1
|
Li Y, Li F, Sun Z, Li J. A review of literature: role of long noncoding RNA TPT1-AS1 in human diseases. CLINICAL & TRANSLATIONAL ONCOLOGY : OFFICIAL PUBLICATION OF THE FEDERATION OF SPANISH ONCOLOGY SOCIETIES AND OF THE NATIONAL CANCER INSTITUTE OF MEXICO 2023; 25:306-315. [PMID: 36112261 DOI: 10.1007/s12094-022-02947-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 09/01/2022] [Indexed: 01/27/2023]
Abstract
Human diseases are multifactorial processes mainly driven by the intricate interactions of genetic and environmental factors. Long noncoding RNAs (lncRNAs) represent a type of non-coding RNAs with more than 200 nucleotides. Multiple studies have demonstrated that the dysregulation of lncRNAs is associated with complex biological as well as pathological processes through various mechanism, especially the regulation of gene transcription and related signal transduction pathways. Moreover, an increasing number of studies have explored lncRNA-based clinical applications in different diseases. For instance, the lncRNA Tumor Protein Translationally Controlled 1 (TPT1) Antisense RNA 1 (TPT1-AS1) was found to be dysregulated in several types of disease and strongly associated with patient prognosis and diverse clinical features. Recent studies have also documented that TPT1-AS1 modulates numerous biological processes through multiple mechanisms, including cell proliferation, apoptosis, autophagy, invasion, migration, radiosensitivity, chemosensitivity, stemness, and extracellular matrix (ECM) synthesis. Furthermore, TPT1-AS1 was regarded as a promising biomarker for the diagnosis, prognosis and treatment of several human diseases. In this review, we summarize the role of TPT1-AS1 in human diseases with the aspects of its expression, relevant clinical characteristics, molecular mechanisms, biological functions, and subsequent clinical applications.
Collapse
Affiliation(s)
- Yi Li
- Department of Infectious Diseases, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshedong Road, Erqi District, Zhengzhou, 450052, China
| | - Fulei Li
- Department of Infectious Diseases, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshedong Road, Erqi District, Zhengzhou, 450052, China
| | - Zongzong Sun
- Department of Obstetrics and Gynaecology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Juan Li
- Department of Infectious Diseases, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshedong Road, Erqi District, Zhengzhou, 450052, China.
| |
Collapse
|
2
|
Buchsbaum JC, Jaffray DA, Ba D, Borkon LL, Chalk C, Chung C, Coleman MA, Coleman CN, Diehn M, Droegemeier KK, Enderling H, Espey MG, Greenspan EJ, Hartshorn CM, Hoang T, Hsiao HT, Keppel C, Moore NW, Prior F, Stahlberg EA, Tourassi G, Willcox KE. Predictive Radiation Oncology - A New NCI-DOE Scientific Space and Community. Radiat Res 2022; 197:434-445. [PMID: 35090025 DOI: 10.1667/rade-22-00012.1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 01/10/2022] [Indexed: 11/03/2022]
Abstract
With a widely attended virtual kickoff event on January 29, 2021, the National Cancer Institute (NCI) and the Department of Energy (DOE) launched a series of 4 interactive, interdisciplinary workshops-and a final concluding "World Café" on March 29, 2021-focused on advancing computational approaches for predictive oncology in the clinical and research domains of radiation oncology. These events reflect 3,870 human hours of virtual engagement with representation from 8 DOE national laboratories and the Frederick National Laboratory for Cancer Research (FNL), 4 research institutes, 5 cancer centers, 17 medical schools and teaching hospitals, 5 companies, 5 federal agencies, 3 research centers, and 27 universities. Here we summarize the workshops by first describing the background for the workshops. Participants identified twelve key questions-and collaborative parallel ideas-as the focus of work going forward to advance the field. These were then used to define short-term and longer-term "Blue Sky" goals. In addition, the group determined key success factors for predictive oncology in the context of radiation oncology, if not the future of all of medicine. These are: cross-discipline collaboration, targeted talent development, development of mechanistic mathematical and computational models and tools, and access to high-quality multiscale data that bridges mechanisms to phenotype. The workshop participants reported feeling energized and highly motivated to pursue next steps together to address the unmet needs in radiation oncology specifically and in cancer research generally and that NCI and DOE project goals align at the convergence of radiation therapy and advanced computing.
