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Russo AE, Memon A, Ahmed S. Bladder Cancer and the Urinary Microbiome-New Insights and Future Directions: A Review. Clin Genitourin Cancer 2024; 22:434-444. [PMID: 38220540 DOI: 10.1016/j.clgc.2023.12.015] [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: 10/10/2023] [Revised: 12/26/2023] [Accepted: 12/28/2023] [Indexed: 01/16/2024]
Abstract
The presence of a microbiome in the urinary system has been established through recent advancements in technology and investigation of microbial communities in the human body. The study of the taxonomic and genomic ecology of microbial communities has been greatly improved by the use of metagenomics. The research in this area has expanded our understanding of microbial ecosystems and shows that the urinary tract contains over 100 species from over 50 genera, with Lactobacillus, Gardnerella, and Streptococcus being the most common. Previous studies have suggested that the microbiota in the urinary tract may play a role in carcinogenesis by causing chronic inflammation and genotoxicity, but more research is needed to reach a definite conclusion. This is a narrative review. We conducted a search for relevant publications by using the databases Medline/PubMed and Google Scholar. The search was based on keywords such as "urinary microbiome," "bladder cancer," "carcinogenesis," "urothelial carcinoma," and "next-generation sequencing." The retrieved publications were then reviewed to study the contribution of the urinary microbiome in the development of bladder cancer. The results have been categorized into four sections to enhance understanding of the urinary microbiome and to highlight its role in the emergence of bladder cancer through alterations in the immune response that involve T-cells and antibodies. The immune system and microbiome play crucial roles in maintaining health and preventing disease. Manipulating the immune system is a key aspect of various cancer treatments, and certain gut bacteria have been linked to positive responses to immunotherapies. However, the impact of these treatments on the urinary microbiome, and how diet and lifestyle affect it, are not well understood. Research in this area could have significant implications for improving bladder cancer treatment and patient outcomes.
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Affiliation(s)
- Angela E Russo
- Larner College of Medicine, University of Vermont, Burlington, VT.
| | - Areeba Memon
- Medical College, Aga Khan University, Karachi, Sindh, Pakistan
| | - Shahid Ahmed
- Department of Hematology and Oncology, University of Vermont, Burlington, VT
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Dias CK, Starke R, Pylro VS, Morais DK. Database limitations for studying the human gut microbiome. PeerJ Comput Sci 2020; 6:e289. [PMID: 33816940 PMCID: PMC7924478 DOI: 10.7717/peerj-cs.289] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Accepted: 07/15/2020] [Indexed: 05/12/2023]
Abstract
BACKGROUND In the last twenty years, new methodologies have made possible the gathering of large amounts of data concerning the genetic information and metabolic functions associated to the human gut microbiome. In spite of that, processing all this data available might not be the simplest of tasks, which could result in an excess of information awaiting proper annotation. This assessment intended on evaluating how well respected databases could describe a mock human gut microbiome. METHODS In this work, we critically evaluate the output of the cross-reference between the Uniprot Knowledge Base (Uniprot KB) and the Kyoto Encyclopedia of Genes and Genomes Orthologs (KEGG Orthologs) or the evolutionary genealogy of genes: Non-supervised Orthologous groups (EggNOG) databases regarding a list of species that were previously found in the human gut microbiome. RESULTS From a list which contemplates 131 species and 52 genera, 53 species and 40 genera had corresponding entries for KEGG Database and 82 species and 47 genera had corresponding entries for EggNOG Database. Moreover, we present the KEGG Orthologs (KOs) and EggNOG Orthologs (NOGs) entries associated to the search as their distribution over species and genera and lists of functions that appeared in many species or genera, the "core" functions of the human gut microbiome. We also present the relative abundance of KOs and NOGs throughout phyla and genera. Lastly, we expose a variance found between searches with different arguments on the database entries. Inferring functionality based on cross-referencing UniProt and KEGG or EggNOG can be lackluster due to the low number of annotated species in Uniprot and due to the lower number of functions affiliated to the majority of these species. Additionally, the EggNOG database showed greater performance for a cross-search with Uniprot about a mock human gut microbiome. Notwithstanding, efforts targeting cultivation, single-cell sequencing or the reconstruction of high-quality metagenome-assembled genomes (MAG) and their annotation are needed to allow the use of these databases for inferring functionality in human gut microbiome studies.
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Affiliation(s)
- Camila K Dias
- Departament of Biochemistry, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil
| | - Robert Starke
- Institute of Microbiology of the Czech Academy of Sciences, Prague, Czech Republic
| | - Victor S. Pylro
- Department of Biology, Universidade Federal de Lavras - UFLA, Lavras, Minas Gerais, Brazil
| | - Daniel K. Morais
- Institute of Microbiology of the Czech Academy of Sciences, Prague, Czech Republic
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Sakowski E, Uritskiy G, Cooper R, Gomes M, McLaren MR, Meisel JS, Mickol RL, Mintz CD, Mongodin EF, Pop M, Rahman MA, Sanchez A, Timp W, Vela JD, Wolz CM, Zackular JP, Chopyk J, Commichaux S, Davis M, Dluzen D, Ganesan SM, Haruna M, Nasko D, Regan MJ, Sarria S, Shah N, Stacy B, Taylor D, DiRuggiero J, Preheim SP. Current State of and Future Opportunities for Prediction in Microbiome Research: Report from the Mid-Atlantic Microbiome Meet-up in Baltimore on 9 January 2019. mSystems 2019; 4:e00392-19. [PMID: 31594828 PMCID: PMC6787564 DOI: 10.1128/msystems.00392-19] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Accurate predictions across multiple fields of microbiome research have far-reaching benefits to society, but there are few widely accepted quantitative tools to make accurate predictions about microbial communities and their functions. More discussion is needed about the current state of microbiome analysis and the tools required to overcome the hurdles preventing development and implementation of predictive analyses. We summarize the ideas generated by participants of the Mid-Atlantic Microbiome Meet-up in January 2019. While it was clear from the presentations that most fields have advanced beyond simple associative and descriptive analyses, most fields lack essential elements needed for the development and application of accurate microbiome predictions. Participants stressed the need for standardization, reproducibility, and accessibility of quantitative tools as key to advancing predictions in microbiome analysis. We highlight hurdles that participants identified and propose directions for future efforts that will advance the use of prediction in microbiome research.
