Codon optimization of Col H gene encoding Clostridium
histolyticum collagenase to express in Escherichia coli
Hamzeh Alipour, Abbasali Raz, Navid Dinparast Djadid, Abbas Rami, Seyed Mohammad Amin Mahdian
A given amino acid sequence can be encoded by a huge number of different nucleic acid
sequences. These sequences, however, prove not to be equally useful. The choice of
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sequence can significantly impact the expression of an encoded protein. As regards the
importance of protein-coding sequence and promising industrial and medicinal applications
of Clostridium histolyticum collagenase, this study examined the codon optimization of the
Col H gene so as to enhance collagenase expression in Escherichia coli (E. coli). The coding
region of mature Col H gene was optimized according to the codon usage of E. coli using
Gene Designer software (DNA 2.0). The results revealed that relative frequency of codon
usage in Col H gene was adapted to the most preferred triplets in E. coli in such a way that
codon usage bias in E. coli was enhanced after codon optimization. Similarly, the higher
level of collagenase expression was more likely the result of substituting rare codons with
optimal codons. As has been reported elsewhere, the findings from this study suggest that
codon optimization provides a theoretical improvement in Col H gene expression in E. coli.
In spite of that, experimental research is needed to confirm the improvement.
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1
Alipour Hamzeh1, 2, Raz Abbasali1, Dinparast Djadid Navid1, Rami Abbas1 and Mahdian Seyed
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Mohammad Amin1,*
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1
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Institute of Iran, Tehran, Iran
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2
6
School of Health, Shiraz University of Medical Sciences, Shiraz, Iran
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*
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Research Group (MVRG), Biotechnology Research Center (BRC), Pasteur Institute of Iran, Tehran,
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Iran. Tel: 02636100955 Fax: 02636100955 E-mail: [email protected]
Malaria and Vector Research Group [MVRG], Biotechnology Research Center [BRC], Pasteur
Department of Medical Entomology and Vector Control, Research Centre for Health Sciences,
To whom correspondence should be addressed: Seyed Mohammad Amin Mahdian, Malaria and Vector
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Introduction
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Collagens that exist in a huge number of cell types are the integral component of animal tissues
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such as skin, tendons and cartilage as well as the organic constituent of bones, teeth and cornea.
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In fact, it accounts for about 25 to 33% of the total protein in mammalian organs. Collagen which
14
exists in the connective tissues of nearly all organs is as insoluble fibers. It imbeds in the
15
mucopolysaccharides and protein of the extracellular matrix and plays a vital role in tissues
16
strength (Harrington 1996). A change in its production or degradation has been shown to result in
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a variety of diseases. In such cases, proteolytic enzymes provide a useful clinical way to the
18
treatment of collagen-centered disorders. On the other hand, making use of enzymes as drug
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enjoys two advantages which differentiate them from all other types of drugs. To start with,
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enzymes frequently bind and act on their substrates with a high affinity. In the second place,
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enzymes are catalytic and transform target molecules into certain products. Thus, enzymes are
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more specific and potent drugs than small molecules that can accomplish therapeutic
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biochemistry in the body. These features have given rise to the development of many enzymatic
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drugs for a large variety of disorders (Vellard 2003).
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A tight triple helical structure that makes up collagen causes its resistance to most proteases; all
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the same, collagenases can specifically degrade collagen. Bacterial collagenases, in addition,
27
have been shown to display broader substrate specificity than vertebrate collagenases.
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Collagenases derived from Clostridium histolyticum, namely Col G and Col H, are a case in point
29
since these are capable of easily digesting collagens, no matter what size and type they have
30
(Ohbayashi, Yamagata et al. 2012; Preet Kaur and Azmi 2013).
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Recently, making use of Clostridium collagenases has attracted a great deal of researchers’
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interest as a non-invasive therapeutic procedure. As such, these collagenases have been examined
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for the treatment of Dupuytren's disease (Badalamente, Hurst et al. 2002), Peyronie’s Disease
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(HELLSTROM and BIVALACQUA 2000), Herniated Lumbar Disk (Sussman, Bromley et al.
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1981), retained placenta (Fecteau, Haffner et al. 1998), adhesive capsulitis (Badalamente and
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Wang 2012), wound healing (YAVUZER, LATİFOĞLU et al. 1997),the debridement of burns
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(Klasen 2000) and in the preparation of pancreatic islet cells for transplantation (Vrabelova, Adin
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et al. 2014).
