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  • 1.
    Benfeitas, Rui
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab. Royal Institute of Technology, KTH.
    Bidkhori, Gholamreza
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Mukhopadhyay, Bani
    Klevstig, Martina
    Arif, Muhammad
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Zhang, Cheng
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lee, Sunjae
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Cinar, Resat
    Nielsen, Jens
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Boren, Jan
    Kunos, George
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Characterization of heterogeneous redox responses in hepatocellular carcinoma patients using network analysis2019In: EBioMedicine, E-ISSN 2352-3964Article in journal (Refereed)
  • 2.
    Bidkhori, Gholamreza
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Benfeitas, Rui
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Elmas, Ezgi
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Kararoudi, Meisam Naeimi
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Arif, Muhammad
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Nielsen, Jens
    Chalmers Univ Technol, Dept Biol & Biol Engn, Gothenburg, Sweden..
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Metabolic Network-Based Identification and Prioritization o f Anticancer Targets Based on Expression Data in Hepatocellular Carcinoma2018In: Frontiers in Physiology, ISSN 1664-042X, E-ISSN 1664-042X, Vol. 9, article id 916Article in journal (Refereed)
    Abstract [en]

    Hepatocellular carcinoma (HCC) is a deadly form of liver cancer with high mortality worldwide. Unfortunately, the large heterogeneity of this disease makes it difficult to develop effective treatment strategies. Cellular network analyses have been employed to study heterogeneity in cancer, and to identify potential therapeutic targets. However, the existing approaches do not consider metabolic growth requirements, i.e., biological network functionality, to rank candidate targets while preventing toxicity to non-cancerous tissues. Here, we developed an algorithm to overcome these issues based on integration of gene expression data, genome-scale metabolic models, network controllability, and dispensability, as well as toxicity analysis. This method thus predicts and ranks potential anticancer non-toxic controlling metabolite and gene targets. Our algorithm encompasses both objective-driven and-independent tasks, and uses network topology to finally rank the predicted therapeutic targets. We employed this algorithm to the analysis of transcriptomic data for 50 HCC patients with both cancerous and non-cancerous samples. We identified several potential targets that would prevent cell growth, including 74 anticancer metabolites, and 3 gene targets (PRKACA, PGS1, and CRLS1). The predicted anticancer metabolites showed good agreement with existing FDA-approved cancer drugs, and the 3 genes were experimentally validated by performing experiments in HepG2 and Hep3B liver cancer cell lines. Our observations indicate that our novel approach successfully identifies therapeutic targets for effective treatment of cancer. This approach may also be applied to any cancer type that has tumor and non-tumor gene or protein expression data.

  • 3.
    Bidkhori, Gholamreza
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Benfeitas, Rui
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Klevstig, Martina
    Zhang, Cheng
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Nielsen, Jens
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Boren, Jan
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Metabolic network-based stratification of hepatocellular carcinoma reveals three distinct tumor subtypes2018In: Proceedings of the National Academy of Sciences of the United States of America, ISSN 0027-8424, E-ISSN 1091-6490Article in journal (Refereed)
  • 4. Bosley, Jim
    et al.
    Borén, Christofer
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lee, Sunjae
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Grotli, Morten
    Nielsen, Jens
    KTH, Centres, Science for Life Laboratory, SciLifeLab. Chalmers University of Technology, Sweden.
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Boren, Jan
    Mardinoglu, Adil
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab. Chalmers University of Technology, Sweden.
    Improving the economics of NASH/NAFLD treatment through the use of systems biology2017In: Drug Discovery Today, ISSN 1359-6446, E-ISSN 1878-5832, Vol. 22, no 10, p. 1532-1538Article, review/survey (Refereed)
    Abstract [en]

    Nonalcoholic steatohepatitis (NASH) is a severe form of nonalcoholic fatty liver disease (NAFLD). We surveyed NASH therapies currently in development, and found a significant variety of targets and approaches. Evaluation and clinical testing of these targets is an expensive and time-consuming process. Systems biology approaches could enable the quantitative evaluation of the likely efficacy and safety of different targets. This motivated our review of recent systems biology studies that focus on the identification of targets and development of effective treatments for NASH. We discuss the potential broader use of genome-scale metabolic models and integrated networks in the validation of drug targets, which could facilitate more productive and efficient drug development decisions for the treatment of NASH.

  • 5. Cadenas, Cristina
    et al.
    Vosbeck, Sonja
    Edlund, Karolina
    Grgas, Katharina
    Madjar, Katrin
    Hellwig, Birte
    Adawy, Alshaimaa
    Glotzbach, Annika
    Stewart, Joanna D.
    Lesjak, Michaela S.
    Franckenstein, Dennis
    Claus, Maren
    Hayen, Heiko
    Schriewer, Alexander
    Gianmoena, Kathrin
    Thaler, Sonja
    Schmidt, Marcus
    Micke, Patrick
    Ponten, Fredrik
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Zhang, Cheng
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Käfferlein, Keiko U.
    Watzl, Carsten
    Frank, Sasa
    Rahnenfuhrer, Jörg
    Marchan, Rosemarie
    Hengstler, Jan G.
    LIPG-promoted lipid storage mediates adaptation to oxidative stress in breast cancer2019In: International Journal of Cancer, ISSN 0020-7136, E-ISSN 1097-0215, Vol. 145, no 4, p. 901-915Article in journal (Refereed)
    Abstract [en]

    Endothelial lipase (LIPG) is a cell surface associated lipase that displays phospholipase A1 activity towards phosphatidylcholine present in high-density lipoproteins (HDL). LIPG was recently reported to be expressed in breast cancer and to support proliferation, tumourigenicity and metastasis. Here we show that severe oxidative stress leading to AMPK activation triggers LIPG upregulation, resulting in intracellular lipid droplet accumulation in breast cancer cells, which supports survival. Neutralizing oxidative stress abrogated LIPG upregulation and the concomitant lipid storage. In human breast cancer, high LIPG expression was observed in a limited subset of tumours and was significantly associated with shorter metastasis-free survival in node-negative, untreated patients. Moreover, expression of PLIN2 and TXNRD1 in these tumours indicated a link to lipid storage and oxidative stress. Altogether, our findings reveal a previously unrecognized role for LIPG in enabling oxidative stress-induced lipid droplet accumulation in tumour cells that protects against oxidative stress, and thus supports tumour progression.

  • 6. Cao, Junyue
    et al.
    Packer, Jonathan
    Waterston, Robert
    Trapnell, Cole
    Shendure, Jay
    Rajaram, Satwik
    Wu, Lani F.
    Altschuler, Steven J.
    Liang, Jackson
    O'Brien, Lucy Erin
    Eizenberg-Magar, Inbal
    Rimer, Jacob
    Friedman, Nir
    Metzl-Raz, Eyal
    Kafri, Moshe
    Yaakov, Gilad
    Soifer, Ilya
    Gurvich, Yonat
    Barkai, Naama
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Biotechnology (BIO).
    Ponten, Fredrik
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Biotechnology (BIO).
    Rahi, Sahand Jamal
    Cross, Frederick R.
    Baumgart, Meike
    Noack, Stephan
    Principles of Systems Biology, No. 212017In: CELL SYSTEMS, ISSN 2405-4712, Vol. 5, no 3, p. 158-160Article in journal (Other academic)
    Abstract [en]

    This month: relating single cells to populations (Cao/Packer, Wu/Altschuler, O'Brien, Friedman), an excess of ribosomes (Barkai), human pathology atlas (Uhlen), signatures of feedback (Rahi), and major genome redesign (Baumgart).

