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. 2022 Dec 9;17(12):e0278699. doi: 10.1371/journal.pone.0278699

A quality improvement study on the relationship between intranasal povidone-iodine and anesthesia and the nasal microbiota of surgery patients

Eric N Hammond 1,2,#, Ashley E Kates 2,3,#, Nathan Putman-Buehler 4,#, Lauren Watson 5,#, Jared J Godfrey 2,3,#, Nicole Brys 6,#, Courtney Deblois 7,8,#, Andrew J Steinberger 7,8,#, Madison S Cox 7,8,#, Joseph H Skarlupka 7,8,#, Ambar Haleem 2,#, Michael L Bentz 9,#, Garret Suen 7,#, Nasia Safdar 2,3,*,#
Editor: Rajesh P Shastry10
PMCID: PMC9733847  PMID: 36490265

Abstract

Introduction

The composition of the nasal microbiota in surgical patients in the context of general anesthesia and nasal povidone-iodine decolonization is unknown. The purpose of this quality improvement study was to determine: (i) if general anesthesia is associated with changes in the nasal microbiota of surgery patients and (ii) if preoperative intranasal povidone-iodine decolonization is associated with changes in the nasal microbiota of surgery patients.

Materials and methods

One hundred and fifty-one ambulatory patients presenting for surgery were enrolled in a quality improvement study by convenience sampling. Pre- and post-surgery nasal samples were collected from patients in the no intranasal decolonization group (control group, n = 54). Pre-decolonization nasal samples were collected from the preoperative intranasal povidone-iodine decolonization group (povidone-iodine group, n = 97). Intranasal povidone-iodine was administered immediately prior to surgery and continued for 20 minutes before patients proceeded for surgery. Post-nasal samples were then collected. General anesthesia was administered to both groups. DNA from the samples was extracted for 16S rRNA sequencing on an Illumina MiSeq.

Results

In the control group, there was no evidence of change in bacterial diversity between pre- and post-surgery samples. In the povidone-iodine group, nasal bacterial diversity was greater in post-surgery, relative to pre-surgery (Shannon’s Diversity Index (P = 0.038), Chao’s richness estimate (P = 0.02) and Inverse Simpson index (P = 0.027). Among all the genera, only the relative abundance of the genus Staphylococcus trended towards a decrease in patients after application (FDR adjusted P = 0.06). Abundant genera common to both povidone-iodine and control groups included Staphylococcus, Bradyrhizobium, Corynebacterium, Dolosigranulum, Lactobacillus, and Moraxella.

Conclusions

We found general anesthesia was not associated with changes in the nasal microbiota. Povidone-iodine treatment was associated with nasal microbial diversity and decreased abundance of Staphylococcus. Future studies should examine the nasal microbiota structure and function longitudinally in surgical patients receiving intranasal povidone-iodine.

Introduction

The human nasal microbiota structure is complex. It comprises diverse microbial communities [1] that play a vital role in the health of an individual [2,3]. Commensal bacteria (Corynebacterium spp. and Staphylococcus epidermidis) help to control and maintain nasal microbial community diversity [4,5]. Despite the presence of these commensals, pathogenic organisms such as Staphylococcus aureus can evade the nasal immune system and reside in the nares for a long time without causing disease to the host. However, when the healthy nasal microbiota is disrupted, such as during surgery, each member of the microbial community competes for adhesion sites, space, and nutrients [2,3,6]. This results in rapid multiplication of opportunistic pathogens, allowing them to readily spread from the nares to other body sites and cause infections [2,3,7] in addition to surgical site infections (SSIs) [7].

According to the Centers for Disease Control and Prevention’s (CDC) definition, SSI is an infection that occurs after surgery in the part of the body where the surgery took place [8]. The most prevalent pathogen associated with nasal SSI is S. aureus [9–11]. Genotypic studies have shown up to 80% of S. aureus infections are caused by a patient’s own nasal microbiota [12]. Across the United States between 20% and 40% of patients have nasal carriage of S. aureus [13] increasing their risk of SSI [8,14].

In healthcare settings, S. aureus colonization of surgical wounds can occur through the patient’s own nasal microbiota [12], through the hands of a healthcare worker, from patient to patient, and from contaminated healthcare environments [15]. S. aureus can cause invasive infections such as bloodstream infection and sepsis [9,16,17]. In addition, S. aureus can form biofilms which impede and complicate wound healing [18], rendering S. aureus infections extremely difficult to treat.

Given these dangers, and the World Health Organization’s recommendations [14], many surgical units have recently implemented multiple strategies directed at S. aureus to prevent SSIs. For instance, prior to surgery, patients are recommended to decolonize their nares at least twice per day for 5 days with Mupirocin ointment before surgery. On the day of surgery, patients are also administered with an antibiotic prophylaxis complemented with chlorhexidine gluconate (CHG) body bath or wipe, and antiseptic mouth rinsing with Hibiclens (CHG 0.12%) or povidone-iodine (PI) (1%) [19].

Consistent adherence to 5 days of nasal Mupirocin application can be challenging for patients [20] and there have been reports of Mupirocin resistance [21–23]. Thus, there is a need to investigate the effectiveness of an alternative intranasal non-antibiotic-based decolonization agent, such as PI, to prevent SSIs.

PI has a broad antibacterial spectrum [24], minimal adverse effects, is easy to use, and can be applied within an hour prior to surgery by a surgical nurse [25]. Additionally, studies have shown that PI can prevent and disrupt microbial biofilms [26,27]. This suggests that PI administered nasally may help to reduce SSIs. As a result, many hospitals in the United States, have introduced the use of intranasal PI as part of standard preoperative protocol. The general operative protocol involves general anesthesia to enhance the procedure. Here, it is unclear what impact anesthesia alone has on the nasal microbiota.

To date, there is no study evaluating the effect of anesthesia on the nasal microbiota. Recent studies on PI are predominately based on the effect of PI on SSI rates [28–30]; however, there is limited research on the impact of PI on the nasal microbiota of surgical patients. In this quality improvement (QI) study, we sought to determine: (i) if general anesthesia can modulate the nasal microbiota and (ii) if PI decolonization is associated with changes in the nasal microbiota.

Materials and methods

Ethical approval

We conducted this quality improvement (QI) study on surgical patients due for surgery at the ambulatory surgery center (an academic medical center), located in the midwestern United States. We applied the quasi-experimental study design. The study was designed to improve presurgical treatment in the academic medical center and specimens were not connected to any patient identifiers. The study protocol was reviewed and approved by the academic medical center quality improvement committee. The University of Wisconsin Health Sciences Institutional Review Board exempted this study as a quality improvement project (approval number 2019–0466, May 6, 2019) and the nurses obtained verbal consent from each patient.

Patient populations

The nasal samples were collected between May—July 2019 and January—February 2020. The patients were divided into two groups (PI group and general anesthesia only group). All patients received general anesthesia and antibiotic prophylaxis during surgery. Patients were administered with intravenous antimicrobial prophylaxis: cefazolin or clindamycin if allergic to the former. Patients with known Methicillin-resistant Staphylococcus aureus colonization had vancomycin 15mg/kg in addition to cefazolin [25]. The PI group were those who received intranasal PI decolonization which lasted for 20 minutes before proceeding for surgery. The general anesthesia only group, were those who received only general anesthesia without PI or any other nasal decolonizing agents prior to surgery. The general anesthesia only group also served as controls and hereinafter will be referred to as the control group. These patients received anesthetic gases inhaled through nose or mouth with a mask or breathing tube supplemented with propofol, which was administered intravenously in the arm. The type of anesthesia administered was selected on a case-by-case basis by the anesthesiologist.

