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A Computational Analysis of Antisense Off-Targets in Prokaryotic Organisms
Thomas O. Worley-Morse, and Claudia K. Gunsch*
Department of Civil and Environmental Engineering, Duke University, Box 90287, Durham, NC
*Correspondence author phone: 919-660-5208; fax: 919-660-5219; e-mail: ckgunsch@duke.edu
ABSTRACT
27708, USA
The adoption of antisense gene silencing as a novel disinfectant for prokaryotic organisms is hindered by poor silencing efficiencies. Few studies have considered the effects of off-targets on silencing efficiencies, especially in prokaryotic organisms. In this computational study, a novel algorithm was developed that determined and sorted the number of off-targets as a function of alignment length in Escherichia coli K-12 MG1655 and Mycobacterium tuberculosis H37Rv.
The mean number of off-targets per a single location was calculated to be 14.1 + 13.3 and 36.1 +
58.5 for the genomes of E. coli K-12 MG1655 and M. tuberculosis H37Rv, respectively.
Furthermore, when the entire transcriptome was analyzed, it was found that there was no general gene location that could be targeted to minimize or maximize the number of off-targets. In an effort to determine the effects of off-targets on silencing efficiencies, previously published studies were used. Analyses with acpP , ino1 , and marORAB revealed a statistically significant relationship between the number of short alignment length off-targets hybrids and the efficacy of the antisense gene silencing, suggesting that the minimization of off-targets may be beneficial for antisense gene silencing in prokaryotic organisms.

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KEYWORDS: antisense gene silencing, bacteria, disinfection
1. Introduction
The problems of antibiotic resistance and pathogens in the environment are of unremitting concern for society. Traditional methods to remove and inactivate pathogens have included chlorine, UV, ozone, antibiotics, and a variety of physical and chemical processes. However, we are witnessing the emergence of pathogens that are resistant to these traditional methods. For example, emerging environmental pathogens such as Mycobacterium avium have been identified,
which are resistant to traditional chlorine based treatments [1]. Furthermore, antibiotics are no
longer as effective due to the emergence of multiple types of antibiotic resistance, and there is
concern of entering a post-antibiotic era [2, 3]. These problems are further compounded because
in many cases the use of a broad acting biocide in the environment harms the indigenous microbiota and fauna. Clearly, there is a need to investigate alternative disinfection strategies for controlling pathogens in the environment and in engineered waste streams.
One novel approach to controlling environmental pathogens is the use of antisense gene silencing, which has received much attention in the medical field as a way to specifically inhibit
the expression of a desired gene [4, 5]. Antisense gene silencing is a biochemical technique used
to inhibit the translation of messenger RNA (mRNA) by the Watson-Crick base pairing of a reverse complementary oligonucleotide strand to its respective mRNA. Protein expression is inhibited either through the steric hindrances of the oligonucleotide mRNA complex or through cleavage of the mRNA through RNase H activity. While initial efforts with antisense gene

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52 silencing focused on drug target evaluation and functional genomic studies, there has been a recent interest in using antisense oligonucleotides for molecular therapeutics and for controlling
pathogens [4, 6, 7]. The use of antisense oligonucleotides as a disinfection agent involves the
specific targeting and inhibition of essential genes, which inhibits the cellular molecular machinery, and causes cell death. Furthermore, antisense gene silencing is a candidate for controlling persistent environmental pathogens because oligonucleotides can specifically target the desired organism of interest and they do not interfere with additional organisms or the environment.
Previous work using antisense oligonucleotides in prokaryotic organisms has mainly focused on targeting essential and non-essential genes in E. coli and M. tuberculosis
[8-12]. While these
initial results have been promising and demonstrated the potential use of antisense oligonucleotides as novel antibiotic therapeutic agents, significant challenges remain before
antisense technologies can be applied in the field [13]. These problems have been attributed to
oligonucleotide stability, delivery, mRNA secondary structure, and the thermodynamics between
the mRNA and the oligonucleotide [4, 5]. In order to address these challenges, there have been
multiple improvements to the oligonucleotide chemistry over the first few generations of oligonucleotides. Recent advances such as phosphorodiamidate morpholino oligomer (PMOs) and peptide nucleic acids (PNAs), which have modified the backbone of the oligonucleotide,
have greatly improved the stability and efficaciousness of the oligonucleotides [5, 14-17]. There
have also been significant advances in improving oligonucleotide delivery; for example, both naked DNA and bacterial cells have a negative charge, and both are unlikely to aggregate due to electrostatic repulsion. To improve oligonucleotide uptake, cationic polymers have been attached

