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Household washing machines (WMs) launder soiled clothes and textiles, but do not sterilize them. We investigated the microbial exchange occurring in five household WMs. Samples from a new cotton T-shirt were laundered together with a normal laundry load. Analyses were performed on the influent water and the ingoing cotton samples, as well as the greywater and the washed cotton samples. The number of living bacteria was generally not lower in the WM effluent water as compared to the influent water. The laundering process caused a microbial exchange of influent water bacteria, skin-, and clothes-related bacteria and biofilm-related bacteria in the WM. A variety of biofilm-producing bacteria were enriched in the effluent after laundering, although their presence in the cotton sample was low. Nearly all bacterial genera detected on the initial cotton sample were still present in the washed cotton samples. A selection for typical skin- and clothes-related microbial species occurred in the cotton samples after laundering. Accordingly, malodour-causing microbial species might be further distributed to other clothes. The bacteria on the ingoing textiles contributed for a large part to the microbiome found in the textiles after laundering.
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ORIGINAL RESEARCH
published: 08 December 2015
doi: 10.3389/fmicb.2015.01381
Edited by:
Yuj i Mo rita,
Aichi Gakuin University, Japan
Reviewed by:
Dirk Bockmühl,
Rhine-Waal University of Applied
Sciences, Germany
Tiago Palladino Delforno,
Universidade Estadual de Campinas,
Brazil
*Correspondence:
Nico Boon
nico.boon@ugent.be
These authors have contributed
equally to this work.
Specialty section:
This article was submitted to
Infectious Diseases,
a section of the journal
Frontiers in Microbiology
Received: 11 August 2015
Accepted: 20 November 2015
Published: 08 December 2015
Citation:
Callewaert C, Van Nevel S, Kerckhof
F-M, Granitsiotis MS and Boon N
(2015) Bacterial Exchange
in Household Washing Machines.
Front. Microbiol. 6:1381.
doi: 10.3389/fmicb.2015.01381
Bacterial Exchange in Household
Washing Machines
Chris Callewaert1, Sam Van Nevel1, Frederiek-Maarten Kerckhof1,
Michael S. Granitsiotis2and Nico Boon 1*
1Laboratory of Microbial Ecology and Technology, Department of Biochemical and Microbial Technology, Faculty of
Bioscience Engineering, Ghent University, Ghent, Belgium, 2Research Unit Environmental Genomics, Department of
Environmental Science, Helmholtz Zentrum München, Neuherberg, Germany
Household washing machines (WMs) launder soiled clothes and textiles, but do not
sterilize them. We investigated the microbial exchange occurring in five household
WMs. Samples from a new cotton T-shirt were laundered together with a normal
laundry load. Analyses were performed on the influent water and the ingoing cotton
samples, as well as the greywater and the washed cotton samples. The number of
living bacteria was generally not lower in the WM effluent water as compared to the
influent water. The laundering process caused a microbial exchange of influent water
bacteria, skin-, and clothes-related bacteria and biofilm-related bacteria in the WM.
A variety of biofilm-producing bacteria were enriched in the effluent after laundering,
although their presence in the cotton sample was low. Nearly all bacterial genera
detected on the initial cotton sample were still present in the washed cotton samples.
A selection for typical skin- and clothes-related microbial species occurred in the cotton
samples after laundering. Accordingly, malodour-causing microbial species might be
further distributed to other clothes. The bacteria on the ingoing textiles contributed for a
large part to the microbiome found in the textiles after laundering.
Keywords: skin, fabrics, microbiome, domestic, washing machine
INTRODUCTION
Bacteria enter the washing machine (WM) via worn clothing, household linen and influent
water, while the laundry process is expected to deliver both visually and hygienically clean
laundry (O’Toole et al., 2009;Teufel et al., 2010). In the past decades, laundry processes
and detergents were substantially adjusted for environmental and economic reasons. The
introduction of enzymes and the concomitant lower washing temperature, a decreased water
consumption and the use of liquid detergents without disinfecting bleaching agents are some
of the main adaptations in the laundry processes (Terpstra, 1998;Munk et al., 2001;Laitala
and Jensen, 2010). These adjustments have impacted the hygienic quality of the laundry
process.
In Europe, colored laundry is most often washed at temperatures between 30 to 40C(Arild
et al., 2003), offering good circumstances for bacteria to survive, or even, to grow. In China, South-
Korea, Japan, and the USA, cold water is the most preferred water type (Pakula and Stamminger,
2010). Only an estimated 5% of the household laundering in the USA is done at 60Corhigher,
an advised temperature for effectively killing possibly pathogenic bacteria (Munk et al., 2001;
Bloomfield et al., 2007;Gerba and Kennedy, 2007). Any lower washing temperature offers survival
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Callewaert et al. Bacterial Exchange in Washing Machines
conditions for bacteria and induces cross-contamination in the
laundry. Staphylococcus aureus and S. epidermidis, for example,
have been shown to survive laundry programs at 50C(Munk
et al., 2001).
A lower washing temperature of 40Cisonlysucientfor
disinfection when bleaches are used in the detergents (Bloomfield
et al., 2007). These bleaches are mainly chlorine and peroxide
based, with sodium hypochlorite being most used. They are still
commonly added in hospital laundering, contrary to household
laundering (Patel et al., 2006). Normal household detergents
are developed primarily for removing dirt and stains, not for
disinfection. At lower washing temperatures, the bleach activity
strongly decreases and becomes insufficient (Ainsworth and
Davis, 1989;Davis and Ainsworth, 1989). A thorough drying of
the laundry can effectively decrease the bacterial load, although a
slow drying process results in a considerable amount of bacterial
growth, giving rise to malodour formation in the laundry (Munk
et al., 2001).
The surviving bacteria build up biofilms in the WM and have
higher resistance toward the used detergents. Modern machines
often contain numerous plastic parts, which is ideal for adhesion
and development of biofilms (Sheane, 2000). WM biofilms
are shown to harbor many possible human pathogens like
Pseudomonas aeruginosa and Klebsiella pneumoniae, sometimes
even considerably more than toilets. Next to the pathogen risks,
they induce malodour in WMs and freshly washed clothes (Munk
et al., 2001;Gattlen et al., 2010). In order to counteract the
biofilm build-up, most WM producers advice a maintenance
wash, a monthly wash at high temperatures, preferably involving
a bleaching agent.
Despite the existing knowledge, the understanding of
bacterial exchange in WMs remains inadequate. Next-generation
sequencing is the new standard to characterize bacterial
communities and offers an in-depth analysis of the microbiome
(Turnbaugh et al., 2007). This study focused on gaining
knowledge on the microbiome before and after a WM cycle and
the possible cross-contamination within a WM. We characterized
the bacterial communities of the influent and effluent water of the
WMs from five different families after running a similar washing
program and load. Additionally, we included a newly bought
cotton T-shirt and examined the microbial community of the
fabric both before and after the laundering programs (Figure 1).
MATERIALS AND METHODS
Experimental Design
The microbiome of five different household washing machines
(WM 1–5) were studied (Figure 1). This study focussed on the
bacterial flows throughout the WMs (no yeasts or fungi). To
allow meaningful comparisons, a similar worn laundry load was
washed on the same day. Samples were taken from the influent
water (tap water or rainwater), the effluent water after washing
(greywater) and a cotton sample from a never worn T-shirt,
before and after laundered in the WM. Absolute quantification
(actual cell count) of bacterial cells was performed using the
‘old’ and ‘new’ standard technique: agar plating and flow
cytometry. Relative quantification (ratio of specified sequencing
read counts to total sequencing read counts) and identification
was performed using the ‘old’ and ‘new’ standard technique:
Denaturing Gradient Gel Electrophoresis (DGGE) and amplicon
pyrosequencing. Descriptive α-andβ-diversity analyses were
performed on the results.
