1
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Trecarten S, Fongang B, Liss M. Current Trends and Challenges of Microbiome Research in Prostate Cancer. Curr Oncol Rep 2024; 26:477-487. [PMID: 38573440 DOI: 10.1007/s11912-024-01520-x] [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] [Accepted: 03/18/2024] [Indexed: 04/05/2024]
Abstract
PURPOSE OF REVIEW The role of the gut microbiome in prostate cancer is an emerging area of research interest. However, no single causative organism has yet been identified. The goal of this paper is to examine the role of the microbiome in prostate cancer and summarize the challenges relating to methodology in specimen collection, sequencing technology, and interpretation of results. RECENT FINDINGS Significant heterogeneity still exists in methodology for stool sampling/storage, preservative options, DNA extraction, and sequencing database selection/in silico processing. Debate persists over primer choice in amplicon sequencing as well as optimal methods for data normalization. Statistical methods for longitudinal microbiome analysis continue to undergo refinement. While standardization of methodology may help yield more consistent results for organism identification in prostate cancer, this is a difficult task due to considerable procedural variation at each step in the process. Further reproducibility and methodology research is required.
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Affiliation(s)
- Shaun Trecarten
- Department of Urology, UT Health San Antonio, 7703 Floyd Curl Dr, San Antonio, TX, 78229, USA
| | - Bernard Fongang
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, UT Health San Antonio, San Antonio, TX, USA
- Department of Biochemistry and Structural Biology, UT Health San Antonio, San Antonio, TX, USA
- Department of Population Health Sciences, UT Health San Antonio, San Antonio, TX, USA
| | - Michael Liss
- Department of Urology, UT Health San Antonio, 7703 Floyd Curl Dr, San Antonio, TX, 78229, USA.
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2
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Blanco-Míguez A, Beghini F, Cumbo F, McIver LJ, Thompson KN, Zolfo M, Manghi P, Dubois L, Huang KD, Thomas AM, Nickols WA, Piccinno G, Piperni E, Punčochář M, Valles-Colomer M, Tett A, Giordano F, Davies R, Wolf J, Berry SE, Spector TD, Franzosa EA, Pasolli E, Asnicar F, Huttenhower C, Segata N. Extending and improving metagenomic taxonomic profiling with uncharacterized species using MetaPhlAn 4. Nat Biotechnol 2023; 41:1633-1644. [PMID: 36823356 PMCID: PMC10635831 DOI: 10.1038/s41587-023-01688-w] [Citation(s) in RCA: 140] [Impact Index Per Article: 140.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 01/20/2023] [Indexed: 02/25/2023]
Abstract
Metagenomic assembly enables new organism discovery from microbial communities, but it can only capture few abundant organisms from most metagenomes. Here we present MetaPhlAn 4, which integrates information from metagenome assemblies and microbial isolate genomes for more comprehensive metagenomic taxonomic profiling. From a curated collection of 1.01 M prokaryotic reference and metagenome-assembled genomes, we define unique marker genes for 26,970 species-level genome bins, 4,992 of them taxonomically unidentified at the species level. MetaPhlAn 4 explains ~20% more reads in most international human gut microbiomes and >40% in less-characterized environments such as the rumen microbiome and proves more accurate than available alternatives on synthetic evaluations while also reliably quantifying organisms with no cultured isolates. Application of the method to >24,500 metagenomes highlights previously undetected species to be strong biomarkers for host conditions and lifestyles in human and mouse microbiomes and shows that even previously uncharacterized species can be genetically profiled at the resolution of single microbial strains.
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Affiliation(s)
| | | | - Fabio Cumbo
- Department CIBIO, University of Trento, Trento, Italy
| | - Lauren J McIver
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kelsey N Thompson
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Moreno Zolfo
- Department CIBIO, University of Trento, Trento, Italy
| | - Paolo Manghi
- Department CIBIO, University of Trento, Trento, Italy
| | | | - Kun D Huang
- Department CIBIO, University of Trento, Trento, Italy
| | | | - William A Nickols
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Elisa Piperni
- Department CIBIO, University of Trento, Trento, Italy
- IEO, European Institute of Oncology IRCCS, Milan, Italy
| | | | | | - Adrian Tett
- Department CIBIO, University of Trento, Trento, Italy
- Centre for Microbiology and Environmental Systems Science, University of Vienna, Vienna, Austria
| | | | | | | | - Sarah E Berry
- Department of Nutritional Sciences, King's College London, London, UK
| | - Tim D Spector
- Department of Twin Research, King's College London, London, UK
| | - Eric A Franzosa
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Edoardo Pasolli
- Department of Agricultural Sciences, University of Naples, Naples, Italy
| | | | - Curtis Huttenhower
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nicola Segata
- Department CIBIO, University of Trento, Trento, Italy.
