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Lemas DJ, Du X, Dado-Senn B, Xu K, Dobrowolski A, Magalhães M, Aristizabal-Henao JJ, Young BE, Francois M, Thompson LA, Parker LA, Neu J, Laporta J, Misra BB, Wane I, Samaan S, Garrett TJ. Untargeted Metabolomic Analysis of Lactation-Stage-Matched Human and Bovine Milk Samples at 2 Weeks Postnatal. Nutrients 2023; 15:3768. [PMID: 37686800 PMCID: PMC10490210 DOI: 10.3390/nu15173768] [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: 07/26/2023] [Revised: 08/20/2023] [Accepted: 08/21/2023] [Indexed: 09/10/2023] Open
Abstract
Epidemiological data demonstrate that bovine whole milk is often substituted for human milk during the first 12 months of life and may be associated with adverse infant outcomes. The objective of this study is to interrogate the human and bovine milk metabolome at 2 weeks of life to identify unique metabolites that may impact infant health outcomes. Human milk (n = 10) was collected at 2 weeks postpartum from normal-weight mothers (pre-pregnant BMI < 25 kg/m2) that vaginally delivered term infants and were exclusively breastfeeding their infant for at least 2 months. Similarly, bovine milk (n = 10) was collected 2 weeks postpartum from normal-weight primiparous Holstein dairy cows. Untargeted data were acquired on all milk samples using high-resolution liquid chromatography-high-resolution tandem mass spectrometry (HR LC-MS/MS). MS data pre-processing from feature calling to metabolite annotation was performed using MS-DIAL and MS-FLO. Our results revealed that more than 80% of the milk metabolome is shared between human and bovine milk samples during early lactation. Unbiased analysis of identified metabolites revealed that nearly 80% of milk metabolites may contribute to microbial metabolism and microbe-host interactions. Collectively, these results highlight untargeted metabolomics as a potential strategy to identify unique and shared metabolites in bovine and human milk that may relate to and impact infant health outcomes.
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Affiliation(s)
- Dominick J. Lemas
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32608, USA; (X.D.); (K.X.); (A.D.); (M.F.); (L.A.T.); (I.W.); (S.S.)
- Department of Obstetrics and Gynecology, College of Medicine, University of Florida, Gainesville, FL 32608, USA;
- Center for Perinatal Outcomes Research, College of Medicine, University of Florida, Gainesville, FL 32608, USA;
| | - Xinsong Du
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32608, USA; (X.D.); (K.X.); (A.D.); (M.F.); (L.A.T.); (I.W.); (S.S.)
| | - Bethany Dado-Senn
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA;
| | - Ke Xu
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32608, USA; (X.D.); (K.X.); (A.D.); (M.F.); (L.A.T.); (I.W.); (S.S.)
| | - Amanda Dobrowolski
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32608, USA; (X.D.); (K.X.); (A.D.); (M.F.); (L.A.T.); (I.W.); (S.S.)
| | - Marina Magalhães
- Department of Behavioral Nursing Science, College of Nursing, University of Florida, Gainesville, FL 32603, USA;
| | - Juan J. Aristizabal-Henao
- Department of Physiological Science, Center for Environmental and Human Toxicology, College of Veterinary Science, University of Florida, Gainesville, FL 32608, USA;
| | - Bridget E. Young
- Division of Breastfeeding and Lactation Medicine, University of Rochester Medical Center, Rochester, NY 14642, USA;
| | - Magda Francois
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32608, USA; (X.D.); (K.X.); (A.D.); (M.F.); (L.A.T.); (I.W.); (S.S.)
| | - Lindsay A. Thompson
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32608, USA; (X.D.); (K.X.); (A.D.); (M.F.); (L.A.T.); (I.W.); (S.S.)
| | - Leslie A. Parker
- Center for Perinatal Outcomes Research, College of Medicine, University of Florida, Gainesville, FL 32608, USA;
| | - Josef Neu
- Department of Pediatrics, College of Medicine, University of Florida, Gainesville, FL 32608, USA;
| | - Jimena Laporta
- Department of Obstetrics and Gynecology, College of Medicine, University of Florida, Gainesville, FL 32608, USA;
| | | | - Ismael Wane
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32608, USA; (X.D.); (K.X.); (A.D.); (M.F.); (L.A.T.); (I.W.); (S.S.)
| | - Samih Samaan
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32608, USA; (X.D.); (K.X.); (A.D.); (M.F.); (L.A.T.); (I.W.); (S.S.)
| | - Timothy J. Garrett
- Department of Pathology, Immunology and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, FL 32608, USA;
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2
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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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3
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Jadhav PD, Shim YY, Paek OJ, Jeon JT, Park HJ, Park I, Park ES, Kim YJ, Reaney MJT. A Metabolomics and Big Data Approach to Cannabis Authenticity (Authentomics). Int J Mol Sci 2023; 24:ijms24098202. [PMID: 37175910 PMCID: PMC10179091 DOI: 10.3390/ijms24098202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 04/13/2023] [Accepted: 05/01/2023] [Indexed: 05/15/2023] Open
Abstract
With the increasing accessibility of cannabis (Cannabis sativa L., also known as marijuana and hemp), its products are being developed as extracts for both recreational and therapeutic use. This has led to increased scrutiny by regulatory bodies, who aim to understand and regulate the complex chemistry of these products to ensure their safety and efficacy. Regulators use targeted analyses to track the concentration of key bioactive metabolites and potentially harmful contaminants, such as metals and other impurities. However, the metabolic complexity of cannabis metabolic pathways requires a more comprehensive approach. A non-targeted metabolomic analysis of cannabis products is necessary to generate data that can be used to determine their authenticity and efficacy. An authentomics approach, which involves combining the non-targeted analysis of new samples with big data comparisons to authenticated historic datasets, provides a robust method for verifying the quality of cannabis products. To meet International Organization for Standardization (ISO) standards, it is necessary to implement the authentomics platform technology and build an integrated database of cannabis analytical results. This study is the first to review the topic of the authentomics of cannabis and its potential to meet ISO standards.
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Affiliation(s)
- Pramodkumar D Jadhav
- Department of Food and Bioproduct Sciences, University of Saskatchewan, Saskatoon, SK S7N 5A8, Canada
| | - Youn Young Shim
- Department of Food and Bioproduct Sciences, University of Saskatchewan, Saskatoon, SK S7N 5A8, Canada
- Prairie Tide Diversified Inc., Saskatoon, SK S7J 0R1, Canada
- Department of Food and Biotechnology, Korea University, Sejong 30019, Republic of Korea
| | - Ock Jin Paek
- Herbal Medicines Research Division, Ministry of Food and Drug Safety, Cheongju 28159, Republic of Korea
| | - Jung-Tae Jeon
- Yuhan Care R&D Center, Yuhan Care Co., Ltd., Yongin 17084, Republic of Korea
| | - Hyun-Je Park
- Yuhan Care R&D Center, Yuhan Care Co., Ltd., Yongin 17084, Republic of Korea
- Yuhan Natural Product R&D Center, Yuhan Care Co., Ltd., Andong 36618, Republic of Korea
| | - Ilbum Park
- Yuhan Care R&D Center, Yuhan Care Co., Ltd., Yongin 17084, Republic of Korea
| | - Eui-Seong Park
- Yuhan Care R&D Center, Yuhan Care Co., Ltd., Yongin 17084, Republic of Korea
| | - Young Jun Kim
- Department of Food and Biotechnology, Korea University, Sejong 30019, Republic of Korea
| | - Martin J T Reaney
- Department of Food and Bioproduct Sciences, University of Saskatchewan, Saskatoon, SK S7N 5A8, Canada
- Prairie Tide Diversified Inc., Saskatoon, SK S7J 0R1, Canada
- Department of Food and Biotechnology, Korea University, Sejong 30019, Republic of Korea
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4
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Shaver AO, Garcia BM, Gouveia GJ, Morse AM, Liu Z, Asef CK, Borges RM, Leach FE, Andersen EC, Amster IJ, Fernández FM, Edison AS, McIntyre LM. An anchored experimental design and meta-analysis approach to address batch effects in large-scale metabolomics. Front Mol Biosci 2022; 9:930204. [PMID: 36438654 PMCID: PMC9682135 DOI: 10.3389/fmolb.2022.930204] [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: 04/27/2022] [Accepted: 10/10/2022] [Indexed: 11/27/2022] Open
Abstract
Untargeted metabolomics studies are unbiased but identifying the same feature across studies is complicated by environmental variation, batch effects, and instrument variability. Ideally, several studies that assay the same set of metabolic features would be used to select recurring features to pursue for identification. Here, we developed an anchored experimental design. This generalizable approach enabled us to integrate three genetic studies consisting of 14 test strains of Caenorhabditis elegans prior to the compound identification process. An anchor strain, PD1074, was included in every sample collection, resulting in a large set of biological replicates of a genetically identical strain that anchored each study. This enables us to estimate treatment effects within each batch and apply straightforward meta-analytic approaches to combine treatment effects across batches without the need for estimation of batch effects and complex normalization strategies. We collected 104 test samples for three genetic studies across six batches to produce five analytical datasets from two complementary technologies commonly used in untargeted metabolomics. Here, we use the model system C. elegans to demonstrate that an augmented design combined with experimental blocks and other metabolomic QC approaches can be used to anchor studies and enable comparisons of stable spectral features across time without the need for compound identification. This approach is generalizable to systems where the same genotype can be assayed in multiple environments and provides biologically relevant features for downstream compound identification efforts. All methods are included in the newest release of the publicly available SECIMTools based on the open-source Galaxy platform.
