A mathematical kinetic model of memory in Bacillus subtilis spore germination

Abstract

Dormant Bacillus subtilis spores germinate through interaction of germinants with germinant receptors (GRs). Subsequently, the activated GR signal is transduced to SpoVA proteins that constitute transport channels. The opening of the SpoVA channel then leads to calcium dipicolinic acid (CaDPA) release and then completion of spore germination. Spores are known to exhibit memory in germination, as spores given an initial short germinant pulse respond more readily to a second pulse. We have developed a mathematical model to identify a minimal reconstructed molecular network that is crucial for germination kinetics leading to memory of germinant exposure; the model reproduces experimental double germinant pulse germination curves. Analysis of the reconstructed network indicates that a minimal set of inactive and active GRs and a SpoVA channel in three states - closed inactive, closed active and open - is needed to reproduce memory. Spore germination memory is introduced in the network by the activation and deactivation rates of GRs, and by the interplay between activation of closed SpoVA channels and their rate of opening and closing. Author summaryDormant bacterial spores return to life in germination when exposed to germinants which are recognized by germinant receptors (GRs). Spores have memory of germinant exposure, such that more spores germinate after a 2nd germinant pulse than after the 1st one, if intervals between pulses are short. In this work, a mathematical model has been developed that reproduces experimental memory behavior. Computer simulations of the model reveal the crucial factors in spore memory of germinant exposure as: i) active and inactive GR states and ii) a membrane channel for a major spore core molecule, Ca-dipicolinic acid (CaDPA), with the channel existing in closed inactive, closed active and open states, all of which are indispensable for spore memory. The model will be part of an iterative cycle of modelling of network dynamics and generating hypotheses for further experimentation.