  • 7. Casey, John R.
    et al.
    Mardinoglu, Adil
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.
    Nielsen, Jens
    KTH, Centres, Science for Life Laboratory, SciLifeLab. Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark.
    Karl, David M.
    Adaptive Evolution of Phosphorus Metabolism in Prochlorococcus2016In: MSYSTEMS, ISSN 2379-5077, Vol. 1, no 6, article id UNSP e00065Article in journal (Refereed)
    Abstract [en]

    Inorganic phosphorus is scarce in the eastern Mediterranean Sea, where the high-light-adapted ecotype HLI of the marine picocyanobacterium Prochlorococcus marinus thrives. Physiological and regulatory control of phosphorus acquisition and partitioning has been observed in HLI both in culture and in the field; however, the optimization of phosphorus metabolism and associated gains for its phosphorus-limited-growth (PLG) phenotype have not been studied. Here, we reconstructed a genome-scale metabolic network of the HLI axenic strain MED4 (iJC568), consisting of 568 metabolic genes in relation to 794 reactions involving 680 metabolites distributed in 6 subcellular locations. iJC568 was used to quantify metabolic fluxes under PLG conditions, and we observed a close correspondence between experimental and computed fluxes. We found that MED4 has minimized its dependence on intracellular phosphate, not only through drastic depletion of phosphorus-containing biomass components but also through network-wide reductions in phosphate-reaction participation and the loss of a key enzyme, succinate dehydrogenase. These alterations occur despite the stringency of having relatively few pathway redundancies and an extremely high proportion of essential metabolic genes (47%; defined as the percentage of lethal in silico gene knockouts). These strategies are examples of nutrient-controlled adaptive evolution and confer a dramatic growth rate advantage to MED4 in phosphorus-limited regions. IMPORTANCE Microbes are known to employ three basic strategies to compete for limiting elemental resources: (i) cell quotas may be adjusted by alterations to cell physiology or by substitution of a more plentiful resource, (ii) stressed cells may synthesize high-affinity transporters, and (iii) cells may access more costly sources from internal stores, by degradation, or by petitioning other microbes. In the case of phosphorus, a limiting resource in vast oceanic regions, the cosmopolitan cyanobacterium Prochlorococcus marinus thrives by adopting all three strategies and a fourth, previously unknown strategy. By generating a detailed model of its metabolism, we found that strain MED4 has evolved a way to reduce its dependence on phosphate by minimizing the number of enzymes involved in phosphate transformations, despite the stringency of nearly half of its metabolic genes being essential for survival. Relieving phosphorus limitation, both physiologically and throughout intermediate metabolism, substantially improves phosphorus-specific growth rates.

  • 8.
    Danielsson, Frida
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Fasterius, Erik
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101).
    Sullivan, Devin
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hases, Linnea
    KTH, Centres, Science for Life Laboratory, SciLifeLab. Karolinska Institute, Huddinge, Sweden.
    Sanli, Kemal
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Zhang, Cheng
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Al-Khalili Szigyarto, Cristina
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101).
    Huss, M.
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101). KTH, Centres, Science for Life Laboratory, SciLifeLab. Technical University of Denmark, Hørsholm, Denmark.
    Williams, Cecilia
    KTH, Centres, Science for Life Laboratory, SciLifeLab. Karolinska Institute, Huddinge, Sweden.
    Lundberg, Emma
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Transcriptome profiling of the interconnection of pathways involved in malignant transformation and response to hypoxia2018In: OncoTarget, ISSN 1949-2553, E-ISSN 1949-2553, Vol. 9, no 28, p. 19730-19744Article in journal (Refereed)
    Abstract [en]

    In tumor tissues, hypoxia is a commonly observed feature resulting from rapidly proliferating cancer cells outgrowing their surrounding vasculature network. Transformed cancer cells are known to exhibit phenotypic alterations, enabling continuous proliferation despite a limited oxygen supply. The four-step isogenic BJ cell model enables studies of defined steps of tumorigenesis: the normal, immortalized, transformed, and metastasizing stages. By transcriptome profiling under atmospheric and moderate hypoxic (3% O2) conditions, we observed that despite being highly similar, the four cell lines of the BJ model responded strikingly different to hypoxia. Besides corroborating many of the known responses to hypoxia, we demonstrate that the transcriptome adaptation to moderate hypoxia resembles the process of malignant transformation. The transformed cells displayed a distinct capability of metabolic switching, reflected in reversed gene expression patterns for several genes involved in oxidative phosphorylation and glycolytic pathways. By profiling the stage-specific responses to hypoxia, we identified ASS1 as a potential prognostic marker in hypoxic tumors. This study demonstrates the usefulness of the BJ cell model for highlighting the interconnection of pathways involved in malignant transformation and hypoxic response.

  • 9. Harms, Matthew J.
    et al.
    Li, Qian
    Lee, Sunjae
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Zhang, Cheng
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Kull, Bengt
    Hallen, Stefan
    Thorell, Anders
    Alexandersson, Ida
    Hagberg, Carolina E.
    Peng, Xiao-Rong
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Spalding, Kirsty L.
    Boucher, Jeremie
    Mature Human White Adipocytes Cultured under Membranes Maintain Identity, Function, and Can Transdifferentiate into Brown-like Adipocytes2019In: Cell reports, ISSN 2211-1247, E-ISSN 2211-1247Article in journal (Refereed)
  • 10.
    Haversen, Liliana
    et al.
    Univ Gothenburg, Dept Mol & Clin Med, Wallenberg Lab, Gothenburg, Sweden.;Sahlgrens Univ Hosp, Gothenburg, Sweden..
    Sundelin, Jeanna Perman
    AstraZeneca, Strateg Planning & Operat, Cardiovasc & Metab Dis, IMED Biotech Unit, Gothenburg, Sweden..
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH). Kings Coll London, Ctr Host Microbiome Interact, Dent Inst, London SE1 9RT, England..
    Rutberg, Mikael
    Univ Gothenburg, Dept Mol & Clin Med, Wallenberg Lab, Gothenburg, Sweden.;Sahlgrens Univ Hosp, Gothenburg, Sweden..
    Stahlman, Marcus
    Univ Gothenburg, Dept Mol & Clin Med, Wallenberg Lab, Gothenburg, Sweden.;Sahlgrens Univ Hosp, Gothenburg, Sweden..
    Wilhelmsson, Ulrika
    Univ Gothenburg, Dept Clin Neurosci, Ctr Brain Repair, Gothenburg, Sweden..
    Hulten, Lillemor Mattsson
    Univ Gothenburg, Dept Mol & Clin Med, Wallenberg Lab, Gothenburg, Sweden.;Sahlgrens Univ Hosp, Gothenburg, Sweden..
    Pekny, Milos
    Univ Gothenburg, Dept Clin Neurosci, Ctr Brain Repair, Gothenburg, Sweden..
    Fogelstrand, Per
    Univ Gothenburg, Dept Mol & Clin Med, Wallenberg Lab, Gothenburg, Sweden.;Sahlgrens Univ Hosp, Gothenburg, Sweden..
    Fog Bentzon, Jacob
    Aarhus Univ, Dept Clin Med, Aarhus, Denmark.;Ctr Nacl Invest Cardiovasc Carlos III CNIC, Madrid, Spain..
    Levin, Malin
    Univ Gothenburg, Dept Mol & Clin Med, Wallenberg Lab, Gothenburg, Sweden.;Sahlgrens Univ Hosp, Gothenburg, Sweden..
    Boren, Jan
    Univ Gothenburg, Dept Mol & Clin Med, Wallenberg Lab, Gothenburg, Sweden.;Sahlgrens Univ Hosp, Gothenburg, Sweden..
    Vimentin deficiency in macrophages induces increased oxidative stress and vascular inflammation but attenuates atherosclerosis in mice2018In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 8, article id 16973Article in journal (Refereed)
    Abstract [en]

