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Griffiths CD, Shah M, Shao W, Borgman CA, Janes KA. Three Modes of Viral Adaption by the Heart. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.28.587274. [PMID: 38585853 PMCID: PMC10996681 DOI: 10.1101/2024.03.28.587274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Viruses elicit long-term adaptive responses in the tissues they infect. Understanding viral adaptions in humans is difficult in organs such as the heart, where primary infected material is not routinely collected. In search of asymptomatic infections with accompanying host adaptions, we mined for cardio-pathogenic viruses in the unaligned reads of nearly one thousand human hearts profiled by RNA sequencing. Among virus-positive cases (~20%), we identified three robust adaptions in the host transcriptome related to inflammatory NFκB signaling and post-transcriptional regulation by the p38-MK2 pathway. The adaptions are not determined by the infecting virus, and they recur in infections of human or animal hearts and cultured cardiomyocytes. Adaptions switch states when NFκB or p38-MK2 are perturbed in cells engineered for chronic infection by the cardio-pathogenic virus, coxsackievirus B3. Stratifying viral responses into reversible adaptions adds a targetable systems-level simplification for infections of the heart and perhaps other organs.
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Affiliation(s)
- Cameron D. Griffiths
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Millie Shah
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - William Shao
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Cheryl A. Borgman
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Kevin A. Janes
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
- Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA
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2
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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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3
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Elkjaer ML, Simon L, Frisch T, Bente LM, Kacprowski T, Thomassen M, Reynolds R, Baumbach J, Röttger R, Illes Z. Hypothesis of a potential BrainBiota and its relation to CNS autoimmune inflammation. Front Immunol 2022; 13:1043579. [PMID: 36532064 PMCID: PMC9756883 DOI: 10.3389/fimmu.2022.1043579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 11/16/2022] [Indexed: 12/03/2022] Open
Abstract
Infectious agents have been long considered to play a role in the pathogenesis of neurological diseases as part of the interaction between genetic susceptibility and the environment. The role of bacteria in CNS autoimmunity has also been highlighted by changes in the diversity of gut microbiota in patients with neurological diseases such as Parkinson's disease, Alzheimer disease and multiple sclerosis, emphasizing the role of the gut-brain axis. We discuss the hypothesis of a brain microbiota, the BrainBiota: bacteria living in symbiosis with brain cells. Existence of various bacteria in the human brain is suggested by morphological evidence, presence of bacterial proteins, metabolites, transcripts and mucosal-associated invariant T cells. Based on our data, we discuss the hypothesis that these bacteria are an integral part of brain development and immune tolerance as well as directly linked to the gut microbiome. We further suggest that changes of the BrainBiota during brain diseases may be the consequence or cause of the chronic inflammation similarly to the gut microbiota.
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Affiliation(s)
- Maria L. Elkjaer
- Department of Neurology, Odense University Hospital, Odense, Denmark,BRIDGE, Department of Clinical Research, University of Southern Denmark, Odense, Denmark,Institute of Molecular Medicine, University of Southern Denmark, Odense, Denmark,*Correspondence: Maria L. Elkjaer, ; Zsolt Illes,
| | - Lukas Simon
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Tobias Frisch
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Lisa-Marie Bente
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics, Technische Universität Braunschweig and Hannover Medical School, Braunschweig, Germany,Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Braunchweig, Germany
| | - Tim Kacprowski
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics, Technische Universität Braunschweig and Hannover Medical School, Braunschweig, Germany,Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Braunchweig, Germany
| | - Mads Thomassen
- BRIDGE, Department of Clinical Research, University of Southern Denmark, Odense, Denmark,Research Unit of Human Genetics, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Richard Reynolds
- Department of Brain Sciences, Imperial College, London, United Kingdom,Centre for Molecular Neuropathology, LKC School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Jan Baumbach
- Chair of Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - Richard Röttger
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Zsolt Illes
- Department of Neurology, Odense University Hospital, Odense, Denmark,BRIDGE, Department of Clinical Research, University of Southern Denmark, Odense, Denmark,Institute of Molecular Medicine, University of Southern Denmark, Odense, Denmark,*Correspondence: Maria L. Elkjaer, ; Zsolt Illes,
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4
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Privitera GF, Alaimo S, Ferro A, Pulvirenti A. Virus finding tools: current solutions and limitations. Brief Bioinform 2022; 23:6618234. [PMID: 35753694 DOI: 10.1093/bib/bbac235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 05/02/2022] [Accepted: 05/20/2022] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION The study of the Human Virome remains challenging nowadays. Viral metagenomics, through high-throughput sequencing data, is the best choice for virus discovery. The metagenomics approach is culture-independent and sequence-independent, helping search for either known or novel viruses. Though it is estimated that more than 40% of the viruses found in metagenomics analysis are not recognizable, we decided to analyze several tools to identify and discover viruses in RNA-seq samples. RESULTS We have analyzed eight Virus Tools for the identification of viruses in RNA-seq data. These tools were compared using a synthetic dataset of 30 viruses and a real one. Our analysis shows that no tool succeeds in recognizing all the viruses in the datasets. So we can conclude that each of these tools has pros and cons, and their choice depends on the application domain. AVAILABILITY Synthetic data used through the review and raw results of their analysis can be found at https://zenodo.org/record/6426147. FASTQ files of real data can be found in GEO (https://www.ncbi.nlm.nih.gov/gds) or ENA (https://www.ebi.ac.uk/ena/browser/home). Raw results of their analysis can be downloaded from https://zenodo.org/record/6425917.
