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  • 1.
    Akan, Pelin
    et al.
    KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Alexeyenko, Andrey
    KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Costea, Paul Igor
    KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hedberg, Lilia
    KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Werne Solnestam, Beata
    KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lundin, Sverker
    KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hallman, Jimmie
    Lundberg, Emma
    KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lundeberg, Joakim
    KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Comprehensive analysis of the genome transcriptome and proteome landscapes of three tumor cell lines2012In: Genome Medicine, ISSN 1756-994X, E-ISSN 1756-994X, Vol. 4, p. 86-Article in journal (Refereed)
    Abstract [en]

    We here present a comparative genome, transcriptome and functional network analysis of three human cancer cell lines (A431, U251MG and U2OS), and investigate their relation to protein expression. Gene copy numbers significantly influenced corresponding transcript levels; their effect on protein levels was less pronounced. We focused on genes with altered mRNA and/or protein levels to identify those active in tumor maintenance. We provide comprehensive information for the three genomes and demonstrate the advantage of integrative analysis for identifying tumor-related genes amidst numerous background mutations by relating genomic variation to expression/protein abundance data and use gene networks to reveal implicated pathways.

  • 2.
    Alekseenko, Andrej
    et al.
    KTH, School of Engineering Sciences (SCI), Applied Physics, Biophysics.
    Pall, Szilard
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC.
    Lindahl, Erik
    KTH, School of Engineering Sciences (SCI), Applied Physics, Biophysics.
    Experiences with Adding SYCL Support to GROMACS2021In: IWOCL'21: Proceedings International Workshop on OpenCL IWOCL 2021, Association for Computing Machinery (ACM) , 2021Conference paper (Refereed)
    Abstract [en]

    GROMACS is an open-source, high-performance molecular dynamics (MD) package primarily used for biomolecular simulations, accounting for 5% of HPC utilization worldwide. Due to the extreme computing needs of MD, significant efforts are invested in improving the performance and scalability of simulations. Target hardware ranges from supercomputers to laptops of individual researchers and volunteers of distributed computing projects such as Folding@Home. The code has been designed both for portability and performance by explicitly adapting algorithms to SIMD and data-parallel processors. A SIMD intrinsic abstraction layer provides high CPU performance. Explicit GPU acceleration has long used CUDA to target NVIDIA devices and OpenCL for AMD/Intel devices. In this talk, we discuss the experiences and challenges of adding support for the SYCL platform into the established GROMACS codebase and share experiences and considerations in porting and optimization. While OpenCL offers the benefits of using the same code to target different hardware, it suffers from several drawbacks that add significant development friction. Its separate-source model leads to code duplication and makes changes complicated. The need to use C99 for kernels, while the rest of the codebase uses C++17, exacerbates these issues. Another problem is that OpenCL, while supported by most GPU vendors, is never the main framework and thus is not getting the primary support or tuning efforts. SYCL alleviates many of these issues, employing a single-source model based on the modern C++ standard. In addition to being the primary platform for Intel GPUs, the possibility to target AMD and NVIDIA GPUs through other implementations (e.g., hipSYCL) might make it possible to reduce the number of separate GPU ports that have to be maintained. Some design differences from OpenCL, such as flow directed acyclic graphs (DAGs) instead of in-order queues, made it necessary to reconsider the GROMACS's task scheduling approach and architectural choices in the GPU backend. Additionally, supporting multiple GPU platforms presents a challenge of balancing performance (low-level and hardware-specific code) and maintainability (more generalization and code-reuse). We will discuss the limitations of the existing codebase and interoperability layers with regards to adding the new platform; the compute performance and latency comparisons; code quality considerations; and the issues we encountered with SYCL implementations tested. Finally, we will discuss our goals for the next release cycle for the SYCL backend and the overall architecture of GPU acceleration code in GROMACS.

  • 3.
    Alexeyenko, Andrey
    et al.
    KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lee, Woojoo
    Pernemalm, Maria
    Guegan, Justin
    Dessen, Philippe
    Lazar, Vladimir
    Lehtio, Janne
    Pawitan, Yudi
    Network enrichment analysis: extension of gene-set enrichment analysis to gene networks2012In: BMC Bioinformatics, E-ISSN 1471-2105, Vol. 13, p. 226-Article in journal (Refereed)
    Abstract [en]

    Background: Gene-set enrichment analyses (GEA or GSEA) are commonly used for biological characterization of an experimental gene-set. This is done by finding known functional categories, such as pathways or Gene Ontology terms, that are over-represented in the experimental set; the assessment is based on an overlap statistic. Rich biological information in terms of gene interaction network is now widely available, but this topological information is not used by GEA, so there is a need for methods that exploit this type of information in high-throughput data analysis. Results: We developed a method of network enrichment analysis (NEA) that extends the overlap statistic in GEA to network links between genes in the experimental set and those in the functional categories. For the crucial step in statistical inference, we developed a fast network randomization algorithm in order to obtain the distribution of any network statistic under the null hypothesis of no association between an experimental gene-set and a functional category. We illustrate the NEA method using gene and protein expression data from a lung cancer study. Conclusions: The results indicate that the NEA method is more powerful than the traditional GEA, primarily because the relationships between gene sets were more strongly captured by network connectivity rather than by simple overlaps.

