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Aplakidou E, Vergoulidis N, Chasapi M, Venetsianou NK, Kokoli M, Panagiotopoulou E, Iliopoulos I, Karatzas E, Pafilis E, Georgakopoulos-Soares I, Kyrpides NC, Pavlopoulos GA, Baltoumas FA. Visualizing metagenomic and metatranscriptomic data: A comprehensive review. Comput Struct Biotechnol J 2024; 23:2011-2033. [PMID: 38765606 PMCID: PMC11101950 DOI: 10.1016/j.csbj.2024.04.060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 04/25/2024] [Accepted: 04/25/2024] [Indexed: 05/22/2024] Open
Abstract
The fields of Metagenomics and Metatranscriptomics involve the examination of complete nucleotide sequences, gene identification, and analysis of potential biological functions within diverse organisms or environmental samples. Despite the vast opportunities for discovery in metagenomics, the sheer volume and complexity of sequence data often present challenges in processing analysis and visualization. This article highlights the critical role of advanced visualization tools in enabling effective exploration, querying, and analysis of these complex datasets. Emphasizing the importance of accessibility, the article categorizes various visualizers based on their intended applications and highlights their utility in empowering bioinformaticians and non-bioinformaticians to interpret and derive insights from meta-omics data effectively.
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Affiliation(s)
- Eleni Aplakidou
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece
- Department of Informatics and Telecommunications, Data Science and Information Technologies program, University of Athens, 15784 Athens, Greece
| | - Nikolaos Vergoulidis
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece
| | - Maria Chasapi
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece
- Department of Informatics and Telecommunications, Data Science and Information Technologies program, University of Athens, 15784 Athens, Greece
| | - Nefeli K. Venetsianou
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece
| | - Maria Kokoli
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece
| | - Eleni Panagiotopoulou
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece
- Department of Informatics and Telecommunications, Data Science and Information Technologies program, University of Athens, 15784 Athens, Greece
| | - Ioannis Iliopoulos
- Department of Basic Sciences, School of Medicine, University of Crete, 71003 Heraklion, Greece
| | - Evangelos Karatzas
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Evangelos Pafilis
- Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Heraklion, Greece
| | - Ilias Georgakopoulos-Soares
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Nikos C. Kyrpides
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Georgios A. Pavlopoulos
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA
- Center of New Biotechnologies & Precision Medicine, Department of Medicine, School of Health Sciences, National and Kapodistrian University of Athens, Greece
- Hellenic Army Academy, 16673 Vari, Greece
| | - Fotis A. Baltoumas
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece
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Ejaz MR, Badr K, Hassan ZU, Al-Thani R, Jaoua S. Metagenomic approaches and opportunities in arid soil research. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 953:176173. [PMID: 39260494 DOI: 10.1016/j.scitotenv.2024.176173] [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: 05/08/2024] [Revised: 09/04/2024] [Accepted: 09/07/2024] [Indexed: 09/13/2024]
Abstract
Arid soils present unique challenges and opportunities for studying microbial diversity and bioactive potential due to the extreme environmental conditions they bear. This review article investigates soil metagenomics as an emerging tool to explore complex microbial dynamics and unexplored bioactive potential in harsh environments. Utilizing advanced metagenomic techniques, diverse microbial populations that grow under extreme conditions such as high temperatures, salinity, high pH levels, and exposure to metals and radiation can be studied. The use of extremophiles to discover novel natural products and biocatalysts emphasizes the role of functional metagenomics in identifying enzymes and secondary metabolites for industrial and pharmaceutical purposes. Metagenomic sequencing uncovers a complex network of microbial diversity, offering significant potential for discovering new bioactive compounds. Functional metagenomics, connecting taxonomic diversity to genetic capabilities, provides a pathway to identify microbes' mechanisms to synthesize valuable secondary metabolites and other bioactive substances. Contrary to the common perception of desert soil as barren land, the metagenomic analysis reveals a rich diversity of life forms adept at extreme survival. It provides valuable findings into their resilience and potential applications in biotechnology. Moreover, the challenges associated with metagenomics in arid soils, such as low microbial biomass, high DNA degradation rates, and DNA extraction inhibitors and strategies to overcome these issues, outline the latest advancements in extraction methods, high-throughput sequencing, and bioinformatics. The importance of metagenomics for investigating diverse environments opens the way for future research to develop sustainable solutions in agriculture, industry, and medicine. Extensive studies are necessary to utilize the full potential of these powerful microbial communities. This research will significantly improve our understanding of microbial ecology and biotechnology in arid environments.
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Affiliation(s)
- Muhammad Riaz Ejaz
- Environmental Science Program, Department of Biological and Environmental Sciences, College of Arts and Science, Qatar University, P.O. Box 2713, Doha, Qatar
| | - Kareem Badr
- Environmental Science Program, Department of Biological and Environmental Sciences, College of Arts and Science, Qatar University, P.O. Box 2713, Doha, Qatar
| | - Zahoor Ul Hassan
- Environmental Science Program, Department of Biological and Environmental Sciences, College of Arts and Science, Qatar University, P.O. Box 2713, Doha, Qatar
| | - Roda Al-Thani
- Environmental Science Program, Department of Biological and Environmental Sciences, College of Arts and Science, Qatar University, P.O. Box 2713, Doha, Qatar
| | - Samir Jaoua
- Environmental Science Program, Department of Biological and Environmental Sciences, College of Arts and Science, Qatar University, P.O. Box 2713, Doha, Qatar.
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Azizpour A, Balaji A, Treangen TJ, Segarra S. Graph-based self-supervised learning for repeat detection in metagenomic assembly. Genome Res 2024; 34:1468-1476. [PMID: 39029947 DOI: 10.1101/gr.279136.124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 07/15/2024] [Indexed: 07/21/2024]
Abstract
Repetitive DNA (repeats) poses significant challenges for accurate and efficient genome assembly and sequence alignment. This is particularly true for metagenomic data, in which genome dynamics such as horizontal gene transfer, gene duplication, and gene loss/gain complicate accurate genome assembly from metagenomic communities. Detecting repeats is a crucial first step in overcoming these challenges. To address this issue, we propose GraSSRep, a novel approach that leverages the assembly graph's structure through graph neural networks (GNNs) within a self-supervised learning framework to classify DNA sequences into repetitive and nonrepetitive categories. Specifically, we frame this problem as a node classification task within a metagenomic assembly graph. In a self-supervised fashion, we rely on a high-precision (but low-recall) heuristic to generate pseudolabels for a small proportion of the nodes. We then use those pseudolabels to train a GNN embedding and a random forest classifier to propagate the labels to the remaining nodes. In this way, GraSSRep combines sequencing features with predefined and learned graph features to achieve state-of-the-art performance in repeat detection. We evaluate our method using simulated and synthetic metagenomic data sets. The results on the simulated data highlight GraSSRep's robustness to repeat attributes, demonstrating its effectiveness in handling the complexity of repeated sequences. Additionally, experiments with synthetic metagenomic data sets reveal that incorporating the graph structure and the GNN enhances the detection performance. Finally, in comparative analyses, GraSSRep outperforms existing repeat detection tools with respect to precision and recall.
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Affiliation(s)
- Ali Azizpour
- Department of Electrical and Computer Engineering, Houston, Texas 77005, USA;
| | - Advait Balaji
- Department of Computer Science, Rice University, Houston, Texas 77005, USA;
| | - Todd J Treangen
- Department of Computer Science, Rice University, Houston, Texas 77005, USA;
- Ken Kennedy Institute, Rice University, Houston, Texas 77005, USA
| | - Santiago Segarra
- Department of Electrical and Computer Engineering, Houston, Texas 77005, USA;
- Ken Kennedy Institute, Rice University, Houston, Texas 77005, USA
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Tan JH, Liew KJ, Goh KM. Dataset of 313 metagenome-assemble genomes from streamer hot spring water. Data Brief 2024; 56:110829. [PMID: 39252782 PMCID: PMC11382323 DOI: 10.1016/j.dib.2024.110829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2024] [Revised: 07/17/2024] [Accepted: 08/06/2024] [Indexed: 09/11/2024] Open
Abstract
This data report presents prokaryotic metagenome-assembled genomes (MAGs) from a hot spring stream with temperatures between 64 and 100°C. The stream water was filtered and the extracted total DNA was sequenced using the Illumina HiSeq 2500 platform. Approximately 80 Gb of raw data were generated, which were subsequently assembled using MEGAHIT v1.2.9. The MAGs were generated using MetaWRAP with binning approaches of MetaBAT2, CONCOCT and MaxBin2. We constructed 25 medium-quality and 24 high-quality archaeal MAGs, and 152 medium-quality and 112 high-quality bacterial MAGs. The fasta files of these MAGs are available in the NCBI database as well as Mendeley Data. Major phyla identified include Bacteroidota, Chloroflexota, Desulfobacterota, Firmicutes, Patescibacteria, Proteobacteria, Spirochaetota, Verrucomicrobiota, Armatimonadota, Nitrospirota, Acidobacteriota, Elusimicrobiota, Planctomycetota, Candidate division WOR-3, Aquificota, Thermoproteota, and Micrarchaeota. This dataset is valuable for studies on thermophilic genomes, reconstruction of biochemical pathways and gene discovery.
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Affiliation(s)
- Jia Hao Tan
- Faculty of Science, Universiti Teknologi Malaysia, Skudai 81310, Johor, Malaysia
| | - Kok Jun Liew
- Faculty of Science, Universiti Teknologi Malaysia, Skudai 81310, Johor, Malaysia
- Codon Genomics, 42300 Seri Kembangan, Selangor, Malaysia
| | - Kian Mau Goh
- Faculty of Science, Universiti Teknologi Malaysia, Skudai 81310, Johor, Malaysia
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Yepes-García J, Falquet L. Metagenome quality metrics and taxonomical annotation visualization through the integration of MAGFlow and BIgMAG. F1000Res 2024; 13:640. [PMID: 39360247 PMCID: PMC11445639 DOI: 10.12688/f1000research.152290.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/03/2024] [Indexed: 10/04/2024] Open
Abstract
Background Building Metagenome-Assembled Genomes (MAGs) from highly complex metagenomics datasets encompasses a series of steps covering from cleaning the sequences, assembling them to finally group them into bins. Along the process, multiple tools aimed to assess the quality and integrity of each MAG are implemented. Nonetheless, even when incorporated within end-to-end pipelines, the outputs of these pieces of software must be visualized and analyzed manually lacking integration in a complete framework. Methods We developed a Nextflow pipeline (MAGFlow) for estimating the quality of MAGs through a wide variety of approaches (BUSCO, CheckM2, GUNC and QUAST), as well as for annotating taxonomically the metagenomes using GTDB-Tk2. MAGFlow is coupled to a Python-Dash application (BIgMAG) that displays the concatenated outcomes from the tools included by MAGFlow, highlighting the most important metrics in a single interactive environment along with a comparison/clustering of the input data. Results By using MAGFlow/BIgMAG, the user will be able to benchmark the MAGs obtained through different workflows or establish the quality of the MAGs belonging to different samples following the divide and rule methodology. Conclusions MAGFlow/BIgMAG represents a unique tool that integrates state-of-the-art tools to study different quality metrics and extract visually as much information as possible from a wide range of genome features.
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Affiliation(s)
- Jeferyd Yepes-García
- Swiss Institute of Bioinformatics, Lausanne, Vaud, 1015, Switzerland
- Department of Biology, University of Fribourg, Fribourg, Canton of Fribourg, 1700, Switzerland
| | - Laurent Falquet
- Swiss Institute of Bioinformatics, Lausanne, Vaud, 1015, Switzerland
- Department of Biology, University of Fribourg, Fribourg, Canton of Fribourg, 1700, Switzerland
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Duan B, Zeng X, Peng J. Advances in genotypic antimicrobialresistance testing: a comprehensive review. SCIENCE CHINA. LIFE SCIENCES 2024:10.1007/s11427-023-2570-4. [PMID: 39300049 DOI: 10.1007/s11427-023-2570-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 03/15/2024] [Indexed: 09/22/2024]
Abstract
Antimicrobial resistance (AMR) represents a substantial threat to global public health, complicating the treatment of common infections and leading to prolonged illness and escalated healthcare expenses. To effectively combat AMR, timely and accurate detection is crucial for AMR surveillance and individual-based therapy. Phenotypic antibiotic resistance testing (AST) has long been considered the gold standard in clinical applications, serving as the foundation for clinical AMR diagnosis and optimized therapy. It has significantly contributed to ensuring patients' health and the development of novel antimicrobials. Despite advancements in automated culture-based AST technologies, inherent limitations impede the widespread use of phenotypic AST in AMR surveillance. Genotypic AST technologies offer a promising alternative option, exhibiting advantages of rapidity, high sensitivity, and specificity. With the continuous advancement and expanding applications of genotypic AST technologies, such as microfluidics, mass spectrometry, and high-resolution melting curve analysis, new vigor has been injected into the development and clinical implementation of genotypic AST technologies. In this narrative review, we discuss the principles, applications, and advancements of emerging genotypic AST methods in clinical settings. The comprehensive review aims to highlight the significant scientific potential of emerging genotypic AST technologies in clinical AMR diagnosis, providing insights to enhance existing methods and explore novel approaches.
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Affiliation(s)
- Boheng Duan
- Huan Kui College of Nanchang University, Nanchang, 330031, China
| | - Xianjun Zeng
- Department of Imaging, The Second Affiliated Hospital of Nanchang University, Nanchang, 330038, China
| | - Junping Peng
- NHC Key Laboratory of Systems Biology of Pathogens, National Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 102629, China.
- Key Laboratory of Respiratory Disease Pathogenomics, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 102629, China.
- Key Laboratory of Pathogen Infection Prevention and Control (Ministry of Education), State Key Laboratory of Respiratory Health and Multimorbidity, National Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 102629, China.
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Su LD, Chiu CY, Gaston D, Hogan CA, Miller S, Simon DW, Thakur KT, Yang S, Piantadosi A. Clinical Metagenomic Next-Generation Sequencing for Diagnosis of Central Nervous System Infections: Advances and Challenges. Mol Diagn Ther 2024; 28:513-523. [PMID: 38992308 DOI: 10.1007/s40291-024-00727-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/18/2024] [Indexed: 07/13/2024]
Abstract
Central nervous system (CNS) infections carry a substantial burden of morbidity and mortality worldwide, and accurate and timely diagnosis is required to optimize management. Metagenomic next-generation sequencing (mNGS) has proven to be a valuable tool in detecting pathogens in patients with suspected CNS infection. By sequencing microbial nucleic acids present in a patient's cerebrospinal fluid, brain tissue, or samples collected outside of the CNS, such as plasma, mNGS can detect a wide range of pathogens, including rare, unexpected, and/or fastidious organisms. Furthermore, its target-agnostic approach allows for the identification of both known and novel pathogens. This is particularly useful in cases where conventional diagnostic methods fail to provide an answer. In addition, mNGS can detect multiple microorganisms simultaneously, which is crucial in cases of mixed infections without a clear predominant pathogen. Overall, clinical mNGS testing can help expedite the diagnostic process for CNS infections, guide appropriate management decisions, and ultimately improve clinical outcomes. However, there are key challenges surrounding its use that need to be considered to fully leverage its clinical impact. For example, only a few specialized laboratories offer clinical mNGS due to the complexity of both the laboratory methods and analysis pipelines. Clinicians interpreting mNGS results must be aware of both false negatives-as mNGS is a direct detection modality and requires a sufficient amount of microbial nucleic acid to be present in the sample tested-and false positives-as mNGS detects environmental microbes and their nucleic acids, despite best practices to minimize contamination. Additionally, current costs and turnaround times limit broader implementation of clinical mNGS. Finally, there is uncertainty regarding the best practices for clinical utilization of mNGS, and further work is needed to define the optimal patient population(s), syndrome(s), and time of testing to implement clinical mNGS.