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Affiliation(s)
- Grete Francesca Privitera
- Department of Physics and Astronomy, University of Catania, Viale A. Doria, 6, 95125, Catania, Italy
| | - Salvatore Alaimo
- Department of Clinical and Experimental Medicine, University of Catania, c/o Dept. of Math. and Comp. Science Viale A. Doria, 6, 95125, Catania, Italy
| | - Alfredo Ferro
- Department of Clinical and Experimental Medicine, University of Catania, c/o Dept. of Math. and Comp. Science Viale A. Doria, 6, 95125, Catania, Italy
| | - Alfredo Pulvirenti
- Department of Clinical and Experimental Medicine, University of Catania, c/o Dept. of Math. and Comp. Science Viale A. Doria, 6, 95125, Catania, Italy
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5
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Fulci V. Meta'omics: Challenges and Applications. Int J Mol Sci 2022; 23:ijms23126486. [PMID: 35742929 PMCID: PMC9224421 DOI: 10.3390/ijms23126486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 06/08/2022] [Indexed: 02/05/2023] Open
Affiliation(s)
- Valerio Fulci
- Dipartimento di Medicina Molecolare, Università di Roma "La Sapienza", 00161 Rome, Italy
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6
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Ye S, Lu C, Qiu Y, Zheng H, Ge X, Wu A, Xia Z, Jiang T, Zhu H, Peng Y. An atlas of human viruses provides new insights into diversity and tissue tropism of human viruses. Bioinformatics 2022; 38:3087-3093. [PMID: 35435220 DOI: 10.1093/bioinformatics/btac275] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 03/25/2022] [Accepted: 04/12/2022] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Viruses continue to threaten human health. Yet, the complete viral species carried by humans and their infection characteristics have not been fully revealed. RESULTS This study curated an atlas of human viruses from public databases and literature, and built the Human Virus Database (HVD). The HVD contains 1,131 virus species of 54 viral families which were more than twice the number of the human-infecting virus species reported in previous studies. These viruses were identified in human samples including 68 human tissues, the excreta and body fluid. The viral diversity in humans was age-dependent with a peak in the infant and a valley in the teenager. The tissue tropism of viruses was found to be associated with several factors including the viral group (DNA, RNA or reverse-transcribing viruses), enveloped or not, viral genome length and GC content, viral receptors and the virus-interacting proteins. Finally, the tissue tropism of DNA viruses was predicted using a random-forest algorithm with a middle performance. Overall, the study not only provides a valuable resource for further studies of human viruses, but also deepens our understanding towards the diversity and tissue tropism of human viruses. AVAILABILITY The HVD is available at http://computationalbiology.cn/humanVirusBase/#/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Sifan Ye
- Bioinformatics Center, College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha, China
| | - Congyu Lu
- Bioinformatics Center, College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha, China
| | - Ye Qiu
- Bioinformatics Center, College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha, China
| | - Heping Zheng
- Bioinformatics Center, College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha, China
| | - Xingyi Ge
- Bioinformatics Center, College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha, China
| | - Aiping Wu
- Center of System Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.,Suzhou Institute of Systems Medicine, Suzhou, China
| | - Zanxian Xia
- Department of Cell Biology, Hunan Key Laboratory of Animal Models for Human Diseases and Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, 410013, China
| | - Taijiao Jiang
- Center of System Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.,Suzhou Institute of Systems Medicine, Suzhou, China
| | - Haizhen Zhu
- Bioinformatics Center, College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha, China
| | - Yousong Peng
- Bioinformatics Center, College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha, China
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7
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Zhao L, Grimes SM, Greer SU, Kubit M, Lee H, Nadauld LD, Ji HP. Characterization of the consensus mucosal microbiome of colorectal cancer. NAR Cancer 2022; 3:zcab049. [PMID: 34988460 PMCID: PMC8693571 DOI: 10.1093/narcan/zcab049] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Revised: 11/18/2021] [Accepted: 12/08/2021] [Indexed: 12/13/2022] Open
Abstract
Dysbioisis is an imbalance of an organ's microbiome and plays a role in colorectal cancer pathogenesis. Characterizing the bacteria in the microenvironment of a cancer through genome sequencing has advantages compared to culture-based profiling. However, there are notable technical and analytical challenges in characterizing universal features of tumor microbiomes. Colorectal tumors demonstrate microbiome variation among different studies and across individual patients. To address these issues, we conducted a computational study to determine a consensus microbiome for colorectal cancer, analyzing 924 tumors from eight independent RNA-Seq data sets. A standardized meta-transcriptomic analysis pipeline was established with quality control metrics. Microbiome profiles across different cohorts were compared and recurrently altered microbial shifts specific to colorectal cancer were determined. We identified cancer-specific set of 114 microbial species associated with tumors that were found among all investigated studies. Firmicutes, Bacteroidetes, Proteobacteria and Actinobacteria were among the four most abundant phyla for the colorectal cancer microbiome. Member species of Clostridia were depleted and Fusobacterium nucleatum was one of the most enriched bacterial species in tumors. Associations between the consensus species and specific immune cell types were noted. Our results are available as a web data resource for other researchers to explore (https://crc-microbiome.stanford.edu).
