WhitePaper_16S

16S metagenomics powers human, animal, and environmental microbiome studies of any scale. Dynamic host-microbe interactions have shaped the evolution of life. Virtually all plants and animals are col - onized by microbiota, which are eco - logical communities of commensal, symbiotic, and pathogenic microor - ganisms. And it is increasingly recog - nized that the biological processes of hosts and their associated micro - bial communities function in tandem, often as biological partners forming a collective entity known as a metaor - ganism [1]. Microorganisms form very diverse communities and a character - istic of these communities is that a few taxa dominate them, while a very large number of taxa occur with lower frequency [2]. Furthermore, taxa that cannot be cultivated may also occur and therefore such taxa cannot be detected by classical methods. The rapidly growing interest in micro - biome research has been reinforced by the ability to profile different microbial communities using Next Generation Sequencing (NGS). This culture-free, high-throughput technology enables the identification and comparison of entire microbial communities, which is known as metagenomics [3]. Metagenomics typically involves two different sequencing strategies: the first sequencing strategy is amplicon sequencing, which is usually of the 16S rRNA gene as a phylogenetic marker, while the second sequencing strategy is shotgun metagenome sequencing, and this is a whole genome sequenc - ing approach [3]. Therefore, metagen - omics provides comprehensive answers to a range of important ques - tions, including the influence of the human intestinal flora on health. The use of the 16S ribosomal RNA gene in prokaryotes and the ITS sequence (internal transcribed spacers of rDNA) in fungi as phylogenetic markers has proven to be an efficient and cost-ef - fective strategy for microbiome anal - ysis. In fact, experiments revolving around 16S rRNA allow even the impu - tation of functional contents based on taxon frequencies [4] [5]. On the other hand, shotgun metagen - omics enables researchers to measure the functional relationships between hosts and bacteria by directly deter - mining the functional content of samples. In addition, shotgun metagenomics has a theoretically unbiased coverage of all taxonomies found in a DNA sample. However, contamination with host DNA and the occurrence of low frequency taxa requires very deep sequencing if one is to achieve the same taxonomic res - olution as 16S rRNA sequencing. This means a manifold increase in both the costs and the data load. In this White Paper we will limit ourselves to 16S amplicon sequencing and the factors that need to be taken into account when conducting it. Amplicon Metagenome Analyses of Microbial Communities – Doing It the Right Way Microbiota and NGS - a Dream Team Typical Barcoding Loci for Bacteria and Fungi 16S rDNA as barcoding locus in prokaryotes The 16S rRNA gene is the DNA sequence corresponding to ribosomal RNA and it is essential for the synthe - sis of all prokaryotic proteins. The 16S rRNA gene occurs in all bacteria and it is highly conserved and highly spe - cific. The internal structure of the 16S rRNA gene is composed of variable regions (see Figure 1 ). Having varying Microsynth AG, SwitzerlandSchützenstrasse 15 ? P.O. Box ? CH - 9436 Balgach ? Phone + 41fi71fi722fi83 33 ? Fax + 41fi71fi722fi87 58 ? info @microsynth.ch ? www.microsynth.ch THE SWISS DNA COMPANY White Paper ? Next Generation Sequencing Figure 1: Overview of ribosomal gene loci commonly used for the taxonomic analysis of microbial communities. Hypervariable regions are marked in green, while conserved regions are marked in gray. A. Structure of the prokaryotic 16S rRNA gene showing the nine hypervariable regions (V1-V9) and the regions targeted by the commonly used primer systems. B. Organization of the fungal rRNA gene operon showing two internal transcribed spacer regions (ITS). ITS2 is used most often for profiling fungal communities. C. 3D structural model of the 16S rRNA of Escherichia coli according to Tung et al [10]. The variable regions in the 16S rRNA are shown in green. D. Secondary structure of the 16S rRNA with variable regions in green. Choosing the Right Primer Set is the Key to Success! Amplicon metagenomics is based on NGS sequencing of the microbial 16S rRNA gene. Since NGS single read lengths are limited to 300 base pairs (600 bp for paired end reads) when using Illumina high-throughput plat - forms, only parts of the 16S rRNA gene can be amplified and sequenced. In prokaryotes, the analysis targets hypervariable regions (V1-9) on the 16S rRNA gene. Meanwhile, in fungi the internal transcribed spacer