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  • 1.
    Fasterius, Erik
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Analysis of public RNA-sequencing data reveals biological consequences of genetic heterogeneity in cell line populations2018In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 8, article id 11226Article in journal (Refereed)
    Abstract [en]

    Meta-analysis of datasets available in public repositories are used to gather and summarise experiments performed across laboratories, as well as to explore consistency of scientific findings. As data quality and biological equivalency across samples may obscure such analyses and consequently their conclusions, we investigated the comparability of 85 public RNA-seq cell line datasets. Thousands of pairwise comparisons of single nucleotide variants in 139 samples revealed variable genetic heterogeneity of the eight cell line populations analysed as well as variable data quality. The H9 and HCT116 cell lines were found to be remarkably stable across laboratories (with median concordances of 99.2% and 98.5%, respectively), in contrast to the highly variable HeLa cells (89.3%). We show that the genetic heterogeneity encountered greatly affects gene expression between same-cell comparisons, highlighting the importance of interrogating the biological equivalency of samples when comparing experimental datasets. Both the number of differentially expressed genes and the expression levels negatively correlate with the genetic heterogeneity. Finally, we demonstrate how comparing genetically heterogeneous datasets affect gene expression analyses and that high dissimilarity between same-cell datasets alters the expression of more than 300 cancer-related genes, which are often the focus of studies using cell lines.

  • 2.
    Fasterius, Erik
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    seqCAT: a Bioconductor R-package for variant analysis of high throughput sequencing dataManuscript (preprint) (Other academic)
  • 3.
    Fasterius, Erik
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Uhlén, Mathias
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Al-Khalili Szigyarto, Cristina
    KTH, Superseded Departments (pre-2005), Biotechnology.
    Single cell RNA-seq variant analysis for exploration of inter- and intra-tumour genetic heterogeneityManuscript (preprint) (Other academic)
  • 4.
    Jahn, Michael
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology. KTH, Centres, Science for Life Laboratory, SciLifeLab. K.
    Vialas, Vital
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Karlsen, Jan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Maddalo, Gianluca
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Edfors, Fredrik
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Forsström, Björn
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Uhlén, Mathias
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Käll, Lukas
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hudson, Elton P.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Growth of Cyanobacteria Is Constrained by the Abundance of Light and Carbon Assimilation Proteins2018In: Cell reports, ISSN 2211-1247, E-ISSN 2211-1247, Vol. 25, no 2, p. 478-+Article in journal (Refereed)
    Abstract [en]

    Cyanobacteria must balance separate demands for energy generation, carbon assimilation, and biomass synthesis. We used shotgun proteomics to investigate proteome allocation strategies in the model cyanobacterium Synechocystis sp. PCC 6803 as it adapted to light and inorganic carbon (C-i) limitation. When partitioning the proteome into seven functional sectors, we find that sector sizes change linearly with growth rate. The sector encompassing ribosomes is significantly smaller than in E. coli, which may explain the lower maximum growth rate in Synechocystis. Limitation of light dramatically affects multiple proteome sectors, whereas the effect of C-i limitation is weak. Carbon assimilation proteins respond more strongly to changes in light intensity than to C-i. A coarse-grained cell economy model generally explains proteome trends. However, deviations from model predictions suggest that the large proteome sectors for carbon and light assimilation are not optimally utilized under some growth conditions and may constrain the proteome space available to ribosomes.

  • 5.
    Karlsen, Jan
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Asplund-Samuelsson, Johannes
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Thomas, Quentin
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH). KTH, Centres, Science for Life Laboratory, SciLifeLab. Univ Copenhagen, Copenhagen Plant Sci Ctr, Dept Plant & Environm Sci, Frederiksberg, Denmark..
    Jahn, Michael
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hudson, Elton Paul
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Ribosome Profiling of Synechocystis Reveals Altered Ribosome Allocation at Carbon Starvation2018In: MSYSTEMS, ISSN 2379-5077, Vol. 3, no 5, article id e00126-18Article in journal (Refereed)
    Abstract [en]

