Multi-omics examination of Q fever fatigue syndrome identifies similarities with chronic fatigue syndrome.
Multi-omics examination of Q fever fatigue syndrome identifies similarities with chronic fatigue syndrome.
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DOI：
10.1186/s12967-020-02585-5
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发表时间：
2020-11-26
影响因子：
7.4
通讯作者：
Xu CJ
中科院分区：
文献类型：
--
作者：
Raijmakers RPH;Roerink ME;Jansen AFM;Keijmel SP;Gacesa R;Li Y;Joosten LAB;van der Meer JWM;Netea MG;Bleeker-Rovers CP;Xu CJ
Q热疲劳综合征（QFS）的特征是在20%的急性Q热感染中观察到的长期疲劳状态，并具有重大的健康相关后果。QFS背后的分子机制在很大程度上还不清楚。为了更好地了解其发病机制，我们应用多组学方法研究了QFS患者的肠道微生物组，血液代谢组和炎症蛋白质组的模式，并将其与慢性疲劳综合征（CFS）患者和健康对照（HC）进行了比较。研究人群包括31名QFS患者、50名CFS患者和72名HC患者。所有受试者的年龄、性别和一般地理区域（荷兰东南部）均匹配。使用Illumina HiSeq平台通过宏基因组测序评估肠道微生物组组成。共92个循环炎症标志物进行了测量，使用接近延伸试验和1607代谢特征进行了评估，与高通量非靶向代谢组学方法。炎症标志物，包括4E-BP1（P = 9.60 - 16和1.41 - 7）和MMP-1（P = 7.09 - 9和3.51 - 9），在QFS和CFS患者中的表达显著高于HC。当比较QFS（319种代谢物）和CFS（441种代谢物）患者与HC时，血液代谢物谱显示出显著差异，并且在鞘脂（P = 0.0256和0.0033）代谢等途径中显著富集。当比较QFS和CFS患者时，几乎没有发现代谢组的显著差异。QFS和CFS患者的微生物组分类与HC的微生物组分类的比较显示拟杆菌属（Bacteroidetes）（重点是拟杆菌属（Bacteroides）和Alistiples spp.）和厚壁菌门和放线菌门（重点是瘤胃球菌和双歧杆菌属）。当我们比较QFS患者和CFS患者时，在微生物组分类中发现了惊人的相似性并且几乎没有任何显著差异。我们表明，QFS和CFS患者在三个不同的组学层中是相似的，并且4E-BP1和MMP-1具有区分QFS和CFS患者与HC的潜力。
Q fever fatigue syndrome (QFS) is characterised by a state of prolonged fatigue that is seen in 20% of acute Q fever infections and has major health-related consequences. The molecular mechanisms underlying QFS are largely unclear. In order to better understand its pathogenesis, we applied a multi-omics approach to study the patterns of the gut microbiome, blood metabolome, and inflammatory proteome of QFS patients, and compared these with those of chronic fatigue syndrome (CFS) patients and healthy controls (HC). The study population consisted of 31 QFS patients, 50 CFS patients, and 72 HC. All subjects were matched for age, gender, and general geographical region (South-East part of the Netherlands). The gut microbiome composition was assessed by Metagenomic sequencing using the Illumina HiSeq platform. A total of 92 circulating inflammatory markers were measured using Proximity Extension Essay and 1607 metabolic features were assessed with a high-throughput non-targeted metabolomics approach. Inflammatory markers, including 4E-BP1 (P = 9.60–16 and 1.41–7) and MMP-1 (P = 7.09–9 and 3.51–9), are significantly more expressed in both QFS and CFS patients compared to HC. Blood metabolite profiles show significant differences when comparing QFS (319 metabolites) and CFS (441 metabolites) patients to HC, and are significantly enriched in pathways like sphingolipid (P = 0.0256 and 0.0033) metabolism. When comparing QFS to CFS patients, almost no significant differences in metabolome were found. Comparison of microbiome taxonomy of QFS and CFS patients with that of HC, shows both in- and decreases in abundancies in Bacteroidetes (with emphasis on Bacteroides and Alistiples spp.), and Firmicutes and Actinobacteria (with emphasis on Ruminococcus and Bifidobacterium spp.). When we compare QFS patients to CFS patients, there is a striking resemblance and hardly any significant differences in microbiome taxonomy are found. We show that QFS and CFS patients are similar across three different omics layers and 4E-BP1 and MMP-1 have the potential to distinguish QFS and CFS patients from HC.
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