Large and healthy snail (A) and its crop fluid (B), feces from field (C) or reared (D) animals are shown.

<p>Schematic representation of the <i>Achatina</i> digestive tract (E).</p

Match between bacterial communities comparing crop vs. feces (A) and wild vs. reared snails (B).

<p>Principal coordinates plots (PCoA) were generated using the pair wise unweighted UniFrac distances.</p

Distribution of sequences in bacterial phyla classified by the RDP Database (A) and proportion (B) of <i>Firmicutes</i> and <i>Bacteroidetes</i> in the crop and intestine (feces) microbiota of wild vs. reared snails.

<p>Clones were designated FC to indicate field-collected snails; RL, reared in the laboratory; C, crop; and I, intestine (feces). The datasets were compared against the following MG-RAST (metagenomics.anl.gov) metagenomic projects: Fish gut (4441695.3); Lean (4440463.3) and Obese Mouse (4440464.3); Red kangaroo (4461325.3); Capybara (4461352.3); Giraffe (4461358.3); Horse (4461321.3); Chicken cecum (4440285.3); Cow rumen (4441682.3) and Human (4440941.3). The sequences from planorbid snails <i>Biomphalaria pfeifferi</i> (FJ228890–FJ229104), <i>Bulinus africanus</i> (FJ228740–FJ228889), and <i>Helisoma duryi</i> (FJ229105–FJ229355) were obtained from NCBI dataset.</p

Metagenomic Analysis of the Microbiota from the Crop of an Invasive Snail Reveals a Rich Reservoir of Novel Genes

<div><p>The shortage of petroleum reserves and the increase in CO₂ 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 <em>Achatina fulica</em> 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 <em>Proteobacteria</em>, followed by <em>Bacteroidetes</em> and <em>Firmicutes</em>. <em>Viruses</em>, <em>Fungi</em>, and <em>Archaea</em> 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.</p> </div

Digestive system of the snail.

<p>(A) Photograph of the African giant snail <i>Achatina fulica</i>. (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).</p

Comparison of the glycoside hydrolase (GH) profiles targeting plant structural polysaccharides in the snail, termite, giant panda, wallaby, and human metagenomes.

<p>Data are presented with the GHs grouped according to their major function roles in the degradation of plant fiber, as classified in Allgaier <i>et al</i>., 2010 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0048505#pone.0048505-Allgaier1" target="_blank">[70]</a>. The numbers in parentheses represent the percentages of these groups relative to the total number of GHs identified in the metagenomic datasets [2590 for snail, 872 for wallaby, 1117 for termite, 227 for panda (from 3 samples), and for human (from 2 samples)].</p

Analysis of composition of the snail microbial community.

<p>(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.</p

Dendrogram showing the relationships among metagenomes obtained through SPSS hierarchical clustering analysis.

<p><i>Achatina fulica</i> crop microbiome (SNAIL) was compared with fungus garden microbial communities from <i>Atta colombica</i> in top (ACOFUNT) and bottom (ACOFUNB), <i>Atta cephalotes</i> (ACEFUN), <i>Cyphomyrmex longiscapus</i> (CLOFUN), <i>Trachymyrmex</i> (TRAFUN), and <i>Acromyrmex echinatior</i> (FUNCOMB); fecal microbiome of swine (SWI267 and SWI266); P3 luminal contents of <i>Nasutitermes</i> (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 <i>Macropus eugenii</i> (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 <i>Trichonympha</i> from termites (TERMITE2); soil microbial communities from farm silage (SOILM), dark crust (SOILD), distinct crusts (SOILL), and switchgrass rhizosphere (SWITGRA); <i>Xyleborus affinis</i> from adult (XAAD) and larvae (XALARY); <i>Sirex noctilio</i> (SNOCT); fungus-growing termite (FUNTER); <i>Dendroctonus ponderosae</i> (BEETLE); and endophytic microbiome from rice (RICE).</p