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  • 1. Azimi, A.
    et al.
    Caramuta, S.
    Seashore-Ludlow, B.
    Boström, J.
    Robinson, J. L.
    Edfors, Fredrik
    KTH, Skolan för bioteknologi (BIO). KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Tuominen, R.
    Kemper, K.
    Krijgsman, O.
    Peeper, D. S.
    Nielsen, J.
    Hansson, J.
    Egyhazi Brage, S.
    Altun, M.
    Uhlén, Mathias
    KTH, Centra, Science for Life Laboratory, SciLifeLab. KTH, Skolan för bioteknologi (BIO).
    Maddalo, G.
    Targeting CDK2 overcomes melanoma resistance against BRAF and Hsp90 inhibitors2018Ingår i: Molecular Systems Biology, ISSN 1744-4292, E-ISSN 1744-4292, Vol. 14, nr 3, artikel-id e7858Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Novel therapies are undergoing clinical trials, for example, the Hsp90 inhibitor, XL888, in combination with BRAF inhibitors for the treatment of therapy-resistant melanomas. Unfortunately, our data show that this combination elicits a heterogeneous response in a panel of melanoma cell lines including PDX-derived models. We sought to understand the mechanisms underlying the differential responses and suggest a patient stratification strategy. Thermal proteome profiling (TPP) identified the protein targets of XL888 in a pair of sensitive and unresponsive cell lines. Unbiased proteomics and phosphoproteomics analyses identified CDK2 as a driver of resistance to both BRAF and Hsp90 inhibitors and its expression is regulated by the transcription factor MITF upon XL888 treatment. The CDK2 inhibitor, dinaciclib, attenuated resistance to both classes of inhibitors and combinations thereof. Notably, we found that MITF expression correlates with CDK2 upregulation in patients; thus, dinaciclib would warrant consideration for treatment of patients unresponsive to BRAF-MEK and/or Hsp90 inhibitors and/or harboring MITF amplification/overexpression. 

  • 2. Buchser, William J.
    et al.
    Slepak, Tatiana I.
    Gutierrez Arenas, Omar
    University of Miami.
    Bixby, John L.
    Lemmon, Vance P.
    Kinase/phosphatase overexpression reveals pathways regulating hippocampal neuron morphology2010Ingår i: Molecular Systems Biology, ISSN 1744-4292, E-ISSN 1744-4292, Vol. 6, s. 391-Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Development and regeneration of the nervous system requires the precise formation of axons and dendrites. Kinases and phosphatases are pervasive regulators of cellular function and have been implicated in controlling axodendritic development and regeneration. We undertook a gain-of-function analysis to determine the functions of kinases and phosphatases in the regulation of neuron morphology. Over 300 kinases and 124 esterases and phosphatases were studied by high-content analysis of rat hippocampal neurons. Proteins previously implicated in neurite growth, such as ERK1, GSK3, EphA8, FGFR, PI3K, PKC, p38, and PP1a, were confirmed to have effects in our functional assays. We also identified novel positive and negative neurite growth regulators. These include neuronal-developmentally regulated kinases such as the activin receptor, interferon regulatory factor 6 (IRF6) and neural leucine-rich repeat 1 (LRRN1). The protein kinase N2 (PKN2) and choline kinase alpha (CHKA) kinases, and the phosphatases PPEF2 and SMPD1, have little or no established functions in neuronal function, but were sufficient to promote neurite growth. In addition, pathway analysis revealed that members of signaling pathways involved in cancer progression and axis formation enhanced neurite outgrowth, whereas cytokine-related pathways significantly inhibited neurite formation.