Collapse
Affiliation(s)
| | - David A Jaffray
- The University of Texas, MD Anderson Cancer Center, Houston, Texas 77030
| | - Demba Ba
- Harvard University, Cambridge, Massachusetts 02138
| | - Lynn L Borkon
- Frederick National Laboratory for Cancer Research, Frederick, Maryland, 21701
| | | | - Caroline Chung
- The University of Texas, MD Anderson Cancer Center, Houston, Texas 77030
| | | | | | | | | | - Heiko Enderling
- H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida 33612
| | | | | | | | - Thuc Hoang
- U.S. Department of Energy, Washington, DC 20585
| | - H Timothy Hsiao
- American Society for Radiation Oncology (ASTRO), Arlington, Virginia 22202
| | | | - Nathan W Moore
- Sandia National Laboratories, Albuquerque, New Mexico 87123
| | - Fred Prior
- University of Arkansas for Medical Sciences, Little Rock, Arkansas 72205
| | - Eric A Stahlberg
- Frederick National Laboratory for Cancer Research, Frederick, Maryland, 21701
| | | | | |
Collapse
|
3
|
Buphamalai P, Kokotovic T, Nagy V, Menche J. Network analysis reveals rare disease signatures across multiple levels of biological organization. Nat Commun 2021; 12:6306. [PMID: 34753928 PMCID: PMC8578255 DOI: 10.1038/s41467-021-26674-1] [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: 04/30/2021] [Accepted: 10/19/2021] [Indexed: 01/26/2023] Open
Abstract
Rare genetic diseases are typically caused by a single gene defect. Despite this clear causal relationship between genotype and phenotype, identifying the pathobiological mechanisms at various levels of biological organization remains a practical and conceptual challenge. Here, we introduce a network approach for evaluating the impact of rare gene defects across biological scales. We construct a multiplex network consisting of over 20 million gene relationships that are organized into 46 network layers spanning six major biological scales between genotype and phenotype. A comprehensive analysis of 3,771 rare diseases reveals distinct phenotypic modules within individual layers. These modules can be exploited to mechanistically dissect the impact of gene defects and accurately predict rare disease gene candidates. Our results show that the disease module formalism can be applied to rare diseases and generalized beyond physical interaction networks. These findings open up new venues to apply network-based tools for cross-scale data integration.
Collapse
Affiliation(s)
- Pisanu Buphamalai
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT 25.3, 1090, Vienna, Austria
- Department of Structural and Computational Biology, Max Perutz Labs, University of Vienna, Campus Vienna BioCenter 5, 1030, Vienna, Austria
| | - Tomislav Kokotovic
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT 25.3, 1090, Vienna, Austria
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Lazarettgasse 14, AKH BT 25.3, 1090, Vienna, Austria
- Department of Neurology, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Vanja Nagy
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT 25.3, 1090, Vienna, Austria
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Lazarettgasse 14, AKH BT 25.3, 1090, Vienna, Austria
- Department of Neurology, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Jörg Menche
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT 25.3, 1090, Vienna, Austria.
- Department of Structural and Computational Biology, Max Perutz Labs, University of Vienna, Campus Vienna BioCenter 5, 1030, Vienna, Austria.
- Faculty of Mathematics, University of Vienna, Oskar-Morgenstern-Platz 1, 1090, Vienna, Austria.