Washing and Sampling
Washing machines of five different Belgian households located
in East and West Flanders were studied (Supplementary Table
S1). All members of these households were in good health
and none of them had taken antibiotics for at least 1 month.
The WMs were operated at a similar washing program: 30C,
delicate washing program, no pre-wash, centrifuging at 500 rpm,
45 g Le Chat powder detergent (Henkel, Germany), without
fabric softener. The washing powder did not contain bleaching
agents. A representative filling of the WM was obtained by
laundering a load made up of colored textiles which have been
worn by the household members: one pair of jeans, five pairs
of socks, five pieces of underwear, and five T-shirts. The cross-
contamination in the WM was studied by adding a 25 cm2
piece of a newly bought cotton T-shirt to these clothes. The
washing was performed on the same day in Spring. After the
completion of the complete washing program and during the last
centrifugation, the first liter of greywater effluent was discarded
after which a sample was collected. The initial influent water
used in the WM was also sampled. Tap water was used in two
of the five WMs (WM 1–2), while the other three machines
used rainwater (WM 3–5). The rainwater tanks and piping were
localized underground; tap water originated from the Belgian
public water supply, as well with underground piping. The cotton
sample was analyzed before and after washing in an aseptic
manner. After washing, the cotton sample was placed into a
sealed plastic bag and stored at 4C prior to analysis. Cotton was
chosen as a representative clothing textile.
Plating
A sequential series of 1:10 dilutions of the liquid samples was
made in sterile saline solution (8.5 g/L NaCl) and plated by
pour plating on Nutrient Agar (NA), Mc Conkey Agar (MCA),
and Mannitol Salt Agar (MSA) to estimate total plate count
(TPC), total Gram-negative bacteria, and total Staphylococcus
and Micrococcus count, respectively. Incubation of all plates was
performed for 72 h at 28C under aerobic conditions.
Flow Cytometry
For the counting of intact bacterial cells, two fluorescent dyes,
SYBR R
Green I (SG) and propidium iodide (PI; Invitrogen,
Belgium) were used for staining (Wang et al., 2010). When
necessary, samples were diluted in 0.22 μm filtered bottled
mineral water prior to staining. The staining solution was
prepared as followed: PI (20 mM in dimethyl sulfoxide, DMSO)
was diluted 50 times and SG (10,000 times concentrate in
DMSO) was diluted 100 times in sterile DMSO. All samples
were stained with 10 μlml
1staining solution and 10 μlml
1
EDTA (pH 8, 500 mM) for outer membrane permeabilization.
10 μlml
1CytoCount counting beads (Dako, Belgium) were
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Callewaert et al. Bacterial Exchange in Washing Machines
FIGURE 1 | Study design: one pair of jeans, five T-shirts, five pairs of socks, and five pieces of underwear, worn by the family members, were
laundered using a delicate washing program, at 30C, with a detergent without bleaching agents. Samples were taken from the influent and effluent water,
and the unused cotton samples before and after laundering.
added as internal standard. Prior to flow cytometry analysis, the
stained samples were incubated for 5 min in the dark at 37C.
Flow cytometry was performed using a CyanTM ADP LX flow
cytometer as described by before (Boon et al., 2006). Only intact
bacterial cells were counted. Dust particles and non-bacterial cells
were excluded using the appropriate software.
DNA Extraction
Liquid samples were brought into a 50 ml sterile reaction tube.
50mlTNEbuer(10mMTris-HClpH8.0,10mMNaCl,
10 mM EDTA) was added to the sealed plastic bag with the
cotton sample and vortexed for 10 min (Callewaert et al., 2014).
The buffer was transferred into a 50 ml sterile reaction tube.
DNA was extracted using the MoBio UltraClean Water DNA
Isolation Kit (MO BIO Laboratories, Canada), according to
manufacturer’s protocol. DNA samples were stored at –20Cuntil
further processing. The DNA samples were used for DGGE and
amplicon pyrosequencing.
PCR and DGGE Analysis
The 16S rRNA gene region was amplified by PCR using 338F
and 518R primers targeting the V3 region (Muyzer et al., 1993;
Ovreas et al., 1997). A GC clamp of 40 bp (Muyzer et al., 1993;
Ovreas et al., 1997) was added to the forward primer. The PCR
program consisted of 10 min 95C; 35 cycles of 1 min 94C, 1 min
of 53C, 2 min of 72C; and a final elongation for 10 min at
72C. Amplification products were analyzed by electrophoresis in
1.5% (wt/vol) agarose gels stained with ethidium bromide. DGGE
based on the protocol of Muyzer et al. (1993) was performed
using the INGENYphorU System (Ingeny International BV,
The Netherlands). PCR fragments were loaded onto 8% (w/v)
polyacrylamide gels in 1 ×TAE bu er (20 m M T ris, 10 m M
acetate, 0.5 mM EDTA pH 7.4). To process and compare the
different gels, a homemade marker of different PCR fragments
was loaded on each gel (Boon et al., 2002). The polyacrylamide
gels were made with denaturing gradients ranging from 40 to 60%
(where 100% denaturant contains 7 M urea and 40% formamide).
The electrophoresis was run for 16 h at 60C and 120 V. Staining
and analysis of the gels was performed as described previously
(Boon et al., 2000). The normalization and analysis of DGGE gel
patterns was done with the BioNumerics software 5.10 (Applied
Maths, Sint-Martens-Latem, Belgium).
PCR and 454 Pyrosequencing
Amplicon pyrosequencing was performed on a 454 XL+
Titanium system (Roche, Penzberg, Germany) as described
before (Pilloni et al., 2012). Barcoded amplicons for multiplexing
were prepared using the Ba27F and Ba519R primers, amplifying
for the V1, V2, and V3 region of the 16S rRNA gene, extended
with the respective A or B adapters, key sequence and multiplex
identifiers (MID) as recommended by the manufacturer. Pyrotag
PCR was performed in a Mastercycler ep gradient (Eppendorf,
Hamburg, Germany) with the following cycling conditions:
initial denaturation (94C, 5 min), followed by 28 cycles of
denaturation, annealing, and elongation (94C – 30 s, 52C–
30 s and 70C – 60 s), followed by a final elongation (5 min –
70C). For each sample the PCR reaction was performed in
triplicates, in a final volume of 50 μl containing 1 ×PCR
buffer, 1.5 mM MgCl2, 0.1 mM dNTPs, 1.25 U recombinant Taq
polymerase (Fermentas, St. Leon-Rot, Germany), 0.2 μgml
1
bovine serum albumin (Roche), 0.3 mM of each MID-
primer (Biomers, Ulm, Germany) and approximately 50 ng
of template DNA. The triplicate amplicons were pooled
together and purified using PCRExtract Mini kit (5 PRIME,
Hilden, Germany) following the manufacturer instructions.
Libraries were quantified by the Quant-iT PicoGreen dsDNA
quantification kit (Invitrogen, Paisley, UK), diluted accordingly
and pooled in an equimolar ratio of 109molecules ml1.
