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  • 1.
    Akan, Pelin
    et al.
    KTH, Skolan för bioteknologi (BIO), Genteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Alexeyenko, Andrey
    KTH, Skolan för bioteknologi (BIO), Genteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Costea, Paul Igor
    KTH, Skolan för bioteknologi (BIO), Genteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Hedberg, Lilia
    KTH, Skolan för bioteknologi (BIO), Genteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Werne Solnestam, Beata
    KTH, Skolan för bioteknologi (BIO), Genteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Lundin, Sverker
    KTH, Skolan för bioteknologi (BIO), Genteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Hallman, Jimmie
    Lundberg, Emma
    KTH, Skolan för bioteknologi (BIO), Genteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Uhlén, Mathias
    KTH, Skolan för bioteknologi (BIO), Proteomik (stängd 20130101). KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Lundeberg, Joakim
    KTH, Skolan för bioteknologi (BIO), Genteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Comprehensive analysis of the genome transcriptome and proteome landscapes of three tumor cell lines2012Ingår i: Genome Medicine, ISSN 1756-994X, E-ISSN 1756-994X, Vol. 4, s. 86-Artikel i tidskrift (Refereegranskat)
    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.
    Lindstrand, Anna
    et al.
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden..
    Eisfeldt, Jesper
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden.;Karolinska Inst, Sci Life Lab, Stockholm, Sweden..
    Pettersson, Maria
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden..
    Carvalho, Claudia M. B.
    Baylor Coll Med, Dept Mol & Human Genet, Houston, TX 77030 USA..
    Kvarnung, Malin
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden..
    Grigelioniene, Giedre
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden..
    Anderlid, Britt-Marie
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden..
    Bjerin, Olof
    Karolinska Inst, Dept Womens & Childrens Hlth, Stockholm, Sweden..
    Gustavsson, Peter
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden..
    Hammarsjö, Anna
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden..
    Georgii-Hemming, Patrik
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden..
    Iwarsson, Erik
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden..
    Johansson-Soller, Maria
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden..
    Lagerstedt-Robinson, Kristina
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden..
    Lieden, Agne
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden..
    Magnusson, Mans
    Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Sci Life Lab, Stockholm, Sweden.;Karolinska Univ Hosp, Ctr Inherited Metab Dis, Stockholm, Sweden..
    Martin, Marcel
    Stockholm Univ, Dept Biochem & Biophys, Sci Life Lab, Natl Bioinformat Infrastruct Sweden, Stockholm, Sweden..
    Malmgren, Helena
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden..
    Nordenskjöld, Magnus
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden..
    Norling, Ameli
    Karolinska Inst, Dept Womens & Childrens Hlth, Stockholm, Sweden..
    Sahlin, Ellika
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden..
    Stranneheim, Henrik
    Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden.;Karolinska Univ Hosp, Ctr Inherited Metab Dis, Stockholm, Sweden..
    Tham, Emma
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden..
    Wincent, Josephine
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden..
    Ygberg, Sofia
    Karolinska Univ Hosp, Ctr Inherited Metab Dis, Stockholm, Sweden..
    Wedell, Anna
    Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Univ Hosp, Ctr Inherited Metab Dis, Stockholm, Sweden..
    Wirta, Valtteri
    KTH, Centra, Science for Life Laboratory, SciLifeLab. KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Genteknologi.
    Nordgren, Ann
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden..
    Lundin, Johanna
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden.;Baylor Coll Med, Dept Mol & Human Genet, Houston, TX 77030 USA..
    Nilsson, Daniel
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden.;Karolinska Inst, Sci Life Lab, Stockholm, Sweden..
