Materials and Methods

Materials and Methods

Sequencing

1 Sample collection

Family members provided fully informed consent to participate in the study by providing samples and related information. Saliva samples were collected by directly expectorating into a 15 ml tube and immediately frozen for storage.

2 Extraction of genome DNA

Extracted total genome DNA from samples using Nucleo spin DNA stool kit (cat No. 740472.50) according to the manufacturer’s instructions. DNA concentration and purity were monitored on 1% agarose gels. According to the concentration, DNA was diluted to 1ng/μL using sterile water.

3 Amplicon Generation

Amplified 16S rRNA genes of 16SV4 region used specific primer(16S V4: 515F-806R) with the barcode. All PCR reactions were carried out with Phusion® High-Fidelity PCR Master Mix (New England Biolabs).

4 PCR Products quantification and qualification

Mix the same volume of 1X loading buffer (contained SYB green) with PCR products and operate electrophoresis on 2% agarose gel for detection. Samples with a bright main strip between 400-450bp were chosen for further experiments.

5 PCR Products Mixing and Purification

PCR products were mixed in equal density ratios. Then, mixture PCR products were purified with the Qiagen Gel Extraction Kit (Qiagen, Germany).

6 Library preparation and sequencing

Sequencing libraries were generated using TruSeq® DNA PCR-Free Sample Preparation Kit (Illumina, USA) following the manufacturer’s recommendations and index codes were added. The library quality was assessed on the Qubit@ 2.0 Fluorometer (Thermo Scientific) and Agilent Bioanalyzer 2100 system. At last, the library was sequenced on an Illumina HiSeq 2500 platform (Illumina, USA)  and 250 bp paired-end reads were generated.

Data analysis

1 Paired-end reads assembly and quality control
1.1 Data split

Assigned paired-end reads to samples based on their unique barcode and truncated by cutting off the barcode and primer sequence.

1.2 Sequence assembly

Paired-end reads were merged using FLASH (V1.2.7) (Magoc and Salzberg, 2011), a very fast and accurate analysis tool, which was designed to merge paired-end reads when at least some of the reads overlap the read generated from the opposite end of the same DNA fragment, and the splicing sequences were called raw tags.

1.3 Data Filtration:

Quality filtering on the raw tags were performed under specific filtering conditions to obtain the high-quality clean tags according to the Qiime (V1.7.0) (Kuczynski et al., 2011) quality controlled process.

1.4 Chimera removal:

The tags were compared with the reference database(Gold database using UCHIME algorithm (Edgar et al., 2011) to detect chimera sequences, and then the chimera sequences were removed. Then the Effective Tags finally obtained.

2 OTU cluster and Species annotation
2.1 OTU Production:

Sequences analysis were performed by Uparse software (Uparse v7.0.1001) (Edgar, 2013)Sequences with ≥97% similarity were assigned to the same OTUs. Representative sequence for each OTU was screened for further annotation.

2.2 Species annotation:

For each representative sequence, the GreenGene Database (McDonald et al., 2012) was used based on RDP classifier(Version 2.2)  (Wang et al., 2007) algorithm to annotate taxonomic information.

2.3 Phylogenetic relationship Construction:

In order to study the phylogenetic relationship of different OTUs, and the difference of the dominant species in different samples(groups), multiple sequence alignment were conducted using the PyNAST software(Version 1.2)  (Caporaso et al., 2010) against the “Core Set” dataset in the GreenGene database.

2.4 Data Normalization:

OTUs abundance information were normalized using a standard of sequence number corresponding to the sample with the least sequences. Subsequent analysis of alpha diversity and beta diversity were all performed basing on this output normalized data.

3 Alpha Diversity

Alpha diversity is applied in analyzing complexity of species diversity for a sample through6 indices, including Observed-species, Chao1, Shannon, Simpson, ACE, Good-coverage. All these indices in our samples were calculated with QIIME(Version 1.7.0) (Kuczynski et al., 2011) and displayed with R software(Version 2.15.3).

Two indices were selected to identify Community richness:

Chao – the Chao1 estimator (Chao, 1984);

ACE – the ACE estimator (Chao et al., 1992) ;

Two indices were used to identify Community diversity:

Shannon – the Shannon index (Seaby R. M. & Henderson, 2006);

Simpson – the Simpson index (Harper, 1999);

One index to characterized Sequencing depth:

Coverage – the Good’s coverage (Seaby R. M. & Henderson, 2006)

4 Beta Diversity

Beta diversity analysis was used to evaluate differences of samples in species complexity, Beta diversity on both weighted and unweighted unifrac were calculated by QIIME software (Version 1.7.0) (Kuczynski et al., 2011).

Non-metric multidimensional scaling(NMDS) was performed to get principal coordinates and visualize from complex, multidimensional data. A distance matrix of weighted or unweighted unifrac among samples obtained before was transformed to a new set of orthogonal axes, by which the maximum variation factor is demonstrated by first principal coordinate, and the second maximum one by the second principal coordinate, and so on. NMDS analysis was displayed by the WGCNA package, stat packages, and ggplot2 package in R software(Version 2.15.3).

Unweighted Pair-group Method with Arithmetic Means(UPGMA) Clustering was performed as a type of hierarchical clustering method to interpret the distance matrix using average linkage and was conducted by QIIME software (Version 1.7.0) (Kuczynski et al., 2011).

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(The above study point to the right place, oral cavity. However, Allergies are not caused by the pathogens but by lack of probiotics.)

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(The above article emphasizes gut microbiota where is not the cause of allergies.)

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(Antibiotic usage may be one of the biggest reasons that cause child allergies.)

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