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用于治疗后莱姆病的基因集预测器。

Gene set predictor for post-treatment Lyme disease.

机构信息

Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA.

Lyme Disease Research Center, Division of Rheumatology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

出版信息

Cell Rep Med. 2022 Nov 15;3(11):100816. doi: 10.1016/j.xcrm.2022.100816.

DOI:10.1016/j.xcrm.2022.100816
PMID:36384094
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9729821/
Abstract

Lyme disease (LD) is tick-borne disease whose post-treatment sequelae are not well understood. For this study, we enrolled 152 individuals with symptoms of post-treatment LD (PTLD) to profile their peripheral blood mononuclear cells (PBMCs) with RNA sequencing (RNA-seq). Combined with RNA-seq data from 72 individuals with acute LD and 44 uninfected controls, we investigated differences in differential gene expression. We observe that most individuals with PTLD have an inflammatory signature that is distinguished from the acute LD group. By distilling gene sets from this study with gene sets from other sources, we identify a subset of genes that are highly expressed in the cohorts but are not already established as biomarkers for inflammatory response or other viral or bacterial infections. We further reduce this gene set by feature importance to establish an mRNA biomarker set capable of distinguishing healthy individuals from those with acute LD or PTLD as a candidate for translation into an LD diagnostic.

摘要

莱姆病(LD)是一种蜱传疾病,其治疗后的后遗症尚不清楚。在这项研究中,我们招募了 152 名有治疗后 LD(PTLD)症状的个体,用 RNA 测序(RNA-seq)对其外周血单核细胞(PBMC)进行分析。结合来自 72 名急性 LD 患者和 44 名未感染对照者的 RNA-seq 数据,我们研究了差异基因表达。我们观察到,大多数 PTLD 患者的炎症特征与急性 LD 组不同。通过从这项研究中提取基因集,并与其他来源的基因集进行蒸馏,我们确定了一组在这些队列中高度表达的基因,但尚未被确定为炎症反应或其他病毒或细菌感染的生物标志物。我们通过特征重要性进一步缩小了这个基因集,以建立一个能够将健康个体与急性 LD 或 PTLD 患者区分开来的 mRNA 生物标志物集,作为转化为 LD 诊断的候选物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/78b3/9729821/f3962a1b54de/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/78b3/9729821/49b42b35121f/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/78b3/9729821/a9799445e745/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/78b3/9729821/5e6d2eb99674/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/78b3/9729821/c0b37fa76cff/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/78b3/9729821/bb58d2385f71/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/78b3/9729821/6452162f0fcc/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/78b3/9729821/5284261be1d0/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/78b3/9729821/f3962a1b54de/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/78b3/9729821/49b42b35121f/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/78b3/9729821/a9799445e745/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/78b3/9729821/5e6d2eb99674/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/78b3/9729821/c0b37fa76cff/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/78b3/9729821/bb58d2385f71/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/78b3/9729821/6452162f0fcc/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/78b3/9729821/5284261be1d0/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/78b3/9729821/f3962a1b54de/gr7.jpg

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本文引用的文献

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2
Optimizing use of multi-antibody assays for Lyme disease diagnosis: A bioinformatic approach.优化多抗体检测在莱姆病诊断中的应用:一种生物信息学方法。
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3
Recent Progress in Lyme Disease and Remaining Challenges.
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Cells. 2024 Aug 19;13(16):1378. doi: 10.3390/cells13161378.
4
Lyme borreliosis diagnosis: state of the art of improvements and innovations.莱姆病诊断:改进和创新的最新进展。
BMC Microbiol. 2023 Aug 1;23(1):204. doi: 10.1186/s12866-023-02935-5.
莱姆病的最新进展及尚存挑战
Front Med (Lausanne). 2021 Aug 18;8:666554. doi: 10.3389/fmed.2021.666554. eCollection 2021.
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Toward comprehensive functional analysis of gene lists weighted by gene essentiality scores.针对基于基因重要性评分加权的基因列表进行全面的功能分析。
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