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Validation de marqueurs liés à des gènes d'intérêt en vue de l'établissement d'une base de données pour la sélection assistée chez le blé tendre.
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... The top 5 most abundant ARG hosts were Burkholderiaceae (6.05%), Halothiobacillaceae (3.5%), Xanthobacteraceae (2.59%), Pelobacteraceae (2.3%), and Desulfocapsaceae (2.03%). Burkholderiaceae, Rhodocyclaceae, Sphingomonadaceae, and Steroidobacteraceae have previously been identified as important ARG hosts (Liu and Pop, 2009;Selvaraj et al., 2018;Singh Phd and Singh, 2008). Most importantly, the Burkholderiaceae family, which comprises environmental saprophytic organisms, phytopathogens, opportunistic pathogens, was identified as the host of 24 ARG subtypes. ...
A comprehensive global profile of the distribution of ARGs in freshwater biofilms is lacking. We utilized metagenomic approaches to reveal the diversity, abundance, transferability and hosts of ARGs in 96 freshwater biofilm samples from 38 sampling sites across four countries. The abundant ARGs were associated with bacitracin, multidrug, polymyxin macrolide-lincosamide-streptogramin (MLS) aminoglycoside, β-lactam, chloramphenicol, sulfonamide and tetracycline resistance, consistent with the spectrum of antibiotics commonly used in human or veterinary medicine. As expected, the resistome in freshwater biofilm habitats was significantly influenced by geographical location and human footprint. Based on the co-occurrence pattern revealed by network analysis, mdtC, kdpE, and emrB were proposed as ARG indicators in freshwater biofilms that can be used to evaluate the abundance of 46 other co-occurring ARG subtypes quantitatively. Metagenomic assembly analysis revealed that the identified ARGs were hosted by more than 46 bacterial phyla, including various pathogens, which greatly expands the knowledge of resistome diversity in freshwater biofilms. Our study points to the central roles of biofilms in harbouring ARGs. The results could enhance understanding the distribution of ARGs in freshwater habitats, thereby strengthening the global environmental risk assessment and management of ARGs.
... Rhodocyclaceae, Sphingomonadaceae, and Steroidobacteraceae, families reported to be the important hosts of ARGs previously (Liu and Pop, 2008;Selvaraj et al., 2018;Singh et al., 2008), harbored > 20 ARGs (Table S10). Similarly, these bacterial families also harbored most diverse expressed ARGs (Table S11). ...
Wastewater treatment plants (WWTPs) are a source and reservoir for subsequent spread of various antibiotic resistance genes (ARGs). However, little is known about the activity and hosts of ARGs in WWTPs. Here, we utilized both metagenomic and metatranscriptomic approaches to comprehensively reveal the diversity, abundance , expression and hosts of ARGs in activated sludge (AS) from three conventional WWTPs in Taiwan. Based on deep sequencing data and a custom-made ARG database, a total of 360 ARGs associated with 24 classes of antibiotics were identified from the three AS metagenomes, with an abundance range of 7.06 × 10 −1-1.20 × 10 −4 copies of ARG/copy of 16S rRNA gene. Differential coverage binning analysis revealed that > 22 bacterial phyla were the putative hosts of the identified ARGs. Surprisingly, genus Mycobacterium and family Burkholderiaceae were observed as multi-drug resistant harboring 14 and 50 ARGs. Metatranscriptome analysis showed 65.8% of the identified ARGs were being expressed, highlighting that ARGs were not only present, but also transcriptionally active in AS. Remarkably, 110 identified ARGs were annotated as plasmid-associated and displayed a close to twofold increased likelihood of being transcriptionally expressed compared to those ARGs found exclusively within bacterial chromosomes. Further analysis showed the transcript abundance of aminoglycoside, sulfonamide, and tetracycline resistance genes was mainly contributed by plasmid-borne ARGs. Our approach allowed us to specifically link ARGs to their transcripts and genetic context, providing a comprehensive insight into the prevalence, expression and hosts of ARGs in AS. Overall, results of this study enhance our understanding of the distribution and dissemination of ARGs in WWTPs, which benefits environmental risk assessment and management of ARB and ARGs.
... A Non-Redundant Beta-Lactamase Dataset (NRBLD) was constructed with protein sequences retrieved from 7 different antibiotic resistance databases. Four databases are specific for BLs: (1) Comprehensive Beta-lactamase Molecular Annotation Resource (CBMAR, downloaded on August 2015), 19 (2) The Institute Pasteur Database (downloaded on October 2015), 20 (3) DLact Antimicrobial Resistance Gene Database (downloaded on October 2015), 21 and (4) Lactamase Engineering Database (LacED, downloaded on August 2015). 22 The Metallo-Beta-Lactamase Engineering Database (MBLED, release 1.0) is the only specific for MBLs. ...
