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  • ISSN 1674-8301
  • CN 32-1810/R
Volume 33 Issue 6
Nov.  2019
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Sun Meiqing, Ding Zhanlin, Wang Hong, Yu Guangping, Feng Zhe, Li Bingzhi, Li Penghui. Gelatin filter capture-based high-throughput sequencing analysis of microbial diversity in haze particulate matter[J]. The Journal of Biomedical Research, 2019, 33(6): 414-421. DOI: 10.7555/JBR.33.20180121
Citation: Sun Meiqing, Ding Zhanlin, Wang Hong, Yu Guangping, Feng Zhe, Li Bingzhi, Li Penghui. Gelatin filter capture-based high-throughput sequencing analysis of microbial diversity in haze particulate matter[J]. The Journal of Biomedical Research, 2019, 33(6): 414-421. DOI: 10.7555/JBR.33.20180121
shu

Gelatin filter capture-based high-throughput sequencing analysis of microbial diversity in haze particulate matter

  • 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

More Information
  • Corresponding author:

    Bingzhi Li, Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China. Tel: +86-22-27402503, E-mail: bzli@tju.edu.cn

    Penghui Li, School of Environmental Science and Safety Engineering, Tianjin University of Technology, Tianjin 300384, China. Tel: +86-22-60214184, E-mail: lipenghui406@163.com

  • Received Date: December 03, 2018
  • Revised Date: February 09, 2019
  • Accepted Date: February 27, 2019
  • Available Online: April 29, 2019
    Graphical Abstract
    • 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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  • [1]
    Feng SL, Gao D, Liao F, et al. The health effects of ambient PM2.5 and potential mechanisms[J]. Ecotoxicol Environ Saf, 2016, 128: 67–74. doi: 10.1016/j.ecoenv.2016.01.030
    [2]
    Wang FW, Lin T, Li YY, et al. Comparison of PM2.5 carbonaceous pollutants between an urban site in Shanghai and a background site in a coastal east China sea island in summer: concentration, composition and sources[J]. Environ Sci Processes Impacts, 2017, 19(6): 833–842. doi: 10.1039/C7EM00129K
    [3]
    Cao SJ, Kong XR, Li LY, et al. An investigation of the PM2.5 and NO2 concentrations and their human health impacts in the metro subway system of Suzhou, China[J]. Environ Sci Processes Impacts, 2017, 19(5): 666–675. doi: 10.1039/C6EM00655H
    [4]
    Fang CS, Zhang ZD, Jin MY, et al. Pollution characteristics of PM2.5 aerosol during haze periods in Changchun, China[J]. Aerosol Air Qual Res, 2017, 17(4): 888–895. doi: 10.4209/aaqr.2016.09.0407
    [5]
    Gao Y, Guo XY, Ji HB, et al. Potential threat of heavy metals and PAHs in PM2.5 in different urban functional areas of Beijing[J]. Atmos Res, 2016, 178-179: 6–16. doi: 10.1016/j.atmosres.2016.03.015
    [6]
    Yeh CF, Lee CL, Brimblecombe P. Effects of seasonality and transport route on chemical characteristics of PM2.5-10 in the East Asian Pacific Rim region[J]. Aerosol Air Qual Res, 2017, 17(12): 2988–3005. doi: 10.4209/aaqr.2016.12.0569
    [7]
    Mohammed MOA, Song WW, Ma YL, et al. Distribution patterns, infiltration and health risk assessment of PM2.5-bound PAHs in indoor and outdoor air in cold zone[J]. Chemosphere, 2016, 155: 70–85. doi: 10.1016/j.chemosphere.2016.04.023
    [8]
    Cheng Z, Chen LJ, Li HH, et al. Characteristics and health risk assessment of heavy metals exposure via household dust from urban area in Chengdu, China[J]. Sci Total Environ, 2018, 619-620: 621–629. doi: 10.1016/j.scitotenv.2017.11.144
    [9]
    Cao C, Jiang WJ, Wang BY, et al. Inhalable microorganisms in Beijing's PM2.5 and PM10 pollutants during a severe smog event[J]. Environ Sci Technol, 2014, 48(3): 1499–1507. doi: 10.1021/es4048472
    [10]
    Wang YC, Fu Y, Wang C, et al. Dissimilar emission characteristics between bioaerosol and suspended particles from gaseous biofilters and bioaerosol health risk evaluation[J]. Aerosol Air Qual Res, 2018, 18(7): 1874–1885. doi: 10.4209/aaqr.2017.11.0485
    [11]
    Zeller M, Giroud M, Royer C, et al. Air pollution and cardiovascular and cerebrovascular disease: epidemiologic data[J]. Presse Med, 2006, 35: 1517–1522. doi: 10.1016/S0755-4982(06)74844-8
    [12]
    Leiva GMA, Santibañez DA, Ibarra ES, et al. A five-year study of particulate matter (PM2.5) and cerebrovascular diseases[J]. Environ Pollut, 2013, 181: 1–6. doi: 10.1016/j.envpol.2013.05.057
    [13]
    Yoo K, Lee TK, Choi EJ, et al. Molecular approaches for the detection and monitoring of microbial communities in bioaerosols: a review[J]. J Environ Sci, 2017, 51: 234–247. doi: 10.1016/j.jes.2016.07.002
    [14]
    Douwes J, Thorne P, Pearce N, et al. Bioaerosol health effects and exposure assessment: progress and prospects[J]. Ann Occup Hyg, 2003, 47(3): 187–200.
