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. 2012;7(11):e48505.
doi: 10.1371/journal.pone.0048505. Epub 2012 Nov 1.

Metagenomic analysis of the microbiota from the crop of an invasive snail reveals a rich reservoir of novel genes

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Metagenomic analysis of the microbiota from the crop of an invasive snail reveals a rich reservoir of novel genes

Alexander M Cardoso et al. PLoS One. 2012.

Abstract

The shortage of petroleum reserves and the increase in CO(2) emissions have raised global concerns and highlighted the importance of adopting sustainable energy sources. Second-generation ethanol made from lignocellulosic materials is considered to be one of the most promising fuels for vehicles. The giant snail Achatina fulica is an agricultural pest whose biotechnological potential has been largely untested. Here, the composition of the microbial population within the crop of this invasive land snail, as well as key genes involved in various biochemical pathways, have been explored for the first time. In a high-throughput approach, 318 Mbp of 454-Titanium shotgun metagenomic sequencing data were obtained. The predominant bacterial phylum found was Proteobacteria, followed by Bacteroidetes and Firmicutes. Viruses, Fungi, and Archaea were present to lesser extents. The functional analysis reveals a variety of microbial genes that could assist the host in the degradation of recalcitrant lignocellulose, detoxification of xenobiotics, and synthesis of essential amino acids and vitamins, contributing to the adaptability and wide-ranging diet of this snail. More than 2,700 genes encoding glycoside hydrolase (GH) domains and carbohydrate-binding modules were detected. When we compared GH profiles, we found an abundance of sequences coding for oligosaccharide-degrading enzymes (36%), very similar to those from wallabies and giant pandas, as well as many novel cellulase and hemicellulase coding sequences, which points to this model as a remarkable potential source of enzymes for the biofuel industry. Furthermore, this work is a major step toward the understanding of the unique genetic profile of the land snail holobiont.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Digestive system of the snail.
(A) Photograph of the African giant snail Achatina fulica. (B) Whole digestive system dissected. The black arrow indicates the crop filled with juice. DNA for the metagenome analysis was extracted from this fluid. (C) Spread out digestive system of the snail showing the salivary gland (SG), empty crop (C), esophagus (O), stomach (S), intestine (I), digestive gland (DG) and rectum (R).
Figure 2
Figure 2. Analysis of composition of the snail microbial community.
(A) Phylogenetic diversity of metagenomic sequences, computed by MEGAN4 based on a BLASTX using an e-value cut-off of 1e−5 comparison for filtered (red) and non-filtered (blue) sequences. The size of the circles is scaled logarithmically to represent the number of reads assigned to each taxon. (B) Bacteria, (C) Archaea, and (D) Eukaryota.
Figure 3
Figure 3. Subsystems-based annotation (SEED) analysis of the snail crop microbiome.
A) Percent of genes assigned by MG-RAST to each SEED subsystem. B) Percent of genes in the “Stress Response” subsystem, which includes sequences mainly associated with the protection against oxidative and osmotic stress, as well as heat shock. C) Genes assigned to the “Virulence, Disease and Defense” subsystem. Seventy-one percent of the sequences correspond to genes providing resistance to antibiotics and other toxic compounds, including multi-drug efflux pumps and genes for detoxification of a wide variety of heavy metals (cobalt, zinc, cadmium, chromium, copper and mercury). D) and E) Percent of genes in the subsystems “Amino Acids and Derivatives” and “Cofactors, Vitamins, Prosthetic Groups, Pigments”, respectively.
Figure 4
Figure 4. Dendrogram showing the relationships among metagenomes obtained through SPSS hierarchical clustering analysis.
Achatina fulica crop microbiome (SNAIL) was compared with fungus garden microbial communities from Atta colombica in top (ACOFUNT) and bottom (ACOFUNB), Atta cephalotes (ACEFUN), Cyphomyrmex longiscapus (CLOFUN), Trachymyrmex (TRAFUN), and Acromyrmex echinatior (FUNCOMB); fecal microbiome of swine (SWI267 and SWI266); P3 luminal contents of Nasutitermes (TERMITE1); intestinal micobiome of lean (MGUTL) and obese (MGUTOB) mice; fecal microbiome of healthy adult humans (HGUT7 and HGUT8); feces from wild pandas (PANDA2, PANDA5, and PANDA1); forestomach from Macropus eugenii (WALLABY); Richmond acid mine drainage (ACID); oral microbiome from humans (SUBGIN); aquatic environment from mesopelagic (MESO), oxygen minimum layer (OMIN), and planktonic zones (EUPHO) in Hawaii; microbiome of Trichonympha from termites (TERMITE2); soil microbial communities from farm silage (SOILM), dark crust (SOILD), distinct crusts (SOILL), and switchgrass rhizosphere (SWITGRA); Xyleborus affinis from adult (XAAD) and larvae (XALARY); Sirex noctilio (SNOCT); fungus-growing termite (FUNTER); Dendroctonus ponderosae (BEETLE); and endophytic microbiome from rice (RICE).

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