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Publications (10 of 14) Show all publications
Arif, M., Klevstig, M., Benfeitas, R., Doran, S., Turkez, H., Uhlén, M., . . . Boren, J. (2021). Integrative transcriptomic analysis of tissue-specific metabolic crosstalk after myocardial infarction. eLIFE, 10
Open this publication in new window or tab >>Integrative transcriptomic analysis of tissue-specific metabolic crosstalk after myocardial infarction
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2021 (English)In: eLIFE, E-ISSN 2050-084X, Vol. 10Article in journal (Refereed) Published
Abstract [en]

Myocardial infarction (MI) promotes a range of systemic effects, many of which are unknown. Here, we investigated the alterations associated with MI progression in heart and other metabolically active tissues (liver, skeletal muscle, and adipose) in a mouse model of MI (induced by ligating the left ascending coronary artery) and sham-operated mice. We performed a genome-wide transcriptomic analysis on tissue samples obtained 6- and 24-hours post MI or sham operation. By generating tissue-specific biological networks, we observed: (1) dysregulation in multiple biological processes (including immune system, mitochondrial dysfunction, fatty-acid beta-oxidation, and RNA and protein processing) across multiple tissues post MI; and (2) tissue-specific dysregulation in biological processes in liver and heart post MI. Finally, we validated our findings in two independent MI cohorts. Overall, our integrative analysis highlighted both common and specific biological responses to MI across a range of metabolically active tissues.Competing Interest StatementJW, MC, DE are employees at AstraZeneca. The other authors declare no conflict of interest.

Place, publisher, year, edition, pages
eLife Sciences Publications, Ltd, 2021
National Category
Bioinformatics and Computational Biology Cardiology and Cardiovascular Disease
Research subject
Biotechnology
Identifiers
urn:nbn:se:kth:diva-294182 (URN)10.7554/eLife.66921 (DOI)000661322200001 ()33972017 (PubMedID)2-s2.0-85108030129 (Scopus ID)
Note

QC 20210518

Available from: 2021-05-11 Created: 2021-05-11 Last updated: 2025-02-10Bibliographically approved
Lam, S., Hartmann, N., Benfeitas, R., Zhang, C., Arif, M., Turkez, H., . . . Mardinoglu, A. (2021). Systems Analysis Reveals Ageing-Related Perturbations in Retinoids and Sex Hormones in Alzheimer's and Parkinson's Diseases. Biomedicines, 9(10), Article ID 1310.
Open this publication in new window or tab >>Systems Analysis Reveals Ageing-Related Perturbations in Retinoids and Sex Hormones in Alzheimer's and Parkinson's Diseases
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2021 (English)In: Biomedicines, E-ISSN 2227-9059, Vol. 9, no 10, article id 1310Article in journal (Refereed) Published
Abstract [en]

Neurodegenerative diseases, including Alzheimer's (AD) and Parkinson's diseases (PD), are complex heterogeneous diseases with highly variable patient responses to treatment. Due to the growing evidence for ageing-related clinical and pathological commonalities between AD and PD, these diseases have recently been studied in tandem. In this study, we analysed transcriptomic data from AD and PD patients, and stratified these patients into three subclasses with distinct gene expression and metabolic profiles. Through integrating transcriptomic data with a genome-scale metabolic model and validating our findings by network exploration and co-analysis using a zebrafish ageing model, we identified retinoids as a key ageing-related feature in all subclasses of AD and PD. We also demonstrated that the dysregulation of androgen metabolism by three different independent mechanisms is a source of heterogeneity in AD and PD. Taken together, our work highlights the need for stratification of AD/PD patients and development of personalised and precision medicine approaches based on the detailed characterisation of these subclasses.

