metatranscriptomics metatranscriptomics 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, metatrascriptomics 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. a quantitative pipeline for transcriptomic analysis was developed by li and dewey and called rsem (rna-seq by expectation maximization). it can work as stand-alone software or as a plug-in for trinity. rsem starts with a reference transcriptome or assembly along with rna-seq reads generated from the sample and calculates normalized transcript abundance (meaning the number of rna-seq reads cor-responding to each reference transcriptome or assembly). although both trinity and rsem were designed for transcriptomic datasets (i.e., obtained from a single organism), it may be possible to apply them to metatranscriptomic data (i.e., obtained from a whole microbial community). bioinformatics the use of computational analysis tools has become more important as dna sequencing capabilities have grown, particularly in metagenomic and metatranscriptomic analysis, which can generate a huge volume of data. many different bioinformatic pipelines have been developed for these purposes, often as open source platforms such as humann and the more recent humann2, metatrans, samsa, leimena-2013 and motus2. humann2 humann2 is a bioinformatic pipeline designed from the previous humann software, which was developed during the human microbiome project (hmp), implementing a “tiered search” approach. in the first tier, humann2 screens dna or rna reads with metaphlan2 in order to identify already-known microbes and constructing a sample-specific database by merging pangenomes of annotated species; in the second tier, the algorithm performs a mapping of the reads against the assembled pangenome database; in the third tier, non-aligned reads are used for a translated search against a protein database. metatrans metatrans is a pipeline that exploits multithreading to improve efficiency. data is obtained from paired-end rna-seq, mainly from 16s rna for taxonomy and mrna for gene expression levels. the pipeline is divided in 4 major steps. firstly, paired-end reads are filtered for quality control purposes, then sorted and filtered for taxonomic analysis (by removal of trna sequences) or functional analysis (by removal of both trna and rrna reads). for the taxonomic analysis, sequences are mapped against 16s rrna greengenes v13.5 database using soap2, while for functional analysis sequences are mapped against a functional database such as metahit-2014 always by using soap2 tool. this pipeline is highly flexible, since it offers the possibility to use third-party tools and improve single modules as long as the general structure is preserved. samsa this pipeline is designed specifically for metatranscriptomics data analysis, by working in conjunction with the mg-rast server for metagenomics. this pipeline is simple to use, requires low technical preparation and computational power and can be applied to a wide range of microbes. first, sequences from raw sequencing data are filtered for quality and then submitted to mg-rast (which performs further steps such as quality control, gene calling, clustering of amino acid sequences and use of sblat on each cluster to detect the best matches). matches are then aggregated for taxonomic and functional analysis purposes. leimena-2013 this pipeline does not have an official name and is usually referred to using the first author of the article in which it is described. this algorithm foresees the implementation of alignment tools such as blast and megablast. reads are clustered in groups of identical sequences and then processed for in-silico removal of trna and rrna sequences. remaining reads are then mapped to ncbi databases using blast and megablast, then classified by their bitscore. sequences with higher bitscores are used to predict phylogenetic origin and function, and lower-score reads are aligned with the more sensitive blastx and eventually can be aligned in protein databases so that their function can be characterized. motus2 the motus2 profiler, which is based on essential housekeeping genes, is demonstrably well-suited for quantification of basal transcriptional activity of microbial community members. depending on environmental conditions, the number of transcripts per cell varies for most genes. an exception to this are housekeeping genes that are expressed constitutively and with low variability under different conditions. thus, the abundance of transcripts from such genes strongly correlate with the abundance of active cells in a community. microarrays another method that can be exploited for metatranscriptomic purposes is tiling microarrays. in particular, microarrays have been used to measure microbial transcription levels, to detect new transcripts and to obtain information about the structure of mrnas (for instance, the utr boundaries). recently, it has also been used to find new regulatory ncrna. however, microarrays are affected by some pitfalls: requirement of probe design low sensitivity prior knowledge of gene targets. rna-seq can overcome these limitations: it does not require any previous knowledge about the genomes that have to be analysed and it provides high throughput validation of genes prediction, structure, expression. thus, by combining the two approaches it is possible to have a more complete representation of bacterial transcriptome. limitations with its dominating abundance, ribosomal rna strongly reduces the coverage of mrna (usually the main focus of transcriptomic studies) in the total collected rna. extraction of high-quality rna from some biological or environmental samples (such as feces) can be difficult. instability of mrna that compromises sample integrity even before sequencing. experimental issues can affect the quantification of differences in expression among multiple samples: they can influence integrity and input rna, as well as the amount of rrna remaining in the samples, size section and gene models. moreover, molecular base techniques are very prone to artefacts. difficulties in differentiating between host and microbial rna, although commercial kits for microbial enrichment are available. this may also be done in silico if a reference genome is available for the host. transcriptome reference databases are limited in their coverage. generally, large populations of cells are exploited in metatranscriptomic analysis, so it is difficult to resolve important variances that can exist between subpopulations. high variability in pathogen populations was demonstrated to affect disease progression and virulence. both for microarray and rna-seq, it is difficult to set a real threshold to classify genes as “expressed”, due to the high dynamic range in gene expression. the presence of mrna is not always associated with the actual presence of the respective protein. applications human gut microbiome