A generalized quantitative antibody homeostasis model: regulation of B-cell development by BCR saturation and novel insights into bone marrow function

Single-residue linear and conformational B cell epitopes prediction using random and ESM-2 based projections

B cell epitope prediction methods are separated into linear sequence-based predictors and conformational epitope predictions that typically use the measured or predicted protein structure. Most linear predictions rely on the translation of the sequence to biologically based representations and the applications of machine learning on these representations. We here present CALIBER 'Conformational And LInear B cell Epitopes pRediction', and show that a bidirectional long short-term memory with random projection produces a more accurate prediction (test set AUC=0.789) than all current linear methods. The same predictor when combined with an Evolutionary Scale Modeling-2 projection also improves on the state of the art in conformational epitopes (AUC = 0.776). The inclusion of the graph of the 3D distances between residues did not increase the prediction accuracy. However, the long-range sequence information was essential for high accuracy. While the same model structure was applicable for linear and conformational epitopes, separate training was required for each. Combining the two slightly increased the linear accuracy (AUC 0.775 versus 0.768) and reduced the conformational accuracy (AUC = 0.769).

The Binding Landscape of Serum Antibodies: How Physical and Mathematical Concepts Can Advance Systems Immunology

Antibodies constitute a major component of serum on protein mass basis. We also know that the structural diversity of these antibodies exceeds that of all other proteins in the body and they react with an immense number of molecular targets. What we still cannot quantitatively describe is how antibody abundance is related to affinity, specificity, and cross reactivity. This ignorance has important practical consequences: we also do not have proper biochemical units for characterizing polyclonal serum antibody binding. The solution requires both a theoretical foundation, a physical model of the system, and technology for the experimental confirmation of theory. Here we argue that the quantitative characterization of interactions between serum antibodies and their targets requires systems-level physical chemistry approach and generates results that should help create maps of antibody binding landscape.

Network Organization of Antibody Interactions in Sequence and Structure Space: the RADARS Model

Adaptive immunity in vertebrates is a complex self-organizing network of molecular interactions. While deep sequencing of the immune-receptor repertoire may reveal clonal relationships, functional interpretation of such data is hampered by the inherent limitations of converting sequence to structure to function. In this paper, a novel model of antibody interaction space and network, termed radial adjustment of system resolution, RAdial ADjustment of System Resolution (RADARS), is proposed. The model is based on the radial growth of interaction affinity of antibodies towards an infinity of directions in structure space, each direction corresponding to particular shapes of antigen epitopes. Levels of interaction affinity appear as free energy shells of the system, where hierarchical B-cell development and differentiation takes place. Equilibrium in this immunological thermodynamic system can be described by a power law distribution of antibody-free energies with an ideal network degree exponent of phi square, representing a scale-free fractal network of antibody interactions. Plasma cells are network hubs, memory B cells are nodes with intermediate degrees, and B1 cells function as nodes with minimal degree. Overall, the RADARS model implies that a finite number of antibody structures can interact with an infinite number of antigens by immunologically controlled adjustment of interaction energy distribution. Understanding quantitative network properties of the system should help the organization of sequence-derived predicted structural data.

Establishment of a protein biochip to detect serum IgG antibodies against IL-2 and soluble CD25 in hemophagocytic lymphohistiocytosis

BACKGROUND

Interleukin-2 (IL-2) and soluble CD25 (sCD25) are among the most important cytokines and diagnostic biomarkers in hemophagocytic lymphohistiocytosis (HLH). Detecting serum level of IL-2 and sCD25 is valuable for making clinical diagnosis and treatment decision in HLH.

METHODS

Since tests showing serum IgG antibody against IL-2 or sCD25 have never been carried out, a new protein biochip, which was modified with cysteine and activated sophorolipid (Cys-SL), was developed.

RESULTS

Limits of detection on the biochip were 78 pg/ml for IL-2 and 39 pg/ml for sCD25, respectively. The data showed that on-chip seroimmunological responses to IL-2 and sCD25 proteins were 20.8% and 83.1% and the seroprevalence of IL-2 and sCD25 IgG antibodies were 45.5% and 57.2%, respectively. Data collection for the seroprevalence of serum antigen-antibody complex of sCD25 was 68.8%. The new biochip model shared similar sensitivity and specificity to chemiluminescent immunoassay (CLIA) in its measuring capacity of serum sCD25.

