Transcriptomic analysis of pathways associated with ITGAV/alpha(v) integrin-dependent autophagy in human B cells

Prediction of Future Parkinson Disease Using Plasma Proteins Combined With Clinical-Demographic Measures

BACKGROUND AND OBJECTIVES

Identification of individuals at high risk of developing Parkinson disease (PD) several years before diagnosis is crucial for developing treatments to prevent or delay neurodegeneration. This study aimed to develop predictive models for PD risk that combine plasma proteins and easily accessible clinical-demographic variables.

METHODS

Using data from the UK Biobank (UKB), which recruited participants across the United Kingdom, we conducted a longitudinal study to identify predictors for incident PD. Participants with baseline plasma proteins and no PD were included. Through machine learning, we narrowed down predictors from a pool of 1,463 plasma proteins and 93 clinical-demographic. These predictors were then externally validated using the Parkinson's Progression Marker Initiative (PPMI) cohort. To further investigate the temporal trends of predictors, a nested case-control study was conducted within the UKB.

RESULTS

A total of 52,503 participants without PD (median age 58, 54% female) were included. Over a median follow-up duration of 14.0 years, 751 individuals were diagnosed with PD (median age 65, 37% female). Using a forward selection approach, we selected a panel of 22 plasma proteins for optimal prediction. Using an ensemble tree-based Light Gradient Boosting Machine (LightGBM) algorithm, the model achieved an area under the receiver operating characteristic curve (AUC) of 0.800 (95% CI 0.785-0.815). The LightGBM prediction model integrating both plasma proteins and clinical-demographic variables demonstrated enhanced predictive accuracy, with an AUC of 0.832 (95% CI 0.815-0.849). Key predictors identified included age, years of education, history of traumatic brain injury, and serum creatinine. The incorporation of 11 plasma proteins (neurofilament light, integrin subunit alpha V, hematopoietic PGD synthase, histamine N-methyltransferase, tubulin polymerization promoting protein family member 3, ectodysplasin A2 receptor, Latexin, interleukin-13 receptor subunit alpha-1, BAG family molecular chaperone regulator 3, tryptophanyl-TRNA synthetase, and secretogranin-2) augmented the model's predictive accuracy. External validation in the PPMI cohort confirmed the model's reliability, producing an AUC of 0.810 (95% CI 0.740-0.873). Notably, alterations in these predictors were detectable several years before the diagnosis of PD.

DISCUSSION

Our findings support the potential utility of a machine learning-based model integrating clinical-demographic variables with plasma proteins to identify individuals at high risk for PD within the general population. Although these predictors have been validated by PPMI, additional validation in a more diverse population reflective of the general community is essential.

Integrin αvβ3 Limits Cytokine Production by Plasmacytoid Dendritic Cells and Restricts TLR-Driven Autoimmunity

Plasmacytoid dendritic cells (pDCs) are strongly implicated as a major source of IFN-I in systemic lupus erythematosus (SLE), triggered through TLR-mediated recognition of nucleic acids released from dying cells. However, relatively little is known about how TLR signaling and IFN-I production are regulated in pDCs. In this article, we describe a role for integrin αvβ3 in regulating TLR responses and IFN-I production by pDCs in mouse models. We show that αv and β3-knockout pDCs produce more IFN-I and inflammatory cytokines than controls when stimulated through TLR7 and TLR9 in vitro and in vivo. Increased cytokine production was associated with delayed acidification of endosomes containing TLR ligands, reduced LC3 conjugation, and increased TLR signaling. This dysregulated TLR signaling results in activation of B cells and promotes germinal center (GC) B cell and plasma cell expansion. Furthermore, in a mouse model of TLR7-driven lupus-like disease, deletion of αvβ3 from pDCs causes accelerated autoantibody production and pathology. We therefore identify a pDC-intrinsic role for αvβ3 in regulating TLR signaling and preventing activation of autoreactive B cells. Because αvβ3 serves as a receptor for apoptotic cells and cell debris, we hypothesize that this regulatory mechanism provides important contextual cues to pDCs and functions to limit responses to self-derived nucleic acids.

Autophagy Induced by Toll-like Receptor Ligands Regulates Antigen Extraction and Presentation by B Cells

The engagement of B cells with surface-tethered antigens triggers the formation of an immune synapse (IS), where the local secretion of lysosomes can facilitate antigen uptake. Lysosomes intersect with other intracellular processes, such as Toll-like Receptor (TLR) signaling and autophagy coordinating immune responses. However, the crosstalk between these processes and antigen presentation remains unclear. Here, we show that TLR stimulation induces autophagy in B cells and decreases their capacity to extract and present immobilized antigens. We reveal that TLR stimulation restricts lysosome repositioning to the IS by triggering autophagy-dependent degradation of GEF-H1, a Rho GTP exchange factor required for stable lysosome recruitment at the synaptic membrane. GEF-H1 degradation is not observed in B cells that lack αV integrins and are deficient in TLR-induced autophagy. Accordingly, these cells show efficient antigen extraction in the presence of TLR stimulation, confirming the role of TLR-induced autophagy in limiting antigen extraction. Overall, our results suggest that resources associated with autophagy regulate TLR and BCR-dependent functions, which can finetune antigen uptake by B cells. This work helps to understand the mechanisms by which B cells are activated by surface-tethered antigens in contexts of subjacent inflammation before antigen recognition, such as sepsis.