Hai-Hua Chuang1,2,3,4,5
Chung-Guei Huang6,7
Shih-Hsuan Chou8,9
Hsueh-Yu Li2,3,10Chin-Chia Lee11
Li-Ang Lee2,3,4,10*- 1Department of Family Medicine, Chang Gung Memorial Hospital, Taipei Branch and Linkou Main Branch, Taoyuan, Taiwan
- 2School of Medicine, College of Medicine, Chang Gung University, Taoyuan, Taiwan
- 3Sleep Center, Metabolism and Obesity Institute, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan
- 4School of Medicine, College of Life Science and Medicine, National Tsing Hua University, Hsinchu, Taiwan
- 5Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei, Taiwan
- 6Department of Laboratory Medicine, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan
- 7Department of Medical Biotechnology and Laboratory Science, Chang Gung University, Taoyuan, Taiwan
- 8Graduate Institute of Biomedical and Pharmaceutical Science, Fu Jen Catholic University, New Taipei City, Taiwan
- 9Biotools Co., Ltd., New Taipei City, Taiwan
- 10Department of Otorhinolaryngology - Head and Neck Surgery, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan
- 11Taipei Wego Private Bilingual Senior High School, Taipei, Taiwan
Background: Analyzing bacterial microbiomes consistently using next-generation sequencing (NGS) is challenging due to the diversity of synthetic platforms for 16S rRNA genes and their analytical pipelines. This study compares the efficacy of full-length (V1–V9 hypervariable regions) and partial-length (V3–V4 hypervariable regions) sequencing of synthetic 16S rRNA genes from human gut microbiomes, with a focus on childhood obesity.
Methods: In this observational and comparative study, we explored the differences between these two sequencing methods in taxonomic categorization and weight status prediction among twelve children with obstructive sleep apnea.
Results: The full-length NGS method by Pacbio® identified 118 genera and 248 species in the V1–V9 regions, all with a 0% unclassified rate. In contrast, the partial-length NGS method by Illumina® detected 142 genera (with a 39% unclassified rate) and 6 species (with a 99% unclassified rate) in the V3–V4 regions. These approaches showed marked differences in gut microbiome composition and functional predictions. The full-length method distinguished between obese and non-obese children using the Firmicutes/Bacteroidetes ratio, a known obesity marker (p = 0.046), whereas the partial-length method was less conclusive (p = 0.075). Additionally, out of 73 metabolic pathways identified through full-length sequencing, 35 (48%) were associated with level 1 metabolism, compared to 28 of 61 pathways (46%) identified through the partial-length method. The full-length NGS also highlighted complex associations between body mass index z-score, three bacterial species (Bacteroides ovatus, Bifidobacterium pseudocatenulatum, and Streptococcus parasanguinis ATCC 15912), and 17 metabolic pathways. Both sequencing techniques revealed relationships between gut microbiota composition and OSA-related parameters, with full-length sequencing offering more comprehensive insights into associated metabolic pathways than the V3–V4 technique.
Conclusion: These findings highlight disparities in NGS-based assessments, emphasizing the value of full-length NGS with amplicon sequence variant analysis for clinical gut microbiome research. They underscore the importance of considering methodological differences in future meta-analyses.
1 Introduction
The gut microbiota, primarily composed of Firmicutes and Bacteroidetes bacteria, plays a pivotal role in maintaining health and metabolic processes (1, 2). Existing evidence suggests that the gut microbiome holds a central role in regulating organismal energy balance (3), which includes the involvement of intestinal microbiota composition in intestinal plasticity and metabolism to sustain energy equilibrium (4). The gut microbiota of the human body is influenced by multiple exogenous and endogenous factors, such as genetic disposition, sex, age, diet (5), physical activity (6), sleep, pollutants (7), and others, leading to substantial variability across individuals and populations. An imbalance or disruption in the natural microbiota composition, referred to as dysbiosis, has been linked to a range of health conditions (8), including gastrointestinal disorders, autoimmune diseases, and metabolic conditions, such as obesity (9).
The Firmicutes/Bacteroidetes (F/B) ratio has been proposed as an obesity marker by some studies (10–12). Increased Firmicutes abundance and the F/B ratio in subjects with obesity are associated with disrupted energy metabolism (13). Reductions in Bacteroides and Lactobacillus induce lipid synthesis and storage in individuals with obesity via decreased bile acid concentrations (14). The altered gut microbiota and related metabolites contribute to weight gain by modulating central appetite and feeding behavior (15). In children, an increased presence of the Firmicutes phylum and a decreased presence of the Bacteroidetes phylum have been associated with high body mass index (BMI) (10). However, the association of the F/B ratio with obesity is contentious due to inherent methodological biases in microbiome analyses (12).
The 16S ribosomal RNA (rRNA) gene, a hallmark of prokaryotic life, encodes the RNA component of the ribosome’s small subunit and is essential for protein synthesis from mRNA. The gene encompasses conserved and variable regions (V1–V9) (16), enabling the 16S rRNA sequencing technique to serve as a cornerstone for microbial taxonomy and phylogeny (16, 17), reliably classifying bacteria up to the genus level (18). However, the precision of next-generation sequencing (NGS) for microbiome analysis varies with the sequencing approach, database, and targeted gene regions (19). Partial sequencing frequently lacks specificity beyond the genus level due to the selection of variable regions and read length inconsistencies (20–22). Although combining several hypervariable regions (usually V3–V4) may enhance resolution (23, 24), it can also lead to taxonomic misclassifications (25).
