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Spatz S, Afonso CL. Non-Targeted RNA Sequencing: Towards the Development of Universal Clinical Diagnosis Methods for Human and Veterinary Infectious Diseases. Vet Sci 2024; 11:239. [PMID: 38921986 DOI: 10.3390/vetsci11060239] [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: 04/16/2024] [Revised: 05/22/2024] [Accepted: 05/23/2024] [Indexed: 06/27/2024] Open
Abstract
Metagenomics offers the potential to replace and simplify classical methods used in the clinical diagnosis of human and veterinary infectious diseases. Metagenomics boasts a high pathogen discovery rate and high specificity, advantages absent in most classical approaches. However, its widespread adoption in clinical settings is still pending, with a slow transition from research to routine use. While longer turnaround times and higher costs were once concerns, these issues are currently being addressed by automation, better chemistries, improved sequencing platforms, better databases, and automated bioinformatics analysis. However, many technical options and steps, each producing highly variable outcomes, have reduced the technology's operational value, discouraging its implementation in diagnostic labs. We present a case for utilizing non-targeted RNA sequencing (NT-RNA-seq) as an ideal metagenomics method for the detection of infectious disease-causing agents in humans and animals. Additionally, to create operational value, we propose to identify best practices for the "core" of steps that are invariably shared among many human and veterinary protocols. Reference materials, sequencing procedures, and bioinformatics standards should accelerate the validation processes necessary for the widespread adoption of this technology. Best practices could be determined through "implementation research" by a consortium of interested institutions working on common samples.
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Affiliation(s)
- Stephen Spatz
- Southeast Poultry Research Laboratory, Agricultural Research Service, United States Department of Agriculture, 934 College Station Road, Athens, GA 30605, USA
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2
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Wanghu H, Li Y, Huang J, Pu K, Guo F, Zhong P, Wang T, Yuan J, Yu Y, Chen J, Liu J, Chen JJ, Hu C. A novel synthetic nucleic acid mixture for quantification of microbes by mNGS. Microb Genom 2024; 10:001199. [PMID: 38358316 PMCID: PMC10926700 DOI: 10.1099/mgen.0.001199] [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: 09/24/2023] [Accepted: 01/29/2024] [Indexed: 02/16/2024] Open
Abstract
Metagenomic next-generation sequencing (mNGS) provides considerable advantages in identifying emerging and re-emerging, difficult-to-detect and co-infected pathogens; however, the clinical application of mNGS remains limited primarily due to the lack of quantitative capabilities. This study introduces a novel approach, KingCreate-Quantification (KCQ) system, for quantitative analysis of microbes in clinical specimens by mNGS, which co-sequence the target DNA extracted from the specimens along with a set of synthetic dsDNA molecules used as Internal-Standard (IS). The assay facilitates the conversion of microbial reads into their copy numbers based on IS reads utilizing a mathematical model proposed in this study. The performance of KCQ was systemically evaluated using commercial mock microbes with varying IS input amounts, different proportions of human genomic DNA, and at varying amounts of sequence analysis data. Subsequently, KCQ was applied in microbial quantitation in 36 clinical specimens including blood, bronchoalveolar lavage fluid, cerebrospinal fluid and oropharyngeal swabs. A total of 477 microbe genetic fragments were screened using the bioinformatic system. Of these 83 fragments were quantitatively compared with digital droplet PCR (ddPCR), revealing a correlation coefficient of 0.97 between the quantitative results of KCQ and ddPCR. Our study demonstrated that KCQ presents a practical approach for the quantitative analysis of microbes by mNGS in clinical samples.
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Affiliation(s)
- Hailing Wanghu
- Guangzhou KingCreate Biotechnology Co., Ltd., Guangzhou, Guangdong, 510005, PR China
| | - Yingzhen Li
- Guangzhou KingCreate Biotechnology Co., Ltd., Guangzhou, Guangdong, 510005, PR China
| | - Jin Huang
- Guangzhou KingCreate Biotechnology Co., Ltd., Guangzhou, Guangdong, 510005, PR China
| | - Kangze Pu
- Guangzhou KingCreate Biotechnology Co., Ltd., Guangzhou, Guangdong, 510005, PR China
| | - Fengming Guo
- Guangzhou KingCreate Biotechnology Co., Ltd., Guangzhou, Guangdong, 510005, PR China
| | - Peiwen Zhong
- Guangzhou KingCreate Biotechnology Co., Ltd., Guangzhou, Guangdong, 510005, PR China
| | - Ting Wang
- Guangzhou KingCreate Biotechnology Co., Ltd., Guangzhou, Guangdong, 510005, PR China
| | - Jianying Yuan
- Guangzhou KingCreate Biotechnology Co., Ltd., Guangzhou, Guangdong, 510005, PR China
| | - Yan Yu
- Changsha KingMed Diagnostics Group Co., Ltd., Changsha, Huna, 410000, PR China
| | - Jiachang Chen
- Guangzhou KingCreate Biotechnology Co., Ltd., Guangzhou, Guangdong, 510005, PR China
| | - Jun Liu
- Guangzhou KingCreate Biotechnology Co., Ltd., Guangzhou, Guangdong, 510005, PR China
| | - Jason J. Chen
- Guangzhou KingCreate Biotechnology Co., Ltd., Guangzhou, Guangdong, 510005, PR China
- KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, Guangdong, 511436, PR China
| | - Chaohui Hu
- Guangzhou KingCreate Biotechnology Co., Ltd., Guangzhou, Guangdong, 510005, PR China
- KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, Guangdong, 511436, PR China
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3
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Huttenhower C, Finn RD, McHardy AC. Challenges and opportunities in sharing microbiome data and analyses. Nat Microbiol 2023; 8:1960-1970. [PMID: 37783751 DOI: 10.1038/s41564-023-01484-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 08/28/2023] [Indexed: 10/04/2023]
Abstract
Microbiome data, metadata and analytical workflows have become 'big' in terms of volume and complexity. Although the infrastructure and technologies to share data have been established, the interdisciplinary and multi-omic nature of the field can make resources difficult to identify and use. Following best practices for data deposition requires substantial effort, with sometimes little obvious reward. Gaps remain where microbiome-specific resources for data sharing or reproducibility do not yet exist. We outline available best practices, challenges to their adoption and opportunities in data sharing in microbiome research. We showcase examples of best practices and advocate for their enforcement and incentivization for data sharing. This includes recognition of data curation and sharing endeavours by individuals, institutions, journals and funders. Opportunities for progress include enabling microbiome-specific databases to incorporate future methods for data analysis, integration and reuse.
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Affiliation(s)
- Curtis Huttenhower
- Harvard Chan Microbiome in Public Health Center, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Departments of Biostatistics and Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Robert D Finn
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Alice Carolyn McHardy
- Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany.
- Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Braunschweig, Germany.
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4
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Ahmed YW, Alemu BA, Bekele SA, Gizaw ST, Zerihun MF, Wabalo EK, Teklemariam MD, Mihrete TK, Hanurry EY, Amogne TG, Gebrehiwot AD, Berga TN, Haile EA, Edo DO, Alemu BD. Epigenetic tumor heterogeneity in the era of single-cell profiling with nanopore sequencing. Clin Epigenetics 2022; 14:107. [PMID: 36030244 PMCID: PMC9419648 DOI: 10.1186/s13148-022-01323-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 08/12/2022] [Indexed: 11/29/2022] Open
Abstract
Nanopore sequencing has brought the technology to the next generation in the science of sequencing. This is achieved through research advancing on: pore efficiency, creating mechanisms to control DNA translocation, enhancing signal-to-noise ratio, and expanding to long-read ranges. Heterogeneity regarding epigenetics would be broad as mutations in the epigenome are sensitive to cause new challenges in cancer research. Epigenetic enzymes which catalyze DNA methylation and histone modification are dysregulated in cancer cells and cause numerous heterogeneous clones to evolve. Detection of this heterogeneity in these clones plays an indispensable role in the treatment of various cancer types. With single-cell profiling, the nanopore sequencing technology could provide a simple sequence at long reads and is expected to be used soon at the bedside or doctor's office. Here, we review the advancements of nanopore sequencing and its use in the detection of epigenetic heterogeneity in cancer.