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Affiliation(s)
- LingHui David Su
- British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada
- The Consortium for Clinical Metagenomics in Infectious Diseases, Nashville, TN, USA
| | - Charles Y Chiu
- The Consortium for Clinical Metagenomics in Infectious Diseases, Nashville, TN, USA
- Department of Laboratory Medicine and Department of Medicine, Division of Infectious Diseases, University of California San Francisco, San Francisco, CA, USA
| | - David Gaston
- The Consortium for Clinical Metagenomics in Infectious Diseases, Nashville, TN, USA
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Catherine A Hogan
- British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada
- The Consortium for Clinical Metagenomics in Infectious Diseases, Nashville, TN, USA
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Steve Miller
- The Consortium for Clinical Metagenomics in Infectious Diseases, Nashville, TN, USA
- Delve Bio, Inc., San Francisco, CA, USA
- Department of Laboratory Medicine, University of California, San Francisco, CA, USA
| | - Dennis W Simon
- The Consortium for Clinical Metagenomics in Infectious Diseases, Nashville, TN, USA
- Department of Pediatric Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Kiran T Thakur
- The Consortium for Clinical Metagenomics in Infectious Diseases, Nashville, TN, USA
- Department of Neurology, Columbia University Irving Medical Center-New York Presbyterian Hospital, New York, NY, USA
| | - Shangxin Yang
- The Consortium for Clinical Metagenomics in Infectious Diseases, Nashville, TN, USA
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Anne Piantadosi
- The Consortium for Clinical Metagenomics in Infectious Diseases, Nashville, TN, USA.
- Department of Pathology and Laboratory Medicine, and Department of Medicine, Division of Infectious Diseases, Emory University School of Medicine, 101 Woodruff Circle, Atlanta, GA, USA.
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Aminu S, Ascandari A, Laamarti M, Safdi NEH, El Allali A, Daoud R. Exploring microbial worlds: a review of whole genome sequencing and its application in characterizing the microbial communities. Crit Rev Microbiol 2024; 50:805-829. [PMID: 38006569 DOI: 10.1080/1040841x.2023.2282447] [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: 05/22/2023] [Revised: 10/20/2023] [Accepted: 11/06/2023] [Indexed: 11/27/2023]
Abstract
The classical microbiology techniques have inherent limitations in unraveling the complexity of microbial communities, necessitating the pivotal role of sequencing in studying the diversity of microbial communities. Whole genome sequencing (WGS) enables researchers to uncover the metabolic capabilities of the microbial community, providing valuable insights into the microbiome. Herein, we present an overview of the rapid advancements achieved thus far in the use of WGS in microbiome research. There was an upsurge in publications, particularly in 2021 and 2022 with the United States, China, and India leading the metagenomics research landscape. The Illumina platform has emerged as the widely adopted sequencing technology, whereas a significant focus of metagenomics has been on understanding the relationship between the gut microbiome and human health where distinct bacterial species have been linked to various diseases. Additionally, studies have explored the impact of human activities on microbial communities, including the potential spread of pathogenic bacteria and antimicrobial resistance genes in different ecosystems. Furthermore, WGS is used in investigating the microbiome of various animal species and plant tissues such as the rhizosphere microbiome. Overall, this review reflects the importance of WGS in metagenomics studies and underscores its remarkable power in illuminating the variety and intricacy of the microbiome in different environments.
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Affiliation(s)
- Suleiman Aminu
- Chemical and Biochemical Sciences-Green Process Engineering, University Mohammed VI Polytechnic, Ben Guerir, Morocco
- Department of Biochemistry, Ahmadu Bello University, Zaria, Nigeria
| | - AbdulAziz Ascandari
- Chemical and Biochemical Sciences-Green Process Engineering, University Mohammed VI Polytechnic, Ben Guerir, Morocco
| | - Meriem Laamarti
- Faculty of Medical Sciences, University Mohammed VI Polytechnic, Ben Guerir, Morocco
| | - Nour El Houda Safdi
- AgroBioSciences Program, College for Sustainable Agriculture and Environmental Science, University Mohammed VI Polytechnic, Ben Guerir, Morocco
| | - Achraf El Allali
- Bioinformatics Laboratory, College of Computing, University Mohammed VI Polytechnic, Ben Guerir, Morocco
| | - Rachid Daoud
- Chemical and Biochemical Sciences-Green Process Engineering, University Mohammed VI Polytechnic, Ben Guerir, Morocco
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Ma T, Zhuang Y, Lu W, Tu Y, Diao Q, Fan X, Zhang N. Seven hundred and ninety-seven metagenome-assembled genomes from the goat rumen during early life. Sci Data 2024; 11:897. [PMID: 39154041 PMCID: PMC11330487 DOI: 10.1038/s41597-024-03703-4] [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: 01/24/2024] [Accepted: 07/30/2024] [Indexed: 08/19/2024] Open
Abstract
The rumen microbiome plays an important role in providing energy and protein to the host. Manipulation of rumen microbiome during early life may have a long-term beneficial effect on the health, growth performance, and feed efficiency of ruminants. To better understand the profiles and functional potentials of rumen microbiome in young ruminants, metagenomic binning was performed to investigate the rumen microbiome of goat kids from one to 84 days of age. A total of 797 metagenome-assembled genomes (MAGs) were recovered from the rumen of 42 Laiwu black goat kids. Our findings provide fundamental knowledge of the rumen microbiome during early life based on metagenomic binning, which may provide insights into effective strategies to achieve long-term beneficial effects on animal health and production.
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Affiliation(s)
- Tao Ma
- Key Laboratory of Feed Biotechnology of the Ministry of Agriculture, Institute of Feed Research, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
| | - Yimin Zhuang
- Key Laboratory of Feed Biotechnology of the Ministry of Agriculture, Institute of Feed Research, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
- State Key Laboratory of Animal Nutrition, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Wei Lu
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100091, China
| | - Yan Tu
- Key Laboratory of Feed Biotechnology of the Ministry of Agriculture, Institute of Feed Research, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Qiyu Diao
- Key Laboratory of Feed Biotechnology of the Ministry of Agriculture, Institute of Feed Research, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Xia Fan
- Institute of Quality Standard and Testing Technology for Agro-Products, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Naifeng Zhang
- Key Laboratory of Feed Biotechnology of the Ministry of Agriculture, Institute of Feed Research, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
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10
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Mallawaarachchi V, Wickramarachchi A, Xue H, Papudeshi B, Grigson SR, Bouras G, Prahl RE, Kaphle A, Verich A, Talamantes-Becerra B, Dinsdale EA, Edwards RA. Solving genomic puzzles: computational methods for metagenomic binning. Brief Bioinform 2024; 25:bbae372. [PMID: 39082646 PMCID: PMC11289683 DOI: 10.1093/bib/bbae372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 06/05/2024] [Accepted: 07/15/2024] [Indexed: 08/03/2024] Open
Abstract
Metagenomics involves the study of genetic material obtained directly from communities of microorganisms living in natural environments. The field of metagenomics has provided valuable insights into the structure, diversity and ecology of microbial communities. Once an environmental sample is sequenced and processed, metagenomic binning clusters the sequences into bins representing different taxonomic groups such as species, genera, or higher levels. Several computational tools have been developed to automate the process of metagenomic binning. These tools have enabled the recovery of novel draft genomes of microorganisms allowing us to study their behaviors and functions within microbial communities. This review classifies and analyzes different approaches of metagenomic binning and different refinement, visualization, and evaluation techniques used by these methods. Furthermore, the review highlights the current challenges and areas of improvement present within the field of research.
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Affiliation(s)
- Vijini Mallawaarachchi
- Flinders Accelerator for Microbiome Exploration, College of Science and Engineering, Flinders University, Adelaide, SA 5042, Australia
| | - Anuradha Wickramarachchi
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Westmead, NSW 2145, Australia
| | - Hansheng Xue
- School of Computing, National University of Singapore, Singapore 119077, Singapore
| | - Bhavya Papudeshi
- Flinders Accelerator for Microbiome Exploration, College of Science and Engineering, Flinders University, Adelaide, SA 5042, Australia
| | - Susanna R Grigson
- Flinders Accelerator for Microbiome Exploration, College of Science and Engineering, Flinders University, Adelaide, SA 5042, Australia
| | - George Bouras
- Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA 5005, Australia
- The Department of Surgery—Otolaryngology Head and Neck Surgery, University of Adelaide and the Basil Hetzel Institute for Translational Health Research, Central Adelaide Local Health Network, Adelaide, SA 5011, Australia
| | - Rosa E Prahl
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Westmead, NSW 2145, Australia
| | - Anubhav Kaphle
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Westmead, NSW 2145, Australia
| | - Andrey Verich
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Westmead, NSW 2145, Australia
- The Kirby Institute, The University of New South Wales, Randwick, Sydney, NSW 2052, Australia
| | - Berenice Talamantes-Becerra
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Westmead, NSW 2145, Australia
| | - Elizabeth A Dinsdale
- Flinders Accelerator for Microbiome Exploration, College of Science and Engineering, Flinders University, Adelaide, SA 5042, Australia
| | - Robert A Edwards
- Flinders Accelerator for Microbiome Exploration, College of Science and Engineering, Flinders University, Adelaide, SA 5042, Australia
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11
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Sato Y. Transcriptome analysis: a powerful tool to understand individual microbial behaviors and interactions in ecosystems. Biosci Biotechnol Biochem 2024; 88:850-856. [PMID: 38749545 DOI: 10.1093/bbb/zbae064] [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: 02/05/2024] [Accepted: 05/06/2024] [Indexed: 07/23/2024]
Abstract
Transcriptome analysis is a powerful tool for studying microbial ecology, especially individual microbial functions in an ecosystem and their interactions. With the development of high-throughput sequencing technology, great progress has been made in analytical methods for microbial communities in natural environments. 16S rRNA gene amplicon sequencing (ie microbial community structure analysis) and shotgun metagenome analysis have been widely used to determine the composition and potential metabolic capability of microorganisms in target environments without requiring culture. However, even if the types of microorganisms present and their genes are known, it is difficult to determine what they are doing in an ecosystem. Gene expression analysis (transcriptome analysis; RNA-seq) is a powerful tool to address these issues. The history and basic information of gene expression analysis, as well as examples of studies using this method to analyze microbial ecosystems, are presented.
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Affiliation(s)
- Yuya Sato
- Environmental Management Research Institute, National Institute of Advanced Industrial Science and Technology, Tsukuba, Ibaraki, Japan
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12
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Zhao Z, Marotta F, Wu M. Thanos: An R Package for the Gene-Centric Analysis of Functional Potential in Metagenomic Samples. Microorganisms 2024; 12:1264. [PMID: 39065033 PMCID: PMC11278725 DOI: 10.3390/microorganisms12071264] [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: 06/04/2024] [Revised: 06/13/2024] [Accepted: 06/18/2024] [Indexed: 07/28/2024] Open
Abstract
As the amount of metagenomic sequencing continues to increase, there is a growing need for tools that help biologists make sense of the data. Specifically, researchers are often interested in the potential of a microbial community to carry out a metabolic reaction, but this analysis requires knitting together multiple software tools into a complex pipeline. Thanos offers a user-friendly R package designed for the pathway-centric analysis and visualization of the functions encoded within metagenomic samples. It allows researchers to go beyond taxonomic profiles and find out, quantitatively, which pathways are prevalent in an environment, as well as comparing different environments in terms of their functional potential. The analysis is based on the sequencing depth of the genes of interest, either in the metagenome-assembled genomes (MAGs) or in the assembled reads (contigs), using a normalization strategy that enables comparison across samples. The package can import the data from multiple formats and offers functions for the visualization of the results as bar plots of the functional profile, box plots of compare functions across samples, and annotated pathway graphs. By streamlining the analysis of the functional potential encoded in microbial communities, Thanos can enable impactful discoveries in all the fields touched by metagenomics, from human health to the environmental sciences.
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Affiliation(s)
- Zhe Zhao
- College of Life Sciences, Zhejiang University, Hangzhou 310058, China;
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany;
| | - Federico Marotta
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany;
| | - Min Wu
- College of Life Sciences, Zhejiang University, Hangzhou 310058, China;
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13
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Lange E, Kranert L, Krüger J, Benndorf D, Heyer R. Microbiome modeling: a beginner's guide. Front Microbiol 2024; 15:1368377. [PMID: 38962127 PMCID: PMC11220171 DOI: 10.3389/fmicb.2024.1368377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 05/27/2024] [Indexed: 07/05/2024] Open
Abstract
Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.
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Affiliation(s)
- Emanuel Lange
- Multidimensional Omics Data Analysis, Department for Bioanalytics, Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
- Graduate School Digital Infrastructure for the Life Sciences, Bielefeld Institute for Bioinformatics Infrastructure (BIBI), Faculty of Technology, Bielefeld University, Bielefeld, Germany
| | - Lena Kranert
- Institute for Automation Engineering, Otto von Guericke University Magdeburg, Magdeburg, Germany
| | - Jacob Krüger
- Engineering of Software-Intensive Systems, Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Dirk Benndorf
- Applied Biosciences and Bioprocess Engineering, Anhalt University of Applied Sciences, Köthen, Germany
| | - Robert Heyer
- Multidimensional Omics Data Analysis, Department for Bioanalytics, Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
- Graduate School Digital Infrastructure for the Life Sciences, Bielefeld Institute for Bioinformatics Infrastructure (BIBI), Faculty of Technology, Bielefeld University, Bielefeld, Germany
- Multidimensional Omics Data Analysis, Faculty of Technology, Bielefeld University, Bielefeld, Germany
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14
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Agustinho DP, Fu Y, Menon VK, Metcalf GA, Treangen TJ, Sedlazeck FJ. Unveiling microbial diversity: harnessing long-read sequencing technology. Nat Methods 2024; 21:954-966. [PMID: 38689099 DOI: 10.1038/s41592-024-02262-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 03/29/2024] [Indexed: 05/02/2024]
Abstract
Long-read sequencing has recently transformed metagenomics, enhancing strain-level pathogen characterization, enabling accurate and complete metagenome-assembled genomes, and improving microbiome taxonomic classification and profiling. These advancements are not only due to improvements in sequencing accuracy, but also happening across rapidly changing analysis methods. In this Review, we explore long-read sequencing's profound impact on metagenomics, focusing on computational pipelines for genome assembly, taxonomic characterization and variant detection, to summarize recent advancements in the field and provide an overview of available analytical methods to fully leverage long reads. We provide insights into the advantages and disadvantages of long reads over short reads and their evolution from the early days of long-read sequencing to their recent impact on metagenomics and clinical diagnostics. We further point out remaining challenges for the field such as the integration of methylation signals in sub-strain analysis and the lack of benchmarks.
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Affiliation(s)
- Daniel P Agustinho
- Human Genome Sequencing center, Baylor College of Medicine, Houston, TX, USA
| | - Yilei Fu
- Department of Computer Science, Rice University, Houston, TX, USA
| | - Vipin K Menon
- Human Genome Sequencing center, Baylor College of Medicine, Houston, TX, USA
- Senior research project manager, Human Genetics, Genentech, South San Francisco, CA, USA
| | - Ginger A Metcalf
- Human Genome Sequencing center, Baylor College of Medicine, Houston, TX, USA
| | - Todd J Treangen
- Department of Computer Science, Rice University, Houston, TX, USA
- Department of Bioengineering, Rice University, Houston, TX, USA
| | - Fritz J Sedlazeck
- Human Genome Sequencing center, Baylor College of Medicine, Houston, TX, USA.
- Department of Computer Science, Rice University, Houston, TX, USA.
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15
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Zhang Z, Xiao J, Wang H, Yang C, Huang Y, Yue Z, Chen Y, Han L, Yin K, Lyu A, Fang X, Zhang L. Exploring high-quality microbial genomes by assembling short-reads with long-range connectivity. Nat Commun 2024; 15:4631. [PMID: 38821971 PMCID: PMC11143213 DOI: 10.1038/s41467-024-49060-z] [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: 08/20/2023] [Accepted: 05/17/2024] [Indexed: 06/02/2024] Open
Abstract
Although long-read sequencing enables the generation of complete genomes for unculturable microbes, its high cost limits the widespread adoption of long-read sequencing in large-scale metagenomic studies. An alternative method is to assemble short-reads with long-range connectivity, which can be a cost-effective way to generate high-quality microbial genomes. Here, we develop Pangaea, a bioinformatic approach designed to enhance metagenome assembly using short-reads with long-range connectivity. Pangaea leverages connectivity derived from physical barcodes of linked-reads or virtual barcodes by aligning short-reads to long-reads. Pangaea utilizes a deep learning-based read binning algorithm to assemble co-barcoded reads exhibiting similar sequence contexts and abundances, thereby improving the assembly of high- and medium-abundance microbial genomes. Pangaea also leverages a multi-thresholding algorithm strategy to refine assembly for low-abundance microbes. We benchmark Pangaea on linked-reads and a combination of short- and long-reads from simulation data, mock communities and human gut metagenomes. Pangaea achieves significantly higher contig continuity as well as more near-complete metagenome-assembled genomes (NCMAGs) than the existing assemblers. Pangaea also generates three complete and circular NCMAGs on the human gut microbiomes.
