Gut Microbiome Differentiates Levels of Consciousness in Patients with Prolonged Disorders of Consciousness after Brain Injury

Abstract

Differentiation of levels of consciousness in patients with prolonged disorders of consciousness (DOC) remains a major challenge, especially differentiation between vegetative state (VS) and minimally conscious state (MCS). This study was to investigate the alterations of gut microbiota in patients with DOC and to identify potential microbiome biomarkers that can differentiate levels of consciousness. In this study, we collected fecal samples from patients with prolonged DOC, including 19 patients in MCS and 14 patients in VS; 16S-rRNA sequencing was used to investigate the gut microbiome of patients. Gut microbiota diversity, composition, and discriminant bacterial taxa were analyzed to identify potential biomarkers for differentiating levels of consciousness. We found that diversity and composition of gut microbiome were significantly altered in patients with DOC, and decreased alpha diversity was associated with lower levels of consciousness. Specific bacterial taxa including Firmicutes, Escherichia_shigella, Raecalibacterium, Lachnospiraceae, and Ruminococcaceae_UCG_013 were more abundant in patients in MCS, whereas Clostridiales were more abundant in patients in VS. In conclusion, the study results demonstrated that patients with DOC exhibited distinct diversity and composition of gut microbiota. And there was a decreasing trend of alpha diversity of gut microbiota from patients in MCS to patients in VS, which indicates that lower alpha diversity was associated with more severe level of unconsciousness. Specific bacterial taxa may be potential biomarkers to differentiate MCS and VS.

Introduction

The brain injury, including traumatic and nontraumatic causes, is one of the leading causes of death and long-term disability worldwide.¹ The fatality rate of brain-injured patients has decreased significantly due to the advances in clinical treatment and intensive care techniques. However, some patients would fall into varied degrees of disorders of consciousness (DOC)² including coma, vegetative state (VS), and minimally conscious state (MCS). Individuals in VS remain unresponsive to external stimulation or only show spontaneous movements,³ whereas patients in MCS can show fluctuating but discernible behaviors of self or environmental awareness.⁴ The differentiation between MCS and VS can be challenging since voluntary and reflexive behaviors can be difficult to distinguish and subtle signs of consciousness may be missed. Therefore, the accurate diagnosis and classification of patients with DOC are of great significance, which can not only determine the therapeutic options but also affect the prognosis of patients with DOC.

At present, the “gold standard” to assess prolonged DOC is the Coma Recovery Scale-Revised (CRS-R),⁵ with six subscales including auditory, visual, motor, oromotor, communication, and arousal to evaluate the consciousness state. However, CRS-R score is based on patients’ behavioral response that can be affected by the fluctuations in arousal level, underlying sensory and motor impairments, as well as the subjective judgment of the operator may lead to a high rate of clinical misdiagnosis.⁶ Therefore, to develop a more objective and accurate method for diagnosing patients with DOC, a large amount of studies have focused on neuroimaging and electrophysiology techniques in the past two decades, such as functional magnetic resonance imaging (MRI) and electroencephalogram (EEG). For instance, neuroimaging techniques such as MRI,^7,8 18F-fluorodeoxyglucose positron emission tomography (PET),⁹ and diffusion tensor imaging^10,11,12 showed some promising value in evaluating patients with DOC. And EEG measures also demonstrated its utility in classifying different states of DOC.^13,14 Nevertheless, neuroimaging techniques are expensive and less practical in patients with DOC, and some task-based experiments required the cooperation of patients. With regard to bedside EEG, the complex paradigms, analysis algorithms, and feature extraction make it difficult to be widely utilized by clinicians. Therefore, to find a cheaper, accurate, and easily performed method to diagnose patients with DOC has always been one of the main focuses of researches in DOC.

Recently, compelling evidence from experimental and clinical studies has indicated the significance of microbiome in human health, and the association between the gut microbiota and neurological diseases has attracted great attention. It is well known that the central nervous system (CNS) and the gastrointestinal system can communicate bi-directionally via various pathways, which is also called brain–gut axis.¹⁵ The brain–gut axis, which integrates neural, hormonal, metabolic, and immunological signals between the central nervous system (CNS) and the gut, provides a pathway for gut microbiota and their metabolites to access the brain.¹⁶ Brain disease can also alter the gastrointestinal and immune function through top-to-bottom pathways.¹⁷ With deeper understanding of the role of gut microbiota, researchers found that it can contribute to the integrity of the intestinal barrier, inflammatory response, and neuroendocrine functions and can produce metabolites with important physiological and pathological significance.¹⁸ Furthermore, the gut microbiota are also involved in the regulation of the physiological processes¹⁹ of the CNS.

A number of studies have reported the important roles of gut microbiota in CNS disorders, including Alzheimer’s disease,²⁰ Parkinson’s disease,²¹ traumatic brain injury (TBI),^22,23 and stroke,^24
–26 making it a promising therapeutic target for these conditions. Our previous study found that gut microbiota changed significantly accompanied by alteration of bile acid metabolism in the acute stage of TBI, with dynamic changes in alpha diversity.²⁷ However, little information is known about the gut microbiota in patients with prolonged DOC after brain injury, Until recently, limited data existed on the gut microbiota in patients with prolonged disorders of consciousness (DOC) following brain injury. However, a recent study suggested that gut microbiota could differentiate DOC patients, though these findings require further validation.²⁸ Therefore, we hypothesize that patients with DOC after brain injury exhibited alterations in gut microbiome, which may be associated with different levels of consciousness. In addition, potential biomarkers in gut microbiome can be identified to distinguish MCS from VS, thereby promoting the diagnostic accuracy and recovery of patients with prolonged DOC.

