UC Davis EVE161 Lecture 9 by @phylogenomics

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

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


!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


!3

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

!


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


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

Methods

Study Location


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


environmental selection, dispersal, diversification, and ecological

The biological processes described above can be fundamentally

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.


Architectural Design Data


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.


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.

PLOS ONE | www.plosone.org

3

January 2014 | Volume 9 | Issue 1 | e87093


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.


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.


• Then a semi-standard rRNA PCR workflow



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).

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

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


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).

[49] in R. Slides for UCthe consequences of beta-diversity
We also assessed Davis EVE161 Course Taught by Jonathan
Results

Eisen Winter 2014

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.



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.

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

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
taxa that were present in near
Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 This core microbiome was dom

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.

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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)

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.


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:

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


Methods


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Methods

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n
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ls
ens.
bs
mon,
m,
outM
hin
as
bs,
se
ll
d

of
is
o

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
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
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


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

Results


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


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.



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


Unclassified

Incubators

0.00

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.

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.


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.


Genomes


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


(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.


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.


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.



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_casseliflavus_EC20


Enterococcus_hirae_ATCC_9790

Enterococcus_faecium_Aus0004
100

Enterococcus_faecium_NRRL_B-2354

100
100


Enterococcus_faecalis_V583
95


Enterococcus_faecalis_62

95

Enterococcus_faecalis_TX0645


Enterococcus_faecalis_SLO2C-1



100




Enterococcus_faecalis_D811610-10




Enterococcus_faecalis_B83616-1

Enterococcus_faecalis_KI-6-1-110608-1


Enterococcus_faecalis_ERV63


Infant1_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

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

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_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



100


1

100


95

95



100









Staphylococcus_aureus_aureus_11819-97

100

Staphylococcus_epidermidis_ATCC_12228

Staphylococcus_epidermidis_RP62A

Klebsiella_pneumoniae_NTUH-K2044

0.1


0.01

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