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Volume 33 Issue 6
Nov.  2019
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Gelatin filter capture-based high-throughput sequencing analysis of microbial diversity in haze particulate matter

  • Airborne particulate matter (PM), especially PM2.5, can be easily adsorbed by human respiratory system. Their roles in carrying pathogens for spreading epidemic diseases has attracted great concern. Herein, we developed a novel gelatin filter-based and culture-independent method for investigation of the microbial diversity in PM samples during a haze episode in Tianjin, China. This method involves particle capture by gelatin filters, filter dissolution for DNA extraction, and high-throughput sequencing for analysis of the microbial diversity. A total of 584 operational taxonomic units (OTUs) of bacteria and 370 OTUs of fungi at the genus level were identified during hazy days. The results showed that both bacterial and fungal diversities could be evaluated by this method. This study provides a convenient strategy for investigation of microbial biodiversity in haze, facilitating accurate evaluation of airborne epidemic diseases.
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Gelatin filter capture-based high-throughput sequencing analysis of microbial diversity in haze particulate matter

    Corresponding author: Bingzhi Li, bzli@tju.edu.cn
    Corresponding author: Penghui Li, lipenghui406@163.com
  • 1. Wuqing District Center for Disease Control and Prevention, Tianjin 301700, China
  • 2. Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
  • 3. School of Environmental Science and Safety Engineering, Tianjin University of Technology, Tianjin 300384, China

Abstract: Airborne particulate matter (PM), especially PM2.5, can be easily adsorbed by human respiratory system. Their roles in carrying pathogens for spreading epidemic diseases has attracted great concern. Herein, we developed a novel gelatin filter-based and culture-independent method for investigation of the microbial diversity in PM samples during a haze episode in Tianjin, China. This method involves particle capture by gelatin filters, filter dissolution for DNA extraction, and high-throughput sequencing for analysis of the microbial diversity. A total of 584 operational taxonomic units (OTUs) of bacteria and 370 OTUs of fungi at the genus level were identified during hazy days. The results showed that both bacterial and fungal diversities could be evaluated by this method. This study provides a convenient strategy for investigation of microbial biodiversity in haze, facilitating accurate evaluation of airborne epidemic diseases.

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    Introduction
    • We breathe in, more or less, airborne particulate matter (PM), e.g., PM2.5 and PM10. As compared to PM10 that can be readily removed by mucosal clearance, PM2.5[13] can be adsorbed into human pulmonary parenchyma owing to their small sizes. Moreover, the higher specific surface area of PM2.5 facilitates their role as carrier for numerous contaminants, such as water soluble inorganic ions, heavy metals, polycyclic aromatic hydrocarbons (PAHs), pathogens and bioaerosols[410]. After entering the human body, these particles can release antigenic compounds, microbial toxins, and viruses into the bloodstream, leading to serious cerebrovascular and cardiovascular diseases[1112].

      There is evidence suggesting that pathogenic microorganisms bound onto the PM, such as Streptococcus pneumoniae, Streptococcus pyogenes, Mycoplasma pneumoniae, Haemophilus influenzae, Klebsiella pneumoniae, Pseudomonas aeruginosa and Mycobacterium tuberculosis, are becoming a threat to public health[13]. Inhalation of these pathogens may cause allergic reactions and severe respiratory infections. High concentrations of these pathogens also cause skin diseases and even cancers[14]. Accordingly, their potential impacts on the environment and the human health have attracted great concerns.

      Up to now, culture-based methods remain the prevalent strategy for the investigation of microbial diversity and evaluation of pathogen risks in airborne samples[15]. For example, Fang et al identified 789 airborne bacteria, which were distributed across 55 genera and 184 species according to a culturable method[16]. However, most of the microorganisms cannot be cultured by common growth conditions, resulting in underestimation of microbial complexity in these samples. Therefore, it is urgent to develop culture-independent techniques for the investigation of airborne microorganisms. In terms of airborne sampling methods, traditional sampling processes involve air pump-mediated compression of air, followed by impact of microbes on the surface of quartz filters[17]. However, the impact process may lead to the escape of abundant microbes from the filters, also resulting in undervaluation of microbial integrity[18].