Place, publisher, year, edition, pages
MDPI AG, 2021
Keywords
neurodegeneration, Alzheimer's, Parkinson's, ageing, systems biology
National Category
Neurosciences
Identifiers
urn:nbn:se:kth:diva-304753 (URN)10.3390/biomedicines9101310 (DOI)000711745500001 ()34680427 (PubMedID)2-s2.0-85116067057 (Scopus ID)
Note

QC 20211112

Available from: 2021-11-12 Created: 2021-11-12 Last updated: 2024-03-15Bibliographically approved
Mohammadi, E., Benfeitas, R., Turkez, H., Boren, J., Nielsen, J., Uhlén, M. & Mardinoglu, A. (2020). Applications of Genome-Wide Screening and Systems Biology Approaches in Drug Repositioning. Cancers, 12(9), Article ID 2694.
Open this publication in new window or tab >>Applications of Genome-Wide Screening and Systems Biology Approaches in Drug Repositioning
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2020 (English)In: Cancers, ISSN 2072-6694, Vol. 12, no 9, article id 2694Article, review/survey (Refereed) Published
Abstract [en]

Simple Summary Drug repurposing is an accelerated route for drug development and a promising approach for finding medications for orphan and common diseases. Here, we compiled databases that comprise both computationally- or experimentally-derived data, and categorized them based on quiddity and origin of data, further focusing on those that present high throughput omic data or drug screens. These databases were then contextualized with genome-wide screening methods such as CRISPR/Cas9 and RNA interference, as well as state of art systems biology approaches that enable systematic characterizations of multi-omic data to find new indications for approved drugs or those that reached the latest phases of clinical trials. Modern drug discovery through de novo drug discovery entails high financial costs, low success rates, and lengthy trial periods. Drug repositioning presents a suitable approach for overcoming these issues by re-evaluating biological targets and modes of action of approved drugs. Coupling high-throughput technologies with genome-wide essentiality screens, network analysis, genome-scale metabolic modeling, and machine learning techniques enables the proposal of new drug-target signatures and uncovers unanticipated modes of action for available drugs. Here, we discuss the current issues associated with drug repositioning in light of curated high-throughput multi-omic databases, genome-wide screening technologies, and their application in systems biology/medicine approaches.

Place, publisher, year, edition, pages
MDPI, 2020
Keywords
drug repositioning, genomic screens, machine learning, systems pharmacology, systems medicine
National Category
Basic Medicine
Identifiers
urn:nbn:se:kth:diva-285642 (URN)10.3390/cancers12092694 (DOI)000580111800001 ()32967266 (PubMedID)2-s2.0-85091672930 (Scopus ID)
Note

QC 20201110

Available from: 2020-11-10 Created: 2020-11-10 Last updated: 2024-03-15Bibliographically approved
Zhang, C., Bjornson, E., Arif, M., Abdellah, T., Lovric, A., Benfeitas, R., . . . Mardinoglu, A. (2020). The acute effect of metabolic cofactor supplementation: a potential therapeutic strategy against non-alc33oholic fatty liver disease. Molecular Systems Biology, 16(4)
Open this publication in new window or tab >>The acute effect of metabolic cofactor supplementation: a potential therapeutic strategy against non-alc33oholic fatty liver disease
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2020 (English)In: Molecular Systems Biology, ISSN 1744-4292, E-ISSN 1744-4292, Vol. 16, no 4Article in journal (Refereed) Published
Abstract [en]

The prevalence of non-alcoholic fatty liver disease (NAFLD) continues to increase dramatically, and there is no approved medication for its treatment. Recently, we predicted the underlying molecular mechanisms involved in the progression of NAFLD using network analysis and identified metabolic cofactors that might be beneficial as supplements to decrease human liver fat. Here, we first assessed the tolerability of the combined metabolic cofactors including l-serine, N-acetyl-l-cysteine (NAC), nicotinamide riboside (NR), and l-carnitine by performing a 7-day rat toxicology study. Second, we performed a human calibration study by supplementing combined metabolic cofactors and a control study to study the kinetics of these metabolites in the plasma of healthy subjects with and without supplementation. We measured clinical parameters and observed no immediate side effects. Next, we generated plasma metabolomics and inflammatory protein markers data to reveal the acute changes associated with the supplementation of the metabolic cofactors. We also integrated metabolomics data using personalized genome-scale metabolic modeling and observed that such supplementation significantly affects the global human lipid, amino acid, and antioxidant metabolism. Finally, we predicted blood concentrations of these compounds during daily long-term supplementation by generating an ordinary differential equation model and liver concentrations of serine by generating a pharmacokinetic model and finally adjusted the doses of individual metabolic cofactors for future human clinical trials.

