Publications by authors named "Sunjae Lee"

33 Publications

A Systems Biology Approach to Investigating the Interaction between Serotonin Synthesis by Tryptophan Hydroxylase and the Metabolic Homeostasis.

Int J Mol Sci 2021 Feb 28;22(5). Epub 2021 Feb 28.

Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju 61005, Korea.

Obesity has become a global public health and economic problem. Obesity is a major risk factor for a number of complications, such as type 2 diabetes, cardiovascular disease, fatty liver disease, and cancer. Serotonin (5-hydroxytryptamine [5-HT]) is a biogenic monoamine that plays various roles in metabolic homeostasis. It is well known that central 5-HT regulates appetite and mood. Several 5-HT receptor agonists and selective serotonin receptor uptake inhibitors (SSRIs) have shown beneficial effects on appetite and mood control in clinics. Although several genetic polymorphisms related to 5-HT synthesis and its receptors are strongly associated with obesity, there is little evidence of the role of peripheral 5-HT in human metabolism. In this study, we performed a systemic analysis of transcriptome data from the Genotype-Tissue Expression (GTEX) database. We investigated the expression of 5-HT and tryptophan hydroxylase (TPH), the rate-limiting enzyme of 5-HT biosynthesis, in the human brain and peripheral tissues. We also performed differential gene expression analysis and predicted changes in metabolites by comparing gene expressions of tissues with high TPH expression to the gene expressions of tissues with low TPH expression. Our analyses provide strong evidence that serotonin plays an important role in the regulation of metabolic homeostasis in humans.
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http://dx.doi.org/10.3390/ijms22052452DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7957782PMC
February 2021

Systematic analysis of gut microbiome reveals the role of bacterial folate and homocysteine metabolism in Parkinson's disease.

Cell Rep 2021 Mar;34(9):108807

Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT London, UK; Science for Life Laboratory (SciLifeLab), KTH - Royal Institute of Technology, Tomtebodavägen 23, 171 65 Solna, Stockholm, Sweden. Electronic address:

Parkinson's disease (PD) is the most common progressive neurological disorder compromising motor functions. However, nonmotor symptoms, such as gastrointestinal (GI) dysfunction, precede those affecting movement. Evidence of an early involvement of the GI tract and enteric nervous system highlights the need for better understanding of the role of gut microbiota in GI complications in PD. Here, we investigate the gut microbiome of patients with PD using metagenomics and serum metabolomics. We integrate these data using metabolic modeling and construct an integrative correlation network giving insight into key microbial species linked with disease severity, GI dysfunction, and age of patients with PD. Functional analysis reveals an increased microbial capability to degrade mucin and host glycans in PD. Personalized community-level metabolic modeling reveals the microbial contribution to folate deficiency and hyperhomocysteinemia observed in patients with PD. The metabolic modeling approach could be applied to uncover gut microbial metabolic contributions to PD pathophysiology.
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http://dx.doi.org/10.1016/j.celrep.2021.108807DOI Listing
March 2021

Acute kidney injury leading to CKD is associated with a persistence of metabolic dysfunction and hypertriglyceridemia.

iScience 2021 Feb 9;24(2):102046. Epub 2021 Jan 9.

Renal Sciences, Department of Inflammation Biology, School of Immunology & Microbial Sciences, Faculty of Life Sciences and Medicine, King's College London, SE5 9NU London, UK.

Fibrosis is the pathophysiological hallmark of progressive chronic kidney disease (CKD). The kidney is a highly metabolically active organ, and it has been suggested that disruption in its metabolism leads to renal fibrosis. We developed a longitudinal mouse model of acute kidney injury leading to CKD and an model of epithelial to mesenchymal transition to study changes in metabolism, inflammation, and fibrosis. Using transcriptomics, metabolic modeling, and serum metabolomics, we observed sustained fatty acid metabolic dysfunction in the mouse model from early to late stages of CKD. Increased fatty acid biosynthesis and downregulation of catabolic pathways for triglycerides and diacylglycerides were associated with a marked increase in these lipids in the serum. We therefore suggest that the kidney may be the source of the abnormal lipid profile seen in patients with CKD, which may provide insights into the association between CKD and cardiovascular disease.
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http://dx.doi.org/10.1016/j.isci.2021.102046DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7843454PMC
February 2021

Calcium carbonate synthesis from waste concrete for carbon dioxide capture: From laboratory to pilot scale.

