Metatranscriptomic Reanalysis of Alzheimer's Brains Identifies Low-Biomass Microbial Signals Including Enrichment of <i>Acinetobacter radioresistens</i>.

Overview

1. Department of Bioengineering, Institut Químic de Sarrià (IQS), Universitat Ramon Llull (URL), 08017 Barcelona, Spain

Abstract

Alzheimer’s disease (AD) is characterized by progressive cognitive decline and the accumulation of amyloid-β (Aβ) plaques and tau neurofibrillary tangles. Beyond genetic and proteostatic mechanisms, infection- and dysbiosis-based models of AD have gained renewed attention, including the antimicrobial protection hypothesis, in which Aβ may participate in innate immune defense. Here, we reanalyzed ribosomal depleted (Ribo-Zero) RNA-seq data from dorsolateral prefrontal cortex (DLPFC) samples from the Mount Sinai Brain Bank cohort (GSE53697) to screen for non-human transcripts. Reads underwent quality control and adapter trimming, taxonomic classification with Kraken2, abundance re-estimation with Bracken, and differential abundance testing with edgeR. Across 17 samples (9 advanced AD and 8 controls), we detected low-biomass microbial signals, with Acinetobacter radioresistens showing enrichment in the AD group (FDR = 0.018). Several additional taxa showed suggestive group differences but did not remain significant after multiple testing correction, including Lactobacillus iners (FDR = 0.051). We also performed an exploratory in silico analysis of an A. radioresistens biofilm-associated protein homolog, identifying predicted amyloidogenic motifs and surface-exposed regions that may be relevant to cross-seeding hypotheses, although no mechanistic inference can be drawn without experimental validation. Given the technical challenges of inferring microbial signals from post-mortem brain RNA-seq data, including contamination risk, low microbial biomass, and overwhelming host background, these findings should be interpreted as hypothesis-generating and warrant orthogonal validation in larger, microbiome-aware cohorts.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• geo:GSE53697, at NCBI GEO; found in the text, “Informed Consent Statement”

Data Availability Statement

All sequencing data analyzed in this study are publicly available from the NCBI Gene Expression Omnibus under accession GSE53697 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE53697) and associated SRA records. Analysis steps were performed using the Galaxy platform and standard open-source tools as described in the Methods.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 1 author, 10 keywords, 8 MeSH terms, 1 funder, 52 references.

Cite

This paper

Guix, F. X. (2026). Metatranscriptomic Reanalysis of Alzheimer's Brains Identifies Low-Biomass Microbial Signals Including Enrichment of <i>Acinetobacter radioresistens</i>. International journal of molecular sciences, 27(8), 3430. https://doi.org/10.3390/ijms27083430

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