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Grants
- This research was partially supported by the Young Collaborative Research Grant (C2004-23Y, L.Z.), HMRF (11221026, L.Z.), the open project of BGI-Shenzhen, Shenzhen 518000, China (BGIRSZ20220012, L.Z.), the Hong Kong Research Grant Council Early Career Scheme (HKBU 22201419, L.Z.), HKBU Start-up Grant Tier 2 (RC-SGT2/19-20/SCI/007, L.Z.), HKBU IRCMS (No. IRCMS/19-20/D02, L.Z.).
- This research was partially supported by the open project of BGI-Shenzhen, Shenzhen 518000, China (BGIRSZ20220014, KJ.Y.).
- The study were partially supported by the Science Technology and Innovation Committee of Shenzhen Municipality, China (SGDX20190919142801722, XD.F.),
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Affiliation(s)
- Zhenmiao Zhang
- Department of Computer Science, Hong Kong Baptist University, Hong Kong, China
| | - Jin Xiao
- Department of Computer Science, Hong Kong Baptist University, Hong Kong, China
| | - Hongbo Wang
- Department of Computer Science, Hong Kong Baptist University, Hong Kong, China
| | - Chao Yang
- Department of Computer Science, Hong Kong Baptist University, Hong Kong, China
| | | | - Zhen Yue
- BGI Research, Sanya, 572025, China
| | - Yang Chen
- State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese, Guangzhou, China
| | - Lijuan Han
- Department of Scientific Research, Kangmeihuada GeneTech Co., Ltd (KMHD), Shenzhen, China
| | - Kejing Yin
- Department of Computer Science, Hong Kong Baptist University, Hong Kong, China
- Institute for Research and Continuing Education, Hong Kong Baptist University, Shenzhen, China
| | - Aiping Lyu
- School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China
| | - Xiaodong Fang
- BGI Research, Shenzhen, 518083, China
- BGI Research, Sanya, 572025, China
- Department of Scientific Research, Kangmeihuada GeneTech Co., Ltd (KMHD), Shenzhen, China
| | - Lu Zhang
- Department of Computer Science, Hong Kong Baptist University, Hong Kong, China.
- Institute for Research and Continuing Education, Hong Kong Baptist University, Shenzhen, China.
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16
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Vuong P, Griffiths AP, Barbour E, Kaur P. The buzz about honey-based biosurveys. NPJ BIODIVERSITY 2024; 3:8. [PMID: 39242847 PMCID: PMC11332087 DOI: 10.1038/s44185-024-00040-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 02/08/2024] [Indexed: 09/09/2024]
Abstract
Approximately 1.8 million metric tonnes of honey are produced globally every year. The key source behind this output, the honey bee (Apis mellifera), works tirelessly to create the delicious condiment that is consumed worldwide. The honey that finds its way into jars on store shelves contains a myriad of information about its biogeographical origins, such as the bees that produced it, the botanical constituents, and traces of other organisms or pathogens that have come in contact with the product or its producer. With the ongoing threat of honey bee decline and overall global biodiversity loss, access to ecological information has become an key factor in preventing the loss of species. This review delves into the various molecular techniques developed to characterize the collective DNA harnessed within honey samples, and how it can be used to elucidate the ecological interactions between honey bees and the environment. We also explore how these DNA-based methods can be used for large-scale biogeographical studies through the environmental DNA collected by foraging honey bees. Further development of these techniques can assist in the conservation of biodiversity by detecting ecosystem perturbations, with the potential to be expanded towards other critical flying pollinators.
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Affiliation(s)
- Paton Vuong
- UWA School of Agriculture & Environment, University of Western Australia, Perth, Australia
| | - Anna Poppy Griffiths
- UWA School of Agriculture & Environment, University of Western Australia, Perth, Australia
| | - Elizabeth Barbour
- UWA School of Agriculture & Environment, University of Western Australia, Perth, Australia
| | - Parwinder Kaur
- UWA School of Agriculture & Environment, University of Western Australia, Perth, Australia.
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17
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Littleford-Colquhoun B, Kartzinel TR. A CRISPR-based strategy for targeted sequencing in biodiversity science. Mol Ecol Resour 2024; 24:e13920. [PMID: 38153158 DOI: 10.1111/1755-0998.13920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 11/10/2023] [Accepted: 12/13/2023] [Indexed: 12/29/2023]
Abstract
Many applications in molecular ecology require the ability to match specific DNA sequences from single- or mixed-species samples with a diagnostic reference library. Widely used methods for DNA barcoding and metabarcoding employ PCR and amplicon sequencing to identify taxa based on target sequences, but the target-specific enrichment capabilities of CRISPR-Cas systems may offer advantages in some applications. We identified 54,837 CRISPR-Cas guide RNAs that may be useful for enriching chloroplast DNA across phylogenetically diverse plant species. We tested a subset of 17 guide RNAs in vitro to enrich plant DNA strands ranging in size from diagnostic DNA barcodes of 1,428 bp to entire chloroplast genomes of 121,284 bp. We used an Oxford Nanopore sequencer to evaluate sequencing success based on both single- and mixed-species samples, which yielded mean chloroplast sequence lengths of 2,530-11,367 bp, depending on the experiment. In comparison to mixed-species experiments, single-species experiments yielded more on-target sequence reads and greater mean pairwise identity between contigs and the plant species' reference genomes. But nevertheless, these mixed-species experiments yielded sufficient data to provide ≥48-fold increase in sequence length and better estimates of relative abundance for a commercially prepared mixture of plant species compared to DNA metabarcoding based on the chloroplast trnL-P6 marker. Prior work developed CRISPR-based enrichment protocols for long-read sequencing and our experiments pioneered its use for plant DNA barcoding and chloroplast assemblies that may have advantages over workflows that require PCR and short-read sequencing. Future work would benefit from continuing to develop in vitro and in silico methods for CRISPR-based analyses of mixed-species samples, especially when the appropriate reference genomes for contig assembly cannot be known a priori.
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Affiliation(s)
- Bethan Littleford-Colquhoun
- Department of Ecology, Evolution, and Organismal Biology, Brown University, Providence, Rhode Island, USA
- Institute at Brown for Environment and Society, Brown University, Providence, Rhode Island, USA
| | - Tyler R Kartzinel
- Department of Ecology, Evolution, and Organismal Biology, Brown University, Providence, Rhode Island, USA
- Institute at Brown for Environment and Society, Brown University, Providence, Rhode Island, USA
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18
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Mwakibete L, Greening SS, Kalantar K, Ahyong V, Anis E, Miller EA, Needle DB, Oglesbee M, Thomas WK, Sevigny JL, Gordon LM, Nemeth NM, Ogbunugafor CB, Ayala AJ, Faith SA, Neff N, Detweiler AM, Baillargeon T, Tanguay S, Simpson SD, Murphy LA, Ellis JC, Tato CM, Gagne RB. Metagenomics for Pathogen Detection During a Mass Mortality Event in Songbirds. J Wildl Dis 2024; 60:362-374. [PMID: 38345467 DOI: 10.7589/jwd-d-23-00109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 01/02/2024] [Indexed: 04/06/2024]
Abstract
Mass mortality events in wildlife can be indications of an emerging infectious disease. During the spring and summer of 2021, hundreds of dead passerines were reported across the eastern US. Birds exhibited a range of clinical signs including swollen conjunctiva, ocular discharge, ataxia, and nystagmus. As part of the diagnostic investigation, high-throughput metagenomic next-generation sequencing was performed across three molecular laboratories on samples from affected birds. Many potentially pathogenic microbes were detected, with bacteria forming the largest proportion; however, no singular agent was consistently identified, with many of the detected microbes also found in unaffected (control) birds and thus considered to be subclinical infections. Congruent results across laboratories have helped drive further investigation into alternative causes, including environmental contaminants and nutritional deficiencies. This work highlights the utility of metagenomic approaches in investigations of emerging diseases and provides a framework for future wildlife mortality events.
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Affiliation(s)
| | - Sabrina S Greening
- Department of Pathobiology, Wildlife Futures Program, University of Pennsylvania School of Veterinary Medicine, New Bolton Center, Kennett Square, Pennsylvania 19348, USA
| | | | - Vida Ahyong
- Chan Zuckerberg Biohub, San Francisco, California 94158, USA
| | - Eman Anis
- Department of Pathobiology, Wildlife Futures Program, University of Pennsylvania School of Veterinary Medicine, New Bolton Center, Kennett Square, Pennsylvania 19348, USA
- Department of Pathobiology, PADLS New Bolton Center, University of Pennsylvania School of Veterinary Medicine, New Bolton Center, Kennett Square, Pennsylvania 19348, USA
| | - Erica A Miller
- Department of Pathobiology, Wildlife Futures Program, University of Pennsylvania School of Veterinary Medicine, New Bolton Center, Kennett Square, Pennsylvania 19348, USA
| | - David B Needle
- New Hampshire Veterinary Diagnostic Lab, University of New Hampshire, Durham, New Hampshire 03824, USA
| | - Michael Oglesbee
- Infectious Diseases Institute, The Ohio State University, Columbus, Ohio 43210, USA
| | - W Kelley Thomas
- Hubbard Center for Genome Studies, University of New Hampshire, Durham, New Hampshire 03824, USA
| | - Joseph L Sevigny
- Hubbard Center for Genome Studies, University of New Hampshire, Durham, New Hampshire 03824, USA
| | - Lawrence M Gordon
- Hubbard Center for Genome Studies, University of New Hampshire, Durham, New Hampshire 03824, USA
| | - Nicole M Nemeth
- Southeastern Cooperative Wildlife Disease Study and Department of Pathology, College of Veterinary Medicine, University of Georgia, Athens, Georgia 30602, USA
- Department of Pathology, College of Veterinary Medicine, University of Georgia, Georgia 30602, USA
| | - C Brandon Ogbunugafor
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, Connecticut 06511, USA
| | - Andrea J Ayala
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, Connecticut 06511, USA
| | - Seth A Faith
- Infectious Diseases Institute, The Ohio State University, Columbus, Ohio 43210, USA
| | - Norma Neff
- Chan Zuckerberg Biohub, San Francisco, California 94158, USA
| | | | - Tessa Baillargeon
- New Hampshire Veterinary Diagnostic Lab, University of New Hampshire, Durham, New Hampshire 03824, USA
| | - Stacy Tanguay
- New Hampshire Veterinary Diagnostic Lab, University of New Hampshire, Durham, New Hampshire 03824, USA
| | - Stephen D Simpson
- Hubbard Center for Genome Studies, University of New Hampshire, Durham, New Hampshire 03824, USA
| | - Lisa A Murphy
- Department of Pathobiology, Wildlife Futures Program, University of Pennsylvania School of Veterinary Medicine, New Bolton Center, Kennett Square, Pennsylvania 19348, USA
- Department of Pathobiology, PADLS New Bolton Center, University of Pennsylvania School of Veterinary Medicine, New Bolton Center, Kennett Square, Pennsylvania 19348, USA
| | - Julie C Ellis
- Department of Pathobiology, Wildlife Futures Program, University of Pennsylvania School of Veterinary Medicine, New Bolton Center, Kennett Square, Pennsylvania 19348, USA
| | - Cristina M Tato
- Chan Zuckerberg Biohub, San Francisco, California 94158, USA
| | - Roderick B Gagne
- Department of Pathobiology, Wildlife Futures Program, University of Pennsylvania School of Veterinary Medicine, New Bolton Center, Kennett Square, Pennsylvania 19348, USA
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19
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Niya B, Yaakoubi K, Beraich FZ, Arouch M, Meftah Kadmiri I. Current status and future developments of assessing microbiome composition and dynamics in anaerobic digestion systems using metagenomic approaches. Heliyon 2024; 10:e28221. [PMID: 38560681 PMCID: PMC10979216 DOI: 10.1016/j.heliyon.2024.e28221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 03/12/2024] [Accepted: 03/13/2024] [Indexed: 04/04/2024] Open
Abstract
The metagenomic approach stands as a powerful technique for examining the composition of microbial communities and their involvement in various anaerobic digestion (AD) systems. Understanding the structure, function, and dynamics of microbial communities becomes pivotal for optimizing the biogas process, enhancing its stability and improving overall performance. Currently, taxonomic profiling of biogas-producing communities relies mainly on high-throughput 16S rRNA sequencing, offering insights into the bacterial and archaeal structures of AD assemblages and their correlations with fed substrates and process parameters. To delve even deeper, shotgun and genome-centric metagenomic approaches are employed to recover individual genomes from the metagenome. This provides a nuanced understanding of collective functionalities, interspecies interactions, and microbial associations with abiotic factors. The application of OMICs in AD systems holds the potential to revolutionize the field, leading to more efficient and sustainable waste management practices particularly through the implementation of precision anaerobic digestion systems. As ongoing research in this area progresses, anticipations are high for further exciting developments in the future. This review serves to explore the current landscape of metagenomic analyses, with focus on advancing our comprehension and critically evaluating biases and recommendations in the analysis of microbial communities in anaerobic digesters. Its objective is to explore how contemporary metagenomic approaches can be effectively applied to enhance our understanding and contribute to the refinement of the AD process. This marks a substantial stride towards achieving a more comprehensive understanding of anaerobic digestion systems.
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Affiliation(s)
- Btissam Niya
- Plant and Microbial Biotechnology Center, Moroccan Foundation of Advanced Science Innovation and Research MAScIR, Mohammed VI Polytechnic University (UM6P), Lot 660, Hay Moulay Rachid, 43150, Benguerir, Morocco
- Engineering, Industrial Management & Innovation Laboratory IMII, Faculty of Science and Technics (FST), Hassan 1st University of Settat, Morocco
| | - Kaoutar Yaakoubi
- Plant and Microbial Biotechnology Center, Moroccan Foundation of Advanced Science Innovation and Research MAScIR, Mohammed VI Polytechnic University (UM6P), Lot 660, Hay Moulay Rachid, 43150, Benguerir, Morocco
| | - Fatima Zahra Beraich
- Biodome.sarl, Research and Development Design Office of Biogas Technology, Casablanca, Morocco
| | - Moha Arouch
- Engineering, Industrial Management & Innovation Laboratory IMII, Faculty of Science and Technics (FST), Hassan 1st University of Settat, Morocco
| | - Issam Meftah Kadmiri
- Plant and Microbial Biotechnology Center, Moroccan Foundation of Advanced Science Innovation and Research MAScIR, Mohammed VI Polytechnic University (UM6P), Lot 660, Hay Moulay Rachid, 43150, Benguerir, Morocco
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20
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Balcha ES, Macey MC, Gemeda MT, Cavalazzi B, Woldesemayat AA. Mining the microbiome of Lake Afdera to gain insights into microbial diversity and biosynthetic potential. FEMS MICROBES 2024; 5:xtae008. [PMID: 38560625 PMCID: PMC10979467 DOI: 10.1093/femsmc/xtae008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 01/24/2024] [Accepted: 03/05/2024] [Indexed: 04/04/2024] Open
Abstract
Microorganisms inhabiting hypersaline environments have received significant attention due to their ability to thrive under poly-extreme conditions, including high salinity, elevated temperatures and heavy metal stress. They are believed to possess biosynthetic gene clusters (BGCs) that encode secondary metabolites as survival strategy and offer potential biotechnological applications. In this study, we mined BGCs in shotgun metagenomic sequences generated from Lake Afdera, a hypersaline lake in the Afar Depression, Ethiopia. The microbiome of Lake Afdera is predominantly bacterial, with Acinetobacter (18.6%) and Pseudomonas (11.8%) being ubiquitously detected. A total of 94 distinct BGCs were identified in the metagenomic data. These BGCs are found to encode secondary metabolites with two main categories of functions: (i) potential pharmaceutical applications (nonribosomal peptide synthase NRPs, polyketide synthase, others) and (ii) miscellaneous roles conferring adaptation to extreme environment (bacteriocins, ectoine, others). Notably, NRPs (20.6%) and bacteriocins (10.6%) were the most abundant. Furthermore, our metagenomic analysis predicted gene clusters that enable microbes to defend against a wide range of toxic metals, oxidative stress and osmotic stress. These findings suggest that Lake Afdera is a rich biological reservoir, with the predicted BGCs playing critical role in the survival and adaptation of extremophiles.
