16S Metagenomics report

First、Project information

Second、Workflow

1. Sequencing process

16S metagenomics library was constructed using two rounds of PCR reactions. The PCR primers,

forward

(5’-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG-3’)

and reverse

(5’-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGGACTACHVGGGTWTCTAAT-3’),

were designed to amplify the V3V4 domain of bacterial 16S ribosomal DNA as well as adding adaptor sequences. Illumina index sequences were subsequently attached into the amplicons in the second PCR.

PCR amplification was performed in a 50 μl reaction volume containing 25 μl 2X Phusion High-Fidelity PCR Master Mix (New Enagland Biolabs), 0.2 μM of each forward and reverse primer, and 100 ng DNA template. The reaction conditions consisted of an initial 98°C for 30sec, followed by 30 cycles of 98°C for 10 sec, 55°C for 30 sec, and 72°C for 30 sec, as well as a final extension of 72°C for 5 min. The number of cycle for second PCR was 10.

Next, amplified products were checked by 2 % agarose gel electrophoresis and were purified using the AMPure XP PCR Purification Kit (Agencourt). Quantification was performed by using Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific) on Qubit 2.0 Fluorometer (Thermo Fisher Scientific) and Light cycler 96 (Roche)- all according to respective manufacturer instructions. The final purified libraries were applied for cluster generation and sequencing on the MiSeq using V3 600 cycles kit.

2. Raw data process

3. Data analysis workflow

Third、Analysis results

1. QC results

SampleID	Raw.Reads	QC	QC.rate	Merge	Merge.rate	OTU.cluster	OTU.cluster.rate	Alignment	Alignment.rate	Represent.OTU	Mapped.OTU	Mapped.OTU.rate	Number.of.Tax
5-2-F10	88539	74129	83.72%	74057	99.90%	39095	52.79%	38774	99.18%	12	11	91.67%	9
5-2-F11	143479	132752	92.52%	132317	99.67%	118977	89.92%	109463	92.00%	67	59	88.06%	44
5-2-F12	140652	122229	86.90%	122116	99.91%	73277	60.01%	62604	85.43%	40	32	80.00%	27
5-2-F13	117505	102245	87.01%	102057	99.82%	52123	51.07%	48877	93.77%	23	20	86.96%	19
5-2-F14	144270	124658	86.41%	124546	99.91%	73044	58.65%	66811	91.47%	24	21	87.50%	17
5-2-F8	105788	97708	92.36%	97180	99.46%	83329	85.75%	76551	91.87%	67	62	92.54%	51
5-2-F9	128612	110167	85.66%	110033	99.88%	71066	64.59%	71066	100.00%	12	12	100.00%	10
5-2-M1	146199	125347	85.74%	125208	99.89%	70072	55.96%	65419	93.36%	24	19	79.17%	18
5-2-M2	96796	82039	84.75%	81904	99.84%	41094	50.17%	37850	92.11%	22	17	77.27%	15
5-2-M3	95485	71051	74.41%	70918	99.81%	34848	49.14%	32115	92.16%	21	19	90.48%	15
5-2-M4	116560	100152	85.92%	100057	99.91%	51865	51.84%	49116	94.70%	22	19	86.36%	15
5-2-M5	108369	93771	86.53%	93684	99.91%	51858	55.35%	45594	87.92%	41	37	90.24%	30
5-2-M6	120432	102599	85.19%	102411	99.82%	49687	48.52%	45650	91.88%	29	25	86.21%	20
5-2-M7	113999	97404	85.44%	97335	99.93%	61089	62.76%	61083	99.99%	19	18	94.74%	15
5-20-F10	127201	108755	85.50%	108668	99.92%	57792	53.18%	57191	98.96%	23	21	91.30%	20
5-20-F11	109253	93779	85.84%	93698	99.91%	50007	53.37%	47572	95.13%	26	22	84.62%	18
5-20-F12	150373	128307	85.33%	128164	99.89%	75561	58.96%	72493	95.94%	33	30	90.91%	29
5-20-F13	151224	128241	84.80%	128136	99.92%	71281	55.63%	70059	98.29%	32	30	93.75%	27
5-20-F14	140689	119599	85.01%	119479	99.90%	63151	52.86%	58574	92.75%	34	30	88.24%	26
5-20-F8	136456	117599	86.18%	117525	99.94%	62035	52.78%	59418	95.78%	31	25	80.65%	25
5-20-F9	114879	97230	84.64%	97131	99.90%	48557	49.99%	47724	98.28%	18	16	88.89%	14
5-20-M1	140154	119488	85.25%	119396	99.92%	70010	58.64%	67861	96.93%	20	16	80.00%	15
5-20-M2	150905	130488	86.47%	130413	99.94%	77993	59.80%	76477	98.06%	21	18	85.71%	18
5-20-M3	130723	113331	86.70%	113263	99.94%	59241	52.30%	55331	93.40%	21	17	80.95%	15
5-20-M4	178332	153427	86.03%	153324	99.93%	99067	64.61%	86607	87.42%	45	41	91.11%	34
5-20-M5	144602	124905	86.38%	124816	99.93%	67653	54.20%	60245	89.05%	33	27	81.82%	26
5-20-M6	147064	125541	85.36%	125405	99.89%	70268	56.03%	63354	90.16%	40	36	90.00%	31
5-20-M7	177935	153409	86.22%	153298	99.93%	92849	60.57%	90826	97.82%	24	21	87.50%	17

