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Zitnik M, Li MM, Wells A, Glass K, Morselli Gysi D, Krishnan A, Murali TM, Radivojac P, Roy S, Baudot A, Bozdag S, Chen DZ, Cowen L, Devkota K, Gitter A, Gosline SJC, Gu P, Guzzi PH, Huang H, Jiang M, Kesimoglu ZN, Koyuturk M, Ma J, Pico AR, Pržulj N, Przytycka TM, Raphael BJ, Ritz A, Sharan R, Shen Y, Singh M, Slonim DK, Tong H, Yang XH, Yoon BJ, Yu H, Milenković T. Current and future directions in network biology. BIOINFORMATICS ADVANCES 2024; 4:vbae099. [PMID: 39143982 PMCID: PMC11321866 DOI: 10.1093/bioadv/vbae099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 05/31/2024] [Accepted: 07/08/2024] [Indexed: 08/16/2024]
Abstract
Summary Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology. Availability and implementation Not applicable.
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Affiliation(s)
- Marinka Zitnik
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, United States
| | - Michelle M Li
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, United States
| | - Aydin Wells
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, United States
- Lucy Family Institute for Data and Society, University of Notre Dame, Notre Dame, IN 46556, United States
- Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, United States
| | - Kimberly Glass
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Deisy Morselli Gysi
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, United States
- Department of Statistics, Federal University of Paraná, Curitiba, Paraná 81530-015, Brazil
- Department of Physics, Northeastern University, Boston, MA 02115, United States
| | - Arjun Krishnan
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, United States
| | - T M Murali
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, United States
| | - Predrag Radivojac
- Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, United States
| | - Sushmita Roy
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53715, United States
- Wisconsin Institute for Discovery, Madison, WI 53715, United States
| | - Anaïs Baudot
- Aix Marseille Université, INSERM, MMG, Marseille, France
| | - Serdar Bozdag
- Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, United States
- Department of Mathematics, University of North Texas, Denton, TX 76203, United States
| | - Danny Z Chen
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, United States
| | - Lenore Cowen
- Department of Computer Science, Tufts University, Medford, MA 02155, United States
| | - Kapil Devkota
- Department of Computer Science, Tufts University, Medford, MA 02155, United States
| | - Anthony Gitter
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53715, United States
- Morgridge Institute for Research, Madison, WI 53715, United States
| | - Sara J C Gosline
- Biological Sciences Division, Pacific Northwest National Laboratory, Seattle, WA 98109, United States
| | - Pengfei Gu
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, United States
| | - Pietro H Guzzi
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro, 88100, Italy
| | - Heng Huang
- Department of Computer Science, University of Maryland College Park, College Park, MD 20742, United States
| | - Meng Jiang
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, United States
| | - Ziynet Nesibe Kesimoglu
- Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, United States
- National Center of Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20814, United States
| | - Mehmet Koyuturk
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH 44106, United States
| | - Jian Ma
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, United States
| | - Alexander R Pico
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA 94158, United States
| | - Nataša Pržulj
- Department of Computer Science, University College London, London, WC1E 6BT, England
- ICREA, Catalan Institution for Research and Advanced Studies, Barcelona, 08010, Spain
- Barcelona Supercomputing Center (BSC), Barcelona, 08034, Spain
| | - Teresa M Przytycka
- National Center of Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20814, United States
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ 08544, United States
| | - Anna Ritz
- Department of Biology, Reed College, Portland, OR 97202, United States
| | - Roded Sharan
- School of Computer Science, Tel Aviv University, Tel Aviv, 69978, Israel
| | - Yang Shen
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, United States
| | - Mona Singh
- Department of Computer Science, Princeton University, Princeton, NJ 08544, United States
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, United States
| | - Donna K Slonim
- Department of Computer Science, Tufts University, Medford, MA 02155, United States
| | - Hanghang Tong
- Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
| | - Xinan Holly Yang
- Department of Pediatrics, University of Chicago, Chicago, IL 60637, United States
| | - Byung-Jun Yoon
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, United States
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973, United States
| | - Haiyuan Yu
- Department of Computational Biology, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, United States
| | - Tijana Milenković
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, United States
- Lucy Family Institute for Data and Society, University of Notre Dame, Notre Dame, IN 46556, United States
- Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, United States
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Lalli G, Li Z, Melograna F, Collier J, Moreau Y, Raimondi D, Van Steen K. A python library for the fast and scalable computation of biologically meaningful individual specific networks. Sci Rep 2024; 14:18243. [PMID: 39107347 PMCID: PMC11303555 DOI: 10.1038/s41598-024-69067-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 07/31/2024] [Indexed: 08/10/2024] Open
Abstract
Individual Specific Networks (ISNs) are a tool used in computational biology to infer Individual Specific relationships between biological entities from omics data. ISNs provide insights into how the interactions among these entities affect their respective functions. To address the scarcity of solutions for efficiently computing ISNs on large biological datasets, we present ISN-tractor, a data-agnostic, highly optimized Python library to build and analyse ISNs. ISN-tractor demonstrates superior scalability and efficiency in generating Individual Specific Networks (ISNs) when compared to existing methods such as LionessR, both in terms of time and memory usage, allowing ISNs to be used on large datasets. We show how ISN-tractor can be applied to real-life datasets, including The Cancer Genome Atlas (TCGA) and HapMap, showcasing its versatility. ISN-tractor can be used to build ISNs from various -omics data types, including transcriptomics, proteomics, and genotype arrays, and can detect distinct patterns of gene interactions within and across cancer types. We also show how Filtration Curves provided valuable insights into ISN characteristics, revealing topological distinctions among individuals with different clinical outcomes. Additionally, ISN-tractor can effectively cluster populations based on genetic relationships, as demonstrated with Principal Component Analysis on HapMap data.
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Affiliation(s)
- Giada Lalli
- BIO3 - Systems Medicine Lab, Department of Human Genetics, KU Leuven, Leuven, Belgium.
| | - Zuqi Li
- BIO3 - Systems Medicine Lab, Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Federico Melograna
- BIO3 - Systems Medicine Lab, Department of Human Genetics, KU Leuven, Leuven, Belgium
| | | | | | | | - Kristel Van Steen
- BIO3 - Systems Medicine Lab, Department of Human Genetics, KU Leuven, Leuven, Belgium
- BIO3 - Systems Genetics Lab, GIGA-R Medical Genomics, University of Liège, Liège, Belgium
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Benincasa G, Napoli C, DeMeo DL. Transgenerational Epigenetic Inheritance of Cardiovascular Diseases: A Network Medicine Perspective. Matern Child Health J 2024; 28:617-630. [PMID: 38409452 DOI: 10.1007/s10995-023-03886-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/19/2023] [Indexed: 02/28/2024]
Abstract
INTRODUCTION The ability to identify early epigenetic signatures underlying the inheritance of cardiovascular risk, including trans- and intergenerational effects, may help to stratify people before cardiac symptoms occur. METHODS Prospective and retrospective cohorts and case-control studies focusing on DNA methylation and maternal/paternal effects were searched in Pubmed from 1997 to 2023 by using the following keywords: DNA methylation, genomic imprinting, and network analysis in combination with transgenerational/intergenerational effects. RESULTS Maternal and paternal exposures to traditional cardiovascular risk factors during critical temporal windows, including the preconceptional period or early pregnancy, may perturb the plasticity of the epigenome (mainly DNA methylation) of the developing fetus especially at imprinted loci, such as the insulin-like growth factor type 2 (IGF2) gene. Thus, the epigenome is akin to a "molecular archive" able to memorize parental environmental insults and predispose an individual to cardiovascular diseases onset in later life. Direct evidence for human transgenerational epigenetic inheritance (at least three generations) of cardiovascular risk is lacking but it is supported by epidemiological studies. Several blood-based association studies showed potential intergenerational epigenetic effects (single-generation studies) which may mediate the transmittance of cardiovascular risk from parents to offspring. DISCUSSION In this narrative review, we discuss some relevant examples of trans- and intergenerational epigenetic associations with cardiovascular risk. In our perspective, we propose three network-oriented approaches which may help to clarify the unsolved issues regarding transgenerational epigenetic inheritance of cardiovascular risk and provide potential early biomarkers for primary prevention.
