微生物测序可变区V4和V3V4如何选择，实例数据

目前针对扩增子测序可选择的测序平台和方案很多，不同平台的读长和适用的测序区段以及优势各有不同。16s测序主要的测序区段包括v4、v3v4，v1v2，v6，此外还有全长等不同的区段选择，不同可变区或全长由于引物的不同以及不同种属相应区段内的变异多样性差异，对菌属的丰度评估会有一定的差异。

从长度来看，全长16s长度为1.5kb左右，单菌落的16s全长sanger一代测序仍然是菌种鉴定的主要手段，纳米孔和pacbio的三代测序可以高通量的获得全长序列，对于希望更高分辨率的分析菌种的研究有一定优势。三代的测序准确度目前逐渐改进，直接测序准确度可以在90%以上，纠错后可以提高到97~99%以上，已足够提供高精度的分类。三代目前主要问题在于建库成本相对较高，通过使用barcode可以降低部分但仍然偏高，此外普遍测序深度相对于二代测序要低许多。

目前最主要的可变区选择是v4区和v3v4区，v4区长度为256bp左右，加上两侧引物长度为290bp左右，使用双端2x250bp或2x150bp可以测通，此外如454、life、illumina hiseq 4000的测序平台读长也可以主要涵盖该区段读长。例如采用illumina hiseq测序平台对该项目进行双端测序(paired-end)，测序得到了fastq格式的原始数据（样本对应一对序列s_1.fastq和s_2.fastq）。再配对拼接成单条序列。其引物通用性相对是所有可变区中最高的，大量的大规模菌群调查研究都采用v4区作为检测区域，包括人体菌群研究如：hmp，肠道菌群如美国肠道计划agp，欧洲的fgfp等，以及全球土壤菌群调查，目前仍然是国际研究中使用最广泛和认可的检测区域。

illumina的miseq提供了长达2x300bp以及hiseq2500和最近的novoseq提供有2x250bp的测序方案，为进一步利用读长，目前有相当一部分研究选择v3v4区，该区段长度在460bp左右，相较于v4度多出了v3区段约100bp左右的片段，在少部分菌属中可以增加一定分辨率。经过对比，v3v4区的检测结果和v4区在绝大部分菌属中的丰度一致，但由于引物不同，在少量菌属中丰度会有不同偏向，v3v4从otu层面上并未发现较v4区有明显增加。引物的选择和提取、储存方法是影响菌群检测丰度构成的主要因素，不同研究之间的比较需要考虑到实验方案的一致，相同的方案可以直接比较。

同一批小鼠粪便样本v4（10万 clean reads）和 v3v4(5万clean reads)测序数据比较:

原始序列数据：

v4（下表）

v3v4（下表）

以上两表是对原始序列数据进行统计，表中可以看出有效序列tags、高质量序列clean_tags、otus数量  v4区都远高于v3v4区。v4区测序获得下机数据在13万条左右，v4区测序获得的下机数据在5万条左右。

alpha多样性指数比较：

v4（下表）

v3v4（下表）

以上两个表分别是对alpha多样性指数计算的结果比较

chao1 指数和ace指数是用来评估样本中所含otu数目的指数，从chao1 指数和ace指数可以看出，用 v4测序获得的结果要明显大于v3v4的结果。这是因为v4测序通量更高，测序深度更好，每个样下机的测序数据可以到10万条以上，一般在13万条左右，所以经过序列比对获得的otu数目更多，相比较用v3v4测序每个样下机的数据大约在4到5万条左右，经过序列比对获得的otu相对少一点。

shannon指数和simpson指数是用来评估菌群的丰富度和均一度 的。从shannon指数和simpson指数，用v4和v3v4测序指数相差不大，或v4比v3v4略高一点，证明两种测序之间菌群的丰富度多样性和均一度叫接近。

物种主要构成比较：

v4（下表）

v4属水平前10个物种构成：lactobacillus、akkermansia、helicobacter、allobaculum、desulfovibrio、adlercreutzia、odoribacter、bacteroides、prevotella、[prevotella]

v3v4（下表）

v3v4属水平前10个物种构成：lactobacillus、adlercreutzia、flexispira、allobaculum、desulfovibrio、prevotella、odoribater、oscillospira、[prevotella]、bacteroides

从前10个物种构成来看，有8个是相同的，物种的主要构成基本一致，测序的稳定性较好。从种类来看，v3v4测到的属水平个数稍多一点。

各分类水平鉴定到的物种种类比较： 

v4（下表）

v3v4(下表)

