1. Introduction
Febrile seizure (FS) is a convulsion caused by a fever. It commonly occurs in children (6 months–6 years old), with a global prevalence of approximately 2% to 14%.[1] Notably, this period overlaps with the essential cranial nerve and motor development stages. Therefore, repeated FS accompanied by brain hypoxia may adversely affect the intelligence, learning/memory, and behavioral development of children.[2,3]
Recent evidence has linked the pathogenesis of nervous system diseases with intestinal flora, and researchers attach great importance to the impact of intestinal flora on the brain-gut axis.[4] Intestinal flora affects the progression of various disorders, such as Alzheimer’s disease, autism, and emotional disorders, by controlling brain functions via the brain-gut axis, neuroimmune system, and neuroendocrine system.[5,6] Metabonomics analyzes the changes of endogenous small molecule metabolites under the joint action of genetics, biology, and environment through high-throughput technology. Metabonomics, a noninvasive method using advanced analytical techniques to determine toxicity biomarkers,[7] enables high-throughput while characterizing the changes in endogenous small molecule metabolites from cells, tissues, or biofluids. It has been widely used in toxicity research, disease prediction, and efficacy evaluation.[8,9]
This study explored the changes in intestinal flora in children with FS by high-throughput sequencing of the 16S rDNA and liquid chromatography-mass spectrometry (LC-MS). We also analyzed the differences in the intestinal flora and metabonomics between healthy children and those with FS and identified the relationship between some exceptional flora and different metabolites and FS. These findings will further our knowledge of FS pathogenesis and contribute to improving FS prognosis.
2. Materials and methods
2.1. Research object and sample collection
Children (6 months–6 years old) meeting the FS diagnostic criteria in our hospital were randomly selected as the FS group (n = 15). Children with central nervous system infections, gastrointestinal dysfunctions, or genetic metabolic disorders were excluded. The participating children had no history of FS and had not received antibiotics or probiotics within the past 2 weeks.
The normal control (NC) group consisted of 15 healthy children (6 months–6 years old) who visited the Department of Children’s Healthcare in our hospital during the same period. The fecal samples of anal swabs from children in the FS and NC groups from June 2022 to November 2022 were collected and stored at −80℃ for further investigation. All recruited healthy children passed the physical examination and had no history of taking antibiotics or probiotics for 2 weeks before the recruitment. In addition, all participating children obtained informed consent from their guardians.
2.2. Sixteen S rDNA amplification and sequencing
The DNA was extracted from fecal samples using an E.Z.N.A. Soil DNA extraction kit (Promega), and the DNA purity and integrity were evaluated using a UV microspectrophotometer (ThermoFisher Scientific). The hypervariable region (V3–V4) of the bacterial 16S rRNA gene was amplified using specific primers (Forward: 5’-ACTCCTACGGGAGGCAGCAG-3’ Reverse: 5’-GGACTACHVGGGTWTCTAAT-3’)[10] in a thermocycler (GeneAmp 9700, Applied Biosystems) using the KAPA HiFi HotStart ReadyMix PCR kit (Roche) according to the manufacturer’s instructions. Subsequently, the products were separated by electrophoresis and extracted by an AxyPrep DNA gel Recovery Kit (Axygen Scientific). The qualified library quality was quantified using a Qubit fluorometer (ThermoFisher Scientific) and was subjected to sequencing in an Illumina MiSeq PE250 platform (Illumina). The difference in the intestinal flora abundance between the 2 groups was conducted by the Wilcoxon rank-sum test (https://metastats.cbcb.umd.edu/). Linear discriminant analysis (LDA) of effect size (LEfSe) was used to identify the most discriminant taxa between the 2 groups (LDA score ≥ 3).
