1
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Liebhoff AM, Menden K, Laschtowitz A, Franke A, Schramm C, Bonn S. Pathogen detection in RNA-seq data with Pathonoia. BMC Bioinformatics 2023; 24:53. [PMID: 36803415 PMCID: PMC9938591 DOI: 10.1186/s12859-023-05144-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 01/10/2023] [Indexed: 02/19/2023] Open
Abstract
BACKGROUND Bacterial and viral infections may cause or exacerbate various human diseases and to detect microbes in tissue, one method of choice is RNA sequencing. The detection of specific microbes using RNA sequencing offers good sensitivity and specificity, but untargeted approaches suffer from high false positive rates and a lack of sensitivity for lowly abundant organisms. RESULTS We introduce Pathonoia, an algorithm that detects viruses and bacteria in RNA sequencing data with high precision and recall. Pathonoia first applies an established k-mer based method for species identification and then aggregates this evidence over all reads in a sample. In addition, we provide an easy-to-use analysis framework that highlights potential microbe-host interactions by correlating the microbial to the host gene expression. Pathonoia outperforms state-of-the-art methods in microbial detection specificity, both on in silico and real datasets. CONCLUSION Two case studies in human liver and brain show how Pathonoia can support novel hypotheses on microbial infection exacerbating disease. The Python package for Pathonoia sample analysis and a guided analysis Jupyter notebook for bulk RNAseq datasets are available on GitHub.
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Affiliation(s)
- Anna-Maria Liebhoff
- Institute for Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany. .,Department of Computer Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, USA.
| | - Kevin Menden
- grid.424247.30000 0004 0438 0426Department of Genome Biology of Neurodegenerative Diseases, DZNE, Tübingen, Germany
| | - Alena Laschtowitz
- grid.13648.380000 0001 2180 3484I. Department of Medicine, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Andre Franke
- grid.9764.c0000 0001 2153 9986Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Christoph Schramm
- grid.13648.380000 0001 2180 3484I. Department of Medicine, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany ,grid.13648.380000 0001 2180 3484Martin Zeitz Center for Rare Diseases, University Medical Center Hamburg-Eppendorf, Hamburg, Germany ,grid.13648.380000 0001 2180 3484Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Stefan Bonn
- grid.13648.380000 0001 2180 3484Institute for Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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2
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Aparicio-Puerta E, Hirsch P, Schmartz GP, Fehlmann T, Keller V, Engel A, Kern F, Hackenberg M, Keller A. isomiRdb: microRNA expression at isoform resolution. Nucleic Acids Res 2022; 51:D179-D185. [PMID: 36243964 PMCID: PMC9825445 DOI: 10.1093/nar/gkac884] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 09/19/2022] [Accepted: 09/30/2022] [Indexed: 01/29/2023] Open
Abstract
A significant fraction of mature miRNA transcripts carries sequence and/or length variations, termed isomiRs. IsomiRs are differentially abundant in cell types, tissues, body fluids or patients' samples. Not surprisingly, multiple studies describe a physiological and pathophysiological role. Despite their importance, systematically collected and annotated isomiR information available in databases remains limited. We thus developed isomiRdb, a comprehensive resource that compiles miRNA expression data at isomiR resolution from various sources. We processed 42 499 human miRNA-seq datasets (5.9 × 1011 sequencing reads) and consistently analyzed them using miRMaster and sRNAbench. Our database provides online access to the 90 483 most abundant isomiRs (>1 RPM in at least 1% of the samples) from 52 tissues and 188 cell types. Additionally, the full set of over 3 million detected isomiRs is available for download. Our resource can be queried at the sample, miRNA or isomiR level so users can quickly answer common questions about the presence/absence of a particular miRNA/isomiR in tissues of interest. Further, the database facilitates to identify whether a potentially interesting new isoform has been detected before and its frequency. In addition to expression tables, isomiRdb can generate multiple interactive visualisations including violin plots and heatmaps. isomiRdb is free to use and publicly available at: https://www.ccb.uni-saarland.de/isomirdb.
