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Gurumurthy P, Alagarsamy M, Kuppusamy S, Ponnusamy NC. M2AI-CVD: Multi-modal AI approach cardiovascular risk prediction system using fundus images. NETWORK (BRISTOL, ENGLAND) 2024; 35:319-346. [PMID: 38279811 DOI: 10.1080/0954898x.2024.2306988] [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: 10/11/2023] [Accepted: 12/11/2023] [Indexed: 01/29/2024]
Abstract
Cardiovascular diseases (CVD) represent a significant global health challenge, often remaining undetected until severe cardiac events, such as heart attacks or strokes, occur. In regions like Qatar, research focused on non-invasive CVD identification methods, such as retinal imaging and dual-energy X-ray absorptiometry (DXA), is limited. This study presents a groundbreaking system known as Multi-Modal Artificial Intelligence for Cardiovascular Disease (M2AI-CVD), designed to provide highly accurate predictions of CVD. The M2AI-CVD framework employs a four-fold methodology: First, it rigorously evaluates image quality and processes lower-quality images for further analysis. Subsequently, it uses the Entropy-based Fuzzy C Means (EnFCM) algorithm for precise image segmentation. The Multi-Modal Boltzmann Machine (MMBM) is then employed to extract relevant features from various data modalities, while the Genetic Algorithm (GA) selects the most informative features. Finally, a ZFNet Convolutional Neural Network (ZFNetCNN) classifies images, effectively distinguishing between CVD and Non-CVD cases. The research's culmination, tested across five distinct datasets, yields outstanding results, with an accuracy of 95.89%, sensitivity of 96.89%, and specificity of 98.7%. This multi-modal AI approach offers a promising solution for the accurate and early detection of cardiovascular diseases, significantly improving the prospects of timely intervention and improved patient outcomes in the realm of cardiovascular health.
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Affiliation(s)
- Premalatha Gurumurthy
- Department of Electronics and Communication Engineering, Prathyusha Engineering College, Tiruvallur, Tamil Nadu, India
| | - Manjunathan Alagarsamy
- Department of Electronics and Communication Engineering, K.Ramakrishnan College of Technology, Trichy, Tamil Nadu, India
| | - Sangeetha Kuppusamy
- Department of Computer Science and Engineering, Kongu Engineering College, Perundurai, Erode, Tamil Nadu, India
| | - Niranjana Chitra Ponnusamy
- Department of Electronics and Communication Engineering, Kongunadu College of Engineering and Technology, Trichy, Tamil Nadu, India
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WU E, QIAO L. [Microbial metaproteomics--From sample processing to data acquisition and analysis]. Se Pu 2024; 42:658-668. [PMID: 38966974 PMCID: PMC11224941 DOI: 10.3724/sp.j.1123.2024.02009] [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: 02/13/2024] [Indexed: 07/06/2024] Open
Abstract
Microorganisms are closely associated with human diseases and health. Understanding the composition and function of microbial communities requires extensive research. Metaproteomics has recently become an important method for throughout and in-depth study of microorganisms. However, major challenges in terms of sample processing, mass spectrometric data acquisition, and data analysis limit the development of metaproteomics owing to the complexity and high heterogeneity of microbial community samples. In metaproteomic analysis, optimizing the preprocessing method for different types of samples and adopting different microbial isolation, enrichment, extraction, and lysis schemes are often necessary. Similar to those for single-species proteomics, the mass spectrometric data acquisition modes for metaproteomics include data-dependent acquisition (DDA) and data-independent acquisition (DIA). DIA can collect comprehensive peptide information from a sample and holds great potential for future development. However, data analysis for DIA is challenged by the complexity of metaproteome samples, which hinders the deeper coverage of metaproteomes. The most important step in data analysis is the construction of a protein sequence database. The size and completeness of the database strongly influence not only the number of identifications, but also analyses at the species and functional levels. The current gold standard for metaproteome database construction is the metagenomic sequencing-based protein sequence database. A public database-filtering method based on an iterative database search has been proven to have strong practical value. The peptide-centric DIA data analysis method is a mainstream data analysis strategy. The development of deep learning and artificial intelligence will greatly promote the accuracy, coverage, and speed of metaproteomic analysis. In terms of downstream bioinformatics analysis, a series of annotation tools that can perform species annotation at the protein, peptide, and gene levels has been developed in recent years to determine the composition of microbial communities. The functional analysis of microbial communities is a unique feature of metaproteomics compared with other omics approaches. Metaproteomics has become an important component of the multi-omics analysis of microbial communities, and has great development potential in terms of depth of coverage, sensitivity of detection, and completeness of data analysis.
