Journal
MOLECULAR PSYCHIATRY
Volume 26, Issue 8, Pages 4277-4287Publisher
SPRINGERNATURE
DOI: 10.1038/s41380-020-0652-5
Keywords
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Funding
- University of Florida Clinical and Translational Science Institute from the National Center For Advancing Translational Sciences of the National Institutes of Health [UL1TR, 427]
- National Institute of Health (NIH) [HL33610, HL56921]
- Gatorade Trust
- PCORI-OneFlorida Clinical Research Consortium [CDRN1501-26692]
- University of Florida Department of Physiology and Functional Genomics
- [UM1 HL087366]
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This study used high throughput microbiome sequencing data combined with machine learning methods to investigate the human gut microbiome, demonstrating that individuals with a depression phenotype can be accurately identified using ASV data from the gut microbiome. Differential abundance analysis and metabolic pathway network analysis can significantly differentiate between individuals with a depression phenotype and healthy reference subjects based on microbiome data.
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.
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