Next-generation DNA sequencing offers diagnostic advantages over traditional culture testing

Annabelle Damerum Zymo Research Corporation, Irvine, CA

Search for other papers by Annabelle Damerum in
Current site
Google Scholar
PubMed
Close
 PhD
,
Shachar Malka Long Island Bird and Exotics Veterinary Clinic, Great Neck, NY

Search for other papers by Shachar Malka in
Current site
Google Scholar
PubMed
Close
 DVM, ABVP, ACEPM
,
Nicolle Lofgren Mountain West Veterinary Specialists, Layton, UT

Search for other papers by Nicolle Lofgren in
Current site
Google Scholar
PubMed
Close
 DVM, DACEPM
,
Gina Vecere Long Island Bird and Exotics Veterinary Clinic, Great Neck, NY

Search for other papers by Gina Vecere in
Current site
Google Scholar
PubMed
Close
 DVM, ACEPM
, and
Janina A. Krumbeck Zymo Research Corporation, Irvine, CA
MiDOG LLC, Tustin, CA

Search for other papers by Janina A. Krumbeck in
Current site
Google Scholar
PubMed
Close
 PhD

Abstract

OBJECTIVE

While the clinical utility of next-generation DNA sequencing (NGS) as a diagnostic tool for infections in humans and traditional pets has been demonstrated, there is a lack of data regarding its utility for exotic animals. For exotic patients, traditional culturing is especially challenging for anaerobic and fungal pathogens. Therefore, diagnosis often relies on PCR, which provides a high degree of sensitivity and specificity, although it targets only a predetermined, finite pathogen panel. NGS provides the same benefits as PCR, while also offering de novo identification and quantification of all bacteria and fungi present in a clinical sample, including novel pathogen discovery.

PROCEDURES

Clinical samples from 78 exotic animal patients were collected simultaneously for conventional culture testing and NGS analysis. Results provided by each laboratory were compared for the presence and absence of bacterial and fungal pathogens and commensals.

RESULTS

Results showed large bacterial and fungal species diversity in the study cohort and a lack of sensitivity of microbial culture testing. Culture failed to grow 15% of putative bacterial and 81% of putative fungal pathogens that were identified by NGS. The probability of a “no growth” diagnosis was 14% higher for bacteria and 49% higher for fungi with culture versus NGS testing if fungal culture was conducted.

CLINICAL RELEVANCE

Culture testing failed to diagnose a substantial number of both bacterial and fungal pathogens, which were detected by NGS. This highlights the limitations of traditional culture-based testing and displays the clinically advanced utility of NGS-based diagnostics in exotic animal medicine.

Abstract

OBJECTIVE

While the clinical utility of next-generation DNA sequencing (NGS) as a diagnostic tool for infections in humans and traditional pets has been demonstrated, there is a lack of data regarding its utility for exotic animals. For exotic patients, traditional culturing is especially challenging for anaerobic and fungal pathogens. Therefore, diagnosis often relies on PCR, which provides a high degree of sensitivity and specificity, although it targets only a predetermined, finite pathogen panel. NGS provides the same benefits as PCR, while also offering de novo identification and quantification of all bacteria and fungi present in a clinical sample, including novel pathogen discovery.

PROCEDURES

Clinical samples from 78 exotic animal patients were collected simultaneously for conventional culture testing and NGS analysis. Results provided by each laboratory were compared for the presence and absence of bacterial and fungal pathogens and commensals.

RESULTS

Results showed large bacterial and fungal species diversity in the study cohort and a lack of sensitivity of microbial culture testing. Culture failed to grow 15% of putative bacterial and 81% of putative fungal pathogens that were identified by NGS. The probability of a “no growth” diagnosis was 14% higher for bacteria and 49% higher for fungi with culture versus NGS testing if fungal culture was conducted.

CLINICAL RELEVANCE

Culture testing failed to diagnose a substantial number of both bacterial and fungal pathogens, which were detected by NGS. This highlights the limitations of traditional culture-based testing and displays the clinically advanced utility of NGS-based diagnostics in exotic animal medicine.

Accurate clinical diagnosis requires correct pathogen identification to select the most appropriate treatment for the patient. Current diagnostic methods available to the clinician include culture and sensitivity and PCR or quantitative PCR (qPCR) testing. While culture and sensitivity testing is still considered the gold standard and allow for the phenotypic antibiotic resistance panel and minimal inhibitory concentration (MIC) tests, there are significant limitations associated with this approach. Due to the large variety of pathogenic bacterial and fungal organisms, the incubation media, temperatures, oxygen levels, water contents, and incubation time need to be carefully considered to address the different growth requirements of individual pathogens.1 Especially for the diagnosis of infectious diseases in wildlife, zoo animals, and exotic pets, the microbial growth requirements can be vastly different from traditional veterinary diagnostic care, which most often covers canine and feline patients. Thus, the diagnostic laboratory performing culture testing needs to be equipped with special supplies to cover the diagnosis of pathogens of exotic animals efficiently, such as anaerobic incubation, unique media, and allowance for longer incubation times for slow-growing bacteria and fungi.2 For example, clinically relevant bacterial pathogens, such as Flavobacterium spp, Pseudomonas spp, Mycobacteria spp, or Mycoplasma spp that are considered common among avian and reptilian pets, grow significantly slower in the artificial laboratory setting than other bacteria.35 If fast-growing bacteria overtake the agar plate, slow growers may be missed, and the patient’s may be infection misdiagnosed.3 Additionally, some small exotic pet mammals and most avian pets have a higher internal body temperature compared to felines and canines, while reptiles, amphibians, fish, and invertebrates have a lower body temperature.4,6,7 Therefore, microbial samples collected from these species may require different temperatures for culture growth.

Reported case numbers of fungal infections have risen, and the clinical diagnosis of these infections remains a considerable challenge, even in human clinical care.8,9 Fungi can take several weeks to grow, which limits the diagnostic value of culture testing, as treatments need to be prescribed in a timely manner.8 This has led to appreciable reductions in orders of fungal culture testing in the past, which in turn can result in a misrepresentation and underrepresentation of actual fungal infections in wildlife and exotic pets. Furthermore, many anaerobic bacteria and fastidious organisms, such as mycobacteria and other acid-fast organisms, fail to grow using traditional culture methods and require special conditions that are not readily available in most commercial labs.1,1012

Due to the development of DNA and RNA sequencing-based microorganism detection methods, it is estimated that 1030 microbes live on planet earth, of which only approximately 1 to 3% are currently culturable in a laboratory setting.13,14 Thus, 97% to 99% of microbes are considered unculturable, including both commensals and pathogens. PCR and qPCR testing overcomes the issue of difficult growth requirements by using DNA and/or RNA as the analyte to identify and quantify bacterial, fungal, viral, and parasitic pathogens. As a culture-independent molecular-based assay, this approach offers high sensitivity and specificity toward the selected targets with results potentially being ready within hours.15 However, the PCR target panels are often limited to 8 to 20 targets, which are preselected by the clinician16 and can return with all negative results if the correct targets are not selected. This results in a secondary testing further delaying efficient treatment recommendations for the patient.