Advances in third-generation NGS technologies, characterized by long-read capabilities, allow for the sequencing of the complete 16S rRNA gene, which enhances the resolution of taxonomic classification (26). At the species level, full-length 16S rRNA gene sequencing has been shown to provide higher resolution compared to the V3–V4 regions, with improvements noted in alpha diversity, the frequency of relative abundance, and accuracy of identification (27). However, this technique is not without its challenges, which include the handling of unique tag sequences and the accurate identification of low-abundance variants (26, 28, 29). These technical hurdles contribute to an ongoing discourse regarding the relative merits of partial versus full-length sequencing for microbial analysis. This discussion highlights the necessity for methodological comparative studies, aiming to ascertain the most reliable approaches for analyzing the gut microbiome.
Obstructive sleep apnea (OSA) is a prevalent sleep disorder in children, with obesity increasingly recognized as a major risk factor alongside adenotonsillar hypertrophy (30). Obesity significantly correlates with the apnea-hypopnea index (AHI) in school-aged children with OSA (31). Emerging evidence suggests a genetic link between gut microbiota and OSA development (32), warranting further research into the effects of different 16S rRNA gene sequencing platforms and analytical pipelines on understanding the gut microbiota’s role in obesity among children with OSA.
We hypothesized that the full-length (V1–V9 region) synthetic 16S rRNA gene sequencing method will surpass the partial (V3–V4 region) method in (1) identifying and classifying the human gut microbiome, and (2) distinguishing patients with overweight or obesity (OWO) from those with normal weight (NW) using microbiota markers associated with obesity. In a sample of pediatric OSA patients with varying weight statuses, this study aimed to: (1) compare the results between full-length (V1-V9 region) and partial (V3–V4 region) 16S rRNA gene sequencing methods in analyzing the gut microbiome, and (2) investigate the implications of both methods on the identification of obesity-associated microbiota markers.
2 Methods
2.1 Study design and participants
This was an observation and comparative study. The participants were prospectively enrolled in an investigation of tonsil and gut microbiomes at the Department of Otolaryngology, Chang Gung Memorial Hospital (Linkou Main Branch, Taoyuan, Taiwan) between March 2017 and January 2019 (33). The Institutional Review Board of Chang Gung Medical Foundation approved the study (Approval No.: 201507279A3). Both parents and participants aged 6 years or older provided written informed consent. Our research adhered to the revised Declaration of Helsinki (34) and complied with the STROBE guidelines (35).
Children aged 5-12 years, exhibiting an AHI of ≥ 5.0 events/hour or an AHI of ≥ 2.0 events/hour with at least one associated morbidity (e.g., elevated blood pressure, daytime sleepiness, growth retardation), were considered eligible (36). We excluded patients with craniofacial, neuromuscular, or chronic inflammatory disorders (37). Those with acute inflammation or conditions requiring antibiotic treatment were only eligible for stool sample collection after a minimum of two weeks following remission.
From a database of 66 children (33), we meticulously selected six children with OWO and another six with NW based on age, sex, and AHI. The participants were subsequently categorized based on their BMI z-scores into the ‘OWO’ group (BMI z-score ≥ 1.0) and the ‘NW’ group (BMI z-score > -2.0 and < 1.0) (38). Figure 1 displays the study flow diagram.
Figure 1 Participant flowchart. Following a rigorous matching process that considered age, sex, and apnea-hypopnea index (AHI), six children diagnosed with obstructive sleep apnea (OSA) and concurrent overweight/obesity (OWO) were meticulously selected. Correspondingly, an equivalent number of six children with OSA and normal weight (NW) were also included as participants in the present study. These participants were chosen from a total of 66 children who had been diagnosed with OSA.
2.2 Polysomnography
Our study assessed various factors linked to OSA severity, including AHI, apnea index, arousal index, mean blood oxygen saturation (SaO2), minimum SaO2, total sleep time, and sleep stages. Following the 2012 American Academy of Sleep Medicine Manual guidelines (39), we employed standard full-night in-lab polysomnography. The detailed protocol for this polysomnography has been previously documented (37, 40, 41).
2.3 Stool sample collection and DNA preparation
Stool samples were collected, snap-frozen in liquid nitrogen, and stored at -80°C. DNA extraction utilized a fecal DNA isolation kit (MoBio Laboratories, USA). DNA concentrations were ascertained with a NanoPhotometer P360 system (Implen, USA) and standardized to 1 ng/ul for subsequent analyses of full-length or 5 ng/μl for subsequent analyses of partial length 16S rRNA amplicon sequencing.
2.4 Full-length 16S rRNA amplicon sequencing and taxonomy classification
The full-length 16S genes encompassing V1–V9 hypervariable regions were amplified using barcoded 16S gene-specific primers as detailed in PacBio®’s guide (42). Each primer is designed to contain a 5’ buffer sequence (GCATC) with a 5’ phosphate modification, a 16-base barcode and the degenerate 16S gene-specific forward or reverse primer sequences (Forward:5’Phos/GCATC- 16-base barcode - AGRGTTYGATYMTGGCTCAG -3’, Reverse: 5’Phos/GCATC- 16-base barcode – RGYTACCTTGTTACGACTT -3’) (43). The degenerate base identities are defined as follows: R = A or G; Y = C or T; M = A or C. A total of 2 ng of gDNA was used for the polymerase chain reaction (PCR), employing the KAPA HiFi HotStart ReadyMix (Roche, USA) under specified PCR conditions. Post-reaction, the PCR products were examined on a 1% agarose gel. Samples exhibiting a prominent band around 1,500 bp were selected and purified using AMPure PB Beads for subsequent library preparation.
