Advances in animal cell recombinant protein production: GS-NS0 expression system

The production of recombinant proteins using mammalian cell expression systems is of growing importance within biotechnology, largely due to the ability of specific mammalian cells to carry out post-translational modifications of the correct fidelity. The Glutamine Synthetase-NS0 system is now one such industrially important expression system.Glutamine synthetase catalyses the formation ofglutamine from glutamate and ammonia. NS0 cellscontain extremely low levels of endogenous glutaminesynthetase activity, therefore exogenous glutaminesynthetase can be used efficiently as a selectablemarker to identify successful transfectants in theabsence of glutamine in the media. In addition, theinclusion of methionine sulphoximine, an inhibitor ofglutamine synthetase activity, enables furtherselection of those clones producing relatively highlevels of transfected glutamine synthetase and henceany heterologous gene which is coupled to it. Theglutamine synthetase system technology has been usedfor research and development purposes during thisdecade and its importance is clearly demonstrated nowthat two therapeutic products produced using thissystem have reached the market place.     

Gaussian mixture clustering and imputation of microarray data

MOTIVATION: In microarray experiments, missing entries arise from blemishes on the chips. In large-scale studies, virtually every chip contains some missing entries and more than 90% of the genes are affected. Many analysis methods require a full set of data. Either those genes with missing entries are excluded, or the missing entries are filled with estimates prior to the analyses. This study compares methods of missing value estimation. RESULTS: Two evaluation metrics of imputation accuracy are employed. First, the root mean squared error measures the difference between the true values and the imputed values. Second, the number of mis-clustered genes measures the difference between clustering with true values and that with imputed values; it examines the bias introduced by imputation to clustering. The Gaussian mixture clustering with model averaging imputation is superior to all other imputation methods, according to both evaluation metrics, on both time-series (correlated) and non-time series (uncorrelated) data sets.     

Developing a cyclin blueprint as a tool for mapping the cell cycle in GS-NS0

The cell cycle is at the center of growth, productivity, and death of mammalian cell cultures. There exists a need to identify and quantify major landmarks in the cell cycle of industrially relevant mammalian cell lines and its association with productivity; central for designing productivity optimization strategies. Herein, we studied the expression of three cyclins, under both perturbed and unperturbed growth, by flow cytometry in batch cultures of GS-NS0. The perturbed systems involved two different DNA synthesis inhibitors, thymidine and dimethyl sulfoxide (DMSO). This approach enables the establishment of characteristic cyclin profiles, timings, and thresholds. In particular, two G₁ class cyclins (D1 and E1), and one G₂ cyclin (B1) were investigated. Cyclin B1 showed a clear cell cycle phase-specific expression increasing during G₂ phase where it was approximately 40% higher when compared to G₁ phase. Similarly, cyclin E1 showed a clear pattern being expressed approximately 10% higher in G₁ compared to G₂ phase and decreased through S phase. Cyclin D1 expression was fairly invariable throughout the cell cycle phases. The observed patterns provide a blueprint of the cell line's cell cycle, which can be used for the development of biologically accurate and experimentally validated distributed cell cycle models.     

A comparison of imputation procedures and statistical tests for the analysis of two-dimensional electrophoresis data

Background  

Numerous gel-based softwares exist to detect protein changes potentially associated with disease. The data, however, are abundant with technical and structural complexities, making statistical analysis a difficult task. A particularly important topic is how the various softwares handle missing data. To date, no one has extensively studied the impact that interpolating missing data has on subsequent analysis of protein spots.     

Family-based approaches: design,imputation, analysis,and beyond

Participants in the family-based analysis group at Genetic Analysis Workshop 19 addressed diverse topics, all of which used the family data. Topics addressed included questions of study design and data quality control (QC), genotype imputation to augment available sequence data, and linkage and/or association analyses. Results show that pedigree-based tests that are sensitive to genotype error may be useful for QC. Imputation quality improved with inclusion of small amounts of pedigree information used to phase the data in evaluation of 5 commonly used approaches for imputation in samples of (typically) unrelated subjects. It improved still further when pedigree-based imputation using larger pedigrees was also added. An important distinction was made between methods that do versus do not make use of Mendelian transmission in pedigrees, because this serves as a key difference between underlying models and assumptions. Methods that model relatedness generally had higher power in association testing than did analyses that carry out testing in the presence of a transmission model, but this may reflect details of implementation and/or ability of more general methods to jointly include data from larger pedigrees. In either case, for single nucleotide polymorphism–set approaches, weights that incorporate information on functional effects may be more useful than those that are based only on allele frequencies. The overall results demonstrate that family data continue to provide important information in the search for trait loci.     

