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. 2024 Oct 10;56(1):2412834. doi: 10.1080/07853890.2024.2412834

Investigation of gut microbiota disorders in norovirus infected children patients based on 16s rRNA sequencing

Jie Li a,#, Nan Jiang b,#, Hui Zheng c, Xiao Zheng d, Yi Xu a, Yongqing Weng e, Feijian Jiang f, Chong Wang g, Peiliang Chang h,✉
PMCID: PMC11469441  PMID: 39387550

Abstract

Background

Norovirus is the leading cause of sporadic viral gastroenteritis cases and outbreaks. Gut microbiota plays a key role in maintaining immune homeostasis. We aimed to investigate the composition and functional effects of gut microbiota in children infected with norovirus.

Methods

Stool samples were collected from 31 children infected with norovirus and 25 healthy children. The gut microbiota was analyzed by 16S rRNA gene sequencing, followed by composition, correlation network, functional and phenotype prediction analyses.

Results

Gut microbiota in children infected with norovirus was characterized by lower species richness and diversity. Veillonella is the dominant gut microbiota specie in norovirus infection. Blautia was significantly lower in norovirus infection. There was a positive correlation between Faecalibacterium, Blautia, Subdoligranulum, Eubacterium_hallii_group, Fusicatenibacter, Agathobacter, Roseburia and Dorea. Functionally, secondary metabolites biosynthesis, transport and catabolism, selenocysteine lyase and peroxiredoxin were the most significantly higher functional compositions of gut microbiota in norovirus infection. However, sn-glycerol-1-phosphate dehydrogenase and fermentation were the most significantly lower functional compositions in norovirus infection group. Phenotype analysis showed that Contains_Mobile_Elements had the highest level of phenotypes in the gut microbiota of norovirus infection.

Conclusion

Norovirus infection may lead to dysregulation of the gut microbiome in children.

Keywords: Norovirus infection, gut microbiota, functional prediction, phenotype prediction

Background

Norovirus is responsible for the majority of gastroenteritis outbreaks [1]. The clinical symptoms of norovirus infection include vomiting, diarrhea, nausea, and fever [2]. The faecal-oral route is the major transmission mode [3–6]. Norovirus infection has severe complications in elderly and young individuals [7–9]. According to statistics, norovirus infection causes over 685 million illnesses and 212,000 deaths worldwide [10]. No antivirals or vaccines are available to prevent norovirus infection [11].

The microbiota is important for the maintenance of gastrointestinal physiology [12]. It has been reported that most norovirus-infected individuals showed significantly higher abundance of Proteobacteria and lower abundance of Bacteroidetes [13]. Thus, identification of potential gut bacteria after norovirus infection is needed. In view of this, 16S rRNA gene sequencing and other analysis was performed to study the composition and function of gut microbiome in children after norovirus infection in this study.

Methods

Patients

A power analysis was performed prior to sample collection using the pwr package in R. The parameters are as follows: (1) d = 0.8 (effect size); (2) sig.level = 0.05 (significance level); (3) power = 0.8 (power level); (4) alternative = two-sided (statistical test). The results showed that the number of samples required for each group was 25. In this study, 31 children infected with norovirus and 25 healthy children from Taizhou City, Zhejiang Province, China, were enrolled. All children infected with norovirus were diagnosed based on the result of real-time polymerase chain reaction (PCR) using the norovirus assay kit. The amplification curve for the norovirus GII gene showed norovirus positive. The detailed inclusion criteria of children infected with norovirus were as follows: (1) children were diagnosed based on the Chinese protocol for diagnosis and treatment of diarrhoeal diseases and amoebic dysentery (WS207-2008); (2) children under 14 years old; (3) children vomits within 24 h (watery or loose stools more than 3 times); (4) no blood or mucus in the stool. In addition, those children over 14 years of age and with malignant tumours or other pathogens infection were excluded from the study. Healthy children were recruited for the study. All samples were collected in the same season (Autumn). Samples relative to symptom onset/duration were collected at the time of seeking medical attention for relevant symptoms. Written informed consent was obtained from parents of all individuals. The Ethics Committee of Taizhou Municipal Hospital approved this study (2022-LWYJ-013). All methods were performed in accordance with the relevant guidelines and regulations.