The SMRTbell library preparation followed the PacBio®’s guidelines as above. Briefly, an equal molar amount of each barcoded PCR product was pooled, and between 500-1,000 ng of the pooled amplicon sample underwent DNA damage repair. This was followed by end-repair, A-tailing, and ligation steps to attach the universal hairpin adapters to double-stranded DNA region. After purification with AMPure PB beads to discard adapter dimers, the SMRTbell library was prepped with Sequel II primer 3.1 and Sequel II Binding Kit 3.1 for primer annealing and polymerase binding. Finally, sequencing was executed in the circular consensus sequence (CCS) mode on a PacBio® Sequel IIe instrument to yield HiFi reads with a predicted accuracy (Phred Scale) of 30.
CCS reads were determined based on a minimum predicted accuracy of 0.9, with the least number of passes set at three, as per PacBio®’s official workflow using the SMRT Link software. Only CCS reads exceeding a quality score of Q30, termed Q30 HiFi reads, advanced to the succeeding phase. Post-demultiplexing, the HiFi reads underwent further processing using DADA2 (version 1.20; https://qiime2.org/) to extract amplicons with single-nucleotide resolution (44). Trimming and filtering were set to a maximum of two expected errors per read. The DADA2 algorithm can discern exact amplicon sequence variants (ASVs) from the full-length 16S rRNA gene with near-zero error rates. For each distinct sequence, tools such as the feature-classifier (45) and classify-consensus-vsearch (46) algorithm in QIIME 2 (v2022.11) were employed to annotate taxonomy classifications based on data from the National Center for Biotechnology Information (NCBI) 16S ribosomal RNA database (47). To evaluate sequence similarities among diverse ASVs, a multiple sequence alignment was executed using QIIME 2’s MAFFT (48) tool against the NCBI 16S ribosomal RNA database. A phylogenetic tree, illustrating the relationship of representative ASV sequences, was constructed using QIIME 2’s phylogeny FastTree (49).
2.5 Partial length 16S rRNA amplicon sequencing and taxonomy classification
We targeted the V3–V4 hypervariable region of the 16S rRNA gene for sequencing, amplified with specific primers: 341F (5’-CCTACGGGNGGCWGCAG-3’) and 806R (5’-GACTACHVGGGTAT CTAATCC -3’). Amplification followed the 16S Metagenomic Sequencing Library Preparation protocol (Illumina®, USA). PCR was carried out with 12.5 ng of genomic DNA, using KAPA HiFi HotStart ReadyMix (Roche, USA). The PCR products were verified via 1.5% agarose gel electrophoresis. DNA from samples with a prominent ~500 bp band was purified using AMPure XP beads (Beckman Coulter, USA). Quality assessment of the indexed PCR products was conducted using the Qubit 4.0 Fluorometer (Thermo Scientific, USA) and the Qsep100TM system (BiOptic, Taiwan). The sequencing library was constructed as per the aforementioned Illumina® protocol, and sequencing was executed on an Illumina® MiSeq platform, yielding paired 300-bp reads.
Post-sequencing, raw reads were demultiplexed based on barcodes. The paired-end reads underwent primer and adapter sequence removal via QIIME 2 cutadapt plugin (50). ASV construction involved the QIIME 2 DADA2 plugin (v2021.4), which facilitated quality filtering, dereplication, denoising, and more (44). Taxonomy classification and phylogenetic tree were performed as above.
2.6 Data analysis of gut microbiome
To account for sequence depth variations, ASVs were rarefied to the minimal sequence depth using QIIME’s script (single_rarefaction.py). Both alpha and beta diversity analyses utilized this normalized data. The community’s relative abundance and evenness accounting for alpha diversity were assessed using the Shannon, Simpson, and Peilou indices (51). For beta diversity, weighted and unweighted UniFrac were calculated (52). Statistical evaluations employed principal coordinate analysis (53) and non-metric multidimensional scaling (NMDS) (54). Furthermore, Welch’s t-test with the Benjamini-Hochberg procedure was performed to control false discovery rate using the STAMP software (v2.1.3) (55). The presence of statistically significant biomarkers was ascertained using the linear discriminant analysis effect size (LEfSe) analysis (56). Community structure differences were determined using analysis of similarity (57) and permutational multivariate analysis of variance (58). Functional abundances were predicted from full-length 16S rRNA data using Tax4Fun2 and the V3–V4 region for the Tax4Fun2 (59) and Kyoto Encyclopedia of Genes and Genomes (KEGG) orthology database (60). For highlighting pivotal functional profiles, the ggplot2 and microeco R packages were coupled with metastat statistics (61).
2.7 Statistical analysis
The normality of the continuous variables was assessed using the Kolmogorov-Smirnov test. For variables that followed a normal distribution, results were presented as the mean with standard deviation (SD). In cases where the distribution was non-normal, we reported the median alongside the interquartile range (IQR). To evaluate the differences between the two weight statuses, the unpaired Student t-test or Mann-Whitney U test were employed for continuous variables, while the Fisher exact test was used for categorical variables. For within-group comparisons, we applied either the paired Student t-test or the Wilcoxon signed-rank test, depending on the nature of the continuous variables. All statistical procedures were performed using multiple software tools: R (versions 4.3.1, R Foundation for Statistical Computing, Vienna, Austria), SPSS (version 27.0, IBM Corp., Armonk, NY, USA), and GraphPad Prism 10.0 for Windows (Graph Pad Software Inc., San Diego, CA, USA).
