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The gut microbiota and metabolite profiles are altered in patients with spinal cord injury

The gut microbiota and metabolite profiles are altered in patients with spinal cord injury Background Metabolites secreted by the gut microbiota may play an essential role in microbiota–gut–central nerv- ous system crosstalk. In this study, we explored the changes occurring in the gut microbiota and their metabolites in patients with spinal cord injury (SCI) and analyzed the correlations among them. Methods The structure and composition of the gut microbiota derived from fecal samples collected from patients with SCI (n = 11) and matched control individuals (n = 10) were evaluated using 16S rRNA gene sequencing. Addi- tionally, an untargeted metabolomics approach was used to compare the serum metabolite profiles of both groups. Meanwhile, the association among serum metabolites, the gut microbiota, and clinical parameters (including injury duration and neurological grade) was also analyzed. Finally, metabolites with the potential for use in the treatment of SCI were identified based on the differential metabolite abundance analysis. Results The composition of the gut microbiota was different between patients with SCI and healthy controls. At the genus level, compared with the control group, the abundance of UBA1819, Anaerostignum, Eggerthella, and Entero- coccus was significantly increased in the SCI group, whereas that of Faecalibacterium, Blautia, Escherichia–Shigella, Agathobacter, Collinsella, Dorea, Ruminococcus, Fusicatenibacter, and Eubacterium was decreased. Forty-one named metabolites displayed significant differential abundance between SCI patients and healthy controls, including 18 that were upregulated and 23 that were downregulated. Correlation analysis further indicated that the variation in gut microbiota abundance was associated with changes in serum metabolite levels, suggesting that gut dysbiosis is an important cause of metabolic disorders in SCI. Finally, gut dysbiosis and serum metabolite dysregulation was found to be associated with injury duration and severity of motor dysfunction after SCI. Conclusions We present a comprehensive landscape of the gut microbiota and metabolite profiles in patients with SCI and provide evidence that their interaction plays a role in the pathogenesis of SCI. Furthermore, our findings sug- gested that uridine, hypoxanthine, PC(18:2/0:0), and kojic acid may be important therapeutic targets for the treatment of this condition. Keywords Spinal cord injury, Gut microbiota, 16S rRNA gene sequencing, Untargeted metabolomics † 3 Ganggang Kong and Wenwu Zhang contributed equally to this work Department of Neurosurgery, The First Affiliated Hospital of Sun Yat-Sen University, No. 58, Zhong Shan Er Lu, Guangzhou 510080, Guangdong, *Correspondence: China Baoshu Xie Department of Anesthesiology, Bazhong Central Hospital, Bazhong, xiebsh3@mail.sysu.edu.cn China Guangdong Provincial Key Laboratory of Orthopedics and Traumatology, Department of Spinal Surgery, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China Department of Rehabilitation Medicine, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China © The Author(s) 2023. 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The Creative Commons Public Domain Dedication waiver (http://creativecom- mons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Kong et al. Molecular Brain (2023) 16:26 Page 2 of 14 microbiome becomes dysregulated after SCI, which exac- Introduction erbates nerve injury and spinal cord pathology [18, 19]. Spinal cord injury (SCI), usually resulting from severe Dysregulation of the gut microbiome activates the TLR4/ trauma such as falls and traffic accidents, is one of the Myd88 signaling pathway, which has been reported to most severe forms of injury to the central nervous system aggravate SCI [19]. (CNS). SCI is associated with a high rate of disability and The exact mechanisms by which gut dysbiosis regulates serious complications [1, 2]. Patients with SCI typically SCI and the biological mediators of these effects remain present with severe neurogenic intestinal dysfunction largely unknown. Studies have shown that short-chain due to intestinal denervation [3]. In addition to changes fatty acids (SCFAs), the primary metabolites produced by in bowel habits, such as the occurrence of constipation bacterial fermentation of dietary fiber, may regulate brain and diarrhea, the gut microbiota of patients with SCI is function through immune, endocrine, vagal, and other also significantly disturbed, which has a marked impact humoral pathways [20]. In addition, it has been reported on the quality of life of affected individuals [4]. The rela - that the metabolite 4-ethylphenyl sulfate influences the tionship between SCI and intestinal dysfunction has been behavior of mice by affecting oligodendrocyte func - extensively studied with the aim of developing novel and tion and myelin patterning [21]. These findings suggest effective management methods for intestinal impair - that gut microbiome-derived metabolites are important ment. The CNS affects intestinal function via several mediators of microbiota–gut–brain crosstalk. However, mechanisms, including the regulation of intestinal motil- relatively few studies have investigated the changes in ity, intestinal transport time, intestinal permeability, and overall metabolite abundance in patients with SCI. hormone secretion [5]. Consequently, abnormal gastroin- In this study, we conducted an omics analysis of gut testinal function in patients with SCI can lead to changes microbiota structure in patients with SCI as well as a in intestinal permeability and, consequently, the migra- quantitative analysis of metabolites in serum samples. tion of intestinal bacteria to the bloodstream and dysbi- We further examined the relationship between the gut osis [6]. Although the gut microbiome is thought to be microbiota and changes in serum metabolites. confined to the intestinal lumen, it has been shown that it can also modulate the function of distant organs [7]. Materials and methods These observations highlight the importance of investi - Study design and sample collection gating the relationship between SCI and gut microbiota Eleven patients with SCI and 10 healthy individuals were composition. recruited for this study. The inclusion criteria for the The gut microbiota is characterized as a collection of SCI group were (1) cervical or thoracic SCI confirmed microorganisms that colonize the digestive tract. It is an via medical history and imaging examination; (2) aged indispensable “microbial organ” in the human body and 14  years or more; and (3) American Spinal Cord Injury plays a vital role in the health of the host, including in the Association (ASIA) neurological function scale ranging CNS [8–11]. For decades, studies investigating the pos- from A to D. The exclusion criteria were the presence sible impact of microbes and viruses in SCI have been of cauda equina injury,  infectious disease, severe diges- largely constrained by technical limitations; however, tive disease, tumor, diabetes, immune metabolic disease, with the development of gut microbiota sequencing tech- craniocerebral injury, and mental disorder; and patients nology, the correlation between the complex gut micro- receiving antibiotics or probiotics one month before the biome and the CNS has been gradually revealed [12]. The study. Healthy individuals (age > 14 years) were recruited gut microbiome and its metabolites regulate the normal based on the same exclusion criteria. development of the CNS, such as blood–brain barrier Data relating to gender, age, ASIA grade, injury dura- formation, myelination, neurogenesis, and microglia tion, and site of enrolment were collected. Neurologi- maturation [13, 14]. The gut microbiome produces neu - cal function grade was based on the standardized ASIA roactive metabolites that can cross the intestinal barrier Impairment Scale, as previously described [22]. and enter the systemic circulation, where they can influ - ence neural activity and promote neuroinflammation [15, Sample collection and preparation 16]. A balanced microbiome is critical for its symbiotic Approximately 10 g of fresh stool samples were collected relationship with the host. Dysbiosis occurs when the from SCI patients and healthy individuals using sterile composition of the gut microbiome changes, particularly plastic spoons and placed in test tubes. Fresh stool sam- when there are fewer non-pathogenic bacteria or more ples were frozen at − 80  °C for 16S rRNA gene sequenc- pathogenic, proinflammatory bacteria. Evidence gar - ing within 2 h of collection. Venous blood samples (2 mL) nered over recent years has indicated that gut dysbiosis were obtained from patients with SCI and healthy indi- is related to secondary injury and the clinical symptoms viduals and centrifuged at 3,000 × g for 10 min for serum of SCI [6, 17]. Studies in mice have shown that the gut Kong  et al. Molecular Brain (2023) 16:26 Page 3 of 14 extraction. The serum samples were stored at − 80  °C for change [FC] and T-tests) analysis. Mean metabolite con- UPLC–Q–TOF/MS analysis. The data were analyzed on centrations in each group were used to calculate FC val- the free online Majorbio cloud platform (www. major bio. ues. Differentially expressed metabolites were identified com). using variable importance in projection (VIP) scores > 1 and p < 0.05 as criteria. 