sense direction; and 2 increased on the lagging strand in the AS direction. For the most part, the abundance of sense-directed transcripts is proportionately distributed on the both the leading and lagging strands for scores between 2 1 and 2 8 ; however, the distribution shows that lagging strand genes tend to be expressed in the sense direction at slightly lower levels, with more leading strand genes having the highest scores.
Here, a sharp bias toward higher expresion for the AS signals on the lagging strand is apparent for all four samples. The consistency of these trends across four unique growth conditions suggests that perhaps much of the greater abundance of AS transcription on the lagging strand could be due to stochastic noise generated by the predicted enzyme collisions that occur during RNA transcription and DNA replication when the respective polymerase enzymes are moving in opposite directions  ,  , with only a subset of AS expression existing as possible overt gene regulation.
The higher abundance of lagging strand AS transcription and a possible gene dosage effect can be seen in Figure 2D—E.
The graphs illustrate the median gene expression normalized square root transformed scores with interquartile ranges for gene expression grouped by their proximity to the origin of replication per Figure 2A.
This is true for both the leading and lagging strands for sense transcription, but only on the lagging strand for AS transcription. Figure 2E further highlights the paucity of leading strand AS abundance compared to the lagging strand for all samples see differences in y-axes.
These highly expressed genes also had extremely low raw AS scores ranging from 0. Thus, the most highly expressed genes in all samples had virtually no transcription from the non-coding strand. These very abundant genes included many crucial housekeeping genes e. The frequency plots illustrated in Figures 1 , 2 and S2 categorize sense and AS scores globally but separately, and so do not convey the balance of sense and AS activity together for individual genes i.
We also had not yet considered transcriptional activity of intergenic regions. Thus, we asked if the patterns of combined sense plus AS transcription for all genes were randomly distributed across individual gene regions, and what the expression profiles looked like when considering surrounding regions.
Note that the percentages were calculated using the raw, normalized scores as opposed to the square root transformed numbers, since the latter calculation would likely exaggerate the trend. We then considered transcriptional patterns of various classes of genes and visually inspected transcriptional patterns within several genomic regions of interest.
Also, because the digital nature of RNA-seq allows intrasample quantification of gene expression levels, we calculated an estimated range of scores that could be expected for transcripts present at a certain copy per cell in the coding direction. These confidence intervals were then used as a rough estimation of transcript abundance based on score within 1 growth condition. Note that the y-axis represents the log 10 of the average number of times that each nucleotide was counted as being present in an RNA present under a specific growth condition i.
AS signals were also mapped across most of the opposite strand of this gene, resulting in an overall AS score of 2. It is generally believed that AS RNA in a cell will bind efficiently to sense copies of its corresponding mRNA, decreasing its ability to act as a template for translation and perhaps lowering its stability as well  ,  ,  , . Also, it is possible that unexpressed and potentially misannotated genes could confound the analysis. It should be noted, however, that a recent study of these RNA-seq data combined with a new protein prediction algorithm suggested that the current B.
Because gene expression has been traditionally viewed only from a sense-strand perspective, we created a novel nomenclature for RNA-seq gene expression that encompasses the complex potential of sense plus antisense expression as follows Table 3 : i No expression 0. Figure 3B—D illustrates the variety of AS patterns that were identified with RNA-seq including transcriptional activity from the surrounding region several of which were confirmed independently with NanoString technology assays Table S5.
Figure 3B shows expression plots for two very highly expressed genes, the flagellin gene left and the sodA1 gene right , both of which displayed a very low or non-existent AS signal within the genes themselves and in the surrounding genes all plots from Control sample — average transformed nucleotide score for all 4 biological replicates. Note that in the flagellin region, a gene that had been originally annotated, but subsequently removed, appears to be an actively transcribed region former GBAA — a phenomenon that has been seen before  , .
Conversely, gene regions pictured in Figure 3C show an extremely unexpected pattern, where AS transcription is almost equal to the sense transcription. For the sigA;dnaG genes, this does not appear to be run-on transcription from the opposite strand; rather, it appears as if both strands are being specifically transcribed. This is particularly interesting, since sigA is the gene for the putative housekeeping RNA polymerase of Bacillus species  , and it seems counterintuitive that there would be an abundant AS species that may destabilize or inhibit the translation of the sigA mRNA.
The plot on the right side of Figure 3C shows that the maoC gene has a very high AS signal that may be run-on from transcription of a hypothetical gene being expressed on the opposite strand.
The dual-stranded transcription was confirmed with the alternative NanoString assay for maoC which detected high signals for both strands of these genes, where the AS signal was slightly lower than the sense signal Tables S4 and S5. Lastly, an unusual group of genes had higher expression from the AS strand than in the sense direction.
Sense in antisense?
Previous efforts to characterize conservation between the human and mouse genomes focused largely on sequence comparisons. These studies are inherently limited because they don't account for gene structure differences, which may exist despite genomic sequence conservation. Recent high-throughput transcriptome studies have revealed widespread and extensive overlaps between genes, and transcripts, encoded on both strands of the genomic sequence. This overlapping gene organization, which produces sense-antisense SAS gene pairs, is capable of effecting regulatory cascades through established mechanisms. We present an evolutionary conservation assessment of SAS pairs, on three levels: genomic, transcriptomic, and structural. From a genome-wide dataset of human SAS pairs, we first identified orthologous loci in the mouse genome, then assessed their transcription in the mouse, and finally compared the genomic structures of SAS pairs expressed in both species. We found that approximately half of human SAS loci have single orthologous locations in the mouse genome; however, only half of those orthologous locations have SAS transcriptional activity in the mouse.
Although genome-wide transcriptional analysis has been used for many years to study bacterial gene expression, many aspects of the bacterial transcriptome remain undefined. One example is antisense transcription, which has been observed in a number of bacteria, though the function of antisense transcripts, and their distribution across the bacterial genome, is still unclear. Single-stranded RNA-seq results revealed a widespread and non-random pattern of antisense transcription covering more than two thirds of the B. Our analysis revealed a variety of antisense structural patterns, suggesting multiple mechanisms of antisense transcription. The data revealed several instances of sense and antisense expression changes in different growth conditions, suggesting that antisense transcription may play a role in the ways in which B.
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Sense in antisense?
A correspondence between open reading frames in sense and antisense strands is expected from the hypothesis that the prototypic triplet code was of general form RNY, where R is a purine base, N is any base, and Y is a pyrimidine. The similar Codon frequencies found in sense and antisense strands can be attributed to the wide distribution of inverted repeats stem-loop potential in natural DNA sequences. This is a preview of subscription content, access via your institution. Rent this article via DeepDyve.
The antisense strand is thus responsible for the RNA that is later translated to protein, while the sense strand possesses a nearly identical makeup to that of the mRNA. Note that for each segment of double stranded DNA, there will possibly be two sets of sense and antisense, depending on which direction one reads since sense and antisense is relative to perspective. It is ultimately the gene product, or mRNA, that dictates which strand of one segment of dsDNA we call sense or antisense. But keep in mind that sometimes, such as in prokaryotes, overlapping genes on opposite strands means the sense for one mRNA can be the antisense for another mRNA.
Antisense is the non-coding DNA strand of a gene. Antisense can also refer to a method for silencing genes. To silence a target gene, a second gene is introduced that produces an mRNA complementary to that produced from the target gene.
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