Loss of AXIN1 drives acquired resistance to WNT pathway blockade in colorectal cancer cells carrying RSPO3 fusions.

In colorectal cancer (CRC), WNT pathway activation by genetic rearrangements of RSPO3 is emerging as a promising target. However, its low prevalence severely limits availability of preclinical models for in-depth characterization. Using a pipeline designed to suppress stroma-derived signal, we find that RSPO3 "outlier" expression in CRC samples highlights translocation and fusion transcript expression. Outlier search in 151 CRC cell lines identified VACO6 and SNU1411 cells as carriers of, respectively, a canonical PTPRK(e1)-RSPO3(e2) fusion and a novel PTPRK(e13)-RSPO3(e2) fusion. Both lines displayed marked in vitro and in vivo sensitivity to WNT blockade by the porcupine inhibitor LGK974, associated with transcriptional and morphological evidence of WNT pathway suppression. Long-term treatment of VACO6 cells with LGK974 led to the emergence of a resistant population carrying two frameshift deletions of the WNT pathway inhibitor AXIN1, with consequent protein loss. Suppression of AXIN1 in parental VACO6 cells by RNA interference conferred marked resistance to LGK974. These results provide the first mechanism of secondary resistance to WNT pathway inhibition.

isomiR-SEA: an RNA-Seq analysis tool for miRNAs/isomiRs expression level profiling and miRNA-mRNA interaction sites evaluation.

BACKGROUND: Massive parallel sequencing of transcriptomes, revealed the presence of many miRNAs and miRNAs variants named isomiRs with a potential role in several cellular processes through their interaction with a target mRNA. Many methods and tools have been recently devised to detect and quantify miRNAs from sequencing data. However, all of them are implemented on top of general purpose alignment methods, thus providing poorly accurate results and no information concerning isomiRs and conserved miRNA-mRNA interaction sites. RESULTS: To overcome these limitations we present a novel algorithm named isomiR-SEA, that is able to provide users with very accurate miRNAs expression levels and both isomiRs and miRNA-mRNA interaction sites precise classifications. Tags are mapped on the known miRNAs sequences thanks to a specialized alignment algorithm developed on top of biological evidence concerning miRNAs structure. Specifically, isomiR-SEA checks for miRNA seed presence in the input tags and evaluates, during all the alignment phases, the positions of the encountered mismatches, thus allowing to distinguish among the different isomiRs and conserved miRNA-mRNA interaction sites. CONCLUSIONS: isomiR-SEA performances have been assessed on two public RNA-Seq datasets proving that the implemented algorithm is able to account for more reliable and accurate miRNAs expression levels with respect to those provided by two compared state of the art tools. Moreover, differently from the few methods currently available to perform isomiRs detection, the proposed algorithm implements the evaluation of isomiRs and conserved miRNA-mRNA interaction sites already in the first alignment phases, thus avoiding any additional filtering stages potentially responsible for the loss of useful information.

Convergent mutations and kinase fusions lead to oncogenic STAT3 activation in anaplastic large cell lymphoma.

A systematic characterization of the genetic alterations driving ALCLs has not been performed. By integrating massive sequencing strategies, we provide a comprehensive characterization of driver genetic alterations (somatic point mutations, copy number alterations, and gene fusions) in ALK(-) ALCLs. We identified activating mutations of JAK1 and/or STAT3 genes in ∼20% of 88 [corrected] ALK(-) ALCLs and demonstrated that 38% of systemic ALK(-) ALCLs displayed double lesions. Recurrent chimeras combining a transcription factor (NFkB2 or NCOR2) with a tyrosine kinase (ROS1 or TYK2) were also discovered in WT JAK1/STAT3 ALK(-) ALCL. All these aberrations lead to the constitutive activation of the JAK/STAT3 pathway, which was proved oncogenic. Consistently, JAK/STAT3 pathway inhibition impaired cell growth in vitro and in vivo.

VDJSeq-Solver: in silico V(D)J recombination detection tool.

In this paper we present VDJSeq-Solver, a methodology and tool to identify clonal lymphocyte populations from paired-end RNA Sequencing reads derived from the sequencing of mRNA neoplastic cells. The tool detects the main clone that characterises the tissue of interest by recognizing the most abundant V(D)J rearrangement among the existing ones in the sample under study. The exact sequence of the clone identified is capable of accounting for the modifications introduced by the enzymatic processes. The proposed tool overcomes limitations of currently available lymphocyte rearrangements recognition methods, working on a single sequence at a time, that are not applicable to high-throughput sequencing data. In this work, VDJSeq-Solver has been applied to correctly detect the main clone and identify its sequence on five Mantle Cell Lymphoma samples; then the tool has been tested on twelve Diffuse Large B-Cell Lymphoma samples. In order to comply with the privacy, ethics and intellectual property policies of the University Hospital and the University of Verona, data is available upon request to supporto.utenti@ateneo.univr.it after signing a mandatory Materials Transfer Agreement. VDJSeq-Solver JAVA/Perl/Bash software implementation is free and available at http://eda.polito.it/VDJSeq-Solver/.

