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Pre-packed small scale chromatography columns are increasingly used for process development, for determination of design space in bioprocess development, and for post-licence process verifications. The packing quality of 30,000 pre-packed columns delivered to customers over a period 10 years has been analyzed by advanced statistical tools. First, the data were extracted and checked for inconsistencies, and then were tabulated and made ready for statistical processing using the programming language Perl (https://www.perl.org/) and the statistical computing environment R (https://www.r-project.org/). Reduced HETP and asymmetry were plotted over time to obtain a trend of packing quality over 10 years. The obtained data were used as a visualized coefficient of variation analysis (VCVA), a process that has often been applied in other industries such as semiconductor manufacturing. A typical fluctuation of reduced HETP was seen. A Tsunami effect in manufacturing, the effect of propagation of manufacturing deviations leading to out-of-specification products, was not observed with these pre-packed columns. Principal component analysis (PCA) showed that all packing materials cluster. Our data analysis showed that the current commercially available chromatography media used for biopharmaceutical manufacturing can be reproducibly and uniformly packed in polymer-based chromatography columns, which are designed for ready-to-use purposes. Although the number of packed columns has quadrupled over one decade the packing quality has remained stable.

Different degrees of protein purity have been observed in immobilized metal affinity chromatography ranging from extremely high purity to moderate and low purity. It has been hypothesized that the host cell protein composition and the metal ligands are factors governing the purity of a protein obtained after immobilized metal affinity chromatography (IMAC). Ni nitrilotriacetic acid (NTA) has become the first choice for facile His-tagged protein purification, but alternative ligands such as iminodiacetic acid (IDA) with other immobilized metal ions such as Zn, Cu and Co are valuable options when the expected purity or binding capacity is not reached. Especially Cu and Zn are very attractive, due to their reduced environmental and safety concerns compared to Ni. Co and Zn are more selective than Ni and Cu. This increased selectivity comes at the cost of weaker binding. In this work, the influence of ligand choice on protein purity after IMAC was evaluated by several methods, including peptide mapping. His-tagged GFP was used as model protein. We found that host cell protein (HCP) content varies drastically between ligands, as IDA eluates generally showing higher HCP concentrations than NTA. The relative content of the key amino acids His, Cys and Trp in the sequence of the co-eluted protein does not suffice to explain co-eluting propensity. The co-elution of HCPs is mostly influenced by metal binding clusters on the protein surface and not by total content or surface concentration of metal interacting amino acids. Prediction of co-elution is not dependent on these clusters alone, due to protein-protein interactions, indicted by a relative low metal binding cluster score but high co-elution propensity and in a lot of cases these proteins are often part of complex such as ribosome and chaperones. The different co-eluting proteins were presented by a heatmap with a dendrogram. Ward's linkage method was used to calculate the distance between groups of co-eluting proteins. Clustering of co-eluting HCPs was observed according to ligand and by metal ions, with Zn and Co forming one cluster and Ni and Cu another. The co-elution of host cell proteins can be explained by clusters of metal interacting amino acids on the protein surface and by protein-protein interactions. While Ni NTA still appears to be highly advantageous, it might not be the cure-all for all applications.

Summary: A model class of finite mixtures of linear additive models is presented. The component-specific parameters in the regression models are estimated using regularized likelihood methods. The advantages of the regularization are that (i) the pre-specified maximum degrees of freedom for the splines is less crucial than for unregularized estimation and that (ii) for each component individually a suitable degree of freedom is selected in an automatic way. The performance is evaluated in a simulation study with artificial data as well as on a yeast cell cycle dataset of gene expression levels over time. Availability: The latest release version of the R package flexmix is available from CRAN (http://cran.r-project.org/). Contact: Bettina.Gruen@jku.at

Background/aimElevated microsatellite instability at selected tetranucleotide repeats (EMAST) is a genetic signature in certain cases of sporadic colorectal cancer and has been linked to MSH3-deficiency. It is currently controversial whether EMAST is associated with oncogenic properties in humans, specifically as cancer development in Msh3-deficient mice is not enhanced. However, a mutator phenotype is different between species as the genetic positions of repetitive sequences are not conserved. Here we studied the molecular effects of human MSH3-deficiency.MethodsHCT116 and HCT116+chr3 (both MSH3-deficient) and primary human colon epithelial cells (HCEC, MSH3-wildtype) were stably transfected with an EGFP-based reporter plasmid for the detection of frameshift mutations within an [AAAG]17 repeat. MSH3 was silenced by shRNA and changes in protein expression were analyzed by shotgun proteomics. Colony forming assay was used to determine oncogenic transformation and double strand breaks (DSBs) were assessed by Comet assay.ResultsDespite differential MLH1 expression, both HCT116 and HCT116+chr3 cells displayed comparable high mutation rates (about 4×10(-4)) at [AAAG]17 repeats. Silencing of MSH3 in HCECs leads to a remarkable increased frameshift mutations in [AAAG]17 repeats whereas [CA]13 repeats were less affected. Upon MSH3-silencing, significant changes in the expression of 202 proteins were detected. Pathway analysis revealed overexpression of proteins involved in double strand break repair (MRE11 and RAD50), apoptosis, L1 recycling, and repression of proteins involved in metabolism, tRNA aminoacylation, and gene expression. MSH3-silencing did not induce oncogenic transformation and DSBs increased 2-fold.ConclusionsMSH3-deficiency in human colon epithelial cells results in EMAST, formation of DSBs and significant changes of the proteome but lacks oncogenic transformation. Thus, MSH3-deficiency alone is unlikely to drive human colon carcinogenesis.

