Single nucleotide exact amplicon sequence variants (ASV) of the human gut microbiome were used to evaluate if individuals with a depression phenotype (DEPR) could be identified from healthy reference subjects (NODEP). Microbial DNA in stool samples obtained from 40 subjects were characterized using high throughput microbiome sequence data processed via DADA2 error correction combined with PIME machine-learning de-noising and taxa binning/parsing of prevalent ASVs at the single nucleotide level of resolution. Application of ALDEx2 differential abundance analysis with assessed effect sizes and stringent PICRUSt2 predicted metabolic pathways. This multivariate machine-learning approach significantly differentiated DEPR (n = 20) vs. NODEP (n = 20) (PERMANOVA P < 0.001) based on microbiome taxa clustering and neurocircuit-relevant metabolic pathway network analysis for GABA, butyrate, glutamate, monoamines, monosaturated fatty acids, and inflammasome components. Gut microbiome dysbiosis using ASV prevalence data may offer the diagnostic potential of using human metaorganism biomarkers to identify individuals with a depression phenotype.

中文翻译：

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通过使用人类肠道微生物组的单核苷酸精确扩增子序列变体鉴定抑郁症表型。

人类肠道微生物组的单核苷酸精确扩增子序列变异 (ASV) 用于评估是否可以从健康参考受试者 (NODEP) 中识别出具有抑郁表型 (DEPR) 的个体。使用通过 DADA2 纠错结合 PIME 机器学习去噪和单核苷酸分辨率水平的流行 ASV 分类单元分类/解析处理的高通量微生物组序列数据，对从 40 名受试者获得的粪便样本中的微生物 DNA 进行了表征。ALDEx2 差异丰度分析与评估效应大小和严格的 PICRUSt2 预测代谢途径的应用。这种多元机器学习方法显着区分了 DEPR (n = 20) 与 NODEP (n = 20) (PERMANOVA P < 0。001) 基于 GABA、丁酸、谷氨酸、单胺、单饱和脂肪酸和炎性小体成分的微生物分类群聚类和神经回路相关代谢通路网络分析。使用 ASV 流行数据的肠道微生物群失调可能提供使用人类代谢生物标志物来识别具有抑郁表型的个体的诊断潜力。