Microbiomics
 

Microbiomics Overview

Microbiome, the community of microorganisms that inhabits a defined environment, shapes the environment in which it lives and with which it interacts. The microbial flora within and outside the human body have an important relationship to an organism’s health status1. For example, dysbiosis in the gut microflora is an established contributor to the development of pathological conditions such as chronic inflammatory bowel diseases, diet-induced obesity, Types 1 and 2 diabetes, colorectal cancer, and peptic ulcer disease2-7. Furthermore, these imbalances are implicated in liver disease progression, AIDS development in HIV-infected patients, autism spectrum disorders, depression, multiple sclerosis, and rheumatic diseases7-12.

The impact of the microbiome on environments ranging from soil to host organisms including plants and humans is being uncovered at an accelerating rate, thanks in large part to advancements in massively parallel sequencing technologies. These technologies (offered by ORB) have enabled profiling of the microbiome DNA composition (metagenomics) and of the microbiome RNA composition (metatranscriptomics) at unprecedented scales allowing for the discovery of invaluable taxonomic and functional profiles of the microbial community.

Metagenomics

Metagenomics is the study of the DNA sequences comprising a population of organisms making up specific environmental niches including specific soil types, water samples, or human skin or gut microflora. Sequencing of total DNA and focused sequencing of specific evolutionarily conserved regions have both been utilized extensively to answer specific questions about the metagenome. However, focused sequencing of 16S ribosomal DNA (rDNA) is most commonly used to characterize a population of organisms, especially when bacteria and archaea are the focus, due to the simplicity, reliability, and lower cost of this method relative to whole genome sequencing 13.

Among microorganisms, the 16S ribosomal DNA (rDNA) gene contains genotypically divergent hypervariable regions (HVR) flanked by highly conserved stretches of DNA (Fig. 1). The conserved regions are suitable for the annealing of degenerate primers for universal amplification of bacterial rDNA while analysis of the amplified sequences, containing the bacterial HVRs, allows for the quantification and characterization of prokaryotic genera present in a bio-sample.14. ORB’s 16S rDNA sequencing targets the most well-characterized of the hypervariable regions (V3 and V4) allowing for a comprehensive picture of your sample’s microbiome.
 

Microbiomics
Figure 1. Map of prokaryotic 16s rDNA gene.
 

ORB offers sequencing with primer sets that span both V3 and V4 HVRs which have been recommended by Illumina (BK primer set)15, or an alternative set that spans V4 only and which has been utilized by the Earth Microbiome Project (PA primer set)16, 17, the later having been optimized for detection of Archaea. ORB can also implement custom primer sets for clients with unique experimental goals. Both primers sets have been validated in ORB's laboratory and typical results are shown in Figure 2.

ORB’s metagenomic analysis service starts from DNA isolation using the ZymoBIOMICS DNA/RNA Miniprep kit which is designed for uniform and efficient lysis of many different organisms (e.g. Gram-negative/positive bacteria, fungus, protozoans, and algae) from a variety of sample inputs. Next the V3-V4 or V4 regions of the 16S rDNA are amplified with either the Bakt or Parada primer sets. The 16S rDNA amplicons are then adapted with Illumina-instrument compatible PCR priming sites via an additional 8 cycles of PCR. Amplicons are sequenced on the MiSeq or HiSeq 2500 sequencing instruments from Illumina.

ORB performs comprehensive metagenomic analysis for elucidating taxonomic diversity and phylogenetic relationships within various sample types. See more details about ORBs Metagenomic bioinformatics services along with our comprehensive rDNA demo analysis package!

Fig. 2 Genus Diversity.
Figure 2 : Evaluation of observed abundances of genera present in the Zymo Research Microbial Standard and DNA Standard obtained by DNA extraction, amplification, and sequencing at ORB versus expected genera composition for Bakt (BK) (A) and Parada (PA) (B) primer sets. Experimental sample names are defined with an acronym starting with the letter M followed by a sample number, the primer type (BK, PA) and the bead beating time during homogenization in minutes (10, 20, 40). Samples without a bead beating time were direct amplified from the Zymo Research Microbial DNA standard. ZymoDNA and ZymoMicro represent the theoretical composition in the Microbial Standard and DNA Standard, respectively, provided by Zymo Research.