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Affiliation(s)
- Lan Zhao
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Susan M Grimes
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Stephanie U Greer
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Matthew Kubit
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - HoJoon Lee
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Lincoln D Nadauld
- Intermountain Precision Genomics Program, Intermountain Healthcare, Saint George, UT 84790, USA
| | - Hanlee P Ji
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
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8
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Chen S, Ren C, Zhai J, Yu J, Zhao X, Li Z, Zhang T, Ma W, Han Z, Ma C. CAFU: a Galaxy framework for exploring unmapped RNA-Seq data. Brief Bioinform 2021; 21:676-686. [PMID: 30815667 PMCID: PMC7299299 DOI: 10.1093/bib/bbz018] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Revised: 01/23/2019] [Accepted: 01/27/2019] [Indexed: 12/13/2022] Open
Abstract
A widely used approach in transcriptome analysis is the alignment of short reads to a reference genome. However, owing to the deficiencies of specially designed analytical systems, short reads unmapped to the genome sequence are usually ignored, resulting in the loss of significant biological information and insights. To fill this gap, we present Comprehensive Assembly and Functional annotation of Unmapped RNA-Seq data (CAFU), a Galaxy-based framework that can facilitate the large-scale analysis of unmapped RNA sequencing (RNA-Seq) reads from single- and mixed-species samples. By taking advantage of machine learning techniques, CAFU addresses the issue of accurately identifying the species origin of transcripts assembled using unmapped reads from mixed-species samples. CAFU also represents an innovation in that it provides a comprehensive collection of functions required for transcript confidence evaluation, coding potential calculation, sequence and expression characterization and function annotation. These functions and their dependencies have been integrated into a Galaxy framework that provides access to CAFU via a user-friendly interface, dramatically simplifying complex exploration tasks involving unmapped RNA-Seq reads. CAFU has been validated with RNA-Seq data sets from wheat and Zea mays (maize) samples. CAFU is freely available via GitHub: https://github.com/cma2015/CAFU.
