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  • 1.
    Benfeitas, Rui
    et al.
    KTH, Centra, Science for Life Laboratory, SciLifeLab. Royal Institute of Technology, KTH.
    Bidkhori, Gholamreza
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Mukhopadhyay, Bani
    Klevstig, Martina
    Arif, Muhammad
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Proteinvetenskap, Systembiologi.
    Zhang, Cheng
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Lee, Sunjae
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Cinar, Resat
    Nielsen, Jens
    Uhlén, Mathias
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Boren, Jan
    Kunos, George
    Mardinoglu, Adil
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Characterization of heterogeneous redox responses in hepatocellular carcinoma patients using network analysis2019Ingår i: EBioMedicine, E-ISSN 2352-3964Artikel i tidskrift (Refereegranskat)
  • 2.
    Bidkhori, Gholamreza
    et al.
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Benfeitas, Rui
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Klevstig, Martina
    Zhang, Cheng
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Nielsen, Jens
    Uhlén, Mathias
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Boren, Jan
    Mardinoglu, Adil
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Metabolic network-based stratification of hepatocellular carcinoma reveals three distinct tumor subtypes2018Ingår i: Proceedings of the National Academy of Sciences of the United States of America, ISSN 0027-8424, E-ISSN 1091-6490Artikel i tidskrift (Refereegranskat)
  • 3. 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, Centra, Science for Life Laboratory, SciLifeLab.
    Zhang, Cheng
    KTH, Centra, 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 cancer2019Ingår i: International Journal of Cancer, ISSN 0020-7136, E-ISSN 1097-0215, Vol. 145, nr 4, s. 901-915Artikel i tidskrift (Refereegranskat)
    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.

  • 4.
    Danielsson, Frida
    et al.
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Fasterius, Erik
    KTH, Skolan för bioteknologi (BIO), Proteomik (stängd 20130101).
    Sullivan, Devin
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Hases, Linnea
    KTH, Centra, Science for Life Laboratory, SciLifeLab. Karolinska Institute, Huddinge, Sweden.
    Sanli, Kemal
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Zhang, Cheng
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Mardinoglu, Adil
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Al-Khalili Szigyarto, Cristina
    KTH, Skolan för bioteknologi (BIO), Proteomik (stängd 20130101).
    Huss, M.
    Uhlén, Mathias
    KTH, Skolan för bioteknologi (BIO), Proteomik (stängd 20130101). KTH, Centra, Science for Life Laboratory, SciLifeLab. Technical University of Denmark, Hørsholm, Denmark.
    Williams, Cecilia
    KTH, Centra, Science for Life Laboratory, SciLifeLab. Karolinska Institute, Huddinge, Sweden.
    Lundberg, Emma
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Transcriptome profiling of the interconnection of pathways involved in malignant transformation and response to hypoxia2018Ingår i: OncoTarget, ISSN 1949-2553, E-ISSN 1949-2553, Vol. 9, nr 28, s. 19730-19744Artikel i tidskrift (Refereegranskat)
    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.

