| BackgroundDiabetes mellitus is a chronic disease that seriously threatens health.According to estimates by the International Diabetes Federation,the total number of adults with diabetes in the world exceeds 463 million currently.By the end of 2030,this number will increase to 578 million.By the end of 2045,it will reach 700 million.As the prevalence of diabetes has increased constantly,the disease has become a worldwide epidemic.At the same time,the incidence of diabetes-related complications is also increasing and are regarded as a major problem that clinicians are facing.Diabetic foot infection(DFI)is the most common destructive complication of diabetes.The risk of hospitalization of patients with DFI is 55.7 times higher than that of patients with diabetic foot(DF)disease without infection,and the risk of amputation is 154.5 times higher in patients with DFI than patients with DF disease without infection.Poor anti-infective treatment results in the gradual progression of infection,eventually invading the bones,this condition is called diabetic foot osteomyelitis(DFO),which is the most severe stage of DF.More than 20%of patients with severe DFI and 50%-60%of patients with moderate DFI develop DFO.Approximately 59.4%of patients with DFO will suffer amputations,and the 5-year survival rate of patients after amputation is only 50%.Therefore,timely and effective infection control is the key point to reducing the amputation rate and mortality of patients with DFO.The premise of improving the efficacy of anti-infection treatment is to improve the timeliness and comprehensiveness of microbial identification in wounds,which is a problem that urgently needs to be addressed around the world.At present,the traditional culture method is routinely used to identify the microorganisms in osteomyelitis wounds.This method has a long history,established culture procedures,and low expenditure.Our team has also retrospectively analyzed the difference in microbial composition between DFO wounds and superficial infection wounds using the culture method,and found that DFO wounds are mainly Gram-negative(G-)bacteria,while superficial infection wounds are mainly Gram-positive(G+)bacteria.It is found that culture method takes a long time(ordinary culture takes 3-5 days,drug sensitivity takes 1-2 weeks)and has low sensitivity.Moreover,it is difficult to identify unknown species and microorganisms that have highly demanding environments.Therefore,it is impossible to guide the selection and application of clinical antibiotics in a timely and correct manner.The empirical use of antibiotics based on previous culture results is not satisfactory.It was estimated that 56%of DFI patients changed their treatment plan due to poor empirical anti-infective treatment and wasted valuable treatment time,which caused the optimal treatment time was missed,making the situation worse.There is an urgent need to explore new methods and technologies that are faster,more comprehensive,and more sensitive for bacterial identification to fully understand the microbial composition of osteomyelitis wounds and guide the use of clinical antibiotics.High-throughput sequencing technology does not rely on culture,it can quickly and comprehensively obtain the composition and function information of wound microbes,providing a new development direction in the field of microbiology research,making it possible to fully and quickly identify microbes of DFO.16S rRNA sequencing is currently the most widely used in analyzing bacterial composition.The 16S rRNA gene is commonly found in prokaryotic microorganisms and is the most widely used molecular marker for studying the diversity of prokaryotic microorganisms and bacterial types.16S rRNA sequencing technology uses PCR to amplify specific regions of the 16S rRNA gene(one or more of the V1 to V9 hypervariable regions is selected according to experimental needs),these regions are sequenced and then compared with the Ribosome Database Project(RDP)database to obtain information about the composition and abundance of bacteria in wounds.Compared with the traditional culture method,the biggest advantage of this technique is that bacterial culture does not need to be performed,and the method is not restricted by conditions needed for bacterial growth.Moreover,the speed of sequencing is fast,and this method results in a more comprehensive understanding of the bacteria in wounds.Illumina is the most widely used and fastest running sequencing platform currently.To analyze the composition of the bacteria in wounds,16S rRNA sequencing of infected bone specimens based on the Illumina sequencing platform was performed,and the standard sampling procedure was strictly implemented.The selective amplification of the target gene fragment by 16S rRNA sequencing results in limited information.Even after continuous optimization,bacteria can only be identified to the genus level,and specific information at the species level cannot be obtained.The emergence of metagenome sequencing has solved this problem.Compared with 16S rRNA sequencing,metagenome sequencing is a technique that involves