Altered gut microbiome in a mouse model of Gulf War Illness causes neuroinflammation and intestinal injury via leaky gut and TLR4 activation

Abstract

Many of the symptoms of Gulf War Illness (GWI) that include neurological abnormalities, neuroinflammation, chronic fatigue and gastrointestinal disturbances have been traced to Gulf War chemical exposure. Though the association and subsequent evidences are strong, the mechanisms that connect exposure to intestinal and neurological abnormalities remain unclear. Using an established rodent model of Gulf War Illness, we show that chemical exposure caused significant dysbiosis in the gut that included increased abundance of phylum Firmicutes and Tenericutes, and decreased abundance of Bacteroidetes. Several gram negative bacterial genera were enriched in the GWI-model that included Allobaculum sp. Altered microbiome caused significant decrease in tight junction protein Occludin with a concomitant increase in Claudin-2, a signature of a leaky gut. Resultant leaching of gut caused portal endotoxemia that led to upregulation of toll like receptor 4 (TLR4) activation in the small intestine and the brain. TLR4 knock out mice and mice that had gut decontamination showed significant decrease in tyrosine nitration and inflammatory mediators IL1β and MCP-1 in both the small intestine and frontal cortex. These events signified that gut dysbiosis with simultaneous leaky gut and systemic endotoxemia-induced TLR4 activation contributes to GW chemical-induced neuroinflammation and gastrointestinal disturbances.

Fig 1. Gulf war chemical exposure alters gut microbiome.

A. Proportional abundance of phyla: Graphical representation of the most abundant taxa of bacteria at the phylum level. Groups compared are gulf war chemical exposed group (GW-T) and control group fed with vehicle (GW-Con). -. Groups include individual samples numbered at the time of V4 16S rRNA sequencing. GW-T show marked higher percent relative Phylum-level abundance of Firmicutes, and Tenericutes over Bacteroidetes (KW p-value: 0.03) compared to GW-Con. B. Proportional abundance of families: Graphical representation of most abundant taxa at the family level in GW-T compared to GW-Con groups. GW-T show a significant increase in Ruminococcaceae and 2 other unclassified/unnamed Operational taxonomic units (OTUs) (KW p-value: 0.01) All profiles are inter-compared in a pair-wise fashion to determine a dissimilarity score and store it in a distance dissimilarity matrix. Distance functions produce low dissimilarity scores when comparing similar samples. Abundance-weighted sample pair-wise differences were calculated using the Bray-Curtis dissimilarity. Bray-Curtis dissimilarity is calculated by the ratio of the summed absolute differences in counts to the sum of abundances in the two samples (Bray and Curtis 1957). The binary dissimilarity values were calculated with the Jaccard index. This metric compares the number of mismatches (OTUs present in one but absent in the other) in two samples relative to the number of OTUs present in at least one of the samples (Jaccard 1912). Kruskal-Wallis rank sum test on top 8 most abundant phyla. Percent relative abundance means are provided.

Fig 2

Alpha diversity estimates: Gulf war chemical exposure alters bacterial diversity in the intestinal lumen while there is no change among the individual groups themselves A. Left panel: OTU richness. Graphical representation of the number of OTUs present in each sample. Chao1 calculates the estimated sample richness (number of OTUs) based on sequencing depth and taking into account rare taxa that may be present in a sample GW-T has significant increase in OTU-richness compared to GW-Con (KW p-value: 0.01). Right panel: Graphical representation of Shannon diversity differences between GW-T and GW-Con. Shannon diversity utilizes the richness of a sample along with the relative abundance of the present OTUs to calculate a diversity index. There is an observed significant increase in Shannon diversity of GW-T over GW-Con (KW p-value: 0.01) B. Weighted ordination of GW-Con and GW-T groups. Dimensional reduction of the Bray-Curtis distance between microbiome samples, using the PCoA ordination method. Samples were separated according to groups along Axis 2 (PERMANOVA p-value = 0.005).

Fig 3. Differentially abundant features in GW-T vs. GW-Con: Each point represents an OTU belonging to each Genus.

Features were considered significant if their FDR-corrected p-value was less than or equal to 0.05, and the absolute value of the Log-2 fold change was greater than or equal to 1. There were 109 significantly different features detected out of 577 tests. Only 18 features that were able to be classified at the genus level are shown in the plot.

Fig 4. GW chemical-induced altered gut microbiome modulates gap junction protein levels in the small intestine.