    [15]
    Li YP, Fu HL, Wang W, et al. Characteristics of bacterial and fungal aerosols during the autumn haze days in Xi'an, China[J]. Atmos Environ, 2015, 122: 439–447. doi: 10.1016/j.atmosenv.2015.09.070
    [16]
    Fang ZG, Yao WC, Lou XQ, et al. Profile and characteristics of culturable airborne bacteria in Hangzhou, southeast of China[J]. Aerosol Air Qual Res, 2016, 16(7): 1690–1700. doi: 10.4209/aaqr.2014.11.0274
    [17]
    Brodie EL, DeSantis TZ, Parker JPM, et al. Urban aerosols harbor diverse and dynamic bacterial populations[J]. Proc Natl Acad Sci USA, 2007, 104(1): 299–304. doi: 10.1073/pnas.0608255104
    [18]
    Azhar EI, Hashem AM, El-Kafrawy SA, et al. Detection of the middle east respiratory syndrome coronavirus genome in an air sample originating from a camel barn owned by an infected patient[J]. mBio, 2014, 5(4): e01450–14.
    [19]
    Magoč T, Salzberg SL. Flash: fast length adjustment of short reads to improve genome assemblies[J]. Bioinformatics, 2011, 27(21): 2957–2963. doi: 10.1093/bioinformatics/btr507
    [20]
    Grice EA, Kong HH, Conlan S, et al. Topographical and temporal diversity of the human skin microbiome[J]. Science, 2009, 324(5931): 1190–1192. doi: 10.1126/science.1171700
    [21]
    Caporaso JG, Kuczynski J, Stombaugh J, et al. QIIME allows analysis of high-throughput community sequencing data[J]. Nat Methods, 2010, 7(5): 335–336. doi: 10.1038/nmeth.f.303
    [22]
    Fitzpatrick F, Roche F, Cunney R, et al. Challenges of implementing national guidelines for the control and prevention of methicillin‐resistant Staphylococcus aureus colonization or infection in acute care hospitals in the republic of Ireland[J]. Infect Control Hosp Epidemiol, 2009, 30(3): 277–281. doi: 10.1086/595734
    [23]
    Gupta AK, Batra R, Bluhm R, et al. Skin diseases associated with Malassezia species[J]. J Am Acad Dermatol, 2004, 51(5): 785–798. doi: 10.1016/j.jaad.2003.12.034
    [24]
    Lee SA, Liao CH. Size-selective assessment of agricultural workers' personal exposure to airborne fungi and fungal fragments[J]. Sci Total Environ, 2014, 466-467: 725–732. doi: 10.1016/j.scitotenv.2013.07.104
    [25]
    Williams L, Ulrich CM, Larson T, et al. Fine particulate matter (PM2.5) air pollution and immune status among women in the Seattle area[J]. Arch Environ Occup Health, 2011, 66(3): 155–165. doi: 10.1080/19338244.2010.539636
    [26]
    Xie ZS, Fan CL, Lu R, et al. Characteristics of ambient bioaerosols during haze episodes in China: a review[J]. Environ Pollut, 2018, 243: 1930–1942. doi: 10.1016/j.envpol.2018.09.051
    [27]
    Khreis H, Kelly C, Tate J, et al. Exposure to traffic-related air pollution and risk of development of childhood asthma: a systematic review and meta-analysis[J]. Environ Int, 2017, 100: 1–31. doi: 10.1016/j.envint.2016.11.012
    [28]
    Kim SY, Peel JL, Hannigan MP, et al. The temporal lag structure of short-term associations of fine particulate matter chemical constituents and cardiovascular and respiratory hospitalizations[J]. Environ Health Perspect, 2012, 120(8): 1094–1099. doi: 10.1289/ehp.1104721
    [29]
    Shahsavan S, Jabalameli L, Maleknejad P, et al. Molecular analysis and antimicrobial susceptibility of methicillin resistant Staphylococcus aureus in one of the hospitals of Tehran university of medical sciences: high prevalence of sequence type 239 (ST239) clone[J]. Acta Microbiol Immunol Hung, 2011, 58(1): 31–39. doi: 10.1556/AMicr.58.2011.1.4
    [30]