Participants and Methods

Participants and study design

Patients with prolonged disorders of consciousness after brain injury were admitted to the Neurosurgery Department of the First Affiliated Hospital of Zhejiang University School of Medicine and the Rehabilitation Department of Hangzhou Wujing hospital between October 2019 and December 2020. Ethics approval for this study was obtained from the Ethics Committee of the First Affiliated Hospital of Zhejiang University School of Medicine, and informed consent was obtained from patients’ caregivers.

Inclusion criteria were as follows: (1) in line with previous research, patients were at least 3 months post injury and diagnosed as suffering from DOC based on the CRS-R, including MCS and VS^29
–31; (2) a history of brain injury, including TBI, cerebral hemorrhage, and ischemic stroke; (3) age 15 years or older; (4) patients with stable vital signs and intracranial conditions, without any indications for emergency surgery such as intracranial hematoma or hydrocephalus; (5) healthy volunteers with age and gender matched were enrolled as the control group; and (6) the consent of patients’ relatives or guardians was taken to enroll in the study. Exclusion criteria were as follows: (1) age 14 years or younger; (2) intravenous or oral antibiotics used within 1 month; (3) a diagnosis of intestinal disease; and (4) coexisted with other serious systemic organ dysfunctions or malignant diseases. In this study, older adolescents (aged from 15 to 18) who were also included for the study showed that gut microbiota in adolescents and adults were relatively stable.³²

Data collection and CRS-R evaluation

Demographic and clinical data were extracted from case records, including gender, age, ethnicity, etiology, past medical history, CRS-R score, diagnosis, and other general physical examination. Prior to study enrollment, the best of three consecutive behavioral assessments using the CRS-R were conducted within a 1-week period. Then the patients were diagnosed as MCS and VS based on CRS-R score. The demographic data of healthy controls (HC) were also collected.

Fecal sample collection and DNA extraction

We collected 2 g stool (about the size of a little finger) from each patient during the first bowel movement of the day. Fecal samples were collected and stored at −80°C, and bacterial DNA was extracted using E.Z.N. Fecal DNA Kit (D4015, Omega, USA), following the manufacturer’s instructions. The total DNA was eluted in 200 μL elution buffer.

16s-rRNA sequencing and quantitative polymerase chain reaction

The gene-specific sequences used in this protocol target the 16S V3 and V4 regions, and amplicons were generated using primers corresponding to the hypervariable regions (forward: CCTACGGRRBGCASCAGKVRVGAAT; reverse: GGACTACNVGGGTWTCTAATCC) for the first polymerase chain reaction (PCR). PCR amplicons were purified in accordance with the manufacturer’s instructions. Each reaction mixture (25 μL) contained 20 ng template DNA, 0.5 μL DNA polymerase, 2.5 μL dNTPs, 2.5 μL buffer solution, 1 μL of each primer, and PCR-grade water to adjust the volume. The cycling conditions were as follows: initial denaturation at 95°C for 3 mins, 24 cycles of denaturation at 94°C for 5 sec, annealing at 57°C for 90 sec, extension at 72°C for 10 sec. The PCR products were quantified to 10 nM by Qubit 3.0 Fluorometer (Invitrogen, USA). The library was sequenced on an Illumina MiSeq (Illumina, USA) platform, and 250 bp/300 bp paired-end reads were generated. DNA sequencing and data analysis were mainly completed by specialized biological sequencing companies.

Data processing and analysis

Two paired-end reads were merged based on overlapping sequences, and merged reads were trimmed and quality filtered using QIIME (version 1.9) and then dereplicated and clustered at 97% identity, generating an operational taxonomic units (OTUs) table. The DADA2 algorithm was used to denoise demultiplexed sequences. In this way, the final feature table and feature sequence were obtained, and subsequent analyses of diversity and species differences were all performed based on these normalized output data. Alpha diversity was applied to analyze complexity of species diversity through five indexes, including Observed-species, Chao1, Shannon, Simpson, and Good-coverage. The beta diversity as depicted in principal coordinate components analysis (PCoA) and clustering analysis (UPGMA) can reveal the diversity of species between different environmental communities. Furthermore, we used the SILVA (Release 132, https://www.arb-silva.de/documentation/release-132/) and the NT-16s database to accurately analyze the species composition and make the species abundance tables at the level of kingdom, phylum, class, order, family, genus, and species. LEfSe (linear discriminant analysis effect size) software was used to compare species differences among groups, and linear discriminant analysis was used to obtain the final differential species (i.e., biomarker). PICRUSt2 was used to predict functional abundances based only on marker gene sequences. The diagrams were implemented using the R package (V3.5.2).

Results

Baseline characteristics

A total of 33 patients with DOC after brain injury were included in this study, including 19 patients in MCS and 14 patients in VS based on CRS-R assessment. The average age of all 33 patients was 47.1 ± 17.6 years, and 21 (63.6%) patients were male. The average duration from brain injury to fecal sample collection was 6.6 months. Thirty HC with age and gender matched were included with an average age of 47.5 ± 16.7 years, and 17 (56.7%) of them were male. There were no significant differences in age, gender, etiology distribution, and time interval since brain injury between DOC and HC groups and between MCS and VS groups. Baseline characteristics of the patients are summarized in Table 1.

Table 1.