16S rRNA amplicon sequencing Total genomic DNA was extracted from each sample and Correlation analysis purified using the cetyltrimethyl ammonium bromide Correlations among the abundance of gut microbiota at (CTAB) method [23]. DNA purity and concentration the genus level, the levels of serum metabolites, injury were determined by agarose gel electrophoresis. The V3– duration, and neurological grading were visualized as V4 region of the 16S rRNA gene was PCR amplified using a heatmap constructed using SCIPY (Python; Version specific primers. PCR was performed in a 30-μL reaction 1.0.0). volume, which included 15 μL of Phusion High-Fidelity PCR Master Mix (New England Biolabs, Ipswich, MA, Statistical analysis USA), 0.2  μM of each of the forward and reverse prim- Data were analyzed using SPSS, version 22 (IBM, ers, and 10 ng of template DNA. The PCR products were Armonk, NY, USA). Continuous variables were expressed analyzed using 2% agarose gel electrophoresis, purified as means ± standard deviation and independent samples with an AxyPrepDNA Gel Extraction Kit (Axygen Biosci- t-tests were employed for comparisons between groups. ence, Union City, NJ, USA), and subjected to paired-end Categorical variables were expressed as rates and the chi- sequencing on an Illumina MiSeq/HiSeq 2500 platform. square test was used for comparisons between groups. Reads were clustered into operational taxonomic units p-values < 0.05 were considered significant. (OTUs) at a 97% sequence similarity threshold based on Ribosomal Database Project (RDP) classification. Beta Results diversity analysis, including principal component analy- Baseline data for the two groups sis (PCA), principal coordinate analysis (PCoA), and par- The participants, all from Guangdong Province, had tial least squares discriminant analysis (PLS-DA), was similar dietary habits and were given standard dietary performed with the Quantitative Insights into Microbial guidance for three days before the study. There were no Ecology (QIIME) software package. Differences between significant differences in age and gender between the SCI groups were analyzed using T-tests, linear discriminant group and the control group, which minimized the influ - analysis (LDA) effect size (LEfSe), and analysis of similar - ence of confounding factors on the study results. Detailed ity (ANOSIM). data for the SCI patients are shown in Table 1. Serum metabolomics The gut microbiota profiles of the two groups Serum samples (100 µL) were centrifuged at 14,000×g for To investigate whether the gut microbiota profile was 20  min at 4 ℃ with an equal volume of pre-cooled ace- changed in patients with SCI, 16S rRNA gene sequenc- tonitrile/methanol (1:1, v/v) and the supernatant was col- ing was performed on fecal samples from both the SCI lected. For LC–MS analysis, the samples were separated and Control groups. A total of 1,900,745 sequences were using ultra-high-performance liquid chromatography obtained. The OTU similarity level for index assessment (UHPLC, 1290 Infinity LC, Agilent Technologies, Santa was 97%. The richness and evenness of the gut microbi - Clara, CA, USA). Electrospray ionization (ESI) was used ome of the two groups were analyzed using rank–abun- for detection in both positive and negative ion modes. dance curves (Fig. 1a). The rarefaction curve had obvious Mass spectrometric analysis and metabolite identifica - asymptotes, the OUT coverage was 98.98% (Fig. 1b), and tion were performed using an Agilent 6550 iFunnel Q– the core species curve had leveled off (Fig.  1c). These TOF spectrometer (Agilent Technologies, Santa Clara, results indicated that the community was adequately CA, USA) and a Triple TOF 6600 mass spectrometer sampled. Beta diversity analysis (PCoA and PLS-DA) (SCIEX, Framingham, MA, USA), respectively. results showed a significant separation of the gut micro - The raw data were converted to the mzXML format biota between the SCI and Control groups (Fig.  1d, e). using ProteoWizard (http:// prote owiza rd. sourc eforge. ANOSIM analysis demonstrated that the gut microbiota net/) and imported into XCMS software for further anal- composition of the two groups was statistically different, ysis, including retention time correction, peak alignment, suggesting that SCI induced gut dysbiosis (Fig. 1f ). and picking. Following Pareto-scaling preprocessing, the Further analysis was performed at different taxo - data were subjected to multivariate (PCA, PLS-DA, and nomic levels based on the annotated species results. orthogonal PLS-DA [OPLS-DA]) and univariate (fold Firmicutes, Actionbacteriota, Bacteroidetes, and Kong et al. Molecular Brain (2023) 16:26 Page 4 of 14 Table 1 Comparison of baseline data between patients with SCI and healthy controls Control (n = 10) SCI (n = 11) Statistics p-value Year 40.70 ± 14.41 49.00 ± 20.51 t = − 1.062 0.301 Gender (male/female) 6/4 6/5 – 1.000 Injury duration (months) 0 22.81 ± 1.15 t = 0.836 0.405 Injury site Cervical cord NA 5 Thoracic cord NA 6 ASIA grade A 0 4 B 0 0 C 0 3 D 0 4 E 10 0 Proteobacteria were the most abundant phyla among The serum metabolite profile of both groups the gut microbiota of both groups (Fig.  2a). In addi- To determine the extent of metabolic disorder result- tion, compared with the healthy controls, the abun- ing from SCI, untargeted metabolomics analysis was dance of Synergistota was significantly increased in used to evaluate the differences in metabolite abundance patients with SCI, whereas that of Firmicutes was sig- between serum samples of the SCI group (n = 10) and nificantly decreased (Fig.  2b). At the genus level, the those of the Control group (n = 10). In the metabolic pro- abundance of UBA1819 (LDA = 4.54) and Eggerthella files of all the samples, 5,039 positive and 4,894 negative (LDA = 3.88) was significantly increased in SCI patients model features were identified. As shown in Fig.  3a, when relative to that in the healthy controls. The results also the relative standard deviation (RSD) was < 0.3, the peak showed marked decreases in the abundances of Blautia proportion was > 70%, indicating that the sample size was (LDA = 4.51), Faecalibacterium (LDA = 4.57), Escheri- appropriate. A  comprehensive multivariate statistical chia–Shigella (LDA = 4.41), Agathobacter (LDA = 4.13), analysis of cations and anions was undertaken using PLS- Collinsella (LDA = 4.04), Dorea (LDA = 3.88), Rose- DA and OPLS-DA. In the PLS-DA (Fig. 3b, c) and OPLS- buria (LDA = 3.97), Lachnospiraceae_ NK4A136 group DA (Fig.  3e, f ) score plots, a significant separation was (LDA = 3.82), Fusicatenibacter (LDA = 3.80), Holde- observed between the Control and SCI groups, indicat- manella (LDA = 3.87), Ruminococcus (LDA = 3.81), ing that SCI led to metabolic dysfunction. Furthermore, UCG-002 (LDA = 3.74), and Clostridia_UCG-014 permutation tests showed that the PLS-DA (Fig. 3d) and (LDA = 3.84) (Fig . 2c). OPLS-DA (Fig. 3g) patterns had good reliability. To further determine the specific gut microbiota A total of 1511 differential metabolites (p < 0.05, VIP components associated with SCI, LEfSe analysis was score > 1) were detected. Furthermore, 41 named differ - used to identify the gut microbiota components of ential metabolites were quantified. Forty-one metabo - both groups. The results revealed 68 components lites exhibited significant differential abundance between with different classification levels, 20 of which were the SCI patients and healthy controls, 18 of which were enriched in SCI patients and 48 in the Control group upregulated and 23 downregulated (Fig.  4a). The differ - (LDA > 3;  p < 0.05, Fig .  2d, e). Classification results entially abundant metabolites are listed in Table  2. The showed that the 20 species enriched in the SCI group metabolites exhibiting significant differential abundance belonged to the phyla Firmicutes (n = 16), Proteobacte- between the two groups are shown in the cluster heat- ria (n = 2), Actinobacteriota (n = 1), and Bacteroidota map in Fig. 4b. (n = 1), while the 48 species enriched in the Control group belonged to the phyla Firmicutes (n = 36), Proteo- Differential metabolites and KEGG pathway enrichment bacteria (n = 6), Actinobacteriota (n = 4), and Bacteroi- analysis dota (n = 2). The correlation between fecal microbiota We next applied KEGG pathway enrichment analysis to the structure and fecal samples is shown in Fig. 2f. 41 named differential metabolites. The results suggested Kong  et al. Molecular Brain (2023) 16:26 Page 5 of 14 Fig. 1 Detection of fecal sample quality and differences in gut microbiota composition between groups. a Rank–abundance curves for fecal samples from the control and spinal cord injury (SCI) groups. The abscissa represents the rank of the number of operational taxonomic units (OTUs) and the ordinate represents the relative percentage of OTU number. b Sobs index of rarefaction curves at the OTU level between the two groups of samples detected using a 97% similarity threshold. c Core curves. The horizontal axis represents the number of observed samples and the vertical axis represents the number of all core species at the OTU level. d Principal coordinate analysis (PCoA) score plots. e Partial least squares discriminant analysis (PLS-DA) score plots. f Weighted UniFrac distances Kong et al. Molecular Brain (2023) 16:26 Page 6 of 14 that the altered metabolites were mainly related to amino the abundance of Dorea (C = − 