    Bernard K. The genus Corynebacterium and other medically relevant coryneform-like bacteria[J]. J Clin Microbiol, 2012, 50(10): 3152–3158. doi: 10.1128/JCM.00796-12
    [31]
    Wozniak TM. Clinical management of drug-resistant bacteria in Australian hospitals: an online survey of doctors' opinions[J]. Infect Dis Health, 2018, 23(1): 41–48. doi: 10.1016/j.idh.2017.11.003
    [32]
    Longhin E, Holme JA, Gutzkow KB, et al. Cell cycle alterations induced by urban PM2.5 in bronchial epithelial cells: characterization of the process and possible mechanisms involved[J]. Part Fibre Toxicol, 2013, 10: 63. doi: 10.1186/1743-8977-10-63
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    1. Hou W, Guan F, Chen W, et al. Breastfeeding, genetic susceptibility, and the risk of asthma and allergic diseases in children and adolescents: a retrospective national population-based cohort study. BMC Public Health, 2024, 24(1): 3056. DOI:10.1186/s12889-024-20501-0
    2. Nandi S, Varotariya K, Luhana S, et al. GWAS for identification of genomic regions and candidate genes in vegetable crops. Funct Integr Genomics, 2024, 24(6): 203. DOI:10.1007/s10142-024-01477-x
    3. Sung HL, Lin WY. Causal effects of cardiovascular health on five epigenetic clocks. Clin Epigenetics, 2024, 16(1): 134. DOI:10.1186/s13148-024-01752-5
    4. Kang HY, Choe EK. Clinical Strategies in Gene Screening Counseling for the Healthy General Population. Korean J Fam Med, 2024, 45(2): 61-68. DOI:10.4082/kjfm.23.0254
    5. Lee SB, Choi JE, Hong KW, et al. Genetic Variants Linked to Myocardial Infarction in Individuals with Non-Alcoholic Fatty Liver Disease and Their Potential Interaction with Dietary Patterns. Nutrients, 2024, 16(5): 602. DOI:10.3390/nu16050602
    6. Zhang S, Jiang Z, Zeng P. Incorporating genetic similarity of auxiliary samples into eGene identification under the transfer learning framework. J Transl Med, 2024, 22(1): 258. DOI:10.1186/s12967-024-05053-6
    7. Seo H, Park JH, Hwang JT, et al. Epigenetic Profiling of Type 2 Diabetes Mellitus: An Epigenome-Wide Association Study of DNA Methylation in the Korean Genome and Epidemiology Study. Genes (Basel), 2023, 14(12): 2207. DOI:10.3390/genes14122207
    8. Han J, Zhang L, Yan R, et al. CoNet: Efficient Network Regression for Survival Analysis in Transcriptome-Wide Association Studies-With Applications to Studies of Breast Cancer. Genes (Basel), 2023, 14(3): 586. DOI:10.3390/genes14030586
    9. Padilla-Martinez F, Szczerbiński Ł, Citko A, et al. Testing the Utility of Polygenic Risk Scores for Type 2 Diabetes and Obesity in Predicting Metabolic Changes in a Prediabetic Population: An Observational Study. Int J Mol Sci, 2022, 23(24): 16081. DOI:10.3390/ijms232416081
    10. Muneeb M, Feng S, Henschel A. Transfer learning for genotype-phenotype prediction using deep learning models. BMC Bioinformatics, 2022, 23(1): 511. DOI:10.1186/s12859-022-05036-8
    11. Qiao J, Shao Z, Wu Y, et al. Detecting associated genes for complex traits shared across East Asian and European populations under the framework of composite null hypothesis testing. J Transl Med, 2022, 20(1): 424. DOI:10.1186/s12967-022-03637-8
    12. Shao Z, Wang T, Qiao J, et al. A comprehensive comparison of multilocus association methods with summary statistics in genome-wide association studies. BMC Bioinformatics, 2022, 23(1): 359. DOI:10.1186/s12859-022-04897-3
    13. Roh H. A genome-wide association study of the occurrence of genetic variations in Edwardsiella piscicida, Vibrio harveyi, and Streptococcus parauberis under stressed environments. J Fish Dis, 2022, 45(9): 1373-1388. DOI:10.1111/jfd.13668
