1
|
Grabert G, Dehncke D, More T, List M, Kraft ARM, Cornberg M, Hiller K, Kacprowski T. MeTEor: an R Shiny app for exploring longitudinal metabolomics data. BIOINFORMATICS ADVANCES 2024; 4:vbae178. [PMID: 39659589 PMCID: PMC11631383 DOI: 10.1093/bioadv/vbae178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 10/06/2024] [Accepted: 11/12/2024] [Indexed: 12/12/2024]
Abstract
Motivation The availability of longitudinal omics data is increasing in metabolomics research. Viewing metabolomics data over time provides detailed insight into biological processes and fosters understanding of how systems react over time. However, the analysis of longitudinal metabolomics data poses various challenges, both in terms of statistical evaluation and visualization. Results To make explorative analysis of longitudinal data readily available to researchers without formal background in computer science and programming, we present MEtabolite Trajectory ExplORer (MeTEor). MeTEor is an R Shiny app providing a comprehensive set of statistical analysis methods. To demonstrate the capabilities of MeTEor, we replicated the analysis of metabolomics data from a previously published study on COVID-19 patients. Availability and implementation MeTEor is available as an R package and as a Docker image. Source code and instructions for setting up the app can be found on GitHub (https://github.com/scibiome/meteor). The Docker image is available at Docker Hub (https://hub.docker.com/r/gordomics/meteor). MeTEor has been tested on Microsoft Windows, Unix/Linux, and macOS.
Collapse
Affiliation(s)
- Gordon Grabert
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig, Lower Saxony 38106, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Braunschweig, Lower Saxony 38106, Germany
| | - Daniel Dehncke
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig, Lower Saxony 38106, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Braunschweig, Lower Saxony 38106, Germany
| | - Tushar More
- Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Braunschweig, Lower Saxony 38106, Germany
- Department of Bioinformatics and Biochemistry, BRICS, Technische Universität Braunschweig, Braunschweig, Lower Saxony 38106, Germany
| | - Markus List
- Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Munich, Bavaria 85354, Germany
| | - Anke R M Kraft
- Centre for Individualised Infection Medicine (CiiM), Joint Venture Between Helmholtz Centre for Infection Research (HZI) and Hannover Medical School (MHH), Hannover, Lower Saxony 30625, Germany
- Department of Gastroenterology, Hepatology, Infectious Diseases and Endocrinology, Hannover Medical School (MHH), Hannover, Lower Saxony 30625, Germany
- German Centre for Infection Research (DZIF), Partner Site Hannover-Braunschweig, Hannover, Lower Saxony 38124, Germany
| | - Markus Cornberg
- Centre for Individualised Infection Medicine (CiiM), Joint Venture Between Helmholtz Centre for Infection Research (HZI) and Hannover Medical School (MHH), Hannover, Lower Saxony 30625, Germany
- Department of Gastroenterology, Hepatology, Infectious Diseases and Endocrinology, Hannover Medical School (MHH), Hannover, Lower Saxony 30625, Germany
- German Centre for Infection Research (DZIF), Partner Site Hannover-Braunschweig, Hannover, Lower Saxony 38124, Germany
| | - Karsten Hiller
- Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Braunschweig, Lower Saxony 38106, Germany
- Department of Bioinformatics and Biochemistry, BRICS, Technische Universität Braunschweig, Braunschweig, Lower Saxony 38106, Germany
| | - Tim Kacprowski
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig, Lower Saxony 38106, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Braunschweig, Lower Saxony 38106, Germany
| |
Collapse
|
2
|
Zheng M, Charvat J, Zwart SR, Mehta SK, Crucian BE, Smith SM, He J, Piermarocchi C, Mias GI. Time-resolved molecular measurements reveal changes in astronauts during spaceflight. Front Physiol 2023; 14:1219221. [PMID: 37520819 PMCID: PMC10376710 DOI: 10.3389/fphys.2023.1219221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 06/28/2023] [Indexed: 08/01/2023] Open
Abstract
From the early days of spaceflight to current missions, astronauts continue to be exposed to multiple hazards that affect human health, including low gravity, high radiation, isolation during long-duration missions, a closed environment and distance from Earth. Their effects can lead to adverse physiological changes and necessitate countermeasure development and/or longitudinal monitoring. A time-resolved analysis of biological signals can detect and better characterize potential adverse events during spaceflight, ideally preventing them and maintaining astronauts' wellness. Here we provide a time-resolved assessment of the impact of spaceflight on multiple astronauts (n = 27) by studying multiple biochemical and immune measurements before, during, and after long-duration orbital spaceflight. We reveal space-associated changes of astronauts' physiology on both the individual level and across astronauts, including associations with bone resorption and kidney function, as well as immune-system dysregulation.