  • 4.
    Alexeyenko, Andrey
    et al.
    KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Nystedt, Björn
    Vezzi, Francesco
    Sherwood, Ellen
    Ye, Rosa
    Knudsen, Bjarne
    Simonsen, Martin
    Turner, Benjamin
    de Jong, Pieter
    Wu, Cheng-Cang
    Lundeberg, Joakim
    KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Efficient de novo assembly of large and complex genomes by massively parallel sequencing of Fosmid pools2014In: BMC Genomics, E-ISSN 1471-2164, Vol. 15, no 1, p. 439-Article in journal (Refereed)
    Abstract [en]

    Background: Sampling genomes with Fosmid vectors and sequencing of pooled Fosmid libraries on the Illumina platform for massive parallel sequencing is a novel and promising approach to optimizing the trade-off between sequencing costs and assembly quality. Results: In order to sequence the genome of Norway spruce, which is of great size and complexity, we developed and applied a new technology based on the massive production, sequencing, and assembly of Fosmid pools (FP). The spruce chromosomes were sampled with similar to 40,000 bp Fosmid inserts to obtain around two-fold genome coverage, in parallel with traditional whole genome shotgun sequencing (WGS) of haploid and diploid genomes. Compared to the WGS results, the contiguity and quality of the FP assemblies were high, and they allowed us to fill WGS gaps resulting from repeats, low coverage, and allelic differences. The FP contig sets were further merged with WGS data using a novel software package GAM-NGS. Conclusions: By exploiting FP technology, the first published assembly of a conifer genome was sequenced entirely with massively parallel sequencing. Here we provide a comprehensive report on the different features of the approach and the optimization of the process. We have made public the input data (FASTQ format) for the set of pools used in this study: ftp://congenie.org/congenie/Nystedt_2013/Assembly/ProcessedData/FosmidPools/.(alternatively accessible via http://congenie.org/downloads).The software used for running the assembly process is available at http://research.scilifelab.se/andrej_alexeyenko/downloads/fpools/.

  • 5.
    Alexeyenko, Andrey
    et al.
    KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Schmitt, Thomas
    Tjärnberg, Andreas
    Stockholm University, Science for Life Laboratory.
    Guala, Dmitri
    Stockholm University, Science for Life Laboratory.
    Frings, Oliver
    Stockholm University, Science for Life Laboratory.
    Sonnhammer, Erik L. L.
    Stockholm University, Science for Life Laboratory.
    Comparative interactomics with Funcoup 2.02012In: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 40, no D1, p. D821-D828Article in journal (Refereed)
    Abstract [en]

    FunCoup (http://FunCoup.sbc.su.se) is a database that maintains and visualizes global gene/protein networks of functional coupling that have been constructed by Bayesian integration of diverse high-throughput data. FunCoup achieves high coverage by orthology-based integration of data sources from different model organisms and from different platforms. We here present release 2.0 in which the data sources have been updated and the methodology has been refined. It contains a new data type Genetic Interaction, and three new species: chicken, dog and zebra fish. As FunCoup extensively transfers functional coupling information between species, the new input datasets have considerably improved both coverage and quality of the networks. The number of high-confidence network links has increased dramatically. For instance, the human network has more than eight times as many links above confidence 0.5 as the previous release. FunCoup provides facilities for analysing the conservation of subnetworks in multiple species. We here explain how to do comparative interactomics on the FunCoup website.

