Metatranscriptomics is the process of analyzing messenger RNA (mRNA) present in microbial communities. It is used in conjunction with metagenomics, extraction of DNA. The data from metagenomic studies is used as a reference data set for the genes present in the community [1,2,3]. Metagenomics circumvents the need to individually culture many bacteria to examine their genes. This has lead to the discovery of many novel genes of unculturable organisms .
DNA is transcribed into mRNA, therefore all mRNA found from the metatranscriptomics study must be accounted for in the metagenomic study . It is believed that metatranscriptomics is a better tool to use in analyzing microbial communities reactions to environmental insults than metaproteomics, proteins [1, 2]. This is because bacteria regulate transcription more tightly and easily than translation. Therefore the immediate changes should be reflected in mRNA not proteins.This gives scientists the ability to alter the environment immediately before harvesting their sample . Proteins are also more abundant than mRNA, probably because of their longer half-life. Since proteins stick around longer then they may be present at the time of insult but may not be present in response to the insult .
Researchers have been trying to understand how microbial communities interact for a long time. This has always been a challenging field because of the lack of techniques available. It is difficult to culture complex microbial communities in the lab [1, 2, 3]. With the advancement of metatranscirptomics we are now able to analyze how bacterium respond to other bacteria, as well as environmental changes: pH, temperature, salinity, etc .
Soil RNA extraction Edit
This is not as simple as it sounds. mRNA is unstable, thus having a short half-life. This is one the three main hurdles for metatranscriptomics. The stability of mRNA also varies between organisms and can be affected by nutrient availability. There is a difference in the rate of degradation between housekeeping genes, and genes being expressed because of an insult. Since the half-life of mRNA is anywhere from seconds to minutes it is crucial to preserve the mRNA at the time of collection, (i.e. flash freezing or RNA preservative solution). It is also important to collect numerous samples overtime. This allows for comparison between relative stability of transcripts and also time of expression (i.e. housekeeping gene, or response to change). Extracting the mRNA from the cells is also challenging. There are commercial kits to help with cell lysis, solubility, pH, etc.
mRNA enrichment/reverse transcription Edit
Only 1-5% of RNA in a cell is mRNA, therefore the mRNA needs be extracted and then amplified to a useable concentration. There are multiple methods in which to do this. Many techniques employ the poly-A tail of bacterial RNA to degrade eukaryotic RNA. There are also methods to isolate mRNA from tRNA and rRNA. Once there is only mRNA then it is amplified by reverse transcription [1, 2].
The last three steps are DNA fragmentation, size selection and then high-throughput sequencing. The most important ideas for selecting and using a high-throughput sequencing method is to optimize the spatial arrangement and to allow a large number of individual reactions at once [1, 2].
Analysis of Data Edit
First the data needs to be combed through and the short and/or sloppy sequences need to be thrown away. There are programs available to help with identifying these sequences. Now the remaining sequences can be mapped to the metagenomic reference set. Gene regulation, up or down, can only be inferred if the data reference set is of good quality and from a similar community. It is best with the metagenomic and metatranscriptomic datasets a coupled .
The discovery and affordability of DNA and RNA sequencing has completely changed the field of metagenomics. The studies are now much easier, quicker, and cheaper . Metatrancriptomics has allowed us to analyze gene expression and how it changes with the environment . We are now able to examine the functional dynamics of microbial communities .
1. Carvalhais, L.C., Dennis, P.G., Tyson G.W., Schenk, P.M. 2012. Application of metatranscriptomics to soil environments. Journal of Microbiological Methods. 91, 246-251. PMID:22963791
2. Moran, M.A., Satinsky, B., Gifford, S.M., Luo, H., Rivers, A., Chan, L., Meng, J., Durham, B.P., Shen, C., Varaljay, V.A., Smith, C.B., Yager, P.L., and Hopkinson, B.M. 2013. Sizing up metatranscriptomics. The ISM Journal. 7, 237-243. PMID: 22931831
3. Simon, C and Daniel, R. 2011. Metagemoic analyses: past and future trends. Appl Environ Microbiol. 77(4)-1153-61. PMID:21169428