Altering the ribosomal subunit ratio in yeast maximizes recombinant protein yield
- Nicklas Bonander†1,
- Richard AJ Darby†1,
- Ljuban Grgic1,
- Nagamani Bora1,
- Jikai Wen2,
- Saverio Brogna2,
- David R Poyner1,
- Michael AA O'Neill3 and
- Roslyn M Bill1Email author
© Bonander et al; licensee BioMed Central Ltd. 2009
Received: 04 December 2008
Accepted: 29 January 2009
Published: 29 January 2009
The production of high yields of recombinant proteins is an enduring bottleneck in the post-genomic sciences that has yet to be addressed in a truly rational manner. Typically eukaryotic protein production experiments have relied on varying expression construct cassettes such as promoters and tags, or culture process parameters such as pH, temperature and aeration to enhance yields. These approaches require repeated rounds of trial-and-error optimization and cannot provide a mechanistic insight into the biology of recombinant protein production. We published an early transcriptome analysis that identified genes implicated in successful membrane protein production experiments in yeast. While there has been a subsequent explosion in such analyses in a range of production organisms, no one has yet exploited the genes identified. The aim of this study was to use the results of our previous comparative transcriptome analysis to engineer improved yeast strains and thereby gain an understanding of the mechanisms involved in high-yielding protein production hosts.
We show that tuning BMS1 transcript levels in a doxycycline-dependent manner resulted in optimized yields of functional membrane and soluble protein targets. Online flow microcalorimetry demonstrated that there had been a substantial metabolic change to cells cultured under high-yielding conditions, and in particular that high yielding cells were more metabolically efficient. Polysome profiling showed that the key molecular event contributing to this metabolically efficient, high-yielding phenotype is a perturbation of the ratio of 60S to 40S ribosomal subunits from approximately 1:1 to 2:1, and correspondingly of 25S:18S ratios from 2:1 to 3:1. This result is consistent with the role of the gene product of BMS1 in ribosome biogenesis.
This work demonstrates the power of a rational approach to recombinant protein production by using the results of transcriptome analysis to engineer improved strains, thereby revealing the underlying biological events involved.
Advances in understanding cellular function rely on improving our knowledge of protein behaviour, protein-protein interactions, and the complex interplay of proteins with other biomolecules. Whilst structures have been solved for many individual proteins, the challenge now is to expand this specific knowledge more generically to physiologically-important, difficult-to-study eukaryotic proteins and to understand the interplay between them in complex systems. Understanding the structure and function of human proteins, and particularly membrane proteins, will not only disclose the underlying structural basis of human function but is vital in the development of new drugs in the fight against human disease .
As they are not naturally highly abundant, membrane proteins and many soluble eukaryotic proteins must be over-produced for the detailed studies that will reveal their biochemical, functional and structural characteristics. Therefore obtaining high yields of functional, recombinant protein remains a major bottleneck in contemporary bioscience . We have shown that the root of the problem is the host organism , and the lack of knowledge about the intricate cellular biology within. Typically eukaryotic protein production experiments have relied on varying either promoter and fusion tag combinations in expression constructs  or culture process parameters such as pH, temperature and aeration  to enhance yields. These approaches require repeated rounds of trial-and-error optimization and cannot provide a mechanistic insight into the biology of recombinant protein production as only external parameters are varied. This is also true of approaches which rely on the mutation of the protein target to improve its production yields . The genomics revolution, however, has allowed us to take a broader but still rational approach to such optimization, which we previously adopted for recombinant membrane protein production  where we reported 39 host cell (S. cerevisiae) genes whose expression was significantly altered when the glycerol facilitator, Fps1, was produced under high-yielding conditions (20°C, pH5) compared to low-yielding standard growth conditions (30°C, pH5). Although similar studies were also subsequently performed in other hosts [7, 8], mechanistic insight into successful recombinant protein production has remained elusive.
Building on our previous transcriptome analysis , we show here how we identified high-yielding strains for the well-characterized [9–11] eukaryotic glycerol facilitator, Fps1, which is a non-trivial production target for further structural study. Specifically, we characterized spt3Δ, srb5Δ and gcn5Δ as effective production hosts for Fps1, where the yield improvement was up to a factor of 9 over the corresponding wild-type control. Improved yields of Fps1 were not explained by changes in promoter activity or FPS1 transcript number, but a post-transcriptional mechanism was suggested by the observation that each strain had elevated levels of BMS1 transcript compared to wild-type, as Bms1, the gene product of BMS1, is involved in ribosome biogenesis . Subsequent overexpression of BMS1 in a doxycycline-dependent manner revealed that maximal membrane protein yield is correlated with an optimum level of BMS1 transcript for Fps1 and can be specifically tuned to maximize yields of other functional membrane (human adenosine 2A receptor) as well as soluble (green fluorescent protein) protein targets. By altering the amount of BMS1 transcript, the metabolism of high-yielding cultures changed substantially as determined by on-line flow microcalorimetry. This coincided with the ratio of 60S and 40S ribosomal subunits being perturbed, which we propose is the key to maximizing recombinant protein yields. This work demonstrates the power of a rational approach to recombinant protein production by finally offering an insight into the actual mechanisms involved.
