Microbiology of diabetic foot infections: from Louis Pasteur to ‘crime scene investigation’

Wound infection: definition, process and prognosis

Foot wounds are an increasingly common problem in people with diabetes and now constitute
the most frequent diabetes-related cause of hospitalization 2]. People with diabetes have about a 25% chance of developing a foot ulcer in their
lifetime 3], about half of which are clinically infected at presentation 2],4]. DFIs cause substantial morbidity and at least one in five results in a lower extremity
amputation 5]. Amputation is even more likely when DFI and foot ischemia coexist. 4],6] In fact, DFIs are now the predominant proximate trigger for lower extremity amputations
worldwide 7].

The pathophysiology of foot infections in persons with diabetes is quite complex,
but their prevalence and severity are largely a consequence of host-related disturbances
(immunopathy, neuropathy and arteriopathy) and secondarily, pathogen-related factors
(virulence, antibiotic-resistance and microbial load) 2],8]. Typically, an insensate, deformed foot develops an ulcer when some form of trauma
disrupts the protective skin envelope. The underlying subcutaneous tissues then quickly
become colonized with bacteria, which may lead to infection, often initially clinically
unapparent 7]. Infection is defined by overgrowth of microorganisms within a wound that promotes
deleterious inflammation or tissue destruction 9]. Infection usually begins as a local process, manifested by the classic signs and
symptoms of inflammation (redness, warmth, pain, tenderness, induration) 10]. If not controlled, infection typically spreads—mostly often contiguously—to deeper
tissues. A host systemic inflammatory response syndrome (for example, fever, chills,
hypotension, tachycardia, delirium, leukocytosis) may accompany this process 10]. In some patients, especially those with peripheral neuropathy or vasculopathy, these
symptoms and signs may be diminished 11],12], leading some to advocate defining infection by the presence of ‘secondary’ findings,
such as foul odor, friable or discolored granulation tissue and rim undermining 13].

Some wound specialists believe that the presence of a high concentration of microorganisms
(usually defined as 10⁵ colony forming units [CFU] per gram of host tissue 14]) in the absence of clinical evidence of infection represents ‘increased bioburden’
or ‘critical colonization.’ They assert this may indicate wound infection 15],16], or at least a degree of colonization that impairs wound healing 12], and may overwhelm host defenses without triggering a generalized immunological reaction
8]. There is, however, no agreed means to define critical colonization, no routine laboratory
availability for quantitative bacteriology and no convincing evidence of its association
with adverse clinical outcomes, for example, failure of healing or development of
overt infection.

One study did find that in neuropathic diabetic foot ulcers (DFUs) there was a significant
inverse relationship between exudate CFU count and rate of wound healing 17]. But, in a cross-sectional study of 64 patients with a non-ischemic DFU, no single
sign or symptom generally recognized as suggestive of infection, or any combination
of them, correlated well with the quantitative microbial load 16]. Unfortunately, this lack of correlation does not clarify whether clinical or microbiological
results are most useful in defining infection. It may be that the presence of specific
types, or combinations, of bacteria, or their acquisition of certain virulence factors,
leads to clinical infection 12]. A prospective study of 77 patients with a neuropathic DFU and no clinical signs
of infection found that none of the three dimensions of bioburden (that is, microbial
load, microbial diversity and presence of potential pathogens) correlated with DFU
outcomes. Some limitations of this study included the fact that specimens were obtained
from the ulcer by swab (using ‘Levine’s technique’) and bioburden analysis was done
by culture-based methods 18].

Diabetic foot infection: bacteriology

Because many different organisms, alone or in combination, can cause a DFI, selecting
the most appropriate antibiotic therapy requires defining the specific causative pathogens
8],10],12]. Clinicians should avoid antibiotic therapy that is unnecessary, overly broad-spectrum
or excessively prolonged, as it may cause drug-related adverse effects, incurs financial
cost and encourages antimicrobial resistance 10].