Quantitative analysis of the intra- and inter-individual variability of the normal urinary proteome

Urine is a readily and noninvasively obtainable body fluid. Mass spectrometry (MS)-based proteomics has shown that urine contains thousands of proteins. Urine is a potential source of biomarkers for diseases of proximal and distal tissues but it is thought to be more variable than the more commonly used plasma. By LC-MS/MS analysis on an LTQ-Orbitrap without prefractionation we characterized the urinary proteome of seven normal human donors over three consecutive days. Label-free quantification of triplicate single runs covered the urinary proteome to a depth of more than 600 proteins. The median coefficient of variation (cv) of technical replicates was 0.18. Interday variability was markedly higher with a cv of 0.48 and the overall variation of the urinary proteome between individuals was 0.66. Thus technical variability in our data was 7.5%, whereas intrapersonal variability contributed 45.5% and interpersonal variability contributed 47.1% to total variability. Determination of the normal fluctuation of individual urinary proteins should be useful in establishing significance thresholds in biomarker studies. Our data also allowed definition of a common and abundant set of 500 proteins that were readily detectable in all studied individuals. This core urinary proteome has a high proportion of secreted, membrane, and relatively high-molecular weight proteins.     

The variability of the hepatitis B virus genome: statistical analysis and biological implications

A statistical analysis of the nucleotide sequence variability in 14 published hepatitis B virus (HBV) genomes was carried out using parametric and nonparametric methods. A parametric statistical model revealed that the different regions of the genome differed significantly in their variability. The conclusion was supported by a nonparametric kernel-density model of the HBV genome. Genes S, C, and P, region X, the precore region, and the pre-S2/pre-S1 regions were ranked in order of increasing variability. In many instances, conserved regions of the genome identified with sequences of known function in HBV biology. However, other characterized regions (such as pre-S) showed much variability despite the involvement of their encoded peptides in specific functions. Point mutations that may result in the formation of stop codons and amino acid changes may affect the clinical picture of HBV infection and may be reflected in atypical serological patterns.      

Quantitative analysis of representative proteome components and clustering of Helicobacter pylori clinical strains

BACKGROUND: Several Helicobacter pylori proteins have been reported to be associated with severe symptoms of gastric disease. However, expression levels of most of these disease-associated proteins require further evaluation in order to clarify their relationships with gastric disease patterns. Representative proteome components of 71 clinical isolates of H. pylori were analyzed quantitatively to determine whether the protein expression levels were associated with gastric diseases and to cluster clinical isolates. METHODS: After two-dimensional electrophoresis (2-DE) of H. pylori isolates, spot intensities were analyzed using pdquest 2-D Gel Analysis Software. The intensities of 10 representative protein spots, identified by peptide fingerprinting using matrix assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF-MS) or peptide sequencing using quadrupole TOF MS, were subjected to the nonparametric Mann-Whitney test and hierarchical agglomerative cluster analysis. The relationship between clusters and gastric diseases was analyzed by the chi-squared test. RESULTS: Although the spot intensities of the 10 representative proteins were highly variable within each gastric disease group, the expression levels of CagA, UreB, GroEL, EF-Tu, EF-P, TagD, and FldA showed some significant differences among the gastric disease patterns. On the basis of the 10 target protein intensities, hierarchical agglomerative cluster analysis generated a dendrogram with clusters indicative of chronic gastritis/gastric cancers and gastric/duodenal ulcers. CONCLUSION: These results indicated that quantitative analysis of proteome components is a feasible method for examining disease-associated proteins and clustering clinical strains of H. pylori.     