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Affiliation(s)
- Yohannis Wondwosen Ahmed
- Department of Medical Biochemistry, School of Medicine, College of Health Sciences, Addis Ababa University, P.O. Box: 9086, Addis Ababa, Ethiopia.
| | - Berhan Ababaw Alemu
- Department of Medical Biochemistry, School of Medicine, St. Paul's Hospital, Millennium Medical College, Addis Ababa, Ethiopia
| | - Sisay Addisu Bekele
- Department of Medical Biochemistry, School of Medicine, College of Health Sciences, Addis Ababa University, P.O. Box: 9086, Addis Ababa, Ethiopia
| | - Solomon Tebeje Gizaw
- Department of Medical Biochemistry, School of Medicine, College of Health Sciences, Addis Ababa University, P.O. Box: 9086, Addis Ababa, Ethiopia
| | - Muluken Fekadie Zerihun
- Department of Medical Biochemistry, School of Medicine, College of Health Sciences, Addis Ababa University, P.O. Box: 9086, Addis Ababa, Ethiopia
| | - Endriyas Kelta Wabalo
- Department of Medical Biochemistry, School of Medicine, College of Health Sciences, Addis Ababa University, P.O. Box: 9086, Addis Ababa, Ethiopia
| | - Maria Degef Teklemariam
- Department of Medical Biochemistry, School of Medicine, College of Health Sciences, Addis Ababa University, P.O. Box: 9086, Addis Ababa, Ethiopia
| | - Tsehayneh Kelemu Mihrete
- Department of Medical Biochemistry, School of Medicine, College of Health Sciences, Addis Ababa University, P.O. Box: 9086, Addis Ababa, Ethiopia
| | - Endris Yibru Hanurry
- Department of Medical Biochemistry, School of Medicine, College of Health Sciences, Addis Ababa University, P.O. Box: 9086, Addis Ababa, Ethiopia
| | - Tensae Gebru Amogne
- Department of Medical Biochemistry, School of Medicine, College of Health Sciences, Addis Ababa University, P.O. Box: 9086, Addis Ababa, Ethiopia
| | - Assaye Desalegne Gebrehiwot
- Department of Medical Anatomy, School of Medicine, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia
| | - Tamirat Nida Berga
- Department of Medical Biochemistry, School of Medicine, College of Health Sciences, Addis Ababa University, P.O. Box: 9086, Addis Ababa, Ethiopia
| | - Ebsitu Abate Haile
- Department of Medical Biochemistry, School of Medicine, College of Health Sciences, Addis Ababa University, P.O. Box: 9086, Addis Ababa, Ethiopia
| | - Dessiet Oma Edo
- Department of Medical Biochemistry, School of Medicine, College of Health Sciences, Addis Ababa University, P.O. Box: 9086, Addis Ababa, Ethiopia
| | - Bizuwork Derebew Alemu
- Department of Statistics, College of Natural and Computational Sciences, Mizan Tepi University, Tepi, Ethiopia
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5
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van Vliet EA, Hildebrand MS, Mills JD, Brennan GP, Eid T, Masino SA, Whittemore V, Bindila L, Wang KK, Patel M, Perucca P, Reid CA. A companion to the preclinical common data elements for genomics, transcriptomics, and epigenomics data in rodent epilepsy models. A report of the TASK3-WG4 omics working group of the ILAE/AES joint translational TASK force. Epilepsia Open 2022. [PMID: 35950645 DOI: 10.1002/epi4.12640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Accepted: 02/22/2022] [Indexed: 11/06/2022] Open
Abstract
The International League Against Epilepsy/American Epilepsy Society (ILAE/AES) Joint Translational Task Force established the TASK3 working groups to create common data elements (CDEs) for various preclinical epilepsy research disciplines. The aim of the CDEs is to improve the standardization of experimental designs across a range of epilepsy research-related methods. Here, we have generated CDE tables with key parameters and case report forms (CRFs) containing the essential contents of the study protocols for genomics, transcriptomics, and epigenomics in rodent models of epilepsy, with a specific focus on adult rats and mice. We discuss the important elements that need to be considered for genomics, transcriptomics, and epigenomics methodologies, providing a rationale for the parameters that should be collected. This is the first in a two-part series of omics papers with the second installment to cover proteomics, lipidomics, and metabolomics in adult rodents.
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Affiliation(s)
- Erwin A van Vliet
- Center for Neuroscience, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands
- Amsterdam UMC location University of Amsterdam, Department of (Neuro)Pathology, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Michael S Hildebrand
- Epilepsy Research Centre, Department of Medicine (Austin Health), The University of Melbourne, Heidelberg, Victoria, Australia
- Murdoch Children's Research Institute, The Royal Children's Hospital, Parkville, Victoria, Australia
| | - James D Mills
- Amsterdam UMC location University of Amsterdam, Department of (Neuro)Pathology, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Gary P Brennan
- UCD School of Biomolecular and Biomedical Science, Conway Institute, University College Dublin, Dublin, Ireland
- FutureNeuro Research Centre, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Tore Eid
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Susan A Masino
- Neuroscience Program and Psychology Department, Life Sciences Center, Trinity College, Hartford, Connecticut, USA
| | - Vicky Whittemore
- Division of Neuroscience, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA
| | - Laura Bindila
- Clinical Lipidomics Unit, Institute of Physiological Chemistry, University Medical Center of the Johannes Gutenberg University of Mainz, Mainz, Germany
| | - Kevin K Wang
- Department of Emergency Medicine, Psychiatry and Neuroscience, University of Florida, Gainesville, Florida, USA
- Brain Rehabilitation Research Center, Malcom Randall VA Medical Center, North Florida/South Georgia Veterans Health System, Gainesville, Florida, USA
| | - Manisha Patel
- Department of Pharmaceutical Sciences, University of Colorado, Aurora, Colorado, USA
| | - Piero Perucca
- Epilepsy Research Centre, Department of Medicine (Austin Health), The University of Melbourne, Heidelberg, Victoria, Australia
- Bladin-Berkovic Comprehensive Epilepsy Program, Austin Health, Heidelberg, Victoria, Australia
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
- Department of Neurology, The Royal Melbourne Hospital, Melbourne, Victoria, Australia
- Department of Neurology, Alfred Health, Melbourne, Victoria, Australia
| | - Christopher A Reid
- Epilepsy Research Centre, Department of Medicine (Austin Health), The University of Melbourne, Heidelberg, Victoria, Australia
- Murdoch Children's Research Institute, The Royal Children's Hospital, Parkville, Victoria, Australia
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
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6
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Billington C, Kingsbury JM, Rivas L. Metagenomics Approaches for Improving Food Safety: A Review. J Food Prot 2022; 85:448-464. [PMID: 34706052 DOI: 10.4315/jfp-21-301] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 10/21/2021] [Indexed: 11/11/2022]
Abstract
ABSTRACT Advancements in next-generation sequencing technology have dramatically reduced the cost and increased the ease of microbial whole genome sequencing. This approach is revolutionizing the identification and analysis of foodborne microbial pathogens, facilitating expedited detection and mitigation of foodborne outbreaks, improving public health outcomes, and limiting costly recalls. However, next-generation sequencing is still anchored in the traditional laboratory practice of the selection and culture of a single isolate. Metagenomic-based approaches, including metabarcoding and shotgun and long-read metagenomics, are part of the next disruptive revolution in food safety diagnostics and offer the potential to directly identify entire microbial communities in a single food, ingredient, or environmental sample. In this review, metagenomic-based approaches are introduced and placed within the context of conventional detection and diagnostic techniques, and essential considerations for undertaking metagenomic assays and data analysis are described. Recent applications of the use of metagenomics for food safety are discussed alongside current limitations and knowledge gaps and new opportunities arising from the use of this technology. HIGHLIGHTS
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Affiliation(s)
- Craig Billington
- Institute of Environmental Science and Research, 27 Creyke Road, Ilam, Christchurch 8041, New Zealand
| | - Joanne M Kingsbury
- Institute of Environmental Science and Research, 27 Creyke Road, Ilam, Christchurch 8041, New Zealand
| | - Lucia Rivas
- Institute of Environmental Science and Research, 27 Creyke Road, Ilam, Christchurch 8041, New Zealand
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7
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Goll JB, Bosinger SE, Jensen TL, Walum H, Grimes T, Tharp GK, Natrajan MS, Blazevic A, Head RD, Gelber CE, Steenbergen KJ, Patel NB, Sanz P, Rouphael NG, Anderson EJ, Mulligan MJ, Hoft DF. The Vacc-SeqQC project: Benchmarking RNA-Seq for clinical vaccine studies. Front Immunol 2022; 13:1093242. [PMID: 36741404 PMCID: PMC9893923 DOI: 10.3389/fimmu.2022.1093242] [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: 11/08/2022] [Accepted: 12/30/2022] [Indexed: 01/20/2023] Open
Abstract
Introduction Over the last decade, the field of systems vaccinology has emerged, in which high throughput transcriptomics and other omics assays are used to probe changes of the innate and adaptive immune system in response to vaccination. The goal of this study was to benchmark key technical and analytical parameters of RNA sequencing (RNA-seq) in the context of a multi-site, double-blind randomized vaccine clinical trial. Methods We collected longitudinal peripheral blood mononuclear cell (PBMC) samples from 10 subjects before and after vaccination with a live attenuated Francisella tularensis vaccine and performed RNA-Seq at two different sites using aliquots from the same sample to generate two replicate datasets (5 time points for 50 samples each). We evaluated the impact of (i) filtering lowly-expressed genes, (ii) using external RNA controls, (iii) fold change and false discovery rate (FDR) filtering, (iv) read length, and (v) sequencing depth on differential expressed genes (DEGs) concordance between replicate datasets. Using synthetic mRNA spike-ins, we developed a method for empirically establishing minimal read-count thresholds for maintaining fold change accuracy on a per-experiment basis. We defined a reference PBMC transcriptome by pooling sequence data and established the impact of sequencing depth and gene filtering on transcriptome representation. Lastly, we modeled statistical power to detect DEGs for a range of sample sizes, effect sizes, and sequencing depths. Results and Discussion Our results showed that (i) filtering lowly-expressed genes is recommended to improve fold-change accuracy and inter-site agreement, if possible guided by mRNA spike-ins (ii) read length did not have a major impact on DEG detection, (iii) applying fold-change cutoffs for DEG detection reduced inter-set agreement and should be used with caution, if at all, (iv) reduction in sequencing depth had a minimal impact on statistical power but reduced the identifiable fraction of the PBMC transcriptome, (v) after sample size, effect size (i.e. the magnitude of fold change) was the most important driver of statistical power to detect DEG. The results from this study provide RNA sequencing benchmarks and guidelines for planning future similar vaccine studies.
