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The study of insect-associated microbial communities is a field of great importance in agriculture, principally because of the role insects play as pests. In addition, there is a recent focus on the potential of the insect gut microbiome in areas such as biotechnology, given some microorganisms produce molecules with biotechnological and industrial applications, and also in biomedicine, since some bacteria and fungi are a reservoir of antibiotic resistance genes (ARGs). To date, most studies aiming to characterize the role of the gut microbiome of insects have been based on high-throughput sequencing of the 16S rRNA gene and/or metagenomics. However, recently functional approaches such as metatranscriptomics, metaproteomics and metabolomics have also been employed. Besides providing knowledge about the taxonomic distribution of microbial populations, these techniques also reveal their functional and metabolic capabilities. This information is essential to gain a better understanding of the role played by microbes comprising the microbial communities in their hosts, as well as to indicate their possible exploitation. This review provides an overview of how far we have come in characterizing insect gut functionality through omics, as well as the challenges and future perspectives in this field.
Insecta, Microbiota, Gastrointestinal Microbiome, Insects, Metaproteomics, RNA, Ribosomal, 16S, Metabolomics, Animals, Metagenomics, Symbiosis
Insecta, Microbiota, Gastrointestinal Microbiome, Insects, Metaproteomics, RNA, Ribosomal, 16S, Metabolomics, Animals, Metagenomics, Symbiosis
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 69 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 1% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 1% |
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