  • 5. Gu, Deqing
    et al.
    Jian, Xingxing
    Zhang, Cheng
    Hua, Qiang
    Reframed genome-scale metabolic model to facilitate genetic design and integration with expression data2016Ingår i: IEEE/ACM Transactions on Computational Biology & Bioinformatics, ISSN 1545-5963, E-ISSN 1557-9964Artikel i tidskrift (Refereegranskat)
  • 6. Gu, Deqing
    et al.
    Zhang, Cheng
    Zhou, Shengguo
    Wei, Liujing
    Hua, Qiang
    IdealKnock: A framework for efficiently identifying knockout strategiesleading to targeted overproduction2016Ingår i: Computational biology and chemistry (Print), ISSN 1476-9271, E-ISSN 1476-928XArtikel i tidskrift (Refereegranskat)
  • 7. Harms, Matthew J.
    et al.
    Li, Qian
    Lee, Sunjae
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Zhang, Cheng
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Kull, Bengt
    Hallen, Stefan
    Thorell, Anders
    Alexandersson, Ida
    Hagberg, Carolina E.
    Peng, Xiao-Rong
    Mardinoglu, Adil
    KTH, Centra, 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 Adipocytes2019Ingår i: Cell reports, ISSN 2211-1247, E-ISSN 2211-1247Artikel i tidskrift (Refereegranskat)
  • 8. Jian, Xingxing
    et al.
    Li, Ningchuan
    Zhang, Cheng
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Hua, Qiang
    In silico profiling of cell growthand succinate production in Escherichia coli NZN1112016Ingår i: Bioresources and BioprocessArtikel i tidskrift (Refereegranskat)
  • 9. Jian, Xingxing
    et al.
    Zhou, Shengguo
    Zhang, Cheng
    Hua, Qiang
    In silico identification of gene amplification targets based on analysisof production and growth coupling2016Ingår i: Biosystems (Amsterdam. Print), ISSN 0303-2647, E-ISSN 1872-8324Artikel i tidskrift (Refereegranskat)
  • 10.
    Lee, Sunjae
    et al.
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab. Korea Adv Inst Sci & Technol, South Korea.
    Mardinoglu, Adil
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab. Chalmers, Sweden.
    Zhang, Cheng
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Lee, Doheon
    Nielsen, Jens
    KTH, Skolan för bioteknologi (BIO), Genteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab. Chalmers, Sweden.
    Dysregulated signaling hubs of liver lipid metabolism reveal hepatocellular carcinoma pathogenesis2016Ingår i: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 44, nr 12, s. 5529-5539Artikel i tidskrift (Refereegranskat)
    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.

  • 11.
    Lee, Sunjae
    et al.
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Zhang, Cheng
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Arif, Muhammad
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Proteinvetenskap, Systembiologi.
    Liu, Zhengtao
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Benfeitas, Rui
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Bidkhori, Gholamreza
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Deshmukh, Sumit
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Shobky, Mohamed AI
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Lovric, Alen
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Boren, Jan
    Nielsen, Jens
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Uhlén, Mathias
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Mardinoglu, Adil
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    TCSBN: a database of tissue and cancer specific biological networks2017Ingår i: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962Artikel i tidskrift (Refereegranskat)
  • 12.
    Lee, Sunjae
    et al.
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Zhang, Cheng
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Kilicarslan, Murat
    Piening, Brian D.
    Björnson, Elias
    Hallström, Björn M.
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Groen, Albert K.
    Ferrannini, Ele
    Laakso, Markku
    Snyder, Michael
    Bluher, Matthias
    Uhlèn, Mathias
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Nielsen, Jens
    KTH, Skolan för bioteknologi (BIO), Genteknologi. Chalmers, Sweden.
    Smith, Ulf
    Serlie, Mireille J.
    Boren, Jan
    Mardinoglu, Adil
    Integrated Network Analysis Reveals an Association between Plasma Mannose Levels and Insulin Resistance2016Ingår i: Cell Metabolism, ISSN 1550-4131, E-ISSN 1932-7420, Vol. 24, nr 1, s. 172-184Artikel i tidskrift (Refereegranskat)
    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.

