1
|
Walker KA, Chen J, Shi L, Yang Y, Fornage M, Zhou L, Schlosser P, Surapaneni A, Grams ME, Duggan MR, Peng Z, Gomez GT, Tin A, Hoogeveen RC, Sullivan KJ, Ganz P, Lindbohm JV, Kivimaki M, Nevado-Holgado AJ, Buckley N, Gottesman RF, Mosley TH, Boerwinkle E, Ballantyne CM, Coresh J. Proteomics analysis of plasma from middle-aged adults identifies protein markers of dementia risk in later life. Sci Transl Med 2023; 15:eadf5681. [PMID: 37467317 PMCID: PMC10665113 DOI: 10.1126/scitranslmed.adf5681] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 06/28/2023] [Indexed: 07/21/2023]
Abstract
A diverse set of biological processes have been implicated in the pathophysiology of Alzheimer's disease (AD) and related dementias. However, there is limited understanding of the peripheral biological mechanisms relevant in the earliest phases of the disease. Here, we used a large-scale proteomics platform to examine the association of 4877 plasma proteins with 25-year dementia risk in 10,981 middle-aged adults. We found 32 dementia-associated plasma proteins that were involved in proteostasis, immunity, synaptic function, and extracellular matrix organization. We then replicated the association between 15 of these proteins and clinically relevant neurocognitive outcomes in two independent cohorts. We demonstrated that 12 of these 32 dementia-associated proteins were associated with cerebrospinal fluid (CSF) biomarkers of AD, neurodegeneration, or neuroinflammation. We found that eight of these candidate protein markers were abnormally expressed in human postmortem brain tissue from patients with AD, although some of the proteins that were most strongly associated with dementia risk, such as GDF15, were not detected in these brain tissue samples. Using network analyses, we found a protein signature for dementia risk that was characterized by dysregulation of specific immune and proteostasis/autophagy pathways in adults in midlife ~20 years before dementia onset, as well as abnormal coagulation and complement signaling ~10 years before dementia onset. Bidirectional two-sample Mendelian randomization genetically validated nine of our candidate proteins as markers of AD in midlife and inferred causality of SERPINA3 in AD pathogenesis. Last, we prioritized a set of candidate markers for AD and dementia risk prediction in midlife.
Collapse
Affiliation(s)
- Keenan A. Walker
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program, Baltimore, MD 21224, USA
| | - Jingsha Chen
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21210, USA
| | - Liu Shi
- Novo Nordisk Research Centre Oxford (NNRCO), Oxford OX3 7FZ, UK
| | - Yunju Yang
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School and Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School and Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Linda Zhou
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21210, USA
| | - Pascal Schlosser
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21210, USA
| | - Aditya Surapaneni
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21210, USA
| | - Morgan E. Grams
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21210, USA
- Division of Nephrology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21210, USA
| | - Michael R. Duggan
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program, Baltimore, MD 21224, USA
| | - Zhongsheng Peng
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program, Baltimore, MD 21224, USA
| | - Gabriela T. Gomez
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21210, USA
| | - Adrienne Tin
- MIND Center and Division of Nephrology, University of Mississippi Medical Center, Jackson, MS 39216, USA
| | - Ron C. Hoogeveen
- Section of Cardiovascular Research, Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA
| | - Kevin J. Sullivan
- Department of Medicine, Division of Geriatrics, University of Mississippi Medical Center, Jackson, MS 39216, USA
| | - Peter Ganz
- Department of Medicine, University of California-San Francisco, San Francisco, CA 94115, USA
| | - Joni V. Lindbohm
- Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02142, USA
| | - Mika Kivimaki
- Department of Mental Health of Older People, Faculty of Brain Sciences, University College London, London WC1E 6BT, UK
- Clinicum, Faculty of Medicine, University of Helsinki, Helsinki 00100, Finland
| | | | - Noel Buckley
- Department of Psychiatry, University of Oxford, Oxford OX1 2JD, UK
| | - Rebecca F. Gottesman
- National Institute of Neurological Disorders and Stroke, Intramural Research Program, Bethesda, MD 20892, USA
| | - Thomas H. Mosley
- Department of Medicine, Division of Geriatrics, University of Mississippi Medical Center, Jackson, MS 39216, USA
| | - Eric Boerwinkle
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston; Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Christie M. Ballantyne
- Section of Cardiovascular Research, Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21210, USA
| |
Collapse
|
2
|
Slenter DN, Hemel IMGM, Evelo CT, Bierau J, Willighagen EL, Steinbusch LKM. Extending inherited metabolic disorder diagnostics with biomarker interaction visualizations. Orphanet J Rare Dis 2023; 18:95. [PMID: 37101200 PMCID: PMC10131334 DOI: 10.1186/s13023-023-02683-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 04/02/2023] [Indexed: 04/28/2023] Open
Abstract
BACKGROUND Inherited Metabolic Disorders (IMDs) are rare diseases where one impaired protein leads to a cascade of changes in the adjacent chemical conversions. IMDs often present with non-specific symptoms, a lack of a clear genotype-phenotype correlation, and de novo mutations, complicating diagnosis. Furthermore, products of one metabolic conversion can be the substrate of another pathway obscuring biomarker identification and causing overlapping biomarkers for different disorders. Visualization of the connections between metabolic biomarkers and the enzymes involved might aid in the diagnostic process. The goal of this study was to provide a proof-of-concept framework for integrating knowledge of metabolic interactions with real-life patient data before scaling up this approach. This framework was tested on two groups of well-studied and related metabolic pathways (the urea cycle and pyrimidine de-novo synthesis). The lessons learned from our approach will help to scale up the framework and support the diagnosis of other less-understood IMDs. METHODS Our framework integrates literature and expert knowledge into machine-readable pathway models, including relevant urine biomarkers and their interactions. The clinical data of 16 previously diagnosed patients with various pyrimidine and urea cycle disorders were visualized on the top 3 relevant pathways. Two expert laboratory scientists evaluated the resulting visualizations to derive a diagnosis. RESULTS The proof-of-concept platform resulted in varying numbers of relevant biomarkers (five to 48), pathways, and pathway interactions for each patient. The two experts reached the same conclusions for all samples with our proposed framework as with the current metabolic diagnostic pipeline. For nine patient samples, the diagnosis was made without knowledge about clinical symptoms or sex. For the remaining seven cases, four interpretations pointed in the direction of a subset of disorders, while three cases were found to be undiagnosable with the available data. Diagnosing these patients would require additional testing besides biochemical analysis. CONCLUSION The presented framework shows how metabolic interaction knowledge can be integrated with clinical data in one visualization, which can be relevant for future analysis of difficult patient cases and untargeted metabolomics data. Several challenges were identified during the development of this framework, which should be resolved before this approach can be scaled up and implemented to support the diagnosis of other (less understood) IMDs. The framework could be extended with other OMICS data (e.g. genomics, transcriptomics), and phenotypic data, as well as linked to other knowledge captured as Linked Open Data.
Collapse
Affiliation(s)
- Denise N Slenter
- Department of Bioinformatics (BiGCaT), NUTRIM, Maastricht University, Maastricht, The Netherlands.
| | - Irene M G M Hemel
- Department of Bioinformatics (BiGCaT), NUTRIM, Maastricht University, Maastricht, The Netherlands
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
| | - Chris T Evelo
- Department of Bioinformatics (BiGCaT), NUTRIM, Maastricht University, Maastricht, The Netherlands
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
| | - Jörgen Bierau
- Department of Clinical Genetics, Maastricht University Medical Center, Maastricht, The Netherlands
- Department of Clinical Genetics, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Egon L Willighagen
- Department of Bioinformatics (BiGCaT), NUTRIM, Maastricht University, Maastricht, The Netherlands
| | - Laura K M Steinbusch
- Department of Clinical Genetics, Maastricht University Medical Center, Maastricht, The Netherlands
| |
Collapse
|
3
|
Rothfels K, Milacic M, Matthews L, Haw R, Sevilla C, Gillespie M, Stephan R, Gong C, Ragueneau E, May B, Shamovsky V, Wright A, Weiser J, Beavers D, Conley P, Tiwari K, Jassal B, Griss J, Senff-Ribeiro A, Brunson T, Petryszak R, Hermjakob H, D'Eustachio P, Wu G, Stein L. Using the Reactome Database. Curr Protoc 2023; 3:e722. [PMID: 37053306 PMCID: PMC11184634 DOI: 10.1002/cpz1.722] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/15/2023]
Abstract
Pathway databases provide descriptions of the roles of proteins, nucleic acids, lipids, carbohydrates, and other molecular entities within their biological cellular contexts. Pathway-centric views of these roles may allow for the discovery of unexpected functional relationships in data such as gene expression profiles and somatic mutation catalogues from tumor cells. For this reason, there is a high demand for high-quality pathway databases and their associated tools. The Reactome project (a collaboration between the Ontario Institute for Cancer Research, New York University Langone Health, the European Bioinformatics Institute, and Oregon Health & Science University) is one such pathway database. Reactome collects detailed information on biological pathways and processes in humans from the primary literature. Reactome content is manually curated, expert-authored, and peer-reviewed and spans the gamut from simple intermediate metabolism to signaling pathways and complex cellular events. This information is supplemented with likely orthologous molecular reactions in mouse, rat, zebrafish, worm, and other model organisms. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Browsing a Reactome pathway Basic Protocol 2: Exploring Reactome annotations of disease and drugs Basic Protocol 3: Finding the pathways involving a gene or protein Alternate Protocol 1: Finding the pathways involving a gene or protein using UniProtKB (SwissProt), Ensembl, or Entrez gene identifier Alternate Protocol 2: Using advanced search Basic Protocol 4: Using the Reactome pathway analysis tool to identify statistically overrepresented pathways Basic Protocol 5: Using the Reactome pathway analysis tool to overlay expression data onto Reactome pathway diagrams Basic Protocol 6: Comparing inferred model organism and human pathways using the Species Comparison tool Basic Protocol 7: Comparing tissue-specific expression using the Tissue Distribution tool.
Collapse
Affiliation(s)
- Karen Rothfels
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Marija Milacic
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | | | - Robin Haw
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Cristoffer Sevilla
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Marc Gillespie
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
- College of Pharmacy and Health Sciences, St. John's University, Queens, New York
| | - Ralf Stephan
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Chuqiao Gong
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Eliot Ragueneau
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Bruce May
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | | | - Adam Wright
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Joel Weiser
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | | | | | - Krishna Tiwari
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Bijay Jassal
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Johannes Griss
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Andrea Senff-Ribeiro
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
- Universidade Federal do Paraná, Curitiba, Brazil
| | | | - Robert Petryszak
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
- Oregon Health and Science University, Portland, Oregon
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | | | - Guanming Wu
- Oregon Health and Science University, Portland, Oregon
| | - Lincoln Stein
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| |
Collapse
|
4
|
Microbiome and Metabolomics in Liver Cancer: Scientific Technology. Int J Mol Sci 2022; 24:ijms24010537. [PMID: 36613980 PMCID: PMC9820585 DOI: 10.3390/ijms24010537] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 12/12/2022] [Accepted: 12/19/2022] [Indexed: 12/30/2022] Open
Abstract
Primary liver cancer is a heterogeneous disease. Liver cancer metabolism includes both the reprogramming of intracellular metabolism to enable cancer cells to proliferate inappropriately and adapt to the tumor microenvironment and fluctuations in regular tissue metabolism. Currently, metabolomics and metabolite profiling in liver cirrhosis, liver cancer, and hepatocellular carcinoma (HCC) have been in the spotlight in terms of cancer diagnosis, monitoring, and therapy. Metabolomics is the global analysis of small molecules, chemicals, and metabolites. Metabolomics technologies can provide critical information about the liver cancer state. Here, we review how liver cirrhosis, liver cancer, and HCC therapies interact with metabolism at the cellular and systemic levels. An overview of liver metabolomics is provided, with a focus on currently available technologies and how they have been used in clinical and translational research. We also list scalable methods, including chemometrics, followed by pathway processing in liver cancer. We conclude that important drivers of metabolomics science and scientific technologies are novel therapeutic tools and liver cancer biomarker analysis.