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Affiliation(s)
| | | | - Georges P Schmartz
- Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
| | - Tobias Fehlmann
- Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany,Rejuvenome, Astera Institute, Berkeley, CA 94705, USA
| | - Verena Keller
- Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany,Department for Internal Medicine II, Saarland University Hospital, 66421 Homburg, Germany
| | - Annika Engel
- Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
| | - Fabian Kern
- Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany,Helmholtz Institute for Pharmaceutical Research Saarland (HIPS)–Helmholtz Centre for Infection Research (HZI), Saarland University Campus, 66123 Saarbrücken, Germany
| | - Michael Hackenberg
- Genetics Department, Faculty of Science, Universidad de Granada, 18071 Granada, Spain
| | - Andreas Keller
- To whom correspondence should be addressed. Tel: +49 681 30268611; Fax: +49 681 30268610;
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3
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Alsop E, Meechoovet B, Kitchen R, Sweeney T, Beach TG, Serrano GE, Hutchins E, Ghiran I, Reiman R, Syring M, Hsieh M, Courtright-Lim A, Valkov N, Whitsett TG, Rakela J, Pockros P, Rozowsky J, Gallego J, Huentelman MJ, Shah R, Nakaji P, Kalani MYS, Laurent L, Das S, Van Keuren-Jensen K. A Novel Tissue Atlas and Online Tool for the Interrogation of Small RNA Expression in Human Tissues and Biofluids. Front Cell Dev Biol 2022; 10:804164. [PMID: 35317387 PMCID: PMC8934391 DOI: 10.3389/fcell.2022.804164] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 01/28/2022] [Indexed: 12/20/2022] Open
Abstract
One promising goal for utilizing the molecular information circulating in biofluids is the discovery of clinically useful biomarkers. Extracellular RNAs (exRNAs) are one of the most diverse classes of molecular cargo, easily assayed by sequencing and with expressions that rapidly change in response to subject status. Despite diverse exRNA cargo, most evaluations from biofluids have focused on small RNA sequencing and analysis, specifically on microRNAs (miRNAs). Another goal of characterizing circulating molecular information, is to correlate expression to injuries associated with specific tissues of origin. Biomarker candidates are often described as being specific, enriched in a particular tissue or associated with a disease process. Likewise, miRNA data is often reported to be specific, enriched for a tissue, without rigorous testing to support the claim. Here we provide a tissue atlas of small RNAs from 30 different tissues and three different blood cell types. We analyzed the tissues for enrichment of small RNA sequences and assessed their expression in biofluids: plasma, cerebrospinal fluid, urine, and saliva. We employed published data sets representing physiological (resting vs. acute exercise) and pathologic states (early- vs. late-stage liver fibrosis, and differential subtypes of stroke) to determine differential tissue-enriched small RNAs. We also developed an online tool that provides information about exRNA sequences found in different biofluids and tissues. The data can be used to better understand the various types of small RNA sequences in different tissues as well as their potential release into biofluids, which should help in the validation or design of biomarker studies.