J Hazard Mater 2021 02 2;403:123862. Epub 2020 Sep 2.

Center for Environment, Health and Welfare Research, Korea Institute of Science and Technology, Hwarang-ro 14, Seongbuk-gu, Seoul 02792, Republic of Korea; Graduate School of Energy and Environment (KU-KIST GREEN SCHOOL), Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea. Electronic address:

This research article explains the synthesis and scale-up of calcium carbonate (CaCO) from waste concrete as calcium-rich material by an inorganic carbonation process. The operating parameters include S/L ratio, HCl concentration, contact time, and extraction pH were investigated. The calcium hydroxide (Ca(OH)) was synthesized by reaction between calcium chloride (CaCl) and sodium hydroxide (NaOH), which induced the spontaneous reaction of CaCO without additional energy consumption. The productivity of CaCO was 1 kg/d in the laboratory scale experiment, and the CaCO productivity was scale-up to 20 kg/d through pilot scale process by same way as the laboratory scale. The approximately 4800 g of CaCO was produced and 2112 g of CO was captured per each cycle operation. Consequently, considered power consumption, the estimated amount of reduced CO was 465 g of CO in the pilot-scale reactor per cycle and produced CaCO with a purity of 99.0 %.
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http://dx.doi.org/10.1016/j.jhazmat.2020.123862DOI Listing
February 2021

Systems Biology: A Multi-Omics Integration Approach to Metabolism and the Microbiome.

Endocrinol Metab (Seoul) 2020 09 22;35(3):507-514. Epub 2020 Sep 22.

Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, UK.

The complex and dynamic nature of human physiology, as exemplified by metabolism, has often been overlooked due to the lack of quantitative and systems approaches. Recently, systems biology approaches have pushed the boundaries of our current understanding of complex biochemical, physiological, and environmental interactions, enabling proactive medicine in the near future. From this perspective, we review how state-of-the-art computational modelling of human metabolism, i.e., genome-scale metabolic modelling, could be used to identify the metabolic footprints of diseases, to guide the design of personalized treatments, and to estimate the microbiome contributions to host metabolism. These state-of-the-art models can serve as a scaffold for integrating multi-omics data, thereby enabling the identification of signatures of dysregulated metabolism by systems approaches. For example, increased plasma mannose levels due to decreased uptake in the liver have been identified as a potential biomarker of early insulin resistance by multi-omics approaches. In addition, we also review the emerging axis of human physiology and the human microbiome, discussing its contribution to host metabolism and quantitative approaches to study its variations in individuals.
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http://dx.doi.org/10.3803/EnM.2020.303DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7520591PMC
September 2020

Compositional and functional differences of the mucosal microbiota along the intestine of healthy individuals.

Sci Rep 2020 09 11;10(1):14977. Epub 2020 Sep 11.

Centre for Host-Microbiome Interactions, Dental Institute, King's College London, London, UK.

Gut mucosal microbes evolved closest to the host, developing specialized local communities. There is, however, insufficient knowledge of these communities as most studies have employed sequencing technologies to investigate faecal microbiota only. This work used shotgun metagenomics of mucosal biopsies to explore the microbial communities' compositions of terminal ileum and large intestine in 5 healthy individuals. Functional annotations and genome-scale metabolic modelling of selected species were then employed to identify local functional enrichments. While faecal metagenomics provided a good approximation of the average gut mucosal microbiome composition, mucosal biopsies allowed detecting the subtle variations of local microbial communities. Given their significant enrichment in the mucosal microbiota, we highlight the roles of Bacteroides species and describe the antimicrobial resistance biogeography along the intestine. We also detail which species, at which locations, are involved with the tryptophan/indole pathway, whose malfunctioning has been linked to pathologies including inflammatory bowel disease. Our study thus provides invaluable resources for investigating mechanisms connecting gut microbiota and host pathophysiology.
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http://dx.doi.org/10.1038/s41598-020-71939-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7486370PMC
September 2020

Integration of molecular profiles in a longitudinal wellness profiling cohort.