Emulsion PCR, emulsion breaking and sequencing were
performed by applying the GS FLX Titanium chemistry
following supplier protocols. Pyrosequencing was performed
in a Picotiter Plate, in a pool with other samples, with 26
samples per quarter of a plate. The influent water, greywater
and cotton samples of WM1, WM3, and WM4 as well
as the unwashed cotton samples and the influent water
of WM2 and WM5 were analyzed by means of amplicon
pyrosequencing. The other samples did not succeed for PCR and
pyrosequencing.
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Callewaert et al. Bacterial Exchange in Washing Machines
16S rRNA Gene Sequence Analysis
Quality filtering of the pyrosequencing reads was performed
using the automatic amplicon pipeline of the GS Run Processor
(Roche), with a modification of the valley filter (vfScanAll-
Flows false instead of TiOnly) to extract sequences. The raw
reads were further quality trimmed using the TRIM function
of GreenGenes (DeSantis et al., 2006) with default settings.
Flows shorter than 250 bp after trimming and with incorrect
primers sequences were excluded from further analysis. Sequence
classification was done for combined forward and reverse reads
for each library using the RDP classifier (Wang et al., 2007).
Prior of classification, chimeric sequences were removed using
Uchime (Edgar et al., 2011) in an in-house Mothur and R/Sweave
pipeline (Schloss et al., 2009). The non-chimeric sequences were
classified using RDP’s command line MultiClassifier (version
2.7) tool (Cole et al., 2014) with confidence threshold set to
80%. Contigs of dominating amplicons were assembled with
SEQMAN II software (DNAStar, Madison, WI, USA), using
forward- and reverse-reads, as described before (Pilloni et al.,
2011). Thresholds of read assembly into one contig were set
to at least 98% sequence similarity for a minimum overlap of
50 bp. Contigs within one library with at least one forward and
one reverse read were excluded from further analysis. Additional
chimeric check was performed with Uchime as described. A total
of 35152 sequence reads were obtained. The abundances per
sample were calculated relative to the total sequence read per
sample.
Statistical Analysis
Statistical analysis was performed in SPSS (IBM Inc.,
USA) and significant cut-off values were set at the 95%
confidence level (p<0.05). Descriptive α-andβ-diversity
statistics were calculated using Mothur (Schloss et al., 2009)
andvisualizedwithR
1.Theα-diversity was calculated to
characterize the diversity of one individual sample. The
Chao 1 richness estimator and the Shannon diversity
index were calculated using the RDP pyrosequencing
pipeline (Cole et al., 2014). To assess the completeness
of sampling also a rarefaction analysis was performed.
The richness and evenness of the DGGE samples was
estimated by means of the total band count and the Gini
coefficient. The Gini coefficient was calculated based on
the Pareto–Lorenz curve and was constructed based on
the DGGE profiles (Marzorati et al., 2008). The β-diversity
analysis was calculated to study the difference between the
different microbial communities. Clustering of the DGGE
samples was performed based on Pearson correlation and
unweighted pair group with mathematical averages dendrogram
method.
Accession Codes
The sequences have been deposited in the NCBI Sequence Read
Archive2under study accession number SRR1619235.
1http://www.r-project.org/
2http://www.ncbi.nlm.nih.gov/Traces/sra
FIGURE 2 | Bacterial cell counts based on plating on NA (total plate
count), MCA (Gram-negative bacteria), and MSA (staphylococci and
Micrococcaceae) in the greywater samples; and intact bacterial cell
counts based on flow cytometry (FCM) of the influent and greywater
samples of the different washing machines (WM). ‘Other bacteria’ refer
to all bacteria detected on NA, not detected on MCA and MSA.
RESULTS
Bacterial Cell Counts
Absolute bacterial abundances in the greywater were studied
using TPC and selective plating, while the intact cells in both
influent and greywater effluent were counted by flow cytometry.
Plating revealed a TPC between 1.32 ×104and 4.06 ×105
culturable cells ml1(Figure 2). The total bacterial counts were
the highest (105cells ml1) for WM2 and WM4. The bacterial
counts were a magnitude lower (104cells ml1)forWM1,
WM3, and WM5. The Gram-negative bacteria (Mc Conkey agar)
accounted for 11 to 65% of the TPC-bacteria in the greywater.
WM1 and WM3 contained no staphylococci or Micrococcaceae
(measuredbyMSAagar),WM2andWM4contained8and
17%, respectively, while WM5 contained 60% staphylococci and
Micrococcaceae. Using flow cytometry, we detected 7 to 157 times
more intact cells in the greywater samples compared to plating
(Figure 2). The two tap water samples had similar cell counts;
6.9 ×104and 6.1 ×104cells ml1. The rainwater showed
variable cell counts of 9.2 ×104up to 4.6 ×106cells ml1.
A higher cell number count was retrieved after washing, with
6.0 ×105to 3.4 ×106cells ml1present in the five greywater
samples.
Bacterial Community Analysis According
to 454 Pyrosequencing
In- and outgoing samples from different WMs were collected
and amplified with conserved 16S rRNA gene primers generating
515-bp amplicons. Two WMs had tap water as influent
water; three other WMs used rainwater (Supplementary Table
S1). The samples clustered together based on sample type
(influent water versus greywater and cotton samples). The
greywater and cotton samples clustered together according to
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Callewaert et al. Bacterial Exchange in Washing Machines
FIGURE 3 | Clustering of the pyrosequencing results of the different
sample types from different household WMs. Clustering according to the
Sorenson index and unweighted pair group with mathematical averages
dendrogram method.
the WM (Figure 3). Large differences were observed for the
different WM influent samples, depending on location. In the
tap water samples, 12 unique phyla were observed for both
locations. The rainwater samples contained 9–13 unique phyla
per sample. A high diversity was observed for the influent
samples, with a Shannon diversity index of 5.61 ±0.45
and Chao1 richness of 1412 ±576 OTUs (see Tab l e 1 and
Supplementary Figures S1 and S2). The diversity in the rainwater
samples was lower, as compared to the tap water samples. Most
sequences were assigned to Proteobacteria and Bacteroidetes,
with an average of 59 and 22% of the total microbiome,
respectively. Limnohabitans sp. were often encountered as
relatively abundant species in the rainwater samples (12, 9.5,
and 5.3% of the total microbiome for WM4, WM5, WM3,
respectively). Also, Flavobacterium and Flectobacillus sp. were
found in relative large quantities in the rainwater sample of
WM3. In contrast, lower abundances were noticed in tap
water samples, with one exception of Ferribacterium sp., which
represented 16% of the total tap water microbiome of WM1
(Figure 4, Supplementary Table S2).
The washed and unwashed cotton samples contained 8
to 12 unique phyla per sample. The cotton samples overall
contained a lower richness of bacteria, with a Chao1 of
953 ±198 OTUs per sample. The unwashed reference cotton
sample contained a comparable richness as the cotton samples
washed in the WMs. The reference cotton sample contained a
microbiome probably due to the handling. The cotton samples
had a Shannon diversity index of 4.78 ±0.57. Species with
high relative abundances were found in the cotton samples
with an average of 2 OTUs representing 30% of the total
bacterial abundance, ranging from 29% for the unwashed to
72% for washed WM3 cotton sample. The majority of OTUs
belonged to the Proteobacteria (77%), Actinobacteria (12%),
and Firmicutes phylum (5.4%). Within the Proteobacteria
phylum, Enhydrobacter, and Acinetobacter were the genera
with the highest abundances (up to 67 and 24% of the
total microbiome, respectively). Also the Gram-positive
Propionibacterium,Staphylococcus,Corynebacterium, and
Micrococcus bacteria were present in relative abundant quantities
(Tab l e 2 ). Remarkably, the same dominant phyla occurred in
the unwashed reference cotton sample (Proteobacteria, 47%;
Actinobacteria, 26%, and Firmicutes, 22%). The unwashed
cotton sample contained a similar diversity as in the laundered
cotton samples. Other frequently identified species included
Pseudomonas sp., Flavobacterium sp., Sphingomonas sp.,
Albidiferax sp., Brevundimonas sp., Limnohabitans sp.,
TABLE 1 | Detailed α-diversity indices based on 454 pyrosequencing and DGGE results.
