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Autologous expression of recombinant human proteins in human cells for biomedical research and product development is often hampered by low expression yields limiting subsequent structural and functional analyses. Following RNA and codon optimization, 50 candidate genes representing five classes of human proteins – transcription factors, ribosomal and polymerase subunits, protein kinases, membrane proteins and immunomodulators – all showed reliable, and 86% even elevated expression. Analysis of three representative examples showed no detrimental effect on protein solubility while unaltered functionality was demonstrated for JNK1, JNK3 and CDC2 using optimized constructs. Molecular analysis of a sequence-optimized transgene revealed positive effects at transcriptional, translational, and mRNA stability levels. Since improved expression was consistent in HEK293T, CHO and insect cells, it was not restricted to distinct mammalian cell systems. Additionally, optimized genes represent powerful tools in functional genomics, as demonstrated by the successful rescue of an siRNA-mediated knockdown using a sequence-optimized counterpart. This is the first large-scale study addressing the influence of multiparameter optimization on autologous human protein expression.

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The codon Adaptation Index--a measure of directional synonymous codon usage bias, and its potential applications.

P. Sharp, W Li (1987)

A simple, effective measure of synonymous codon usage bias, the Codon Adaptation Index, is detailed. The index uses a reference set of highly expressed genes from a species to assess the relative merits of each codon, and a score for a gene is calculated from the frequency of use of all codons in that gene. The index assesses the extent to which selection has been effective in moulding the pattern of codon usage. In that respect it is useful for predicting the level of expression of a gene, for assessing the adaptation of viral genes to their hosts, and for making comparisons of codon usage in different organisms. The index may also give an approximate indication of the likely success of heterologous gene expression.

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Tissue-Specific Differences in Human Transfer RNA Expression

Kimberly Dittmar, Jeffrey Goodenbour, Tao Pan (2006)