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Affiliation(s)
- Eric Alsop
- Neurogenomics Division, The Translational Genomics Research Institute, Phoenix, AZ, United States
| | - Bessie Meechoovet
- Neurogenomics Division, The Translational Genomics Research Institute, Phoenix, AZ, United States
| | - Robert Kitchen
- Cardiovascular Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Thadryan Sweeney
- Cardiovascular Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Thomas G. Beach
- Banner Sun Health Research Institute, Sun City, AZ, United States
| | - Geidy E. Serrano
- Banner Sun Health Research Institute, Sun City, AZ, United States
| | - Elizabeth Hutchins
- Neurogenomics Division, The Translational Genomics Research Institute, Phoenix, AZ, United States
| | - Ionita Ghiran
- Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, United States
| | - Rebecca Reiman
- Neurogenomics Division, The Translational Genomics Research Institute, Phoenix, AZ, United States
| | - Michael Syring
- Neurogenomics Division, The Translational Genomics Research Institute, Phoenix, AZ, United States
| | - Michael Hsieh
- Neurogenomics Division, The Translational Genomics Research Institute, Phoenix, AZ, United States
| | - Amanda Courtright-Lim
- Neurogenomics Division, The Translational Genomics Research Institute, Phoenix, AZ, United States
| | - Nedyalka Valkov
- Cardiovascular Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Timothy G. Whitsett
- Neurogenomics Division, The Translational Genomics Research Institute, Phoenix, AZ, United States
| | | | - Paul Pockros
- Division of Gastroenterology/Hepatology, Scripps Clinic, La Jolla, CA, United States
| | - Joel Rozowsky
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, United States
| | - Juan Gallego
- Institute for Behavioral Science, The Feinstein Institute for Medical Research, Manhasset, NY, United States
- Division of Psychiatry Research, The Zucker Hillside Hospital, Glen Oaks, NY, United States
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Matthew J. Huentelman
- Neurogenomics Division, The Translational Genomics Research Institute, Phoenix, AZ, United States
| | - Ravi Shah
- Cardiovascular Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Peter Nakaji
- Department of Neurosurgery, Banner Health, Phoenix, AZ, United States
| | - M. Yashar S. Kalani
- Department of Neurosurgery, St. John Medical Center, Tulsa, OK, United States
| | - Louise Laurent
- Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Diego, San Diego, CA, United States
| | - Saumya Das
- Cardiovascular Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
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4
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Hanusek K, Poletajew S, Kryst P, Piekiełko-Witkowska A, Bogusławska J. piRNAs and PIWI Proteins as Diagnostic and Prognostic Markers of Genitourinary Cancers. Biomolecules 2022; 12:biom12020186. [PMID: 35204687 PMCID: PMC8869487 DOI: 10.3390/biom12020186] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 01/14/2022] [Accepted: 01/18/2022] [Indexed: 12/30/2022] Open
Abstract
piRNAs (PIWI-interacting RNAs) are small non-coding RNAs capable of regulation of transposon and gene expression. piRNAs utilise multiple mechanisms to affect gene expression, which makes them potentially more powerful regulators than microRNAs. The mechanisms by which piRNAs regulate transposon and gene expression include DNA methylation, histone modifications, and mRNA degradation. Genitourinary cancers (GC) are a large group of neoplasms that differ by their incidence, clinical course, biology, and prognosis for patients. Regardless of the GC type, metastatic disease remains a key therapeutic challenge, largely affecting patients’ survival rates. Recent studies indicate that piRNAs could serve as potentially useful biomarkers allowing for early cancer detection and therapeutic interventions at the stage of non-advanced tumour, improving patient’s outcomes. Furthermore, studies in prostate cancer show that piRNAs contribute to cancer progression by affecting key oncogenic pathways such as PI3K/AKT. Here, we discuss recent findings on biogenesis, mechanisms of action and the role of piRNAs and the associated PIWI proteins in GC. We also present tools that may be useful for studies on the functioning of piRNAs in cancers.
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Affiliation(s)
- Karolina Hanusek
- Centre of Postgraduate Medical Education, Department of Biochemistry and Molecular Biology, 01-813 Warsaw, Poland;
| | - Sławomir Poletajew
- Centre of Postgraduate Medical Education, II Department of Urology, 01-813 Warsaw, Poland; (S.P.); (P.K.)
| | - Piotr Kryst
- Centre of Postgraduate Medical Education, II Department of Urology, 01-813 Warsaw, Poland; (S.P.); (P.K.)
| | - Agnieszka Piekiełko-Witkowska
- Centre of Postgraduate Medical Education, Department of Biochemistry and Molecular Biology, 01-813 Warsaw, Poland;
- Correspondence: (A.P.-W.); (J.B.)
| | - Joanna Bogusławska
- Centre of Postgraduate Medical Education, Department of Biochemistry and Molecular Biology, 01-813 Warsaw, Poland;
- Correspondence: (A.P.-W.); (J.B.)