454 pyrosequencing DGGE
Sample type Sample ID N(total sequence
reads)
Observed richness
(sequence reads)
Chao1 richness
(sequence reads)
Shannon
diversity
Richness
(# bands)
Gini
evenness
Influent WM1 3464 453 696 5.28 20 0.67
Influent WM2 3052 1590 2378 6.37 8 0.82
Influent WM3 3672 592 1009 5.10 26 0.54
Influent WM4 4144 802 1629 5.83 33 0.47
Influent WM5 4260 693 1349 5.45 27 0.58
Cotton sample Ref 2705 439 696 5.20 n.a. n.a.
Cotton sample WM1 4387 561 962 4.50 9 0.82
Cotton sample WM2 n.a. n.a. n.a. n.a. 11 0.77
Cotton sample WM3 3089 493 904 3.99 18 0.73
Cotton sample WM4 6151 693 1250 5.42 15 0.78
Cotton sample WM5 n.a. n.a. n.a. n.a. 14 0.72
Greywater WM1 2568 804 1460 5.49 18 0.61
Greywater WM2 n.a. n.a. n.a. n.a. 22 0.52
Greywater WM3 4565 768 1754 5.34 24 0.56
Greywater WM4 2726 632 1299 4.92 25 0.62
Greywater WM5 n.a. n.a. n.a. n.a. 16 0.68
n.a., not available.
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Callewaert et al. Bacterial Exchange in Washing Machines
FIGURE 4 | Overview of the relative abundant bacterial classes in the different samples for WM1 (A), WM3 (B), and WM4 (C) displayed according to
the study design.
Janthinobacterium sp. and Flectobacillus sp., although in
lower quantities.
In the greywater samples, most sequences were assigned to
Proteobacteria (59%), Bacteriodetes (12%), and the Actinobacteria
(8.7%). The samples contained 8–10 unique phyla per sample.
A high diversity was observed for the greywater samples,
with a Shannon diversity index of 5.25 ±0.24 and a Chao1
estimated richness of 1504 ±188 OTUs per sample. An
average of four OTUs represented >20% of the total bacterial
abundance. Typically, a mixture of influent water-related
bacterial species and skin-related bacterial species was found
in the greywater samples. Among the typical skin-related
bacteria, Enhydrobacter,Staphylococcus, and Corynebacterium
sp. were found in relatively high abundances. Among the
typical water-related bacteria, Aldibiferax,Luteolibacter,TM7,
Schlesneria,Polynucleobacter,Phenylobacterium,Flectobacillus,
Brevundimonas, and Flavobacterium sp. were found in relatively
high quantities (Figure 4, Supplementary Tables S2–S4). Also
Pseudomonas sp. were frequently found in high amounts and
were enriched in two of the three WMs effluents, compared to
the influent water. Enhydrobacter sp. were generally enriched
in the greywater, however, this probably originated from the
other clothes brought into the WM. Full overview of the species
with the highest relative abundances and rarefaction curves can
be found in the Supplementary Figures S3 and S4. Detailed
α-diversity characteristics are displayed in Ta b l e 1 .
Bacterial Community Analysis According
to the DGGE
The diversity of the samples was characterized by means of
the richness, determined as the number of bands observed on
gel, and the evenness, calculated as Gini coefficient. Finger
printing analyses indicated a large bacterial diversity, mainly
in the influent and greywater samples (Figure 5). The samples
mainly clustered together based on sample type (influent
water, greywater, and cotton samples). Relatively low Pearson
similarities were found between the different samples from the
same household (20 ±16%). Rainwater influents displayed
the highest richness (29 ±3 bands) and highest evenness
(Gini =0.53 ±0.04). High Pearson similarities were found
between the rainwater influents on the different locations
(41 ±2%). The richness and evenness of tap water samples
varied, i.e., 8 and 20 bands with Gini coefficient of 0.82 and
0.67, respectively. Irrespective of the influent water, the effluent
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Callewaert et al. Bacterial Exchange in Washing Machines
TABLE 2 | Relative abundances of the abundant genera from assembled contigs in the cotton samples.
Phylum Genus Unwashed cotton
sample
Cotton sample
WM1
Cotton sample
WM3
Cotton sample
WM4
Proteobacteria Enhydrobacter G– 2.07% 47.45% 66.66% 5.31%
Proteobacteria Acinetobacter G– 19.45% 15.29% 6.31% 24.34%
Actinobacteria Propionibacterium G+9.50% 0.71% 0.09% 6.22%
Firmicutes Staphylococcus G+4.29% 2.62% 0.04% 7.74%
Actinobacteria Corynebacterium G+3.36% 1.98% 0.04% 8.00%
Actinobacteria Micrococcus G+2.48% 2.30% 1.34% 0.84%
Proteobacteria Pseudomonas G– 1.96% 0.93% 0.04% 2.65%
Bacteroidetes Flavobacterium G– 0.30% 0.64% 3.20% 0.10%
Proteobacteria Sphingomonas G– 3.55% 0.00% 0.04% 0.23%
Proteobacteria Albidiferax G– 0.00% 0.00% 2.41% 0.62%
Proteobacteria Brevundimonas G– 2.03% 0.32% 0.00% 0.13%
Proteobacteria Limnohabitans G– 0.00% 0.00% 0.39% 1.94%
Proteobacteria Janthinobacterium G– 1.29% 0.00% 0.11% 0.52%
Bacteroidetes Flectobacillus G– 0.00% 0.00% 1.29% 0.00%
greywater displayed comparable richness and evenness (22 ±4
fortapwaterand20±2 bands for rainwater with a Gini
coefficient of 0.62 ±0.05 and 0.57 ±0.05, respectively). Similar
bacterial bands were found in the greywater as in the influent
water, indicating a bacterial transfer from influent to greywater
(especially for WM3). The cotton samples were determined with
the lowest richness (13 ±3 bands) and the lowest evenness
(Gini =0.76 ±0.04). The cotton samples clustered separately
from the influent and effluent samples, with a high Pearson
similarity (44 ±28%) found within the cotton samples. Although
the cotton samples were not used before, certain bacterial bands
were associated with the cotton, which were generally not seen in
the water samples. Full α-diversity characteristics are displayed in
Tab l e 1 .
DISCUSSION
Little information exists about the microbial communities in
clothes and WMs. In this study, we analyzed the microbial flows
in different WMs. Of the 13 phyla detected in the greywater
samples, OTUs belonging to the Proteobacteria dominated the
microbial community. OTUs belonging to the Bacteroidetes
were another important group in the in- and effluent samples.
Proteobacteria and Bacteroidetes are known for their dominant
presence in drinking water supplies (Kwon et al., 2011). For the
cotton samples, however, the Actinobacteria and the Firmicutes
were the second and third most important bacterial groups before
the Bacteroidetes.
