Temporal variations in bacterial community diversity and composition throughout intensive care unit renovations

In-hospital areas with distinct maintenance and staff/patient traffic have specific microbiome profiles, functions, and resistomes

Hospitals are subject to strict microbial control. Stringent cleaning and confinement measures in hospitals lead to a decrease in microbial diversity, but an increase in resistance genes. Given the rise of antimicrobial resistances and healthcare-associated infections, understanding the hospital microbiome and its resistome is crucial. This study compared the microbiome and resistome at different levels of confinement (CL) within a single hospital. Using amplicon sequencing, shotgun metagenomics, and genome/plasmid reconstruction, we demonstrate that microbial composition differs in a stable way between the CLs and that the most restrictive confinement level CL1 had the lowest microbial but the highest functional diversity. This CL also exhibited a greater abundance of functions related to virulence, disease, defense, and stress response. Comparison of antibiotic resistance also showed differences among CLs in terms of the selection process and specific mechanisms for antimicrobial/antibiotic resistance. The resistances found in the samples of CL1 were mostly mediated via antibiotic efflux pumps and were mainly located on chromosomes, whereas in the other, less restrictive CL antibiotic resistances were more present on plasmids. This could be of particular importance for patient-related areas (CL2), as the potential spread of antibiotic resistances could be a major concern in this area. Our results show that there are differences in the microbiome and resistome even within a single hospital, reflecting room utilization and confinement. Since restrictive confinement selects for resistant microorganisms, strategies are required to deepen our understanding of dynamic processes of microbiome and resistome within hospital environments.

IMPORTANCE

Effective measures to combat antibiotic resistances and healthcare-associated infections are urgently needed, including optimization of microbial control. However, previous studies have indicated that stringent control can lead to an increase in the resistance capacities of microbiomes on surfaces. This study adds to previous knowledge by focusing on the conditions in a single hospital, resolving the microbiomes and their resistomes in three different confinement levels (CL): operating room, patient-related areas, and non-patient-related areas. We were able to identify stable key taxa; profiled the capacities of taxa, functions, and antimicrobial resistances (AMR); and reconstruct genomes and plasmids in each CL. Our results show that the most restrictive CL indeed had the highest functional diversity, but that resistances were mostly encoded on chromosomes, indicating a lower possibility of resistance spread. However, clever strategies are still required to strike a balance between microbial control and selective pressures in environments where patients need protection.

Intra-hospital microbiome variability is driven by accessibility and clinical activities

The hospital environmental microbiome, which can affect patients' and healthcare workers' health, is highly variable and the drivers of this variability are not well understood. In this study, we collected 37 surface samples from the neonatal intensive care unit (NICU) in an inpatient hospital before and after the operation began. Additionally, healthcare workers collected 160 surface samples from five additional areas of the hospital. All samples were analyzed using 16S rRNA gene amplicon sequencing, and the samples collected by healthcare workers were cultured. The NICU samples exhibited similar alpha and beta diversities before and after opening, which indicated that the microbiome there was stable over time. Conversely, the diversities of samples taken after opening varied widely by area. Principal coordinate analysis (PCoA) showed the samples clustered into two distinct groups: high alpha diversity [the pediatric intensive care unit (PICU), pathology lab, and microbiology lab] and low alpha diversity [the NICU, pediatric surgery ward, and infection prevention and control (IPAC) office]. Least absolute shrinkage and selection operator (LASSO) classification models identified 156 informative amplicon sequence variants (ASVs) for predicting the sample's area of origin. The testing accuracy ranged from 86.37% to 100%, which outperformed linear and radial support vector machine (SVM) and random forest models. ASVs of genera that contain emerging pathogens were identified in these models. Culture experiments had identified viable species among the samples, including potential antibiotic-resistant bacteria. Though area type differences were not noted in the culture data, the prevalences and relative abundances of genera detected positively correlated with 16S sequencing data. This study brings to light the microbial community temporal and spatial variation within the hospital and the importance of pathogenic and commensal bacteria to understanding dispersal patterns for infection control.