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Affiliation(s)
- Johannes B Goll
- Department of Biomedical Data Science and Bioinformatics, The Emmes Company, LLC, Rockville, MD, United States
| | - Steven E Bosinger
- Division of Microbiology & Immunology, Emory National Primate Research Center, Emory University, Atlanta, GA, United States.,Department of Pathology & Laboratory Medicine, School of Medicine, Emory University, Atlanta, GA, United States.,Emory NPRC Genomics Core, Emory National Primate Research Center, Emory University, Atlanta, GA, United States.,Emory Vaccine Center, Emory University School of Medicine, Atlanta, GA, United States
| | - Travis L Jensen
- Department of Biomedical Data Science and Bioinformatics, The Emmes Company, LLC, Rockville, MD, United States
| | - Hasse Walum
- Division of Microbiology & Immunology, Emory National Primate Research Center, Emory University, Atlanta, GA, United States
| | - Tyler Grimes
- Department of Biomedical Data Science and Bioinformatics, The Emmes Company, LLC, Rockville, MD, United States
| | - Gregory K Tharp
- Emory NPRC Genomics Core, Emory National Primate Research Center, Emory University, Atlanta, GA, United States
| | - Muktha S Natrajan
- Emory Vaccine Center, Emory University School of Medicine, Atlanta, GA, United States.,Hope Clinic of the Emory Vaccine Center, Emory University, Atlanta, GA, United States
| | - Azra Blazevic
- Division of Infectious Diseases, Allergy, and Immunology, Department of Internal Medicine, Saint Louis University School of Medicine, St. Louis, MO, United States
| | - Richard D Head
- McDonnell Genome Institute, Washington University, St. Louis, MO, United States
| | - Casey E Gelber
- Department of Biomedical Data Science and Bioinformatics, The Emmes Company, LLC, Rockville, MD, United States
| | - Kristen J Steenbergen
- Department of Biomedical Data Science and Bioinformatics, The Emmes Company, LLC, Rockville, MD, United States
| | - Nirav B Patel
- Emory NPRC Genomics Core, Emory National Primate Research Center, Emory University, Atlanta, GA, United States
| | - Patrick Sanz
- Office of Biodefense, Research Resources and Translational Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD, United States
| | - Nadine G Rouphael
- Emory Vaccine Center, Emory University School of Medicine, Atlanta, GA, United States.,Hope Clinic of the Emory Vaccine Center, Emory University, Atlanta, GA, United States.,Department of Medicine, Division of Infectious Diseases, Emory University School of Medicine, Emory University, Atlanta, GA, United States
| | - Evan J Anderson
- Department of Medicine, Division of Infectious Diseases, Emory University School of Medicine, Emory University, Atlanta, GA, United States.,Center for Childhood Infections and Vaccines (CCIV) of Children's Healthcare of Atlanta and Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, United States
| | - Mark J Mulligan
- Emory Vaccine Center, Emory University School of Medicine, Atlanta, GA, United States.,Hope Clinic of the Emory Vaccine Center, Emory University, Atlanta, GA, United States.,Department of Medicine, Division of Infectious Diseases, Emory University School of Medicine, Emory University, Atlanta, GA, United States.,New York University Vaccine Center, New York, NY, United States
| | - Daniel F Hoft
- Division of Infectious Diseases, Allergy, and Immunology, Department of Internal Medicine, Saint Louis University School of Medicine, St. Louis, MO, United States.,Department of Molecular Microbiology & Immunology, Saint Louis University, St. Louis, MO, United States
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8
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Pleiss J. Standardized Data, Scalable Documentation, Sustainable Storage – EnzymeML As A Basis For FAIR Data Management In Biocatalysis. ChemCatChem 2021. [DOI: 10.1002/cctc.202100822] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Affiliation(s)
- Jürgen Pleiss
- Institute of Biochemistry and Technical Biochemistry University of Stuttgart Allmandring 31 70569 Stuttgart Germany
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9
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Moore KJM, Cahill J, Aidelberg G, Aronoff R, Bektaş A, Bezdan D, Butler DJ, Chittur SV, Codyre M, Federici F, Tanner NA, Tighe SW, True R, Ware SB, Wyllie AL, Afshin EE, Bendesky A, Chang CB, Dela Rosa R, Elhaik E, Erickson D, Goldsborough AS, Grills G, Hadasch K, Hayden A, Her SY, Karl JA, Kim CH, Kriegel AJ, Kunstman T, Landau Z, Land K, Langhorst BW, Lindner AB, Mayer BE, McLaughlin LA, McLaughlin MT, Molloy J, Mozsary C, Nadler JL, D'Silva M, Ng D, O'Connor DH, Ongerth JE, Osuolale O, Pinharanda A, Plenker D, Ranjan R, Rosbash M, Rotem A, Segarra J, Schürer S, Sherrill-Mix S, Solo-Gabriele H, To S, Vogt MC, Yu AD, Mason CE. Loop-Mediated Isothermal Amplification Detection of SARS-CoV-2 and Myriad Other Applications. J Biomol Tech 2021; 32:228-275. [PMID: 35136384 PMCID: PMC8802757 DOI: 10.7171/jbt.21-3203-017] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
As the second year of the COVID-19 pandemic begins, it remains clear that a massive increase in the ability to test for SARS-CoV-2 infections in a myriad of settings is critical to controlling the pandemic and to preparing for future outbreaks. The current gold standard for molecular diagnostics is the polymerase chain reaction (PCR), but the extraordinary and unmet demand for testing in a variety of environments means that both complementary and supplementary testing solutions are still needed. This review highlights the role that loop-mediated isothermal amplification (LAMP) has had in filling this global testing need, providing a faster and easier means of testing, and what it can do for future applications, pathogens, and the preparation for future outbreaks. This review describes the current state of the art for research of LAMP-based SARS-CoV-2 testing, as well as its implications for other pathogens and testing. The authors represent the global LAMP (gLAMP) Consortium, an international research collective, which has regularly met to share their experiences on LAMP deployment and best practices; sections are devoted to all aspects of LAMP testing, including preanalytic sample processing, target amplification, and amplicon detection, then the hardware and software required for deployment are discussed, and finally, a summary of the current regulatory landscape is provided. Included as well are a series of first-person accounts of LAMP method development and deployment. The final discussion section provides the reader with a distillation of the most validated testing methods and their paths to implementation. This review also aims to provide practical information and insight for a range of audiences: for a research audience, to help accelerate research through sharing of best practices; for an implementation audience, to help get testing up and running quickly; and for a public health, clinical, and policy audience, to help convey the breadth of the effect that LAMP methods have to offer.
