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Städtische
Block-Architektur: Mehr Keime in
Korridoren und Wohnungen
Auch Architektur
kann krank machen
http://www.srf.ch/gesundheit/alltag-umwelt/auch-architektur-kann-krank-machen
<Studie zeigt: Je städtischer wir
leben, desto mehr potenziell
krankmachende Bazillen umgeben
uns.>
Nicht nur unser Darm oder unsere Haut
sind von Mikroorganismen bevölkert,
sondern auch unsere Wohnräume. Doch je
nach Art des Wohnraums sind das ganz
unterschiedliche Mikroorganismen. Das
hat jetzt ein
amerikanisch-brasilianisches
Forscherteam herausgefunden. Und zwar
untersuchte es Mikroben für einen
systematischen Vergleich in vier sehr
verschiedenen Lebensräumen des
Amazonas-Gebietes:
- Strohgedeckte Waldhütten ohne
Aussenwände
- Häuser mit Wänden, jedoch ohne
Aussentür
- Häuser in einer Kleinstadt
- Häuser in der Grossstadt Manaos
An der University of Puerto Rico
wurden dann die Unterschiede in der
Zusammensetzung der Mikroorganismen
analysiert. Resultat: Je
architektonisch organisierter der
Lebensraum (wie das in Städten der
Fall ist), desto höher die
Konzentration potenziell
krankmachender Bazillen. Auch
Architektur kann also
gesundheitsschädigend sein.
ambm>
ENGLISCH: Walls
talk: Microbial biogeography of
homes spanning urbanization
Abstract
(englisch)
http://advances.sciencemag.org/content/2/2/e1501061
Westernization has propelled
changes in urbanization and
architecture, altering our
exposure to the outdoor
environment from that experienced
during most of human evolution.
These changes might affect the
developmental exposure of infants
to bacteria, immune development,
and human microbiome diversity.
Contemporary urban humans spend
most of their time indoors, and
little is known about the microbes
associated with different designs
of the built environment and their
interaction with the human immune
system. This study addresses the
associations between architectural
design and the microbial
biogeography of households across
a gradient of urbanization in
South America. Urbanization was
associated with households’
increased isolation from outdoor
environments, with additional
indoor space isolation by walls.
Microbes from house walls and
floors segregate by location, and
urban indoor walls contain human
bacterial markers of space use.
Urbanized spaces uniquely increase
the content of human-associated
microbes—which could increase
transmission of potential
pathogens—and decrease exposure to
the environmental microbes with
which humans have coevolved.>
INTRODUCTION
Urbanization of
traditional villages—the villages
developing in more urban form, and
historical villagers migrating to
towns and cities—is occurring
concurrently with a global
convergence toward a more
Westernized urban plan and
life-style (1).
This process occurs as human
societies integrate from
hunter-gatherers into first rural
and then urban life-styles.
Urbanization also involves more
people spending most of their
lives in indoor built environments
(2,
3).
A large proportion of
the microbes found in the built
environment are shed by humans (4–7)
or animals (8),
and with natural ventilation,
microbes can also be transported
from outdoors (5,
6,
9).
Understanding the consequences of
architectural changes on
environmental exposures, including
microbial exposures, is therefore
important in improving home design
and ultimately human health. Here,
we determine the changes in
architectural design and the
resulting microbial communities of
houses spanning a range of
modernization within the Amazon
River basin. We measured community
demographics and architectural
parameters, and characterized the
microbial communities of 10 houses
and their inhabitants from each of
four locations: a traditional
jungle village of hunter-gatherers
near the border between Peru and
Ecuador, a rural village further
east along a similar latitude, the
large Peruvian town of Iquitos,
and, finally, the modern Brazilian
city of Manaus (Fig.
1A).
RESULTS
The jungle village of
Checherta is a 21-house Achuar
community of hunter-gatherers
(tables S1 and S2, and fig. S1, A
and B). Homes are organized around
a central area, including a
communal building. This community
design is retained in the 25-house
rural village of Puerto Almendras,
with the homes surrounding a
soccer field (fig. S1C and table
S1). Iquitos has 0.4 million
inhabitants and is the largest
urban population in the world not
accessible by roads (fig. S1C and
tables S1 and S2). Manaus, the
capital of Amazonas State in
Brazil, is a contemporary Western
city with 1.8 million inhabitants
(fig. S1D and tables S1 and S2).
Although no significant
environmental differences were
found across the urbanization
gradient, large architectural
changes were observed (Fig.
1). No significant
differences were found across the
studied locations in outdoor
temperature (mean variation,
<2°C; table S3) or relative
humidity, and all locations had
high ventilation rates (air
exchange rates of 25 to 100 h−1
in the jungle village, 7 to 20 h−1
in the rural village, 4 to 17 h−1
in the town, and 0.8 to 15 h−1
in the city). The jungle village
homes of Checherta are open huts
made of wood and reeds, and are
generally single open-plan spaces
composed of two functional areas (Fig.
