Studying on the Difference Between Two Kinds of Osteomyelitis
NCT ID: NCT04240964
Last Updated: 2020-01-27
Study Results
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Basic Information
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COMPLETED
28 participants
OBSERVATIONAL
2017-07-01
2018-12-01
Brief Summary
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Detailed Description
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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.
Conditions
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Study Design
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CASE_CONTROL
PROSPECTIVE
Study Groups
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Dd group
Patients with diabetic foot osteomyelitis
ND group
Foot osteomyelitis without diabetes
Interventions
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Eligibility Criteria
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Inclusion Criteria
2. diagnosed as foot osteomyelitis, and wounds were located below the knee joint.
3. nfected bone exposure or positive probe-to-bone test.
4. The patients were good physical condition.
5. Patients were able to tolerate debridement or operation treatment.
6. patients and their families agreed to participate in the study.
Exclusion Criteria
2. tumors affecting the wound of osteomyelitis;
3. long-term use of immunosuppressive therapy before admission;
4. patients that do not cooperate.
18 Years
80 Years
ALL
No
Sponsors
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Nanfang Hospital, Southern Medical University
OTHER
Responsible Party
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Ying Cao
associate professor
Principal Investigators
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ying cao, doctor
Role: PRINCIPAL_INVESTIGATOR
Nanfang Hospital, Southern Medical University
Locations
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Department of Endocrinology and Metabolism, NanFang Hospital, Southern Medical University
Guangzhou, Guangdong, China
Countries
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References
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Lavery LA, Peters EJ, Armstrong DG, Wendel CS, Murdoch DP, Lipsky BA. Risk factors for developing osteomyelitis in patients with diabetic foot wounds. Diabetes Res Clin Pract. 2009 Mar;83(3):347-52. doi: 10.1016/j.diabres.2008.11.030. Epub 2008 Dec 30.
Berendt AR, Peters EJ, Bakker K, Embil JM, Eneroth M, Hinchliffe RJ, Jeffcoate WJ, Lipsky BA, Senneville E, Teh J, Valk GD. Diabetic foot osteomyelitis: a progress report on diagnosis and a systematic review of treatment. Diabetes Metab Res Rev. 2008 May-Jun;24 Suppl 1:S145-61. doi: 10.1002/dmrr.836.
Valabhji J, Oliver N, Samarasinghe D, Mali T, Gibbs RG, Gedroyc WM. Conservative management of diabetic forefoot ulceration complicated by underlying osteomyelitis: the benefits of magnetic resonance imaging. Diabet Med. 2009 Nov;26(11):1127-34. doi: 10.1111/j.1464-5491.2009.02828.x.
Huang Y, Cao Y, Zou M, Luo X, Jiang Y, Xue Y, Gao F. A Comparison of Tissue versus Swab Culturing of Infected Diabetic Foot Wounds. Int J Endocrinol. 2016;2016:8198714. doi: 10.1155/2016/8198714. Epub 2016 Mar 30.
Goda A, Maruyama F, Michi Y, Nakagawa I, Harada K. Analysis of the factors affecting the formation of the microbiome associated with chronic osteomyelitis of the jaw. Clin Microbiol Infect. 2014 May;20(5):O309-17. doi: 10.1111/1469-0691.12400. Epub 2013 Nov 11.
Redel H, Gao Z, Li H, Alekseyenko AV, Zhou Y, Perez-Perez GI, Weinstock G, Sodergren E, Blaser MJ. Quantitation and composition of cutaneous microbiota in diabetic and nondiabetic men. J Infect Dis. 2013 Apr;207(7):1105-14. doi: 10.1093/infdis/jit005. Epub 2013 Jan 8.
Zou M, Cai Y, Hu P, Cao Y, Luo X, Fan X, Zhang B, Wu X, Jiang N, Lin Q, Zhou H, Xue Y, Gao F. Analysis of the Composition and Functions of the Microbiome in Diabetic Foot Osteomyelitis Based on 16S rRNA and Metagenome Sequencing Technology. Diabetes. 2020 Nov;69(11):2423-2439. doi: 10.2337/db20-0503. Epub 2020 Aug 14.
Other Identifiers
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NFEC-2017-013
Identifier Type: -
Identifier Source: org_study_id
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