Traumatic brain injury (TBI) is remarkably common with an estimated 600 TBI-related hospitalizations and more than 170 TBI-related deaths each day in the United States (Peterson and Thomas, 2021; Read, n.d.). In an Ohio based study, 1 in 5 adults reported a TBI with loss of consciousness and for 10% of respondents the TBI occurred before they were 15 years old (Corrigan et al., 2018). TBI are associated with long-term health issues including increased risk of stroke (Esterov et al., 2023), early onset dementia (Bellomo et al., 2022), depression (Lambert et al., 2022), and decreased olfaction (Tai et al., 2022). Post-TBI sequelae frequently include fatigue and altered cognition that compromise quality of life and productivity as described by the brain injury associated fatigue and altered cognition syndrome (BIAFAC) (Yuen et al., 2020). BIAFAC is characterized clinically by abnormal growth hormone (GH) levels after glucagon stimulation and hypoaminoacidemia (Børsheim et al., 2007; Durham et al., 2017; Mossberg et al., 2017; Wright et al., 2020; Yuen et al., 2020) raising the possibility that BIAFAC and other TBI-related sequelae may be caused by a dysbiotic gastrointestinal microbiome. We have studied the potential role of the microbial community structure in chronic TBI through fecal sampling followed by bacterial 16S rDNA, tuf gene and shot-gun, whole genome sequencing (WGS). These data directed creation of a customized qPCR array to provide absolute abundance of selected microbiota (Urban et al., 2020).

In clinical samples and animal models the fecal microbiome (FMB) is significantly altered soon after even mild TBI, and develops into chronic dysbiosis in some patients (Treangen et al., 2018; Nicholson et al., 2019; Brenner et al., 2020; Pathare et al., 2020; Urban et al., 2020; Yuen et al., 2020; Opeyemi et al., 2021; Aghakhani, 2022; Soriano et al., 2022). Previously reported WGS data from fecal samples from patients who had a moderate to severe TBI predicted functional alterations in amino acid (AA) metabolism, short chain fatty acid (SCFA) biosynthesis/oxidation and communication with the nervous system including the pituitary (Urban et al., 2020). Dysbiotic FMB communities are increasingly associated with a variety of other neurocognitive and neuropathological conditions (Sochocka et al., 2019; Elfil et al., 2020; Johnson et al., 2020; Sun et al., 2020; Alharthi et al., 2022; Dinan and Dinan, 2022). Comparing the dysbiotic FMBs from individuals suffering with Parkinson’s disease, autism spectrum disorder, Alzheimer’s disease, and other pathologies has revealed an interesting list of bacterial genera that may produce detrimental metabolites and/or metabolize critical dietary components including essential AAs. This may lead to deficiencies that have not yet understood consequences on nervous system function.

It appears that the TBI FMB may cause similar impacts on the gut-brain axis (Sundman et al., 2017). We previously reported that the FMB communities in samples from participants with moderate to severe, debilitating TBI that occurred, on average, >20 years prior, were distinct from subjects without TBI in two care facilities located in California and Texas (Urban et al., 2020). Our findings illustrated changes in abundance of species in the Bacteroides and Prevotella genera; consistent with findings reported more recently for other neuropathologies (Rogers et al., 2016; Brenner et al., 2020; George et al., 2021; Hanscom et al., 2021). Our WGS analysis predicted substantial changes in molecular functions of the FMB communities in TBI subjects relative to control subjects including AA metabolism, SCFA metabolism, and energy utilization (Urban et al., 2020).

