Novel reference genes for quantifying transcriptional responses of Escherichia coli to protein overexpression by quantitative PCR
© Zhou et al; licensee BioMed Central Ltd. 2011
Received: 21 January 2011
Accepted: 23 April 2011
Published: 23 April 2011
Accurate interpretation of quantitative PCR (qPCR) data requires normalization using constitutively expressed reference genes. Ribosomal RNA is often used as a reference gene for transcriptional studies in E. coli. However, the choice of reliable reference genes has not been systematically validated. The objective of this study is to identify a set of reliable reference genes for transcription analysis in recombinant protein over-expression studies in E. coli.
In this study, the meta-analysis of 240 sets of single-channel Affymetrix microarray data representing over-expressions of 63 distinct recombinant proteins in various E. coli strains identified twenty candidate reference genes that were stably expressed across all conditions. The expression of these twenty genes and two commonly used reference genes, rrsA encoding ribosomal RNA 16S and ihfB, was quantified by qPCR in E. coli cells over-expressing four genes of the 1-Deoxy-D-Xylulose 5-Phosphate pathway. From these results, two independent statistical algorithms identified three novel reference genes cysG, hcaT, and idnT but not rrsA and ihfB as highly invariant in two E. coli strains, across different growth temperatures and induction conditions. Transcriptomic data normalized by the geometric average of these three genes demonstrated that genes of the lycopene synthetic pathway maintained steady expression upon enzyme overexpression. In contrast, the use of rrsA or ihfB as reference genes led to the mis-interpretation that lycopene pathway genes were regulated during enzyme over-expression.
This study identified cysG/hcaT/idnT to be reliable novel reference genes for transcription analysis in recombinant protein producing E. coli.
Recently, transcriptomic studies using DNA microarray and qPCR identified gene expression changes in E. coli[1–3]. Accurate quantification of transcriptomic changes requires reliable normalization methods to minimize technical variations, such as the quality/quantity of samples and instrumental bias. To date, normalization with internal reference genes is the most frequently used and reliable method for qPCR data [4, 5]. To the best of our knowledge, there has been no systematic study to identify reference genes for qPCR in E. coli. To date, rrsA encoding ribosomal RNA 16S [6, 7] and ihfB[2, 8–13] are the two most frequently used reference genes in E. coli. However, the stability of these two genes has not been validated.
E. coli has been extensively used in biotechnology for the production of proteins, therapeutic metabolites, and biofuels [1, 14, 15]. Recombinant DNA technology has provided various means to express proteins with diverse functions and for the over-production of metabolites in E. coli. As a result, a set of invariant reference genes for qPCR normalization during recombinant protein production is highly desired in E. coli.
In the present study, we aim to identify and validate a set of reference genes for the accurate normalization of transcription analysis in recombinant protein producing E. coli cells. Candidate reference genes were systematically selected from public microarray database. The temporal expressions of these twenty genes, rrsA and ihfB were quantified in two different E. coli strains induced to express enzymes of the 1-deoxy-D-xylulose 5-phosphate (DXP) pathway at two different temperatures. Two independent statistical algorithms 'geNorm'  and 'NormFinder'  were utilized to identify reliable reference genes stably expressed under the conditions tested. Further analysis examined if normalization factors derived from these novel reference genes or that of rrsA or ihfB allowed accurate quantification of the expressions of genes producing lycopene. This study illustrates the importance of the use of validated reference genes in transcriptional studies in E. coli.
High protein overexpression inhibits metabolite production
Selection of candidate reference genes from microarray
Selection of candidate reference genes from microarray data.
