Nasopharyngeal Microbiome of COVID-19 Patients Using 16S Amplicon Analysis Revealed Distinct Bacterial Profile in Demised and Recovered Individuals

doi:10.21203/rs.3.rs-966672/v1

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Nasopharyngeal Microbiome of COVID-19 Patients Using 16S Amplicon Analysis Revealed Distinct Bacterial Profile in Demised and Recovered Individuals

https://doi.org/10.21203/rs.3.rs-966672/v1

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Background

COVID-19 pandemic has shaken the entire world and the socio-economic life on the planet. The implications of the bacterial co-infections in SARS-CoV-2 patients remained the least explored subject of clinical manifestations among the COVID-19. Identification and association of the nasopharyngeal microbial community structure within SARS-CoV-2 infected patients could reveal microbiota dynamics that may determine and/or influence the disease outcome in the COVID-19 patients.

Results

Here, in this study, we present distinct nasopharyngeal microbiome in the demised (N=48) and recovered (N=29) COVID-19 patients as compared to the control group (N=33). The nasal microbiome composition in the three groups vary significantly (PERMANOVA, p-value <0.001), where demised patients showed higher species richness as compared to the recovered and control groups. Pathogenic genera including Corynebacterium (LAD score 5.51), Staphylococcus, Serratia, Klebsiella and their corresponding species were found as biomarkers (p-value <0.05, LDA cutoff 4.0) in the patients those who demised due to SARS-CoV-2 infection. Except for a few pathogenic species in the genera, Ochrobactrum (LDA score 5.79), and Burkholderia (LDA 5.29), the recovered group harbors ordinal bacteria (p-value <0.05, LDA-4.0) as biomarkers. Similarly, Pseudomonas (LDA score 6.19), and several healthy oral/nasal cavity commensal including Veillonella, and Porphyroonas, were biomarkers for the control individuals.

Conclusions

Dysbiosis in the commensal nasopharyngeal microbiota, especially due to infection of opportunistic pathogens could lead to more complex and severe COVID-19 disease dynamics. Whereas, healthy commensal bacteria may trigger the immune response and alter the viral infection susceptibility and thus, may help in preventing the viral infection and possible recovery which is yet to be explored. The findings in the present study provide key considerations and have significant implications for the COVID-19 treatment and control measures. However, further study is required to address the microbiome based therapeutic aspects.

General Microbiology

COVID-19

Dysbiosis

Nasopharyngeal Microbiome

Opportunistic pathogens

SARS-CoV-2

The COVID-19 pandemic caused huge loss of life across the globe especially with the emergence of the variant of concerns (VoCs) as defined by the set of mutations with higher transmissibility, pathogenesis, and virulence as compared to the reference strain. The second wave in India was caused by the sudden rise in cases due to increase in the Delta variant (B.1.617.2 lineage) circulation. The additional risk of microbial coinfections in the COVID-19 patients remained an understudied subject till date. The concerns were raised due to the bacterial co-infections, opportunistic pathogens and fungal infections in the COVID-19 patients. However, the rise in cases of Mucormycosis, commonly known as “black fungus”, were reported from several hospitalized patients on the path to recovery [1]. Recent studies demonstrated the alterations in the richness and diversity of gut microbiome of the SARS-CoV-2 infected patients in follow-up studies [2,3]. Only a few studies explored the dynamics of the nasal microbiome analysis of the COVID-19 patients [4–6]. The temporal association between the healthy, infected and demised patients’ microbiomes is of greater significance in terms of the possible therapeutic interventions that might require additional medical treatments for complete recovery. However, the quantifiable data is not available to estimate the precise burden of the bacterial co-infections among the covid-19 patients [7].

COVID-19 pandemics is one of the most complex health challenges in recent times due to several factors. SARS-CoV-2 virus infections can severely damage the epithelial barriers, the primary line of the body's defense mechanisms. This damage in the cells is an opportunity for the pathogenic bacteria to invade and cause further disease complications in the predisposed and immunocompromised individuals. Nasopharyngeal cavity is an important interface and the well-known transmission route of the SARS-CoV-2 infection. Further, it is also a rich habitat for the diversity of microbial communities. Microbial diversity is a reflective determinant of the persistent bacterial population within a host and has characteristic signatures of the health of an individual. The influence of the human microbiome on health and disease outcome has been recognized with the recent evidence based studies and discussed in continuum of the possible role in patient survival [8]. However, it is less studied in supplement with the viral infections and probably under hospitalized conditions.

Even the loss of smell and taste in the COVID-19 patients are reported but have not been systematically explored to identify the cause and preventative measures in management of the early infections. However, the olfactory modulations such as sense of smell can be due to the altered microbiome pathophysiology of the infected patients [9]. Some research studies explored the dynamics of host genetic and environmental factors for the nasal microbiomes and colonization of Staphylococcus aureus in the nasal cavity. Further authors examined the significance and role of nasal microbiota in S. aureus nasal colonization process [10]. Similarly, another study explored the dynamics of the nasal microbiome in asthma patients and compared with the control and healthy groups. These studies found the composition differences among the different groups and argued that microbiome composition can alter the pathobiology of the diseased and control groups [11,12]. Further, these implications need to be examined in relevance to the SARS-CoV-2 infections. These studies have valuable implications for even the high risk groups with the comorbidity and hygiene conditions would further help in better treatment and control measures. The health issues of the predisposed comorbid patients further deteriorate the patient survival outcomes and recovery. The modulation of host immune response by the microbiomes in early infections may certainly be examined and may have relevant implications for the susceptibility of the COVID-19. In this research study, we explored the nasopharyngeal microbiome of the recovered and demised COVID-19 patients and compared it with the control group.

Sample collection

For this study, a total 97 nasopharyngeal swabs samples of the COVID-19 patient’s and 44 swabs from the control individuals (may or may not have flu like symptoms however, confirmed SARS-CoV-2 RT-PCR negative) were collected. Except 13 samples, all the samples were collected during the period of April-June, 2021, i.e. peak time of the second wave of COVID-19 in India. For this study, we did not do any special sampling; however, we used the diagnostics samples as well as samples which come for SARS-CoV-2 genome sequencing to Gujrat Biotechnology Research Centre (GBRC). GBRC is an authorized COVID-19 testing center by RT-PCR as approved by Indian Council of Medical (ICMR) and also a member of Indian SARS-COV-2 Genome Sequencing Consortium (INSACOG), Government of India. All the samples were collected before any medical treatments were given to the patients. The confirmed COVID-19 patients were further followed till the final outcome of the disease progression i.e. either the patient recovered or demised. The control samples were also confirmed negative for SARS-CoV-2 infection throughout RT-PCR. In this way, we had total control (n=44), recovered (SARS-CoV-2 infected but recovered, n=43), demised (SARS-CoV-2 infected but demised, n=54).

Real-Time PCR for SARS-CoV-2 detection in diagnostic samples

For the RNA extraction, we used QIAamp Viral RNA Mini Kit (QIAGEN, Germany). A 300 µL of the nasopharyngeal swabs samples were used for the RNA extraction by following the manufacturer’s instruction and RNA was eluted into a 30 µL of elution buffer. For SARS-CoV-2 RNA detection we used QUANTIPLUS KIT from Huwel Lifesciences, (India) and samples were run on Applied Biosystems 7500 fast Dx Real-Time PCR instrument (Thermo Fisher Scientific, MA USA).

DNA extraction and 16S amplicon sequencing

For DNA extraction, 250-300 µL from each nasopharyngeal swab sample was processed using QIAamp® DNA Mini Kit (QIAGEN, Germany) by following the manufacturer's instructions. DNA was eluted into a 30 µL of elution buffer supplied along with the kit. For DNA quantification we used Qubit™ dsDNA broad range assay kit and Qubit fluorometer v4.0 (Thermo Fisher Scientific, MA USA). From each sample, 20-25ng of DNA was used for amplification of 16S rDNA V2-V3 region. For this, we used 101F (5′ACTGGCGGACGGGTGAGTAA3′) and 518R (5′CGTATTACCGCGGCTGCTGG3′) universal primer which were already tagged with Ion library adaptor, key sequence and a unique barcode sequence. A 20µL PCR reaction mixture contain 20-25ng DNA, 10µL, 2X EmeraldAmp® GT PCR master mix (Takara Bio, Japan), 1µL each forward and reverse primer (5pM), 1µL bovine serum albumin (2mg/mL) and nuclease free water. The thermal cycling conditions were as follows; 95°C for 5 minutes, followed by 25 cycles of denaturation at 95°C for 1 minute, annealing at 60°C for 30seconds, and extension at 72°C 1 minute, with final extension at 72°C for 5 minutes. Each PCR reaction mixture was loaded into 1.5% agarose gel to see the amplification and positive samples were purified using Invitrogen E-Gel EX precast 2% agarose gel (Thermo Fisher Scientific, MA USA). The purified 16S amplicon libraries were quantified using Qubit™ 1X dsDNA high sensitivity assay kit and Qubit fluorometer v4.0 (Thermo Fisher Scientific, MA USA). Each library was diluted to 100pM and pooled into equal molar concentration. The pool was further diluted to 12pM and emulsion PCR was carried out using Ion 520™ & Ion 530™ Kit-OT2 and sequencing was performed on the Ion S5 Plus system with 400 bp chemistry and Ion 530 chip.

Data processing and bioinformatics analysis

The obtained raw sequences were filtered using the PRINSEQ [13] where the minimum quality threshold was set at Q20. The polished reads were uploaded to MG-RAST server v4.0.3 for the taxonomic analysis and annotation. For the taxonomic classification of the amplicon sequencing reads, we used the SILVA SSU database. The annotation results were exported with the criteria of representative hit’, e-value 10^-5, and minimum identity 60%. The taxonomy profile of each reads was downloaded and subjected to statistical analysis using various statistical analysis tools.

Statistical analysis

We used STAMP [14], for calculating the relative abundance of the taxa in each group. For this, the data was analyzed at genus level and we applied Kruskal-Wallis-H test and Benjamini-Hochberg FDR test for multiple test correction. For two group comparisons, we applied Welch’s t-test (Two-side) with 0.95 CI and Benjamini-Hochberg FDR for multiple test correction. MicrobiomeAnalyst server [15] was used for the rest of the analysis where low count data (prevalence in samples=20%) and low variance features were filtered based on inter-quantile range to improve downstream statistical analysis. Further, the data was also rarified to even sequencing depth. To compare the alpha diversity between two groups and among three groups, we used observed features, Shannon, Simpson, Cho1 and Abundance-based Coverage Estimator (ACE) indices, where, Kruskal-Wallis statistics and t-test, was applied for three and two group comparison, respectively. Similarly, beta diversity was estimated using Principal Coordinates Analysis (PCoA) and Non-Metric Multidimensional Scaling (NMDS) with Bray-Curtis dissimilarity and Permutation multivariate analysis of variance (PERMANOVA) statistics. Linear Discriminant Analysis (LDA) Effect Size LEfSe (p-value <0.05, LDA-4.0) was used to identify most significant taxa within each group. For dendrogram analysis we used Bray-Curtis distance measure and applied two different clustering algorithms i.e. Ward and Jaccard. Random forest classification was used to predict an error rate in each group and evaluate the classification performance of genera. For Random forest classification, 1000 trees were grown with consideration of the group as an experimental factor.

