1
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Xavier JB. Machine learning of cellular metabolic rewiring. Biol Methods Protoc 2024; 9:bpae048. [PMID: 39011352 PMCID: PMC11249387 DOI: 10.1093/biomethods/bpae048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 06/14/2024] [Accepted: 07/01/2024] [Indexed: 07/17/2024] Open
Abstract
Metabolic rewiring allows cells to adapt their metabolism in response to evolving environmental conditions. Traditional metabolomics techniques, whether targeted or untargeted, often struggle to interpret these adaptive shifts. Here, we introduce MetaboLiteLearner, a lightweight machine learning framework that harnesses the detailed fragmentation patterns from electron ionization (EI) collected in scan mode during gas chromatography/mass spectrometry to predict changes in the metabolite composition of metabolically adapted cells. When tested on breast cancer cells with different preferences to metastasize to specific organs, MetaboLiteLearner predicted the impact of metabolic rewiring on metabolites withheld from the training dataset using only the EI spectra, without metabolite identification or pre-existing knowledge of metabolic networks. Despite its simplicity, the model learned captured shared and unique metabolomic shifts between brain- and lung-homing metastatic lineages, suggesting cellular adaptations associated with metastasis to specific organs. Integrating machine learning and metabolomics paves the way for new insights into complex cellular adaptations.
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Affiliation(s)
- Joao B Xavier
- Program for Computational and Systems Biology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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2
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Qu Y, Chen M, Wang Y, Qu L, Wang R, Liu H, Wang L, Nie Z. Rapid screening of infertility-associated gynecological conditions via ambient glow discharge mass spectrometry utilizing urine metabolic fingerprints. Talanta 2024; 274:125969. [PMID: 38608629 DOI: 10.1016/j.talanta.2024.125969] [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: 12/04/2023] [Revised: 02/29/2024] [Accepted: 03/20/2024] [Indexed: 04/14/2024]
Abstract
Infertility presents a widespread challenge for many families worldwide, often arising from various gynecological diseases (GDs) that hinder successful pregnancies. Current diagnostic methods for GDs have disadvantages such as low efficiency, high cost, misdiagnose, invasive injury and etc. This paper introduces a rapid, non-invasive, efficient, and straightforward analytical method that utilizes desorption, separation, and ionization mass spectrometry (DSI-MS) platform in conjunction with machine learning (ML) to detect urine metabolite fingerprints in patients with different GDs. We analyzed 257 samples from patients diagnosed with polycystic ovary syndrome (PCOS), premature ovarian insufficiency (POI), diminished ovarian reserve (DOR), endometriosis (EMS), recurrent pregnancy loss (RPL), recurrent implantation failure (RIF), and 87 samples from healthy control (HC) individuals. We identified metabolite differences and dysregulated pathways through dimensionality reduction methods, with the result of the discovery of 7 potential biomarkers for GDs diagnosis. The ML method effectively distinguished subtle differences in urine metabolite fingerprints. We anticipate that this innovative approach will offer a patient-friendly, rapid screening, and differentiation method for infertility-related GDs patients.
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Affiliation(s)
- Yijiao Qu
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Ming Chen
- Centre of Reproductive Medicine, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, 518000, China; Department of Gynecology and Obstetrics, Guangxi University of Chinese Medicine, Nanning, 530200, China
| | - Yiran Wang
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Liangliang Qu
- School of Life Sciences, Nanchang University, Nanchang, 330031, China
| | - Ruiyue Wang
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Huihui Liu
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Liping Wang
- Centre of Reproductive Medicine, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, 518000, China.
| | - Zongxiu Nie
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100190, China.
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3
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Leong JV, Mezzomo P, Kozel P, Volfová T, de Lima Ferreira P, Seifert CL, Butterill PT, Freiberga I, Michálek J, Matos-Maraví P, Weinhold A, Engström MT, Salminen JP, Segar ST, Sedio BE, Volf M. Effects of individual traits vs. trait syndromes on assemblages of various herbivore guilds associated with central European Salix. Oecologia 2024:10.1007/s00442-024-05569-0. [PMID: 38829402 DOI: 10.1007/s00442-024-05569-0] [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: 10/04/2023] [Accepted: 05/13/2024] [Indexed: 06/05/2024]
Abstract
Plants employ diverse anti-herbivore defences that can covary to form syndromes consisting of multiple traits. Such syndromes are hypothesized to impact herbivores more than individual defences. We studied 16 species of lowland willows occurring in central Europe and explored if their chemical and physical traits form detectable syndromes. We tested for phylogenetic trends in the syndromes and explored whether three herbivore guilds (i.e., generalist leaf-chewers, specialist leaf-chewers, and gallers) are affected more by the detected syndromes or individual traits. The recovered syndromes showed low phylogenetic signal and were mainly defined by investment in concentration, richness, or uniqueness of structurally related phenolic metabolites. Resource acquisition traits or inducible volatile organic compounds exhibited a limited correlation with the syndromes. Individual traits composing the syndromes showed various correlations to the assemblages of herbivores from the three studied guilds. In turn, we found some support for the hypothesis that defence syndromes are composed of traits that provide defence against various herbivores. However, individual traits rather than trait syndromes explained more variation for all studied herbivore assemblages. The detected negative correlations between various phenolics suggest that investment trade-offs may occur primarily among plant metabolites with shared metabolic pathways that may compete for their precursors. Moreover, several traits characterizing the recovered syndromes play additional roles in willows other than defence from herbivory. Taken together, our findings suggest that the detected syndromes did not solely evolve as an anti-herbivore defence.
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Affiliation(s)
- Jing V Leong
- Biology Centre of the Czech Academy of Sciences, Ceske Budejovice, Czech Republic.
- Faculty of Science, Department of Zoology, University of South Bohemia, Ceske Budejovice, Czech Republic.
| | - Priscila Mezzomo
- Biology Centre of the Czech Academy of Sciences, Ceske Budejovice, Czech Republic
- Faculty of Science, Department of Zoology, University of South Bohemia, Ceske Budejovice, Czech Republic
| | - Petr Kozel
- Biology Centre of the Czech Academy of Sciences, Ceske Budejovice, Czech Republic
- Faculty of Science, Department of Zoology, University of South Bohemia, Ceske Budejovice, Czech Republic
| | - Tereza Volfová
- Biology Centre of the Czech Academy of Sciences, Ceske Budejovice, Czech Republic
- Faculty of Science, Department of Zoology, University of South Bohemia, Ceske Budejovice, Czech Republic
| | - Paola de Lima Ferreira
- Biology Centre of the Czech Academy of Sciences, Ceske Budejovice, Czech Republic
- Department of Biology, Aarhus University, Aarhus, Denmark
| | - Carlo L Seifert
- Department of Forest Nature Conservation, Faculty of Forest Sciences and Forest Ecology, Georg-August-University of Göttingen, Göttingen, Germany
| | - Phillip T Butterill
- Biology Centre of the Czech Academy of Sciences, Ceske Budejovice, Czech Republic
| | - Inga Freiberga
- Biology Centre of the Czech Academy of Sciences, Ceske Budejovice, Czech Republic
| | - Jan Michálek
- Biology Centre of the Czech Academy of Sciences, Ceske Budejovice, Czech Republic
- Institute of Microbiology, Centre Algatech Czech Academy of Sciences, Trebon, Czech Republic
| | - Pável Matos-Maraví
- Biology Centre of the Czech Academy of Sciences, Ceske Budejovice, Czech Republic
| | - Alexander Weinhold
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
- Institute of Biodiversity, Friedrich Schiller University Jena, Jena, Germany
| | - Marica T Engström
- Bioanalytical Laboratory, Institute of Biomedicine, University of Turku, Turku, Finland
| | - Juha-Pekka Salminen
- Natural Chemistry Research Group, Department of Chemistry, University of Turku, Turku, Finland
| | - Simon T Segar
- Agriculture and Environment Department, Harper Adams University, Newport, United Kingdom
| | - Brian E Sedio
- Department of Integrative Biology, University of Texas at Austin, Austin, TX, United States of America
- Smithsonian Tropical Research Institute, Ancón, Panama
| | - Martin Volf
- Biology Centre of the Czech Academy of Sciences, Ceske Budejovice, Czech Republic
- Faculty of Science, Department of Zoology, University of South Bohemia, Ceske Budejovice, Czech Republic
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4
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Sierra AM, Meléndez O, Bethancourt R, Bethancourt A, Rodríguez-Castro L, López CA, Sedio BE, Saltonstall K, Villarreal A JC. Leaf Endophytes Relationship with Host Metabolome Expression in Tropical Gymnosperms. J Chem Ecol 2024:10.1007/s10886-024-01511-z. [PMID: 38809282 DOI: 10.1007/s10886-024-01511-z] [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: 12/04/2023] [Revised: 05/07/2024] [Accepted: 05/17/2024] [Indexed: 05/30/2024]
Abstract
Plant-microbe interactions play a pivotal role in shaping host fitness, especially concerning chemical defense mechanisms. In cycads, establishing direct correlations between specific endophytic microbes and the synthesis of highly toxic defensive phytochemicals has been challenging. Our research delves into the intricate relationship between plant-microbe associations and the variation of secondary metabolite production in two closely related Zamia species that grow in distinct habitats; terrestrial and epiphytic. Employing an integrated approach, we combined microbial metabarcoding, which characterize the leaf endophytic bacterial and fungal communities, with untargeted metabolomics to test if the relative abundances of specific microbial taxa in these two Zamia species were associated with different metabolome profiles. The two species studied shared approximately 90% of the metabolites spanning diverse biosynthetic pathways: alkaloids, amino acids, carbohydrates, fatty acids, polyketides, shikimates, phenylpropanoids, and terpenoids. Co-occurrence networks revealed positive associations among metabolites from different pathways, underscoring the complexity of their interactions. Our integrated analysis demonstrated to some degree that the intraspecific variation in metabolome profiles of the two host species was associated with the abundance of bacterial orders Acidobacteriales and Frankiales, as well as the fungal endophytes belonging to the orders Chaetothyriales, Glomerellales, Heliotiales, Hypocreales, and Sordariales. We further associate individual metabolic similarity with four specific fungal endophyte members of the core microbiota, but no specific bacterial taxa associations were identified. This study represents a pioneering investigation to characterize leaf endophytes and their association with metabolomes in tropical gymnosperms, laying the groundwork for deeper inquiries into this complex domain.
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Affiliation(s)
- Adriel M Sierra
- Département de Biologie, Université Laval, Québec, (QC), G1V 0A6, Canada.
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, (QC), G1V 0A6, Canada.
| | - Omayra Meléndez
- Departamento de Microbiología y Parasitología, Facultad de Ciencias Naturales, Exactas y Tecnología, Universidad de Panamá, Panamá, Panamá
- Smithsonian Tropical Research Institute, Ancón, Panamá
| | - Rita Bethancourt
- Departamento de Microbiología y Parasitología, Facultad de Ciencias Naturales, Exactas y Tecnología, Universidad de Panamá, Panamá, Panamá
| | - Ariadna Bethancourt
- Departamento de Microbiología y Parasitología, Facultad de Ciencias Naturales, Exactas y Tecnología, Universidad de Panamá, Panamá, Panamá
| | - Lilisbeth Rodríguez-Castro
- Departamento de Microbiología, Facultad de Ciencias Naturales, Exactas y Tecnología, Universidad de Panamá, Panamá, Panamá
- Smithsonian Tropical Research Institute, Ancón, Panamá
| | - Christian A López
- Smithsonian Tropical Research Institute, Ancón, Panamá
- Department of Integrative Biology, University of Texas at Austin, Austin, TX, USA
| | - Brian E Sedio
- Smithsonian Tropical Research Institute, Ancón, Panamá
- Department of Integrative Biology, University of Texas at Austin, Austin, TX, USA
| | | | - Juan Carlos Villarreal A
- Département de Biologie, Université Laval, Québec, (QC), G1V 0A6, Canada.
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, (QC), G1V 0A6, Canada.
- Smithsonian Tropical Research Institute, Ancón, Panamá.
- Canada Research Chair in Genomics of Tropical Symbioses, Department of Biology, Université Laval, Québec, G1V 0A6, Canadá.
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5
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Bauermeister A, Furtado LC, Ferreira EG, Moreira EA, Jimenez PC, Lopes NP, Araújo WL, Olchanheski LR, Monteiro da Cruz Lotufo T, Costa-Lotufo LV. Chemical and microbial diversity of a tropical intertidal ascidian holobiont. MARINE ENVIRONMENTAL RESEARCH 2024; 194:106303. [PMID: 38150785 DOI: 10.1016/j.marenvres.2023.106303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 12/07/2023] [Accepted: 12/10/2023] [Indexed: 12/29/2023]
Abstract
The tropical ascidian Eudistoma vannamei, endemic to the northeastern coast of Brazil, is considered a prolific source of secondary metabolites and hosts Actinomycetota that produce bioactive compounds. Herein, we used an omics approach to study the ascidian as a holobiont, including the microbial diversity through 16S rRNA gene sequencing and metabolite production using mass spectrometry-based metabolomics. Gene sequencing analysis revealed all samples of E. vannamei shared about 50% of the observed ASVs, and Pseudomonadota (50.7%), Planctomycetota (9.58%), Actinomycetota (10.34%), Bacteroidota (12.05%) were the most abundant bacterial phyla. Analysis of tandem mass spectrometry (MS/MS) data allowed annotation of compounds, including phospholipids, amino acids, and pyrimidine alkaloids, such as staurosporine, a member of a well-known chemical class recognized as a microbial metabolite. Isolated bacteria, mainly belonging to Streptomyces and Micromonospora genera, were cultivated and extracted with ethyl acetate. MS/MS analysis of bacterial extracts allowed annotation of compounds not detected in the ascidian tissue, including marineosin and dihydroergotamine, yielding about 30% overlapped ions between host and isolated bacteria. This study reveals E. vannamei as a rich source of microbial and chemical diversity and, furthermore, highlights the importance of omic tools for a comprehensive investigation of holobiont systems.
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Affiliation(s)
- Anelize Bauermeister
- Departamento de Farmacologia, Instituto de Ciências Biomédicas, Universidade de São Paulo, São Paulo, SP, 05508-000, Brazil; Departamento de Física e Química, Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, SP, 14040-903, Brazil
| | - Luciana Costa Furtado
- Departamento de Farmacologia, Instituto de Ciências Biomédicas, Universidade de São Paulo, São Paulo, SP, 05508-000, Brazil
| | - Elthon G Ferreira
- Departamento de Química Orgânica e Inorgânica, Universidade Federal do Ceará, Fortaleza, CE, 60451-970, Brazil
| | - Eduarda Antunes Moreira
- Departamento de Física e Química, Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, SP, 14040-903, Brazil
| | | | - Norberto Peporine Lopes
- Departamento de Física e Química, Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, SP, 14040-903, Brazil
| | - Welington Luiz Araújo
- Departamento de Microbiologia, Instituto de Ciências Biomédicas, Universidade de São Paulo, São Paulo, SP, 05508-900, Brazil
| | - Luiz Ricardo Olchanheski
- Departamento de Microbiologia, Instituto de Ciências Biomédicas, Universidade de São Paulo, São Paulo, SP, 05508-900, Brazil
| | | | - Leticia Veras Costa-Lotufo
- Departamento de Farmacologia, Instituto de Ciências Biomédicas, Universidade de São Paulo, São Paulo, SP, 05508-000, Brazil.