    Cyanobacteria experience both rapid and periodic fluctuations in light and inorganic carbon (C-i) and have evolved regulatory mechanisms to respond to these, including extensive posttranscriptional gene regulation. We report the first genome-wide ribosome profiling data set for cyanobacteria, where ribosome occupancy on mRNA is quantified with codon-level precision. We measured the transcriptome and translatome of Synechocystis during autotrophic growth before (high carbon [HC] condition) and 24 h after removing CO2 from the feedgas (low carbon [LC] condition). Ribosome occupancy patterns in the 5' untranslated region suggest that ribosomes can assemble there and slide to the Shine-Dalgarno site, where they pause. At LC, total translation was reduced by 80% and ribosome pausing was increased at stop and start codons and in untranslated regions, which may be a sequestration mechanism to inactivate ribosomes in response to rapid C-i depletion. Several stress response genes, such as thioredoxin M (sll1057), a putative endonuclease (slr0915), protease HtrA (slr1204), and heat shock protein HspA (sll1514) showed marked increases in translational efficiency at LC, indicating translational control in response to Ci depletion. Ribosome pause scores within open reading frames were mostly constant, though several ribosomal proteins had significantly altered pause score distributions at LC, which might indicate translational regulation of ribosome biosynthesis in response to Ci depletion. We show that ribosome profiling is a powerful tool to decipher dynamic gene regulation strategies in cyanobacteria. IMPORTANCE Ribosome profiling accesses the translational step of gene expression via deep sequencing of ribosome-protected mRNA footprints. Pairing of ribosome profiling and transcriptomics data provides a translational efficiency for each gene. Here, the translatome and transcriptome of the model cyanobacterium Synechocystis were compared under carbon-replete and carbon starvation conditions. The latter may be experienced when cyanobacteria are cultivated in poorly mixed bioreactors or engineered to be product-secreting cell factories. A small fraction of genes (<200), including stress response genes, showed changes in translational efficiency during carbon starvation, indicating condition-dependent translation-level regulation. We observed ribosome occupancy in untranslated regions, possibly due to an alternative translation initiation mechanism in Synechocystis. The higher proportion of ribosomes residing in untranslated regions during carbon starvation may be a mechanism to quickly inactivate superfluous ribosomes. This work provides the first ribosome profiling data for cyanobacteria and reveals new regulation strategies for coping with nutrient limitation.

  • 6. Karnevi, E.
    et al.
    Dror, L. B.
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Elebro, J.
    Heby, M.
    Olofsson, S. -E
    Nodin, B.
    Eberhard, J.
    Gallagher, W.
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science.
    Jirström, K.
    Translational study reveals a two-faced role of RBM3 in pancreatic cancer and suggests its potential value as a biomarker for improved patient stratification2018In: OncoTarget, ISSN 1949-2553, E-ISSN 1949-2553, Vol. 9, no 5, p. 6188-6200Article in journal (Refereed)
    Abstract [en]

    Periampullary adenocarcinoma, including pancreatic cancer, is a heterogeneous group of tumors with dismal prognosis, partially due to lack of reliable targetable and predictive biomarkers. RNA-binding motif protein 3 (RBM3) has previously been shown to be an independent prognostic and predictive biomarker in several types of cancer. Herein, we examined the prognostic value of RBM3 in periampullary adenocarcinoma, as well as the effects following RBM3 suppression in pancreatic cancer cells in vitro. RBM3 mRNA levels were examined in 176 pancreatic cancer patients from The Cancer Genome Atlas. Immunohistochemical expression of RBM3 was analyzed in tissue microarrays with primary tumors and paired lymph node metastases from 175 consecutive patients with resected periampullary adenocarcinoma. Pancreatic cancer cells were transfected with anti-RBM3 siRNA in vitro and the influence on cell viability following chemotherapy, transwell migration and invasion was assessed. The results demonstrated that high mRNA-levels of RBM3 were significantly associated with a reduced overall survival (p = 0.026). RBM3 protein expression was significantly higher in lymph node metastases than in primary tumors (p = 0.005). High RBM3 protein expression was an independent predictive factor for the effect of adjuvant chemotherapy and an independent negative prognostic factor in untreated patients (p for interaction = 0.003). After siRNA suppression of RBM3 in vitro, pancreatic cancer cells displayed reduced migration and invasion compared to control, as well as a significantly increased resistance to chemotherapy. In conclusion, the strong indication of a positive response predictive effect of RBM3 expression in pancreatic cancer may be highly relevant in the clinical setting and merits further validation.

