Mobile phones are hazardous microbial platforms warranting robust public health and biosecurity protocols

Participant features and questionnaire findings

In total, there were 26 health care workers who participated in this study: 16 nurses, 8 doctors, 1 outpatient clinical staff and 1 unspecified participant. 16 staff members were from the General Paediatric Department and 10 were from the Paediatric Intensive Care Unit. Majority of the participants (77%; N = 20/26) were completing their shift and whilst 23% (n/N = 6/26) commencing their shift. 77% (20/26) reported using their mobile phones at work with 88% (23/26) believing their mobile phones were essential tools for their job. 96% (25/26) of participants believed their mobile phones would harbor potentially pathogenic microorganisms. Concerning the hygiene habits associated with mobile phone use in the professional setting, 46% (12/26) of the participants had recently used their mobile phones in the bathroom. Of the medical staff using mobile phones in the bathroom, 58% (7/12) reported using their devices for social media access, 25% (3/12) did not specify the purpose of use and 16% (2/12) reported using their phone for work-related purposes. Over half of the participants (54%; n/N = 14/26) of participants had never cleaned their mobile phone. Of the 46% (12/26) of participants who had cleaned their mobile phones at some point, 25% (3/12) did so within the past year, 33% (4/12) did so within the past month, 16 % (2/12) did so within the past week and 25% (3/12) did so within the past day. Of those who reported cleaning their phones, 41% (5/12) used an alcohol-based wipe and 33% (4/12) used a disinfectant spray.

Illumina derived next generation sequencing datasets


The average amount of sequencing reads per mobile phone was approximately 53 million reads. Sample 26 (NS313-110) contained the lowest (33 million) and sample 12 (NS250-72) the highest number of reads (156 million) respectively.

The sequencing fastq dataset files of all sequencing samples of this study are available and processed in the SRA database with the SRA BioProject accession number PRJNA828402 that can be available in Enter ( Each detailed accession number of the 26 datasets generated and analyzed during the current study are available in the NCBI repository, (PRJNA828402—SRA—NCBI(”

Sequencing reads and metagenomic overview

A total of 11,163 microorganisms and 2096 genes coding for antibiotic and virulent factors were identified in this metagenomic shotgun next generation sequencing study. In total, there were 5714 bacteria, 675 fungi, 93 protists, 228 viruses, 4453 bacteriophages, 560 antibiotic resistant genes and 1536 virulence factor genes identified across the 26 mobile phones from GPD and PICU (Table 1).

Table 1 Number of all microorganisms and genes found on each mobile phone (per ward) via shotgun-metagenomic sequencing.

On average, mobile phones from the GPD contained higher amounts of pathogens and genes, compared to the phones sampled from PICU. Additionally, mobile phones of nurses contained in average a slightly higher number of microbes compared to doctors with 460.2 and 403.6 respectively. Across all 26 mobile phones, the average number of micro-organisms was calculated to be 429 with an average of 477.7 on the GPD phones and 361.6 in the PICU phones. Microbial numbers ranged from 138 to 669 per phone and genes (ARG and VRG) ranged from 7 to 144 per phone. (Table 1). Bacteria and bacteriophages represented the largest proportion of the microorganism distribution (Fig. 1).

Figure 1
figure 1

Distribution of different types of microorganisms across the 26 mobile phone samples.

Bacterial identification

1307 bacterial different strains were found with a richness across all 26 mobile phones accounting for 5714 hits. Clinically relevant species were found and include bacteria responsible for nosocomial diseases. 143 ‘ESKAPE’ type bacteria were found and consisted of Enterobacteriaceae: [46 hits on 19 phones (73%; 19/26) ], Staphylococcus aureus [25 hits; 25 phones (96%; 25/26)], Klebsiella pneumoniae [2 hits; 2 phones (7.7%; 2/26)], Acinetobacter baumannii [33 hits; 22 mobile phones (84.6%; 22/26)], Pseudomonas aeruginosa [21 hits, 21 mobile phones (80.8%; 21/26], Enterococcus faecalis/E.faecium [14 hits; with 50% of all 26 phones contaminated]. Of note, different strains of Pseudomonas and Acinetobacter species accounted for 187 and 205 wealth hits respectively across the 26 mobile phones.

Additionally, community-acquired pathogenic HACEK group gram-negative bacteria accounted for 180 wealth hits across the mobile phones swabbed. The highest hits were attributed to Haemophilus spp Aggregatibacter spp with 110 and 38 hits respectively while Cardiobacterium hominis, Eikenella corrodensand kingella spp corresponded to 14, 12 and 6 hits respectively. Every single phone swab harbored at least one Haemophilus spp.

Coagulase negative staphylococci (CONS) was found on all the mobile phones accounting for a total of 272 wealth hits. All phones within that study harbored CONS with S. epidermidis, S. hominis, S.warneri., S.haemolyticus. S. lugdunensis was identified on 92% (24/26) of mobile phones. While S. capitis and S. pasteuri in 88% and 81% of phones respectively.

Neisseria spp were identified with 152 wealth hits. N. flavescens, N.subflava, N. elongate, N. siccaand N. mucosa were the most represented with 21, 16, 16, 16 and 14 hits respectively. Noteworthy, N.meningitidis were present on 27% of phones (7/26) and N. gonorrhoeae was retrieved from one phone.

Streptococci strains accounted for 404 wealth hits across the 26 mobile phones and included S.thermophilus, S. sanguninis, S.parasanguinis, S. salivarius, S. pseudopnemoniae, S.oralis, S. mitis, S. intermedius, S. infantis, S. infantarius, S. cristatus, S. australis, S.anginosusand S. agalactiae. S.pneumoniae was found on the surface of 81% of the mobile phones (21/26) (Fig. 2).