| |
Collapse
|
4
|
Pin C, Collins T, Gibbs M, Kimko H. Systems Modeling to Quantify Safety Risks in Early Drug Development: Using Bifurcation Analysis and Agent-Based Modeling as Examples. AAPS JOURNAL 2021; 23:77. [PMID: 34018069 PMCID: PMC8137611 DOI: 10.1208/s12248-021-00580-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Accepted: 03/09/2021] [Indexed: 11/30/2022]
Abstract
Quantitative Systems Toxicology (QST) models, recapitulating pharmacokinetics and mechanism of action together with the organic response at multiple levels of biological organization, can provide predictions on the magnitude of injury and recovery dynamics to support study design and decision-making during drug development. Here, we highlight the application of QST models to predict toxicities of cancer treatments, such as cytopenia(s) and gastrointestinal adverse effects, where narrow therapeutic indexes need to be actively managed. The importance of bifurcation analysis is demonstrated in QST models of hematologic toxicity to understand how different regions of the parameter space generate different behaviors following cancer treatment, which results in asymptotically stable predictions, yet highly irregular for specific schedules, or oscillating predictions of blood cell levels. In addition, an agent-based model of the intestinal crypt was used to simulate how the spatial location of the injury within the crypt affects the villus disruption severity. We discuss the value of QST modeling approaches to support drug development and how they align with technological advances impacting trial design including patient selection, dose/regimen selection, and ultimately patient safety.
Collapse
Affiliation(s)
- Carmen Pin
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge Science Park, Milton Road, Cambridge, UK
| | - Teresa Collins
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge Science Park, Milton Road, Cambridge, UK
| | - Megan Gibbs
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Gaithersburg, Maryland, USA
| | - Holly Kimko
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Gaithersburg, Maryland, USA.
| |
Collapse
|
5
|
Wang H, Pujos-Guillot E, Comte B, de Miranda JL, Spiwok V, Chorbev I, Castiglione F, Tieri P, Watterson S, McAllister R, de Melo Malaquias T, Zanin M, Rai TS, Zheng H. Deep learning in systems medicine. Brief Bioinform 2021; 22:1543-1559. [PMID: 33197934 PMCID: PMC8382976 DOI: 10.1093/bib/bbaa237] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 08/25/2020] [Accepted: 08/26/2020] [Indexed: 12/11/2022] Open
Abstract
Systems medicine (SM) has emerged as a powerful tool for studying the human body at the systems level with the aim of improving our understanding, prevention and treatment of complex diseases. Being able to automatically extract relevant features needed for a given task from high-dimensional, heterogeneous data, deep learning (DL) holds great promise in this endeavour. This review paper addresses the main developments of DL algorithms and a set of general topics where DL is decisive, namely, within the SM landscape. It discusses how DL can be applied to SM with an emphasis on the applications to predictive, preventive and precision medicine. Several key challenges have been highlighted including delivering clinical impact and improving interpretability. We used some prototypical examples to highlight the relevance and significance of the adoption of DL in SM, one of them is involving the creation of a model for personalized Parkinson's disease. The review offers valuable insights and informs the research in DL and SM.
Collapse
Affiliation(s)
| | - Estelle Pujos-Guillot
- metabolomic platform dedicated to metabolism studies in nutrition and health in the French National Research Institute for Agriculture, Food and Environment
| | - Blandine Comte
- French National Research Institute for Agriculture, Food and Environment
| | - Joao Luis de Miranda
- (ESTG/IPP) and a Researcher (CERENA/IST) in optimization methods and process systems engineering
| | - Vojtech Spiwok
- Molecular Modelling Researcher applying machine learning to accelerate molecular simulations
| | - Ivan Chorbev
- Faculty for Computer Science and Engineering, University Ss Cyril and Methodius in Skopje, North Macedonia working in the area of eHealth and assistive technologies
| | | | - Paolo Tieri
- National Research Council of Italy (CNR) and a lecturer at Sapienza University in Rome, working in the field of network medicine and computational biology
| | | | - Roisin McAllister
- Research Associate working in CTRIC, University of Ulster, Derry, and has worked in clinical and academic roles in the fields of molecular diagnostics and biomarker discovery
| | | | - Massimiliano Zanin
- Researcher working in the Institute for Cross-Disciplinary Physics and Complex Systems, Spain, with an interest on data analysis and integration using statistical physics techniques
| | - Taranjit Singh Rai