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Xu W, Yuan G, Fang Y, Liu X, Ma X, Zhu K. Coumarin Glycosides Reverse Enterococci-Facilitated Enteric Infections. RESEARCH (WASHINGTON, D.C.) 2024; 7:0374. [PMID: 38756989 PMCID: PMC11096794 DOI: 10.34133/research.0374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 04/10/2024] [Indexed: 05/18/2024]
Abstract
Commensal enterococci with pathogenic potential often facilitate the growth of diverse pathogens, thereby exacerbating infections. However, there are few effective therapeutic strategies to prevent and intervene in enterococci-mediated polymicrobial infections. Here, we find that enterococci at high density drive the expansion and pathogenicity of enteric Salmonella enterica serotype Typhimurium (S. Tm). Subsequently, we show that the driving role of enterococci in such infections is counteracted by dietary coumarin glycosides in vivo. Enterococci, which are tolerant of iron-deficient environments, produce β-glucosidases to hydrolyze coumarin glycosides into bioactive aglycones, inhibiting S. Tm growth and ameliorating the severity of S. Tm-induced symptoms by inducing iron limitation. Overall, we demonstrate that coumarin glycosides as a common diet effectively reverse enterococci-facilitated enteric infections, providing an alternative intervention to combat polymicrobial infections.
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Affiliation(s)
- Wenjiao Xu
- National Key Laboratory of Veterinary Public Health and Safety, College of Veterinary Medicine,
China Agricultural University, Beijing 100193, China
- Engineering Research Center of Animal Innovative Drugs and Safety Evaluation, Ministry of Education, College of Veterinary Medicine,
China Agricultural University, Beijing 100193, China
| | - Guixin Yuan
- Ministry of Agriculture and Rural Affairs Key Laboratory for Crop Pest Monitoring and Green Control,
China Agricultural University, Beijing 100193, China
| | - Yuwen Fang
- National Key Laboratory of Veterinary Public Health and Safety, College of Veterinary Medicine,
China Agricultural University, Beijing 100193, China
- Engineering Research Center of Animal Innovative Drugs and Safety Evaluation, Ministry of Education, College of Veterinary Medicine,
China Agricultural University, Beijing 100193, China
| | - Xiaojia Liu
- National Key Laboratory of Veterinary Public Health and Safety, College of Veterinary Medicine,
China Agricultural University, Beijing 100193, China
- Engineering Research Center of Animal Innovative Drugs and Safety Evaluation, Ministry of Education, College of Veterinary Medicine,
China Agricultural University, Beijing 100193, China
| | - Xiaowei Ma
- National Key Laboratory of Veterinary Public Health and Safety, College of Veterinary Medicine,
China Agricultural University, Beijing 100193, China
- Engineering Research Center of Animal Innovative Drugs and Safety Evaluation, Ministry of Education, College of Veterinary Medicine,
China Agricultural University, Beijing 100193, China
| | - Kui Zhu
- National Key Laboratory of Veterinary Public Health and Safety, College of Veterinary Medicine,
China Agricultural University, Beijing 100193, China
- Engineering Research Center of Animal Innovative Drugs and Safety Evaluation, Ministry of Education, College of Veterinary Medicine,
China Agricultural University, Beijing 100193, China
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Chen J, Sun Y, Li J, Lyu M, Yuan L, Sun J, Chen S, Hu C, Wei Q, Xu Z, Guo T, Cheng X. In-depth metaproteomics analysis of tongue coating for gastric cancer: a multicenter diagnostic research study. MICROBIOME 2024; 12:6. [PMID: 38191439 PMCID: PMC10773145 DOI: 10.1186/s40168-023-01730-8] [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: 08/06/2023] [Accepted: 11/21/2023] [Indexed: 01/10/2024]
Abstract
BACKGROUND Our previous study revealed marked differences in tongue images between individuals with gastric cancer and those without gastric cancer. However, the biological mechanism of tongue images as a disease indicator remains unclear. Tongue coating, a major factor in tongue appearance, is the visible layer on the tongue dorsum that provides a vital environment for oral microorganisms. While oral microorganisms are associated with gastric and intestinal diseases, the comprehensive function profiles of oral microbiota remain incompletely understood. Metaproteomics has unique strength in revealing functional profiles of microbiota that aid in comprehending the mechanism behind specific tongue coating formation and its role as an indicator of gastric cancer. METHODS We employed pressure cycling technology and data-independent acquisition (PCT-DIA) mass spectrometry to extract and identify tongue-coating proteins from 180 gastric cancer patients and 185 non-gastric cancer patients across 5 independent research centers in China. Additionally, we investigated the temporal stability of tongue-coating proteins based on a time-series cohort. Finally, we constructed a machine learning model using the stochastic gradient boosting algorithm to identify individuals at high risk of gastric cancer based on tongue-coating microbial proteins. RESULTS We measured 1432 human-derived proteins and 13,780 microbial proteins from 345 tongue-coating samples. The abundance of tongue-coating proteins exhibited high temporal stability within an individual. Notably, we observed the downregulation of human keratins KRT2 and KRT9 on the tongue surface, as well as the downregulation of ABC transporter COG1136 in microbiota, in gastric cancer patients. This suggests a decline in the defense capacity of the lingual mucosa. Finally, we established a machine learning model that employs 50 microbial proteins of tongue coating to identify individuals at a high risk of gastric cancer, achieving an area under the curve (AUC) of 0.91 in the independent validation cohort. CONCLUSIONS We characterized the alterations in tongue-coating proteins among gastric cancer patients and constructed a gastric cancer screening model based on microbial-derived tongue-coating proteins. Tongue-coating proteins are shown as a promising indicator for identifying high-risk groups for gastric cancer. Video Abstract.