Pegasus: a comprehensive annotation and prediction tool for detection of driver gene fusions in cancer.

BACKGROUND: The extraordinary success of imatinib in the treatment of BCR-ABL1 associated cancers underscores the need to identify novel functional gene fusions in cancer. RNA sequencing offers a genome-wide view of expressed transcripts, uncovering biologically functional gene fusions. Although several bioinformatics tools are already available for the detection of putative fusion transcripts, candidate event lists are plagued with non-functional read-through events, reverse transcriptase template switching events, incorrect mapping, and other systematic errors. Such lists lack any indication of oncogenic relevance, and they are too large for exhaustive experimental validation. RESULTS: We have designed and implemented a pipeline, Pegasus, for the annotation and prediction of biologically functional gene fusion candidates. Pegasus provides a common interface for various gene fusion detection tools, reconstruction of novel fusion proteins, reading-frame-aware annotation of preserved/lost functional domains, and data-driven classification of oncogenic potential. Pegasus dramatically streamlines the search for oncogenic gene fusions, bridging the gap between raw RNA-Seq data and a final, tractable list of candidates for experimental validation. CONCLUSION: We show the effectiveness of Pegasus in predicting new driver fusions in 176 RNA-Seq samples of glioblastoma multiforme (GBM) and 23 cases of anaplastic large cell lymphoma (ALCL).

Gelsius: a literature-based workflow for determining quantitative associations between genes and biological processes.

An effective knowledge extraction and quantification methodology from biomedical literature would allow the researcher to organize and analyze the results of high-throughput experiments on microarrays and next-generation sequencing technologies. Despite the large amount of raw information available on the web, a tool able to extract a measure of the correlation between a list of genes and biological processes is not yet available. In this paper, we present Gelsius, a workflow that incorporates biomedical literature to quantify the correlation between genes and terms describing biological processes. To achieve this target, we build different modules focusing on query expansion and document cononicalization. In this way, we reached to improve the measurement of correlation, performed using a latent semantic analysis approach. To the best of our knowledge, this is the first complete tool able to extract a measure of genes-biological processes correlation from literature. We demonstrate the effectiveness of the proposed workflow on six biological processes and a set of genes, by showing that correlation results for known relationships are in accordance with definitions of gene functions provided by NCI Thesaurus. On the other side, the tool is able to propose new candidate relationships for later experimental validation. The tool is available at >http://bioeda1.polito.it:8080/medSearchServlet/.

Acceleration of coarse grain molecular dynamics on GPU architectures.

Coarse grain (CG) molecular models have been proposed to simulate complex systems with lower computational overheads and longer timescales with respect to atomistic level models. However, their acceleration on parallel architectures such as graphic processing units (GPUs) presents original challenges that must be carefully evaluated. The objective of this work is to characterize the impact of CG model features on parallel simulation performance. To achieve this, we implemented a GPU-accelerated version of a CG molecular dynamics simulator, to which we applied specific optimizations for CG models, such as dedicated data structures to handle different bead type interactions, obtaining a maximum speed-up of 14 on the NVIDIA GTX480 GPU with Fermi architecture. We provide a complete characterization and evaluation of algorithmic and simulated system features of CG models impacting the achievable speed-up and accuracy of results, using three different GPU architectures as case studies.

Bellerophontes: an RNA-Seq data analysis framework for chimeric transcripts discovery based on accurate fusion model.

MOTIVATION: Next-generation sequencing technology allows the detection of genomic structural variations, novel genes and transcript isoforms from the analysis of high-throughput data. In this work, we propose a new framework for the detection of fusion transcripts through short paired-end reads which integrates splicing-driven alignment and abundance estimation analysis, producing a more accurate set of reads supporting the junction discovery and taking into account also not annotated transcripts. Bellerophontes performs a selection of putative junctions on the basis of a match to an accurate gene fusion model. RESULTS: We report the fusion genes discovered by the proposed framework on experimentally validated biological samples of chronic myelogenous leukemia (CML) and on public NCBI datasets, for which Bellerophontes is able to detect the exact junction sequence. With respect to state-of-art approaches, Bellerophontes detects the same experimentally validated fusions, however, it is more selective on the total number of detected fusions and provides a more accurate set of spanning reads supporting the junctions. We finally report the fusions involving non-annotated transcripts found in CML samples. AVAILABILITY AND IMPLEMENTATION: Bellerophontes JAVA/Perl/Bash software implementation is free and available at http://eda.polito.it/bellerophontes/.

