Global Transcriptome Analysis Identifies a Diagnostic Signature for Early Disseminated Lyme Disease and Its Resolution

ABSTRACT A bioinformatics approach was employed to identify transcriptome alterations in the peripheral blood mononuclear cells of well-characterized human subjects who were diagnosed with early disseminated Lyme disease (LD) based on stringent microbiological and clinical criteria. Transcriptomes w...

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Autores principales:	Mary M. Petzke, Konstantin Volyanskyy, Yong Mao, Byron Arevalo, Raphael Zohn, Johanna Quituisaca, Gary P. Wormser, Nevenka Dimitrova, Ira Schwartz
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2020
Materias:	Borrelia burgdorferi Lyme disease diagnostics random forest transcriptome Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/50b7810118a643f6b380c02da1539ef3
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spelling	oai:doaj.org-article:50b7810118a643f6b380c02da1539ef32021-11-15T15:57:02ZGlobal Transcriptome Analysis Identifies a Diagnostic Signature for Early Disseminated Lyme Disease and Its Resolution10.1128/mBio.00047-202150-7511https://doaj.org/article/50b7810118a643f6b380c02da1539ef32020-04-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mBio.00047-20https://doaj.org/toc/2150-7511ABSTRACT A bioinformatics approach was employed to identify transcriptome alterations in the peripheral blood mononuclear cells of well-characterized human subjects who were diagnosed with early disseminated Lyme disease (LD) based on stringent microbiological and clinical criteria. Transcriptomes were assessed at the time of presentation and also at approximately 1 month (early convalescence) and 6 months (late convalescence) after initiation of an appropriate antibiotic regimen. Comparative transcriptomics identified 335 transcripts, representing 233 unique genes, with significant alterations of at least 2-fold expression in acute- or convalescent-phase blood samples from LD subjects relative to healthy donors. Acute-phase blood samples from LD subjects had the largest number of differentially expressed transcripts (187 induced, 54 repressed). This transcriptional profile, which was dominated by interferon-regulated genes, was sustained during early convalescence. 6 months after antibiotic treatment the transcriptome of LD subjects was indistinguishable from that of healthy controls based on two separate methods of analysis. Return of the LD expression profile to levels found in control subjects was concordant with disease outcome; 82% of subjects with LD experienced at least one symptom at the baseline visit compared to 43% at the early convalescence time point and only a single patient (9%) at the 6-month convalescence time point. Using the random forest machine learning algorithm, we developed an efficient computational framework to identify sets of 20 classifier genes that discriminated LD from other bacterial and viral infections. These novel LD biomarkers not only differentiated subjects with acute disseminated LD from healthy controls with 96% accuracy but also distinguished between subjects with acute and resolved (late convalescent) disease with 97% accuracy. IMPORTANCE Lyme disease (LD), caused by Borrelia burgdorferi, is the most common tick-borne infectious disease in the United States. We examined gene expression patterns in the blood of individuals with early disseminated LD at the time of diagnosis (acute) and also at approximately 1 month and 6 months following antibiotic treatment. A distinct acute LD profile was observed that was sustained during early convalescence (1 month) but returned to control levels 6 months after treatment. Using a computer learning algorithm, we identified sets of 20 classifier genes that discriminate LD from other bacterial and viral infections. In addition, these novel LD biomarkers are highly accurate in distinguishing patients with acute LD from healthy subjects and in discriminating between individuals with active and resolved infection. This computational approach offers the potential for more accurate diagnosis of early disseminated Lyme disease. It may also allow improved monitoring of treatment efficacy and disease resolution.Mary M. PetzkeKonstantin VolyanskyyYong MaoByron ArevaloRaphael ZohnJohanna QuituisacaGary P. WormserNevenka DimitrovaIra SchwartzAmerican Society for MicrobiologyarticleBorrelia burgdorferiLyme diseasediagnosticsrandom foresttranscriptomeMicrobiologyQR1-502ENmBio, Vol 11, Iss 2 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Borrelia burgdorferi Lyme disease diagnostics random forest transcriptome Microbiology QR1-502