      In this paper, we reported an approach by developing a novel gelatin filter-based and culture-independent method for the investigation of the microbial diversity in PM samples. This method involves rapid gas compression by air pumps, thorough particle capture on gelatin filters, convenient dissolution of the filters for DNA extraction, and high-throughput sequencing for analysis of the microbial diversity. Hence, this study provides a new and convenient strategy for the investigation of biodiversity in haze, facilitating accurate evaluation of airborne epidemic diseases.

    Materials and methods

      Sample collection

    • The PM samples were obtained each day by MD8 Air Scan sampling device (Air Scan Sartorius AG, Gottingen, Germany) with the average flow rate of 40 L/hour, through the sterile gelatin filters (diameter of 80 mm, pore size of 3 µm, 17528-80-ACD, Sartorius) during November 30 to December 21 of 2016 in different functional regions of Wuqing District, Tianjin (Table 1), including: the control groups sampled in clean days (Cle1 from hospitals, and Cle2 from traffic hubs), Group 1 sampled from hospitals (Hosp1, Hosp2, and Hosp3), Group 2 sampled from traffic hubs(Trans1, Trans2, and Trans3), Group 3 sampled from schools (Sch1, Sch2, and Sch3), and traffic hubs (Tran1, Tran2, and Tran3). Initially, we also sampled the materials in clean days from the school as the third control (Cle3). Unfortunately, the sequencing of this sample was failed. Therefore, we did not include this control sample to the manuscript. All the samplers were placed 1.5 m above the ground. The variations of PM2.5 and PM10 concentrations during the sampling periods were made available online (China national environmental monitoring center). The gelatin filters were stored at −20 °C prior to extraction of total DNA.

      Groups Functional areas Abbreviations of sampling sites
      Control Clean days of hospitals and traffic hubs Cle1 (hospital areas), Cle2 (traffic hubs)
      Group1 Hospitals Hosp1, Hosp2, Hosp3
      Group2 Traffic hubs Trans1, Trans2, Trans3
      Group3 Schools Sch1, Sch2, Sch3

      Table 1.  Sampling groups of the different functional areas

    • Electron microscopy

    • The morphology of PM samples on the gelatin membranes was characterized by a Phenom scanning electron microscope (SEM) supported with image software (SEM, ProX, Holland).

    • DNA extraction

    • The gelatin filters were dissolved in 5 mL sterile water (preheated to 37 °C). The pathogenic microorganisms captured by gelatin filter were lysed by the alkaline lysing liquid (NaOH 50 mmol/L, SDS 1%, Protease K 10 mg/L, RNase 20 mg/L), the total DNA was enriched using magnetic nanoparticles modified by polyquaternary amino salt polymers, and the total DNA was eluted by elution (Jinping Biotech, China) according to the instruction. The extracted DNA was stored at −80 °C for further use.

    • Amplification of 16S and 18S rDNA gene sequences

    • The 16S rDNA V3+V4 region was amplified using the forward primer 338F (5′-ACTCCTACGGGAGGCAGCA-3′) and the reverse primer 806R (5′-GGACTACHVGGGTWTCTAAT-3′), the 18S rDNA ITS region was amplified using primers ITS1 (5′-CTTGGTCATTTAGAGGAAGTAA-3′) and ITS2 (5′-GCTGCGTTCTTCATCGATGC-3′). The primers were synthesized by Sangon Ltd., Beijing. The PCR reaction procedure is shown in Supplementary Table 1, available online.

    • High-throughput sequencing of 16S rDNA

    • The obtained total 16S rDNA and 18S rDNA were sequenced by double end sequencing (Paired-End) method through FLASH v1.2.7 using the Illumina HiSeq 2500 sequencing platform (BioMarker Technologies Co., Ltd., Beijing, China)[19]. Bioinformatics analysis of sequences was conducted using the QIIME (V1.7.0) software package. Sequences with similarities greater than or equal to 97% were grouped into operational taxonomic units (OTUs). The Shannon index was used to estimate the biodiversity of bacteria in a single sample[20]. The principal component analysis (PCA) and the cluster heatmap analysis were also performed to assess the bacterial composition of the samples[21].