- IEO, European Institute of Oncology IRCCS, Milan, Italy.
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3
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Kropochev AI, Lashin SA, Matushkin YG, Klimenko AI. Trait-Based Method of Quantitative Assessment of Ecological Functional Groups in the Human Intestinal Microbiome. BIOLOGY 2023; 12:biology12010115. [PMID: 36671807 PMCID: PMC9855786 DOI: 10.3390/biology12010115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 12/15/2022] [Accepted: 12/30/2022] [Indexed: 01/15/2023]
Abstract
We propose the trait-based method for quantifying the activity of functional groups in the human gut microbiome based on metatranscriptomic data. It allows one to assess structural changes in the microbial community comprised of the following functional groups: butyrate-producers, acetogens, sulfate-reducers, and mucin-decomposing bacteria. It is another way to perform a functional analysis of metatranscriptomic data by focusing on the ecological level of the community under study. To develop the method, we used published data obtained in a carefully controlled environment and from a synthetic microbial community, where the problem of ambiguity between functionality and taxonomy is absent. The developed method was validated using RNA-seq data and sequencing data of the 16S rRNA amplicon on a simplified community. Consequently, the successful verification provides prospects for the application of this method for analyzing natural communities of the human intestinal microbiota.
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Affiliation(s)
- Andrew I. Kropochev
- Institute of Cytology and Genetics, Novosibirsk 630090, Russia
- Kurchatov Genomic Center of ICG SB RAS, Novosibirsk 630090, Russia
- Correspondence:
| | - Sergey A. Lashin
- Institute of Cytology and Genetics, Novosibirsk 630090, Russia
- Kurchatov Genomic Center of ICG SB RAS, Novosibirsk 630090, Russia
- Department of Natural Sciences, Novosibirsk State University, Novosibirsk 630090, Russia
| | - Yury G. Matushkin
- Institute of Cytology and Genetics, Novosibirsk 630090, Russia
- Department of Natural Sciences, Novosibirsk State University, Novosibirsk 630090, Russia
| | - Alexandra I. Klimenko
- Institute of Cytology and Genetics, Novosibirsk 630090, Russia
- Kurchatov Genomic Center of ICG SB RAS, Novosibirsk 630090, Russia
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4
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Strain level microbial detection and quantification with applications to single cell metagenomics. Nat Commun 2022; 13:6430. [PMID: 36307411 PMCID: PMC9616933 DOI: 10.1038/s41467-022-33869-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 10/04/2022] [Indexed: 12/25/2022] Open
Abstract
Computational identification and quantification of distinct microbes from high throughput sequencing data is crucial for our understanding of human health. Existing methods either use accurate but computationally expensive alignment-based approaches or less accurate but computationally fast alignment-free approaches, which often fail to correctly assign reads to genomes. Here we introduce CAMMiQ, a combinatorial optimization framework to identify and quantify distinct genomes (specified by a database) in a metagenomic dataset. As a key methodological innovation, CAMMiQ uses substrings of variable length and those that appear in two genomes in the database, as opposed to the commonly used fixed-length, unique substrings. These substrings allow to accurately decouple mixtures of highly similar genomes resulting in higher accuracy than the leading alternatives, without requiring additional computational resources, as demonstrated on commonly used benchmarking datasets. Importantly, we show that CAMMiQ can distinguish closely related bacterial strains in simulated metagenomic and real single-cell metatranscriptomic data.