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5
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Rincón-Riveros A, Morales D, Rodríguez JA, Villegas VE, López-Kleine L. Bioinformatic Tools for the Analysis and Prediction of ncRNA Interactions. Int J Mol Sci 2021; 22:11397. [PMID: 34768830 PMCID: PMC8583695 DOI: 10.3390/ijms222111397] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Revised: 09/30/2021] [Accepted: 09/30/2021] [Indexed: 12/16/2022] Open
Abstract
Noncoding RNAs (ncRNAs) play prominent roles in the regulation of gene expression via their interactions with other biological molecules such as proteins and nucleic acids. Although much of our knowledge about how these ncRNAs operate in different biological processes has been obtained from experimental findings, computational biology can also clearly substantially boost this knowledge by suggesting possible novel interactions of these ncRNAs with other molecules. Computational predictions are thus used as an alternative source of new insights through a process of mutual enrichment because the information obtained through experiments continuously feeds through into computational methods. The results of these predictions in turn shed light on possible interactions that are subsequently validated experimentally. This review describes the latest advances in databases, bioinformatic tools, and new in silico strategies that allow the establishment or prediction of biological interactions of ncRNAs, particularly miRNAs and lncRNAs. The ncRNA species described in this work have a special emphasis on those found in humans, but information on ncRNA of other species is also included.
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Affiliation(s)
- Andrés Rincón-Riveros
- Bioinformatics and Systems Biology Group, Universidad Nacional de Colombia, Bogotá 111221, Colombia;
| | - Duvan Morales
- Centro de Investigaciones en Microbiología y Biotecnología-UR (CIMBIUR), Facultad de Ciencias Naturales, Universidad del Rosario, Bogotá 111221, Colombia;
| | - Josefa Antonia Rodríguez
- Grupo de Investigación en Biología del Cáncer, Instituto Nacional de Cancerología, Bogotá 111221, Colombia;
| | - Victoria E. Villegas
- Centro de Investigaciones en Microbiología y Biotecnología-UR (CIMBIUR), Facultad de Ciencias Naturales, Universidad del Rosario, Bogotá 111221, Colombia;
| | - Liliana López-Kleine
- Department of Statistics, Faculty of Science, Universidad Nacional de Colombia, Bogotá 111221, Colombia
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6
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Keller A, Gröger L, Tschernig T, Solomon J, Laham O, Schaum N, Wagner V, Kern F, Schmartz GP, Li Y, Borcherding A, Meier C, Wyss-Coray T, Meese E, Fehlmann T, Ludwig N. miRNATissueAtlas2: an update to the human miRNA tissue atlas. Nucleic Acids Res 2021; 50:D211-D221. [PMID: 34570238 PMCID: PMC8728130 DOI: 10.1093/nar/gkab808] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 08/26/2021] [Accepted: 09/07/2021] [Indexed: 12/12/2022] Open
Abstract
Small non-coding RNAs (sncRNAs) are pervasive regulators of physiological and pathological processes. We previously developed the human miRNA Tissue Atlas, detailing the expression of miRNAs across organs in the human body. Here, we present an updated resource containing sequencing data of 188 tissue samples comprising 21 organ types retrieved from six humans. Sampling the organs from the same bodies minimizes intra-individual variability and facilitates the making of a precise high-resolution body map of the non-coding transcriptome. The data allow shedding light on the organ- and organ system-specificity of piwi-interacting RNAs (piRNAs), transfer RNAs (tRNAs), microRNAs (miRNAs) and other non-coding RNAs. As use case of our resource, we describe the identification of highly specific ncRNAs in different organs. The update also contains 58 samples from six tissues of the Tabula Muris collection, allowing to check if the tissue specificity is evolutionary conserved between Homo sapiens and Mus musculus. The updated resource of 87 252 non-coding RNAs from nine non-coding RNA classes for all organs and organ systems is available online without any restrictions (https://www.ccb.uni-saarland.de/tissueatlas2).