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Bulut M, Wendenburg R, Bitocchi E, Bellucci E, Kroc M, Gioia T, Susek K, Papa R, Fernie AR, Alseekh S. A comprehensive metabolomics and lipidomics atlas for the legumes common bean, chickpea, lentil and lupin. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2023; 116:1152-1171. [PMID: 37285370 DOI: 10.1111/tpj.16329] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 05/09/2023] [Accepted: 05/24/2023] [Indexed: 06/09/2023]
Abstract
Legumes represent an important component of human and livestock diets; they are rich in macro- and micronutrients such as proteins, dietary fibers and polyunsaturated fatty acids. Whilst several health-promoting and anti-nutritional properties have been associated with grain content, in-depth metabolomics characterization of major legume species remains elusive. In this article, we used both gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) to assess the metabolic diversity in the five legume species commonly grown in Europe, including common bean (Phaseolus vulgaris), chickpea (Cicer arietinum), lentil (Lens culinaris), white lupin (Lupinus albus) and pearl lupin (Lupinus mutabilis), at the tissue level. We were able to detect and quantify over 3400 metabolites covering major nutritional and anti-nutritional compounds. Specifically, the metabolomics atlas includes 224 derivatized metabolites, 2283 specialized metabolites and 923 lipids. The data generated here will serve the community as a basis for future integration to metabolomics-assisted crop breeding and facilitate metabolite-based genome-wide association studies to dissect the genetic and biochemical bases of metabolism in legume species.
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Affiliation(s)
- Mustafa Bulut
- Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam-Golm, Germany
| | - Regina Wendenburg
- Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam-Golm, Germany
| | - Elena Bitocchi
- Department of Agricultural, Food and Environmental Sciences, Polytechnic University of Marche, via Brecce Bianche, Ancona, 60131, Italy
| | - Elisa Bellucci
- Department of Agricultural, Food and Environmental Sciences, Polytechnic University of Marche, via Brecce Bianche, Ancona, 60131, Italy
| | - Magdalena Kroc
- Legume Genomics Team, Institute of Plant Genetics, Polish Academy of Sciences, Strzeszynska 34, Poznan, 60-479, Poland
| | - Tania Gioia
- School of Agricultural, Forestry, Food and Environmental Sciences, University of Basilicata, Potenza, 85100, Italy
| | - Karolina Susek
- Legume Genomics Team, Institute of Plant Genetics, Polish Academy of Sciences, Strzeszynska 34, Poznan, 60-479, Poland
| | - Roberto Papa
- Department of Agricultural, Food and Environmental Sciences, Polytechnic University of Marche, via Brecce Bianche, Ancona, 60131, Italy
| | - Alisdair R Fernie
- Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam-Golm, Germany
- Center for Plant Systems Biology and Biotechnology, 4000, Plovdiv, Bulgaria
| | - Saleh Alseekh
- Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam-Golm, Germany
- Center for Plant Systems Biology and Biotechnology, 4000, Plovdiv, Bulgaria
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7
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Xavier JB. Machine learning of cellular metabolic rewiring. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.11.552957. [PMID: 37645838 PMCID: PMC10462012 DOI: 10.1101/2023.08.11.552957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Metabolic rewiring allows cells to adapt their metabolism in response to evolving environmental conditions. Traditional metabolomics techniques, whether targeted or untargeted, often struggle to interpret these adaptive shifts. Here, we introduce MetaboLiteLearner, a machine learning framework that harnesses the detailed fragmentation patterns from electron ionization (EI) collected in scan mode during gas chromatography/mass spectrometry (GC/MS) to predict abundance changes in metabolically adapted cells. When tested on breast cancer cells with different preferences to metastasize to specific organs, MetaboLiteLearner predicted the impact of metabolic rewiring on metabolites withheld from the training dataset using only the EI spectra, without metabolite identification or pre-existing knowledge of metabolic networks. The model learned captures shared and unique metabolomic shifts between brain- and lung-homing metastatic lineages, suggesting potential organ-tailored cellular adaptations. Integrating machine learning and metabolomics paves the way for new insights into complex cellular adaptations.
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Affiliation(s)
- Joao B Xavier
- Program for Computational and Systems Biology, Sloan Kettering Institute for Cancer Research
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8
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Baker JL. Illuminating the oral microbiome and its host interactions: recent advancements in omics and bioinformatics technologies in the context of oral microbiome research. FEMS Microbiol Rev 2023; 47:fuad051. [PMID: 37667515 PMCID: PMC10503653 DOI: 10.1093/femsre/fuad051] [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: 01/31/2023] [Revised: 08/02/2023] [Accepted: 09/01/2023] [Indexed: 09/06/2023] Open
Abstract
The oral microbiota has an enormous impact on human health, with oral dysbiosis now linked to many oral and systemic diseases. Recent advancements in sequencing, mass spectrometry, bioinformatics, computational biology, and machine learning are revolutionizing oral microbiome research, enabling analysis at an unprecedented scale and level of resolution using omics approaches. This review contains a comprehensive perspective of the current state-of-the-art tools available to perform genomics, metagenomics, phylogenomics, pangenomics, transcriptomics, proteomics, metabolomics, lipidomics, and multi-omics analysis on (all) microbiomes, and then provides examples of how the techniques have been applied to research of the oral microbiome, specifically. Key findings of these studies and remaining challenges for the field are highlighted. Although the methods discussed here are placed in the context of their contributions to oral microbiome research specifically, they are pertinent to the study of any microbiome, and the intended audience of this includes researchers would simply like to get an introduction to microbial omics and/or an update on the latest omics methods. Continued research of the oral microbiota using omics approaches is crucial and will lead to dramatic improvements in human health, longevity, and quality of life.
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Affiliation(s)
- Jonathon L Baker
- Department of Oral Rehabilitation & Biosciences, School of Dentistry, Oregon Health & Science University, 3181 Sam Jackson Park Road, Portland, OR 97202, United States
- Genomic Medicine Group, J. Craig Venter Institute, La Jolla, CA 92037, United States
- Department of Pediatrics, UC San Diego School of Medicine, La Jolla, CA 92093, United States
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9
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Volf M, Leong JV, de Lima Ferreira P, Volfová T, Kozel P, Matos-Maraví P, Hörandl E, Wagner ND, Luntamo N, Salminen JP, Segar ST, Sedio BE. Contrasting levels of β-diversity and underlying phylogenetic trends indicate different paths to chemical diversity in highland and lowland willow species. Ecol Lett 2023; 26:1559-1571. [PMID: 37345539 DOI: 10.1111/ele.14273] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 05/16/2023] [Accepted: 05/26/2023] [Indexed: 06/23/2023]
Abstract
Diverse specialised metabolites contributed to the success of vascular plants in colonising most terrestrial habitats. Understanding how distinct aspects of chemical diversity arise through heterogeneous environmental pressures can help us understand the effects of abiotic and biotic stress on plant evolution and community assembly. We examined highland and lowland willow species within a phylogenetic framework to test for trends in their chemical α-diversity (richness) and β-diversity (variation among species sympatric in elevation). We show that differences in chemistry among willows growing at different elevations occur mainly through shifts in chemical β-diversity and due to convergence or divergence among species sharing their elevation level. We also detect contrasting phylogenetic trends in concentration and α-diversity of metabolites in highland and lowland willow species. The resulting elevational patterns contribute to the chemical diversity of willows and suggest that variable selective pressure across ecological gradients may, more generally, underpin complex changes in plant chemistry.
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Affiliation(s)
- Martin Volf
- Biology Centre of the Czech Academy of Sciences, Ceske Budejovice, Czech Republic
- Faculty of Science, University of South Bohemia, Ceske Budejovice, Czech Republic
| | - Jing Vir Leong
- Biology Centre of the Czech Academy of Sciences, Ceske Budejovice, Czech Republic
- Faculty of Science, University of South Bohemia, Ceske Budejovice, Czech Republic
| | - Paola de Lima Ferreira
- Biology Centre of the Czech Academy of Sciences, Ceske Budejovice, Czech Republic
- Department of Biology, Aarhus University, Aarhus, Denmark
| | - Tereza Volfová
- Biology Centre of the Czech Academy of Sciences, Ceske Budejovice, Czech Republic
- Faculty of Science, University of South Bohemia, Ceske Budejovice, Czech Republic
| | - Petr Kozel
- Biology Centre of the Czech Academy of Sciences, Ceske Budejovice, Czech Republic
- Faculty of Science, University of South Bohemia, Ceske Budejovice, Czech Republic
| | - Pável Matos-Maraví
- Biology Centre of the Czech Academy of Sciences, Ceske Budejovice, Czech Republic
| | - Elvira Hörandl
- Department of Systematics, Biodiversity and Evolution of Plants (with Herbarium), University of Goettingen, Göttingen, Germany
| | - Natascha D Wagner
- Department of Systematics, Biodiversity and Evolution of Plants (with Herbarium), University of Goettingen, Göttingen, Germany
| | - Niko Luntamo
- Natural Chemistry Research Group, Department of Chemistry, University of Turku, Turku, Finland
| | - Juha-Pekka Salminen
- Natural Chemistry Research Group, Department of Chemistry, University of Turku, Turku, Finland
| | - Simon T Segar
- Agriculture and Environment Department, Harper Adams University, Newport, UK
| | - Brian E Sedio
- Department of Integrative Biology, University of Texas at Austin, Austin, Texas, USA
- Smithsonian Tropical Research Institute, Ancón, Panama
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10
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Mannochio-Russo H, Swift SOI, Nakayama KK, Wall CB, Gentry EC, Panitchpakdi M, Caraballo-Rodriguez AM, Aron AT, Petras D, Dorrestein K, Dorrestein TK, Williams TM, Nalley EM, Altman-Kurosaki NT, Martinelli M, Kuwabara JY, Darcy JL, Bolzani VS, Wegley Kelly L, Mora C, Yew JY, Amend AS, McFall-Ngai M, Hynson NA, Dorrestein PC, Nelson CE. Microbiomes and metabolomes of dominant coral reef primary producers illustrate a potential role for immunolipids in marine symbioses. Commun Biol 2023; 6:896. [PMID: 37653089 PMCID: PMC10471604 DOI: 10.1038/s42003-023-05230-1] [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: 12/01/2022] [Accepted: 08/08/2023] [Indexed: 09/02/2023] Open
Abstract
The dominant benthic primary producers in coral reef ecosystems are complex holobionts with diverse microbiomes and metabolomes. In this study, we characterize the tissue metabolomes and microbiomes of corals, macroalgae, and crustose coralline algae via an intensive, replicated synoptic survey of a single coral reef system (Waimea Bay, O'ahu, Hawaii) and use these results to define associations between microbial taxa and metabolites specific to different hosts. Our results quantify and constrain the degree of host specificity of tissue metabolomes and microbiomes at both phylum and genus level. Both microbiome and metabolomes were distinct between calcifiers (corals and CCA) and erect macroalgae. Moreover, our multi-omics investigations highlight common lipid-based immune response pathways across host organisms. In addition, we observed strong covariation among several specific microbial taxa and metabolite classes, suggesting new metabolic roles of symbiosis to further explore.
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Affiliation(s)
- Helena Mannochio-Russo
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA.
- Department of Biochemistry and Organic Chemistry, Institute of Chemistry, São Paulo State University, Araraquara, SP, 14800-060, Brazil.
| | - Sean O I Swift
- Daniel K. Inouye Center for Microbial Oceanography: Research and Education, Department of Oceanography and Sea Grant College Program, University of Hawai'i at Mānoa, Honolulu, HI, 96822, USA.
| | - Kirsten K Nakayama
- Pacific Biosciences Research Center, University of Hawai'i at Mānoa, Honolulu, HI, 96822, USA
| | - Christopher B Wall
- Pacific Biosciences Research Center, University of Hawai'i at Mānoa, Honolulu, HI, 96822, USA
- Ecology Behavior and Evolution Section, Department of Biological Sciences, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Emily C Gentry
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Morgan Panitchpakdi
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Andrés M Caraballo-Rodriguez
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Allegra T Aron
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA
- Department of Chemistry and Biochemistry, University of Denver, Denver, CO, 80210, USA
| | - Daniel Petras
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA
- Cluster of Excellence "Controlling Microbes to Fight Infections" (CMFI), University of Tuebingen, Tuebingen, Germany
| | - Kathleen Dorrestein
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA
| | | | - Taylor M Williams
- Marine Option Program, University of Hawai'i at Mānoa, Honolulu, HI, 96822, USA
| | - Eileen M Nalley
- Hawai'i Sea Grant College Program, University of Hawai'i at Mānoa, Honolulu, HI, 96822, USA
| | - Noam T Altman-Kurosaki
- School of Biological Sciences, Georgia Institute of Technology, 311 Ferst Drive, Atlanta, GA, 30332, USA
| | | | - Jeff Y Kuwabara
- Marine Option Program, University of Hawai'i at Mānoa, Honolulu, HI, 96822, USA
| | - John L Darcy
- Pacific Biosciences Research Center, University of Hawai'i at Mānoa, Honolulu, HI, 96822, USA
| | - Vanderlan S Bolzani
- Department of Biochemistry and Organic Chemistry, Institute of Chemistry, São Paulo State University, Araraquara, SP, 14800-060, Brazil
| | - Linda Wegley Kelly
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, CA, USA
| | - Camilo Mora
- Geography, University of Hawai'i at Mānoa, Honolulu, HI, 96822, USA
| | - Joanne Y Yew
- Pacific Biosciences Research Center, University of Hawai'i at Mānoa, Honolulu, HI, 96822, USA
| | - Anthony S Amend
- Pacific Biosciences Research Center, University of Hawai'i at Mānoa, Honolulu, HI, 96822, USA
| | - Margaret McFall-Ngai
- Pacific Biosciences Research Center, University of Hawai'i at Mānoa, Honolulu, HI, 96822, USA
| | - Nicole A Hynson
- Pacific Biosciences Research Center, University of Hawai'i at Mānoa, Honolulu, HI, 96822, USA
| | - Pieter C Dorrestein
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Craig E Nelson
- Daniel K. Inouye Center for Microbial Oceanography: Research and Education, Department of Oceanography and Sea Grant College Program, University of Hawai'i at Mānoa, Honolulu, HI, 96822, USA
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11
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Fu T, Huan T, Rahman G, Zhi H, Xu Z, Oh TG, Guo J, Coulter S, Tripathi A, Martino C, McCarville JL, Zhu Q, Cayabyab F, Low B, He M, Xing S, Vargas F, Yu RT, Atkins A, Liddle C, Ayres J, Raffatellu M, Dorrestein PC, Downes M, Knight R, Evans RM. Paired microbiome and metabolome analyses associate bile acid changes with colorectal cancer progression. Cell Rep 2023; 42:112997. [PMID: 37611587 PMCID: PMC10903535 DOI: 10.1016/j.celrep.2023.112997] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 05/08/2023] [Accepted: 08/01/2023] [Indexed: 08/25/2023] Open
Abstract
Colorectal cancer (CRC) is driven by genomic alterations in concert with dietary influences, with the gut microbiome implicated as an effector in disease development and progression. While meta-analyses have provided mechanistic insight into patients with CRC, study heterogeneity has limited causal associations. Using multi-omics studies on genetically controlled cohorts of mice, we identify diet as the major driver of microbial and metabolomic differences, with reductions in α diversity and widespread changes in cecal metabolites seen in high-fat diet (HFD)-fed mice. In addition, non-classic amino acid conjugation of the bile acid cholic acid (AA-CA) increased with HFD. We show that AA-CAs impact intestinal stem cell growth and demonstrate that Ileibacterium valens and Ruminococcus gnavus are able to synthesize these AA-CAs. This multi-omics dataset implicates diet-induced shifts in the microbiome and the metabolome in disease progression and has potential utility in future diagnostic and therapeutic developments.
