Comparison of 16S rRNA gene phylogeny and functional tfdA gene distribution in thirty-one different 2,4-dichlorophenoxyacetic acid and 4-chloro-2-methylphenoxyacetic acid degraders

2010 ◽  
Vol 33 (2) ◽  
pp. 67-70 ◽  
Author(s):  
Jacob Bælum ◽  
Carsten S. Jacobsen ◽  
William E. Holben
Author(s):  
Soon Dong Lee ◽  
Yeong-Sik Byeon ◽  
Sung-Min Kim ◽  
Hong Lim Yang ◽  
In Seop Kim

Taxonomic positions of four Gram-negative bacterial strains, which were isolated from larvae of two insects in Jeju, Republic of Korea, were determined by a polyphasic approach. Strains CWB-B4, CWB-B41 and CWB-B43 were recovered from larvae of Protaetia brevitarsis seulensis, whereas strain BWR-B9T was from larvae of Allomyrina dichotoma. All the isolates grew at 10–37 °C, at pH 5.0–9.0 and in the presence of 4 % (w/v) NaCl. The 16S rRNA gene phylogeny showed that the four isolates formed two distinct sublines within the order Enterobacteriales and closely associated with members of the genus Jinshanibacter . The first group represented by strain CWB-B4 formed a tight cluster with Jinshanibacter xujianqingii CF-1111T (99.3 % sequence similarity), whereas strain BWR-B9T was most closely related to Jinshanibacter zhutongyuii CF-458T (99.5 % sequence similarity). The 92 core gene analysis showed that the isolates belonged to the family Budviciaceae and supported the clustering shown in 16S rRNA gene phylogeny. The genomic DNA G+C content of the isolates was 45.2 mol%. A combination of overall genomic relatedness and phenotypic distinctness supported that three isolates from Protaetia brevitarsis seulensis are different strains of Jinshanibacter xujianqingii , whereas one isolate from Allomyrina dichotoma represents a new species of the genus Jinshanibacter . On the basis of results obtained here, Jinshanibacter allomyrinae sp. nov. (type strain BWR-B9T=KACC 22153T=NBRC 114879T) and Insectihabitans xujianqingii gen. nov., comb. nov. are proposed, with the emended descriptions of the genera Jinshanibacter , Limnobaculum and Pragia .


2018 ◽  
Author(s):  
Hugo R Barajas de la Torre ◽  
Miguel Romero ◽  
Shamayim Martínez-Sánchez ◽  
Luis D Alcaraz

Background. Comparative genomics between closely related bacterial strains can distinguish important features determining pathogenesis, antibiotic resistance, and phylogenetic structure. The Streptococcus genus is relevant to public health and food safety and it is well-represented (>100 genomes) in databases of publicly available databases. Streptococci are cosmopolitan, with multiple sources of isolation, from humans to dairy products. The Streptococcus genus has been classified by morphology, serotypes, 16S rRNA gene, and Multi Locus Sequence Types (MLST). The Genomic Similarity Score (GSS) is proposed as a tool to quantify genome level relatedness between species of Streptococcus. The Streptococcus core genome can be used to assess strain specific abundances in metagenomic sequences. Methods. A 16S rRNA gene phylogeny was calculated for 108 strains, belonging to 16 Streptococcus species and compared to a dendrogram using GSS pairwise distances for the same genomes. The core and pan-genome were calculated for these 108 genomes. The core genome sequences were analyzed and used as a resource to discriminate homologous fragment reads from closely related strains in metagenomic samples. Results. A total of 404 proteins are shared by all 108 Streptococcus genomes, which is the core genome. The pairwise amino acid identity values of the core proteins for all the compared strains and outgroups are reported. Lower sequence identity variation (90-100%) is predominantly found in core clusters containing ribosomal and translation-related proteins. For 48 core proteins (11.8%) no functional assignment could be made and those proteins have larger sequence identity variations than other core proteins. The sequence identity of the core genome diminishes as GSS score between species decreases. The GSS dendrogram recovers most of the clades in the 16S rRNA gene phylogeny while distinguishing between 16S polytomies (unresolved nodes). Finally, the core genome was used to distinguish between closely related species within human oral metagenomes. Discussion. The Streptococcus genus provides a benchmark dataset for comparative genomic studies due to the breath depth of genomic coverage. Comparing metagenomic shotgun fragment reads to the core genome using rapid alignment tools allows species-specific abundance estimates in metagenomic samples. Understanding of genomic variability and strains relatedness is the goal of tools like GSS, which make use of both pairwise shared core and pan-genomic homologous shared sequences for its calculation.