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5
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Hoarfrost A, Aptekmann A, Farfañuk G, Bromberg Y. Deep learning of a bacterial and archaeal universal language of life enables transfer learning and illuminates microbial dark matter. Nat Commun 2022; 13:2606. [PMID: 35545619 PMCID: PMC9095714 DOI: 10.1038/s41467-022-30070-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 03/30/2022] [Indexed: 12/22/2022] Open
Abstract
The majority of microbial genomes have yet to be cultured, and most proteins identified in microbial genomes or environmental sequences cannot be functionally annotated. As a result, current computational approaches to describe microbial systems rely on incomplete reference databases that cannot adequately capture the functional diversity of the microbial tree of life, limiting our ability to model high-level features of biological sequences. Here we present LookingGlass, a deep learning model encoding contextually-aware, functionally and evolutionarily relevant representations of short DNA reads, that distinguishes reads of disparate function, homology, and environmental origin. We demonstrate the ability of LookingGlass to be fine-tuned via transfer learning to perform a range of diverse tasks: to identify novel oxidoreductases, to predict enzyme optimal temperature, and to recognize the reading frames of DNA sequence fragments. LookingGlass enables functionally relevant representations of otherwise unknown and unannotated sequences, shedding light on the microbial dark matter that dominates life on Earth. Computational methods to analyse microbial systems rely on reference databases which do not capture their full functional diversity. Here the authors develop a deep learning model and apply it using transfer learning, creating biologically useful models for multiple different tasks.
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Affiliation(s)
- A Hoarfrost
- Department of Marine and Coastal Sciences, Rutgers University, 71 Dudley Road, New Brunswick, NJ, 08873, USA. .,NASA Ames Research Center, Moffett Field, CA, 94035, USA.
| | - A Aptekmann
- Department of Biochemistry and Microbiology, Rutgers University, 76 Lipman Dr, New Brunswick, NJ, 08901, USA
| | - G Farfañuk
- Department of Biological Chemistry, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Y Bromberg
- Department of Biochemistry and Microbiology, Rutgers University, 76 Lipman Dr, New Brunswick, NJ, 08901, USA.
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6
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Ekim B, Berger B, Chikhi R. Minimizer-space de Bruijn graphs: Whole-genome assembly of long reads in minutes on a personal computer. Cell Syst 2021; 12:958-968.e6. [PMID: 34525345 PMCID: PMC8562525 DOI: 10.1016/j.cels.2021.08.009] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 08/01/2021] [Accepted: 08/19/2021] [Indexed: 10/20/2022]
Abstract
DNA sequencing data continue to progress toward longer reads with increasingly lower sequencing error rates. Here, we define an algorithmic approach, mdBG, that makes use of minimizer-space de Bruijn graphs to enable long-read genome assembly. mdBG achieves orders-of-magnitude improvement in both speed and memory usage over existing methods without compromising accuracy. A human genome is assembled in under 10 min using 8 cores and 10 GB RAM, and 60 Gbp of metagenome reads are assembled in 4 min using 1 GB RAM. In addition, we constructed a minimizer-space de Bruijn graph-based representation of 661,405 bacterial genomes, comprising 16 million nodes and 45 million edges, and successfully search it for anti-microbial resistance (AMR) genes in 12 min. We expect our advances to be essential to sequence analysis, given the rise of long-read sequencing in genomics, metagenomics, and pangenomics. Code for constructing mdBGs is freely available for download at https://github.com/ekimb/rust-mdbg/.
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Affiliation(s)
- Barış Ekim
- Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA; Department of Mathematics, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
| | - Bonnie Berger
- Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA; Department of Mathematics, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA.
| | - Rayan Chikhi
- Department of Computational Biology, Institut Pasteur, Paris 75015, France.
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7
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Nielsen SD, Pearson NM, Seidler K. The link between the gut microbiota and Parkinson's Disease: A systematic mechanism review with focus on α-synuclein transport. Brain Res 2021; 1769:147609. [PMID: 34371014 DOI: 10.1016/j.brainres.2021.147609] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 07/29/2021] [Accepted: 08/01/2021] [Indexed: 10/20/2022]
Abstract
INTRODUCTION Research has suggested a link between the gut microbiota and Parkinson's Disease (PD), and an early involvement of gastrointestinal dysfunction has been reported in patients. A mechanism review was performed to investigate whether the neurodegenerative cascade begins in the gut; mediated by gut dysbiosis and retrograde transport of α-synuclein. This review provides a summary of microbiome composition associated with PD, and evaluates pathophysiological mechanisms from animal and in vitro models of PD. METHOD A systematic literature search was performed in PubMed; 82 of 299 papers met the inclusion criteria. RESULTS All twenty-two human case-control studies demonstrated an altered gut microbiota in PD compared to healthy controls, with results suggesting a proinflammatory phenotype present in PD. A germ-free animal study has demonstrated that gut microbiota are required for microglia activation, α-synuclein pathology and motor deficits. Accumulation of phosphorylated α-synuclein has been observed in the enteric nervous system prior to the onset of motor symptoms in animal models of PD, and there is data to support retrograde transport of α-synuclein from the gut to the brain. Different animal models of PD have demonstrated neuroinflammation, microglial activation and loss of dopaminergic neurons in the brain. CONCLUSION Evidence from this review supports the hypothesis that pathology spreads from the gut to the brain. Future animal studies using oral LPS or microbiota transplants from human PD cases could provide further insight into the entire mechanism. Prospective longitudinal microbiome studies and novel modelling approaches could help to identify functional dysbiosis and early biomarkers for PD.
