Characterization of SARS-CoV-2 distribution and microbial succession in a clinical microbiology testing facility during the SARS-CoV-2 pandemic

doi:10.21203/rs.3.rs-1670856/v2

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Characterization of SARS-CoV-2 distribution and microbial succession in a clinical microbiology testing facility during the SARS-CoV-2 pandemic

https://doi.org/10.21203/rs.3.rs-1670856/v2

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Background

The exchange of microbes between humans and the built environment is a bidirectional and dynamic process that has significant impact on health. Most studies exploring the microbiome of the built environment have been predicated on improving our understanding of pathogen emergence, persistence, and transmission. Previous studies have demonstrated that SARS-CoV-2 presence significantly correlates with the proportional abundance of specific bacteria on surfaces in the built environment. However, in these studies, SARS-CoV-2 originated from infected patients. Here we perform a similar assessment for a clinical microbiology lab while staff were handling SARS-CoV-2 infected swab samples for RT-qPCR analysis. The goal of this study was to understand the distribution and dynamics of microbial population on various surfaces within different sections of a clinical microbiology lab during a short period of 2020 Coronavirus disease (COVID-19) pandemic.

Results

We sampled floors, benches and sinks in three sections of an active clinical microbiology lab (bacteriology, molecular microbiology, and COVID) over a 3-month period. Although floor samples harbored SARS-CoV-2, it was rarely identified on other surfaces, and bacterial diversity was significantly greater in floor samples than sinks and benches. The floor-associated microbiota comprised greater proportions of bacteria commonly associated with natural environments (e.g., soils), and benchtops harbored a greater proportion of human-associated microbes, including Staphylococcus and Streptococcus, which may have originated from either laboratory staff or clinical samples. Finally, we show that the microbial composition of these surfaces did not change over time and remained stable.

Conclusion

Despite of finding viruses on the floors, no lab-acquired infections were reported during the study period, which suggests that lab safety protocols and sanitation practices were sufficient to prevent pathogen exposures. Like other BEs and health care facilities the most common bacteria colonizing the surfaces in a clinical microbiology lab primarily consist of bacterial taxa of environmental and human origin. Although our analysis here does not identify the source of infection in any given case, it does help to elucidate microbes that colonize these surfaces and have the potential to cause LAIs.

Built environment

SARS-CoV-2

RT-qPCR

microbiota

clinical microbiology lab

Clinical laboratories routinely conduct a variety of diagnostic tests to provide reliable data to assist physicians in diagnosing diseases in their patients. Due to the nature of the service provided, clinical laboratories usually are near hospitals or housed in a separate section of the same building. According to the United States Department of Labor, an estimated 500,000 workers are employed in clinical laboratories across the USA. These workers, particularly those working in microbiology sections, are at greater risk of infections caused by a wide variety of microorganisms that fall under the category of occupational hazards. Laboratory-acquired infections (LAIs) are defined as infections acquired through laboratory-related activities [1] and could potentially be symptomatic depending on the microbe acquired. According to a survey conducted by doctoral-level clinical microbiology laboratory directors in 2005, 33% of laboratories reported at least one LAI over the period of three years from 2002 to 2004 [2]. These employees may acquire LAIs through improper use of personal protective equipment, contaminated work surfaces, or a lack of adherence to safety protocols. There have been several outbreaks of Salmonella over the last decade in microbiology laboratories despite their rigorous safety protocols [3–5]. Nevertheless, LAIs have been decreasing in recent years, likely due to improved ventilation, process changes, and greater adherence to training and safety protocols [6, 7]. Some of the most concerning causative agents of LAIs include Brucella sp., Shigella sp., Salmonella sp., Mycobacterium tuberculosis, and Neisseria meningitidis [8].

Contaminated surfaces can act as sources of pathogen transmissions between individuals [9] and can substantially impact human health. Viruses are frequently transmitted, particularly in indoor environments; depending on the virus and the host, symptoms may range from none to severe life-threatening conditions [10]. Most viruses causing respiratory tract infections (e.g., coronavirus, coxsackie virus, influenza virus, respiratory syncytial virus, and rhinovirus) can persist on surfaces for days, and disseminate infection if the surfaces are not properly disinfected [11]. Coronavirus Induced Disease 19 (COVID-19) is caused by the Severe Acute Respiratory Syndrome Coronavirus type 2 (SARS-CoV-2), which has been responsible for 520 million cases and 6.2 million deaths worldwide between January 2020 and April 2022 [12]. The primary mode of SARS-CoV-2 transmission is respiratory droplets released into the air by coughing, sneezing, speaking and singing [13–16]. Towards the beginning of the pandemic, some early laboratory studies revealed the persistence of SARS-CoV-2 virus on human skin, plastic, glass, cloth, stainless steel, and other surfaces for hours to days [17–19]. A plethora of research has been done on the prevalence of SARS-CoV-2 virus and RNA was found on every conceivable object/surface which suggested the possibility of fomite borne viral dissemination [20–23]. In some studies of viral persistence in controlled laboratory environments, and viral detection in real-world settings, majority of swab samples showed positive PCR tests, however only a handful showed limited cytopathic effect [24–30] suggesting that virus was not viable on surfaces and therefore illustrates that SARS-CoV-2 surface transmission may be a possibility but not a rule [31].

Microbes are ubiquitously present in nature, and their distribution, diversity and dispersal are shaped by their ability to adapt and compete within their surrounding environment. Modern humans spend approximately 90% of their time inside the built environment (BE) [32–34], and the microbiology of these environments are primarily shaped by the microbial profiles of the individuals inhabiting them [35]. The microbes that inhabit the BE are probably most important in healthcare settings, where chronically ill patients are at high risk of acquiring hospital-acquired infections. These types of infections are among the leading causes of patient deaths [36–39]; however, there have been relatively few studies characterizing the microbes that exist in healthcare environments [40–52]. Prior studies have demonstrated that both pathogen outbreaks and pathogen exposures can occur in a clinical microbiology laboratory setting, because the people in the laboratory work to cultivate and/or detect these pathogens. Interestingly, there have been no prior reports detailing the microbiology of surfaces within the clinical microbiology laboratory to help determine whether these surfaces could harbor potential pathogens.

There has been a rapid growth in the discovery and application of culture-independent techniques such as high-throughput DNA sequencing that has greatly increased our understanding of the complex microbial communities that inhabit the BE [53, 54]. Specifically, molecular investigations using the 16S rRNA marker gene have enabled the identification of novel, previously uncultivable bacterial species under normal laboratory conditions [55]. In recent years, 16S rRNA amplicon sequencing has facilitated the study of microbes inhabiting a variety of BEs [56, 57]. The hospital microbiome is primarily shaped by patients and health workers, and may be more diverse and dynamic compared to other BEs [48, 58]. Likewise, studies focusing on ICUs have reported increased abundances of skin associated microbes [49], but with reduced diversity compared to nonpatient care portions of the hospital [45, 48, 59]. We recently characterized the microbial composition of different surfaces in the ICU during different stages of a renovation [44]. Our results demonstrated that microbial composition is significantly influenced by environmental and humans-associated bacteria at each stage of renovation.

Clinical laboratories have been operating and performing diagnostic tests for decades, and the makeup of microbes on their surfaces have not been thoroughly examined. When working surfaces are contaminated with pathogens, they may serve as an indirect source of disease transmission. Therefore, identifying pathogens that potentially inhabit these surfaces is of critical importance. Such information may also be critical in assessing the reliability of laboratory safety and sanitation practices to lower any potential risk of exposures. During times where laboratories are working with novel pathogens such as with the arrival of the SARS-CoV-2 pandemic, such studies to characterize where these pathogens may reside within the clinical laboratory may help to elucidate exposure risks and inform sanitation practices. Here, we examined the surfaces of a clinical microbiology facility to determine whether surfaces within the laboratory may present potential exposure risks and to identify whether succession of microbes in the laboratory is human associated. We chose several different parts of the laboratory, including a basic bacterial culture section, a molecular microbiology section, and a SARS-CoV-2 testing section to identify microbiological trends that may be informative. We analyzed bacterial succession longitudinally using 16S rRNA amplicon sequencing and tested for the presence of SARS-CoV-2 on laboratory surfaces using RT-qPCR.