  • 7. Kennedy, S
    et al.
    Fasterius, Erik
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Al-Khalili Szigyarto, Cristina
    KTH, Superseded Departments (pre-2005), Biotechnology.
    Kolch, W
    et al.,
    Adaptive rewiring of protein-protein interactions and signal flow in the EGFR signaling network by mutant RASManuscript (preprint) (Other academic)
  • 8. Piening, Brian D.
    et al.
    Zhou, Wenyu
    Contrepois, Kevin
    Rost, Hannes
    Urban, Gucci Jijuan Gu
    Mishra, Tejaswini
    Hanson, Blake M.
    Bautista, Eddy J.
    Leopold, Shana
    Yeh, Christine Y.
    Spakowicz, Daniel
    Banerjee, Imon
    Chen, Cynthia
    Kukurba, Kimberly
    Perelman, Dalia
    Craig, Colleen
    Colbert, Elizabeth
    Salins, Denis
    Rego, Shannon
    Lee, Sunjae
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Nano Biotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Zhang, Cheng
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Nano Biotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Wheeler, Jessica
    Sailani, M. Reza
    Liang, Liang
    Abbott, Charles
    Gerstein, Mark
    Mardinoglu, Adil
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology. KTH, Centres, Science for Life Laboratory, SciLifeLab. Chalmers University of Technology, Sweden.
    Smith, Ulf
    Rubin, Daniel L.
    Pitteri, Sharon
    Södergren, Erica
    McLaughlin, Tracey L.
    Weinstock, George M.
    Snyder, Michael P.
    Integrative Personal Omics Profiles during Periods of Weight Gain and Loss2018In: Cell Systems, ISSN 2405-4712, Vol. 6, no 2, p. 157-170.e8Article in journal (Refereed)
    Abstract [en]

    Advances in omics technologies now allow an unprecedented level of phenotyping for human diseases, including obesity, in which individual responses to excess weight are heterogeneous and unpredictable. To aid the development of better understanding of these phenotypes, we performed a controlled longitudinal weight perturbation study combining multiple omics strategies (genomics, transcriptomics, multiple proteomics assays, metabolomics, and microbiomics) during periods of weight gain and loss in humans. Results demonstrated that: (1) weight gain is associated with the activation of strong inflammatory and hypertrophic cardiomyopathy signatures in blood; (2) although weight loss reverses some changes, a number of signatures persist, indicative of long-term physiologic changes; (3) we observed omics signatures associated with insulin resistance that may serve as novel diagnostics; (4) specific biomolecules were highly individualized and stable in response to perturbations, potentially representing stable personalized markers. Most data are available open access and serve as a valuable resource for the community.

  • 9.
    Shabestary, Kiyan
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Anfelt, Josefin
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Ljungqvist, Emil
    KTH.
    Jahn, Michael
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Yao, Lun
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hudson, Elton P.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Targeted Repression of Essential Genes To Arrest Growth and Increase Carbon Partitioning and Biofuel Titers in Cyanobacteria2018In: ACS Synthetic Biology, E-ISSN 2161-5063, Vol. 7, no 7, article id diva2:1239079Article in journal (Refereed)
    Abstract [en]

