Downloaded from on May 20, 2020 by guest6 86 Results 87 Characterization of bacterial and fungal...

1

Microbiomes in Dishwashers: Analysis of the microbial diversity and putative opportunistic 1

pathogens in dishwasher biofilm communities 2

Prem Krishnan Raghupathi a, c*

, Jerneja Zupančič b*

, Asker Daniel Brejnrod a, Samuel 3

Jacquiod a, Kurt Houf

c, Mette Burmølle

a, Nina Gunde-Cimerman

b#, Søren J. Sørensen

a# 4

*Shared First Authorship 5

Molecular Microbial Ecology Group, Section of Microbiology, Department of Biology, 6

University of Copenhagen, Universitetsparken 15, bldg. 1, DK2100 Copenhagen, Denmark a

; 7

Department of Biology, Biotechnical Faculty, University of Ljubljana, Jamnikarjeva 101, 8

1000 Ljubljana, Sloveniab; Department of Veterinary Public Health and Food Safety, Faculty 9

of Veterinary Medicine, Ghent University, Salisburylaan 133, 9820 Merelbeke, Belgium c 10

Running Head: Microbial diversity in dishwasher biofilm communities 11

P.K.R and J.Z. contributed equally to this work. 12

#Corresponding authors: 13

Søren J. Sørensen, [email protected] 14

Nina Gunde-Cimerman, [email protected] 15

AEM Accepted Manuscript Posted Online 12 January 2018Appl. Environ. Microbiol. doi:10.1128/AEM.02755-17Copyright © 2018 Raghupathi et al.This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

on June 4, 2020 by guesthttp://aem

.asm.org/

Dow

nloaded from

http://aem.asm.org/

2

ABSTRACT 16

Extreme habitats are not only limited to natural environments, but also apply to man-made 17

systems, for instance household appliances such as dishwashers. Limiting factors, such as 18

high temperatures, high and low pH, high NaCl concentrations, presence of detergents and 19

shear force from water during washing cycles define the microbial survival in this extreme 20

system. Fungal and bacterial diversity in biofilms isolated from rubber seals of 24 different 21

household dishwashers were investigated using next generation sequencing. Bacterial genera 22

such as Pseudomonas, Escherichia and Acinetobacter, known to include opportunistic 23

pathogens, were represented in most samples. The most frequently encountered fungal genera 24

in these samples belonged to Candida, Cryptococcus and Rhodotorula, also known to include 25

opportunistic pathogenic representatives. This study showed how specific conditions of the 26

dishwashers impact the abundance of microbial groups, and investigated on the inter- and 27

intra-kingdom interactions that shape these biofilms. The age, the usage frequency and 28

hardness of incoming tap water of dishwashers had significant impact on bacterial and fungal 29

composition. Representatives of Candida spp. were found at highest prevalence (100%) in all 30

dishwashers and are assumingly one of the first colonizers in recent dishwashers. Pairwise 31

correlations in tested microbiome showed that certain bacterial groups co-occur and so did the 32

fungal groups. In mixed bacterial-fungal biofilms, early adhesion, contact and interactions 33

were vital in the process of biofilm formation, where mixed complexes of the two, bacteria 34

and fungi, could provide a preliminary biogenic structure for the establishment of these 35

biofilms. 36

IMPORTANCE 37

Worldwide demand for household appliances, such as dishwashers and washing machines, is 38

increasing, as well as the number of immune-compromised individuals. The harsh conditions 39


.asm.org/

Dow

nloaded from

http://aem.asm.org/

3

in household dishwashers should prevent growth of most microorganisms. However, our 40

research shows that persisting poly-extremotolerant groups of microorganisms in household 41

appliances are well established under these unfavourable conditions, supported by the biofilm 42

mode of growth. The significance of our research is in identifying the microbial composition 43

of biofilms formed on dishwasher rubber seals, how diverse abiotic conditions affects 44

microbiota and which key members were represented in early colonisation and contamination 45

of dishwashers, as these appliances can present a source of domestic cross-contamination 46

leading to broader medical impacts. 47


.asm.org/

Dow

nloaded from

http://aem.asm.org/

4

Introduction 48

Extreme natural environments have for decades attracted the interest of microbiologists. 49

However, only fairly recently have microbial communities in extreme environments in 50

households and common household appliances been studied. The selection pressure within 51

some of these is reflected in reduced microbial diversity allowing only the most fitted species 52

to withstand the stressful conditions. In recent years, in addition to understanding the 53

biodiversity of extreme natural habitats (1–5), ecological investigations into various man-54

made ecosystems like kitchens (6), bathrooms (7), trash bins (8), tap water pipes (9–11), 55

automated teller machines (12), coffee-machines (13), washing machines (14, 15) and 56

dishwashers (16–19) have gained momentum. 57

Amongst different household ecosystems studied so far, kitchens are colonized with the 58

broadest diversity of extremotolerant microorganisms (20–22). And only recently, it was 59

discovered that some microbes can survive and grow even under extreme conditions in certain 60

domestic appliances (13, 16) as for instance dishwashers (DWs). DWs are extreme habitats 61

with constantly fluctuating conditions, where only microbial communities with poly-62

extremotolerant properties can survive. The individual community members, as well as the 63

whole community itself must possess key phenotypic traits that enable them to resist 64

alternating wet and dry periods, frequent changes of temperatures during the washing cycles 65

(from 20°C and up to 74°C), oxidative detergents elevating the pH from 6.5 to 12, high 66

organic loads, high NaCl concentrations and shearing generated by water sprinklers. The 67

metal, plastic and rubber parts of DWs may enable the establishment and growth of mixed 68

bacterial/fungal communities that are protected by copious amounts of extracellular polymeric 69

substances (EPS) and thus, confer on the biofilm communities, the extremotolerant properties 70

that go beyond the extremotolerance of each individual species (17, 18, 23). 71


.asm.org/

Dow

nloaded from

http://aem.asm.org/

5

The obvious choice for microbes when exposed with extreme conditions in DWs is the 72

biofilm mode of growth, providing shelter against external stresses (24) and where intimate 73

cross-species boundaries may occur (25). Such surface communities (26, 27) may also 74

provide a link to emerging disease pathogenesis (28), since biofilms formed in DWs (and 75

other appliances) could contribute to the dispersion and persistence of bacterial/fungal groups 76

outside the common spectrum of saprobes (16). Fungi, able to cause opportunistic infections 77

in humans, have been documented inside DWs (17–19) and the incidence of domestically-78

sourced fungal infections has been increasing steadily over the last decades (17, 29, 30). 79

However, to date, the diversity of the whole microbiota in DWs has not been investigated. 80

Therefore, we have studied both the bacterial and fungal communities (with a special focus on 81

mixed biofilms) associated with household DWs, using high-throughput sequencing. 82

Furthermore, we studied how specific conditions of the DWs impact the abundance of certain 83

microbial groups and how well inter- and intra-kingdom interactions shape the structure of 84

microbial communities within DWs. 85 on June 4, 2020 by guesthttp://aem

.asm.org/

Dow

nloaded from

http://aem.asm.org/

6

Results 86

Characterization of bacterial and fungal communities in DW-associated biofilms 87

Twenty-four biofilms grown on rubber seals of DWs (Fig 1) were sampled to assess both 88

bacterial and fungal communities. DWs characteristics varied in terms of years, frequency of 89

use, temperature of washing cycles and influent water hardness (WH) as shown in Table 1. In 90

the first PCR reactions, 21 samples generated DNA fragments that were processed by a 91

second PCR and further sequenced. A total of 221, 032 partial 16S rRNA gene and 313, 420 92

ITS rRNA gene transcript sequences were obtained from 21 DW samples, respectively. Raw 93

sequences of all the DWs are available from the NCBI Sequence Read Archive (SRA) under 94

the Bioproject IDs: PRJNA315977 for bacterial and PRJNA317625 for fungal reads, 95

respectively. Overall, sequence reads were assigned to 309 bacterial OTUs and 194 fungal 96

unique OTU classifications. The predicted number of bacterial genera ranged from 29 to 150 97

across all samples and the fungal genera ranged from 15 to 104 (Supplementary information, 98

Table S1). 99

Alpha diversity were calculated based on rarefied sequences and are presented 100

(Supplementary information, Table S2). Richness and evenness of the bacterial community 101

were not affected by the DW conditions (Supplementary information, Table S3). However, 102

the alpha diversity indices of fungal community were found to be significantly influenced by 103

DW conditions. The years of usage and DW with influent hard water had significant impact 104

on species richness, species evenness and abundance indicating that these specific 105

environmental conditions enrich fungal community profiles (Wilcoxon-Mann-Whitney tests, 106

p < 0.05) (Supplementary information, Table S4). 107

Microbial phyla in DW associated biofilms 108


.asm.org/

Dow

nloaded from

http://aem.asm.org/

7

DWs biofilms were composed of diverse fungal and bacterial phyla. Based on non-rarefied 109

16S rRNA gene reads, the sequences were assigned to 16 different bacterial phyla, of which, 110

Proteobacteria were dominating across all the samples followed by Actinobacteria and 111

Firmicutes (Supplementary information, Fig S1). All DW samples also contained sequences 112

representing Bacteroidetes. The remaining phyla, (Chloroflexi, Cyanobacteria, Deinococcus-113