Study site and sampling scheme in diagnostic laboratory sections

Samples were collected from the University of California San Diego Center for Advanced Lab Medicine (UCSD CALM) in La Jolla, California. We sampled the clinical microbiology laboratory with two separate goals in mind. The first was to understand the succession of bacteria throughout the facility over time, and the second was to understand where the SARS-CoV-2 virus may be present on surfaces in a laboratory performing routine SARS-CoV-2 PCR testing. We collected swab samples from the Accessioning benches (A-BN), Benches (BN), Sinks (SK) and Floors (FL) surfaces (Fig. 1A and 1B) from each laboratory section: bacteriology, molecular microbiology, and COVID overflow within the microbiology laboratory between 07/20/2020 and 10/30/2020, coinciding with a time during which there was substantial SARS-CoV-2 transmission in the USA. Briefly, pre-moistened sterile swab (BD Swube-Dual Swab Sterile cat# 281130) were rubbed for 30 seconds over the surfaces. An additional site that acts as the entry point for the bacteriology lab, here called the accessioning bench, where samples are received from hospitals and clinics, was also sampled. The samples were collected from each lab section during the day shift working hours. The swabbed sink areas include the front, sides, and bottom of the sink basin, which are likely to have contact with runoff water from employees’ hands during hand washing. The swabbed floor areas include spaces near the working benches. Collected samples were stored at -80°C until further processing.

Nucleic acid extraction and high throughput DNA sequencing

The collected swabs were homogenized in sterile PBS buffer for 10 minutes followed by brief vortexing to dislodge bacteria and any particulate matter from the swabs. 500 µl resuspended PBS buffer from the tube was subjected to total nucleic acid (DNA/RNA) extraction and concentration using PureLink™ Viral RNA/DNA Mini Kit (Invitrogen, cat# 12280050) and Zymo gDNA Clean and Concentrator-10 kit (Zymo research cat# 11–316), respectively. Additionally, unused sterile swabs were used for negative control during the extraction to ensure no DNA contamination occurred during the process. The nucleic acid was subjected for PCR amplification of V3-V4 hypervariable region of the 16S rRNA gene using Kapa Hifi Hotstart Readymix (Kapa Biosystems; KK2602) with forward primer 5′-TCG TCG GCA GCG TCA GAT GTG TAT AAG AGA CAG CCT ACG GGN GGC WGC AG-3′ and reverse primer 5′-GTC TCG TGG GCT CGG AGA TGT GTA TAA GAG ACA GGA CTA CHV GGG TAT CTA ATC C-3′ [60] using the following cycling parameters: 95°C for 3 min, followed by 35 cycles of 95°C for 30 s, 55°C for 30 s, 72°C for 30 s, and a final elongation step of 72°C for 5 min. Ampure XP beads (Beckman-Coulter Life Sciences product# A63881) were used to clean PCR amplicons. The cleaned PCR amplicons were indexed using Nextera XT index kit v2 (Illumina; San Diego, CA) followed by cleaning by Ampure XP beads. Indexed and cleaned PCR products were then analyzed using High Sensitivity DNA Chips (Agilent Technologies part# 5067 − 4626) on an Agilent 2100 Bioanalyzer (Agilent Technologies; Palo Alto, CA) and quantified using Qubit dsDNA HS Assay Kit (Invitrogen cat# Q32851) on a Qubit 2.0 Fluorometer (Thermo Fisher Scientific; USA). Finally, Samples were normalized and pooled into equal molar concentration and sequenced using MiSeq Reagent Kit v3 (600-cycle)

on the Illumina MiSeq platform (Illumina San Diego, CA).

Realtime qPCR for COVID screening

Samples were tested for the presence of SARS-CoV-2 viral RNA using 7500 Fast Dx Real-Time PCR instrument (Thermo Fisher Scientific) and the TaqPath COVID-19 Combo Kit (Thermo Fisher Scientific). The MagMax Viral/Pathogen Nucleic Acid Isolation kit was used for nucleic acid extraction in conjunction with the KingFisher Flex Instrument (Thermo Fisher Scientific). A 25 µl reaction was prepared for qualitative detection of SARS-CoV-2 by RT-qPCR utilizing 5 µl of MagMax extracted RNA, 15ul of reaction mix, which included TaqPath 1-Step Multiplex Master Mix and COVID-19 Real Time PCR Assay Multiplex reagent (Thermo Fisher Scientific), which included primers and probe sequences targeting ORF1ab, N Protein, and S Protein, according to manufacturer’s instructions. Thermal cycling was performed at 25°C for 2 min, 53°C for 10 min for reverse transcription, followed by 95°C for 2 min and then 40 cycles of 95°C for 3 s, 60°C for 30 s. The cut-off threshold (C_t value) for each viral target to be considered positive was ≤ 37, and at least 2 of the 3 genes had to be detected. Some specimens tested were indeterminate (N = 26) and had to be repeated. Samples were considered indeterminate if only 1 out of 3 genes were detected. Upon repeat, only 4 specimens remained indeterminate and were excluded from the study.

16S rRNA gene sequencing analysis

We sequenced a total of 4,449,418 reads from 329 swab samples over the 15-week course of this study. There were approximately 17,813 reads per specimen with a median of 10,799 per sample. For further analysis, we chose a sampling depth parameter of 6,469 sequence reads, which retained 2,128,301 (50.00%) reads in 329 (86.58%) samples which were grouped by laboratory section (130 for bacteriology, 100 for molecular, and 99 for COVID), or by sampling surfaces (33 for accessioning bench, 99 for benches, 94 for floors, and 103 for sinks).

Sequenced reads were processed with Quantitative Insights Into Microbial Ecology 2 (QIIME2; version 2021.4) [61]. The Deblur plugin in QIIME2 was used for quality filtering and denoising the data [62]. Taxonomy classification was generated using the QIIME feature-classifier, with Naive Bayes classifiers trained on the SILVA database (version 138) [63]. Alpha diversity was analyzed using Observed Amplicon Sequence Variants (ASVs), Faith’s Phylogenetic Diversity [64], and Shannon Index [65] as measures of alpha-diversity metrics and beta diversity was measured using Bray-Curtis diversity metric by QIIME2 core-metrics phylogenetic pipeline (sampling-depth parameter 6,469). Additionally, due to the compositional nature of the data, we implemented a quantitative beta diversity metric, robust Aitchison PCA, using the DEICODE plugin with taxonomic biplot overlays [66]. The results were imported and visualized using the qiime2R (available at https://github.com/jbisanz/qiime2R) and ggplot2 packages in R-Studio (version 1.4.1717) [67]. Comparison of the relative abundances at the family level was visualized using ggplot2 with the exported QIIME2 taxonomy tables. The QIIME2 core-feature function, with the maximum fraction set to 90%, was used to define the “core microbiome” for each of the sample sources (Bench, Floor, Sink) which identified the features (e.g., amplicon sequence variant) present in at least 90% of samples from each source. Finally, the Analysis of Compositions of Microbiomes (ANCOM) test in the q2-composition plugin was used to assess the differential abundance of the bacterial taxa at genus level [68].

