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. 2022 Aug 10;13(8):719.
doi: 10.3390/insects13080719.

The Role of Feeding Characteristics in Shaping Gut Microbiota Composition and Function of Ensifera (Orthoptera)

Xiang Zheng  1   2 , Qidi Zhu  1 , Meng Qin  2   3 , Zhijun Zhou  1 , Chunmao Liu  2 , Liyuan Wang  2 , Fuming Shi  1
Affiliations

The Role of Feeding Characteristics in Shaping Gut Microbiota Composition and Function of Ensifera (Orthoptera)

Xiang Zheng et al. Insects. .

Abstract

Feeding habits were the primary factor affecting the gut bacterial communities in Ensifera. However, the interaction mechanism between the gut microbiota and feeding characteristics is not precisely understood. Here, the gut microbiota of Ensifera with diverse feeding habits was analyzed by shotgun metagenomic sequencing to further clarify the composition and function of the gut microbiota and its relationship with feeding characteristics. Our results indicate that under the influence of feeding habits, the gut microbial communities of Ensifera showed specific characteristics. Firstly, the gut microbial communities of the Ensifera with different feeding habits differed significantly, among which the gut microbial diversity of the herbivorous Mecopoda niponensis was the highest. Secondly, the functional genes related to feeding habits were in high abundance. Thirdly, the specific function of the gut microbial species in the omnivorous Gryllotalpa orientalis showed that the more diverse the feeding behavior of Ensifera, the worse the functional specificity related to the feeding characteristics of its gut microbiota. However, feeding habits were not the only factors affecting the gut microbiota of Ensifera. Some microorganisms' genes, whose functions were unrelated to feeding characteristics but were relevant to energy acquisition and nutrient absorption, were detected in high abundance. Our results were the first to report on the composition and function of the gut microbiota of Ensifera based on shotgun metagenomic sequencing and to explore the potential mechanism of the gut microbiota's association with diverse feeding habits.

Keywords: CAZymes; Ensifera; KEGG; feeding habits; gut microbiota; metagenomic.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Figure 1
Figure 1
Gut microbiota composition of the three species. Relative abundance of gut microbial composition, (A) Alpha diversity of the gut microbial community based on Shannon, Chao1, Simpson, and observed_species (ns, p > 0.05; *, p < 0.05). (B) Beta diversity of PCoA analysis based on Bray–Curtis distances to compare differences between species.
Figure 2
Figure 2
A stacked bar chart revealing the relative abundance of gut bacterial and eukaryotic composition at the (A,C) phylum and (B,D) species levels. The results show the phylum and species of the gut microbiota with the highest abundance.
Figure 3
Figure 3
Characteristics of gut microbiota composition. UpSet analysis of the shared and unique microorganisms at the (A) phylum and (B) species levels between species. Distribution diagram of the LEfSe analysis based on the LDA score of (C) bacteria and (D) archaea to screen the biomarkers.
Figure 4
Figure 4
Functional and pathway annotation comparison of the genes assigned to the KEGG database. (A) Function annotation at KEGG level 1. (B) Function annotation at KEGG level 2. (C) Gene abundance comparison in the fructose and mannose metabolism pathway. (D) Gene abundance comparison in the starch and sucrose metabolism pathway. Notes for C and D: The genes belonging to the three samples with significant differences in the metabolic pathway map are marked by a color, in which Mec, Oce, and Gry are from left to right. The red to green color represents the high to low gene abundance. (https://www.omicstudio.cn/tool, accessed on 22 May 2022).
Figure 4
Figure 4
Functional and pathway annotation comparison of the genes assigned to the KEGG database. (A) Function annotation at KEGG level 1. (B) Function annotation at KEGG level 2. (C) Gene abundance comparison in the fructose and mannose metabolism pathway. (D) Gene abundance comparison in the starch and sucrose metabolism pathway. Notes for C and D: The genes belonging to the three samples with significant differences in the metabolic pathway map are marked by a color, in which Mec, Oce, and Gry are from left to right. The red to green color represents the high to low gene abundance. (https://www.omicstudio.cn/tool, accessed on 22 May 2022).
Figure 4
Figure 4
Functional and pathway annotation comparison of the genes assigned to the KEGG database. (A) Function annotation at KEGG level 1. (B) Function annotation at KEGG level 2. (C) Gene abundance comparison in the fructose and mannose metabolism pathway. (D) Gene abundance comparison in the starch and sucrose metabolism pathway. Notes for C and D: The genes belonging to the three samples with significant differences in the metabolic pathway map are marked by a color, in which Mec, Oce, and Gry are from left to right. The red to green color represents the high to low gene abundance. (https://www.omicstudio.cn/tool, accessed on 22 May 2022).
Figure 5
Figure 5
Comparison of the genes assigned to the CAZymes database. (A) Classification of CAZyme families in the samples. (B) The different abundant genes assigned to CAZy level 2. (C) Correlation analysis between microbiota and CAZymes (*, p < 0.05; **, p < 0.01).
Figure 5
Figure 5
Comparison of the genes assigned to the CAZymes database. (A) Classification of CAZyme families in the samples. (B) The different abundant genes assigned to CAZy level 2. (C) Correlation analysis between microbiota and CAZymes (*, p < 0.05; **, p < 0.01).
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