Integrated chromosomal and plasmid sequence analyses reveal diverse modes of carbapenemase gene spread among Klebsiella pneumoniae
Molecular and genomic surveillance systems for bacterial pathogens currently rely on tracking clonally evolving lineages. By contrast, plasmids are usually excluded or analyzed with low-resolution techniques, despite being the primary vectors of antibiotic resistance genes across many key pathogens....
Saved in:
Published in | Proceedings of the National Academy of Sciences - PNAS Vol. 117; no. 40; pp. 25043 - 25054 |
---|---|
Main Authors | , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
National Academy of Sciences
06.10.2020
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | Molecular and genomic surveillance systems for bacterial pathogens currently rely on tracking clonally evolving lineages. By contrast, plasmids are usually excluded or analyzed with low-resolution techniques, despite being the primary vectors of antibiotic resistance genes across many key pathogens. Here, we used a combination of long- and short-read sequence data of Klebsiella pneumoniae isolates (n = 1,717) from a European survey to perform an integrated, continent-wide study of chromosomal and plasmid diversity. This revealed three contrasting modes of dissemination used by carbapenemase genes, which confer resistance to last-line carbapenems. First, bla
OXA-48-like genes have spread primarily via the single epidemic pOXA-48–like plasmid, which emerged recently in clinical settings and spread rapidly to numerous lineages. Second, bla
VIM and bla
NDM genes have spread via transient associations of many diverse plasmids with numerous lineages. Third, bla
KPC genes have transmitted predominantly by stable association with one successful clonal lineage (ST258/512) yet have been mobilized among diverse plasmids within this lineage. We show that these plasmids, which include pKpQIL-like and IncX3 plasmids, have a long association (and are coevolving) with the lineage, although frequent recombination and rearrangement events between them have led to a complex array of mosaic plasmids carrying bla
KPC. Taken altogether, these results reveal the diverse trajectories of antibiotic resistance genes in clinical settings, summarized as using one plasmid/multiple lineages, multiple plasmids/multiple lineages, and multiple plasmids/one lineage. Our study provides a framework for the much needed incorporation of plasmid data into genomic surveillance systems, an essential step toward a more comprehensive understanding of resistance spread. |
---|---|
AbstractList | In many clinically important bacteria, antibiotic resistance genes are primarily carried on plasmids. These can spread horizontally between different strains and species. However, current surveillance systems track chromosomal lineages of bacteria only, leading to an incomplete understanding of how resistance spreads, from within an individual hospital to across country borders. We present an integrated, high-resolution analysis of both chromosome and plasmid sequences using
Klebsiella pneumoniae
isolates sampled during a European survey. We show that carbapenemase genes, which confer resistance to last-line antibiotics, have spread in diverse ways including via one plasmid/multiple lineages (
bla
OXA-48-like
), multiple plasmids/multiple lineages (
bla
VIM
,
bla
NDM
), and multiple plasmids/one lineage (
bla
KPC
). These different trajectories must be considered in genomic surveillance systems and the design of new interventions.
Molecular and genomic surveillance systems for bacterial pathogens currently rely on tracking clonally evolving lineages. By contrast, plasmids are usually excluded or analyzed with low-resolution techniques, despite being the primary vectors of antibiotic resistance genes across many key pathogens. Here, we used a combination of long- and short-read sequence data of
Klebsiella pneumoniae
isolates (
n
= 1,717) from a European survey to perform an integrated, continent-wide study of chromosomal and plasmid diversity. This revealed three contrasting modes of dissemination used by carbapenemase genes, which confer resistance to last-line carbapenems. First,
bla
OXA-48-like
genes have spread primarily via the single epidemic pOXA-48–like plasmid, which emerged recently in clinical settings and spread rapidly to numerous lineages. Second,
bla
VIM
and
bla
NDM
genes have spread via transient associations of many diverse plasmids with numerous lineages. Third,
bla
KPC
genes have transmitted predominantly by stable association with one successful clonal lineage (ST258/512) yet have been mobilized among diverse plasmids within this lineage. We show that these plasmids, which include pKpQIL-like and IncX3 plasmids, have a long association (and are coevolving) with the lineage, although frequent recombination and rearrangement events between them have led to a complex array of mosaic plasmids carrying
