Biology subjects

Biniaminov, N.

Publications and source records attributed to Biniaminov, N..

2 recordsLinked to original sources

bioRxiv2026

Dual-Layer Serological Encoding: Integrating Pathogen-Derived and Host-Reactive Peptide Signatures Reveals Complementary Dimensions of HIV Immune Response

Serological diagnostics traditionally rely on pathogen-derived antigens to detect infection-specific antibody responses. Chronic infections also induce systemic immune remodeling that may be reflected in global antibody reactivity patterns beyond antigen specificity. Here we evaluate a dual-layer serological framework combining HIV-derived peptides with a host-derived peptide library designed to capture distributed antibody reactivity patterns. Using strict nested cross-validation in a cohort of 105 individuals, pathogen-derived 12-mer peptides achieved high classification performance with an AUC of 0.891, whereas the 10-mer host-based peptide library alone yielded moderate but statistically significant discrimination with an AUC of 0.805. Integration via regularized stacking resulted in only a modest additive improvement, reaching an AUC of 0.897, indicating partial redundancy in diagnostic ranking. In contrast, entropy and inequality analyses revealed substantial immune repertoire restructuring in HIV-positive individuals, characterized by reduced Shannon entropy and significant correlations between classifier probability and repertoire concentration. These findings support a dual-layer model of serology in which the integration of pathogen-derived and host-derived peptides into a meta model encode antigen specificity, whereas host-reactive signatures reflect systemic immune topology. Distinguishing diagnostic ranking from immune-state encoding provides a conceptual framework for multi-layer serological diagnostics.

immunology↗

bioRxiv2026

High-density peptide arrays detect tuberculosis through immune remodeling, not only antigen recognition alone

Serological diagnostics for tuberculosis rely on pathogen-derived antigens to detect infection-specific antibodies. Whether chronic TB infection also reshapes the global topology of the antibody repertoire remains largely unexplored. Here we profile serum antibody binding across 6,936 peptides in 105 individuals from three countries using two complementary libraries: Mycobacterium tuberculosis peptides (TBC) and a resemblance-ranking library representing the human self-proteome (RRL). We construct a five-dimensional immune state vector from distributional binding properties and map individual sera into an immune phase space. A remodeling classifier achieves virtually identical performance on pathogen-derived and host-derived peptides (AUC 0.63-0.73), demonstrating that the diagnostic signal arises from global repertoire restructuring rather than antigen-specific recognition. HIV co-infection partially masks this signal; restricting analysis to HIV-negative individuals increases AUC to 0.73 (permutation p = 0.005) and enables detection of smear-negative TB (AUC = 0.83, specificity 0.95 with three peptides). Phase-space projections reveal that TB severity maps onto a continuous remodeling gradient, with smear-negative patients occupying intermediate positions between healthy controls and smear-positive cases. These findings position high-density peptide arrays as sensors of antibody repertoire topology, enabling detection of chronic immune states beyond antigen-specific recognition.

immunology↗