0.691, p = 0.001), Blau- acid metabolism, digestive system, nucleotide metabolism, tia (C = 0.575, p = 0.006), Faecalibacterium (C = − 0.618, and membrane transport (Fig. 4c). The 41 metabolites were p = 0.003), Agathobacter (C = 0.652, p = 0.001), and Col- enriched in 20 KEGG pathways (p < 0.05). Histidine metab- linsella (C = 0.646, p = 0.002). Meanwhile, injury duration olism: M and FoxO signaling pathway: EIP were the two was significantly and negatively correlated with the level most significantly enriched pathways (p < 0.01, Fig. 4d). of 2-methylbutyroylcarnitine (C = − 0.730, p = 0.000) and deoxycholic acid (C = 0.642, p = 0.002). The overall correla - Analysis of the correlations among gut dysbiosis, altered tion results suggested that gut microorganisms UBA1819, serum metabolites, and clinical parameters Dorea, Faecalibacterium, Agathobacter, and Collinsella Spearman’s correlation was used to investigate the rela- and the metabolites kojic acid, 12-hydroxydodecanoic tionship between gut microbiota and metabolites. The acid, gamma-D-glutamylglycine, asparaginyl-hydroxypro- relationship between the 20 most differentially expressed line, and allocholic acid were associated with neurological metabolites and the 20 most differentially abundant gut grade. The results further suggested that the gut microbes microbiota at the genus level was analyzed in patients with UBA1819, Dorea, Blautia, Faecalibacterium, Agathobacter, SCI (Fig.  5a). Significant correlations were found between and Collinsella and the metabolites 2-methylbutyroylcarni- UBA1819 and uridine (C = 0.609, p = 0.004), Lachno- tine and deoxycholic acid were related to injury duration. spiraceae and hypoxanthine (C = 0.595, p = 0.006), Blautia and PC (18:2/0:0) (C = 0.659, p = 0.002), and Akkermansia Discussion and kojic acid (C = 0.628, p = 0.003). Meanwhile, the abun- In this study, we compared the gut microbiota and serum dance of Akkermansia was significantly and negatively metabolites of patients with SCI and healthy individu- correlated with that of (5-{8-[1-(2,4-dihydroxyphenyl)- als using 16S rRNA sequencing and metabolomics. The 3-(3,4-dihydroxyphenyl)-2-hydroxypropyl]-3,5,7-trih- patients and the controls recruited for our study were age- ydr oxy -3,4-dihydr o-2H-1-b enzopy ran-2- yl}-2-hy - and gender-matched and were from the same geographi- droxyphenyl) oxidanesulfonic acid in serum (C = 0.609, cal location. Despite these similarities, we found that gut p = 0.004). microbiota and metabolite composition was significantly To explore the clinical significance of gut microbiome altered in SCI patients compared with that in the con- and metabolite dysregulation in patients with SCI, the trols, and the changes were correlated. The main findings putative correlations among gut microbiota abundance, of our study were as follows: (1) The composition of the metabolite abundance, and clinical parameters (includ- gut microbiota differed between SCI patients and healthy ing injury duration and neurological grade) were analyzed individuals, suggesting that SCI induced gut dysbiosis; (2) using Spearman’s correlation (Fig.  5b, c). We found that the serum metabolite profile of patients with SCI differed neurological grade was significantly and positively corre - from that of healthy individuals, implying that SCI patients lated with the abundance of Dorea (C = 0.655, p = 0.001), experience metabolic abnormalities; (3) the gut microbiota Faecalibacterium (C = 0.587, p = 0.005), Agathobacter dysregulation that occurs following SCI is closely related (C = 0.567, p = 0.007), and Collinsella (C = 0.575, p = 0.006) to the changes in the serum metabolite profile as well as and significantly and negatively correlated with the abun - clinical parameters (including injury duration and neuro- dance of UBA1819 (C = − 0.709, p = 0.000). Simul- logical grade). The metabolomics analysis identified many taneously, neurological grade was identified as being metabolites that are closely associated with CNS diseases significantly and positively correlated with the level of and may be important therapeutic targets for the treatment 12-hydroxydodecanoic acid (C = 0.455, p = 0.001), gamma- of SCI, a notion that merits further investigation. D-glutamylglycine (C = 0.712, p = 0.000), asparaginyl- Increasing evidence, both basic and clinical, has indi- hydroxyproline (C = 0.613, p = 0.004), and allocholic acid cated that the gut microbiome is involved in regulating a (C = 0.581, p = 0.007) and significantly and negatively cor - variety of cellular and molecular processes under both related with the level of kojic acid (C = − 0.617, p = 0.004). physiological and pathological conditions. The gut micro - In addition, injury duration showed a significant positive biome and its metabolites can migrate from the gut to correlation with the abundance of UBA1819 (C = 0.660, the intestinal wall and cross the intestinal barrier, thereby p = 0.001) and a significant negative correlation with promoting inflammation and affecting other organs [24]. (See figure on next page.) Fig. 2 Alterations in the gut microbiota at the phylum and genus levels between the two groups. a The six most abundant species at the phylum level in the two groups. b, c Microbiota displaying significantly different abundances at the phylum and genus levels (*p < 0.05, **p < 0.01, ***p < 0.001). d A cladogram of linear discriminant analysis (LDA) effect size (LEfSe) results in the Control and spinal cord injury (SCI) groups. e Histogram of the LDA scores calculated for a differential abundance of functional profiles in the two groups. A LDA score cutoff of 3.0 was used to indicate a significant difference. Different colors represent different groups. f Correlation between fecal microbiota structure and samples Kong  et al. Molecular Brain (2023) 16:26 Page 7 of 14 Fig. 2 (See legend on previous page.) Kong et al. Molecular Brain (2023) 16:26 Page 8 of 14 Fig. 3 Changes in serum metabolite abundance in the Control and spinal cord injury (SCI) groups. a Relative standard deviation (RSD) distribution plot. b, c Partial least squares discriminant analysis (PLS-DA) score plots in positive ion mode and negative ion mode, respectively. d Model verification map of PLS-DA (permutation test). e, f Orthogonal PLS-DA (OPLS-DA) score plots in positive ion mode and negative ion mode, respectively. g Model verification map of OPLS-DA (permutation test) Kong  et al. Molecular Brain (2023) 16:26 Page 9 of 14 Fig. 4 Differential metabolite extraction and KEGG pathway enrichment analysis. a Volcano plot of the differentially abundant metabolites. The abscissa is the multiple change value of the expression difference of metabolites between the two groups, and the ordinate is the statistical test value of the expression difference of metabolites (p-value). Each point in the figure represents a specific metabolite. b Heatmap of the differentially abundant metabolites between the two groups (variable importance in projection [ VIP] scores > 3, p < 0.05). The color represents the relative abundance of the metabolites in the samples. c Level 1 and 2 KEGG pathways related to the differentially expressed metabolites. The ordinate is the name of the level 2 pathway and the abscissa is the number of metabolites related to that pathway. Different colors represent different level 1 pathways. d KEGG pathway enrichment column chart. The abscissa is the name of the level 3 pathway. CP cellular processes, EIP environmental information processing, GIP genetic information processing, HD human diseases, M metabolism, OS organismal systems However, the precise mechanisms underlying these effects on the role of gut microbes in regulating CNS func- are unclear. Immune-, endocrine-, metabolism-, and neu- tion, especially in animal models. One study showed that rotransmitter-related pathways are considered to be the dysregulation of the gut microbiome is associated with main mechanisms via which the gut microbiota influence impaired functional recovery and increased anxiety-like the occurrence and development of CNS diseases. In the behavior after SCI [25]. Meanwhile, Jing et  al. found that last decade, an increasing number of studies have focused fecal microbiota transplantation can help restore the gut Kong et al. Molecular Brain (2023) 16:26 Page 10 of 14 Table 2 Differentially expressed serum metabolites between patients with SCI and healthy controls Metabolite Formula Adducts VIP FC p-value Mode PC(18:2/0:0) C26H50NO7P M + H-H2O, M + Na, M + H 1.05 0.99 0.037 pos Benzyl ethyl ether C9H12O M + H, M + NH4, M + H-H2O 3.71 1.28 0.004 pos Hypoxanthine C5H4N4O M + H-H2O, M + H 1.90 1.05 0.018 pos 3-Hexenoic acid C6H10O2 M + ACN + H, 2 M + K 2.59 0.87 0.021 pos L-Nicotianine C10H12N2O4 M + H-H2O 2.79 0.86 0.009 pos Indoleacetyl glutamine C15H17N3O4 M + H 2.45 0.84 0.043 pos Hypoglycin C7H11NO2 M + H 1.12 1.02 0.002 pos Pipericine C22H41NO M + H 1.04 1.02 0.023 pos Xestoaminol C C14H31NO M + H 1.93 0.96 0.001 pos 12-Hydroxydodecanoic acid C12H24O3 2M + NH4 1.40 0.98 0.000 pos MG(0:0/15:0/0:0) C18H36O4 M + NH4 4.37 0.78 0.000 pos 6-Hydroxypentadecanedioic acid C15H28O5 M + ACN + H 1.99 0.92 0.022 pos DL-2-Aminooctanoic acid C8H17NO2 M + H 2.23 0.92 0.004 pos 2-Methylbutyroylcarnitine C12H23NO4 M + H 1.30 0.97 0.009 pos Glycylglycylglycine C6H11N3O4 M + H-2H2O 1.57 1.05 0.012 pos Hydroxyprolyl-proline C10H16N2O4 M + H-H2O 2.60 1.13 0.003 pos Dopaquinone C9H9NO4 M + ACN + H 1.61 0.95 0.017 pos