sequencing and analyzing the entire genome of a specimen,and more detailed information on the taxonomy and genes of microorganisms can be obtained with this method.The sequencing information can not only identify bacteria at the species level and even the strain level but also obtain functional information of the microbiome in specimens by comparison with functional databases.Previous studies have used metagenome sequencing to analyze microorganisms in the intestinal tract,oral cavity,environment,and chronic ulcer,confirming the feasibility of this sequencing technology.However,this technology has not been used to analyze the composition and functions of microorganisms in infected bone specimens.Therefore,this study further explored the feasibility of performing metagenome sequencing to determine the composition and functions of the microbiome in infected bone specimens and provided evidence for the clinical use of this method.At the same time,it identified bacteria to the species level accurately,and obtained more detailed bacterial information through the correlation analysis of species and clinical indicators of patients to guide clinical empirical anti-infective treatment.performed annotation analysis on the functions of the microbiome.Simultaneously,the functions of microorganisms in the wound is analyzed.These not only answer the question of what microbes are in the wounds,but also what they can do.Most of the literatures related to microorganisms in DFO wounds in the past have used microorganisms in superficial wounds of DFI as control.There have been no studies compared the differences of infected bone specimens between patients with diabetes and those without diabetes.Therefore,this study selected posttraumatic foot osteomyelitis(PFO)without diabetes as a control to analyze the similarities and differences of microbial composition and functions with DFO.Chapter 1 Analysis of the Composition of Bacteria in Patients with Diabetic Foot Osteomyelitis Based on 16S rRNA Sequencing TechnologyObjectivesTo verify the feasibility of 16S rRNA high-throughput sequencing technology in the identification of bacteria in infected bone specimens.To provide a reference for clinical empirical antibiotic selection and rapid identification of bacteria.MethodsFrom September 2018 to April 2019,17 consecutive patients with confirmed DFO(Dd group:Ddl,Dd2,Dd3...Dd17)and 11 patients with confirmed PFO(ND group:ND1,ND2,ND3...ND11)were prospectively included.Infected bone specimens were collected and divided into two parts under sterile conditions.The culture method and 16S rRNA high-throughput sequencing technology were used to analyze the composition of the bacteria in wounds.Results1.A total of 1034689 counts and 7555 OTUs were obtained by sequencingUsing the Illumina sequencing platform and PE250 sequencing protocol to perform 16S rRNA sequencing of 28 infected bone specimens,1034689 counts and 7555 OTUs were obtained.The sparse curves of the diversity index tended to be level,indicating that the sequencing depth was sufficient and that the sequencing data were reasonable and reliable.More sequencing will not find new species.2.The diversity of bacteria Dd was higher than that of ND,and the bacterial difference between groups was significantBacterial diversity index in the group:The Shannon index(t=1.996;P=0.058)and Simpson index(t=2.913;P=0.009)of group Dd were higher than those of group ND.Bacterial richness index in the group:The ACE index(t=-3.329;P=0.003),Chao1 index(t=-3.560;P=0.002),and observed OTUs(t=-3.423;P=0.002)of group Dd were lower than those of group ND.The PCoA graph based on the weighted UniFrac distance showed significant differences in bacteria between groups(F=6.248;P=0.001).3.16S rRNA sequencing identified more bacteria than culture method,mainly anaerobic and gram-negative(G-)bacteriaBacteria in infected bone specimens of group Dd identified by the culture method belonged to Proteobacteria and Firmicutes,which are G-and gram-positive(G+)bacteria,respectively.16S rRNA sequencing of infected bone specimens from group Dd identified 21 phylum and 5 dominant phylum.Ten genera were cultured from the infected bone specimens in group Dd:Enterococcus,Streptococcus,Staphylococcus,Escherichia,Pseudomonas,Proteus,Klebsiella,Enterobacter,Serratia and Citrobacter.The most frequently occurring genera were Streptococcus(4/23;17.4%)and Enterococcus(4/23;17.4%).A total of 242 genera and 18 dominant genera were identified by 16S rRNA sequencing.It contained all the bacteria from the culture method,Prevotella was the most frequent genus in group Dd.Among the genera obtained by the culture method,G+bacteria accounted for 47.8%(11/23),G-bacteria accounted for 52.2%(12/23),and there were no anaerobes.Among the dominant genera identified by 16S rRNA sequencing,G-bacteria accounted for 61.1%(11/18),G+bacteria accounted for 38.9%(7/18),aerobes accounted for 22.2%(4/18),facultative anaerobes accounted for 27.8%(5/18),and obligate anaerobes accounted for 50.0%(9/18).4.The most abundant genera in groups Dd and ND were Prevotella and Halomonas,respectivelyFirmicutes