Aerobic, Gram-positive cocci are the predominant organisms responsible for acute DFI,
with Staphylococcus aureus the most commonly isolated pathogen 10],19],20]. In wounds that are chronic, especially in patients who have recently been treated
with antimicrobial therapy, infections are more frequently polymicrobial and the causative
pathogens are more diverse, often including aerobic gram-negative bacilli and obligate
anaerobic bacteria 10],21]. The presence of a mixture of bacterial types appears to predispose to the production
of virulence factors, such as hemolysins, proteases and collagenases, as well as short-chain
fatty acids; these cause inflammation, impede wound healing and contribute to the
chronicity of the infection 19],22]. In chronic, clinically uninfected wounds, the presence of some microbes is potentially
advantageous, inducing passive resistance, metabolic cooperation, quorum sensing systems
and DNA sharing 23].

New data derived using molecular techniques demonstrate that chronic wounds contain
many different microorganisms some of which were not previously recognized using standard
culture methods. We are only in the early stages of understanding the specific roles
these microorganisms play in chronic wounds 23]. Moreover, recent studies from less developed countries, especially in hot, humid
climates, report that even with standard microbiological methods aerobic gram-negative
bacilli, especially Pseudomonas aeruginosa more often cause DFIs 24]. While not yet adequately investigated, these findings are probably related to various
environmental, hygienic and cultural issues. To better interpret culture results and
provide optimal antimicrobial therapy, clinicians must be familiar with the microbial
isolates in their own region of practice. An additional pathogenic property of many
organisms is their ability to become enveloped biofilm. This has been best studied
in S. aureus skin biofilms, which appear to inhibit wound healing, diminish localized immunity
and enable other microorganisms to colonize and infect the wound 23]. Furthermore, consortia of genotypically distinct bacteria may symbiotically produce
a pathogenic community, referred to as functionally equivalent pathogroups 25].

In the past few decades a major problem in treating DFIs has been the increased rate
of isolation of antibiotic-resistant pathogens, particularly methicillin-resistant
S. aureus (MRSA), and to a lesser degree glycopeptide-intermediate S. aureus (GISA), vancomycin-resistant enterococci (VRE), extended-spectrum ?-lactamase- (ESBL)
or carbapenamase–producing gram-negative bacilli and highly resistant strains of P. aeruginosa. The rates of isolation of these multi-drug resistant pathogens vary widely by geographical
area and treatment center. But, the potential presence of such resistant isolates
emphasizes the importance of obtaining optimal specimens for culture and sensitivity
testing for infected DFIs 10],26], as well as avoiding the excessive antibiotic therapy that drives this resistance.

Evaluation of S. aureus virulence genes in DFU/DFI

Staphylococci, in addition to being the most frequent, are perhaps the most virulent
pathogens in DFI 10],19],20]. Studies in France have demonstrated a correlation between specific virulence genotypic
markers in S. aureus isolates from DFU and ulcer outcome 27]-29]. Using a miniaturized oligonucleotide array to identify genes encoding resistance
determinants, toxins and species-specific sequences of S. aureus, Sotto et al. sought to differentiate colonized from infected wounds in diabetic patients with
a foot ulcer that was culture-positive for only S. aureus. Virulence genes were absent in 20 of 22 (92%) clinically uninfected ulcers, but present
in 49 of 50 (98%) infected ulcers 27]. In a follow-up study with similar inclusion criteria, these investigators used polymerase
chain reaction (PCR) assays to detect genetic markers in both clinically uninfected
and infected diabetic foot ulcers. Analyzing for the presence of 31 of the most prevalent
virulence-associated S. aureus genes they noted that a five-gene combination of capsular type 8 (cap8), Staphylococcus enterotoxin A (sea), Staphylococcus enterotoxin I (sei), LukDE leukocidin (lukD/lukE) and ?-hemolysin V (hlgv) was most predictive of clinical infection 28]. Then, using a new generation of miniaturized oligonucleotide arrays for genotyping
S. aureus that covered a larger number of genes, they compared the presence of each gene in
S. aureus strains to the grades and outcomes of diabetic foot ulcers. Using logistic analyses
they found that lukDE was the gene most predictive of a favorable outcome of infection resolution or healing
of uninfected DFU 29]. These data demonstrate the potential of molecular methods for identifying virulence
factors in isolates from DFIs.