The different influent waters used in this study showed
major differences. Flow cytometry revealed cell counts of
6–7 ×104cells ml1in the tap water, while much higher cell
counts were obtained for the rainwater samples, reaching up
to 5 ×106cells ml1. Roof-harvested rainwater can contain
a higher concentration of contaminants, due to rinsing of
airborne dust, bird faeces, and other organic material, and
subsequent collection in the water tank (Kaushik et al., 2014). The
identity, diversity, and dominance of the reported genera differed
for every household location and water source. The genera
present in the rainwater were generally well-known bacteria
to inhabit rainwater storage tanks (Evans et al., 2009). The
genera detected in the tap water are known to inhabit different
environments and were previously reported in aquatic samples,
lake sediments, activated sludge, among others (Hong et al.,
2010). Opportunistic pathogens were identified in some cases,
such as Leptospira,Sphingomonas, and Legionella sp. (Edelstein
et al., 2003;Evangelista and Coburn, 2010;Ryan and Adley, 2010),
although their abundances were generally not higher than 1% of
the total microbiome.
Although low relative bacterial abundances were found in
the cotton samples after laundering, a considerable diversity
was observed. Certain bacterial bands found on DGGE were
associated with cotton, which were generally not observed in
the water samples. The initial cotton samples were unused
before laundering, but were retrieved with a high diversity of
typical skin-, textile- and WM-related bacteria after laundering,
as identified by pyrosequencing. In general, all types of bacteria
detected on the initial cotton sample were still present in the
washed cotton samples (Figure 4). Additionally, an apparent
enrichment occurred with the skin-related Enhydrobacter,
Acinetobacter,Corynebacterium,Staphylococcus sp. and the
biofilm-related Pseudomonas sp. The laundering process resulted
in a microbial exchange with other worn clothes and the influent
water, with a favored selectivity for typical skin- and clothes-
related bacteria. Micrococcus sp. were not enriched, however,
they were retrieved in abundances of 0.84 to 2.30% in the textile
after laundering. Previous research showed that micrococci were
generally solely enriched on polyester textiles (Callewaert et al.,
2014). Overall, a high occurrence of the Moraxellaceae family was
apparent in all cotton samples, dominated by Enhydrobacter sp.
and Acinetobacter sp. These were enriched in high amounts in the
cotton textiles (up to 67 and 24% of the total textile microbiome,
respectively). Both are Gram-negative and ubiquitous skin
commensal bacteria (Seifert et al., 1997;Gao et al., 2007).
High abundances of Enhydrobacter sp. were identified in the
greywater, indicating their presence in some peoples skin and
Frontiers in Microbiology | www.frontiersin.org 7December 2015 | Volume 6 | Article 1381
Callewaert et al. Bacterial Exchange in Washing Machines
FIGURE 5 | Clustering of the DGGE bacterial fingerprinting results according to the Pearson correlation and unweighted pair group with
mathematical averages dendrogram method.
clothes microbiome. A recent study showed that Moraxellaceae
were found among the relative abundant bacteria in the axillary
region (Callewaert et al., 2013). Acinetobacter sp. are typically
retrieved from clothing textiles (Loh et al., 2000;Pilonetto et al.,
2004;Callewaert et al., 2014). Only low quantities of Moraxella
sp. were found in the WMs, which were formerly identified
as a malodour causing species in WMs in Japan (Kubota
et al., 2012). Additionally, the Gram-positive Corynebacterium,
Staphylococcus,Propionibacterium, and Micrococcus sp. were
identified in relatively high abundances in the laundered cotton
samples (up to 8.0, 7.7, 6.2, and 2.3% of the total textile
microbiome, respectively). These bacteria are considered as
typical skin commensals (Kloos and Musselwhite, 1975;Gao
et al., 2007;Grice et al., 2009) and were brought into the
WM through the worn clothing textiles. Corynebacterium and
Staphylococcus sp. reside in high quantities in the axillary region
(Callewaert et al., 2013), and thus can be transferred in high
abundances to the shirts. For WM4, an apparent enrichment
of Corynebacterium sp. occurred in the laundered cotton piece,
suggesting its presence on the skin and textiles of this family.
Corynebacterium sp. are known to play a key role in body
odor development (Leyden et al., 1981). As such, malodorous
bacteria can be further distributed to other clothing textiles via
the WM. As shown previously, Staphylococcus and Micrococcus
sp. were present in high amounts on clothing textiles (Callewaert
et al., 2014). Some of the Gram-positive skin-related species
were enriched in the cotton samples, indicating a microbial
exchange with other clothing textiles in the WM. None of
the typical influent water bacterial species were enriched in
the cotton samples. Staphylococci and Micrococcaceae were
likewise reflected in the greywater samples. For WM4, agar
plating indicated 17%, while pyrosequencing indicated 11%
staphylococci and Micrococcaceae (Supplementary Table S4).
For WM1 and WM3, agar plating did not indicate any, while
pyrosequencing correspondingly found very low numbers of
staphylococci and Micrococcaceae (1.2 and 0.9%, respectively).
The number of intact bacteria in the greywater was
comparable or higher than that of the influent water. The number
in greywater ranged from 104to 105cells ml1(plate counts) to
106cells ml1(flow cytometry). The TPC were relatively higher
for the effluent greywater from WM2 and WM4 (105CFU ml1
of which 66% were Gram-negative bacteria). The DGGE results
of WM2 additionally indicated a large bacterial enrichment from
influent to effluent water. This high bacterial concentration might
be explained by the fact that WM2 was in use in a household
with children and pets in the house. Household dust of houses
with indoor pets is shown to carry a more rich and diverse
bacterial community (Fujimura et al., 2010), although the effect
on absolute cell numbers remains unknown. Among the relative
abundant species, no typical dog-related species were found, as
compared to literature (Hoffmann et al., 2014). The bacterial
community found in the rainwater influent and to a lower
extent in the tap water was likewise found in the greywater
effluent. A high bacterial transfer was noted from influent to
greywater (as seen by the DGGE and pyrosequencing results).
Most bacterial species present in the influent water were found
in lower quantities in the greywater. As an example, the alpha-
and beta-proteobacteria present in the influent water return
in high amounts in the effluent water, whilst only marginally
present in the cotton samples after laundering (Figure 4). It
seemed that the water-related bacterial species were generally
completely washed out with the greywater and did not adhere to
fabrics or WM parts. Nonetheless, certain species were enriched
in the greywater, such as Albidiferax,TM7,Schlesneria and
Luteolibacter sp., which represented more than 10% of the total
identified microbiome. Other enriched species included Sphing
omonas,Phenylobacterium,Brevundimonas,Flavobacterium,
Aquabacterium,Polynucleobacter,Undibacterium, and Legionella
sp. It is suggested that these species formed a biofilm in the WM.
All of the enriched bacterial species are indeed known for their
biofilm forming capacities (Pang and Liu, 2006;Basson et al.,
2008;Miller et al., 2009;Egert et al., 2010;Gattlen et al., 2010;
Kwon et al., 2011;Liao et al., 2013;Zhu et al., 2014). Interestingly,
the enriched bacterial species differed for every WM, except for
Sphingomonas sp., which was enriched in two out of three WMs.
The pathogenic Legionella sp. were enriched in the greywater
Frontiers in Microbiology | www.frontiersin.org 8December 2015 | Volume 6 | Article 1381
Callewaert et al. Bacterial Exchange in Washing Machines
of one WM (1.25% for WM1), while its presence in the cotton
sample was nonetheless very low (0.14%). The latter were not
found in the greywater from the other WMs, although they
were initially present in low quantities in the influent water.