Introduction Human tissues contain common and distinct macromolecular components in varying amounts. Large-scale, high-throughput analyses of mRNA expression in human tissues show tissue-specific gene expression (e.g., [1–3]). Transfer RNA (tRNA) plays a central role in translating the mRNA sequence into the protein sequence. Approximately 450 tRNA genes have been annotated in the human genome [4,5] (http://lowelab.ucsc.edu/GtRNAdb/Hsapi). These tRNA genes are scattered throughout the genome and are present on all but the Y chromosome. Twenty-two additional tRNA genes are present in human mitochondrial DNA [6,7]. Thus, there are approximately 473 human tRNAs that are grouped into 49 isoacceptor families to decode the 21 amino acids specified by the genetic code (20 standard amino acids and selenocysteine). To our knowledge, no systematic studies of tRNA expression among human tissues have been published. The dearth of information on tRNA expression is the result of technical and intellectual obstacles. Accurate quantitation of individual tRNA species is challenging due to the extensive secondary and tertiary structure of tRNA and numerous post-transcriptional modifications [8], both of which interfere with reverse transcription and hybridization of short oligonucleotides. Enthusiasm for tackling this challenge was low because prior to the human genome sequencing project, human tRNAs were considered to be no more diverse than those in unicellular organisms [8,9]. Complete sequencing of the human genome revealed, however, that over 270 different tRNA sequences are present among approximately 450 tRNA genes. Since only 61 possible anticodons are specified by the triplet code, there are many distinct tRNA species with identical anticodons, but they contain sequence differences in the tRNA body. This sequence diversity is not an inevitable result of genetic drift in multicopy genes over the course of evolution but may also be of some functional relevance [10]. In stark contrast to vertebrate genomes, there are only 51 different tRNA sequences among 274 yeast tRNA genes [4,5,11]. Why should the human genome contain such a diverse array of tRNA sequences? A compelling explanation is that controlling expression of individual tRNA species enables another level of translational control for specific gene products. In bacteria and yeast, differences in the relative abundance of tRNA isoacceptors for a given amino acid clearly impact the synthesis of highly expressed proteins [12,13]. Codon bias in tissue-specifically expressed genes have been reported, prompting the insight that such biases may be related to potential tissue-dependent differences in tRNA expression [14]. tRNA is also the dominant ligand for the elongation factor 1α (EF-1α). Given the myriad supratranslational functions of EF-1α [15,16] in cellular physiology, variations in tRNA expression could influence these processes which include bundling of actins and disassembly of microtubules [17,18]. For these reasons, even a rudimentary analysis of tRNA expression in human tissues could reveal novel aspects of human tRNA biology. Here we describe the comparative analysis of tRNA levels in eight human tissues and two human cell lines using a microarray method adapted from our previously developed arrays for bacterial tRNAs [19,20]. Our human tRNA microarray contains 42 probes for nuclear encoded tRNAs and 21 probes for mitochondrial encoded tRNAs. These probes cover tRNAs for all amino acids with enough sequence differences to be uniquely distinguished. Our results show that tRNA levels vary widely among human tissues and coordinate according to the properties of their cognate amino acids. The differences in relative expression of tRNA isoacceptors in several tissues show statistically significant correlation to codon usage of a group of approximately 15 to 40 of tissue-specific genes that are expressed at the highest levels among tissue-specific genes. Results/Discussion Design and Specificity of Microarrays for Human tRNA tRNA was quantified by taking advantage of its universally conserved 3′CCA sequence to attach a fluorescently labeled probe to tRNA present in total RNA prepared from tissues or cell lines. tRNA labeled in this manner was hybridized to DNA probes arrayed on glass slides. The brain sample was included in all hybridizations to correct for the variations in fluorescence labeling and array manufacturing. We used probes that are 70 to 80 nucleotides long, covering the length of the entire tRNA minus the conserved 3′CCA sequence. Probes at these lengths significantly increase hybridization efficiency and eliminate the sensitivity to potential variations in post-transcriptional modifications [19]. We designed the probes for nuclear tRNA genes to distinguish expression between tRNA isoacceptors, i.e., tRNAs with different anticodon sequences that read the codons for the same amino acid. Often, an isoacceptor family is encoded by multiple highly homologous genes. For example, five tRNAArg isoacceptors read the six arginine codons. Our work on bacterial tRNAs showed that two tRNAs having more than ten different residues can be generally distinguished on a microarray, whereas significant cross-hybridization occurs when the sequence difference between two tRNAs is less than eight [19]. Sequence alignment by Clustal X [21] shows that there are sufficient sequence differences between tRNAArg isoacceptors (more than ten among 70 to 75 residues) to enable design of three isoacceptor probes that separately cover tRNA genes with ACG (modified to ICG [22]), CCT, or TCT anticodons. Since sequence differences between tRNAArg with CCG and TCG anticodons are insufficient (fewer than eight in 70 to 75 residues) to allow the design of two distinct probes, a single probe is used for these tRNAs. Thirty-seven probes are designed in this way to cover the 49 human tRNA isoacceptors plus the initiator tRNAMet (Tables S1 and S2). The gene sequences for tRNALys(TTT), tRNALeu(TAA), tRNAThr(TGT), and tRNAThr(CGT) isoacceptors are distinct enough that separate probes can be designed for their individual tRNA genes. The mitochondrial encoded tRNA sequences are sufficiently different so that 21 probes are designed to cover all mitochondrial tRNA genes except tRNAGlu (Tables S3 and S4). We established the specificity of the human tRNA array by examining the cross-hybridization of probes designed to detect tRNAs from different organisms (Figure 1). Based on sequence conservation, most of the 42 probes for human nuclear encoded tRNAs should hybridize to mouse tRNAs as well as to some Drosophila and Caenorhabditis elegans tRNAs [4,5]. Mitochondrial tRNA genes between human and mouse are sufficiently unique that 18 distinct probes for mouse mitochondrial tRNAs are present on the array. Approximately two-thirds of the Drosophila tRNAs and one-third of the C. elegans tRNAs have sufficient sequence similarity to human tRNAs that the same probes are used for these tRNAs. Ten separate probes for Drosophila and 34 probes for C. elegans tRNAs are also included on the array. The microarray was tested using total RNA isolated from HeLa cells, mouse kidney, and entire C. elegans. Figure 1B shows one of the representative 32 blocks on the microarray. This block contains two to four repeats each of nine human probes, two mouse mitochondrial probes, six Drosophila or C. elegans probes, three probes as negative controls, and four blank spots. When HeLa total RNA was used, eight of nine human probes showed signals ranging from weak to strong. No signals were detected from the two mouse mitochondrial tRNA probes. Weak signals could be seen on one of the three negative control probes and two of six Drosophila and C. elegans probes. When mouse kidney total RNA was applied to the array, the two mouse mitochondrial tRNA probes showed intermediate to strong signals. When C. elegans total RNA was applied, all ten homologous probes showed weak to strong signals, with only a lone Drosophila probe showing a weak signal among the non–C. elegans probes. Using HeLa RNA, probes for human tRNAs show significantly higher hybridization signals than nonhuman probes (Figure 1C). Hybridization to all but one nonhuman probe yields signals that are lower than 10% of the strongest signals among the human tRNA probes. A single exception is the strong hybridization to the C. elegans tRNAHis probe which is not predicted by sequence similarity. These results show that the specificity of the microarray is sufficiently high given the extensive conservation among tRNAs between these organisms and supports its validity for measuring specific differences in tRNA expression. To determine the appropriate dynamic range for the fluorescent dye ratios, serial dilutions of total RNA from HeLa were performed (Figure 1D). The dynamic range is at least two orders of magnitude as the dye ratios remain within a constant threshold of 1.25-fold. This result also suggests a detection limit of 