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  • 1.
    Benfeitas, Rui
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab. Royal Institute of Technology, KTH.
    Bidkhori, Gholamreza
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Mukhopadhyay, Bani
    Klevstig, Martina
    Arif, Muhammad
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Zhang, Cheng
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lee, Sunjae
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Cinar, Resat
    Nielsen, Jens
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Boren, Jan
    Kunos, George
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Characterization of heterogeneous redox responses in hepatocellular carcinoma patients using network analysis2019In: EBioMedicine, E-ISSN 2352-3964Article in journal (Refereed)
  • 2.
    Bidkhori, Gholamreza
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Benfeitas, Rui
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Klevstig, Martina
    Zhang, Cheng
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Nielsen, Jens
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Boren, Jan
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Metabolic network-based stratification of hepatocellular carcinoma reveals three distinct tumor subtypes2018In: Proceedings of the National Academy of Sciences of the United States of America, ISSN 0027-8424, E-ISSN 1091-6490Article in journal (Refereed)
  • 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, Centres, Science for Life Laboratory, SciLifeLab.
    Zhang, Cheng
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Käfferlein, Keiko U.
    Watzl, Carsten
    Frank, Sasa
    Rahnenfuhrer, Jörg
    Marchan, Rosemarie
    Hengstler, Jan G.
    LIPG-promoted lipid storage mediates adaptation to oxidative stress in breast cancer2019In: International Journal of Cancer, ISSN 0020-7136, E-ISSN 1097-0215, Vol. 145, no 4, p. 901-915Article in journal (Refereed)
    Abstract [en]

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

  • 4. Gu, Deqing
    et al.
    Jian, Xingxing
    Zhang, Cheng
    Hua, Qiang
    Reframed genome-scale metabolic model to facilitate genetic design and integration with expression data2016In: IEEE/ACM Transactions on Computational Biology & Bioinformatics, ISSN 1545-5963, E-ISSN 1557-9964Article in journal (Refereed)
  • 5. Gu, Deqing
    et al.
    Zhang, Cheng
    Zhou, Shengguo
    Wei, Liujing
    Hua, Qiang
    IdealKnock: A framework for efficiently identifying knockout strategiesleading to targeted overproduction2016In: Computational biology and chemistry (Print), ISSN 1476-9271, E-ISSN 1476-928XArticle in journal (Refereed)
  • 6. 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)
  • 7. Jian, Xingxing
    et al.
    Li, Ningchuan
    Zhang, Cheng
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hua, Qiang
    In silico profiling of cell growthand succinate production in Escherichia coli NZN1112016In: Bioresources and BioprocessArticle in journal (Refereed)
  • 8. Jian, Xingxing
    et al.
    Zhou, Shengguo
    Zhang, Cheng
    Hua, Qiang
    In silico identification of gene amplification targets based on analysisof production and growth coupling2016In: Biosystems (Amsterdam. Print), ISSN 0303-2647, E-ISSN 1872-8324Article in journal (Refereed)
  • 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.
    Lee, Sunjae
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Zhang, Cheng
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Liu, Zhengtao
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Klevstig, Martina
    Mukhopadhyay, Bani
    Bergentall, Mattias
    Cinar, Resat
    Ståhlman, Marcus
    Sikanic, Natasa
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science.
    Park, Joshua K.
    Deshmukh, Sumit
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science.
    Harzandi, Azadeh M.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Kuijpers, Tim
    KTH.
    Grotli, Morten
    Elsässer, Simon J.
    Piening, Brian D.
    Snyder, Michael
    Smith, Ulf
    Nielsen, Jens
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Bäckhed, Fredrik
    Kunos, George
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Boren, Jan
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Network analyses identify liver-specific targets for treating liver diseases2017In: Molecular Systems Biology, ISSN 1744-4292, E-ISSN 1744-4292Article in journal (Refereed)
  • 11.
    Liu, Zhengtao
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Zhang, Cheng
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lee, Sunjae
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Kim, Woonghee
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Klevstig, Martina
    Harzandi, Azadeh M.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Sikanic, Natasa
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science.
    Arif, Muhammad
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Ståhlman, Marcus
    Nielsen, Jens
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Boren, Jan
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Pyruvate kinase L/R is a regulator of lipid metabolism and mitochondrial function2019In: Metabolic engineering, ISSN 1096-7176, E-ISSN 1096-7184Article in journal (Refereed)
  • 12. Lundgren, Sebastian
    et al.
    Fagerström-Vahman, Helena
    Zhang, Cheng
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Ben-Dror, Liv
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Nodin, Björn
    Jirström, Karin
    Discovery of KIRREL as a biomarker for prognostic stratification of patients within melanoma2019In: Biomarker Research, ISSN 0961-088X, E-ISSN 1475-925XArticle in journal (Refereed)
  • 13.
    Mahdessian, Diana
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics.
    Sullivan, D. P.
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics.
    Danielsson, Frida
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Arif, Muhammad
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Zhang, Cheng
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Åkesson, Lovisa
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Gnann, Christian
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Shutten, Rutger
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH).
    Thul, Peter
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics.
    Carja, Oana
    Department of Genetics, Stanford University, Stanford, CA 94305, USA. ; Chan Zuckerberg Biohub, San Francisco, San Francisco, CA 94158, USA..
    Ayoglu, Burcu
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics.
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology. Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, United Kingdom.
    Pontén, Fredrik
    Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, SE-751 85 Uppsala, Sweden.
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Lindskog, Cecilia
    Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, SE-751 85 Uppsala, Sweden..
    Lundberg, Emma
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. Department of Genetics, Stanford University, Stanford, CA 94305, USA. ; Chan Zuckerberg Biohub, San Francisco, San Francisco, CA 94158, USA..
    Spatiotemporal dissection of the cell cycle regulated human proteomeManuscript (preprint) (Other academic)
    Abstract [en]