    Photoautotrophic production of fuels and chemicals by cyanobacteria typically gives lower volumetric productivities and titers than heterotrophic production. Cyanobacteria cultures become light limited above an optimal cell density, so that this substrate is not supplied to all cells sufficiently. Here, we investigate genetic strategies for a two-phase cultivation, where biofuel-producing Synechocystis cultures are limited to an optimal cell density through inducible CRISPR interference (CRISPRi) repression of cell growth. Fixed CO2 is diverted to ethanol or n-butanol. Among the most successful strategies was partial repression of citrate synthase gltA. Strong repression (>90%) of gitA at low culture densities increased carbon partitioning to n-butanol 5-fold relative to a nonrepression strain, but sacrificed volumetric productivity due to severe growth restriction. CO2 fixation continued for at least 3 days after growth was arrested. By targeting sgRNAs to different regions of the gitA gene, we could modulate GItA expression and carbon partitioning between growth and product to increase both specific and volumetric productivity. These growth arrest strategies can be useful for improving performance of other photoautotrophic processes.

  • 10.
    Sjöstedt, Evelina
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Towards a deeper understanding of the human brain2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Identifying the proteome variation in different parts of the body provides fundamental molecular details, enabling further findings and mapping of tissue specific proteins. By combining quantitative transcriptomics with qualitative antibody based proteomics, the Human Protein Atlas (HPA) project aims to protein profile each human protein-coding gene. Genes with varying expression levels in the different tissue types are categorized as tissue elevated in one tissue compared to others, thus connecting genes to potential tissue specific functions. This thesis focuses on the most complex organ in the human body, the brain. With its billions of neurons specifically organized and interconnected, the ability of not only controlling the body but also responsible for higher cognitive functions, the brain is still not fully understood.

    In my search for brain important proteins, genes were classified at different stages based on expression levels. In Paper I and II the transcriptome of cerebral cortex was compared with peripheral organs to classify genes with elevated expression in the brain. Brain expression information was expanded by including external data (GTEx and FANTOM5) into the analysis, in Paper III. Thereafter, in Paper IV, the three datasets (HPA, GTEx and FANTOM5) were aligned and combined, enabling a consensus classification with an improved representation of the brain complexity. The most recent classification provided whole body gene expression profiles and out of the 19,670 protein-coding genes, 2,501 were expressed at elevated levels in the brain compared to the other tissue types. Twelve individual regions represented the brain as an organ, and were further analyzed and compared for regional classification of gene expression. One thousand genes showed regional variation in expression level, thus classified as regionally elevated within the brain. Interestingly, less than 500 of the genes classified as brain elevated on the whole body level, and were also regionally elevated in the brain. Many genes with regionally variable expression within the brain showed higher expression in a peripheral organ than in the brain when comparing whole body expression. Based on elevated expression in the brain or brain regions, more than 3,000 genes were suggested to be of high importance to the brain.

    In addition, this high-throughput approach to combine transcriptomics and protein profiles in tissues and cells further generated new knowledge in several different other aspects: better understanding of uncharacterized and “missing proteins” (Paper III), validation of an antibody improving classification of pituitary adenoma (Paper V) and in Paper VI the possibility to explore cancer specific expression in relation to clinical data and normal tissue expression.

    There are multiple diseases of the brain that are poorly understood on both a cellular and molecular level. While my work mainly focused on identifying and understanding the molecular organization of the normal brain, the ultimate goal of mapping and studying the normal expression baseline is to understand the molecular aspects of disease and identify ways to prevent, treat and cure diseases.

