REPORT Comparative Test for bacterial DNA extraction€¦ · 2 COST Action PiGutNet FA1401 ‐ Comparative test for bacterial DNA extraction for 16S rRNA sequencing combination of

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COST Action PiGutNet FA1401 ‐ Comparative test for bacterial DNA extraction for 16S rRNA sequencing

REPORT

PiGutNet FA1401 ‐ Comparative test for bacterial DNA extraction

for 16S rRNA sequencing

BACKGROUND and AIMS

The 16S rRNA gene amplicon sequencing is currently the most cost‐effective means to comprehensively

assign bacterial taxonomy and to characterize the microbial profile.

Although standardized primers and computational workflows have been developed, sample processing for

DNA extraction remains a continued source of variability across studies. Indeed, even if all methods are

based on chemical or mechanical disruption and lysis with detergents, or a mixture of these approaches,

many different DNA extraction kits and protocols are used, each with its potential error. In addition, part of

the variability ascribed to laboratories are not well characterized and defined in the standardized workflow.

Taking advantage of the PiGutNet network, we set up a ring test including 11 partners (see the list in

Appendix I) with the main objective to test and verify the reproducibility of the microbial sequencing data

from intestinal content among different laboratory in Europe. Furthermore, a second purpose of the ring

test was to highlight the difference between different DNA extraction kits commonly used for the 16S rRNA

gene.

SAMPLE PREPARATION and DNA EXTRACTION

A total of 9 replicates (600 mg each) of 3 pools were prepared by mixing a total of 12 lyophilized cecum

samples (3mg per sample) obtained from heavy pig by one of the partners (UNIBO). A single replicate of

each poll was delivered to the 9 PiGutNet partners (Table 1). Among the different laboratories of the

partners, 8 different protocols were used including 4 different commercial DNA extraction kits, a

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combination of commercial and customised RNA extraction kit, and a protocol with TRIzol as reported in

Table 1. In details, one partner used the QuickExtract™ Bacterial DNA Extraction Kit (Epicentre, Madison,

WI) using the manufacturer’s instructions; two partners used the FastDNA™ SPIN Kit for Soil (MP

Biomedical, Solon, OH, USA) following the manufacturer’s instructions; one partner used a combination of

Repeated Bead Beating (RBB) with S.T.A.R buffer (Roche Molecular Systems, USA) and Maxwell automated

system with customised RNA extraction kit; one partner used the Purelink Microbiome DNA Extraction kit

(Invitrogen); one partner used the Omega E.Z.N.A. Stool Kit (Omega Bio‐Tek, Norcross, Georgia, USA)

according to the manufacturer’s instruction; four partners used the QIAamp® DNA Stool mini kit (Qiagen,

Hilden, Germany) according to the manufacturer’s instruction, while two partners used the QIAamp® DNA

Stool mini modifying the standard protocol by the addition of two bead‐beating steps (FastPrep‐24, MP

Biomedicals, Illkirsh, France), as described by Leblois et al. (2017); one partner used the protocol of TRIzol®

reagent from Invitrogen (Silva and Fricke 2018).

The DNA extraction was carried out in triplicate for each pool for a total of 117 samples. The extracted DNA

samples were then frozen and stored at ‐20 until further analysis.

MICROBIOTA ANALYSIS

Total Bacteria DNA was delivered from all partners to one of the partners of PiGutNet at the INRA, Jouy‐en‐

Josas, France. Cecum DNA concentration and its purity (absorbance ratio 260/230 and 260/280) were

measured by Nanodrop mass spectrophotometry before dilution the dilution to 50 ng/μl for library

preparation. The library formation and sequencing analysis of the of the V3–V4 [V3fwd:

TACGGRAGGCAGCAG, V4rev: GGACTACCAGGGTATCTAAT; (Wilson et al., 1990)] region of 16S rRNA gene

were carried out using an MiSeq® Reagent Kit V3‐V4 on an MiSeq‐Illumina® platform (INRA, Jouy‐en‐Josas,

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France) during the training school “Analysis of porcine metagenomic” organized by the PiGutNt in February

2016.

