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Nested mobile genetic elements mediating antimicrobial resistance genes mobility

within and between

Acinetobacter

isolates

Kenneth Bongulto, Ngure Kagia, Hisamichi Tauchi, Satoru Suzuki, Kozo Watanabe

PII:

S2589-0042(26)01023-0

DOI:

https://doi.org/10.1016/j.isci.2026.115648

Reference:

ISCI 115648

To appear in:

iScience

Received Date: 8 December 2025
Revised Date: 25 February 2026
Accepted Date: 6 April 2026

Please cite this article as: Bongulto, K., Kagia, N., Tauchi, H., Suzuki, S., Watanabe, K., Nested mobile

genetic elements mediating antimicrobial resistance genes mobility within and between

Acinetobacter

isolates,

iScience

(2026), doi:

https://doi.org/10.1016/j.isci.2026.115648

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Nested mobile genetic elements mediating antimicrobial resistance genes mobility

within and between Acinetobacter isolates

Kenneth Bongulto

1,2

, Ngure Kagia

1,2

, Hisamichi Tauchi

, Satoru Suzuki

, Kozo

Watanabe

1, *

Center for Marine Environmental Studies (CMES), Ehime University, Bunkyo-cho 3,

Matsuyama, Ehime 790-8577, Japan

Graduate School of Science and Engineering, Ehime University, Bunkyo-cho 3,

Matsuyama, Ehime 790-8577, Japan

Ehime University Graduate School of Medicine, Toon, Ehime, Japan

ADDRESS CORRESPONDENCE TO LEAD CONTACT

Kozo Watanabe (watanabe.kozo.mj@ehime-u.ac.jp)

KEYWORDS

Acinetobacter

, mobile genetic elements, antimicrobial resistance genes, intracellular

MGEs, intercellular MGEs, genomic island

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SUMMARY

Mobile genetic elements (MGEs) play a central role in the acquisition and dissemination

of antibiotic resistance genes (ARGs). This study analyzed the distribution of MGEs using

the whole-genome sequences of 38

Acinetobacter

isolates from patient, environmental,

and pig waste samples. Pig waste isolates exhibited the highest mean number of

plasmids, while prophages were more prevalent in environment-associated isolates.

Interestingly, we observed a significant positive correlation between number of plasmids

and number of defense systems. Co-localization of multiple ARGs within a single plasmid

was observed, with up to 10 distinct ARGs observed within a single p

dif

module.

Additionally, putative genomic resistance islands (GRIs) were identified in non-baumannii

Acinetobacter

species, representing the first documentation of GRIs outside the

Acinetobacter calcoaceticus-baumannii

(Acb) complex. This study provides new insights

into the mechanisms of ARG dissemination in

Acinetobacter

, particularly the role of MGEs

in facilitating hierarchical gene transfer processes.

INTRODUCTION

Horizontal gene transfer (HGT) is a fundamental driver of bacterial evolution, enabling

the rapid acquisition of beneficial genetic traits that enhance fitness and survival.

Through various mechanisms including transformation, conjugation, transduction, and

vesiduction, bacteria can acquire diverse functional genes.

Mobile genetic elements

(MGEs) serve as key mediators of these processes, facilitating gene transfer both within

and between cells.

3,4

These gene transfers are significant for bacterial adaptation,

particularly in the development of antimicrobial resistance.

The prevalence of MGEs,

including intracellular types (e.g. insertion sequences (IS) elements, transposons, and

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integrons) and intercellular types (e.g. conjugative and mobilizable plasmids, prophages,

integrative conjugative elements (ICEs), integrative mobilizable elements (IMEs),

underscores their crucial role in bacterial evolution and adaptation to changing

environments.

6,7

Acinetobacter

species are well known opportunistic pathogens,

inhabiting various environments such as hospital settings, human and animal wastewater

and environmental waters. Infection caused by multidrug-resistant

Acinetobacter

has

been a significant concern. Research focusing on the frequency of MGEs and their

associated antibiotic resistance genes (ARGs) is essential to mitigate the risk of multidrug

resistance development in the genus

Acinetobacter.

While MGEs are present across all bacterial species, the frequency of different MGE

types can

vary significantly depending on the host bacterium’s ecology and environment.

For instance, prophages, an intercellular MGE type, display variable abundance between

Staphylococcus aureus

isolates, being present in human isolates but absent in pig

isolates.

Similarly, plasmids, which are extrachromosomal MGEs, are frequently

detected in livestock wastes.

The abundance of MGE types is also influenced by the

presence of defense systems that control HGT.

10,11

Bacteria utilize sophisticated defense

systems, such as CRISPR-Cas

12,13

and restriction modification systems,

which

recognize specific DNA sequences to target phages and plasmids.

The prevalence of ARGs varies across different MGE types. Previous studies have

shown that ARG prevalence is primarily elevated in intracellular MGEs types, such as

transposable elements (TEs).

15,16

Among intracellular MGE types, IS elements have been

shown to have higher ARG prevalence compared to integrons.

Among intercellular MGE

types, higher ARG prevalence has been reported in plasmids compared to

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prophages.

17,18

Plasmids can be categorized as conjugative, mobilizable, or

nonmobilizable depending on the presence of conjugative genes such as relaxase and

conjugative coupling proteins.

Although plasmids are well-known ARG vectors, over

70% of plasmids lack ARGs.

Based on these observations, we hypothesize that

plasmids harbor a high frequency of ARGs due to the presence of IS elements that

accumulate ARGs.

Nested MGE structures, or intracellular MGEs embedded within an intercellular MGE,

mediate both intracellular and intercellular ARG transfer, forming sophisticated networks

of gene transfer.

21-23

One example of nested MGE structures is plasmids harboring IS

elements. Since IS elements are only capable of intracellular movement, a nested MGE

structure is vital for their efficient intercellular transfer. For example, IS elements can

mediate the intracellular transfer of ARGs from chromosomes to plasmids, while the

plasmids that receive these ARGs can mediate the intercellular transfer of ARGs. Notably,

IS elements demonstrate distinct distribution patterns between genomic compartments,

with plasmids showing significantly higher IS density compared to chromosomal

regions.

However, the frequency of IS elements among different types of intercellular

MGEs has yet to be fully characterized.

Genomic resistance island (GRI) are variable regions in the bacterial genome formed

through intracellular and intercellular ARG transfer.

These regions integrate MGEs,

including integrons and transposons, along with associated ARGs. In

Acinetobacter

baumannii

, GRIs have been extensively characterized, contributing to the development

of multidrug resistance by harboring different ARGs.