Primary multi-fun term
cell processes -> protection -> drug resistance/sensitivity
RnpB RNA; catalytic subunit of RNAse P
information transfer -> RNA related -> RNA degradation
probable pyruvate formate lyase 2 activating enzyme
information transfer -> protein related -> posttranslational modification
hydrogenase 3 and formate hydrogenlyase complex
metabolism -> energy metabolism, carbon -> anaerobic respiration
metabolism -> carbon utilization -> carbon compounds
YgjD, target for YeaZ protease
regulation -> type of regulation -> posttranscriptional -> proteases, cleavage of compounds
information transfer -> RNA related -> Transcription related
YajR MFS transporter
cell processes -> protection -> drug resistance/sensitivity
asparagine synthetase A
metabolism -> biosynthesis of building blocks -> amino acids -> asparagine
HcaT MFS transporter
cell structure -> membrane
cell structure -> membrane
conserved inner membrane protein
cell structure -> membrane
glycerophosphodiester phosphodiesterase, cytosolic
metabolism -> central intermediary metabolism -> misc. glycerol metabolism
aspartate kinase/homoserine dehydrogenase
metabolism -> biosynthesis of building blocks -> amino acids -> homoserine
putative peptidoglycan enzyme
cell structure -> murein
IlvY DNA binding transcriptional dual regulator
information transfer -> RNA related -> Transcription related
metabolism -> metabolism of other compounds -> phosphorous metabolism
uroporphyrin III C-methyltransferase [multifunctional]
metabolism -> biosynthesis of building blocks -> cofactors, small molecule carriers -> heme, porphyrine
information transfer -> RNA related -> RNA modification
rrsA 16S ribosomal RNA
cell structure -> ribosomes
integration host factor β subunit
Temporal expression of candidate reference genes in E. coli cells over-expressing metabolic pathway genes
Stabilities of candidate reference genes and common housekeeping genes
Two independent statistical algorithms, geNorm and NormFinder, were used to analyze the gene expressions. geNorm is based on pairwise variation analysis and assumes none of the reference genes are co-regulated. NormFinder is a model based method that analyzes the stability of each candidate gene, assuming all input samples are of equal quality and quantity. The "M value" (geNorm) or "Stability Value" (NormFinder) are inversely correlated to the stability of the candidate gene. The stability of the 17 candidate genes, rrsA and ihfB across all experimental conditions was analyzed by the two methods. Interestingly, both approaches identified cysG, idnT, and hcaT as the most stable reference genes (Additional file 1: supplementary figure S3). On the contrary, rrsA and ihfB were poorly ranked (Additional file 1: supplementary figure S3). In addition, pairwise variation analysis by geNorm showed that the combination of cysG, idnT, and hcaT is sufficiently stable (V3/4 = 0.116, less than the proposed cut-off of 0.15) to serve as normalizer.
Further analysis of candidate gene stabilities in cells grown at different temperatures or at specific time point (Additional file 1: supplementary table S2) revealed that the stability rankings of candidate genes do vary among different subgroups. However, cysG, idnT and hcaT were consistently more stable than rrsA and ihfB under all subgroups examined. The results suggested that the three novel candidate genes may serve as better normalizers than the commonly used rrsA and ihfB for gene expression profiling in BL21 cells.
Comparison of the normalization factors generated by different reference gene(s)
Choice of reference genes affects the interpretation of target gene regulation
Further validation of novel reference genes in an E. coli K-12 derivative strain
Besides E. coli BL21 (DE3), E. coli K-12 derivatives were also frequently used for recombinant protein production and metabolic engineering. Unlike BL21 (DE3) utilizing T7 system from bacteriophage, the K-12 derivatives employ endogenous expression system. To test if the same set of reference genes is applicable across strains, the expression stabilities of the 15 candidate genes (two genes were not detectable in the newly tested strain as compared to BL21), rrsA and ihfB were evaluated in a K-12 derivative, DH10B. Similar to that of BL21, DH10B cells were engineered to over-express dxs, idi, ispD, and ispF controlled by araBAD system at 28°C. Sample preparation and data analysis were performed as described for BL21.
Normalization of gene expression is critical for the accurate interpretation of transcriptional changes determined by qPCR. The use of total RNA as a reference has been explored as an alternative method to normalize qPCR gene expression but this approach was found not to be suitable as ribosomal RNA, the main constituent, was found to be regulated [24–26]. The use of multiple reference genes is deemed as the preferred method, where technical variations are taken into account [4, 5]. In this study we aimed to identify reliable reference genes for qPCR in recombinant protein producing E. coli, since over expression of recombinant proteins in E. coli is extensively used in biotechnology.