Three host strains deleted for genes involved in transcriptional regulation give substantially higher Fps1 yields than the wild-type parent strain
43 deletion strains were screened in shake-flasks to test the effect of genes that we had previously shown to be down-regulated under high-yielding production conditions of our target protein, Fps1 , or that were known to be from related pathways, especially in cases where a deletion strain was non-viable. The amount of membrane-bound Fps1 from late-log phase shake-flask cultures was quantified from immunoblots relative to the wild-type yield, with all immunoblot signals being below saturation. A cut-off point twice that of the wild-type was set for selecting strains for further study resulting in three strains being analyzed in bioreactors. The three deleted genes are known to be components of the transcriptional SAGA (GCN5, SPT3) and mediator (SRB5) complexes . It has been hypothesized that SAGA may have a role in the transcription of 10% of genes, most of which seem to be stress-induced and that this might reflect the need to balance inducible stress responses with the steady output of housekeeping genes . In contrast, the mediator complex appears to be required for all transcriptional events and transmits regulatory signals from transcription factors to RNA polymerase II, interacting directly with the unphosphorylated carboxy-terminal domain of RNA polymerase II, forming part of the pre-initiation complex, thus stimulating transcription .
Fps1 yield correlates with BMS1 transcript number in the host cell
Up-regulation of BMS1 is a marker of high-yielding conditions
BMS1 up-regulation can be used as a marker for selecting high-yielding strains
Copies BMS1 mRNA/cell
Fps1 yield relative to wild-type
The BMS1 overexpression strain, yTHCBMS1, can be further tuned to improve the functional yield of the G-protein coupled receptor, hA2aR, and soluble green fluorescent protein
On-line flow microcalorimetry data indicate different metabolic activity in high-yielding versus low-yielding strains
As expected, the heat outputs in all but the maximal Fps1-yielding conditions were indistinguishable. A natural log plot of the glucose growth phases for the low-yielding strains had highly similar pseudo-first order rate constants of 0.39 h-1, 0.37 h-1and 0.32 h-1, for the wild-type in the absence or presence of doxycycline, and yTHCBMS1 in its absence, respectively, yielding a mean growth rate of 0.36 h-1 (where the standard deviation is 0.04). This is in excellent agreement with the mean growth rate of 0.34 h-1 calculated from OD600 values and dry weights (Figure 4, lower insert) and is in line with typical growth rates for glucose-grown S. cerevisiae in bioreactors . In contrast the mean growth rate for the maximal Fps1-yielding conditions (yTHCBMS1 in the presence of 0.5 μg/mL doxycycline) was calculated to be 0.21 h-1 from triplicate determinations (standard deviation is 0.02), indistinguishable from 0.19 h-1 calculated from OD600 values and dry weights (Figure 4). This is 58% of the mean growth rate for the low-yielding strains and is clearly reflected in the differing peak areas of the glucose growth phases (Figure 4): the corresponding heat yields for yTHCBMS1 in the presence and absence of 0.5 μg/mL were 5.4 kJ/g and 6.5 kJ/g, respectively. This suggested a substantially different metabolism in high-yielding compared to low-yielding cultures, consistent with the different ethanol profiles (Figure 4, upper insert).
Maximum protein yield is accompanied by an altered 60S:40S subunit ratio in the host cell
A recent analysis of membrane protein production in E. coli  reported that increases in levels of chaperones and proteases were associated with increased membrane protein production and it was speculated that low yields were due to limited Sec translocon capacity. Our previous transcript analysis identified up-regulation of SEC62 in high yielding protein production. Sec62 is an essential subunit of the Sec63 complex (Sec63, Sec62, Sec66 and Sec72) and with the Sec61 complex, Kar2/BiP and Lhs1 forms a channel competent for SRP-dependent and post-translational SRP-independent protein targeting and import into the ER . We found a SEC63 over-expression strain from the Open Biosystems yTHC S. cerevisiae collection did not give improved recombinant protein yields. Additionally, we found that SRP102, which encodes the signal recognition particle receptor β subunit, was down-regulated in high-yielding experiments and again that a srp102Δ strain gave only wild-type yields of Fps1. Whilst this does not preclude the eukaryotic secretory pathway from having a limiting effect in S. cerevisiae translational efficiency it does highlight clear differences between prokaryotic and eukaryotic cells.