  • 13.
    Lee, Sunjae
    et al.
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Zhang, Cheng
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Liu, Zhengtao
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Klevstig, Martina
    Mukhopadhyay, Bani
    Bergentall, Mattias
    Cinar, Resat
    Ståhlman, Marcus
    Sikanic, Natasa
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Proteinvetenskap.
    Park, Joshua K.
    Deshmukh, Sumit
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Proteinvetenskap.
    Harzandi, Azadeh M.
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Kuijpers, Tim
    KTH.
    Grotli, Morten
    Elsässer, Simon J.
    Piening, Brian D.
    Snyder, Michael
    Smith, Ulf
    Nielsen, Jens
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Bäckhed, Fredrik
    Kunos, George
    Uhlén, Mathias
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Boren, Jan
    Mardinoglu, Adil
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Network analyses identify liver-specific targets for treating liver diseases2017Ingår i: Molecular Systems Biology, ISSN 1744-4292, E-ISSN 1744-4292Artikel i tidskrift (Refereegranskat)
  • 14.
    Liu, Zhengtao
    et al.
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Zhang, Cheng
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Lee, Sunjae
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Kim, Woonghee
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Proteinvetenskap, Systembiologi.
    Klevstig, Martina
    Harzandi, Azadeh M.
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Sikanic, Natasa
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Proteinvetenskap.
    Arif, Muhammad
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Proteinvetenskap, Systembiologi.
    Ståhlman, Marcus
    Nielsen, Jens
    Uhlén, Mathias
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Boren, Jan
    Mardinoglu, Adil
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Pyruvate kinase L/R is a regulator of lipid metabolism and mitochondrial function2019Ingår i: Metabolic engineering, ISSN 1096-7176, E-ISSN 1096-7184Artikel i tidskrift (Refereegranskat)
  • 15. Lundgren, Sebastian
    et al.
    Fagerström-Vahman, Helena
    Zhang, Cheng
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Ben-Dror, Liv
    Mardinoglu, Adil
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Uhlén, Mathias
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Nodin, Björn
    Jirström, Karin
    Discovery of KIRREL as a biomarker for prognostic stratification of patients within melanoma2019Ingår i: Biomarker Research, ISSN 0961-088X, E-ISSN 1475-925XArtikel i tidskrift (Refereegranskat)
  • 16.
    Mahdessian, Diana
    et al.
    KTH, Centra, Science for Life Laboratory, SciLifeLab. KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Proteinvetenskap, Cellulär och klinisk proteomik.
    Sullivan, D. P.
    KTH, Centra, Science for Life Laboratory, SciLifeLab. KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Proteinvetenskap, Cellulär och klinisk proteomik.
    Danielsson, Frida
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Proteinvetenskap, Cellulär och klinisk proteomik. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Arif, Muhammad
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Proteinvetenskap, Systembiologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Zhang, Cheng
    KTH, Centra, Science for Life Laboratory, SciLifeLab. KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Proteinvetenskap, Systembiologi.
    Åkesson, Lovisa
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Proteinvetenskap, Cellulär och klinisk proteomik. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Gnann, Christian
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Proteinvetenskap, Cellulär och klinisk proteomik. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Shutten, Rutger
    KTH, Centra, Science for Life Laboratory, SciLifeLab. KTH, Skolan för kemi, bioteknologi och hälsa (CBH).
    Thul, Peter
    KTH, Centra, Science for Life Laboratory, SciLifeLab. KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Proteinvetenskap, Cellulär och klinisk proteomik.
    Carja, Oana
    Department of Genetics, Stanford University, Stanford, CA 94305, USA. ; Chan Zuckerberg Biohub, San Francisco, San Francisco, CA 94158, USA..
    Ayoglu, Burcu
    KTH, Centra, Science for Life Laboratory, SciLifeLab. KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Proteinvetenskap, Cellulär och klinisk proteomik.
    Mardinoglu, Adil
    KTH, Centra, Science for Life Laboratory, SciLifeLab. KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Proteinvetenskap, Systembiologi. 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, Centra, Science for Life Laboratory, SciLifeLab. KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Proteinvetenskap, Systembiologi.
    Lindskog, Cecilia
    Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, SE-751 85 Uppsala, Sweden..
    Lundberg, Emma
    KTH, Centra, Science for Life Laboratory, SciLifeLab. KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Proteinvetenskap, Cellulär och klinisk proteomik. 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 proteomeManuskript (preprint) (Övrigt vetenskapligt)
    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.

  • 17.
    Mardinoglu, Adil
    et al.
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Bjornson, Elias
    Zhang, Cheng
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Klevstig, Martina
    Soderlund, Sanni
    Stahlman, Marcus
    Adiels, Martin
    Hakkarainen, Antti
    Lundbom, Nina
    Kilicarslan, Murat
    Hallstrom, Bjorn M.
    Lundbom, Jesper
    Verges, Bruno
    Barrett, Peter Hugh R.
    Watts, Gerald F.
    Serlie, Mireille J.
    Nielsen, Jens
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Uhlén, Mathias
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Smith, Ulf
    Marschall, Hanns-Ulrich
    Taskinen, Marja-Riitta
    Boren, Jan
    Personal model-assisted identification of NAD(+) and glutathione metabolism as intervention target in NAFLD2017Ingår i: Molecular Systems Biology, ISSN 1744-4292, E-ISSN 1744-4292, Vol. 13, nr 3, artikel-id 916Artikel i tidskrift (Refereegranskat)
    Abstract [en]