  • 3.
    Edfors, Fredrik
    et al.
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Danielsson, Frida
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Hallström, Björn
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Käll, Lukas
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Lundberg, Emma
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Ponten, Fredrik
    Department of Immunology, Genetics and Pathology, Rudbeck Laboratory, Uppsala University, SE-751 85 Uppsala, Sweden.
    Forsström, Björn
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Uhlén, Mathias
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab. Technical University of Denmark, Denmark.
    Gene specific correlation of RNA and protein levels in human cells and tissues2016Ingår i: Molecular Systems Biology, ISSN 1744-4292, E-ISSN 1744-4292Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    An important issue for molecular biology is to establish if transcript levels of a given gene can be used as proxies for the corresponding protein levels. Here, we have developed a targeted proteomics approach for a set of human non-secreted proteins based on Parallel Reaction Monitoring to measure, at steady-state conditions, absolute protein copy numbers across human tissues and cell lines and compared these levels with the corresponding mRNA levels using transcriptomics. The study shows that the transcript and protein levels do not correlate well unless a gene-specific RNA-to-protein (RTP) conversion factor independent of the tissue-type is introduced, thus significantly enhancing the predictability of protein copy numbers from RNA levels. The results show that the RTP-ratio varies significantly with a few hundred copies per mRNA molecule for some genes to several hundred thousands protein copies per mRNA molecule for others. In conclusion, our data suggests that transcriptome analysis can be used as a tool to predict the protein copy numbers per cell, thus forming an attractive link between the field of genomics and proteomics. 

  • 4.
    Edfors, Fredrik
    et al.
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Danielsson, Frida
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Hallström, Björn M.
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Käll, Lukas
    KTH, Skolan för bioteknologi (BIO), Genteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Lundberg, Emma
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Ponten, Fredrik
    Forsström, Björn
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Uhlén, Mathias
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab. Technical University of Denmark, Denmark.
    Gene-specific correlation of RNA and protein levels in human cells and tissues2016Ingår i: Molecular Systems Biology, ISSN 1744-4292, E-ISSN 1744-4292, Vol. 12, nr 10, artikel-id 883Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    An important issue for molecular biology is to establish whether transcript levels of a given gene can be used as proxies for the corresponding protein levels. Here, we have developed a targeted proteomics approach for a set of human non-secreted proteins based on parallel reaction monitoring to measure, at steady-state conditions, absolute protein copy numbers across human tissues and cell lines and compared these levels with the corresponding mRNA levels using transcriptomics. The study shows that the transcript and protein levels do not correlate well unless a gene-specific RNA-to-protein (RTP) conversion factor independent of the tissue type is introduced, thus significantly enhancing the predictability of protein copy numbers from RNA levels. The results show that the RTP ratio varies significantly with a few hundred copies per mRNA molecule for some genes to several hundred thousands of protein copies per mRNA molecule for others. In conclusion, our data suggest that transcriptome analysis can be used as a tool to predict the protein copy numbers per cell, thus forming an attractive link between the field of genomics and proteomics.