We found instead that a common theme in our high-yielding strains was the up-regulation of BMS1 by a factor of 6–7 over wild-type (Table 1) suggesting the importance of ribosome biogenesis in maximizing production yield. Bms1 is an essential nucleolar protein that is evolutionarily conserved throughout the eukaryotic kingdom and has been suggested to have a regulatory role in the biogenesis of the 40S subunit  as well as being a GTP-binding protein . Further experiments have led to the current model of Bms1 binding to the product of a second essential gene, Rcl1, in a GTP-dependent manner and shuttling Rcl1 to pre-ribosomes via its affinity for U3 snoRNA . Our unpublished data suggest that over-expression of RCL1 in a doxycycline-dependent manner does not lead to improved yields, although this could be a result of Bms1 being limiting in a Bms1/Rcl1 complex. Nonetheless, recombinant protein translation was clearly enhanced by the overexpression of BMS1 alone (Figure 2).
Interestingly, it has previously been reported that mice in which deletion of both genes for ribosomal protein S6 led specifically to a lack of 40S synthesis, but normal 60S synthesis, in the liver survived for several weeks. Their livers could respond to fasting and re-feeding cycles, in which the mass of the liver nearly doubles. However, whereas partial hepatectomy in normal animals leads to rapid re-growth and cell division, the livers of mice defective in the production of 40S ribosomal subunits did not re-grow and showed no signs of cell division . These results imply that a lack of 40S ribosome biogenesis can induce a checkpoint control that prevents cell cycle progression. This is entirely consistent with our observed slowing of growth that accompanied high-yielding protein production which has been widely reported by others as a feature of successful protein production hosts  and measured by on-line gas analysis . Our own data support an interpretation that the yTHCBMS1 strain at 0.5 μg/mL doxycycline (high yielding conditions) has low fermentative activity, since it produces 16% less ethanol than the wild-type strain under the same conditions. Dry weights in the high yielding condition were also 6.5% lower than in poor yielding conditions over a full growth curve (122 h) in agreement with the observation that 1 g ethanol leads to 0.25 g dry weight in a typical yeast fermentation , and so 16% less ethanol should lead to 4% less dry weight. Furthermore the yTHCBMS1 strain had a lower average exponential phase RQ in the presence of 0.5 μg/mL doxycycline (RQ = 3.6) than in its absence (RQ = 4.1; in excellent agreement with literature values for wild-type strains grown on glucose ).
These observations are supported by our on-line flow microcalorimetry data. Kemp and co-workers were amongst the first to apply this technique to recombinant systems  and recently Gill and colleagues reported 'thermal profiling' as a method for on-line analysis of the growth of E. coli cultures expressing cyclohexanone monooxygenase under the control of an L-arabinose promoter in a multi-well format . In the study presented here, we took the next step by using thermal profiles to rationalize the underlying mechanisms at work in our high-yielding cultures. The reduction in heat yield we observed suggests that high-yielding cells have a more efficient metabolism than low-yielding cells. As ribosome biogenesis is a major consumer of cellular energy resources  and its regulation is intimately linked to cell size, which in turn affects cell growth  as well as influencing other features such as the way that cells respond to stress , we hypothesized that the mechanism underlying this changed metabolism could be due to changes in ribosomal composition. The polysome profile for the high-yielding strain in which BMS1 is overexpressed in the presence of optimal levels of doxycycline for maximal Fps1 yield (at 0.5 μg/mL, Figure 5A) showed elevated levels of 60S subunits without any significant decrease in the levels of 80S or polysome. On further probing this by disassociating the ribosomes with EDTA, it could be seen that the ratio of 60S to 40S had been perturbed from wild-type levels of close to 1:1 to 2:1. This might suggest a previously unreported role (direct or indirect) for Bms1 in 60S biogenesis and also the possibility of an underlying mechanism in which binding and release of the 60S ribosomal subunit might be rate-limiting. We speculate that perturbing the ratio of the ribosomal subunits is key to maximizing recombinant protein yields by making protein production a more efficient cellular process. This might be influenced by an altered dynamic equilibrium between the transcript:40S complex and the 60S subunit which is available in greater amounts in the high-yielding strain. Of course it is also possible that some other component is affected by the perturbed 60S:40S ratio; both possibilities are the subject of our ongoing investigations.
Our data show that recombinant protein production can be rationalized, guided by the results of transcriptome analysis of the host strain response. We specifically identify BMS1 as a gene whose expression can be tuned to facilitate high yielding protein production experiments. Under these conditions, there is a perturbation in the ratio of ribosomal subunits and the metabolism of the host cell is more efficient with respect to protein production.
The FPS1 gene was tagged at its 3' end replacing the carboxy-terminal threonine residue with a sequence encoding three HA epitopes to permit immunodetection: SGRIFYPYDVPDYA GYPYDVPDYA GYPYDVPDYA AQCGR. The HA sequences are underlined. The construct was expressed from the TPI promoter in the 2 μpYX212 and pYX222 vectors (Novagen; now discontinued) which contain the URA3 and HIS3 selection markers respectively. The gene was cloned into the Bam H1 and Hin dIII sites and the vectors transformed into S. cerevisiae using the lithium acetate method. The GFP (GenBank U62636) and hA2aR  genes were amplified by PCR and cloned into the Hin dIII and Xma I sites and the Hin dIII and Sal I sites of pYX212 and pYX222, respectively. The GFP gene was cloned with an α-secretion factor to facilitate secretion into the culture medium.
