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Chilimoniuk J, Grzesiak K, Kała J, Nowakowski D, Krętowski A, Kolenda R, Ciborowski M, Burdukiewicz M. imputomics: web server and R package for missing values imputation in metabolomics data. Bioinformatics 2024; 40:btae098. [PMID: 38377398 PMCID: PMC10918629 DOI: 10.1093/bioinformatics/btae098] [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: 10/20/2023] [Revised: 01/26/2024] [Accepted: 02/19/2024] [Indexed: 02/22/2024] Open
Abstract
MOTIVATION Missing values are commonly observed in metabolomics data from mass spectrometry. Imputing them is crucial because it assures data completeness, increases the statistical power of analyses, prevents inaccurate results, and improves the quality of exploratory analysis, statistical modeling, and machine learning. Numerous Missing Value Imputation Algorithms (MVIAs) employ heuristics or statistical models to replace missing information with estimates. In the context of metabolomics data, we identified 52 MVIAs implemented across 70 R functions. Nevertheless, the usage of those 52 established methods poses challenges due to package dependency issues, lack of documentation, and their instability. RESULTS Our R package, 'imputomics', provides a convenient wrapper around 41 (plus random imputation as a baseline model) out of 52 MVIAs in the form of a command-line tool and a web application. In addition, we propose a novel functionality for selecting MVIAs recommended for metabolomics data with the best performance or execution time. AVAILABILITY AND IMPLEMENTATION 'imputomics' is freely available as an R package (github.com/BioGenies/imputomics) and a Shiny web application (biogenies.info/imputomics-ws). The documentation is available at biogenies.info/imputomics.
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Affiliation(s)
| | - Krystyna Grzesiak
- Clinical Research Centre, Medical University of Białystok, Białystok, Poland
- Faculty of Mathematics and Computer Science, University of Wrocław, Wrocław, Poland
| | - Jakub Kała
- Clinical Research Centre, Medical University of Białystok, Białystok, Poland
| | - Dominik Nowakowski
- Department of Biostatistics and Medical Informatics, Medical University of Białystok, Białystok, Poland
| | - Adam Krętowski
- Clinical Research Centre, Medical University of Białystok, Białystok, Poland
| | - Rafał Kolenda
- Quadram Institute Biosciences, Norwich Research Park, Norwich, United Kingdom
- Faculty of Veterinary Medicine, Wrocław University of Environmental and Life Sciences, Wrocław, Poland
| | - Michał Ciborowski
- Clinical Research Centre, Medical University of Białystok, Białystok, Poland
| | - Michał Burdukiewicz
- Clinical Research Centre, Medical University of Białystok, Białystok, Poland
- Institute of Biotechnology and Biomedicine, Autonomous University of Barcelona, Cerdanyola del Vallès, Spain
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van Vorstenbosch R, Mommers A, Pachen D, van Schooten FJ, Smolinska A. The optimization and comparison of two high-throughput faecal headspace sampling platforms: the microchamber/thermal extractor and hi-capacity sorptive extraction probes (HiSorb). J Breath Res 2024; 18:026007. [PMID: 38237170 DOI: 10.1088/1752-7163/ad2002] [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] [Received: 07/21/2023] [Accepted: 01/18/2024] [Indexed: 02/06/2024]
Abstract
Disease detection and monitoring using volatile organic compounds (VOCs) is becoming increasingly popular. For a variety of (gastrointestinal) diseases the microbiome should be considered. As its output is to large extent volatile, faecal volatilomics carries great potential. One technical limitation is that current faecal headspace analysis requires specialized instrumentation which is costly and typically does not work in harmony with thermal desorption units often utilized in e.g. exhaled breath studies. This lack of harmonization hinders uptake of such analyses by the Volatilomics community. Therefore, this study optimized and compared two recently harmonized faecal headspace sampling platforms:High-capacity Sorptive extraction (HiSorb) probesand theMicrochamber thermal extractor (Microchamber). Statistical design of experiment was applied to find optimal sampling conditions by maximizing reproducibility, the number of VOCs detected, and between subject variation. To foster general applicability those factors were defined using semi-targeted as well as untargeted metabolic profiles. HiSorb probes were found to result in a faster sampling procedure, higher number of detected VOCs, and higher stability. The headspace collection using the Microchamber resulted in a lower number of detected VOCs, longer sampling times and decreased stability despite a smaller number of interfering VOCs and no background signals. Based on the observed profiles, recommendations are provided on pre-processing and study design when using either one of both platforms. Both can be used to perform faecal headspace collection, but altogether HiSorb is recommended.
