NGS phylogenetic analysis using target amplicon

Microbial floral analysis from environment, food and feces

Using microbial DNA coverage analysis (following “16S rRNA phylogenetic analysis”), we can provide the layout that characterizes the phylogeny of bacteria. This is done by extracting DNA from samples (soil, water, gut, food samples,etc.) and sequencing the species-specific 16S rRNA region. Furthermore, we can provide insights to the functionality of the organisms from the metadata we obtained though our own analysis platform.

The required amount and delivery method will differ among the types of samples. Please contact us for details.

PlanSequencerCost/Sample
(1〜4 Samples)
Cost/Sample
(5~12 Samples)
Cost/Sample
(13~24 Samples)
Cost/Sample
(>25 Samples)
Metagenome Experiment and AnalysisMiniSeq (V4 Amplicon)
MiSeq (V3-4Amplicon)
$1,000$950$750$550
Experiment ONLY
(until raw data in fast. format)
MiniSeq (V4Amplicon)
MiSeq (V3-4Amplicon)
$950$750$550$350
Analysis ONLYMiniSeq (V4Amplicon)
MiSeq (V3-4Amplicon)
$950$750$550$350

*For MiSeq analysis (V3-4 amplicon), there will be an additional charge of $950 for every sample requested.。

About 16S rRNA phylogenetic analysis

We analyze the sequence pattern diversity of the coding region of 16S rRNA (small subunit of ribosome) common to all microorganisms and clarify the microflora distribution in the sample.

* Phylogenetic analysis targeting the V1 – V3 and V3 – V4 areas is also available

Example of 16S rRNA phylogenetic analysis ~ Microorganisms of Atacama desert ~

【Overview】

The ecological analysis of the soil microbial community in the world has revealed that desert microorganisms are phylogenetically and functionally different from other biological communities and that diversity of function related to nutrient circulation is relatively low. In order to properly assess the impact of climate change on land productivity, it is necessary to determine the resistance of these low diversity microorganisms and deepen their understanding of the potential response to the dry environment. Soil microbial analysis requires analysis across the gradient of dryness. In this study, we utilize such extreme condition present in the Atacama Desert, located in northern part of Chile, to identify the deciding factors that explain the changes in soil microbial community. Soil samples were taken from two transects (Yungay, Baquedano) crossing the Atacama desert from the east to the west. Plot coverage, geochemical measurements, soil relative humidity and temperature are recorded at each point.

【Required Data】

・Metadata from obtained samples

File name:sample-metadata.tsv

https://data.qiime2.org/2018.2/tutorials/atacama-soils/sample_metadata.tsv

・Sequence Data

Only 10% of the actual data will be used here.

File name:forward.fastq.gz

https://data.qiime2.org/2018.2/tutorials/atacama-soils/10p/forward.fastq.gz

File name:reverse.fastq.gz

https://data.qiime2.org/2018.2/tutorials/atacama-soils/10p/reverse.fastq.gz

File name:barcodes.fastq.gz

https://data.qiime2.org/2018.2/tutorials/atacama-soils/10p/barcodes.fastq.gz

・Full Length Sequence OTUs (99%) from Greengenes

File name:gg-13-8-99-nb-classifier.qza

https://data.qiime2.org/2018.2/common/gg-13-8-99-nb-classifier.qza

Analysis result

Sampling place.
Soil samples were collected from two parallel west-east zones across the Atacama desert, from the Pacific near Antofagasta to the slope of the Andean western part near the Argentine border (Figure 1). From an area of 1,000 to 2,000 m in altitude with no vegetation for a few million years(rainfall less than 5 mm) to the dry area located on the west slope of Andean with vegetation with precipitation of 36 to 115 mm It is extending to the east, we can see that the transect is extending to the east. For the location, total of 22 points (12 points in Yungay (YUN; Antofagasta to Paso de Socompa) and 10 points in Baquedano (BAQ; Baquedano to Paso Jama) were chosen for sampling. We sampled three soil microbial communities (amplicon analysis of 16S) at each point.

Figure 1. Locations for sampling (Julia W. Neilson et al. 2017)

Separating sequence data.
Based on the sample barcode described in the metadata, we will distribute the sequence data for each sample. Here, we can obtain information such as how many reads for each sample were obtained, and the quality of the array (Figure 2A, B, Figure 3).

In the obtained table, array/feature information (OTU in QIIME 1) after quality control of each sample are described below.

Figure 2A. Quality scatter plot on Forward array

Figure 2B. Quality scatter plot on Reverse array

Figure 3. Reads per Sample

Figure 4.  Sequence Feature Information

・Performing composition analysis of species

We used Naïve Bayes classifier to assign seeds to array data. Here Greengenes 13.8 with 99% OTUs was used. We also visualized the relative abundance of microorganisms as a bar chart. We then sorted the sample classification level by “Taxnonomic Level” and sample by metadata category “Sort Samples By”.

When samples were classified by mean relative humidity of the soil, archaebacteria were not found in the most dry places (Figure 5A). In addition, bacteria such as Acidobacteria, Proteobacteria, Verrucomicrobia, Nitrospirae, and Elusimicrobia showed a decreasing trend when relative humidity also dropped in the soil (Figure 5B).

