Publications by authors named "Anna Ritz"

24 Publications

  • Page 1 of 1

The Rab GAP RN-tre cross-talks with the Rho1 signaling pathway to regulate nonmuscle myosin II localization and function.

Mol Biol Cell 2020 10 20;31(21):2379-2397. Epub 2020 Aug 20.

Department of Biology, Reed College, Portland, OR 97202.

To identify novel regulators of nonmuscle myosin II (NMII) we performed an image-based RNA interference screen using stable S2 cells expressing the enhanced green fluorescent protein (EGFP)-tagged regulatory light chain (RLC) of NMII and mCherry-Actin. We identified the Rab-specific GTPase-activating protein (GAP) RN-tre as necessary for the assembly of NMII RLC into contractile actin networks. Depletion of RN-tre led to a punctate NMII phenotype, similar to what is observed following depletion of proteins in the Rho1 pathway. Depletion of RN-tre also led to a decrease in active Rho1 and a decrease in phosphomyosin-positive cells by immunostaining, while expression of constitutively active Rho or Rho-kinase (Rok) rescues the punctate phenotype. Functionally, RN-tre depletion led to an increase in actin retrograde flow rate and cellular contractility in S2 and S2R+ cells, respectively. Regulation of NMII by RN-tre is only partially dependent on its GAP activity as overexpression of constitutively active Rabs inactivated by RN-tre failed to alter NMII RLC localization, while a GAP-dead version of RN-tre partially restored phosphomyosin staining. Collectively, our results suggest that RN-tre plays an important regulatory role in NMII RLC distribution, phosphorylation, and function, likely through Rho1 signaling and putatively serving as a link between the secretion machinery and actomyosin contractility.
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http://dx.doi.org/10.1091/mbc.E20-03-0181DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7851959PMC
October 2020

Integrating protein localization with automated signaling pathway reconstruction.

BMC Bioinformatics 2019 Dec 2;20(Suppl 16):505. Epub 2019 Dec 2.

Biology Department, Reed College, Portland, OR 97202, USA.

Background: Understanding cellular responses via signal transduction is a core focus in systems biology. Tools to automatically reconstruct signaling pathways from protein-protein interactions (PPIs) can help biologists generate testable hypotheses about signaling. However, automatic reconstruction of signaling pathways suffers from many interactions with the same confidence score leading to many equally good candidates. Further, some reconstructions are biologically misleading due to ignoring protein localization information.

Results: We propose LocPL, a method to improve the automatic reconstruction of signaling pathways from PPIs by incorporating information about protein localization in the reconstructions. The method relies on a dynamic program to ensure that the proteins in a reconstruction are localized in cellular compartments that are consistent with signal transduction from the membrane to the nucleus. LocPL and existing reconstruction algorithms are applied to two PPI networks and assessed using both global and local definitions of accuracy. LocPL produces more accurate and biologically meaningful reconstructions on a versatile set of signaling pathways.

Conclusion: LocPL is a powerful tool to automatically reconstruct signaling pathways from PPIs that leverages cellular localization information about proteins. The underlying dynamic program and signaling model are flexible enough to study cellular signaling under different settings of signaling flow across the cellular compartments.
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http://dx.doi.org/10.1186/s12859-019-3077-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6886211PMC
December 2019

Distance measures for tumor evolutionary trees.

Bioinformatics 2020 04;36(7):2090-2097

Department of Computer Science, Carleton College, Northfield, MN 55057, USA.

Motivation: There has been recent increased interest in using algorithmic methods to infer the evolutionary tree underlying the developmental history of a tumor. Quantitative measures that compare such trees are vital to a number of different applications including benchmarking tree inference methods and evaluating common inheritance patterns across patients. However, few appropriate distance measures exist, and those that do have low resolution for differentiating trees or do not fully account for the complex relationship between tree topology and the inheritance of the mutations labeling that topology.

Results: Here, we present two novel distance measures, Common Ancestor Set distance (CASet) and Distinctly Inherited Set Comparison distance (DISC), that are specifically designed to account for the subclonal mutation inheritance patterns characteristic of tumor evolutionary trees. We apply CASet and DISC to multiple simulated datasets and two breast cancer datasets and show that our distance measures allow for more nuanced and accurate delineation between tumor evolutionary trees than existing distance measures.

