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The transfer network of bacterial mobile resistome connecting animal and human microbiome



Importance: The development of antibiotic resistance threatens our modern medical achievements. The dissemination of antibiotic resistance can be largely attributed to the transfer of bacterial mobile antibiotic resistance genes (ARGs). Revealing the transfer network of these genes in bacteria and the forces driving the gene flow is of great importance for controlling and predicting the emergence of antibiotic resistance in the clinic. Here, by analyzing tens of thousands of bacterial genomes and millions of human and animal gut bacterial genes, we reveal that the transfer of mobile ARGs is mainly controlled by bacterial phylogeny, but under ecological constraints. We also found that dozens of ARGs are transferred between the human and animal gut and human pathogens. This work is the first demonstration of the whole profile of mobile ARGs and their transfer network in bacteria and provides further insight into the evolution and spread of antibiotic resistance in the nature.
The transfer network of bacterial mobile resistome connecting animal and human 1
microbiome 2
Yon gf ei H u, a,b,c Xi Yang,a Jing Li,a,b Na Lv, a,b Fei Liu,a,b,c Jun Wu,a,b Ivan YC. Lin,a,b Na Wu,d Bart C. 3
Weimer,e George F. Gao,a,f Yulan Liu,d* Baoli Zhua,b,c*
CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of 5
Sciences, Beijing 100101, Chinaa; Beijing Key Laboratory of Microbial Drug Resistance and Resistome, Beijing 6
100101, Chinab; Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First 7
Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310006, Chinac; Department of 8
Gastroenterology, Peking University People’s Hospital, Beijing 100044, Chinad; Department of Population Health 9
and Reproduction, 100K Pathogen Genome Project, School of Veterinary Medicine, University of California, Davis, 10
California 95616, United States of Americae; Office of Director-General, Chinese Center for Disease Control and 11
Prevention, Beijing 102206, Chinaf. 12
Running title: Transfer network of bacterial mobile resistome 15
Keywords: bacterial phylogeny; horizontal gene transfer; mobile resistome; antibiotic 17
resistance; gut microbiomes 18
*Corresponding author. Mailing address for Baoli Zhu: CAS Key Laboratory of Pathogenic 24
Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing 25
100101, China. Phone: (86)10-64807362. Fax: (86)10-64807358. E-mail: 26
Mailing address for Yulan Liu: Department of Gastroenterology, Peking University People’s 27
Hospital, Beijing 100044, China. E-mail: 28
AEM Accepted Manuscript Posted Online 9 September 2016
Appl. Environ. Microbiol. doi:10.1128/AEM.01802-16
Copyright © 2016, American Society for Microbiology. All Rights Reserved.
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Horizontally acquired antibiotic resistance genes (ARGs) in bacteria are highly mobile 30
and have been ranked as principal risk resistance determinants. However, the 31
transfer network of the mobile resistome and the forces driving mobile ARG transfer 32
are largely unknown. Here, we present the whole profile of the mobile resistome in 33
23,425 bacterial genomes and explore the effects of phylogeny and ecology on the 34
recent transfer (≥99% nucleotide identity) of mobile ARGs. We found that mobile 35
ARGs are mainly present in four bacterial phyla and are significantly enriched in 36
Proteobacteria. The recent mobile ARG transfer network, which comprises 703 37
bacterial species and 16,859 species pairs, is shaped by the bacterial phylogeny, 38
while an ecological barrier also exists, especially when interrogating bacteria 39
colonizing different human body sites. Phylogeny is still a driving force for the 40
transfer of mobile ARGs between farm animals and the human gut, and, interestingly, 41
the mobile ARGs that are shared between the human and animal gut microbiome are 42
also harbored by diverse human pathogens. Together, we suggest that phylogeny and 43
ecology are complementary in shaping the bacterial mobile resistome and exert 44
synergistic effects on the development of antibiotic resistance in human pathogens. 45
on October 17, 2016 by UC DAVIS SHIELDS LIBRARY from
The development of antibiotic resistance threatens our modern medical 54
achievements. The dissemination of antibiotic resistance can be largely attributed to 55
the transfer of bacterial mobile antibiotic resistance genes (ARGs). Revealing the 56
transfer network of these genes in bacteria and the forces driving the gene flow is of 57
great importance for controlling and predicting the emergence of antibiotic 58
resistance in the clinic. Here, by analyzing tens of thousands of bacterial genomes 59
and millions of human and animal gut bacterial genes, we reveal that the transfer of 60
mobile ARGs is mainly controlled by bacterial phylogeny, but under ecological 61
constraints. We also found that dozens of ARGs are transferred between the human 62
and animal gut and human pathogens. This work is the first demonstration of the 63
whole profile of mobile ARGs and their transfer network in bacteria and provides 64
further insight into the evolution and spread of antibiotic resistance in the nature. 65
on October 17, 2016 by UC DAVIS SHIELDS LIBRARY from
Antibiotic resistance is a growing global threat to human public health, and the 78
continuing emergence of drug-resistant bacteria has led to the potential for a 79
post-antibiotic era (1). Bacteria have evolved a dynamic array of antimicrobial 80
resistance mechanisms to withstand antibiotic attacks, such as altering drug target(s), 81
synthesizing drug-inactivating enzymes, changing cell membrane permeability and 82
exporting drugs via efflux pumps (2). A large number of antibiotic resistance genes 83
(ARGs) involved in these resistance mechanisms have been discovered and are 84
collected in various ARG databases, including ARG-ANOTT (3), CARD (4), ResFINDER 85
(5) and ARDB (6). Because antibiotic resistance can be endogenous or exogenous, 86
ARGs are generally classified as intrinsic (chromosomally encoded in specific species 87
or genera) and acquired (via specific point mutations or horizontal gene transfer 88
(HGT)) ARGs (7). ResFINDER is a repository of ARGs that covers only the horizontally 89
acquired ARGs but not the resistance caused by mutations, according to public 90
sources and published papers (5). 91
ARGs, including the cryptic resistance genes in bacteria, have been collectively 93
referred to as the antibiotic resistome (8, 9), and accordingly, intrinsic and 94
horizontally acquired ARGs have been named the intrinsic and mobile resistome, 95
respectively (10, 11). In recent years, the antibiotic resistome from both 96
environmental as well as host-associated microbiomes has been explored (either 97
function- or sequence-based), leading to the discovery of numerous ARGs (8, 12-15). 98
However, the risk of these identified ARGs has been considered to be over-estimated 99
because most studies have failed to differentiate between intrinsic ARGs and mobile 100
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ARGs (16). The mobile ARGs that are frequently associated with mobile genetic 101
elements (MGEs) and disseminated by HGT have been considered to have the 102
highest risk of developing resistance among the antibiotic resistome (16). 103
HGT is considered a very important mechanism that contributes to bacterial genome 105
mutability and evolution (17, 18). Revealing associated factors, such as transfer 106
barriers of HGT, has been a continued research interest because significant adaptive 107
traits of bacteria such as virulence, beneficial metabolic properties and antibiotic 108
resistance can be rapidly disseminated through HGT. Phylogenetic, ecological and 109
functional barriers have all been highlighted to exert an influence on HGT (19). 110
Regarding ARGs, bacterial phylogeny (20) and ecology (21, 22) both have been shown 111
to be driving forces that shape the antibiotic resistome. Unfortunately, these studies 112
targeted all ARGs but did not focus on the mobile ones. An in-depth understanding of 113
the trends and barriers for HGT of the mobile resistome is undoubtedly essential for 114
predicting the emergence of antibiotic resistance in human pathogens. 115
To provide a comprehensive view of the mobile resistome in bacteria and elucidate 117