Collapse
|
5
|
Liu Y, Pan X, Bao Y, Wei L, Gao Y. Many kinds of oxidized proteins are present more in the urine of the elderly. Clin Proteomics 2022; 19:22. [PMID: 35733114 PMCID: PMC9214981 DOI: 10.1186/s12014-022-09360-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Accepted: 06/08/2022] [Indexed: 11/22/2022] Open
Abstract
Background Many studies have shown an association between aging and oxidation. To our knowledge, there have been no studies exploring aging-related urine proteome modifications. The purpose of this study was to explore differences in global chemical modifications of urinary protein at different ages. Methods Discovery (n=38) cohort MS data including children, young and old groups were downloaded from three published studies, and this data was analyzed using open-pFind for identifying modifications. Verification cohort human samples (n=28) including young, middle-aged, and old groups, rat samples (n=7) at three-time points after birth, adulthood, and old age were collected and processed in the laboratory simultaneously based on label-free quantification combined with pFind. Results Discovery cohort: there were 28 kinds of differential oxidations in the old group that were higher than those in the young or children group in. Verification cohort: there were 17 kinds of differential oxidations of 49 oxidized proteins in the middle and old groups, which were significantly higher than those in the young group. Both oxidations and oxidized proteins distinguished different age groups well. There were also 15 kinds of differential oxidations in old age higher than others in the rat cohort. The results showed that the validation experiment was basically consistent with the results of the discovery experiment, showing that the level of oxidized proteins in urine increased significantly with age. Conclusions Our study is the first to show that oxidative proteins occur in urine and that oxidations are higher in older than younger ages. Perhaps improving the degree of excretion of oxidative protein in vivo through the kidney is helpful for maintaining the homeostasis of the body’s internal environment, delaying aging and the occurrence of senile diseases. Supplementary Information The online version contains supplementary material available at 10.1186/s12014-022-09360-2.
Collapse
Affiliation(s)
- Yongtao Liu
- Department of Biochemistry and Molecular Biology, Beijing Key Laboratory of Gene Engineering Drug and Biotechnology, Beijing Normal University, Beijing, 100875, China
| | - Xuanzhen Pan
- Department of Biochemistry and Molecular Biology, Beijing Key Laboratory of Gene Engineering Drug and Biotechnology, Beijing Normal University, Beijing, 100875, China
| | - Yijin Bao
- Department of Biochemistry and Molecular Biology, Beijing Key Laboratory of Gene Engineering Drug and Biotechnology, Beijing Normal University, Beijing, 100875, China
| | - Lilong Wei
- Clinical Laboratory, China-Japan Friendship Hospital, Beijing, 100029, China
| | - Youhe Gao
- Department of Biochemistry and Molecular Biology, Beijing Key Laboratory of Gene Engineering Drug and Biotechnology, Beijing Normal University, Beijing, 100875, China.
| |
Collapse
|
6
|
Miller RA, Kutmon M, Bohler A, Waagmeester A, Evelo CT, Willighagen EL. Understanding signaling and metabolic paths using semantified and harmonized information about biological interactions. PLoS One 2022; 17:e0263057. [PMID: 35436299 PMCID: PMC9015122 DOI: 10.1371/journal.pone.0263057] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 01/11/2022] [Indexed: 11/22/2022] Open
Abstract
To grasp the complexity of biological processes, the biological knowledge is often translated into schematic diagrams of, for example, signalling and metabolic pathways. These pathway diagrams describe relevant connections between biological entities and incorporate domain knowledge in a visual format making it easier for humans to interpret. Still, these diagrams can be represented in machine readable formats, as done in the KEGG, Reactome, and WikiPathways databases. However, while humans are good at interpreting the message of the creators of diagrams, algorithms struggle when the diversity in drawing approaches increases. WikiPathways supports multiple drawing styles which need harmonizing to offer semantically enriched access. Particularly challenging, here, are the interactions between the biological entities that underlie the biological causality. These interactions provide information about the biological process (metabolic conversion, inhibition, etc.), the direction, and the participating entities. Availability of the interactions in a semantic and harmonized format is essential for searching the full network of biological interactions. We here study how the graphically-modelled biological knowledge in diagrams can be semantified and harmonized, and exemplify how the resulting data is used to programmatically answer biological questions. We find that we can translate graphically modelled knowledge to a sufficient degree into a semantic model and discuss some of the current limitations. We then use this to show that reproducible notebooks can be used to explore up- and downstream targets of MECP2 and to analyse the sphingolipid metabolism. Our results demonstrate that most of the graphical biological knowledge from WikiPathways is modelled into the semantic layer with the semantic information intact and connectivity information preserved. Being able to evaluate how biological elements affect each other is useful and allows, for example, the identification of up or downstream targets that will have a similar effect when modified.
Collapse
Affiliation(s)
- Ryan A. Miller
- Department of Bioinformatics (BiGCaT), NUTRIM, Maastricht University, Maastricht, The Netherlands
- * E-mail:
| | - Martina Kutmon
- Department of Bioinformatics (BiGCaT), NUTRIM, Maastricht University, Maastricht, The Netherlands
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
| | - Anwesha Bohler
- Department of Bioinformatics (BiGCaT), NUTRIM, Maastricht University, Maastricht, The Netherlands
| | - Andra Waagmeester
- Department of Bioinformatics (BiGCaT), NUTRIM, Maastricht University, Maastricht, The Netherlands
- Micellio, Antwerp, Belgium
| | - Chris T. Evelo
- Department of Bioinformatics (BiGCaT), NUTRIM, Maastricht University, Maastricht, The Netherlands
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
| | - Egon L. Willighagen
- Department of Bioinformatics (BiGCaT), NUTRIM, Maastricht University, Maastricht, The Netherlands
| |
Collapse
|
7
|
Ahmed MM, Tazyeen S, Haque S, Alsulimani A, Ali R, Sajad M, Alam A, Ali S, Bagabir HA, Bagabir RA, Ishrat R. Network-Based Approach and IVI Methodologies, a Combined Data Investigation Identified Probable Key Genes in Cardiovascular Disease and Chronic Kidney Disease. Front Cardiovasc Med 2022; 8:755321. [PMID: 35071341 PMCID: PMC8767007 DOI: 10.3389/fcvm.2021.755321] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Accepted: 11/17/2021] [Indexed: 01/28/2023] Open
Abstract
In fact, the risk of dying from CVD is significant when compared to the risk of developing end-stage renal disease (ESRD). Moreover, patients with severe CKD are often excluded from randomized controlled trials, making evidence-based therapy of comorbidities like CVD complicated. Thus, the goal of this study was to use an integrated bioinformatics approach to not only uncover Differentially Expressed Genes (DEGs), their associated functions, and pathways but also give a glimpse of how these two conditions are related at the molecular level. We started with GEO2R/R program (version 3.6.3, 64 bit) to get DEGs by comparing gene expression microarray data from CVD and CKD. Thereafter, the online STRING version 11.1 program was used to look for any correlations between all these common and/or overlapping DEGs, and the results were visualized using Cytoscape (version 3.8.0). Further, we used MCODE, a cytoscape plugin, and identified a total of 15 modules/clusters of the primary network. Interestingly, 10 of these modules contained our genes of interest (key genes). Out of these 10 modules that consist of 19 key genes (11 downregulated and 8 up-regulated), Module 1 (RPL13, RPLP0, RPS24, and RPS2) and module 5 (MYC, COX7B, and SOCS3) had the highest number of these genes. Then we used ClueGO to add a layer of GO terms with pathways to get a functionally ordered network. Finally, to identify the most influential nodes, we employed a novel technique called Integrated Value of Influence (IVI) by combining the network's most critical topological attributes. This method suggests that the nodes with many connections (calculated by hubness score) and high spreading potential (the spreader nodes are intended to have the most impact on the information flow in the network) are the most influential or essential nodes in a network. Thus, based on IVI values, hubness score, and spreading score, top 20 nodes were extracted, in which RPS27A non-seed gene and RPS2, a seed gene, came out to be the important node in the network.
Collapse
Affiliation(s)
- Mohd Murshad Ahmed
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India
| | - Safia Tazyeen
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India
| | - Shafiul Haque
- Research and Scientific Unit, College of Nursing and Allied Health Science, Jazan University, Jazan, Saudi Arabia
| | - Ahmad Alsulimani
- Department of Medical Laboratory Technology, College of Applied Medical Sciences, Jazan University, Jazan, Saudi Arbia
| | - Rafat Ali
- Department of Bioscience, Jamia Millia Islamia, New Delhi, India
| | - Mohd Sajad
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India
| | - Aftab Alam
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India
| | - Shahnawaz Ali
- Centre for Stem Cell & Regenerative Medicine, KING' College London, Guy's Hospital, London, United Kingdom
| | - Hala Abubaker Bagabir
- Department of Medical Physiology, Faculty of Medicine, King Abdulaziz University, Rabigh, Saudi Arabia
| | - Rania Abubaker Bagabir
- Department of Hematology and Immunology, College of Medicine, Umm-Al-Qura University, Mecca, Saudi Arabia
| | - Romana Ishrat
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India,*Correspondence: Romana Ishrat ; orcid.org/0000-0001-9744-9047
| |
Collapse
|
8
|
Ostaszewski M, Niarakis A, Mazein A, Kuperstein I, Phair R, Orta‐Resendiz A, Singh V, Aghamiri SS, Acencio ML, Glaab E, Ruepp A, Fobo G, Montrone C, Brauner B, Frishman G, Monraz Gómez LC, Somers J, Hoch M, Kumar Gupta S, Scheel J, Borlinghaus H, Czauderna T, Schreiber F, Montagud A, Ponce de Leon M, Funahashi A, Hiki Y, Hiroi N, Yamada TG, Dräger A, Renz A, Naveez M, Bocskei Z, Messina F, Börnigen D, Fergusson L, Conti M, Rameil M, Nakonecnij V, Vanhoefer J, Schmiester L, Wang M, Ackerman EE, Shoemaker JE, Zucker J, Oxford K, Teuton J, Kocakaya E, Summak GY, Hanspers K, Kutmon M, Coort S, Eijssen L, Ehrhart F, Rex DAB, Slenter D, Martens M, Pham N, Haw R, Jassal B, Matthews L, Orlic‐Milacic M, Senff Ribeiro A, Rothfels K, Shamovsky V, Stephan R, Sevilla C, Varusai T, Ravel J, Fraser R, Ortseifen V, Marchesi S, Gawron P, Smula E, Heirendt L, Satagopam V, Wu G, Riutta A, Golebiewski M, Owen S, Goble C, Hu X, Overall RW, Maier D, Bauch A, Gyori BM, Bachman JA, Vega C, Grouès V, Vazquez M, Porras P, Licata L, Iannuccelli M, Sacco F, Nesterova A, Yuryev A, de Waard A, Turei D, Luna A, Babur O, Soliman S, Valdeolivas A, Esteban‐Medina M, Peña‐Chilet M, Rian K, Helikar T, Puniya BL, Modos D, Treveil A, Olbei M, De Meulder B, Ballereau S, Dugourd A, Naldi A, Noël V, Calzone L, Sander C, Demir E, Korcsmaros T, Freeman TC, Augé F, Beckmann JS, Hasenauer J, Wolkenhauer O, Wilighagen EL, Pico AR, Evelo CT, Gillespie ME, Stein LD, Hermjakob H, D'Eustachio P, Saez‐Rodriguez J, Dopazo J, Valencia A, Kitano H, Barillot E, Auffray C, Balling R, Schneider R. COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms. Mol Syst Biol 2021; 17:e10387. [PMID: 34664389 PMCID: PMC8524328 DOI: 10.15252/msb.202110387] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 08/25/2021] [Accepted: 08/26/2021] [Indexed: 12/13/2022] Open
Abstract
We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.