KOJIC ACID C6H6O4 M + H 1.28 1.02 0.000 pos 2-Amino-4-butanoic acid C19H25N3O7S M + 2Na-H 2.22 0.90 0.019 pos ZAPA C4H6N2O2S M + H 1.54 1.05 0.039 pos Oxidanesulfonic acid C30H28O14S M + 2Na-H 1.16 0.98 0.020 pos 13′-Carboxy-gamma-tocopherol C28H46O4 M-H, M + Na-2H 1.51 0.96 0.039 neg Glutamylvaline C10H18N2O5 M-H2O-H 3.02 1.16 0.001 neg Uridine C9H12N2O6 M-H 1.16 1.03 0.049 neg 3b,12a-Dihydroxy-5a-cholanoic acid C24H40O4 M + FA-H 2.17 0.91 0.008 neg Deoxycholic acid C24H40O4 M-H 1.70 0.94 0.039 neg Topaquinone C9H9NO5 M-H2O-H 1.54 1.07 0.046 neg Allocholic acid C24H40O5 M-H 2.20 0.92 0.012 neg 1,10-Bisaboladiene-3,4-diol C15H26O2 M + FA-H 1.34 1.04 0.008 neg Triptohypol F C31H52O2 M + FA-H 1.39 0.96 0.043 neg Malathion monocarboxylic acid C8H15O6PS2 M + FA-H 1.35 0.96 0.032 neg 20-Dihydrodydrogesterone C23H34 M + Na-2H 2.51 1.20 0.023 neg 7a,17-dimethyl-5b-Androstane- C21H36O2 M + FA-H 1.45 0.96 0.046 neg 3a,17b-diol Taurochenodeoxycholate-7-sulfate C26H45NO9S2 M-2H 2.54 1.10 0.000 neg Acetyl-DL-Valine C7H13NO3 M-H 1.08 1.03 0.036 neg Hydantoin-5-propionic acid C6H8N2O4 M-H 2.00 0.92 0.009 neg Asparaginyl-hydroxyproline C9H15N3O5 M + FA-H 6.25 0.53 0.001 neg Gamma-D-glutamylglycine C7H12N2O5 M-H 1.47 0.97 0.001 neg D-Apiose C5H10O5 M-H 1.75 1.07 0.020 neg Hexadecanedioic acid C16H30O4 M-H 1.61 1.05 0.002 neg L-Glutamate C5H9NO4 M-H 1.04 1.02 0.048 neg microbiota and the metabolite profile of mice with SCI, thus alleviating neuropathology and promoting motor thereby improving intestinal and neurological function recovery in the animals [18]. The same study also demon - [26]. A different study reported that the feeding of com - strated that lactic acid supplementation facilitates func- mercial probiotics to mice promoted anti-inflammatory tional recovery after SCI. These results suggested that the responses by increasing the number of regulatory T cells, gut microbiome is a key regulator of SCI pathogenesis. The Kong  et al. Molecular Brain (2023) 16:26 Page 11 of 14 Fig. 5 Analysis of the correlation among the gut microbiota, serum metabolites, and clinical parameters. a Heatmap of the correlations between differentially abundant species and metabolites (associations between 41 differentially abundant serum metabolites and the 20 most abundant genera). b Heatmap of the correlations between the gut microbiome and clinical parameters. c Heatmap of the correlations between metabolites and clinical parameters modulation of the gut microbiome and derived metabolites abundance of some constituents of the gut microbiome in SCI patients is expected to reduce functional impair- was reported to be altered in patients with SCI [29]. In ment and promote nerve regeneration. a Chinese cohort study involving patients with chronic At the phylum level, the gut microbiome primarily con- traumatic complete SCI, Zhang et  al. observed that the sists of Firmicutes, Bacteroidetes, Actinobacteria, Pro- proportions of Proteobacteria and Verrucomicrobia were teobacteria, and Verrucomicrobia [27]. Firmicutes and increased, while that of Bacteroidetes was decreased [6]. Bacteroidetes were reported to account for approximately Another clinical study that enrolled 54 Turkish people (41 90% of all bacteria in the gut [28]. These bacteria fer - patients with SCI and 13 healthy individuals) reported ment indigestible polysaccharides and produce metabo- that SCI patients had a significantly reduced abundance lites that can be used as energy by the host. The relative of Firmicutes compared with healthy individuals [17]. Kong et al. Molecular Brain (2023) 16:26 Page 12 of 14 However, results from animal experiments have indicated UBA1819 abundance observed in patients with SCI was that the abundance of Firmicutes is decreased and that of positively correlated with injury duration, but negatively Bacteroidetes increased in mice with SCI and that Fir- correlated with motor function. Importantly, the abun- micutes is positively correlated with motor recovery [14, dance of UAB1819 was positively correlated with serum 30]. Similarly, the number of Bacteroidetes was reported uridine levels. UBA1819 belongs to the family Rumino- to be increased in mice with acute or subacute SCI [18, coccaceae [35], the members of which produce SCFAs 31]. Consistent with these studies, we also found signifi - that help maintain the health of the digestive tract [36]. cant changes in the composition of the gut microbiome It has been shown that Ruminococcaceae is enriched in in patients with SCI, namely, the abundance of Firmicutes behavior-deficient mice and is closely associated with was found to be decreased, whereas that of Synergistota anxiety-like behavior [37]. In our study, we found that was increased, in SCI patients. Our results related to the UBA1819 abundance was increased after SCI, which changes in gut microbiota composition after SCI are not may be associated with chronic motor dysfunction entirely consistent with those of previous studies. Possi- and depression. Uridine is a precursor of the pyrimi- ble reasons for this include (1) differences in the sever - dine nucleotides necessary for RNA synthesis as well as ity of SCI, such as between complete and incomplete an essential starting material for many metabolic pro- injury [32]; (2) differences in diet, environment, and gut cesses. Uridine has many critical biological functions. microbe composition among individuals; (3) differences For instance, it has been reported that uridine can reduce in antibiotic intake; and (4) experimental bias. To reduce inflammation and oxidative stress levels [38], reduce the influence of confounding factors on our results, the cytotoxicity [39], and improve neurophysiological func- two groups of participants enrolled in our study were tions [40, 41]. Uridine can also influence regeneration matched for gender and age and all originated from the in a variety of mammalian tissues and promote stem cell same region. Additionally, dietary management was activity [42]. These observations imply that UBA1819 standardized before sampling. may play a role in SCI by influencing uridine metabolism The gut microbiota regulates several key metabolic and that countering oxidative stress and promoting nerve processes in the body; accordingly, gut microbiota imbal- regeneration may serve to ameliorate the pathology of ance is a major contributor to metabolic disorders in the SCI. host [27].  Clinical studies and studies involving animals In addition to uridine, hypoxanthine and kojic acid, have demonstrated that changes in the gut microbiome identified as being differentially abundant in this study, after SCI are closely linked to metabolic abnormalities. are also closely related to neurological diseases.  Hypox- For instance, changes in the gut microbiome of patients anthine increases the expression of proinflammatory with thoracic SCI were reported to be correlated with cytokines and that of NF-κB, decreases nitrite levels, and differences in serum biomarker levels, suggesting that induces microglia and astrocyte activation [43]. Hypox- gut dysbiosis was associated with multiple metabolic anthine and kojic acid have both been associated with the processes [32]. It has also been suggested that changes induction of oxidative stress. Moreover, kojic acid was in the abundance of Bacteroidetes and Firmicutes after reported to exert a significant inhibitory effect on glial SCI may cause or lead to chronic metabolic disorder [32, cell activation and the release of inflammatory factors 33]. Moreover, studies have shown that people with para- [44, 45]. These metabolites may serve as important thera - plegia or quadriplegia have significantly impaired meta - peutic targets for SCI, a possibility that will be examined bolic function and higher body fat content than healthy in subsequent studies. people [33, 34]. Similar to the results of previous stud- Despite the importance of our findings, this study nev - ies, our untargeted metabolomics analysis indicated that ertheless had several limitations. The first was that it is the serum metabolite profile was significantly altered not possible to eliminate the influence of confounding in patients with SCI relative to that in the healthy con- factors given that the factors that can affect the composi - trols and that this was correlated with changes in the gut tion of the gut microbiota and metabolite abundance are microbiome. extremely complex. The second limitation of this study was In our study, 68 differentially abundant microbial that, although the results indicated that the sampling of the strains and 41 differentially expressed metabolites were microbial community was adequate, the sample size was selected for association analysis and a combined analy- still small. The third limitation was the lack of verification sis with clinical indicators. The abundance of UBA1819, of the differential flora and metabolite abundance. How - Lachnospiraceae, Blautia, and Akkermansia was ever, this study was the first to use serum metabolomics found to be significantly and positively correlated with combined with the gut microbiota to study the relationship that of serum uridine, kojic acid, hypoxanthine, and between the intestinal environment and clinical parameters PC(18:2/0:0), respectively. Additionally, the increase in in patients with SCI. Importantly, we found that uridine, Kong  et al. Molecular Brain (2023) 16:26 Page 13 of 14 2. Trgovcevic S, Milicevic M, Nedovic G, Jovanic G. Health condition and glutamate, hypoxanthine, and kojic acid are closely related quality of life in persons with spinal cord injury. Iran J Public Health. to SCI and represent potentially important targets for the 2014;43:1229–38. treatment of this condition. Our data provided a novel per- 3. Benevento BT, Sipski ML. Neurogenic bladder, neurogenic bowel, and sexual dysfunction in people with spinal cord injury. 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The gut microbiota and metabolite profiles are altered in patients with spinal cord injury

