Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Mar 24;11(1):6781.
doi: 10.1038/s41598-021-86378-w.

Evaluation of 16S rRNA primer sets for characterisation of microbiota in paediatric patients with autism spectrum disorder

Affiliations

Evaluation of 16S rRNA primer sets for characterisation of microbiota in paediatric patients with autism spectrum disorder

L Palkova et al. Sci Rep. .

Abstract

intestinal microbiota is becoming a significant marker that reflects differences between health and disease status also in terms of gut-brain axis communication. Studies show that children with autism spectrum disorder (ASD) often have a mix of gut microbes that is distinct from the neurotypical children. Various assays are being used for microbiota investigation and were considered to be universal. However, newer studies showed that protocol for preparing DNA sequencing libraries is a key factor influencing results of microbiota investigation. The choice of DNA amplification primers seems to be the crucial for the outcome of analysis. In our study, we have tested 3 primer sets to investigate differences in outcome of sequencing analysis of microbiota in children with ASD. We found out that primers detected different portion of bacteria in samples especially at phylum level; significantly higher abundance of Bacteroides and lower Firmicutes were detected using 515f/806r compared to 27f/1492r and 27f*/1495f primers. So, the question is whether a gold standard of Firmicutes/Bacteroidetes ratio is a valuable and reliable universal marker, since two primer sets towards 16S rRNA can provide opposite information. Moreover, significantly higher relative abundance of Proteobacteria was detected using 27f/1492r. The beta diversity of sample groups differed remarkably and so the number of observed bacterial genera.

PubMed Disclaimer

Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Comparative analysis of bacterial community at genus level of stool samples of children with ASD analysed by three different primer sets visualized by PCA. Analysis was performed at genus level using all detected (2731) bacterial genera. Set#1—515f/806r, set#2—27f/1492r, set#3—27f*/1495r. Unit variance scaling is applied to rows; SVD with imputation is used to calculate principal components. X and Y axis show principal component 1 and principal component 2 that explain 30.4% and 9.7% of the total variance, respectively. Prediction ellipses are such that with probability 0.95, a new observation from the same group will fall inside the ellipse. N = 29 data points.
Figure 2
Figure 2
Differences in relative abundance of bacterial phyla in paediatric ASD samples amplified with different primer sets. Primer set#1 (515f/806r) adhered significantly more to Bacteroidetes than primer set#2 (27f/1492r) and set#3 (27f*/1495r). For Firmicutes detection primer set#3 was the most favourable followed by set#2. Primer set#3 represented balanced ratio of detected OTUs between Bacteroidetes and Firmicutes phyla. The highest abundance of Proteobacteria was detected by primer set#2 compared to set#1 and 3. The level of significance ≤ 0.01 was applied.
Figure 3
Figure 3
Firmicutes to Bacteroidetes ratio in faecal samples of ASD patients visualized by heatmap. Increased relative abundance of Bacteroidetes compared to Firmicutes in samples in that for amplification of 16S rRNA primer set#1 was used, compared to samples processed with primer set#2 and set#3.
Figure 4
Figure 4
Number of detected bacterial genera in stool samples of paediatric ASD patients determined with three primer sets. Primer set#2 (27f/1492r) matched significantly to more genera than primer set#1 (515f/806r) and set#3 (27f*/1495r). The level of significance ≤ 0.01 was applied.
Figure 5
Figure 5
Comparison of relative abundance of bacterial composition between three primer sets amplifying 16S rRNA based on Spearman rank correlation coefficient. The most similar results are obtained with the primer set#2 and primer set#3. For the analysis 244 the most abundant bacterial genera and inclusion criteria for their presence in all samples was applied.
Figure 6
Figure 6
Comparison of the detection capability of three primer sets based on the least abundant bacterial genera with significantly different abundance among groups. For data analysis student´s t-test was performed. Bacterial genera with significantly altered abundance between compared datasets (p ≤ 0.005) were visualized using heatmap.
Figure 7
Figure 7
(a) Relative abundance of bacterial species that have been observed to be elevated in children with ASD. The level of significance ≤ 0.01 was applied. (b) Relative abundance of bacterial species that have been observed to be decreased in children with ASD. The level of significance ≤ 0.01 was applied.

Similar articles

Cited by

References

    1. Turnbaugh PJ, Ley RE, Hamady M, Fraser-Liggett CM, Knight R, Gordon JI. The human microbiome project. Nature. 2007;449:804–810. doi: 10.1038/nature06244. - DOI - PMC - PubMed
    1. Lane DJ, Pace B, Olsen GJ, Stahlt DA, Sogint ML, Pace NR. Rapid determination of 16S ribosomal RNA sequences for phylogenetic analyses. Proc. Natl. Acad. Sci. U. S. A. 1985;82:6955–6959. doi: 10.1073/pnas.82.20.6955. - DOI - PMC - PubMed
    1. Wu W, Chen C, Panyod S, Chen R, Wu M, Sheen L. Optimization of fecal sample processing for microbiome study d The journey from bathroom to bench. J. Formos Med. Assoc. 2019;118:545–555. doi: 10.1016/j.jfma.2018.02.005. - DOI - PubMed
    1. Lim MY, Song E, Kim SH, Lee J, Nam Y. Comparison of DNA extraction methods for human gut microbial community profiling. Syst. Appl. Microbiol. 2018;41:151–157. doi: 10.1016/j.syapm.2017.11.008. - DOI - PubMed
    1. Milani C, Hevia A, Foroni E, Duranti S, Turroni F, Luigi GA, Sanchey B, Martín R, Gueimonde M, Van SD, et al. Assessing the fecal microbiota: an optimized ion torrent 16S rRNA gene-based analysis protocol. PLoS ONE. 2013;8:e68739. doi: 10.1371/journal.pone.0068739. - DOI - PMC - PubMed

Publication types

Substances