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to the oligonucleotides [14, 18]. Furthermore, delivery has been improved with the use of spherical nucleic acids [19].
While these improvements have greatly increased the efficacy of antisense gene silencing, one overlooked reason for poor performance of antisense gene silencing in prokaryotic organisms is
off-target effects [20]. For example, if the oligonucleotides bind with multiple unintended gene
targets, the effective concentration of available silencing strands is decreased. Previous research has shown that with RNA interference (RNAi), a similar antisense gene silencing system in
eukaryotic organisms, off-target effects are problematic [21]. Additionally, a suite of tools has
been developed for RNAi to select optimal antisense gene silencing targets that account for
possible off-targets [22-24]. While there has been a significant effort to characterize off-target
effects in eukaryotic organisms in RNAi, there has yet to be a study of off-targets with antisense oligonucleotides in prokaryotic organisms.
We hypothesize that off-target effects are responsible for some of the poor performance of in vivo antisense gene silencing. To test this hypothesis, a novel asynchronous algorithm was constructed that calculates the number of off-targets and then groups them as a function of the mRNA oligonucleotide alignment length for each possible location on the mRNA. This analysis was performed for the transcriptomes of Escherichia coli K-12 MG1655 and Mycobacterium tuberculosis H37Rv to determine the mean and standard deviation number of off-targets for the two different organisms. Additionally, this algorithm was used to determine if certain locations on the gene, such as beginning, middle, or the end, are more preferential for silencing targets with reduced numbers of off-targets. Finally, using previously published data, the effects of off-

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95 targets on silencing efficiencies were elucidated using linear models to relate silencing efficiencies to the number of off-targets for a variety of alignment lengths.
2. Experimental
2.1 Programs and Data Used
The main algorithm was written in Perl and is available at http://gunsch.pratt.duke.edu/antisense
[25]. The Perl scripts were coupled with a locally installed Blast program [26]. Soligo was used to create the thermodynamic output files for each gene [27]. The transcriptomes used in this
study were obtained from the following genomes of E. coli K-12 MG1655 and M. tuberculosis
H37Rv using the NCBI send to coding sequence feature [28, 29]. The four genes targeted in this
research were acpP ,
β
-lactamase, and marORAB for E. coli K-12 MG1655 and ino1 for M. tuberculosis
H37Rv [9, 12, 15].
2.2 Program Algorithm
The program algorithm is composed of two main processes: the first process finds all possible antisense targets on the gene; and, the second process bins the off-targets as a function of the alignment length. Briefly, a FASTA text file of the gene encoding DNA sequence is loaded into the Perl script, which fragments the gene into small sections of 21 nucleotides and proceeds stepwise for every nucleotide along the sequence and runs the algorithm similar to work by
Henschel et al. [22]. For example, if a gene is 100 base pairs (bp) in length and the
oligonucleotide is 21 bp in length, the result from the algorithm would return 80 possible target locations for the gene. This is calculated from (length of gene - oligonucleotide length + 1). For each target location the algorithm calls BLASTn with the antisense sequence against the

