Application of machine learning methods to pathogen safety evaluation in biological manufacturing processes.

The production of recombinant therapeutic proteins from animal or human cell lines entails the risk of endogenous viral contamination from cell substrates and adventitious agents from raw materials and environment. One of the approaches to control such potential viral contamination is to ensure the manufacturing process can adequately clear the potential viral contaminants. Viral clearance for production of human monoclonal antibodies is achieved by dedicated unit operations, such as low pH inactivation, viral filtration, and chromatographic separation. The process development of each viral clearance step for a new antibody production requires significant effort and resources invested in wet laboratory experiments for process characterization studies. Machine learning methods have the potential to help streamline the development and optimization of viral clearance unit operations for new therapeutic antibodies. The current work focuses on evaluating the usefulness of machine learning methods for process understanding and predictive modeling for viral clearance via a case study on low pH viral inactivation.

The EDLL motif: a potent plant transcriptional activation domain from AP2/ERF transcription factors.

In plants, the ERF/EREBP family of transcriptional regulators plays a key role in adaptation to various biotic and abiotic stresses. These proteins contain a conserved AP2 DNA-binding domain and several uncharacterized motifs. Here, we describe a short motif, termed 'EDLL', that is present in AtERF98/TDR1 and other clade members from the same AP2 sub-family. We show that the EDLL motif, which has a unique arrangement of acidic amino acids and hydrophobic leucines, functions as a strong activation domain. The motif is transferable to other proteins, and is active at both proximal and distal positions of target promoters. As such, the EDLL motif is able to partly overcome the repression conferred by the AtHB2 transcription factor, which contains an ERF-associated amphiphilic repression (EAR) motif. We further examined the activation potential of EDLL by analysis of the regulation of flowering time by NF-Y (nuclear factor Y) proteins. Genetic evidence indicates that NF-Y protein complexes potentiate the action of CONSTANS in regulation of flowering in Arabidopsis; we show that the transcriptional activation function of CONSTANS can be substituted by direct fusion of the EDLL activation motif to NF-YB subunits. The EDLL motif represents a potent plant activation domain that can be used as a tool to confer transcriptional activation potential to heterologous DNA-binding proteins.

Plant nuclear factor Y (NF-Y) B subunits confer drought tolerance and lead to improved corn yields on water-limited acres.

Commercially improved crop performance under drought conditions has been challenging because of the complexity of the trait and the multitude of factors that influence yield. Here we report the results of a functional genomics approach that identified a transcription factor from the nuclear factor Y (NF-Y) family, AtNF-YB1, which acts through a previously undescribed mechanism to confer improved performance in Arabidopsis under drought conditions. An orthologous maize transcription factor, ZmNF-YB2, is shown to have an equivalent activity. Under water-limited conditions, transgenic maize plants with increased ZmNF-YB2 expression show tolerance to drought based on the responses of a number of stress-related parameters, including chlorophyll content, stomatal conductance, leaf temperature, reduced wilting, and maintenance of photosynthesis. These stress adaptations contribute to a grain yield advantage to maize under water-limited environments. The application of this technology has the potential to significantly impact maize production systems that experience drought.

Effect of various normalization methods on Applied Biosystems expression array system data.

BACKGROUND: DNA microarray technology provides a powerful tool for characterizing gene expression on a genome scale. While the technology has been widely used in discovery-based medical and basic biological research, its direct application in clinical practice and regulatory decision-making has been questioned. A few key issues, including the reproducibility, reliability, compatibility and standardization of microarray analysis and results, must be critically addressed before any routine usage of microarrays in clinical laboratory and regulated areas can occur. In this study we investigate some of these issues for the Applied Biosystems Human Genome Survey Microarrays. RESULTS: We analyzed the gene expression profiles of two samples: brain and universal human reference (UHR), a mixture of RNAs from 10 cancer cell lines, using the Applied Biosystems Human Genome Survey Microarrays. Five technical replicates in three different sites were performed on the same total RNA samples according to manufacturer's standard protocols. Five different methods, quantile, median, scale, VSN and cyclic loess were used to normalize AB microarray data within each site. 1,000 genes spanning a wide dynamic range in gene expression levels were selected for real-time PCR validation. Using the TaqMan assays data set as the reference set, the performance of the five normalization methods was evaluated focusing on the following criteria: (1) Sensitivity and reproducibility in detection of expression; (2) Fold change correlation with real-time PCR data; (3) Sensitivity and specificity in detection of differential expression; (4) Reproducibility of differentially expressed gene lists. CONCLUSION: Our results showed a high level of concordance between these normalization methods. This is true, regardless of whether signal, detection, variation, fold change measurements and reproducibility were interrogated. Furthermore, we used TaqMan assays as a reference, to generate TPR and FDR plots for the various normalization methods across the assay range. Little impact is observed on the TP and FP rates in detection of differentially expressed genes. Additionally, little effect was observed by the various normalization methods on the statistical approaches analyzed which indicates a certain robustness of the analysis methods currently in use in the field, particularly when used in conjunction with the Applied Biosystems Gene Expression System.

Evaluation of DNA microarray results with quantitative gene expression platforms.

We have evaluated the performance characteristics of three quantitative gene expression technologies and correlated their expression measurements to those of five commercial microarray platforms, based on the MicroArray Quality Control (MAQC) data set. The limit of detection, assay range, precision, accuracy and fold-change correlations were assessed for 997 TaqMan Gene Expression Assays, 205 Standardized RT (Sta)RT-PCR assays and 244 QuantiGene assays. TaqMan is a registered trademark of Roche Molecular Systems, Inc. We observed high correlation between quantitative gene expression values and microarray platform results and found few discordant measurements among all platforms. The main cause of variability was differences in probe sequence and thus target location. A second source of variability was the limited and variable sensitivity of the different microarray platforms for detecting weakly expressed genes, which affected interplatform and intersite reproducibility of differentially expressed genes. From this analysis, we conclude that the MAQC microarray data set has been validated by alternative quantitative gene expression platforms thus supporting the use of microarray platforms for the quantitative characterization of gene expression.

The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements.

Over the last decade, the introduction of microarray technology has had a profound impact on gene expression research. The publication of studies with dissimilar or altogether contradictory results, obtained using different microarray platforms to analyze identical RNA samples, has raised concerns about the reliability of this technology. The MicroArray Quality Control (MAQC) project was initiated to address these concerns, as well as other performance and data analysis issues. Expression data on four titration pools from two distinct reference RNA samples were generated at multiple test sites using a variety of microarray-based and alternative technology platforms. Here we describe the experimental design and probe mapping efforts behind the MAQC project. We show intraplatform consistency across test sites as well as a high level of interplatform concordance in terms of genes identified as differentially expressed. This study provides a resource that represents an important first step toward establishing a framework for the use of microarrays in clinical and regulatory settings.