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1 Supplementary Data Supplementary Methods Sequencing DNA Was BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance Supplemental material placed on this supplemental material which has been supplied by the author(s) Ann Rheum Dis 1 Supplementary data Supplementary methods Sequencing DNA was extracted from one swab from each sample using an initial bead beating step followed by extraction using the Maxwell 16 Tissue DNA Purification Kit and Maxwell 16 Research Instrument System (Promega, USA). DNA concentration was measured using a Qubit dsDNA BR Assay Kit (ThermoFisher, USA) and normalized to a concentration of 5 ng/ul. The 16S rRNA gene encompassing the V5 to V8 regions was targeted using the 803F (TTAGAKACCCBNGTAGTC) and 1392wR (ACGGGCGGTGWGTRC) primers, modified to contain Illumina specific adapter sequence (803F: TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGTTAGAKACCCBNGTAGTC, 1392wR: GTCTCGTGGGCTCGGGTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGACGGGCGGTGWGTRC) [1]. Preparation of the library was performed as described, using the workflow outlined by Illumina (#15044223 Rev.B). In the first stage, PCR products of ~590 bp were amplified according to the specified workflow with an alteration in polymerase used to substitute Q5 Hot Start High-Fidelity 2X Master Mix (New England BioLabs, USA) in standard PCR conditions. Resulting PCR amplicons were purified using Agencourt AMPure XP beads (Beckman Coulter, USA). Purified DNA was indexed with unique 8 bp barcodes using the Illumina Nextera XT 384 sample Index Kit A-D (Illumina FC-131-1002) in standard PCR conditions with Q5 Hot Start High-Fidelity 2X Master Mix. Indexed amplicons were pooled together in equimolar concentrations and sequenced on MiSeq Sequencing System (Illumina, USA) using paired end sequencing with V3 300 bp chemistry, according to manufacturer’s instructions, by the Australian Centre for Ecogenomics. Sequence processing and statistical analysis The sequences were initially processed using QIIME[2]. Sequences were demultiplexed, then quality filtered using Trimmomatic single end (SLIDINGWINDOW:4:15 LEADING:10 HEADCROP:23 MINLEN:250)[3]. The forward and reverse reads could not be joined therefore forward reads were used for subsequent analyses. Operational taxonomic units (OTUs) were picked by open-reference against a Greengenes (v. 13_5)/ Silva (v.119) database using the 97% cut off for percent identity. OTUs with counts higher than 10 in the extraction control (lysis buffer) were removed. The resulting OTU table was further filtered and normalized using the mixOmics package in R[4]. The prefiltering step removes low abundance OTUs with proportional counts across all samples below 0.01% [4]. The data were normalized using total sum scaling (TSS) then transformed as centered log-ratio (CLR) to account for their compositional nature [4]. Moentadj R, et al. Ann Rheum Dis 2021;0:1–9. doi: 10.1136/annrheumdis-2020-219009 BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance Supplemental material placed on this supplemental material which has been supplied by the author(s) Ann Rheum Dis 2 We applied multivariate sparse PLS discriminant analysis (sPLS-DA [4]) to identify an OTU signature that best classified RA vs HC groups. Repeated cross-validation was used to optimally choose each sPLS-DA signature and assess its performance based on balanced classification error rate, to take into account the minority number of samples within a patient group. The importance of each OTU in the signature was displayed using loading barplots, where the length of the bar indicates the contribution or importance of each OTU to define the sPLS-DA components, and the colour indicates the treatment group where each OTU is most abundant. A sPLS-DA component is a linear combination of selected OTUs weighted by their loading weights. As such, an individual dysbiosis score can be obtained for each subject based on a linear combination of their OTU signature. Similarly, a predicted dysbiosis score was then calculated based on the same sPLS-DA model and OTU signature, but on the FDR group. sPLS-DA analyses, receiver operating characteristic (ROC) curve and Area under the curve (AUC) were obtained using the mixOmics package in R [4]. Semi supervised hierarchical clusterings using Euclidean distance and Ward linkage method were plotted with the R base package. Stacked bar plots of phylogeny were plotted in R using the phyloseq, dplyr, tidyr, magrittr and ggplot2 packages [5-9]. For analysis of functional effects of heat-killed Streptococci and SCW, multiple groups were compared by ANOVA, having tested for skewness, using GraphPad Prism v8. Axenic cultures generated from oral swabs. Oral swabs (n =3 selected from the