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Affiliation(s)
- Giuditta Benincasa
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", 80138, Naples, Italy
| | - Claudio Napoli
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", 80138, Naples, Italy.
| | - Dawn L DeMeo
- Channing Division of Network Medicine and the Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
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Welsh N, Disano K, Linzey M, Pike SC, Smith AD, Pachner AR, Gilli F. CXCL10/IgG1 Axis in Multiple Sclerosis as a Potential Predictive Biomarker of Disease Activity. NEUROLOGY(R) NEUROIMMUNOLOGY & NEUROINFLAMMATION 2024; 11:e200200. [PMID: 38346270 DOI: 10.1212/nxi.0000000000200200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 11/16/2023] [Indexed: 02/15/2024]
Abstract
BACKGROUND AND OBJECTIVES Multiple sclerosis (MS) is a heterogeneous disease, and its course is difficult to predict. Prediction models can be established by measuring intrathecally synthesized proteins involved in inflammation, glial activation, and CNS injury. METHODS To determine how these intrathecal proteins relate to the short-term, i.e., 12 months, disease activity in relapsing-remitting MS (RRMS), we measured the intrathecal synthesis of 46 inflammatory mediators and 14 CNS injury or glial activation markers in matched serum and CSF samples from 47 patients with MS (pwMS), i.e., 23 RRMS and 24 clinically isolated syndrome (CIS), undergoing diagnostic lumbar puncture. Subsequently, all pwMS were followed for ≥12 months in a retrospective follow-up study and ultimately classified into "active", i.e., developing clinical and/or radiologic disease activity, n = 18) or "nonactive", i.e., not having disease activity, n = 29. Disease activity in patients with CIS corresponded to conversion to RRMS. Thus, patients with CIS were subclassified as "converters" or "nonconverters" based on their conversion status at the end of a 12-month follow-up. Twenty-seven patients with noninflammatory neurologic diseases were included as negative controls. Data were subjected to differential expression analysis and modeling techniques to define the connectivity arrangement (network) between neuroinflammation and CNS injury relevant to short-term disease activity in RRMS. RESULTS Lower age and/or higher CXCL13 levels positively distinguished active/converting vs nonactive/nonconverting patients. Network analysis significantly improved the prediction of short-term disease activity because active/converting patients featured a stronger positive connection between IgG1 and CXCL10. Accordingly, analysis of disease activity-free survival demonstrated that pwMS, both RRMS and CIS, with a lower or negative IgG1-CXCL10 correlation, have a higher probability of activity-free survival than the patients with a significant correlation (p < 0.0001, HR ≥ 2.87). DISCUSSION Findings indicate that a significant IgG1-CXCL10 positive correlation predicts the risk of short-term disease activity in patients with RRMS and CIS. Thus, the present results can be used to develop a predictive model for MS activity and conversion to RRMS.
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Affiliation(s)
- Nora Welsh
- From the Integrative Neuroscience (N.W., M.L., S.C.P.), Dartmouth College, Hanover, NH; Neurology (N.W., K.D., S.C.P., A.D.S., A.R.P., F.G.), Dartmouth Hitchcock Medical Center, Lebanon, NH; and Veteran Affairs Medical Center (K.D.), White River Junction, VT
| | - Krista Disano
- From the Integrative Neuroscience (N.W., M.L., S.C.P.), Dartmouth College, Hanover, NH; Neurology (N.W., K.D., S.C.P., A.D.S., A.R.P., F.G.), Dartmouth Hitchcock Medical Center, Lebanon, NH; and Veteran Affairs Medical Center (K.D.), White River Junction, VT
| | - Michael Linzey
- From the Integrative Neuroscience (N.W., M.L., S.C.P.), Dartmouth College, Hanover, NH; Neurology (N.W., K.D., S.C.P., A.D.S., A.R.P., F.G.), Dartmouth Hitchcock Medical Center, Lebanon, NH; and Veteran Affairs Medical Center (K.D.), White River Junction, VT
| | - Steven C Pike
- From the Integrative Neuroscience (N.W., M.L., S.C.P.), Dartmouth College, Hanover, NH; Neurology (N.W., K.D., S.C.P., A.D.S., A.R.P., F.G.), Dartmouth Hitchcock Medical Center, Lebanon, NH; and Veteran Affairs Medical Center (K.D.), White River Junction, VT
| | - Andrew D Smith
- From the Integrative Neuroscience (N.W., M.L., S.C.P.), Dartmouth College, Hanover, NH; Neurology (N.W., K.D., S.C.P., A.D.S., A.R.P., F.G.), Dartmouth Hitchcock Medical Center, Lebanon, NH; and Veteran Affairs Medical Center (K.D.), White River Junction, VT
| | - Andrew R Pachner
- From the Integrative Neuroscience (N.W., M.L., S.C.P.), Dartmouth College, Hanover, NH; Neurology (N.W., K.D., S.C.P., A.D.S., A.R.P., F.G.), Dartmouth Hitchcock Medical Center, Lebanon, NH; and Veteran Affairs Medical Center (K.D.), White River Junction, VT
| | - Francesca Gilli
- From the Integrative Neuroscience (N.W., M.L., S.C.P.), Dartmouth College, Hanover, NH; Neurology (N.W., K.D., S.C.P., A.D.S., A.R.P., F.G.), Dartmouth Hitchcock Medical Center, Lebanon, NH; and Veteran Affairs Medical Center (K.D.), White River Junction, VT
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Clift CL, Blaser MC, Gerrits W, Turner ME, Sonawane A, Pham T, Andresen JL, Fenton OS, Grolman JM, Campedelli A, Buffolo F, Schoen FJ, Hjortnaes J, Muehlschlegel JD, Mooney DJ, Aikawa M, Singh SA, Langer R, Aikawa E. Intracellular proteomics and extracellular vesiculomics as a metric of disease recapitulation in 3D-bioprinted aortic valve arrays. SCIENCE ADVANCES 2024; 10:eadj9793. [PMID: 38416823 PMCID: PMC10901368 DOI: 10.1126/sciadv.adj9793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 01/25/2024] [Indexed: 03/01/2024]
Abstract
In calcific aortic valve disease (CAVD), mechanosensitive valvular cells respond to fibrosis- and calcification-induced tissue stiffening, further driving pathophysiology. No pharmacotherapeutics are available to treat CAVD because of the paucity of (i) appropriate experimental models that recapitulate this complex environment and (ii) benchmarking novel engineered aortic valve (AV)-model performance. We established a biomaterial-based CAVD model mimicking the biomechanics of the human AV disease-prone fibrosa layer, three-dimensional (3D)-bioprinted into 96-well arrays. Liquid chromatography-tandem mass spectrometry analyses probed the cellular proteome and vesiculome to compare the 3D-bioprinted model versus traditional 2D monoculture, against human CAVD tissue. The 3D-bioprinted model highly recapitulated the CAVD cellular proteome (94% versus 70% of 2D proteins). Integration of cellular and vesicular datasets identified known and unknown proteins ubiquitous to AV calcification. This study explores how 2D versus 3D-bioengineered systems recapitulate unique aspects of human disease, positions multiomics as a technique for the evaluation of high throughput-based bioengineered model systems, and potentiates future drug discovery.