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Affiliation(s)
- Sophie D Nielsen
- Centre for Nutrition Education and Lifestyle Management (CNELM), Chapel Garden, 14 Rectory Road, Wokingham, Berkshire RG40 1DH, UK.
| | - Nicola M Pearson
- Centre for Nutrition Education and Lifestyle Management (CNELM), Chapel Garden, 14 Rectory Road, Wokingham, Berkshire RG40 1DH, UK
| | - Karin Seidler
- Centre for Nutrition Education and Lifestyle Management (CNELM), Chapel Garden, 14 Rectory Road, Wokingham, Berkshire RG40 1DH, UK
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8
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Zhang Y, Jing G, Chen Y, Li J, Su X. Hierarchical Meta-Storms enables comprehensive and rapid comparison of microbiome functional profiles on a large scale using hierarchical dissimilarity metrics and parallel computing. BIOINFORMATICS ADVANCES 2021; 1:vbab003. [PMID: 36700101 PMCID: PMC9710644 DOI: 10.1093/bioadv/vbab003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 05/06/2021] [Indexed: 01/28/2023]
Abstract
Functional beta-diversity analysis on numerous microbiomes interprets the linkages between metabolic functions and their meta-data. To evaluate the microbiome beta-diversity, widely used distance metrices only count overlapped gene families but omit their inherent relationships, resulting in erroneous distances due to the sparsity of high-dimensional function profiles. Here we propose Hierarchical Meta-Storms (HMS) to tackle such problem. HMS contains two core components: (i) a dissimilarity algorithm that comprehensively measures functional distances among microbiomes using multi-level metabolic hierarchy and (ii) a fast Principal Co-ordinates Analysis (PCoA) implementation that deduces the beta-diversity pattern optimized by parallel computing. Results showed HMS can detect the variations of microbial functions in upper-level metabolic pathways, however, always missed by other methods. In addition, HMS accomplished the pairwise distance matrix and PCoA for 20 000 microbiomes in 3.9 h on a single computing node, which was 23 times faster and 80% less RAM consumption compared to existing methods, enabling the in-depth data mining among microbiomes on a high resolution. HMS takes microbiome functional profiles as input, produces their pairwise distance matrix and PCoA coordinates. Availability and implementation It is coded in C/C++ with parallel computing and released in two alternative forms: a standalone software (https://github.com/qdu-bioinfo/hierarchical-meta-storms) and an equivalent R package (https://github.com/qdu-bioinfo/hrms). Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
- Yufeng Zhang
- College of Computer Science and Technology, Qingdao University, Qingdao, Shandong 266071, China
| | - Gongchao Jing
- Single-Cell Center, Qingdao Institute of BioEnergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong 266101, China
| | - Yuzhu Chen
- College of Computer Science and Technology, Qingdao University, Qingdao, Shandong 266071, China
| | - Jinhua Li
- College of Computer Science and Technology, Qingdao University, Qingdao, Shandong 266071, China,To whom correspondence should be addressed. or Jinhua Li
| | - Xiaoquan Su
- College of Computer Science and Technology, Qingdao University, Qingdao, Shandong 266071, China,Single-Cell Center, Qingdao Institute of BioEnergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong 266101, China,To whom correspondence should be addressed. or Jinhua Li
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9
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Beghini F, McIver LJ, Blanco-Míguez A, Dubois L, Asnicar F, Maharjan S, Mailyan A, Manghi P, Scholz M, Thomas AM, Valles-Colomer M, Weingart G, Zhang Y, Zolfo M, Huttenhower C, Franzosa EA, Segata N. Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3. eLife 2021; 10:65088. [PMID: 33944776 PMCID: PMC8096432 DOI: 10.7554/elife.65088] [Citation(s) in RCA: 704] [Impact Index Per Article: 234.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Accepted: 04/21/2021] [Indexed: 02/06/2023] Open
Abstract
Culture-independent analyses of microbial communities have progressed dramatically in the last decade, particularly due to advances in methods for biological profiling via shotgun metagenomics. Opportunities for improvement continue to accelerate, with greater access to multi-omics, microbial reference genomes, and strain-level diversity. To leverage these, we present bioBakery 3, a set of integrated, improved methods for taxonomic, strain-level, functional, and phylogenetic profiling of metagenomes newly developed to build on the largest set of reference sequences now available. Compared to current alternatives, MetaPhlAn 3 increases the accuracy of taxonomic profiling, and HUMAnN 3 improves that of functional potential and activity. These methods detected novel disease-microbiome links in applications to CRC (1262 metagenomes) and IBD (1635 metagenomes and 817 metatranscriptomes). Strain-level profiling of an additional 4077 metagenomes with StrainPhlAn 3 and PanPhlAn 3 unraveled the phylogenetic and functional structure of the common gut microbe Ruminococcus bromii, previously described by only 15 isolate genomes. With open-source implementations and cloud-deployable reproducible workflows, the bioBakery 3 platform can help researchers deepen the resolution, scale, and accuracy of multi-omic profiling for microbial community studies.
