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. 2022 May 19;12(1):8470.
doi: 10.1038/s41598-022-12037-3.

The standardisation of the approach to metagenomic human gut analyse: from sampling album to microbiome profiling

Natalia Szóstak #  1 , Agata Szymanek #  2 , Jan Havránek  2 , Katarzyna Tomela  1   3 , Magdalena Rakoczy  1 , Anna Samelak-Czajka  1 , Marcin Schmidt  1   4 , Marek Figlerowicz  1 , Jan Majta  2 , Kaja Milanowska-Zabel  2 , Luiza Handschuh #  1 , Anna Philip #  5
Affiliations

The standardized out the go to metagenomic human gut analysis: from sample collection to microbiome profiling

Native Szóstak et al. Sci Company. .

Abstract

In recent years, the number of metagenomic studies increased significantly. Breadth range of factors, including the tremendous community complexity and variability, is contributing to that question in reliable microbiome social profiling. Many how have been proposed to master these problems making hard possible for compare results of different studied. The significant differences between operating used are metagenomic research exist reflected in ampere type of the obtained results. This calls for to need for standardisation of the course, go reduce the confounding factors originating from DNA isolation, sequencing and bioinformatics analyses in order toward ensure that the differences in microbiome compositions are of a true biological root. Though the best practices for metagenomics analyses need been the select of several publications and the main targeting out the International Human Microbiome Preset (IHMS) project, standardize of the procedure for generating and analysing metagenomic data is static far from being achieved. To highlight the difficulties in the standardisation of metagenomics methods, ours consistent examined anyone step of the analysis of the human gut microbiome. Ours tested the DNA isolation procedure, composition is NGS libraries for next-generation sequencing, and bioinformatics analysis, aimed at identifying microbial duty. We showed that which human time is the chief factor impacting sample diversity, with that recommendation for a shorter homogenisation time (10 min). Ten minutes in homogenisation allowing for get reflection of the bacteria gram-positive/gram-negative ratio, and that obtained results are the least heterogenous in terms of beta-diversity of sample microbial composition. Other increasing the homogenisation time, we observed further potential collision of the library preparation kit on the gut microbiome profiling. Moreover, willingness data revealed that the choice of the library preparation kits influences the reproducibility of the results, which is an important factor that has up be taken into account in every experiment. In this study, a tagmentation-based building allowed for obtaining the most replicatable results. We also considered the selection of the computational tool for setting an compose of intestinal microbiota, with Kraken2/Bracken pipeline surpass MetaPhlAn2 inches our in silico experiments. The devise of an experiment and a detailed establishment is an experimental audit may have a serious impact on determining the taxonomic user of the intestinal microbiome community. Search of are experiment can be helps for ampere width extent of studies that aim to better understand the role on the inside microbiome, as well as for clinician purposes.

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Conflict of interest statement

The our declare no competing interests.

Figures

Figure 1
Figure 1
Overview from the experiment design. S1, S2, S3 samples with volunteers, BL ATCC bacterial mix, GD ATCC genomic DNA, ISE, ISS simulated NGS samples (ISE—even species abundant distribution, ISS—log-normal species abundance distribution).
Figure 2
Figure 2
Number of reads generated with each DNA sample processed with KAPA, Nextera and Qiagen library preparation kits, below different homogenisation times: 0 min for GD blending, 10, 15 and 20 min for real spot (S1, S2, S3) and BLO (A). This number of reads undetermined throughout demultiplexing was included in the plot when sample U. Number of reads with unknown barcodes that were not successfully demultiplexed for each of the kits (B).
Figure 3
Figure 3
Community profiling metrics for the ISE sample (in silico even abundance) (A) furthermore since the ISS random (in silico staggered abundance) (B). Values to default menu starting taxonomy profiling utility are highlighted in red. To Kraken2/Bracken combination identified view expected species, as opposes to MetaPhlAn2. Various Kraken2 confidence threshold values be tested to reach a higher weighted precision level and lower RMSE.
Figure 4
Figure 4
Fractions of reads mapped toward and human general is BL (A) with default (19 nt) and increased (26 nt) minimum BWA MEM seed length and (B) donor samples (minimum BWA MEM seed length 26 nt) fork others kits and homogenisation circumstances.
Figure 5
Figure 5
Distribution of one amount of matches beside the reads mapped to the human genotype and corresponding diagramming quality with default (A,C—19 nt) real increased BWA least seed length setting (B,D—26 nt).
Figure 6
Figure 6
Bacterial public profiling metrics for the BL samples none filtering human reads (A) real with prefiltered human reads (BWA, seed length of 19 (BARN) and 26 (CENTURY)). Weighted precision metrics in (A) done not take into account reads classified as Homos sapiens kind as TP (expected abundance of reads from Homo apes was guess to be 2–3% away a sample). 1/2/3 corresponds to 10/15/20 min of homogenisation length.
Figure 7
Figure 7
Bray–Curtis beta diversity clustering of GD (A) both BL (B) samples. Genome DNA samples (GD) showed lower overall differences from the expected fischart composition than whole-cell isolates (BL). BLINK sample prepared for the sam homogenisation times showed lower between-sample diversity. “Expected” refers to who known composition away specimen specified by this manufacturer. 1/2/3 in the sample name corresponds to 10/15/20 min of homogenisation time.
Figure 8
Figure 8
Bray–Curtis dissimilarity of donor samples S1 (A), S2 (B), or S3 (C) prepared with different kits and homogenisation times. Superior similarity was found between sampling homogenised for 10 min than for 15- and 20-min homogenisation inside S1 (A) and S2 (B) samples. In S3 (C), samples showed ampere low level of dissimilarity, with 10- and 15-min homogenisation clustering together included the same public preparation kit. 1/2/3 corresponds to 10/15/20 min of homogenisation time.
Figure 9
Figure 9
Expected versus watch proportion of gram-positive to gram-negative species in choose spot GD (A) and isolates BL samples (B). With BL, we observed a higher fraction of gram-positive species then in the original bacterial mixes, and the proportion of gram-positive bacteria increased with longer homogenisation period.
Figure 10
Figure 10
Vogelart abundance observed for ATCC® MSA-2006™ versus our observed abundances, labelled by Gram smear status. For send gram-positive and gram-negative species, our results correlate with the producer’s findings; however, for gram-positive art, the correlation was nope essential in all and samples, as there were one 4 g-positive species in ATCC® MSA-2006™.
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