25-33

However, while studies have

documented GRIs in

A. baumannii

from several parts of the world, knowledge gaps

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persist regarding the types of GRIs in non-

baumannii

species. Recent investigations have

identified genomic islands known as

A. baumannii

resistance islands (AbaR)-type GRIs

in non-

baumannii

species, such as

A. nosocomialis

and

A. seifertii

, demonstrating

interspecies transfer capabilities.

Notably, a significant knowledge gap exists regarding

the characteristics and prevalence of GRIs in non-

baumannii

species, particularly the

pathogenic

Acinetobacter calcoaceticus

– baumannii

(Acb) complex group.

The

present

study

examines

the

mechanisms

ARG

mobility

Acinetobacter

species from various sources (i.e., patient, environmental, and pig waste

samples), through comprehensive

in silico

analysis of genomic sequences, with particular

emphasis on MGEs and their role in ARG acquisition and dissemination. The aim of this

study is to characterize the hierarchical process of ARG transfer mediated by intracellular

and intercellular MGEs. Specifically, we investigated whether the frequencies of MGE

types in

Acinetobacter

species are influenced by source origins and their defense

systems. Additionally, we examined the ARG prevalence among different types of MGEs

and the co-localization patterns of ARGs within MGEs. Subsequently, we also analyzed

the nested MGE structures and investigated which type of intercellular MGEs exhibits a

high IS element frequency. Based on these results, we tested the hypothesis that ARGs

100

are frequently present in plasmids because IS elements frequently harbor ARGs and are

101

highly abundant in plasmids. Lastly, we discuss the intracellular and intercellular

102

movement of ARGs and how it contributes to the formation of genomic islands in

103

Acinetobacter.

104

RESULTS

105

Prevalence of mobile genetic elements in Acinetobacter isolates

106

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Of the 38

Acinetobacter

isolates examined, IS elements (

= 1045) were the most

107

prevalent MGEs, followed by plasmids (

= 193) and prophages (

= 103)

(Figure S1A)

108

Integrative conjugative elements (ICEs) and integrative mobilizable elements (IMEs) were

109

the least prevalent (

= 28 and

= 13, respectively). Significant differences in the

110

frequencies of each MGE type were observed between environment- and pig waste-

111

associated isolates, specifically for IS, prophage, and plasmid

(Figure 1A)

. However,

112

there was no significant difference in the frequency of ICE and IME carriage among

113

Acinetobacter

isolates

IS elements were significantly more abundant in environment-

114

associated isolates than pig waste-associated isolates (

= 0.01, Kruskal-Wallis).

115

Additionally, environment-associated isolates had more prophages than pig waste-

116

associated isolates (

<0.01, Kruskal-Wallis). In contrast, plasmids were significantly

117

more prevalent in pig waste-associated isolates than in patient- and environment-

118

associated isolates (

< 0.0001, Kruskal-Wallis). We also observed a significant

119

difference in the mean frequencies of defense systems among

Acinetobacter

isolates

120

from different sources. Pig waste-associated isolates had a higher frequency of defense

121

systems than both environment- (

< 0.05, Kruskal-Wallis) and patient-associated

122

isolates (

<0.01, Kruskal-Wallis)

(Figure 1B)

. Furthermore, we observed a significant

123

positive correlation (

=0.001, Spearman correlation) between the number of plasmids

124

and the number of defense systems

(Figure 1C)

. Interestingly, we also found a significant

125

inverse correlation (

p =

0.002, Spearman correlation) between the number of defense

126

systems and the number of prophages in the

Acinetobacter

genomes

(Figure 1D)

. Our

127

analysis also revealed a distinct distribution pattern of defense systems between bacterial

128

chromosomes and plasmids. Specifically, plasmids predominantly harbor BREX_I,

129

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SanaTA, and Retron_VI systems, while chromosomes encode diverse array of defense

130

mechanisms including Gabija, Gao_Mza, multiple restriction modification (RM) types (I,

131

II, IIG, III, IV), and Wadjet_I

(Figure S2)

132

ARG distribution, co-occurrence, and enrichment in plasmids

133

The extent of ARG carriage in different MGE types was also investigated. Most

134

ARGs were found to be associated with 42 plasmids and one integrative mobilizable

135

element (IME), with no ARGs detected in prophages or ICE regions. A total of 193

136

plasmids were identified in the 38

Acinetobacter

isolates. Most of the isolates' plasmid

137

repertoire consists of nonmobilizable plasmids (n = 105, 54.4%), mobilizable plasmids (n

138

= 81, 42.0%), and conjugative plasmids (n = 7, 3.6%)

(Figure S3A)

. Plasmid sizes

139

exhibited remarkable diversity, ranging from 1.4 kb to 657 kb

(Figure S3B)

. One hundred

140

thirty-nine plasmids (72%) could not be assigned to a plasmid typing scheme using the

141

rep gene

(Figure S3C)

. Similarly, relaxase were not detected in 105 plasmids (54.0%)

142

using the mob typing scheme

(Figure S3D)

143

A total of 42 plasmids were detected harboring various ARGs

(Figure 2A)

. The

144

majority (40 out of 193 plasmids; 95.2%) were found in pig waste-associated isolates.

145

Large size plasmids (>100 kb) tend to harbor more ARGs (2-16 ARGs) and were

146

categorized as mobilizable or nonmobilizable. The proportion of plasmids harboring

147

ARGs was 28.6% (2/7) among conjugative plasmids, 29.6% (24/81) among mobilizable

148

plasmids, and 15.2% (16/105) among nonmobilizable plasmids

(Table S2)

. We revealed

149

different patterns of ARG co-localization in plasmids

(Figure 2B)

. Macrolide resistance

150

genes (

mph(E)

and

msr(E

))

and aminoglycoside resistance genes (

aph(3

”)-Ib

and

aph(6)-

151

)

tend to co-occur in 40.5% and 38.1% of ARG-carrying plasmids, respectively. While

152

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sul2

and

aph(6)-

Id and

bla

OXA-58

and

aph(3”)-

Ia tend to co-occur in 33.3% and 16.67% of

153

ARG-carrying plasmids, respectively. Further analysis revealed that 19 pairs of ARGs

154

were co-localized on a single plasmid, showing significant associations (

= <0.05, using

155

Fisher’s test)

(Table S3).

Up to 14 different ARGs were detected on a single plasmid.

156

Nine prophage regions were also detected in eight plasmids. However, no ARGs were

157

directly encoded within the prophage regions of these plasmids.