Our clinical research and the field broadly support continued investigation of the bi-directional signaling between the gut and brain and determination of whether FMB dysbiosis is sustained following TBI. FMB stability in large human cohorts has been reported across time spans of >4 years (Faith et al., 2013; Chen et al., 2021; Frost et al., 2021). Collective data indicated that FMBs with greater alpha-diversity had higher stability and that several bacterial genera including Bacteroides were among the most stable over time. To address our theory that TBI FMB dysbiosis is similarly stable, we analyzed the TBI FMB community profile 5 years after our initial study in a small cohort of past participants with moderate to severe debilitating TBI living continuously in the Centre for Neuro Skills (CNS) facility (Bakersfield, CA). In this first kinetic study of TBI FMBs, analysis by qPCR array confirmed the stability of the TBI FMB metagenomic community structure over the 5-year sampling window.

To better understand the molecular consequences of the shifted metagenome, we also completed bioinformatic analyses of the metatranscriptome in the TBI FMB samples and compared that profile to healthy controls. This novel approach identified microbiota that were most contributory to the TBI FMB metatranscriptome and those molecular pathways that were significantly different between the groups. The results illustrated that some metagenomically identified bacteria did not contribute measurable RNA while other novel bacteria were recognized as important contributors but were not noted as having significantly altered abundance in past qPCR metagenomic comparisons. Application of these newer technologies and bioinformatic workflows will focus future study designs on only those bacteria that contribute to functional distinctions. Differences in expression of microbial biochemical pathways confirmed and extended prior WGS predictions including differences in specific AA metabolism pathways, glycolysis and altered energy usage, and revealed impacts on nucleotide metabolism and metabolism of lipids and SCFAs. Collectively the data add confidence that subjects with moderate to severe TBI have stable dysbiotic FMB community structures with distinct functional profiles that may contribute to chronic sequelae and increased risk for pathology. A greater understanding of these alterations is critical to design effective interventions to improve quality of life for individuals suffering long-term consequences of TBI.

Acute TBI has been shown to cause FMB dysbiosis in both clinical and animal model studies (Treangen et al., 2018; Nicholson et al., 2019; Brenner et al., 2020; Pathare et al., 2020; Opeyemi et al., 2021; Soriano et al., 2022). Based on studies of individuals with debilitating moderate to severe TBIs, we continue to test the hypothesis that the dysbiotic TBI FMB becomes a stabilized norm in some patients and may cause or contribute to long-term fatigue and altered cognition years after injury. Using multi-omic approaches, the stability of the dysbiotic FMB was shown in five individuals who provided a fecal sample 5 years after the initial study and, on average, 25 years after TBI. Each TBI FMB kinetic pair was found to be significantly similar supporting the conclusion that a stable dysbiotic relationship was formed between FMB community members and the TBI GI environment. Further, by comparing the metagenomic profiles to a new cohort of age- and gender-matched controls, FMB organisms common to the prior and current comparisons were identified, revealing several genera associated with TBI dysbiosis and refining prior outcomes (Urban et al., 2020). Ongoing work led to additional qPCR targets identified in the average TBI FMB community including R. pickettii. The genomic DNA for R. pickettii was detected in all five TBI FMB communities while only one control sample had detectable DNA suggesting a potential role in TBI-related dysbiosis and symptoms. By comparison to the new control cohort, L. fermentum and P. goldsteinii were found almost exclusively in controls. Finally, control FMBs had higher abundance of Blautia spp., Collinsella spp., F. prausnitzii, Lachnoclostridium spp., and P. merdae genomes consistent with prior observations (Urban et al., 2020) leading to the prediction that these organisms may provide metabolic functions associated with eubiosis that are reduced or lost in the dysbiotic TBI FMB.