  • 5.
    Eraslan, Basak
    et al.
    Tech Univ Munich, Dept Informat, Computat Biol, Munich, Germany.;Ludwig Maximilians Univ Munchen, Grad Sch Quantitat Biosci QBM, Munich, Germany..
    Wang, Dongxue
    Tech Univ Munich, Chair Prote & Bioanalyt, Freising Weihenstephan, Germany..
    Gusic, Mirjana
    Tech Univ Munich, Inst Human Genet, Munich, Germany.;Helmholtz Zentrum Munchen, Inst Human Genet, Neuherberg, Germany..
    Prokisch, Holger
    Tech Univ Munich, Inst Human Genet, Munich, Germany.;Helmholtz Zentrum Munchen, Inst Human Genet, Neuherberg, Germany..
    Hallström, Björn M.
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Uhlén, Mathias
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Asplund, Anna
    Uppsala Univ, Dept Immunol Genet & Pathol, Sci Life Lab, Uppsala, Sweden..
    Ponten, Frederik
    Uppsala Univ, Dept Immunol Genet & Pathol, Sci Life Lab, Uppsala, Sweden..
    Wieland, Thomas
    Tech Univ Munich, Chair Prote & Bioanalyt, Freising Weihenstephan, Germany..
    Hopf, Thomas
    Tech Univ Munich, Chair Prote & Bioanalyt, Freising Weihenstephan, Germany..
    Hahne, Hannes
    OmicScouts GmbH, Freising Weihenstephan, Germany..
    Kuster, Bernhard
    Tech Univ Munich, Chair Prote & Bioanalyt, Freising Weihenstephan, Germany.;CIPSM, Munich, Germany..
    Gagneur, Julien
    Tech Univ Munich, Dept Informat, Computat Biol, Munich, Germany..
    Quantification and discovery of sequence determinants of protein-per-mRNA amount in 29 human tissues2019Ingår i: Molecular Systems Biology, ISSN 1744-4292, E-ISSN 1744-4292, Vol. 15, nr 2, artikel-id e8513Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Despite their importance in determining protein abundance, a comprehensive catalogue of sequence features controlling protein-to-mRNA (PTR) ratios and a quantification of their effects are still lacking. Here, we quantified PTR ratios for 11,575 proteins across 29 human tissues using matched transcriptomes and proteomes. We estimated by regression the contribution of known sequence determinants of protein synthesis and degradation in addition to 45 mRNA and 3 protein sequence motifs that we found by association testing. While PTR ratios span more than 2 orders of magnitude, our integrative model predicts PTR ratios at a median precision of 3.2-fold. A reporter assay provided functional support for two novel UTR motifs, and an immobilized mRNA affinity competition-binding assay identified motif-specific bound proteins for one motif. Moreover, our integrative model led to a new metric of codon optimality that captures the effects of codon frequency on protein synthesis and degradation. Altogether, this study shows that a large fraction of PTR ratio variation in human tissues can be predicted from sequence, and it identifies many new candidate post-transcriptional regulatory elements.

  • 6. Jornsten, Rebecka
    et al.
    Abenius, Tobias
    Kling, Teresia
    Schmidt, Linnea
    Johansson, Erik
    Nordling, Torbjorn E. M.
    KTH, Skolan för elektro- och systemteknik (EES), Reglerteknik.
    Nordlander, Bodil
    Sander, Chris
    Gennemark, Peter
    Funa, Keiko
    Nilsson, Bjorn
    Lindahl, Linda
    Nelander, Sven
    Network modeling of the transcriptional effects of copy number aberrations in glioblastoma2011Ingår i: Molecular Systems Biology, ISSN 1744-4292, E-ISSN 1744-4292, Vol. 7, s. 486-Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    DNA copy number aberrations (CNAs) are a hallmark of cancer genomes. However, little is known about how such changes affect global gene expression. We develop a modeling framework, EPoC (Endogenous Perturbation analysis of Cancer), to (1) detect disease-driving CNAs and their effect on target mRNA expression, and to (2) stratify cancer patients into long-and short-term survivors. Our method constructs causal network models of gene expression by combining genome-wide DNA-and RNA-level data. Prognostic scores are obtained from a singular value decomposition of the networks. By applying EPoC to glioblastoma data from The Cancer Genome Atlas consortium, we demonstrate that the resulting network models contain known disease-relevant hub genes, reveal interesting candidate hubs, and uncover predictors of patient survival. Targeted validations in four glioblastoma cell lines support selected predictions, and implicate the p53-interacting protein Necdin in suppressing glioblastoma cell growth. We conclude that large-scale network modeling of the effects of CNAs on gene expression may provide insights into the biology of human cancer. Free software in MATLAB and R is provided.