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Affiliation(s)
- Ting Fu
- Gene Expression Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Tao Huan
- Department of Chemistry, UBC Faculty of Science, Vancouver Campus, Vancouver, BC V6T 1Z4, Canada
| | - Gibraan Rahman
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Hui Zhi
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Zhenjiang Xu
- UCSD Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA
| | - Tae Gyu Oh
- Gene Expression Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Jian Guo
- Department of Chemistry, UBC Faculty of Science, Vancouver Campus, Vancouver, BC V6T 1Z4, Canada
| | - Sally Coulter
- Storr Liver Centre, Westmead Institute for Medical Research and Sydney Medical School, University of Sydney, Westmead, NSW 2145, Australia
| | - Anupriya Tripathi
- UCSD Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA
| | - Cameron Martino
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; UCSD Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA
| | - Justin L McCarville
- Molecular and Systems Physiology Laboratory, Gene Expression Laboratory, NOMIS Center for Immunobiology and Microbial Pathogenesis, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Qiyun Zhu
- UCSD Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA
| | - Fritz Cayabyab
- Gene Expression Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Brian Low
- Department of Chemistry, UBC Faculty of Science, Vancouver Campus, Vancouver, BC V6T 1Z4, Canada
| | - Mingxiao He
- Gene Expression Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Shipei Xing
- Department of Chemistry, UBC Faculty of Science, Vancouver Campus, Vancouver, BC V6T 1Z4, Canada
| | - Fernando Vargas
- UCSD Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA
| | - Ruth T Yu
- Gene Expression Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Annette Atkins
- Gene Expression Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Christopher Liddle
- Storr Liver Centre, Westmead Institute for Medical Research and Sydney Medical School, University of Sydney, Westmead, NSW 2145, Australia
| | - Janelle Ayres
- Molecular and Systems Physiology Laboratory, Gene Expression Laboratory, NOMIS Center for Immunobiology and Microbial Pathogenesis, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Manuela Raffatellu
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; UCSD Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA; Chiba University-UC San Diego Center for Mucosal Immunity, Allergy, and Vaccines (CU-UCSD cMAV), La Jolla, CA 92093, USA
| | - Pieter C Dorrestein
- UCSD Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA; Department of Engineering, University of California, San Diego, La Jolla, CA 92093, USA; Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA 92093, USA
| | - Michael Downes
- Gene Expression Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Rob Knight
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; UCSD Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA; Department of Engineering, University of California, San Diego, La Jolla, CA 92093, USA.
| | - Ronald M Evans
- Gene Expression Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA.
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12
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Bartmanski BJ, Rocha M, Zimmermann-Kogadeeva M. Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism. Curr Opin Chem Biol 2023; 75:102324. [PMID: 37207402 PMCID: PMC10410306 DOI: 10.1016/j.cbpa.2023.102324] [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: 12/28/2022] [Revised: 04/15/2023] [Accepted: 04/18/2023] [Indexed: 05/21/2023]
Abstract
With the rapid progress in metabolomics and sequencing technologies, more data on the metabolome of single microbes and their communities become available, revealing the potential of microorganisms to metabolize a broad range of chemical compounds. The analysis of microbial metabolomics datasets remains challenging since it inherits the technical challenges of metabolomics analysis, such as compound identification and annotation, while harboring challenges in data interpretation, such as distinguishing metabolite sources in mixed samples. This review outlines the recent advances in computational methods to analyze primary microbial metabolism: knowledge-based approaches that take advantage of metabolic and molecular networks and data-driven approaches that employ machine/deep learning algorithms in combination with large-scale datasets. These methods aim at improving metabolite identification and disentangling reciprocal interactions between microbes and metabolites. We also discuss the perspective of combining these approaches and further developments required to advance the investigation of primary metabolism in mixed microbial samples.
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Affiliation(s)
| | - Miguel Rocha
- Centre of Biological Engineering, University of Minho, Campus of Gualtar, Braga, Portugal
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13
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Gaudêncio SP, Bayram E, Lukić Bilela L, Cueto M, Díaz-Marrero AR, Haznedaroglu BZ, Jimenez C, Mandalakis M, Pereira F, Reyes F, Tasdemir D. Advanced Methods for Natural Products Discovery: Bioactivity Screening, Dereplication, Metabolomics Profiling, Genomic Sequencing, Databases and Informatic Tools, and Structure Elucidation. Mar Drugs 2023; 21:md21050308. [PMID: 37233502 DOI: 10.3390/md21050308] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 05/11/2023] [Accepted: 05/12/2023] [Indexed: 05/27/2023] Open
Abstract
Natural Products (NP) are essential for the discovery of novel drugs and products for numerous biotechnological applications. The NP discovery process is expensive and time-consuming, having as major hurdles dereplication (early identification of known compounds) and structure elucidation, particularly the determination of the absolute configuration of metabolites with stereogenic centers. This review comprehensively focuses on recent technological and instrumental advances, highlighting the development of methods that alleviate these obstacles, paving the way for accelerating NP discovery towards biotechnological applications. Herein, we emphasize the most innovative high-throughput tools and methods for advancing bioactivity screening, NP chemical analysis, dereplication, metabolite profiling, metabolomics, genome sequencing and/or genomics approaches, databases, bioinformatics, chemoinformatics, and three-dimensional NP structure elucidation.
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Affiliation(s)
- Susana P Gaudêncio
- Associate Laboratory i4HB-Institute for Health and Bioeconomy, NOVA School of Science and Technology, NOVA University Lisbon, 2819-516 Caparica, Portugal
- UCIBIO-Applied Molecular Biosciences Unit, Chemistry Department, NOVA School of Science and Technology, NOVA University of Lisbon, 2819-516 Caparica, Portugal
| | - Engin Bayram
- Institute of Environmental Sciences, Room HKC-202, Hisar Campus, Bogazici University, Bebek, Istanbul 34342, Turkey
| | - Lada Lukić Bilela
- Department of Biology, Faculty of Science, University of Sarajevo, 71000 Sarajevo, Bosnia and Herzegovina
| | - Mercedes Cueto
- Instituto de Productos Naturales y Agrobiología-CSIC, 38206 La Laguna, Spain
| | - Ana R Díaz-Marrero
- Instituto de Productos Naturales y Agrobiología-CSIC, 38206 La Laguna, Spain
- Instituto Universitario de Bio-Orgánica (IUBO), Universidad de La Laguna, 38206 La Laguna, Spain
| | - Berat Z Haznedaroglu
- Institute of Environmental Sciences, Room HKC-202, Hisar Campus, Bogazici University, Bebek, Istanbul 34342, Turkey
| | - Carlos Jimenez
- CICA- Centro Interdisciplinar de Química e Bioloxía, Departamento de Química, Facultade de Ciencias, Universidade da Coruña, 15071 A Coruña, Spain
| | - Manolis Mandalakis
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, HCMR Thalassocosmos, 71500 Gournes, Crete, Greece
| | - Florbela Pereira
- LAQV, REQUIMTE, Chemistry Department, NOVA School of Science and Technology, NOVA University of Lisbon, 2819-516 Caparica, Portugal
| | - Fernando Reyes
- Fundación MEDINA, Avda. del Conocimiento 34, 18016 Armilla, Spain
| | - Deniz Tasdemir
- GEOMAR Centre for Marine Biotechnology (GEOMAR-Biotech), Research Unit Marine Natural Products Chemistry, GEOMAR Helmholtz Centre for Ocean Research Kiel, Am Kiel-Kanal 44, 24106 Kiel, Germany
- Faculty of Mathematics and Natural Science, Kiel University, Christian-Albrechts-Platz 4, 24118 Kiel, Germany
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14
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Qiu S, Cai Y, Yao H, Lin C, Xie Y, Tang S, Zhang A. Small molecule metabolites: discovery of biomarkers and therapeutic targets. Signal Transduct Target Ther 2023; 8:132. [PMID: 36941259 PMCID: PMC10026263 DOI: 10.1038/s41392-023-01399-3] [Citation(s) in RCA: 99] [Impact Index Per Article: 99.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 03/01/2023] [Accepted: 03/03/2023] [Indexed: 03/22/2023] Open
Abstract
Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.
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Affiliation(s)
- Shi Qiu
- International Advanced Functional Omics Platform, Scientific Experiment Center, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), College of Chinese Medicine, Hainan Medical University, Xueyuan Road 3, Haikou, 571199, China
| | - Ying Cai
- Graduate School, Heilongjiang University of Chinese Medicine, Harbin, 150040, China
| | - Hong Yao
- First Affiliated Hospital, Harbin Medical University, Harbin, 150081, China
| | - Chunsheng Lin
- Second Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, 150001, China
| | - Yiqiang Xie
- International Advanced Functional Omics Platform, Scientific Experiment Center, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), College of Chinese Medicine, Hainan Medical University, Xueyuan Road 3, Haikou, 571199, China.
| | - Songqi Tang
- International Advanced Functional Omics Platform, Scientific Experiment Center, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), College of Chinese Medicine, Hainan Medical University, Xueyuan Road 3, Haikou, 571199, China.
| | - Aihua Zhang
- International Advanced Functional Omics Platform, Scientific Experiment Center, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), College of Chinese Medicine, Hainan Medical University, Xueyuan Road 3, Haikou, 571199, China.
- Graduate School, Heilongjiang University of Chinese Medicine, Harbin, 150040, China.
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15
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Petrén H, Köllner TG, Junker RR. Quantifying chemodiversity considering biochemical and structural properties of compounds with the R package chemodiv. THE NEW PHYTOLOGIST 2023; 237:2478-2492. [PMID: 36527232 DOI: 10.1111/nph.18685] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 12/11/2022] [Indexed: 06/17/2023]
Abstract
Plants produce large numbers of phytochemical compounds affecting plant physiology and interactions with their biotic and abiotic environment. Recently, chemodiversity has attracted considerable attention as an ecologically and evolutionary meaningful way to characterize the phenotype of a mixture of phytochemical compounds. Currently used measures of phytochemical diversity, and related measures of phytochemical dissimilarity, generally do not take structural or biosynthetic properties of compounds into account. Such properties can be indicative of the compounds' function and inform about their biosynthetic (in)dependence, and should therefore be included in calculations of these measures. We introduce the R package chemodiv, which retrieves biochemical and structural properties of compounds from databases and provides functions for calculating and visualizing chemical diversity and dissimilarity for phytochemicals and other types of compounds. Our package enables calculations of diversity that takes the richness, relative abundance and - most importantly - structural and/or biosynthetic dissimilarity of compounds into account. We illustrate the use of the package with examples on simulated and real datasets. By providing the R package chemodiv for quantifying multiple aspects of chemodiversity, we hope to facilitate investigations of how chemodiversity varies across levels of biological organization, and its importance for the ecology and evolution of plants and other organisms.
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Affiliation(s)
- Hampus Petrén
- Evolutionary Ecology of Plants, Department of Biology, Philipps-University Marburg, 35043, Marburg, Germany
| | - Tobias G Köllner
- Department of Natural Product Biosynthesis, Max Planck Institute for Chemical Ecology, 07745, Jena, Germany
| | - Robert R Junker
- Evolutionary Ecology of Plants, Department of Biology, Philipps-University Marburg, 35043, Marburg, Germany
- Department of Environment and Biodiversity, University of Salzburg, 5020, Salzburg, Austria
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16
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Morrison CR, Rhodes AC, Bowman EA, Plowes RM, Sedio BE, Gilbert LE. Adding insult to injury: Light competition and allelochemical weapons interact to facilitate grass invasion. Ecosphere 2023. [DOI: 10.1002/ecs2.4438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/11/2023] Open
Affiliation(s)
- Colin R. Morrison
- Department of Integrative Biology The University of Texas at Austin Austin Texas USA
- Brackenridge Field Laboratory The University of Texas at Austin Austin Texas USA
| | - Aaron C. Rhodes
- Department of Integrative Biology The University of Texas at Austin Austin Texas USA
- Brackenridge Field Laboratory The University of Texas at Austin Austin Texas USA
| | - Elizabeth A. Bowman
- Department of Integrative Biology The University of Texas at Austin Austin Texas USA
- Brackenridge Field Laboratory The University of Texas at Austin Austin Texas USA
| | - Robert M. Plowes
- Department of Integrative Biology The University of Texas at Austin Austin Texas USA
- Brackenridge Field Laboratory The University of Texas at Austin Austin Texas USA
| | - Brian E. Sedio
- Department of Integrative Biology The University of Texas at Austin Austin Texas USA
- Smithsonian Tropical Research Institute Panama Republic of Panama
| | - Lawrence E. Gilbert
- Department of Integrative Biology The University of Texas at Austin Austin Texas USA
- Brackenridge Field Laboratory The University of Texas at Austin Austin Texas USA
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17
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Schlossarek D, Zhang Y, Sokolowska EM, Fernie AR, Luzarowski M, Skirycz A. Don't let go: co-fractionation mass spectrometry for untargeted mapping of protein-metabolite interactomes. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2023; 113:904-914. [PMID: 36575913 DOI: 10.1111/tpj.16084] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 12/20/2022] [Accepted: 12/23/2022] [Indexed: 06/17/2023]
Abstract
The chemical complexity of metabolomes goes hand in hand with their functional diversity. Small molecules have many essential roles, many of which are executed by binding and modulating the function of a protein partner. The complex and dynamic protein-metabolite interaction (PMI) network underlies most if not all biological processes, but remains under-characterized. Herein, we highlight how co-fractionation mass spectrometry (CF-MS), a well-established approach to map protein assemblies, can be used for proteome and metabolome identification of the PMIs. We will review recent CF-MS studies, discuss the main advantages and limitations, summarize the available CF-MS guidelines, and outline future challenges and opportunities.
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Affiliation(s)
- Dennis Schlossarek
- Depeartment One, Max-Planck-Institute of Molecular Plant Physiology, 14476, Potsdam, Germany
| | - Youjun Zhang
- Depeartment One, Max-Planck-Institute of Molecular Plant Physiology, 14476, Potsdam, Germany
| | - Ewelina M Sokolowska
- Depeartment One, Max-Planck-Institute of Molecular Plant Physiology, 14476, Potsdam, Germany
| | - Alisdair R Fernie
- Depeartment One, Max-Planck-Institute of Molecular Plant Physiology, 14476, Potsdam, Germany
| | - Marcin Luzarowski
- Center for Molecular Biology Heidelberg, Heidelberg University, Heidelberg, Germany
| | - Aleksandra Skirycz
- Depeartment One, Max-Planck-Institute of Molecular Plant Physiology, 14476, Potsdam, Germany
- Boyce Thompson Institute, Ithaca, NY, 14850, USA
- School of Integrative Plant Science, Cornell University, Ithaca, NY, 14850, USA
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18
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Gomes PWP, de Tralia Medeiros TC, Maimone NM, Leão TF, de Moraes LAB, Bauermeister A. Microbial Metabolites Annotation by Mass Spectrometry-Based Metabolomics. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1439:225-248. [PMID: 37843811 DOI: 10.1007/978-3-031-41741-2_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
Since the discovery of penicillin, microbial metabolites have been extensively investigated for drug discovery purposes. In the last decades, microbial derived compounds have gained increasing attention in different fields from pharmacognosy to industry and agriculture. Microbial metabolites in microbiomes present specific functions and can be associated with the maintenance of the natural ecosystems. These metabolites may exhibit a broad range of biological activities of great interest to human purposes. Samples from either microbial isolated cultures or microbiomes consist of complex mixtures of metabolites and their analysis are not a simple process. Mass spectrometry-based metabolomics encompass a set of analytical methods that have brought several improvements to the microbial natural products field. This analytical tool allows the comprehensively detection of metabolites, and therefore, the access of the chemical profile from those biological samples. These analyses generate thousands of mass spectra which is challenging to analyse. In this context, bioinformatic metabolomics tools have been successfully employed to accelerate and facilitate the investigation of specialized microbial metabolites. Herein, we describe metabolomics tools used to provide chemical information for the metabolites, and furthermore, we discuss how they can improve investigation of microbial cultures and interactions.
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Affiliation(s)
- Paulo Wender P Gomes
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Talita Carla de Tralia Medeiros
- Departamento de Química, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Naydja Moralles Maimone
- Departamento de Ciências Exatas, Escola Superior de Agricultura 'Luiz de Queiroz', Universidade de São Paulo, Piracicaba, São Paulo, Brazil
| | - Tiago F Leão
- Centro de Energia Nuclear na Agricultura, Universidade de São Paulo, Piracicaba, São Paulo, Brazil
| | - Luiz Alberto Beraldo de Moraes
- Departamento de Química, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Anelize Bauermeister
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA.
- Instituto de Ciências Biomédicas, Universidade de São Paulo, São Paulo, Brazil.