Defining DFI microbiota – a methodological metamorphosis?

Over the past forty years studies to identify pathogens in DFI have used standard
microbiological methods, despite their significant time to perform (two to three days
for preliminary results with final sensitivities often taking longer), bias in species
detected, lack of sensitivity (for example, for fastidious organisms) and lack of
information on the relative prevalence of various pathogens and their potential virulence.
The recent availability of molecular techniques has shed new light on the microbial
world of diabetic foot wounds. They have generally revealed the presence of many more
organisms and considerably more species (especially obligate anaerobes) than found
with standard cultures. However, molecular methods also have some limitations, including
high cost and the need for substantial technician time for some methods. Although
advances in technology have produced new desktop sequencers that are easy and somewhat
quicker to operate, these machines lack the sequencing throughput required for microbial
community sequencing provided by larger-scale sequencers (for example, Life Technology
Ion Proton System or Illumina Hiseq X Ten), which are generally available only in
large-scale centers. Moreover, the clinical significance of these microbiological
findings is as yet unclear 30]. For example, although we advocate selecting as focused an antimicrobial regime as
possible, we do not know if antibiotic treatment must be directed at each isolated
organism, or only at presumed bacterial ‘ringleaders’, or even at organisms that were
once considered probable non-pathogenic ‘lab weeds’ 31]. Thus, to better understand the microbial diversity of wounds in DFI and to identify
the relative proportion and types of species in the wound some studies have examined
the results of using ‘crime scene investigation (CSI)’-era technology (for example,
small subunit ribosomal RNA sequencing methods and real time PCR) to standard culture
methods 25],32]-34].

Standard sample collection and bacterial culture

When obtaining a specimen for culture and sensitivity testing, it is key to collect
material that is not contaminated with colonizing flora, but contains the true pathogens.
Since prior antibiotic therapy can cause false-negative cultures, it is best if specimens
can be obtained before such therapy is begun. In some chronic infections, such as
osteomyelitis, it is possible to safely discontinue antibiotic therapy for at least
a few days (or even weeks) before obtaining deep cultures 35]. Specimens should be obtained only after cleansing (with non-antimicrobial substances)
and debriding the wound. While swabs of open wounds are easy to obtain, most studies
comparing them to tissue specimens have shown that they are more apt to grown contaminants
and less likely to yield true pathogens 21], especially when sampling bone 36]. Optimal specimens for culture include tissue obtained by curettage of debrided ulcer
or a biopsy 37]. It is also important to ensure the specimen is placed in an appropriate sterile
transport container, is rapidly sent to the microbiology laboratory and once there
is quickly processed.

Despite optimal specimen collection and processing, culture-based techniques select
for species that flourish under typical nutritional and physiological conditions of
the microbiology laboratory but are potentially not the most abundant or clinically
important pathogens 30]. These standard methods may fail to identify slow-growing, fastidious or anaerobic
organisms 34]. Performing the 130-year-old method of the Gram-stained smear of a wound specimen
can provide rapid information about the presence and type of microorganism and their
relative abundance in the tissue. Finally, we now have newer, rapid tests that may
provide a more accurate snapshot of the wound microbiological milieu and are widely
used in clinical microbiology 7]. But, has the promise of molecular microbiology been fulfilled yet? Let us review
concepts of molecular microbiology as a step forward in identifying and better defining
the microbiome of DFI.

