Fuller, L. | Search bioRxiv

bioRxiv2024

Rickettsia β-peptide Reactivity with Immune IgM

Spotted Fever Group (SFG) Rickettsia species maintain an s-layer with two major outer membrane proteins (OmpA and OmpB) aligned within a lipopolysaccharide (LPS) matrix external to the bacterial outer membrane. While the two typhus group species (R. typhi and R. prowazekii) maintain a similar s-layer containing only OmpB with its own specific form of LPS. A major component of these two types of OmpB is a specific and highly conserved beta peptide ({beta}-peptide) non-covalently attached to the respective OmpB passenger domain. This {beta}-peptide is initially translated as the c-terminus of the 168 kDa polypeptide and initially forms a membrane pore to allow the larger passenger domain to exit the bacterial cytoplasm through the outer membrane. Prior to the full exit of this passenger domain from the cytoplasm the long polypeptide chain is cleaved by a peptidase, leaving the c-terminus remaining as a beta-folded membrane pore and the N-terminal passenger domain exiting to the space between the bacterial membrane and the s-layer. The fate of this pore structure and the attachment of {beta}-peptide to the OmpB continues to be examined and will be discussed as another protective component of the Rickettsia. An EIA IgM assay has been developed to utilize this antigen for clinical diagnostic use to accurately detect acute rickettsial infection in testing labs and lead to the availability of rapid test (lateral flow) formats for more immediate testing.

immunology↗

bioRxiv2021

Reduced Dimension, Biophysical Neuron Models Constructed From Observed Data

Using methods from nonlinear dynamics and interpolation techniques from applied mathematics, we show how to use data alone to construct discrete time dynamical rules that forecast observed neuron properties. These data may come from from simulations of a Hodgkin-Huxley (HH) neuron model or from laboratory current clamp experiments. In each case the reduced dimension data driven forecasting (DDF) models are shown to predict accurately for times after the training period. When the available observations for neuron preparations are, for example, membrane voltage V(t) only, we use the technique of time delay embedding from nonlinear dynamics to generate an appropriate space in which the full dynamics can be realized. The DDF constructions are reduced dimension models relative to HH models as they are built on and forecast only observables such as V(t). They do not require detailed specification of ion channels, their gating variables, and the many parameters that accompany an HH model for laboratory measurements, yet all of this important information is encoded in the DDF model. As the DDF models use only voltage data and forecast only voltage data, they can be used in building networks with biophysical connections. Both gap junction connections and ligand gated synaptic connections among neurons involve presynaptic voltages and induce postsynaptic voltage response. Biophysically based DDF neuron models can replace other reduced dimension neuron models, say of the integrate-and-fire type, in developing and analyzing large networks of neurons. When one does have detailed HH model neurons for network components, a reduced dimension DDF realization of the HH voltage dynamics may be used in network computations to achieve computational efficiency and the exploration of larger biological networks.

biophysics↗