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19
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de Jonge NF, Mildau K, Meijer D, Louwen JJR, Bueschl C, Huber F, van der Hooft JJJ. Good practices and recommendations for using and benchmarking computational metabolomics metabolite annotation tools. Metabolomics 2022; 18:103. [PMID: 36469190 PMCID: PMC9722809 DOI: 10.1007/s11306-022-01963-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 11/18/2022] [Indexed: 12/12/2022]
Abstract
BACKGROUND Untargeted metabolomics approaches based on mass spectrometry obtain comprehensive profiles of complex biological samples. However, on average only 10% of the molecules can be annotated. This low annotation rate hampers biochemical interpretation and effective comparison of metabolomics studies. Furthermore, de novo structural characterization of mass spectral data remains a complicated and time-intensive process. Recently, the field of computational metabolomics has gained traction and novel methods have started to enable large-scale and reliable metabolite annotation. Molecular networking and machine learning-based in-silico annotation tools have been shown to greatly assist metabolite characterization in diverse fields such as clinical metabolomics and natural product discovery. AIM OF REVIEW We highlight recent advances in computational metabolite annotation workflows with a special focus on their evaluation and comparison with other tools. Whilst the progress is substantial and promising, we also argue that inconsistencies in benchmarking different tools hamper users from selecting the most appropriate and promising method for their research. We summarize benchmarking strategies of the different tools and outline several recommendations for benchmarking and comparing novel tools. KEY SCIENTIFIC CONCEPTS OF REVIEW This review focuses on recent advances in mass spectral library-based and machine learning-supported metabolite annotation workflows. We discuss large-scale library matching and analogue search, the current bloom of mass spectral similarity scores, and how molecular networking has changed the field. In addition, the potentials and challenges of machine learning-supported metabolite annotation workflows are highlighted. Overall, recent developments in computational metabolomics have started to fundamentally change metabolomics workflows, and we expect that as a community we will be able to overcome current method performance ambiguities and annotation bottlenecks.
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Affiliation(s)
- Niek F. de Jonge
- Bioinformatics Group, Wageningen University, Wageningen, the Netherlands
| | - Kevin Mildau
- Department of Analytical Chemistry, Biochemical Network Analysis Lab, University of Vienna, Vienna, Austria
| | - David Meijer
- Bioinformatics Group, Wageningen University, Wageningen, the Netherlands
| | - Joris J. R. Louwen
- Bioinformatics Group, Wageningen University, Wageningen, the Netherlands
| | - Christoph Bueschl
- Department of Analytical Chemistry, Biochemical Network Analysis Lab, University of Vienna, Vienna, Austria
| | - Florian Huber
- Centre for Digitalization and Digitality (ZDD), University of Applied Sciences Düsseldorf, Düsseldorf, Germany
| | - Justin J. J. van der Hooft
- Bioinformatics Group, Wageningen University, Wageningen, the Netherlands
- Department of Biochemistry, University of Johannesburg, Johannesburg, South Africa
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20
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Gauglitz JM, West KA, Bittremieux W, Williams CL, Weldon KC, Panitchpakdi M, Di Ottavio F, Aceves CM, Brown E, Sikora NC, Jarmusch AK, Martino C, Tripathi A, Meehan MJ, Dorrestein K, Shaffer JP, Coras R, Vargas F, Goldasich LD, Schwartz T, Bryant M, Humphrey G, Johnson AJ, Spengler K, Belda-Ferre P, Diaz E, McDonald D, Zhu Q, Elijah EO, Wang M, Marotz C, Sprecher KE, Vargas-Robles D, Withrow D, Ackermann G, Herrera L, Bradford BJ, Marques LMM, Amaral JG, Silva RM, Veras FP, Cunha TM, Oliveira RDR, Louzada-Junior P, Mills RH, Piotrowski PK, Servetas SL, Da Silva SM, Jones CM, Lin NJ, Lippa KA, Jackson SA, Daouk RK, Galasko D, Dulai PS, Kalashnikova TI, Wittenberg C, Terkeltaub R, Doty MM, Kim JH, Rhee KE, Beauchamp-Walters J, Wright KP, Dominguez-Bello MG, Manary M, Oliveira MF, Boland BS, Lopes NP, Guma M, Swafford AD, Dutton RJ, Knight R, Dorrestein PC. Enhancing untargeted metabolomics using metadata-based source annotation. Nat Biotechnol 2022; 40:1774-1779. [PMID: 35798960 PMCID: PMC10277029 DOI: 10.1038/s41587-022-01368-1] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 05/20/2022] [Indexed: 01/30/2023]
Abstract
Human untargeted metabolomics studies annotate only ~10% of molecular features. We introduce reference-data-driven analysis to match metabolomics tandem mass spectrometry (MS/MS) data against metadata-annotated source data as a pseudo-MS/MS reference library. Applying this approach to food source data, we show that it increases MS/MS spectral usage 5.1-fold over conventional structural MS/MS library matches and allows empirical assessment of dietary patterns from untargeted data.
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Affiliation(s)
- Julia M Gauglitz
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Kiana A West
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Wout Bittremieux
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Candace L Williams
- Beckman Center for Conservation Research, San Diego Zoo Wildlife Alliance, Escondido, CA, USA
| | - Kelly C Weldon
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
- Center for Microbiome Innovation, Joan and Irwin Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA
| | - Morgan Panitchpakdi
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Francesca Di Ottavio
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, USA
| | - Christine M Aceves
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Elizabeth Brown
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
- Division of Biological Sciences, University of California San Diego, La Jolla, CA, USA
| | - Nicole C Sikora
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Alan K Jarmusch
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Cameron Martino
- Center for Microbiome Innovation, Joan and Irwin Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
| | - Anupriya Tripathi
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
- Division of Biological Sciences, University of California San Diego, La Jolla, CA, USA
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Michael J Meehan
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Kathleen Dorrestein
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Justin P Shaffer
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Roxana Coras
- Division of Rheumatology, Allergy & Immunology, Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Fernando Vargas
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
- Division of Biological Sciences, University of California San Diego, La Jolla, CA, USA
| | | | - Tara Schwartz
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - MacKenzie Bryant
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Gregory Humphrey
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Abigail J Johnson
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Katharina Spengler
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, USA
| | - Pedro Belda-Ferre
- Center for Microbiome Innovation, Joan and Irwin Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Edgar Diaz
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Daniel McDonald
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Qiyun Zhu
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Emmanuel O Elijah
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Mingxun Wang
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Clarisse Marotz
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Kate E Sprecher
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO, USA
- Department of Population Health Sciences, University of Wisconsin-Madison, Madison, WI, USA
| | - Daniela Vargas-Robles
- Servicio Autónomo Centro Amazónico de Investigación y Control de Enfermedades Tropicales Simón Bolívar, Puerto Ayacucho, Amazonas, Venezuela
| | - Dana Withrow
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO, USA
| | - Gail Ackermann
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Lourdes Herrera
- Department of Pediatrics, Billings Clinic, Billings, MT, USA
| | - Barry J Bradford
- Department of Animal Science, Michigan State University, East Lansing, MI, USA
| | - Lucas Maciel Mauriz Marques
- Department of Pharmacology, Ribeirão Preto Medicinal School, Center of Research in Inflammatory Diseases, University of São Paulo, Ribeirão Preto, Sao Paolo, Brazil
| | - Juliano Geraldo Amaral
- Multidisciplinary Health Institute, Federal University of Bahia, Vitória da Conquista, Bahia, Brazil
| | - Rodrigo Moreira Silva
- NPPNS, Department of Biomolecular Sciences, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Ribeirão Preto, Sao Paolo, Brazil
| | - Flavio Protasio Veras
- Department of Pharmacology, Ribeirão Preto Medicinal School, Center of Research in Inflammatory Diseases, University of São Paulo, Ribeirão Preto, Sao Paolo, Brazil
| | - Thiago Mattar Cunha
- Department of Pharmacology, Ribeirão Preto Medicinal School, Center of Research in Inflammatory Diseases, University of São Paulo, Ribeirão Preto, Sao Paolo, Brazil
| | - Rene Donizeti Ribeiro Oliveira
- Department of Internal Medicine, Ribeirão Preto Medical School, Center of Research in Inflammatory Diseases, University of São Paulo, Ribeirão Preto, Sao Paolo, Brazil
| | - Paulo Louzada-Junior
- Department of Internal Medicine, Ribeirão Preto Medical School, Center of Research in Inflammatory Diseases, University of São Paulo, Ribeirão Preto, Sao Paolo, Brazil
| | - Robert H Mills
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
- Department of Pharmacology, University of California San Diego, La Jolla, CA, USA
| | - Paulina K Piotrowski
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Stephanie L Servetas
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Sandra M Da Silva
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Christina M Jones
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Nancy J Lin
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Katrice A Lippa
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Scott A Jackson
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Rima Kaddurah Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, Durham, NC, USA
- Department of Medicine, Duke University, Durham, NC, USA
- Duke Institute of Brain Sciences, Duke University, Durham, NC, USA
| | - Douglas Galasko
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
| | - Parambir S Dulai
- Division of Gastroenterology, Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | | | - Curt Wittenberg
- Department of Molecular Medicine, The Scripps Research Institute, La Jolla, CA, USA
| | - Robert Terkeltaub
- Division of Rheumatology, Allergy & Immunology, Department of Medicine, University of California San Diego, La Jolla, CA, USA
- San Diego VA Healthcare System, San Diego, CA, USA
| | - Megan M Doty
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
- Division of Neonatology, Department of Pediatrics, Kapi'olani Medical Center for Women and Children, John A. Burns School of Medicine, Honolulu, Hawaii, USA
| | - Jae H Kim
- Division of Neonatology, Perinatal Institute, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Kyung E Rhee
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Julia Beauchamp-Walters
- Division of Pediatric Hospital Medicine, Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Kenneth P Wright
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO, USA
| | - Maria Gloria Dominguez-Bello
- Department of Biochemistry and Microbiology, School of Environmental and Biological Sciences; Rutgers, The State University of New Jersey, New Brunswick, NJ, USA
| | - Mark Manary
- Department of Pediatrics, Washington University, St. Louis, MO, USA
| | - Michelli F Oliveira
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Brigid S Boland
- Division of Gastroenterology, Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Norberto Peporine Lopes
- NPPNS, Department of Biomolecular Sciences, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Ribeirão Preto, Sao Paolo, Brazil
| | - Monica Guma
- Division of Rheumatology, Allergy & Immunology, Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Austin D Swafford
- Center for Microbiome Innovation, Joan and Irwin Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA
| | - Rachel J Dutton
- Division of Biological Sciences, University of California San Diego, La Jolla, CA, USA
| | - Rob Knight
- Center for Microbiome Innovation, Joan and Irwin Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA.
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
- Department of Medicine, University of California San Diego, La Jolla, CA, USA.
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA.
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA.
| | - Pieter C Dorrestein
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, USA.
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA.
- Center for Microbiome Innovation, Joan and Irwin Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA.
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
- Department of Pharmacology, University of California San Diego, La Jolla, CA, USA.
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21
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Zhao XJG, Cao H. Linking research of biomedical datasets. Brief Bioinform 2022; 23:6712704. [PMID: 36151775 DOI: 10.1093/bib/bbac373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 08/03/2022] [Accepted: 08/08/2022] [Indexed: 12/14/2022] Open
Abstract
Biomedical data preprocessing and efficient computing can be as important as the statistical methods used to fit the data; data processing needs to consider application scenarios, data acquisition and individual rights and interests. We review common principles, knowledge and methods of integrated research according to the whole-pipeline processing mechanism diverse, coherent, sharing, auditable and ecological. First, neuromorphic and native algorithms integrate diverse datasets, providing linear scalability and high visualization. Second, the choice mechanism of different preprocessing, analysis and transaction methods from raw to neuromorphic was summarized on the node and coordinator platforms. Third, combination of node, network, cloud, edge, swarm and graph builds an ecosystem of cohort integrated research and clinical diagnosis and treatment. Looking forward, it is vital to simultaneously combine deep computing, mass data storage and massively parallel communication.
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Affiliation(s)
- Xiu-Ju George Zhao
- Wuhan Institute of Physics and Mathematics (WIPM), China.,Wuhan Polytechnic University, China
| | - Hui Cao
- Wuhan Polytechnic University, China
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22
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Gonzalez CG, Mills RH, Zhu Q, Sauceda C, Knight R, Dulai PS, Gonzalez DJ. Location-specific signatures of Crohn's disease at a multi-omics scale. MICROBIOME 2022; 10:133. [PMID: 35999575 PMCID: PMC9400277 DOI: 10.1186/s40168-022-01331-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 07/15/2022] [Indexed: 06/01/2023]
Abstract
BACKGROUND Crohn's disease (CD), an inflammatory bowel disease (IBD) subtype, results from pathologic interactions between host cells and its resident gut microbes. CD manifests in both isolated disease locations (ileum or colon) or a combination of locations (ileocolonic). To date, a comprehensive understanding of how isolated CD subtypes influence molecular profiles remains outstanding. To address this, we sought to define CD location signatures by leveraging a large cross-sectional feature set captured from the stool of over 200 IBD patients and healthy controls using metaproteomics, shotgun metagenomics, 16S rRNA sequencing, metabolomic profiling, and host genetics paired with clinical endoscopic assessments. RESULTS Neither metagenomic nor host genetics alone distinguished CD location subtypes. In contrast, ileal and colonic CD were distinguished using mass spectrometry-based methods (metabolomics or metaproteomics) or a combined multi-omic feature set. This multi-omic feature set revealed colonic CD was strongly associated with neutrophil-related proteins. Additionally, colonic CD displayed a disease-severity-related association with Bacteroides vulgatus. Colonic CD and ulcerative colitis profiles harbored strikingly similar feature enrichments compared to ileal CD, including neutrophil-related protein enrichments. Compared to colonic CD, ileal CD profiles displayed increased primary and secondary bile acid levels and concomitant shifts in taxa with noted sensitivities such as Faecalibacterium prausnitzii or affinities for bile acid-rich environments, including Gammaproteobacteria and Blautia sp. Having shown robust molecular and microbial distinctions tied to CD locations, we leveraged these profiles to generate location-specific disease severity biomarkers that surpass the performance of Calprotectin. CONCLUSIONS When compared using multi-omics features, colonic- and ileal-isolated CD subtypes display striking differences that suggest separate location-specific pathologies. Colonic CD's strong similarity to ulcerative colitis, including neutrophil and Bacteroides vulgatus involvement, is also evidence of a shared pathology for colonic-isolated IBD subtypes, while ileal CD maintains a unique, bile acid-driven profile. More broadly, this study demonstrates the power of multi-omics approaches for IBD biomarker discovery and elucidating the underlying biology. Video Abstract.
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Affiliation(s)
- Carlos G Gonzalez
- Department of Pharmacology, University of California San Diego, San Diego, CA, 92093, USA
- Department of Pediatrics, University of California San Diego, San Diego, CA, 92093, USA
- School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, 92093, USA
- Center for Microbiome Innovation, University of California San Diego, San Diego, CA, 92093, USA
- Department of Bioengineering, University of California San Diego, San Diego, CA, 92093, USA
- Department of Computer Science & Engineering, University of California San Diego, San Diego, CA, 92093, USA
| | - Robert H Mills
- Department of Pharmacology, University of California San Diego, San Diego, CA, 92093, USA
- Department of Pediatrics, University of California San Diego, San Diego, CA, 92093, USA
- School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, 92093, USA
- Center for Microbiome Innovation, University of California San Diego, San Diego, CA, 92093, USA
| | - Qiyun Zhu
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
- Biodesign Center for Fundamental and Applied Microbiomics, Arizona State University, Tempe, AZ, USA
| | - Consuelo Sauceda
- Department of Pharmacology, University of California San Diego, San Diego, CA, 92093, USA
- School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, 92093, USA
- Center for Microbiome Innovation, University of California San Diego, San Diego, CA, 92093, USA
| | - Rob Knight
- Department of Pediatrics, University of California San Diego, San Diego, CA, 92093, USA
- Center for Microbiome Innovation, University of California San Diego, San Diego, CA, 92093, USA
- Department of Bioengineering, University of California San Diego, San Diego, CA, 92093, USA
- Department of Computer Science & Engineering, University of California San Diego, San Diego, CA, 92093, USA
| | - Parambir S Dulai
- Department of Medicine, Division of Gastroenterology and Hepatology, Feinberg School of Medicine Northwestern University, Chicago, IL, 60061, USA.
| | - David J Gonzalez
- Department of Pharmacology, University of California San Diego, San Diego, CA, 92093, USA.
- School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, 92093, USA.
