*4.1.3 Metabolomics*

*Human Microbiome*

*4.1.1 Metatranscriptomics*

*4.1.2 Metaproteomics*

metatranscriptomics, metaproteomics, and metabolomics, which have helped us in the functional analysis of metagenome represented by a whole microbial community [54]. These methods offered a huge amount of genomic data stored in different

In fact, transcriptomics is the analysis of the whole gamut of RNA molecules expressed by a particular cell. There are many RNA molecules including mRNA, rRNA, tRNA, and other noncoding RNA transcribed in a microbial ecosystem which play an important role in the gene expression or metagenome expression in the case of the microbial community. Traditionally, the transcriptomics analysis is carried out by measuring the level of RNA expression by using cDNA-based microarray chip. To study microbial communities, thousands of fluorescent probes were required to be immobilized on the microarray chip surface. Actually, metatranscriptomics is the studies of RNA molecules encoded by a metagenome present in a local ecosystem, for example, gut microbiota. Recently, metatranscriptome is studied with the help of the RNA-seq method; this technique is extremely suitable to confirm the gene expression of complete metagenome in the sample which provides the basic data for proteomics and metabolomics [55]. Metatranscriptomics is highly sensitive methods which can even differentiate between dead and live bacterial cell present in a sample. The major drawback of the method is its high cost and it requires great care during the design and execution of experiments because of the momentary stability of mRNA and its contaminations. There are several demerits associated with this method, for example, less amount of mRNA in bacteria, and hence, it creates an experimental problem. Recently, metatranscriptomics methods have been used to identify the pathway of carbohydrate metabolism and energy

databases that can be integrated with the help of bioinformatics tools.

extraction and physiological functions regulated by a metagenome [56].

The proteome is the complete protein complement expressed by a cell or tissue at a particular moment, and the study of the proteome is known as "proteomics." The metaproteomics or community proteomics is the variant of proteomics in the sense that it is the protein complement expressed by a metagenome from a microbial community. Currently, a small number of reports are available on gut community meta-proteomics that is attributed to the small amount of proteins available in the sample, and its detection makes it further a less applied method in comparison to metagenomics and metatranscriptome. There are still lacking standardized protocols related to protein extraction and its downstream processing. The detection of low abundant proteins in the sample is still a challenge. Moreover, its high cost, time-consuming, and labor-intensive nature further restricted its applications. But many labs have applied metaproteomics in the study of functional analysis of hostmicrobiome interactions and proteins expressed by gut metagenome. There are two types of proteomics methods, i.e., gel-dependent and gel-independent methods. First, the category of protocols includes the combination of 2D gel electrophoresis, mass spectroscopy, and various bioinformatics tools. Second, categories, namely, shotgun proteomics, mainly depend on most expensive and more sophisticated instruments like two-dimensional liquid chromatography (LC) coupled with nanospray tandem mass spectrometry (nano 2D LC–MS/MS) and powerful bioinformatics data analysis pipeline. Both types of technologies have provided large-scale protein analysis data in the case of the human gut proteome [57]. Currently,

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Metabolites are the final outcome of the gene expression process; they are highly unique in the case of the gut microbiota. Large numbers of metabolites are produced by gut microbiota, which can act as pharmaceutical agents or bioactive products. The metabolomics is a high-throughput omics-based method that mainly deals with the identification and quantification of total metabolites produced in a cell, tissue, and organ which are also called the metabolome. The "meta-metabolome" is the whole complement of metabolites and is produced by a specific microbial community. The analysis of meta-metabolomics requires a set of very sophisticated tools and techniques like matrix-assisted laser desorption/ionization time-of-flight, secondary ion mass spectrometry (SIMS), and Fourier transform ion cyclotron resonance MS that are used for metabolome analysis [59]. The complete annotation of the metabolome produced by a metagenome will help us to understand the physiology and functionality of a microbial community. Inside the human gut, fermentation of short-chain fatty acid is carried out by specific bacteria and produced many types of metabolites that participate in host metabolism and influence the physiology of both host-microbial communities inside the gut. The metabolome analysis offered the investigation of functional gene products in a sample that is helpful in functional analysis of microbes present a microbial niche. Currently, many unique metabolites are identified that are produced by gut microbiota.

### *4.1.4 Bioinformatics and multi-omics data integration*

In the last two decades, bioinformatics has provided much needed help to annotate the complex genome sequences and metagenomic data. The microbial bioinformatics offers help to understand microbial agents of the microbial ecosystem and their mutual and host-microbes interactions. Recently, community-based bioinformatics platforms and pipelines are developed like Mothur and QIIME which help in downstreaming of high-throughput genome sequencing data of variable regions of bacterial 16S ribosomal genes or amplicons. These platforms also help in data analysis and visualization of gut microbiome composition. The high-throughput method like shotgun sequencing and WGS metagenomics produced a huge amount of data, and its annotation is a great challenge in the field of microbiome analysis [60].

In order to know the functions of a particular microbial community, it requires integrating data from other studies such as metatranscriptomics sequencing, metagenomics, metatranscriptomics, metaproteomics, metabolomics, and other techniques. The integration of data provides holistic knowledge of a gut community in terms of its structure and functions [61]. For example, any perturbation such as antibiotics or heavy metal toxicities leads to the change in gut microbial community that can be studied at the level of metabolite production and protein expression. Multi-omics data integration is the uphill task and requires a highly advanced level of computational skill, but current few tools have been developed, e.g., XCMS is a new web-based tool that integrates transcriptome, proteome, and metabolome data [62]. The new systems-level integration can also provide valuable insights, especially when they are combined with community surveys and metagenomics (**Table 1**).