1.25-fold for measured changes in tRNA abundance between two human samples. In bacteria and yeast, the relative abundance between tRNA members in an isoacceptor family ranges from 1-fold to 20-fold [23–26]. Our microarray is clearly capable of detecting differences within this range. tRNA Expression in Human Tissues and Cell Lines In unicellular prokaryotes and eukaryotes, the abundance of tRNA isoacceptors is correlated with codon preferences among genes encoding highly expressed proteins, e.g., ribosomal proteins [11,27,28]. Mining mRNA microarray expression data, Plotkin et al. [14] reported the existence of tissue-based codon bias in paralogous genes; they proposed that this codon bias is related to tissue-specific differences in the abundance of corresponding decoding tRNAs. To explore this intriguing hypothesis, we used our microarray to measure the relative tRNA expression between eight tissues: brain, liver, vulva, testis and ovary, thymus, lymph node, and spleen. We included the latter three immune tissues based on an unexpected consequence of human genome sequencing: the single largest cluster of tRNA genes resides in the gene cluster of the major histocompatibility complex (also known as the human leukocyte antigen complex) [29]. The existence of one-third of all human tRNA genes in the human leukocyte antigen complex suggests that expression of these genes may be related to immune system function. For example, a high expression level of tRNA in this region may facilitate high expression of histocompatibility complex genes following a signaling event [29]. Microarray results show overall variations in the expression levels of tRNA among different tissues (Figures 2 and S1). For example, all tRNAs in ovary have lower levels relative to brain. Some tRNAs in spleen have higher, while others have lower levels, compared to those in brain (Figure 2A). Within individual tissues, the maximal differences between the relative tRNA levels can be as large as approximately tenfold (e.g., vulva, thymus) or only approximately threefold (e.g., testis). Nuclear and mitochondrial encoded tRNA levels can be approximated separately by the mean and median tissue-to-brain ratio (Figure 2B and 2C). Liver and vulva have approximately two-thirds the amount of tRNA present in brain, while the reproductive tissues of testis and ovary express approximately one-third as much tRNA as the brain. Among the three immune tissues, thymus is comparable to liver and vulva, lymph node is similar to the reproductive tissues, while spleen has similar tRNA levels as the brain. On the other hand, the relative mitochondrial encoded tRNA levels in all seven tissues are lower than that in brain, a result that may reflect high mitochondrial translation activity in the brain (e.g., [30]). Since mature tRNA in human is thought to be very stable, total tRNA levels likely reflect tRNA transcription rates [28]. tRNA is transcribed by multisubunit complexes of RNA polymerase III, TFIIIB and TFIIIC [31,32]. Varying the abundance of one or more of these protein factors as suggested by results from mRNA expression arrays [1,2] could lead to varying levels of transcription among tRNA genes. On the other hand, the levels or activities of these subunit components can also be controlled post-trascriptionally and post-translationally. Understanding the basis for tissue specific differences in total tRNA awaits direct experimental examination of all of the potential factors. Large differences in the relative abundance of individual tRNAs from brain versus other tissues (e.g., ovary and spleen, Figure 2A) are observed. For example, both tRNAIle isoacceptors in ovary are expressed at only one-tenth of the level in brain, but these tRNAs in spleen are expressed above their levels in brain. To facilitate evaluation of tissue-specific differences in relative levels of individual tRNAs (Figure 3), we normalized fluorescent ratios internally to the separate median values among the nuclear- (Figure 2B) and the mitochondrial- (Figure 2C) encoded tRNAs. Variations in the relative expression of tRNA isoacceptors among tissues are readily observed, suggesting a possible relationship between tRNA abundance and codon usage among different tissues (Figure 3 and Table 1). For example, among the members of the tRNAArg isoacceptor family, four probes separate the five isoacceptor groups according to their codon-reading capabilities: Arg-ICG reads CGU/C; Arg-YCG reads CGA/G; Arg-CCT reads only AGG; and Arg-TCT reads primarily AGA codons. A clear difference among these tRNAArg isoacceptors can be seen among the brain and the liver, thymus, and lymph node. Arg-TCT and Arg-CCT are preferred over Arg-ICG and Arg-YCG in these nonbrain tissues, suggesting a possible preference in reading AGA and AGG codons. Another example is the tRNALys isoacceptor family. The AAG-reading isoacceptor is present in higher amounts than the AAA-reading isoacceptor in almost all tissues. This difference is particularly pronounced in vulva, thymus, and lymph node. The same type of analysis can be applied to tRNA isoacceptors for the glycine, isoleucine, leucine, serine, threonine, and valine families (Table 1). The relative expression of nuclear encoded tRNAs in two commonly used cell lines (HeLa and HEK293) differs by less than twofold and is quite similar among the isoacceptors (Figures 3 and S1 and Table 1). This result suggests that tRNA isoacceptor levels do not play a major role in differential protein expression in these cell lines, even though they are derived from different tissues (cervix and embryonic kidney). This may also be a result of nontissue processes such as the immortalized nature of these lines. Interesting trends can be observed on the basis of the cognate amino acid properties of tRNA (Figure 3B). tRNAs and their corresponding amino acids can be divided into four groups: hydrophobic (Ile, Leu, Met, Phe, Trp, and Val), small (Ala, Cys, Gly, and Pro), charged (Arg, Asp, Glu, His, and Lys), and polar (Asn, Gln, Ser, Se-Cys, Thr, and Tyr). Among the nuclear encoded tRNAs, the three immune tissues (thymus, lymph node, and spleen) contain increased levels of tRNA for the hydrophobic group and decreased levels of tRNA for the charged group compared to brain. In contrast, the two reproductive tissues (testis and ovary) contain decreased levels of tRNA for the hydrophobic group and increased levels of tRNA for the small group. Trends for the liver and vulva are more similar to those of the immune tissues, with the increase in the tRNA for the hydrophobic group less pronounced. In general, brain contains increased levels of tRNA for the charged group (except for some tRNAArg isoacceptors) and for the polar group (except for some tRNAThr isoacceptors). The mitochondrial encoded tRNAs show distinctly different trends compared to the nuclear encoded tRNAs. The three immune tissues have decreased levels of mitochondrial tRNA for the hydrophobic group but comparable or increased levels of mitochondrial tRNA for the polar group relative to those in brain. The two reproductive tissues have decreased levels of mitochondrial tRNA for the small group than those in brain. These differences may reflect the amino acid availability in these tissues, although how this affects RNA polymerase III transcription is unclear. Correlating Relative tRNA Abundance to Codon Usage of Tissue-Specific Gene The differential expression of tRNA isoacceptors can potentially be used to control translation via the codon usage of specific genes. However, it is unclear to which genes and at which stages of cellular development and differentiation this mechanism may be applied. Codon preferences are clearly present in some tissue-specifically expressed genes [14], although this bias has also been suggested to represent regional variations rather than selection for translational performance [33]. We attempted to find potential correlations between the codon usage of tissue-specifically expressed genes to the relative tRNA abundance among these tissues (Figure 4). In bacteria and yeast, correlations of the abundance of tRNA isoacceptors to codon usage are derived primarily from genes that are translated at high levels (e.g., ribosomal proteins). Adapting this strategy, thresholds were algorithmically established to determine the top 13 to 43 tissue-specific transcripts according to the Human GeneAtlas GNF1H gcRMA dataset at http://symatlas.gnf.org/SymAtlas [2]. Gene sequences for the most highly expressed tissue-specific transcripts were then compiled and analyzed for codon content at http://bioinformatics.org/sms [34]. Some gene information was not included due to ambiguity of data or nomenclature. Table 2 lists the tissue-specific threshold expression levels used for this analysis and the tabulated gene and codon counts. The dataset contains 7,737 to 21,163 codons for six tissues: brain, liver, testis, ovary, thymus, and lymph node (we were unable to carry out this analysis for vulva