Lecture 9:

EVE 161:

Microbial Phylogenomics
!

Lecture #9:
Era II: rRNA Case Study
!
UC Davis, Winter 2014
Instructor: J...
Where we are going and where we have been

• Previous lecture:
! 8: Era II: rRNA ecology
• Current Lecture:
! 9: rRNA Case...
Where we are going and where we have been

• Previous lecture:
! 8: Era II: rRNA ecology
• Current Lecture:
! 9: rRNA Case...
Microbial Ecology of the Built Environment
• New Sloan Foundation Program
• Culture independent microbial studies linked t...
Why Care?
• Humans spend most of their time in built environments
• Most microbial ecology studies have focused on natural...
organization of spaces from a classroom and office building to understand how design choices influence the biogeography
of...
Methods

Study Location

Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
Biogeography of Indoor Bacterial Communities

Figure 1. Architectural layout for two of four floors in Lillis Hall. Restro...
Methods
Study Location
We analyzed bacterial communities in dust collected from 155 spaces in the Lillis Hall, a four-stor...
Architectural Design Data

Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
Architectural Design Data
Data on architectural design attributes of each space including function, form, and organization...
Architectural Design Data
Data on architectural design attributes of each space including function, form, and organization...
From a study design perspective, diverse space types, occupancy
levels, and building management strategies were located in...
Human use patterns are a product of functional classification, but they also dictate form and organizational attributes of...
Biological Sampling
Sampling of dust was carried out with a Shop-Vac® 9.4L Hang Up vacuum (www.shopvac.com; #215726) fitte...
• Then a semi-standard rRNA PCR workflow

Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
Biogeography of Indoor Bacterial Communities

Building-scale Design Influences on the Built Environment Microbiome

Figure...
Building-scale Design Influences on the Built Environment Microbiome

Biogeography of Indoor Bacterial Communi

Table 1. V...
Biogeography of Indoor Bacterial Communities

Figure 4. Dust communities within a building cluster by space type and are s...
Design Influences on the Built Environment Microbiome within a Space Type

The large number of office spaces (73 offices) ...
Biogeography of Indoor Bacterial Communities

Design Influences on the Built Environment Microbiome within a Space Type

F...
Biogeogr

P = 0.002; from a Mantel tes
connectance distance). This as
building scale, regardless of sp

Discussion

In thi...
SFig1. High degree of correlation between three beta-diversity metrics. Multivariate community analysis was carried
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Conclusion
Churchill famously stated that “[w]e shape our buildings, and afterwards our buildings shape us.” Humans help t...
Brooks et al. Microbiome 2014, 2:1
http://www.microbiomejournal.com/content/2/1/1

RESEARCH

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Methods

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Brooks et al. Microbiome 2014, 2:1
http://www.microbiomejournal.com/content/2/1/1

Page 5 of 16

Table 2 Sample collection...
Results

Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
Stability

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lassifier [43] at a confidence
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Brooks et al. Microbiome 2014, 2:1
http://www.microbiomejournal.com/content/2/1/1

Page 6 of 16

Time Series of Rooms

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Time Series of Rooms

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3 6 9 12 15 18 21 24 27 30

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Figure 2 Principle coordina...
Figure 2 Principle coordinates analysis (PCoA) based on UniFrac scores of room and gut microbes. Analysis reveals four dis...
Genomes

http://www.microbiomejournal.com/content/2/1/1

Table 4 Genome summaries
Taxa

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bp

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Brooks et al. Microbiome 2014, 2:1
http://www.microbiomejournal.com/content/2/1/1

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Page 9 of 16

Fecal Sinks Tubes Ha...
The NICU as a reservoir for gut colonists

Figure 5 summarizes the gut colonizing organisms found
in room samples at the g...
To further explore similarity of shared strains, reads

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UC Davis EVE161 Lecture 9 by @phylogenomics

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UC Davis EVE161 Lecture 9 by @phylogenomics

