Mechanistically Inspired Kinetic Approach to Describe Interactions During Co‐Culture Growth of Carnobacterium maltaromaticum and Listeria monocytogenes

Cannabinoid control of gingival immune activation in chronically SIV-infected rhesus macaques involves modulation of the indoleamine-2,3-dioxygenase-1 pathway and salivary microbiome

BACKGROUND

HIV/SIV-associated periodontal disease (gingivitis/periodontitis) (PD) represents a major comorbidity affecting people living with HIV (PLWH) on combination anti-retroviral therapy (cART). PD is characterized by chronic inflammation and dysbiosis. Nevertheless, the molecular mechanisms and use of feasible therapeutic strategies to reduce/reverse inflammation and dysbiosis remain understudied and unaddressed.

METHODS

Employing a systems biology approach, we report molecular, metabolome and microbiome changes underlying PD and its modulation by phytocannabinoids [delta-9-tetrahydrocannabinol (Δ9-THC)] in uninfected and SIV-infected rhesus macaques (RMs) untreated (VEH-untreated/SIV) or treated with vehicle (VEH/SIV) or Δ9-THC (THC/SIV).

FINDINGS

VEH- untreated/SIV but not THC/SIV RMs showed significant enrichment of genes linked to anti-viral defense, interferon-β, NFκB, RIG-1, and JAK-STAT signaling. We focused on the anti-microbial DUOX1 and immune activation marker IDO1 that were reciprocally regulated in the gingiva of VEH-untreated/SIV RMs. Both proteins localized to the gingival epithelium and CD163+ macrophages, and showed differential expression in the gingiva of THC/SIV and VEH/SIV RMs. Additionally, inflammation-associated miR-21, miR-142-3p, miR-223, and miR-125a-5p showed significantly higher expression in the gingiva of VEH/SIV RMs. In human primary gingival epithelial cells, miR-125a-5p post-transcriptionally downregulated DUOX1 and THC inhibited IDO1 protein expression through a cannabinoid receptor-2 mediated mechanism. Interestingly, THC/SIV RMs showed relatively reduced plasma levels of kynurenine, kynurenate, and the neurotoxic quinolinate compared to VEH/SIV RMs at 5 months post SIV infection (MPI). Most importantly, THC blocked HIV/SIV-induced depletion of Firmicutes and Bacteroidetes, and reduced Gammaproteobacteria abundance in saliva. Reduced IDO1 protein expression was associated with significantly (p<0.05) higher abundance of Prevotella, Lactobacillus (L. salivarius, L. buchneri, L. fermentum, L. paracasei, L. rhamnosus, L. johnsonii) and Bifidobacteria and reduced abundance of the pathogenic Porphyromonas cangingivalis and Porphyromonas macacae at 5MPI.

INTERPRETATION

The data provides deeper insights into the molecular mechanisms underlying HIV/SIV-induced PD and more importantly, the anti-inflammatory and anti-dysbiotic properties of THC in the oral cavity. Overall, these translational findings suggest that phytocannabinoids may help reduce gingival/systemic inflammation, salivary dysbiosis and potentially metabolic disease/syndrome in PLWH on cART and those with no access to cART or do not suppress the virus under cART.

FUNDING

Research reported in this publication was supported by the National Institutes of Health Award Numbers R01DA052845 (MM and SNB), R01DA050169 (MM and CO), R01DA042524 and R56DE026930 (MM), and P51OD011104 and P51OD011133. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Genetic algorithm applied to simultaneous parameter estimation in bacterial growth

Several mathematical models have been developed to understand the interactions of microorganisms in foods and predict their growth. The resulting model equations for the growth of interacting cells include several parameters that must be determined for the specific conditions to be modeled. In this study, these parameters were determined by using inverse engineering and a multi-objective optimization procedure that allows fitting more than one experimental growth curve simultaneously. A genetic algorithm was applied to obtain the best parameter values of a model that permit the construction of the front of Pareto with 50 individuals or phenotypes. The method was applied to three experimental data sets of simultaneous growth of lactic acid bacteria (LAB) and Listeria monocytogenes (LM). Then, the proposed method was compared with a conventional mono-objective sequential fit. We concluded that the multi-objective fit by the genetic algorithm gives superior results with more parameter identifiability than the conventional sequential approach.

Modeling the Growth and Interaction Between Brochothrix thermosphacta, Pseudomonas spp., and Leuconostoc gelidum in Minced Pork Samples

The aim of this study was to obtain the growth parameters of specific spoilage micro-organisms previously isolated in minced pork (MP) samples and to develop a three-spoilage species interaction model under different storage conditions. Naturally contaminated samples were used to validate this approach by considering the effect of the food microbiota. Three groups of bacteria were inoculated on irradiated samples, in mono- and in co-culture experiments (n = 1152): Brochothrix thermosphacta, Leuconostoc gelidum, and Pseudomonas spp. (Pseudomonas fluorescens and Pseudomonas fragi). Samples were stored in two food packaging [food wrap and modified atmosphere packaging (CO2 30%/O2 70%)] at three isothermal conditions (4, 8, and 12°C). Analysis was carried out by using both 16S rRNA gene amplicon sequencing and classical microbiology in order to estimate bacterial counts during the storage period. Growth parameters were obtained by fitting primary (Baranyi) and secondary (square root) models. The food packaging shows the highest impact on bacterial growth rates, which in turn have the strongest influence on the shelf life of food products. Based on these results, a three-spoilage species interaction model was developed by using the modified Jameson-effect model and the Lotka Volterra (prey-predator) model. The modified Jameson-effect model showed slightly better performances, with 40-86% out of the observed counts falling into the Acceptable Simulation Zone (ASZ). It only concerns 14-48% for the prey-predator approach. These results can be explained by the fact that the dynamics of experimental and validation datasets seems to follow a Jameson behavior. On the other hand, the Lotka Volterra model is based on complex interaction factors, which are included in highly variable intervals. More datasets are probably needed to obtained reliable factors, and so better model fittings, especially for three- or more-spoilage species interaction models. Further studies are also needed to better understand the interaction of spoilage bacteria between them and in the presence of natural microbiota.