- Open Access
Fecal Aliquot Straw Technique (FAST) allows for easy and reproducible subsampling: assessing interpersonal variation in trimethylamine-N-oxide (TMAO) accumulation
© The Author(s). 2018
- Received: 11 October 2017
- Accepted: 5 April 2018
- Published: 18 May 2018
Convenient, reproducible, and rapid preservation of unique biological specimens is pivotal to their use in microbiome analyses. As an increasing number of human studies incorporate the gut microbiome in their design, there is a high demand for streamlined sample collection and storage methods that are amenable to different settings and experimental needs. While several commercial kits address collection/shipping needs for sequence-based studies, these methods do not preserve samples properly for studies that require viable microbes.
We describe the Fecal Aliquot Straw Technique (FAST) of fecal sample processing for storage and subsampling. This method uses a straw to collect fecal material from samples recently voided or preserved at low temperature but not frozen (i.e., 4 °C). Different straw aliquots collected from the same sample yielded highly reproducible communities as disclosed by 16S rRNA gene sequencing; operational taxonomic units that were lost, or gained, between the two aliquots represented very low-abundance taxa (i.e., < 0.3% of the community). FAST-processed samples inoculated into germ-free animals resulted in gut communities that retained on average ~ 80% of the donor’s bacterial community. Assessment of choline metabolism and trimethylamine-N-oxide accumulation in transplanted mice suggests large interpersonal variation.
Overall, FAST allows for repetitive subsampling without thawing of the specimens and requires minimal supplies and storage space, making it convenient to utilize both in the lab and in the field. FAST has the potential to advance microbiome research through easy, reproducible sample processing.
- Fecal Aliquot Straw Technique
The human intestine harbors diverse and dynamic microbial communities encompassing species from the three domains of life [1, 2]. These microbial communities encode metabolic functions that complement the human genome and play key roles in our biology and health. While many of these functions are shared among communities from different individuals, large interpersonal differences have been reported . Identifying the consequences of this variation as it relates to host immune responses, drug effectiveness, and metabolism is key to fully understand how microbes modulate our biology and for the successful implementation of precision medicine strategies.
DNA sequencing approaches, such as 16S rRNA gene profiling or shotgun metagenomics, which have dominated the field in the last decade , have been instrumental in illuminating the degree of interpersonal variation in gut communities. These methods, however, provide limited insights into the metabolic capabilities of microbes, or how members in the community interact with each other and the host. Germ-free mice colonized with synthetic microbial mixtures provide a complementary platform for dissecting the role(s) of single species or a specific microbial metabolic pathway on host biology. However, most studies taking this approach use constructed communities of low diversity [5–7], in which colonizing species are represented at higher levels than naturally found in the host. While highly tractable, this reductionist approach may overestimate the impact a microbe/pathway has on host biology; or fail to reveal microbial interactions that occur in conditions of high microbial complexity or that may only occur between species that have co-evolved within a specific host. Thus, it is important to validate synthetic community findings with naturally occurring complex communities.
Currently, our ability to move towards larger model communities is limited by the ability to grow many microbes in parallel [8, 9]. One alternative to this limitation is to use uncultured non-defined microbial communities to colonize germ-free mice (i.e., fecal transplants). This approach has been extremely useful in inferring the causal role of microbial dysbiosis with many host phenotypes (e.g., adiposity, insulin resistance) [9, 10]. Key to the success of this approach is the ability to collect samples that can be stored long-term and reproducibly subsampled without compromising cell viability. Currently, this is a laborious process that can be done effectively only on a relatively small scale. Moving to larger studies presents several challenges including shipping/storage/subsampling of material in ways that preserve cell viability and allows for reproducible and repetitive testing of samples.