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Affiliation(s)
- Ermias Sissay Balcha
- School of Medical Laboratory Science, College of Health Sciences, Hawassa University, 16417, Hawassa, Ethiopia
- Biotechnology and Bioprocess Center of Excellence, College of Biological and Chemical Engineering, Addis Ababa Science and Technology University, 16417, Addis Ababa, Ethiopia
| | - Michael C Macey
- Astrobiology OU, School of Environment, Earth and Ecosystem Sciences, The Open University, Milton Keynes, MK7 6AA, United Kingdom
| | - Mesfin Tafesse Gemeda
- Biotechnology and Bioprocess Center of Excellence, College of Biological and Chemical Engineering, Addis Ababa Science and Technology University, 16417, Addis Ababa, Ethiopia
| | - Barbara Cavalazzi
- Dipartimento di Scienze Biologiche, Geologiche e Ambientali, Università di Bologna, Bologna, Italy
- Department of Geology, University of Johannesburg, Johannesburg, South Africa
| | - Adugna Abdi Woldesemayat
- Biotechnology and Bioprocess Center of Excellence, College of Biological and Chemical Engineering, Addis Ababa Science and Technology University, 16417, Addis Ababa, Ethiopia
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21
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Hui X, Yang J, Sun J, Liu F, Pan W. MCSS: microbial community simulator based on structure. Front Microbiol 2024; 15:1358257. [PMID: 38516019 PMCID: PMC10956353 DOI: 10.3389/fmicb.2024.1358257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 02/20/2024] [Indexed: 03/23/2024] Open
Abstract
De novo assembly plays a pivotal role in metagenomic analysis, and the incorporation of third-generation sequencing technology can significantly improve the integrity and accuracy of assembly results. Recently, with advancements in sequencing technology (Hi-Fi, ultra-long), several long-read-based bioinformatic tools have been developed. However, the validation of the performance and reliability of these tools is a crucial concern. To address this gap, we present MCSS (microbial community simulator based on structure), which has the capability to generate simulated microbial community and sequencing datasets based on the structure attributes of real microbiome communities. The evaluation results indicate that it can generate simulated communities that exhibit both diversity and similarity to actual community structures. Additionally, MCSS generates synthetic PacBio Hi-Fi and Oxford Nanopore Technologies (ONT) long reads for the species within the simulated community. This innovative tool provides a valuable resource for benchmarking and refining metagenomic analysis methods. Code available at: https://github.com/panlab-bio/mcss.
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Affiliation(s)
- Xingqi Hui
- Zhengzhou Research Base, State Key Laboratory of Cotton Biology, School of Agricultural Sciences, Zhengzhou University, Zhengzhou, China
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences (ICR, CAAS), Shenzhen, China
| | - Jinbao Yang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences (ICR, CAAS), Shenzhen, China
- College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Jinhuan Sun
- Key Laboratory of Plant Molecular Physiology, CAS Center for Excellence in Molecular Plant Sciences, Institute of Botany, Chinese Academy of Sciences, Beijing, China
| | - Fang Liu
- Zhengzhou Research Base, State Key Laboratory of Cotton Biology, School of Agricultural Sciences, Zhengzhou University, Zhengzhou, China
- National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences (ICR, CAAS), Anyang, China
| | - Weihua Pan
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences (ICR, CAAS), Shenzhen, China
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22
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Liu X, Zheng J, Ding J, Wu J, Zuo F, Zhang G. When Livestock Genomes Meet Third-Generation Sequencing Technology: From Opportunities to Applications. Genes (Basel) 2024; 15:245. [PMID: 38397234 PMCID: PMC10888458 DOI: 10.3390/genes15020245] [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: 12/23/2023] [Revised: 01/30/2024] [Accepted: 02/10/2024] [Indexed: 02/25/2024] Open
Abstract
Third-generation sequencing technology has found widespread application in the genomic, transcriptomic, and epigenetic research of both human and livestock genetics. This technology offers significant advantages in the sequencing of complex genomic regions, the identification of intricate structural variations, and the production of high-quality genomes. Its attributes, including long sequencing reads, obviation of PCR amplification, and direct determination of DNA/RNA, contribute to its efficacy. This review presents a comprehensive overview of third-generation sequencing technologies, exemplified by single-molecule real-time sequencing (SMRT) and Oxford Nanopore Technology (ONT). Emphasizing the research advancements in livestock genomics, the review delves into genome assembly, structural variation detection, transcriptome sequencing, and epigenetic investigations enabled by third-generation sequencing. A comprehensive analysis is conducted on the application and potential challenges of third-generation sequencing technology for genome detection in livestock. Beyond providing valuable insights into genome structure analysis and the identification of rare genes in livestock, the review ventures into an exploration of the genetic mechanisms underpinning exemplary traits. This review not only contributes to our understanding of the genomic landscape in livestock but also provides fresh perspectives for the advancement of research in this domain.
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Affiliation(s)
- Xinyue Liu
- College of Animal Science and Technology, Southwest University, Rongchang, Chongqing 402460, China; (X.L.); (J.Z.); (J.D.); (J.W.); (F.Z.)
| | - Junyuan Zheng
- College of Animal Science and Technology, Southwest University, Rongchang, Chongqing 402460, China; (X.L.); (J.Z.); (J.D.); (J.W.); (F.Z.)
| | - Jialan Ding
- College of Animal Science and Technology, Southwest University, Rongchang, Chongqing 402460, China; (X.L.); (J.Z.); (J.D.); (J.W.); (F.Z.)
| | - Jiaxin Wu
- College of Animal Science and Technology, Southwest University, Rongchang, Chongqing 402460, China; (X.L.); (J.Z.); (J.D.); (J.W.); (F.Z.)
| | - Fuyuan Zuo
- College of Animal Science and Technology, Southwest University, Rongchang, Chongqing 402460, China; (X.L.); (J.Z.); (J.D.); (J.W.); (F.Z.)
- Beef Cattle Engineering and Technology Research Center of Chongqing, Southwest University, Rongchang, Chongqing 402460, China
| | - Gongwei Zhang
- College of Animal Science and Technology, Southwest University, Rongchang, Chongqing 402460, China; (X.L.); (J.Z.); (J.D.); (J.W.); (F.Z.)
- Beef Cattle Engineering and Technology Research Center of Chongqing, Southwest University, Rongchang, Chongqing 402460, China
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Banchi E, Corre E, Del Negro P, Celussi M, Malfatti F. Genome-resolved metagenomics of Venice Lagoon surface sediment bacteria reveals high biosynthetic potential and metabolic plasticity as successful strategies in an impacted environment. MARINE LIFE SCIENCE & TECHNOLOGY 2024; 6:126-142. [PMID: 38433960 PMCID: PMC10902248 DOI: 10.1007/s42995-023-00192-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 09/05/2023] [Indexed: 03/05/2024]
Abstract
Bacteria living in sediments play essential roles in marine ecosystems and deeper insights into the ecology and biogeochemistry of these largely unexplored organisms can be obtained from 'omics' approaches. Here, we characterized metagenome-assembled-genomes (MAGs) from the surface sediment microbes of the Venice Lagoon (northern Adriatic Sea) in distinct sub-basins exposed to various natural and anthropogenic pressures. MAGs were explored for biodiversity, major marine metabolic processes, anthropogenic activity-related functions, adaptations at the microscale, and biosynthetic gene clusters. Starting from 126 MAGs, a non-redundant dataset of 58 was compiled, the majority of which (35) belonged to (Alpha- and Gamma-) Proteobacteria. Within the broad microbial metabolic repertoire (including C, N, and S metabolisms) the potential to live without oxygen emerged as one of the most important features. Mixotrophy was also found as a successful lifestyle. Cluster analysis showed that different MAGs encoded the same metabolic patterns (e.g., C fixation, sulfate oxidation) thus suggesting metabolic redundancy. Antibiotic and toxic compounds resistance genes were coupled, a condition that could promote the spreading of these genetic traits. MAGs showed a high biosynthetic potential related to antimicrobial and biotechnological classes and to organism defense and interactions as well as adaptive strategies for micronutrient uptake and cellular detoxification. Our results highlighted that bacteria living in an impacted environment, such as the surface sediments of the Venice Lagoon, may benefit from metabolic plasticity as well as from the synthesis of a wide array of secondary metabolites, promoting ecosystem resilience and stability toward environmental pressures. Supplementary Information The online version contains supplementary material available at 10.1007/s42995-023-00192-z.
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Affiliation(s)
- Elisa Banchi
- National Institute of Oceanography and Applied Geophysics OGS, Trieste, Italy
| | - Erwan Corre
- FR2424, Station Biologique de Roscoff, Plateforme ABiMS (Analysis and Bioinformatics for Marine Science), Sorbonne Université CNRS, 29680 Roscoff, France
| | - Paola Del Negro
- National Institute of Oceanography and Applied Geophysics OGS, Trieste, Italy
| | - Mauro Celussi
- National Institute of Oceanography and Applied Geophysics OGS, Trieste, Italy
| | - Francesca Malfatti
- National Institute of Oceanography and Applied Geophysics OGS, Trieste, Italy
- Department of Life Sciences, University of Trieste, Trieste, Italy
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Zhang Y, Yin XL, Ji M, Chen Y, Chai Z. Decoupling the dynamic mechanism revealed by FGFR2 mutation-induced population shift. J Biomol Struct Dyn 2024; 42:1940-1951. [PMID: 37254996 DOI: 10.1080/07391102.2023.2217924] [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: 03/01/2023] [Accepted: 04/08/2023] [Indexed: 06/01/2023]
Abstract
The fibroblast growth factor receptor 2 (FGFR2) is a key component in cellular signaling networks, and its dysfunctional activation has been implicated in various diseases including cancer and developmental disorders. Mutations at the activation loop (A-loop) have been suggested to trigger an increased basal kinase activity. However, the molecular mechanism underlying this highly dynamic process has not been fully understood due to the limitation of static structural information. Here, we conducted multiple, large-scale Gaussian accelerated molecular dynamics simulations of five (K659E, K659N, K659M, K659Q, and K659T) FGFR2 mutants at the A-loop, and comprehensively analyzed the dynamic molecular basis of FGFR2 activation. The results quantified the population shift of each system, revealing that all mutants had a higher proportion of active-like states. Using Markov state models, we extracted the representative structure of different conformational states and identified key residues related to the increased kinase activity. Furthermore, community network analysis showed enhanced information connections in the mutants, highlighting the long-range allosteric communication between the A-loop and the hinge region. Our findings may provide insights into the dynamic mechanism for FGFR2 dysfunctional activation and allosteric drug discovery.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Yuxiang Zhang
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiao-Lan Yin
- Department of Radiotherapy, Shanghai 411 Hospital, China RongTong Medical Healthcare Group Co. Ltd, Shanghai, China
| | - Mingfei Ji
- Department of Urology, The Second Affiliated Hospital of Navy Medical University, Shanghai, China
| | - Yi Chen
- Department of Ultrasound interventional, Eastern Hepatobiliary Surgery Hospital, Navy Medical University, Shanghai, China
| | - Zongtao Chai
- Department of Liver Surgery and Transplantation, Key Laboratory of Carcinogenesis and Cancer Invasion of Ministry of Education, Liver Cancer Institute and Zhongshan Hospital, Fudan University, Shanghai, China
- Department of Hepatic Surgery, Shanghai Geriatric Medical Center, Shanghai, China
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Hosokawa M, Nishikawa Y. Tools for microbial single-cell genomics for obtaining uncultured microbial genomes. Biophys Rev 2024; 16:69-77. [PMID: 38495448 PMCID: PMC10937852 DOI: 10.1007/s12551-023-01124-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 08/23/2023] [Indexed: 03/19/2024] Open
Abstract
The advent of next-generation sequencing technologies has facilitated the acquisition of large amounts of DNA sequence data at a relatively low cost, leading to numerous breakthroughs in decoding microbial genomes. Among the various genome sequencing activities, metagenomic analysis, which entails the direct analysis of uncultured microbial DNA, has had a profound impact on microbiome research and has emerged as an indispensable technology in this field. Despite its valuable contributions, metagenomic analysis is a "bulk analysis" technique that analyzes samples containing a wide diversity of microbes, such as bacteria, yielding information that is averaged across the entire microbial population. In order to gain a deeper understanding of the heterogeneous nature of the microbial world, there is a growing need for single-cell analysis, similar to its use in human cell biology. With this paradigm shift in mind, comprehensive single-cell genomics technology has become a much-anticipated innovation that is now poised to revolutionize microbiome research. It has the potential to enable the discovery of differences at the strain level and to facilitate a more comprehensive examination of microbial ecosystems. In this review, we summarize the current state-of-the-art in microbial single-cell genomics, highlighting the potential impact of this technology on our understanding of the microbial world. The successful implementation of this technology is expected to have a profound impact in the field, leading to new discoveries and insights into the diversity and evolution of microbes.
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Affiliation(s)
- Masahito Hosokawa
- Department of Life Science and Medical Bioscience, Waseda University, 2-2 Wakamatsu-Cho, Shinjuku-Ku, Tokyo, 162-8480 Japan
- Computational Bio Big-Data Open Innovation Laboratory, National Institute of Advanced Industrial Science and Technology, 3-4-1 Okubo, Shinjuku-Ku, Tokyo, 169-8555 Japan
- Research Organization for Nano and Life Innovation, Waseda University, 513 Wasedatsurumaki-Cho, Shinjuku-Ku, Tokyo, 162-0041 Japan
- Institute for Advanced Research of Biosystem Dynamics, Waseda Research Institute for Science and Engineering, 3-4-1 Okubo, Shinjuku-Ku, Tokyo, 169-8555 Japan
- bitBiome, Inc., 513 Wasedatsurumaki-Cho, Shinjuku-Ku, Tokyo, 162-0041 Japan
| | - Yohei Nishikawa
- Computational Bio Big-Data Open Innovation Laboratory, National Institute of Advanced Industrial Science and Technology, 3-4-1 Okubo, Shinjuku-Ku, Tokyo, 169-8555 Japan
- Research Organization for Nano and Life Innovation, Waseda University, 513 Wasedatsurumaki-Cho, Shinjuku-Ku, Tokyo, 162-0041 Japan
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26
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Qi W, Xue MY, Jia MH, Zhang S, Yan Q, Sun HZ. - Invited Review - Understanding the functionality of the rumen microbiota: searching for better opportunities for rumen microbial manipulation. Anim Biosci 2024; 37:370-384. [PMID: 38186256 PMCID: PMC10838668 DOI: 10.5713/ab.23.0308] [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: 08/17/2023] [Accepted: 11/03/2023] [Indexed: 01/09/2024] Open
Abstract
Rumen microbiota play a central role in the digestive process of ruminants. Their remarkable ability to break down complex plant fibers and proteins, converting them into essential organic compounds that provide animals with energy and nutrition. Research on rumen microbiota not only contributes to improving animal production performance and enhancing feed utilization efficiency but also holds the potential to reduce methane emissions and environmental impact. Nevertheless, studies on rumen microbiota face numerous challenges, including complexity, difficulties in cultivation, and obstacles in functional analysis. This review provides an overview of microbial species involved in the degradation of macromolecules, the fermentation processes, and methane production in the rumen, all based on cultivation methods. Additionally, the review introduces the applications, advantages, and limitations of emerging omics technologies such as metagenomics, metatranscriptomics, metaproteomics, and metabolomics, in investigating the functionality of rumen microbiota. Finally, the article offers a forward-looking perspective on the new horizons and technologies in the field of rumen microbiota functional research. These emerging technologies, with continuous refinement and mutual complementation, have deepened our understanding of rumen microbiota functionality, thereby enabling effective manipulation of the rumen microbial community.
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Affiliation(s)
- Wenlingli Qi
- Key Laboratory of Dairy Cow Genetic Improvement and Milk Quality Research of Zhejiang Province, College of Animal Sciences, Zhejiang University, Hangzhou 310058, China
| | - Ming-Yuan Xue
- Key Laboratory of Dairy Cow Genetic Improvement and Milk Quality Research of Zhejiang Province, College of Animal Sciences, Zhejiang University, Hangzhou 310058, China
| | - Ming-Hui Jia
- Key Laboratory of Dairy Cow Genetic Improvement and Milk Quality Research of Zhejiang Province, College of Animal Sciences, Zhejiang University, Hangzhou 310058, China
| | - Shuxian Zhang
- CAS Key Laboratory of Agro-Ecological Processes in Subtropical Region, Hunan Provincial Key Laboratory of Animal Nutritional Physiology and Metabolic Process, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China
| | - Qiongxian Yan
- CAS Key Laboratory of Agro-Ecological Processes in Subtropical Region, Hunan Provincial Key Laboratory of Animal Nutritional Physiology and Metabolic Process, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China
| | - Hui-Zeng Sun
- Key Laboratory of Dairy Cow Genetic Improvement and Milk Quality Research of Zhejiang Province, College of Animal Sciences, Zhejiang University, Hangzhou 310058, China
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Zulfiqar M, Singh V, Steinbeck C, Sorokina M. Review on computer-assisted biosynthetic capacities elucidation to assess metabolic interactions and communication within microbial communities. Crit Rev Microbiol 2024:1-40. [PMID: 38270170 DOI: 10.1080/1040841x.2024.2306465] [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: 03/13/2023] [Accepted: 01/12/2024] [Indexed: 01/26/2024]
Abstract
Microbial communities thrive through interactions and communication, which are challenging to study as most microorganisms are not cultivable. To address this challenge, researchers focus on the extracellular space where communication events occur. Exometabolomics and interactome analysis provide insights into the molecules involved in communication and the dynamics of their interactions. Advances in sequencing technologies and computational methods enable the reconstruction of taxonomic and functional profiles of microbial communities using high-throughput multi-omics data. Network-based approaches, including community flux balance analysis, aim to model molecular interactions within and between communities. Despite these advances, challenges remain in computer-assisted biosynthetic capacities elucidation, requiring continued innovation and collaboration among diverse scientists. This review provides insights into the current state and future directions of computer-assisted biosynthetic capacities elucidation in studying microbial communities.