Metagenomic WGS analyses predict encoded functions that may contribute to dysbiosis but fail to confirm that specific operons are expressed so metaTx comparisons were completed. These analyses identified both contributing bacteria and pathways enhancing understanding of the TBI FMB dysbiosis. This small, focused study revealed significant differences in multiple core metabolic functions and further refined the organisms most likely involved in the dysbiosis by mapping DEGs. Unexpectedly, these analyses illustrated that the TBI FMB had increased numbers of expressed genes and pathways suggesting enriched functional diversity and alternate pathway usage compared to FMBs from healthy subjects. Several genera (e.g., Prevotella) previously identified metagenomically (Urban et al., 2020) failed to show functional contributions while others (e.g., Ralstonia) proved distinctive in both genomic and transcriptomic outcomes. Mapping detected sequences revealed that Parabacteroides spp. contributed the most RNA to the control profile. However, significant DEGs were not mapped to this genus suggesting that this genus contributed only to common core metabolic processes. In contrast, the distinction in the Bacteroides genus was substantial and complicated. Bacteroides spp. contributed the second highest amount of RNA to the control cohort that was indistinguishable from the amount in TBI profiles (both ∼6.5%; Table 3). However, when considering only significant DEGs and pathways that mapped to Bacteroides, 52% mapped to Bacteroides spp. in the controls while only 6% mapped to this genus in TBI profiles (Table 5) supporting a strong eubiotic contribution consistent with other reports (Elfil et al., 2020; Dinan and Dinan, 2022).

Fecal microbiome bacteria produce a variety of metabolites that influence or directly signal neuronal and endocrine changes (Sarkar et al., 2018; Dinan and Dinan, 2022). The complex web of metabolite usage within and between community members leads to selection pressures that favor some bacteria while forcing others to seek alternate means for survival. The metaTx data established 5 broad differences represented by the 30 differently expressed pathways including major differences in glycolysis pathway expression (Figures 2, 3). Of the 30 pathways, the 3 that were significantly more abundant in the control profile (pyruvate fermentation to propanoate I, adenosine ribonucleotides de novo biosynthesis and glycolysis IV) revealed contributions missing from the TBI FMB.

Pyruvate fermentation to SCFAs and nucleotide levels (potentially contributed by commensal bacteria) have both been connected to improved outcomes for TBI recovery (Lusardi, 2009; Yanckello et al., 2022a,b; Zhao et al., 2023). Interestingly, as reviewed by Gomes et al. (2011), work on extracellular adenosine in a variety of brain disorders has led to recognition that adenosine receptor antagonists positively promote synaptic plasticity that may ultimately involve control of glutamatergic transmission or a variety of other brain homeostasis pathways. Remarkably, research on adenosine receptor antagonists, including caffeine and other methylxanthine compounds, led to the approval of istradefylline as a treatment for Parkinson’s disease (Berger et al., 2020). Reduced abundance of the adenosine pathway in the TBI profile and increase in pyrimidine synthesis related pathways (SPRB and flavin biosynthesis) suggests environmental differences leading to FMB adaptation. Finally, elevated expression of the pentose phosphate pathway also relates to carbon usage and homeostasis consistent with the more diverse metaTx requirements for the TBI FMB.

Substantial differences in the types of glycolysis-associated RNAs between cohorts may be central to the dysbiotic functions associated with the TBI FMB. Glycolysis is a central amphibolic metabolic process in bacteria contributing both energy (catabolic) and molecular precursors (anabolic) to other pathways in the form of pyruvate. Different types of bacterial glycolysis metabolize different sugars, ultimately cleaving glucose into as many as four ATP molecules and two pyruvates (Wolfe, 2015). Glycolysis (pathway IV) was a common pathway when comparing the metaTx profiles but alternative versions of glycolysis (glycolysis III and VI) were distinct to the top 10 most abundant pathways in the controls. This suggests that TBI FMB communities must be using alternate means of ATP generation and may ultimately contribute less pyruvate to the metabolome, limiting available precursors for other pathways. These differences could be produced by environmental activation of different operons or distinctly encoded pathways based on the bacterial genomes present. In support of distinctive sugar usage, three pathways that degraded glucarate or galactarate were exclusively observed in each of the individual TBI FMBs (Figure 3). Finally, increased abundance of the glyoxylate cycle RNAs in the TBI FMB suggest the need to create additional glucose in support of bacterial functions perhaps addressing predicted pyruvate deficiency.