bla
KPC
. Taken altogether, these results reveal the diverse trajectories of antibiotic resistance genes in clinical settings, summarized as using one plasmid/multiple lineages, multiple plasmids/multiple lineages, and multiple plasmids/one lineage. Our study provides a framework for the much needed incorporation of plasmid data into genomic surveillance systems, an essential step toward a more comprehensive understanding of resistance spread. Molecular and genomic surveillance systems for bacterial pathogens currently rely on tracking clonally evolving lineages. By contrast, plasmids are usually excluded or analyzed with low-resolution techniques, despite being the primary vectors of antibiotic resistance genes across many key pathogens. Here, we used a combination of long- and short-read sequence data of Klebsiella pneumoniae isolates (n = 1,717) from a European survey to perform an integrated, continent-wide study of chromosomal and plasmid diversity. This revealed three contrasting modes of dissemination used by carbapenemase genes, which confer resistance to last-line carbapenems. First, blaOXA-48-like genes have spread primarily via the single epidemic pOXA-48–like plasmid, which emerged recently in clinical settings and spread rapidly to numerous lineages. Second, blaVIM and blaNDM genes have spread via transient associations of many diverse plasmids with numerous lineages. Third, blaKPC genes have transmitted predominantly by stable association with one successful clonal lineage (ST258/512) yet have been mobilized among diverse plasmids within this lineage. We show that these plasmids, which include pKpQIL-like and IncX3 plasmids, have a long association (and are coevolving) with the lineage, although frequent recombination and rearrangement events between them have led to a complex array of mosaic plasmids carrying blaKPC. Taken altogether, these results reveal the diverse trajectories of antibiotic resistance genes in clinical settings, summarized as using one plasmid/multiple lineages, multiple plasmids/multiple lineages, and multiple plasmids/one lineage. Our study provides a framework for the much needed incorporation of plasmid data into genomic surveillance systems, an essential step toward a more comprehensive understanding of resistance spread. Molecular and genomic surveillance systems for bacterial pathogens currently rely on tracking clonally evolving lineages. By contrast, plasmids are usually excluded or analyzed with low-resolution techniques, despite being the primary vectors of antibiotic resistance genes across many key pathogens. Here, we used a combination of long- and short-read sequence data of isolates ( = 1,717) from a European survey to perform an integrated, continent-wide study of chromosomal and plasmid diversity. This revealed three contrasting modes of dissemination used by carbapenemase genes, which confer resistance to last-line carbapenems. First, genes have spread primarily via the single epidemic pOXA-48-like plasmid, which emerged recently in clinical settings and spread rapidly to numerous lineages. Second, and genes have spread via transient associations of many diverse plasmids with numerous lineages. Third, genes have transmitted predominantly by stable association with one successful clonal lineage (ST258/512) yet have been mobilized among diverse plasmids within this lineage. We show that these plasmids, which include pKpQIL-like and IncX3 plasmids, have a long association (and are coevolving) with the lineage, although frequent recombination and rearrangement events between them have led to a complex array of mosaic plasmids carrying Taken altogether, these results reveal the diverse trajectories of antibiotic resistance genes in clinical settings, summarized as using one plasmid/multiple lineages, multiple plasmids/multiple lineages, and multiple plasmids/one lineage. Our study provides a framework for the much needed incorporation of plasmid data into genomic surveillance systems, an essential step toward a more comprehensive understanding of resistance spread. Molecular and genomic surveillance systems for bacterial pathogens currently rely on tracking clonally evolving lineages. By contrast, plasmids are usually excluded or analyzed with low-resolution techniques, despite being the primary vectors of antibiotic resistance genes across many key pathogens. Here, we used a combination of long- and short-read sequence data of Klebsiella pneumoniae isolates (n = 1,717) from a European survey to perform an integrated, continent-wide study of chromosomal and plasmid diversity. This revealed three contrasting modes of dissemination used by carbapenemase genes, which confer resistance to last-line carbapenems. First, bla OXA-48-like genes have spread primarily via the single epidemic pOXA-48–like plasmid, which emerged recently in clinical settings and spread rapidly to numerous lineages. Second, bla VIM and