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10.1186/s13041-023-01014-0
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Abstract

Background Metabolites secreted by the gut microbiota may play an essential role in microbiota–gut–central nerv- ous system crosstalk. In this study, we explored the changes occurring in the gut microbiota and their metabolites in patients with spinal cord injury (SCI) and analyzed the correlations among them. Methods The structure and composition of the gut microbiota derived from fecal samples collected from patients with SCI (n = 11) and matched control individuals (n = 10) were evaluated using 16S rRNA gene sequencing. Addi- tionally, an untargeted metabolomics approach was used to compare the serum metabolite profiles of both groups. Meanwhile, the association among serum metabolites, the gut microbiota, and clinical parameters (including injury duration and neurological grade) was also analyzed. Finally, metabolites with the potential for use in the treatment of SCI were identified based on the differential metabolite abundance analysis. Results The composition of the gut microbiota was different between patients with SCI and healthy controls. At the genus level, compared with the control group, the abundance of UBA1819, Anaerostignum, Eggerthella, and Entero- coccus was significantly increased in the SCI group, whereas that of Faecalibacterium, Blautia, Escherichia–Shigella, Agathobacter, Collinsella, Dorea, Ruminococcus, Fusicatenibacter, and Eubacterium was decreased. Forty-one named metabolites displayed significant differential abundance between SCI patients and healthy controls, including 18 that were upregulated and 23 that were downregulated. Correlation analysis further indicated that the variation in gut microbiota abundance was associated with changes in serum metabolite levels, suggesting that gut dysbiosis is an important cause of metabolic disorders in SCI. Finally, gut dysbiosis and serum metabolite dysregulation was found to be associated with injury duration and severity of motor dysfunction after SCI. Conclusions We present a comprehensive landscape of the gut microbiota and metabolite profiles in patients with SCI and provide evidence that their interaction plays a role in the pathogenesis of SCI. Furthermore, our findings sug- gested that uridine, hypoxanthine, PC(18:2/0:0), and kojic acid may be important therapeutic targets for the treatment of this condition. Keywords Spinal cord injury, Gut microbiota, 16S rRNA gene sequencing, Untargeted metabolomics † 3 Ganggang Kong and Wenwu Zhang contributed equally to this work Department of Neurosurgery, The First Affiliated Hospital of Sun Yat-Sen University, No. 58, Zhong Shan Er Lu, Guangzhou 510080, Guangdong, *Correspondence: China Baoshu Xie Department of Anesthesiology, Bazhong Central Hospital, Bazhong, xiebsh3@mail.sysu.edu.cn China Guangdong Provincial Key Laboratory of Orthopedics and Traumatology, Department of Spinal Surgery, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China Department of Rehabilitation Medicine, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecom- mons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Kong et al. Molecular Brain (2023) 16:26 Page 2 of 14 microbiome becomes dysregulated after SCI, which exac- Introduction erbates nerve injury and spinal cord pathology [18, 19]. Spinal cord injury (SCI), usually resulting from severe Dysregulation of the gut microbiome activates the TLR4/ trauma such as falls and traffic accidents, is one of the Myd88 signaling pathway, which has been reported to most severe forms of injury to the central nervous system aggravate SCI [19]. (CNS). SCI is associated with a high rate of disability and The exact mechanisms by which gut dysbiosis regulates serious complications [1, 2]. Patients with SCI typically SCI and the biological mediators of these effects remain present with severe neurogenic intestinal dysfunction largely unknown. Studies have shown that short-chain due to intestinal denervation [3]. In addition to changes fatty acids (SCFAs), the primary metabolites produced by in bowel habits, such as the occurrence of constipation bacterial fermentation of dietary fiber, may regulate brain and diarrhea, the gut microbiota of patients with SCI is function through immune, endocrine, vagal, and other also significantly disturbed, which has a marked impact humoral pathways [20]. In addition, it has been reported on the quality of life of affected individuals [4]. The rela - that the metabolite 4-ethylphenyl sulfate influences the tionship between SCI and intestinal dysfunction has been behavior of mice by affecting oligodendrocyte func - extensively studied with the aim of developing novel and tion and myelin patterning [21]. These findings suggest effective management methods for intestinal impair - that gut microbiome-derived metabolites are important ment. The CNS affects intestinal function via several mediators of microbiota–gut–brain crosstalk. However, mechanisms, including the regulation of intestinal motil- relatively few studies have investigated the changes in ity, intestinal transport time, intestinal permeability, and overall metabolite abundance in patients with SCI. hormone secretion [5]. Consequently, abnormal gastroin- In this study, we conducted an omics analysis of gut testinal function in patients with SCI can lead to changes microbiota structure in patients with SCI as well as a in intestinal permeability and, consequently, the migra- quantitative analysis of metabolites in serum samples. tion of intestinal bacteria to the bloodstream and dysbi- We further examined the relationship between the gut osis [6]. Although the gut microbiome is thought to be microbiota and changes in serum metabolites. confined to the intestinal lumen, it has been shown that it can also modulate the function of distant organs [7]. Materials and methods These observations highlight the importance of investi - Study design and sample collection gating the relationship between SCI and gut microbiota Eleven patients with SCI and 10 healthy individuals were composition. recruited for this study. The inclusion criteria for the The gut microbiota is characterized as a collection of SCI group were (1) cervical or thoracic SCI confirmed microorganisms that colonize the digestive tract. It is an via medical history and imaging examination; (2) aged indispensable “microbial organ” in the human body and 14  years or more; and (3) American Spinal Cord Injury plays a vital role in the health of the host, including in the Association (ASIA) neurological function scale ranging CNS [8–11]. For decades, studies investigating the pos- from A to D. The exclusion criteria were the presence sible impact of microbes and viruses in SCI have been of cauda equina injury,  infectious disease, severe diges- largely constrained by technical limitations; however, tive disease, tumor, diabetes, immune metabolic disease, with the development of gut microbiota sequencing tech- craniocerebral injury, and mental disorder; and patients nology, the correlation between the complex gut micro- receiving antibiotics or probiotics one month before the biome and the CNS has been gradually revealed [12]. The study. Healthy individuals (age > 14 years) were recruited gut microbiome and its metabolites regulate the normal based on the same exclusion criteria. development of the CNS, such as blood–brain barrier Data relating to gender, age, ASIA grade, injury dura- formation, myelination, neurogenesis, and microglia tion, and site of enrolment were collected. Neurologi- maturation [13, 14]. The gut microbiome produces neu - cal function grade was based on the standardized ASIA roactive metabolites that can cross the intestinal barrier Impairment Scale, as previously described [22]. and enter the systemic circulation, where they can influ - ence neural activity and promote neuroinflammation [15, Sample collection and preparation 16]. A balanced microbiome is critical for its symbiotic Approximately 10 g of fresh stool samples were collected relationship with the host. Dysbiosis occurs when the from SCI patients and healthy individuals using sterile composition of the gut microbiome changes, particularly plastic spoons and placed in test tubes. Fresh stool sam- when there are fewer non-pathogenic bacteria or more ples were frozen at − 80  °C for 16S rRNA gene sequenc- pathogenic, proinflammatory bacteria. Evidence gar - ing within 2 h of collection. Venous blood samples (2 mL) nered over recent years has indicated that gut dysbiosis were obtained from patients with SCI and healthy indi- is related to secondary injury and the clinical symptoms viduals and centrifuged at 3,000 × g for 10 min for serum of SCI [6, 17]. Studies in mice have shown that the gut Kong  et al. Molecular Brain (2023) 16:26 Page 3 of 14 extraction. The serum samples were stored at − 80  °C for change [FC] and T-tests) analysis. Mean metabolite con- UPLC–Q–TOF/MS analysis. The data were analyzed on centrations in each group were used to calculate FC val- the free online Majorbio cloud platform (www. major bio. ues. Differentially expressed metabolites were identified com). using variable importance in projection (VIP) scores > 1 and p < 0.05 as criteria. 