158

We assessed the presence of p

dif

sites or plasmid site-specific recombination sites

159

in all ARG-carrying plasmids, as well as investigating whether these sites were associated

160

with the acquisition of ARGs. As many as 20 p

dif

sites were detected in a single ARG-

161

bearing plasmid. Multiple ARG types were flanked by these p

dif

sites, forming ARG p

dif

162

modules (

Figure S4A-S4G)

. In total, seven different ARG p

dif

modules were identified:

163

dif-

(

msr(E)-mph(E)

), p

dif

tet(39)

), p

dif

bla

OXA-58

), p

dif

aph(3”)-Ib – aph(6)-Id – tet(Y) –

164

aph(3

’’)-Ia

), p

dif

aph(3

”)-Ia – tet(Y) – aph(6)-Id – aph(3”)-Ib – floR – mph(B) – sul2

), p

dif

165

(

sul2

– aph(3”)-Ib – aph(6)-Id

), and p

dif

aph(4)-Ia

– aac(3)-IVa – aph(3”)-Ia – aph(3”)-Ib

166

– aph(6)-Id – tet(Y) – tet(X5) – floR – dfrA1 – sul2

). The number of ARGs in p

dif

modules

167

ranged from 1 to 10 per module. Plasmids with ARGs had more p

dif

sites than those

168

without ARG (

= 2.2 x 10

-16

, Wilcoxon test)

(Figure 2C)

. Furthermore, there was a

169

significant association between the number of p

dif

sites and the number of ARGs in ARG-

170

carrying plasmids (

= 2.3 x 10

-25

, using Fisher's test).

171

Nested MGE structures in Acinetobacter

172

A total of 29 types of IS elements were detected among 193 plasmids

(Figure

173

S5A)

. The most prevalent IS elements were ISAba14 (n=23) and IS17 (n=23), followed

174

by ISOur1 (n=20), ISAba34 (n=19), and IS1006 (n=18). We examined whether ARG

175

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enrichment in plasmids is associated with the presence of IS elements. Plasmid-bearing

176

ARGs possessed more IS elements compared to plasmids without ARGs (

p =

0.0012,

177

Wilcoxon test)

(Figure 3A)

. The number of IS elements was positively correlated with the

178

plasmid size

(Figure S5B)

. The Sankey plot analysis showed that nine ARGs existed in

179

TEs on plasmids (

Figure 3B)

. The aminoglycoside resistance genes,

aph(6)-Id

and

180

aph(3”)-Ib,

both occurred on Tn6205, while

aph(3”)-Ia

was found on Tn4352.

181

Furthermore, ARGs such as

dfrA1

bla

GES-4

aac(6’)-31

aadA2

, and

catB6

were identified

182

within gene cassettes associated with integrons.

183

Among the four types of intercellular MGEs, IS elements were predominantly found

184

in conjugative elements such as plasmids and ICEs. The frequency of IS carriage was

185

significantly different between plasmids and prophages (

<0.0001, Kruskal-Wallis), as

186

well as between ICEs and prophages (

<0.001, Kruskal-Wallis)

(Figure 3C)

. The

187

frequency of IS elements also varies among plasmid types, with mobilizable and non-

188

mobilizable plasmids possessing more IS elements (

Figure S5C

). Furthermore, no

189

significant difference was observed in the frequency of IS element carriage among

190

plasmids from different isolation sources (patient, pig waste, environment) (

= 0.6,

191

Kruskal-Wallis) (

Figure S5D)

. However, the types of IS elements varied among plasmids

192

isolated from different isolation sources, with some types being exclusively found in

193

plasmids from a specific isolation source (

Figure S6)

194

Duplication of ARGs on the same plasmid was also observed. Some of the ARGs

195

that showed duplication were aminoglycoside resistance genes, such as

aph(3”)-Ib,

196

aph(6)-

Id, ant(2”)-Ia,

and macrolide resistance genes

msr(E),

and

mph(E) (Table S4)

197

Additionally, we revealed that several plasmids from different bacterial hosts possessed

198

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similar set of ARGs (

Figure S7A)

or possessed similar plasmids with the same set of

199

ARGs and other functional genes

(Figure S7B

). We demonstrated the potential

200

movement of macrolide resistance genes (

msr(E)

and

mph(E

)) and aminoglycoside

201

resistance genes (

aph(3”)-Ib

and

aph(6)-Id

) between different plasmids in different hosts.

202

A similar set of ARGs and functional genes was also observed between co-residing

203

plasmids. Specifically, this was observed for the

aph(3”)-Ib

and

aph(6)-Id

sul2,

and

204

msr(E)

and

mph(E)

genes (

Figure S8A-S8C)

205

Nine prophage regions, ranging in size from 44 to 183 kilobases, were identified

206

across eight plasmids. Individual plasmids harbor from one or two prophages. Notably,

207

these prophages were not associated with

Acinetobacter

as their host bacterium; rather,

208

they represented various phage types, including Escher_RCS47, Stx2_c_1717,

209

Escher_PA28, Entero_VT2phl, Stx2_II, Staphy_Spbeta, Faecal_FP_Taranis, and

210

Salmon_epsilon15 (

Table S5)

. Out of the 8 prophages detected in plasmids, 6 (75%)

211

were found to be intact or complete. Furthermore, we observed that prophage regions in

212

plasmids exhibited a significantly higher frequency of IS elements than those located

213

within the chromosomes (

p =

7.5e-13, Wilcoxon test)

(Figure S9A)

. We also found that

214

intact prophages had a significantly higher frequency of IS elements compared to

215

incomplete prophages (

p =

0.03, Kruskal-Wallis)

(Figure S9B)

216

Genomic islands in non-baumannii species

217

We detected four putative genomic islands in non-

baumannii

pig waste-associated

218

isolates possibly mediated by intracellular ARG transfer from plasmids to chromosomes.

219

The ARG synteny analysis revealed that mobilizable ARG regions in plasmids of the pig

220

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waste-associated isolates showed high sequence similarity with the ARG regions in the

221

chromosome of the host bacteria. Furthermore, transposable elements in plasmids

222

showed homologous regions within the chromosome. The first GRI type was detected in

223

a plasmid of

A. towneri

(EA24) flanked by IS1006 and containing a sulfonamide

224

resistance gene, as well as cobalt, zinc, and cadmium resistance gene

czcA

gene

(Figure

225

4A)

. The second GRI type, observed in

A. towneri

(EA13), was marked by an IS3 family-

226

bound GRI containing resistance genes (

aph(3

”)-Ib, aph(6)-Id,

and

sul2

), metal resistance

227

genes (

copA, copB, merR, zitB,

czcABD

), and a toxin/anti-toxin system

(Figure 4B)

. The

228

third GRI type included an integron class 2, associated with the Tn7 transposon family,

229

and was identified in

A. towneri

(EA14), harboring a gene cassette bearing a trimethoprim

230

resistance gene (

dfrA1

)

(Figure 4C)

. The fourth GRI type, observed in

A. haemolyticus

231

(EA17), featured an ISAba1-bound GRI that contained aminoglycoside resistance genes

232

(

aph(3

”)-Ia

aph(3

”)-Ib

, and

aph(6)-Id

); a tetracycline resistance gene (

tet(X3)

); and

233

sulfonamide resistance gene (

sul2

)

(Figure 4D)

234

DISCUSSION

235

Mobile genetic elements (MGEs) are abundant, and their distribution varies in the

236

bacterial genome. A notable differentiation in the distribution of MGEs based on the

237

isolation source was observed in our study. Plasmids were abundant in pig waste-

238

associated isolates, whereas they were rare in patient- or environment-associated

239

isolates. These findings are consistent with previous studies, which have reported the

240

widespread plasmids and demonstrated a strong positive correlation between the

241

numbers of plasmids and ARGs per cell.