IMPORTANCE

We sampled surface samples from a newly built inpatient hospital in multiple areas, including areas accessed by only healthcare workers. Our analysis of the neonatal intensive care unit (NICU) showed that the microbiome was stable before and after the operation began, possibly due to access restrictions. Of the high-touch samples taken after opening, areas with high diversity had more potential external seeds (long-term patients and clinical samples), and areas with low diversity and had fewer (short-term or newborn patients). Classification models performed at high accuracy and identified biomarkers that could be used for more targeted surveillance and infection control. Though culturing data yielded viability and antibiotic-resistance information, it disproportionately detected the presence of genera relative to 16S data. This difference reinforces the utility of 16S sequencing in profiling hospital microbiomes. By examining the microbiome over time and in multiple areas, we identified potential drivers of the microbial variation within a hospital.

16S rRNA amplicon sequencing and antimicrobial resistance profile of intensive care units environment in 41 Brazilian hospitals

Introduction

Infections acquired during healthcare setting stay pose significant public health threats. These infections are known as Healthcare-Associated Infections (HAI), mostly caused by pathogenic bacteria, which exhibit a wide range of antimicrobial resistance. Currently, there is no knowledge about the global cleaning process of hospitals and the bacterial diversity found in ICUs of Brazilian hospitals contributing to HAI.

Objective

Characterize the microbiome and common antimicrobial resistance genes present in high-touch Intensive Care Unit (ICU) surfaces, and to identify the potential contamination of the sanitizers/processes used to clean hospital surfaces.

Methods

In this national, multicenter, observational, and prospective cohort, bacterial profiles and several antimicrobial resistance genes from 41 hospitals across 16 Brazilian states were evaluated. Using high-throughput 16S rRNA amplicon sequencing and real-time PCR, the bacterial abundance and resistance genes presence were analyzed in both ICU environments and cleaning products.

Results

We identified a wide diversity of microbial populations with a recurring presence of HAI-related bacteria among most of the hospitals. The median bacterial positivity rate in surface samples was high (88.24%), varying from 21.62 to 100% in different hospitals. Hospitals with the highest bacterial load in samples were also the ones with highest HAI-related abundances. Streptococcus spp., Corynebacterium spp., Staphylococcus spp., Bacillus spp., Acinetobacter spp., and bacteria from the Flavobacteriaceae family were the microorganisms most found across all hospitals. Despite each hospital particularities in bacterial composition, clustering profiles were found for surfaces and locations in the ICU. Antimicrobial resistance genes mecA, bla KPC-like, bla NDM-like, and bla OXA-23-like were the most frequently detected in surface samples. A wide variety of sanitizers were collected, with 19 different active principles in-use, and 21% of the solutions collected showed viable bacterial growth with antimicrobial resistance genes detected.

Conclusion

This study demonstrated a diverse and spread pattern of bacteria and antimicrobial resistance genes covering a large part of the national territory in ICU surface samples and in sanitizers solutions. This data should contribute to the adoption of surveillance programs to improve HAI control strategies and demonstrate that large-scale epidemiology studies must be performed to further understand the implications of bacterial contamination in hospital surfaces and sanitizer solutions.

Biocontrol in built environments to reduce pathogen exposure and infection risk

The microbiome of the built environment comprises bacterial, archaeal, fungal, and viral communities associated with human-made structures. Even though most of these microbes are benign, antibiotic-resistant pathogens can colonize and emerge indoors, creating infection risk through surface transmission or inhalation. Several studies have catalogued the microbial composition and ecology in different built environment types. These have informed in vitro studies that seek to replicate the physicochemical features that promote pathogenic survival and transmission, ultimately facilitating the development and validation of intervention techniques used to reduce pathogen accumulation. Such interventions include using Bacillus-based cleaning products on surfaces or integrating bacilli into printable materials. Though this work is in its infancy, early research suggests the potential to use microbial biocontrol to reduce hospital- and home-acquired multidrug-resistant infections. Although these techniques hold promise, there is an urgent need to better understand the microbial ecology of built environments and to determine how these biocontrol solutions alter species interactions. This review covers our current understanding of microbial ecology of the built environment and proposes strategies to translate that knowledge into effective biocontrol of antibiotic-resistant pathogens.

