Critical Networks Exhibit Maximal Information Diversity in Structure-Dynamics Relationships

Network structure strongly constrains the range of dynamic behaviors available to a complex system. These system dynamics can be classified based on their response to perturbations over time into two distinct regimes, ordered or chaotic, separated by a critical phase transition. Numerous studies have shown that the most complex dynamics arise near the critical regime. Here we use an information theoretic approach to study structure-dynamics relationships within a unified framework and how that these relationships are most diverse in the critical regime

Novel ZNF414 activity characterized by integrative analysis of ChIP-exo, ATAC-seq and RNA-seq data

Transcription factor binding to DNA is a central mechanism regulating gene expression. Thus, thorough characterization of this process is essential for understanding cellular biology in both health and disease. We combined data from three sequencing-based methods to unravel the DNA binding function of the novel ZNF414 protein in cells representing two tumor types. ChIP-exo served to map protein binding sites, ATAC-seq allowed identification of open chromatin, and RNA-seq examined the transcriptome. We show that ZNF414 is a DNAbinding protein that both induces and represses gene expression. This transcriptional response has an impact on cellular processes related to proliferation and other malignancy-associated functions, such as cell migration and DNA repair. Approximately 20% of the differentially expressed genes harbored ZNF414 binding sites in their promoters in accessible chromatin, likely representing direct targets of ZNF414. De novo motif discovery revealed several putative ZNF414 binding sequences, one of which was validated using EMSA. In conclusion, this study illustrates a highly efficient integrative approach for the characterization of the DNA binding and transcriptional activity of transcription factors.Peer reviewe

Independent and cumulative coeliac disease-susceptibility loci are associated with distinct disease phenotypes

The phenotype of coeliac disease varies considerably for incompletely understood reasons. We investigated whether established coeliac disease susceptibility variants (SNPs) are individually or cumulatively associated with distinct phenotypes. We also tested whether a polygenic risk score (PRS) based on genome-wide associated (GWA) data could explain the phenotypic variation. The phenotypic association of 39 non-HLA coeliac disease SNPs was tested in 625 thoroughly phenotyped coeliac disease patients and 1817 controls. To assess their cumulative effects a weighted genetic risk score (wGRS39) was built, and stratified by tertiles. In our PRS model in cases, we took the summary statistics from the largest GWA study in coeliac disease and tested their association at eight P value thresholds (P-T) with phenotypes. Altogether ten SNPs were associated with distinct phenotypes after correction for multiple testing (P-EMP2 1.62 for having coeliac disease-related symptoms during childhood, a more severe small bowel mucosal damage, malabsorption and anaemia. PRS was associated only with dermatitis herpetiformis (P-T = 0.2, P-EMP2 = 0.02). Independent coeliac disease-susceptibility loci are associated with distinct phenotypes, suggesting that genetic factors play a role in determining the disease presentation. Moreover, the increased number of coeliac disease susceptibility SNPs might predispose to a more severe disease course.Peer reviewe

PTPRD and CNTNAP2 as markers of tumor aggressiveness in oligodendrogliomas

Oligodendrogliomas are typically associated with the most favorable prognosis among diffuse gliomas. However, many of the tumors progress, eventually leading to patient death. To characterize the changes associated with oligodendroglioma recurrence and progression, we analyzed two recurrent oligodendroglioma tumors upon diagnosis and after tumor relapse based on whole-genome and RNA sequencing. Relapsed tumors were diagnosed as glioblastomas with an oligodendroglioma component before the World Health Organization classification update in 2016. Both patients died within 12 months after relapse. One patient carried an inactivating POLE mutation leading to a clearly hypermutated progressed tumor. Strikingly, both relapsed tumors carried focal chromosomal rearrangements in PTPRD and CNTNAP2 genes with associated decreased gene expression. TP53 mutation was also detected in both patients after tumor relapse. In The Cancer Genome Atlas (TCGA) diffuse glioma cohort, PTPRD and CNTNAP2 expression decreased by tumor grade in oligodendrogliomas and PTPRD expression also in IDH-mutant astrocytomas. Low expression of the genes was associated with poor overall survival. Our analysis provides information about aggressive oligodendrogliomas with worse prognosis and suggests that PTPRD and CNTNAP2 expression could represent an informative marker for their stratification.publishedVersionPeer reviewe

A Linear Model for Transcription Factor Binding Affinity Prediction in Protein Binding Microarrays