Thermus, Spirochaetes, TM7, Verrucomicrobia, Synergistetes and Acidobacteria) contributed 114

up to 9% of the total prokaryotic sequences. Among the subclasses of Proteobacteria, the 115

bacterial community was dominated by α-Proteobacteria (46 ± 7 %) and γ-Proteobacteria (45 116

± 7 %) in all the DW biofilm samples. Bacilli were the most abundant subclass of Firmicutes 117

dominating in all biofilm samples. The most abundant taxa in all DNA samples belonged to 118

the genera Gordonia, Wautersiella, Rhodobacter, Nesterenkonia, Stenotrophomonas, 119

Exiguobacterium, Acinetobacter and Pseudomonas. Bacterial OTU represented by the genus 120

Meiothermus belonging to the phylum Deinococcus-Thermus and the phyla TM7 were 121

present in 19/21 DW samples. The genera Escherichia/Shigella and Pseudomonas were 122

identified in 62% and 67% of DWs, respectively (Fig 2). 123

OTUs based on non-rarefied ITS rRNA gene reads were assigned across four fungal phyla 124

(Supplementary information, Fig S1). Ascomycota dominated in the samples followed by 125

Basidiomycota. Among the subclasses of Ascomycota, the fungal biofilm community was 126

dominated by Saccharomycetes characterized by genera Candida, Debaryomyces, and 127

Saccharomyces (Fig 2) present in all DWs. Filamentous fungal genera like Cladosporium, 128

Fusarium and Aspergillus were present in more than 16 DW samples, and the black yeast 129

genera, Aureobasidium and Exophiala, were present in 33% of DWs. Basidiomycota 130

represented by genera Rhodotorula and Cryptococcus were present in 90% and 86% of DWs, 131

respectively. The genera Wallemia and Trichosporon were present in more than half of DWs. 132


.asm.org/

Dow

nloaded from

http://aem.asm.org/

8

Microbiota based on absolute sample count i.e. the bacterial and fungal taxa classified at the 133

genus level that occurred in N > 5 samples, where 21 ≥ N ≥ 5 is shown in Fig 2. 134

Abiotic conditions of DW affect microbial composition 135

Changes in the structure of microbial communities were investigated by multivariate beta-136

diversity analysis. Redundancy analyses (RDA) were performed to test the relationship 137

between different DW conditions and their impact on the microbial community composition. 138

RDA with two included factors (years of use and frequency of use) showed that the DWs 139

years of use was the most significant driving force (ANOVA, p<0.05) affecting bacterial 140

communities followed by frequency of use (Freq). RDA explained 84% total variance, of 141

which 43.8 % is described by RDA1 and RDA2 (respectively, 27.32% and 16.44%, Fig 3A & 142

B). The most explanatory variables were frequency of use (27%) and the number of years in 143

use (23%) (Supplementary information, Table S5). RDA with three included factors (year, 144

water hardness and frequency of use) showed that years of use, frequency of use and WH 145

significantly affected fungal community (ANOVA, p<0.05). Out of 38% total variance, 146

23.5% was explained by the first two components (respectively, 15.43% and 8.08%, Fig 4A, 147

B&C). In this case, WH was found to be the most explanatory variable (18.9%) (Table S5, 148

Supplementary information). PERMANOVA on Bray-Curtis dissimilarity index confirmed 149

the observed trends where DWs frequency of use (14%) and WH (17%) were the most 150

significant factors (p<0.05) impacting the bacterial and fungal community profiles, 151

respectively (Supplementary information, Table S6). 152

RD scatter-plot shows DW with bacterial composition grouped into recent DW used between 153

0-4; old DWs at 5-7/8 years (Fig 3A). Based on frequency of use, DWs were grouped under 154

three categories: low frequency (1-3 times / week), intermediate frequency (7 times /week) 155

and high frequency (14 times /week) (Fig 3B). Samples with fungal composition were also 156


.asm.org/

Dow

nloaded from

http://aem.asm.org/

9

grouped based on DW conditions (year, frequency of use and influent WH) as shown in Fig 157

4A, B & C, respectively. 158

The influence of DW’s age and frequency of use on bacterial communities 159

Based on the above results, further analyses of DW groups on taxonomic profiles were 160

performed in the software package STAMP. Actinobacteria, Firmicutes and Proteobacteria 161

were the three major phyla significantly affected by DW’s age (ANOVA, p< 0.05) and 162

Firmicutes further influenced by frequency of use (ANOVA, p< 0.05). Recent DWs (0-4 163

years) contained Proteobacteria (48%), Actinobacteria (29%) and Firmicutes (13%) indicating 164

that these phyla members were the early settlers in biofilms of recent DWs. DWs between 5-7 165

years of age seem later to be increasingly populated by Actinobacteria (36%) and Firmicutes 166

(34%). DWs at 8 years of age showed increased levels of Actinobacteria (49%) with further 167

reduction in Proteobacteria and Firmicutes (Fig 5A). DWs that were used more often 168

(frequency) had reductional shift in Firmicutes diversity (Fig 5B). Thus, bacterial members 169

belonging to Actinobacteria and Proteobacteria groups could dominate in DWs and 170

Firmicutes being susceptible to operational conditions of DWs. 171

Investigation on abundance shift patterns at genus level between different DW age groups is 172

shown in Fig 5C. A total of 11 bacterial taxa showed significant prevalence levels according 173

to the three DW age groups (ANOVA, p< 0.05). Most young DWs (0-4 years) had varying 174

levels of bacterial abundance represented by genera Rhizobium, Escherichia/Shigella, 175

Azospira, Exiguobacterium, Chryseobacterium and Staphylococcus. Taxa belonging to genera 176

Exiguobacterium, Arthrobacter, Staphylococcus, Aerococcus, Treponema and Lactobacillus 177

were represented in DWs 5-7 years. Further, genera like Aeromicrobium, Salana, 178

Ancylobacter, Starkeya, Kaistella, Brevibacterium and Ancylobacter dominated in DWs used 179

for 8 years. Genera Azospira, Escherichia/Shigella and Rhizobium dominated in younger 180


.asm.org/

Dow

nloaded from

http://aem.asm.org/

10

DWs (Welch’s t-test, p< 0.05) whereas, genera Aeromicrobium and Brevibacterium 181

dominated in older DWs used for 5-7 and 8 years (Welch’s t-test, p< 0.05, respectively). The 182

frequency of use showed 9 bacterial taxa to be significantly influenced (ANOVA, p<0.05) 183

between the three levels (Fig 5D). The genera Brevibacterium, Aeromicrobium, Roseomonas, 184

Xanthobacter, Helicobacter and Cellulosimicrobium were prevalent in DW used more 185

frequently compared to DWs used at low frequencies (1-3times /week) (Welch’s t- test, p < 186

0.05). 187

Fungal community of DWs rubber seal is influenced by age, frequency of use and 188

hardness of incoming tap water 189

The conditions of DWs and their impact on fungal taxonomic profiles revealed a significant 190

difference between the three fungal phyla, Ascomycota and Basidiomycota (ANOVA, 191

p<0.05). Young dishwashers (0-4 years) were abundant in Ascomycota (92%) with 192

Basidiomycota contributing only 5%. Over time, the two phyla became more equally 193

represented at 55% and 43%, respectively (Fig 6A). A higher shift in abundance between the 194

phyla, Ascomycota and Basidiomycota was observed under two frequency levels (Welch’s t-195

test, p< 0.05, respectively) (Fig 6B). This could indicate Basidiomycota susceptible to decline 196

in DWs used more frequently while ascomycetous fungi remain well established. The impacts 197

of WH were shown to affect the phyla Ascomycota, Basidiomycota and Zygomycota between 198

the four levels of water harness (ANOVA, p< 0.05). Hard and moderately hard water 199

influenced fungal genera belonging to Basidiomycota and Zygomycota compared to soft and 200

slightly hard water (Welch’s t-tests, p< 0.05) (Fig 6C). 201

At genus level, 6 taxa were influenced on abundance levels by years of use (Fig 6D). Most 202

DWs used between 5-7 years were shown to be colonized by the genera Wallemia, 203

Rhodotorula, Candida, Aureobasidium and Cryptococcus. Though recent DWs had varying 204


.asm.org/

Dow

nloaded from

http://aem.asm.org/

11

fungal representations between these genera; Candida was significantly abundant in recent 205

DWs (0-4 years) and Rhodotorula was significantly higher in abundance in DWs used for 5-7 206

years compared to DWs at 8 years (Welch’s t-test, p<0.05), respectively. DWs frequency of 207

use had significant impact on the fungal genera Candida and Rhodotorula where, most 208

frequently used DWs enriched Candida and less frequently used DWs were settled with 209

Rhodotorula (Fig 6E) (Welch’s t- test, p<0.05). The DW samples with incoming WH also 210

influenced the fungal biota as shown in Fig 6F. Genera Phoma, Thelebolus, Stagonaspora, 211

Neobulgaria, Perisporiopsis Cladosporium were represented significantly at higher 212

abundance in samples with hard water compared to samples with other WH characteristics 213