    Results

      Concentration and diameter of fine particles during the haze events

    • Fig. 1 shows the variations of PM2.5 and PM10 concentrations during the sampling periods. The concentrations of PM2.5 were higher than 200 μg/m3 in 8 days, and the concentrations of PM10 were higher than 200 μg/m3 in 10 days, with the highest PM10 concentration being almost 350 μg/m3, which far exceeded the PRC National Standard PM standards: 50 μg/m3 for PM10, and 35 μg/m3 for PM2.5, respectively (PRC National Standard, 2012). As shown in Fig. 2, the statistical mean diameters of the sampled particles were in the ranges of 100–900 nm and 1.0 –2.5 μm, confirming that the particles were in the scope of fine particulate matter.

      Figure 1.  Daily average PM2.5 and PM10 concentrations estimated from the samples collected from November 30 to December 21, 2016.

      Figure 2.  Scanning electron microscopy characterization of the gelatin filter after PM sampling in different functional areas and the control group.

    • Microbial species richness and diversity

    • Rarefaction curves derived from the observed OTU number and shannon index were flattened (Fig. 3), showing that our sequencing depth was sufficient to cover the vast majority of bacteria and fungi in the samples. The statistics of OTU species in different ranks of the bacteria and the fungi are shown in Table 2 and 3, and a total of 584 OTUs of bacteria and 370 OTUs of fungi at the genus level were identified during hazy days.

      Figure 3.  Rarefaction curves and Shannon index derived from the observed OTU numbers.

      Sample Kindom Phylum Class Order Family Genus Species
      Cle1 1 20 52 83 142 205 143
      Cle2 2 20 50 79 135 220 158
      Hosp1 2 31 74 120 226 524 362
      Hosp2 1 26 55 100 203 507 316
      Hosp3 1 31 79 125 243 487 328
      Sch1 1 23 52 88 157 261 175
      Sch2 1 27 70 114 187 340 240
      Sch3 1 39 105 170 318 584 420
      Tran1 1 22 50 80 134 221 151
      Tran2 1 22 58 112 221 521 336
      Tran3 1 26 71 128 253 582 396
      OUTs: operational taxonomic units.

      Table 2.  Statistics of OTU species of bacteria in different ranks

      Sample Kindom Phylum Class Order Family Genus Species
      Cle1 4 18 39 87 209 348 335
      Cle2 4 18 41 88 210 339 361
      Hosp1 3 8 22 44 93 131 152
      Hosp2 3 9 127 63 134 192 254
      Hosp3 4 16 41 83 197 334 319
      Sch1 4 17 39 81 198 330 313
      Sch2 4 16 39 86 197 331 325
      Sch3 4 16 39 79 197 345 377
      Tran1 4 16 40 87 213 370 373
      Tran2 1 6 19 34 58 85 81
      Tran3 3 10 28 64 146 223 259
      OUTs: operational taxonomic units.

      Table 3.  Statistics of OTU species of fungi in different ranks

      As shown in Fig. 4, in all samples, Proteobacteria was the most abundant phylum, and four other dominant phyla were Firmicutes, Bacteroidetes, Actinobacteria, and Cyanobacteria (Fig. 4A). Compared to the samples from the clear days, the samples from hospitals and traffic hubs during hazy days contain much higher levels of human health-related bacteria, such as Acinetobacter, Staphylococcus, Corynebacterium, Lactobacillus, and Duganella (Fig. 4B). Especially, Staphylococcus and Corynebacterium were the predominant pathogens around the high-density-population functional areas, such as hospital and transportation areas. For example, Staphylococcus on PM collected around the hospital areas was the dominant bacteria (18% in Hosp2 samples), which may be derived from the high density of patients and health care personnel[22]. Nevertheless, the average abundance of Staphylococcus in Sch2 was only 1.2% because of the regular population. The dominant bacteria were Corynebacterium (34.3%) in Tran2 and Lachnospiraceae_Nk4A136 (10.5%) in Tran3. In Sch3 areas, the dominant bacteria were Lactobacillus (13.5%) and Klebsiella (11.8%). Interestingly, the genus Sphingomonas is detected on both the clear days and hazy days, which are the environment-associated bacteria widely found in surface water, the rhizosphere, sediments, and even soils. Fig. 4C shows that the dominant fungi were Malassezia (58.0%) in Hosp1 and (54.9%) in Hosp2, Alternaria (55.5%) and Cladosporium (9.17%) in Tran3. The genus Malassezia has been associated with a number of diseases affecting the human skin, such as pityriasis versicolor, Malassezia (Pityrosporum) folliculitis, seborrheic dermatitis and dandruff, atopic dermatitis, and psoriasis. Malassezia yeasts are a part of the normal micro-flora, but under certain conditions they can cause superficial skin infection[23]. Cladosporium, Aspergillus and Alternaria could enter the deep lung and cause respiratory diseases[24].

