Molecular characterization of head and neck infection causing bacterial communities using 16S rRNA in eastern Indian population

doi:10.21203/rs.3.rs-3100101/v1

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Molecular characterization of head and neck infection causing bacterial communities using 16S rRNA in eastern Indian population

https://doi.org/10.21203/rs.3.rs-3100101/v1

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published 01 Jun, 2024

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Head and neck infections (HNI) are dangerous illnesses caused by a variety of bacterial species. Due to either incorrect diagnosis or excessive antibiotic usage, multidrug resistance (MDR) poses a problem on a global scale. We aimed this study to identify and compare their relationship with eastern Indian MDR bacteria.We collected 264 HNI clinical samples (2020–2022) from the eastern Indian state of Odisha to investigate the bacterial isolates associated with HNI, as well as the characterization was performed with their drug susceptibility and assessment of their diversity by comparison with worldwide bacterial isolates. There are 218 positive bacterial samples. Using the 16S rRNA gene, we performed molecular characterization and phylogenetic analysis on 25 multidrug-resistant bacteria. According to population genetic factors, the haplotype (Hd) and nucleotide diversity (π) ranged from 0.558 to 0.828 and 0.03236 to 0.28428.The prevalence of these neglected pathogens within this rarely focused geographical area and the transboundary potential to find better therapeutic approaches can be possible through this study.

Biological sciences/Biotechnology

Biological sciences/Microbiology

Biological sciences/Molecular biology

Health sciences/Medical research

Infections and abscesses at the head and neck region in humans are frequently caused by bacteria. The head and neck infection (HNI) associated sites include otitis media, odontogenic region, oral cavity, pharynx, larynx, paranasal sinus, orbit, parotid gland, sub-mandibular gland, and the deep neck. Infections that are localized initially can spread into the deep neck and even descend into the mediastinum, due to the connection of the cervical fascial spaces. Most HNIs initially originated from the otitis, sino-nasal, odontogenic, oral, and pharyngeal regions [1]. The infection is also associated with blepharitis and dacryocystitis, conjunctivitis, keratitis, preseptal (periorbital) and orbital cellulitis, endophthalmitis, chorioretinitis syndromes, oropharyngeal diseases, acute and chronic sinusitis as well as otitis externa and interna [2, 3]. Although HNIs are prevented by the widespread use of antibiotics, the serious illness still deteriorates over time, especially when it descends into the mediastinum. In this case, the mortality rate becomes 10–40% due to sepsis and organ failure [4]. Bacterial colonization within the brain results in a brain abscess which leads to serious consequences.

The pathogens usually colonize two main areas heavily during their progression. Those included the skin and the upper respiratory tract. The flora of pathogens included Staphylococci, Corynebacterium spp, Haemophilusspp, saprophytic Neisseria spp, Micrococci, nonhemolytic and anaerobes like Propionibacterium, Peptostreptococcus, Veillonella, and Lactobacillus spp. Except these, a wide range of other pathogenic organisms such as beta-hemolytic Streptococci, Haemophilus influenza (H. influenza), Streptococcus pneumoniae (S.pneumoniae),Staphylococcus aureus (S.aureus), Neisseria meningitidis(N. meningitidis), Moraxella catarrhalis(M. catarrhalis ), Fusobacterium, and Actinomyces spp commonly found at these sites [5]. Spirochetes spp. usually present in the oral cavity and initiate the infection there [6]. However, some DNA viruses named herpes simplex virus (HSV), cytomegalovirus (CMV), and varicella zoster virus (VZV) maintain the latent stage for a long period and become reactivated during the opportunity. Due to their enormous diversity, complex anatomic structures, and environmental variability, the presence of normal flora varies at the site of infection. However, the presence of a range of pathogens during infection also depends upon many factors such as age, health condition, hygiene, use of antibiotics, smoking, and other conditions [5]. The major risk factors for HNI are the pathogenic microbial community, which is adhered to through various unusual lifestyles. Previously reported studies have shown that the most frequently isolated bacterial species were S. aureus, Klebsiella pneumoniae (K. pneumoniae), Escherichia coli (E. coli), Pseudomonas putida (P. putida), Pseudomonas aeruginosa, (P. aeruginosa), Acinetobacterbaumannii (A.baumannii), Citrobacterfreundii (C. freundii), Streptococcusgordonii (S. gordonii), H. influenza and Enterococcus spp.[7].