Next-generation DNA sequencing (NGS) offers a potential solution for infectious disease diagnostics that combines the benefits of both qPCR and culture and sensitivity testing.1719 Like PCR, microbial DNA or RNA is used as the analyte, thus offering high sensitivity and specificity. Unlike PCR, however, no target panel selection of specific bacteria and fungi is needed, as the primers target 2 highly conserved regions of the bacterial and fungal genomes, the 16S rRNA and nuclear ribosomal internal transcribed spacer (ITS) region, respectively. Thus, all members of the bacterial and fungal kingdoms present in a clinical sample can be identified based on their genetic profile down to the species level.20 Due to this broad approach of targeting all bacteria and fungi, novel pathogens are often discovered using this tool,18,19 as well as microbiome markers for health and disease of humans21,22 and animals.12,20,23,24 A wide body of literature has reported that NGS testing offers clinical advantages over traditional testing, especially for the diagnosis of uncommon, novel, or chronic infections, as reviewed in Pak and Kasarskis17 and Gu et al.18 However, most of these studies have been conducted in human medicine, leaving a considerable knowledge gap regarding the clinical utility of this technique in veterinary medicine.

The aim of this study was to compare the clinical utility of NGS-based infectious disease testing as a diagnostic tool to traditional microbial culture testing for samples derived from exotic animals suffering from suspected infections.

Materials and Methods

Samples were collected at 13 different clinical locations across the United States over 4 years (August 2019 to March 2023; Table 1). Two samples from each animal included in this study were collected in parallel, 1 for traditional culture and sensitivity testing and 1 for NGS testing. Traditional culture and sensitivity testing was carried out by the clinic’s standard culture testing lab and NGS testing by a commercial NGS-testing laboratory (MiDOG LLC). Inclusion criteria for sample collection were that the individual was an exotic pet or zoo/park wildlife animal and that the sample was taken due to a confirmed or suspected infection in the individual. Samples were collected from any relevant bodily locations using swabs from wounds, abscesses, skin, ocular, gastrointestinal, urogenital, and oral infections etc. A full list of animal species and sample types included in this study is listed (Table 2). A total of 78 matched samples were included in this study. For culture testing, aerobic bacterial incubation was ordered for all cases (78/78 [100%]) and additional anaerobic culturing was ordered for 65 patients (65/78 [83%]). For a subset of the samples (53/78 [68%]), fungal incubation was requested by the clinician. Specific details about each sample can be found elsewhere, including the type of incubation and testing laboratory (Supplementary Table S1). Sterile swabs for culturing were supplied by the clinician’s lab routinely. Fungal and bacterial NGS was performed for each sample. A sterile DNA preservation buffer (DNA/RNA Shield; catalog No. R1100-50, Zymo Research Corp; 1-ml volume) was provided by the NGS-testing laboratory that stabilized the samples at room temperature until they were shipped to the testing facility. This solution can preserve the microbial DNA present in the specimen at room temperature for up to 30 days. A sterile, DNA-free swab (HydraFlock; Puritan; catalog No. 25-3406-H) was provided as well as a standardized protocol, which instructed participants to aseptically remove the swab from the pouch and twist and twirl the swab 10 times gently back-and-forth over the sampled location. All participating clinics used the same collection devices for the NGS-based analysis.

Table 1

Names of sample contributing clinics and hospitals for this study.

Clinic/institution name
Griffin Exotics
Parrish Creek Veterinary Hospital & Diagnostic Center
Veterinary Diagnostic Laboratories
Six Flag Discovery Kingdom
Pacific Marine Mammal Center
Saddleback Animal Hospital
Sea World San Diego
Long Island Bird & Exotics Veterinary Clinic
Cheyenne Mountain Zoo
Oklahoma State University
Animal Dermatology Clinic Tustin
WEL Wildlife Epidemiology Laboratory
Tampa’s Lowry Park Zoo
Table 2

Summary of samples included in this study.

Animal subtype Count (n) Species Sample type(s)
Bird 20 Amazon parrot (1), Canada goose (1), Catalina macaw (1), chicken (1), Congo African grey (5), double yellow-headed Amazon (1), electus parrot (1), kea (1), Moluccan cockatoo (1), peacock (1), pigeon (1), Timneh African grey (1), Von der Decken’s hornbill (1), white-bellied caique (1), yellow-naped Amazon parrot (1) Blood (1), eye (1), feces (4), foot (1), nail bed (1), naso-channel flush (4), oviduct (1), renal mass (1), skin (4), trachea (1), wound (2)
Fish 1 Not specified Skin (1)
Mammal 37 Bongo (1), ferret (2), guinea pig (7), hamster (1), North American porcupine (1), pilot whale (1), rabbit (20), rat (2), sloth (1), sugar glider (1), tiger (1) Abscess (7), BAL lung (4), bladder (1), ear (5), eye (1), face (1), feces (3), joint (1), nasal (5), scent gland (1), skin (5), ulcer (corneal) (1), urine (1), vulva (1), wound (1)
Reptile 14 Bearded dragon (3), chameleon (1), leopard gecko (1), Russian tortoise (1), snake (5), sulcata tortoise (3) Abscess (2), ear (1), eye (1), glottis (1), laceration (1), oral (4), skin (3), trachea (1)

DNA sequencing analysis was conducted as previously described, targeting the 16S ribosomal RNA taxonomic marker gene for bacterial and the ITS marker for fungal identification and quantification.20 Several positive (ZymoBIOMICS microbial community standard; catalog No. D6300 and D6305; Zymo Research Corp) and negative controls are part of the standardized protocol adhered to by the NGS-testing laboratory.