Yeast strains and culturing conditions
S. cerevisiae BY4741 is the parental strain for the deletion mutants spt3Δ, srb5Δ and gcn5Δ from the EUROSCARF collection http://web.uni-frankfurt.de/fb15/mikro/euroscarf and the yTHCBMS1 strain (Open Biosystems) used in this study, and as such provided the wild-type control. Yeast cells were cultured in 2.5 L bioreactors containing 2 L of either CSM ± myo-inositol or 2 × CBS. CSM was composed of 1.7 g/L yeast nitrogen base (YNB) without amino acids, 5 g/L ammonium sulphate supplemented with 2% glucose ± 10 μg/mL additional myo-inositol, 2 × DO solution minus histidine or minus uracil (Clontech Yeast Protocols Handbook Version PR13103) and 10 mM MES pH 6. 2 × CBS was composed of 10 g/L ammonium sulphate, 6 g/L potassium dihydrogen phosphate, 1 g/L magnesium sulphate supplemented with 2% glucose, 2 mL/L each of trace element solution and vitamin stock solution (recipes shown below) and 2 × DO solution minus histidine or minus uracil. The pH was adjusted to, and maintained at, 6 via the online addition of 0.5 M NaOH. The agitation, aeration and temperature of the cultures were maintained at 700 rpm, 1 L per min and 30°C respectively. 1L trace element solution was composed of the following: 15 g EDTA, 4.5 g ZnSO4·7H20, 1 g MnCl2·4H2O, 0.3 g CoCl2·6H2O, 0.3 g CuSO4·5H2O, 0.4 g Na2MoO4·2H2O, 4.5 g CaCl2·2H2O, 3 g FeSO4·7H2O, 1 g H3BO3 and 0.1 g KI. The pH was maintained at 6.0 with 1 M NaOH throughout the addition and finally adjusted to pH 4 with 1 M HCl prior to autoclave sterilisation and storage at 4°C. 1 L vitamin solution was composed of the following: 0.05 g D-biotin, 1 g Ca D (+) panthothenate, 1 g nicotinic acid, 25 g myo-inositol, 1 g thiamine hydrochloride, 1 g pyridoxol hydrochloride and 0.2 g D-amino benzoic acid. pH maintained at 6.5 with 1 M HCl. The vitamin solution was filter sterilized and stored as 20 mL aliquots at 4°C. Plasmid retention was verified by the plating of the cells onto CSM + inositol agar in the absence of histidine or uracil and incubation at 30°C for 4 days. To initiate each experiment, 50 mL of a given medium were inoculated with fresh yeast cells in a baffled shake flask and cultured for up to 72 h in a shaking incubator at 30°C, 220 rpm. This pre-culture was subsequently used to inoculate 200 mL of the same medium in a baffled shake flask and cultured under the conditions outlined above to an OD600 of 1. This was then used to inoculate the bioreactors to initial OD600 = 0.05. For the production of hA2aR, the strains BY4714 and yTHCBMS1 were transformed with hA2aR vectors and cultured in 1.75 L 2 × CBS supplemented with 10 mM theophylline and 0.5, 1.0 or 10.0 μg/mL doxycycline. The cells were harvested by centrifugation once the glucose concentration of the cultures was in the range 5 – 10 mM.
Sampling, extracellular substrate determination and membrane preparation
Samples were withdrawn at various points in both the glucose and ethanol phases. 15 – 100 mL culture were centrifuged at 5000 × g, 4°C for 5 min. 0.5 mL of the supernatant was stored at -20°C for glucose and ethanol analyses. The cell pellets were frozen in liquid nitrogen and stored at -80°C for subsequent membrane preparation. Ethanol analysis (10176290035, R-Biopharm, Germany) was performed according to the manufacturer's instructions. Glucose concentrations were calculated with an Accu-Chek Active glucose analyzer (Roche Diagnostics, UK). Cell pellets were fractionated with glass beads (1:1 ratio) in 2 mL cell breaking buffer (50 mM potassium phosphate pH 7.4, 100 mM NaCl, 0.5 mM EDTA, 5% glycerol, 4 mM PMSF). The cells were agitated in a Fast Prep (Thermo Fisher Scientific, UK) at speed 6.5, employing 6 × 40 s pulses with 2 min incubations on ice between pulses. The samples were clarified at 10,000 × g, 4°C for 30 min and the total membrane pellet recovered from the supernatant at 100,000 × g, 4°C for 60 min. Total membranes were re-suspended in 50 μL of Buffer A (20 mM Hepes pH 8, 50 mM NaCl, 10% glycerol w/v) and the total protein concentration determined using a Bio-Rad (Hemel Hempstead, UK) Bradford-based assay with bovine serum albumin as standard. Dry-weight determinations were performed by collecting two samples of 5 mL by centrifugation for 5 min at 5,000 g. The cells were washed once in 5 mL water, dried for 24 h at 110°C, and stored in a desiccator for 24 h before being weighed. Typical dry weights were 2.1 g/L for low-yielding conditions and 2.0 g/L for high yielding conditions after completion of the glucose growth phase.