Traditional biochemical method of bacterial identification is labor-intensive, time-consuming, and expensive, and can produce false results. When the microbial species are likely similar to their phenotypic characters, a biochemical assay cannot adequately determine the strain of interest [8]. As a result, advanced techniques such as Vitek 2 identification and 16S rRNA gene sequencing can be used for proper antibiotic susceptibility patterns and to better assign strain-level identification [9]. In the past few decades, scientists have found several molecular markers that can be used to identify each microbial species and place it in a certain phylogenetic group. These markers belong to certain protein-coding/structural genes or their combinations. Among them, 16S rRNA is mostly used due to its universal amplification and assessment of both close and far-related bacterial species [10]. This typical gene composite of 1500bp nucleotide sequences encodes a catalytic RNA found in the 30S subunit of the ribosome [11]. This study is to investigate the prevalence of pathogenic bacteria associated with HNI utilizing 16S rRNA gene-based identification and phylogeny of the eastern Indian population.

Study subjects

A sum of 264 head and neck infected swab samples were obtained from different purulent discharge sites of patients admitted to the Otorhinolaryngology Department of the Institute of Medical Sciences and SUM Hospital in Bhubaneswar, Odisha between 2020–2022. Informed consent was obtained from the subjects/ or their legal guardians from whom the swab samples were taken. Patients having profuse as well as scanty purulent discharge were selected for further evaluation. This study was approved by the Institutional ethical committee (Ref. no/IEC/IMS.SH/SOA/2022/403). This study has been performed in accordance with the Declaration of Helsinki.

Isolation, identification, and antibiotic susceptibility profiling of the collected sample

After aseptic collection of purulent discharged swab samples, those were immediately streaked on Blood Agar (BA), MacConkey Agar (MAC), and Sabouraud dextrose Agar (SDA), and then incubated overnight at 37⁰C. Swabs were also incubated overnight with 2ml of nutrient broth (NB) for short-term storage. Preliminary biochemical identification, Vitek 2, and antibiotic profiling were carried out using standard procedures [12, 13].

Molecular Identification and characterization

Genomic DNA extraction

The genomic DNA of all resistant bacteria was extracted using the QIAamp DNA Micro Kit. A single colony from pure cultured nutrient agar plates was inoculated in a fresh and sterile 2ml nutrient broth tube for the preparation of bacterial liquid cultures. The liquid culture tubes were placed in a shaker at 180 rpm and incubated at 37⁰ C overnight. Freshly prepared broth cultures were taken for DNA isolation according to the kit procedure. After extraction, the quality and quantity of DNA were checked using 0.8% agarose gel electrophoresis. For any further analysis, the extracted DNA was stored at -20⁰C.

PCR amplification using 16S rRNA gene

Partial gene sequences of 16S rRNA were amplified using universal primers 8F (5^/AGAGTTTGATCCTGGCTCAG3^/) and reverse primer 1541R (5^/AAGGAGGTGATCCAGCCGCA3^/) [14]. The following components were used in a thermal cycler to perform PCR amplification in a 25 µl reaction volume: 1X PCR buffer, 0.25mM of dNTP mix, 10 picomoles of each primer, 0.25U of Taq polymerase, and 50 ng/µl genomic DNA. The PCR reaction conditions were as follows: initial denaturation at 94°C for 4 min, then 35 cycles of denaturation for 30 sec, annealing for 1 min, and extension for 2 min, followed by a final extension at 72°C for 7 min. After the PCR products were verified using 1% agarose gel, the most intense and exact products were chosen for sequencing. The amplicons were purified with a Qiagen PCR purification kit. The Sanger sequencing was carried out on an automated DNA sequencer (ABI Prism 310 genetic analyzer) using a universal primer.