- Lecturer in cellular ageing at the Centre for Stratified Medicine. Dr Rai’s research interests are in cellular senescence, which is thought to promote cellular and tissue ageing in disease, and the development of senolytic compounds to restrict this process
| | - Huiru Zheng
- Professor of computer sciences at Ulster University
| |
Collapse
|
6
|
Zanin M, Aitya NA, Basilio J, Baumbach J, Benis A, Behera CK, Bucholc M, Castiglione F, Chouvarda I, Comte B, Dao TT, Ding X, Pujos-Guillot E, Filipovic N, Finn DP, Glass DH, Harel N, Iesmantas T, Ivanoska I, Joshi A, Boudjeltia KZ, Kaoui B, Kaur D, Maguire LP, McClean PL, McCombe N, de Miranda JL, Moisescu MA, Pappalardo F, Polster A, Prasad G, Rozman D, Sacala I, Sanchez-Bornot JM, Schmid JA, Sharp T, Solé-Casals J, Spiwok V, Spyrou GM, Stalidzans E, Stres B, Sustersic T, Symeonidis I, Tieri P, Todd S, Van Steen K, Veneva M, Wang DH, Wang H, Wang H, Watterson S, Wong-Lin K, Yang S, Zou X, Schmidt HH. An Early Stage Researcher's Primer on Systems Medicine Terminology. NETWORK AND SYSTEMS MEDICINE 2021; 4:2-50. [PMID: 33659919 PMCID: PMC7919422 DOI: 10.1089/nsm.2020.0003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/27/2020] [Indexed: 12/19/2022] Open
Abstract
Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic.
Collapse
Affiliation(s)
- Massimiliano Zanin
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain
| | - Nadim A.A. Aitya
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - José Basilio
- Center for Physiology and Pharmacology, Institute of Vascular Biology and Thrombosis Research, Medical University of Vienna, Vienna, Austria
| | - Jan Baumbach
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Arriel Benis
- Faculty of Technology Management, Holon Institute of Technology (HIT), Holon, Israel
| | - Chandan K. Behera
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Magda Bucholc
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Filippo Castiglione
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy
| | - Ioanna Chouvarda
- Lab of Computing, Medical Informatics, and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Blandine Comte
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Tien-Tuan Dao
- Biomechanics and Bioengineering Laboratory (UMR CNRS 7338), Université de Technologie de Compiègne, Compiègne, France
- Labex MS2T “Control of Technological Systems-of-Systems,” CNRS and Université de Technologie de Compiègne, Compiègne, France
| | - Xuemei Ding
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Estelle Pujos-Guillot
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Nenad Filipovic
- Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia
- Bioengineering Research and Development Center (BioIRC), Kragujevac, Serbia
- Steinbeis Advanced Risk Technologies Institute doo Kragujevac, Kragujevac, Serbia
| | - David P. Finn
- Pharmacology and Therapeutics, School of Medicine, Galway Neuroscience Centre, National University of Ireland, Galway, Republic of Ireland
| | - David H. Glass
- School of Computing, Ulster University, Ulster, United Kingdom
| | - Nissim Harel
- Faculty of Sciences, Holon Institute of Technology (HIT), Holon, Israel
| | - Tomas Iesmantas
- Department of Mathematics and Natural Sciences, Kaunas University of Technology, Kaunas, Lithuania
| | - Ilinka Ivanoska
- Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Skopje, Macedonia
| | - Alok Joshi
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Karim Zouaoui Boudjeltia
- Laboratory of Experimental Medicine (ULB 222), Medicine Faculty, Université libre de Bruxelles, CHU de Charleroi, Charleroi, Belgium
| | - Badr Kaoui
- Biomechanics and Bioengineering Laboratory (UMR CNRS 7338), Université de Technologie de Compiègne, Compiègne, France
- Labex MS2T “Control of Technological Systems-of-Systems,” CNRS and Université de Technologie de Compiègne, Compiègne, France
| | - Daman Kaur
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, Ulster University, Ulster, United Kingdom
| | - Liam P. Maguire
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Paula L. McClean
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, Ulster University, Ulster, United Kingdom
| | - Niamh McCombe
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - João Luís de Miranda
- Escola Superior de Tecnologia e Gestão, Instituto Politécnico de Portalegre, Portalegre, Portugal
- Centro de Recursos Naturais e Ambiente (CERENA), Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
| | | | | | - Annikka Polster
- Centre for Molecular Medicine Norway (NCMM), Forskningparken, Oslo, Norway
| | - Girijesh Prasad
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Damjana Rozman
- Centre for Functional Genomics and Bio-Chips, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Ioan Sacala
- Faculty of Automatic Control and Computers, University Politehnica of Bucharest, Bucharest, Romania
| | - Jose M. Sanchez-Bornot
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Johannes A. Schmid