regions (ITS) are used for taxonomic profiling (see Figure 1 ). For 16S/ITS amplicon metagenom - ics, it is important to give high prior - ity to the choice of primers. An ideal primer system should be sufficiently universal to cover a broad range of taxonomic groups, while the result - ing amplicon must provide sufficient taxonomic information for a reliable taxonomic classification. Based on our experience and the validation of our 16S/ITS analysis pipeline, we rec - ommend the V34 primer system for a broad and accurate bacterial classifi - cation. However, if Archaea are also expected, the V4 primer system should be used in order to obtain a good tax - onomic classification. It should be emphasized that our service is not limited to the presented marker genes and primer systems; other phyloge - netic marker genes (e.g. cytochrome c oxidase I) and primer systems can also be used. Furthermore, a pilot study can be very helpful in terms of finding the best primer system for your specific research question. Finally, metagen - ome analyses should only compare communities generated with identi - cal primer sets. This is because of the biases of the different primer systems. Besides the choice of the locus specific PCR primers, the isolation of high-qual - ity DNA from environmental samples has a major impact on the outcome of the study. 5 C D ITS regions as barcoding locus for fungi The ITS region has become the gold standard for the classification of fungi [7]. With few exceptions [8], this locus is suitable for distinguishing fungi up to species level. In addition to the ITS regions, other loci have been used for barcoding fungi, although the data basis is much smaller [9]. degrees of difference among the dif - ferent bacteria makes it possible to identify the taxonomic identity of microbes, at the genus level and often at the species level. Since it is not prac - tical to sequence the complete 16S rDNA using NGS, only a part of the vari - able regions is sequenced. Microsynth currently offers different standard primer sets that have been developed based on the recommen - dations of the Human Microbiome Project Consortium [6]. Specifically, the V4 and V34 locus primers are suit - able for most bacterial metacom - munity projects. It has been demon - strated in several studies that the aforementioned primers facilitate the detection of a broad range of taxa (see Figure  1A ). Depending on the project requirements, customer-spe - cific primer sets can also be applied or even developed and validated. Microsynth AG, SwitzerlandSchützenstrasse 15 ? P.O. Box ? CH - 9436 Balgach ? Phone + 41fi71fi722fi83 33 ? Fax + 41fi71fi722fi87 58 ? info @microsynth.ch ? www.microsynth.ch THE SWISS DNA COMPANY White Paper ? Next Generation Sequencing The first step in an amplicon metage - nome project is deciding which primer set is the most promising. At Microsynth, the question of whether to work with a standard primer set or to instead develop a customer-spe - cific primer set is discussed in detail with the customer. In addition, it must be defined whether DNA is to be iso - lated internally or whether it should be outsourced (see Figure 2 ). The DNA is then amplified by PCR and Microsynth uses a “two-step” PCR protocol for the amplification. In a first step, the locus is amplified with short template spe - cific primers as well as an Illumina tail adapter. From there, in a second step, the so-called NGS fusion primers, including an index for multiplexing as well as an Illumina adapter, are used. Previous studies have shown that this protocol generates high quality mul - tiplex amplicon libraries and ensures high reproducibility [11]. Instead of only using a single forward and reverse primer for first step ampli - fication, some protocols use several forward primers that differ in length by adding various numbers of degen - erate bases (wobbles, Ns) at the 5’ end of the locus specific primer. An alter - native but similar approach is to use a fixed number of degenerate bases (see Figure 3 ). Both concepts aim at adding sequence diversity as this improves the quality and quantity of reads gen - erated on an Illumina MiSeq platform (see Figure 4 ). The fixed length degen - erate bases are less effective in terms of introducing sequence diversity compared to the staggered degen - erate base approach. However, they represent a good trade-off in practice and are also easier to handle in down - stream analysis. These protocols are especially useful for high-throughput projects where sequencing through - put is particularly critical and many samples are pooled. For projects involving very low amounts of starting material we rec - ommend a three-step PCR protocol including two