Nat Commun 2020 09 8;11(1):4487. Epub 2020 Sep 8.

Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden.

An important aspect of precision medicine is to probe the stability in molecular profiles among healthy individuals over time. Here, we sample a longitudinal wellness cohort with 100 healthy individuals and analyze blood molecular profiles including proteomics, transcriptomics, lipidomics, metabolomics, autoantibodies and immune cell profiling, complemented with gut microbiota composition and routine clinical chemistry. Overall, our results show high variation between individuals across different molecular readouts, while the intra-individual baseline variation is low. The analyses show that each individual has a unique and stable plasma protein profile throughout the study period and that many individuals also show distinct profiles with regards to the other omics datasets, with strong underlying connections between the blood proteome and the clinical chemistry parameters. In conclusion, the results support an individual-based definition of health and show that comprehensive omics profiling in a longitudinal manner is a path forward for precision medicine.
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http://dx.doi.org/10.1038/s41467-020-18148-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7479148PMC
September 2020

Publisher Correction: MEMOTE for standardized genome-scale metabolic model testing.

Nat Biotechnol 2020 Apr;38(4):504

Science for Life Laboratory, KTH -Royal Institute of Technology, Stockholm, Sweden.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.
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http://dx.doi.org/10.1038/s41587-020-0477-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7138757PMC
April 2020

Abundance and diversity of resistomes differ between healthy human oral cavities and gut.

Nat Commun 2020 02 4;11(1):693. Epub 2020 Feb 4.

Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King's College London, London, UK.

The global threat of antimicrobial resistance has driven the use of high-throughput sequencing techniques to monitor the profile of resistance genes, known as the resistome, in microbial populations. The human oral cavity contains a poorly explored reservoir of these genes. Here we analyse and compare the resistome profiles of 788 oral cavities worldwide with paired stool metagenomes. We find country and body site-specific differences in the prevalence of antimicrobial resistance genes, classes and mechanisms in oral and stool samples. Within individuals, the highest abundances of antimicrobial resistance genes are found in the oral cavity, but the oral cavity contains a lower diversity of resistance genes compared to the gut. Additionally, co-occurrence analysis shows contrasting ARG-species associations between saliva and stool samples. Maintenance and persistence of antimicrobial resistance is likely to vary across different body sites. Thus, we highlight the importance of characterising the resistome across body sites to uncover the antimicrobial resistance potential in the human body.
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http://dx.doi.org/10.1038/s41467-020-14422-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7000725PMC
February 2020

The Intestine Harbors Functionally Distinct Homeostatic Tissue-Resident and Inflammatory Th17 Cells.

Immunity 2019 07 19;51(1):77-89.e6. Epub 2019 Jun 19.

The Francis Crick Institute, 1 Midland Road London NW1 1AT, UK. Electronic address:

T helper 17 (Th17) cells are pathogenic in many inflammatory diseases, but also support the integrity of the intestinal barrier in a non-inflammatory manner. It is unclear what distinguishes inflammatory Th17 cells elicited by pathogens and tissue-resident homeostatic Th17 cells elicited by commensals. Here, we compared the characteristics of Th17 cells differentiating in response to commensal bacteria (SFB) to those differentiating in response to a pathogen (Citrobacter rodentium). Homeostatic Th17 cells exhibited little plasticity towards expression of inflammatory cytokines, were characterized by a metabolism typical of quiescent or memory T cells, and did not participate in inflammatory processes. In contrast, infection-induced Th17 cells showed extensive plasticity towards pro-inflammatory cytokines, disseminated widely into the periphery, and engaged aerobic glycolysis in addition to oxidative phosphorylation typical for inflammatory effector cells. These findings will help ensure that future therapies directed against inflammatory Th17 cells do not inadvertently damage the resident gut population.
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http://dx.doi.org/10.1016/j.immuni.2019.05.004DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6642154PMC
July 2019

Mature Human White Adipocytes Cultured under Membranes Maintain Identity, Function, and Can Transdifferentiate into Brown-like Adipocytes.