3 Results
3.1 Clinical characteristics of children with OSA and various weight status
Of the 66 children initially diagnosed with OSA, 36 were excluded because their stool samples were collected after December 31, 2017. Of the 30 children that remained, 17 had both OSA and OWO, and 13 had OSA with NW. Further exclusion based on age, sex, and weight status criteria left 12 participants for the final analysis: 6 children with concurrent OSA and OWO, and another 6 with OSA and NW, as illustrated in Figure 1. The final cohort consisted of 10 boys and 2 girls, with a median age of 6 years (IQR: 5–10 years). This is detailed in Table 1. The clinical variables were similar between the OWO and NW groups, except for the BMI z-score, which showed a significant difference (p = 0.002).
Table 1 Clinical characteristics of twelve pediatric patients with obstructive sleep apnea: a comparison between overweight/obesity and normal weight.
3.2 Full-length 16S rRNA sequencing offers enhanced coverage but fewer ASVs
Table 2 outlines the ASV counts, and corresponding sequence counts for each sample. With a Q score threshold of 30, the full-length 16S rRNA sequence via PacBio® yielded significantly fewer ASVs than the V3–V4 region sequenced with Illumina® (16044 (15554–18552) vs. 147952 (141946–152740), p < 0.001). Notably, almost all ASVs derived from full-length 16S rRNA sequencing were classified, resulting in a negligible rate of unassigned sequences (0–0.2% range) across phylum to species levels. In contrast, the V3–V4 region had a progressive increase in unassigned ASVs, especially from family (0.8%–14.5%) to species tiers (97.9%–99.9%). Moreover, the coverage rates from full-length V1–V9 region surpassed those of V3–V4 across all taxonomic levels (all p < 0.01).
Table 2 Microbial community analysis.
3.3 Full-length 16S rRNA sequencing unveils greater bacterial species richness and elevated microbial diversities
Upon normalization by relative abundance, there were no significant disparities in the taxonomic profiles between full-length 16S rRNA sequencing and V3–V4 region from the phylum to the genus levels (Figures 2A–E; all p > 0.05). However, distinctions emerged at the species level (Figure 2F; p < 0.001). In terms of alpha diversity, the Simpson and Pielou indexes were both appreciably higher for full-length 16S rRNA sequencing as compared to the V3–V4 region (Figure 3A; p = 0.021 and p = 0.001, respectively). Delving further, it was observed that both unweighted (Figure 3B) and weighted (Figure 3C) UniFrac distances, as computed for individual samples from patients with NW and OWO, exhibited pronounced differences between the two NGS methodologies (p = 0.001 and p = 0.002, respectively). Regarding beta diversity (Figure 3D), the unweighted UniFrac distances associated with the full-length 16S rRNA sequencing were considerably lower than those of the V3–V4 region (p = 0.001). Conversely, the weighted UniFrac distances recorded for the full-length 16S rRNA sequencing surpassed those for the V3–V4 region (p = 0.001).
Figure 2 Comparative analysis of microbial taxonomic composition. Using the analysis of similarity test, taxonomic profiles showed no significant difference between the full-length and V3–V4 region of the 16S rRNA genes at the (A) phylum, (B) class, (C) order, (D) family, and (E) genus levels. Notably, (F) the species level exhibited marked variations between the full-length and V3–V4 region. Taxa that did not rank within the top ten most frequent and unassigned data are collectively termed “others”.
Figure 3 Differential analysis of alpha and beta diversity between sequencing methods. (A) The Wilcoxon signed-rank test revealed that the Shannon index did not differ significantly, while the Simpson and Pielou indexes were notably higher for the full-length method compared to the V3–V4 region. The principal coordinate analysis (PCoA) plots showcase the distributions of (B) unweighted and (C) weighted UniFrac distances for both sequencing methods. Their differences were found to be significant when analyzed with the permutational multivariate analysis of variance test. (D) Based on the Wilcoxon signed-rank test, the beta index for the full-length 16S rRNA sequencing was lower than the V3–V4 region using unweighted UniFrac distances. However, when weighted, the beta index was notably higher for the full-length method compared to the V3–V4 region. All data are presented as medians with interquartile ranges, and ellipses signify 95% confidence intervals.
3.4 Full-length 16S rRNA sequencing offers a deeper insight into the disparities in gut microbiota between children with OWO and those with NW compared to the V3–V4 16S rRNA sequencing
There were notable differences in ASVs at both the phylum and genus levels between the OWO and NW groups, as evidenced by both the full-length 16S rRNA sequencing and the V3–V4 region methodologies (Figures 4A, B; all p < 0.05). However, the full-length method provided deeper insights compared to the V3–V4 region. This distinction was apparent when evaluating the F/B ratio (Figure 4C), the alpha diversity (Figure 4D), beta diversity (Figure 4E), and the number of differentiated ASVs (Figures 5A, B), helping to better distinguish children with OWO from those with NW.