35,36

Specifically, integrons and conjugative

242

plasmids, which are widely distributed in livestock waste, serve as key vectors for ARG

243

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dissemination via HGT.

37,38

The extensive use of antibiotics in pig farming imposes

244

significant selective pressure, favoring bacterial populations that harbor ARG-bearing

245

plasmids.

246

In contrast, prophages were observed at a higher frequency in environment-

247

associated isolates compared to those from patients or pig waste. This trend corresponds

248

with prior studies that identified a high prevalence of prophages in environments such as

249

wastewater treatment plants (WWTPs), which harbor diverse bacterial populations of

250

human origin.

A large-scale analysis of prophages from 13,713 complete prokaryotic

251

genomes also identified genera such as

Acinetobacter

Enterobacter

, and

Pseudomonas

252

as major prophage reservoirs.

Furthermore, it has been reported that prophages were

253

frequently detected in human-derived

Staphylococcus aureus

isolates compared to those

254

from pig origin.

While this study revealed a significant difference in terms of MGE

255

frequency among

Acinetobacter

isolates from different sources,

the limited sample size

256

precludes definitive conclusions about the general distribution of MGEs across the

257

Acinetobacter

population.

258

To further investigate the factors influencing the frequency of MGEs, we examined

259

the presence of bacterial defense systems within each

Acinetobacter

genome. Notably, a

260

negative correlation was observed between the number of defense systems and the

261

number of prophages, highlighting a possible role of defense systems in restricting

262

prophage acquisition. This observation is consistent with findings from Kohgay et al.

who

263

reported fewer phage-related genes in bacterial genomes with a higher abundance of

264

defense systems.

Similarly, Costa et al. demonstrated that an increase in defense

265

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systems enhances bacterial resistance to phage infections, further supporting their

266

involvement in limiting prophage acquisition.

267

Interestingly, we observed a compartmentalized organization of bacterial defense

268

systems between the bacterial chromosomes and plasmids. Isolates derived from pig

269

waste exhibited the highest frequency of defense systems and plasmids. Additionally, we

270

observed a significant positive correlation between the numbers of defense systems and

271

plasmids. Notably, the plasmid-specific enrichment of BREX_I, SanaTA, and Retron_VI

272

systems were observed. Plasmids, while known as principal vectors of ARG

273

dissemination, also carry diverse defense systems, which protect host bacteria from

274

phage infections. Recent studies (emphasized the strong association between plasmids

275

and bacterial defense systems, with plasmids playing a pivotal role in the accumulation

276

and dissemination of these systems across bacterial genomes.

44,45

277

Based on these observations, we propose a hypothetical model wherein isolates

278

originating from pig waste environments acquire plasmids carrying multiple ARGs under

279

the selective pressure of high antibiotic use. These plasmids may additionally harbor

280

defense systems that protect the host bacteria against phages. Future experimental

281

studies are needed to validate this hypothesis and further elucidate the interplay between

282

plasmids, defense systems, and prophage dynamics in different environmental contexts.

283

Our analysis also revealed that ARGs in

Acinetobacter

isolates were

284

predominantly associated with plasmids. Notably, the ARG carriage accounted for only

285

21% of the total plasmids identified across all isolates, consistent with former findings

286

indicating that more than 70% of plasmids lack known resistance genes.

Interestingly,

287

most ARGs in this study were found in mobilizable and non-mobilizable plasmids, in

288

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contrast to other studies showing an enrichment of ARGs in conjugative plasmids.

289

Distinct patterns of ARG co-localization were observed in plasmids. For instance, beta-

290

lactamase gene

bla

OXA-58

was co-localized with aminoglycoside resistance gene (

aph

(

”)-

291

), while

sul2

was co-localized with

aph

(

)-Id. Such co-localization suggests

292

mechanisms of co-resistance or the encoding of multidrug resistance, potentially

293

promoting the survival and emergence of multidrug-resistant

Acinetobacter

strains. The

294

conservation of such ARG clusters indicates that they may be transferred or inherited as

295

a single unit.

296

Furthermore, ARG enrichment was identified within p

dif

modules in plasmids

297

carrying ARGs. The

dif

site comprised a 28 bp DNA sequence featuring two 11 bp Xer

298

protein binding motifs arranged in an inverted repeat configuration, with a 6 bp central

299

region.

In plasmids, this

dif

site is referred to as the p

dif

module, often occurring in

300

multiple copies throughout the plasmid DNA. Previous studies have reported p

dif

module-

301

associated ARG enrichment in

Acinetobacter

plasmids.

48,49

However, this study, to the

302

best of our knowledge, is the first to document up to 10 different ARGs localized within a

303

single p

dif

module.

304

While ARGs are predominantly associated with transposable elements (TEs) and

305

plasmids, their occurrence within prophage regions showed considerable variation across

306

bacterial species. Comprehensive genomic studies of clinical isolates generally report a

307

lack of ARGs in the prophage regions of

Pseudomonas aeruginosa

Escherichia coli

308

and

Staphylococcus aureus

However, contrary findings have demonstrated ARG

309

presence within prophage regions of

A. baumannii

isolates.

In contrast to the findings

310

of Loh et al.

and consistent with earlier studies on other bacterial species,

50-52

our

311

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analysis detected no ARGs in prophage regions of

Acinetobacter

genomes. This absence

312

may be attributed to the size constraints of phages, which limit their ability to carry

313

accessory genes like ARGs.

Additionally, the discrepancy with previous studies may

314

stem from advancements in prophage detection tools. Our study utilized the latest

315

iteration of PHASTEST for prophage prediction, which integrates the Prodigal algorithm,

316

characterized by reduced false-positive and false-negative rates for open reading frame

317

(ORF) identification. Previous studies employed an earlier version of PHASTER,

318

potentially leading to the detection of ARG-containing prophages that are not confirmed

319

with the updated toolset. This difference highlights the need for methodological

320

standardization in prophage analysis. Nonetheless, the limited presence of ARGs in

321

prophage regions supports the notion that plasmids are the primary vehicles ARG transfer

322

Acinetobacter

323

We then analyzed the potential role of the nested structure of MGEs in the

324

intracellular and intercellular movement of ARGs

in silico

. We observed that ARGs in

325

plasmids were primarily associated with intracellular MGEs, consistent with previous

326

findings that ARGs are frequently linked to transposable elements and integrons.