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Affiliation(s)
| | - Lauren J McIver
- Harvard T.H. Chan School of Public Health, Boston, United States
| | | | | | | | - Sagun Maharjan
- Harvard T.H. Chan School of Public Health, Boston, United States.,The Broad Institute of MIT and Harvard, Cambridge, United States
| | - Ana Mailyan
- Harvard T.H. Chan School of Public Health, Boston, United States.,The Broad Institute of MIT and Harvard, Cambridge, United States
| | - Paolo Manghi
- Department CIBIO, University of Trento, Trento, Italy
| | - Matthias Scholz
- Department of Food Quality and Nutrition, Research and Innovation Center, Edmund Mach Foundation, San Michele all'Adige, Italy
| | | | | | - George Weingart
- Harvard T.H. Chan School of Public Health, Boston, United States.,The Broad Institute of MIT and Harvard, Cambridge, United States
| | - Yancong Zhang
- Harvard T.H. Chan School of Public Health, Boston, United States.,The Broad Institute of MIT and Harvard, Cambridge, United States
| | - Moreno Zolfo
- Department CIBIO, University of Trento, Trento, Italy
| | - Curtis Huttenhower
- Harvard T.H. Chan School of Public Health, Boston, United States.,The Broad Institute of MIT and Harvard, Cambridge, United States
| | - Eric A Franzosa
- Harvard T.H. Chan School of Public Health, Boston, United States.,The Broad Institute of MIT and Harvard, Cambridge, United States
| | - Nicola Segata
- Department CIBIO, University of Trento, Trento, Italy.,IEO, European Institute of Oncology IRCCS, Milan, Italy
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10
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Xing Z, Zhang Y, Li M, Guo C, Mi S. RBUD: A New Functional Potential Analysis Approach for Whole Microbial Genome Shotgun Sequencing. Microorganisms 2020; 8:E1563. [PMID: 33050530 PMCID: PMC7650719 DOI: 10.3390/microorganisms8101563] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 10/04/2020] [Accepted: 10/06/2020] [Indexed: 11/16/2022] Open
Abstract
Whole metagenome shotgun sequencing is a powerful approach to detect the functional potential of microbial communities. Currently, the read-based metagenomics profiling for established database (RBED) method is one of the two kinds of conventional methods for species and functional annotations. However, the databases, which are established based on test samples or specific reference genomes or protein sequences, limit the coverage of global microbial diversity. The other assembly-based metagenomics profiling for unestablished database (ABUD) method has a low utilization rate of reads, resulting in a lot of biological information loss. In this study, we proposed a new method, read-based metagenomics profiling for unestablished database (RBUD), based on Metagenome Database of Global Microorganisms (MDGM), to solve the above problems. To evaluate the accuracy and effectiveness of our method, the intestinal bacterial composition and function analyses were performed in both avian colibacillosis chicken cases and type 2 diabetes mellitus patients. Comparing to the existing methods, RBUD is superior in detecting proteins, percentage of reads mapping and ontological similarity of intestinal microbes. The results of RBUD are in better agreement with the classical functional studies on these two diseases. RBUD also has the advantages of fast analysis speed and is not limited by the sample size.