We used an open-label strategy in which patients, surgical nurses, and researchers knew the type of nasal decolonization agent assigned to a given patient. We used convenience sampling to select and enroll patients as they reported to the preoperative room for surgery. In the preoperative room, the surgical nurses explained the relevance of PI decolonization, potential side effects, and nasal sample collection process to the patients. Once a patient agreed to participate and provided verbal consent, they were enrolled into the study [25]. Patients included those scheduled for cosmetic plastic surgery–breast reduction, breast augmentation, and tummy tuck. Patients scheduled for a major nasal surgery were excluded. In the PI group, patients with known allergies to iodine were also excluded. Patients without general anesthesia and those who refused to provide two nasal samples (pre- and post-surgery) were also excluded from the study.

Pre- and post-surgery nasal sample collection

All pre- and post-surgical nurses working in the academic medical center were trained in nasal sample collection, documentation, labelling, and sample storage. To obtain nasal samples, trained surgical nurses gently inserted a dry dual-headed BD BBL™ CultureSwab™ Sterile, Media-free Swab (Becton, Dickinson and Company (BD), Sparks, Maryland, USA) tip into the widest part (about 3–5 mm) of the anterior nares of the patient and gently spun 4 times with slight pressure to collect the sample. Swabs were immediately placed in transport tubes pre-labelled with a unique study identifier and stored at 4°C until laboratory processing. The first time timepoint (pre-) occurred immediately prior to application of the nasal PI solution (before the patient underwent surgery). The second time point (post-) occurred after surgery but before the patient was discharged. There was no specific time allocated to post-surgery nasal sample collection.

Sample storage and transportation

Samples were transported at 4°C in a cooler from the academic medical center to the University of Wisconsin-Madison Infection Disease Research Laboratory (IDR), where they were immediately stored at 4°C. The IDR Lab received all samples within 48 hours of collection. Each sample was processed by cutting off each head with sterilized scissors into a sterile 2.0 mL bead beating tube containing 500 mg of 0.1 mm diameter zirconia/silica beads (Biospec Products, Oklahoma, USA) aseptically and stored at -80°C until DNA extraction and sequencing.

DNA extraction, amplification, and sequencing

We followed a previously published protocol [31] with some modifications to extract DNA. Briefly, each nasal swab specimen was exposed to enzymatic lysis and mechanical lysis using silica beads. Extract was further purified and quantified on BioTek Synergy 2 plate reader (BioTek, Winooski, VT, USA) using Qubit fluorometric quantitation reagents (Thermo Fisher Scientific, Waltham, MA, USA).

The fourth hypervariable (V4) region of the bacterial 16S rRNA gene was amplified using the one-step polymerase chain reaction (PCR) approach with barcoded V4 primers (F- GTGCCAGCMGCCGCGGTAA; R- GGACTACHVGGGTWTCTAAT). Each of these primers were barcoded with individual custom indices to facilitate demultiplexing, as described in Kozich et al. (2013) [32]. Each PCR reaction consisted of 12.5 μl KAPA 2x HiFi Master Mix (KAPA Biosystems, Wilmington, MA, USA), 0.5 μl of 10 μM forward primer, 0.5 μl of 10 μM reverse primer and up to 11.5 μl of 10ng/μl DNA to a total volume of 25 μl with nuclease-free water (IDT, Coralville, Iowa, USA). Amplification conditions on a C1000 Touch™ thermal cycler (Bio-Rad Laboratories, Hercules, CA, USA) were 95°C for 3 min, 35 cycles of 95° for 30 s, 55°C for 30 s, and 72°C for 30 s, followed by a final extension at 72°C for 5 min. The PCR products were purified by running on a 1% low-melt agarose gel stained with SYBR Safe DNA Gel Stain (Invitrogen, Waltham, CA) to isolate amplicons of the expected size. DNA bands of ~380 bp were excised and purified with the Zymo Gel DNA Recovery Kit (Zymo Research, Irvine, CA, United States). Purified PCR products were equimolar pooled, then sequenced on an Illumina MiSeq (Illumina, San Diego, CA, USA) with 10% PhiX control using a 500-cycle v2 sequencing kit and custom sequencing primers [32].

Quality control

For quality control purposes negative and positive controls were sequenced alongside with patients’ samples to validate: (i) DNA isolation, (ii) DNA purification, and (iii) sequencing and data analysis. We used positive controls, ZymoBIOMICS® Gut Microbiome Standard (Zymo Research Corp., Irvine, CA) and negative controls (non-sample blanks).

Microbiota analysis

Sequence data clean-up in mothur

The raw sequences were demultiplexed according to their sample-specific indices on the Illumina MiSeq. We subsequently cleaned-up the data in the program mothur (v1.44.2) [32]. Paired end reads were computationally stitched together to form contigs and aligned against the SILVA 16S rRNA gene reference alignment database, release 138 [33,34]. Contigs that were not aligned to the V4 region were eliminated from analysis. We performed pre-clustering to reduce sequencing errors with a defect value of 2. The UCHIME algorithm [35] in mothur was used to identify and removed chimeric sequences. Singletons were removed from the dataset before performing operational taxonomic unit (OTU) clustering.

The remaining high-quality sequences were clustered into OTUs with 97% similarity threshold using the cluster.split (method = opti) in mothur [36]. Bacterial sequences were classified against the SILVA database (v. 138) with a bootstrap cutoff of 80. Sequence coverage was calculated in mothur with Good’s coverage index [37] to assess if enough sequencing was done to have an accurate picture of true sample diversity. Samples with a pre-normalization Good’s coverage of less than 77% were excluded from the analysis.

After cleanup in mothur, we predicted and removed suspected contaminants with the decontam [38] package in R (version 3.6.1) [39] using the prevalence method [40]. We also removed all quality control samples prior to normalization. Finally, we normalized our raw reads count to 400 reads/sample in mothur, which represented the lowest number of sequences that ensured sufficient coverage for all samples. This is to account for differences in sequencing depth between samples prior to statistical analysis [41]. Alpha diversity metrics were calculated in mothur.

Statistical analysis

Statistical analyses were performed in R (version 3.6.1) [39] using RStudio v1.2.1335 [42]. Any patient without both pre- and post-samples after normalization were removed to allow for paired analysis. The Wilcoxon signed rank test was applied to determine differences in alpha diversity (within-sample-diversity) metrics between paired pre- and post-surgery samples in PI group and the control group separately. We also used Wilcoxon rank-sum test to compare changes between PI-post and control-post. For the exploratory analysis, we randomly subsampled PI group to equal the number of samples in the control group (n = 46 per group) to determine changes in alpha diversity. Beta diversity (between-sample diversity) was assessed using the Bray-Curtis dissimilarity metric [43] and visualized using non-metric multidimensional scaling (nMDS, vegan::metaMDS) plots of square root transformed data. We further added the standard error ellipses around the plotted points to better illustrate the behavior of pre and post centroid points within groups. The Beta dispersion test (vegan::betadisper) was used to determine equality of group variance and the analysis to perform. We used PERMANOVA [44] (vegan::adonis2) to test if beta diversity centroids differed among the pre and post samples in both control and PI groups. We then identified OTUs that abundantly contributed to the differences observed between pre- and post- for each treatment using the similarity or percentages (SIMPER) function (vegan::simper). OTUs that contributed to more than 1% of the differences between groups were subject to Kruskal-Wallis rank sum tests with false discovery rate (fdr) P-value correction to confirm differential abundance and control for false positives. An exploratory analysis was additionally performed to directly compare the changes in Staphylococcus relative abundances between control and PI groups.