Collapse
Affiliation(s)
- Marek Ostaszewski
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Anna Niarakis
- Université Paris‐SaclayLaboratoire Européen de Recherche pour la Polyarthrite rhumatoïde ‐ GenhotelUniv EvryEvryFrance
- Lifeware GroupInria Saclay‐Ile de FrancePalaiseauFrance
| | - Alexander Mazein
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Inna Kuperstein
- Institut CuriePSL Research UniversityParisFrance
- INSERMParisFrance
- MINES ParisTechPSL Research UniversityParisFrance
| | - Robert Phair
- Integrative Bioinformatics, Inc.Mountain ViewCAUSA
| | - Aurelio Orta‐Resendiz
- Institut PasteurUniversité de Paris, Unité HIVInflammation et PersistanceParisFrance
- Bio Sorbonne Paris CitéUniversité de ParisParisFrance
| | - Vidisha Singh
- Université Paris‐SaclayLaboratoire Européen de Recherche pour la Polyarthrite rhumatoïde ‐ GenhotelUniv EvryEvryFrance
| | - Sara Sadat Aghamiri
- Inserm‐ Institut national de la santé et de la recherche médicaleParisFrance
| | - Marcio Luis Acencio
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Enrico Glaab
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Andreas Ruepp
- Institute of Experimental Genetics (IEG)Helmholtz Zentrum München‐German Research Center for Environmental Health (GmbH)NeuherbergGermany
| | - Gisela Fobo
- Institute of Experimental Genetics (IEG)Helmholtz Zentrum München‐German Research Center for Environmental Health (GmbH)NeuherbergGermany
| | - Corinna Montrone
- Institute of Experimental Genetics (IEG)Helmholtz Zentrum München‐German Research Center for Environmental Health (GmbH)NeuherbergGermany
| | - Barbara Brauner
- Institute of Experimental Genetics (IEG)Helmholtz Zentrum München‐German Research Center for Environmental Health (GmbH)NeuherbergGermany
| | - Goar Frishman
- Institute of Experimental Genetics (IEG)Helmholtz Zentrum München‐German Research Center for Environmental Health (GmbH)NeuherbergGermany
| | - Luis Cristóbal Monraz Gómez
- Institut CuriePSL Research UniversityParisFrance
- INSERMParisFrance
- MINES ParisTechPSL Research UniversityParisFrance
| | - Julia Somers
- Department of Molecular and Medical GeneticsOregon Health & Sciences UniversityPortlandORUSA
| | - Matti Hoch
- Department of Systems Biology and BioinformaticsUniversity of RostockRostockGermany
| | | | - Julia Scheel
- Department of Systems Biology and BioinformaticsUniversity of RostockRostockGermany
| | - Hanna Borlinghaus
- Department of Computer and Information ScienceUniversity of KonstanzKonstanzGermany
| | - Tobias Czauderna
- Faculty of Information TechnologyDepartment of Human‐Centred ComputingMonash UniversityClaytonVic.Australia
| | - Falk Schreiber
- Department of Computer and Information ScienceUniversity of KonstanzKonstanzGermany
- Faculty of Information TechnologyDepartment of Human‐Centred ComputingMonash UniversityClaytonVic.Australia
| | | | | | - Akira Funahashi
- Department of Biosciences and InformaticsKeio UniversityYokohamaJapan
| | - Yusuke Hiki
- Department of Biosciences and InformaticsKeio UniversityYokohamaJapan
| | - Noriko Hiroi
- Graduate School of Media and GovernanceResearch Institute at SFCKeio UniversityKanagawaJapan
| | - Takahiro G Yamada
- Department of Biosciences and InformaticsKeio UniversityYokohamaJapan
| | - Andreas Dräger
- Computational Systems Biology of Infections and Antimicrobial‐Resistant PathogensInstitute for Bioinformatics and Medical Informatics (IBMI)University of TübingenTübingenGermany
- Department of Computer ScienceUniversity of TübingenTübingenGermany
- German Center for Infection Research (DZIF), partner siteTübingenGermany
| | - Alina Renz
- Computational Systems Biology of Infections and Antimicrobial‐Resistant PathogensInstitute for Bioinformatics and Medical Informatics (IBMI)University of TübingenTübingenGermany
- Department of Computer ScienceUniversity of TübingenTübingenGermany
| | - Muhammad Naveez
- Department of Systems Biology and BioinformaticsUniversity of RostockRostockGermany
- Institute of Applied Computer SystemsRiga Technical UniversityRigaLatvia
| | - Zsolt Bocskei
- Sanofi R&DTranslational SciencesChilly‐MazarinFrance
| | - Francesco Messina
- Dipartimento di Epidemiologia Ricerca Pre‐Clinica e Diagnostica AvanzataNational Institute for Infectious Diseases 'Lazzaro Spallanzani' I.R.C.C.S.RomeItaly
- COVID‐19 INMI Network Medicine for IDs Study GroupNational Institute for Infectious Diseases 'Lazzaro Spallanzani' I.R.C.C.SRomeItaly
| | - Daniela Börnigen
- Bioinformatics Core FacilityUniversitätsklinikum Hamburg‐EppendorfHamburgGermany
| | - Liam Fergusson
- Royal (Dick) School of Veterinary MedicineThe University of EdinburghEdinburghUK
| | - Marta Conti
- Faculty of Mathematics and Natural SciencesUniversity of BonnBonnGermany
| | - Marius Rameil
- Faculty of Mathematics and Natural SciencesUniversity of BonnBonnGermany
| | - Vanessa Nakonecnij
- Faculty of Mathematics and Natural SciencesUniversity of BonnBonnGermany
| | - Jakob Vanhoefer
- Faculty of Mathematics and Natural SciencesUniversity of BonnBonnGermany
| | - Leonard Schmiester
- Faculty of Mathematics and Natural SciencesUniversity of BonnBonnGermany
- Center for MathematicsChair of Mathematical Modeling of Biological SystemsTechnische Universität MünchenGarchingGermany
| | - Muying Wang
- Department of Chemical and Petroleum EngineeringUniversity of PittsburghPittsburghPAUSA
| | - Emily E Ackerman
- Department of Chemical and Petroleum EngineeringUniversity of PittsburghPittsburghPAUSA
| | - Jason E Shoemaker
- Department of Chemical and Petroleum EngineeringUniversity of PittsburghPittsburghPAUSA
- Department of Computational and Systems BiologyUniversity of PittsburghPittsburghPAUSA
| | | | | | | | | | | | - Kristina Hanspers
- Institute of Data Science and BiotechnologyGladstone InstitutesSan FranciscoCAUSA
| | - Martina Kutmon
- Department of Bioinformatics ‐ BiGCaTNUTRIMMaastricht UniversityMaastrichtThe Netherlands
- Maastricht Centre for Systems Biology (MaCSBio)Maastricht UniversityMaastrichtThe Netherlands
| | - Susan Coort
- Department of Bioinformatics ‐ BiGCaTNUTRIMMaastricht UniversityMaastrichtThe Netherlands
| | - Lars Eijssen
- Department of Bioinformatics ‐ BiGCaTNUTRIMMaastricht UniversityMaastrichtThe Netherlands
- Maastricht University Medical CentreMaastrichtThe Netherlands
| | - Friederike Ehrhart
- Department of Bioinformatics ‐ BiGCaTNUTRIMMaastricht UniversityMaastrichtThe Netherlands
- Maastricht University Medical CentreMaastrichtThe Netherlands
| | | | - Denise Slenter
- Department of Bioinformatics ‐ BiGCaTNUTRIMMaastricht UniversityMaastrichtThe Netherlands
| | - Marvin Martens
- Department of Bioinformatics ‐ BiGCaTNUTRIMMaastricht UniversityMaastrichtThe Netherlands
| | - Nhung Pham
- Department of Bioinformatics ‐ BiGCaTNUTRIMMaastricht UniversityMaastrichtThe Netherlands
| | - Robin Haw
- MaRS CentreOntario Institute for Cancer ResearchTorontoONCanada
| | - Bijay Jassal
- MaRS CentreOntario Institute for Cancer ResearchTorontoONCanada
| | | | | | - Andrea Senff Ribeiro
- MaRS CentreOntario Institute for Cancer ResearchTorontoONCanada
- Universidade Federal do ParanáCuritibaBrasil
| | - Karen Rothfels
- MaRS CentreOntario Institute for Cancer ResearchTorontoONCanada
| | | | - Ralf Stephan
- MaRS CentreOntario Institute for Cancer ResearchTorontoONCanada
| | - Cristoffer Sevilla
- European Bioinformatics Institute (EMBL‐EBI)European Molecular Biology LaboratoryHinxton, CambridgeshireUK
| | - Thawfeek Varusai
- European Bioinformatics Institute (EMBL‐EBI)European Molecular Biology LaboratoryHinxton, CambridgeshireUK
| | - Jean‐Marie Ravel
- INSERM UMR_S 1256Nutrition, Genetics, and Environmental Risk Exposure (NGERE)Faculty of Medicine of NancyUniversity of LorraineNancyFrance
- Laboratoire de génétique médicaleCHRU NancyNancyFrance
| | - Rupsha Fraser
- Queen's Medical Research InstituteThe University of EdinburghEdinburghUK
| | - Vera Ortseifen
- Senior Research Group in Genome Research of Industrial MicroorganismsCenter for BiotechnologyBielefeld UniversityBielefeldGermany
| | - Silvia Marchesi
- Department of Surgical ScienceUppsala UniversityUppsalaSweden
| | - Piotr Gawron
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
- Institute of Computing SciencePoznan University of TechnologyPoznanPoland
| | - Ewa Smula
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Laurent Heirendt
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Venkata Satagopam
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Guanming Wu
- Department of Medical Informatics and Clinical EpidemiologyOregon Health & Science UniversityPortlandORUSA
| | - Anders Riutta
- Institute of Data Science and BiotechnologyGladstone InstitutesSan FranciscoCAUSA
| | | | - Stuart Owen
- Department of Computer ScienceThe University of ManchesterManchesterUK
| | - Carole Goble
- Department of Computer ScienceThe University of ManchesterManchesterUK
| | - Xiaoming Hu
- Heidelberg Institute for Theoretical Studies (HITS)HeidelbergGermany
| | - Rupert W Overall
- German Center for Neurodegenerative Diseases (DZNE) DresdenDresdenGermany
- Center for Regenerative Therapies Dresden (CRTD)Technische Universität DresdenDresdenGermany
- Institute for BiologyHumboldt University of BerlinBerlinGermany
| | | | | | - Benjamin M Gyori
- Harvard Medical SchoolLaboratory of Systems PharmacologyBostonMAUSA
| | - John A Bachman
- Harvard Medical SchoolLaboratory of Systems PharmacologyBostonMAUSA
| | - Carlos Vega
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Valentin Grouès
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | | | - Pablo Porras
- European Bioinformatics Institute (EMBL‐EBI)European Molecular Biology LaboratoryHinxton, CambridgeshireUK
| | - Luana Licata
- Department of BiologyUniversity of Rome Tor VergataRomeItaly
| | | | - Francesca Sacco
- Department of BiologyUniversity of Rome Tor VergataRomeItaly
| | | | | | | | - Denes Turei
- Institute for Computational BiomedicineHeidelberg UniversityHeidelbergGermany
| | - Augustin Luna
- cBio Center, Divisions of Biostatistics and Computational BiologyDepartment of Data SciencesDana‐Farber Cancer InstituteBostonMAUSA
- Department of Cell BiologyHarvard Medical SchoolBostonMAUSA
| | - Ozgun Babur
- Computer Science DepartmentUniversity of Massachusetts BostonBostonMAUSA
| | | | - Alberto Valdeolivas
- Institute for Computational BiomedicineHeidelberg UniversityHeidelbergGermany
| | - Marina Esteban‐Medina
- Clinical Bioinformatics AreaFundación Progreso y Salud (FPS)Hospital Virgen del RocioSevillaSpain
- Computational Systems Medicine GroupInstitute of Biomedicine of Seville (IBIS)Hospital Virgen del RocioSevillaSpain
| | - Maria Peña‐Chilet
- Clinical Bioinformatics AreaFundación Progreso y Salud (FPS)Hospital Virgen del RocioSevillaSpain
- Computational Systems Medicine GroupInstitute of Biomedicine of Seville (IBIS)Hospital Virgen del RocioSevillaSpain