Cell Rep 2019 04;27(1):213-225.e5

Cardiovascular, Renal and Metabolism, IMED Biotech Unit, AstraZeneca, Gothenburg, Sweden; The Lundberg Laboratory for Diabetes Research, University of Gothenburg, Sweden; Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Sweden. Electronic address:

White adipose tissue (WAT) is a central factor in the development of type 2 diabetes, but there is a paucity of translational models to study mature adipocytes. We describe a method for the culture of mature white adipocytes under a permeable membrane. Compared to existing culture methods, MAAC (membrane mature adipocyte aggregate cultures) better maintain adipogenic gene expression, do not dedifferentiate, display reduced hypoxia, and remain functional after long-term culture. Subcutaneous and visceral adipocytes cultured as MAAC retain depot-specific gene expression, and adipocytes from both lean and obese patients can be cultured. Importantly, we show that rosiglitazone treatment or PGC1α overexpression in mature white adipocytes induces a brown fat transcriptional program, providing direct evidence that human adipocytes can transdifferentiate into brown-like adipocytes. Together, these data show that MAAC are a versatile tool for studying phenotypic changes of mature adipocytes and provide an improved translational model for drug development.
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http://dx.doi.org/10.1016/j.celrep.2019.03.026DOI Listing
April 2019

Simplified Intestinal Microbiota to Study Microbe-Diet-Host Interactions in a Mouse Model.

Cell Rep 2019 03;26(13):3772-3783.e6

Wallenberg Laboratory, Department of Molecular and Clinical Medicine, University of Gothenburg, Gothenburg, 41345, Sweden; Novo Nordisk Foundation Center for Basic Metabolic Research, Section for Metabolic Receptology and Enteroendocrinology, Faculty of Health Sciences, University of Copenhagen, Copenhagen, 2200, Denmark. Electronic address:

The gut microbiota can modulate human metabolism through interactions with macronutrients. However, microbiota-diet-host interactions are difficult to study because bacteria interact in complex food webs in concert with the host, and many of the bacteria are not yet characterized. To reduce the complexity, we colonize mice with a simplified intestinal microbiota (SIM) composed of ten sequenced strains isolated from the human gut with complementing pathways to metabolize dietary fibers. We feed the SIM mice one of three diets (chow [fiber rich], high-fat/high-sucrose, or zero-fat/high-sucrose diets [both low in fiber]) and investigate (1) how dietary fiber, saturated fat, and sucrose affect the abundance and transcriptome of the SIM community, (2) the effect of microbe-diet interactions on circulating metabolites, and (3) how microbiota-diet interactions affect host metabolism. Our SIM model can be used in future studies to help clarify how microbiota-diet interactions contribute to metabolic diseases.
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http://dx.doi.org/10.1016/j.celrep.2019.02.090DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6444000PMC
March 2019

Pyruvate kinase L/R is a regulator of lipid metabolism and mitochondrial function.

Metab Eng 2019 03 4;52:263-272. Epub 2019 Jan 4.

Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden; Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden; Centre for Host-Microbiome Interactions, Dental Institute, King's College London, London SE1 9RT, United Kingdom. Electronic address:

The pathogenesis of non-alcoholic fatty liver disease (NAFLD) and hepatocellular carcinoma (HCC) has been associated with altered expression of liver-specific genes including pyruvate kinase liver and red blood cell (PKLR), patatin-like phospholipase domain containing 3 (PNPLA3) and proprotein convertase subtilisin/kexin type 9 (PCSK9). Here, we inhibited and overexpressed the expression of these three genes in HepG2 cells, generated RNA-seq data before and after perturbation and revealed the altered global biological functions with the modulation of these genes using integrated network (IN) analysis. We found that modulation of these genes effects the total triglycerides levels within the cells and viability of the cells. Next, we generated IN for HepG2 cells, identified reporter transcription factors based on IN and found that the modulation of these genes affects key metabolic pathways associated with lipid metabolism (steroid biosynthesis, PPAR signalling pathway, fatty acid synthesis and oxidation) and cancer development (DNA replication, cell cycle and p53 signalling) involved in the progression of NAFLD and HCC. Finally, we observed that inhibition of PKLR lead to decreased glucose uptake and decreased mitochondrial activity in HepG2 cells. Hence, our systems level analysis indicated that PKLR can be targeted for development efficient treatment strategy for NAFLD and HCC.
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http://dx.doi.org/10.1016/j.ymben.2019.01.001DOI Listing
March 2019

Characterization of heterogeneous redox responses in hepatocellular carcinoma patients using network analysis.

