Page 1 of 32 Diabetes
Analysis of the Composition and Functions of the Microbiome in Diabetic Foot
Osteomyelitis based on 16S rRNA and Metagenome Sequencing Technology
Zou Mengchen1*; Cai Yulan2*; Hu Ping3*; Cao Yin1; Luo Xiangrong1; Fan Xinzhao1; Zhang
Bao4; Wu Xianbo4; Jiang Nan5; Lin Qingrong5; Zhou Hao6; Xue Yaoming1; Gao Fang1#
1Department of Endocrinology and Metabolism, Nanfang Hospital, Southern Medical University,
Guangzhou, China
2Department of Endocrinology, Affiliated Hospital of Zunyi Medical College, Zunyi, China
3Department of Geriatric Medicine, Xiaogan Central Hospital, Xiaogan, China
4School of Public Health and Tropic Medicine, Southern Medical University, Guangzhou, China
5Department of Orthopaedics & Traumatology, Nanfang Hospital, Southern Medical University,
Guangzhou, China
6Department of Hospital Infection Management of Nanfang Hospital, Southern Medical
University, Guangzhou, China
*Zou mengchen, Cai yulan and Hu ping contributed equally to this work.
Running title: Microbiome of Diabetic Foot Osteomyelitis
Word count: 3915
Figures/Tables Count: 4Figures / 3 Tables
References: 26
Diabetes Publish Ahead of Print, published online August 14, 2020 Diabetes Page 2 of 32
Keywords: diabetic foot osteomyelitis; microbiome; 16S rRNA sequencing; metagenome sequencing
#Corresponding author: Gao Fang, E-mail: [email protected], Tel: 13006871226 Page 3 of 32 Diabetes
ABSTRACT
Metagenome sequencing has not been used in infected bone specimens. This study aimed to
analyze the microbiome and its functions. This prospective observational study explored the
microbiome and its functions of DFO (group DM) and posttraumatic foot osteomyelitis (PFO)
(group NDM) based on 16S rRNA sequencing and metagenome sequencing technologies.
Spearman analysis was used to explore the correlation between dominant species and clinical
indicators of patients with DFO. High-throughput sequencing showed that all the specimens
were polymicrobial. The microbial diversity was significantly higher in DM than in NDM.
Firmicutes, Prevotellaceae, and Prevotella were the most abundant microbes in DM. The most
abundant microbes in NDM were Proteobacteria, Halomonadaceae, and Halomonas. Prevotella
denticola, Prevotella jejuni and Prevotella fusca had positive correlation with the duration of
diabetic foot infection (DFI_d). Proteus vulgaris was positively correlated with the infection
index, while Bacteroides fragilis was negatively correlated. The microbial functional genes were
more abundant in DM than in NDM. Metagenome sequencing is feasible for the analysis of the
microbiome in infected bone specimens. G- bacteria and anaerobes are dominant in DFO.
Clinical trials registration: NCT04240964, ClinicalTrials.gov Diabetes Page 4 of 32
Diabetes-related lower-extremity complications are a large and growing contributor to the disability burden worldwide (1). Diabetic foot osteomyelitis (DFO) is the most severe stage of
DF. More than 20% of patients with severe diabetic foot infection (DFI) and 50%-60% of patients with moderate DFI develop DFO. The minor amputation rate of patients with DFO reaches 59.4% (2). Five year mortality for patients undergoing minor amputations is 46.2%.
Long-term survival was worse in patients who underwent a major amputation with a 5-year mortality of 65.6%. Which exceeds the reported 5-year overall mortality rate of cancer by 31.0%.
DFO is common, complicated and costly. In 2017, the direct cost of diabetes care was $237 billion, and more than one-third of the direct cost was attributed to the lower extremities (3). In recent years, the importance of antibiotics in DFO treatment has gradually been recognized, but the application of antibiotics is limited due to the limitations and one-sidedness of microbial identification technology (4). Fast and comprehensive analysis of the microbiome in wounds is a prerequisite for reducing the amputation rate and mortality. Although culture methods have a long history and have been widely used, shortcomings remain (e.g. time consumption and low sensitivity) (5). These deficiencies have hindered a correct understanding of the microbiome and made it difficult to meet the needs of clinical application and the development of modern biological research. Consequently, technological updates are urgently needed in the field of microbial research. High-throughput sequencing technology does not rely on culture and can be used to examine the microbiome and its functions in wounds quickly and comprehensively, providing a new direction for the development of microbial research. The most widely used high-throughput sequencing technology for identifying bacteria is 16S rRNA sequencing. Page 5 of 32 Diabetes
Among its advantages, 16S rRNA sequencing does not require culture and is simple, fast,
low-cost and widely applied. However, 16S rRNA sequencing provides only partial bacterial
genome information. Even after full optimization, it is impossible to obtain detailed information
at the species level, and it is difficult to analyze the functions of microorganisms in detail.
Compared with 16S rRNA sequencing, metagenome sequencing is a revolutionary method that
can examine all DNA in specimens without the use of culture or PCR amplification.
Metagenome sequencing can provide sufficient and accurate species information, thus
compensating for the shortcomings of the above two methods (6). However, there have been no
studies detecting microorganisms in infected bone specimens based on metagenome sequencing.
We therefore conducted a prospective study with the goal of better observing the microbiome
and its functions in wounds of DFO based on cultivation, 16S rRNA sequencing and
metagenome sequencing.
Research Design and Methods
Population
No study has analyzed the differences and similarities between the microorganisms in wounds of
DFO and posttraumatic foot osteomyelitis (PFO). Over a one-year period, we prospectively
enrolled 28 consecutive patients aged over 18 years who presented with osteomyelitis at Nanfang
Hospital from September 2017 to September 2018. The patients were divided into two groups,
namely, the group DM (DFO, 17 patients) and the group NDM (PFO, 11 patients), according to Diabetes Page 6 of 32
whether or not they had diabetes mellitus. Osteomyelitis was suspected based on the patient's clinical manifestations, laboratory tests, and imaging examination: 1) clinical manifestations: bone exposure (especially if the area was >2 cm2) or probe-to-bone test was positive, erythema and hardening of the toes (sausage-like toes) occurred; 2) laboratory tests: erythrocyte sedimentation rate (ESR) >70 mm/h; C-reactive protein (CRP), procalcitonin (PCT) ,white blood cell (WBC) levels increased and microorganisms could be cultivated from specimens; 3)
Radiographs examination: Radiographs showed loss of cortical bone, accompanied by bone erosion or demineralization, focal trabecular morphology loss or loss of bone marrow radio permeability; 4) MRI examination: MRI images showed T1-weighted low-focus signals,
T2-weighted high-focus signals, and high bone marrow short-term inversion recovery (STIR) sequence signals (4). Patients were included if osteomyelitis was diagnosed, microorganisms could be cultivated from specimens, and the infected bone was exposed. Furthermore, the patients exhibited no obvious skin lesions on the wound surface or surroundings, and they were in good health and can withstand debridement. The patients and/or his/her family were informed and agreed to participate in the study. The patients were ≥18 years old. Patients were excluded if they had an immune system that could not tolerate debridement or had taken immunosuppressants within 3 months before admission. The study was designed and implemented in accordance with the Helsinki declaration (2013), approved by the ethics committee of Nanfang Hospital (NFEC-2017-013), and registered on the Clinical Trials website
(NCT04240964). Informed consent was obtained from all patients. Page 7 of 32 Diabetes
Specimen collection
After the wounds were rinsed with sterile saline and hydrogen peroxide solution, 17 bone
specimens were collected in the clinic setting from patients with DFO who required debridement
to manage their osteomyelitis after removal of necrotic tissue from the surface to avoid soft
tissue or sinus tract contamination, which was not predictive of the presence of pathogen with
sufficient accuracy (9). And after the wounds were rinsed with sterile saline and iodophor
solution, 11 bone specimens were collected in an operating room from patients with PFO
routinely. We obtained bone specimens and divided them into two parts under sterile conditions.
