View Cart | Checkout
Menu

Impact of Method Variables on Microbiota Sample Disruption

Jaclyn Zecca and David Burden

OPS Diagnostics LLC

Abstract

Population studies require representative sampling, which is the case for DNA extraction for microbiome analysis. Sample processing for nucleic acid isolation can be a factor in data error. Even though there have been efforts at protocol standardization, researchers still tend to modify existing protocols or create their own. As bead beating is a popular method used to disrupt microbiota, it is important to consider the effects of different bead sizes and composition, as well as homogenizer type, on the efficiency of cell disruption. Generally, prokaryotes are best disrupted with small (100 micron) zirconium beads using high speed linear or oscillating homogenizers. Not all organisms may respond to bead beating alone, thus chemical and enzymatic methods can be included in combined methods to improve DNA isolation and sample representation.

Introduction

Early studies of microbiota typically included assaying parameters that measured the population, whether it was ATP (luciferase assay), total protein (via Lowry or Bradford assay), or microbial counts. Several decades later, the analytical focus has shifted from generalized population parameters to metagenomics, in large part due to the advent of PCR and NextGen sequencing. However, the same basic challenge faces both analytical approaches, ensuring that the initial extraction of analyte, whether ATP, protein, or nucleic acid, is representative of the entire population.

When measuring microbial diversity in samples, whether fecal, soil, or sediments, obtaining ribosomal RNA genes that are representative of the population is critical. Like any population study, sampling errors can skew results; therefore the methods used to extract DNA must yield representative DNA. The laboratories at OPS Diagnostics have analyzed variations associated with sample processing and found the overall method, equipment applied, and choice of accessory items can all dramatically affect data (Burden, 2012). A limited survey of microbiome DNA extraction procedures shows significant variation in methodology (Table 1).

Table 1. Sampling of Methods Used to Isolate Microbiome.

Sample Type Method Reference
Feces Modified QIAamp® DNA Stool Kit without bead beating Ramadass et al., 2017
Feces ZR Fecal DNA Mini Prep Kit™ with FastPrep® bead beating Guard et al., 2017
Feces Manual precipitation method with bead beating Ericsson et al., 2016
Feces PowerSoil® DNA Isolation Kit with vortexing Galloway-Peña et al., 2017
Feces Modified DNeasy® Blood & Tissue Kit with enzyme treatment Tamecki et al., 2016
Soil PowerMax® Soil DNA Kit with vortexing Silva et al., 2017
Soil Modified FastDNA™ Spin Kit method with bead beating Neilson et al., 2017
Soil Modified 96 well Earth Microbiome Project protocol with bead beating Gibbons et al., 2017
Soil Omega E.Z.N.A.® Soil Kit without bead beating with vortexing Jie et al., 2016
Plant Leaves High throughput bead beating paired with KingFisher extraction Sapkota et al., 2015
Sediments Homegrown method with bead beating Durbin and Teske, 2011

 

Guidelines have been devised to standardize sample processing in microbiome research. The Earth Microbiome Project, International Human Microbiome Standards Consortium, and NIH Human Microbiome Project have each published recommended protocols for sample disruption and DNA isolation. Many researchers have, at minimum modified these protocols to fit the complexity of their samples or to make use of available materials. An extended survey shows that less than 10% of the methods adhere modestly to the recommended guidelines (data not shown), and resulted in numerous variations on the theme.

Most of the research includes some form of bead beating to liberate nucleic acids, followed by purification with either homegrown methods, nucleic acid purification kits, or some modification of nucleic acid purification kit protocols (Table 1). Many reports fail to specify the details of sample disruption by bead beating. Our laboratory has routinely demonstrated that bead size and composition are important for efficient homogenization, as is the type of homogenizer used for processing. As an example, the disruption of yeast with 400 micron silica or zirconium beads in a vortexer or high throughput homogenizer (HT Homogenizer™) shows the impact of bead and homogenizer choice (Table 2) on the resulting homogenate.

Table 2. Comparison of Yeast Lysed Using 400 µm Silica and Zirconium Beads on a Linear Homogenizer vs. Vortexer*. (Click image to enlarge)

* The lysis efficiency of yeast cells is calculated as gray “ghost” cells as a percentage of total cells.  Ghost cells are cells that are cracked open.  Intact cells refract light while disrupted cells appear as shadows.

The homogenizer propels the yeast and grinding beads in a linear direction, resulting in direct collisions between beads, yeast, and tube. The higher density zirconium beads have greater force resulting in higher lysis efficiency. Vortexers swirl the beads and yeast in unison, a motion that imparts much less direct impact on the cells, and lysis efficiency drops. Bead size is also vital, as yeast require larger beads (400 micron) while prokaryotes need smaller 100 micron beads (Burden, 2012). Microbiome analysis that includes eukaryotic DNA would need a mixture of small and large beads during sample disruption. Sample types must be matched with the appropriate beads/balls (see link for details).