QC: # of sequencing reads passed by the quality threshold

Merge: # of merged sequencing reads

OTU.cluster: # of clustered sequencing reads

Alignment: # of sequencing reads mapped to Silva database

Represent.OTU: # of clusters

Mapped.OTU: # of clusters mapped to Silva database

Number.of.Tax: # of Taxonomy

Path: ./result/OTU & QC/

2. Taxonomy results

Barplot

According to taxonomy annotation, top 15 bacteria with relative abundance in each level (Phylum, Class, Order, family, Genus, Species) are selected for each sample or group and generate a histogram of relative abundance. Bacteria with lower relative abundance are merged into “Others”.

Path: ./result/Abundance/

Case & Control

Phylum

Class

Order

Family

Genus

Species

Cas0_BF

Phylum

Class

Order

Family

Genus

Species

Cas1_BF

Phylum

Class

Order

Family

Genus

Species

Gender

Phylum

Class

Order

Family

Genus

Species

Heatmap

The heatmap is drawn according to the taxonomy annotation and relative abundance of all samples (groups) in each level. “Square root” is applied to numerical conversion for relative abundance.

Path: ./result/Heatmap/

Case & Control

Phylum

Species

Cas0_BF

Phylum

Species

Cas1_BF

Phylum

Species

Gender

Phylum

Species

3. Alpha Diversity

Alpha Diversity analysis is to evaluate the diversity of the microbial community in a sample or grouping. The diversity analysis of a single sample can reflect the microbial diversity (Chao index) and the main bacterial diversity (Shannon index, Inverse Simpson) of the microbial community in the sample. The definition of “Chao index” is regardless of the relative abundance of bacteria, whether they are present or not, the codes of 0 and 1 are used to evaluate the number of bacteria. Diversity of main bacteria, calculate the number of bacteria with high relative abundance, bacteria are ignored in the measurement due to low relative abundance, mainly used to evaluate the number of bacteria with high relative abundance.

Path: ./result/ALPHA/

Chao

Case & Control

Cas0_BF

Cas1_BF

Gender

Inverse-Simpson

Case & Control

Cas0_BF

Cas1_BF

Gender

Shannon

Case & Control

Cas0_BF

Cas1_BF

Gender

Rarefaction curve

The rarefaction curve randomly select a certain number of individuals among the samples, count the number of species represented by these individuals, and construct the curve with the number of individuals and species. It can be used to compare the abundance of species in samples with different amounts of sequencing data, and it can also be used to show whether the amount of sequencing data of a sample is reasonable. Using the method of random sampling of sequences, construct a rareaction curve based on the number of sequences drawn and the number of OTUs they can represent. When the curve tends to be flatten, it means that the amount of sequencing data is reasonable, and more data will only generate a small amount of new data. On the contrary, it indicates that more new OTUs may be generated by continuing the sequencing.