and spleen). To mimic the relative tRNA abundance measurements, the raw codon count is normalized to the total number of codons in every tissue, and the sum of each normalized codon count from nonbrain tissues is divided by the sum of the same count from brain (Tables S5 and S6). The correlation of codon usage versus relative tRNA abundance (not divided by median values) was explored in three ways. First, all data points from the same tissue are plotted. For liver and brain, a linear fit of this plot gives an r-value of 0.78 and a p-value of <0.0001, a statistically significant correlation (Figure 4A). Similar correlations for testis, ovary, thymus, lymph node, and brain are much weaker and not statistically relevant (unpublished data). The fold over median expression threshold for liver is the highest among all tissues examined (Table 2), suggesting that the liver-specific genes are expressed at much higher levels than other tissue-specific genes. The high expression levels of liver-specific genes may explain why a significant tRNA abundance–codon usage correlation is only found in liver versus brain. Second, data points for each tRNA isoacceptor are plotted across five tissues. Linear correlations for three tRNA isoacceptors [tRNALeu(CAG); tRNALys(CTT), and tRNAVal(mAC)] can be found with r-values from 0.90 to 0.94 and p-values from 0.016 to 0.039 (Figure 4B). Similar correlations for other isoacceptors are not statistically significant. Third, only two amino acids (arginine and leucine) have more than three data points and they are plotted for each tissue. Linear correlations among the four tRNAArg for two tissues (liver and thymus) can be found with r-values of 0.93 and 0.97 and p-values of 0.067 and 0.033, respectively (Figure 4C). Similar correlations for other tissues are much weaker. Conclusions We have found that human tRNA expression varies by as much as tenfold among human tissues. Given the central role of tRNA in protein synthesis, this wide variation of tRNA abundance may reflect translational control via the availability of certain tRNAs. Since tRNA is the dominant ligand for the multitasking protein EF-1α, variations in tRNA levels may provide a mechanism to link translation with the dynamics of the cytoskeleton. Transcriptional control of tRNA genes may therefore play a role in the function of human tissues or possibly in cellular development and differentiation. tRNA abundance may also play a role in translational control of highly expressed, tissue-specific genes via their codon usage. Determination of tRNA abundance and charging levels [20] for differentiating cells or cells undergoing adaptation may reveal previously unseen connections between translation and other cellular processes. Materials and Methods Materials. Total RNA from eight human tissues was purchased from Stratagene (http://www.stratagene.com): brain (No. 540005, No. 735006), liver (No. 735017), vulva (No. 735067), testis (No. 735064), ovary (No. 735260), thymus (No. 540141), lymph node (No. 540021), and spleen (No. 540035). After the microarray measurements were completed, it was found that the tissue RNA samples from Stratagene underwent an LiCl precipitation step which is known to result in the loss of small RNAs [35]. We subsequently found that the loss of tRNA is quite minimal when the concentration of the total RNA was greater than 1.5 μg/μl prior to the addition of LiCl and that the total amount of tRNA in brain was similar to that in HeLa isolated using a different protocol (Figure S2). The Stratagene RNAs we used are certified for microRNA studies. It is clear that under certain conditions, LiCl can precipitate microRNAs that are 3.5 times smaller than tRNAs. These results lend confidence that it is possible to perform LiCl precipitation and not affect tRNA studies. The total RNA from the HeLa and HEK293 cell lines were obtained using RNAwiz (Ambion, http://www.ambion.com) according to manufacturer's manuals. This procedure does not include LiCl precipitation or other known steps biasing against tRNAs. The three tRNA standards, E. coli tRNALys (No. R6018), E. coli tRNATyr (No. R0258), and yeast tRNAPhe (No. R4018), were purchased from Sigma-Aldrich (http://www.sigmaaldrich.com) and used without further purifications. tRNA microarrays. The microarray experiment consists of four steps starting from total RNA: (i) deacylation to remove remaining amino acids attached to the tRNA, (ii) selective Cy3/Cy5 labeling of tRNA, (iii) hybridization with prefabricated arrays, and (iv) data analysis. For deacylation, 0.25 μg/μl total RNA premixed with the three tRNA standards at 0.17 μM each was incubated in 100 mM Tris-HCl (pH 9.0) at 37 °C for 30 min. The solution was neutralized by the addition of an equal volume of 100 mM Na-acetate/acetic acid (pH 4.8) plus 100 mM NaCl, followed by ethanol precipitation. Deacylated total RNA was dissolved in water, and its integrity was examined using agarose gel electrophoresis. For Cy3/Cy5 labeling, tRNA in the total RNA mixture was selectively labeled with either Cy3 or Cy5 fluorophore using an enzymatic ligation method described previously [19,20]. The ligation reaction depends on the presence of the universally conserved 3′CCA nucleotides in every tRNA. Two different labeling oligonucleotides for the T4 DNA ligase were used in this work (Figure 1A). Both oligonucleotides are designed and shown to have insignificant bias for Cy3 and Cy5 dyes (Figure 1B and unpublished data). Oligo-1 contains 5-aminoallyl-U, and the Cy3 and Cy5 fluorophores were attached after the ligation step upon reactions with succimidyl esters of Cy3 or Cy5 (Amersham Biosciences, http://www.amersham.com, described in [20]). Oligo-2 contains an 8–base pair hybrid helix and either a Cy3 or Cy5 fluorophore preattached in the loop. The ligation reaction used an approximately 1 μM concentration of purified T4 DNA ligase for Oligo-1 but only 0.5 U/μl T4 DNA ligase (US Biochemicals, http://www.usbweb.com) for Oligo-2. Hence, the ligation of Oligo-2 required substantially less T4 DNA ligase compared to Oligo-1. Hybridization was performed at 60 °C overnight with 1 μg each of Cy3- or Cy5-labeled total RNA mixture using Oligo-1 and 1 μg of Cy3-labeled total RNA and 2.5 μg of Cy5-labeled total RNA using Oligo-2 (because only 40% of Oligo-2 used in this work contained the Cy5 fluorophore). Multiple arrays were run using the brain reference sample labeled with either Cy3 or Cy5. The microarray printing and hybridization conditions were the same as those in bacterial tRNA studies [19,20]. DNA oligonucleotide probes were designed on the basis of the 2001 version of the human genome [36]. Subsequent revision of the human genome sequences and tRNA annotations showed that 136 probes are useful for our study. They included 42 probes for human nuclear encoded tRNA genes, 21 probes for human mitochondrial encoded tRNA genes, 18 probes for mouse mitochondrial encoded tRNA genes, ten probes for Drosophila nuclear tRNA genes, 34 probes for C. elegans nuclear tRNA genes, three probes for bacterial and yeast tRNA standards, and eight probes for human tRNA hybridization controls. Nonhuman probes were used as specificity controls for hybridization of human samples. Eighteen replicates of each probe were printed on each array. The descriptions and sequences of the DNA oligonucleotide probes used for human nuclear and mitochondrial tRNA genes are provided in Tables S1 through S4. For data analysis, arrays were scanned using GenePix 4000b scanner (Axon Instruments, http://www.axon.com) to obtain fluorescence intensities and the Cy5/Cy3 ratio per pixel at each probe spot. The averaged Cy5/Cy3 ratio per pixel at each probe spot was first normalized to an averaged value of the three tRNA standards prior to subsequent analysis. For all tissue samples, the brain total RNA was used as the reference sample at equal amounts of total RNA as determined by the UV absorbance. tRNA constitutes up to 15% of total RNA. Supporting Information Figure S1 Relative Ratios of Each Human tRNA Probe for Liver, Vulva, Testis, Thymus, and Lymph Node versus Brain (55 KB PDF) Click here for additional data file. Figure S2 Recovery of tRNA Following LiCl Precipitation (36 KB PDF) Click here for additional data file. Table S1 Human Probes for Chromosomal Encoded tRNA Genes (10 KB PDF) Click here for additional data file. Table S2 Sequences of Human Probes for Chromosomal Encoded tRNA Genes (17 KB PDF) Click here for additional data file. Table S3 Human Probes for Mitochondrial Encoded tRNA Genes (8 KB PDF) Click here for additional data file. Table S4 Sequences of Human Probes for Mitochondrial Encoded tRNA Genes (12 KB PDF) Click here for additional data file. Table S5 Gene Sequences for Tissue-Specifically Expressed Gene According to mRNA Expression Data (429 KB XLS) Click here for additional data file. Table S6 Averaged Ratios of tRNA Abundance for Seven Tissues Relative to Brain (34 KB XLS) Click here for additional data file.