the forces shaping ARG transfer, we examined 23,425 bacterial genomes (including 118
2,768 complete genomes and 20,657 draft genomes available in August 2014 in 119
GenBank) for known mobile ARGs and analyzed their recent HGT network and the 120
roles of phylogeny and ecology in the transfer of the mobile resistome. We also 121
sequenced the gut microbiome of three representative farm animals and compared 122
the identified mobile ARGs with those in the recently released human gut gene 123
catalog (approximately 9.8 million genes) (23) to investigate the transfer of mobile 124
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ARGs at the bacterial community level. 125
Datasets. A total of 23,425 bacterial genomes, including 2,768 complete and 20,657 128
draft genomes (as of August 2014), as well as their taxonomic affiliations were 129
downloaded from the National Center for Biotechnology Information (NCBI, 130 GenBank. Concurrently, 1,680 mobile ARGs, including 131
88 van genes belonging to 19 vancomycin resistance operons with a total of 2134 132
nucleotide sequences, were retrieved from the ResFinder database (5). Metadata 133
defining the “Ecosystem Category” andDisease” status, among others, of the strains 134
were obtained from the Integrated Microbial Genomes database (IMG) 135
( A dataset consisting of mobility genes of 180,787 136
integrase/recombinase and transposase was generated by searching the Universal 137
Protein Resource (UniProt) (24) uniref90 database using corresponding keywords; the 138
dataset was subsequently used to search the flanking regions of the mobile ARGs, 139
which we termed the adjacent mobility genes. The human gut microbiome 140
integrated gene catalog (IGC, 9,879,896 genes) and the separate European gene 141
catalog (EGC, 8,096,991 genes), Chinese gene catalog (CGC, 3,547,396 genes) and 142
American gene catalog (AGC, 2,681,342 genes), as well as the Gene Taxonomic 143
Assignment Profiles, were downloaded from the GigaScience Database 144
( The 889 reference bacterial genomes of 145
different human body sites (airways, blood, gastrointestinal tract, oral cavity, skin, 146
and urogenital tract) from the Human Microbiome Project (HMP) were downloaded 147
from the Data Analysis and Coordination Center (DACC) ( The 148
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three animal gut gene datasets were generated as described below. 149
Searching for mobile ARGs and evaluating the ARG diversity. To s ea r ch f or mobile 151
ARGs, the bacterial genomes and the animal and human gut microbiome gene sets 152
were subjected to BLAST using the mobile ARGs in the ResFinder database (5). To 153
guarantee the ARGs we annotated were accurate and homologous, we used 154
relatively strict cutoff value of 95% nucleotide identity and 90% coverage for the 155
BLAST search, a criterion that has also been adopted to establish gene catalogues 156
such as those for the human gut microbiome (23, 25). The Mann-Whitney U-test was 157
used for statistical comparisons of the median number of mobile ARGs contained in 158
species from different phyla. 159
For the vancomycin resistance Van operons consisting of more than one structural 161
gene, for example, the vanB operon consisting of structural genes of vanA-B, vanH-B, 162
vanR-B, vanS-B, vanW-B, vanX-B and vanY-B, only at least three-fourths of the 163
structural genes were presented simultaneously, it was deemed to be a real mobile 164
van operon, and the operon was regarded as a single gene when counting the mobile 165
ARG number and in the subsequent analyses. 166
To evaluate the diversity of mobile ARGs in individual bacterial species, a rarefaction 168
curve plotting the observed number of mobile ARGs against the number of genomes 169
in each species was generated. Only those species with more than 50 genome 170
sequences and 10 genomes containing mobile ARGs were used to generate the 171
curve. 172
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Analysis of the ARG cluster and the adjacent mobility genes. To de t er mi n e wh i ch 174
mobile ARGs were adjacent to each other in a specific region of a genome, and to 175
what extent they were associated with MGEs, we searched the flanking regions of 176
each mobile ARG for adjacent ARGs or mobility genes such as integrase/recombinase 177
and/or transposase. First, we selected fragments containing the mobile ARGs and 178
both their upstream and downstream 5-Kb flanking regions in each bacterial genome; 179
then, if an ARG was adjacent to another ARG ( 5 Kb), the overlapping regions were 180
merged to generate a longer fragment harboring the two mobile ARGs, and the two 181
ARGs were considered to be clustered together (ARG cluster containing 2 mobile 182
ARGs), and this process was repeated. The mobility genes were found with protein 183
homology of 70% protein identity using the long fragments that contained clustered 184
ARGs. If observed within this search parameter they were considered to be adjacent 185
to mobile ARGs. 186
Determination of the recent HGT of the mobile ARGs and the HGT frequency. A 188
stringent cut-off of 99% nucleotide identity was used to determine the recent HGT 189
of the mobile ARGs between species pairs (20, 21). That is, if a mobile ARG was 190
shared between two species with 99% nucleotide identity the ARG was considered 191
to be a recent HGT. The HGT number of a species was defined as the number of ARGs 192
shared between this species and all others. To c al c ul a te the intra- and inter-taxon 193
HGT frequencies of the mobile ARGs, the intra- and inter-taxon HGT number of a 194
species were counted separately, and the intra- and inter-taxon HGT frequencies 195
were calculated by dividing the HGT number of a species by the total number of 196
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species containing the mobile ARGs within and outside the taxon, respectively. For 197
cross ecology analysis, the strains were first assigned to different ecological 198
categories according to the isolation source. Subsequently, the intra-category 199
(eco_same) and inter-category (eco_diff) HGT frequencies in the same taxon 200
(taxon_same) and different taxa (taxon_diff) were calculated separately, generating 201
the HGT frequencies in four combinations: taxon_same and eco_same, taxon_same 202
and eco_diff, taxon_diff and eco_same, and taxon_diff and eco_diff. Cross body site 203
analysis was done using the intra-category and inter-category HGT frequencies and 204
calculated along the strains phylogenetic distance with the maximum of 2% 16S 205
rRNA gene sequence divergence. The Mann-Whitney U-test was used to determine 206
significant differences between the medians of the HGT frequencies among different 207
groups. 208
Definition of environments/pathogens. The environment and pathogenicity 210
information for all genomes were obtained from IMG. We generalized the 211
environment information into four categories via the Ecosystemand Ecosystem 212
category tokens: env_aqua, ecosystem “Environment and ecosystem category 213
Aquatic; env_terr, ecosystem “Environmentand ecosystem category Te rres t r ial; 214
host_homo, ecosystem “Host-associatedand ecosystem category Human”; and 215
host_anim, ecosystem Host-associated and ecosystem category Animal, 216
Mammalsor Birds. The pathogenicity information of genomes was extracted via 217
the Diseases, “Phenotypeand Relevancetokens. If any token had a defined 218
patho-” as a defined keyword, the genome was considered to be pathogenic. The 219
HGT frequencies of the mobile ARGs across phylogenetic hierarchies and across 220
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different environments were calculated as described above. 221
Sequencing the animal gut microbiomes. Fresh stool samples of chicken, pig and 223
cattle were collected from suburban farms in Beijing, China. The samples were 224
collected and subsequently used for DNA extraction with the PSP® Spin Stool DNA 225
Plus Kit according to the manufacturer’s instructions. Paired-end Illumina sequencing 226
DNA libraries with an insertion length of 350-400 bp (4 samples each for chicken and 227
pig and 5 samples for cattle) were constructed following the manufacturer’s 228
instructions (Illumina), followed by sequencing on an Illumina HiSeq 2000 platform to 229
generate approximately 5 G data for each sample (see Table S1 in the supplemental 230
material). The raw reads were trimmed until three contiguous bases with a 231