- Bioinformatics in Rare Diseases (BiER)Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER)FPS, Hospital Virgen del RocíoSevillaSpain
| | - Kinza Rian
- Clinical Bioinformatics AreaFundación Progreso y Salud (FPS)Hospital Virgen del RocioSevillaSpain
- Computational Systems Medicine GroupInstitute of Biomedicine of Seville (IBIS)Hospital Virgen del RocioSevillaSpain
| | - Tomáš Helikar
- Department of BiochemistryUniversity of Nebraska‐LincolnLincolnNEUSA
| | | | - Dezso Modos
- Quadram Institute BioscienceNorwichUK
- Earlham InstituteNorwichUK
| | - Agatha Treveil
- Quadram Institute BioscienceNorwichUK
- Earlham InstituteNorwichUK
| | - Marton Olbei
- Quadram Institute BioscienceNorwichUK
- Earlham InstituteNorwichUK
| | | | - Stephane Ballereau
- Cancer Research UK Cambridge InstituteUniversity of CambridgeCambridgeUK
| | - Aurélien Dugourd
- Institute for Computational BiomedicineHeidelberg UniversityHeidelbergGermany
- Institute of Experimental Medicine and Systems BiologyFaculty of Medicine, RWTHAachen UniversityAachenGermany
| | | | - Vincent Noël
- Institut CuriePSL Research UniversityParisFrance
- INSERMParisFrance
- MINES ParisTechPSL Research UniversityParisFrance
| | - Laurence Calzone
- Institut CuriePSL Research UniversityParisFrance
- INSERMParisFrance
- MINES ParisTechPSL Research UniversityParisFrance
| | - Chris Sander
- cBio Center, Divisions of Biostatistics and Computational BiologyDepartment of Data SciencesDana‐Farber Cancer InstituteBostonMAUSA
- Department of Cell BiologyHarvard Medical SchoolBostonMAUSA
| | - Emek Demir
- Department of Molecular and Medical GeneticsOregon Health & Sciences UniversityPortlandORUSA
| | | | - Tom C Freeman
- The Roslin InstituteUniversity of EdinburghEdinburghUK
| | - Franck Augé
- Sanofi R&DTranslational SciencesChilly‐MazarinFrance
| | | | - Jan Hasenauer
- Helmholtz Zentrum München – German Research Center for Environmental HealthInstitute of Computational BiologyNeuherbergGermany
- Interdisciplinary Research Unit Mathematics and Life SciencesUniversity of BonnBonnGermany
| | - Olaf Wolkenhauer
- Department of Systems Biology and BioinformaticsUniversity of RostockRostockGermany
| | - Egon L Wilighagen
- Department of Bioinformatics ‐ BiGCaTNUTRIMMaastricht UniversityMaastrichtThe Netherlands
| | - Alexander R Pico
- Institute of Data Science and BiotechnologyGladstone InstitutesSan FranciscoCAUSA
| | - Chris T Evelo
- Department of Bioinformatics ‐ BiGCaTNUTRIMMaastricht UniversityMaastrichtThe Netherlands
- Maastricht Centre for Systems Biology (MaCSBio)Maastricht UniversityMaastrichtThe Netherlands
| | - Marc E Gillespie
- MaRS CentreOntario Institute for Cancer ResearchTorontoONCanada
- St. John’s University College of Pharmacy and Health SciencesQueensNYUSA
| | - Lincoln D Stein
- MaRS CentreOntario Institute for Cancer ResearchTorontoONCanada
- Department of Molecular GeneticsUniversity of TorontoTorontoONCanada
| | - Henning Hermjakob
- European Bioinformatics Institute (EMBL‐EBI)European Molecular Biology LaboratoryHinxton, CambridgeshireUK
| | | | | | - Joaquin Dopazo
- Clinical Bioinformatics AreaFundación Progreso y Salud (FPS)Hospital Virgen del RocioSevillaSpain
- Computational Systems Medicine GroupInstitute of Biomedicine of Seville (IBIS)Hospital Virgen del RocioSevillaSpain
- Bioinformatics in Rare Diseases (BiER)Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER)FPS, Hospital Virgen del RocíoSevillaSpain
- FPS/ELIXIR‐esHospital Virgen del RocíoSevillaSpain
| | - Alfonso Valencia
- Barcelona Supercomputing Center (BSC)BarcelonaSpain
- Institució Catalana de Recerca i Estudis Avançats (ICREA)BarcelonaSpain
| | - Hiroaki Kitano
- Systems Biology InstituteTokyoJapan
- Okinawa Institute of Science and Technology Graduate SchoolOkinawaJapan
| | - Emmanuel Barillot
- Institut CuriePSL Research UniversityParisFrance
- INSERMParisFrance
- MINES ParisTechPSL Research UniversityParisFrance
| | - Charles Auffray
- Cancer Research UK Cambridge InstituteUniversity of CambridgeCambridgeUK
| | - Rudi Balling
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Reinhard Schneider
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | | |
Collapse
|
9
|
Walker KA, Chen J, Zhang J, Fornage M, Yang Y, Zhou L, Grams ME, Tin A, Daya N, Hoogeveen RC, Wu A, Sullivan KJ, Ganz P, Zeger SL, Gudmundsson EF, Emilsson V, Launer LJ, Jennings LL, Gudnason V, Chatterjee N, Gottesman RF, Mosley TH, Boerwinkle E, Ballantyne CM, Coresh J. Large-scale plasma proteomic analysis identifies proteins and pathways associated with dementia risk. NATURE AGING 2021; 1:473-489. [PMID: 37118015 PMCID: PMC10154040 DOI: 10.1038/s43587-021-00064-0] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 04/02/2021] [Indexed: 04/30/2023]
Abstract
The plasma proteomic changes that precede the onset of dementia could yield insights into disease biology and highlight new biomarkers and avenues for intervention. We quantified 4,877 plasma proteins in nondemented older adults in the Atherosclerosis Risk in Communities cohort and performed a proteome-wide association study of dementia risk over five years (n = 4,110; 428 incident cases). Thirty-eight proteins were associated with incident dementia after Bonferroni correction. Of these, 16 were also associated with late-life dementia risk when measured in plasma collected nearly 20 years earlier, during mid-life. Two-sample Mendelian randomization causally implicated two dementia-associated proteins (SVEP1 and angiostatin) in Alzheimer's disease. SVEP1, an immunologically relevant cellular adhesion protein, was found to be part of larger dementia-associated protein networks, and circulating levels were associated with atrophy in brain regions vulnerable to Alzheimer's pathology. Pathway analyses for the broader set of dementia-associated proteins implicated immune, lipid, metabolic signaling and hemostasis pathways in dementia pathogenesis.
Collapse
Affiliation(s)
- Keenan A Walker
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program, Baltimore, MD, USA.
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Jingsha Chen
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Jingning Zhang
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School and Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Yunju Yang
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School and Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Linda Zhou
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Morgan E Grams
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Division of Nephrology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Adrienne Tin
- MIND Center and Division of Nephrology, University of Mississippi Medical Center, Jackson, MS, USA
| | - Natalie Daya
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Ron C Hoogeveen
- Section of Cardiovascular Research, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Aozhou Wu
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Kevin J Sullivan
- Department of Medicine, Division of Geriatrics, University of Mississippi Medical Center, Jackson, MS, USA
| | - Peter Ganz
- Department of Medicine, University of California-San Francisco, San Francisco, CA, USA
| | - Scott L Zeger
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | | | - Lenore J Launer
- Laboratory of Epidemiology and Population Sciences, Intramural Research Program, National Institute on Aging, Bethesda, MD, USA
| | - Lori L Jennings
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | - Vilmundur Gudnason
- Icelandic Heart Association, Kopavogur, Iceland
- University of Iceland, Reykjavik, Iceland
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Rebecca F Gottesman
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Thomas H Mosley
- Department of Medicine, Division of Geriatrics, University of Mississippi Medical Center, Jackson, MS, USA
| | - Eric Boerwinkle
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Christie M Ballantyne
- Section of Cardiovascular Research, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
| |
Collapse
|
10
|
Chia R, Sabir MS, Bandres-Ciga S, Saez-Atienzar S, Reynolds RH, Gustavsson E, Walton RL, Ahmed S, Viollet C, Ding J, Makarious MB, Diez-Fairen M, Portley MK, Shah Z, Abramzon Y, Hernandez DG, Blauwendraat C, Stone DJ, Eicher J, Parkkinen L, Ansorge O, Clark L, Honig LS, Marder K, Lemstra A, St George-Hyslop P, Londos E, Morgan K, Lashley T, Warner TT, Jaunmuktane Z, Galasko D, Santana I, Tienari PJ, Myllykangas L, Oinas M, Cairns NJ, Morris JC, Halliday GM, Van Deerlin VM, Trojanowski JQ, Grassano M, Calvo A, Mora G, Canosa A, Floris G, Bohannan RC, Brett F, Gan-Or Z, Geiger JT, Moore A, May P, Krüger R, Goldstein DS, Lopez G, Tayebi N, Sidransky E, Norcliffe-Kaufmann L, Palma JA, Kaufmann H, Shakkottai VG, Perkins M, Newell KL, Gasser T, Schulte C, Landi F, Salvi E, Cusi D, Masliah E, Kim RC, Caraway CA, Monuki ES, Brunetti M, Dawson TM, Rosenthal LS, Albert MS, Pletnikova O, Troncoso JC, Flanagan ME, Mao Q, Bigio EH, Rodríguez-Rodríguez E, Infante J, Lage C, González-Aramburu I, Sanchez-Juan P, Ghetti B, Keith J, Black SE, Masellis M, Rogaeva E, Duyckaerts C, Brice A, Lesage S, Xiromerisiou G, Barrett MJ, Tilley BS, Gentleman S, Logroscino G, Serrano GE, Beach TG, McKeith IG, Thomas AJ, Attems J, Morris CM, Palmer L, Love S, Troakes C, Al-Sarraj S, Hodges AK, Aarsland D, Klein G, Kaiser SM, Woltjer R, Pastor P, Bekris LM, Leverenz JB, Besser LM, Kuzma A, Renton AE, Goate A, Bennett DA, Scherzer CR, Morris HR, Ferrari R, Albani D, Pickering-Brown S, Faber K, Kukull WA, Morenas-Rodriguez E, Lleó A, Fortea J, Alcolea D, Clarimon J, Nalls MA, Ferrucci L, Resnick SM, Tanaka T, Foroud TM, Graff-Radford NR, Wszolek ZK, Ferman T, Boeve BF, Hardy JA, Topol EJ, Torkamani A, Singleton AB, Ryten M, Dickson DW, Chiò A, Ross OA, Gibbs JR, Dalgard CL, Traynor BJ, Scholz SW. Genome sequencing analysis identifies new loci associated with Lewy body dementia and provides insights into its genetic architecture. Nat Genet 2021; 53:294-303. [PMID: 33589841 PMCID: PMC7946812 DOI: 10.1038/s41588-021-00785-3] [Citation(s) in RCA: 184] [Impact Index Per Article: 61.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 01/12/2021] [Indexed: 01/30/2023]
Abstract
The genetic basis of Lewy body dementia (LBD) is not well understood. Here, we performed whole-genome sequencing in large cohorts of LBD cases and neurologically healthy controls to study the genetic architecture of this understudied form of dementia, and to generate a resource for the scientific community. Genome-wide association analysis identified five independent risk loci, whereas genome-wide gene-aggregation tests implicated mutations in the gene GBA. Genetic risk scores demonstrate that LBD shares risk profiles and pathways with Alzheimer's disease and Parkinson's disease, providing a deeper molecular understanding of the complex genetic architecture of this age-related neurodegenerative condition.