PeerJ ◽  
2019 ◽  
Vol 6 ◽  
pp. e6233 ◽  
Author(s):  
Hugo R. Barajas ◽  
Miguel F. Romero ◽  
Shamayim Martínez-Sánchez ◽  
Luis D. Alcaraz

Background The Streptococcus genus is relevant to both public health and food safety because of its ability to cause pathogenic infections. It is well-represented (>100 genomes) in publicly available databases. Streptococci are ubiquitous, with multiple sources of isolation, from human pathogens to dairy products. The Streptococcus genus has traditionally been classified by morphology, serum types, the 16S ribosomal RNA (rRNA) gene, and multi-locus sequence types subject to in-depth comparative genomic analysis. Methods Core and pan-genomes described the genomic diversity of 108 strains belonging to 16 Streptococcus species. The core genome nucleotide diversity was calculated and compared to phylogenomic distances within the genus Streptococcus. The core genome was also used as a resource to recruit metagenomic fragment reads from streptococci dominated environments. A conventional 16S rRNA gene phylogeny reconstruction was used as a reference to compare the resulting dendrograms of average nucleotide identity (ANI) and genome similarity score (GSS) dendrograms. Results The core genome, in this work, consists of 404 proteins that are shared by all 108 Streptococcus. The average identity of the pairwise compared core proteins decreases proportionally to GSS lower scores, across species. The GSS dendrogram recovers most of the clades in the 16S rRNA gene phylogeny while distinguishing between 16S polytomies (unresolved nodes). The GSS is a distance metric that can reflect evolutionary history comparing orthologous proteins. Additionally, GSS resulted in the most useful metric for genus and species comparisons, where ANI metrics failed due to false positives when comparing different species. Discussion Understanding of genomic variability and species relatedness is the goal of tools like GSS, which makes use of the maximum pairwise shared orthologous sequences for its calculation. It allows for long evolutionary distances (above species) to be included because of the use of amino acid alignment scores, rather than nucleotides, and normalizing by positive matches. Newly sequenced species and strains could be easily placed into GSS dendrograms to infer overall genomic relatedness. The GSS is not restricted to ubiquitous conservancy of gene features; thus, it reflects the mosaic-structure and dynamism of gene acquisition and loss in bacterial genomes.


2020 ◽  
Vol 70 (4) ◽  
pp. 2740-2749 ◽  
Author(s):  
Prashant Singh ◽  
Jana Šnokhousová ◽  
Aniket Saraf ◽  
Archana Suradkar ◽  
Josef Elster

Cyanobacterial strain ARC8 was isolated from seepage coming into the river Dračice, Františkov, Czech Republic, and was characterized using a polyphasic approach. Strain ARC8 showed a typical Nostoc -like morphology and in-depth morphological characterization indicated that it is a member of the genus Nostoc . Furthermore, in the 16S rRNA gene phylogeny inferred using Bayesian inference, maximum likelihood and neighbour joining methods, strain ARC8 clustered within the Nostoc sensu stricto clade. The phylogenetic distance and the positioning of strain ARC8 also indicated that it is a member of the genus Nostoc . Furthermore, the rbcL gene phylogeny along with the 16S–23S ITS secondary structure analysis also supported the findings from the 16S rRNA gene tree. In accordance with the International Code of Nomenclature for Algae, Fungi and Plants we describe a novel species of Nostoc with the name Nostoc neudorfense sp. nov.