The results provided by each laboratory were compared to the matched samples for the presence of microbes detected and the identified bacterial and fungal species. When no fungal cultivation was ordered for traditional culture testing, but fungal pathogens were diagnosed using NGS for that sample, this was highlighted in the results and discussion. Specific pathogens of clinical interest in exotic pets, wildlife, and zoo animals, determined by literature search47,10 were highlighted if they were detected in the sample by either method. Those pathogens of special clinical interest are listed (Table 3). Routinely, commensals are either not listed in culture reports or summarized as “commensals detected.” If commensals were reported by either culture or NGS, those results were also compared, since commensals can be of clinical importance in certain samples.

Table 3

Organisms of interest and frequency of detection for culture and NGS test.

Species Gram Respiration Culture (+) NGS (+)
Bacteria
   Acinetobacter spp Aerobe 6, 7.7% 4, 5.1%
    Acinetobacter baumanii Aerobe 4, 5.1% 1, 1.3%
   Bacteroides pyogenes Obligately anaerobe 1, 1.3% 4, 5.1%
   Citrobacter spp Facultative anaerobe 1, 1.3% 1, 1.3%
   Corynebacterium spp + Aerobe 4, 5.1% 26, 33.3%
   Enterobacter cloacae Facultative anaerobe 4, 5.1% 4, 5.1%
   Enterococcus spp + Facultative anaerobe 11, 14.1% 15, 19.2%
    Enterococcus faecalis + Facultative anaerobe 5, 6.4% 7, 9.0%
    Enterococcus faecium + Facultative anaerobe 2, 2.6% 4, 5.1%
   Escherichia coli Facultative anaerobe 11, 14.1% 8, 10.3%
   Fusobacterium spp Anaerobe 2, 2.6% 13 (12c), 16.7%
   Helicobacter spp Anaerobe Not detected 2 (1c), 2.6%
   Klebsiella spp Facultative anaerobe 12, 15.4% 4, 5.1%
   Mycobacterium sp + Aerobe Not detected 1, 1.3%
   Micrococcus spp + Aerobe 1, 1.3% 4, 5.1%
   Mycoplasma spp No gram stains Aerobe Not detected 8, 10.3%
   Pseudomonas aeruginosa + Aerobe (facultative anaerobe) 14, 17.9% 15, 19.2%
   Salmonella entericaa Facultative anaerobe 1, 1.3% 3, 3.8%
   Staphylococcus spp + Facultative anaerobe 9, 11.5% 23, 29.5%
    Staphylococcus aureus + Facultative anaerobe 2, 2.6% 5, 6.4%
    Staphylococcus pseudintermedius + Facultative anaerobe 2, 2.6% 3, 3.8%
   Streptococcus spp + Facultative anaerobe 6, 7.7% 19, 24.4%
    Streptococcus pneumoniae + Facultative anaerobe 2, 2.6% 2, 2.6%
   Vibrio spp Facultative anaerobe 1, 1.3% 1, 1.3%
Fungib
   Alternaria spp Not detected 12, 22.6%
   Aspergillus spp 1, 1.9% 18, 33.9%
   Candida spp 3, 5.8% 16, 30.2%
    Candida albicans 2, 3.8% 3, 5.7%
   Cryptococcus spp 1, 1.9% 2, 3.8%
   Fusarium spp Not detected 7, 13.2%
   Malassezia restricta Not detected 22, 41.5%
   Nannizziopsis chlamydospora Not detected 1, 1.9%
   Trichosporon spp Not detected 11, 20.8%

Shown are the number of positive cases followed by the percentage for this dataset of 78 samples.

a

Classified to genus level only by culture test.

b

Subset of 53 samples tested by fungal culture.

c

For 2 cases no anaerobic culture testing was conducted, but NGS testing identified anaerobe pathogens in the samples.

Results

The study dataset included 78 samples, collected from 15 different bird species (n = 22), fish (1), 11 mammalian species (40), and 6 reptilian species (15; Table 2). The most common species investigated were rabbit (n = 22), guinea pig (7), Congo African grey (5), python (4), and sulcata tortoise (3). The most common sample collection sites were skin swabs (n = 15), feces (7), ear swabs (7), facial abscesses (6), bronchoalveolar lavage (BAL) lung fluids (4), and nasal swabs (4; Table 2; Supplementary Table S1). Samples were collected in duplicate for either traditional culture-based testing, including aerobic bacterial culture for all samples and anaerobic culture for 65 cases (83%), or NGS-based testing. A subset of the samples (n = 53, 68%) was also sent for fungal culture testing. All NGS testing was conducted by the same laboratory, but participating clinics submitted the samples for culturing at their preferred reference lab, which included 2 major US-based animal diagnostic laboratories (n = 56, 72% of samples), as well as local private laboratories (19, 24%) and university laboratories (3, 4%). Specific details on which culture laboratory analyzed which sample and the microbes detected in each test are provided (Supplementary Table S1).

Bacterial species were reported in 83% of samples by culture (n = 65), of which 63 (81%) cases included a putative pathogen, and 97% of samples by the NGS test (76), of which all cases included a putative pathogen (Figure 1; Table 3). Of the 53 samples tested both by fungal culture and NGS, growth was detected by culture in 7 cases (9%), 6 of which reported a putative pathogen and the seventh reported an unidentifiable yeast species; the NGS test detected fungal species in 58% of samples (n = 45), with 43 cases reporting a putative pathogen. Considering the sample set as a whole, including the additional samples which were not tested by fungal culture, NGS detected fungi in 88% of cases (n = 70). In summary, culture results failed to grow 15% of putative bacterial and 81% of fungal pathogens that were identified by NGS testing.

Figure 1
Figure 1

A—Graphical summary of study design and results for the matched sample comparisons. B—Codetection and type of organisms (bacterial and/or fungal) detected in the specimen by NGS testing (left) and culture testing (right).