2.3. Metabolomic analysis
As previously reported, fecal samples from each group (n = 6) were randomly selected for metabolomic analysis.[11] In brief, stool samples (50 mg) were solved in 400 µL methanol: water (4:1, v/v) solution containing 0.02 mg/mL L-2-chlorophenylalanin as internal standard. The mixtures were then ground by a tissue crusher (Wonbio-96c, Wanbo biotechnology, 50 Hz, 6 minutes, −10ºC) followed by ultrasonic homogenization (40 kHz, 30 minutes, 5ºC). Subsequently, the samples were settled at −20ºC for 30 minutes and centrifuged for 15 minutes (13,000 g, 4ºC). The cleared supernatants were collected and analyzed using a UHPLC-Q Exactive system (ThermoFisher Scientific) integrated with an electrospray ionization source in either negative or positive ion mode. The raw data were analyzed by Progenesis QI software (Waters Corporation) for baseline filtering, peak detection, integration, retention time correction, alignment, and sum normalization. Afterward, a data matrix containing mass/charge values and peak intensity was generated. The data were processed following the 80% rule, and the variables with relative standard deviation > 30% of QC samples were removed. The mass spectra of the metabolites were determined based on the exact mass number, and the metabolite profile was quantified using the human metabolome databases (http://www.hmdb.ca/), Metlin (https://metlin.scripps.edu/), and Majorbio Database (Majorbio Bio-Pharm Technology, https://could.majorbio.com). The standardized metabolomics data was uploaded to the Majorbio cloud for data analysis.
R package “ropls” (Version 1.6.2) was used to perform the principal component analysis to determine the overall sample distribution. The orthogonal least partial squares discriminant analysis (OPLS-DA) was used to compare the metabolite difference between groups, and a 7-cycle interactive validation was used to evaluate the model stability. Metabolites with the variable importance of the projection (VIP) generated from the OPLS-DA were employed to determine the differential metabolites (VIP > 1 and P < .01). The differential metabolites were mapped into their metabolic pathways through pathway enrichment analysis based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) mapper[12] (http://www.genome.jp/kegg/). These metabolites were classified according to their functions or the pathways involved, and scipy. stats (Python packages) (https://docs.scipy.org/doc/scipy/) was used to identify significantly enriched pathways using Fisher exact test. Spearman rank and Pearson coefficients were used to determine the correlation between environmental factors and selected species, and the results were visualized as a heatmap using the R package “pheatmap.” The color gradient is proportional to the data value.
2.4. Statistical analysis
Data were processed by SPSS 27.0 statistical software; the Student t test was used for pairwise comparisons; and variance multiple analysis was used for multiple comparisons. The value of P < .05 is considered statistically significant and indicated as asterisk marks (* P ≤ .05, ** P ≤ .01, *** P ≤ .001). Annotation of metabolites with the value of VIP >1 was validated using a reference MS spectrum of the Metlin database.
3. Results
3.1. Clinical characteristics of 2 cohorts
According to the inclusion and exclusion criteria, 15 children were included in the FS and NC groups, respectively. As shown in Table 1, there was no significant gender and age difference between the 2 groups.
Table 1 -
Patient information.
| Group |
Number |
Age |
Gender |
| Male |
Female |
| FS group |
15 |
5.63 ± 3.57 |
7 |
8 |
| NC group |
15 |
5.42 ± 2.34 |
8 |
7 |
| F |
|
2.62 |
2.34 |
2.51 |
|
P value |
|
>.05 |
>.05 |
>.05 |
FS = febrile seizure, NC = normal control.
3.2. Community structure analysis
Analysis at the phylum level revealed that the Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria are the dominant phyla of intestinal flora in both cohorts. However, the abundance is different among the 2 groups. The detailed results are shown in Figure 1. The intestinal Pathescibacteria in normal children is significantly higher than in children with FS (P = .02883), suggesting that the changes in Pathescibacteria may be related to FS.
;)
Figure 1.: Schematic graph showing the results of intestinal flora community structure analysis in the FS group and NC group. A1: FS group; A2: NC group. FS = febrile seizure, NC = normal control.
3.3. LEfSe analysis of samples from the FS group and NC group
The LDA LEfSe analysis was performed to identify the vital bacterial species involved in FS. The LDA score diagram shows that the intestinal flora in children from the FS and the NC groups is significantly different (Fig. 2). Amongst, the Bacteroides abundance increased significantly in the FS group. Meanwhile, we noticed that the Brevundimonas_diminuta, Brevundimonas, and Erysipelotrichale were highly abundant in healthy children. These findings suggest that Bacteroides are essential for FS initiation/progression, while Brevundimonas_diminuta, Brevundimonas, and Erysipelotrichale are critical in maintaining normal/healthy conditions.