Availability And Implementation: Implementations of CASet and DISC are freely available at: https://bitbucket.org/oesperlab/stereodist.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btz869DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7141873PMC
April 2020

Hypergraph-based connectivity measures for signaling pathway topologies.

PLoS Comput Biol 2019 10 25;15(10):e1007384. Epub 2019 Oct 25.

Biology Department, Reed College, Portland, Oregon, United States of America.

Characterizing cellular responses to different extrinsic signals is an active area of research, and curated pathway databases describe these complex signaling reactions. Here, we revisit a fundamental question in signaling pathway analysis: are two molecules "connected" in a network? This question is the first step towards understanding the potential influence of molecules in a pathway, and the answer depends on the choice of modeling framework. We examined the connectivity of Reactome signaling pathways using four different pathway representations. We find that Reactome is very well connected as a graph, moderately well connected as a compound graph or bipartite graph, and poorly connected as a hypergraph (which captures many-to-many relationships in reaction networks). We present a novel relaxation of hypergraph connectivity that iteratively increases connectivity from a node while preserving the hypergraph topology. This measure, B-relaxation distance, provides a parameterized transition between hypergraph connectivity and graph connectivity. B-relaxation distance is sensitive to the presence of small molecules that participate in many functionally unrelated reactions in the network. We also define a score that quantifies one pathway's downstream influence on another, which can be calculated as B-relaxation distance gradually relaxes the connectivity constraint in hypergraphs. Computing this score across all pairs of 34 Reactome pathways reveals pairs of pathways with statistically significant influence. We present two such case studies, and we describe the specific reactions that contribute to the large influence score. Finally, we investigate the ability for connectivity measures to capture functional relationships among proteins, and use the evidence channels in the STRING database as a benchmark dataset. STRING interactions whose proteins are B-connected in Reactome have statistically significantly higher scores than interactions connected in the bipartite graph representation. Our method lays the groundwork for other generalizations of graph-theoretic concepts to hypergraphs in order to facilitate signaling pathway analysis.
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http://dx.doi.org/10.1371/journal.pcbi.1007384DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6834280PMC
October 2019

MsPAC: a tool for haplotype-phased structural variant detection.

Bioinformatics 2020 02;36(3):922-924

Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.

Summary: While next-generation sequencing (NGS) has dramatically increased the availability of genomic data, phased genome assembly and structural variant (SV) analyses are limited by NGS read lengths. Long-read sequencing from Pacific Biosciences and NGS barcoding from 10x Genomics hold the potential for far more comprehensive views of individual genomes. Here, we present MsPAC, a tool that combines both technologies to partition reads, assemble haplotypes (via existing software) and convert assemblies into high-quality, phased SV predictions. MsPAC represents a framework for haplotype-resolved SV calls that moves one step closer to fully resolved, diploid genomes.

Availability And Implementation: https://github.com/oscarlr/MsPAC.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btz618DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7523683PMC
February 2020

Invasive ductal carcinoma detected within a fibroadenolipoma through digital breast tomosynthesis.

Acta Radiol Open 2019 Jul 25;8(7):2058460119865905. Epub 2019 Jul 25.

Kantonsspital Baden AG, Institute of Radiology, Baden, Switzerland.

A 52-year-old patient referred to our hospital for a screening mammogram showed a suspicious new architectural distortion. Previously, a fibroadenolipoma within the right breast was diagnosed clinically and radiologically. Further work-up with tomosynthesis, magnetic resonance imaging, and magnetic resonance-guided biopsy showed an invasive ductal carcinoma within the fibroadenolipoma, which are usually benign breast lesions not associated with malignancy. This case report offers a review of the literature and a discussion of signs, which should alert the radiologist.
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http://dx.doi.org/10.1177/2058460119865905DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6659190PMC
July 2019

Network-based prediction of polygenic disease genes involved in cell motility.

BMC Bioinformatics 2019 Jun 20;20(Suppl 12):313. Epub 2019 Jun 20.

Biology Department, Reed College, Portland, OR, USA.

Background: Schizophrenia and autism are examples of polygenic diseases caused by a multitude of genetic variants, many of which are still poorly understood. Recently, both diseases have been associated with disrupted neuron motility and migration patterns, suggesting that aberrant cell motility is a phenotype for these neurological diseases.