BIOINFORMATIC AND STATISTICAL ANALYSIS

The sequencing data analysis for microbiota community was carried out with a workflow employing the

Quantitative Insights Into Microbial Ecology (QIIME) pipeline (Caporaso et al., 2010). De‐multiplexing and

initial sequence quality filtering were done with the “split_libraries.py” script using the default settings of

QIIME. The generated sequences were analysed using a subsampled open‐reference OTU strategy with

default settings in QIIME (v1.9.1) and clustering of Operational Taxonomic Units (OTUs) was performed

using UCLUST with 97% sequence similarity. Representative sequences were assigned taxonomy against the

Greengenes database V13_8 using the UCLUST method with a 90% confidence threshold.

Data were low‐abundance filtered by removal of OTUs with a relative abundance ≤0.005 % across all

samples (Bokulich et al., 2013) and chimera checked using the Blast_fragments approach in QIIME (Altschul

et al., 1990). The samples that reported low sequencing yield (less than 15000 reads after quality check)

were excluded from the analysis. Finally, the rarefaction was carried out to get an even number of reads

per sample. The final dataset was composed of 100 samples and 1362 OTUs.

Shannon’s index (for alpha diversity index) and Bray Curtis distance matrix (for Beta diversity) were

calculated and differences were tested using an ANOVA model including laboratory and protocol of

extraction and their interaction as factors. The same ANOVA model was fitted separately to DNA

yield and quality (A260/280 and A60/230) measurements. The influence of DNA quantity and quality on

Shannon’s index and Bray Curtis distance matrix was tested using a further ANOVA model including DNA

concentration and A260/280 and A60/230 values as factors. To assess the variability between the pools an

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ANOVA model was carried out on overall samples including pool (I, II, III) as factor. In addition, to assess the

repeatability and reproducibility of values obtained in the ring test, the variance decomposition was

performed following the methods reported by Youden and Steiner (1975).

Biostatistical analyses on Alpha and Beta Diversity and Taxonomic Composition were performed in R v3.3.2

using the phyloSeq (McMurdie et al., 2013) and Vegan (Dixon, 2003) packages.

RESULTS

Quality and Quantity of Genomic DNA

Table 2 reports the DNA yields and quality among laboratories and protocols. Spectrophotometric

measurements of the extracted DNA showed differences in quantity (ng per μl of DNA) according to both

laboratories (P<0.0001). The highest quantities of DNA were obtained with the Omega E.Z.N.A. Stool

Kit used by Aarhus (4632 ±938.5 ng/μl) and with the customized protocol used by WUR (415.6±62.2 ng/μl)

compared to the others Laboratories.

The DNA quality (260/280 and 260/230 values) was influenced by both the Laboratory and the protocol.

None of the DNA extracts had a ratio of absorbance at 260 nm and 280 nm (A260/280) below 1.8,

indicating no presence of contaminants (typically proteins and/or phenol) that absorb at a slightly higher

wavelength than DNA. Some of the DNA extracts had a ratio of absorbance at 260 nm and 230 nm

(A260/230) higher than 2.0 (maximal value or pure DNA) particularly the samples extracted by Aarhus

(Omega E.Z.N.A. kit) and IATA‐CSIC (Epicentre kit), while others samples had a very low A260/230 including

samples extracted by HOHENHEIM and UNIBO (Soilkit).

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Taxa composition

A total of 1,746,300 quality checked reads were obtained, which returned a total of 1,362 different OTUs,

distributed among the 100 samples.

Nine different Phyla were identified in the cecal samples, most of which had a low abundance (<1%), the

dominant Phyla on overall samples were Firmicutes (49.5%) and Bacteroidetes (45.2%) (Figure 1). At the

Family level of taxonomic classification, 43 different Family were assigned and the most abundant on

overall samples were Prevotellaceae (39.7%), Clostridiaceae (14.7%) and Lachnospiraceae (12.1%) (Figure

2). At the genus level of taxonomic classification, 76 different genera were assigned and the most abundant

on overall samples were Prevotella (39.5%), Unclassified Clostridiaceae (13.6%), Unclassified

Lachnospiraceae (7.8%), Unclassified Clostridiales (5.0%) and Streptococcus (4.3%) (Figure 3).