(Welch’s t- test, p< 0.05). 214

Positive microbial interactions may shape the DWs biofilm communities 215

Microbes sharing the same environmental niche may co-exist or exclude each other while 216

competing for the same resources (40–42). Correlation analyses were performed to explore 217

relationships among the microbial flora associated with DW’s biofilm communities, as 218

positive pairwise correlations may indicate microbial collaboration or dependencies. The 219

survey revealed 140 significant interactions at the genus level in biofilm samples including 220

both fungal and bacterial genera (Spearman rank correlation cut-off, r > |0.65|, permutation 221

test, p<0.05) with 90% (125/140) of the predicted interactions positively correlated (Table 2). 222

Surprisingly, 95% (119/125) of the predicted positive correlations were intra-kingdom 223

bacterial interactions dominated by Proteobacteria, Actinobacteria and Firmicutes. 224

Representatives of genera detected in these biofilms were shown to positively correlate with 225

other taxa. For example, γ-Proteobacteria genus Escherichia/Shigella spp. was positively 226

correlated with Ochrobactrum spp.; Staphylococcus spp. was positively correlated with γ-227

Proteobacteria genus Pseudomonas. Enterococcus spp. was positively correlated with the γ-228

Proteobacteria genus Stenotrophomonas. Positive inter-kingdom correlations i.e. interaction 229


.asm.org/

Dow

nloaded from

http://aem.asm.org/

12

between bacterial and fungal members were observed only in 2.5% of total positive 230

interactions (N=125). Example includes Saccharomycetes (Candida spp.) and α-231

Proteobacteria in one case and between β–Proteobacteria and Dothideomycetes in two cases. 232

Fungal members tend to mutually co-occur in DWs community (2.5% (3/125)) as no negative 233

correlations within the fungal taxa were observed. In fungi, positive co-occurrence was 234

observed between Rhodotorula spp. and Cladosporium spp.; Cryptococcus spp. and 235

Cladosporium spp.; Debaryomyces spp. and yeasts classified as Saccharomycetes. However, 236

mutual exclusions among inter-kingdom interactions accounted for 46% (7/15) of the total 237

negative correlations (N=15). Cross-domain negative interactions i.e. mutual exclusions 238

between bacteria and fungi were observed between the fungal phylum Ascomycota and the 239

bacterial phyla Actinobacteria and Bacteroidetes. Basidiomycota negatively correlated with 240

γ–Proteobacteria; α-Proteobacteria and Sphingobacteria (Supplementary information, Fig S2). 241

Table 2: Pair-wise correlations between the bacterial and fungal OTUs observed in 18 DW 242

biofilms samples. Interactions shown here are based on Spearman Rank correlations (r > 243

cutoff=|0.65|, permutation test, p<0.05) 244


.asm.org/

Dow

nloaded from

http://aem.asm.org/

13

Discussion 245

Indoor and household environments, including household appliances, offer diverse habitats 246

for microorganisms to adapt and flourish. Most current knowledge on DWs microbiology is 247

focussed on opportunistic pathogenic black yeasts and other members of DWs mycobiota 248

based on classical cultivation techniques (16, 17, 19). To date, the molecular approach to 249

identify and characterize the microbial diversity of DWs was documented in a single study 250

that was limited to the presence of fungi in new and old biofilms (17). This study, using high 251

throughput sequencing, assessed both bacterial and fungal community coexistence in 252

biofilms, which were developed on the rubber seals of DWs. These mixed bacterial/fungal 253

biofilms are very likely to enable protection against harsh environmental conditions 254

contributing to persistence. The formation of biofilms on DWs rubber seals also reflects 255

invasion, settlement and growth of microorganisms under extreme conditions in these 256

appliances. 257

Combination of different stress factors within DWs allows for the survival of only well 258

adapted and complementary microbial species that enable the formation of biofilms. We 259

found that on rubber seals in DWs, most abundant OTUs were dominated by Gram-positive 260

members like Gordonia spp., Micrococcus spp., Chryseobacterium spp., and 261

Exiguobacterium spp. The presence of these genera are usually associated with natural 262

environments in which biotic conditions are extreme (31–36). Few species within these 263

genera were earlier reported as halotolerant and tolerate a broad range of pH (5-11), high 264

levels of UV radiation and heavy metal stress (including arsenic) (31–33). The most abundant 265

bacterial genus Gordonia was found in all DWs sampled in this study. These representatives 266

were reported to degrade different polymers and xenobiotics (35) that presumably facilitate 267

their presence on DW rubber seals. Bacterial taxa represented by putative thermophilic genus, 268

Meiothermus and phylum TM7 were present in most DW samples. Both of these thermophilic 269


.asm.org/

Dow

nloaded from

http://aem.asm.org/

14

genera could be expected in biofilms formed on DW rubber seals, since the bacterial members 270

of these genera can tolerate short periods of up to 70 °C and grow optimally from 50 to 65°C 271

and at alkaline conditions (pH ~8.0) (36). This study reports the detection of putative 272

Meiothermus spp. within a domestic system. 273

Microbial diversity within indoor environments is influenced by human effects. The 274

abundance of bacterial composition of indoor environment was shown to closely mirror the 275

microbial profiles of its human residents (37). Sampled DWs included a subset of bacterial 276

genera known to have representatives associated with humans, for example Staphylococcus, 277

Streptococcus, Lactobacillus, Corynebacterium and Enterococcus. These bacterial genera, 278

common on human skin and in the gut, have been detected in other studies investigating the 279

domestic microbiome (30, 38, 39), yet their presence within the extreme conditions has not 280

been reported. Furthermore, our study revealed the presence of sequences affiliated to genera 281

known to harbor some of the most common and potential human opportunistic pathogens, 282

namely Escherichia/Shigella and Pseudomonas as integral members of DWs microbial 283

biofilms. In fact, more than 60% of samples contained genera like Acinetobacter, 284

Escherichia/Shigella and Pseudomonas, indicating that DWs could shelter these bacterial 285

groups in private homes. However, it should be noted that the presence of potential bacterial 286

pathogens in household DW can be tempered by the fact that pathogenicity can be strain 287

specific (the methods used here do not provide such resolution). In addition, the approach 288

applied in our study cannot distinguish between cells that were alive/dead/spores. 289

Multispecies biofilm formation may help these well-adapted (and other) opportunistic bacteria 290

to survive in harsh environment of DWs, being protected with large amounts of bacterial and 291

fungal EPS protecting them in immediate close vicinity (40, 41). EPS immobilizes individual 292

cells in biofilms and keep them in close proximity, allowing interactions including cell-cell 293

communication and the formation of synergistic micro-consortia (40) and thereby, determine 294


.asm.org/

Dow

nloaded from

http://aem.asm.org/

15

the development and structure of multi-species biofilms (42). The primary colonizers of any 295

surface are predominantly bacteria (43), which modify surface characteristics, enabling 296

subsequent colonization by secondary microorganisms (44, 45). Recent studies of microbial 297

biofilm communities on natural and artificial substrates (ceramic, glass, plastic, aluminium, 298

and coral skeleton) showed that early-stage biofilms were established by successive 299

colonization of Proteobacteria, Firmicutes and Actinobacteria (46–48). Similarly, in this 300

study, it was observed that in recent dishwashers, the main biofilm community members 301

belonged to Proteobacteria, Actinobacteria and Firmicutes. α-Proteobacteria that prevail in 302

aquatic-related biofilms (49) were well represented besides γ-Proteobacteria in DWs. γ-303

Proteobacteria were found to be the major contributors to biofouling (49, 50) formed on 304

polymers (51) and their presence in DWs could constitute to different polymers used in DW 305

components. 306

Taxonomic identification of the fungal DW community by gene marker-based amplicon 307

sequencing showed similar results to previous cultivation based approaches (16, 17, 19). The 308

prevalence of Ascomycota in relation to Basidiomycota was confirmed (17). However, in this 309

study, we report differences in distribution at the genus level. Sequencing results from fungi 310

in DWs biofilms from a previous study showed an higher abundance of Cryptococcus (17), 311

while in this study the dominance of Candida is reported. Also, the prevalence of fungi 312

belonging to genera Rhodotorula, Cryptococcus was higher; and genera Exophiala and 313

Aureobasidium, the two black yeast-like fungi classified as opportunistic pathogens were also 314

detected. The presence of black yeast-like fungi was lower than in previous cultivation based 315

studies (16–19). This reduced incidence rate of these colonizers in DWs could be due to the 316

consequence of less efficient DNA extraction from black yeast cells due to the presence of 317

melanin in cell walls, large polysaccharide production or meristematic growth forms (52–54). 318

In addition, lower or higher incidence of microbial composition can also result from the 319


.asm.org/

Dow

nloaded from

http://aem.asm.org/

16

chosen methodology as sequencing analyses will also detect non-viable cells (55), resulting in 320

an increased microbial richness. 321

The density of microbial settlement on 1 cm2

rubber seals in DWs is only known for fungal 322

community (17) and not for bacterial community. The main colonizer black yeast E. 323

dermatitidis was detected at up to 106 CFU/cm

2; E. phaeomuriformis, R. mucilaginosa and C. 324

parapsilosis were detected in the range between 102 and 10

5 CFU/cm

2 (17). Ascomycetous 325

fungi, namely Candida dominated the recent DWs. These early fungal colonisers were 326

followed by other co-occurring Ascomycota and Basidiomycota members. It cannot be 327

excluded that early colonisers may benefit from the so-called “priority effect” (56), giving 328

them the advantage to occupy the surface first, and subsequently filter/chose the new comers, 329

resulting in differential community assemblies. The hardness of tap water also significantly 330

affected the fungal community in DWs where, more diverse fungal species were present in 331