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Affiliation(s)
- Robert van Vorstenbosch
- Department of Pharmacology and Toxicology, NUTRIM School of Nutrition and Translational Research, Maastricht University, Maastricht, The Netherlands
| | - Alex Mommers
- Department of Pharmacology and Toxicology, NUTRIM School of Nutrition and Translational Research, Maastricht University, Maastricht, The Netherlands
| | - Daniëlle Pachen
- Department of Pharmacology and Toxicology, NUTRIM School of Nutrition and Translational Research, Maastricht University, Maastricht, The Netherlands
| | - Frederik-Jan van Schooten
- Department of Pharmacology and Toxicology, NUTRIM School of Nutrition and Translational Research, Maastricht University, Maastricht, The Netherlands
| | - Agnieszka Smolinska
- Department of Pharmacology and Toxicology, NUTRIM School of Nutrition and Translational Research, Maastricht University, Maastricht, The Netherlands
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3
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Anwardeen NR, Diboun I, Mokrab Y, Althani AA, Elrayess MA. Statistical methods and resources for biomarker discovery using metabolomics. BMC Bioinformatics 2023; 24:250. [PMID: 37322419 DOI: 10.1186/s12859-023-05383-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Accepted: 06/09/2023] [Indexed: 06/17/2023] Open
Abstract
Metabolomics is a dynamic tool for elucidating biochemical changes in human health and disease. Metabolic profiles provide a close insight into physiological states and are highly volatile to genetic and environmental perturbations. Variation in metabolic profiles can inform mechanisms of pathology, providing potential biomarkers for diagnosis and assessment of the risk of contracting a disease. With the advancement of high-throughput technologies, large-scale metabolomics data sources have become abundant. As such, careful statistical analysis of intricate metabolomics data is essential for deriving relevant and robust results that can be deployed in real-life clinical settings. Multiple tools have been developed for both data analysis and interpretations. In this review, we survey statistical approaches and corresponding statistical tools that are available for discovery of biomarkers using metabolomics.
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Affiliation(s)
- Najeha R Anwardeen
- Research and Graduate Studies, Biomedical Research Center, Qatar University, P.O. Box 2713, Doha, Qatar
| | - Ilhame Diboun
- Department of Human Genetics, Sidra Medicine, Doha, Qatar
| | - Younes Mokrab
- Department of Human Genetics, Sidra Medicine, Doha, Qatar
| | - Asma A Althani
- Research and Graduate Studies, Biomedical Research Center, Qatar University, P.O. Box 2713, Doha, Qatar
- QU Health, Qatar University, Doha, Qatar
| | - Mohamed A Elrayess
- Research and Graduate Studies, Biomedical Research Center, Qatar University, P.O. Box 2713, Doha, Qatar.
- QU Health, Qatar University, Doha, Qatar.
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Souza MCO, Cruz JC, Rocha BA, Maria Oliveira Souza J, Devóz PP, Santana A, Campíglia AD, Barbosa F. The influence of the co-exposure to polycyclic aromatic hydrocarbons and toxic metals on DNA damage in brazilian lactating women and their infants: A cross-sectional study using machine learning approaches. CHEMOSPHERE 2023; 334:138975. [PMID: 37224977 DOI: 10.1016/j.chemosphere.2023.138975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 03/29/2023] [Accepted: 05/16/2023] [Indexed: 05/26/2023]
Abstract
Polycyclic aromatic hydrocarbons (PAHs) and toxic metals are widely spread pollutants of public health concern. The co-contamination of these chemicals in the environment is frequent, but relatively little is known about their combined toxicities. In this context, this study aimed to evaluate the influence of the co-exposure to PAHs and toxic metals on DNA damage in Brazilian lactating women and their infants using machine learning approaches. Data were collected from an observational, cross-sectional study with 96 lactating women and 96 infants living in two cities. The exposure to these pollutants was estimated by determining urinary levels of seven mono-hydroxylated PAH metabolites and the free form of three toxic metals. 8-Hydroxydeoxyguanosine (8-OHdG) levels in the urine were used as the oxidative stress biomarker and set as the outcome. Individual sociodemographic factors were also collected using questionnaires. Sixteen machine learning algorithms were trained using 10-fold cross-validation to investigate the associations of urinary OH-PAHs and metals with 8-OHdG levels. This approach was also compared with models attained by multiple linear regression. The results showed that the urinary concentration of OH-PAHs was highly correlated between the mothers and their infants. Multiple linear regression did not show a statistically significant association between the contaminants and urinary 8OHdG levels. Machine learning models indicated that all investigated variables did not present predictive performance on 8-OHdG concentrations. In conclusion, PAHs and toxic metals were not associated with 8-OHdG levels in Brazilian lactating women and their infants. These novelty and originality results were achieved even after applying sophisticated statistical models to capture non-linear relationships. However, these findings should be interpreted cautiously because the exposure to the studied contaminants was considerably low, which may not reflect other populations at risk.