Sequence analysis and construction of the phylogenetic tree

The raw sequence data of specific bacteria derived during this study were annotated and to get the accession numbers they were submitted to GenBank. Furthermore, to infer the relationship of the present isolates with global isolates, fifty gene-specific sequences were retrieved from GenBank, and to obtain a consensus sequence, they were aligned using the Bioedit software version 7.0.5.3. Using DnaSP version 5.10.01, population genetics parameters for this specific bacterial evolution, including nucleotide diversity (π), haplotype diversity (Hd), selection pressure (θ = dN/dS), and neutrality test (D), were obtained [15]. The Kimura's two parameter with bootstrap value 1000 was implemented in MEGA 10 to construct species specific phylogenetic tree [16].

Demographic Characteristics

At our tertiary care health center, only 264 HNI individuals were included in the analysis. There were 84 women (31.81%) and 180 men (68.18%). The patient's ages ranged from 9-72, with a mean of 40.62 (standard deviation: 17.58) years. Female patients ranged in age from 9-65 (mean 48.45, SD 18.566) years, whereas male patients ranged in age from 16-72 (mean 49.7, SD 17.1). HNIs were most common in those aged 25–50, followed by those above 50. The submandibular area, parotid area, buccal/oral area, sino-nasal area, otitis media, neck region, and lateral pharyngeal region were targeted for sample collection.

Microbiological Analysis and Bacterial Isolations

In total, 218 (82.58%) positive bacterial isolates were obtained from 264 head and neck infection samples, and the rest 46 (17.42%), did not meet our inclusion criteria. Therefore, 218 cultures were processed for biochemical identification, and 46 samples were excluded from the study. The biochemical analysis identified 8 different types of bacteria, of which the abundance of P. aeruginosa (26.15%) was the highest followed by K. pneumoniae (21.56%), S. aureus (17.89%), A. baumanii (16.51%), E. faecalis (8.26%), S. pyogenes (5.05%), C. freundii (3.21%) and K. oxytoca (1.38%) (Figure1).

Antibiotic susceptibility

The antibiotic susceptibility pattern was analysed, where the resistance pattern was determined according to the zone of inhibition produced by different antibiotics. The susceptibility pattern of phenotypically characterized bacteria was noted differently for gram-negative and gram-positive bacteria. Sixteen different antibiotics were used for both groups of bacteria. Ticarcillin was highly resistant (70.21%) to K. pneumoniae in the case of gram-negative bacteria and the beta-lactam group of antibiotics (Ticarcillin and Piperacillin), followed by monobactam (aztreonam) 65.96% and carbapenem (meropenem) 53.19%. P. aeruginosa revealed the highest resistance to tetracycline at 85.45% and tigecycline at 70.91%. A. baumannii was resistant to monobactam (aztreonam) (76.47%) and 73.53%, whereas C. freundii and K. oxytoca were highly resistant to tetracycline 71.43% and beta-lactam group (ticarcillin) 100%, respectively (Table 1).

According to the antibiotic susceptibility pattern of gram-positive bacteria, the cephalosporin group of antibiotics was highly resistant (97.44%) against S. aureus, amoxicillin had a 93.75% resistant pattern against E. faecalis, and the cyclic-lipopeptide group of antibiotics (daptomycin) had 100% resistance against S. pyogenes (Table 2). According to the statistical analysis, P. aeruginosa (26.15%) is the most resistant isolate, followed by K. oxytocawith the least resistance (1.38%), with a mean and standard deviation 28.94±8.13 and 0.63±0.81 respectively (Table 3).

Bacterial identification results obtained from Vitek 2 procedure remained as usual for the biochemical identification. But the susceptibility pattern revealed higher resistance activity with the same antibiotics in Vitek 2 MIC analysis, where a particular concentration of antibiotics showed their mode of action towards the bacteria (Table 4).

Molecular identification and phylogenetic analysis

The 25 clinical HNI samples were amplified with primers specific to the 16S rRNA gene, to produce 604 bp amplicons (Figure 2). GenBank accession numbers OP881606- OP889581 were assigned to the corresponding sequences (Table 5). The similarities among the existing isolates were found to be between 99-100% in terms of the nucleic acid sequence.