Molecular microbiology

PCR

This is a molecular method to amplify a genomic region of interest. When followed
by DNA sequencing, the abundance and genetic composition of a gene of interest can
be determined. The small subunit (SSU) ribosomal RNA (rRNA) gene in bacteria, called
16S rRNA, is a useful gene target given that it is conserved across all prokaryotes
(bacteria) but not eukaryotes (for example, humans). In a clinical specimen, using
universal primers for bacteria in highly conserved regions of this gene permits broad-range
amplification by PCR of bacterial SSU rRNA genes, but not human host genes. Simultaneously,
identifying species-specific hypervariable regions in the 16S rRNA gene allows for
taxonomic classification of bacteria 38]. Following amplification by PCR, 16S rRNA gene fragments are sequenced and analyzed
using various methods to assess the taxonomic composition and abundance of bacterial
communities 25],30],39],40]. Thus, the combination of conserved primer-binding sites and intervening variable
sequences facilitates the identification and quantification of microorganisms at the
level of genus and species, to permit a better understanding of a DFU microbiome 30].

Currently, 16S rRNA quantitative PCR (qPCR) is used to determine the biodiversity
of wounds and estimate bacterial load. Techniques for determining biodiversity include
full ribosomal amplification, cloning and Sanger sequencing (FRACS), partial ribosomal
amplification with a gel band identification and Sanger sequencing (PRADS), and density
gradient gel electrophoresis (DGGE) 40]. Similarly, PCR assay or oligonucleotide array sequence analyses (hybridization of
a nucleic acid sample to a large set of probes for gene mapping) can assess virulence-associated
genes 28],29],34],41]. Overall, PCR amplification and sequencing allows for the quantification and analysis
of specific genes (or genomic regions) of interest.

Metagenomics

In the past decade numerous molecular methods have been introduced for detecting microorganisms
from clinical specimens in a wound. Perhaps the most revolutionary are those used
to sequence DNA directly from a sample, known as metagenomics 42]. Metagenomic methods can potentially provide not only the names of the pathogens
present in an infected wound, but information on their virulence and their antibiotic
susceptibility patterns (to selected agents in some cases, but to all drugs when needed),
all within a time frame that would allow replacing most empirical antibiotic selections
with evidence-based therapy 26],41]. This targeted diagnosis is fundamental for preventing the overuse of broad-spectrum
antibiotics that is one of the causes of the emergence of bacterial resistance.

Metagenomic techniques allow for the complete characterization of all bacteria, archaea,
fungi and viruses within a sample (that is, the microbiome). Studies with this method
suggest that cultivable bacteria comprise only a small fraction (1%) of the total
bacterial diversity 43]. These culture independent methods are revolutionizing clinical microbiology by providing
a first glimpse into microbial community structure and function relative to human
health and disease 44]-46]. The introduction of next-generation sequencing (NGS) technologies (for example,
454/Roche pyrosequencing, Illumina and Ion Torrent) 47]-49] allows for the generation of DNA sequence data more quickly and at decreased cost,
which should lead metagenomics from the ‘research’ realm into the clinical microbiology
laboratory. Metagenomic methods differ from other molecular methods in the steps involved
to prepare samples for sequencing, time it takes to obtain results, number of sequences
generated and bacterial diversity observable 23],40].

There are now two metagenomic methods for analyzing the microbes within a specimen,
that is, community profiling (using a single gene assay such as 16S rRNA) and functional
metagenomics (using total DNA), as shown in Figure 1 and further described in Table 150]. Metagenomic community profiles are produced by amplifying regions in the SSU rRNA
gene from genomic DNA in a clinical sample and can be more accurate than culture-based
approaches 51],52]. Conversely, emerging functional metagenomics methods provide a comprehensive look
at bacterial communities by sequencing all genomic DNA in a sample rather than a single
gene, such as the 16S rRNA gene 53]. This allows for the characterization of bacteria and their biological processes,
including pathogenicity islands (that is, the genetic element of an organism responsible
for its capacity to cause disease), virulence factors, and antibiotic resistance 54]. Furthermore, organisms can be classified with better taxonomic resolution than single
gene assays, such as 16S rRNA 44],55].