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Affiliation(s)
- Andreas Keller
- Clinical Bioinformatics, Center for Bioinformatics, Saarland University, 66123 Saarbrücken, Germany.,Department of Neurology and Neurobiology, Stanford University, CA 94305, USA
| | - Laura Gröger
- Center for Human and Molecular Biology, Junior Research Group Human Genetics, Saarland University, 66421 Homburg, Germany
| | - Thomas Tschernig
- Institute for Anatomy, Saarland University, 66421 Homburg, Germany
| | - Jeffrey Solomon
- Clinical Bioinformatics, Center for Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
| | - Omar Laham
- Clinical Bioinformatics, Center for Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
| | - Nicholas Schaum
- Department of Neurology and Neurobiology, Stanford University, CA 94305, USA
| | - Viktoria Wagner
- Clinical Bioinformatics, Center for Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
| | - Fabian Kern
- Clinical Bioinformatics, Center for Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
| | - Georges Pierre Schmartz
- Clinical Bioinformatics, Center for Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
| | - Yongping Li
- Clinical Bioinformatics, Center for Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
| | | | - Carola Meier
- Institute for Anatomy, Saarland University, 66421 Homburg, Germany
| | - Tony Wyss-Coray
- Department of Neurology and Neurobiology, Stanford University, CA 94305, USA
| | - Eckart Meese
- Department of Human Genetics, Saarland University, 66421 Homburg, Germany
| | - Tobias Fehlmann
- Clinical Bioinformatics, Center for Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
| | - Nicole Ludwig
- Center for Human and Molecular Biology, Junior Research Group Human Genetics, Saarland University, 66421 Homburg, Germany
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7
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Kavakiotis I, Alexiou A, Tastsoglou S, Vlachos IS, Hatzigeorgiou AG. DIANA-miTED: a microRNA tissue expression database. Nucleic Acids Res 2021; 50:D1055-D1061. [PMID: 34469540 PMCID: PMC8728140 DOI: 10.1093/nar/gkab733] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 08/27/2021] [Indexed: 12/15/2022] Open
Abstract
microRNAs (miRNAs) are short (∼23nt) single-stranded non-coding RNAs that act as potent post-transcriptional gene expression regulators. Information about miRNA expression and distribution across cell types and tissues is crucial to the understanding of their function and for their translational use as biomarkers or therapeutic targets. DIANA-miTED is the most comprehensive and systematic collection of miRNA expression values derived from the analysis of 15 183 raw human small RNA-Seq (sRNA-Seq) datasets from the Sequence Read Archive (SRA) and The Cancer Genome Atlas (TCGA). Metadata quality maximizes the utility of expression atlases, therefore we manually curated SRA and TCGA-derived information to deliver a comprehensive and standardized set, incorporating in total 199 tissues, 82 anatomical sublocations, 267 cell lines and 261 diseases. miTED offers rich instant visualizations of the expression and sample distributions of requested data across variables, as well as study-wide diagrams and graphs enabling efficient content exploration. Queries also generate links towards state-of-the-art miRNA functional resources, deeming miTED an ideal starting point for expression retrieval, exploration, comparison, and downstream analysis, without requiring bioinformatics support or expertise. DIANA-miTED is freely available at http://www.microrna.gr/mited.