  1. 1. Lecture 9: EVE 161:
 Microbial Phylogenomics ! Lecture #9: Era II: rRNA Case Study ! UC Davis, Winter 2014 Instructor: Jonathan Eisen Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 !1
  2. 2. Where we are going and where we have been • Previous lecture: ! 8: Era II: rRNA ecology • Current Lecture: ! 9: rRNA Case Study - Built Environment • Next Lecture: ! 10: Genome Sequencing Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 !2
  3. 3. Where we are going and where we have been • Previous lecture: ! 8: Era II: rRNA ecology • Current Lecture: ! 9: rRNA Case Study - Built Environment • Next Lecture: ! 10: Genome Sequencing Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 !3
  4. 4. Microbial Ecology of the Built Environment • New Sloan Foundation Program • Culture independent microbial studies linked to building science • Many facilities being looked at including schools, homes, hospitals, offices, planes, cars • More information at http://microBE.net ! Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
  5. 5. Why Care? • Humans spend most of their time in built environments • Most microbial ecology studies have focused on natural environments • Building design being governed by esthetic and engineering aspects and some health aspects but generally little microbiology taken into account • Likely an important source of microbiomes of humans and other organisms in the built environment Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
  6. 6. organization of spaces from a classroom and office building to understand how design choices influence the biogeography of the built environment microbiome. Results: Sequencing of the bacterial 16S gene from dust samples revealed that indoor bacterial communities were extremely diverse, containing more than 32,750 OTUs (operational taxonomic units, 97% sequence similarity cutoff), but most communities were dominated by Proteobacteria, Firmicutes, and Deinococci. Architectural design characteristics related to space type, building arrangement, human use and movement, and ventilation source had a large influence on the structure of bacterial communities. Restrooms contained bacterial communities that were highly distinct from all other rooms, and spaces with high human occupant diversity and a high degree of connectedness to other spaces via ventilation or human movement contained a distinct set of bacterial taxa when compared to spaces with low occupant diversity and low connectedness. Within offices, the source of ventilation air had the greatest effect on bacterial community structure. Architectural Design Drives the Biogeography of Indoor Bacterial Communities Conclusions: Our study indicates that humans have a guiding impact on the microbial biodiversity in buildings, both Steven W. Kembel1,2,3., James F. Meadow2,3*., Timothy K. O’Connor2,3,4, Gwynne Mhuireach2,5, indirectly through the effects of architectural design on microbial community structure, and more directly through the 2,5 2,3 Dale Northcutt2,5, Jeff Kline2,5, Maxwell on the microbes G. Z. Brown2,5,6,spaces and space types. The impact of effects of human occupancy and use patterns Moriyama , found in different Brendan J. M. Bohannan , design L. Green2,3,7 Jessica decisions in structuring the indoor microbiome offers the possibility to use ecological knowledge to shape our buildings in a way that will select for an indoor microbiome that promotes our health and well-being. ´partement des sciences biologiques, Universite du Que ´ ´bec a Montre Montre Que ` ´al, ´al, ´bec, Canada, 2 Biology and the Built Environment Center, University of Oregon, 1 De Eugene, Oregon, United States of America, 3 Institute of Ecology and Evolution, University of Oregon, Eugene, Oregon, United States of America, 4 Department of Ecology Citation: Kembel Biology, University O’Connor TK, Mhuireach G, United States of America, 5 Architectural Design DrivesLaboratory, University of Oregon, Eugene, and Evolutionary SW, Meadow JF, of Arizona, Tucson, Arizona, Northcutt D, et al. (2014) Energy Studies in Buildings the Biogeography of Indoor Bacterial Communities. PLoS ONE 9(1): e87093. doi:10.1371/journal.pone.0087093 of Oregon, Eugene, Oregon, United States of America, 7 Santa Fe Institute, Santa Fe, New Oregon, United States of America, 6 Department of Architecture, University Mexico, United States of America Editor: Bryan A. White, University of Illinois, United States of America Received July 18, 2013; Accepted December 18, 2013; Published January 29, 2014 Copyright: ß 2014 Kembel et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits Abstract unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This researchArchitectural adesign the Biology potential to influence the from the Alfred P. Sloan Foundationenvironment, with Background: was funded by grant to has the and the Built Environment Center microbiology of the built Microbiology for the Built Environment Program (http://www.sloan.org/major-program-areas/basic-research/microbiology-of-the-built-environment/). The funders had no role in study implications for human health and well-being, but the impact of design on the microbial biogeography of buildings remains design, data collection and analysis, decision to publish, or preparation of the manuscript. poorly understood. In this study we combined microbiological data with information on the function, form, and how design choices influence the biogeography of the built environment microbiome. * E-mail: jfmeadow@gmail.com Competing Interests: The authors have declared thatandcompeting interests exist. organization of spaces from a classroom no office building to understand . These authors contributed equally to this work. Results: Sequencing of the bacterial 16S gene from dust samples revealed that indoor bacterial communities were extremely diverse, containing more than 32,750 OTUs (operational taxonomic units, 97% sequence similarity cutoff), but most communities were dominated by Proteobacteria, Firmicutes, and Deinococci. Architectural design characteristics built environment microbiome (the microbial communities related to Introduction space type, building arrangement, human use and movement, and ventilation source had a large influence on the within buildings) [6]. Third, evidence is growing that the microbes structure of bacterial communities. Restrooms contained bacterial communities that were highly distinct from all other living Biologists and designerswith high human occupant diversity and a highand on people, the human microbiome, playventilation role in rooms, and spaces are beginning to collaborate in a new in degree of connectedness to other spaces via a critical field focused on the microbiology of the built environment [1,2].taxa when compared to well-being [7–9].occupant diversity and or human movement contained a distinct set of bacterial human health and spaces with low Together, these observations low connectedness. Within offices, the source of ecology had the greatest be possible to influence the human microbiome These collaborations, which integrate perspectives fromventilation airsuggest that it mayeffect on bacterial community structure. and evolution, architecture, engineering and building science, are and ultimately human health, by modifying the built environment the Eisen Winter 2014 driven Conclusions: Our study indicates that humansFirst, it guiding impact on through architectural design. buildings, both by a number of Slides for UC Davis EVE161 Course Taught by Jonathanmicrobial biodiversity in interrelated observations. have a is microbiome
  7. 7. Methods Study Location Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
  8. 8. Biogeography of Indoor Bacterial Communities Figure 1. Architectural layout for two of four floors in Lillis Hall. Restrooms (brown), offices (blue) and classrooms (yellow) are shown to illustrate space type distribution throughout Lillis. The first two floors of the building are primarily devoted to classrooms and share a similar floorplan. The 3rd and 4th floors contain most offices in the building and also share a similar floor-plan. The building has a basement and penthouse spaces; these are largely building support spaces, including mechanical rooms and storage. doi:10.1371/journal.pone.0087093.g001 Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 environmental selection, dispersal, diversification, and ecological The biological processes described above can be fundamentally
  9. 9. Methods Study Location We analyzed bacterial communities in dust collected from 155 spaces in the Lillis Hall, a four-story classroom and office building on the University of Oregon campus in Eugene, Oregon, USA. This building was chosen as a study site for several reasons. Architecturally, Lillis Hall was designed to accommodate natural ventilation for both fresh air and cooling; the building is thin, allowing most rooms access to the building skin for supplying outside air directly through windows and louvers, and it has a central atrium used for exhausting air through stack ventilation. From a study design perspective, diverse space types, occupancy levels, and building management strategies were located in close proximity within the same building, making it possible to compare their relative influences on indoor biogeography. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
  10. 10. Architectural Design Data Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
  11. 11. Architectural Design Data Data on architectural design attributes of each space including function, form, and organization were obtained using architectural plans, field observation, and a building information model (Fig. 1). Spaces in the building were classified into one of seven space types. This classification system was developed for the present study based on the Oregon University System’s space type codes and definitions [40]. These categories are based on the overall architectural design and intended human use pattern for each space, and include circulation (e.g. hallways, atria), classrooms, classroom support (e.g. reading and practice rooms), offices, office support (e.g. most storage spaces, conference rooms), building support (e.g. mechanical equipment rooms, janitor closets), and restrooms. We measured numerous spatial and architectural attributes of each space including level (floor), wing (east versus west), size (net floor area), air handling unit (AHU) (13 different AHUs supply air to different rooms, so AHU is a categorical variable with 15 levels, one for each AHU as well as a ‘none’ category for rooms without mechanically supplied air, and a ‘multiple’ category for circulation spaces fed by multiple supply sources), and a separate binary variable denoting whether the space was only capable of being naturally ventilated by unfiltered outside air (e.g. via windows or louvers; 41 rooms) or by dedicated mechanical AHU supply (114 rooms). Metrics related to form and organization were quantified using network analysis (Fig. 2) and information from building construction drawings. Spaces were considered to be spatially connected if they shared a doorway or other physical connection that would permit a person to move directly between the two spaces. The network of spatial connections among spaces was used to calculate two measures of network centrality [22], [41] for each space in the building: betweenness, a measure of the fraction of shortest paths among all spaces in the building that would pass through a space, and degree, the number of connections a space has to other spaces. The network of spatial connections between spaces was also used to define a connectance distance between all pairs of spaces in the building, defined as the minimum number of spaces a person would need to travel through to move between two spaces. We considered using ventilation-based distance (how much duct length separates two connected spaces) as a connectance distance, however preliminary investigation indicated that connectance distance and ventilation distance were strongly correlated. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