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High Guanine and Cytosine Content Increases mRNA Levels in Mammalian Cells

Grzegorz Kudla, Leszek Lipinski, Fanny Caffin … (2006)

Introduction In the standard genetic code, all U↔C and almost all A↔G substitutions in the third positions of codons are synonymous. Consequently, every protein sequence can be encoded by a large number of different nucleotide sequences, ranging from nearly 0%–100% G and C nucleotides in the third codon positions. In most organisms, the variation in guanine and cytosine (GC) content among genes is modest; for example, 90% of Saccharomyces cerevisiae genes have GC3 contents (proportion of G and C in the third positions of codons) between 30% and 50%. The diversity of codon usage in humans and other mammals is larger than in most other species. The GC3 content of human genes ranges from 20% to more than 95% ( Figure 1). It is believed that this broad variation in nucleotide usage is caused by the large-scale variation of nucleotide composition (isochore structure) of mammalian genomes. Genes located in GC-rich isochores tend to be more GC-rich than genes located in the GC-poor isochores [ 1, 2], and the GC content of pseudogenes increases following their insertion into GC-rich isochores [ 3]. This suggests that the same evolutionary force is responsible for the isochore structure of mammalian genomes and for the codon usage of genes. However, the precise mechanism that underlies the formation of isochores and the diversification of nucleotide usage in genes is not yet clear. The question of selection on synonymous sites in mammalian genes is widely debated (recently reviewed in [ 4]). In the early studies, silent (synonymous) sites in mammals were assumed to evolve neutrally, and it is still believed that a large majority of silent mutations are neutral. The strongest support for this view comes from an analysis of evolutionary rates at silent sites. Synonymous sites are believed to evolve as fast as the genomic average [ 5], ancient repeats [ 6], and introns [ 7, 8], although some authors report lower silent evolutionary rates [ 9, 10]. Silent substitution rates are also uncorrelated with gene expression breadth and tissue-specificity [ 11]. These results suggest that most synonymous mutations are not opposed by purifying selection in mammals. Furthermore, it is known from studies of bacteria, yeast, and flies that selection intensity on silent sites is correlated with gene expression level, leading to increased codon bias in highly expressed genes in these organisms [ 12– 16]. The lack of clear correlation between codon usage and expression level or breadth in mammals (reviewed in [ 17]) further supports the neutral evolution of silent sites. And yet, some observations lend support to the existence of selection on silent sites in mammals. The frequency distributions of silent polymorphisms in mammalian genes are compatible with nucleotide usage being determined by selection (or biased gene conversion), but not by regional mutation bias [ 18, 19]. The average GC content is higher at silent sites than in neighboring non-coding regions [ 20], suggesting that high GC content in coding regions could confer some selective advantage. The patterns of tissue-specificity in the codon usage of human genes [ 21, 22], although weak, could indicate translational selection on silent sites. Local codon bias in human genes depends on the position relative to splice sites [ 23, 24], and, as demonstrated in the CFTR gene, a surprisingly high proportion of synonymous mutations results in exon skipping and protein inactivation [ 25]. Many human diseases are caused by synonymous mutations resulting in aberrant splicing [ 4]. Finally, synonymous substitution rates vary within mammalian genes, and a case of unusually high sequence conservation at synonymous sites in the BRCA1 gene has been attributed to selection [ 26]. Most of these arguments are indirect, highlighting the need for experimental studies of mammalian codon usage evolution. Selection on silent sites requires the existence of functional differences between synonymous genes. Although several cases of such differences have been demonstrated in mammals, they are mostly related to differential splicing of synonymous gene variants. On the other hand, little is known about the effects of nucleotide usage at silent sites on gene expression efficiency. Several recent studies reported weak positive or negative correlations between the GC content and expression levels of mammalian genes [ 17, 27– 32]. All these works relied on estimations of expression levels of endogenous genes using microarrays or analyses of EST or SAGE databases. These are powerful approaches in terms of the amounts of experimental data analyzed. However, since gene expression depends on many factors other than codon usage—such as transcriptional regulation or mRNA UTRs—these studies provide only very indirect insight into the possible effects of codon usage on expression. To eliminate all factors other than nucleotide usage itself, one needs to compare directly the expression of GC-rich and adenine and thymine-rich GC-poor genes, placed in the context of identical promoters and UTR sequences. Here we use this direct experimental approach to study the effects of GC content on the expression of Hsp70 , green fluorescent protein (GFP), and IL2 genes in mammalian cells. Results In the first set of experiments, we compared the expression of genes from the mammalian Hsp70 family. We have recently shown that despite the very high similarity of their encoded proteins, mammalian Hsp70-family genes display large differences in their nucleotide usage [ 33, 34]. We used the human HSPA1A gene (GC3 = 92%, encoding heat-inducible Hsp70) and the human HSPA8 gene (GC3 = 46%, encoding constitutive Hsc70). The coding regions of both genes have similar lengths (1,920 and 1,926 nucleotides) and their encoded proteins share 85% identity. To enable a direct comparison of HSPA1A and HSPA8 expression, independent of their genomic context, we cloned their cDNA coding regions into pcDNA3.1 mammalian expression vectors. The 5′- and 3′- UTRs were comprised of the pcDNA3.1 vector sequence, and they were identical in both vectors. HA tags were used to enable easy comparison of protein expression levels, and the first three codons in HSPA8 were replaced by HSPA1A codons to avoid differences in the Kozak translation initiation sequence. We transfected HeLa cells using equal amounts of pcDNA3-Hsp70-HA or pcDNA3-Hsc70-HA vectors. Following 24 h of incubation at 37 °C, the cells were harvested and the Hsp70-HA and Hsc70-HA proteins were quantified by Western blotting using an anti-HA antibody. The Hsp70-HA protein, encoded by the GC-rich gene, was at least ten times more abundant than Hsc70-HA ( Figure 2A). The difference was consistently observed over a 3-fold range of plasmid concentrations ( Figure S1A) and was apparent as soon as 3 h post-transfection, when the Hsp70-HA protein first appeared (unpublished data). Identical results were obtained using 293T cells ( Figure S1B). Since HSPA1A is a heat-inducible gene, we tested whether high GC content facilitates its expression at high temperatures. We found that the ratio of Hsp70-HA to Hsc70-HA protein levels did not change with temperature in the range from 28 °C to 42 °C (unpublished data), suggesting that HSPA1A expression is enhanced independently of temperature. Among the 641 codons in the HSPA1A gene, 77% are the preferred human codons, i.e., those that are most frequently used in human genes. In comparison, only 39% of the codons in HSPA8 are preferred human codons. We therefore hypothesized that the difference in Hsp70-HA and Hsc70-HA protein abundance in cells might be due to different translation rates of these proteins. To explore this possibility, we performed in vitro translation experiments. Equal amounts of Hsp70 and Hsc70 mRNA (1.5 μg each) were used for translation in rabbit reticulocyte lysates in the presence of 35S-methionine. The only detectable protein products in the translation reactions corresponded to complete Hsp70 and Hsc70 polypeptides. The Hsp70 and Hsc70 proteins both appeared between 12 and 14 min after the reaction started ( Figure 2B). There was no detectible difference in the rates of Hsp70 and Hsc70 translation. If the translation rates of Hsp70 and Hsc70 are similar, then their different cellular protein levels could arise from a difference in mRNA abundance. To test this possibility, we quantified Hsp70-HA and Hsc70-HA mRNA using real-time RT-PCR, by amplifying a fragment of the 3′ UTR identical in both mRNAs. 24 h after the transfection of HeLa cells, the amount of Hsp70-HA mRNA was over 10-fold higher than the amount of Hsc70-HA mRNA ( Figure 2C). No Hsp70-HA or Hsc70-HA mRNA was detected in untransfected HeLa cells ( Figure 2C). To control for possible differences in transfection efficiencies, we quantified the mRNA of the neomycin resistance (neo) gene expressed from both plasmids. The neo mRNA levels were identical in the Hsp70 and Hsc70-transfected cells, suggesting that both plasmids were transfected with equal efficiencies ( Figure 2D). The equal loading of total mRNA in all samples was also confirmed using a cellular housekeeping gene, GAPDH ( Figure 2E). Thus, the difference in the Hsp70 and Hsc70 cellular mRNA levels results from their different transcription efficiency or mRNA stability. Similar results were obtained using 293T cells ( Figure S1C–E). Taken together, these results lead to the hypothesis that GC content may strongly affect the expression efficiency of HSPA1A and HSPA8 genes. To test the possibility that high GC content might increase gene expression in mammalian cells, we used plasmids encoding either a modified GC-poor jellyfish GFP gene (GC3 = 35%) or a GC-rich version of the gene, EGFP (GC3 = 96%). The Kozak sequences of both genes, the encoded protein sequences, and the plasmid sequences around the genes were identical. 