Alpha diversity indices

The OTUs distribution fairly constant for the experimental factors and the Shannon indices values did not

show significant differences for laboratory, extraction protocols and their interaction (Tables 3; Figure 4).

No significant differences in alpha diversity indices were observed among laboratories which used the same

protocol (Figure 5). A weaktest, attention was focused on the variability but significant effect of A260/280

was observed on the Shannon index (P = 0.04; R2 = 0.04). No significant differences in the alpha index

among samples were identified (Figure 6). The repeatability of the analytical method (pure error) was 0.227

(as standard deviation of Shannon index), the reproducibility (which includes the variance of pure error, the

one of Lab and their interaction) was 0.480 (as standard deviation of Shannon index). The calculated mean

standard error of Shannon index distribution was 0.480 for N = 1 replicate (only one replicate per each of

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the 3 samples) and decreased to 0.342 and to 0.281 by increasing the number of analysed replicates for the

same sample to 2 and 3 respectively.

Beta diversity indices

No significant differences for laboratory nor for protocol or their interaction were observed for the Beta

diversity (Bray‐Curtis distance) by using the Adonis test. Among laboratories which used the same protocol

(QIAamp® DNA Stool mini kit and FastDNA™ SPIN Kit for Soil) no difference was observed. No well‐defined

clusters were showed in Non‐Metric Multidimensional Scaling (NMDS) plot of the Bray‐Curtis distance

matrix for laboratory neither for the protocol (Figures 7 ‐ 8). In addition, no defined cluster for the

laboratory was visualised among samples extracted using common DNA extraction protocol (Figure 9). The

DNA quantity (P = 0.01), as well as the A260/280 values (P = 0.03), influenced the Bray‐Curtis distance,

although a not well‐defined trend can be observed in the NMDS (Figure 10). No significant differences in

the beta diversity among samples were identified (Figure 11).

DISCUSSION

The increasing interest on the microbial profile characterisation through sequencing of 16S rRNA gene

regions made necessary standardization of the protocols to obtain these data. Variability among studies

can be ascribed to the extraction of DNA, the selected sequenced region of the 16S rRNA gene, the selected

primers, the sequencing machine as well as the bioinformatics pipeline. In this ring test, attention was

focused on the variability ascribed to the DNA extraction, while all others factors were fixed.

The protocols for bacterial DNA extraction included in the ring test employed both chemical and

mechanical disruption of cells by bead beat. Our data showed a wide variability among laboratories for the

quantity of extracted DNA. A higher quantity was observed in protocols which included the RBB, however

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that observation was not confirmed by the results of Qiagen kit with or without RBB modification step,

indicating that additional factors such as the agitation speeds may have modified the obtained DNA. All

DNA of the extracted samples had a good A260/280 ratio, while some samples had a low A 260/A230 ratio

which may be due to the residual phenol from nucleic acid extraction or the residual guanidine (often used

in column based kits).

Determining the bacterial community structure in the hind‐gut samples using amplification and NGS

analysis of extracted DNA has reformed gastrointestinal microbiology research over recent years. Previous

studies have demonstrated differences among DNA extraction kits and protocols from human faecal

samples, evidencing controversial results by detection with PCR (McOrist et al., 2002), qPCR (Maukonen et

al., 2012) and more recently using the sequencing of 16S rRNA regions (Kennedy et al., 2014; Wu et al.,

2010).

Our study evidenced a heterogeneous distribution in the mean numbers of Taxa and OTUs among the

tested protocols and among laboratories, indicating a large variation in the capability of recovering a

certain given bacterial community. However, in contrast with the previous results of Wu et al (2010) and

Burbach et al. (2016) in which differences in terms of alpha and beta diversity indices were observed, in our

ring test no significant differences were detected. However, the lack of differences among protocols in

terms of alpha and beta indices could be due to the high similarity among samples, since no experimental

factor (i.e. different diet or health status) affecting the samples was included; thus we cannot assert that

different protocols do not influence the microbial community profile under experimental condition.