  12. 12. Architectural Design Data Data on architectural design attributes of each space including function, form, and organization were obtained using architectural plans, field observation, and a building information model (Fig. 1). Spaces in the building were classified into one of seven space types. This classification system was developed for the present study based on the Oregon University System’s space type codes and definitions [40]. These categories are based on the overall architectural design and intended human use pattern for each space, and include circulation (e.g. hallways, atria), classrooms, classroom support (e.g. reading and practice rooms), offices, office support (e.g. most storage spaces, conference rooms), building support (e.g. mechanical equipment rooms, janitor closets), and restrooms. We measured numerous spatial and architectural attributes of each space including level (floor), wing (east versus west), size (net floor area), air handling unit (AHU) (13 different AHUs supply air to different rooms, so AHU is a categorical variable with 15 levels, one for each AHU as well as a ‘none’ category for rooms without mechanically supplied air, and a ‘multiple’ category for circulation spaces fed by multiple supply sources), and a separate binary variable denoting whether the space was only capable of being naturally ventilated by unfiltered outside air (e.g. via windows or louvers; 41 rooms) or by dedicated mechanical AHU supply (114 rooms). Metrics related to form and organization were quantified using network analysis (Fig. 2) and information from building construction drawings. Spaces were considered to be spatially connected if they shared a doorway or other physical connection that would permit a person to move directly between the two spaces. The network of spatial connections among spaces was used to calculate two measures of network centrality [22], [41] for each space in the building: betweenness, a measure of the fraction of shortest paths among all spaces in the building that would pass through a space, and degree, the number of connections a space has to other spaces. The network of spatial connections between spaces was also used to define a connectance distance between all pairs of spaces in the building, defined as the minimum number of spaces a person would need to travel through to move between two spaces. We considered using ventilation-based distance (how much duct length separates two connected spaces) as a connectance distance, however preliminary investigation indicated that connectance distance and ventilation distance were strongly correlated. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
  13. 13. From a study design perspective, diverse space types, occupancy levels, and building management strategies were located in close proximity within the same building, making it possible to compare their relative influences on indoor biogeography. spaces fed by multiple supply sources), and a separate binary variable denoting whether the space was only capable of being naturally ventilated by unfiltered outside air (e.g. via windows or louvers; 41 rooms) or by dedicated mechanical AHU supply (114 rooms). Figure 2. Network analysis metrics used to quantify spatial arrangement of spaces within Lillis Hall. Examples in the left column follow classic network representation, while those in the right column embody the architectural translation of networks. Shaded nodes and building spaces correspond to centrality measures [22] of betweenness (the number of shortest paths between all pairs of spaces that pass through a given space over the sum of all shortest paths between all pairs of spaces in the building) and degree (the number of connections a space has to other spaces); connectance distance (the number of doors between any two spaces) is a pairwise metric, shown here as the range of connectance distance values for each complete network/building. Since betweenness and degree strongly co-vary and are both measures of network centrality [22], they are considered together in some analyses. doi:10.1371/journal.pone.0087093.g002 PLOS ONE | www.plosone.org 3 January 2014 | Volume 9 | Issue 1 | e87093 Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
  14. 14. Human use patterns are a product of functional classification, but they also dictate form and organizational attributes of building design. In this study, human use patterns for each space were estimated based on a qualitative assessment of the expected patterns of human diversity and annual occupied hours in each space. Briefly, human diversity was defined on a three-point scale, ranging from low human diversity (spaces likely to be occupied by at most a single individual during a typical day; e.g. a closet) to high human diversity (spaces likely to be occupied by numerous different individuals during a typical day; e.g. a hallway). Annual occupied hours (person-hours per year) were similarly defined along a three-point scale from low (spaces that are typically vacant or occupied at low density; e.g. a mechanical support space) to high (spaces that are frequently occupied at relatively high density; e.g. administrative offices). Both of these human occupancy variables are explained in more detail in Table S1. At the time of microbial community sampling, ambient air temperature and relative humidity measurements were taken from each space. Relative humidity measurements were detrended using daily mean values to account for temporal changes over the sampling period. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
  15. 15. Biological Sampling Sampling of dust was carried out with a Shop-Vac® 9.4L Hang Up vacuum (www.shopvac.com; #215726) fitted with a Dustream™ Collector vacuum filter sampling device (www.inbio.com/dustream.html). Dust samples were collected by vacuuming an area of approximately 2m2 on horizontal surfaces above head level for 2 minutes in each space. We preferentially chose these surfaces for sampling since they minimized the frequency of disturbance by cleaning, and thus likely serve as a long-term sample of airborne particles in each space [21]. All samples were collected during June 22–24, 2012. Building construction was completed in 2003, and dust has presumably been accumulating in some sampled spaces since that time. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
  16. 16. • Then a semi-standard rRNA PCR workflow Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
  17. 17. Biogeography of Indoor Bacterial Communities Building-scale Design Influences on the Built Environment Microbiome Figure 3. The taxonomic composition of bacterial communities sampled from dust in Lillis Hall. Samples are organized by space type, and relative abundances are shown for groups comprising more than 1% (for phylum and class level) and 4% (for order level). doi:10.1371/journal.pone.0087093.g003 Slides form and Davis EVE161 Course design. In this study, human use patterns for each Metrics related to for UC organization were quantified usingTaught by Jonathan Eisen Winter 2014 space were
  18. 18. Building-scale Design Influences on the Built Environment Microbiome Biogeography of Indoor Bacterial Communi Table 1. Variance in biological dissimilarity among bacterial communities from all spaces, as well as just offices, (Canberra distance) explained by different variables in Lillis Hall. R2 P-value Room types all rooms Analysis of the variance in bacterial community composition explained by different factors (Table 1; PERMANOVA on Canberra distances) indicated that space type and air handling unit (AHU) explained the greatest proportion of variance (R2 = 0.06 & 0.13, respectively; both P = 0.001). Explanatory variable Space type 0.06 0.001 Air source - air handling unit (AHU) 0.13 0.001 Building floor 0.01 0.001 Space size 0.01 0.001 Building wing - East/West 0.01 0.341 Building side - North/South 0.01 0.001 Occupant diversity 0.01 0.001 Annual occupied hours 0.01 0.015 Centrality (betweenness) 0.01 0.001 Centrality (degree) 0.01 0.001 Temperature 0.01 0.024 Relative Humidity* 0.01 0.001 Natural ventilation capability 0.01 0.001 Air source - air handling unit (AHU) 0.07 0.001 Building floor 0.07 0.001 Space size 0.02 0.025 Building wing - East/West 0.01 0.541 Centrality (betweenness) 0.02 0.005 Centrality (degree) 0.02 0.016 Temperature 0.02 0.002 Relative Humidity* 0.01 0.786 Natural ventilation capability offices 0.02 0.001 2 Variance explained (R ) and statistical significance (P-value) quantified with a PERMANOVA test; since P-values are from permutational tests involving 999 permutations, they are only reported down to 0.001. All variables and their respective units are described in the methods section and Table S1. *detrended using daily averages. doi:10.1371/journal.pone.0087093.t001 Biological Sampling protocol consisted of two PCRs. The first amplified the V4/ region using the primers 59-AYTGGGYDTAAAGNG-39 and CCGTCAATTYYTTTRAGTTT-39 [42,43] and appended 6 bp barcode and partial Illumina sequencing adaptor. Forw and reverse strands were labeled with different barcodes, and unique combination of these barcodes was used to pool sample post-processing. All extracted samples were amplified in triplicate for PCR1 a triplicates were pooled before PCR2. PCR1 (25 mL total volu per reaction) consisted of the following ingredients: 5 mL 5x buffer (Thermo Fisher Scientific, U.S.A.), 0.5 mL dNTPs (10 m 0.25 mL Phusion Hotstart II polymerase (Thermo Fisher Sci tific, U.S.A.), 13.25 mL certified nucleic-acid free water, 0.5 forward primer (10 uM), 0.5 mL reverse primer (10 uM), and 5 template DNA. The PCR1 conditions were as follows: ini denaturation for 30 s at 98uC; 20 cycles of 20 s at 98uC, 30 50uC and 30 s at 72uC; and 72uC for 10 min for final extensi After PCR1, the triplicate reactions were pooled and cleaned w the QIAGEN Minelute PCR Purification Kit according to manufacturers protocol (QIAGEN, Germantown, MD). Amplif products from PCR1 were eluted in 11.5 mL of Buffer EB. PCR2, a single primer pair was used to add the remain Illumina adaptor segments to the ends of the concentra amplicons of PCR1. The PCR2 (25 mL volume per reacti consisted of the same combination of reagents that was used PCR1, along with 5 mL concentrated PCR1 product as templ The PCR 2 conditions were as follows: 30 s denaturation at 98 15 cycles of 10 s at 98uC, 30 s at 64uC and 30 s at 72uC; a 10 min at 72uC for final extension. Amplicons were size-selected by gel electrophoresis: gel band c. 500bp were extracted and concentrated, using the ZR Zymoclean Gel DNA Recovery Kit (ZYMO Research, Irvi CA), following manufacturer’s instructions, quantified using Qubit Fluorometer (Invitrogen, NY), and pooled in equimo concentrations for library preparation for sequencing. Result libraries were sequenced in two multiplexed Illumina MiSeq la (paired-end 150 base pair sequencing) at the Dana Farber Can Institute (Boston, MA). All sequence data and metadata have b deposited in the open-access data repository Figshare (http figshare.com/articles/Lillis_Dust_Sequencing_Data/709596). Slides for UC Davis EVE161was carried out with a Shop-VacH 9.4L Hang Sequence2014 Sampling of dust Course Taught by Jonathan Eisen Winter Processing
  19. 19. Biogeography of Indoor Bacterial Communities Figure 4. Dust communities within a building cluster by space type and are strongly correlated with building centrality and human occupancy. Points represent centroids (6SE) from distance based redundancy analysis (DB-RDA). Space types hold significantly different communities (P = 0.005), though this is driven primarily by restrooms. Bacterial OTUs that have the strongest influence in sample dissimilarities are shown at the margins; numbers in parentheses indicate multiple OTUs in the same genus. Centrality (along y-axis) represents network betweenness and degree; human occupancy (along x-axis) represents annual occupied hours and human diversity. All four correlates (simple linear models as a factor of ordination axis) are significant along their respective axes (all P,0.001). doi:10.1371/journal.pone.0087093.g004 [49] in R. Slides for UCthe consequences of beta-diversity We also assessed Davis EVE161 Course Taught by Jonathan Results Eisen Winter 2014