  • 6. Brownstein, Catherine A.
    et al.
    Beggs, Alan H.
    Homer, Nils
    Merriman, Barry
    Yu, Timothy W.
    Flannery, Katherine C.
    DeChene, Elizabeth T.
    Towne, Meghan C.
    Savage, Sarah K.
    Price, Emily N.
    Holm, Ingrid A.
    Luquette, Lovelace J.
    Lyon, Elaine
    Majzoub, Joseph
    Neupert, Peter
    McCallie, David, Jr.
    Szolovits, Peter
    Willard, Huntington F.
    Mendelsohn, Nancy J.
    Temme, Renee
    Finkel, Richard S.
    Yum, Sabrina W.
    Medne, Livija
    Sunyaev, Shamil R.
    Adzhubey, Ivan
    Cassa, Christopher A.
    de Bakker, Paul I. W.
    Duzkale, Hatice
    Dworzynski, Piotr
    Fairbrother, William
    Francioli, Laurent
    Funke, Birgit H.
    Giovanni, Monica A.
    Handsaker, Robert E.
    Lage, Kasper
    Lebo, Matthew S.
    Lek, Monkol
    Leshchiner, Ignaty
    MacArthur, Daniel G.
    McLaughlin, Heather M.
    Murray, Michael F.
    Pers, Tune H.
    Polak, Paz P.
    Raychaudhuri, Soumya
    Rehm, Heidi L.
    Soemedi, Rachel
    Stitziel, Nathan O.
    Vestecka, Sara
    Supper, Jochen
    Gugenmus, Claudia
    Klocke, Bernward
    Hahn, Alexander
    Schubach, Max
    Menzel, Mortiz
    Biskup, Saskia
    Freisinger, Peter
    Deng, Mario
    Braun, Martin
    Perner, Sven
    Smith, Richard J. H.
    Andorf, Janeen L.
    Huang, Jian
    Ryckman, Kelli
    Sheffield, Val C.
    Stone, Edwin M.
    Bair, Thomas
    Black-Ziegelbein, E. Ann
    Braun, Terry A.
    Darbro, Benjamin
    DeLuca, Adam P.
    Kolbe, Diana L.
    Scheetz, Todd E.
    Shearer, Aiden E.
    Sompallae, Rama
    Wang, Kai
    Bassuk, Alexander G.
    Edens, Erik
    Mathews, Katherine
    Moore, Steven A.
    Shchelochkov, Oleg A.
    Trapane, Pamela
    Bossler, Aaron
    Campbell, Colleen A.
    Heusel, Jonathan W.
    Kwitek, Anne
    Maga, Tara
    Panzer, Karin
    Wassink, Thomas
    Van Daele, Douglas
    Azaiez, Hela
    Booth, Kevin
    Meyer, Nic
    Segal, Michael M.
    Williams, Marc S.
    Tromp, Gerard
    White, Peter
    Corsmeier, Donald
    Fitzgerald-Butt, Sara
    Herman, Gail
    Lamb-Thrush, Devon
    McBride, Kim L.
    Newsom, David
    Pierson, Christopher R.
    Rakowsky, Alexander T.
    Maver, Ales
    Lovrecic, Luca
    Palandacic, Anja
    Peterlin, Borut
    Torkamani, Ali
    Wedell, Anna
    Huss, Mikael
    Alexeyenko, Andrey
    KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab. Stockholm Bioinformatics Centre, Science for Life Laboratory, Solna, Sweden .
    Lindvall, Jessica M.
    Magnusson, Mans
    Nilsson, Daniel
    Stranneheim, Henrik
    Taylan, Fulya
    Gilissen, Christian
    Hoischen, Alexander
    van Bon, Bregje
    Yntema, Helger
    Nelen, Marcel
    Zhang, Weidong
    Sager, Jason
    Zhang, Lu
    Blair, Kathryn
    Kural, Deniz
    Cariaso, Michael
    Lennon, Greg G.
    Javed, Asif
    Agrawal, Saloni
    Ng, Pauline C.
    Sandhu, Komal S.
    Krishna, Shuba
    Veeramachaneni, Vamsi
    Isakov, Ofer
    Halperin, Eran
    Friedman, Eitan
    Shomron, Noam
    Glusman, Gustavo
    Roach, Jared C.
    Caballero, Juan
    Cox, Hannah C.
    Mauldin, Denise
    Ament, Seth A.
    Rowen, Lee
    Richards, Daniel R.
    San Lucas, F. Anthony
    Gonzalez-Garay, Manuel L.
    Caskey, C. Thomas
    Bai, Yu
    Huang, Ying
    Fang, Fang
    Zhang, Yan
    Wang, Zhengyuan
    Barrera, Jorge
    Garcia-Lobo, Juan M.
    Gonzalez-Lamuno, Domingo
    Llorca, Javier
    Rodriguez, Maria C.
    Varela, Ignacio
    Reese, Martin G.
    De la Vega, Francisco M.
    Kiruluta, Edward
    Cargill, Michele
    Hart, Reece K.
    Sorenson, Jon M.
    Lyon, Gholson J.
    Stevenson, David A.
    Bray, Bruce E.
    Moore, Barry M.
    Eilbeck, Karen
    Yandell, Mark
    Zhao, Hongyu
    Hou, Lin
    Chen, Xiaowei
    Yan, Xiting
    Chen, Mengjie
    Li, Cong
    Yang, Can
    Gunel, Murat
    Li, Peining
    Kong, Yong
    Alexander, Austin C.
    Albertyn, Zayed I.
    Boycott, Kym M.
    Bulman, Dennis E.
    Gordon, Paul M. K.
    Innes, A. Micheil
    Knoppers, Bartha M.
    Majewski, Jacek
    Marshall, Christian R.
    Parboosingh, Jillian S.
    Sawyer, Sarah L.
    Samuels, Mark E.
    Schwartzentruber, Jeremy
    Kohane, Isaac S.
    Margulies, David M.
    An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge2014In: Genome Biology, ISSN 1465-6906, E-ISSN 1474-760X, Vol. 15, no 3, p. R53-Article in journal (Refereed)
    Abstract [en]