327

Although IS-harboring plasmids carrying ARGs accounted for only 8% of the analyzed

328

plasmids, the presence of IS elements highlights their role in facilitating gene shuffling

329

and replication both within and between plasmids.

330

IS elements can also transfer from plasmids to chromosomes, enabling bacteria

331

to maintain HGT capabilities while reducing the metabolic burden with plasmid

332

maintenance.

IS elements linked to ARGs are key indicators of resistance transfer

333

risk.

4,56

Interestingly, we observed ARG-bearing plasmids had a higher frequency of IS

334

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elements compared to plasmids without ARGs. This aligns with previous studies showing

335

that higher density of IS elements in plasmids is associated with ARG carriage.

5,57

These

336

findings may indicate that nested MGEs facilitate hierarchical ARG transfer, potentially

337

promoting the movement of ARGs between plasmids, chromosomes, and even across

338

cells, enhancing overall ARG dissemination.

339

We also examined the presence of prophages within plasmids, often referred to as

340

“phage-plasmids,” which have been identified in notable pathogens such as

Escherichia,

341

Acinetobacter, Klebsiella,

and

Salmonella

58,59

For instance, in

E. coli

, the plasmid

342

p1108-IncY exhibits high nucleotide sequence identity with phage P1, suggesting

343

lysogenization via genetic integration.

Similarly, prophage regions have been identified

344

A. baumannii

plasmids.

In our study, we detected phage-plasmids in non-

345

baumannii

Acinetobacter

species, such as

A. towneri

and

A. guillouiae.

346

Subsequently, our synteny analysis revealed a potential intracellular transfer of

347

multiple ARGs from plasmids to chromosomes playing a significant role in the formation

348

of genomic islands (GIs). These are variable chromosomal regions that, when enriched

349

with resistance genes, are referred to as genomic resistance islands (GRIs). This transfer

350

is primarily mediated by transposable elements, especially when ARGs are co-localized

351

with transposons or integrons. These findings suggest that ARG movement within

352

bacterial cells (e.g., from plasmids to chromosomes) is a key driver in the formation of

353

new GRIs. While these co-localizations suggest potential horizontal gene transfer (HGT),

354

they represent indirect indicators rather than definitive proof of active gene movement.

355

These findings should be interpreted cautiously as markers of shared mobile genetic

356

structures rather than confirmed transfer events. Direct experimental verification through

357

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conjugation or transformation assays would be necessary to definitively establish the

358

mobility of these genetic elements.

359

GRIs have been extensively studied in

Acinetobacter

, especially in clinical and

360

pathogenic isolates. In

A. baumannii

, numerous GRIs have been identified and

361

characterized.

27,28,30,31,62-65

. However, studies on GRIs in non-

baumannii

species are

362

limited and have mainly focused on clinical isolates from the

Acinetobacter calcoaceticus-

363

baumannii

(Acb) complex, including

A. nosocomialis

and

A. seifertii

364

In this study, we identified GRIs in non-

baumannii

strains isolated from pig farm

365

wastewater and discovered GRI structures that distinctly differ from those previously

366

reported in clinical isolates.

30,31,63

Remarkably, this represents the first report of GRIs in

367

non-

baumannii

species outside the Acb complex. The discovery of these GRI structures

368

in non-

baumannii

isolates highlights their adaptation to distinct selective pressures

369

encountered in non-clinical environments, such as those characteristics of pig waste.

370

Plasmids isolated from pig waste-associated isolates exhibited a notable enrichment of

371

distinct ARG types not commonly observed in patient- and environment-associated

372

isolates. Transposition of these ARGs from plasmid to the chromosome perhaps gives

373

rise to the formation of putative genomic resistance islands.

374

Limitations of the study

375

This work has several limitations that should be acknowledged when interpreting

376

the findings. The relatively small number of

Acinetobacter

isolates (i.e. number of clinical

377

isolates) reduces statistical power, hindering the detection of rare mobile genetic

378

elements and low-frequency ARG-MGE associations, and it limits the generalizability of

379

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the results across different ecological niches, geographic regions, or clinical contexts.

380

Furthermore, the reported relationships among MGEs, ARGs, and defense systems are

381

correlative and do not establish causality. Experimental validation is required to determine

382

whether the co-occurrence reflects direct mechanistic interactions or is driven by shared

383

selective pressures or population structure. Overall, these limitations underscore the need

384

for larger, more diverse datasets and complementary experimental approaches to

385

strengthen confidence in the inferred ARG-MGE dynamics.

386

387

RESOURCE AVAILABILITY

388

Lead contact

389

Request for additional information or resources should be addressed to the lead contact,

390

Professor Kozo Watanabe (watanabe.kozo.mj@ehime-u.ac.jp), who will handle all such

391

inquiries.

392

Materials availability

393

Materials will be made available upon request.

394

Data and code availability

395

•

Data: The study did not generate new set of sequences.

396

•

Code: This paper does not report any original code. All details related to

397

supplementary software used for the analysis are listed in the key resource table.

398

•

All other items: The study sought approval from the Ehime University Hospital

399

Clinical Research Ethics Review Committee with approval number 2511001. Any

400

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further details necessary to reproduce the data presented in this study can be

401

obtained from the corresponding lead contact upon request.

402

AUTHOR CONTRIBUTIONS

403

Conceptualization, K.A.B, S.S., and K.W.; methodology, K.A.B, N.K., H.T., S.S.,

404

investigation, K.A.B., formal analysis, K.A.B., N.G., and K.W.; writing- original draft,

405

K.A.B.; writing- reviewing and editing, K.A.B., N.G., H.T., S.S., and K.W.; project

406

administration, K.A.B. and K.W. visualization; K.A.B.; funding acquisition, K.W.;

407

supervision, S.S. and K.W.

408

409

ACKNOWLEDGEMENTS

410

This research was performed by the Environment Research and Technology

411

Development Fund (SII-12-1 (3)) of the Environmental Restoration and Conservation

412

Agency provided by Ministry of the Environment of Japan. This research was supported

413

by the Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Scientific

414

Research (A) (20H00633).

415

416

DECLARATION OF INTERESTS

417

The authors declare no competing interests.

418

419

420

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FIGURES TITLES & LEGENDS:

421

Figure 1. Frequency of mobile genetic elements (MGEs) and defense systems

422

across different isolation sources.

The frequency of integrative conjugative

423

elements (ICEs) and integrative mobilizable elements (IMEs) in

Acinetobacter

424

chromosomes. Frequency of insertion sequence (IS) elements in the chromosome of

425

Acinetobacter

species (*

<0.05). Plasmid counts among patient-, pig waste- and

426

environment-associated isolates (***

<0.001). Number of prophages integrated in the

427

chromosome of

Acinetobacter

isolates (**

<0.01). Data are presented as median with

428

interquartile range.

The frequency of defense systems in

Acinetobacter

species across

429

different isolation sources (

<0.05, **

<0.01). Kruskal-Wallis test was performed to

430

compare the average number of MGEs and defense systems among

Acinetobacter

431

isolates. Data are presented as median with interquartile range.

Correlation plot of the

432

number of plasmids and number of defense systems using Spearman’s rank correlation

433

(R=0.51,

=0.0012).

Correlation plot of the number of prophages and number of

434

defense systems using Spearman’s rank correlation (R=-0.49,

=0.0017). Each dot

435

represents a genome (n=38).

436

Figure 2. ARG distribution, co-occurrence, and enrichment in Acinetobacter

437

plasmid. A.

Chord diagram showing the association of ARGs with different plasmid types.

438

Upset plot showing the ARG number in blue bar plots, ARG co-localization patterns

439

shown in interconnected dots and number of plasmids having the ARG co-localization

440

pattern in black bar plots.

Comparison of the number of p

dif

sites between ARG-

441

bearing plasmids and plasmids without ARGs (

<2.22e-16). Wilcoxon test was

442

performed to compare the average number of IS elements and number of p

dif

sites

443

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between ARG-bearing plasmids (red) and plasmids without ARGs (blue). Data are

444

presented as median with interquartile range.

445

Figure 3. Nested MGE structures in Acinetobacter. A.

Comparison of IS element

446

carriage between ARG-bearing plasmids and plasmids without ARGs (

= 0.0012). Data

447

are presented as median with interquartile range.

Sankey plot showing the association

448

of antimicrobial resistance genes (green) in nested MGEs - transposable elements

449

(orange) and different plasmid types (blue).

Frequency of IS elements among different

450

intercellular MGE types (**

<0.01, ****

<0.0001). Kruskal-Wallis test was performed to

451

compare the frequency of IS elements in A and C. Data are presented as median with

452

interquartile range.

453

Figure 4. Genomic resistance islands (GRIs) in non-baumannii isolates. A.

ARG

454

synteny analysis showing homologous region of

sul2

gene between plasmid pAtoEA24f

455

(

A. towneri

) and chromosome of

A. indicus

EA-11.

. ARG movement from a co-residing

456

plasmid pAtoEA13i (

A. towneri

) into the chromosome (

A. towneri

EA-13)

ARG synteny

457

analysis showing homologous region of

dfrA1

gene in an integron of plasmid pAtoEA23i

458

(

towneri

) and a plasmid pAtoEA14c from a different bacterial host (

A. towneri

). The

459

same region was found integrated into the chromosome of

A. towneri

EA-14

. D.

ARG

460

synteny analysis of ARGs from plasmid pAtoEA13i (

A. towneri

) and genomic island in

461

haemolyticus

EA-17. Red lines indicate high sequence similarity matches while gray lines

462

indicate reverse or inverted synteny.

463

464

STAR

★

METHODS

465

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EXPERIMENTAL MODEL AND STUDY PARTICIPANTS DETAILS

466

Microbe Strains

467

The study involves 38

Acinetobacter

strains from different isolation sources. Nine

468

strains including

A. baumannii, A. junii, A. pittii, A. guillouiae, A. bereziniae,

and

A. ursingii

469

were obtained from the Ehime University Hospital. All clinical isolates were prepared in

470

glycerol stocks and were anonymized. Eighteen strains such as

A. indicus, A.

471

haemolyticus, A. johnsonii, A. amyesii,

and

A. towneri

were isolated from pig wastewater

472

sample. One strain was isolated from the river water (

A. pittii

) and coastal area (

473

johnsonii

). While 9 strains including

A. baumannii, A. junii, A. calcoaceticus,

and

A. pittii

474

were isolated from municipal wastewater sample.

475

Bioethical Clearance

476

The study sought approval from the Ehime University Hospital Clinical Research

477

Ethics Review Committee with approval number 2511001. The clinical isolates from

478

glycerol stock cultures used in this study were anonymized.

479

480

METHOD DETAILS

481

Data Collection

482

The datasets, namely plasmid sequences and chromosome sequences of 38

483

Acinetobacter

species from the different co-selection patterns of ARGs and virulence

484

genes,

were retrieved from the NCBI database under the BioProject accession number

485

PRJNA1184881. We note that the previous study did not probe into the composition and

486

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association of MGEs and ARGs. Our set of 38

Acinetobacter

isolates originated from

487

patients (

=6), pig wastewater (

=18), municipal wastewater (

=9), natural surface water

488

(

=2), and hospital environment (

=3)

(Table S1)

We categorized the 38

Acinetobacter

489

strains into patient-associated (

=6), pig waste-associated (

=18), and environment-

490

associated (

=14) groups. The patient-associated isolates include strains isolated from

491

patients’ sputum, stool, and saliva. Strains isolated from hospital environment, municipal

492

wastewater treatment plants, river, and coastal area were classified into the environment-

493

associated isolates. Lastly, strains isolated from the pig farm wastewater were considered

494

as pig waste-associated isolates since the influent sample primaril

y originated from pigs’

495

waste

496

Two complementary sequencing approaches were employed in the previous

497

study

. First, long-read sequencing was performed using Oxford Nanopore Technology,

498

where genomic DNA (~1µg) was subjected to library preparation following the Oxford

499

Nanopore Technologies’ protocol for environmental bacteria. The DNA library was

500

prepared using the Ligation Sequencing Kit protocol (SQK-LSK109) and the long-read

501

sequences were generated using the MinION FLO-MIN106D flow cell and a MinION

502

MK1B sequencing device (Oxford Nanopore Technology). Finally, MinKNOW v23.07.12

503

software was used for data acquisition. To complement the long-read sequencing data,

504

short-read sequencing was conducted using a NovaSeq 6000 S4 Reagent Kit (2 x 150

505

bp paired-end read setting) and sequencing was performed on an Illumina NovaSeq 6000

506

sequencer at Eurofins Genomic Ltd.

507

The hybrid assembled genome of

Acinetobacter

isolates were used to extract MGE

508

sequences. The plasmid dataset (

=193) only includes circularized DNA and complete

509

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sequences. Other intercellular MGEs (e.g. prophages, integrative conjugative elements,

510

integrative mobilizable elements) were extracted from the chromosome sequences.