  • 7.
    Lee, Sunjae
    et al.
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Zhang, Cheng
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Liu, Zhengtao
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Klevstig, Martina
    Mukhopadhyay, Bani
    Bergentall, Mattias
    Cinar, Resat
    Ståhlman, Marcus
    Sikanic, Natasa
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Proteinvetenskap.
    Park, Joshua K.
    Deshmukh, Sumit
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Proteinvetenskap.
    Harzandi, Azadeh M.
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Kuijpers, Tim
    KTH.
    Grotli, Morten
    Elsässer, Simon J.
    Piening, Brian D.
    Snyder, Michael
    Smith, Ulf
    Nielsen, Jens
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Bäckhed, Fredrik
    Kunos, George
    Uhlén, Mathias
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Boren, Jan
    Mardinoglu, Adil
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Network analyses identify liver-specific targets for treating liver diseases2017Ingår i: Molecular Systems Biology, ISSN 1744-4292, E-ISSN 1744-4292Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We performed integrative network analyses to identify targets that can be used for effectively treating liver diseases with minimal side effects. We first generated co-expression networks (CNs) for 46 human tissues and liver cancer to explore the functional relationships between genes and examined the overlap between functional and physical interactions. Since increased de novo lipogenesis is a characteristic of nonalcoholic fatty liver disease (NAFLD) and hepatocellular carcinoma (HCC), we investigated the liver-specific genes co-expressed with fatty acid synthase (FASN). CN analyses predicted that inhibition of these liver-specific genes decreases FASN expression. Experiments in human cancer cell lines, mouse liver samples, and primary human hepatocytes validated our predictions by demonstrating functional relationships between these liver genes, and showing that their inhibition decreases cell growth and liver fat content. In conclusion, we identified liver-specific genes linked to NAFLD pathogenesis, such as pyruvate kinase liver and red blood cell (PKLR), or to HCC pathogenesis, such as PKLR, patatin-like phospholipase domain containing 3 (PNPLA3), and proprotein convertase subtilisin/kexin type 9 (PCSK9), all of which are potential targets for drug development.

  • 8.
    Lundberg, Emma
    et al.
    KTH, Centra, Science for Life Laboratory, SciLifeLab. KTH, Skolan för bioteknologi (BIO), Proteomik (stängd 20130101).
    Fagerberg, Linn
    KTH, Skolan för bioteknologi (BIO), Proteomik (stängd 20130101).
    Klevebring, Daniel
    KTH, Skolan för bioteknologi (BIO), Genteknologi.
    Matic, Ivan
    Geiger, Tamar
    Cox, Juergen
    Älgenäs, Cajsa
    KTH, Skolan för bioteknologi (BIO), Proteomik (stängd 20130101).
    Lundeberg, Joakim
    KTH, Centra, Science for Life Laboratory, SciLifeLab. KTH, Skolan för bioteknologi (BIO), Genteknologi.
    Mann, Matthias
    Uhlén, Mathias
    KTH, Centra, Science for Life Laboratory, SciLifeLab. KTH, Skolan för bioteknologi (BIO), Proteomik (stängd 20130101).
    Defining the transcriptome and proteome in three functionally different human cell lines2010Ingår i: Molecular Systems Biology, ISSN 1744-4292, E-ISSN 1744-4292, Vol. 6, s. 450-Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    An essential question in human biology is how cells and tissues differ in gene and protein expression and how these differences delineate specific biological function. Here, we have performed a global analysis of both mRNA and protein levels based on sequence-based transcriptome analysis (RNA-seq), SILAC-based mass spectrometry analysis and antibody-based confocal microscopy. The study was performed in three functionally different human cell lines and based on the global analysis, we estimated the fractions of mRNA and protein that are cell specific or expressed at similar/different levels in the cell lines. A highly ubiquitous RNA expression was found with > 60% of the gene products detected in all cells. The changes of mRNA and protein levels in the cell lines using SILAC and RNA ratios show high correlations, even though the genome-wide dynamic range is substantially higher for the proteins as compared with the transcripts. Large general differences in abundance for proteins from various functional classes are observed and, in general, the cell-type specific proteins are low abundant and highly enriched for cell-surface proteins. Thus, this study shows a path to characterize the transcriptome and proteome in human cells from different origins.