Collapse
Affiliation(s)
- Minzhang Zheng
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, United States
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, United States
| | | | - Sara R. Zwart
- University of Texas Medical Branch, Galveston, TX, United States
| | | | | | | | - Jin He
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, United States
| | - Carlo Piermarocchi
- Department of Physics and Astronomy, Michigan State University, East Lansing, MI, United States
| | - George I. Mias
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, United States
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, United States
- Department of Physics and Astronomy, Michigan State University, East Lansing, MI, United States
| |
Collapse
|
3
|
Zheng M, Charvat J, Zwart SR, Mehta S, Crucian BE, Smith SM, He J, Piermarocchi C, Mias GI. Time-resolved molecular measurements reveal changes in astronauts during spaceflight. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.17.530234. [PMID: 36993537 PMCID: PMC10055136 DOI: 10.1101/2023.03.17.530234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
From the early days of spaceflight to current missions, astronauts continue to be exposed to multiple hazards that affect human health, including low gravity, high radiation, isolation during long-duration missions, a closed environment and distance from Earth. Their effects can lead to adverse physiological changes and necessitate countermeasure development and/or longitudinal monitoring. A time-resolved analysis of biological signals can detect and better characterize potential adverse events during spaceflight, ideally preventing them and maintaining astronauts' wellness. Here we provide a time-resolved assessment of the impact of spaceflight on multiple astronauts (n=27) by studying multiple biochemical and immune measurements before, during, and after long-duration orbital spaceflight. We reveal space-associated changes of astronauts' physiology on both the individual level and across astronauts, including associations with bone resorption and kidney function, as well as immune-system dysregulation.
Collapse
|
4
|
Xue S, Rogers LR, Zheng M, He J, Piermarocchi C, Mias GI. Applying differential network analysis to longitudinal gene expression in response to perturbations. Front Genet 2022; 13:1026487. [PMID: 36324501 PMCID: PMC9618823 DOI: 10.3389/fgene.2022.1026487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 10/03/2022] [Indexed: 11/17/2022] Open
Abstract
Differential Network (DN) analysis is a method that has long been used to interpret changes in gene expression data and provide biological insights. The method identifies the rewiring of gene networks in response to external perturbations. Our study applies the DN method to the analysis of RNA-sequencing (RNA-seq) time series datasets. We focus on expression changes: (i) in saliva of a human subject after pneumococcal vaccination (PPSV23) and (ii) in primary B cells treated ex vivo with a monoclonal antibody drug (Rituximab). The DN method enabled us to identify the activation of biological pathways consistent with the mechanisms of action of the PPSV23 vaccine and target pathways of Rituximab. The community detection algorithm on the DN revealed clusters of genes characterized by collective temporal behavior. All saliva and some B cell DN communities showed characteristic time signatures, outlining a chronological order in pathway activation in response to the perturbation. Moreover, we identified early and delayed responses within network modules in the saliva dataset and three temporal patterns in the B cell data.
Collapse
Affiliation(s)
- Shuyue Xue
- Department of Physics and Astronomy, Michigan State University, East Lansing, MI, United States
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, United States
| | - Lavida R.K. Rogers
- Department of Biological Sciences, University of the Virgin Islands, St Thomas, US Virgin Islands
| | - Minzhang Zheng
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, United States
| | - Jin He
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, United States
| | - Carlo Piermarocchi
- Department of Physics and Astronomy, Michigan State University, East Lansing, MI, United States
| | - George I. Mias
- Department of Physics and Astronomy, Michigan State University, East Lansing, MI, United States
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, United States
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, United States
| |
Collapse
|
5
|
Temporal response characterization across individual multiomics profiles of prediabetic and diabetic subjects. Sci Rep 2022; 12:12098. [PMID: 35840765 PMCID: PMC9284494 DOI: 10.1038/s41598-022-16326-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 07/08/2022] [Indexed: 11/08/2022] Open
Abstract
Longitudinal deep multiomics profiling, which combines biomolecular, physiological, environmental and clinical measures data, shows great promise for precision health. However, integrating and understanding the complexity of such data remains a big challenge. Here we utilize an individual-focused bottom-up approach aimed at first assessing single individuals’ multiomics time series, and using the individual-level responses to assess multi-individual grouping based directly on similarity of their longitudinal deep multiomics profiles. We used this individual-focused approach to analyze profiles from a study profiling longitudinal responses in type 2 diabetes mellitus. After generating periodograms for individual subject omics signals, we constructed within-person omics networks and analyzed personal-level immune changes. The results identified both individual-level responses to immune perturbation, and the clusters of individuals that have similar behaviors in immune response and which were associated to measures of their diabetic status.
Collapse
|
6
|
Anžel A, Heider D, Hattab G. MOVIS: A multi-omics software solution for multi-modal time-series clustering, embedding, and visualizing tasks. Comput Struct Biotechnol J 2022; 20:1044-1055. [PMID: 35284047 PMCID: PMC8886009 DOI: 10.1016/j.csbj.2022.02.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 02/14/2022] [Accepted: 02/14/2022] [Indexed: 11/28/2022] Open
Abstract
Thanks to recent advances in sequencing and computational technologies, many researchers with biological and/or medical backgrounds are now producing multiple data sets with an embedded temporal dimension. Multi-modalities enable researchers to explore and investigate different biological and physico-chemical processes with various technologies. Motivated to explore multi-omics data and time-series multi-omics specifically, the exploration process has been hindered by the separation introduced by each omics-type. To effectively explore such temporal data sets, discover anomalies, find patterns, and better understand their intricacies, expertise in computer science and bioinformatics is required. Here we present MOVIS, a modular time-series multi-omics exploration tool with a user-friendly web interface that facilitates the data exploration of such data. It brings into equal participation each time-series omic-type for analysis and visualization. As of the time of writing, two time-series multi-omics data sets have been integrated and successfully reproduced. The resulting visualizations are task-specific, reproducible, and publication-ready. MOVIS is built on open-source software and is easily extendable to accommodate different analytical tasks. An online version of MOVIS is available under https://movis.mathematik.uni-marburg.de/ and on Docker Hub (https://hub.docker.com/r/aanzel/movis).