β-lactamases, the enzymes responsible for resistance to β-lactam antibiotics, are widespread among prokaryotic genera. However, current β-lactamase classification schemes do not represent their present diversity. Here, we propose a workflow to identify and classify β-lactamases. Initially, a set of curated sequences was used as a model for the construction of profiles Hidden Markov Models (HMM), specific for each β-lactamase class. An extensive, nonredundant set of β-lactamase sequences was constructed from 7 different resistance proteins databases to test the methodology. The profiles HMM were improved for their specificity and sensitivity and then applied to fully assembled genomes. Five hierarchical classification levels are described, and a new class of β-lactamases with fused domains is proposed. Our profiles HMM provide a better annotation of β-lactamases, with classes and subclasses defined by objective criteria such as sequence similarity. This classification offers a solid base to the elaboration of studies on the diversity, dispersion, prevalence, and evolution of the different classes and subclasses of this critical enzymatic activity.
... Although, the information available in the previously published resources are useful (Thai and Pleiss 2010;Thai et al., 2009), such resources do not provide the information for all types of beta-lactamases. Moreover, other resources (Liu and Pop, 2009;Singh et al., 2008) lack the information about the 3D structures and inhibitors resistance profile of the beta-lactamases. Beta-lactamase database (BLAD) represents the most extensive catalog for maximum type of beta-lactamases (Class A-D) along with other unique features such as search by source, country, type, variant along with local alignment, restriction enzyme digestion analysis tools, which are lacking in previously published databases of this kind. ...
Beta-lactamases confer resistance to a broad range of antibiotics and inhibitors by accumulating mutations. The number of beta-lactamases and their variants is steadily increasing. The horizontal gene transfer (HGT) likely plays a major role in dissemination of these markers to new environments and hosts. Moreover, information about the beta-lactamase classes and their variants was scattered. Categorizing all these classes and their associated variants along with their epidemiology and resistance pattern information on one platform could be helpful to the researcher working on multidrug resistant bacteria. Thus, the BLAD (Beta-lactamases database) has been developed to provide comprehensive information (epidemiology and resistance pattern) on beta-lactamases. Beta-lactamase gene sequences in BLAD are linked with structural data, phenotypic data (i.e. antibiotic resistance), and literature references to experimental studies. In summary, BLAD integrates information which may provide insight into the epidemiology of multidrug resistance and enable the designing of novel drug candidates.
The database can be accessed from the website www.blad.co.in.
... However, the above mentioned classification and identified class-specific motifs are useful; these have been identified using a limited set of sequences. Recently we have developed a database of beta-lactamase genes, identified from sequenced bacterial genomes and plasmids [9]. This database contains 2020 beta-lactamase genes from 457 bacterial strains and offered us an opportunity to study diversity of lactamase genes and to identify molecular signatures of lactamase family. ...
... Protein and DNA sequences of 2020 lactamase genes available in DLact database [9] were used in this study. The lactamase sequences in DLact database were identified using experimentally identified lactamase proteins. ...
Beta-lactamases are one of the most serious threats to public health. In order to combat this threat we need to study the molecular and functional diversity of these enzymes and identify signatures specific to these enzymes. These signatures will enable us to develop inhibitors and diagnostic probes specific to lactamases. The existing classification of beta-lactamases was developed nearly 30 years ago when few lactamases were available. DLact database contain more than 2000 beta-lactamase, which can be used to study the molecular diversity and to identify signatures specific to this family.
A set of 2020 beta-lactamase proteins available in the DLact database http://59.160.102.202/DLact were classified using graph-based clustering of Best Bi-Directional Hits. Non-redundant (> 90 percent identical) protein sequences from each group were aligned using T-Coffee and annotated using information available in literature. Motifs specific to each group were predicted using PRATT program.
The graph-based classification of beta-lactamase proteins resulted in the formation of six groups (Four major groups containing 191, 726, 774 and 73 proteins while two minor groups containing 50 and 8 proteins). Based on the information available in literature, we found that each of the four major groups correspond to the four classes proposed by Ambler. The two minor groups were novel and do not contain molecular signatures of beta-lactamase proteins reported in literature. The group-specific motifs showed high sensitivity (> 70%) and very high specificity (> 90%). The motifs from three groups (corresponding to class A, C and D) had a high level of conservation at DNA as well as protein level whereas the motifs from the fourth group (corresponding to class B) showed conservation at only protein level.
The graph-based classification of beta-lactamase proteins corresponds with the classification proposed by Ambler, thus there is no need for formulating a new classification. However, further characterization of two small groups may require updating the existing classification scheme. Better sensitivity and specificity of group-specific motifs identified in this study, as compared to PROSITE motifs, and their proximity to the active site indicates that these motifs represents group-specific signature of beta-lactamases and can be further developed into diagnostics and therapeutics.
