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Robinson W, Stone JK, Schischlik F, Gasmi B, Kelly MC, Seibert C, Dadkhah K, Gertz EM, Lee JS, Zhu K, Ma L, Wang XW, Sahinalp SC, Patro R, Leiserson MDM, Harris CC, Schäffer AA, Ruppin E. Identification of intracellular bacteria from multiple single-cell RNA-seq platforms using CSI-Microbes. SCIENCE ADVANCES 2024; 10:eadj7402. [PMID: 38959321 PMCID: PMC11221508 DOI: 10.1126/sciadv.adj7402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 05/29/2024] [Indexed: 07/05/2024]
Abstract
The study of the tumor microbiome has been garnering increased attention. We developed a computational pipeline (CSI-Microbes) for identifying microbial reads from single-cell RNA sequencing (scRNA-seq) data and for analyzing differential abundance of taxa. Using a series of controlled experiments and analyses, we performed the first systematic evaluation of the efficacy of recovering microbial unique molecular identifiers by multiple scRNA-seq technologies, which identified the newer 10x chemistries (3' v3 and 5') as the best suited approach. We analyzed patient esophageal and colorectal carcinomas and found that reads from distinct genera tend to co-occur in the same host cells, testifying to possible intracellular polymicrobial interactions. Microbial reads are disproportionately abundant within myeloid cells that up-regulate proinflammatory cytokines like IL1Β and CXCL8, while infected tumor cells up-regulate antigen processing and presentation pathways. These results show that myeloid cells with bacteria engulfed are a major source of bacterial RNA within the tumor microenvironment (TME) and may inflame the TME and influence immunotherapy response.
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Affiliation(s)
- Welles Robinson
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20910, USA
- Department of Computer Science, University of Maryland, College Park, MD 20910, USA
- Surgery Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
- Tumour Immunogenomics and Immunosurveillance Laboratory, Department of Oncology, University College London, London, UK
| | - Joshua K. Stone
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Fiorella Schischlik
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Billel Gasmi
- Surgery Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Michael C. Kelly
- Center for Cancer Research Single Cell Analysis Facility, Frederick National Laboratory for Cancer Research, Bethesda, MD 20701, USA
| | - Charlie Seibert
- Center for Cancer Research Single Cell Analysis Facility, Frederick National Laboratory for Cancer Research, Bethesda, MD 20701, USA
| | - Kimia Dadkhah
- Center for Cancer Research Single Cell Analysis Facility, Frederick National Laboratory for Cancer Research, Bethesda, MD 20701, USA
| | - E. Michael Gertz
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Joo Sang Lee
- Department of Artificial Intelligence and Department of Precision Medicine, School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Kaiyuan Zhu
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
- Department of Computer Science, Indiana University, Bloomington, IN 47408, USA
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Lichun Ma
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Xin Wei Wang
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - S. Cenk Sahinalp
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Rob Patro
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20910, USA
- Department of Computer Science, University of Maryland, College Park, MD 20910, USA
| | - Mark D. M. Leiserson
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20910, USA
- Department of Computer Science, University of Maryland, College Park, MD 20910, USA
| | - Curtis C. Harris
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Alejandro A. Schäffer
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Eytan Ruppin
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
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Mario-Vasquez JE, Bagal UR, Lowe E, Morgulis A, Phan J, Sexton DJ, Shiryev S, Slatkevičius R, Welsh R, Litvintseva AP, Blumberg M, Agarwala R, Chow NA. Finding Candida auris in public metagenomic repositories. PLoS One 2024; 19:e0291406. [PMID: 38241320 PMCID: PMC10798454 DOI: 10.1371/journal.pone.0291406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 01/04/2024] [Indexed: 01/21/2024] Open
Abstract
Candida auris is a newly emerged multidrug-resistant fungus capable of causing invasive infections with high mortality. Despite intense efforts to understand how this pathogen rapidly emerged and spread worldwide, its environmental reservoirs are poorly understood. Here, we present a collaborative effort between the U.S. Centers for Disease Control and Prevention, the National Center for Biotechnology Information, and GridRepublic (a volunteer computing platform) to identify C. auris sequences in publicly available metagenomic datasets. We developed the MetaNISH pipeline that uses SRPRISM to align sequences to a set of reference genomes and computes a score for each reference genome. We used MetaNISH to scan ~300,000 SRA metagenomic runs from 2010 onwards and identified five datasets containing C. auris reads. Finally, GridRepublic has implemented a prospective C. auris molecular monitoring system using MetaNISH and volunteer computing.