Figure 1. Overview of methods for community profiling and functional metagenomics. Patient tissue samples contain a mixture of human and microbial DNA. Microbial DNA
is derived from a community of bacteria and other organisms present at their relative
abundance in the sample, indicated here using different colors. Once DNA has been
extracted from the sample, two metagenomic methods can be applied. In functional metagenomics the total DNA is sequenced and analyzed by comparing it to databases of known genomes
(for example, NCBI and IMG) and 16S rRNA genes (for example, RDP, Green Genes and
Silva) to identify bacterial taxa and their abundance. Sequences are also compared
to known proteins (for example, SIMAP, MG-RAST, KEGG) for functional analysis of genes,
pathways and relative frequency. In community profiling , hypervariable regions of the 16S rRNA gene from bacteria are amplified and sequenced.
Highly similar sequences are binned by operational taxonomic units and compared to
databases of 16S rRNA genes from known bacteria (for example, RDP, Green Genes and
Silva) to identify bacterial taxa and their frequency. 16S rRNA gene sequences can
be used in subsequent analyses of phylogenetic diversity in the sample. IMG: Integrated
Microbial Genomes; KEGG: Kyoto Encyclopedia of Genes and Genomes; MG-RAST: Metagenomic
Rapid Annotations using Subsystems Technology; NCBI: National Center for Biotechnology
Information; OTU: Operational Taxonomic Unit; RDP: Ribosomal Database Project; SIMAP:
Similarity Matrix of Proteins.

Table 1. Metagenomic methods: community profiling versus functional metagenomics

Limitations of molecular microbiology

Despite their great possible clinical utility, each of these molecular techniques
(especially PCR based and functional metagenomics) has recognized pitfalls that may
block translation from research laboratory to clinical practice. Overall, diagnostic
tests based on PCR are subject to issues related to detection sensitivity and specificity,
which may lead to an inaccurate portrayal of bacterial communities in wounds 56]-58]. Specifically, PCR amplification of genomic fragments requires that PCR primers are
unique, bind specifically to a region of interest and bind efficiently enough to produce
a PCR product. Given these criteria, PCR primer design relies on a priori knowledge of genomic sequences of bacteria in a wound (that may not be cultivable
and, therefore, amenable to genome sequencing) and may not be broad enough to account
for natural variation in bacteria in polymicrobial wound samples. As a result, genomic
fragments that are amplified by PCR may be affected by primer bias, leading to inaccurate
representation of the bacterial community.

Even when PCR is successful, the targeted gene must have enough discriminatory power
to differentiate related microorganisms. In particular, community profiling based
on single gene assays, such as 16S rRNA, may yield inconclusive results for closely
related species that lack variation in this highly conserved gene. Moreover, diagnostics
based on a single gene, such as 16S rRNA, are limited to bacterial community composition
analysis, thereby failing to capture clinically important functional information,
such as on virulence factors or antibiotic resistance genes. Lastly, errors in sequencing,
such as well-documented issues with homopolymer regions in 454/Roche pyrosequencing
59], could lead to misrepresentation of bacterial communities.

In contrast to 16S rRNA community profiling, functional metagenomics holds great promise
in assessing DFI, given that: sequencing is unbiased, allowing for accurate measurement
of species and bioburden in the sample; it can be used for viruses that lack conserved
genes, such as 16S rRNA; it can be used to analyze multiple genes at one time, including
virulence and antibiotic resistance factors; and, it can be used to discover new pathogens
or virulence factors. Major concerns with this approach are that datasets are more
costly to produce and larger and more complex to analyze. Moreover, it is challenging
to separate human-host DNA from microbial DNA in skin samples and to obtain enriched
microbial genomic DNA for sequencing 60]. Of the total DNA in a skin sample, approximately 90% is human. Thus, defining microbial
DNA requires deep sequencing at a higher cost, with subsequent in silico (computer) removal of human sequence contaminants, making functional metagenomics
not conducive to rapid diagnosis. Because of this limitation all studies to date on
DFU and DFI have been based entirely on 16S rRNA community profiling.