Results: We formulate the POLYGENIC DISEASE PHENOTYPE Problem which seeks to identify candidate disease genes that may be associated with a phenotype such as cell motility. We present a machine learning approach to solve this problem for schizophrenia and autism genes within a brain-specific functional interaction network. Our method outperforms peer semi-supervised learning approaches, achieving better cross-validation accuracy across different sets of gold-standard positives. We identify top candidates for both schizophrenia and autism, and select six genes labeled as schizophrenia positives that are predicted to be associated with cell motility for follow-up experiments.

Conclusions: Candidate genes predicted by our method suggest testable hypotheses about these genes’ role in cell motility regulation, offering a framework for generating predictions for experimental validation.
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http://dx.doi.org/10.1186/s12859-019-2834-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6584515PMC
June 2019

A Cell-based Assay to Investigate Non-muscle Myosin II Contractility via the Folded-gastrulation Signaling Pathway in Drosophila S2R+ Cells.

J Vis Exp 2018 08 19(138). Epub 2018 Aug 19.

Department of Biology, Reed College;

We have developed a cell-based assay using Drosophila cells that recapitulates apical constriction initiated by folded gastrulation (Fog), a secreted epithelial morphogen. In this assay, Fog is used as an agonist to activate Rho through a signaling cascade that includes a G-protein-coupled receptor (Mist), a Gα12/13 protein (Concertina/Cta), and a PDZ-domain-containing guanine nucleotide exchange factor (RhoGEF2). Fog signaling results in the rapid and dramatic reorganization of the actin cytoskeleton to form a contractile purse string. Soluble Fog is collected from a stable cell line and applied ectopically to S2R+ cells, leading to morphological changes like apical constriction, a process observed during developmental processes such as gastrulation. This assay is amenable to high-throughput screening and, using RNAi, can facilitate the identification of additional genes involved in this pathway.
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http://dx.doi.org/10.3791/58325DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6128210PMC
August 2018

Metabolic reprogramming ensures cancer cell survival despite oncogenic signaling blockade.

Genes Dev 2017 10 14;31(20):2067-2084. Epub 2017 Nov 14.

Knight Comprehensive Cancer Institute, Oregon Health and Science University, Portland, Oregon 97239, USA.

There is limited knowledge about the metabolic reprogramming induced by cancer therapies and how this contributes to therapeutic resistance. Here we show that although inhibition of PI3K-AKT-mTOR signaling markedly decreased glycolysis and restrained tumor growth, these signaling and metabolic restrictions triggered autophagy, which supplied the metabolites required for the maintenance of mitochondrial respiration and redox homeostasis. Specifically, we found that survival of cancer cells was critically dependent on phospholipase A2 (PLA2) to mobilize lysophospholipids and free fatty acids to sustain fatty acid oxidation and oxidative phosphorylation. Consistent with this, we observed significantly increased lipid droplets, with subsequent mobilization to mitochondria. These changes were abrogated in cells deficient for the essential autophagy gene Accordingly, inhibition of PLA2 significantly decreased lipid droplets, decreased oxidative phosphorylation, and increased apoptosis. Together, these results describe how treatment-induced autophagy provides nutrients for cancer cell survival and identifies novel cotreatment strategies to override this survival advantage.
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http://dx.doi.org/10.1101/gad.305292.117DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5733498PMC
October 2017

Pathway Analysis with Signaling Hypergraphs.

IEEE/ACM Trans Comput Biol Bioinform 2017 Sep-Oct;14(5):1042-1055

Signaling pathways play an important role in the cell's response to its environment. Signaling pathways are often represented as directed graphs, which are not adequate for modeling reactions such as complex assembly and dissociation, combinatorial regulation, and protein activation/inactivation. More accurate representations such as directed hypergraphs remain underutilized. In this paper, we present an extension of a directed hypergraph that we call a signaling hypergraph. We formulate a problem that asks what proteins and interactions must be involved in order to stimulate a specific response downstream of a signaling pathway. We relate this problem to computing the shortest acyclic B-hyperpath in a signaling hypergraph-an NP-hard problem-and present a mixed integer linear program to solve it. We demonstrate that the shortest hyperpaths computed in signaling hypergraphs are far more informative than shortest paths, Steiner trees, and subnetworks containing many short paths found in corresponding graph representations. Our results illustrate the potential of signaling hypergraphs as an improved representation of signaling pathways and motivate the development of novel hypergraph algorithms.
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http://dx.doi.org/10.1109/TCBB.2015.2459681DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5810418PMC
May 2018

GraphSpace: stimulating interdisciplinary collaborations in network biology.