- Center for Microbiome Innovation, University of California San Diego, San Diego, CA, 92093, USA.
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23
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Volf M, Volfová T, Hörandl E, Wagner ND, Luntamo N, Salminen J, Sedio BE. Abiotic stress rather than biotic interactions drives contrasting trends in chemical richness and variation in alpine willows. Funct Ecol 2022. [DOI: 10.1111/1365-2435.14169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Martin Volf
- Biology Centre of the Czech Academy of Sciences Ceske Budejovice Czech Republic
- Faculty of Science University of South Bohemia Ceske Budejovice Czech Republic
| | - Tereza Volfová
- Biology Centre of the Czech Academy of Sciences Ceske Budejovice Czech Republic
- Faculty of Science University of South Bohemia Ceske Budejovice Czech Republic
| | - Elvira Hörandl
- Department of Systematics, Biodiversity and Evolution of Plants (With Herbarium) University of Goettingen Göttingen Germany
| | - Natascha D. Wagner
- Department of Systematics, Biodiversity and Evolution of Plants (With Herbarium) University of Goettingen Göttingen Germany
| | - Niko Luntamo
- Natural Chemistry Research Group, Department of Chemistry University of Turku Turku Finland
| | - Juha‐Pekka Salminen
- Natural Chemistry Research Group, Department of Chemistry University of Turku Turku Finland
| | - Brian E. Sedio
- Department of Integrative Biology University of Texas at Austin Austin TX USA
- Smithsonian Tropical Research Institute Ancón Republic of Panama
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24
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Boiko DA, Kozlov KS, Burykina JV, Ilyushenkova VV, Ananikov VP. Fully Automated Unconstrained Analysis of High-Resolution Mass Spectrometry Data with Machine Learning. J Am Chem Soc 2022; 144:14590-14606. [PMID: 35939718 DOI: 10.1021/jacs.2c03631] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Mass spectrometry (MS) is a convenient, highly sensitive, and reliable method for the analysis of complex mixtures, which is vital for materials science, life sciences fields such as metabolomics and proteomics, and mechanistic research in chemistry. Although it is one of the most powerful methods for individual compound detection, complete signal assignment in complex mixtures is still a great challenge. The unconstrained formula-generating algorithm, covering the entire spectra and revealing components, is a "dream tool" for researchers. We present the framework for efficient MS data interpretation, describing a novel approach for detailed analysis based on deisotoping performed by gradient-boosted decision trees and a neural network that generates molecular formulas from the fine isotopic structure, approaching the long-standing inverse spectral problem. The methods were successfully tested on three examples: fragment ion analysis in protein sequencing for proteomics, analysis of the natural samples for life sciences, and study of the cross-coupling catalytic system for chemistry.
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Affiliation(s)
- Daniil A Boiko
- Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Leninsky Prospekt 47, Moscow 119991, Russia
| | - Konstantin S Kozlov
- Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Leninsky Prospekt 47, Moscow 119991, Russia
| | - Julia V Burykina
- Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Leninsky Prospekt 47, Moscow 119991, Russia
| | - Valentina V Ilyushenkova
- Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Leninsky Prospekt 47, Moscow 119991, Russia
| | - Valentine P Ananikov
- Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Leninsky Prospekt 47, Moscow 119991, Russia
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25
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Yan S, Bhawal R, Yin Z, Thannhauser TW, Zhang S. Recent advances in proteomics and metabolomics in plants. MOLECULAR HORTICULTURE 2022; 2:17. [PMID: 37789425 PMCID: PMC10514990 DOI: 10.1186/s43897-022-00038-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Accepted: 06/20/2022] [Indexed: 10/05/2023]
Abstract
Over the past decade, systems biology and plant-omics have increasingly become the main stream in plant biology research. New developments in mass spectrometry and bioinformatics tools, and methodological schema to integrate multi-omics data have leveraged recent advances in proteomics and metabolomics. These progresses are driving a rapid evolution in the field of plant research, greatly facilitating our understanding of the mechanistic aspects of plant metabolisms and the interactions of plants with their external environment. Here, we review the recent progresses in MS-based proteomics and metabolomics tools and workflows with a special focus on their applications to plant biology research using several case studies related to mechanistic understanding of stress response, gene/protein function characterization, metabolic and signaling pathways exploration, and natural product discovery. We also present a projection concerning future perspectives in MS-based proteomics and metabolomics development including their applications to and challenges for system biology. This review is intended to provide readers with an overview of how advanced MS technology, and integrated application of proteomics and metabolomics can be used to advance plant system biology research.
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Affiliation(s)
- Shijuan Yan
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, China
| | - Ruchika Bhawal
- Proteomics and Metabolomics Facility, Institute of Biotechnology, Cornell University, 139 Biotechnology Building, 526 Campus Road, Ithaca, NY, 14853, USA
| | - Zhibin Yin
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, China
| | | | - Sheng Zhang
- Proteomics and Metabolomics Facility, Institute of Biotechnology, Cornell University, 139 Biotechnology Building, 526 Campus Road, Ithaca, NY, 14853, USA.
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He L, Wang X, Chen J, Li Y, Wang L, Xiong C, Nie Z. Biofluids Metabolic Profiling Based on PS@Fe 3O 4-NH 2 Magnetic Beads-Assisted LDI-MS for Liver Cancer Screening. Anal Chem 2022; 94:10367-10374. [PMID: 35839421 DOI: 10.1021/acs.analchem.2c00654] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Liver cancer (LC) is the third frequent cause of death worldwide, so early diagnosis of liver cancer patients is crucial for disease management. Herein, we applied NH2-coated polystyrene@Fe3O4 magnetic beads (PS@Fe3O4-NH2 MBs) as a matrix material in laser desorption/ionization mass spectrometry (LDI-MS). Rapid, sensitive, and selective metabolic profiling of the native biofluids was achieved without any inconvenient enrichment or purification. Then, based on the selected m/z features, LC patients were discriminated from healthy controls (HCs) by machine learning, with the high area under the curve (AUC) values for urine and serum assessments (0.962 and 0.935). Moreover, initial-diagnosed and subsequent-visited LC patients were also differentiated, which indicates potential applications of this method in early diagnosis. Furthermore, among these identified compounds by FT-ICR MS, the expression level of some metabolites changed from HCs to LCs, including 29 and 12 characteristic metabolites in human urine and serum samples, respectively. These results suggest that PS@Fe3O4-NH2 MBs-assisted LDI-MS coupled with machine learning is feasible for LC clinical diagnosis.
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Affiliation(s)
- Liuying He
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100190, China
| | - Xiao Wang
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100190, China
| | - Junyu Chen
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100190, China
| | - Yuze Li
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100190, China
| | - Liping Wang
- Centre of Reproductive Medicine, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen 518000, China
| | - Caiqiao Xiong
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
| | - Zongxiu Nie
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100190, China
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Schenck CA, Busta L. Using interdisciplinary, phylogeny-guided approaches to understand the evolution of plant metabolism. PLANT MOLECULAR BIOLOGY 2022; 109:355-367. [PMID: 34816350 DOI: 10.1007/s11103-021-01220-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 11/05/2021] [Indexed: 06/13/2023]
Abstract
To cope with relentless environmental pressures, plants produce an arsenal of structurally diverse chemicals, often called specialized metabolites. These lineage-specific compounds are derived from the simple building blocks made by ubiquitous core metabolic pathways. Although the structures of many specialized metabolites are known, the underlying metabolic pathways and the evolutionary events that have shaped the plant chemical diversity landscape are only beginning to be understood. However, with the advent of multi-omics data sets and the relative ease of studying pathways in previously intractable non-model species, plant specialized metabolic pathways are now being systematically identified. These large datasets also provide a foundation for comparative, phylogeny-guided studies of plant metabolism. Comparisons of metabolic traits and features like chemical abundances, enzyme activities, or gene sequences from phylogenetically diverse plants provide insights into how metabolic pathways evolved. This review highlights the power of studying evolution through the lens of comparative biochemistry, particularly how placing metabolism into a phylogenetic context can help a researcher identify the metabolic innovations enabling the evolution of structurally diverse plant metabolites.
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Affiliation(s)
- Craig A Schenck
- Department of Biochemistry, University of Missouri, Columbia, MO, USA.
| | - Lucas Busta
- Department of Chemistry and Biochemistry, University of Minnesota Duluth, Duluth, MN, USA
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28
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Aksenov AA, Salido RA, Melnik AV, Brennan C, Brejnrod A, Caraballo-Rodríguez AM, Gauglitz JM, Lejzerowicz F, Farmer DK, Vance ME, Knight R, Dorrestein PC. The molecular impact of life in an indoor environment. SCIENCE ADVANCES 2022; 8:eabn8016. [PMID: 35749501 PMCID: PMC9232106 DOI: 10.1126/sciadv.abn8016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 05/10/2022] [Indexed: 06/15/2023]
Abstract
The chemistry of indoor surfaces and the role of microbes in shaping and responding to that chemistry are largely unexplored. We found that, over 1 month, people's presence and activities profoundly reshaped the chemistry of a house. Molecules associated with eating/cooking, bathroom use, and personal care were found throughout the entire house, while molecules associated with medications, outdoor biocides, and microbially derived compounds were distributed in a location-dependent manner. The house and its microbial occupants, in turn, also introduced chemical transformations such as oxidation and transformations of foodborne molecules. The awareness of and the ability to observe the molecular changes introduced by people should influence future building designs.
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Affiliation(s)
- Alexander A. Aksenov
- Skaggs of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093, USA
- Department of Chemistry, University of Connecticut, Storrs, CT 06269, USA
| | - Rodolfo A. Salido
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA 92093, USA
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Alexey V. Melnik
- Skaggs of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093, USA
- Department of Chemistry, University of Connecticut, Storrs, CT 06269, USA
| | - Caitriona Brennan
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA 92093, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA 92093, USA
| | - Asker Brejnrod
- Skaggs of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Andrés Mauricio Caraballo-Rodríguez
- Skaggs of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Julia M. Gauglitz
- Skaggs of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Franck Lejzerowicz
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA 92093, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA 92093, USA
| | - Delphine K. Farmer
- Department of Chemistry, Colorado State University, Fort Collins, CO 80523, USA
| | - Marina E. Vance
- Department of Mechanical Engineering, University of Colorado Boulder, Boulder, CO 80309, USA
| | - Rob Knight
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA 92093, USA
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA 92093, USA
- Department of Computer Science, University of California San Diego, La Jolla, CA, 92093, USA
| | - Pieter C. Dorrestein
- Skaggs of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093, USA
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29
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Skirycz A, Fernie AR. Past accomplishments and future challenges of the multi-omics characterization of leaf growth. PLANT PHYSIOLOGY 2022; 189:473-489. [PMID: 35325227 PMCID: PMC9157134 DOI: 10.1093/plphys/kiac136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 01/25/2022] [Indexed: 06/14/2023]
Abstract
The advent of omics technologies has revolutionized biology and advanced our understanding of all biological processes, including major developmental transitions in plants and animals. Here, we review the vast knowledge accumulated concerning leaf growth in terms of transcriptional regulation before turning our attention to the historically less well-characterized alterations at the protein and metabolite level. We will then discuss how the advent of biochemical methods coupled with metabolomics and proteomics can provide insight into the protein-protein and protein-metabolite interactome of the growing leaves. We finally highlight the substantial challenges in detection, spatial resolution, integration, and functional validation of the omics results, focusing on metabolomics as a prerequisite for a comprehensive understanding of small-molecule regulation of plant growth.
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Affiliation(s)
- Aleksandra Skirycz
- Max-Planck-Institute of Molecular Plant Physiology, Potsdam-Golm 14476, Germany
- Boyce Thompson Institute, Ithaca, New York 14853, USA
- Cornell University, Ithaca, New York 14853, USA
| | - Alisdair R Fernie
- Max-Planck-Institute of Molecular Plant Physiology, Potsdam-Golm 14476, Germany
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30
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Chen K, Xiang Y, Yan X, Li Z, Qin R, Sun J. Global Tracking of Transformation Products of Environmental Contaminants by 2H-Labeled Stable Isotope-Assisted Metabolomics. Anal Chem 2022; 94:7255-7263. [PMID: 35510918 DOI: 10.1021/acs.analchem.2c00500] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Stable isotope-assisted metabolomics (SIAM) enables global tracking of isotopic labels in nontargeted metabolomics in living organisms. However, its application in tracking transformation products (TPs, as metabolites of contaminants) of environmental contaminants is still a challenge due to limits in methodology, unmatured algorithms, and the high cost of 13C-labeled contaminants. Therefore, we developed a 2H-SIAM pipeline coupled with a highly flexible algorithm 2H-SIAM(1.0) (https://github.com/kechen1984/2H-SIAM), facilitating tracking TPs of contaminants in the environmental matrix. A detailed discussion illustrates the theory, behavior, and prospect of 2H-SIAM. We demonstrate that the proposed 2H-SIAM pipeline has unique advantages over 13C-SIAM, for example, cost-effective 2H-labeled contaminants, easy synthesis of 2H-labeled emerging contaminants, and providing more structural information. A pyrene soil degradation study further confirmed its high performance. It efficiently discarded 99% of noise signals and extracted 52 features from the nontargeted high resolution mass spectrometry (HRMS) data. Among them, 13 features were annotated as TPs of pyrene with identification confidence from Level 2a to Level 5, and 5 TPs were reported for the first time. In conclusion, the proposed 2H-SIAM pipeline is powerful in tracking potential TPs of environmental contaminants with unique advantages.
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Affiliation(s)
- Ke Chen
- Key Laboratory of Resources Conversion and Pollution Control of the State Ethnic Affairs Commission, College of Resources and Environmental Science, South-Central Minzu University, Wuhan 430074, P.R. China
| | - Yuhui Xiang
- Key Laboratory of Resources Conversion and Pollution Control of the State Ethnic Affairs Commission, College of Resources and Environmental Science, South-Central Minzu University, Wuhan 430074, P.R. China
| | - Xiaoyu Yan
- Department of Chemistry, Renmin University of China, Beijing 100872, P.R. China
| | - Zhenghui Li
- School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan, Hubei 430074, P.R. China
| | - Rui Qin
- College of Life Sciences, South-Central Minzu University, Wuhan 430068, P.R. China
| | - Jie Sun
- Key Laboratory of Resources Conversion and Pollution Control of the State Ethnic Affairs Commission, College of Resources and Environmental Science, South-Central Minzu University, Wuhan 430074, P.R. China
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31
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Gregor R, Probst M, Eyal S, Aksenov A, Sasson G, Horovitz I, Dorrestein PC, Meijler MM, Mizrahi I. Mammalian gut metabolomes mirror microbiome composition and host phylogeny. THE ISME JOURNAL 2022; 16:1262-1274. [PMID: 34903850 PMCID: PMC9038745 DOI: 10.1038/s41396-021-01152-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 10/18/2021] [Accepted: 11/09/2021] [Indexed: 12/19/2022]
Abstract
In the past decade, studies on the mammalian gut microbiome have revealed that different animal species have distinct gut microbial compositions. The functional ramifications of this variation in microbial composition remain unclear: do these taxonomic differences indicate microbial adaptations to host-specific functionality, or are these diverse microbial communities essentially functionally redundant, as has been indicated by previous metagenomics studies? Here, we examine the metabolic content of mammalian gut microbiomes as a direct window into ecosystem function, using an untargeted metabolomics platform to analyze 101 fecal samples from a range of 25 exotic mammalian species in collaboration with a zoological center. We find that mammalian metabolomes are chemically diverse and strongly linked to microbiome composition, and that metabolome composition is further correlated to the phylogeny of the mammalian host. Specific metabolites enriched in different animal species included modified and degraded host and dietary compounds such as bile acids and triterpenoids, as well as fermentation products such as lactate and short-chain fatty acids. Our results suggest that differences in microbial taxonomic composition are indeed translated to host-specific metabolism, indicating that taxonomically distant microbiomes are more functionally diverse than redundant.