subsequent locus-spe - cific PCRs to increase the yield of sequenceable amplicons. After equi - molar pooling of the PCR products, NGS sequencing is performed on the Illumina MiSeq. Meanwhile, the final and perhaps most important step is the bioinformatic analysis. For this step, we have established a designated pipeline that comprises state-of-the- art algorithms and software designed to extract as much valuable informa - tion as possible from the generated data and to visualize the data in a cap - tivating form. In the following section we will show in more detail how a bio - informatic analysis and its output in metagenome projects can look. What Does a Typical Project Schedule Look Like? 16S/ITS Metagenomics AnalysisProject Output: Total DNA Isolation Second Step PCR Illumina MiSeq Sequencing Option I: Samples Option II: Isolated DNA Project Input: Report Generation Option IV: Bioinformatics only Experimental Design Option I: Library Prep only Option II: Raw Data only Option III: Full Report First Step PCR with Custom or Standard Primer Set Option III: First Step Products Figure 2: Schematic representation of a project procedure for metagenome analysis of microbial commu - nities. Depending on the initial situation and the problem, the first step is to determine the suitable primer set. However, should the customer request it, a new primer set can be developed. Furthermore, either the entire process - including DNA isolation - can be outsourced to Microsynth, or the DNA can be isolated by the customer. At the end of the process the customer will receive a clearly structured report that can be used for further analysis. Figure 3: Principle of the degenerate base approach for primers. A: In the first step PCR, the target is amplified and the tail adapter is appended. 5 degenerate bases are appended between the tail adapter and the locus specific primer. B: In the second step PCR the index and the adapter are appended. The second PCR product binds to the Illumina flow cell and it is sequenced starting at the degenerate bases, which ensures sequence diversity. A B f o rw ard prim er l o cu s sp eci? c r e ver se prim er l o cu s sp eci? c deg en erate bases d eg en erate bases t a il a d ap ter t a il a d ap ter in dex a d ap ter ad ap ter i n dex Microsynth AG, SwitzerlandSch?tzenstrasse 15 ? P.O. Box ? CH - 9436 Balgach ? Phone + 41 71 722 83 33 ? Fax + 41 71 722 87 58 ? info @microsynth.ch ? www.microsynth.ch THE SWISS DNA COMPANY White Paper ? Next Generation Sequencing State of the Art - the Bioinformatics Analysis Pipeline from Microsynth For the bioinformatic analysis of the sequencing data, the sequenced paired-end reads are first subjected to de-multiplexing and trimming of Illumina adaptor residuals. In a second preparation step, paired-end reads are filtered for their quality and their locus specific primers are trimmed as well. From there, the remain - ing paired-end reads are de-noised [12] to form operational taxonomic units (OTUs), while in the process dis - carding singletons and chimeras. The resulting OTU abundances are then filtered for possible barcode bleed-in contaminations [13] to reduce noise. Following this, the OTU sequences are compared to a reference sequence database, such as RDP [14], in order to predict their taxonomies and cor - responding confidence scores. The resulting metagenome is visualized by an interactive Krona chart [15] (see Figure 5 ) that provides a quick and easy overview of the data. This enables the scientist to intuitively explore the intricacies of the analyzed bacte - rial community. The diversity of the metagenomic community is expressed in simple and comparable terms as alpha and beta diversity scores. The alpha diversity describes the intra-di - versity of each sample, while the beta diversity describes the inter-diversity of all samples together [16]. Different and widely used alpha diversity scoring metrics are displayed in Figure 6B . On the X axis the analyzed samples are annotated, and on the Y axis their individual scores for each metric are displayed. Rarefaction curves are cal - culated in order to estimate if a micro - biome has been sufficiently character - ized. If the curves end in a plateau, this signifies that the microbiome was suf - ficiently covered (see Figure 6A ). The complex interaction of multiple bac - terial communities in a given envi - ronment is described by beta diver - sity based on a distance metric such as Unifrac [17]. Principal component analysis (PCA) helps in terms of simpli - fying, understanding, and visualizing such interactions (see Figure 7A ). In Figure 4: These charts show the effect of degenerate bases on the quality of the sequencing. A: In the chart of the