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Affiliation(s)
- Amanda O. Shaver
- Department of Genetics, University of Georgia, Athens, GA, United States,Complex Carbohydrate Research Center, University of Georgia, Athens, GA, United States
| | - Brianna M. Garcia
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, United States,Department of Chemistry, University of Georgia, Athens, GA, United States
| | - Goncalo J. Gouveia
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, United States,Department of Biochemistry, University of Georgia, Athens, GA, United States
| | - Alison M. Morse
- Department of Molecular Genetics and Microbiology, University of Florida, Gainesville, FL, United States
| | - Zihao Liu
- Department of Molecular Genetics and Microbiology, University of Florida, Gainesville, FL, United States
| | - Carter K. Asef
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, United States
| | - Ricardo M. Borges
- Walter Mors Institute of Research on Natural Products, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Franklin E. Leach
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, United States,Department of Environmental Health Science, University of Georgia, Athens, GA, United States
| | - Erik C. Andersen
- Department of Molecular Biosciences, Northwestern University, Evanston, IL, United States
| | - I. Jonathan Amster
- Department of Chemistry, University of Georgia, Athens, GA, United States
| | - Facundo M. Fernández
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, United States
| | - Arthur S. Edison
- Department of Genetics, University of Georgia, Athens, GA, United States,Complex Carbohydrate Research Center, University of Georgia, Athens, GA, United States,Department of Biochemistry, University of Georgia, Athens, GA, United States
| | - Lauren M. McIntyre
- Department of Molecular Genetics and Microbiology, University of Florida, Gainesville, FL, United States,University of Florida Genetics Institute, University of Florida, Gainesville, FL, United States,*Correspondence: Lauren M. McIntyre,
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5
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Pan Z, Mao B, Zhang Q, Tang X, Yang B, Zhao J, Cui S, Zhang H. Postbiotics Prepared Using Lactobacillus paracasei CCFM1224 Prevent Nonalcoholic Fatty Liver Disease by Modulating the Gut Microbiota and Liver Metabolism. Int J Mol Sci 2022; 23:ijms232113522. [PMID: 36362307 PMCID: PMC9653709 DOI: 10.3390/ijms232113522] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 10/31/2022] [Accepted: 11/02/2022] [Indexed: 11/06/2022] Open
Abstract
Postbiotics are rich in a variety of bioactive components, which may have beneficial effects in inhibiting hepatic lipid accumulation. In this study, we investigated the preventive effects of postbiotics (POST) prepared from Lactobacillus paracasei on non-alcoholic fatty liver disease (NAFLD). Our results showed that when mice ingested a high-fat diet (HFD) and POST simultaneously, weight gain was slowed, epididymal white fat hypertrophy and insulin resistance were suppressed, serum biochemical indicators related to blood lipid metabolism were improved, and hepatic steatosis and liver inflammation decreased. Bacterial sequencing showed that POST modulated the gut microbiota in HFD mice, increasing the relative abundance of Akkermansia and reducing the relative abundance of Lachnospiraceae NK4A136 group, Ruminiclostridium and Bilophila. Spearman’s correlation analysis revealed significant correlations between lipid metabolism parameters and gut microbes. Functional prediction results showed that the regulation of gut microbiota was associated with the improvement of metabolic status. The metabolomic analysis of the liver revealed that POST-regulated liver metabolic pathways, such as glycerophospholipid and ether lipid metabolism, pantothenate and CoA biosynthesis, some parts of amino acid metabolism, and other metabolic pathways. In addition, POST regulated the gene expression in hepatocytes at the mRNA level, thereby regulating lipid metabolism. These findings suggest that POST plays a protective role against NAFLD and may exert its efficacy by modulating the gut microbiota and liver metabolism, and these findings may be applied to related functional foods.
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Affiliation(s)
- Zhenghao Pan
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- School of Food Science and Technology, Jiangnan University, Wuxi 214122, China
| | - Bingyong Mao
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- School of Food Science and Technology, Jiangnan University, Wuxi 214122, China
| | - Qiuxiang Zhang
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- School of Food Science and Technology, Jiangnan University, Wuxi 214122, China
| | - Xin Tang
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- School of Food Science and Technology, Jiangnan University, Wuxi 214122, China
| | - Bo Yang
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- School of Food Science and Technology, Jiangnan University, Wuxi 214122, China
| | - Jianxin Zhao
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- School of Food Science and Technology, Jiangnan University, Wuxi 214122, China
| | - Shumao Cui
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- School of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- Correspondence: ; Tel.: +86-0510-85912155
| | - Hao Zhang
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- School of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- National Engineering Research Center for Functional Food, Jiangnan University, Wuxi 214122, China
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6
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McIntyre LM, Huertas F, Morse AM, Kaletsky R, Murphy CT, Kalia V, Miller GW, Moskalenko O, Conesa A, Mor DE. GAIT-GM integrative cross-omics analyses reveal cholinergic defects in a C. elegans model of Parkinson's disease. Sci Rep 2022; 12:3268. [PMID: 35228596 PMCID: PMC8885929 DOI: 10.1038/s41598-022-07238-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Accepted: 01/24/2022] [Indexed: 11/09/2022] Open
Abstract
Parkinson’s disease (PD) is a disabling neurodegenerative disorder in which multiple cell types, including dopaminergic and cholinergic neurons, are affected. The mechanisms of neurodegeneration in PD are not fully understood, limiting the development of therapies directed at disease-relevant molecular targets. C. elegans is a genetically tractable model system that can be used to disentangle disease mechanisms in complex diseases such as PD. Such mechanisms can be studied combining high-throughput molecular profiling technologies such as transcriptomics and metabolomics. However, the integrative analysis of multi-omics data in order to unravel disease mechanisms is a challenging task without advanced bioinformatics training. Galaxy, a widely-used resource for enabling bioinformatics analysis by the broad scientific community, has poor representation of multi-omics integration pipelines. We present the integrative analysis of gene expression and metabolite levels of a C. elegans PD model using GAIT-GM, a new Galaxy tool for multi-omics data analysis. Using GAIT-GM, we discovered an association between branched-chain amino acid metabolism and cholinergic neurons in the C. elegans PD model. An independent follow-up experiment uncovered cholinergic neurodegeneration in the C. elegans model that is consistent with cholinergic cell loss observed in PD. GAIT-GM is an easy to use Galaxy-based tool for generating novel testable hypotheses of disease mechanisms involving gene-metabolite relationships.