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121 transcriptome. The resulting BLASTn output is further processed. To minimize erroneous offtarget calls, all results with greater than two bp mismatches and gaps are filtered from the results.
The alignment matches with gaps were removed so that only the alignments with consecutive sets of bases were reported. Furthermore, the limit on bp mismatches was set to two so that all consecutive alignments would have no greater than two bp mismatches, as a high number of bp
mismatches has been shown to inhibit antisense gene silencing efficacy [30].
The filtered results are then grouped as a function of alignment length in bins of 10 - 13 bp, 14 -
17 bp, and 18 - 21 bp. The program proceeds sequentially until all of the possible targets have been searched. To verify that only antisense oligonucleotide and sense mRNA hybrids were called from the BLASTn function, the strand minus option was selected. Furthermore, the parameters of the BLASTn query and the program can be adjusted to include shorter oligonucleotides, and the BLASTn specificity can be tuned to increase or decrease the target outputs. The output results include the following: the target location; the target; the antisense oligonucleotide; the total number of off-targets; the binned number of off-targets; and, the required concentration adjustment to obtain the desired effective concentration (which corresponds to the product of the number of off-targets and the original desired concentration).
The final data is output in plain text to facilitate use in a variety of programs along with the concentration increase necessary to offset the number of off-targets present.
2.3 Data Analysis
For all statistical analysis, the R statistical program was used [31]. To assess the relationship
between the number of off-targets, location, and silencing efficiencies linear models were

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149 constructed. All statistically significant variables had at least a p < 0.10. To test the validity of
the algorithm, previously published in vivo silencing results were used [9, 12, 15].
3 Results and Discussions
3.1 Off-Target Variability as a Function of Location
The number of off-targets for the genes marORAB in E. coli K-12 MG1655 and ino1 in M. tuberculosis H37Rv are shown in Figure 1. For both genes there is significant off-target variability as a function of location with a maximum number of off-targets of 48 and 155 for marORAB and ino1 , respectively. The mean and standard deviation number of off-targets for each location on the transcriptome is 9.0 + 7.6 and 34.0 + 30.2 for E. coli K-12 MG1655 and M. tuberculosis H37Rv, respectively. Since the standard deviations are of similar magnitude to the mean values, this suggests a high degree of locational variability concerning the number of offtargets, as some locations have very little and others have significantly more alignment matches.
The high variability in the number of off-targets in these results suggests that it is possible to select locations on the gene that minimizes off-targets.
Interestingly, the transcriptome from E. coli K-12 MG1655 has a lower mean number of offtargets compared to the M. tuberculosis H37Rv transcriptome, which has approximately 3.8 times the mean number of off-targets per a location compared to E. coli K-12 MG1655.
Furthermore, the relative number of off-targets for genes in a genome might be another indication for the heterogeneity or homogeneity of the genome. Homogenous genomes are
known to have more highly similar sequences compared to more heterogeneous genomes [32].
This would suggest that homogenous genomes, which are more similar to each other, have

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167 higher rates of off-targets, and therefore, it would be more difficult to design targets that are less susceptible to off-targets. In contrast, this observation also suggests that genomes with greater heterogeneity would have lower amounts of off-targets, and therefore, should be easier to target with antisense gene silencing.
Taken together, these results indicate that there is a large variability in the number of off-targets as a function of gene length for prokaryotic organisms and that the mean frequency of the number of off-targets is specific to each organism. Therefore, this suggests that organisms with higher numbers of off-targets such as M. tuberculosis H37Rv require greater concentrations of silencing oligonucleotides to reach the desired concentration, as compared to organisms with lower numbers of off-targets such as E. coli K-12 MG1655.
3.2 Size Distribution of the Off-target Alignment Lengths
To obtain all possible off-targets, the blast algorithm expectation value (E) was set to 10, so that a larger list of alignment matches was returned. This setting resulted in a wide variety of target locations with a large variability in alignment lengths. For the transcriptome of E. coli K-12
MG1655, the mean and standard deviation for the binned number of total off-targets was 11.3 +
10.5, 2.6 + 3.1, and 0.3 + 0.8 for the bins of 10-13, 14-17, and 18-21 bp, respectively (Figure 2).
For the transcriptome of M. tuberculosis H37Rv, the mean and standard deviation for the binned number of total off-targets was 25.4 + 30.8, 8.9 + 24.3, and 1.8 + 13.8 for the bins of 10-13, 14-
17, and 18-21 bp, respectively.

