A methodical approach to ultra-scale-down of process sequences: application to casein removal from the milk of transgenic animals

Scale-down models of individual operations are widely used in biopharmaceutical process development to obtain information about the performance of production-scale equipment on the basis of inexpensive and efficient laboratory-scale tests, for the purposes of validation or optimization or characterization studies. We have investigated the ability of scale-down models of whole process sequences to provide reliable information for process scale-up from laboratory- to pilot-scales of operation. Using the example of the recovery of a protein from transgenic milk, we have conducted an a priori scale-down analysis of a projected pilot-scale process sequence. A systematic approach was developed to ensure that all critical aspects of process behavior were included in the scale-down model, resulting in the creation of an accurate and reliable scale-down representation of the pilot-scale process. The data from scale-down process trials conducted at 70 and 200 mL scales of operation served to highlight crucial factors determining process performance, and proved reliable in predicting the performance of the pilot-scale process over a scaling factor of 1000.     

Prediction and verification of centrifugal dewatering of P. pastoris fermentation cultures using an ultra scale-down approach

Recent years have seen a dramatic rise in fermentation broth cell densities and a shift to extracellular product expression in microbial cells. As a result, dewatering characteristics during cell separation is of importance, as any liquor trapped in the sediment results in loss of product, and thus a decrease in product recovery. In this study, an ultra scale-down (USD) approach was developed to enable the rapid assessment of dewatering performance of pilot-scale centrifuges with intermittent solids discharge. The results were then verified at scale for two types of pilot-scale centrifuges: a tubular bowl equipment and a disk-stack centrifuge. Initial experiments showed that employing a laboratory-scale centrifugal mimic based on using a comparable feed concentration to that of the pilot-scale centrifuge, does not successfully predict the dewatering performance at scale (P-value <0.05). However, successful prediction of dewatering levels was achieved using the USD method (P-value ≥0.05), based on using a feed concentration at small-scale that mimicked the same height of solids as that in the pilot-scale centrifuge. Initial experiments used Baker's yeast feed suspensions followed by fresh Pichia pastoris fermentation cultures. This work presents a simple and novel USD approach to predict dewatering levels in two types of pilot-scale centrifuges using small quantities of feedstock (<50 mL). It is a useful tool to determine optimal conditions under which the pilot-scale centrifuge needs to be operated, reducing the need for repeated pilot-scale runs during early stages of process development.     

Pilot-scale verification of a computer-based simulation for fractional protein precipitation

The development and experimental verification at pilot scale of a suite of models for the batch precipitation by two-cut ammonium sulphate salting-out of total protein and alcohol dehydrogenase from yeast homogenate is presented. The model consists of two elements: protein and enzyme solubility prediction and precipitate phase particle property prediction. An isotherm-based approach has been used successfully to describe solubility behaviour for a range of operating conditions typical of those obtained at a process scale. Estimation of the precipitate phase particle size distributions has been achieved through a discretized population balance approach using simplified terms to account for particle breakage and aggregation. The developed model accounts for the effects of average shear rate and residence time in the precipitation vessel across a two orders of magnitude range of scale. The size-density relationship for the precipitate phase has been defined. Results of simulations are compared with pilot scale verification data to confirm the validity of the models developed.     

Modeling Huntington disease in yeast: Perspectives and future directions

Yeast have been extensively used to model aspects of protein folding diseases, yielding novel mechanistic insights and identifying promising candidate therapeutic targets. In particular, the neurodegenerative disorder Huntington disease (HD), which is caused by the abnormal expansion of a polyglutamine tract in the huntingtin (htt) protein, has been widely studied in yeast. This work has led to the identification of several promising therapeutic targets and compounds that have been validated in mammalian cells, Drosophila and rodent models of HD. Here we discuss the development of yeast models of mutant htt toxicity and misfolding, as well as the mechanistic insights gleaned from this simple model. The role of yeast prions in the toxicity/misfolding of mutant htt is also highlighted. Furthermore, we provide an overview of the application of HD yeast models in both genetic and chemical screens, and the fruitful results obtained from these approaches. Finally, we discuss the future of yeast in neurodegenerative research, in the context of HD and other diseases.     