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Affiliation(s)
- Ioannis Kavakiotis
- DIANA-Lab, Dept. of Computer Science and Biomedical Informatics, Univ. of Thessaly, 35131 Lamia, Greece
| | - Athanasios Alexiou
- DIANA-Lab, Dept. of Computer Science and Biomedical Informatics, Univ. of Thessaly, 35131 Lamia, Greece.,Hellenic Pasteur Institute, 11521 Athens, Greece
| | - Spyros Tastsoglou
- DIANA-Lab, Dept. of Computer Science and Biomedical Informatics, Univ. of Thessaly, 35131 Lamia, Greece.,Hellenic Pasteur Institute, 11521 Athens, Greece
| | - Ioannis S Vlachos
- Cancer Research Institute
- Harvard Medical School Initiative for RNA Medicine, Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Artemis G Hatzigeorgiou
- DIANA-Lab, Dept. of Computer Science and Biomedical Informatics, Univ. of Thessaly, 35131 Lamia, Greece.,Hellenic Pasteur Institute, 11521 Athens, Greece
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8
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Zhao Y, Panzer U, Bonn S, Krebs CF. Single-cell biology to decode the immune cellular composition of kidney inflammation. Cell Tissue Res 2021; 385:435-443. [PMID: 34125286 PMCID: PMC8200789 DOI: 10.1007/s00441-021-03483-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Accepted: 06/03/2021] [Indexed: 12/26/2022]
Abstract
Single-cell biology is transforming the ability of researchers to understand cellular signaling and identity across medical and biological disciplines. Especially for immune-mediated diseases, a single-cell look at immune cell subtypes, signaling, and activity might yield fundamental insights into the disease etiology, mechanisms, and potential therapeutic interventions. In this review, we highlight recent advances in the field of single-cell RNA profiling and their application to understand renal function in health and disease. With a focus on the immune system, in particular on T cells, we propose some key directions of understanding renal inflammation using single-cell approaches. We detail the benefits and shortcomings of the various technological approaches outlined and give advice on potential pitfalls and challenges in experimental setup and computational analysis. Finally, we conclude with a brief outlook into a promising future for single-cell technologies to elucidate kidney function.
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Affiliation(s)
- Yu Zhao
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
- Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
- Translational Immunology, III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
| | - Ulf Panzer
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Translational Immunology, III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Stefan Bonn
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Christian F Krebs
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
- Translational Immunology, III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
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9
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Meng Q, Chu Y, Shao C, Chen J, Wang J, Gao Z, Yu J, Kang Y. Roles of host small RNAs in the evolution and host tropism of coronaviruses. Brief Bioinform 2021; 22:1096-1105. [PMID: 33587745 PMCID: PMC7929378 DOI: 10.1093/bib/bbab027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Revised: 12/30/2020] [Accepted: 01/28/2021] [Indexed: 12/13/2022] Open
Abstract
Human coronaviruses (CoVs) can cause respiratory infection epidemics that sometimes expand into globally relevant pandemics. All human CoVs have sister strains isolated from animal hosts and seem to have an animal origin, yet the process of host jumping is largely unknown. RNA interference (RNAi) is an ancient mechanism in many eukaryotes to defend against viral infections through the hybridization of host endogenous small RNAs (miRNAs) with target sites in invading RNAs. Here, we developed a method to identify potential RNAi-sensitive sites in the viral genome and discovered that human-adapted coronavirus strains had deleted some of their sites targeted by miRNAs in human lungs when compared to their close zoonic relatives. We further confirmed using a phylogenetic analysis that the loss of RNAi-sensitive target sites could be a major driver of the host-jumping process, and adaptive mutations that lead to the loss-of-target might be as simple as point mutation. Up-to-date genomic data of severe acute respiratory syndrome coronavirus 2 and Middle-East respiratory syndromes-CoV strains demonstrate that the stress from host miRNA milieus sustained even after their epidemics in humans. Thus, this study illustrates a new mechanism about coronavirus to explain its host-jumping process and provides a novel avenue for pathogenesis research, epidemiological modeling, and development of drugs and vaccines against coronavirus, taking into consideration these findings.