    Background: There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. Results: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. Conclusions: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups.

  • 7. Frings, O.
    et al.
    Alexeyenko, Andrey
    KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab. Stockholm Bioinformatics Centre, Science for Life Laboratory, Solna, Sweden .
    Sonnhammer, E. L. L.
    MGclus: Network clustering employing shared neighbors2013In: Molecular BioSystems, ISSN 1742-206X, Vol. 9, no 7, p. 1670-1675Article in journal (Refereed)
    Abstract [en]

    Network analysis is an important tool for functional annotation of genes and proteins. A common approach to discern structure in a global network is to infer network clusters, or modules, and assume a functional coherence within each module, which may represent a complex or a pathway. It is however not trivial to define optimal modules. Although many methods have been proposed, it is unclear which methods perform best in general. It seems that most methods produce far from optimal results but in different ways. MGclus is a new algorithm designed to detect modules with a strongly interconnected neighborhood in large scale biological interaction networks. In our benchmarks we found MGclus to outperform other methods when applied to random graphs with varying degree of noise, and to perform equally or better when applied to biological protein interaction networks. MGclus is implemented in Java and utilizes the JGraphT graph library. It has an easy to use command-line interface and is available for download from http://sonnhammer.sbc.su.se/download/software/ MGclus/.

  • 8.
    Frings, Oliver
    et al.
    Stockholm Univ, Science for life laboratory.
    Mank, Judith E.
    Alexeyenko, Andrey
    KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Sonnhammer, Erik L. L.
    Network Analysis of Functional Genomics Data: Application to Avian Sex-Biased Gene Expression2012In: Scientific World Journal, E-ISSN 1537-744X, p. 130491-Article in journal (Refereed)
    Abstract [en]

    Gene expression analysis is often used to investigate the molecular and functional underpinnings of a phenotype. However, differential expression of individual genes is limited in that it does not consider how the genes interact with each other in networks. To address this shortcoming we propose a number of network-based analyses that give additional functional insights into the studied process. These were applied to a dataset of sex-specific gene expression in the chicken gonad and brain at different developmental stages. We first constructed a global chicken interaction network. Combining the network with the expression data showed that most sex-biased genes tend to have lower network connectivity, that is, act within local network environments, although some interesting exceptions were found. Genes of the same sex bias were generally more strongly connected with each other than expected. We further studied the fates of duplicated sex-biased genes and found that there is a significant trend to keep the same pattern of sex bias after duplication. We also identified sex-biased modules in the network, which reveal pathways or complexes involved in sex-specific processes. Altogether, this work integrates evolutionary genomics with systems biology in a novel way, offering new insights into the modular nature of sex-biased genes.

  • 9. McCormack, T.
    et al.
    Frings, O.
    Alexeyenko, Andrey
    KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Sonnhammer, E. L. L.
    Statistical Assessment of Crosstalk Enrichment between Gene Groups in Biological Networks2013In: PLOS ONE, E-ISSN 1932-6203, Vol. 8, no 1, p. e54945-Article in journal (Refereed)
    Abstract [en]

    Motivation: Analyzing groups of functionally coupled genes or proteins in the context of global interaction networks has become an important aspect of bioinformatic investigations. Assessing the statistical significance of crosstalk enrichment between or within groups of genes can be a valuable tool for functional annotation of experimental gene sets. Results: Here we present CrossTalkZ, a statistical method and software to assess the significance of crosstalk enrichment between pairs of gene or protein groups in large biological networks. We demonstrate that the standard z-score is generally an appropriate and unbiased statistic. We further evaluate the ability of four different methods to reliably recover crosstalk within known biological pathways. We conclude that the methods preserving the second-order topological network properties perform best. Finally, we show how CrossTalkZ can be used to annotate experimental gene sets using known pathway annotations and that its performance at this task is superior to gene enrichment analysis (GEA). Availability and Implementation: CrossTalkZ (available at http://sonnhammer.sbc.su.se/download/software/CrossTalkZ/) is implemented in C++, easy to use, fast, accepts various input file formats, and produces a number of statistics. These include z-score, p-value, false discovery rate, and a test of normality for the null distributions.