    The aim was to clarify the role of vimentin, an intermediate filament protein abundantly expressed in activated macrophages and foam cells, in macrophages during atherogenesis. Global gene expression, lipid uptake, ROS, and inflammation were analyzed in bone-marrow derived macrophages from vimentin-deficient (Vim(-/-)) and wild-type (Vim(-/-)) mice. Atherosclerosis was induced in Ldlr(-/-) mice transplanted with a PCSK9 gain-of-function virus. The mice were fed an atherogenic diet for 12-15 weeks. We observed impaired uptake of native LDL but increased uptake of oxLDL in Vim(-/-) macrophages. FACS analysis revealed increased surface expression of the scavenger receptor CD36 on Vim(-/-) macrophages. Vim(-/-) macrophages also displayed increased markers of oxidative stress, activity of the transcription factor NF-kappa B, secretion of proinflammatory cytokines and GLUT1-mediated glucose uptake. Vim(-/- )mice displayed decreased atherogenesis despite increased vascular inflammation and increased CD36 expression on macrophages in two mouse models of atherosclerosis. We demonstrate that vimentin has a strong suppressive effect on oxidative stress and that Vim(-/-) mice display increased vascular inflammation with increased CD36 expression on macrophages despite decreased subendothelial lipid accumulation. Thus, vimentin has a key role in regulating inflammation in macrophages during atherogenesis.

  • 11. Heinonen, Sini
    et al.
    Muniandy, Maheswary
    Buzkova, Jana
    Mardinoglu, Adil
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab. Chalmers University of Technology, Sweden.
    Rodriguez, Amaia
    Fruhbeck, Gema
    Hakkarainen, Antti
    Lundbom, Jesper
    Lundbom, Nina
    Kaprio, Jaakko
    Rissanen, Aila
    Pietilainen, Kirsi H.
    Mitochondria-related transcriptional signature is downregulated in adipocytes in obesity: a study of young healthy MZ twins2017In: Diabetologia, ISSN 0012-186X, E-ISSN 1432-0428, Vol. 60, no 1, p. 169-181Article in journal (Refereed)
    Abstract [en]

    Low mitochondrial activity in adipose tissue is suggested to be an underlying factor in obesity and its metabolic complications. We aimed to find out whether mitochondrial measures are downregulated in obesity also in isolated adipocytes. We studied young adult monozygotic (MZ) twin pairs discordant (n = 14, intrapair difference Delta BMI ae<yen> 3 kg/m(2)) and concordant (n = 5, Delta BMI < 3 kg/m(2)) for BMI, identified from ten birth cohorts of 22- to 36-year-old Finnish twins. Abdominal body fat distribution (MRI), liver fat content (magnetic resonance spectroscopy), insulin sensitivity (OGTT), high-sensitivity C-reactive protein, serum lipids and adipokines were measured. Subcutaneous abdominal adipose tissue biopsies were obtained to analyse the transcriptomics patterns of the isolated adipocytes as well as of the whole adipose tissue. Mitochondrial DNA transcript levels in adipocytes were measured by quantitative real-time PCR. Western blots of oxidative phosphorylation (OXPHOS) protein levels in adipocytes were performed in obese and lean unrelated individuals. The heavier (BMI 29.9 +/- 1.0 kg/m(2)) co-twins of the discordant twin pairs had more subcutaneous, intra-abdominal and liver fat and were more insulin resistant (p < 0.01 for all measures) than the lighter (24.1 +/- 0.9 kg/m(2)) co-twins. Altogether, 2538 genes in adipocytes and 2135 in adipose tissue were significantly differentially expressed (nominal p < 0.05) between the co-twins. Pathway analysis of these transcripts in both isolated adipocytes and adipose tissue revealed that the heavier co-twins displayed reduced expression of genes relating to mitochondrial pathways, a result that was replicated when analysing the pathways behind the most consistently downregulated genes in the heavier co-twins (in at least 12 out of 14 pairs). Consistently upregulated genes in adipocytes were related to inflammation. We confirmed that mitochondrial DNA transcript levels (12S RNA, 16S RNA, COX1, ND5, CYTB), expression of mitochondrial ribosomal protein transcripts and a major mitochondrial regulator PGC-1 alpha (also known as PPARGC1A) were reduced in the heavier co-twins' adipocytes (p < 0.05). OXPHOS protein levels of complexes I and III in adipocytes were lower in obese than in lean individuals. Subcutaneous abdominal adipocytes in obesity show global expressional downregulation of oxidative pathways, mitochondrial transcripts and OXPHOS protein levels and upregulation of inflammatory pathways. The datasets analysed and generated during the current study are available in the figshare repository.

  • 12. Huvila, J.
    et al.
    Laajala, T. D.
    Edqvist, P. -H
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab. Department of Biology and Biological Engineering, Chalmers University of Technology, 41296 Gothenburg, Sweden.
    Talve, L.
    Pontén, F.
    Grénman, S.
    Carpén, O.
    Aittokallio, T.
    Auranen, A.
    Combined ASRGL1 and p53 immunohistochemistry as an independent predictor of survival in endometrioid endometrial carcinoma2018In: Gynecologic Oncology, ISSN 0090-8258, E-ISSN 1095-6859, Vol. 149, no 1, p. 173-180Article in journal (Refereed)
    Abstract [en]

    Objective: In clinical practise, prognostication of endometrial cancer is based on clinicopathological risk factors. The use of immunohistochemistry-based markers as prognostic tools is generally not recommended and a systematic analysis of their utility as a panel is lacking. We evaluated whether an immunohistochemical marker panel could reliably assess endometrioid endometrial cancer (EEC) outcome independent of clinicopathological information. Methods: A cohort of 306 EEC specimens was profiled using tissue microarray (TMA). Cost- and time-efficient immunohistochemical analysis of well-established tissue biomarkers (ER, PR, HER2, Ki-67, MLH1 and p53) and two new biomarkers (L1CAM and ASRGL1) was carried out. Statistical modelling with embedded variable selection was applied on the staining results to identify minimal prognostic panels with maximal prognostic accuracy without compromising generalizability. Results: A panel including p53 and ASRGL1 immunohistochemistry was identified as the most accurate predictor of relapse-free and disease-specific survival. Within this panel, patients were allocated into high- (5.9%), intermediate- (29.5%) and low- (64.6%) risk groups where high-risk patients had a 30-fold risk (P < 0.001) of dying of EEC compared to the low-risk group. Conclusions: P53 and ASRGL1 immunoprofiling stratifies EEC patients into three risk groups with significantly different outcomes. This simple and easily applicable panel could provide a useful tool in EEC risk stratification and guiding the allocation of treatment modalities. 