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Affiliation(s)
- Cassandra L Clift
- Division of Cardiovascular Medicine, Department of Medicine, Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Mark C Blaser
- Division of Cardiovascular Medicine, Department of Medicine, Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Willem Gerrits
- Division of Cardiovascular Medicine, Department of Medicine, Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
- Department of Cardiothoracic Surgery, University Medical Center Utrecht, Utrecht, Netherlands
| | - Mandy E Turner
- Division of Cardiovascular Medicine, Department of Medicine, Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Abhijeet Sonawane
- Division of Cardiovascular Medicine, Department of Medicine, Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Tan Pham
- Division of Cardiovascular Medicine, Department of Medicine, Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Jason L Andresen
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Owen S Fenton
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Division of Pharmacoengineering and Molecular Pharmaceutics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Joshua M Grolman
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02134, USA
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA 02115, USA
- Materials Science and Engineering, The Technion-Israel Institute of Technology, Haifa, Israel
| | - Alesandra Campedelli
- Division of Cardiovascular Medicine, Department of Medicine, Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Fabrizio Buffolo
- Division of Cardiovascular Medicine, Department of Medicine, Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
- Division of Internal Medicine and Hypertension Unite, Department of Medical Sciences, University of Torin, Turin, Italy
| | - Frederick J Schoen
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Jesper Hjortnaes
- Department of Cardiothoracic Surgery, Leiden University Medical Center (LUMC), Leiden, Netherlands
| | - Jochen D Muehlschlegel
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - David J Mooney
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02134, USA
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA 02115, USA
| | - Masanori Aikawa
- Division of Cardiovascular Medicine, Department of Medicine, Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
- Division of Cardiovascular Medicine, Department of Medicine, Center for Excellence in Vascular Biology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Sasha A Singh
- Division of Cardiovascular Medicine, Department of Medicine, Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Robert Langer
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Harvard and MIT Division of Health Science and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Elena Aikawa
- Division of Cardiovascular Medicine, Department of Medicine, Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
- Division of Cardiovascular Medicine, Department of Medicine, Center for Excellence in Vascular Biology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
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Duroux D, Wohlfart C, Van Steen K, Vladimirova A, King M. Graph-based multi-modality integration for prediction of cancer subtype and severity. Sci Rep 2023; 13:19653. [PMID: 37949935 PMCID: PMC10638406 DOI: 10.1038/s41598-023-46392-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 10/31/2023] [Indexed: 11/12/2023] Open
Abstract
Personalised cancer screening before therapy paves the way toward improving diagnostic accuracy and treatment outcomes. Most approaches are limited to a single data type and do not consider interactions between features, leaving aside the complementary insights that multimodality and systems biology can provide. In this project, we demonstrate the use of graph theory for data integration via individual networks where nodes and edges are individual-specific. We showcase the consequences of early, intermediate, and late graph-based fusion of RNA-Seq data and histopathology whole-slide images for predicting cancer subtypes and severity. The methodology developed is as follows: (1) we create individual networks; (2) we compute the similarity between individuals from these graphs; (3) we train our model on the similarity matrices; (4) we evaluate the performance using the macro F1 score. Pros and cons of elements of the pipeline are evaluated on publicly available real-life datasets. We find that graph-based methods can increase performance over methods that do not study interactions. Additionally, merging multiple data sources often improves classification compared to models based on single data, especially through intermediate fusion. The proposed workflow can easily be adapted to other disease contexts to accelerate and enhance personalized healthcare.
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Affiliation(s)
- Diane Duroux
- BIO3 - Systems Genetics, GIGA-R Medical Genomics, University of Liège, 4000, Liège, Belgium.
- Post-Doctoral Fellow, ETH AI center, Zürich, Switzerland.
| | | | - Kristel Van Steen
- BIO3 - Systems Genetics, GIGA-R Medical Genomics, University of Liège, 4000, Liège, Belgium
- Department of Human Genetics, BIO3 - Systems Medicine, 3000, Leuven, Belgium
| | - Antoaneta Vladimirova
- Roche Information Solutions, Roche Diagnostics Corporation, Santa Clara, California, United States of America
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Yousefi B, Firoozbakht F, Melograna F, Schwikowski B, Van Steen K. PLEX.I: a tool to discover features in multiplex networks that reflect clinical variation. Front Genet 2023; 14:1274637. [PMID: 37928248 PMCID: PMC10620964 DOI: 10.3389/fgene.2023.1274637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 10/09/2023] [Indexed: 11/07/2023] Open
Abstract
Molecular profiling technologies, such as RNA sequencing, offer new opportunities to better discover and understand the molecular networks involved in complex biological processes. Clinically important variations of diseases, or responses to treatment, are often reflected, or even caused, by the dysregulation of molecular interaction networks specific to particular network regions. In this work, we propose the R package PLEX.I, that allows quantifying and testing variation in the direct neighborhood of a given node between networks corresponding to different conditions or states. We illustrate PLEX.I in two applications in which we discover variation that is associated with different responses to tamoxifen treatment and to sex-specific responses to bacterial stimuli. In the first case, PLEX.I analysis identifies two known pathways i) that have already been implicated in the same context as the tamoxifen mechanism of action, and ii) that would have not have been identified using classical differential gene expression analysis.
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Affiliation(s)
- Behnam Yousefi
- Computational Systems Biomedicine Lab, Institut Pasteur, Université Paris Cité, Paris, France
- École Doctorale Complexite du Vivant, Sorbonne Université, Paris, France
- BIO3—Laboratory for Systems Medicine, KU Leuven, Leuven, Belgium
| | - Farzaneh Firoozbakht
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | | | - Benno Schwikowski
- Computational Systems Biomedicine Lab, Institut Pasteur, Université Paris Cité, Paris, France
| | - Kristel Van Steen
- BIO3—Laboratory for Systems Medicine, KU Leuven, Leuven, Belgium
- BIO3—Laboratory for Systems Genetics, GIGA-R Medical Genomics, University of Liège, Liège, Belgium
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Hsieh PH, Lopes-Ramos CM, Zucknick M, Sandve GK, Glass K, Kuijjer ML. Adjustment of spurious correlations in co-expression measurements from RNA-Sequencing data. Bioinformatics 2023; 39:btad610. [PMID: 37802917 PMCID: PMC10598588 DOI: 10.1093/bioinformatics/btad610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 08/05/2023] [Accepted: 10/05/2023] [Indexed: 10/08/2023] Open
Abstract
MOTIVATION Gene co-expression measurements are widely used in computational biology to identify coordinated expression patterns across a group of samples. Coordinated expression of genes may indicate that they are controlled by the same transcriptional regulatory program, or involved in common biological processes. Gene co-expression is generally estimated from RNA-Sequencing data, which are commonly normalized to remove technical variability. Here, we demonstrate that certain normalization methods, in particular quantile-based methods, can introduce false-positive associations between genes. These false-positive associations can consequently hamper downstream co-expression network analysis. Quantile-based normalization can, however, be extremely powerful. In particular, when preprocessing large-scale heterogeneous data, quantile-based normalization methods such as smooth quantile normalization can be applied to remove technical variability while maintaining global differences in expression for samples with different biological attributes. RESULTS We developed SNAIL (Smooth-quantile Normalization Adaptation for the Inference of co-expression Links), a normalization method based on smooth quantile normalization specifically designed for modeling of co-expression measurements. We show that SNAIL avoids formation of false-positive associations in co-expression as well as in downstream network analyses. Using SNAIL, one can avoid arbitrary gene filtering and retain associations to genes that only express in small subgroups of samples. This highlights the method's potential future impact on network modeling and other association-based approaches in large-scale heterogeneous data. AVAILABILITY AND IMPLEMENTATION The implementation of the SNAIL algorithm and code to reproduce the analyses described in this work can be found in the GitHub repository https://github.com/kuijjerlab/PySNAIL.