EBioMedicine 2019 Feb 31;40:471-487. Epub 2018 Dec 31.

Science for Life Laboratory, KTH - Royal Institute of Technology, SE-171 21 Stockholm, Sweden; Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden; Centre for Host-Microbiome Interactions, Dental Institute, King's College London, London, UK. Electronic address:

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.
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http://dx.doi.org/10.1016/j.ebiom.2018.12.057DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6412169PMC
February 2019

ESS: A Tool for Genome-Scale Quantification of Essentiality Score for Reaction/Genes in Constraint-Based Modeling.

Front Physiol 2018 28;9:1355. Epub 2018 Sep 28.

Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden.

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 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.
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http://dx.doi.org/10.3389/fphys.2018.01355DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6173058PMC
September 2018

An Integrated Understanding of the Rapid Metabolic Benefits of a Carbohydrate-Restricted Diet on Hepatic Steatosis in Humans.

Cell Metab 2018 03 15;27(3):559-571.e5. Epub 2018 Feb 15.

Department of Molecular and Clinical Medicine, University of Gothenburg, and Sahlgrenska University Hospital, Gothenburg, Sweden. Electronic address:

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 β-hydroxybutyrate concentrations, reflecting increased mitochondrial β-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.
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http://dx.doi.org/10.1016/j.cmet.2018.01.005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6706084PMC
March 2018

Integrative Personal Omics Profiles during Periods of Weight Gain and Loss.

Cell Syst 2018 Feb 17;6(2):157-170.e8. Epub 2018 Jan 17.

Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA. Electronic address:

Advances in omics technologies now allow an unprecedented level of phenotyping for human diseases, including obesity, in which individual responses to excess weight are heterogeneous and unpredictable. To aid the development of better understanding of these phenotypes, we performed a controlled longitudinal weight perturbation study combining multiple omics strategies (genomics, transcriptomics, multiple proteomics assays, metabolomics, and microbiomics) during periods of weight gain and loss in humans. Results demonstrated that: (1) weight gain is associated with the activation of strong inflammatory and hypertrophic cardiomyopathy signatures in blood; (2) although weight loss reverses some changes, a number of signatures persist, indicative of long-term physiologic changes; (3) we observed omics signatures associated with insulin resistance that may serve as novel diagnostics; (4) specific biomolecules were highly individualized and stable in response to perturbations, potentially representing stable personalized markers. Most data are available open access and serve as a valuable resource for the community.
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http://dx.doi.org/10.1016/j.cels.2017.12.013DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6021558PMC
February 2018

TCSBN: a database of tissue and cancer specific biological networks.

Nucleic Acids Res 2018 01;46(D1):D595-D600

Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden.

Biological networks provide new opportunities for understanding the cellular biology in both health and disease states. We generated tissue specific integrated networks (INs) for liver, muscle and adipose tissues by integrating metabolic, regulatory and protein-protein interaction networks. We also generated human co-expression networks (CNs) for 46 normal tissues and 17 cancers to explore the functional relationships between genes as well as their relationships with biological functions, and investigate the overlap between functional and physical interactions provided by CNs and INs, respectively. These networks can be employed in the analysis of omics data, provide detailed insight into disease mechanisms by identifying the key biological components and eventually can be used in the development of efficient treatment strategies. Moreover, comparative analysis of the networks may allow for the identification of tissue-specific targets that can be used in the development of drugs with the minimum toxic effect to other human tissues. These context-specific INs and CNs are presented in an interactive website http://inetmodels.com without any limitation.
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http://dx.doi.org/10.1093/nar/gkx994DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5753183PMC
January 2018

Network analyses identify liver-specific targets for treating liver diseases.

Mol Syst Biol 2017 08 21;13(8):938. Epub 2017 Aug 21.

Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden

We performed integrative network analyses to identify targets that can be used for effectively treating liver diseases with minimal side effects. We first generated co-expression networks (CNs) for 46 human tissues and liver cancer to explore the functional relationships between genes and examined the overlap between functional and physical interactions. Since increased lipogenesis is a characteristic of nonalcoholic fatty liver disease (NAFLD) and hepatocellular carcinoma (HCC), we investigated the liver-specific genes co-expressed with fatty acid synthase (FASN). CN analyses predicted that inhibition of these liver-specific genes decreases FASN expression. Experiments in human cancer cell lines, mouse liver samples, and primary human hepatocytes validated our predictions by demonstrating functional relationships between these liver genes, and showing that their inhibition decreases cell growth and liver fat content. In conclusion, we identified liver-specific genes linked to NAFLD pathogenesis, such as pyruvate kinase liver and red blood cell (PKLR), or to HCC pathogenesis, such as PKLR, patatin-like phospholipase domain containing 3 (PNPLA3), and proprotein convertase subtilisin/kexin type 9 (PCSK9), all of which are potential targets for drug development.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5572395PMC
http://dx.doi.org/10.15252/msb.20177703DOI Listing
August 2017

A pathology atlas of the human cancer transcriptome.

Science 2017 08;357(6352)

Department of Immunology Genetics and Pathology, Uppsala University, Uppsala, Sweden.

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.
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http://dx.doi.org/10.1126/science.aan2507DOI Listing
August 2017

Improving the economics of NASH/NAFLD treatment through the use of systems biology.

Drug Discov Today 2017 10 20;22(10):1532-1538. Epub 2017 Jul 20.

Science for Life Laboratory, KTH - Royal Institute of Technology, SE-171 21, Stockholm, Sweden; Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden. Electronic address:

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.
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http://dx.doi.org/10.1016/j.drudis.2017.07.005DOI Listing
October 2017

A subcellular map of the human proteome.

Science 2017 05 11;356(6340). Epub 2017 May 11.

Science for Life Laboratory, School of Biotechnology, KTH Royal Institute of Technology, SE-171 21 Stockholm, Sweden.

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.
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http://dx.doi.org/10.1126/science.aal3321DOI Listing
May 2017

Investigating the Combinatory Effects of Biological Networks on Gene Co-expression.

Front Physiol 2016 2;7:160. Epub 2016 May 2.

State Key Laboratory of Bioreactor Engineering, East China University of Science and TechnologyShanghai, China; Shanghai Collaborative Innovation Center for Biomanufacturing TechnologyShanghai, China.

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 Saccharomyces 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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http://dx.doi.org/10.3389/fphys.2016.00160DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4916787PMC
July 2016

Integrated Network Analysis Reveals an Association between Plasma Mannose Levels and Insulin Resistance.

Cell Metab 2016 07 23;24(1):172-84. Epub 2016 Jun 23.

Science for Life Laboratory, KTH-Royal Institute of Technology, 171 21 Stockholm, Sweden; Department of Biology and Biological Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden. Electronic address:

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.
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http://dx.doi.org/10.1016/j.cmet.2016.05.026DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6666317PMC
July 2016

Dysregulated signaling hubs of liver lipid metabolism reveal hepatocellular carcinoma pathogenesis.

Nucleic Acids Res 2016 07 23;44(12):5529-39. Epub 2016 May 23.

Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, SE-412 96, Sweden

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.
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http://dx.doi.org/10.1093/nar/gkw462DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4937331PMC
July 2016

Predicting unintended effects of drugs based on off-target tissue effects.

Biochem Biophys Res Commun 2016 Jan 2;469(3):399-404. Epub 2015 Dec 2.

Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea; Bio-Synergy Research Center, Daejeon, Republic of Korea. Electronic address:

Unintended effects of drugs can be caused by various mechanisms. Conventional analysis of unintended effects has focused on the target proteins of drugs. However, an interaction with off-target tissues of a drug might be one of the unintended effect-related mechanisms. We propose two processes to predict a drug's unintended effects by off-target tissue effects: 1) identification of a drug's off-target tissue and; 2) tissue protein - symptom relation identification (tissue protein - symptom matrix). Using this method, we predicted that 1,177 (10.7%) side-effects were related to off-target tissue effects in 11,041 known side-effects. Off-target tissues and unintended effects of successful repositioning drugs were also predicted. The effectiveness of relations of the proposed tissue protein - symptom matrix were evaluated by using the literature mining method. We predicted unintended effects of drugs as well as those effect-related off-target tissues. By using our prediction, we are able to reduce drug side-effects on off-target tissues and provide a chance to identify new indications of drugs of interest.
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http://dx.doi.org/10.1016/j.bbrc.2015.11.095DOI Listing
January 2016

Anomalies in network bridges involved in bile Acid metabolism predict outcomes of colorectal cancer patients.