  • 11.
    Sjöstedt, Evelina
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Fagerberg, Linn
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Mitsios, Nicholas
    Karolinska Institutet.
    Adori, Csaba
    Karolinska Institutet.
    Oksvold, Per
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Limiszewka, Agnieszka
    Karolinska Insititutet.
    Kheder, Sania
    Karolinska Insitutiet.
    Norradin, Feria Hikmet
    Department of Immunology, Genetics and Pathology, Uppsala University.
    Lindskog, Cecilia
    Department of Immunology, Genetics and Pathology, Uppsala University.
    Pontén, Fredrik
    Department of immunology, genetics and pathology, Uppsala Univesity.
    Hökfelt, Tomas
    Karolinska Institutet.
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Mulder, Jan
    Karolinska institutet.
    The transcriptomic landscape of mammalian brainManuscript (preprint) (Other academic)
  • 12.
    Sjöstedt, Evelina
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Sivertsson, Åsa
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Norradin, Feria Hikmet
    Department of Immunology, Genetics and Pathology, Uppsala University.
    Katona, Borbala
    Department of Immunology, Genetics and Pathology, Uppsala University.
    Näsström, Åsa
    Rudbeck Laboratory, Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Sweden.
    Vuu, Jimmy
    Department of Immunology, Genetics and Pathology, Uppsala university.
    Kesti, Dennis
    Department of Immunology, Genetics and Pathology, Uppsala University.
    Oksvold, Per
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Edqvist, Per-Henrik
    Department of Immunology, Genetics and Pathology, Uppsala University.
    Olsson, Ingmarie
    Department of Immunology, Genetics and Pathology, Uppsala University.
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Lindskog, Cecilia
    Department of Immunology, Genetics and Pathology, Uppsala University.
    Integration of Transcriptomics and Antibody-Based Proteomics for Exploration of Proteins Expressed in Specialized Tissues2018In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907Article in journal (Refereed)
    Abstract [en]

    A large portion of human proteins are referred to as missing proteins, defined as protein-coding genes that lack experimental data on the protein level due to factors such as temporal expression, expression in tissues that are difficult to sample, or they actually do not encode functional proteins. In the present investigation, an integrated omics approach was used for identification and exploration of missing proteins. Transcriptomics data from three different sourcesthe Human Protein Atlas (HPA), the GTEx consortium, and the FANTOM5 consortiumwere used as a starting point to identify genes selectively expressed in specialized tissues. Complementing the analysis with profiling on more specific tissues based on immunohistochemistry allowed for further exploration of cell-type-specific expression patterns. More detailed tissue profiling was performed for >300 genes on complementing tissues. The analysis identified tissue-specific expression of nine proteins previously listed as missing proteins (POU4F1, FRMD1, ARHGEF33, GABRG1, KRTAP2-1, BHLHE22, SPRR4, AVPR1B, and DCLK3), as well as numerous proteins with evidence of existence on the protein level that previously lacked information on spatial resolution and cell-type- specific expression pattern. We here present a comprehensive strategy for identification of missing proteins by combining transcriptomics with antibody-based proteomics. The analyzed proteins provide interesting targets for organ-specific research in health and disease.

  • 13.
    Spitali, Pietro
    et al.
    Leiden Univ, Med Ctr, Dept Human Genet, Albinusdreef 2, NL-2333 ZA Leiden, Netherlands..
    Hettne, Kristina
    Leiden Univ, Med Ctr, Dept Human Genet, Albinusdreef 2, NL-2333 ZA Leiden, Netherlands..
    Tsonaka, Roula
    Leiden Univ, Med Ctr, Dept Med Stat & Bioinformat, Leiden, Netherlands..
    Charrout, Mohammed
    Leiden Univ, Med Ctr, Dept Human Genet, Albinusdreef 2, NL-2333 ZA Leiden, Netherlands..
    van den Bergen, Janneke
    Leiden Univ, Med Ctr, Dept Neurol, Leiden, Netherlands..
    Koeks, Zaida
    Leiden Univ, Med Ctr, Dept Neurol, Leiden, Netherlands..
    Kan, Hermien E.
    Leiden Univ, Med Ctr, Dept Radiol, CJ Gorter Ctr High Field MRI, Leiden, Netherlands..
    Hooijmans, Melissa T.
    Leiden Univ, Med Ctr, Dept Radiol, CJ Gorter Ctr High Field MRI, Leiden, Netherlands..
    Roos, Andreas
    Univ Newcastle, Inst Med Genet, MRC Ctr Neuromuscular Dis, John Walton Muscular Dystrophy Res Ctr, Newcastle Upon Tyne, Tyne & Wear, England..
    Straub, Volker
    Univ Newcastle, Inst Med Genet, MRC Ctr Neuromuscular Dis, John Walton Muscular Dystrophy Res Ctr, Newcastle Upon Tyne, Tyne & Wear, England..
    Muntoni, Francesco
    UCL, Great Ormond St Inst Child Hlth, Dubowitz Neuromuscular Ctr, London, England..
    Al-Khalili Szigyarto, Cristina
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Koel-Simmelink, Marleen J. A.
    Vrije Univ Amsterdam Med Ctr, Amsterdam Neurosci, Dept Clin Chem, Neurochem Lab & Biobank, Amsterdam, Netherlands..
    Teunissen, Charlotte E.
    Vrije Univ Amsterdam Med Ctr, Amsterdam Neurosci, Dept Clin Chem, Neurochem Lab & Biobank, Amsterdam, Netherlands..
    Lochmueller, Hanns
    Univ Newcastle, Inst Med Genet, MRC Ctr Neuromuscular Dis, John Walton Muscular Dystrophy Res Ctr, Newcastle Upon Tyne, Tyne & Wear, England..
    Niks, Erik H.
    Leiden Univ, Med Ctr, Dept Neurol, Leiden, Netherlands..
    Aartsma-Rus, Annemieke
    Leiden Univ, Med Ctr, Dept Human Genet, Albinusdreef 2, NL-2333 ZA Leiden, Netherlands.;Leiden Univ, Med Ctr, Dept Radiol, CJ Gorter Ctr High Field MRI, Leiden, Netherlands..
    Tracking disease progression non-invasively in Duchenne and Becker muscular dystrophies2018In: Journal of Cachexia, Sarcopenia and Muscle, ISSN 2190-5991, E-ISSN 2190-6009, Vol. 9, no 4, p. 715-726Article in journal (Refereed)
    Abstract [en]