We first analyzed single platform and uniformly normalized microarray expression data from a substantial number of recombinant protein over-expression studies. From these, we selected 20 candidate reference genes based on their variation of signal intensities across all selected arrays. The expression of the 20 candidate genes and 2 commonly used reference genes were then measured by qPCR under a variety of conditions, including different strains, growth stages, temperatures, and induction levels. The expression of cysG, idnT and hcaT were found to be most constant as ranked by geNorm and NormFinder. Furthermore, scaling with the geometric average of these three genes (cysG/idnT/hcaT) provided accurate data interpretation under all tested conditions. The genes, idnT and hcaT, encode transporters for idonate and 3-phenylpropionate, respectively and cysG encodes a metabolic enzyme involved in siro-heme synthesis. Putative regulations of these gene expressions were predicted in EcoCyc http://ecocyc.org/, but only the control of idnT expression by its substrate idonate was experimentally verified in E. coli. Idonate is not a commonly used ingredient in E. coli media, which might explain the constant expression of idnT in the diverse microarray studies which we have analyzed and the qPCR study carried out herein.
Normalization of gene expression using the scaling factor derived from the three most stable genes (cysG/hcaT/idnT) revealed that the induction of the 4 enzymes (dxs, idi, ispD, and ispF) of the DXP pathway did not inhibit the expression of the enzymes involved in the lycopene production. The lycopene biosynthetic genes expressed from multiple copy plasmid and some of endogenous DXP pathway genes were found to maintain constant transcription levels during high induction levels. In contrast to the empirically validated reference genes described here, rrsA[6, 7] encoding ribosomal RNA 16S and ihfB [2, 8–13], were unvalidated but yet commonly used as reference genes in E. coli. Disturbingly, we found that ihfB and rrsA were less stable than cysG/idnT/hcaT under many conditions tested. The expression of lycopene biosynthetic genes normalized with ihfB appeared to be upregulated by up to 5 fold during induction, while rrsA overestimated gene downregulation by up to 8 fold (Figure 4 and additional file 1: supplementary figure S4). This type of incorrect interpretation can lead to faulty conclusions with regard to transcriptional regulations and is especially problematic in guiding further genetic manipulations. These results showed that the E. coli transcription machinery is fairly robust, and the lycopene production inhibition is not due to altered transcriptions of genes in the pathway examined. The results also illustrate the importance of the use of reliable, validated reference genes for qPCR analysis in E. coli.
In addition, the stabilities of gene expressions of rrsA in two tested strains were not consistent. We found that rrsA expression was fairly stable in DH10B but not in BL21 (DE3). This could be due to the strain-specific expression systems of the two cells. BL21 (DE3) was engineered to produce recombinant T7 RNA polymerase for target protein expression whereas DH10B uses E. coli endogenous expression systems. Another possibility could be due to the differences in their genetic backgrounds. Nonetheless, this example clearly highlights the necessity of evaluating the suitability of reference genes in different experimental contexts. Extending the study herein, it will be of interest to examine the use of the normalization factor of these three genes (cysG/hcaT/idnT) in more specific applications including conditions in biotechnology such as heat/cold-shock stresses, the presence of alcohols, and knockout of metabolic genes, and the study of pathogenic strains.
This systematic study included the meta-analysis of public microarray data and extensive in house qPCR analysis. Twenty candidate genes were identified and their expression stabilities were analyzed and compared with two commonly used reference genes rrsA and ihfB. The expression of cysG/hcaT/idnT was found to be most constant in the two recombinant protein producing E. coli strains across different growth stages, growth temperature, and inducer concentrations. Only the normalization factors derived from cysG/hcaT/idnT but not the ones derived from rrsA or ihfB provided accurate interpretation of transcriptional responses in the tested conditions. The identified reference genes in this study could be useful for other studies involving recombinant protein producing E. coli.