quality >20 were found, and all read lengths shorter than 50 bp were discarded. 232
Sequence assembly and gene prediction. High-quality reads were assembled using 234
SOAPdenovo program ( with automatic determination 235
of the K-mer size; the resulting contigs shorter than 200 bp were discarded. Coding 236
sequence (CDS) extraction was performed using MetaGeneAnnotator (26), and only 237
those CDSs >100 bp were retained for further analysis. The CDSs from chicken, pig 238
and cattle were pooled, respectively, into non-redundant datasets using the CD-HIT 239
as described by Qin et al (25). 240
Tax on om ic c la ss if ic ati on of metagenomic sequences. The non-redundant CDS 242
sequences were first aligned with the NCBI non-redundant protein database using 243
NCBI BLAST. The tabular results were analyzed using the MEGAN (27) for the 244
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assignment of taxonomy. Spearman correlations were used to test the similarity of 245
community structure between gut microbiomes, and Pearson correlations were used 246
to examine the relationship between the similarity of community structures and the 247
number of mobile genes shared between the communities. 248
Accession number. All sequencing data were deposited in the NCBI Sequence Read 250
Archive database under accession number SRP062937. 251
Mobile ARGs are enriched in Proteobacteria. Using a nucleotide identity cutoff value 254
of 95% for the BLAST search against the ResFINDER database, we identified a total 255
of 515 mobile ARGs distributed across 790 species among 6,335 bacterial species (Fig. 256
1 and Dataset S1A). These mobile ARGs were mainly harbored by four bacterial phyla: 257
Proteobacteria (399 genes), Firmicutes (86 genes), Bacteroidetes (46 genes) and 258
Actinobacteria (40 genes); a small portion of the ARGs were shared among these 259
phyla (see below). A comparison of the number of mobile ARGs harbored by each 260
bacterial species showed that Proteobacteria species were highly enriched with 261
mobile ARGs (P=2.9×10-4, Mann-Whitney U-test) compared with all species in the 262
other three phyla. In detail, among 16 species that harbored more than 20 mobile 263
ARGs, 10 species belonged to the phylum Proteobacteria, and most importantly, 7 of 264
these species harboring more than 40 mobile ARGs were all from Proteobacteria. In 265
addition, the rarefaction curves showed that the ARG diversity in Proteobacterial 266
bacteria such as Klebsiella pneumoniae (119 mobile ARGs) and Pseudomonas 267
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aeruginosa (49 mobile ARGs) were far from saturation (see Fig. S1 in the 268
supplemental material). 269
Mobile ARGs are frequently associated with MGEs. To de t er mi n e w hi ch mobile 271
ARGs clustered together in a specific region of a genome and to what extent they 272
were associated with MGEs, we searched the 5-Kb flanking regions of each mobile 273
ARG for adjacent mobile ARGs or integrase/recombinase and/or transposase (see 274
Methods). We observed that ARG clusters containing 1, 2, and 3 mobile ARGs (19,569, 275
4,999 and 1,636 occurrences, respectively, in all genomes) accounted for the vast 276
majority of the total ARG clusters, while ARG clusters containing more than 3 mobile 277
ARGs accounted for a small proportion (656 occurrences) (see Fig. S2A in the 278
supplemental material), suggesting that long multi-gene-containing ARG clusters are 279
not frequently present in bacteria. Furthermore, 67.3% of the total ARG clusters 280
(single-gene or multi-gene) were associated with known mobility-associated genes 281
such as integrase/recombinase and/or transposase, and most ARG clusters co-existed 282
with both integrase/recombinase and transposase genes (see Fig. S2B in the 283
supplemental material). In addition, the occurrence of mobility-associated genes 284
increased with the number of mobile ARGs in the ARG cluster. These results 285
emphasized the contribution of mobility-associated genes to the transfer of mobile 286
ARGs. 287
ARGs that are clustered within a MGE in a bacterial genome are usually referred to as 289
resistance islands (RIs) (28). We found a total of 656 ARG clusters, presumably 290
potential RIs, each containing at least four different acquired ARGs (data not shown). 291
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The largest cluster containing 18 mobile ARGs was located in the largest resistance 292
island known to date, AbaR1 (86-kb), which was discovered in Acinetobacter 293
baumannii strain AYE (29); the second largest one harboring 12 ARGs was from the 294
plasmid of a highly invasive and resistant zoonotic pathogen, Salmonella enterica 295
serovar Choleraesuis (30); and the third was from K. pneumoniae MGH 43 296
(JCNT01000000). Among all ARG clusters, the sulfonamide resistance gene sul1, 297
which is always associated with class 1 integrons, was observed in the neighborhood 298
with the greatest number of different types of ARGs (99 different ARGs). This 299
association reflects not only the widespread of class 1 integrons in resistance islands, 300
but also their important role in acquiring and disseminating ARGs in bacteria (31). 301
A recent HGT network of the mobile resistome. To determine recently horizontally 303
transferred ARGs, we compared all 515 of the mobile ARGs obtained from the 304
genomes using cutoff of ≥99% nucleotide identity (20, 21) and a HGT network of 305
these mobile ARGs at the species level (703 nodes, 16,859 species pairs) was 306
generated (see Fig. S3A in the supplemental material). In general, the recent 307
exchanges of the mobile ARGs were mainly detected in Proteobacteria, Firmicutes, 308
Bacteroidetes and Actinobacteria, and the exchanges were more active in 309
Proteobacteria (see Fig. S3B in the supplemental material). We found a total of 11 310
species that each shared at least one mobile ARG with more than 200 other species. 311
The top three species displaying the most number of connections were E. coli, 312
Bacteroides fragilis, and Staphylococcus aureus, which shared ARGs with 302, 266 313
and 260 species, respectively. Moreover, among 16,859 species pairs, 1,376 shared at 314
least three different mobile ARGs (Fig. 2A). E. coli and K. pneumoniae shared the 315
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largest number of mobile ARGs (60 individual genes), followed by E. coli and S. 316
enterica (38 genes), A. baumannii and K. pneumoniae (38 genes), and K. oxytoca and 317
K. pneumoniae (36 genes) (Dataset S1B). 318
Next, we added to the network all 222 mobile ARGs that were transferred mutually 320
between at least two different species (Fig. 2B). The tetracycline resistance genes, 321
tet(M) and tet(Q), and the integron-associated sulfonamide resistance gene, sul1, 322
were the top three widely transferred mobile ARGs at the species level (Dataset S2), 323
while tet (C), tet (W) and sul1 were the top three at the genus level. Notably, a total 324
of 37 mobile ARGs had been found transferred, even between different phyla. The 325
exchanges of tet(C) and blaTEM-116 genes spanned seven and six different phyla, 326
respectively, and aph(3)-III, catA1 and erm(B) were all transferred across five 327
different phyla. These results revealed that mobile ARGs are frequently transferred 328
between not only closely related bacteria but also phylogenetically distant species. 329
However, a preferred exchange pattern of the recently exchanged mobile ARGs was 330
found along the bacterial lineage (Fig. 2B). For examples, tet(Q), erm(F) and cfxA5 331
were transferred solely within Bacteroidetes, tet(M), tet(O) and erm(B) were mainly 332
transferred in Firmicutes, and sul1, sul2, strA and strB were mainly transferred in 333
Proteobacteria, suggesting that there is a potential phylogenetic barrier for HGT of 334
the mobile resistome. 335
The recent HGT network of mobile ARGs is mainly shaped by bacterial phylogeny. 337
To te s t t h e phylogenetic barrier prediction, we computed the HGT frequency of the 338
mobile ARGs at different phylogenetic hierarchies (see Methods). As expected, the 339
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intra-taxon HGT frequency of the mobile ARGs was significantly higher than the 340
inter-taxon HGT frequency at each phylogenetic level (see Fig. S4 in the supplemental 341
material), which suggests the presence of a strong phylogenetic barrier. We further 342
