Metatranscriptomics

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Metatranscriptomics is the set of techniques used to study gene expression of microbes within natural environments, i.e., the metatranscriptome. [1]

Contents

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. [2]

Introduction

The microbiome has been defined as a microbial community occupying a well-defined habitat. [3] 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. [4] 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:

The last two strategies are not recommended as they have been reported to be highly biased. [6]

Computational analysis

A typical metatranscriptome analysis pipeline:

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. [7] 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. [8]

A quantitative pipeline for transcriptomic analysis was developed by Li and Dewey [9] 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). [10] [11]

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). [12] [13] [14] [15] [16] [17]

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. [18]

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. [19]

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. [20]

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. [21]

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. [12]

mOTUs2

The mOTUs2 profiler, [22] which is based on essential housekeeping genes, is demonstrably well-suited for quantification of basal transcriptional activity of microbial community members.[ citation needed ] 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.[ citation needed ] 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:

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. [1]

Limitations

Applications

Human 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. [23] [24] [25] [26] [27] [28] [29] 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 changes in the expression of the genes, metatrascriptomics can generate 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 [30] [31]

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. [32] 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. [33] [34] 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. [35]

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. [33] 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. [36] Metatranscriptomics has the advantage of allowing researchers to skip the assaying of biochemical products in situ (like mucus or oxygen) and enables evaluation of 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. [33] This suggests that microbial dysbiosis affect the gut health through changing in the transcriptional programmes in a stable community. In this way, metatranscriptomic 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

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:

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).

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. [37] [38]

Related Research Articles

<span class="mw-page-title-main">Human microbiome</span> Microorganisms in or on human skin and biofluids

The human microbiome is the aggregate of all microbiota that reside on or within human tissues and biofluids along with the corresponding anatomical sites in which they reside, including the skin, mammary glands, seminal fluid, uterus, ovarian follicles, lung, saliva, oral mucosa, conjunctiva, biliary tract, and gastrointestinal tract. Types of human microbiota include bacteria, archaea, fungi, protists, and viruses. Though micro-animals can also live on the human body, they are typically excluded from this definition. In the context of genomics, the term human microbiome is sometimes used to refer to the collective genomes of resident microorganisms; however, the term human metagenome has the same meaning.

<span class="mw-page-title-main">Omics</span> Suffix in biology

The branches of science known informally as omics are various disciplines in biology whose names end in the suffix -omics, such as genomics, proteomics, metabolomics, metagenomics, phenomics and transcriptomics. Omics aims at the collective characterization and quantification of pools of biological molecules that translate into the structure, function, and dynamics of an organism or organisms.

The transcriptome is the set of all RNA transcripts, including coding and non-coding, in an individual or a population of cells. The term can also sometimes be used to refer to all RNAs, or just mRNA, depending on the particular experiment. The term transcriptome is a portmanteau of the words transcript and genome; it is associated with the process of transcript production during the biological process of transcription.

<span class="mw-page-title-main">Metagenomics</span> Study of genes found in the environment

Metagenomics is the study of genetic material recovered directly from environmental or clinical samples by a method called sequencing. The broad field may also be referred to as environmental genomics, ecogenomics, community genomics or microbiomics.

<span class="mw-page-title-main">Gut microbiota</span> Community of microorganisms in the gut

Gut microbiota, gut microbiome, or gut flora are the microorganisms, including bacteria, archaea, fungi, and viruses, that live in the digestive tracts of animals. The gastrointestinal metagenome is the aggregate of all the genomes of the gut microbiota. The gut is the main location of the human microbiome. The gut microbiota has broad impacts, including effects on colonization, resistance to pathogens, maintaining the intestinal epithelium, metabolizing dietary and pharmaceutical compounds, controlling immune function, and even behavior through the gut–brain axis.

Dysbiosis is characterized by a disruption to the microbiome resulting in an imbalance in the microbiota, changes in their functional composition and metabolic activities, or a shift in their local distribution. For example, a part of the human microbiota such as the skin flora, gut flora, or vaginal flora, can become deranged, with normally dominating species underrepresented and normally outcompeted or contained species increasing to fill the void. Similar to the human gut microbiome, diverse microbes colonize the plant rhizosphere, and dysbiosis in the rhizosphere, can negatively impact plant health. Dysbiosis is most commonly reported as a condition in the gastrointestinal tract or plant rhizosphere.

<span class="mw-page-title-main">Human Microbiome Project</span> Former research initiative

The Human Microbiome Project (HMP) was a United States National Institutes of Health (NIH) research initiative to improve understanding of the microbiota involved in human health and disease. Launched in 2007, the first phase (HMP1) focused on identifying and characterizing human microbiota. The second phase, known as the Integrative Human Microbiome Project (iHMP) launched in 2014 with the aim of generating resources to characterize the microbiome and elucidating the roles of microbes in health and disease states. The program received $170 million in funding by the NIH Common Fund from 2007 to 2016.

<span class="mw-page-title-main">Microbiota</span> Community of microorganisms

Microbiota are the range of microorganisms that may be commensal, mutualistic, or pathogenic found in and on all multicellular organisms, including plants. Microbiota include bacteria, archaea, protists, fungi, and viruses, and have been found to be crucial for immunologic, hormonal, and metabolic homeostasis of their host.

<span class="mw-page-title-main">RNA-Seq</span> Lab technique in cellular biology

RNA-Seq is a technique that uses next-generation sequencing to reveal the presence and quantity of RNA molecules in a biological sample, providing a snapshot of gene expression in the sample, also known as transcriptome.

