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Systems and Network-based Approaches to Complex Metabolic Diseases
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology. KTH, Centres, Science for Life Laboratory, SciLifeLab. (SysMedicine)ORCID iD: 0000-0003-2261-0881
2021 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The future of healthcare is personalized medicine, in which disease treatments are tailored based on the individual characteristics of each patient. To reach that objective, we need to obtain a better understanding of diseases. The main facilitator of personalized medicine is systems and data-driven biology, which makes omics data a top commodity in this era. Coupled with computational and biological expertise, omics data can be a useful asset for obtaining mechanistic insights into the biological conundrum, particularly in disease-related contexts. This thesis describes systems biology approaches and their applications in disease-specific contexts. Systems biology assists us in systematically and comprehensively understanding complex biological systems as a whole interconnected system.

The first part of the thesis describes the generation of more than 100 biological networks based on personalized data originated from several different omics, usually referred to as multiomics data, including clinical data and metabolomics, proteomics, and metagenomics data collected from the same individuals. Moreover, we present a web-based multiomics biological network database and visualization platform called iNetModels.

In the second part of the thesis, we describe systems biology frameworks and their applications to the study of various biological questions in disease contexts using single- and multiomics data. First, we present our findings on the integrative view of metabolic activities from multiple tissues after myocardial infarction using transcriptomics data from the heart and other metabolically active tissues. Second, we used transcriptomics data to describe the mechanistic effect of lifelong training on skeletal muscle in both men and women and the role of short-term training in reversing damage from metabolic-related diseases. Third, we deciphered the molecular mechanism of nonalcoholic fatty liver disease (NAFLD) based on clinical data, plasma metabolomics, plasma inflammatory proteomics, and oral and gut metagenomics data. Finally, we elucidated the mechanism of action of CMA supplementation, a potential treatment for NAFLD, based on proteomics and metabolomics data.

In summary, this thesis presents a novel platform for biological network analysis and proven systems biology frameworks to provide mechanistic and systematic understandings of specific diseases using single- and multiomics data.

Abstract [sv]

Framtiden för hälsovård är precisionsmedicin; behandling av sjukdomskräddarsys baserat på de individualla egenskaper hos varje enskildpatient. För att nå detta mål behöver vi öka vår kunskap om sjukdomar.Det främsta hjälpmedlet för att utveckla precisionsmedicin är system- ochdatadriven biologi, vilket i sin tur gör omikdata till en viktig resurs isamtiden. Omikdata kan kombineras med expertis inomberäkningsbiologi för att på så vis vara en värdeful tillgång för att få insyni biologiska mekanismer, särskilt inom sjukdomskontext. Dennaavhandling beskriver strategier inom systembiologi, och deras appliceringför specifika sjukdomar.

Den första delen av avhandlingen beskriver utvecklandet av mer än 100biologiska nätverk baserade på personaliserad multiomik-data, inklusiveklinisk data samt metabolomik-, proteomik-, och metagenomikdata,insamlat från samma individer. Dessutom presenterar vi en webb-baseraddatabas innehållande biologiska nätverk byggda från multiomik-data,samt en visualiseringsplatform vid namn iNetModels.

I den andra delen av avhandlingen beskriver vi systembiologiska ramverkoch deras applicering för studier av olika sorters biologiska frågor inomsjukdomskontext, genom att använda en eller flera sorters omikdata. Förstpresenterar vi våra fynd om den integrativa vyn av metaboliska aktiviteterfrån flertalet vävnader efter hjärtinfarkt, genom att användatranskriptomikdata både från hjärtat och andra metaboliskt aktivavävnader. Sedan använde vi transkriptomikdata för att beskriva denmekanistiska effekten av livslång träning av skelettmuskel i både män ochkvinnor, samt vilken roll kortsiktig träning har i att läka skador frånmetabolismrelaterade sjukdomar. Efter det dechiffrerade vi denmolekylära mekanismen bakom nonalcoholic fatty liver disease (NAFLD),eller fettlever, baserat på kliniska data, plasma-metabolomik,inflammatorisk plasma-proteomik, samt metagenomikdata från månhålaoch tarmkanal. Till sist tydliggjorde vi mekanismen av CMAsupplementrering, en potentiell behandling av NAFLD, baserat påproteomik- och metabolomikdata.

Sammanfattningsvis beskriver denna avhandling en ny plattform förbiologisk nätverksanalys och bevisade systembiologiska ramverk för attutröna mekanistisk och systematisk förståelse för specifika sjukdomar,genom att använda singel- eller multiomikdata.

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2021. , p. 75
Publication channel
978-91-7873-880-9
Series
TRITA-CBH-FOU ; 2021:23
National Category
Bioinformatics and Systems Biology
Research subject
Biotechnology
Identifiers
URN: urn:nbn:se:kth:diva-294200ISBN: 978-91-7873-880-9 (print)OAI: oai:DiVA.org:kth-294200DiVA, id: diva2:1554002
Public defence
2021-06-11, https://kth-se.zoom.us/webinar/register/WN_Si0EW3vKRKSYkKek533ohQ, Stockholm, 13:00 (English)
Opponent
Supervisors
Note

QC 2021-05-11

Available from: 2021-05-11 Created: 2021-05-11 Last updated: 2022-06-25Bibliographically approved
List of papers
1. TCSBN: a database of tissue and cancer specific biological networks
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 1362-4962, Vol. 46, no D1, p. D595-D600Article in journal (Refereed) Published
Abstract [en]

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 integratingmetabolic, 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.