was the most abundant phylum in group Dd,followed by bacteroidetes,and Proteobacteria was the most abundant phylum in group ND.At the family level,Prevotellaceae had the highest abundance in group Dd,and the most abundant family in group ND was Halomonadaceae.At the genus level,the most abundant genus in group Dd was Prevotella.The most abundant genus in group ND was Halomonas.The relative abundance of Prevotella in group Dd was significantly higher than that in group ND(t=-3.817;P=0.002).The relative abundance of Halomonas in group ND was significantly higher than that in group Dd(t=-5.074;P<0.001).5.Clostridiales and Prevotella were the markerd bacteria of group DdLEfSe difference analysis showed that Firmicutes(LDA SCORE=5.25;P=0.001),Clostridia belonging to Firmicutes(LDA SCORE=5.03;P=0.029),and Clostridiales belonging to Clostridia(LDA SCORE=5.03;P=0.029)were the top three most representative core bacteria in group Dd,followed by Prevotellaceae(LDA SCORE=5.02;P=0.022)and Prevotella belonging to Prevotellaceae(LDA SCORE=5.02;P=0.022).Oceanospirillales(LDA SCORE=5.46;P<0.001),Halomonadaceae belonging to Oceanospirillales(LDA SCORE=5.46;P<0.001),Halomonas belonging to Halomonadaceae(LDA SCORE=5.46;P<0.001)and Proteobacteria(LDA SCORE=5.46;P=0.009)containing Oceanospirillales were the most representative core bacteria in group ND.Conclusions1.16S rRNA high-throughput sequencing technology can be used as an effective method for the identification of bacteria in infected bone specimens.2.16S rRNA high-throughput sequencing technology has great advantages in showing the diversity of bacteria,and many hard-to-culture bacteria can be identified with this method.G-bacteria and anaerobes are dominant in DFO.3.The most abundant,most widely distributed and marked bacteria are G-anaerobes in DFO and G-aerobes in PFO.It is necessary to formulate anti-infective treatment programs based on the bacterial characteristics of the two types of osteomyelitis.Chapter 2 Analysis of the Composition and Functions of the Microbiome in Patients with Diabetic Foot Osteomyelitis Based on Metagenome Sequencing TechnologyObjectivesTo establish a method for the analysis of microorganisms in infected bone specimens by metagenome sequencing,providing reference for the comprehensive and fast identification of microorganisms in wounds.The composition of microorganisms of DFO was analyzed,the clinical indicators that may be related to DFO were explored to guide the clinical empirical anti-infection treatment.The functions of microorganisms were elaborated to fully understand microbes in wounds.MethodsFrom September 2018 to April 2019,5 patients diagnosed with DFO(Dd group:Dd7,Dd8,Dd10,Ddll,and Ddl5)and 5 patients diagnosed with PFO(ND group:ND2,ND4,ND5,ND7,and ND10)were enrolled in this study prospectively and continuously.Infected bone specimens were collected and divided into two parts under sterile conditions.The microbial composition of the two groups was analyzed with metagenome sequencing technology.Spearman correlation was used to analyze the correlation between species and clinical indicators of patients with DFO.Functional information of the microbiome was obtained by blasting against the KEGG database,CAZy database,eggNOG database,ARDB and VFDB.Results1.A total of 129 G data were obtained after metagenome sequencing,and a total of 127 G data were obtained after filteringA total of 1.29349×1011 bp bases and 861,351,778 reads were obtained by metagenome sequencing based on Illumina NovaSeq for 10 bone specimens,totaling 129 G data,and the average for each sample was 12.9 G.After filtering,a total of 1.2726×1011 bp bases and 846819798 reads were obtained,totaling 127 G data,and the average for each sample was 12.7 G.After assembly,95609 contigs were obtained,with an average length of 3151.68 bp,and the N50 was 7698 bp.2.The diversity and abundance of species in group Dd were higher than those in group NDThe venn diagram of species showed 19.51%overlap between group Dd and group ND,and 60.49%of species were unique in group Dd.The heatmap showed that the abundance of species in group ND was lower than that in group Dd.3.By metagenome sequencing,22 dominant species were obtained,6 of which belonged to the PrevotellaKlebsiella pneumoniae was the most abundant species in group Dd and ND,followed by Veillonella parvula.The third most abundant species in group Dd was Prevotella intermedia,followed by Prevotella denticola,Thielavia terrestris and Yersinia enterocolitica.In group ND,the third most abundant species was Yersinia enterocolitica,followed by Thielavia terrestris,Kluyveromyces marxianus and Prevotella denticola.Among the 22 dominant species,6 species belonged to Prevotella,and the total relative abundance of Prevotella in group Dd reached 13.6%.It had the highest proportion of all species in group Dd,which is consistent with the results of 16S rRNA sequencing,verifying the accuracy of metagenome sequencing.Among the dominant species in group Dd ny metagenome sequencing,G-bacteria accounted for 85.71%,G+bacteria accounted for 14.29%,anaerobes accounted for 60.00%,facultative