bla NDM genes have spread via transient associations of many diverse plasmids with numerous lineages. Third, bla KPC genes have transmitted predominantly by stable association with one successful clonal lineage (ST258/512) yet have been mobilized among diverse plasmids within this lineage. We show that these plasmids, which include pKpQIL-like and IncX3 plasmids, have a long association (and are coevolving) with the lineage, although frequent recombination and rearrangement events between them have led to a complex array of mosaic plasmids carrying bla KPC. Taken altogether, these results reveal the diverse trajectories of antibiotic resistance genes in clinical settings, summarized as using one plasmid/multiple lineages, multiple plasmids/multiple lineages, and multiple plasmids/one lineage. Our study provides a framework for the much needed incorporation of plasmid data into genomic surveillance systems, an essential step toward a more comprehensive understanding of resistance spread. |
Author | Feil, Edward J. Giani, Tommaso Grundman, Hajo Aanensen, David M. David, Sophia Sheppar, Anna E. Rossolini, Gian Maria Cohen, Victoria Parkhill, Julian Reuter, Sandra |
Author_xml | – sequence: 1 givenname: Sophia surname: David fullname: David, Sophia – sequence: 2 givenname: Victoria surname: Cohen fullname: Cohen, Victoria – sequence: 3 givenname: Sandra surname: Reuter fullname: Reuter, Sandra – sequence: 4 givenname: Anna E. surname: Sheppar fullname: Sheppar, Anna E. – sequence: 5 givenname: Tommaso surname: Giani fullname: Giani, Tommaso – sequence: 6 givenname: Julian surname: Parkhill fullname: Parkhill, Julian – sequence: 9 givenname: Gian Maria surname: Rossolini fullname: Rossolini, Gian Maria – sequence: 10 givenname: Edward J. surname: Feil fullname: Feil, Edward J. – sequence: 11 givenname: Hajo surname: Grundman fullname: Grundman, Hajo – sequence: 12 givenname: David M. surname: Aanensen fullname: Aanensen, David M. |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32968015$$D View this record in MEDLINE/PubMed |
BookMark | eNpdkc1v1DAQxS1URLeFMyeQJS5c0o4dO44vSFVFoaISFzhbjj3ZZpXYwc6u1Ct_Od5uWT5Ott78POM374ychBiQkNcMLhio-nIONl9wgFqAYkw9IysGmlWN0HBCVgBcVa3g4pSc5bwBAC1beEFOa66bFphckZ-3YcF1sgt66u5TnGKOkx2pDZ7Oo83T4GnGH1sMDotox4eMmSbcYYH8sMOUkU7RFzH21NnU2RkDTrbI63KheU5oPbVTDGv6ZcQuDziOls4Bt0UbLL4kz3s7Znz1dJ6T7zcfv11_ru6-frq9vrqrnBD1UjnPgDe-R98Bg64VaLETHLjjEmQxr_qOM8m1qlWnG-WZVrZ3qLSTLZNNfU4-HPrO225C7zAsyY5mTsNk04OJdjD_VsJwb9ZxZ5RsFeeqNHj_1CDFspG8mGnIbu8mYNxmw4WQWgn-OOvdf-gmblNZ356SoNpWgS7U5YFyKeacsD9-hoHZ52v2-Zo_-ZYXb__2cOR_B1qANwdgk5eYjnXe6EYLJetfR8Sv6Q |
CitedBy_id | crossref_primary_10_1038_s41467_023_39915_2 crossref_primary_10_1093_jacamr_dlac084 crossref_primary_10_2217_fmb_2022_0109 crossref_primary_10_1016_j_foodcont_2024_110296 crossref_primary_10_2807_1560_7917_ES_2024_29_22_2300666 crossref_primary_10_1099_mgen_0_000892 crossref_primary_10_1128_spectrum_01574_21 crossref_primary_10_1128_msystems_01169_21 crossref_primary_10_1128_Spectrum_01141_21 crossref_primary_10_1016_j_jgar_2023_07_004 crossref_primary_10_3389_fmicb_2023_1291540 crossref_primary_10_3389_fcimb_2021_722536 crossref_primary_10_1128_AAC_00574_21 crossref_primary_10_1016_S2666_5247_22_00338_X crossref_primary_10_1093_jac_dkab131 crossref_primary_10_1371_journal_pcbi_1010018 crossref_primary_10_1128_mbio_03054_23 crossref_primary_10_1038_s41467_024_45761_7 crossref_primary_10_1016_j_gpb_2022_02_005 crossref_primary_10_1099_mgen_0_001032 crossref_primary_10_1099_mgen_0_000741 crossref_primary_10_1128_msphere_00170_23 crossref_primary_10_1128_msystems_01275_22 crossref_primary_10_3389_fsci_2024_1298248 crossref_primary_10_1128_spectrum_03395_22 crossref_primary_10_2807_1560_7917_ES_2023_28_27_2200774 crossref_primary_10_1016_j_diagmicrobio_2024_116370 crossref_primary_10_3389_fmicb_2021_629139 crossref_primary_10_1016_S2666_5247_23_00285_9 crossref_primary_10_1038_s41467_024_49349_z crossref_primary_10_1099_mgen_0_001082 crossref_primary_10_1099_mgen_0_000509 crossref_primary_10_3390_antibiotics13060535 crossref_primary_10_1186_s13073_021_00960_5 crossref_primary_10_1007_s00103_023_03713_4 crossref_primary_10_1016_j_scitotenv_2022_153632 crossref_primary_10_1099_mgen_0_000634 crossref_primary_10_1186_s13073_023_01260_w crossref_primary_10_1093_femsre_fuac044 crossref_primary_10_1099_mgen_0_000798 crossref_primary_10_1186_s44280_023_00033_9 crossref_primary_10_1016_j_jgar_2022_06_021 crossref_primary_10_1099_mgen_0_001008 crossref_primary_10_3390_antibiotics10030322 crossref_primary_10_1080_26895293_2024_2350414 crossref_primary_10_1099_mgen_0_001048 crossref_primary_10_1038_s41598_023_38647_z crossref_primary_10_1128_mSphere_00850_21 crossref_primary_10_1099_mgen_0_001127 crossref_primary_10_1128_msystems_00924_23 crossref_primary_10_1128_jcm_01080_22 crossref_primary_10_1128_spectrum_02158_21 crossref_primary_10_3390_antibiotics12101549 crossref_primary_10_1093_jac_dkad337 