Current methods of fecal collection and processing for purposes beyond DNA sequencing are cumbersome, messy, and inefficient for handling large number of samples [11–15]. For example, a standard procedure for aliquoting fecal samples entails scooping small amounts of the specimens into multiple 2-ml screw-cap tubes that have been previously labeled. These aliquots are typically for a single use, thus many of them need to be prepared from each specimen (e.g., 20 aliquots), and significant amounts of freezer space is needed for their storage. Importantly, the process of aliquoting samples this way exposes a large fraction of the microbes to air, which decreases viability of many strict anaerobes. While excessive exposure to oxygen could be avoided by processing samples inside an anaerobic chamber, this would add time, the standard burden associated with working in an anaerobic chamber, and the need for an anaerobic chamber dedicated to fecal processing. Furthermore, once the samples are frozen, retrieving a consistent amount of material from each tube for follow-up procedures (e.g., screen in 96-well plate) is quite cumbersome as samples stick to the tube (i.e., they do not come out), their removal require the use a small spatula (a new spatula for each sample), and in our experience by the time the operator has managed to scoop the sample out of the tube a large fraction of it has thawed, which is not desirable. Moreover, while immediate sample processing and full homogenization are preferred, these methods are not always possible in the field or for geographically disperse samples.
Here, we present the Fecal Aliquot Straw Technique (FAST), a novel fecal processing and storage method. FAST allows easy, reproducible fecal processing and storage with minimal supplies. FAST-processed samples can be efficiently stored at ultra-low temperatures for extended periods of time and easily subsampled without thawing. 16S rRNA gene profiling confirmed the ability for highly reproducible subsampling within a subject. Furthermore, transplantation of these samples into germ-free mice recovered on average ~ 80% of the donor bacterial community composition, retained donor individuality, and revealed large interpersonal variation in microbial choline metabolism.
Fecal Aliquot Straw Technique (FAST)
FAST enables consistent and convenient subsampling of frozen fecal material
FAST-processed samples were removed from the freezer and subsampled by slicing 1 cm sections with a sterile razor blade, while keeping the straw on a weigh boat on top of dry ice to prevent thawing. Two randomly selected straws were sampled for each subject, from here on referred to as “S1” and “S2”. S1 was used only for DNA extraction while S2 was used for preparing an oral gavage used to colonize germ-free (GF) mice (see “Methods” section). We obtained 2.5 million high-quality sequences (62,600 ± 62,700 SD per sample) of the variable 4 region of the 16S rRNA gene. This resulted in Good’s coverage > 98% for all samples (Additional file 1: Table S1) and 2723 unique 97% operational taxonomic units (OTUs) in the dataset (Additional file 2: Table S2). Rarified datasets used in alpha- and beta-diversity analyses were normalized to 4500 sequences per sample, which resulted in Good’s coverage > 98% and 809 OTUs.
Inoculation of mice using FAST-processed samples
Transplanted bacterial communities were more similar to that of their donor than to any other human sample in the dataset (Fig. 3e, Kruskal-Wallis P < 0.01). However, mouse selected communities clustered distinctly from human ones (Additional file 4: Figure S2) suggesting the largest selection on bacterial taxa occurred during colonization of the mouse gut rather than in processing. Consistently in all samples, taxa belonging to the Bacteroidetes and Verrucomicrobia phyla bloomed following transplantation into mice while taxa belonging to the Firmicutes and Proteobacteria phyla decreased in relative abundance (Additional file 5: Figure S3A). Similarly, shifts in relative abundance were observed at the family (Additional file 5: Figure S3B) and genus (Additional file 5: Figure S3C) level but the direction and amplitude remained similar among subjects. There were several genera that were rarely abundant in donor samples, e.g., Bifidobacterium, Collinsella, Dialister, Lachnospira, and Streptococcus that failed to colonize mice. However, Akkermansia, Bacteroides, and Sutterella, when present in the donor sample, tended to increase in representation in mouse samples (Additional file 2: Table S2). Lost OTUs may reflect evolutionary (i.e., host adaptation), diet imparted-constraints, or result from reduced cell viability associated with the handling and freezing of the samples.