Background Many different cluster methods are frequently used in gene expression data analysis to find groups of co-expressed genes. However, cluster algorithms with the ability to visualize the resulting clusters are usually preferred. The visualization of gene clusters gives practitioners an understanding of the cluster structure of their data and makes it easier to interpret the cluster results. Results In this paper recent extensions of R package gcExplorer are presented. gcExplorer is an interactive visualization toolbox for the investigation of the overall cluster structure as well as single clusters. The different visualization options including arbitrary node and panel functions are described in detail. Finally the toolbox can be used to investigate the quality of a given clustering graphically as well as theoretically by testing the association between a partition and a functional group under study. Conclusion It is shown that gcExplorer is a very helpful tool for a general exploration of microarray experiments. The identification of potentially interesting gene candidates or functional groups is substantially accelerated and eased. Inferential analysis on a cluster solution is used to judge its ability to provide insight into the underlying mechanistic biology of the experiment.

Summary: Cluster analysis plays an important role in the analysis of gene expression data since the early beginning of microarray studies and is routinely used to find groups of genes with common expression pattern. In order to make cluster analysis helpful for users, visualization of cluster solutions is of utmost importance. Here, we present the new R package gcExplorer for the interactive exploration of gene clusters. gcExplorer facilitates the interpretation of cluster results and allows to investigate extensive information about clusters. Availability: The latest release of gcExplorer is always available at the Comprehensive R Archive Network CRAN: http://cran.R-project.org/package=gcExplorer. See the README file in the package for detailed installation instructions. Contact: theresa.scharl@ci.tuwien.ac.at Supplementary information: Supplementary data are available at Bioinformatics online.

Escherichia coli K-12 and B strains are among the most frequently used bacterial hosts for production of recombinant proteins on an industrial scale. To improve existing processes and to accelerate bioprocess development, we performed a detailed host analysis. We investigated the different behaviors of the E. coli production strains BL21, RV308, and HMS174 in response to high-glucose concentrations. Tightly controlled cultivations were conducted under defined environmental conditions for the in-depth analysis of physiological behavior. In addition to acquisition of standard process parameters, we also used DNA microarray analysis and differential gel electrophoresis (Ettan(TM) DIGE). Batch cultivations showed different yields of the distinct strains for cell dry mass and growth rate, which were highest for BL21. In addition, production of acetate, triggered by excess glucose supply, was much higher for the K-12 strains compared to the B strain. Analysis of transcriptome data showed significant alteration in 347 of 3882 genes common among all three hosts. These differentially expressed genes included, for example, those involved in transport, iron acquisition, and motility. The investigation of proteome patterns additionally revealed a high number of differentially expressed proteins among the investigated hosts. The subsequently selected 38 spots included proteins involved in transport and motility. The results of this comprehensive analysis delivered a full genomic picture of the three investigated strains. Differentially expressed groups for targeted host modification were identified like glucose transport or iron acquisition, enabling potential optimization of strains to improve yield and process quality. Dissimilar growth profiles of the strains confirm different genotypes. Furthermore, distinct transcriptome patterns support differential regulation at the genome level. The identified proteins showed high agreement with the transcriptome data and suggest similar regulation within a host at both levels for the identified groups. Such host attributes need to be considered in future process design and operation.

Summary: Finite mixture models are routinely applied to time course microarray data. Due to the complexity and size of this type of data, the choice of good starting values plays an important role. So far initialization strategies have only been investigated for data from a mixture of multivariate normal distributions. In this work several initialization procedures are evaluated for mixtures of regression models with and without random effects in an extensive simulation study on different artificial datasets. Finally, these procedures are also applied to a real dataset from Escherichia coli. Availability: The latest release versions of R packages flexmix, gcExplorer and kernlab are always available from CRAN (http://cran.r-project.org/). Contact: theresa.scharl@ci.tuwien.ac.at Supplementary information: Supplementary data are available at Bioinformatics online.