Collapse
Affiliation(s)
- Ruth Chia
- Neuromuscular Diseases Research Section, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA
| | - Marya S Sabir
- Neurodegenerative Diseases Research Unit, Laboratory of Neurogenetics, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
| | - Sara Bandres-Ciga
- Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA
| | - Sara Saez-Atienzar
- Neuromuscular Diseases Research Section, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA
| | - Regina H Reynolds
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
- NIHR Great Ormond Street Hospital Biomedical Research Centre, University College London, London, UK
- Great Ormond Street Institute of Child Health, Genetics and Genomic Medicine, University College London, London, UK
| | - Emil Gustavsson
- NIHR Great Ormond Street Hospital Biomedical Research Centre, University College London, London, UK
- Great Ormond Street Institute of Child Health, Genetics and Genomic Medicine, University College London, London, UK
| | - Ronald L Walton
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA
| | - Sarah Ahmed
- Neurodegenerative Diseases Research Unit, Laboratory of Neurogenetics, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
| | - Coralie Viollet
- Collaborative Health Initiative Research Program, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD, USA
| | - Jinhui Ding
- Computational Biology Group, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA
| | - Mary B Makarious
- Neurodegenerative Diseases Research Unit, Laboratory of Neurogenetics, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
| | - Monica Diez-Fairen
- Memory and Movement Disorders Units, Department of Neurology, University Hospital Mutua de Terrassa, Barcelona, Spain
| | - Makayla K Portley
- Neurodegenerative Diseases Research Unit, Laboratory of Neurogenetics, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
| | - Zalak Shah
- Neurodegenerative Diseases Research Unit, Laboratory of Neurogenetics, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
| | - Yevgeniya Abramzon
- Neuromuscular Diseases Research Section, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA
- Reta Lila Weston Institute, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Dena G Hernandez
- Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA
| | - Cornelis Blauwendraat
- Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA
| | | | - John Eicher
- Genetics and Pharmacogenomics, Merck & Co., Inc., West Point, PA, USA
| | - Laura Parkkinen
- Nuffield Department of Clinical Neurosciences, Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
| | - Olaf Ansorge
- Nuffield Department of Clinical Neurosciences, Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
| | - Lorraine Clark
- Taub Institute for Alzheimer Disease and the Aging Brain, and Department of Pathology and Cell Biology, Columbia University, New York, NY, USA
| | - Lawrence S Honig
- Taub Institute for Alzheimer Disease and the Aging Brain, G. H. Sergievsky Center and Department of Neurology, Columbia University, New York, NY, USA
| | - Karen Marder
- Taub Institute for Alzheimer Disease and the Aging Brain, G. H. Sergievsky Center and Department of Neurology, Columbia University, New York, NY, USA
| | - Afina Lemstra
- Department of Neurology and Alzheimer Center, Neuroscience Campus Amsterdam, Amsterdam, the Netherlands
| | - Peter St George-Hyslop
- Department of Clinical Neurosciences, Cambridge Institute of Medical Research, University of Cambridge, Cambridge, UK
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Elisabet Londos
- Clinical Memory Research Unit, Institution of Clinical Sciences Malmo, Lund University, Lund, Sweden
| | - Kevin Morgan
- Human Genetics, School of Life Sciences, Queens Medical Centre, University of Nottingham, Nottingham, UK
| | - Tammaryn Lashley
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
- Queen Square Brain Bank for Neurological Disorders, Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Thomas T Warner
- Reta Lila Weston Institute, UCL Queen Square Institute of Neurology, University College London, London, UK
- Queen Square Brain Bank for Neurological Disorders, Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Zane Jaunmuktane
- Queen Square Brain Bank for Neurological Disorders, Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Douglas Galasko
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
- Veterans Affairs San Diego Healthcare System, La Jolla, CA, USA
| | - Isabel Santana
- Neurology Service, University of Coimbra Hospital, Coimbra, Portugal
- Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal
| | - Pentti J Tienari
- Translational Immunology, Research Programs Unit, University of Helsinki, Helsinki, Finland
- Department of Neurology, Helsinki University Hospital, Helsinki, Finland
| | - Liisa Myllykangas
- Department of Pathology, Medicum, University of Helsinki, Helsinki, Finland
- HUS Diagnostic Center, Helsinki University Hospital, Helsinki, Finland
| | - Minna Oinas
- Department of Clinical Medicine, Faculty of Health, UiT The Arctic University of Norway, Tromsø, Norway
| | - Nigel J Cairns
- Knight Alzheimer's Disease Research Center, Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | - John C Morris
- Knight Alzheimer's Disease Research Center, Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Glenda M Halliday
- Neuroscience Research Australia, Sydney, New South Wales, Australia
- School of Medical Sciences, Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia
- Brain and Mind Centre, Sydney Medical School, University of Sydney, Sydney, New South Wales, Australia
| | - Vivianna M Van Deerlin
- Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Disease Research, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - John Q Trojanowski
- Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Disease Research, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Maurizio Grassano
- Neuromuscular Diseases Research Section, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA
- Rita Levi Montalcini Department of Neuroscience, University of Turin, Turin, Italy
| | - Andrea Calvo
- Rita Levi Montalcini Department of Neuroscience, University of Turin, Turin, Italy
- Azienda Ospedaliero Universitaria Città della Salute e della Scienza, Turin, Italy
| | - Gabriele Mora
- Istituti Clinici Scientifici Maugeri, IRCCS, Milan, Italy
| | - Antonio Canosa
- Rita Levi Montalcini Department of Neuroscience, University of Turin, Turin, Italy
- Azienda Ospedaliero Universitaria Città della Salute e della Scienza, Turin, Italy
| | - Gianluca Floris
- Department of Neurology, University Hospital of Cagliari, Cagliari, Italy
| | - Ryan C Bohannan
- Department of Neurobiology and Behavior, University of California Irvine, Irvine, CA, USA
| | - Francesca Brett
- Dublin Brain Bank, Neuropathology Department, Beaumont Hospital, Dublin, Ireland
| | - Ziv Gan-Or
- Montreal Neurological Institute and Hospital, Department of Neurology & Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Joshua T Geiger
- Neurodegenerative Diseases Research Unit, Laboratory of Neurogenetics, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
| | - Anni Moore
- Computational Biology Group, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA
| | - Patrick May
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Rejko Krüger
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Transversal Translational Medicine, Luxembourg Institute of Health, Strassen, Luxembourg
- Parkinson Research Clinic, Centre Hospitalier de Luxembourg, Luxembourg City, Luxembourg
| | - David S Goldstein
- Clinical Neurocardiology Section, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
| | - Grisel Lopez
- Medical Genetics Branch, National Human Genome Research Institute, Bethesda, MD, USA
| | - Nahid Tayebi
- Medical Genetics Branch, National Human Genome Research Institute, Bethesda, MD, USA
| | - Ellen Sidransky
- Medical Genetics Branch, National Human Genome Research Institute, Bethesda, MD, USA
| | | | - Jose-Alberto Palma
- Department of Neurology, New York University School of Medicine, New York, NY, USA
| | - Horacio Kaufmann
- Department of Neurology, New York University School of Medicine, New York, NY, USA
| | - Vikram G Shakkottai
- Department of Neurology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Matthew Perkins
- Michigan Brain Bank, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Kathy L Newell
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Thomas Gasser
- Department of Neurodegenerative Diseases, Center of Neurology and Hertie-Institute for Clinical Brain Research, University of Tübingen and German Center for Neurodegenerative Diseases, Tübingen, Germany
| | - Claudia Schulte
- Department of Neurodegenerative Diseases, Center of Neurology and Hertie-Institute for Clinical Brain Research, University of Tübingen and German Center for Neurodegenerative Diseases, Tübingen, Germany
| | - Francesco Landi
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS Università Cattolica del Sacro Cuore, Rome, Italy
| | - Erika Salvi
- Neuroalgology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Daniele Cusi
- Bio4Dreams-Business Nursery for Life, Milan, Italy
| | - Eliezer Masliah
- Molecular Neuropathology Section, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA
| | - Ronald C Kim
- Department of Neuropathology, School of Medicine, University of California Irvine, Irvine, CA, USA
| | - Chad A Caraway
- Institute for Memory Impairments and Neurological Disorders, University of California Irvine, Irvine, CA, USA
| | - Edwin S Monuki
- Department of Pathology & Laboratory Medicine, School of Medicine, University of California Irvine, Irvine, CA, USA
| | - Maura Brunetti
- Rita Levi Montalcini Department of Neuroscience, University of Turin, Turin, Italy
| | - Ted M Dawson
- Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD, USA
- Neuroregeneration and Stem Cell Programs, Institute of Cell Engineering, Johns Hopkins University Medical Center, Baltimore, MD, USA
- Department of Pharmacology and Molecular Science, Johns Hopkins University Medical Center, Baltimore, MD, USA
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins University Medical Center, Baltimore, MD, USA
| | - Liana S Rosenthal
- Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD, USA
| | - Marilyn S Albert
- Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD, USA
| | - Olga Pletnikova
- Department of Pathology (Neuropathology), Johns Hopkins University Medical Center, Baltimore, MD, USA
- Department of Pathology and Anatomical Sciences, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Juan C Troncoso
- Department of Pathology (Neuropathology), Johns Hopkins University Medical Center, Baltimore, MD, USA
| | - Margaret E Flanagan
- Mesulam Center for Cognitive Neurology and Alzheimer's Disease, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Qinwen Mao
- Mesulam Center for Cognitive Neurology and Alzheimer's Disease, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Eileen H Bigio
- Mesulam Center for Cognitive Neurology and Alzheimer's Disease, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Eloy Rodríguez-Rodríguez
- Neurology Service, University Hospital Marqués de Valdecilla-IDIVAL-UC-CIBERNED, Santander, Spain
| | - Jon Infante
- Neurology Service, University Hospital Marqués de Valdecilla-IDIVAL-UC-CIBERNED, Santander, Spain
| | - Carmen Lage
- Neurology Service, University Hospital Marqués de Valdecilla-IDIVAL-UC-CIBERNED, Santander, Spain
| | - Isabel González-Aramburu
- Neurology Service, University Hospital Marqués de Valdecilla-IDIVAL-UC-CIBERNED, Santander, Spain
| | - Pascual Sanchez-Juan
- Neurology Service, University Hospital Marqués de Valdecilla-IDIVAL-UC-CIBERNED, Santander, Spain
| | - Bernardino Ghetti
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Julia Keith
- Department of Anatomical Pathology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Sandra E Black
- Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Division of Neurology, Department of Medicine, University of Toronto, Toronto, Ontario, Canada
- Heart and Stroke Foundation Canadian Partnership for Stroke Recovery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
- Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada
- LC Campbell Cognitive Neurology Research Unit, Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Mario Masellis
- Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada
- LC Campbell Cognitive Neurology Research Unit, Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada
- Cognitive & Movement Disorders Clinic, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
- Department of Medicine, Division of Neurology, University of Toronto, Toronto, Ontario, Canada
| | - Ekaterina Rogaeva
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, Ontario, Canada
| | - Charles Duyckaerts
- Department of Neuropathology Escourolle, Paris Brain Institute, Sorbonne Universités, Paris, France
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, AP-HP, Hôpital Pitié-Salpêtrière, DMU Neuroscience 6, Paris, France
| | - Alexis Brice
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, AP-HP, Hôpital Pitié-Salpêtrière, DMU Neuroscience 6, Paris, France
| | - Suzanne Lesage
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, AP-HP, Hôpital Pitié-Salpêtrière, DMU Neuroscience 6, Paris, France
| | - Georgia Xiromerisiou
- Department of Neurology, University Hospital of Larissa, University of Thessalia, Larissa, Greece
| | - Matthew J Barrett
- Department of Neurology, Virginia Commonwealth University, Richmond, VA, USA
| | - Bension S Tilley
- Neuropathology Unit, Department of Brain Sciences, Imperial College London, London, UK
| | - Steve Gentleman
- Neuropathology Unit, Department of Brain Sciences, Imperial College London, London, UK
| | - Giancarlo Logroscino
- Department of Basic Medicine Neurosciences and Sense Organs, University Aldo Moro, Bari, Italy
- Center for Neurodegenerative Diseases and the Aging Brain - Department of Clinical Research in Neurology of the University of Bari at 'Pia Fondazione Card G. Panico' Hospital Tricase (Le), Bari, Italy
| | - Geidy E Serrano
- Civin Laboratory for Neuropathology, Banner Sun Health Research Institute, Sun City, AZ, USA
| | - Thomas G Beach
- Civin Laboratory for Neuropathology, Banner Sun Health Research Institute, Sun City, AZ, USA
| | - Ian G McKeith
- Newcastle Brain Tissue Resource, Translational and Clinical Research Institute, Biomedical Research Building, Newcastle University, Newcastle upon Tyne, UK
| | - Alan J Thomas
- Newcastle Brain Tissue Resource, Translational and Clinical Research Institute, Biomedical Research Building, Newcastle University, Newcastle upon Tyne, UK
| | - Johannes Attems
- Newcastle Brain Tissue Resource, Translational and Clinical Research Institute, Biomedical Research Building, Newcastle University, Newcastle upon Tyne, UK
| | - Christopher M Morris
- Newcastle Brain Tissue Resource, Translational and Clinical Research Institute, Biomedical Research Building, Newcastle University, Newcastle upon Tyne, UK
| | - Laura Palmer
- South West Dementia Brain Bank, Bristol Medical School, University of Bristol, Bristol, UK
| | - Seth Love
- Dementia Research Group, Bristol Medical School, University of Bristol, Bristol, UK
| | - Claire Troakes
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Safa Al-Sarraj
- Department of Clinical Neuropathology and London Neurodegenerative Diseases Brain Bank, Institute of Psychiatry, Psychology and Neuroscience, King's College Hospital and King's College London, London, UK
| | - Angela K Hodges
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Dag Aarsland
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway
| | - Gregory Klein
- Rush Alzheimer's Disease Center, Rush University, Chicago, IL, USA
| | - Scott M Kaiser
- Department of Neuropathology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Randy Woltjer
- Department of Neurology, Oregon Health & Sciences University, Portland, OR, USA
| | - Pau Pastor