explored the ecological impact on mobile ARG transfer by comparing the HGT 343
frequency across ecologies (human, animal, aquatic and terrestrial) and across 344
phylogenetic hierarchies. We found that at each phylogenetic level, the HGT 345
frequencies of the mobile ARGs between strains with the same phylogenetic taxa 346
(whether eco_same or eco_diff) were significantly higher than those with different 347
phylogenetic taxa (whether eco_same or eco_diff); in contrast, the HGT frequencies 348
in strains from the same ecology were only slightly higher than those from different 349
ecologies (e.g., mean values of 26.4% vs 22.8% at the phylum level) (Fig. 3A). To a vo id 350
bias introduced by summarizing the ecological affiliation of bacteria according to the 351
general descriptions of bacterial sources in the database, we next analyzed the HMP 352
dataset comprising 889 reference bacterial genomes with defined sources of 353
different human body sites. We also analyzed the HGT frequency over a range of 354
bacterial phylogenetic distances for these genomes. We found that phylogeny played 355
a leading role in the recent ARG transfer; that is, the closer the 16S distance of the 356
strains, the more frequently the transfer occurred (Fig. 3B). However, the HGT 357
frequency was observably higher between strains from the same body site compared 358
with those from different body sites, especially when the strains reached 359
approximately more than 90% 16S identity (Fig. 3B). Taken together, we suggest that 360
bacterial phylogeny dominates the recent HGT network of the mobile resistome, but 361
existing ecological barriers also supply a contribution. 362
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The transfer of mobile ARGs between human and animal gut microbiomes. We 364
compared the occurrence of mobile ARG transfer in strains with different origins 365
(restricted to those with defined isolation sources). Among strains that were either 366
human- or animal-associated, approximately 35% carried mobile ARGs, while the 367
ratio was relatively low for strains of aquatic or terrestrial origin at only 368
approximately 5% (see Fig. S5A in the supplemental material). Furthermore, among 369
the strains with human and animal origins carrying mobile ARGs, 98% and 95% 370
shared mobile ARGs with strains from other sources, respectively, while the ratios 371
were 88% and 64% for aquatic and terrestrial isolations, respectively. The HGT 372
frequency of the mobile ARGs in animal-associated bacteria was the highest, 373
followed by human, aquatic and terrestrial bacteria (see Fig. S5B in the supplemental 374
material). Interestingly, the mobile ARGs were exchanged most frequently between 375
animal and human bacteria, followed by between animal and aquatic and then 376
between animal and terrestrial bacteria (see Fig. S5C in the supplemental material). 377
These findings may indicate that animal bacteria represent a more powerful mobile 378
ARG pool that can impact both human and environmental mobile resistomes. 379
To further investigate the shared mobile ARGs between animals and humans at the 381
bacterial community level, we focused on gut bacteria as an example and sequenced 382
the gut microbiomes of three representative farm animals, including chicken, pig and 383
cattle (see Table S1 in the supplemental material), and compared the mobile ARGs in 384
these microbiomes with the integrated gene catalog (IGC) of the human gut 385
microbiome (approximately 9.8 million genes) (23). We found that the recent mobile 386
ARG transfer (≥99% nucleotide identity) indeed occurred between human and animal 387
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gut microbiomes, and most intriguingly, the human IGC shared the most mobile ARGs 388
with the chicken gut microbiome (36 genes) and the fewest with cattle (10 genes) 389
(see Fig. S6A in the supplemental material). Because the animals sequenced herein 390
were all from China, to investigate whether a country-specific ARG transfer occurred 391
between animals and humans, we constructed a network using separated datasets of 392
the European gene catalog (EGC), Chinese gene catalog (CGC) and American gene 393
catalog (AGC) and the animals. The results showed that the EGC and CGC shared the 394
largest number of mobile ARGs (69 genes), but unexpectedly, each of the three gene 395
catalogs still shared more mobile ARGs with the chicken gut microbiome compared 396
with the other animals (see Fig. S6B in the supplemental material). 397
We further computed the correlation between the similarity of the bacterial 399
community structure (reflecting the phylogenetic similarity between bacterial 400
communities) and the shared gene number. Regarding the relative abundance at 401
both phylum and genus levels, the microbiome profile of any of the three human gut 402
microbiomes was more similar to chicken than to pig or cattle (Fig. 4A and B), and a 403
positive correlation (Pearsons r=0.512 (P=0.051) and 0.778 (P=6.4×10- 4) at the 404
phylum and genus levels, respectively) was observed between the similarity of 405
community structures (Spearman correlation) and the number of shared genes (Fig. 406
4C and D). These results suggest that, at the levels of bacterial communities that are 407
not closely ecologically connected, bacterial phylogeny may also contribute to the 408
transfer of the mobile resistome, although there is a possibility that some of the 409
shared mobile ARGs we observed were caused by the exchange of bacteria. 410
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Mobile ARGs shared between the human and animal gut microbiomes are also 412
harbored by human pathogens. We found a total of 84 mobile ARGs that were 413
shared between at least two gut datasets (Fig. 5A). Among them, 41 genes were 414
recently transferred between human and animal guts (human-animal-shared mobile 415
ARGs). These human-animal-shared mobile ARGs covered six major antibiotic classes, 416
including tetracyclines (11 genes), aminoglycosides (10 genes), 417
macrolide-lincosamide-streptogramin B (MLSB) (9 genes), chloramphenicols (5 418
genes), beta-lactams (3 genes) and sulfonamides (3 genes) (Dataset S3). In addition, 419
eleven mobile ARGs were shared among all six gut datasets, among which seven 420
were tetracycline resistance genes, indicating that the transfer of tetracycline 421
resistance genes is more common between human and animals. 422
We furt her examined 2,613 genomes of the human-associated pathogens (see 424
Methods) for human-animal-shared mobile ARGs (≥99% nucleotide identity). 425
Interestingly, we found that 33 of the 41 shared mobile ARGs were harbored by 426
various human pathogens or opportunistic pathogens worldwide, spanning 47 427
species and 403 genomes (Dataset S4). The tetracycline resistance gene tet(M) was 428
the most widely distributed; it was detected in 13 species and was over-represented 429
in Streptococcus agalactiae (group B streptococcus) isolates (95 genomes), a 430
causative agent of a wide range of human disease, especially in newborns (32). S. 431
agalactiae, E. coli and S. suis were the top three species harboring the greatest 432
number of ARGs (eight genes each) that were shared between human and animal 433
guts. These pathogen-carried human-animal-shared mobile ARGs were moved via 434
different mobile genetic elements, as reflected by the different regions surrounding 435
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the same genes and the nearby mobility-associated genes. For example, the MLSB 436
resistance gene erm(F) shared between cattle and humans was also carried by 437
pathogens such as Bibersteinia trehalosi 192 and by known MGEs such as the 438
Bacteroides thetaiotaomicron transposon CTnDOT (Fig. 5B). The genetic 439
environments of erm(F) differed between different bacterial hosts, and the gene was 440
frequently flanked by other ARGs as well as different genes that facilitate the transfer 441
process, such as integrase and transposase, indicating that the gene has been 442
subjected to various recombination events. A similar phenomenon has been 443
observed for the aminoglycoside resistance gene ant(6)-Ia, which is shared between 444
pig and human guts (Fig. 5C), emphasizing the contributions of MGEs to the mobility 445
of mobile ARGs. Taken together, our results provide evidence for the recent exchange 446
of mobile ARGs among human, animal and clinical pathogens, though we are 447