Method application: assessing inter-individual variation in microbial choline metabolism
We used the mice mentioned above to assess inter-personal variation in microbial choline consumption and accumulation of the pro-atherogenic molecule trimethylamine-N-oxide (TMAO) . Microbial metabolism of choline results in the production of trimethylamine (TMA), a compound that is absorbed by the host and further converted in the liver to TMAO . Accumulation of TMAO thereby provides an indirect measure of microbial metabolic activity allowing us to assess functional recovery following transplantation. Colonized mice maintained on a standard chow for 2 weeks were transitioned onto a 1% (wt/wt) choline diet for an additional 2 weeks after fecal collection. Following 2 weeks on the choline-supplemented diet, serum was collected from non-fasted mice and TMAO and choline quantified using HPLC/MS.
Notably, mouse cohorts colonized months apart with different subsamples of an individual’s FAST samples reproducibly captured microbial metabolic activity as indicated by TMAO accumulation. For example, a non-TMA producing community (subject 1) consistently did not accumulate TMAO when transplanted into GF mice whereas a TMA-producing community (subject 8) retained TMA-production activity, as indicated by the consistently high levels of TMAO accumulation (Additional file 6: Figure S4).
Changes in the specific composition (and encoded functions) of the gut microbiota have been associated with various disorders from obesity to cognitive dysfunction [9, 18]. While many of these associations still lack mechanistic details, such correlations have prompted a growing number of human longitudinal studies to incorporate human microbiome analyses into their vast metadata collections. Until now, these efforts have been largely limited to 16S rRNA gene sequencing due to both cost and processing techniques. The Fecal Aliquot Straw Technique (FAST) presented here provides an easy, inexpensive, reproducible workflow for processing and storing fecal samples (human or other animals) that can be completed in remote locations. It reduces sample volumes for shipping and enables repetitive subsampling (straw pieces) from valuable specimens. FAST minimizes risks associated with previous sampling methods [11–15, 19] including repetitive freeze-thaw cycles and overall provides a novel solution to future collection endeavors.
All experiments involving mice were performed using protocols approved by the University of Wisconsin-Madison Animal Care and Use Committee. For completion of fecal transplants, female C57BL/6 (B6) germ-free mice were gavaged with ~ 200 μl of fecal inocula. Fecal suspensions were prepared under anaerobic conditions in Hungate tubes using a 1 cm piece of frozen FAST straw material and 5 ml mega media . Mice were maintained on the autoclaved chow diets for 2 weeks after humanization and on the irradiated 1% Choline diet (Envigo TD.140179) for two additional weeks. Serum for TMAO and choline measurements were taken from non-fasted mice.
Samples collected for microbiota analysis included a 1 cm piece of a FAST straw (Straw1 or S1), 1 ml of the aforementioned gavage slurry (Straw2 or S2), and 3 fecal pellets collected from mice 2 weeks after humanization (mouse or M). DNA was extracted from samples according to published bead-beating procedures . In short, samples were resuspended in a solution containing 500 μl of 2× extraction buffer [200 mM Tris (pH 8.0), 200 mM NaCl, 20 mM EDTA], 210 μl of 20% SDS, 500 μl phenol:chloroform:isoamyl alcohol (pH 7.9, 25:24:1), and 500 μl of 0.1-mm diameter zirconia/silica beads. Cells were mechanically disrupted using a bead beater (BioSpec Products, Barlesville, OK, USA) for 3 min at room temperature. The aqueous layer was removed and DNA precipitated using 600 μl isopropanol and 60 μl 3 M Na-acetate. Pellets were dried with ethanol and resuspended in TE. NucleoSpin Gel and PCR Clean-up Kit (Macherey-Nagel, Bethlehem, PA, USA) was used to remove contaminants. Isolated DNA was stored at − 80 °C until downstream processing.