Bacteria strains and plasmids
E. coli BL21-Gold (DE3) (Stratagen) [E. coli B F- ompT hsdS (rB- mB-) dcm+ Tetr gal λ(DE3) endA Hte], and DH10B (New England Biolabs) [araD139 Δ(ara, leu)7697 fhuA lacX74 galK (ϕ80 Δ(lacZ) M15) mcrA galU recA1 endA1 nupG rpsL Δ(mrr-hsdRMS-mcrBC)] were used for lycopene production with pACLYC plasmid . The dxs-idi-ispDF operon was amplified by polymerase chain reaction (PCR) from p20T7MEP . Primers used were SacI-Ec_dxs: GCTTAGAGCTCAGTTTTGATATTGCCAAATA and Ec_ispF-XhoI: GTAACCTCGAGTCATTTTGTTGCCTTAATGA. The purified PCR product was ligated into a modified pBAD-B vector (Invitrogen), engineered to contain SacI & XhoI restriction sites. The resulting plasmid was termed pBAD-SIDF. p20T7MEP was transformed into BL21-Gold (DE3) and pBAD-SIDF was transformed into DH10B.
Bacteria growth and induction of protein expression
Four colonies were picked from agar plates, inoculated into 2xPY medium (20 g/L Peptone, 10 g/L Yeast extract, and 10 g/L NaCl, pH = 7) containing 34 μg/mL Chloramphenicol and 100 μg/mL Ampicillin, and incubated overnight. Ten micro-liter aliquots of overnight grown cell culture were inoculated into 1 mL 2xPY medium in 14 mL BD Falcon™ tube. Cells were grown at 37°C with shaking (300rpm) till OD595 reached the range of 0.5~1.0. The cells were then induced with various concentrations of L-Arabinose/IPTG and grown at 28°C or 37°C for indicated time before collected for RNA/protein extraction or Lycopene assay.
Cell suspension, equivalent to 0.5 mL OD595 = 1.0 cells, were withdrawn from each biological replicate 4 h after induction for protein assay. The cells were centrifuged at 20,000 g for 1 min, and pellets were resuspended in 100 μL 2% SDS solution and incubated for 10 min at 95°C, The protein samples were separated on a 12% denaturing polyacrylamide gel and the protein bands were visualized by Instant Blue (Gentaur).
Twenty microliters of cell suspension were sampled from each biological replicate at 4 h, 8 h, and 24 h after induction, and OD595 was recorded. The cells were centrifuged at 2,800 g for 2 mins, washed twice with PBS, and resuspended in 200 μL acetone. Resuspended cells were vortexed for 10 mins and centrifuged at 2,800 g for 2 mins. One hundred microliters of supernatant was mixed with equal volume of Ethanol and transferred to 96 Well Optical Bottom Plates (NUNC). Lycopene content was determined by interpolating from a standard dilution of lycopene (Sigma), based on absorbance at 472 nm (Spectra Max 190, Molecular Devices). Cell dry weight was estimated from cell density (OD595).
Meta-analysis of microarray data
Two hundred and forty sets of Affymetrix microarray expression data (RMA normalized) were extracted from the Many Microbe Microarray Databases (M3DE_coli_v4_Build_6). The data belongs to five different recombinant protein over-expression projects (upregulation_low_norfloxacin, upregulation_high_norfloxacin, metabolic_burden_response, recombinant_fermenter, and stringent_response) that include over-expression of 63 different proteins. The mean expression value and standard deviation were computed for each gene across 240 sets of microarray data. The coefficient of variation (CV = Standard Deviation/Mean) of signal intensity was calculated. The top fifty genes with the lowest CV (CV, 0.78% - 2.72%) were selected for further analysis of their genetic loci ecocyc.org and functional classifications .
The gene symbol and sequence for each candidate reference gene was retrieved from the affymetrix microarray probe set and compared to the NCBI E. coli BL21 (DE3) genomic sequence (NC_012971.1). Vector NTI Advance 10 (Invitrogen) was used to design two sets of primers for each target gene. The first set of primers generated amplicons of ~300 bp which were used as standard templates for qPCR of the targeted gene. The second set of primers was used for qPCR assays and was designed to amplify a ~100 bp region within each ~300 bp template. All products generated after amplifications were verified by gel-electrophoresis.