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These results suggest that most off-target hybrids have short alignments with lengths between 10
and 13 bp, and therefore, these hybrids would not likely induce phenotype changes [33]. The
data also suggests that off-target matches between 18-21 bp are infrequent for E. coli K-12
MG1655 and are not of great concern. However, for M. tuberculosis H37Rv it appears that certain target locations have higher numbers of off-targets with longer alignment length lengths, and therefore, these target locations are not appropriate for antisense gene silencing.
Furthermore, assuming that each oligonucleotide that is delivered into the cell binds with its respective mRNA off-target, this suggests that short alignment matches reduce the effective concentration of silencing strands by 91% and 96%, respectively, for E. co li K-12 MG1655 and
M. tuberculosis H37Rv due to the competition from off-target matches, calculated as ((1 -
1/(number of off-targets)) * 100). While these calculations assumed a simple stoichiometric relationship between the concentrations of the oligonucleotides and the off-target transcripts, these complex cellular interactions are not so simple. For example, the concentrations of the offtarget transcripts will determine the transcriptomic competition for the oligonucleotide.
Therefore, if there is a higher concentration of off-target transcripts, there will be a greater competition for the oligonucleotides, and the effective oligonucleotide concentration may be reduced. However, if there are lower concentrations of the off-target transcripts, we hypothesize that off-targets will not strongly interfere with the silencing of the desired gene. Ultimately, further research is needed to quantify the concentration of off-target transcripts, which would allow for a more quantitative approach to determine the effective oligonucleotide concentration reduction due to short off-target alignment matches.

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3.3 Overall Effect of Location on the Number of Off-Targets
Previous work has demonstrated the efficacy in targeting the beginning regions of the gene and the Shine-Dalgarno start codon region, and it is known that targeting downstream regions of the
mRNA do not result in strong silencing effects [15]. In order to further explore the effect of
location on the number of off-targets, and to determine if there was an optimal location on the gene, such as the beginning, middle, or end, which minimized off-targets, a combined dataset using each organism’s transcriptome was created. To compare the genes of different lengths, the length of each gene was normalized so that each normalized gene was 100 target locations in length. For each target location, the mean and standard deviation number of the off-targets was calculated and plotted for the normalized gene length (Figure 3).
For both the E. coli K-12 MG1655 and the M. tuberculosis H37Rv transcriptomes, the number of off-targets remained stable over the length on the gene. On average, there was a slight decrease in the number of off-targets at both the beginning and the end of each gene, which is in agreement with previous results that suggest the beginning regions of the gene are optimal for targeting. Interestingly, there was no statistically significant difference for a preferential location between the beginning, middle, or end ( p > 0.05). Performing a similar analysis for each of the binned number of off-targets to determine if a preferential location for shorter or longer alignment lengths could be identified, no trends were observed except that the mean number of off-targets decreased as the alignment length bin size increased (Figure 4 and Figure 5). Thus, while it appears that both E. coli K-12 MG1655 and M. tuberculosis H37Rv have a decreased number of off-targets at the beginning and end of each gene, these results suggest that there are

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239 no general rules for minimizing off-targets and that the analysis needs to be repeated for each gene in question.
3.4 Impacts of Off-Targets on Prokaryotic Antisense Gene Silencing
Previous work has verified the presence of off-targets through computational and experimental
means in eukaryotic organisms [21, 34]. It has been hypothesized that off-target effects inhibit
antisense gene silencing in prokaryotic organisms. However, it is difficult to quantify the effects of off-targets on silencing efficiencies. For example, it is difficult to separate the response due to
off-targets from the other factors that affect silencing outcomes [20]. Furthermore, the studies
that determined the importance of minimizing the Gibbs free energy between the oligonucleotide and mRNA for increased silencing efficiencies were performed in vitro with cell free extracts,
and the effects of off-targets could not be determined [4, 35]. Unfortunately, most studies
involving in vivo silencing are only able to locate a few successful target locations due to the complexities in oligonucleotide delivery and quantifying the silencing efficiency.
However, one possible strategy to obtain enough relevant gene locations and silencing results is to analyze the reported data from multiple studies. To this end, experimental silencing results from the following genes were used: acpP ,
β
-lactamase , ino1, and marORAB
[9, 12, 15]. These
studies were selected because they included multiple oligonucleotides and the associated silencing efficiencies for each oligonucleotide. To assess the effects of off-targets for longer oligonucleotides in the range of 18-21 bp, data from the genes of marORAB and ino1 were combined and used; to assess the effects of off-targets for shorter length oligonucleotides, the data from the genes of acpP and
β
-lactamase were used. Using the published oligonucleotides