Place, publisher, year, edition, pages
EMBO, 2020
Keywords
NAFLD, l-serine, N-acetyl-l-cysteine (NAC), nicotinamide riboside (NR), and l-carnitine, systems medicine
National Category
Biochemistry Molecular Biology
Identifiers
urn:nbn:se:kth:diva-277220 (URN)10.15252/msb.209495 (DOI)000530421100005 ()32337855 (PubMedID)2-s2.0-85084170451 (Scopus ID)
Note

QC 20200714

Available from: 2020-07-14 Created: 2020-07-14 Last updated: 2025-02-20Bibliographically approved
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, 40, 471-487
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 2352-3964, Vol. 40, p. 471-487Article in journal (Refereed) Published
Abstract [en]

Background: Redox metabolism is often considered a potential target for cancer treatment, but a systematic examination of redox responses in hepatocellular carcinoma (HCC) is missing. Methods: Here, we employed systems biology and biological network analyses to reveal key roles of genes associated with redox metabolism in HCC by integrating multi-omics data. Findings: We found that several redox genes, including 25 novel potential prognostic genes, are significantly co-expressed with liver-specific genes and genes associated with immunity and inflammation. Based on an integrative analysis, we found that HCC tumors display antagonistic behaviors in redox responses. The two HCC groups are associated with altered fatty acid, amino acid, drug and hormone metabolism, differentiation, proliferation, and NADPH-independent vs - dependent antioxidant defenses. Redox behavior varies with known tumor subtypes and progression, affecting patient survival. These antagonistic responses are also displayed at the protein and metabolite level and were validated in several independent cohorts. We finally showed the differential redox behavior using mice transcriptomics in HCC and noncancerous tissues and associated with hypoxic features of the two redox gene groups. Interpretation: Our integrative approaches highlighted mechanistic differences among tumors and allowed the identification of a survival signature and several potential therapeutic targets for the treatment of HCC. (C) 2018 Published by Elsevier B.V.

Keywords
Hepatocellular carcinoma, Redox metabolism, Systems biology, Precision medicine, Cancer, Transcriptomics, Liver cancer
National Category
Physical Sciences
Identifiers
urn:nbn:se:kth:diva-270854 (URN)10.1016/j.ebiom.2018.12.057 (DOI)000460696900057 ()30606699 (PubMedID)2-s2.0-85059240987 (Scopus ID)
Note

QC 20200316

Available from: 2020-03-16 Created: 2020-03-16 Last updated: 2023-12-07Bibliographically approved
Turanli, B., Zhang, C., Kim, W., Benfeitas, R., Uhlén, M., Yalcin Arga, K. & Mardinoglu, A. (2019). Discovery of therapeutic agents for prostate cancer using genome-scale metabolic modeling and drug repositioning. EBioMedicine, 42, 386-396
Open this publication in new window or tab >>Discovery of therapeutic agents for prostate cancer using genome-scale metabolic modeling and drug repositioning
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2019 (English)In: EBioMedicine, E-ISSN 2352-3964, Vol. 42, p. 386-396Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier, 2019
National Category
Medical and Health Sciences Bioinformatics and Computational Biology
Identifiers
urn:nbn:se:kth:diva-248689 (URN)10.1016/j.ebiom.2019.03.009 (DOI)000466175100052 ()30905848 (PubMedID)2-s2.0-85063114920 (Scopus ID)
Note

QC 20190424

Available from: 2019-04-09 Created: 2019-04-09 Last updated: 2025-02-05Bibliographically approved
Lovric, A., Graner, M., Bjornson, E., Arif, M., Benfeitas, R., Nyman, K., . . . Boren, J. (2018). Characterization of different fat depots in NAFLD using inflammation-associated proteome, lipidome and metabolome. Scientific Reports, 8, Article ID 14200.
Open this publication in new window or tab >>Characterization of different fat depots in NAFLD using inflammation-associated proteome, lipidome and metabolome
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2018 (English)In: Scientific Reports, E-ISSN 2045-2322, Vol. 8, article id 14200Article in journal (Refereed) Published
Abstract [en]

