Rational optimization of culture conditions for the most efficient ethanol production in Scheffersomyces stipitis using design of experiments

Optimization of culture parameters for achieving the most efficient ethanol fermentation is challenging due to multiple variables involved. Here we presented a rationalized methodology for multi‐variables optimization through the design of experiments DoE approach. Three critical parameters, pH, temperature, and agitation speed, affecting ethanol fermentation in S. stipitis was investigated. A predictive model showed that agitation speed significantly affected ethanol synthesis. Reducing pH and temperature also improved ethanol production. The model identified the optimum culture conditions for the most efficient ethanol production with the yield and productivity of 0.46 g/g and 0.28 g/l h, respectively, which is consistent with experimental observation. The results also indicated the scalability of the model from shake flask to bioreactor. Thus, DoE is a promising tool permitting the rapid establishment of culture conditions for the most efficient ethanol fermentation in S. stipitis. The approach could be useful to reduce process development time in lignocellulosic ethanol industry. © 2012 American Institute of Chemical Engineers Biotechnol. Prog., 2012     

Statistical vs. Stochastic experimental design: An experimental comparison on the example of protein refolding

Optimization of experimental problems is a challenging task in both engineering and science. In principle, two different design of experiments (DOE) strategies exist: statistical and stochastic methods. Both aim to efficiently and precisely identify optimal solutions inside the problem‐specific search space. Here, we evaluate and compare both strategies on the same experimental problem, the optimization of the refolding conditions of the lipase from Thermomyces lanuginosus with 26 variables under study. Protein refolding is one of the main bottlenecks in the process development for recombinant proteins. Despite intensive effort, the prediction of refolding from sequence information alone is still not applicable today. Instead, suitable refolding conditions are typically derived empirically in large screening experiments. Thus, protein refolding should constitute a good performance test for DOE strategies. We compared an iterative stochastic optimization applying a genetic algorithm and a standard statistical design consisting of a D‐optimal screening step followed by an optimization via response surface methodology. Our results revealed that only the stochastic optimization was able to identify optimal refolding conditions (～1.400 U g^?1 refolded activity), which were 3.4‐fold higher than the standard. Additionally, the stochastic optimization proved quite robust, as three independent optimizations performed similar. In contrast, the statistical DOE resulted in a suboptimal solution and failed to identify comparable activities. Interactions between process variables proved to be pivotal for this optimization. Hence, the linear screening model was not able to identify the most important process variables correctly. Thereby, this study highlighted the limits of the classic two‐step statistical DOE. © 2012 American Institute of Chemical Engineers Biotechnol. Prog., 2012     

Predictive models for the accumulation of a fluorescent marker protein in tobacco leaves according to the promoter/5′UTR combination

The promoter and 5′‐untranslated region (5′UTR) play a key role in determining the efficiency of recombinant protein expression in plants. Comparative experiments are used to identify suitable elements but these are usually tested in transgenic plants or in transformed protoplasts/suspension cells, so their relevance in whole‐plant transient expression systems is unclear given the greater heterogeneity in expression levels among different leaves. Furthermore, little is known about the impact of promoter/5′UTR interactions on protein accumulation. We therefore established a predictive model using a design of experiments (DoE) approach to compare the strong double‐enhanced Cauliflower mosaic virus 35S promoter (CaMV 35SS) and the weaker Agrobacterium tumefaciens Ti‐plasmid nos promoter in whole tobacco plants transiently expressing the fluorescent marker protein DsRed. The promoters were combined with one of three 5′UTRs (one of which was tested with and without an additional protein targeting motif) and the accumulation of DsRed was measured following different post‐agroinfiltration incubation periods in all leaves and at different leaf positions. The model predictions were quantitative, allowing the rapid identification of promoter/5′UTR combinations stimulating the highest and quickest accumulation of the marker protein in all leaves. The model also suggested that increasing the incubation time from 5 to 8 days would reduce batch‐to‐batch variability in protein yields. We used the model to identify promoter/5′UTR pairs that resulted in the least spatiotemporal variation in expression levels. These ideal pairs are suitable for the simultaneous, balanced production of several proteins in whole plants by transient expression. Biotechnol. Bioeng. 2013; 110: 471–482. © 2012 Wiley Periodicals, Inc.     