Pyruvate is a precursor in many pathways, is utilized to synthesize AAs (e.g., alanine, valine, and leucine) and is converted to propionate by Corynebacteria, Propionibacteria, and Bifidobacteria (Wolfe, 2015). Other glycolysis metabolites are used to synthesize histidine and serine and drive production of lipids and vitamins. Bacteroides pathway analyses found reduced abundance of glycolysis in the TBI FMB as well as increased degradation of selected AAs (e.g., histidine) (Table 6). This increases the likelihood of reduced systemic availability of specific AAs based on both reduced synthesis and increased degradation for energy production that could contribute to the clinical hypoaminoacidemia observed following TBI (Børsheim et al., 2007; Dai et al., 2011; Durham et al., 2017). At the species level, B. uniformis and B. vulgatus represented the largest proportion of mapped Bacteroides RNAs in the controls, consistent with eubiotic functions (Wang et al., 2021; Dinan and Dinan, 2022). Genomic levels of B. uniformis were higher in the TBI FMB illustrating the limitation of metagenomics and exemplifying the complexity surrounding development of probiotics. Similarly, B. vulgatus is associated with ulcerative colitis (Mills et al., 2022) suggesting that the contextual influences of environment and community composition must be considered when attempting to assign probiotic status.

Among the AA metabolism differences (Figure 3), degradation pathways observed in the TBI profile utilized aspartate, glutamate, and glutathione (NAD biosynthesis, heme biosynthesis, and gamma-glutamyl cycle) as precursors supplying products for other pathways including AA synthesis and transport. In contrast, several biosynthesis pathways for specific AAs were more abundant in the TBI metaTx (tryptophan, arginine, and seleno AAs). The chorismate biosynthesis pathway (Figure 3, CB3D) can lead to synthesis of aromatic AAs including phenylalanine, tyrosine, and tryptophan consistent with the observed activation of the biosynthetic pathways. Finally, the 4 aminonbutanoate (GABA) degradation V pathway exclusively expressed in the TBI FMBs suggests a direct effect of the dysbiotic community on the availability of this major inhibitory neurotransmitter that will require additional study. The increased abundance of AA metabolism pathways in the TBI FMB and reduced abundance of pathways for pyruvate fermentation to produce lipids and SCFAs further illustrate adaptations that reflect responses to the TBI GI environment and/or the stable FMB selection.

The fifth group of more abundant pathways in the TBI FMB included bacterial cell wall component biosynthetic machinery for peptidoglycan and coenzyme A (Figure 3) that likely favors reproduction of pathobiont species. CoA serves as a cofactor for production of phospholipids, SCFAs and TCA cycle components illustrating the complexity of overlap associated with the adaptive transcription in FMB communities. Bacterial peptidoglycan in cell walls protects against environmental stress but also triggers inflammatory responses through interactions with pathogen recognition receptors. The role of inflammation remains unclear with some reports indicating it enhances neuroprotection while others show associated damage and post-TBI sequelae leading to controversial connections and inconclusive therapeutic strategies (Cederberg and Siesjö, 2010; Corps et al., 2015; Hanscom et al., 2021). Careful analysis of larger FMB metaTx datasets will be needed to confidently identify pathobionts adding inflammatory molecules to the systemic milieu.