Bacterial Profile and Their Antimicrobial Susceptibility Pattern of Isolates Recovered from Intensive Care Unit Environments at Wachemo University Nigist Ellen Mohammed Memorial Comprehensive Specialized Hospital, Southern Ethiopia

Background

Bacterial contamination of indoor hospitals, especially in intensive care units is a serious health hazard in the world with a high morbidity and mortality rates. Particularly, multidrug-resistant bacteria can cross-contaminate medical devices, inanimate surfaces, health care providers, and patients in the intensive care unit. This study was aimed to assess the bacterial profile and their antimicrobial susceptibility patterns of bacterial isolates from intensive care unit at WUNEMMCSH (Wachemo University Nigest Ellen Mohammed Memorial Compressive Specialized Hospital), Southern Ethiopia.

Methods

A hospital-based cross sectional study was conducted on 180 intensive care unit environmental samples at WUNEMMCSH from August 1, 2022, to October 30, 2022. In this study, a total of 180 swab samples were collected from medical devices, inanimate surfaces, patients, and health care providers from the intensive care unit by using sterile cotton-tipped swabs moistened with normal saline. Then, bacterial isolates were identified using the standard culture method, Gram stain, and biochemical tests. Antimicrobial susceptibility tests for bacterial isolates were performed by using the Kirby Bauer disk diffusion method. Data were entered into EpiData Version 4.6 cleanup and exported to SPSS V25 for analysis.

Results

A total of 180 swab samples were processed from intensive care unit environments, and 143 (79.4%) were found to have been contaminated by at least one potential pathogenic bacterial isolate. A total of two hundred and thirty-eight bacteria were isolated. Of these, the predominant bacteria were coagulase-negative Staphylococci 72 (30.3%), S. aureus 61 (25.6%), E. coli 41 (17.2%), and K. pneumoniae 30 (12.6%). Seventy (49%) out of all swabbed samples were contaminated with mixed isolates. In the antimicrobial susceptibility tests, 19 (86%) Gram-positive bacteria and 25 (76%) Gram-negative bacterial isolates were susceptible to nitrofurantoin, respectively. Vancomycin was sensitive to 83% of Gram-positive isolates. Gram-positive and Gram-negative isolates from irrespective sources showed multidrug resistance in 56.4% and 76.2%, respectively.

Conclusion

The inanimate hospital environments, medical device, health care providers, and patients in ICU rooms of WUNEMMCSH (Wachemo University Nigest Ellen Mohammed Memorial Comprehensive Specialized Hospital) were colonized with 143 (79.4%) of potential pathogenic bacterial isolate, which can cause nosocomial infections with high rates of morbidity and mortality among patients. The frequencies of multidrug-resistant 159 (66.8%) bacterial pathogens were alarmingly high. Therefore, to reduce the risk of bacterial contamination and MDR, strict adherence to hospital infection prevention and control measures should be enforced. These measures include regularly performing hand hygiene, periodic disinfection, and sterilization of medical equipment.

Characterization of SARS-CoV-2 Distribution and Microbial Succession in a Clinical Microbiology Testing Facility during the SARS-CoV-2 Pandemic