Carry-over of pathogens via WMs was reported before (Wiksell
et al., 1973;Munk et al., 2001). Pseudomonas sp. were often
identified in relative abundant quantities in the cotton samples
(up to 2.7% of the total textile microbiome). In the greywater,
they were enriched in two of the three WMs, compared to the
influent water. It is suggested that these enrichments occurred
due to its presence in the WM, as they have been identified as
typical biofilm-forming bacteria in WMs (Gattlen et al., 2010).
Especially Pseudomonas putida sp. were found as important
biofilm producers (Gattlen et al., 2010). The biofilm formation
can give rise to a number of problems, such as unpleasant odors,
fabric staining, and deterioration, reducing the lifetime of the
WM or even skin infections (Szostak-Kotowa, 2004). Chemical
disinfection (use of bleaching agents), thermal disinfection (use
of higher washing temperatures) and/or physical removal (extra
centrifuging) can reduce biofilm formation and bacterial loads
in the WM. The use of the three washing mechanisms together
can have a strong synergistic effect (Arild et al., 2003). In this
study, the washing powder did not contain bleach, the laundry
machine was operated at low temperatures (30C) and with a
delicate program (no extra centrifuging), which can explain the
high (living) bacterial loads retrieved after washing. Although
many biofilm-producing bacteria were enriched in the effluent
water after laundering, their presence was generally not reflected
in the cotton samples. Only a few biofilm forming bacteria, such
as Pseudomonas and Sphingomonas sp., were observed in low
quantities in the cotton samples. We can conclude that the water-
related biofilms did not transfer their microbial constituents to
the laundered clothes. The microbial load brought into the WM
by means of the worn clothes seemed more important for the
laundered textiles. It seems that certain species have a higher
specificity to adhere to clothing textiles.
In this study, four different analysis methods were
employed: the molecular-based techniques DGGE and
amplicon pyrosequencing, as well as the non-molecular bacterial
enumeration techniques agar plating and flow cytometry. DGGE
served as a first screening technique for the relative abundant
species. Amplicon pyrosequencing added a more thorough
insight, with subsequent identification and quantification of the
bacterial community (Edwards et al., 2006). Diversity differences
between DGGE and amplicon pyrosequencing were observed
(Tab l e 1 ). The results from amplicon pyrosequencing were
supposed to be more reliable, as DGGE can only visualize
bacteria present for at least 1% of the bacterial community
(Muyzer and Smalla, 1998). A (weak) positive correlation was
observed between the observed richness of the DGGE results
with the Chao 1 richness of the pyrosequencing results (see
Supplementary Figure S5). The diversity indices derived from
DGGE can at least be considered indicative for the diversity
found with amplicon pyrosequencing. The molecular-based
techniques focused on bacterial DNA from living as well as
dead bacterial cells, while the bacterial enumeration techniques
focused on the living bacterial cells. When comparing the
enumeration techniques, we found large discrepancies between
agar plating and flow cytometry. Flow cytometry detected 7 to
157 times more cells in the greywater as compared to plating
(Figure 2). The agar plating technique differentiated between
living, cultivable cells and uncultivable and dead cells. The
applied flow cytometry technique differentiated between intact
and damaged bacterial cells. It can be concluded that the applied
washing program left high amounts of intact bacterial cells in
the greywater, whereas only a small portion was able to grow on
agar plate. It was previously shown that flow cytometry detects
more cells in drinking water samples as compared to plate
counts (Hammes et al., 2008), a given also known as “the great
plate count anomaly” (Staley and Konopka, 1985). From this
study, flow cytometry can be regarded as a useful technique to
enumerate bacterial cells.
This study revealed that the household low-temperature
laundering process created a bacterial mixing in the laundered
clothing textiles. An enrichment of a variety of biofilm-forming
bacteria was observed in the studied WMs; however, most of
these bacteria were washed out with the greywater. The textiles
brought into the WM were found to be more important in the
determination of the microbiome of the laundered clothes. The
cotton pieces in the WM selected for typical skin- and textile-
related bacterial microbiota. Previous research indeed showed
that the composition of clothing fibers determined a selective
bacterial enrichment (Callewaert et al., 2014). It is suggested
that the cause for malodour generation in WMs and clothes
is related to the bacteria present in the textiles. It is expected
that a household WM plays a role in the specification of the
skin microbiome of the household family members. A previous
study confirmed that cohabiting family members have large
similarities in their –especially skin– microbiome (Song et al.,
2013). The laundering process can lead to a mix-up of skin- and
clothes-related bacteria between clothes of family members. By
means of the WM, malodour-causing microbial species might
be further distributed to other clothing textiles. This study gave
more insight into the microbial communities and their exchange
in household WMs.
AUTHOR CONTRIBUTIONS
NB, CC, SVN, F-MK designed the experiments. CC and SVN
wrote the main manuscript text. MSG prepared the amplicon
libraries and performed the 454 pyrosequencing. F-MK and MSG
analyzed the pyrosequencing results. CC and SV performed the
experiments and analyzed the flow cytometry, plating and DGGE
analyses. All authors reviewed the manuscript.
ACKNOWLEDGMENTS
CC was supported by the Flemish Government, through
his assistantship, and the FWO grant FWO15/PDO/033.
SVN was supported by the FWO grant no. G.0808.10N
and the Inter-University Attraction Pole (IUAP) “μ
Manager” by the Belgian Science Policy (BELSPO, 305
P7/25). F-MK was supported by a research grant from the
Frontiers in Microbiology | www.frontiersin.org 9December 2015 | Volume 6 | Article 1381
Callewaert et al. Bacterial Exchange in Washing Machines
Geconcerteerde Onderzoeksactie (GOA) of Ghent University
(BOF09/GOA/005). We thank Tim Lacoere for his assistance
during the molecular work. We thank Hugo Roume and Jessica
Benner for their critical review of the manuscript and the
inspiring discussions.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: http://journal.frontiersin.org/article/10.3389/fmicb.
2015.01381
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Conflict of Interest Statement: The authors declare that the research was
conducted in the absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
Copyright © 2015 Callewaert, Van Nevel, Kerckhof, Granitsiotis and Boon. This
is an open-access article distributed under the terms of the Creative Commons
Attribution License (CC BY). The use, distribution or reproduction in other forums
is permitted, provided the original author(s) or licensor are credited and that the
original publication in this journal is cited, in accordance with accepted academic
practice. No use, distribution or reproduction is permitted which does not comply
with these terms.
Frontiers in Microbiology | www.frontiersin.org 11 December 2015 | Volume 6 | Article 1381
... Malodour formation in domestic laundry has become a relevant problem for many consumers, especially since wash temperatures have steadily decreased [1][2][3][4]. Although there are several types of odours that can be associated with textiles [5], this study focuses on the "wet-and-dirty-dustcloth-like" or "wet fabric" malodour [6][7][8]. ...
... Moreover, this combination comprised a mixture of bacteria from different sources related to laundering. While Micrococci can routinely be found in washing machines and on worn textiles [4,42], P. aeruginosa has been isolated from washing machine biofilms [28,42] and S. hominis has been associated with the formation of body odour due to the formation of thioalcohols [43]. ...
... Nix et al., who analysed the microbial communities in a domestic washing machine, were able to identify Micrococcus luteus as a frequent coloniser of the rubber seal and the detergent chamber [28]. Likewise, Callewaert et al. (2015) found Micrococcus sp. on worn cotton clothes [4], whereas Gattlen et al. in a culture-dependent approach could not isolate Micrococci from a washing machine but could isolate Staphylococci [42]. Moreover, Callewaert et al. (2015) suggested that skin-derived Corynebacteria and Staphylococci might be enriched on the textiles during laundering, while Micrococci will remain quite abundant [4]. ...