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Affiliation(s)
- Keith J M Moore
- School of Science and Engineering, Ateneo de Manila University, Quezon City 1108, Philippines
| | | | - Guy Aidelberg
- Université de Paris, INSERM U1284, Center for Research and Interdisciplinarity (CRI), 75006 Paris, France
- Just One Giant Lab, Centre de Recherches Interdisciplinaires (CRI), 75004 Paris, France
| | - Rachel Aronoff
- Just One Giant Lab, Centre de Recherches Interdisciplinaires (CRI), 75004 Paris, France
- Action for Genomic Integrity Through Research! (AGiR!), Lausanne, Switzerland
- Association Hackuarium, Lausanne, Switzerland
| | - Ali Bektaş
- Oakland Genomics Center, Oakland, CA 94609, USA
| | - Daniela Bezdan
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, 72076 Tübingen, Germany
- NGS Competence Center Tübingen (NCCT), University of Tübingen, 72076 Tübingen, Germany
- Poppy Health, Inc, San Francisco, CA 94158, USA
- Institute of Medical Virology and Epidemiology of Viral Diseases, University Hospital, 72076 Tübingen, Germany
| | - Daniel J Butler
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10065, USA
| | - Sridar V Chittur
- Center for Functional Genomics, Department of Biomedical Sciences, School of Public Health, University at Albany, State University of New York, Rensselaer, 12222, USA
| | - Martin Codyre
- GiantLeap Biotechnology Ltd, Wicklow A63 Kv91, Ireland
| | - Fernan Federici
- ANID, Millennium Science Initiative Program, Millennium Institute for Integrative Biology (iBio), Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago 8331150, Chile
| | | | | | - Randy True
- FloodLAMP Biotechnologies, San Carlos, CA 94070, USA
| | - Sarah B Ware
- Just One Giant Lab, Centre de Recherches Interdisciplinaires (CRI), 75004 Paris, France
- BioBlaze Community Bio Lab, 1800 W Hawthorne Ln, Ste J-1, West Chicago, IL 60185, USA
- Blossom Bio Lab, 1800 W Hawthorne Ln, Ste K-2, West Chicago, IL 60185, USA
| | - Anne L Wyllie
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
| | - Evan E Afshin
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10065, USA
- The WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY 10065, USA
| | - Andres Bendesky
- Department of Ecology, Evolution and Environmental Biology, Columbia University, New York, NY 10027, USA
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Connie B Chang
- Department of Chemical and Biological Engineering, Montana State University, Bozeman, 59717, USA
- Center for Biofilm Engineering, Montana State University, Bozeman, 59717, USA
| | - Richard Dela Rosa
- School of Science and Engineering, Ateneo de Manila University, Quezon City 1108, Philippines
| | - Eran Elhaik
- Department of Biology, Lund University, Sölvegatan 35, Lund, Sweden
| | - David Erickson
- Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14850, USA
| | | | - George Grills
- Department of Microbiology, University of Pennsylvania, Philadelphia, 19104, USA
| | - Kathrin Hadasch
- Université de Paris, INSERM U1284, Center for Research and Interdisciplinarity (CRI), 75006 Paris, France
- Department of Biology, Membrane Biophysics, Technische Universität Darmstadt, 64289 Darmstadt, Germany
- Lab3 eV, Labspace Darmstadt, 64295 Darmstadt, Germany
- IANUS Verein für Friedensorientierte Technikgestaltung eV, 64289 Darmstadt, Germany
| | - Andrew Hayden
- Center for Functional Genomics, Department of Biomedical Sciences, School of Public Health, University at Albany, State University of New York, Rensselaer, 12222, USA
| | | | - Julie A Karl
- Department of Pathology and Laboratory Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, Madison 53705, USA
| | | | | | | | - Zeph Landau
- Department of Computer Science, University of California, Berkeley, Berkeley, 94720, USA
| | - Kevin Land
- Mologic, Centre for Advanced Rapid Diagnostics, (CARD), Bedford Technology Park, Thurleigh MK44 2YA, England
- Department of Electrical, Electronic and Computer Engineering, University of Pretoria, 0028 Pretoria, South Africa
| | | | - Ariel B Lindner
- Université de Paris, INSERM U1284, Center for Research and Interdisciplinarity (CRI), 75006 Paris, France
| | - Benjamin E Mayer
- Department of Biology, Membrane Biophysics, Technische Universität Darmstadt, 64289 Darmstadt, Germany
- Lab3 eV, Labspace Darmstadt, 64295 Darmstadt, Germany
| | | | - Matthew T McLaughlin
- Department of Pathology and Laboratory Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, Madison 53705, USA
| | - Jenny Molloy
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, England
| | - Christopher Mozsary
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10065, USA
| | - Jerry L Nadler
- Department of Pharmacology, New York Medical College, Valhalla, 10595, USA
| | - Melinee D'Silva
- Department of Pharmacology, New York Medical College, Valhalla, 10595, USA
| | - David Ng
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - David H O'Connor
- Department of Pathology and Laboratory Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, Madison 53705, USA
| | - Jerry E Ongerth
- University of Wollongong, Environmental Engineering, Wollongong NSW 2522, Australia
| | - Olayinka Osuolale
- Applied Environmental Metagenomics and Infectious Diseases Research (AEMIDR), Department of Biological Sciences, Elizade University, Ilara Mokin, Nigeria
| | - Ana Pinharanda
- Department of Biological Sciences, Columbia University, New York, NY 10027, USA
| | - Dennis Plenker
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Ravi Ranjan
- Genomics Resource Laboratory, Institute for Applied Life Sciences, University of Massachusetts, Amherst, 01003, USA
| | - Michael Rosbash
- Howard Hughes Medical Institute and Department of Biology, Brandeis University, Waltham, MA 02453, USA
| | | | | | | | - Scott Sherrill-Mix
- Department of Microbiology, University of Pennsylvania, Philadelphia, 19104, USA
| | | | - Shaina To
- School of Science and Engineering, Ateneo de Manila University, Quezon City 1108, Philippines
| | - Merly C Vogt
- Department of Biological Sciences, Columbia University, New York, NY 10027, USA
| | - Albert D Yu
- Howard Hughes Medical Institute and Department of Biology, Brandeis University, Waltham, MA 02453, USA
| | - Christopher E Mason
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10065, USA
- The WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY 10065, USA
- The Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10065, USA
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10
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Mirizio E, Tabib T, Wang X, Chen W, Liu C, Lafyatis R, Jacobe H, Torok KS. Single-cell transcriptome conservation in a comparative analysis of fresh and cryopreserved human skin tissue: pilot in localized scleroderma. Arthritis Res Ther 2020; 22:263. [PMID: 33168063 PMCID: PMC7654179 DOI: 10.1186/s13075-020-02343-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 10/04/2020] [Indexed: 01/15/2023] Open
Abstract
Background The purpose of this study was to assess variability in cell composition and cell-specific gene expression in the skin of patients with localized scleroderma (LS) utilizing CryoStor® CS10 in comparison to RPMI to produce adequate preservation of tissue samples and cell types of interest for use in large-scale multi-institutional collaborations studying localized scleroderma and other skin disorders. Methods We performed single-cell RNA sequencing on paired skin biopsy specimens from 3 patients with LS. Each patient with one sample cryopreserved in CryoStor® CS10 and one fresh in RPMI media using 10× Genomics sequencing. Results Levels of cell viability and yield were comparable between CryoStor® CS10 (frozen) and RPMI (fresh) preserved cells. Furthermore, gene expression between preservation methods was collectively significantly correlated and conserved across all 18 identified cell cluster populations. Conclusion Comparable cell population and transcript expression yields between CryoStor® CS10 and RPMI preserved cells support the utilization of cryopreserved skin tissue in single-cell analysis. This suggests that employing standardized cryopreservation protocols for the skin tissue will help facilitate multi-site collaborations looking to identify mechanisms of disease in disorders characterized by cutaneous pathology.
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Affiliation(s)
- Emily Mirizio
- Division of Rheumatology, Department of Pediatrics, Children's Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, PA, USA
| | - Tracy Tabib
- Division of Rheumatology, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Xinjun Wang
- Division of Pediatric Pulmonary Medicine, UPMC Children's Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, PA, USA
| | - Wei Chen
- Division of Pediatric Pulmonary Medicine, UPMC Children's Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, PA, USA
| | - Christopher Liu
- Division of Rheumatology, Department of Pediatrics, Children's Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, PA, USA
| | - Robert Lafyatis
- Division of Rheumatology, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Heidi Jacobe
- Department of Dermatology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Kathryn S Torok
- Division of Rheumatology, Department of Pediatrics, Children's Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, PA, USA. .,Division of Rheumatology, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA. .,University of Pittsburgh Medical Center Children's Hospital of Pittsburgh Faculty Pavilion, 3rd floor, Office 3117 4401 Penn Avenue, PA, 15237, Pittsburgh, USA.