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Affiliation(s)
- Lauren M McIntyre
- University of Florida Genetics Institute, Gainesville, FL, USA. .,Department of Molecular Genetics and Microbiology, University of Florida, Gainesville, FL, USA.
| | - Francisco Huertas
- University of Florida Genetics Institute, Gainesville, FL, USA.,Department of Microbiology and Cell Science, University of Florida, Gainesville, FL, 32610, USA
| | - Alison M Morse
- University of Florida Genetics Institute, Gainesville, FL, USA.,Department of Molecular Genetics and Microbiology, University of Florida, Gainesville, FL, USA
| | - Rachel Kaletsky
- Department of Molecular Biology and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08544, USA
| | - Coleen T Murphy
- Department of Molecular Biology and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08544, USA
| | - Vrinda Kalia
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, 10032, USA
| | - Gary W Miller
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, 10032, USA
| | - Olexander Moskalenko
- University of Florida Research Computing, University of Florida, Gainesville, FL, 32610, USA
| | - Ana Conesa
- Department of Microbiology and Cell Science, University of Florida, Gainesville, FL, 32610, USA. .,Institute for Integrative Systems Biology, Spanish National Research Council, 46980, Paterna, Spain.
| | - Danielle E Mor
- Department of Neuroscience and Regenerative Medicine, Medical College of Georgia at Augusta University, Augusta, GA, 30912, USA.
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7
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Loftfield E, Stepien M, Viallon V, Trijsburg L, Rothwell JA, Robinot N, Biessy C, Bergdahl IA, Bodén S, Schulze MB, Bergman M, Weiderpass E, Schmidt JA, Zamora-Ros R, Nøst TH, Sandanger TM, Sonestedt E, Ohlsson B, Katzke V, Kaaks R, Ricceri F, Tjønneland A, Dahm CC, Sánchez MJ, Trichopoulou A, Tumino R, Chirlaque MD, Masala G, Ardanaz E, Vermeulen R, Brennan P, Albanes D, Weinstein SJ, Scalbert A, Freedman ND, Gunter MJ, Jenab M, Sinha R, Keski-Rahkonen P, Ferrari P. Novel Biomarkers of Habitual Alcohol Intake and Associations With Risk of Pancreatic and Liver Cancers and Liver Disease Mortality. J Natl Cancer Inst 2021; 113:1542-1550. [PMID: 34010397 PMCID: PMC8562969 DOI: 10.1093/jnci/djab078] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 02/24/2021] [Accepted: 04/09/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Alcohol is an established risk factor for several cancers, but modest alcohol-cancer associations may be missed because of measurement error in self-reported assessments. Biomarkers of habitual alcohol intake may provide novel insight into the relationship between alcohol and cancer risk. METHODS Untargeted metabolomics was used to identify metabolites correlated with self-reported habitual alcohol intake in a discovery dataset from the European Prospective Investigation into Cancer and Nutrition (EPIC; n = 454). Statistically significant correlations were tested in independent datasets of controls from case-control studies nested within EPIC (n = 280) and the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC; n = 438) study. Conditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for associations of alcohol-associated metabolites and self-reported alcohol intake with risk of pancreatic cancer, hepatocellular carcinoma (HCC), liver cancer, and liver disease mortality in the contributing studies. RESULTS Two metabolites displayed a dose-response association with self-reported alcohol intake: 2-hydroxy-3-methylbutyric acid and an unidentified compound. A 1-SD (log2) increase in levels of 2-hydroxy-3-methylbutyric acid was associated with risk of HCC (OR = 2.54, 95% CI = 1.51 to 4.27) and pancreatic cancer (OR = 1.43, 95% CI = 1.03 to 1.99) in EPIC and liver cancer (OR = 2.00, 95% CI = 1.44 to 2.77) and liver disease mortality (OR = 2.16, 95% CI = 1.63 to 2.86) in ATBC. Conversely, a 1-SD (log2) increase in questionnaire-derived alcohol intake was not associated with HCC or pancreatic cancer in EPIC or liver cancer in ATBC but was associated with liver disease mortality (OR = 2.19, 95% CI = 1.60 to 2.98) in ATBC. CONCLUSIONS 2-hydroxy-3-methylbutyric acid is a candidate biomarker of habitual alcohol intake that may advance the study of alcohol and cancer risk in population-based studies.
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Affiliation(s)
- Erikka Loftfield
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute,National Institutes of Health, Bethesda, MD, USA
| | - Magdalena Stepien
- Nutritional Epidemiology Group, International Agency for Research on Cancer (IARC-WHO), Lyon, France
| | - Vivian Viallon
- Nutritional Methodology and Biostatistics Group, International Agency for Research on Cancer (IARC-WHO), Lyon, France
| | - Laura Trijsburg
- Nutritional Methodology and Biostatistics Group, International Agency for Research on Cancer (IARC-WHO), Lyon, France
| | - Joseph A Rothwell
- Nutritional Epidemiology Group, International Agency for Research on Cancer (IARC-WHO), Lyon, France
- Gustave Roussy, F-94805, Villejuif, France
- Biomarkers Group, International Agency for Research on Cancer (IARC-WHO), Lyon, France
| | - Nivonirina Robinot
- Centre for Epidemiology and Population Health (U1018), Generations and Health team, Faculté de Médecine, Université Paris-Saclay, UVSQ, INSERM, Villejuif, France
| | - Carine Biessy
- Nutritional Methodology and Biostatistics Group, International Agency for Research on Cancer (IARC-WHO), Lyon, France
| | | | - Stina Bodén
- Department of Radiation Sciences, Oncology, Umeå University, Umeå, Sweden
| | - Matthias B Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
- Institute of Nutritional Science, University of Potsdam, Nuthetal, Germany
| | - Manuela Bergman
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
- Institute of Nutritional Science, University of Potsdam, Nuthetal, Germany
| | | | - Julie A Schmidt
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Raul Zamora-Ros
- Unit of Nutrition and Cancer, Epidemiology Research Program, Catalan Institute of Oncology, Bellvitge Biomedical Research Institute (IDIBELL), Hospitalet de Llobregat, Barcelona, Spain
| | - Therese H Nøst
- Department of Community Medicine, UiT- The Arctic University of Norway, Tromsø, Norway
| | - Torkjel M Sandanger
- Department of Community Medicine, UiT- The Arctic University of Norway, Tromsø, Norway
| | - Emily Sonestedt
- Department of Clinical Sciences in Malmö, Lund University, Malmö, Sweden
| | - Bodil Ohlsson
- Department of Clinical Sciences in Malmö, Lund University, Malmö, Sweden
| | - Verena Katzke
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Fulvio Ricceri
- Department of Clinical and Biological Sciences, University of Turin, Italy; Unit of Epidemiology, Regional Health Service ASL TO3, Grugliasco, TO, Italy
| | - Anne Tjønneland
- Danish Cancer Society Research Center; University of Copenhagen, Department of Public Health
| | | | - Maria-Jose Sánchez
- Escuela Andaluza de Salud Pública (EASP), Granada, Spain; Instituto de Investigación Biosanitaria ibs. GRANADA, Granada, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Granada, Granada, Spain
| | | | - Rosario Tumino
- Cancer Registry and Histopathology Department, Provincial Health Authority (ASP 7), Ragusa, Italy
| | - María-Dolores Chirlaque
- Department of Epidemiology, Regional Health Council, IMIB-Arrixaca, Murcia University, Murcia, Spain
- CIBER in Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Giovanna Masala
- Cancer Risk Factors and Life-Style Epidemiology Unit, Institute for Cancer Research, Prevention and Clinical Network—ISPRO, Florence, Italy
| | - Eva Ardanaz
- Navarra Public Health Institute, Pamplona, Spain
- IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
- CIBER Epidemiology and Public Health CIBERESP, Madrid, Spain
| | - Roel Vermeulen
- Institute for Risk Assessment Sciences, Division of Environmental Epidemiology, Utrecht University, Utrecht, the Netherlands
| | - Paul Brennan
- Genetic Epidemiology Group, International Agency for Research on Cancer (IARC-WHO), Lyon, France
| | - Demetrius Albanes
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute,National Institutes of Health, Bethesda, MD, USA
| | - Stephanie J Weinstein
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute,National Institutes of Health, Bethesda, MD, USA
| | - Augustin Scalbert
- Centre for Epidemiology and Population Health (U1018), Generations and Health team, Faculté de Médecine, Université Paris-Saclay, UVSQ, INSERM, Villejuif, France
| | - Neal D Freedman
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute,National Institutes of Health, Bethesda, MD, USA
| | - Marc J Gunter
- Nutritional Epidemiology Group, International Agency for Research on Cancer (IARC-WHO), Lyon, France
| | - Mazda Jenab
- Nutritional Epidemiology Group, International Agency for Research on Cancer (IARC-WHO), Lyon, France
| | - Rashmi Sinha
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute,National Institutes of Health, Bethesda, MD, USA
| | - Pekka Keski-Rahkonen
- Centre for Epidemiology and Population Health (U1018), Generations and Health team, Faculté de Médecine, Université Paris-Saclay, UVSQ, INSERM, Villejuif, France
| | - Pietro Ferrari
- Nutritional Methodology and Biostatistics Group, International Agency for Research on Cancer (IARC-WHO), Lyon, France
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8
