Comparative genome analysis reveals key genetic factors associated with probiotic property in Enterococcus faecium strains

Ghattargi, Vikas C.; Gaikwad, Meghana A.; Meti, Bharati S.; Nimonkar, Yogesh; Dixit, Kunal; Prakash, Om; Shouche, Yogesh S.; Pawar, Sharad; Dhotre, Dhiraj

doi:10.1186/s12864-018-5043-9

Cited by 47 publications

(45 citation statements)

References 113 publications

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“…The minute influence on the size of core genes with the addition of genomes indicated that the analyzed E. cecorum genomes were sufficient to construct the representative core-genome. The results were in agreement with previous studies of E. faecalis [ 53 , 54 ]. The small pan-genome size and inclusion of 43% gene families in core genes in the clinical isolates showed their conserved nature.…”

Section: Discussionsupporting

confidence: 93%

Comparison of Antimicrobial Resistance and Pan-Genome of Clinical and Non-Clinical Enterococcus cecorum from Poultry Using Whole-Genome Sequencing

Sharma

Gupta

Barrett

et al. 2020

Foods

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Enterococcus cecorum is an emerging avian pathogen, particularly in chickens, but can be found in both diseased (clinical) and healthy (non-clinical) poultry. To better define differences between E. cecorum from the two groups, whole-genome sequencing (WGS) was used to identify and compare antimicrobial resistance genes as well as the pan-genome among the isolates. Eighteen strains selected from our previous study were subjected to WGS using Illumina MiSeq and comparatively analyzed. Assembled contigs were analyzed for resistance genes using ARG-ANNOT. Resistance to erythromycin was mediated by ermB, ermG, and mefA, in clinical isolates and ermB and mefA, in non-clinical isolates. Lincomycin resistance genes were identified as linB, lnuB, lnuC, and lnuD with lnuD found only in non-clinical E. cecorum; however, lnuB and linB were found in only one clinical isolate. For both groups of isolates, kanamycin resistance was mediated by aph3-III, while tetracycline resistance was conferred by tetM, tetO, and tetL. No mutations or known resistance genes were found for isolates resistant to either linezolid or chloramphenicol, suggesting possible new mechanisms of resistance to these drugs. A comparison of WGS results confirmed that non-clinical isolates contained more resistance genes than clinical isolates. The pan-genome of clinical and non-clinical isolates resulted in 3651 and 4950 gene families, respectively, whereas the core gene sets were comprised of 1559 and 1534 gene families in clinical and non-clinical isolates, respectively. Unique genes were found more frequently in non-clinical isolates than clinical. Phylogenetic analysis of the isolates and all the available complete and draft genomes showed no correlation between healthy and diseased poultry. Additional genomic comparison is required to elucidate genetic factors in E. cecorum that contribute to disease in poultry.

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Section: Discussionsupporting

confidence: 93%

Comparison of Antimicrobial Resistance and Pan-Genome of Clinical and Non-Clinical Enterococcus cecorum from Poultry Using Whole-Genome Sequencing

Sharma

Gupta

Barrett

et al. 2020

Foods

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“…The pan-genome contained a family of 3,915 genes, of which 1,959 genes were conserved across strains (Figures 2D,E). As the number of comparative strains increased, the size of the pan genome gradually increased, while the size of the core genome stabilized similar to the previous study of the E. faecium (Mikalsen et al, 2015;Ghattargi et al, 2018). Based on this result, we found an open pan-genome structure in 10 adjacent E. faecium strains including BIOPOP-3.…”

Section: Discussionsupporting

confidence: 87%

Complete Genomic Analysis of Enterococcus faecium Heat-Resistant Strain Developed by Two-Step Adaptation Laboratory Evolution Method

Min

Yoo

Lee

et al. 2020

Front. Bioeng. Biotechnol.

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Stress resistance is an important trait expected of lactic acid bacteria used in food manufacturing. Among the various sources of stress, high temperature is a key factor that interrupts bacterial growth. In this regards, constant efforts are made for the development of heat-resistant strains, but few studies were done accompanying genomic analysis to identify the causal factors of the resistance mechanisms. Furthermore, it is also thought that tolerance to multiple stresses are equally important. Herein, we isolated one Enterococcus faecium strain named BIOPOP-3 and completed a full-length genome sequence. Using this strain, a two-step adaptive laboratory evolution (ALE) method was applied to obtain a heat-resistant strain, BIOPOP-3 ALE. After sequencing the whole genome, we compared the two full-length sequences and identified one non-synonymous variant and four indel variants that could potentially confer heat resistance, which were technically validated by resequencing. We experimentally verified that the evolved strain was significantly enhanced in not only heat resistance but also acid and bile resistance. We demonstrated that the developed heat-resistant strain can be applied in animal feed manufacturing processes. The multistress-resistant BIOPOP-3 ALE strain developed in this study and the two-step ALE method are expected to be widely applied in industrial and academic fields. In addition, we expect that the identified variants which occurred specifically in heat-resistant strain will enhance molecular biological understanding and be broadly applied to the biological engineering field.