Pathogenomics is a field which uses high-throughput screening technology and bioinformatics to study encoded microbe resistance, as well as virulence factors (VFs), which enable a microorganism to infect a host and possibly cause disease. This includes studying genomes of pathogens which cannot be cultured outside of a host. In the past, researchers and medical professionals found it difficult to study and understand pathogenic traits of infectious organisms. With newer technology, pathogen genomes can be identified and sequenced in a much shorter time and at a lower cost, thus improving the ability to diagnose, treat, and even predict and prevent pathogenic infections and disease. It has also allowed researchers to better understand genome evolution events - gene loss, gain, duplication, rearrangement - and how those events impact pathogen resistance and ability to cause disease. This influx of information has created a need for bioinformatics tools and databases to analyze and make the vast amounts of data accessible to researchers, and it has raised ethical questions about the wisdom of reconstructing previously extinct and deadly pathogens in order to better understand virulence.

Metaproteomics is an umbrella term for experimental approaches to study all proteins in microbial communities and microbiomes from environmental sources. Metaproteomics is used to classify experiments that deal with all proteins identified and quantified from complex microbial communities. Metaproteomics approaches are comparable to gene-centric environmental genomics, or metagenomics.

Single-cell sequencing examines the nucleic acid sequence information from individual cells with optimized next-generation sequencing technologies, providing a higher resolution of cellular differences and a better understanding of the function of an individual cell in the context of its microenvironment. For example, in cancer, sequencing the DNA of individual cells can give information about mutations carried by small populations of cells. In development, sequencing the RNAs expressed by individual cells can give insight into the existence and behavior of different cell types. In microbial systems, a population of the same species can appear genetically clonal. Still, single-cell sequencing of RNA or epigenetic modifications can reveal cell-to-cell variability that may help populations rapidly adapt to survive in changing environments.

<span class="mw-page-title-main">Microbiome</span> Microbial community assemblage and activity

A microbiome is the community of microorganisms that can usually be found living together in any given habitat. It was defined more precisely in 1988 by Whipps et al. as "a characteristic microbial community occupying a reasonably well-defined habitat which has distinct physio-chemical properties. The term thus not only refers to the microorganisms involved but also encompasses their theatre of activity". In 2020, an international panel of experts published the outcome of their discussions on the definition of the microbiome. They proposed a definition of the microbiome based on a revival of the "compact, clear, and comprehensive description of the term" as originally provided by Whipps et al., but supplemented with two explanatory paragraphs. The first explanatory paragraph pronounces the dynamic character of the microbiome, and the second explanatory paragraph clearly separates the term microbiota from the term microbiome.

The microbiota are the sum of all symbiotic microorganisms living on or in an organism. The fruit fly Drosophila melanogaster is a model organism and known as one of the most investigated organisms worldwide. The microbiota in flies is less complex than that found in humans. It still has an influence on the fitness of the fly, and it affects different life-history characteristics such as lifespan, resistance against pathogens (immunity) and metabolic processes (digestion). Considering the comprehensive toolkit available for research in Drosophila, analysis of its microbiome could enhance our understanding of similar processes in other types of host-microbiota interactions, including those involving humans. Microbiota plays key roles in the intestinal immune and metabolic responses via their fermentation product, acetate.

Hologenomics is the omics study of hologenomes. A hologenome is the whole set of genomes of a holobiont, an organism together with all co-habitating microbes, other life forms, and viruses. While the term hologenome originated from the hologenome theory of evolution, which postulates that natural selection occurs on the holobiont level, hologenomics uses an integrative framework to investigate interactions between the host and its associated species. Examples include gut microbe or viral genomes linked to human or animal genomes for host-microbe interaction research. Hologenomics approaches have also been used to explain genetic diversity in the microbial communities of marine sponges.

<span class="mw-page-title-main">Pharmacomicrobiomics</span>

Pharmacomicrobiomics, proposed by Prof. Marco Candela for the ERC-2009-StG project call, and publicly coined for the first time in 2010 by Rizkallah et al., is defined as the effect of microbiome variations on drug disposition, action, and toxicity. Pharmacomicrobiomics is concerned with the interaction between xenobiotics, or foreign compounds, and the gut microbiome. It is estimated that over 100 trillion prokaryotes representing more than 1000 species reside in the gut. Within the gut, microbes help modulate developmental, immunological and nutrition host functions. The aggregate genome of microbes extends the metabolic capabilities of humans, allowing them to capture nutrients from diverse sources. Namely, through the secretion of enzymes that assist in the metabolism of chemicals foreign to the body, modification of liver and intestinal enzymes, and modulation of the expression of human metabolic genes, microbes can significantly impact the ingestion of xenobiotics.

Machine learning in bioinformatics is the application of machine learning algorithms to bioinformatics, including genomics, proteomics, microarrays, systems biology, evolution, and text mining.

Transcriptomics technologies are the techniques used to study an organism's transcriptome, the sum of all of its RNA transcripts. The information content of an organism is recorded in the DNA of its genome and expressed through transcription. Here, mRNA serves as a transient intermediary molecule in the information network, whilst non-coding RNAs perform additional diverse functions. A transcriptome captures a snapshot in time of the total transcripts present in a cell. Transcriptomics technologies provide a broad account of which cellular processes are active and which are dormant. A major challenge in molecular biology is to understand how a single genome gives rise to a variety of cells. Another is how gene expression is regulated.

<span class="mw-page-title-main">Microbiome-wide association study</span>

A microbiome-wide association study (MWAS), otherwise known as a metagenome-wide association study (MGWAS), is a statistical methodology used to examine the full metagenome of a defined microbiome in various organisms to determine if some feature of the microbiome is associated with a host trait. MWAS has been adopted by the field of metagenomics from the widely used genome-wide association study (GWAS).

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