Median age of the sampling group was 46 years with minimum 12 and maximum 88 years, in which, there were 107 males and 34 females. Wherever possible, we have also collected information of the symptoms and co-morbidities of the COVID-19 patients. Many of the demised patients have comorbidities such as heart disease and hypertension. Except for two patients, none of in the recovered group have any comorbidity. Cough, fever, and breathlessness, were the most common symptoms in the demised COVID-19 patients and fever, body ache, and loss of smell were most common among the recovered patients. Additionally, we also sequenced SARS-CoV-2 genome from all the recovered and demised patients (82 samples), where, except 8 demised patients, all the patients were found to be infected with B.1.617.2 (Delta) variants (Additional file 2: Table S1). For this study, we extracted total DNA from 141 nasopharyngeal swab samples, which included 54, 43, and 44 demised, recovered and control samples, respectively. Out of 141 samples, 14 samples (8 demised and 7 recovered samples) failed in PCR amplification. From the remaining 126 samples, 16 samples were discarded based on either having insufficient reads or they are outliers. Finally, a total of 110 samples which include 48 demised, 29 recovered and 33 control samples were analyzed and reported in this study. In total, we generated 42.7 million reads with an average size of 336bp. Details of sequencing reads generated for each sample is provided in Additional file 2: Table S2.

Microbial diversity analysis

Rarefaction curve (Additional file 1: Figure S1), clearly depicting that, in all the samples the curve almost reached a plateau, means the generated reads are sufficient to capture the bacterial diversity present in the samples. Beta-diversity (Genus level) determined based on PCoA (Fig. 1A) (PERMANOVA; F-value: 6.5886; R²: 0.10965; p-value <0.001) NMDS (Fig. 1B) (PERMANOVA; F-value: 6.5886; R²: 0.10965; p-value <0.001 [NMDS] Stress = 0.20839) based on Bray-Curtis distance matrix revealed significant difference in the microbiota composition among the three groups. PCoA explained overall 22.5% and 17.1% variation on Axis1 and Axis 2, respectively. PCoA plots for between two groups comparison are shown in Additional file 1: Figure S2, where also we observed significant difference (p-value <0.001) between control-demised, control-recovered and demised-recovered groups. Dendrogram (Additional file 1: Figure S3) also showed distinct clustering of three groups except few samples which did not show clustering according to group. Microbial species richness assessed based on Cho1 (Fig. 1C), and ACE (Fig. 1D), and observed features (Figure 1E) index showed higher diversity in the demised patients group, however, the difference among the three groups was not significant [Cho1: p-value: 0.41324; (Kruskal-Wallis) statistic: 1.7674; ACE: p-value: 0.39582; (Kruskal-Wallis) statistic: 1.8536 and Observed: p-value: 0.067215; (Kruskal-Wallis) statistic: 5.3997]. Also, there was no significant difference in the Shannon and Simpson indices among the three groups. However, in two group comparison, we observed a significant difference in specie richness i.e. Observed; p-value: 0.017453; [T-test] statistic: 2.4342, Chao 1: p-value: 0.030277; [T-test] statistic: 2.2084, and ACE: p-value: 0.021; [T-test] statistic: 2.3579 while comparing demised and recovered patients group. However, there was no significant differences between control-demised and control-recovered COVID-19 patients group (Additional file 1: figure S4).

Microbiome composition

In all the samples, unclassified bacteria were present with an abundance >23.0% with highest (35.2%) in the group of the patients who recovered. Genus Pseudomonas was most prevalent in the control group i.e. 31.34%, followed by, 4.11% in demised, and only 1.14% in recovered group. Similarly, Prevotella was also found to be abundant in the control group as compared to the other two groups. Coming to the demised patients, here, Corynebacterium (6.58%), Enterococcus (5.21%), Acinetobacter (3.89%), Streptococcus (3.05%), Pseudoalteromonas (2.36%), Propionibacterium (2.6%), Staphylococcus (2.47%) and others were abundant as compared to the rest two groups. Likewise, unclassified bacteria (35.23%), Ochrobactrum (12.15%), Burkholderia (4.79%), Brevundimonas (2.88%), Leptotrichia (2.15%) and some others were abundant in the patients those who received from COVID-19 (Additional file 1: Figure S5 and Additional file 2: Table S3). An extended error plots at genus level showing between group comparisons are provided in Additional file 1: Figure S6. Differential abundances of various genera across two groups comparison are shown as a heat-map trees as well as dot plot from LEfSe analysis in Fig. 2 and listed in Additional file 2: Table S4. Total 25 and 13 genera and 55 and 33 species with >0.1% abundance, were exclusively present in demised and recovered patients, respectively (Additional file 1: Figure S7, Additional file 2: Tables S5 and S6).