For all four bacteria, the Hd and π ranged from 0.558-0.828 and 0.03236-0.28428, respectively (Supplementary file 1). The S. aureus population had the highest Hd (0.828), followed by K. pneumoniae (0.776), P. aeruginosa (0.598), and A. baumannii (0.558), respectively. In this case, K. pneumoniae (0.28428) has the highest nucleotide diversity followed by A. baumannii (0.14254), S. aureus (0.06180), and P. aeruginosa (0.03236) genes. As the value of selection pressure analysis (θ = dN/dS) < 1, all bacterium of the present study has undergone purifying selection. The negative value of Tajima's D test of neutrality revealed these four bacteria laid on a period of evolutionary expansion. Four species-specific phylogenetic trees were constructed (Figure 3, a-d) utilizing the sequences submitted during this study along with fifty global isolates retrieved from GenBank. Interestingly all the present isolates derived during this study related to Chinese isolates indicating the transboundary potential of these bacteria.

Bacteria associated with HNI are prevalent throughout the world [17]. But in India, these neglected pathogens have got very less attention. However, few cases were reported concerning these bacteria. That enhanced our curiosity to get an insight into these bacteria prevalence in the eastern part of India. Identifying the causative agent is crucial for the development of effective treatment strategies. Thus, various methods are usually applied to investigate pathogens that include the biochemical method, molecular methods, etc. Each method has its advantage and disadvantage. Although the biochemical method is easy and less time-consuming, sometimes it is hard to obtain the best culture medium for the growth of diverse bacterial species [8]. In that case, molecular detection became a gold standard method to disseminate the bacterial community. However, another advantage of the molecular method includes the estimation of genetic diversity [18]. Culture methods and the 16S rRNA gene sequencing methods were commonly utilized to study the simultaneous identification and characterization of bacterial communities in HNI samples across age groups at our tertiary care health facility.

The isolates included in the present study were both pathogenic and nonpathogenic bacteria. For every 218 isolates, 68 were gram-positive aerobes (25.75%), 150 were gram-negative aerobes (56.81%), and 46 (17.42%) were non-bacterial growth. There was a notable difference between conventional and Vitek 2 antibiotic susceptibility patterns on this score. The number of antibiotics revealed resistant activity obtained from minimum inhibitory concentration of Vitek 2 was higher compared to conventional measurement of the zone of inhibition. There should be a correlation between these two techniques as the breakpoint of antibiotic intake can be determined through MIC analysis [19]. Methicillin-resistant S. aureus (MRSA), K. pneumoniae, and K. oxytoca, highly pathogenic bacteria isolated from healthcare-associated infections, [20] were closely related to the bacteria of the present study. P. aeruginosa, the most often isolated bacterium in this investigation, was found in 57 individuals (26.15%), followed by K. pneumoniae, A. baumannii, C. freundii, K. oxytoca, S. aureus, and S. pyogenes. This goes against the findings of previous investigations, in which S. viridans was the highest isolate in cases among gram-positive bacteria [21, 22]. That unbiasedness might be possible due to the introduction of a new strain or complex host-pathogen interaction, which requires further in-depth research.

It was found that amikacin and gentamycin (36%) were the most effective antibiotics against the gram-negative bacteria examined here, while amikacin was shown to be effective against both P. aeruginosa and K. pneumoniae, and ineffective against A. baumannii, which was sensitive to penicillin, ticarcillin, and meropenem. The significance of gram-positive as well as gram-negative organisms in HNI infections is substantial. As a result of their fast progression, abscess drainage through surgery combined with intravenous antibiotics in the treatment of HNIs can be considerable. This emphasizes the need for epidemiological research on HNIs such that the appropriate empirical therapy may be determined for a certain location [23]. However, our study revealed the prevalence of various bacterial species within the patients which will lead to the use of various combinations of antibiotics for the prevention of this infection in the future.