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11
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Hu Y, Fang L, Nicholson C, Wang K. Implications of Error-Prone Long-Read Whole-Genome Shotgun Sequencing on Characterizing Reference Microbiomes. iScience 2020; 23:101223. [PMID: 32563152 PMCID: PMC7305381 DOI: 10.1016/j.isci.2020.101223] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Revised: 05/09/2020] [Accepted: 05/28/2020] [Indexed: 01/16/2023] Open
Abstract
Long-read sequencing techniques, such as the Oxford Nanopore Technology, can generate reads that are tens of kilobases in length and are therefore particularly relevant for microbiome studies. However, owing to the higher per-base error rates than typical short-read sequencing, the application of long-read sequencing on microbiomes remains largely unexplored. Here we deeply sequenced two human microbiota mock community samples (HM-276D and HM-277D) from the Human Microbiome Project. We showed that assembly programs consistently achieved high accuracy (∼99%) and completeness (∼99%) for bacterial strains with adequate coverage. We also found that long-read sequencing provides accurate estimates of species-level abundance (R = 0.94 for 20 bacteria with abundance ranging from 0.005% to 64%). Our results not only demonstrate the feasibility of characterizing complete microbial genomes and populations from error-prone Nanopore sequencing data but also highlight necessary bioinformatics improvements for future metagenomics tool development.
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Affiliation(s)
- Yu Hu
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Li Fang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Christopher Nicholson
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kai Wang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
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12
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Mische SM, Fisher NC, Meyn SM, Sol-Church K, Hegstad-Davies RL, Weis-Garcia F, Adams M, Ashton JM, Delventhal KM, Dragon JA, Holmes L, Jagtap P, Kubow KE, Mason CE, Palmblad M, Searle BC, Turck CW, Knudtson KL. A Review of the Scientific Rigor, Reproducibility, and Transparency Studies Conducted by the ABRF Research Groups. J Biomol Tech 2020; 31:11-26. [PMID: 31969795 PMCID: PMC6959150 DOI: 10.7171/jbt.20-3101-003] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Shared research resource facilities, also known as core laboratories (Cores), are responsible for generating a significant and growing portion of the research data in academic biomedical research institutions. Cores represent a central repository for institutional knowledge management, with deep expertise in the strengths and limitations of technology and its applications. They inherently support transparency and scientific reproducibility by protecting against cognitive bias in research design and data analysis, and they have institutional responsibility for the conduct of research (research ethics, regulatory compliance, and financial accountability) performed in their Cores. The Association of Biomolecular Resource Facilities (ABRF) is a FASEB-member scientific society whose members are scientists and administrators that manage or support Cores. The ABRF Research Groups (RGs), representing expertise for an array of cutting-edge and established technology platforms, perform multicenter research studies to determine and communicate best practices and community-based standards. This review provides a summary of the contributions of the ABRF RGs to promote scientific rigor and reproducibility in Cores from the published literature, ABRF meetings, and ABRF RGs communications.
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Affiliation(s)
- Sheenah M. Mische
- New York University (NYU) Langone Medical Center, New
York, New York 10016, USA
| | - Nancy C. Fisher
- University of North Carolina at Chapel Hill, Chapel
Hill, North Carolina 27599, USA
| | - Susan M. Meyn
- Vanderbilt University Medical Center, Nashville,
Tennessee 37212, USA
| | - Katia Sol-Church
- University of Virginia School of Medicine,
Charlottesville, Virginia 22908, USA
| | | | | | - Marie Adams
- Van Andel Institute, Grand Rapids, Michigan 49503,
USA
| | - John M. Ashton
- University of Rochester Medical Center, West
Henrietta, New York 14642, USA
| | - Kym M. Delventhal
- Stowers Institute for Medical Research, Kansas City,
Missouri 64110, USA
| | | | - Laura Holmes
- Stowers Institute for Medical Research, Kansas City,
Missouri 64110, USA
| | - Pratik Jagtap
- University of Minnesota, Minneapolis, Minnesota
55455, USA
| | | | | | - Magnus Palmblad
- Leiden University Medical Center, Leiden 2333, The
Netherlands
| | - Brian C. Searle
- Institute for Systems Biology, Seattle, Washington
98109, USA
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13
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Martellacci L, Quaranta G, Patini R, Isola G, Gallenzi P, Masucci L. A Literature Review of Metagenomics and Culturomics of the Peri-implant Microbiome: Current Evidence and Future Perspectives. MATERIALS 2019; 12:ma12183010. [PMID: 31533226 PMCID: PMC6766346 DOI: 10.3390/ma12183010] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 09/06/2019] [Accepted: 09/16/2019] [Indexed: 02/07/2023]
Abstract
Background and objectives: In recent years, many different culture-independent molecular techniques have been developed with the aim of investigating the not yet cultivated part of the resident flora of the oral cavity and of analyzing the peri-implant and periodontal flora both in healthy and diseased sites. The most used technologies are Roche 454 pyrosequencing, Illumina HiSeq/MiSeq, ABI SOLiD and Ion Torrent. Due to these methods, two different approaches are available: Metagenomics and the 16S gene analysis. A complementary strategy was also recently developed: Culturomics. Culturomics consists of different culture conditions that allow a very rapid bacterial identification. The focused question of this review was developed in PICO format in order to investigate the role of metagenomics, 16S gene analysis and culturomics (interventions) in the differential study (comparison) of the peri-implant and periodontal microbiome (outcome) in humans (participants). The secondary aim was the characterization of currents limits and future applications of the three techniques. Methods: The authors performed a literature search on three databases (Web of Science, Scopus and PubMed) from 01/01/2003 to 31/06/2019. Date of last search was: 25/08/19. Any type of article dealing with the analysis of periodontal and peri-implant flora with metagenomic, culturomic or 16S gene analysis was included. No language restrictions were applied. Risk of bias for RCT was assessed using the Cochrane collaboration's tool whereas case-control and cohort studies were evaluated through the Newcastle-Ottawa scale. Results: The initial search resulted in 330 titles in total. After careful evaluation of all results no studies were found to satisfy the primary outcome of the present review. Hence a narrative review dealing with the secondary aim was performed. Conclusions: Metagenomic and 16S gene analysis approaches contributed in clarifying some crucial aspects of the oral microbiome. Based on the reported evidence some bacteria could be found around teeth and implants even in the absence of signs of inflammation and other species are more frequently found in supragingival peri-implant biofilm. Teeth and implants (even if adjacent) seem not to share the same microbiome and healthy teeth have a more diversified one. The same analyses also highlighted that the oral biofilm of smokers is composed by more periodontopathogen bacteria compared to non-smokers and that geographical location and ethnicity seem to play a role in bacterial composition. Culturomics, which has not yet been applied to the study of oral microbiota, consists of the use of different culture conditions and of the identification by matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) with the aim of increasing the bacterial repertoire and avoiding the limits of molecular methods. In order to better evaluate perspectives and limits of the all presented approaches further studies comparing the different molecular techniques are encouraged. This review received no funding.
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Affiliation(s)
- Leonardo Martellacci
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Institute of Dentistry and Maxillofacial Surgery, Università Cattolica del Sacro Cuore, 00168 Rome, Italy.
| | - Gianluca Quaranta
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Institute of Microbiology and Virology, Università Cattolica del Sacro Cuore, 00168 Rome, Italy.
| | - Romeo Patini
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Institute of Dentistry and Maxillofacial Surgery, Università Cattolica del Sacro Cuore, 00168 Rome, Italy.
| | - Gaetano Isola
- Department of General Surgery and Surgical-Medical Specialties, School of Dentistry, University of Catania, 95124 Catania, Italy.
| | - Patrizia Gallenzi
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Institute of Dentistry and Maxillofacial Surgery, Università Cattolica del Sacro Cuore, 00168 Rome, Italy.
| | - Luca Masucci
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Institute of Microbiology and Virology, Università Cattolica del Sacro Cuore, 00168 Rome, Italy.
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14
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Nicholls SM, Quick JC, Tang S, Loman NJ. Ultra-deep, long-read nanopore sequencing of mock microbial community standards. Gigascience 2019; 8:giz043. [PMID: 31089679 PMCID: PMC6520541 DOI: 10.1093/gigascience/giz043] [Citation(s) in RCA: 131] [Impact Index Per Article: 26.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Revised: 02/22/2019] [Accepted: 03/27/2019] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Long sequencing reads are information-rich: aiding de novo assembly and reference mapping, and consequently have great potential for the study of microbial communities. However, the best approaches for analysis of long-read metagenomic data are unknown. Additionally, rigorous evaluation of bioinformatics tools is hindered by a lack of long-read data from validated samples with known composition. FINDINGS We sequenced 2 commercially available mock communities containing 10 microbial species (ZymoBIOMICS Microbial Community Standards) with Oxford Nanopore GridION and PromethION. Both communities and the 10 individual species isolates were also sequenced with Illumina technology. We generated 14 and 16 gigabase pairs from 2 GridION flowcells and 150 and 153 gigabase pairs from 2 PromethION flowcells for the evenly distributed and log-distributed communities, respectively. Read length N50 ranged between 5.3 and 5.4 kilobase pairs over the 4 sequencing runs. Basecalls and corresponding signal data are made available (4.2 TB in total). Alignment to Illumina-sequenced isolates demonstrated the expected microbial species at anticipated abundances, with the limit of detection for the lowest abundance species below 50 cells (GridION). De novo assembly of metagenomes recovered long contiguous sequences without the need for pre-processing techniques such as binning. CONCLUSIONS We present ultra-deep, long-read nanopore datasets from a well-defined mock community. These datasets will be useful for those developing bioinformatics methods for long-read metagenomics and for the validation and comparison of current laboratory and software pipelines.