The exchange of microbes between humans and the built environment is a 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 samples. 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. We sampled floors, benches, and sinks in 3 sections (bacteriology, molecular microbiology, and COVID) of an active clinical microbiology lab 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 on floors than sinks and benches. The floors were primarily colonized by bacteria common to natural environments (e.g., soils), and benchtops harbored a greater proportion of human-associated microbes, including Staphylococcus and Streptococcus. Finally, we show that the microbial composition of these surfaces did not change over time and remained stable. Despite 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. IMPORTANCE For decades, diagnostic clinical laboratories have been an integral part of the health care systems that perform diagnostic tests on patient's specimens in bulk on a regular basis. Understanding their microbiota should assist in designing and implementing disinfection, and cleaning regime in more effective way. To our knowledge, there is a lack of information on the composition and dynamics of microbiota in the clinical laboratory environments, and, through this study, we have tried to fill that gap. This study has wider implications as understanding the makeup of microbes on various surfaces within clinical laboratories could help identify any pathogenic bacterial taxa that could have colonized these surfaces, and might act as a potential source of laboratory-acquired infections. Mapping the microbial community within these built environments may also be critical in assessing the reliability of laboratory safety and sanitation practices to lower any potential risk of exposures to health care workers.

How Does Hospital Microbiota Contribute to Healthcare-Associated Infections?

Healthcare-associated infections (HAIs) are still a global public health concern, associated with high mortality and increased by the phenomenon of antimicrobial resistance. Causative agents of HAIs are commonly found in the hospital environment and are monitored in epidemiological surveillance programs; however, the hospital environment is a potential reservoir for pathogenic microbial strains where microorganisms may persist on medical equipment surfaces, on the environment surrounding patients, and on corporal surfaces of patients and healthcare workers (HCWs). The characterization of hospital microbiota may provide knowledge regarding the relatedness between commensal and pathogenic microorganisms, their role in HAIs development, and the environmental conditions that favor its proliferation. This information may contribute to the effective control of the dissemination of pathogens and to improve infection control programs. In this review, we describe evidence of the contribution of hospital microbiota to HAI development and the role of environmental factors, antimicrobial resistance, and virulence factors of the microbial community in persistence on hospital surfaces.

The Microbiome of the Built Environment: The Nexus for Urban Regeneration for the Cities of Tomorrow

Built environments are, for most of us, our natural habitat. In the last 50 years, the built-up area has more than doubled, with a massive biodiversity loss. The undeniable benefits of a city providing all the basic needs to a growing population showed longer-term and less obvious costs to human health: autoimmune and non-communicable diseases, as well as antimicrobial resistance, have reached unprecedented and alarming levels. Humans coevolved with microbes, and this long-lasting alliance is affected by the loss of connection with natural environments, misuse of antibiotics, and highly sanitized environments. Our aim is to direct the focus onto the microbial communities harbored by the built environments we live in. They represent the nexus for urban regeneration, which starts from a healthy environment. Planning a city means considering, in a two-fold way, the ecosystem health and the multidimensional aspects of wellbeing, including social, cultural, and aesthetic values. The significance of this perspective is inspiring guidelines and strategies for the urban regeneration of the cities of tomorrow, exploiting the invaluable role of microbial biodiversity and the ecosystem services that it could provide to create the robust scientific knowledge that is necessary for a bioinformed design of buildings and cities for healthy and sustainable living.

Temporal variation in bacterial community profile on patients' bedsheets in a primary healthcare unit

Fabrics serve as fomites in spreading nosocomial infections. As a patient is in close contact with bedsheets, it is important to assess the seasonal variation in bacterial diversity on these in healthcare units. The study was conducted to characterise the bacterial diversity on patients' bedsheets across 7 months in a primary healthcare unit. Polyester-cotton blend fabric was stitched on bedsheets, and temporal dynamics of bacterial communities was assessed from May to November 2019. qPCR and amplicon sequencing of 16S rRNA gene was performed for profiling of bacterial community. Results revealed the dominance of Bacillota followed by Pseudomonadota, and Actinomycetota. A seasonal variation was observed in the bacterial load, with maximum values in June. This indicates the impact of environmental conditions on bacterial abundance and composition on fabrics in healthcare unit. The presence of priority pathogens on the patient bedsheets is a human health concern reiterating the need for season-specific laundering protocol.