  • 13. 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.

  • 14.
    Lee, Sunjae
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Zhang, Cheng
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Arif, Muhammad
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Liu, Zhengtao
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Benfeitas, Rui
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Bidkhori, Gholamreza
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Deshmukh, Sumit
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Shobky, Mohamed AI
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lovric, Alen
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Boren, Jan
    Nielsen, Jens
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    TCSBN: a database of tissue and cancer specific biological networks2017In: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962Article in journal (Refereed)
  • 15.
    Lee, Sunjae
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Zhang, Cheng
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Liu, Zhengtao
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Klevstig, Martina
    Mukhopadhyay, Bani
    Bergentall, Mattias
    Cinar, Resat
    Ståhlman, Marcus
    Sikanic, Natasa
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science.
    Park, Joshua K.
    Deshmukh, Sumit
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science.
    Harzandi, Azadeh M.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Kuijpers, Tim
    KTH.
    Grotli, Morten
    Elsässer, Simon J.
    Piening, Brian D.
    Snyder, Michael
    Smith, Ulf
    Nielsen, Jens
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Bäckhed, Fredrik
    Kunos, George
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Boren, Jan
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Network analyses identify liver-specific targets for treating liver diseases2017In: Molecular Systems Biology, ISSN 1744-4292, E-ISSN 1744-4292Article in journal (Refereed)
  • 16.
    Liu, Zhengtao
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab. Key Laboratory of Combined Multi-Organ Transplantation Ministry of Public Health First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China; Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Hangzhou, China.
    Que, Shuping
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.
    Rediscussion on Linearity Between Fibrosis Stages and Mortality Risk in Nonalcoholic Fatty Liver Disease Patients2017In: Hepatology, ISSN 0270-9139, E-ISSN 1527-3350, Vol. 66, no 4, p. 1357-1358Article in journal (Refereed)
  • 17.
    Liu, Zhengtao
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Zhang, Cheng
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lee, Sunjae
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Kim, Woonghee
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Klevstig, Martina
    Harzandi, Azadeh M.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Sikanic, Natasa
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science.
    Arif, Muhammad
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Ståhlman, Marcus
    Nielsen, Jens
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Boren, Jan
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Pyruvate kinase L/R is a regulator of lipid metabolism and mitochondrial function2019In: Metabolic engineering, ISSN 1096-7176, E-ISSN 1096-7184Article in journal (Refereed)
  • 18.
    Lovric, Alen
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Graner, Marit
    Univ Helsinki, Heart & Lung Ctr, Div Cardiol, Cent Hosp, Helsinki, Finland.;Univ Helsinki, Helsinki, Finland..
    Bjornson, Elias
    Chalmers Univ Technol, Dept Biol & Biol Engn, Gothenburg, Sweden.;Univ Gothenburg, Wallenberg Lab, Dept Mol & Clin Med, Gothenburg, Sweden.;Sahlgrens Univ Hosp, Gothenburg, Sweden..
    Arif, Muhammad
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Benfeitas, Rui
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Nyman, Kristofer
    Univ Helsinki, Helsinki, Finland.;Univ Helsinki, HUS Med Imaging Ctr, Dept Radiol, Cent Hosp, Helsinki, Finland..
    Stahlman, Marcus
    Univ Gothenburg, Wallenberg Lab, Dept Mol & Clin Med, Gothenburg, Sweden.;Sahlgrens Univ Hosp, Gothenburg, Sweden..
    Pentikainen, Markku O.
    Univ Helsinki, Heart & Lung Ctr, Div Cardiol, Cent Hosp, Helsinki, Finland.;Univ Helsinki, Helsinki, Finland..
    Lundborn, Jesper
    Univ Helsinki, Helsinki, Finland.;Univ Helsinki, HUS Med Imaging Ctr, Dept Radiol, Cent Hosp, Helsinki, Finland..
    Hakkarainen, Antti
    Univ Helsinki, Helsinki, Finland.;Univ Helsinki, HUS Med Imaging Ctr, Dept Radiol, Cent Hosp, Helsinki, Finland..
    Siren, Reijo
    Univ Helsinki, Helsinki, Finland.;Hlth Care Ctr City Helsinki, Dept Gen Practice & Primary Hlth Care, Helsinki, Finland..
    Nieminen, Markku S.
    Univ Helsinki, Heart & Lung Ctr, Div Cardiol, Cent Hosp, Helsinki, Finland.;Univ Helsinki, Helsinki, Finland..
    Lundborns, Nina
    Univ Helsinki, Helsinki, Finland.;Univ Helsinki, HUS Med Imaging Ctr, Dept Radiol, Cent Hosp, Helsinki, Finland..
    Lauermas, Kirsi
    Univ Helsinki, Helsinki, Finland.;Univ Helsinki, HUS Med Imaging Ctr, Dept Radiol, Cent Hosp, Helsinki, Finland..
    Taskinen, Marja-Riitta
    Univ Helsinki, Heart & Lung Ctr, Div Cardiol, Cent Hosp, Helsinki, Finland.;Univ Helsinki, Helsinki, Finland..
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Boren, Jan
    Univ Gothenburg, Wallenberg Lab, Dept Mol & Clin Med, Gothenburg, Sweden.;Sahlgrens Univ Hosp, Gothenburg, Sweden..
    Characterization of different fat depots in NAFLD using inflammation-associated proteome, lipidome and metabolome2018In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 8, article id 14200Article in journal (Refereed)
    Abstract [en]

    Non-alcoholic fatty liver disease (NAFLD) is recognized as a liver manifestation of metabolic syndrome, accompanied with excessive fat accumulation in the liver and other vital organs. Ectopic fat accumulation was previously associated with negative effects at the systemic and local level in the human body. Thus, we aimed to identify and assess the predictive capability of novel potential metabolic biomarkers for ectopic fat depots in non-diabetic men with NAFLD, using the inflammation-associated proteome, lipidome and metabolome. Myocardial and hepatic triglycerides were measured with magnetic spectroscopy while function of left ventricle, pericardial and epicardial fat, subcutaneous and visceral adipose tissue were measured with magnetic resonance imaging. Measured ectopic fat depots were profiled and predicted using a Random Forest algorithm, and by estimating the Area Under the Receiver Operating Characteristic curves. We have identified distinct metabolic signatures of fat depots in the liver (TAG50:1, glutamate, diSM18:0 and CE20:3), pericardium (N-palmitoyl-sphinganine, HGF, diSM18:0, glutamate, and TNFSF14), epicardium (sphingomyelin, CE20:3, PC38:3 and TNFSF14), and myocardium (CE20:3, LAPTGF-beta 1, glutamate and glucose). Our analyses highlighted non-invasive biomarkers that accurately predict ectopic fat depots, and reflect their distinct metabolic signatures in subjects with NAFLD.