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Affiliation(s)
- Ping-Han Hsieh
- Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, Oslo 0318, Norway
- Department of Informatics, University of Oslo, Oslo 0316, Norway
| | - Camila Miranda Lopes-Ramos
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, United States
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA 02115, United States
| | - Manuela Zucknick
- Oslo Centre for Biostatistics and Epidemiology, Institute of Basic Medical Sciences, University of Oslo, Oslo 0317, Norway
| | | | - Kimberly Glass
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, United States
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA 02115, United States
| | - Marieke Lydia Kuijjer
- Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, Oslo 0318, Norway
- Department of Pathology, Leiden University Medical Center, Leiden 2300RC, The Netherlands
- Leiden Center of Computational Oncology, Leiden University Medical Center,Leiden 2300RC, The Netherlands
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9
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Yousefi B, Melograna F, Galazzo G, van Best N, Mommers M, Penders J, Schwikowski B, Van Steen K. Capturing the dynamics of microbial interactions through individual-specific networks. Front Microbiol 2023; 14:1170391. [PMID: 37256048 PMCID: PMC10225591 DOI: 10.3389/fmicb.2023.1170391] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 04/21/2023] [Indexed: 06/01/2023] Open
Abstract
Longitudinal analysis of multivariate individual-specific microbiome profiles over time or across conditions remains dauntin. Most statistical tools and methods that are available to study microbiomes are based on cross-sectional data. Over the past few years, several attempts have been made to model the dynamics of bacterial species over time or across conditions. However, the field needs novel views on handling microbial interactions in temporal analyses. This study proposes a novel data analysis framework, MNDA, that combines representation learning and individual-specific microbial co-occurrence networks to uncover taxon neighborhood dynamics. As a use case, we consider a cohort of newborns with microbiomes available at 6 and 9 months after birth, and extraneous data available on the mode of delivery and diet changes between the considered time points. Our results show that prediction models for these extraneous outcomes based on an MNDA measure of local neighborhood dynamics for each taxon outperform traditional prediction models solely based on individual-specific microbial abundances. Furthermore, our results show that unsupervised similarity analysis of newborns in the study, again using the notion of a taxon's dynamic neighborhood derived from time-matched individual-specific microbial networks, can reveal different subpopulations of individuals, compared to standard microbiome-based clustering, with potential relevance to clinical practice. This study highlights the complementarity of microbial interactions and abundances in downstream analyses and opens new avenues to personalized prediction or stratified medicine with temporal microbiome data.
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Affiliation(s)
- Behnam Yousefi
- Computational Systems Biomedicine Lab, Institut Pasteur, University Paris City, Paris, France
- École Doctorale Complexite du vivant, Sorbonne University, Paris, France
- BIO3—Laboratory for Systems Medicine, Department of Human Genetics, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Federico Melograna
- BIO3—Laboratory for Systems Medicine, Department of Human Genetics, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Gianluca Galazzo
- Department of Medical Microbiology, Infectious Diseases and Infection Prevention, School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, Maastricht, Netherlands
| | - Niels van Best
- Department of Medical Microbiology, Infectious Diseases and Infection Prevention, School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, Maastricht, Netherlands
- Institute of Medical Microbiology, Rhine-Westphalia Technical University of Aachen, RWTH University, Aachen, Germany
| | - Monique Mommers
- Department of Epidemiology, Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, Netherlands
| | - John Penders
- Department of Medical Microbiology, Infectious Diseases and Infection Prevention, School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, Maastricht, Netherlands
- Department of Medical Microbiology, Infectious Diseases and Infection Prevention, Care and Public Health Research Institute (CAPHRI), Maastricht University Medical Center+, Maastricht, Netherlands
| | - Benno Schwikowski
- Computational Systems Biomedicine Lab, Institut Pasteur, University Paris City, Paris, France
| | - Kristel Van Steen
- BIO3—Laboratory for Systems Medicine, Department of Human Genetics, Katholieke Universiteit Leuven, Leuven, Belgium
- BIO3—Laboratory for Systems Genetics, GIGA-R Medical Genomics, University of Lièvzge, Liège, Belgium
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10
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Melograna F, Li Z, Galazzo G, van Best N, Mommers M, Penders J, Stella F, Van Steen K. Edge and modular significance assessment in individual-specific networks. Sci Rep 2023; 13:7868. [PMID: 37188794 PMCID: PMC10185658 DOI: 10.1038/s41598-023-34759-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 05/07/2023] [Indexed: 05/17/2023] Open
Abstract
Individual-specific networks, defined as networks of nodes and connecting edges that are specific to an individual, are promising tools for precision medicine. When such networks are biological, interpretation of functional modules at an individual level becomes possible. An under-investigated problem is relevance or "significance" assessment of each individual-specific network. This paper proposes novel edge and module significance assessment procedures for weighted and unweighted individual-specific networks. Specifically, we propose a modular Cook's distance using a method that involves iterative modeling of one edge versus all the others within a module. Two procedures assessing changes between using all individuals and using all individuals but leaving one individual out (LOO) are proposed as well (LOO-ISN, MultiLOO-ISN), relying on empirically derived edges. We compare our proposals to competitors, including adaptions of OPTICS, kNN, and Spoutlier methods, by an extensive simulation study, templated on real-life scenarios for gene co-expression and microbial interaction networks. Results show the advantages of performing modular versus edge-wise significance assessments for individual-specific networks. Furthermore, modular Cook's distance is among the top performers across all considered simulation settings. Finally, the identification of outlying individuals regarding their individual-specific networks, is meaningful for precision medicine purposes, as confirmed by network analysis of microbiome abundance profiles.
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Affiliation(s)
- Federico Melograna
- BIO3 - Laboratory for Systems Medicine, Department of Human Genetics, KU Leuven, Leuven, Belgium.
| | - Zuqi Li
- BIO3 - Laboratory for Systems Medicine, Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Gianluca Galazzo
- School of Nutrition and Translational Research in Metabolism (NUTRIM), Department of Medical Microbiology Infectious Diseases and Infection Prevention, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Niels van Best
- Institute of Medical Microbiology, RWTH University Hospital Aachen, RWTH University, Aachen, Germany
- Department of Epidemiology, Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, The Netherlands
| | - Monique Mommers
- Department of Epidemiology, Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, The Netherlands
| | - John Penders
- School of Nutrition and Translational Research in Metabolism (NUTRIM), Department of Medical Microbiology Infectious Diseases and Infection Prevention, Maastricht University Medical Center+, Maastricht, The Netherlands
- Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, The Netherlands
| | - Fabio Stella
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, 20126, Milan, Italy
| | - Kristel Van Steen
- BIO3 - Laboratory for Systems Medicine, Department of Human Genetics, KU Leuven, Leuven, Belgium
- BIO3 - Laboratory for Systems Genetics, GIGA-R Medical Genomics, University of Liège, Liège, Belgium
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11
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Ben Guebila M, Wang T, Lopes-Ramos CM, Fanfani V, Weighill D, Burkholz R, Schlauch D, Paulson JN, Altenbuchinger M, Shutta KH, Sonawane AR, Lim J, Calderer G, van IJzendoorn DGP, Morgan D, Marin A, Chen CY, Song Q, Saha E, DeMeo DL, Padi M, Platig J, Kuijjer ML, Glass K, Quackenbush J. The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks. Genome Biol 2023; 24:45. [PMID: 36894939 PMCID: PMC9999668 DOI: 10.1186/s13059-023-02877-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 02/15/2023] [Indexed: 03/11/2023] Open
Abstract
Inference and analysis of gene regulatory networks (GRNs) require software that integrates multi-omic data from various sources. The Network Zoo (netZoo; netzoo.github.io) is a collection of open-source methods to infer GRNs, conduct differential network analyses, estimate community structure, and explore the transitions between biological states. The netZoo builds on our ongoing development of network methods, harmonizing the implementations in various computing languages and between methods to allow better integration of these tools into analytical pipelines. We demonstrate the utility using multi-omic data from the Cancer Cell Line Encyclopedia. We will continue to expand the netZoo to incorporate additional methods.