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Affiliation(s)
- Mahnoor Zulfiqar
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, Jena, Germany
| | - Vinay Singh
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
| | - Christoph Steinbeck
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, Jena, Germany
| | - Maria Sorokina
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
- Data Science and Artificial Intelligence, Research and Development, Pharmaceuticals, Bayer, Berlin, Germany
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28
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Wu S, Feng T, Tang W, Qi C, Gao J, He X, Wang J, Zhou H, Fang Z. metaProbiotics: a tool for mining probiotic from metagenomic binning data based on a language model. Brief Bioinform 2024; 25:bbae085. [PMID: 38487846 PMCID: PMC10940841 DOI: 10.1093/bib/bbae085] [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] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 01/26/2024] [Accepted: 02/15/2024] [Indexed: 03/18/2024] Open
Abstract
Beneficial bacteria remain largely unexplored. Lacking systematic methods, understanding probiotic community traits becomes challenging, leading to various conclusions about their probiotic effects among different publications. We developed language model-based metaProbiotics to rapidly detect probiotic bins from metagenomes, demonstrating superior performance in simulated benchmark datasets. Testing on gut metagenomes from probiotic-treated individuals, it revealed the probioticity of intervention strains-derived bins and other probiotic-associated bins beyond the training data, such as a plasmid-like bin. Analyses of these bins revealed various probiotic mechanisms and bai operon as probiotic Ruminococcaceae's potential marker. In different health-disease cohorts, these bins were more common in healthy individuals, signifying their probiotic role, but relevant health predictions based on the abundance profiles of these bins faced cross-disease challenges. To better understand the heterogeneous nature of probiotics, we used metaProbiotics to construct a comprehensive probiotic genome set from global gut metagenomic data. Module analysis of this set shows that diseased individuals often lack certain probiotic gene modules, with significant variation of the missing modules across different diseases. Additionally, different gene modules on the same probiotic have heterogeneous effects on various diseases. We thus believe that gene function integrity of the probiotic community is more crucial in maintaining gut homeostasis than merely increasing specific gene abundance, and adding probiotics indiscriminately might not boost health. We expect that the innovative language model-based metaProbiotics tool will promote novel probiotic discovery using large-scale metagenomic data and facilitate systematic research on bacterial probiotic effects. The metaProbiotics program can be freely downloaded at https://github.com/zhenchengfang/metaProbiotics.
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Affiliation(s)
- Shufang Wu
- Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Tao Feng
- Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Waijiao Tang
- Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Cancan Qi
- Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Jie Gao
- Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China
- Department of Gastroenterology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xiaolong He
- Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Jiaxuan Wang
- Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Hongwei Zhou
- Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Zhencheng Fang
- Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China
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29
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Kharnaior P, Tamang JP. Microbiome and metabolome in home-made fermented soybean foods of India revealed by metagenome-assembled genomes and metabolomics. Int J Food Microbiol 2023; 407:110417. [PMID: 37774634 DOI: 10.1016/j.ijfoodmicro.2023.110417] [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: 08/08/2023] [Revised: 09/10/2023] [Accepted: 09/22/2023] [Indexed: 10/01/2023]
Abstract
Grep-chhurpi, peha, peron namsing and peruñyaan are lesser-known home-made fermented soybean foods prepared by the native people of Arunachal Pradesh in India. Present work aims to study the microbiome, their functional annotations, metabolites and recovery of metagenome-assembled genomes (MAGs) in these four fermented soybean foods. Metagenomes revealed the dominance of bacteria (97.80 %) with minor traces of viruses, eukaryotes and archaea. Bacillota is the most abundant phylum with Bacillus subtilis as the abundant species. Metagenome also revealed the abundance of lactic acid bacteria such as Enterococcus casseliflavus, Enterococcus faecium, Mammaliicoccus sciuri and Staphylococcus saprophyticus in all samples. B. subtilis was the major species found in all products. Predictive metabolic pathways showed the abundance of genes associated with metabolisms. Metabolomics analysis revealed both targeted and untargeted metabolites, which suggested their role in flavour development and therapeutic properties. High-quality MAGs, identified as B. subtilis, Enterococcus faecalis, Pediococcus acidilactici and B. velezensis, showed the presence of several biomarkers corresponding to various bio-functional properties. Gene clusters of secondary metabolites (antimicrobial peptides) and CRISPR-Cas systems were detected in all MAGs. This present work also provides key elements related to the cultivability of identified species of MAGs for future use as starter cultures in fermented soybean food product development. Additionally, comparison of microbiome and metabolites of grep-chhurpi, peron namsing and peruñyaan with that of other fermented soybean foods of Asia revealed a distinct difference.
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Affiliation(s)
- Pynhunlang Kharnaior
- Department of Microbiology, Sikkim University, Science Building, Tadong 737102, Gangtok, Sikkim, India
| | - Jyoti Prakash Tamang
- Department of Microbiology, Sikkim University, Science Building, Tadong 737102, Gangtok, Sikkim, India.
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30
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Balcha ES, Gómez F, Gemeda MT, Bekele FB, Abera S, Cavalazzi B, Woldesemayat AA. Shotgun Metagenomics-Guided Prediction Reveals the Metal Tolerance and Antibiotic Resistance of Microbes in Poly-Extreme Environments in the Danakil Depression, Afar Region. Antibiotics (Basel) 2023; 12:1697. [PMID: 38136731 PMCID: PMC10740858 DOI: 10.3390/antibiotics12121697] [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: 10/30/2023] [Revised: 11/19/2023] [Accepted: 11/22/2023] [Indexed: 12/24/2023] Open
Abstract
The occurrence and spread of antibiotic resistance genes (ARGs) in environmental microorganisms, particularly in poly-extremophilic bacteria, remain underexplored and have received limited attention. This study aims to investigate the prevalence of ARGs and metal resistance genes (MRGs) in shotgun metagenome sequences obtained from water and salt crust samples collected from Lake Afdera and the Assale salt plain in the Danakil Depression, northern Ethiopia. Potential ARGs were characterized by the comprehensive antibiotic research database (CARD), while MRGs were identified by using BacMetScan V.1.0. A total of 81 ARGs and 39 MRGs were identified at the sampling sites. We found a copA resistance gene for copper and the β-lactam encoding resistance genes were the most abundant the MRG and ARG in the study area. The abundance of MRGs is positively correlated with mercury (Hg) concentration, highlighting the importance of Hg in the selection of MRGs. Significant correlations also exist between heavy metals, Zn and Cd, and ARGs, which suggests that MRGs and ARGs can be co-selected in the environment contaminated by heavy metals. A network analysis revealed that MRGs formed a complex network with ARGs, primarily associated with β-lactams, aminoglycosides, and tetracyclines. This suggests potential co-selection mechanisms, posing concerns for both public health and ecological balance.
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Affiliation(s)
- Ermias Sissay Balcha
- School of Medical Laboratory Science, College of Medicine and Health Sciences, Hawassa University, Hawassa P.O. Box 1560, Ethiopia;
- Department of Biotechnology, College of Biological and Chemical Engineering, Addis Ababa Science and Technology University, Addis Ababa P.O. Box 16417, Ethiopia;
| | - Felipe Gómez
- Centro de Astrobiología (INTA-CSIC) Crtera, Ajalvir km 4 Torrejón de Ardoz, P.O. Box 28850 Madrid, Spain;
| | - Mesfin Tafesse Gemeda
- Department of Biotechnology, College of Biological and Chemical Engineering, Addis Ababa Science and Technology University, Addis Ababa P.O. Box 16417, Ethiopia;
| | - Fanuel Belayneh Bekele
- School of Public Health, College of Medicine and Health Sciences, Hawassa University, Hawassa P.O. Box 1560, Ethiopia;
| | - Sewunet Abera
- Department of Microbial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), P.O. Box 50, 6700 AB Wageningen, The Netherlands;
- Institute of Biology, Leiden University, P.O. Box 9500, 2300 RA Leiden, The Netherlands
- Ethiopian Institute of Agricultural Research (EIAR), Addis Ababa P.O. Box 2003, Ethiopia
| | - Barbara Cavalazzi
- Dipartimento di Scienze Biologiche, Geologiche e Ambientali, Università di Bologna, 40100 Bologna, Italy;
- Department of Geology, University of Johannesburg, Johannesburg P.O. Box 524, South Africa
| | - Adugna Abdi Woldesemayat
- Department of Biotechnology, College of Biological and Chemical Engineering, Addis Ababa Science and Technology University, Addis Ababa P.O. Box 16417, Ethiopia;
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31
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Pernigoni N, Guo C, Gallagher L, Yuan W, Colucci M, Troiani M, Liu L, Maraccani L, Guccini I, Migliorini D, de Bono J, Alimonti A. The potential role of the microbiota in prostate cancer pathogenesis and treatment. Nat Rev Urol 2023; 20:706-718. [PMID: 37491512 DOI: 10.1038/s41585-023-00795-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/15/2023] [Indexed: 07/27/2023]
Abstract
The human body hosts a complex and dynamic population of trillions of microorganisms - the microbiota - which influences the body in homeostasis and disease, including cancer. Several epidemiological studies have associated specific urinary and gut microbial species with increased risk of prostate cancer; however, causal mechanistic data remain elusive. Studies have associated bacterial generation of genotoxins with the occurrence of TMPRSS2-ERG gene fusions, a common, early oncogenic event during prostate carcinogenesis. A subsequent study demonstrated the role of the gut microbiota in prostate cancer endocrine resistance, which occurs, at least partially, through the generation of androgenic steroids fuelling oncogenic signalling via the androgen receptor. These studies present mechanistic evidence of how the host microbiota might be implicated in prostate carcinogenesis and tumour progression. Importantly, these findings also reveal potential avenues for the detection and treatment of prostate cancer through the profiling and modulation of the host microbiota. The latter could involve approaches such as the use of faecal microbiota transplantation, prebiotics, probiotics, postbiotics or antibiotics, which can be used independently or combined with existing treatments to reverse therapeutic resistance and improve clinical outcomes in patients with prostate cancer.
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Affiliation(s)
- Nicolò Pernigoni
- Institute of Oncology Research, Bellinzona, Switzerland
- Faculty of Biomedical Sciences, Università della Svizzera Italiana, Lugano, Switzerland
| | - Christina Guo
- Institute of Cancer Research, London, UK
- Royal Marsden Hospital, London, UK
| | | | - Wei Yuan
- Institute of Cancer Research, London, UK
| | - Manuel Colucci
- Institute of Oncology Research, Bellinzona, Switzerland
- Faculty of Biomedical Sciences, Università della Svizzera Italiana, Lugano, Switzerland
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Martina Troiani
- Institute of Oncology Research, Bellinzona, Switzerland
- Faculty of Biomedical Sciences, Università della Svizzera Italiana, Lugano, Switzerland
| | - Lei Liu
- Institute of Oncology Research, Bellinzona, Switzerland
- Faculty of Biomedical Sciences, Università della Svizzera Italiana, Lugano, Switzerland
| | - Luisa Maraccani
- Institute of Oncology Research, Bellinzona, Switzerland
- Faculty of Biomedical Sciences, Università della Svizzera Italiana, Lugano, Switzerland
- Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Padova, Italy
- Veneto Institute of Molecular Medicine, Padova, Italy
| | - Ilaria Guccini
- Institute of Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
| | - Denis Migliorini
- Department of Oncology, Geneva University Hospitals, Geneva, Switzerland
- Center for Translational Research in Onco-Hematology, University of Geneva, Geneva, Switzerland
- Swiss Cancer Center Léman, Lausanne and Geneva, Geneva, Switzerland
- AGORA Cancer Research Center, Lausanne, Switzerland
| | - Johann de Bono
- Institute of Cancer Research, London, UK
- Royal Marsden Hospital, London, UK
| | - Andrea Alimonti
- Institute of Oncology Research, Bellinzona, Switzerland.
- Faculty of Biomedical Sciences, Università della Svizzera Italiana, Lugano, Switzerland.
- Veneto Institute of Molecular Medicine, Padova, Italy.
- Oncology Institute of Southern Switzerland, EOC, Bellinzona, Switzerland.
- Department of Medicine, University of Padova, Padova, Italy.
- Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.
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Xu D, Zhang X, Yuan X, Han H, Xue Y, Guo X. Hazardous risk of antibiotic resistance genes: Host occurrence, distribution, mobility and vertical transmission from different environments to corn silage. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 338:122671. [PMID: 37788797 DOI: 10.1016/j.envpol.2023.122671] [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: 07/19/2023] [Revised: 09/25/2023] [Accepted: 09/30/2023] [Indexed: 10/05/2023]
Abstract
Antibiotic resistance genes (ARGs) are one of the emerging contaminants posing a great deal of hazardous risk to public health. This study employed metagenomics and deciphered the potential risk of the antibiotic resistome and their vertical transfer to ensiled whole-crop corn silage harvested from six climate zones: 1. Warm temperate-fully humid-hot summer (Cfa), 2. Arid-desert-cold arid (BWk), 3. Snow-desert-cold summer (Dwc), 4. Snow-desert-hot summer (Dwa), 5. Arid-steppe-cold arid (BSk), and 6. Equatorial-desert (Aw) based on the Köppen-Geiger climate classification in China. The findings demonstrate a high diversity of ARGs, which is related to the drug classes of tetracycline, ciprofloxacin, lincosamide, fosfomycin, and beta lactam. Resistome variations are mostly related to variations in microbial composition and fermentation characteristics of the silages from different climate zones, which are indirectly influenced by environmental conditions. The most dominating ARGs in corn silage were tetM, acrA, H-NS, lnuA, emrR, and KpnG, which is primarily hosted by Klebsiella and Lactobacilli. There were 5 high-risk ARGs (tetM, bacA, SHV-1, dfrA17, and QnrS1) in silage from different climate zones, and the tetM was the most prevalent high-risk ARG. However, throughout the ensiling process, the abundance of ARGs, and mobile ARGs were reduced. The resistome contamination in silage from Tibet (Dwc) with high altitude and harsh environment was relatively low due to the low variety and abundance of ARGs, the low abundance of mobile ARGs and high-risk ARGs. In addition, most of the bacteria responsible for the silage fermentation were also found to be the hosts to the ARGs, although their abundance decreased after 90 d of silage fermentation. Hence, we alert the existence of ARGs-related biosafety risk in silages and call for more attention to the silage ARGs, their hosts, and mobile genetic elements in order to curtail their possible risk to public health.
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Affiliation(s)
- Dongmei Xu
- School of Life Sciences, Lanzhou University, Lanzhou, 730000, PR China
| | - Xingguo Zhang
- Bioyi Biotechnology Co., Ltd., Wuhan, 430075, PR China
| | - Xianjun Yuan
- Institute of Ensiling and Processing of Grass, Nanjing Agricultural University, Nanjing, 210095, PR China
| | - Hongyan Han
- The Research Center for Laboratory Animal Science, College of Life Science, Inner Mongolia University, Hohhot, 010070, PR China
| | - Yanlin Xue
- Inner Mongolia Engineering Research Center of Development and Utilization of Microbial Resources in Silage, Inner Mongolia Academy of Agriculture and Animal Husbandry Science, Hohhot, 010031, PR China
| | - Xusheng Guo
- School of Life Sciences, Lanzhou University, Lanzhou, 730000, PR China.