- Memory and Movement Disorders Units, Department of Neurology, University Hospital Mutua de Terrassa, Barcelona, Spain
| | - Lynn M Bekris
- Genomic Medicine Institute, Cleveland Clinic, Cleveland, OH, USA
| | - James B Leverenz
- Cleveland Lou Ruvo Center for Brain Health, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Lilah M Besser
- Institute for Human Health and Disease Intervention, Florida Atlantic University, Boca Raton, FL, USA
| | - Amanda Kuzma
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Alan E Renton
- Ronald M. Loeb Center for Alzheimer's Disease, Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alison Goate
- Ronald M. Loeb Center for Alzheimer's Disease, Nash Family Department of Neuroscience, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University, Chicago, IL, USA
| | - Clemens R Scherzer
- Precision Neurology Program, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Huw R Morris
- Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Raffaele Ferrari
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Diego Albani
- Department of Neuroscience, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Stuart Pickering-Brown
- Division of Neuroscience and Experimental Psychology, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Kelley Faber
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Walter A Kukull
- National Alzheimer's Coordinating Center, Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Estrella Morenas-Rodriguez
- Biomedizinisches Centrum, Biochemie, Ludwig-Maximilians-Universität München & Deutsches Zentrum für Neurodegenerative Erkrankungen, Munich, Germany
- Sant Pau Biomedical Research Institute, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
- The Network Center for Biomedical Research in Neurodegenerative Diseases, Madrid, Spain
| | - Alberto Lleó
- Sant Pau Biomedical Research Institute, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
- The Network Center for Biomedical Research in Neurodegenerative Diseases, Madrid, Spain
| | - Juan Fortea
- Sant Pau Biomedical Research Institute, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
- The Network Center for Biomedical Research in Neurodegenerative Diseases, Madrid, Spain
| | - Daniel Alcolea
- Sant Pau Biomedical Research Institute, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
- The Network Center for Biomedical Research in Neurodegenerative Diseases, Madrid, Spain
| | - Jordi Clarimon
- Sant Pau Biomedical Research Institute, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
- The Network Center for Biomedical Research in Neurodegenerative Diseases, Madrid, Spain
| | - Mike A Nalls
- Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA
- Data Tecnica International, Glen Echo, MD, USA
- Center for Alzheimer's and Related Dementias, National Institute on Aging, Bethesda, MD, USA
| | - Luigi Ferrucci
- Longitudinal Studies Section, National Institute on Aging, Baltimore, MD, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Toshiko Tanaka
- Longitudinal Studies Section, National Institute on Aging, Baltimore, MD, USA
| | - Tatiana M Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | | | | | - Tanis Ferman
- Department of Psychiatry and Psychology, Mayo Clinic, Jacksonville, FL, USA
| | | | - John A Hardy
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
- Reta Lila Weston Institute, UCL Queen Square Institute of Neurology, University College London, London, UK
- UK Dementia Research Institute of UCL, UCL Institute of Neurology, University College London, London, UK
- UCL Movement Disorders Centre, University College London, London, UK
- Institute for Advanced Study, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Eric J Topol
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA, USA
| | - Ali Torkamani
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA, USA
| | - Andrew B Singleton
- Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA
- Center for Alzheimer's and Related Dementias, National Institute on Aging, Bethesda, MD, USA
| | - Mina Ryten
- NIHR Great Ormond Street Hospital Biomedical Research Centre, University College London, London, UK
- Great Ormond Street Institute of Child Health, Genetics and Genomic Medicine, University College London, London, UK
| | | | - Adriano Chiò
- Rita Levi Montalcini Department of Neuroscience, University of Turin, Turin, Italy
- Azienda Ospedaliero Universitaria Città della Salute e della Scienza, Turin, Italy
- Institute of Cognitive Sciences and Technologies, C.N.R., Rome, Italy
| | - Owen A Ross
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA
- Department of Clinical Genomics, Mayo Clinic, Jacksonville, FL, USA
| | - J Raphael Gibbs
- Computational Biology Group, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA
| | - Clifton L Dalgard
- Department of Anatomy, Physiology & Genetics, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
- The American Genome Center, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Bryan J Traynor
- Neuromuscular Diseases Research Section, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA
- Reta Lila Weston Institute, UCL Queen Square Institute of Neurology, University College London, London, UK
- Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD, USA
| | - Sonja W Scholz
- Neurodegenerative Diseases Research Unit, Laboratory of Neurogenetics, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA.
- Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD, USA.
| |
Collapse
|
11
|
Rampler E, Abiead YE, Schoeny H, Rusz M, Hildebrand F, Fitz V, Koellensperger G. Recurrent Topics in Mass Spectrometry-Based Metabolomics and Lipidomics-Standardization, Coverage, and Throughput. Anal Chem 2021; 93:519-545. [PMID: 33249827 PMCID: PMC7807424 DOI: 10.1021/acs.analchem.0c04698] [Citation(s) in RCA: 81] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Evelyn Rampler
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
- Vienna Metabolomics Center (VIME), University of Vienna, Althanstraße 14, 1090 Vienna, Austria
- University of Vienna, Althanstraße 14, 1090 Vienna, Austria
| | - Yasin El Abiead
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
| | - Harald Schoeny
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
| | - Mate Rusz
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
- Institute of Inorganic
Chemistry, University of Vienna, Währinger Straße 42, 1090 Vienna, Austria
| | - Felina Hildebrand
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
| | - Veronika Fitz
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
| | - Gunda Koellensperger
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
- Vienna Metabolomics Center (VIME), University of Vienna, Althanstraße 14, 1090 Vienna, Austria
- University of Vienna, Althanstraße 14, 1090 Vienna, Austria
| |
Collapse
|
12
|
Martens M, Ammar A, Riutta A, Waagmeester A, Slenter D, Hanspers K, A. Miller R, Digles D, Lopes E, Ehrhart F, Dupuis LJ, Winckers LA, Coort S, Willighagen EL, Evelo CT, Pico AR, Kutmon M. WikiPathways: connecting communities. Nucleic Acids Res 2021; 49:D613-D621. [PMID: 33211851 PMCID: PMC7779061 DOI: 10.1093/nar/gkaa1024] [Citation(s) in RCA: 455] [Impact Index Per Article: 151.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 10/13/2020] [Accepted: 10/19/2020] [Indexed: 12/17/2022] Open
Abstract
WikiPathways (https://www.wikipathways.org) is a biological pathway database known for its collaborative nature and open science approaches. With the core idea of the scientific community developing and curating biological knowledge in pathway models, WikiPathways lowers all barriers for accessing and using its content. Increasingly more content creators, initiatives, projects and tools have started using WikiPathways. Central in this growth and increased use of WikiPathways are the various communities that focus on particular subsets of molecular pathways such as for rare diseases and lipid metabolism. Knowledge from published pathway figures helps prioritize pathway development, using optical character and named entity recognition. We show the growth of WikiPathways over the last three years, highlight the new communities and collaborations of pathway authors and curators, and describe various technologies to connect to external resources and initiatives. The road toward a sustainable, community-driven pathway database goes through integration with other resources such as Wikidata and allowing more use, curation and redistribution of WikiPathways content.
Collapse
Affiliation(s)
- Marvin Martens
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, the Netherlands
| | - Ammar Ammar
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, the Netherlands
| | - Anders Riutta
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA 94158, USA
| | | | - Denise N Slenter
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, the Netherlands
| | - Kristina Hanspers
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA 94158, USA
| | - Ryan A. Miller
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, the Netherlands
| | - Daniela Digles
- Department of Pharmaceutical Chemistry/Pharmacoinformatics Research Group, University of Vienna, 1090 Vienna, Austria
| | - Elisson N Lopes
- Instituto de Ciencias Biologicas, Departamento de Bioquimica e Imunologia, Universidade Federal de Minas Gerais, Belo Horizonte, 31270-901, Brazil
| | - Friederike Ehrhart
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, the Netherlands
| | - Lauren J Dupuis
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, the Netherlands
| | - Laurent A Winckers
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, the Netherlands
| | - Susan L Coort
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, the Netherlands
| | - Egon L Willighagen
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, the Netherlands
| | - Chris T Evelo
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, the Netherlands
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, 6229 EN Maastricht, the Netherlands
| | - Alexander R Pico
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA 94158, USA
| | - Martina Kutmon
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, the Netherlands
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, 6229 EN Maastricht, the Netherlands
| |
Collapse
|
13
|
Tamblyn JA, Jeffery LE, Susarla R, Lissauer DM, Coort SL, Garcia AM, Knoblich K, Fletcher AL, Bulmer JN, Kilby MD, Hewison M. Transcriptomic analysis of vitamin D responses in uterine and peripheral NK cells. Reproduction 2020; 158:211-221. [PMID: 31163399 DOI: 10.1530/rep-18-0509] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Accepted: 06/03/2019] [Indexed: 12/19/2022]
Abstract
Vitamin D deficiency is prevalent in pregnant women and is associated with adverse pregnancy outcomes, in particular disorders of malplacentation. The active form of vitamin D, 1,25-dihydroxyvitamin D3 (1,25(OH)2D3), is a potent regulator of innate and adaptive immunity, but its immune effects during pregnancy remain poorly understood. During early gestation, the predominant immune cells in maternal decidua are uterine natural killer cells (uNK), but the responsivity of these cells to 1,25(OH)2D3 is unknown despite high levels of 1,25(OH)2D3 in decidua. Transcriptomic responses to 1,25(OH)2D3 were characterised in paired donor uNK and peripheral natural killer cells (pNK) following cytokine (CK) stimulation. RNA-seq analyses indicated 911 genes were differentially expressed in CK-stimulated uNK versus CK-stimulated pNK in the absence of 1,25(OH)2D3, with predominant differentially expressed pathways being associated with glycolysis and transforming growth factor β (TGFβ). RNA-seq also showed that the vitamin D receptor (VDR) and its heterodimer partner retinoid X receptor were differentially expressed in CK-stimulated uNK vs CK-stimulated pNK. Further analyses confirmed increased expression of VDR mRNA and protein, as well as VDR-RXR target in CK-stimulated uNK. RNA-seq analysis showed that in CK-stimulated pNK, 1,25(OH)2D3 induced 38 and suppressed 33 transcripts, whilst in CK-stimulated uNK 1,25(OH)2D3 induced 46 and suppressed 19 genes. However, multiple comparison analysis of transcriptomic data indicated that 1,25(OH)2D3 had no significant overall effect on gene expression in either CK-stimulated pNK or uNK. These data indicate that CK-stimulated uNK are transcriptionally distinct from pNK and, despite expressing abundant VDR, neither pNK nor uNK are sensitive targets for vitamin D.
Collapse
Affiliation(s)
- J A Tamblyn
- Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK.,Centre for Women's & Newborn Health, Birmingham Health Partners, Birmingham Women's & Children's Foundation Hospital, Edgbaston, Birmingham, UK.,Centre for Endocrinology, Diabetes and Metabolism, Birmingham Health Partners, Birmingham, UK
| | - L E Jeffery
- Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - R Susarla
- Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - D M Lissauer
- Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK.,Centre for Women's & Newborn Health, Birmingham Health Partners, Birmingham Women's & Children's Foundation Hospital, Edgbaston, Birmingham, UK
| | - S L Coort
- Department of Bioinformatics-BiGCaT, NUTRIM School for Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, Netherlands
| | - A Muñoz Garcia
- Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK.,Department of Bioinformatics-BiGCaT, NUTRIM School for Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, Netherlands
| | - K Knoblich
- Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia
| | - A L Fletcher
- Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia
| | - J N Bulmer
- Reproductive and Vascular Biology Group, Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, UK
| | - M D Kilby
- Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK.,Centre for Women's & Newborn Health, Birmingham Health Partners, Birmingham Women's & Children's Foundation Hospital, Edgbaston, Birmingham, UK.,Centre for Endocrinology, Diabetes and Metabolism, Birmingham Health Partners, Birmingham, UK.,Fetal Medicine Centre, Birmingham Women's & Children's Foundation Trust, Edgbaston, Birmingham, UK
| | - M Hewison
- Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK.,Centre for Endocrinology, Diabetes and Metabolism, Birmingham Health Partners, Birmingham, UK
| |
Collapse
|
14
|
Ivanisevic J, Want EJ. From Samples to Insights into Metabolism: Uncovering Biologically Relevant Information in LC-HRMS Metabolomics Data. Metabolites 2019; 9:metabo9120308. [PMID: 31861212 PMCID: PMC6950334 DOI: 10.3390/metabo9120308] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 12/09/2019] [Accepted: 12/12/2019] [Indexed: 12/31/2022] Open
Abstract
Untargeted metabolomics (including lipidomics) is a holistic approach to biomarker discovery and mechanistic insights into disease onset and progression, and response to intervention. Each step of the analytical and statistical pipeline is crucial for the generation of high-quality, robust data. Metabolite identification remains the bottleneck in these studies; therefore, confidence in the data produced is paramount in order to maximize the biological output. Here, we outline the key steps of the metabolomics workflow and provide details on important parameters and considerations. Studies should be designed carefully to ensure appropriate statistical power and adequate controls. Subsequent sample handling and preparation should avoid the introduction of bias, which can significantly affect downstream data interpretation. It is not possible to cover the entire metabolome with a single platform; therefore, the analytical platform should reflect the biological sample under investigation and the question(s) under consideration. The large, complex datasets produced need to be pre-processed in order to extract meaningful information. Finally, the most time-consuming steps are metabolite identification, as well as metabolic pathway and network analysis. Here we discuss some widely used tools and the pitfalls of each step of the workflow, with the ultimate aim of guiding the reader towards the most efficient pipeline for their metabolomics studies.