Venn-diagram

Venn diagrams can be used to count the number of common and unique species (such as OTU) in multiple groups or samples, and can more intuitively show the similarity and overlap of the number of species (such as OTU) in environmental samples. Usually,16S analysis uses a similar level of 97% OTU or other taxonomic level sample tables.

2groups

3groups

4groups

4. Beta Diversity

Beta diversity (Distance matrix between samples) is to compare the composition of microbial communities between different samples. The larger value represents the greater the difference in species distribution between samples. According to taxonomy annotations and OTUs abundance information, merge the OTUs information of the same classification to obtain an abundance table. The systematic evolution and relative abundance relationship between microbial sequences in each sample are used to calculate the distance matrix between samples or groups. Principal Coordinates Analysis (PCoA, Principal Coordinates Analysis) is used to reduce the dimensionality of the sample group.

Path: ./result/BETA/

Unweighted

Weighted

5. Differential analysis

Significant difference analysis

The Kruskal-Wallis test (also known as the H test method) in the non-matrix method is used for analysis to determine whether there is a difference in the median of two or more groups. If the chi-square value of the overall test is statistically significant, then the null hypothesis is rejected, which means that at least one pair of groups has an unequal average level. As for which pairs are different, a post-mortem comparison is required. The number of samples in each group for KW verification must be at least 5 or more.

In this analysis, three conditions were used for significance screening: (a) Select the H test method p.value <0.05 (b) Fold change more than doubled (c) Case group or Control group, the average relative abundance of at least one group exceeds 0.5% .

Path: ./result/DEG/Sig_Bac_Visual

Cas0_BF

Cas1_BF

Cas0 vs Cas1

Con0 vs Con1

Gender

Summarize_Table

X	Orientation	Mean_seperation	KW.P.value	KW.FDR	ANOVA.P.value	ANOVA.FDR	FC	FC_MEAN_UP	FC_MEAN_DOWN	FC_SEM_UP	FC_SEM_DOWN	UP	DOWN	Sign.KW	Sign.ANOVA	Sign.Both
k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Lactobacillaceae;g__Lactobacillus;s__Lactobacillus animalis	Cas0_Cas1	Overall	0.1116108	0.5258346	0.0342102	0.5144687	0.2620754	0.042240	0.161175	0.0131833	0.0760204	Cas1	Cas0	0	1	1
k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Clostridiaceae;g__Clostridium;s__Clostridium disporicum	Cas0_Cas1	Overall	0.0344914	0.5258346	0.0376441	0.5144687	Inf	0.134360	0.000000	0.0354972	0.0000000	Cas1	Cas0	1	1	1
k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Clostridiaceae;g__Clostridium;s__Clostridium saudiense	Cas0_Cas1	Overall	0.0031599	0.1295557	0.0020703	0.0848838	0.0000000	0.000000	0.026950	0.0000000	0.0116491	Cas1	Cas0	1	1	1
k__Bacteria;p__Firmicutes;c__Erysipelotrichia;o__Erysipelotrichales;f__Erysipelotrichaceae;gErysipelatoclostridium;s[Clostridium] cocleatum	Cas0_Cas1	Overall	0.0479527	0.5258346	0.0730814	0.5866132	0.2291892	0.001060	0.004625	0.0008207	0.0020878	Cas1	Cas0	1	0	1
k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Rikenellaceae;g__Alistipes;s__Alistipes senegalensis	Cas0_BF	Overall	0.0138744	0.2081161	0.0304154	0.4562312	Inf	0.041050	0.000000	0.0145647	0.0000000	After	Before	1	1	1
k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Clostridiaceae;g__Clostridium;s__Clostridium disporicum	Cas0_BF	Overall	0.0138744	0.2081161	0.0003426	0.0102788	0.0000000	0.000000	0.156100	0.0000000	0.0214490	After	Before	1	1	1
k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Clostridiaceae;g__Clostridium;s__Clostridium saudiense	Cas0_BF	Overall	0.0472209	0.2833254	0.0599814	0.4633202	Inf	0.026950	0.000000	0.0116491	0.0000000	After	Before	1	0	1
k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;gLachnoclostridium;s[Clostridium] scindens	Cas0_BF	Overall	0.0472209	0.2833254	0.0770693	0.4633202	Inf	0.008925	0.000000	0.0041876	0.0000000	After	Before	1	0	1
k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Ruminococcaceae;g__Neglecta;s__Neglecta timonensis	Cas0_BF	Overall	0.0472209	0.2833254	0.1092441	0.4633202	Inf	0.014250	0.000000	0.0075822	0.0000000	After	Before	1	0	1
k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Tannerellaceae;g__Parabacteroides;s__Parabacteroides goldsteinii	Cas1_BF	Overall	0.0283060	0.4251487	0.0275870	0.4407532	0.2823173	0.045710	0.161910	0.0150630	0.0460742	After	Before	1	1	1
k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Lactobacillaceae;g__Lactobacillus;s__Lactobacillus animalis	Cas1_BF	Overall	0.1597867	0.4251487	0.0423044	0.4407532	0.2965043	0.042240	0.142460	0.0131833	0.0439188	After	Before	0	1	1
k__Bacteria;p__Firmicutes;c__Erysipelotrichia;o__Erysipelotrichales;f__Erysipelotrichaceae;gErysipelatoclostridium;s[Clostridium] cocleatum	Cas1_BF	Overall	0.0022695	0.0998583	0.0723346	0.4407532	0.0490968	0.001060	0.021590	0.0008207	0.0107229	After	Before	1	0	1

