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      Longitudinal Transcriptome Analysis Reveals a Sustained Differential Gene Expression Signature in Patients Treated for Acute Lyme Disease

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          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

          Lyme disease is a tick-borne illness caused by the bacterium Borrelia burgdorferi, and approximately 10 to 20% of patients report persistent symptoms lasting months to years despite appropriate treatment with antibiotics. To gain insights into the molecular basis of acute Lyme disease and the ensuing development of post-treatment symptoms, we conducted a longitudinal transcriptome study of 29 Lyme disease patients (and 13 matched controls) enrolled at the time of diagnosis and followed for up to 6 months. The differential gene expression signature of Lyme disease following the acute phase of infection persisted for at least 3 weeks and had fewer than 44% differentially expressed genes (DEGs) in common with other infectious or noninfectious syndromes. Early Lyme disease prior to antibiotic therapy was characterized by marked upregulation of Toll-like receptor signaling but lack of activation of the inflammatory T-cell apoptotic and B-cell developmental pathways seen in other acute infectious syndromes. Six months after completion of therapy, Lyme disease patients were found to have 31 to 60% of their pathways in common with three different immune-mediated chronic diseases. No differential gene expression signature was observed between Lyme disease patients with resolved illness to those with persistent symptoms at 6 months post-treatment. The identification of a sustained differential gene expression signature in Lyme disease suggests that a panel of selected human host-based biomarkers may address the need for sensitive clinical diagnostics during the “window period” of infection prior to the appearance of a detectable antibody response and may also inform the development of new therapeutic targets.

          IMPORTANCE

          Lyme disease is the most common tick-borne infection in the United States, and some patients report lingering symptoms lasting months to years despite antibiotic treatment. To better understand the role of the human host response in acute Lyme disease and the development of post-treatment symptoms, we conducted the first longitudinal gene expression (transcriptome) study of patients enrolled at the time of diagnosis and followed up for up to 6 months after treatment. Importantly, we found that the gene expression signature of early Lyme disease is distinct from that of other acute infectious diseases and persists for at least 3 weeks following infection. This study also uncovered multiple previously undescribed pathways and genes that may be useful in the future as human host biomarkers for diagnosis and that constitute potential targets for the development of new therapies.

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          Most cited references 53

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          limma powers differential expression analyses for RNA-sequencing and microarray studies

          limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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            Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks.

            Recent advances in high-throughput cDNA sequencing (RNA-seq) can reveal new genes and splice variants and quantify expression genome-wide in a single assay. The volume and complexity of data from RNA-seq experiments necessitate scalable, fast and mathematically principled analysis software. TopHat and Cufflinks are free, open-source software tools for gene discovery and comprehensive expression analysis of high-throughput mRNA sequencing (RNA-seq) data. Together, they allow biologists to identify new genes and new splice variants of known ones, as well as compare gene and transcript expression under two or more conditions. This protocol describes in detail how to use TopHat and Cufflinks to perform such analyses. It also covers several accessory tools and utilities that aid in managing data, including CummeRbund, a tool for visualizing RNA-seq analysis results. Although the procedure assumes basic informatics skills, these tools assume little to no background with RNA-seq analysis and are meant for novices and experts alike. The protocol begins with raw sequencing reads and produces a transcriptome assembly, lists of differentially expressed and regulated genes and transcripts, and publication-quality visualizations of analysis results. The protocol's execution time depends on the volume of transcriptome sequencing data and available computing resources but takes less than 1 d of computer time for typical experiments and ∼1 h of hands-on time.
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              A comparison of normalization methods for high density oligonucleotide array data based on variance and bias.

              When running experiments that involve multiple high density oligonucleotide arrays, it is important to remove sources of variation between arrays of non-biological origin. Normalization is a process for reducing this variation. It is common to see non-linear relations between arrays and the standard normalization provided by Affymetrix does not perform well in these situations. We present three methods of performing normalization at the probe intensity level. These methods are called complete data methods because they make use of data from all arrays in an experiment to form the normalizing relation. These algorithms are compared to two methods that make use of a baseline array: a one number scaling based algorithm and a method that uses a non-linear normalizing relation by comparing the variability and bias of an expression measure. Two publicly available datasets are used to carry out the comparisons. The simplest and quickest complete data method is found to perform favorably. Software implementing all three of the complete data normalization methods is available as part of the R package Affy, which is a part of the Bioconductor project http://www.bioconductor.org. Additional figures may be found at http://www.stat.berkeley.edu/~bolstad/normalize/index.html
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                Author and article information

                Journal
                mBio
                MBio
                mbio
                mbio
                mBio
                mBio
                American Society of Microbiology (1752 N St., N.W., Washington, DC )
                2150-7511
                12 February 2016
                Jan-Feb 2016
                : 7
                : 1
                Affiliations
                [a ]Department of Laboratory Medicine, University of California, San Francisco, San Francisco, California, USA
                [b ]Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
                [c ]Department of Biology, San Francisco State University, San Francisco, California, USA
                [d ]Qiagen Bioinformatics, Redwood City, California, USA
                Author notes
                Address correspondence to John Aucott, jaucott@ 123456jhmi.edu , or Charles Y. Chiu, charles.chiu@ 123456ucsf.edu .

                J.B., M.J.S., J.A., and C.Y.C. conceived the project and designed the research. J.B., M.J.S., A.S., and B.K. performed the experiments. J.A. and A.R. enrolled patients in the SLICE study and collected clinical and laboratory data. J.B., M.J.S., S.F., J.-N.B., R.H., M.B., C.C., and C.Y.C. analyzed next-generation sequencing and microarray data. J.B. and C.Y.C. wrote the manuscript.

                Editor Alan G. Barbour, University of California Irvine

                Article
                mBio00100-16
                10.1128/mBio.00100-16
                4791844
                26873097
                Copyright © 2016 Bouquet et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

                Counts
                supplementary-material: 8, Figures: 4, Tables: 2, Equations: 0, References: 53, Pages: 11, Words: 9335
                Product
                Funding
                Funded by: Swartz Foundation
                Award Recipient : Charles Y. Chiu
                Funded by: Bay Area Lyme Foundation
                Award Recipient : Jerome Bouquet Award Recipient : Charles Y. Chiu
                Funded by: Stabler Foundation
                Award Recipient : Mark J. Soloski
                Funded by: Lyme Research Alliance
                Award Recipient : Jerome Bouquet Award Recipient : John N. Aucott Award Recipient : Charles Y. Chiu
                Funded by: Lyme Disease Research Foundation, Inc.
                Award Recipient : John N. Aucott
                Funded by: Office of Extramural Research, National Institutes of Health (OER) http://dx.doi.org/10.13039/100006955
                Award ID: HL105704
                Award Recipient : Charles Y. Chiu
                Funded by: Office of Extramural Research, National Institutes of Health (OER) http://dx.doi.org/10.13039/100006955
                Award ID: P30-AR05350
                Award Recipient : Mark J. Soloski
                Funded by: Abbott Laboratories http://dx.doi.org/10.13039/100001316
                Award Recipient : Charles Y. Chiu
                The funders of this publication had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Custom metadata
                January/February 2016

                Life sciences

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