Citation: American Journal of Veterinary Research 84, 8; 10.2460/ajvr.23.03.0054

Organisms were considered of interest to this study if they have previously been reported as either a confirmed pathogen or an opportunistic pathogen. A total of 83 bacterial genera and 29 fungal genera were identified from the 2 diagnostic approaches, with important pathogens, as listed (Table 2). The most prevalent bacterial species identified by both culture and NGS was Pseudomonas aeruginosa (culture, n = 14; NGS, 15; Table 3). Other commonly observed bacteria included Staphylococcus spp (including S aureus, S chromogenes, S cohnii, S epidermidis, S felis, S haemolyticus, S hominis, S massiliensis, S pseudintermedius, S saprophyticus, and S xylosus [culture, n = 9, 12%; NGS, 27, 35%]), Corynebacterium spp, (including C aurimucosum, C diphtheriae, C jeikeium, C kroppenstedtii, C pseudogenitalium, and C urealyticum [culture, 4, 5%; NGS, 26, 33%]), Escherichia coli (culture, 11, 14%; NGS, 7, 9%), Enterococcus spp (including E avium, E canintestini, E faecalis, and E faecium [culture, 12, 15%; NGS, 15, 19%]), and Klebsiella spp (including K oxytoca and K Pneumoniae [culture, 12, 15%; NGS, 4, 5%]). The most prevalent potentially pathogenic fungal species detected were Malassezia restricta (culture, not detected; NGS, n = 22, 42%), Alternaria sp (culture, not detected; NGS, 12, 23%), Aspergillus spp [including A fumigatus, A niger, A ostianus, A restrictus, A steynii, and A unguis [culture, 1, 2%; NGS, 18, 34%]), Trichosporon sp (culture, not detected; NGS, 11, 21%), and Candida spp (including C albicans, C catenulata, C parapsilosis, and C tropicalis [culture, 3, 4%; NGS, 16, 30%]). Significant pathogens including Mycobacterium, Mycoplasma spp (M cavipharyngis and M gallisepticums), and Helicobacter spp (H trogontum and unknown species) and the fungal genera Alternaria, Malassezia (including M caprae, M furfur, M globosa, M restricta, and M slooffiae), and Nannizziopsis chlamydospora were not detected by culture test but were detected by NGS (Table 3).

Organisms detected by each test were compared by performing a matched sample comparison (Table 4). Agreement between culture and NGS results for bacterial species varied depending on species: For Streptococcus pneumoniae and Vibrio spp, culture and NGS were in 100% concurrence, meaning these organisms were detected by both methods in all cases, and for S pseudintermedius, there was only 1 case that was positive for NGS but negative for culture. For organisms including P aeruginosa, E coli, A baumanii, E faecium, and E faecalis, concurrence was ≥ 37%; however, concordance was low (< 30%) for organisms including Corynebacterium spp, E cloacae, Fusobacterium spp, S enterica, Staphylococcus spp, and Streptococcus spp (Table 4). No concordance was reported for Citrobacter spp, Helicobacter spp, Klebsiella spp, Micrococcus spp, or Mycoplasma spp.

Table 4

Matched sample comparison of organisms of interest.

Species Culture (+)/NGS (−) Culture (−)/NGS (+) Culture (+)/NGS (+) Culture (−)/NGS (−)
Bacteria
   Acinetobacter spp 4, 5.1% 2, 2.6% 3, 3.8% 69, 88.5%
      Acinetobacter baumanii 3, 3.8% 0, 0% 1, 1.3% 74, 94.9%
   Bacteroides pyogenes 0, 0% 2, 2.6% 2, 2.6% 74, 94.9%
   Citrobacter spp 1, 1.3% 1, 1.3% 0, 0% 76, 97.4%
   Corynebacterium spp 3, 3.8% 25, 32.1% 1, 1.3% 49, 62.8%
   Enterobacter cloacae 3, 3.8% 3, 3.8% 1, 1.3% 71, 91.0%
   Enterococcus spp 6, 7.7% 8, 10.3% 7, 9.0% 57, 73.1%
      Enterococcus faecalis 2, 2.6% 3, 3.8% 3, 3.8% 70, 89.7%
      Enterococcus faecium 0, 0% 2, 2.6% 2, 2.6% 74, 94.9%
   Escherichia coli 6, 7.7% 3, 3.8% 5, 6.4% 64, 82.1%
   Fusobacterium spp 0, 0% 12, 15.4% 2, 2.6% 64, 82.1%
   Helicobacter spp 0, 0% 2, 2.6% 0, 0% 76, 97.4%
   Klebsiella spp 11, 14.1% 3, 3.8% 2, 2.6% 62, 79.5%
   Mycobacterium sp 0, 0% 1, 1.3% 0, 0% 77, 98.7%
   Micrococcus spp 1, 1.3% 4, 5.1% 0, 0% 73, 93.6%
   Mycoplasma spp 0, 0% 8, 10.3% 0, 0% 70, 89.7%
   Pseudomonas aeruginosa 4, 5.1% 4, 5.1% 10, 12.8% 60, 76.9%
   Salmonella entericaa 0, 0% 3, 3.8% 1, 1.3% 74, 94.9%
   Staphylococcus spp 4, 5.1% 18, 23.1% 5, 6.4% 51, 65.4%
      Staphylococcus aureus 1, 1.3% 5, 6.4% 1, 1.3% 71, 91.0%
   Staphylococcus pseudintermedius 0, 0% 0, 0% 3, 3.8% 75, 96.2%
   Streptococcus spp 1, 1.3% 14, 17.9% 5, 6.4% 58, 74.4%
      Streptococcus pneumoniae 0, 0% 0, 0% 2, 2.6% 76, 97.4%
   Vibrio spp 0, 0% 0, 0% 1, 1.3% 77, 98.7%
Fungib
   Alternaria spp 0, 0% 7 (12c), 13.2% 0, 0% 46
   Aspergillus spp 0, 0% 8 (18c), 15.1% 1, 1.9% 44
Candida spp 2, 3.8% 13 (15c), 24.5% 1, 1.9% 37
   Candida albicans 2, 3.8% 1 (3c), 1.9% 0, 0% 51
   Cryptococcus spp 1, 1.9% 1, 1.9% 0, 0% 51
   Fusarium spp 0, 0% 5 (7c), 9.4% 0, 0% 48
   Malassezia spp 0, 0% 18 (21c), 34.0% 0, 0% 35
      Malassezia restricta 0, 0% 18 (21c), 34.0% 0, 0% 35
   Nannizziopsis chlamydospora 0, 0% 1, 1.9% 0, 0% 52
   Trichosporon spp 0, 0% 11, 20.8% 0, 0% 42
c

Total number of observations by NGS, including samples not tested by fungal culture.

See Table 3 for remainder of key.