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257 and target locations, the off-target algorithm was used to determine the number of off-targets for each silencing location. A linear regression was performed comparing the silencing efficiencies to the number of off-targets. Furthermore, if sufficient data were present, a linear regression was performed to determine the relationship between the target location on the gene and the respective silencing efficiency.
Based on the analysis of off-targets for longer oligonucleotides between 18 – 21 bp, for genes ino1 and marORAB , the total number of off-targets was found to explain approximately 45% of the variation of the antisense gene silencing efficiency (Figure 6a). However, there was no statistically significant relationship between the total number of off-targets and the silencing
efficiency. This is also in agreement with previous work by Mondhe et al. [36], which observed
no relationship between the number of off-targets and the silencing efficiencies, when targeting axenic and mixed cultures. This suggests that the overall number of off-targets may not be an important indicator of the effects of off-targets on silencing efficiencies. In order to verify this data analysis approach, the same analysis was performed using thermodynamic data for each oligonucleotide tested. The Soligo program was used to calculate the binding energy in kcal/mol for each target location on the gene, and these data were combined with the silencing efficiencies
from previously published results [27]. A regression analysis relating the silencing efficiencies to
the thermodynamic data revealed that the binding energy between the mRNA and the oligonucleotide was able to explain 83% of the variability and was statistically significant at p <
0.05 (Figure 6b). This is in agreement with previous reports, which demonstrated that the thermodynamic binding energies and the mRNA secondary structure play an important role in

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silencing efficiencies [4]. These results suggest that in this situation the above analysis method
was appropriate.
To further explore the effect of the length of the off-target matches, the same analysis was performed with the binned number of off-targets (Figure 7). No statistically significant relationships were found for the bins of 18 - 21 bp and 14 - 17 bp (Figure 7a and Figure 7b).
However, for the bin of 10 - 13 bp there was a statistically significant relationship between the number of off-targets and the silencing efficiencies, and the binned number of off-targets between 10 - 13 bp explained 77% of the variance with p < 0.10 (Figure 7c). This suggests that the high number of short off-target alignment matches for certain targets may explain the reduced efficacy of the target location, which may be attributed to a reduced oligonucleotide concentration due to the competition from off-target transcripts. Furthermore, it is important to note that all of the above target locations for genes ino1 and marORAB were near the beginning of the genes, and that no effective locations were reported from the later parts of the genes.
To broaden the above analyses and include shorter silencing oligonucleotides ranging between
10 - 12 bp, data targeting the acpP and β -lactamase genes in E. coli K12 were analyzed (Figure
8). When analyzing the effects of off-targets on silencing efficiencies for the acpP gene, the effects of off-targets explained 58% of the variance and were statistically significant with p <
0.01 (data not shown). This is in agreement with the previous analysis, which suggested that the shorter off-target matches play a more important role in the outcome of the silencing efficiencies.
The effect of the target location was also statistically significant with p < 0.01 (data not shown).
Since the MIC values of greater than or equal to 10 μM were reported as 10 μM for the acpP

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302 gene, it is likely that the linear relationships were skewed. Therefore, to eliminate this uncertainty, the analyses were performed again with only the first 7 data points. Figure 8a represents the relationship between the number of off-targets and the MIC, and Figure 8b represents the effect of location on the MIC. While the predictive power of the updated models slightly decreased, which is likely due to the lower number of points in the model, we felt that the updated models more accurately represented these interactions.
In contrast to these results, when analyzing the
β
-lactamase gene and determining the effects of off-targets for oligonucleotides of 10 bp, there was no statistically significant relationship between the number of short off-targets and the silencing efficiency (Figure 8c). However, the effect of location was statistically significant with p < 0.01 (Figure 8d). While the previous analyses demonstrated a relationship between the short length off-target matches and the silencing efficiency, these contrasting results suggest that more work is needed in order to fully understand the effects of off-targets. For example, future work should be aimed at determining the effective mRNA concentrations of the off-target genes. With the concentrations of the offtarget transcripts and the oligonucleotide concentrations, computational tools such as NUPACK and piRNApredictor could be used to determine the equilibrium concentrations of the oligonucleotide-target complexes and improve quantitative predictions on the effective
oligonucleotide concentration reduction [37, 38].
These results confirm the previously published work that demonstrated the importance of thermodynamics and location in determining the outcome of antisense gene silencing. It is also the first work to demonstrate a relationship between short alignment off-target matches of 10-13

