Metatranscriptomics is the science that studies gene expression of microbes within natural environments. It also allows to obtain whole gene expression profiling of complex microbial communities.
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 which appear to be the same in terms of microbe composition.
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 which appear to be the same in terms of microbe composition.
Introduction
The microbiome has been defined as a microbial community occupying a well-defined habitat.
They are ubiquitous and extremely relevant for the maintenance of” the
characteristic of the environment in which they reside and an imbalance
in these communities can affect negatively the activity 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 allows to obtain 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 allows to
understand 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 by performing a whole metatranscriptomics shotgun sequencing.
Tools and techniques
Although
microarrays can be exploited to determine the gene expression profiles
of some model organisms, NGS is the preferred technique 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 trickiest parts. 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 softwares. So, its limits
depend on the software that is used for the assembly.
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
Given
the huge amount of data obtained from metagenomic and
metatranscriptomic analysis, the use of bioinformatic tools have become
of greater importance in the last decades. In order to achieve so, many
different bioinformatic pipelines have been developed, often as open
source platforms, such as HUMAnN and the most recent HUMAnN2, MetaTrans,
SAMSA, Leimena-2013 and mOTUs2.
HUMAnN2
HUMAnN2 is a bioinformatic pipeline designed from the latter HUMAnN 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 multi-threading computers to improve
metagenomic and metatranscriptomic analysis. 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, to be
thereafter sorted for taxonomic analysis (by removal of tRNA sequences)
or functional analysis (by removal of both tRNA and rRNA sequencing).
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. The algorithm is divided in 4 steps. At first,
sequences from raw sequencing data are selected on quality basis and are
then submitted to MG-RAST (which foresee different steps such as
quality control check, 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, that
usually follow up as last steps of the process.
Leimena-2013
This
pipeline actually does not have a name so that it is usually reckoned
with the first name of the author of the article in which it is
described. This algorithm foresees the implementation of alignment tools
such as BLAST and MegaBLAST. Reads, usually obtained by Illumina
sequencing, are clustered in identical-reads clusters and are then
processed for in-silico removal of t-RNA and r-RNA sequences. Remaining
reads are then mapped on NCBI databases by using BLAST and MegaBLAST
tools and classified by their bitscore. Higher bitscore sequences are
thereby interpreted to predict phylogenetic origin and function. Lower
score reads instead are aligned with BLASTX (higher sensitivity) 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.
Microarray
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.
Limits of the metatranscriptomic techniques
- With its dominating abundance, ribosomal RNA strongly reduces the coverage of mRNA (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 the 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. Actually, 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 “cut off” in order to consider the genes as “express”, 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.
Metatrascriptomics and Gut Microbiome
The
gut microbiome has emerged in recent years as an important player in
human health. Its prevalent functions are related to the fermentation of
indigestible food components, competitions with pathogen, strengthening
of the intestinal barrier, stimulation and regulation of the immune
system.
Although much has been learnt about the microbiome community in the last
years, the wide diversity of microorganisms and molecules in the gut
requires new tools to enable new discoveries.
By focusing on the changes in the expression of the genes,
metatrascriptomics allows to take a more dynamic picture of the state
and activity of the microbiome than metagenomics. It has been observed
that metatranscriptomic functional profiles are more variable than what
might have been reckoned only by metagenomic information. This suggests
that non-housekeeping genes are not stably expressed in situ.
One example of metatranscriptomic application is in the study of the gut
microbiome in inflammatory bowel disease. Inflammatory bowel disease
(IBD) is a group of chronic diseases of the digestive tract that affects
millions of people worldwide.
Several human genetic mutations have been linked to an increased
susceptibility to IBD, but additional factors are needed for the full
development of the disease.
Regarding the relationship between IBD and gut microbiome, it is known
that there is a dysbiosis in patients with IBD but microbial taxonomic
profiles can be highly different among patients, making it difficult to
implicate specific microbial species or strains in disease onset and
progression. In addition, the gut microbiome composition presents a high
variability over time among people, with more pronounced variations in
patient with IBD.
