Tero Aittokallio

A community effort to assess and improve drug sensitivity prediction algorithms

Predicting the best treatment strategy from genomic information is a core goal of precision medicine. Here we focus on predicting drug response based on a cohort of genomic, epigenomic and proteomic profiling data sets measured in human breast cancer cell lines. Through a collaborative effort between the National Cancer Institute (NCI) and the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we analyzed a total of 44 drug sensitivity prediction algorithms. The top-performing approaches modeled nonlinear relationships and incorporated biological pathway information. We found that gene expression microarrays consistently provided the best predictive power of the individual profiling data sets; however, performance was increased by including multiple, independent data sets. We discuss the innovations underlying the top-performing methodology, Bayesian multitask MKL, and we provide detailed descriptions of all methods. This study establishes benchmarks for drug sensitivity prediction and identifies approaches that can be leveraged for the development of new methods.

Quantitative Subcellular Proteome and Secretome Profiling of Influenza A Virus-Infected Human Primary Macrophages

Influenza A viruses are important pathogens that cause acute respiratory diseases and annual epidemics in humans. Macrophages recognize influenza A virus infection with their pattern recognition receptors, and are involved in the activation of proper innate immune response. Here, we have used high-throughput subcellular proteomics combined with bioinformatics to provide a global view of host cellular events that are activated in response to influenza A virus infection in human primary macrophages. We show that viral infection regulates the expression and/or subcellular localization of more than one thousand host proteins at early phases of infection. Our data reveals that there are dramatic changes in mitochondrial and nuclear proteomes in response to infection. We show that a rapid cytoplasmic leakage of lysosomal proteins, including cathepsins, followed by their secretion, contributes to inflammasome activation and apoptosis seen in the infected macrophages. Also, our results demonstrate that P2X7 receptor and src tyrosine kinase activity are essential for inflammasome activation during influenza A virus infection. Finally, we show that influenza A virus infection is associated with robust secretion of different danger-associated molecular patterns (DAMPs) suggesting an important role for DAMPs in host response to influenza A virus infection. In conclusion, our high-throughput quantitative proteomics study provides important new insight into host-response against influenza A virus infection in human primary macrophages.

A practical comparison of methods for detecting transcription factor binding sites in ChIP-seq experiments

BackgroundChromatin immunoprecipitation coupled with massively parallel sequencing (ChIP-seq) is increasingly being applied to study transcriptional regulation on a genome-wide scale. While numerous algorithms have recently been proposed for analysing the large ChIP-seq datasets, their relative merits and potential limitations remain unclear in practical applications.ResultsThe present study compares the state-of-the-art algorithms for detecting transcription factor binding sites in four diverse ChIP-seq datasets under a variety of practical research settings. First, we demonstrate how the biological conclusions may change dramatically when the different algorithms are applied. The reproducibility across biological replicates is then investigated as an internal validation of the detections. Finally, the predicted binding sites with each method are compared to high-scoring binding motifs as well as binding regions confirmed in independent qPCR experiments.ConclusionsIn general, our results indicate that the optimal choice of the computational approach depends heavily on the dataset under analysis. In addition to revealing valuable information to the users of this technology about the characteristics of the binding site detection approaches, the systematic evaluation framework provides also a useful reference to the developers of improved algorithms for ChIP-seq data.

Improving missing value estimation in microarray data with gene ontology

MOTIVATION Gene expression microarray experiments produce datasets with frequent missing expression values. Accurate estimation of missing values is an important prerequisite for efficient data analysis as many statistical and machine learning techniques either require a complete dataset or their results are significantly dependent on the quality of such estimates. A limitation of the existing estimation methods for microarray data is that they use no external information but the estimation is based solely on the expression data. We hypothesized that utilizing a priori information on functional similarities available from public databases facilitates the missing value estimation. RESULTS We investigated whether semantic similarity originating from gene ontology (GO) annotations could improve the selection of relevant genes for missing value estimation. The relative contribution of each information source was automatically estimated from the data using an adaptive weight selection procedure. Our experimental results in yeast cDNA microarray datasets indicated that by considering GO information in the k-nearest neighbor algorithm we can enhance its performance considerably, especially when the number of experimental conditions is small and the percentage of missing values is high. The increase of performance was less evident with a more sophisticated estimation method. We conclude that even a small proportion of annotated genes can provide improvements in data quality significant for the eventual interpretation of the microarray experiments. AVAILABILITY Java and Matlab codes are available on request from the authors. SUPPLEMENTARY MATERIAL Available online at http://users.utu.fi/jotatu/GOImpute.html.

Genome-wide Profiling of Interleukin-4 and STAT6 Transcription Factor Regulation of Human Th2 Cell Programming

Dissecting the molecular mechanisms by which T helper (Th) cells differentiate to effector Th2 cells is important for understanding the pathogenesis of immune-mediated diseases, such as asthma and allergy. Because the STAT6 transcription factor is an upstream mediator required for interleukin-4 (IL-4)-induced Th2 cell differentiation, its targets include genes important for this process. Using primary human CD4(+) T cells, and by blocking STAT6 with RNAi, we identified a number of direct and indirect targets of STAT6 with ChIP sequencing. The integration of these data sets with detailed kinetics of IL-4-driven transcriptional changes showed that STAT6 was predominantly needed for the activation of transcription leading to the Th2 cell phenotype. This integrated genome-wide data on IL-4- and STAT6-mediated transcription provide a unique resource for studies on Th cell differentiation and, in particular, for designing interventions of human Th2 cell responses.