crossref_primary_10_1128_spectrum_03833_22 crossref_primary_10_1128_aac_00787_22 crossref_primary_10_7554_eLife_85302 crossref_primary_10_1038_s41597_022_01463_7 crossref_primary_10_1093_jacamr_dlab015 crossref_primary_10_1016_j_ijheh_2022_113968 crossref_primary_10_3390_microorganisms11041050 crossref_primary_10_1093_cid_ciab777 crossref_primary_10_3201_eid2808_212542 crossref_primary_10_3389_fmed_2022_827474 crossref_primary_10_1080_20477724_2022_2121362 crossref_primary_10_1099_mgen_0_000924 crossref_primary_10_1080_14787210_2024_2305854 crossref_primary_10_1099_mgen_0_001257 crossref_primary_10_1099_mgen_0_001016 crossref_primary_10_1128_aac_00860_23 crossref_primary_10_3390_microorganisms9040762 crossref_primary_10_1038_s41564_022_01146_4 crossref_primary_10_1099_mgen_0_001138 crossref_primary_10_1186_s13073_022_01040_y crossref_primary_10_3389_fcimb_2021_792305 crossref_primary_10_3390_antibiotics12121727 crossref_primary_10_1128_msphere_00608_23 crossref_primary_10_1146_annurev_micro_032521_022006 crossref_primary_10_3390_antibiotics9120862 crossref_primary_10_1016_j_micres_2021_126894 crossref_primary_10_1093_jac_dkac114 crossref_primary_10_1128_spectrum_00148_22 crossref_primary_10_1093_jac_dkab463 crossref_primary_10_1128_AAC_00206_21 crossref_primary_10_3390_microorganisms10081592 crossref_primary_10_1038_s41467_024_48296_z crossref_primary_10_3390_microorganisms9122443 |
Cites_doi | 10.1128/AAC.01889-16 10.1101/gr.215087.116 10.1093/bioinformatics/btv421 10.1371/journal.pcbi.1005595 10.1093/jac/dkw227 10.1093/jac/dkz366 10.1101/456897 10.1093/bioinformatics/bti553 10.1111/j.1469-0691.2011.03532.x 10.1093/nar/gkw290 10.1186/gb-2004-5-2-r12 10.1093/jac/dkw106 10.1126/science.aao2136 10.1128/AAC.48.1.15-22.2004 10.1128/AAC.02412-14 10.1128/AAC.05289-11 10.1073/pnas.1321364111 10.1016/S0022-2836(05)80360-2 10.1371/journal.pone.0123063 10.1128/AAC.00175-10 10.1093/bioinformatics/btt086 10.1016/S1473-3099(18)30605-4 10.1093/nar/gku1196 10.1093/bioinformatics/btu153 10.1016/S1473-3099(13)70190-7 10.1093/bioinformatics/btl446 10.1128/AAC.00120-14 10.1128/CMR.00116-14 10.1128/AAC.05202-11 10.1038/s41564-019-0492-8 10.1128/AAC.01019-15 10.1128/mBio.00444-16 10.1128/AAC.03900-14 10.1093/bioinformatics/btp324 10.1128/AAC.04292-14 10.1016/S1473-3099(18)30225-1 10.1128/AAC.00464-16 10.1016/S1473-3099(16)30257-2 10.1145/2543629 10.1093/bioinformatics/btp352 10.1093/jac/dkx264 10.1126/science.1182395 10.1128/mBio.00204-11 10.1093/jac/dkx141 10.2807/1560-7917.ES2013.18.31.20549 10.1016/j.tim.2014.09.003 10.1093/jac/dkx513 |
ContentType | Journal Article |
Contributor | Jakobsen, Lotte McGrath, Elaine Sjöström, Karin Glupczynski, Youri Kaase, Martin Maikanti-Charalampous, Panagiota Balode, Arta Literacka, Elżbieta Hartl, Rainer Dortet, Laurent Apfalter, Petra Ivanova, Marina Żabicka, Dorota Miciuleviciene, Jolanta Pirs, Mateja Marteva-Proevska, Yuliya Woodford, Neil Caniça, Manuela Pieridou-Bagatzouni, Despo Jelesic, Zora Wiuff, Camilla Lopicic, Milena Hrabak, Jaroslav Lacej, Denada Boo, Teck Wee Österblad, Monica Mierauskaite, Aiste Niks, Milan Werner, Guido Couto, Natacha Kurti, Arsim Lixandru, Brandusa Cakar, Aslı Hopkins, Katie Strateva, Tanya Pavelkovich, Anastasia Giske, Christian Hammerum, Anette Zemlickova, Helena Cerar, Tjasa Samuelsen, Ørjan Kaftandzieva, Ana Brown, Derek J Schreterova, Eva Perrin-Weniger, Monique Pérez-Vázquez, María Saule, Mara Trajkovska-Dokic, Elena Pantosti, Annalisa Oteo-Iglesias, Jesús Vatopoulos, Alkiviadis Raka, Lul Gür, Deniz Jalava, Jari Damian, Maria Vaux, Sophie Tryfinopoulou, Kyriaki Debattista, Sonia Trudic, Anika Haldorsen, Bjørg Tóth, Ákos Nestorova, Nina Carmeli, Yehu |
Contributor_xml | – sequence: 1 givenname: Andi surname: Koraqi fullname: Koraqi, Andi – sequence: 2 givenname: Denada surname: Lacej fullname: Lacej, Denada – sequence: 3 givenname: Petra surname: Apfalter fullname: Apfalter, Petra – sequence: 4 givenname: Rainer surname: Hartl fullname: Hartl, Rainer – sequence: 5 givenname: Youri surname: Glupczynski fullname: Glupczynski, Youri – sequence: 6 givenname: Te-Din surname: Huang fullname: Huang, Te-Din – sequence: 7 givenname: Tanya surname: Strateva fullname: Strateva, Tanya – sequence: 8 givenname: Yuliya surname: Marteva-Proevska fullname: Marteva-Proevska, Yuliya – sequence: 9 givenname: Arjana Tambic surname: Andrasevic fullname: Andrasevic, Arjana Tambic – sequence: 10 givenname: Iva surname: Butic fullname: Butic, Iva – sequence: 11 givenname: Despo surname: Pieridou-Bagatzouni fullname: Pieridou-Bagatzouni, Despo – sequence: 12 givenname: Panagiota surname: Maikanti-Charalampous fullname: Maikanti-Charalampous, Panagiota – sequence: 13 givenname: Jaroslav surname: Hrabak fullname: Hrabak, Jaroslav – sequence: 14 givenname: Helena surname: Zemlickova fullname: Zemlickova, Helena – sequence: 15 givenname: Anette surname: Hammerum fullname: Hammerum, Anette – sequence: 16 givenname: Lotte surname: Jakobsen fullname: Jakobsen, Lotte – sequence: 17 givenname: Marina surname: Ivanova fullname: Ivanova, Marina – sequence: 18 givenname: Anastasia surname: Pavelkovich fullname: Pavelkovich, Anastasia – sequence: 19 givenname: Jari surname: Jalava fullname: Jalava, Jari – sequence: 20 givenname: Monica surname: Österblad fullname: Österblad, Monica – sequence: 21 givenname: Laurent surname: Dortet fullname: Dortet, Laurent – sequence: 22 givenname: Sophie surname: Vaux fullname: Vaux, Sophie – sequence: 23 givenname: Martin surname: Kaase fullname: Kaase, Martin – sequence: 24 givenname: Sören G surname: Gatermann fullname: Gatermann, Sören G – sequence: 25 givenname: Alkiviadis surname: Vatopoulos fullname: Vatopoulos, Alkiviadis – sequence: 26 givenname: Kyriaki surname: Tryfinopoulou fullname: Tryfinopoulou, Kyriaki – sequence: 27 givenname: Ákos surname: Tóth fullname: Tóth, Ákos – sequence: 28 givenname: Laura surname: Jánvári fullname: Jánvári, Laura – sequence: 29 givenname: Teck Wee surname: Boo fullname: Boo, Teck Wee – sequence: 30 givenname: Elaine surname: McGrath fullname: McGrath, Elaine – sequence: 31 givenname: Yehuda surname: Carmeli fullname: Carmeli, Yehuda – sequence: 32 givenname: Amos surname: Adler fullname: Adler, Amos – sequence: 33 givenname: Annalisa surname: Pantosti fullname: Pantosti, Annalisa – sequence: 34 givenname: Monica surname: Monaco fullname: Monaco, Monica – sequence: 35 givenname: Lul surname: Raka fullname: Raka, Lul – sequence: 36 givenname: Arsim surname: Kurti fullname: Kurti, Arsim – sequence: 37 givenname: Arta surname: Balode fullname: Balode, Arta – sequence: 38 givenname: Mara surname: Saule fullname: Saule, Mara – sequence: 39 givenname: Jolanta surname: Miciuleviciene fullname: Miciuleviciene, Jolanta – sequence: 40 givenname: Aiste surname: Mierauskaite fullname: Mierauskaite, Aiste – sequence: 41 givenname: Monique surname: Perrin-Weniger fullname: Perrin-Weniger, Monique – sequence: 42 givenname: Paul surname: Reichert fullname: Reichert, Paul – sequence: 43 givenname: Nina surname: Nestorova fullname: Nestorova, Nina – sequence: 44 givenname: Sonia surname: Debattista fullname: Debattista, Sonia – sequence: 45 givenname: Gordana surname: Mijovic fullname: Mijovic, Gordana – sequence: 46 givenname: Milena surname: Lopicic fullname: Lopicic, Milena – sequence: 47 givenname: Ørjan surname: Samuelsen fullname: Samuelsen, Ørjan – sequence: 48 givenname: Bjørg surname: Haldorsen fullname: Haldorsen, Bjørg – sequence: 49 givenname: Dorota surname: Żabicka fullname: Żabicka, Dorota – sequence: 50 givenname: Elżbieta surname: Literacka fullname: Literacka, Elżbieta – sequence: 51 givenname: Manuela surname: Caniça fullname: Caniça, Manuela – sequence: 52 givenname: Vera surname: Manageiro fullname: Manageiro, Vera – sequence: 53 givenname: Ana surname: Kaftandzieva fullname: Kaftandzieva, Ana – sequence: 54 givenname: Elena surname: Trajkovska-Dokic fullname: Trajkovska-Dokic, Elena – sequence: 55 givenname: Maria surname: Damian fullname: Damian, Maria – sequence: 56 givenname: Brandusa surname: Lixandru fullname: Lixandru, Brandusa – sequence: 57 givenname: Zora surname: Jelesic fullname: Jelesic, Zora – sequence: 58 givenname: Anika surname: Trudic fullname: Trudic, Anika – sequence: 59 givenname: Milan surname: Niks fullname: Niks, Milan – sequence: 60 givenname: Eva surname: Schreterova fullname: Schreterova, Eva – sequence: 61 givenname: Mateja surname: Pirs fullname: Pirs, Mateja – sequence: 62 givenname: Tjasa surname: Cerar fullname: Cerar, Tjasa – sequence: 63 givenname: Jesús surname: Oteo-Iglesias fullname: Oteo-Iglesias, Jesús – sequence: 64 givenname: María surname: Pérez-Vázquez fullname: Pérez-Vázquez, María – sequence: 65 givenname: Christian surname: Giske fullname: Giske, Christian – sequence: 66 givenname: Karin surname: Sjöström fullname: Sjöström, Karin – sequence: 67 givenname: Deniz surname: Gür fullname: Gür, Deniz – sequence: 68 givenname: Aslı surname: Cakar fullname: Cakar, Aslı – sequence: 69 givenname: Neil surname: Woodford fullname: Woodford, Neil – sequence: 70 givenname: Katie surname: Hopkins fullname: Hopkins, Katie – sequence: 71 givenname: Camilla surname: Wiuff fullname: Wiuff, Camilla – sequence: 72 givenname: Derek J surname: Brown fullname: Brown, Derek J – sequence: 73 givenname: Guido surname: Werner fullname: Werner, Guido – sequence: 74 givenname: Natacha surname: Couto fullname: Couto, Natacha |
Copyright | Copyright © 2020 the Author(s). Published by PNAS. Copyright National Academy of Sciences Oct 6, 2020 Copyright © 2020 the Author(s). Published by PNAS. 2020 |
Copyright_xml | – notice: Copyright © 2020 the Author(s). Published by PNAS. – notice: Copyright National Academy of Sciences Oct 6, 2020 – notice: Copyright © 2020 the Author(s). Published by PNAS. 2020 |