PLoS One 2014 26;9(9):e107925. Epub 2014 Sep 26.

Department of Bio and Brain Engineering, KAIST, Yuseong-gu, Daejeon, Republic of Korea.

Biomarkers prognostic for colorectal cancer (CRC) would be highly desirable in clinical practice. Proteins that regulate bile acid (BA) homeostasis, by linking metabolic sensors and metabolic enzymes, also called bridge proteins, may be reliable prognostic biomarkers for CRC. Based on a devised metric, "bridgeness," we identified bridge proteins involved in the regulation of BA homeostasis and identified their prognostic potentials. The expression patterns of these bridge proteins could distinguish between normal and diseased tissues, suggesting that these proteins are associated with CRC pathogenesis. Using a supervised classification system, we found that these bridge proteins were reproducibly prognostic, with high prognostic ability compared to other known markers.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0107925PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4178056PMC
June 2015

Rule-based multi-scale simulation for drug effect pathway analysis.

BMC Med Inform Decis Mak 2013 5;13 Suppl 1:S4. Epub 2013 Apr 5.

Department of Bio and Brain Engineering, KAIST, Daejeon, South Korea.

Background: Biological systems are robust and complex to maintain stable phenotypes under various conditions. In these systems, drugs reported the limited efficacy and unexpected side-effects. To remedy this situation, many pharmaceutical laboratories have begun to research combination drugs and some of them have shown successful clinical results. Complementary action of multiple compounds could increase efficacy as well as reduce side-effects through pharmacological interactions. However, experimental approach requires vast cost of preclinical experiments and tests as the number of possible combinations of compound dosages increases exponentially. Computer model-based experiments have been emerging as one of the most promising solutions to cope with such complexity. Though there have been many efforts to model specific molecular pathways using qualitative and quantitative formalisms, they suffer from unexpected results caused by distant interactions beyond their localized models.

Results: In this work, we propose a rule-based multi-scale modelling platform. We have tested this platform with Type 2 diabetes (T2D) model, which involves the malfunction of numerous organs such as pancreas, circulation system, liver, and adipocyte. We have extracted T2D-related 190 rules by manual curation from literature, pathway databases and converting from different types of existing models. We have simulated twenty-two T2D drugs. The results of our simulation show drug effect pathways of T2D drugs and whether combination drugs have efficacy or not and how combination drugs work on the multi-scale model.

Conclusions: We believe that our simulation would help to understand drug mechanism for the drug development and provide a new way to effectively apply existing drugs for new target. It also would give insight for identifying effective combination drugs.
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http://dx.doi.org/10.1186/1472-6947-13-S1-S4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3618249PMC
July 2013

Prioritization of SNPs for genome-wide association studies using an interaction model of genetic variation, gene expression, and trait variation.

Mol Cells 2012 Apr 28;33(4):351-61. Epub 2012 Mar 28.

Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 305-701, Korea.

The identification of true causal loci to unravel the statistical evidence of genotype-phenotype correlations and the biological relevance of selected single-nucleotide polymorphisms (SNPs) is a challenging issue in genome-wide association studies (GWAS). Here, we introduced a novel method for the prioritization of SNPs based on p-values from GWAS. The method uses functional evidence from populations, including phenotype-associated gene expressions. Based on the concept of genetic interactions, such as perturbation of gene expression by genetic variation, phenotype and gene expression related SNPs were prioritized by adjusting the p-values of SNPs. We applied our method to GWAS data related to drug-induced cytotoxicity. Then, we prioritized loci that potentially play a role in druginduced cytotoxicity. By generating an interaction model, our approach allowed us not only to identify causal loci, but also to find intermediate nodes that regulate the flow of information among causal loci, perturbed gene expression, and resulting phenotypic variation.
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http://dx.doi.org/10.1007/s10059-012-2264-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3887803PMC
April 2012