Collapse
Affiliation(s)
- Aleksandar Anžel
- Department of Mathematics and Computer Science, University of Marburg, Hans-Meerwein-Strasse 6, Marburg 35032, Hesse, Germany
| | - Dominik Heider
- Department of Mathematics and Computer Science, University of Marburg, Hans-Meerwein-Strasse 6, Marburg 35032, Hesse, Germany
| | - Georges Hattab
- Department of Mathematics and Computer Science, University of Marburg, Hans-Meerwein-Strasse 6, Marburg 35032, Hesse, Germany
| |
Collapse
|
7
|
Visibility graph based temporal community detection with applications in biological time series. Sci Rep 2021; 11:5623. [PMID: 33707481 PMCID: PMC7952737 DOI: 10.1038/s41598-021-84838-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Accepted: 02/22/2021] [Indexed: 12/16/2022] Open
Abstract
Temporal behavior is an essential aspect of all biological systems. Time series have been previously represented as networks. Such representations must address two fundamental problems on how to: (1) Create appropriate networks to reflect the characteristics of biological time series. (2) Detect characteristic dynamic patterns or events as network temporal communities. General community detection methods use metrics comparing the connectivity within a community to random models, or are based on the betweenness centrality of edges or nodes. However, such methods were not designed for network representations of time series. We introduce a visibility-graph-based method to build networks from time series and detect temporal communities within these networks. To characterize unevenly sampled time series (typical of biological experiments), and simultaneously capture events associated to peaks and troughs, we introduce the Weighted Dual-Perspective Visibility Graph (WDPVG). To detect temporal communities in individual signals, we first find the shortest path of the network between start and end nodes, identifying high intensity nodes as the main stem of our community detection algorithm that act as hubs for each community. Then, we aggregate nodes outside the shortest path to the closest nodes found on the main stem based on the closest path length, thereby assigning every node to a temporal community based on proximity to the stem nodes/hubs. We demonstrate the validity and effectiveness of our method through simulation and biological applications.
Collapse
|
8
|
Veziroglu EM, Mias GI. Characterizing Extracellular Vesicles and Their Diverse RNA Contents. Front Genet 2020; 11:700. [PMID: 32765582 PMCID: PMC7379748 DOI: 10.3389/fgene.2020.00700] [Citation(s) in RCA: 170] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Accepted: 06/09/2020] [Indexed: 12/15/2022] Open
Abstract
Cells release nanometer-scale, lipid bilayer-enclosed biomolecular packages (extracellular vesicles; EVs) into their surrounding environment. EVs are hypothesized to be intercellular communication agents that regulate physiological states by transporting biomolecules between near and distant cells. The research community has consistently advocated for the importance of RNA contents in EVs by demonstrating that: (1) EV-related RNA contents can be detected in a liquid biopsy, (2) disease states significantly alter EV-related RNA contents, and (3) sensitive and specific liquid biopsies can be implemented in precision medicine settings by measuring EV-derived RNA contents. Furthermore, EVs have medical potential beyond diagnostics. Both natural and engineered EVs are being investigated for therapeutic applications such as regenerative medicine and as drug delivery agents. This review focuses specifically on EV characterization, analysis of their RNA content, and their functional implications. The NIH extracellular RNA communication (ERC) program has catapulted human EV research from an RNA profiling standpoint by standardizing the pipeline for working with EV transcriptomics data, and creating a centralized database for the scientific community. There are currently thousands of RNA-sequencing profiles hosted on the Extracellular RNA Atlas alone (Murillo et al., 2019), encompassing a variety of human biofluid types and health conditions. While a number of significant discoveries have been made through these studies individually, integrative analyses of these data have thus far been limited. A primary focus of the ERC program over the next five years is to bring higher resolution tools to the EV research community so that investigators can isolate and analyze EV sub-populations, and ultimately single EVs sourced from discrete cell types, tissues, and complex biofluids. Higher resolution techniques will be essential for evaluating the roles of circulating EVs at a level which impacts clinical decision making. We expect that advances in microfluidic technologies will drive near-term innovation and discoveries about the diverse RNA contents of EVs. Long-term translation of EV-based RNA profiling into a mainstay medical diagnostic tool will depend upon identifying robust patterns of circulating genetic material that correlate with a change in health status.
Collapse
Affiliation(s)
- Eren M. Veziroglu
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, United States
- Department of Biomedical Engineering, Michigan State University, East Lansing, MI, United States
| | - George I. Mias
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, United States
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, United States
| |
Collapse
|