16S rRNA amplicon sequencing Total genomic DNA was extracted from each sample and Correlation analysis purified using the cetyltrimethyl ammonium bromide Correlations among the abundance of gut microbiota at (CTAB) method [23]. DNA purity and concentration the genus level, the levels of serum metabolites, injury were determined by agarose gel electrophoresis. The V3– duration, and neurological grading were visualized as V4 region of the 16S rRNA gene was PCR amplified using a heatmap constructed using SCIPY (Python; Version specific primers. PCR was performed in a 30-μL reaction 1.0.0). volume, which included 15 μL of Phusion High-Fidelity PCR Master Mix (New England Biolabs, Ipswich, MA, Statistical analysis USA), 0.2  μM of each of the forward and reverse prim- Data were analyzed using SPSS, version 22 (IBM, ers, and 10 ng of template DNA. The PCR products were Armonk, NY, USA). Continuous variables were expressed analyzed using 2% agarose gel electrophoresis, purified as means ± standard deviation and independent samples with an AxyPrepDNA Gel Extraction Kit (Axygen Biosci- t-tests were employed for comparisons between groups. ence, Union City, NJ, USA), and subjected to paired-end Categorical variables were expressed as rates and the chi- sequencing on an Illumina MiSeq/HiSeq 2500 platform. square test was used for comparisons between groups. Reads were clustered into operational taxonomic units p-values < 0.05 were considered significant. (OTUs) at a 97% sequence similarity threshold based on Ribosomal Database Project (RDP) classification. Beta Results diversity analysis, including principal component analy- Baseline data for the two groups sis (PCA), principal coordinate analysis (PCoA), and par- The participants, all from Guangdong Province, had tial least squares discriminant analysis (PLS-DA), was similar dietary habits and were given standard dietary performed with the Quantitative Insights into Microbial guidance for three days before the study. There were no Ecology (QIIME) software package. Differences between significant differences in age and gender between the SCI groups were analyzed using T-tests, linear discriminant group and the control group, which minimized the influ - analysis (LDA) effect size (LEfSe), and analysis of similar - ence of confounding factors on the study results. Detailed ity (ANOSIM). data for the SCI patients are shown in Table 1. Serum metabolomics The gut microbiota profiles of the two groups Serum samples (100 µL) were centrifuged at 14,000×g for To investigate whether the gut microbiota profile was 20  min at 4 ℃ with an equal volume of pre-cooled ace- changed in patients with SCI, 16S rRNA gene sequenc- tonitrile/methanol (1:1, v/v) and the supernatant was col- ing was performed on fecal samples from both the SCI lected. For LC–MS analysis, the samples were separated and Control groups. A total of 1,900,745 sequences were using ultra-high-performance liquid chromatography obtained. The OTU similarity level for index assessment (UHPLC, 1290 Infinity LC, Agilent Technologies, Santa was 97%. The richness and evenness of the gut microbi - Clara, CA, USA). Electrospray ionization (ESI) was used ome of the two groups were analyzed using rank–abun- for detection in both positive and negative ion modes. dance curves (Fig. 1a). The rarefaction curve had obvious Mass spectrometric analysis and metabolite identifica - asymptotes, the OUT coverage was 98.98% (Fig. 1b), and tion were performed using an Agilent 6550 iFunnel Q– the core species curve had leveled off (Fig.  1c). These TOF spectrometer (Agilent Technologies, Santa Clara, results indicated that the community was adequately CA, USA) and a Triple TOF 6600 mass spectrometer sampled. Beta diversity analysis (PCoA and PLS-DA) (SCIEX, Framingham, MA, USA), respectively. results showed a significant separation of the gut micro - The raw data were converted to the mzXML format biota between the SCI and Control groups (Fig.  1d, e). using ProteoWizard (http:// prote owiza rd. sourc eforge. ANOSIM analysis demonstrated that the gut microbiota net/) and imported into XCMS software for further anal- composition of the two groups was statistically different, ysis, including retention time correction, peak alignment, suggesting that SCI induced gut dysbiosis (Fig. 1f ). and picking. Following Pareto-scaling preprocessing, the Further analysis was performed at different taxo - data were subjected to multivariate (PCA, PLS-DA, and nomic levels based on the annotated species results. orthogonal PLS-DA [OPLS-DA]) and univariate (fold Firmicutes, Actionbacteriota, Bacteroidetes, and Kong et al. Molecular Brain (2023) 16:26 Page 4 of 14 Table 1 Comparison of baseline data between patients with SCI and healthy controls Control (n = 10) SCI (n = 11) Statistics p-value Year 40.70 ± 14.41 49.00 ± 20.51 t = − 1.062 0.301 Gender (male/female) 6/4 6/5 – 1.000 Injury duration (months) 0 22.81 ± 1.15 t = 0.836 0.405 Injury site Cervical cord NA 5 Thoracic cord NA 6 ASIA grade A 0 4 B 0 0 C 0 3 D 0 4 E 10 0 Proteobacteria were the most abundant phyla among The serum metabolite profile of both groups the gut microbiota of both groups (Fig.  2a). In addi- To determine the extent of metabolic disorder result- tion, compared with the healthy controls, the abun- ing from SCI, untargeted metabolomics analysis was dance of Synergistota was significantly increased in used to evaluate the differences in metabolite abundance patients with SCI, whereas that of Firmicutes was sig- between serum samples of the SCI group (n = 10) and nificantly decreased (Fig.  2b). At the genus level, the those of the Control group (n = 10). In the metabolic pro- abundance of UBA1819 (LDA = 4.54) and Eggerthella files of all the samples, 5,039 positive and 4,894 negative (LDA = 3.88) was significantly increased in SCI patients model features were identified. As shown in Fig.  3a, when relative to that in the healthy controls. The results also the relative standard deviation (RSD) was < 0.3, the peak showed marked decreases in the abundances of Blautia proportion was > 70%, indicating that the sample size was (LDA = 4.51), Faecalibacterium (LDA = 4.57), Escheri- appropriate. A  comprehensive multivariate statistical chia–Shigella (LDA = 4.41), Agathobacter (LDA = 4.13), analysis of cations and anions was undertaken using PLS- Collinsella (LDA = 4.04), Dorea (LDA = 3.88), Rose- DA and OPLS-DA. In the PLS-DA (Fig. 3b, c) and OPLS- buria (LDA = 3.97), Lachnospiraceae_ NK4A136 group DA (Fig.  3e, f ) score plots, a significant separation was (LDA = 3.82), Fusicatenibacter (LDA = 3.80), Holde- observed between the Control and SCI groups, indicat- manella (LDA = 3.87), Ruminococcus (LDA = 3.81), ing that SCI led to metabolic dysfunction. Furthermore, UCG-002 (LDA = 3.74), and Clostridia_UCG-014 permutation tests showed that the PLS-DA (Fig. 3d) and (LDA = 3.84) (Fig . 2c). OPLS-DA (Fig. 3g) patterns had good reliability. To further determine the specific gut microbiota A total of 1511 differential metabolites (p < 0.05, VIP components associated with SCI, LEfSe analysis was score > 1) were detected. Furthermore, 41 named differ - used to identify the gut microbiota components of ential metabolites were quantified. Forty-one metabo - both groups. The results revealed 68 components lites exhibited significant differential abundance between with different classification levels, 20 of which were the SCI patients and healthy controls, 18 of which were enriched in SCI patients and 48 in the Control group upregulated and 23 downregulated (Fig.  4a). The differ - (LDA > 3;  p < 0.05, Fig .  2d, e). Classification results entially abundant metabolites are listed in Table  2. The showed that the 20 species enriched in the SCI group metabolites exhibiting significant differential abundance belonged to the phyla Firmicutes (n = 16), Proteobacte- between the two groups are shown in the cluster heat- ria (n = 2), Actinobacteriota (n = 1), and Bacteroidota map in Fig. 4b. (n = 1), while the 48 species enriched in the Control group belonged to the phyla Firmicutes (n = 36), Proteo- Differential metabolites and KEGG pathway enrichment bacteria (n = 6), Actinobacteriota (n = 4), and Bacteroi- analysis dota (n = 2). The correlation between fecal microbiota We next applied KEGG pathway enrichment analysis to the structure and fecal samples is shown in Fig. 2f. 41 named differential metabolites. The results suggested Kong  et al. Molecular Brain (2023) 16:26 Page 5 of 14 Fig. 1 Detection of fecal sample quality and differences in gut microbiota composition between groups. a Rank–abundance curves for fecal samples from the control and spinal cord injury (SCI) groups. The abscissa represents the rank of the number of operational taxonomic units (OTUs) and the ordinate represents the relative percentage of OTU number. b Sobs index of rarefaction curves at the OTU level between the two groups of samples detected using a 97% similarity threshold. c Core curves. The horizontal axis represents the number of observed samples and the vertical axis represents the number of all core species at