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**5. New advancements**

The advancements made in the area of NGS also coincide with machine learning in the last two decades. Machine learning, a branch of artificial intelligence, is based on computational and statistical principles and is recently applied to various fields of genomics including microbiome genomics. Machine learning deals with the development and testing of algorithms to identify, classify, and forecast patterns that emerged from a huge data set [63]. The gut microbial community is comprised of trillion of microbes which further affected various types of factors such as diet, drugs, age, environment, and even lifestyles. To extract the information from such an intricate system cannot be carried out by humans but rather require machine intervention. The machine learning methods such as deep learning and neural network are used to predict severity and susceptibility gingivitis on the basis of the oral microbiome. The two most important machine learning algorithms, random forest and SourceTracker, are applied to know the effect of antibiotics on the genomic and metagenomic studies [64]. In the near future, machine learning can be used to know the host-trait prediction.

*Summary of various techniques used for phylogenetic classification and functional characterization of the* 

**5.1 Machine learning**

*human gut microbiome.*

**Table 1.**

*Genomic Techniques Used to Investigate the Human Gut Microbiota*

Random shearing of genome. Then assemble genomes on the basis of overlapping sequences by bioinformatics methods

molecules encoded by a metagenome through NGS-based RNA-seq

encoded by metagenome by applying nano 2D LC–MS/MS

metabolites encoded by metagenome using MALDI-TOF, SIMS, and Fourier transform ion cyclotron resonance MS

data analysis pipelines/ platforms are developed QIIME and XCMS

methods

Metaproteomics Detection of all proteins

**Technique Basis of techniques Advantage Disadvantage**

Useful for phylogenetic identification of new species and suitable for microbiome studies

Can identify the metabolism encoded by metagenome

Can identify the unique proteins and enzymes encoded by metagenome

The method can be used to identify noble metabolites and metabolic pathways imparted by a microbial community

Integration of omics-based data, it provides holistic knowledge about gut microbiome

Method is costly and not suitable for phylogenetic classification of a new bacterial species

Expensive and requires technical knowledge to conduct experiments

Difficult to protein extraction and its downstream processing

Highly expensive and sophisticated, lack of standard protocols so far

Need high level of computational skill

*DOI: http://dx.doi.org/10.5772/intechopen.91808*

Shotgun cloning of microbiome genome/ metagenome

*Multi-omics methods*

**Method for functional analysis**

Metatranscriptomics Sequenced RNA

Metabolomics Detection of all

Bioinformatics Various web-based

*Genomic Techniques Used to Investigate the Human Gut Microbiota DOI: http://dx.doi.org/10.5772/intechopen.91808*


#### **Table 1.**

*Human Microbiome*

**Method for phylogenetic classifications**

Culturomics Culture of microbes

Microfluidics assays Microchips based on

Quantitative PCR Fluorescent dyes bind

DGGE/TGGE Separation of 16S

T-RFLP Fragmentation of 16S

DNA microarray Fluorescent probes

Colony features, microscopic and biochemical studies

and MALDI-TOF mass spectroscopy-based investigations

biochemical reactions

with 16S rRNA gene and quantification of DNA

rRNA amplicons on electrophoresis based on DNA denaturants and temperature gradients

rRNA amplicons by one or more restriction enzymes followed by electrophoretic isolation

The 16S rRNA ampliconspecific fluorescent probes and flow cytometry

immobilized on DNA chip hybridized with 16S rRNA gene. Fluorescence intensity is measured

by laser

Amplification of 16sRNA gene by PCR- and Sanger-based sequencing by capillary electrophoresis

Sequencing of 16S rRNA amplicons by fast NGS methods, e.g., 454 pyrosequencing, Illumina, SOLiD, singlemolecule real-time, Pacific Biosciences and nanopore sequencing

methods

methods

Sequencing of the whole genome by NGS-based

*Culture-dependent methods* Culture of bacteria and microscopic studies

*Culture-independent*

FISH (fluorescence in situ hybridization)

Cloning of 16sRNA gene (*classical metagenomics*)

Direct sequencing of 16sRNA amplicon (*modern metagenomics*)

Whole-genome sequencing of bacterial species

**Technique Basis of techniques Advantage Disadvantage**

Low cost Not suitable for

Appropriate for uncultured microbes

Co-culture of microbes

Highly suitable for phylogenetic classification

Fast and semiquantitative

Used for phylogenetic analysis

Phylogenetic identification is possible, highthroughput method, a semiquantitative method which is very fast

Highly suitable for phylogenetic classifications and microbiota composition

Cheap, fast, suitable for phylogenetic identification of unknown microbes

Suitable for phylogenetic identification of new

species

Fast, less-expensive, and semiquantitative microbiota studies

Extremely costly

Need high technical knowledge

Not suitable to identify new bacterial species and biased due to PCR steps

Results also affected by

identification, results are affected with PCR biases

Unable to identify a new bacterial species. Free from PCR-based bias

Possibility of cross hybridization, PCR biases, detect low-level species in gut microbiota

Affected with the PCR/cloning bias, time-consuming, and extremely expensive

PCR biases, expensive, laborious, and computer

Expensive and computer

intensive

intensive

PCR biases

Not suitable for phylogenetic

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*Summary of various techniques used for phylogenetic classification and functional characterization of the human gut microbiome.*