Protein binding microarrays (PBM) are a high throughput technology used to characterize protein-DNA binding. The arrays measure a protein's affinity toward thousands of double-stranded DNA sequences at once, producing a comprehensive binding specificity catalog. We present a linear model for predicting the binding affinity of a protein toward DNA sequences based on PBM data. Our model represents the measured intensity of an individual probe as a sum of the binding affinity contributions of the probe's subsequences. These subsequences characterize a DNA binding motif and can be used to predict the intensity of protein binding against arbitrary DNA sequences. Our method was the best performer in the Dialogue for Reverse Engineering Assessments and Methods 5 (DREAM5) transcription factor/DNA motif recognition challenge. For the DREAM5 bonus challenge, we also developed an approach for the identification of transcription factors based on their PBM binding profiles. Our approach for TF identification achieved the best performance in the bonus challenge

The evolutionary history of lethal metastatic prostate cancer

Cancers emerge from an ongoing Darwinian evolutionary process, often leading to multiple competing subclones within a single primary tumour. This evolutionary process culminates in the formation of metastases, which is the cause of 90% of cancer-related deaths. However, despite its clinical importance, little is known about the principles governing the dissemination of cancer cells to distant organs. Although the hypothesis that each metastasis originates from a single tumour cell is generally supported, recent studies using mouse models of cancer demonstrated the existence of polyclonal seeding from and interclonal cooperation between multiple subclones. Here we sought definitive evidence for the existence of polyclonal seeding in human malignancy and to establish the clonal relationship among different metastases in the context of androgen-deprived metastatic prostate cancer. Using whole-genome sequencing, we characterized multiple metastases arising from prostate tumours in ten patients. Integrated analyses of subclonal architecture revealed the patterns of metastatic spread in unprecedented detail. Metastasis-to-metastasis spread was found to be common, either through de novo monoclonal seeding of daughter metastases or, in five cases, through the transfer of multiple tumour clones between metastatic sites. Lesions affecting tumour suppressor genes usually occur as single events, whereas mutations in genes involved in androgen receptor signalling commonly involve multiple, convergent events in different metastases. Our results elucidate in detail the complex patterns of metastatic spread and further our understanding of the development of resistance to androgen-deprivation therapy in prostate cancer.This is an ICGC Prostate Cancer study funded by: Cancer Research UK (2011-present); NIH NCI Intramural Program (2013-2014); Academy of Finland (2011-present); Cancer Society of Finland (2013-present); PELICAN Autopsy Study family members and friends (1998-2004); John and Kathe Dyson (2000); US National Cancer Institute CA92234 (2000-2005); American Cancer Society (1998-2000); Johns Hopkins University Department of Pathology (1997-2011); Women's Board of Johns Hopkins Hospital (1998); The Grove Foundation (1998); Association for the Cure of Cancer of the Prostate (1994-1998); American Foundation for Urologic Disease (1991-1994); Bob Champion Cancer Trust (2013-present); Research Foundation – Flanders (FWO) [FWO-G.0687.12] (2012-present). E.P. is a European Hematology Association Research Fellow

POMO - Plotting Omics analysis results for Multiple Organisms

Background Systems biology experiments studying different topics and organisms produce thousands of data values across different types of genomic data. Further, data mining analyses are yielding ranked and heterogeneous results and association networks distributed over the entire genome. The visualization of these results is often difficult and standalone web tools allowing for custom inputs and dynamic filtering are limited. Results We have developed POMO (http://pomo.cs.tut.fi), an interactive web-based application to visually explore omics data analysis results and associations in circular, network and grid views. The circular graph represents the chromosome lengths as perimeter segments, as a reference outer ring, such as cytoband for human. The inner arcs between nodes represent the uploaded network. Further, multiple annotation rings, for example depiction of gene copy number changes, can be uploaded as text files and represented as bar, histogram or heatmap rings. POMO has built-in references for human, mouse, nematode, fly,yeast, zebrafish, rice, tomato, Arabidopsis, and Escherichia coli. In addition, POMO provides custom options that allow integrated plotting of unsupported strains or closely related species associations, such as human and mouse orthologs or two yeast wild types, studied together within a single analysis. The web application also supports interactive label and weight filtering. Every iterative filtered result in POMO can be exported as image file and text file for sharing or direct future input. Conclusions The POMO web application is a unique tool for omics data analysis, which can be used to visualize and filter the genome-wide networks in the context of chromosomal locations as well as multiple network layouts. With the several illustration and filtering options the tool supports the analysis and visualization of any heterogeneous omics data analysis association results for many organisms. POMO is freely available and does not require any installation or registration