Abstract
Microbial diversity loss has been associated with urban landscapes and might play an important role in the asthma and other respiratory disease epidemics seen in western countries [1]. Thus, analysing the impact of urban features on the built environment (BE) microbiome is key to build healthier cities.
To study the biodiversity across gradients of urbanisation, air microbiome samples were collected from a city farm and a rural dairy farm over a 6-month period. Microbial and viral DNA were extracted for whole-genome sequencing. Sequences were then analysed using a non-assembled approach against the RefSeq PlusPF database using Kraken2 [2]. Potentially contaminant species were removed using the decontam [3] R package. Statistical analyses were carried out in R using centered log-ratio (CLR) transformations and Euclidean distances [4].
Preliminary diversity analyses showed that microbial composition was significantly associated with farm type and room type, but not with the season when the samples were taken. This implies that the whole microbial diversity was influenced by the location of the farm, animal presence and room features, but it does not have a seasonal component. However, when analysed individually, fungal communities appeared to cluster by season, while bacterial and archaeal communities did not.
These preliminary results shed light on the BE microbiome composition and dynamics and will be the basis of future functional analyses of air microbiomes in rural and urban farms. This study has the potential to understand microbiome composition in relation to building design to prevent microbial diversity loss for the benefit of future generations.
[1] Kirjavainen, P. v. et al. Farm-like indoor microbiota in non-farm homes protects children from asthma development. Nat Med 25, 1089–1095 (2019).
[2] Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol 20, (2019).
[3] Davis, N. M., Proctor, Di. M., Holmes, S. P., Relman, D. A. & Callahan, B. J. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 6, (2018).
[4] Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: And this is not optional. Frontiers in Microbiology vol. 8 Preprint at https://doi.org/10.3389/fmicb.2017.02224 (2017).
To study the biodiversity across gradients of urbanisation, air microbiome samples were collected from a city farm and a rural dairy farm over a 6-month period. Microbial and viral DNA were extracted for whole-genome sequencing. Sequences were then analysed using a non-assembled approach against the RefSeq PlusPF database using Kraken2 [2]. Potentially contaminant species were removed using the decontam [3] R package. Statistical analyses were carried out in R using centered log-ratio (CLR) transformations and Euclidean distances [4].
Preliminary diversity analyses showed that microbial composition was significantly associated with farm type and room type, but not with the season when the samples were taken. This implies that the whole microbial diversity was influenced by the location of the farm, animal presence and room features, but it does not have a seasonal component. However, when analysed individually, fungal communities appeared to cluster by season, while bacterial and archaeal communities did not.
These preliminary results shed light on the BE microbiome composition and dynamics and will be the basis of future functional analyses of air microbiomes in rural and urban farms. This study has the potential to understand microbiome composition in relation to building design to prevent microbial diversity loss for the benefit of future generations.
[1] Kirjavainen, P. v. et al. Farm-like indoor microbiota in non-farm homes protects children from asthma development. Nat Med 25, 1089–1095 (2019).
[2] Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol 20, (2019).
[3] Davis, N. M., Proctor, Di. M., Holmes, S. P., Relman, D. A. & Callahan, B. J. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 6, (2018).
[4] Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: And this is not optional. Frontiers in Microbiology vol. 8 Preprint at https://doi.org/10.3389/fmicb.2017.02224 (2017).
| Original language | English |
|---|---|
| Number of pages | 1 |
| Publication status | Unpublished - 2023 |
| Event | Microbiology Society Annual Conference - Birmingham ICC Duration: 17 Apr 2023 → 20 Apr 2023 https://microbiologysociety.org/event/annual-conference/annual-conference-2023.html |
Conference
| Conference | Microbiology Society Annual Conference |
|---|---|
| Period | 17/04/23 → 20/04/23 |
| Internet address |