In Table 2 we have summarized the potential advantages, selected disadvantages and costs of
molecular methods that are more apt to be taken up in the clinical microbiology laboratory
(metagenomic community profiling, qPCR and virulence genes assays). Given limitations
associated with the various methods described, we propose a three-pronged molecular
approach: 1) identification of bacterial diversity; 2) quantification of microbial
load; and 3) identification of virulence factors of any S. aureus isolates. This methodology may allow for a rapid and broader understanding of the
microbiological factors affecting a DFU (see Figure 2).

Table 2. Key features of molecular methods for characterizing microorganisms from a diabetic
foot infection

Figure 2. Proposed algorithm for diabetic foot or other chronic wound infection management using
molecular microbiology methodology.

Finally, translating large-scale metagenomic datasets into a clinical report requires
the synthesis of bacterial abundance, virulence factors and antibiotic resistance
towards understanding the full susceptibility profile of pathogens. As such, we speculate
on what a report might look like based on community profiling (Figure 3) and functional metagenomics (Figure 4) to demonstrate the increased resolution that clinicians might expect to see when
moving from Pasteur to CSI.

Figure 3. Example of a potential microbiology report produced using the results of 16S rRNA
(NGS) data. Example of a potential microbiology report produced using the results of 16S rRNA
NGS data from an actual patient specimen from the Southern Arizona Limb Salvage Alliance
clinic. A) Patient and specimen information, B) Test description and overview, C) Sample preparation requirements, D) List of any resistance or virulence factors detected (note that this test does not
yield these data), E) Bacterial taxonomic profile, F) Antibiotic susceptibility profile based on the bacterial taxa detected in this sample.
NGS, next-generation sequencing.

Figure 4. Example of a potential microbiology report based on hypothetical functional metagenomic
next generation sequencing (NGS) data. Example of a potential microbiology report based on hypothetical functional metagenomic
NGS data from a patient specimen. A) Patient and specimen information, B) Test description and overview, C) Sample preparation requirements, D) List of any resistance or virulence factors detected, E) Bacterial taxonomic profile, F) Antibiotic susceptibility profile based on bacterial taxa detected and antibiotic
resistance and virulence factors detected.

Microbial diversity and bacterial load in patients with DFU: molecular versus culture
techniques

Several recent studies have compared standard culture to molecular community profiling
techniques to assess the effectiveness of each approach in characterizing bacterial
diversity and microbial load in DFU and DFI 34],40],61]. Although the types of wounds and methodology differed in the studies, we will focus
our discussion on the analysis of diabetic foot ulcers.

Dowd et al. performed a comprehensive survey of bacterial diversity on three groups of patients
with chronic wounds with pathogenic biofilms, including one group with DFU 40]. Analyses of a single pooled sample of the 10 patients in the DFU group for 16S partial
ribosomal amplification and 454/Roche pyrosequencing generated approximately 36,000
sequences. Rhoads et al. compared results of parallel samples processed by aerobic culture versus 16S rRNA
partial ribosomal amplification and 454/Roche pyrosequencing from 168 patients with
chronic wounds, including 40 on the lower extremity of diabetic patients 61]. Gardner et al. compared the results detected by community profiling versus culture of three dimensions
of DFU bioburden (microbial diversity, microbial load and pathogenicity) in 52 patients
with DFUs. Microbial diversity was defined as the number of bacterial taxa present
using 16S rRNA community profiling and microbial load was defined as the total quantity
of microbes present using quantitative real time PCR. Sequences were assigned to operational
taxonomic units (OTU), molecular proxies for describing organisms based on their phylogenetic
relationship to other organisms. Because pathophysiologically distinct DFUs likely
lead to confounding identification of microbial diversity, all 52 subjects selected
had only a specific homogeneous type of wound, that is, a neuropathic nonischemic
DFU. Roche/454 pyrosequencing showed an average of 5,634 sequences generated per sample
34].