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Affiliation(s)
- Siyuan Chen
- State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest Agriculture and Forestry University
| | - Chengzhi Ren
- State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest Agriculture and Forestry University
| | - Jingjing Zhai
- State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest Agriculture and Forestry University
| | - Jiantao Yu
- College of Information Engineering, Northwest Agriculture and Forestry University
| | - Xuyang Zhao
- College of Information Engineering, Northwest Agriculture and Forestry University
| | - Zelong Li
- State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest Agriculture and Forestry University
| | - Ting Zhang
- State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest Agriculture and Forestry University
| | - Wenlong Ma
- State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest Agriculture and Forestry University
| | - Zhaoxue Han
- State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest Agriculture and Forestry University
| | - Chuang Ma
- State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest Agriculture and Forestry University
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9
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Development of a generalizable natural language processing pipeline to extract physician-reported pain from clinical reports: Generated using publicly-available datasets and tested on institutional clinical reports for cancer patients with bone metastases. J Biomed Inform 2021; 120:103864. [PMID: 34265451 DOI: 10.1016/j.jbi.2021.103864] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 06/30/2021] [Accepted: 07/04/2021] [Indexed: 11/22/2022]
Abstract
OBJECTIVE The majority of cancer patients suffer from severe pain at the advanced stage of their illness. In most cases, cancer pain is underestimated by clinical staff and is not properly managed until it reaches a critical stage. Therefore, detecting and addressing cancer pain early can potentially improve the quality of life of cancer patients. The objective of this research project was to develop a generalizable Natural Language Processing (NLP) pipeline to find and classify physician-reported pain in the radiation oncology consultation notes of cancer patients with bone metastases. MATERIALS AND METHODS The texts of 1249 publicly-available hospital discharge notes in the i2b2 database were used as a training and validation set. The MetaMap and NegEx algorithms were implemented for medical terms extraction. Sets of NLP rules were developed to score pain terms in each note. By averaging pain scores, each note was assigned to one of the three verbally-declared pain (VDP) labels, including no pain, pain, and no mention of pain. Without further training, the generalizability of our pipeline in scoring individual pain terms was tested independently using 30 hospital discharge notes from the MIMIC-III database and 30 consultation notes of cancer patients with bone metastasis from our institution's radiation oncology electronic health record. Finally, 150 notes from our institution were used to assess the pipeline's performance at assigning VDP. RESULTS Our NLP pipeline successfully detected and quantified pain in the i2b2 summary notes with 93% overall precision and 92% overall recall. Testing on the MIMIC-III database achieved precision and recall of 91% and 86% respectively. The pipeline successfully detected pain with 89% precision and 82% recall on our institutional radiation oncology corpus. Finally, our pipeline assigned a VDP to each note in our institutional corpus with 84% and 82% precision and recall, respectively. CONCLUSION Our NLP pipeline enables the detection and classification of physician-reported pain in our radiation oncology corpus. This portable and ready-to-use pipeline can be used to automatically extract and classify physician-reported pain from clinical notes where the pain is not otherwise documented through structured data entry.
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10
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Melnick M, Gonzales P, LaRocca TJ, Song Y, Wuu J, Benatar M, Oskarsson B, Petrucelli L, Dowell RD, Link CD, Prudencio M. Application of a bioinformatic pipeline to RNA-seq data identifies novel viruslike sequence in human blood. G3-GENES GENOMES GENETICS 2021; 11:6259144. [PMID: 33914880 PMCID: PMC8661426 DOI: 10.1093/g3journal/jkab141] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 04/20/2021] [Indexed: 12/11/2022]
Abstract
Numerous reports have suggested that infectious agents could play a role in neurodegenerative diseases, but specific etiological agents have not been convincingly demonstrated. To search for candidate agents in an unbiased fashion, we have developed a bioinformatic pipeline that identifies microbial sequences in mammalian RNA-seq data, including sequences with no significant nucleotide similarity hits in GenBank. Effectiveness of the pipeline was tested using publicly available RNA-seq data and in a reconstruction experiment using synthetic data. We then applied this pipeline to a novel RNA-seq dataset generated from a cohort of 120 samples from amyotrophic lateral sclerosis patients and controls, and identified sequences corresponding to known bacteria and viruses, as well as novel virus-like sequences. The presence of these novel virus-like sequences, which were identified in subsets of both patients and controls, were confirmed by quantitative RT-PCR. We believe this pipeline will be a useful tool for the identification of potential etiological agents in the many RNA-seq datasets currently being generated.
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Affiliation(s)
- Marko Melnick
- Integrative Physiology, University of Colorado, Boulder, Colorado, 80303, USA
| | - Patrick Gonzales
- Integrative Physiology, University of Colorado, Boulder, Colorado, 80303, USA
| | - Thomas J LaRocca
- Department of Health and Exercise Science, Center for Healthy Aging, Colorado State University, Fort Collins, Colorado, 80523, USA
| | - Yuping Song
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, Florida, 32224, USA
| | - Joanne Wuu
- Department of Neurology, University of Miami, Miami, Florida, 33136, USA
| | - Michael Benatar
- Department of Neurology, University of Miami, Miami, Florida, 33136, USA
| | - Björn Oskarsson
- Department of Neurology, Mayo Clinic, 4500 San Pablo Road, Jacksonville FL, 32224, USA
| | - Leonard Petrucelli
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, Florida, 32224, USA.,Neuroscience Graduate Program, Mayo Clinic Graduate School of Biomedical Sciences, Jacksonville, Florida, 32224, USA
| | - Robin D Dowell
- BioFrontiers Institute and Department of Molecular, Cellular and Developmental Biology, University of Colorado, Boulder, Colorado, 80303, USA
| | - Christopher D Link
- Integrative Physiology, University of Colorado, Boulder, Colorado, 80303, USA.,Institute for Behavioral Genetics, University of Colorado, Boulder, Colorado, 80303, USA
| | - Mercedes Prudencio
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, Florida, 32224, USA.,Neuroscience Graduate Program, Mayo Clinic Graduate School of Biomedical Sciences, Jacksonville, Florida, 32224, USA
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11
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Charting Extracellular Transcriptomes in The Human Biofluid RNA Atlas. Cell Rep 2020; 33:108552. [PMID: 33378673 DOI: 10.1016/j.celrep.2020.108552] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 10/14/2020] [Accepted: 12/03/2020] [Indexed: 02/06/2023] Open
Abstract
Extracellular RNAs present in biofluids have emerged as potential biomarkers for disease. Where most studies focus on blood-derived fluids, other biofluids may be more informative. We present an atlas of messenger, circular, and small RNA transcriptomes of a comprehensive collection of 20 human biofluids. By means of synthetic spike-in controls, we compare RNA content across biofluids, revealing a 10,000-fold difference in concentration. The circular RNA fraction is increased in most biofluids compared to tissues. Each biofluid transcriptome is enriched for RNA molecules derived from specific tissues and cell types. Our atlas enables an informed selection of the most relevant biofluid to monitor particular diseases. To verify the biomarker potential in these biofluids, four validation cohorts representing a broad spectrum of diseases were profiled, revealing numerous differential RNAs between case and control subjects. Spike-normalized data are publicly available in the R2 web portal for further exploration.