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Affiliation(s)
- Rachel Gregor
- Department of Chemistry, Ben-Gurion University of the Negev, Be'er Sheva, Israel
- National Institute of Biotechnology in the Negev, Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Maraike Probst
- National Institute of Biotechnology in the Negev, Ben-Gurion University of the Negev, Be'er Sheva, Israel
- Department of Life Sciences, Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Stav Eyal
- National Institute of Biotechnology in the Negev, Ben-Gurion University of the Negev, Be'er Sheva, Israel
- Department of Life Sciences, Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Alexander Aksenov
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Goor Sasson
- National Institute of Biotechnology in the Negev, Ben-Gurion University of the Negev, Be'er Sheva, Israel
- Department of Life Sciences, Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Igal Horovitz
- The Zoological Center Tel Aviv-Ramat Gan, Ramat Gan, Israel
| | - Pieter C Dorrestein
- Collaborative Mass Spectrometry Innovation Center, 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
- Department of Pharmacology, School of Medicine, University of California San Diego, La Jolla, CA, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Michael M Meijler
- Department of Chemistry, Ben-Gurion University of the Negev, Be'er Sheva, Israel.
- National Institute of Biotechnology in the Negev, Ben-Gurion University of the Negev, Be'er Sheva, Israel.
| | - Itzhak Mizrahi
- National Institute of Biotechnology in the Negev, Ben-Gurion University of the Negev, Be'er Sheva, Israel.
- Department of Life Sciences, Ben-Gurion University of the Negev, Be'er Sheva, Israel.
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Mannochio-Russo H, de Almeida RF, Nunes WDG, Bueno PCP, Caraballo-Rodríguez AM, Bauermeister A, Dorrestein PC, Bolzani VS. Untargeted Metabolomics Sheds Light on the Diversity of Major Classes of Secondary Metabolites in the Malpighiaceae Botanical Family. FRONTIERS IN PLANT SCIENCE 2022; 13:854842. [PMID: 35498703 PMCID: PMC9047359 DOI: 10.3389/fpls.2022.854842] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 03/23/2022] [Indexed: 06/14/2023]
Abstract
Natural products produced by plants are one of the most investigated natural sources, which substantially contributed to the development of the natural products field. Even though these compounds are widely explored, the literature still lacks comprehensive investigations aiming to explore the evolution of secondary metabolites produced by plants, especially if classical methodologies are employed. The development of sensitive hyphenated techniques and computational tools for data processing has enabled the study of large datasets, being valuable assets for chemosystematic studies. Here, we describe a strategy for chemotaxonomic investigations using the Malpighiaceae botanical family as a model. Our workflow was based on MS/MS untargeted metabolomics, spectral searches, and recently described in silico classification tools, which were mapped into the latest molecular phylogeny accepted for this family. The metabolomic analysis revealed that different ionization modes and extraction protocols significantly impacted the chemical profiles, influencing the chemotaxonomic results. Spectral searches within public databases revealed several clades or genera-specific molecular families, being potential chemical markers for these taxa, while the in silico classification tools were able to expand the Malpighiaceae chemical space. The classes putatively annotated were used for ancestral character reconstructions, which recovered several classes of metabolites as homoplasies (i.e., non-exclusive) or synapomorphies (i.e., exclusive) for all sampled clades and genera. Our workflow combines several approaches to perform a comprehensive evolutionary chemical study. We expect it to be used on further chemotaxonomic investigations to expand chemical knowledge and reveal biological insights for compounds classes in different biological groups.
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Affiliation(s)
- Helena Mannochio-Russo
- NuBBE, Department of Biochemistry and Organic Chemistry, Institute of Chemistry, São Paulo State University (UNESP), Araraquara, Brazil
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA, United States
| | - Rafael F. de Almeida
- Royal Botanical Gardens Kew, Science, Ecosystem Stewardship, Diversity and Livelihoods, Richmond, United Kingdom
- Department of Biological Sciences, Lamol Lab, Feira de Santana State University (UEFS), Feira de Santana, Brazil
| | - Wilhan D. G. Nunes
- Federal Institute of Education, Science and Technology of Rondônia (IFRO), Ji-Paraná, Brazil
| | - Paula C. P. Bueno
- Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
- Institute of Chemistry, Federal University of Alfenas (UNIFAL), Alfenas, Brazil
| | - Andrés M. Caraballo-Rodríguez
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA, United States
| | - Anelize Bauermeister
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA, United States
| | - Pieter C. Dorrestein
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA, United States
| | - Vanderlan S. Bolzani
- NuBBE, Department of Biochemistry and Organic Chemistry, Institute of Chemistry, São Paulo State University (UNESP), Araraquara, Brazil
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Gaudry A, Huber F, Nothias LF, Cretton S, Kaiser M, Wolfender JL, Allard PM. MEMO: Mass Spectrometry-Based Sample Vectorization to Explore Chemodiverse Datasets. FRONTIERS IN BIOINFORMATICS 2022; 2:842964. [PMID: 36304329 PMCID: PMC9580960 DOI: 10.3389/fbinf.2022.842964] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Accepted: 02/16/2022] [Indexed: 11/13/2022] Open
Abstract
In natural products research, chemodiverse extracts coming from multiple organisms are explored for novel bioactive molecules, sometimes over extended periods. Samples are usually analyzed by liquid chromatography coupled with fragmentation mass spectrometry to acquire informative mass spectral ensembles. Such data is then exploited to establish relationships among analytes or samples (e.g., via molecular networking) and annotate metabolites. However, the comparison of samples profiled in different batches is challenging with current metabolomics methods since the experimental variation—changes in chromatographical or mass spectrometric conditions - hinders the direct comparison of the profiled samples. Here we introduce MEMO—MS2 BasEd SaMple VectOrization—a method allowing to cluster large amounts of chemodiverse samples based on their LC-MS/MS profiles in a retention time agnostic manner. This method is particularly suited for heterogeneous and chemodiverse sample sets. MEMO demonstrated similar clustering performance as state-of-the-art metrics considering fragmentation spectra. More importantly, such performance was achieved without the requirement of a prior feature alignment step and in a significantly shorter computational time. MEMO thus allows the comparison of vast ensembles of samples, even when analyzed over long periods of time, and on different chromatographic or mass spectrometry platforms. This new addition to the computational metabolomics toolbox should drastically expand the scope of large-scale comparative analysis.
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Affiliation(s)
- Arnaud Gaudry
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland
- School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland
| | - Florian Huber
- Center for Digitalization and Digitality, HSD—Düsseldorf University of Applied Sciences, Düsseldorf, Germany
| | - Louis-Félix Nothias
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland
- School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland
| | - Sylvian Cretton
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland
- School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland
| | - Marcel Kaiser
- Department of Medical and Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, University of Basel, Basel, Switzerland
- Faculty of Science, University of Basel, Basel, Switzerland
| | - Jean-Luc Wolfender
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland
- School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland
| | - Pierre-Marie Allard
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland
- School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland
- Department of Biology, University of Fribourg, Fribourg, Switzerland
- *Correspondence: Pierre-Marie Allard,
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34
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Aleti G, Kohn JN, Troyer EA, Weldon K, Huang S, Tripathi A, Dorrestein PC, Swafford AD, Knight R, Hong S. Salivary bacterial signatures in depression-obesity comorbidity are associated with neurotransmitters and neuroactive dipeptides. BMC Microbiol 2022; 22:75. [PMID: 35287577 PMCID: PMC8919597 DOI: 10.1186/s12866-022-02483-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 02/25/2022] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND Depression and obesity are highly prevalent, often co-occurring conditions marked by inflammation. Microbiome perturbations are implicated in obesity-inflammation-depression interrelationships, but how the microbiome mechanistically contributes to pathology remains unclear. Metabolomic investigations into microbial neuroactive metabolites may offer mechanistic insights into host-microbe interactions. Using 16S sequencing and untargeted mass spectrometry of saliva, and blood monocyte inflammation regulation assays, we identified key microbes, metabolites and host inflammation in association with depressive symptomatology, obesity, and depressive symptomatology-obesity comorbidity. RESULTS Gram-negative bacteria with inflammation potential were enriched relative to Gram-positive bacteria in comorbid obesity-depression, supporting the inflammation-oral microbiome link in obesity-depression interrelationships. Oral microbiome was more highly predictive of depressive symptomatology-obesity co-occurrences than of obesity or depressive symptomatology independently, suggesting specific microbial signatures associated with obesity-depression co-occurrences. Mass spectrometry analysis revealed significant changes in levels of signaling molecules of microbiota, microbial or dietary derived signaling peptides and aromatic amino acids among depressive symptomatology, obesity and comorbid obesity-depression. Furthermore, integration of the microbiome and metabolomics data revealed that key oral microbes, many previously shown to have neuroactive potential, co-occurred with potential neuropeptides and biosynthetic precursors of the neurotransmitters dopamine, epinephrine and serotonin. CONCLUSIONS Together, our findings offer novel insights into oral microbial-brain connection and potential neuroactive metabolites involved.
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Affiliation(s)
- Gajender Aleti
- Department of Psychiatry, University of California San Diego, La Jolla, CA, 92093, USA
| | - Jordan N Kohn
- Department of Psychiatry, University of California San Diego, La Jolla, CA, 92093, USA
| | - Emily A Troyer
- Department of Psychiatry, University of California San Diego, La Jolla, CA, 92093, USA
| | - Kelly Weldon
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, 92093, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
| | - Shi Huang
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, 92093, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA, 92093, USA
| | - Anupriya Tripathi
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA, 92093, USA
| | - Pieter C Dorrestein
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, 92093, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, 92093, USA
| | - Austin D Swafford
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, 92093, USA
| | - Rob Knight
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, 92093, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA, 92093, USA
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, 92093, USA
- Department of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA
| | - Suzi Hong
- Department of Psychiatry, University of California San Diego, La Jolla, CA, 92093, USA.
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, 92093, USA.
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Morozumi S, Ueda M, Okahashi N, Arita M. Structures and functions of the gut microbial lipidome. Biochim Biophys Acta Mol Cell Biol Lipids 2022; 1867:159110. [PMID: 34995792 DOI: 10.1016/j.bbalip.2021.159110] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 12/19/2021] [Accepted: 12/24/2021] [Indexed: 12/26/2022]
Abstract
Microbial lipids provide signals that are responsible for maintaining host health and controlling disease. The differences in the structures of microbial lipids have been shown to alter receptor selectivity and agonist/antagonist activity. Advanced lipidomics is an emerging field that helps to elucidate the complex bacterial lipid diversity. The use of cutting-edge technologies is expected to lead to the discovery of new functional metabolites involved in host homeostasis. This review aims to describe recent updates on functional lipid metabolites derived from gut microbiota, their structure-activity relationships, and advanced lipidomics technologies.
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Affiliation(s)
- Satoshi Morozumi
- Laboratory for Metabolomics, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan; Division of Physiological Chemistry and Metabolism, Graduate School of Pharmaceutical Sciences, Keio University, 1-5-30 Shibakoen, Minato-ku, Tokyo 105-8512, Japan
| | - Masahiro Ueda
- Laboratory for Metabolomics, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan; JSR Bioscience and Informatics R&D Center, JSR Corporation, 3-103-9 Tonomachi, Kawasaki-ku, Kawasaki, Kanagawa 210-0821, Japan
| | - Nobuyuki Okahashi
- Laboratory for Metabolomics, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan; Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Makoto Arita
- Laboratory for Metabolomics, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan; Division of Physiological Chemistry and Metabolism, Graduate School of Pharmaceutical Sciences, Keio University, 1-5-30 Shibakoen, Minato-ku, Tokyo 105-8512, Japan; Cellular and Molecular Epigenetics Laboratory, Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.
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36
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Bauermeister A, Mannochio-Russo H, Costa-Lotufo LV, Jarmusch AK, Dorrestein PC. Mass spectrometry-based metabolomics in microbiome investigations. Nat Rev Microbiol 2022; 20:143-160. [PMID: 34552265 PMCID: PMC9578303 DOI: 10.1038/s41579-021-00621-9] [Citation(s) in RCA: 150] [Impact Index Per Article: 75.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/03/2021] [Indexed: 02/08/2023]
Abstract
Microbiotas are a malleable part of ecosystems, including the human ecosystem. Microorganisms affect not only the chemistry of their specific niche, such as the human gut, but also the chemistry of distant environments, such as other parts of the body. Mass spectrometry-based metabolomics is one of the key technologies to detect and identify the small molecules produced by the human microbiota, and to understand the functional role of these microbial metabolites. This Review provides a foundational introduction to common forms of untargeted mass spectrometry and the types of data that can be obtained in the context of microbiome analysis. Data analysis remains an obstacle; therefore, the emphasis is placed on data analysis approaches and integrative analysis, including the integration of microbiome sequencing data.
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Affiliation(s)
- Anelize Bauermeister
- Institute of Biomedical Science, Universidade de São Paulo, São Paulo, SP, Brazil,Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA, USA
| | - Helena Mannochio-Russo
- Department of Biochemistry and Organic Chemistry, Institute of Chemistry, São Paulo State University, Araraquara, SP, Brazil,Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA, USA
| | | | - Alan K. Jarmusch
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA, USA
| | - Pieter C. Dorrestein
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA, USA.,Department of Pediatrics, University of California, San Diego, CA, USA.,Center for Microbiome Innovation, University of California, San Diego, CA, USA
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37
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Mills RH, Dulai PS, Vázquez-Baeza Y, Sauceda C, Daniel N, Gerner RR, Batachari LE, Malfavon M, Zhu Q, Weldon K, Humphrey G, Carrillo-Terrazas M, Goldasich LD, Bryant M, Raffatellu M, Quinn RA, Gewirtz AT, Chassaing B, Chu H, Sandborn WJ, Dorrestein PC, Knight R, Gonzalez DJ. Multi-omics analyses of the ulcerative colitis gut microbiome link Bacteroides vulgatus proteases with disease severity. Nat Microbiol 2022; 7:262-276. [PMID: 35087228 PMCID: PMC8852248 DOI: 10.1038/s41564-021-01050-3] [Citation(s) in RCA: 108] [Impact Index Per Article: 54.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 12/15/2021] [Indexed: 12/19/2022]
Abstract
Ulcerative colitis (UC) is driven by disruptions in host-microbiota homoeostasis, but current treatments exclusively target host inflammatory pathways. To understand how host-microbiota interactions become disrupted in UC, we collected and analysed six faecal- or serum-based omic datasets (metaproteomic, metabolomic, metagenomic, metapeptidomic and amplicon sequencing profiles of faecal samples and proteomic profiles of serum samples) from 40 UC patients at a single inflammatory bowel disease centre, as well as various clinical, endoscopic and histologic measures of disease activity. A validation cohort of 210 samples (73 UC, 117 Crohn's disease, 20 healthy controls) was collected and analysed separately and independently. Data integration across both cohorts showed that a subset of the clinically active UC patients had an overabundance of proteases that originated from the bacterium Bacteroides vulgatus. To test whether B. vulgatus proteases contribute to UC disease activity, we first profiled B. vulgatus proteases found in patients and bacterial cultures. Use of a broad-spectrum protease inhibitor improved B. vulgatus-induced barrier dysfunction in vitro, and prevented colitis in B. vulgatus monocolonized, IL10-deficient mice. Furthermore, transplantation of faeces from UC patients with a high abundance of B. vulgatus proteases into germfree mice induced colitis dependent on protease activity. These results, stemming from a multi-omics approach, improve understanding of functional microbiota alterations that drive UC and provide a resource for identifying other pathways that could be inhibited as a strategy to treat this disease.