CONCLUSIONS

We addressed and confirmed the involvement of serum IgG antibodies against IL-2 and sCD25 as well as Ag-Ab complex of sCD25 in HLH patients. Therefore, this biochip platform would offer a new technological substitution for clinical serological diagnosis of HLH.

A generalized quantitative antibody homeostasis model: maintenance of global antibody equilibrium by effector functions

The homeostasis of antibodies can be characterized as a balanced production, target-binding and receptor-mediated elimination regulated by an interaction network, which controls B-cell development and selection. Recently, we proposed a quantitative model to describe how the concentration and affinity of interacting partners generates a network. Here we argue that this physical, quantitative approach can be extended for the interpretation of effector functions of antibodies. We define global antibody equilibrium as the zone of molar equivalence of free antibody, free antigen and immune complex concentrations and of dissociation constant of apparent affinity: [Ab]=[Ag]=[AbAg]=K_D. This zone corresponds to the biologically relevant K_D range of reversible interactions. We show that thermodynamic and kinetic properties of antibody–antigen interactions correlate with immunological functions. The formation of stable, long-lived immune complexes correspond to a decrease of entropy and is a prerequisite for the generation of higher-order complexes. As the energy of formation of complexes increases, we observe a gradual shift from silent clearance to inflammatory reactions. These rules can also be applied to complement activation-related immune effector processes, linking the physicochemical principles of innate and adaptive humoral responses. Affinity of the receptors mediating effector functions shows a wide range of affinities, allowing the continuous sampling of antibody-bound antigen over the complete range of concentrations. The generation of multivalent, multicomponent complexes triggers effector functions by crosslinking these receptors on effector cells with increasing enzymatic degradation potential. Thus, antibody homeostasis is a thermodynamic system with complex network properties, nested into the host organism by proper immunoregulatory and effector pathways. Maintenance of global antibody equilibrium is achieved by innate qualitative signals modulating a quantitative adaptive immune system, which regulates molecular integrity of the host by tuning the degradation and recycling of molecules from silent removal to inflammatory elimination.

Evolutionary genetics of immunological supertypes reveals two faces of the Red Queen

Red Queen host-parasite co-evolution can drive adaptations of immune genes by positive selection that erodes genetic variation (Red Queen arms race) or results in a balanced polymorphism (Red Queen dynamics) and long-term preservation of genetic variation (trans-species polymorphism). These two Red Queen processes are opposite extremes of the co-evolutionary spectrum. Here we show that both Red Queen processes can operate simultaneously by analysing the major histocompatibility complex (MHC) in guppies (Poecilia reticulata and P. obscura) and swamp guppies (Micropoecilia picta). Sub-functionalisation of MHC alleles into 'supertypes' explains how polymorphisms persist during rapid host-parasite co-evolution. Simulations show the maintenance of supertypes as balanced polymorphisms, consistent with Red Queen dynamics, whereas alleles within supertypes are subject to positive selection in a Red Queen arms race. Building on the divergent allele advantage hypothesis, we show that functional aspects of allelic diversity help to elucidate the evolution of polymorphic genes involved in Red Queen co-evolution.

A generalized quantitative antibody homeostasis model: antigen saturation, natural antibodies and a quantitative antibody network

In a pair of articles, we present a generalized quantitative model for the homeostatic function of clonal humoral immune system. In this second paper, we describe how antibody production controls the saturation of antigens and the network of antibody interactions that emerges in the epitome space with the establishment of the immune system. Efficient control of antigens, be it self or foreign, requires the maintenance of antibody concentrations that saturate antigen to relevant levels. Simple calculations suggest that the observed diverse recognition of antigens by natural antibodies is only possible by cross-reactivity whereby particular clones of antibodies bind to diverse targets and shared recognition of particular antigens by multiple antibody clones contribute to the maintenance of antigen control. We also argue that natural antibodies are none else than the result of thymus-independent responses against immunological self. We interpret and explain antibody production and function in a virtual molecular interaction space and as a network of interactions. Indeed, the general quantitative (GQM) model we propose is in agreement with earlier models, confirms some assumptions and presumably provides the theoretical basis for the construction of a real antibody network using the sequence and interaction database data.