the OTU level. d Principal coordinate analysis (PCoA) score plots. e Partial least squares discriminant analysis (PLS-DA) score plots. f Weighted UniFrac distances Kong et al. Molecular Brain (2023) 16:26 Page 6 of 14 that the altered metabolites were mainly related to amino the abundance of Dorea (C = − 0.691, p = 0.001), Blau- acid metabolism, digestive system, nucleotide metabolism, tia (C = 0.575, p = 0.006), Faecalibacterium (C = − 0.618, and membrane transport (Fig. 4c). The 41 metabolites were p = 0.003), Agathobacter (C = 0.652, p = 0.001), and Col- enriched in 20 KEGG pathways (p < 0.05). Histidine metab- linsella (C = 0.646, p = 0.002). Meanwhile, injury duration olism: M and FoxO signaling pathway: EIP were the two was significantly and negatively correlated with the level most significantly enriched pathways (p < 0.01, Fig. 4d). of 2-methylbutyroylcarnitine (C = − 0.730, p = 0.000) and deoxycholic acid (C = 0.642, p = 0.002). The overall correla - Analysis of the correlations among gut dysbiosis, altered tion results suggested that gut microorganisms UBA1819, serum metabolites, and clinical parameters Dorea, Faecalibacterium, Agathobacter, and Collinsella Spearman’s correlation was used to investigate the rela- and the metabolites kojic acid, 12-hydroxydodecanoic tionship between gut microbiota and metabolites. The acid, gamma-D-glutamylglycine, asparaginyl-hydroxypro- relationship between the 20 most differentially expressed line, and allocholic acid were associated with neurological metabolites and the 20 most differentially abundant gut grade. The results further suggested that the gut microbes microbiota at the genus level was analyzed in patients with UBA1819, Dorea, Blautia, Faecalibacterium, Agathobacter, SCI (Fig.  5a). Significant correlations were found between and Collinsella and the metabolites 2-methylbutyroylcarni- UBA1819 and uridine (C = 0.609, p = 0.004), Lachno- tine and deoxycholic acid were related to injury duration. spiraceae and hypoxanthine (C = 0.595, p = 0.006), Blautia and PC (18:2/0:0) (C = 0.659, p = 0.002), and Akkermansia Discussion and kojic acid (C = 0.628, p = 0.003). Meanwhile, the abun- In this study, we compared the gut microbiota and serum dance of Akkermansia was significantly and negatively metabolites of patients with SCI and healthy individu- correlated with that of (5-{8-[1-(2,4-dihydroxyphenyl)- als using 16S rRNA sequencing and metabolomics. The 3-(3,4-dihydroxyphenyl)-2-hydroxypropyl]-3,5,7-trih- patients and the controls recruited for our study were age- ydr oxy -3,4-dihydr o-2H-1-b enzopy ran-2- yl}-2-hy - and gender-matched and were from the same geographi- droxyphenyl) oxidanesulfonic acid in serum (C = 0.609, cal location. Despite these similarities, we found that gut p = 0.004). microbiota and metabolite composition was significantly To explore the clinical significance of gut microbiome altered in SCI patients compared with that in the con- and metabolite dysregulation in patients with SCI, the trols, and the changes were correlated. The main findings putative correlations among gut microbiota abundance, of our study were as follows: (1) The composition of the metabolite abundance, and clinical parameters (includ- gut microbiota differed between SCI patients and healthy ing injury duration and neurological grade) were analyzed individuals, suggesting that SCI induced gut dysbiosis; (2) using Spearman’s correlation (Fig.  5b, c). We found that the serum metabolite profile of patients with SCI differed neurological grade was significantly and positively corre - from that of healthy individuals, implying that SCI patients lated with the abundance of Dorea (C = 0.655, p = 0.001), experience metabolic abnormalities; (3) the gut microbiota Faecalibacterium (C = 0.587, p = 0.005), Agathobacter dysregulation that occurs following SCI is closely related (C = 0.567, p = 0.007), and Collinsella (C = 0.575, p = 0.006) to the changes in the serum metabolite profile as well as and significantly and negatively correlated with the abun - clinical parameters (including injury duration and neuro- dance of UBA1819 (C = − 0.709, p = 0.000). Simul- logical grade). The metabolomics analysis identified many taneously, neurological grade was identified as being metabolites that are closely associated with CNS diseases significantly and positively correlated with the level of and may be important therapeutic targets for the treatment 12-hydroxydodecanoic acid (C = 0.455, p = 0.001), gamma- of SCI, a notion that merits further investigation. D-glutamylglycine (C = 0.712, p = 0.000), asparaginyl- Increasing evidence, both basic and clinical, has indi- hydroxyproline (C = 0.613, p = 0.004), and allocholic acid cated that the gut microbiome is involved in regulating a (C = 0.581, p = 0.007) and significantly and negatively cor - variety of cellular and molecular processes under both related with the level of kojic acid (C = − 0.617, p = 0.004). physiological and pathological conditions. The gut micro - In addition, injury duration showed a significant positive biome and its metabolites can migrate from the gut to correlation with the abundance of UBA1819 (C = 0.660, the intestinal wall and cross the intestinal barrier, thereby p = 0.001) and a significant negative correlation with promoting inflammation and affecting other organs [24]. (See figure on next page.) Fig. 2 Alterations in the gut microbiota at the phylum and genus levels between the two groups. a The six most abundant species at the phylum level in the two groups. b, c Microbiota displaying significantly different abundances at the phylum and genus levels (*p < 0.05, **p < 0.01, ***p < 0.001). d A cladogram of linear discriminant analysis (LDA) effect size (LEfSe) results in the Control and spinal cord injury (SCI) groups. e Histogram of the LDA scores calculated for a differential abundance of functional profiles in the two groups. A LDA score cutoff of 3.0 was used to indicate a significant difference. Different colors represent different groups. f Correlation between fecal microbiota structure and samples Kong  et al. Molecular Brain (2023) 16:26 Page 7 of 14 Fig. 2 (See legend on previous page.) Kong et al. Molecular Brain (2023) 16:26 Page 8 of 14 Fig. 3 Changes in serum metabolite abundance in the Control and spinal cord injury (SCI) groups. a Relative standard deviation (RSD) distribution plot. b, c Partial least squares discriminant analysis (PLS-DA) score plots in positive ion mode and negative ion mode, respectively. d Model verification map of PLS-DA (permutation test). e, f Orthogonal PLS-DA (OPLS-DA) score plots in positive ion mode and negative ion mode, respectively. g Model verification map of OPLS-DA (permutation test) Kong  et al. Molecular Brain (2023) 16:26 Page 9 of 14 Fig. 4 Differential metabolite extraction and KEGG pathway enrichment analysis. a Volcano plot of the differentially abundant metabolites. The abscissa is the multiple change value of the expression difference of metabolites between the two groups, and the ordinate is the statistical test value of the expression difference of metabolites (p-value). Each point in the figure represents a specific metabolite. b Heatmap of the differentially abundant metabolites between the two groups (variable importance in projection [ VIP] scores > 3, p < 0.05). The color represents the relative abundance of the metabolites in the samples. c Level 1 and 2 KEGG pathways related to the differentially expressed metabolites. The ordinate is the name of the level 2 pathway and the abscissa is the number of metabolites related to that pathway. Different colors represent different level 1 pathways. d KEGG pathway enrichment column chart. The abscissa is the name of the level 3 pathway. CP cellular processes, EIP environmental information processing, GIP genetic information processing, HD human diseases, M metabolism, OS organismal systems However, the precise mechanisms underlying these effects on the role of gut microbes in regulating CNS func- are unclear. Immune-, endocrine-, metabolism-, and neu- tion, especially in animal models. One study showed that rotransmitter-related pathways are considered to be the dysregulation of the gut microbiome is associated with main mechanisms via which the gut microbiota influence impaired functional recovery and increased anxiety-like the occurrence and development of CNS diseases. In the behavior after SCI [25]. Meanwhile, Jing et  al. found that last decade, an increasing number of studies have focused fecal microbiota transplantation can help restore the gut Kong et al. Molecular Brain (2023) 16:26 Page 10 of 14 Table 2 Differentially expressed serum metabolites between patients with SCI and healthy controls Metabolite Formula Adducts VIP FC p-value Mode PC(18:2/0:0) C26H50NO7P M + H-H2O, M + Na, M + H 1.05 0.99 0.037 pos Benzyl ethyl ether C9H12O M + H, M + NH4, M + H-H2O 3.71 1.28 0.004 pos Hypoxanthine C5H4N4O M + H-H2O, M + H 1.90 1.05 0.018 pos 3-Hexenoic acid C6H10O2 M + ACN + H, 2 M + K 2.59 0.87 0.021 pos L-Nicotianine C10H12N2O4 M + H-H2O 2.79 0.86 0.009 pos Indoleacetyl glutamine C15H17N3O4 M + H 2.45 0.84 0.043 pos Hypoglycin C7H11NO2 M + H 1.12 1.02 0.002 pos Pipericine C22H41NO M + H 1.04 1.02 0.023 pos Xestoaminol C C14H31NO M + H 1.93 0.96 0.001 pos 12-Hydroxydodecanoic acid C12H24O3 2M + NH4 1.40 0.98 0.000 pos MG(0:0/15:0/0:0) C18H36O4 M + NH4 4.37 0.78 0.000 pos 6-Hydroxypentadecanedioic acid C15H28O5 M + ACN + H 1.99 0.92 0.022 