    From cytogenetics to cytogenomics: whole-genome sequencing as a first-line test comprehensively captures the diverse spectrum of disease-causing genetic variation underlying intellectual disability2019Ingår i: Genome Medicine, ISSN 1756-994X, E-ISSN 1756-994X, Vol. 11, nr 1, artikel-id 68Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    BackgroundSince different types of genetic variants, from single nucleotide variants (SNVs) to large chromosomal rearrangements, underlie intellectual disability, we evaluated the use of whole-genome sequencing (WGS) rather than chromosomal microarray analysis (CMA) as a first-line genetic diagnostic test.MethodsWe analyzed three cohorts with short-read WGS: (i) a retrospective cohort with validated copy number variants (CNVs) (cohort 1, n=68), (ii) individuals referred for monogenic multi-gene panels (cohort 2, n=156), and (iii) 100 prospective, consecutive cases referred to our center for CMA (cohort 3). Bioinformatic tools developed include FindSV, SVDB, Rhocall, Rhoviz, and vcf2cytosure.ResultsFirst, we validated our structural variant (SV)-calling pipeline on cohort 1, consisting of three trisomies and 79 deletions and duplications with a median size of 850kb (min 500bp, max 155Mb). All variants were detected. Second, we utilized the same pipeline in cohort 2 and analyzed with monogenic WGS panels, increasing the diagnostic yield to 8%. Next, cohort 3 was analyzed by both CMA and WGS. The WGS data was processed for large (>10kb) SVs genome-wide and for exonic SVs and SNVs in a panel of 887 genes linked to intellectual disability as well as genes matched to patient-specific Human Phenotype Ontology (HPO) phenotypes. This yielded a total of 25 pathogenic variants (SNVs or SVs), of which 12 were detected by CMA as well. We also applied short tandem repeat (STR) expansion detection and discovered one pathologic expansion in ATXN7. Finally, a case of Prader-Willi syndrome with uniparental disomy (UPD) was validated in the WGS data.Important positional information was obtained in all cohorts. Remarkably, 7% of the analyzed cases harbored complex structural variants, as exemplified by a ring chromosome and two duplications found to be an insertional translocation and part of a cryptic unbalanced translocation, respectively.ConclusionThe overall diagnostic rate of 27% was more than doubled compared to clinical microarray (12%). Using WGS, we detected a wide range of SVs with high accuracy. Since the WGS data also allowed for analysis of SNVs, UPD, and STRs, it represents a powerful comprehensive genetic test in a clinical diagnostic laboratory setting.

  • 3. Varemo, Leif
    et al.
    Henriksen, Tora Ida
    Scheele, Camilla
    Broholm, Christa
    Pedersen, Maria
    Uhlen, Mathias
    Pedersen, Bente Klarlund
    Nielsen, Jens
    KTH, Skolan för bioteknologi (BIO), Genteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Type 2 diabetes and obesity induce similar transcriptional reprogramming in human myocytes2017Ingår i: Genome Medicine, ISSN 1756-994X, E-ISSN 1756-994X, Vol. 9, artikel-id 47Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Background: Skeletal muscle is one of the primary tissues involved in the development of type 2 diabetes (T2D). The close association between obesity and T2D makes it difficult to isolate specific effects attributed to the disease alone. Therefore, here we set out to identify and characterize intrinsic properties of myocytes, associated independently with T2D or obesity. Methods: We generated and analyzed RNA-seq data from primary differentiated myotubes from 24 human subjects, using a factorial design (healthy/T2D and non-obese/obese), to determine the influence of each specific factor on genome-wide transcription. This setup enabled us to identify intrinsic properties, originating from muscle precursor cells and retained in the corresponding myocytes. Bioinformatic and statistical methods, including differential expression analysis, gene-set analysis, and metabolic network analysis, were used to characterize the different myocytes. Results: We found that the transcriptional program associated with obesity alone was strikingly similar to that induced specifically by T2D. We identified a candidate epigenetic mechanism, H3K27me3 histone methylation, mediating these transcriptional signatures. T2D and obesity were independently associated with dysregulated myogenesis, down-regulated muscle function, and up-regulation of inflammation and extracellular matrix components. Metabolic network analysis identified that in T2D but not obesity a specific metabolite subnetwork involved in sphingolipid metabolism was transcriptionally regulated. Conclusions: Our findings identify inherent characteristics in myocytes, as a memory of the in vivo phenotype, without the influence from a diabetic or obese extracellular environment, highlighting their importance in the development of T2D.

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