DWs with hard and moderately hard water. Hard and moderately hard water have increasing 332

contents of Ca2+

and Mg2+

ions. Though water disinfection procedures have also shown to 333

influence fungal diversity (58, 59), it is noteworthy that the existence and morphology of 334

certain fungi depends on the presence of some ions, in particularly cations, such as Ca2+

(57). 335

Other ions, such as Cl−, generated by water chlorination, generally do not affect fungi (60, 336

61). Our results indicate that establishment and diversity of fungi within DW biofilms will be 337

greater with hard water, while in soft water fungal biomass tends to be dominated by the 338

fungal phyla Ascomycota. Further, fungal composition could also be influenced by their 339

adaption to different disinfectants used during wash cycles in DWs. 340

In most natural environments, individual organisms do not live in isolation, but rather form a 341

complex community of different species that shape the structure, community itself and the 342

evolution of the individual species (62). In mixed bacterial-fungal biofilms, the early contact 343

and adhesion are likely to be important in the process of biofilm formation where mixed 344


.asm.org/

Dow

nloaded from

http://aem.asm.org/

17

complexes of the two, bacteria or fungi might provide biotic support for the establishment of 345

biofilms (63–65). Microbes that live in the same ecological habitat may co-occur or exclude 346

each other. Studies have shown that coexistence can facilitate interspecies interactions in 347

biofilms (41, 66). This aspect was investigated as the fungal and bacterial communities from 348

18 samples in this study that shared the same habitat and that they therefore could provide 349

insights into their possible interactions. Positive pairwise correlations indicate mutual co-350

occurrence which also may point to symbiosis, mutualism or commensalism, whereas 351

negative pairwise correlations indicate indicate mutual exclusion which may reflect 352

competition, mutual exclusion or parasitism (67). Our results indicate that bacterial groups co-353

occur with each other and so do the fungal groups with other fungal members. Interestingly, 354

cross-kingdom pairwise correlations between fungi and bacteria were dominated by negative 355

correlations which may reflect that they occupy different locations on the biofilms. 356

However, in-vitro studies have shown close interactions between yeast cells forming the 357

biofilm core, and bacteria in the biofilm periphery create a protective coating for yeasts cells 358

and pseudo-hyphae (68, 69). Stressful conditions in DWs and the presence of bacteria could 359

stimulate growth of fungi like Candida spp. as pseudo-hyphae, thus enabling formation of the 360

multispecies biofilm core on which further bacteria could associate. This could be attributed 361

to positive cross-kingdom correlation seen between Saccharomycetes (mostly represented by 362

the genus Candida) and Proteobacteria (α-Proteobacteria). This suggests that the early 363

members of the DW biofilm community and their associations have possibly developed 364

overtime in these environments. These could possibly support the idea of strong priority 365

effects, where first colonizers will strongly determine the chrono-succession of events leading 366

to establishment of successful biofilm structures in DWs. 367

Conclusion 368


.asm.org/

Dow

nloaded from

http://aem.asm.org/

18

In this study, we investigated diverse bacterial and fungal communities in biofilms formed on 369

different DW rubber seals. Furthermore, abiotic conditions in DWs were shown to influence 370

the microbial community composition and several putative microbial pathogens that are 371

important to food safety and human health were presented. This study confirms that 372

household appliances like dishwashers, colonized by poly-extremotolerant bacteria and fungi, 373

could present potential sources of domestically-sourced infections. To understand and 374

possibly prevent these phenomena, more studies should investigate fungal interactions on 375

bacterial physiology and vice-versa in biofilms formed on household appliances. 376


.asm.org/

Dow

nloaded from

http://aem.asm.org/

19

Materials and methods 377

Dishwasher Sample Information 378

Microbial biofilm grown on the rubber seals of 24 different DWs in private dwellings across 379

different Slovenian cities was sampled in this study (Table 1). The water supply at source of 380

these DWs was characterized into soft or hard water based on ion analysis method as 381

previously performed (10). Final concentrations were determined following the method from 382

ISO Standard SIST EN ISO 11885:2009. 383

Table 1: Different dishwasher machines and its associated characteristics from which 384

biofilm samples were obtained. ‘Ticks’ represent the samples that were sequenced based on 385

16S rRNA genes /ITS rRNA region. S1-S24: 24 sampled dishwashers; 16S rRNA: gene for 386

16S rRNA, partial sequence obtained within sample; ITS rRNA: ITS rRNA gene partial 387

sequence obtained within sample; Age: years used; Freq. of use: Frequency of use i.e. the no. 388

of times the DW was used per week; Washing cycle: Temperature of washing cycle; Water 389

hardness characteristics: H – hard (above 2.0 mmol/L CaCO3); MH – moderately hard (1.5- 390

2 mmol/L CaCO3); SH – slightly hard (1.0 -1.5 mmol/L CaCO3); MS- moderately soft (0.5 -1 391

mmol/L CaCO3); S- soft (below 0.5 mmol/L CaCO3). 392

Biofilm sampling and genomic DNA extraction 393

Biofilm formed on up to 1 cm2 of the rubber seal surface (Fig 1) was scraped off using sterile 394

scalpel and the collected biomass was placed into a sterile tube and stored at -20°C till use. 395

Genomic DNA extraction from 50 – 100 mg of biofilm biomass was performed using MoBio 396

Power Biofilm DNA isolation Kit (Carlsbad, CA, USA) according to the manufacturer’s 397

instructions. Extraction controls were included during DNA extraction. Negative control 398

contained ultrapure water (Milli-Q) in the same quantity as dishwasher biofilm biomass. The 399

processing of the sample and the negative control were performed simultaneously and in the 400


.asm.org/

Dow

nloaded from

http://aem.asm.org/

20

same way, according to manufacturer’s instructions. DNA concentrations were quantified for 401

all samples using Qubit® dsDNA HS Assay and measuring the fluorescence on Qubit® 402

fluorometer (InvitrogenTM, UK). 403

16S rRNA gene and nuclear ribosomal internal transcribed spacer (ITS) rRNA 404

amplicon based sequencing 405

To determine the microbial diversity in biofilms associated with the selected DW rubber 406

seals, amplicon sequencing based on 16S rRNA gene and nuclear ribosomal internal 407

transcribed spacer (ITS) region for bacterial and fungal diversity, respectively, was applied. 408

The concentration of extracted DNA was quantified and adjusted to 5 ng /µl for all samples 409

using ultrapure water (Milli-Q). For the PCR reaction, 1 µl of above prepared DNA was used. 410

The variable regions V3 and V4 were used for bacterial identification by primers targeting the 411

flanking conserved regions and amplified using the primers PRK341F (5’-CCT AYG GGR 412

BGC ASCAG-3’) and MPRK806R (5’-GGATCTACNNGGTATSTAAT-3’) (70). The 413

general eukaryote primers ITS7 (5’-GTGAATCATCGAATCTTTG-3’) (71) and ITS4 (5’-414

CAGACTTRTAYATGGTCCAG-3’) (72) were used to amplify the ITS2 region for 415

sequencing. Blank was included as a negative control. PCR amplifications were done in two 416

steps. 417

The first PCR amplification was done using PuRe Taq ready-To-Go PCR Beads (GE 418

Healthcare, United Kingdom) containing 1µl of each primer. Bacterial PCR-I mix was 419

amplified according to following conditions: 94°C for 2 min, 35 cycles of 94°C for 20 s, 56°C 420

for 20s and 68°C for 30s, and final extension at 68°C for 5 min. Eukaryotic PCR-I 421

amplifications were 94°C for 2 min, 35 cycles of 94°C for 30 s, 56°C for 30 s, 72°C for 30 s, 422

followed by 72°C for 5 min. The final products were then cooled on ice to minimize 423

hybridization between specific PCR products and short nonspecific amplicons. Products were 424


.asm.org/

Dow

nloaded from

http://aem.asm.org/

21

checked by running 5 μl on a 1.5% agarose gel. Sequencing primers and adaptors were added 425

to the amplicon products in the second PCR step: 2.0 µl 10x AccuPrime™ PCR Buffer 426

containing 15 mM MgCl2, Invitrogen), 0.15 µl (MSM2) AccuPrime™ Taq DNA Polymerase 427

(2 units/µl, Life Technologies), 1.0 µl of each fusion primers, 2 µl of 10X diluted PCR 428

product from first PCR and water to a total of 20 µl reaction volume. The PCR-II conditions 429

were: 94°C for 2 min, followed by 15 cycles of 94°C for 30 s, 56°C for 30s and 68°C for 30s, 430

and final extension at 68°C for 5 min. Amplicons were size separated on a 1 % agarose gel 431

and purified using Montage Gel Extraction Kit (Millipore, Billerica, MA, USA). Amplicon 432

concentrations were quantified for all samples using Qubit® dsDNA HS Assay and 433

measuring the fluorescence on Qubit® fluorometer (InvitrogenTM, UK). Samples were 434

sequenced using an Illumina MiSeq sequencer, employing paired-end reads, as described 435

previously (73). De-multiplexing was performed by the Miseq Controller Software. Raw fastq 436

files for both 16S and ITS data were processed with qiime_pipe, a wrapper around the QIIME 437