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

  • 14.
    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, Centres, 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 Children2018In: Cell, ISSN 0092-8674, E-ISSN 1097-4172, Vol. 174, no 5, p. 1277-+Article in journal (Refereed)
    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.

  • 15. Sanchez, Benjamin J.
    et al.
    Zhang, Cheng
    KTH, Centres, 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 constraints2017In: Molecular Systems Biology, ISSN 1744-4292, E-ISSN 1744-4292, Vol. 13, no 8, article id 935Article in journal (Refereed)
    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.

  • 16. Svensson, Maria C.
    et al.
    Borg, David
    Zhang, Cheng
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hedner, Charlotta
    Nodin, Björn
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Leandersson, Karin
    Jirström, Karin
    Expression of PD-L1 and PD-1 in Chemoradiotherapy-Naïve Esophageal and Gastric Adenocarcinoma: Relationship With Mismatch Repair Status and Survival2019In: Frontiers in Oncology, ISSN 2234-943X, E-ISSN 2234-943XArticle in journal (Refereed)
  • 17.
    Turanli, Beste
    et al.
    KTH.
    Zhang, Cheng
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Kim, Woonghee
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Benfeitas, Rui
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Yalcin Arga, Kazim
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Discovery of therapeutic agents for prostate cancer using genome-scale metabolic modeling and drug repositioning2019In: EBioMedicine, E-ISSN 2352-3964, Vol. 42, p. 386-396Article in journal (Refereed)
    Abstract [sv]

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

  • 18.
    Wei, S.
    et al.
    China.
    Jian, X.
    China.
    Chen, J.
    China.
    Zhang, Cheng
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hua, Q.
    China.
    Reconstruction of genome-scale metabolic model of Yarrowia lipolytica and its application in overproduction of triacylglycerol2017In: Bioresources and Bioprocessing, ISSN 2197-4365, Vol. 4, no 1, article id 51Article in journal (Refereed)
    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.].

  • 19.
    Zhang, Cheng
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Aldrees, Mohammed
    Arif, Muhammad
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Li, Xiangyu
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Aziz, Mohammad Azhar
    Elucidating the Reprograming of Colorectal Cancer Metabolism Using Genome-Scale Metabolic Modeling2019In: Frontiers in Oncology, ISSN 2234-943X, E-ISSN 2234-943X, Vol. 9, article id 681Article in journal (Refereed)
    Abstract [en]

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

  • 20.
    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.

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  • asciidoc
  • rtf