One part was used for routine microbial culture, and the other was used for high-throughput
sequencing.
16S rRNA sequencing
Genomic DNA was extracted using a DNA extraction kit (YiRui, ShenZhen, China) according to
the manufacturer's instructions. The extracted DNA was quantified and quality controlled by 1%
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 forward and reverse fusion
primers (341F: 5’-CCTAYGGGRBGCASCAG-3’ and 806R:
5’-GGACTACNNGGGTATCTAAT-3’). PCR was performed in a total volume of 20 μl
containing 4 μl of 10x PCR buffer, 2 μl of 2.5 mM dNTPs, 0.8 μl of forward primer (5 M), 0.8
μl of reverse primer (5 M), 0.4 μl of FastPfu polymerase, template DNA (10 μl) and H2O (2 μl).
PCR amplification was conducted under the following conditions: initial denaturation at 95°C Diabetes Page 8 of 32
for 2 min, followed by 27 cycles of 95°C for 30 s, 55°C for 30 s, and 72°C for 45 s. A final extension was performed at 72°C for 10 min. Then, we purified and quantified the PCR products, followed by 16S rRNA sequencing on the Illumina sequencing platform with the PE250 sequencing protocol. The data were obtained in fastq format after sequencing. Then, we separated the sample data from the irregular sequencing data according to the barcode sequences.
To obtain high-quality clean reads, raw reads were filtered according to the following rules: reads containing 10% unknown nucleotides and reads containing less than 80% bases with quality (Q-value) >20 were removed. The filtered reads were then assembled into raw tags according to overlap between paired-end reads with more than 10 bp overlap and less than 2% mismatch. Clean tags were obtained after filtering out short, low-quality and chimeric raw tags.
Then, we clustered raw tags into operational taxonomic units (OTUs). Taxonomy and abundance were assigned to OTUs by blasting against the RDP database.
Metagenome sequencing
We also selected a subset of specimens after MicroPITA analysis for metagenome sequencing. In brief, DNA fragments of the appropriate length (approximately 350 bp) were obtained by sonication. Then, the fragments were A-tailed and ligated to adapters. NovaSeq sequencing systems (Illumina, America) were used for sequencing and library validation. The raw data obtained by sequencing had a certain proportion of low-quality data, which were removed to obtain clean data. The remaining reads were mapped to the human genome, and the matching reads were removed as contaminants. The De Bruijn graph assembly method was used to splice Page 9 of 32 Diabetes
clean reads by single-sample assembly and mixed assembly, and contigs were obtained. Contigs
less than 1000 bp in length were filtered out. This study was based on a BLAST of the
non-redundant contigs to the NCBI database to obtain taxonomic annotation information for the
microbiome.
The next step was gene prediction. We first predicted the open reading frames (ORFs) of
the assembled sequences. Then, we filtered out redundant genes to obtain non-redundant gene
sets. Genes with similarity greater than 95% were clustered together, and the longest gene in the
same class after clustering was the representative gene. The gene sequences were compared with
5 functional databases (KEGG/EggNOG/CAZy/ARDB/VFDB) to obtain functional information.
Statistical analysis
Patient demographics, laboratory and clinical data were examined using chi-square and t
tests. Spearman correlation analysis was used to analyze the correlations between the dominant
species and the clinical indicators of DFO patients. For all comparisons, the level of significance
was set at 0.05.
Data and Resource Availability
The data generated and analyzed during the current study is available from the corresponding
author upon reasonable request.
Results
Patient demographics Diabetes Page 10 of 32
The main characteristics of the included patients are summarized in Table 1. Among these
patients, 17 were diagnosed with DFO (group DM), and 11 patients were diagnosed with PFO
(group NDM). There were 9 males and 8 females in DM, and the average age was 60.59±9.39
years. The 7 males and 4 females in NDM had an average age of 46.90±13.62 years. There was
no significant difference in gender composition (P=0.580), the locations of the wounds
(P=0.157), and antibiotic use (P=0.157) between the two groups. In terms of infection indicators,
the WBC (P=0.002) and neutrophil (N; P<0.001), PCT (P=0.003), CRP (P<0.001) and ESR
(P=<0.001) levels were significantly higher in DM than in NDM (Table 1).
Culture and 16S rRNA sequencing results of infected bone specimens of DFO
Microorganisms obtained from cultured infected bone specimens of DFO belonged to
Proteobacteria and Firmicutes, corresponding to Gram-negative (G-) and Gram-positive (G+)
bacteria, respectively. Twenty-three isolates from ten genera were cultured from the infected
bone specimens of DFO. The most frequently occurring genera were Streptococcus (4/23; 17.4%)
and Enterococcus (4/23; 17.4%). 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. A total of 41.2% of the cultured infected bone specimens showed polymicrobial
infections (Table 2). 16S rRNA sequencing of infected bone specimens yielded 5 dominant phyla,
including Proteobacteria and Firmicutes. A total of 242 genera were obtained, 18 of which were dominant; 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), obligate Page 11 of 32 Diabetes
anaerobes accounted for 50.0% (8/18), and all samples showed polymicrobial infections (Table
3).
Composition and diversity of the microbiome in DFO and PFO determined by 16S rRNA
sequencing
16S rRNA sequencing generated 1034689 counts, which were clustered at 97% similarity and
indicated 7555 unique OTUs. The Shannon and Simpson index values were significantly higher
in DM than in NDM (P<0.001) (Figure 1A and B). We found two obvious groups of microbes
using Weighted UniFrac distance-based principal coordinate analysis (PCoA), which showed
that the microbiome of DM was different from that of NDM ([PERMANOVA] F-value: 10.091;
R-squared: 0.32457; p-value<0.001) (Figure 1C). A clustered heatmap was utilized according to
the relative abundance profiles of the top 200 genera. The microbiota was more evenly
distributed in the NDM group than in the DM group (Figure 1D). In total, 18 genera that had a
relative abundance of greater than 1% in samples from at least two subjects were authenticated.
Prevotella was the most abundant genus in DM, followed by Streptococcus, Anaerococcus,
Staphylococcus and Enterococcus. Halomonas was the most abundant genus in NDM, followed
by Streptococcus, Bacteroides, Corynebacterium, Providencia and Bradyrhizobium (Figure 1E).