LEfSe analysis

LDA Effect Size (LEfSe) is an algorithm for discovering biomarkers and features in high-dimensional data. Firstly, the Kruskal-Wallis (KW) sumrank test was used to test the abundance difference of each species between the two groups, and then the Linear Discriminant Analysis (LDA) method was used to evaluate the classification utility of all the different species for the groups.

Cut off :

Kruskal-Wallis test p value > 0.05

|LDA score| < 2

Path: ./result/DEG/lefse

Barplot

Cladogram

6. Co-occurence network

Co-occurence analysis use FastSpar to calculate the correlation coefficient between different level species, and then use the absolute value of the correlation coefficient 0.6 to determine the positive correlation and the negative correlation to make a network diagram.

(This example uses a correlation coefficient of 0.1)

Co-occurence network Edge ( |correlation value| > 0.6)

◎ Green: positive

◎ Red: negitave

Node Size: Depend on degree number

Edge width: Depend on correlation coefficient value

Path: ./result/Network/

Con

Cas

7. Advanced analysis

Tax4fun_pathway

The KEGG (Kyoto Encyclopedia of Genes and Genomes) database was launched in 1995 and has since developed into one of the most representative biological metabolic pathway database in the biological world. The database divides metabolic pathways into six categories, namely Cellular Processes, Environmental Information Processing, Genetic Information Processing, Human Diseases, Metabolism, Organismal Systems.

This analysis is based on 16S sequencing data, Silva database and Tax4fun tools to predict the metabolic pathway function of KEGG database. Use Kruskal Wallis two-sample test for P.value, and use 1.5 times the Fold change of Pathway activity as the screening criteria.