Place, publisher, year, edition, pages
Oxford University Press, 2017
National Category
Bioinformatics and Systems Biology
Identifiers
urn:nbn:se:kth:diva-248672 (URN)10.1093/nar/gkx994 (DOI)000419550700090 ()29069445 (PubMedID)2-s2.0-85040915985 (Scopus ID)
Note

QC 20190423

Available from: 2019-04-09 Created: 2019-04-09 Last updated: 2024-03-15Bibliographically approved
2. iNetModels 2.0: an interactive visualization and database of multi-omics data.
Open this publication in new window or tab >>iNetModels 2.0: an interactive visualization and database of multi-omics data.
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2021 (English)In: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 49, no W1, p. W271-W276, article id gkab254Article in journal (Refereed) Published
Abstract [en]

It is essential to reveal the associations between various omics data for a comprehensive understanding of the altered biological process in human wellness and disease. To date, very few studies have focused on collecting and exhibiting multi-omics associations in a single database. Here, we present iNetModels, an interactive database and visualization platform of Multi-Omics Biological Networks (MOBNs). This platform describes the associations between the clinical chemistry, anthropometric parameters, plasma proteomics, plasma metabolomics, as well as metagenomics for oral and gut microbiome obtained from the same individuals. Moreover, iNetModels includes tissue- and cancer-specific Gene Co-expression Networks (GCNs) for exploring the connections between the specific genes. This platform allows the user to interactively explore a single feature's association with other omics data and customize its particular context (e.g. male/female specific). The users can also register their data for sharing and visualization of the MOBNs and GCNs. Moreover, iNetModels allows users who do not have a bioinformatics background to facilitate human wellness and disease research. iNetModels can be accessed freely at https://inetmodels.com without any limitation.

Place, publisher, year, edition, pages
Oxford University Press (OUP), 2021
National Category
Bioinformatics and Systems Biology
Identifiers
urn:nbn:se:kth:diva-294064 (URN)10.1093/nar/gkab254 (DOI)000672775800036 ()33849075 (PubMedID)2-s2.0-85110396987 (Scopus ID)
Note

QC 20210518

Available from: 2021-05-06 Created: 2021-05-06 Last updated: 2023-12-07Bibliographically approved
3. Integrative transcriptomic analysis of tissue-specific metabolic crosstalk after myocardial infarction
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 Systems Biology Cardiac and Cardiovascular Systems
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: 2023-12-07Bibliographically approved
4. Skeletal Muscle Transcriptomic Comparison between Long-Term Trained and Untrained Men and Women
Open this publication in new window or tab >>Skeletal Muscle Transcriptomic Comparison between Long-Term Trained and Untrained Men and Women
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2020 (English)In: Cell Reports, E-ISSN 2211-1247, Vol. 31, no 12, article id UNSP 107808Article in journal (Refereed) Published
Abstract [en]

To better understand the health benefits of lifelong exercise in humans, we conduct global skeletal muscle transcriptomic analyses of long-term endurance- (9 men, 9 women) and strength-trained (7 men) humans compared with age-matched untrained controls (7 men, 8 women). Transcriptomic analysis, Gene Ontology, and genome-scale metabolic modeling demonstrate changes in pathways related to the prevention of metabolic diseases, particularly with endurance training. Our data also show prominent sex differences between controls and that these differences are reduced with endurance training. Additionally, we compare our data with studies examining muscle gene expression before and after a months-long training period in individuals with metabolic diseases, This analysis reveals that training shifts gene expression in individuals with impaired metabolism to become more similar to our endurance-trained group. Overall, our data provide an extensive examination of the accumulated transcriptional changes that occur with decades-long training and identify important "exercise-responsive" genes that could attenuate metabolic disease.

Place, publisher, year, edition, pages
Elsevier BV, 2020
National Category
Sport and Fitness Sciences
Identifiers
urn:nbn:se:kth:diva-278667 (URN)10.1016/j.celrep.2020.107808 (DOI)000543381400023 ()32579934 (PubMedID)2-s2.0-85087057792 (Scopus ID)
Note

QC 20200720

Available from: 2020-07-20 Created: 2020-07-20 Last updated: 2024-01-17Bibliographically approved
5. Multi-omics analysis reveals the influence of the oral and gut microbiome on host metabolism in non-alcoholic fatty liver disease
Open this publication in new window or tab >>Multi-omics analysis reveals the influence of the oral and gut microbiome on host metabolism in non-alcoholic fatty liver disease
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

Non-alcoholic fatty liver disease (NAFLD) is a complex disease involving alterations in multiple biological processes regulated by the interactions between obesity, genetic background and environmental factors including the microbiome. To decipher hepatic steatosis (HS) pathogenesis by excluding critical confounding factors including genetic variants, obesity and diabetes, we characterized 56 heterogeneous NAFLD patients by generating multi-omics data including oral and gut metagenomics as well as plasma metabolomics and inflammatory proteomics data. We explored the dysbiosis in the oral and gut microbiome and revealed host-microbiome interactions based on global metabolic and inflammatory processes. We integrated this multi-omics data using the biological network and identified HS's key features using multi-omics data. We finally predicted HS using these key features and validated our findings in a validation dataset, where we characterized 22 subjects with varying degree of HS 

Keywords
Non-alcoholic fatty liver disease: Multi-omics analysis; Metabolomics; Proteomics; gut and oral metagenomics; Systems Biology; Systems Medicine
National Category
Bioinformatics and Systems Biology
Research subject
Biotechnology
Identifiers
urn:nbn:se:kth:diva-294196 (URN)
Note

QC 20210527

Available from: 2021-05-11 Created: 2021-05-11 Last updated: 2023-12-07Bibliographically approved
6. The acute effect of metabolic cofactor supplementation: a potential therapeutic strategy against non-alc33oholic fatty liver disease
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 and 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: 2024-03-18Bibliographically approved

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