The most abundant microorganisms were G- anaerobes in DM and G- aerobes in NDM. To
further characterize the impact of DM and NDM on the circulating microbiome, a discriminative
features cladogram and histogram based on an effect size cutoff of 3.6 for the logarithmic LDA
score were plotted. Firmicutes (LDA score=5.25; P=0.001), Clostridia (LDA score=5.03; Diabetes Page 12 of 32
P=0.029), and Clostridiales (LDA score=5.03; P=0.029) were the top three identified
microorganisms in DM, followed by Prevotellaceae (LDA score=5.02; P=0.022) and Prevotella
(LDA score=5.02; P=0.022). Oceanospirillales (LDA score=5.46; P<0.001), Halomonadaceae
(LDA score=5.46; P<0.001), and Halomonas (LDA score=5.46; P<0.001) were the most widely identified microorganisms in ND (Figure 1F and G).
Composition of the microbiome in wounds of DFO and PFO determined by metagenome sequencing
After filtering, a total of 1.2726×1011 bp and 846819798 reads were obtained, totaling 127G of data (12.7G per sample). After assembly, 95609 contigs were obtained, with an average length of
3151.68 bp and N50 of 7698 bp. In addition to the overview of the species in DM and NDM, a heatmap containing the top 200 relative abundances of species was graphed. Compared with the species in NDM, the species in DM showed a significantly high abundance (Figure 2A). At the phylum level, the relative abundance of Firmicutes was significantly higher in DM than in NDM
(t=3.671; P=0.001). The relative abundance of Proteobacteria was significantly higher in NDM
than in DM (t=-4.132; P<0.001) (Figure 2B). Prevotella was the most abundant genus in DM,
and its relative abundance was significantly higher than that in NDM (t=-3.817; P=0.002).
Halomonas was the most abundant genus in NDM, and its relative abundance was significantly
higher than that in DM (t=-5.074; P<0.001) (Figure 2C). Among the 22 dominant species, 6
belonged to Prevotella, the total relative abundance of which reached 13.6% in DM. This genus
had the highest proportion among all species in DM. The most abundant species in DM and Page 13 of 32 Diabetes
NDM was Klebsiella pneumoniae, followed by Veillonella parvula. The third most abundant
species in DM was Prevotella intermedia, followed by Prevotella denticola, Thielavia terrestris,
and Yersinia enterocolitica. The third most abundant species in NDM was Yersinia
enterocolitica, followed by T. terrestris, Kluyveromyces marxianus, and P. denticola (Figure 2D).
Based on linear discriminant analysis (LDA) effect size (LEfSe), Prevotellaceae (LDA
score=5.49; P=0.028) and P. intermedia (LDA score=5.20; P=0.009) were the two most widely
identified microorganisms in DM. No notable species were found in NDM (Figure 2E and F).
Correlations between species in DFO wounds and clinical parameters of DFO patients
P. intermedia (ρ=0.31), P. denticola (ρ=0.36), Prevotella jejuni (ρ=0.46) and Prevotella fusca
(ρ=0.46) were all positively correlated with the duration of diabetic foot infection (DFI_d). K.
pneumoniae was positively correlated with Wagner classification (ρ=0.87) and negatively
correlated with DFI_d (ρ=-0.46). Bacteroides fragilis was negatively correlated with WBC
(ρ=-0.70), N (ρ=-0.70), and CRP (ρ=-0.70). Proteus vulgaris was positively correlated with
WBC (ρ=0.94), N (ρ=0.92), and CRP (ρ=0.68) (Figure 3).
Functional differences between DM and NDM
The microbiome of DM had more functional genes than that of NDM (Figure 4A). The most
abundant virulence factor (VF) in DM was Hsp60, followed by ClpC and ClpE (Figure 4B).
Antibiotic resistance (AR) in pathogens through the acquisition of resistance genes or mutations
makes infections difficult to treat (8). The number of AR genes was much higher in DM than in Diabetes Page 14 of 32
NDM (P=0.030). The most abundant AR gene was the streptomycin resistance genes, followed
by the lincosamide, macrolide and tetracycline resistance genes (Figure 4C). The Kyoto
Encyclopedia of Genes and Genomes (KEGG) database is a collection of large-scale
molecule-level data sets (9). After blasting the filtered genes against the KEGG database, 4,626
KOs and 75,630 pathways were obtained. The most abundant pathway was signal transduction
and glycolysis gluconeogenesis in the two groups (Figure 4D). Evolutionary genealogy of genes:
Nonsupervised Orthologous Groups (eggNOG) is a public database of orthologous relationships
(8). In the DM group, replication and repair-related functional genes were the most abundant,
followed by translation (Figure 4E). Genes involved in complex carbohydrate metabolism are
listed in the Carbohydrate-Active enZYmes (CAZy) database (11). The abundance of CAZymes
was higher in DM than in NDM (Figure 4F).
Discussion
For over 150 years, clinicians have defined causative organisms based on the results of
culture-based experiments (12). Almost every European and North American study reports
Staphylococcus aureus as the most common microorganism cultured from DFO, followed by
Staphylococcus epidermidis (12-15). These findings are not surprising; culture-based methods select only species that flourish under the typical nutritional conditions in microbiological laboratories, not the most abundant species. Therefore, Staphylococcus grows more easily than
much-needed bacteria such as anaerobes, especially in media that are commonly used in clinical
microbiological laboratories within short periods of time (16). Aerobic culture was used in this Page 15 of 32 Diabetes
study, and no anaerobes were cultured. In fact, due to the difficulty in maintaining the anaerobic
environment, many clinical microbiology laboratories have not carried out anaerobic culture.
The culture method is not sensitive enough to identify species and lacks the judgment of
microbial abundance. It cannot reflect the composition of microorganisms in the wound in a
timely and comprehensive manner, which will delay the treatment. Over the past decade, the
accuracy of culture results has been doubted in the field of molecular microbiology (18), with a
larger number of bacterial communities being identified through the development of new
molecular technology (19).
The most widely used high-throughput sequencing technique for bacterial identification is 16S
rRNA sequencing. The sensitivity of high-throughput sequencing detection methods to
microorganisms is significantly higher than that of culture-based methods (20). Furthermore, the
number of microorganisms cultured per sample was small. In this study, 16S rRNA sequencing
showed that all specimens were polymicrobial, but only 41.18% of the specimens were
polymicrobial by culture method. The number of bacteria obtained by sequencing is much higher
than that obtained by culture method. Lance B. Price et al. (21) utilized anaerobic culture,
aerobic culture method and 16S rRNA sequencing showed that the number of bacteria obtained
by sequencing was 4 times than that of the culture method, which further verified the outstanding
advantages of 16S rRNA sequencing in demonstrating bacterial diversity. It has been also
reported that osteomyelitis is caused not by single pathogens but by a variety of bacteria,
including aerobes and anaerobes, many of which cannot be cultured by traditional culture
methods (22). Coincidentally, Brook I and colleagues showed a significant increase in the Diabetes Page 16 of 32
mortality of mice when anaerobes were mixed with aerobes (23). Anaerobes play an important role in the development of DFO and may cooperate with aerobes to promote progression of disease. In this study, the highest relative abundance in DFO was Prevotella based on the 16S rRNA sequencing, while that was Halomonas in PFO. Compared with DM group, patients in
NDM group seems to be much healthier. The possible reason is that patients in NDM group were younger and had fewer systemic diseases, the abundance of VFs in NDM is also lower. In general, G-bacteria and anaerobes are dominant in DFO, while G-bacteria and aerobes are dominant in PFO, providing reference for clinical empirical selection of antibiotics.