Figure 2.: The Results of LEfSe analysis represented LAD scores demonstrating the bacterial species associated with children from different groups. A1: FS group; A2: NC group. FS = febrile seizure, LEfSe = linear discriminant analysis of effect size, NC = normal control.
3.4. The OPLS-DA score of fecal metabolic profile of children with FS and NCs
The OPLS-DA analysis was used to remove the classification-unrelated information, and the full spectrum of feces of children with FS and NCs was analyzed. The results showed that the 2 groups of samples could be significantly separated. Following the permutation test, the results indicated that the OPLS-DA model did not lead to overfitting, as the R2 and Q2 values were less than the original point. Moreover, the Q2 intercept was <0, specifically under the electrospray ion positive (+) mode (Fig. 3A), R2y = 0.366, Q2 = −0.361. Under the electrospray ion (−) mode (Fig. 3B), R2 and Q2 were 0.0895 and −0.705, respectively. The figure shows that children with FS and NC have a significant separation trend. Results also showed that the metabolic spectrum of children with FS was changed compared with that of normal children.
Figure 3.: Statistical validation of the OPLS-DA model in random permutation tests under ESI + (A) and ESI− (B) modes. ESI = electrospray ion, OPLS-DA = orthogonal partial least squares discriminant analysis.
3.5. Identification of potential biomarkers
LC-MS has become the mainstream technology in metabolomics research, primarily due to its direct analyzing potency on various biopsies, including body fluid and tissue extracts, and its capability of acquiring accurate molecular and structural information of compounds. We have identified 121 metabolites from the tested samples and 10 differential metabolites that were considered as potential FS markers, which are (Table 2): xanthosine, (S)-abscisic acid, N-palmitoylglycine, (+/−)-2-(5-methyl-5-vinyl-tetrahydrofuran-2-yl) propionaldehyde, (R)-3-hydroxybutyrylcarnitine, lauroylcarnitine, oleoylethanolamide, tetradecanoyl carnitine, taurine, and lysoPC (18:1 [9z]/0:0).
Table 2 -
Significantly changed
metabolites in fecal samples from the FS group.
| Name |
VIP_ PLS-DA |
FC (a1/a2) |
P value |
Regulate |
| Xanthosine |
2.8041 |
1.5281 |
.003835 |
Up |
| (S)-Abscisic acid |
3.1112 |
1.8337 |
.00799 |
Up |
| N-palmitoylglycine |
2.7459 |
1.5036 |
.005671 |
Up |
| (+/−)-2-(5-methyl-5-vinyl-tetrahydrofuran-2-yl) propionaldehyde |
2.6149 |
1.3696 |
.005328 |
Up |
| (R)-3-hydroxybutyrylcarnitine |
2.5038 |
1.357 |
.009318 |
Up |
| Lauroylcarnitine |
2.5437 |
1.5906 |
.009892 |
Up |
| Oleoylethanolamide |
1.8296 |
1.318 |
.002053 |
Up |
| Tetradecanoylcarnitine |
2.5148 |
1.3529 |
.0007826 |
Up |
| Taurine |
2.2467 |
1.3717 |
.001572 |
Up |
| LysoPC (18:1 [9z]/0:0) |
3.1032 |
1.5757 |
.0008164 |
Up |
FS = febrile seizure, VIP = variable importance of the projection.
3.6. Cluster analysis of FS biomarkers
Cluster analysis was performed on the LC/MS-identified metabolites to further interrogate the relationship between potential biomarkers and FS. First, a heatmap of the identified metabolites significantly different in healthy and FS children’s fecal samples indicated the clustering of the 2 cohorts (Fig. 4). Additionally, combined with cluster analysis, the metabolic map can be divided into 2 categories, consistent with the clinical diagnosis of these FS children. These findings demonstrate that metabolomics can be used for FS diagnosis by classifying FS and non-FS children.
Figure 4.: Heat map representing identified metabolites significantly different between the fecal samples from children with or without FS (n = 6). Groups are indicated at the bottom of the figure (A1: FS group; A2: normal control group). Correlation thermogram of FS group and normal control group. FS = febrile seizure.