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Affiliation(s)
- Marouen Ben Guebila
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Tian Wang
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Present Address: Biology Department, Boston College, Chestnut Hill, MA, USA
| | - Camila M Lopes-Ramos
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Viola Fanfani
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Des Weighill
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Present Address: Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Rebekka Burkholz
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Present Address: CISPA Helmholtz Center for Information Security, Saarbrücken, Germany
| | - Daniel Schlauch
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Present Address: Genospace, LLC, Boston, MA, USA
| | - Joseph N Paulson
- Department of Biochemistry and Molecular Biology, Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Michael Altenbuchinger
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Present Address: Department of Medical Bioinformatics, University Medical Center Göttingen, Göttingen, Germany
| | - Katherine H Shutta
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Abhijeet R Sonawane
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Present Address: Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - James Lim
- Department of Molecular and Cellular Biology, University of Arizona, Tucson, AZ, USA
- Present Address: Monoceros Biosystems, LLC, San Diego, CA, USA
| | - Genis Calderer
- Center for Molecular Medicine Norway, Nordic EMBL Partnership, University of Oslo, Oslo, Norway
| | - David G P van IJzendoorn
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
- Present Address: Department of Pathology, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Daniel Morgan
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Present Address: School of Biomedical Sciences, Hong Kong University, Pokfulam, Hong Kong
| | | | - Cho-Yi Chen
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Dana-Farber Cancer Institute, Boston, MA, USA
- Present Address: Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan
| | - Qi Song
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Present Address: Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Enakshi Saha
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Dawn L DeMeo
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Megha Padi
- Department of Molecular and Cellular Biology, University of Arizona, Tucson, AZ, USA
| | - John Platig
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Marieke L Kuijjer
- Center for Molecular Medicine Norway, Nordic EMBL Partnership, University of Oslo, Oslo, Norway
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
- Leiden Center for Computational Oncology, Leiden University, Leiden, The Netherlands
| | - Kimberly Glass
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - John Quackenbush
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Dana-Farber Cancer Institute, Boston, MA, USA.
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12
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Keyl P, Bischoff P, Dernbach G, Bockmayr M, Fritz R, Horst D, Blüthgen N, Montavon G, Müller KR, Klauschen F. Single-cell gene regulatory network prediction by explainable AI. Nucleic Acids Res 2023; 51:e20. [PMID: 36629274 PMCID: PMC9976884 DOI: 10.1093/nar/gkac1212] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 11/16/2022] [Accepted: 12/06/2022] [Indexed: 01/12/2023] Open
Abstract
The molecular heterogeneity of cancer cells contributes to the often partial response to targeted therapies and relapse of disease due to the escape of resistant cell populations. While single-cell sequencing has started to improve our understanding of this heterogeneity, it offers a mostly descriptive view on cellular types and states. To obtain more functional insights, we propose scGeneRAI, an explainable deep learning approach that uses layer-wise relevance propagation (LRP) to infer gene regulatory networks from static single-cell RNA sequencing data for individual cells. We benchmark our method with synthetic data and apply it to single-cell RNA sequencing data of a cohort of human lung cancers. From the predicted single-cell networks our approach reveals characteristic network patterns for tumor cells and normal epithelial cells and identifies subnetworks that are observed only in (subgroups of) tumor cells of certain patients. While current state-of-the-art methods are limited by their ability to only predict average networks for cell populations, our approach facilitates the reconstruction of networks down to the level of single cells which can be utilized to characterize the heterogeneity of gene regulation within and across tumors.
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Affiliation(s)
- Philipp Keyl
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Philip Bischoff
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Berlin, Charitéplatz 1, 10117 Berlin, Germany
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Anna-Louisa-Karsch-Straße 2, 10178 Berlin, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Berlin partner site, Germany
| | - Gabriel Dernbach
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Berlin, Charitéplatz 1, 10117 Berlin, Germany
- BIFOLD – Berlin Institute for the Foundations of Learning and Data, Berlin, Germany
| | - Michael Bockmayr
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Berlin, Charitéplatz 1, 10117 Berlin, Germany
- Department of Pediatric Hematology and Oncolog, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246 Hamburg, Germany
- Mildred Scheel Cancer Career Center HaTriCS4, University Medical Center Hamburg-Eppendorf Martinistr. 52, 20246 Hamburg, Germany
| | - Rebecca Fritz
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - David Horst
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Berlin, Charitéplatz 1, 10117 Berlin, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Berlin partner site, Germany
| | - Nils Blüthgen
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Berlin, Charitéplatz 1, 10117 Berlin, Germany
- Institut für Biologie, Humboldt University, Free University of Berlin, Unter den Linden 6, 10099 Berlin, Germany
| | - Grégoire Montavon
- BIFOLD – Berlin Institute for the Foundations of Learning and Data, Berlin, Germany
- Machine Learning Group, Technical University of Berlin, Marchstr. 23, 10587 Berlin, Germany
| | - Klaus-Robert Müller
- BIFOLD – Berlin Institute for the Foundations of Learning and Data, Berlin, Germany
- Machine Learning Group, Technical University of Berlin, Marchstr. 23, 10587 Berlin, Germany
- Department of Artificial Intelligence, Korea University, Seoul 136-713, South Korea
- Max-Planck-Institute for Informatics, Stuhlsatzenhausweg 4, 66123 Saarbrücken, Germany
| | - Frederick Klauschen
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Berlin, Charitéplatz 1, 10117 Berlin, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Berlin partner site, Germany
- BIFOLD – Berlin Institute for the Foundations of Learning and Data, Berlin, Germany
- Institute of Pathology, Ludwig-Maximilians-University Munich, Thalkirchner Str. 36, 80337 München, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Munich partner site, Germany
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13
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Yao S, Campbell PT, Ugai T, Gierach G, Abubakar M, Adalsteinsson V, Almeida J, Brennan P, Chanock S, Golub T, Hanash S, Harris C, Hathaway CA, Kelsey K, Landi MT, Mahmood F, Newton C, Quackenbush J, Rodig S, Schultz N, Tearney G, Tworoger SS, Wang M, Zhang X, Garcia-Closas M, Rebbeck TR, Ambrosone CB, Ogino S. Proceedings of the fifth international Molecular Pathological Epidemiology (MPE) meeting. Cancer Causes Control 2022; 33:1107-1120. [PMID: 35759080 PMCID: PMC9244289 DOI: 10.1007/s10552-022-01594-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 05/20/2022] [Indexed: 01/19/2023]
Abstract
Cancer heterogeneities hold the key to a deeper understanding of cancer etiology and progression and the discovery of more precise cancer therapy. Modern pathological and molecular technologies offer a powerful set of tools to profile tumor heterogeneities at multiple levels in large patient populations, from DNA to RNA, protein and epigenetics, and from tumor tissues to tumor microenvironment and liquid biopsy. When coupled with well-validated epidemiologic methodology and well-characterized epidemiologic resources, the rich tumor pathological and molecular tumor information provide new research opportunities at an unprecedented breadth and depth. This is the research space where Molecular Pathological Epidemiology (MPE) emerged over a decade ago and has been thriving since then. As a truly multidisciplinary field, MPE embraces collaborations from diverse fields including epidemiology, pathology, immunology, genetics, biostatistics, bioinformatics, and data science. Since first convened in 2013, the International MPE Meeting series has grown into a dynamic and dedicated platform for experts from these disciplines to communicate novel findings, discuss new research opportunities and challenges, build professional networks, and educate the next-generation scientists. Herein, we share the proceedings of the Fifth International MPE meeting, held virtually online, on May 24 and 25, 2021. The meeting consisted of 21 presentations organized into the three main themes, which were recent integrative MPE studies, novel cancer profiling technologies, and new statistical and data science approaches. Looking forward to the near future, the meeting attendees anticipated continuous expansion and fruition of MPE research in many research fronts, particularly immune-epidemiology, mutational signatures, liquid biopsy, and health disparities.
