Gut Microbiota Analysis and In Silico Biomarker Detection of Children with Autism Spectrum Disorder across Cohorts
Abstract
:1. Introduction
2. Materials and Methods
2.1. Gut Microbiome Data Acquisition
2.2. Microbiome Bioinformatics
2.3. Microbiome Data Analysis
2.4. Prediction
- Processing input data. The input data of this paper are the microbial species and their relative abundance information from each sample generated after species annotation, where “features” are each microbial species and their relative abundance, and “labels” is the category of each sample, including neurotypical individuals and those with ASD.
- Learning or training model. This step is mainly to find the optimal parameters of the model by repeating the sub-steps of “parameter estimation,” “model performance evaluation,” and “error identification and correction”.
- Once the optimal parameters are determined in step 2, the model with the optimal parameters is used to predict with the new input data.
3. Results
3.1. Species Composition of Gut Microbiota
3.2. The ASD Group Was More Heterogeneous than the TD Group
3.3. No Biomarker Was Observed in the Species with Low Abundance
3.4. Correlations in the ASD Group Were More Complex than Those in the TD Group
3.5. Prediction Model Based on Random Forest Algorithm
3.6. Potential Biomarkers of ASD Diagnosis
4. Discussion
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Model | Number of Samples | Country or Region | Sequencing Methods | Manifestation of Species Disorder (ASD) | Reference |
---|---|---|---|---|---|
children | TD: 10; ASD: 10 | Slovakia | Realtime-PCR | phyla Bacteroidetes/Firmicutes: ↓ *; Lactobacillus: ↑; Bifidobacterium/Lactobacillus, Streptococcus thermophillus, the total bacteria content: - | [38] |
children | TD: 45; ASD: 45 | China | 16S rRNA V3-V4 | At phylum level: -; genera Lachnoclostridium, Tyzzerella subgroup 4, Flavonifractor, unidentified_Lachnospiraceae: ↓ | [39] |
children | TD: 20; ASD: 20 | - | 16S rRNA V2-V3 | genera Prevotella, Coprococcus, and unclassified_Veillonellaceae: ↓ | [40] |
children | TD: 35; ASD: 6 | China | 16S rRNA V3-V4 | phyla Bacteroidetes/Firmicutes; genera Sutterella, Odoribacter and Butyricimonas: ↑ genera Veillonella and Streptococcuse: ↓ | [41] |
children ASD | TD: 40; ASD: 40 | Italy | 16S rRNA V3-V5 | phylum Bacteroidetes, genera Alistipes, Bilophila, Dialister, Parabacteroides, Veillonella: ↓; phyla Firmicutes/Bacteroidetes; genera Collinsella, Corynebacterium, Dorea, and Lactobacillus; Escherichia/Shigella and Clostridium cluster XVII; fungal: genus Candida: ↑ | [42] |
mice | TD: 10; ASD: 10 | USA | 16S rRNA V3-V5 | classes Bacteroidia, Clostridia: ↑ | [43] |
children | TD: 3; ASD: 3 | UK | FISH-FCM | phyla Clostridium spp.: ↑ Bifidobacterial: ↓ | [44] |
children | TD: 20; ASD: 18 | USA | 16S rRNA V4 | genera Bifidobacterium, Desulfovibrio: ↓ | [23] |
mice | TD: 21; ASD: 25 | Canada | qRT-PCR | phyla Firmicutes: ↓ Bacteroidetes: ↑ | [45] |
children and mothers | TD: 30; ASD: 59 | China | 16S rRNA V1-V2 | Children: phylum Proteobacteria: ↑; genera Enhydrobacter, Chryseobacterium, Streptococcus, and Acinetobacter: ↑; species Acinetobacter rhizosphaerae, Acinetobacter johnsonii, Prevotella melaninogenica: ↓ Mother: families Moraxellaceae and Enterobacteriaceae, genus Faecalibacterium: ↓ | [46] |
minors | TD: 450 ASD: 569 | China, Ecuador, Italy, Korean | 16S rRNA V3-V4, V4, V4-V5 | Results were variable according to different analysis methods and parameter settings. | [47] |
children | TD: 31 ASD: 43 | China | Shotgun metagenomic sequencing | phylum Actinobacteria: ↑; three Clostridium taxons, two Eggerthella taxons, two Klebsiella taxons: ↑; taxons Bacteroides vulgatus, Betaproteobacteria, Campylobacter jejuni subsp. jejuni 81–176, Campylobacter jejuni subsp. jejuni ICDCCJ07001, Candidatus Chloracidobacterium thermophilum B, Coraliomargarita akajimensis DSM 45221, Proteus mirabilis, and HI4320 Spirochaeta thermophila DSM 6192: ↓ | [24] |
children | TD: 20 ASD: 30 | Moscow | Shotgun metagenomic sequencing | species Enterococcus faecium, Megasphaera elsdenii, Bacteroides fragilis: ↑ | [28] |
Characteristic | Moscow Cohort | Shenzhen Cohort |
---|---|---|
Subjects of ASD (n) | 30 | 43 |
Subjects of TD (n) | 20 | 31 |
Age (years) | 3–5 | 2–7 |
Sequencing instruments | Illumina NovaSeq 6000 | Illumina HiSeq 4000 |
Layout | PAIRED | PAIRED |
AvgSpotLen | 300 | 300 |
Bytes (Gb) | 1.92–4.08 | 0.526–4.09 |
Model A | Model B | Model C | Model D | Model E | Average | |
---|---|---|---|---|---|---|
1st iteration | 136 | 88 | 111 | 109 | 140 | 117 |
2nd iteration | 78 | 49 | 68 | 65 | 90 | 70 |
3rd iteration | 60 | 38 | 54 | 44 | 60 | 51 |
4th iteration | 48 | 34 | 46 | 38 | 38 | 41 |
5th iteration | 43 | 28 | 43 | 36 | 35 | 37 |
6th iteration | 36 | 24 | 34 | 35 | 30 | 32 |
Model A | Model B | Model C | Model D | Model E | Average | |
---|---|---|---|---|---|---|
1st iteration | 112 | 125 | 110 | 116 | 109 | 114 |
2nd iteration | 93 | 96 | 73 | 80 | 84 | 85 |
3rd iteration | 59 | 77 | 56 | 72 | 71 | 67 |
4th iteration | 51 | 61 | 50 | 62 | 53 | 55 |
5th iteration | 48 | 56 | 45 | 58 | 51 | 52 |
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Wang, W.; Fu, P. Gut Microbiota Analysis and In Silico Biomarker Detection of Children with Autism Spectrum Disorder across Cohorts. Microorganisms 2023, 11, 291. https://doi.org/10.3390/microorganisms11020291
Wang W, Fu P. Gut Microbiota Analysis and In Silico Biomarker Detection of Children with Autism Spectrum Disorder across Cohorts. Microorganisms. 2023; 11(2):291. https://doi.org/10.3390/microorganisms11020291
Chicago/Turabian StyleWang, Wenjuan, and Pengcheng Fu. 2023. "Gut Microbiota Analysis and In Silico Biomarker Detection of Children with Autism Spectrum Disorder across Cohorts" Microorganisms 11, no. 2: 291. https://doi.org/10.3390/microorganisms11020291
APA StyleWang, W., & Fu, P. (2023). Gut Microbiota Analysis and In Silico Biomarker Detection of Children with Autism Spectrum Disorder across Cohorts. Microorganisms, 11(2), 291. https://doi.org/10.3390/microorganisms11020291