Metatranscriptomics

ORB’s metatranscriptomics services facilitates gene expression studies at a global scale, revealing the transcriptionally active portion of the microbiome, i.e. the portion that contributes to the functional capacity of the microbiome. The utility of metatranscriptomics to elucidate important and underappreciated biological functions of the microbiome has been demonstrated in a wide array of fields. ORB’s metatranscriptome services are offered for all stages of your microbiomic project. We perform RNA extraction, library preparation, sequencing, and analysis of your complex metatranscriptomic data including interactive taxonomic classification reports, as well as gene-level expression analysis and visualization.
 

  • Determine drug treatment responses with measurable changes in microflora composition and gene expression
  • Microflora RNA signatures can be associated with specific-disease subtypes and enable patient stratification, including identification of non-responders.
  • Study induction or repression of specific metabolic pathways in response to changes in diet or during disease development.
  • General mechanism of action studies.
  • Improve diagnostics for infectious disease status and susceptibility.
  • Study interactions between symbiotic bacteria and host.
 

Sample Processing for Metatranscriptomic Analysis at ORB

Advances in sample preparation, sequencing, and bioinformatics have enabled effective and efficient analysis of complex microorganism communities colonizing in vivo samples18. ORB has developed well-validated protocols for extraction of RNA from common and unique biospecimen. Examples of accepted sample types are given below. Contact us to discuss options for your samples even if they are not listed below!

  • Stool / Feces
  • Tissue biopsies
  • Cells
  • Saliva
  • Bronchial lavage
  • Sputum
  • Buccal swabs
  • Oral biofilms
  • Soil
  • Plants
  • Food items
 

Following RNA isolation, prokaryotic and mammalian ribosomal RNAs are depleted from the total RNA sample and then libraries are prepared using a method that incorporates random-priming of cDNA synthesis. This methodology allows sequencing of cDNA derived from not only microbial symbionts and parasites but also the host mammalian cells. High coverage sequencing is achieved using either the NextSeq 500, HiSeq 4000, or NovaSeq 6000 instruments from Illumina.
 

Sample Submission Guidance

Stool is of the most common sample type chosen for microbiomic research. The correct preparation of stool samples is critical to obtaining long RNA of sufficient yield and quality for microbiomic analysis19. Fecal matter generally contains high level of nucleases and endogenous substances which can serve as inhibitors to downstream processing (e.g. complex polysaccharides, immunoglobulins, glycogens, lipids, and metabolites)20-22. ORB recommends flash-freezing fecal samples to ensure the best results from RNA sequencing applications. A minimum submission of 0.25 mg of feces per sample is needed. Please find directions here for sample submission guidance of additional samples types for your microbiomic studies. When shipping, enclose a completed sample submission checklist in a separate dry compartment with the sample shipment. Also send a digital file version of the sample submission checklist to array@oceanridgebio.com and provide notification of the sample shipment to by calling 754-600-5128 on the day samples are shipped.
 

Contact Us about your microbiome study or interest for a free consultation!

 