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Schwaiger-Haber M, Stancliffe E, Arends V, Thyagarajan B, Sindelar M, Patti GJ. A Workflow to Perform Targeted Metabolomics at the Untargeted Scale on a Triple Quadrupole Mass Spectrometer. ACS MEASUREMENT SCIENCE AU 2021; 1:35-45. [PMID: 34476422 PMCID: PMC8377714 DOI: 10.1021/acsmeasuresciau.1c00007] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Indexed: 05/25/2023]
Abstract
The thousands of features commonly observed when performing untargeted metabolomics with quadrupole time-of-flight (QTOF) and Orbitrap mass spectrometers often correspond to only a few hundred unique metabolites of biological origin, which is in the range of what can be assayed in a single targeted metabolomics experiment by using a triple quadrupole (QqQ) mass spectrometer. A major benefit of performing targeted metabolomics with QqQ mass spectrometry is the affordability of the instruments relative to high-resolution QTOF and Orbitrap platforms. Optimizing targeted methods to profile hundreds of metabolites on a QqQ mass spectrometer, however, has historically been limited by the availability of authentic standards, particularly for "unknowns" that have yet to be structurally identified. Here, we report a strategy to develop multiple reaction monitoring (MRM) methods for QqQ instruments on the basis of high-resolution spectra, thereby enabling us to use data from untargeted metabolomics to design targeted experiments without the need for authentic standards. We demonstrate that using high-resolution fragmentation data alone to design MRM methods results in the same quantitative performance as when methods are optimized by measuring authentic standards on QqQ instruments, as is conventionally done. The approach was validated by showing that Orbitrap ID-X data can be used to establish MRM methods on a Thermo TSQ Altis and two Agilent QqQs for hundreds of metabolites, including unknowns, without a dependence on standards. Finally, we highlight an application where metabolite profiling was performed on an ID-X and a QqQ by using the strategy introduced here, with both data sets yielding the same result. The described approach therefore allows us to use QqQ instruments, which are often associated with targeted metabolomics, to profile knowns and unknowns at a comprehensive scale that is typical of untargeted metabolomics.
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Affiliation(s)
- Michaela Schwaiger-Haber
- Department
of Chemistry, Washington University in St.
Louis, St. Louis, Missouri 63130, United States
- Department
of Medicine, Washington University in St.
Louis, St. Louis, Missouri 63130, United States
| | - Ethan Stancliffe
- Department
of Chemistry, Washington University in St.
Louis, St. Louis, Missouri 63130, United States
- Department
of Medicine, Washington University in St.
Louis, St. Louis, Missouri 63130, United States
| | - Valerie Arends
- Department
of Laboratory Medicine and Pathology, University
of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Bharat Thyagarajan
- Department
of Laboratory Medicine and Pathology, University
of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Miriam Sindelar
- Department
of Chemistry, Washington University in St.
Louis, St. Louis, Missouri 63130, United States
- Department
of Medicine, Washington University in St.
Louis, St. Louis, Missouri 63130, United States
| | - Gary J. Patti
- Department
of Chemistry, Washington University in St.
Louis, St. Louis, Missouri 63130, United States
- Department
of Medicine, Washington University in St.
Louis, St. Louis, Missouri 63130, United States
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9
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Gouveia GJ, Shaver AO, Garcia BM, Morse AM, Andersen EC, Edison AS, McIntyre LM. Long-Term Metabolomics Reference Material. Anal Chem 2021; 93:9193-9199. [PMID: 34156835 PMCID: PMC8996483 DOI: 10.1021/acs.analchem.1c01294] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
The use of quality control samples in metabolomics ensures data quality, reproducibility, and comparability between studies, analytical platforms, and laboratories. Long-term, stable, and sustainable reference materials (RMs) are a critical component of the quality assurance/quality control (QA/QC) system; however, the limited selection of currently available matrix-matched RMs reduces their applicability for widespread use. To produce an RM in any context, for any matrix that is robust to changes over the course of time, we developed iterative batch averaging method (IBAT). To illustrate this method, we generated 11 independently grown Escherichia coli batches and made an RM over the course of 10 IBAT iterations. We measured the variance of these materials by nuclear magnetic resonance (NMR) and showed that IBAT produces a stable and sustainable RM over time. This E. coli RM was then used as a food source to produce a Caenorhabditis elegans RM for a metabolomics experiment. The metabolite extraction of this material, alongside 41 independently grown individual C. elegans samples of the same genotype, allowed us to estimate the proportion of sample variation in preanalytical steps. From the NMR data, we found that 40% of the metabolite variance is due to the metabolite extraction process and analysis and 60% is due to sample-to-sample variance. The availability of RMs in untargeted metabolomics is one of the predominant needs of the metabolomics community that reach beyond quality control practices. IBAT addresses this need by facilitating the production of biologically relevant RMs and increasing their widespread use.
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Affiliation(s)
- Goncalo J Gouveia
- Department of Biochemistry & Molecular Biology, University of Georgia, Green Street, Athens, Georgia 30602, United States
- Complex Carbohydrate Research Center, University of Georgia, 315, Riverbend Road, Athens, Georgia 30602, United States
| | - Amanda O Shaver
- Department of Genetics, University of Georgia, Green Street, Athens, Georgia 30602, United States
- Complex Carbohydrate Research Center, University of Georgia, 315, Riverbend Road, Athens, Georgia 30602, United States
| | - Brianna M Garcia
- Department of Chemistry, University of Georgia, 140, Cedar Street, Athens, Georgia 30602, United States
- Complex Carbohydrate Research Center, University of Georgia, 315, Riverbend Road, Athens, Georgia 30602, United States
| | - Alison M Morse
- Department of Molecular Genetics and Microbiology, University of Florida Genetics Institute, University of Florida, Mowry Road, Gainesville, Florida 32610, United States
| | - Erik C Andersen
- Department of Molecular Biosciences, Northwestern University, 2205, Tech Drive, Evanston, Illinois 60208, United States
| | - Arthur S Edison
- Department of Biochemistry & Molecular Biology, University of Georgia, Green Street, Athens, Georgia 30602, United States
- Department of Genetics, University of Georgia, Green Street, Athens, Georgia 30602, United States
- Complex Carbohydrate Research Center, University of Georgia, 315, Riverbend Road, Athens, Georgia 30602, United States
| | - Lauren M McIntyre
- Department of Molecular Genetics and Microbiology, University of Florida Genetics Institute, University of Florida, Mowry Road, Gainesville, Florida 32610, United States
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10
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Tan SK, Mahmud I, Fontanesi F, Puchowicz M, Neumann CKA, Griswold AJ, Patel R, Dispagna M, Ahmed HH, Gonzalgo ML, Brown JM, Garrett TJ, Welford SM. Obesity-Dependent Adipokine Chemerin Suppresses Fatty Acid Oxidation to Confer Ferroptosis Resistance. Cancer Discov 2021; 11:2072-2093. [PMID: 33757970 DOI: 10.1158/2159-8290.cd-20-1453] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 02/15/2021] [Accepted: 03/18/2021] [Indexed: 12/13/2022]
Abstract
Clear cell renal cell carcinoma (ccRCC) is characterized by accumulation of neutral lipids and adipogenic transdifferentiation. We assessed adipokine expression in ccRCC and found that tumor tissues and patient plasma exhibit obesity-dependent elevations of the adipokine chemerin. Attenuation of chemerin by several approaches led to significant reduction in lipid deposition and impairment of tumor cell growth in vitro and in vivo. A multi-omics approach revealed that chemerin suppresses fatty acid oxidation, preventing ferroptosis, and maintains fatty acid levels that activate hypoxia-inducible factor 2α expression. The lipid coenzyme Q and mitochondrial complex IV, whose biogeneses are lipid-dependent, were found to be decreased after chemerin inhibition, contributing to lipid reactive oxygen species production. Monoclonal antibody targeting chemerin led to reduced lipid storage and diminished tumor growth, demonstrating translational potential of chemerin inhibition. Collectively, the results suggest that obesity and tumor cells contribute to ccRCC through the expression of chemerin, which is indispensable in ccRCC biology. SIGNIFICANCE: Identification of a hypoxia-inducible factor-dependent adipokine that prevents fatty acid oxidation and causes escape from ferroptosis highlights a critical metabolic dependency unique in the clear cell subtype of kidney cancer. Targeting lipid metabolism via inhibition of a soluble factor is a promising pharmacologic approach to expand therapeutic strategies for patients with ccRCC.See related commentary by Reznik et al., p. 1879.This article is highlighted in the In This Issue feature, p. 1861.