Predictive models for transient protein expression in tobacco (Nicotiana tabacum L.) can optimize process time, yield, and downstream costs

The transient expression of recombinant biopharmaceutical proteins in plants can suffer inter‐batch variation, which is considered a major drawback under the strict regulatory demands imposed by current good manufacturing practice (cGMP). However, we have achieved transient expression of the monoclonal antibody 2G12 and the fluorescent marker protein DsRed in tobacco leaves with ～15% intra‐batch coefficients of variation, which is within the range reported for transgenic plants. We developed models for the transient expression of both proteins that predicted quantitative expression levels based on five parameters: The OD_600nm of Agrobacterium tumefaciens (from 0.13 to 2.00), post‐inoculation incubation temperature (15–30°C), plant age (harvest at 40 or 47 days after seeding), leaf age, and position within the leaf. The expression models were combined with a model of plant biomass distribution and extraction, generating a yield model for each target protein that could predict the amount of protein in specific leaf parts, individual leaves, groups of leaves, and whole plants. When the yield model was combined with a cost function for the production process, we were able to perform calculations to optimize process time, yield, or downstream costs. We illustrate this procedure by transferring the cost function from a production process using transgenic plants to a hypothetical process for the transient expression of 2G12. Our models allow the economic evaluation of new plant‐based production processes and provide greater insight into the parameters that affect transient protein expression in plants. Biotechnol. Bioeng. 2012; 109: 2575–2588. © 2012 Wiley Periodicals, Inc.     

Optimization of the operating conditions of a lab scale Aljet mill using lactose and sucrose: A technical note

Conclusion  The results of the experiments have revealed that the optimal operating conditions for a lab scale Aljet mill are at the high level (110 psi) of the pushing nozzle and the low level (65 psi) of both grinding nozzles, or vice versa. Operating the Aljet mill at high pushing and grinding pressures also produces small particle size; however, the high pressures require more gaseous fluid making the process less efficient. At a very low pushing nozzle pressure as compared with the grinding nozzle pressure, the material kicks back from the mill, reducing the yield. Optimization of the lab scale Aljet mill operating conditions will be very useful in particle size reduction of poorly water-soluble compounds and is particularly beneficial at early stages of drug development when the drug quantity is very limited.     

What is the minimum number of letters required to fold a protein?

Experimental studies have shown that the full sequence complexity of naturally occurring proteins is not required to generate rapidly folding and functional proteins, i.e. proteins can be designed with fewer than 20 letters. This raises the question of what is the minimum number of amino acid types required to encode complex protein folds? Here, we investigate this issue from three aspects. First, we study the minimum sequence complexity that can reserve the necessary structural information for detection of distantly related homologues. Second, we compare the ability of designing foldable model sequences over a wide range of reduced amino acid alphabets, which find the minimum number of letters that have the similar design ability as 20. Finally, we survey the lower bound of alphabet size of globular proteins in a non-redundant protein database. These different approaches give a remarkably consistent view, that the minimum number of letters required to fold a protein is around ten.     