    Background Analysis of muscle biopsies allowed to characterize the pathophysiological changes of Duchenne and Becker muscular dystrophies (D/BMD) leading to the clinical phenotype. Muscle tissue is often investigated during interventional dose finding studies to show in situ proof of concept and pharmacodynamics effect of the tested drug. Less invasive readouts are needed to objectively monitor patients' health status, muscle quality, and response to treatment. The identification of serum biomarkers correlating with clinical function and able to anticipate functional scales is particularly needed for personalized patient management and to support drug development programs. Methods A large-scale proteomic approach was used to identify serum biomarkers describing pathophysiological changes (e.g. loss of muscle mass), association with clinical function, prediction of disease milestones, association with in vivo(31)P magnetic resonance spectroscopy data and dystrophin levels in muscles. Cross-sectional comparisons were performed to compare DMD patients, BMD patients, and healthy controls. A group of DMD patients was followed up for a median of 4.4years to allow monitoring of individual disease trajectories based on yearly visits. Results Cross-sectional comparison enabled to identify 10 proteins discriminating between healthy controls, DMD and BMD patients. Several proteins (285) were able to separate DMD from healthy, while 121 proteins differentiated between BMD and DMD; only 13 proteins separated BMD and healthy individuals. The concentration of specific proteins in serum was significantly associated with patients' performance (e.g. BMP6 serum levels and elbow flexion) or dystrophin levels (e.g. TIMP2) in BMD patients. Analysis of longitudinal trajectories allowed to identify 427 proteins affected over time indicating loss of muscle mass, replacement of muscle by adipose tissue, and cardiac involvement. Over-representation analysis of longitudinal data allowed to highlight proteins that could be used as pharmacodynamic biomarkers for drugs currently in clinical development. Conclusions Serum proteomic analysis allowed to not only discriminate among DMD, BMD, and healthy subjects, but it enabled to detect significant associations with clinical function, dystrophin levels, and disease progression.

