A signal transduction score flow algorithm for cyclic cellular pathway analysis, which combines transcriptome and ChIP-seq data

Determination of cell signalling behaviour is crucial for understanding the physiological response to a specific stimulus or drug treatment. Current approaches for large-scale data analysis do not effectively incorporate critical topological information provided by the signalling network. We herein describe a novel model- and data-driven hybrid approach, or signal transduction score flow algorithm, which allows quantitative visualization of cyclic cell signalling pathways that lead to ultimate cell responses such as survival, migration or death. This score flow algorithm translates signalling pathways as a directed graph and maps experimental data, including negative and positive feedbacks, onto gene nodes as scores, which then computationally traverse the signalling pathway until a pre-defined biological target response is attained. Initially, experimental data-driven enrichment scores of the genes were computed in a pathway, then a heuristic approach was applied using the gene score partition as a solution for protein node stoichiometry during dynamic scoring of the pathway of interest. Incorporation of a score partition during the signal flow and cyclic feedback loops in the signalling pathway significantly improves the usefulness of this model, as compared to other approaches. Evaluation of the score flow algorithm using both transcriptome and ChIP-seq data-generated signalling pathways showed good correlation with expected cellular behaviour on both KEGG and manually generated pathways. Implementation of the algorithm as a Cytoscape plug-in allows interactive visualization and analysis of KEGG pathways as well as user-generated and curated Cytoscape pathways. Moreover, the algorithm accurately predicts gene-level and global impacts of single or multiple in silico gene knockouts.Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich

PATZ1 is a DNA damage-responsive transcription factor that inhibits p53 function

Insults to cellular health cause p53 protein accumulation, and loss of p53 function leads to tumorigenesis. Thus, p53 has to be tightly controlled. Here we report that the BTB/POZ domain transcription factor PATZ1 (MAZR), previously known for its tran- scriptional suppressor functions in T lymphocytes, is a crucial regulator of p53. The novel role of PATZ1 as an inhibitor of the p53 protein marks its gene as a proto-oncogene. PATZ1-deficient cells have reduced proliferative capacity, which we assessed by transcriptome sequencing (RNA-Seq) and real-time cell growth rate analysis. PATZ1 modifies the expression of p53 target genes associated with cell proliferation gene ontology terms. Moreover, PATZ1 regulates several genes involved in cellular adhesion and morphogenesis. Significantly, treatment with the DNA damage-inducing drug doxorubicin results in the loss of the PATZ1 transcription factor as p53 accumulates. We find that PATZ1 binds to p53 and inhibits p53-dependent transcription activation. We examine the mechanism of this functional inhibitory interaction and demonstrate that PATZ1 excludes p53 from DNA bind- ing. This study documents PATZ1 as a novel player in the p53 pathway

Molecular Biology of Liver Cancer

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer‐related mortality worldwide. Recent advances in the molecular profiling of HCC emphasize its intratumoral heterogeneity and reveal how cellular pathways are altered in favor of tumor progression. Malignant transformation of primary liver cancer is achieved through the acquisition of cancer hallmark capabilities that promote the uncontrolled proliferation of hepatocytes. In this review, the characteristics and acquired capabilities of human primary liver cancer, based on the HCC‐specific genetic and epigenetic alterations, are described and discusse

Identification of Novel Reference Genes Based on MeSH Categories

Transcriptome experiments are performed to assess protein abundance through mRNA expression analysis. Expression levels of genes vary depending on the experimental conditions and the cell response. Transcriptome data must be diverse and yet comparable in reference to stably expressed genes, even if they are generated from different experiments on the same biological context from various laboratories. In this study, expression patterns of 9090 microarray samples grouped into 381 NCBI-GEO datasets were investigated to identify novel candidate reference genes using randomizations and Receiver Operating Characteristic (ROC) curves. The analysis demonstrated that cell type specific reference gene sets display less variability than a united set for all tissues. Therefore, constitutively and stably expressed, origin specific novel reference gene sets were identified based on their coefficient of variation and percentage of occurrence in all GEO datasets, which were classified using Medical Subject Headings (MeSH). A large number of MeSH grouped reference gene lists are presented as novel tissue specific reference gene lists. The most commonly observed 17 genes in these sets were compared for their expression in 8 hepatocellular, 5 breast and 3 colon carcinoma cells by RT-qPCR to verify tissue specificity. Indeed, commonly used housekeeping genes GAPDH, Actin and EEF2 had tissue specific variations, whereas several ribosomal genes were among the most stably expressed genes in vitro. Our results confirm that two or more reference genes should be used in combination for differential expression analysis of large-scale data obtained from microarray or next generation sequencing studies. Therefore context dependent reference gene sets, as presented in this study, are required for normalization of expression data from diverse technological backgrounds

Large-scale automated function prediction of protein sequences and an experimental case study validation on PTEN transcript variants

Recent advances in computing power and machine learning empower functional annotation of protein sequences and their transcript variations. Here, we present an automated prediction system UniGOPred, for GO annotations and a database of GO term predictions for proteomes of several organisms in UniProt Knowledgebase (UniProtKB). UniGOPred provides function predictions for 514 molecular function (MF), 2909 biological process (BP), and 438 cellular component (CC) GO terms for each protein sequence. UniGOPred covers nearly the whole functionality spectrum in Gene Ontology system and it can predict both generic and specific GO terms. UniGOPred was run on CAFA2 challenge target protein sequences and it is categorized within the top 10 best performing methods for the molecular function category. In addition, the performance of UniGOPred is higher compared to the baseline BLAST classifier in all categories of GO. UniGOPred predictions are compared with UniProtKB/TrEMBL database annotations as well. Furthermore, the proposed tool's ability to predict negatively associated GO terms that defines the functions that a protein does not possess, is discussed. UniGOPred annotations were also validated by case studies on PTEN protein variants experimentally and on CHD8 protein variants with literature. UniGOPred protein functional annotation system is available as an open access tool at

Graphs of coefficient of variations and relative expression levels of 17 reference genes in RT-qPCR.

<p>Coefficient of Variation (CV) was calculated based on the relative expression (efficiency^−ΔCq) of each housekeeping gene in (<b>A</b>) Liver, (<b>B</b>) Breast, (<b>C</b>) Colon and (<b>D</b>) All cancer cell lines.</p