Monitoring microbial communities in intensive care units over one year in China

Healthcare-associated infections (HAIs) seriously threaten patient health in intensive care units (ICUs). Profiling the microbial composition and diversity in ICU is important to prevent HAI-related spreading. Given that microbial communities vary across different environments, the time-scale characteristics of pathogens in ICUs have not been explored in China. In our study, to study the bacterial communities of two different ICUs in China, we proceeded dynamic monitoring using 16S rRNA sequencing for a whole year among the bed sheets, bed rails, shared pulse oximeters, bedside lockers, nurses' hands, floor, and carts. Our results showed that the microbial composition significantly changed within months. Significant differences in alpha and beta diversities were also observed among the 12 sampling months in each ICU. Additionally, we found the persistence of several HAI-related bacteria, including Acinetobacter, Pseudomonas, Staphylococcus, Escherichia, and Enterococcus. Source tracking analysis showed that most bacteria in both ICUs came from buildings or human skin. With deep investigations of hospital microbial surveillance on a long-term time-scale, we hope that these results will provide constructive guidelines to prevent the spread of HAIs in ICUs.

Direct-PCR from rectal swabs and environmental reservoirs: A fast and efficient alternative to detect blaOXA-48 carbapenemase genes in an Enterobacter cloacae outbreak setting

Carbapenemase-producing bacteria are a risk factor in clinical settings worldwide. The aim of the study was to accelerate the time to results during an outbreak situation with bla_OXA-48-positive Enterobacter cloacae by using a real-time multiplex quantitative PCR (qPCR) directly on rectal swab specimens and on wastewater samples to detect carbapenemase-producing bacteria. Thus, we analyzed 681 rectal swabs and 947 environmental samples during a five-month period by qPCR and compared the results to culture screening. The qPCR showed a sensitivity of 100% by testing directly from rectal swabs and was in ten cases more sensitive than the culture-based methods. Environmental screening for bla_OXA-48-carbapenemase genes by qPCR revealed reservoirs of different carbapenemase genes that are potential sources of transmission and might lead to new outbreaks. The rapid identification of patients colonized with those isolates and screening of the hospital environment is essential for earlier patient treatment and eliminating potential sources of nosocomial infections.

Environmental dynamics of hospital microbiome upon transfer from a major hospital to a new facility

BACKGROUND

Infection control is critical to safe hospital care. However, how bacteria within nosocomial environments relate to space utilisation and occupancy remains poorly understood. Our aim was to characterise the hospital microbiome in the context of the closure of a tertiary hospital and the opening of a new facility.

METHODS

Environmental swabs were collected from common and inpatient areas in the old and new hospitals during a 12-month transition period. Microbiota characteristics were determined by 16S rRNA gene sequencing and quantitative (q)PCR. Targeted assays were used to detect Methicillin-resistant Staphylococcus aureus (MRSA) and vanB-positive Vancomycin-Resistant Enterococci (VRE).

RESULTS

The transition to full occupancy in the new facility was associated with an increase in bacterial load (inpatient areas, 3 months p = 0.001; common areas, 6 months p = 0.039) and a change in microbiota composition (baseline-12 months, PERMANOVA p = 0.002). These changes were characterised by an increase in human microbiota-associated taxa, including Acinetobacter and Veillonella. Closure of the existing facility was associated with a decrease in bacterial load (p = 0.040). Detection of MRSA did not differ significantly between sites.

CONCLUSIONS

Occupancy is a major determinant of bacterial dispersion within hospital environments. Steady-state bacterial levels and microbiota composition provide a basis for assessment of infection control measures.

Healthcare-Associated Infections-Related Bacteriome and Antimicrobial Resistance Profiling: Assessing Contamination Hotspots in a Developing Country Public Hospital