Interestingly, there appeared to be a high incidence of multifactorial infections, with multiple putative pathogens detected within a single sample. Out of 64 samples measured by the culture test to contain a putative bacterial pathogen, 30 (47%) had > 1 pathogens reported, and of the 76 pathogen-positive samples detected by NGS, 69 (91%) had > 1 pathogens. While fungal culture only detected 1 incidence of multifactorial fungal infection, 50% of NGS reports detected more than 1 putative fungal pathogen. While culture testing rarely reported commensal species (2/78, 2.6%), NGS testing reports all organisms present enabling the analysis of all species present as a whole. A high incidence of commensal species reported alongside pathogenic species was observed by the NGS test (Table 5). In the majority of cases, putative bacterial pathogens were detected alongside commensal species (80%), and pathogenic species were rarely reported in isolation (10%; Table 5). Fungal pathogens were also more likely to be detected together with commensal species (62%). The majority of cases reported both bacterial and fungal pathogens present in the samples according to NGS analysis (55 cases, 1%), and 8 cases found bacterial pathogens along with fungal commensals (10%).

Table 5

Incidence of pathogenic and commensal species detected by the NGS test.

NGS testing Number of samples (%)
Bacteria
   Commensal (+)/pathogen (+) 69 (79.3%)
   Commensal (+)/pathogen (−) 0
   Commensal (−)/pathogen (+) 8 (10.3%)
   Commensal (−)/pathogen (−) 1 (1.3%)
Fungi
   Commensal (+)/pathogen (+) 48 (61.5%)
   Commensal (+)/pathogen (−) 10 (12.8%)
   Commensal (−)/pathogen (+) 6 (7.7%)
   Commensal (−)/pathogen (−) 14 (17.9%)

Other rare pathogens detected in the dataset included Chelonobacter oris, which causes respiratory disease in tortoises,25 Trueperella pyogenes, which causes infections in swine, cows, and domestic animals,26,27 and Capnocytophaga canimorsus, which is zoonotic in humans but a commensal in dogs and cats28 (Supplementary Table S1).

Direct comparison of the reports highlighted that the probability of getting a “no growth” diagnosis was 14% higher for bacteria and 49% higher for fungi with traditional culture versus NGS testing, even if fungal culturing was conducted. If all fungal-positive cases are taken into account, the probability of getting a no growth diagnosis is at 74%.

Discussion

Successful infection treatment depends on the correct identification of the pathogen to provide the most appropriate therapies for the patient. While culture is still considered the gold standard for clinical microorganism identification, recent investigations have highlighted that culture-free, NGS-based technologies may be less biased in characterizing microbial profiles.29 In this comparative study, we investigated the results obtained from culture-based and NGS-based tests for 78 samples collected from exotic pets and zoo animals, across a range of body sites and geographical locations.

The data presented here showed that the probability of getting a no growth diagnosis was 14% higher for bacteria and 49% higher for fungi with traditional culture versus NGS testing (and 81% if culture reports that did not conduct fungal testing are taken into account). Only 1 NGS report indicated neither bacterial nor fungal species detected (1%), which could be due to the sampling method or detection limitations of the assay; however, for culture testing 8 samples (15.1%) reported no detection of any bacteria or any fungi, even though fungal culture was ordered. On average, NGS testing reported more pathogens, both bacterial and fungal, and more commensals. Specifically, several clinically relevant pathogens were detected using NGS but not reported by culture. Those pathogens included Mycobacterium, Mycoplasma, and Helicobacter for bacteria and Alternaria, Malassezia, Nannizziopsis, Trichosporon, Finegoldia magna, and Fusarium for fungi. In addition to these well-known pathogens, novel putative pathogens such as Riemerella anatipestifer,30,31 Granulicatella adiacens,32 Corynebacterium xerosis,33 Corynebacterium aurimucosum,34 Fusarium,20 Anaerosporobacter spp (noted by Vecere et al, DVM, unpublished data, 2023), Clostridium disporicum,35,36 and Bibersteinia trehalosi37,38 were detected by NGS and undetected by culture. A benefit of NGS diagnostics is that genomic databases describing novel bacterial and fungal pathogens isolated from patients are constantly updated by researchers worldwide, allowing for pathogen identification of even novel and rare organisms.

When added together, culture failed to identify 15% of putative bacterial pathogens. Specifically, strict and facultative anaerobic pathogens were underrepresented in culture reports, such as Enterococcus, Salmonella, and Helicobacter. Anaerobic bacteria require special handling from the moment the sample is collected to avoid cell death and an accurate representation of the sample profile in the lab.2

Interestingly, culture testing reported a higher incidence of the gram-negative bacteria E coli and Klebsiella spp than NGS, while NGS detected important gram-positive bacteria such as Corynebacterium spp. While the true composition of the samples at the time of collection is unknown due to the nature of a clinical sample, it might be possible that those bacteria grew disproportionately during transit to the culture laboratory or are contaminants. Amir et al39 investigated the effects of sample storage on the microbial profile of human fecal samples and observed microbial “blooms,” in which the taxonomic profiles of samples changed over time due to growth of particular taxa, including those in the Gammaproteobacteria class. The DNA preservation buffer used for NGS-based sample collection in this study does not allow for growth or decay of any organism as the cells are efficiently inactivated, thus taking a snapshot of the sample at the time of collection.40 Therefore, a microbial “blooming” during transit cannot be observed in this NGS-based approach.

Slow-growing and fastidious organisms provide a challenge for traditional culture testing as fast-growing organisms requiring simple growth matrices may overtake the petri dish, thus causing the slow-growing organisms to be missed in the clinical diagnosis. Two examples of such slow-growing pathogens were detected in this study, Corynebacterium urealyticum, which causes cystitis, pyelonephritis, and bacteremia,41 and Mycoplasma species (M cavipharyngis and M gallisepticum). These samples highlighted that such slow growers can be identified based on their DNA, while not being detected by culturing. Interestingly, in 1 case the slow-growing Capnocytophaga ochracea pathogen was reported in culture testing, but not using NGS. Instead, NGS reported Fusobacterium nucleatum to be present in this sample, which has a similar colony morphology to C ochracea, and may have been misidentified by culture.

Four rabbit cases presented in this study showed several pathogens of interest. One abscess showed high cell counts of Granulicatella adiacens by NGS testing but no bacterial growth in culture. This bacterium has fastidious growth requirements that make cultivation challenging. While G adiacens is considered a commensal in humans, it has also been shown to cause endocarditis, bacteremia, meningitis, and peritonitis.42 To date, G adiacens has not been reported as a pathogen in rabbits. Another nonculturable species detected in a rabbit nasal sample was Treponema pallidum-paraluiscuniculi, which causes venereal spirochetosis.43 Furthermore, 2 rabbit cases showed Helicobacter sp in the NGS reports but not the culture reports.