Demographic and Clinical Characteristics of Patients with DOC

Case number	Gender	Age	Etiology	CRS-R score	Time since brain injury (month)	Diagnosis
1	Male	66	Ischemic stroke	2–3–5–1–0–2	7	MCS
2	Male	44	Hemorrhagic stroke	1–3–1–0–0–2	5	MCS
3	Male	66	Hemorrhagic stroke	2–3–5–0–0–2	4	MCS
4	Male	61	TBI	1–1–5–1–0–2	8	MCS
5	Male	25	Ischemic stroke	1–0–3–2–0–2	10	MCS
6	Female	70	Hemorrhagic stroke	3–3–3–2–0–3	5	MCS
7	Male	46	TBI	1–0–2–1–0–2	6	VS
8	Male	70	TBI	0–0–2–0–0-0	5	VS
9	Female	64	Hemorrhagic stroke	2–3–4–2–1–3	5	MCS
10	Male	64	Hemorrhagic stroke	1–1–2–0–0–2	14	VS
11	Female	44	Hemorrhagic stroke	0–0–2–0–0–2	4	VS
12	Male	15	TBI	0–0–2–0–0–2	6	VS
13	Male	35	Hemorrhagic stroke	2–3–3–1–0–2	4	MCS
14	Male	66	Ischemic stroke	1–0–2–0–0–2	6	VS
15	Male	23	TBI	0–0–2–0–0–2	16	VS
16	Male	69	TBI	2–3–5–1–1–2	5	MCS
17	Male	42	TBI	0–0–3–2–0–1	3	MCS
18	Female	60	Hemorrhagic stroke	1–0–2–0–0–2	6	VS
19	Male	40	Hemorrhagic stroke	3–4–5–1–0–2	4	MCS
20	Female	18	TBI	2–3–5–2–0–2	4	MCS
21	Female	33	TBI	1–1–2–0–0–2	5	VS
22	Male	37	TBI	2–3–5–2–1–2	4	MCS
23	Male	22	TBI	2–1–2–2–0–2	7	VS
24	Female	59	TBI	0–0–1–0–0–2	3	VS
25	Male	43	Hemorrhagic stroke	0–3–2–0–0–2	3	MCS
26	Female	45	Hemorrhagic stroke	0–0–2–0–0–2	7	VS
27	Female	55	TBI	1–2–3–0–0–2	11	MCS
28	Female	70	Ischemic stroke	3–4–5–1–1–2	5	MCS
29	Male	51	TBI	0–0–2–0–0–2	4	VS
30	Female	16	TBI	2–3–2–0–0–1	9	MCS
31	Male	44	Hemorrhagic stroke	2–3–2–0–0–1	20	MCS
32	Female	26	Ischemic stroke	1–0–2–1–0–2	4	VS
33	Male	66	Hemorrhagic stroke	2–3–5–1–0–2	9	MCS

CRS-R, Coma Recovery Scale-Revised; DOC, disorders of consciousness; MCS, minimally conscious state; TBI, traumatic brain injury; VS, vegetative state.

Decreased alpha diversity of gut microbiota was associated with levels of consciousness in patients with DOC

Alpha diversity analysis was performed to investigate the community richness and coverage within gut microbiome, and it was measured by Chao1, Goods_coverage, observed_otus, Shannon, and Simpson indexes. In this study, we found that Goods_coverage index (p < 0.01) was significantly lower in patients with DOC compared with HC. However, there were no significant differences in Chao1 (p = 0.064), observed_otus (p = 0.064), Shannon (p = 0.84), and Simpson (p = 0.97) indexes between the two groups (Fig. 1A–E). When we further compared these indexes among HC, MCS, and VS groups, we found that Chao1 and observed_otus of the HC group were higher than that of the VS group, while goods_coverage index in the HC group was lower than the VS group. There were no significant differences in the indexes between the MCS and the VS group. However, a descending trend of Chao1 and observed_otus was observed from the HC to the VS group, which indicated decreased alpha diversity was associated with the decreasing level of consciousness (Fig. 2A–E). These results suggested that a lower alpha diversity of gut microbiota may be an indicator of a more severe level of unconsciousness in patients with DOC.

FIG. 1.

Alterations of gut microbiota diversity in patients with DOC. Goods_coverage index was significantly lower in patients with DOC compared with HC (B). There were no significant differences in Chao1 (A), observed_otus (C), Shannon (D), and Simpson (E) indexes between these two groups. Beta diversity depicted by PCA (F) and PCoA (G) showed that composition of gut microbiota in patients with DOC differed significantly from HC, while there was no significant differences in NMDS (H) between the two groups. DOC, disorders of consciousness; PCoA, principal coordinate components analysis.

FIG. 2.

Alterations of gut microbiota diversity in patients in MCS and patients in VS. Chao1 (A) and observed_otus (C) indexes were significantly lower in patients in VS compared with HC, while goods_coverage (B) was higher in patients in VS. There were no significant differences in Shannon (D) and Simpson (E) indexes among the groups. Beta diversity depicted by PCA (F) and PCoA (G) showed that composition of gut microbiota in patients in MCS and patients in VS differed significantly from HC, while there were no significant differences in NMDS (H) among the groups. HC, healthy controls; MCS, minimally conscious state; PCoA, principal coordinate components analysis; VS, vegetative state.