    14. Zhang M, Qiao J, Zhang S, et al. Exploring the association between birthweight and breast cancer using summary statistics from a perspective of genetic correlation, mediation, and causality. J Transl Med, 2022, 20(1): 227. DOI:10.1186/s12967-022-03435-2
    15. Yamamoto A, Shibuya T. More practical differentially private publication of key statistics in GWAS. Bioinform Adv, 2021, 1(1): vbab004. DOI:10.1093/bioadv/vbab004
    16. Mkize N, Maiwashe A, Dzama K, et al. Suitability of GWAS as a Tool to Discover SNPs Associated with Tick Resistance in Cattle: A Review. Pathogens, 2021, 10(12): 1604. DOI:10.3390/pathogens10121604
    17. Lu H, Qiao J, Shao Z, et al. A comprehensive gene-centric pleiotropic association analysis for 14 psychiatric disorders with GWAS summary statistics. BMC Med, 2021, 19(1): 314. DOI:10.1186/s12916-021-02186-z
    18. Monnot S, Desaint H, Mary-Huard T, et al. Deciphering the Genetic Architecture of Plant Virus Resistance by GWAS, State of the Art and Potential Advances. Cells, 2021, 10(11): 3080. DOI:10.3390/cells10113080
    19. Lu H, Wei Y, Jiang Z, et al. Integrative eQTL-weighted hierarchical Cox models for SNP-set based time-to-event association studies. J Transl Med, 2021, 19(1): 418. DOI:10.1186/s12967-021-03090-z
    20. Gao Y, Zhang J, Zhao H, et al. Instrumental Heterogeneity in Sex-Specific Two-Sample Mendelian Randomization: Empirical Results From the Relationship Between Anthropometric Traits and Breast/Prostate Cancer. Front Genet, 2021, 12: 651332. DOI:10.3389/fgene.2021.651332
    21. Petersen KS, Kris-Etherton PM, McCabe GP, et al. Perspective: Planning and Conducting Statistical Analyses for Human Nutrition Randomized Controlled Trials: Ensuring Data Quality and Integrity. Adv Nutr, 2021, 12(5): 1610-1624. DOI:10.1093/advances/nmab045
    22. Muneeb M, Henschel A. Eye-color and Type-2 diabetes phenotype prediction from genotype data using deep learning methods. BMC Bioinformatics, 2021, 22(1): 198. DOI:10.1186/s12859-021-04077-9
    23. O'Rielly DD, Rahman P. Genetic Epidemiology of Complex Phenotypes. Methods Mol Biol, 2021, 2249: 335-367. DOI:10.1007/978-1-0716-1138-8_19
    24. Scossa F, Fernie AR. Ancestral sequence reconstruction - An underused approach to understand the evolution of gene function in plants?. Comput Struct Biotechnol J, 2021, 19: 1579-1594. DOI:10.1016/j.csbj.2021.03.008
    25. Lu H, Zhang J, Jiang Z, et al. Detection of Genetic Overlap Between Rheumatoid Arthritis and Systemic Lupus Erythematosus Using GWAS Summary Statistics. Front Genet, 2021, 12: 656545. DOI:10.3389/fgene.2021.656545
    26. McGuire D, Jiang Y, Liu M, et al. Model-based assessment of replicability for genome-wide association meta-analysis. Nat Commun, 2021, 12(1): 1964. DOI:10.1038/s41467-021-21226-z
    27. Dennis JK, Sealock JM, Straub P, et al. Clinical laboratory test-wide association scan of polygenic scores identifies biomarkers of complex disease. Genome Med, 2021, 13(1): 6. DOI:10.1186/s13073-020-00820-8
    28. Ramanan VK, Wang X, Przybelski SA, et al. Variants in PPP2R2B and IGF2BP3 are associated with higher tau deposition. Brain Commun, 2020, 2(2): fcaa159. DOI:10.1093/braincomms/fcaa159
    29. Chen H, Wang T, Yang J, et al. Improved Detection of Potentially Pleiotropic Genes in Coronary Artery Disease and Chronic Kidney Disease Using GWAS Summary Statistics. Front Genet, 2020, 11: 592461. DOI:10.3389/fgene.2020.592461
