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  • 1.
    Lee, Sunjae
    et al.
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab. Korea Adv Inst Sci & Technol, South Korea.
    Mardinoglu, Adil
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab. Chalmers, Sweden.
    Zhang, Cheng
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lee, Doheon
    Nielsen, Jens
    KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab. Chalmers, Sweden.
    Dysregulated signaling hubs of liver lipid metabolism reveal hepatocellular carcinoma pathogenesis2016In: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 44, no 12, p. 5529-5539Article in journal (Refereed)
    Abstract [en]

    Hepatocellular carcinoma (HCC) has a high mortality rate and early detection of HCC is crucial for the application of effective treatment strategies. HCC is typically caused by either viral hepatitis infection or by fatty liver disease. To diagnose and treat HCC it is necessary to elucidate the underlying molecular mechanisms. As a major cause for development of HCC is fatty liver disease, we here investigated anomalies in regulation of lipid metabolism in the liver. We applied a tailored network-based approach to identify signaling hubs associated with regulation of this part of metabolism. Using transcriptomics data of HCC patients, we identified significant dysregulated expressions of lipid-regulated genes, across many different lipid metabolic pathways. Our findings, however, show that viral hepatitis causes HCC by a distinct mechanism, less likely involving lipid anomalies. Based on our analysis we suggest signaling hub genes governing overall catabolic or anabolic pathways, as novel drug targets for treatment of HCC that involves lipid anomalies.

  • 2.
    Lee, Sunjae
    et al.
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Zhang, Cheng
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Kilicarslan, Murat
    Piening, Brian D.
    Björnson, Elias
    Hallström, Björn M.
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Groen, Albert K.
    Ferrannini, Ele
    Laakso, Markku
    Snyder, Michael
    Bluher, Matthias
    Uhlèn, Mathias
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Nielsen, Jens
    KTH, School of Biotechnology (BIO), Gene Technology. Chalmers, Sweden.
    Smith, Ulf
    Serlie, Mireille J.
    Boren, Jan
    Mardinoglu, Adil
    Integrated Network Analysis Reveals an Association between Plasma Mannose Levels and Insulin Resistance2016In: Cell Metabolism, ISSN 1550-4131, E-ISSN 1932-7420, Vol. 24, no 1, p. 172-184Article in journal (Refereed)
    Abstract [en]

    To investigate the biological processes that are altered in obese subjects, we generated cell-specific integrated networks (INs) by merging genome-scale metabolic, transcriptional regulatory and protein-protein interaction networks. We performed genome-wide transcriptomics analysis to determine the global gene expression changes in the liver and three adipose tissues from obese subjects undergoing bariatric surgery and integrated these data into the cell-specific INs. We found dysregulations in mannose metabolism in obese subjects and validated our predictions by detecting mannose levels in the plasma of the lean and obese subjects. We observed significant correlations between plasma mannose levels, BMI, and insulin resistance (IR). We also measured plasma mannose levels of the subjects in two additional different cohorts and observed that an increased plasma mannose level was associated with IR and insulin secretion. We finally identified mannose as one of the best plasma metabolites in explaining the variance in obesity-independent IR.

  • 3.
    Mardinoglu, Adil
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Bjornson, Elias
    Zhang, Cheng
    KTH, Centres, 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, Centres, Science for Life Laboratory, SciLifeLab.
    Uhlén, Mathias
    KTH, Centres, 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 NAFLD2017In: Molecular Systems Biology, ISSN 1744-4292, E-ISSN 1744-4292, Vol. 13, no 3, article id 916Article in journal (Refereed)
    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.

  • 4.
    Mardinoglu, Adil
    et al.
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab. Chalmers University of Technology, Sweden.
    Shoaie, Saeed
    Bergentall, Mattias
    Ghaffari, Pouyan
    Zhang, Cheng
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Larsson, Erik
    Backhed, Fredrik
    Nielsen, Jens
    KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab. Chalmers University of Technology, Sweden.
    The gut microbiota modulates host amino acid and glutathione metabolism in mice2015In: Molecular Systems Biology, ISSN 1744-4292, E-ISSN 1744-4292, Vol. 11, no 10, article id 834Article in journal (Refereed)
    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.