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Pérez G, Krause SMB, Bodelier PLE, Meima-Franke M, Pitombo L, Irisarri P. Interactions between Cyanobacteria and Methane Processing Microbes Mitigate Methane Emissions from Rice Soils. Microorganisms 2023; 11:2830. [PMID: 38137974 PMCID: PMC10745823 DOI: 10.3390/microorganisms11122830] [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: 10/16/2023] [Revised: 11/16/2023] [Accepted: 11/18/2023] [Indexed: 12/24/2023] Open
Abstract
Cyanobacteria play a relevant role in rice soils due to their contribution to soil fertility through nitrogen (N2) fixation and as a promising strategy to mitigate methane (CH4) emissions from these systems. However, information is still limited regarding the mechanisms of cyanobacterial modulation of CH4 cycling in rice soils. Here, we focused on the response of methane cycling microbial communities to inoculation with cyanobacteria in rice soils. We performed a microcosm study comprising rice soil inoculated with either of two cyanobacterial isolates (Calothrix sp. and Nostoc sp.) obtained from a rice paddy. Our results demonstrate that cyanobacterial inoculation reduced CH4 emissions by 20 times. Yet, the effect on CH4 cycling microbes differed for the cyanobacterial strains. Type Ia methanotrophs were stimulated by Calothrix sp. in the surface layer, while Nostoc sp. had the opposite effect. The overall pmoA transcripts of Type Ib methanotrophs were stimulated by Nostoc. Methanogens were not affected in the surface layer, while their abundance was reduced in the sub surface layer by the presence of Nostoc sp. Our results indicate that mitigation of methane emission from rice soils based on cyanobacterial inoculants depends on the proper pairing of cyanobacteria-methanotrophs and their respective traits.
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Affiliation(s)
- Germán Pérez
- Department of Microbial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), 6708 PB Wageningen, The Netherlands or (G.P.); (S.M.B.K.); (M.M.-F.)
- Laboratory of Microbiology, Department of Plant Biology, Agronomy Faculty, University of the Republic, Montevideo 12900, Uruguay;
| | - Sascha M. B. Krause
- Department of Microbial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), 6708 PB Wageningen, The Netherlands or (G.P.); (S.M.B.K.); (M.M.-F.)
- School of Ecology and Environmental Sciences, East China Normal University, Shanghai 200062, China
| | - Paul L. E. Bodelier
- Department of Microbial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), 6708 PB Wageningen, The Netherlands or (G.P.); (S.M.B.K.); (M.M.-F.)
| | - Marion Meima-Franke
- Department of Microbial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), 6708 PB Wageningen, The Netherlands or (G.P.); (S.M.B.K.); (M.M.-F.)
| | - Leonardo Pitombo
- Department of Environmental Sciences, Federal University of São Carlos (UFSCar), São Paulo 18052-780, Brazil;
| | - Pilar Irisarri
- Laboratory of Microbiology, Department of Plant Biology, Agronomy Faculty, University of the Republic, Montevideo 12900, Uruguay;
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Heinken A, Hulshof TO, Nap B, Martinelli F, Basile A, O'Brolchain A, O’Sullivan NF, Gallagher C, Magee E, McDonagh F, Lalor I, Bergin M, Evans P, Daly R, Farrell R, Delaney RM, Hill S, McAuliffe SR, Kilgannon T, Fleming RM, Thinnes CC, Thiele I. APOLLO: A genome-scale metabolic reconstruction resource of 247,092 diverse human microbes spanning multiple continents, age groups, and body sites. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.02.560573. [PMID: 37873072 PMCID: PMC10592896 DOI: 10.1101/2023.10.02.560573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Computational modelling of microbiome metabolism has proved instrumental to catalyse our understanding of diet-host-microbiome-disease interactions through the interrogation of mechanistic, strain- and molecule-resolved metabolic models. We present APOLLO, a resource of 247,092 human microbial genome-scale metabolic reconstructions spanning 19 phyla and accounting for microbial genomes from 34 countries, all age groups, and five body sites. We explored the metabolic potential of the reconstructed strains and developed a machine learning classifier able to predict with high accuracy the taxonomic strain assignments. We also built 14,451 sample-specific microbial community models, which could be stratified by body site, age, and disease states. Finally, we predicted faecal metabolites enriched or depleted in gut microbiomes of people with Crohn's disease, Parkinson disease, and undernourished children. APOLLO is compatible with the human whole-body models, and thus, provide unprecedented opportunities for systems-level modelling of personalised host-microbiome co-metabolism. APOLLO will be freely available under https://www.vmh.life/.
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Affiliation(s)
- Almut Heinken
- School of Medicine, University of Galway, Galway, Ireland
- Ryan Institute, University of Galway, Galway, Ireland
- Inserm UMRS 1256 NGERE, University of Lorraine, Nancy, France
| | - Timothy Otto Hulshof
- School of Medicine, University of Galway, Galway, Ireland
- Ryan Institute, University of Galway, Galway, Ireland
| | - Bram Nap
- School of Medicine, University of Galway, Galway, Ireland
- Ryan Institute, University of Galway, Galway, Ireland
| | - Filippo Martinelli
- School of Medicine, University of Galway, Galway, Ireland
- Ryan Institute, University of Galway, Galway, Ireland
| | - Arianna Basile
- School of Medicine, University of Galway, Galway, Ireland
- Department of Biology, University of Padova, Padova, Italy
| | | | | | | | | | | | - Ian Lalor
- University of Galway, Galway, Ireland
| | | | | | | | | | | | | | | | | | | | - Cyrille C. Thinnes
- School of Medicine, University of Galway, Galway, Ireland
- Ryan Institute, University of Galway, Galway, Ireland
| | - Ines Thiele
- School of Medicine, University of Galway, Galway, Ireland
- Ryan Institute, University of Galway, Galway, Ireland
- Division of Microbiology, University of Galway, Galway, Ireland
- APC Microbiome Ireland, Cork, Ireland
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Hassa J, Tubbesing TJ, Maus I, Heyer R, Benndorf D, Effenberger M, Henke C, Osterholz B, Beckstette M, Pühler A, Sczyrba A, Schlüter A. Uncovering Microbiome Adaptations in a Full-Scale Biogas Plant: Insights from MAG-Centric Metagenomics and Metaproteomics. Microorganisms 2023; 11:2412. [PMID: 37894070 PMCID: PMC10608942 DOI: 10.3390/microorganisms11102412] [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: 08/28/2023] [Revised: 09/15/2023] [Accepted: 09/18/2023] [Indexed: 10/29/2023] Open
Abstract
The current focus on renewable energy in global policy highlights the importance of methane production from biomass through anaerobic digestion (AD). To improve biomass digestion while ensuring overall process stability, microbiome-based management strategies become more important. In this study, metagenomes and metaproteomes were used for metagenomically assembled genome (MAG)-centric analyses to investigate a full-scale biogas plant consisting of three differentially operated digesters. Microbial communities were analyzed regarding their taxonomic composition, functional potential, as well as functions expressed on the proteome level. Different abundances of genes and enzymes related to the biogas process could be mostly attributed to different process parameters. Individual MAGs exhibiting different abundances in the digesters were studied in detail, and their roles in the hydrolysis, acidogenesis and acetogenesis steps of anaerobic digestion could be assigned. Methanoculleus thermohydrogenotrophicum was an active hydrogenotrophic methanogen in all three digesters, whereas Methanothermobacter wolfeii was more prevalent at higher process temperatures. Further analysis focused on MAGs, which were abundant in all digesters, indicating their potential to ensure biogas process stability. The most prevalent MAG belonged to the class Limnochordia; this MAG was ubiquitous in all three digesters and exhibited activity in numerous pathways related to different steps of AD.
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Affiliation(s)
- Julia Hassa
- Genome Research of Industrial Microorganisms, Center for Biotechnology (CeBiTec), Bielefeld University, Universitätsstrasse 27, 33615 Bielefeld, Germany; (J.H.)
| | - Tom Jonas Tubbesing
- Computational Metagenomics Group, Center for Biotechnology (CeBiTec), Bielefeld University, Universitätsstraße 27, 33615 Bielefeld, Germany; (T.J.T.)
| | - Irena Maus
- Genome Research of Industrial Microorganisms, Center for Biotechnology (CeBiTec), Bielefeld University, Universitätsstrasse 27, 33615 Bielefeld, Germany; (J.H.)
| | - Robert Heyer
- Multidimensional Omics Data Analyses Group, Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., Bunsen-Kirchhoff-Straße 11, Dortmund 44139, Germany
- Multidimensional Omics Data Analyses Group, Faculty of Technology, Bielefeld University, Universitätsstraße 25, 33615 Bielefeld, Germany
| | - Dirk Benndorf
- Biosciences and Process Engineering, Anhalt University of Applied Sciences, Bernburger Straße 55, Postfach 1458, 06366 Köthen, Germany
- Bioprocess Engineering, Otto von Guericke University, Universitätsplatz 2, 39106 Magdeburg, Germany
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstraße 1, 39106 Magdeburg, Germany
| | - Mathias Effenberger
- Bavarian State Research Center for Agriculture, Institute for Agricultural Engineering and Animal Husbandry, Vöttinger Straße 36, 85354 Freising, Germany
| | - Christian Henke
- Computational Metagenomics Group, Center for Biotechnology (CeBiTec), Bielefeld University, Universitätsstraße 27, 33615 Bielefeld, Germany; (T.J.T.)
| | - Benedikt Osterholz
- Computational Metagenomics Group, Center for Biotechnology (CeBiTec), Bielefeld University, Universitätsstraße 27, 33615 Bielefeld, Germany; (T.J.T.)
| | - Michael Beckstette
- Computational Metagenomics Group, Center for Biotechnology (CeBiTec), Bielefeld University, Universitätsstraße 27, 33615 Bielefeld, Germany; (T.J.T.)
| | - Alfred Pühler
- Genome Research of Industrial Microorganisms, Center for Biotechnology (CeBiTec), Bielefeld University, Universitätsstrasse 27, 33615 Bielefeld, Germany; (J.H.)
| | - Alexander Sczyrba
- Computational Metagenomics Group, Center for Biotechnology (CeBiTec), Bielefeld University, Universitätsstraße 27, 33615 Bielefeld, Germany; (T.J.T.)
| | - Andreas Schlüter
- Genome Research of Industrial Microorganisms, Center for Biotechnology (CeBiTec), Bielefeld University, Universitätsstrasse 27, 33615 Bielefeld, Germany; (J.H.)
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Adams AK, Kristy BD, Gorman M, Balint-Kurti P, Yencho GC, Olukolu BA. Qmatey: an automated pipeline for fast exact matching-based alignment and strain-level taxonomic binning and profiling of metagenomes. Brief Bioinform 2023; 24:bbad351. [PMID: 37824740 PMCID: PMC10569747 DOI: 10.1093/bib/bbad351] [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: 06/06/2023] [Revised: 08/23/2023] [Accepted: 09/16/2023] [Indexed: 10/14/2023] Open
Abstract
Metagenomics is a powerful tool for understanding organismal interactions; however, classification, profiling and detection of interactions at the strain level remain challenging. We present an automated pipeline, quantitative metagenomic alignment and taxonomic exact matching (Qmatey), that performs a fast exact matching-based alignment and integration of taxonomic binning and profiling. It interrogates large databases without using metagenome-assembled genomes, curated pan-genes or k-mer spectra that limit resolution. Qmatey minimizes misclassification and maintains strain level resolution by using only diagnostic reads as shown in the analysis of amplicon, quantitative reduced representation and shotgun sequencing datasets. Using Qmatey to analyze shotgun data from a synthetic community with 35% of the 26 strains at low abundance (0.01-0.06%), we revealed a remarkable 85-96% strain recall and 92-100% species recall while maintaining 100% precision. Benchmarking revealed that the highly ranked Kraken2 and KrakenUniq tools identified 2-4 more taxa (92-100% recall) than Qmatey but produced 315-1752 false positive taxa and high penalty on precision (1-8%). The speed, accuracy and precision of the Qmatey pipeline positions it as a valuable tool for broad-spectrum profiling and for uncovering biologically relevant interactions.
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Affiliation(s)
- Alison K Adams
- Department of Entomology and Plant Pathology, University of Tennessee, Knoxville, TN 37996, USA
- UT-ORNL Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN 37996, USA
| | - Brandon D Kristy
- Department of Integrative Biology, Michigan State University, East Lansing, MI, USA
- W.K. Kellogg Biological Station, Michigan State University, Hickory Corners, MI, USA
| | - Myranda Gorman
- Department of Animal Science, University of Tennessee, Knoxville, TN 37996, USA
- College of Veterinary Medicine, University of Tennessee, Knoxville, TN 37996, USA
| | - Peter Balint-Kurti
- Department of Entomology and Plant Pathology, NC State University, Raleigh, NC 27695-7613, USA
- Plant Science Research Unit, USDA-ARS, Raleigh, NC, USA
| | - G Craig Yencho
- Department of Horticultural Science, NC State University, Raleigh, NC 27695-7609, USA
| | - Bode A Olukolu
- Department of Entomology and Plant Pathology, University of Tennessee, Knoxville, TN 37996, USA
- UT-ORNL Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN 37996, USA
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37
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Seong HJ, Kim JJ, Sul WJ. ACR: metagenome-assembled prokaryotic and eukaryotic genome refinement tool. Brief Bioinform 2023; 24:bbad381. [PMID: 37889119 DOI: 10.1093/bib/bbad381] [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: 06/20/2023] [Revised: 09/16/2023] [Accepted: 10/03/2023] [Indexed: 10/28/2023] Open
Abstract
Microbial genome recovery from metagenomes can further explain microbial ecosystem structures, functions and dynamics. Thus, this study developed the Additional Clustering Refiner (ACR) to enhance high-purity prokaryotic and eukaryotic metagenome-assembled genome (MAGs) recovery. ACR refines low-quality MAGs by subjecting them to iterative k-means clustering predicated on contig abundance and increasing bin purity through validated universal marker genes. Synthetic and real-world metagenomic datasets, including short- and long-read sequences, evaluated ACR's effectiveness. The results demonstrated improved MAG purity and a significant increase in high- and medium-quality MAG recovery rates. In addition, ACR seamlessly integrates with various binning algorithms, augmenting their strengths without modifying core features. Furthermore, its multiple sequencing technology compatibilities expand its applicability. By efficiently recovering high-quality prokaryotic and eukaryotic genomes, ACR is a promising tool for deepening our understanding of microbial communities through genome-centric metagenomics.
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Affiliation(s)
- Hoon Je Seong
- Korean Medicine Data Division, Korea Institute of Oriental Medicine, Daejeon, Republic of Korea
| | - Jin Ju Kim
- Department of Systems Biotechnology, Chung-Ang University, Anseong, Republic of Korea
| | - Woo Jun Sul
- Department of Systems Biotechnology, Chung-Ang University, Anseong, Republic of Korea
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Cui Z, Wu Y, Zhang QH, Wang SG, He Y, Huang DS. MV-CVIB: a microbiome-based multi-view convolutional variational information bottleneck for predicting metastatic colorectal cancer. Front Microbiol 2023; 14:1238199. [PMID: 37675425 PMCID: PMC10477591 DOI: 10.3389/fmicb.2023.1238199] [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: 06/11/2023] [Accepted: 08/02/2023] [Indexed: 09/08/2023] Open
Abstract
Introduction Imbalances in gut microbes have been implied in many human diseases, including colorectal cancer (CRC), inflammatory bowel disease, type 2 diabetes, obesity, autism, and Alzheimer's disease. Compared with other human diseases, CRC is a gastrointestinal malignancy with high mortality and a high probability of metastasis. However, current studies mainly focus on the prediction of colorectal cancer while neglecting the more serious malignancy of metastatic colorectal cancer (mCRC). In addition, high dimensionality and small samples lead to the complexity of gut microbial data, which increases the difficulty of traditional machine learning models. Methods To address these challenges, we collected and processed 16S rRNA data and calculated abundance data from patients with non-metastatic colorectal cancer (non-mCRC) and mCRC. Different from the traditional health-disease classification strategy, we adopted a novel disease-disease classification strategy and proposed a microbiome-based multi-view convolutional variational information bottleneck (MV-CVIB). Results The experimental results show that MV-CVIB can effectively predict mCRC. This model can achieve AUC values above 0.9 compared to other state-of-the-art models. Not only that, MV-CVIB also achieved satisfactory predictive performance on multiple published CRC gut microbiome datasets. Discussion Finally, multiple gut microbiota analyses were used to elucidate communities and differences between mCRC and non-mCRC, and the metastatic properties of CRC were assessed by patient age and microbiota expression.