Non-alcoholic fatty liver disease (NAFLD) is recognized as a liver manifestation of metabolic syndrome, accompanied with excessive fat accumulation in the liver and other vital organs. Ectopic fat accumulation was previously associated with negative effects at the systemic and local level in the human body. Thus, we aimed to identify and assess the predictive capability of novel potential metabolic biomarkers for ectopic fat depots in non-diabetic men with NAFLD, using the inflammation-associated proteome, lipidome and metabolome. Myocardial and hepatic triglycerides were measured with magnetic spectroscopy while function of left ventricle, pericardial and epicardial fat, subcutaneous and visceral adipose tissue were measured with magnetic resonance imaging. Measured ectopic fat depots were profiled and predicted using a Random Forest algorithm, and by estimating the Area Under the Receiver Operating Characteristic curves. We have identified distinct metabolic signatures of fat depots in the liver (TAG50:1, glutamate, diSM18:0 and CE20:3), pericardium (N-palmitoyl-sphinganine, HGF, diSM18:0, glutamate, and TNFSF14), epicardium (sphingomyelin, CE20:3, PC38:3 and TNFSF14), and myocardium (CE20:3, LAPTGF-beta 1, glutamate and glucose). Our analyses highlighted non-invasive biomarkers that accurately predict ectopic fat depots, and reflect their distinct metabolic signatures in subjects with NAFLD.

Place, publisher, year, edition, pages
Nature Publishing Group, 2018
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:kth:diva-235879 (URN)10.1038/s41598-018-31865-w (DOI)000445276000044 ()30242179 (PubMedID)2-s2.0-85053722070 (Scopus ID)
Funder
Science for Life Laboratory - a national resource center for high-throughput molecular bioscience
Note

QC 20181008

Available from: 2018-10-08 Created: 2018-10-08 Last updated: 2024-03-18Bibliographically 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, 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 ()30323767 (PubMedID)2-s2.0-85068559447 (Scopus ID)
Funder
Knut and Alice Wallenberg Foundation
Note

QC 20181016

Available from: 2018-10-16 Created: 2018-10-16 Last updated: 2024-03-18Bibliographically approved
Bidkhori, G., Benfeitas, R., Elmas, E., Kararoudi, M. N., Arif, M., Uhlén, M., . . . Mardinoglu, A. (2018). Metabolic Network-Based Identification and Prioritization o f Anticancer Targets Based on Expression Data in Hepatocellular Carcinoma. Frontiers in Physiology, 9, Article ID 916.
Open this publication in new window or tab >>Metabolic Network-Based Identification and Prioritization o f Anticancer Targets Based on Expression Data in Hepatocellular Carcinoma
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2018 (English)In: Frontiers in Physiology, E-ISSN 1664-042X, Vol. 9, article id 916Article in journal (Refereed) Published
Abstract [en]

