Socioeconomic drivers of the human microbiome footprint in global sewage

doi:10.1007/s11783-024-1889-z

Front. Environ. Sci. Eng.

2024, Vol. 18

Issue (10) : 129 https://doi.org/10.1007/s11783-024-1889-z

Socioeconomic drivers of the human microbiome footprint in global sewage

Minglei Ren^1,², Shaojuan Du³, Jianjun Wang^1,²(

)

¹. Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
². State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
³. Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, China

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Abstract

● We built a read-mapping framework to profile human microbes from sewages (HSM).

● There were 95.03% human microbial species successfully recaptured from sewages.

● The HSM composition showed a distance-decay pattern at a global scale.

● The HSM communities from developed regions were separated from developing regions.

● Economy was the key socioeconomic factors driving the HSM diversity.

The human microbiome leaves a legacy in sewage ecosystems, also referred to as the human sewage microbiomes (HSM), and could cause potential risk to human health and ecosystem service. However, these host-associated communities remain understudied, especially at a global scale, regarding microbial diversity, community composition and the underlying drivers. Here, we built a metagenomic read mapping-based framework to estimate HSM abundance in 243 sewage samples from 60 countries across seven continents. Our approach revealed that 95.03% of human microbiome species were identified from global sewage, demonstrating the potential of sewage as a lens to explore these human-associated microbes while bypassing the limitations of human privacy concerns. We identified significant biogeographic patterns for the HSM community, with species richness increasing toward high latitudes and composition showing a distance-decay relationship at a global scale. Interestingly, the HSM communities were mainly clustered by continent, with those from Europe and North America being separated from Asia and Africa. Furthermore, global HSM diversity was shown to be shaped by both climate and socioeconomic variables. Specifically, the average annual temperature was identified as the most important factor for species richness (33.18%), whereas economic variables such as country export in goods and services contributed the most to the variation in community composition (27.53%). Economic and other socioeconomic variables, such as education, were demonstrated to have direct effects on the HSM, as indicated by structural equation modeling. Our study provides the global biogeography of human sewage microbiomes and highlights the economy as an important socioeconomic factor driving host-associated community composition.

Keywords Human sewage microbiome Biogeography Socioeconomic factors Climate factors

Corresponding Author(s): Jianjun Wang

Issue Date: 02 August 2024

Cite this article:

Minglei Ren,Shaojuan Du,Jianjun Wang. Socioeconomic drivers of the human microbiome footprint in global sewage[J]. Front. Environ. Sci. Eng., 2024, 18(10): 129.

URL:

https://academic.hep.com.cn/fese/EN/10.1007/s11783-024-1889-z
https://academic.hep.com.cn/fese/EN/Y2024/V18/I10/129

Fig.1 The overview of the framework for exploring HSM. The framework includes three steps highlighted in panels with distinct colors: (a) The taxonomic profiling of the human-related microbiome in sewage (HSM), (b) The collection of the relevant variables from the curated databases, (c) Statistical analyses.

Fig.2 Global distribution and diversity of the HMS. (a) Latitudinal distribution of the HMS diversity. The points represent the HMS species richness. (b) Distance-decay relationships based on the Bray-Curtis dissimilarity of the HMS. (c) NMDS ordinations showing the distribution patterns of HMS communities across global continents.

Fig.3 The correlation network of the HMS and variables. The network represents the relationships between species abundance and both two types of variables (a). The distributions of average linking numbers and degrees across different types of variables were shown in panels (b, c).

Fig.4 The relative importance of the variables to HMS diversity. The relative contributions of the variables to HMS species richness and community composition based on the random forest model results (a, b). The effects of the significant variables on the HMS were shown based on redundancy analysis (c).

Fig.5 The drivers of the HMS diversity revealed through SEMs. The SEMs showed the relationships among the variables and HMS species richness and community composition (a, b). The width of the arrows represents the strength of influence between variables, with numbers denoting the standardized path coefficients.

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