spellingShingle	Borrelia burgdorferi Lyme disease diagnostics random forest transcriptome Microbiology QR1-502 Mary M. Petzke Konstantin Volyanskyy Yong Mao Byron Arevalo Raphael Zohn Johanna Quituisaca Gary P. Wormser Nevenka Dimitrova Ira Schwartz Global Transcriptome Analysis Identifies a Diagnostic Signature for Early Disseminated Lyme Disease and Its Resolution
description	ABSTRACT A bioinformatics approach was employed to identify transcriptome alterations in the peripheral blood mononuclear cells of well-characterized human subjects who were diagnosed with early disseminated Lyme disease (LD) based on stringent microbiological and clinical criteria. Transcriptomes were assessed at the time of presentation and also at approximately 1 month (early convalescence) and 6 months (late convalescence) after initiation of an appropriate antibiotic regimen. Comparative transcriptomics identified 335 transcripts, representing 233 unique genes, with significant alterations of at least 2-fold expression in acute- or convalescent-phase blood samples from LD subjects relative to healthy donors. Acute-phase blood samples from LD subjects had the largest number of differentially expressed transcripts (187 induced, 54 repressed). This transcriptional profile, which was dominated by interferon-regulated genes, was sustained during early convalescence. 6 months after antibiotic treatment the transcriptome of LD subjects was indistinguishable from that of healthy controls based on two separate methods of analysis. Return of the LD expression profile to levels found in control subjects was concordant with disease outcome; 82% of subjects with LD experienced at least one symptom at the baseline visit compared to 43% at the early convalescence time point and only a single patient (9%) at the 6-month convalescence time point. Using the random forest machine learning algorithm, we developed an efficient computational framework to identify sets of 20 classifier genes that discriminated LD from other bacterial and viral infections. These novel LD biomarkers not only differentiated subjects with acute disseminated LD from healthy controls with 96% accuracy but also distinguished between subjects with acute and resolved (late convalescent) disease with 97% accuracy. IMPORTANCE Lyme disease (LD), caused by Borrelia burgdorferi, is the most common tick-borne infectious disease in the United States. We examined gene expression patterns in the blood of individuals with early disseminated LD at the time of diagnosis (acute) and also at approximately 1 month and 6 months following antibiotic treatment. A distinct acute LD profile was observed that was sustained during early convalescence (1 month) but returned to control levels 6 months after treatment. Using a computer learning algorithm, we identified sets of 20 classifier genes that discriminate LD from other bacterial and viral infections. In addition, these novel LD biomarkers are highly accurate in distinguishing patients with acute LD from healthy subjects and in discriminating between individuals with active and resolved infection. This computational approach offers the potential for more accurate diagnosis of early disseminated Lyme disease. It may also allow improved monitoring of treatment efficacy and disease resolution.
format	article
author	Mary M. Petzke Konstantin Volyanskyy Yong Mao Byron Arevalo Raphael Zohn Johanna Quituisaca Gary P. Wormser Nevenka Dimitrova Ira Schwartz
author_facet	Mary M. Petzke Konstantin Volyanskyy Yong Mao Byron Arevalo Raphael Zohn Johanna Quituisaca Gary P. Wormser Nevenka Dimitrova Ira Schwartz
author_sort	Mary M. Petzke
title	Global Transcriptome Analysis Identifies a Diagnostic Signature for Early Disseminated Lyme Disease and Its Resolution
title_short	Global Transcriptome Analysis Identifies a Diagnostic Signature for Early Disseminated Lyme Disease and Its Resolution
title_full	Global Transcriptome Analysis Identifies a Diagnostic Signature for Early Disseminated Lyme Disease and Its Resolution
title_fullStr	Global Transcriptome Analysis Identifies a Diagnostic Signature for Early Disseminated Lyme Disease and Its Resolution
title_full_unstemmed	Global Transcriptome Analysis Identifies a Diagnostic Signature for Early Disseminated Lyme Disease and Its Resolution
title_sort	global transcriptome analysis identifies a diagnostic signature for early disseminated lyme disease and its resolution
publisher	American Society for Microbiology
publishDate	2020
url	https://doaj.org/article/50b7810118a643f6b380c02da1539ef3
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Global Transcriptome Analysis Identifies a Diagnostic Signature for Early Disseminated Lyme Disease and Its Resolution

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