Vaidya et al. (2018) showed that RBB steps can influence the bacterial community of liquid rumen fractions

of dairy cows. In contrast to Vaidya et al., (2018) our data did not evidence this difference, especially in the

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case of QIAamp DNA Stool Minikit kit, which was used by partners following the manufactural instruction or

by adding the RBB modifications. On the other hand, our data indicated that DNA quantity and quality,

which may result influenced by the RBB steps or by the different solvent used for the DNA extraction, can

weakly affect the beta diversity index on cecum microbial community. However, since a not clear trend can

be observed in the NMDS plot, further studies are needed to confirm our observation.

Furthermore, our ring test highlights that different laboratory did not influence the cecum microbial profile,

without contribution in the general bias of DNA extraction. This could mostly be appreciated by the

comparison of alpha and beta indices in the four partners which used the QIAamp DNA Stool Minikit and by

the two partners which use the FastDNA™ SPIN Kit for Soil.

In addition, it is important to note that a wide variability was also observed between the replicates of the

same samples in the majority of the laboratories. However, one of the partners (Aarhus) obtained a lower

variability between the replicates, suggesting that internal source of variation can be reduced, i.e. by

improving the standardization of internal laboratory protocol for bacterial DNA extraction. We could also

infer that variance within replicates appear quite wide because the pools were similar to each other (no

factor of variation during animals’ life) and no outgroup to rescale the dispersion were included. In

addition, the results of the variance partitioning for Shannon index indicate that an increase in the number

of replicates for a sample from one to two allows reducing the mean standard error by 28% supporting that

this can be a way to improve the power of tests done on pig intestinal samples, although this would raise

the costs of an experiment. More attention should be paid to reduce the pure analytical error (the sample

repeatability) that affected the results more than the reproducibility, although this statistical method has

not commonly applied in NGS microbial studies. A possible reason is that this test can be applied to

univariate data, such as Shannon index, while it is not easily applicable to multivariate microbiological data.

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CONCLUSION

Whilst the DNA extraction protocols affected the DNA quantity and quality, no single extraction protocol

could be concluded to be more effective in influencing the apparent bacterial community composition. This

lack could be also related to the low variability between the tested samples joined to the averagely high

variability between the replicates within each sample and, finally, to the low representability of kits.

Further researches on this field are needed to improve the protocol standardization among studies, as

already implemented for other matrix and species, following the increasing interest in microbial profile

characterization in pigs.

REFERENCES

Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. Journal of Molecular

Biology. 1990 Oct;215(3):403–10.

Bokulich NA, Subramanian S, Faith JJ, Gevers D, Gordon JI, Knight R, et al. Quality‐filtering vastly improves

diversity estimates from Illumina amplicon sequencing. Nature methods. 2013;10(1):57–9.

Burbach K, Seifert J, Pieper DH, Camarinha‐Silva A. Evaluation of DNA extraction kits and phylogenetic

diversity of the porcine gastrointestinal tract based on Illumina sequencing of two hypervariable regions.

MicrobiologyOpen. 2016;5(1):70–82.

Camarinha‐Silva A., Fricke W.F. (2018) Molecular Microbiome Analysis. In: Haller D. (eds) The Gut

Microbiome in Health and Disease. Springer, Cham.

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Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F. D., Costello, E. K., et al. (2010). QIIME

allows analysis of high‐throughput community sequencing data. Nat. Methods 7, 335–336. doi:

10.1038/nmeth.f.303

Dixon P. VEGAN, a package of R functions for community ecology. Journal of Vegetation Science. Blackwell

Publishing Ltd; 2003 Dec;14(6):927–30.

Kennedy NA, Walker AW, Berry SH, Duncan SH, Farquarson FM, Louis P, et al. The impact of different DNA

extraction kits and laboratories upon the assessment of human gut microbiota composition by 16S rRNA

gene sequencing. PLoS ONE. 2014;9(2):1–9.

Maukonen J, Simo˜es C, Saarela M (2012) The currently used commercial DNA‐ extraction methods give

different results of clostridial and actinobacterial populations derived from human fecal samples. FEMS

Microbiol Ecol 79: 697– 708. doi:10.1111/j.1574–6941.2011.01257.x.

