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Holien, J. K.

Publications and source records attributed to Holien, J. K..

2 recordsLinked to original sources

Leukocyte Immunoglobulin-Like Receptor B1 and its Interactions with Human Leukocyte Antigens

Interactions between Human Leukocyte Antigen (HLA) molecules and their cognate immunoreceptors are essential for regulating innate and adaptive immune cell functions. Leukocyte Immunoglobulin-like Receptors (LILRs) are key regulators of HLA-mediated immune responses, owing to their broad expression across immune cell populations and their ability to modulate both immune activation and tolerance. Among these, LILRB1-HLA interactions are increasingly recognised as important in transplantation, chronic infection and cancer therapies. Unlike other HLA-binding receptors, which recognise epitopes specific to HLA subsets, LILRB1 primarily engages the relatively conserved 3 and {beta}2-microglobulin components of HLA molecules, supporting its role as a broad regulator of pan-HLA class I-mediated functions. Nonetheless, there have been conflicting findings regarding the breadth of LILRB1-HLA-I interactions. While direct affinity studies on a limited subset of HLA-I molecules have revealed no significant differences in LILRB1 binding, broader analyses using single-antigen bead arrays suggest underlying variability. Here, we show through a broad binding assay that, while LILRB1 is a broad HLA-I-binding receptor, it exhibits differential preferences across HLA-I allotypes. Molecular dynamics analyses of the HLA-I-LILRB1 interface suggest that HLA-3 domain dynamism underlies these binding differences. We further determined the crystal structure of LILRB1 and used it to highlight intrinsic structural flexibility within its domains. Finally, these structural insights were leveraged to refine our understanding of the binding modalities of therapeutic monoclonal antibodies currently described. Together, our findings establish structural and mechanistic bases for differential HLA-I recognition by LILRB1 and provide insights into immunotherapeutic targeting of LILRB1.

immunology↗

CharacTERT: A machine learning tool for classifying hTERT missense variants

Missense mutations in TERT, the gene encoding the human telomerase catalytic subunit hTERT, are associated with Telomere Biology Disorders (TBDs). Experimentally elucidating the effects of all possible missense variants would be time-consuming and technically challenging. Moreover, current computational predictors are not hTERT-specific and primarily rely on sequence information, failing to capture the complex biological and structural context of the telomerase enzyme. In this work, we developed three machine learning models integrating both sequence- and structure-based features to account for the biological mechanisms of hTERT. Compared to state-of-the-art methods, our best-performing models achieved a higher Matthews Correlation Coefficient of 0.88 on ClinVar and gnomAD curated variants and demonstrated robust sensitivity (0.75) on a dataset curated according to guidelines from the American College of Medical Genetics and Genomics and Association for Molecular Pathology (ACMG/AMP). Feature interpretation highlighted hTERT residue conservation and changes in hydrophobic and weak polar interactions as critical determinants of pathogenicity. Finally, in silico saturation mutagenesis was performed to present a mutational landscape of TERT, available in a user-friendly web server, CharacTERT, which could offer valuable insights into the molecular mechanisms driving TBDs, aid in early diagnosis, as well as guide personalized treatment strategies. CharacTERT is freely available at https://biosig.lab.uq.edu.au/charactert/.

bioinformatics↗