Illumina MiSeq sequencing

In this study, 3–5 ml stool samples were collected and cryopreserved at −20 °C for Illumina MiSeq sequencing. In the process, positive and negative controls were run during sequencing and analyzed. The genomic DNA was amplified by ABI GeneAmp for library construction. The primers used were 338 F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806 R (5′-ACTCCTACGGGAGGCAGCAG-3′). The experiment used TransGen AP221-02: TransStart FastPfu DNA Polymerase 20 μL reaction system. To produce single-stranded DNA fragments, PCR products were denatured by Sodium hydroxide. Detailed process of Miseq sequencing was as follows: (1) one end of the DNA fragment was fixed to the chip; (2) the fixed base sequence was used as primer for PCR synthesis; (3) the other end of the DNA fragment randomly complemented with another nearby primer; (4) PCR amplification was carried out to produce DNA cluster; (5) the DNA amplicon was linearized into the single strand; (6) the modified DNA polymerase and dNTP were added to synthesize base; (7) the surface of the reaction plate was scanned with a laser; (8) the ‘fluorophorer’ and ‘terminator’ were chemically cleaved to polymerize the second nucleotide; (9) the results of fluorescence signal collected were counted.

Data processing

The QIIME2 software was used to process the 16S amplicon reads. The paired-end reads were first spliced. OTU cluster analysis (97% similarity) and species taxonomy analysis (reference database: Silva, http://www.arb-silva.de) were performed after the samples were distinguished. The data of each sample were distinguished. The extracted data were saved in FASTQ format. In the process of removing clutter from data, the 50 bp window was set. According to the overlap relation between PE reads, pairs of reads were merged into a sequence. The samples were differentiated based on the barcode and primers.

Annotation of gut microbiota species in norovirus infection

A Partial Least Squares Discriminant Analysis (PLS-DA) was performed. Changes in total/core gut microbiota species as the sample size increased, analysis of community abundance and diversity, Rank-Abundance, Pan/Core and Alpha diversity (vegan package) were performed using R language.

Composition analysis of gut microbiota species in norovirus infection

A Venn diagram was used to count the number of common/unique gut microbiota species. A heatmap was used to present the community composition information. The Circos diagram describes the corresponding relationship between samples and microbiota species using R language.

Gut microbiota species in norovirus infection

According to community abundance, a related analysis method was used to detect microbial community diversity. Multiple testing correction between the two groups was applied to screen for different microbiota species in norovirus infection. The screening criterion was a false discovery rate (FDR) < 0.05.

Correlation network analysis in norovirus infection

Cytoscape is a visual analysis software for molecular interaction networks. The species correlation network diagram mainly reflects the correlation of various species. The classification level was set as species, and the Spearman method was used to calculate the correlation between different microbiota species. The correlation coefficient |r| > 0.8 was set to construct the correlation network.

PICRUSt functional prediction of differential microbiota species in norovirus infection

The functional prediction of differential species was to standardize the OTU abundance table using PICRUSt. The abundance of COG and KO was calculated. Evolutionary Genealogy of Genes is an internationally recognized professional annotation database of homologous clustering gene groups. It includes functional classifications from primitive COG/KOG and taxonomic-based functional annotations. KEGG database contacts genome information and function of a large knowledge base. KEGG includes a number of functional units including Enzyme, KO and Module.

BugBase phenotype prediction analysis of differential gut microbiota species in norovirus infection

BugBase, a type of microbial group analysis tool, can determine high levels of phenotype in microbial groups of samples [14]. The phenotype includes gram positive, gram negative, biofilm forming, pathogenic, mobile element containing, oxygen utilizing.

Results

Clinical characteristics of children infected with norovirus

This study included 31 children infected with norovirus and 25 healthy children. The clinical information of all the individuals is listed in Table 1. There were no significant difference in age, weight, and sex between healthy children and children infected with norovirus.