Immunoblotting and yield analysis
30 – 75 μg of total membranes were loaded per lane on an 8% polyacrylamide gel and separated by SDS-PAGE at 150 V for 1.25 h. Proteins were subsequently transferred to a nitrocellulose membrane (ProTran; Geneflow, UK) at 100 V for 1 h. The membrane was blocked with phosphate-buffered saline (PBS) containing 5% milk overnight at 4°C before incubating with mouse monoclonal anti-HA (clone 12CA5; Roche Diagnostics, UK) at a 1:5,000 dilution in PBS/5% milk for 1 h at room temperature with gentle agitation. The membrane was subsequently washed twice with PBS/0.2% Tween 20 for 5 min before incubating with goat anti-mouse horseradish peroxidase-conjugated secondary monoclonal antibody (Sigma-Aldrich, UK) at a 1:5,000 dilution in PBS/5% milk for 1 h at room temperature with gentle agitation. The membrane was washed as above and developed using an enhanced chemiluminesence detection kit (Geneflow, UK) following the manufacturer's instructions and visualized with a Chemidoc (UVItech, UK). The signal from each lane was quantified using either UVIband or the ImageGauge programme and was expressed as the factor improvement over our internal control (which is the previously-reported reference yield of Fps1 per μg of total membrane ) and was corrected for the amount of total membranes loaded per lane.
Radioligand binding assay
Harvested cells were re-suspended in 30 mL breaking buffer and disrupted at 30,000 psi for 10 min using an Avestin C3. The samples were clarified by centrifugation at 10,000 × g, 4°C for 30 min and total membranes recovered from the supernatant at 100,000 × g, 4°C for 60 min. Total membranes were re-suspended in 2.5 mL Buffer A, the protein concentration determined using a NanoDrop 1000 (Thermo Fisher Scientific, UK) and 0.5 mL aliquots stored at -80°C. Membrane bound hA2aR was then determined using a radioligand binding assay based on the protocol of Fraser . Membranes at 0.5 mg/ml with 0.1 U of adenosine deaminase were incubated with varying concentrations of 3H ZM241385 for 60 min at 30°C and non-specific binding was defined by including 1 μM ZM241385 in the incubations. Assays were terminated by centrifugation at 14,000 rpm in a bench-top centrifuge for 5 min. The supernatant was discarded, the pellets washed superficially with water and solublilized with Soluene, which was added to scintillation fluid and then counted to determine bound radioactivity. Binding was analyzed using PRISM Graphpad v 4.0 to determine Kd and binding capacity.
GFP fluorescence measurements
50 mL of 2 × CBS medium were inoculated with individual yeast colonies transformed with the GFP vector and cultured for up to 48 h in the presence or absence of various doxycycline concentrations at 220 rpm and 30°C. These cultures were used to inoculate fresh 50 mL 2 × CBS medium to a final OD600 of 0.1 and were cultured as above for 16 – 20 h. 1 mL samples were withdrawn and the cells pelletted at 5,000 × g, 4°C for 5 min and the supernatant collected. 200 μL supernatant were loaded in triplicate in a black Nunc MaxiSorp 96-well plate and the fluorescence recorded on a SpectraMax Gemini XS plate reader (Molecular Devices, Wokingham, UK) with excitation and emission wavelengths of 390 nm and 510 nm respectively, and a cut-off of 495 nm. Doxycycline fluorescence accounted for less than 5% of the signal up to concentrations of 10 μg/mL.
RNA preparation and real time quantitative PCR
Yeast cells (60 mL) from two biological replicates and two technical replicates were harvested and frozen in liquid nitrogen. Total RNA was then prepared using the RNeasy kit from Qiagen with on-column DNAse treatment, following the manufacturer's instructions. Analysis of mRNA was performed using real time quantitative PCR (Q-PCR). 1.1 μg RNA was used in the cDNA reaction using the iScript cDNA Synthesis Kit (Bio-Rad, UK). Each sample was amplified using up to 30 cycles (20 s 94°C; 20 s 60°C; 20 s 72°C) in a Bio-Rad iCycler iQ, and the data were analyzed using iCycler IQ version 3.0. The data were normalized using the reference genes PDA1 and ACT1 and the signal was scaled to mRNA copies/cell according to a SAGE study  in which copies of mRNA/cell of all the reference genes had previously been determined.