Figure 4 Comparative microbial composition in Overweight/Obese (OWO) and Normal Weight (NW) groups. (A, B) Using the analysis of similarity test, significant disparities in taxonomic profiles were found at both the phylum (A) and genus (B) levels when comparing the full-length and V3–V4 region of the 16S rRNA genes. (C) Specifically, the Firmicutes/Bacteroidetes ratio in the OWO group, as determined using the full-length methodology, was significantly elevated compared to the NW group (unpaired Student t-test). However, using the V3–V4 region methodology, the Firmicutes/Bacteroidetes ratios were similar between both groups. (D, E) Per the unpaired Mann-Whitney U test, the OWO group displayed markedly greater alpha (D) and beta (E) diversities than the NW group with the full-length method. However, using the V3–V4 region methodology, the majority of alpha and beta diversity metrics were analogous for both groups, save for the weighted UniFrac distance. Taxa not ranking within the top ten most frequent and all unassigned data are collectively labeled as “others”. Continuous data are depicted as either means ± standard deviations or as medians accompanied by interquartile ranges.
Figure 5 Cladograms comparing gut microbial composition in Overweight/Obesity (OWO) and Normal Weight (NW) groups. (A) The cladogram highlights 29 representative amplicon sequence variants having a linear discriminant analysis score ≥ 4, identified via the full-length method and LEfSe. (B) In contrast, the V3–V4 region methodology identified merely 13 representative amplicon sequence variants.
3.5 Both the full-length and V3–V4 16S rRNA sequencing methods reveal unique functional predictions of gut microbiota across weight categories
Using the Tax4Fun2-based NMDS plot, a marked differentiation in the habitat-specific functional profile of gut microbiota emerged between children with OWO and those of NW status. This was clearly highlighted when analyzing data via the full-length 16S rRNA sequencing, paired with the level 3 pathway of the KEGG orthology database (stress value = 0.065) (Figure 6A). In parallel, the Tax4Fun NMDS plot, derived from V3–V4 segment data and aligned with the same functional prediction pathway, particularly pronounced divergences in functional profiles between the two weight groups (stress value = 0.021) (Figure 6B).
Figure 6 Functional profile analysis of gut microbiomes in children with Overweight/Obese (OWO) and Normal Weight (NW). (A) By integrating Tax4Fun2 with the NMDS test, we observed a distinct variation in the functional profiles of the two weight categories as per the KEGG orthology database. The stress values, which range from ≥ 0.05 to < 0.1, confirm the trustworthy representation of data after dimensionality reduction. (B) Using the older version, Tax4Fun, in conjunction with the NMDS test, the functional profile differences between the weight groups became evident once more. This reliable difference is accentuated by the strong representation in the visual plot with a stress value of < 0.05. (C) The UpSet plot showcases the overlap and unique pathways: 73 derived from the full-length 16S rRNA genes and another 61 from the V3–V4 segments. Notably, 18 predictive functions were consistently observed across both NGS methods.
By harnessing the Welch’s t-test in tandem with the Benjamini-Hochberg procedure to manage the false discovery rate, we pinpointed 73 distinct level 3 pathways using full-length 16S rRNA genes and another 61 pathways using the V3–V4 region (Figure 6C). All pathways were identified with both p- and q-values falling below 0.05. Intriguingly, both NGS methods shared 18 predictive functions. Difference in the proportion of overlapping level 3 functional predictions were not significant (25% vs 30%, p = 0.562). Figure 7 delves deeper into these shared 18 level 3 pathways. While the majority (n = 16) exhibited consistent distribution patterns across both methods, two pathways — specifically, fructose and mannose metabolism, and glycosaminoglycan biosynthesis-chondroitin sulfate — displayed distinct patterns between the two methods.
Figure 7 Comparison of predicted metabolic pathway abundance in gut microbiomes from children with Overweight/Obesity (OWO) and children with Overweight/Obesity (OWO) and Children with Normal Weight (NW). Through the columnar visualization, we get a clearer view of the pathway distributions, making it simpler to contrast and compare across the weight classifications and the two NGS technologies.
Additionally, of the 73 identified through full-length methodology, 35 (48%) were affiliated with the level 1 metabolism pathway, while from the 61 pathways discerned through the V3–V4 region, 28 (46%) resonated with the same category. Interestingly, five metabolism pathways—comprising fructose and mannose metabolism, galactose metabolism, tetracycline biosynthesis, steroid hormone biosynthesis, and pentose and glucuronate interconversions—exhibited consistency between the two methodologies. The proportion of overlapping level 1 metabolism pathways were similar (86% vs 82%, p = 0.737).
3.6 Association between weight-related gut bacterial species and metabolic pathways
Utilizing the Spearman correlation test, a set of bacteria—Bacteroides ovatus, Bifidobacterium pseudocatenulatum, and Streptococcus parasanguinis ATCC 15912—were identified in association with BMI z-score. These were discerned through the combined use of Welch’s t-test and the Benjamini-Hochberg procedure. Additionally, Bacteroides uniformis was singled out via the LEfSe method as having relevance to BMI z-score. Concurrently, an analysis using Tax4Fun2 highlighted 17 metabolic pathways that bore associations with the BMI z-score (Figure 8).
Figure 8 Heatmap illustrating the spearman correlations Between BMI Z-score, specific gut bacteria, and metabolic pathways. By consolidating findings from Welch’s t-test, LEfSe, and Tax4Fun2, we identified intricate associations between the BMI z-score, four gut bacterial species, and 17 metabolic pathways. The Spearman correlation test facilitated this observation.