  • 9.
    Mardinoglu, Adil
    et al.
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Bjornson, Elias
    Zhang, Cheng
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Klevstig, Martina
    Soderlund, Sanni
    Stahlman, Marcus
    Adiels, Martin
    Hakkarainen, Antti
    Lundbom, Nina
    Kilicarslan, Murat
    Hallstrom, Bjorn M.
    Lundbom, Jesper
    Verges, Bruno
    Barrett, Peter Hugh R.
    Watts, Gerald F.
    Serlie, Mireille J.
    Nielsen, Jens
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Uhlén, Mathias
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Smith, Ulf
    Marschall, Hanns-Ulrich
    Taskinen, Marja-Riitta
    Boren, Jan
    Personal model-assisted identification of NAD(+) and glutathione metabolism as intervention target in NAFLD2017Ingår i: Molecular Systems Biology, ISSN 1744-4292, E-ISSN 1744-4292, Vol. 13, nr 3, artikel-id 916Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    To elucidate the molecular mechanisms underlying non-alcoholic fatty liver disease (NAFLD), we recruited 86 subjects with varying degrees of hepatic steatosis (HS). We obtained experimental data on lipoprotein fluxes and used these individual measurements as personalized constraints of a hepatocyte genome-scale metabolic model to investigate metabolic differences in liver, taking into account its interactions with other tissues. Our systems level analysis predicted an altered demand for NAD(+) and glutathione (GSH) in subjects with high HS. Our analysis and metabolomic measurements showed that plasma levels of glycine, serine, and associated metabolites are negatively correlated with HS, suggesting that these GSH metabolism precursors might be limiting. Quantification of the hepatic expression levels of the associated enzymes further pointed to altered de novo GSH synthesis. To assess the effect of GSH and NAD(+) repletion on the development of NAFLD, we added precursors for GSH and NAD(+) biosynthesis to the Western diet and demonstrated that supplementation prevents HS in mice. In a proof-of-concept human study, we found improved liver function and decreased HS after supplementation with serine (a precursor to glycine) and hereby propose a strategy for NAFLD treatment.

  • 10.
    Mardinoglu, Adil
    et al.
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab. Chalmers University of Technology, Sweden.
    Shoaie, Saeed
    Bergentall, Mattias
    Ghaffari, Pouyan
    Zhang, Cheng
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Larsson, Erik
    Backhed, Fredrik
    Nielsen, Jens
    KTH, Skolan för bioteknologi (BIO), Genteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab. Chalmers University of Technology, Sweden.
    The gut microbiota modulates host amino acid and glutathione metabolism in mice2015Ingår i: Molecular Systems Biology, ISSN 1744-4292, E-ISSN 1744-4292, Vol. 11, nr 10, artikel-id 834Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The gut microbiota has been proposed as an environmental factor that promotes the progression of metabolic diseases. Here, we investigated how the gut microbiota modulates the global metabolic differences in duodenum, jejunum, ileum, colon, liver, and two white adipose tissue depots obtained from conventionally raised (CONV-R) and germ-free (GF) mice using gene expression data and tissue-specific genome-scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue-specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV-R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism. To validate our predictions, we measured the level of AAs and N-acetylated AAs in the hepatic portal vein of CONV-R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV-R and GF mice accounting for the content of the diet and relative gene expression differences. Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice.

  • 11. Mardinoglu, Adil
    et al.
    Ågren, Rasmus
    Kampf, Caroline
    Asplund, Anna
    Nookaew, Intawat
    Jacobson, Peter
    Walley, Andrew J.
    Froguel, Philippe
    Carlsson, Lena M.
    Uhlén, Mathias
    KTH, Skolan för bioteknologi (BIO), Proteomik.
    Nielsen, Jens
    Integration of clinical data with a genome-scale metabolic model of the human adipocyte2013Ingår i: Molecular Systems Biology, ISSN 1744-4292, E-ISSN 1744-4292, Vol. 9, s. 649-Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We evaluated the presence/absence of proteins encoded by 14 077 genes in adipocytes obtained from different tissue samples using immunohistochemistry. By combining this with previously published adipocyte-specific proteome data, we identified proteins associated with 7340 genes in human adipocytes. This information was used to reconstruct a comprehensive and functional genome-scale metabolic model of adipocyte metabolism. The resulting metabolic model, iAdipocytes1809, enables mechanistic insights into adipocyte metabolism on a genome-wide level, and can serve as a scaffold for integration of omics data to understand the genotype-phenotype relationship in obese subjects. By integrating human transcriptome and fluxome data, we found an increase in the metabolic activity around androsterone, ganglioside GM2 and degradation products of heparan sulfate and keratan sulfate, and a decrease in mitochondrial metabolic activities in obese subjects compared with lean subjects. Our study hereby shows a path to identify new therapeutic targets for treating obesity through combination of high throughput patient data and metabolic modeling.