Table 1.

Clinical information of enrolled 56 individuals.

Group Number Sex Age (month) Weight (Kg) Allergic history Feeding history (breast/artificial/mixed feeding) Admission temperature (°C) Abdominal pain Diarrhea Nausea Vomiting Dehydration
Normal control 1 Male 24 11.5 No Mixed feeding NA NA NA NA NA NA
2 Male 12 10 No Mixed feeding NA NA NA NA NA NA
3 Female 14 9 No Mixed feeding NA NA NA NA NA NA
4 Female 11 9 No Mixed feeding NA NA NA NA NA NA
5 Female 24 10.5 No Mixed feeding NA NA NA NA NA NA
6 Female 13 8.5 No Mixed feeding NA NA NA NA NA NA
7 Male 17 11.5 No Mixed feeding NA NA NA NA NA NA
8 Female 24 10 No Mixed feeding NA NA NA NA NA NA
9 Female 36 14.5 No Breast feeding NA NA NA NA NA NA
10 Female 19 10.85 No Breast feeding NA NA NA NA NA NA
11 Male 72 20 No Breast feeding NA NA NA NA NA NA
12 Female 2 5 No Breast feeding NA NA NA NA NA NA
13 Female 24 12 No Breast feeding NA NA NA NA NA NA
14 Male 12 10 No Breast feeding NA NA NA NA NA NA
15 Male 12 10.5 No Breast feeding NA NA NA NA NA NA
16 Female 36 No No Breast feeding NA NA NA NA NA NA
17 Female 11 9 No Breast feeding NA NA NA NA NA NA
18 Male 8 7.5 No Breast feeding NA NA NA NA NA NA
19 Female 60 21 No Breast feeding NA NA NA NA NA NA
20 Male 24 12 No Breast feeding NA NA NA NA NA NA
21 Male 12 11 No Breast feeding NA NA NA NA NA NA
22 Male 12 12 No Breast feeding NA NA NA NA NA NA
23 Female 36 15 No Breast feeding NA NA NA NA NA NA
24 Male 84 7 No Artificial feeding NA NA NA NA NA NA
25 Male 9 10 No Breast feeding NA NA NA NA NA NA
Norovirus infection 1 Male 24 12.5 No Breast feeding 12.5 No Yes Yes Yes No
2 Male 12 9.5 No Breast feeding 37 No Yes No No No
3 Female 24 12 No Breast feeding 36.5 No Yes No No No
4 Male 8 8.5 No Mixed feeding 37.8 No Yes No No No
5 Female 72 16 No Mixed feeding 37.6 Yes Yes No No No
6 Male 12 11 No Breast feeding 36.5 No Yes No Yes No
7 Male 12 10 No Mixed feeding 37.6 No Yes No No No
8 Male 72 20 No Breast feeding 37.1 No Yes No Yes No
9 Female 36 15 No Mixed feeding 36.8 No Yes No No No
10 Female 2 5.5 No Mixed feeding 37.3 No Yes No No No
11 Male 84 22 No Breast feeding 37.6 No Yes No No No
12 Female 24 13.5 No Breast feeding 36.6 No Yes No No No
13 Female 12 10 No Breast feeding 36.7 No Yes No No No
14 Female 60 20 No Breast feeding 37 No Yes No No No
15 Female 12 12 No Breast feeding 37 No Yes No No No
16 Male 17 11.5 No Breast feeding 38.2 No Yes No No No
17 Female 24 11 No Breast feeding 37 No Yes No No No
18 Male 12 11 No Breast feeding 37 No Yes No No No
19 Female 36 16.5 No Breast feeding 37.8 No Yes No No No
20 Female 16 8 No Artificial feeding 39 No Yes No No No
21 Female 12 9 No Mixed feeding 36.8 No Yes No No No
22 Female 16 10.5 No Breast feeding 36.3 No Yes No No No
23 Male 11 10 No Breast feeding 37 No Yes No No No
24 Male 11 9 No Breast feeding 36.3 No Yes No No No
25 Male 24 12.5 No Breast feeding 36.6 No Yes No Yes No
26 Male 14 15 No Mixed feeding 37.2 No Yes No No No
27 Male 18 10 No Breast feeding 36.1 No Yes No No No
28 Male 7 7 No Breast feeding 36.6 No Yes No No No
29 Female 132 45 No Mixed feeding 38.5 No Yes No No No
30 Male 12 14 No Breast feeding 36.5 No Yes No No No
31 Male 15 12 No Breast feeding 36.5 No Yes No Yes No
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NA, not applicable.