以上两张表代表了每个样本在各分类水平上鉴定到的物种种类数。从整体上来看，分别用v4和v3v4测序得到的数据，在各分类水平上鉴定到的物种个数相对比较稳定和接近，（尤其在目水平和科水平上）。用v3v4测序获得的物种数比v4相对稍多一点，但是在属水平和种水平则反而是v4更丰富，最终鉴定到的物种个数也跟该样本的测序质量有关。

最后附几篇顶级杂志发表的16s v4区的文章

 Poyet, M., et al. "A library of human gut bacterial isolates paired with longitudinal multiomics data enables mechanistic microbiome research." Nature medicine 25.9 (2019): 1442-1452.

16S library preparation and sequencing. 16S rRNA gene libraries targeting the V4 region of the 16S rRNA gene were prepared by first normalizing template concentrations and determining optimal cycle number by way of qPCR. Two 25 µL reactions for each sample were amplified with 0.5 units of Phusion with 1X High Fidelity buffer, 200 μM of each dNTP, 0.3 μM of 515 F( 5′- AATGATACGGCGACCACCGAGATCTACACTATGGTAATTGTGTGCCAGCMGCCGCGGTAA-3′) and 806rcbc0 (5′- CAAGCAGAAGACGGCATACGAGATTCCCTTGTCTCCAGTCAGTCAGCCGGACTACHVGGGTWTCTAAT-3′).

Tito, Raul Y., et al. "Population-level analysis of Blastocystis subtype prevalence and variation in the human gut microbiota." Gut 68.7 (2019): 1180-1189.

We profiled stool samples from 616 healthy individuals from the FGFP cohort as well as 107 patients with IBD using amplicon sequencing targeting the V4 variable region of the 16S rRNA and 18S rRNA genes.

Call, Lee, et al. "Metabolomic signatures distinguish the impact of formula carbohydrates on disease outcome in a preterm piglet model of NEC." Microbiome 6.1 (2018): 111.

Gut contents and mucosal samples were collected and analyzed for microbial profiles by sequencing the V4 region of the 16S rRNA gene. Metabolomic profiles of cecal contents and plasma were analyzed by LC/GC mass spectrometry

Wang, Chao, et al. "High-salt diet has a certain impact on protein digestion and gut microbiota: a sequencing and proteome combined study." Frontiers in Microbiology 8 (2017): 1838.

In this study, C57BL/6J mice were fed low- or high-salt diets (0.25 vs. 3.15% NaCl) for 8 weeks, and then gut contents and feces were collected. Fecal microbiota was identified by sequencing the V4 region of 16S ribosomal RNA gene.

Bai, J., Y. Hu, and D. W. Bruner. "Composition of gut microbiota and its association with body mass index and lifestyle factors in a cohort of 7–18 years old children from the American Gut Project." Pediatric obesity 14.4 (2019): e12480.

AGP sequenced the V4 region of 16S rRNA gene

Luthold, Renata V., et al. "Gut microbiota interactions with the immunomodulatory role of vitamin D in normal individuals." Metabolism 69 (2017): 76-86.

The association between 25(OH)D and fecal microbiota (16S rRNA sequencing, V4 region) was tested by multiple linear regression.

Iszatt, Nina, et al. "Environmental toxicants in breast milk of Norwegian mothers and gut bacteria composition and metabolites in their infants at 1 month." Microbiome 7.1 (2019): 34.

Child fecal samples were characterized by 16S rRNA gene amplicon sequencing of the V4 region. We used Deblur, a novel sub-operational taxonomic-unit (sub-OTU) approach that provides a higher resolution than OTU-based analyses.

Vangay, Pajau, et al. "US immigration westernizes the human gut microbiome." Cell 175.4 (2018): 962-972.

We performed amplicon-based sequencing of the 16S rRNA gene V4 region on 550 stool samples (one sample per participant).

Suez, Jotham, et al. "Post-antibiotic gut mucosal microbiome reconstitution is impaired by probiotics and improved by autologous FMT." Cell 174.6 (2018): 1406-1423.

For 16S amplicon pyrosequencing, PCR amplification was performed spanning the V4 region using the primers 515F/806R of the 16S rRNA gene and subsequently sequenced using 2X250 bp paired-end sequencing (Illumina MiSeq).