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Affiliation(s)
- Qingren Meng
- Southern University of Science and Technology School of Medicine, China
| | - Yanan Chu
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Changjun Shao
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Jing Chen
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Jian Wang
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | | | - Jun Yu
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Yu Kang
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
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10
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Dexheimer PJ, Wang J, Cochella L. Two MicroRNAs Are Sufficient for Embryonic Patterning in C. elegans. Curr Biol 2020; 30:5058-5065.e5. [PMID: 33125867 PMCID: PMC7758728 DOI: 10.1016/j.cub.2020.09.066] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 08/25/2020] [Accepted: 09/21/2020] [Indexed: 11/17/2022]
Abstract
MicroRNAs (miRNAs) are a class of post-transcriptional repressors with diverse roles in animal development and physiology [1]. The Microprocessor complex, composed of Drosha and Pasha/DGCR8, is necessary for the biogenesis of all canonical miRNAs and essential for the early stages of animal embryogenesis [2, 3, 4, 5, 6, 7, 8]. However, the cause for this requirement is largely unknown. Animals often express hundreds of miRNAs, and it remains unclear whether the Microprocessor is required to produce one or few essential miRNAs or many individually non-essential miRNAs. Additionally, both Drosha and Pasha/DGCR8 bind and cleave a variety of non-miRNA substrates [9, 10, 11, 12, 13, 14, 15], and it is unknown whether these activities account for the Microprocessor’s essential requirement. To distinguish between these possibilities, we developed a system in C. elegans to stringently deplete embryos of Microprocessor activity. Using a combination of auxin-inducible degradation (AID) and RNA interference (RNAi), we achieved Drosha and Pasha/DGCR8 depletion starting in the maternal germline, resulting in Microprocessor and miRNA-depleted embryos, which fail to undergo morphogenesis or form organs. Using a Microprocessor-bypass strategy, we show that this early embryonic arrest is rescued by the addition of just two miRNAs, one miR-35 and one miR-51 family member, resulting in morphologically normal larvae. Thus, just two out of ∼150 canonical miRNAs are sufficient for morphogenesis and organogenesis, and the processing of these miRNAs accounts for the essential requirement for Drosha and Pasha/DGCR8 during the early stages of C. elegans embryonic development. Video Abstract
Depletion of Drosha and Pasha results in embryos that fail to undergo morphogenesis The mirtron pathway enables expression of miRNAs in the absence of Drosha and Pasha Two miRNAs are sufficient to rescue embryogenesis in the absence of Drosha and Pasha miR-35 and miR-51 play an unexplored, likely conserved role in animal development
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Affiliation(s)
- Philipp J Dexheimer
- Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Campus-Vienna-Biocenter 1, 1030 Vienna, Austria
| | - Jingkui Wang
- Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Campus-Vienna-Biocenter 1, 1030 Vienna, Austria
| | - Luisa Cochella
- Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Campus-Vienna-Biocenter 1, 1030 Vienna, Austria.
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Fiosina J, Fiosins M, Bonn S. Explainable Deep Learning for Augmentation of Small RNA Expression Profiles. J Comput Biol 2020; 27:234-247. [PMID: 31855058 PMCID: PMC7047095 DOI: 10.1089/cmb.2019.0320] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
The lack of well-structured metadata annotations complicates the reusability and interpretation of the growing amount of publicly available RNA expression data. The machine learning-based prediction of metadata (data augmentation) can considerably improve the quality of expression data annotation. In this study, we systematically benchmark deep learning (DL) and random forest (RF)-based metadata augmentation of tissue, age, and sex using small RNA (sRNA) expression profiles. We use 4243 annotated sRNA-Seq samples from the sRNA expression atlas database to train and test the augmentation performance. In general, the DL machine learner outperforms the RF method in almost all tested cases. The average cross-validated prediction accuracy of the DL algorithm for tissues is 96.5%, for sex is 77%, and for age is 77.2%. The average tissue prediction accuracy for a completely new data set is 83.1% (DL) and 80.8% (RF). To understand which sRNAs influence DL predictions, we employ backpropagation-based feature importance scores using the DeepLIFT method, which enable us to obtain information on biological relevance of sRNAs.
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Affiliation(s)
- Jelena Fiosina
- Clausthal University of Technology, Institute of Informatics, Clausthal-Zellerfeld, Germany
| | - Maksims Fiosins
- German Center for Neurodegenerative Diseases, Tübingen, Germany.,Institute for Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Genevention GmbH, Göttingen, Germany.,Address correspondence to: Dr. Maksims Fiosins, German Center for Neurodegenerative Diseases, Otfried-Müller Str. 23, 72076 Tübingen, Germany
| | - Stefan Bonn
- German Center for Neurodegenerative Diseases, Tübingen, Germany.,Institute for Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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