  • 19. Lundgren, Sebastian
    et al.
    Fagerström-Vahman, Helena
    Zhang, Cheng
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Ben-Dror, Liv
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Nodin, Björn
    Jirström, Karin
    Discovery of KIRREL as a biomarker for prognostic stratification of patients within melanoma2019In: Biomarker Research, ISSN 0961-088X, E-ISSN 1475-925XArticle in journal (Refereed)
  • 20.
    Mahdessian, Diana
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics.
    Sullivan, D. P.
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics.
    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.
    Arif, Muhammad
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Zhang, Cheng
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    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.
    Gnann, Christian
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Shutten, Rutger
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH).
    Thul, Peter
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics.
    Carja, Oana
    Department of Genetics, Stanford University, Stanford, CA 94305, USA. ; Chan Zuckerberg Biohub, San Francisco, San Francisco, CA 94158, USA..
    Ayoglu, Burcu
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics.
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology. Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, United Kingdom.
    Pontén, Fredrik
    Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, SE-751 85 Uppsala, Sweden.
    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, Science for Life Laboratory, Uppsala University, SE-751 85 Uppsala, Sweden..
    Lundberg, Emma
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. Department of Genetics, Stanford University, Stanford, CA 94305, USA. ; Chan Zuckerberg Biohub, San Francisco, San Francisco, CA 94158, USA..
    Spatiotemporal dissection of the cell cycle regulated human proteomeManuscript (preprint) (Other academic)
    Abstract [en]

    Here we present a spatiotemporal dissection of proteome single cell heterogeneity in human cells, performed with subcellular resolution over the course of a cell cycle. We identify 17% of the human proteome to display cell-to-cell variability, of which we could attribute 25% as correlated to cell cycle progression, and present the first evidence of cell cycle association for 258 proteins. A key finding is that the variance, of many of the cell cycle associated proteins, is only partially explained by the cell cycle, which hints at cross-talk between the cell cycle and other signaling pathways. We also demonstrate that several of the identified cell cycle regulated proteins may be clinically significant in proliferative disorders. This spatially resolved proteome map of the cell cycle, integrated into the Human Protein Atlas, serves as a valuable resource to accelerate the molecular knowledge of the cell cycle and opens up novel avenues for the understanding of cell proliferation.

  • 21.
    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.

  • 22.
    Mardinoglu, Adil
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab. Chalmers Univ Technol, Dept Biol & Biol Engn, Gothenburg, Sweden..
    Boren, Jan
    Univ Gothenburg, Dept Mol & Clin Med, Gothenburg, Sweden.;Sahlgrens Univ Hosp, Gothenburg, Sweden..
    Smith, Ulf
    Univ Gothenburg, Dept Mol & Clin Med, Gothenburg, Sweden.;Sahlgrens Univ Hosp, Gothenburg, Sweden..
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Nielsen, Jens
    KTH, Centres, Science for Life Laboratory, SciLifeLab. Chalmers Univ Technol, Dept Biol & Biol Engn, Gothenburg, Sweden..
    Systems biology in hepatology: approaches and applications2018In: Nature Reviews. Gastroenterology & Hepatology, ISSN 1759-5045, E-ISSN 1759-5053, Vol. 15, no 6, p. 365-377Article, review/survey (Refereed)
    Abstract [en]

    Detailed insights into the biological functions of the liver and an understanding of its crosstalk with other human tissues and the gut microbiota can be used to develop novel strategies for the prevention and treatment of liver-associated diseases, including fatty liver disease, cirrhosis, hepatocellular carcinoma and type 2 diabetes mellitus. Biological network models, including metabolic, transcriptional regulatory, protein-protein interaction, signalling and co-expression networks, can provide a scaffold for studying the biological pathways operating in the liver in connection with disease development in a systematic manner. Here, we review studies in which biological network models were used to integrate multiomics data to advance our understanding of the pathophysiological responses of complex liver diseases. We also discuss how this mechanistic approach can contribute to the discovery of potential biomarkers and novel drug targets, which might lead to the design of targeted and improved treatment strategies. Finally, we present a roadmap for the successful integration of models of the liver and other human tissues with the gut microbiota to simulate whole-body metabolic functions in health and disease.

  • 23.
    Mardinoglu, Adil
    et al.
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Bosley, Jim
    Integrated Network Modeling for Novel Target Searches and Better Predictive Models2016In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 43, p. S88-S88Article in journal (Refereed)
  • 24.
    Mardinoglu, Adil
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Gogg, Silvia
    Lotta, Luca A.
    Stancakova, Alena
    Nerstedt, Annika
    Boren, Jan
    Blueher, Matthias
    Ferrannini, Ele
    Langenberg, Claudia
    Wareham, Nicholas J.
    Laakso, Markku
    Smith, Ulf
    Elevated Plasma Levels of 3-Hydroxyisobutyric Acid Are Associated With Incident Type 2 Diabetes2018In: EBioMedicine, ISSN 0360-0637, E-ISSN 2352-3964, Vol. 27, p. 151-155Article in journal (Refereed)
    Abstract [en]

    Branched-chain amino acids (BCAAs) metabolite, 3-Hydroxyisobutyric acid (3-HIB) has been identified as a secreted mediator of endothelial cell fatty acid transport and insulin resistance (IR) using animal models. To identify if 3-HIB is a marker of human IR and future risk of developing Type 2 diabetes (T2D), we measured plasma levels of 3-HIB and associated metabolites in around 10,000 extensively phenotyped individuals. The levels of 3-HIB were increased in obesity but not robustly associated with degree of IR after adjusting for BMI. Nevertheless, also after adjusting for obesity and plasma BCAA, 3-HIB levels were associated with future risk of incident T2D. We also examined the effect of 3-HIB on fatty acid uptake in human cells and found that both HUVEC and human cardiac endothelial cells respond to 3-HIB whereas human adipose tissue-derived endothelial cells do not respond to 3-HIB. In conclusion, we found that increased plasma level of 3-HIB is a marker of future risk of T2D and 3-HIB may be important for the regulation of metabolic flexibility in heart and muscles.