An ideal homogenizer for lysing microbial cells will impart the same motion on all the samples. Oscillating homogenizers which hold pre-filled disruption tubes (e.g.,HT 24™, FastPrep®, Precellys®) process samples equally well. Linear motion homogenizers (HT Homogenizer™, 2010 Geno/Grinder®), also work well, with the additonal option of processing samples in high throughput formats. Figure “8” homogenizers tend to have higher sample variation.

Bead beating is a major method used for disrupting microbiota, but there is no single homogenization method that is suitable for all sample types. Alternatively, chemical methods and sonication are also widely used for sample disruption. Sonication has been found to be effective in the lysis of E. coli, B. subtilis, and S. cerevisiae for enzyme analysis, but each species requires different optimal power settings for the sonicator. Meaning that if the cells were in mixed culture, lysis of the individual species would be differential (OPS Diagnostics, unpublished data).

The effectiveness of chemical lysis methods, including lysing enzymes, detergents and/or chaotropes, can be very dependent upon the species. A simple study conducted in our laboratory on four common bacteria shows that detergents and chaotropes alone are fractionally as efficient as bead beating. An exception was the lysozyme/CTAB treatment of Bacillus subtilis, which was more effective than bead beating (Figure 1). This comparison is only meant to demonstrate that lysis efficiency can be species dependent.

Figure 1.  Relative effectiveness of isolating DNA from four species of bacteria using different lysis methods, which is mechanical bead beating versus chemical buffers (CTAB, SDS, Guanidine, and Lysozyme/CTAB).  For each bacterium, the yield of DNA was divided by the method that had the highest yield, with that method equaling 100%.  This calculation normalizes the data between the species and nullifies differences in culture density and genome size. (Click image to enlarge)

Some investigators have linked multiple methods in order to target the widest range of species in sample preparation. Bag et al. (2016) used a combination of three lytic enzymes to generate spheroplasts,which were then lysed with chaotropes and detergents, subsequently bead beaten, and  heated to burst remaining cells. This combined method was efficient, though also very involved. A good process needs to be effective, practical, and yield a representative DNA sample of the population.

Summary

Analysis of the microbiome requires that isolation methods yield DNA representative of the entire microbial population. No one method has shown universal application to sample disruption, however of the choices, bead beating is the most efficient. A method that is a combination of chemical and mechanical lysis would be best, although the choice of methods will be dependent upon the sample type (e.g., soil, feces, plants, tissues). Any enzymes, detergents, and/or chaotropes employed must be optimized for the sample. This is also true for the type of homogenizer, as well as the grinding media composition and size. Different size grinding beads and balls may also be used for samples containing cells that are both eukaryotes and prokaryotes.

Methods

Yeast Bead Beating – Yeast were cultured on YPD broth overnight at 28°C on a shaking incubator, 5 ml of culture was pelleted by centrifugation and resuspended in water. Pre-filled disruption tubes with 400 µm glass beads or 400 µm zirconium beads were filled with 600 µl of yeast suspension.Yeast were processed on a vortexer (Cell Disruptor Genie, Scientific Industries) or linear homogenizer (HT Homogenizer™, OPS Diagnostics) on high for 2 min. Samples were removed from the disruption tube and observed using phase contrast microscopy at 400X.Cell counts were done using a hemocytometer. Disruption efficiency was calculated by dividing number of ghost cells (dark, broken cells) by total number of cells multiplied by 100.

Chemical Lysis Comparison - Cultures of Escherichia coli, Staphylococcus epidermidis, Enterobacter aerogenes, and Bacillus subtilis were prepared by inoculating tryptic soy broth and shaking overnight at 30°C.  Cultures (1 ml) we centrifuged to pellet and the supernatant replaced with one of five buffers: 1) SDS buffer (0.5% SDS, 200 mM Tris, pH 8,25 mM EDTA, 250 mM NaCl), 2) Guanidine buffer (4M Guanidine HCl, 0.1M Tris, pH 8, 50 mM NaCl), 3) CTAB buffer (2% cetyl trimethylammonium bromide, 1% polyvinyl pyrrolidone, 100 mM Tris, pH 8, 1.4 M NaCl, 20 mM EDTA), 4) TE buffer with lysozyme (10 mM Tris, pH 8, 1 mM EDTA, 20 mg/ml lysozyme), and 5) TE buffer (10 mM Tris, pH 8, 1 mM EDTA). Pellets were resuspended by vortexing.

The guanidine, SDS, and CTAB buffer samples were vortexed, incubated for 15 min. at room temperature, centrifuged and then the supernatants processed as described below.  The TE/lysozyme samples were incubated at 37°C for 30 min. followed by the addition of 500 µl CTAB buffer. The solution was centrifuged and the supernatant was processed as described below.

The cells in TE were transferred to a 100 µm zirconium pre-filled disruption tube (PFAW 100-100-02) and bead beaten for 2 min. in an HT Mini™. Cell debris was pelleted by centrifugation and the supernatant was transferred to a new tube. One tenth (1/10) volume of 5 M NaCl was added and mixed, then processed as described below.