uncertain the directions of the transfer. 448
Our analysis showed that both the abundance and the HGT frequency of the mobile 451
ARGs were over-represented in Proteobacteria, and this finding was largely in 452
accordance with our previous analysis showing that in the human gut microbiota, 453
compared with other genes, ARGs are more enriched in Proteobacteria (13). An 454
excessive number of ARGs carried by Proteobacteria may partially explain the 455
expansion of this group in human and mouse gut microbiomes following exposure to 456
antibiotics (33, 34). It is also interesting to note that a large majority of 457
antibiotic-subsisting soil bacteria belong to the Proteobacteria (87% clonal isolates, 458
each resistant to multiple antibiotics) (35). Tak en to geth er, th es e r es ul ts suggest that 459
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Proteobacteria probably constitute a major ARG pool, potentially even a mobile ARG 460
pool in the nature, regardless of the origin of their ARGs. The enrichment of mobile 461
ARGs in Proteobacteria can be interpreted in part, by the fact that many human 462
clinical pathogens are Proteobacteria (36), which presumably faced with more 463
antibiotic selection pressures due to clinical treatments. 464
It is reasonable to deduce that the recent transfer of the mobile resistome is 466
generally under ecological constraint; the success of HGT depends on contact 467
between the donor and recipient residing in the same niche (37). This trend is clearly 468
demonstrated by our analysis targeting bacterial communities from different sites in 469
the human body. However, the mobile ARG transfers are clearly controlled by 470
bacterial phylogeny regardless of whether the ecology (or the body site) is the same 471
or not (Fig. 3). This observation was consistent when we assessed mobile ARG 472
exchange between animal and human gut microbiomes (Fig. 4). These findings 473
suggest that mobile ARGs have a tendency to spread among phylogenetically closely 474
related bacteria, i.e., phylogenetic evolutionary pressure may be a primary force that 475
structures the bacterial mobile resistome. Therefore, we propose that antibiotic 476
exposure is the source of selection, ecology provides a physical barrier, and 477
phylogeny determines the HGT network of the mobile resistome. We cannot exclude 478
the possibility that the sharing of mobile ARGs between different ecosystems 479
(microbiomes) was caused by the exchange of bacteria harboring mobile ARGs; 480
however, we propose that once the ecological barrier is overcome, the mobile ARGs 481
were primarily expanded among phylogenetically similar populations. 482
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Our analysis indicated that not only the number of bacteria harboring mobile ARGs 484
but also the HGT frequency of the ARGs between animal-associated bacteria is 485
higher than human and environmental origin bacteria (see Fig. S5A and B in the 486
supplemental material). One consideration, from the worldwide ecological view, is 487
that bacteria of animal origin may face more antibiotic selection pressure because 488
more antibiotics (nearly 80% in the United States (38)) are consumed by animals as 489
growth-promoters, infection prevention, and clinical treatments. The high exchange 490
frequency of mobile ARGs between animals and humans or environmental bacteria is 491
also noteworthy (see Fig. S5C in the supplemental material). Because we were 492
unable to distinguish the direction of ARG transfer, we are not certain if this hints 493
that the animal-associated bacteria has a larger contribution to the dissemination of 494
ARGs to humans and the environment. However, evidence of the transfer of ARGs 495
from animal bacteria to humans and the environment has been published elsewhere 496
(39-41). It has also been suggested that the use of antibiotics in animals has 497
contributed to the emergence of ARGs in human gut microbiomes (14, 42). However, 498
if the use of antibiotics in animals leads to the dissemination of mobile ARGs to 499
humans and the environment, we would speculate that there would be fewer mobile 500
ARGs in "organically" raised farm animals that are not subjected to routine antibiotic 501
exposure compared with traditionally raised farm animals. A future comparison of 502
the resistome in animals with different husbandry with that of the human 503
microbiome will provide more information regarding the transfer of ARGs among 504
different ecological niches. 505
Previous studies investigating the bacterial resistome (not discriminating between 507
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intrinsic and mobile ARGs) in metagenomes emphasize that the discovered ARGs 508
represent a high risk for human pathogens because of HGT (8, 15, 43, 44). As 509
suggested, however, if every gene indicates a risk, there are no differential risks and 510
no workable interventions (16). Here, we focused on the mobile resistome and 511
showed that identical mobile ARGs were shared between animal and human gut 512
microbiomes and human pathogens, supporting the occurrence of real ARG transfers 513
or bacterial exchange events, although we remain uncertain about the spreading 514
routes. 515
Very recently, a new mobile ARG, named mcr-1, encoding resistance to colistin was 517
found in animals and patients in China (45). We were surprised to find that this new 518
emerging mobile ARG has been transferred to the healthy Chinese human gut 519
microbiota through a potential food chain dissemination pathway (46), which is more 520
likely the case for several other mobile ARGs that are shared between the animals 521
and humans we observed in this study. In addition, mcr-1 is currently found in or 522
circulated among E. coli, K. pneumoniae and S. enterica, which support our results 523
that the mobile ARGs are exchanged most frequently among these three bacterial 524
(Fig. 2A and Dataset S1B). As demonstrated in the ARG transfer network, the 525
exchange of mobile ARGs between K. pneumoniae and A. baumannii occurs at a 526
considerable amount. We speculate that A. baumannii may become the next mcr-1 527
carrier. 528
Finally, the HGT network of the mobile ARGs presented here was most likely formed 530
by gene flow from a common mobile ARG reservoir, but less likely by direct gene 531
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exchange, for the bacteria or microbiomes used for analysis were not in physical 532
proximity to each other. Therefore, the observations point to the commonalities 533
among different populations to be exposed to the source. Though statistical 534
normalization was considered when processing the genome data, we cannot exclude 535
potential bias in our results due to the database bias and lack of specific organisms 536
within public resources. For example, more antibiotic resistance genes and more 537
genomes from human and animal pathogens are included in the databases than 538
those from commensal or environmental bacteria. This may lead to, for example, an 539
overrepresentation of mobile ARGs in Proteobacteria and an overestimation of the 540
exchange of mobile ARGs between human and animal. However, we believe that the 541
distribution of mobile ARGs in bacteria and the phylogeny-driven, 542
ecology-constrained features of mobile ARG transfer largely reflect the real situation 543
in the nature. For the reasons that a) all of the known genomes included herein, 544
especially the drug-resistant ones, are representative of current antibiotic resistance 545
profiles; b) the mobile ARG transfer analysis is relatively less susceptible to database 546
bias, as we showed that, at each phylogenetic level, the intra-taxon HGT frequency of 547
the mobile ARGs was significantly higher than the inter-taxon HGT frequency, 548
regardless of whether the ecology was the same; and c) to reduce bias from the 549
unevenly sampled genomes in the different habitats, we investigated the ARG 550
transfer analysis within a single ecology (i.e., human body) and we obtained similar 551
results that led to the same conclusions. Overall, large genome sequencing efforts 552
are still needed to reduce the database bias and thus contribute to elucidating the 553
detailed profiles of the mobile ARG transfer network among bacteria and 554
microbiomes. 555
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This work was supported by the National Basic Research Program of China (973 685