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Affiliation(s)
- Zhen Cui
- Institute of Machine Learning and Systems Biology, College of Electronics and Information Engineering, Tongji University, Shanghai, China
| | - Yan Wu
- College of Electronics and Information Engineering, Tongji University, Shanghai, China
| | - Qin-Hu Zhang
- EIT Institute for Advanced Study, Ningbo, Zhejiang, China
| | - Si-Guo Wang
- Institute of Machine Learning and Systems Biology, College of Electronics and Information Engineering, Tongji University, Shanghai, China
| | - Ying He
- Institute of Machine Learning and Systems Biology, College of Electronics and Information Engineering, Tongji University, Shanghai, China
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Martinez-Hernandez JE, Berrios P, Santibáñez R, Cuesta Astroz Y, Sanchez C, Martin AJM, Trombert AN. First metagenomic analysis of the Andean condor ( Vultur gryphus) gut microbiome reveals microbial diversity and wide resistome. PeerJ 2023; 11:e15235. [PMID: 37434868 PMCID: PMC10332357 DOI: 10.7717/peerj.15235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 03/28/2023] [Indexed: 07/13/2023] Open
Abstract
Background The Andean condor (Vultur gryphus) is the largest scavenger in South America. This predatory bird plays a crucial role in their ecological niche by removing carcasses. We report the first metagenomic analysis of the Andean condor gut microbiome. Methods This work analyzed shotgun metagenomics data from a mixture of fifteen captive Chilean Andean condors. To filter eukaryote contamination, we employed BWA-MEM v0.7. Taxonomy assignment was performed using Kraken2 and MetaPhlAn v2.0 and all filtered reads were assembled using IDBA-UD v1.1.3. The two most abundant species were used to perform a genome reference-guided assembly using MetaCompass. Finally, we performed a gene prediction using Prodigal and each gene predicted was functionally annotated. InterproScan v5.31-70.0 was additionally used to detect homology based on protein domains and KEGG mapper software for reconstructing metabolic pathways. Results Our results demonstrate concordance with the other gut microbiome data from New World vultures. In the Andean condor, Firmicutes was the most abundant phylum present, with Clostridium perfringens, a potentially pathogenic bacterium for other animals, as dominating species in the gut microbiome. We assembled all reads corresponding to the top two species found in the condor gut microbiome, finding between 94% to 98% of completeness for Clostridium perfringens and Plesiomonas shigelloides, respectively. Our work highlights the ability of the Andean condor to act as an environmental reservoir and potential vector for critical priority pathogens which contain relevant genetic elements. Among these genetic elements, we found 71 antimicrobial resistance genes and 1,786 virulence factors that we associated with several adaptation processes.
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Affiliation(s)
- J. Eduardo Martinez-Hernandez
- Laboratorio de Redes Biológicas, Centro Científico y Tecnológico de Excelencia Ciencia & Vida, Fundación Ciencia & Vida, Santiago, Chile
- CGNA (Agriaquaculture Nutritional Genomic Center), Temuco, Chile
| | - Pablo Berrios
- Escuela de Biotecnología, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago, Región Metropolitana, Chile
| | - Rodrigo Santibáñez
- Laboratorio de Redes Biológicas, Centro Científico y Tecnológico de Excelencia Ciencia & Vida, Fundación Ciencia & Vida, Santiago, Chile
| | - Yesid Cuesta Astroz
- Instituto Colombiano de Medicina Tropical, Universidad CES, Sabaneta, Colombia
| | - Carolina Sanchez
- Centro de Oncología de Precisión, Escuela de Medicina, Universidad Mayor, Santiago, Chile
- Advanced Genomics Core, Universidad Mayor, Santiago, Chile
| | - Alberto J. M. Martin
- Laboratorio de Redes Biológicas, Centro Científico y Tecnológico de Excelencia Ciencia & Vida, Fundación Ciencia & Vida, Santiago, Chile
- Escuela de Ingeniería, Facultad de Ingeniería, Arquitectura y Diseño, Universidad San Sebastián, Santiago, Chile
| | - Annette N. Trombert
- Escuela de Biotecnología, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago, Región Metropolitana, Chile
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Shangpliang HNJ, Tamang JP. Metagenome-assembled genomes for biomarkers of bio-functionalities in Laal dahi, an Indian ethnic fermented milk product. Int J Food Microbiol 2023; 402:110300. [PMID: 37364321 DOI: 10.1016/j.ijfoodmicro.2023.110300] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Revised: 06/15/2023] [Accepted: 06/18/2023] [Indexed: 06/28/2023]
Abstract
Laal dahi is a sweetened and soft pudding-like fermented milk product of the Eastern regions of India, which has not been studied for its microbial community structures and health promoting functionality in terms of 'omics' approaches. We applied metagenomic and metagenomes-assembled genomes (MAGs) tools to decipher the biomarkers for genes encoding for different health promoting functionalities in laal dahi. Abundance of bacterial domains was observed with negligible presence of eukaryotes and viruses. Bacillota was the most abundant phylum with different bacterial species viz., Enterococcus italicus, Lactococcus raffinolactis, Lactobacillus helveticus, Bifidobacterium mongoliense, Hafnia alvei, Lactococcus lactis, Acetobacter okinawensis, Streptococcus thermophilus, Thermus thermophilus, Leuconostoc citreum, Leuconostoc pseudomesenteroides, Acetobacter orientalis, Lactobacillus gallinarum, Lactococcus chungangensis and Lactobacillus delbrueckii. Comparison of laal dahi microbiome with that of similar fermented milk products was also carried out after retrieving the metagenomic datasets from public databases. Significant abundance of Lb. helveticus, E. italicus, Lc. raffinolactis and Lc. lactis in laal dahi. Interestingly, Bifidobacterium mongoliense, Lb. gallinarum, Lc. chungangensis and Acetobacter okinawensis were only detected in laal dahi but Streptococcus infantarius, Lacticaseibacillus rhamnosus and Lb. johnsonii were absent. Reconstruction of putative single environment-specific genomes from metagenomes in addition to subsampling of the abundant species resulted in five high-quality MAGs identified as Lactobacillus delbrueckii, Lactobacillus helveticus, Lactococcus chungangensis, Lactococcus lactis and Streptococcus thermophilus. All MAGs showed the presence of various genes with several putative functions corresponding to different probiotic and prebiotic functions, short-chain fatty acids production, immunomodulation, antitumor genes, essential amino acid and vitamin biosynthesis. Genes for γ-Aminobutyric acid (GABA) production were only detected in MAG of Lactococcus lactis. Gene clusters for secondary metabolites (antimicrobial peptides) were detected in all MAGs except Lc. chungangensis. Additionally, detection of clustered regularly interspaced short palindromic repeats (CRISPR)-associated (Cas) elements was observed only in Lactobacillus delbrueckii and Streptococcus thermophilus. Annotation of several genes with potential health beneficial properties in all five MAGs may support the need to explore the culturability of these MAGs for future use in controlled fermentation of functional dairy products.
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Affiliation(s)
| | - Jyoti Prakash Tamang
- Department of Microbiology, School of Life Sciences, Sikkim University, Tadong, Gangtok 737102, Sikkim, India.
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Wirth R, Bagi Z, Shetty P, Szuhaj M, Cheung TTS, Kovács KL, Maróti G. Inter-kingdom interactions and stability of methanogens revealed by machine-learning guided multi-omics analysis of industrial-scale biogas plants. THE ISME JOURNAL 2023:10.1038/s41396-023-01448-3. [PMID: 37286740 DOI: 10.1038/s41396-023-01448-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 05/23/2023] [Accepted: 05/26/2023] [Indexed: 06/09/2023]
Abstract
Multi-omics analysis is a powerful tool for the detection and study of inter-kingdom interactions, such as those between bacterial and archaeal members of complex biogas-producing microbial communities. In the present study, the microbiomes of three industrial-scale biogas digesters, each fed with different substrates, were analysed using a machine-learning guided genome-centric metagenomics framework complemented with metatranscriptome data. This data permitted us to elucidate the relationship between abundant core methanogenic communities and their syntrophic bacterial partners. In total, we detected 297 high-quality, non-redundant metagenome-assembled genomes (nrMAGs). Moreover, the assembled 16 S rRNA gene profiles of these nrMAGs showed that the phylum Firmicutes possessed the highest copy number, while the representatives of the archaeal domain had the lowest. Further investigation of the three anaerobic microbial communities showed characteristic alterations over time but remained specific to each industrial-scale biogas plant. The relative abundance of various microorganisms as revealed by metagenome data was independent from corresponding metatranscriptome activity data. Archaea showed considerably higher activity than was expected from their abundance. We detected 51 nrMAGs that were present in all three biogas plant microbiomes with different abundances. The core microbiome correlated with the main chemical fermentation parameters, and no individual parameter emerged as a predominant shaper of community composition. Various interspecies H2/electron transfer mechanisms were assigned to hydrogenotrophic methanogens in the biogas plants that ran on agricultural biomass and wastewater. Analysis of metatranscriptome data revealed that methanogenesis pathways were the most active of all main metabolic pathways.
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Affiliation(s)
- Roland Wirth
- Institute of Plant Biology, Biological Research Centre, Szeged, Hungary
- Department of Biotechnology, University of Szeged, Szeged, Hungary
| | - Zoltán Bagi
- Department of Biotechnology, University of Szeged, Szeged, Hungary
| | - Prateek Shetty
- Institute of Plant Biology, Biological Research Centre, Szeged, Hungary
| | - Márk Szuhaj
- Department of Biotechnology, University of Szeged, Szeged, Hungary
| | | | - Kornél L Kovács
- Department of Biotechnology, University of Szeged, Szeged, Hungary
| | - Gergely Maróti
- Institute of Plant Biology, Biological Research Centre, Szeged, Hungary.
- Faculty of Water Sciences, University of Public Service, Baja, Hungary.
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Nam NN, Do HDK, Loan Trinh KT, Lee NY. Metagenomics: An Effective Approach for Exploring Microbial Diversity and Functions. Foods 2023; 12:2140. [PMID: 37297385 PMCID: PMC10252221 DOI: 10.3390/foods12112140] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 05/21/2023] [Accepted: 05/24/2023] [Indexed: 06/12/2023] Open
Abstract
Various fields have been identified in the "omics" era, such as genomics, proteomics, transcriptomics, metabolomics, phenomics, and metagenomics. Among these, metagenomics has enabled a significant increase in discoveries related to the microbial world. Newly discovered microbiomes in different ecologies provide meaningful information on the diversity and functions of microorganisms on the Earth. Therefore, the results of metagenomic studies have enabled new microbe-based applications in human health, agriculture, and the food industry, among others. This review summarizes the fundamental procedures on recent advances in bioinformatic tools. It also explores up-to-date applications of metagenomics in human health, food study, plant research, environmental sciences, and other fields. Finally, metagenomics is a powerful tool for studying the microbial world, and it still has numerous applications that are currently hidden and awaiting discovery. Therefore, this review also discusses the future perspectives of metagenomics.
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Affiliation(s)
- Nguyen Nhat Nam
- Biotechnology Center, School of Agriculture and Aquaculture, Tra Vinh University, Tra Vinh City 87000, Vietnam
| | - Hoang Dang Khoa Do
- NTT Hi-Tech Institute, Nguyen Tat Thanh University, Ward 13, District 04, Ho Chi Minh City 72820, Vietnam
| | - Kieu The Loan Trinh
- Department of BioNano Technology, Gachon University 1342 Seongnam-daero, Sujeong-gu, Seongnam-si 13120, Republic of Korea;
| | - Nae Yoon Lee
- Department of BioNano Technology, Gachon University 1342 Seongnam-daero, Sujeong-gu, Seongnam-si 13120, Republic of Korea;
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Chen Y, Knight R, Gallo RL. Evolving approaches to profiling the microbiome in skin disease. Front Immunol 2023; 14:1151527. [PMID: 37081873 PMCID: PMC10110978 DOI: 10.3389/fimmu.2023.1151527] [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] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 03/14/2023] [Indexed: 04/22/2023] Open
Abstract
Despite its harsh and dry environment, human skin is home to diverse microbes, including bacteria, fungi, viruses, and microscopic mites. These microbes form communities that may exist at the skin surface, deeper skin layers, and within microhabitats such as the hair follicle and sweat glands, allowing complex interactions with the host immune system. Imbalances in the skin microbiome, known as dysbiosis, have been linked to various inflammatory skin disorders, including atopic dermatitis, acne, and psoriasis. The roles of abundant commensal bacteria belonging to Staphylococcus and Cutibacterium taxa and the fungi Malassezia, where particular species or strains can benefit the host or cause disease, are increasingly appreciated in skin disorders. Furthermore, recent research suggests that the interactions between microorganisms and the host's immune system on the skin can have distant and systemic effects on the body, such as on the gut and brain, known as the "skin-gut" or "skin-brain" axes. Studies on the microbiome in skin disease have typically relied on 16S rRNA gene sequencing methods, which cannot provide accurate information about species or strains of microorganisms on the skin. However, advancing technologies, including metagenomics and other functional 'omic' approaches, have great potential to provide more comprehensive and detailed information about the skin microbiome in health and disease. Additionally, inter-species and multi-kingdom interactions can cause cascading shifts towards dysbiosis and are crucial but yet-to-be-explored aspects of many skin disorders. Better understanding these complex dynamics will require meta-omic studies complemented with experiments and clinical trials to confirm function. Evolving how we profile the skin microbiome alongside technological advances is essential to exploring such relationships. This review presents the current and emerging methods and their findings for profiling skin microbes to advance our understanding of the microbiome in skin disease.
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Affiliation(s)
- Yang Chen
- Department of Dermatology, University of California San Diego, La Jolla, CA, United States
- Department of Pediatrics, University of California San Diego, La Jolla, CA, United States
- Biomedical Sciences Graduate Program, University of California San Diego, La Jolla, CA, United States
| | - Rob Knight
- Department of Pediatrics, University of California San Diego, La Jolla, CA, United States
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, United States
- Department of Bioengineering, University of California San Diego, La Jolla, CA, United States
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, United States
| | - Richard L. Gallo
- Department of Dermatology, University of California San Diego, La Jolla, CA, United States
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, United States
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Kruasuwan W, Jenjaroenpun P, Arigul T, Chokesajjawatee N, Leekitcharoenphon P, Foongladda S, Wongsurawat T. Nanopore Sequencing Discloses Compositional Quality of Commercial Probiotic Feed Supplements. Sci Rep 2023; 13:4540. [PMID: 36941307 PMCID: PMC10027865 DOI: 10.1038/s41598-023-31626-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Accepted: 03/15/2023] [Indexed: 03/23/2023] Open
Abstract
The market for the application of probiotics as a livestock health improvement supplement has increased in recent years. However, most of the available products are quality-controlled using low-resolution techniques and un-curated databases, resulting in misidentification and incorrect product labels. In this work, we deployed two workflows and compared results obtained by full-length 16S rRNA genes (16S) and metagenomic (Meta) data to investigate their reliability for the microbial composition of both liquid and solid forms of animal probiotic products using Oxford Nanopore long-read-only (without short-read). Our result revealed that 16S amplicon data permits to detect the bacterial microbiota even with the low abundance in the samples. Moreover, the 16S approach has the potential to provide species-level resolution for prokaryotes but not for assessing yeast communities. Whereas, Meta data has more power to recover of high-quality metagenome-assembled genomes that enables detailed exploration of both bacterial and yeast populations, as well as antimicrobial resistance genes, and functional genes in the population. Our findings clearly demonstrate that implementing these workflows with long-read-only monitoring could be applied to assessing the quality and safety of probiotic products for animals and evaluating the quality of probiotic products on the market. This would benefit the sustained growth of the livestock probiotic industry.
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Affiliation(s)
- Worarat Kruasuwan
- Division of Medical Bioinformatics, Research Department, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
- Siriraj Long-Read Lab (Si-LoL), Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Piroon Jenjaroenpun
- Division of Medical Bioinformatics, Research Department, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
- Siriraj Long-Read Lab (Si-LoL), Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Tantip Arigul
- Division of Medical Bioinformatics, Research Department, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
- Siriraj Long-Read Lab (Si-LoL), Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Nipa Chokesajjawatee
- National Center for Genetic Engineering and Biotechnology (BIOTEC), 113 Thailand Science Park, Phahonyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani, Thailand
| | - Pimlapas Leekitcharoenphon
- Research Group for Genomic Epidemiology, National Food Institute, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark
| | - Suporn Foongladda
- Department of Microbiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Thidathip Wongsurawat
- Division of Medical Bioinformatics, Research Department, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.