In our second experiment, sequencing of the 16S rRNA gene was used to decipher bacterial community. 16S rRNA gene sequencing has been widely used to decipher the pathogens of infections from normally sterile sites[24, 25]. Using the same methods on human lung specimens, Einarsson et al., 2016 showed that bronchial lavages exhibited a preponderance of Firmicutes, Actinobacteria, Bacteroidetes, and Proteobacteria[26]. In our study, we observed four multidrug-resistant species. The haplotype and nucleotide diversity is used as key parameter to determine the evolution of the species[27]. More or less similar diversity was measured throughout all species. So, we can assume the parallel evolution of these species. Furthermore, we have constructed four species-specific phylogenetic trees to infer the relationship of present isolates with other global isolates. Interestingly all the isolates (four species) revealed their relationship with Chinese isolates. We can assume the globalization that leads to ease of human travel might be responsible for the transboundary property of these pathogens.

The 16S rRNA gene revealed more potential than the standard biochemical methods, but it cannot deliver information about strain or genotype[24]. It has a low potential to deliver a clear phylogenetic network for genotype or strain discrimination [24, 28]. For example, two species of Bacillus, such as B. globisporus and B. psychrophilus contribute 99.5% nucleotide sequence similarity in terms of their 16S rRNA genes, but at the entire genome level, they share only 23 to 50% relatedness[29].Similarly, the study carried out by Janda et al.(2007)[30] in Edwardsiellaspecies revealed the presence of 99.35 to 99.81% nucleotide similarities within 16SrRNA, which cannot differentiate the species. As the resolution of 16S rRNA gene sequencing depends upon sequence similarity or nucleotide BLAST scores, 16SrRNA gene sequence data cannot give a proper answer and it cannot infer the relationship between recently diverged species[31]. This kind of ambiguity was observed within the 16S rRNA gene sequence of the Streptococcus mitis group[30]. Due to these disadvantages, a robust molecular tool should be developed to reveal the species and their genotypes. Thus, the implementation of molecular markers such as Variable Number of Tandem Repeats (VNTRs) also known as Simple Sequence Repeats (SSRs) may be investigated followed by their implementation to make a multiplex panel kit to solve this problem. This marker will target the region that is more prone to mutation rather than the more conserved region of the species like 16S rRNA.

Our study gave an insight into the pathogen prevalence in eastern India that may help to elucidate more scientific information for the development of an effective strategy to eradicate this disease. Bacteria are prone to mutation, genetic recombination, and the elimination of non-essential genes or the introduction of host genes to produce novel strains. As a result, the introduction of new genotypes mimics the host factor and controls the pathogen's pathogenicity. It is challenging to learn more about this pathogen due to the lack of adequate genomic resources and whole genome sequences from India. To build an effective strategy for disease prevention interventions, our next goal will be to disseminate the complete genome of Indian strains.

In conclusion, multidrug-resistant bacteria usually developed through bad chemotherapeutic practices and the use of unapproved medicines. The high level of resistance to the most popular antibiotics is alarming. When diagnosing a bacterial infection, conventional biochemical identification tests must be double-checked using molecular approaches, such as 16S gene sequencing. 16S rRNA sequencing has a major effect on reducing the need for antibiotics in the treatment of hospital-based infections. It has the potential to be a helpful clinical tool for making a diagnosis in cases where no culture is available. Furthermore, continuous surveillance should be maintained to obtain a clear picture concerning the genotype of those pathogens which regulate the severity of the disease.

Additional informations

Acknowledgments: Authors would like to thank Prof. (Dr.) Sanghamitra Mishra, Dean, IMS & SUM Hospital, Siksha ‘O’ Anusandhan deemed to be University, Bhubaneswar for her support and all technical hands of Medical Research Laboratory, IMS & SUM Hospital

Authors Contributions: Conceptualization, S.L., and S.K.S.; methodology, S.K.S.; software, data curation, resources, B.P.S; R.K.L.; writing—original draft preparation, S.L., and B.P.S; writing—review and editing, K.K.S.; visualization, D.D.; supervision, S.K.S; project administration, S.K.S; funding acquisition, S.K.S.; and D.D.; validation, M.C.S.; formal analysis, D.D.; investigation, S.L. All authors have critically reviewed and approved the final draft and are responsible for the content and similarity index of the manuscript.