All supernatants from the lysis steps above were further purified using spin columns (SSC 100-01).  Supernatants were transferred to new tubes and 0.7 volume of isopropanol was added and mixed.  The tubes were incubated at -20°C for 15 min.  The solutions were then added to spin columns, and centrifuged to bind the DNA.  The columns were then washed twice with 250 µl cold 70% ethanol that was removed by centrifugation.  The DNA was eluted with 50 µl of water that was collected in a new tube by centrifugation.  Yields and purities were measured using a DeNovix spectrophotometer at 260 and 280 nm.

References

Bag, S., Saha, B., Mehta, O., Anbumani, D., Kumar, N., Dayal, M., … Das, B. (2016). An Improved Method for High Quality Metagenomics DNA Extraction from Human and Environmental Samples.Scientific Reports,6, 26775. http://doi.org/10.1038/srep26775

Burden, D. (2012).  Guide to the Disruption of Biological Samples – 2012.  Retrieved from https://opsdiagnostics.com/applications/samplehomogenization/homogenizationguidepart1.html

Durbin, A. M. and Teske, A. (2011), Microbial diversity and stratification of South Pacific abyssal marine sediments. Environmental Microbiology, 13: 3219–3234. doi:10.1111/j.1462-2920.2011.02544.x

Ericsson, A. C., Personett, A. R., Grobman, M. E., Rindt, H., & Reinero, C. R. (2016). Composition and Predicted Metabolic Capacity of Upper and Lower Airway Microbiota of Healthy Dogs in Relation to the Fecal Microbiota. PLoS ONE, 11(5), e0154646. http://doi.org/10.1371/journal.pone.0154646

Galloway-Peña, J. R., Smith, D. P., Sahasrabhojane, P., Wadsworth, W. D., Fellman, B. M., Ajami, N. J., … Shelburne, S. A. (2017). Characterization of oral and gut microbiome temporal variability in hospitalized cancer patients.Genome Medicine, 9, 21. http://doi.org/10.1186/s13073-017-0409-1

Guard, B. C., Mila, H., Steiner, J. M., Mariani, C., Suchodolski, J. S., & Chastant-Maillard, S. (2017). Characterization of the fecal microbiome during neonatal and early pediatric development in puppies. PLoS ONE,12(4), e0175718. http://doi.org/10.1371/journal.pone.0175718

Gibbons, S. M., Lekberg, Y., Mummey, D. L., Sangwan, N., Ramsey, P. W., & Gilbert, J. A. (2017). Invasive Plants Rapidly Reshape Soil Properties in a Grassland Ecosystem.mSystems,2(2), e00178–16. http://doi.org/10.1128/mSystems.00178-16

Jie, S., Li, M., Gan, M., Zhu, J., Yin, H., & Liu, X. (2016). Microbial functional genes enriched in the Xiangjiang River sediments with heavy metal contamination. BMC Microbiology, 16, 179. http://doi.org/10.1186/s12866-016-0800-x

Köberl, M., Erlacher, A., Ramadan, E. M., El-Arabi, T. F., Müller, H., Bragina, A., & Berg, G. (2016). Comparisons of diazotrophic communities in native and agricultural desert ecosystems reveal plants as important drivers in diversity. FEMS Microbiology Ecology, 92(2), fiv166. http://doi.org/10.1093/femsec/fiv166

Lavoie, K. H., Winter, A. S., Read, K. J. H., Hughes, E. M., Spilde, M. N., & Northup, D. E. (2017). Comparison of bacterial communities from lava cave microbial mats to overlying surface soils from Lava Beds National Monument, USA. PLoS ONE, 12(2), e0169339. http://doi.org/10.1371/journal.pone.0169339

Neilson, J. W., Califf, K., Cardona, C., Copeland, A., van Treuren, W., Josephson, K. L., … Maier, R. M. (2017). Significant Impacts of Increasing Aridity on the Arid Soil Microbiome. mSystems, 2(3), e00195–16. http://doi.org/10.1128/mSystems.00195-16

Ramadass, B., Rani, B. S., Pugazhendhi, S., John, K. R., & Ramakrishna, B. S. (2017). Faecal microbiota of healthy adults in south India: Comparison of a tribal & a rural population. The Indian Journal of Medical Research, 145(2), 237–246. http://doi.org/10.4103/ijmr.IJMR_639_14

Sapkota, R., Knorr, K., Jørgensen, L. N., O'Hanlon, K. A. and Nicolaisen, M. (2015), Host genotype is an important determinant of the cereal phyllosphere mycobiome. New Phytol, 207: 1134–1144. doi:10.1111/nph.13418

Silva, U. C., Medeiros, J. D., Leite, L. R., Morais, D. K., Cuadros-Orellana, S., Oliveira, C. A., … Dos Santos, V. L. (2017). Long-Term Rock Phosphate Fertilization Impacts the Microbial Communities of Maize Rhizosphere. Frontiers in Microbiology, 8, 1266. http://doi.org/10.3389/fmicb.2017.01266

Tarnecki, A. M., Patterson, W. F., & Arias, C. R. (2016). Microbiota of wild-caught Red Snapper Lutjanus campechanus. BMC Microbiology, 16, 245. http://doi.org/10.1186/s12866-016-0864-7