RA, FDR and HC groups) were cultured to obtain purified bacterial (axenic) Streptococcal colonies. Swabs were streaked onto selective nutritious Brain Heart Infusion (BHI) agar plates supplemented with 10% defibrinated horse blood. A total of 20 isolates of different bacterial colonies, separated based on their morphology (Supplementary Figure 1), were streaked on o the second batch of BHI plates. After 24 h incubation at 37 C with 5% CO2, single colonies from BHI plates were streaked in triplicate onto Mitis Salivarius agar mixed with 1% potassium tellurite, which is selective for Streptococcus and Enterococcus. After two further rounds on Mitis plates, a single colony was inoculated into BHI broth for DNA extraction. Streptococcus genus verification. Moentadj R, et al. Ann Rheum Dis 2021;0:1–9. doi: 10.1136/annrheumdis-2020-219009 BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance Supplemental material placed on this supplemental material which has been supplied by the author(s) Ann Rheum Dis 3 DNA extracted from bacterial isolates was quantified with a NanoDrop Lite Spectrophotometer (Thermo Scientific, Massachusetts, United States). Axenic cultures were verified as Streptococcus species, using universal Streptococcus PCR primers (Supplementary Table 2, StrepGen) [10], with inclusion of mock-inoculated medium and no template controls for amplification of extracted DNA. For gram staining and microscopy, glass slides prepared with overnight-cultured bacteria were stained with Crystal Violet, Iodine mordant, 95% ethanol and Safranin then analyzed and photographed. Phylogenetic Sanger Sequencing. To determine the different Streptococcus species present in the axenic cultures, extracted DNA was sequenced (Australian Genome Research Facility, University of Queensland) after amplification in the presence of universal 16S rRNA gene targeting PCR primers 27F and 1492R. Streptococcus species were characterised with five universal 16S rRNA gene targeting PCR primers (Supplementary Table 3) (Integrated DNA Technologies, Iowa, United States) [11]. The intervening regions contain variable sequence data, which can be used to determine the best species match. The Sanger sequencing data were assembled using the Staden Package, and manually verified [12]. The consensus sequences were blasted against the bacterial 16S rRNA gene databases GreenGenes and Ribosomal Database Project to obtain the best Streptococcus match at the species level [13, 14]. Streptococcal sequencing and characterisation Library preparation was performed using the Illumina Nextera XT DNA Library Preparation Kit (Illumina, CA). Libraries were sequenced at the Australian Centre for Ecogenomics using the Illumina NextSeq 500 platform with V2 chemistry generating approximately 1 Gb of 150 bp paired-end reads per isolate. Reads were quality and adapter trimmed using Trimmomatic v0.36 [3] with default settings plus a head crop of 10. Trimmed reads were merged using BBMerge (https://sourceforge.net/projects/bbmap/). Assembly of each isolate was performed using SPAdes v3.11.1 [15] using k-mers 21, 33, 55 & 77. Genome completeness and contamination was assessed using CheckM v1.0.7 [16]. Taxonomic affiliation determined using GTDB-Tk v1.2.0 [17] against the GTDB database release 04-RS89 [18, 19]. Assembled genomes were annotated using Prokka v1.12 [20]. Orthology between annotations was assessed using Roary v3.11.0 [21] with minimum percentage identity of 70%. Maximum-likelihood tree generated using IQ-TREE v1.6.9 [22] with ModelFinder based on alignment of 120 marker genes Moentadj R, et al. Ann Rheum Dis 2021;0:1–9. doi: 10.1136/annrheumdis-2020-219009 BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance Supplemental material placed on this supplemental material which has been supplied by the author(s) Ann Rheum Dis 4 identified using GTDB-Tk [17]. Alignment filtered for sites conserved in a minimum of 25% of genomes. SNPs and indels were identified using Snippy v4.4.3 (https://github.com/tseemann/snippy) based on alignment to the S. parasalivarius species representative GCF_001556435.1. Bedtools v2.26.0 [23] intersect was used to compare call lists between isolates. Mappings and mutations were visualised using IGV v2.4.9 [24]. SnpEff v4.3 [25] was used to predict the effect of mutations on annotated proteins as implemented in Snippy. EggNOG and COG annotations from the v5.0 database were assigned to predicted proteins using EggNOG mapper v2.0.1 [22, 26]. Streptococcal cell wall isolation BHI broth was inoculated with thawed axenic cultures of streptococcal isolates from RA patients (21.1, 22.1, 23.2), FDRs (16.1 and 11.1) and a healthy control subject (2.1). After 24 hr incubation bacteria were streaked on blood
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