Links between airborne microbiome, meteorology, and chemical composition in northwestern Turkey
Graphical abstract
Introduction
The atmosphere can be a significant transport vector for microorganisms (Archer et al., 2019b; Caliz et al., 2018; Reche et al., 2018) including bacteria, protists, fungi, viruses, algae; collectively, these are also termed bioaerosols (Burrows et al., 2009; Fröhlich-Nowoisky et al., 2016). Their emission into the atmosphere may follow an independent, free particle transport, or an agglomeration with other airborne particles. In the atmosphere, bioaerosols, traveling across oceans and continents may experience significant changes due to interactions with gasses, radiation or water vapor (Estillore et al., 2016; Morris et al., 2014; Tang et al., 2018). When settled, bioaerosols may significantly impact the new environment as they may mobilize microbial genes such as virulence factors (Schaumburg et al., 2011), functional (Rahav et al., 2016), or antibiotic resistance genes (ARGs) from distant sources (Abulreesh et al., 2017; Peterson and Kaur, 2018). Possible horizontal gene transfer of ARGs into local microorganisms may allow pathogens to develop immunity against antibiotic treatments (Martinez, 2008).
The chemical characterization of the atmospheric aerosol sampled in Bolu, a province at the northwestern part of Turkey, have been previously reported in a few studies. Increased levels of NH4+ in local particulate matter <10 μm (PM10) were linked with domestic heating, as well as with emissions from poultry farms that are usually more active during autumn (Keleş, 2014). Domestic heating and the prevailing lower atmospheric boundary layer height during the winter months increase the ratio between organic and elemental carbon in the aerosols, shifting it towards higher organic fraction (Ozturk and Keles, 2016). Contribution of sporadic Saharan dust outbreaks to the local particulate matter (PM) increase the levels of soil-related metals (mainly, Mg, Si and Al) in ambient samples (Ozturk and Keles, 2016). In addition, the long-term trends of SO2 and PM10 concentrations have significantly decreased from 2006 to 2017, most probably due to implementation of more strict air pollution control policies in Turkey (Öztürk and Keleş Özgül, 2019).
Genomic characterization is a powerful tool that has been recently adopted for the identification of the microbial content and diversity in atmospheric samples (Archer et al., 2019a; Archer et al., 2019b; Bowers et al., 2011; Caliz et al., 2018; DeLeon-Rodriguez et al., 2013; Gat et al., 2017). Nevertheless, atmospheric microbiome studies are still relatively rare, mostly due to challenges in obtaining high quality DNA amounts, and sampling limitations (Hospodsky et al., 2010; Nieto-Caballero et al., 2019). Previous studies have suggested that the atmospheric microbiome changes seasonally (Caliz et al., 2018; Du et al., 2018), differs between air-mass origin (Gat et al., 2017), PM10 loadings (Mazar et al., 2016) and atmospheric path (Archer et al., 2019a). Some studies indicate that bacteria may remain viable and even active in the atmosphere (Amato et al., 2019). However, significant knowledge gap regarding aerial transport of microorganisms through the atmosphere remains. As the atmospheric air masses are advected, and not affixed to a specific location, the environmental characterization of the air mass source, the origin of the sampled biomass, as well as the chemical composition, are all needed in order to retrieve biogeographical information of the sampled microorganisms (Lang-Yona et al., 2014). Deciphering the transport of microorganisms in the atmosphere may shed light on global ecological and epidemiological processes.
In this study, we explored the atmospheric microbiome composition, patterns, and distribution in Bolu (Turkey), and identified the linking factors that indicate on differences in microbial populations. We explored both bacterial and fungal gene abundances using amplicon sequencing analyses of DNA extracted from airborne particulates (<10 μm), sampled in July–August 2017 and February 2018. In addition, we focused on specific antibiotic resistance gene abundances. Integrating the genomic along with the chemical composition of the sampled air masses and their sources, as well as the ARGs' abundance, introduces a new dimension in the characterization of air masses and in understanding the origin and transport of microorganisms in the environment.
Section snippets
Filter preparation and aerosol sampling
Ambient air was sampled in Bolu, Turkey (40.73°N–31.60°E and 741 m asl, see Fig. S1) between July and August 2017 (13 sampled filters) and on February 2018 (3 sampled filters, see Table 1). The sampling station was located 2.5 m above the ground in an urban environment, rich with poultry farms and with little local industry. The city is located downwind of Kocaeli province, one of the most industrialized cities of Turkey, and the mega-city Istanbul, and is covered by 77% productive forests. The
Air mass flow and meteorological conditions
Ambient PM samples were collected during July–August 2017 (13 sampled filters) and February 2018 (3 sampled filters), as described in Table 1. Air mass trajectory clustering analysis, based on annual trajectories between January 1st to February 28th, 2018, shows that 20% of the air flow come from the south, 46% from south-west (Clusters 2 and 3), and 22% come from the west (Fig. S2). Clustering analysis of the summer air masses, based on annual trajectories between July 1st to August 15th, 2017
Author contributions
P.Z, M.T, and NM performed part of the chemical analyses of the collected air samples. M.A, E.D, and F·O collected the air samples, performed the chemical and back trajectory analyses, and provided the meteorological information. N.L.-Y. extracted DNA and performed the metabarcoding data analysis and the integration of the results. D.G., Y.R., F.O. and N.L-Y. discussed the results and wrote the manuscript. A.B. and P.B·K-K. provided the air sampler and supported in the field campaign. All
CRediT authorship contribution statement
Naama Lang-Yona: Conceptualization, Methodology, Investigation, Data curation, Writing - original draft, Visualization. Fatma Öztürk: Resources, Funding acquisition, Data curation, Investigation, Methodology, Supervision, Writing - review & editing. Daniella Gat: Formal analysis, Writing - review & editing. Merve Aktürk: Data curation, Investigation. Emre Dikmen: Methodology, Investigation. Pavlos Zarmpas: Methodology, Investigation. Maria Tsagkaraki: Methodology, Investigation. Nikolaos
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgment
This study was partially supported by the Israel Science Foundation (grant # 236/16) and by a grant from the Estate of David Levinson. Bolu Abant İzzet Baysal University (BAİBU) Scientific Research Projects Coordination Office (BAP) supported this project (grant # BAP–2017.9.04.1162). N.L-Y. acknowledges support from the Women Bridging position, and the Sustainability and Energy Research Initiative (SAERI), Weizmann Institute of Science.
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