(1.7) pipeline available at https://github.com/maasha/qiime_pipe. The preprocess_illumina.rb 438

script handles quality control, and for both datasets this was done using the same parameters. 439

Merging of paired-end reads was done with a maximum of 20% mismatches and minimum 440

length overlaps of 15bp. Primers were identified with 2 maximum mismatches and the 441

amplicon was trimmed to only contain the sequence between the primers. Sequences were 442

discarded if the average quality was less than 30. Chimera checks were performed with the 443

QIIME script identify_chimeric_seqs.py using the usearch61 method. For 16S, it was checked 444

against GreenGenes (4 Feb. 2011) and for ITS, against UNITE (09 Feb 2014 dynamic 445

version) databases. OTU picking were done at 97% for both datasets with QIIMEs 446

pick_otu.py, and representatives of these were picked with pick_rep_set.py, both at default 447

settings. For 16S, trees were generated by aligning to the GreenGenes set using PyNast and 448

FastTree through the QIIME wrappers. Representative sequences were classified through the 449


.asm.org/

Dow

nloaded from

http://aem.asm.org/

22

RDP classifier at the default 0.8 confidence threshold. The same databases were used for 450

classification as well as for chimera checking. 451

Microbial Interaction Network 452

Positive correlations signifying co-occurrence and negative correlations signifying mutual 453

exclusions were characterized by generating the Spearman co-occurrence network. The 454

network based on normalized abundance was generated using the CoNet 1.0b6 plugin for 455

Cytoscape 3.2.1 on the basis of the in-built nonparametric Spearman correlation coefficient 456

with a minimal cut-off threshold of r ≥ |0.65| (P≪0.01, Bonferroni corrected) (74, 75). In this 457

study, we present the correlation data for bacterial and fungal members that were coexisting in 458

the same dishwashers (N=18). OTUs with ≥50% sample representation were selected for the 459

microbial network (n≥9, N=18). 460

Data Analysis 461

Alpha diversity analyses were performed in the PAST software ver.2.17 (76). Alpha diversity 462

indices were calculated on OTU counts rarefied to 4000 counts per sample for bacterial 463

sequences and samples below 4000 bacterial counts were not included in this analysis. Fungal 464

counts were rarefied to 400 counts per sample for this particular analysis. Fungal sequences 465

rarefied to the lowest sequencing depth allowed DW samples with replicate conditions to be 466

maintained. The following indices were used to assess the diversity: the sample richness, the 467

Shannon (H), the Chao-1. The effect of DW conditions on alpha diversity indices were 468

statistically assessed using t-test (Wilcoxon-Mann-Whitney, p< 0.05). 469

Multivariate beta-diversity analyses were done using non-rarefied counts (77). As the 470

contingency tables featured 1000-folds variation in abundance, a log10 transformation was 471

applied to have all the information possible and to satisfy distributional assumptions. The 472

transformed compositional dataset was subject to a Redundancy analysis (RDA) using DW 473


.asm.org/

Dow

nloaded from

http://aem.asm.org/

23

conditions as factors. The significance of the model, of the RDA axes and factors were 474

estimated by ANOVA on Euclidean distances and 999 permutations, p<0.05 significance. In 475

addition, PERMANOVA using 999 permutations and Bray-Curtis dissimilarity index was 476

performed to assess the significance of DW conditions. Selections of taxa (phyla and genus 477

level) with significant changes in prevalence grouped by DW conditions were performed in 478

STAMP 2.1.3 (78) using multiple and group comparisons and significance checked using in-479

built ANOVA (Tukey-Kramer post-hoc tests) and Welch’s t- test (Bonferroni corrected). The 480

selected significant taxa were plotted in heatmap using dissimilarity indices computed based 481

on euclidean distances, average clustering and scaled counts. The heat maps were clustered 482

based on ‘coniss’. RDA, PERMANOVA, principal component (PC) plots and heatmaps were 483

generated using various R packages: gplots, vegan, rioja and Rcolorbrewer available for Rgui 484

3.2.0 (79). 485

Declarations 486

Acknowledgements 487

Our acknowledgements go to all the people who kindly provided samples from their 488

dishwashers. We also thank Karin Vestberg for her assistance with NGS and prof. Børge 489

Diderichsen for careful and critical reading of the manuscript. 490

Ethics approval and consent to participate 491

In this study, field sampling was performed, and to our knowledge, no endangered or 492

protected species were involved. All of the samples studied here were obtained from the 493

discussed sampling areas, for which permission was obtained from the owners. 494

Consent for publication 495

All authors read and approved the final manuscript. 496


.asm.org/

Dow

nloaded from

http://aem.asm.org/

24

Availability of data and materials 497

The sequence data sets generated during and analysed during the current study are available at 498

the NCBI Sequence Read Archive (SRA) under the Bioproject IDs: PRJNA315977 for 499

bacterial reads and PRJNA317625 for fungal reads. 500

Competing interest 501

The authors declare that they have no conflicts of interest. 502

Funding 503

This research was funded by the Ministry of Higher Education, Science and Technology of 504

the Republic of Slovenia, as a Young Researcher grant to JZ (grant no. 382228-1/2013). We 505

also thank the Slovenian Research Agency (Infrastructural Centre Mycosmo, MRIC UL) and 506

the Danish Council for Independent Research grant: 1323 00235 for providing financial 507

support. The funding bodies had no influence on the design of the study and collection, 508

analysis, and interpretation of data and in writing the manuscript. 509

Authors’ contributions 510

SJS, NGC and MB designed the experimental setup. PKR and JZ performed the experiments. 511

ADB, SJ and PKR analysed the data. PKR, JZ, ADB, SJ, KH, NGC, MB, and SJS compiled 512

the manuscript. 513


.asm.org/

Dow

nloaded from

http://aem.asm.org/

25

References 514

1. de Gannes V, Eudoxie G, Bekele I, Hickey WJ. 2015. Relations of microbiome 515

characteristics to edaphic properties of tropical soils from Trinidad. Frontiers in 516

Microbiology 6:1045. 517

2. Frey B, Rime T, Phillips M, Stierli B, Hajdas I, Widmer F, Hartmann M. 2016. 518

Microbial diversity in European alpine permafrost and active layers. FEMS 519

microbiology ecology 92: 10.1093/femsec/fiw018. 520

3. Ventosa A, de la Haba RR, Sanchez-Porro C, Papke RT. 2015. Microbial diversity 521

of hypersaline environments: a metagenomic approach. Current opinion in 522

microbiology 25:80–87. 523

4. Crits-Christoph A, Robinson CK, Barnum T, Fricke WF, Davila AF, Jedynak B, 524

McKay CP, Diruggiero J. 2013. Colonization patterns of soil microbial communities 525

in the Atacama Desert. Microbiome 1:28-10.1186/2049-2618-1-28. 526

5. Bhullar K, Waglechner N, Pawlowski A, Koteva K, Banks ED, Johnston MD, 527

Barton HA, Wright GD. 2012. Antibiotic Resistance Is Prevalent in an Isolated Cave 528

Microbiome. PLoS ONE 7:e34953. 529

6. Flores GE, Bates ST, Caporaso JG, Lauber CL, Leff JW, Knight R, Fierer N. 530

2013. Diversity, distribution and sources of bacteria in residential kitchens. 531

Environmental microbiology 15:588–596. 532

7. Hamada N, Abe N. 2009. Physiological characteristics of 13 common fungal species 533

in bathrooms. Mycoscience 50:421. 534

8. Naegele A, Reboux G, Vacheyrou M, Valot B, Millon L, Roussel S. 2015. 535

Microbiological consequences of indoor composting. Indoor Air 26:605–613. 536

9. Ren H, Wang W, Liu Y, Liu S, Lou L, Cheng D, He X, Zhou X, Qiu S, Fu L, Liu 537

J, Hu B. 2015. Pyrosequencing analysis of bacterial communities in biofilms from 538

different pipe materials in a city drinking water distribution system of East China. 539

Applied Microbiology and Biotechnology 99:10713–10724. 540

10. Babič MN, Zalar P, Gunde-Cimerman N. 2013. Black yeasts enter household 541

appliances via water, p. 15. In ISHAM. Guangzhou. 542

11. Gambino M, Cappitelli F. 2016. Mini-review: Biofilm responses to oxidative stress. 543

Biofouling 32:167–178. 544

12. Bik HM, Maritz JM, Luong A, Shin H, Dominguez-Bello MG, Carlton JM. 2016. 545

Microbial Community Patterns Associated with Automated Teller Machine Keypads in 546

New York City. mSphere 1: e00226-16. 547

13. Vilanova C, Iglesias A, Porcar M. 2015. The coffee-machine bacteriome: biodiversity 548

and colonisation of the wasted coffee tray leach. Scientific Reports 549

5:10.1038/srep17163. 550

14. Callewaert C, Van Nevel S, Kerckhof FM, Granitsiotis MS, Boon N. 2015. 551

Bacterial exchange in household washing machines. Frontiers in Microbiology 6: 1381. 552