Sample comparative analysis in norovirus infection

PLS-DA analysis showed that a clear separation between the norovirus infection group and normal controls group (Figure 1). It is indicated that the structure of gut microbiota was significantly different between two groups.

Figure 1.

Figure 1.

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PLS-DA Analysis between norovirus infection group and normal controls group. NV, norovirus infection group; NOR, no norovirus infection group.

Microbiota species annotation in norovirus infection

Gut microbiota species in the norovirus infection group were evenly distributed (Figure 2). In the Pan/Core analysis, as the number of samples increased, the total number of microbiota species was higher. However, the number of core gut microbiota species was lower in the norovirus infection group (Figure 3). Community richness and diversity were remarkably lower in the norovirus infection group (Figure 4).

Figure 2.

Figure 2.

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Rank-Abundance analysis in norovirus infection group. X and Y-axis represent the OTU level rank and relative abundance, respectively. NV, norovirus infection group; NOR, no norovirus infection group.

Figure 3.

Figure 3.

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Pan/core analysis in norovirus infection group. X and Y-axis, respectively, represent the number of observed samples and the number of OTU shared by all samples in a grouping category; pan OTU and core OTU, respectively, represent the union number of common OTU and the intersection number of common OTU. NV, norovirus infection group; NOR, no norovirus infection group.

Figure 4.

Figure 4.

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Alpha diversity analysis in norovirus infection group. X and Y-axis, respectively, represent group name and index of OTU level; ***p < 0.001. NV, norovirus infection group; NOR, no norovirus infection group.

Species composition in norovirus infection

There were only 19 unique gut microbiota species in the norovirus infection group (Figure 5A), which showed a lower gut microbiota species diversity in the norovirus infection group. The heat map of community richness in the norovirus infection group is showed in Figure 5B. Veillonella was the dominant gut microbiota species in the norovirus infection group at both the genus and species levels (Figure 6).

Figure 5.

Figure 5.

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Venn Diagrams and heat map. (A) Venn diagrams of norovirus infection group and normal control group. NV, norovirus infection group; NOR, no norovirus infection group. (B) The heat map of top 50 gut microbiota species in total abundance of OTU classification level in norovirus infection group. The colour scale illustrates the relative richness of composition: red, below the reference channel; blue, higher than the reference. L/C prefix in column names stand for case or controls. NV, norovirus infection group; NOR, no norovirus infection group.

Figure 6.

Figure 6.

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The predominant dominant gut microbiota species at genus (A) and species (B) levels in Circos in norovirus infection group. The innermost layer of the circle represented the percentage distribution of the microbiota species between two groups. NV, norovirus infection group; NOR, no norovirus infection group.

Species difference in norovirus infection

At the genus and species levels, 136 and 244 gut microbiota species were significantly different, respectively. Subsequently, histograms were drawn for the top 30 species with significant differences at the genus (Figure 7A) and species (Figure 7B) levels, respectively. Blautia was significantly lower at both the genus and species level.

Figure 7.

Figure 7.

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Differential species analysis of gut microbiota in norovirus infection group at the genus (A) and species (B) levels. The histogram only shows the top 30 species with significant differences. *0.01 < p < 0.05, **0.01 < p < 0.001, ***p < 0.001. NV, norovirus infection group; NOR, no norovirus infection group.