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Affiliation(s)
- Samuel M Nicholls
- Institute of Microbiology and Infection, School of Biosciences, University of Birmingham, Edgbaston, B15 2TT, UK
| | - Joshua C Quick
- Institute of Microbiology and Infection, School of Biosciences, University of Birmingham, Edgbaston, B15 2TT, UK
| | - Shuiquan Tang
- Zymo Research Corporation, 17062 Murphy Ave., Irvine, CA 92614, USA
| | - Nicholas J Loman
- Institute of Microbiology and Infection, School of Biosciences, University of Birmingham, Edgbaston, B15 2TT, UK
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15
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Goldman SL, MacKay M, Afshinnekoo E, Melnick AM, Wu S, Mason CE. The Impact of Heterogeneity on Single-Cell Sequencing. Front Genet 2019; 10:8. [PMID: 30881372 PMCID: PMC6405636 DOI: 10.3389/fgene.2019.00008] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2018] [Accepted: 01/09/2019] [Indexed: 12/28/2022] Open
Abstract
The importance of diversity and cellular specialization is clear for many reasons, from population-level diversification, to improved resiliency to unforeseen stresses, to unique functions within metazoan organisms during development and differentiation. However, the level of cellular heterogeneity is just now becoming clear through the integration of genome-wide analyses and more cost effective Next Generation Sequencing (NGS). With easy access to single-cell NGS (scNGS), new opportunities exist to examine different levels of gene expression and somatic mutational heterogeneity, but these assays can generate yottabyte scale data. Here, we model the importance of heterogeneity for large-scale analysis of scNGS data, with a focus on the utilization in oncology and other diseases, providing a guide to aid in sample size and experimental design.
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Affiliation(s)
- Samantha L Goldman
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY, United States.,The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, United States
| | - Matthew MacKay
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY, United States.,The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, United States
| | - Ebrahim Afshinnekoo
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY, United States.,The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, United States.,WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY, United States
| | - Ari M Melnick
- Department of Medicine, Weill Cornell Medicine, New York, NY, United States
| | - Shuxiu Wu
- Hangzhou Cancer Institute, Hangzhou Cancer Hospital, Hangzhou, China.,Department of Radiation Oncology, Hangzhou Cancer Hospital, Hangzhou, China
| | - Christopher E Mason
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY, United States.,The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, United States.,WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY, United States.,The Feil Family Brain and Mind Research Institute, New York, NY, United States
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16
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Danko DC, Meleshko D, Bezdan D, Mason C, Hajirasouliha I. Minerva: an alignment- and reference-free approach to deconvolve Linked-Reads for metagenomics. Genome Res 2019; 29:116-124. [PMID: 30523036 PMCID: PMC6314158 DOI: 10.1101/gr.235499.118] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Accepted: 11/26/2018] [Indexed: 12/20/2022]
Abstract
Emerging Linked-Read technologies (aka read cloud or barcoded short-reads) have revived interest in short-read technology as a viable approach to understand large-scale structures in genomes and metagenomes. Linked-Read technologies, such as the 10x Chromium system, use a microfluidic system and a specialized set of 3' barcodes (aka UIDs) to tag short DNA reads sourced from the same long fragment of DNA; subsequently, the tagged reads are sequenced on standard short-read platforms. This approach results in interesting compromises. Each long fragment of DNA is only sparsely covered by reads, no information about the ordering of reads from the same fragment is preserved, and 3' barcodes match reads from roughly 2-20 long fragments of DNA. However, compared to long-read technologies, the cost per base to sequence is far lower, far less input DNA is required, and the per base error rate is that of Illumina short-reads. In this paper, we formally describe a particular algorithmic issue common to Linked-Read technology: the deconvolution of reads with a single 3' barcode into clusters that represent single long fragments of DNA. We introduce Minerva, a graph-based algorithm that approximately solves the barcode deconvolution problem for metagenomic data (where reference genomes may be incomplete or unavailable). Additionally, we develop two demonstrations where the deconvolution of barcoded reads improves downstream results, improving the specificity of taxonomic assignments and of k-mer-based clustering. To the best of our knowledge, we are the first to address the problem of barcode deconvolution in metagenomics.
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Affiliation(s)
- David C Danko
- Tri-Institutional Computational Biology and Medicine Program, Weill Cornell Medicine of Cornell University, New York, New York 10065, USA
- Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine of Cornell University, New York, New York 10065, USA
| | - Dmitry Meleshko
- Tri-Institutional Computational Biology and Medicine Program, Weill Cornell Medicine of Cornell University, New York, New York 10065, USA
- Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine of Cornell University, New York, New York 10065, USA
| | - Daniela Bezdan
- Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine of Cornell University, New York, New York 10065, USA
| | - Christopher Mason
- Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine of Cornell University, New York, New York 10065, USA
- The Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York 10065, USA
| | - Iman Hajirasouliha
- Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine of Cornell University, New York, New York 10065, USA
- Englander Institute for Precision Medicine, The Meyer Cancer Center, Weill Cornell Medicine, New York, New York 10065, USA
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17
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Abstract
Shotgun metagenomics is a powerful platform to characterize human microbiomes. However, to translate such survey data into consumer-relevant products or services, it is critical to have a robust metagenomics workflow. We present a tool – spike-in DNA – to assess performance of metagenomics workflows. The spike-in is DNA from two organisms – Alivibrio fischeri and Rhodopseudomonas palustris, in a ratio of 4:1 added to samples before DNA extraction. With a valid workflow, the output ratio of relative abundances of these organisms should be close to 4. This expectation was tested in samples of varying diversities (n = 110), and the mean ratio was 4.73 (99% CI [4.0, 5.24]). We anticipate this tool to be a relevant community resource for assessing the quality of shotgun metagenomics workflows and thereby enable robust characterization of microbiomes.
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18
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Minogue TD, Koehler JW, Stefan CP, Conrad TA. Next-Generation Sequencing for Biodefense: Biothreat Detection, Forensics, and the Clinic. Clin Chem 2018; 65:383-392. [PMID: 30352865 DOI: 10.1373/clinchem.2016.266536] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Accepted: 06/22/2018] [Indexed: 11/06/2022]
Abstract
BACKGROUND Next-generation sequencing (NGS) is revolutionizing a variety of molecular biology fields including bioforensics, biosurveillance, and infectious disease diagnostics. For pathogen detection, the ability to sequence all nucleic acids in a sample allows near limitless multiplexability, free from a priori knowledge regarding an etiologic agent as is typically required for targeted molecular assays such as real-time PCR. Furthermore, sequencing capabilities can generate in depth genomic information, allowing detailed molecular epidemiological studies and bioforensics analysis, which is critical for source agent identification in a biothreat outbreak. However, lack of analytical specificity, inherent to NGS, presents challenges for regulated applications such as clinical diagnostics and molecular attribution. CONTENT Here, we discuss NGS applications in the context of preparedness and biothreat readiness. Specifically, we investigate current and future applications of NGS technologies to affect the fields of biosurveillance, bioforensics, and clinical diagnostics with specific focus on biodefense. SUMMARY Overall, there are many advantages to the implementation of NGS for preparedness and readiness against biowarfare agents, from forensics to diagnostics. However, appropriate caveats must be associated with any technology. This includes NGS. While NGS is not the panacea replacing all molecular techniques, it will greatly enhance the ability to detect, characterize, and diagnose biowarfare agents, thus providing an excellent addition to the biodefense toolbox of biosurveillance, bioforensics, and biothreat diagnosis.