Results

A total of 151 surgical patients were involved in our quality improvement (QI) study. Patients were divided into two groups: a PI group (97 patients with 194 nasal samples collected) and a control group (54 patients with 108 nasal samples collected). In the PI group, PI stayed in patients’ nares throughout the duration of surgery. In the PI group, the average time between nasal swabs was 3 hours (range:1.15–9 hours). Duration of PI was determined by the projected length of the surgery. In the control group, the average time between nasal swabs was 3.5 hours (range:1.25–6 hours). A study schematic diagram outlines various components in Fig 1.

Fig 1. Study design components.

Fig 1

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A quality improvement study schematic outlining sample collection, nasal povidone-iodine (PI) application, and sequencing method.

In total, 3,967,385 raw sequences were generated from the 357 samples. Following decontamination and clean up in mothur, 2,990,013 sequences remained (mean = 8,398.91, SD = 14,638.16) resulting in 14,200 OTUs. Post-normalization, a total of 130 patients with 260 sequenced nasal samples representing 2,867,890 bacteria sequences were retained in the final dataset for analysis. This comprised of 84 PI patients with 168 samples (1,239,198 sequences, mean = 7,376.179, SD = 9,532.848) and 46 control patients with 92 samples (1,628,692 sequences, mean = 1,7703.17, SD = 2,4492.52). We excluded 42 samples (PI = 26, control = 16) from analysis due to insufficient DNA yield, failure to pass normalization, and lack of paired samples. All positive and negative control samples were also excluded from the final data for analysis. A summary of sample collection results can be found in S1–S3 Tables.

Response of nasal bacterial diversity to anesthesia

In the control group, we observed diverse microbial communities with varied bacterial community compositions (Fig 2A and 2C and S4 Table). Some of the phyla present included Actinobacteriota, Firmicutes, Proteobacteria, and Bacteriodota. The genera detected included Staphylococcus, Pseudomonas, Corynebacterium and Lactobacillus. See S4 Table for relative abundance data.

Fig 2. Taxa composition with relative abundance.

Fig 2

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Bar plots display relative abundance (%) of top 10 phyla and genera composition between pre- and post-surgery nasal microbiota in the control group (A and C) and PI group (B and D). Rare taxa are classified as “uncultured”. The legend displays the color coding of genera and phyla to which these taxa belong.

We assessed alpha diversity (within-sample diversity) of patient nasal community samples pre- and post-surgery. In the control group there was no change in diversity between pre- and post-surgery patients with respect to Chao’s richness (P = 0.739), Shannon’s diversity index (P = 0.241) or Inverse Simpson index (P = 0.122) Fig 3A, 3C and 3E). The Bray-Curtis dissimilarity showed that the overall bacterial community composition between pre- and post-control samples were not different from each other (P = 0.974, PERMANOVA, nMDS stress = 0.25). A similar bacteria composition was observed in the nasal samples of the control group (Fig 4A).

Fig 3. Alpha diversity of bacteria community.

Fig 3

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Box and whisker plots to compare bacteria community alpha diversity between pre-surgery and post-surgery in control (Prec, and Postc) and povidone-iodine (pi) (Prepi and Postpi) groups. (A) and (B) Chao’s richness, (C) and (D) Shannon’s diversity, and (E) and (F) Inverse Simpson’s diversity. P-value was based on pairwise sample comparison in Wilcoxon signed-rank test. Significant values (p-values) between pre- and post-surgery are displayed on the plots. P-values <0.05 are considered significant.

Fig 4. Beta diversity of bacterial community composition.

Fig 4

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Differences of nasal bacterial community compositions across treatments groups. Non-metric multidimensional scaling (nMDS) plot of the Bray-Curtis dissimilarity for bacterial community of nasal samples collected pre- and post-surgery from (A) control group and (B) povidone-iodine group. Ellipses illustrates standard error around centroid of pre- and post- nasal community samples in each treatment group and colored by treatment. P- value <0.05 was considered a significant difference between bacterial community composition between pre- and post-surgery samples. The nMDS stress levels for PI group = 0.20 and control group = 0.25.

The SIMPER analysis predicted a total of 14 OTUs from the control group that were the primary drivers influencing the beta diversity differences between pre- and post-surgery nasal communities (S5 Table). The impact of general anesthesia on relative abundance of each genus is shown in (Fig 2C; S5 Table). Of the 14 SIMPER-detected OTUs from control group comparisons, OTU1 had the highest contribution to differences between pre- and post-surgery samples and was classified to the genus Staphylococcus. OTU1 in the control group comprised on average 21.65% (standard error (SE) = 0.06) of the reads in the pre-surgery control samples and 18.79% (SE = 0.06) in the post-surgery control samples, representing a 13.2% decrease. However, this difference was not statistically significant (FDR adjusted P = 0.991). In the control group, OTU2, classified to the genus Corynebacterium, and its abundance increased from 16.48% (SE = 0.05) pre-surgery to 18.29% (SE = 0.06) post-surgery, representing a 11.0% increase. Similarly, this difference was not significant (FDR adjusted P = 0.944) S5 Table.

The other SIMPER-detected OTUs that explained differences between pre- and post-surgery samples in control group included bacteria classified to the genera Ralstonia, Dolosigranulum, Lactobacillus, Bradyrhizobium, Moraxella, Lawsonella, Streptococcus, Janthinobacterium, Neisseriaceae;uncultured, Pseudomonas, Succiniclasticum, and Succinivibrionaceae_UCG-001 (S5 Table). None of these were found to be differentially abundant after correcting for multiple testing (P > 0.05) (S5 Table).

Response of nasal bacterial diversity to PI

In the PI group, we observed diverse bacterial communities with varied community compositions (Fig 2B and 2D and S6 Table). Some of the phyla included Actinobacteriota, Firmicutes, Proteobacteria, and Fusobacteria. We also observed bacteria in the genera Staphylococcus, Moraxella, Corynebacterium and Lactobacillus.

In the PI group, changes in bacterial diversity between pre- and post-surgery nasal samples were observed (Fig 3B, 3D and 3F). All three alpha diversity metrics—Chao’s richness estimate (P = 0.02), Shannon’s Diversity Index (P = 0.038) and Inverse Simpson index (P = 0.027)–were increased in the post-surgery patient samples, relative to pre-surgery (Fig 3B, 3D and 3F).

Comparison of the Bray-Curtis dissimilarity showed that the overall bacterial community composition between pre- and post-samples were different (P = 0.024, PERMANOVA, nMDS stress = 0.20) (Fig 4B). A similar species composition was also observed in the nasal samples in the PI group (Fig 4B).

Our SIMPER analysis predicted 10 OTUs from PI group to be primary drivers influencing the differences between pre- and post-surgery nasal community (S6 Table). The impact of PI on the relative abundance of each genus is shown in (Fig 2B and 2D and S6 Table). In the PI group’s differential abundance analysis, OTU1, which classified to the genus Staphylococcus, had the greatest contribution to the differences observed between pre- and post-surgery samples (Fig 2D, S6 Table). The mean relative abundance of Staphylococcus decreased from 22.98% (SE = 0.07) pre-surgery to 13.77% (SE = 0.04) post-surgery, representing 40.1% decrease. This trended towards being significantly reduced in the PI post-surgery group (FDR adjusted P = 0.06, S3 Table).

OTU2, classified to the genus Corynebacterium, was found in lower abundance (20.1% (SE = 0.06)) pre-surgery compared to post-surgery (14.54% (SE = 0.05)), representing 27.75% decrease in PI group. However, this difference was not statistically significant (FDR adjusted P = 0.197, S6 Table).

Other OTUs classified to the genera Lactobacillus, Dolosigranulum, Moraxella, Bradyrhizobium, Succinivibrionaceae_UCG-001, Lawsonella, Prevotella, Succiniclasticum were shown to has contributed to observed community-level differences between pre- and post-surgery nasal samples in the PI group (S6 Table). Of these, insufficient evidence of abundant change was observed between pre- and post-surgery samples (FDR adjusted P > 0.05, S6 Table).

Exploratory analysis between control and PI groups

The alpha diversity exploratory analysis showed significant difference in diversity between control-post and PI-post for Chao’s richness estimate (P = 0.019) and Shannon’s diversity index (P = 0.047), however the Inverse Simpson index did not show a significant difference (P = 0.113). As expected, there was no significant difference between diversity in the 46 pre-surgery samples of the control group and the randomly selected subset of 46 pre-surgery of PI samples: (Chao’s richness estimate P = 0.67, Shannon’s diversity Index P = 0.821, and Inverse Simpson index P = 0.561).

Impact of anesthesia and PI on Staphylococcus relative abundance

In the exploratory analysis to further determine the impact of anesthesia and PI on Staphylococcus relative abundance, we compared 46 control post-surgery with 46 post-surgery subsampled PI group. The PI group post-surgery had 12.59% (SE = 0.04) Staphylococcus, which was 33.16% lower than the control post-surgery group’s relative abundance of 18.79% (SE = 0.06). The difference was insufficient to show significant difference (FDR adjusted P = 0.165). The abundance changes between 46 control group pre-surgery (21.65% (SE = 0.06)) and 46 PI group pre-surgery (25.53% (SE = 0.08)) increased by 17%. This difference was insufficient to show a significant difference (FDR adjusted P = 0.958). For quality control assessment, the phyla and genera (S1 Fig) observed were comparable to the positive control, as expected [45].

Discussion

In this QI study, we investigated the relationship between general anesthesia and preoperative intranasal PI decolonization on nasal microbiota diversity using 16S rRNA gene amplification sequencing analysis. To the best of our knowledge, this QI study is the first of its kind. Research in this area has primarily focused on the effect of PI on SSI rates but not specifically on the nasal microbiota. The relationship between microbial diversity and infection risk has been well studied. Reports show that patients with greater microbial diversity are more resistant to infection compared to those with lower microbial diversity [46,47].

Response of nasal microbiota to general anesthesia

In this study, all patients received general anesthesia. However, the type of anesthesia administered varied from patient to patient due to the difference in medical history and the procedure required to improve the quality of life of the patient. General anesthesia was administered by either inhalation via mask or endotracheal tube, through an intravenous line (IV), or a combination of these. In this group, we identified both pathogen-containing and non-pathogen-containing genera with high relative abundance in the anterior nares of patients pre- and post-surgery. The nasal bacterial community composition was similar to the nasal microbiota of previous studies [48–50].

Data from the control group demonstrated that general anesthesia did not significantly alter the diversity of the nasal bacterial community. Additionally, the pre-surgery abundance of each genus was not significantly different from post-surgery samples. This suggests that the type of anesthesia administered to patients in the academic center did not significantly affect a patients’ nasal microbiota at the time samples were taken. Therefore, it may not severely deplete the nasal microbiota diversity to increase risk of opportunistic pathogens spreading to other sites of the body to potentially cause infections. Although anesthesia did not exhibit significant change in nasal microbiota diversity, the presence of genera with known pathogenic species in the control group highlights the need for decolonizing pre-surgery patients with intranasal PI prior to surgery, as recommended [14]. The control group also serves as a baseline to assess if the changes in diversity that occurred after PI application was influenced by anesthesia or not.

Response of nasal microbiota to PI

Unlike the control group, an increased bacterial diversity was detected among the PI group at the end of surgery. Further evidence depicts that the PI post-surgery compared to control post-surgery also yielded a similar outcome. These suggest the activity of PI against bacteria varied and PI may play a vital role in promoting the increased diversity. Research shows that PI can readily penetrate the cell wall of S. aureus within 30 seconds to kill or inhibit growth [51]. On the other hand, commensals such as Corynebacterium spp. can hinder PI from penetrating the cell wall due to the high mycolic acid content [52]. This may support commensals such as Corynebacterium spp., allowing them to recover rapidly and colonize faster than pathogens to control diversity [52]. In addition, bacteria including Corynebacterium species have antagonistic mechanisms against S. aureus proliferation [53,54]. The increased bacteria community richness and evenness observed also supported the stability of the diversity at the end of the surgery. The persistent inhibition and bactericidal activity of PI on pathogens (e.g. Methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-susceptible Staphylococcus aureus (MSSA) [51,55,56] may also be a contributing factor for commensals to thrive, compete for colonization [12,53] and maintain diversity. As such, harmful invaders may be contained by commensals from spreading to other body sites [57] especially during surgery.

Impact of PI on abundance and prevalence of bacterial taxa

The role of an individual bacterial taxa present in the nasal community is vital [54,58]. We identified specific bacterial taxa community members that may drive the differences seen between taxa communities with and without PI. While anesthesia did not significantly impact individual genera in the control group, PI impacted individual genera differently. In the PI and control groups, the genus Staphylococcus (phylum Firmicutes) was the most abundant bacterial taxa across PI and control samples pre-surgery. Post-surgery, PI samples showed a significant decrease in the relative abundance of Staphylococcus. However, there was no such decrease in this genus between the pre- and post-surgery in the control group. This demonstrates the potential of PI to reduce the relative abundances of S. aureus, strains of which are recognized as the main causative agent of SSI. Other species in the genus Staphylococcus which do not cause serious infections are known to modulate the abundance of S. aureus [58]. We also observed that the genus Corynebacterium (phylum Actinobacteriota) was the second most abundant genus responsible for community-level differences between pre- and post-surgery nasal bacterial communities. A report showed Corynebacterium species play an integral role in controlling S. aureus by the human cell binding competition mechanism [53]. The PI penetrability of bacteria cell walls may also play an important role [52].

PI has broad antimicrobial spectrum activity and its impact on the relative abundance of the genus Corynebacterium, and other commensal genera (Prevotella, Lactobaccilus, Dolosigranulum) in the PI group, varied and was insufficient to detect significant differences. In addition to the antimicrobial activity of PI, the high abundance of multiple commensals; Corynebacterium, Prevotella, Lactobaccilus, Dolosigranulum may contribute to the striking decrease in abundance of the genus Staphylococcus after PI application. These findings support previous studies that Corynebacterium [53], Lactobaccilus [59] and Dolosigranulum [60] negatively affect S. aureus.

This study had several limitations. The samples in this QI study were collected from a single academic center, not randomized, not adequately powered, and therefore, may not be generalizable. The QI study did not follow patients to determine if the nasal microbial diversity change by PI led to a reduction in rate of SSI. Therefore, it was impossible to directly relate changes in the nasal microbiota to the rate of SSI among patients. Since QI study does not allow for the collection of patient identifiers, we were unable to collect any patient-level and provider-level confounding variables such as prior MRSA infections or colonization, extra nasal colonization, co-morbidities, type of surgery, duration of surgery, age, gender, antibiotics, ethnicity and diet. We were unable to connect the type of anesthesia administered to each patient’s nasal sample. Therefore, could not stratify the general anesthesia administration mode into mask via nose or mouth, tube, or others.

Conclusions

Our data showed that intranasal PI decolonization prior to surgery increases nasal bacterial diversity among surgery patients. Therefore, preoperative intranasal PI may serve as a potential option to prevent SSI by nasal bacterial community modulation. Additionally, general anesthesia administration did not significantly alter the nasal bacterial community, evidence that it has no potential impact on the risk of escalating opportunistic pathogens from a patient’s nares to other body sites to cause infections. This study was conducted in one ambulatory surgical setting with patients having limited variety of surgical procedures, therefore further studies may be required to fully understand the impact of PI decolonization nasal microbial communities.

Supporting information

S1 Fig. Microbial community standard composition.

Stacked bar plots of the absolute abundance of top 10 phyla and genera composition in positive control (ZymoBIOMICS® Gut Microbiome). Rare taxa are classified as “uncultured”. The legend displays the color coding of genera and phyla to which these taxa belong.

(TIF)

Click here for additional data file. (492KB, tif)
S1 Table. A table of samples, their coverage, read counts, alpha diversity metrics, and metadata.

(XLSX)

Click here for additional data file. (51KB, xlsx)
S2 Table. A table of samples and their operational taxonomy units (OTUs) in the control group.

(XLSX)

Click here for additional data file. (783.8KB, xlsx)
S3 Table. A table of samples and their operational taxonomy units (OTUs) in the PI group.

(XLSX)

Click here for additional data file. (1.4MB, xlsx)
S4 Table. The relative abundance of the top 10 phyla and genera.

The relative abundance of the top 10 phyla and genera found in pre-surgery and post-surgery samples in control and povidone-iodine groups.

(XLSX)

Click here for additional data file. (12.2KB, xlsx)
S5 Table. The SIMPER analysis for the control group.

The predicted OTUs by SIMPER analysis drive differences found between pre- and post-surgery in control group.

(XLSX)

Click here for additional data file. (11.8KB, xlsx)
S6 Table. The SIMPER analysis for the PI group.

The predicted OTUs by SIMPER analysis to drive differences found between pre- and post-surgery in PI group.

(XLSX)

Click here for additional data file. (10.4KB, xlsx)
S7 Table. List of OTUs and their taxonomy.

(XLSX)

Click here for additional data file. (289.9KB, xlsx)

Acknowledgments

The authors would like to thank Ms. Michele Zimbric (Division of Infectious Diseases, Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison), Dr. Evelyn Hammond (Institute of Natural Resources, University of Wisconsin Madison Division of Extension), Ms. Edna Chiang, Mr. Ibrahim Zuniga Chaves (Department of Bacteriology, University of Wisconsin-Madison), and the Suen Lab for their technical support, careful reading of this manuscript, and invaluable suggestions during the study.

Data Availability

All sequences associated with this study were deposited into the National Center for Biotechnological Information’s Short Read Archive (NCBI SRA database) and are available under BioProject Accession PRJNA859807.

Funding Statement

The author(s) received no specific funding for this work.

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Decision Letter 0

Rajesh P Shastry

12 Sep 2022

PONE-D-22-21286A quality improvement study on the relationship between intranasal povidone-iodine and anesthesia and the nasal microbiota of surgery patientsPLOS ONE

Dear Dr. Hammond,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Reviewer #2: Yes

Reviewer #3: Partly

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Reviewer #2: Yes

Reviewer #3: No

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Reviewer #1: The authors found that povidone-iodine decolonization of the nasal cavity during surgery altered the composition of the nasal flora compared to its absence. Specifically, we have found that povidone-iodine sterilization reduces the proportion of staphylococci and increases bacterial diversity. On the other hand, general anesthesia did not alter the composition of the nasal flora.

Staphylococci are bacteria highly associated with surgical site infections. It is conceivable that staphylococci, which exist abundantly in the nasal cavity, which is the inhalation part of general anesthesia, diffuse into the body through the respiratory system due to anesthesia, increasing the risk of infection during surgery. Our results suggest that intranasal use of povidone-iodine during anesthesia may reduce the composition of staphylococcal bacteria and reduce infections during surgery. In that sense, the present results are considered to be significant.

However, this paper leaves some unclear questions.

1. Age and gender distribution of subjects is unknown. It is necessary to examine whether these two conditions are factors for changes in the bacterial flora. It should also be investigated whether the differences in the microbiota with and without povidone-iodine use differ by age and gender.

2. The subject was on antibiotic prophylaxis during surgery, but the drug was not specified. It is conceivable that the type of antibiotics affects the compositional changes of the nasal flora after surgery. Therefore, it is necessary to clarify which antibiotics were used and to clearly demonstrate the effect of different antibiotics on postoperative changes in the composition of the bacterial flora.

3. The fourth variable region of the bacterial 16sRNA gene is targeted for the analysis of bacterial flora from the nasal cavity. Whether or not this is the most suitable method for compositional analysis of the bacterial flora in the nasal cavity should be explained in detail using various literatures and findings.

4. Are subjects with chronic or acute sinusitis, various respiratory/nasal diseases, and allergic rhinitis included in the subject exclusion criteria? Do these differ in composition of the nasal flora compared to healthy subjects in the nasal cavity? Or are the proportions of these affected individuals different in the two groups? The above should be clearly indicated. If you have not considered this point, you should explain in detail why you did not consider it, or why you did not need to consider it.

5. Overall, the figures are not clean and the letters are too small. The figure should be modified to make it clear.

Reviewer #2: Dear Authors,

Thank you for conducting this study entitled "A quality improvement study on the relationship between intranasal povidone-iodine and anesthesia and the nasal microbiota of surgery patients" for possible publication in the esteemed journal "PLOS ONE". The manuscript needs major revision because of the following comments:

Materials and methods

1. You should mention the design of the study. I see only the title "Study design and participants".

2. You should define the study groups. Because this sentence "(PI group and general anesthesia only group)." is confusing for the readers.

3. Please discuss the way of randomization.

4. More detail is needed about the inclusion and exclusion criteria.

5. Please provide the reference number and date of your IRB approval letter.

6. You didn't mention the data information regarding the age, gender, comorbidities, type of operation, etc. (these might affect the results).

7. How do you calculate the sample size?

Results

1. This sentence "Patients included those scheduled for cosmetic plastic surgery – breast reduction, breast augmentation, and tummy tuck." Belongs to the inclusion criteria in the materials and methods section.

2. The writing in Figure 1 is not clear. Besides, the other figures are blurred.

3. The Figure 1 legend should be below the figure. Please do the same for other figures.

Reviewer #3: Title of the manuscript: A quality improvement study on the relationship between intranasal povidone-iodine and anesthesia and the nasal microbiota of surgery patients

Review comments

The manuscript described nasal bacterial composition on treatment with povidine-iodine treatment in surgery patients. In my opinion, the manuscript should provide more detailed information and be improved overall. The nasal tract of humans consists of a very complex and dynamic microbial ecosystem. A series of exogenous and endogenous factors can affect the establishment and nature of the microbial composition in the tract. Such as, the age, gender, diet, habits, ethnicity, the surrounding environment and so on, they are very critical factors that affect the initial colonization and the subsequent establishment process. The author should have a very clear explanation of the above-mentioned factors.

It has novel findings about the bacterial differences between the two groups, but the data analysis doesn’t seem to be appropriate to represent the real diversity. Most of all, the number of reads and OTUs seem to be underestimated. The discussion needs to be enforced. For example, the distinguished taxa between groups and genera should be discussed more. The authors have focused majorly on the generic level but the higher taxonomic level variations and similarity have to be added. The SIMPER results have to be corroborated using other statistical approaches like ASCA, Lefse or ANCOM

Specific comments

1. The metadata of the patients and their biochemical profile is missing in the MS which is a big drawback of the study. The gender/ethnicity/diet or the overall health of the individual is known the drive the variations in microbiome and it needs to be provided for the benefit of the scientific community and readers.

2. Why a V4 region was preferred over V3-V4 or V4-V5 region

3. The total number of OTU's are not provided in the results section

4. Why rarefaction curves for the groups has not been provided?

5. The sample sequencing shows too much difference. There is a wide disparity between the reads. Some sample in treated group has reads of 418, while the max ranges upto 51400. In the control group, the same ranges from 440 to 1,47,000. What is the reason for this much variance?

6. Authors have used the metaMDS package of vegan to plot the NMDS plot. The ellipse color can be transparent allowing to see the grouping of sample points.

7. Figure 2A-D can be made better by converting the same in to a stacked barplot which will help the reader. The y axis of the images is in different scales which is misleading the reader.

8. There is no data on the OTU classification and their actual numbers in the samples as a OTU table– This data needs to be added as a supplementary.

9. What was the stress value for nMDS analysis? That should be provided in the manuscript

10. PERMANOVA results are confusing. The lines 334-335 indicates that there is difference between the PI and control groups – this could be due to the diverse patient profile which could be the reason for this difference.

Lines 88-89 “On the day of surgery, patients are also administered with an antibiotic prophylaxis”. What antibiotics were administered to the patients and whether this was the same for all the patients?

Was the antibiotic history of the patients collected as this can affect the overall outcome of the study?

Lines 254-256, when mentioning the groups, provide relative abundance in the parenthesis.

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Reviewer #2: No

Reviewer #3: No

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PLoS One. 2022 Dec 9;17(12):e0278699. doi: 10.1371/journal.pone.0278699.r002

Author response to Decision Letter 0

31 Oct 2022

Additional requirements:

2. Please provide additional details regarding participant consent. In the ethics statement in the Methods and online submission information, please ensure that you have specified whether:

i) whether the ethics committee approved the verbal/oral consent procedure,

Authors’ reply: The University of Wisconsin Health Sciences Institutional Review Board exempted this study as a quality improvement project and therefore did not request a verbal consent statement for approval. We have indicated this on Page Lines 107-115.

ii) why written consent could not be obtained, and

Authors’ reply: As noted above, we did not obtain written consent because the University of Wisconsin Health Sciences Institutional Review Board exempted this study as a quality improvement project. Moreover, a written consent is not mandatory in the quality improvement project.

iii) how verbal/oral consent was recorded. If your study included minors, please state whether you obtained consent from parents or guardians in these cases. If the need for consent was waived by the ethics committee, please include this information.

Authors’ reply: Although the nurse obtained verbal consent it was not recorded. Minors were not involved in this study.

3. Please upload a new copy of Figures 2 to 4 as the detail is not clear.

Authors’ reply: We have corrected these figures to be clearer and have uploaded the revisions

Reviewer #1 comments

Reviewer #1 comment 1: Age and gender distribution of subjects is unknown. It is necessary to examine whether these two conditions are factors for changes in the bacterial flora. It should also be investigated whether the differences in the microbiota with and without povidone-iodine use differ by age and gender .

Authors’ reply: While we agree with the reviewer, this study was considered a quality improvement project. Because quality improvement projects are not human subjects research, we were unable to collect any patient identifiers (such as age and gender) as indicated in lines 111-112. We have also stated in lines 422-429 that “Since QI study does not allow for the collection of patient identifiers, we were unable to collect patient-level and provider-level confounding variables such as prior MRSA infections or colonization, extra nasal colonization, co-morbidities, type of surgery, and duration of surgery”. To clarify potential confounding variables, we have added age, gender, antibiotics, ethnicity, and diet (Page 18, Lines 425-426). Based on these limitations we were unable to stratify our data by age or gender or ethnicity or antibiotic use.

Reviewer #1 comment 2: The subject was on antibiotic prophylaxis during surgery, but the drug was not specified. It is conceivable that the type of antibiotics affects the compositional changes of the nasal flora after surgery. Therefore, it is necessary to clarify which antibiotics were used and to clearly demonstrate the effect of different antibiotics on postoperative changes in the composition of the bacterial flora.

Authors’ reply: We have updated our manuscript to include: “Patients were administered with intravenous antimicrobial prophylaxis: cefazolin or clindamycin if allergic to the former. Patients with known Methicillin-resistant Staphylococcus aureus colonization had vancomycin 15mg/kg in addition to cefazolin[1].” (Page 6, Lines 119-122). We were unable to clearly demonstrate the effect of different antibiotics on postoperative changes in the composition of the bacterial flora because we were not able to collect data on which patients received which treatment (this would be considered identifiable information).

Reviewer #1 comment 3: The fourth variable region of the bacterial 16sRNA gene is targeted for the analysis of bacterial flora from the nasal cavity. Whether or not this is the most suitable method for compositional analysis of the bacterial flora in the nasal cavity should be explained in detail using various literatures and findings.

Authors’ reply: We chose the fourth variable region (V4 region) because the V4 region is well-accepted for its maximum nucleotide heterogeneity and specificity to bacterial characterization. In addition, we were interested in comparing specific target regions such as hypervariable regions (V4) to ensure consistency and comparable regions of the 16S rRNA. However, V3-V4 or V4-V5 have different target hypervariable regions of the 16S rRNA gene. Based on these, our lab uses the V4 region to characterize bacteria in many sample types and have been published in major scientific journals (Yatsunenko et al. 2012; Eggers et al 2018). Moreover, we want to keep the data reproducible with the lab protocols and to be able to compare it with future projects. V4 region is a well-accepted region for bacterial classification and characterization in any sample. So far there is no standard protocol indicating that a specific hypervariable region should be used for a specific sample. Some investigators use V4 region (Lehtinen et al, 2018), V3-V4 region (Chen et al 2022; Gan et al 2021), V1-V2 regions (Chen et al 2019) to assess the status of nasal microbiota.

Reference:

1. Yatsunenko T, Rey FE, Manary MJ, Trehan I, Dominguez-Bello MG, Contreras M, et al. Human gut microbiome viewed across age and geography. Nature. Nature Publishing Group; 2012;486:222–7.

2. Eggers S, Malecki KM, Peppard P, Mares J, Shirley D, Shukla SK, et al. Wisconsin microbiome study, a cross-sectional investigation of dietary fibre, microbiome composition and antibiotic-resistant organisms: rationale and methods. BMJ Open. 2018;8:e019450.

3. Lehtinen MJ, Hibberd AA, Männikkö S, Yeung N, Kauko T, Forssten S, et al. Nasal microbiota clusters associate with inflammatory response, viral load, and symptom severity in experimental rhinovirus challenge. Sci Rep. Nature Publishing Group; 2018;8:1–12.