Path: ./result/PATHWAY/

Cas0_BF

Cas1_BF

Gender

Summarize_Table

Pathway	Orientation	Mean_seperation	KW.P.value	KW.FDR	ANOVA.P.value	ANOVA.FDR	FC	FC_MEAN_UP	FC_MEAN_DOWN	FC_SEM_UP	FC_SEM_DOWN	N_UP	N_DOWN	UP	DOWN	Sign.ANOVA
ko00100; Steroid biosynthesis	Cas0_BF	Overall	0.0209213	0.0415233	0.0034788	0.0111406	2.4802253	0.0000057	0.0000023	0.0000003	7.00e-07	4	4	After	Before	1
ko00140; Steroid hormone biosynthesis	Cas0_BF	Overall	0.0209213	0.0415233	0.0008187	0.0084341	2.3645966	0.0007303	0.0003089	0.0000594	3.34e-05	4	4	After	Before	1
ko00540; Lipopolysaccharide biosynthesis	Cas0_BF	Overall	0.0209213	0.0415233	0.0012049	0.0084341	2.4545654	0.0029277	0.0011927	0.0002913	7.87e-05	4	4	After	Before	1
ko00601; Glycosphingolipid biosynthesis - lacto and neolacto series	Cas0_BF	Overall	0.0209213	0.0415233	0.0014072	0.0084341	2.9032122	0.0000895	0.0000308	0.0000103	1.90e-06	4	4	After	Before	1
ko03015; mRNA surveillance pathway	Cas0_BF	Overall	0.0209213	0.0415233	0.0067704	0.0176030	2.1289147	0.0000097	0.0000046	0.0000012	3.00e-07	4	4	After	Before	1
ko04113; Meiosis - yeast	Cas0_BF	Overall	0.0209213	0.0415233	0.0007715	0.0084341	3.2704205	0.0008542	0.0002612	0.0000792	5.19e-05	4	4	After	Before	1
ko04142; Lysosome	Cas0_BF	Overall	0.0209213	0.0415233	0.0086779	0.0204942	2.1488534	0.0022379	0.0010414	0.0003053	6.70e-05	4	4	After	Before	1
ko04144; Endocytosis	Cas0_BF	Overall	0.0209213	0.0415233	0.0089213	0.0204942	0.4286896	0.0000007	0.0000015	0.0000001	2.00e-07	4	4	After	Before	1
ko04210; Apoptosis	Cas0_BF	Overall	0.0209213	0.0415233	0.0035505	0.0111406	11.2872505	0.0003950	0.0000350	0.0000775	5.00e-06	4	4	After	Before	1
ko04612; Antigen processing and presentation	Cas0_BF	Overall	0.0209213	0.0415233	0.0019340	0.0084341	2.4579523	0.0000646	0.0000263	0.0000073	6.00e-07	4	4	After	Before	1
ko04723; Retrograde endocannabinoid signaling	Cas0_BF	Overall	0.0209213	0.0415233	0.0010217	0.0084341	4.2206170	0.0001510	0.0000358	0.0000184	6.00e-06	4	4	After	Before	1
ko04912; GnRH signaling pathway	Cas0_BF	Overall	0.0209213	0.0415233	0.0089213	0.0204942	0.4286896	0.0000007	0.0000015	0.0000001	2.00e-07	4	4	After	Before	1
ko04914; Progesterone-mediated oocyte maturation	Cas0_BF	Overall	0.0209213	0.0415233	0.0019340	0.0084341	2.4579523	0.0000646	0.0000263	0.0000073	6.00e-07	4	4	After	Before	1
ko04974; Protein digestion and absorption	Cas0_BF	Overall	0.0209213	0.0415233	0.0022972	0.0087833	4.2660718	0.0006629	0.0001554	0.0001001	4.60e-06	4	4	After	Before	1
ko04975; Fat digestion and absorption	Cas0_BF	Overall	0.0209213	0.0415233	0.0028337	0.0103770	38.0761257	0.0000026	0.0000001	0.0000005	0.00e+00	4	4	After	Before	1
ko05215; Prostate cancer	Cas0_BF	Overall	0.0209213	0.0415233	0.0021661	0.0087833	3.2311396	0.0000849	0.0000263	0.0000114	6.00e-07	4	4	After	Before	1
ko03015; mRNA surveillance pathway	Cas1_BF	Overall	0.0081510	0.1640263	0.0260165	0.1878971	3.7760112	0.0000190	0.0000050	0.0000057	6.00e-07	10	10	After	Before	1
ko03015; mRNA surveillance pathway	Gender	Overall	0.0308082	0.6791520	0.0337744	0.4082970	0.3676912	0.0000057	0.0000155	0.0000007	4.30e-06	14	14	Male	Female	1