Macaubas, C. | Search bioRxiv

bioRxiv2025

Peripheral Complement C4 Protein in Schizophrenia: Association with Gene Copy Number and Immune Cell Subtypes

The lack of disease-modifying treatments for schizophrenia necessitates the exploration of novel aspects of its pathophysiology, including innate immune mechanisms in the periphery. C4 protein activation, associated with the complement cascade of innate immunity, associates with symptoms and predicts outcomes. However, C4 protein activation does not coincide with expected changes to other proteins in the complement cascade, suggesting another source of C4 protein activation. Using a combination of fresh whole blood from ten anonymous donors and a large set of publicly available microarray data, we show, for the first time, that C4 protein is found and expressed primarily in neutrophils and monocytes. Then, we compared the correlation between C4 protein in neutrophils, classical monocytes, plasma and the number of C4A gene copies. We determined the number of C4A genes using digital droplet PCR, C4 protein in neutrophils (15 patients/21 controls) and plasma (30 patients/38 controls) using western blotting, and classical monocytes (30 patients/38 controls) using flow cytometry. We found a moderate positive correlation between the number of C4A gene copies and the amount of C4 protein only in neutrophils and only in the schizophrenia group (Spearmans rho, r = 0.64, p = 0.01, Cohens d [~]1.67). Our results indicate a convergence of innate immunity mechanisms associated with schizophrenia. This novel mechanism of innate immunity in schizophrenia deserves further study to determine whether it could be a useful drug target.

immunology↗

bioRxiv2020

Multicohort Analysis of Publicly-available Monocyte Expression Data Identifies Gene Signatures to Accurately Monitor Subset-specific Changes in Human Diseases

Monocytes and monocyte-derived cells play important roles in the regulation of inflammation, both as precursors as well as effector cells. Monocytes are heterogeneous and characterized by three distinct subsets in humans. Classical and non-classical monocytes represent the most abundant subsets, each carrying out distinct biological functions. Consequently, altered frequencies of different subsets have been associated with inflammatory conditions, such as infections and autoimmune disorders including lupus, rheumatoid arthritis, inflammatory bowel disease, and, more recently, COVID-19. Dissecting the contribution of different monocyte subsets to disease is currently limited by samples and cohorts, often resulting in underpowered studies and, consequently, poor reproducibility. Public transcriptomes provide an alternative source of data characterized by high statistical power and real world heterogeneity. However, most transcriptome datasets profile bulk blood or tissue samples, requiring the use of in silico approaches to quantify changes in the levels of specific cell types. Here, we integrated 853 publicly available microarray expression profiles of sorted human monocyte subsets from 45 independent studies to identify robust and parsimonious gene expression signatures, consisting of 10 genes specific to each subset. These signatures, although derived using only datasets profiling healthy individuals, maintain their accuracy independent of the disease state in an independent cohort profiled by RNA-sequencing (AUC = 1.0). Furthermore, we demonstrate that our signatures are specific to monocyte subsets compared to other immune cells such as B, T, dendritic cells (DCs) and natural killer (NK) cells (AUC = 0.87~0.88, p<2.2e-16). This increased specificity results in estimated monocyte subset levels that are strongly correlated with cytometry-based quantification of cellular subsets (r = 0.69, p = 6.7e-4). Consequently, we show that these monocyte subset-specific signatures can be used to quantify changes in monocyte subsets levels in expression profiles from patients in clinical trials. Finally, we show that proteins encoded by our signature genes can be used in cytometry-based assays to specifically sort monocyte subsets. Our results demonstrate the robustness, versatility, and utility of our computational approach and provide a framework for the discovery of new cellular markers.

systems biology↗