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The indoor air microbiome in
        classrooms
     DeninaHospodsky, Naomichi Yamamoto, Kyle
  Bibby, Dana Miller, SisiraGorthala, William Nazaroff,
                     Jordan Peccia

             Yale University, UC Berkeley
Background

 How does the human occupant shape the indoor
  air microbiome?
 Classrooms
     Defined times of occupancy and vacancy
     Constant numbers of occupants
     Public places
     Children, a susceptible population, spend
      considerable amount of time in classrooms
Goals: Toidentifysources and calculate emission
          rates of indoor (bio)aerosols
 Objectives:
    To combine biomolecular techniques and particle modeling to
     assess the influence of the human occupant on shaping the
     indoor air microbiome
    To describe and model these two processes for biological and
     total particles
    To identify sources of bacteria and fungi in indoor air using
     phylogentic information
    Understand how building design and operation influences the
     indoor air microbiome                               shedding



                          resuspension

                                                             resuspension
Eleven sampling sites- classrooms, 3-4 days of continuous occupied
                   and unoccupied sampling each

                       Arhus
                       Berlin
                       Lyngby



New Haven
                                                Lanzhou 1
Salinas
                                                Lanzhou 2
Study design

 Floor dust as well as indoor and outdoor air collection
  during times of occupancy and vacancy using
  multistage Andersen cascade impactor in eleven
  classrooms
 Quantitative data from qPCR for all samples
    Bacterial and fungal emission rate calculations
 Fungal ITS region from (almost) all air and dust samples
 Bacterial 16S region for floor dust and occupied air
  samples from eleven sampling sites
OccIn   Occ out   UO in UO out   Floor     Floor   Floor   Floor
    Location          (Air)    (Air)    (Air)  (Air)     1         2       3       4

     Salinas
  New Haven 1
  New Haven 2
  New Haven 3
     Arhus
     Berlin
  Copenhagen
Lanzhou 1 summer
Lanzhou 2 winter
Lanzhou 1 summer
Lanzhou 2 winter

                                                 Bacteria 16S
     Fungi ITS only      Bacteria 16S only                                 No sample
                                                 and Fungi ITS
Study design “metadata”

 Air temperature (minute)
 Humidity of air (minute)
 Particle mass and particle number concentration (all time averaged
  and per minute)
 Bacterial and fungal number concentrations in air and on dust (all
  time averaged)
 # of occupants and their activity rate (minute and time averaged
  per day)
 CO2 concentration and air exchange rate (minute and time averaged
  per day)
 Particle, bacterial, and fungal emission rate (all time averaged)


                             Time scale
1min        1hour         1day      2days         3days         4days
Data and metadata sharing

 Previous sequencing data is available on MG
  RAST (and via MG RAST on MoBeDac)
   metadata documentedin SI or text of publications.
 New sequencing data will be submitted to
  Qiime DB and MG RAST including metadata
 No human subjects restrictions
Sequence data analysis

                                                  Study needs:
                                                   Easy access to
                                                    sequence data
                                                    from other
                                                    groups
                                                   Toolsets that
                                                    allow graphical
                                                    and analytical
                                                    comparison of
                                                    samples from
                                                    our and other
                                                    groups

Costello et al, Science 2009
Lauber et al, AEM 2009
What do you hope to get out of the QIIME/VAMPS workshop?

1. Data deposition
      Clear guidelines about where to deposit sequencing data when
       publication date is nearing (MoBeDac? QIIME DB? MG RAST?)
      Convenient data deposition with and without “metadata”
       templates
2. Share sequence data with others
    Easy access to published sequencing data of other groups,
     especially within Sloan
    Access to raw data and processed data
    Clear communication on how raw sequence data was processed
3. Effective data pooling when sequence regions do not
   overlap
    Alignment tools for 16S and ITS region

 Enhanced tools for ITS sequences
Thank you!

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Peccia qiime vamps_workshop

  • 1. The indoor air microbiome in classrooms DeninaHospodsky, Naomichi Yamamoto, Kyle Bibby, Dana Miller, SisiraGorthala, William Nazaroff, Jordan Peccia Yale University, UC Berkeley
  • 2. Background  How does the human occupant shape the indoor air microbiome?  Classrooms  Defined times of occupancy and vacancy  Constant numbers of occupants  Public places  Children, a susceptible population, spend considerable amount of time in classrooms
  • 3. Goals: Toidentifysources and calculate emission rates of indoor (bio)aerosols  Objectives:  To combine biomolecular techniques and particle modeling to assess the influence of the human occupant on shaping the indoor air microbiome  To describe and model these two processes for biological and total particles  To identify sources of bacteria and fungi in indoor air using phylogentic information  Understand how building design and operation influences the indoor air microbiome shedding resuspension resuspension
  • 4. Eleven sampling sites- classrooms, 3-4 days of continuous occupied and unoccupied sampling each Arhus Berlin Lyngby New Haven Lanzhou 1 Salinas Lanzhou 2
  • 5. Study design  Floor dust as well as indoor and outdoor air collection during times of occupancy and vacancy using multistage Andersen cascade impactor in eleven classrooms  Quantitative data from qPCR for all samples  Bacterial and fungal emission rate calculations  Fungal ITS region from (almost) all air and dust samples  Bacterial 16S region for floor dust and occupied air samples from eleven sampling sites
  • 6. OccIn Occ out UO in UO out Floor Floor Floor Floor Location (Air) (Air) (Air) (Air) 1 2 3 4 Salinas New Haven 1 New Haven 2 New Haven 3 Arhus Berlin Copenhagen Lanzhou 1 summer Lanzhou 2 winter Lanzhou 1 summer Lanzhou 2 winter Bacteria 16S Fungi ITS only Bacteria 16S only No sample and Fungi ITS
  • 7. Study design “metadata”  Air temperature (minute)  Humidity of air (minute)  Particle mass and particle number concentration (all time averaged and per minute)  Bacterial and fungal number concentrations in air and on dust (all time averaged)  # of occupants and their activity rate (minute and time averaged per day)  CO2 concentration and air exchange rate (minute and time averaged per day)  Particle, bacterial, and fungal emission rate (all time averaged) Time scale 1min 1hour 1day 2days 3days 4days
  • 8. Data and metadata sharing  Previous sequencing data is available on MG RAST (and via MG RAST on MoBeDac)  metadata documentedin SI or text of publications.  New sequencing data will be submitted to Qiime DB and MG RAST including metadata  No human subjects restrictions
  • 9. Sequence data analysis Study needs:  Easy access to sequence data from other groups  Toolsets that allow graphical and analytical comparison of samples from our and other groups Costello et al, Science 2009 Lauber et al, AEM 2009
  • 10. What do you hope to get out of the QIIME/VAMPS workshop? 1. Data deposition  Clear guidelines about where to deposit sequencing data when publication date is nearing (MoBeDac? QIIME DB? MG RAST?)  Convenient data deposition with and without “metadata” templates 2. Share sequence data with others  Easy access to published sequencing data of other groups, especially within Sloan  Access to raw data and processed data  Clear communication on how raw sequence data was processed 3. Effective data pooling when sequence regions do not overlap  Alignment tools for 16S and ITS region  Enhanced tools for ITS sequences

Editor's Notes

  1. Slight change in title. We are not working on microbes and fungi in the indoor environment, instead the working title for today is THE INDOOR AIR MICROBIOME IN CLASSROOMS
  2. ~200 air and dust libraries for fungi~120 air and dust libraries for bacteria
  3. What to do with the dataHow to get access to other pplssequencesHow to use alignment tools for ITS and 16STools for ITS beta diversity