Figure 2
figure 2

Clinically relevant pathogenic bacteria identified across all 26 mobile phones.

Mobile phones microbial composition varied with a subset of microbes uniquely present in either department: 170 and 317 bacteria in PICU and GPD respectively. These unique ward bacterial signatures showed different bacterial phylum profiles with the bacterial Actinobacteria phylum demonstrating the larger signature subset of PICU derived mobile phones while Bacteroidetes, Firmicutesand Proteobacteria phylum were predominant in GPD derived devices (Fig. 3).

Figure 3
picture 3

Phylum distribution of exclusive bacteria found present on General Paediatric Department or Paediatric Intensive Care Unit derived mobile phones.

Bacteriophage identification

In total there were 512 different bacteriophage viruses accounting for 4453 hits. Figure 4 illustrates the various bacteriophages identified from mobile phones of the GPD and PICU hospital departments. The highest hits corresponded to Propionibacterium virus, Streptococcus virus, Lactococcus virus, Staphylococcus virus, Pseudomonas virus with 29% (1,283/4,453), ~17.5% (777/4,453), ~17% (755/4,453), ~14.5% (646/4,453), ~3% (128/4,453) respectively (Fig. 4).

Figure 4
figure 4

Distribution of bacteriophages identified across the 26 mobile phones.

A significant difference in the number of bacteriophages was observed between the two wards (GPD and PICU) (P-value: 0.0022) (Wilcoxon Rank Sum Test) (Fig. 5).

Figure 5
figure 5

Boxplot of bacteriophages in GPD versus PICU wards (CHAO1 representation).

Viral identification

Sixty-seven different viruses accounting for 228 wealth hits was found on the mobile phones. Seven different human herpes viruses (HHV) were identified and corresponded to a total richness of 29 hits. 15 phones had at least one HHV and in one phone alone 5 HHVs could be retrieved [Herpes Simplex virus 1, Epstein bar Virus, cytomegalovirus, Roseolovirus 6 and 7]. Twenty-nine different strains of human papillomavirus were found which corresponded to 95 total hit richness across the mobile phones swabbed in this experiment. Seven pathogenic Human Papilloma Viruses (HPVs) (24%;7/29) were present and these accounted for 45% (43/95 hits) of all the 95 HPV hits. Of note, one phone alone had 5 pathogenic HPVs (HPV-3, -4, -5, -9 and -49). Polyomaviruses such as the Human polyomavirus 6, MW and polyomavirus STL were identified. Noteworthy, the Merkel cell polyomavirus was retrieved on six mobile phones.

Protist identification

12 different protists were found representing 93 total hits. Figure 6 highlights the range of protozoa identified with several amoebae of the protozoal group Sarcodina with Acanthamoeba polyphaga, Acanthamoeba palestinensis, Naegleria fowleri, Entamoeba dispar, Entamoeba histolytica (Fig. 6).

Figure 6
figure 6

Distribution of protists identified across 26 mobile phones.

Resistome and viruloma

Antibiotic resistance genes

The metagenomic analysis revealed the presence of 134 different (distinct) antibiotic resistance genes with a cumulative richness number across all the mobile phones of 560 ARGs. Figure 7 represents the distribution of grouped antibiotic resistant genes. Resistance genes to macrolides (19 genes), beta-lactams (32 genes), aminoglycosides (26 genes), and tetracycline (13 genes) corresponded to richness hits of 167, 98, 97 and 50 respectively (Fig. 7). Multi-type of antibiotics was targeted by efflux pumps (17 genes) and pump-regulator genes (13 genes) which together accounted for 89 wealth hits. Less richness was found for other antibiotics resistance genes acting on bacterial metabolism (sul2 gene acting on Sulphonamides; dfrC and dfrG genes acting on Trimethoprim), on cell wall (PBP1b/2b and vanXY genes acting on transpeptidases and vancomycin), on bacterial DNA ( norA, oqxA, bleomycin binding protein genes) and on protein translation [genes like cmx, dha1, cm acting on phenicols; fusC gene acting on the bacterial elongation factor (EF)] Fig. 8 (and Supplementary Fig. 1).

Picture 7
figure 7

Antibiotic Resistant gene distribution across all wards of 26 mobile phones.

Figure 8
figure 8

Heatmap representation of antibiotic resistant genes found on mobile phones owned by health care staff (heatmap clustered by staff occupation).

Virulence factor genes (GVGs)

Across the mobile phones swabbed, this study identified 419 different (distinct) virulent factor genes with 1536 hits. 35% of all these hits (552/1536) were attributed to 28 different VFGs genes that were all in at least 50% of mobile phones and included Klebsiella pneumoniae GENE tnpA, Proteus mirabilis GENE tnpA, Enterococcus faecalis GENE repB & GENE mob, Enterococcus faecium GENE ermB, Streptococcus pyogenes GENE msrD, Staphylococcus epidermidis GeneID SEA1545, Staphylococcus lentus GENE tetK & GENE repL & GENE repC & GENE pre & GENE ermC, Staphylococcus aureus GENE qacC & GENE dfrA & GENE blaZ & GENE blaR1 & GENE blaI & GENE thyA (Fig. 9 and Supplementary Fig. 2).

Picture 9
figure 9

Heatmap representation by healthcare occupation of the 419 distinct virulence factor genes identified on mobile phones by means of metagenomic analysis.

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