Multi-model statistical process monitoring and diagnosis of a sequencing batch reactor

Biological processes exhibit different behavior depending on the influent loads, temperature, microorganism activity, and so on. It has been shown that a combination of several models can provide a suitable approach to model such processes. In the present study, we developed a multiple statistical model approach for the monitoring of biological batch processes. The proposed method consists of four main components: (1) multiway principal component analysis (MPCA) to reduce the dimensionality of data and to remove collinearity; (2) multiple models with a posterior probability for modeling different operating regions; (3) local batch monitoring by the T(2)- and Q-statistics of the specific local model; and (4) a new discrimination measure (DM) to identify when the system has shifted to a new operating condition. Under this approach, local monitoring by multiple models divides the entire historical data set into separate regions, which are then modeled separately. Then, these local regions can be supervised separately, leading to more effective batch monitoring. The proposed method is applied to a pilot-scale 80-L sequencing batch reactor (SBR) for biological wastewater treatment. This SBR is characterized by nonstationary, batchwise, and multiple operation modes. The results obtained for the pilot-scale SBR indicate that the proposed method has the ability to model multiple operating conditions, to identify various operating regions, and also to determine whether the biosystem has shifted to a new operating condition. Our findings show that the local monitoring approach can give more reliable and higher resolution monitoring results than the global model.     

Resveratrol and rapamycin: are they anti‐aging drugs?

Studies of the basic biology of aging have advanced to the point where anti‐aging interventions, identified from experiments in model organisms, are beginning to be tested in people. Resveratrol and rapamycin, two compounds that target conserved longevity pathways and may mimic some aspects of dietary restriction, represent the first such interventions. Both compounds have been reported to slow aging in yeast and invertebrate species, and rapamycin has also recently been found to increase life span in rodents. In addition, both compounds also show impressive effects in rodent models of age‐associated diseases. Clinical trials are underway to assess whether resveratrol is useful as an anti‐cancer treatment, and rapamycin is already approved for use in human patients. Compounds such as these, identified from longevity studies in model organisms, hold great promise as therapies to target multiple age‐related diseases by modulating the molecular causes of aging.     

Optimization of Phaffia rhodozyma continuous culture through response surface methodology

Response surface methodology was applied to optimize the growth of the yeast Phaffia rhodozyma in continuous fermentation using peat hydrolysates as substrate. A second-order, complete, factorial design of the experiments was used to develop empirical models providing a quantitative interpretation of the relationships between the two variables studied, dilution rate and pH. Maximum biomass concentration in the fermentor was obtained by employing the following predicted optimum fermentation conditions: a dilution rate of 0.017/h and a pH level of 7.19. A verification experiment, conducted at previously optimized conditions for maximum biomass volumetric productivity (a dilution rate of 0.022/h, and a pH level of 6.90), produced values for biomass concentration, residual substrate concentration, biomass yield, and biomass volumetric productivity that were very close to the predicted values, indicating the reliability of the empirical model. The concentration of the pigment astaxanthin produced by the yeast under the optimized growth conditions was found to be 544 mg astaxanthin/kg dry cell biomass.     

Genomic approaches to the initiation of DNA replication and chromatin structure reveal a complex relationship

The mechanisms regulating the coordinate activation of tens of thousands of replication origins in multicellular organisms remain poorly explored. Recent advances in genomics have provided valuable information about the sites at which DNA replication is initiated and the selection mechanisms of specific sites in both yeast and vertebrates. Studies in yeast have advanced to the point that it is now possible to develop convincing models for origin selection. A general model has emerged, but yeast data have also revealed an unsuspected diversity of strategies for origin positioning. We focus here on the ways in which chromatin structure may affect the formation of pre-replication complexes, a prerequisite for origin activation. We also discuss the need to exercise caution when trying to extrapolate yeast models directly to more complex vertebrate genomes.     

Chromosomal Integration and Expression of Two Bacterial alpha-Acetolactate Decarboxylase Genes in Brewer's Yeast

A bacterial gene encoding alpha-acetolactate decarboxylase, isolated from Klebsiella terrigena or Enterobacter aerogenes, was expressed in brewer's yeast. The genes were expressed under either the yeast phosphoglycerokinase (PGK1) or the alcohol dehydrogenase (ADH1) promoter and were integrated by gene replacement by using cotransformation into the PGK1 or ADH1 locus, respectively, of a brewer's yeast. The expression level of the alpha-acetolactate decarboxylase gene of the PGK1 integrant strains was higher than that of the ADH1 integrants. Under pilot-scale brewing conditions, the alpha-acetolactate decarboxylase activity of the PGK1 integrant strains was sufficient to reduce the formation of diacetyl below the taste threshold value, and no lagering was needed. The brewing properties of the recombinant yeast strains were otherwise unaltered, and the quality (most importantly, the flavor) of the trial beers produced was as good as that of the control beer.     