sequence content across all bases, you can clearly see the 5 degenerate bases at the beginning of the sequence due to their typical distribution (approximately 25%). B: When no degenerate bases were included, the beginning of the sequencing was always the same. C: The chart of the per base quality shows high quality scores for all bases when degenerated bases were used. D: When no degenerate bases were used in the primers, the quality score soon decreased for longer reads. A B C D Microsynth AG, SwitzerlandSchützenstrasse 15 ? P.O. Box ? CH - 9436 Balgach ? Phone + 41fi71fi722fi83 33 ? Fax + 41fi71fi722fi87 58 ? info @microsynth.ch ? www.microsynth.ch THE SWISS DNA COMPANY White Paper ? Next Generation Sequencing
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?????????? ?????????? ??# Figure 6. Examples of alpha diversity results. 6A. Rarefaction curves indicating whether sampling and sequencing covered the sample richness (the x axis displays the sampled number of reads, while the y axis displays the number of detected OTUs). 6B. Alpha diversity measures for the analyzed community including observed rich - ness; the Chao 1 index and the Shannon diversity index. Figure 5. Interactive Krona chart of the bacteria represented by 16S rRNA gene amplicon-based bac - terial diversity in a feces sample. Each circle repre - sents the phylum, class, order, family, genus, and species from the inside to the outside of the circle, respectively. In addition, the relative abundance of each taxa is annotated on the chart. Figure 7A , the PCA was able to explain 90% of the variance of the original data with just two components (75% on the first principal component and 15% on the second principal component). If an experimental design exists, dividing samples into different categories such as treated and control samples, the dif - ferential OTU analysis reveals detailed changes in the microbiome of the analyzed groups [18] (see Figure  7B ). Finally, functional profiles are pre - dicted [19] (see Table 1 ) using various publicly available databases. The path - ways and their abundance within the different samples are shown in Table 1 . Microsynth AG, SwitzerlandSchützenstrasse 15 ? P.O. Box ? CH - 9436 Balgach ? Phone + 41fi71fi722fi83 33 ? Fax + 41fi71fi722fi87 58 ? info @microsynth.ch ? www.microsynth.ch THE SWISS DNA COMPANY White Paper ? Next Generation Sequencing Conclusion The complexity of metagenome analysis has increased in line with technological progress. In order to perform meaningful metagenome analyses, our experience has taught us that the following success criteria are important: • It must be considered which variable DNA region (e.g. on the 16S rDNA or ITS regions) is best suited for the intended study. Based on these considerations, the optimal primer set is determined (and, if possible, it is pre-tested in a pilot study). • To be able to amplify the different taxa representatively and to sequence them afterwards, a robust and functional DNA isolation procedure must be available. On the other hand, we recommend a “two-step” protocol if possible, to enable the use of high-quality amplicon libraries for the subsequent sequencing. • Data analysis should be performed using a few but meaningful bioinformatics tools. • If you are lacking experience in one or more of the above criteria, we recommend outsourcing the study to an experi - enced service provider. Microsynth has more than 10 years of experience. pathwa yd escrip ?o nS ample_ V3 4_1a Samp le_V 34_1 bSample_ V3 4_1c NO NOXIPE NT-PWY pentos e phospha te pa thwa y (n on -oxida ?v e br an ch)4 55004630 04 6500 CA LVIN-PWY Calvin -B en son- Bassha m cycl e3 9700 4020 04 0400 PW Y-7220 aden osine de oxyribonuc leo? des de novo biosynth esis II 397003 9800 40100 PW Y-7222 guanosine de oxyribonuc leo? des de novo biosynth esis II 397003 9800 40100 PW Y-7663 gondo ate bi osynth esis (a na erob ic )3 8200 3860 03 8800 PW Y-6737 starch de grad a? on V3 7400 3750 03 7800 PW Y-5101 L-isol eu cine bi osynth esis II 371003 7800 37800 GLYC OCAT-PWY glyc og en de grad a? on I (b ac terial )3 6800 3760 03 7300 PW Y-7229 superpathw ay of ad en osine nuc leo? des de novo biosynth esis I3 6500 3700 03 7200 ANAG LYCO LYSIS- PWYg lyco lysis III (fro m gluc ose) 361003 6600 36700 Showing 1 to 10 of 336 entrie s Table 1. Functional profiles predicted according to OTUs, their predicted taxonomies, and their abundance in each of the samples. ?