24 h following transfection of HeLa cells, the overall EGFP fluorescence was 20–30 times higher than GFP fluorescence ( Figure 3A–C). The same result was seen in 293T cells and at times ranging from 6–36 h post-transfection (unpublished data), in agreement with previous reports [ 35]. We next investigated the amounts of GFP or EGFP mRNA produced in transiently transfected HeLa cells. mRNA was quantified by real-time RT-PCR, using a fragment of the 3′ UTR that was identical in both genes. As shown in Figure 3D, the steady-state level of EGFP mRNA was 20–50 times higher than that of GFP mRNA in HeLa cells. As a control, neo mRNA levels were similar for both plasmids, suggesting that the transfection efficiencies of pGFP-N2 and pEGFP-N2 plasmids did not differ ( Figure 3E). The same results were obtained in 293T cells (unpublished data). Since the ratio of EGFP to GFP mRNA levels was similar to the ratio of their protein levels, it is reasonable to conclude that mRNA level and not translation rate is responsible for the efficient EGFP protein synthesis in human cells. It is usually believed that selection on silent sites does not significantly affect codon usage in mammals. Thus, even if a gene becomes GC-poor and inefficiently expressed, perhaps because of its location in a GC-poor isochore, selective forces are not strong enough to improve the codon usage of that gene. It follows that many mammalian genes may have codon usage patterns that do not support their efficient expression. We analyzed human genes used in biotechnology or pharmaceutical industry. Several of them have GC3 contents below 60%, the median GC3 content of human genes ( Table S1). To test whether the expression of these genes could be modulated by changing their GC content, we used synthetic nucleotide usage variants of the IL2 gene. The IL2 protein is produced by T cells in response to antigenic stimulation. It performs a variety of immunostimulatory functions, including the induction of proliferation of T and B lymphocytes [ 36]. Recombinant IL2 (as Proleukin) is used in therapy of metastatic renal cell carcinoma and metastatic melanoma, and cancer gene therapy trials using IL2 cDNA are ongoing [ 37– 39]. An important factor in gene therapy and biotechnology is the efficiency of therapeutic gene expression. Since the original human IL2 gene has a low GC content (GC3 = 41%) that could potentially hamper its expression, we attempted to enhance IL2 expression using a synthetic version of the gene, eIL2 (enhanced IL2, GC3 = 100%). To provide additional controls for the relationship between GC content and expression, we used wIL2 (weakened IL2, GC3 = 7%), and a fusion gene containing half of the IL2 gene and half of the eIL2 gene ( IL2-eIL2, GC3 = 70%). All four IL2 constructs encode exactly the same protein sequence, and they were cloned into the pcDNA3.1 vector using the same restriction sites. The production of IL2 protein from the four constructs was measured by ELISA in cell culture supernatants. As expected, IL2 protein synthesis increased with increasing GC content of the genes ( Figure 4A and 4C). In HeLa cells, the eIL2 gene was expressed 5-fold stronger, and the IL2-eIL2 hybrid 3-fold stronger than the original IL2 gene. The expression of the wIL2 gene was so weak that the protein was not detectable in the HeLa cell culture media. In Saos-2 cells, protein synthesis of eIL2 and IL2-eIL2 was 13-fold and 3-fold stronger, respectively, than of wild-type IL2, while protein synthesis of wIL2 was five times lower than wild-type. Real-time RT-PCR experiments demonstrated a very similar positive correlation between GC content and mRNA levels, both in HeLa and Saos-2 cells ( Figure 4B and 4D). These experiments support the hypothesis that the nucleotide usage of mammalian genes can be modified to increase their mRNA levels. We next wanted to check whether GC content would affect the expression of genes integrated into mammalian chromosomes, as opposed to genes expressed from plasmids. Stable integration of transgenes eliminates many problems potentially associated with transient transfection, such as unequal plasmid concentration or purity and different transfection efficiency. We first used the MCF-7 human breast cancer cell line to integrate the various GFP and IL2 constructs into random genomic locations. As a negative control, we stably transfected MCF-7 cells with an empty pcDNA3.1 plasmid. Clones were selected using G418, and three to five clones of each type were used for measurements of mRNA and protein levels. As shown in Figure 5A, all the clones containing the GC-rich EGFP gene produced 10-fold to 100-fold more fluorescence than the clones expressing the GC-poor GFP gene, indicating increased EGFP protein levels. A similar result was obtained when comparing GFP and EGFP mRNA levels ( Figure 5B). The IL2 mRNA and protein levels also correlated very strongly with the GC contents of stably integrated IL2 gene variants ( Figure 5C and 5D). In this case, the variation in expression levels spanned several orders of magnitude, and considerable variation existed even among clones expressing the same gene (see e.g., Figure 5D, IL2 gene). However, none of the clones transfected with the GC-poorest wIL2 gene produced significant amounts of IL2 mRNA or protein ( Figure 5C and 5D). Random genomic integration of transgenes often results in a large variation of expression between clones, due to differences in integration sites or transgene copy numbers. In order to avoid this variation, we next used the Flp-In T-Rex-293 cell line to integrate single copies of the GFP or IL2 variants into a specific genomic location. The Flp-In T-Rex-293 cells also contain a Tet-ON inducible expression system. To eliminate possible artifacts caused by constitutive transgene expression during the selection process, clones were selected in the absence of tetracycline. We then measured the transgene expression following tetracycline addition. After 12–24 h following induction, EGFP mRNA and protein levels were around ten times higher than GFP levels ( Figure 6A and 6B). eIL2-transfected cells produced 5- to 10-fold more transgenic mRNA and protein than IL2-transfected cells ( Figure 6C and 6D). In contrast, the wIL2 protein and mRNA levels barely exceeded background measurements in the parental Flp-In T-Rex-293 cell line. Similar results were also obtained with TM3-FRT cells, with site-directed integration and constitutive, CMV promoter-driven expression of the GFP and IL2 transgenes (unpublished data). As expected, the variation between clones was much lower in the cells with site-directed transgene integration than in the cells with random integration sites ( Figures 5 and 6 and unpublished data). To test whether slow degradation or efficient synthesis caused the increased steady-state levels of GC-rich mRNA, we performed mRNA stability studies using an inhibitor of transcription, actinomycin D. HeLa cells were transfected with Hsp70, Hsc70, or with the GC-rich or GC-poor versions of GFP or IL2 genes. 20 h following transfection, the cells were treated with actinomycin D for 0–7 h, and mRNA was quantified by real-time RT-PCR. Two cellular mRNA species: GAPDH (stable) and c-myc (unstable) were also quantified to control the proper transcription inhibition by actinomycin D. As expected, the measured half-life of GAPDH mRNA was around 7 h, while the half-life of c-myc mRNA was below 1 h ( Figure 7). The stabilities of GC-rich and GC-poor mRNA species were similar in all cases ( Figure 7). The mRNA half-lives were: Hsp70, 2.9 h; Hsc70, 3.8 h; EGFP, 4.8 h; GFP, 3.3 h; eIL2, 4.5; IL2, 3.9 h. These slight differences in mRNA stabilities lifetimes are unlikely to account for the large difference in steady-state levels of GC-rich and GC-poor mRNA species. This result suggests that enhanced mRNA transcription or co-transcriptional processing accounts for the increased expression of GC-rich genes in mammalian cells. Discussion We have shown that the efficiency of mRNA production from GFP, IL2, and Hsp70-family genes in mammalian cells correlates with the silent-site GC content of these genes. Although the origin of GC content variability in human genes attracts much interest, the effect of GC content on gene expression in mammalian cells has not been previously addressed in a direct experimental way. However, previous studies on codon optimization provide some insight into the relationship between nucleotide usage and expression. In mammalian expression systems, the codon optimization strategy consists in increasing the proportion of preferred (i.e., most frequently used) mammalian codons in target genes. Since all of the preferred mammalian codons have G or C nucleotides in the third positions, codon-optimized genes are necessarily GC-rich. We reevaluated the results of published codon optimization experiments by analyzing the effects of GC content on gene expression ( Table 1). All these results support the higher expression of GC-rich genes as compared to adenine and thymine-rich genes. The ratio of GC-rich to adenine and thymine-rich gene expression levels varies from 2.5-fold to over 1,000-fold ( Table 1). This large variation is understandable, considering the different degrees of codon usage modification and different methods for quantifying gene expression. It has often been assumed that the increased expression of codon-optimized genes was caused by a translational mechanism [ 40– 42], although this possibility has not been thoroughly tested experimentally (but see [ 43]). Here we suggest that most of the observed codon optimization effects in mammalian cells may be attributed to expression changes at the mRNA level. For example, optimization of GFP has been assumed to enhance its translation rate [ 40]; instead, we have shown that codon optimization increases GFP mRNA levels. In some of the previous studies, increased mRNA levels contributed to the enhanced protein levels of codon-optimized (GC-rich) genes ( Table 1, [ 44– 46]). A codon-optimized version of HIV-1 gag (GC3 = 98%), was expressed in H1299 cells 100-fold more efficiently than wild-type gag (GC3 = 38%), both at mRNA and protein levels [ 44]. Unlike the wild-type gene, codon-optimized gag was expressed independently of the cis-acting mRNA regulatory elements, and did not require the RNA-interacting protein Rev for efficient expression [ 44, 47]. Similar effects of codon optimization were also shown for the HIV-1 gag-pol gene [ 45] and for HIV-1 vif and vpu genes [ 46]. The latter study demonstrated that codon optimization enhanced nuclear export, but not the transcriptional efficiency of vif and vpu mRNA. Furthermore, the GC-poor HPV-16 L1 (GC3 = 26%) and L2 genes (GC3 = 16%) as well as BPV-1 L1 and L2 genes (GC3 = 36%) have been shown to contain potent cis-acting mRNA-down-regulating elements in their open reading frames (ORF) [ 48, 49]. The HPV-16 L2 elements operate in an orientation-dependent manner, and their effect is partially explained by cytoplasmic RNA destabilization [ 48]. Most interestingly, the effects of L1 and L2 inhibitory elements could be overcome by T7 polymerase-driven