.asm.org/

Dow

nloaded from

http://aem.asm.org/

26

15. Babič MN, Zalar P, Ženko B, Schroers HJ, Džeroski S, Gunde-Cimerman N. 553

2015. Candida and Fusarium species known as opportunistic human pathogens from 554

customer-accessible parts of residential washingmachines. Fungal Biology 119:95–113. 555

16. Zalar P, Novak M, de Hoog GS, Gunde-Cimerman N. 2011. Dishwashers – A man-556

made ecological niche accommodating human opportunistic fungal pathogens. Fungal 557

Biology 115:997–1007. 558

17. Zupančič J, Babič MN, Zalar P, Gunde-Cimerman N. 2016. The black yeast 559

Exophiala dermatitidis and other selected opportunistic human fungal pathogens spread 560

from dishwashers to kitchens. PLoS ONE 11: e0148166. 561

18. Döğen A, Kaplan E, Oksüz Z, Serin MS, Ilkit M, de Hoog GS. 2013. Dishwashers 562

are a major source of human opportunistic yeast-like fungi in indoor environments in 563

Mersin, Turkey. Medical mycology 51:493–8. 564

19. Gümral R, Özhak-Baysan B, Tümgör A, Saraçlı MA, Yıldıran ŞT, Ilkit M, 565

Zupančič J, Novak-Babič M, Gunde-Cimerman N, Zalar P, de Hoog GS. 2016. 566

Dishwashers provide a selective extreme environment for human-opportunistic yeast-567

like fungi. Fungal Diversity 76:1–9. 568

20. Ojima M, Toshima Y, Koya E, Ara K, Kawai S, Ueda N. 2002. Bacterial 569

contamination of Japanese households and related concern about sanitation. 570

International journal of environmental health research 12:41–52. 571

21. Sinclair RG, Gerba CP. 2011. Microbial contamination in kitchens and bathrooms of 572

rural Cambodian village households. Letters in applied microbiology 52:144–149. 573

22. Ojima M, Toshima Y, Koya E, Ara K, Tokuda H, Kawai S, Kasuga F, Ueda N. 574

2002. Hygiene measures considering actual distributions of microorganisms in 575

Japanese households. Journal of applied microbiology 93:800–809. 576

23. Limoli DH, Jones CJ, Wozniak DJ. 2015. Bacterial Extracellular Polysaccharides in 577

Biofilm Formation and Function. Microbiology spectrum 3: 578

10.1128/microbiolspec.MB-0011-2014. 579

24. Burmølle M, Ren D, Bjarnsholt T, Sørensen SJ. 2016. Interactions in multispecies 580

biofilms: do they actually matter? Trends in Microbiology 22:84–91. 581

25. Oliveira NM, Martinez-Garcia E, Xavier J, Durham WM, Kolter R, Kim W, 582

Foster KR. 2015. Biofilm Formation As a Response to Ecological Competition. PLoS 583

Biol 13:e1002191. 584

26. Jabra-Rizk MA, Falkler WA, Meiller TF. 2004. Fungal Biofilms and Drug 585

Resistance. Emerging Infectious Diseases 10:14–19. 586

27. Donlan RM. 2002. Biofilms: Microbial life on surfaces. Emerging Infectious Diseases. 587

8:881-90 588

28. Parsek MR, Singh PK. 2003. Bacterial biofilms: an emerging link to disease 589

pathogenesis. Annual review of microbiology 57:677–701. 590

29. Jones KE, Patel NG, Levy MA, Storeygard A, Balk D, Gittleman JL, Daszak P. 591

2008. Global trends in emerging infectious diseases. Nature 451:990–993. 592


.asm.org/

Dow

nloaded from

http://aem.asm.org/

27

30. Dannemiller KC, Gent JF, Leaderer BP, Peccia J. 2016. Influence of housing 593

characteristics on bacterial and fungal communities in homes of asthmatic children. 594

Indoor air 26:179–192. 595

31. Ordonez OF, Lanzarotti E, Kurth D, Gorriti MF, Revale S, Cortez N, Vazquez 596

MP, Farias ME, Turjanski AG. 2013. Draft Genome Sequence of the 597

Polyextremophilic Exiguobacterium sp. Strain S17, Isolated from Hyperarsenic Lakes 598

in the Argentinian Puna. Genome announcements 1: e00480-13. 599

32. Chaturvedi P, Shivaji S. 2006. Exiguobacterium indicum sp. nov., a psychrophilic 600

bacterium from the Hamta glacier of the Himalayan mountain ranges of India. 601

International journal of systematic and evolutionary microbiology 56:2765–2770. 602

33. Cabria GLB, Argayosa VB, Lazaro JEH, Argayosa AM, Arcilla CA. 2014. Draft 603

Genome Sequence of Haloalkaliphilic Exiguobacterium sp. AB2 from Manleluag 604

Ophiolitic Spring, Philippines. Genome Announcements 2:e00840-14. 605

34. Vishnivetskaya TA, Kathariou S, Tiedje JM. 2009. The Exiguobacterium genus: 606

biodiversity and biogeography. Extremophiles : life under extreme conditions 13:541–607

555. 608

35. Arenskotter M, Broker D, Steinbuchel A. 2004. Biology of the metabolically diverse 609

genus Gordonia. Applied and environmental microbiology 70:3195–3204. 610

36. Nobre MF, Trueper HG, DA Costa MS. 1996. Transfer of Thermus ruber (Loginova 611

et al. 1984), Thermus silvanus (Tenreiro et al. 1995), and Thermus chliarophilus 612

(Tenreiro et al. 1995) to Meiothermus gen. nov. as Meiothermus ruber comb. nov., 613

Meiothermus silvanus comb. nov., and Meiothermus chliarop. International Journal of 614

Systematic and Evolutionary Microbiology 46:604–606. 615

37. Lax S, Smith DP, Hampton-Marcell J, Owens SM, Handley KM, Scott NM, 616

Gibbons SM, Larsen P, Shogan BD, Weiss S, Metcalf JL, Ursell LK, Vazquez-617

Baeza Y, Van Treuren W, Hasan NA, Gibson MK, Colwell R, Dantas G, Knight 618

R, Gilbert JA. 2014. Longitudinal analysis of microbial interaction between humans 619

and the indoor environment. Science (New York, NY) 345:1048–1052. 620

38. Taubel M, Rintala H, Pitkaranta M, Paulin L, Laitinen S, Pekkanen J, Hyvarinen 621

A, Nevalainen A. 2009. The occupant as a source of house dust bacteria. The Journal 622

of allergy and clinical immunology 124:834–40.e47. 623

39. Adams RI, Miletto M, Lindow SE, Taylor JW, Bruns TD. 2014. Airborne Bacterial 624

Communities in Residences: Similarities and Differences with Fungi. PLOS One 625

9:e91283. 626

40. Flemming H-C, Wingender J. 2010. The biofilm matrix. Nat Rev Micro 8:623–633. 627

41. Madsen JS, Røder HL, Russel J, Sørensen H, Burmølle M, Sørensen SJ. 2016. 628

Coexistence facilitates interspecific biofilm formation in complex microbial 629

communities. Environmental Microbiology 18:2565–74. 630

42. Yang L, Liu Y, Wu H, Hoiby N, Molin S, Song Z. 2011. Current understanding of 631

multi-species biofilms. International journal of oral science 3:74–81. 632

43. Chen X, Suwarno SR, Chong TH, McDougald D, Kjelleberg S, Cohen Y, Fane 633


.asm.org/

Dow

nloaded from

http://aem.asm.org/

28

AG, Rice SA. 2013. Dynamics of biofilm formation under different nutrient levels and 634

the effect on biofouling of a reverse osmosis membrane system. Biofouling 29:319–635

330. 636

44. Dang H, Lovell CR. 2016. Microbial Surface Colonization and Biofilm Development 637

in Marine Environments. Microbiology and molecular biology reviews : MMBR 638

80:91–138. 639

45. Rampadarath S, Bandhoa K, Puchooa D, Jeewon R, Bal S. 2017. Early bacterial 640

biofilm colonizers in the coastal waters of Mauritius. Electronic Journal of 641

Biotechnology 29:13–21. 642

46. Loviso CL, Lozada M, Guibert LM, Musumeci MA, Sarango Cardenas S, Kuin R 643

V, Marcos MS, Dionisi HM. 2015. Metagenomics reveals the high polycyclic 644

aromatic hydrocarbon-degradation potential of abundant uncultured bacteria from 645

chronically polluted subantarctic and temperate coastal marine environments. Journal 646

of applied microbiology 119:411–424. 647

47. Mueller RS, Bryson S, Kieft B, Li Z, Pett-Ridge J, Chavez F, Hettich RL, Pan C, 648

Mayali X. 2015. Metagenome Sequencing of a Coastal Marine Microbial Community 649

from Monterey Bay, California. Genome Announcements 3: 10.1128. 650

48. Oberbeckmann S, Osborn AM, Duhaime MB. 2016. Microbes on a Bottle: 651

Substrate, Season and Geography Influence Community Composition of Microbes 652

Colonizing Marine Plastic Debris. PLOS One 11:e0159289. 653

49. Dobretsov S, Abed RMM, Teplitski M. 2013. Mini-review: Inhibition of biofouling 654

by marine microorganisms. Biofouling 29:423–441. 655

50. Lachnit T, Fischer M, Kunzel S, Baines JF, Harder T. 2013. Compounds associated 656

with algal surfaces mediate epiphytic colonization of the marine macroalga Fucus 657

vesiculosus. FEMS microbiology ecology 84:411–420. 658

51. Lakshmi K, Muthukumar T, Doble M, Vedaprakash L, Kruparathnam, 659

Dineshram R, Jayaraj K, Venkatesan R. 2012. Influence of surface characteristics 660

on biofouling formed on polymers exposed to coastal sea waters of India. Colloids and 661

surfaces B, Biointerfaces 91:205–211. 662

52. Sterflinger K. 2006. Black Yeasts and Meristematic Fungi: Ecology, Diversity and 663

Identification BT - Biodiversity and Ecophysiology of Yeasts, p. 501–514. In Péter, G, 664