The intricate interconnections amongst BMI z-score, the four gut bacterial species, and the metabolic pathways are vividly depicted in the network graph. This representation underscores the multifaceted associations between BMI z-score and these critical components of the gut microbiome. Strikingly, the trio of bacteria—Bacteroides ovatus, Bifidobacterium pseudocatenulatum, and Streptococcus parasanguinis ATCC 15912—showcased active interactions with the metabolic pathways (Figure 9).
Figure 9 Network interactions illustrating the correlation between BMI Z-score, gut bacteria, and metabolic pathways. This network graph elucidates the complex relationships between BMI z-score, the gut bacteria Bacteroides ovatus, Bifidobacterium pseudocatenulatum, and Streptococcus parasanguinis ATCC 15912, and various metabolic pathways. Nodes represent either bacterial species or metabolic pathways, with red ellipses indicating associations with overweight/obesity and blue for normal weight. The interconnecting lines represent correlation strengths, with thicker lines denoting stronger associations (correlation coefficient of r-value ≥ 0.60).
3.7 Correlations between gut microbiota composition, functional predictions, and polysomnographic parameters
Comprehensive 16S rRNA gene sequencing revealed that alpha diversity indices were not correlated with polysomnographic measures. However, distinct genera demonstrated significant associations: Bifidobacterium levels correlated with AHI and Escherichia with AI. Additionally, Phocaeicola abundance was inversely related to AI, and Bifidobacterium abundance was inversely associated with minimal SaO2. Extended analysis using the V3–V4 regions of the 16S rRNA gene confirmed these relationships and further identified a significant correlation between Bifidobacterium and AI (Table 3).
Table 3 Spearman correlations of alpha diversity indices and top 10 gut microbiota genera with polysomnographic parameters in pediatric obstructive sleep apnea.
Metabolic function predictions based on full-length 16S rRNA gene sequencing indicated significant associations between microbial metabolism in diverse environments, propanoate metabolism, and AI, as well as between glycine, serine, and threonine metabolism and mean SaO2. Conversely, sequencing of the V3–V4 regions elucidated a notable association of peptidoglycan biosynthesis with minimal SaO2 (Table 4).
Table 4 Spearman correlations between top 20 predicted level 3 metabolic functions of gut microbiota and polysomnographic parameters in pediatric obstructive sleep apnea.
4 Discussion
This study investigated the gut microbiome of twelve pediatric OSA patients, half with OWO and half without, using both the V3-V4 region and full-length 16S rRNA sequencing methods. The clinical characteristics were comparable the two groups except for the BMI z-score. The full-length sequencing, despite producing fewer ASVs, outperformed the V3–V4 region in terms of taxonomic coverage. It also resulted in fewer unassigned sequences and exhibited significant variations in both alpha and beta diversities. Interestingly, the full-length sequencing provided deeper insights into the differences in gut microbiota composition between children with OWO and children with NW compared to the V3–V4 approach. Functionally, each method highlighted unique metabolic pathways associated with gut microbiota across the different weight categories. The full-length sequencing was particularly effective in identifying bacterial species linked to BMI z-score, emphasizing their potential significance in the dynamics of weight-related gut microbiome. Table 5 provides a comprehensive comparison of these two NGS methods, including microbiota bioinformatics, correlation with weight status, and functional predictions of microbial communities in stool samples. Subsequent sections will delve deeper into the detailed findings.
Table 5 Comparison of full-length and V3–V4 region 16S rRNA gene sequencing methods in assessing gut microbiome across different weight groups in children.
4.1 16S rRNA sequencing methods: full-length or V3–V4 region?
In this research, we focused on comparing two sequencing methods: full-length sequencing and the V3–V4 region sequencing of the 16S rRNA gene. One of the most striking observations was that full-length sequencing demonstrated superior taxonomic coverage compared to the V3–V4 region as previous studies (27, 62). This suggests that the full-length method captures a broader spectrum of microbial diversity, potentially providing a more comprehensive picture of the microbial community. This comprehensive taxonomic resolution could be particularly beneficial in studies where fine-scale differences in microbial communities are of interest (63).
While the full-length sequencing yielded fewer ASVs after denoising, it’s crucial to consider the quality and relevance of these ASVs. Fewer ASVs doesn’t necessarily imply reduced microbial diversity but may indicate reduced noise or spurious sequences (64). The reduced rate of unassigned sequences in the full-length method further underscores its precision. Unassigned sequences, often a source of ambiguity in microbial studies, can stem from sequencing errors, chimeric sequences, or genuine novel sequences that aren’t yet represented in reference databases (65).
Despite similar relative abundances of both methods, the significant variations in both alpha (within-sample diversity) and beta diversities (between-sample diversity) between the two methods have substantial implications. The higher alpha diversity in the full-length sequencing might suggest that it can detect a wider range of taxa within a single sample, potentially unearthing rare or low-abundance species that might be missed by the V3–V4 method (66). Differences in beta diversity, on the other hand, could imply that community compositions derived from the two methods might not be directly comparable (22). This is pivotal for studies looking at differences between groups, such as those based on health status, geography, or other variables.
While our results highlight the potential advantages of full-length 16S rRNA gene sequencing, researchers should carefully consider their objectives before selecting a method. For instance, studies that require rapid results or are constrained by budget might still benefit from the V3–V4 region due to its established protocols, faster turnaround, and lower per-case costs (67). Conversely, projects demanding in-depth taxonomic resolution might favor the full-length approach.