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Affiliation(s)
- Sze Kiat Tan
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, Florida.,Sheila and David Fuente Graduate Program in Cancer Biology, University of Miami Miller School of Medicine, Miami, Florida
| | - Iqbal Mahmud
- Department of Pathology, Immunology and Laboratory Medicine, UF Health, UF Health Cancer Center, Southeast Center for Integrated Metabolomics, Clinical and Translational Science Institute, College of Medicine, University of Florida, Gainesville, Florida
| | - Flavia Fontanesi
- Department of Biochemistry and Molecular Biology, University of Miami Miller School of Medicine, Miami, Florida
| | - Michelle Puchowicz
- Department of Pediatrics, Metabolic Phenotyping Core, Pediatric Obesity Program, University of Tennessee Health Science Center, Memphis, Tennessee
| | - Chase K A Neumann
- Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio.,Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, Ohio
| | - Anthony J Griswold
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, Florida
| | - Rutulkumar Patel
- Department of Radiation Oncology, Duke University School of Medicine, Durham, North Carolina
| | - Marco Dispagna
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, Florida
| | - Hamzah H Ahmed
- Department of Pathology, Immunology and Laboratory Medicine, UF Health, UF Health Cancer Center, Southeast Center for Integrated Metabolomics, Clinical and Translational Science Institute, College of Medicine, University of Florida, Gainesville, Florida.,Diagnostic Radiology Department, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Mark L Gonzalgo
- Department of Urology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, Florida
| | - J Mark Brown
- Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio.,Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, Ohio.,Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio.,Center for Microbiome and Human Health, Cleveland Clinic Foundation, Cleveland, Ohio
| | - Timothy J Garrett
- Department of Pathology, Immunology and Laboratory Medicine, UF Health, UF Health Cancer Center, Southeast Center for Integrated Metabolomics, Clinical and Translational Science Institute, College of Medicine, University of Florida, Gainesville, Florida
| | - Scott M Welford
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, Florida.
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11
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Koelmel JP, Lin EZ, Nichols A, Guo P, Zhou Y, Godri Pollitt KJ. Head, Shoulders, Knees, and Toes: Placement of Wearable Passive Samplers Alters Exposure Profiles Observed. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:3796-3806. [PMID: 33625210 DOI: 10.1021/acs.est.0c05522] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Chemical exposures are a major risk factor for many diseases. Comprehensive characterization of personal exposures is necessary to highlight chemicals of concern and factors that influence these chemical exposure dynamics. For this purpose, wearable passive samplers can be applied to assess longitudinal personal exposures to airborne contaminants. Questions remain regarding the impact of sampler placement at different locations of the body on the exposure profiles observed and how these placements affect the monitoring of seasonal dynamics in exposures. This study assessed personal air contaminant exposure using passive samplers worn in parallel across 32 participant's wrists, chest, and shoes over 24 h. Samplers were analyzed by thermal desorption gas chromatography high-resolution mass spectrometry. Personal exposure profiles were similar for about one-third of the 275 identified chemicals, irrespective of sampler placement. Signals of certain semivolatile organic compounds (SVOCs) were enhanced in shoes and, to a lesser extent, wrist samplers, as compared to those in chest samplers. Signals of volatile organic compounds were less impacted by sampler placement. Results showed that chest samplers predominantly captured more volatile exposures, as compared to those of particle-bound exposures, which may indicate predominant monitoring of chemicals via the inhalation route of exposure for chest samplers. In contrast, shoe samplers were more sensitive to particle-bound SVOCs. Seventy-one chemicals changed across participants between winter and summer in the same manner for two or more different sampler placements on the body, whereas 122 chemicals were observed to have seasonal differences in only one placement. Hence, the placement in certain cases significantly impacts exposure dynamics observed. This work shows that it is essential in epidemiological studies undertaking exposure assessment to consider the consequence of the placement of exposure monitors.
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Affiliation(s)
- Jeremy P Koelmel
- Department of Environmental Health Sciences, Yale School of Public Health, 60 College Street, New Haven, Connecticut 06510, United States
| | - Elizabeth Z Lin
- Department of Environmental Health Sciences, Yale School of Public Health, 60 College Street, New Haven, Connecticut 06510, United States
| | - Amy Nichols
- Department of Chemical and Environmental Engineering, Yale University, 17 Hillhouse Avenue, New Haven, Connecticut 06520, United States
| | - Pengfei Guo
- Department of Environmental Health Sciences, Yale School of Public Health, 60 College Street, New Haven, Connecticut 06510, United States
| | - Yakun Zhou
- Department of Environmental Health Sciences, Yale School of Public Health, 60 College Street, New Haven, Connecticut 06510, United States
| | - Krystal J Godri Pollitt
- Department of Environmental Health Sciences, Yale School of Public Health, 60 College Street, New Haven, Connecticut 06510, United States
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12
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Richard-Forget F, Atanasova V, Chéreau S. Using metabolomics to guide strategies to tackle the issue of the contamination of food and feed with mycotoxins: A review of the literature with specific focus on Fusarium mycotoxins. Food Control 2021. [DOI: 10.1016/j.foodcont.2020.107610] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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13
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Amer B, Baidoo EEK. Omics-Driven Biotechnology for Industrial Applications. Front Bioeng Biotechnol 2021; 9:613307. [PMID: 33708762 PMCID: PMC7940536 DOI: 10.3389/fbioe.2021.613307] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 01/11/2021] [Indexed: 12/11/2022] Open
Abstract
Biomanufacturing is a key component of biotechnology that uses biological systems to produce bioproducts of commercial relevance, which are of great interest to the energy, material, pharmaceutical, food, and agriculture industries. Biotechnology-based approaches, such as synthetic biology and metabolic engineering are heavily reliant on "omics" driven systems biology to characterize and understand metabolic networks. Knowledge gained from systems biology experiments aid the development of synthetic biology tools and the advancement of metabolic engineering studies toward establishing robust industrial biomanufacturing platforms. In this review, we discuss recent advances in "omics" technologies, compare the pros and cons of the different "omics" technologies, and discuss the necessary requirements for carrying out multi-omics experiments. We highlight the influence of "omics" technologies on the production of biofuels and bioproducts by metabolic engineering. Finally, we discuss the application of "omics" technologies to agricultural and food biotechnology, and review the impact of "omics" on current COVID-19 research.