Bioinformatics 2017 Oct;33(19):3134-3136

Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA.

Summary: Networks have become ubiquitous in systems biology. Visualization is a crucial component in their analysis. However, collaborations within research teams in network biology are hampered by software systems that are either specific to a computational algorithm, create visualizations that are not biologically meaningful, or have limited features for sharing networks and visualizations. We present GraphSpace, a web-based platform that fosters team science by allowing collaborating research groups to easily store, interact with, layout and share networks.

Availability And Implementation: Anyone can upload and share networks at http://graphspace.org. In addition, the GraphSpace code is available at http://github.com/Murali-group/graphspace if a user wants to run his or her own server.

Contact: murali@cs.vt.edu.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btx382DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5860550PMC
October 2017

Pathways on demand: automated reconstruction of human signaling networks.

NPJ Syst Biol Appl 2016 3;2:16002. Epub 2016 Mar 3.

Department of Computer Science, Virginia Tech, Blacksburg, VA, USA.

Signaling pathways are a cornerstone of systems biology. Several databases store high-quality representations of these pathways that are amenable for automated analyses. Despite painstaking and manual curation, these databases remain incomplete. We present PATHLINKER, a new computational method to reconstruct the interactions in a signaling pathway of interest. PATHLINKER efficiently computes multiple short paths from the receptors to transcriptional regulators (TRs) in a pathway within a background protein interaction network. We use PATHLINKER to accurately reconstruct a comprehensive set of signaling pathways from the NetPath and KEGG databases. We show that PATHLINKER has higher precision and recall than several state-of-the-art algorithms, while also ensuring that the resulting network connects receptor proteins to TRs. PATHLINKER's reconstruction of the Wnt pathway identified CFTR, an ABC class chloride ion channel transporter, as a novel intermediary that facilitates the signaling of Ryk to Dab2, which are known components of Wnt/β-catenin signaling. In HEK293 cells, we show that the Ryk-CFTR-Dab2 path is a novel amplifier of β-catenin signaling specifically in response to Wnt 1, 2, 3, and 3a of the 11 Wnts tested. PATHLINKER captures the structure of signaling pathways as represented in pathway databases better than existing methods. PATHLINKER's success in reconstructing pathways from NetPath and KEGG databases point to its applicability for complementing manual curation of these databases. PATHLINKER may serve as a promising approach for prioritizing proteins and interactions for experimental study, as illustrated by its discovery of a novel pathway in Wnt/β-catenin signaling. Our supplementary website at http://bioinformatics.cs.vt.edu/~murali/supplements/2016-sys-bio-applications-pathlinker/ provides links to the PATHLINKER software, input datasets, PATHLINKER reconstructions of NetPath pathways, and links to interactive visualizations of these reconstructions on GraphSpace.
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http://dx.doi.org/10.1038/npjsba.2016.2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5516854PMC
March 2016

Characterization of structural variants with single molecule and hybrid sequencing approaches.

Bioinformatics 2014 Dec 28;30(24):3458-66. Epub 2014 Oct 28.

Department of Computer Science, Brown University, RI Department of Genetics and Genomic Sciences, Icahn School of Medicine, Mount Sinai, NY Institute for Genomics and Multiscale Biology, Icahn School of Medicine, Mount Sinai, NY School of Natural Sciences, University of California, Merced, CA Pacific Biosciences, Menlo Park, CA Center for Computational Molecular Biology, Brown University, RI Department of Computer Science, Brown University, RI Department of Genetics and Genomic Sciences, Icahn School of Medicine, Mount Sinai, NY Institute for Genomics and Multiscale Biology, Icahn School of Medicine, Mount Sinai, NY School of Natural Sciences, University of California, Merced, CA Pacific Biosciences, Menlo Park, CA Center for Computational Molecular Biology, Brown University, RI.

Motivation: Structural variation is common in human and cancer genomes. High-throughput DNA sequencing has enabled genome-scale surveys of structural variation. However, the short reads produced by these technologies limit the study of complex variants, particularly those involving repetitive regions. Recent 'third-generation' sequencing technologies provide single-molecule templates and longer sequencing reads, but at the cost of higher per-nucleotide error rates.