On-line flow microcalorimetry
where Φ is the calorimetric output (Js-1); F is the flow rate (dm3s-1); C is the concentration of reactant (mol dm-3); H is the enthalpy of reaction (kJ mol-1); k is the rate constant (s-1); τ is the residence time of reacting solution (s); t is time (s). By plotting the natural logarithm of power against time, a pseudo 1st order rate constant for exponential growth under the conditions employed was obtained. This was validated against the value calculated from OD600 and dry weight measurements. Peak areas were calculated using MicroCal Origin 7.0 using the calculus function embedded in the software. Integration limits were chosen to omit the initial 10 min of data to minimize errors associated with the initiation of the experiment. The high limit was taken as being the very bottom of the down-curve of the glucose peak.
50 mL of 2 × CBS medium were inoculated with individual yeast colonies and cultured for up to 72 h in the presence or absence of 0.5 μg/mL doxycycline at 220 rpm and 30°C. These cultures were used to inoculate fresh 50 mL 2 × CBS medium to a final OD600 of 0.1 and were cultured as above for 24 h. 50 mL 2 × CBS were inoculated to a final OD600 of 0.1 from these cultures and grown as stated above to a final OD600 of 1. Cycloheximide was then added to the cultures to a final concentration of 10 μg/mL and incubated for 10 min. Cells were recovered at 5,000 × g, 4°C, 5 min, stored on ice and then suspended in 0.4 mL lysis buffer (10 mM Tris-HCl pH 7.4, 100 mM NaCl, 30 mM MgCl2, 200 μg/mL heparin, 50 μg/mL cycloheximide, 1 mM DTT, 1 μl/mL RNase inhibitor and one EDTA-free protease inhibitor cocktail per 10 mL buffer) prepared in fresh DEPC-treated water with an equivalent volume of acid-washed glass beads. The cells were then disrupted in a Precellys 24 (Bertin Technology, France) at a speed of 6,500 rpm for 10 s. Samples were cooled on ice for 2 min followed by 2 additional rounds of disruption. The samples were clarified twice at 5,000 × g, 4°C, 5 min and 10,000 × g, 4°C, 20 min and the supernatant recovered. The RNA was subsequently quantified at 260 nm on a Nanodrop (Thermo Fisher Scientific, UK) and 5 – 10 OD600 units loaded on a freshly-prepared 10 mL 10 – 50% sucrose gradient. Gradients were ultracentrifuged at 37,000 rpm, 4°C in a SW40 Beckman rotor for 2 h 40 min. The OD254 profile of the gradients were recorded by chart recorder (Pharmacia LKB REC 102, GE Healthcare Life Sciences, UK) by passing the gradient (from the bottom of the tube to the top of the tube) through an AmershamPharmacia UV detector (GE Healthcare Life Sciences, UK) at a speed of 1.8 mL/min with the simultaneous collection of 0.7 mL fractions. These fractions were stored at -20°C for later analysis. For EDTA polysome disruption profiling, yeast cultures were prepared as above and the cells recovered in the absence of cycloheximide. Cells were resuspended in 0.4 mL lysis buffer lacking cyclohexamide, and disrupted and clarified as above. The RNA was quantified as previously stated and 10 OD600 units were mixed with 50 mM EDTA and loaded on top of freshly-prepared 10 mL 7.5 – 20% sucrose gradients. The gradients were then ultracentrifuged and processed as described above.
Analysis of ribosomal RNA
50 mL 2 × CBS medium were inoculated with individual yeast colonies transformed with the Fps1-HA3 vector and cultured for up to 48 h in the presence or absence of various doxycycline concentrations at 220 rpm and 30°C. These cultures were used to inoculate fresh 50 mL 2 × CBS medium containing various doxycycline concentrations to a final OD600 of 0.1 and were cultured as above to a final OD600 of 1 – 3. Cells from 0.5 mL culture were collected by centrifugation at 1,000 × g, 4°C for 5 min and the supernatant discarded. The pellet was then processed and the RNA collected with an RNeasy Mini Kit (Qiagen) according to the standard enzymatic lysis protocol for yeast. The RNA was quantified and samples diluted to 150 ng/μL. The RNA samples were heated to 70°C for 3 min prior to loading 150 ng samples in triplicate on an Agilent RNA 6000 Nanochip and running on an Agilent Bioanalyzer 2100 according to the manufacturer's instructions.
We gratefully acknowledge Drs Richard Kemp and Christer Larsson for their critical appraisal of our data, and Dr Martin Wilks for advice on the presentation of Figure 2. This work was supported by European Commission contracts LSHG-CT-2004-504601 (E-MeP) and LSHG-CT-2006-037793 (OptiCryst), and Technology Strategy Board grant TP/7/BIO/6/S/M0014B to RMB. The BBSRC supports bioreactors and a flow microcalorimeter in the RMB laboratory through an REI award. The work described here is the subject of UK patent application number 08132253.2.