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12
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Ma A, Sun M, McDermaid A, Liu B, Ma Q. MetaQUBIC: a computational pipeline for gene-level functional profiling of metagenome and metatranscriptome. Bioinformatics 2020; 35:4474-4477. [PMID: 31116375 DOI: 10.1093/bioinformatics/btz414] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2018] [Revised: 04/15/2019] [Accepted: 05/14/2019] [Indexed: 01/16/2023] Open
Abstract
MOTIVATION Metagenomic and metatranscriptomic analyses can provide an abundance of information related to microbial communities. However, straightforward analysis of this data does not provide optimal results, with a required integration of data types being needed to thoroughly investigate these microbiomes and their environmental interactions. RESULTS Here, we present MetaQUBIC, an integrated biclustering-based computational pipeline for gene module detection that integrates both metagenomic and metatranscriptomic data. Additionally, we used this pipeline to investigate 735 paired DNA and RNA human gut microbiome samples, resulting in a comprehensive hybrid gene expression matrix of 2.3 million cross-species genes in the 735 human fecal samples and 155 functional enriched gene modules. We believe both the MetaQUBIC pipeline and the generated comprehensive human gut hybrid expression matrix will facilitate further investigations into multiple levels of microbiome studies. AVAILABILITY AND IMPLEMENTATION The package is freely available at https://github.com/OSU-BMBL/metaqubic. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Anjun Ma
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA
| | - Minxuan Sun
- Department of Computer Science, South Dakota State University, Brookings, SD, USA
| | - Adam McDermaid
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA.,Department of Mathematics and Statistics, South Dakota State University, Brookings, SD, USA
| | - Bingqiang Liu
- School of Mathematics, Shandong University, Jinan, China
| | - Qin Ma
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA
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Nia AM, Khanipov K, Barnette BL, Ullrich RL, Golovko G, Emmett MR. Comparative RNA-Seq transcriptome analyses reveal dynamic time-dependent effects of 56Fe, 16O, and 28Si irradiation on the induction of murine hepatocellular carcinoma. BMC Genomics 2020; 21:453. [PMID: 32611366 PMCID: PMC7329445 DOI: 10.1186/s12864-020-06869-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 06/24/2020] [Indexed: 01/04/2023] Open
Abstract
Background One of the health risks posed to astronauts during deep space flights is exposure to high charge, high-energy (HZE) ions (Z > 13), which can lead to the induction of hepatocellular carcinoma (HCC). However, little is known on the molecular mechanisms of HZE irradiation-induced HCC. Results We performed comparative RNA-Seq transcriptomic analyses to assess the carcinogenic effects of 600 MeV/n 56Fe (0.2 Gy), 1 GeV/n 16O (0.2 Gy), and 350 MeV/n 28Si (0.2 Gy) ions in a mouse model for irradiation-induced HCC. C3H/HeNCrl mice were subjected to total body irradiation to simulate space environment HZE-irradiation, and liver tissues were extracted at five different time points post-irradiation to investigate the time-dependent carcinogenic response at the transcriptomic level. Our data demonstrated a clear difference in the biological effects of these HZE ions, particularly immunological, such as Acute Phase Response Signaling, B Cell Receptor Signaling, IL-8 Signaling, and ROS Production in Macrophages. Also seen in this study were novel unannotated transcripts that were significantly affected by HZE. To investigate the biological functions of these novel transcripts, we used a machine learning technique known as self-organizing maps (SOMs) to characterize the transcriptome expression profiles of 60 samples (45 HZE-irradiated, 15 non-irradiated control) from liver tissues. A handful of localized modules in the maps emerged as groups of co-regulated and co-expressed transcripts. The functional context of these modules was discovered using overrepresentation analysis. We found that these spots typically contained enriched populations of transcripts related to specific immunological molecular processes (e.g., Acute Phase Response Signaling, B Cell Receptor Signaling, IL-3 Signaling), and RNA Transcription/Expression. Conclusions A large number of transcripts were found differentially expressed post-HZE irradiation. These results provide valuable information for uncovering the differences in molecular mechanisms underlying HZE specific induced HCC carcinogenesis. Additionally, a handful of novel differentially expressed unannotated transcripts were discovered for each HZE ion. Taken together, these findings may provide a better understanding of biological mechanisms underlying risks for HCC after HZE irradiation and may also have important implications for the discovery of potential countermeasures against and identification of biomarkers for HZE-induced HCC.