16S rRNA gene V4 amplication and sequencing
PCR was performed using primers for the variable 4 (V4) region of the bacterial 16S rRNA gene . PCR reactions contained 12.5 ng DNA, 10 μM each primer, 12.5 μl 2× HotStart ReadyMix (KAPA Biosystems, Wilmington, MA, USA), and water to 25 μl. Cycling conditions were 95 °C for 3 min, then 25 cycles of 95 °C for 30 s, 55 °C for 30 s, and 72 °C for 30 s, and finally 72 °C for 5 min. PCR products were purified by gel extraction from a 1% low-melt agarose gel using a ZR-96 Zymoclean Gel DNA Recovery Kit (Zymo Research, Irvine, CA, USA). Individual samples were quantified by Qubit Fluorometer (Invitrogen, Carlsbad, CA, USA) and were equimolar pooled. The pool plus 5% PhiX control DNA was sequenced with the MiSeq 2 × 250 v2 kit (Illumina, San Diego, CA, USA) using custom sequencing primers . All DNA sequences have been deposited in NCBI’s Short Read Archive (PRJNA393465).
16S rRNA gene V4 sequence analysis
Sequences were demultiplexed on the Illumina MiSeq, and sequence clean-up was completed in mothur v.1.39.0  following steps as described in . Briefly, paired-end sequences were combined, and poor quality sequences were removed. The remaining sequences were aligned to the SILVA 16S rRNA gene reference alignment database , and very similar sequences (differences ≤2) were pre-clustered. Chimera detection and removal were performed. Sequences were grouped into 97% operational taxonomic units (OTUs) by uncorrected pairwise distances and OptiClust clustering . OTUs were classified using GreenGenes . Coverage was calculated using Good’s coverage.
All statistical analyses of sequence data were performed in R v3.4.0 (http://www.r-project.org/). Beta-diversity measures were calculated using data rarified to 4500 sequences per sample with vegan (Bray-Curtis and Jaccard; https://CRAN.R-project.org/package=vegan) and phyloseq (UniFrac ) packages in R. Differences in beta-diversity were visualized by non-metric multidimensional scaling (nMDS) plots and assessed by permutational ANOVA (vegan::adonis) stratified by subject when needed. For factors with more than 2 levels, pairwise adonis was performed with the Bonferroni correction for multiple comparisons. When group sizes differed greatly, beta-diversity was assessed using Kruskal-Wallis rank sum tests. Alpha-diversity was calculated using the rarified dataset in mothur, and differences were assessed by ANOVA or linear mixed effects models with subject as a random effect (nlme::lme; https://CRAN.R-project.org/package=nlme). Pairwise alpha-diversity tests were performed using multiple comparisons for parametric models with Tukey’s correction for multiple comparisons (multcomp::glht) . All tests were assessed at significance P < 0.05, and values are expressed as mean ± standard error, unless otherwise noted.
Number of taxa recovered was calculated at the phylum, order, family, genus, and OTU levels using un-rarified data. For each subject, only taxa present to at least 0.1% abundance in at least one of the two samples of interest (S1 vs. S2 or S2 vs. M) were included. Percent relative abundance recovered was calculated using all OTUs present in the reference sample (S1 or S2). All code is available at https://github.com/kdillmcfarland/FAST_method.
HPLC metabolite measurements
Serum choline and TMAO levels were measured according published methods . In brief, serum samples were prepared for analysis by precipitating proteins with 4 volumes of ice-cold methanol spiked with 2.5 μM deuterium-labeled choline and deuterium-labeled TMAO internal standards. Samples were centrifuged at 18,213×g at 4 °C for 3 min. The recovered supernatants were diluted 1:1 in uHPLC-grade water prior to screening. After sample preparation, identification and quantitation of TMAO and choline were performed using a uHPLC (Thermo Scientific/Dionex 3000) coupled to a high-resolution mass spectrometer (Thermo Scientific Q Exactive). Liquid chromatography separation was achieved on a Dikma Bio-Bond C4 column (150 mm × 2.1 mm; 3-μm particle size) using a 7 min isocratic gradient (50:50 methanol [MeOH] − water, 5 mM ammonium formate, and 0.1% formic acid). Quantitation of TMAO (76.0762) and d9-TMAO (85.1318) was performed via targeted MS/MS in positive mode using the following fragments masses: TMAO (58.0659) and d9-TMAO (68.1301). Quantitation of choline (104.1075) and d9-choline (113.1631) was performed in positive mode with full-MS scan by monitoring their exact masses.