  • 14.
    The, Matthew
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Edfors, Fredrik
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Perez-Riverol, Yasset
    EBI, EMBL, Wellcome Trust Genome Campus, Cambridge CB10 1SD, England..
    Payne, Samuel H.
    Pacific Northwest Natl Lab, Biol Sci Div, Richland, WA 99352 USA..
    Hoopmann, Michael R.
    Inst Syst Biol, Seattle, WA 98109 USA..
    Palmblad, Magnus
    Leiden Univ, Med Ctr, Ctr Prote & Metabol, NL-2300 RC Leiden, Netherlands..
    Forsström, Björn
    KTH, School of Biotechnology (BIO), Centres, Albanova VinnExcellence Center for Protein Technology, ProNova. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Käll, Lukas
    KTH, Centres, SeRC - Swedish e-Science Research Centre. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    A Protein Standard That Emulates Homology for the Characterization of Protein Inference Algorithms2018In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 17, no 5, p. 1879-1886Article in journal (Refereed)
    Abstract [en]

    A natural way to benchmark the performance of an analytical experimental setup is to use samples of known measured analytes are peptides and not the actual proteins one of the inherent problems of interpreting data is that the composition and see to what degree one can correctly infer the content of such a sample from the data. For shotgun proteomics, themselves. As some proteins share proteolytic peptides, there might be more than one possible causative set of proteins resulting in a given set of peptides and there is a need for mechanisms that infer proteins from lists of detected peptides. A weakness of commercially available samples of known content is that they consist of proteins that are deliberately selected for producing tryptic peptides that are unique to a single protein. Unfortunately, such samples do not expose any complications in protein inference. Hence, for a realistic benchmark of protein inference procedures, there is a need for samples of known content where the present proteins share peptides with known absent proteins. Here, we present such a standard, that is based on E. coli expressed human protein fragments. To illustrate the application of this standard, we benchmark a set of different protein inference procedures on the data. We observe that inference procedures excluding shared peptides provide more accurate estimates of errors compared to methods that include information from shared peptides, while still giving a reasonable performance in terms of the number of identified proteins. We also demonstrate that using a sample of known protein content without proteins with shared tryptic peptides can give a false sense of accuracy for many protein inference methods.

  • 15.
    Thul, Peter
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Åkesson, Lovisa
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Mahdessian, Diana
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Bäckström, Anna
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Danielsson, Frida
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Gnann, Christian
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hjelmare, Martin
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Schutten, Rutger
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Stadler, Charlotte
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Sullivan, Devin
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Winsnes, Casper
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Galea, Gabriella
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Pepperkok, R.
    Uhlén, Mathias
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lundberg, Emma
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Exploring the Proteome of Multilocalizing Proteins2017In: Molecular Biology of the Cell, ISSN 1059-1524, E-ISSN 1939-4586, Vol. 28Article in journal (Other academic)
  • 16.
    Zhang, Cheng
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Bidkhori, Gholamreza
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Benfeitas, Rui
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lee, Sunjae
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Arif, Muhammad
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Uhlen, Mathias
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    ESS: A Tool for Genome-Scale Quantification of Essentiality Score for Reaction/Genes in Constraint-Based Modeling2018In: Frontiers in Physiology, ISSN 1664-042X, E-ISSN 1664-042X, Vol. 9, article id 1355Article in journal (Refereed)
    Abstract [en]

    Genome-scale metabolic models (GEMs) are comprehensive descriptions of cell metabolism and have been extensively used to understand biological responses in health and disease. One such application is in determining metabolic adaptation to the absence of a gene or reaction, i.e., essentiality analysis. However, current methods do not permit efficiently and accurately quantifying reaction/gene essentiality. Here, we present Essentiality Score Simulator (ESS), a tool for quantification of gene/reaction essentialities in GEMs. ESS quantifies and scores essentiality of each reaction/gene and their combinations based on the stoichiometric balance using synthetic lethal analysis. This method provides an option to weight metabolic models which currently rely mostly on topologic parameters, and is potentially useful to investigate the metabolic pathway differences between different organisms, cells, tissues, and/or diseases. We benchmarked the proposed method against multiple network topology parameters, and observed that our method displayed higher accuracy based on experimental evidence. In addition, we demonstrated its application in the wild-type and ldh knock-out E. coli core model, as well as two human cell lines, and revealed the changes of essentiality in metabolic pathways based on the reactions essentiality score. ESS is available without any limitation at https://sourceforge.net/projects/essentiality-score-simulator.

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