  20. 20. Design Influences on the Built Environment Microbiome within a Space Type The large number of office spaces (73 offices) made it possible to test for drivers of microbial community variation among offices. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
  21. 21. Biogeography of Indoor Bacterial Communities Design Influences on the Built Environment Microbiome within a Space Type Figure 5. Offices contain significantly different dust microbial communities depending on ventilation source. a) The first axis is constrained by whether or not offices have operable window louvers (blue) or not (red). Taxon names on either side are grouped from the 25 strongest weighting OTUs in either direction. b) Deinococcus were 1.7 times more abundant in mechanically ventilated offices compared to window ventilated offices. c) The opposite pattern was observed for Methylobacterium OTUs, which were 1.8 times more abundant in window ventilated offices. Boxplots delineate (from bottom) minimum, Q1, median, Q3, and maximum values; notches indicate 95% confidence intervals. d) Crosssectional view of representative Lillis Hall offices. Offices on the south side of the building (left) received primarily mechanically ventilated air, while offices on the north side of the building (right) are equipped with operable windows as a primary ventilation air source. doi:10.1371/journal.pone.0087093.g005 Slides 1; PERMANOVA on Canberra distances) different factors (Table for UC Davis EVE161 Course Taught by Jonathan Eisen Winternumber of of human occupants (defined as a high 2014 different
  22. 22. Biogeogr P = 0.002; from a Mantel tes connectance distance). This as building scale, regardless of sp Discussion In this paper we first asked: a function, form and organization environment microbiome? Ou yes. In architecture, function Lillis Hall was the strongest throughout the building. D architectural design, function and organization of spaces thr organization are necessarily dif and organization are distinct did not attempt to draw a analyses, since nearly every b both. In Lillis Hall, design cho that greatly differed in terms o which were related to variatio sition at the building-scale. W most common space type in Figure 6. Offices in Lillis Hall show a strong distance-decay asked which aspects of form an pattern. When only considering a single space type, biological built environment microbiome similarity (y-axis; 1 - Canberra distance) decreases with connectance betweenness, building floor, spa distance (number of intermediate space boundaries [e.g., doors] one the strongest predictors for would walk through to travel the shortest distance between any two holding function constant. spaces) (Mantel test; R = 0.189; P = 0.002). The same pattern was also Despite the microbiome observed at the whole-building scale (not shown; Mantel test; R = 0.112; P = 0.001). detected a core built environm doi:10.1371/journal.pone.0087093.g006 taxa that were present in near Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 This core microbiome was dom
  23. 23. SFig1. High degree of correlation between three beta-diversity metrics. Multivariate community analysis was carried out with the Canberra taxonomic metric; this choice results in de-emphasis of the most abundant species (as opposed to using the Bray-Curtis dissimilarity metric), and also ignores nuanced evolutionary relationships between bacterial OTUs (as opposed to using the phylogenetic Weighted UniFrac distance). While the choice of a beta-diversity metric can impact results, the three potential candidates that we explored resulted in largely the same distance between samples in multivariate space. All three metrics are bounded between 0 and 1. Pearson’s correlations (r) are given in the upper right panels. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
  24. 24. ac s illu e cto bac m or 3 4% cte r er ia ba old teo rkh Fi rm icu ro tap Bu cilli Be   5% Bacteria a s illu c iba ysin   L %  1 ore 6m lus acil    B 1% occu eroc s  Ana 2%   2 more 6 more De teo les illa e pir ea os od rac Rh cte ba eto Ac ro ap ph eae s ia ete bacter roid cte Ba utes eric Ten es ial tes ...r ae llicu ing Mo ce ra ph st S cte la iba Chlorop x Fle cte ba Cyano ria Rhiz obia les 13%    D ein oco ccu s Acidobacteria   0.6% %   4 s  Actinomycetales   0.5%     2% a on %    7% 0.8 erium ore 2m lobact r te ore Methy ac ob e    en Chlamydiales   0.2% Fusobacteriaceae   0.2% Verrucomicrobia   0.1% ore 2m 4m sma la irop 4m ore Rh izo bia cea SC4   0.3% m Hy  Sp phyta trepto 2%   S Ro 2%   om se ph ta   S ci ceae Ba Al    3% omonas Sphing nadales monadac Sphingo 5 more Proteobacteri eae   3% ales te s Sphingomo Caulo b actera 1% Rhodobacterac bacill ia les ae    5 % ndimo nas    Lacto acil la Ae ceae roc oc ca ce ae ae Lachno...race eae edis illac st...S Bac e Clo e ales ea Clostridi ea ...c ac Plan cc co lo hy es lal cil a Ba Clostridi Ox alo ba cte u cc co ylo tob coc % Lac ap St e    3 Brevu cea ella M s ae ale ace ad ae on cteri ce om roba ud Ente na e e Ps lig ria ca cte Al oba ote apr mm Ga ea rac e Streptococcace ae s x ora ino e or m e ad    L a ore 4 or m am on 8%   Streptococc us 6 more 11 m e  3% s   na mo do eu Ps 2 Co m Planctomycetes   0.08% Chloroflexi   0.03% Figure S2. The taxonomic composition of bacterial communities sampled from dust in the Lillis Business Complex. The relative abundance of sequences assigned to taxa at different taxonomic levels is indicated by the relative width of categories at each level. Bacterial taxonomy was visualized using Krona (http://sourceforge.net/projects/krona/; Ondov et al. 2011). doi:10.1371/journal.pone.0087093.s002 (PDF) Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
  25. 25. Conclusion Churchill famously stated that “[w]e shape our buildings, and afterwards our buildings shape us.” Humans help to direct microbial biodiversity patterns in buildings – not only as building occupants, but also through architectural design strategies. The impact of human design decisions in structuring the indoor microbiome offers the possibility to use ecological knowledge to shape our buildings in a way that will select for an indoor microbiome that promotes our health and well-being. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
  26. 26. Brooks et al. Microbiome 2014, 2:1 http://www.microbiomejournal.com/content/2/1/1 RESEARCH Open Access Microbes in the neonatal intensive care unit resemble those found in the gut of premature infants Brandon Brooks1, Brian A Firek2, Christopher S Miller1,3, Itai Sharon1, Brian C Thomas1, Robyn Baker4, Michael J Morowitz2 and Jillian F Banfield1* Abstract Background: The source inoculum of gastrointestinal tract (GIT) microbes is largely influenced by delivery mode in full-term infants, but these influences may be decoupled in very low birth weight (VLBW, <1,500 g) neonates via conventional broad-spectrum antibiotic treatment. We hypothesize the built environment (BE), specifically room surfaces frequently touched by humans, is a predominant source of colonizing microbes in the gut of premature VLBW infants. Here, we present the first matched fecal-BE time series analysis of two preterm VLBW neonates housed in a neonatal intensive care unit (NICU) over the first month of life. Results: Fresh fecal samples were collected every 3 days and metagenomes sequenced on an Illumina HiSeq2000 Slides for approximately 33 Course Taught by Jonathan Eisen Winter 2014 device. For each fecal sample, UC Davis EVE161swabs were collected from each NICU room from 6 specified areas:
  27. 27. set of n an LBW emely ition, 19]. It d the e first ource These es es- ICU gens. mon, U outwithin samples were collected after signed guardian consent was obtained, as outlined in our protocol to the ethical Table 1 Health profile of premature infant cohort Characteristic Infant 1 Infant 2 Gestational age 26 3/7 weeks 28 2/7 weeks 951 g 1,148 g No Twin Vaginal Vaginal Chorioamnionitis Yes Yes Day of life (DOL) 1 to 7 antibiotics Ampicillin, gentamycin Ampicillin, gentamycin Other antibiotics No DOL 14 to 16, vancomycin, cefotaxime Feeding initiated DOL 3, maternal milk DOL 8, artificial formula Yes Yes Weight Multiple gestation Delivery mode Survive to discharge Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
  28. 28. Methods Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
  29. 29. man the r in we BE’s udy ants our Methods dies and to uisiflun in ject of an BW B mely n ion, ]. It at the h irst rurce ts hese ). esby n CU ls ens. bs mon, m, outM hin as bs, se ll d of is o http://www.microbiomejournal.com/content/2/1/1 station electronics (keyboard, mouse,a and cell phone). All EMIRGE assembly of full-length 16S rRNA genespike-in control phylogeny frequently touched by humans, is cleaned with thesource nally, reconstructed sequences from a clustered combined across gradients and predominant QIAquick operational taxonomic units (OTUs) were amplicons at samples were placed in a sterile transport tube and stored EMIRGE is an iterativelevel using USEARCH [33] for down- For phylogenetic resolution beyond the 16S rR >97% identity not shown) were removed and of colonizing microbes(Qiagen, Hilden, Germany) as directed theexperiment (data template-guided assembler that re-an in the GI tract of premature PCR Purification Kit within 30 minutes at -80°C until further processing. quantified OTUa table Microbiome 2014, 2:1 using sequences pick_otus_ 32 highly conserved, single copy ribosomal prot liesBrooks et al.analysis. 16S rRNA gene in the analysis are pubon database of Sequences used QIIME’s to probinfants.the manufacturer. Cleaned amplicons were stream was constructed by http://www.microbiomejournal.com/content/2/1/1 abilistically generateas a script. An adjusted OTU table that used from infant 1 and 2’s assemblies (RpL10, 1 through_otu_table.py project16S rRNA gene http://ggkbase. licly available full-length attachment at sequences via board of Technologies, Carlsbad, CA, (IRB fragmented research Qubit (Life the amplification of Pittsburgh USA) and provide to anrelative abundance of these sequences in University 17, 18, 19, 2, 20, 21, 22, 24, 27, 29, 3, 30, and berkeley.edu/NICU-Micro/. bp using the Biorupthe average size of 225 DNA extraction andIllumina library preparation pipeline. incorporated EMIRGE generated abundances was conMethodsinto an PCR input PRO11060238). This consent included ice and collection the NGS (Diagenode, Seraing, Belgium), and sheared fragsample 0.25 g of tor assayed consortia [31]. For the reference database, we RpS10, 11, 12, 13, 15, 16, 17, 18, 19, 20, 5, 6, 7 Frozen fecal samples were thawed on structed using an in-house script [29] and is publicly Sample collection permissions and consent to publish study findings. lysis used versionused in athe SILVA SSU preparation protocol ments were 108 of robotic library database, filtered to same genes from recently sequenced E. fae thawed samplesadded to tubes and prewarmed (65°C) on with available as a project attachment at http://ggkbase.berkeley. Fecal sample were collected every third day, starting pipeline using the aforementioned database. After Page final the 3 Brooks Sequencing preparationfrom sequencing Microbiome 2014, 2:1 All et al.from were PowerSoil DNA private-style NICU at exclude sequences <1,200 bp and >1,900 Pleasanton,of resamples the obtained a Isolation Kit (MoBio usingMetagenomic EMIRGE assembly of 16S rRNA gene16iter- nomes, in [46]. Genome completeness distant the Appollo 324 robot (Integenx, bp [32]. ToCA, from more was d solution day of life, for 1 month from standard