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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.
    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)
  • 5.
    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.

  • 6.
    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.

  • 7.
    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.

  • 8.
    Zhang, Cheng
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Bidkhori, Gholamreza
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Benfeitas, Rui
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lee, Sunjae
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Arif, Muhammad
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Uhlen, Mathias
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    ESS: A Tool for Genome-Scale Quantification of Essentiality Score for Reaction/Genes in Constraint-Based Modeling2018In: Frontiers in Physiology, ISSN 1664-042X, E-ISSN 1664-042X, Vol. 9, article id 1355Article in journal (Refereed)
    Abstract [en]

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

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