Program; 2015CB554200), the National Natural Science Foundation of China (NSFC) 686
(81401701 and 31471203), the Beijing Municipal Natural Science Foundation 687
(5152019), the Youth Innovation Promotion Association of Chinese Academy of 688
Sciences and the 100K Foodborne Pathogen Genome Project. 689
Figure legends 692
Fig. 1 Heat map showing the distribution of mobile ARGs in different taxonomic ranks. Inner to 693
outer circles indicate sequentially the phylum, class, order, family, genus and species. Each bar in 694
the outermost circle represents a species (n=790), and the sizes of the other sectors representing 695
different taxa are scaled according to the number of species assigned to that taxon. The scale bar 696
indicates the gene number. Species harboring more than 20 mobile ARGs are shown beside the 697
species bars, and species with more than 40 ARGs are highlighted in bold. The phylogenetic 698
affiliation was based on NCBI taxonomy. 699
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Fig. 2 Recent HGT networks of the mobile resistome. (A) The network of species sharing mobile 701
ARGs. Each node represents a species, and the node size as well as the labels is scaled according 702
to the number of mobile ARGs detected in that species. The edge between any two nodes 703
indicates that there are at least 3 shared mobile ARGs (≥99% nucleotide identity) by the species 704
pair, with greater thickness and width of the edge with a larger number of shared mobile ARGs. 705
The same color of nodes indicates species belonging to the same family, and the same shape 706
denotes the same phylum. The entire profile of this network can be found in Supplementary Fig. 707
S4A. (B) The network of mobile ARGs shared between species. Dots and triangles represent 708
species (n=703) and ARGs (n=222), respectively. Edges linking species and ARGs indicate that the 709
ARGs were shared among those species. The size of the gene labels is scaled based on the 710
number of species harboring the gene. The names of the ARGs shared among less than 20 711
species are not labeled. 712
Fig. 3 The HGT frequency of the mobile ARGs across phylogenetic hierarchies and ecologies. (A) 714
Intra- and inter-taxon HGT frequency of the mobile ARGs across human, animal, aquatic and 715
terrestrial environments. The phylogenetic affiliation was based on NCBI taxonomy. Statistics 716
were performed using the Mann-Whitney U-test; **P ≤0.01, ***P ≤0.001. From left to right: 717
phylum, class, order, family and genus. (B) The 16S rRNA gene distance-based HGT frequency of 718
the mobile ARGs across bacterial communities of different human body sites. The HGT frequency 719
in (B) is calculated in bins of 2% 16S rRNA gene sequence divergence; the data shown are mean 720
values, and light shading denotes the interquartile range (IQR) between the first and third 721
quartiles. See methods for the detailed calculation process. 722
Fig. 4 Structures of human and animal gut bacterial communities are correlated with mobile ARG 724
transfer. Bacterial composition at the phylum level (A) and the genus level (B). Correlation of the 725
shared mobile ARG number with the similarity of the bacterial community at the phylum level (C) 726
on October 17, 2016 by UC DAVIS SHIELDS LIBRARY from
and the genus level (D). 727
Fig. 5 Shared mobile ARGs among human and animal gut microbiomes and human pathogens. (A) 729
Network of the shared mobile ARGs. Edges linking species/gut and ARGs indicate that the ARGs 730
were shared among those species/gut microbiomes. Number of human pathogen species: n=47; 731
number of shared mobile ARGs between humans and animals: n=41; number of shared mobile 732
ARGs among humans, animals and human pathogens: n=33. Comparison of representative 733
animal and human gut assembled contigs harboring the macrolide-lincosamide-streptogramin B 734
resistance gene erm(F) (B) and the aminoglycoside resistance gene ant(6)-Ia (C) with 735
representative human pathogen genomes and known mobile genetic elements. 736
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... With a surge in the need for food and goods from animals, it is anticipated that a high volume would expand over time. Antimicrobials may be expelled in water and animal excrement up to 90% of the time after injection without being digested, which allows them to spread to different environmental media due to humans, animals, and plant interactions [16] Table 2 shows the different types of microorganisms possible in different food and a particular time. ...
... Preprints ( | NOT PEER-REVIEWED | Posted: 10 July 2023 doi:10.20944/preprints202307.0568.v116 ...
Full-text available
Our ability to cure common diseases is still in danger due to the creation and spread of bacteria and fungi that are resistant to drugs and has developed new resistance mechanisms. The increasing global expansion of multi- and pan-resistant bacteria commonly referred to as "superbugs," which cause diseases that cannot be treated with current antimicrobial medications like antibiotics, is particularly concerning. Indirect and direct risks to public health are posed by microorganisms that are resistant to antibiotics in food. Food can get contaminated via cross-contamination, intentionally introducing resistance genes during food preparation, and antibiotic-resistant bacteria in food. Consuming unprocessed or unpreserved food products increases the danger of transferring antimicrobial resistance to people and other animals. Stressed cells in food can have an impact on food quality, safety, and security. They can also contribute to the development of resistance and afterwards spread it to unintended organisms in the food chain. These reviews look at a variety of topics to address the question of how antimicrobial resistance mechanisms work. The various types of bacteria that make up infections are examined, as well as the usage of antimicrobial agents and the requirement for alternative antimicrobial agents that will discourage the use of synthetic antimicrobial agents. Additionally, specific details are provided regarding the uniqueness, interdependence, and phylogenicity of organisms as well as their interactions with various ecological media. Thus, it is clear why it is important to work with food producers and handlers to stop the spread of pathogenic organisms.
... Ce phénomène de transfert est d'autant plus important qu'il existe une grande diversité d'éléments génétiques mobiles (plasmides, transposons, cassettes d'intégrons, ICEs (éléments conjugatifs intégratifs, IS (séquences d'insertion), etc.) et que le nombre de bactéries présentes dans l'environnement (sols, stations d'épuration) ou dans le tube digestif de l'homme et de l'animal est très élevé (des milliards de bactéries par gramme de sol, de boues de stations d'épuration ou de fèces) [4]. La très grande majorité de ces bactéries est d'ailleurs non cultivable avec les milieux de culture usuellement utilisés, ce qui rend difficile l'identification de ces transferts par culture microbienne. ...
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The fight against antibiotic resistance must incorporate the "One Health" concept to be effective. This means having a holistic approach embracing the different ecosystems, human, animal, and environment. Transfers of resistance genes may exist between these three domains and different stresses related to the exposome may influence these transfers. Various targeted or pan-genomic molecular biology techniques can be used to better characterise the dissemination of bacterial clones and to identify exchanges of genes and mobile genetic elements between ecosystems.
... Many studies have tended to focus on pathogens, with limited focus on nonpathogenic bacteria that are regularly excluded from surveillance programs, yet may serve as reservoirs of AMR in the environment and the food chains, underscoring the need for more comprehensive analyses and monitoring of food environmental and animal reservoirs of AMR [30,31]. With the threat of antimicrobial resistance and untreatable infections as a big global concern, the role of animals and environment reservoirs in sustaining and disseminating AMR can no longer be ignored, as recent evidence suggests that AMR genes in animal and environmental bacteria can be rapidly acquired by human-associated and pathogenic bacteria [32][33][34]. ...