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325 bp and reduced silencing efficiencies. This work suggests it may be beneficial to minimize short alignment off-target matches to improve silencing efficiencies. However, this relationship was not observed in all data samples. One possible mechanism of this observation is that the effect of off-targets depends on the concentration of the mRNA transcripts, and it is possible that organisms in some environmental conditions may have different stringency and expression
requirements, which may have unattended effects on the antisense gene silencing [39].
Therefore, additional work is needed to explore how gene stringency and expression affects the competition for antisense oligonucleotides in prokaryotic organisms.
3.5 Specific Advantages and Limitations
The enclosed algorithm represents an improvement in the current methods of designing antisense oligonucleotides that minimize off-targets in prokaryotic organisms. Previously, each target location was blasted individually to find the number of off-targets, whereas with the algorithm presented herein, the number of off-targets is returned for each location on the gene along with the required concentration increase to reach the desired effective concentration. One specific advantage of this algorithm is that it locates target positions with lower numbers of off-targets, which results in an effective increase in the active oligonucleotide concentration. This may lead to significant cost savings, especially in antisense gene silencing applications using advanced forms of oligonucleotides, such as PNAs. Furthermore, there are specific situations in which the design of oligonucleotides with multiple off-targets would be advantageous, such as when
designing wide spectrum antisense oligonucleotides for multiple gene targets or organisms [36].

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Furthermore, this algorithm could be used for the rational design of PCR primer sets with increased specificity. For example, in many PCR reactions, the product contains multiple bands due to nonspecific binding in the PCR reaction, and it is desirable to target a region with a lower number of possible locations with non-specific binding. It is feasible to use the algorithm discussed herein to minimize the potential off-targets for a section of a gene in regard to the entire genome. This algorithm could be incorporated with previous PCR primer design algorithms to assist in selecting a primer set that had not only good binding efficiency with the target, but also minimized the possible nonspecific binding in the PCR reaction to minimize the number of PCR product bands. However, further research is needed to verify this possibility.
Concerning limitations with antisense gene silencing, the most significant limitation of this method is that it requires that the desired organism has been sequenced and that the genes have been annotated to determine the possible targets. Therefore, this suggests that antisense gene silencing as a disinfection process is most applicable to treat bacterial contamination where the microbial contaminant is known in advance, such as that occurring in a chronic contamination in an industrial setting. Furthermore, there is a need to determine what gene targets are most preferential for cellular inactivation. For example, in E. coli K-12 MG1655 it was determined
that there were 620 essential genes and 3,126 non-essential genes [40]. This is problematic
because there is little understanding of which of the 620 genes are most preferential for cellular inactivation, and a rational framework is needed to rank target genes in order of their effectiveness. Fortunately, recent work has demonstrated that with E. coli , antisense gene silencing can reveal the degree of gene expression required for cell viability, which will give more gene stringency information when designing targets and assist in developing a framework

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for the rational design of antisense oligonucleotides [39]. Additionally, this algorithm could be
improved by returning the number of essential gene targets for each location, which would aid in decision-making. For example, the inclusion of the DEG 5.0 database would significantly help
in designing efficacious targets [41]. Further modifications could include of thermodynamic
calculations to account for the binding energy between the oligonucleotide and the mRNA and
also the secondary structure of the mRNA [22].
Furthermore, a majority of the antisense work in prokaryotic organisms has been focused on targeting plasmid targets instead of genomic targets. This suggests that it might be more advantageous to target plasmid based genes. For example, one system that has potential is the addiction modules that determine cell death in bacterial cultures. Specifically, in many bacterial species, the addiction system expresses two genes (a stable toxin and a labile antitoxin); it might be advantageous to focus on testing antisense gene silencing on cultures with addiction systems
by targeting the labile antitoxin genes [42].
Finally, it is also important to note that the environmental conditions may also affect the oligonucleotide stability. For example, in warmer environments the stability of the oligonucleotide complex decreases, and therefore would result in fewer off-target hybrids and also a reduced binding affinity with the actual target. This suggests that using antisense oligonucleotides as a disinfection process may be limited to environmental conditions with cooler temperatures. However, further work is needed to verify the effect of temperature on silencing efficacy.