Collapse
Affiliation(s)
- Julijana Ivanisevic
- Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Rue du Bugnon 19, 1005 Lausanne, Switzerland
- Correspondence: (J.I.); (E.J.W.)
| | - Elizabeth J. Want
- Section of Biomolecular Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK
- Correspondence: (J.I.); (E.J.W.)
| |
Collapse
|
15
|
Slenter DN, Kutmon M, Hanspers K, Riutta A, Windsor J, Nunes N, Mélius J, Cirillo E, Coort SL, Digles D, Ehrhart F, Giesbertz P, Kalafati M, Martens M, Miller R, Nishida K, Rieswijk L, Waagmeester A, Eijssen LMT, Evelo CT, Pico AR, Willighagen EL. WikiPathways: a multifaceted pathway database bridging metabolomics to other omics research. Nucleic Acids Res 2019; 46:D661-D667. [PMID: 29136241 PMCID: PMC5753270 DOI: 10.1093/nar/gkx1064] [Citation(s) in RCA: 578] [Impact Index Per Article: 115.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2017] [Accepted: 10/25/2017] [Indexed: 02/06/2023] Open
Abstract
WikiPathways (wikipathways.org) captures the collective knowledge represented in biological pathways. By providing a database in a curated, machine readable way, omics data analysis and visualization is enabled. WikiPathways and other pathway databases are used to analyze experimental data by research groups in many fields. Due to the open and collaborative nature of the WikiPathways platform, our content keeps growing and is getting more accurate, making WikiPathways a reliable and rich pathway database. Previously, however, the focus was primarily on genes and proteins, leaving many metabolites with only limited annotation. Recent curation efforts focused on improving the annotation of metabolism and metabolic pathways by associating unmapped metabolites with database identifiers and providing more detailed interaction knowledge. Here, we report the outcomes of the continued growth and curation efforts, such as a doubling of the number of annotated metabolite nodes in WikiPathways. Furthermore, we introduce an OpenAPI documentation of our web services and the FAIR (Findable, Accessible, Interoperable and Reusable) annotation of resources to increase the interoperability of the knowledge encoded in these pathways and experimental omics data. New search options, monthly downloads, more links to metabolite databases, and new portals make pathway knowledge more effortlessly accessible to individual researchers and research communities.
Collapse
Affiliation(s)
- Denise N Slenter
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, The Netherlands
| | - Martina Kutmon
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, The Netherlands.,Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, 6229 ER Maastricht, The Netherlands
| | | | - Anders Riutta
- Gladstone Institutes, San Francisco, California, CA 94158, USA
| | - Jacob Windsor
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, The Netherlands
| | - Nuno Nunes
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, The Netherlands
| | - Jonathan Mélius
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, The Netherlands
| | - Elisa Cirillo
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, The Netherlands
| | - Susan L Coort
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, The Netherlands
| | - Daniela Digles
- University of Vienna, Department of Pharmaceutical Chemistry, 1090 Vienna, Austria
| | - Friederike Ehrhart
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, The Netherlands
| | - Pieter Giesbertz
- Chair of Nutritional Physiology, Technische Universität München, 85350 Freising, Germany
| | - Marianthi Kalafati
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, The Netherlands.,Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, 6229 ER Maastricht, The Netherlands
| | - Marvin Martens
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, The Netherlands
| | - Ryan Miller
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, The Netherlands
| | - Kozo Nishida
- Laboratory for Biochemical Simulation, RIKEN Quantitative Biology Center, Suita, Osaka 565-0874, Japan
| | - Linda Rieswijk
- Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, CA 94720, USA
| | - Andra Waagmeester
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, The Netherlands.,Micelio, Antwerp, Belgium
| | - Lars M T Eijssen
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, The Netherlands.,School for Mental Health and Neuroscience, Department of Psychiatry and Neuropsychology, Maastricht University Medical Centre, 6229 ER Maastricht, The Netherlands
| | - Chris T Evelo
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, The Netherlands.,Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, 6229 ER Maastricht, The Netherlands
| | | | - Egon L Willighagen
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, The Netherlands
| |
Collapse
|
16
|
Wang G, Wang YZ, Yu Y, Wang JJ, Yin PH, Xu K. Triterpenoids Extracted fromRhus chinensis MillAct Against Colorectal Cancer by Inhibiting Enzymes in Glycolysis and Glutaminolysis: Network Analysis and Experimental Validation. Nutr Cancer 2019; 72:293-319. [PMID: 31267795 DOI: 10.1080/01635581.2019.1631858] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Affiliation(s)
- Gang Wang
- Department of Pharmaceutics, Shanghai Eighth People's Hospital, Jiangsu University, Shanghai, China
| | - Yu-Zhu Wang
- Department of Medicine, Jiangsu University, Zhenjiang City, China
| | - Yang Yu
- Department of Medicine, Jiangsu University, Zhenjiang City, China
| | - Jun-Jie Wang
- Department of Pharmaceutics, Shanghai Eighth People's Hospital, Jiangsu University, Shanghai, China
| | - Pei-Hao Yin
- Central laboratory, Putuo Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Ke Xu
- Central laboratory, Putuo Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| |
Collapse
|
17
|
Domingo-Fernández D, Mubeen S, Marín-Llaó J, Hoyt CT, Hofmann-Apitius M. PathMe: merging and exploring mechanistic pathway knowledge. BMC Bioinformatics 2019; 20:243. [PMID: 31092193 PMCID: PMC6521546 DOI: 10.1186/s12859-019-2863-9] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Accepted: 04/29/2019] [Indexed: 12/12/2022] Open
Abstract
Background The complexity of representing biological systems is compounded by an ever-expanding body of knowledge emerging from multi-omics experiments. A number of pathway databases have facilitated pathway-centric approaches that assist in the interpretation of molecular signatures yielded by these experiments. However, the lack of interoperability between pathway databases has hindered the ability to harmonize these resources and to exploit their consolidated knowledge. Such a unification of pathway knowledge is imperative in enhancing the comprehension and modeling of biological abstractions. Results Here, we present PathMe, a Python package that transforms pathway knowledge from three major pathway databases into a unified abstraction using Biological Expression Language as the pivotal, integrative schema. PathMe is complemented by a novel web application (freely available at https://pathme.scai.fraunhofer.de/) which allows users to comprehensively explore pathway crosstalk and compare areas of consensus and discrepancies. Conclusions This work has harmonized three major pathway databases and transformed them into a unified schema in order to gain a holistic picture of pathway knowledge. We demonstrate the utility of the PathMe framework in: i) integrating pathway landscapes at the database level, ii) comparing the degree of consensus at the pathway level, and iii) exploring pathway crosstalk and investigating consensus at the molecular level. Electronic supplementary material The online version of this article (10.1186/s12859-019-2863-9) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Daniel Domingo-Fernández
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53754, Sankt Augustin, Germany. .,Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115, Bonn, Germany.
| | - Sarah Mubeen
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53754, Sankt Augustin, Germany.,Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115, Bonn, Germany
| | - Josep Marín-Llaó
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53754, Sankt Augustin, Germany
| | - Charles Tapley Hoyt
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53754, Sankt Augustin, Germany.,Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115, Bonn, Germany
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53754, Sankt Augustin, Germany.,Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115, Bonn, Germany
| |
Collapse
|
18
|
Sole head transcriptomics reveals a coordinated developmental program during metamorphosis. Genomics 2019; 112:592-602. [PMID: 31071460 DOI: 10.1016/j.ygeno.2019.04.011] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2018] [Revised: 03/07/2019] [Accepted: 04/16/2019] [Indexed: 12/21/2022]
Abstract
Most teleosts undergo a thyroid hormone (TH) regulated larval to juvenile transition known as metamorphosis. In Pleuronectiformes (flatfish), metamorphosis is most dramatic, and one eye of the symmetric pelagic larvae migrates to the opposite side of the head, giving rise to an asymmetric benthic juvenile with both eyes on the same side of the body. Asymmetric development occurs mostly in the head. To understand the genetic mechanisms underlying this developmental change we have generated a Solea senegalensis metamorphosing flatfish head transcriptome. Our results reveal that THs acting as integrative factors direct a stepwise genetic program that initiates a specific organismal level response followed by cell specific responses that lead to the long-term changes that characterise the post-metamorphic identity and physiology of the head. Flatfish head asymmetric development during metamorphosis and its TH dependency is conserved thus we consider the findings in sole most likely representative of all flatfish species.
Collapse
|
19
|
Ostaszewski M, Gebel S, Kuperstein I, Mazein A, Zinovyev A, Dogrusoz U, Hasenauer J, Fleming RMT, Le Novère N, Gawron P, Ligon T, Niarakis A, Nickerson D, Weindl D, Balling R, Barillot E, Auffray C, Schneider R. Community-driven roadmap for integrated disease maps. Brief Bioinform 2019; 20:659-670. [PMID: 29688273 PMCID: PMC6556900 DOI: 10.1093/bib/bby024] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Revised: 03/02/2018] [Indexed: 01/07/2023] Open
Abstract
The Disease Maps Project builds on a network of scientific and clinical groups that exchange best practices, share information and develop systems biomedicine tools. The project aims for an integrated, highly curated and user-friendly platform for disease-related knowledge. The primary focus of disease maps is on interconnected signaling, metabolic and gene regulatory network pathways represented in standard formats. The involvement of domain experts ensures that the key disease hallmarks are covered and relevant, up-to-date knowledge is adequately represented. Expert-curated and computer readable, disease maps may serve as a compendium of knowledge, allow for data-supported hypothesis generation or serve as a scaffold for the generation of predictive mathematical models. This article summarizes the 2nd Disease Maps Community meeting, highlighting its important topics and outcomes. We outline milestones on the roadmap for the future development of disease maps, including creating and maintaining standardized disease maps; sharing parts of maps that encode common human disease mechanisms; providing technical solutions for complexity management of maps; and Web tools for in-depth exploration of such maps. A dedicated discussion was focused on mathematical modeling approaches, as one of the main goals of disease map development is the generation of mathematically interpretable representations to predict disease comorbidity or drug response and to suggest drug repositioning, altogether supporting clinical decisions.
Collapse
Affiliation(s)
- Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine, Universite du Luxembourg, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| | - Stephan Gebel
- Luxembourg Centre for Systems Biomedicine, Universite du Luxembourg, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| | - Inna Kuperstein
- Institut Curie, PSL Research University, INSERM U900, F-75005 Paris, France and CBIO-Centre for Computational Biology, MINES ParisTech, PSL Research University, F-75006 Paris, France
| | - Alexander Mazein
- European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
| | - Andrei Zinovyev
- Institut Curie, PSL Research University, INSERM U900, F-75005 Paris, France and CBIO-Centre for Computational Biology, MINES ParisTech, PSL Research University, F-75006 Paris, France
| | - Ugur Dogrusoz
- Computer Engineering Department, Faculty of Engineering, Bilkent University, Ankara 06800, Turkey
| | - Jan Hasenauer
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - Ronan M T Fleming
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Faculty of Science, Leiden University, Leiden, Netherlands
| | - Nicolas Le Novère
- The Babraham Institute, Babraham Research Campus, Cambridge CB22 3AT, United Kingdom
| | - Piotr Gawron
- Luxembourg Centre for Systems Biomedicine, Universite du Luxembourg, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| | - Thomas Ligon
- Faculty of Physics and Center for NanoScience (CeNS), Ludwig-Maximilians-Universität, 80539 München, Germany
| | - Anna Niarakis
- GenHotel EA3886, Univ Evry, Université Paris-Saclay, Evry 91025, France
| | - David Nickerson
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Daniel Weindl
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - Rudi Balling
- Luxembourg Centre for Systems Biomedicine, Universite du Luxembourg, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| | - Emmanuel Barillot
- Institut Curie, PSL Research University, INSERM U900, F-75005 Paris, France and CBIO-Centre for Computational Biology, MINES ParisTech, PSL Research University, F-75006 Paris, France
| | - Charles Auffray
- European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine, Universite du Luxembourg, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| |
Collapse
|
20
|
Domingo-Fernández D, Hoyt CT, Bobis-Álvarez C, Marín-Llaó J, Hofmann-Apitius M. ComPath: an ecosystem for exploring, analyzing, and curating mappings across pathway databases. NPJ Syst Biol Appl 2018; 5:3. [PMID: 30564458 PMCID: PMC6292919 DOI: 10.1038/s41540-018-0078-8] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Revised: 10/31/2018] [Accepted: 11/02/2018] [Indexed: 11/09/2022] Open
Abstract
Although pathways are widely used for the analysis and representation of biological systems, their lack of clear boundaries, their dispersion across numerous databases, and the lack of interoperability impedes the evaluation of the coverage, agreements, and discrepancies between them. Here, we present ComPath, an ecosystem that supports curation of pathway mappings between databases and fosters the exploration of pathway knowledge through several novel visualizations. We have curated mappings between three of the major pathway databases and present a case study focusing on Parkinson’s disease that illustrates how ComPath can generate new biological insights by identifying pathway modules, clusters, and cross-talks with these mappings. The ComPath source code and resources are available at https://github.com/ComPath and the web application can be accessed at https://compath.scai.fraunhofer.de/.