4. Chen F, Gao W, Yu C, Li J, Yu F, Xia M, et al. Age-Associated Changes of Nasal Bacterial Microbiome in Patients With Chronic Rhinosinusitis. Frontiers in Cellular and Infection Microbiology [Internet]. Frontiers Media SA; 2022 [cited 2022 Oct 12];12. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8891534/

5. Gan W, Zhang H, Yang F, Liu S, Liu F, Meng J. The influence of nasal microbiome diversity and inflammatory patterns on the prognosis of nasal polyps. Sci Rep. Nature Publishing Group; 2021;11:6364.

6. Chen C-H, Liou M-L, Lee C-Y, Chang M-C, Kuo H-Y, Chang T-H. Diversity of nasal microbiota and its interaction with surface microbiota among residents in healthcare institutes. Sci Rep. Nature Publishing Group; 2019;9:1–10.

Reviewer #1 comment 4: Are subjects with chronic or acute sinusitis, various respiratory/nasal diseases, and allergic rhinitis included in the subject exclusion criteria? Do these differ in composition of the nasal flora compared to healthy subjects in the nasal cavity? Or are the proportions of these affected individuals different in the two groups? The above should be clearly indicated. If you have not considered this point, you should explain in detail why you did not consider it, or why you did not need to consider it.

Authors’ reply: We agree that sinusitis can alter nasal microbiota. However, this data would be considered a patient identifier and as such we were not permitted to collect it for this quality improvement study. Because of this, we could not exclude patients with such conditions except those who were allergic to povidone-iodine. As a result of this we mentioned in our limitations that “we were unable to collect patient-level and provider-level confounding variables such as prior MRSA infections or colonization, extra nasal colonization, co-morbidities, type of surgery, and duration of surgery” (page18 and 19, lines 424-429). Generally, patients who attended the academic center for surgery were considered healthy.

Reviewer #1 comment 5: Overall, the figures are not clean and the letters are too small. The figure should be modified to make it clear..

Authors’ reply: We agree with reviewer’s comment and have corrected the figures.

Reviewer #2 comments

(Materials and methods)

Reviewer #2 comment 1. You should mention the design of the study. I see only the title "Study design and participants".

Authors’ reply: We thank the reviewer for this comment. We have inserted “We applied the quasi-experimental study design” (Page 6, Line 110).

Reviewer #2 comment: 2. You should define the study groups. Because this sentence "(PI group and general anesthesia only group)." is confusing for the readers.

Authors’ reply: We appreciate the reviewer’s comment. The definitions can be found on Page 6, lines 122-129. "The general anesthesia only group, were those who received only general anesthesia without PI or any other nasal decolonizing agents prior to surgery. The general anesthesia only group also served as controls and hereinafter will be referred to as the control group."

Reviewer #2 comment 3: Please discuss the way of randomization.

Authors’ reply: This study is a quality improvement study, and we applied a convenience sample to assign patients to treatments (Page7, Lines 131-132). Therefore, we did not randomize our patients.

Reviewer #2 comment 4: More detail is needed about the inclusion and exclusion criteria.

Authors’ reply: We have moved the statement: “Patients included those scheduled for cosmetic plastic surgery – breast reduction, breast augmentation, and tummy tuck.” from the results section and inserted it in the materials and methods section (Page, 7, Lines 135 - 137). Others that were not included were considered as a limitation of our study.

Reviewer #2 comment 5: Please provide the reference number and date of your IRB approval letter.

Authors’ reply: We have revised the ethical approval section and inserted “(approval number 2019-0466, May 6, 2019)” (Page 6, Lines 114).

Reviewer #2 comment 6: You didn't mention the data information regarding the age, gender, comorbidities, type of operation, etc. (these might affect the results).

Authors’ reply: We have addressed this comment in reviewer #1 comment 1 above. While we agree that addressing potentially confounding variables would strengthen our results, we were not able to collect any patient identifiable information for this study due to the designation as quality improvement by our IRB.

Reviewer #2 comment 7: How do you calculate the sample size?

Authors’ reply: Due to the nature of quality improvement studies, there is no minimum sample size we hoped to achieve. However, the research community usually considers a sample of 25-50 as sufficient to obtain effect size estimates. Moreover, it is not required to calculate power in quality improvement studies. We chose a sample size of 151 due to the feasibility and practicality of enrolling individuals to undertake such a study. The goal was to obtain effect size estimates, so formal sample size is unnecessary. We discussed this as part of the limitation of this study (Page 18, Line 421).

Results:

Reviewer #2 comment 1(Results): This sentence "Patients included those scheduled for cosmetic plastic surgery – breast reduction, breast augmentation, and tummy tuck." Belongs to the inclusion criteria in the materials and methods section.

Authors’ reply: We agree and have moved it to this materials and methods section (Page, 7, Lines 135 - 147).

Reviewer #2 comment 2(Results). The writing in Figure 1 is not clear. Besides, the other figures are blurred.

Authors’ reply: We agree with reviewer’s comment and have corrected the figures.

Reviewer #2 comment 3 (Results): The Figure 1 legend should be below the figure. Please do the same for other figures.

Authors’ reply: We followed PLOS ONE submission guidelines to separate the figures from the legend. The legend should be inserted in the text and the figure should stand alone.

 

Reviewer #3 comments:

Reviewer #3 comment 1: The metadata of the patients and their biochemical profile is missing in the MS which is a big drawback of the study. The gender/ethnicity/diet or the overall health of the individual is known the drive the variations in microbiome and it needs to be provided for the benefit of the scientific community and readers.

Authors’ reply: Please see the response to reviewer 1 above.

Reviewer #3 comment: 2. Why a V4 region was preferred over V3-V4 or V4-V5 region

Authors’ reply: We have addressed this comment in reviewer #1 comment 3 above.

Reviewer #3 comment: 3. The total number of OTU's are not provided in the results section.

Authors’ reply: We have updated our manuscript to reflect this as follows:

We have inserted “prior to decontamination” (Page 11, Line 243).

We delete “After clean-up in mothur, 3,061,702 high-quality reads remained (mean = 9,876.46, SD = 16,235.97).” (Page 11, line 244). We noticed the statement was redundant and was not part of the analysis.

We inserted “Following decontamination and clean up in mothur, 2,990,013 sequences (mean = 8,398.91, SD = 14,638.16) with 14,200 OTUs remained.”(Pages 11, Lines 244-245), to provide detail information on the total raw sequences and total OTU used in our analysis.

Reviewer #3 comment 4: Why rarefaction curves for the groups has not been provided?

Authors’ reply: We did not use rarefaction curves to determine normalization cutoff point. We chose a well-accepted approach, Good’s coverage which does not required rarefaction curve to determine the best sampling depth. This approach is consistent with our lab protocol and our numerous published manuscripts in major scientific journals such as (Chiang et al. 2022; Pilch et al 2021).

References:

Chiang E, Deblois CL, Carey HV, Suen G. Characterization of captive and wild 13-lined ground squirrel cecal microbiotas using Illumina-based sequencing. Animal Microbiome. 4: 1.

Pilch HE, Steinberger AJ, Sockett DC, Aulik N, Suen G, Czuprynski CJ. Assessing the microbiota of recycled bedding sand on a Wisconsin dairy farm. Journal of Animal Science and Biotechnology. 12: 114.

Reviewer #3 comment 5: The sample sequencing shows too much difference. There is a wide disparity between the reads. Some sample in treated group has reads of 418, while the max ranges up to 51400. In the control group, the same ranges from 440 to 1,47,000. What is the reason for this much variance?

