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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. 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.

  • 3. 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)
  • 4. Kim, D.
    et al.
    Lee, J.
    Lee, Sunjae
    KTH, Centres, Science for Life Laboratory, SciLifeLab. Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, South Korea.
    Park, J.
    Lee, D.
    Predicting unintended effects of drugs based on off-target tissue effects2016In: Biochemical and Biophysical Research Communications - BBRC, ISSN 0006-291X, E-ISSN 1090-2104, Vol. 469, no 3, p. 399-404Article in journal (Refereed)
    Abstract [en]

    Unintended effects of drugs can be caused by various mechanisms. Conventional analysis of unintended effects has focused on the target proteins of drugs. However, an interaction with off-target tissues of a drug might be one of the unintended effect-related mechanisms. We propose two processes to predict a drug’s unintended effects by off-target tissue effects: 1) identification of a drug’s off-target tissue and; 2) tissue protein - symptom relation identification (tissue protein - symptom matrix). Using this method, we predicted that 1,177 (10.7%) side-effects were related to off-target tissue effects in 11,041 known side-effects. Off-target tissues and unintended effects of successful repositioning drugs were also predicted. The effectiveness of relations of the proposed tissue protein - symptom matrix were evaluated by using the literature mining method. We predicted unintended effects of drugs as well as those effect-related off-target tissues. By using our prediction, we are able to reduce drug side-effects on off-target tissues and provide a chance to identify new indications of drugs of interest.

  • 5.
    Lee, Sunjae
    et al.
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab. Korea Adv Inst Sci & Technol, South Korea.
    Mardinoglu, Adil
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab. Chalmers, Sweden.
    Zhang, Cheng
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lee, Doheon
    Nielsen, Jens
    KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab. Chalmers, Sweden.
    Dysregulated signaling hubs of liver lipid metabolism reveal hepatocellular carcinoma pathogenesis2016In: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 44, no 12, p. 5529-5539Article in journal (Refereed)
    Abstract [en]

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

  • 6.
    Lee, Sunjae
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Zhang, Cheng
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Arif, Muhammad
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    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.
    Al Shobky, Mohamed
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lovric, Alen
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Boren, J.
    Nielsen, J.
    KTH, Centres, Science for Life Laboratory, SciLifeLab. Department of Biology and Biological Engineering, Chalmers University of Technology.
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab. Department of Biology and Biological Engineering, Chalmers University of Technology.
    TCSBN: A database of tissue and cancer specific biological networks2018In: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 46, no D1, p. D595-D600Article in journal (Refereed)
    Abstract [en]

    Biological networks provide new opportunities for understanding the cellular biology in both health and disease states. We generated tissue specific integrated networks (INs) for liver, muscle and adipose tissues by integrating metabolic, regulatory and protein-protein interaction networks. We also generated human co-expression networks (CNs) for 46 normal tissues and 17 cancers to explore the functional relationships between genes as well as their relationships with biological functions, and investigate the overlap between functional and physical interactions provided by CNs and INs, respectively. These networks can be employed in the analysis of omics data, provide detailed insight into disease mechanisms by identifying the key biological components and eventually can be used in the development of efficient treatment strategies. Moreover, comparative analysis of the networks may allow for the identification of tissue-specific targets that can be used in the development of drugs with the minimum toxic effect to other human tissues. These context-specific INs and CNs are presented in an interactive website http://inetmodels.com without any limitation. 

  • 7.
    Lee, Sunjae
    et al.
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Zhang, Cheng
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Kilicarslan, Murat
    Piening, Brian D.
    Björnson, Elias
    Hallström, Björn M.
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Groen, Albert K.
    Ferrannini, Ele
    Laakso, Markku
    Snyder, Michael
    Bluher, Matthias
    Uhlèn, Mathias
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Nielsen, Jens
    KTH, School of Biotechnology (BIO), Gene Technology. Chalmers, Sweden.
    Smith, Ulf
    Serlie, Mireille J.
    Boren, Jan
    Mardinoglu, Adil
    Integrated Network Analysis Reveals an Association between Plasma Mannose Levels and Insulin Resistance2016In: Cell Metabolism, ISSN 1550-4131, E-ISSN 1932-7420, Vol. 24, no 1, p. 172-184Article in journal (Refereed)
    Abstract [en]