Article
Full-text available
Although malodour formation on textiles and in washing machines has been reported to be a very relevant problem in domestic laundry, the processes leading to bad odours have not been studied intensively. In particular, the smell often described as “wet-and-dirty-dustcloth-like malodour” had not been reproduced previously. We developed a lab model based on a bacterial mixture of Micrococcus luteus, Staphylococcus hominis, and Corynebacterium jeikeium, which can produce this odour type and which might allow the detailed investigation of this problem and the development of counteractions. The model uses bacterial strains that have been isolated from malodourous textiles. We could also show that the three volatile compounds dimethyl disulfide, dimethyl trisulfide, and indole contribute considerably to the “wet-fabric-like” malodour. These substances were not only found to be formed in the malodour model but have already been identified in the literature as relevant malodourous substances.
... Various studies have shown that washing machines are colonized by a considerable diversity of microbes, often capable of forming biofilms [3,[7][8][9][10]. For instance, Nix and co-workers [10] investigated pro-and eukaryotic microorganisms on the rubber door seal and the detergent drawer using 16S rRNA gene and ITS1 region pyrosequencing. ...
... Regarding bacteria, washing machines are indeed mainly populated by the phyla Proteobacteria, Actinobacteria, Firmicutes, and Bacteroidetes [7,9,10]. They largely enter the machine via soiled clothing, tap water, and maybe also air [2,4]. ...
... In accordance with previous molecular studies [7,9,10], Proteobacteria (29%), Actinobacteria (27%), Firmicutes (26%), and Bacteroidetes (0.5%) also represented the most abundant phyla here. In accordance with the relatively most abundant species found in our previous molecular study [9], Pseudomonas oleovorans, Acinetobacter parvus, and Moraxella osloensis were also detected here by cultivation in the door seal, while Rhizobium radiobacter was detected in the detergent drawer (Table 1) [9]. ...
Article
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Detergent drawer and door seal represent important sites for microbial life in domestic washing machines. Interestingly, quantitative data on the microbial contamination of these sites is scarce. Here, 10 domestic washing machines were swab-sampled for subsequent bacterial cultivation at four different sampling sites: detergent drawer and detergent drawer chamber, as well as the top and bottom part of the rubber door seal. The average bacterial load over all washing machines and sites was 2.1 ± 1.0 × 104 CFU cm-2 (average number of colony forming units ± standard error of the mean (SEM)). The top part of the door seal showed the lowest contamination (11.1 ± 9.2 × 101 CFU cm-2), probably due to less humidity. Out of 212 isolates, 178 (84%) were identified on the genus level, and 118 (56%) on the species level using matrix-assisted laser desorption/ionization (MALDI) Biotyping, resulting in 29 genera and 40 identified species across all machines. The predominant bacterial genera were Staphylococcus and Micrococcus, which were found at all sites. 22 out of 40 species were classified as opportunistic pathogens, emphasizing the need for regular cleaning of the investigated sites.
... Using the molecular approach of 16S rRNA gene pyrosequencing, we recently showed that the relatively most abundant sequence types in domestic washing machines were closely related to potentially pathogenic bacteria, such as Brevundimonas vesicularis or Pseudomonas aeruginosa inside the detergent drawer, and Moraxella osloensis or Acinetobacter parvus inside the door seal [7]. While this and other structural studies have looked at the microbial community composition of washing machines and laundry items [3,[8][9][10][11][12][13][14], studies on the metabolic activities of the laundry microbiota are often limited to distinct functionalities, such as the formation and prevention of malodor [2,15,16]. Malodor is often associated with a lack of hygiene, and can negatively affect the life cycle of a textile [17]. ...
... Based on the transcript counts, several bacterial genera known to be typical for washing machines and laundered textiles [7,10,13] were detected, such as Acinetobacter (48.5%, 51.7%), Aeromonas (26.1%, 21.6%), Rhizobium (6.0%, 6.5%), Agrobacterium (2.9%, 2.4%), Moraxella (1.8%, 2.1%) and Pseudomonas (0.4%, 0.4%) (the brackets show the relative abundances based on the Spades and Trinity assemblies, respectively, averaged over all of the samples). However, we also detected genera which were, to the best of our knowledge, previously not reported as being typical for washing machines or laundered textiles, such as Sphingorhabdus (9.9%, not detected), Anderseniella (2.1%, 12.1%), Epilithonimonas (0.9%, 1.0%), Haematobacter (0.5%, 0.6%) and Escherichia (0.04%, 0.3%). ...
Article
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Microbially contaminated washing machines and mild laundering conditions facilitate the survival and growth of microorganisms on laundry, promoting undesired side effects such as malodor formation. Clearly, a deeper understanding of the functionality and hygienic relevance of the laundry microbiota necessitates the analysis of the microbial gene expression on textiles after washing, which—to the best of our knowledge—has not been performed before. In this pilot case study, we used single-end RNA sequencing to generate de novo transcriptomes of the bacterial communities remaining on polyester and cotton fabrics washed in a domestic washing machine in mild conditions and subsequently incubated under moist conditions for 72 h. Two common de novo transcriptome assemblers were used. The final assemblies included 22,321 Trinity isoforms and 12,600 Spades isoforms. A large part of these isoforms could be assigned to the SwissProt database, and was further categorized into “molecular function”, “biological process” and “cellular component” using Gene Ontology (GO) terms. In addition, differential gene expression was used to show the difference in the pairwise comparison of the two tissue types. When comparing the assemblies generated with the two assemblers, the annotation results were relatively similar. However, there were clear differences between the de novo assemblies regarding differential gene expression.
... While being effective in reducing our environmental footprint, these ecological trends also bear some less desirable consequences. In recent years, several studies reported on the formation of biofilms in home environments [4] and household appliances such as washing machines [5,6], dishwashers [7], coffee makers [8], and food-processing equipment [9]. During operation, microorganisms are introduced by contaminated tap water [6] or the dirty load (dishes, laundry etc.) [5] and, if environmental conditions are favorable, can lead to Microorganisms 2021, 9,992 2 of 18 the growth of bacteria on internal and external surface. ...
... In recent years, several studies reported on the formation of biofilms in home environments [4] and household appliances such as washing machines [5,6], dishwashers [7], coffee makers [8], and food-processing equipment [9]. During operation, microorganisms are introduced by contaminated tap water [6] or the dirty load (dishes, laundry etc.) [5] and, if environmental conditions are favorable, can lead to Microorganisms 2021, 9,992 2 of 18 the growth of bacteria on internal and external surface. This bacteria accumulation, or cohabitation, commonly referred to as biofilm, comprises microorganisms in their own ecosystem [10], which can stick to almost any surface in an aqueous environment [11]. ...
Article
Full-text available
New ecological trends and changes in consumer behavior are known to favor biofilm formation in household appliances, increasing the need for new antimicrobial materials and surfaces. Their development requires laboratory-cultivated biofilms, or biofilm model systems (BMS), which allow for accelerated growth and offer better understanding of the underlying formation mechanisms. Here, we identified bacterial strains in wildtype biofilms from a variety of materials from domestic appliances using matrix-assisted laser desorption/ionization-time of flight mass spectroscopy (MALDI-TOF-MS). Staphylococci and pseudomonads were identified by MALDI-TOF-MS as the main genera in the habitats and were analyzed for biofilm formation using various in vitro methods. Standard quantitative biofilm assays were combined with scanning electron microscopy (SEM) to characterize biofilm formation. While Pseudomonas putida, a published lead germ, was not identified in any of the collected samples, Pseudomonas aeruginosa was found to be the most dominant biofilm producer. Water-born Pseudomonads were dominantly found in compartments with water contact only, such as in detergent compartment and detergent enemata. Furthermore, materials in contact with the washing load are predominantly colonized with bacteria from the human.
