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Publications (6 of 6) Show all publications
Benfeitas, R., Bidkhori, G., Mukhopadhyay, B., Klevstig, M., Arif, M., Zhang, C., . . . Mardinoglu, A. (2019). Characterization of heterogeneous redox responses in hepatocellular carcinoma patients using network analysis. EBioMedicine
Open this publication in new window or tab >>Characterization of heterogeneous redox responses in hepatocellular carcinoma patients using network analysis
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2019 (English)In: EBioMedicine, E-ISSN 2352-3964Article in journal (Refereed) Published
National Category
Bioinformatics and Systems Biology
Identifiers
urn:nbn:se:kth:diva-248702 (URN)
Note

QC 20190423

Available from: 2019-04-09 Created: 2019-04-09 Last updated: 2019-04-23Bibliographically approved
Zhang, C., Aldrees, M., Arif, M., Li, X., Mardinoglu, A. & Aziz, M. A. (2019). Elucidating the Reprograming of Colorectal Cancer Metabolism Using Genome-Scale Metabolic Modeling. Frontiers in Oncology, 9, Article ID 681.
Open this publication in new window or tab >>Elucidating the Reprograming of Colorectal Cancer Metabolism Using Genome-Scale Metabolic Modeling
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2019 (English)In: Frontiers in Oncology, ISSN 2234-943X, E-ISSN 2234-943X, Vol. 9, article id 681Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2019
Keywords
colorectal cancer, genome scale metabolic model, polyamine metabolism, personalized medicine, transcriptomics
National Category
Cancer and Oncology
Identifiers
urn:nbn:se:kth:diva-255737 (URN)10.3389/fonc.2019.00681 (DOI)000477876200001 ()2-s2.0-85072220274 (Scopus ID)
Note

QC 20190814

Available from: 2019-08-14 Created: 2019-08-14 Last updated: 2019-10-04Bibliographically approved
Liu, Z., Zhang, C., Lee, S., Kim, W., Klevstig, M., Harzandi, A. M., . . . Mardinoglu, A. (2019). Pyruvate kinase L/R is a regulator of lipid metabolism and mitochondrial function. Metabolic engineering
Open this publication in new window or tab >>Pyruvate kinase L/R is a regulator of lipid metabolism and mitochondrial function
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2019 (English)In: Metabolic engineering, ISSN 1096-7176, E-ISSN 1096-7184Article in journal (Refereed) Published
National Category
Bioinformatics and Systems Biology
Identifiers
urn:nbn:se:kth:diva-248703 (URN)
Note

QC 20190425

Available from: 2019-04-09 Created: 2019-04-09 Last updated: 2019-04-25Bibliographically approved
Zhang, C., Bidkhori, G., Benfeitas, R., Lee, S., Arif, M., Uhlen, M. & Mardinoglu, A. (2018). ESS: A Tool for Genome-Scale Quantification of Essentiality Score for Reaction/Genes in Constraint-Based Modeling. Frontiers in Physiology, 9, Article ID 1355.
Open this publication in new window or tab >>ESS: A Tool for Genome-Scale Quantification of Essentiality Score for Reaction/Genes in Constraint-Based Modeling
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2018 (English)In: Frontiers in Physiology, ISSN 1664-042X, E-ISSN 1664-042X, Vol. 9, article id 1355Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2018
Keywords
constraint-based modeling, gene essentiality, genome-scale metabolic models, reaction essentiality, systems biology
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:kth:diva-236008 (URN)10.3389/fphys.2018.01355 (DOI)000445930500001 ()
Funder
Knut and Alice Wallenberg Foundation
Note

QC 20181016

Available from: 2018-10-16 Created: 2018-10-16 Last updated: 2019-04-26Bibliographically approved
Lee, S., Zhang, C., Arif, M., Liu, Z., Benfeitas, R., Bidkhori, G., . . . Mardinoglu, A. (2017). TCSBN: a database of tissue and cancer specific biological networks. Nucleic Acids Research
Open this publication in new window or tab >>TCSBN: a database of tissue and cancer specific biological networks
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2017 (English)In: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962Article in journal (Refereed) Published
National Category
Bioinformatics and Systems Biology
Identifiers
urn:nbn:se:kth:diva-248672 (URN)
Note

QC 20190423

Available from: 2019-04-09 Created: 2019-04-09 Last updated: 2019-04-25Bibliographically approved
Mahdessian, D., Sullivan, D. P., Danielsson, F., Arif, M., Zhang, C., Åkesson, L., . . . Lundberg, E.Spatiotemporal dissection of the cell cycle regulated human proteome.
Open this publication in new window or tab >>Spatiotemporal dissection of the cell cycle regulated human proteome
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(English)Manuscript (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.

Keywords
Proteomics, Single cell variation, Immunofluorescence, Human Protein Atlas, Cell cycle, Cell cycle dependent proteome
National Category
Natural Sciences Medical Biotechnology
Research subject
Biotechnology
Identifiers
urn:nbn:se:kth:diva-261234 (URN)10.1101/54323 (DOI)
Funder
Science for Life Laboratory - a national resource center for high-throughput molecular bioscience
Note

QC 20191007

Available from: 2019-10-03 Created: 2019-10-03 Last updated: 2019-10-07Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-2261-0881

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