Results: We present MultiBreak-SV, an algorithm to detect structural variants (SVs) from single molecule sequencing data, paired read sequencing data, or a combination of sequencing data from different platforms. We demonstrate that combining low-coverage third-generation data from Pacific Biosciences (PacBio) with high-coverage paired read data is advantageous on simulated chromosomes. We apply MultiBreak-SV to PacBio data from four human fosmids and show that it detects known SVs with high sensitivity and specificity. Finally, we perform a whole-genome analysis on PacBio data from a complete hydatidiform mole cell line and predict 1002 high-probability SVs, over half of which are confirmed by an Illumina-based assembly.
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http://dx.doi.org/10.1093/bioinformatics/btu714DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4253835PMC
December 2014

Signaling hypergraphs.

Trends Biotechnol 2014 Jul 22;32(7):356-62. Epub 2014 May 22.

Department of Computer Science, Virginia Tech, Blacksburg, VA, USA; ICTAS Center for Systems Biology of Engineered Tissues, Virginia Tech, Blacksburg, VA, USA. Electronic address:

Signaling pathways function as the information-passing mechanisms of cells. A number of databases with extensive manual curation represent the current knowledge base for signaling pathways. These databases motivate the development of computational approaches for prediction and analysis. Such methods require an accurate and computable representation of signaling pathways. Pathways are often described as sets of proteins or as pairwise interactions between proteins. However, many signaling mechanisms cannot be described using these representations. In this opinion, we highlight a representation of signaling pathways that is underutilized: the hypergraph. We demonstrate the usefulness of hypergraphs in this context and discuss challenges and opportunities for the scientific community.
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http://dx.doi.org/10.1016/j.tibtech.2014.04.007DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4299695PMC
July 2014

Quantitative phosphoproteomics reveals SLP-76 dependent regulation of PAG and Src family kinases in T cells.

PLoS One 2012 11;7(10):e46725. Epub 2012 Oct 11.

Department of Chemistry, Brown University, Providence, Rhode Island, United States of America.

The SH2-domain-containing leukocyte protein of 76 kDa (SLP-76) plays a critical scaffolding role in T cell receptor (TCR) signaling. As an adaptor protein that contains multiple protein-binding domains, SLP-76 interacts with many signaling molecules and links proximal receptor stimulation to downstream effectors. The function of SLP-76 in TCR signaling has been widely studied using the Jurkat human leukaemic T cell line through protein disruption or site-directed mutagenesis. However, a wide-scale characterization of SLP-76-dependant phosphorylation events is still lacking. Quantitative profiling of over a hundred tyrosine phosphorylation sites revealed new modes of regulation of phosphorylation of PAG, PI3K, and WASP while reconfirming previously established regulation of Itk, PLCγ, and Erk phosphorylation by SLP-76. The absence of SLP-76 also perturbed the phosphorylation of Src family kinases (SFKs) Lck and Fyn, and subsequently a large number of SFK-regulated signaling molecules. Altogether our data suggests unique modes of regulation of positive and negative feedback pathways in T cells by SLP-76, reconfirming its central role in the pathway.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0046725PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3469622PMC
April 2013

Reconstructing cancer genomes from paired-end sequencing data.

BMC Bioinformatics 2012 Apr 19;13 Suppl 6:S10. Epub 2012 Apr 19.

Department of Computer Science, Brown University, Providence, RI, USA.

Background: A cancer genome is derived from the germline genome through a series of somatic mutations. Somatic structural variants - including duplications, deletions, inversions, translocations, and other rearrangements - result in a cancer genome that is a scrambling of intervals, or "blocks" of the germline genome sequence. We present an efficient algorithm for reconstructing the block organization of a cancer genome from paired-end DNA sequencing data.