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Affiliation(s)
- Jiahui Chen
- Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, China
| | - Yingying Sun
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- School of Medicine, School of Life Sciences, Westlake University, Hangzhou, China
- Research Center for Industries of the Future, Westlake University, Hangzhou, China
| | - Jie Li
- Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing, China
- MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China
| | - Mengge Lyu
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- School of Medicine, School of Life Sciences, Westlake University, Hangzhou, China
- Research Center for Industries of the Future, Westlake University, Hangzhou, China
| | - Li Yuan
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, China
| | - Jiancheng Sun
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Shangqi Chen
- Department of General Surgery, HwaMei Hospital, University of Chinese Academy of Sciences, Ningbo, China
| | - Can Hu
- Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, China
| | - Qing Wei
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, China
| | - Zhiyuan Xu
- Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.
- Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, China.
| | - Tiannan Guo
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.
- School of Medicine, School of Life Sciences, Westlake University, Hangzhou, China.
- Research Center for Industries of the Future, Westlake University, Hangzhou, China.
| | - Xiangdong Cheng
- Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.
- Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, China.
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Wang XL, Xu HW, Liu NN. Oral Microbiota: A New Insight into Cancer Progression, Diagnosis and Treatment. PHENOMICS (CHAM, SWITZERLAND) 2023; 3:535-547. [PMID: 37881320 PMCID: PMC10593652 DOI: 10.1007/s43657-023-00124-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 08/04/2023] [Accepted: 08/07/2023] [Indexed: 10/27/2023]
Abstract
The polymorphic microbiome has been defined as one of the "Hallmarks of Cancer". Extensive studies have now uncovered the role of oral microbiota in cancer development and progression. Bacteria, fungi, archaea, and viruses in the oral cavity interact dynamically with the oral microenvironment to maintain the oral micro-ecological homeostasis. This complex interaction is influenced by many factors, such as maternal transmission, personal factors and environmental factors. Dysbiosis of oral microbiota can disturbed this host-microbiota interaction, leading to systemic diseases. Numerous studies have shown the potential associations between oral microbiota and a variety of cancers. However, the underlying mechanisms and therapeutic insights are still poorly understood. In this review, we mainly focus on the following aspects: (1) the factors affect oral microbiota composition and function; (2) the interaction between microenvironment and oral microbiota; (3) the role of multi-kingdom oral microbiota in human health; (4) the potential underlying mechanisms and therapeutic benefits of oral microbiota against cancer. Finally, we aim to describe the impact of oral microbiota on cancer progression and provide novel therapeutic insights into cancer prevention and treatment by targeting oral microbiota.