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Nowadays, culturing C. Histolyticum and subsequent purifying all of the produced bacterial
40
proteins is a widely used method of producing collagenases for clinical applications (Bertuzzi,
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Cuttitta et al. 2014). Nevertheless, the isolation and extraction of enzyme from natural sources
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due to low expression levels and the intracellular localization of enzyme faces technical problems
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and increases the costs of production. For commercial success, decreasing the cost of production
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is necessary which, in turn, depends on the expression level of enzyme and the purification costs.
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As such, there has been growing interest in producing enzyme by recombinant methods (Katrolia,
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Yan et al. 2011). Prokaryotic expression systems, especially E. coli, are one of the most common
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systems for the industrial production of proteins of therapeutic or commercial applications. E.
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coli has advantages including growth on inexpensive media, rapid biomass accumulation,
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convenient genetic manipulation, and simple scale-up (Baneyx and Mujacic 2004). Nonetheless,
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the production of heterologous protein in the organism may be decreased by codon bias
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phenomenon, in which proteins of interest contain codons that are rarely used in E. coli (Burgess-
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Brown, Sharma et al. 2008). It has revealed that the presence of rare codons results in decreasing
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translation speed and inducing translational errors (Plotkin and Kudla 2010; Elena, Ravasi et al.
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2014). The rarest codons in E. coli are illustrated in Table 1. In addition to rare codons, GC
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content can affect expression levels. GC-rich mRNAs can contribute to forming powerful
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secondary structures and, especially in bacteria, such a powerful structure near ribosome-binding
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site obstructs translation initiation. On the other hand, GC-poor mRNAs cannot fold strongly and
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frequently carry sequence elements limiting expression. For instance, low GC content has been
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seen to restrict the expression of Plasmodium falciparum genes in E. coli. Such mRNAs seem to
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be targets for RNase E cleaving AU-rich sequence (Plotkin and Kudla 2010). Codon optimization
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has been considered to be a common strategy to improve translation efficiency and accuracy
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(Gingold and Pilpel 2011). Codon optimization, indeed, is to alter rare codons in target gene so as
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to adapt them to the codon usage of specific expression host. Recently, a huge number of studies
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have reported an increase in expression levels by codon optimization (Gustafsson, Govindarajan
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et al. 2004; Zhou, Schnake et al. 2004; Lee, Koh et al. 2010). Here, we explored codon
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optimization of Col H gene to express in E. coli.
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Material and methods:
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Codon optimization of Col H
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The protein sequence of Col H was taken from UniProt database and 40 amino acids of putative
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signal peptide (MKRKCLSKRLMLAITMATIFTVNSTLPIYAAVDKNNATAA) were removed
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in order to generate a mature enzyme. Then, the coding region of mature Col H gene was
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optimized according to the codon usage of E. coli using Gene Designer software (DNA 2.0). This
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software using proprietary algorithms replaces rare codons and eliminates problematic mRNA
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structures and repetitive sequences.
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Gene Sequence Analyses
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The sequence analysis of native and optimized Col H gene was performed using online software
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involving Rare Codon Analysis Tool and Sequence Analysis which are available on web sites
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www.genscript.com and www.bioinformatics.org, respectively.
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Results and Discussion
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Codon optimization of Col H
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The native gene used tandem rare codons such as AGA AGG which have been shown to greatly
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affect heterologous expression in E. coli. These effects include ribosome pausing and co-
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translational cleavage of mRNA, ribosomal frame shifting or amino acid misincorporation
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(Burgess-Brown, Sharma et al. 2008; Plotkin and Kudla 2010). Codon optimization substituted
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such codons and adapted frequency of codon usage to the most preferred triplets in E. coli so that
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its codon usage bias was enhanced. Moreover, the sequence of native (GenBank accession
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number D29981) and codon-optimized genes were aligned, indicating that codon optimization
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did not alter the amino acid sequence and that 666 out of 982 codons (67.82%) were substituted
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(Figure 1).