Altered beta diversity demonstrated different composition of gut microbiota in patients with DOC

Beta diversity of gut microbiota refers to the potential differences in different bacterial communities. In our study, PCA, PCoA, and NMDS were used to analyze the beta diversity. In patients with DOC, a shift in microbial composition was observed in PCA1 and PCoA1, which accounted for 15.47% and 35.08% of the inter-sample variation. NMDS analysis showed that there was a marked difference between the HC and patients with DOC patients (Fig. 1F–H). Then we further compared the beta diversity between MCS, VS, and HC. The results of PCA, PCoA, and NMDS analysis showed that there were significant differences in the composition of gut microbiota among them. These results indicated that patients with DOC with different levels of consciousness exhibited distinguished gut microbial composition (Fig. 2F–H).

Patients with DOC exhibited a different spectrum of abundant bacterial taxa

The top 30 species with the highest abundance in each taxonomic hierarchy are shown in Figure 3. At the phylum level, we observed decrease of abundance in two phyla, including Firmicutes and Actinobacteria and increase in four phyla (Bacteroidetes, Proteobacteria, Verrucomicrobia, and Fusobacteria) in patients with DOC when compared with HC. In further comparison between MCS and VS, the relative abundance of Firmicutes, Proteobacteria, and Fusobacteria was lower in patients in VS than in patients in MCS, while that of Bacteroidetes, Actinobacteria, and Verrucomicrobia was higher in VS than in MCS. At the genus level, Bacteroides, Klebsiella, and Escherichia_shigella were more abundant in patients with DOC, while Bifidobacterium, Faecalibacterium, prevotella_9, and Lachnospira were less abundant in patients with DOC than HC. In further comparison between MCS and VS, the relative abundance of Faecalibacterium and Klebsiella was lower in patients in VS than patients in MCS, while that of Bacteroides and Bifidobacterium was higher in VS than MCS. Due to the limited depth of 16S-RDNA sequencing, we were unable to account for all of the detected sequences at species level.

FIG. 3.

Taxonomy stacked bar displaying the relative abundance of bacteria taxa in each group at different levels. DOC and HC at phylum level (A), genus level (B), and species level (C); Patients in MCS, patients in VS, and HC at phylum level (D), genus level (E), and species level (F). DOC, disorders of consciousness; HC, healthy controls; MCS, minimally conscious state; VS, vegetative state.

LefSe analysis identified discriminant taxa in patients with DOC

LefSe analysis was used to identify discriminant bacterial taxa among different groups; we compared the species differences between the patients with DOC and HC, as well as MCS and VS groups. Thirty differentially expressed taxa were obtained in patients with DOC. The top 10 taxa included Bacteroides, Bacteroidaceae, Proteobacteria, Gammaproteobacterial, Enterobacteriales, Enterobacteriaceae, Klebsiella_unclassified, Verrucomicrobia, Verrucomicrobiae, and Akkermansiaceae. Comparing patients in minimally conscious states and vegetative states, we found higher abundances of five taxa—Firmicutes, Escherichia-Shigella, uncultured_Faecalibacterium_sp, Lachnospiraceae_unclassified, and Ruminococcaceae_UCG_013 in MCS patients, while Clostridiales_unclassified abundance was higher in VS patients. (Fig. 4).

FIG. 4.

Linear discriminant analysis of gut microbiota effect in DOC and HC group (A) and MCS and VS group (B). LDA score >3.0, p < 0.05. DOC, disorders of consciousness; HC, healthy controls; LDA, linear discriminant analysis; MCS, minimally conscious state; VS, vegetative state.

Functional analysis of gut microbiota biomarkers identified potential pathways involved in DOC after brain injury

We further used PICRUSt2 analysis to predict the potential functions of differently abundant genera. And the results revealed that in the HC group, abundant bacterial taxa were associated with adenosine triphosphate synthesis and bacterial cell division, while abundant genera in DOC group were associated with multidrug efflux pump, heat shock protein, and beta lactamase family. In comparison of patients in MCS and patients in VS, we found that abundant genera in the VS group were associated with galactosidase function, while abundant genera in the MCS group were associated with sugar fermentation and protein biosynthesis (Fig. 5). These results indicated that these biological pathways may be involved in DOC after brain injury.

FIG. 5.

Functional analysis of gut microbiota biomarkers. Functional prediction of abundant genera in DOC and HC group (A) and functional prediction of abundant genera in MCS and VS group (B). DOC, disorders of consciousness; HC, healthy controls; MCS, minimally conscious state; VS, vegetative state.

Discussion

In this study, the results showed that the diversity and composition of gut microbiome were significantly altered in patients with DOC, and the alpha diversity was significantly lower in patients with DOC when compared with the HC. And the alpha diversity of gut microbiota in patients in VS was also lower compared with patients in MCS though not reaching statistical significance. Moreover, we found that specific bacterial taxa including Firmicutes, Escherichia_shigella, Faecalibacterium, Lachnospiraceae, and Ruminococcaceae_UCG_013 were more abundant in patients in MCS, whereas Clostridiales were more abundant in patients in VS. Collectively, our findings demonstrated the alterations of gut microbiome in patients with DOC and identified potential bacterial biomarkers for diagnosis and evaluation of patients with DOC.