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Affiliation(s)
- Song Yao
- Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Elm & Carlton Streets, Buffalo, NY, 14263, USA.
| | - Peter T Campbell
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Tomotaka Ugai
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Gretchen Gierach
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Mustapha Abubakar
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | | | - Jonas Almeida
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Paul Brennan
- International Agency for Research On Cancer (IARC/WHO), Genomic Epidemiology Branch, Lyon, France
| | - Stephen Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Todd Golub
- Broad Institute of MIT and Harvard, Boston, MA, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Samir Hanash
- Department of Clinical Cancer Prevention, MD Anderson Cancer Institute, Houston, TX, USA
| | - Curtis Harris
- Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Cassandra A Hathaway
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Karl Kelsey
- Department of Epidemiology, Brown School of Public Health, Brown University, Providence, RI, USA
| | - Maria Teresa Landi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Faisal Mahmood
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Christina Newton
- Department of Population Science, American Cancer Society, Atlanta, GA, USA
| | - John Quackenbush
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Scott Rodig
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Nikolaus Schultz
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Guillermo Tearney
- Department of Pathology and Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, MA, USA
| | - Shelley S Tworoger
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Molin Wang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Xuehong Zhang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | | | - Timothy R Rebbeck
- Zhu Family Center for Global Cancer Prevention, Harvard T.H. Chan School of Public Health and Dana-Farber Cancer Institute, Boston, MA, USA
| | - Christine B Ambrosone
- Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Elm & Carlton Streets, Buffalo, NY, 14263, USA
| | - Shuji Ogino
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Boston, MA, USA.
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
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14
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Altered Transcriptional Regulation of Glycolysis in Circulating CD8+ T Cells of Rheumatoid Arthritis Patients. Genes (Basel) 2022; 13:genes13071216. [PMID: 35886000 PMCID: PMC9323564 DOI: 10.3390/genes13071216] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 07/01/2022] [Accepted: 07/04/2022] [Indexed: 12/04/2022] Open
Abstract
Peripheral T lymphocytes of rheumatoid arthritis (RA) patients show pathological changes in their metabolic pathways, especially glycolysis. These changes may drive the increased proliferation and tissue invasiveness of RA T cells. In order to study the transcriptional regulation underlying these alterations, we analysed publicly available RNA sequencing data from circulating T lymphocyte subsets of healthy individuals, untreated RA patients, and patients undergoing treatment for RA. Differential co-expression networks were created using sample-wise edge weights from an analysis called “linear interpolation to obtain network estimates for single sample” (lionessR), and annotated using the Gene Transcription Regulation Database (GTRD). Genes with high centrality scores were identified. CD8+ effector memory cells (Tem) and CD8+CD45RA+ effector memory cells (Temra) showed large changes in the transcriptional regulation of glycolysis in untreated RA. PFKFB3 and GAPDH were differentially regulated and had high centrality scores in CD8+ Tem cells. PFKFB3 downregulation may be due to HIF1A post transcriptional inhibition. Tocilizumab treatment partially reversed the RA-associated differential expression of several metabolic and regulatory genes. MYC was upregulated and had high centrality scores in RA CD8+ Temra cells; however, its glycolysis targets were unaltered. The upregulation of the PI3K-AKT and mTOR pathways may explain MYC upregulation.
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15
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Patient-level proteomic network prediction by explainable artificial intelligence. NPJ Precis Oncol 2022; 6:35. [PMID: 35672443 PMCID: PMC9174200 DOI: 10.1038/s41698-022-00278-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 04/15/2022] [Indexed: 11/08/2022] Open
Abstract
Understanding the pathological properties of dysregulated protein networks in individual patients’ tumors is the basis for precision therapy. Functional experiments are commonly used, but cover only parts of the oncogenic signaling networks, whereas methods that reconstruct networks from omics data usually only predict average network features across tumors. Here, we show that the explainable AI method layer-wise relevance propagation (LRP) can infer protein interaction networks for individual patients from proteomic profiling data. LRP reconstructs average and individual interaction networks with an AUC of 0.99 and 0.93, respectively, and outperforms state-of-the-art network prediction methods for individual tumors. Using data from The Cancer Proteome Atlas, we identify known and potentially novel oncogenic network features, among which some are cancer-type specific and show only minor variation among patients, while others are present across certain tumor types but differ among individual patients. Our approach may therefore support predictive diagnostics in precision oncology by inferring “patient-level” oncogenic mechanisms.
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16
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Singh A, Anderssen E, Fenton CG, Paulssen RH. Identifying anti-TNF response biomarkers in ulcerative colitis using a diffusion-based signalling model. BIOINFORMATICS ADVANCES 2021; 1:vbab017. [PMID: 36700114 PMCID: PMC9710619 DOI: 10.1093/bioadv/vbab017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 07/21/2021] [Indexed: 01/28/2023]
Abstract
Motivation Resistance to anti-TNF therapy in subgroups of ulcerative colitis (UC) patients is a major challenge and incurs significant treatment costs. Identification of patients at risk of nonresponse to anti-TNF is of major clinical importance. To date, no quantitative computational framework exists to develop a complex biomarker for the prognosis of UC treatment. Modelling patient-wise receptor to transcription factor (TF) network connectivity may enable personalized treatment. Results We present an approach for quantitative diffusion analysis between receptors and TFs using gene expression data. Key TFs were identified using pandaR. Network connectivities between immune-specific receptor-TF pairs were quantified using network diffusion in UC patients and controls. The patient-specific network could be considered a complex biomarker that separates anti-TNF treatment-resistant and responder patients both in the gene expression dataset used for model development and separate independent test datasets. The model was further validated in rheumatoid arthritis where it successfully discriminated resistant and responder patients to tocilizumab treatment. Our model may contribute to prognostic biomarkers that may identify treatment-resistant and responder subpopulations of UC patients. Availability and implementation Software is available at https://github.com/Amy3100/receptor2tfDiffusion. Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
- Amrinder Singh
- Clinical Bioinformatics Research Group, Department of Clinical Medicine, UiT-The Arctic University of Norway, Tromsø N-9037, Norway
| | - Endre Anderssen
- Genomics Support Centre Tromsø (GSCT), Department of Clinical Medicine, UiT-The Arctic University of Norway, Tromsø N-9037, Norway
| | - Christopher G Fenton
- Genomics Support Centre Tromsø (GSCT), Department of Clinical Medicine, UiT-The Arctic University of Norway, Tromsø N-9037, Norway
| | - Ruth H Paulssen
- Clinical Bioinformatics Research Group, Department of Clinical Medicine, UiT-The Arctic University of Norway, Tromsø N-9037, Norway
- Genomics Support Centre Tromsø (GSCT), Department of Clinical Medicine, UiT-The Arctic University of Norway, Tromsø N-9037, Norway
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17
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Ovens K, Eames BF, McQuillan I. Comparative Analyses of Gene Co-expression Networks: Implementations and Applications in the Study of Evolution. Front Genet 2021; 12:695399. [PMID: 34484293 PMCID: PMC8414652 DOI: 10.3389/fgene.2021.695399] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 07/19/2021] [Indexed: 11/13/2022] Open
Abstract
Similarities and differences in the associations of biological entities among species can provide us with a better understanding of evolutionary relationships. Often the evolution of new phenotypes results from changes to interactions in pre-existing biological networks and comparing networks across species can identify evidence of conservation or adaptation. Gene co-expression networks (GCNs), constructed from high-throughput gene expression data, can be used to understand evolution and the rise of new phenotypes. The increasing abundance of gene expression data makes GCNs a valuable tool for the study of evolution in non-model organisms. In this paper, we cover motivations for why comparing these networks across species can be valuable for the study of evolution. We also review techniques for comparing GCNs in the context of evolution, including local and global methods of graph alignment. While some protein-protein interaction (PPI) bioinformatic methods can be used to compare co-expression networks, they often disregard highly relevant properties, including the existence of continuous and negative values for edge weights. Also, the lack of comparative datasets in non-model organisms has hindered the study of evolution using PPI networks. We also discuss limitations and challenges associated with cross-species comparison using GCNs, and provide suggestions for utilizing co-expression network alignments as an indispensable tool for evolutionary studies going forward.