As part of a working model of FMB contribution to long-term TBI sequelae, we hypothesize that the observed increase in histidine degradation (significantly more abundant in Bacteroides-specific TBI FMB pathways; Table 6), and distinct degradation and synthesis of other AAs as well as increased gamma-glutamyl cycle pathway expression support a role for AA metabolism in fatigue and cognitive issues following TBI (graphically summarized in Figure 5). A reduction in available histidine would be predicted to reduce conversion to histamine, a potent neurotransmitter. Histidine is converted to histamine by histaminergic neurons in the tuberomammillary nucleus located in the hypothalamus that serves a hub to link key brain regions associated with arousal, cognition, sleep, and GH secretion (Haas and Panula, 2003; Haas et al., 2008). Individuals with a history of TBI and BIAFAC symptoms had reduced max GH values (<10 ng/ml) following a glucagon stimulation test (Mossberg et al., 2017; Wright et al., 2020; Yuen et al., 2020) and hypoaminoacidemia following a standardized meal (Durham et al., 2017). GH replacement therapy improved symptoms (Mossberg et al., 2017; Wright et al., 2020; Yuen et al., 2020). In animal model studies, dietary and systemically administered histidine substantially increased plasma GH levels via pathways dependent on functional conversion to histamine by histidine decarboxylase (Maeda and Frohman, 1978; Cacabelos, 1991; Bruno et al., 1993). There has been almost a decade of connection between the histidine/histamine system and nervous system dysfunction including associations with autism spectrum disorder, Tourette’s syndrome, and other neuropathologies (Wright et al., 2017; Moro et al., 2020). Very recently, elegant mapping of histaminergic neuron fibers revealed a heterogenic projection pattern that reached a large number of brain regions with little overlap (Lin et al., 2023) suggesting histidine deficiency could have substantial impacts. Disruption of the histaminergic system has been associated with dysregulation of sleep-wake cycle (fatigue) and cognitive functions (Passani et al., 2000; Tashiro et al., 2002; Alvarez, 2009; Yoshikawa et al., 2021). Collectively these data support additional testing of the hypothesis that TBI FMB dysbiosis creates chronic histidine deficiency leading to dysregulated release of GH ultimately contributing to the observed profound fatigue and cognition issues in subjects with BIAFAC (Figure 5).

Histidine degradation and other AA metabolism pathways were mapped back to a select set of bacteria associated with greater contributions in TBI FMBs including R. pickettii. A member of the Burkholderiales order, R. pickettii was originally isolated from moist soil and water samples. It has since been reported as a FMB community member that impacts host functions (Udayappan et al., 2017; Kim et al., 2021) but has not been reported in the context of TBI. It is possible that the observed DEGs in our study were incorrectly mapped based on the 75 bp reads so additional studies will be required to confirm the functional role for R. pickettii and the other bacteria to establish if their transcriptional contributions are causally associated with TBI sequelae including abnormal GH and hypoaminoacidemia.

Among the limitations of this focused characterization study was the small cohort of subjects who provided a follow up fecal sample. Despite the small cohort, we confirmed lasting stable distinctions in the TBI FMB metagenome with strong statistically significant functional impacts identifying several metabolic consequences related to neuroendocrine dysfunction. Larger studies are warranted with focused analyses of the identified differences in core metabolic processes as well as functional reconstruction studies in ex vivo and germ-free mouse model systems. The RNA sequence mapping also must be confirmed by alternate methods to address inaccuracies cause by conservation of gene sequences that complicate accurate assignment to species levels. Associating multiple DEGs with differently expressed pathways reduces such errors and increases confidence in this screening method that supports development of testable hypotheses and directing next steps.

Translationally, completion of these studies will not only help to elucidate the impacts of the dysbiotic TBI FMB but should identify novel targets for prebiotic, probiotic, and molecular approaches targeted to the bacteria in the GI tract. There are a number of studies that tested general probiotics or FMB transplants in the context of TBI or other neurotrauma that showed mixed results (Kigerl et al., 2016; Brenner et al., 2017; Rice et al., 2019; Yi et al., 2019; Yanckello et al., 2022a,b) supporting a need for greater specificity in targeting. Based on the observed SCFA metabolism differences in the TBI FMB, we recently completed a trial of a prebiotic fructooligosaccharide dietary supplement in subjects who have moderate to severe TBI (NCT04949607). Such prebiotics create SCFAs through fermentation by several bacterial genera including some identified in this study. Data are being analyzed but the supplement was tolerated and appears to have had positive impacts on fatigue and cognitive measures (unpublished findings). Based on the current data implicating multiple metabolic processes, larger cohorts, and additional metaTx analyses will be needed to select optimal combinations of prebiotics, probiotics, and targeted molecules for optimal designs for clinical testing.

