Profiling Community-Wide Gene Expression

The microbial flora within and and on the surface of the human body, especially the gut, are well-established to have an important relationship to human health status1. Dysbiosis in the gut microflora can contribute to the development of pathological conditions such as chronic inflammatory bowel disease, diet-induced obesity, Type 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.

While the transcriptome is a complete collection of RNAs encoded by an organism’s genome, the metatranscriptome includes all RNAs encoded by a group of organisms. Metatranscriptomics analyses are relevant to biomarker and drug discovery studies in numerous ways. Some major applications include:

  • Determine drug treatment responses with measurable changes in microflora composition and gene expression
  • Identify microflora RNA signatures which can be associated with specific-disease subtypes and enable patient stratification, including identification of non-responders
  • Investigations of induction or repression of specific metabolic pathways in response to diet changes or 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 and rRNA depletion, sequencing, and bioinformatics have enabled effective and efficient analysis of complex microorganism communities colonizing in vivo samples13. Well-validated protocols for extraction of RNA from feces and other common biospecimens have been developed by ORB scientists. Examples of accepted types are given below.

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

The Illumina ScriptSeq Epidemiology kit is used to prepare sequencing libraries depleted of RNA arising from both prokaryotic and eukaryotic sources. 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 2500, or HiSeq 4000 instruments from Illumina.

Bioinformatic Services for Metatranscriptomic Data Sets

                Metatranscriptomics
                    Bioinformatics

ORB offers comprehensive processing and analysis of metatranscriptomic sequencing data using up to three independent analytic approaches including taxonomic classification using Kraken software, de novo transcriptome assembly and counting, and alignment and counting using a customized database of expected microbial genomes. Analysis can remain focused or cover all three domains of living organisms (archea, prokaryote, and eukaryotes) as well as viruses. Analysis reports include interactive taxonomic classification tables and graphs providing the relative abundance of detectable species, as well as gene-level expression analysis and visualization. Visit the metatranscriptomics bioinformatics page for more information.

Sample Submission Instructions

The preparation of stool samples is critical to obtaining long RNA of sufficient yield and quality for metatranscriptomics analysis14. 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 metabolites15-17). ORB recommends flash-freezing fecal samples to ensure the best results from RNA sequencing applications. A minimum submission of 0.25 milligrams of feces per sample is needed. Please complete a sample submission checklist prior to shipping samples.

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 to discuss processing other sample types using ORB’s metatranscriptomic analysis services!

Related Applications and Services


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. 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 online12(Suppl 1), 5.
  14. 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 microbiology12(1), 158.
  15. Wilson, I. G. (1997). Inhibition and facilitation of nucleic acid amplification. Applied and environmental microbiology63(10), 3741.
  16. 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 One7(7), e39455.
  17. Schrader, C., Schielke, A., Ellerbroek, L., & Johne, R. (2012). PCR inhibitors–occurrence, properties and removal. Journal of applied microbiology113(5), 1014-1026.