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Affiliation(s)
- Eric Sakowski
- Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Gherman Uritskiy
- Department of Biology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Rachel Cooper
- Molecular and Comparative Pathobiology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Maya Gomes
- Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, Maryland, USA
| | - Michael R McLaren
- Department of Population Health and Pathobiology, North Carolina State University, Raleigh, North Carolina, USA
| | - Jacquelyn S Meisel
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA
| | | | - C David Mintz
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Emmanuel F Mongodin
- University of Maryland School of Medicine, Institute for Genome Sciences, Baltimore, Maryland, USA
| | - Mihai Pop
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA
| | | | - Alvaro Sanchez
- Department of Ecology and Evolutionary Biology, Yale University, New Haven Connecticut, USA
| | - Winston Timp
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Jeseth Delgado Vela
- Department of Civil and Environmental Engineering, Howard University, Washington, DC, USA
| | - Carly Muletz Wolz
- Center for Conservation Genomics, Smithsonian National Zoological Park & Conservation Biology Institute, Washington, DC, USA
| | - Joseph P Zackular
- Department of Pathology and Laboratory Medicine, University of Pennsylvania and Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Jessica Chopyk
- School of Public Health, University of Maryland, College Park, Maryland, USA
| | - Seth Commichaux
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA
| | - Meghan Davis
- Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Douglas Dluzen
- Department of Biology, Morgan State University, Baltimore, Maryland, USA
| | - Sukirth M Ganesan
- National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, Maryland, USA
| | - Muyideen Haruna
- Department of Biology, Morgan State University, Baltimore, Maryland, USA
| | - Dan Nasko
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA
| | - Mary J Regan
- University of Maryland School of Nursing, Baltimore, Maryland, USA
| | - Saul Sarria
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA
| | - Nidhi Shah
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA
| | - Brook Stacy
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA
| | - Dylan Taylor
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA
| | | | - Sarah P Preheim
- Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, Maryland, USA
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Piper AM, Batovska J, Cogan NOI, Weiss J, Cunningham JP, Rodoni BC, Blacket MJ. Prospects and challenges of implementing DNA metabarcoding for high-throughput insect surveillance. Gigascience 2019; 8:giz092. [PMID: 31363753 PMCID: PMC6667344 DOI: 10.1093/gigascience/giz092] [Citation(s) in RCA: 82] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Revised: 06/25/2019] [Accepted: 07/09/2019] [Indexed: 12/21/2022] Open
Abstract
Trap-based surveillance strategies are widely used for monitoring of invasive insect species, aiming to detect newly arrived exotic taxa as well as track the population levels of established or endemic pests. Where these surveillance traps have low specificity and capture non-target endemic species in excess of the target pests, the need for extensive specimen sorting and identification creates a major diagnostic bottleneck. While the recent development of standardized molecular diagnostics has partly alleviated this requirement, the single specimen per reaction nature of these methods does not readily scale to the sheer number of insects trapped in surveillance programmes. Consequently, target lists are often restricted to a few high-priority pests, allowing unanticipated species to avoid detection and potentially establish populations. DNA metabarcoding has recently emerged as a method for conducting simultaneous, multi-species identification of complex mixed communities and may lend itself ideally to rapid diagnostics of bulk insect trap samples. Moreover, the high-throughput nature of recent sequencing platforms could enable the multiplexing of hundreds of diverse trap samples on a single flow cell, thereby providing the means to dramatically scale up insect surveillance in terms of both the quantity of traps that can be processed concurrently and number of pest species that can be targeted. In this review of the metabarcoding literature, we explore how DNA metabarcoding could be tailored to the detection of invasive insects in a surveillance context and highlight the unique technical and regulatory challenges that must be considered when implementing high-throughput sequencing technologies into sensitive diagnostic applications.
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Affiliation(s)
- Alexander M Piper
- Agriculture Victoria Research, AgriBio Centre, 5 Ring Road, Bundoora 3083, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora 3083, VIC, Australia
| | - Jana Batovska
- Agriculture Victoria Research, AgriBio Centre, 5 Ring Road, Bundoora 3083, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora 3083, VIC, Australia
| | - Noel O I Cogan
- Agriculture Victoria Research, AgriBio Centre, 5 Ring Road, Bundoora 3083, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora 3083, VIC, Australia
| | - John Weiss
- Agriculture Victoria Research, AgriBio Centre, 5 Ring Road, Bundoora 3083, VIC, Australia
| | - John Paul Cunningham
- Agriculture Victoria Research, AgriBio Centre, 5 Ring Road, Bundoora 3083, VIC, Australia
| | - Brendan C Rodoni
- Agriculture Victoria Research, AgriBio Centre, 5 Ring Road, Bundoora 3083, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora 3083, VIC, Australia
| | - Mark J Blacket
- Agriculture Victoria Research, AgriBio Centre, 5 Ring Road, Bundoora 3083, VIC, Australia
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