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Affiliation(s)
- Robert H Mills
- Department of Pharmacology, University of California, San Diego, CA, USA.,Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA, USA.,Department of Pediatrics, University of California, San Diego, CA, USA
| | - Parambir S Dulai
- Division of Gastroenterology, University of California, San Diego, CA, USA
| | - Yoshiki Vázquez-Baeza
- Department of Pediatrics, University of California, San Diego, CA, USA.,Department of Computer Science and Engineering, University of California, San Diego, CA, USA.,Center for Microbiome Innovation, University of California, San Diego, CA, USA
| | - Consuelo Sauceda
- Department of Pharmacology, University of California, San Diego, CA, USA.,Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA, USA
| | - Noëmie Daniel
- INSERM U1016, team Mucosal microbiota in chronic inflammatory diseases, CNRS UMR 8104, Université de Paris, Paris, France
| | - Romana R Gerner
- Department of Pediatrics, University of California, San Diego, CA, USA.,Division of Host-Microbe Systems and Therapeutics, University of California, San Diego, CA, USA
| | | | - Mario Malfavon
- Department of Pharmacology, University of California, San Diego, CA, USA
| | - Qiyun Zhu
- Department of Pediatrics, University of California, San Diego, CA, USA.,School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | - Kelly Weldon
- Center for Microbiome Innovation, University of California, San Diego, CA, USA
| | - Greg Humphrey
- Department of Pediatrics, University of California, San Diego, CA, USA
| | - Marvic Carrillo-Terrazas
- Department of Pharmacology, University of California, San Diego, CA, USA.,Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA, USA.,Department of Pathology, University of California, San Diego, CA, USA
| | | | - MacKenzie Bryant
- Department of Pediatrics, University of California, San Diego, CA, USA
| | - Manuela Raffatellu
- Center for Microbiome Innovation, University of California, San Diego, CA, USA.,Division of Host-Microbe Systems and Therapeutics, University of California, San Diego, CA, USA
| | - Robert A Quinn
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, USA
| | - Andrew T Gewirtz
- Center for Inflammation, Immunity and Infection, Institute for Biomedical Sciences, Georgia State University, Atlanta, GA, USA
| | - Benoit Chassaing
- INSERM U1016, team Mucosal microbiota in chronic inflammatory diseases, CNRS UMR 8104, Université de Paris, Paris, France
| | - Hiutung Chu
- Department of Pathology, University of California, San Diego, CA, USA
| | - William J Sandborn
- Division of Gastroenterology, University of California, San Diego, CA, USA
| | - Pieter C Dorrestein
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA, USA.,Department of Pediatrics, University of California, San Diego, CA, USA.,Center for Microbiome Innovation, University of California, San Diego, CA, USA
| | - Rob Knight
- Department of Pediatrics, University of California, San Diego, CA, USA. .,Department of Computer Science and Engineering, University of California, San Diego, CA, USA. .,Center for Microbiome Innovation, University of California, San Diego, CA, USA.
| | - David J Gonzalez
- Department of Pharmacology, University of California, San Diego, CA, USA. .,Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA, USA. .,Center for Microbiome Innovation, University of California, San Diego, CA, USA.
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Advances in Microbiome-Derived Solutions and Methodologies Are Founding a New Era in Skin Health and Care. Pathogens 2022; 11:pathogens11020121. [PMID: 35215065 PMCID: PMC8879973 DOI: 10.3390/pathogens11020121] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 01/11/2022] [Accepted: 01/12/2022] [Indexed: 12/04/2022] Open
Abstract
The microbiome, as a community of microorganisms and their structural elements, genomes, metabolites/signal molecules, has been shown to play an important role in human health, with significant beneficial applications for gut health. Skin microbiome has emerged as a new field with high potential to develop disruptive solutions to manage skin health and disease. Despite an incomplete toolbox for skin microbiome analyses, much progress has been made towards functional dissection of microbiomes and host-microbiome interactions. A standardized and robust investigation of the skin microbiome is necessary to provide accurate microbial information and set the base for a successful translation of innovations in the dermo-cosmetic field. This review provides an overview of how the landscape of skin microbiome research has evolved from method development (multi-omics/data-based analytical approaches) to the discovery and development of novel microbiome-derived ingredients. Moreover, it provides a summary of the latest findings on interactions between the microbiomes (gut and skin) and skin health/disease. Solutions derived from these two paths are used to develop novel microbiome-based ingredients or solutions acting on skin homeostasis are proposed. The most promising skin and gut-derived microbiome interventional strategies are presented, along with regulatory, safety, industrial, and technical challenges related to a successful translation of these microbiome-based concepts/technologies in the dermo-cosmetic industry.
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39
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Yuan H, Cao G, Hou X, Huang M, Du P, Tan T, Zhang Y, Zhou H, Liu X, Liu L, Jiangfang Y, Li Y, Liu Z, Fang C, Zhao L, Fernie AR, Luo J. Development of a widely targeted volatilomics method for profiling volatilomes in plants. MOLECULAR PLANT 2022; 15:189-202. [PMID: 34509640 DOI: 10.1016/j.molp.2021.09.003] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 08/22/2021] [Accepted: 09/09/2021] [Indexed: 05/26/2023]
Abstract
Volatile organic compounds play essential roles in plant environment interactions as well as determining the fragrance of plants. Although gas chromatography-mass spectrometry-based untargeted metabolomics is commonly used to assess plant volatiles, it suffers from high spectral convolution, low detection sensitivity, a limited number of annotated metabolites, and relatively poor reproducibility. Here, we report a widely targeted volatilomics (WTV) method that involves using a "targeted spectra extraction" algorithm to address spectral convolution, constructing a high-coverage MS2 spectral tag library to expand volatile annotation, adapting a multiple reaction monitoring mode to improve sensitivity, and using regression models to adjust for signal drift. The newly developed method was used to profile the volatilome of rice grains. Compared with the untargeted method, the newly developed WTV method shows higher sensitivity (for example, the signal-to-noise ratio of guaicol increased from 4.1 to 18.8), high annotation coverage (the number of annotated volatiles increased from 43 to 132), and better reproducibility (the number of volatiles in quality control samples with relative standard deviation value below 30.0% increased from 14 to 92 after normalization). Using the WTV method, we studied the metabolic responses of tomato to environmental stimuli and profiled the volatilomes of different rice accessions. The results identified benzothiazole as a potential airborne signal priming tomato plants for enhanced defense and 2-nonanone and 2-heptanone as novel aromatic compounds contributing to rice fragrance. These case studies suggest that the widely targeted volatilomics method is more efficient than those currently used and may considerably promote plant volatilomics studies.
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Affiliation(s)
- Honglun Yuan
- College of Tropical Crops, Hainan University, Haikou 570288, China; Sanya Nanfan Research Institute of Hainan University, Hainan Yazhou Bay Seed Laboratory, Sanya, 572025, China
| | - Guangping Cao
- College of Tropical Crops, Hainan University, Haikou 570288, China; Sanya Nanfan Research Institute of Hainan University, Hainan Yazhou Bay Seed Laboratory, Sanya, 572025, China
| | - Xiaodong Hou
- College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen 518060, China
| | - Menglan Huang
- College of Tropical Crops, Hainan University, Haikou 570288, China; Sanya Nanfan Research Institute of Hainan University, Hainan Yazhou Bay Seed Laboratory, Sanya, 572025, China
| | - Pengmeng Du
- College of Tropical Crops, Hainan University, Haikou 570288, China; Sanya Nanfan Research Institute of Hainan University, Hainan Yazhou Bay Seed Laboratory, Sanya, 572025, China
| | - Tingting Tan
- College of Tropical Crops, Hainan University, Haikou 570288, China; Sanya Nanfan Research Institute of Hainan University, Hainan Yazhou Bay Seed Laboratory, Sanya, 572025, China
| | - Youjin Zhang
- College of Tropical Crops, Hainan University, Haikou 570288, China; Sanya Nanfan Research Institute of Hainan University, Hainan Yazhou Bay Seed Laboratory, Sanya, 572025, China
| | - Haihong Zhou
- College of Tropical Crops, Hainan University, Haikou 570288, China; Sanya Nanfan Research Institute of Hainan University, Hainan Yazhou Bay Seed Laboratory, Sanya, 572025, China
| | - Xianqing Liu
- College of Tropical Crops, Hainan University, Haikou 570288, China; Sanya Nanfan Research Institute of Hainan University, Hainan Yazhou Bay Seed Laboratory, Sanya, 572025, China
| | - Ling Liu
- College of Tropical Crops, Hainan University, Haikou 570288, China; Sanya Nanfan Research Institute of Hainan University, Hainan Yazhou Bay Seed Laboratory, Sanya, 572025, China
| | - Yiding Jiangfang
- College of Tropical Crops, Hainan University, Haikou 570288, China; Sanya Nanfan Research Institute of Hainan University, Hainan Yazhou Bay Seed Laboratory, Sanya, 572025, China
| | - Yufei Li
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
| | - Zhenhuan Liu
- College of Tropical Crops, Hainan University, Haikou 570288, China; Sanya Nanfan Research Institute of Hainan University, Hainan Yazhou Bay Seed Laboratory, Sanya, 572025, China
| | - Chuanying Fang
- College of Tropical Crops, Hainan University, Haikou 570288, China; Sanya Nanfan Research Institute of Hainan University, Hainan Yazhou Bay Seed Laboratory, Sanya, 572025, China
| | - Liqing Zhao
- College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen 518060, China
| | - Alisdair R Fernie
- Department of Molecular Physiology, Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany
| | - Jie Luo
- College of Tropical Crops, Hainan University, Haikou 570288, China; Sanya Nanfan Research Institute of Hainan University, Hainan Yazhou Bay Seed Laboratory, Sanya, 572025, China; National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China.
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40
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Wang X, Li Y, Fan J, He L, Chen J, Liu H, Nie Z. Rapid screening for genitourinary cancers: mass spectrometry-based metabolic fingerprinting of urine. Chem Commun (Camb) 2022; 58:9433-9436. [DOI: 10.1039/d2cc02329f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Genitourinary (GU) cancers are among the most common malignant diseases in men. Rapid screening is the key to GU cancers management for early diagnosis and treatment. Urine is a highly...
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41
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Abstract
Motivation Untargeted metabolomics experiments rely on spectral libraries for structure annotation, but these libraries are vastly incomplete; in silico methods search in structure databases, allowing us to overcome this limitation. The best-performing in silico methods use machine learning to predict a molecular fingerprint from tandem mass spectra, then use the predicted fingerprint to search in a molecular structure database. Predicted molecular fingerprints are also of great interest for compound class annotation, de novo structure elucidation, and other tasks. So far, kernel support vector machines are the best tool for fingerprint prediction. However, they cannot be trained on all publicly available reference spectra because their training time scales cubically with the number of training data. Results We use the Nyström approximation to transform the kernel into a linear feature map. We evaluate two methods that use this feature map as input: a linear support vector machine and a deep neural network (DNN). For evaluation, we use a cross-validated dataset of 156 017 compounds and three independent datasets with 1734 compounds. We show that the combination of kernel method and DNN outperforms the kernel support vector machine, which is the current gold standard, as well as a DNN on tandem mass spectra on all evaluation datasets. Availability and implementation The deep kernel learning method for fingerprint prediction is part of the SIRIUS software, available at https://bio.informatik.uni-jena.de/software/sirius.
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Affiliation(s)
- Kai Dührkop
- To whom correspondence should be addressed. E-mail:
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42
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Multi-omics of human plasma reveals molecular features of dysregulated inflammation and accelerated aging in schizophrenia. Mol Psychiatry 2022; 27:1217-1225. [PMID: 34741130 PMCID: PMC9054664 DOI: 10.1038/s41380-021-01339-z] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 09/09/2021] [Accepted: 10/01/2021] [Indexed: 11/08/2022]
Abstract
Schizophrenia is a devastating psychiatric illness that detrimentally affects a significant portion of the worldwide population. Aging of schizophrenia patients is associated with reduced longevity, but the potential biological factors associated with aging in this population have not yet been investigated in a global manner. To address this gap in knowledge, the present study assesses proteomics and metabolomics profiles in the plasma of subjects afflicted with schizophrenia compared to non-psychiatric control patients over six decades of life. Global, unbiased analyses of circulating blood plasma can provide knowledge of prominently dysregulated molecular pathways and their association with schizophrenia, as well as features of aging and gender in this disease. The resulting data compiled in this study represent a compendium of molecular changes associated with schizophrenia over the human lifetime. Supporting the clinical finding of schizophrenia's association with more rapid aging, both schizophrenia diagnosis and age significantly influenced the plasma proteome in subjects assayed. Schizophrenia was broadly associated with prominent dysregulation of inflammatory and metabolic system components. Proteome changes demonstrated increased abundance of biomarkers for risk of physiologic comorbidities of schizophrenia, especially in younger individuals. These findings advance our understanding of the molecular etiology of schizophrenia and its associated comorbidities throughout the aging process.
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43
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The Diversity, Metabolomics Profiling, and the Pharmacological Potential of Actinomycetes Isolated from the Estremadura Spur Pockmarks (Portugal). Mar Drugs 2021; 20:md20010021. [PMID: 35049876 PMCID: PMC8780274 DOI: 10.3390/md20010021] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 12/05/2021] [Accepted: 12/14/2021] [Indexed: 01/24/2023] Open
Abstract
The Estremadura Spur pockmarks are a unique and unexplored ecosystem located in the North Atlantic, off the coast of Portugal. A total of 85 marine-derived actinomycetes were isolated and cultured from sediments collected from this ecosystem at a depth of 200 to 350 m. Nine genera, Streptomyces, Micromonospora, Saccharopolyspora, Actinomadura, Actinopolymorpha, Nocardiopsis, Saccharomonospora, Stackebrandtia, and Verrucosispora were identified by 16S rRNA gene sequencing analyses, from which the first two were the most predominant. Non-targeted LC-MS/MS, in combination with molecular networking, revealed high metabolite diversity, including several known metabolites, such as surugamide, antimycin, etamycin, physostigmine, desferrioxamine, ikarugamycin, piericidine, and rakicidin derivatives, as well as numerous unidentified metabolites. Taxonomy was the strongest parameter influencing the metabolite production, highlighting the different biosynthetic potentials of phylogenetically related actinomycetes; the majority of the chemical classes can be used as chemotaxonomic markers, as the metabolite distribution was mostly genera-specific. The EtOAc extracts of the actinomycete isolates demonstrated antimicrobial and antioxidant activity. Altogether, this study demonstrates that the Estremadura Spur is a source of actinomycetes with potential applications for biotechnology. It highlights the importance of investigating actinomycetes from unique ecosystems, such as pockmarks, as the metabolite production reflects their adaptation to this habitat.
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Shrivastava AD, Swainston N, Samanta S, Roberts I, Wright Muelas M, Kell DB. MassGenie: A Transformer-Based Deep Learning Method for Identifying Small Molecules from Their Mass Spectra. Biomolecules 2021; 11:1793. [PMID: 34944436 PMCID: PMC8699281 DOI: 10.3390/biom11121793] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 11/14/2021] [Accepted: 11/27/2021] [Indexed: 12/15/2022] Open
Abstract
The 'inverse problem' of mass spectrometric molecular identification ('given a mass spectrum, calculate/predict the 2D structure of the molecule whence it came') is largely unsolved, and is especially acute in metabolomics where many small molecules remain unidentified. This is largely because the number of experimentally available electrospray mass spectra of small molecules is quite limited. However, the forward problem ('calculate a small molecule's likely fragmentation and hence at least some of its mass spectrum from its structure alone') is much more tractable, because the strengths of different chemical bonds are roughly known. This kind of molecular identification problem may be cast as a language translation problem in which the source language is a list of high-resolution mass spectral peaks and the 'translation' a representation (for instance in SMILES) of the molecule. It is thus suitable for attack using the deep neural networks known as transformers. We here present MassGenie, a method that uses a transformer-based deep neural network, trained on ~6 million chemical structures with augmented SMILES encoding and their paired molecular fragments as generated in silico, explicitly including the protonated molecular ion. This architecture (containing some 400 million elements) is used to predict the structure of a molecule from the various fragments that may be expected to be observed when some of its bonds are broken. Despite being given essentially no detailed nor explicit rules about molecular fragmentation methods, isotope patterns, rearrangements, neutral losses, and the like, MassGenie learns the effective properties of the mass spectral fragment and valency space, and can generate candidate molecular structures that are very close or identical to those of the 'true' molecules. We also use VAE-Sim, a previously published variational autoencoder, to generate candidate molecules that are 'similar' to the top hit. In addition to using the 'top hits' directly, we can produce a rank order of these by 'round-tripping' candidate molecules and comparing them with the true molecules, where known. As a proof of principle, we confine ourselves to positive electrospray mass spectra from molecules with a molecular mass of 500Da or lower, including those in the last CASMI challenge (for which the results are known), getting 49/93 (53%) precisely correct. The transformer method, applied here for the first time to mass spectral interpretation, works extremely effectively both for mass spectra generated in silico and on experimentally obtained mass spectra from pure compounds. It seems to act as a Las Vegas algorithm, in that it either gives the correct answer or simply states that it cannot find one. The ability to create and to 'learn' millions of fragmentation patterns in silico, and therefrom generate candidate structures (that do not have to be in existing libraries) directly, thus opens up entirely the field of de novo small molecule structure prediction from experimental mass spectra.