cytoplasmic transcription in a vaccinia virus-based system, suggesting that most of the mRNA down-regulation takes place at the stage of transcription or nuclear export [ 48, 50]. A recent study of the mammalian GC-poor L1 retrotransposon expression shows that its mRNA is down-regulated at posttranscriptional or transcriptional levels depending on the ORF sense or antisense orientation [ 51, 52]. Increasing the proportion of TpA dinucleotides in the human DRD2 gene lowered its mRNA stability, while increasing the proportion of CpG dinucleotides increased the stability [ 53]. Finally, two codon optimization studies failed to detect differences in the levels of GC-rich and adenine and uracil (AU)-rich mRNAs [ 43, 54]. In one of these works, different probes were used to compare GC-rich and AU-rich mRNA abundance without correction for hybridization efficiency, weakening the conclusions [ 43]. Taken all together, results obtained in most prior studies are compatible with our hypothesis that high GC content enhances mRNA levels in human cells. The increased mRNA levels of GC-rich genes detected in this and previous studies can result from two mechanisms, not mutually exclusive: increased mRNA synthesis or decreased mRNA degradation. Interestingly, both RNA synthesis and degradation could potentially be affected by GC content in coding regions. It is well documented that AU-rich elements located in the 3′ UTRs can act to destabilize mRNA [ 55– 57]. cis-acting RNA-destabilizing elements have also been detected in the coding regions of several genes [ 50, 58– 61], but they remain poorly characterized. Most of the mRNAs that harbor coding region instability elements happen to be GC-poor (i.e., factor VIII, IL2, c-myc, c-fos, HPV, and HIV-1 mRNAs), but it is not known whether a general correlation exists between cellular mRNA lifetime and GC3 content. On the other hand, low GC content might also be associated with low transcription or RNA processing efficiency. The efficient expression of GC-poor genes in a T7 polymerase-driven transcription system in mammalian cells supports this type of mechanism. Low RNA-DNA duplex stability [ 62] and runs of uridines [ 63] have been implicated in abnormal pausing and arrest of mammalian RNA polymerase II, and U-rich motifs as well as the conserved AAUAAA signal play a role in normal transcription termination [ 64]. High GC content could also facilitate DNA transitions from B to A or Z conformation [ 65, 66], thereby affecting transcription factor binding or RNA polymerase processivity [ 67, 68]. The possible effects of DNA conformational transitions on nucleotide usage evolution have been previously described [ 69]. To distinguish between the effects of GC content on mRNA synthesis versus mRNA degradation in this study, we performed actinomycin D chase experiments. We demonstrated that GC content does not significantly affect the cellular mRNA lifetimes of GFP, IL2, and Hsp70 genes. Further, we have shown that destruction of the single AU-rich element-like sequence element in the GFP coding region does not enhance GFP expression (unpublished data), suggesting that AU-rich element-mediated RNA destabilization is not responsible for the low GFP expression. Taken together, these results suggest that the high expression of GC-rich genes results primarily from the efficient production of polyadenylated mRNA, through efficient transcription or co-transcriptional processing. The observation that codon usage optimization can enhance gene expression in homologous systems (as in the case of the IL2 optimization) may have important implications for biotechnology and medicine. It is important to note that we have neither proved nor disproved the idea that selection determines codon usage in mammalian genes. While the differences in expression levels between GC-rich and GC-poor genes are very important, single AT↔GC substitutions may only cause minor changes in expression. The selective coefficients associated with such minor changes may be too small to affect the evolutionary outcomes in small mammalian populations. It might be tempting to hypothesize that the paucity of GC-poor (GC3 95% cell viability was routinely achieved, as detected by fluorescence microscopy, immunofluorescence microscopy, and flow cytometry. For transfection of 293T cells, 8 × 10 4 cells per well were used in a 24-well plate. For each well, 0.4 μg pure plasmid DNA was mixed with 25 μL DMEM without FBS, and 0.8 μL 1 mg/mL polyethyleneimine (PEI, Polysciences Incorporated, Warrington, Pennsylvania, United States) in H 2O was added to this mixture, incubated 10 min at room temperature and the solution was added onto the cells. The transfection efficiency and cell viability was similar as for HeLa cells. For transfection of Saos-2 cells, 1.6 × 10 5 cells per well were seeded in a 12-well plate. For each well, 0.8 μg DNA was mixed with 50 μL DMEM, 1.6 μL of 1 mg/mL PEI was added, incubated 10 min, and spread on the cells. Transfection efficiency was 20%. For mRNA quantification, all transfections were scaled up to 6-well plates. SDS-PAGE and Western blotting. Cells were washed once with ice-cold PBS and lysed directly in the wells in 70 μL 1 × SDS sample buffer, boiled for 5 min and amounts corresponding to about 5 μg total protein per lane were loaded on 10% poliacrylamide gels. A prestained protein ladder (PAGE-Ruler, Fermentas, Burlington, Ontario, Canada) was routinely used. Following electrophoresis, proteins were transferred onto a nitrocellulose membrane (Pall) using a Bio-Rad blotting system (Bio-Rad, Hercules, California, United States). The following antibodies were used for detection: rabbit anti-HA, sc-805 (Santa Cruz Biotechnology), 1:2000; rabbit anti-GAPDH, sc-25778 (Santa Cruz Biotechnology), 1:6000; goat anti-rabbit IgG-HRP conjugated, 401393 (Calbiochem, San Diego, California, United States), 1:6000. The membranes were soaked in the chemiluminescence reagent immediately before exposure to a Kodak BioMax film. Flow cytometry. Cells were trypsinized, washed with medium containing 10% FBS, resuspended in PBS with 5% DMSO, and stored at −70 °C. The flow cytometry analysis was performed using BD FACS Calibur. Forward scatter and side scatter measurements were used to define a homogenous population of living cells, and the FL1 channel was used to detect the GFP or EGFP fluorescence. For fluorescence quantification, the arithmetic mean of all events corresponding to living cells was used. IL2 ELISA. 24 h following transfection, cell culture media were gathered and centrifuged 1 min at 14,000 rpm. Supernatants were diluted to the appropriate concentration with PBS + 10% heat-inactivated FBS, and IL2 concentrations were measured using the OptEIA human IL2 ELISA set (BD Biosciences, Palo Alto, California, United States) according to the manufacturer's instructions. In vitro transcription and translation. Capped Hsp70 and Hsc70 mRNA was produced in vitro using the T7 Cap Scribe kit (Roche, Basel, Switzerland) according to the manufacturer's instructions. The mRNA was analyzed by 1% agarose gel electrophoresis to confirm the absence of degradation. The in vitro translations were performed at 28 °C using the Reticulocyte Translation Kit Type II (Roche) and 35S-labeled Methionine (Amersham Biosciences, Little Chalfont, United Kingdom). The reactions contained 1–2 μg Hsp70 or Hsc70 mRNA, 2 μL translation reaction mix without methionine, 50 mM potassium acetate, 1.25 mM magnesium acetate, 2 μL 35S-Met (10 mCi/mL), and 10 μL rabbit reticulocyte lysate, in a total reaction volume of 25 μL. The reactions were started by the addition of rabbit reticulocyte lysate, and stopped after the desired time by addition of SDS sample buffer, followed by SDS-PAGE and autoradiography. mRNA quantification. Total cellular RNA was purified using the NucleoSpin kit (Macherey Nagel, Germany) according to the manufacturer's instructions. The NucleoSpin purification procedure comprises on-column DNA digestion using DNAse I. On several occasions, we verified the absence of contaminating plasmid DNA in our RNA preparations by omitting the reverse transcriptase in the RT reactions and then performing the real-time PCR. We never observed any significant contamination with this purification method. RNA concentration was measured spectrophotometrically, and approximately 1.5 μg of total RNA was used in each cDNA synthesis reaction. cDNA synthesis was performed using the RevertAid kit (Fermentas) with (dT) 18 primers. Real-time PCR cDNA quantification was performed using Light-Cycler (Roche) with Sybr Green II (Sigma). The primer sequences are shown in the Table S2. The equal transfection efficiency in transient transfection experiments was controlled using the neomycin resistance gene (neo), present in all our experimental constructs. The neo gene cDNA from the pEGFP-N2 and pGFP-N2 plasmids was amplified using the neo(GFP) primers, and the neo gene cDNA from the pcDNA3-Hsp70-HA, pcDNA3-Hsc70-HA, and all the pcDNA3-IL2 plasmids—using the neo(pcDNA) primers. The IL2 and GFP variants expressed in the Flp-In cells were quantified using the pcDNA5-UTR-U and pcDNA5-UTR-L primers. For RNA stability assays, cells were treated with 10 μg/mL actinomycin D (Sigma) for 0–7 h before RNA isolation. mRNA half-lives were determined by fitting exponential decay curves to experimental data points. Supporting Information Dataset S1 Sequences of the IL2 and GFP Gene Variants (3 KB TXT) Click here for additional data file. Figure S1 Expression of Hsp70 (GC3 = 92%) and Hsc70 (GC3 = 46%) in HeLa and 293T Cells (A) HeLa cells were transfected using 0.1, 0.2, or 0.3 μg of pcDNA3-Hsp70-HA or pcDNA3-Hsc70-HA plasmids and the protein expression levels 24 h after transfection were analyzed by Western blotting. (B) Same as (A), using 293T cells. (C–E) 293T cells were transfected with equal amounts of pcDNA3-Hsp70-HA or pcDNA3-Hsc70-HA plasmids. After 24 h, total cellular RNA was isolated and analyzed by qRT-PCR. The graphs represent Hsp/c70 (C), neo (D), and GAPDH (E) mRNA amounts. Hsp70, cells transfected with pcDNA3-Hsp70-HA; Hsc70 cells, transfected with pcDNA3-Hsc70-HA; control, untransfected cells. The mRNA amounts were normalized to the amounts in the Hsp70-transfected cells. The error bars represent standard deviations from 3–4 independent transfections. (71 KB PDF) Click here for additional data file. Table S1 Human GC-Poor Genes (GC3 < 60%) Used in Therapy (35 KB DOC) Click here for additional data file. Table S2 Primers Used for Cloning and Real-Time RT-PCR (44 KB DOC) Click here for additional data file.