  • 25.
    Mardinoglu, Adil
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab. Chalmers Univ Technol, Dept Biol & Biol Engn, S-41260 Gothenburg, Sweden..
    Stancakova, Alena
    Univ Eastern Finland, Inst Clin Med, Internal Med, Kuopio 70210, Finland.;Kuopio Univ Hosp, SF-70210 Kuopio, Finland..
    Lotta, Luca A.
    Univ Cambridge, MRC, Epidemiol Unit, Cambridge CB2 0QQ, England..
    Kuusisto, Johanna
    Univ Eastern Finland, Inst Clin Med, Internal Med, Kuopio 70210, Finland.;Kuopio Univ Hosp, SF-70210 Kuopio, Finland..
    Boren, Jan
    Univ Gothenburg, Dept Mol & Clin Med, S-41345 Gothenburg, Sweden.;Sahlgrens Univ Hosp, S-41345 Gothenburg, Sweden..
    Blueher, Matthias
    Univ Leipzig, Dept Med, D-04103 Leipzig, Germany..
    Wareham, Nicholas J.
    Univ Cambridge, MRC, Epidemiol Unit, Cambridge CB2 0QQ, England..
    Ferrannini, Ele
    CNR, Inst Clin Physiol, I-56126 Pisa, Italy..
    Groop, Per Henrik
    Folkhalsan Res Ctr, Biomedicum, Folkhalsan Inst Genet, Helsinki 00290, Finland.;Univ Helsinki, Abdominal Ctr Nephrol, Helsinki 00029, Finland.;Helsinki Univ Hosp, Helsinki 00029, Finland.;Baker IDI Heart & Diabet Inst, Melbourne, Vic 3004, Australia..
    Laakso, Markku
    Univ Eastern Finland, Inst Clin Med, Internal Med, Kuopio 70210, Finland.;Kuopio Univ Hosp, SF-70210 Kuopio, Finland..
    Langenberg, Claudia
    Univ Cambridge, MRC, Epidemiol Unit, Cambridge CB2 0QQ, England..
    Smith, Ulf
    Univ Gothenburg, Dept Mol & Clin Med, S-41345 Gothenburg, Sweden.;Sahlgrens Univ Hosp, S-41345 Gothenburg, Sweden..
    Plasma Mannose Levels Are Associated with Incident Type 2 Diabetes and Cardiovascular Disease2017In: Cell Metabolism, ISSN 1550-4131, E-ISSN 1932-7420, Vol. 26, no 2, p. 281-283Article in journal (Refereed)
  • 26.
    Mardinoglu, Adil
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Uhlen, Mathias
    Boren, Jan
    Broad Views of Non-alcoholic Fatty Liver Disease2018In: CELL SYSTEMS, ISSN 2405-4712, Vol. 6, no 1, p. 7-9Article in journal (Other academic)
    Abstract [en]

    Multi-omics multi-tissue data are used to interpret genome-wide association study results from mice to identify key driver genes of non-alcoholic fatty liver disease. Non-alcoholic fatty liver disease (NAFLD) is the accumulation of fat (steatosis) in the liver due to causes other than excessive alcohol consumption. The disease may progress to more severe forms of liver diseases, including non-alcoholic steatohepatitis, cirrhosis, and hepatocellular carcinoma. In this issue of Cell Systems, Krishnan et al. (2018) reveal mechanisms underlying NAFLD by generating multi-omics data using liver and adipose tissues obtained from the Hybrid Mouse Diversity Panel, consisting of 113 mouse strains with various degrees of NAFLD. The study identified key driver genes of NAFLD that can be used in the development of efficient treatment strategies and illustrates the potential utility of systematic analysis of multi-layer biological networks.

  • 27.
    Mardinoglu, Adil
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Ural, Dilek
    Koc Univ, Sch Med, TR-34450 Istanbul, Turkey..
    Zeybel, Mujdat
    Koc Univ, Sch Med, Dept Gastroenterol & Hepatol, TR-34450 Istanbul, Turkey..
    Yuksel, Hatice Hilal
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Biotechnology (BIO), Centres, Albanova VinnExcellence Center for Protein Technology, ProNova. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science.
    Boren, Jan
    Univ Gothenburg, Dept Mol & Clin Med, S-41345 Gothenburg, Sweden.;Sahlgrenska Univ Hosp Gothenburg, S-41345 Gothenburg, Sweden..
    The Potential Use of Metabolic Cofactors in Treatment of NAFLD2019In: Nutrients, ISSN 2072-6643, E-ISSN 2072-6643, Vol. 11, no 7, article id 1578Article, review/survey (Refereed)
    Abstract [en]

    Non-alcoholic fatty liver disease (NAFLD) is caused by the imbalance between lipid deposition and lipid removal from the liver, and its global prevalence continues to increase dramatically. NAFLD encompasses a spectrum of pathological conditions including simple steatosis and non-alcoholic steatohepatitis (NASH), which can progress to cirrhosis and liver cancer. Even though there is a multi-disciplinary effort for development of a treatment strategy for NAFLD, there is not an approved effective medication available. Single or combined metabolic cofactors can be supplemented to boost the metabolic processes altered in NAFLD. Here, we review the dosage and usage of metabolic cofactors including l-carnitine, Nicotinamide riboside (NR), l-serine, and N-acetyl-l-cysteine (NAC) in human clinical studies to improve the altered biological functions associated with different human diseases. We also discuss the potential use of these substances in treatment of NAFLD and other metabolic diseases including neurodegenerative and cardiovascular diseases of which pathogenesis is linked to mitochondrial dysfunction.

  • 28.
    Mardinoglu, Adil
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Wu, Hao
    Björnson, Elias
    Zhang, Cheng
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hakkarainen, Antti
    Rasanen, Sari M.
    Lee, Sunjae
    Mancina, Rosellina M.
    Bergentall, Mattias
    Pietilainen, Kirsi H.
    Söderlund, Sanni
    Matikainen, Niina
    Stahlman, Marcus
    Bergh, Per-Olof
    Adiels, Martin
    Piening, Brian D.
    Graner, Marit
    Lundbom, Nina
    Williams, Kevin J.
    Romeo, Stefano
    Nielsen, Jens
    Snyder, Michael
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Bergström, Göran
    Perkins, Rosie
    Marschall, Hanns-Ulrich
    Backhed, Fredrik
    Taskinen, Marja-Riitta
    Boren, Jan
    An Integrated Understanding of the Rapid Metabolic Benefits of a Carbohydrate-Restricted Diet on Hepatic Steatosis in Humans2018In: Cell Metabolism, ISSN 1550-4131, E-ISSN 1932-7420, Vol. 27, no 3, p. 559-571.e5Article in journal (Refereed)
    Abstract [en]

    A carbohydrate-restricted diet is a widely recommended intervention for non-alcoholic fatty liver disease (NAFLD), but a systematic perspective on the multiple benefits of this diet is lacking. Here, we performed a short-term intervention with an isocaloric low-carbohydrate diet with increased protein content in obese subjects with NAFLD and characterized the resulting alterations in metabolism and the gut microbiota using a multi-omics approach. We observed rapid and dramatic reductions of liver fat and other cardiometabolic risk factors paralleled by (1) marked decreases in hepatic de novo lipogenesis; (2) large increases in serum beta-hydroxybutyrate concentrations, reflecting increased mitochondrial beta-oxidation; and (3) rapid increases in folate-producing Streptococcus and serum folate concentrations. Liver transcriptomic analysis on biopsy samples from a second cohort revealed downregulation of the fatty acid synthesis pathway and upregulation of folate-mediated one-carbon metabolism and fatty acid oxidation pathways. Our results highlight the potential of exploring diet-microbiota interactions for treating NAFLD.