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Commensal Escherichia coli with broad repertoire of virulence and antimicrobial resistance (AMR) genes pose serious public health risks as reservoirs of AMR and virulence. This study undertook whole genome characterization of commensal E. coli from food-producing animals in Uganda to investigate their genome variability (resistome and virulome). We established that the E. coli had high genomic diversity with 38 sequence types, 24 FimH types, and 33 O-antigen serotypes randomly distributed within three phylogroups (A, B1, and E). A greater proportion (≥93.65%) of the E. coli were resistant to amoxicillin/clavulanate and ampicillin antibiotics. The isolates were AmpC beta-lactamase producers dominated by blaEC-15 (71.88%) and tet(A) (20.31%) antimicrobial resistant genes besides a diverse armory of virulence-associated genes in the class of exotoxin, adhesins, iron uptake, and serine protease autotransporters which varied by host species. Cattle were found to be the major source of E. coli carrying Shiga toxin genes, whereas swine was the main source of E. coli carrying colicin-like Usp toxin gene. The study underscores the importance of livestock as the carrier of E. coli with antimicrobial resistance and a large repertoire of virulence traits with a potential of causing disease in animals and humans by acquiring more genetic traits.
... The analysis of mobile genetic elements that can be transferred from animaladapted clones to human-adapted ones is also of relevance, because these clones can coexist for instance in manure or wastewater. Direct sequencing of isolates from animals and humans have shown that the same resistance elements can be found in both (de Been et al., 2014;Hu et al., 2016). More recent analysis have shown that plasmids presenting the same backbones, but different accessory genes can be found in a variety of habitats, indicating the existence of global plasmid populations that can differently evolve in each habitat and later on transferred to different hosts and ecosystems (Matlock, et al., 2023). ...
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As recognized by several international agencies, antibiotic resistance is nowadays one of the most relevant problems for human health. While this problem was alleviated with the introduction of new antibiotics into the market in the golden age of antimicrobial discovery, nowadays few antibiotics are in the pipeline. Under these circumstances, a deep understanding on the mechanisms of emergence, evolution and transmission of antibiotic resistance, as well as on the consequences for the bacterial physiology of acquiring resistance is needed to implement novel strategies, beyond the development of new antibiotics or the restriction in the use of current ones, to more efficiently treat infections. There are still several aspects in the field of antibiotic resistance that are not fully understood. In the current article, we make a non-exhaustive critical review of some of them that we consider of special relevance, in the aim of presenting a snapshot of the studies that still need to be done to tackle antibiotic resistance.
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The spread of antimicrobial resistance genes (ARGs) is a major public health crisis, with the ongoing spread of ARGs leading to reduced efficacy of antibiotic treatments. The gut microbiome is a key reservoir for ARGs, and because diet shapes the gut microbiome, diet also has the potential to shape the resistome. This diet–gut microbiome–resistome relationship may also be important in infants and young children. This narrative review examines what is known about the interaction between the infant gut microbiome, the infant resistome, and infant nutrition, including exploring the potential of diet to mitigate infant ARG carriage. While more research is needed, diet has the potential to reduce infant and toddler carriage of ARGs, an important goal as part of maintaining the efficacy of available antibiotics and preserving infant and toddler health.
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Multidrug-resistant (MDR) bacteria are a significant contributor to the global antibiotic resistance crisis, which is predicted to kill more people than cancer by 2050. Livestock production is a contributing factor as it has been one of the fastest-growing industries in the previous century but has the most harmful effects on the environment and human health. The pig is the most widely raised and consumed food-producing animal globally, with an upward trend. The derived residues and the meat products constitute an important reservoir of antibiotic resistance genes (ARGs) that can be transmitted to humans through consumption, direct contact, the environment, or poor handling, leading to relevant zoonotic diseases, especially enteric ones. It is essential to know the diversity, abundance, and distribution of ARGs to have better control and monitoring of their dispersion. In the present study, the ARGs and Mobile Genetic Elements (MGEs) of five enteric and pathogenic species commonly present in the microbiota of both pigs and humans were examined by bioinformatic analysis. This analysis showed that 157 ARGs were distributed across 1869 genomes of five bacterial species, ranked from highest to lowest diversity of ARGs: Klebsiella pneumoniae , Escherichia coli , Enterococcus faecium , Salmonella enterica , and Enterococcus faecalis . This study contributes to better management of antibiotics, which directly impact the health of both humans and animals.
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Water quality monitoring of a particular river is crucially important to determine if the river water meets the requirement for its designated uses. Therefore, an assessment of the physiochemical water quality at Satow Waterfall, Sarawak, and the prevalence of antibiotic-resistant bacteria (ARB) was conducted to corroborate the safety of the water from the waterfall. A total of 42 water samples were collected at the subsurface water of the upstream, midstream, and downstream on two sampling trips in June 2021 (dry season) and December 2021 (wet season), with six months intervals. The water quality index (WQI) of the sampling stations ranged from 92.60 to 95, classifying the water under Class I. However, the water is microbiologically polluted, with the highest coliform count of 2.59 × 105 CFU/mL recorded at the middle stream during the wet season, which has surpassed the regulatory standard set by the Malaysian Department of Environment (DOE). A total of 54 bacterial isolates were chosen as representatives and identified by 16S rRNA sequencing, which confirmed the existence of 21 bacterial genera. All identified isolates were tested against 15 antibiotics of various classes employing the Clinical and Laboratory Standards Institute (CLSI) protocols. The antibiotic susceptibility test (AST) demonstrated substantial resistance to sulfamethoxazole (48%) and erythromycin (48%), as well as high susceptibility to chloramphenicol (84%), levofloxacin (60%) and ofloxacin (60%). The Multiple Antibiotic Resistance Index (MARI) evaluations demonstrated the MARI varied from 0-0.60, with 33% of the isolates having a MARI greater than 0.2. In this study, the occurrence of ARB in a water environment where there is an undisclosed source of antibiotic utilization is still inevitable, highlighting the necessity to constantly monitor the water quality and the pervasiveness of ARB in recreational water.
This study developed a high efficiency compound enzyme (Fungal mash) produced in situ from food waste (FW) used for improving hydrolysis and anaerobic digestion (AD) efficiency of FW. Results showed that the soluble COD and methane yield were respectively increased by 67.80% and 16.58% after 24 h in situ enzymatic hydrolysis of food waste by fungal mash. Furthermore, most of target ARGs in FW were also reduced by 45-94% after 24 h in situ enzymatic hydrolysis, while the total tested ARGs and intI1 were respectively further removed by 44-55% and 21-73% in subsequent AD process. In-depth analysis showed that fungal mash could effectively reduce potential hosts and control the horizontal transfer of ARGs during the in situ enzymatic hydrolysis and AD process. Ultimately, correlation analysis and redundancy analysis indicated that the evolution of bacterial communities and changes in intI1 where the common driving forces for the fate of ARGs.
Technical Report
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xxii, 232 p. Summary report published as technical document with reference number: WHO/HSE/PED/AIP/2014.2 World Health Organization. (‎2014)‎. Antimicrobial resistance: global report on surveillance. World Health Organization.
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Antibiotic resistance is a dire clinical problem with important ecological dimensions. While antibiotic resistance in human pathogens continues to rise at alarming rates, the impact of environmental resistance on human health is still unclear. To investigate the relationship between human-associated and environmental resistomes, we analyzed functional metagenomic selections for resistance against 18 clinically relevant antibiotics from soil and human gut microbiota as well as a set of multidrug-resistant cultured soil isolates. These analyses were enabled by Resfams, a new curated database of protein families and associated highly precise and accurate profile hidden Markov models, confirmed for antibiotic resistance function and organized by ontology. We demonstrate that the antibiotic resistance functions that give rise to the resistance profiles observed in environmental and human-associated microbial communities significantly differ between ecologies. Antibiotic resistance functions that most discriminate between ecologies provide resistance to β-lactams and tetracyclines, two of the most widely used classes of antibiotics in the clinic and agriculture. We also analyzed the antibiotic resistance gene composition of over 6000 sequenced microbial genomes, revealing significant enrichment of resistance functions by both ecology and phylogeny. Together, our results indicate that environmental and human-associated microbial communities harbor distinct resistance genes, suggesting that antibiotic resistance functions are largely constrained by ecology.The ISME Journal advance online publication, 8 July 2014; doi:10.1038/ismej.2014.106.
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Many analyses of the human gut microbiome depend on a catalog of reference genes. Existing catalogs for the human gut microbiome are based on samples from single cohorts or on reference genomes or protein sequences, which limits coverage of global microbiome diversity. Here we combined 249 newly sequenced samples of the Metagenomics of the Human Intestinal Tract (MetaHit) project with 1,018 previously sequenced samples to create a cohort from three continents that is at least threefold larger than cohorts used for previous gene catalogs. From this we established the integrated gene catalog (IGC) comprising 9,879,896 genes. The catalog includes close-to-complete sets of genes for most gut microbes, which are also of considerably higher quality than in previous catalogs. Analyses of a group of samples from Chinese and Danish individuals using the catalog revealed country-specific gut microbial signatures. This expanded catalog should facilitate quantitative characterization of metagenomic, metatranscriptomic and metaproteomic data from the gut microbiome to understand its variation across populations in human health and disease.
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Ancient and diverse antibiotic resistance genes (ARGs) have previously been identified from soil, including genes identical to those in human pathogens. Despite the apparent overlap between soil and clinical resistomes, factors influencing ARG composition in soil and their movement between genomes and habitats remain largely unknown. General metagenome functions often correlate with the underlying structure of bacterial communities. However, ARGs are proposed to be highly mobile, prompting speculation that resistomes may not correlate with phylogenetic signatures or ecological divisions. To investigate these relationships, we performed functional metagenomic selections for resistance to 18 antibiotics from 18 agricultural and grassland soils. The 2,895 ARGs we discovered were mostly new, and represent all major resistance mechanisms. We demonstrate that distinct soil types harbour distinct resistomes, and that the addition of nitrogen fertilizer strongly influenced soil ARG content. Resistome composition also correlated with microbial phylogenetic and taxonomic structure, both across and within soil types. Consistent with this strong correlation, mobility elements (genes responsible for horizontal gene transfer between bacteria such as transposases and integrases) syntenic with ARGs were rare in soil by comparison with sequenced pathogens, suggesting that ARGs may not transfer between soil bacteria as readily as is observed between human pathogens. Together, our results indicate that bacterial community composition is the primary determinant of soil ARG content, challenging previous hypotheses that horizontal gene transfer effectively decouples resistomes from phylogeny.
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In this review, we focus on a group of mobile genetic elements designated pathogenicity islands (PAI). These elements play a pivotal role in the virulence of bacterial pathogens of humans and are also essential for virulence in pathogens of animals and plants. Characteristic molecular features of PAI of important human pathogens and their role in pathogenesis are described. The availability of a large number of genome sequences of pathogenic bacteria and their benign relatives currently offers a unique opportunity for the identification of novel pathogen-specific genomic islands. However, this knowledge has to be complemented by improved model systems for the analysis of virulence functions of bacterial pathogens. PAI apparently have been acquired during the speciation of pathogens from their nonpathogenic or environmental ancestors. The acquisition of PAI not only is an ancient evolutionary event that led to the appearance of bacterial pathogens on a timescale of millions of years but also may represent a mechanism that contributes to the appearance of new pathogens within a human life span. The acquisition of knowledge about PAI, their structure, their mobility, and the pathogenicity factors they encode not only is helpful in gaining a better understanding of bacterial evolution and interactions of pathogens with eukaryotic host cells but also may have important practical implications such as providing delivery systems for vaccination, tools for cell biology, and tools for the development of new strategies for therapy of bacterial infections.
Background: Until now, polymyxin resistance has involved chromosomal mutations but has never been reported via horizontal gene transfer. During a routine surveillance project on antimicrobial resistance in commensal Escherichia coli from food animals in China, a major increase of colistin resistance was observed. When an E coli strain, SHP45, possessing colistin resistance that could be transferred to another strain, was isolated from a pig, we conducted further analysis of possible plasmid-mediated polymyxin resistance. Herein, we report the emergence of the first plasmid-mediated polymyxin resistance mechanism, MCR-1, in Enterobacteriaceae. Methods: The mcr-1 gene in E coli strain SHP45 was identified by whole plasmid sequencing and subcloning. MCR-1 mechanistic studies were done with sequence comparisons, homology modelling, and electrospray ionisation mass spectrometry. The prevalence of mcr-1 was investigated in E coli and Klebsiella pneumoniae strains collected from five provinces between April, 2011, and November, 2014. The ability of MCR-1 to confer polymyxin resistance in vivo was examined in a murine thigh model. Findings: Polymyxin resistance was shown to be singularly due to the plasmid-mediated mcr-1 gene. The plasmid carrying mcr-1 was mobilised to an E coli recipient at a frequency of 10(-1) to 10(-3) cells per recipient cell by conjugation, and maintained in K pneumoniae and Pseudomonas aeruginosa. In an in-vivo model, production of MCR-1 negated the efficacy of colistin. MCR-1 is a member of the phosphoethanolamine transferase enzyme family, with expression in E coli resulting in the addition of phosphoethanolamine to lipid A. We observed mcr-1 carriage in E coli isolates collected from 78 (15%) of 523 samples of raw meat and 166 (21%) of 804 animals during 2011-14, and 16 (1%) of 1322 samples from inpatients with infection. Interpretation: The emergence of MCR-1 heralds the breach of the last group of antibiotics, polymyxins, by plasmid-mediated resistance. Although currently confined to China, MCR-1 is likely to emulate other global resistance mechanisms such as NDM-1. Our findings emphasise the urgent need for coordinated global action in the fight against pan-drug-resistant Gram-negative bacteria. Funding: Ministry of Science and Technology of China, National Natural Science Foundation of China.
Metagenomic studies have shown that antibiotic resistance genes are ubiquitous in the environment, which has led to the suggestion that there is a high risk that these genes will spread to bacteria that cause human infections. If this is true, estimating the real risk of dissemination of resistance genes from environmental reservoirs to human pathogens is therefore very difficult. In this Opinion article, we analyse the current definitions of antibiotic resistance and antibiotic resistance genes, and we describe the bottlenecks that affect the transfer of antibiotic resistance genes to human pathogens. We propose rules for estimating the risks associated with genes that are present in environmental resistomes by evaluating the likelihood of their introduction into human pathogens, and the consequences of such events for the treatment of infections