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Affiliation(s)
- Anna M Nia
- Biochemistry and Molecular Biology, University of Texas Medical Branch, 301 University Blvd, Galveston, TX, 77550, USA
| | - Kamil Khanipov
- Pharmacology and Toxicology, University of Texas Medical Branch, 301 University Blvd, Galveston, TX, 77550, USA
| | - Brooke L Barnette
- Biochemistry and Molecular Biology, University of Texas Medical Branch, 301 University Blvd, Galveston, TX, 77550, USA
| | - Robert L Ullrich
- The Radiation Effects Research Foundation (RERF), Hiroshima, Japan
| | - George Golovko
- Pharmacology and Toxicology, University of Texas Medical Branch, 301 University Blvd, Galveston, TX, 77550, USA
| | - Mark R Emmett
- Biochemistry and Molecular Biology, University of Texas Medical Branch, 301 University Blvd, Galveston, TX, 77550, USA. .,Pharmacology and Toxicology, University of Texas Medical Branch, 301 University Blvd, Galveston, TX, 77550, USA.
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14
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Kumata R, Ito J, Takahashi K, Suzuki T, Sato K. A tissue level atlas of the healthy human virome. BMC Biol 2020; 18:55. [PMID: 32493363 PMCID: PMC7269688 DOI: 10.1186/s12915-020-00785-5] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Accepted: 04/22/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Human-resident microbes can influence both health and disease. Investigating the microbiome using next-generation sequencing technology has revealed examples of mutualism and conflict between microbes and humans. Comparing to bacteria, the viral component of the microbiome (i.e., the "virome") is understudied. Somatic tissues of healthy individuals are usually inaccessible for the virome sampling; therefore, there is limited understanding of the presence and distribution of viruses in tissues in healthy individuals and how virus infection associates with human gene expression and perturbs immunological homeostasis. RESULTS To characterize the human virome in a tissue-specific manner, here we performed meta-transcriptomic analysis using the RNA-sequencing dataset from the Genotype-Tissue Expression (GTEx) Project. We analyzed the 8991 RNA-sequencing data obtained from 51 somatic tissues from 547 individuals and successfully detected 39 viral species in at least one tissue. We then investigated associations between virus infection and human gene expression and human disease onset. We detected some expected relationships; for instance, hepatitis C virus infection in the liver was strongly associated with interferon-stimulated gene upregulation and pathological findings of chronic hepatitis. The presence of herpes simplex virus type 1 in one subject's brain strongly associated with immune gene expression. While torque teno virus was detected in a broad range of human tissues, it was not associated with interferon responses. Being notable in light of its association with lymphoproliferative disorders, Epstein-Barr virus infection in the spleen and blood was associated with an increase in plasma cells in healthy subjects. Human herpesvirus 7 was often detected in the stomach; intriguingly, it associated with the proportion of human leukocytes in the stomach as well as digestive gene expression. Moreover, virus infections in the local tissues associated with systemic immune responses in circulating blood. CONCLUSIONS To our knowledge, this study is the first comprehensive investigation of the human virome in a variety of tissues in healthy individuals through meta-transcriptomic analysis. Further investigation of the associations described here, and application of this analytical pipeline to additional datasets, will be useful to reveal the impact of viral infections on human health.
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Affiliation(s)
- Ryuichi Kumata
- Division of Systems Virology, Department of Infectious Disease Control, International Research Center for Infectious Diseases, Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 1088639, Japan
| | - Jumpei Ito
- Division of Systems Virology, Department of Infectious Disease Control, International Research Center for Infectious Diseases, Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 1088639, Japan
| | - Kenta Takahashi
- Department of Pathology, National Institute of Infectious Diseases, Tokyo, 1628640, Japan
| | - Tadaki Suzuki
- Department of Pathology, National Institute of Infectious Diseases, Tokyo, 1628640, Japan
| | - Kei Sato
- Division of Systems Virology, Department of Infectious Disease Control, International Research Center for Infectious Diseases, Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 1088639, Japan. .,CREST, Japan Science and Technology Agency, Saitama, 3220012, Japan.