  • 12. Ponten, Fredrik
    et al.
    Gry, Marcus
    KTH, Skolan för bioteknologi (BIO), Proteomik.
    Fagerberg, Linn
    KTH, Skolan för bioteknologi (BIO), Proteomik.
    Lundberg, Emma
    KTH, Skolan för bioteknologi (BIO), Proteomik.
    Asplund, Anna
    Berglund, Lisa
    KTH, Skolan för bioteknologi (BIO), Proteomik.
    Oksvold, Per
    KTH, Skolan för bioteknologi (BIO), Proteomik.
    Björling, Erik
    KTH, Skolan för bioteknologi (BIO), Proteomik.
    Hober, Sophia
    KTH, Skolan för bioteknologi (BIO), Proteomik.
    Kampf, Caroline
    Navani, Sanjay
    Nilsson, Peter
    KTH, Skolan för bioteknologi (BIO), Proteomik.
    Ottosson, Jenny
    KTH, Skolan för bioteknologi (BIO), Proteomik.
    Persson, Anja
    KTH, Skolan för bioteknologi (BIO), Proteomik.
    Wernérus, Henrik
    KTH, Skolan för bioteknologi (BIO), Proteomik.
    Wester, Kenneth
    Uhlén, Mathias
    KTH, Skolan för bioteknologi (BIO), Proteomik.
    A global view of protein expression in human cells, tissues, and organs2009Ingår i: Molecular Systems Biology, ISSN 1744-4292, E-ISSN 1744-4292, Vol. 5Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Defining the protein profiles of tissues and organs is critical to understanding the unique characteristics of the various cell types in the human body. In this study, we report on an anatomically comprehensive analysis of 4842 protein profiles in 48 human tissues and 45 human cell lines. A detailed analysis of over 2 million manually annotated, high-resolution, immunohistochemistry- based images showed a high fraction (>65%) of expressed proteins in most cells and tissues, with very few proteins (<2%) detected in any single cell type. Similarly, confocal microscopy in three human cell lines detected expression of more than 70% of the analyzed proteins. Despite this ubiquitous expression, hierarchical clustering analysis, based on global protein expression patterns, shows that the analyzed cells can be still subdivided into groups according to the current concepts of histology and cellular differentiation. This study suggests that tissue specificity is achieved by precise regulation of protein levels in space and time, and that different tissues in the body acquire their unique characteristics by controlling not which proteins are expressed but how much of each is produced. Molecular Systems Biology 5: 337; published online 22 December 2009; doi:10.1038/msb.2009.93

  • 13. Sanchez, Benjamin J.
    et al.
    Zhang, Cheng
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Nilsson, Avlant
    Lahtvee, Petri-Jaan
    Kerkhoven, Eduard J.
    Nielsen, Jens
    Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints2017Ingår i: Molecular Systems Biology, ISSN 1744-4292, E-ISSN 1744-4292, Vol. 13, nr 8, artikel-id 935Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Genome-scale metabolic models (GEMs) are widely used to calculate metabolic phenotypes. They rely on defining a set of constraints, the most common of which is that the production of metabolites and/or growth are limited by the carbon source uptake rate. However, enzyme abundances and kinetics, which act as limitations on metabolic fluxes, are not taken into account. Here, we present GECKO, a method that enhances a GEM to account for enzymes as part of reactions, thereby ensuring that each metabolic flux does not exceed its maximum capacity, equal to the product of the enzyme's abundance and turnover number. We applied GECKO to a Saccharomyces cerevisiae GEM and demonstrated that the new model could correctly describe phenotypes that the previous model could not, particularly under high enzymatic pressure conditions, such as yeast growing on different carbon sources in excess, coping with stress, or overexpressing a specific pathway. GECKO also allows to directly integrate quantitative proteomics data; by doing so, we significantly reduced flux variability of the model, in over 60% of metabolic reactions. Additionally, the model gives insight into the distribution of enzyme usage between and within metabolic pathways. The developed method and model are expected to increase the use of model-based design in metabolic engineering.

