Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Clouds in a crowd: deciphering individual contributions to the human microbial cloud (ISME16 poster)

222 views

Published on

Presented by Roxana Hickey (Postdoctoral Scholar, University of Oregon) at the 16th International Symposium on Microbial Ecology (#ISME16) in Montreal, Quebec, Canada on August 21-26, 2016

Published in: Science
  • Be the first to comment

  • Be the first to like this

Clouds in a crowd: deciphering individual contributions to the human microbial cloud (ISME16 poster)

  1. 1. BACKGROUND METHODS RESULTS SUMMARY & NEXT STEPS @ROXANA_HICKEY CLOUDS IN A CROWD: DECIPHERING INDIVIDUAL CONTRIBUTIONS TO THE HUMAN MICROBIAL CLOUD ROXANA HICKEY, JAMES MEADOW, ASHLEY BATEMAN, CLARISSE BETANCOURT-ROMAN & JESSICA GREEN1 1 1 1,22 [1] BIOLOGY & THE BUILT ENVIRONMENT CENTER, UNIVERSITY OF OREGON, EUGENE, OR; [2] PHYLAGEN, SAN FRANCISCO, CA ● ● ● ● ● ● ● ● ●● ●● Air filters (a) NMDS 1 NMDS2 ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● Settling dishes (c) NMDS 1 NMDS2 ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● occupied unoccupied Subject 1 Subject 2 Subject 3 (b) ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● (d) (e) 0.03 0.04 0.05 0.06 CanberraSimilarity self other self other self other All Samples Air Filters Settling Dishes Figure 1. Occupied and unoccupied rooms are significantly different in bioaerosols collected on air filters (a, b) and settled dust (c, d) during a 4-hour sampling period. Occupant microbial clouds were more similar to other samples from the same person than to other occupants, regardless of sampling method. (Previously published in Meadow et al. PeerJ 2015; DOI: 10.7717/peerj.1258) The human microbiome is highly individualized, with each person harboring a unique assortment of microbes at various sites throughout the body. This individuality appears to extend to the microbial cloud of bacteria-laden particles emitted from a person while inhabiting a room, even after a relatively short period of occupancy (Meadow et al. PeerJ 2015). The ability to identify individuals from bioaerosols has potential forensic applications and, more fundamentally, can help us understand the dispersal of human-associated microbes in the built environment. We performed an experiment at the crowd-scale to characterize the composition and spatial organization of individuals’ contributions to the microbial cloud under variable occupancy and ventilation. a b c Figure 2. Experimental setup in the Energy Studies in Buildings Laboratory in Portland, OR. (a) The Climate Chamber is an experimental room with radiant heating panels and customizable ventilation system. (b) L: three occupants sat in chairs spaced equidistantly across the room while bioaerosols were collected onto 0.2µm filters for 90 min. Each occupant sat for a solo run as well as a group run. R: air samples were collected in the supply and exhaust air ducts. (c) Schematic diagrams from the top (L) and side (R) of the chamber. Photographs and diagrams courtesy of ESBL. S01 S02 S03 S01 S02 S03 Solo run: 90 min @ 1 ACH Group run: 90 min @ 1 ACH Skin: pre/post run, nylon swabs Air: c. 24.5 L/min, 0.2µm filters Sample Collection 3 x 3 = 9 subjects, 3 groups Figure 4. Heatmap of top 100 OTUs across a subset of skin and air samples. A subset of samples from Group B (subjects S04, S05, S06; both solo and group runs) are shown here and organized by sample type. OTU read counts were normalized using variance stabilizing transformation and are labeled according to the genus assigned by the RDP Classifier. skin pre supply chamber (box) exhaust skin post Figure 3.Principal coordinates analysis of skin and airborne microbial communities in three groups of occupants. (a) PCoA (Bray-Curtis distance) on 69 skin and 203 air microbial communities (supply, chamber and exhaust) from 9 solo-occupant runs and 3 group- occupant runs. OTU read counts were normalized using variance stabilizing transformation in the DESeq2 package (Love et al. Genome Biology 2014). (b) PCoA (Bray-Curtis) on only skin samples. (c) PCoA (Bray-Curtis) on only air samples. (d) PCoA from part (a) split into three co-occupant groups. Each panel features a subset of samples from co-occupants on the same ordination space. a b c d Reported results are preliminary and analyses are actively ongoing. Next steps will utilize approaches such as oligotyping (Murat Eren, merenlab.org) and metagenomic codes (Franzosa et al. PNAS 2015; http://huttenhower.sph.harvard.edu/idability) to trace bacterial strains sourced from individuals' skin microbiome to bioaerosols captured in the chamber and exhaust air. Additionally, we have data to determine whether higher ventilation rates under realistic indoor conditions (3, 10 and 20 air changes per hour) dilutes the microbial cloud signal such that bioaerosolized bacteria cannot be detected or distinguished on an individual basis. Sample & Data Processing Extract DNA (MoBio PL/PS) Amplify 16S rRNA V4 (515f / 806r) Generate sequences (NextSeq PE 150) Quality filter, cluster OTUs, classify (Flash, USEARCH, RDP) Analyze data (phyloseq, DESeq2, vegan) Preliminary observations reveal clear community dissimilarity between skin and air, but no obvious patterns emerge for supply vs. occupied air or solo vs. group runs. Additional analyses are needed and will rely on finer-resolution techniques and statistical approaches to assess whether individuals can be distinguished by specific components of the microbial cloud.

×