Studying on the Difference Between Two Kinds of Osteomyelitis
Condition: Diabetic Foot Osteomyelitis; Diabetic Foot Infection; Microbiome
MeSH terms: Osteomyelitis; Diabetic Foot
Study type: Observational

Summary: 
      To explore the microbial differences of diabetic foot osteomyelitis and osteomyelitis without
      diabetes.
    

Detailed description: 
      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.
    

Observational model: Case-Control
Time perspective: Prospective
Sampling method: Non-Probability Sample
Study population: 
        patients with osteomyelitis
      
Keywords: Diabetic Foot Osteomyelitis; 16S rRNA; Metagenomes; Microbiome