RNA purification and cDNA synthesis
Total RNA from E. coli was prepared using TRIzol® reagent (Invitrogen) according to the manufacturer's instructions. Total RNA was collected from samples in quadruplicate at each treatment time point. RNA concentration was quantified using a NanoDrop ND-1000 spectrophotometer (Thermo Scientific), and the 260/280 and 260/230 ratios were examined for protein and solvent contamination. The integrities of all RNA samples were confirmed by formaldehyde agarose gel. Eight hundred nanograms of total RNA were reverse transcribed in a total volume of 40 μL containing ImpromII (Promega) for 60 min at 42°C according to the manufacturer's instructions. The reaction was terminated by heating at 70°C for 5 min.
Quantitative real-time PCR
The cDNA levels were then analyzed using a Biorad iCycler 4 Real-Time PCR Detection System (Bio-Rad) with SYBR Green I detection. Each sample was measured in duplicate in a 96-well plate (Bio-rad) in a reaction mixture (30 μL final volume) containing 1× XtensaMix-SG (BioWORKS), 200 nM primer mix, 2.5 mM MgCl2, 0.75 U of iTaq DNA polymerase (iDNA). Realtime PCR was performed with an initial denaturation of 3 min at 95°C, followed by 40 cycles of 20 s at 95°C, 20 s at 60°C, and 20s at 72°C. Fluorescent detection was performed at the annealing phase and during subsequent dissociation curve analysis to confirm that a single product had been amplified. The threshold cycles (Ct) were calculated using the iQ5 Optical system software version 2.0. Primer dimers in all the assays showed distinct melt characteristics from the desired amplicons. All real-time PCR quantifications were performed simultaneously with PCR amplified standards and no-template controls. As PCR is an exponential process, it can be described by the equation, Nn = N0(1 +ε)n, where Nn is the number of target molecules at cycle n, N0 is the initial number of target molecules, ε is the efficiency of amplification and n is the number of cycles. Target amplification efficiency of an assay was determined from the slope of a plot of Ct (Threshold cycle) versus -log10 concentration of the initial number of target molecules. High efficiency of amplification has a slope approaching the value of 3.32 cycles (log210) for every 10-fold dilution of the target. Absolute gene copy numbers for each gene were interpolated from standard curves. All Real-time PCR experiments were compliant with the MIQE (Minimum Information for Publication of Quantitative Real-Time PCR Experiments) guidelines (MIQE form is included as additional file 4).
Data from four biological replicates were averaged for the analysis. The numbers of biological replicates for 4 hours, 8 hours, 28°C, 37°C subgroups were 30, 32, 29, 40. Gene expression stability analysis using two publicly available software tools, geNorm http://medgen.ugent.be/genorm/ and NormFinder http://www.mdl.dk/ were carried out according to authors' instructions.
Financial support from Singapore-MIT Alliance is acknowledged.
- Brynildsen MP, Liao JC: An integrated network approach identifies the isobutanol response network of Escherichia coli. Mol Syst Biol. 2009, 5: 277-PubMed CentralView ArticlePubMedGoogle Scholar
- Baez-Viveros JL, Flores N, Juarez K, Castillo-Espana P, Bolivar F, Gosset G: Metabolic transcription analysis of engineered Escherichia coli strains that overproduce L-phenylalanine. Microb Cell Fact. 2007, 6: 30- 10.1186/1475-2859-6-30PubMed CentralView ArticlePubMedGoogle Scholar
- Kizer L, Pitera DJ, Pfleger B, Keasling JD: Functional Genomics for Pathway Optimization: Application to Isoprenoid Production. Applied and environmental microbiology. 2008Google Scholar
- Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F: Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002, 3 (7): RESEARCH0034-PubMed CentralView ArticlePubMedGoogle Scholar
- Andersen CL, Jensen JL, Orntoft TF: Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Res. 2004, 64 (15): 5245-5250. 10.1158/0008-5472.CAN-04-0496View ArticlePubMedGoogle Scholar
- Wang S, Deng K, Zaremba S, Deng X, Lin C, Wang Q, Tortorello ML, Zhang W: Transcriptomic response of Escherichia coli O157:H7 to oxidative stress. Appl Environ Microbiol. 2009, 75 (19): 6110-6123. 10.1128/AEM.00914-09PubMed CentralView ArticlePubMedGoogle Scholar
- Ishii N, Nakahigashi K, Baba T, Robert M, Soga T, Kanai A, Hirasawa T, Naba M, Hirai K, Hoque A: Multiple high-throughput analyses monitor the response of E. coli to perturbations. Science. 2007, 316 (5824): 593-597. 10.1126/science.1132067View ArticlePubMedGoogle Scholar
- Lara AR, Leal L, Flores N, Gosset G, Bolivar F, Ramirez OT: Transcriptional and metabolic response of recombinant Escherichia coli to spatial dissolved oxygen tension gradients simulated in a scale-down system. Biotechnol Bioeng. 2006, 93 (2): 372-385. 10.1002/bit.20704View ArticlePubMedGoogle Scholar
- Caspeta L, Flores N, Perez NO, Bolivar F, Ramirez OT: The effect of heating rate on Escherichia coli metabolism, physiological stress, transcriptional response, and production of temperature-induced recombinant protein: a scale-down study. Biotechnol Bioeng. 2009, 102 (2): 468-482. 10.1002/bit.22084View ArticlePubMedGoogle Scholar
- Baez A, Flores N, Bolivar F, Ramirez OT: Metabolic and transcriptional response of recombinant Escherichia coli to elevated dissolved carbon dioxide concentrations. Biotechnol Bioeng. 2009, 104 (1): 102-110. 10.1002/bit.22379View ArticlePubMedGoogle Scholar
- Orencio-Trejo M, Flores N, Escalante A, Hernandez-Chavez G, Bolivar F, Gosset G, Martinez A: Metabolic regulation analysis of an ethanologenic Escherichia coli strain based on RT-PCR and enzymatic activities. Biotechnol Biofuels. 2008, 1 (1): 8- 10.1186/1754-6834-1-8PubMed CentralView ArticlePubMedGoogle Scholar
- Flores N, Escalante A, de Anda R, Baez-Viveros JL, Merino E, Franco B, Georgellis D, Gosset G, Bolivar F: New insights into the role of sigma factor RpoS as revealed in escherichia coli strains lacking the phosphoenolpyruvate:carbohydrate phosphotransferase system. J Mol Microbiol Biotechnol. 2008, 14 (4): 176-192. 10.1159/000109945View ArticlePubMedGoogle Scholar
- Flores N, Flores S, Escalante A, de Anda R, Leal L, Malpica R, Georgellis D, Gosset G, Bolivar F: Adaptation for fast growth on glucose by differential expression of central carbon metabolism and gal regulon genes in an Escherichia coli strain lacking the phosphoenolpyruvate:carbohydrate phosphotransferase system. Metab Eng. 2005, 7 (2): 70-87. 10.1016/j.ymben.2004.10.002View ArticlePubMedGoogle Scholar
- Makrides SC: Strategies for achieving high-level expression of genes in Escherichia coli. Microbiol Rev. 1996, 60 (3): 512-538.PubMed CentralPubMedGoogle Scholar
- Ajikumar PK, Xiao WH, Tyo KE, Wang Y, Simeon F, Leonard E, Mucha O, Phon TH, Pfeifer B, Stephanopoulos G: Isoprenoid pathway optimization for Taxol precursor overproduction in Escherichia coli. Science. 2010, 330 (6000): 70-74. 10.1126/science.1191652PubMed CentralView ArticlePubMedGoogle Scholar
- Baneyx F, Mujacic M: Recombinant protein folding and misfolding in Escherichia coli. Nat Biotechnol. 2004, 22 (11): 1399-1408. 10.1038/nbt1029View ArticlePubMedGoogle Scholar
