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. 2024 Mar 13:17:1341808.
doi: 10.3389/fnmol.2024.1341808. eCollection 2024.

The altered TBI fecal microbiome is stable and functionally distinct

Richard B Pyles  1 , Aaron L Miller  1 , Randall J Urban  2 , Melinda Sheffield-Moore  2 , Traver J Wright  2 , Carrie A Maxwell  3 , Kathleen M Randolph  2 , Christopher P Danesi  2 , Kristen A McGovern  2 , Jayson Vargas  4 , Peyton Armstrong  4 , Lisa Kreber  5 , Giuliana Cumpa  5 , Kevin Randall  5 , Melissa Morrison  5 , Brent E Masel  5   6
Affiliations

The altered TBI fecal microbiome is stable and functionally distinct

Richard B Pyles et al. Front Mol Neurosci. .

Abstract

Introduction: Patients who suffer a traumatic brain injury (TBI) often experience chronic and sometimes debilitating sequelae. Recent reports have illustrated both acute and long-term dysbiosis of the gastrointestinal microbiome with significant alterations in composition and predicted functional consequences.

Methods: Working with participants from past research, metagenomic stability of the TBI- associated fecal microbiome (FMB) was evaluated by custom qPCR array comparing a fecal sample from 2015 to one collected in 2020. Metatranscriptomics identified differently expressed bacterial genes and biochemical pathways in the TBI FMB. Microbiota that contributed the largest RNA amounts identified a set of core bacteria most responsible for functional consequences of the TBI FMB.

Results: A remarkably stable FMB metagenome with significant similarity (two-tail Spearman nonparametric correlation p < 0.001) was observed between 2015 and 2020 fecal samples from subjects with TBI. Comparing the 2020 TBI FMB metagenome to FMBs from healthy controls confirmed and extended the dysbiotic genera and species. Abundance differences between average TBI and healthy FMBs revealed Bacteroides caccae, B. uniformis, Blautia spp., Collinsella spp., Dialister spp., and Ordoribacter spp. were significantly different. Functionally, the Parabacteroides genus contributed the highest percentage of RNA sequences in control FMBs followed by the Bacteroides genus as the second highest contributor. In the TBI FMB, the Corynebacterium genus contributed the most RNA followed by the Alistipes genus. Corynebacterium and Pseudomonas were distinct in the top 10 contributing genera in the TBI FMB while Parabacteroides and Ruminococcus were unique to the top 10 in controls. Comparing RNA profiles, TBI samples had ∼1.5 fold more expressed genes with almost 700 differently expressed genes (DEGs) mapped to over 100 bacterial species. Bioinformatic analysis associated DEGs with pathways led identifying 311 functions in the average TBI FMB profile and 264 in the controls. By average profile comparison, 30 pathways had significantly different abundance (p < 0.05, t-test) or were detected in >80% of the samples in only one of the cohorts (binary distinction).

Discussion: Functional differences between TBI and healthy control FMBs included amino acid metabolism, energy and carbon source usage, fatty acid metabolism, bacterial cell wall component production and nucleic acid synthesis and processing pathways. Together these data shed light on the functional consequences of the dysbiotic TBI FMB decades after injury.

Keywords: BIAFAC; fecal microbiome; metatranscriptome; microbiome; traumatic brain injury.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Spearman nonparametric correlation (two-tail) plots of the FMB metagenomic profiles of five subjects with TBI. Similarity indices (Spearman rho value) based on the results from the 96 target custom qPCR array for TBI FMB are shown (r). The x-axis depicts the original profile (2015) and the y-axis shows the 2020 sample distribution. For hypothesis testing the p-value was set to 0.05.
FIGURE 2
FIGURE 2
A heat map depiction of the 30 significantly differently expressed biochemical pathways identified by comparison of the average TBI and control metatranscriptomic data. Individual expression levels for each of the FMB profiles is shown (red is high expression and blue is low expression). TBI samples are clustered on the left (blue) and controls on the right (red). Three pathways were significantly more abundant in controls (top 3 labels on the right) and 27 were more abundant in the TBI (p < 0.05 multiple t-test). Additional detail for specific values can be found in Figure 3. Pathway names are assigned by the ASAIM workflow and match the MetaCyc database. ARB, adenosine ribonucleotides de novo biosynthesis; CDGB, CDP diacylglycerol biosynthesis pathway I or II; UAPB, UDP N acetylmuramoyl pentapeptide biosynthesis; SGGD, superpathway of D glucarate and galactarate degradation; UAGB, UDP N acetyl D glucosamine biosynthesis I; CB3D, chorismate biosynthesis from 3 dehydroquinate; SPRB, superpathway of pyrimidine ribonucleotides de novo biosynthesis; PCoAB, pantothenate and coenzyme A biosynthesis I.
FIGURE 3
FIGURE 3
Differently expressed pathways grouped by core metabolic process. The top left panel depicts a PCA of the distinctions based on the expression pattern of the 30 pathways between TBI (blue) and control (red) FMB metatranscriptome data. The other five panels are scatterplots illustrating the average expression level of each pathway as a percent of total mapped RNA sequences with circles denoting individual profile values. SEM values are shown as whiskers and p-values are shown (unpaired Welch’s t-test with p < 0.05 considered significant). Pathway abbreviations are as shown in the legend to Figure 2.
FIGURE 4
FIGURE 4
Heat map and PCA plots of the 12 differently expressed pathways based on Bacteroides contributions only. The individual Bacteroides pathway profiles for subjects with TBI (blue) and healthy controls (red) are depicted as a heat map representing the 12 that showed significant differences in abundance (p < 0.05 multiple t-test; red to blue scale bar shows percent of total). The PCA illustrates the distinction provided illustrating the complexity of the transcriptional adaptation of this genus to the TBI and health control GI environments.
FIGURE 5
FIGURE 5
A graphic summary of the metaTx findings and potential neuroendocrine impacts. This image depicts a working model focused on altered TBI FMB metabolism of histidine, other AAs and nucleotides leading to impacts on histaminergic neuron function in the tuberomammillary nucleus (TMN) of the hypothalamus. Reduced production of histamine in the TMN would have a cascade effect including dysregulation of pituitary functions including growth hormone (GH) secretion. Created with BioRender.com.
See this image and copyright information in PMC

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