Rosa, C (eds.), . Springer Berlin Heidelberg, Berlin, Heidelberg. 665

53. Langfelder K, Streibel M, Jahn B, Haase G, Brakhage AA. 2003. Biosynthesis of 666

fungal melanins and their importance for human pathogenic fungi. Fungal genetics and 667

biology : FG & B 38:143–158. 668

54. Yurlova NA, de Hoog GS. 2002. Exopolysaccharides and capsules in human 669

pathogenic Exophiala species. Mycoses 45:443–448. 670

55. Carini P, Marsden PJ, Leff JW, Morgan EE, Strickland MS, Fierer N. 2016. Relic 671

DNA is abundant in soil and obscures estimates of soil microbial diversity. Nature 672

microbiology 2:16242. 673

56. Fukami T. 2015. Historical Contingency in Community Assembly: Integrating Niches, 674


.asm.org/

Dow

nloaded from

http://aem.asm.org/

29

Species Pools, and Priority Effects. Annual Review of Ecology, Evolution, and 675

Systematics 46:1–23. 676

57. Karuppayil SM, Szaniszlo PJ. 1997. Importance of calcium to the regulation of 677

polymorphism in Wangiella (Exophiala) dermatitidis. Journal of medical and 678

veterinary mycology : bi-monthly publication of the International Society for Human 679

and Animal Mycology 35:379–388. 680

58. Ma X, Baron JL, Vikram A, Stout JE, Bibby K. 2015. Fungal diversity and presence 681

of potentially pathogenic fungi in a hospital hot water system treated with on-site 682

monochloramine. Water Research 71: 197–206. 683

59. Pereira VJ, Marques R, Marques M, Benoliel MJ, Barreto Crespo MT. 2013. Free 684

chlorine inactivation of fungi in drinking water sources. Water Research 71: 1517–523. 685

60. Babič NM, Zalar P, Ženko B, Džeroski S, Gunde-Cimerman N. 2016. Yeasts and 686

yeast-like fungi in tap water and groundwater, and their transmission to household 687

appliances. Fungal Ecology 20:30–39. 688

61. Al-Gabr HM, Zheng T, Yu X. 2014. Fungi contamination of drinking water. Reviews 689

of environmental contamination and toxicology 228:121–139. 690

62. Losos JB, Leal M, Glor RE, de Queiroz K, Hertz PE, Rodriguez Schettino L, 691

Chamizo Lara A, Jackman TR, Larson A. 2003. Niche lability in the evolution of a 692

Caribbean lizard community. Nature 424:542–545. 693

63. Hogan, DA, Wargo, MJ and Beck N. 2007. Bacterial biofilms on fungal surfaces, p. 694

235–245. In S. Kjelleberg and M. Givskov (ed.), The biofilm mode of life: mechanisms 695

and adaptations. Horizon Scientific Press, Norfolk,UK. 696

64. Seneviratne G, Zavahir JS, Bandara WMMS, Weerasekara MLMAW. 2007. 697

Fungal-bacterial biofilms: their development for novel biotechnological applications. 698

World Journal of Microbiology and Biotechnology 24:739. 699

65. Frey-Klett P, Burlinson P, Deveau A, Barret M, Tarkka M, Sarniguet A. 2011. 700

Bacterial-Fungal Interactions: Hyphens between Agricultural, Clinical, Environmental, 701

and Food Microbiologists. Microbiology and Molecular Biology Reviews 75:583–609. 702

66. Hansen LBS, Ren D, Burmolle M, Sorensen SJ. 2017. Distinct gene expression 703

profile of Xanthomonas retroflexus engaged in synergistic multispecies biofilm 704

formation. The ISME journal 11:300–303. 705

67. Roggenbuck M, Bærholm Schnell I, Blom N, Bælum J, Bertelsen MF, Sicheritz-706

Pontén T, Sørensen SJ, Gilbert MTP, Graves GR, Hansen LH. 2014. The 707

microbiome of New World vultures. Nature Communications 5:5498. 708

68. Kalan L, Loesche M, Hodkinson BP, Heilmann K, Ruthel G, Gardner SE, Grice 709

EA. 2016. Redefining the Chronic-Wound Microbiome: Fungal Communities Are 710

Prevalent, Dynamic, and Associated with Delayed Healing. mBio 7:e01058-16. 711

69. Ghannoum M. 2016. Cooperative Evolutionary Strategy between the Bacteriome and 712

Mycobiome. mBio 7: 10.1128/mBio.01951-16. 713

70. Yu Y, Lee C, Kim J, Hwang S. 2005. Group-specific primer and probe sets to detect 714


.asm.org/

Dow

nloaded from

http://aem.asm.org/

30

methanogenic communities using quantitative real-time polymerase chain reaction. 715

Biotechnology and bioengineering 89:670–679. 716

71. Ihrmark K, Bodeker ITM, Cruz-Martinez K, Friberg H, Kubartova A, Schenck J, 717

Strid Y, Stenlid J, Brandstrom-Durling M, Clemmensen KE, Lindahl BD. 2012. 718

New primers to amplify the fungal ITS2 region--evaluation by 454-sequencing of 719

artificial and natural communities. FEMS microbiology ecology 82:666–677. 720

72. White TJ, Bruns S, Lee S, Taylor J. 1990. Amplification and direct sequencing of 721

fungal ribosomal RNA genes for phylogenetics. PCR Protocols: A Guide to Methods 722

and Applications: 315-322. 723

73. Mortensen MS, Brejnrod AD, Roggenbuck M, Abu Al-Soud W, Balle C, Krogfelt 724

KA, Stokholm J, Thorsen J, Waage J, Rasmussen MA, Bisgaard H, Sørensen SJ. 725

2016. The developing hypopharyngeal microbiota in early life. Microbiome 4:70. 726

74. Faust K, Sathirapongsasuti JF, Izard J, Segata N, Gevers D, Raes J, Huttenhower 727

C. 2012. Microbial co-occurrence relationships in the human microbiome. PLoS 728

computational biology 8:e1002606. 729

75. Saito R, Smoot ME, Ono K, Ruscheinski J, Wang P-L, Lotia S, Pico AR, Bader 730

GD, Ideker T. 2012. A travel guide to Cytoscape plugins. Nature methods 9:1069–731

1076. 732

76. Hammer Ø, Harper DAT, Ryan PD. 2001. PAST : Paleontological Statistics 733

Software Package for Education and Data Analysis. Palaeontologia Electronica 4:9. 734

77. McMurdie PJ, Holmes S. 2014. Waste Not, Want Not: Why Rarefying Microbiome 735

Data Is Inadmissible. PLOS Computational Biology 10:e1003531. 736

78. Parks DH, Tyson GW, Hugenholtz P, Beiko RG. 2014. STAMP: Statistical analysis 737

of taxonomic and functional profiles. Bioinformatics 30:3123–3124. 738

79. R Development Core Team. 2014. R: A Language and Environment for Statistical 739

Computing Vienna, Austria R Foundation for Statistical Computing ISBN 3-900051-740

07-0, http://www.R-project.org/ (22 August 2016, date last accessed) 741


.asm.org/

Dow

nloaded from

http://aem.asm.org/

31

Figure legends 742

Fig 1: Biofilm formed on the rubber seal in residential DWs. Microbial biofilm formation 743

on DW rubber seal, the square (in red) represents the 1cm2 sampling site. Biofilm samples 744

from 1 cm2 were collected by scrapping the surface of rubber seal with sterile scalpel for 745

DNA extraction and further analysis. Sampling was done in-situ, with the seal at its original 746

place. 747

748

Fig 2: The different bacterial and fungal genera that were represented across 21 DW 749

samples. The numbers represent sample count i.e. the number of DW samples that contained 750

the representative genera. 751

752

Fig 3: Principal component plots of redundancy analysis (RDA) performed to log10-753

transformed 16S rRNA amplicon sequencing data using (A) year and (B) frequency of 754

use as explanatory factors. Significance of the model, axes and factors was determined by 755