1, A and B, and fig. S2): a
dormitory containing one platform
bed per family, and a fire area
for cooking and socializing. Up to
six core families, among extended
family members, share a home. As
urbanization increases, a
progressive separation of the
indoor environment from the
outdoor occurs first, followed by
internal division of home spaces
and the use of a wider variety of
building materials (table S4). In
the rural village, a toilet
appears as an external latrine,
which in the town and city becomes
a piped indoor bathroom. Town and
city houses typically have
additional spaces differentiated
by functional purpose (living
room, kitchen, and bathroom) and
segregated by walls (Fig.
1B).
Houses in the most
urbanized conditions are more
variable in design, but in
general, there is an
urbanization-associated increase
in the number of rooms per person
(privacy index) (Fig.
1C, fig. S3, and table S4),
house area, and its variance (P
< 0.005; Fig.
1C and table S4). The
average house occupancy (persons
per square meter) decreases with
urbanization (P <
0.005; Fig.
1C and table S4), which is
consistent with higher area and
smaller families.
Remarkably,
classification of house functional
spaces using microbes was possible
(Fig.
1D and fig. S4), and the
probability of correct assignment
given the wall bacterial
composition increased with
urbanization (Fig.
1E). We tested for
differences in the types and
diversity of household bacteria
across locations. Microbial
richness (α diversity) did not
change with urbanization (Fig.
2 and figs. S5 and S6), but
bacterial composition was markedly
different (Fig.
2, A and B, and figs. S7 and
S8) with houses becoming more
microbially distinct along the
gradient. Bacterial community
structure in samples from floor
and walls converged with
urbanization (Fig.
2B). At the jungle end of
the gradient, floors were made of
dirt and people walked barefoot,
and walls were wood columns; at
the city end, floors and walls
were made of synthetic materials,
and people walked with shoes (in
all but one house). Moreover, wall
microbes better differentiated the
kitchen and bathroom functional
spaces in urban than in rural
houses (Fig.
1D and fig. S9). The 10 most
important operational taxonomic
units (OTUs) that help
discriminate among rooms in Manaus
comprise several taxa normally
associated with the human oral
cavity, including Streptococcus,
Neisseria, Actinomyces,
and Veillonella dispar,
as well as taxa normally
associated with the human gut such
as Enterobacteriaceae.
 Despite lower occupant
density in urban houses,
“humanization” of the houses
occurred with increased
urbanization (Fig.
2D), associated with home
enclosure—isolation from the
outdoor environment—especially in
dwellings sealed for air
conditioning. Human bacteria were
enriched in the town and city
houses, with Prevotella,
Verrucomicrobia, and Serratia
on the walls (figs. S7 and S10),
and skin taxa on the floors,
consistent with human shedding (7,
10–12)
and with the isolation of homes
from bacterial sources from
outdoor environments.
Environmental bacteria were
proportionally higher in the
jungle and rural village house
floors and included soil bacteria
[for example, Mesorhizobium
and Luteimonas from
water sources and Rickettsiella
from arthropods (figs. S7 and
S10)]. The environmental bacterium
found in walls included Acidobacteriales,
Bradyrhizobium, Dactylosporangium,
Actinomycetospora, Actinoalloteichus,
Saccharopolyspora, Pedomicrobium,
and Rickettsiella (figs.
S7 and S10). As we move from the
rural to the urban locations,
there is a shift within
Actinobacteria, from Brachybacterium
and Brevibacterium
commonly found in the environment
to Corynebacterium,
common in human skin (Fig.
2B).
A Bayesian approach
called SourceTracker allowed the
estimation of proportion of each
community (that is, sample) that
are likely to originate from each
of a specified set of source
environments (9).
This analysis further confirmed
the presence of a partially
oral-like community on the urban
bathroom walls (Fig.
3); these traces of human
oral microbes from bathrooms and
traces of water-associated
microbes on kitchen countertops
and walls likely contribute to the
increased ability to identify both
the houses and the indoor
functional spaces.
 We found no systematic
association between the bacterial
communities and many other
parameters measured in the study
including the structural materials
in the households, number of
people living in the house, number
of pets (Fig.
2C), temperature variations,
light incidence, frequency of
cleaning, number of outsiders at
sampling time, date of last rain,
and time of day samples were
collected (P > 0.05 in
all cases). In particular,
consistent with recent studies (11,
13),
we find that samples within a
house with different materials are
more similar to one another than
samples from the same material
across different houses and that,
in all communities, the
inhabitants of each house are a
major source of bacteria (Fig.
3).
