Biology subjects

Guikema, J.

Publications and source records attributed to Guikema, J..

2 recordsLinked to original sources

bioRxiv2025

Levamisole-Mediated Suppression of B-Cell Proliferation and Antibody Production Reveals Mechanistic Insights into Idiopathic Nephrotic Syndrome Therapy

Levamisole (LMS) is a small imidazole derivative with immunomodulatory properties. Despite its longstanding clinical use in Idiopathic Nephrotic Syndrome (INS), the mechanisms underlying its immune-regulating effects remain poorly defined. In this study, we investigated the in vitro effects of LMS treatment on human B-cells, and in patients included from the LEARNS (LEvamisole as Adjuvant therapy to Reduce relapses of Nephrotic Syndrome) clinical trial. In vitro experiments showed that LMS directly suppresses activation and proliferation in both T-cell dependent and T-cell independent stimulated B-cells, without inducing cytotoxicity. In agreement, transcriptomic profiling demonstrated downregulation of cell cycle-associated genes and genes involved in immunoglobulin synthesis, while genes involved in terminal B-cell differentiation were upregulated, including SLAMF7. Consistently, LMS treatment decreased immunoglobulin expression and secretion while simultaneously inducing the expression of factors associated with a more terminal B-cell phenotype (CD138/CD319). Flowcytometry analysis of blood samples from LMS-treated INS patients revealed reduced circulating B-cell counts. Collectively, these data suggest that LMS acts as a potent modulator of B-cell function that inhibits proliferation and immunoglobulin synthesis, providing mechanistic support for its therapeutic efficacy in INS management.

immunology↗

bioRxiv2022

Quantifying B-cell Clonal Diversity In Repertoire Data

The adaptive immune system has the extraordinary ability to produce a broad range of immunoglobulins that can bind a wide variety of antigens. During adaptive immune responses, activated B cells duplicate and undergo somatic hypermutation in their B-cell receptor (BCR) genes, resulting in clonal families of diversified B-cells that can be related back to a common ancestor. Advances in high-throughput sequencing technologies have enabled the high-throughput characterization of B-cell repertoires, however, the accurate identification of clonally related BCR sequences remains a major challenge. In this study, we compare three different clone identification methods on both simulated and experimental data, and investigate their impact on the characterization of B-cell diversity. We find that different methods may lead to different clonal definitions, which in turn can affect the quantification of clonal diversity in repertoire data. Interestingly, we find the Shannon entropy to be overall the most robust diversity index in regard to different clonal identification. Our analysis also suggests that the traditional germline gene alignment-based method for clonal identification remains the most accurate when the complete information about the sequence is known, but that alignment-free methods may be preferred for shorter read length. We make our implementation freely available as a Python library cdiversity.

bioinformatics↗