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Affiliation(s)
- Bashar Amer
- Lawrence Berkeley National Laboratory, Joint BioEnergy Institute, Emeryville, CA, United States
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
| | - Edward E. K. Baidoo
- Lawrence Berkeley National Laboratory, Joint BioEnergy Institute, Emeryville, CA, United States
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
- U.S. Department of Energy, Agile BioFoundry, Emeryville, CA, United States
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14
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Koelmel JP, Lin EZ, Guo P, Zhou J, He J, Chen A, Gao Y, Deng F, Dong H, Liu Y, Cha Y, Fang J, Beecher C, Shi X, Tang S, Godri Pollitt KJ. Exploring the external exposome using wearable passive samplers - The China BAPE study. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 270:116228. [PMID: 33360595 DOI: 10.1016/j.envpol.2020.116228] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 12/02/2020] [Accepted: 12/03/2020] [Indexed: 06/12/2023]
Abstract
Environmental exposures are one of the greatest threats to human health, yet we lack tools to answer simple questions about our exposures: what are our personal exposure profiles and how do they change overtime (external exposome), how toxic are these chemicals, and what are the sources of these exposures? To capture variation in personal exposures to airborne chemicals in the gas and particulate phases and identify exposures which pose the greatest health risk, wearable exposure monitors can be deployed. In this study, we deployed passive air sampler wristbands with 84 healthy participants (aged 60-69 years) as part of the Biomarkers for Air Pollutants Exposure (China BAPE) study. Participants wore the wristband samplers for 3 days each month for five consecutive months. Passive samplers were analyzed using a novel gas chromatography high resolution mass spectrometry data-processing workflow to overcome the bottleneck of processing large datasets and improve confidence in the resulting identified features. The toxicity of chemicals observed frequently in personal exposures were predicted to identify exposures of potential concern via inhalation route or other routes of airborne contaminant exposure. Three exposures were highlighted based on elevated toxicity: dichlorvos from insecticides (mosquito/malaria control), naphthalene partly from mothballs, and 183 polyaromatic hydrocarbons from multiple sources. Other exposures explored in this study are linked to diet and personal care products, cigarette smoke, sunscreen, and antimicrobial soaps. We highlight the potential for this workflow employing wearable passive samplers for prioritizing chemicals of concern at both the community and individual level, and characterizing sources of exposures for follow up interventions.
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Affiliation(s)
- Jeremy P Koelmel
- Department of Environmental Health Sciences, School of Public Health, Yale University, New Haven, CT, 06520, USA
| | - Elizabeth Z Lin
- Department of Environmental Health Sciences, School of Public Health, Yale University, New Haven, CT, 06520, USA
| | - Pengfei Guo
- Department of Environmental Health Sciences, School of Public Health, Yale University, New Haven, CT, 06520, USA
| | - Jieqiong Zhou
- Department of Environmental Health Sciences, School of Public Health, Yale University, New Haven, CT, 06520, USA
| | - Jucong He
- Department of Environmental Health Sciences, School of Public Health, Yale University, New Haven, CT, 06520, USA
| | - Alex Chen
- Department of Computer Science, Yale University, New Haven, CT, 06520, USA
| | - Ying Gao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Fuchang Deng
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Haoran Dong
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Yuanyuan Liu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Yu'e Cha
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Jianlong Fang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | | | - Xiaoming Shi
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China; Center for Global Health, Nanjing Medical University, Nanjing, Jiangsu, 211166, China
| | - Song Tang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China; Center for Global Health, Nanjing Medical University, Nanjing, Jiangsu, 211166, China
| | - Krystal J Godri Pollitt
- Department of Environmental Health Sciences, School of Public Health, Yale University, New Haven, CT, 06520, USA.
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15
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Monascus purpureus-fermented common buckwheat protects against dyslipidemia and non-alcoholic fatty liver disease through the regulation of liver metabolome and intestinal microbiome. Food Res Int 2020; 136:109511. [DOI: 10.1016/j.foodres.2020.109511] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 06/05/2020] [Accepted: 06/29/2020] [Indexed: 02/07/2023]
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16
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Domanskyi S, Piermarocchi C, Mias GI. PyIOmica: longitudinal omics analysis and trend identification. Bioinformatics 2020; 36:2306-2307. [PMID: 31778155 PMCID: PMC7141865 DOI: 10.1093/bioinformatics/btz896] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Revised: 10/26/2019] [Accepted: 11/26/2019] [Indexed: 12/16/2022] Open
Abstract
SUMMARY PyIOmica is an open-source Python package focusing on integrating longitudinal multiple omics datasets, characterizing and categorizing temporal trends. The package includes multiple bioinformatics tools including data normalization, annotation, categorization, visualization and enrichment analysis for gene ontology terms and pathways. Additionally, the package includes an implementation of visibility graphs to visualize time series as networks. AVAILABILITY AND IMPLEMENTATION PyIOmica is implemented as a Python package (pyiomica), available for download and installation through the Python Package Index (https://pypi.python.org/pypi/pyiomica), and can be deployed using the Python import function following installation. PyIOmica has been tested on Mac OS X, Unix/Linux and Microsoft Windows. The application is distributed under an MIT license. Source code for each release is also available for download on Zenodo (https://doi.org/10.5281/zenodo.3548040). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics.
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Affiliation(s)
- Sergii Domanskyi
- Department of Physics and Astronomy, East Lansing, MI 48824, USA
| | | | - George I Mias
- Department of Physics and Astronomy, East Lansing, MI 48824, USA.,Department of Biochemistry and Molecular Biology, East Lansing, MI 48824, USA.,Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
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17
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Patterson Rosa L, Mallicote MF, Long MT, Brooks SA. Metabogenomics reveals four candidate regions involved in the pathophysiology of Equine Metabolic Syndrome. Mol Cell Probes 2020; 53:101620. [PMID: 32659253 DOI: 10.1016/j.mcp.2020.101620] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 06/01/2020] [Accepted: 06/14/2020] [Indexed: 02/02/2023]
Abstract
An analogous condition to human metabolic syndrome, Equine Metabolic Syndrome (EMS) is defined by several clinical signs including obesity, hyperinsulinemia, and peripheral insulin dysregulation (ID). Affected horses may also exhibit hypertension, hyperlipemia and systemic inflammation. Measures of ID typically comprise the gold-standard for diagnosis in veterinary care. Yet, the dynamic nature of insulin homeostasis and complex procedures of typical assays make accurate quantification of ID and EMS challenging. This work aimed to investigate new strategies for identification of biochemical markers and correlated genes in EMS. To quantify EMS risk within this population, we utilized a composite score derived from nine common diagnostic variables. We applied a global liquid chromatography/mass spectroscopy approach (HPLC/MS) to whole plasma collected from 49 Arabian horses, resulting in 3392 high-confidence features and identification of putative metabolites in public databases. We performed a genome wide association analysis with genotypes from the 670k Affymetrix Equine SNP array utilizing EMS-correlated metabolites as phenotypes. We discovered four metabolite features significantly correlated with EMS score (P < 1.474 × 10-5). GWAs for these features results (P = 6.787 × 10-7, Bonferroni) identified four unique candidate regions (r2 > 0.4) containing 63 genes. Significant genomic markers capture 43.52% of the variation in the original EMS score phenotype. The identified genomic loci provide insight into the pathways controlling variation in EMS and the origin of genetic predisposition to the condition. Rapid, feasible and accurate diagnostic tools derived from metabogenomics can be translated into measurable benefits in the timeline and quality of preventative management practices to preserve health in horses.
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Affiliation(s)
- Laura Patterson Rosa
- Department of Animal Sciences, University of Florida, Gainesville, FL, United States of America, PO Box 110910, Gainesville, FL, 32611, USA
| | - Martha F Mallicote
- Department of Large Animal Clinical Sciences, College of Veterinary Medicine, University of Florida, PO Box 100136, Gainesville, FL, 32610, USA
| | - Maureen T Long
- Department of Infectious Diseases and Pathology, College of Veterinary Medicine, University of Florida, PO Box 100123, Gainesville, FL, 32610, USA
| | - Samantha A Brooks
- Department of Animal Sciences and UF Genetics Institute, University of Florida, Gainesville, FL, United States of America, PO Box 110910, Gainesville, FL, 32611, USA.
