The intestinal microbiota constitutes a complex ecosystem in constant reciprocal interactions with the immune, neuroendocrine, and neural systems of the host. Recent molecular technological advances allow for the exploration of this living organ and better facilitates our understanding of the biological importance of intestinal microbes in health and disease. Clinical and experimental studies demonstrate that intestinal microbes may be intimately involved in the progression of diseases of the central nervous system (CNS), including those of affective and psychiatric nature. Gut microbes regulate neuroinflammatory processes, play a role in balancing the concentrations of neurotransmitters and could provide beneficial effects against neurodegeneration. In this review, we explore some of these reciprocal interactions between gut microbes and the CNS during experimental disease and suggest that therapeutic approaches impacting the gut-brain axis may represent the next avenue for the treatment of psychiatric disorders.
Intestinal dysbiosis is being evaluated as an influencing factor in human diseases. Our laboratory works on the general hypothesis that the association is bidirectional and that disease induction affects the composition of the microbiota. Non-obese diabetic (NOD) mice develop spontaneous diabetes unless active experimental autoimmune encephalomyelitis (EAE), a murine model of multiple sclerosis, is induced. It was proposed that the complete Freund’s adjuvant used for active induction of EAE protect against diabetes. Interestingly, although oral antibiotics treatment reduces EAE severity in SJL and C57BL/6 mice, others showed that the same treatment exacerbates diabetes in adult NOD mice. We questioned whether two different diseases affecting one animal strain, the NOD mice, affected the microbiota composition differently. We hypothesized that the gut microbiota differs between the acute inflammatory and chronic progressive stages that characterize the NOD EAE model. Further, we proposed differences in the microbiota/disease axis in EAE and diabetes. We observed significant changes in the microbiota of NOD mice that developed a severe secondary form of EAE when compared with healthy control mice. The genera Coprococcus, Ruminococcus, and Akkermansia were found elevated in EAE mice while undetermined members of the families Lactobacillaceae and Christensenellaceae were significantly reduced. Furthermore, we compared the EAE and diabetes microbiome and evaluated the effects of transplantation stool samples obtained from EAE, diabetes and control mice on diabetes progression. Our findings support the hypothesis that there are reciprocal effects between disease induction and the modification of the microbiome.
Many advancements in the understanding of multiple sclerosis (MS) have been made through the use of laboratory models. One commonly used model is experimental autoimmune encephalomyelitis (EAE), a mouse model characterized by central nervous system (CNS) inflammation and demyelination, allowing for symptoms resembling some of the most prominent features of the human disease. Although the exact etiology of MS is still being investigated, experiments with EAE have shown that the NLRP3 inflammasome complex of the innate immune system is critical and necessary for disease development. The inflammasome complex can be assembled in all innate immune cells, including microglia and astrocytes in the CNS. Dysregulation of inflammasome activity can result in uncontrolled inflammation, which underlies many chronic diseases, and metabolic and autoimmune disorders such as MS. Our lab has shown that farnesol a 15-carbon organic sesquiterpene and primary alcohol, reduced EAE disease severity and onset and also decreased T-cell infiltration into the CNS. However, the mechanisms of its action have yet to be fully defined. Therefore, in-vitro work on murine macrophages is being conducted to investigate how farnesol may be potentially affecting the pathway of the inflammasome complex and providing this protection. Furthermore, since farnesol is a quorum-sensing molecule that impacts biofilm formation, other studies of ours are aimed at evaluating how farnesol affects the gut-brain axis and specifically the gut microbiome of EAE mice.
Background Biologically relevant insights into cellular disease mechanisms of neurons and glia can be obtained by complimentary molecular profiling of the transcriptome and proteome of these cells. While mutually exclusive pipelines are available, the information surveyed is often from different samples leading to poor correlation between the transcriptome and proteome. Our goal was to develop a method for concomitant cell type‐specific analyses of RNA and protein. Method We utilized a proximity‐labeling strategy that uses the biotin ligase, TurboID, to efficiently label the proteome, in a mouse microglial BV2 cell line. We created a stable BV2‐TurboID cell line that expresses TurboID fused to a nuclear export sequence. Biotin treatment of BV2‐TurboID cells resulted in robust biotinylation of the cellular proteome as confirmed by Western blot and by label‐free quantitation mass spectrometry (LFQ‐MS) of biotinylated proteins enriched with streptavidin beads. LFQ‐MS revealed that TurboID biotinylates several RNA‐binding proteins (RBPs) including ribosomal units, suggesting that transcripts associated with RBPs may also be pulled down simultaneously with proteins. To test this, we homogenized BV2‐Turbo and control BV2 cells, enriched biotinylated proteins with streptavidin beads while maintaining RNA‐protein interactions, and then eluted RNA. Result Quality control studies showed negligible mRNA from streptavidin pulldowns from control BV2 cells, while BV2‐TurboID pulldowns had larger mRNA yields with high quality. NanoString neuroinflammatory profiling (800 genes) of whole cell RNA from control BV2 and BV2‐TurboID cells and streptavidin pulldowns from both cell types were performed. We observed a 23‐fold higher mRNA yield in the BV2‐TurboID pulldowns compared to control BV2 cells for 550 genes included in the analyses. Transcript abundances from total RNA and BV2‐TurboID pulldowns were highly comparable (R2=0.97; no differentially expressed genes) with equivalent abundance of microglial genes (e.g., Spp1 and Apoe) suggesting a faithful transcriptome capture. Conclusion Our novel TurboID proximity labeling approach can simultaneously capture cell type‐specific transcriptomes and proteomes. We are now validating this method in other cell types using RNA‐sequencing and MS approaches. Once validated, this concurrent RNA and protein profiling approach can be applied to in vivo and ex vivo model systems to investigate the distinct roles brain cell types play in development, aging, and disease.
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