优化的复合自动诱导培养基（CAI-4）对外源蛋白在大肠杆菌中表达的影响

[目的]原核表达系统是目前最为广泛使用的一种外源蛋白表达系统。在利用原核表达系统表达目的蛋白的过程中,可溶性外源蛋白的产量是决定成本和效率的决定性因素。[方法]本项研究中利用本实验室构建的7种不同的重组质粒(-1、p-2、p-3、p-4、p-5、p-6、p-7),检测其在复合自动诱导培养基中的表达情况,评价哪一种培养基更适合于外源蛋白的表达,提高目的蛋白的产量。[结果]结果显示,这7种重组蛋白在复合自动诱导培养基的表达量是普通LB培养基的4~8倍;并在此基础上,对复合培养基的成份进行进一步优化,形成了一种优化培养基(改良培养基-4),P-1、P-2、P-3这3种融合蛋白在这种改良培养基中的表达量比优化前提高了至少2倍。     

鼠羧肽酶原B在毕赤酵母中的表达、纯化与鉴定

为了在毕赤酵母中表达鼠羧肽酶原B(procarboxypeptidaseB,proCPB)蛋白,以RT-PCR法从SD鼠胰腺细胞中克隆了proCPB基因,将其插入pPIC9载体,PEG1000介导转入毕赤酵母GS115细胞,在甲醇的诱导下,实现了proCPB在毕赤酵母中的成功表达。通过发酵条件的优化,使用BMGY(pH6·0)培养基,添加0·5%的酪蛋白水解物,于28℃,在起始OD600达10·0时,每隔12h补加0·5%的甲醇,重组酵母GS115-proCPB表达的产物量可达到最高(500mg/L),表达时间可达120h,表达的目的蛋白占总蛋白的94%以上。通过纯化条件的优化,采用两步疏水层析,可使目的蛋白的纯度达96%以上,蛋白得率达38%,重组proCPB活化后所得CPB的比活力可达110u/mg(CPB标准品为180u/mg)。相对分子量测定表明重组蛋白的分子量与理论值极相近,N-端氨基酸测序进一步表明proCPB基因在毕赤酵母中得到了正确的表达和翻译后加工修饰。     

Reconstitution conditions for dried probiotic powders represent a critical step in determining cell viability

Aims: Resuscitation of dried cultures represents a critical control point in obtaining active and effective probiotic strains. This study examined the effects of various rehydration conditions on the viability of Bifidobacterium longum NCC3001 and Lactobacillus johnsonii La1. Methods and Results: Reconstitution conditions for these strains were optimized using a multivariate experimental design approach. Furthermore, using flow cytometry, the cell integrity was followed during reconstitution. By adjusting the pH, availability of a metabolizable sugar, reconstitution duration, powder matrix and ratio of powder to reconstitution solution, the recovery of Bif. longum NCC3001 and Lact. johnsonii La1 following reconstitution was increased eight‐ and two‐fold, respectively, over standard reconstitution in maximum recovery diluent. It was shown that pH had a significant effect on the recovery of Bif. longum NCC3001 and Lact. johnsonii La1. Conclusions: The recovery of dried probiotic cultures is greatly dependent on the reconstitution conditions. The maximum recovery of 11·7 ¹⁰log CFU g^?1Bif. longum NCC3001 was achieved at 30‐min reconstitution at pH 8, in the presence of 2%l ‐arabinose and a ratio of 1 : 100 of powder to diluent. Lact. johnsonii La1 showed highest recovery (9·3 ¹⁰log CFU g^?1) after reconstitution, when mixed with maltodextrin at pH 4. Significance and Impact of the Study: To achieve accurate viable probiotic numbers from dried probiotic cultures, the reconstitution conditions should be optimized for the strain used.     

Design and Analysis of Two-Phase Experiments for Gene Expression Microarrays—Part I

Summary .   Gene expression microarray experiments are intrinsically two-phase experiments. Messenger RNA (mRNA), required for the microarray experiment, must first be derived from plants or animals that are exposed to a set of treatments in a previous experiment (Phase 1). The mRNA is then used in the subsequent laboratory-based microarray experiment (Phase 2) from which gene expression is measured and ultimately analyzed. We show that obtaining a valid test for the effects of treatments on gene expression depends on the design of both the Phase 1 and Phase 2 experiments. Examples show that the multiple dye-swap design at Phase 2 is more robust than the alternating loop design in the absence of prior knowledge of the relative size of variation in the Phase 1 and Phase 2 experiments.