It is known that accurate evaluation of levels of consciousness in DOC is the prerequisite for following effective treatment. However, there was still a high rate of misdiagnosis in these patients, and some studies reported that about 30–40% of patients in MCS were misdiagnosed as in VS.^33,34 Electrophysiological and neuroimaging techniques have been proposed to aid the diagnosis of patients with DOC. For example, studies reported that EEG networks had a discriminative capacity (80%) for the differentiation between VS and MCS.^35,36 And resting-state PET has also been used to distinguish MCS from VS.^36,37 In addition, some researchers found that TMS-EEG based on PCI can classify individuals in VS, individuals in MCS, and healthy individuals.^38,39 However, all these existing methods suffered from some shortcomings, such as high expense and inconvenience in clinical use. In recent years, increasing knowledge about microbiota and gut–brain axis shed some insights into the evaluation of patients with DOC. For instance, Lucidi et al. found that Christensenellaceae is a marker of disease progression in bipolar disorder, and decrease in relative abundance of Faecalis was associated with disease severity.⁴⁰ Li et al. found that changes in the gut microbiota may occur several years before the onset of dementia, even in the mild cognitive impairment stage.⁴¹ Inspired by these studies, we investigated gut microbiome in patients with DOC and tried to identify some potential biomarkers for evaluating these patients in this study. And encouragingly, we found that patients with had decreased alpha diversity in gut microbiota compared with HC. Further analysis revealed a decreasing trend in alpha diversity (Chao1 index) across groups: highest in healthy controls (HC), followed by MCS patients, and lowest in VS patients. This suggests that reduced alpha diversity may correlate with the severity of consciousness impairment. Therefore, our results indicated that alpha diversity was a potential biomarker in the evaluation of patients with DOC and provided the preliminary evidence of association between gut microbiome alpha diversity and levels of consciousness.

By further analyzing the gut microbiota at different levels, we found that at phylum level, increased abundance of Bacteroides and Proteobacteria was observed in patients with DOC. At the genus level, Bacteroides, Klebsiella, and Escherichia_shigella were more abundant in patients with DOC. While the abundance of Firmicutes, Proteobacteria, and Fusobacteria was lower in patients in VS than in patients in MCS, while that of Bacteroidetes, Actinobacteria, and Verrucomicrobia was higher in VS than MCS. At the genus level, abundance of Faecalibacterium and Klebsiella was lower in patients in VS than in patients in MCS, while that of Bacteroides and Bifidobacterium was higher in VS than MCS, which was similar to previous studies in experimental TBI.^42,43 For instance, Nicholson et al. found that the relative abundance of traditional beneficial bacteria decreased after TBI, especially in the families Trichospiraceae, Mogamiaceae, and Ruminococcaceae within Firmicutes. On the contrary, the number of Bacteroidetes, Proteobacteriaceae, and Enterobacteriaceae increased after TBI.⁴² Bacteroidetes were found to be involved in the inflammatory process, also known as opportunistic pathogens.²⁰ Moreover, increasing brain injury severity was associated with decreased Firmicutes abundance, increased Proteobacteria abundance, and a significant reduction in alpha diversity.⁴² Therefore, the results of our study suggested that with the increase of disease severity, the probiotics in gut microbiome decreased while opportunistic pathogens increased. This also suggested that intervention of intestinal flora, such as probiotics,⁴⁴ may be a potential method in the future to inhibit the growth of mechanistic pathogenic bacteria, thus reducing secondary injury and improving prognosis of patients with prolonged DOC. In addition, LefSe analysis identified that specific taxa Firmicutes, Escherichia_shigella, Faecalibacterium, Lachnospiraceae, and Ruminococcaceae_UCG_013 were more abundant in patients in MCS, whereas Clostridiales were more abundant in patients in VS. This was partly consistent with the previous study reporting Faecailbacterium, Enterococcus, and Methanobrevibacter as specific bacterial taxa to distinguish between MSC and VS.²⁸

Another important goal of our study was to explore the biological functions and potential effects of discriminant bacteria taxa in patients with DOC, such as corresponding inflammatory, immune, or metabolic pathways related to these alterations. Therefore, we performed functional analysis of these specific taxa and found that abundant bacterial taxa in the DOC group were associated with multidrug efflux pump, heat shock protein, and beta lactamase family. A comparison of patients in MCS and patients in VS revealed that abundant taxa in VS group were associated with galactosidase function, while abundant taxa in MCS group were associated with sugar fermentation and protein biosynthesis. Previous studies have demonstrated that the gut microbiota are involved in regulating immune and inflammatory responses in both acute and chronic neurological diseases and can further influence patients’ outcome.^45,46 For instance, the abundance of Clostridiales after 24 h TBI was found to be closely related to the intestinal immune response through modulating the migration of TH17 cells,^47,48 While in ischemic brain injury, reduced Proteobacteria levels and elevated Firmicutes levels led to the increase of anti-inflammatory Treg cells as well as the decrease of pro-inflammatory γδ T cells, which will eventually produce certain neuroprotective effects.⁴⁹ In addition, some intestinal bacteria influence the brain function through microbial metabolites, such as short-chain fatty acids,^50,51 bile acids,^52,53 and amino acids.²³ One study demonstrated that multiple sclerosis can induce the BA metabolism alterations, and the supplement of bile acids can alleviate the neuroinflammation through inhibiting polarization of astrocytes and microglia toward neurotoxic phenotypes.⁵³ Our previous study also found that TBI-induced intestinal microbiome dysregulation promoted intestinal inflammation by reducing the level of secondary bile acids. The change of bile acid to lithocholic acid was negatively correlated with Staphylococcus, and tauroursodeoxycholic acid was positively correlated with Lachnospiraceae. ²⁷ Moreover, a retrospective study by our team also found that the serum bile acid level of patients after TBI was significantly lower than that in healthy volunteers, and TBI was an independent factor of the decreased level of serum bile acid.⁵⁴ Gut microbiota such as Lachnospiraceae and Firmicutes ^55,56 might affect intestinal inflammation and barrier function by changing bile acid metabolism. And peripheral inflammation can stimulate the activation of microglia and astrocytes in the brain,⁵⁷ leading to the release of cytokines in the brain, resulting in the death of neurons, the destruction of the integrity of the blood–brain barrier, and even the disorders of consciousness. Taken together, we speculated that serum bile acid level may be an important factor that links gut microbiome and DOC after brain injury. However, further confirmatory studies utilizing fecal transplantation will be required to investigate the potential mechanisms underlying gut microbiome alterations and prognosis of DOC.