protocols edu/NICU-Micro/. OTUs were aligned to thewere used in of based on addition to genessingle-copy genes the Illumina library construction followed third infants. Ination, 153,980 reads, spanning16 fecal samples onsamall samples, Greengenes Metagenomic sequencing of instructions. Each 1 lane taxa, were the number of the JGI IMG datab http://www.microbiomejournal.com/content/2/1/1 two Pittsburgh Magee-Womens Hospital of USA). The incubation was move closely related sequences, we clustered the database of USA) manufacturer’s obtained from Laboratories,University inCA, the UniversityDNA Technologies [39]following thefecal 2000 produced approximately 350 Mbp conserved genes [49,50] identified in each bin. Carlsbad, reference fants were enrolled of the study based on the criteria at Center. Room California Davis an Illuminaalignment (gg_97_otus_4feb2011.fasta) using Medicalthefor 5 minutessamples were collected protocol at 97% identitywith unique barcodes consisting of Downstream gether, each gene set was aligned using MUSC concur- ple reconstructingHiSeq 16S rRNA sequences. six nuclewas tagged with USEARCH [33]. A total of 1 million conducted were <31 weeks’ gestation, <1,250 g at birth, and the manufacturer’s theof 101 bp aligner [40] reads. aTrimmed rRNA gene PyNAST paired-end and phylogenetic tree built thatCore they 16S rently with Facility (http://dnatech.genomecenter.ucdavis.edu) filtering andto the adapterbarcodedalibraryreads were input [51,52] and manually curated toin each samp fecal samples and spanned four timepoints otides internal analysis of reconstructedseparate indexing se- tive abundance of each organism remove am read as followed previously Swab heads followed thelocation within paired-end reads from defaultwith default that were sam[41] and as thereafter. in the same physical same proced- were using FastTree v.2.1.3each parameters run for 80 iterations culated by mapping reads to unique region were housed described [29]. Briefly, amplicons quences fromto and samples followedwere 12 cycles room aligned regions and end gaps [53]. The curat of the into ligated fecal fragment. There parameters. Beta EMIRGE each on except collection (9:00, 12:00, read, randomly ure, days ofheads were first month of13:00,Aand 16:00). pled and was average replacement using the using of diversity similar Fast the NICU during life. summary of assembled genomes. Metagenomic assemblies samples. research board of thecut with sterilized scissors into the fragmented to an without sizefrom fragments accommodate University were determined by PCR enriched forcalculated of 225 bp to trees BiorupPittsburgh (IRB Most frequently touched surfaces adapter-ligated before were concatenated to computational restrictions associated and shearedlibrary with use of the extraction tube beforeconsent included sample collection is torUniFrac scores and visualized with principle coordinates ments annotations are publicly form a 32-gene health-related This starting the protocol. metadata including antibiotics exposure PRO11060238). NGS (Diagenode, Seraing, Fecal samples underwent the Belgium), fragtheir available likeliho visual observation andswabs was pooled such interviews in full dataset. Reads from the subsample from each lihealth care provider that the four quantification and validation. 4,101-position alignment. A maximum at http: DNA extracted from Fecal samples were analysis (PCoA) [42]. Taxonomy was assigned to each OTU provided and consent to publish study collected using berkeley.edu/NICU-Micro/. alignment was c Community analysis two exceptions: (1)samples DNA permissionsin Table 1.to sample collection.findings. cells ments preparationin a robotic library preparation protocol were used with of room and fecal genomic the weeks leading up Microbial same were ogeny for the concatenated timepointsal. Microbiome 1 dayperineal private-style NICU 16 using thecommunity analysis,(Integenx, Pleasanton, CA, 16 at and/or species level using the [34] of Ribosomal a previously Brooks et sampled in 2014, per environment were consoliFor genera DNA trimmed using Sickle Page 4 for All removed established 2:1 usingstimulation procedure brary the andstringentlyrobot EMIRGE-reconstructed Page of at were samples were obtained from a was used as template quality scores >30324 was fragmented to Trimmed reads se- using PhyML under the LG + α + γ model of from surfaces DNA foam tipped 3swabs was used Appollo and length >60 bp. 550 bp. Libraries (2) dated into one sample. -80°C within 10 minutes [16]. All http://www.microbiomejournal.com/content/2/1/1 Database Project (RDP) classifier [43] at Each samand were stored at Pooled Magee-Womens Hospital of the University of Pittsburgh USA) following the manufacturer’s instructions. a confidence with 100 bootstrap replicates. quences (BBL CultureSwab EZ Collection 16S Transport System, were added,were input into the standard QIIME 1.5.0 workand were input into an amplicon-optimized version of equimolar amounts, Illumina HiSeq for amplification of the full-lengthwere rRNA geneconcur- pleinterval ofin0.8 presence/absence to thesamerepresentative Enterococcus faecalis concatenated ribosomal pr samples were Room samples signed guardian with 27 collected after consent flow [38]. For unique barcodes consisting Greengenes Medical Center.NJ, USA) and a sampling buffer of 0.15 M 2000 platform. Paired-end sequences the obtained with collected was tagged with and trained with analyses,of six nucleFranklin Lakes, were parameters. A F was with fecal samples and our protocol timepoints (5’-AGAGTTTGATCCTGGCTCAG-3’) and the ethical EMIRGE [29] to the adapter read(OTUs) were clustered at phylogeny database. OTUs were visualized default in operational taxonomic units across Casava version rently obtained, as outlined Six spanned four to1492R (5’- otidescycles andfor assembly using as with room-infant pairs NaCl and 0.1% Tween20. frequently [30]. To limit total internaliterations processed a separate indexing 100 of 80 the data GGTTACCTTGTTACGACTT-3’) primers touched areas read, andspring-weighted,wereusing USEARCH each plot by Results in a >97% identity level performed network sub- an For phylogenetic resolution beyond the 16S rR edge-embedded for [33] of on days of collection (9:00, 12:00, 13:00, and 16:00). cycles the ligated to each been deposited were NCBI Short were bias,Health perPCR of prematurebp feeding units/μL iter- sample. EMIRGE-reconstructed There in the 12determined processed profile was performed with 5the Biorupand 1.8.2. Raw read data has fragment. sequences withoutand Table 1 gradient infant room: sink, Aftercohort intubinfant pipelinetable was constructed fragments before pick_otus_ 32 highly NICU room samples over time and sp [46]. adapter-ligated using QIIME’s with using the aforementioned of 225 final of Ns PCR frequently touched surfaces were determined by PCR enriched for Genome completeness was [38] librarythe Stability of conserved, single copy ribosomal pro fragmented to an average size database. using using QIIME’s make_otu_network.py script OTU Most tubing, hands of healthcare providers and parents, Read Archive (accession number SRP033353). ation (Diagenode, Seraing, 1 all samples, were 7 dif- and with an estimated of single-copy genes and other used sample preparation, assemblies (RpL10, Characteristic Infant Infant based on OTU table as script. An of 0.01% or table that After from infant 1 and 2’s 57 and 36 samples ation, spanning provider interviews in TaKaRa Ex Taq™ (Takara Bio Inc., Otsu, and sheared used tor NGS 153,980 reads, health Belgium), Japan) across2frag- in quantificationthe numberabundanceadjusted OTU greater modified and validation. Fecal samples underwent the input. through_otu_table.py visual observation and knobs care incubator, and nurse general surfaces,temperatures weeksthepreparation protocol access16S rRNA the followingDownstream were kept for analysis. Putative chimeras were removed on sequences. 2/7 weeks 1 17, 18, 19, 2, were subsequently 29, 3, 30, conserved genes [49,50] identified in(1) each bin. The relareconstructing Gestational used 28 reaction: ferent wereage ments annealing fecalrobotic library a 26 3/7 collection. Microbial successfully and 20, 21, 22, 24, 27, analyzed fo the weeks leadingin(keyboard, with sample DNA station electronicsup toof of 1 minute at 94°C, 30 s at cellsse- same preparation EMIRGE exceptions: eachgenomic was calmouse, and cell phone). All by incorporated with two generatedtwo gene amplicons EMIRGE assembly of each organism in abundances was conof full-length 16S rRNA chimera detecRpS10, 11, 12, 13, 15, 16, 17, 18, 19, 20, 5, 6, tive abundance sample filtering and analysis rRNA g CA, using the intersectionfragmented 550 bp. minute at 94°C; from surfaces (Integenx, Pleasanton, 48°C 35 cycles 951 using the Appollo 324 reconstructed 16Stipped swabs was structed using an was and gene prediction Libraries Weight and infant 2, respectively (Table 2). EMIRGE Metagenomic DNA assembly between and were removed placed in robot gusing foamtube 1,148 gene samples (7°C temperature sterile followed1 that of thesamwere the manufacturer’s instructions. Eachstored tionused and (2) DECIPHER [35] scriptUCHIME v6.0publicly same genes from recently sequenced E. fa a gradient) and minute at 72°C; EMIRGE is anmappingin-houseto and to[29]regionsthat retransport and room culated by iterative template-guided assembler is on the programs, [36] toquences from EZ Collection 58°C USA) following fecal samples and Transport System, were added, in were constructed usinghttp://ggkbase.berkeley. approximately 12,000 full-length 16S rRNA s Assemblies equimolarreads and Multiple gestation No Twin attachment unique at (BBL CultureSwab at -80°C until further processing. amounts, to idba_ud [44] prob-an within tagged with liesavailable as a projectMetagenomic the Illumina HiSeq nomes, in for each room-infant more distan assembled genomes. OTUsof Velvet sequences to idba_ud samples. searched against the 16S sequences were to the Greengenes and OTUs addition to genes from pair (cluster database [37]. with and a final extension for 7 minutes at 72°C. Amplicons M ple was 30 minutes uniqueand a sampling buffer Vaginal were 2000on a database of 2011rRNA geneassemblies along Fibarcodes consisting ofof 0.15 six nucleiterative implementationGreengenes[45,46]. For with Franklin Lakes, NJ, USA) Vaginal platform. Paired-end were rRNA gene sequences obtained edu/NICU-Micro/. Delivery mode abilistically generateare publicly16Saligned at http://ggkbase. full-length availablespike-in control taxa, were obtained from the JGI IMG data their reference alignment (gg_97_otus_4feb2011.fasta) nally, reconstructed sequences from a Casava version combined 0.1% gradients Six frequently touched areas otides and acrossTween20. and cleaned separate indexing internal to the adapter read as assemblies, trimmed reads were with NaCl extraction and PCR amplification a with the QIAquick 100 [39] annotations data abundance assembled using default 97% nucleotide identity level). Broadly speakin Chorioamnionitis Yes and fecal samplesYes and cycles and thenot shown) wereof these sequences using gether, each gene set was aligned using MUSC provide (data the relative processed removed for downin DNA Purification Kit (Qiagen, Hilden, Germany) as directed berkeley.edu/NICU-Micro/. Community analysis of experiment data the Velvet assemblies, sequence cover- richness decreased from electronics > sinks > s PCR read, processed to infantroom cycles parameters. For has [40] were and ligatedper each fragment.sink, feeding 12 0.25 g of 1.8.2.assayed consortia [31]. Forand a phylogenetic treewe room: There were and intubRaw read NCBI Short Frozen life (DOL)for adapter-ligated fragments and gentamycin thethe PyNAST alignerbeen deposited in the database, built fecal Day of manufacturer. were gentamycin Ampicillin, library For tubing, samples healthcare providers before analysis, amplicons and parents, seare pubby enrichedhands of Cleaned EMIRGE-reconstructed of stream analysis. Sequences used in default the dataset Beta [51,52] and manually curated to remove am PCRthe community Ampicillin, thawed on icewere quantified Read Archive (accession numberthe referenceparameters.were incubators > hands > tubes, a finding that was age bins representing [41] genomes in ation 7 antibiotics added to tubes with prewarmed (65°C) lysis used version 108 of the major with the analysisfiltered to using FastTree v.2.1.3 SILVA SRP033353). 