Studying on the Difference Between Foot Osteomyelitis Patients With or Without Diabetes
Studying on the Difference Between Foot Osteomyelitis Patients With or Without Diabetes
To explore the microbial differences of diabetic foot osteomyelitis and osteomyelitis without diabetes.
Diabetic foot osteomyelitis (Dd group) subjects (sample size ≥ 10 cases) and foot osteomyelitis without diabetes (ND group) subjects (sample size ≥ 10 cases) were collected in accordance with the inclusion criteria. Surgeons collected intra-operative bone specimens from all 28 patients who required surgical intervention (debridement or amputation) for management of their osteomyelitis[10]. Avoided soft tissue or sinus tract cultures, because they were not sufficiently accurate in predicting bone pathogens[11]. Following surgical debridement of infected or necrotic tissue, cleansed by sterile saline solution, deep bone specimens were harvested using sterile instruments. We routinely obtained adequate bone specimens and divided them into three parts. One for routine microbiology culture and antibiotic sensitivity testing, one for the histopathology, and the other one for DNA sequencing analysis. This process to obtain bone specimens could reduce chances of contamination by wound tissue colonizing bacteria.
16S rRNA high-throughput sequencing Two specimens were sent to the laboratory for conventional culturing and histopathological tests. The bone specimen left were placed in sterile pipes without any transport medium and stored at 4℃ for 24 hours and then frozen at -80C until DNA extraction. Genomic DNA was extracted using DNA extraction kit (YiRui,ShenZhen,China) according to the manufacturer's instructions. Extracted DNA was quantitative and quality control by agarose gel electrophoresis(JS-power 300, PeiQin, ShangHai, China). Then we amplified the V3-V4 variable region of the 16S rRNA gene for sequencing using a forward and a reverse fusion primer-（341F：5'-CCTAYGGGRBGCASCAG-3' and 806R：5'-GGACTACNNGGGTATCTAAT-3）(ABI GeneAmp 9700 PCR Instrument). PCR products were amplified by removing short sequences, singleton sequences and noisy reads. The PCR reaction was performed in a total volume of 60 μl, containing 6 μl of 10× Ex Tap PCR buffer, 6 μl of dNTP mixture, 0.6μl of bovine serum albumin (BSA), 0.3 μl Ex Tag, 1μl DNA, 1.2μl forward and reverse primers, and 43.7 μlH 2O. The PCR amplification was conducted under following conditions: initial denaturing was conducted at 94°Cfor5min, which was followed by 27 cycles at94°Cfor30s, 55°Cfor30s, and72°C for 45s. A final extension was performed at 72°C for 10min from 28 samples (including DFO and SFO patients) were sequenced over two separate runs on Illumina Miseq. To get high-quality clean reads, raw reads were filtered according to the following rules: (1) remove reads containing morethan10% ofunknown nucleotides and(2) removereads containing less than 80% of bases with quality (Q-value)>20. The filtered reads were then assembled into tags according to overlap between paired-end reads with more than 10bp overlap, and less than 2% mismatch. The software Mothur (v.1.34.0) was used to remove the redundant tags to get unique tags. The obtained unique tags were then used to calculate the abundance.Then we clustered sequences into operational taxonomic units (OTUs) using the Greengene. Taxonomy was assigned to OTUs using the BLASTto the Greengene database at 97% similarity to identify microorganisms at the genera level (species level where possible). A phylogenetic tree was built from aligned representative OTU sequences using figtree.The total species diversity in a landscape was determined by two different parameters, the mean species diversity in sites or habitats at a more local scale (alpha diversity) and the differentiation among those habitats (beta diversity). Alpha diversity included both community diversity and richness: community richness was represented by the ACE estimator or the Chao1 estimator. Beta diversity was the calculation of differences(distance) between microbiome community structure and membership based on the evolution of species. This kind of distance was calculated using Weighted Unifrac and can be performanced by Principal Co-ordinates Analysis diagram.
Metagenomes Analysis A metagenome DNA libraries from specimens were constructed using TruSeq Nano DNA kit (FC-121-4002) according to the manufacturer's instructions, with slight modifications. In brief, the length conformed DNA(350 bp) was obtained by sonication. DNA fragments were end-repaired and the appropriate library size was selected, then the samples were A-tailed and ligated to adapters. The NovaSeq 6000 sequencing systems（Illumina）were used for sequencing and library validation Raw Data obtained by sequencing have a certain proportion of low-quality dataaccording to the following rules: (1) remove reads containing morethan10% ofunknown nucleotides and(2) removereads containing less than 80% of bases with quality (Q-value)>20 . Megahit was used to splice the sequences（clean data） after quality control, and contigs were obtained.Contigs were filtered below 1000bp. The vector and host sequences were filtered by BLASTN, with an E-value cutoff of 1e-5. The remaining reads were mapped to the human genome by SOAP alignment, and the matching reads were removed as being contaminants from the host genome.The taxonomic classifications were performed on assembled contigs and singletons using BLAST against the NCBI database. And the best BLAST hit was used to refer the taxonomic rank of each sequence. All the analyses have been performed in R and p values were corrected for multiple testing with the false discovery rate method.
Inclusion Criteria:
Exclusion Criteria: