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Fig. 2. Principal coordinates analysis (PCoA) of bacterial communities on stethoscopes in the medical intensive care unit (ICU). Bacterial communities were analyzed using unweighted (A) and weighted (B) UniFrac and were visualized by PCoA. UniFrac compares communities based on the phylogenetic relatedness of their constituent bacteria. Unweighted considers the presence or absence of bacteria, whereas the weighted approach also considers their abundance within each community. The PCoA provides a means of visualizing these relationships, and each symbol on the PCoA plot represents an individual bacterial community derived from one stethoscope or background control sample. All community types (practitioner stethoscopes, patient-room stethoscopes, clean stethoscopes and background controls) were significantly different by unweighted UniFrac (P<.05; PERMANOVA). By weighted UniFrac, the 2 in-use stethoscope types (practitioner and patient-room) did not differ from each other, nor did the 2 background samples (clean stethoscopes and background controls), but the 2 in-use stethoscopes were significantly different the 2 types of background samples (P<.05; PERMANOVA).
We then compared the bacterial community composition of practitioner Set C before and after practitioner cleaning (Fig. 5). In the unweighted Unifrac PCoA, which is equally impacted by high- and low-abundance taxa (Fig. 5(A)), the pre- and post- cleaning samples were significantly different from one another (P=.001; PERMANOVA) and were significantly different from the clean stethoscopes and background controls (P<.001; PERMA- NOVA). In contrast, no significance difference was observed between the pre- and post-practitioner cleaning in the weighted Unifrac PCoA, in which taxa are weighted based on their relative abundances (P=.274; PERMANOVA) (Fig. 5(B)). These results suggest that cleaning does not have a substantial effect on community structure and that low abundance (minority) taxa are mainly responsible for differences between the pre- and post-cleaning communities seen in the unweighted but not weighted analysis.
Fig. 3. Top 1% taxa that drive ordination on the weighted PCoA. To visualize the bacteria that are most responsible for differences between the groups of samples, taxa present at>1% abundance that are responsible for separation on the PCoA are shown, with the length of the vector proportional to its power to explain the separation. Practitioner and patient-room stethoscopes are distinguished by the presence of taxa such as Streptococcus, Staphylococcus, Propionibacterium, and other skin and gut flora, whereas clean stethoscopes and background samples are distinguished by detection of Methylobacterium, Pseudomonas,and Acinetobacter.
usually use between patients. Bacterial contamination was quantified by the amount of 16S rRNA gene amplicon following barcoded PCR end-point amplification. As shown in Fig. 4(A), standardized cleaning resulted in a
significant decrease in stethoscope bacteria based on 16S rRNA gene amplification (P=5.7×10−5; Student t test). Of the 10 practitioner stethoscopes, 5 dropped below the mean level seen on clean stethoscopes. In the practitioner-preferred cleaning method group (Fig. 4(B)), there was also a significant difference between pre- and post-cleaning measured by 16S rRNA quantification (P=.002, Student t test). However, only 2 of 20 stethoscopes dropped below the threshold of clean stethoscopes after practi- tioner cleaning. Similar results were obtained when bacterial quantification was done by 16S qPCR (Fig. S2).
Identification of nosocomial genera
We next investigated the presence of bacteria that are potential nosocomial pathogens. Samples from practitioner stethoscopes in sets A and C (prior to cleaning) were queried for relevant bacteria (Table 1). Both the V1V2 and V4 16S rRNA gene-variable regions were targeted to minimize the impact of potential primer bia- ses.19,20 In addition to aligning 16S rRNA gene sequences through the QIIME pipeline, sequences were manually aligned to data- bases by BLAST to optimize assignment. Sequences were identified from genera that are commonly asso-
ciated with hospital-associated infections including Staphylococcus, Pseudomonas, Acinetobacter, Clostridium, Enterococcus, Stenotrophomonas, and Burkholderia. Most taxa could only be assigned at the genus level by both V1V2 and V4 16S rRNA gene sequences, although some Staphylococcus sequences could be iden- tified at the species level and included S. aureus. All stethoscopes had Staphylococcus spp., and more than half of them were confirmed to carry S. aureus. Most stethoscopes also carried Pseudomonas and Acinetobacter. Approximately half of the stethoscopes had Enterococcus, Stenotrophomonas, and Clostridium,while Burkholderia was less frequent. The V4 results were overall highly concordant with
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