While the findings of our study demonstrated the association between DOC and gut microbiome, some limitations should be recognized. First, this is a preliminary study with a relatively small sample size to investigate potential biomarkers in gut microbiome to distinguish levels of consciousness in patients with prolonged DOC. There results based on the differential expression should be interpreted with caution. Second, the 16s-rRNA sequencing we used could only account for changes in the flora at phylum and genus levels but not at species level. So further studies in metagenomic sequencing are necessary to explore the alterations of gut microbiota at species level in patients with DOC. Third, patients with brain injury of varied etiologies were included in this study, and investigation of the impact of etiologies on gut microbiome was not performed due to the relatively small sample size. Nevertheless, previous studies also indicated that it is the brain function status, not the etiology, that played a more important role in modulating gut microbiome. In conclusion, future studies with larger sample size, combined with metagenomic, metabolomics, and fecal transplantation, are needed to further elucidate the value of gut microbiome as biological markers for diagnosis and evaluation of patients with DOC, as well as the possibility of potential therapeutic targets.

Transparency, Rigor, and Reproducibility Statement

This study was conducted to investigate the alterations of gut microbiota in patients with DOC and to identify potential microbiome biomarkers that can differentiate levels of consciousness. This was an exploratory study with a relatively small sample size, so the study was not formally registered. The analysis plan was not formally pre-registered, but the team member with primary responsibility for the analysis certifies that the analysis plan was prespecified. A sample size of 30 subjects per group was planned based on the availability of patients with DOC. As a result, we included 33 patients with DOC and 30 HC in the study. The authors agree to provide the full content of the article on request.

Footnotes

Acknowledgment

The authors thank the participants and their families for their participation in this study.

Authors’ Contributions

W.Y.: Conceptualization, methodology, investigation, data analysis, and writing—original draft. Y.J.L. and A.W.: Sample collection and data analysis. J.C. and L.W.: Language improvement. Y.X.L.: Article revision. X.Y.: Writing—review and editing. Z.W.: Conceptualization, supervision, investigation, and writing—review and editing.

Author Disclosure Statement

The authors declare that they have no competing interests.

Funding Information

The study was supported by research projects (2023YSJYX-ZD-2, 2022-YJRC3935) and Fujian Technology Innovation Joint Project (2023Y9080).

References

Feigin

, Nichols

, Alam

, et al. Global, regional, and national burden of neurological disorders, 1990–2016: A systematic analysis for the Global Burden of Disease Study 2016. The Lancet Neurology, 2019; 18(5):459–480; doi: 10.1016/s1474-4422(18)30499-x

Giacino

, Fins

, Laureys

, et al. Disorders of consciousness after acquired brain injury: The state of the science. Nat Rev Neurol, 2014; 10(2):99–114; doi: 10.1038/nrneurol.2013.279

Laureys

, Celesia

, Cohadon

, et al.; European Task Force on Disorders of Consciousness. Unresponsive wakefulness syndrome: A new name for the vegetative state or apallic syndrome. BMC Med, 2010; 8:68; doi: 10.1186/1741-7015-8-68

Giacino

, Ashwal

, Childs

, et al. The minimally conscious state: Definition and diagnostic criteria. Neurology, 2002; 58(3):349–353; doi: 10.1212/wnl.58.3.349

Giacino

, Kalmar

, Whyte

. The JFK Coma Recovery Scale-Revised: Measurement characteristics and diagnostic utility. Arch Phys Med Rehabil, 2004; 85(12):2020–2029; doi: 10.1016/j.apmr.2004.02.033

Giacino

, Schnakers

, Rodriguez-Moreno

, et al. Behavioral assessment in patients with disorders of consciousness: Gold standard or fool’s gold? Prog Brain Res, 2009; 177:33–48.

Luppi

, Craig

, Pappas

, et al. Consciousness-specific dynamic interactions of brain integration and functional diversity. Nat Commun, 2019; 10(1):4616; doi: 10.1038/s41467-019-12658-9

Snider

, Edlow

. MRI in disorders of consciousness. Curr Opin Neurol, 2020; 33(6):676–683; doi: 10.1097/WCO.0000000000000873

Thibaut

, Panda

, Annen

, et al. Preservation of brain activity in unresponsive patients identifies MCS star. Ann Neurol, 2021; 90(1):89–100; doi: 10.1002/ana.26095

10.

, Zhang

, Cui

, et al. White matter deficits underlying the impaired consciousness level in patients with disorders of consciousness. Neurosci Bull, 2018; 34(4):668–678; doi: 10.1007/s12264-018-0253-3

11.

Bodart

, Amico

, Gomez

, et al. Global structural integrity and effective connectivity in patients with disorders of consciousness. Brain Stimul, 2018; 11(2):358–365; doi: 10.1016/j.brs.2017.11.006

12.

Jang

, Kwon

. Neuroimaging characterization of recovery of impaired consciousness in patients with disorders of consciousness. Neural Regen Res, 2019; 14(7):1202–1207; doi: 10.4103/1673-5374.251299

13.