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Percentage of non-optimal and optimal codons before and after codon optimization
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The native and optimized codon sequences of Col H enzyme are compared using Sequence
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Analysis software. The gene coding collagenase contains 2946 bp encoded the protein 982 amino
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acids. Before codon optimization, the numbers of rare codon GGA and GGG (codons encoding
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glycine) were 38 and 6, respectively. On the other hand, the codons GGC and GGT that are
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optimal for glycine were 3 and 26, respectively. After codon optimization, the rare codons
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reached 0 and the optimal codons GGC and GGT increased to 37 and 36, respectively. Moreover,
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the rare codons AGA and AGG (coding arginine) that were 23 and 5 fell to zero after
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optimization. In contrast, the codons CGT and CGC which are optimal for arginine increased
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from 2 and 1 to 20 and 11, respectively, after optimization. Additionally, the numbers of rare
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codons for leucine, i.e. TTA and CTA, decreased from 42 and 4 to zero. On the contrary, codon
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optimization raised the optimal codon CTG to 65. Furthermore, the codons AGT and TCA that
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are rare for serine diminished to zero and, in contrast, increased numbers in the optimal codons
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TCT, TCC, and AGC emerged from codon optimization. In addition, after codon optimization,
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the rare codon ACA encoding threonine declined from 23 to 0 and the optimal codon ACC
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increased from 4 to 40. As far as isoleucine amino acid is concerned, the rare codon ATA reduced
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from 41 to 0 and the optimal codons ATC and ATT rose to 35 and 23, respectively. As for proline,
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the rare codon CCC decreased from 1 to 0 and optimal codon CCG increased from 1 to 33. As to
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other amino acids, it has also been shown that codons biased towards more frequent codons via
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codon optimization. As an example, less frequent codon AAT that encode asparagine reduced
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from 57 to 10 and, contrarily, more frequent codon AAC increased from 12 to 59 (Table 2)
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Codon Adaption Index
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Codon Adaption Index (CAI) assesses the extent of bias in favor of codons which are involved in
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highly expressed genes (Moriyama 2003). The levels of protein expression and CAI are known to
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be correlated. A CAI of > 0.8 is regarded to be optimum for expression in the expression host of
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interest. As shown in Figure 2, the CAI value for original gene was 0.62; however, codon
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optimization increased the CAI of the coding sequence to 0.84 that is ideal for expression in E.
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coli.
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Frequency of optimal codon
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The simplest method for measuring species-specific codon usage bias is the frequency of optimal
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codons (Fop):
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where Xop and Xnon are the number of optimal and non-optimal codons in a gene, respectively.
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Codons excluded from the calculation contain stop codons and codons for tryptophan and
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methionine. Optimal codons for E. coli were originally determined based on the availability of
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tRNA and the nature of the codon-anticodon interplay. These codons are thought to be
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translationally optimal and are more frequently involved in genes expressed highly than lowly
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expressed genes (Moriyama 2003). Non-optimal codon content is currently known to limit the
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expression of heterologous proteins owing to restricting available cognate tRNAs in the
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expression host (Angov 2011). As can be seen from Figure 3, the 44% of Col H codons showed
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the value of 100 before codon optimization whilst they increased to 64% with codon
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optimization. Additionally, the percentage of rare codons (codons with values lower than 30) was
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12, which decreased to 0 following codon optimization.
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GC content adjustment
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Original gene encoding Col H showed a GC low content, resulting in low expression. In spite of
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that, codon optimization increased it from 30.99% to 47.46% (Figure 4).
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Conclusion
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In order to quantify translation efficiency, two measures are available. The first type of measures
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evaluates the codon bias of genes which CAI is a good case in point. A further kind is based on
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the availability of tRNA at each codon along the gene which Fop is a case in point. A privileged
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status of the Fop over the CAI is that it reduces the need to identify a set of highly expressed
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genes as a reference. On the contrary, it only needs the recognition of all tRNA genes in the
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genome and their classification in accordance with their anti-codon (Gingold and Pilpel 2011).
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Here, the Gene designer stand-alone software applied to optimize codons considers both
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methods. Furthermore, GC content in the Col H gene was balanced using the software. As such,
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better results are expected to have been obtained using the software.
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As has been reported elsewhere [e.g. (Hu, Shi et al. 1996; Johansson, Bolton-Grob et al. 1999;
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Zhou, Schnake et al. 2004; Burgess-Brown, Sharma et al. 2008)] the findings from this study
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suggest that codon optimization provides a theoretical improvement in Col H gene expression in
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E. coli. In spite of that, experimental research is needed to confirm the improvement. This is
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because although codon bias greatly impacts gene expression, it is not the only contributing
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factor. The selection of expression vectors and transcriptional promoters is also imperative
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(Gustafsson, Govindarajan et al. 2004). In addition, a sequence motif in the vicinity of the
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initiation AUG (Gingold and Pilpel 2011) and mRNA stability at the 5´terminus (Maertens,
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Spriestersbach et al. 2010) were shown to play a role in the gene expression.
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Acknowledgments
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We would like to thank Dr. Ramin Rahmany for reviewing and editing the manuscript.
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Competing interests
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The authors declare that they have no competing interests.
