Biology subjects

Baer, A.

Publications and source records attributed to Baer, A..

3 recordsLinked to original sources

bioRxiv2024

Receptor-based protein binding in the supramolecular network of velvet worm slime

The slime of velvet worms (Onychophora) is a protein-based bioadhesive that undergoes rapid, yet reversible transition from a fluid into stiff fibers used for prey capture and defense, but the mechanism by which this phase transition functions is largely unknown. Here, integrating transcriptomic and proteomic approaches with AI-guided structure predictions, we discover a group of evolutionarily conserved leucine-rich repeat (LRR) proteins in velvet worm slime that readily adopt a receptor-like, protein-binding "horseshoe" structure. Our structural predictions suggest dimerization of LRR proteins and support their interactions with conserved {beta}-sheets-rich domains of high-molecular-weight proteins, the primary building blocks of velvet worm slime fibers. This previously unknown functional context of LRR proteins is presumably involved in reversible, receptor-based supramolecular network formation in these adhesive biofibers and provides possible new avenues for fabricating fully recyclable (bio)polymeric materials. Significance StatementAnalyzing structure-function-relationships underlying reversible fiber formation in velvet worm slime may inspire avenues for the sustainable fabrication of protein-based polymeric materials. Here, we present evidence for an evolutionarily conserved mechanism of reversible fiber formation in velvet worm slime based on the receptor-like binding of fiber forming proteins by a leucine-rich repeat (LRR) protein. The structures of both protein components are highly conserved evolutionarily in the two distantly related velvet worm subgroups, indicating pervasive presence of this mechanism across species that has been maintained through the last [~]380 MY. Our results suggest that the ubiquitously occurring LRR motif--better known for its innate immunity and developmental roles--has a novel identified function in processing a biological material, which might contribute to the development of sustainable bio-inspired materials.

biochemistry↗

bioRxiv2021

The DNA sensors AIM2 and IFI16 are NET-binding SLE autoantigens

Nucleic acid binding proteins are frequently targeted as autoantigens in systemic lupus erythematosus (SLE) and other interferon (IFN)-linked rheumatic diseases. The AIM-like receptors (ALRs) are IFN-inducible innate sensors that form supramolecular assemblies along double-stranded DNA of various origins. Here, we identify the ALR Absent in melanoma 2 (AIM2) as a novel autoantigen in SLE, with similar properties to the established ALR autoantigen interferon-inducible protein 16 (IFI16). Our SLE cohort revealed a frequent co-occurrence of anti-AIM2, anti-IFI16 and anti-DNA antibodies, and higher clinical measures of disease activity in patients positive for antibodies against these ALRs. We examined neutrophil extracellular traps (NETs) as DNA scaffolds on which these antigens might interact in a pro-immune context, finding that both ALRs bind NETs in vitro and in SLE renal tissues. We demonstrate that ALR binding causes NETs to resist degradation by DNase I, suggesting a mechanism whereby extracellular ALR-NET interactions may promote sustained IFN signaling. Our work suggests that extracellular ALRs bind NETs, leading to DNase resistant nucleoprotein fibers that are targeted as autoantigens in SLE.

immunology↗

bioRxiv2018

Improved Analysis of Phage ImmunoPrecipitation Sequencing (PhIP-Seq) Data Using a Z-score Algorithm

Phage ImmunoPrecipitation Sequencing (PhIP-Seq) is a massively multiplexed, phage-display based methodology for analyzing antibody binding specificities, with several advantages over existing techniques, including the uniformity and completeness of proteomic libraries, as well as high sample throughput and low cost. Data generated by the PhIP-Seq assay are unique in many ways. The only published analytical approach for these data suffers from important limitations. Here, we propose a new statistical framework with several improvements. Using a set of replicate mock immunoprecipitations (negative controls lacking antibody input) to generate background binding distributions, we establish a statistical model to quantify antibody-dependent changes in phage clone abundance. Our approach incorporates robust regression of experimental samples against the mock IPs as a means to calculate the expected phage clone abundance, and provides a generalized model for calculating each clones expected abundance-associated standard deviation. In terms of bias removal and detection sensitivity, we demonstrate that this z-score algorithm outperforms the previous approach. Further, in a large cohort of autoantibody-defined Sjogrens Syndrome (SS) patient sera, PhIP-Seq robustly identified Ro52, Ro60, and SSB/La as known autoantigens associated with SS. In an effort to identify novel SS-specific binding specificities, SS z-scores were compared with z-scores obtained by screening Ropositive sera from patients with systemic lupus erythematosus (SLE). This analysis did not yield any commonly targeted SS-specific autoantigens, suggesting that if they exist at all, their epitopes are likely to be discontinuous or post-translationally modified. In summary, we have developed an improved algorithm for PhIP-Seq data analysis, which was validated using a large set of sera with clinically characterized autoantibodies. This z-score approach will substantially improve the ability of PhIP-Seq to detect and interpret antibody binding specificities. The associated Python code is freely available for download here: https://github.com/LarmanLab/PhIP-Seq-Analyzer.

bioinformatics↗