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“…These direct threshold methods ensure that sequences that are more dissimilar than the threshold do not appear in the same cluster, and are extremely fast. CD-HIT has been used for pan-genome clustering for different microbial species (3,8,9), while UCLUST is the default clustering algorithm in the Bacterial Pan Genome Analysis tool (BPGA) (10), which is also used for multiple species’ pan-genome analysis (11–15). Importantly, when using direct-threshold methods , the correct value of the threshold may be difficult or impossible to determine, and an incorrectly chosen threshold value directly impacts clustering accuracy.…”

Section: Introductionmentioning

confidence: 99%

Boundary-Forest Clustering: Large-Scale Consensus Clustering of Biological Sequences

Surujon

Bento

Opijnen

2020

Preprint

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10Bacterial species with large sequence diversity enable studies focused on comparative genomics, 11 population genetics and pan-genome evolution. In such analyses it is key to determine whether 12 sequences (e.g. genes) from different strains, are the same or different. This is often achieved by 13 clustering orthologous genes based on sequence similarity. Importantly, one limitation of existing 14 pan-genome clustering methods is that they do not assign a confidence score to the identified 15 clusters. Given that clustering ground truth is unavailable when working with pan-genomes, the 16 absence of confidence scores makes performance evaluation on real data an open challenge. 17 Moreover, most pan-genome clustering solutions do not accommodate cluster augmentation, 18 which is the addition of new sequences to an already clustered set of sequences. Finally, the pan-19 genome size of many organisms prevents direct application of powerful clustering techniques that 20 do not scale to large datasets. Here, we present Boundary-Forest Clustering (BFClust), a method 21 that addresses these challenges in three main steps: 1) The approximate-nearest-neighbor retrieval 22 method Boundary-Forest is used as a representative selection step; 2) Downstream clustering of 23 the representatives is performed using Markov Clustering (MCL); 3) Consensus clustering is 24 applied across the Boundary-Forest, improving clustering accuracy and enabling confidence score 25 calculation. First, MCL is favorably benchmarked against 6 powerful clustering methods. To 26 explore the strengths of the entire BFClust approach, it is applied to 4 different datasets of the 27 bacterial pathogen Streptococcus pneumoniae, and compared against 4 other pan-genome 28 clustering tools. Unlike existing approaches, BFClust is fast, accurate, robust to noise and allows 29 augmentation. Moreover, BFClust uniquely identifies low-confidence clusters in each dataset, 30 which can negatively impact downstream analyses and interpretation of pan-genomes. Being the 31 first tool that outputs confidence scores both when clustering de novo, and during cluster 32 augmentation, BFClust offers a way of automatically evaluating and eliminating ambiguity in pan-33 genomes. 34 35 3 36 Author Summary 37Clustering of biological sequences is a critical step in studying bacterial species with large 38 sequence diversity. Existing clustering approaches group sequences together based on similarity. 39However, these approaches do not offer a way of evaluating the confidence of their output. This 40 makes it impossible to determine whether the clustering output reflect biologically relevant 41 clusters. Most existing methods also do not allow cluster augmentation, which is the quick 42 incorporation and clustering of newly available sequences with an already clustered set. We 43 present Boundary-Forest Clustering (BFClust) as a method that can generate cluster confidence 44 scores, as well as allow cluster augmentation. In addition to having these additional key 45 functio...

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Comparative genome analysis reveals key genetic factors associated with probiotic property in Enterococcus faecium strains

Cited by 47 publications

References 113 publications

Comparison of Antimicrobial Resistance and Pan-Genome of Clinical and Non-Clinical Enterococcus cecorum from Poultry Using Whole-Genome Sequencing

Comparison of Antimicrobial Resistance and Pan-Genome of Clinical and Non-Clinical Enterococcus cecorum from Poultry Using Whole-Genome Sequencing

Complete Genomic Analysis of Enterococcus faecium Heat-Resistant Strain Developed by Two-Step Adaptation Laboratory Evolution Method

Boundary-Forest Clustering: Large-Scale Consensus Clustering of Biological Sequences

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