- Schlyer S, Horuk R: I want a new drug: G-protein-coupled receptors in drug development. Drug Discov Today. 2006, 11 (11–12): 481-493. 10.1016/j.drudis.2006.04.008.View ArticleGoogle Scholar
- Forstner M, Leder L, Mayr LM: Optimization of protein expression systems for modern drug discovery. Expert Rev Proteomics. 2007, 4 (1): 67-78. 10.1586/147894184.108.40.206.View ArticleGoogle Scholar
- Bonander N, Hedfalk K, Larsson C, Mostad P, Chang C, Gustafsson L, Bill RM: Design of improved membrane protein production experiments: Quantitation of the host response. Protein Sci. 2005, 14 (7): 1729-1740. 10.1110/ps.051435705.View ArticleGoogle Scholar
- Andre N, Cherouati N, Prual C, Steffan T, Zeder-Lutz G, Magnin T, Pattus F, Michel H, Wagner R, Reinhart C: Enhancing functional production of G protein-coupled receptors in Pichia pastoris to levels required for structural studies via a single expression screen. Protein Sci. 2006, 15 (5): 1115-1126. 10.1110/ps.062098206.View ArticleGoogle Scholar
- Islam RS, Tisi D, Levy MS, Lye GJ: Framework for the rapid optimization of soluble protein expression in Escherichia coli combining microscale experiments and statistical experimental design. Biotechnol Prog. 2007, 23 (4): 785-793.View ArticleGoogle Scholar
- Martinez Molina D, Wetterholm A, Kohl A, McCarthy AA, Niegowski D, Ohlson E, Hammarberg T, Eshaghi S, Haeggstrom JZ, Nordlund P: Structural basis for synthesis of inflammatory mediators by human leukotriene C4 synthase. Nature. 2007, 448 (7153): 613-616. 10.1038/nature06009.View ArticleGoogle Scholar
- Wagner S, Baars L, Ytterberg AJ, Klussmeier A, Wagner CS, Nord O, Nygren PA, van Wijk KJ, de Gier JW: Consequences of membrane protein overexpression in Escherichia coli. Mol Cell Proteomics. 2007, 6 (9): 1527-1550. 10.1074/mcp.M600431-MCP200.View ArticleGoogle Scholar
- Wagner S, Klepsch MM, Schlegel S, Appel A, Draheim R, Tarry M, Hogbom M, van Wijk KJ, Slotboom DJ, Persson JO, et al: Tuning Escherichia coli for membrane protein overexpression. Proc Natl Acad Sci USA. 2008Google Scholar
- Bill RM: Yeast – a panacea for the structure-function analysis of membrane proteins?. Curr Genet. 2001, 40 (3): 157-171. 10.1007/s002940100252.View ArticleGoogle Scholar
- Hedfalk K, Bill RM, Mullins JG, Karlgren S, Filipsson C, Bergstrom J, Tamas MJ, Rydstrom J, Hohmann S: A regulatory domain in the C-terminal extension of the yeast glycerol channel Fps1p. J Biol Chem. 2004, 279 (15): 14954-14960. 10.1074/jbc.M313126200.View ArticleGoogle Scholar
- Tamas MJ, Karlgren S, Bill RM, Hedfalk K, Allegri L, Ferreira M, Thevelein JM, Rydstrom J, Mullins JG, Hohmann S: A short regulatory domain restricts glycerol transport through yeast Fps1p. J Biol Chem. 2003, 278 (8): 6337-6345. 10.1074/jbc.M209792200.View ArticleGoogle Scholar
- Wegierski T, Billy E, Nasr F, Filipowicz W: Bms1p, a G-domain-containing protein, associates with Rcl1p and is required for 18S rRNA biogenesis in yeast. RNA. 2001, 7 (9): 1254-1267. 10.1017/S1355838201012079.View ArticleGoogle Scholar
- Wu PYJ, Ruhlmann C, Winston F, Schultz P: Molecular architecture of the S. cerevisiae SAGA complex. Mol Cell. 2004, 15 (2): 199-208. 10.1016/j.molcel.2004.06.005.View ArticleGoogle Scholar
- Huisinga KL, Pugh BF: A genome-wide housekeeping role for TFIID and a highly regulated stress-related role for SAGA in Saccharomyces cerevisiae. Mol Cell. 2004, 13 (4): 573-585. 10.1016/S1097-2765(04)00087-5.View ArticleGoogle Scholar
- Kornberg RD: Mediator and the mechanism of transcriptional activation. Trends Biochem Sci. 2005, 30 (5): 235-239. 10.1016/j.tibs.2005.03.011.View ArticleGoogle Scholar
- Thompson CM, Koleske AJ, Chao DM, Young RA: A Multisubunit Complex-Associated with the RNA Polymerase-II CTD and TATA-Binding Protein in Yeast. Cell. 1993, 73 (7): 1361-1375. 10.1016/0092-8674(93)90362-T.View ArticleGoogle Scholar
- Kidane A, Guan Y, Evans P, Kaderbhai M, Kemp R: Comparison of heat flux in wild-type and genetically-engineered chinese hamster ovary cells. J Thermal Anal. 1997, 49: 771-783. 10.1007/BF01996760. 10.1007/BF01996760.View ArticleGoogle Scholar
