ABSTRACT
BACKGROUND: Existing evidence suggests that gut microbiota represent a significant environmental risk factor for various forms of dementia, including Alzheimer's dementia, vascular dementia, and dementia in other diseases classified elsewhere. However, the exact causal relationships between gut microbiota and the different forms of dementia or their subtypes remain unclear. AIM: To investigate putative causal relationships between gut microbiota and dementia or its subtypes using Mendelian randomization (MR) analysis. METHODS: A bidirectional, two-sample, MR analysis was conducted utilizing publicly available gut microbiota-related genome-wide association study (GWAS) summary data from the MiBioGen consortium alongside GWAS summary statistics for dementia and its subtypes from the FinnGen consortium. Instrumental variables were selected according to the fundamental tenets of MR and their strengths were evaluated using the F-statistic. Five MR methods were employed, and the robustness of our findings was validated. To account for multiple comparisons, we applied the Bonferroni method for P-value adjustment. RESULTS: We identified several gut microbiota taxa exhibiting putative causal relationships with dementia or its subtypes, potentially serving as risk or protective factors for the disease. In addition, reverse MR analysis indicated that the relative abundance of several gut microbiota taxa might be influenced by dementia or its subtypes. An exhaustive sensitivity analysis confirmed the absence of heterogeneity and horizontal pleiotropy. After applying correction for multiple testing, we observed that the order Bacillales (odds ratio: 0.830, 95% confidence interval: 0.740-0.932, P = 0.00155, Padjust = 0.0311) exhibited a strong association with Alzheimer's disease-related dementia. CONCLUSION: The results suggest that gut microbiota is causally associated with dementia. Our findings provide novel insights into the pathophysiology of dementia and have important implications for its treatment and prevention.
ABSTRACT
Current biotechnologies can simultaneously measure multiple high-dimensional modalities (e.g., RNA, DNA accessibility, and protein) from the same cells. A combination of different analytical tasks (e.g., multi-modal integration and cross-modal analysis) is required to comprehensively understand such data, inferring how gene regulation drives biological diversity and functions. However, current analytical methods are designed to perform a single task, only providing a partial picture of the multi-modal data. Here, we present UnitedNet, an explainable multi-task deep neural network capable of integrating different tasks to analyze single-cell multi-modality data. Applied to various multi-modality datasets (e.g., Patch-seq, multiome ATAC + gene expression, and spatial transcriptomics), UnitedNet demonstrates similar or better accuracy in multi-modal integration and cross-modal prediction compared with state-of-the-art methods. Moreover, by dissecting the trained UnitedNet with the explainable machine learning algorithm, we can directly quantify the relationship between gene expression and other modalities with cell-type specificity. UnitedNet is a comprehensive end-to-end framework that could be broadly applicable to single-cell multi-modality biology. This framework has the potential to facilitate the discovery of cell-type-specific regulation kinetics across transcriptomics and other modalities.