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Affiliation(s)
- Katie Ovens
- Augmented Intelligence & Precision Health Laboratory (AIPHL), Research Institute of the McGill University Health Centre, Montreal, QC, Canada
| | - B. Frank Eames
- Department of Anatomy, Physiology, & Pharmacology, University of Saskatchewan, Saskatoon, SK, Canada
| | - Ian McQuillan
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
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18
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Cannoodt R, Saelens W, Deconinck L, Saeys Y. Spearheading future omics analyses using dyngen, a multi-modal simulator of single cells. Nat Commun 2021; 12:3942. [PMID: 34168133 PMCID: PMC8225657 DOI: 10.1038/s41467-021-24152-2] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Accepted: 05/27/2021] [Indexed: 02/05/2023] Open
Abstract
We present dyngen, a multi-modal simulation engine for studying dynamic cellular processes at single-cell resolution. dyngen is more flexible than current single-cell simulation engines, and allows better method development and benchmarking, thereby stimulating development and testing of computational methods. We demonstrate its potential for spearheading computational methods on three applications: aligning cell developmental trajectories, cell-specific regulatory network inference and estimation of RNA velocity.
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Affiliation(s)
- Robrecht Cannoodt
- grid.11486.3a0000000104788040Data Mining and Modelling for Biomedicine group, VIB Center for Inflammation Research, Ghent, Belgium ,grid.5342.00000 0001 2069 7798Department of Applied Mathematics, Computer Science, and Statistics, Ghent University, Ghent, Belgium ,Data Intuitive, Lebbeke, Belgium
| | - Wouter Saelens
- grid.11486.3a0000000104788040Data Mining and Modelling for Biomedicine group, VIB Center for Inflammation Research, Ghent, Belgium ,grid.5342.00000 0001 2069 7798Department of Applied Mathematics, Computer Science, and Statistics, Ghent University, Ghent, Belgium ,grid.5333.60000000121839049Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Louise Deconinck
- grid.11486.3a0000000104788040Data Mining and Modelling for Biomedicine group, VIB Center for Inflammation Research, Ghent, Belgium ,grid.5342.00000 0001 2069 7798Department of Applied Mathematics, Computer Science, and Statistics, Ghent University, Ghent, Belgium
| | - Yvan Saeys
- grid.11486.3a0000000104788040Data Mining and Modelling for Biomedicine group, VIB Center for Inflammation Research, Ghent, Belgium ,grid.5342.00000 0001 2069 7798Department of Applied Mathematics, Computer Science, and Statistics, Ghent University, Ghent, Belgium
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19
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Weighill D, Ben Guebila M, Glass K, Platig J, Yeh JJ, Quackenbush J. Gene Targeting in Disease Networks. Front Genet 2021; 12:649942. [PMID: 33968133 PMCID: PMC8103030 DOI: 10.3389/fgene.2021.649942] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 03/15/2021] [Indexed: 01/12/2023] Open
Abstract
Profiling of whole transcriptomes has become a cornerstone of molecular biology and an invaluable tool for the characterization of clinical phenotypes and the identification of disease subtypes. Analyses of these data are becoming ever more sophisticated as we move beyond simple comparisons to consider networks of higher-order interactions and associations. Gene regulatory networks (GRNs) model the regulatory relationships of transcription factors and genes and have allowed the identification of differentially regulated processes in disease systems. In this perspective, we discuss gene targeting scores, which measure changes in inferred regulatory network interactions, and their use in identifying disease-relevant processes. In addition, we present an example analysis for pancreatic ductal adenocarcinoma (PDAC), demonstrating the power of gene targeting scores to identify differential processes between complex phenotypes, processes that would have been missed by only performing differential expression analysis. This example demonstrates that gene targeting scores are an invaluable addition to gene expression analysis in the characterization of diseases and other complex phenotypes.
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Affiliation(s)
- Deborah Weighill
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Harvard University, Boston, MA, United States
| | - Marouen Ben Guebila
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Harvard University, Boston, MA, United States
| | - Kimberly Glass
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Harvard University, Boston, MA, United States
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States
- Harvard Medical School, Harvard University, Boston, MA, United States
| | - John Platig
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States
- Harvard Medical School, Harvard University, Boston, MA, United States
| | - Jen Jen Yeh
- Departments of Surgery and Pharmacology, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - John Quackenbush
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Harvard University, Boston, MA, United States
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States
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20
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Unlocking immune-mediated disease mechanisms with transcriptomics. Biochem Soc Trans 2021; 49:705-714. [PMID: 33843974 PMCID: PMC8106500 DOI: 10.1042/bst20200652] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Revised: 03/08/2021] [Accepted: 03/18/2021] [Indexed: 01/10/2023]
Abstract
The transcriptome represents the entire set of RNA transcripts expressed in a cell, reflecting both the underlying genetic and epigenetic landscape and environmental influences, providing a comprehensive view of functional cellular states at any given time. Recent technological advances now enable the study of the transcriptome at the resolution of individual cells, providing exciting opportunities to characterise cellular and molecular events that underpin immune-medicated diseases. Here, we draw on recent examples from the literature to highlight the application of advanced bioinformatics tools to extract mechanistic insight and disease biology from bulk and single-cell transcriptomic profiles. Key considerations for the use of available analysis techniques are presented throughout.
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21
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Mac Aogáin M, Narayana JK, Tiew PY, Ali NABM, Yong VFL, Jaggi TK, Lim AYH, Keir HR, Dicker AJ, Thng KX, Tsang A, Ivan FX, Poh ME, Oriano M, Aliberti S, Blasi F, Low TB, Ong TH, Oliver B, Giam YH, Tee A, Koh MS, Abisheganaden JA, Tsaneva-Atanasova K, Chalmers JD, Chotirmall SH. Integrative microbiomics in bronchiectasis exacerbations. Nat Med 2021; 27:688-699. [PMID: 33820995 DOI: 10.1038/s41591-021-01289-7] [Citation(s) in RCA: 106] [Impact Index Per Article: 35.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 02/16/2021] [Indexed: 02/01/2023]
Abstract
Bronchiectasis, a progressive chronic airway disease, is characterized by microbial colonization and infection. We present an approach to the multi-biome that integrates bacterial, viral and fungal communities in bronchiectasis through weighted similarity network fusion ( https://integrative-microbiomics.ntu.edu.sg ). Patients at greatest risk of exacerbation have less complex microbial co-occurrence networks, reduced diversity and a higher degree of antagonistic interactions in their airway microbiome. Furthermore, longitudinal interactome dynamics reveals microbial antagonism during exacerbation, which resolves following treatment in an otherwise stable multi-biome. Assessment of the Pseudomonas interactome shows that interaction networks, rather than abundance alone, are associated with exacerbation risk, and that incorporation of microbial interaction data improves clinical prediction models. Shotgun metagenomic sequencing of an independent cohort validated the multi-biome interactions detected in targeted analysis and confirmed the association with exacerbation. Integrative microbiomics captures microbial interactions to determine exacerbation risk, which cannot be appreciated by the study of a single microbial group. Antibiotic strategies probably target the interaction networks rather than individual microbes, providing a fresh approach to the understanding of respiratory infection.