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Affiliation(s)
- Aditya Divyakant Shrivastava
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Crown St, Liverpool L69 7ZB, UK; (A.D.S.); (N.S.); (S.S.); (I.R.); (M.W.M.)
- Department of Computer Science and Engineering, Nirma University, Ahmedabad 382481, India
| | - Neil Swainston
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Crown St, Liverpool L69 7ZB, UK; (A.D.S.); (N.S.); (S.S.); (I.R.); (M.W.M.)
- Mellizyme Biotechnology Ltd., Liverpool Science Park IC1, 131 Mount Pleasant, Liverpool L3 5TF, UK
| | - Soumitra Samanta
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Crown St, Liverpool L69 7ZB, UK; (A.D.S.); (N.S.); (S.S.); (I.R.); (M.W.M.)
| | - Ivayla Roberts
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Crown St, Liverpool L69 7ZB, UK; (A.D.S.); (N.S.); (S.S.); (I.R.); (M.W.M.)
| | - Marina Wright Muelas
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Crown St, Liverpool L69 7ZB, UK; (A.D.S.); (N.S.); (S.S.); (I.R.); (M.W.M.)
| | - Douglas B. Kell
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Crown St, Liverpool L69 7ZB, UK; (A.D.S.); (N.S.); (S.S.); (I.R.); (M.W.M.)
- Mellizyme Biotechnology Ltd., Liverpool Science Park IC1, 131 Mount Pleasant, Liverpool L3 5TF, UK
- Novo Nordisk Foundation Centre for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kongens Lyngby, Denmark
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45
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Aggarwal S, Peng WK, Srivastava S. Multi-Omics Advancements towards Plasmodium vivax Malaria Diagnosis. Diagnostics (Basel) 2021; 11:2222. [PMID: 34943459 PMCID: PMC8700291 DOI: 10.3390/diagnostics11122222] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 11/21/2021] [Accepted: 11/22/2021] [Indexed: 11/16/2022] Open
Abstract
Plasmodium vivax malaria is one of the most lethal infectious diseases, with 7 million infections annually. One of the roadblocks to global malaria elimination is the lack of highly sensitive, specific, and accurate diagnostic tools. The absence of diagnostic tools in particular has led to poor differentiation among parasite species, poor prognosis, and delayed treatment. The improvement necessary in diagnostic tools can be broadly grouped into two categories: technologies-driven and omics-driven progress over time. This article discusses the recent advancement in omics-based malaria for identifying the next generation biomarkers for a highly sensitive and specific assay with a rapid and antecedent prognosis of the disease. We summarize the state-of-the-art diagnostic technologies, the key challenges, opportunities, and emerging prospects of multi-omics-based sensors.
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Affiliation(s)
- Shalini Aggarwal
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, Maharashtra, India;
| | - Weng Kung Peng
- Songshan Lake Materials Laboratory, Building A1, University Innovation Park, Dongguan 523808, China
- Precision Medicine-Engineering Group, International Iberian Nanotechnology Laboratory, 4715-330 Braga, Portugal
| | - Sanjeeva Srivastava
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, Maharashtra, India;
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46
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Caesar LK, Montaser R, Keller NP, Kelleher NL. Metabolomics and genomics in natural products research: complementary tools for targeting new chemical entities. Nat Prod Rep 2021; 38:2041-2065. [PMID: 34787623 PMCID: PMC8691422 DOI: 10.1039/d1np00036e] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Covering: 2010 to 2021Organisms in nature have evolved into proficient synthetic chemists, utilizing specialized enzymatic machinery to biosynthesize an inspiring diversity of secondary metabolites. Often serving to boost competitive advantage for their producers, these secondary metabolites have widespread human impacts as antibiotics, anti-inflammatories, and antifungal drugs. The natural products discovery field has begun a shift away from traditional activity-guided approaches and is beginning to take advantage of increasingly available metabolomics and genomics datasets to explore undiscovered chemical space. Major strides have been made and now enable -omics-informed prioritization of chemical structures for discovery, including the prospect of confidently linking metabolites to their biosynthetic pathways. Over the last decade, more integrated strategies now provide researchers with pipelines for simultaneous identification of expressed secondary metabolites and their biosynthetic machinery. However, continuous collaboration by the natural products community will be required to optimize strategies for effective evaluation of natural product biosynthetic gene clusters to accelerate discovery efforts. Here, we provide an evaluative guide to scientific literature as it relates to studying natural product biosynthesis using genomics, metabolomics, and their integrated datasets. Particular emphasis is placed on the unique insights that can be gained from large-scale integrated strategies, and we provide source organism-specific considerations to evaluate the gaps in our current knowledge.
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Affiliation(s)
- Lindsay K Caesar
- Department of Chemistry, Northwestern University, Evanston, IL, USA.
| | - Rana Montaser
- Department of Chemistry, Northwestern University, Evanston, IL, USA.
| | - Nancy P Keller
- Department of Medical Microbiology and Immunology and Bacteriology, University of Wisconsin-Madison, Madison, WI, USA
| | - Neil L Kelleher
- Department of Chemistry, Northwestern University, Evanston, IL, USA.
- Department of Molecular Biosciences, Northwestern University, Evanston, IL, USA
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47
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Beniddir MA, Kang KB, Genta-Jouve G, Huber F, Rogers S, van der Hooft JJJ. Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches. Nat Prod Rep 2021; 38:1967-1993. [PMID: 34821250 PMCID: PMC8597898 DOI: 10.1039/d1np00023c] [Citation(s) in RCA: 67] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Indexed: 12/13/2022]
Abstract
Covering: up to the end of 2020Recently introduced computational metabolome mining tools have started to positively impact the chemical and biological interpretation of untargeted metabolomics analyses. We believe that these current advances make it possible to start decomposing complex metabolite mixtures into substructure and chemical class information, thereby supporting pivotal tasks in metabolomics analysis including metabolite annotation, the comparison of metabolic profiles, and network analyses. In this review, we highlight and explain key tools and emerging strategies covering 2015 up to the end of 2020. The majority of these tools aim at processing and analyzing liquid chromatography coupled to mass spectrometry fragmentation data. We start with defining what substructures are, how they relate to molecular fingerprints, and how recognizing them helps to decompose complex mixtures. We continue with chemical classes that are based on the presence or absence of particular molecular scaffolds and/or functional groups and are thus intrinsically related to substructures. We discuss novel tools to mine substructures, annotate chemical compound classes, and create mass spectral networks from metabolomics data and demonstrate them using two case studies. We also review and speculate about the opportunities that NMR spectroscopy-based metabolome mining of complex metabolite mixtures offers to discover substructures and chemical classes. Finally, we will describe the main benefits and limitations of the current tools and strategies that rely on them, and our vision on how this exciting field can develop toward repository-scale-sized metabolomics analyses. Complementary sources of structural information from genomics analyses and well-curated taxonomic records are also discussed. Many research fields such as natural products discovery, pharmacokinetic and drug metabolism studies, and environmental metabolomics increasingly rely on untargeted metabolomics to gain biochemical and biological insights. The here described technical advances will benefit all those metabolomics disciplines by transforming spectral data into knowledge that can answer biological questions.
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Affiliation(s)
- Mehdi A Beniddir
- Université Paris-Saclay, CNRS, BioCIS, 5 rue J.-B Clément, 92290 Châtenay-Malabry, France
| | - Kyo Bin Kang
- Research Institute of Pharmaceutical Sciences, College of Pharmacy, Sookmyung Women's University, Seoul 04310, Republic of Korea
| | - Grégory Genta-Jouve
- Laboratoire de Chimie-Toxicologie Analytique et Cellulaire (C-TAC), UMR CNRS 8038, CiTCoM, Université de Paris, 4, Avenue de l'Observatoire, 75006, Paris, France
- Laboratoire Ecologie, Evolution, Interactions des Systèmes Amazoniens (LEEISA), USR 3456, Université De Guyane, CNRS Guyane, 275 Route de Montabo, 97334 Cayenne, French Guiana, France
| | - Florian Huber
- Netherlands eScience Center, 1098 XG Amsterdam, The Netherlands
| | - Simon Rogers
- School of Computing Science, University of Glasgow, Glasgow G12 8QQ, UK
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48
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Armstrong G, Cantrell K, Huang S, McDonald D, Haiminen N, Carrieri AP, Zhu Q, Gonzalez A, McGrath I, Beck KL, Hakim D, Havulinna AS, Méric G, Niiranen T, Lahti L, Salomaa V, Jain M, Inouye M, Swafford AD, Kim HC, Parida L, Vázquez-Baeza Y, Knight R. Efficient computation of Faith's phylogenetic diversity with applications in characterizing microbiomes. Genome Res 2021; 31:2131-2137. [PMID: 34479875 PMCID: PMC8559715 DOI: 10.1101/gr.275777.121] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 09/01/2021] [Indexed: 02/01/2023]
Abstract
The number of publicly available microbiome samples is continually growing. As data set size increases, bottlenecks arise in standard analytical pipelines. Faith's phylogenetic diversity (Faith's PD) is a highly utilized phylogenetic alpha diversity metric that has thus far failed to effectively scale to trees with millions of vertices. Stacked Faith's phylogenetic diversity (SFPhD) enables calculation of this widely adopted diversity metric at a much larger scale by implementing a computationally efficient algorithm. The algorithm reduces the amount of computational resources required, resulting in more accessible software with a reduced carbon footprint, as compared to previous approaches. The new algorithm produces identical results to the previous method. We further demonstrate that the phylogenetic aspect of Faith's PD provides increased power in detecting diversity differences between younger and older populations in the FINRISK study's metagenomic data.
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Affiliation(s)
- George Armstrong
- Department of Pediatrics, School of Medicine, University of California, San Diego, California 92093, USA
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, California 92093, USA
- Bioinformatics and Systems Biology Program, University of California, San Diego, California 92093, USA
| | - Kalen Cantrell
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, California 92093, USA
| | - Shi Huang
- Department of Pediatrics, School of Medicine, University of California, San Diego, California 92093, USA
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, California 92093, USA
| | - Daniel McDonald
- Department of Pediatrics, School of Medicine, University of California, San Diego, California 92093, USA
| | - Niina Haiminen
- IBM T. J. Watson Research Center, Yorktown Heights, New York 10562, USA
| | | | - Qiyun Zhu
- School of Life Sciences, Arizona State University, Tempe, Arizona 85281, USA
- Biodesign Center for Fundamental and Applied Microbiomics, Arizona State University, Tempe, Arizona 85281, USA
| | - Antonio Gonzalez
- Department of Pediatrics, School of Medicine, University of California, San Diego, California 92093, USA
| | - Imran McGrath
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, California 92093, USA
- Division of Biological Sciences, University of California San Diego, La Jolla, California 92093, USA
| | - Kristen L Beck
- IBM Almaden Research Center, San Jose, California 95120, USA
| | - Daniel Hakim
- Department of Pediatrics, School of Medicine, University of California, San Diego, California 92093, USA
- Bioinformatics and Systems Biology Program, University of California, San Diego, California 92093, USA
| | - Aki S Havulinna
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki 00271, Finland
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki 00014, Finland
| | - Guillaume Méric
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia
- Department of Infectious Diseases, Central Clinical School, Monash University, Melbourne, Victoria 3800, Australia
| | - Teemu Niiranen
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki 00271, Finland
- Department of Internal Medicine, University of Turku, Turku 20014, Finland
- Division of Medicine, Turku University Hospital, Turku 20014, Finland
| | - Leo Lahti
- Department of Computing, University of Turku, Turku 20014, Finland
| | - Veikko Salomaa
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki 00271, Finland
| | - Mohit Jain
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, California 92093, USA
- Department of Medicine, University of California, San Diego, California 92093, USA
- Department of Pharmacology, University of California, San Diego, California 92093, USA
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia
- Department of Public Health and Primary Care, Cambridge University, Cambridge CB2 1TN, United Kingdom
| | - Austin D Swafford
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, California 92093, USA
| | - Ho-Cheol Kim
- IBM Almaden Research Center, San Jose, California 95120, USA
| | - Laxmi Parida
- IBM T. J. Watson Research Center, Yorktown Heights, New York 10562, USA
| | - Yoshiki Vázquez-Baeza
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, California 92093, USA
| | - Rob Knight
- Department of Pediatrics, School of Medicine, University of California, San Diego, California 92093, USA
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, California 92093, USA
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, California 92093, USA
- Department of Bioengineering, University of California, San Diego, La Jolla, California 92093, USA
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Thomas GH. Microbial Musings - September 2021. MICROBIOLOGY (READING, ENGLAND) 2021; 167. [PMID: 34672917 PMCID: PMC8698185 DOI: 10.1099/mic.0.001115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Affiliation(s)
- Gavin H. Thomas
- Department of Biology, University of York, YO10 5YW, UK
- *Correspondence: Gavin H. Thomas,
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50
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Undabarrena A, Pereira CF, Kruasuwan W, Parra J, Sélem-Mojica N, Vind K, Schniete JK. Integrating perspectives in actinomycete research: an ActinoBase review of 2020-21. MICROBIOLOGY (READING, ENGLAND) 2021; 167:001084. [PMID: 34515628 PMCID: PMC8549240 DOI: 10.1099/mic.0.001084] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 07/26/2021] [Indexed: 12/12/2022]
Abstract
Last year ActinoBase, a Wiki-style initiative supported by the UK Microbiology Society, published a review highlighting the research of particular interest to the actinomycete community. Here, we present the second ActinoBase review showcasing selected reports published in 2020 and early 2021, integrating perspectives in the actinomycete field. Actinomycetes are well-known for their unsurpassed ability to produce specialised metabolites, of which many are used as therapeutic agents with antibacterial, antifungal, or immunosuppressive activities. Much research is carried out to understand the purpose of these metabolites in the environment, either within communities or in host interactions. Moreover, many efforts have been placed in developing computational tools to handle big data, simplify experimental design, and find new biosynthetic gene cluster prioritisation strategies. Alongside, synthetic biology has provided advances in tools to elucidate the biosynthesis of these metabolites. Additionally, there are still mysteries to be uncovered in understanding the fundamentals of filamentous actinomycetes' developmental cycle and regulation of their metabolism. This review focuses on research using integrative methodologies and approaches to understand the bigger picture of actinomycete biology, covering four research areas: i) technology and methodology; ii) specialised metabolites; iii) development and regulation; and iv) ecology and host interactions.
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Affiliation(s)
- Agustina Undabarrena
- Laboratorio de Microbiología Molecular y Biotecnología Ambiental, Departamento de Química & Centro de Biotecnología Daniel Alkalay Lowitt, Universidad Técnica Federico Santa María, Valparaíso 2340000, Chile
| | - Camila F Pereira
- School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Ribeirão Preto, Brazil
| | - Worarat Kruasuwan
- Microbial Cell Factory Research Team, National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, 113 Thailand Science Park, Khlong Nueng, Khlong Luang, Pathum Thani 12120, Thailand
| | - Jonathan Parra
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, G4 0RE, UK
| | - Nelly Sélem-Mojica
- Centro de Ciencias Matemáticas, Antigua Carretera a Pátzcuaro # 8701, Col. Ex Hacienda San José de la Huerta, Morelia C.P. 58089, Michoacán, México
| | - Kristiina Vind
- NAICONS Srl, Viale Ortles 22/4, 20139 Milan (MI), Italy
- Host-Microbe Interactomics Group, Wageningen University, De Elst 1 6708 WD, Wageningen, Netherlands
| | - Jana K. Schniete
- Biology, Edge Hill University, St Helens Road, Ormskirk, L39 4QP, UK
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