- Rodriguez-Villalon A, Perez-Gil J, Rodriguez-Concepcion M: Carotenoid accumulation in bacteria with enhanced supply of isoprenoid precursors by upregulation of exogenous or endogenous pathways. J Biotechnol. 2008, 135 (1): 78-84. 10.1016/j.jbiotec.2008.02.023View ArticlePubMedGoogle Scholar
- Yuan LZ, Rouviere PE, Larossa RA, Suh W: Chromosomal promoter replacement of the isoprenoid pathway for enhancing carotenoid production in E. coli. Metabolic engineering. 2006, 8 (1): 79-90. 10.1016/j.ymben.2005.08.005View ArticlePubMedGoogle Scholar
- Kim SW, Keasling JD: Metabolic engineering of the nonmevalonate isopentenyl diphosphate synthesis pathway in Escherichia coli enhances lycopene production. Biotechnology and bioengineering. 2001, 72 (4): 408-415. 10.1002/1097-0290(20000220)72:4<408::AID-BIT1003>3.0.CO;2-HView ArticlePubMedGoogle Scholar
- Yoon SH, Lee YM, Kim JE, Lee SH, Lee JH, Kim JY, Jung KH, Shin YC, Keasling JD, Kim SW: Enhanced lycopene production in Escherichia coli engineered to synthesize isopentenyl diphosphate and dimethylallyl diphosphate from mevalonate. Biotechnology and bioengineering. 2006, 94 (6): 1025-1032. 10.1002/bit.20912View ArticlePubMedGoogle Scholar
- Faith JJ, Driscoll ME, Fusaro VA, Cosgrove EJ, Hayete B, Juhn FS, Schneider SJ, Gardner TS: Many Microbe Microarrays Database: uniformly normalized Affymetrix compendia with structured experimental metadata. Nucleic Acids Res. 2008, D866-870. 36 DatabaseGoogle Scholar
- Karp PD, Keseler IM, Shearer A, Latendresse M, Krummenacker M, Paley SM, Paulsen I, Collado-Vides J, Gama-Castro S, Peralta-Gil M: Multidimensional annotation of the Escherichia coli K-12 genome. Nucleic Acids Res. 2007, 35 (22): 7577-7590. 10.1093/nar/gkm740PubMed CentralView ArticlePubMedGoogle Scholar
- Zhou L, Lim QE, Wan G, Too HP: Normalization with genes encoding ribosomal proteins but not GAPDH provides an accurate quantification of gene expressions in neuronal differentiation of PC12 cells. BMC Genomics. 2010, 11: 75- 10.1186/1471-2164-11-75PubMed CentralView ArticlePubMedGoogle Scholar
- Hendriks-Balk MC, Michel MC, Alewijnse AE: Pitfalls in the normalization of real-time polymerase chain reaction data. Basic Res Cardiol. 2007, 102 (3): 195-197. 10.1007/s00395-007-0649-0PubMed CentralView ArticlePubMedGoogle Scholar
- Tricarico C, Pinzani P, Bianchi S, Paglierani M, Distante V, Pazzagli M, Bustin SA, Orlando C: Quantitative real-time reverse transcription polymerase chain reaction: normalization to rRNA or single housekeeping genes is inappropriate for human tissue biopsies. Anal Biochem. 2002, 309 (2): 293-300. 10.1016/S0003-2697(02)00311-1View ArticlePubMedGoogle Scholar
- de Kok JB, Roelofs RW, Giesendorf BA, Pennings JL, Waas ET, Feuth T, Swinkels DW, Span PN: Normalization of gene expression measurements in tumor tissues: comparison of 13 endogenous control genes. Lab Invest. 2005, 85 (1): 154-159.View ArticlePubMedGoogle Scholar
- Bausch C, Peekhaus N, Utz C, Blais T, Murray E, Lowary T, Conway T: Sequence analysis of the GntII (subsidiary) system for gluconate metabolism reveals a novel pathway for L-idonic acid catabolism in Escherichia coli. J Bacteriol. 1998, 180 (14): 3704-3710.PubMed CentralPubMedGoogle Scholar
- Cunningham FX, Sun Z, Chamovitz D, Hirschberg J, Gantt E: Molecular structure and enzymatic function of lycopene cyclase from the cyanobacterium Synechococcus sp strain PCC7942. The Plant cell. 1994, 6 (8): 1107-1121.PubMed CentralView ArticlePubMedGoogle Scholar
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