Data Availability Statement: The nucleotide sequence generated during this study was submitted to GenBank (https://www.ncbi.nlm.nih.gov/) to get the following accession number for the respective species. Pseudomonas aeruginosa: OP881606, OP881607, OP881608, OP881609, OP881610, OP881611, OP881612; Acenetobacterbaumannii: OP889558, OP889559, OP889560, OP889561, OP889562, OP889563, OP889564; Staphylococcus aureus: OP889672, OP889673, OP889674, OP889675; Klebsiella pneumoniae: OP889575, OP889576, OP889577, OP889578, OP889579, OP889580, OP889581

Source of Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Conflicts of Interest:Authors declared no conflict of interest.

Ethical approval: The study was conducted following the approval of the Institutional Ethics Committee, of the Institute of Medical Sciences & SUM Hospital, Siksha ‘O’ Anusandhan deemed to be University, Bhubaneswar. (Ref.no/IEC/IMS.SH/SOA/2022/403).

Supplementary Materials: The following supporting information is in (supplementary file 1) Table S1: Table illustrating, population genetics parameters evaluated for the respective species of bacteria globally and supplementary file 2: the pdf version of the gel figure are uploaded.

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Table 1: Resistance % of gram-negative bacteria

Organisms	Beta-lactam		Cephalosporin			Monobactam	Carbapenem			Aminoglycoside		Fluroquinolones		Tetracyclin		Polymixin
Organisms	TI	PI	CAZ	CPZ	CPM	AT	DOR	IPM	MRP	AK	GEN	CIP	LE	TE	TGC	CL
Klebsiella pneumonaea	70.21	48.94	38.30	44.68	53.19	65.96	48.94	44.68	53.19	23.40	23.40	25.53	46.81	48.94	38.30	36.17
Pseudomonas aeruginosa	60	61.82	43.64	67.27	54.55	50.91	30.91	38.18	40	43.64	52.73	52.73	58.18	85.45	70.91	30.91
Acenetobacterbaumannii	55.88	41.18	44.12	61.76	73.53	76.47	64.71	41.18	35.29	50	52.94	50	50	64.71	55.88	55.88
Citrobacter freundii	0	14.29	42.86	28.57	14.29	28.57	28.57	42.86	28.57	57.14	14.29	42.86	14.29	71.43	57.14	28.57
Klebsiella oxytoca	100	33.33	0	0	33.33	33.33	33.33	33.33	33.33	0	0	0	0	33.33	0	0

*TI-Ticarcillin, PI-Piperacillin, CAZ- Ceftazidime, CPM-Cefepime, AT- Aztreonam, DOR- Doripenem, MOR- Meropenem, AK- Amikacin, GEN- Gentamycin, CIP-Ciprofloxacin, LE- Levofloxacin, TE-Tetracyclin, CL-Colistin.

Table 2: Resistance % of gram-positive bacteria

*Organisms*	Cephalosporin	Beta-lactam			Amino-glycoside	Quinolone			Macrolid	Lincomycin	Oxazoli-dinones	Cyclic lipopeptide	Glycopeptide		Tetracyclin	Glycylcycline
*Organisms*	CX	P	OX	AMX	GEN	CIP	LE	MO	E	CD	LZ	DAP	TEI	VA	TE	TGC
Staphylococcus aureus	97.44	69.23	25.64	82.05	89.74	82.05	58.97	12.82	2.56	5.13	10.26	30.77	17.95	33.33	33.33	38.46
Enterococcus faecalis	75	31.25	62.5	93.75	81.25	31.25	68.75	18.75	18.75	18.75	31.25	68.75	25	37.5	37.5	50
Streptococcus pyogenes	36.36	54.55	72.73	45.45	90.91	45.45	36.36	72.73	36.36	36.36	90.91	100	81.82	36.36	63.64	63.64

*CX-Cefixime, P-Penicillin, OX-Oxacillin, AMX-Amoxicillin, GEN-Gentamycin, CIP-Ciprofloxacin, LE-Levofloxacin, MO-Moxifloxacin, E-Erythromycin, CD-Clindamycin, LZ-Linezolid, DAP-Daptomycin, TEI-Teicoplanin, VA-Vancomycin, TE-Tetracycline, TGC-Tigecycline

Table 3: Determination of resistance mean and standard deviation of biochemically identified bacteria.