In 81% of cases, important fungal pathogens were not identified by culture, thus indicating that fungal infections are more likely to be misdiagnosed as bacterial infections and in general are underreported. This dataset specifically highlighted that reptiles quite often suffer from fungal infections, which were undiagnosed with traditional methods. Severe fungal pathogens detected here included Nannizziopsis, Fusarium, Malassezia, Trichosporon, Candida, Aspergillus, and Alternaria.

Potential causes for the no growth results by culture test for both bacteria and fungi could be that the bacterial and fungal cells are no longer viable after transit due to the transport media utilized or that growth did not occur due to the type of culture media used or the incubation temperature applied. Incubation temperature is often set to 37 °C, which is optimized for samples from feline and canine patients. However, the body temperature of exotic animals is significantly higher (ie, most avian pet and small exotic pet mammals) or lower (ie, reptiles, amphibians, fish, and invertebrates)4,6,7 than 37 °C. Therefore, microbial cells that are viable and causing infections in vivo may be unculturable in vitro due to the applied incubation temperature. It should also be noted that the use of different culture laboratories utilized in this study may have impacted the results obtained via the culture test, although all labs were accredited and no correlation between a no growth observation and the commercial laboratory that performed the testing was noted.

A commonly expressed concern about NGS-based diagnostics is the high amount of data being provided, possibly masking the clinically relevant information with commensal organisms, the fact that nonviable microbes are reported, and potential contaminants. A high amount of clinically irrelevant information is a valid concern; however, most diagnostic laboratories filter the information for the clinicians, highlighting clinically relevant organisms, as with culture testing. While culture testing may report “commensals detected” without further specification, knowing which commensals are detected could provide valuable clinical information, as under certain circumstances commensals can be opportunistic pathogens.44,45 Microbiome research performed on a diversity of animal species and body locations has demonstrated that a diverse commensal community is significantly associated with a clinically healthy individual20,23,4648 and that a lack of a diverse commensal community can be an indicator of an infection about to manifest itself. Especially in exotic medicine there is a lack of microbial reference ranges that define the microbiome profile of a clinically healthy individual, which species are commensals, and at which cell counts.10 In some cases, even high densities of otherwise nonpathogenic species can compromise a patient’s health.10 Often, traditional culturing refers to commensals based on previous knowledge obtained from human and veterinary medicine of common domestic species and this may be different for exotic pet species.11 Furthermore, a commensal nonpathogenic bacterium in 1 organ system such as skin or the gut can be pathogenic once populating another organ such as the auricular bulla.12

NGS testing does provide the opportunity to further understand the clinically healthy microbiome of exotic animals for all body locations and guide ultimately the clinicians on which species are of clinical concern and warrant a treatment. Environmental contaminants are a concern of all microbial tests, including culture and PCR testing, and are thus not unique to NGS-based testing. As previously discussed by Eisenhofer et al,49 NGS testing in a clinical diagnostic setting requires a high-quality control system designed to ensure immaculate sample preparation and analysis with the highest standard of precision, data accuracy, and controls.

Limitations of this study include the nature of the opportunistic sample collection. Ideally, a consistent sample host and type would have allowed a stronger signal of the pathogens reported or potentially missed by either diagnostic tool. In addition, the culture testing was not standardized, as each participating clinic submitted the samples for aerobic and anaerobic culturing at their preferred reference lab. Fungal culturing was only ordered in a subset of samples (68%), which makes a direct comparison of the results challenging. The geographic location of the patient may also have influence on the types of infections more prevalent in the given region.

The true composition of the microbial profile of the samples at the time of collection remains unknown. An unbiased comparison between the 2 diagnostic tools could be achieved by performing both tests on predefined mock community samples that contain a known mixture of pathogens and commensals at known concentrations. A limitation of this approach, however, would be that the host matrix and other confounding factors that can affect the success of the diagnostic test would not be accounted for in such an artificial setting. Opportunistic sample collection and comparison still offer an insight into the day-to-day clinical operations across the country and present a real-life perspective on the diagnostic results provided by each method. The data provided here may be used as a pilot study and provide some guidance toward a future systematic nationwide study.

In conclusion, this article provided the first study directly comparing culture- and NGS-based diagnostics for exotic animals suffering from suspected infectious diseases. Specifically, slow-growing pathogens, fungal pathogens, strict and facultatively anaerobic bacterial pathogens, novel and rare pathogens, and gram-positive bacteria were often not identified by culture but were diagnosed using NGS. Culture results failed to grow 15% of putative bacterial and 81% of putative fungal pathogens that were identified by NGS.

Supplementary Materials

Supplementary materials are posted online at the journal website: avmajournals.avma.org.

Acknowledgments

S. Malka, A. Damerum, and G. Vecere declare no conflict of interest. J. A. Krumbeck is an employee of the NGS-based testing laboratory.

The authors thank all the participating doctors, clinics, and institutions for their contributions to this study.

References

  • 1.

    Stewart EJ. Growing unculturable bacteria. J Bacteriol. 2012;194(16):41514160. doi:10.1128/JB.00345-12S.

  • 2.

    Nagy E, Boyanova L, Justesen US. How to isolate, identify and determine antimicrobial susceptibility of anaerobic bacteria in routine laboratories. Clin Microbiol Infect. 2018;24(11):11391148. doi:10.1016/j.cmi.2018.02.008

    • Search Google Scholar
    • Export Citation
  • 3.

    Clarridge JE. Impact of 16S rRNA gene sequence analysis for identification of bacteria on clinical microbiology and infectious diseases. Clin Microbiol Rev. 2004;17(4):840862. doi:10.1128/CMR.17.4.840-862.2004

    • Search Google Scholar
    • Export Citation
  • 4.

    Heatley JJ, Russell KE. Exotic Animal Laboratory Diagnosis. 1st ed. John Wiley & Sons; 2020.

  • 5.

    Saggese MD. Veterinary clinics of North America exotic animal practice. In: Saggese MD, ed. Mycobacteriosis, an Issue of Veterinary Clinics: Exotic Animal Practice. 1st ed. Vol 15. Elsevier; 2012.

    • Search Google Scholar
    • Export Citation
  • 6.

    Seifert HS. Tropical Animal Health. 2nd ed. Kluwer Academic Publishers; 1996.

  • 7.

    Raidal S, Cross G, Fenwick S, et al. Aquatic Animal Health Exotic Disease Training Manual. Fisheries Research and Development Corp and Murdoch University; 2004. https://researchportal.murdoch.edu.au/esploro/outputs/book/Aquatic-animal-health-exotic-disease-training/991005544187507891#file-0

  • 8.

