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Additional file 1. Fig. S1: Sampling point on Pacific Ocean. Fig. S2: Exotoxin genes of which different richness were observed in the microbiome of the Mariana Trench and Pacific basin sites. (a) and (b) show exotoxin genes which exist higher abundance in sediment than in water. Similarly, (c) and (d) show exotoxins which are more present in water. Fig. S3: Relative abundance of virulence factor genes in the microbiome of the Mariana Trench and adjacent basin. 50 virulence factors with the highest abundance in these metagenomes were shown in (a). Color of each box is correlated with the relative abundance of each virulence factor in samples. (b) shows number of type of found virulence factors in each metagenome. Fig. S4: Relative abundance and richness of ARGs in the sediment microbiome of the Mariana Trench. The left Y axis and bar charts showed the relative percentage of ARG subtypes while the right Y axis and lines denoted the abundance sum (as copies per 16S rRNA gene copy, GP16S, b, c and d) of ARG subtypes. SP5856, SM6038 and SM10898 represented sediment metagenomes from different depths. The symbols before gene names, i.e., *, ** and !, denoted the degrees of fold change in abundance from SM10898 to SP5856 of ≥ 1, ≥ 10 and positive infinity, respectively. Fig. S5: Relative distribution of the taxonomy (in phylum-level) of toxin gene-carrying resistance contigs taxonomy. Fig. S6: Principal component analysis of relative abundance of ARGs which was annotation by predicted ORFs, % variance explained shown in parentheses. SP5856, SM6038 and SM10898 represented sediment metagenomes from different depths. WM9600, WM10400, WM10500 represented seawater metagenomes from different depths.

Additional file 1: Figure S1. Microbial taxonomic profiles of seawater samples at the domain, phylum and class levels. Figure S2. Potential eukaryotic sources of DMSP in the natural (T0) coastal seawater. Figure S3. Potential prokaryotic sources of DMSP in the natural (T0) coastal seawater. Figure S4. Maximum likelihood phylogenetic tree of DmdA proteins. Figure S5. Maximum likelihood phylogenetic tree of DddP proteins. Figure S6. Maximum likelihood phylogenetic trees of DddQ, DddL, DddK, DddY, DddW and DddX proteins. Figure S7. 16S rRNA gene profiles of seawater samples enriched with DMSP analysed by DGGE. Figure S8. Bacterial community profiles of coastal seawater samples analysed by 16S rRNA gene sequencing. Figure S9. Relative abundance and taxonomy of ancillary genes from the DMSP demethylation pathway in coastal seawater samples. Figure S10. Relative abundance and taxonomic affiliation of ancillary genes from the DMSP cleavage pathway in coastal seawater metagenomes. Figure S11. Relative abundance and taxonomic affiliation of DMS cycling genes in coastal seawater samples. Table S1. Relative abundance of genes encoding proteins involved in the cycling of DMSP, DMS and related compounds in metagenomes from seawater samples. Table S2. Relative abundance (RA) of main bacterial genera from seawater samples analysed by 16S rRNA gene amplicon (16S) and metagenomics (MG) sequencing. Table S3. Dominant bacterial genera in T0 seawater samples analysed by 16S rRNA gene amplicon sequencing. Table S4. Relative abundance (RA) of main bacterial orders from seawater samples analysed by 16S rRNA gene amplicon (16S) and metagenomics (MG) sequencing. Table S5. Metagenome-assembled genomes (MAGs) with homologous sequences to genes involved in DMSP cycling reconstructed from metagenomes from seawater samples. Table S6. Characteristics of bacterial strains with DMSP-degrading activity isolated from seawater incubations with DMSP. Table S7. 16S rRNA gene amplicon sequencing results for coastal seawater samples. Table S8. Statistics for metagenomic sequencing and assemblies. Table S9. Accession numbers of previously ratified enzymes involved in the cycling of DMSP and related compounds.

Ru-Tian Hao, Chen Zheng, Chen-Yong Wu, Er-Jie Xia, Xiao-Fen Zhou, Rui-Da Quan, Xiao-Hua ZhangDepartment of Thyroid and Breast Surgery, The First Affiliated Hospital of Wenzhou, Medical University, Wenzhou, Zhejiang, People’s Republic of ChinaAbstract: Papillary thyroid cancer (PTC) is the most frequent type of malignant thyroid cancer, but its molecular mechanisms remain unknown. To better understand the tumorigenesis and progression of PTC, we conducted a comprehensive analysis of the whole-transcriptome resequencing of paired PTC and normal thyroid tissues. Nectin cell adhesion molecule 4 (NECTIN4) was significantly overexpressed in thyroid carcinoma compared with that in matched normal tissue. We also assessed the relation between the expression level of NECTIN4 and the clinicopathological features of PTC in The Cancer Genome Atlas database, and results showed that upregulated NECTIN4 is associated with lymph node metastasis (P