However, 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. This study first utilized metagenome sequencing to analyze the microbiome of infected bone specimens, which confirmed the feasibility of this technique in identification of microbiome in bone specimens. That lays a solid foundation for promoting metagenome sequencing from the research field to clinical application as soon as possible.
Metagenome sequencing identified a greater variety of microorganisms than culture-based Page 17 of 32 Diabetes
methods. The majority of microbes identified by metagenome sequencing were not identified by
culture, but almost all bacteria identified by culture were also authenticated by metagenome
sequencing. In this study, 832 species were obtained from the metagenome sequencing of 10
infected bone specimens, including 22 dominant species. Among them, 6 species belong to
Prevotella, which is the highest proportion of all species in DM. This result was consistent with
the results of 16S rRNA sequencing, thus verifying the accuracy of metagenome sequencing.
It was reported that elevated CRP values are highly predictive of amputations and may also be
associated with G- bacterial infection (25). The species most positively correlated with CRP in
this study was P. vulgaris, suggesting that the appearance of P. vulgaris may be related to poor
prognosis of patients, such as amputation. And the species with the greatest negative correlation
with PCT and CRP value is Bacteroides fragilis, suggesting that Bacteroides fragilis may exist
in wounds of DFO in the early stage of infection, so it is necessary to select sensitive antibiotics
for treatment at that time. Gardner et al. (14) analyzed the correlation between three dimensions
of microorganisms in DFO wounds and six clinical variables and found that the duration of
ulcers was positively correlated with bacterial diversity and the abundance of P. vulgaris and
negatively correlated with the abundance of Staphylococcus. Ulcer depth was positively
correlated with the abundance of anaerobes and negatively correlated with the abundance of
Staphylococcus. The results of this study show that bacteria belonging to Prevotella are
positively correlated with the duration of DFI, suggesting that the longer the duration of DFI, the
greater the chance of emergence of G- anaerobes, and treatment should be strengthened
accordingly. Diabetes Page 18 of 32
The rise in morbidity and mortality associated with drug-resistant microbial infections is a major global threat facing humans today. Improved comprehension of AR will provide a new perspective for redirecting antibiotic therapy and reducing bacterial resistance, which could minimize complications arising from treatment with broad-spectrum antibiotics and facilitate appropriate treatment. To avoid prolonging the patient's condition due to poor efficacy and increased levels of drug-resistant bacteria, streptomycin, lincosamide and macrolide are not used as first-line empirical anti-infective treatment for osteomyelitis. However, this study merely defined a range of possible microbial drug resistance in wounds. Due to the complexity and diversity of bacterial drug resistance mechanisms, the evaluation of drug resistance is far from simple. It is not possible to determine the resistance of certain bacteria only by identifying drug resistance genes, further proof must be obtained through drug sensitivity experiments and transcriptomics.
This is the first study of the DFO microbiome conducted by utilizing metagenome sequencing. The total DNA was analyzed with sufficient depth in the samples to provide a high level of detail, pinpoint bacteria at the species level and provide information regarding their functions. Moreover, the biases introduced by assembly, database construction, and reference database annotation in metagenome sequencing are more easily understood than those in 16S rRNA sequencing. However, the ability of metagenome sequencing to provide information regarding the actual functions of the microbiome is limited because this method cannot distinguish between expressed and non-expressed genes. To overcome this limitation, metagenome sequencing should be combined with other molecular approaches, such as Page 19 of 32 Diabetes
transcriptomics and proteomics, to identify biological characteristics that control the expression
of metabolic activity in microbial communities (26).
DFO is a major stress to health care systems and leads to significant morbidity and
mortality. The economic and social burden of DFO is as the global incidence of diabetes
increases. Therefore, new treatment and management methods are urgently needed. Here, we
examined specimens of DFO with metagenome sequencing to determine microbial taxonomy
and its functions. We combined this analysis with clinical data to demonstrate the role of the
wound microbiome in host response and wound healing. These insights may lead to improved
management and treatment based on the wound microbiome. Further studies with larger
populations are needed to fully understand the DFO microbiome.
Acknowledgments
Founding. This work was supportedby the National Natural Science Foundation of China for
Young Scholars (81600648).
Author Contributions. Zou mengchen and Cai yulan collected specimens, analyzed data and
wrote the manuscript. Hu ping collected specimens and researched data. Cao yin, Zhang bao, and
Wu xianbo analyzed data. Luo xiangrong, Jiang nan, and Lin qingrong collected specimens.
Zhou hao and Fan xinzhao contributed to the discussion. Xue yaoming and Gao fang reviewed
the manuscript.
Duality of Interest. The authors declare that they have no conflict of interest. No potential
conflicts of interest relevant to this article were reported. Diabetes Page 20 of 32
Conflict of Interest Statement. Gao Fang is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Page 21 of 32 Diabetes