- Center for Physiology and Pharmacology, Institute of Vascular Biology and Thrombosis Research, Medical University of Vienna, Vienna, Austria
| | - Trevor Sharp
- Department of Pharmacology, University of Oxford, Oxford, United Kingdom
| | - Jordi Solé-Casals
- Data and Signal Processing Research Group, University of Vic–Central University of Catalonia, Vic, Spain
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- College of Artificial Intelligence, Nankai University, Tianjin, China
| | - Vojtěch Spiwok
- Department of Biochemistry and Microbiology, University of Chemistry and Technology, Prague, Czech Republic
| | - George M. Spyrou
- The Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Egils Stalidzans
- Computational Systems Biology Group, Institute of Microbiology and Biotechnology, University of Latvia, Riga, Latvia
| | - Blaž Stres
- Department of Animal Science, Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
- Faculty of Civil and Geodetic Engineering, University of Ljubljana, Ljubljana, Slovenia
- Department of Automation, Biocybernetics and Robotics, Jozef Stefan Institute, Ljubljana, Slovenia
| | - Tijana Sustersic
- Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia
- Bioengineering Research and Development Center (BioIRC), Kragujevac, Serbia
- Steinbeis Advanced Risk Technologies Institute doo Kragujevac, Kragujevac, Serbia
| | - Ioannis Symeonidis
- Center for Research and Technology Hellas, Hellenic Institute of Transport, Thessaloniki, Greece
| | - Paolo Tieri
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy
| | - Stephen Todd
- Altnagelvin Area Hospital, Western Health and Social Care Trust, Altnagelvin, United Kingdom
| | - Kristel Van Steen
- BIO3-Systems Genetics, GIGA-R, University of Liege, Liege, Belgium
- BIO3-Systems Medicine, Department of Human Genetics, KU Leuven, Leuven, Belgium
| | | | - Da-Hui Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, and School of Systems Science, Beijing Normal University, Beijing, China
| | - Haiying Wang
- School of Computing, Ulster University, Ulster, United Kingdom
| | - Hui Wang
- School of Computing, Ulster University, Ulster, United Kingdom
| | - Steven Watterson
- Northern Ireland Centre for Stratified Medicine, Ulster University, Londonderry, United Kingdom
| | - KongFatt Wong-Lin
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Su Yang
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Xin Zou
- Shanghai Centre for Systems Biomedicine, Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
| | - Harald H.H.W. Schmidt
- Faculty of Health, Medicine & Life Science, Maastricht University, Maastricht, The Netherlands
| |
Collapse
|
7
|
Voit EO. Metabolic Systems. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11619-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
|
8
|
Gerdle B, Ghafouri B. Proteomic studies of common chronic pain conditions - a systematic review and associated network analyses. Expert Rev Proteomics 2020; 17:483-505. [DOI: 10.1080/14789450.2020.1797499] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Björn Gerdle
- Pain and Rehabilitation Centre, and Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Bijar Ghafouri
- Pain and Rehabilitation Centre, and Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| |
Collapse
|
9
|
Stres B, Kronegger L. Shift in the paradigm towards next-generation microbiology. FEMS Microbiol Lett 2020; 366:5533319. [PMID: 31314103 PMCID: PMC6759065 DOI: 10.1093/femsle/fnz159] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Accepted: 07/15/2019] [Indexed: 12/14/2022] Open
Abstract
In this work, the position of contemporary microbiology is considered from the perspective of scientific success, and a list of historical points and lessons learned from the fields of medical microbiology, microbial ecology and systems biology is presented. In addition, patterns in the development of top-down research topics that emerged over time as well as overlapping ideas and personnel, which are the first signs of trans-domain research activities in the fields of metagenomics, metaproteomics, metatranscriptomics and metabolomics, are explored through analysis of the publication networks of 28 654 papers using the computer programme Pajek. The current state of affairs is defined, and the need for meta-analyses to leverage publication biases in the field of microbiology is put forward as a very important emerging field of microbiology, especially since microbiology is progressively dealing with multi-scale systems. Consequently, the need for cross-fertilisation with other fields/disciplines instead of ‘more microbiology’ is needed to advance the field of microbiology as such. The reader is directed to consider how novel technologies, the introduction of big data approaches and artificial intelligence have transformed microbiology into a multi-scale field and initiated a shift away from its history of mostly manual work and towards a largely technology-, data- and statistics-driven discipline that is often coupled with automation and modelling.