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Affiliation(s)
- Timothy D Minogue
- Diagnostic Systems Division, United States Army Medical Research Institute of Infectious Diseases, Fort Detrick, MD.
| | - Jeffrey W Koehler
- Diagnostic Systems Division, United States Army Medical Research Institute of Infectious Diseases, Fort Detrick, MD
| | - Christopher P Stefan
- Diagnostic Systems Division, United States Army Medical Research Institute of Infectious Diseases, Fort Detrick, MD
| | - Turner A Conrad
- Diagnostic Systems Division, United States Army Medical Research Institute of Infectious Diseases, Fort Detrick, MD
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19
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Hardwick SA, Chen WY, Wong T, Kanakamedala BS, Deveson IW, Ongley SE, Santini NS, Marcellin E, Smith MA, Nielsen LK, Lovelock CE, Neilan BA, Mercer TR. Synthetic microbe communities provide internal reference standards for metagenome sequencing and analysis. Nat Commun 2018; 9:3096. [PMID: 30082706 PMCID: PMC6078961 DOI: 10.1038/s41467-018-05555-0] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2018] [Accepted: 06/20/2018] [Indexed: 12/12/2022] Open
Abstract
The complexity of microbial communities, combined with technical biases in next-generation sequencing, pose a challenge to metagenomic analysis. Here, we develop a set of internal DNA standards, termed “sequins” (sequencing spike-ins), that together constitute a synthetic community of artificial microbial genomes. Sequins are added to environmental DNA samples prior to library preparation, and undergo concurrent sequencing with the accompanying sample. We validate the performance of sequins by comparison to mock microbial communities, and demonstrate their use in the analysis of real metagenome samples. We show how sequins can be used to measure fold change differences in the size and structure of accompanying microbial communities, and perform quantitative normalization between samples. We further illustrate how sequins can be used to benchmark and optimize new methods, including nanopore long-read sequencing technology. We provide metagenome sequins, along with associated data sets, protocols, and an accompanying software toolkit, as reference standards to aid in metagenomic studies. Complex microbial communities pose a challenge to metagenomic analysis. Here the authors develop ‘sequins’, internal DNA standards that represent a synthetic community of artificial genomes.
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Affiliation(s)
- Simon A Hardwick
- Garvan Institute of Medical Research, Sydney, 2010, NSW, Australia.,St. Vincent's Clinical School, Faculty of Medicine, UNSW Sydney, Sydney, 2052, NSW, Australia
| | - Wendy Y Chen
- Garvan Institute of Medical Research, Sydney, 2010, NSW, Australia.,St. Vincent's Clinical School, Faculty of Medicine, UNSW Sydney, Sydney, 2052, NSW, Australia
| | - Ted Wong
- Garvan Institute of Medical Research, Sydney, 2010, NSW, Australia
| | | | - Ira W Deveson
- Garvan Institute of Medical Research, Sydney, 2010, NSW, Australia.,St. Vincent's Clinical School, Faculty of Medicine, UNSW Sydney, Sydney, 2052, NSW, Australia
| | - Sarah E Ongley
- School of Biotechnology and Biomolecular Sciences, UNSW Sydney, Sydney, 2052, NSW, Australia.,School of Environmental and Life Sciences, The University of Newcastle, Callaghan, 2308, NSW, Australia
| | - Nadia S Santini
- Centre for Marine Bioinnovation UNSW Sydney, Sydney, 2052, NSW, Australia.,Instituto de Ecologia, Universidad Nacional Autonoma de Mexico, Mexico City, 04500, Mexico
| | - Esteban Marcellin
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, 4072, Queensland, Australia
| | - Martin A Smith
- Garvan Institute of Medical Research, Sydney, 2010, NSW, Australia.,St. Vincent's Clinical School, Faculty of Medicine, UNSW Sydney, Sydney, 2052, NSW, Australia
| | - Lars K Nielsen
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, 4072, Queensland, Australia
| | - Catherine E Lovelock
- School of Biological Sciences, The University of Queensland, Brisbane, 4072, QLD, Australia
| | - Brett A Neilan
- School of Biotechnology and Biomolecular Sciences, UNSW Sydney, Sydney, 2052, NSW, Australia.,School of Environmental and Life Sciences, The University of Newcastle, Callaghan, 2308, NSW, Australia
| | - Tim R Mercer
- Garvan Institute of Medical Research, Sydney, 2010, NSW, Australia. .,St. Vincent's Clinical School, Faculty of Medicine, UNSW Sydney, Sydney, 2052, NSW, Australia. .,Altius Institute for Biomedical Sciences, Seattle, 98121, WA, USA.
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20
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Poussin C, Sierro N, Boué S, Battey J, Scotti E, Belcastro V, Peitsch MC, Ivanov NV, Hoeng J. Interrogating the microbiome: experimental and computational considerations in support of study reproducibility. Drug Discov Today 2018; 23:1644-1657. [PMID: 29890228 DOI: 10.1016/j.drudis.2018.06.005] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Revised: 05/03/2018] [Accepted: 06/06/2018] [Indexed: 12/12/2022]
Abstract
The microbiome is an important factor in human health and disease and is investigated to develop novel therapeutics. Metagenomics leverages advances in sequencing technologies and computational analysis to identify and quantify the microorganisms present in a sample. This field has, however, not yet reached maturity and the international metagenomics community, aware of the current limitations and of the necessity for standardization, has started investigating sources of variability in experimental and computational workflows. The first studies have already resulted in the identification of crucial steps and factors affecting metagenomics data quality, quantification and interpretation. This review summarizes experimental and computational considerations for interrogating the microbiome and establishing reproducible and robust analysis workflows.
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Affiliation(s)
- Carine Poussin
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchâtel, Switzerland
| | - Nicolas Sierro
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchâtel, Switzerland
| | - Stéphanie Boué
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchâtel, Switzerland
| | - James Battey
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchâtel, Switzerland
| | - Elena Scotti
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchâtel, Switzerland
| | - Vincenzo Belcastro
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchâtel, Switzerland
| | - Manuel C Peitsch
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchâtel, Switzerland
| | - Nikolai V Ivanov
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchâtel, Switzerland
| | - Julia Hoeng
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchâtel, Switzerland.
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21
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D'Argenio V. The High-Throughput Analyses Era: Are We Ready for the Data Struggle? High Throughput 2018; 7:E8. [PMID: 29498666 PMCID: PMC5876534 DOI: 10.3390/ht7010008] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2017] [Revised: 02/16/2018] [Accepted: 02/27/2018] [Indexed: 12/23/2022] Open
Abstract
Recent and rapid technological advances in molecular sciences have dramatically increased the ability to carry out high-throughput studies characterized by big data production. This, in turn, led to the consequent negative effect of highlighting the presence of a gap between data yield and their analysis. Indeed, big data management is becoming an increasingly important aspect of many fields of molecular research including the study of human diseases. Now, the challenge is to identify, within the huge amount of data obtained, that which is of clinical relevance. In this context, issues related to data interpretation, sharing and storage need to be assessed and standardized. Once this is achieved, the integration of data from different -omic approaches will improve the diagnosis, monitoring and therapy of diseases by allowing the identification of novel, potentially actionably biomarkers in view of personalized medicine.
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Affiliation(s)
- Valeria D'Argenio
- CEINGE-Biotecnologie Avanzate, via G. Salvatore 486, 80145 Naples, Italy.
- Department of Molecular Medicine and Medical Biotechnologies, University of Naples Federico II, via Pansini 5, 80131 Naples, Italy.
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22
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Shamarina D, Stoyantcheva I, Mason CE, Bibby K, Elhaik E. Communicating the promise, risks, and ethics of large-scale, open space microbiome and metagenome research. MICROBIOME 2017; 5:132. [PMID: 28978331 PMCID: PMC5628477 DOI: 10.1186/s40168-017-0349-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Accepted: 09/20/2017] [Indexed: 05/07/2023]
Abstract
The public commonly associates microorganisms with pathogens. This suspicion of microorganisms is understandable, as historically microorganisms have killed more humans than any other agent while remaining largely unknown until the late seventeenth century with the works of van Leeuwenhoek and Kircher. Despite our improved understanding regarding microorganisms, the general public are apt to think of diseases rather than of the majority of harmless or beneficial species that inhabit our bodies and the built and natural environment. As long as microbiome research was confined to labs, the public's exposure to microbiology was limited. The recent launch of global microbiome surveys, such as the Earth Microbiome Project and MetaSUB (Metagenomics and Metadesign of Subways and Urban Biomes) project, has raised ethical, financial, feasibility, and sustainability concerns as to the public's level of understanding and potential reaction to the findings, which, done improperly, risk negative implications for ongoing and future investigations, but done correctly, can facilitate a new vision of "smart cities." To facilitate improved future research, we describe here the major concerns that our discussions with ethics committees, community leaders, and government officials have raised, and we expound on how to address them. We further discuss ethical considerations of microbiome surveys and provide practical recommendations for public engagement.