Results: By aligning paired reads from a cancer genome - and a matched germline genome, if available - to the human reference genome, we derive: (i) a partition of the reference genome into intervals; (ii) adjacencies between these intervals in the cancer genome; (iii) an estimated copy number for each interval. We formulate the Copy Number and Adjacency Genome Reconstruction Problem of determining the cancer genome as a sequence of the derived intervals that is consistent with the measured adjacencies and copy numbers. We design an efficient algorithm, called Paired-end Reconstruction of Genome Organization (PREGO), to solve this problem by reducing it to an optimization problem on an interval-adjacency graph constructed from the data. The solution to the optimization problem results in an Eulerian graph, containing an alternating Eulerian tour that corresponds to a cancer genome that is consistent with the sequencing data. We apply our algorithm to five ovarian cancer genomes that were sequenced as part of The Cancer Genome Atlas. We identify numerous rearrangements, or structural variants, in these genomes, analyze reciprocal vs. non-reciprocal rearrangements, and identify rearrangements consistent with known mechanisms of duplication such as tandem duplications and breakage/fusion/bridge (B/F/B) cycles.

Conclusions: We demonstrate that PREGO efficiently identifies complex and biologically relevant rearrangements in cancer genome sequencing data. An implementation of the PREGO algorithm is available at http://compbio.cs.brown.edu/software/.
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http://dx.doi.org/10.1186/1471-2105-13-S6-S10DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3358655PMC
April 2012

Integrated genomics of ovarian xenograft tumor progression and chemotherapy response.

BMC Cancer 2011 Jul 22;11:308. Epub 2011 Jul 22.

Molecular Therapeutics Laboratory, Program in Women’s Oncology, Department of Obstetrics and Gynecology, Women and Infants7 Hospital, Alpert Medical School of Brown University, Providence, RI 02905, USA.

Background: Ovarian cancer is the most deadly gynecological cancer with a very poor prognosis. Xenograft mouse models have proven to be one very useful tool in testing candidate therapeutic agents and gene function in vivo. In this study we identify genes and gene networks important for the efficacy of a pre-clinical anti-tumor therapeutic, MT19c.

Methods: In order to understand how ovarian xenograft tumors may be growing and responding to anti-tumor therapeutics, we used genome-wide mRNA expression and DNA copy number measurements to identify key genes and pathways that may be critical for SKOV-3 xenograft tumor progression. We compared SKOV-3 xenografts treated with the ergocalciferol derived, MT19c, to untreated tumors collected at multiple time points. Cell viability assays were used to test the function of the PPARγ agonist, Rosiglitazone, on SKOV-3 cell growth.

Results: These data indicate that a number of known survival and growth pathways including Notch signaling and general apoptosis factors are differentially expressed in treated vs. untreated xenografts. As tumors grow, cell cycle and DNA replication genes show increased expression, consistent with faster growth. The steroid nuclear receptor, PPARγ, was significantly up-regulated in MT19c treated xenografts. Surprisingly, stimulation of PPARγ with Rosiglitazone reduced the efficacy of MT19c and cisplatin suggesting that PPARγ is regulating a survival pathway in SKOV-3 cells. To identify which genes may be important for tumor growth and treatment response, we observed that MT19c down-regulates some high copy number genes and stimulates expression of some low copy number genes suggesting that these genes are particularly important for SKOV-3 xenograft growth and survival.

Conclusions: We have characterized the time dependent responses of ovarian xenograft tumors to the vitamin D analog, MT19c. Our results suggest that PPARγ promotes survival for some ovarian tumor cells. We propose that a combination of regulated expression and copy number can identify genes that are likely important for chemotherapy response. Our findings suggest a new approach to identify candidate genes that are critical for anti-tumor therapy.
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http://dx.doi.org/10.1186/1471-2407-11-308DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3155912PMC
July 2011

Detection of recurrent rearrangement breakpoints from copy number data.

BMC Bioinformatics 2011 Apr 21;12:114. Epub 2011 Apr 21.

Department of Computer Science, Brown University, Providence, RI, USA.

Background: Copy number variants (CNVs), including deletions, amplifications, and other rearrangements, are common in human and cancer genomes. Copy number data from array comparative genome hybridization (aCGH) and next-generation DNA sequencing is widely used to measure copy number variants. Comparison of copy number data from multiple individuals reveals recurrent variants. Typically, the interior of a recurrent CNV is examined for genes or other loci associated with a phenotype. However, in some cases, such as gene truncations and fusion genes, the target of variant lies at the boundary of the variant.