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Affiliation(s)
- Xiu-Li Wang
- State Key Laboratory of Systems Medicine for Cancer, Center for Single-Cell Omics, School of Public Health, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025 China
| | - Hua-Wen Xu
- State Key Laboratory of Systems Medicine for Cancer, Center for Single-Cell Omics, School of Public Health, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025 China
| | - Ning-Ning Liu
- State Key Laboratory of Systems Medicine for Cancer, Center for Single-Cell Omics, School of Public Health, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025 China
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Kalló G, Bertalan PM, Márton I, Kiss C, Csősz É. Salivary Chemical Barrier Proteins in Oral Squamous Cell Carcinoma-Alterations in the Defense Mechanism of the Oral Cavity. Int J Mol Sci 2023; 24:13657. [PMID: 37686462 PMCID: PMC10487546 DOI: 10.3390/ijms241713657] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 08/28/2023] [Accepted: 09/01/2023] [Indexed: 09/10/2023] Open
Abstract
Oral squamous cell carcinoma (OSCC) is one of the most frequent types of head and neck cancer. Despite the genetic and environmental risk factors, OSCC is also associated with microbial infections and/or dysbiosis. The secreted saliva serves as the chemical barrier of the oral cavity and, since OSCC can alter the protein composition of saliva, our aim was to analyze the effect of OSCC on the salivary chemical barrier proteins. Publicly available datasets regarding the analysis of salivary proteins from patients with OSCC and controls were collected and examined in order to identify differentially expressed chemical barrier proteins. Network analysis and gene ontology (GO) classification of the differentially expressed chemical barrier proteins were performed as well. One hundred and twenty-seven proteins showing different expression pattern between the OSCC and control groups were found. Protein-protein interaction networks of up- and down-regulated proteins were constructed and analyzed. The main hub proteins (IL-6, IL-1B, IL-8, TNF, APOA1, APOA2, APOB, APOC3, APOE, and HP) were identified and the enriched GO terms were examined. Our study highlighted the importance of the chemical barrier of saliva in the development of OSCC.
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Affiliation(s)
- Gergő Kalló
- Proteomics Core Facility, Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Debrecen, Egyetem tér 1, 4032 Debrecen, Hungary; (P.M.B.); (I.M.); (É.C.)
- Biomarker Research Group, Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Debrecen, Egyetem tér 1, 4032 Debrecen, Hungary
| | - Petra Magdolna Bertalan
- Proteomics Core Facility, Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Debrecen, Egyetem tér 1, 4032 Debrecen, Hungary; (P.M.B.); (I.M.); (É.C.)
- Biomarker Research Group, Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Debrecen, Egyetem tér 1, 4032 Debrecen, Hungary
- Doctoral School of Molecular Cell and Immune Biology, University of Debrecen, Egyetem tér 1, 4032 Debrecen, Hungary
| | - Ildikó Márton
- Proteomics Core Facility, Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Debrecen, Egyetem tér 1, 4032 Debrecen, Hungary; (P.M.B.); (I.M.); (É.C.)
| | - Csongor Kiss
- Division of Pediatric Hematology-Oncology, Department of Pediatrics, Faculty of Medicine, University of Debrecen, Nagyerdei krt. 98, 4032 Debrecen, Hungary;
| | - Éva Csősz
- Proteomics Core Facility, Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Debrecen, Egyetem tér 1, 4032 Debrecen, Hungary; (P.M.B.); (I.M.); (É.C.)
- Biomarker Research Group, Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Debrecen, Egyetem tér 1, 4032 Debrecen, Hungary
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Mehta S, Bernt M, Chambers M, Fahrner M, Föll MC, Gruening B, Horro C, Johnson JE, Loux V, Rajczewski AT, Schilling O, Vandenbrouck Y, Gustafsson OJR, Thang WCM, Hyde C, Price G, Jagtap PD, Griffin TJ. A Galaxy of informatics resources for MS-based proteomics. Expert Rev Proteomics 2023; 20:251-266. [PMID: 37787106 DOI: 10.1080/14789450.2023.2265062] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 09/06/2023] [Indexed: 10/04/2023]
Abstract
INTRODUCTION Continuous advances in mass spectrometry (MS) technologies have enabled deeper and more reproducible proteome characterization and a better understanding of biological systems when integrated with other 'omics data. Bioinformatic resources meeting the analysis requirements of increasingly complex MS-based proteomic data and associated multi-omic data are critically needed. These requirements included availability of software that would span diverse types of analyses, scalability for large-scale, compute-intensive applications, and mechanisms to ease adoption of the software. AREAS COVERED The Galaxy ecosystem meets these requirements by offering a multitude of open-source tools for MS-based proteomics analyses and applications, all in an adaptable, scalable, and accessible computing environment. A thriving global community maintains these software and associated training resources to empower researcher-driven analyses. EXPERT OPINION The community-supported Galaxy ecosystem remains a crucial contributor to basic biological and clinical studies using MS-based proteomics. In addition to the current status of Galaxy-based resources, we describe ongoing developments for meeting emerging challenges in MS-based proteomic informatics. We hope this review will catalyze increased use of Galaxy by researchers employing MS-based proteomics and inspire software developers to join the community and implement new tools, workflows, and associated training content that will add further value to this already rich ecosystem.
