Molecular crypsis by pathogenic fungi using human factor H. A numerical model

Abstract

Molecular mimicry is the formation of specific molecules by microbial pathogens to avoid recognition and attack by the immune system of the host. Several pathogenic Ascomycota and Zygomycota show such a behaviour by utilizing human complement factor H to hide in the blood stream. We call this type of mimicry molecular crypsis. Such a crypsis can reach a point where the immune system can no longer clearly distinguish between self and non-self cells. Thus, a trade-off between attacking disguised pathogens and erroneously attacking host cells has to be made. Based on signalling theory and protein-interaction modelling, we here present a mathematical model of molecular crypsis of pathogenic fungi using the example of Candida albicans . We tackle the question whether perfect crypsis is feasible, which would imply that protection of human cells by complement factors would be useless. The model identifies pathogen abundance relative to host cell abundance as the predominant factor influencing successful or unsuccessful molecular crypsis. If pathogen cells gain a (locally) quantitative advantage over host cells, even autoreactivity may occur. Our new model enables insights into the mechanisms of candidiasis-induced sepsis and complement-associated autoimmune diseases.

A computational model for the evaluation of complement system regulation under homeostasis, disease, and drug intervention

“…The complexity of the complement system comes from the number of proteins and their networks of interactions involved in all three pathways. However, recent mathematical models targeting complement pathways have been developed to aid our understanding in the dynamics involved in mediating immune response through activation and regulation of complement species [ 12 – 16 ]. To further these efforts, we previously developed a comprehensive model of the alternative pathway divided into four modules: (i) initiation (fluid phase); (ii) amplification (surfaces); (iii) termination (pathogen); and (iv) regulation (host cell and fluid phase) [ 17 ].…”

Trends in mathematical modeling of host–pathogen interactions

“…Perfect resemblance of the model signals by the mimic is possible in principle, no matter how sophisticated the classifier is, but may never be achieved. This is because the dupe and mimic constantly adapt and there is always an arms race in identifying and hiding, being attacked and evading [77,81]. For example, in the case of complement evasion, most microorganisms are able to remove complement factors (which would identify them as pathogens) by utilizing the host-protecting complement factor H [137].…”

“…The term 'molecular mimicry' had been introduced by Damian in 1964 [22]. Recently, Lang et al used signaling theory and ODEs to describe and analyze the attack decision by the host in the case of molecular crypsis [77]. That decision is non-trivial, because the two criteria of minimizing false negatives (foreign cells erroneously recognized as self) and minimizing false positives (own cells erroneously recognized as foreign) cannot be met simultaneously.…”

“…Only a high investment is effective and leads to the global maximum. For further explanations, see section "Molecular mimicry and crypsis" and Lang et al [77] employed to study the dynamics of bacteria [19,88], fungi [79], viruses (for review, see [8,12,25,52,101,139]), and has also been applied to other areas such as cell division mechanisms (e.g., [53, 63, 65-67, 129, 130]).…”

Multiscale Solutions to Quantitative Systems Biology Models

“…In defining a biological network in a quantitative manner, ODE models can enable to predict concentrations, kinetics and behavior of the network components, building hypotheses on disease causation, progression and interference, which can be tested experimentally (Enderling and Chaplain, 2014). In line with this, models of the complement system based on ODEs have been designed to mechanistically deconstruct segments of the complement system under homeostasis and infection (Hirayama et al, 1996; Korotaevskiy et al, 2009; Liu et al, 2011; Zewde et al, 2016; Sagar et al, 2017; Lang et al, 2019).…”