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Affiliation(s)
- Marília Cristina Oliveira Souza
- ASTox Lab - Analytical and System Toxicology Laboratory, Department of Clinical Analyses, Toxicology and Food Sciences, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Avenida Do Café S/n, 14040-903, Ribeirão Preto, São Paulo, Brazil.
| | - Jonas Carneiro Cruz
- ASTox Lab - Analytical and System Toxicology Laboratory, Department of Clinical Analyses, Toxicology and Food Sciences, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Avenida Do Café S/n, 14040-903, Ribeirão Preto, São Paulo, Brazil
| | - Bruno Alves Rocha
- ASTox Lab - Analytical and System Toxicology Laboratory, Department of Clinical Analyses, Toxicology and Food Sciences, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Avenida Do Café S/n, 14040-903, Ribeirão Preto, São Paulo, Brazil
| | - Juliana Maria Oliveira Souza
- Department of Biochemistry, Biological Sciences Institute, University of Juiz de Fora, Campus Universitário, Rua José Lourenço Kelmer, S/n - São Pedro, Juiz de Fora, MG, 36036-900, Brazil
| | - Paula Pícoli Devóz
- ASTox Lab - Analytical and System Toxicology Laboratory, Department of Clinical Analyses, Toxicology and Food Sciences, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Avenida Do Café S/n, 14040-903, Ribeirão Preto, São Paulo, Brazil
| | - Anthony Santana
- Department of Chemistry, University of Central Florida, Orlando, FL, 32816, USA
| | | | - Fernando Barbosa
- ASTox Lab - Analytical and System Toxicology Laboratory, Department of Clinical Analyses, Toxicology and Food Sciences, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Avenida Do Café S/n, 14040-903, Ribeirão Preto, São Paulo, Brazil
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Zaid A, Hassan NH, Marriott PJ, Wong YF. Comprehensive Two-Dimensional Gas Chromatography as a Bioanalytical Platform for Drug Discovery and Analysis. Pharmaceutics 2023; 15:pharmaceutics15041121. [PMID: 37111606 PMCID: PMC10140985 DOI: 10.3390/pharmaceutics15041121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 03/26/2023] [Accepted: 03/27/2023] [Indexed: 04/05/2023] Open
Abstract
Over the last decades, comprehensive two-dimensional gas chromatography (GC×GC) has emerged as a significant separation tool for high-resolution analysis of disease-associated metabolites and pharmaceutically relevant molecules. This review highlights recent advances of GC×GC with different detection modalities for drug discovery and analysis, which ideally improve the screening and identification of disease biomarkers, as well as monitoring of therapeutic responses to treatment in complex biological matrixes. Selected recent GC×GC applications that focus on such biomarkers and metabolite profiling of the effects of drug administration are covered. In particular, the technical overview of recent GC×GC implementation with hyphenation to the key mass spectrometry (MS) technologies that provide the benefit of enhanced separation dimension analysis with MS domain differentiation is discussed. We conclude by highlighting the challenges in GC×GC for drug discovery and development with perspectives on future trends.
