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Publications (10 of 11) 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
Harms, M. J., Li, Q., Lee, S., Zhang, C., Kull, B., Hallen, S., . . . Boucher, J. (2019). Mature Human White Adipocytes Cultured under Membranes Maintain Identity, Function, and Can Transdifferentiate into Brown-like Adipocytes. Cell reports
Open this publication in new window or tab >>Mature Human White Adipocytes Cultured under Membranes Maintain Identity, Function, and Can Transdifferentiate into Brown-like Adipocytes
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2019 (English)In: Cell reports, ISSN 2211-1247, E-ISSN 2211-1247Article in journal (Refereed) Published
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:kth:diva-248688 (URN)10.1016/j.celrep.2019.03.026 (DOI)000463187700018 ()2-s2.0-85063422988 (Scopus ID)
Note

QC 20190423

Available from: 2019-04-09 Created: 2019-04-09 Last updated: 2019-05-16Bibliographically 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
Uhlén, M., Zhang, C., Lee, S., Sjöstedt, E., Fagerberg, L., Bidkhori, G., . . . Ponten, F. (2017). A pathology atlas of the human cancer transcriptome. Science, 357(6352), 660-+
Open this publication in new window or tab >>A pathology atlas of the human cancer transcriptome
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2017 (English)In: Science, ISSN 0036-8075, E-ISSN 1095-9203, Vol. 357, no 6352, p. 660-+Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
American Association for the Advancement of Science, 2017
National Category
Medical Biotechnology
Identifiers
urn:nbn:se:kth:diva-214334 (URN)10.1126/science.aan2507 (DOI)000407793600028 ()2-s2.0-85028362951 (Scopus ID)
Funder
Swedish Cancer SocietyScience for Life Laboratory - a national resource center for high-throughput molecular bioscienceKnut and Alice Wallenberg FoundationSwedish Research Council
Note

QC 20170913

Available from: 2017-09-13 Created: 2017-09-13 Last updated: 2018-10-17Bibliographically approved
Thul, P. J., Åkesson, L., Wiking, M., Mahdessian, D., Geladaki, A., Ait Blal, H., . . . Lundberg, E. (2017). A subcellular map of the human proteome. Science, 356(6340), Article ID 820.
Open this publication in new window or tab >>A subcellular map of the human proteome
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2017 (English)In: Science, ISSN 0036-8075, E-ISSN 1095-9203, Vol. 356, no 6340, article id 820Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
American Association for the Advancement of Science, 2017
Keywords
antibody, proteome, biology, cells and cell components, disease incidence, image analysis, physiological response, protein, proteomics, spatial distribution, Article, cell organelle, cellular distribution, human, human cell, immunofluorescence microscopy, mass spectrometry, priority journal, protein analysis, protein localization, protein protein interaction, single cell analysis, transcriptomics
National Category
Cell Biology
Identifiers
urn:nbn:se:kth:diva-216588 (URN)10.1126/science.aal3321 (DOI)000401957900032 ()2-s2.0-85019201137 (Scopus ID)
Note

QC 20171208

Available from: 2017-12-08 Created: 2017-12-08 Last updated: 2017-12-08Bibliographically approved
Bosley, J., Borén, C., Lee, S., Grotli, M., Nielsen, J., Uhlén, M., . . . Mardinoglu, A. (2017). Improving the economics of NASH/NAFLD treatment through the use of systems biology. Drug Discovery Today, 22(10), 1532-1538
Open this publication in new window or tab >>Improving the economics of NASH/NAFLD treatment through the use of systems biology
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2017 (English)In: Drug Discovery Today, ISSN 1359-6446, E-ISSN 1878-5832, Vol. 22, no 10, p. 1532-1538Article, review/survey (Refereed) Published
Abstract [en]

Nonalcoholic steatohepatitis (NASH) is a severe form of nonalcoholic fatty liver disease (NAFLD). We surveyed NASH therapies currently in development, and found a significant variety of targets and approaches. Evaluation and clinical testing of these targets is an expensive and time-consuming process. Systems biology approaches could enable the quantitative evaluation of the likely efficacy and safety of different targets. This motivated our review of recent systems biology studies that focus on the identification of targets and development of effective treatments for NASH. We discuss the potential broader use of genome-scale metabolic models and integrated networks in the validation of drug targets, which could facilitate more productive and efficient drug development decisions for the treatment of NASH.

Place, publisher, year, edition, pages
ELSEVIER SCI LTD, 2017
National Category
Pharmacology and Toxicology
Identifiers
urn:nbn:se:kth:diva-217446 (URN)10.1016/j.drudis.2017.07.005 (DOI)000413799500008 ()28736156 (PubMedID)2-s2.0-85026322326 (Scopus ID)
Note

QC 20171117

Available from: 2017-11-17 Created: 2017-11-17 Last updated: 2018-01-13Bibliographically approved
Lee, S., Zhang, C., Liu, Z., Klevstig, M., Mukhopadhyay, B., Bergentall, M., . . . Mardinoglu, A. (2017). Network analyses identify liver-specific targets for treating liver diseases. Molecular Systems Biology
Open this publication in new window or tab >>Network analyses identify liver-specific targets for treating liver diseases
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2017 (English)In: Molecular Systems Biology, ISSN 1744-4292, E-ISSN 1744-4292Article in journal (Refereed) Published
National Category
Bioinformatics and Systems Biology
Identifiers
urn:nbn:se:kth:diva-248643 (URN)
Note

QC 20190425

Available from: 2019-04-09 Created: 2019-04-09 Last updated: 2019-04-25Bibliographically 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
Zhang, C., Lee, S., Mardinoglu, A. & Hua, Q. (2016). Investigating the Combinatory Effects of Biological Networks on Gene Co-expression. Frontiers in Physiology, 7, Article ID 160.
Open this publication in new window or tab >>Investigating the Combinatory Effects of Biological Networks on Gene Co-expression
2016 (English)In: Frontiers in Physiology, ISSN 1664-042X, E-ISSN 1664-042X, Vol. 7, article id 160Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Frontiers Media, 2016
Keywords
Saccharomyces cerevisiae, co-expression, co-regulation, transcriptional regulatory network, protein-protein interaction network
National Category
Bioinformatics and Systems Biology Physiology
Identifiers
urn:nbn:se:kth:diva-187330 (URN)10.3389/fphys.2016.00160 (DOI)000375185600001 ()2-s2.0-84974652956 (Scopus ID)
Funder
Knut and Alice Wallenberg FoundationScience for Life Laboratory - a national resource center for high-throughput molecular bioscience
Note

QC 20160523

Available from: 2016-05-23 Created: 2016-05-20 Last updated: 2019-04-23Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-6428-5936

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