CorporateAuthor | European Survey of Carbapenemase-Producing Enterobacteriaceae (EuSCAPE) Working Group ESCMID Study Group for Epidemiological Markers (ESGEM) the ESCMID Study Group for Epidemiological Markers (ESGEM) the European Survey of Carbapenemase-Producing Enterobacteriaceae (EuSCAPE) Working Group |
CorporateAuthor_xml | – sequence: 0 name: ESCMID Study Group for Epidemiological Markers (ESGEM) – sequence: 0 name: European Survey of Carbapenemase-Producing Enterobacteriaceae (EuSCAPE) Working Group – sequence: 0 name: the ESCMID Study Group for Epidemiological Markers (ESGEM) – sequence: 0 name: the European Survey of Carbapenemase-Producing Enterobacteriaceae (EuSCAPE) Working Group – name: ESCMID Study Group for Epidemiological Markers (ESGEM) – name: European Survey of Carbapenemase-Producing Enterobacteriaceae (EuSCAPE) Working Group |
DBID | CGR CUY CVF ECM EIF NPM AAYXX CITATION 7QG 7QL 7QP 7QR 7SN 7SS 7T5 7TK 7TM 7TO 7U9 8FD C1K FR3 H94 M7N P64 RC3 7X8 5PM |
DOI | 10.1073/pnas.2003407117 |
DatabaseName | Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed CrossRef Animal Behavior Abstracts Bacteriology Abstracts (Microbiology B) Calcium & Calcified Tissue Abstracts Chemoreception Abstracts Ecology Abstracts Entomology Abstracts (Full archive) Immunology Abstracts Neurosciences Abstracts Nucleic Acids Abstracts Oncogenes and Growth Factors Abstracts Virology and AIDS Abstracts Technology Research Database Environmental Sciences and Pollution Management Engineering Research Database AIDS and Cancer Research Abstracts Algology Mycology and Protozoology Abstracts (Microbiology C) Biotechnology and BioEngineering Abstracts Genetics Abstracts MEDLINE - Academic PubMed Central (Full Participant titles) |
DatabaseTitle | MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) CrossRef Virology and AIDS Abstracts Oncogenes and Growth Factors Abstracts Technology Research Database Nucleic Acids Abstracts Ecology Abstracts Neurosciences Abstracts Biotechnology and BioEngineering Abstracts Environmental Sciences and Pollution Management Entomology Abstracts Genetics Abstracts Animal Behavior Abstracts Bacteriology Abstracts (Microbiology B) Algology Mycology and Protozoology Abstracts (Microbiology C) AIDS and Cancer Research Abstracts Chemoreception Abstracts Immunology Abstracts Engineering Research Database Calcium & Calcified Tissue Abstracts MEDLINE - Academic |
DatabaseTitleList | CrossRef Virology and AIDS Abstracts MEDLINE |
Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Sciences (General) |
EISSN | 1091-6490 |
EndPage | 25054 |
ExternalDocumentID | 10_1073_pnas_2003407117 32968015 26969475 |
Genre | Research Support, Non-U.S. Gov't Journal Article |
GrantInformation_xml | – fundername: Wellcome Trust grantid: 099202 – fundername: Wellcome Trust grantid: 098051 – fundername: Department of Health grantid: 16/136/111 – fundername: Wellcome grantid: 098051 – fundername: Wellcome grantid: 099202 – fundername: DH | National Institute for Health Research (NIHR) grantid: 16/136/111 |
GroupedDBID | --- -DZ -~X .55 0R~ 123 29P 2AX 2FS 2WC 4.4 53G 5RE 5VS 85S AACGO AAFWJ AANCE ABBHK ABOCM ABPLY ABPPZ ABTLG ABXSQ ABZEH ACGOD ACIWK ACNCT ACPRK ADACV AENEX AEUPB AEXZC AFFNX AFOSN AFRAH ALMA_UNASSIGNED_HOLDINGS AQVQM BKOMP CS3 D0L DCCCD DIK DU5 E3Z EBS F5P FRP GX1 H13 HH5 HYE IPSME JAAYA JBMMH JENOY JHFFW JKQEH JLS JLXEF JPM JSG JST KQ8 L7B LU7 N9A N~3 O9- OK1 PNE PQQKQ R.V RHF RHI RNA RNS RPM RXW SA0 SJN TAE TN5 UKR VQA W8F WH7 WOQ WOW X7M XSW Y6R YBH YKV YSK ZCA ~02 ~KM CGR CUY CVF ECM EIF NPM AAYXX CITATION 7QG 7QL 7QP 7QR 7SN 7SS 7T5 7TK 7TM 7TO 7U9 8FD C1K FR3 H94 M7N P64 RC3 7X8 5PM |
ID | FETCH-LOGICAL-c443t-cd1026dfedb010b84eaeb4202c25051177fb21529737b967d197afce79c581563 |
IEDL.DBID | RPM |
ISSN | 0027-8424 |
IngestDate | Tue Sep 17 21:25:24 EDT 2024 Fri Oct 25 05:14:58 EDT 2024 Thu Oct 10 18:32:55 EDT 2024 Fri Dec 06 02:30:32 EST 2024 Sat Sep 28 08:17:32 EDT 2024 Tue Dec 10 23:44:57 EST 2024 |
IsDoiOpenAccess | true |
IsOpenAccess | true |
IsPeerReviewed | true |
IsScholarly | true |
Issue | 40 |
Keywords | genomics carbapenemase genes plasmids carbapenem resistance Klebsiella pneumoniae |
Language | English |
License | Copyright © 2020 the Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY). |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-c443t-cd1026dfedb010b84eaeb4202c25051177fb21529737b967d197afce79c581563 |
Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Author contributions: S.D. and D.M.A. designed research; S.D. and V.C. performed research; E.S.C.-P.E.W.G. and E.S.G.E.M. contributed new reagents/analytic tools; S.D., S.R., A.E.S., T.G., J.P., G.M.R., E.J.F., H.G., and D.M.A. analyzed data; E.S.C.-P.E.W.G. collected the bacterial isolates and epidemiological data; E.S.G.E.M. facilitated the training and capacity building for the collection of bacterial isolates; and S.D., G.M.R., E.J.F., H.G., and D.M.A. wrote the paper. Edited by Rita R. Colwell, University of Maryland, College Park, MD, and approved August 17, 2020 (received for review February 23, 2020) 3H.G. and D.M.A. contributed equally to this work. |