Marine Drugs ◽  
2018 ◽  
Vol 16 (9) ◽  
pp. 303 ◽  
Author(s):  
Ronald Garcia ◽  
James La Clair ◽  
Rolf Müller

Over the last two decades, halophilic (organisms that thrive at high salt concentrations) and halotolerant (organisms that have adapted to high salt concentrations) myxobacteria emerged as an important source of structurally diverse secondary metabolites from the marine environment. This review explores the advance of metagenomics analysis and 16S rRNA gene phylogeny of the cultured and uncultured myxobacteria from marine and other salt-environments up to July 2018. The diversity of novel groups of myxobacteria in these environments appears unprecedented, especially in the Sorangiineae and Nannocystineae suborders. The Sandaracinaceae related clade in the Sorangiineae suborder seems more widely distributed compared to the exclusively marine myxobacterial cluster. Some of the previously identified clones from metagenomic studies were found to be related to the Nannocystineae suborder. This understanding provides the foundation for a vital, unexplored resource. Understanding the conditions required to cultivate these yet “uncultured” myxobacteria in the laboratory, while a key next step, offers a significant potential to further expand access to diverse secondary metabolites.


2019 ◽  
Vol 1 (3) ◽  
Author(s):  
Usha Ahirwar ◽  
Bharati Kollah ◽  
Garima Dubey ◽  
Santosh Ranjan Mohanty

2018 ◽  
Author(s):  
Hugo R Barajas de la Torre ◽  
Miguel Romero ◽  
Shamayim Martínez-Sánchez ◽  
Luis D Alcaraz

Background. Comparative genomics between closely related bacterial strains aids to distinguish important features like pathogenesis, antibiotic resistance, and phylogenetic structure. Streptococcus is relevant because public health and food safety and it are well-represented (>100 genomes ) in databases of publicly available databases. Streptococci are cosmopolitan, and there are multiple sources of isolation, from humans to dairy products. The Streptococcus have been classified by morphology, serum types, 16S rRNA gene, and Multi Locus Sequence Types (MLST). The Genomic Similarity Score (GSS) is proposed as a tool to quantify genome level relatedness between Streptococcus and using their core genome as a simplified tool to assess strain specific abundances in metagenomic sequences. Methods. A 16S rRNA gene phylogeny has been calculated for 108 strains, belonging to 16 Streptococcus species and compared the results to a dendrogram using the GSS with all homologous shared information available in the genomes. Additionally, genus core and pan-genome were calculated. The core genome sequences identity was analyzed and the core genome was used as a seed to discriminate abundances between close related strains in metagenomic samples. Results. A total of 404 proteins are shared by all 108 Streptococcus genomes, which are the core genome. The core identity values ranges across all the compared strains and outgroups are reported. Lower sequence identity variation (90-100%) within the core belongs to ribosomal and translation-related proteins. It was found out that 48 proteins (11.8%) of the core genome are considered a hypothetical protein and those proteins host the larger sequence identity variations within the core. The sequence identity of the core genome identity diminishes as GSS score between species increases. The GSS dendrogram recovers most of the clades in the 16S rRNA gene phylogeny with the advantage to distinguish between 16S polytomies (unresolved nodes). Finally, our proposed core genome was used to distinguish the abundances of close related strains within human oral metagenomes being able to get strain relative abundances between healthy and caries infected (with S. mutans) individuals. Discussion. The clinical and food safety importance of Streptococcus genus gives a playground to test multiple comparative genomic scenarios due to its excellent genomic coverage. Understanding of genomic variability and strains relatedness is the goal of tools like GSS, which make use of both pairwise shared core and pan-genomic homologous shared sequences for its calculation. Combination of core genome and rapid alignment tools allows to estimate abundance and discriminate in a strain-specific manner in metagenomic samples. Here it is shared with the community both GSS genomic dendrogram and core genome to explore possibilities within streptococci.


Sign in / Sign up

Export Citation Format

Share Document