  • 10.
    Nystedt, Björn
    et al.
    Stockholm University.
    Vezzi, Francesco
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Alekseenko, Andrey
    KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Sahlin, Kristoffer
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hällman, Jimmie
    KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Käller, Max
    KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Rilakovic, Nemanja
    KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Arvestad, Lars
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lundeberg, Joakim
    KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    et, al,
    The Norway spruce genome sequence and conifer genome evolution2013In: Nature, ISSN 0028-0836, E-ISSN 1476-4687, Vol. 497, no 7451, p. 579-584Article in journal (Refereed)
    Abstract [en]

    Conifers have dominated forests for more than 200 million years and are of huge ecological and economic importance. Here we present the draft assembly of the 20-gigabase genome of Norway spruce (Picea abies), the first available for any gymnosperm. The number of well-supported genes (28,354) is similar to the >100 times smaller genome of Arabidopsis thaliana, and there is no evidence of a recent whole-genome duplication in the gymnosperm lineage. Instead, the large genome size seems to result from the slow and steady accumulation of a diverse set of long-terminal repeat transposable elements, possibly owing to the lack of an efficient elimination mechanism. Comparative sequencing of Pinus sylvestris, Abies sibirica, Juniperus communis, Taxus baccata and Gnetum gnemon reveals that the transposable element diversity is shared among extant conifers. Expression of 24-nucleotide small RNAs, previously implicated in transposable element silencing, is tissue-specific and much lower than in other plants. We further identify numerous long (>10,000 base pairs) introns, gene-like fragments, uncharacterized long non-coding RNAs and short RNAs. This opens up new genomic avenues for conifer forestry and breeding.

  • 11. Szatmári, T.
    et al.
    Mundt, F.
    Heidari-Hamedani, G.
    Zong, F.
    Ferolla, E.
    Alexeyenko, Andrey
    KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hjerpe, A.
    Dobra, K.
    Novel Genes and Pathways Modulated by Syndecan-1: Implications for the Proliferation and Cell-Cycle Regulation of Malignant Mesothelioma Cells2012In: PLOS ONE, E-ISSN 1932-6203, Vol. 7, no 10, p. e48091-Article in journal (Refereed)
    Abstract [en]

    Malignant pleural mesothelioma is a highly malignant tumor, originating from mesothelial cells of the serous cavities. In mesothelioma the expression of syndecan-1 correlates to epithelioid morphology and inhibition of growth and migration. Our previous data suggest a complex role of syndecan-1 in mesothelioma cell proliferation although the exact underlying molecular mechanisms are not completely elucidated. The aim of this study is therefore to disclose critical genes and pathways affected by syndecan-1 in mesothelioma; in order to better understand its importance for tumor cell growth and proliferation. We modulated the expression of syndecan-1 in a human mesothelioma cell line via both overexpression and silencing, and followed the transcriptomic responses with microarray analysis. To project the transcriptome analysis on the full-dimensional picture of cellular regulation, we applied pathway analysis using Ingenuity Pathway Analysis (IPA) and a novel method of network enrichment analysis (NEA) which elucidated signaling relations between differentially expressed genes and pathways acting via various molecular mechanisms. Syndecan-1 overexpression had profound effects on genes involved in regulation of cell growth, cell cycle progression, adhesion, migration and extracellular matrix organization. In particular, expression of several growth factors, interleukins, and enzymes of importance for heparan sulfate sulfation pattern, extracellular matrix proteins and proteoglycans were significantly altered. Syndecan-1 silencing had less powerful effect on the transcriptome compared to overexpression, which can be explained by the already low initial syndecan-1 level of these cells. Nevertheless, 14 genes showed response to both up- and downregulation of syndecan-1. The "cytokine - cytokine-receptor interaction", the TGF-β, EGF, VEGF and ERK/MAPK pathways were enriched in both experimental settings. Most strikingly, nearly all analyzed pathways related to cell cycle were enriched after syndecan-1 silencing and depleted after syndecan-1 overexpression. Syndecan-1 regulates proliferation in a highly complex way, although the exact contribution of the altered pathways necessitates further functional studies.

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