Interestingly, treatment of medically refractory epilepsy has included implementation of a ketogenic diet since the 1920s (Peterman, 1924) suggesting the reported dysbiosis associated with epilepsy may also reflect alternate energy metabolism and even distinct amino acid utilization as expertly reviewed by several groups (Holmes et al., 2020; Lum et al., 2020; Chatzikonstantinou et al., 2021). The published studies in clinical cohorts and in animal models primarily employed metagenomic analyses and were, like this report, limited by relatively small cohorts but illustrated some common findings that need to be further studied. In particular, several indicated reductions in the relative abundance of Bacteroides spp. (Lum et al., 2020; Chatzikonstantinou et al., 2021) and functional predictions of impacts on the acetyl-CoA pathway and subsequent potential for degradation of essential amino acids and consequential changes in GABA and other neurotransmitters. The intriguing similarities as well as connections to other neuropathologies support completion of addition studies including the use of metatranscriptomics to better address functional profile differences.

Long-term sequelae related to TBI are relatively common with nearly 30% of survey participants reporting fatigue and cognitive symptoms within 6 months of a TBI and over 15% still experiencing symptoms 5 years later (Wright et al., in review, PLoS One). Others have reported that most patients with a TBI have lingering symptoms that slowly improve over the 5 years following injury (Nelson et al., 2023). Such data strongly support a TBI initiated cascade that may create a stable dysbiotic FMB impact. Working to address the stability of the dysbiotic TBI FMB we had the benefit of five subjects who were willing to provide a fecal sample 5 years after our original analysis which confirmed that dysbiosis associated with TBI decades earlier remained stable. By comparison to two temporal and geographically disparate control cohorts, we refined the list of organisms that define TBI FMB dysbiosis and identified potential prebiotic/probiotic intervention opportunities that will need to be carefully studied as effectively reviewed in the past (Brenner et al., 2017). Through metaTx analyses, functional impacts consistent with clinical deficiencies in essential AAs, SCFAs and altered energy utilization were confirmed. Although the gut-brain axis involves well established correlations, causative mechanisms are not understood. Using an established model of FMB dysbiosis and neurologic symptoms, we identified metabolic pathways expressed in the FMB that affect bioavailability of key signaling molecules, essential nutrients, and neurotransmitter precursors. These findings help initiate directed studies to better understand the underlying issues and the potential for interventions to address the long-term consequences of TBI on the gut-brain axis in some patients.

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/, PRJNA1005267.

The studies involving humans were approved by the Advarra IRB (Pro00027812) for subjects at the CNS facility (Bakersfield, CA) or by the University of Texas Medical Branch (UTMB) IRB for control samples collected in Galveston County, TX. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

RP: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing. AM: Data curation, Formal analysis, Investigation, Methodology, Writing – review & editing. RU: Conceptualization, Funding acquisition, Investigation, Project administration, Writing – review & editing. MS-M: Conceptualization, Investigation, Project administration, Supervision, Writing – review & editing. TW: Data curation, Formal analysis, Writing – review & editing. CM: Data curation, Investigation, Methodology, Writing – review & editing. KMR: Investigation, Project administration, Supervision, Validation, Writing – review & editing. CD: Data curation, Investigation, Project administration, Writing – review & editing. KM: Investigation, Methodology, Writing – review & editing. JV: Formal analysis, Investigation, Methodology, Writing – review & editing. PA: Formal analysis, Investigation, Methodology, Writing – review & editing. LK: Investigation, Methodology, Project administration, Writing – review & editing. GC: Data curation, Investigation, Methodology, Writing – review & editing. KR: Investigation, Project administration, Validation, Writing – review & editing. MM: Investigation, Methodology, Writing – review & editing. BM: Investigation, Methodology, Project administration, Resources, Writing – review & editing, Conceptualization, Funding acquisition.