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

  • 18.
    Mardinoglu, Adil
    et al.
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab. Chalmers University of Technology, Sweden.
    Shoaie, Saeed
    Bergentall, Mattias
    Ghaffari, Pouyan
    Zhang, Cheng
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Larsson, Erik
    Backhed, Fredrik
    Nielsen, Jens
    KTH, Skolan för bioteknologi (BIO), Genteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab. Chalmers University of Technology, Sweden.
    The gut microbiota modulates host amino acid and glutathione metabolism in mice2015Ingår i: Molecular Systems Biology, ISSN 1744-4292, E-ISSN 1744-4292, Vol. 11, nr 10, artikel-id 834Artikel i tidskrift (Refereegranskat)
    Abstract [en]

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

  • 19.
    Mardinoglu, Adil
    et al.
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Wu, Hao
    Björnson, Elias
    Zhang, Cheng
    KTH, Centra, 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, Centra, 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 Humans2018Ingår i: Cell Metabolism, ISSN 1550-4131, E-ISSN 1932-7420, Vol. 27, nr 3, s. 559-571.e5Artikel i tidskrift (Refereegranskat)
    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.

  • 20.
    Olin, Axel
    et al.
    Karolinska Inst, Dept Womens & Childrens Hlth, Sci Life Lab, S-17121 Solna, Sweden..
    Henckel, Ewa
    Karolinska Inst, Dept Clin Sci Intervent & Technol, S-14152 Solna, Sweden.;Karolinska Univ Hosp, Dept Neonatol, S-17176 Solna, Sweden..
    Chen, Yang
    Karolinska Inst, Dept Womens & Childrens Hlth, Sci Life Lab, S-17121 Solna, Sweden..
    Lakshmikanth, Tadepally
    Karolinska Inst, Dept Womens & Childrens Hlth, Sci Life Lab, S-17121 Solna, Sweden..
    Pou, Christian
    Karolinska Inst, Dept Womens & Childrens Hlth, Sci Life Lab, S-17121 Solna, Sweden..
    Mikes, Jaromir
    Karolinska Inst, Dept Womens & Childrens Hlth, Sci Life Lab, S-17121 Solna, Sweden..
    Gustafsson, Anna
    Karolinska Inst, Dept Clin Sci Intervent & Technol, S-14152 Solna, Sweden.;Karolinska Univ Hosp, Dept Neonatol, S-17176 Solna, Sweden..
    Bernhardsson, Anna Karin
    Karolinska Inst, Dept Womens & Childrens Hlth, Sci Life Lab, S-17121 Solna, Sweden.;Karolinska Univ Hosp, Dept Neonatol, S-17176 Solna, Sweden..
    Zhang, Cheng
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Bohlin, Kajsa
    Karolinska Inst, Dept Clin Sci Intervent & Technol, S-14152 Solna, Sweden.;Karolinska Univ Hosp, Dept Neonatol, S-17176 Solna, Sweden..
    Brodin, Petter
    Karolinska Inst, Dept Womens & Childrens Hlth, Sci Life Lab, S-17121 Solna, Sweden.;Karolinska Univ Hosp, Dept Neonatol, S-17176 Solna, Sweden..
    Stereotypic Immune System Development in Newborn Children2018Ingår i: Cell, ISSN 0092-8674, E-ISSN 1097-4172, Vol. 174, nr 5, s. 1277-+Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Epidemiological data suggest that early life exposures are key determinants of immune-mediated disease later in life. Young children are also particularly susceptible to infections, warranting more analyses of immune system development early in life. Such analyses mostly have been performed in mouse models or human cord blood samples, but these cannot account for the complex environmental exposures influencing human newborns after birth. Here, we performed longitudinal analyses in 100 newborn children, sampled up to 4 times during their first 3 months of life. From 100 mu L of blood, we analyze the development of 58 immune cell populations by mass cytometry and 267 plasma proteins by immunoassays, uncovering drastic changes not predictable from cord blood measurements but following a stereotypic pattern. Preterm and term children differ at birth but converge onto a shared trajectory, seemingly driven by microbial interactions and hampered by early gut bacterial dysbiosis.