Results: We introduce Neighborhood Breakpoint Conservation (NBC), an algorithm for identifying rearrangement breakpoints that are highly conserved at the same locus in multiple individuals. NBC detects recurrent breakpoints at varying levels of resolution, including breakpoints whose location is exactly conserved and breakpoints whose location varies within a gene. NBC also identifies pairs of recurrent breakpoints such as those that result from fusion genes. We apply NBC to aCGH data from 36 primary prostate tumors and identify 12 novel rearrangements, one of which is the well-known TMPRSS2-ERG fusion gene. We also apply NBC to 227 glioblastoma tumors and predict 93 novel rearrangements which we further classify as gene truncations, germline structural variants, and fusion genes. A number of these variants involve the protein phosphatase PTPN12 suggesting that deregulation of PTPN12, via a variety of rearrangements, is common in glioblastoma.

Conclusions: We demonstrate that NBC is useful for detection of recurrent breakpoints resulting from copy number variants or other structural variants, and in particular identifies recurrent breakpoints that result in gene truncations or fusion genes. Software is available at http://http.//cs.brown.edu/people/braphael/software.html.
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http://dx.doi.org/10.1186/1471-2105-12-114DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3112242PMC
April 2011

Gremlin: an interactive visualization model for analyzing genomic rearrangements.

IEEE Trans Vis Comput Graph 2010 Nov-Dec;16(6):918-26

Computer Science Department, Brown University, Providence, RI 02912, USA.

In this work we present, apply, and evaluate a novel, interactive visualization model for comparative analysis of structural variants and rearrangements in human and cancer genomes, with emphasis on data integration and uncertainty visualization. To support both global trend analysis and local feature detection, this model enables explorations continuously scaled from the high-level, complete genome perspective, down to the low-level, structural rearrangement view, while preserving global context at all times. We have implemented these techniques in Gremlin, a genomic rearrangement explorer with multi-scale, linked interactions, which we apply to four human cancer genome data sets for evaluation. Using an insight-based evaluation methodology, we compare Gremlin to Circos, the state-of-the-art in genomic rearrangement visualization, through a small user study with computational biologists working in rearrangement analysis. Results from user study evaluations demonstrate that this visualization model enables more total insights, more insights per minute, and more complex insights than the current state-of-the-art for visual analysis and exploration of genome rearrangements.
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http://dx.doi.org/10.1109/TVCG.2010.163DOI Listing
December 2010

Structural variation analysis with strobe reads.

Bioinformatics 2010 May 8;26(10):1291-8. Epub 2010 Apr 8.

Department of Computer Science, Brown University, Providence, RI 02912, USA.

Motivation: Structural variation including deletions, duplications and rearrangements of DNA sequence are an important contributor to genome variation in many organisms. In human, many structural variants are found in complex and highly repetitive regions of the genome making their identification difficult. A new sequencing technology called strobe sequencing generates strobe reads containing multiple subreads from a single contiguous fragment of DNA. Strobe reads thus generalize the concept of paired reads, or mate pairs, that have been routinely used for structural variant detection. Strobe sequencing holds promise for unraveling complex variants that have been difficult to characterize with current sequencing technologies.

Results: We introduce an algorithm for identification of structural variants using strobe sequencing data. We consider strobe reads from a test genome that have multiple possible alignments to a reference genome due to sequencing errors and/or repetitive sequences in the reference. We formulate the combinatorial optimization problem of finding the minimum number of structural variants in the test genome that are consistent with these alignments. We solve this problem using an integer linear program. Using simulated strobe sequencing data, we show that our algorithm has better sensitivity and specificity than paired read approaches for structural variation identification.

Contact: braphael@brown.edu
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http://dx.doi.org/10.1093/bioinformatics/btq153DOI Listing
May 2010

A new approach for quantitative phosphoproteomic dissection of signaling pathways applied to T cell receptor activation.

Mol Cell Proteomics 2009 Nov 14;8(11):2418-31. Epub 2009 Jul 14.

Department of Molecular Biology, Brown University, Providence, Rhode Island 02912, USA.