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15
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Swanson GM, Moskovtsev S, Librach C, Pilsner JR, Goodrich R, Krawetz SA. What human sperm RNA-Seq tells us about the microbiome. J Assist Reprod Genet 2020; 37:359-368. [PMID: 31902104 PMCID: PMC7056791 DOI: 10.1007/s10815-019-01672-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Accepted: 12/19/2019] [Indexed: 10/25/2022] Open
Abstract
PURPOSE The study was designed to assess the capacity of human sperm RNA-seq data to gauge the diversity of the associated microbiome within the ejaculate. METHODS Semen samples were collected, and semen parameters evaluated at time of collection. Sperm RNA was isolated and subjected to RNA-seq. Microbial composition was determined by aligning sequencing reads not mapped to the human genome to the NCBI RefSeq bacterial, viral and archaeal genomes following RNA-Seq. Analysis of microbial assignments utilized phyloseq and vegan. RESULTS Microbial composition within each sample was characterized as a function of microbial associated RNAs. Bacteria known to be associated with the male reproductive tract were present at similar levels in all samples representing 11 genera from four phyla with one exception, an outlier. Shannon diversity index (p < 0.001) and beta diversity (unweighted UniFrac distances, p = 9.99e-4; beta dispersion, p = 0.006) indicated the outlier was significantly different from all other samples. The outlier sample exhibited a dramatic increase in Streptococcus. Multiple testing indicated two operational taxonomic units, S. agalactiae and S. dysgalactiae (p = 0.009), were present. CONCLUSION These results provide a first look at the microbiome as a component of human sperm RNA sequencing that has sufficient sensitivity to identify contamination or potential pathogenic bacterial colonization at least among the known contributors.
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Affiliation(s)
- Grace M Swanson
- Department of Obstetrics and Gynecology, Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, 275 E. Hancock, Detroit, MI, 48202, USA
| | | | | | - J Richard Pilsner
- Department of Environmental Health Sciences, University of Massachusetts Amherst School of Public Health and Health Sciences, Amherst, MA, 01003, USA
| | - Robert Goodrich
- Department of Obstetrics and Gynecology, Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, 275 E. Hancock, Detroit, MI, 48202, USA
| | - Stephen A Krawetz
- Department of Obstetrics and Gynecology, Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, 275 E. Hancock, Detroit, MI, 48202, USA.
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16
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Rahman RU, Liebhoff AM, Bansal V, Fiosins M, Rajput A, Sattar A, Magruder DS, Madan S, Sun T, Gautam A, Heins S, Liwinski T, Bethune J, Trenkwalder C, Fluck J, Mollenhauer B, Bonn S. SEAweb: the small RNA Expression Atlas web application. Nucleic Acids Res 2020; 48:D204-D219. [PMID: 31598718 PMCID: PMC6943056 DOI: 10.1093/nar/gkz869] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Revised: 09/14/2019] [Accepted: 10/01/2019] [Indexed: 12/12/2022] Open
Abstract
We present the Small RNA Expression Atlas (SEAweb), a web application that allows for the interactive querying, visualization and analysis of known and novel small RNAs across 10 organisms. It contains sRNA and pathogen expression information for over 4200 published samples with standardized search terms and ontologies. In addition, SEAweb allows for the interactive visualization and re-analysis of 879 differential expression and 514 classification comparisons. SEAweb's user model enables sRNA researchers to compare and re-analyze user-specific and published datasets, highlighting common and distinct sRNA expression patterns. We provide evidence for SEAweb's fidelity by (i) generating a set of 591 tissue specific miRNAs across 29 tissues, (ii) finding known and novel bacterial and viral infections across diseases and (iii) determining a Parkinson's disease-specific blood biomarker signature using novel data. We believe that SEAweb's simple semantic search interface, the flexible interactive reports and the user model with rich analysis capabilities will enable researchers to better understand the potential function and diagnostic value of sRNAs or pathogens across tissues, diseases and organisms.