  • 21. Piening, Brian D.
    et al.
    Zhou, Wenyu
    Contrepois, Kevin
    Röst, Hannes
    Urban, Cucci 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
    Zhang, Cheng
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Wheeler, Jessica
    Sailani, M. Reza
    Liang, Liang
    Abbott, Charles
    Gerstein, Mark
    Mardinoglu, Adil
    Smith, Ulf
    Rubin, Daniel L.
    Pitteri, Sharon
    Sodergren, Erica
    McLaughlin, Tracey L.
    Weinstrock, George M.
    Snyder, Michael P.
    Integrative Personal Omics Profiles during Periods of Weight Gain and Loss2018Ingår i: Cell SystemsArtikel i tidskrift (Refereegranskat)
  • 22. Pineau, C.
    et al.
    Hikmet, F.
    Zhang, Cheng
    KTH, Centra, Science for Life Laboratory, SciLifeLab. KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Proteinvetenskap, Systembiologi.
    Oksvold, Per
    KTH, Centra, Science for Life Laboratory, SciLifeLab. KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Proteinvetenskap, Systembiologi.
    Chen, Shuqi
    KTH, Centra, Science for Life Laboratory, SciLifeLab. KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Proteinvetenskap, Systembiologi.
    Fagerberg, Linn
    KTH, Centra, Science for Life Laboratory, SciLifeLab. KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Proteinvetenskap, Systembiologi.
    Uhlén, Mathias
    KTH, Centra, Science for Life Laboratory, SciLifeLab. KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Proteinvetenskap, Systembiologi.
    Lindskog, C.
    Cell Type-Specific Expression of Testis Elevated Genes Based on Transcriptomics and Antibody-Based Proteomics2019Ingår i: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    One of the most complex organs in the human body is the testis, where spermatogenesis takes place. This physiological process involves thousands of genes and proteins that are activated and repressed, making testis the organ with the highest number of tissue-specific genes. However, the function of a large proportion of the corresponding proteins remains unknown and testis harbors many missing proteins (MPs), defined as products of protein-coding genes that lack experimental mass spectrometry evidence. Here, an integrated omics approach was used for exploring the cell type-specific protein expression of genes with an elevated expression in testis. By combining genome-wide transcriptomics analysis with immunohistochemistry, more than 500 proteins with distinct testicular protein expression patterns were identified, and these were selected for in-depth characterization of their in situ expression in eight different testicular cell types. The cell type-specific protein expression patterns allowed us to identify six distinct clusters of expression at different stages of spermatogenesis. The analysis highlighted numerous poorly characterized proteins in each of these clusters whose expression overlapped with that of known proteins involved in spermatogenesis, including 88 proteins with an unknown function and 60 proteins that previously have been classified as MPs. Furthermore, we were able to characterize the in situ distribution of several proteins that previously lacked spatial information and cell type-specific expression within the testis. The testis elevated expression levels both at the RNA and protein levels suggest that these proteins are related to testis-specific functions. In summary, the study demonstrates the power of combining genome-wide transcriptomics analysis with antibody-based protein profiling to explore the cell type-specific expression of both well-known proteins and MPs. The analyzed proteins constitute important targets for further testis-specific research in male reproductive disorders. Copyright

  • 23.
    Rosario, Dorines
    et al.
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Benfeitas, Rui
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Bidkhori, Gholamreza
    KTH, Centra, Science for Life Laboratory, SciLifeLab. Royal Inst Technol, Sci Life Lab, Stockholm, Sweden..
    Zhang, Cheng
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Uhlén, Mathias
    KTH, Centra, 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, Centra, 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 Modeling2018Ingår i: Frontiers in Physiology, ISSN 1664-042X, E-ISSN 1664-042X, Vol. 9, artikel-id 775Artikel i tidskrift (Refereegranskat)
    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.