Engemann

, Raimondo

, King

, et al. Robust EEG-based cross-site and cross-protocol classification of states of consciousness. Brain, 2018; 141(11):3179–3192; doi: 10.1093/brain/awy251

14.

Lutkenhoff

, Nigri

, Rossi Sebastiano

, et al. EEG Power spectra and subcortical pathology in chronic disorders of consciousness. Psychol Med, 2022; 52(8):1491–1500; doi: 10.1017/S003329172000330X

15.

, Xing

, Long

, et al. Impact of microbiota on central nervous system and neurological diseases: The gut-brain axis. J Neuroinflammation, 2019; 16(1):53; doi: 10.1186/s12974-019-1434-3

16.

Collins

, Surette

, Bercik

. The interplay between the intestinal microbiota and the brain. Nat Rev Microbiol, 2012; 10(11):735–742; doi: 10.1038/nrmicro2876

17.

Moloney

, Johnson

, O’Mahony

, et al. Stress and the microbiota-gut-brain axis in visceral pain: Relevance to irritable bowel syndrome. CNS Neurosci Ther, 2016; 22(2):102–117; doi: 10.1111/cns.12490

18.

Fung

, Olson

, Hsiao

. Interactions between the microbiota, immune and nervous systems in health and disease. Nat Neurosci, 2017; 20(2):145–155; doi: 10.1038/nn.4476

19.

Sharon

, Sampson

, Geschwind

, et al. The central nervous system and the gut microbiome. Cell, 2016; 167(4):915–932; doi: 10.1016/j.cell.2016.10.027

20.

Lin

, Chen

, Chiang

, et al. Altered gut microbiota and inflammatory cytokine responses in patients with Parkinson’s disease. J Neuroinflammation, 2019; 16(1):129; doi: 10.1186/s12974-019-1528-y

21.

Wang

, Davis

, Qi

, et al. Gut-microbiota-microglia-brain interactions in Alzheimer’s disease: Knowledge-based, multi-dimensional characterization. Alzheimers Res Ther, 2021; 13(1):177; doi: 10.1186/s13195-021-00917-1

22.

Oft

, Simon

, Sun

. New insights into metabolism dysregulation after TBI. J Neuroinflammation, 2024; 21(1):184; doi: 10.1186/s12974-024-03177-6

23.

Urban

, Pyles

, Stewart

, et al. Altered fecal microbiome years after traumatic brain injury. J Neurotrauma, 2020; 37(8):1037–1051; doi: 10.1089/neu.2019.6688

24.

Winek

, Dirnagl

, Meisel

. The gut microbiome as therapeutic target in central nervous system diseases: Implications for stroke. Neurotherapeutics, 2016; 13(4):762–774; doi: 10.1007/s13311-016-0475-x

25.

Houlden

, Goldrick

, Brough

, et al. Brain injury induces specific changes in the caecal microbiota of mice via altered autonomic activity and mucoprotein production. Brain Behav Immun, 2016; 57:10–20; doi: 10.1016/j.bbi.2016.04.003

26.

, Zhou

, Shao

, et al. Gut microbiota dysbiosis induced by intracerebral hemorrhage aggravates neuroinflammation in mice. Front Microbiol, 2021; 12:647304; doi: 10.3389/fmicb.2021.647304

27.

You

, Zhu

, Wei

, et al. Traumatic brain injury induces gastrointestinal dysfunction and dysbiosis of gut microbiota accompanied by alterations of bile acid profile. J Neurotrauma, 2022; 39(1–2):227–237; doi: 10.1089/neu.2020.7526

28.

, Cheng

, He

, et al. Altered intestinal microbiomes and lipid metabolism in patients with prolonged disorders of consciousness. Front Immunol, 2022; 13:781148; doi: 10.3389/fimmu.2022.781148

29.

Fingelkurts

, Fingelkurts

, Bagnato

, et al. Long-term (six years) clinical outcome discrimination of patients in the vegetative state could be achieved based on the operational architectonics EEG analysis: A pilot feasibility study. Open Neuroimag J, 2016; 10:69–79; doi: 10.2174/1874440001610010069

30.

Cavinato

, Freo

, Ori

, et al. Post-acute P300 predicts recovery of consciousness from traumatic vegetative state. Brain Inj, 2009; 23(12):973–980; doi: 10.3109/02699050903373493

31.

Martens

, Deltombe

, Foidart-Dessalle

, et al. Clinical and electrophysiological investigation of spastic muscle over activity in patients with disorders of consciousness following severe brain injury. Clin Neurophysiol, 2019; 130(2):207–213; doi: 10.1016/j.clinph.2018.10.021

32.

Odamaki

, Kato

, Sugahara

, et al. Age-related changes in gut microbiota composition from newborn to centenarian: A cross-sectional study. BMC Microbiol, 2016; 16:90; doi: 10.1186/s12866-016-0708-5

33.

Andrews

, Murphy

, Munday

, et al. Misdiagnosis of the vegetative state retrospective study in a rehabilitation unit. BMJ, 1996; 313(7048):13–16.

34.

Schnakers

, Vanhaudenhuyse

, Giacino

, et al. Diagnostic accuracy of the vegetative and minimally conscious state: Clinical consensus versus standardized neurobehavioral assessment. BMC Neurol, 2009; 9:35; doi: 10.1186/1471-2377-9-35

35.

Demertzi

, Antonopoulos

, Heine

, et al. Intrinsic functional connectivity differentiates minimally conscious from unresponsive patients. Brain, 2015; 138(Pt 9):2619–2631; doi: 10.1093/brain/awv169

36.