Multiscale modeling of cellular actin filaments: from atomistic molecular to coarse-grained dynamics.

In this article, we present a computational multiscale model for the characterization of subcellular proteins. The model is encoded inside a simulation tool that builds coarse-grained (CG) force fields from atomistic simulations. Equilibrium molecular dynamics simulations on an all-atom model of the actin filament are performed. Then, using the statistical distribution of the distances between pairs of selected groups of atoms at the output of the MD simulations, the force field is parameterized using the Boltzmann inversion approach. This CG force field is further used to characterize the dynamics of the protein via Brownian dynamics simulations. This combination of methods into a single computational tool flow enables the simulation of actin filaments with length up to 400 nm, extending the time and length scales compared to state-of-the-art approaches. Moreover, the proposed multiscale modeling approach allows to investigate the relationship between atomistic structure and changes on the overall dynamics and mechanics of the filament and can be easily (i) extended to the characterization of other subcellular structures and (ii) used to investigate the cellular effects of molecular alterations due to pathological conditions.

miREE: miRNA recognition elements ensemble.

BACKGROUND: Computational methods for microRNA target prediction are a fundamental step to understand the miRNA role in gene regulation, a key process in molecular biology. In this paper we present miREE, a novel microRNA target prediction tool. miREE is an ensemble of two parts entailing complementary but integrated roles in the prediction. The Ab-Initio module leverages upon a genetic algorithmic approach to generate a set of candidate sites on the basis of their microRNA-mRNA duplex stability properties. Then, a Support Vector Machine (SVM) learning module evaluates the impact of microRNA recognition elements on the target gene. As a result the prediction takes into account information regarding both miRNA-target structural stability and accessibility. RESULTS: The proposed method significantly improves the state-of-the-art prediction tools in terms of accuracy with a better balance between specificity and sensitivity, as demonstrated by the experiments conducted on several large datasets across different species. miREE achieves this result by tackling two of the main challenges of current prediction tools: (1) The reduced number of false positives for the Ab-Initio part thanks to the integration of a machine learning module (2) the specificity of the machine learning part, obtained through an innovative technique for rich and representative negative records generation. The validation was conducted on experimental datasets where the miRNA:mRNA interactions had been obtained through (1) direct validation where even the binding site is provided, or through (2) indirect validation, based on gene expression variations obtained from high-throughput experiments where the specific interaction is not validated in detail and consequently the specific binding site is not provided. CONCLUSIONS: The coupling of two parts: a sensitive Ab-Initio module and a selective machine learning part capable of recognizing the false positives, leads to an improved balance between sensitivity and specificity. miREE obtains a reasonable trade-off between filtering false positives and identifying targets. miREE tool is available online at http://didattica-online.polito.it/eda/miREE/

A molecular dynamics study of a miRNA:mRNA interaction.

In this paper we present a methodology to evaluate the binding free energy of a miRNA:mRNA complex through molecular dynamics (MD)-thermodynamic integration (TI) simulations. We applied our method to the Caenorhabditis elegans let-7 miRNA:lin-41 mRNA complex-a validated miRNA:mRNA interaction-in order to estimate the energetic stability of the structure. To make the miRNA:mRNA simulation possible and realistic, the methodology introduces specific solutions to overcome some of the general challenges of nucleic acid simulations and binding free energy computations that have been discussed widely in many previous research reports. The main features of the proposed methodology are: (1) positioning of the restraints imposed on the simulations in order to guarantee complex stability; (2) optimal sampling of the phase space to achieve satisfactory accuracy in the binding energy value; (3) determination of a suitable trade-off between computational costs and accuracy of binding free energy computation by the assessment of the scalability characteristics of the parallel simulations required for the TI. The experiments carried out demonstrate that MD simulations are a viable strategy for the study of miRNA binding characteristics, opening the way to the development of new computational target prediction methods based on three-dimensional structure information.

Automated segmentation of cells with IHC membrane staining.

This study presents a fully automated membrane segmentation technique for immunohistochemical tissue images with membrane staining, which is a critical task in computerized immunohistochemistry (IHC). Membrane segmentation is particularly tricky in immunohistochemical tissue images because the cellular membranes are visible only in the stained tracts of the cell, while the unstained tracts are not visible. Our automated method provides accurate segmentation of the cellular membranes in the stained tracts and reconstructs the approximate location of the unstained tracts using nuclear membranes as a spatial reference. Accurate cell-by-cell membrane segmentation allows per cell morphological analysis and quantification of the target membrane proteins that is fundamental in several medical applications such as cancer characterization and classification, personalized therapy design, and for any other applications requiring cell morphology characterization. Experimental results on real datasets from different anatomical locations demonstrate the wide applicability and high accuracy of our approach in the context of IHC analysis.