Statistical tests

Statistical significance for alpha diversity among sampling sites were determined using pairwise Kruskal-Wallis test. Pearson correlation coefficients were calculated in R-Studio (2022.02.1) to identify associations between the sampling days and the alpha diversity metric (Shannon Index). Beta diversity significance was determined using ANOSIM tests with 999 permutations in R-studio and the significance of the Aitchison PCA was quantified by the PERMANOVA test with 999 permutations using DEICODE plugin in QIIME2.

SARS-CoV-2 testing

The COVID overflow section did not officially open until 10/13/2020 and was used primarily as a control for a laboratory that had very little influence from people working on its premises. The bacteriology section, where bacterial cultures are performed routinely, also included the main A-BN, which is the entry point for all the specimens into the laboratory. A separate part of the main bacteriology section was devoted to serological testing for antibodies and antigens. Diagnostic tests in the molecular microbiology section included work related to detection of DNA, RNA, proteins, and other small molecules from patient samples using molecular techniques such as PCR and hybridization. Even though the laboratory developed a separate space for some of the SARS-CoV-2 diagnostics (COVID overflow lab section), most of the SARS-CoV-2 testing occurred in the molecular microbiology section throughout the study. We used the TaqPath™ COVID-19 (Thermo Fisher Scientific, Waltham, MA, USA) quantitative PCR test for SARS-CoV-2 detection from each of the swab specimens. This assay evaluates the presence of three gene targets from the orf1a/b, S, and N regions of the SARS-CoV-2 genome to determine their presence/absence in a sample. At least two of the three genes must be amplified for any specimen to be called positive. We found that the floor in the molecular microbiology laboratory had the greatest presence of SARS-CoV-2 of all the surfaces tested, with 16 out of 38 samples tested positive (42.10%). Similarly, the floor samples from bacteriology COVID overflow laboratory had 13.2% (5 out of 38 samples positive) and the COVID overflow lab had 2.6% (one out of 38 samples positive) of samples tested positive, respectively (Fig. 1C; Table 1). We only identified a single positive swab specimen taken from the accessioning bench out of 38 tested (2.6%). Of note, the A-BN is the access point where all the specimens are received and enter the laboratory. Swab specimens from other surfaces within each of the laboratory sections were found to be free of virus, including the other benches and sinks.

Next, we performed a longitudinal analysis to evaluate the prevalence of SARS-CoV-2 on the laboratory floor surfaces over the entire 15-weeks of the study period. In the first week, we confirmed 46.67% of all the floor swab samples tested to be positive (7 of 15 samples) which declined to 40% and 16.67% in the 2nd and 3rd week respectively. The cases declined over the weeks with an unexpected hike to 66.67% (4 of 6 samples) in week five (Fig. 1D). We found that the majority of SARS CoV-2 positive floor samples were from the molecular lab section, which highlights the role of molecular diagnostic technique as a potential source of viral dispersal and contamination in laboratory settings.

Table 1

SARS-CoV-2 positive RT-PCR test showing Ct value for the three genes.
				Ct Value
SN	Sample ID	Date	Surface	ORF1a/b gene	S gene	N gene
1	6	7/20/20	Molecular Floor	31.4	33.2	30.6
2	13	7/21/20	Bacteriology Floor	34.2	-	33.25
3	16	7/21/20	Molecular Floor	27.4	29.2	26.6
4	26	7/22/20	Molecular Floor	32.222652	30.856493	31.555956
5	33	7/23/20	Bacteriology Floor	31	32.7	29
6	36	7/23/20	Molecular Floor	27.6	29.6	27
7	46	7/24/20	Molecular Floor	35	-	33.2
8	56	7/27/20	Molecular Floor	30.8	32.9	29.8
9	63	7/28/20	Bacteriology Floor	33.6	-	32.5
10	66	7/28/20	Molecular Floor	34.3	-	33
11	76	7/29/20	Molecular Floor	31.5	33.3	30.2
12	83	7/30/20	Bacteriology Floor	34	-	32.4
13	86	7/30/20	Molecular Floor	36	-	32.9
14	91	7/31/20	Accessioning Bench	33.965828	-	33.068256
15	106	8/3/20	Molecular Floor	33.952847	29.280006	34.526432
16	143	8/17/20	Bacteriology Floor	35.750957	-	32.96266
17	146	8/17/20	Molecular Floor	32.23212	23.150824	31.549265
18	149	8/17/20	COVID Floor	30.58805	24.853996	30.653702
19	156	8/21/20	Molecular Floor	32.55478	35.73658	32.464104
20	186	8/31/20	Molecular Floor	30.767782	36.80289	31.005854
21	196	9/4/20	Molecular Floor	33.51642	37.045574	34.668537
22	226	9/14/20	Molecular Floor	33.93188	36.080032	34.11979
23	316	10/22/20	Molecular Floor	33.85367	-	36.618362

Alpha diversity among surfaces differed significantly

In each laboratory section (bacteriology, molecular, and COVID) and sampling surfaces (A-BN, BN, FL, and SK), we measured alpha-diversity of the bacterial communities i.e., the Shannon index (a quantitative measure that accounts for the number of species living in a habitat (richness), their relative abundance (evenness), and Faith’s PD (a qualitative measure of community richness that incorporates phylogenetic differences between species). Regardless of the metrics used, there were significant differences in the alpha diversity among sampling surfaces for all the lab sections (Fig. 2) as indicated by Kruskal-Wallis test (Faith’s PD; p_adj < 0.05). Pairwise comparison between sampling surfaces within individual lab sections indicates that microbial diversity of FL was invariably higher as compared to the other surfaces across all the lab sections (See Table 2). Likewise, we noticed that the pairwise difference in alpha diversity between all the other surfaces was significant (Table 2) except between A-BN and SK within the Bacteriology Lab section (H = 1.30, p_adj = 0.253). Surprisingly, we noted a significant difference in the alpha diversity between A-BN and BN surfaces (Fig. 2; Table 2; H = 9.135, p_adj < 0.05). This could be due to the fact that although both A-BN and BN represent benches, the BN in the other sections of the lab represent surfaces where microbial cultivation and PCR of the microbial cultures takes place, while the A-BN simply represents an access point for specimens into the clinical microbiology lab. Furthermore, to test the differences in microbial diversity within lab sections, we performed alpha diversity analysis with lab sections as groups and found that lab sections differed significantly from each other in relation to their benches and sinks but not the floors (Fig S1). Finally, we studied the longitudinal variation in alpha diversity within individual lab sections and did not find any significant change over the sampling period (Fig S2) as indicated by Kruskal-Wallis test (Faith’s PD; p_adj > 0.05).

Table 2

Faith’s PD Kruskal-Wallis (pairwise) test
Bacteriology Lab
Group 1	Group 2	H-value	p_adj-value
Accessioning Bench (n = 33)	Bench (n = 34)	9.13510538	0.00376127
Accessioning Bench (n = 33)	Floor (n = 29)	39.680002	8.98E-10
Accessioning Bench (n = 33)	Sink (n = 34)	1.3024536	0.25376553
Bench (n = 34)	Floor (n = 29)	36.1490872	3.66E-09
Bench (n = 34)	Sink (n = 34)	6.32240108	0.01430665
Floor (n = 29)	Sink (n = 34)	42.0068458	5.46E-10
Molecular Lab
Bench (n = 34)	Floor (n = 32)	43.6608319	5.86E-11
Bench (n = 34)	Sink (n = 34)	25.0434783	5.61E-07
Floor (n = 32)	Sink (n = 34)	46.9418349	2.19E-11
COVID Lab
Bench (n = 31)	Floor (n = 33)	27.2379427	1.80E-07
Bench (n = 31)	Sink (n = 35)	30.8779834	4.12E-08
Floor (n = 33)	Sink (n = 35)	48.8357237	8.35E-12

