Metatranscriptomics is the set of techniques used to study gene expression of microbes within natural environments, i.e., the metatranscriptome.
While metagenomics focuses on studying the genomic content and on identifying which microbes are present within a community, metatranscriptomics can be used to study the diversity of the active genes within such community, to quantify their expression levels and to monitor how these levels change in different conditions (e.g., physiological vs. pathological conditions in an organism). The advantage of metatranscriptomics is that it can provide information about differences in the active functions of microbial communities that would otherwise appear to have similar make-up.
Introduction
The microbiome has been defined as a microbial community occupying a well-defined habitat. These communities are ubiquitous and can play a key role in maintenance of the characteristics of their environment, and an imbalance in these communities can negatively affect the activities of the setting in which they reside. To study these communities, and to then determine their impact and correlation with their niche, different omics approaches have been used. While metagenomics can help researchers generate a taxonomic profile of the sample, metatranscriptomics provides a functional profile by analysing which genes are expressed by the community. It is possible to infer what genes are expressed under specific conditions, and this can be done using functional annotations of expressed genes.
Function
Since metatranscriptomics focuses on what genes are expressed, it enables the characterization of the active functional profile of the entire microbial community. The overview of the gene expression in a given sample is obtained by capturing the total mRNA of the microbiome and performing whole-metatranscriptomics shotgun sequencing.
Tools and techniques
Although microarrays can be exploited to determine the gene expression profiles of some model organisms, next-generation sequencing and third-generation sequencing are the preferred techniques in metatranscriptomics. The protocol that is used to perform a metatranscriptome analysis may vary depending on the type of sample that needs to be analysed. Indeed, many different protocols have been developed for studying the metatranscriptome of microbial samples. Generally, the steps include sample harvesting, RNA extraction (different extraction methods for different kinds of samples have been reported in the literature), mRNA enrichment, cDNA synthesis and preparation of metatranscriptomic libraries, sequencing and data processing and analysis. mRNA enrichment is one of the most technically challenging steps, for which different strategies have been proposed: removing rRNA through Ribosomal RNA capture using a 5-3 exonuclease to degrade processed RNAs (mostly rRNA and tRNA) adding poly(A) to mRNAs by using a polyA polymerase (in E. coli) using antibodies to capture mRNAs that bind to specific proteins The last two strategies are not recommended as they have been reported to be highly biased.
Computational analysis
A typical metatranscriptome analysis pipeline: maps reads to a reference genome, or performs de novo assembly of the reads into transcript contigs and supercontigs The first strategy maps reads to reference genomes in databases, to collect information that is useful to deduce the relative expression of the single genes. Metatranscriptomic reads are mapped against databases using alignment tools, such as Bowtie2, BWA, and BLAST. Then, the results are annotated using resources, such as GO, KEGG, COG, and Swiss-Prot. The final analysis of the results is carried out depending on the aim of the study. One of the latest metatranscriptomics techniques is stable isotope probing (SIP), which has been used to retrieve specific targeted transcriptomes of aerobic microbes in lake sediment. The limitation of this strategy is its reliance on the information of reference genomes in databases.
The second strategy retrieves the abundance in the expression of the different genes by assembling metatranscriptomic reads into longer fragments called contigs using different software. The Trinity software for RNA-seq, in comparison with other de novo transcriptome assemblers, was reported to recover more full-length transcripts over a broad range of expression levels, with a sensitivity similar to methods that rely on genome alignments. This is particularly important in the absence of a reference genome.