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

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

  • 25. Svensson, Maria C.
    et al.
    Borg, David
    Zhang, Cheng
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Hedner, Charlotta
    Nodin, Björn
    Uhlén, Mathias
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Mardinoglu, Adil
    KTH, Centra, 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 Survival2019Ingår i: Frontiers in Oncology, ISSN 2234-943X, E-ISSN 2234-943XArtikel i tidskrift (Refereegranskat)
  • 26.
    Thul, Peter J.
    et al.
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Åkesson, Lovisa
    KTH, Skolan för bioteknologi (BIO). KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Wiking, Mikaela
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Mahdessian, Diana
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Geladaki, A.
    Ait Blal, Hammou
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Alm, Tove L.
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Asplund, A.
    Björk, Lars
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Breckels, L. M.
    Bäckström, Anna
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Danielsson, Frida
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Fagerberg, Linn
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Fall, Jenny
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Gatto, L.
    Gnann, Christian
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Hober, Sophia
    KTH, Skolan för bioteknologi (BIO), Proteinteknologi.
    Hjelmare, Martin
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Johansson, Fredric
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Lee, Sunjae
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Lindskog, C.
    Mulder, J.
    Mulvey, C. M.
    Nilsson, Peter
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Oksvold, Per
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Rockberg, Johan
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi.
    Schutten, Rutger
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Schwenk, Jochen M.
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Sivertsson, Åsa
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Sjöstedt, E.
    Skogs, Marie
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Stadler, Charlotte
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Sullivan, Devin P.
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Tegel, Hanna
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi.
    Winsnes, Casper F.
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Zhang, Cheng
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Zwahlen, Martin
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Mardinoglu, Adil
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Pontén, F.
    von Feilitzen, Kalle
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Lilley, K. S.
    Uhlén, Mathias
    KTH, Centra, Science for Life Laboratory, SciLifeLab. KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi.
    Lundberg, Emma
    KTH, Centra, Science for Life Laboratory, SciLifeLab. KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi.
    A subcellular map of the human proteome2017Ingår i: Science, ISSN 0036-8075, E-ISSN 1095-9203, Vol. 356, nr 6340, artikel-id 820Artikel i tidskrift (Refereegranskat)
    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.

  • 27.
    Turanli, Beste
    et al.
    KTH.
    Zhang, Cheng
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Kim, Woonghee
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Proteinvetenskap, Systembiologi.
    Benfeitas, Rui
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Uhlén, Mathias
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Yalcin Arga, Kazim
    Mardinoglu, Adil
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Discovery of therapeutic agents for prostate cancer using genome-scale metabolic modeling and drug repositioning2019Ingår i: EBioMedicine, E-ISSN 2352-3964, Vol. 42, s. 386-396Artikel i tidskrift (Refereegranskat)
    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.

  • 28.
    Uhlén, Mathias
    et al.
    KTH, Centra, Science for Life Laboratory, SciLifeLab. KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. Center for Biosustainability, Danish Technical University, Copenhagen, Denmark..
    Zhang, Cheng
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Lee, Sunjae
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Sjöstedt, Evelina
    KTH, Centra, Science for Life Laboratory, SciLifeLab. Department of Immunology Genetics and Pathology, Uppsala University, Uppsala, Sweden..
    Fagerberg, Linn
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Bidkhori, Gholamreza
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Benfeitas, Rui
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Arif, Muhammad
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Liu, Zhengtao
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Edfors, Fredrik
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Sanli, Kemal
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    von Feilitzen, Kalle
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Oksvold, Per
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Lundberg, Emma
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Hober, Sophia
    KTH, Skolan för bioteknologi (BIO).
    Nilsson, Peter
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Mattsson, Johanna
    Schwenk, Jochen M.
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Brunnstrom, Hans
    Glimelius, Bengt
    Sjoblom, Tobias
    Edqvist, Per-Henrik
    Djureinovic, Dijana
    Micke, Patrick
    Lindskog, Cecilia
    Mardinoglu, Adil
    KTH, Centra, Science for Life Laboratory, SciLifeLab. KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi.
    Ponten, Fredrik
    A pathology atlas of the human cancer transcriptome2017Ingår i: Science, ISSN 0036-8075, E-ISSN 1095-9203, Vol. 357, nr 6352, s. 660-+Artikel i tidskrift (Refereegranskat)
    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.