1 to SSU database, thawed sample Technologies, standard QIIMEUSA) and quences (Life access knobs 1.5.0 the via Qubit were input into theonCarlsbad, underwent work- licly available as a project attachment at http://ggkbase. quantification and validation. Fecal samples CA, and nurse identified byfaecalis concatenatedsimilar trees permissive rated with several alpha gaps [53]. indexes ( first running from program with using Fast aligned regions and end diversity The cura the ribosomal protein diversity was <1,200 bp generalantibiotics the PowerSoil DNA Isolation Kit (MoBio exclude sequencescalculated and >1,900 bp [32]. To resurfaces, the incubator, Enterococcus solution from Other [38]. For No 16, ments were concatenated to form a the N flow input electronicswith library preparation genomictoDNA same preparation presence/absence analyses,DOL 14 cefotaxime berkeley.edu/NICU-Micro/. k-mer rRNA covered coordinates Nearly 300 genera were detected in 32-gene parameters in which the UniFrac station into an Illumina two exceptions: (1)cell representative EMIRGE assembly ofand visualized with principle database (keyboard, mouse, The incubation All and pipeline. was move closely relatedfull-length 16S clustered the the whole phone). phylogenyscores sequences, we size gene amplicons vancomycin, Laboratories, (2) DNA was units (OTUs) 550 bp. Libraries at Carlsbad, CA, USA). to were clustered 4,101-position alignment. A maximum likelih operational was used and taxonomicsterile transport tube and stored fragmented range ofan coverages. was summed samples were placed in a 3,and the milk DOL 8, artificial formula EMIRGE is (PCoA) [42]. Taxonomy We A total oftothat k-mer broadly visualize temporal stability of envi analysis observed template-guided assembler the gene, Feeding initiated 5 minutes maternal manufacturer’s protocol DOL atFor phylogenetic resolution beyond rRNA gene each OTU ogeny for the concatenated alignment was 97% identity with USEARCH [33]. assigned 1 million conducted for equimolarand sequencing Illumina HiSeqan Metagenomic iterative assembly of 16S the 16S rRNA rethe 30 minutes at level using to processing. Sequencing identity-80°C until furtherthe EMIRGE were added, preparation amounts,USEARCH [33] and for and/or species ribosomal at thedatabase of 16S rRNA gene sequencesassembly to within >97% in liescoveragesconserved, singlegenerated by thisthe Ribosomal across time and space, the phylum level class on a 32 highly readsall contigs copylevel using proteins paired-endgenera from each barcoded libraryon to lanewere wereprobsamfollowed thereafter. Swab sequences QIIME’s Yes Survive tolibrary construction followed standard protocols Yes OTU discharge pick_otus_ Metagenomiccoverage(RDP) 1616S rRNA gene sequences Illuminatable Paired-endheads usingwere obtained with sequencing of classifier 2000 platform. was constructed followed the same proced- abilistically generate 1 and 2’s(each of which contains 14,of or are plotted in Figure 1. Actinobacteria, Firmic define the infant full-length fecal samples a 1confidence using PhyML under the LG + α + γ model of Database Project bins assemblies to at 13, one16, usedrandomly without replacement [43] accommodate from (RpL10, ure, cycles and the data processed with scissors tablethe an Illumina HiSeq 2000 produced approximately 350 Mbp except heads were cut with Davis DNA OTU version sterilized Casava into that pled with 100 bootstrap replicates. atthrough_otu_table.py script. 100the University of CaliforniaAn adjusted Technologies and interval19, 2,relative abundance of bin-specificGreengenes Proteobacteria dominate the sampled environm more provided 29, same provide of restrictions the 20, 21,trained 27, these use expected DNA extraction and PCR amplification 17, 18, genomes). This 22, 24,with the 3,sequences in 5, and computational 0.8 andreads. associated reads30, 4,of the with were input extraction read data has been deposited in the NCBI Short tube before starting the protocol. incorporated EMIRGE thawed abundances was of Core fecal 101 bp consortia [31]. ForTrimmed and 1.8.2. Raw samples weregeneratedon ice and 0.25 g con- ofRpS10, 11,k-mer size, 16, 17, 18, across 5, coverage pairs areas most exposed to human skin deposition h colassayed 12, 13,were the the referencefrom 7, 8).we database, liFrozen Facility (http://dnatech.genomecenter.ucdavis.edu) thecoverage,paired-end coverage cutoff,20,room-infant The 15, 6, DNA extracted an in-house script [29] and is publicly fulldatabase. OTUsdefault visualized 19, asstructed using from swabs with prewarmed (65°C) were into dataset. 108 of parametersSSU the iterative each to previously added to number Briefly, such described [29]. pooled amplicons four and from edge-embedded Read Archive (accession tubes wasSRP033353). that thelysis used EMIRGE Reads the SILVA subsamplefor 80 iterations by most variation over time. At lower taxonomic lev lectionspring-weighted, parametersdatabase, filtered gethreshold recently sequenced network plot for run E. faecalis assembly. inversion Results thawed sample samewere stringently trimmed using Sickle [34] for timepoints as project 1 day per environment were consoli- brary a genes from available sampled in attachment at http://ggkbase.berkeley. After QIIME’s <1,200genes from more distantly related solution from athe PowerSoil DNA Isolation Kit template exclude sequences make_otu_network.py script [38]there- the lar trendsof NICU room sampleson the 20 mosts (MoBio usingscores >30 and length >60 specific [32]. To binStability are observed. Based over time and nomes,each iteration targeting >1,900 Trimmed with in addition to bp and a bp. bp bin, reads dated into one sample. Pooled DNA was to the Greengenes quality edu/NICU-Micro/. OTUs were aligned used as EMIRGE assembly of full-length 16S rRNA gene amplicons families, frequently touched 57 and 36 samples specific reads were removed from the dataset.database modified After sample preparation, surfaces are distinct Laboratories, Carlsbad, full-length 16S rRNA gene with 27 move closely OTU table as input. JGI IMG the CA, USA). The incubation taxa, were obtained from the were input related sequences, we clustered database. Toamplicon-optimized version of for amplification of thetemplate-guided assembler thatwas [39] reference minutes and the manufacturer’s protocol EMIRGE isfor 5 alignment (gg_97_otus_4feb2011.fasta) using at gether, each into an was aligned using MUSCLE 3.8.31 an iterative reTime-series-coverage-based[33]. A total self-organizing frequently touched surfaces (Figure 1). UniFrac emergent of 1 million successfully and were subsequently analyzed fo 97% identity gene USEARCH with set conducted [29] for assembly default parameters. A F the PyNAST aligner [40] andgene sequences tree built lies(5’-AGAGTTTGATCCTGGCTCAG-3’) and 1492R (5’- EMIRGE and manually usedusingto predictionambiguously on a database Swab rRNA a phylogenetic procedmaps followed thereafter. of 16Sheads followed the [30]. to prob- paired-end80 iterations each barcodedremove were sam- by basedinfant 2, respectively (Table 2).reveals fou same To limit total of (ESOMs)from were to bin scaffolds generated and community composition PCoA EMIRGE Metagenomic were curated gene library [51,52] reads assembly andperformed for each subGGTTACCTTGTTACGACTT-3’) default gene sequences using FastTree v.2.1.3 [41] with rRNAscissors into Beta abilistically heads were cut with 16S primers parameters.the pled randomly were constructedGenes to accommodate an ible ecosystem types (skin associated communit generate full-length sterilized metagenomic withoutend[47]. using The curated alignassemblyreplacement were predicted and approximately 12,000 full-length 16S rRNA Assemblies ure, except gradient PCR was performed with 5 units/μL of sample. EMIRGE-reconstructed [53]. idba_ud [44] and aligned regions and gaps sequences without Ns PCR bias, diversitytube calculated from similar and provide the relative abundance of these sequences in translated implementation of Velvet 32-gene, 39-taxa, [48]. and and feces) each room-infant pair (cluste iterative restrictions to form 0.01% For extractionEx wasbefore startingInc., protocol. trees using dif- computationalestimated abundance ofusing Prodigalthe the Otsu, Japan) across 7 Fast and withwereinto protein sequences awith use of idba_ud tubes,OTUs for and confirms clustering of samp ments an concatenatedassociated [45,46]. or greater TaKaRa consortia visualized with principle coordinates UniFrac Taq™ (Takara Bio was pooled such that the four and swabs the assayedscoresfrom[31]. For the reference database, we fullFunctionalReads fromreads subsample fromusing default to skin depositionidentity level). Broadly speakin annotation the were assembled aneach phylwas maximum likelihood liadded with in-house dataset. DNA extracted 97% nucleotide via touching (Figure 2). assemblies, trimmed 4,101-position alignment. A removed ferent annealing of the SILVA SSU assigned filtered to analysis (PCoA) [42]. Taxonomy the following reaction: 1 were were stringently trimmedchimeras sequence cover- richness decreased from electronics > sinks > used version 108temperatures withwasdatabase, to each OTU brary kept for analysis. Putativeassemblies, were [34] for For the Velvet using timepoints 94°C; 35 in 1 day perminute at 94°C, 30 s at 48°C by parameters.intersection between two Sickle conducted was usingfor minute sampled cycles of 1 environment were consoliSlides for UC Davis ogenyscoresthe concatenated >60 Course Taught alignmentchimera detecWinter 2014 at into one sample. Pooled DNA was bp as To reexclude atgenera and/or species level using [32].Ribosomal EVE161the >30 and the LGby Jonathan Eisen were incubators > hands > tubes, a finding that was age dated thesequences <1,200 bp and >1,900usedthe template quality bins representinglength + α +bp.model of evolution using PhyML under major genomesTrimmed reads γ in the dataset