The authors thank the University of Wisconsin Biotechnology Center DNA Sequencing Facility for providing sequencing access and support services, and the University of Wisconsin Center for High Throughput Computing (CHTC) in the Department of Computer Sciences for providing computational resources, support, and assistance. Our gratitude to Kelsea Jewell of Mina Lunga Illustrations for provided graphical assistance. We thank David Stevenson for technical support.
This work was supported in part by the National Institutes for Health DK108259-01 (F.E.R.) AG041868 (P.H), the Center for the Demography of Health and Aging (AG017266, PH), and the National Institute of Food and Agriculture, U.S. Department of Agriculture, under award number 2016-67017-24416 (F.E.R.) and Hatch WIS01901 (F.E.R). Additional support was provided by the Vice Chancellor for Research and Graduate Education (VCGRE) at the University of Wisconsin-Madison. KAR was supported by an NLM training grant to the Computation and Informatics in Biology and Medicine Training Program (NLM5T15LM007359). The authors declare no competing financial interests.
Availability of data and materials
The data sets generated and/or analyzed during the current study are available in NCBI’s Short Read Archive (PRJNA393465) or are included in this published article and its supplementary information files.
FER, RLK, and PH designed the research. KAR, KK, RLK, and EIV completed the sample processing and gnotobiotic experiments. KADM performed the sequencing and analyzed the data. Initial manuscript written is by KAR, KADM, and FER. Metabolomic analysis made possible by DAN. All authors contributed to and approved the final manuscript.
The use of fecal microbiota data was approved by the Institutional Review Board at the University of Wisconsin-Madison (2017-0600). All experiments involving mice were performed using protocols approved by the University of Wisconsin-Madison Animal Care and Use Committee.
The authors declare that they have no competing interests.
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- Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010;464:59–65.View ArticlePubMedPubMed CentralGoogle Scholar
- Marsland BJ, Gollwitzer ES. Host–microorganism interactions in lung diseases. Nat Rev Immunol. 2014;14:827–35.View ArticlePubMedGoogle Scholar
- Turnbaugh PJ, Hamady M, Yatsunenko T, Cantarel BL, Duncan A, Ley RE, et al. A core gut microbiome in obese and lean twins. Nature. 2009;457:480–4.View ArticlePubMedGoogle Scholar
- Kuczynski J, Lauber CL, Walters WA, Parfrey LW, Clemente JC, Gevers D, et al. Experimental and analytical tools for studying the human microbiome. Nat Rev Genet. 2011;13:47–58.View ArticlePubMedPubMed CentralGoogle Scholar
- Romano KA, Vivas EI, Amador-Noguez D, Rey FE. Intestinal microbiota composition modulates choline bioavailability from diet and accumulation of the proatherogenic metabolite trimethylamine-N-oxide. MBio. 2015;6:e02481–14.View ArticlePubMedPubMed CentralGoogle Scholar
- Faith JJ, Rey FE, O’Donnell D, Karlsson M, McNulty NP, Kallstrom G, et al. Creating and characterizing communities of human gut microbes in gnotobiotic mice. ISME J. 2010;4:1094–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Romano KA, Martinez-del Campo A, Kasahara K, Chittim CL, Vivas EI, Amador-Noguez D, et al. Metabolic, epigenetic, and transgenerational effects of gut bacterial choline consumption. Cell Host Microbe. 2017;22(3):279–290.e7.Google Scholar