ORCID | 0000-0003-1592-6394 0000-0002-9971-522X 0000-0002-0115-0954 0000-0002-7069-5958 |
OpenAccessLink | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7587227/ |
PMID | 32968015 |
PQID | 2450788709 |
PQPubID | 42026 |
PageCount | 12 |
ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_7587227 proquest_miscellaneous_2445974256 proquest_journals_2450788709 crossref_primary_10_1073_pnas_2003407117 pubmed_primary_32968015 jstor_primary_26969475 |
PublicationCentury | 2000 |
PublicationDate | 2020-10-06 |
PublicationDateYYYYMMDD | 2020-10-06 |
PublicationDate_xml | – month: 10 year: 2020 text: 2020-10-06 day: 06 |
PublicationDecade | 2020 |
PublicationPlace | United States |
PublicationPlace_xml | – name: United States – name: Washington |
PublicationTitle | Proceedings of the National Academy of Sciences - PNAS |
PublicationTitleAlternate | Proc Natl Acad Sci U S A |
PublicationYear | 2020 |
Publisher | National Academy of Sciences |
Publisher_xml | – sequence: 0 name: National Academy of Sciences – name: National Academy of Sciences |
References | e_1_3_4_3_2 e_1_3_4_1_2 e_1_3_4_9_2 Sheppard A. E. (e_1_3_4_19_2) 2018; 4 e_1_3_4_7_2 e_1_3_4_40_2 e_1_3_4_5_2 e_1_3_4_23_2 e_1_3_4_44_2 e_1_3_4_21_2 e_1_3_4_42_2 e_1_3_4_27_2 e_1_3_4_48_2 e_1_3_4_25_2 e_1_3_4_46_2 e_1_3_4_29_2 e_1_3_4_30_2 e_1_3_4_51_2 e_1_3_4_34_2 e_1_3_4_32_2 e_1_3_4_15_2 e_1_3_4_38_2 e_1_3_4_13_2 e_1_3_4_36_2 e_1_3_4_17_2 e_1_3_4_2_2 e_1_3_4_8_2 e_1_3_4_6_2 e_1_3_4_4_2 e_1_3_4_22_2 e_1_3_4_45_2 e_1_3_4_20_2 e_1_3_4_43_2 e_1_3_4_26_2 e_1_3_4_49_2 e_1_3_4_24_2 e_1_3_4_47_2 e_1_3_4_28_2 Hunt M. (e_1_3_4_41_2) 2017; 3 e_1_3_4_50_2 e_1_3_4_12_2 e_1_3_4_33_2 e_1_3_4_10_2 e_1_3_4_31_2 e_1_3_4_16_2 e_1_3_4_37_2 e_1_3_4_14_2 e_1_3_4_35_2 George S. (e_1_3_4_11_2) 2017; 3 e_1_3_4_18_2 e_1_3_4_39_2 |
References_xml | – ident: e_1_3_4_25_2 doi: 10.1128/AAC.01889-16 – ident: e_1_3_4_17_2 doi: 10.1101/gr.215087.116 – ident: e_1_3_4_38_2 doi: 10.1093/bioinformatics/btv421 – ident: e_1_3_4_10_2 doi: 10.1371/journal.pcbi.1005595 – ident: e_1_3_4_34_2 doi: 10.1093/jac/dkw227 – volume: 3 start-page: e000131 year: 2017 ident: e_1_3_4_41_2 article-title: ARIBA: Rapid antimicrobial resistance genotyping directly from sequencing reads publication-title: Microb. Genom. contributor: fullname: Hunt M. – ident: e_1_3_4_30_2 doi: 10.1093/jac/dkz366 – ident: e_1_3_4_32_2 doi: 10.1101/456897 – ident: e_1_3_4_44_2 doi: 10.1093/bioinformatics/bti553 – ident: e_1_3_4_29_2 doi: 10.1111/j.1469-0691.2011.03532.x – ident: e_1_3_4_1_2 – ident: e_1_3_4_51_2 doi: 10.1093/nar/gkw290 – ident: e_1_3_4_43_2 doi: 10.1186/gb-2004-5-2-r12 – ident: e_1_3_4_28_2 doi: 10.1093/jac/dkw106 – ident: e_1_3_4_8_2 doi: 10.1126/science.aao2136 – ident: e_1_3_4_24_2 doi: 10.1128/AAC.48.1.15-22.2004 – ident: e_1_3_4_13_2 doi: 10.1128/AAC.02412-14 – ident: e_1_3_4_14_2 doi: 10.1128/AAC.05289-11 – ident: e_1_3_4_49_2 doi: 10.1073/pnas.1321364111 – ident: e_1_3_4_36_2 doi: 10.1016/S0022-2836(05)80360-2 – ident: e_1_3_4_27_2 doi: 10.1371/journal.pone.0123063 – ident: e_1_3_4_18_2 doi: 10.1128/AAC.00175-10 – ident: e_1_3_4_40_2 doi: 10.1093/bioinformatics/btt086 – ident: e_1_3_4_2_2 doi: 10.1016/S1473-3099(18)30605-4 – volume: 3 start-page: e000118 year: 2017 ident: e_1_3_4_11_2 article-title: Resolving plasmid structures in Enterobacteriaceae using the MinION nanopore sequencer: Assessment of MinION and MinION/Illumina hybrid data assembly approaches publication-title: Microb. Genom. contributor: fullname: George S. – ident: e_1_3_4_50_2 doi: 10.1093/nar/gku1196 – ident: e_1_3_4_37_2 doi: 10.1093/bioinformatics/btu153 – ident: e_1_3_4_22_2 doi: 10.1016/S1473-3099(13)70190-7 – ident: e_1_3_4_48_2 doi: 10.1093/bioinformatics/btl446 – ident: e_1_3_4_33_2 doi: 10.1128/AAC.00120-14 – ident: e_1_3_4_23_2 doi: 10.1128/CMR.00116-14 – ident: e_1_3_4_20_2 doi: 10.1128/AAC.05202-11 – ident: e_1_3_4_3_2 doi: 10.1038/s41564-019-0492-8 – ident: e_1_3_4_31_2 doi: 10.1128/AAC.01019-15 – ident: e_1_3_4_7_2 doi: 10.1128/mBio.00444-16 – ident: e_1_3_4_16_2 doi: 10.1128/AAC.03900-14 – ident: e_1_3_4_45_2 doi: 10.1093/bioinformatics/btp324 – ident: e_1_3_4_15_2 doi: 10.1128/AAC.04292-14 – ident: e_1_3_4_9_2 doi: 10.1016/S1473-3099(18)30225-1 – ident: e_1_3_4_6_2 doi: 10.1128/AAC.00464-16 – ident: e_1_3_4_12_2 doi: 10.1016/S1473-3099(16)30257-2 – ident: e_1_3_4_39_2 doi: 10.1145/2543629 – ident: e_1_3_4_47_2 doi: 10.1093/bioinformatics/btp352 – ident: e_1_3_4_5_2 doi: 10.1093/jac/dkx264 – ident: e_1_3_4_46_2 doi: 10.1126/science.1182395 – ident: e_1_3_4_4_2 doi: 10.1128/mBio.00204-11 – ident: e_1_3_4_35_2 doi: 10.1093/jac/dkx141 – ident: e_1_3_4_26_2 doi: 10.2807/1560-7917.ES2013.18.31.20549 – ident: e_1_3_4_21_2 doi: 10.1016/j.tim.2014.09.003 – volume: 4 start-page: e000232 year: 2018 ident: e_1_3_4_19_2 article-title: TETyper: A bioinformatic pipeline for classifying variation and genetic contexts of transposable elements from short-read whole-genome sequencing data publication-title: Microb. Genom. contributor: fullname: Sheppard A. E. – ident: e_1_3_4_42_2 doi: 10.1093/jac/dkx513 |
