受抗逆转录病毒药物控制的艾滋病病毒感染者肠道微生物组中胰岛素敏感性相关类群的大量减少。

IF 2.1 4区 医学 Q3 GENETICS & HEREDITY BMC Medical Genomics Pub Date : 2024-08-13 DOI:10.1186/s12920-024-01978-5
Eugeni Belda, Jacqueline Capeau, Jean-Daniel Zucker, Emmanuelle Le Chatelier, Nicolas Pons, Florian Plaza Oñate, Benoit Quinquis, Rohia Alili, Soraya Fellahi, Christine Katlama, Karine Clément, Bruno Fève, Stéphane Jaureguiberry, Cécile Goujard, Olivier Lambotte, Joël Doré, Edi Prifti, Jean-Philippe Bastard
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引用次数: 0

摘要

背景:与非感染者相比,艾滋病病毒感染者(PWH)的肠道微生物组发生了改变(普雷沃特氏菌丰度较高,芽孢杆菌属和反刍球菌属丰度较低)。这些改变有些与性偏好有关,有些则与艾滋病毒感染有关。这些菌系与代谢改变之间的关系尚未得到深入研究:在这项研究中,我们通过单变量分析和机器学习方法,比较了 25 名抗逆转录病毒治疗(ART)控制的 PWH 和 25 名非感染匹配个体组成的三个独立对照组的粪便元基因组。此外,我们还使用了两个外部数据集来验证 PWH 分类的预测模型。接下来,我们研究了临床和生物代谢参数与微生物组分类和功能特征之间的关联。最后,我们比较了 7 名 PWH 在接受为期 17 周的抗逆转录病毒疗法(ART)转换为雷特格韦/马拉维若后的肠道微生物组:结果:三大肠道菌型(普雷沃特氏菌、乳酸杆菌和反刍球菌科)存在于所有组别中。第一种普雷沃特氏菌肠型在 PWH 中富集,其中几个特征菌系与代谢状况不佳有关(低高密度脂蛋白和脂联素、高胰岛素抵抗(HOMA-IR))。与此相反,在 PWH 中,丁酸盐产生的肠系明显减少,这与性偏好无关,而且与较好的代谢状况有关(较高的高密度脂蛋白和脂肪连接蛋白以及较低的 HOMA-IR)。相应地,PWH 代谢状况最差的丁酸盐生产和氨基酸降解模块与高密度脂蛋白、高脂联素和低 HOMA-IR 相关。根据分类丰度对 PWH 和对照组进行分类的随机森林模型在两个外部保留数据集上显示出较高的泛化性能(ROC AUC 为 80-82%)。最后,在改用雷特格韦/马拉维若治疗后,没有观察到微生物组组成的明显变化:高分辨率元基因组分析显示,与三个独立的匹配对照组相比,抗逆转录病毒疗法控制的 PWH 患者的肠道微生物组存在重大差异。观察到的明显胰岛素抵抗既可能是普雷沃特氏菌系富集的结果,也可能是产生丁酸并参与氨基酸降解的菌种减少的结果,而这种菌种的减少与艾滋病病毒感染有关。
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Major depletion of insulin sensitivity-associated taxa in the gut microbiome of persons living with HIV controlled by antiretroviral drugs.

Background: Persons living with HIV (PWH) harbor an altered gut microbiome (higher abundance of Prevotella and lower abundance of Bacillota and Ruminococcus lineages) compared to non-infected individuals. Some of these alterations are linked to sexual preference and others to the HIV infection. The relationship between these lineages and metabolic alterations, often present in aging PWH, has been poorly investigated.

Methods: In this study, we compared fecal metagenomes of 25 antiretroviral-treatment (ART)-controlled PWH to three independent control groups of 25 non-infected matched individuals by means of univariate analyses and machine learning methods. Moreover, we used two external datasets to validate predictive models of PWH classification. Next, we searched for associations between clinical and biological metabolic parameters with taxonomic and functional microbiome profiles. Finally, we compare the gut microbiome in 7 PWH after a 17-week ART switch to raltegravir/maraviroc.

Results: Three major enterotypes (Prevotella, Bacteroides and Ruminococcaceae) were present in all groups. The first Prevotella enterotype was enriched in PWH, with several of characteristic lineages associated with poor metabolic profiles (low HDL and adiponectin, high insulin resistance (HOMA-IR)). Conversely butyrate-producing lineages were markedly depleted in PWH independently of sexual preference and were associated with a better metabolic profile (higher HDL and adiponectin and lower HOMA-IR). Accordingly with the worst metabolic status of PWH, butyrate production and amino-acid degradation modules were associated with high HDL and adiponectin and low HOMA-IR. Random Forest models trained to classify PWH vs. control on taxonomic abundances displayed high generalization performance on two external holdout datasets (ROC AUC of 80-82%). Finally, no significant alterations in microbiome composition were observed after switching to raltegravir/maraviroc.

Conclusion: High resolution metagenomic analyses revealed major differences in the gut microbiome of ART-controlled PWH when compared with three independent matched cohorts of controls. The observed marked insulin resistance could result both from enrichment in Prevotella lineages, and from the depletion in species producing butyrate and involved into amino-acid degradation, which depletion is linked with the HIV infection.

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来源期刊
BMC Medical Genomics
BMC Medical Genomics 医学-遗传学
CiteScore
3.90
自引率
0.00%
发文量
243
审稿时长
3.5 months
期刊介绍: BMC Medical Genomics is an open access journal publishing original peer-reviewed research articles in all aspects of functional genomics, genome structure, genome-scale population genetics, epigenomics, proteomics, systems analysis, and pharmacogenomics in relation to human health and disease.
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