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Table 1(on next page)
table of Codons rarely used in E. coli
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Codons rarely used in E. coli
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Rare codon(s)
AGG, AGA, CGG, CGA
GGA, GGG
ATA
CTA, TTA
CCC
TCG, TCA, AGT
ACA
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Amino acid
Arginine
Glycine
Isoleucine
Leucine
Proline
Serine
Threonine
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Table 2(on next page)
table of results for the codon analysis of Col H gene before and after optimization
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Results for the codon analysis of Col H gene before and after optimization
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Amino Acid
Ala
Ala
Ala
Ala
Arg
Arg
Arg
Arg
Arg
Arg
Asn
Asn
Asp
Asp
Cys
Cys
Gln
Gln
Glu
Glu
Gly
Gly
Gly
Gly
His
His
Ile
Ile
Ile
Leu
Leu
Leu
Leu
Leu
Leu
Lys
Lys
Met
Phe
Phe
Pro
Pro
Pro
Pro
Ser
Ser
Ser
Ser
Ser
Ser
Thr
Thr
Thr
Thr
Trp
Before codon optimization
Codon
Number
/1000
GCG
1.00
1.02
GCA
22.00
22.40
GCT
17.00
17.31
GCC
2.00
2.04
AGG
5.00
5.09
AGA
23.00
23.42
CGG
0.00
0.00
CGA
0.00
0.00
CGT
2.00
2.04
CGC
1.00
1.02
AAT
57.00
58.04
AAC
12.00
12.22
GAT
59.00
60.08
GAC
12.00
12.22
TGT
3.00
3.05
TGC
0.00
0.00
CAG
3.00
3.05
CAA
20.00
20.37
GAG
10.00
10.18
GAA
60.00
61.10
GGG
6.00
6.11
GGA
38.00
38.70
GGT
26.00
26.48
GGC
3.00
3.05
CAT
16.00
16.29
CAC
1.00
1.02
ATA
41.00
41.75
ATT
11.00
11.20
ATC
6.00
6.11
TTG
4.00
4.07
TTA
42.00
42.77
CTG
0.00
0.00
CTA
4.00
4.07
CTT
14.00
14.26
CTC
1.00
1.02
AAG
32.00
32.59
AAA
59.00
60.08
ATG
19.00
19.35
TTT
24.00
24.44
TTC
13.00
13.24
CCG
1.00
1.02
CCA
23.00
23.42
CCT
15.00
15.27
CCC
1.00
1.02
AGT
25.00
25.46
AGC
5.00
5.09
TCG
0.00
0.00
TCA
16.00
16.29
TCT
21.00
21.38
TCC
2.00
2.04
ACG
1.00
1.02
ACA
23.00
23.42
ACT
34.00
34.62
ACC
4.00
4.07
TGG
9.00
9.16
Fraction
0.02
0.52
0.40
0.05
0.16
0.74
0.00
0.00
0.06
0.03
0.83
0.17
0.83
0.17
1.00
0.00
0.13
0.87
0.14
0.86
0.08
0.52
0.36
0.04
0.94
0.06
0.71
0.19
0.10
0.06
0.65
0.00
0.06
0.22
0.02
0.35
0.65
1.00
0.65
0.35
0.03
0.57
0.38
0.03
0.36
0.07
0.00
0.23
0.30
0.03
0.02
0.37
0.55
0.06
1.00
Amino Acid
Ala
Ala
Ala
Ala
Arg
Arg
Arg
Arg
Arg
Arg
Asn
Asn
Asp
Asp
Cys
Cys
Gln
Gln
Glu
Glu
Gly
Gly
Gly
Gly
His
His
Ile
Ile
Ile
Leu
Leu
Leu
Leu
Leu
Leu
Lys
Lys
Met
Phe
Phe
Pro
Pro
Pro
Pro
Ser
Ser
Ser
Ser
Ser
Ser
Thr
Thr
Thr
Thr
Trp
After codon optimization
Codon
Number
/1000
GCG
14.00
14.26
GCA
13.00
13.24
GCT
8.00
8.15
GCC
7.00
7.13
AGG
0.00
0.00
AGA
0.00
0.00
CGG
0.00
0.00
CGA
0.00
0.00
CGT
20.00
20.37
CGC
11.00
11.20
AAT
10.00
10.18
AAC
59.00
60.08
GAT
35.00
35.64
GAC
36.00
36.66
TGT
1.00
1.02
TGC
2.00
2.04
CAG
16.00
16.29
CAA
7.00
7.13
GAG
19.00
19.35
GAA
51.00
51.93
GGG
0.00
0.00
GGA
0.00
0.00
GGT
36.00
36.66
GGC
37.00
37.68
CAT
5.00
5.09
CAC
12.00
12.22
ATA
0.00
0.00
ATT
23.00
23.42
ATC
35.00
35.64
TTG
0.00
0.00
TTA
0.00
0.00
CTG
65.00
66.19
CTA
0.00
0.00
CTT
0.00
0.00
CTC
0.00
0.00
AAG
25.00