References

  1. Clemente, J. C., Ursell, L. K., Parfrey, L. W., & Knight, R. (2012). The impact of the gut microbiota on human health: an integrative view. Cell148(6), 1258-1270.
  2. Jostins, L., Ripke, S., Weersma, R. K., Duerr, R. H., McGovern, D. P., Hui, K. Y., & Essers, J. (2012). International IBD Genetics Consortium (IIBDGC)(2012). Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature491(7422), 119-124.
  3. Armougom, F., Henry, M., Vialettes, B., Raccah, D., & Raoult, D. (2009). Monitoring bacterial community of human gut microbiota reveals an increase in Lactobacillus in obese patients and Methanogens in anorexic patients. PloS one4(9), e7125.
  4. Brown, C. T., Davis-Richardson, A. G., Giongo, A., Gano, K. A., Crabb, D. B., Mukherjee, N., & Hyöty, H. (2011). Gut microbiome metagenomics analysis suggests a functional model for the development of autoimmunity for type 1 diabetes. PloS one6(10), e25792.
  5. Larsen, N., Vogensen, F. K., van den Berg, F. W., Nielsen, D. S., Andreasen, A. S., Pedersen, B. K., & Jakobsen, M. (2010). Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults. PloS one5(2), e9085.
  6. Weir, T. L., Manter, D. K., Sheflin, A. M., Barnett, B. A., Heuberger, A. L., & Ryan, E. P. (2013). Stool microbiome and metabolome differences between colorectal cancer patients and healthy adults. PloS one8(8), e70803.
  7. Zhang, Y. J., Li, S., Gan, R. Y., Zhou, T., Xu, D. P., & Li, H. B. (2015). Impacts of gut bacteria on human health and diseases. International journal of molecular sciences16(4), 7493-7519.
  8. Mutlu, E. A., Keshavarzian, A., Losurdo, J., Swanson, G., Siewe, B., Forsyth, C., & Cox, S. (2014). A compositional look at the human gastrointestinal microbiome and immune activation parameters in HIV infected subjects. PLoS Pathog10(2), e1003829.
  9. Cao, X., Lin, P., Jiang, P., & Li, C. (2013). Characteristics of the gastrointestinal microbiome in children with autism spectrum disorder: a systematic review. Shanghai Arch Psychiatry25(6), 342-53.
  10. Lyte, M. (2013). Microbial endocrinology in the microbiome-gut-brain axis: how bacterial production and utilization of neurochemicals influence behavior. PLoS Pathog9(11), e1003726.
  11. Jangi, S., Gandhi, R., Cox, L. M., Li, N., Von Glehn, F., Yan, R., ... & Cook, S. (2016). Alterations of the human gut microbiome in multiple sclerosis. Nature Communications7.
  12. Brusca, S. B., Abramson, S. B., & Scher, J. U. (2014). Microbiome and mucosal inflammation as extra-articular triggers for rheumatoid arthritis and autoimmunity. Current opinion in rheumatology26(1), 101.
  13. SG, Hugenholtz P. (2008) A renaissance for the pioneering 16S rRNA gene.  Curr Opin Microbiol. 11(5),442–446.
  14. Yang B, Wang Y, Qian PY. Sensitivity and correlation of hypervariable regions in 16S rRNA genes in phylogenetic analysis. (2016)  BMC Bioinformatics. 22(17):135. doi: 10.1186/s12859-016-0992-y.
  15. Herlemann DP, Labrenz M, Jürgens K, Bertilsson S, Waniek JJ, Andersson AF. (2011) Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea.  ISME J. 5(10),1571-1579.
  16. Parada AE, Needham DM, Fuhrman JA. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. (2016)  Environ. Microbiol., 18(5):1403-1414.
  17. Apprill A, McNally SP, Parsons R & Weber L. (2015) Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton.  Aquatic Microbial Ecology. 75:129-137.
  18. Aguiar-Pulido, V., Huang, W., Suarez-Ulloa, V., Cickovski, T., Mathee, K., & Narasimhan, G. (2016). Metagenomics, metatranscriptomics, and metabolomics approaches for microbiome analysis. Evolutionary bioinformatics online, 12(Suppl 1), 5.
  19. Cardona, S., Eck, A., Cassellas, M., Gallart, M., Alastrue, C., Dore, J., & Manichanh, C. (2012). Storage conditions of intestinal microbiota matter in metagenomic analysis. BMC microbiology, 12 (1), 158.
  20. Wilson, I. G. (1997). Inhibition and facilitation of nucleic acid amplification. Applied and environmental microbiology, 63(10), 3741.
  21. Shulman, L. M., Hindiyeh, M., Muhsen, K., Cohen, D., Mendelson, E., & Sofer, D. (2012). Evaluation of four different systems for extraction of RNA from stool suspensions using MS-2 coliphage as an exogenous control for RT-PCR inhibition. PLoS One, 7(7), e39455.
  22. Schrader, C., Schielke, A., Ellerbroek, L., & Johne, R. (2012). PCR inhibitors–occurrence, properties and removal. Journal of applied microbiology, 113(5), 1014-1026.