Microbiome signature of the COVID-19 patients

LEfSe analysis is widely used to determine the biomarkers across the different samples. Therefore, in this study, LEfSe analysis was used to identify the biomarker genera as a signature of the nasopharyngeal microbiome among the control, infected but recovered and infected but demised COVID-19 patients. Total 32 genera (15, 10, and 7 in demised, recovered and control patients, respectively) and 46 species (22, 13, and 11 in demised, recovered and control patients, respectively) were identified as biomarkers with LDA score > 4.0 and p-value <0.05 (Fig. 3 and Additional file 2: Tables S7 and S8). Genera, Corynebacterium, with the LDA score 5.51(highest in the demised group) and Staphylococcus, Serratia, Micrococcus, and Klebsiella along with their, pathogenic or opportunistic pathogenic species such as C. xerosis, S. epidermidis, S. liquefaciens, K. pneumoniae were picked out as a biomarker for demised COVID-19 patients. In a similar manner, Ochrobactrum (LDA core 5.79, highest in the recovered group), Burkholderia (LDA core 5.29), unclassified Betaproteobacteria, and their pathogenic or opportunistic pathogenic species such as O. anthropi, and O. tritici, and B. cepacia were spotted as biomarkers for recovered COVID-19 patients. Pseudomoans with highest LDA core 6.19 among the three group was picked as a biomarker for control group. Apart from this, several health oral and nasal cavity commensal including Fusobacterium (F. periodonticum), Veillonella (V. parvula), Porphyromonas (P. catoniae), and Bulleidia (Bulleidia extructa) along with some pathogenic bacteria like, Neisseria (N. flavescens) were biomarkers. Fig. 4 depicts the Log transformed abundance of the important biomarkers genera. Random forest classification showed an excellent judgement of the guessing the samples in their respective group, with 44/48, 19/29 and 29/33 correct prediction of demised, recovered and control patients, respectively in their corresponding group with an overall 16.4% out of bag (OOB) error rate (Fig. 5).

SARS-CoV-2 is an evolving pathogenic virus, as we have observed the dominance of the delta variant (B.1.617.2 lineage) during the second wave in India. This variant is far more contagious, highly transmissible, and virulent than the original strain of the Wuhan virus. In the present study, we are able to demonstrate the signature microbiome profile of the, demised and recovered COVID-19 patients and compared it with health controls. In the light of perturbation in microbial communities induced by the viral infections is of significant importance due to the health risk assessment and survival outcomes, apart from the therapeutic and medical treatment considerations. The impact on the healthy microflora of the nasopharyngeal microbiome due to the SARS-CoV-2 infections might be the key driver and transition between the mild to the severe disease conditions at the initial stage of disease progression and may also provide new avenue in terms of the therapeutic considerations before the patient might become the severely ill or advance stage of the health infections and related complications.

The results of the present study highlighted the difference in the microbial community structure, abundance and their probable functional role in the disease severity, patient survival outcomes and bacterial co-infections by opportunistic pathogens and prolonged health complications due to SARS-CoV-2 infections. The PCoA and NMDS plots clearly indicated that there is substantial shift in the nasal microbiome composition in the SARS-CoV-2 infected individuals, which is in parallel with the previously reported studies [16–18]. However, we could not observe any significant difference in the alpha diversity measured based on Shannon, Simpson, Cho1 and ACE indices among the three groups as well as between two groups. This was again in concordance with the previous report [17]. Although there was no significant difference in the different alpha diversity indices, we observed that the species richness was highest in the demised patient followed by recovers and control group. Previous studies have reported decrease in the microbial diversity in the COVID-19 patients [16,19]. Apart from dysbiosis in the nasopharyngeal microbiota due to SARS-COV-2 infection [18] also reported alteration in the oral and gut microbiota with respect to SARS-CoV-2 viral load in the COVID-19 patients. Collectively, all these results suggest that SARS-CoV-2 infection literally creates a dysbiosis in the healthy human nasopharyngeal microbiota which may lead to more complex disease severity and outcome especially when pathogenic microbes overwhelm the normal/beneficial bacteria.