Beta diversity amongst the clinical laboratory sections

Bray Curtis dissimilarity metric showed no significant difference in beta diversity between the lab sections (Fig S3A; ANOSIM, R = 0.01074, p = 0.093). However, the diversity between the sampling surfaces were significantly different as evidenced by separate clustering of Floor and Bench surfaces (Fig S3B; ANOSIM; R = 0.1196, p = 0.001). Next, we stratified the samples by sampling surfaces and lab sections and performed beta diversity analysis using Bray Curtis and Weighted Unifrac metrics. We found that the microbial composition and phylogeny of the sink was very different from floors and benches in the Bacteriology lab (Fig. 3A), and that the floor microbiota was distinct from other surfaces in case of the Molecular and COVID labs (Fig. 3B and C). Furthermore, even though we did not see a clear difference in beta diversity between lab sections when samples are not stratified (Fig S3A), floors and sinks showed significant difference in beta diversity between lab sections with distinct clustering (Fig S4). We also examined differences in microbial communities across sampling surfaces from individual lab sections for each sampling day to determine whether microbial communities on surfaces become more or less similar over time. We used Compositional Tensor Factorization (CTF) analysis [69], to examine whether the sampled surfaces within each laboratory section were similar in composition longitudinally. We found that across the surfaces in each of the different laboratory sections, microbial compositions were highly similar at each time point. For example, there was little variation observed at each time point for the molecular (Fig S5B) and the COVID (Fig S5C) sections. Comparatively, we observed a mild degree of compositional difference between different surfaces in the bacteriology section (Fig S5A), which was not significant.

Taxonomic profiles and core bacterial taxa by laboratory section

Approximately 75% of the microbes we could identify on laboratory surfaces belonged to the families including Streptococcaceae, Staphylococcaceae, Lachnospiraceae, Clostridiaceae, Xanthomonadaceae, Nocardiaceae, Comamonadaceae, Sphingobacteriaceae, Pectobacteriaceae and Planococcaceae (Fig. 4). We identified significantly greater relative abundances of bacteria from families Comamonadaceae, Nocardiaceae, Staphylococcaceae and class Gammaproteobacteria, while identifying significantly fewer Clostridiaceae, Lachnospiraceae and Pectobacteriaceae on the sink compared to the benches (Kruskal-Wallis; p < 0.05) in the bacteriology lab section (Fig. 4A). Similarly, we saw a significant reduction in the relative abundances of families Clostridiaceae and Lachnospiraceae, while also observing an increase in Nocardiaceae on floor surfaces as compared to benches (Kruskal-Wallis; p < 0.05) in the bacteriology lab section (Fig. 4A). There was also a significant reduction in the relative abundances of Planococcaceae, Xanthomonadaceae, Comamonadaceae, Sphingobacteriaceae, and Clostridiaceae, accompanied by an increase in the relative abundance of Nocardiaceae on the floor compared to benches and sinks (Kruskal-Wallis; p < 0.05) in molecular section (Fig. 4B). We generally observed less variation in taxonomic profiles on the different surfaces in the COVID section (Fig. 4C) compared to bacteriology (Fig. 4A) and molecular sections (Fig. 4B).

To further understand the core microbial taxa present in different laboratory sections, we computed the “core microbiome,” represented by the taxa present in at least 90% of samples from each group. These taxa belonged to the genera Dickeya, Comamonas, Sphingobacterium, families Lachnospiraceae, Xanthomonadaceae, and classes Alphaproteobacteria and Gammaproteobacteria. Actinobacteria was the unique core taxa found on the surfaces of the bacteriology section, while members of the class Alphaproteobacteria were commonly found on the surfaces of all the three lab sections. Lachnospiraceae was the only core taxa unique to the molecular lab section.

Next, we evaluated the relative abundance of microbes on different surfaces from these lab sections over the course of the study. These largely included environmental microbes such as Rhizobium, Nocardia, Actinobacteria, Dickeya, Xanthomonadaceae, and Sphingobacterium, but also human associated microbes including Staphylococcus, and Streptococcus (Fig S6). There were also microbes such as gamma-proteobacteria, Lactobacillales, and Lachnospiraceae, which could be derived from humans or from other environmental sources. Many of the taxa identified had relatively stable relative abundances over time regardless of the surface being examined. Certain microbes such as Dickeya, Clostridium, Lachnospiraceae, and gamma-proteobacteria were relatively common to all surfaces in each of the laboratories. However, Staphylococcus and Streptococcus, which were common to the bacteriology section (Fig S6A), were less common in the molecular (Fig S6B) and COVID (Fig S6C) laboratories.

Differentially abundant bacterial taxa

We performed differential abundance analysis of the microbes found on various laboratory surfaces using ANCOM test [70] to identify bacterial taxa that significantly differed between laboratory sections. Results are presented in the form of a volcano plot which summarizes how different a species is on one surface as compared to the other. We found 90 species that were differentially abundant in the bacteriology lab section with a confidence limit of > 95% (Fig. 5A), with two of these being different species of Staphylococcus. The presence of Staphylococcus could reflect the skin bacteria of people working in these s but could also represent the fact that Staphylococcus is the most common bacterium identified in cultures in this part of the laboratory. We also found that Nocardia and Comamonas were among differentially abundant taxa in the bacteriology lab section, which are common soil bacteria [71, 72], but also known to cause infections in immunocompromised individuals [73, 74]. The number of differentially abundant taxa was much lower in the molecular and COVID overflow lab sections than was for the bacteriology section (Table S1). In the molecular lab section (Fig. 5B), we identified different species of Staphylococcus, Streptococcus, and Cutibacterium, which are common human-associated bacteria. We also found environmental bacteria such as Nocardia and Actinobacteria. In the COVID overflow laboratory section (Fig. 5C), which was not in operation until 10/13/2020, we did identify some Staphylococcus and Streptococcus, but more commonly identified bacteria that probably were associated with the environment, including Nocardia, Rhizobium, Rhizobiales, Comamonas, Zymomonas, and other alpha- and gamma-proteobacteria.

Finally, we evaluated each laboratory section to identify bacteria that may be driving the differences between the surfaces. We performed Aitchison PCA [75], which is commonly used to identify taxa that are primarily responsible for sample clustering [66, 76]. As we found using PCoA (Fig. 3), there was significant clustering of samples from different surfaces within individual laboratory sections (PERMANOVA; p < 0.05; Fig. 6). We measured pseudo-F value (a measure of degree of group separation) for each laboratory section and found that the bacteriology section (pseudo-F = 109.21) had higher degree of separation between surfaces than molecular (30.79) or COVID (17.73), which suggests that the greatest beta diversity observed was in the bacteriology section (Fig. 6). We also overlaid each PCoA with DEICODE biplots [66], which identified significant taxa responsible for driving clustering within each group. We found that many of the taxa driving diversity within the laboratory sections were associated with human skin or originated from the environment. For example, we found Staphylococcus along with environmental microbes Dickeya, Comamonas, and gamma-proteobacteria as drivers of diversity in the bacteriology section (Fig. 6A). Similarly, Streptococcus, Comamonas, Dickeya were also identified as drivers of diversity in the molecular and COVID overflow sections, accompanied by Clostridium, Nocardia, Lachnospiraceae and Actinobacteria (Fig. 6B and 6C).