The functional potential of an organism, meaning the genes and pathways
encoded in its genome, provides only indirect information about the
level or extent of activation of such functions. So, the measurement of
functional activity (gene expression) is critical to understand the
mechanism of the gut microbiome dysbiosis.
Alterations in transcriptional activity in IBD, established on the rRNA
expression, indicate that some bacterial populations are active in
patients with IBD, while other groups are inactive or latent.
A metatranscriptomics analysis measuring the functional activity of the
gut microbiome reveals insights only partially observable in metagenomic
functional potential, including disease-linked observations for IBD. It
has been reported that many IBD-specific signals are either more
pronounced or only detectable on the RNA level.
These altered expression profiles are potentially the result of changes
in the gut environment in patients with IBD, which include increased
levels of inflammation, higher concentrations of oxygen and a diminished
mucous layer.
Metatranscriptomics has the advantage of allowing to skip the assaying
of biochemical products in situ (like mucus or oxygen) and allows to
study the effects of environmental changes on microbial expression
patterns in vivo for large human populations.
In addition, it can be coupled with longitudinal sampling to associate
modulation of activity with the disease progression. Indeed, it has been
shown that while a particular path may remain stable over time at the
genomic level, the corresponding expression varies with the disease
severity.
This suggests that microbial dysbiosis affect the gut health through
changing in the transcriptional programmes in a stable community. In
this way, metatracriptomic profiling emerges as an important tool for
understanding the mechanisms of that relationship.
Some technical limitations of the RNA measurements in stool are related
to the fact that the extracted RNA can be degraded and, if not, it still
represents only the organisms presents in the stool sample.
Other applications of metagenomics:
- Directed culturing: it was used to understand nutritional preferences of organisms in order to allow the preparation of a proper culture medium, resulting in a successful isolation of microbes in vitro.
- Identify potential virulence factors: through comparative transcriptomics, in order to compare different transcriptional responses of related strains or species after specific stimuli.
- Identify host-specific biological processes and interactions For this purpose, it’s important to develop new technologies which allow the detection, at the same time, of changes in the expression levels of some genes.
Examples of techniques applied:
Microarrays: allow the monitoring of changes in the expression levels of
many genes in parallel for both host and pathogen. First microarray
approaches have shown the first global analysis of gene expression
changes in pathogens such as Vibrio cholerae, Borrelia burgdorferi,
Chlamydia trachomatis, Chlamydia pneumoniae and Salmonella enterica,
revealing the strategies that are used by these microorganisms to adapt
to the host.
In addition, microarrays only provide the first global insights about
the host innate immune response to PAMPs, as the effects of bacterial
infection on the expression of various host factor.
Anyway, the detection through microarrays of both organisms at the same
time could be problematic.
Problems:
- Probe selection (hundreds of millions of different probes)
- Cross-hybridization
- Need of expensive chips (with the proper design; high-density arrays)
- Require the pathogen and host cells to be physically separated before gene expression analysis (eukaryotic cells’ transcriptomes are larger in comparison to the pathogens’ ones, so could happen that the signal from pathogens’ RNAs is hidden).
- Loss of RNA molecules during the eukaryotic cells lysis.
Dual RNA-Seq: this technique allows the simultaneous study of both host and pathogen transcriptomes as well. It is possible to monitor the expression of genes at different time points of the infection process; in this way could it be possible to study the changes in cellular networks in both organisms starting from the initial contact until the manipulation of the host (interplay host-patogen).
- Potential: No need of expensive chips
- Probe-independent approach (RNA-seq provides transcript information without prior knowledge of mRNA sequences)
- High sensitivity.
- Possibility of studying the expression levels of even unknown genes under different conditions
Moreover, RNA-Seq is an important approach for identifying
coregulated genes, enabling the organization of pathogen genomes into
operons. Indeed, genome annotation has been done for some eukaryotic
pathogens, such as Candida albicans, Trypanosoma brucei and Plasmodium
falciparum.
Despite the increasing sensitivity and depth of sequencing now
available, there are still few published RNA-Seq studies concerning the
response of the mammalian host cell to the infection.