DOI: 10.5061/dryad.s7h44j1d4
title: Metagenomic analysis of gut microbiome illuminates the mechanisms and evolution of lignocellulose degradation in mangrove herbivorous crabs

description: Background: Sesarmid crabs dominate mangrove habitat as the major primary
 consumers, which facilitates the trophic link and nutrient recycling in
 the ecosystem. Therefore, the adaptations and mechanisms of sesarmid crabs
 to herbivory is not only crucial to terrestrialization and its
 evolutionary success, but also to the healthy functioning of mangrove
 forest ecosystems. Although endogenous cellulases expressions were
 reported in crab species, it remains unknown if the endogenous enzymes
 alone can complete the whole lignocellulolytic pathway, or they also
 depend on the contribution from their intestinal microbiome. We attempt to
 investigate the role of gut symbiotic microbes of mangrove-feeding
 sesarmid crabs in plant digestion using a comparative metagenomic
 approach. Results: Metagenomics analyses on 43 crab gut samples from 23
 species of mangrove crabs revealed a wide coverage of 127 CAZy families
 and nine KOs targeting lignocellulose and their derivatives in all species
 analyzed, including predominantly carnivorous species, suggesting the crab
 species gut microbiome have lignocellulolytic capacity regardless of
 dietary preference. Microbial cellulase, hemicellulase and pectinase genes
 in herbivorous and detritivorous crabs were differentially more abundant
 when compared to omnivorous and carnivorous crabs, indicating the
 importance of gut symbionts in lignocellulose degradation in mangrove
 crabs and the enrichment of lignocellulolytic microbes in response to diet
 with higher lignocellulose content. The herbivorous and detritivorous
 crabs showed highly similar CAZyme composition compared to dissimilarities
 observed in taxonomic profiles observed in both groups, suggesting a
 stronger selection force to gut microbiota by its functional capacity than
 by taxonomy. The gut microbiota in herbivorous sesarmid crabs were also
 enriched with nitrogen reduction and fixation genes, implying possible
 roles of the gut microbiota in supplementing nitrogen that is deficient in
 plant diet. Conclusions: Endosymbiotic cellulolytic microbes play an
 important role in lignocellulose degradation in most crab species but
 their abundance is strongly correlated with dietary preference, and they
 are highly enriched in herbivorous sesarmids, thus enhancing their
 capacity for digestion of mangrove leaves. Dietary preference is a
 stronger driver in determining the microbial CAZyme composition and
 taxonomic profile in mangrove crab microbiome, resulting in functional
 redundancy of endosymbiotic microbes. Our results showed that crabs
 implement a mixed mode of digestion utilizing both endogenous and
 microbial enzymes in lignocellulose degradation, as observed in most of
 the more advanced herbivorous invertebrate species.
description: # Metagenomic analysis of gut microbiome illuminates the mechanisms and
 evolution of lignocellulose degradation in mangrove herbivorous crabs.
 [Access this dataset on Dryad](https://doi.org/10.5061/dryad.s7h44j1d4)
 This dataset contains the assembled metagenomes of 43 gut microbiome
 biological samples in 23 species of mangrove crabs in Hong Kong collected
 during 2019-2021. The raw metagenomic reads are available at NCBI SRA
 under accession PRJNA1017629. The assembly was conducted using MEGAHIT
 1.2.9. The unnormalized bacterial read counts tables on bacterial phyla
 and functional features in CAZy (release V10, 07292021) and KEGG databases
 (release v58) were generated using an integrated pipeline SqueezeMeta
 v1.4.0, which includes ORF prediction with Prodigal v2.6.3,
 taxonomic/functional read annotation with DIAMOND v2.0.8.146, and read
 mapping to annotations with Bowtie2 v2.3.4.1. The raw count table were
 further processed using SQMtools to filter only bacterial read counts. To
 repeat the analysis, CoDaSeq package should be used to remove features
 with average &lt;1000 counts. ## Description of the data and file
 structure The metagenomic assemblies were under following naming
 convention \[Abbreviated 6 character species name].megahit.fasta The
 functional/ taxonomic read counts were under following naming convention
 prok_[cazy|KEGG]*abund.txt prok_taxa*{taxa level}_abund.txt
 doi_10_5061_dryad_s7h44j1d4.zip \|- Clivil.megahit.fasta \|-
 Epifro.megahit.fasta \|- Epiver.megahit.fasta \|- Etilae.megahit.fasta \|-
 Fasfas.megahit.fasta \|- Gelbor.megahit.fasta \|- Hempen.megahit.fasta \|-
 Lepaff.megahit.fasta \|- Macdef.megahit.fasta \|- Metfro.megahit.fasta \|-
 Metlon.megahit.fasta \|- Mettre.megahit.fasta \|- Micbre.megahit.fasta \|-
 Ocycer.megahit.fasta \|- Orideh.megahit.fasta \|- Oriint.megahit.fasta \|-
 Orisin.megahit.fasta \|- Paraff.megahit.fasta \|- Parcon.megahit.fasta \|-
 Parspl.megahit.fasta \|- Scypar.megahit.fasta \|- Thrdan.megahit.fasta \|-
 Tubarc.megahit.fasta \|- README.md \|- prok_cazy_abund.txt \|-
 prok_KEGG_abund.txt \|- prok_taxa_class_abund.txt \|-
 prok_taxa_family_abund.txt \|- prok_taxa_genus_abund.txt \|-
 prok_taxa_order_abund.txt \|- prok_taxa_phylum_abund.txt \|-
 prok_taxa_species_abund.txt ## Sharing/Access information Data was derived
 from the following sources: * Bioproject accession PRJNA1017629
 ([https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA1017629](https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA1017629)) ## Code/Software Squeezemeta ([https://github.com/jtamames/SqueezeMeta](https://github.com/jtamames/SqueezeMeta)) CoDaSeq ([https://github.com/ggloor/CoDaSeq](https://github.com/ggloor/CoDaSeq)) To filter tables by read counts (and occurrence) threshold in R: ``` library(CoDaSeq) codaSeq.filter(f, min.reads=1000, min.occurrence=0.99, samples.by.row=FALSE) ``` For downstream DA analyses please refer to the supplementary .Rmd from Gloor, Macklaim, Pawlowsky-Glahn & Egozcue (2017)( [https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2017.02224/full](https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2017.02224/full) [https://github.com/ggloor/Frontiers_2017](https://github.com/ggloor/Frontiers_2017)), as well as documentations from DeSeq2 ([https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#plot-counts](https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#plot-counts)) and ANCOM-BC ([https://www.bioconductor.org/packages/release/bioc/vignettes/ANCOMBC/inst/doc/ANCOMBC.html](https://www.bioconductor.org/packages/release/bioc/vignettes/ANCOMBC/inst/doc/ANCOMBC.html))