Identification of Novel IL-4/Stat6-Regulated Genes in T Lymphocytes

IL-4, primarily produced by T cells, mast cells, and basophiles, is a cytokine which has pleiotropic effects on the immune system. IL-4 induces T cells to differentiate to Th2 cells and activated B lymphocytes to proliferate and to synthesize IgE and IgG1. IL-4 is particularly important for the development and perpetuation of asthma and allergy. Stat6 is the protein activated by signal transduction through the IL-4R, and studies with knockout mice demonstrate that Stat6 is critical for a number of IL-4-mediated functions including Th2 development and production of IgE. In the present study, novel IL-4- and Stat6-regulated genes were discovered by using Stat6−/− mice and Affymetrix oligonucleotide arrays. Genes regulated by IL-4 were identified by comparing the gene expression profile of the wild-type T cells induced to polarize to the Th2 direction (CD3/CD28 activation + IL-4) to gene expression profile of the cells induced to proliferate (CD3/CD28 activation alone). Stat6-regulated genes were identified by comparing the cells isolated from the wild-type and Stat6−/− mice; in this experiment the cells were induced to differentiate to the Th2 direction (CD3/CD28 activation + IL-4). Our study demonstrates that a number a novel genes are regulated by IL-4 through Stat6-dependent and -independent pathways. Moreover, elucidation of kinetics of gene expression at early stages of cell differentiation reveals several genes regulated rapidly during the process, suggesting their importance for the differentiation process.

Alignment of LC‐MS images, with applications to biomarker discovery and protein identification

LC‐MS‐based approaches have gained considerable interest for the analysis of complex peptide or protein mixtures, due to their potential for full automation and high sampling rates. Advances in resolution and accuracy of modern mass spectrometers allow new analytical LC‐MS‐based applications, such as biomarker discovery and cross‐sample protein identification. Many of these applications compare multiple LC‐MS experiments, each of which can be represented as a 2‐D image. In this article, we survey current approaches to LC‐MS image alignment. LC‐MS image alignment corrects for experimental variations in the chromatography and represents a computational key technology for the comparison of LC‐MS experiments. It is a required processing step for its two major applications: biomarker discovery and protein identification. Along with descriptions of the computational analysis approaches, we discuss their relative merits and potential pitfalls.

GOlorize: a Cytoscape plug-in for network visualization with Gene Ontology-based layout and coloring

UNLABELLED We have implemented a graph layout algorithm that exposes Gene Ontology (GO) class structure on the network nodes. It can be used in conjunction with BiNGO plug-in to Cytoscape, which finds the GO categories over-represented in a given network. Our plug-in, named GOlorize, first highlights the class members with category-specific color-coding and then constructs an enhanced visualization of the network using a class-directed layout algorithm. AVAILABILITY http://www.cytoscape.org/plugins2.php. SUPPLEMENTARY INFORMATION Installation instructions and tutorial at http://www.cytoscape.org/plugins/GOlorize/GOlorizeUserGuide.pdf.

Systematic construction of gene coexpression networks with applications to human T helper cell differentiation process

MOTIVATION Coexpression networks have recently emerged as a novel holistic approach to microarray data analysis and interpretation. Choosing an appropriate cutoff threshold, above which a gene-gene interaction is considered as relevant, is a critical task in most network-centric applications, especially when two or more networks are being compared. RESULTS We demonstrate that the performance of traditional approaches, which are based on a pre-defined cutoff or significance level, can vary drastically depending on the type of data and application. Therefore, we introduce a systematic procedure for estimating a cutoff threshold of coexpression networks directly from their topological properties. Both synthetic and real datasets show clear benefits of our data-driven approach under various practical circumstances. In particular, the procedure provides a robust estimate of individual degree distributions, even from multiple microarray studies performed with different array platforms or experimental designs, which can be used to discriminate the corresponding phenotypes. Application to human T helper cell differentiation process provides useful insights into the components and interactions controlling this process, many of which would have remained unidentified on the basis of expression change alone. Moreover, several human-mouse orthologs showed conserved topological changes in both systems, suggesting their potential importance in the differentiation process. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Dealing with missing values in large-scale studies: microarray data imputation and beyond

High-throughput biotechnologies, such as gene expression microarrays or mass-spectrometry-based proteomic assays, suffer from frequent missing values due to various experimental reasons. Since the missing data points can hinder downstream analyses, there exists a wide variety of ways in which to deal with missing values in large-scale data sets. Nowadays, it has become routine to estimate (or impute) the missing values prior to the actual data analysis. After nearly a decade since the publication of the first missing value imputation methods for gene expression microarray data, new imputation approaches are still being developed at an increasing rate. However, what is lagging behind is a systematic and objective evaluation of the strengths and weaknesses of the different approaches when faced with different types of data sets and experimental questions. In this review, the present strategies for missing value imputation and the measures for evaluating their performance are described. The imputation methods are first reviewed in the context of gene expression microarray data, since most of the methods have been developed for estimating gene expression levels; then, we turn to other large-scale data sets that also suffer from the problems posed by missing values, together with pointers to possible imputation approaches in these settings. Along with a description of the basic principles behind the different imputation approaches, the review tries to provide practical guidance for the users of high-throughput technologies on how to choose the imputation tool for their data and questions, and some additional research directions for the developers of imputation methodologies.