Figure 5A. Bar graph showing the relative abundance in the bacterial community (at Kingdom level)

Figure 5B. Bar graph showing relative abundance of bacterial community (Phylum level)

Analyze α diversity and β diversity

α diversity
· Shannon Diversity Index Number of OTUs
· Phylogenetic diversity of Faith
· Evenness (a measure of community uniformity)
Calculates by default.

β diversity
· Jaccard distance (qualitative measure of community dissimilarity)/Bray-Curtis distance (a quantitative measure of community dissimilarities)
· Unweighted UniFrac distance (Qualitative measure of community dissimilarity incorporating systematic relationships between features)
UniFrac distance (quantitative measure of community dissimilarity incorporating the phylogenetic relation between features)

There were no specific bacterial communities among the samples obtained with the two transects of Yungay and Baquedano, and the bacterial community between the two transects was similar (Fig. 6, 7).

Drawing the rarefaction curve based on the diversity of the microbial community (Faith’s phylogenetic diversity index) the analysis revealed that the diversity of the bacterial community declined with the decrease in the average soil relative humidity (Figure 8). Suggesting that the effect of increased dryness on soil microorganisms is amplified in more dry ecosystems.

Figure 6. Scatter plot of PCoA at unweighted UniFrac distance

Figure 7. Box plot at unweighted UniFrac distance

Figure 8. Rare faction curve classified by mean soil relative humidity

Reference

  1. Neilson JW, Califf K, Cardona C, Copeland A, van Treuren W, Josephson KL, Knight R, Gilbert JA, Quade J, Caporaso JG, Maier RM. Significant Impacts of Increasing Aridity on the Arid Soil Microbiome. mSystems. 2017 May 30;2(3). pii: e00195-16. doi: 10.1128/mSystems.00195-16. eCollection 2017 May-Jun. PubMed PMID: 28593197; PubMed Central PMCID: PMC5451488.
    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5451488/#
  2. “Atacama soil microbiome” tutorial
    https://docs.qiime2.org/2018.2/tutorials/atacama-soils/
  3.  Fierer N, Leff JW, Adams BJ, Nielsen UN, Bates ST, Lauber CL, Owens S, Gilbert JA, Wall DH, Caporaso JG. Cross-biome metagenomic analyses of soil microbial communities and their functional attributes. Proc Natl Acad Sci U S A. 2012 Dec 26;109(52):21390-5. doi: 10.1073/pnas.1215210110. Epub 2012 Dec 10. PubMed PMID: 23236140; PubMed Central PMCID: PMC3535587.
    https://www.ncbi.nlm.nih.gov/pubmed/23236140
  4. 微生物群集解析ツール QIIME 2 を使う.Part1.(インストール,ファイルインポート編)
    https://qiita.com/kohei-108/items/5d29bd8bbe1d19fdeec5
  5. 微生物群集解析ツール QIIME 2 を使う.Part2.(QC,FeatureTable生成編)
    https://qiita.com/kohei-108/items/547b2fbdf28fb04c28ea

Example of 16S rRNA phylogenetic analysis ~ Fecal microbial transplantation of children with autism and gastrointestinal disorders

【Overview】

Autism Spectrum Disorder (ASD) is a complex neurobiological disorder that affects social interactions and communication, behavior, interest, and generally associated with repetitive action and restricted patterns. It is involved in complicated interactions between environment and genetic factors, including mutation inherited from his/her mothers (1). Several studies have shown that the severity of ASD correlates with severe gastrointestinal symptoms (GI) such as constipation and diarrhea (2) (3). Many children suffering from ASD are thought to frequently receive oral antibiotic therapy during the first 3 years of age, making the intestinal microbial community unstable (4). Children with ASD have shown that children with ASD have a change in the profile of intestinal microbial communities compared to children without ASD (5) (6) ( 7). Thus researchers have hypothesized that the lack of a beneficial intestinal microbial community impairs neurological health because of the low abundance of fermenting bacteria and the low overall bacterial diversity (8). Here we take into account of Dae-Wook Kang’s study in which the influence of microbial transfer therapy on the composition of the intestinal microbial community of 18 children diagnosed with ASD and GI and ASD symptoms is evaluated (9). In this amplicon analysis, we will introduce analysis method using his data with reference to fecal microbiota transplant (FMT) study: an exercise described in QIIME 2 docs.

【Required Data】

File name:sample-metadata.tsv

https://data.qiime2.org/2018.2/tutorials/fmt/sample_metadata.tsv

File name:fmt-tutorial-demux-1.qza (Only 10% of the actual data will be used here)

https://data.qiime2.org/2018.2/tutorials/fmt/fmt-tutorial-demux-1-10p.qza

File name:fmt-tutorial-demux-2.qza (Only 10% of the actual data will be used here)

https://data.qiime2.org/2018.2/tutorials/fmt/fmt-tutorial-demux-2-10p.qza

【Analysis result】Sequence’s Quality Score

1. Here, we will check the quality of each sample from the fastq file and remove only the low-quality array. The number of leads and quality of the array are displayed as follows (Figure 1A, B).

