Exploring the transcriptomic variation caused by the Finnish founder mutation of lysinuric protein intolerance (LPI).

Lysinuric protein intolerance (LPI) is an autosomal recessive disorder caused by mutations in cationic amino acid transporter gene SLC7A7. Although all Finnish patients share the same homozygous mutation, their clinical manifestations vary greatly. The symptoms range from failure to thrive, protein aversion, anemia and hyperammonaemia, to immunological abnormalities, nephropathy and pulmonary alveolar proteinosis. To unravel the molecular mechanisms behind those symptoms not explained directly by the primary mutation, gene expression profiles of LPI patients were studied using genome-wide microarray technology. As a result, we discovered 926 differentially-expressed genes, including cationic and neutral amino acid transporters. The functional annotation analysis revealed a significant accumulation of such biological processes as inflammatory response, immune system processes and apoptosis. We conclude that changes in the expression of genes other than SLC7A7 may be linked to the various symptoms of LPI, indicating a complex interplay between amino acid transporters and various cellular processes.

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. RESULTS: We show that this discrepancy can mostly be attributed to the way in which imputation methods have traditionally been developed and evaluated. By comparing a number of advanced imputation methods on recent microarray datasets, we show that even when there are marked differences in the measurement-level imputation accuracies across the datasets, these differences become negligible when the methods are evaluated in terms of how well they can reproduce the original gene clusters or their biological interpretations. Regardless of the evaluation approach, however, imputation always gave better results than ignoring missing data points or replacing them with zeros or average values, emphasizing the continued importance of using more advanced imputation methods. CONCLUSION: The results demonstrate that, while missing values are still severely complicating microarray data analysis, their impact on the discovery of biologically meaningful gene groups can - up to a certain degree - be reduced by using readily available and relatively fast imputation methods, such as the Bayesian Principal Components Algorithm (BPCA).

Biochemical and clinical approaches in evaluating the prognosis of colon cancer.

BACKGROUND: Colorectal adenocarcinoma is a common malignant neoplasm in the Western world. To achieve optimal treatment results, the risk estimation of recurrence should be as accurate as possible. MATERIALS AND METHODS: Tissue material from tumour and normal mucosa was taken from six patients and was analysed to screen aberrantly expressed genes using cDNA microarray. Selected up-regulated genes were chosen for further analysis by immunohistochemistry. For this purpose a tissue array material of 114 colorectal cancer patients was obtained. In addition to the routinely used proliferation marker Ki-67, the analysed proteins included securin and CDC25B. RESULTS: Processes such as cellular defense, cell structure, motility and cell division were found to be notably represented among the most deregulated genes. A significant portion of the overexpressed genes included those functioning in the cell cycle. Immunohistochemical stainings of securin and CDC25B showed a consistent expression pattern with that of cDNA microarray analysis. There was no statistical association between the studied proliferation markers and survival. Instead, there was a significant association between the Dukes' class and the histological grade (p=0.04), but not between histological grade and survival. The survival of Dukes' B patients was significantly poorer if no regional lymph nodes were studied compared with the Dukes' B patients with even a single lymph node was studied (p=0.04, hazard ratio 2.7). CONCLUSION: Tumour stage is superior in estimating the prognosis of patients with colonic cancer compared with the grading of cell cycle regulators or histological grade of the cancer. The study of regional lymph nodes is essential to identify the patients who would benefit from adjuvant chemotherapy.

A multilevel layout algorithm for visualizing physical and genetic interaction networks, with emphasis on their modular organization.

BACKGROUND: Graph drawing is an integral part of many systems biology studies, enabling visual exploration and mining of large-scale biological networks. While a number of layout algorithms are available in popular network analysis platforms, such as Cytoscape, it remains poorly understood how well their solutions reflect the underlying biological processes that give rise to the network connectivity structure. Moreover, visualizations obtained using conventional layout algorithms, such as those based on the force-directed drawing approach, may become uninformative when applied to larger networks with dense or clustered connectivity structure. METHODS: We implemented a modified layout plug-in, named Multilevel Layout, which applies the conventional layout algorithms within a multilevel optimization framework to better capture the hierarchical modularity of many biological networks. Using a wide variety of real life biological networks, we carried out a systematic evaluation of the method in comparison with other layout algorithms in Cytoscape. RESULTS: The multilevel approach provided both biologically relevant and visually pleasant layout solutions in most network types, hence complementing the layout options available in Cytoscape. In particular, it could improve drawing of large-scale networks of yeast genetic interactions and human physical interactions. In more general terms, the biological evaluation framework developed here enables one to assess the layout solutions from any existing or future graph drawing algorithm as well as to optimize their performance for a given network type or structure. CONCLUSIONS: By making use of the multilevel modular organization when visualizing biological networks, together with the biological evaluation of the layout solutions, one can generate convenient visualizations for many network biology applications.

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.