Hepatocellular carcinoma (HCC) is a deadly form of liver cancer with high mortality worldwide. Unfortunately, the large heterogeneity of this disease makes it difficult to develop effective treatment strategies. Cellular network analyses have been employed to study heterogeneity in cancer, and to identify potential therapeutic targets. However, the existing approaches do not consider metabolic growth requirements, i.e., biological network functionality, to rank candidate targets while preventing toxicity to non-cancerous tissues. Here, we developed an algorithm to overcome these issues based on integration of gene expression data, genome-scale metabolic models, network controllability, and dispensability, as well as toxicity analysis. This method thus predicts and ranks potential anticancer non-toxic controlling metabolite and gene targets. Our algorithm encompasses both objective-driven and-independent tasks, and uses network topology to finally rank the predicted therapeutic targets. We employed this algorithm to the analysis of transcriptomic data for 50 HCC patients with both cancerous and non-cancerous samples. We identified several potential targets that would prevent cell growth, including 74 anticancer metabolites, and 3 gene targets (PRKACA, PGS1, and CRLS1). The predicted anticancer metabolites showed good agreement with existing FDA-approved cancer drugs, and the 3 genes were experimentally validated by performing experiments in HepG2 and Hep3B liver cancer cell lines. Our observations indicate that our novel approach successfully identifies therapeutic targets for effective treatment of cancer. This approach may also be applied to any cancer type that has tumor and non-tumor gene or protein expression data.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2018
Keywords
hepatocellular carcinoma, genome-scale metabolic model, network analysis, biological networks, cancer, gene expression, protein expression, systems biology and network biology
National Category
Physiology and Anatomy
Identifiers
urn:nbn:se:kth:diva-232768 (URN)10.3389/fphys.2018.00916 (DOI)000438974200001 ()30065658 (PubMedID)2-s2.0-85050120958 (Scopus ID)
Funder
Knut and Alice Wallenberg FoundationScience for Life Laboratory - a national resource center for high-throughput molecular bioscience
Note

QC 20180807

Available from: 2018-08-06 Created: 2018-08-06 Last updated: 2025-02-10Bibliographically approved
Bidkhori, G., Benfeitas, R., Klevstig, M., Zhang, C., Nielsen, J., Uhlén, M., . . . Mardinoglu, A. (2018). Metabolic network-based stratification of hepatocellular carcinoma reveals three distinct tumor subtypes. Proceedings of the National Academy of Sciences of the United States of America
Open this publication in new window or tab >>Metabolic network-based stratification of hepatocellular carcinoma reveals three distinct tumor subtypes
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2018 (English)In: Proceedings of the National Academy of Sciences of the United States of America, ISSN 0027-8424, E-ISSN 1091-6490Article in journal (Refereed) Published
Abstract [en]

Hepatocellular carcinoma (HCC) is one of the most frequent forms of liver cancer, and effective treatment methods are limited due to tumor heterogeneity. There is a great need for comprehensive approaches to stratify HCC patients, gain biological insights into subtypes, and ultimately identify effective therapeutic targets. We stratified HCC patients and characterized each subtype using transcriptomics data, genome-scale metabolic networks and network topology/controllability analysis. This comprehensive systems-level analysis identified three distinct subtypes with substantial differences in metabolic and signaling pathways reflecting at genomic, transcriptomic, and proteomic levels. These subtypes showed large differences in clinical survival associated with altered kynurenine metabolism, WNT/beta-catenin-associated lipid metabolism, and PI3K/AKT/mTOR signaling. Integrative analyses indicated that the three subtypes rely on alternative enzymes (e.g., ACSS1/ACSS2/ACSS3, PKM/PKLR, ALDOB/ALDOA, MTHFD1L/MTHFD2/MTHFD1) to catalyze the same reactions. Based on systems-level analysis, we identified 8 to 28 subtype-specific genes with pivotal roles in controlling the metabolic network and predicted that these genes may be targeted for development of treatment strategies for HCC subtypes by performing in silico analysis. To validate our predictions, we performed experiments using HepG2 cells under normoxic and hypoxic conditions and observed opposite expression patterns between genes expressed in high/moderate/low-survival tumor groups in response to hypoxia, reflecting activated hypoxic behavior in patients with poor survival. In conclusion, our analyses showed that the heterogeneous HCC tumors can be stratified using a metabolic network-driven approach, which may also be applied to other cancer types, and this stratification may have clinical implications to drive the development of precision medicine.

Keywords
hepatocellular carcinoma; biological networks; personalized medicine; genome-scale metabolic models; systems biology
National Category
Bioinformatics and Computational Biology
Identifiers
urn:nbn:se:kth:diva-248680 (URN)10.1073/pnas.1807305115 (DOI)000452866000035 ()30482855 (PubMedID)2-s2.0-85058364905 (Scopus ID)
Note

QC 20190423

Available from: 2019-04-09 Created: 2019-04-09 Last updated: 2025-02-07Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0001-7972-0083

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