Insights into the Profile of the Human Expiratory Microbiota and Its Associations with Indoor Microbiotas

Height-Resolved Analysis of Indoor Airborne Microbiome: Comparison with Floor Dust-Borne Microbiome and the Significance of Shoe Sole Dust

Exposure to the indoor airborne microbiome is closely related to the air that individuals breathe. However, the floor dust-borne microbiome is commonly used as a proxy for indoor airborne microbiome, and the spatial distribution of indoor airborne microbiome is less well understood. This study aimed to characterize indoor airborne microorganisms at varying heights and compare them with those in floor dust. An assembly of three horizontally and three vertically positioned Petri dishes coated with mineral oil was applied for passive air sampling continuously at three heights without interruption. The airborne microbiomes at the three different heights showed slight stratification and differed significantly from those found in the floor dust. Based on the apportionment results from the fast expectation-maximization algorithm (FEAST), shoe sole dust contributed approximately 4% to indoor airborne bacteria and 14% to airborne fungi, a contribution that is comparable to that from the floor dust-borne microbiome. The results indicated that floor dust may not be a reliable proxy for indoor airborne microbiome. Moreover, the study highlights the need for height-resolved studies of indoor airborne microbiomes among humans in different activity modes and life states. Additionally, shoe sole-dust-associated microorganisms could potentially be a source to "re-wild" the indoor microbiota.

Structure and assembly process of skin fungal communities among bat species in northern China

Background

The skin fungal communities of animals play a crucial role in maintaining host health and defending against pathogens. Because fungal infections can affect the skin microbiota of bats, gaining a comprehensive understanding of the characteristics of healthy bat skin fungal communities and the ecological processes driving them provides valuable insights into the interactions between pathogens and fungi.

Methods

We used Kruskal-Wallis tests and Permutational Multivariate Analysis of Variance (PERMANOVA) to clarify differences in skin fungal community structure among bat species. A Generalized Linear Model (GLM) based on a quasi-Poisson distribution and partial distance-based redundancy analysis (db-RDA) was performed to assess the influence of variables on skin fungal communities. Using community construction models to explore the ecological processes driving fungal community changes, t-tests and Wilcoxon tests were used to compare the alpha diversity and species abundance differences between the fungal structure on bat species' skin and the environmental fungal pool.

Results

We found significant differences in the composition and diversity of skin fungal communities among bat species influenced by temperature, sampling site, and body mass index. Trophic modes and skin fungal community complexity also varied among bat species. Null model and neutral model analysis demonstrated that deterministic processes dominated the assembly of skin fungal communities, with homogeneous selection as the predominant process. Skin fungal communities on bat species were impacted by the environmental fungal reservoir, and actively selected certain amplicon sequence variants (ASVs) from the environmental reservoir to adhere to the skin.

Conclusion

In this study, we revealed the structure and the ecological process driving the skin fungal community across bat species in northern China. Overall, these results broaden our knowledge of skin fungal communities among bat species, which may be beneficial to potential strategies for the protection of bats in China.

A case study showing highly traceable sources of bacteria on surfaces of university buildings

University students predominantly spend their time indoors, where prolonged exposure raises the risk of contact with microorganisms of concern. However, our knowledge about the microbial community characteristics on university campus and their underpinnings is limited. To address it, we characterized bacterial communities from the surfaces of various built environments typical of a university campus, including cafeterias, classrooms, dormitories, offices, meeting rooms, and restrooms, in addition to human skin. The classrooms harbored the highest α-diversity, while the cafeterias had the lowest α-diversity. The bacterial community composition varied significantly across different building types. Proteobacteria, Actinobacteria, Firmicutes, Bacteroidetes, and Cyanobacteria were common phyla in university buildings, accounting for more than 90 % of total abundance. Staphylococcus aureus was the most abundant potential pathogen in classrooms, dormitories, offices, restrooms, and on human skin, indicating a potential risk for skin disease infections in these buildings. We further developed a new quantitative pathogenic risk assessment method according to the threat of pathogens to humans and found that classrooms exhibited the highest potential risk. The fast expectation-maximization algorithm identified 59 %-86 % of bacterial sources in buildings, with the human skin as the largest bacterial source for most buildings. As the sources of bacteria were highly traceable, we showed that homogeneous selection, dispersal limitation, and ecological drift were major ecological forces that drove community assembly. Our findings have important implications for predicting the distribution and sources of indoor dust bacterial communities on university campus.