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Affiliation(s)
- Jorge E. Mario-Vasquez
- Mycotic Diseases Branch, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | | | - Elijah Lowe
- General Dynamics Information Technology Inc., Atlanta, Georgia, United States of America
| | - Aleksandr Morgulis
- National Center for Biotechnology Information, Bethesda, Maryland, United States of America
| | - John Phan
- General Dynamics Information Technology Inc., Atlanta, Georgia, United States of America
| | - D. Joseph Sexton
- Mycotic Diseases Branch, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Sergey Shiryev
- National Center for Biotechnology Information, Bethesda, Maryland, United States of America
| | | | - Rory Welsh
- Mycotic Diseases Branch, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Anastasia P. Litvintseva
- Mycotic Diseases Branch, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Matthew Blumberg
- GridRepublic, Cambridge, Massachusetts, United States of America
| | - Richa Agarwala
- National Center for Biotechnology Information, Bethesda, Maryland, United States of America
| | - Nancy A. Chow
- Mycotic Diseases Branch, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
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Hussen BM, Abdullah ST, Salihi A, Sabir DK, Sidiq KR, Rasul MF, Hidayat HJ, Ghafouri-Fard S, Taheri M, Jamali E. The emerging roles of NGS in clinical oncology and personalized medicine. Pathol Res Pract 2022; 230:153760. [PMID: 35033746 DOI: 10.1016/j.prp.2022.153760] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 12/29/2021] [Accepted: 01/06/2022] [Indexed: 02/07/2023]
Abstract
Next-generation sequencing (NGS) has been increasingly popular in genomics studies over the last decade, as new sequencing technology has been created and improved. Recently, NGS started to be used in clinical oncology to improve cancer therapy through diverse modalities ranging from finding novel and rare cancer mutations, discovering cancer mutation carriers to reaching specific therapeutic approaches known as personalized medicine (PM). PM has the potential to minimize medical expenses by shifting the current traditional medical approach of treating cancer and other diseases to an individualized preventive and predictive approach. Currently, NGS can speed up in the early diagnosis of diseases and discover pharmacogenetic markers that help in personalizing therapies. Despite the tremendous growth in our understanding of genetics, NGS holds the added advantage of providing more comprehensive picture of cancer landscape and uncovering cancer development pathways. In this review, we provided a complete overview of potential NGS applications in scientific and clinical oncology, with a particular emphasis on pharmacogenomics in the direction of precision medicine treatment options.
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Affiliation(s)
- Bashdar Mahmud Hussen
- Department Pharmacognosy, College of Pharmacy, Hawler Medical University, Kurdistan Region, Erbil, Iraq; Center of Research and Strategic Studies, Lebanese French University, Kurdistan Region, Erbil, Iraq
| | - Sara Tharwat Abdullah
- Department of Pharmacology and Toxicology, College of Pharmacy, Hawler Medical University, Erbil, Iraq
| | - Abbas Salihi
- Center of Research and Strategic Studies, Lebanese French University, Kurdistan Region, Erbil, Iraq; Department of Biology, College of Science, Salahaddin University, Kurdistan Region, Erbil, Iraq
| | - Dana Khdr Sabir
- Department of Medical Laboratory Sciences, Charmo University, Kurdistan Region, Iraq
| | - Karzan R Sidiq
- Department of Biology, College of Education, University of Sulaimani, Sulaimani 334, Kurdistan, Iraq
| | - Mohammed Fatih Rasul
- Department of Medical Analysis, Faculty of Applied Science, Tishk International University, Kurdistan Region, Erbil, Iraq
| | - Hazha Jamal Hidayat
- Department of Biology, College of Education, Salahaddin University, Kurdistan Region, Erbil, Iraq
| | - Soudeh Ghafouri-Fard
- Department of Medical Genetics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammad Taheri
- Institute of Human Genetics, Jena University Hospital, Jena, Germany; Urology and Nephrology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Elena Jamali
- Skull Base Research Center, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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Schäffer AA, Dominguez DA, Chapman LM, Gertz EM, Budhu A, Forgues M, Chaisaingmongkol J, Rabibhadana S, Pupacdi B, Wu X, Bayarsaikhan E, Harris CC, Ruchirawat M, Ruppin E, Wang XW. Integration of adeno-associated virus (AAV) into the genomes of most Thai and Mongolian liver cancer patients does not induce oncogenesis. BMC Genomics 2021; 22:814. [PMID: 34763675 PMCID: PMC8588581 DOI: 10.1186/s12864-021-08098-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 10/19/2021] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Engineered versions of adeno-associated virus (AAV) are commonly used in gene therapy but evidence revealing a potential oncogenic role of natural AAV in hepatocellular carcinoma (HCC) has raised concerns. The frequency of potentially oncogenic integrations has been reported in only a few populations. AAV infection and host genome integration in another type of liver cancer, cholangiocarcinoma (CCA), has been studied only in one cohort. All reported oncogenic AAV integrations in HCC come from strains resembling the fully sequenced AAV2 and partly sequenced AAV13. When AAV integration occurs, only a fragment of the AAV genome is detectable in later DNA or RNA sequencing. The integrated fragment is typically from the 3' end of the AAV genome, and this positional bias has been only partly explained. Three research