      Figure 4.  Microbial species richness and diversity.

      The variation of bacteria in collected samples were analyzed by two methods: the PCA analysis and the clustering heatmap analysis. PCA analysis showed that the high-population-density functional areas (Hosp1, Hosp2, Tran2, and Tran3 but not Tran1) had a similar bacterial diversity (Fig. 4D). However, the samples from Trans1 and Sch1 displayed little similarity in the bacterial composition. The clustering heat map analysis also demonstrated the similarity of the bacterial composition between the hospital areas and the transportation areas (Fig. 5). One cluster was composed of the hospital areas and the transportation areas; another cluster was composed of the samples from control and the school areas (samples Cle1, Cle2, Sch1, and Sch3). The bacterial diversity around school areas (Sch1 and Sch2) is similar to the control group, but not always the case: the sample Sch3 showed partially similar bacterial diversity to the samples from the hubs and hospitals, which may be attributed to transportation of PM from hubs and hospitals.

      Figure 5.  The cluster heatmap analysis of different functional areas.

    Discussion
    • Numerous epidemiologic studies have documented that PM is associated with inflammation-related diseases. For example, PM may result in the alteration of immune functions, such as IgA, IgG, IgM, IgE and lymphocyte profiles (CD4+ T cells, CD8+ T cells, CD4+/CD8+ T cells) in blood[25], which might link to various adverse health effects, including asthma, pulmonary infectious diseases, diabetes mellitus and obstructive bronchitis[26]. In China, numerous studies have been conducted to monitor regional air pollution[27], and the exposure-response relationships between airborne PM and cardiovascular diseases. All these studies have focused on the particle concentration effects, the airborne gas and other chemical substances[28]. However, little information has been available on the association of particle-bound pathogenic bacteria with the prevalence of infectious diseases and epidemics. In this paper, we found that the amounts of Staphylococcus and Corynebacterium are high on the PM collected around hospital areas and traffic hubs. Staphylococcus may cause both endemics and epidemic infections, such as neumonia, pseudomembranous colitis, pericarditis, and sepsis, especially outbreak of severe acute pneumonia[29]. Corynebacterium genus is also able to cause various types of healthcare-associated infections in immunocompromised hosts, such as pneumonia, pharyngitis and epidemic disease[30]. Except Staphylococcus and Corynebacterium, other prevailing pathogenic bacteria are Acinetobacter, Pseudomonas, which often cause large multifacility, nosocomial outbreaks and are frequently evolved into drug-resistant bacteria[31]. Our data indicate that densely populated traffic areas show strong evidence of bacteria and the presence of some potentially pathogenic organisms. In addition, PM2.5 exposure may alter and impair the normal immune responses of the lung, and decrease the phagocytosis of alveolar macrophages through disrupting the normal physical and immunological function of the lung, induce disorder of inflammatory cytokine[32]. Plus these more active pathogens, all of these effects would lead to the decline in immunity and facilitate infectious diseases.

      In conclusions, the pathogenic microorganisms in PM are closely related to their surrounding environmental conditions and population density, mobility and activities. In this study, we developed a convenient culture-independent method for the investigation of the microbial diversity in PM samples, which is composed of gas compression, particle capture, DNA extraction, and high-throughput sequencing. The results showed that both bacterial and fungal diversities could be accurately evaluated by this method. The results demonstrated the presence of some pathogenic bacteria, such as Staphylococcus, Corynebacterium, Acinetobacter and Pseudomonas, which may affect immunocompromised populations (e.g., the elderly, children and postoperative convalescence patients). This study provides a new strategy for the evaluation and surveillance of airborne epidemic diseases that increasingly threaten urban population.

    Acknowledgments
    • This work is supported by Project of Science and Technology Development in Wuqing District, Tianjin (No. WQKJ201614), Tianjin 131 innovative talent training project, Postdoctoral Science Foundation.

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