酵母细胞自然衰老分子作用机理的研究进展

衰老是任何生物都无法避免的生理现象,它由多种因素引起,其过程极其复杂.酵母细胞是目前衰老研究领域公认的模式生物,一系列影响衰老的分子作用机理及调控因素的发现均源自于对酵母细胞的研究.自然衰老是酵母细胞的衰老模式之一,由于该衰老过程与其他高等真核细胞(特别是哺乳动物细胞)极为相似,近年来受到广泛关注.全面比较酵母细胞衰老的两种模式,详细介绍自然衰老过程中分子作用机理的研究进展,重点阐述其复杂的自然寿命调控通路,包括卡路里限制以及药物添加对Ras/PKA、Sch9、Tor等营养依赖型调控通路的影响,并展望未来该领域需要解决的重要科学问题,为全面深入了解高等生物,特别是人类自身的衰老机理提供参考.     

Plasmid stability in budding yeast populations: Steady-state growth with selection pressure

Plasmid gene product accumulation in a cell population depends on the fraction of plasmid-containing cells and the distribution of single-cell plasmid content. These important population properties have been related to plasmid replication regulation and kinetics and to plasmid segregation rules at the single-cell level using population balance mathematical models. Budding yeast populations are considered in detail because of the practical potential of yeast host-vector systems and because of the model complications introduced by the asymmetric division pattern observed for Saccharomyces cerevisiae at all but the largest growth rates. Solutions are presented for several different reasonable models of plasmid replication and segregation. The results offer potential for identification of important qualitative features of yeast plasmid replication and of model parameter values from average and segregated experimental data on yeast populations.     

Morphologically-structured models of growing budding yeast populations

It has been well recognized that many key aspects of cell cycle regulation are encoded into the size distributions of growing budding yeast populations due to the tight coupling between cell growth and cell division present in this organism. Several attempts have been made to model the cell size distribution of growing yeast populations in order to obtain insight on the underlying control mechanisms, but most were based on the age structure of asymmetrically dividing populations. Here we propose a new framework that couples a morphologically-structured representation of the population with population balance theory to formulate a dynamic model for the size distribution of growing yeast populations. An advantage of the presented framework is that it allows derivation of simpler models that are directly identifiable from experiments. We show how such models can be derived from the general framework and demonstrate their utility in analyzing yeast population data. Finally, by employing a recently proposed numerical scheme, we proceed to integrate numerically the full distributed model to provide predictions of dynamics of the cell size structure of growing yeast populations.     

Neuronal protection by sirtuins in Alzheimer's disease

Silent information regulator 2, a member of NAD+-dependent histone deacetylase in yeast, and its homologs in mice and humans, participate in numerous important cell functions, including cell protection and cell cycle regulation. The sirtuin family members are highly conserved evolutionarily, and are predicted to have a role in cell survival. The science of sirtuins is an emerging field and is expected to contribute significantly to the role of sirtuins in healthy aging in humans. The role of sirtuins in neuronal protection has been studied in lower organisms, such as yeast, worms, flies and rodents. Both yeast Sir2 and mammalian sirtuin proteins are up-regulated under calorie-restricted and resveratrol treatments. Increased sirtuin expression protects cells from various insults. Caloric restriction and antioxidant treatments have shown useful effects in mouse models of aging and Alzheimer's disease (AD) and in limited human AD clinical trials. The role sirtuins may play in modifying and protecting neurons in patients with neurodegenerative diseases is still unknown. However, a recent report of Huntington's disease revealed that Sirtuin protects neurons in a Huntington's disease mouse model, suggesting that sirtuins may protect neurons in patients with neurodegenerative diseases, such as AD. In this review, we discuss the possible mechanisms of sirtuins involved in neuronal protection and the potential therapeutic value of sirtuins in healthy aging and AD.     