4.2 Potential applications in studying weight-related gut microbiome
The observed superiority of full-length sequencing in discerning the differences in gut microbiota composition and functional prediction between OWO and NW groups versus the V3–V4 approach is a significant finding. The enhanced depth offered by full-length sequencing might be analogous to viewing an image in higher resolution (66). Where the V3–V4 approach might provide a general view of the microbiota landscape, full-length sequencing offers a detailed map. In this study, the performance of the full-length sequencing to differentiate children with OWO from children with NW was better than that of the V3–V4 approach, regarding F/B ratio, alpha diversity, and beta diversity. Therefore, the full-length sequencing is particularly cost-effective when the sample size was relatively small. This can be especially significant when identifying minor bacterial taxa that may have substantial impacts on host physiology (67, 68). For example, the full-length method revealed six bacterial species of 29 representative ASVs, whereas the V3–V4 region methodology identified 0 bacterial species of 13 representative ASVs. Furthermore, Bacteroides ovatus, Bacteroides uniformis, Bifidobacterium pseudocatenulatum, and Streptococcus parasanguinis ATCC 15912 were closely related to BMI z-score by using the full-length method.
The differences in functional predictions identified by each sequencing method underscore the complex and multifaceted nature of the gut microbiome’s role in host metabolism (69). In this study, we pinpointed 73 distinct level 3 pathways using full-length 16S rRNA genes and another 61 pathways using the V3–V4 region. Differences in gut microbiota composition can impact nutrient absorption, gut barrier function, inflammation, and interactions with other organ systems (70). Furthermore, the metabolic pathways, whether involved in short-chain fatty acid production, amino acid metabolism, or other processes, might hold the key to understanding how gut microbiota influence weight and overall health (71–73). Using the full-length method, 35 differentiated metabolic pathways were identified, whereas the V3–V4 region method revealed 28 differentiated metabolic pathways. Furthermore, Bacteroides ovatus, Bifidobacterium pseudocatenulatum, and Streptococcus parasanguinis ATCC 15912 showcased active interactions with the metabolic pathways and closely related to BMI z-score by using the full-length method. Therefore, beyond just identifying which bacterial species are present, the full-length sequencing might offer insights into the potential functions of these bacteria. Moreover, understanding how various strains and species are related can give insights into how the gut microbiome might have evolved in response to dietary, environmental, or other external factors in children with OWO versus children with NW.
The ability of full-length sequencing to associate specific bacterial species with the BMI z-score is not just a technical achievement but also a clinically relevant finding. Such associations might provide the foundation for personalized therapeutic interventions. For instance, seaweed Undaria pinnatifida administration significantly increased lean-related Bacteroides ovatus and short-chain fatty acids and tricarboxylic acid cycle intermediates and decrease high-fat diet-induced body weight gain in high-fat diet-fed mice (74). As mentioned above, carbohydrate, amino acids, and lipid metabolisms are weight-related metabolic pathways. Interestingly, the role of Bifidobacterium pseudocatenulatum on these pathways is strain dependent. For example, Bifidobacterium pseudocatenulatum CECT 7765 can reduce both metabolic and immunological dysfunctions related to obesity in HFD-fed mice (75), whereas Bifidobacterium pseudocatenulatum JCLA3 involves the carbohydrate and amino acids metabolism (76). Streptococcus parasanguinis ATCC 15912 is an atypical viridans streptococcus (77) and its clinical significance is not well studied. In this study, Streptococcus parasanguinis ATCC 15912 is positively associated with BMI z-score and involves amino acid and lipid metabolic pathways. This is a novel finding; however, its exact mechanism of weight gaining should be further investigated. Nevertheless, understanding the metabolic pathways prevalent in different weight categories could guide dietary recommendations.
While the study focuses on weight differences, it indirectly sheds light on the broader interactions between the host and its microbiota. The gut microbiota’s influence isn’t limited to weight but extends to the immune system, mental health, and more (78, 79). The identified differences in gut microbiota composition and function could have ripple effects across various physiological systems. These findings set the stage for further studies. Longitudinal analyses could explore whether these microbiota differences precede weight changes or result from them. Additionally, mechanistic studies could elucidate how the identified bacterial species influence the BMI z-score and whether modulating their populations can have tangible health benefits.
4.3 Difference in analyzing OSA-related gut microbiome with two 16S rRNA sequencing approaches
Our study offers preliminary evidence that full-length and V3–V4 16S rRNA sequencing methods identify significant correlations between certain gut microbiota and polysomnographic parameters in pediatric OSA. Both techniques demonstrated the capability to discern relationships between the composition of gut microbiota and OSA-related parameters. Notably, taxa such as Bifidobacterium, Escherichia, and Phocaeicola, which are either anaerobic or facultative anaerobic and typically reside in the human gastrointestinal tract, were examined. Previous research suggests that anaerobic conditions may promote the proliferation of Bifidobacterium and Escherichia (80, 81), corroborating our findings of their positive correlations with OSA severity indicators. Conversely, Phocaeicola, known for its distinctive oxygen tolerance (82), demonstrates an inverse correlation with the AI, supporting our observations of its relationship with OSA severity. Hence, NGS methods are valuable in elucidating the contribution of specific gut microbes to OSA pathogenesis.