  • 29.
    Wei, S.
    et al.
    China.
    Jian, X.
    China.
    Chen, J.
    China.
    Zhang, Cheng
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Hua, Q.
    China.
    Reconstruction of genome-scale metabolic model of Yarrowia lipolytica and its application in overproduction of triacylglycerol2017Ingår i: Bioresources and Bioprocessing, ISSN 2197-4365, Vol. 4, nr 1, artikel-id 51Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Background: Yarrowia lipolytica is widely studied as a non-conventional model yeast owing to the high level of lipid accumulation. Therein, triacylglycerol (TAG) is a major component of liposome. In order to investigate the TAG biosynthesis mechanism at a systematic level, a novel genome-scale metabolic model of Y. lipolytica was reconstructed based on a previous model iYL619_PCP published by our lab and another model iYali4 published by Kerkhoven et al. Results: The novel model iYL_2.0 contains 645 genes, 1083 metabolites, and 1471 reactions, which was validated more effective on simulations of specific growth rate. The precision of 29 carbon sources utilities reached up to 96.6% when simulated by iYL_2.0. In minimal growth medium, 111 genes were identified as essential for cell growth, whereas 66 essential genes were identified in yeast extract medium, which were verified by database of essential genes, suggesting a better prediction ability of iYL_2.0 in comparison with other existing models. In addition, potential metabolic engineering targets of improving TAG production were predicted by three in silico methods developed in-house, and the effects of amino acids supplementation were investigated based on model iYL_2.0. Conclusions: The reconstructed model iYL_2.0 is a powerful platform for efficiently optimizing the metabolism of TAG and systematically understanding the physiological mechanism of Y. lipolytica. [Figure not available: see fulltext.].

  • 30.
    Zhang, Cheng
    East China University of Science and Technology, China; Chalmers University of Technology,Sweden.
    Logical transformation of genome-scale metabolic models for gene level applications and analysis2015Ingår i: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 31, nr 14, s. 2324-2331Artikel i tidskrift (Refereegranskat)
    Abstract [en]

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

  • 31.
    Zhang, Cheng
    et al.
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Aldrees, Mohammed
    Arif, Muhammad
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Li, Xiangyu
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Mardinoglu, Adil
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Aziz, Mohammad Azhar
    Elucidating the Reprograming of Colorectal Cancer Metabolism Using Genome-Scale Metabolic Modeling2019Ingår i: Frontiers in Oncology, ISSN 2234-943X, E-ISSN 2234-943X, Vol. 9, artikel-id 681Artikel i tidskrift (Refereegranskat)
    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.

  • 32.
    Zhang, Cheng
    et al.
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Bidkhori, Gholamreza
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Benfeitas, Rui
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Lee, Sunjae
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Arif, Muhammad
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Proteinvetenskap, Systembiologi.
    Uhlen, Mathias
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Proteinvetenskap, Systembiologi.
    Mardinoglu, Adil
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    ESS: A Tool for Genome-Scale Quantification of Essentiality Score for Reaction/Genes in Constraint-Based Modeling2018Ingår i: Frontiers in Physiology, ISSN 1664-042X, E-ISSN 1664-042X, Vol. 9, artikel-id 1355Artikel i tidskrift (Refereegranskat)
    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.

  • 33. Zhang, Cheng
    et al.
    Hua, Qiang
    Applications of Genome-Scale Metabolic Models in Biotechnology and Systems Medicine: Application of GEMs2016Ingår i: Frontiers in Physiology, ISSN 1664-042X, E-ISSN 1664-042X, Vol. 6, nr January, artikel-id 413Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Genome-scale metabolic models (GEMs) have become a popular tool for systems biology, and they have been used in many fields such as industrial biotechnology and systems medicine. Since more and more studies are being conducted using GEMs, they have recently received considerable attention. In this review, we introduce the basic concept of GEMs and provide an overview of their applications in biotechnology, systems medicine, and some other fields. In addition, we describe the general principle of the applications and analyses built on GEMs. The purpose of this review is to introduce the application of GEMs in biological analysis and to promote its wider use by biologists.

  • 34.
    Zhang, Cheng
    et al.
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Lee, Sunjae
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Mardinoglu, Adil
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab. Chalmers, Dept Biol & Biol Engn, Sweden.
    Hua, Qiang
    Investigating the Combinatory Effects of Biological Networks on Gene Co-expression2016Ingår i: Frontiers in Physiology, ISSN 1664-042X, E-ISSN 1664-042X, Vol. 7, artikel-id 160Artikel i tidskrift (Refereegranskat)
    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.

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