McMurdie PJ, Holmes S, Kindt R, Legendre P, O’Hara R. phyloseq: An R Package for Reproducible Interactive

Analysis and Graphics of Microbiome Census Data. Watson M, editor. PLoS ONE. Public Library of Science;

2013; 8(4):e61217.

McOrist AL, Jackson M, Bird AR (2002) A comparison of five methods for extraction of bacterial DNA from

human faecal samples. J Microbiol Methods 50: 131–139. doi:10.1016/S0167‐7012(02)00018‐0.

Vaidya Jueeli D., van den Bogert Bartholomeus, Edwards Joan E., Boekhorst Jos, van Gastelen Sanne,

Saccenti Edoardo, Plugge Caroline M., Smidt Hauke. Effect of DNA extraction methods on observed

microbial communities from fibrous and liquid rumen fractions of dairy cows. Frontiers in Microbiology

2018, 9: 92. DOI=10.3389/fmicb.2018.00092

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Wilson, K.H., Blitchington, R.B., and Greene, R.C. (1990) Amplification of bacterial 16S ribosomal DNA with

polymer‐ ase chain reaction. J Clin Microbiol 28: 1942–1946.

Wu GD, Lewis JD, Hoffmann C, Chen Y, Knight R, Bittinger K, et al. Sampling and pyrosequencing methods

for characterizing bacterial communities in the human gut using 16S sequence tags. Bmc Microbiology.

2010;10:206

Youden, W. J., and Steiner, E. H. (1975). Statistical manual of the association of official analytical chemists

(No. LC‐0111). Aoac International.

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Figure 1. Taxonomy composition at Phylum level among different protocols of DNA extraction (A) and different laboratories (B).

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Figure 2. Taxonomy composition at Family level among different protocols of DNA extraction (A) and different laboratories (B).

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Figure 3. Taxonomy composition at Family level among different protocols of DNA extraction (A) and different laboratories (B).

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Figure 4. Alpha diversity indices among different laboratories of PiGutNet partners

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Figure 5. Alpha diversity indices among different laboratories of PiGutNet partners using the same bacterial DNA extraction protocol.

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Figure 6. Alpha diversity indices among the three different pool samples

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Figure 7. Non‐Metric Multidimensional Scaling (NMDS) plot on Bray‐Curtis distance matrix among different laboratories.

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Figure 8. Non‐Metric Multidimensional Scaling (NMDS) plot on Bray‐Curtis distance matrix among different protocols.

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Figure 9. Non‐Metric Multidimensional Scaling (NMDS) plot on Bray‐Curtis distance matrix between laboratories using the same protocol for bacterial DNA extraction.

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Figure 10. Non‐Metric Multidimensional Scaling (NMDS) plot on Bray‐Curtis distance matrix evidencing the influence of DNA concentration and of A260/280 values.

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Figure 11. Results of the Anosism test for on Bray Curtis distance matrix among three different polls. The red box plot represents the between samples variability. Blue, green and purple boxplot represent the within variability in pool 1,2,3 respectively

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Table 1. PiGutNet partners involved in the ring test and relative kit and protocols used for bacterial DNA extraction.

KIT Producer Protocol Label N. of

Laboratory Partner

QuickExtract™ Bacterial DNA Extraction Kit

Epicentre manufacturer’s instructions Epicentre 1 IATA‐CSIC

FastDNA_Soil MP

Biomedical manufacturer’s instructions Soilkit 2 UNIBO; HOHENHEIM

Customised RNA extraction kit WUR confidential MIB 1 WUR

Purelink Microbiome DNA Extraction kit

Invitrogen manufacturer’s instructions Purelink 1 UCO

Omega E.Z.N.A. Stool Kit Omega Bio‐

Tek manufacturer’s instructions + two bead‐beating steps

Omega 1 Aarhus

QIAamp DNA Stool Mini Kit Qiagen manufacturer’s instructions Qiagen 4 CREA; TEAGASC; ULG; IATA‐

CSIC

QIAamp DNA Stool Mini Kit Qiagen manufacturer’s instructions + two bead‐beating steps

Qiagen_BB 1 TEAGASC; ULG

Trizol Invitrogen Trizol protocol Trizol 1 HOHENHEIM

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Table 2. Purity and yield of genomic DNA extracted from pig cecum samples