Distinctly, the full-length sequencing provided a more comprehensive insight into potential OSA-associated metabolic pathways compared to the V3–V4 technique. The full-length approach has been instrumental in identifying correlations between microbial metabolism in diverse environments and AI, as well as in discovering and characterizing new microbial lineages that significantly enrich our understanding of microbial diversity (83). The associations of propanoate metabolism and the metabolism of glycine, serine, and threonine with OSA were also observed (84, 85). The impact of peptidoglycan biosynthesis, vital for bacterial structural integrity and stress tolerance (86), in relation to OSA and hypoxic conditions is novel and merits further exploration. These observed differences likely reflect the distinct genomic regions targeted by each sequencing method, indicating that each technique may provide a unique perspective on microbiome complexity. Furthermore, full-length 16S rRNA sequencing could offer a more nuanced understanding of the pathophysiological interplay between OSA and the gut microbiota.
4.4 Study limitations
The comparison underscores the importance of method selection in microbiome studies. There are some study limitations. First, the choice of sequencing method and analytic pipeline might lead researchers to different conclusions or emphasize certain findings over others (67, 87). It’s a reminder that while partial-length (such as V3–V4) sequencing is more common and might be more cost-effective, there might be trade-offs in terms of the depth and breadth of data acquired (88). Additionally, expanding the scope to include larger cohorts and other disease-associated microbiomes could enrich the preliminary results presented herein. The observed differences in microbial compositions between the two groups, as revealed by the full-length sequencing, can pave the way for longitudinal studies. Second, although certain bacterial species were more predominant in children with OWO than in children with NW and might play roles in metabolism, appetite regulation, or other physiological processes linked to weight, the cause-effect relationships between gut bacteria and weight categories need further investigations. These studies can assess if the microbial differences are a cause or consequence of being overweight or obese and if modulating the microbiota can have therapeutic benefits. Targeted interventions, such as probiotics or dietary changes, could potentially be designed to modulate the gut microbiota in favor of weight regulation.
4.5 Conclusions
The full-length 16S rRNA sequencing offers a promising avenue for microbial research, particularly when high taxonomic resolution is required. However, the choice between it and the V3–V4 region should be context-driven, factoring in the study’s goals, available resources, and the potential trade-offs of each method. Furthermore, the observed advantages of full-length sequencing in differentiating gut microbiota and predictive functions between children with OWO and children with NW, coupled with its ability to correlate these functions with OSA-related indicators, highlight its potential in advancing our understanding of the complex relationship between the gut microbiome and weight. It emphasizes the need for methodological rigor in microbiome meta-analysis and suggests a rich area for future investigations. The associations drawn between gut microbiota composition, metabolic pathways, and BMI z-score offer a promising avenue to understand weight management and overall health better.
Data availability statement
The original contributions presented in the study are publicly available. This data can be found here: https://doi.org/10.6084/m9.figshare.25959100.
Ethics statement
The studies involving humans were approved by the Institutional Review Board of Chang Gung Medical Foundation. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation in this study was provided by the participants’ legal guardians/next of kin.
Author contributions
H-HC: Conceptualization, Data curation, Investigation, Methodology, Writing – original draft, Writing – review & editing. C-GH: Conceptualization, Data curation, Investigation, Methodology, Writing – review & editing. S-HC: Data curation, Investigation, Visualization, Writing – original draft. H-YL: Investigation, Supervision, Writing – review & editing. C-CL: Investigation, Writing – original draft. L-AL: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Project administration, Visualization, Writing – original draft, Writing – review & editing.
Funding
The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was supported by the National Science and Technology Council, Taiwan (grant number 109-2314-B-182-083-MY3) and the Chang Gung Medical Foundation, Taiwan (grant numbers CMRPG3F1091-3 and CMRPG3L0811-2).
Acknowledgments
The authors would like to express their sincere appreciation to Ruo-Chi Wang and Chung-Fang Hsiao from the Department of Otorhinolaryngology, Head and Neck Surgery, at Linkou Chang Gung Memorial Hospital in Taoyuan City, Taiwan, for their invaluable technical assistance. Special thanks are also extended to Ai-Ling Ho of Biotools Co., Ltd., New Taipei City, Taiwan, for her indispensable help with bioinformatics analysis. The contributions of all individuals acknowledged herein significantly enhanced the quality and integrity of this research, and their support is gratefully acknowledged.
Conflict of interest
S-HC was employed by Biotools Co., Ltd.
The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.
Publisher’s note
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Keywords: gut microbiome, synthetic 16S rRNA gene, read lengths, analysis pipelines, Firmicutes/Bacteroidetes ratio, obesity, obstructive sleep apnea
Citation: Chuang H-H, Huang C-G, Chou S-H, Li H-Y, Lee C-C and Lee L-A (2024) Comparative analysis of gut microbiota in children with obstructive sleep apnea: assessing the efficacy of 16S rRNA gene sequencing in metabolic function prediction based on weight status. Front. Endocrinol. 15:1344152. doi: 10.3389/fendo.2024.1344152
Received: 25 November 2023; Accepted: 13 May 2024;
Published: 14 June 2024.
Edited by:
Elpis Vlachopapadopoulou, Panagiotis & Aglaia Kyriakou Children’s Hospital, GreeceReviewed by:
Jiaojiao Huang, Qingdao Agricultural University, ChinaJuliana Cardoso, Universidade Federal de Goiás, Brazil
Copyright © 2024 Chuang, Huang, Chou, Li, Lee and Lee. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Li-Ang Lee, 5738@cgmh.org.tw