Collapse
Affiliation(s)
- Daniel Domingo-Fernández
- 1Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53754 Sankt Augustin, Germany.,2Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115 Bonn, Germany
| | - Charles Tapley Hoyt
- 1Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53754 Sankt Augustin, Germany.,2Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115 Bonn, Germany
| | - Carlos Bobis-Álvarez
- 3Faculty of Medicine and Health Sciences, University of Oviedo, 33006 Oviedo, Spain
| | - Josep Marín-Llaó
- 1Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53754 Sankt Augustin, Germany.,4Rovira i Virgili University, 43003 Tarragona, Spain
| | - Martin Hofmann-Apitius
- 1Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53754 Sankt Augustin, Germany.,2Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115 Bonn, Germany
| |
Collapse
|
21
|
Kornakiewicz A, Czarnecka AM, Khan MI, Krasowski P, Kotrys AV, Szczylik C. Effect of Everolimus on Heterogenous Renal Cancer Cells Populations Including Renal Cancer Stem Cells. Stem Cell Rev Rep 2018; 14:385-397. [PMID: 29508215 DOI: 10.1007/s12015-018-9804-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The aim of this study was to compare effect of everolimus on growth of different renal cell carcinoma (RCC) populations and develop experimental design to measure the early response of everolimus in clear cell RCC (ccRCC) cell lines including renal cancer stem cells. Effect of everolimus on RCC cell lines which include primary (786-0) and metastatic (ACHN) RCC cell lines as well as heterogenous populations of tumor cells of different histological RCC subtypes (clear cell RCC and papillary RCC) was measured when treated with everolimus in the range of 1-9 µM. Gene expression profiling using microarray was performed to determine the early response to everolimus in ccRCC cell lines after optimizing concentration of drug. Gene Set Enrichment Analysis (GSEA) was done which mainly focused on basic genes related to mTOR, hormonal and metabolic pathways. Everolimus acts on RCC cells in a dose-dependent manner. In all examined cell lines IC50 dose was possible to calculate after the third day of treatment. In ccRCC lines (parental and stem cell) everolimus changes expression of mTOR complexes elements and elements of related pathways when treated with optimized doses of drug. Characteristic expression profile for ccRCC cells at an early exposure time to everolimus is to elucidate. Wevarie include some basic observations derived from data analysis in the context of mechanism of action of drug with a view to better understand biology of renal cancer cells.
Collapse
Affiliation(s)
- Anna Kornakiewicz
- Molecular Oncology Laboratory, Department of Oncology, Military Institute of Medicine, ul. Szaserów 128, 04-141, Warsaw, Poland. .,Postgraduate School of Molecular Medicine, Warsaw Medical University, Żwirki i Wigury 61, 02-091, Warsaw, Poland.
| | - Anna M Czarnecka
- Molecular Oncology Laboratory, Department of Oncology, Military Institute of Medicine, ul. Szaserów 128, 04-141, Warsaw, Poland
| | - Mohammed I Khan
- Molecular Oncology Laboratory, Department of Oncology, Military Institute of Medicine, ul. Szaserów 128, 04-141, Warsaw, Poland
| | - Paweł Krasowski
- Molecular Oncology Laboratory, Department of Oncology, Military Institute of Medicine, ul. Szaserów 128, 04-141, Warsaw, Poland
| | - Anna V Kotrys
- Institute of Biochemistry and Biophysics Polish Academy of Sciences, Laboratory of RNA Biology and Functional Genomics, Pawińskiego 5A, 02-106, Warsaw, Poland
| | - Cezary Szczylik
- Molecular Oncology Laboratory, Department of Oncology, Military Institute of Medicine, ul. Szaserów 128, 04-141, Warsaw, Poland.,Warsaw Medical University, Żwirki i Wigury 61, 02-091, Warsaw, Poland
| |
Collapse
|
22
|
Abstract
The diversity and huge omics data take biology and biomedicine research and application into a big data era, just like that popular in human society a decade ago. They are opening a new challenge from horizontal data ensemble (e.g., the similar types of data collected from different labs or companies) to vertical data ensemble (e.g., the different types of data collected for a group of person with match information), which requires the integrative analysis in biology and biomedicine and also asks for emergent development of data integration to address the great changes from previous population-guided to newly individual-guided investigations.Data integration is an effective concept to solve the complex problem or understand the complicate system. Several benchmark studies have revealed the heterogeneity and trade-off that existed in the analysis of omics data. Integrative analysis can combine and investigate many datasets in a cost-effective reproducible way. Current integration approaches on biological data have two modes: one is "bottom-up integration" mode with follow-up manual integration, and the other one is "top-down integration" mode with follow-up in silico integration.This paper will firstly summarize the combinatory analysis approaches to give candidate protocol on biological experiment design for effectively integrative study on genomics and then survey the data fusion approaches to give helpful instruction on computational model development for biological significance detection, which have also provided newly data resources and analysis tools to support the precision medicine dependent on the big biomedical data. Finally, the problems and future directions are highlighted for integrative analysis of omics big data.
Collapse
Affiliation(s)
- Xiang-Tian Yu
- Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Chinese Academy Science, Shanghai, China
| | - Tao Zeng
- Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Chinese Academy Science, Shanghai, China.
| |
Collapse
|
23
|
Shen H, Liang Z, Zheng S, Li X. Pathway and network-based analysis of genome-wide association studies and RT-PCR validation in polycystic ovary syndrome. Int J Mol Med 2017; 40:1385-1396. [PMID: 28949383 PMCID: PMC5627882 DOI: 10.3892/ijmm.2017.3146] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2016] [Accepted: 09/07/2017] [Indexed: 01/25/2023] Open
Abstract
The purpose of this study was to identify promising candidate genes and pathways in polycystic ovary syndrome (PCOS). Microarray dataset GSE345269 obtained from the Gene Expression Omnibus database includes 7 granulosa cell samples from PCOS patients, and 3 normal granulosa cell samples. Differentially expressed genes (DEGs) were screened between PCOS and normal samples. Pathway enrichment analysis was conducted for DEGs using ClueGO and CluePedia plugin of Cytoscape. A Reactome functional interaction (FI) network of the DEGs was built using ReactomeFIViz, and then network modules were extracted, followed by pathway enrichment analysis for the modules. Expression of DEGs in granulosa cell samples was measured using quantitative RT-PCR. A total of 674 DEGs were retained, which were significantly enriched with inflammation and immune-related pathways. Eight modules were extracted from the Reactome FI network. Pathway enrichment analysis revealed significant pathways of each module: module 0, Regulation of RhoA activity and Signaling by Rho GTPases pathways shared ARHGAP4 and ARHGAP9; module 2, GlycoProtein VI-mediated activation cascade pathway was enriched with RHOG; module 3, Thromboxane A2 receptor signaling, Chemokine signaling pathway, CXCR4-mediated signaling events pathways were enriched with LYN, the hub gene of module 3. Results of RT-PCR confirmed the finding of the bioinformatic analysis that ARHGAP4, ARHGAP9, RHOG and LYN were significantly upregulated in PCOS. RhoA-related pathways, GlycoProtein VI-mediated activation cascade pathway, ARHGAP4, ARHGAP9, RHOG and LYN may be involved in the pathogenesis of PCOS.
Collapse
Affiliation(s)
- Haoran Shen
- Department of Gynecology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, P.R. China
| | - Zhou Liang
- Department of Assisted Reproduction, Shanghai Ninth People's Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, P.R. China
| | - Saihua Zheng
- Department of Gynecology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, P.R. China
| | - Xuelian Li
- Department of Gynecology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, P.R. China
| |
Collapse
|
24
|
Talikka M, Bukharov N, Hayes WS, Hofmann-Apitius M, Alexopoulos L, Peitsch MC, Hoeng J. Novel approaches to develop community-built biological network models for potential drug discovery. Expert Opin Drug Discov 2017; 12:849-857. [PMID: 28585481 DOI: 10.1080/17460441.2017.1335302] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
INTRODUCTION Hundreds of thousands of data points are now routinely generated in clinical trials by molecular profiling and NGS technologies. A true translation of this data into knowledge is not possible without analysis and interpretation in a well-defined biology context. Currently, there are many public and commercial pathway tools and network models that can facilitate such analysis. At the same time, insights and knowledge that can be gained is highly dependent on the underlying biological content of these resources. Crowdsourcing can be employed to guarantee the accuracy and transparency of the biological content underlining the tools used to interpret rich molecular data. Areas covered: In this review, the authors describe crowdsourcing in drug discovery. The focal point is the efforts that have successfully used the crowdsourcing approach to verify and augment pathway tools and biological network models. Technologies that enable the building of biological networks with the community are also described. Expert opinion: A crowd of experts can be leveraged for the entire development process of biological network models, from ontologies to the evaluation of their mechanistic completeness. The ultimate goal is to facilitate biomarker discovery and personalized medicine by mechanistically explaining patients' differences with respect to disease prevention, diagnosis, and therapy outcome.
Collapse
Affiliation(s)
- Marja Talikka
- a Philip Morris International R&D , Philip Morris Products S.A. , Neuchâtel , Switzerland
| | - Natalia Bukharov
- b Translational Data Management Services, Clarivate Analytics (Formerly the IP & Science Business of Thomson Reuters) , Boston , MA , USA
| | - William S Hayes
- c Data Sciences , Applied Dynamic Solutions, LLC , Rahway , NJ , USA
| | - Martin Hofmann-Apitius
- d Department of Bioinformatics , Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven , Sankt Augustin , Germany
| | - Leonidas Alexopoulos
- e Systems Bioengineering Lab , National Technical University of Athens , Zografou , Greece.,f Protavio Ltd , Stevenage , UK
| | - Manuel C Peitsch
- a Philip Morris International R&D , Philip Morris Products S.A. , Neuchâtel , Switzerland
| | - Julia Hoeng
- a Philip Morris International R&D , Philip Morris Products S.A. , Neuchâtel , Switzerland
| |
Collapse
|
25
|
Golubev A, Hanson AD, Gladyshev VN. Non-enzymatic molecular damage as a prototypic driver of aging. J Biol Chem 2017; 292:6029-6038. [PMID: 28264930 PMCID: PMC5391736 DOI: 10.1074/jbc.r116.751164] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023] Open
Abstract
The chemical potentialities of metabolites far exceed metabolic requirements. The required potentialities are realized mostly through enzymatic catalysis. The rest are realized spontaneously through organic reactions that (i) occur wherever appropriate reactants come together, (ii) are so typical that many have proper names (e.g. Michael addition, Amadori rearrangement, and Pictet-Spengler reaction), and (iii) often have damaging consequences. There are many more causes of non-enzymatic damage to metabolites than reactive oxygen species and free radical processes (the "usual suspects"). Endogenous damage accumulation in non-renewable macromolecules and spontaneously polymerized material is sufficient to account for aging and differentiates aging from wear-and-tear of inanimate objects by deriving it from metabolism, the essential attribute of life.
Collapse
Affiliation(s)
- Alexey Golubev
- From the Department of Biochemistry, Saint-Petersburg State University, Saint Petersburg 199034, Russia,
| | - Andrew D Hanson
- the Horticultural Sciences Department, University of Florida, Gainesville, Florida 32611, and
| | - Vadim N Gladyshev
- the Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115
| |
Collapse
|