- Barford JP: A general model for aerobic yeast growth: Batch growth. Biotechnol Bioeng. 1990, 35 (9): 907-920. 10.1002/bit.260350908.View ArticleGoogle Scholar
- Mutiu AI, Brandl CJ: RNA isolation from yeast using silica matrices. J Biomol Tech. 2005, 16 (4): 316-317.Google Scholar
- Young BP, Craven RA, Reid PJ, Willer M, Stirling CJ: Sec63p and Kar2p are required for the translocation of SRP-dependent precursors into the yeast endoplasmic reticulum in vivo. EMBO J. 2001, 20 (1–2): 262-271. 10.1093/emboj/20.1.262.View ArticleGoogle Scholar
- Gelperin D, Horton L, Beckman J, Hensold J, Lemmon SK: Bms1p, a novel GTP-binding protein, and the related Tsr1p are required for distinct steps of 40S ribosome biogenesis in yeast. RNA. 2001, 7 (9): 1268-1283. 10.1017/S1355838201013073.View ArticleGoogle Scholar
- Karbstein K, Doudna JA: GTP-dependent formation of a ribonucleoprotein subcomplex required for ribosome biogenesis. J Mol Biol. 2006, 356 (2): 432-443. 10.1016/j.jmb.2005.11.052.View ArticleGoogle Scholar
- Volarevic S, Stewart MJ, Ledermann B, Zilberman F, Terracciano L, Montini E, Grompe M, Kozma SC, Thomas G: Proliferation, but not growth, blocked by conditional deletion of 40S ribosomal protein S6. Science. 2000, 288 (5473): 2045-2047. 10.1126/science.288.5473.2045.View ArticleGoogle Scholar
- Hoffmann F, Weber J, Rinas U: Metabolic adaptation of Escherichia coli during temperature-induced recombinant protein production: 1. Readjustment of metabolic enzyme synthesis. Biotechnol Bioeng. 2002, 80 (3): 313-319. 10.1002/bit.10379.View ArticleGoogle Scholar
- Hoffmann F, Rinas U: On-line estimation of the metabolic burden resulting from the synthesis of plasmid-encoded and heat-shock proteins by monitoring respiratory energy generation. Biotechnol Bioeng. 2001, 76 (4): 333-340. 10.1002/bit.10098.View ArticleGoogle Scholar
- Henricsson C, de Jesus Ferreira MC, Hedfalk K, Elbing K, Larsson C, Bill RM, Norbeck J, Hohmann S, Gustafsson L: Engineering of a novel Saccharomyces cerevisiae wine strain with a respiratory phenotype at high external glucose concentrations. Appl Environ Microbiol. 2005, 71 (10): 6185-6192. 10.1128/AEM.71.10.6185-6192.2005.View ArticleGoogle Scholar
- Gill NK, Appleton M, Lye GJ: Thermal profiling for parallel on-line monitoring of biomass growth in miniature stirred bioreactors. Biotechnol Lett. 2008, 30 (9): 1571-1575. 10.1007/s10529-008-9719-0.View ArticleGoogle Scholar
- Rudra D, Warner JR: What better measure than ribosome synthesis?. Genes Dev. 2004, 18 (20): 2431-2436. 10.1101/gad.1256704.View ArticleGoogle Scholar
- Jorgensen P, Nishikawa JL, Breitkreutz BJ, Tyers M: Systematic identification of pathways that couple cell growth and division in yeast. Science. 2002, 297 (5580): 395-400. 10.1126/science.1070850.View ArticleGoogle Scholar
- Smirnova JB, Selley JN, Sanchez-Cabo F, Carroll K, Eddy AA, McCarthy JE, Hubbard SJ, Pavitt GD, Grant CM, Ashe MP: Global gene expression profiling reveals widespread yet distinctive translational responses to different eukaryotic translation initiation factor 2B-targeting stress pathways. Mol Cell Biol. 2005, 25 (21): 9340-9349. 10.1128/MCB.25.21.9340-9349.2005.View ArticleGoogle Scholar
- Fraser NJ: Expression and functional purification of a glycosylation deficient version of the human adenosine 2a receptor for structural studies. Protein Expr Purif. 2006, 49 (1): 129-137. 10.1016/j.pep.2006.03.006.View ArticleGoogle Scholar
- Velculescu VE, Zhang L, Zhou W, Vogelstein J, Basrai MA, Bassett DE, Hieter P, Vogelstein B, Kinzler KW: Characterization of the yeast transcriptome. Cell. 1997, 88 (2): 243-251. 10.1016/S0092-8674(00)81845-0.View ArticleGoogle Scholar
- Poore V, Beezer A: Systematic errors in the measurement of heat by flow microcalorimetry. Thermochimica Acta. 1983, 63: 133-144. 10.1016/0040-6031(83)80078-1.View ArticleGoogle Scholar
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