Genomic Analyses of &gt;3,100 Nasopharyngeal Pneumococci Revealed Significant Differences Between Pneumococci Recovered in Four Different Geographical Regions

Tonder, Andries J. van; Bray, James E.; Jolley, Keith A.; Rensburg, Melissa Jansen van; Quirk, Sigríður Júlía; Haraldsson, Gunnsteinn; Maiden, Martin C. J.; Bentley, Stephen D.; Haraldsson, Ásgeir; Erlendsdóttir, Helga; Kristinsson, Karl G.; Brueggemann, Angela B.

doi:10.3389/fmicb.2019.00317

Cited by 13 publications

(11 citation statements)

References 38 publications

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“…Therefore, in this section we compare different clustering methods to each other and see whether they yield consistent outputs, in addition to exploring the cluster confidence scores generated by BFClust. Previously, core and pan-genome analyses using Roary had revealed that across different datasets of pneumococcal isolates, the core genome is not conserved, and the size of the pan-genome is not the same across datasets (3). However, it is unclear whether this is a consequence of the datasets (which come from different types of populations that are also geographically separated) being different from one another, or an artifact of the clustering method used.…”

Section: Resultsmentioning

confidence: 99%

“…For example, any given strain of the human respiratory bacterial pathogen Streptococcus pneumoniae has about 2,100 genes in its genome, but two strains can differ by the presence or absence of hundreds of genes. In fact, the core genome (the genes shared by all strains) is estimated to be anywhere between 15-50% of the pangenome (the entire genetic repertoire of the species, thought to contain between 5,000-10,000 genes) (1–3). In species such as S. pneumoniae where there is a large amount of genetic diversity, phylogenetic studies or studies that compare multiple strains first necessitate identifying which genetic elements are the same across the different strains.…”

Section: Introductionmentioning

confidence: 99%

“…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%

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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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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Surujon

Bento

Opijnen

2020

Preprint

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“…The CHAP domain-containing endolysin (WP_057595562; 288 aa) found in the genome of S. pneumoniae strain SMRU392 (serotype 35F) is encoded by one of the PPH080 group of phages, and is identical to that of prophage 33888. The sequence of the latter was recently reported in an independent study (van Tonder et al, 2019) (Table S4). Since the host strain of prophage 33888 was the same as in the present study (SMRU392), and the Ints were also identical (WP_ 050256063), both prophages are probably the same.…”

Section: Pph Endolysinsmentioning

confidence: 71%

Streptococcus pneumoniae: a Plethora of Temperate Bacteriophages With a Role in Host Genome Rearrangement

Martín-Galiano

Garcı́a

2021

Front. Cell. Infect. Microbiol.

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Bacteriophages (phages) are viruses that infect bacteria. They are the most abundant biological entity on Earth (current estimates suggest there to be perhaps 1031 particles) and are found nearly everywhere. Temperate phages can integrate into the chromosome of their host, and prophages have been found in abundance in sequenced bacterial genomes. Prophages may modulate the virulence of their host in different ways, e.g., by the secretion of phage-encoded toxins or by mediating bacterial infectivity. Some 70% of Streptococcus pneumoniae (the pneumococcus)—a frequent cause of otitis media, pneumonia, bacteremia and meningitis—isolates harbor one or more prophages. In the present study, over 4000 S. pneumoniae genomes were examined for the presence of prophages, and nearly 90% were found to contain at least one prophage, either defective (47%) or present in full (43%). More than 7000 complete putative integrases, either of the tyrosine (6243) or serine (957) families, and 1210 full-sized endolysins (among them 1180 enzymes corresponding to 318 amino acid-long N-acetylmuramoyl-L-alanine amidases [LytAPPH]) were found. Based on their integration site, 26 different pneumococcal prophage groups were documented. Prophages coding for tRNAs, putative virulence factors and different methyltransferases were also detected. The members of one group of diverse prophages (PPH090) were found to integrate into the 3’ end of the host lytASpn gene encoding the major S. pneumoniae autolysin without disrupting it. The great similarity of the lytASpnand lytAPPH genes (85–92% identity) allowed them to recombine, via an apparent integrase-independent mechanism, to produce different DNA rearrangements within the pneumococcal chromosome. This study provides a complete dataset that can be used to further analyze pneumococcal prophages, their evolutionary relationships, and their role in the pathogenesis of pneumococcal disease.

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“…In our study, ANI values between pairs of Sphingopyxis isolates were further calculated ( [7]. The findings indicated that genus Sphingopyxis was more conservative than its neighboring genera such as Novosphingobium and Sphingomonas with respect to their gene content [46]. In other words, core-genome and pan-genome had a pronounced heterogeneity in these neighboring genera, which suggested that caution should be taken when scaling from a single genus to the larger neighboring genera.…”

Section: Prediction Of Mobile Genetic Elements (Mges)mentioning

confidence: 72%

Genome‐Wide Analysis Reveals Genetic Potential for Aromatic Compounds Biodegradation of Sphingopyxis

Yang

Feng

Massey

et al. 2020

BioMed Research International

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Members of genus Sphingopyxis are frequently found in diverse eco-environments worldwide and have been traditionally considered to play vital roles in the degradation of aromatic compounds. Over recent decades, many aromatic-degrading Sphingopyxis strains have been isolated and recorded, but little is known about their genetic nature related to aromatic compounds biodegradation. In this study, bacterial genomes of 19 Sphingopyxis strains were used for comparative analyses. Phylogeny showed an ambiguous relatedness between bacterial strains and their habitat specificity, while clustering based on Cluster of Orthologous Groups suggested the potential link of functional profile with substrate-specific traits. Pan-genome analysis revealed that 19 individuals were predicted to share 1,066 orthologous genes, indicating a high genetic homogeneity among Sphingopyxis strains. Notably, KEGG Automatic Annotation Server results suggested that most genes pertaining aromatic compounds biodegradation were predicted to be involved in benzoate, phenylalanine, and aminobenzoate metabolism. Among them, β-ketoadipate biodegradation might be the main pathway in Sphingopyxis strains. Further inspection showed that a number of mobile genetic elements varied in Sphingopyxis genomes, and plasmid-mediated gene transfer coupled with prophage- and transposon-mediated rearrangements might play prominent roles in the evolution of bacterial genomes. Collectively, our findings presented that Sphingopyxis isolates might be the promising candidates for biodegradation of aromatic compounds in pollution sites.

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Genomic Analyses of >3,100 Nasopharyngeal Pneumococci Revealed Significant Differences Between Pneumococci Recovered in Four Different Geographical Regions

Cited by 13 publications

References 38 publications

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

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

Streptococcus pneumoniae: a Plethora of Temperate Bacteriophages With a Role in Host Genome Rearrangement

Genome‐Wide Analysis Reveals Genetic Potential for Aromatic Compounds Biodegradation of Sphingopyxis

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