- Siriraj Long-Read Lab (Si-LoL), Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
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Baltoumas FA, Karatzas E, Paez-Espino D, Venetsianou NK, Aplakidou E, Oulas A, Finn RD, Ovchinnikov S, Pafilis E, Kyrpides NC, Pavlopoulos GA. Exploring microbial functional biodiversity at the protein family level-From metagenomic sequence reads to annotated protein clusters. FRONTIERS IN BIOINFORMATICS 2023; 3:1157956. [PMID: 36959975 PMCID: PMC10029925 DOI: 10.3389/fbinf.2023.1157956] [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: 02/07/2023] [Accepted: 02/21/2023] [Indexed: 03/06/2023] Open
Abstract
Metagenomics has enabled accessing the genetic repertoire of natural microbial communities. Metagenome shotgun sequencing has become the method of choice for studying and classifying microorganisms from various environments. To this end, several methods have been developed to process and analyze the sequence data from raw reads to end-products such as predicted protein sequences or families. In this article, we provide a thorough review to simplify such processes and discuss the alternative methodologies that can be followed in order to explore biodiversity at the protein family level. We provide details for analysis tools and we comment on their scalability as well as their advantages and disadvantages. Finally, we report the available data repositories and recommend various approaches for protein family annotation related to phylogenetic distribution, structure prediction and metadata enrichment.
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Affiliation(s)
- Fotis A. Baltoumas
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari, Greece
| | - Evangelos Karatzas
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari, Greece
| | - David Paez-Espino
- Lawrence Berkeley National Laboratory, DOE Joint Genome Institute, Berkeley, CA, United States
| | - Nefeli K. Venetsianou
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari, Greece
| | - Eleni Aplakidou
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari, Greece
| | - Anastasis Oulas
- The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Robert D. Finn
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, United Kingdom
| | - Sergey Ovchinnikov
- John Harvard Distinguished Science Fellowship Program, Harvard University, Cambridge, MA, United States
| | - Evangelos Pafilis
- Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Heraklion, Greece
| | - Nikos C. Kyrpides
- Lawrence Berkeley National Laboratory, DOE Joint Genome Institute, Berkeley, CA, United States
| | - Georgios A. Pavlopoulos
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari, Greece
- Center of New Biotechnologies and Precision Medicine, Department of Medicine, School of Health Sciences, National and Kapodistrian University of Athens, Athens, Greece
- Hellenic Army Academy, Vari, Greece
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Mendoza RM, Kim SH, Vasquez R, Hwang IC, Park YS, Paik HD, Moon GS, Kang DK. Bioinformatics and its role in the study of the evolution and probiotic potential of lactic acid bacteria. Food Sci Biotechnol 2023; 32:389-412. [PMID: 36911331 PMCID: PMC9992694 DOI: 10.1007/s10068-022-01142-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 06/30/2022] [Accepted: 07/13/2022] [Indexed: 11/04/2022] Open
Abstract
Due to their numerous well-established applications in the food industry, there have been many studies regarding the adaptation and evolution of lactic acid bacteria (LAB) in a wide variety of hosts and environments. Progress in sequencing technology and continual decreases in its costs have led to the availability of LAB genome sequence data. Bioinformatics has been central to the extraction of valuable information from these raw genome sequence data. This paper presents the roles of bioinformatics tools and databases in understanding the adaptation and evolution of LAB, as well as the bioinformatics methods used in the initial screening of LAB for probiotic potential. Moreover, the advantages, challenges, and limitations of employing bioinformatics for these purposes are discussed.
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Affiliation(s)
- Remilyn M. Mendoza
- Department of Animal Resources Science, Dankook University, 119 Dandae-ro, Cheonan, 31116 Republic of Korea
| | - Sang Hoon Kim
- Department of Animal Resources Science, Dankook University, 119 Dandae-ro, Cheonan, 31116 Republic of Korea
| | - Robie Vasquez
- Department of Animal Resources Science, Dankook University, 119 Dandae-ro, Cheonan, 31116 Republic of Korea
| | - In-Chan Hwang
- Department of Animal Resources Science, Dankook University, 119 Dandae-ro, Cheonan, 31116 Republic of Korea
| | - Young-Seo Park
- Department of Food Science and Biotechnology, Gachon University, Seongnam, 13120 Republic of Korea
| | - Hyun-Dong Paik
- Department of Food Science and Biotechnology of Animal Resource, Konkuk University, Seoul, 05029 Republic of Korea
| | - Gi-Seong Moon
- Division of Food Science and Biotechnology, Korea National University of Transportation, Jeungpyeong, 27909 Republic of Korea
| | - Dae-Kyung Kang
- Department of Animal Resources Science, Dankook University, 119 Dandae-ro, Cheonan, 31116 Republic of Korea
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47
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O’Connor L, Heyderman R. The challenges of defining the human nasopharyngeal resistome. Trends Microbiol 2023:S0966-842X(23)00056-2. [DOI: 10.1016/j.tim.2023.02.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Revised: 02/16/2023] [Accepted: 02/20/2023] [Indexed: 04/03/2023]
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Ibañez-Lligoña M, Colomer-Castell S, González-Sánchez A, Gregori J, Campos C, Garcia-Cehic D, Andrés C, Piñana M, Pumarola T, Rodríguez-Frias F, Antón A, Quer J. Bioinformatic Tools for NGS-Based Metagenomics to Improve the Clinical Diagnosis of Emerging, Re-Emerging and New Viruses. Viruses 2023; 15:v15020587. [PMID: 36851800 PMCID: PMC9965957 DOI: 10.3390/v15020587] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 02/16/2023] [Accepted: 02/17/2023] [Indexed: 02/24/2023] Open
Abstract
Epidemics and pandemics have occurred since the beginning of time, resulting in millions of deaths. Many such disease outbreaks are caused by viruses. Some viruses, particularly RNA viruses, are characterized by their high genetic variability, and this can affect certain phenotypic features: tropism, antigenicity, and susceptibility to antiviral drugs, vaccines, and the host immune response. The best strategy to face the emergence of new infectious genomes is prompt identification. However, currently available diagnostic tests are often limited for detecting new agents. High-throughput next-generation sequencing technologies based on metagenomics may be the solution to detect new infectious genomes and properly diagnose certain diseases. Metagenomic techniques enable the identification and characterization of disease-causing agents, but they require a large amount of genetic material and involve complex bioinformatic analyses. A wide variety of analytical tools can be used in the quality control and pre-processing of metagenomic data, filtering of untargeted sequences, assembly and quality control of reads, and taxonomic profiling of sequences to identify new viruses and ones that have been sequenced and uploaded to dedicated databases. Although there have been huge advances in the field of metagenomics, there is still a lack of consensus about which of the various approaches should be used for specific data analysis tasks. In this review, we provide some background on the study of viral infections, describe the contribution of metagenomics to this field, and place special emphasis on the bioinformatic tools (with their capabilities and limitations) available for use in metagenomic analyses of viral pathogens.
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Affiliation(s)
- Marta Ibañez-Lligoña
- Liver Diseases-Viral Hepatitis, Liver Unit, Vall d’Hebron Institut de Recerca (VHIR), Vall d’Hebron Hospital Universitari, Vall d’Hebron Barcelona Hospital Campus, Passeig Vall d’Hebron 119-129, 08035 Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Instituto de Salud Carlos III, Av. Monforte de Lemos, 3-5, 28029 Madrid, Spain
- Biochemistry and Molecular Biology Department, Universitat Autònoma de Barcelona (UAB), Campus de la UAB, Plaça Cívica, 08193 Bellaterra, Spain
| | - Sergi Colomer-Castell
- Liver Diseases-Viral Hepatitis, Liver Unit, Vall d’Hebron Institut de Recerca (VHIR), Vall d’Hebron Hospital Universitari, Vall d’Hebron Barcelona Hospital Campus, Passeig Vall d’Hebron 119-129, 08035 Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Instituto de Salud Carlos III, Av. Monforte de Lemos, 3-5, 28029 Madrid, Spain
- Biochemistry and Molecular Biology Department, Universitat Autònoma de Barcelona (UAB), Campus de la UAB, Plaça Cívica, 08193 Bellaterra, Spain
| | - Alejandra González-Sánchez
- Microbiology Department, Vall d’Hebron Institut de Recerca (VHIR), Vall d’Hebron Hospital Universitari, Vall d’Hebron Barcelona Hospital Campus, Passeig Vall d’Hebron 119-129, 08035 Barcelona, Spain
| | - Josep Gregori
- Liver Diseases-Viral Hepatitis, Liver Unit, Vall d’Hebron Institut de Recerca (VHIR), Vall d’Hebron Hospital Universitari, Vall d’Hebron Barcelona Hospital Campus, Passeig Vall d’Hebron 119-129, 08035 Barcelona, Spain
| | - Carolina Campos
- Liver Diseases-Viral Hepatitis, Liver Unit, Vall d’Hebron Institut de Recerca (VHIR), Vall d’Hebron Hospital Universitari, Vall d’Hebron Barcelona Hospital Campus, Passeig Vall d’Hebron 119-129, 08035 Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Instituto de Salud Carlos III, Av. Monforte de Lemos, 3-5, 28029 Madrid, Spain
- Biochemistry and Molecular Biology Department, Universitat Autònoma de Barcelona (UAB), Campus de la UAB, Plaça Cívica, 08193 Bellaterra, Spain
| | - Damir Garcia-Cehic
- Liver Diseases-Viral Hepatitis, Liver Unit, Vall d’Hebron Institut de Recerca (VHIR), Vall d’Hebron Hospital Universitari, Vall d’Hebron Barcelona Hospital Campus, Passeig Vall d’Hebron 119-129, 08035 Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Instituto de Salud Carlos III, Av. Monforte de Lemos, 3-5, 28029 Madrid, Spain
| | - Cristina Andrés
- Microbiology Department, Vall d’Hebron Institut de Recerca (VHIR), Vall d’Hebron Hospital Universitari, Vall d’Hebron Barcelona Hospital Campus, Passeig Vall d’Hebron 119-129, 08035 Barcelona, Spain
| | - Maria Piñana
- Microbiology Department, Vall d’Hebron Institut de Recerca (VHIR), Vall d’Hebron Hospital Universitari, Vall d’Hebron Barcelona Hospital Campus, Passeig Vall d’Hebron 119-129, 08035 Barcelona, Spain
| | - Tomàs Pumarola
- Microbiology Department, Vall d’Hebron Institut de Recerca (VHIR), Vall d’Hebron Hospital Universitari, Vall d’Hebron Barcelona Hospital Campus, Passeig Vall d’Hebron 119-129, 08035 Barcelona, Spain
- Microbiology Department, Universitat Autònoma de Barcelona (UAB), Campus de la UAB, Plaça Cívica, 08193 Bellaterra, Spain
| | - Francisco Rodríguez-Frias
- Liver Diseases-Viral Hepatitis, Liver Unit, Vall d’Hebron Institut de Recerca (VHIR), Vall d’Hebron Hospital Universitari, Vall d’Hebron Barcelona Hospital Campus, Passeig Vall d’Hebron 119-129, 08035 Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Instituto de Salud Carlos III, Av. Monforte de Lemos, 3-5, 28029 Madrid, Spain
- Department of Basic Sciences, Universitat Internacional de Catalunya, Sant Cugat del Vallès, 08195 Barcelona, Spain
| | - Andrés Antón
- Microbiology Department, Vall d’Hebron Institut de Recerca (VHIR), Vall d’Hebron Hospital Universitari, Vall d’Hebron Barcelona Hospital Campus, Passeig Vall d’Hebron 119-129, 08035 Barcelona, Spain
- Microbiology Department, Universitat Autònoma de Barcelona (UAB), Campus de la UAB, Plaça Cívica, 08193 Bellaterra, Spain
| | - Josep Quer
- Liver Diseases-Viral Hepatitis, Liver Unit, Vall d’Hebron Institut de Recerca (VHIR), Vall d’Hebron Hospital Universitari, Vall d’Hebron Barcelona Hospital Campus, Passeig Vall d’Hebron 119-129, 08035 Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Instituto de Salud Carlos III, Av. Monforte de Lemos, 3-5, 28029 Madrid, Spain
- Biochemistry and Molecular Biology Department, Universitat Autònoma de Barcelona (UAB), Campus de la UAB, Plaça Cívica, 08193 Bellaterra, Spain
- Correspondence:
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Keithley AE, Ryu H, Gomez-Alvarez V, Harmon S, Bennett-Stamper C, Williams D, Lytle DA. Comprehensive characterization of aerobic groundwater biotreatment media. WATER RESEARCH 2023; 230:119587. [PMID: 36638728 PMCID: PMC10119871 DOI: 10.1016/j.watres.2023.119587] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 01/04/2023] [Accepted: 01/05/2023] [Indexed: 06/17/2023]
Abstract
Aerobic biotreatment systems can treat multiple reduced inorganic contaminants in groundwater, including ammonia (NH3), arsenic (As), iron (Fe), and manganese (Mn). While individual systems treating multiple contaminants simultaneously have been characterized and several systems treating one contaminant have been compared, a comparison of systems treating co-occurring contaminants is lacking. This study assessed the treatment performance and microbial communities within 7 pilot- and full-scale groundwater biotreatment systems in the United States that treated waters with pH 5.6-7.8, 0.1-2.0 mg/L dissolved oxygen, 75-376 mg CaCO3/L alkalinity, < 0.03-3.79 mg NH3-N/L, < 4-31 µg As/L, < 0.01-9.37 mg Fe/L, 2-1220 µg Mn/L, and 0.1-5.6 mg/L total organic carbon (TOC). Different reactor configurations and media types were represented, allowing for a broad assessment of linkages between water quality and microbial communities via microscopy, biofilm quantification, and molecular methods. Influent NH3, TOC, and pH contributed to differences in the microbial communities. Mn oxidase gene copy numbers were slightly negatively correlated with the influent Mn concentration, but no significant relationships between gene copy number and influent concentration were observed for the other contaminants. Extracellular enzyme activities, community composition, and carbon transformation pathways suggested heterotrophic bacteria may be important in nitrifying biofilters. Aerobic groundwater biofilters are complex, and improved understanding could lead to engineering enhancements.
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Affiliation(s)
- Asher E Keithley
- ORD, CESER, WID, Drinking Water Management Branch, U.S. Environmental Protection Agency, 26W. Martin Luther King Dr., Cincinnati, OH 45268, United States.
| | - Hodon Ryu
- ORD, CESER, WID, Drinking Water Management Branch, U.S. Environmental Protection Agency, 26W. Martin Luther King Dr., Cincinnati, OH 45268, United States
| | - Vicente Gomez-Alvarez
- ORD, CESER, WID, Drinking Water Management Branch, U.S. Environmental Protection Agency, 26W. Martin Luther King Dr., Cincinnati, OH 45268, United States
| | - Stephen Harmon
- ORD, CESER, WID, Drinking Water Management Branch, U.S. Environmental Protection Agency, 26W. Martin Luther King Dr., Cincinnati, OH 45268, United States
| | - Christina Bennett-Stamper
- ORD, CESER, WID, Drinking Water Management Branch, U.S. Environmental Protection Agency, 26W. Martin Luther King Dr., Cincinnati, OH 45268, United States
| | - Daniel Williams
- ORD, CESER, WID, Drinking Water Management Branch, U.S. Environmental Protection Agency, 26W. Martin Luther King Dr., Cincinnati, OH 45268, United States
| | - Darren A Lytle
- ORD, CESER, WID, Drinking Water Management Branch, U.S. Environmental Protection Agency, 26W. Martin Luther King Dr., Cincinnati, OH 45268, United States
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50
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Metagenomic Antimicrobial Susceptibility Testing from Simulated Native Patient Samples. Antibiotics (Basel) 2023; 12:antibiotics12020366. [PMID: 36830277 PMCID: PMC9952719 DOI: 10.3390/antibiotics12020366] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 02/06/2023] [Accepted: 02/08/2023] [Indexed: 02/12/2023] Open
Abstract
Genomic antimicrobial susceptibility testing (AST) has been shown to be accurate for many pathogens and antimicrobials. However, these methods have not been systematically evaluated for clinical metagenomic data. We investigate the performance of in-silico AST from clinical metagenomes (MG-AST). Using isolate sequencing data from a multi-center study on antimicrobial resistance (AMR) as well as shotgun-sequenced septic urine samples, we simulate over 2000 complicated urinary tract infection (cUTI) metagenomes with known resistance phenotype to 5 antimicrobials. Applying rule-based and machine learning-based genomic AST classifiers, we explore the impact of sequencing depth and technology, metagenome complexity, and bioinformatics processing approaches on AST accuracy. By using an optimized metagenomics assembly and binning workflow, MG-AST achieved balanced accuracy within 5.1% of isolate-derived genomic AST. For poly-microbial infections, taxonomic sample complexity and relatedness of taxa in the sample is a key factor influencing metagenomic binning and downstream MG-AST accuracy. We show that the reassignment of putative plasmid contigs by their predicted host range and investigation of whole resistome capabilities improved MG-AST performance on poly-microbial samples. We further demonstrate that machine learning-based methods enable MG-AST with superior accuracy compared to rule-based approaches on simulated native patient samples.
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