The response of GS-NS0 myeloma cells to single and multiple pH perturbations

Animal cells are cultured in several types of vessels at laboratory and industrial scale the most common being the stirred tank and the air-lift. Economically, it is preferable to culture animal cells at the largest possible scale but the perceived sensitivity of animal cells to hydrodynamic shear has, until now, limited the aeration and agitation rates used. This has been reported to cause inhomogeneities in operational parameters such as dissolved oxygen concentration, temperature and pH. pH is of special interest during the latter stages of many animal cell fermentation because alkali additions, used for pH control, can cause large local pH perturbations of varying size and duration. The effect of single and multiple pH perturbations on the cell growth of a widely used GS-NS0 mouse myeloma cell line grown in batch culture was investigated. The effect of perturbation amplitude and duration was investigated using a single stirred tank reactor (STR). In the single STR system cells were subjected to one pH 8.0 or 9.0 perturbation ranging in duration from 0-90 minutes. No measurable decrease in viable cell number was seen for pH 8.0 perturbations of any duration whereas pH 9.0 perturbations lasting for 10 minutes caused a 15% decrease in viable cell number. The proportion of viable cells decreased with increasing perturbation time and a 90-minute exposure killed all of the cells. The effect of multiple pH perturbations on GS-NS0 cells was investigated using two connected STR's. More specifically the number of perturbations and the perturbation frequency were investigated. Cells were subjected to between 0 and 100 perturbations at pH 8.0; the time between each perturbation (frequency) was 6 minutes and each perturbation lasted for 200 seconds. Viable cell number decreased with increasing perturbation number, with 100 perturbations causing death of 27.5% of cells. Cells were also exposed to 10 perturbations at pH 9.0, each of 200 second duration at frequencies of either 6, 18 or 60 minutes. Approximately 8 times more cells were killed with perturbations at a 6-minute frequency (28.3% cell death) than at a 60-minute frequency (3.4% cell death).     

Towards clustering of incomplete microarray data without the use of imputation

MOTIVATION: Clustering technique is used to find groups of genes that show similar expression patterns under multiple experimental conditions. Nonetheless, the results obtained by cluster analysis are influenced by the existence of missing values that commonly arise in microarray experiments. Because a clustering method requires a complete data matrix as an input, previous studies have estimated the missing values using an imputation method in the preprocessing step of clustering. However, a common limitation of these conventional approaches is that once the estimates of missing values are fixed in the preprocessing step, they are not changed during subsequent processes of clustering; badly estimated missing values obtained in data preprocessing are likely to deteriorate the quality and reliability of clustering results. Thus, a new clustering method is required for improving missing values during iterative clustering process. RESULTS: We present a method for Clustering Incomplete data using Alternating Optimization (CIAO) in which a prior imputation method is not required. To reduce the influence of imputation in preprocessing, we take an alternative optimization approach to find better estimates during iterative clustering process. This method improves the estimates of missing values by exploiting the cluster information such as cluster centroids and all available non-missing values in each iteration. To test the performance of the CIAO, we applied the CIAO and conventional imputation-based clustering methods, e.g. k-means based on KNNimpute, for clustering two yeast incomplete data sets, and compared the clustering result of each method using the Saccharomyces Genome Database annotations. The clustering results of the CIAO method are more significantly relevant to the biological gene annotations than those of other methods, indicating its effectiveness and potential for clustering incomplete gene expression data. AVAILABILITY: The software was developed using Java language, and can be executed on the platforms that JVM (Java Virtual Machine) is running. It is available from the authors upon request.     

Responses of GS-NS0 Myeloma cells to osmolality: Cell growth, intracellular mass metabolism, energy metabolism, and antibody production

The influence of osmolality on growth, metabolism, and antibody production of mammalian cells has been widely reported in the past. However, more information about the responses of GS-NS0 Myeloma cells to osmolality, especially regarding the intracellular mass and energy metabolism, has not been available in detail. Fed-batch cultures started at different osmolalities in the range of 280∼370 mOsm/kg were designed to investigate the effects. As the osmolality and cell status changed during the process, cell performance was evaluated in the comparable periods with similar growth rates, nutrition concentrations, and relatively consistent environments. Metabolic flux analysis indicated most of extra consumed glucose at higher osmolalities flowed into lactate formation pathway. The proportion of glucose flux flowed into glycolysis pathway remained approximately 90% and the need of glucose for biomass synthesis was constantly. Also, more than 88% of the glutamine was used in biomass synthesis and the absolute flux remained constant. The specific consumption rate of glutamine declined significantly when cells were cultured in hypo-osmolality (276 mOsm/kg) and a portion of glutamine was synthesized from glutamate. Furthermore, cells were in the state of high energy production at osmolality of 276 mOsm/kg. More glucose flowed into TCA circle with the high efficiency of energy production to meet the demand. Thus, the IVC, the specific antibody production rate, and maximal antibody concentration in fed-batch culture started at 280 mOsm/kg decreased by 35, 36, and 48% compared to those in the culture started at 330 mOsm/kg.     