... Mechanical removal includes fabric agitation assisted by surfactant properties of detergents, while inactivation processes can occur as a consequence of elevated water temperature combined with laundry additives such as sodium hypochlorite. Among relevant studies, Callewaert et al. (2015) documented microbial exchanges among clothing articles during washing. Nordstrom et al. (2012) found that home-washed hospital scrubs had increased prevalence of bacterial species compared to those laundered in hospitals, presumably due to low temperature washing. ...
Chapter
There is growing evidence that the clothing is an important source of exposure to various chemicals and particles on a daily basis. Emerging knowledge suggests that everyday clothing harbors various contaminants, which if inhaled, ingested, or dermally absorbed, could carry significant health risks. This chapter summarizes the state of the most recent knowledge regarding how clothing, during wear, influences exposure to molecular chemicals, abiotic particles, and biotic particles, including microbes and allergens. The underlying processes that govern the acquisition, retention, and transmission of clothing-associated contaminants and the consequences of these for subsequent exposures are explored. Chemicals of concern have been identified in clothing, including byproducts of their manufacture and chemicals that adhere to clothing during use and care. Analogously, clothing acts as a reservoir for biotic and abiotic particles acquired from occupational and environmental sources. Evidence suggests that while clothing can be protective by acting as a physical or chemical barrier, clothing-mediated exposures can be substantial in certain circumstances and may have adverse health consequences. This complex process is influenced by the type and history of the clothing, the nature of the contaminant, and by wear, care, and storage practices. This chapter also summarizes the most pressing knowledge gaps that are important for better quantification, prediction, and control of clothing-mediated exposures.
... Malodorous VCs, including ammonia and hydrogen sulfide as well as short chain fatty acids, arise from these processes (Denawaka et al., 2016). VCs generated from the human body can also come from external sources, such as the washing machine and the drying environment (Callewaert et al., 2015;Stapleton et al., 2013). ...
Article
The persistence of sebum after low‐water‐temperature washing can contribute to malodor and microbial growth during subsequent use; thus, this work focuses on improved sebum removal. The detergency of sebum at various hydrophobic–lipophilic deviation (HLD) values was performed using 0.1 w/v% C12‐13‐8PO‐SO4Na and C8‐4PO‐1EO‐SO4Na at 1:1 molar ratio. The detergency of synthetic sebum on 87/13 polyester/spandex was relatively poor (70% removal) at HLD = 0. Various additives (heptanol, dipropylene glycol n‐butyl ether, decyltrimethyl ammonium bromide or sodium benzoate) were explored and it was found that none of them could facilitate sebum removal on the 87/13 polyester/spandex surface. On the other hand, adding low molecular weight primary amines (ethylene diamine, or monoethanolamine [MEA]) in the surfactant system without salt showed sebum removal of 70%–80% depending on the amine molecule. Adding MEA as a detergency additive with salt appeared to achieve good detergency (>80% removal) at all studied HLD numbers between −1.0 and 1.1 and the maximum detergency (approximately 90% removal) was observed at the optimum formulation (HLD = 0). The formulation pH with added MEA decreased from 11 to roughly 9. Detergency performance with added MEA was not dependent on pH within the studied basic conditions. The principal cold water sebum removal mechanism was found to be detachment of solid sebum fractions, dispersed in the detergent bath or floating on the bath surface.
... Examples include work clothes from individuals employed in healthcare, wastewater, agriculture and food processing industries. In the case of enteric illnesses, consideration should also be given to processing clothing separately from ill individuals and professional clothing from other household clothing to reduce the possibility of crosscontamination of other washed laundry (Callewaert et al., 2015;Nordstrom et al., 2012). Contaminated clothing from these households should also be washed and dried at the hottest temperatures allowed without damaging the items in question as heat can play a role in inactivating pathogenic microorganisms (Bockmühl et al., 2019;Riley et al., 2017). ...
Article
Aims: Contaminated laundry can spread infections. However, current directives for safe laundering are limited to healthcare settings and not reflective of domestic conditions. We aimed to use quantitative microbial risk assessment to evaluate household laundering practices (e.g., detergent selection, washing and drying temperatures, and sanitizer use) relative to log10 reductions in pathogens and infection risks during the clothes sorting, washer/dryer loading, folding, and storing steps. Methods and results: Using published data, we characterized laundry infection risks for respiratory and enteric pathogens relative to a single user contact scenario and a 1.0 x 10-6 acceptable risk threshold. For respiratory pathogens, risks following cold water wash temperatures (e.g., median 14.4°C) and standard detergents ranged from 2.2 x 10-5 to 2.2 x 10-7 . Use of advanced, enzymatic detergents reduced risks to 8.6 x 10-8 and 2.2 x 10-11 , respectively. For enteric pathogens, however, hot water, advanced detergents, sanitizing agents, and drying are needed to reach risk targets. Significance and impact of study: Conclusions provide guidance for household laundry practices to achieve targeted risk reductions, given a single user contact scenario. A key finding was that hand hygiene implemented at critical control points in the laundering process was the most significant driver of infection prevention, additionally reducing infection risks by up to six log10 .
Chapter
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LAUNDRY DEPARTMENT 335 A. Define important terms related to the laundry service 335 B. Identify risks of infection related to the laundry service. 335 335 C. Recommend necessary infection prevention and control measures. 336 336 APPENDIX 18 Classification of human etiologic agents on the basis of hazards 341 References 341
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Washed textiles can remain malodorous and dingy due to the recalcitrance of soils. Recent work has found that ‘invisible’ soils such as microbial extracellular DNA (eDNA) play a key role in the adhesion of extracellular polymeric substances that form matrixes contributing to these undesirable characteristics. Here we report the application of an immunostaining method to illustrate the cleaning mechanism of a nuclease (DNase I) acting upon eDNA. Extending previous work that established a key role for eDNA in anchoring these soil matrixes, this work provides new insights into the presence and effective removal of eDNA deposited on fabrics using high-resolution in-situ imaging. Using a monoclonal antibody specific to Z-DNA, we showed that when fabrics are washed with DNase I, the incidence of microbial eDNA is reduced. As well as a quantitative reduction in microbial eDNA, the deep cleaning benefits of this enzyme are shown using confocal microscopy and imaging analysis of T-shirt fibers. To the best of our knowledge, this is the first time the use of a molecular probe has been leveraged for fabric and homecare-related R&D to visualize eDNA and evaluate its removal from textiles by a new-to-laundry DNase enzyme. The approaches described in the current work also have scope for re-application to identify further cleaning technology.
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The skin microbiome has become a hot field of research in the last few years. The emergence of next-generation sequencing has given unprecedented insights into the impact and involvement of microbiota in skin conditions. More and more cosmetics contain probiotics or bacteria as an active ingredient, with or without scientific data. This research is also acknowledged by the textile industry. There has been a more holistic approach on how the skin and textile microbiome interacts and how they influence the pH, moisture content and odour generation. To date, most of the ingredients have a broad-spectrum antibacterial action. This manuscript covers the current research and industry developments in the field of skin and textiles. It explores the nature of antimicrobial finishing in textiles which can disrupt the skin microbiome, and the benefits of more natural and microbiome friendly therapies to combat skin conditions, malodour and skin infection.
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