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Journal ID (nlm-ta): PLoS One

Journal ID (publisher-id): plos

Journal ID (pmc): plosone

Title: PLoS ONE

Publisher: Public Library of Science (San Francisco, USA )

ISSN (Electronic): 1932-6203

Publication date Collection: 2011

Publication date (Electronic): 3 March 2011

Volume: 6

Issue: 3

Electronic Location Identifier: e17596

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[1 ]Geneart AG, BioPark, Regensburg, Germany

[2 ]Molecular Microbiology and Gene Therapy Unit, Institute of Medical Microbiology and Hygiene, University of Regensburg, Regensburg, Germany

[3 ]QIAGEN GmbH, Hilden, Germany

University of Edinburgh, United Kingdom

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* E-mail: ralf.wagner@ 123456geneart.com

Conceived and designed the experiments: SF APB ML AS BM PH FS MG RW. Performed the experiments: SF APB AS BM PH. Analyzed the data: SF APB AS BM PH. Contributed reagents/materials/analysis tools: SF APB AS BM PH. Wrote the paper: SF CL. Designed, performed and analyzed mammalian expression experiments, functional kinase assays and wrote the manuscript: SF. Designed, performed and analyzed MIP1a experiments: APB. Designed and performed Sf9-expression experiments: AS BM. Performed the CDC2-rescue study: PH. Helped design the experiments: ML. Revised the manuscript: CL. Designed and initiated the study: FS MG RW. Designed and set up the de novo gene synthesis- and optimization approach: MG RW.

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Publisher ID: PONE-D-10-04323

DOI: 10.1371/journal.pone.0017596

PMC ID: 3048298

PubMed ID: 21408612

SO-VID: b7b6494d-c047-4c1e-9fb3-9310b53942e4

Copyright © Fath et al. 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.

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Date received : 14 October 2010

Date accepted : 30 January 2011

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Multiparameter RNA and Codon Optimization: A Standardized Tool to Assess and Enhance Autologous Mammalian Gene Expression

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