Basic Science and Pathogenesis.

Background: Alzheimer's disease (AD) is the most common type of dementia which results in debilitating memory loss as the disease advances. However, among older adults with AD, some may experience rapid cognitive decline while others may maintain a stable cognitive status for years. In addition to the amyloid plaques, tau tangles, and neuronal inflammation characteristic of AD, there is strong evidence of dysregulation in the peripheral immune system, including decreased naïve T cells and increased memory T cells among older adults with AD.

Basic Science and Pathogenesis.

Background: In Alzheimer's disease (AD), changes in intestinal microbiota and systemic inflammation are concomitant with neuroinflammation and cognitive decline. This has led to the theory of microbial communities or infections as being causative in the development of neuroinflammation and immunosenescence seen in AD. Our research has demonstrated a decreased taxonomic diversity and an increased abundance of pathobionts in the gut of AD patients (Haran, mBio 2019), which is sufficient to promote amyloid and tau deposition in a mouse model (Chen, Gut 2023).

Microbial Metabolites Orchestrate a Distinct Multi-Tiered Regulatory Network in the Intestinal Epithelium That Directs P-Glycoprotein Expression.

P-glycoprotein (P-gp) is a key component of the intestinal epithelium playing a pivotal role in removal of toxins and efflux of endocannabinoids to prevent excessive inflammation and sustain homeostasis. Recent studies revealed butyrate and secondary bile acids, produced by the intestinal microbiome, potentiate the induction of functional P-gp expression. We now aim to determine the molecular mechanism by which this functional microbiome output regulates P-gp.

Dynamics of embryonic stem cell differentiation inferred from single-cell transcriptomics show a series of transitions through discrete cell states.

The complexity of gene regulatory networks that lead multipotent cells to acquire different cell fates makes a quantitative understanding of differentiation challenging. Using a statistical framework to analyze single-cell transcriptomics data, we infer the gene expression dynamics of early mouse embryonic stem (mES) cell differentiation, uncovering discrete transitions across nine cell states. We validate the predicted transitions across discrete states using flow cytometry.