A robust method using propensity score stratification for correcting verification bias for binary tests

Sensitivity and specificity are common measures of the accuracy of a diagnostic test. The usual estimators of these quantities are unbiased if data on the diagnostic test result and the true disease status are obtained from all subjects in an appropriately selected sample. In some studies, verification of the true disease status is performed only for a subset of subjects, possibly depending on the result of the diagnostic test and other characteristics of the subjects. Estimators of sensitivity and specificity based on this subset of subjects are typically biased; this is known as verification bias. Methods have been proposed to correct verification bias under the assumption that the missing data on disease status are missing at random (MAR), that is, the probability of missingness depends on the true (missing) disease status only through the test result and observed covariate information. When some of the covariates are continuous, or the number of covariates is relatively large, the existing methods require parametric models for the probability of disease or the probability of verification (given the test result and covariates), and hence are subject to model misspecification. We propose a new method for correcting verification bias based on the propensity score, defined as the predicted probability of verification given the test result and observed covariates. This is estimated separately for those with positive and negative test results. The new method classifies the verified sample into several subsamples that have homogeneous propensity scores and allows correction for verification bias. Simulation studies demonstrate that the new estimators are more robust to model misspecification than existing methods, but still perform well when the models for the probability of disease and probability of verification are correctly specified.     

Modeling Huntington disease in yeast: Perspectives and future directions

Yeast have been extensively used to model aspects of protein folding diseases, yielding novel mechanistic insights and identifying promising candidate therapeutic targets. In particular, the neurodegenerative disorder Huntington disease (HD), which is caused by the abnormal expansion of a polyglutamine tract in the huntingtin (htt) protein, has been widely studied in yeast. This work has led to the identification of several promising therapeutic targets and compounds that have been validated in mammalian cells, Drosophila and rodent models of HD. Here we discuss the development of yeast models of mutant htt toxicity and misfolding, as well as the mechanistic insights gleaned from this simple model. The role of yeast prions in the toxicity/misfolding of mutant htt is also highlighted. Furthermore, we provide an overview of the application of HD yeast models in both genetic and chemical screens, and the fruitful results obtained from these approaches. Finally, we discuss the future of yeast in neurodegenerative research, in the context of HD and other diseases.Key words: Huntington disease, yeast, neurodegeneration, genetic modifiers, prionsThe single-celled eukaryote Saccharomyces cerevisiae has long been involved with the technological advancement of mankind. Commonly known as baker''s yeast, for millennia this organism has been employed for the requisite fermentation in the production of bread, wine, beer and other food products.¹ Louis Pasteur first described the critical role of yeast in fermentation in 1860, and conclusively showed that living yeast cells are required for this process.² Since this time, yeast have been used extensively in biological sciences to explore the fundamental properties of the cell, and have become a vital genetic weapon in the arsenal of modern day medical scientists. This review provides an overview of the development, characterization and utilization of yeast models of Huntington disease (HD). These simple models have provided striking insights into the mechanisms underlying cellular toxicity in this disease, and have also uncovered many promising candidate drug targets for HD, several of which have been validated in animal models and hold great therapeutic promise.     

Molecular biology of translation in yeast

The combination of genetic, molecular and biochemical approaches have made the yeastSaccharomyces cerevisiae a convenient organism to study translation. The sequence similarity of translation factors from yeast and other organisms suggests a high degree of conservation in the translational machineries. This view is also strengthened by a functional analogy of some proteins implicated in translation. Beautiful genetic experiments have confirmed existing models and added new insights in the mechanism of translation. This review summarizes recent experiments using yeast as a model system for the analysis of this complex process.     

Improving the <Emphasis Type="Italic">i</Emphasis>MM904 <Emphasis Type="Italic">S. cerevisiae</Emphasis> metabolic model using essentiality and synthetic lethality data

Background  

Saccharomyces cerevisiae is the first eukaryotic organism for which a multi-compartment genome-scale metabolic model was constructed. Since then a sequence of improved metabolic reconstructions for yeast has been introduced. These metabolic models have been extensively used to elucidate the organizational principles of yeast metabolism and drive yeast strain engineering strategies for targeted overproductions. They have also served as a starting point and a benchmark for the reconstruction of genome-scale metabolic models for other eukaryotic organisms. In spite of the successive improvements in the details of the described metabolic processes, even the recent yeast model (i.e., i MM904) remains significantly less predictive than the latest E. coli model (i.e., i AF1260). This is manifested by its significantly lower specificity in predicting the outcome of grow/no grow experiments in comparison to the E. coli model.     