Chennu

, Annen

, Wannez

, et al. Brain networks predict metabolism, diagnosis and prognosis at the bedside in disorders of consciousness. Brain, 2017; 140(8):2120–2132; doi: 10.1093/brain/awx163

37.

Stender

, Gosseries

, Bruno

M-A

, et al. Diagnostic precision of PET imaging and functional MRI in disorders of consciousness: A clinical validation study. Lancet, 2014; 384(9942):514–522; doi: 10.1016/s0140-6736(14)60042-8

38.

Casarotto

, Comanducci

, Rosanova

, et al. Stratification of unresponsive patients by an independently validated index of brain complexity. Ann Neurol, 2016; 80(5):718–729; doi: 10.1002/ana.24779

39.

Casali

AG GO

, Rosanova

, Boly

, et al. A theoretically based index of consciousness independent of sensory processing and behavior. Sci Transl Med, 2013; 5(198):198ra105.

40.

Lucidi

, Pettorruso

, Vellante

, et al. Gut microbiota and bipolar disorder: An overview on a novel biomarker for diagnosis and treatment. Int J Mol Sci, 2021; 22(7):3723; doi: 10.3390/ijms22073723

41.

, He

, Ma

, et al. Mild cognitive impairment has similar alterations as Alzheimer’s disease in gut microbiota. Alzheimers Dement, 2019; 15(10):1357–1366; doi: 10.1016/j.jalz.2019.07.002

42.

Nicholson

, Watts

, Burmeister

, et al. Moderate traumatic brain injury alters the gastrointestinal microbiome in a time-dependent manner. Shock, 2019; 52(2):240–248; doi: 10.1097/SHK.0000000000001211

43.

, Tang

, Zhou

, et al. Fecal microbiota transplantation is a promising method to restore gut microbiota dysbiosis and relieve neurological deficits after traumatic brain injury. Oxid Med Cell Longev, 2021; 2021:5816837; doi: 10.1155/2021/5816837

44.

Tan

, Zhu

J-C

, Du

, et al. Effects of probiotics on serum levels of Th1Th2 cytokine and clinical outcomes in severe traumatic brain-injured patients a prospective randomized pilot study. Crit Care, 2011; 15(6):R290.

45.

, Liu

, Fu

, et al. Lactobacillus acidophilus exerts neuroprotective effects in mice with traumatic brain injury. J Nutr, 2019; 149(9):1543–1552; doi: 10.1093/jn/nxz105

46.

, Zeng

, Zhao

, et al. Inhibition of dectin-1 ameliorates neuroinflammation by regulating microglia/macrophage phenotype after intracerebral hemorrhage in mice. Transl Stroke Res, 2021; 12(6):1018–1034; doi: 10.1007/s12975-021-00889-2

47.

Schirmer

, Garner

, Vlamakis

, et al. Microbial genes and pathways in inflammatory bowel disease. Nat Rev Microbiol, 2019; 17(8):497–511; doi: 10.1038/s41579-019-0213-6

48.

Nishio

, Honda

. Immunoregulation by the gut microbiota. Cell Mol Life Sci, 2012; 69(21):3635–3650; doi: 10.1007/s00018-012-0993-6

49.

Benakis

, Brea

, Caballero

, et al. Commensal microbiota affects ischemic stroke outcome by regulating intestinal gammadelta T cells. Nat Med, 2016; 22(5):516–523; doi: 10.1038/nm.4068

50.

Erny

, Hrabě de Angelis

, Jaitin

, et al. Host microbiota constantly control maturation and function of microglia in the CNS. Nat Neurosci, 2015; 18(7):965–977; doi: 10.1038/nn.4030

51.

Braniste

VA-AM

, Kowal

, Anuar

, et al. The gut microbiota influences blood-brain barrier permeability in mice. Sci Transl Med, 2014; 6(263):263ra158.

52.

Nho

, Kueider-Paisley

, MahmoudianDehkordi

, et al.; Alzheimer’s Disease Neuroimaging Initiative and the Alzheimer Disease Metabolomics Consortium. Altered bile acid profile in mild cognitive impairment and Alzheimer’s disease: Relationship to neuroimaging and CSF biomarkers. Alzheimers Dement, 2019; 15(2):232–244; doi: 10.1016/j.jalz.2018.08.012

53.

Bhargava

, Smith

, Mische

, et al. Bile acid metabolism is altered in multiple sclerosis and supplementation ameliorates neuroinflammation. J Clin Invest, 2020; 130(7):3467–3482; doi: 10.1172/JCI129401

54.

Zhu

, Chen

, You

, et al. A retrospective clinical analysis of the serum bile acid alteration caused by traumatic brain injury. Front Neurol, 2021; 12:624378; doi: 10.3389/fneur.2021.624378

55.

Ridlon

, Kang

, Hylemon

, et al. Bile acids and the gut microbiome. Curr Opin Gastroenterol, 2014; 30(3):332–338; doi: 10.1097/MOG.0000000000000057

56.

Wahlstrom

, Sayin

, Marschall

, et al. Intestinal crosstalk between bile acids and microbiota and its impact on host metabolism. Cell Metab, 2016; 24(1):41–50; doi: 10.1016/j.cmet.2016.05.005

57.

Block

, Zecca

, Hong

. Microglia-mediated neurotoxicity: Uncovering the molecular mechanisms. Nat Rev Neurosci, 2007; 8(1):57–69; doi: 10.1038/nrn2038