25.46
AAA
66.00
67.21
ATG
19.00
19.35
TTT
12.00
12.22
TTC
25.00
25.46
CCG
33.00
33.60
CCA
6.00
6.11
CCT
1.00
1.02
CCC
0.00
0.00
AGT
0.00
0.00
AGC
17.00
17.31
TCG
0.00
0.00
TCA
0.00
0.00
TCT
34.00
34.62
TCC
18.00
18.33
ACG
6.00
6.11
ACA
0.00
0.00
ACT
16.00
16.29
ACC
40.00
40.73
TGG
9.00
9.16
Fraction
0.33
0.31
0.19
0.17
0.00
0.00
0.00
0.00
0.65
0.35
0.14
0.86
0.49
0.51
0.33
0.67
0.70
0.30
0.27
0.73
0.00
0.00
0.49
0.51
0.29
0.71
0.00
0.40
0.60
0.00
0.00
1.00
0.00
0.00
0.00
0.27
0.73
1.00
0.32
0.68
0.82
0.15
0.03
0.00
0.00
0.25
0.00
0.00
0.49
0.26
0.10
0.00
0.26
0.65
1.00
PeerJ PrePrints | http://dx.doi.org/10.7287/peerj.preprints.754v1 | CC-BY 4.0 Open Access | rec: 21 Dec 2014, publ: 21 Dec 2014
TAT
TAC
GTG
GTA
GTT
GTC
64.00
13.00
2.00
31.00
23.00
0.00
65.17
13.24
2.04
31.57
23.42
0.00
0.83
0.17
0.04
0.55
0.41
0.00
Tyr
Tyr
Val
Val
Val
Val
TAT
TAC
GTG
GTA
GTT
GTC
23.00
54.00
19.00
11.00
19.00
7.00
23.42
54.99
19.35
11.20
19.35
7.13
0.30
0.70
0.34
0.20
0.34
0.13
PrePrints
Tyr
Tyr
Val
Val
Val
Val
PeerJ PrePrints | http://dx.doi.org/10.7287/peerj.preprints.754v1 | CC-BY 4.0 Open Access | rec: 21 Dec 2014, publ: 21 Dec 2014
Figure 1(on next page)
image of sequences alignment
The sequence alignments of original and codon-optimized Col H. 666 out of 982 codons
PrePrints
(67.82%) were substituted.
PeerJ PrePrints | http://dx.doi.org/10.7287/peerj.preprints.754v1 | CC-BY 4.0 Open Access | rec: 21 Dec 2014, publ: 21 Dec 2014
PrePrints
PeerJ PrePrints | http://dx.doi.org/10.7287/peerj.preprints.754v1 | CC-BY 4.0 Open Access | rec: 21 Dec 2014, publ: 21 Dec 2014
2
image of the distribution of codon usage frequency along the coding region of Col H
The distribution of codon usage frequency along the coding region of Col H. (A) and (B)
PrePrints
depict the coding region of Col H before and after codon optimization, respectively.
PeerJ PrePrints | http://dx.doi.org/10.7287/peerj.preprints.754v1 | CC-BY 4.0 Open Access | rec: 21 Dec 2014, publ: 21 Dec 2014
3
image of the percentage distribution of codons on the basis of their qualification
The percentage distribution of codons on the basis of their qualification. The value of 100 is
assigned to the codon with the highest usage frequency for a specific amino acid in the
PrePrints
expression organism of interest. The values of < 30 are assigned to codons which hinder the
expression efficiency and accuracy. (A) and (B) illustrate the percentage of Col H codons
before and after codon optimization, respectively.
PeerJ PrePrints | http://dx.doi.org/10.7287/peerj.preprints.754v1 | CC-BY 4.0 Open Access | rec: 21 Dec 2014, publ: 21 Dec 2014
4
image of GC content adjustment
GC content adjustment. The percentage range of which GC content is optimum is between
30% and 70%. Any peaks exterior of this range will adversely influence transcriptional and
PrePrints
translational efficiency
PeerJ PrePrints | http://dx.doi.org/10.7287/peerj.preprints.754v1 | CC-BY 4.0 Open Access | rec: 21 Dec 2014, publ: 21 Dec 2014