50
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Resolving host–pathogen interactions by dual RNA-seq

      review-article
      1 , 1 , 1 , 2 , *
      PLoS Pathogens
      Public Library of Science

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The transcriptome is a powerful proxy for the physiological state of a cell, healthy or diseased. As a result, transcriptome analysis has become a key tool in understanding the molecular changes that accompany bacterial infections of eukaryotic cells. Until recently, such transcriptomic studies have been technically limited to analyzing mRNA expression changes in either the bacterial pathogen or the infected eukaryotic host cell. However, the increasing sensitivity of high-throughput RNA sequencing now enables “dual RNA-seq” studies, simultaneously capturing all classes of coding and noncoding transcripts in both the pathogen and the host. In the five years since the concept of dual RNA-seq was introduced, the technique has been applied to a range of infection models. This has not only led to a better understanding of the physiological changes in pathogen and host during the course of an infection but has also revealed hidden molecular phenotypes of virulence-associated small noncoding RNAs that were not visible in standard infection assays. Here, we use the knowledge gained from these recent studies to suggest experimental and computational guidelines for the design of future dual RNA-seq studies. We conclude this review by discussing prospective applications of the technique.

          Related collections

          Most cited references65

          • Record: found
          • Abstract: found
          • Article: not found

          Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells

          Recent molecular studies have revealed that, even when derived from a seemingly homogenous population, individual cells can exhibit substantial differences in gene expression, protein levels, and phenotypic output 1–5 , with important functional consequences 4,5 . Existing studies of cellular heterogeneity, however, have typically measured only a few pre-selected RNAs 1,2 or proteins 5,6 simultaneously because genomic profiling methods 3 could not be applied to single cells until very recently 7–10 . Here, we use single-cell RNA-Seq to investigate heterogeneity in the response of bone marrow derived dendritic cells (BMDCs) to lipopolysaccharide (LPS). We find extensive, and previously unobserved, bimodal variation in mRNA abundance and splicing patterns, which we validate by RNA-fluorescence in situ hybridization (RNA-FISH) for select transcripts. In particular, hundreds of key immune genes are bimodally expressed across cells, surprisingly even for genes that are very highly expressed at the population average. Moreover, splicing patterns demonstrate previously unobserved levels of heterogeneity between cells. Some of the observed bimodality can be attributed to closely related, yet distinct, known maturity states of BMDCs; other portions reflect differences in the usage of key regulatory circuits. For example, we identify a module of 137 highly variable, yet co-regulated, antiviral response genes. Using cells from knockout mice, we show that variability in this module may be propagated through an interferon feedback circuit involving the transcriptional regulators Stat2 and Irf7. Our study demonstrates the power and promise of single-cell genomics in uncovering functional diversity between cells and in deciphering cell states and circuits.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Computational methods for transcriptome annotation and quantification using RNA-seq.

            High-throughput RNA sequencing (RNA-seq) promises a comprehensive picture of the transcriptome, allowing for the complete annotation and quantification of all genes and their isoforms across samples. Realizing this promise requires increasingly complex computational methods. These computational challenges fall into three main categories: (i) read mapping, (ii) transcriptome reconstruction and (iii) expression quantification. Here we explain the major conceptual and practical challenges, and the general classes of solutions for each category. Finally, we highlight the interdependence between these categories and discuss the benefits for different biological applications.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Single cell RNA Seq reveals dynamic paracrine control of cellular variation

              High-throughput single-cell transcriptomics offers an unbiased approach for understanding the extent, basis, and function of gene expression variation between seemingly identical cells. Here, we sequence single-cell RNA-Seq libraries prepared from over 1,700 primary mouse bone marrow derived dendritic cells (DCs) spanning several experimental conditions. We find substantial variation between identically stimulated DCs, in both the fraction of cells detectably expressing a given mRNA and the transcript’s level within expressing cells. Distinct gene modules are characterized by different temporal heterogeneity profiles. In particular, a “core” module of antiviral genes is expressed very early by a few “precocious” cells, but is later activated in all cells. By stimulating cells individually in sealed microfluidic chambers, analyzing DCs from knockout mice, and modulating secretion and extracellular signaling, we show that this response is coordinated via interferon-mediated paracrine signaling. Surprisingly, preventing cell-to-cell communication also substantially reduces variability in the expression of an early-induced “peaked” inflammatory module, suggesting that paracrine signaling additionally represses part of the inflammatory program. Our study highlights the importance of cell-to-cell communication in controlling cellular heterogeneity and reveals general strategies that multicellular populations use to establish complex dynamic responses.
                Bookmark

                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Pathog
                PLoS Pathog
                plos
                plospath
                PLoS Pathogens
                Public Library of Science (San Francisco, CA USA )
                1553-7366
                1553-7374
                16 February 2017
                February 2017
                : 13
                : 2
                : e1006033
                Affiliations
                [1 ]RNA Biology Group, Institute for Molecular Infection Biology, University of Würzburg, Würzburg, Germany
                [2 ]Helmholtz Institute for RNA-based Infection Research (HIRI), Würzburg, Germany
                Stony Brook University, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0003-4732-2667
                http://orcid.org/0000-0003-2220-1404
                Article
                PPATHOGENS-D-16-01820
                10.1371/journal.ppat.1006033
                5313147
                28207848
                96835a16-5ef2-4344-bba9-c4a10cf604c4
                © 2017 Westermann et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Page count
                Figures: 4, Tables: 1, Pages: 19
                Funding
                LB was supported by a Research Fellowship from the Alexander von Humboldt Stiftung/Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Review
                Biology and life sciences
                Molecular biology
                Molecular biology techniques
                Sequencing techniques
                RNA sequencing
                Research and analysis methods
                Molecular biology techniques
                Sequencing techniques
                RNA sequencing
                Medicine and Health Sciences
                Infectious Diseases
                Bacterial Diseases
                Salmonellosis
                Biology and Life Sciences
                Computational Biology
                Genome Analysis
                Transcriptome Analysis
                Biology and Life Sciences
                Genetics
                Genomics
                Genome Analysis
                Transcriptome Analysis
                Biology and Life Sciences
                Genetics
                Gene Expression
                Biology and Life Sciences
                Microbiology
                Medical Microbiology
                Microbial Pathogens
                Bacterial Pathogens
                Medicine and Health Sciences
                Pathology and Laboratory Medicine
                Pathogens
                Microbial Pathogens
                Bacterial Pathogens
                Medicine and Health Sciences
                Pathology and Laboratory Medicine
                Pathogenesis
                Host-Pathogen Interactions
                Medicine and Health Sciences
                Infectious Diseases
                Bacterial Diseases
                Salmonella
                Biology and Life Sciences
                Microbiology
                Medical Microbiology
                Microbial Pathogens
                Bacterial Pathogens
                Salmonella
                Medicine and Health Sciences
                Pathology and Laboratory Medicine
                Pathogens
                Microbial Pathogens
                Bacterial Pathogens
                Salmonella
                Biology and Life Sciences
                Organisms
                Bacteria
                Enterobacteriaceae
                Salmonella
                Research and analysis methods
                Extraction techniques
                RNA extraction

                Infectious disease & Microbiology
                Infectious disease & Microbiology

                Comments

                Comment on this article