Indoor/Outdoor airborne microbiome characteristics in residential areas across four seasons and its indoor purification

Bioaerosols are more likely to accumulate in the residential environment, and long-term inhalation may lead to a variety of diseases and allergies. Here, we studied the distribution, influencing factors and diffusion characteristics of indoor and outdoor microbiota pollution in six residential buildings in Guangzhou, southern China over a period of one year. The results showed that the particle sizes of bioaerosol were mainly in the range of inhalable particle size (<4.7 μm) with a small difference among four seasons (74.61 % ± 2.17 %). The microbial communities showed obvious seasonal differences with high abundance in summer, but no obvious geographical differences. Among them, the bacteria were more abundant than the fungi. The dominant microbes in indoor and outdoor environments were similar, with Anoxybacillu, Brevibacillus and Acinetobacter as the dominant bacteria, and Cladosporium, Penicillium and Alternaria as the dominant fungi. The airborne microbiomes were more sensitive to temperature and particulate matter (PM2.5, PM10) concentrations. Based on the Sloan neutral model, bacteria were more prone to random diffusion than fungi, and the airborne microbiome can be randomly distributed in indoor and outdoor environments and between the two environments in each season. Bioaerosol in indoor was mainly from outdoor. The health risk evaluation showed that the indoor inhalation risks were higher than those outdoor. The air purifier had a better removal efficiency on 1.1-4.7 μm microorganisms, and the removal efficiency on Gram-negative bacteria was better than that on Gram-positive bacteria. This study is of great significance for the risk assessment and control of residential indoor bioaerosol exposure.

Impact of climate zones and seasons on indoor airborne microbial communities: Insights from a comprehensive analysis

Bacteria and fungi are ubiquitous throughout built environments and are suspended in the air, potentially affecting human health. However, the impacts of climate zones on the diversity, structure, and stochastic assembly of indoor airborne microbes remain unknown. This study comprehensively analyzed indoor airborne microbes across five climate zones in China during the summer and winter using high-throughput sequencing. The diversity and structure of indoor airborne communities vary across climatic zones. A random forest model was used to identify biomarkers in different climate zones. The results showed no relationship between the biomarkers and their rankings in mean relative abundance. The Sloan neutral model fitting results indicated that the impact of climate zones on the stochastic process in the assembly of indoor airborne microbes was considerably more important than that of seasons. Additionally, the influence of seasons on the diversity, structure, and stochastic assembly process of indoor airborne microbes differed among different climate zones. The diversity, structure, and stochastic assembly processes of bacteria present distinctive outcomes in climate zones and seasons compared with those of fungi. Overall, these findings indicate that customized strategies are necessary to manage indoor airborne microbial communities in each climate zone, season, and for specific microbial species.

Unveiling the overlooked microbial niches thriving on building exteriors

Rapid urbanization in the Asia-Pacific region is expected to place two-thirds of its population in concrete-dominated urban landscapes by 2050. While diverse architectural facades define the unique appearance of these urban systems. There remains a significant gap in our understanding of the composition, assembly, and ecological potential of microbial communities on building exteriors. Here, we examined bacterial and protistan communities on building surfaces along an urbanization gradient (urban, suburban and rural regions), investigating their spatial patterns and the driving factors behind their presence. A total of 55 bacterial and protist phyla were identified. The bacterial community was predominantly composed of Proteobacteria (33.7% to 67.5%). The protistan community exhibited a prevalence of Opisthokonta and Archaeplastida (17.5% to 82.1% and 1.8% to 61.2%, respectively). The composition and functionality of bacterial communities exhibited spatial patterns correlated with urbanization. In urban buildings, factors such as facade type, light exposure, and building height had comparatively less impact on bacterial composition compared to suburban and rural areas. The highest bacterial diversity and lowest Weighted Average Community Identity (WACI) were observed on suburban buildings, followed by rural buildings. In contrast, protists did not show spatial distribution characteristics related to facade type, light exposure, building height and urbanization level. The distinct spatial patterns of protists were primarily shaped by community diffusion and the bottom-up regulation exerted by bacterial communities. Together, our findings suggest that building exteriors serve as attachment points for local microbial metacommunities, offering unique habitats where bacteria and protists exhibit independent adaptive strategies closely tied to the overall ecological potential of the community.