References 1. Zhang Y , Lazzarini P A , Mcphail S M , et al. Global Disability Burdens of Diabetes-Related Lower-Extremity Complications in 1990 and 2016[J]. Diabetes Care, 2020:dc191614 2. Mutluoglu M, Sivrioglu AK, Eroglu M, Uzun G, Turhan V, Ay H, Lipsky BA. The implications of the presence of osteomyelitis on outcomes of infected diabetic foot wounds. Scand J Infect Dis 2013;45:497-503 3. Armstrong D G , Swerdlow M A , Armstrong A A , et al. Five year mortality and direct costs of care for people with diabetic foot complications are comparable to cancer[J]. Journal of Foot and Ankle Research, 2020, 13(1) 4. Lipsky BA, Aragon-Sanchez J, Diggle M, Embil J, Kono S, Lavery L, Senneville E, Urbancic-Rovan V, Van Asten S, Peters EJ. IWGDF guidance on the diagnosis and management of foot infections in persons with diabetes. Diabetes Metab Res Rev 2016;32 Suppl 1:45-74 5. Tuttle MS, Mostow E, Mukherjee P, Hu FZ, Melton-Kreft R, Ehrlich GD, Dowd SE, Ghannoum MA. Characterization of bacterial communities in venous insufficiency wounds by use of conventional culture and molecular diagnostic methods. J Clin Microbiol 2011;49:3812-3819 6. Knight R, Vrbanac A, Taylor BC, Aksenov A, Callewaert C, Debelius J, Gonzalez A, Kosciolek T, McCall LI, McDonald D, Melnik AV, Morton JT, Navas J, Quinn RA, Sanders JG, Swafford AD, Thompson LR, Tripathi A, Xu ZZ, Zaneveld JR, Zhu Q, Caporaso JG, Dorrestein PC. Best practices for analysing microbiomes. Nat Rev Microbiol 2018;16:410-422 7. Lesens O, Desbiez F, Vidal M, Robin F, Descamps S, Beytout J, Laurichesse H, Tauveron I. Culture of per-wound bone specimens: a simplified approach for the medical management of diabetic foot osteomyelitis. Clin Microbiol Infect 2011;17:285-291 8. Goossens H, Ferech M, Stichele RV, Elseviers M. Outpatient antibiotic use in Europe and association with resistance: a cross-national database study. Lancet 2005;365:579-587 9. Kanehisa M, Sato Y. KEGG Mapper for inferring cellular functions from protein sequences. Protein Sci 2020;29:28-35 10. Huerta-Cepas J, Szklarczyk D, Heller D, Hernandez-Plaza A, Forslund SK, Cook H, Mende DR, Letunic I, Rattei T, Jensen LJ, von Mering C, Bork P. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res 2019;47:D309-D314 11. Huang L, Zhang H, Wu P, Entwistle S, Li X, Yohe T, Yi H, Yang Z, Yin Y. dbCAN-seq: a database of carbohydrate-active enzyme (CAZyme) sequence and annotation. Nucleic Acids Res 2018;46:D516-D521 12. Lipsky BA, Richard JL, Lavigne JP. Diabetic foot ulcer microbiome: one small step for molecular microbiology . . . One giant leap for understanding diabetic foot ulcers? Diabetes Page 22 of 32
Diabetes 2013;62:679-681 13. Senneville E, Melliez H, Beltrand E, Legout L, Valette M, Cazaubiel M, Cordonnier M, Caillaux M, Yazdanpanah Y, Mouton Y. Culture of percutaneous bone biopsy specimens for diagnosis of diabetic foot osteomyelitis: concordance with ulcer swab cultures. Clin Infect Dis 2006;42:57-62 14. Wheat J. Diagnostic strategies in osteomyelitis. Am J Med 1985;78:218-224 15. Embil JM, Trepman E. Microbiological evaluation of diabetic foot osteomyelitis. Clin Infect Dis 2006;42:63-65 16. Gardner SE, Hillis SL, Heilmann K, Segre JA, Grice EA. The neuropathic diabetic foot ulcer microbiome is associated with clinical factors. Diabetes 2013;62:923-930 17. Ziment I, Miller LG, Finegold SM. Nonsporulating anaerobic bacteria in osteomyelitis. Antimicrob Agents Chemother (Bethesda) 1967;7:77-85 18. Kong HH, Segre JA. Skin microbiome: looking back to move forward. J Invest Dermatol 2012;132:933-939 19. Bowler PG, Duerden BI, Armstrong DG. Wound microbiology and associated approaches to wound management. Clin Microbiol Rev 2001;14:244-269 20. Han A, Zenilman JM, Melendez JH, Shirtliff ME, Agostinho A, James G, Stewart PS, Mongodin EF, Rao D, Rickard AH, Lazarus GS. The importance of a multifaceted approach to characterizing the microbial flora of chronic wounds. Wound Repair Regen 2011;19:532-541 21. Price L B , Liu C M , Melendez J H , et al. Community Analysis of Chronic Wound Bacteria Using 16S rRNA Gene-Based Pyrosequencing: Impact of Diabetes and Antibiotics on Chronic Wound Microbiota[J]. Plos One, 2009, 4(7):e6462- 22. Gerding DN. Foot infections in diabetic patients: the role of anaerobes. Clin Infect Dis 1995;20 Suppl 2:S283-S288 23. Brook I, Hunter V, Walker RI. Synergistic effect of bacteroides, Clostridium, Fusobacterium, anaerobic cocci, and aerobic bacteria on mortality and induction of subcutaneous abscesses in mice. J Infect Dis 1984;149:924-928 24. Van Asten SA, La Fontaine J, Peters EJ, Bhavan K, Kim PJ, Lavery LA. The microbiome of diabetic foot osteomyelitis. Eur J Clin Microbiol Infect Dis 2016;35:293-298 25. Ng PM, Jin Z, Tan SS, Ho B, Ding JL. C-reactive protein: a predominant LPS-binding acute phase protein responsive to Pseudomonas infection. J Endotoxin Res 2004;10:163-174 26. Sorek R, Cossart P. Prokaryotic transcriptomics: a new view on regulation, physiology and pathogenicity. Nat Rev Genet 2010;11:9-16 Page 23 of 32 Diabetes
Tables Table 1. Patient demographics and laboratory data for patients presenting with osteomyelitis
Parameter DM NDM P Male/female (n) 9 vs. 8 7 vs. 4 0.576 Age (years) 60.59±9.39 46.90±13.62 0.002
BMI（kg/m2） 23.01±2.86 21.21±2.87 0.099 WBC (×109/L) 10.58 (9.09-16.91) 7.33 (5.65-9.51) 0.002 N (×109/L) 8.68 (6.79-14.50) 4.48 (2.75-6.07) <0.001 PCT (mg/L) 0.213 (0.084-0.556) 0.049 (0.031-0.081) 0.003 CRP (mg/L) 85.37 (35.10-205.63) 4.15 (0.85-27.83) <0.001 ESR(mm/h) 85.71±25.86 34.73±31.66 <0.001 Hb (g/L) 100.65±24.46 120.80±20.95 0.017 ALB (g/L) 31.79±6.83 38.22±4.05 0.005 SCr (mg/dL) 98.77±36.71 61.50±10.81 0.005 Location Metatarsal head(%) 14(82.4) 1(0) Planta pedis(%) 2(11.8) 1(18.2) Dorsum pedis(%) 1(5.9) 0(0) Tibia(%) 0(0) 7(63.6) Others(%) 0(0) 2(18.2) 0.157 Antibiotics Cephalosporin(%) 11(64.7) 8(72.7) Vancomycin(%) 1(5.9) 1(9.1) Meropenem(%) 3(17.6) 0(0) Levofloxacin(%) 2(11.8) 2(18.2) 0.157 Wagner(3/4) 10 vs.7 - - duration of diabetes(years) 8.61±6.20 - - uration of DFI(days) 15.0(10.0-45.0) - - HbAlc,%(mmol/mol) 10.76±2.29,(94.08±1.51) - -
BMI: Body Mass Index; WBC: white blood cell; N: neutrophil; PCT: procalcitonin; CRP:
C-reactive protein; ESR: Erythrocyte Sedimentation Rate; Hb: hemoglobin; ALB: albumin; SCr:
serum creatinine. Data are the mean ± SD (coefficient of variation) or median (interquartile
range). Diabetes Page 24 of 32
Table 2. Cultured microorganisms from infected bone specimens of DFO
Number of Phylum Genus Number of isolates Species isolates
Firmicutes Enterococcus faecalis 3 Enterococcus 4 Enterococcus raffinosus 1 Staphylococcus 3 Staphylococcus aureus 3 Streptococcus 2 acidominimus Streptococcus 4 Streptococcus anginosus 1 Streptococcus agalactiae 1 Proteobacteria Pseudomonas 1 Pseudomonas aeruginosa 1 Proteus vulgaris 1 Proteus 3 Proteus mirabilis 2 Klebsiella 2 Klebsiella pneumoniae 2 Serratia 2 Serratia marcescens 2 Escherichia 2 Escherichia coli 2 Citrobacter 1 Citrobacter koseri 1 Enterobacter 1 Enterobacter cloacae 1 Page 25 of 32 Diabetes
Table 3. Microbes in infected bone specimens of DFO determined by 16S rRNA sequencing Gram Gram Dominant phylum Dominant genus Number of isolates stain stain G+ Actinobacteria G+ Streptococcus 14 Firmicutes Anaerococcus 14 G- Bacteroidetes Staphylococcus 13 Fusobacteria Enterococcus 15 Proteobacteria Finegoldia 15 Corynebacterium 10 Dialister 12 G- Prevotella 15 Halomonas 17 Citrobacter 11 Fusobacterium 8 Veillonella 2 Pseudomonas 7 Bacteroides 10 Klebsiella 14 Porphyromonas 6 Bradyrhizobium 16 Providencia 5 Diabetes Page 26 of 32
Figure legends
Figure 1. 16S rRNA sequencing of the DFO and PFO microbiome
(A, B) Alpha diversity, as illustrated by the Shannon and Simpson indexes, was reduced in NDM
(t=9.349, P<0.001).