Since the inception of the pandemic in early 2020, laboratories such as the one at UC San Diego Health have been testing large numbers of samples for the virus that causes the disease COVID-19. Particularly early in the pandemic, there was significant concern that the virus may be harbored on laboratory surfaces and as such present an infection risk to the staff performing such tests. The early testing in this clinical laboratory took place before there was any mask mandate in the facility, and when the World Health Organization announced that the virus could be transmitted via an airborne fashion, there was a significant concern that such an airborne nature could result in a number of workplace exposures. While we had no reports of employees testing positive for SARS-CoV-2 during the sample collection for this study, there was still significant concern for workplace exposures. Some of the highest risk practices occurred when the facility was testing up to 4,500 specimens per day [77], which meant that not all samples could be handled in biosafety cabinets. The simple opening and closing of the caps in each tube promoted the risk of aerosolizing the live virus. It has been recently shown through RT-qPCR that SARS-CoV-2 RNA was present on surfaces of clinical microbiology laboratories indicating a possible role of environmental contamination [78]. There was therefore a significant interest for us to perform such a study to identify where/if SARS-CoV-2 existed outside the collection tubes in the laboratory [79]. The primary test used for detection of SARS-CoV-2 is a RT-qPCR test [80] to detect the presence of virus RNA. Our findings were largely reassuring that we could identify SARS-CoV-2 almost exclusively from the floors of the lab, but not from the sinks or the benches. We did, however, identify a single SARS-CoV-2 positive specimen from A-BN. All the specimens arrive in the laboratory through A-BN and occasionally these specimens leak when the caps are not sufficiently secured. It is possible that the positive A-BN specimen is the result of leakage, rather than A-BN serving as a persistent risk for SARS-CoV-2 transmission.

Bacterial alpha diversity on the floors of the clinical lab section was richer and more diverse (Fig. 2) than those detected on other surfaces. A prior study had also shown that indoor floor materials serve as microbial reservoirs, especially soil borne bacteria [81]. Our finding of greater diversity on the floor surfaces is also supported by previous reports showing a significant correlation between the microbiome of shoe soles and floor surfaces [82, 83]. Floors come in direct contact with shoes which are typically contaminated with microbes from environmental sources such as soil and water. We found that there was no significant difference in alpha diversity among floor samples from all the lab sections (Fig S1). This could potentially be governed by the microbes tracked inside on the bottoms of shoes, combined with commensal microbes already living in these spaces, which may not lead to significant variation between different lab sections.

Significant proportions of potentially environmentally-derived bacteria were present on the floors, such as Actinobacteria and Nocardia (Fig. 6). The 16S rRNA amplicon sequencing analysis could not identify specific taxa, and these microbes are generally found in the environment. While taxa associated with Actinobacteria and Nocardia have been known to cause infections [84–87], there is no evidence to suggest the organisms identified posed any threat to healthcare workers. However, their presence does support the need to continue strict sterile techniques and to regularly sanitize testing areas. We believe that such sanitation practices account for the substantial differences in the representation of human skin-associated organisms between the bacteriology section and other parts of the laboratory. For example, Staphylococcus and Streptococcus were amongst the most abundant microbes identified in the bacteriology section (Fig. 5), indicating the substantial contribution that laboratory workers likely have to the BE microbiome. However, Staphylococcus and Streptococcus are also amongst the most common pathogens identified in patient cultures from this section of the laboratory, so it is not clear the extent to which patient cultures contribute to the BE microbiome in this section. Particularly, where Staphylococcus is so prevalent amongst the patient population and the Methicillin-resistant Staphylococcus aureus (MRSA) variant of Staphylococcus is known to colonize the skin of both patients and laboratory workers. Unfortunately, a much more detailed study examining the movements of Staphylococcus throughout the lab would be necessary to decipher the relative contributions of patients and lab workers to the representation of Staphylococcus. While Staphylococcus was found at high proportions in the bacteriology section, it was less prevalent in both the molecular and COVID overflow sections (Fig. 5). One potential explanation for this is that patient cultures (which do not take place in molecular and COVID overflow sections) may have been contributing to the proportion of Staphylococcus found in the bacteriology section. We were able to identify Staphylococcus and Streptococcus in the COVID overflow laboratory, but they were of relatively low proportion even compared to the molecular section. This difference may be due to the fact that the COVID overflow laboratory testing began after the beginning of this study and was largely unpopulated by workers for much of this time. Thus, we observed an increased proportion of environment-associated bacteria compared to human-associated bacteria in this section (Fig. 6).

Studies suggest that human skin, respiratory tract, gastrointestinal/urogenital associated bacteria, as well as those originating from water and soil habitats are the primary contributors to microbial diversity in many indoor BEs, such as restrooms [88, 89], offices [88, 89], kitchens [90], child-care facilities [53], and airplanes [91]. Interestingly, the microbiota of health-care facilities, including hospitals [49, 92–94], and ICUs [44, 47, 95] is remarkably similar to every other built environment. It has been reported that the most common bacteria associated with indoor surfaces belong to Corynebacterium, Staphylococcus, Streptococcus, Lactobacillus, Mycobacterium, Bacillus, Pseudomonas, Acinetobacter, Sphingomonas, Methylobacterium and other members of the Enterobacteriaceae family [34, 96, 97]. Like other BEs and health care facilities, we found that the most common bacteria colonizing surfaces in a clinical diagnostic laboratory primarily belong to the genera Dickeya, Staphylococcus, Streptococcus, Lactobacillus, Nocardia, Comamonas, Clostridium, and to members of Actinobacteria, Lachnospiraceae and gamma-proteobacteria phyla and families. Clinical laboratories are a specialized BE that house trained medical staff and are exposed to a plethora of human commensal and pathogenic microbes. Surfaces in the clinical laboratory, particularly the floors, come in direct contact with shoes that bring a rich source of environmental microbes into the facility. This study provides a glimpse into the complex microbiota of an important and often neglected healthcare-associated facility.

In this study, we characterized the complex bacterial communities inhabiting the inanimate surfaces in a diagnostic clinical laboratory during the period of SARS-CoV-2 pandemic. While the 16S rRNA gene sequencing method is the most widely used technique to explore the microbial diversity that could otherwise go unrecognized by culture alone, there are some inherent limitations. For example, variation in 16S copy number and primer binding and amplification efficiencies can limit the accuracy in bacterial abundance and diversity estimations [98–102]. Similarly, this technique without modification does not inform about the viability and infectivity of the microbes being assessed. Another important limitation is that the technique based on a small segment of 16S rRNA (V3-V4 hypervariable region) often cannot resolve taxonomic classification for some medically important bacteria. A study utilizing metagenomic sequencing and classification of the bacteria on the laboratory surfaces could potentially resolve taxonomic classifications that were limited in this study.

As far as we can tell, the study described here is the first comprehensive survey of the microbiome of a clinical microbiology laboratory. BEs such as this one likely have not previously been described because the connection between hospital infections and laboratory infections are not always obvious. For example, there have been well documented outbreaks of pathogen infections such as Salmonella and Shigella in clinical laboratories before and pinpointing the source of these infections back to an individual patient is usually obvious. However, when dealing with other more pervasive pathogens such as MRSA, where the source could be a myriad of patients, and the laboratory workers who acquire MRSA infections could have been infected through another means, can be quite difficult. While our analysis here will not pinpoint the source of infection in any given case, it does help to elucidate microbes that colonize these surfaces and have the potential to cause LAIs. What is most favorable about this study is that it took place during a surge in the SARS-CoV-2 pandemic when the laboratory was testing thousands of specimens per day. We had no reports of SARS-CoV-2 LAIs during this period, we do note that along with potential pathogens colonizing laboratory surfaces, there was also SARS-CoV-2 on some surfaces in the lab. This study illuminates that we did not find SARS-CoV-2 on the benches (with 1 exception) or in the sinks of the lab, but instead found it largely on the floors. We believe that the virus arrived on the floors largely through droplets that settled to the ground and were captured by our swabs. Because of the techniques used to identify the presence of this virus, we cannot determine whether the virus from the floors was still infectious. The relative lack of SARS-CoV-2 on the working benches suggests that basic laboratory sanitary practices can help to prevent exposures.