  • 29.
    Rosario, Dorines
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Benfeitas, Rui
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Bidkhori, Gholamreza
    KTH, Centres, Science for Life Laboratory, SciLifeLab. Royal Inst Technol, Sci Life Lab, Stockholm, Sweden..
    Zhang, Cheng
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Shoaie, Saeed
    Kings Coll London, Ctr Host Microbiome Interact, Dent Inst, London, England.;Karolinska Inst, Ctr Translat Microbiome Res, Dept Microbiol Tumor & Cell Biol, Stockholm, Sweden..
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab. Chalmers Univ Technol, Dept Biol & Biol Engn, Gothenburg, Sweden..
    Understanding the Representative Gut Microbiota Dysbiosis in Metformin-Treated Type 2 Diabetes Patients Using Genome-Scale Metabolic Modeling2018In: Frontiers in Physiology, ISSN 1664-042X, E-ISSN 1664-042X, Vol. 9, article id 775Article in journal (Refereed)
    Abstract [en]

    Dysbiosis in the gut microbiome composition may be promoted by therapeutic drugs such as metformin, the world's most prescribed antidiabetic drug. Under metformin treatment, disturbances of the intestinal microbes lead to increased abundance of Escherichia spp., Akkermansia muciniphila, Subdoligranulum variabile and decreased abundance of Intestinibacter bartlettii. This alteration may potentially lead to adverse effects on the host metabolism, with the depletion of butyrate producer genus. However, an increased production of butyrate and propionate was verified in metformin-treated Type 2 diabetes (T2D) patients. The mechanisms underlying these nutritional alterations and their relation with gut microbiota dysbiosis remain unclear. Here, we used Genomescale Metabolic Models of the representative gut bacteria Escherichia spp., I. bartlettii, A. muciniphila, and S. variabile to elucidate their bacterial metabolism and its effect on intestinal nutrient pool, including macronutrients (e.g., amino acids and short chain fatty acids), minerals and chemical elements (e.g., iron and oxygen). We applied flux balance analysis (FBA) coupled with synthetic lethality analysis interactions to identify combinations of reactions and extracellular nutrients whose absence prevents growth. Our analyses suggest that Escherichia sp. is the bacteria least vulnerable to nutrient availability. We have also examined bacterial contribution to extracellular nutrients including short chain fatty acids, amino acids, and gasses. For instance, Escherichia sp. and S. variabile may contribute to the production of important short chain fatty acids (e.g., acetate and butyrate, respectively) involved in the host physiology under aerobic and anaerobic conditions. We have also identified pathway susceptibility to nutrient availability and reaction changes among the four bacteria using both FBA and flux variability analysis. For instance, lipopolysaccharide synthesis, nucleotide sugar metabolism, and amino acid metabolism are pathways susceptible to changes in Escherichia sp. and A. muciniphila. Our observations highlight important commensal and competing behavior, and their association with cellular metabolism for prevalent gut microbes. The results of our analysis have potential important implications for development of new therapeutic approaches in T2D patients through the development of prebiotics, probiotics, or postbiotics.

  • 30.
    Sahebekhtiari, Navid
    et al.
    Univ Helsinki, Diabet & Obes Res Program, Res Programs Unit, Obes Res Unit, FIN-00014 Helsinki, Finland. araswat, Mayank; Joenvaara, Sakari; Renkonen, Risto.
    Saraswat, Mayank
    Joenvaara, Sakari
    Jokinen, Riikka
    Lovric, Alen
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Kaye, Sanna
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Rissanen, Aila
    Kaprio, Jaakko
    Renkonen, Risto
    Pietilainen, Kirsi H.
    Plasma Proteomics Analysis Reveals Dysregulation of Complement Proteins and Inflammation in Acquired Obesity-A Study on Rare BMI-Discordant Monozygotic Twin Pairs2019In: PROTEOMICS - Clinical Applications, ISSN 1862-8346, E-ISSN 1862-8354, Vol. 13, no 4, article id 1800173Article in journal (Refereed)
    Abstract [en]

    Purpose: The purpose of this study is to elucidate the effect of excess body weight and liver fat on the plasma proteome without interference from genetic variation. Experimental Design: The effect of excess body weight is assessed in young, healthy monozygotic twins from pairs discordant for body mass index (intrapair difference (Δ) in BMI > 3 kg m−2, n = 26) with untargeted LC-MS proteomics quantification. The effect of liver fat is interrogated via subgroup analysis of the BMI-discordant twin cohort: liver fat discordant pairs (Δliver fat > 2%, n = 12) and liver fat concordant pairs (Δliver fat < 2%, n = 14), measured by magnetic resonance spectroscopy. Results: Seventy-five proteins are differentially expressed, with significant enrichment for complement and inflammatory response pathways in the heavier co-twins. The complement dysregulation is found in obesity in both the liver fat subgroups. The complement and inflammatory proteins are significantly associated with adiposity measures, insulin resistance and impaired lipids. Conclusions and Clinical Relevance: The early pathophysiological mechanisms in obesity are incompletely understood. It is shown that aberrant complement regulation in plasma is present in very early stages of clinically healthy obese persons, independently of liver fat and in the absence of genetic variation that typically confounds human studies.

  • 31. Svensson, Maria C.
    et al.
    Borg, David
    Zhang, Cheng
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hedner, Charlotta
    Nodin, Björn
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Leandersson, Karin
    Jirström, Karin
    Expression of PD-L1 and PD-1 in Chemoradiotherapy-Naïve Esophageal and Gastric Adenocarcinoma: Relationship With Mismatch Repair Status and Survival2019In: Frontiers in Oncology, ISSN 2234-943X, E-ISSN 2234-943XArticle in journal (Refereed)
  • 32.
    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.

  • 33.
    Turanli, Beste
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab. Istanbul Medeniyet Univ, Dept Bioengn, Istanbul, Turkey.;Marmara Univ, Dept Bioengn, Istanbul, Turkey..
    Grotli, Morten
    Univ Gothenburg, Dept Chem & Mol Biol, Gothenburg, Sweden..
    Boren, Jan
    Univ Gothenburg, Sahlgrenska Univ Hosp, Dept Mol & Clin Med, Gothenburg, Sweden..
    Nielsen, Jens
    Chalmers Univ Technol, Dept Biol & Biol Engn, Gothenburg, Sweden..
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Arga, Kazim Y.
    Marmara Univ, Dept Bioengn, Istanbul, Turkey..
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab. Chalmers Univ Technol, Dept Biol & Biol Engn, Gothenburg, Sweden..
    Drug Repositioning for Effective Prostate Cancer Treatment2018In: Frontiers in Physiology, ISSN 1664-042X, E-ISSN 1664-042X, Vol. 9, article id 500Article, review/survey (Refereed)
    Abstract [en]

    Drug repositioning has gained attention from both academia and pharmaceutical companies as an auxiliary process to conventional drug discovery. Chemotherapeutic agents have notorious adverse effects that drastically reduce the life quality of cancer patients so drug repositioning is a promising strategy to identify non-cancer drugs which have anti-cancer activity as well as tolerable adverse effects for human health. There are various strategies for discovery and validation of repurposed drugs. In this review, 25 repurposed drug candidates are presented as result of different strategies, 15 of which are already under clinical investigation for treatment of prostate cancer (PCa). To date, zoledronic acid is the only repurposed, clinically used, and approved non-cancer drug for PCa. Anti-cancer activities of existing drugs presented in this review cover diverse and also known mechanisms such as inhibition of mTOR and VEGFR2 signaling, inhibition of PI3K/Akt signaling, COX and selective COX-2 inhibition, NF-kappa B inhibition, Wnt/beta - Catenin pathway inhibition, DNMT1 inhibition, and GSK-3 beta inhibition. In addition to monotherapy option, combination therapy with current anti-cancer drugs may also increase drug efficacy and reduce adverse effects. Thus, drug repositioning may become a key approach for drug discovery in terms of time- and cost-efficiency comparing to conventional drug discovery and development process.