DISCUSSION
Our findings indicate
that the bacteria from the
surfaces of house walls are
informative of level of
urbanization based on
architectural design. Floors are
the most informative of the
commonalities found in
individual houses across
urbanization levels, whereas
walls, less perturbed reservoirs
of microbes accumulated through
room usage, provide an indicator
of room function.
Ventilation,
described as a key factor for
microbial community composition
in urban settings (14–16),
was very high in all of the
houses of our study and does not
explain differences in home
microbial composition with
urbanization. Instead, we
propose that the presence of
walls dividing functional spaces
acquires function-dependent
microbes, mostly of human
origin.
The current study is
limited to one geographical
region of the world and is a
small pilot study, and thus,
results may not be
generalizable. Further research
should identify mechanistic
explanations for these
phenomena. Insights into the
chemical signals that bacteria
provide in different sites
within the home are also needed.
These remarkable changes in
house microbial content across
urbanization might translate
into differences in microbial
exposure that may have
developmental health
implications for humans (17),
according to several related
hypotheses [the “hygiene”
hypothesis (18),
the “Old Friends” hypothesis (19),
and the “Disappearing
microbiota” hypothesis (20)],
suggesting that the reduced
pattern of microbial exposure
leads to immune and metabolic
disorders that have become the
new disease paradigm in the
industrialized world.
MATERIALS AND METHODS
Design of the study
We selected four
communities at the same
latitude in the Amazon Basin,
with different degrees of
urbanization (fig. S1): an
isolated jungle village, a
rural community, an urban
town, and an urban city. The
specific locations were
selected to represent four
significantly different
urbanization levels with
similar climate. Ten houses
from each location were
sampled in four sites to
characterize architectural and
microbiological profiles of
house walls and floors. The
sample size of n =
40 per location was based on
estimations using a two-sided
test, for significant
differences in the microbial
composition, with a Cohen’s d
= 0.63, power of 80%, and α =
0.05.
Communities’ description
Four human settings
were studied in this work,
spanning urbanization. Three
of them were in Peru, and one
in Brazil.
The Peruvian rural
community of Checherta is a
traditional, native,
hunter-gatherer, Amerindian
village in the border between
Peru and Ecuador (fig. S1). It
is inhabited by approximately
300 inhabitants, living in
open huts, with the exception
of one house that was enclosed
from the outside by walls
(fig. S11), made of natural
materials (Fig.
1A and fig. S2). It has
a recently made school
consisting of one classroom
and three adjacent latrines,
which remain unused by the
locals. Checherta has no
electricity or potable water
services; water is obtained
from the nearest river, and
the village is highly
inaccessible, requiring travel
by a plane that can land on an
improvised landing field in
Nuevo Andoas and then taking a
2-day trip on small river
boats (table S1).
The second Peruvian
community, Puerto Almendras,
is a rural setting located at
~1-hour drive (12 km) west
from Iquitos. It has ~250
inhabitants that live in
houses with external walls,
made out of both natural and
industrial materials. Most of
the houses were not internally
subdivided, and those spaces
that were remained connected
with adjacent areas because
the walls did not reach the
roof. Puerto Almendras has a
water reservoir (however, no
potable water service),
electricity service, a school,
and a health care center
within walking distance.
Houses are distributed around
a soccer field.
The third Peruvian
community was the town of
Iquitos, the world’s biggest
populated center that is
inaccessible by road—it is
accessible only by plane or
boat (table S1). This town has
371,000 inhabitants, an
international airport, paved
roads, municipally treated
piped water, and electricity.
All houses are enclosed in
external walls that separate
them from the outdoor
environment and are made of
industrial materials. Walls
that divide the inner house do
not always reach the roof.
The fourth location
was Manaus in Brazil, the
biggest city in the Amazon
region, with a population of
1.8 million, accessible by
roads, boats, and planes.
Sampled houses were completely
separated from the environment
and internally divided by
walls. Unlike the Peruvian
communities, this city has
enormous social differences,
and we sampled homes from
middle class families.
Architectural determinations
Sketches of the
houses were created with
measurements collected in the
field, with photographs of
each household (Fig.
1A and figs. S2 and
S11), that provided the basis
for estimations of floor
area/surface, volume, openness
(proportion of apertures,
location in floor plans, and
orientation), human density
(number of people per square
meter), and privacy index for
each household. Additionally,
this information sets the
basis for modeling using a
building modeling program
(Autodesk Revit) to produce
three-dimensional
representations of each
sampled house.
Environmental
variables of temperature and
relative humidity were
collected. A HOBO Micro
Station Data Logger (H21-002)
was used to record 2-min
interval data of temperature
and relative humidity.
Analysis of qualitative data
from both architecture and
environmental variables was
made using the SPSS version 20
program to compare variations
in architecture and
environment between locations.