These studies reported somewhat different taxonomic compositions of DFUs. Dowd et al. found the primary bacterial genera were Staphylococcus (29.7%), Peptoniphilus (6.9%), Rhodopseudomonas (6.9%) and Enterococcus (6.4%) 40]. Facultative and strictly anaerobic gram-positive cocci were the most prevalent isolates.
They used two traditional methods: FRACS showed the overwhelmingly predominant species
was S. aureus, followed by Anaerococcus lactolyticus, Anaerococcus vaginalis, Bacterioides fragilis, Finegoldia
magna and Morganella morganii; PRADS identified Pseudomonas, Haemophilus, Citrobacter and Stenotrophomonas as the predominant species. The molecular methods differ in the number of sequences
generated and the variety of bacteria found with different physiological and phenotypic
preferences. Molecular methods identified all bacterial isolates found on standard
culture, but they were performed during the study, while the cultures were reviewed
retrospectively 40].

In the Rhoads et al. study, the most common genera detected in DFU by molecular testing were Corynebacterium, Peptoniphilus, Staphylococcus, Anaerococcus and Bacteroides, while the most frequent on culture were species of Enterococcus, Staphylococcus, Pseudomonas, Serratia and Proteus61]. Finally, Gardner et al. detected a total of 13 phyla, with the majority of sequences being Firmicutes (67%),
Actinobacteria (14%), Proteobacteria (9.8%), Bacteroidetes (7.3%) and Fusobacteria
(1.4%). The most abundant OTU, Staphylococcus, comprised 29% of all sequences. Culture results showed a much higher relative abundance
of Staphylococcus (46%) and a much lower prevalence of anaerobic bacteria (12%). Furthermore, cultures
substantially underestimated the bacterial load based on qPCR of the 16S rRNA gene
by an average 2.34 logs and, in some cases, by more than 6 logs 34].

Taken together, these three studies strongly suggest that molecular techniques, such
as 16S rRNA community profiling, identify a greater diversity of organisms than do
standard microbiological methods. In particular, they reveal more fastidious anaerobes
and gram-negative species than previously recognized. These results have been affirmed
in clinical case studies that have demonstrated the potential utility of 16S rRNA
community profiling over culture 62].

Translating new technologies for bacterial identification to the clinic – a possible
future use in DFI?

New point-of-care (POC) testing methods may be useful in a variety of clinical settings.
The ideal diagnostic test would be accurate, portable, low cost, and require minimal
technical skills. POC testing could be used by the patient (or care-giver or visiting
nurse) at home in selected circumstances, and could also help determine the level
of care (in the ambulatory or inpatient setting) needed by a patient. In evaluating
a diabetic foot wound in the outpatient setting, POC testing could provide a mechanism
for early detection of infection, allowing clinicians to determine which wounds to
culture and to provide definitive (rather than empiric) antibiotic therapy before
the patient leaves the clinic 63]. We can (or soon will be able to) get all of this clinically useful information in
‘real’ time, before the clinician sits down to write orders for further microbiological
testing or antibiotic treatment. The United States Food and Drug Administration (FDA)
has already approved rapid antigen tests for a variety of selected pathogens. While
viral assays currently have the lion’s share of these approvals 63] it is likely they will be increasingly used for bacterial identification, including
for diagnosing DFI pathogens. Advanced microbiology diagnostic tests provide the promise
of dramatically increased sensitivity of pathogen identification in decreased time.