ANOVA (999 permutations; p < 0.05). Stars stand for the level of significance according to 756

the code: (*) 0.05 ≤ P-values < 0.01; (**) 0.01 ≤ P-values< 0.001. Factors ‘y’ represent years 757

of use (0-3, 5-7 and 8 years) and ‘Freq’ represents frequency of use (1-3, 7 and 14 758

times/week). 759

760

Fig 4: Principal component plots of redundancy analysis (RDA) performed to log10-761

transformed ITS rRNA amplicon sequencing data using (A) year and (B) frequency of 762

use and (C) water hardness as explanatory factors. Significance of the model, axes and 763

factors was determined by ANOVA (999 permutations; p < 0.05). Stars stand for the level of 764

significance according to the code: (*) 0.05 ≤ P-values < 0.01; (**) 0.01 ≤ P-values< 0.001. 765

Factors ‘y’ represent years of use (0-3, 5-7 and 8 years); ‘Freq’ represents frequency of use 766


.asm.org/

Dow

nloaded from

http://aem.asm.org/

32

(1-3, 7 and 14 times/week) and ‘H’ represents hard water, ‘MH’ represents moderately hard 767

water, ‘SH’ represents slightly hard water and ‘MS’ represents moderately soft water. 768

769

Fig 5: Impact of DWs age and frequency of use on the relative abundance of bacterial 770

taxa present in the samples. (A & B) Mean ± S.E.M of percentage abundance in samples 771

grouped by (A) years and (B) frequency of use at the phyla level. (C & D) Heat map of 772

significant bacterial genera in DW samples grouped by (C) years of use (0-4, 5-7 and 8 years) 773

and grouped by (D) frequency of use (1-3, 7 and 14times/week), respectively. 774

775

Fig 6: Impact of DWs age and frequency of use on the relative abundance of fungal taxa 776

present in the samples. (A & B) Mean ± S.E.M of percentage abundance in samples grouped 777

by (A) years, (B) frequency of use and (C) water hardness at the phyla level. (D, E & F) Heat 778

map of significant fungal genera in DW samples grouped by (D) years of use (0-4, 5-7 and 8 779

years); (E) grouped by frequency of use and (F) grouped by influent water hardness, 780

respectively. ‘H’ represents hard water, ‘MH’ represents moderately hard water, ‘SH’ 781

represents slightly hard water and ‘MS’ represents moderately soft water. 782


.asm.org/

Dow

nloaded from

http://aem.asm.org/

33

Table 1 783

Sample S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 S21 S22 S23 S24

16S rRNA

ITS rRNA

Years used 2.5 2.5 7 3 2 8 5 8 5 1 7 0.5 1 2 8 1 1 4 4 3 8 5 1 8

Freq. of

use

7 7 3 14 14 7 3 7 3 2 7 3 3 2 7 7 14 3 3 1 7 7 1 7

Washing

cycle (°C)

60 60 60 60 60 60 60 60 60 65 60 65 50 65 65 65 65 65 65 60 50 60 65 60

Water

Hardness

SH SH MH MH MH MH MH MH MH MH SH SH SH SH MS MH MH MH H SH MS H SH MS


.asm.org/

Dow

nloaded from

http://aem.asm.org/

34

Table 2 784

785

786

787

788

789

790

Figure 1 791

Total interactions 140

Total Positive Interactions 125

Bacterial -Bacterial positive interactions 119

Fungal-Fungal positive Interactions 3

Bacterial-Fungal positive Interactions 3

Total Negative Interactions 15

Bacterial -Bacterial negative interactions 8

Bacterial-Fungal negative Interactions 7


.asm.org/

Dow

nloaded from

http://aem.asm.org/

35

Figure 2 792

Figure 3 793

-6 -4 -2 0 2 4 6

-4-2

02

RDA1 ** - 27.32%

RD

A2

*-

16

.44

%

S24

S17

S22

S19

S6

S15

S21

S13

S1S2

S10

S4

S14

S7

S3

S23

S5

S12

S11

Year 0-4

Year 5-7Year 8

Model P= 0.04*

Variance = 84%

8 5-7

0-4

A

S21

S16

-6 -4 -2 0 2 4 6

-4-2

02

RDA1 ** - 27.32%

RD

A2

*-

16

.44

%

Freq ≥14

Freq 1-3Freq 7

S24

S17

S22

S19

S6

S15

S21

S13

S1S2

S10

S4

S14

S7

S3

S23

S5

S12

S11

Model P= 0.04*

Variance = 84%

≥ 14 7

1-3

B

S21

S16


.asm.org/

Dow

nloaded from

http://aem.asm.org/

36

Figure 4 794

-6 -4 -2 0 2 4

-3-2

-10

12

3

RDA1** - 15.43%

RD

A2

**

-8

.08

%

Year 0-4

Year 5-7

Year 8

S19

S22

S3

S18 S12

S16

S24

S11

S13

S7

S21

S4

S9

S6

S1

S23S2S8

S17S15

S20

Model P= 0.004**

Variance = 38%

-6

8 5-7

0-4

A

-6 -4 -2 0 2 4

-3-2

-10

12

3

RDA1** - 15.43%

RD

A2

**

-8

.08

%

Freq 1-6 Freq 7-14

S19

S22

S3

S18 S12

S16

S24

S11

S13

S7

S21

S4

S9

S6

S1

S23S2S8

S17S15

S20

Model P= 0.004**

Variance = 38%

-6

7-14 1- 6

B

-6 -4 -2 0 2 4

-3-2

-10

12

3

RDA1** - 15.43%

RD

A2

**

-8

.08

%

H

S19

S22

S3

S18 S12

S16

S24

S11

S13

S7

S21

S4

S9

S6

S1

S23S2S8

S17S15

S20

Model P= 0.004**

Variance = 38%

-6

H MH

MS SH

SH

MS

MH

C


.asm.org/

Dow

nloaded from

http://aem.asm.org/

37

Figure 5 795

A B%

Re

lative

Ab

un

da

nce

% R

ela

tive

Ab

un

da

nce ▪

●

Proteobacteria

Firmicutes

Actinobacteria

0

10

20

30

40

50

60

0-4 years 5-7 years 8 years0

10

20

30

40

50

60

1-3 7 ≥14

Rhizobium

Escherichia/Shigella

Azospira

Exiguobacterium

Chryseobacterium

Staphylococcus

Arthrobacter

Treponema

Aerococcus

Brevibacterium

Starkeya

Aeromicrobium

Kaistella

Ancylobacter

Salana

-3 -1 1 3

Relative abundance0- 4

5- 78

C

S1

S1

0

S1

2

S1

3

S1

4

S1

7

S1

9

S2

S2

3

S4

S5

S1

1

S2

2

S3

S7

S1

5

S2

1

S2

4

S6

S2

0

S1

6


.asm.org/

Dow

nloaded from

http://aem.asm.org/

38

-3 -1 1 3

Relative abundance

S2

3

S1

0

S1

2

S1

3

S1

4

S2

0

S1

9

S3

S1

S1

1

S1

5

S1

6

S2

S2

1

S2

2

S2

4

S6

S1

7

S5

S7

S4

1- 3

7

14

D

Cellulosimicrobium

Helicobacter

Brevibacterium

Nesterenkonia

Xanthobacter

Roseomonas

Aeromicrobium

Micrococcus

Leptothrix


.asm.org/

Dow

nloaded from

http://aem.asm.org/

39

Figure 6 796

0

20

40

60

80

100

0-4 years 5-7 years 8 years

%R

ela

tive

ab

un

da

nce

0

20

40

60

80

100

1-6 7-14

%R

ela

tive

ab

un

da

nce

0

20

40

60

80

100

H MH MS SH

%R

ela

tive

ab

un

da

nce

A B C

▪●

Ascomycota

Basidiomycota

Zygomycota

Cryptococcus

Hyphodontia

Aureobasidium

Candida

Rhodotorula

Wallemia

-2 -1 0 1 2

Relative abundance

0- 4

5- 7

8

S1

S1

2

S1

3

S1

6

S1

7

S1

8

S1

9 S2

S2

0

S3

S7

S4

S9

S1

5

S2

1

S2

4

S6

S8

S2

2 S3

S1

1

D

S1

2

S1

3

S1

8

S1

9

S2

0

S2

3

S2

4

S3

S7

S9

S1

S1

1

S1

5

S1

6

S1

7

S2

S2

1

S2

2

S4

S6

S8

Rhodotorula

Candida

1- 6

7- 14

-2 -1 0 1 2

Relative abundance

E


.asm.org/

Dow

nloaded from

http://aem.asm.org/

40

Debaryomyces

Cladosporium

Rhodotorula

Sterigmatomyces

Stagonospora

Thelebolus

Neobulgaria

Perisporiopsis

Mortierella

Cryptococcus

Phoma

Alternaria

Trichosporon

Pilidium

Articulospora

Ascochyta

Dioszegia

Clitopilus

Rhizophagus

Gibberella

Hannaella

Candida

-4 -2 0 2 4

Relative abundance

H

MH

MS

SH

S1

9

S2

2

S1

6

S1

7

S1

8

S3

S4

S6

S7

S8

S9

S1

5

S2

1

S1

S2

4

S1

2

S2

S2

0

S2

3

S1

3

S1

1

F


.asm.org/

Dow

nloaded from

http://aem.asm.org/

Downloaded from on May 20, 2020 by guest6 86 Results 87 Characterization of bacterial and fungal...

Documents

Transcript of Downloaded from on May 20, 2020 by guest6 86 Results 87 Characterization of bacterial and fungal...