Cady, N. | bioRxiv Science

bioRxiv2025

Physiologically Relevant Coculture Model for Oral Microbial-Host Interactions

Understanding microbial-host interactions in the oral cavity is essential for elucidating oral disease pathogenesis and its systemic implications. In vitro bacteria-host cell coculture models have enabled fundamental studies to characterize bacterial infection and host responses in a reductionist yet reproducible manner. However, existing in vitro coculture models fail to replicate the physiological oxygen gradients critical for studying these interactions. Here, we present an asymmetric gas coculture system that simulates the oral microenvironment by maintaining distinct normoxic and anaerobic conditions for gingival epithelial cells and anaerobic bacteria, respectively. Using Fusobacterium nucleatum, a key oral pathobiont, we demonstrate that the system preserves bacterial viability and supports the integrity of telomerase-immortalized gingival keratinocytes. Compared to conventional models, this system enhanced bacterial invasion, elevated intracellular bacterial loads, and elicited more robust host pro-inflammatory responses, including increased secretion of CXCL10, IL-6, and IL-8. Additionally, the model enabled precise evaluation of antibiotic efficacy against intracellular pathogens. These results underscore the utility of this coculture platform for studying oral microbial pathogenesis and screening therapeutics, offering a physiologically relevant approach to advance oral and systemic health research. TOC O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=159 SRC="FIGDIR/small/632380v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@3a5951org.highwire.dtl.DTLVardef@bdf8dcorg.highwire.dtl.DTLVardef@1d444forg.highwire.dtl.DTLVardef@16faae7_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2018

Applying non-parametric testing to discrete transfer entropy

Transfer entropy (TE) is a powerful algorithm which attempts to detect the transfer of information from one system to another. In neuroscience, it has the potential to track the movement of information through complex neuronal systems, and provide powerful insights into their organization and operation. One such application is the ability to infer the existence of causal connectivity (such as synaptic pathways) between neurons in a culture being recorded by micro-electrode array (MEA).\n\nThere are several challenges, however, in applying TE to neurological data; one of these is the ability to robustly classify what experimental TE value qualifies as significant. We find that common methods in spike train analysis such as a Z-test cannot be applied, as their assumptions are not met. Instead, we utilize surrogate data to compute a sample under the null hypothesis (no causal connection), and resample experimental data through Markov chain Monte Carlo (MCMC) methods to create a sample of TE values under experimental conditions. A standard non-parametric test (Mann-Whitney U-test) is then applied to compare these samples, and determine if they represent a significant connection.\n\nWe have applied this methodology to MEA recordings of neuronal cultures developing over a period of roughly a month, and find that it provides a wealth of information regarding the cultures' maturity. This includes features such as the directed graph of causal connections across the MEA and identification of information exchange centers. These results are consistent and carry a well-defined significance level.

neuroscience↗