groups searched for evidence of AAV integration in HCC RNAseq samples in the Cancer Genome Atlas (TCGA) but reported conflicting results. RESULTS We collected and analyzed whole transcriptome and viral capture DNA sequencing in paired tumor and non-tumor samples from two liver cancer Asian cohorts from Thailand (N = 147, 47 HCC and 100 intrahepatic cholangiocarcinoma (iCCA)) and Mongolia (N = 70, all HCC). We found only one HCC patient with a potentially oncogenic integration of AAV, in contrast to higher frequency reported in European patients. There were no oncogenic AAV integrations in iCCA patients. AAV genomic segments are present preferentially in the non-tumor samples of Thai patients. By analyzing the AAV genome positions of oncogenic and non-oncogenic integrated fragments, we found that almost all the putative oncogenic integrations overlap the X gene, which is present and functional only in the strain AAV2 among all fully sequenced strains. This gene content difference could explain why putative oncogenic integrations from other AAV strains have not been reported. We resolved the discrepancies in previous analyses of AAV presence in TCGA HCC samples and extended it to CCA. There are 12 TCGA samples with an AAV segment and none are in Asian patients. AAV segments are present in preferentially in TCGA non-tumor samples, like what we observed in the Thai patients. CONCLUSIONS Our findings suggest a minimal AAV risk of hepatocarcinogenesis in Asian liver cancer patients. The partial genome presence and positional bias of AAV integrations into the human genome has complicated analysis of possible roles of AAV in liver cancer.
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Affiliation(s)
- Alejandro A Schäffer
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Dana A Dominguez
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Lesley M Chapman
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - E Michael Gertz
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Anuradha Budhu
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Liver Cancer Program, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Marshonna Forgues
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jittiporn Chaisaingmongkol
- Laboratory of Chemical Carcinogenesis, Chulabhorn Research Institute, Bangkok, Thailand
- Center of Excellence on Environmental Health and Toxicology, Office of Higher Education Commission, Ministry of Education, Bangkok, Thailand
| | - Siritida Rabibhadana
- Laboratory of Chemical Carcinogenesis, Chulabhorn Research Institute, Bangkok, Thailand
| | - Benjarath Pupacdi
- Laboratory of Chemical Carcinogenesis, Chulabhorn Research Institute, Bangkok, Thailand
| | - Xiaolin Wu
- Frederick National Laboratory for Cancer Research, Leidos Biomedical Research Inc, Frederick, MD, USA
| | | | - Curtis C Harris
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Mathuros Ruchirawat
- Laboratory of Chemical Carcinogenesis, Chulabhorn Research Institute, Bangkok, Thailand
- Center of Excellence on Environmental Health and Toxicology, Office of Higher Education Commission, Ministry of Education, Bangkok, Thailand
| | - Eytan Ruppin
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
| | - Xin Wei Wang
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
- Liver Cancer Program, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
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Morgulis A, Agarwala R. SRPRISM (Single Read Paired Read Indel Substitution Minimizer): an efficient aligner for assemblies with explicit guarantees. Gigascience 2020; 9:giaa023. [PMID: 32315028 PMCID: PMC7172022 DOI: 10.1093/gigascience/giaa023] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Revised: 08/15/2019] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Alignment of sequence reads generated by next-generation sequencing is an integral part of most pipelines analyzing next-generation sequencing data. A number of tools designed to quickly align a large volume of sequences are already available. However, most existing tools lack explicit guarantees about their output. They also do not support searching genome assemblies, such as the human genome assembly GRCh38, that include primary and alternate sequences and placement information for alternate sequences to primary sequences in the assembly. FINDINGS This paper describes SRPRISM (Single Read Paired Read Indel Substitution Minimizer), an alignment tool for aligning reads without splices. SRPRISM has features not available in most tools, such as (i) support for searching genome assemblies with alternate sequences, (ii) partial alignment of reads with a specified region of reads to be included in the alignment, (iii) choice of ranking schemes for alignments, and (iv) explicit criteria for search sensitivity. We compare the performance of SRPRISM to GEM, Kart, STAR, BWA-MEM, Bowtie2, Hobbes, and Yara using benchmark sets for paired and single reads of lengths 100 and 250 bp generated using DWGSIM. SRPRISM found the best results for most benchmark sets with error rate of up to ∼2.5% and GEM performed best for higher error rates. SRPRISM was also more sensitive than other tools even when sensitivity was reduced to improve run time performance. CONCLUSIONS We present SRPRISM as a flexible read mapping tool that provides explicit guarantees on results.
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Affiliation(s)
- Aleksandr Morgulis
- National Center for Biotechnology Information, National Library of Medicine, 8600 Rockville Pike Bethesda, MD 20894, USA
| | - Richa Agarwala
- National Center for Biotechnology Information, National Library of Medicine, 8600 Rockville Pike Bethesda, MD 20894, USA
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