Gram-negative Bacteria	Resistance (Mean ±standard deviation)
Klebsiella pneumoniae	20.88 ±6.34
Acinetobacter baumannii	18.56±3.93
Pseudomonas aeruginosa	28.94±8.13
Citrobacter freundii	2.25±1.34
Klebsiella oxytoca	0.63±0.81
Gram-positive Bacteria
Staphylococcus aureus	16.81±12.53
Enterococcus faecalis	7.44±3.95
Streptococcus pyogenes	6.63±2.47

Table 4: Vitek 2 identified multidrug-resistant bacteria and their MIC (minimum inhibitory concentration) interpretation.

Name of organism

BEN

GEN

CIP

Inducable clindamycin resistant

CLN

LNZ

DAP

TEI

TGC

NIT

RIF

PEN

MRSA

POS

0.5/r

4/r

0.5/s

4/r

POS+

8/r

0.25/r*

2/s

0.25/s

4/s

2/s

16/r

0.25/s

16/s

0.03/s

MRSA

POS

0.5/r

4/r

2/s

2/i

8/r

NEG

8/r

0.5/s

4/s

0.5/s

16/i

2/s

1/s

0.25/s

16/s

0.03/s

MRSA

POS

0.5/r

4/r

16/r

8/r

NEG

8/r

4/r

2/s

1/s

0.5/s

16/r

0.25/s

32/s

4/r

MRSA

POS

0.5/r

4/r

0.5/s

8/r

POS+

8/r

4*/r

2/s

1/s

2/s

1/s

0.12/s

16/s

0.03/s

Name of organism

CAZ

CPZ

CPM

DOR

IMP

MRP

GEN

CIP

TGC

K.pneumonae

128/r

64/r

8/r

16/r

64/r

16/r

4/r

8/r

16/r

4/r

0.5/i

20/s

K.pneumonae

128/r

64/r

8/r

2/r

16/r

64/r

16/r

4/r

8/r

16/r

4/r

0.5/i

320/r

K.pneumonae

128/r

64/r

8/r

1*/*r

4/r

64/r

16/r

4/r

8/r

16/r

0.5/s

0.5/i

320/r

K.pneumonae

128/r

64/r

8/r

4/r

16/r

64/r

2/s

4/r

8/r

16/r

4/r

16/r

320/r

K.pneumonae

128/r

64/r

8/r

2/i

16/r

64/r

2/s

4/r

8/r

16/r

4/r

0.5/i

320/r

K.pneumonae

128/r

64/r

8/r

2/i

16/r

64/r

16/r

4/r

16/r

4/r

0.5/i

20/i

K.pneumonae

128/r

64/r

1/s

8/r

16/r

64/r

16/r

4/r

8/r

8/i

2/r

0.5/i

320/r

Name of organisms

CAZ

CPZ

CPM

DOR

IMP

MRP

GEN

CIP

TGC

A. baumanii

128/r

32/r

32/i

32/r

8/r

16/r

64/r

8/i

4/r

8/r

16/r

8/r

16/r

80/r

A. baumanii

128/r

64/r

8/r

16/r

64/r

16/r

4/r

8/r

8/i

2/s

0.5/i

320/r

A. baumanii

128/r

64/r

8/r

16/r

64/r

16/r

4/r

8/r

16/r

4/i

0.5/i

320/r

A. baumanii

128/r

64/r

4/i

16/r

16/s

16/r

4/r

8/r

16/r

0.5/r

320/r

A. baumanii

128/r

64/r

8/r

16/r

64/r

16/r

4/r

8/r

16/r

2/s

0.5/i

320/r

A. baumanii

128/r

64/r

8/r

16/r

64/r

16/r

4/r

8/r

1/s

2/s

0.5/i

20/s

A. baumanii

128/r

64/r

32/i

64/r

8/r

16/r

8/s

16/r

4/r

8/r

16/r

4/i

0.5/i

20/s

Name of organism

CAZ

CPZ

CPM

DOR

IPM

MRP

GEN

CIP

TGC

P.aeruginosa

128/r

64/r

8/r

16/r

64/r

16/r

4/r

8/r

4/r

80/r

P.aeruginosa

128/r

64/r

8/r

16/r

64/r

16/r

4/r

8/r

16/r

320/r

P.aeruginosa

128/r

64/r

8/r

16/r

64/r

16/r

4/r

8/r

0.5/i

320/r

P.aeruginosa

128/r

64/r

16/r

64/r

16/r

4/r

0.5/i

320/r

P.aeruginosa

128/r

64/r

8/r

16/r

64/r