(C) Principal coordinate analysis (PCoA) of the number of observed operational taxonomic units
(OTUs) demonstrated that individuals in DM were significantly different from those in NDM
([PERMANOVA] F-value: 10.091; R-squared: 0.32457; p-value<0.001).
(D, E) The two hundred most abundant genera are shown as a heatmap, samples of NDM (n=11) are on the left, and those of DM (n=17) are on the right. The uniformity of the genera was higher in NDM than in DM.
(E) Stack graph representing the relative abundance of genera in DM and NDM. Eighteen genera were identified with a relative abundance of greater than 1% in the samples. The analysis of the dominant genus in bone specimens from all patients reflects the total number of identified DNA sequences.
(F, G) LDA effect size (LEfSe) cladogram of the 16S rRNA sequence analysis of whole bone samples for DM and NDM. The cladogram shows the taxonomic levels represented by rings, with phyla at the innermost ring and species at the outermost ring. Each circle is a member within that level. Taxa at each level are shaded green (NDM) or red (DM) based on significance
(P<0.05; LDA score>3.6). LEfSe indicates the differential signatures based on DM and NDM.
Figure 2. Metagenome sequencing of DFO and PFO Page 27 of 32 Diabetes
(A) Clustered heatmap presenting the top 200 relatively abundant species in DM and NDM. In
the figure, red represents high abundance, and blue represents low abundance.
(B, C, D) Differences in phylogenetic relative abundance at the phylum, genus, and species
levels between the DM and NDM groups. Red and green indicate the DM and NDM groups,
respectively. The bar in the middle of the box represents the median, the upper and lower hinges
of the box represent the first and third quartiles, and the whiskers represent the highest and
lowest values within 1.5 times the interquartile range (IQR). Points beyond the whiskers are
those outside 1.5 IQR.
(E, F) LDA effect size (LEfSe) cladogram of the metagenome sequence analysis of whole bone
samples for DM and NDM. The cladogram shows the taxonomic levels represented by rings,
with phyla at the innermost ring and species at the outermost ring. Each circle is a member
within that level. Taxa at each level are shaded green (NDM) or red (DM) based on significance
(P<0.05; LDA score>4). LEfSe indicates the differential signatures based on DM and NDM.
Figure 3. Species in DFO associated with clinical indexes
Correlation between the dominant species and clinical indexes of DFO patients using Spearman
correlation analysis. Red indicates a positive correlation with each index, blue indicates a
negative correlation with each index. The darker the color, the greater the intensity. Correlation
coefficients are marked in the heatmap.
Figure 4. Comparative functional analysis of DFO and PFO
A. Venn diagram of functional genes in the two groups.
B. Comparison between the DM-enriched and NDM-enriched genes for different VFs shown by Diabetes Page 28 of 32
relative abundance (left) and absolute abundance (right). C. Heatmap presenting the abundance of antibiotic resistance (AR) genes in DM and NDM. In the figure, red represents high abundance, and green represents low abundance. D. Distribution of identified pathways (level 2) shown by number. E. Comparison between the DM-enriched and NDM-enriched genes for different eggNOG functional categories shown by number. F. Comparison between the DM-enriched and NDM-enriched genes for different active carbohydrate enzymes shown by number. PageA 29 of 32 B CDiabetes G
DM NDM
DM DM NDM NDM
DM DM NDM NDM
D E F
Prevotella Streptococcus Anaerococcus Staphylococcus Enterococcus Finegoldia Halomonas Citrobacter Dialister Fusobacterium Veillonella Pseudomonas 4 Bacteroides Klebsiella Parvimonas Bulleidia Porphyromonas Peptoniphilus Proteus Actinomyces Peptostreptococcus Morganella Bradyrhizobium Atopobium Pseudoramibacter_Eubacterium Sneathia Serratia Devosia Megasphaera Dermabacter Campylobacter 2 Sharpea Slackia [Prevotella] Gardnerella Gemella Granulicatella Shuttleworthia Nesterenkonia Acinetobacter Trabulsiella Corynebacterium Mogibacterium Arcanobacterium Haemophilus Rhodococcus Filifactor Helcococcus Catonella Rhodobacter Adlercreutzia 0 Clostridium Methylobacterium Sphingomonas Sphingobium Phycicoccus Novosphingobium Elizabethkingia Burkholderia Pseudoalteromonas Oribacterium Lactobacillus Methylotenera Brevundimonas Bacillus Caulobacter Ralstonia Acidovorax Aquabacterium Achromobacter Vagococcus −2 Agrobacterium Selenomonas Facklamia Afipia Leptothrix Oligella Phenylobacterium Blastomonas Propionibacterium Methyloversatilis Rubellimicrobium Yersinia Mycoplana Asticcacaulis Enhydrobacter Lactococcus Salmonella Salinibacterium Kaistobacter −4 Brachybacterium Brevibacillus Anaerovibrio Candidatus Nitrososphaera Providencia Varibaculum Alloscardovia Paludibacter Chryseobacterium Peptococcus Dietzia Dysgonomonas Xanthobacter Diaphorobacter Cupriavidus Amaricoccus Neisseria Sphingobacterium Herbaspirillum Haererehalobacter Spirosoma Flavisolibacter Delftia Pseudoclavibacter Micrococcus Dyadobacter Sphingopyxis Paenibacillus Coprococcus Paracoccus Janthinobacterium Rheinheimera Hyphomicrobium Mesorhizobium Roseateles Hymenobacter Flavobacterium Sediminibacterium Rhodoplanes Eikenella Brochothrix Phyllobacterium Variovorax Hydrogenophilus Cobetia Candidatus Solibacter Pedobacter Brevibacterium Lysinibacillus Oscillospira Hydrogenophaga Vogesella Aeromicrobium Desulfovibrio Kribbella Yaniella Chelativorans Haloplanus Methanobrevibacter Methanolinea Methanosaeta Mobiluncus Geodermatophilus Leucobacter Arthrobacter Mycobacterium Streptomyces Bifidobacterium Eggerthella Parabacteroides Tannerella Butyricimonas Odoribacter Cloacibacterium Olivibacter Chloronema Solibacillus Sporosarcina Exiguobacterium Aerococcus Alloiococcus Turicibacter Butyrivibrio Dorea Roseburia Anaerofilum Anaerotruncus Faecalibacterium Ruminococcus Acidaminococcus Megamonas Fusibacter Gallicola [Eubacterium] Nitrospira Methylosinus Shinella Anaerospora Skermanella Wolbachia Sutterella Comamonas Methylibium Rhodoferax Rubrivivax Thiobacillus Kingella Dechloromonas Bdellovibrio Desulfococcus NDM01 NDM02 NDM03 NDM04 NDM05 NDM06 NDM07 NDM08 NDM09 NDM10 NDM11 DM01 DM02 DM03 DM04 DM05 DM06 DM07 DM08 DM09 DM10 DM11 DM12 DM13 DM14 DM15 DM16 DM17