Collapse
Affiliation(s)
- Blaž Stres
- Center for Clinical Neurophysiology, Faculty of Medicine, University of Ljubljana, Vrazov trg 2, SI-1000 Ljubljana, Slovenia.,Group for Microbiology and Microbial Biotechnology, Department of Animal Science, Biotechnical Faculty, University of Ljubljana, Jamnikarjeva 101, SI-1000 Ljubljana, Slovenia.,Institute of Sanitary Engineering, Faculty of Civil and Geodetic Engineering, University of Ljubljana, Hajdrihova 28, SI-1000 Ljubljana, Slovenia
| | - Luka Kronegger
- Faculty of Social Sciences, University of Ljubljana, Kardeljeva ploščad 5, SI-1000 Ljubljana, Slovenia
| |
Collapse
|
10
|
Stalidzans E, Zanin M, Tieri P, Castiglione F, Polster A, Scheiner S, Pahle J, Stres B, List M, Baumbach J, Lautizi M, Van Steen K, Schmidt HH. Mechanistic Modeling and Multiscale Applications for Precision Medicine: Theory and Practice. NETWORK AND SYSTEMS MEDICINE 2020. [DOI: 10.1089/nsm.2020.0002] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Affiliation(s)
- Egils Stalidzans
- Computational Systems Biology Group, University of Latvia, Riga, Latvia
- Latvian Biomedical Reasearch and Study Centre, Riga, Latvia
| | - Massimiliano Zanin
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, Spain
| | - Paolo Tieri
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy
| | - Filippo Castiglione
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy
| | | | - Stefan Scheiner
- Institute for Mechanics of Materials and Structures, Vienna University of Technology, Vienna, Austria
| | - Jürgen Pahle
- BioQuant, Heidelberg University, Heidelberg, Germany
| | - Blaž Stres
- Department of Animal Science, University of Ljubljana, Ljubljana, Slovenia
- Faculty of Civil and Geodetic Engineering, University of Ljubljana, Ljubljana, Slovenia
- Department of Automation, Biocybernetics and Robotics, Jozef Stefan Institute, Ljubljana, Slovenia
| | - Markus List
- Big Data in BioMedicine Research Group, Chair of Experimental Bioinformatics, TUM School of Weihenstephan, Technical University of Munich, Freising, Germany
| | - Jan Baumbach
- Chair of Experimental Bioinformatics, TUM School of Weihenstephan, Technical University of Munich, Freising, Germany
| | - Manuela Lautizi
- Computational Systems Medicine Research Group, Chair of Experimental Bioinformatics, TUM School of Weihenstephan, Technical University of Munich, Freising, Germany
| | - Kristel Van Steen
- BIO-Systems Genetics, GIGA-R, University of Liège, Liège, Belgium
- BIO3—Systems Medicine, Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Harald H.H.W. Schmidt
- Department of Pharmacology and Personalised Medicine, Faculty of Health, Medicine and Life Science, Maastricht University, Maastricht, The Netherlands
| |
Collapse
|
11
|
Silverman EK, Schmidt HHHW, Anastasiadou E, Altucci L, Angelini M, Badimon L, Balligand JL, Benincasa G, Capasso G, Conte F, Di Costanzo A, Farina L, Fiscon G, Gatto L, Gentili M, Loscalzo J, Marchese C, Napoli C, Paci P, Petti M, Quackenbush J, Tieri P, Viggiano D, Vilahur G, Glass K, Baumbach J. Molecular networks in Network Medicine: Development and applications. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2020; 12:e1489. [PMID: 32307915 DOI: 10.1002/wsbm.1489] [Citation(s) in RCA: 84] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2019] [Revised: 02/29/2020] [Accepted: 03/20/2020] [Indexed: 12/14/2022]
Abstract
Network Medicine applies network science approaches to investigate disease pathogenesis. Many different analytical methods have been used to infer relevant molecular networks, including protein-protein interaction networks, correlation-based networks, gene regulatory networks, and Bayesian networks. Network Medicine applies these integrated approaches to Omics Big Data (including genetics, epigenetics, transcriptomics, metabolomics, and proteomics) using computational biology tools and, thereby, has the potential to provide improvements in the diagnosis, prognosis, and treatment of complex diseases. We discuss briefly the types of molecular data that are used in molecular network analyses, survey the analytical methods for inferring molecular networks, and review efforts to validate and visualize molecular networks. Successful applications of molecular network analysis have been reported in pulmonary arterial hypertension, coronary heart disease, diabetes mellitus, chronic lung diseases, and drug development. Important knowledge gaps in Network Medicine include incompleteness of the molecular interactome, challenges in identifying key genes within genetic association regions, and limited applications to human diseases. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Translational, Genomic, and Systems Medicine > Translational Medicine Analytical and Computational Methods > Analytical Methods Analytical and Computational Methods > Computational Methods.
Collapse
Affiliation(s)
- Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Harald H H W Schmidt
- Department of Pharmacology and Personalized Medicine, School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Science, Maastricht University, Maastricht, The Netherlands
| | - Eleni Anastasiadou
- Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Lucia Altucci
- Department of Precision Medicine, University of Campania 'Luigi Vanvitelli', Naples, Italy
| | - Marco Angelini
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Lina Badimon
- Cardiovascular Program-ICCC, IR-Hospital de la Santa Creu i Sant Pau, CiberCV, IIB-Sant Pau, Autonomous University of Barcelona, Barcelona, Spain
| | - Jean-Luc Balligand
- Pole of Pharmacology and Therapeutics (FATH), Institute for Clinical and Experimental Research (IREC), UCLouvain, Brussels, Belgium
| | - Giuditta Benincasa
- Department of Advanced Clinical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Giovambattista Capasso
- Department of Translational Medical Sciences, University of Campania "L. Vanvitelli", Naples, Italy.,BIOGEM, Ariano Irpino, Italy
| | - Federica Conte
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
| | - Antonella Di Costanzo
- Department of Precision Medicine, University of Campania 'Luigi Vanvitelli', Naples, Italy
| | - Lorenzo Farina
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Giulia Fiscon
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
| | - Laurent Gatto
- de Duve Institute, Brussels, Belgium.,Institute for Experimental and Clinical Research (IREC), UCLouvain, Brussels, Belgium
| | - Michele Gentili
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Joseph Loscalzo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.,Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Cinzia Marchese
- Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Claudio Napoli
- Department of Advanced Clinical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Paola Paci
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Manuela Petti
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - John Quackenbush
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Paolo Tieri
- CNR National Research Council of Italy, IAC Institute for Applied Computing, Rome, Italy
| | - Davide Viggiano
- BIOGEM, Ariano Irpino, Italy.,Department of Medicine and Health Sciences, University of Molise, Campobasso, Italy
| | - Gemma Vilahur
- Cardiovascular Program-ICCC, IR-Hospital de la Santa Creu i Sant Pau, CiberCV, IIB-Sant Pau, Autonomous University of Barcelona, Barcelona, Spain
| | - Kimberly Glass
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Jan Baumbach
- Department of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Maximus-von-Imhof-Forum 3, Freising, Germany.,Institute of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| |
Collapse
|
12
|
Ferreira S, Fisher C, Furlong LI, Laplanche L, Park BK, Pin C, Saez-Rodriguez J, Trairatphisan P. Quantitative Systems Toxicology Modeling To Address Key Safety Questions in Drug Development: A Focus of the TransQST Consortium. Chem Res Toxicol 2020; 33:7-9. [PMID: 31909603 DOI: 10.1021/acs.chemrestox.9b00499] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Omics data have been increasingly generated with limited demonstrated value in drug safety assessment. The TransQST consortium was launched to use omics and other data in mechanistic-based quantitative systems toxicology (QST) models to evaluate their potential use in species translation.