3.7. KEGG enrichment analysis of differentially accumulated metabolites
KEGG enrichment analysis was conducted for differently accumulated metabolites identified from the fecal samples of children with FS. Those metabolites were enriched into 54 metabolic pathways (data not shown). KEGG topology analysis was performed based on the candidate pathways to dissect the potentially important metabolic pathways. The results are demonstrated in the bubble diagram (Fig. 5). The bubble sizes are proportional to the importance of the pathways, which was represented by the impact values. The results revealed 3 vital metabolic pathways: taurine and hypotaurine metabolism (map00430); glycine, serine, and threonine metabolism (map00260); and arginine biosynthesis (map00220).
Figure 5.: The results of the KEGG topology analysis are represented as bubble graphs. Each bubble represents 1 KEGG pathway. The X-axis depicts the metabolites importance in the pathway, and the Y-axis represents the pathway’s enrichment significance, which was displayed as -log10 (P value). The bubble size in the chart represents the impact values, and the color scales demonstrate the enrichment significance. KEGG = Kyoto encyclopedia of genes and genomes.
3.8. Correlation analysis
Correlation analysis was performed to explore the relationship between intestinal flora and the differentially accumulated fecal metabolomics. The results (Fig. 6) revealed that the Bacteroides, which is highly abundant in the FS group, is significantly correlated with the 4 metabolites which are concomitantly upregulated in the FS group [(s)-Abscisic acid (P < .05), lysoPC (P < .05), N-palmitoylglycine (P < .05), and (R)-3-hydroxybutyrylcarnitine] (P < .05). Interestingly, unclassified_k_norank_d_Bacteria was also significantly correlated with taurine (P < .05), (R)-3-hydroxybutyrylcarnitine (P < .01), lauroylcarnitine (P < .05), xanthosine (P < .01), tetradecanoylcarnitine (P < .05), and lysoPC (P < .01). However, the abundance of the unclassified_k_norank_d_Bacteria was negligible. Thus, this finding highlighted the clinical significance of the Bacteroides in children’s FS and revealed the diagnostic value of these metabolites.
Figure 6.: Heat map showing the correlation between intestinal flora and differentially accumulated fecal metabolites in children with FS. The color ranges on the right represent the R values, and the asterisks indicate statistical significance (* P ≤ .05, ** P ≤ .01, *** P ≤ .00). FS = febrile seizure.
4. Discussion
FS is one of the most common emergencies in pediatric departments. However, children from 6 months to 6 years old have a high incidence and recurrence rate due to factors such as young age, convulsion-accompanied hypothermia, long convulsion duration, and family history. Although most cases have an excellent prognosis, 20% to 30% of children can recur after the initial attack. The recurrence rate for children with high-risk factors can reach 75%.[13,14]
Intestinal flora co-exists with the host harmoniously and is equivalent to a functional organ playing essential roles in the human body. For instance, studies have demonstrated intestinal flora significant effects on central nervous system development.[15] Nevertheless, more importantly, accumulating evidence has associated intestinal flora with the occurrence and development of diseases,[16] such as childhood epilepsy,[17] attention deficit hyperactivity disorder,[18] and an autism spectrum disorder.[19] Specially, clinicians noticed that the component of intestinal flora are significantly different between healthy children and children with focal epilepsy, indicating the importance of intestinal microbiota.[20] Moreove, intestinal flora in children is also closely related with childhood development. It has been recently reported that modulating intestinal flora is a promising approach of improving bone mineral density and hight in infants, children, and adolescents.[21] In addition, importance of gut microbiota has also been attached to the childhood development of a variety of organs, such as brain, lung, as well as the immune system.[22] Therefore, a better understanding of the intestinal flora in children’s health and disease conditions will help us to hold the key to better clinical management.