For Peer Review Only NDM DM Page 30 of 32 30 Page Genus F C DM NDM
Group
Proteobacteria
Fusobacteria Actinobacteria Diabetes
Phylum Ascomycota For Peer Review Only
E Bacteroidetes
DM NDM
Firmicutes
esnaenterocolitica Yersinia eloel parvula Veillonella
0.4 0.3 0.2 0.1 0.0 heai terrestris Thielavia
B
hrohlmcsthermophila Thermothelomyces
tetccu oralis Streptococcus
2 1 0 −1 −2 agalactiae Streptococcus nahaamnii Sneathia ymphae n
icho
ithidii r um vulgaris Proteus r ium_c r ans utans mentans r xydans um us r r us o o m e m acheale n yrodida f us mophila r um ans iiv aneensis r e us midis n r ia utylicum alis une acea is r er alens ius ans r v us b visiae r yi r r tum iatum ientale v n ulans ausnitzii u r icus r o i a medius otii e r iodonticum ri ius utylicum megale a_al r um asookii us v v asanguinis v r m r anchiophilum atus ae a oei r m icola r e yicus oltae b ri r ogenes r ibacter_asiaticus o r alis v v istatus w v ucleatum is utans ylolyticus ingens v r yssophila y us r yticus r assostreae istensenii ra opneumoniae tier us_lipocalidus ey nimense oconitis icum media b p m m uchneri aecalis aecium atus r vulum r h obacteroides_pseudotr is micaceticum aginalis y inae r a f f arciminis ype r ium_gallina inireducens vula ri odaii y yces_the imitia r agilis ucosa oodyi r v r m r z uccalis aginalis o elagibacter_ubique ium_o v a_halophila r anobacte r r r ium_rhinot h ium_str k ium_p r liniensis r o o v ium_cu ae r b r w y r P P m f a_elsdenii us_ma r r orsythia ium_damselae b abilis ix_la ix_rhusiopathiae uminicola r ium_b ium_comm ium_gilvum f yces_cere ium_n ium_h ium_pe r idium_sordellii ycoides us_dulcis m yces_marxia yces_lactis r r r r elshime r r ium]_sulci ium]_eligens ium]_rectale io_hungatei r r r ia_hominis vi yces_m yces_pacaensis us_to r ia_gonorrhoeae a ichia_coli ichia_ us_siber r u r via_terrestr ylobacter_ureolyticus idium_aceticum idium_perf idium_chau idium_bo idium_botulin idium_cellul idium_aceto idium_saccharo idium_ idioides_difficile yromonas_gingi yromonas_asaccharolytica ylococcus_haemolyticus ylococcus_condimenti ylococcus_h ylococcus_si ylococcus_epider ylococcus_lugdunensis ylococcus_aureus ylococcus_succin amaella_lignohabitans anella_psychrophila anella_violacea anella_ ia_w erom erom ivibr b r r r r r r r r r r ibacter_splanchnicus p h h mococcus_sibir mothelom otella_inte otella_denticola otella_jejuni otella_fusca otella_enoeca otella_melaninogenica r otella_dentalis otella_ obium_leguminosar v v r atia_marcescens obacter obacter obacter imonas_ inobacter_sala olob agenococcus_halophilus ynebacte yseobacter istensenella_massiliensis tonella_vinsonii nesiella_visce r vimonas_micra p p v v v v v v v v actor_alocis abacteroides_distasonis mentimonas_caenicola r r r r r r ew ew r r ew r r r r r nithobacte yptobacte aconibacter ysipelothr ysipelothr ylorella_equigenitalis e e e e e e e e ischella_per eeksella_virosa aeniclost a aenibacillus_cr aludibacter_propionicigenes a andor elobacter_propionicus edobacter_cr o o et r eillonella_pa eillonella_rodentium e aecalibaculum_rodentium aecalibacte ersinia_enterocolitica et reponema_pedis reponema_denticola a reponema_p annerella_ epidanaerobacter_acetat T Clost F Alkalitalea_saponilacus Ther Salinivirga_c Clost Candidatus_A Bacillus_oceanisediminis Candidatus_Methanomassiliicoccus_intestinalis P Methanococcus_ Pr Pr Thiela Y Candidatus_ Gardnerella_ Bacillus_cohnii Sh Capnocytophaga_sputigena Staph P Staph Halanaerobium_p [Haemophilus]_ducre Sulf Klebsiella_pneumoniae V Streptococcus_or Kluy Bacteroides_f Pr Sneathia_amnii Ther Kluy BeAn_58058_vir Pr Anaerococcus_mediterr Neisse Streptococcus_agalactiae Streptococcus_dysgalactiae Proteus_vulga Pr Pr Leishmania_major Enterococcus_ Streptococcus_anginosus Finegoldia_magna Dialister_pneumosintes Anaerococcus_pre Streptococcus_gallolyticus Bacteroides_thetaiotaomicron Bacteroides_cellulosilyticus Cedecea_nete Er Bordetella_holmesii T Aggregatibacter_aphrophilus Escher Actinom Capnocytophaga_stomatis Flav Sh Halorhodospi P Clost Streptococcus_ Fusobacte Ser Cam Ezakiella_massiliensis P Ma Leadbetterella_ T Buty Lactococcus_lactis Clost Staph T Lactobacillus_b P Staph Acetoanaerobium_sticklandii T Haemophilus_influenzae Clost Lactobacillus_a Rhodothe Bacteroides_helcogenes Staph Streptococcus_pasteu Saccharom Actinom Clost P F F Pr Filif Bacteroides_ Clost P Streptococcus_cr Candidatus_Libe Cr Opitutus_ter Ba Bacillus_m Oleiphilus_messinensis Chelatococcus_daeguensis Rhiz Fusobacte Candidatus_ Ch Acinetobacter_johnsonii Bacteroides_vulgatus Clost Staph Syntrophothe Streptococcus_acidomini Dr Clost Candidatus_Kinetoplastibacte Candida_du Clost Bacteroides_dorei Bacteroides_salanitronis Odo P Or Proteus_mir Mycoplasma_h P Streptococcus_gordonii Megasphae Streptococcus_equi Co [Eubacte Mageeibacillus_indolicus Streptococcus_inter Ba T Streptococcus_constellatus P Staph Streptococcus_halotoler Olsenella_uli Streptococcus_pneumoniae Alistipes_finegoldii Negativicoccus_massiliensis Murdochiella_ Sugiy V Atopobium_pa Pr Bacteroides_caeci Acinetobacter_bereziniae Enterococcus_ P Bordetella_bronchiseptica Capnocytophaga_och Streptococcus_suis Streptococcus_sali Aerococcus_ch Aerococcus_ur Mollivir Ch F [Eubacte Streptococcus_mitis Streptococcus_ Lactobacillus_f Lactobacillus_ginsenosidi Photobacter Lactobacillus_cur Histophilus_somni Slackia_heliot Mycoplasma_flocculare Fusobacte Anaerostipes_had Rose Er Bacillus_endop [Eubacte Leptot Streptococcus_par Ndongobacter_massiliensis Riemerella_anatipestif Proteiniphilum_saccharo Megamonas_ Flav Sh Lister T W Flav Staph Streptococcus_iniae