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

  • 8.
    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
    Stahlman, Marcus
    Sikanic, Natasha
    Park, Joshua K.
    Deshmukh, Sumit
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Harzandi, Azadeh M.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Kuijpers, Tim
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Grotli, Morten
    Elsasser, Simon J.
    Piening, Brian D.
    Snyder, Michael
    Smith, Ulf
    Nielsen, Jens
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Backhed, 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 1744-4292, Vol. 13, no 8, article id 938Article in journal (Refereed)
    Abstract [en]

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

  • 9.
    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)
  • 10.
    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.
    $$$Kim, Woonghee
    KTH Royal Inst Technol, Sci Life Lab, Stockholm, Sweden..
    Klevstig, Martina
    Univ Gothenburg, Dept Mol & Clin Med, Gothenburg, Sweden.;Sahlgrens Univ Hosp, Gothenburg, Sweden..
    $$$Harzandi, Azadeh M.
    KTH Royal Inst Technol, Sci Life Lab, Stockholm, Sweden..
    $$$Sikanic, Natasa
    KTH Royal Inst Technol, Sci Life Lab, Stockholm, Sweden..
    $$$Arif, Muhammad
    KTH Royal Inst Technol, Sci Life Lab, Stockholm, Sweden..
    Stahlman, Marcus
    Univ Gothenburg, Dept Mol & Clin Med, Gothenburg, Sweden.;Sahlgrens Univ Hosp, Gothenburg, Sweden..
    Nielsen, Jens
    Chalmers Univ Technol, Dept Biol & Biol Engn, Gothenburg, Sweden..
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Boren, Jan
    Univ Gothenburg, Dept Mol & Clin Med, Gothenburg, Sweden.;Sahlgrens Univ Hosp, Gothenburg, Sweden..
    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 1096-7184, Vol. 52, p. 263-272Article in journal (Refereed)
    Abstract [en]

    The pathogenesis of non-alcoholic fatty liver disease (NAFLD) and hepatocellular carcinoma (HCC) has been associated with altered expression of liver-specific genes including pyruvate kinase liver and red blood cell (PKLR), patatin-like phospholipase domain containing 3 (PNPLA3) and proprotein convertase subtilisin/kexin type 9 (PCSK9). Here, we inhibited and overexpressed the expression of these three genes in HepG2 cells, generated RNA-seq data before and after perturbation and revealed the altered global biological functions with the modulation of these genes using integrated network (IN) analysis. We found that modulation of these genes effects the total triglycerides levels within the cells and viability of the cells. Next, we generated IN for HepG2 cells, identified reporter transcription factors based on IN and found that the modulation of these genes affects key metabolic pathways associated with lipid metabolism (steroid biosynthesis, PPAR signalling pathway, fatty acid synthesis and oxidation) and cancer development (DNA replication, cell cycle and p53 signalling) involved in the progression of NAFLD and HCC. Finally, we observed that inhibition of PKLR lead to decreased glucose uptake and decreased mitochondrial activity in HepG2 cells. Hence, our systems level analysis indicated that PKLR can be targeted for development efficient treatment strategy for NAFLD and HCC.

  • 11.
    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.

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

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

  • 13.
    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.

  • 14.
    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.

  • 15.
    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.

  • 16.
    Zhang, Cheng
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lee, Sunjae
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Mardinoglu, Adil
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab. Chalmers, Dept Biol & Biol Engn, Sweden.
    Hua, Qiang
    Investigating the Combinatory Effects of Biological Networks on Gene Co-expression2016In: Frontiers in Physiology, ISSN 1664-042X, E-ISSN 1664-042X, Vol. 7, article id 160Article in journal (Refereed)
    Abstract [en]

    Co-expressed genes often share similar functions, and gene co-expression networks have been widely used in studying the functionality of gene modules. Previous analysis indicated that genes are more likely to be co-expressed if they are either regulated by the same transcription factors, forming protein complexes or sharing similar topological properties in protein-protein interaction networks. Here, we reconstructed transcriptional regulatory and protein-protein networks for Saccharornyces cerevisiae using well-established databases, and we evaluated their co-expression activities using publically available gene expression data. Based on our network-dependent analysis, we found that genes that were co-regulated in the transcription regulatory networks and shared similar neighbors in the protein-protein networks were more likely to be co-expressed. Moreover, their biological functions were closely related.

1 - 16 of 16
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