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18
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Maruvada P, Lampe JW, Wishart DS, Barupal D, Chester DN, Dodd D, Djoumbou-Feunang Y, Dorrestein PC, Dragsted LO, Draper J, Duffy LC, Dwyer JT, Emenaker NJ, Fiehn O, Gerszten RE, B Hu F, Karp RW, Klurfeld DM, Laughlin MR, Little AR, Lynch CJ, Moore SC, Nicastro HL, O'Brien DM, Ordovás JM, Osganian SK, Playdon M, Prentice R, Raftery D, Reisdorph N, Roche HM, Ross SA, Sang S, Scalbert A, Srinivas PR, Zeisel SH. Perspective: Dietary Biomarkers of Intake and Exposure-Exploration with Omics Approaches. Adv Nutr 2020; 11:200-215. [PMID: 31386148 PMCID: PMC7442414 DOI: 10.1093/advances/nmz075] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
While conventional nutrition research has yielded biomarkers such as doubly labeled water for energy metabolism and 24-h urinary nitrogen for protein intake, a critical need exists for additional, equally robust biomarkers that allow for objective assessment of specific food intake and dietary exposure. Recent advances in high-throughput MS combined with improved metabolomics techniques and bioinformatic tools provide new opportunities for dietary biomarker development. In September 2018, the NIH organized a 2-d workshop to engage nutrition and omics researchers and explore the potential of multiomics approaches in nutritional biomarker research. The current Perspective summarizes key gaps and challenges identified, as well as the recommendations from the workshop that could serve as a guide for scientists interested in dietary biomarkers research. Topics addressed included study designs for biomarker development, analytical and bioinformatic considerations, and integration of dietary biomarkers with other omics techniques. Several clear needs were identified, including larger controlled feeding studies, testing a variety of foods and dietary patterns across diverse populations, improved reporting standards to support study replication, more chemical standards covering a broader range of food constituents and human metabolites, standardized approaches for biomarker validation, comprehensive and accessible food composition databases, a common ontology for dietary biomarker literature, and methodologic work on statistical procedures for intake biomarker discovery. Multidisciplinary research teams with appropriate expertise are critical to moving forward the field of dietary biomarkers and producing robust, reproducible biomarkers that can be used in public health and clinical research.
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Affiliation(s)
- Padma Maruvada
- National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, USA
| | - Johanna W Lampe
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- School of Public Health, University of Washington, Seattle, WA, USA
| | - David S Wishart
- Departments of Biological Sciences and Computing Science, University of Alberta, Edmonton, Alberta, Canada
| | - Dinesh Barupal
- West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, Davis, CA, USA
| | - Deirdra N Chester
- Division of Nutrition, Institute of Food Safety and Nutrition at the National Institute of Food and Agriculture, USDA, Washington, DC, USA
| | - Dylan Dodd
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA
| | - Yannick Djoumbou-Feunang
- Departments of Biological Sciences and Computing Science, University of Alberta, Edmonton, Alberta, Canada
| | - Pieter C Dorrestein
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, USA
| | - Lars O Dragsted
- Department of Nutrition, Exercise, and Sports, Section of Preventive and Clinical Nutrition, University of Copenhagen, Copenhagen, Denmark
| | - John Draper
- Institute of Biological Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, Ceredigion, United Kingdom
| | - Linda C Duffy
- National Institutes of Health, National Center for Complementary and Integrative Health, Bethesda, MD, USA
| | - Johanna T Dwyer
- National Institutes of Health, Office of Dietary Supplements, Bethesda, MD, USA
| | - Nancy J Emenaker
- National Institutes of Health, National Cancer Institute, Rockville, MD, USA
| | - Oliver Fiehn
- West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, Davis, CA, USA
| | - Robert E Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Frank B Hu
- Departments of Nutrition; Epidemiology and Statistics, Harvard TH Chan School of Public Health, Boston, MA, USA
- Channing Division for Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Robert W Karp
- National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, USA
| | - David M Klurfeld
- Department of Nutrition, Food Safety/Quality, USDA—Agricultural Research Service, Beltsville, MD, USA
| | - Maren R Laughlin
- National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, USA
| | - A Roger Little
- National Institutes of Health, National Institute on Drug Abuse, Bethesda, MD, USA
| | - Christopher J Lynch
- National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, USA
| | - Steven C Moore
- National Institutes of Health, National Cancer Institute, Rockville, MD, USA
| | - Holly L Nicastro
- National Institutes of Health, National Heart, Lung, and Blood Institute, Bethesda, MD, USA
| | - Diane M O'Brien
- Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, AK, USA
| | - José M Ordovás
- Nutrition and Genomics Laboratory, Jean Mayer–USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA, USA
| | - Stavroula K Osganian
- National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, USA
| | - Mary Playdon
- Department of Nutrition and Integrative Physiology, University of Utah and Division of Cancer Population Sciences, Huntsman Cancer Institute, Salt Lake City, UT, USA
| | - Ross Prentice
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- School of Public Health, University of Washington, Seattle, WA, USA
| | - Daniel Raftery
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- School of Medicine, University of Washington, Seattle, WA, USA
| | | | - Helen M Roche
- Nutrigenomics Research Group, School of Public Health, Physiotherapy and Sports Science, UCD Institute of Food and Health, Diabetes Complications Research Centre, University College Dublin, Dublin, Ireland
| | - Sharon A Ross
- National Institutes of Health, National Cancer Institute, Rockville, MD, USA
| | - Shengmin Sang
- Laboratory for Functional Foods and Human Health, Center for Excellence in Post-Harvest Technologies, North Carolina A&T State University, North Carolina Research Campus, Nutrition Research Building, Kannapolis, NC, USA
| | - Augustin Scalbert
- International Agency for Research on Cancer, Nutrition and Metabolism Section, Biomarkers Group, Lyon, France
| | - Pothur R Srinivas
- National Institutes of Health, National Heart, Lung, and Blood Institute, Bethesda, MD, USA
| | - Steven H Zeisel
- Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, USA
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19
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Koelmel JP, Campbell JE, Guingab-Cagmat J, Meke L, Garrett TJ, Stingl U. Re-modeling of foliar membrane lipids in a seagrass allows for growth in phosphorus-deplete conditions. PLoS One 2019; 14:e0218690. [PMID: 31774814 PMCID: PMC6880972 DOI: 10.1371/journal.pone.0218690] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Accepted: 10/10/2019] [Indexed: 11/18/2022] Open
Abstract
In this study, we used liquid chromatography high-resolution tandem mass spectrometry to analyze the lipidome of turtlegrass (Thalassia testudinum) leaves with either extremely high phosphorus content or extremely low phosphorus content. Most species of phospholipids were significantly down-regulated in phosphorus-deplete leaves, whereas diacylglyceryltrimethylhomoserine (DGTS), triglycerides (TG), galactolipid digalactosyldiacylglycerol (DGDG), certain species of glucuronosyldiacylglycerols (GlcADG), and certain species of sulfoquinovosyl diacylglycerol (SQDG) were significantly upregulated, accounting for the change in phosphorus content, as well as structural differences in the leaves of plants growing across regions of varying elemental availability. These data suggest that seagrasses are able to modify the phosphorus content in leaf membranes dependent upon environmental availability.