Correlation network analysis in norovirus infection

Pearson correlation analysis was performed for 244 species at the species level. Interestingly, there was a positive correlation between Faecalibacterium, Blautia, Subdoligranulum, Eubacterium_hallii_group, Fusicatenibacter, Agathobacter, Roseburia and Dorea (Figure 8).

Figure 8.

Figure 8.

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Correlation network analysis of the gut microbiota species at the species level in norovirus infection group. The size of nodes and the red line, respectively, indicate the abundance of gut microbiota species and the positive correlation between gut microbiota species; the thickness of the line indicates the value of correlation coefficient.

PICRUSt functional prediction of differential microbiota species in norovirus infection

In the COG functional composition analysis, Q (secondary metabolites biosynthesis, transport and catabolism) was the most significantly higher functional composition of gut microbiota in norovirus infection group (Supplementary Figure 1A and Supplementary Table 1). In the functional units of Enzyme, 4.4.1.16 (selenocysteine lyase) and 1.1.1.261 (sn-glycerol-1-phosphate dehydrogenase) was, respectively, the most significantly higher and lower functional composition of gut microbiota in norovirus infection group (Supplementary Figure 1B and Supplementary Table 2). In the functional units of KO, K03386 (peroxiredoxin (alkyl hydroperoxide reductase subunit C) [EC:1.11.1.15]) was the most significantly higher functional composition of gut microbiota in norovirus infection group (Supplementary Figure 1C and Supplementary Table 3). In the functional units of Module, M00532 (photorespiration) and M00565 (trehalose biosynthesis, D-glucose 1 P = > trehalose) was, respectively, the most significantly higher and lower functional composition of gut microbiota in norovirus infection group (Supplementary Figure 1D and Supplementary Table 4).

BugBase phenotype analysis of differential microbiota species in norovirus infection

In the BugBase phenotype analysis, Contains_Mobile_Elements was the highest level of phenotypes in gut microbiota of norovirus infection (Figure 9A). It is noted that Enterococcus_faecium (Figure 9B) was the most contributing species in the Contains_Mobile_Elements phenotype.

Figure 9.

Figure 9.

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Analysis of BugBase phenotype (A-B) in norovirus infection group. *0.01 < p < 0.05, **0.01 < p < 0.001, ***0.001 < p < 0.0001. NV, norovirus infection group; NOR, no norovirus infection group.

Discussion

In the present study, 16S rRNA sequencing was performed on 56 individuals, including 31 children infected with norovirus and 25 healthy children. Community richness and diversity were remarkably lower in norovirus infection group. There were only 19 unique gut microbiota species in norovirus infection group, which showed a lower gut microbiota species diversity in norovirus infection group. Significant dysbiosis of gut microbiome composition and function was found in children infected with norovirus, indicating the association of the dysbiosis of gut microbial composition and function to norovirus infection, which may be useful for management or treating norovirus infection.

In the Circos diagram, Veillonella was the dominant microbiota species in norovirus infection group at genus and species levels in norovirus infection. Veillonella, present in all oral, gut, and placenta samples, is significantly elevated in adult patients with diarrhea and Coeliac disease. It is worth mentioning that Veillonella is highly prevalent across all norovirus positive samples with an average of 60.8% [15]. Thus, Veillonella could be regarded as an abundance indicator of children infected with norovirus.

In the correlation network analysis, there was a positive correlation between Faecalibacterium, Blautia, Subdoligranulum, Eubacterium_hallii_group, Fusicatenibacter, Agathobacter, Roseburia and Dorea. Butyrate, produced from Faecalibacterium prausnitzii, has anti-inflammatory properties and prevents intestinal mucosa atrophy. Abundance of Faecalibacterium spp. is negatively associated with anti-norovirus antibody titres in healthy controls. Oysters are one of the main vectors of norovirus transmission, and the abundance of gut microbiota (such as Faecalibacterium, Blautia and Agathobacter) in oysters will decrease with the prolonged accumulation time of human norovirus [16]. Subdoligranulum is the most differentially depleted genera related to Clostridium difficile infection [17]. Eubacterium_hallii_group in Han patients with ulcerative colitis is lower than that in normal controls [18]. There is a significant lower in the genus of Fusicatenibacter in ulcerative colitis patients [19]. Agathobacter is also a characteristic microbiota of peptic ulcer disease and may play an important role in the disease [20]. In gut bacterial handling genes, the elevated risk of inflammatory bowel disease-associated gene mutations is significantly related to the lower Roseburia [21]. Dorea can maintain gut homeostasis by producing anti-inflammatory SCFA propionate [22,23]. This suggested that these species may play roles in norovirus infection in children.