- Goodman AL, Kallstrom G, Faith JJ, Reyes A, Moore A, Dantas G, et al. Extensive personal human gut microbiota culture collections characterized and manipulated in gnotobiotic mice. Proc Natl Acad Sci. 2011;108:6252–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Ridaura VK, Faith JJ, Rey FE, Cheng J, Duncan AE, Kau AL, et al. Gut microbiota from twins discordant for obesity modulate metabolism in mice. Science. 2013;341:–1241214.Google Scholar
- Kreznar JH, Keller MP, Traeger LL, Rabaglia ME, Schueler KL, Stapleton DS, et al. Host genotype and gut microbiome modulate insulin secretion and diet-induced metabolic phenotypes. Cell Rep. 2017;18:1739–50.View ArticlePubMedPubMed CentralGoogle Scholar
- Song SJ, Amir A, Metcalf JL, Amato KR, Xu ZZ, Humphrey G, et al. Preservation methods differ in fecal microbiome stability, affecting suitability for field studies. mSystems. 2016;1:e00021–16.Google Scholar
- Choo JM, Leong LE, Rogers GB. Sample storage conditions significantly influence faecal microbiome profiles. Sci Rep. 2015;5:16350.Google Scholar
- Cardona S, Eck A, Cassellas M, Gallart M, Alastrue C, Dore J, et al. Storage conditions of intestinal microbiota matter in metagenomic analysis. BMC Microbiol. 2012;12:158.View ArticlePubMedPubMed CentralGoogle Scholar
- Carroll IM, Ringel-Kulka T, Siddle JP, Klaenhammer TR, Ringel Y. Characterization of the fecal microbiota using high-throughput sequencing reveals a stable microbial community during storage. PLoS One. 2012;7:e46953. Neufeld J, editorView ArticlePubMedPubMed CentralGoogle Scholar
- Hsieh Y-H, Peterson CM, Raggio A, Keenan MJ, Martin RJ, Ravussin E, et al. Impact of different fecal processing methods on assessments of bacterial diversity in the human intestine. Front Microbiol. 2016;7:1643.Google Scholar
- Herd P, Schaeffer NC, DiLoreto K, Jacques K, Stevenson J, Rey F, et al. The Influence of Social Conditions Across the Life Course on the Human Gut Microbiota: A Pilot Project With the Wisconsin Longitudinal Study. J Gerontol Ser B. 2018;73:124–33.Google Scholar
- Wang Z, Klipfell E, Bennett BJ, Koeth R, Levison BS, DuGar B, et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature. 2011;472:57–63.View ArticlePubMedPubMed CentralGoogle Scholar
- Brandscheid C, Schuck F, Reinhardt S, Schäfer K-H, Pietrzik CU, Grimm M, et al. Altered gut microbiome composition and tryptic activity of the 5xFAD Alzheimer’s mouse model. J Alzheimers Dis. 2017;56:775–88.View ArticlePubMedGoogle Scholar
- Harrison AP. Survival of bacteria upon repeated freezing and thawing. J Bacteriol. 1955;70:711–5.PubMedPubMed CentralGoogle Scholar
- Kozich JJ, Westcott SL, Baxter NT, Highlander SK, Schloss PD. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl Environ Microbiol. 2013;79:5112–20.View ArticlePubMedPubMed CentralGoogle Scholar
- Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol. 2009;75:7537–41.View ArticlePubMedPubMed CentralGoogle Scholar
- Pruesse E, Quast C, Knittel K, Fuchs BM, Ludwig W, Peplies J, et al. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res. 2007;35:7188–96.View ArticlePubMedPubMed CentralGoogle Scholar
- Westcott SL, Schloss PD. OptiClust, an improved method for assigning amplicon-based sequence data to operational taxonomic units. mSphere. 2017;2:e00073–17. Google Scholar
- DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 2006;72:5069–72.View ArticlePubMedPubMed CentralGoogle Scholar
- McMurdie PJ, Holmes S. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One. 2013;8:e61217. Google Scholar
- Hothorn T, Bretz F, Westfall P. Simultaneous inference in general parametric models. Biom J. 2008;50:346–63.View ArticlePubMedGoogle Scholar