A refined atomic scale model of the Saccharomyces cerevisiae K+-translocation protein Trk1p combined with experimental evidence confirms the role of selectivity filter glycines and other key residues

Potassium ion (K⁺) uptake in yeast is mediated mainly by the Trk1/2 proteins that enable cells to survive on external K⁺ concentration as low as a few μM. Fungal Trks are related to prokaryotic TRK and Ktr and plant HKT K⁺ transport systems. Overall sequence similarity is very low, thus requiring experimental verification of homology models. Here a refined structural model of the Saccharomyces cerevisiae Trk1 is presented that was obtained by combining homology modeling, molecular dynamics simulation and experimental verification through functional analysis of mutants. Structural models and experimental results showed that glycines within the selectivity filter, conserved among the K-channel/transporter family, are not only important for protein function, but are also required for correct folding/membrane targeting.A conserved aspartic acid in the P_A helix (D79) and a lysine in the M2_D helix (K1147) were proposed earlier to interact. Our results suggested individual roles of these residues in folding, structural integrity and function. While mutations of D79 completely abolished protein folding, mutations at position 1147 were tolerated to some extent. Intriguingly, a secondary interaction of D79 with R76 could enhance folding/stability of Trk1 and enable a fraction of Trk1[K1147A] to fold.The part of the ion permeation path containing the selectivity filter is shaped similar to that of ion channels. However below the selectivity filter it is obstructed or regulated by a proline containing loop. The presented model could provide the structural basis for addressing the long standing question if Trk1 is a passive or active ion-translocation system.     

From the bakery to the brain business: developing inducible yeast models of human neurodegenerative disorders

In the last decade, the budding yeast Saccharomyces cerevisiae has been used as a model system to study the mechanisms of the human aging process and of age-associated neurodegenerative disorders such as Parkinson's, Huntington's, Alzheimer's, and amyotrophic lateral sclerosis. S. cerevisiae is a facultative aerobic, unicellular yeast, and despite their simplicity, yeast cells possess most of the same basic cellular machinery as neurons in the brain, including pathways required for protein homeostasis and energy metabolism. The power of yeast genetics and the use of high-throughput screening technologies have provided important clues concerning the pathophysiology of these disorders and the identification of candidate therapeutic targets and drugs. The yeast models are based on the expression of human disease proteins in yeast and recapitulate some of the cytotoxic features observed in patients. However, the currently available models mostly suffer from high-level protein expression that results in acute cytotoxicity, and from metabolic constraints when the models are based on extensively used, strong, galactose-inducible promoters. The models would increase their significance if they were based on continuous and tightly regulated gene expression systems for both activation and levels of expression. This would allow for more chronic cytotoxicity that better simulates the timing of events that occur during disease progression. Additionally, the use of metabolism-independent inducers would allow for the study of cell toxicities under conditions where the cells are forced to exclusively respire, thus more reliably modeling the highly oxidative neuronal metabolism. Here we have constructed yeast models of Huntington's disease based on the expression, under the control of different promoters, of the first exon of the huntingtin-containing polyglutamine tracts of both wild-type and mutant lengths. The different models are compared and evaluated.     

Choosing appropriate substitution models for the phylogenetic analysis of protein-coding sequences

Although phylogenetic inference of protein-coding sequences continues to dominate the literature, few analyses incorporate evolutionary models that consider the genetic code. This problem is exacerbated by the exclusion of codon-based models from commonly employed model selection techniques, presumably due to the computational cost associated with codon models. We investigated an efficient alternative to standard nucleotide substitution models, in which codon position (CP) is incorporated into the model. We determined the most appropriate model for alignments of 177 RNA virus genes and 106 yeast genes, using 11 substitution models including one codon model and four CP models. The majority of analyzed gene alignments are best described by CP substitution models, rather than by standard nucleotide models, and without the computational cost of full codon models. These results have significant implications for phylogenetic inference of coding sequences as they make it clear that substitution models incorporating CPs not only are a computationally realistic alternative to standard models but may also frequently be statistically superior.