  30. 30. Brooks et al. Microbiome 2014, 2:1 http://www.microbiomejournal.com/content/2/1/1 Page 5 of 16 Table 2 Sample collection summary and summary of the number of 16S rRNA genes assembled Table 3 Alpha diversity indexes from neonatal intensive care unit (NICU) room and fecal samples Characteristic Infant Infant 1 Infant 2 Shannon 1 No. of samples 2 Simpson 1 Chao 1 2 1 2 Electronics 10 4 Surfaces Surfaces 7 5 Electronics 8.36375 8.27527 0.996905 0.996620 45,519.9 33,602.8 Incubator 8 4 Incubators 8.11070 8.76042 0.996291 0.997674 30,216.9 76,196.9 Sink 9 10 Sinks 8.29052 8.82959 0.996676 0.997687 41,104.6 96,694.1 Hands 8 2 Hands 7.56186 8.60501 0.993397 0.997322 27,708.1 89,233.5 Tubes 6 4 Tubes 5.06097 5.20681 0.961848 0.963895 1,756.60 1,828.00 Fecal 9 7 Fecal 1.71097 2.10295 0.640741 0.747619 9.70000 Total 57 36 Electronics 3,359 1,298 Surfaces 2,440 2,205 Incubators 2,270 1,751 Sinks 2,936 4,766 Hands 1,783 812 Tubes 272 198 Fecal 33 32 Total 13,093 11,062 No. of EMIRGE sequences No. of OTUs Electronics 3,353 1,293 Surfaces 2,436 2,197 Incubators 2,264 1,749 Sinks 2,933 4,762 Hands 1,781 812 Tubes 271 198 Fecal 33 32 Total 13,071 11,043 Shared OTUs 3,822 No. of unique OTUs Electronics 2,486 1,202 Surfaces 2,211 2,015 Incubators 2,048 1,606 Sinks 2,756 4,453 Hands 1,603 801 Tubes 256 185 Fecal 11 11 Total 10,371 10,273 EMIRGE ‘expectation maximization iterative reconstruction of genes from the environment’, OTU operational taxonomic unit. Time-series characterization of fecal samples More than 94% of the reads from Slides samples infant 1’s for UC 8.42848 8.76498 0.997065 0.997677 42,978.9 47,467.2 13.7000 metagenomic data from infant 2 was highly fragmented, and less than 40% of reads could be mapped to the assembled scaffolds. Subsequent reassembly of metagenomic data from infant 2’s samples using the iterative Velvet-based assembly approach [54] generated a significantly better result. As >90% of reads could be mapped to the scaffolds generated by the Velvet assembly, this assembly was chosen for further analysis. The de novo assemblies reconstructed a majority of the genomes for 4 of the 5 and 8 of the 11 most abundant bacterial colonists from infant 1 and infant 2’s metagenomes, respectively. For infant 1, time-series organism abundance patterns in the sample sets analyzed via ESOM (Figure 3) defined five major genome bins for which between 37% and 99% of the single copy genes were identified, based on standard analyses of the single copy gene inventory (Table 4). For infant 2, time-series organism abundance patterns in the sample sets analyzed via ESOM (Figure 3) defined 11 major genome bins for which between 27 and 99% of the single copy genes were identified (Table 4). Infant 1 and infant 2’s gastrointestinal tract (GIT) microbial communities are distinctly different. Infant 1’s colonization pattern echoes the canonical observation in infant GIT succession that facultative anaerobes dominate early phase colonization whereas late stage colonizers are primarily obligate anaerobes [12]. This shift is observed on day of life 12 in infant 1, but is not observed in infant 2, in whom facultative anaerobes were observed throughout the study period. The metagenomic EMIRGE analyses corroborated the binningbased compositional analyses in that no sequences for new taxa were assembled for scaffolds included in the ESOM. Some 16S rRNA genes were identified in the metagenomic assemblies and match EMIRGE generated sequences with approximately 100% identity. The E. Davis EVE161 infant 1 was not identified by faecalis sequence from Course Taught by Jonathan Eisen Winter 2014
  31. 31. Results Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
  32. 32. Stability ies level using the Ribosomal lassifier [43] at a confidence d with the same Greengenes alized across room-infant pairs e-embedded network plot by ks et al. Microbiome 2014, 2:1 network.py script [38] with the //www.microbiomejournal.com/content/2/1/1 ut. ogeny for the concatenated alignment was conducted using PhyML under the LG + α + γ model of evolution with 100 bootstrap replicates. Results Page 5 of 16 Stability of NICU room samples over time and space After sample preparation, 57 and 36 samples amplified successfully and were subsequently analyzed for infant 1 legene prediction 2 Sample collection summary and summary of and infant3 2, respectively (Table 2). EMIRGE generated the Table Alpha diversity indexes from neonatal intensive d mber of 16S rRNA genes assembled care unit (NICU) room and fecal samples approximately Shannon full-length 16S Chao 1 12,000 rRNA sequences ed using idba_ud [44] and an Infant 2 Infant acteristic Infant 1 Simpson of samples 2 1 2 1 2 f Velvet [45,46]. For idba_ud and OTUs for1 each room-infant pair (clustered at the Surfaces 8.42848 8.76498 0.997065 0.997677 42,978.9 47,467.2 ronics 10 4 were assembled using default 97% nucleotide identity level). Broadly speaking, species Electronics 8.36375 8.27527 0.996905 0.996620 45,519.9 33,602.8 ces 7 5 t assemblies, sequence cover- richness decreased from electronics > sinks > surfaces > Incubators 8.11070 8.76042 0.996291 0.997674 30,216.9 76,196.9 bator 8 4 r genomes in the dataset were incubators > hands8.82959 0.996676 0.997687 41,104.6 96,694.1 corroboSinks 8.29052 > tubes, a finding that was 9 10 ds 2 with 7.56186 8.60501 0.993397 0.997322 27,708.1 89,233.5 the program with8 permissive rated Hands several alpha diversity indexes (Table 3). Tubes s 4 genera were detected in the k-mer size covered6 the whole Nearly 300 5.06097 5.20681 0.961848 0.963895 1,756.60 1,828.00 NICU. To Fecal 1.71097 2.10295 0.640741 0.747619 9.70000 13.7000 9 7 ges. We summed the k-mer broadly visualize temporal stability of environments 57 36 generated by this assembly to across time and space, the phylum level classifications of EMIRGE sequences metagenomic data from infant 2 was highly fragmented, are and less than 40% of 1. could be mapped to Firmicutes, and each of which contains one or 1,298 plotted in Figure readsActinobacteria, the asronics 3,359 ces 2,440 Proteobacteria dominate the reassembly environments, with ovided bin-specific expected 2,205 sembled scaffolds. Subsequent sampled of metagenomic bators 2,270 areas Velvet-based assembly approach [54] generated aiterative exposed to 2’s samples using the signifirage cutoff, and coverage col- 1,751 most data from infant human skin deposition having the 2,936 4,766 ers for the iterative assembly. most variation over time. At lowercould be mappedlevels, simicantly better result. As >90% of reads taxonomic ds 1,783 812 to the scaffolds generated by the Velvet assembly, this Based on ting a specific bin, the bin- lar trends are observed.further analysis. the 20 most abundant s 272 198 assembly was chosen for 33 32 families, frequently touched surfacesa are distinct from ind from the dataset. The de novo assemblies reconstructed majority of 13,093 11,062 the genomes for 4 of the 5 and(Figure11 most abun8 of the 1). UniFrac distancesed emergent self-organizing frequently touched surfaces dant bacterial colonists from infant 1 and infant 2’s of OTUs based community composition PCoA reveals four to bin scaffolds generated by 1,293 metagenomes, respectively. For infant 1, time-series or- discernronics 3,353 ible ganism abundance patterns associated communities, sinks, 7]. Genes were predicted and 2,197 ecosystem types (skin in the sample sets analyzed ces 2,436 via ESOM (Figure 3) defined five major genome bins for bators 2,264 tubes, and between 37% confirms clustering of samples prone quences using Prodigal [48]. 1,749 which feces) and and 99% of the single copy genes 2,933 were identified, via on standard analyses of the to as added with an in-house 4,762skin depositionbased touching (Figure 2). single ds 1,781 s 271 33 copy gene inventory (Table 4). For infant 2, time-series organism abundance patterns in the sample sets ana198 for UC lyzed via ESOM (Figure 3) defined 11 major genomeEisen Davis EVE161 Course Taught by Jonathan 32 812 Slides Winter 2014
  33. 33. Brooks et al. Microbiome 2014, 2:1 http://www.microbiomejournal.com/content/2/1/1 Page 6 of 16 Time Series of Rooms Infant 2 Infant 1 Electronics Hands Incubators Electronics Hands Incubator Sinks Surfaces Tubes Sinks Surfaces Tubes 3 6 9 12 15 18 21 24 27 30 3 6 9 12 15 18 21 24 27 30 3 6 9 12 15 18 21 24 27 30 3 6 9 12 15 18 21 24 27 30 3 6 9 12 15 18 21 24 27 30 3 6 9 12 15 18 21 24 27 30 1.00 relative abundance 0.75 0.50 0.25 0.00 1.00 0.75 0.50 0.25 0.00 day of life Phylum Actinobacteria Bacteroidetes Firmicutes Cyanobacteria Fusobacteria Proteobacteria Other Unclassified Electronics Hands Incubators Electronics Hands Incubators Sinks Surfaces Tubes Sinks Surfaces Tubes 1.00 0.75 relative abundance 0.50 0.25 0.00 1.00 0.75 0.50 0.25 0.00 3 6 9 12 15 18 21 24 27 30 3 6 9 12 15 18 21 24 27 30 3 6 9 12 15 18 21 24 27 30 3 6 9 12 15 18 21 24 27 30 3 6 9 12 15 18 21 24 27 30 3 6 9 12 15 18 21 24 27 30 day of life Aerococcaceae Gemellaceae Other Sphingomonadaceae Comamonadaceae Lactobacillaceae Pasteurellaceae Staphylococcaceae Bacillaceae Corynebacteriaceae Micrococcaceae Propionibacteriaceae Streptococcaceae Carnobacteriaceae Enterobacteriaceae Moraxellaceae Pseudomonadaceae Unclassified Caulobacteraceae Family Clostridiaceae Aeromonadaceae Enterococcaceae Neisseriaceae Rhizobiaceae Xanthomonadaceae Figure 1 Taxonomic classification of neonatal intensive care unit (NICU) room microbes for infants 1 and 2. Phylum-level (top) and family-level (bottom) classifications were assigned using the Ribosomal Database Project (RDP) classifier on assembled full-length 16S rRNA genes. Day of life (DOL) is plotted on the X axis and relative abundance, generated by ‘expectation maximization iterative reconstruction of genes from the environment’ (EMIRGE), is plotted on the Y axis. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
  34. 34. Time Series of Rooms Infant 2 Infant 1 Electronics Hands Incubators Electronics Hands Incubator Sinks Surfaces Tubes Sinks Surfaces Tubes 3 6 9 12 15 18 21 24 27 30 3 6 9 12 15 18 21 24 27 30 3 6 9 12 15 18 21 24 27 30 3 6 9 12 15 18 21 24 27 30 3 6 9 12 15 18 21 24 27 30 3 6 9 12 15 18 21 24 27 30 1.00 relative abundance 0.75 0.50 0.25 0.00 1.00 0.75 0.50 0.25 0.00 day of life Phylum Electronics Actinobacteria Bacteroidetes Hands Cyanobacteria Incubators Firmicutes Fusobacteria Electronics Other Proteobacteria Hands 1.00 0.75 dance 0.50 0.25 Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Unclassified Incubators
  35. 35. 0.00 Time Series of Rooms 3 6 9 12 15 18 21 24 27 30 Phylum 3 6 9 12 15 18 21 24 27 30 Actinobacteria 3 6 9 12 15 18 21 24 27 30 3 6 9 12 15 18 21 24 27 30 3 6 9 12 15 18 21 24 27 30 3 6 9 12 15 18 21 24 27 30 day of life Bacteroidetes Firmicutes Cyanobacteria Fusobacteria Proteobacteria Other Unclassified Electronics Hands Incubators Electronics Hands Incubators Sinks Surfaces Tubes Sinks Surfaces Tubes 1.00 0.75 relative abundance 0.50 0.25 0.00 1.00 0.75 0.50 0.25 0.00 3 6 9 12 15 18 21 24 27 30 3 6 9 12 15 18 21 24 27 30 3 6 9 12 15 18 21 24 27 30 3 6 9 12 15 18 21 24 27 30 3 6 9 12 15 18 21 24 27 30 3 6 9 12 15 18 21 24 27 30 day of life Aerococcaceae Gemellaceae Other Sphingomonadaceae Aeromonadaceae Comamonadaceae Lactobacillaceae Pasteurellaceae Staphylococcaceae Bacillaceae Corynebacteriaceae Micrococcaceae Propionibacteriaceae Streptococcaceae Carnobacteriaceae Enterobacteriaceae Moraxellaceae Pseudomonadaceae Unclassified Caulobacteraceae Family Clostridiaceae Enterococcaceae Neisseriaceae Rhizobiaceae Xanthomonadaceae Figure 1 Taxonomic classification of neonatal intensive care unit (NICU) room microbes for infants 1 and 2. Phylum-level (top) and family-level (bottom) classifications were for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Slides assigned using the Ribosomal Database Project (RDP) classifier on assembled full-length 16S rRNA genes.
  36. 36. Room and Gut Infant 1 Infant 2 Hands, Electronics, Surfaces, Incubator, Tubes, Fecal, Sinks Figure 2 Principle coordinates analysis (PCoA) based on UniFrac scores of room and gut microbes. Analysis reveals four discernible ecosystem clusters: skin associated communities, sinks, tubes, and feces. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
  37. 37. Figure 2 Principle coordinates analysis (PCoA) based on UniFrac scores of room and gut microbes. Analysis reveals four discernible ecosystem clusters: skin associated communities, sinks, tubes, and feces. Different Infants Figure 3 Time-series coverage emergent self-organizing maps (ESOMs) reveal discrete genome bins for each infant’s dataset. The underlying ESOMs are shown in a tiled display with each data point colored by its taxonomic assignment. Labels to the left are colored to match their respective data points and numbers in parentheses correspond to the bin numbers in Table 4. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