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Affiliation(s)
- Zhikai Xing
- Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, China National Center for Bioinformation, Beijing 100101, China; (Z.X.); (Y.Z.); (M.L.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yunting Zhang
- Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, China National Center for Bioinformation, Beijing 100101, China; (Z.X.); (Y.Z.); (M.L.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Meng Li
- Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, China National Center for Bioinformation, Beijing 100101, China; (Z.X.); (Y.Z.); (M.L.)
| | - Chongye Guo
- Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, China National Center for Bioinformation, Beijing 100101, China; (Z.X.); (Y.Z.); (M.L.)
| | - Shuangli Mi
- Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, China National Center for Bioinformation, Beijing 100101, China; (Z.X.); (Y.Z.); (M.L.)
- University of Chinese Academy of Sciences, Beijing 100049, China
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11
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Pérez-Cobas AE, Gomez-Valero L, Buchrieser C. Metagenomic approaches in microbial ecology: an update on whole-genome and marker gene sequencing analyses. Microb Genom 2020; 6:mgen000409. [PMID: 32706331 PMCID: PMC7641418 DOI: 10.1099/mgen.0.000409] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Accepted: 06/30/2020] [Indexed: 12/23/2022] Open
Abstract
Metagenomics and marker gene approaches, coupled with high-throughput sequencing technologies, have revolutionized the field of microbial ecology. Metagenomics is a culture-independent method that allows the identification and characterization of organisms from all kinds of samples. Whole-genome shotgun sequencing analyses the total DNA of a chosen sample to determine the presence of micro-organisms from all domains of life and their genomic content. Importantly, the whole-genome shotgun sequencing approach reveals the genomic diversity present, but can also give insights into the functional potential of the micro-organisms identified. The marker gene approach is based on the sequencing of a specific gene region. It allows one to describe the microbial composition based on the taxonomic groups present in the sample. It is frequently used to analyse the biodiversity of microbial ecosystems. Despite its importance, the analysis of metagenomic sequencing and marker gene data is quite a challenge. Here we review the primary workflows and software used for both approaches and discuss the current challenges in the field.
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Affiliation(s)
- Ana Elena Pérez-Cobas
- Institut Pasteur, Biologie des Bactéries Intracellulaires, Paris, France and CNRS UMR 3525, 675724, Paris, France
| | - Laura Gomez-Valero
- Institut Pasteur, Biologie des Bactéries Intracellulaires, Paris, France and CNRS UMR 3525, 675724, Paris, France
| | - Carmen Buchrieser
- Institut Pasteur, Biologie des Bactéries Intracellulaires, Paris, France and CNRS UMR 3525, 675724, Paris, France
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12
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Zhu C, Miller M, Zeng Z, Wang Y, Mahlich Y, Aptekmann A, Bromberg Y. Computational Approaches for Unraveling the Effects of Variation in the Human Genome and Microbiome. Annu Rev Biomed Data Sci 2020. [DOI: 10.1146/annurev-biodatasci-030320-041014] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
The past two decades of analytical efforts have highlighted how much more remains to be learned about the human genome and, particularly, its complex involvement in promoting disease development and progression. While numerous computational tools exist for the assessment of the functional and pathogenic effects of genome variants, their precision is far from satisfactory, particularly for clinical use. Accumulating evidence also suggests that the human microbiome's interaction with the human genome plays a critical role in determining health and disease states. While numerous microbial taxonomic groups and molecular functions of the human microbiome have been associated with disease, the reproducibility of these findings is lacking. The human microbiome–genome interaction in healthy individuals is even less well understood. This review summarizes the available computational methods built to analyze the effect of variation in the human genome and microbiome. We address the applicability and precision of these methods across their possible uses. We also briefly discuss the exciting, necessary, and now possible integration of the two types of data to improve the understanding of pathogenicity mechanisms.
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Affiliation(s)
- Chengsheng Zhu
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey 08873, USA;,
| | - Maximilian Miller
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey 08873, USA;,
| | - Zishuo Zeng
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey 08873, USA;,
| | - Yanran Wang
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey 08873, USA;,
| | - Yannick Mahlich
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey 08873, USA;,
| | - Ariel Aptekmann
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey 08873, USA;,
| | - Yana Bromberg
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey 08873, USA;,
- Department of Genetics, Rutgers University, Piscataway, New Jersey 08854, USA
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