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Affiliation(s)
- Raza-Ur Rahman
- Institute of Medical Systems Biology, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Anna-Maria Liebhoff
- Institute of Medical Systems Biology, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Vikas Bansal
- Institute of Medical Systems Biology, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
- German Center for Neurodegenerative Diseases, 72076 Tübingen, Germany
| | - Maksims Fiosins
- Institute of Medical Systems Biology, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
- German Center for Neurodegenerative Diseases, 72076 Tübingen, Germany
- Genevention GmbH, 37079 Göttingen, Germany
| | - Ashish Rajput
- Institute of Medical Systems Biology, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Abdul Sattar
- Institute of Medical Systems Biology, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Daniel S Magruder
- Institute of Medical Systems Biology, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
- Genevention GmbH, 37079 Göttingen, Germany
| | - Sumit Madan
- Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, 53757 Sankt Augustin, Germany
- Rheinische Friedrich-Wilhelms-Universität Bonn, 53113 Bonn, Germany
| | - Ting Sun
- Institute of Medical Systems Biology, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
- Department of Neurogenetics, Max Planck Institute of Experimental Medicine, 37075 Göttingen, Germany
| | - Abhivyakti Gautam
- Institute of Medical Systems Biology, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Sven Heins
- Institute of Medical Systems Biology, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Timur Liwinski
- Department of Medicine, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Jörn Bethune
- Institute of Medical Systems Biology, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Claudia Trenkwalder
- Paracelsus-Elena-Klinik, 34128 Kassel, Germany
- Department of Neurosurgery, University Medical Center Göttingen, 37075 Göttingen, Germany
| | - Juliane Fluck
- Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, 53757 Sankt Augustin, Germany
- Institute of Geodesy and Geoinformation, University of Bonn, 53115 Bonn, Germany
- German National Library of Medicine (ZB MED) - Information Centre for Life Sciences, 53115 Bonn, Germany
| | - Brit Mollenhauer
- Paracelsus-Elena-Klinik, 34128 Kassel, Germany
- Institute of Neurology, University Medical Center Göttingen, 37075 Göttingen, Germany
| | - Stefan Bonn
- Institute of Medical Systems Biology, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
- German Center for Neurodegenerative Diseases, 72076 Tübingen, Germany
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17
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Simon LM, Karg S, Westermann AJ, Engel M, Elbehery AHA, Hense B, Heinig M, Deng L, Theis FJ. MetaMap: an atlas of metatranscriptomic reads in human disease-related RNA-seq data. Gigascience 2018; 7:5036539. [PMID: 29901703 PMCID: PMC6025204 DOI: 10.1093/gigascience/giy070] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
Background With the advent of the age of big data in bioinformatics, large volumes of data and high-performance computing power enable researchers to perform re-analyses of publicly available datasets at an unprecedented scale. Ever more studies imply the microbiome in both normal human physiology and a wide range of diseases. RNA sequencing technology (RNA-seq) is commonly used to infer global eukaryotic gene expression patterns under defined conditions, including human disease-related contexts; however, its generic nature also enables the detection of microbial and viral transcripts. Findings We developed a bioinformatic pipeline to screen existing human RNA-seq datasets for the presence of microbial and viral reads by re-inspecting the non-human-mapping read fraction. We validated this approach by recapitulating outcomes from six independent, controlled infection experiments of cell line models and compared them with an alternative metatranscriptomic mapping strategy. We then applied the pipeline to close to 150 terabytes of publicly available raw RNA-seq data from more than 17,000 samples from more than 400 studies relevant to human disease using state-of-the-art high-performance computing systems. The resulting data from this large-scale re-analysis are made available in the presented MetaMap resource. Conclusions Our results demonstrate that common human RNA-seq data, including those archived in public repositories, might contain valuable information to correlate microbial and viral detection patterns with diverse diseases. The presented MetaMap database thus provides a rich resource for hypothesis generation toward the role of the microbiome in human disease. Additionally, codes to process new datasets and perform statistical analyses are made available.
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Affiliation(s)
- L M Simon
- Helmholtz Zentrum München, German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany
| | - S Karg
- Helmholtz Zentrum München, German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany
| | - A J Westermann
- Institute of Molecular Infection Biology, University of Würzburg, Würzburg, Germany.,Helmholtz Institute for RNA-Based Infection Research, Würzburg, Germany
| | - M Engel
- Helmholtz Zentrum München, German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany.,Helmholtz Zentrum München, German Research Center for Environmental Health, Scientific Computing Research Unit, Neuherberg, Germany
| | - A H A Elbehery
- Helmholtz Zentrum München, German Research Center for Environmental Health, Institute of Virology, Neuherberg, Germany
| | - B Hense
- Helmholtz Zentrum München, German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany
| | - M Heinig
- Helmholtz Zentrum München, German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany
| | - L Deng
- Helmholtz Zentrum München, German Research Center for Environmental Health, Institute of Virology, Neuherberg, Germany
| | - F J Theis
- Helmholtz Zentrum München, German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany.,Department of Mathematics, Technische Universität München, Munich, Germany
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