    Kozel TR, Wickes B. Fungal diagnostics. Cold Spring Harb Perspect Med. 2014;4(4):a019299. doi:10.1101/cshperspect.a019299

  • 9.

    Zhang SX, Babady NE, Hanson KE, et al. Recognition of diagnostic gaps for laboratory diagnosis of fungal diseases: expert opinion from the Fungal Diagnostics Laboratories Consortium (FDLC). J Clin Microbiol. 2021;59(7):e0178420. doi:10.1128/JCM.01784-20

    • Search Google Scholar
    • Export Citation
  • 10.

    Hadfield CA, Whitaker BR. Amphibian emergency medicine and care. Semin Avian Exotic Pet Med. 2005;14(2):7989. doi:10.1053/j.saep.2005.04.003

    • Search Google Scholar
    • Export Citation
  • 11.

    Flowers L, Grice EA. The skin microbiota: balancing risk and reward. Cell Host Microbe. 2020;28(2):190200. doi:10.1016/j.chom.2020.06.017

    • Search Google Scholar
    • Export Citation
  • 12.

    Vecere G, Malka S, Holden N, Tang S, Krumbeck JA. Comparison of ear canal microbiome in rabbits (Oryctolagus cuniculus domesticus) with and without otitis externa using next generation DNA sequencing. J Exot Pet Med. 2022;42:3541. doi:10.1053/j.jepm.2022.05.002

    • Search Google Scholar
    • Export Citation
  • 13.

    Barcina I, Arana, I. The viable but nonculturable phenotype: a crossroads in the life-cycle of non-differentiating bacteria? Rev Environ Sci Biotechnol. 2009;8(3):254255. doi:10.1007/s11157-009-9159-x

    • Search Google Scholar
    • Export Citation
  • 14.

    Vartoukian S, Palmer R, Wade W. Strategies for culture of “unculturable” bacteria. FEMS Microbiol Lett. 2010;309(1):17.

  • 15.

    Keer JT. Quantitative real-time PCR analysis. In: Keer JT, Burch L, eds. Essentials of Nucleic Acid Analysis: A Robust Approach. RSC Publishing; 2008:132166.

    • Search Google Scholar
    • Export Citation
  • 16.

    Burton M, Krumbeck JA, Wu G, et al. The adult microbiome of healthy and otitis patients: definition of the core healthy and diseased ear microbiomes. PLoS One. 2022;17(11):e0262806. doi:10.1371/journal.pone.0262806

    • Search Google Scholar
    • Export Citation
  • 17.

    Pak TR, Kasarskis A. How next-generation sequencing and multiscale data analysis will transform infectious disease management. Clin Infect Dis. 2015;61(11):16951702. doi:10.1093/cid/civ670

    • Search Google Scholar
    • Export Citation
  • 18.

    Gu W, Miller S, Chiu CY. Clinical metagenomic next-generation sequencing for pathogen detection. Ann Rev Pathol. 2019;14:319338. doi:10.1146/annurev-pathmechdis-012418-012751

    • Search Google Scholar
    • Export Citation
  • 19.

    Goldberg B, Sichtig H, Geyer C, Ledeboer N, Weinstock GM. Making the leap from research laboratory to clinic: challenges and opportunities for next-generation sequencing in infectious disease diagnostics. mBio. 2015;6(6):e01888-15. doi:10.1128/mBio.01888-15

    • Search Google Scholar
    • Export Citation
  • 20.

    Tang S, Prem A, Tjokrosurjo J, et al. The canine skin and ear microbiome: a comprehensive survey of pathogens implicated in canine skin and ear infections using a novel next-generation-sequencing-based assay. Vet Microbiol. 2020;247:108764. doi:10.1016/j.vetmic.2020.108764

    • Search Google Scholar
    • Export Citation
  • 21.

    Manor O, Dai CL, Kornilov SA, et al. Health and disease markers correlate with gut microbiome composition across thousands of people. Nat Commun. 2020;11(1):5206. doi:10.1038/s41467-020-18871-1

    • Search Google Scholar
    • Export Citation
  • 22.

    Lebeer S, Spacova I. Exploring human host-microbiome interactions in health and disease–how to not get lost in translation. Genome Biol. 2019;20(1):56.doi:10.1186/s13059-019-1669-4

    • Search Google Scholar
    • Export Citation
  • 23.

    Krumbeck JA, Reiter AM, Pohl JC, et al. Characterization of oral microbiota in cats: novel insights on the potential role of fungi in feline chronic gingivostomatitis. Pathogens. 2021;10(7). doi:10.3390/pathogens10070904

    • Search Google Scholar
    • Export Citation
  • 24.

    Meason-Smith C, Diesel A, Patterson AP, et al. What is living on your dog’s skin? Characterization of the canine cutaneous mycobiota and fungal dysbiosis in canine allergic dermatitis. FEMS Microbiol Ecol. 2015;91(12):112. doi:10.1093/femsec/fiv139.

    • Search Google Scholar
    • Export Citation
  • 25.

    Kudirkiene E, Hansen MJ, Bojesen AM. Draft genome sequence of Chelonobacter oris strain 1662T, associated with respiratory disease in Hermann’s tortoises. Genome Announc. 2014;2(6):e01322-14. doi:10.1128/genomeA.01322-14

    • Search Google Scholar
    • Export Citation
  • 26.

    Ribeiro MG, Risseti RM, Bolaños CAD, et al. Trueperella pyogenes multispecies infections in domestic animals: a retrospective study of 144 cases (2002 to 2012). Vet Quart. 2015;35(2):8287. doi:10.1080/01652176.2015.1022667

    • Search Google Scholar
    • Export Citation
  • 27.

    Rzewuska M, Kwiecień E, Chrobak-Chmiel D, Kizerwetter-Świda M, Stefańska I, Gieryńska M. Pathogenicity and virulence of Trueperella pyogenes: a review. Int J Mol Sci. 2019;20(11):2737. doi:10.3390/ijms20112737

    • Search Google Scholar
    • Export Citation
  • 28.

    Janda JM, Graves MH, Lindquist D, Probert WS. Diagnosing Capnocytophaga canimorsus infections. Emerg Infect Dis. 2006;12(2):340-342. doi: 10.3201/eid1202.050783

    • Search Google Scholar
    • Export Citation
  • 29.

    Daniels JB. Molecular diagnostics for infectious disease in small animal medicine: an overview from the laboratory. Vet Clin North Am Small Anim Pract. 2013;43(6):13731384. doi:10.1016/j.cvsm.2013.07.006

    • Search Google Scholar
    • Export Citation
  • 30.