Contribution of Particulates to Airborne Disease Transmission and Severity: A Review

The emergence of coronavirus disease 2019 (COVID-19) has catalyzed great interest in the spread of airborne pathogens. Airborne infectious diseases are classified into viral, bacterial, and fungal infections. Environmental factors can elevate their transmission and lethality. Air pollution has been reported as the leading environmental cause of disease and premature death worldwide. Notably, ambient particulates of various components and sizes are harmful pollutants. There are two prominent health effects of particles in the atmosphere: (1) particulate matter (PM) penetrates the respiratory tract and adversely affects health, such as heart and respiratory diseases; and (2) bioaerosols of particles act as a medium for the spread of pathogens in the air. Particulates contribute to the occurrence of infectious diseases by increasing vulnerability to infection through inhalation and spreading disease through interactions with airborne pathogens. Here, we focus on the synergistic effects of airborne particulates on infectious disease. We outline the concepts and characteristics of bioaerosols, from their generation to transformation and circulation on Earth. Considering that microorganisms coexist with other particulates as bioaerosols, we investigate studies examining respiratory infections associated with airborne PM. Furthermore, we discuss four factors (meteorological, biological, physical, and chemical) that may impact the influence of PM on the survival of contagious pathogens in the atmosphere. Our review highlights the significant role of particulates in supporting the transmission of infectious aerosols and emphasizes the need for further research in this area.

Perspectives on Sampling and New Generation Sequencing Methods for Low-Biomass Bioaerosols in Atmospheric Environments

Bioaerosols play essential roles in the atmospheric environment and can affect human health. With a few exceptions (e.g., farm or rainforest environments), bioaerosol samples from wide-ranging environments typically have a low biomass, including bioaerosols from indoor environments (e.g., residential homes, offices, or hospitals), outdoor environments (e.g., urban or rural air). Some specialized environments (e.g., clean rooms, the Earth's upper atmosphere, or the international space station) have an ultra-low-biomass. This review discusses the primary sources of bioaerosols and influencing factors, the recent advances in air sampling techniques and the new generation sequencing (NGS) methods used for the characterization of low-biomass bioaerosol communities, and challenges in terms of the bias introduced by different air samplers when samples are subjected to NGS analysis with a focus on ultra-low biomass. High-volume filter-based or liquid-based air samplers compatible with NGS analysis are required to improve the bioaerosol detection limits for microorganisms. A thorough understanding of the performance and outcomes of bioaerosol sampling using NGS methods and a robust protocol for aerosol sample treatment for NGS analysis are needed. Advances in NGS techniques and bioinformatic tools will contribute toward the precise high-throughput identification of the taxonomic profiles of bioaerosol communities and the determination of their functional and ecological attributes in the atmospheric environment. In particular, long-read amplicon sequencing, viability PCR, and meta-transcriptomics are promising techniques for discriminating and detecting pathogenic microorganisms that may be active and infectious in bioaerosols and, therefore, pose a threat to human health.

Supplementary Information

The online version contains supplementary material available at 10.1007/s41745-023-00380-x.

Characterization of airborne bacteria and fungi at a land-sea transition site in Southern China

Airborne microbe can have impact on regional to global climate as ice nuclei and cloud condensation nuclei. In coastal region, microbial aerosols are simultaneously contributed by terrestrial and marine sources under the influence of land-sea air exchange. We present a study on the characteristics of airborne bacteria and fungi, including their concentrations and communities, at a land-sea transition site in Southern China from December 2019 to December 2020. Seasonal variations of microbial communities were observed with evident profiles in summer, especially for fungal aerosols. The significant enhancement of Basidiomycota abundance in summer was contributed by local biogenic release under the influence of meteorological factors. Terrestrial sources are suggested as the dominant contributors to both bacterial and fungal aerosols rather than marine sources during the whole year period. Source-tracking analysis identified that marine contributions to airborne bacteria and fungi were 3.1-14.2 % and 4-24 %, respectively. It suggests that airborne fungi should be more suitable for long-range transport than airborne bacteria. This study improves the understanding of the conversional contribution of marine and terrestrial sources to airborne microbes in coastal region and the influencing environmental factors under land-sea exchange.