Collapse
Affiliation(s)
- Sofia Ferreira
- Certara UK Limited , Simcyp Division, Level 2-Acero, 1 Concourse Way , Sheffield , S1 2BJ , United Kingdom
| | - Ciarán Fisher
- Certara UK Limited , Simcyp Division, Level 2-Acero, 1 Concourse Way , Sheffield , S1 2BJ , United Kingdom
| | - Laura I Furlong
- Research Programme on Biomedical Informatics (GRIB), IMIM (Hospital del Mar Medical Research Institute) , Pompeu Fabra University (UPF) , Dr. Aiguader 88 , 08003 Barcelona , Spain
| | - Loic Laplanche
- Drug Metabolism Pharmacokinetics and Bioanalysis , AbbVie Deutschland GmbH & Co. KG , D-67008 Ludwigshafen , Germany
| | - Brian Kevin Park
- MRC Centre for Drug Safety Science, Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine , University of Liverpool , Liverpool L69 3GE , United Kingdom
| | - Carmen Pin
- Clinical Pharmacology, ADME, and AI, Clinical Pharmacology and Safety Sciences, Biopharmaceuticals R&D , AstraZeneca , Cambridge CB2 0SL , United Kingdom
| | - Julio Saez-Rodriguez
- Faculty of Medicine, Institute of Computational Biomedicine , Heidelberg University , 69120 Heidelberg , Germany
| | - Panuwat Trairatphisan
- Drug Metabolism Pharmacokinetics and Bioanalysis , AbbVie Deutschland GmbH & Co. KG , D-67008 Ludwigshafen , Germany.,Faculty of Medicine, Institute of Computational Biomedicine , Heidelberg University , 69120 Heidelberg , Germany
| |
Collapse
|
13
|
Changes in inflammatory plasma proteins from patients with chronic pain associated with treatment in an interdisciplinary multimodal rehabilitation program – an explorative multivariate pilot study. Scand J Pain 2019; 20:125-138. [DOI: 10.1515/sjpain-2019-0088] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Accepted: 09/10/2019] [Indexed: 01/04/2023]
Abstract
Abstract
It has been suggested that alterations in inflammation molecules maintain chronic pain although little is known about how these factors influence homeostatic and inflammatory events in common chronic pain conditions. Nonpharmacological interventions might be associated with alterations in inflammation markers in blood. This study of patients with chronic pain investigates whether an interdisciplinary multimodal rehabilitation program (IMMRP) was associated with significant alterations in the plasma pattern of 68 cytokines/chemokines 1 year after rehabilitation and whether such changes were associated with clinical changes. Blood samples and self-reports of pain, psychological distress, and physical activity of 25 complex chronic pain patients were collected pre-IMMRP and at 12-month follow-up. Analyses of inflammatory proteins (cytokines/chemokines/growth factors) were performed directly in plasma using the multiplex immunoassay technology Meso Scale Discovery. This explorative pilot study found that 12 substances, mainly pro-inflammatory, decreased after IMMRP. In two other relatively small IMMRP studies, four of these proinflammatory markers were also associated with decreases. The pattern of cytokines/chemokines pre-IMMRP was associated with changes in psychological distress but not with pain or physical activity. The present study cannot impute cause and effect. These results together with the results of the two previous IMMRP studies suggest that there is a need for larger and more strictly controlled studies of IMMRP with respect to inflammatory markers in blood. Such studies need to consider responders/non-responders, additional therapies, involved pain mechanisms and diagnoses. This and the two other studies open up for developing biologically measurable outcomes from plasma. Such biomarkers will be an important tool for further development of IMMRP and possibly other treatments for patients w ith chronic pain.
Collapse
|