511

Further, chromosome and plasmid sequences were used to identify intracellular MGEs,

512

such as IS elements, transposons, and integrons.

513

514

QUANTIFICATION AND STATISTICAL ANALYSIS

515

Bioinformatics analysis

516

Plasmid identification was conducted by predicting the mob and rep genes.

517

Plasmid types were predicted using the parameter mob_typer in MOB-suite v3.1.8

518

which searched for the presence of the oriT, relaxase, and mate-pair formation in the

519

plasmid sequence. Plasmids possessing either oriT or relaxase but missing the mate-

520

pair formation were considered as mobilizable, while plasmids without an oriT and

521

relaxase were considered nonmobilizable. Plasmid rep typing was conducted using

522

blastn

with the parameter -perc_identity 95 against the

Acinetobacter

Plasmid Typing

523

(APT) scheme v3.0.

Prophage regions in both chromosomes and plasmids were

524

predicted using the default parameter of PHASTEST.

MobileElementFinder

was used

525

to identify IS elements. Transposons were detected using BacAnt

with the minimum

526

identity threshold of 90% and 60% coverage. Integrons were detected using the

527

parameter

–local-max in Integron Finder v.2.0

to allow for a more sensitive search and

528

minimize the false positive rate. Integrative conjugative elements and integrative

529

mobilizable elements were predicted using the default parameter of ICEberg 2.0

530

Genomic islands and p

dif

modules were predicted using the default parameter of

531

Journal Pre-proof

IslandViewer 4

and pdifFinder

, respectively. To predict ARGs in MGE sequences,

532

Abricate

was used with the CARD v3.2.4

and ResFinder v.4.6.0

databases with a

533

minimum identity threshold and minimum coverage threshold of 90%. Lastly, defense

534

genes were predicted using the DefenseFinder tool.

535

Gene synteny analysis

536

Each MGE sequence was annotated using Bakta

and the full database version

537

(v.5.0) was used to obtain the best annotation results. The annotated .gbk files were used

538

for the gene synteny analysis using the R package geneviewer v0.1.6.

The sequences

539

of ARG-bearing plasmids and genomic islands in chromosome were used for the ARG

540

synteny analysis.

541

Visualization and Statistical analysis

542

The frequency of IS elements, plasmids, prophage regions, integrative conjugative

543

elements (ICEs), and integrative mobilizable elements (IMEs), as well as defense

544

systems among patient-, pig waste-, and environment-associated

Acinetobacter

isolates

545

were determined using Kruskal-Wallis test with post hoc Wilcoxon rank-sum test.

546

Statistical significance was considered at

<0.05. Additionally, the correlation between

547

the number of plasmids and number of defense systems, and between number of

548

prophages and number of defense systems were examined. The number of defense

549

systems, plasmids, and prophages were normalized using the total number of protein-

550

coding sequences (CDS). The correlations were evaluated using the cor function in R

551

v.4.2.0. with statistical significance set at

< 0.05. The significant ARG co-localization on

552

a single plasmid was tested using Fisher’s exact test with fisher.test function in R. The

553

Journal Pre-proof

test was performed for all plasmid-bearing ARGs. The upset plot for the co-localization

554

patterns of ARGs were generated in R v.4.2.0. Kruskal-Wallis test was conducted in

555

comparing the frequency of insertion sequence (IS) elements across different intercellular

556

MGEs such as plasmids, prophages, ICEs, and integrative mobilizable elements (IMEs)

557

using the function kruskal.test in R v.4.2.0. Likewise, Wilcoxon rank test was also

558

performed to determine the significant difference in terms of IS element carriage between

559

plasmid-bearing ARGs and plasmid without ARGs. Significance levels are indicated as:

560

****

<0.0001, ***

<0.001, **

<0.01, and *

<0.05.

Further, the relationship between p

dif

561

modules and ARG carriage in plasmid-bearing ARGs and plasmids without ARGs were

562

investigated. Wilcoxon test was also performed with statistical significance set at

<0.05.

563

Sankey plot was built using the networkD3 package in R to show the association of ARGs,

564

intracellular MGEs, and intercellular MGEs.

565

566

SUPPLEMENTARY INFORMATION

567

Document S1. Figure S1

– S9, and Tables S1 – S5.

568

Supplementary Table S1. Species identification, isolation source, and antimicrobial

569

resistance genes composition of the 38

Acinetobacter

species.

570

Supplementary Table S4. Antimicrobial resistance genes (ARGs) in plasmids. ARG

571

duplication in plasmids were colored blue.

572

573

574

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575

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KEY RESOURCES TABLE

The table highlights the reagents, genetically modified organisms and strains, cell lines, software,
instrumentation, and source data

essential

to reproduce results presented in the manuscript. Depending

on the nature of the study, this may include standard laboratory materials (i.e., food chow for metabolism
studies, support material for catalysis studies), but the table is

not

meant to be a comprehensive list of all

materials and resources used (e.g., essential chemicals such as standard solvents, SDS, sucrose, or
standard culture media do not need to be listed in the table).

However, please note that items in the

table must also be reported in the method details section within the context of their use.

ALL references cited in the key resources table must be included in the main references list.

Citations should be formatted as “Author name et al.

” (e.g., Smith et al.

), with the citation number

matching that in the main references list.

Please report the information as follows:

•

REAGENT or RESOURCE:

Provide the full descriptive name of the item so that it can be identified

and linked with its description in the manuscript (e.g., provide version number for software, host
source for antibody, strain name). See the sample tables

at the end of this document for examples of

how to report reagents.

In the

experimental models sections

(applicable only to experimental life science

studies), please include all models used in the paper and describe each line/strain as
model organism: name used for strain/line in paper: genotype (e.g.,
Mouse: OXTR

fl/fl

: B6.129(SJL)-Oxtr

tm1.1Wsy/J

The

Biological samples section

(applicable only to experimental life science studies)

should list all samples obtained in this study or from commercial sources or biological
repositories.

You may list a maximum of 10 oligonucleotides or RNA sequences

in the table. If

there are more than 10 to report, please provide this information as a supplemental
document and reference the file (e.g., See Table S1 for XX) in the key resources table.

Deposited data

should include both newly deposited data from this manuscript and

existing datasets that were used in the manuscript.

Please include software and code mentioned in the method details or data and code

availability section under

software and algorithms

Any item that does not fit the existing subheadings should be added to the

“other”

section.

Please do not add your own subheadings.

•

SOURCE:

Report the company, manufacturer, or individual that provided the item or where the item

can be obtained (e.g., stock center or repository).