SSID | ssj0009580 |
Score | 2.6491294 |
Snippet | Molecular and genomic surveillance systems for bacterial pathogens currently rely on tracking clonally evolving lineages. By contrast, plasmids are usually... In many clinically important bacteria, antibiotic resistance genes are primarily carried on plasmids. These can spread horizontally between different strains... |
SourceID | pubmedcentral proquest crossref pubmed jstor |
SourceType | Open Access Repository Aggregation Database Index Database Publisher |
StartPage | 25043 |
SubjectTerms | Anti-Bacterial Agents - therapeutic use Antibiotic resistance Antibiotics Bacterial Proteins - genetics beta-Lactamases - genetics Biological Sciences Carbapenemase Carbapenems Carbapenems - therapeutic use Cell Lineage - genetics Chromosomes, Bacterial - genetics Drug resistance Drug Resistance, Multiple, Bacterial - drug effects Drug Resistance, Multiple, Bacterial - genetics Epidemics Genes Genome, Bacterial - genetics Humans Klebsiella Klebsiella Infections - drug therapy Klebsiella Infections - genetics Klebsiella Infections - microbiology Klebsiella pneumoniae Klebsiella pneumoniae - genetics Klebsiella pneumoniae - pathogenicity Mathematical analysis Pathogens Plasmids Plasmids - genetics Recombination Sequence Analysis, DNA - methods Surveillance systems |
Title | Integrated chromosomal and plasmid sequence analyses reveal diverse modes of carbapenemase gene spread among Klebsiella pneumoniae |
URI | https://www.jstor.org/stable/26969475 https://www.ncbi.nlm.nih.gov/pubmed/32968015 https://www.proquest.com/docview/2450788709 https://search.proquest.com/docview/2445974256 https://pubmed.ncbi.nlm.nih.gov/PMC7587227 |
Volume | 117 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Nb9QwEB21PXFBFCgESmUkDuWQ7q7txOsjqqgKqIgDlXqL_BU1UpONyO4f4Jcz48SBRZy4-ktRZux5Iz-_AXhXm9pZqWXOXVnkUq1DbtfO5hicuKxFrZbxlevN1_L6Vn6-K-4OoEhvYSJp39nmontoL7rmPnIr-9YtEk9s8e3mEjGu4pizH8Ihht-Uos9Ku-vx3QnH41dymfR8lFj0nYkK3YKymBXV3xNcl3hGF3tRaSQm_gty_s2c_CMUXT2BxxOGZB_Gbz2Gg9A9heNplw7sfJKSfv8Mfn5KahCeuXti3g2bFqeazrMecXPbeJbI1NhI-iS4AKk64SAfKRuBUbGcgW1q5uhuosfFWwx9DD0vsKFH0OlZLFnEvjwEOzREp2J9F3bY1pjwHG6vPn6_vM6nqgu5k1Jsc-cRc5S-Dt5irmbXMphgJV9yR2iJ7nhrS8VwtRLK6lL5lVZo8KC0K0h6RpzAUbfpwktg3CouS-cKYwMCBWWsEqamFI17PEhWGZynv171o7hGFS_FlajIVtVvW2VwEq0yj-OlLrVURQanyUzVtP1wnkSYi8fnUmfwdu7GjUO3IaYLmx2NkZRMIeTL4MVo1Xnx5BYZqD17zwNIlHu_B301inNPvvnqv2e-hkeccnoiKZSncLT9sQtvEPhs7RmFneIsuvsvblcE5g |
link.rule.ids | 230,314,727,780,784,885,27924,27925,53791,53793 |
linkProvider | National Library of Medicine |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lb9QwEB6VcoALooXSQGmNxKEc0t21nXh9RFWrLe1WHFqptyh-RI3UZCOy-wf45cw4D1jEiatfijLj8Tfy528APhd5YY3UMuY2TWKp5j42c2tiPJy4LEShpuGV6_I2XdzLbw_Jww4kw1uYQNq3pjyrn6qzunwM3MqmspOBJzb5vjxHjKs45uzP4HkilJ4NSfqotTvvXp5wDMCSy0HRR4lJU-dBo1tQHjOjCnyC6xSjdLJ1LnXUxH-Bzr-5k38cRpev4VWPItnX7mv3YMfX-7DX79OWnfZi0l_ewM-rQQ_CMftI3Lt2VeHUvHasQeRclY4NdGpsJIUSXIB0nXCQC6QNz6hcTstWBbN0O9Hg4hUefgx9z7O2QdjpWChaxK6fvGlLIlSxpvYbbCtz_xbuLy_uzhdxX3chtlKKdWwdoo7UFd4ZzNbMXPrcG8mn3BJeolvewlA5XK2EMjpVbqYVmtwrbRMSnxEHsFuvan8IjBvFZWptkhuPUEHlRom8oCSNOwwlswhOh7-eNZ28RhauxZXIyFbZb1tFcBCsMo7jqU61VEkER4OZsn4D4jyJQBcD6FRH8Gnsxq1D9yF57VcbGiMpnULQF8G7zqrj4oNbRKC27D0OIFnu7R701iDP3Xvn-_-eeQIvFnfLm-zm6vb6A7zklOETZSE9gt31j43_iDBobY6D0_8CI4gHVQ |
linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lb9QwEB5BkRCXigKlaQsYiUM5pLtrO_HmiAqrltKqByr1FvkVNVKTjZrdP9Bf3hnnQRdx4uqXosx4_I38-RuAL4UurJGZjLlNk1iquY_N3JoYDycuC1GoaXjlenGZnl7LnzfJzZNSX4G0b015XN9Vx3V5G7iVTWUnA09scnVxghhXcczZG1dMnsOLRKCTDYn6qLc7716fcAzCkstB1UeJSVProNMtKJeZURU-wbMUI3WycTZ19MR_Ac-_-ZNPDqTFa9jukST71n3xDjzz9RvY6fdqy456Qemvb-HhbNCEcMzeEv-uXVY4VdeONYieq9KxgVKNjaRSgguQthMOcoG44RmVzGnZsmCWbigaXLzCA5Ch_3nWNgg9HQuFi9j5nTdtSaQq1tR-jW2l9u_gevHj98lp3NdeiK2UYhVbh8gjdYV3BjM2M5deeyP5lFvCTHTTWxgqiZspoUyWKjfLFJrdq8wmJEAjdmGrXtZ-Dxg3isvU2kQbj3BBaaOELihR4w7DySyCo-Gv500nsZGHq3ElcrJV_sdWEewGq4zjeJqlmVRJBIeDmfJ-E-I8iWAXg-g0i-Dz2I3bh-5EdO2XaxojKaVC4BfB-86q4-KDW0SgNuw9DiBp7s0e9Ngg0d176P5_z_wEL6--L_JfZ5fnB_CKU5JPrIX0ELZW92v_AZHQynwMPv8I5nkIaA |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Integrated+chromosomal+and+plasmid+sequence+analyses+reveal+diverse+modes+of+carbapenemase+gene+spread+among+Klebsiella+pneumoniae&rft.jtitle=Proceedings+of+the+National+Academy+of+Sciences+-+PNAS&rft.au=David%2C+Sophia&rft.au=Cohen%2C+Victoria&rft.au=Reuter%2C+Sandra&rft.au=Sheppard%2C+Anna+E&rft.date=2020-10-06&rft.eissn=1091-6490&rft.volume=117&rft.issue=40&rft.spage=25043&rft_id=info:doi/10.1073%2Fpnas.2003407117&rft_id=info%3Apmid%2F32968015&rft.externalDocID=32968015 |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0027-8424&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0027-8424&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0027-8424&client=summon |