  38. 38. Genomes http://www.microbiomejournal.com/content/2/1/1 Table 4 Genome summaries Taxa Bin no. bp Contigs N50 % GC Cvg % SCG Bacteroides fragilis 6 4,551,095 39 249,654 43.3 1,930.3 99 Bacteroides phage1 4 205,842 1 205,842 41.9 2,221.4 0 Infant 1: Bacteroides phage2 5 144,903 1 144,903 42.0 2,060.8 0 Enterococcus faecalis 8 2,649,897 93 40,945 37.8 7.6 99 Clostridium ramosum 7 3,630,043 63 78,436 31.4 23.5 99 Escherichia coli 3 5,035,302 53 218,574 50.5 1,254.1 57 Klebsiella pneumoniae 1 5,447,442 78 189,741 57.3 345.0 37 Staphylococcus epidermidis plasmid 2 20,739 2 11,095 31.5 14.5 0 Actinomyces neuii strain 1 18 1,580,717 37 280,583 56.9 15.6 27 Actinomyces neuii strain 2 24 2,375,188 27 179,095 56.7 17.6 70 Actinomyces sp. 6 2,666,449 11 345,356 59.3 55.4 99 Anaerococcus prevotii 1 1,599,845 13 225,571 33.1 39.2 99 Caudovirales bacteriophage 26 18,308 1 18,308 29.5 1,169.7 0 Dermabacter sp. 4 2,040,279 12 289,797 62.8 51.9 90 Enterococcus faecalis 9 3,011,019 26 499,183 37.1 147.3 99 Enterococcus faecalis phage 14 335,286 39 12,896 34.8 103.7 0 Enterococcus faecalis plasmid 22 8,514 2 4,866 30.4 90.6 0 Finegoldia magna 7 1,729,913 42 78,482 32.0 93.0 99 Finegoldia phage 25 3,168 1 3,168 32.3 138.5 0 Finegoldia plasmid 1 23 7,589 2 3,969 33.0 103.4 0 Finegoldia plasmid 2 21 28,958 3 15,674 55.4 10.9 0 Pseudomonas aeruginosa 5 6,755,599 64 212,603 66.0 51.5 99 Staphylococcus epidermidis 10 1,902,759 82 40,484 33.0 65.4 7 Staphylococcus epidermidis mobile 17 55,503 10 6,452 31.7 54.5 43 Staphylococcus epidermidis phage 2 11 19,082 2 12,983 29.4 84.3 0 Infant 2: Staphylococcus epidermidis strain 3 81,754 9 14,965 29.4 67.1 0 Staphylococcus phage 1 13 216,785 13 8,080 29.5 45.7 0 Staphylococcus phage 2 16 198,742 14 20,782 0.3 79.3 0 Staphylococcus phage 3 and plasmid 15 137,609 12 19,343 29.3 67.8 0 Staphylococcus warneri 8 2,363,750 22 198,467 32.8 33.9 53 Veillonella sp. 2 2,281,484 223 12,637 37.8 56.2 70 Highly Slides for UC Davis connected BE microbes shared by only two samples (fewer edges) are positioned EVE161 Course Taught by Jonathan Eisen Winter 2014 The distribution of shared OTUs across sampled sites was closer to the periphery of the network. The top 5% of most
  39. 39. Brooks et al. Microbiome 2014, 2:1 http://www.microbiomejournal.com/content/2/1/1 (a) Page 9 of 16 Fecal Sinks Tubes Hands Electronics Surfaces Incubators = OTUs = Samples (b) Figure 4 Spring-weighted edge-embedded network plots of room and fecal operational taxonomic units (OTUs). Found in two or more samples (infant 1 (a), infant 2 (b)). Left, the entire network is displayed. To better visualize the distribution of gut colonizers across room samples, only room samples sharing fecal OTUs are shown in the excerpt (right). Triangles represent samples and circles represent OTUs. The spring weight is derived from ‘expectation maximization iterative reconstruction of genes from the environment’ (EMIRGE) generated abundances and edges are colored by environment type. Each OTU has a taxonomic label and asterisks indicate OTUs detected in room samples before detection in the gut. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
  40. 40. The NICU as a reservoir for gut colonists Figure 5 summarizes the gut colonizing organisms found in room samples at the genera level. Typically, for both infants, electronics had the lowest relative abundance of organisms detected in the gut whereas tubing had the Infant 1 Electronics Hands Incubators Sinks Surfaces Tubes 1.00 Fecal 1.00 0.75 relative abundance relative abundance 0.50 0.75 0.50 0.25 0.00 1.00 0.75 0.25 0.50 0.25 0.00 6 9 12 15 18 21 day of life 24 27 30 0.00 Species 3 B. fragilis C. ramosum E. coli E. faecalis K. pneumoniae 6 9 12 15 18 21 24 27 30 3 6 9 12 15 18 21 24 27 30 3 day of life 6 9 12 15 18 21 24 27 30 Phylum / Genus did not colonize gut Bacteroidetes / Bacteroides Infant 2 Proteobacteria / Escherichia Firmicutes / Clostridium Firmicutes / Enterococcus Proteobacteria / Klebsiella Electronics Hands Incubator Sinks Fecal Surfaces Tubes 1.00 1.00 0.75 0.50 relative abundance 0.75 relative abundance Gut vs Room infant 2’s fecal samples fall within the top ten most frequently occurring OTUs in the room environment. Interestingly, infant 2’s most abundant gut colonists, Staphylococcus sp. and E. faecalis, are the two most frequently occurring OTUs in the room environment. 0.50 0.25 0.00 1.00 0.75 0.25 0.50 0.25 0.00 0.00 12 15 18 21 24 27 30 3 6 9 12 15 18 21 24 27 30 Actinomyces sp. Dermabacter sp. E. faecalis F. magna Actinobacteria / Actinomyces did not colonize gut P. aeruginosa 6 9 12 15 18 21 24 27 30 3 6 9 12 15 18 21 24 27 30 Phylum / Genus Species A. prevotti 3 day of life day of life Prochlorothrix sp. S. epidermidis Actinobacteria / Dermabacter Firmicutes / Anaerococcus Cyanobacteria / Prochlorothrix Firmicutes / Enterococcus Firmicutes / Finegoldia Veillonella sp. Firmicutes / Staphylococcus Firmicutes / Veillonella Proteobacteria / Pseudomonas Figure 5 Community composition of gut colonizing microbes and room microbes through the first month of life. Time-series characterization of the fecal microbial community (left) and fecal microbes concurrently collected from the room (right) display discrete reservoirs of gut colonizers in the neonatal intensive care unit. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
  41. 41. To further explore similarity of shared strains, reads SourceTracker to infant 2’s assembled confrom infant 1 were mapped tigs. Infant 1’s reads covered 95% of the length of infant 2’s assembly at an average of 4.66X coverage. Read Infant 1 proteins [57]. Particularly interesting are genes encoding the QacA/B MFS, SugE SMR, and MexA/B RND proteins, which are a growing concern in hospitals due to coselection through the practice of combining two or more types Infant 2 % probability of source 1.00 0.75 0.50 0.25 0.00 6 9 12 15 18 Electronics 21 24 Hands 27 30 day of life Incubators 12 Sinks 15 Surfaces 18 21 Tubes 24 27 30 Unknown Figure 6 The most probable source of gut colonizing microbes. This was generated using the source-sink characterization software, SourceTracker. Neonatal intensive care unit room sequences were designated as putative sources and fecal sequences sinks. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
  42. 42. http://www.microbiomejournal.com/content/2/1/1 E. faecalis - the one taxon shared between infants Streptococcus_suis_JS14 Streptococcus_suis_SC070731 Streptococcus_suis_ST1 Streptococcus_gallolyticus_subsp._gallolyticus_ATCC_BAA-2069 Streptococcus_gallolyticus_subsp._gallolyticus_ATCC_43143_DNA 100 97 Streptococcus_agalactiae_2603V/R 100 Streptococcus_dysgalactiae_subsp._equisimilis_GGS_124_chromosome_1 Streptococcus_equi_subsp._equi_4047 100 Streptococcus_equi_subsp._zooepidemicus Streptococcus_equi_subsp._zooepidemicus_str._MGCS10565_2 Streptococcus_equi_subsp._zooepidemicus_str._MGCS10565 Streptococcus_mutans_NN2025 100 Streptococcus_mutans_GS-5 Streptococcus_mutans_LJ23 Enterococcus_faecalis_06-MB-DW-09 Enterococcus_faecalis_06-MB-DW-09 Enterococcus_casseliflavus_EC20 Enterococcus_casseliflavus_EC20 Enterococcus_hirae_ATCC_9790 Enterococcus_hirae_ATCC_9790 Enterococcus_faecium_Aus0004 100 Enterococcus_faecium_NRRL_B-2354 100 100 Enterococcus_faecium_Aus0004 Enterococcus_faecium_NRRL_B-2354 Enterococcus_faecalis_V583 95 Enterococcus_faecalis_V583 Enterococcus_faecalis_62 Enterococcus_faecalis_62 95 Enterococcus_faecalis_TX0645 Enterococcus_faecalis_TX0645 Enterococcus_faecalis_SLO2C-1 Enterococcus_faecalis_SLO2C-1 Enterococcus_faecalis_TX0312 100 Enterococcus_faecalis_TX0312 Enterococcus_faecalis_TX1346 Enterococcus_faecalis_TX1346 Enterococcus_faecalis_D811610-10 Enterococcus_faecalis_D811610-10 Enterococcus_faecalis_TX0630 Enterococcus_faecalis_TX0630 Enterococcus_faecalis_B83616-1 Enterococcus_faecalis_B83616-1 Enterococcus_faecalis_KI-6-1-110608-1 Enterococcus_faecalis_KI-6-1-110608-1 Enterococcus_faecalis_ERV63 Enterococcus_faecalis_ERV63 Infant1_Enterococcus_faecalis Infant1_Enterococcus_faecalis Infant2_Enterococcus_faecalis Infant2_Enterococcus_faecalis Enterococcus_faecalis_OG1X Enterococcus_faecalis_OG1X Enterococcus_faecalis_Symbioflor_1 Enterococcus_faecalis_Symbioflor_1 Enterococcus_faecalis_OG1RF_ATCC_47077 Enterococcus_faecalis_OG1RF_ATCC_47077 Staphylococcus_aureus_aureus_11819-97 100 Staphylococcus_epidermidis_ATCC_12228 Staphylococcus_epidermidis_RP62A Klebsiella_pneumoniae_NTUH-K2044 0.1 0.01 Figure 7 Enterococcus faecalis phylogeny using 32 concatenated ribosomal proteins reveals closely related strains. The maximum likelihood phylogeny of E. faecalis strains was based on a concatenation of single-copy, highly conserved ribosomal proteins from our data set and available reference genomes. Bootstrap values greater than 50 are shown. An excerpt of the E. faecalis clade is shown to the right. Next-generation sequencing surveys in the ICU have of antibiotic treatments [58]. Resistance to multiple types of antibiotics can arise from a single resistance mechanism reported high levels of community diversity. Poza et al. such as efflux pumping [59]. In addition to antibiotics, found 1,145 distinct OTUs in an ICU in Spain [8] and these pumps can expel quaternary ammonium com- subsequent studies reported 1,621 and 3,925 OTUs in a pounds (QACs), the active biocide in the detergent used NICU in the US and in an Austrian ICU, respectively [9,10]. While comparing these studies is difficult due to to clean hospital surfaces during UC Davis EVE161 notable Taught by Jonathan Eisen Winter 2014 Slides for the study. Other Course
  43. 43. colonists later in ds were nizer, E. ne level using a , singleno acids s strains r, but are ure 7). ns, reads led conof infant ge. Read Streptococcus_equi_subsp._equi_4047 sequence identity. 100 Streptococcus_equi_subsp._zooepidemicus Streptococcus_equi_subsp._zooepidemicus_str._MGCS10565_2 Streptococcus_equi_subsp._zooepidemicus_str._MGCS10565 Streptococcus_mutans_NN2025 100 Streptococcus_mutans_GS-5 Genes relevant to adaptation to the NICU environment Streptococcus_mutans_LJ23 Enterococcus_faecalis_06- Enterococcus_faecalis_06-MB-DW-09 Enterococcus_casseliflavus_ Analysis of reconstructed genomes for gut microorganisms can lend clues as to how organisms detected in the GIT and room environment are able to persist in the NICU, which is subjected to regular cleaning/sterilization. Numerous antibiotic resistance genes were found in genomes of microorganisms in fecal samples of both infants. A large portion of these were efflux pumps, with representatives from all four families of multidrug transporters: major facilitator superfamily (MFS), small multidrug resistant (SMR), resistance-nodulation-cell division (RND), and multidrug and toxic compound extrusion (MATE) Figure 7 Enterococcus faecalis phylogeny are genes encoding proteins [57]. Particularly interestingusing 32 concatenated ribosomal proteins reveals closely r likelihood phylogeny of E. faecalis strains was based on a concatenation of single-copy, highly conserved rib the QacA/B reference genomes. Bootstrap values greater than 50 are shown. An excerpt of the E. faecalis c MFS, SugE SMR, and MexA/B RND proteins, and available which are a growing concern in hospitals due to coselection through the practice of combining two or more types Next-generation sequen of antibiotic treatments [58]. Resistance to multiple types of antibiotics can arise from a single resistance mechanism reported high levels of co such as efflux pumping [59]. In addition to antibiotics, found 1,145 distinct OTU Infant 2 these pumps can expel quaternary ammonium com- subsequent studies report pounds (QACs), the active biocide in the detergent used NICU in the US and in to clean hospital surfaces during the study. Other notable [9,10]. While comparing t Enterococcus_casseliflavus_EC20 Enterococcus_hirae_ATCC_9790 Enterococcus_faecium_Aus0004 100 Enterococcus_faecium_NRRL_B-2354 1 100 Enterococcus_faecalis_V583 Enterococcus_faecalis_62 95 95 Enterococcus_faecalis_TX0645 Enterococcus_faecalis_SLO2C-1 Enterococcus_faecalis_TX0312 100 Enterococcus_faecalis_TX1346 Enterococcus_faecalis_D811610-10 Enterococcus_faecalis_TX0630 Enterococcus_faecalis_B83616-1 Enterococcus_faecalis_KI-6-1-110608-1 Enterococcus_faecalis_ERV63 Infant1_Enterococcus_faecalis Infant2_Enterococcus_faecalis Enterococcus_faecalis_OG1X Enterococcus_faecalis_Symbioflor_1 Enterococcus_faecalis_OG1RF_ATCC_47077 Staphylococcus_aureus_aureus_11819-97 100 Staphylococcus_epidermidis_ATCC_12228 Staphylococcus_epidermidis_RP62A Klebsiella_pneumoniae_NTUH-K2044 0.1 Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 0.01

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