Relative Abundance and Strain Diversity in the Bacterial Endosymbiont Community of a Sap-Feeding Insect Across Its Native and Introduced Geographic Range

Most insects are associated with bacterial symbionts. The bacterial diversity and community composition within hosts may play an important role in shaping insect population biology, ecology and evolution. We focussed on the bacterial microbiome of the Australian fig homotomid Mycopsylla fici (Hemiptera: Psylloidea), which can cause defoliation of its only host tree, Ficus macrophylla. This sap-feeding insect is native to mainland Australia and Lord Howe Island (LHI) but also occurs where its host has been planted, notably in New Zealand. By using a high-throughput 16S rDNA amplicon sequencing approach, we compared the bacterial diversity and community composition in individual adult males of four host populations, Sydney, Brisbane, LHI and Auckland. We also compared males, females and nymphs of the Sydney population. The microbiome of M. fici was simple and consisted mostly of the following three maternally inherited endosymbiont species: the primary endosymbiont Carsonella, a secondary (S-) endosymbiont and Wolbachia. However, the relative abundance of their sequence reads varied between host populations, except for similarities between Sydney and Auckland. In addition, insects from Sydney and Auckland had identical bacterial strains supporting the hypothesis that Sydney is the source population for Auckland. In contrast, mainland and LHI populations harboured the same S-endosymbiont, co-diverged Carsonella but different Wolbachia strains. Besides detecting endosymbiont-specific patterns of either co-evolution or horizontal acquisition, our study highlights that relative abundance of maternally inherited endosymbionts should also be taken into account when studying bacterial communities across host populations, as variations in bacterial density may impact host biology and ecology

MOESM1 of Lignolytic-consortium omics analyses reveal novel genomes and pathways involved in lignin modification and valorization

Additional file 1: Table S1. Compounds identified by gas chromatography–mass spectrometry (GC-MS) in the lignin-waste stream used for establishment of the lignin-degrading microbial community (LigMet). Table S2. Sequencing statistics and data processing of amplicon libraries constructed for profiling LigMet and soil samples analyzed. Table S3. Diversity and richness indices of the LigMet and soil samples based on 16S rRNA and ITS2 region sequences. Table S4. Protozoa identified in LigMet based on 18S rRNA sequencing. Table S5. Assembly statistics from draft genomes recovered from LigMet (all assemblies). Table S6. Genome statistics of Paenarthrobacter sp. str. HW13. Figure S1. Microbial growth was monitored by OD 600 nm, observing exponential growing during the first 40 hours of consortium growth. The consumption of reducing sugars over time as monitored by DNS, the exponential phase was completed after the first 40 hours of growth when monitoring sugar consumption. Figure S2. Rarefaction curves based on targeted sequencing of 16S rRNA gene amplicons derived from the LigMet (A) and sugarcane soil (B) samples. The rarefaction curves of each biological replicate are shown in different colors. Figure S3. Rarefaction curves based on targeted sequencing of the ITS2 region derived from the LigMet sample. The rarefaction curves of each biological replicate are shown in different colors. Figure S4. The taxonomic profiles from LigMet and sugarcane soil samples at the class level based on 16S rRNA gene amplicon. The respective relative abundances of each biological replicate for LigMet and sugarcane soil are shown. Figure S5. The archaeal phylum abundance in LigMet and sugarcane soil sample. The relative abundance is shown in percentage for each biological replicate of the LigMet and sugacarcane soil. Figure S6. Metabolic pathways related to aromatic compound degradation identified in LigMet according to KEGG automatic annotation. Figure S7. Classification of the predicted proteins from the LigMet according to the dbCAN database. Figure S8. Predicted auxiliary activity (AA) and carbohydrate esterase (CE) families from LigMet and draft genomes, based on the dbCAN database. AA and CE families are related to peroxidase activity and break down of lignin ester cross links, respectively. Figure S9. Phylogenetic relationship of the strain HW13 relative to the most closely related strains of the genus Paenarthrobacter. EzBioCloud webserver was used to perform a similarity-based search of HW13 16S rRNA to retrieve the most closely related sequences. The resulting 16S rRNA sequences were aligned using the MAFFT v7.299b software. A phylogenetic tree was inferred using the maximum likelihood method implemented in RAxML v8.2.0, evolutionary distances were based on the GTRGAMMAI model, inferred as the best model by jModelTest2. Numbers at the nodes are percentages of bootstrap values obtained by repeating the analysis 1,000 times. The type strains are marked with a superscript ‘T’. Accession numbers are shown in parentheses. Figure S10. Phylogenetic relationships among feruloyl-CoA synthetase (upper) and Enoyl-CoA hydratase/aldolase. The phylogenetic tree was generated using amino acid sequences retrieved from NCBI and Uniprot database. The sequences were aligned using MAFFT v7.299b software [5]. The phylogenetic tree was reconstructed using maximum likelihood method implemented in RAxML v8.2.0 [6], evolutionary distances were based on the GTRGAMMAI model, inferred as the best model by jModelTest2 [7]. The bootstrap values (1,000 replicate runs, shown as %) higher than 70 % are represented. Accession numbers are listed in parentheses. The FerA_B3 and FerB_B11 amino acid sequence retrieved from LigMet data set is printed in bold

Isolated Neutral Peptides

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