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4. Conclusions and Implications
Antisense gene silencing shows promise for a variety of applications; however, the efficacy of antisense gene silencing still needs to be improved prior to the broader dissemination of this technology. The work enclosed demonstrates a novel method of estimating off-targets effects in prokaryotic organisms and demonstrates that rational design can be used to minimize or maximize the number of off-targets. These results suggest that off-target effects may reduce the effective concentration of the silencing strands and lower the silencing efficiency. This effect is dependent on the antisense concentration, the off-target mRNA concentration, and the chemical reaction constants between the two strands. Future work is needed to characterize how the offtarget mRNA concentrations affect this relationship to confirm this observation experimentally.
For acpP , ino1 , and marORAB, there was a statistically significant relationship between the number of off-targets hybrids of 10 - 13 bp in length and the efficacy of the antisense gene silencing, which suggests that the minimization of off-targets may be beneficial for optimal antisense gene silencing targeting. Therefore, for the rational design of silencing oligonucleotides, it is recommended that the thermodynamic optimization of the binding energy and location be performed first, followed by the minimization of off-targets to improve antisense gene silencing efficiencies in prokaryotic organisms.
Acknowledgements
Funding for this research was provided by the Environmental Protection Agency STAR
Fellowship program. Although the research described in the article has been funded by the
U.S. Environmental Protection Agency, it has not been subjected to any EPA review and

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412 therefore does not necessarily reflect the views of the Agency, and no official endorsement should be inferred.

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Figures
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Figure 1. A) Off-target matches for the marORAB gene in E. coli K-12 MG1655 as a function of location. B) Off-target matches for the ino1 gene in M. tuberculosis H37Rv as a function of location. Closed circles represent previously published targets.

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Figure 2. A) Distribution of the mean number of off-targets as a function of alignment lengths for E. coli K-12 MG1655 and B) M. tuberculosis H37Rv. Error bars correspond to the standard deviation.

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Figure 3. A) Distribution of all off-target matches as function of normalized gene length for the transcriptomes of E. coli K-12 MG1655 and B) M. tuberculosis H37Rv. Error bars correspond to the standard deviation.

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Figure 4. Distribution of off-target matches as function of normalized gene length for the transcriptomes of E. coli K-12 MG1655. A) Bin lengths of 10 - 13 bp, B) Bin lengths of 14 - 17 bp, and C) Bin lengths of 18 - 21 bp. Error bars correspond to the standard deviation.

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Figure 5. Distribution of off-target matches as function of normalized gene length for the transcriptomes of M. tuberculosis H37Rv. A) Bin lengths of 10 - 13 bp, B) Bin lengths of 14 - 17 bp, and C) Bin lengths of 18 - 21 bp. Error bars correspond to the standard deviation.

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Figure 6. Scatterplots and results of the linear models for the effects of A) off-targets and B) the free energy between the mRNA and the oligonucleotide (18 - 21 bp) for genes ino1 and marORAB , expressed as percent silencing efficiency.

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Figure 7. Scatterplots and results of the linear models for the effects of off targets for bin lengths of A) 18 - 21 bp, B) 14 - 17 bp, and C) 10 - 13 bp for gene for genes ino1 and marORAB , expressed as percent silencing efficiency.

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Figure 8. Scatterplots and results of the linear models for the effects of A) the number of offtargets and B) gene location for 10 bp targets of gene acpP ; scatterplots and results of the linear models for C) the number of off-targets and D) gene location for 12 bp targets for the gene
β
lactamase . For graphs A) and B) the linear regressions were fitted through the first seven data points (

), and the remaining points ( ) were not used for the regression because the points with
MIC values of greater than or equal to 10 μM were reported as 10 μM.