pos DL-2-Aminooctanoic acid C8H17NO2 M + H 2.23 0.92 0.004 pos 2-Methylbutyroylcarnitine C12H23NO4 M + H 1.30 0.97 0.009 pos Glycylglycylglycine C6H11N3O4 M + H-2H2O 1.57 1.05 0.012 pos Hydroxyprolyl-proline C10H16N2O4 M + H-H2O 2.60 1.13 0.003 pos Dopaquinone C9H9NO4 M + ACN + H 1.61 0.95 0.017 pos KOJIC ACID C6H6O4 M + H 1.28 1.02 0.000 pos 2-Amino-4-butanoic acid C19H25N3O7S M + 2Na-H 2.22 0.90 0.019 pos ZAPA C4H6N2O2S M + H 1.54 1.05 0.039 pos Oxidanesulfonic acid C30H28O14S M + 2Na-H 1.16 0.98 0.020 pos 13′-Carboxy-gamma-tocopherol C28H46O4 M-H, M + Na-2H 1.51 0.96 0.039 neg Glutamylvaline C10H18N2O5 M-H2O-H 3.02 1.16 0.001 neg Uridine C9H12N2O6 M-H 1.16 1.03 0.049 neg 3b,12a-Dihydroxy-5a-cholanoic acid C24H40O4 M + FA-H 2.17 0.91 0.008 neg Deoxycholic acid C24H40O4 M-H 1.70 0.94 0.039 neg Topaquinone C9H9NO5 M-H2O-H 1.54 1.07 0.046 neg Allocholic acid C24H40O5 M-H 2.20 0.92 0.012 neg 1,10-Bisaboladiene-3,4-diol C15H26O2 M + FA-H 1.34 1.04 0.008 neg Triptohypol F C31H52O2 M + FA-H 1.39 0.96 0.043 neg Malathion monocarboxylic acid C8H15O6PS2 M + FA-H 1.35 0.96 0.032 neg 20-Dihydrodydrogesterone C23H34 M + Na-2H 2.51 1.20 0.023 neg 7a,17-dimethyl-5b-Androstane- C21H36O2 M + FA-H 1.45 0.96 0.046 neg 3a,17b-diol Taurochenodeoxycholate-7-sulfate C26H45NO9S2 M-2H 2.54 1.10 0.000 neg Acetyl-DL-Valine C7H13NO3 M-H 1.08 1.03 0.036 neg Hydantoin-5-propionic acid C6H8N2O4 M-H 2.00 0.92 0.009 neg Asparaginyl-hydroxyproline C9H15N3O5 M + FA-H 6.25 0.53 0.001 neg Gamma-D-glutamylglycine C7H12N2O5 M-H 1.47 0.97 0.001 neg D-Apiose C5H10O5 M-H 1.75 1.07 0.020 neg Hexadecanedioic acid C16H30O4 M-H 1.61 1.05 0.002 neg L-Glutamate C5H9NO4 M-H 1.04 1.02 0.048 neg microbiota and the metabolite profile of mice with SCI, thus alleviating neuropathology and promoting motor thereby improving intestinal and neurological function recovery in the animals [18]. The same study also demon - [26]. A different study reported that the feeding of com - strated that lactic acid supplementation facilitates func- mercial probiotics to mice promoted anti-inflammatory tional recovery after SCI. These results suggested that the responses by increasing the number of regulatory T cells, gut microbiome is a key regulator of SCI pathogenesis. The Kong  et al. Molecular Brain (2023) 16:26 Page 11 of 14 Fig. 5 Analysis of the correlation among the gut microbiota, serum metabolites, and clinical parameters. a Heatmap of the correlations between differentially abundant species and metabolites (associations between 41 differentially abundant serum metabolites and the 20 most abundant genera). b Heatmap of the correlations between the gut microbiome and clinical parameters. c Heatmap of the correlations between metabolites and clinical parameters modulation of the gut microbiome and derived metabolites abundance of some constituents of the gut microbiome in SCI patients is expected to reduce functional impair- was reported to be altered in patients with SCI [29]. In ment and promote nerve regeneration. a Chinese cohort study involving patients with chronic At the phylum level, the gut microbiome primarily con- traumatic complete SCI, Zhang et  al. observed that the sists of Firmicutes, Bacteroidetes, Actinobacteria, Pro- proportions of Proteobacteria and Verrucomicrobia were teobacteria, and Verrucomicrobia [27]. Firmicutes and increased, while that of Bacteroidetes was decreased [6]. Bacteroidetes were reported to account for approximately Another clinical study that enrolled 54 Turkish people (41 90% of all bacteria in the gut [28]. These bacteria fer - patients with SCI and 13 healthy individuals) reported ment indigestible polysaccharides and produce metabo- that SCI patients had a significantly reduced abundance lites that can be used as energy by the host. The relative of Firmicutes compared with healthy individuals [17]. Kong et al. Molecular Brain (2023) 16:26 Page 12 of 14 However, results from animal experiments have indicated UBA1819 abundance observed in patients with SCI was that the abundance of Firmicutes is decreased and that of positively correlated with injury duration, but negatively Bacteroidetes increased in mice with SCI and that Fir- correlated with motor function. Importantly, the abun- micutes is positively correlated with motor recovery [14, dance of UAB1819 was positively correlated with serum 30]. Similarly, the number of Bacteroidetes was reported uridine levels. UBA1819 belongs to the family Rumino- to be increased in mice with acute or subacute SCI [18, coccaceae [35], the members of which produce SCFAs 31]. Consistent with these studies, we also found signifi - that help maintain the health of the digestive tract [36]. cant changes in the composition of the gut microbiome It has been shown that Ruminococcaceae is enriched in in patients with SCI, namely, the abundance of Firmicutes behavior-deficient mice and is closely associated with was found to be decreased, whereas that of Synergistota anxiety-like behavior [37]. In our study, we found that was increased, in SCI patients. Our results related to the UBA1819 abundance was increased after SCI, which changes in gut microbiota composition after SCI are not may be associated with chronic motor dysfunction entirely consistent with those of previous studies. Possi- and depression. Uridine is a precursor of the pyrimi- ble reasons for this include (1) differences in the sever - dine nucleotides necessary for RNA synthesis as well as ity of SCI, such as between complete and incomplete an essential starting material for many metabolic pro- injury [32]; (2) differences in diet, environment, and gut cesses. Uridine has many critical biological functions. microbe composition among individuals; (3) differences For instance, it has been reported that uridine can reduce in antibiotic intake; and (4) experimental bias. To reduce inflammation and oxidative stress levels [38], reduce the influence of confounding factors on our results, the cytotoxicity [39], and improve neurophysiological func- two groups of participants enrolled in our study were tions [40, 41]. Uridine can also influence regeneration matched for gender and age and all originated from the in a variety of mammalian tissues and promote stem cell same region. Additionally, dietary management was activity [42]. These observations imply that UBA1819 standardized before sampling. may play a role in SCI by influencing uridine metabolism The gut microbiota regulates several key metabolic and that countering oxidative stress and promoting nerve processes in the body; accordingly, gut microbiota imbal- regeneration may serve to ameliorate the pathology of ance is a major contributor to metabolic disorders in the SCI. host [27].  Clinical studies and studies involving animals In addition to uridine, hypoxanthine and kojic acid, have demonstrated that changes in the gut microbiome identified as being differentially abundant in this study, after SCI are closely linked to metabolic abnormalities. are also closely related to neurological diseases.  Hypox- For instance, changes in the gut microbiome of patients anthine increases the expression of proinflammatory with thoracic SCI were reported to be correlated with cytokines and that of NF-κB, decreases nitrite levels, and differences in serum biomarker levels, suggesting that induces microglia and astrocyte activation [43]. Hypox- gut dysbiosis was associated with multiple metabolic anthine and kojic acid have both been associated with the processes [32]. It has also been suggested that changes induction of oxidative stress. Moreover, kojic acid was in the abundance of Bacteroidetes and Firmicutes after reported to exert a significant inhibitory effect on glial SCI may cause or lead to chronic metabolic disorder [32, cell activation and the release of inflammatory factors 33]. Moreover, studies have shown that people with para- [44, 45]. These metabolites may serve as important thera - plegia or quadriplegia have significantly impaired meta - peutic targets for SCI, a possibility that will be examined bolic function and higher body fat content than healthy in subsequent studies. people [33, 34]. Similar to the results of previous stud- Despite the importance of our findings, this study nev - ies, our untargeted metabolomics analysis indicated that ertheless had several limitations. The first was that it is the serum metabolite profile was significantly altered not possible to eliminate the influence of confounding in patients with SCI relative to that in the healthy con- factors given that the factors that can affect the composi - trols and that this was correlated with changes in the gut tion of the gut microbiota and metabolite abundance are microbiome. extremely complex. The second limitation of this study was In our study, 68 differentially abundant microbial that, although the results indicated that the sampling of the strains and 41 differentially expressed metabolites were microbial community was adequate, the sample size was selected for association analysis and a combined analy- still small. The third limitation was the lack of verification sis with clinical indicators. The abundance of UBA1819, of the differential flora and metabolite abundance. 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Journal

Molecular BrainSpringer Journals

Published: Feb 20, 2023

Keywords: Spinal cord injury; Gut microbiota; 16S rRNA gene sequencing; Untargeted metabolomics

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