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Affiliation(s)
- Atiqah Zaid
- Centre for Research on Multidimensional Separation Science, School of Chemical Sciences, Universiti Sains Malaysia, Penang 11800, Malaysia
| | - Norfarizah Hanim Hassan
- Centre for Research on Multidimensional Separation Science, School of Chemical Sciences, Universiti Sains Malaysia, Penang 11800, Malaysia
| | - Philip J. Marriott
- Australian Centre for Research on Separation Science, School of Chemistry, Monash University, Wellington Road, Clayton, Melbourne, VIC 3800, Australia
| | - Yong Foo Wong
- Centre for Research on Multidimensional Separation Science, School of Chemical Sciences, Universiti Sains Malaysia, Penang 11800, Malaysia
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Big Data in Gastroenterology Research. Int J Mol Sci 2023; 24:ijms24032458. [PMID: 36768780 PMCID: PMC9916510 DOI: 10.3390/ijms24032458] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 01/18/2023] [Accepted: 01/20/2023] [Indexed: 01/28/2023] Open
Abstract
Studying individual data types in isolation provides only limited and incomplete answers to complex biological questions and particularly falls short in revealing sufficient mechanistic and kinetic details. In contrast, multi-omics approaches to studying health and disease permit the generation and integration of multiple data types on a much larger scale, offering a comprehensive picture of biological and disease processes. Gastroenterology and hepatobiliary research are particularly well-suited to such analyses, given the unique position of the luminal gastrointestinal (GI) tract at the nexus between the gut (mucosa and luminal contents), brain, immune and endocrine systems, and GI microbiome. The generation of 'big data' from multi-omic, multi-site studies can enhance investigations into the connections between these organ systems and organisms and more broadly and accurately appraise the effects of dietary, pharmacological, and other therapeutic interventions. In this review, we describe a variety of useful omics approaches and how they can be integrated to provide a holistic depiction of the human and microbial genetic and proteomic changes underlying physiological and pathophysiological phenomena. We highlight the potential pitfalls and alternatives to help avoid the common errors in study design, execution, and analysis. We focus on the application, integration, and analysis of big data in gastroenterology and hepatobiliary research.
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Higgins Keppler EA, Van Dyke MCC, Mead HL, Lake DF, Magee DM, Barker BM, Bean HD. Volatile Metabolites in Lavage Fluid Are Correlated with Cytokine Production in a Valley Fever Murine Model. J Fungi (Basel) 2023; 9:jof9010115. [PMID: 36675936 PMCID: PMC9864585 DOI: 10.3390/jof9010115] [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: 10/18/2022] [Revised: 01/10/2023] [Accepted: 01/10/2023] [Indexed: 01/18/2023] Open
Abstract
Coccidioides immitis and Coccidioides posadasii are soil-dwelling fungi of arid regions in North and South America that are responsible for Valley fever (coccidioidomycosis). Forty percent of patients with Valley fever exhibit symptoms ranging from mild, self-limiting respiratory infections to severe, life-threatening pneumonia that requires treatment. Misdiagnosis as bacterial pneumonia commonly occurs in symptomatic Valley fever cases, resulting in inappropriate treatment with antibiotics, increased medical costs, and delay in diagnosis. In this proof-of-concept study, we explored the feasibility of developing breath-based diagnostics for Valley fever using a murine lung infection model. To investigate potential volatile biomarkers of Valley fever that arise from host−pathogen interactions, we infected C57BL/6J mice with C. immitis RS (n = 6), C. posadasii Silveira (n = 6), or phosphate-buffered saline (n = 4) via intranasal inoculation. We measured fungal dissemination and collected bronchoalveolar lavage fluid (BALF) for cytokine profiling and for untargeted volatile metabolomics via solid-phase microextraction (SPME) and two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOFMS). We identified 36 volatile organic compounds (VOCs) that were significantly correlated (p < 0.05) with cytokine abundance. These 36 VOCs clustered mice by their cytokine production and were also able to separate mice with moderate-to-high cytokine production by infection strain. The data presented here show that Coccidioides and/or the host produce volatile metabolites that may yield biomarkers for a Valley fever breath test that can detect coccidioidal infection and provide clinically relevant information on primary pulmonary disease severity.
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Affiliation(s)
- Emily A. Higgins Keppler
- School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA
- Center for Fundamental and Applied Microbiomics, The Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA
| | | | - Heather L. Mead
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ 86011, USA
| | - Douglas F. Lake
- School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA
| | - D. Mitchell Magee
- Center for Personalized Diagnostics, The Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA
| | - Bridget M. Barker
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ 86011, USA
| | - Heather D. Bean
- School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA
- Center for Fundamental and Applied Microbiomics, The Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA
- Correspondence:
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Trinklein TJ, Cain CN, Ochoa GS, Schöneich S, Mikaliunaite L, Synovec RE. Recent Advances in GC×GC and Chemometrics to Address Emerging Challenges in Nontargeted Analysis. Anal Chem 2023; 95:264-286. [PMID: 36625122 DOI: 10.1021/acs.analchem.2c04235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Affiliation(s)
- Timothy J Trinklein
- Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195-1700, United States
| | - Caitlin N Cain
- Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195-1700, United States
| | - Grant S Ochoa
- Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195-1700, United States
| | - Sonia Schöneich
- Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195-1700, United States
| | - Lina Mikaliunaite
- Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195-1700, United States
| | - Robert E Synovec
- Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195-1700, United States
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