A multi-stage approach to clustering and imputation of gene expression profiles

MOTIVATION: Microarray experiments have revolutionized the study of gene expression with their ability to generate large amounts of data. This article describes an alternative to existing approaches to clustering of gene expression profiles; the key idea is to cluster in stages using a hierarchy of distance measures. This method is motivated by the way in which the human mind sorts and so groups many items. The distance measures arise from the orthogonal breakup of Euclidean distance, giving us a set of independent measures of different attributes of the gene expression profile. Interpretation of these distances is closely related to the statistical design of the microarray experiment. This clustering method not only accommodates missing data but also leads to an associated imputation method. RESULTS: The performance of the clustering and imputation methods was tested on a simulated dataset, a yeast cell cycle dataset and a central nervous system development dataset. Based on the Rand and adjusted Rand indices, the clustering method is more consistent with the biological classification of the data than commonly used clustering methods. The imputation method, at varying levels of missingness, outperforms most imputation methods, based on root mean squared error (RMSE). AVAILABILITY: Code in R is available on request from the authors.     

Island clustering analysis for the comparison of the membrane and the soluble protein fractions of human brain proteome

A protein identified in multiple separate bands of a 1-D gel reflects variation in the molecular weight caused by alternative splicing, endoproteolytic cleavage, or PTMs, such as glycosylation or ubiquitination. To characterize such a protein distribution over the bands, we defined an entity called an 'island' as the band region including the bands of the same protein identified sequentially. We quantified the island distribution using a new variable called an Iscore. Previously, as described in Park et al.. (Proteomics 2006, 6, 4978-4986.), we analyzed human brain tissue using a multidimensional MS/MS separation method. Here, the new method of island analysis was applied to the previous proteome data. The soluble and membrane protein fractions of human brain tissue were reanalyzed using the island distribution. The proteome of the soluble fraction exhibited more variation in island positions than that of the membrane fraction. Through the island analysis, we identified protein modifications and protein complexes over the 1-D gel bands.     

Missing value imputation improves clustering and interpretation of gene expression microarray data

Background  

Missing values frequently pose problems in gene expression microarray experiments as they can hinder downstream analysis of the datasets. While several missing value imputation approaches are available to the microarray users and new ones are constantly being developed, there is no general consensus on how to choose between the different methods since their performance seems to vary drastically depending on the dataset being used.     

Mind the gaps: overlooking inaccessible regions confounds statistical testing in genome analysis

Background

The current versions of reference genome assemblies still contain gaps represented by stretches of Ns. Since high throughput sequencing reads cannot be mapped to those gap regions, the regions are depleted of experimental data. Moreover, several technology platforms assay a targeted portion of the genomic sequence, meaning that regions from the unassayed portion of the genomic sequence cannot be detected in those experiments. We here refer to all such regions as inaccessible regions, and hypothesize that ignoring these regions in the null model may increase false findings in statistical testing of colocalization of genomic features.

Results

Our explorative analyses confirm that the genomic regions in public genomic tracks intersect very little with assembly gaps of human reference genomes (hg19 and hg38). The little intersection was observed only at the beginning and end portions of the gap regions. Further, we simulated a set of synthetic tracks by matching the properties of real genomic tracks in a way that nullified any true association between them. This allowed us to test our hypothesis that not avoiding inaccessible regions (as represented by assembly gaps) in the null model would result in spurious inflation of statistical significance. We contrasted the distributions of test statistics and p-values of Monte Carlo-based permutation tests that either avoided or did not avoid assembly gaps in the null model when testing colocalization between a pair of tracks. We observed that the statistical tests that did not account for assembly gaps in the null model resulted in a distribution of the test statistic that is shifted to the right and a distribution of p-values that is shifted to the left (indicating inflated significance). We observed a similar level of inflated significance in hg19 and hg38, despite assembly gaps covering a smaller proportion of the latter reference genome.

Conclusion

We provide empirical evidence demonstrating that inaccessible regions, even when covering only a few percentages of the genome, can lead to a substantial amount of false findings if not accounted for in statistical colocalization analysis.