PR1 Lab2 N3 DNA yield (ng/μl)

P‐value DNA purity

A260/280

P‐value DNA purity A260/230

P‐value

PR Lab PR x Lab PR Lab

PR x Lab PR Lab

PR x Lab

Epicentre IATA‐CSIC 3

256.3±126.6 0.30 <.0001

0.90 2.14±0.04 <.001 < .0001 <.001 2.22±0.04 <.0001 <.0001 <.0001

MIB WUR 9 415.6±62.2 1.94±0.01 1.99±0.07

PureLink UCO 9 57.2±18.5 1.85±0.03 1.52±0.17 Omega E.Z.N.A. Aarhus

7 4632 ±938.5 2.13±0.1 2.26±0.02

QiagenBB TEAGASC 9 59.7±12.9 2.38±0.06 0.33±0.06

QiagenBB ULG 9 63.6±15.0 2.23±0.04 1.38±015

Qiagen CREA 9 26.13±8.8 2.02±0.2 0.66±0.19

Qiagen IATA‐CSIC 3 159.7±80.1 1.86±0.02 1.39±0.18

Qiagen TEAGASC 9

109.6±33.85 2.29±0.03 0.54±0.12

Qiagen ULG 9 22.2±7.0 2.35±0.04 0.77±0.17

Soilkit HOHENHEIM 8

272.05±109.8 1.93±0.1 0.3±0.09

Soilkit UNIBO 8 172±43.3 1.92±0.04 0.19±0.05

Trizol HOHENHEIM 8 23.5±24.6 1.93±0.03 0.42±0.23 1PR stands for used protocol for DNA extraction 2 Lab stands for laboratory 3 N stands for number of analysed samples

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Table 3. Alpha diversity indices in different laboratory.

PR1 Lab2 N3 ObservedSD

Observed ShannonSD

Shannon

P‐value PR Lab PR x Lab

Epicentre IATA‐CSIC 3 936.33 187.46 5.12 0.22 0.42 0.97 0.99 MIB WUR 9 1004.00 53.91 5.01 0.33 PureLink UCO 9 994.56 89.85 5.04 0.30 Omega E.Z.N.A Aarhus

7 1045.43 21.12 5.07 0.22

QiagenBB TEAGASC 9 976.44 68.12 5.12 0.18 QiagenBB ULG 9 1022.56 64.55 5.11 0.20 Qiagen CREA 9 983.22 85.33 5.04 0.24 Qiagen IATA‐CSIC 3 1057.67 23.18 5.03 0.19 Qiagen TEAGASC 9 1031.56 63.20 5.10 0.23 Qiagen ULG 9 1006.00 112.71 5.09 0.18 Soilkit HOHENHEIM 8 1006.13 64.63 4.93 0.22 Soilkit UNIBO 8 1003.00 63.32 4.92 0.25 Trizol HOHENHEIM 8 994.75 61.60 4.98 0.24 1PR: stands for used protocol for DNA extraction 2 Lab: stands for laboratory 3 N: stands for number of analysed samples

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Appendix I. List of involved PiGutNet partners

Country Name of Institute Label in the Report

Belgium (BG) University of Liège, Gx A‐B T ULG

Denmark (DK) Aarhus University Aarhus

France (FR) INRA Jouy‐en‐Josas INRA

Germany (GE) University of Hohenheim: Stuttgart HOHENHEIM

Ireland (IR) Teagasc, Agriculture and Food Development Authority TEAGASC

Italy (IT) Parco Tecnologico Padano (PTP) PTP

Italy (IT) Centro di Ricerca per gli Alimenti e la Nutrizione (CREA) CREA

Italy (IT) University of Bologna UNIBO

Netherland (NL) Wageningen University & Research: Wageningen WUR

Spain (SP) University of Cordoba (UCO) UCO

Spain (SP) Instituto de Agroquímica y Tecnología de Alimentos IATA‐CSIC

Documents

REPORT Comparative Test for bacterial DNA extraction€¦ · 2 COST Action PiGutNet FA1401 ‐ Comparative test for bacterial DNA extraction for 16S rRNA sequencing combination of