Besides the dysbiosis of gut microbiome composition, gut microbial functional dysfunction was also found in norovirus infection. In the COG functional composition analysis, secondary metabolites biosynthesis, transport and catabolism was the most significantly higher functional composition of gut microbiota. Stimulation of host endogenous protective defence response can provide multi-layered protections against bacterial contamination. In the functional units of KO, peroxiredoxin (alkyl hydroperoxide reductase subunit C) [EC:1.11.1.15]) was the most significantly higher functional composition of gut microbiota. Some studies have demonstrated that peroxiredoxin of eukaryotic hosts can be induced in response to bacterial, viral, and fungal infections [24–26]. Another peroxiredoxin system is shown to be highly effective in protecting M. tuberculosis against ROS stress. Our study suggested that the above microbiota functional compositions may be associated with norovirus infection in children.

In the BugBase phenotype analysis, Enterococcus_faecium was the most contributing species in the Contains_Mobile_Elements phenotype. Enterococcus is commonly lives in the gastrointestinal tract. Wang et al. found that Enterococcus_faecium could prevent infection by influenza viruses [27]. It is reported that Enterococcus_faecium has a strong action for the prevention of antibiotic-associated diarrhea [28]. Our result indicated that the phenotype of Contains_Mobile_Elements may play important roles in the pathology of norovirus infection in children.

In summary, several norovirus infection-related gut microbiota and functional compositions of gut microbiota have been identified in children. Our study could extend the current knowledge about the role of gut microbiota in norovirus infected children. However, there are some limitations to our study. Firstly, causal links between identified gut microbiota and functional compositions and norovirus infection need a deeper investigation by functional experiment, such as faecal transplant experiment. Secondly, there are also differences in medication use, fever, diarrhea, etc. which all can have an impact on microbiota composition. A comparison with children suffering from diarrhea and fever without norovirus infection is needed in the further study.

Supplementary Material

Supplemental Material
IANN_A_2412834_SM8187.zip (2.2MB, zip)

Acknowledgements

Not applicable.

Funding Statement

This study was funded by Basic Public welfare Research Program of Zhejiang Province of ‘Study on the role of dysfunctional intestinal flora in acute Diarrhea caused by Norovirus based on metagenomic sequencing Technology’ (LGF18H260001).

Authors contributions

Conception and design: Feijian Jiang and Peiliang Chang; Administrative support: Feijian Jiang and Yi Xu; Provision of materials and samples: Nan Jiang, Xiao Zheng, Yongqing Weng and Hui Zheng; Data collection and collation: Jie Li, Peiliang Chang and Chong Wang; Data analysis and interpretation: Peiliang Chang. All authors read and approve the publication of the article.

Ethics approval statement

The procedures used in this study adhere to the tenets of the Declaration of Helsinki. Written informed consent was obtained from all participants’ parents. The Ethics Committee of Taizhou Municipal Hospital approved this study (2022-LWYJ-013).

Consent for publication

The subjects gave written informed consent for the publication of any associated data.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability

In this study, analysis of all omics data was performed using a free online platform of Majorbio Cloud Platform (https://edu.majorbio.com/). The datasets generated and analyzed during the current study are available in the SRA repository (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA788674).

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplemental Material
IANN_A_2412834_SM8187.zip (2.2MB, zip)

Data Availability Statement

In this study, analysis of all omics data was performed using a free online platform of Majorbio Cloud Platform (https://edu.majorbio.com/). The datasets generated and analyzed during the current study are available in the SRA repository (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA788674).

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