4/s

4/r

8/r

16/r

320/r

P.aeruginosa

128/r

64/r

8/r

16/r

4/r

8/r

0.5/i

20/s

P.aeruginosa

128/r

64/r

16/i

64/r

8/r

16/r

64/r

16/r

4/r

8/r

16/r

20/s

*r-resistant, s-sensitive, i-intermediate NA-Not applicable, MRSA- Methicillin resistant S. aureus

Table 5. Details of bacteria isolation sources and NCBI accession number.

S.no	Isolation source	Host species	Name of bacteria	Submission code	Accession no.
1	Neck abscess	Human	P. aeruginosa	PSEAIMSSUMD02	OP881606
2	Otitis media	Human	P. aeruginosa	PSEAIMSSUMD06	OP881607
3	Otitis media	Human	P. aeruginosa	PSEAIMSSUMS35	OP881608
4	Oral cavity	Human	P. aeruginosa	PSEAIMSSUMS36	OP881609
5	Retropharyngeal abscess	Human	P. aeruginosa	PSEAIMSSUMS37	OP881610
6	Oral cavity	Human	P. aeruginosa	PSEAIMSSUMS39	OP881611
7	Oral cavity	Human	P. aeruginosa	PSEAIMSSUMS40	OP881612
8	Neck abscess	Human	A. baumannii	ACENIMSSUMD05	OP889558
9	Neck abscess	Human	A. baumannii	ACENIMSSUMD06	OP889559
10	Parotid gland abscess	Human	A. baumannii	ACENIMSSUMS08	OP889560
11	Submandibular abscess	Human	A. baumannii	ACENIMSSUMS29	OP889561
12	Neck abscess	Human	A. baumannii	ACENIMSSUMS31	OP889562
13	Neck abscess	Human	A. baumannii	ACENIMSSUMS32	OP889563
14	Parotid gland abscess	Human	A. baumannii	ACENIMSSUMS34	OP889564
15	Submandibular abscess	Human	S. aureus	STAPIMSSUMS07	OP889672
16	Neck abscess	Human	S. aureus	STAPIMSSUMS16	OP889673
17	Otitis media	Human	S. aureus	STAPIMSSUMS18	OP889674
18	Otitis media	Human	S. aureus	STAPIMSSUMS30	OP889675
19	Oral cavity	Human	K. pneumoniae	KlebIMSSUMD04	OP889575
20	Retropharyngeal abscess	Human	K. pneumoniae	KlebIMSSUMS09	OP889576
21	Oral cavity	Human	K. pneumoniae	KlebIMSSUMS10	OP889577
22	Oral cavity	Human	K. pneumoniae	KlebIMSSUMS19	OP889578
23	Neck abscess	Human	K. pneumoniae	KlebIMSSUMS20	OP889579
24	Neck abscess	Human	K. pneumoniae	KlebIMSSUMS23	OP889580
25	Neck abscess	Human	K. pneumoniae	KlebIMSSUMS26	OP889581

No competing interests reported.

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Molecular characterization of head and neck infection causing bacterial communities using 16S rRNA in eastern Indian population

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Abstract

Figures

Introduction

Materials and methods

Study subjects

Isolation, identification, and antibiotic susceptibility profiling of the collected sample

Molecular Identification and characterization

Genomic DNA extraction

PCR amplification using 16S rRNA gene

Sequence analysis and construction of the phylogenetic tree

Results

Discussions

Conclusion

Declarations

References

Tables

Additional Declarations

Supplementary Files

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