For materials distributed by Addgene, please cite the article describing the plasmid and

include “Addgene” as part of the identifier.

If an item is from another lab, please include the name of the principal investigator and a

citation if it has been previously published.

If the material is being reported for the first time in the current paper, please indicate as

“this paper.”

For software, please provide the company name if it is commercially available or cite the

paper in which it has been initially described.

•

IDENTIFIER:

Include catalog numbers

(entered in the column as “Cat#” followed by the number,

e.g., Cat#3879S). Where available, please include unique entities such as RRIDs, Model Organism
Database numbers, and accession numbers preceded by database abbreviations such as PDB or

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CCDC). Please ensure the accuracy of the identifiers, as they are essential for generation of
hyperlinks to external sources when available. For more information about data sharing policies and
a list of recommended data repositories for abbreviations, please see the Cell Press

Author’s guide

to data sharing.

For antibodies, if applicable and available, please also include the lot number or clone

identity.

For software or data resources, please include the URL where the resource can be

downloaded.

When listing more than one identifier for the same item, use semicolons to separate them

(e.g., Cat#3879S; RRID: AB_2255011).

If an identifier is not available,

please enter “N/A” in the column.

A NOTE ABOUT RRIDs:

we highly recommend using RRIDs as the identifier (in

particular for antibodies and organisms but also for software tools and databases). For
more details on how to obtain or generate an RRID for existing or newly generated
resources, please visit the RII or search for RRIDs.

Please use the empty table that follows to organize the information under the provided subheadings and
skip sections that are not relevant to your study. To add a row, place the cursor at the end of the row
above where you would like to add the row, just outside the right border of the table. Then press the
ENTER key to add the row. Alternatively, you can right-click on your mouse and choose Insert > Insert
rows above or Insert rows below. Please delete empty rows. Each entry must be on a separate row; do
not list multiple items in a single table cell. Please see the sample tables at the end of this document for
relevant examples in the life and physical sciences of how reagents and instrumentation should be cited.

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TABLE FOR AUTHOR TO COMPLETE

Please do not add custom subheadings. If you wish to make an entry that does not fall into one of the
subheadings below, please contact your handling editor or add it under

the “other

” subheading

. Any subheadings

not relevant to your study can be skipped. (NOTE: references should be in numbered style, e.g., Smith et al.

)

Key resources table

REAGENT or RESOURCE

SOURCE

IDENTIFIER

Antibodies

Bacterial and virus strains

Acinetobacter baumannii

Bongulto et al.

BioProject
Accession:
PRJNA1184881

Acinetobacter pittii

Bongulto et al.

BioProject
Accession:
PRJNA1184881

Acinetobacter junii

Bongulto et al.

BioProject
Accession:
PRJNA1184881

Acinetobacter guillouiae

Bongulto et al.

BioProject
Accession:
PRJNA1184881

Acinetobacter bereziniae

Bongulto et al.

BioProject
Accession:
PRJNA1184881

Acinetobacter ursingii

Bongulto et al.

BioProject
Accession:
PRJNA1184881

Acinetobacter calcoaceticus

Bongulto et al.

BioProject
Accession:
PRJNA1184881

Acinetobacter indicus

Bongulto et al.

BioProject
Accession:
PRJNA1184881

Acinetobacter amyesii

Bongulto et al.

BioProject
Accession:
PRJNA1184881

Acinetobacter johnsonii

Bongulto et al.

BioProject
Accession:
PRJNA1184881

Acinetobacter towneri

Bongulto et al.

BioProject
Accession:
PRJNA1184881

Acinetobacter haemolyticus

Bongulto et al.

BioProject
Accession:
PRJNA1184881

Biological samples

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Chemicals, peptides, and recombinant proteins

Blunt/TA Ligase Master Mix

New England BioLabs

M03367L

NEBNext® Companion Module for Oxford Nanopore
Technologies Ligation Sequencing

New England BioLabs

E7180S

NEBNext® Quick Ligation Reaction Buffer

New England BioLabs

B6058S

NEBNext® FFPE Repair Mix

New England BioLabs

M6630S

NEBNext® Ultra II End repair/dA-tailing Module

New England BioLabs

E7546S

NEBNext® Quick Ligation Module

New England BioLabs

E6056S

Oxford Nanopore Technologies Ligation Sequencing Kit

Oxford Nanopore
Technologies

SQK-LSK109

Flow Cell R9

Oxford Nanopore
Technologies

FLO-MIN106D

Flow Cell Wash Kit

Oxford Nanopore
Technologies

EXP-WSH004

Native Barcoding Expansion 1-12 (PCR free)

Oxford Nanopore
Technologies

EXP-NBD104

Ampure XP Beads

Beckman Coulter Inc.

A63880

Critical commercial assays

Illumina NovaSeq 6000 Whole Genome Sequencing

Eurofins Genomics

https://eurofinsgeno
mics.jp/jp/home/

Deposited data

Whole genome sequences

Bongulto et al.

BioProject
Accession:
PRJNA1184881

Experimental models: Cell lines

Experimental models: Organisms/strains

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Oligonucleotides

Recombinant DNA

Software and algorithms

MOB-suite (v3.1.8)

Robertson et al.

https://github.com/ph
ac-nml/mob-suite

blastn

Acinetobacter

Plasmid Typing (APT) scheme (v3.0)

Lam et al.

https://github.com/M
ehradHamidian/Acin
etobacterPlasmidTy
ping

PHASTEST

Wishart et al.

https://phastest.ca

MobileElementFinder

Johansson et al.

https://cge.food.dtu.d
k/services/MobileEle
mentFinder/

BacAnt

Hua et al.

bacant.net/BacAnt/

Integron Finder

Néron et al.

https://github.com/ge
m-
pasteur/Integron_Fin
der

ICEberg (v2.0)

Liu et al.

http://db-
mml.sjtu.edu.cn/ICE
berg/

IslandViewer 4

Bertelli et al.

https://www.pathoge
nomics.sfu.ca/island
viewer

pdifFinder

Shao et al.

pdif.dmicrobe.cn/pdif
/home/

Abricate

Seeman, T.

https://github.com/ts
eeman/abricate

CARD (v3.2.4)

Alcock et al.

https://github.com/ar
pcard/rgi

ResFinder (v4.6.0)

Florensa et al.

https://github.com/ge
nomicepidemiology/r
esfinder

DefenseFinder

Tesson et al.

https://github.com/m
dmparis/defense-
finder

Bakta (v5.0)

Schwengers et al.

https://github.com/os
chwengers/bakta

Geneviewer (v0.1.6)

Van der Velden, N.

https://nvelden.githu
b.io/geneviewer/

R v4.2.0

R core Team, 2021

https://posit.co/downl
oad/rstudio-desktop/

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