Authors’ reply: There are several factors that may contribute to these differences. This includes sample to sample variation, sequencing error, amplification error and low DNA yield. During DNA equimolar pooling for sequencing, varied concentrations of DNA samples are pooled together to a specific concentration. However, small errors during DNA quantification and equimolar pooling can result in un-even sequencing depth across samples. As such, samples with high concentration yielded higher raw sequencing reads as compared to those with lower concentrations, causing the need for normalization so the data can be confidently compared.

Reviewer #3 comment 6: Authors have used the metaMDS package of vegan to plot the NMDS plot. The ellipse color can be transparent allowing to see the grouping of sample points.

Authors’ reply: We have deleted the nMDS plots without sample points and replaced them with ellipse color that are transparent to allow our readers to see the grouping of sample points (fig 4), as suggested.

Reviewer #3 comment 7. Figure 2A-D can be made better by converting the same in to a stacked barplot which will help the reader. The y axis of the images is in different scales which is misleading the reader.

Authors’ reply: We agree that stacked bar is an effect method to visualize the estimation of individual taxa. However, we changed our stacked bar plots because all our pilot readers indicated that the stacked bar plot is confusing and difficult to read. Moreover, it is difficult to interpret and therefore cannot provide a meaningful interpretation of the plot. Following careful analyzes of the reviewer’s comment, we decided to maintain the bar plots to allow clarity, allow our readers to better read the results directly from the figures without estimating. This will also optimize their understanding and interpret the results accurately without a struggle.

We have deleted figures 2A-D with unequal y-axis scale and replaced them with sharp and readable figures with equal scale.

Reviewer #3 comment 8. There is no data on the OTU classification and their actual numbers in the samples as a OTU table– This data needs to be added as a supplementary.

Authors’ reply: We have added OTU tables for PI and control groups and added taxonomy table to optimize clarity in the supplementary information, as suggested.

Reviewer #3 comment 9. What was the stress value for nMDS analysis? That should be provided in the manuscript

Authors’ reply: We have updated our manuscript and made the following additions:

We have inserted “, nMDS stress = 0.25” for control group (Page 12, Line 267).

We have inserted “, nMDS stress = 0.20” for PI group (Page 13, Lines 293-294).

We have inserted “The nMDS stress levels for PI group = 0.20 and control group = 0.25.” (Page 15 line 328).

Reviewer #3 comment 10. PERMANOVA results are confusing. The lines 334-335 indicates that there is difference between the PI and control groups – this could be due to the diverse patient profile which could be the reason for this difference.

Authors’ reply: We agree and decided to delete, “Comparison of the Bray-Curtis dissimilarity showed that the overall bacterial community composition between control and PI groups samples were different (P < 0.001, PERMANOVA).” The following additional changes were also made:

We have deleted “figure 4C”

We have deleted “(C) combined (A) and (B)”.

We have deleted “and 4C” (Page 13, Line 312)

Reviewer #3 comment 10a. Lines 88-89 “On the day of surgery, patients are also administered with an antibiotic prophylaxis”. What antibiotics were administered to the patients and whether this was the same for all the patients?

Authors’ reply: We have addressed this comment in the reviewer #1 comment 2 above. The antibiotics prophylaxis administered to PI group was not different from that of control group. We were unable to link the type of antibiotics administered to patients.

Reviewer #3 comment 10b. Was the antibiotic history of the patients collected as this can affect the overall outcome of the study ?

Authors’ reply: Because this was a quality improvement study, we were not able to collect which patients received which antibiotic(s) and were not able to conduct any analyses based on antibiotic history.

Reviewer #3 comment 10c. Lines 254-256, when mentioning the groups, provide relative abundance in the parenthesis.

Authors’ reply: On line 254-257 we only mentioned a few of the taxa shown in figs 2A and 2B. After we carefully analyzed the reviewer’s comment, we noticed that adding individual taxon’s pre and post relative abundance may create duplication. To avoid this, instead of adding the relative abundance of each in in-text, we have calculated all the relative abundance and standard error of each phylum and genus in each group in S4 line 257. This also applies to lines 285-287 in addition to fig 2C and 2D which the reviewer did not mention.

We inserted “See S4 Table for relative abundance data.” (Page 12, Line 257). We have also directed our readers to the location of the relative abundance of each taxon.

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file. (59.5KB, docx)

Decision Letter 1

Rajesh P Shastry

22 Nov 2022

A quality improvement study on the relationship between intranasal povidone-iodine and anesthesia and the nasal microbiota of surgery patients

PONE-D-22-21286R1

Dear Dr. Hammond,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Rajesh P. Shastry

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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Reviewer #1: All comments have been addressed

Reviewer #3: All comments have been addressed

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The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #3: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #3: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

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Reviewer #1: Yes

Reviewer #3: Yes

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Reviewer #1: Yes

Reviewer #3: Yes

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Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors responded appropriately to comments and criticisms and revised the manuscript appropriately.

Reviewer #3: All the questions raised by me have been addressed satisfactorily.

I approve the acceptance of this Manuscript.

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Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #3: Yes: Sudeep D Ghate

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Acceptance letter

Rajesh P Shastry

28 Nov 2022

PONE-D-22-21286R1

A quality improvement study on the relationship between intranasal povidone-iodine and anesthesia and the nasal microbiota of surgery patients

Dear Dr. Safdar:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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on behalf of

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Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Fig. Microbial community standard composition.

    Stacked bar plots of the absolute abundance of top 10 phyla and genera composition in positive control (ZymoBIOMICS® Gut Microbiome). Rare taxa are classified as “uncultured”. The legend displays the color coding of genera and phyla to which these taxa belong.

    (TIF)

    Click here for additional data file. (492KB, tif)
    S1 Table. A table of samples, their coverage, read counts, alpha diversity metrics, and metadata.

    (XLSX)

    Click here for additional data file. (51KB, xlsx)
    S2 Table. A table of samples and their operational taxonomy units (OTUs) in the control group.

    (XLSX)

    Click here for additional data file. (783.8KB, xlsx)
    S3 Table. A table of samples and their operational taxonomy units (OTUs) in the PI group.

    (XLSX)

    Click here for additional data file. (1.4MB, xlsx)
    S4 Table. The relative abundance of the top 10 phyla and genera.

    The relative abundance of the top 10 phyla and genera found in pre-surgery and post-surgery samples in control and povidone-iodine groups.

    (XLSX)

    Click here for additional data file. (12.2KB, xlsx)
    S5 Table. The SIMPER analysis for the control group.

    The predicted OTUs by SIMPER analysis drive differences found between pre- and post-surgery in control group.

    (XLSX)

    Click here for additional data file. (11.8KB, xlsx)
    S6 Table. The SIMPER analysis for the PI group.

    The predicted OTUs by SIMPER analysis to drive differences found between pre- and post-surgery in PI group.

    (XLSX)

    Click here for additional data file. (10.4KB, xlsx)
    S7 Table. List of OTUs and their taxonomy.

    (XLSX)

    Click here for additional data file. (289.9KB, xlsx)
    Attachment

    Submitted filename: Response to Reviewers.docx

    Click here for additional data file. (59.5KB, docx)

    Data Availability Statement

    All sequences associated with this study were deposited into the National Center for Biotechnological Information’s Short Read Archive (NCBI SRA database) and are available under BioProject Accession PRJNA859807.

    Articles from PLOS ONE are provided here courtesy of PLOS

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