Skin microbiota interact with microbes on office surfaces

The indoor environment is recognized as a potential contributor to human health impacts through resident microbiomes. Indoor surface microbial communities are formed from several sources, environmental and anthropogenic. In this study, we characterized the bacterial and fungal communities from various sources typical of a working office environment including dust, fingers, and computer keyboards and mice. The composition of the dust bacterial community was significantly different from the other tested surfaces (P < 0.05), whereas the dust fungal community was only significantly different from fingers (P < 0.05). Bacterial and fungal communities were both shaped by deterministic processes, and bacterial communities had a higher migration rate. Results of a network analysis showed that the microbial community interactions of keyboards and mice were mainly competitive. Fast expectation-maximization microbial source tracking (FEAST) identified the sources of > 70 % of the keyboard and mouse microbial communities. Biomarkers for each sample types were identified by LDA Effect Size (LEfSE) analysis, some of which were soil-derived and potential anthropogenic pathogens, indicating the potential for exchange of microbes among outdoor, human and indoor surfaces. The current study shows that the source of microorganisms at the office interface is highly traceable and that their migration is linked to human activity. The migration of potentially pathogenic microbes were identified, emphasising the importance of personal hygiene.

Efficiency and sensitivity optimization of a protocol to quantify indoor airborne SARS-CoV-2 levels

Background

Development of methodologies to quantify airborne micro-organisms is needed for the prevention and control of infections. It is difficult to conclude which is the most efficient and sensitive strategy to assess airborne SARS-CoV-2 RNA levels due to the disparity of results reported in clinical settings.

Aim

To improve our previously reported protocol of measuring SARS-CoV-2 RNA levels, which was based on bioaerosol collection with a liquid impinger and RNA quantification with droplet digital polymerase chain reaction (ddPCR).

Methods

Air samples were collected in COVID-19 patient rooms to assess efficiency and/or sensitivity of different air samplers, liquid collection media, and reverse transcriptases (RT).

Findings

Mineral oil retains airborne RNA better than does hydrophilic media without impairing integrity. SARS-CoV-2 ORF1ab target was detected in 80% of the air samples using BioSampler with mineral oil. No significant differences in effectiveness were obtained with MD8 sampler equipped with gelatine membrane filters, but the SARS-CoV-2 copies/m³ air obtained with the latter were lower (28.4 ± 6.1 vs 9 ± 1.7). SuperScript II RT allows the detection of a single SARS-CoV-2 genome RNA molecule by ddPCR with high efficiency. This was the only RT that allowed the detection of SARS-CoV-2 N1 target in air samples.

Conclusion

The collection efficiency and detection sensivity of a protocol to quantify SARS-CoV-2 RNA levels in indoor air has been improved in the present study. Such optimization is important to improve our understanding of the microbiological safety of indoor air.

Bioaerosol Seasonal Variation and Contribution to Airborne Particulate Matter in Huangshi City of Central China

Ambient bioaerosols affect ecosystems and public health, but their seasonal variations and their contributions to aerosol particles are limitedly understood. Ambient bioaerosols in PM2.5 and PM10 samples were measured in Huangshi City, Hubei Province of China from April 2018 to December 2018. Bioaerosols were measured using a fluorescence microscope after staining with 4′, 6-diamino-2-phenylindole dihydrochloride (DAPI) following a direct staining technique. The bioaerosol number concentrations ranged from 0.12 to 15.69 # cm−3 for PM2.5 and 0.22 to 18.20 # cm−3 for PM10, with averages of 2.79 # cm−3 and 4.66 # cm−3, respectively. The bioaerosol concentrations of PM2.5 and PM10 varied significantly by seasons and were arranged in the following descending order: spring > fall > winter > summer. Bioaerosol numbers were dominated by fine particles of 0.37–2.5 μm diameter, while the spring bioaerosol particles were detected at the peak concentration of 0.56–1 μm diameter. Bioaerosol fractions accounted for 18.3 ± 10.6% PM10 mass and 13.7 ± 12.5% PM2.5 mass. Bioaerosol concentrations were increased during the haze event, but the increased amounts were not as large as those of the dust event, and higher bioaerosol contributions to PM were observed in the dust event than in the haze event. As enhanced emission controls have reduced PM concentrations in China, bioaerosols can be important contributors to PM mass.