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?? ??? ??? ??? ? ?? ? ?? ? ?? Figure 7. Examples of beta diversity results. 7A. Principal component analysis plot to visualize sample clustering. 7B. This excerpt from a results table shows differ - ential abundance of OTUs between two conditions, including statistical measures for differential abundance (log fold change) and significance (adjusted p-value). Microsynth AG, SwitzerlandSchützenstrasse 15 ? P.O. Box ? CH - 9436 Balgach ? Phone + 41fi71fi722fi83 33 ? Fax + 41fi71fi722fi87 58 ? info @microsynth.ch ? www.microsynth.ch THE SWISS DNA COMPANY White Paper ? Next Generation Sequencing Literature [1] Rausch, P., Rühlemann, M., Hermes, B.M. et al. Comparative analysis of amplicon and metagenomic sequenc - ing methods reveals key features in the evolution of animal metaorganisms. Microbiome 7, 133 (2019). https://doi. org/10.1186/s40168-019-0743-1 [2] McGill, B.J., Etienne, R.S., Gray, J.S., Alonso, D., Anderson, M.J., Benecha, H.K., Dornelas, M., Enquist, B.J., Green, J.L., He, F.L., Hurlbert, A.H., Magurran, A.E., Marquet, P.A., Maurer, B.A., Ostling, A., Soykan, C.U., Ugland, K.I. & White, E.P. (2007) Species abundance distributions: moving beyond single prediction the - ories to integration within an ecologi - cal framework. Ecology Letters, 10: 995– 1015. [3] Hugenholtz, P. (2002) Exploring prokaryotic diversity in the genomic era. Genome Biology 3: reviews0003.1– reviews0003.8. [4] Morgan XC, Huttenhower C. Chapter 12: human microbiome analysis. PLoS Comput Biol. 2012;8(12):e1002808. [5] Langille MGI, Zaneveld J, Caporaso JG, McDonald D, Knights D, Reyes JA, Clemente JC, Burkepile DE, Vega Thurber RL, Knight R, et al. Predictive functional profiling of micro - bial communities using 16S rRNA marker gene sequences. Nat Biotechnol. 2013;31(9):814–21. [6] The Human Microbiome Project Consortium (2012) A framework for human microbiome research. Nature, 486: 215-221. [7] Schoch CL, Seifert KA, Huhndorf S, Robert V, Spouge JL, Levesque CA, Chen W, Bergeron MJ, Hamelin RC, Vialle A, and Fungal Barcoding Consortium. (2012) Nuclear ribosomal internal tran - scribed spacer (ITS) region as a uni - versal DNA barcode marker for Fungi. Proceedings of the National Academy of Science 109: 6241-6246. (doi: 10.1073/ pnas.1117018109) [8] Grünig, C.R.; Brunner, P. C.; Duo, A. & Sieber, T. N. (2007) Suitability of methods for species recognition in the Phialocephala fortinii - Acephala appla - nata species complex using DNA analy - sis. Fungal Genetics and Biology, 44: 773- 788. [9] Ratnasingham S, Hebert PDN (2013) A DNA-Based Registry for All Animal Species: The Barcode Index Number (BIN) System. PLoS ONE 8(8): e66213. DOI:10.1371/journal. pone.0066213 [10] Tung, C.-S., Joseph, S. & Sanbonmatsu, K.Y. (2003) All-atom homology model of the Escherichia coli 30S ribosomal subunit. Nature Structural Biology, 9: 750-755 [11] Berry, D., Mahfoudh, K.B., Wagner, M. & Loy, A. (2011) Barcoded primers used in multiplex ampli - con pyrosequencing bias amplifi - cation, Applied and Environmental Microbiology, 77: 7846- 7849. [12] R.C. Edgar (2016), UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing, https://doi.org/10.1101/081257 [13] R.C. Edgar (2018), UNCROSS2: identification of cross-talk in 16S rRNA OTU tables, https://doi. org/10.1101/400762 [14] Cole, J. R., Q. Wang, J. A. Fish, B. Chai, D. M. McGarrell, Y. Sun, C. T. Brown, A. Porras-Alfaro, C. R. Kuske, and J. M. Tiedje. 2014. Ribosomal Database Project: data and tools for high through - put rRNA analysis Nucl. Acids Res. 42(Database issue):D633-D642; doi: 10.1093/nar/gkt1244 [PMID: 24288368] [15] Ondov BD, Bergman NH, and Phillippy AM. Interactive metagenomic visualization in a Web browser. BMC Bioinformatics. 2011 Sep 30; 12(1):385. [16] https://journals.plos.org/ plosone/article?id=10.1371/journal. pone.0061217 [17] Lozupone C, Knight R . (2005). UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol 71: 8228–8235. [18] https://genomebiology. biomedcentral.com/articles/10.1186/ s13059-014-0550-8 (DESeq2) [19] Predictive functional profil - ing of microbial communities using 16S rRNA marker gene sequences. Langille, M. G.I.*; Zaneveld, J.*; Caporaso, J. G.; McDonald, D.; Knights, D.; a Reyes, J.; Clemente, J. C.; Burkepile, D. E.; Vega Thurber, R. L.; Knight, R.; Beiko, R. G.; and Huttenhower, C. Nature Biotechnology, 1-10. 8 2013. Microsynth AG, SwitzerlandSch?tzenstrasse 15 ? P.O. Box ? CH - 9436 Balgach ? Phone + 41 71 722 83 33 ? Fax + 41 71 722 87 58 ? info @microsynth.ch ? www.microsynth.ch THE SWISS DNA COMPANY White Paper ? Next Generation Sequencing Contact Microsynth AG Schützenstrasse 15 CH-9436 Balgach Switzerland phone: +41-71-722 83 33 web: www.microsynth.ch email: genome@microsynth.ch Microsynth AG, SwitzerlandSchützenstrasse 15 ? P.O. Box ? CH - 9436 Balgach ? Phone + 41fi71fi722fi83 33 ? Fax + 41fi71fi722fi87 58 ? info @microsynth.ch ? www.microsynth.ch THE SWISS DNA COMPANY White Paper ? Next Generation Sequencing