anaerobes accounted for 26.67%,and aerobes accounted for only 13.33%.4.Klebsiella pneumoniae was negatively related to the species belonging to PrevotellaThe Spearman-based species correlation analysis showed that Klebsiella pneumoniae had the greatest positive correlation with Streptococcus oralis(p=0.79)and the most negative correlation with Prevotella denticola(p=-0.85)and Veillonella parvula(P=-0.85).Species belonging to Prevotella were all negatively correlated with Klebsiella pneumoniae.5.Prevotellaceae and Prevotella intermedia were marked species in group DdBased on the LEfSe analysis,Prevotellaceae(LDA SCORE=5.49;P=0.028)and Prevotella intermedia(LDA SCORE=5.20;P=0.009)were the two most representative core microorganisms in group Dd,followed by Fusobacteria(LDA SCORE=4.39;P=0.028)and Fusobacteria(LDA SCORE=4.39;P=0.028).6.Species belonging to Prevotella were positively correlated with DFI duration(DFI_d)Prevotella denticola(p=0.36),Prevotella jejuni(p=0.46)and Prevotella fusca(p=0.46)all had the highest positive correlation with DFI_d.The bacteria most positively correlated with DFI_d were Prevotella jejuni(p=0.46)and Prevotella fusca(p=0.46).The clinical indicator most negatively correlated with Klebsiella pneumoniae was DFI_d(p=-0.46).The species with positive correlation with infection indexes such as WBC,N and CRP was Proteus vulgaris,while the species with negative correlation was Bacteroides fragilis.7.The abundance of observed pathway in group Dd was higher than that in group ND,and the glycolysis gluconeogenesis pathway had the highest abundanceThe venn diagram of genes showed that group Dd contained all the genes of group ND,and genes in group ND accounted for 12%of the total genes in group Dd.After blasting the filtered genes to the KEGG database,4,626 KOs and 75,630 pathways were obtained.In Level A,the most abundant was metabolism pathway in the two groups,followed by the genetic information processing pathway.In Level B,the most abundant pathway was transport and catabolism pathway in the two groups.At the Pathway level,the transport and catabolism pathway with the highest abundance in the two groups,followed by the amino acid metabolism and homologous recombination Pathway.8.The most abundant functional gene in group Dd was replication and repairAfter the redundant genes were removed,the remaining genes were blasted to the eggNOG database.In group Dd,functional genes of replication and repair were the most abundant,followed by functional genes of translation,amino acid metabolism and transport,cell membrane biological origin.In group ND,functional genes were of amino acid metabolism and transport was the most abundant,followed by functional genes of replication and repair,and translation.9.The most abundant carbohydrate-active enzymes(CAZymes)were glycosyltransferasesAccording to the annotation of the CAZy database,the abundance of CAZymes in group Dd was higher than that of group ND.The most abundant CAZymes were glycosyltransferases,followed by glycoside hydrolases.10.The abundance of antibiotic resistance genes in group Dd was significantly higher than that in group NDA total of 11,930 antibiotic resistance genes were obtained after blasting the sequencing data to the ARDB.The number of resistance genes in group Dd was much higher than that in group ND(P=0.030).The most abundant antibiotic resistance genes were related to streptomycin,followed by lincosamides,macrolides,and tetracyclines.11.The most abundant virulence factor in group Dd was Hsp60,followed by ClpC and ClpEA total of 120,266 virulence genes and 121 virulence factors were obtained by lasting the metagenome sequencing data with the VFDB.The most abundant virulence factor was Hsp60,followed by ClpC,ClpE.Conclusions1.Metagenome sequencing identified microorganisms to the species level accurately,and a total of 22 dominant species were obtained.G-bacteria and anaerobes were dominant in DFO,which was consistent with the results of 16S rRNA sequencing.It confirms the feasibility of metagenome sequencing technology in the analysis of the microbiome in infected bone specimens.2.When the duration of DFI is long,doctors need to be alert to the emergence of Prevotella.When the infection index value increases,Proteus vulgaris may appear in wounds.and Bacteroides fragilis with higher incidence when the value of infection index is lower.Therefore,the empirical antibiotic anti-infection therapy can be selected according to the clinical indicators of patients with DFO.3.The microorganisms in of DFO contain all the functions of those of PFO wounds,and the abundance of DFO is higher.The microorganisms in DFO have a strong ability to repair their own damage and can adapt to changes of the external environment easily.Carbohydrates may be one of its main energy sources.Moreover,there are abundant antibiotic resistance genes and virulence factors in microorganisms of DFO.This defines a range of possible antibiotic resistance and provides ideas for subsequent fundamental research and the development of new antimicrobial agents. |
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