Figure 1A. Read Frequencies Distribution

Figure 1B. Box plot of sample quality score.

2.  Analysis of microbial communities from obtained samples

We used Naïve Bayes classifier to assign seeds to array data. Here Greengenes 13.8 with 99% OTUs was used. We also visualized the relative abundance of microorganisms as a bar chart. We then sorted the sample classification level by “Taxnonomic Level” and sample by metadata category “Sort Samples By”.

Bacteroides genus and Escherichia genus occupied most of each sample in the control group, donor group, and treatment group, respectively. Both are famous intestinal bacteria and are commonly found commonly from stool samples.

Figure 2. Bar graph showing the relative abundance in the bacterial community

3. Analyze α diversity and β diversity

α diversity
· Shannon Diversity Index Number of OTUs
· Phylogenetic diversity of Faith
· Evenness (a measure of community uniformity)
Calculates by default.

β diversity
· Jaccard distance (qualitative measure of community dissimilarity)/Bray-Curtis distance (a quantitative measure of community dissimilarities)
· Unweighted UniFrac distance (Qualitative measure of community dissimilarity incorporating systematic relationships between features)
UniFrac distance (quantitative measure of community dissimilarity incorporating the phylogenetic relation between features)

The three-dimensional plot of β diversity was color-coded by Treatment-group (Figure 3). Red indicates treatment, blue indicates donor, and orange indicates control. The contribution ratio of Axis 1 (PC 1) is 12.16%, the contribution ratio of Axis 2 (PC 2) is 10.47%, and the contribution ratio of Axis 3 (PC 3) is 8.071%.

We created a box plot of α diversity (Figure 4).

When α, β diversity was divided by Treatment – group, there was no significant difference between treatment, donor, and control.

Figure 3. PCA analysis for β diversity

Figure 4. Box plot for α diversity.

【References】

  1. Dominguez-Bello MG, Costello EK, Contreras M, Magris M, Hidalgo G, Fierer N, et al. Delivery mode shapes the acquisition and structure of the initial microbiota across multiple body habitats in newborns. Proc Natl Acad Sci [Internet]. 2010 Jun 29 [cited 2018 Mar 24];107(26):11971–5. Available from: http://www.ncbi.nlm.nih.gov/pubmed/20566857
  2. Adams JB, Johansen LJ, Powell LD, Quig D, Rubin RA. Gastrointestinal flora and gastrointestinal status in children with autism – comparisons to typical children and correlation with autism severity. BMC Gastroenterol [Internet]. 2011 Dec 16 [cited 2018 Mar 24];11(1):22. Available from: http://www.ncbi.nlm.nih.gov/pubmed/21410934
  3. Chaidez V, Hansen RL, Hertz-Picciotto I. Gastrointestinal Problems in Children with Autism, Developmental Delays or Typical Development. J Autism Dev Disord [Internet]. 2014 May 6 [cited 2018 Mar 24];44(5):1117–27. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24193577
  4. Willing BP, Russell SL, Finlay BB. Shifting the balance: antibiotic effects on host–microbiota mutualism. Nat Rev Microbiol [Internet]. 2011 Apr 28 [cited 2018 Mar 24];9(4):233–43. Available from: http://www.ncbi.nlm.nih.gov/pubmed/21358670
  5. Finegold SM, Dowd SE, Gontcharova V, Liu C, Henley KE, Wolcott RD, et al. Pyrosequencing study of fecal microflora of autistic and control children. Anaerobe [Internet]. 2010 Aug [cited 2018 Mar 24];16(4):444–53. Available from: http://www.ncbi.nlm.nih.gov/pubmed/20603222
  6. Williams BL, Hornig M, Parekh T, Lipkin WI. Application of Novel PCR-Based Methods for Detection, Quantitation, and Phylogenetic Characterization of Sutterella Species in Intestinal Biopsy Samples from Children with Autism and Gastrointestinal Disturbances. MBio [Internet]. 2012 Jan 10 [cited 2018 Mar 24];3(1):e00261-11-e00261-11. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22233678
  7. Song Y, Liu C, Finegold SM. Real-Time PCR Quantitation of Clostridia in Feces of Autistic Children. Appl Environ Microbiol [Internet]. 2004 Nov 1 [cited 2018 Mar 24];70(11):6459–65. Available from: http://www.ncbi.nlm.nih.gov/pubmed/15528506
  8. Kang D-W, Park JG, Ilhan ZE, Wallstrom G, LaBaer J, Adams JB, et al. Reduced Incidence of Prevotella and Other Fermenters in Intestinal Microflora of Autistic Children. Gilbert JA, editor. PLoS One [Internet]. 2013 Jul 3 [cited 2018 Mar 24];8(7):e68322. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23844187
  9. Kang D-W, Adams JB, Gregory AC, Borody T, Chittick L, Fasano A, et al. Microbiota Transfer Therapy alters gut ecosystem and improves gastrointestinal and autism symptoms: an open-label study. Microbiome [Internet]. BioMed Central; 2017 Dec 23 [cited 2018 Mar 24];5(1):10. Available from: http://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-016-0225-7