  • 5.
    Thul, Peter J.
    et al.
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Åkesson, Lovisa
    KTH, School of Biotechnology (BIO). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Wiking, Mikaela
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Mahdessian, Diana
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Geladaki, A.
    Ait Blal, Hammou
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Alm, Tove L.
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Asplund, A.
    Björk, Lars
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Breckels, L. M.
    Bäckström, Anna
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Danielsson, Frida
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Fagerberg, Linn
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Fall, Jenny
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Gatto, L.
    Gnann, Christian
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hober, Sophia
    KTH, School of Biotechnology (BIO), Protein Technology.
    Hjelmare, Martin
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Johansson, Fredric
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lee, Sunjae
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lindskog, C.
    Mulder, J.
    Mulvey, C. M.
    Nilsson, Peter
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Oksvold, Per
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Rockberg, Johan
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Schutten, Rutger
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Schwenk, Jochen M.
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Sivertsson, Åsa
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Sjöstedt, E.
    Skogs, Marie
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Stadler, Charlotte
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Sullivan, Devin P.
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Tegel, Hanna
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Winsnes, Casper F.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Zhang, Cheng
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Zwahlen, Martin
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Mardinoglu, Adil
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Pontén, F.
    von Feilitzen, Kalle
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lilley, K. S.
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Lundberg, Emma
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    A subcellular map of the human proteome2017In: Science, ISSN 0036-8075, E-ISSN 1095-9203, Vol. 356, no 6340, article id 820Article in journal (Refereed)
    Abstract [en]

    Resolving the spatial distribution of the human proteome at a subcellular level can greatly increase our understanding of human biology and disease. Here we present a comprehensive image-based map of subcellular protein distribution, the Cell Atlas, built by integrating transcriptomics and antibody-based immunofluorescence microscopy with validation by mass spectrometry. Mapping the in situ localization of 12,003 human proteins at a single-cell level to 30 subcellular structures enabled the definition of the proteomes of 13 major organelles. Exploration of the proteomes revealed single-cell variations in abundance or spatial distribution and localization of about half of the proteins to multiple compartments. This subcellular map can be used to refine existing protein-protein interaction networks and provides an important resource to deconvolute the highly complex architecture of the human cell.

  • 6.
    Uhlén, Mathias
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. Center for Biosustainability, Danish Technical University, Copenhagen, Denmark..
    Zhang, Cheng
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lee, Sunjae
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Sjöstedt, Evelina
    KTH, Centres, Science for Life Laboratory, SciLifeLab. Department of Immunology Genetics and Pathology, Uppsala University, Uppsala, Sweden..
    Fagerberg, Linn
    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.
    Arif, Muhammad
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Liu, Zhengtao
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Edfors, Fredrik
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Sanli, Kemal
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    von Feilitzen, Kalle
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Oksvold, Per
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lundberg, Emma
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hober, Sophia
    KTH, School of Biotechnology (BIO).
    Nilsson, Peter
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Mattsson, Johanna
    Schwenk, Jochen M.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Brunnstrom, Hans
    Glimelius, Bengt
    Sjoblom, Tobias
    Edqvist, Per-Henrik
    Djureinovic, Dijana
    Micke, Patrick
    Lindskog, Cecilia
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Ponten, Fredrik
    A pathology atlas of the human cancer transcriptome2017In: Science, ISSN 0036-8075, E-ISSN 1095-9203, Vol. 357, no 6352, p. 660-+Article in journal (Refereed)
    Abstract [en]

    Cancer is one of the leading causes of death, and there is great interest in understanding the underlying molecular mechanisms involved in the pathogenesis and progression of individual tumors. We used systems-level approaches to analyze the genome-wide transcriptome of the protein-coding genes of 17 major cancer types with respect to clinical outcome. A general pattern emerged: Shorter patient survival was associated with up-regulation of genes involved in cell growth and with down-regulation of genes involved in cellular differentiation. Using genome-scale metabolic models, we show that cancer patients have widespread metabolic heterogeneity, highlighting the need for precise and personalized medicine for cancer treatment. All data are presented in an interactive open-access database (www.proteinatlas.org/pathology) to allow genome-wide exploration of the impact of individual proteins on clinical outcomes.

  • 7.
    Zhang, Cheng
    East China University of Science and Technology, China; Chalmers University of Technology,Sweden.
    Logical transformation of genome-scale metabolic models for gene level applications and analysis2015In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 31, no 14, p. 2324-2331Article in journal (Refereed)
    Abstract [en]

    Motivation: In recent years, genome-scale metabolic models (GEMs) have played important roles in areas like systems biology and bioinformatics. However, because of the complexity of genereaction associations, GEMs often have limitations in gene level analysis and related applications. Hence, the existing methods were mainly focused on applications and analysis of reactions and metabolites. Results: Here, we propose a framework named logic transformation of model (LTM) that is able to simplify the gene-reaction associations and enables integration with other developed methods for gene level applications. We show that the transformed GEMs have increased reaction and metabolite number as well as degree of freedom in flux balance analysis, but the gene-reaction associations and the main features of flux distributions remain constant. In addition, we develop two methods, OptGeneKnock and FastGeneSL by combining LTM with previously developed reaction-based methods. We show that the FastGeneSL outperforms exhaustive search. Finally, we demonstrate the use of the developed methods in two different case studies. We could design fast genetic intervention strategies for targeted overproduction of biochemicals and identify double and triple synthetic lethal gene sets for inhibition of hepatocellular carcinoma tumor growth through the use of OptGeneKnock and FastGeneSL, respectively.

1 - 7 of 7
CiteExportLink to result list
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