  • 14.
    Uhlén, Mathias
    et al.
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab. Technical University of Denmark, Denmark.
    Hallström, Björn M.
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Lindskog, Cecilia
    Mardinoglu, Adil
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab. Chalmers University of Technology, Sweden.
    Pontén, Fredrik
    Nielsen, Jens
    KTH, Skolan för bioteknologi (BIO), Genteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab. Technical University of Denmark, Denmark; Chalmers University of Technology, Sweden.
    Transcriptomics resources of human tissues and organs2016Ingår i: Molecular Systems Biology, ISSN 1744-4292, E-ISSN 1744-4292, Vol. 12, nr 4, artikel-id 862Artikel, forskningsöversikt (Refereegranskat)
    Abstract [en]

    Quantifying the differential expression of genes in various human organs, tissues, and cell types is vital to understand human physiology and disease. Recently, several large-scale transcriptomics studies have analyzed the expression of protein-coding genes across tissues. These datasets provide a framework for defining the molecular constituents of the human body as well as for generating comprehensive lists of proteins expressed across tissues or in a tissue-restricted manner. Here, we review publicly available human transcriptome resources and discuss body-wide data from independent genome-wide transcriptome analyses of different tissues. Gene expression measurements from these independent datasets, generated using samples from fresh frozen surgical specimens and postmortem tissues, are consistent. Overall, the different genome-wide analyses support a distribution in which many proteins are found in all tissues and relatively few in a tissue-restricted manner. Moreover, we discuss the applications of publicly available omics data for building genome-scale metabolic models, used for analyzing cell and tissue functions both in physiological and in disease contexts.

  • 15.
    Wang, Dongxue
    et al.
    Tech Univ Munich, Chair Prote & Bioanalyt, Freising Weihenstephan, Germany..
    Eraslan, Basak
    Tech Univ Munich, Dept Informat, Computat Biol, Garching, Germany.;Ludwig Maximilians Univ Munchen, Gene Ctr, Dept Biochem, Quantitat Biosci Munich, Munich, Germany..
    Wieland, Thomas
    OmicScouts GmbH, Freising Weihenstephan, Germany..
    Hallström, Björn M.
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Hopf, Thomas
    OmicScouts GmbH, Freising Weihenstephan, Germany..
    Zolg, Daniel Paul
    Tech Univ Munich, Chair Prote & Bioanalyt, Freising Weihenstephan, Germany..
    Zecha, Jana
    Tech Univ Munich, Chair Prote & Bioanalyt, Freising Weihenstephan, Germany..
    Asplund, Anna
    Uppsala Univ, Dept Immunol Genet & Pathol, Sci Life Lab, Uppsala, Sweden..
    Li, Li-hua
    Tech Univ Munich, Chair Prote & Bioanalyt, Freising Weihenstephan, Germany..
    Meng, Chen
    Tech Univ Munich, Chair Prote & Bioanalyt, Freising Weihenstephan, Germany..
    Frejno, Martin
    Tech Univ Munich, Chair Prote & Bioanalyt, Freising Weihenstephan, Germany..
    Schmidt, Tobias
    Tech Univ Munich, Chair Prote & Bioanalyt, Freising Weihenstephan, Germany..
    Schnatbaum, Karsten
    JPT Peptide Technol GmbH, Berlin, Germany..
    Wilhelm, Mathias
    Tech Univ Munich, Chair Prote & Bioanalyt, Freising Weihenstephan, Germany..
    Ponten, Frederik
    Uppsala Univ, Dept Immunol Genet & Pathol, Sci Life Lab, Uppsala, Sweden..
    Uhlén, Mathias
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Gagneur, Julien
    Tech Univ Munich, Dept Informat, Computat Biol, Garching, Germany..
    Hahne, Hannes
    OmicScouts GmbH, Freising Weihenstephan, Germany..
    Kuster, Bernhard
    Tech Univ Munich, Chair Prote & Bioanalyt, Freising Weihenstephan, Germany.;CIPSM, Munich, Germany..
    A deep proteome and transcriptome abundance atlas of 29 healthy human tissues2019Ingår i: Molecular Systems Biology, ISSN 1744-4292, E-ISSN 1744-4292, Vol. 15, nr 2, artikel-id e8503Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Genome-, transcriptome- and proteome-wide measurements provide insights into how biological systems are regulated. However, fundamental aspects relating to which human proteins exist, where they are expressed and in which quantities are not fully understood. Therefore, we generated a quantitative proteome and transcriptome abundance atlas of 29 paired healthy human tissues from the Human Protein Atlas project representing human genes by 18,072 transcripts and 13,640 proteins including 37 without prior protein-level evidence. The analysis revealed that hundreds of proteins, particularly in testis, could not be detected even for highly expressed mRNAs, that few proteins show tissue-specific expression, that strong differences between mRNA and protein quantities within and across tissues exist and that protein expression is often more stable across tissues than that of transcripts. Only 238 of 9,848 amino acid variants found by exome sequencing could be confidently detected at the protein level showing that proteogenomics remains challenging, needs better computational methods and requires rigorous validation. Many uses of this resource can be envisaged including the study of gene/protein expression regulation and biomarker specificity evaluation.