In the demised patients we observed several pathogenic microbes such as Corynebacterium, Staphylococcus, Serratia, Klebsiella, Acinetobacter, and Ralstonia along with their pathogenic or opportunistic pathogenic species notably as C. xerosis, S. epidermidis, S. liquefaciens, K. pneumoniae. Similarly, we detected 55 species (abundance >1.0%), which were exclusively present in the demised patients and several of them are either pathogens or opportunistic pathogens in humans [19] also reported abundance of opportunistic pathogens in the SARS-CoV-2 infected individuals. In this study, the biomarkers species, S. epidermidis, S. liquefaciens, and K. pneumoniae which are found in demised COVID-19 patients are well known pathogens. Similarly, C. xerosis resemble to C. amycolatum [20] which is a very close relative to C. diphtheriae may cause infection in immunocompromised patients [21]. Moreover, C. xerosis itself may cause several types of infections including endocarditis [22]. Similarly, species of Ralstonia, R. picketti and R. mannitolilytica, are also considered as an emerging pathogen and cause infection in immunocompromised patients [23]. Although Acinetobacter baumannii is not identified as a biomarker for demised groups in this study, its proportion is very high in the COVD-19 demised patient. A. baumannii is again an opportunistic pathogen causing infection including lungs and heart [24]. We also found presence of A. calcoaceticus in the demised COVID-19 patients which is associated with the pneumonia [25]. Research findings suggested that, human nasal and oral cavity is the residence of several opportunistic pathogens. Such pathogens may develop respiratory diseases including asthma [26,27], when there is a dysbiosis in the microbiota. Such dysbiosis may interrupt the immune response [28], thus, SARS-CoV-2 susceptibility [16,29]. Linking this with the present study, the data collectively suggests that, SARS-CoV-2 infected patients have a higher possibility of getting secondary respiratory infection from the opportunistic pathogens which cause dysbiosis in the normal nasopharyngeal microflora, which may lead to poor and simultaneously cause critical disease severity and COVID-19 outcome.

In the recovered COVID-19 patients, we observed very few pathogens such as Ochrobactrum (O. anthropi, and O. tritici) and B. cepacia. Instead of pathogens, in this group, we found abundance of many human common oral or nasal and air flora. In contrast to the present study, [5], have reported presence of Burkholderia cepacia complex in critically ill COVID-19 patients. Surprisingly, we observed the presence of Brucella in the recovered patients which was unusual as infection of Brucella in the respiratory tract is very rare [30,31]. However, a case study from Saudi Arabia has reported Brucella bacteremia in a 41 year old COVD-19 patient [32] and brucellosis mimics the COVID-19 symptoms [33]. Normal flora in the human nasal cavity play an important role in the evasion of pathogens via local immune response [26,34,35] and thus may help in preventing disease complexity which may result in healthy outcomes like what healthy gut microbiota do. In summary, absence of pathogens/opportunistic pathogens and presence of normal flora in the recovered COVID-19 patients highlight less disease severity and possible recovery in the SARS-CoV-2 infected patients. The present data also support the very recent hypothesis of transplantation of oral microbiota to combat COVID-19 and other inflammatory diseases [36] as, dysbiosis in the oral microbiota is also responsible for COVID-19 disease severity as it may disrupt the local immune responses [37,38].