  • 34.
    Turanli, Beste
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Karagoz, Kubra
    Rutgers Canc Inst New Jersey, Dept Radiat Oncol, New Brunswick, NJ USA..
    Bidkhori, Gholamreza
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Sinha, Raghu
    Penn State Coll Med, Dept Biochem & Mol Biol, Hershey, PA 17033 USA..
    Gatza, Michael L.
    Rutgers Canc Inst New Jersey, Dept Radiat Oncol, New Brunswick, NJ USA..
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Arga, Kazim Yalcin
    Marmara Univ, Dept Bioengn, Istanbul, Turkey..
    Multi-Omic Data Interpretation to Repurpose Subtype Specific Drug Candidates for Breast Cancer2019In: Frontiers in Genetics, ISSN 1664-8021, E-ISSN 1664-8021, Vol. 10, article id 420Article in journal (Refereed)
    Abstract [en]

    Triple-negative breast cancer (TNBC), which is largely synonymous with the basal-like molecular subtype, is the 5th leading cause of cancer deaths for women in the United States. The overall prognosis for TNBC patients remains poor given that few treatment options exist; including targeted therapies (not FDA approved), and multi-agent chemotherapy as standard-of-care treatment. TNBC like other complex diseases is governed by the perturbations of the complex interaction networks thereby elucidating the underlying molecular mechanisms of this disease in the context of network principles, which have the potential to identify targets for drug development. Here, we present an integrated "omics" approach based on the use of transcriptome and interactome data to identify dynamic/active protein-protein interaction networks (PPINs) in TNBC patients. We have identified three highly connected modules, EED, DHX9, and AURKA, which are extremely activated in TNBC tumors compared to both normal tissues and other breast cancer subtypes. Based on the functional analyses, we propose that these modules are potential drivers of proliferation and, as such, should be considered candidate molecular targets for drug development or drug repositioning in TNBC. Consistent with this argument, we repurposed steroids, anti-inflammatory agents, anti-infective agents, cardiovascular agents for patients with basal-like breast cancer. Finally, we have performed essential metabolite analysis on personalized genome-scale metabolic models and found that metabolites such as sphingosine-1-phosphate and cholesterol-sulfate have utmost importance in TNBC tumor growth.

  • 35.
    Turanli, Beste
    et al.
    KTH.
    Zhang, Cheng
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Kim, Woonghee
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Benfeitas, Rui
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Yalcin Arga, Kazim
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Discovery of therapeutic agents for prostate cancer using genome-scale metabolic modeling and drug repositioning2019In: EBioMedicine, E-ISSN 2352-3964, Vol. 42, p. 386-396Article in journal (Refereed)
    Abstract [sv]

    Background: Genome-scale metabolic models (GEMs)offer insights into cancer metabolism and have been used to identify potential biomarkers and drug targets. Drug repositioning is a time- and cost-effective method of drug discovery that can be applied together with GEMs for effective cancer treatment. Methods: In this study, we reconstruct a prostate cancer (PRAD)-specific GEM for exploring prostate cancer metabolism and also repurposing new therapeutic agents that can be used in development of effective cancer treatment. We integrate global gene expression profiling of cell lines with >1000 different drugs through the use of prostate cancer GEM and predict possible drug-gene interactions. Findings: We identify the key reactions with altered fluxes based on the gene expression changes and predict the potential drug effect in prostate cancer treatment. We find that sulfamethoxypyridazine, azlocillin, hydroflumethiazide, and ifenprodil can be repurposed for the treatment of prostate cancer based on an in silico cell viability assay. Finally, we validate the effect of ifenprodil using an in vitro cell assay and show its inhibitory effect on a prostate cancer cell line. Interpretation: Our approach demonstate how GEMs can be used to predict therapeutic agents for cancer treatment based on drug repositioning. Besides, it paved a way and shed a light on the applicability of computational models to real-world biomedical or pharmaceutical problems.

  • 36.
    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.

  • 37.
    Zhang, Cheng
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Aldrees, Mohammed
    Arif, Muhammad
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Li, Xiangyu
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Aziz, Mohammad Azhar
    Elucidating the Reprograming of Colorectal Cancer Metabolism Using Genome-Scale Metabolic Modeling2019In: Frontiers in Oncology, ISSN 2234-943X, E-ISSN 2234-943X, Vol. 9, article id 681Article in journal (Refereed)
    Abstract [en]

    Colorectal cancer is the third most incidental cancer worldwide, and the response rate of current treatment for colorectal cancer is very low. Genome-scale metabolic models (GEMs) are systems biology platforms, and they had been used to assist researchers in understanding the metabolic alterations in different types of cancer. Here, we reconstructed a generic colorectal cancer GEM by merging 374 personalized GEMs from the Human Pathology Atlas and used it as a platform for systematic investigation of the difference between tumor and normal samples. The reconstructed model revealed the metabolic reprogramming in glutathione as well as the arginine and proline metabolism in response to tumor occurrence. In addition, six genes including ODC1, SMS, SRM, RRM2, SMOX, and SAT1 associated with arginine and proline metabolism were found to be key players in this metabolic alteration. We also investigated these genes in independent colorectal cancer patients and cell lines and found that many of these genes showed elevated level in colorectal cancer and exhibited adverse effect in patients. Therefore, these genes could be promising therapeutic targets for treatment of a specific colon cancer patient group.

  • 38.
    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.

  • 39. Zhang, Xiang
    et al.
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab. Chalmers University of Technology, Sweden.
    Joosten, Leo A. B.
    Kuivenhoven, Jan A.
    Li, Yang
    Netea, Mihai G.
    Groen, Albert K.
    Identification of Discriminating Metabolic Pathways and Metabolites in Human PBMCs Stimulated by Various Pathogenic Agents2018In: Frontiers in Physiology, ISSN 1664-042X, E-ISSN 1664-042X, Vol. 9, article id 139Article in journal (Refereed)
    Abstract [en]

    Immunity and cellular metabolism are tightly interconnected but it is not clear whether different pathogens elicit specific metabolic responses. To address this issue, we studied differential metabolic regulation in peripheral blood mononuclear cells (PBMCs) of healthy volunteers challenged by Candida albicans, Borrelia burgdorferi, lipopolysaccharide, and Mycobacterium tuberculosis in vitro. By integrating gene expression data of stimulated PBMCs of healthy individuals with the KEGG pathways, we identified both common and pathogen-specific regulated pathways depending on the time of incubation. At 4 h of incubation, pathogenic agents inhibited expression of genes involved in both the glycolysis and oxidative phosphorylation pathways. In contrast, at 24 h of incubation, particularly glycolysis was enhanced while genes involved in oxidative phosphorylation remained unaltered in the PBMCs. In general, differential gene expression was less pronounced at 4 h compared to 24 h of incubation. KEGG pathway analysis allowed differentiation between effects induced by Candida and bacterial stimuli. Application of genome-scale metabolic model further generated a Candida-specific set of 103 reporter metabolites (e.g., desmosterol) that might serve as biomarkers discriminating Candida stimulated PBMCs from bacteria-stimuated PBMCs. Our analysis also identified a set of 49 metabolites that allowed discrimination between the effects of Borrelia burgdorferi, lipopolysaccharide and Mycobacterium tuberculosis. We conclude that analysis of pathogen-induced effects on PBMCs by a combination of KEGG pathways and genome-scale metabolic model provides deep insight in the metabolic changes coupled to host defense.

1 - 39 of 39
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