LAIs Laboratory-acquired infections

COVID-19 Coronavirus Induced Disease 19

SARS-CoV-2 Severe Acute Respiratory Syndrome Coronavirus type 2

BE built environment

A-BN Accessioning benches

BN Benches

SK Sinks

FL Floors

ASVs Amplicon Sequence Variants

IQR Interquartile Range

PCoA Principal coordinates analysis

CTF compositional tensor factorization

clr centered log ratio

MRSA Methicillin-resistant Staphylococcus aureus

Ethics approval and consent to participate: Not applicable

Consent for publication: All authors have reviewed and approved this manuscript and provided consent for publication.

Availability of data and material: All sequences included in this study have been deposited in the NCBI Sequence Read Archive under the BioProject accession PRJNA812962.

Competing interests: The authors declare that they have no competing interests.

Funding: This project was supported by startup funds from UC San Diego to D.T.P. No specific government agencies or private foundations participated in the funding of this project.

Authors' contributions:

Conceptualization: J.A.G., R.K., and D.T.P.

Sample Collection: G.K., J.C. and P.K.

Methodology: G.P.S., G.K., J.C., L.H., P.G. and S.R.

Data analysis: G.P.S., G.K., P.D., S.R. and D.T.P.

Data validation and visualization: G.P.S. and D.T.P.

Original Draft writing: G.P.S.

Review and editing: R.K., J.A.G. and D.T.P.

Acknowledgements: We thank the UC San Diego Health Clinical Microbiology Laboratory and Patrick Aziz for their participation in this study.