Reversible protein phosphorylation plays a pivotal role in the regulation of cellular signaling pathways. Current approaches in phosphoproteomics focus on analysis of the global phosphoproteome in a single cellular state or of receptor stimulation time course experiments, often with a restricted number of time points. Although these studies have provided some insights into newly discovered phosphorylation sites that may be involved in pathways, they alone do not provide enough information to make precise predictions of the placement of individual phosphorylation events within a signaling pathway. Protein disruption and site-directed mutagenesis are essential to clearly define the precise biological roles of the hundreds of newly discovered phosphorylation sites uncovered in modern proteomics experiments. We have combined genetic analysis with quantitative proteomic methods and recently developed visual analysis tools to dissect the tyrosine phosphoproteome of isogenic Zap-70 tyrosine kinase null and reconstituted Jurkat T cells. In our approach, label-free quantitation using normalization to copurified phosphopeptide standards is applied to assemble high density temporal data within a single cell type, either Zap-70 null or reconstituted cells, providing a list of candidate phosphorylation sites that change in abundance after T cell stimulation. Stable isotopic labeling of amino acids in cell culture (SILAC) ratios are then used to compare Zap-70 null and reconstituted cells across a time course of receptor stimulation, providing direct information about the placement of newly observed phosphorylation sites relative to Zap-70. These methods are adaptable to any cell culture signaling system in which isogenic wild type and mutant cells have been or can be derived using any available phosphopeptide enrichment strategy.
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http://dx.doi.org/10.1074/mcp.M800307-MCP200DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2773711PMC
November 2009

Discovery of phosphorylation motif mixtures in phosphoproteomics data.

Bioinformatics 2009 Jan 7;25(1):14-21. Epub 2008 Nov 7.

Department of Computer Science, Brown University, Toyota Technological Institute at Chicago, Chicago, IL, USA.

Motivation: Modification of proteins via phosphorylation is a primary mechanism for signal transduction in cells. Phosphorylation sites on proteins are determined in part through particular patterns, or motifs, present in the amino acid sequence.

Results: We describe an algorithm that simultaneously discovers multiple motifs in a set of peptides that were phosphorylated by several different kinases. Such sets of peptides are routinely produced in proteomics experiments.Our motif-finding algorithm uses the principle of minimum description length to determine a mixture of sequence motifs that distinguish a foreground set of phosphopeptides from a background set of unphosphorylated peptides. We show that our algorithm outperforms existing motif-finding algorithms on synthetic datasets consisting of mixtures of known phosphorylation sites. We also derive a motif specificity score that quantifies whether or not the phosphoproteins containing an instance of a motif have a significant number of known interactions. Application of our motif-finding algorithm to recently published human and mouse proteomic studies recovers several known phosphorylation motifs and reveals a number of novel motifs that are enriched for interactions with a particular kinase or phosphatase. Our tools provide a new approach for uncovering the sequence specificities of uncharacterized kinases or phosphatases.
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http://dx.doi.org/10.1093/bioinformatics/btn569DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2638929PMC
January 2009

Quantitative time-resolved phosphoproteomic analysis of mast cell signaling.

J Immunol 2007 Nov;179(9):5864-76

Department of Chemistry, Brown University, Providence, RI 02912, USA.

Mast cells play a central role in type I hypersensitivity reactions and allergic disorders such as anaphylaxis and asthma. Activation of mast cells, through a cascade of phosphorylation events, leads to the release of mediators of the early phase allergic response. Understanding the molecular architecture underlying mast cell signaling may provide possibilities for therapeutic intervention in asthma and other allergic diseases. Although many details of mast cell signaling have been described previously, a systematic, quantitative analysis of the global tyrosine phosphorylation events that are triggered by activation of the mast cell receptor is lacking. In many cases, the involvement of particular proteins in mast cell signaling has been established generally, but the precise molecular mechanism of the interaction between known signaling proteins often mediated through phosphorylation is still obscure. Using recently advanced methodologies in mass spectrometry, including automation of phosphopeptide enrichments and detection, we have now substantially characterized, with temporal resolution as short as 10 s, the sites and levels of tyrosine phosphorylation across 10 min of FcepsilonRI-induced mast cell activation. These results reveal a far more extensive array of tyrosine phosphorylation events than previously known, including novel phosphorylation sites on canonical mast cell signaling molecules, as well as unexpected pathway components downstream of FcepsilonRI activation. Furthermore, our results, for the first time in mast cells, reveal the sequence of phosphorylation events for 171 modification sites across 121 proteins in the MCP5 mouse mast cell line and 179 modification sites on 117 proteins in mouse bone marrow-derived mast cells.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2727709PMC
http://dx.doi.org/10.4049/jimmunol.179.9.5864DOI Listing
November 2007