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Affiliation(s)
- Daria Shamarina
- Department of Molecular Biology and Biotechnology, University of Sheffield, Sheffield, S10 2TN UK
| | - Iana Stoyantcheva
- Department of Molecular Biology and Biotechnology, University of Sheffield, Sheffield, S10 2TN UK
| | - Christopher E. Mason
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10021 USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY 10021 USA
- The Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10021 USA
| | - Kyle Bibby
- University of Notre Dame Department of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, Notre Dameᅟ, IN 46556 USA
| | - Eran Elhaik
- Department of Animal and Plant Sciences, University of Sheffield, Sheffield, S10 2TN UK
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23
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Hassan C, Afshinnekoo E, Li S, Wu S, Mason CE. Genetic and epigenetic heterogeneity and the impact on cancer relapse. Exp Hematol 2017; 54:26-30. [PMID: 28705639 PMCID: PMC5651672 DOI: 10.1016/j.exphem.2017.07.002] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2017] [Revised: 06/30/2017] [Accepted: 07/04/2017] [Indexed: 12/16/2022]
Abstract
Acute myeloid leukemia (AML) is an aggressive hematopoietic malignancy with an exceedingly poor prognosis: a 5-year overall survival rate of 40%-45% in the young and a 5-year survival rate of less than 10% in the elderly (>60 years of age). Although a high percentage of patients enters complete remission after chemotherapeutic intervention, the majority of patients relapse within 3 years. Such stark prognostic outcomes highlight the need for additional clinical research, basic discovery, and molecular delineation of the etiologies and mechanisms behind responses to therapy that lead to relapse. Here, we summarize recent discoveries in tumor heterogeneity at the genetic and epigenetic levels and their independent molecular trajectories and dynamics in response to therapy. These new discoveries may have significant implications for understanding, monitoring, and treating leukemia and other cancers.
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MESH Headings
- Age Factors
- Antineoplastic Agents/therapeutic use
- Chromosome Aberrations
- Drug Resistance, Neoplasm/genetics
- Epigenesis, Genetic
- Gene Expression Regulation, Leukemic
- Genetic Heterogeneity
- Humans
- Leukemia, Myeloid, Acute/diagnosis
- Leukemia, Myeloid, Acute/drug therapy
- Leukemia, Myeloid, Acute/genetics
- Leukemia, Myeloid, Acute/mortality
- Mutation
- Neoplasm Proteins/genetics
- Neoplasm Proteins/metabolism
- Prognosis
- Recurrence
- Remission Induction
- Single-Cell Analysis
- Survival Analysis
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Affiliation(s)
- Ciaran Hassan
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - Ebrahim Afshinnekoo
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA; School of Medicine, New York Medical College, Valhalla, NY, USA
| | - Sheng Li
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA; The Jackson Laboratory Cancer Center, Bar Harbor, Maine, USA; Department of Genetics and Genome Sciences, University of Connecticut Health Center, Farmington, CT, USA
| | - Shixiu Wu
- Hangzhou Cancer Institute in Hangzhou Cancer Hospital, Hangzhou, China; Department of Radiotherapy, Hangzhou Cancer Hospital, Hangzhou, China
| | - Christopher E Mason
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA; The Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA.
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24
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McIntyre ABR, Ounit R, Afshinnekoo E, Prill RJ, Hénaff E, Alexander N, Minot SS, Danko D, Foox J, Ahsanuddin S, Tighe S, Hasan NA, Subramanian P, Moffat K, Levy S, Lonardi S, Greenfield N, Colwell RR, Rosen GL, Mason CE. Comprehensive benchmarking and ensemble approaches for metagenomic classifiers. Genome Biol 2017; 18:182. [PMID: 28934964 PMCID: PMC5609029 DOI: 10.1186/s13059-017-1299-7] [Citation(s) in RCA: 163] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Accepted: 08/16/2017] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND One of the main challenges in metagenomics is the identification of microorganisms in clinical and environmental samples. While an extensive and heterogeneous set of computational tools is available to classify microorganisms using whole-genome shotgun sequencing data, comprehensive comparisons of these methods are limited. RESULTS In this study, we use the largest-to-date set of laboratory-generated and simulated controls across 846 species to evaluate the performance of 11 metagenomic classifiers. Tools were characterized on the basis of their ability to identify taxa at the genus, species, and strain levels, quantify relative abundances of taxa, and classify individual reads to the species level. Strikingly, the number of species identified by the 11 tools can differ by over three orders of magnitude on the same datasets. Various strategies can ameliorate taxonomic misclassification, including abundance filtering, ensemble approaches, and tool intersection. Nevertheless, these strategies were often insufficient to completely eliminate false positives from environmental samples, which are especially important where they concern medically relevant species. Overall, pairing tools with different classification strategies (k-mer, alignment, marker) can combine their respective advantages. CONCLUSIONS This study provides positive and negative controls, titrated standards, and a guide for selecting tools for metagenomic analyses by comparing ranges of precision, accuracy, and recall. We show that proper experimental design and analysis parameters can reduce false positives, provide greater resolution of species in complex metagenomic samples, and improve the interpretation of results.
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Affiliation(s)
- Alexa B R McIntyre
- Tri-Institutional Program in Computational Biology and Medicine, New York, NY, USA
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, 10021, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, 10021, USA
| | - Rachid Ounit
- Department of Computer Science and Engineering, University of California, Riverside, CA, 92521, USA
| | - Ebrahim Afshinnekoo
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, 10021, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, 10021, USA
- School of Medicine, New York Medical College, Valhalla, NY, 10595, USA
| | - Robert J Prill
- Accelerated Discovery Lab, IBM Almaden Research Center, San Jose, CA, 95120, USA
| | - Elizabeth Hénaff
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, 10021, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, 10021, USA
| | - Noah Alexander
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, 10021, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, 10021, USA
| | - Samuel S Minot
- One Codex, Reference Genomics, San Francisco, CA, 94103, USA
| | - David Danko
- Tri-Institutional Program in Computational Biology and Medicine, New York, NY, USA
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, 10021, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, 10021, USA
| | - Jonathan Foox
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, 10021, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, 10021, USA
| | - Sofia Ahsanuddin
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, 10021, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, 10021, USA
| | - Scott Tighe
- University of Vermont, Burlington, VT, 05405, USA
| | - Nur A Hasan
- CosmosID, Inc, Rockville, MD, 20850, USA
- Center for Bioinformatics and Computational Biology, University of Maryland Institute for Advanced Computer Studies (UMIACS), College Park, MD, 20742, USA
| | | | | | - Shawn Levy
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, 35806, USA
| | - Stefano Lonardi
- Department of Computer Science and Engineering, University of California, Riverside, CA, 92521, USA
| | - Nick Greenfield
- One Codex, Reference Genomics, San Francisco, CA, 94103, USA
| | - Rita R Colwell
- CosmosID, Inc, Rockville, MD, 20850, USA
- Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA
| | - Gail L Rosen
- Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, 19104, USA.
| | - Christopher E Mason
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, 10021, USA.
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, 10021, USA.
- The Feil Family Brain and Mind Research Institute, New York, NY, 10065, USA.
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25
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Large-scale differences in microbial biodiversity discovery between 16S amplicon and shotgun sequencing. Sci Rep 2017; 7:6589. [PMID: 28761145 PMCID: PMC5537354 DOI: 10.1038/s41598-017-06665-3] [Citation(s) in RCA: 112] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2017] [Accepted: 06/15/2017] [Indexed: 01/27/2023] Open
Abstract
Modern metagenomic environmental DNA studies are almost completely reliant on next-generation sequencing, making evaluations of these methods critical. We compare two next-generation sequencing techniques – amplicon and shotgun – on water samples across four of Brazil’s major river floodplain systems (Amazon, Araguaia, Paraná, and Pantanal). Less than 50% of phyla identified via amplicon sequencing were recovered from shotgun sequencing, clearly challenging the dogma that mid-depth shotgun recovers more diversity than amplicon-based approaches. Amplicon sequencing also revealed ~27% more families. Overall the amplicon data were more robust across both biodiversity and community ecology analyses at different taxonomic scales. Our work doubles the sampling size in similar environmental studies, and novelly integrates environmental data (e.g., pH, temperature, nutrients) from each site, revealing divergent correlations depending on which data are used. While myriad variants on NGS techniques and bioinformatic pipelines are available, our results point to core differences that have not been highlighted in any studies to date. Given the low number of taxa identified when coupling shotgun data with clade-based taxonomic algorithms, previous studies that quantified biodiversity using such bioinformatic tools should be viewed cautiously or re-analyzed. Nonetheless, shotgun has complementary advantages that should be weighed when designing projects.
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