A. Tissue levels of Claudin-2 in control (GW-Con), treated (GW-T) and antibiotic treated (GW-Ab) samples as observed by immunofluorescent microscopy after labelling the protein with red fluorescent secondary antibody. Nuclear staining is shown by DAPI (blue) stain. B. Tissue levels of Occludin in control (GW-Con), treated (GW-T) and antibiotic treated (GW-Ab) samples as observed by immunofluorescent microscopy after labelling the protein with red fluorescent secondary antibody. Nuclear staining is shown by DAPI (blue) stain. C. Morphometry of fluorescence intensity as observed in the region of interest (ROI) Claudin-2 immunoreactivity and D. occludin immunoreactivity. E. Western blot analysis of Claudin-2 and Occludin in small intestine tissue homogenate. F-G. morphometric analysis of western blot. *(p<0.05). Data is represented as Mean+/- SE.

Fig 5. Portal endotoxemia following gulf war chemical exposure-induced altered gut microbiome.

A. Endotoxin concentration as measured by Limulus amebocyte lysate (LAL) assay from serum of mice exposed to GW-chemicals. B. serum endotoxin levels in mice that were administered antibiotics (Neomycin and Enrofloxacin) to eliminate gut bacteria. *(p<0.05). Data is represented as Mean+/- SE.

Fig 6. TLR4 activation following gulf war chemical exposure-induced altered gut microbiome.

A. Immunofluorescence microscopy of olfactory bulb showing (TLR4 (red) trafficking to the lipid rafts (green), an essential process for TLR4 activation causing a co-localization of TLR4 in flotillin-rich rafts (yellow). B. Representative images of TLR4-flotillin co-localization in the small intestine shown by white arrow heads pointing to the yellow spots created by an overlay of red (TLR4) and green (Flotillin). C. Higher magnification (60x oil) representative image of the co-localization (TLR4 and Flotillin) in the small intestine from GW-treated samples.

Fig 7

A. Graphical analysis of the number of co-localizations represented by yellow in individual groups of tissues (olfactory bulb) per 200 cells counted from randomly chosen microscopic fields. B. Graphical analysis of the number of co-localizations represented by yellow in individual groups of tissues (Small intestine) per 200 cells counted from randomly chosen microscopic fields *(p<0.05). Data is represented as Mean+/- SE.

Fig 8. Oxidative stress as evidenced by tyrosine nitration of brain and intestinal tissues following GW chemical exposure and subsequent changes in the gut microbiome and TLR4 activation.

A. Frontal cortex tissue slices showing immunoreactivity to 3-nitrotyrosine (red) followed by olfactory bulb (B) and small intestine(C). D, E and F shows the quantitative analysis of fluorescence intensities in the region of interest (ROI) in frontal cortex (D), Olfactory Bulb (E) and small intestine (F). *(p<0.05).

Fig 9. Gulf war chemical exposure-induced changes in gut microbiome modulates neuroinflammation and intestinal cytokine release.

A. Frontal cortex tissue slices were probed for IL-1β immunoreactivity in control (GW-Con, treated (GW-T), TLR4 knockout mice treated with GW Chemical exposure (GW-TLR4KO) and antibiotic treated (GW-Ab) groups. Specific immunoreactivity to IL-1β as evident by dark brown spots are indicated by black arrows and areas of interest are outlined by red circles. B. Frontal cortex tissue slices were probed for MCP-1 immunoreactivity in control (GW-Con, treated (GW-T), TLR4 knockout mice treated with GW Chemical exposure (GW-TLR4KO) and antibiotic treated (GW-Ab) groups. Specific immunoreactivity to MCP-1 as evident by dark brown stain/spots are indicated by black arrows and areas of interest are outlined by red circles. C. Small intestine tissue slices were probed for IL-1β immunoreactivity in control (GW-Con, treated (GW-T), TLR4 knockout mice treated with GW Chemical exposure (GW-TLR4KO) and antibiotic treated (GW-Ab) groups. D. Small intestine tissue slices were probed for MCP-1 immunoreactivity in control (GW-Con, treated (GW-T), TLR4 knockout mice treated with GW Chemical exposure (GW-TLR4KO) and antibiotic treated (GW-Ab) groups.

Fig 10

A-D. Morphometry of the immunoreactivities in tissue slices using the stained regions of interest. Data normalized against control and plotted in Microcal-Origin software *(p<0.05). Data is represented as Mean+/- SE.

Fig 11. Schematic representation of dosage pattern:

A. dosage pattern of control group (GW-Con) n = 4, C57BL/6J mice were exposed to vehicle/solvent (Veh) of the Gulf war chemicals and antibiotics. B. C57BL/6J (n = 6) and TLR4 KO (n = 6) mice were exposed to gulf war chemicals Pyridostigmine bromide (Pb), Permethrin (Per) and Corticosterone (Cort). C. C57BL/6J (n = 6) mice were co-exposed to gulf war chemicals (Pb, Per and Cort) and Antibiotics (Ab).