    30. Xiao L, Yuan Z, Jin S, et al. Multiple-Tissue Integrative Transcriptome-Wide Association Studies Discovered New Genes Associated With Amyotrophic Lateral Sclerosis. Front Genet, 2020, 11: 587243. DOI:10.3389/fgene.2020.587243
    31. Jin T, Youn J, Kim AN, et al. Interactions of Habitual Coffee Consumption by Genetic Polymorphisms with the Risk of Prediabetes and Type 2 Diabetes Combined. Nutrients, 2020, 12(8): 2228. DOI:10.3390/nu12082228
    32. Kuo TT, Jiang X, Tang H, et al. iDASH secure genome analysis competition 2018: blockchain genomic data access logging, homomorphic encryption on GWAS, and DNA segment searching. BMC Med Genomics, 2020, 13(Suppl 7): 98. DOI:10.1186/s12920-020-0715-0
    33. Padilla-Martínez F, Collin F, Kwasniewski M, et al. Systematic Review of Polygenic Risk Scores for Type 1 and Type 2 Diabetes. Int J Mol Sci, 2020, 21(5): 1703. DOI:10.3390/ijms21051703
    34. Lan T, Yang B, Zhang X, et al. Statistical Methods and Software for Substance Use and Dependence Genetic Research. Curr Genomics, 2019, 20(3): 172-183. DOI:10.2174/1389202920666190617094930
    35. Gaudillo J, Rodriguez JJR, Nazareno A, et al. Machine learning approach to single nucleotide polymorphism-based asthma prediction. PLoS One, 2019, 14(12): e0225574. DOI:10.1371/journal.pone.0225574
    36. Romagnoni A, Jégou S, Van Steen K, et al. Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data. Sci Rep, 2019, 9(1): 10351. DOI:10.1038/s41598-019-46649-z
    37. Himmerich H, Bentley J, Kan C, et al. Genetic risk factors for eating disorders: an update and insights into pathophysiology. Ther Adv Psychopharmacol, 2019, 9: 2045125318814734. DOI:10.1177/2045125318814734
    38. Sanyal N, Lo MT, Kauppi K, et al. GWASinlps: non-local prior based iterative SNP selection tool for genome-wide association studies. Bioinformatics, 2019, 35(1): 1-11. DOI:10.1093/bioinformatics/bty472
    39. Brinster R, Köttgen A, Tayo BO, et al. Control procedures and estimators of the false discovery rate and their application in low-dimensional settings: an empirical investigation. BMC Bioinformatics, 2018, 19(1): 78. DOI:10.1186/s12859-018-2081-x
    40. Zeng P, Wang T, Huang S. Cis-SNPs Set Testing and PrediXcan Analysis for Gene Expression Data using Linear Mixed Models. Sci Rep, 2017, 7(1): 15237. DOI:10.1038/s41598-017-15055-8
    41. Zeng P, Zhou X, Huang S. Prediction of gene expression with cis-SNPs using mixed models and regularization methods. BMC Genomics, 2017, 18(1): 368. DOI:10.1186/s12864-017-3759-6
    42. Läll K, Mägi R, Morris A, et al. Personalized risk prediction for type 2 diabetes: the potential of genetic risk scores. Genet Med, 2017, 19(3): 322-329. DOI:10.1038/gim.2016.103
    43. Umehara H, Numata S, Tajima A, et al. Calcium Signaling Pathway Is Associated with the Long-Term Clinical Response to Selective Serotonin Reuptake Inhibitors (SSRI) and SSRI with Antipsychotics in Patients with Obsessive-Compulsive Disorder. PLoS One, 2016, 11(6): e0157232. DOI:10.1371/journal.pone.0157232
    44. Zhang Q, Zhao Y, Zhang R, et al. A Comparative Study of Five Association Tests Based on CpG Set for Epigenome-Wide Association Studies. PLoS One, 2016, 11(6): e0156895. DOI:10.1371/journal.pone.0156895
    45. Gasc C, Peyretaillade E, Peyret P. Sequence capture by hybridization to explore modern and ancient genomic diversity in model and nonmodel organisms. Nucleic Acids Res, 2016, 44(10): 4504-18. DOI:10.1093/nar/gkw309

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