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Affiliation(s)
- Jeremy P. Koelmel
- University of Florida, Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, Gainesville, Florida, United States of America
| | - Justin E. Campbell
- Florida International University, Department of Biological Sciences, Institute of Water and Environment, North Miami, FL, United States of America
| | - Joy Guingab-Cagmat
- University of Florida, Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, Gainesville, Florida, United States of America
| | - Laurel Meke
- University of Florida, Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, Gainesville, Florida, United States of America
| | - Timothy J. Garrett
- University of Florida, Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, Gainesville, Florida, United States of America
| | - Ulrich Stingl
- University of Florida, UF/IFAS Fort Lauderdale Research and Education Center, Department of Microbiology & Cell Science, Davie, Florida, United States of America
- * E-mail:
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20
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Lee HJ, Kremer DM, Sajjakulnukit P, Zhang L, Lyssiotis CA. A large-scale analysis of targeted metabolomics data from heterogeneous biological samples provides insights into metabolite dynamics. Metabolomics 2019; 15:103. [PMID: 31289941 PMCID: PMC6616221 DOI: 10.1007/s11306-019-1564-8] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2019] [Accepted: 06/20/2019] [Indexed: 12/31/2022]
Abstract
INTRODUCTION We previously developed a tandem mass spectrometry-based label-free targeted metabolomics analysis framework coupled to two distinct chromatographic methods, reversed-phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC), with dynamic multiple reaction monitoring (dMRM) for simultaneous detection of over 200 metabolites to study core metabolic pathways. OBJECTIVES We aim to analyze a large-scale heterogeneous data compendium generated from our LC-MS/MS platform with both RPLC and HILIC methods to systematically assess measurement quality in biological replicate groups and to investigate metabolite abundance changes and patterns across different biological conditions. METHODS Our metabolomics framework was applied in a wide range of experimental systems including cancer cell lines, tumors, extracellular media, primary cells, immune cells, organoids, organs (e.g. pancreata), tissues, and sera from human and mice. We also developed computational and statistical analysis pipelines, which include hierarchical clustering, replicate-group CV analysis, correlation analysis, and case-control paired analysis. RESULTS We generated a compendium of 42 heterogeneous deidentified datasets with 635 samples using both RPLC and HILIC methods. There exist metabolite signatures that correspond to various phenotypes of the heterogeneous datasets, involved in several metabolic pathways. The RPLC method shows overall better reproducibility than the HILIC method for most metabolites including polar amino acids. Correlation analysis reveals high confidence metabolites irrespective of experimental systems such as methionine, phenylalanine, and taurine. We also identify homocystine, reduced glutathione, and phosphoenolpyruvic acid as highly dynamic metabolites across all case-control paired samples. CONCLUSIONS Our study is expected to serve as a resource and a reference point for a systematic analysis of label-free LC-MS/MS targeted metabolomics data in both RPLC and HILIC methods with dMRM.
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Affiliation(s)
- Ho-Joon Lee
- Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
- Center for Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
- Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI, 48109, USA.
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, 48109, USA.
| | - Daniel M Kremer
- Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Peter Sajjakulnukit
- Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Li Zhang
- Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Michigan Regional Comprehensive Metabolomics Resource Core, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Costas A Lyssiotis
- Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, 48109, USA.
- Department of Internal Medicine, Division of Gastroenterology and Hepatology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
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21
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Bell M, Blais JM. "-Omics" workflow for paleolimnological and geological archives: A review. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 672:438-455. [PMID: 30965259 DOI: 10.1016/j.scitotenv.2019.03.477] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Revised: 03/29/2019] [Accepted: 03/30/2019] [Indexed: 06/09/2023]
Abstract
"-Omics" is a powerful screening method with applications in molecular biology, toxicology, wildlife biology, natural product discovery, and many other fields. Genomics, proteomics, metabolomics, and lipidomics are common examples included under the "-omics" umbrella. This screening method uses combinations of untargeted, semi-targeted, and targeted analyses paired with data mining to facilitate researchers' understanding of the genome, proteins, and small organic molecules in biological systems. Recently, however, the use of "-omics" has expanded into the fields of geology, specifically petrology, and paleolimnology. Specifically, untargeted analyses stand to transform these fields as petroleomics, and sediment-"omics" become more prevalent. "-Omics" facilitates the visualization of small molecule profiles from environmental matrices (i.e. oil and sediment). Small molecule profiles can provide improved understanding of small molecules distributions throughout the environment, and how those compositions can change depending on conditions (i.e. climate change, weathering, etc.). "-Omics" also facilities discovery of next-generation biomarkers that can be used for oil source identification and as proxies for reconstructing past environmental changes. Untargeted analyses paired with data mining and multivariate statistical analyses represents a powerful suite of tools for hypothesis generation, and new method development for environmental reconstructions. Here we present an introduction to "-omics" methodology, technical terms, and examples of applications to paleolimnology and petrology. The purpose of this review is to highlight the important considerations at each step in the "-omics" workflow to produce high quality and statistically powerful data for petrological and paleolimnological applications.
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Affiliation(s)
- Madison Bell
- Laboratory for the Analysis of Natural and Synthetic Environmental Toxicants, Department of Biology, University of Ottawa, Ottawa, ON K1N 6N5, Canada
| | - Jules M Blais
- Laboratory for the Analysis of Natural and Synthetic Environmental Toxicants, Department of Biology, University of Ottawa, Ottawa, ON K1N 6N5, Canada.
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22
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Misra BB, Mohapatra S. Tools and resources for metabolomics research community: A 2017-2018 update. Electrophoresis 2018; 40:227-246. [PMID: 30443919 DOI: 10.1002/elps.201800428] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Revised: 11/09/2018] [Accepted: 11/09/2018] [Indexed: 01/09/2023]
Abstract
The scale at which MS- and NMR-based platforms generate metabolomics datasets for both research, core, and clinical facilities to address challenges in the various sciences-ranging from biomedical to agricultural-is underappreciated. Thus, metabolomics efforts spanning microbe, environment, plant, animal, and human systems have led to continual and concomitant growth of in silico resources for analysis and interpretation of these datasets. These software tools, resources, and databases drive the field forward to help keep pace with the amount of data being generated and the sophisticated and diverse analytical platforms that are being used to generate these metabolomics datasets. To address challenges in data preprocessing, metabolite annotation, statistical interrogation, visualization, interpretation, and integration, the metabolomics and informatics research community comes up with hundreds of tools every year. The purpose of the present review is to provide a brief and useful summary of more than 95 metabolomics tools, software, and databases that were either developed or significantly improved during 2017-2018. We hope to see this review help readers, developers, and researchers to obtain informed access to these thorough lists of resources for further improvisation, implementation, and application in due course of time.
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Affiliation(s)
- Biswapriya B Misra
- Department of Internal Medicine, Section of Molecular Medicine, Medical Center Boulevard, Winston-Salem, NC, USA
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23
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Grape and Wine Metabolomics to Develop New Insights Using Untargeted and Targeted Approaches. FERMENTATION-BASEL 2018. [DOI: 10.3390/fermentation4040092] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Chemical analysis of grape juice and wine has been performed for over 50 years in a targeted manner to determine a limited number of compounds using Gas Chromatography, Mass-Spectrometry (GC-MS) and High Pressure Liquid Chromatography (HPLC). Therefore, it only allowed the determination of metabolites that are present in high concentration, including major sugars, amino acids and some important carboxylic acids. Thus, the roles of many significant but less concentrated metabolites during wine making process are still not known. This is where metabolomics shows its enormous potential, mainly because of its capability in analyzing over 1000 metabolites in a single run due to the recent advancements of high resolution and sensitive analytical instruments. Metabolomics has predominantly been adopted by many wine scientists as a hypothesis-generating tool in an unbiased and non-targeted way to address various issues, including characterization of geographical origin (terroir) and wine yeast metabolic traits, determination of biomarkers for aroma compounds, and the monitoring of growth developments of grape vines and grapes. The aim of this review is to explore the published literature that made use of both targeted and untargeted metabolomics to study grapes and wines and also the fermentation process. In addition, insights are also provided into many other possible avenues where metabolomics shows tremendous potential as a question-driven approach in grape and wine research.
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24
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Systems biology primer: the basic methods and approaches. Essays Biochem 2018; 62:487-500. [PMID: 30287586 DOI: 10.1042/ebc20180003] [Citation(s) in RCA: 79] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2018] [Revised: 08/22/2018] [Accepted: 08/24/2018] [Indexed: 12/16/2022]
Abstract
Systems biology is an integrative discipline connecting the molecular components within a single biological scale and also among different scales (e.g. cells, tissues and organ systems) to physiological functions and organismal phenotypes through quantitative reasoning, computational models and high-throughput experimental technologies. Systems biology uses a wide range of quantitative experimental and computational methodologies to decode information flow from genes, proteins and other subcellular components of signaling, regulatory and functional pathways to control cell, tissue, organ and organismal level functions. The computational methods used in systems biology provide systems-level insights to understand interactions and dynamics at various scales, within cells, tissues, organs and organisms. In recent years, the systems biology framework has enabled research in quantitative and systems pharmacology and precision medicine for complex diseases. Here, we present a brief overview of current experimental and computational methods used in systems biology.
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