Bacterial community
structure determinations
Microbial samples
were collected (using sterile
swabs) from floors and walls
of living rooms, kitchens,
bedrooms, and bathrooms—or
equivalent functional spaces
in jungle houses—of each
household. Metadata
information from each sample
was recorded, including
surface material, sample
height (walls), cleaning
frequency, presence of pets in
the home, light, surface
temperature, and whether
people wore shoes. Shoes were
worn in 36% of family members
in Checherta, 75% in Puerto
Almendras, and 100% in Iquitos
and Manaus.
Cryovial-containing
samples were frozen in a dry
shipper and stored at −80°C
until DNA was extracted using
the MoBio PowerSoil Kit
(following the manufacturer’s
instructions). The V3-V4
regions of the 16S rRNA
gene were sequenced using the
HiSeq Illumina platform.
Sequences were analyzed using
the Qiime pipeline. Sequences
were trimmed at 100 base
pairs, and open-reference OTU
picking (21)
was performed at a 97%
identity to assign taxonomy
using Greengenes version 13_8
(22)
and to characterize novel
taxa. α Diversity was
estimated using PD whole tree
(23)
on rarefied tables at 10,000
sequences per sample for
floors and 2500 sequences per
sample for walls. β Diversity
was measured using unweighted
UniFrac (24)
on the rarefied tables.
Finally, the Bayesian approach
SourceTracker was used to
identify possible sources of
contamination (9).
SUPPLEMENTARY
MATERIALS
Supplementary
material for this article is
available at http://advances.sciencemag.org/cgi/content/full/2/2/e1501061/DC1
Fig. S1. Satellite
view and urban plan of the
communities selected for this
study in the South American
Amazonas.
Fig. S2. Typical
house in the jungle community
of Checherta.
Fig. S3.
Distribution of privacy index
on each location.
Fig. S4.
UniFrac-based PCoA of
microbial communities in the
floors (A and B) and walls (C
and D), colored by village (A
and C) and by room (B and D).
Fig. S5. Bacterial
α diversity calculated with
the PD whole tree metric on
floor and wall samples by
location.
Fig. S6. Bacterial
α diversity calculated with
the PD whole tree metric on
floor and wall samples by home
site in each location.
Fig. S7. Taxonomic
composition of floor and wall
samples from each location.
Fig. S8. UniFrac
distances of the microbial
communities in floors and
walls, and between and within
locations.
Fig. S9.
UniFrac-based PCoA of
microbial communities in the
walls of jungle (Checherta)
and city (Manaus).
Fig. S10.
Discriminative bacteria from
each location based on an LDA
effective size (LEfSe)
analysis on floor and walls
from all villages.
Fig. S11. Blueprint
of a closed, segregated house
from Checherta.
Table S1. Urban
parameters of the four
sampling sites for this study.
Table S2.
Architectural qualitative
descriptions of the studied
settlements.
Table S3. Summary
statistics for indoor
temperature and relative
humidity on the four locations
at 1-min intervals.
Table S4.
Architectural parameters for
the studied settlements
(averages and SDs).
This is an open-access
article distributed under the
terms of the Creative Commons
Attribution-NonCommercial
license, which permits
use, distribution, and
reproduction in any medium, so
long as the resultant use is not
for commercial advantage and
provided the original work is
properly cited.
REFERENCES AND NOTES
- ↵
J. N.
Pieterse, Globalization
and Culture: Global
Mélange (Rowman
& Littlefield, Lanham,
MD, 2015).
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Acknowledgments:
We acknowledge the
collaborators in Peru, the
interpreter J. J. Semu, and the
late Father Luigi for being a
source of information and
inspiration. In Manaus, we had the
valuable help of A. Vasconcelos
and J. Machado with the fieldwork.
Funding: This
work was supported by the Sloan
Foundation, the C&D Fund, and
the Emch Fund for Microbial
Diversity (to M.G.D.-B.). Partial
support was also provided by the
NIH Research Initiative for
Scientific Enhancement Program
2R25GM061151-13 (to J.F.R.-C.). Author
contributions:
M.G.D.-B. and R.K. designed the
study. J.F.R.-C., H.C., A.N.,
J.N.H., R.R., O.H.B., H.P.,
L.C.P., M.J.B., and M.G.D.-B.
collected the data and processed
the specimens. J.F.R.-C., S.J.S.,
R.K., H.C., L.R.P., and M.G.D.-B.
analyzed the data. M.G.D.B.,
J.F.R.-C., H.C., S.J.S., and R.K.
drafted the manuscript. All
authors reviewed the final
manuscript. Competing
interests: The authors
declare that they have no
competing interests. Data
and materials availability:
All data needed to evaluate the
conclusions in the paper are
present in the paper and/or the
Supplementary Materials.
Additional data related to this
paper may be requested from the
authors and/or http://qiita.ucsd.edu/study/description/10333.
- Copyright
© 2016, The Authors
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