The control group samples were represented by the RT-PCR negative patients without any prior COVID-19 infections. In the control group, Pseudomonas, Veillonella, Porphyromonas, Gemella,and Bulleidia were abundant as compared to the demised and recovered patients [16] have reported higher abundance of Veillonella in COVD-19 patients however, in this study it was a biomarker for control patients. Veillonella species are normal human flora of the human oral cavity [39]. Likewise, in contrast to the current study, Prevotella has been reported in the COVID-19 patients [17,40,41] however, although it was abundant in this study, it was not a biomarker for the control group may be because its proportion in the control samples vary. Again Neisseria was predicted as a biomarker for the control group which is again a part of normal oral microflora [18,42,43]. Gemella sanguinis, which is a nasopharyngeal commensal bacterium [44], is also predicated as biomarkers in the control group in this study. The presence of Pseudomonas might have protective role as, Pseudomonas in the respiratory tract produce mucin, which may serve as a physical barrier against viral infection [45]. In summary, the control group in this study mostly harbors normal human commensal as well as high abundance of Pseudomonas which may prevent the SARS-CoV-2 infection. However, as described by [45], the health respiratory microbiota is still undefined, the role of Pseudomonas in respiratory diseases still required more insights, as, Pseudomonas aeruginosa is also an opportunistic pathogen for the respiratory disease especially in the sinus [46,47].

In summary, the present study demonstrated the comparative analysis of the nasopharyngeal microbiome that might be the key driver of the patient outcome and disease using 16S ribosomal RNA (rRNA) amplicon sequencing. However, further experiments needed to the study the specific functional role of the dominant bacterial communities in the demised and recovered patients in order to draw larger conclusions. As evident from the previous research studies where the microbial community structure analysis from the COVID-19 patients and very little literature has demonstrated the key findings with conclusive evidence of the functional role of the bacterial microflora at large scale datasets. Furthermore, clinical profiles including the co-morbidity, age, sampling location, gender etc. need to be further examined with sufficient coverage and sequencing depth in randomized controlled experiments. Yet, this research study provides a glimpse of the bacterial diversity and signature microbiome profile among the different groups of SARS-CoV-2 patients.

The global impact of the SARS-CoV-2 pandemics has certainly taken a toll on human health, disease susceptibility and recovery. Our research study examined the critical aspects of the nasopharyngeal microbiome of the control, recovered and demised COVID-19 patients from Gujarat, India and revealed the signature microbiome within the three different groups. Dysbiosis in the commensal nasopharyngeal microbiota, especially due to infection of opportunistic pathogens could lead to more complex and severe COVID-19 disease dynamics. Whereas, healthy commensal bacteria may trigger the immune response and alter the viral infection susceptibility and thus, may help in preventing the viral infection and possible recovery which is yet to be explored. The findings in the present study provide key considerations and have significant implications for the COVID-19 treatment and control measures. However, further study is required to address the therapeutic aspects of the microbiome based interventions.

Ethical approval

GBRC is an Indian Council of Medical Research (ICMR) approved COVID-19 testing and diagnosis research laboratory. Institutional Ethical Committee of Gujarat Biotechnology Research Centre (GBRC), Gandhinagar, B. J. Medical College and Civil hospital, Ahmedabad, reference No. EC/Approval/38/2020 and GMERS medical College Gandhinagar, reference No. GMERS/MCG/IEC/06/2020.

Consent for publication

Not applicable

Availability of data and materials

The raw 16S ribosomal RNA (rRNA) gene amplicon sequencing data have been deposited in the National Centre for Biotechnology Information (NCBI) Sequence Read Archive (SRA) (https://www.ncbi.nlm.nih.gov/sra) under Bio-project accession number PRJNA763288.

Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this research study.

Funding

This work is funded by the Department of Science and Technology (DST), Government of Gujarat, Gandhinagar, Gujarat, India.

Author Contribution

CJ conceive the experimental design of the study, execute the project and edit manuscript, MJ

involved in execution of the project, manuscript editing, and sample collection, DK and RP did

data analysis and prepared a preliminary draft of the manuscript, ZP, SS and JR performed

DNA extraction, library preparation and sequencing.

Acknowledgement

Authors would like to acknowledge the Department of Science and Technology (DST), Government of Gujarat for the financial assistance and infrastructure support for the research work. Authors also would like to acknowledge and highly appreciate the support of the Commissionerate of Health, Government of Gujarat, Gujarat Medical Education and Research Society (GMERS) and associated medical colleges for providing the clinical samples.

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Nasopharyngeal Microbiome of COVID-19 Patients Using 16S Amplicon Analysis Revealed Distinct Bacterial Profile in Demised and Recovered Individuals

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