This study showed that the intestinal microbial diversity in children with FS significantly differed from healthy children. The 2 groups share dominant intestinal flora: Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria at the phylum level. However, the abundance of these flora differs between groups. The gut microbiota, which is the most complex microbiota in the body, consists of approximately 500 bacterial species. The dominant phyla in the guy microbiota includ Firmicutes, Bacteroidetes, Actinobacteria, and Proteobacteria, which account for the vast majority of bacterial populations. The gut microbiota performs essential functions, including protecting the integrity of the gastrointestinal mucus barrier, regulating inflammatory mediators in the nervous system, and regulating the body’s immune defense.[23,24] For instance, the Patescibacteria in the FS group was significantly lower than that in the NC group. Our findings revealed a significant change in the diversity of intestinal flora in children with FS, which highlights the correlation between the intestinal microbiome and FS occurrence/severity. In addition, the LEfSe analysis indicated that Bacteroides played critical roles in the FS group, shedding light on the importance of intestinal flora in disease progression. In alignment with our observation, it was documented that the Actinobacteria abundance was significantly reduced while the abundance of the Bacteroides was dramatically increased in children with autism spectrum disorders.[25] Consistently, Bacteroides in children with FS also increased significantly in this study. Notably, Bacteroides are associated with the maturation, function, and morphology of microglia that participate in the steady state of the central nervous system. However, microglia are activated under stress or pathological conditions to produce inflammatory factors, such as tumor necrosis factor, interleukin-8, and interleukin-1, leading to neuron injury.[26–28]
Metabolomics systematically evaluates the dynamic changes in multiple metabolic activities by interrogating the bodily fluids.[29,30] This study used LC-MS to determine the metabonomics from human fecal samples and found that FS significantly changed the fecal metabolic spectrum resulting in the upregulation of 10 metabolites: xanthosine, (S)-abscisic acid, N-palmitoylglycine, (+/−)-2-(5-methyl-5-vinyl-tetrahydrofuran-2-yl) propionaldehyde, (R)-3-hydroxybutyrylcarnitine, lauroylcarnitine, oleoylethanolamide, tetradecyl carnitine, taurine, and lysoPC (18:1 [9z]/0:0). These elevated metabolites were considered potential FS markers. Subsequently, the bioinformatics approach enriched the differential metabolites in 54 metabolic pathways. Finally, 3 metabolic pathways stood out based on the KEGG topology analysis: taurine metabolism; glycine, serine, and threonine metabolism; and arginine biosynthesis.
Notably, taurine is an essential metabolism-related molecule that protects neurons from glutamate-mediated damage.[31] Studies show that taurine regulates the central nervous system function and modulates the intestinal micro-ecology.[32–34] Consistently, our study identified that taurine is abnormally accumulated in FS children, indicating its potential clinical value in FS treatment. Additionally, arginine has been documented to induce gut microbiota remodeling, which is closely related to brain development and function.[35,36] Moreover, studies have shown that abnormal glycine, serine, and threonine metabolism affects the body’s pyruvate metabolism, which is directly associated with oxidative stress and inflammatory status in the human body.[37] Furthermore, Bacteroides are significantly associated with 4 metabonomic differential metabolites: (S)-abscisic acid, lysoPC, N-palmitoylglycine, and (R)-3-hydroxybutyrylcarnitine. Although the unclassified_k_norank_d_Bacteria were also significantly correlated with multiple differential metabolites, their abundance in the intestinal flora limited their clinical significance.
Although this study has provided insights into the topic of FS in children, we cannot avoid the limitation of this study. The most significant limitation of this study is the small sample size, which may limit the generalizability of the findings to a larger population. To the best of our knowledge, the annual incidence is 0.35% in our region,[38] which resulted in the limited case number in this study. To address this limitation, our future study will aim to increase the sample size and consider recruiting participants from populations of other geographic districts. This will help us develop more reliable findings that can be generalized to larger populations.
5. Conclusions
This study found significant differences in intestinal microecological structure and metabonomics between children with FS and the NC groups. Adjusting the balance of intestinal flora may be an effective way to prevent and treat FS.
Author contributions
Data curation: Lin Yang.
Formal analysis: Lin Yang.
Funding acquisition: Jianmei Tian.
Investigation: Lin Yang.
Project administration: Lin Yang, Jianmei Tian.
Resources: Lin Yang.
Supervision: Lin Yang, Jianmei Tian.
Validation: Lin Yang.
Visualization: Jianmei Tian.
Writing – original draft: Lin Yang.
Writing – review & editing: Lin Yang.
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