**3.14 Metagenomic analysis of microbiomes**

16S rRNA sequencing became the standard and normal method of determining the structure of a human microbiome population. The V1V3 and V3V5 regions of the hypervariable 16S rRNA gene help to distinguish the taxonomic structure of different bacterial species. To study the composition of microbiota, researchers categorize this gene into Operational Taxonomic Unit (OTU). Sanger sequencing was the primary instrument for sampling the entire amplicon range (16S rDNA). However, people discovered that species diversity can be classified utilizing shorter DNA stretches with higher sequence coverage and thus the developments of NGS, i.e., Roche 454 pyrosequencing, Illumina, and Ion Torrent sequencing are also used

*Role of Mosquito Microbiome in Insecticide Resistance DOI: http://dx.doi.org/10.5772/intechopen.104265*


**Table 1.**

*Examples of insect's source with their enzymes and genes isolated by the metagenomics functional expression analysis.*

for the meta-genomic sequencing. Numerous analytical methods for studying the 16S rRNA sequences of microbes were also developed later to better understand their biology in the microbials. Nonetheless, even though we have strong coverage and longer sequencing reads using 16S rRNA sequencing, it would still be challenging to access the genomic details of low-abundance species. Therefore, recent work has moved to the use of high-throughput data techniques to develop both the qualitative and quantitative microbiome DNA information, mRNA transcripts, metabolites, and microbial community proteins. Metagenomic methods will help give a more detailed functional view of microorganisms and their functions within the microbiome. Shotgun metagenomic sequencing was the first step in this direction in which the whole genomic DNA of human/environmental bacteria samples were analyzed with a view to identifying all species and recognizing the microbe's gene function potential. Another example is the HMP Unified Metabolic Analysis Network (HUMAnN), which performs metabolic and functional metagenomic data reconstructions [126]. This technique was performed on 102 individuals at seven key locations in the human body, namely diarrhea, dorsal tongue, and anterior nares. For various sites, they established the main metabolic pathways, genes, and functional modules that were distinct across individuals. Glycosaminoglycan degradation, phosphate and amino acid transport within this microbiota have been shown to be more involved in the vaginal microbiome; these methods have also been applied for insect's microbiome. Computational modeling strategies such as metabolic genome scale models (GEMs) have been developed to integrate and interpret data for research purpose based on the increased experimental data produced by the high-throughput strategies. Throughout recent years, meta-omics results are used on a genome scale throughout tandem with metabolic models (GEMs). The genome size of metabolic models and metagenomic data were taken as feedback by using MAMBO (Metabolomic Analysis of Metagenomes using fBa and Optimization). The use of in vitro, ex vivo, and in situ laboratory evidence with in silico models serves as an outstanding testing tool for the discovery in human microbiomes of the elusive microbial microbe–microbe and microbe–host relationships that suggest major therapeutic progresses. Each of the respective omic data types provides useful knowledge in characterizing the organism's working, and certain data types are incorporated more directly into the modeling formalism than others. For example, Vanee et al. used a proteomicsderived model to describe the *Thermobifida fusca* microbe's metabolism functionalities where the growth rates seen in experimental and silico results were almost similar [127].

#### **3.15 Homology-based analysis of metagenome sequenced DNA**

Compared with functional/expression analysis, homology-based metagenomics are more precise as they target the gene on the basis of the data present and existing conserved genomics databases. Sequence-based screening methods depend on the existing conserved sequences and hence, may not help to identify brand new nonhomologous enzymes [128]. The sequence-based search combined with powerful bioinformatics tools has led to a higher rate of identification of novel genes than function-based methods do. Bioinformatics tools for sequence mining have been developed, based not only on homology of the primary sequence but also on the predicted protein structures. Gene function can be predicted with the improvement of the protein sorting and modeling tools, the putative active sites. Some tools of gene finding such as MetaGene has been used in order to predict 90% of shotgun sequences [129]. Many recent publications identify metagenome sequence databases that look for genes and enzymes that would be useful for commercial development in prospecting. For example, 71 million base pairs of sequence data were created by sequencing a metagenome library of hindgut microbiota from the largest family of wood-feeding termites. By detecting complete domains using global alignment, over 700 homologous domains of the glycoside hydrolase catalytic site corresponding to 45 different carbohydrate active enzyme families were identified, including a rich diversity of putative cellulases and hemicellulases [130].

#### **3.16 Insecticide resistance**

Numerous studies have shown that the individual mosquito species are involved in multiple mechanisms of resistance. In particular, two mechanisms increased metabolic detoxification of insecticides and reduced target protein sensitivity, which is the most critical target of insecticide. The insensitivity of the target site has been studied very extensively and has been accepted due to its extreme importance. The relationship between the genes related to the resistance on the regulation level of genes has provided with a very excellent example showing that how precisely these resistances develop in the insects. In the coding region, the overexpression and the amplification of mutant result in the structural differences inside the proteins and are linked with the resistance of the insecticides in the populations of mosquitoes. The overexpression at the transcriptional level of these genes shows resistance to the insecticides in mosquitoes. Collectively it is very easy for the researchers to conclude that these resistances are not only being transmitted from one generation to the other, but also it is being regulated at gene level. It is not yet clear which genes are directly or indirectly involved in the resistance and also how many are involved in the phenomenon [131–137].

*Role of Mosquito Microbiome in Insecticide Resistance DOI: http://dx.doi.org/10.5772/intechopen.104265*