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Affiliation(s)
- Micheál Mac Aogáin
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Jayanth Kumar Narayana
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Pei Yee Tiew
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.,Department of Respiratory and Critical Care Medicine, Singapore General Hospital, Singapore, Singapore
| | | | - Valerie Fei Lee Yong
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Tavleen Kaur Jaggi
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Albert Yick Hou Lim
- Department of Respiratory and Critical Care Medicine, Tan Tock Seng Hospital, Singapore, Singapore
| | - Holly R Keir
- School of Medicine, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK
| | - Alison J Dicker
- School of Medicine, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK
| | - Kai Xian Thng
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Akina Tsang
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | | | - Mau Ern Poh
- Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Martina Oriano
- Respiratory Unit and Cystic Fibrosis Adult Center, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.,Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Stefano Aliberti
- Respiratory Unit and Cystic Fibrosis Adult Center, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.,Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Francesco Blasi
- Respiratory Unit and Cystic Fibrosis Adult Center, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.,Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Teck Boon Low
- Department of Respiratory and Critical Care Medicine, Changi General Hospital, Singapore, Singapore
| | - Thun How Ong
- Department of Respiratory and Critical Care Medicine, Singapore General Hospital, Singapore, Singapore
| | - Brian Oliver
- Woolcock Institute of Medical Research, University of Sydney, Sydney, New South Wales, Australia.,School of Life Sciences, University of Technology Sydney, Sydney, New South Wales, Australia
| | - Yan Hui Giam
- School of Medicine, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK
| | - Augustine Tee
- Department of Respiratory and Critical Care Medicine, Changi General Hospital, Singapore, Singapore
| | - Mariko Siyue Koh
- Department of Respiratory and Critical Care Medicine, Singapore General Hospital, Singapore, Singapore
| | | | - Krasimira Tsaneva-Atanasova
- Department of Mathematics and Living Systems Institute, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
| | - James D Chalmers
- School of Medicine, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK
| | - Sanjay H Chotirmall
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
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22
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Škrlj B, Novak MP, Brader G, Anžič B, Ramšak Ž, Gruden K, Kralj J, Kladnik A, Lavrač N, Roitsch T, Dermastia M. New Cross-Talks between Pathways Involved in Grapevine Infection with ' Candidatus Phytoplasma solani' Revealed by Temporal Network Modelling. PLANTS (BASEL, SWITZERLAND) 2021; 10:646. [PMID: 33805409 PMCID: PMC8065506 DOI: 10.3390/plants10040646] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 03/18/2021] [Accepted: 03/24/2021] [Indexed: 12/17/2022]
Abstract
Understanding temporal biological phenomena is a challenging task that can be approached using network analysis. Here, we explored whether network reconstruction can be used to better understand the temporal dynamics of bois noir, which is associated with 'Candidatus Phytoplasma solani', and is one of the most widespread phytoplasma diseases of grapevine in Europe. We proposed a methodology that explores the temporal network dynamics at the community level, i.e., densely connected subnetworks. The methodology offers both insights into the functional dynamics via enrichment analysis at the community level, and analyses of the community dissipation, as a measure that accounts for community degradation. We validated this methodology with cases on experimental temporal expression data of uninfected grapevines and grapevines infected with 'Ca. P. solani'. These data confirm some known gene communities involved in this infection. They also reveal several new gene communities and their potential regulatory networks that have not been linked to 'Ca. P. solani' to date. To confirm the capabilities of the proposed method, selected predictions were empirically evaluated.
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Affiliation(s)
- Blaž Škrlj
- Jožef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia;
- Jožef Stefan Institute, 1000 Ljubljana, Slovenia;
| | - Maruša Pompe Novak
- National Institute of Biology, 1000 Ljubljana, Slovenia; (M.P.N.); (B.A.); (Ž.R.); (K.G.); (M.D.)
- School of Viticulture and Enology, University of Nova Gorica, 5271 Vipava, Slovenia
| | - Günter Brader
- Austrian Institute of Technology, Bioresources Unit, 3430 Tulln, Austria;
| | - Barbara Anžič
- National Institute of Biology, 1000 Ljubljana, Slovenia; (M.P.N.); (B.A.); (Ž.R.); (K.G.); (M.D.)
| | - Živa Ramšak
- National Institute of Biology, 1000 Ljubljana, Slovenia; (M.P.N.); (B.A.); (Ž.R.); (K.G.); (M.D.)
| | - Kristina Gruden
- National Institute of Biology, 1000 Ljubljana, Slovenia; (M.P.N.); (B.A.); (Ž.R.); (K.G.); (M.D.)
| | - Jan Kralj
- Jožef Stefan Institute, 1000 Ljubljana, Slovenia;
| | - Aleš Kladnik
- Department of Biology, Biotechnical Faculty, University of Ljubljana, 1000 Ljubljana, Slovenia;
| | - Nada Lavrač
- Jožef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia;
- Jožef Stefan Institute, 1000 Ljubljana, Slovenia;
| | - Thomas Roitsch
- Department of Plant and Environmental Sciences, University of Copenhagen, 2630 Taastrup, Denmark;
| | - Marina Dermastia
- National Institute of Biology, 1000 Ljubljana, Slovenia; (M.P.N.); (B.A.); (Ž.R.); (K.G.); (M.D.)
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23
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Jahagirdar S, Saccenti E. Evaluation of Single Sample Network Inference Methods for Metabolomics-Based Systems Medicine. J Proteome Res 2020; 20:932-949. [PMID: 33267585 PMCID: PMC7786380 DOI: 10.1021/acs.jproteome.0c00696] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
![]()
Networks
and network analyses are fundamental tools of systems
biology. Networks are built by inferring pair-wise relationships among
biological entities from a large number of samples such that subject-specific
information is lost. The possibility of constructing these sample
(individual)-specific networks from single molecular profiles might
offer new insights in systems and personalized medicine and as a consequence
is attracting more and more research interest. In this study, we evaluated
and compared LIONESS (Linear Interpolation to Obtain Network Estimates
for Single Samples) and ssPCC (single sample network based on Pearson
correlation) in the metabolomics context of metabolite–metabolite
association networks. We illustrated and explored the characteristics
of these two methods on (i) simulated data, (ii) data generated from
a dynamic metabolic model to simulate real-life observed metabolite
concentration profiles, and (iii) 22 metabolomic data sets and (iv)
we applied single sample network inference to a study case pertaining
to the investigation of necrotizing soft tissue infections to show
how these methods can be applied in metabolomics. We also proposed
some adaptations of the methods that can be used for data exploration.
Overall, despite some limitations, we found single sample networks
to be a promising tool for the analysis of metabolomics data.
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Affiliation(s)
- Sanjeevan Jahagirdar
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Stippeneng 4, 6708 WE Wageningen, The Netherlands
| | - Edoardo Saccenti
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Stippeneng 4, 6708 WE Wageningen, The Netherlands
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