    Hess C, Enichlmayr H, Jandreski-Cvetkovic D, Liebhart D, Bilic I, Hess M. Riemerella anatipestifer outbreaks in commercial goose flocks and identification of isolates by MALDI-TOF mass spectrometry. Avian Pathology. 2013;42(2):151156. doi:10.1080/03079457.2013.775401

    • Search Google Scholar
    • Export Citation
  • 31.

    Cha SY, Seo HS, Wei B, et al. Surveillance and characterization of Riemerella anatipestifer from wild birds in South Korea. J Wildl Dis. 2015;51(2):341347. doi:10.7589/2014-05-128

    • Search Google Scholar
    • Export Citation
  • 32.

    Padmaja K, Lakshmi V, Subramanian S, Neeraja M, Krishna SR, Sai Satish O. Infective endocarditis due to Granulicatella adiacens: a case report and review. J Infect Dev Ctries. 2014;8(4):548550. doi:10.3855/jidc.3689

    • Search Google Scholar
    • Export Citation
  • 33.

    Michel P, Janna D. Characterization and identification of bacterial flora from infected equine hooves. Int J Vet Sci Res. 2022;8(2):5056. doi:10.17352/ijvsr.000113

    • Search Google Scholar
    • Export Citation
  • 34.

    Lefèvre CR, Pelletier R, Le Monnier A, et al. Clinical relevance and antimicrobial susceptibility profile of the unknown human pathogen Corynebacterium aurimucosum. J Med Microbiol. 2021;70(3). doi:10.1099/jmm.0.001334

    • Search Google Scholar
    • Export Citation
  • 35.

    Plassart C, Mauvais F, Heurté J, Sautereau J, Legeay C, Bouvet P. First case of intra-abdominal infection with Clostridium disporicum. Anaerobe. 2013;19(1):7778. doi:10.1016/j.anaerobe.2012.12.002

    • Search Google Scholar
    • Export Citation
  • 36.

    McBride JA, Sterkel AK, Rehrauer WM, Smith JA. First described case of prosthetic joint infection with Clostridium disporicum. Anaerobe. 2017;48:5658. doi:10.1016/j.anaerobe.2017.06.022

    • Search Google Scholar
    • Export Citation
  • 37.

    Collins R. Bibersteinia trehalosi in cattle-another component of the bovine respiratory disease complex? Cattle Practice. 2011;19(1):912.

    • Search Google Scholar
    • Export Citation
  • 38.

    Hanthorn CJ, Dewell RD, Cooper VL, et al. Randomized clinical trial to evaluate the pathogenicity of Bibersteinia trehalosi in respiratory disease among calves. BMC Vet Res. 2014;10:89. doi:10.1186/1746-6148-10-89

    • Search Google Scholar
    • Export Citation
  • 39.

    Amir A, McDonald D, Navas-Molina JA, et al. Correcting for microbial blooms in fecal samples during room-temperature shipping. mSystems. 2017;2(2):e00199-16. doi:10.1128/msystems.00199-16

    • Search Google Scholar
    • Export Citation
  • 40.

    Kim JH, Jeon JY, Im YJ, et al. Long-term taxonomic and functional stability of the gut microbiome from human fecal samples. Sci Rep. 2023;13(1):114. doi:10.1038/s41598-022-27033-w

    • Search Google Scholar
    • Export Citation
  • 41.

    Salem N, Salem L, Saber S, Ismail G, Bluth MH. Corynebacterium urealyticum: a comprehensive review of an understated organism. Infect Drug Resist. 2015;8:129145. doi:10.2147/IDR.S74795

    • Search Google Scholar
    • Export Citation
  • 42.

    Gupta S, Garg M, Misra S, Singhal S. Granulicatella adiacens abscess: two rare cases and review. J Lab Physicians. 2018;10(01):121123. doi:10.4103/jlp.jlp_58_17

    • Search Google Scholar
    • Export Citation
  • 43.

    Nowland MH, Brammer DW, Garcia A, Rush HG. Chapter 10–biology and diseases of rabbits. In: Fox J, Anderson L, Otto G, Pritchett-Corning K, Whary M, eds. Laboratory Animal Medicine. 3rd ed. Elsevier Inc; 2015:411461. doi:10.1016/B978-0-12-409527-4.00010-9

    • Search Google Scholar
    • Export Citation
  • 44.

    Derome N, Gauthier J, Boutin S, Llewellyn M. Bacterial opportunistic pathogens of fish. In: Hurst CJ, ed. The Rasputin Effect: When Commensals and Symbionts Become Parasitic. Advances in Environmental Microbiology. Vol 3. Springer; 2016:81108. doi:10.1007/978-3-319-28170-4_4

    • Search Google Scholar
    • Export Citation
  • 45.

    Krawczyk B, Wityk P, Gałęcka M, Michalik M. The many faces of Enterococcus spp.—commensal, probiotic and opportunistic pathogen. Microorganisms. 2021;9(9):1900. doi:10.3390/microorganisms9091900

    • Search Google Scholar
    • Export Citation
  • 46.

    Schmitz S, Suchodolski J. Understanding the canine intestinal microbiota and its modification by pro-, pre- and synbiotics–what is the evidence? Vet Med Sci. 2016;2(2):7194. doi:10.1002/vms3.17

    • Search Google Scholar
    • Export Citation
  • 47.

    Melgarejo T, Oakley BB, Krumbeck JA, Tang S, Krantz A, Linde A. Assessment of bacterial and fungal populations in urine from clinically healthy dogs using next-generation sequencing. J Vet Intern Med. 2021;35(3):14161426. doi:10.1111/jvim.16104

    • Search Google Scholar
    • Export Citation
  • 48.

    Roberts FA, Darveau RP. Microbial protection and virulence in periodontal tissue as a function of polymicrobial communities: symbiosis and dysbiosis. Periodontology. 2015;69(1):1827. doi:https://doi.org/10.1111/prd.12087

    • Search Google Scholar
    • Export Citation
  • 49.

    Eisenhofer R, Minich JJ, Marotz C, Cooper A, Knight R, Weyrich LS. Contamination in low microbial biomass microbiome studies: issues and recommendations. Trends Microbiol. 2019;27(2):105117. doi:10.1016/j.tim.2018.11.003

    • Search Google Scholar
    • Export Citation

Supplementary Materials

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 3634 2310 533
PDF Downloads 2119 1153 84
Advertisement