Zmora, Niv, et al. "Personalized gut mucosal colonization resistance to empiric probiotics is associated with unique host and microbiome features." Cell 174.6 (2018): 1388-1405.

For 16S amplicon pyrosequencing, PCR amplification was performed spanning the V4 region using the primers 515F/806R of the 16S rRNA gene and subsequently sequenced using 2 × 250 bp paired-end sequencing (Illumina MiSeq).

Riquelme, Erick, et al. "Tumor microbiome diversity and composition influence pancreatic cancer outcomes." Cell 178.4 (2019): 795-806.

The 16S rDNA V4 region was amplified by PCR and sequenced in the MiSeq platform (Illumina) using the 2x250 bp paired-end protocol yielding pair-end reads that overlap almost completely. The primers used for amplification contain adapters for MiSeq sequencing and single-index barcodes so that the PCR products may be pooled and sequenced directly (Caporaso et al., 2012), targeting at least 10,000 reads per sample. 16S (variable region 4 [v4]) rRNA gene pipeline data incorporated phylogenetic and alignment based approaches to maximize data resolution.

Matson, Vyara, et al. "The commensal microbiome is associated with anti–PD-1 efficacy in metastatic melanoma patients." Science 359.6371 (2018): 104-108.

Specifically, the V4 region of the 16S rRNA gene (515F-806R) was PCR-amplified with region-specific primers that include sequencer adapter sequences used in the Illumina flowcell.

Raman, Arjun S., et al. "A sparse covarying unit that describes healthy and impaired human gut microbiota development." Science 365.6449 (2019): eaau4735.

Amplicons generated from variable region 4 (V4) of bacterial 16S rRNA genes present in these 2455 fecal samples were sequenced, and the resulting reads were assigned to operational taxonomic units with ≥97% nucleotide sequence identity (97%ID OTUs).

Gehrig, Jeanette L., et al. "Effects of microbiota-directed foods in gnotobiotic animals and undernourished children." Science365.6449 (2019): eaau4732.

Characterizing human fecal microbial communities Methods for V4-16S rRNA gene sequencing and data analysis, calculation of MAZ scores and functional microbiome maturity, and quantification of enteropathogen burden by means of multiplex quantitative polymerase chain reaction (qPCR) are described in the supplementary materials.

Lloyd-Price, Jason, et al. "Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases." Nature 569.7758 (2019): 655.

In brief, bacterial genomic DNA was extracted from the total mass of the biopsied specimens using the MoBIO PowerLyzer Tissue and Cells DNA isolation kit and sterile spatulas for tissue transfer. The 16S rDNA V4 region was amplified from the extracted DNA by PCR and sequenced in the MiSeq platform (Illumina) using the 2 × 250 bp paired-end protocol, yielding pair-end reads that overlapped almost completely.

Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature. 2019

In brief, bacterial genomic DNA was extracted from the total mass of the biopsied specimens using the MoBIO PowerLyzer Tissue and Cells DNA isolation kit and sterile spatulas for tissue transfer. The 16S rDNA V4 region was amplified from the extracted DNA by PCR and sequenced in the MiSeq platform (Illumina) using the 2 × 250 bp paired-end protocol, yielding pair-end reads that overlapped almost completely.

emporal development of the gut microbiome in early childhood from the TEDDY study. Nature. 2019

Bacterial DNA was extracted using the PowerMag Microbiome DNA isolation kit following the manufacturer’s instructions. The V4 region of the 16S rRNA gene was amplified by PCR and sequenced on the MiSeq platform (Illumina) using the 2 × 250 bp paired-end read protocol.

A communal catalogue reveals Earth’s multiscale microbial diversity. Nature. 2018

We surveyed bacterial and archaeal diversity using amplicon sequencing of the 16S rRNA gene, a common taxonomic marker for bacteria and archaea12 that remains a valuable tool for microbial ecology despite the introduction of whole-genome methods (e.g., metagenomics) that capture gene-level functional diversity13. We amplified the 16S rRNA gene (V4 region) using primers14 shown to recover sequences from most bacterial taxa and many archaea.

Root microbiota drive direct integration of phosphate stress and immunity. Nature. 2017.

For wild soil experiment 16S sequencing, we processed libraries according to Caporaso, et al.28. Three sets of index primers were used to amplify the V4 (515F-806R) region of the 16S rRNA gene of each sample. In each case, the reverse primer had a unique molecular barcode for each sample.