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Affiliation(s)
- Subina Mehta
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Matthias Bernt
- Helmholtz Centre for Environmental Research - UFZ, Department Computational Biology, Leipzig, Germany
| | | | - Matthias Fahrner
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Melanie Christine Föll
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Bjoern Gruening
- Bioinformatics Group, Department of Computer Science, Albert-Ludwigs-University Freiburg, Freiburg, Germany
| | - Carlos Horro
- Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
| | - James E Johnson
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Valentin Loux
- Université Paris-Saclay, INRAE, MaIAGE, Jouy-en-Josas, France
- Université Paris-Saclay, INRAE, BioinfOmics, MIGALE bioinformatics facility, Jouy-en-Josas, France
| | - Andrew T Rajczewski
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Oliver Schilling
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | | | - W C Mike Thang
- Queensland Cyber Infrastructure Foundation (QCIF), Australia
- Institute of Molecular Bioscience, University of Queensland, St Lucia, Australia
| | - Cameron Hyde
- Queensland Cyber Infrastructure Foundation (QCIF), Australia
- Sippy Downs, University of the Sunshine Coast, Australia
| | - Gareth Price
- Queensland Cyber Infrastructure Foundation (QCIF), Australia
- Institute of Molecular Bioscience, University of Queensland, St Lucia, Australia
| | - Pratik D Jagtap
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Timothy J Griffin
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
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Couvillion SP, Mostoller KE, Williams JE, Pace RM, Stohel IL, Peterson HK, Nicora CD, Nakayasu ES, Webb-Robertson BJM, McGuire MA, McGuire MK, Metz TO. Interrogating the role of the milk microbiome in mastitis in the multi-omics era. Front Microbiol 2023; 14:1105675. [PMID: 36819069 PMCID: PMC9932517 DOI: 10.3389/fmicb.2023.1105675] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 01/16/2023] [Indexed: 02/05/2023] Open
Abstract
There is growing interest in a functional understanding of milk-associated microbiota as there is ample evidence that host-associated microbial communities play an active role in host health and phenotype. Mastitis, characterized by painful inflammation of the mammary gland, is prevalent among lactating humans and agricultural animals and is associated with significant clinical and economic consequences. The etiology of mastitis is complex and polymicrobial and correlative studies have indicated alterations in milk microbial community composition. Recent evidence is beginning to suggest that a causal relationship may exist between the milk microbiota and host phenotype in mastitis. Multi-omic approaches can be leveraged to gain a mechanistic, molecular level understanding of how the milk microbiome might modulate host physiology, thereby informing strategies to prevent and ameliorate mastitis. In this paper, we review existing studies that have utilized omics approaches to investigate the role of the milk microbiome in mastitis. We also summarize the strengths and challenges associated with the different omics techniques including metagenomics, metatranscriptomics, metaproteomics, metabolomics and lipidomics and provide perspective on the integration of multiple omics technologies for a better functional understanding of the milk microbiome.
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Affiliation(s)
- Sneha P. Couvillion
- Pacific Northwest National Laboratory, Earth and Biological Sciences Directorate, Richland, WA, United States,*Correspondence: Sneha P. Couvillion, ✉
| | - Katie E. Mostoller
- Pacific Northwest National Laboratory, Earth and Biological Sciences Directorate, Richland, WA, United States
| | - Janet E. Williams
- Department of Animal, Veterinary, and Food Sciences, University of Idaho, Moscow, ID, United States
| | - Ryan M. Pace
- Margaret Ritchie School of Family and Consumer Sciences, University of Idaho, Moscow, ID, United States
| | - Izabel L. Stohel
- Pacific Northwest National Laboratory, Earth and Biological Sciences Directorate, Richland, WA, United States
| | - Haley K. Peterson
- Department of Animal, Veterinary, and Food Sciences, University of Idaho, Moscow, ID, United States
| | - Carrie D. Nicora
- Pacific Northwest National Laboratory, Earth and Biological Sciences Directorate, Richland, WA, United States
| | - Ernesto S. Nakayasu
- Pacific Northwest National Laboratory, Earth and Biological Sciences Directorate, Richland, WA, United States
| | - Bobbie-Jo M. Webb-Robertson
- Pacific Northwest National Laboratory, Earth and Biological Sciences Directorate, Richland, WA, United States
| | - Mark A. McGuire
- Department of Animal, Veterinary, and Food Sciences, University of Idaho, Moscow, ID, United States
| | - Michelle K. McGuire
- Margaret Ritchie School of Family and Consumer Sciences, University of Idaho, Moscow, ID, United States
| | - Thomas O. Metz
- Pacific Northwest National Laboratory, Earth and Biological Sciences Directorate, Richland, WA, United States,Thomas O. Metz, ✉
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