Wurtz N, Papa A, Hukic M, Di Caro A, Leparc-Goffart I, Leroy E, et al. Survey of laboratory-acquired infections around the world in biosafety level 3 and 4 laboratories. Eur J Clin Microbiol Infect Dis 2016;35(8):1247–58.
Baron EJ, Miller JM. Bacterial and fungal infections among diagnostic laboratory workers: evaluating the risks. Diagn Microbiol Infect Dis 2008;60(3):241–6.
Control CfD, Prevention. Human Salmonella Typhimurium infections linked to exposure to clinical and teaching microbiology laboratories. Centers for Disease Control and Prevention, Atlanta, GA: http://www cdc gov/salmonella/typhimurium-labs-06-14/[Google Scholar] 2014.
Smith AM, Smouse SL, Tau NP, Bamford C, Moodley VM, Jacobs C, et al. Laboratory-acquired infections of Salmonella enterica serotype Typhi in South Africa: phenotypic and genotypic analysis of isolates. BMC Infectious Diseases 2017;17(1):656.
Alexander DC, Fitzgerald SF, DePaulo R, Kitzul R, Daku D, Levett PN, et al. Laboratory-Acquired Infection with Salmonella enterica Serovar Typhimurium Exposed by Whole-Genome Sequencing. Journal of Clinical Microbiology 2016;54(1):190–3.
DiNardi SR. The occupational environment: its evaluation, control, and management. AIHA Press (American Industrial Hygiene Association) Fairfax; 2003.
Mitchell AH. Engineering Controls and Safer Medical Devices. Preventing Occupational Exposures to Infectious Disease in Health Care. Springer; 2020, p. 101–15.
Singh K. Laboratory-acquired infections. Clin Infect Dis 2009;49(1):142–7.
Barker J, Stevens D, Bloomfield S. Spread and prevention of some common viral infections in community facilities and domestic homes. Journal of Applied Microbiology 2001;91(1):7.
Kronbichler A, Kresse D, Yoon S, Lee KH, Effenberger M, Shin JI. Asymptomatic patients as a source of COVID-19 infections: A systematic review and meta-analysis. International journal of infectious diseases 2020;98:180–6.
Kutter JS, Spronken MI, Fraaij PL, Fouchier RA, Herfst S. Transmission routes of respiratory viruses among humans. Current opinion in virology 2018;28:142–51.
John Hopkins University. Coronavirus resource center, https://coronavirus.jhu.edu/map.html; 2022.
Patel KP, Vunnam SR, Patel PA, Krill KL, Korbitz PM, Gallagher JP, et al. Transmission of SARS-CoV-2: an update of current literature. European Journal of Clinical Microbiology & Infectious Diseases 2020;39(11):2005–11.
Schijven J, Vermeulen LC, Swart A, Meijer A, Duizer E, de Roda Husman AM. Quantitative microbial risk assessment for airborne transmission of SARS-CoV-2 via breathing, speaking, singing, coughing, and sneezing. Environmental health perspectives 2021;129(4):047002.
Katelaris AL, Wells J, Clark P, Norton S, Rockett R, Arnott A, et al. Epidemiologic evidence for airborne transmission of SARS-CoV-2 during church singing, Australia, 2020. Emerging infectious diseases 2021;27(6):1677.
Coleman KK, Tay DJW, Tan KS, Ong SWX, Koh MH, Chin YQ, et al. Viral load of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in respiratory aerosols emitted by patients with coronavirus disease 2019 (COVID-19) while breathing, talking, and singing. Clinical Infectious Diseases 2021.
Van Doremalen N, Bushmaker T, Morris DH, Holbrook MG, Gamble A, Williamson BN, et al. Aerosol and surface stability of SARS-CoV-2 as compared with SARS-CoV-1. New England journal of medicine 2020;382(16):1564–7.
Hirose R, Ikegaya H, Naito Y, Watanabe N, Yoshida T, Bandou R, et al. Survival of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and Influenza Virus on Human Skin: Importance of Hand Hygiene in Coronavirus Disease 2019 (COVID-19). Clinical Infectious Diseases 2020;73(11):e4329-e35.
Chin AW, Chu JT, Perera MR, Hui KP, Yen H-L, Chan MC, et al. Stability of SARS-CoV-2 in different environmental conditions. The Lancet Microbe 2020;1(1):e10.
Wong JCC, Hapuarachchi HC, Arivalan S, Tien WP, Koo C, Mailepessov D, et al. Environmental contamination of SARS-CoV-2 in a non-healthcare setting. International journal of environmental research and public health 2021;18(1):117.
Kozer E, Rinott E, Kozer G, Bar-Haim A, Benveniste-Levkovitz P, Klainer H, et al. Presence of SARS-CoV-2 RNA on playground surfaces and water fountains. Epidemiology & Infection 2021;149.
Caggiano G, Triggiano F, Apollonio F, Diella G, Lopuzzo M, D’Ambrosio M, et al. SARS-CoV-2 RNA and Supermarket Surfaces: A Real or Presumed Threat? International Journal of Environmental Research and Public Health 2021;18(17):9404.
Marotz C, Belda-Ferre P, Ali F, Das P, Huang S, Cantrell K, et al. SARS-CoV-2 detection status associates with bacterial community composition in patients and the hospital environment. Microbiome 2021;9(1):1–15.
Wang J, Feng H, Zhang S, Ni Z, Ni L, Chen Y, et al. SARS-CoV-2 RNA detection of hospital isolation wards hygiene monitoring during the Coronavirus Disease 2019 outbreak in a Chinese hospital. International Journal of Infectious Diseases 2020;94:103–6.
Ong SWX, Lee PH, Tan YK, Ling LM, Ho BCH, Ng CG, et al. Environmental contamination in a coronavirus disease 2019 (COVID-19) intensive care unit—What is the risk? Infection Control & Hospital Epidemiology 2021;42(6):669–77.
Moore G, Rickard H, Stevenson D, Aranega-Bou P, Pitman J, Crook A, et al. Detection of SARS-CoV-2 within the healthcare environment: a multi-centre study conducted during the first wave of the COVID-19 outbreak in England. Journal of Hospital Infection 2021;108:189–96.
Ge T, Lu Y, Zheng S, Zhuo L, Yu L, Ni Z, et al. Evaluation of disinfection procedures in a designated hospital for COVID-19. American journal of infection control 2021;49(4):447–51.
Colaneri M, Seminari E, Novati S, Asperges E, Biscarini S, Piralla A, et al. Severe acute respiratory syndrome coronavirus 2 RNA contamination of inanimate surfaces and virus viability in a health care emergency unit. Clinical Microbiology and Infection 2020;26(8):1094. e1-. e5.
Coil DA, Albertson T, Banerjee S, Brennan G, Campbell AJ, Cohen SH, et al. SARS-CoV-2 detection and genomic sequencing from hospital surface samples collected at UC Davis. PloS one 2021;16(6):e0253578.
Bartlett C, Langsjoen J, Cheng Q, Yingling AV, Weiss M, Bradfute S, et al. COVID-19 global pandemic planning: Presence of SARS-CoV-2 fomites in a university hospital setting. Experimental Biology and Medicine 2021;246(18):2039–45.
Katona P, Kullar R, Zhang K. Bringing Transmission of SARS-CoV-2 to the Surface: Is there a Role for Fomites? Clin Infect Dis 2022.
Custovic A, Taggart S, Woodcock A. House dust mite and cat allergen in different indoor environments. Clinical & Experimental Allergy 1994;24(12):1164–8.
Hoppe P, Martinac I. Indoor climate and air quality. Review of current and future topics in the field of ISB study group 10. Int J Biometeorol 1998;42(1):1–7.
Kelley ST, Gilbert JA. Studying the microbiology of the indoor environment. Genome Biol 2013;14(2):202.
Klepeis NE, Nelson WC, Ott WR, Robinson JP, Tsang AM, Switzer P, et al. The National Human Activity Pattern Survey (NHAPS): a resource for assessing exposure to environmental pollutants. Journal of Exposure Science & Environmental Epidemiology 2001;11(3):231–52.
Klevens RM, Edwards JR, Richards Jr CL, Horan TC, Gaynes RP, Pollock DA, et al. Estimating health care-associated infections and deaths in US hospitals, 2002. Public health reports 2007;122(2):160–6.
McNabb SJ, Jajosky RA, Hall-Baker PA, Adams DA, Sharp P, Anderson WJ, et al. Summary of notifiable diseases --- United States, 2005. MMWR Morb Mortal Wkly Rep 2007;54(53):1–92.
Groseclose SL, Brathwaite WS, Hall PA, Connor FJ, Sharp P, Anderson WJ, et al. Summary of notifiable diseases–United States, 2002. MMWR Morb Mortal Wkly Rep 2004;51(53):1–84.
Anderson RN, Smith BL. Deaths: leading causes for 2002. Natl Vital Stat Rep 2005;53(17):1–89.
Ashokan A, Choo JM, Taylor SL, Lagana D, Shaw DR, Warner MS, et al. Environmental dynamics of hospital microbiome upon transfer from a major hospital to a new facility. Journal of Infection 2021;83(6):637–43.
Bokulich NA, Mills DA, Underwood MA. Surface microbes in the neonatal intensive care unit: changes with routine cleaning and over time. Journal of clinical microbiology 2013;51(8):2617–24.
Brooks B, Firek BA, Miller CS, Sharon I, Thomas BC, Baker R, et al. Microbes in the neonatal intensive care unit resemble those found in the gut of premature infants. Microbiome 2014;2(1):1–16.
Brooks B, Olm MR, Firek BA, Baker R, Thomas BC, Morowitz MJ, et al. Strain-resolved analysis of hospital rooms and infants reveals overlap between the human and room microbiome. Nature communications 2017;8(1):1–7.
Chopyk J, Akrami K, Bavly T, Shin JH, Schwanemann LK, Ly M, et al. Temporal variations in bacterial community diversity and composition throughout intensive care unit renovations. Microbiome 2020;8:1–15.
ElRakaiby MT, Gamal-Eldin S, Amin MA, Aziz RK. Hospital microbiome variations as analyzed by high-throughput sequencing. Omics: a journal of integrative biology 2019;23(9):426–38.
Haak BW, Wiersinga WJ. Uncovering hidden antimicrobial resistance patterns within the hospital microbiome. Nature medicine 2020;26(6):826–8.
Hewitt KM, Mannino FL, Gonzalez A, Chase JH, Caporaso JG, Knight R, et al. Bacterial diversity in two neonatal intensive care units (NICUs). PloS one 2013;8(1):e54703.
Oberauner L, Zachow C, Lackner S, Högenauer C, Smolle K-H, Berg G. The ignored diversity: complex bacterial communities in intensive care units revealed by 16S pyrosequencing. Scientific reports 2013;3(1):1–12.
Rampelotto PH, Sereia AF, de Oliveira LFV, Margis R. Exploring the hospital microbiome by high-resolution 16S rRNA profiling. International journal of molecular sciences 2019;20(12):3099.
