Genome Sequence of Porphyromonas gingivalis Strain A7A1-28

Chastain-Gross, Ryan P.; Xie, Gary; Bélanger, Myriam; Kumar, Dibyendu; Whitlock, Joan A.; Liu, Li; Raines, Sarah M.; Farmerie, William G.; Daligault, Hajnalka E.; Han, Cliff; Brettin, Thomas; Progulske‐Fox, Ann

doi:10.1128/genomea.00021-17

Cited by 5 publications

(3 citation statements)

References 41 publications

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“…One may argue that, as the DNA sequencing technologies are advancing very fast, the problems due to the short read length do not bother us any longer. However, there are still metagenomics experiments currently performed using the ‘old’ sequencers that produce short reads [3, 33]; secondly, it is of scientific importance to analyse the ‘old’ sequencing data with improved methods to draw more biologically meaningful conclusions [15, 28]. There are very large volume of such short reads generated in last decade.…”

Section: Summary and Discussionmentioning

confidence: 99%

Statistical correction for functional metagenomic profiling of a microbial community with short NGS reads

Fang

2018

Journal of Applied Statistics

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By sequence homology search, the list of all the functions found and the counts of reads being aligned to them present the functional profile of a metagenomic sample. However, a significant obstacle has been observed in this approach due to the short read length associated with many next generation sequencing technologies. This includes artificial families, cross-annotations, length bias and conservation bias. The widely applied cutoff methods, such as BLAST E-value, are not able to solve the problems. Following the published successful procedures on the artificial families and the cross-annotation issue, we propose in this paper to use zero-truncated Poisson and Binomial (ZTP-Bin) hierarchical modelling to correct the length bias and the conservation bias. Goodness-of-fit of the modelling and cross-validation for the prediction using a bioinformatic simulated sample show the validity of this approach. Evaluated on an in vitro-simulated data set, the proposed modelling method outperforms other traditional methods. All three steps were then sequentially applied on real-life metagenomic samples to show that the proposed framework will lead to a more accurate functional profile of a short read metagenomic sample.

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Section: Summary and Discussionmentioning

confidence: 99%

Statistical correction for functional metagenomic profiling of a microbial community with short NGS reads

Fang

2018

Journal of Applied Statistics

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“…Although low sequence divergence within genes encoding catalytic domains of gingipains has been found ( 21 ), P. gingivalis clinical isolates exhibited different rates and distribution of Kgp and arginine-specific gingipain (RgpA and RgpB) activities ( 23 ). Sequencing of several P. gingivalis genomes revealed that the bacterium can exchange chromosomal DNA by specific gene rearrangements and allelic exchange through horizontal gene transfer using natural transformation and conjugation ( 20 , 21 , 44 – 46 ). Importantly, the spontaneous generation of phenotypically different sub-strains, caused in part by differences in genes encoding hemagglutinin domains of Kgp and HagA, can also occur, resulting in significant attenuation in a mouse virulence model ( 47 – 50 ).…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of Porphyromonas gingivalis A7436 and ATCC 33277 strains reveals differences in the expression of heme acquisition systems

Śmiga,

Ślęzak,

Olczak

2024

Microbiol Spectr

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Porphyromonas gingivalis strains exhibit different phenotypes in vitro , different virulence potential in animal models, and different associations with human diseases, with strains classified as virulent/more virulent (e.g., A7436 and W83) or as less virulent/avirulent (e.g., ATCC 33277). In this study, we comparatively analyzed the A7436 and ATCC 33277 strains to better understand their variability. Global gene expression analysis in response to heme and iron limitation revealed more pronounced differences in the A7436 than in the ATCC 33277 strain; however, in both strains, the largest changes were observed in genes encoding hypothetical proteins, genes whose products participate in energy metabolism, and in genes encoding proteins engaged in transport and binding proteins. Our results confirmed that variability between P. gingivalis strains is due to differences in the arrangement of their genomes. Analysis of gene expression of heme acquisition systems demonstrated that not only the availability of iron and heme in the external environment but also the ability to store iron intracellularly can influence the P. gingivalis phenotype. Therefore, we assume that differences in virulence potential may also be due to differences in the production of systems involved in iron and heme acquisition, mainly the Hmu system. In addition, our study showed that hemoglobin, in a concentration-dependent manner, differentially influences the virulence potential of P. gingivalis strains. We conclude that iron and heme homeostasis may add to the variability observed between P. gingivalis strains. IMPORTANCE Periodontitis belongs to a group of multifactorial diseases, characterized by inflammation and destruction of tooth-supporting tissues. P. gingivalis is one of the most important microbial factors involved in the initiation and progression of periodontitis. To survive in the host, the bacterium must acquire heme as a source of iron and protoporphyrin IX. P. gingivalis strains respond differently to changing iron and heme concentrations, which may be due to differences in the expression of systems involved in iron and heme acquisition. The ability to accumulate iron intracellularly, being different in more and less virulent P. gingivalis strains, may influence their phenotypes, production of virulence factors (including proteins engaged in heme acquisition), and virulence potential of this bacterium.

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“…gingivalis virulence, as can be seen in invasive wild type P . gingivalis (W83) [31], and in ATCC 33277, A7436, 381, TDC60, HG66, A7A1‐28, and the only non‐invasive AJW4 strain [32–36]. However, even strains that share the same genes do not exhibit the same virulence.…”

Section: Introductionmentioning

confidence: 99%

Clinical significance of ragA, ragB, and PG0982 genes in Porphyromonas gingivalis isolates from periodontitis patients

Bunte

Kühn

Walther

et al. 2021

European J Oral Sciences

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Genome Sequence of Porphyromonas gingivalis Strain A7A1-28

Cited by 5 publications

References 41 publications

Statistical correction for functional metagenomic profiling of a microbial community with short NGS reads

Statistical correction for functional metagenomic profiling of a microbial community with short NGS reads

Comparative analysis of Porphyromonas gingivalis A7436 and ATCC 33277 strains reveals differences in the expression of heme acquisition systems

Clinical significance of ragA, ragB, and PG0982 genes in Porphyromonas gingivalis isolates from periodontitis patients

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