ND10 299 taxon oral sp. Prevotella rvtlajejuni Prevotella
NDM07 Species rvtlaintermedia Prevotella
NDM05 fusca Prevotella rvtlaenoeca Prevotella
NDM04 rvtladenticola Prevotella
NDM02 esei gonorrhoeae Neisseria
NDM15
lyeoye marxianus Kluyveromyces lyeoye lactis Kluyveromyces
DM11 lbilapneumoniae Klebsiella
DM10 adeel vaginalis Gardnerella
DM08
addtsPrir aleyrodidarum Portiera Candidatus en508virus 58058 BeAn
DM07 atrie fragilis Bacteroides 0.0 0.2 0.1
D A Page 31 of 32 Diabetes
For Peer Review Only Diabetes Page 32 of 32
VF0159_Hsp60 1 VF0072_ClpC A A VF0073_ClpE A LPBGEOLM_01201 VF0456_MsrAB VF0394_Flagella 0.6 VF0144_Capsule GGLILJFF_07357&teicoplanin_||_vancomycin VF0074_ClpP VF0244_Hyaluronic acid capsule KHHLKKBP_34172&streptogramin_a VF0444_Lap VF0392_O-antigen 0.8 VF0371_BadA/Vomp 0.4 KHHLKKBP_38913&streptogramin_a VF0361_Capsule VF0043_Capsule KHHLKKBP_34522&streptogramin_a 2 VF0044_LOS VF0136_Yersiniabactin KHHLKKBP_36969&lincosamide_||_macrolide_||_streptogramin_b VF0169_SodB VF0323_Capsule VF0454_KatA 0.6 0.2 KHHLKKBP_37578&streptogramin_a VF0148_Neuraminidase VF0529_PI-1 GGLILJFF_17634&streptogramin_a VF0287_RelA VF0225_Hemolysin KCOKMJAE_26489&lincosamide_||_macrolide_||_streptogramin_b VF0050_Urease DM VF0530_PI-2 KCOKMJAE_24083&lincosamide_||_macrolide_||_streptogramin_b VF0273_Flagella 0 VF0265_MARTX 1 VF0003_Capsule 0.4 KCOKMJAE_28528&vancomycin VF0367_LPS VF0274_Capsule KHHLKKBP_15658&lincosamide_||_macrolide_||_streptogramin_b VF0326_LOS NDM VF0082_Type IV pili VF0227_Chu -0.2 KHHLKKBP_12019&streptogramin_a VF0429_T6SS-1 VF0145_CBPs KHHLKKBP_20292&streptogramin_a VF0101_Vi antigen VF0528_PI-2a 0.2 KHHLKKBP_06389&vancomycin VF0350_BSH VF0033_LPS 0 VF0221_Type 1 fimbriae -0.4 KCOKMJAE_26446&lincosamide_||_macrolide_||_streptogramin_b VF0116_TTSS(SPI-1 encode) VF0256_Shu KHHLKKBP_23846&lincosamide_||_macrolide_||_streptogramin_b VF0220_P fimbriae VF0430_Flagella KCOKMJAE_13550&lincosamide_||_macrolide_||_streptogramin_b VF0436_Capsule I 0 VF0276_
VF0252_DNase Relative Aundance DOPJDDDM_23427&streptogramin_a VF0014_Intercellular adhesion proteins VF0056_LPS JIPJBNCA_08208&lincosamide_||_macrolide_||_streptogramin_b VF0104_Pef VF0408_T3SS1 DOPJDDDM_34829&aminoglycoside_||_chloramphenicol VF0334_HSI-I VF0414_RicA VF0075_Type IV pili DOPJDDDM_22490&aminoglycoside_||_chloramphenicol VF0143_Autolysin VF0224_F1C fimbriae JIPJBNCA_13252&lincosamide_||_macrolide_||_streptogramin_b VF0229_Aerobactin VF0005_CNA JIPJBNCA_00649&lincosamide_||_macrolide_||_streptogramin_b VF0168_KatAB VF0538_Ebp pili VF0275_Lmb DOPJDDDM_10118&lincosamide_||_macrolide_||_streptogramin_b VF0160_FeoAB VF0094_Pyoverdine JIPJBNCA_25781&lincosamide_||_macrolide_||_streptogramin_b VF0324_CiaB VF0156_Dot/Icm DOPJDDDM_33116&tetracycline VF0364_Pili VF0447_InlF VF0157_Flagella DOPJDDDM_22098&tetracycline VF0152_PspA VF0369_VirB/VirD4 type IV secretion system DOPJDDDM_30798&lincosamide_||_macrolide_||_streptogramin_b VF0348_Auto VF0359_Hyaluronidase DOPJDDDM_11058&lincosamide_||_macrolide_||_streptogramin_b VF0147_IgA1 protease VF0270_Hgp VF0451_MtrCDE DM07 DM08 DM10 DM11 DM15 NDM02 NDM05 NDM07 NDM10 NDM04 VF0527_PI-1 VF0349_FbpA VF0149_Pneumolysin VF0268_HitABC VF0354_EfaA VF0403_Type VII secretion system VF0242_M protein VF0243_FBPs VF0247_Streptokinase VF0250_SLO VF0254_GRAB VF0278_C5a peptidase DM NDM DM NDM
A A A
2000 2500 10000
1500 2000 Group 7500
1000 DM Group 1500 Group NDM 5000 DM DM 500 NDM 1000 NDM
0 2500
Number of gene in each Category 500
0 Number of gene in each Category Number of gene in each Category 0
Endocytosis
DNA replication Mismatch repair
Mitophagy - yeast Autophagy - yeast
Signal transduction
ancomycin resistance
Thiamine metabolism Riboflavin metabolism Fatty acid degradation Galactose metabolism N-Glycan biosynthesis Fatty acid biosynthesis Lipoic acid metabolism V Vitamin B6 metabolism beta-Lactam resistance
Sphingolipid metabolism
Environmental adaptation Nucleotide excision repair Cellulosome
Peptidoglycan biosynthesis Homologous recombination Glycolysis Gluconeogenesis
Starch and sucrose metabolism Glycosaminoglycan degradation Lipopolysaccharide biosynthesis Staphylococcus aureus infection Bacterial invasion of epithelial cells Glycoside Hydrolases (GHs) GlycosylTransferases (GTs) Cysteine and methionine metabolism Carbohydrate Esterases (CEs) Polysaccharide Lyases (PLs)
Glycine, serine and threonine metabolism Pentose and glucuronate interconversions
[J]Tranlsation Carbohydrate-Binding Modules (CBMs) [N]Cell motility Alanine, aspartate and glutamate metabolism
[K]Transcription [Z]Cytoskeleton
Cationic antimicrobial peptide (CAMP) resistance [I]Lipid metabolism processing and modification [Y]Nuclear structure
[T]Signal Transduction
[Q]Secondary Structure [H]Coenzyme metabolis [L]Replication and repair [V]Defense mechanisms
[W]Extracellular structures
[D]Cell cycle control and mitosis
[A]RNA
[C]Energy production and conversion [B]Chromatin Structure and dynamics [U]Intracellular trafficing and secretion [E]Amino Acid metabolis and transport
[F]Nucleotide metabolism and transport
[P]Inorganic ion transport and metabolism
[G]Carbohydrate metabolism and transport [M]Cell wall/membrane/envelop biogenesis For Peer Review Only