Lax S, Smith D, Sangwan N, Handley K, Larsen P, Richardson M, et al. Colonization and succession of hospital-associated microbiota. Science translational medicine 2017;9(391).
Ramos T, Dedesko S, Siegel JA, Gilbert JA, Stephens B. Spatial and temporal variations in indoor environmental conditions, human occupancy, and operational characteristics in a new hospital building. PLoS One 2015;10(3):e0118207.
Lax S, Sangwan N, Smith D, Larsen P, Handley KM, Richardson M, et al. Bacterial colonization and succession in a newly opened hospital. Science translational medicine 2017;9(391):eaah6500.
Lee L, Tin S, Kelley ST. Culture-independent analysis of bacterial diversity in a child-care facility. BMC microbiology 2007;7(1):1–13.
Xu Y, Tandon R, Ancheta C, Arroyo P, Gilbert JA, Stephens B, et al. Quantitative profiling of built environment bacterial and fungal communities reveals dynamic material dependent growth patterns and microbial interactions. Indoor air 2021;31(1):188–205.
Lozupone CA, Stombaugh JI, Gordon JI, Jansson JK, Knight R. Diversity, stability and resilience of the human gut microbiota. Nature 2012;489(7415):220–30.
Janda JM, Abbott SL. 16S rRNA gene sequencing for bacterial identification in the diagnostic laboratory: pluses, perils, and pitfalls. Journal of clinical microbiology 2007;45(9):2761–4.
Lax S, Gilbert JA. Hospital-associated microbiota and implications for nosocomial infections. Trends Mol Med 2015;21(7):427–32.
Brooks AW, Kohl KD, Brucker RM, van Opstal EJ, Bordenstein SR. Correction: Phylosymbiosis: Relationships and Functional Effects of Microbial Communities across Host Evolutionary History. PLoS biology 2017;15(1):e1002587.
Poza M, Gayoso C, Gomez MJ, Rumbo-Feal S, Tomas M, Aranda J, et al. Exploring bacterial diversity in hospital environments by GS-FLX Titanium pyrosequencing. 2012.
Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res 2013;41(1):e1.
Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol 2019;37(8):852–7.
Amir A, McDonald D, Navas-Molina JA, Kopylova E, Morton JT, Zech Xu Z, et al. Deblur Rapidly Resolves Single-Nucleotide Community Sequence Patterns. mSystems 2017;2(2).
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 2013;41(Database issue):D590-6.
Faith DP. Conservation evaluation and phylogenetic diversity. Biological conservation 1992;61(1):1–10.
Shannon CE. A mathematical theory of communication. The Bell system technical journal 1948;27(3):379–423.
Martino C, Morton JT, Marotz CA, Thompson LR, Tripathi A, Knight R, et al. A Novel Sparse Compositional Technique Reveals Microbial Perturbations. mSystems 2019;4(1).
Wickham H. ggplot2: Elegant Graphics for Data Analysis. 2016.
Mandal S, Van Treuren W, White RA, Eggesbo M, Knight R, Peddada SD. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb Ecol Health Dis 2015;26:27663.
Martino C, Shenhav L, Marotz CA, Armstrong G, McDonald D, Vázquez-Baeza Y, et al. Context-aware dimensionality reduction deconvolutes gut microbial community dynamics. Nature biotechnology 2021;39(2):165–8.
Mandal S, Van Treuren W, White RA, Eggesbø M, Knight R, Peddada SD. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microbial ecology in health and disease 2015;26(1):27663.
Goodfellow M. Genus Nocardia Trevisan 1889, 9. Bergey's manual of systematic bacteriology 1989:2350-236l.
Willems A, Pot B, Falsen E, Vandamme P, Gillis M, Kersters K, et al. Polyphasic taxonomic study of the emended genus Comamonas: relationship to Aquaspirillum aquaticum, E. Falsen group 10, and other clinical isolates. International Journal of Systematic and Evolutionary Microbiology 1991;41(3):427–44.
Filice GA. Nocardiosis in persons with human immunodeficiency virus infection, transplant recipients, and large, geographically defined populations. Journal of Laboratory and Clinical Medicine 2005;145(3):156–62.
Lennette EH, Balows A, Hausler Jr W, Truant J. Manual of Clinical. Microbiology Washington, DC: American Society for Microbiology 1980:195–219,21.
Aitchison J. Principal component analysis of compositional data. Biometrika 1983;70(1):57–65.
Gloor GB, Macklaim JM, Pawlowsky-Glahn V, Egozcue JJ. Microbiome Datasets Are Compositional: And This Is Not Optional. Front Microbiol 2017;8:2224.
Pride DT. What to do with expanded molecular testing capacity in a post-pandemic world. MLO: Medical Laboratory Observer 2021:34 – 6.
Bloise I, Gómez-Arroyo B, García-Rodríguez J. Detection of SARS-CoV-2 on high-touch surfaces in a clinical microbiology laboratory. Journal of Hospital infection 2020;105(4):784–6.
Belluco S, Mancin M, Marzoli F, Bortolami A, Mazzetto E, Pezzuto A, et al. Prevalence of SARS-CoV-2 RNA on inanimate surfaces: a systematic review and meta-analysis. European journal of epidemiology 2021;36(7):685–707.
Stites EC, Wilen CB. The interpretation of SARS-CoV-2 diagnostic tests. Med 2020.
Gupta M, Lee S, Bisesi M, Lee J. Indoor Microbiome and Antibiotic Resistance on Floor Surfaces: An Exploratory Study in Three Different Building Types. Int J Environ Res Public Health 2019;16(21).
Coil DA, Neches RY, Lang JM, Jospin G, Brown WE, Cavalier D, et al. Bacterial communities associated with cell phones and shoes. PeerJ 2020;8:e9235.
Lax S, Hampton-Marcell JT, Gibbons SM, Colares GB, Smith D, Eisen JA, et al. Forensic analysis of the microbiome of phones and shoes. Microbiome 2015;3:21.
Darazam IA, Shamaei M, Mobarhan M, Ghasemi S, Tabarsi P, Motavasseli M, et al. Nocardiosis: risk factors, clinical characteristics and outcome. Iranian Red Crescent Medical Journal 2013;15(5):436.
Chawla K, Prakash PY. Biology of Pathogenic Actinobacteria: Nocardia and Allied Genera. New and Future Developments in Microbial Biotechnology and Bioengineering. Elsevier; 2018, p. 225–33.
Steinbrink J, Leavens J, Kauffman CA, Miceli MH. Manifestations and outcomes of nocardia infections: comparison of immunocompromised and nonimmunocompromised adult patients. Medicine 2018;97(40).
Roy M, Martial A, Ahmad S. Disseminated Nocardia beijingensis Infection in an Immunocompetent Patient. European Journal of Case Reports in Internal Medicine 2020;7(11).
Flores GE, Bates ST, Knights D, Lauber CL, Stombaugh J, Knight R, et al. Microbial biogeography of public restroom surfaces. PLoS One 2011;6(11):e28132.
Gibbons SM, Schwartz T, Fouquier J, Mitchell M, Sangwan N, Gilbert JA, et al. Ecological succession and viability of human-associated microbiota on restroom surfaces. Appl Environ Microbiol 2015;81(2):765–73.
Flores GE, Bates ST, Caporaso JG, Lauber CL, Leff JW, Knight R, et al. Diversity, distribution and sources of bacteria in residential kitchens. Environ Microbiol 2013;15(2):588–96.
McManus CJ, Kelley ST. Molecular survey of aeroplane bacterial contamination. J Appl Microbiol 2005;99(3):502–8.
Kembel SW, Jones E, Kline J, Northcutt D, Stenson J, Womack AM, et al. Architectural design influences the diversity and structure of the built environment microbiome. ISME J 2012;6(8):1469–79.
Lax S, Sangwan N, Smith D, Larsen P, Handley KM, Richardson M, et al. Bacterial colonization and succession in a newly opened hospital. Sci Transl Med 2017;9(391).
Pereira da Fonseca TA, Pessôa R, Felix AC, Sanabani SS. Diversity of bacterial communities on four frequently used surfaces in a large Brazilian teaching hospital. International Journal of Environmental Research and Public Health 2016;13(2):152.
Poza M, Gayoso C, Gomez MJ, Rumbo-Feal S, Tomas M, Aranda J, et al. Exploring bacterial diversity in hospital environments by GS-FLX Titanium pyrosequencing. PLoS One 2012;7(8):e44105.
Verdier T, Coutand M, Bertron A, Roques C. A review of indoor microbial growth across building materials and sampling and analysis methods. Building and environment 2014;80:136–49.
Gilbert JA, Stephens B. Microbiology of the built environment. Nat Rev Microbiol 2018;16(11):661–70.
Pinto AJ, Raskin L. PCR biases distort bacterial and archaeal community structure in pyrosequencing datasets. 2012.
Lee ZM-P, Bussema III C, Schmidt TM. rrn DB: documenting the number of rRNA and tRNA genes in bacteria and archaea. Nucleic acids research 2009;37(suppl_1):D489-D93.
Kembel SW, Wu M, Eisen JA, Green JL. Incorporating 16S gene copy number information improves estimates of microbial diversity and abundance. PLoS computational biology 2012;8(10):e1002743.
Ide N, Frogner BK, LeRouge CM, Vigil P, Thompson M. What’s on your keyboard? A systematic review of the contamination of peripheral computer devices in healthcare settings. BMJ open 2019;9(3):e026437.
Dabney J, Meyer M. Length and GC-biases during sequencing library amplification: a comparison of various polymerase-buffer systems with ancient and modern DNA sequencing libraries. Biotechniques 2012;52(2):87–94.

No competing interests reported.

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Characterization of SARS-CoV-2 distribution and microbial succession in a clinical microbiology testing facility during the SARS-CoV-2 pandemic

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Abstract

Background

Results

Conclusion

Figures

Introduction

Materials And Methods

Study site and sampling scheme in diagnostic laboratory sections

Nucleic acid extraction and high throughput DNA sequencing

Realtime qPCR for COVID screening

16S rRNA gene sequencing analysis

Statistical tests

Results

SARS-CoV-2 testing

Alpha diversity among surfaces differed significantly

Beta diversity amongst the clinical laboratory sections

Taxonomic profiles and core bacterial taxa by laboratory section

Differentially abundant bacterial taxa

Discussion

List of Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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