Multiomics and artificial intelligence enabled peripheral blood-based prediction of amnestic mild cognitive impairment.

BACKGROUND: Since dementia is preventable with early interventions, biomarkers that assist in diagnosing early stages of dementia, such as mild cognitive impairment (MCI), are urgently needed. METHODS: Multiomics analysis of amnestic MCI (aMCI) peripheral blood (n = 25) was performed covering the transcriptome, microRNA, proteome, and metabolome. Validation analysis for microRNAs was conducted in an independent cohort (n = 12). Artificial intelligence was used to identify the most important features for predicting aMCI. FINDINGS: We found that hsa-miR-4455 is the best biomarker in all omics analyses. The diagnostic index taking a ratio of hsa-miR-4455 to hsa-let-7b-3p predicted aMCI patients against healthy subjects with 97% overall accuracy. An integrated review of multiomics data suggested that a subset of T cells and the GCN (general control nonderepressible) pathway are associated with aMCI. INTERPRETATION: The multiomics approach has enabled aMCI biomarkers with high specificity and illuminated the accompanying changes in peripheral blood. Future large-scale studies are necessary to validate candidate biomarkers for clinical use.

Paenibacillus macerans possesses two types of 16S rDNA copies in a genome with a length difference of twelve base pairs.

Two Paenibacillus macerans strains, JCM 2500T and MCRI 12, exhibited two types of 16S rDNA copies in their genomes, accompanied by a length difference of 12 bp at positions 203 to 214 (Escherichia coli numbering). The long-type sequences were newly identified for P. macerans 16S rDNA, and the copy numbers were different between the two strains. Both types of 16S rRNA were expressed in each strain, and it was predicted that the polymorphism at this position is located in helix H10, based on a comparison with the E. coli 16S rRNA secondary structure model.