  • 16. Ågren, Rasmus
    et al.
    Mardinoglu, Adil
    Asplund, Anna
    Kampf, Caroline
    Uhlén, Mathias
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Nielsen, Jens
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Identification of anticancer drugs for hepatocellular carcinoma through personalized genome-scale metabolic modeling2014Ingår i: Molecular Systems Biology, ISSN 1744-4292, E-ISSN 1744-4292, Vol. 10, nr 3, s. A721-Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Synopsis image Personalized GEMs for six hepatocellular carcinoma patients are reconstructed using proteomics data and a task-driven model reconstruction algorithm. These GEMs are used to predict antimetabolites preventing tumor growth in all patients or in individual patients. The presence of proteins encoded by 15,841 genes in tumors from 27 HCC patients is evaluated by immunohistochemistry. Personalized GEMs for six HCC patients and GEMs for 83 healthy cell types are reconstructed based on HMR 2.0 and the tINIT algorithm for task-driven model reconstruction. 101 antimetabolites are predicted to inhibit tumor growth in all patients. Antimetabolite toxicity is tested using the 83 cell type-specific GEMs. An l-carnitine analog inhibits the proliferation of HepG2 cells. Abstract Genome-scale metabolic models (GEMs) have proven useful as scaffolds for the integration of omics data for understanding the genotype-phenotype relationship in a mechanistic manner. Here, we evaluated the presence/absence of proteins encoded by 15,841 genes in 27 hepatocellular carcinoma (HCC) patients using immunohistochemistry. We used this information to reconstruct personalized GEMs for six HCC patients based on the proteomics data, HMR 2.0, and a task-driven model reconstruction algorithm (tINIT). The personalized GEMs were employed to identify anticancer drugs using the concept of antimetabolites; i.e., drugs that are structural analogs to metabolites. The toxicity of each antimetabolite was predicted by assessing the in silico functionality of 83 healthy cell type-specific GEMs, which were also reconstructed with the tINIT algorithm. We predicted 101 antimetabolites that could be effective in preventing tumor growth in all HCC patients, and 46 antimetabolites which were specific to individual patients. Twenty-two of the 101 predicted antimetabolites have already been used in different cancer treatment strategies, while the remaining antimetabolites represent new potential drugs. Finally, one of the identified targets was validated experimentally, and it was confirmed to attenuate growth of the HepG2 cell line.

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