Hospital-built environment colonization by healthcare-associated infections-related bacteria (HAIrB) and the interaction with their occupants have been studied to support more effective tools for HAI control. To investigate HAIrB dynamics and antimicrobial resistance (AMR) profile we carried out a 6-month surveillance program in a developing country public hospital, targeting patients, hospital environment, and healthcare workers, using culture-dependent and culture-independent 16S rRNA gene sequencing methods. The bacterial abundance in both approaches shows that the HAIrB group has important representativeness, with the taxa Enterobacteriaceae, Pseudomonas, Staphylococcus, E. coli, and A. baumannii widely dispersed and abundant over the time at the five different hospital units included in the survey. We observed a high abundance of HAIrB in the patient rectum, hands, and nasal sites. In the healthcare workers, the HAIrB distribution was similar for the hands, protective clothing, and mobile phones. In the hospital environment, the healthcare workers resting areas, bathrooms, and bed equipment presented a wide distribution of HAIrB and AMR, being classified as contamination hotspots. AMR is highest in patients, followed by the environment and healthcare workers. The most frequently detected beta-lactamases genes were, bla_SHV–like, bla_{OXA–23–like}, bla_{OXA–51–like}, bla_KPC–like, bla_CTX–M–1, bla_CTX–M–8, and bla_CTX–M–9 groups. Our results demonstrate that there is a wide spread of antimicrobial resistance due to HAIrB in the hospital environment, circulating among patients and healthcare workers. The contamination hotspots identified proved to be constant over time. In the fight for patient safety, these findings can reorient practices and help to set up new guidelines for HAI control.

Effects of Age, Diet CP, NDF, EE, and Starch on the Rumen Bacteria Community and Function in Dairy Cattle

To understand the effects of diet and age on the rumen bacterial community and function, forty-eight dairy cattle at 1.5 (M1.5), 6 (M6), 9 (M9), 18 (M18), 23 (M23), and 27 (M27) months old were selected. Rumen fermentation profile, enzyme activity, and bacteria community in rumen fluid were measured. The acetate to propionate ratio (A/P) at M9, M18, and M23 was higher than other ages, and M6 was the lowest (p < 0.05). The total volatile fatty acid (TVFA) at M23 and M27 was higher than at other ages (p < 0.05). The urease at M18 was lower than at M1.5, M6, and M9, and the xylanase at M18 was higher than at M1.5, M23, and M27 (p < 0.05). Thirty-three bacteria were identified as biomarkers of the different groups based on the linear discriminant analysis (LDA) when the LDA score >4. The variation partitioning approach analysis showed that the age and diet had a 7.98 and 32.49% contribution to the rumen bacteria community variation, respectively. The richness of Succinivibrionaceae_UCG-002 and Fibrobacter were positive correlated with age (r > 0.60, p < 0.01) and positively correlated with TVFA and acetate (r > 0.50, p < 0.01). The Lachnospiraceae_AC2044_group, Pseudobutyrivibrio, and Saccharofermentans has a positive correlation (r > 0.80, p < 0.05) with diet fiber and a negative correlation (r < −0.80, p < 0.05) with diet protein and starch, which were also positively correlated with the acetate and A/P (r > 0.50, p < 0.01). The genera of Lachnospiraceae_AC2044_group, Pseudobutyrivibrio, and Saccharofermentans could be worked as the target bacteria to modulate the rumen fermentation by diet; meanwhile, the high age correlated bacteria such as Succinivibrionaceae_UCG-002 and Fibrobacter also should be considered when shaping the rumen function.

Defining the wheat microbiome: Towards microbiome-facilitated crop production

Wheat is one of the world's most important crops, but its production relies heavily on agrochemical inputs which can be harmful to the environment when used excessively. It is well known that a multitude of microbes interact with eukaryotic organisms, including plants, and the sum of microbes and their functions associated with a given host is termed the microbiome. Plant-microbe interactions can be beneficial, neutral or harmful to the host plant. Over the last decade, with the development of next generation DNA sequencing technology, our understanding of the plant microbiome structure has dramatically increased. Considering that defining the wheat microbiome is key to leverage crop production in a sustainable way, here we describe how different factors drive microbiome assembly in wheat, including crop management, edaphic-environmental conditions and host selection. In addition, we highlight the benefits to take a multidisciplinary approach to define and explore the wheat core microbiome to generate solutions based on microbial (synthetic) communities or single inoculants. Advances in plant microbiome research will facilitate the development of microbial strategies to guarantee a sustainable intensification of crop production.