Genomic and phenotypic comparison of polyhydroxyalkanoates producing strains of genus Caldimonas/Schlegelella

Polyhydroxyalkanoates (PHAs) have emerged as an environmentally friendly alternative to conventional polyesters. In this study, we present a comprehensive analysis of the genomic and phenotypic characteristics of three non-model thermophilic bacteria known for their ability to produce PHAs: Schlegelella aquatica LMG 23380T, Caldimonas thermodepolymerans DSM 15264, and C. thermodepolymerans LMG 21645 and the results were compared with the type strain C. thermodepolymerans DSM 15344T. We have assembled the first complete genomes of these three bacteria and performed the structural and functional annotation. This analysis has provided valuable insights into the biosynthesis of PHAs and has allowed us to propose a comprehensive scheme of carbohydrate metabolism in the studied bacteria. Through phylogenomic analysis, we have confirmed the synonymity between Caldimonas and Schlegelella genera, and further demonstrated that S. aquatica and S. koreensis, currently classified as orphan species, belong to the Caldimonas genus

New Multilocus Sequence Typing Scheme for Enterococcus faecium Based on Whole Genome Sequencing Data

ABSTRACT The MLST scheme currently used for Enterococcus faecium typing was designed in 2002 and is based on putative gene functions and Enterococcus faecalis gene sequences available at that time. As a result, the original MLST scheme does not correspond to the real genetic relatedness of E. faecium strains and often clusters genetically distant strains to the same sequence types (ST). Nevertheless, typing has a significant impact on the subsequent epidemiological conclusions and introduction of appropriate epidemiological measures, thus it is crucial to use a more accurate MLST scheme. Based on the genome analysis of 1,843 E. faecium isolates, a new scheme, consisting of 8 highly discriminative loci, was created in this study. These strains were divided into 421 STs using the new MLST scheme, as opposed to 223 STs assigned by the original MLST scheme. The proposed MLST has a discriminatory power of D = 0.983 (CI95% 0.981 to 0.984), compared to the original scheme’s D = 0.919 (CI95% 0.911 to 0.927). Moreover, we identified new clonal complexes with our newly designed MLST scheme. The scheme proposed here is available within the PubMLST database. Although whole genome sequencing availability has increased rapidly, MLST remains an integral part of clinical epidemiology, mainly due to its high standardization and excellent robustness. In this study, we proposed and validated a new MLST scheme for E. faecium, which is based on genome-wide data and thus reflects the tested isolates’ more accurate genetic similarity. IMPORTANCE Enterococcus faecium is one of the most important pathogens causing health care associated infections. One of the main reasons for its clinical importance is a rapidly spreading resistance to vancomycin and linezolid, which significantly complicates antibiotic treatment of infections caused by such resistant strains. Monitoring the spread and relationships between resistant strains causing severe conditions represents an important tool for implementing appropriate preventive measures. Therefore, there is an urgent need to establish a robust method enabling strain monitoring and comparison at the local, national, and global level. Unfortunately, the current, extensively used MLST scheme does not reflect the real genetic relatedness between individual strains and thus does not provide sufficient discriminatory power. This can lead directly to incorrect epidemiological measures due to insufficient accuracy and biased results

Table_1_Using deep learning for gene detection and classification in raw nanopore signals.xlsx

Recently, nanopore sequencing has come to the fore as library preparation is rapid and simple, sequencing can be done almost anywhere, and longer reads are obtained than with next-generation sequencing. The main bottleneck still lies in data postprocessing which consists of basecalling, genome assembly, and localizing significant sequences, which is time consuming and computationally demanding, thus prolonging delivery of crucial results for clinical practice. Here, we present a neural network-based method capable of detecting and classifying specific genomic regions already in raw nanopore signals—squiggles. Therefore, the basecalling process can be omitted entirely as the raw signals of significant genes, or intergenic regions can be directly analyzed, or if the nucleotide sequences are required, the identified squiggles can be basecalled, preferably to others. The proposed neural network could be included directly in the sequencing run, allowing real-time squiggle processing.</p

Table_2_Using deep learning for gene detection and classification in raw nanopore signals.xlsx

Recently, nanopore sequencing has come to the fore as library preparation is rapid and simple, sequencing can be done almost anywhere, and longer reads are obtained than with next-generation sequencing. The main bottleneck still lies in data postprocessing which consists of basecalling, genome assembly, and localizing significant sequences, which is time consuming and computationally demanding, thus prolonging delivery of crucial results for clinical practice. Here, we present a neural network-based method capable of detecting and classifying specific genomic regions already in raw nanopore signals—squiggles. Therefore, the basecalling process can be omitted entirely as the raw signals of significant genes, or intergenic regions can be directly analyzed, or if the nucleotide sequences are required, the identified squiggles can be basecalled, preferably to others. The proposed neural network could be included directly in the sequencing run, allowing real-time squiggle processing.</p

Image_1_Using deep learning for gene detection and classification in raw nanopore signals.JPEG

Recently, nanopore sequencing has come to the fore as library preparation is rapid and simple, sequencing can be done almost anywhere, and longer reads are obtained than with next-generation sequencing. The main bottleneck still lies in data postprocessing which consists of basecalling, genome assembly, and localizing significant sequences, which is time consuming and computationally demanding, thus prolonging delivery of crucial results for clinical practice. Here, we present a neural network-based method capable of detecting and classifying specific genomic regions already in raw nanopore signals—squiggles. Therefore, the basecalling process can be omitted entirely as the raw signals of significant genes, or intergenic regions can be directly analyzed, or if the nucleotide sequences are required, the identified squiggles can be basecalled, preferably to others. The proposed neural network could be included directly in the sequencing run, allowing real-time squiggle processing.</p

Data_Sheet_1_Whole genome sequencing and characterization of Pantoea agglomerans DBM 3797, endophyte, isolated from fresh hop (Humulus lupulus L.).docx

BackgroundThis paper brings new information about the genome and phenotypic characteristics of Pantoea agglomerans strain DBM 3797, isolated from fresh Czech hop (Humulus lupulus) in the Saaz hop-growing region. Although P. agglomerans strains are frequently isolated from different materials, there are not usually thoroughly characterized even if they have versatile metabolism and those isolated from plants may have a considerable potential for application in agriculture as a support culture for plant growth.MethodsP. agglomerans DBM 3797 was cultured under aerobic and anaerobic conditions, its metabolites were analyzed by HPLC and it was tested for plant growth promotion abilities, such as phosphate solubilization, siderophore and indol-3-acetic acid productions. In addition, genomic DNA was extracted, sequenced and de novo assembly was performed. Further, genome annotation, pan-genome analysis and selected genome analyses, such as CRISPR arrays detection, antibiotic resistance and secondary metabolite genes identification were carried out.Results and discussionThe typical appearance characteristics of the strain include the formation of symplasmata in submerged liquid culture and the formation of pale yellow colonies on agar. The genetic information of the strain (in total 4.8 Mb) is divided between a chromosome and two plasmids. The strain lacks any CRISPR-Cas system but is equipped with four restriction-modification systems. The phenotypic analysis focused on growth under both aerobic and anaerobic conditions, as well as traits associated with plant growth promotion. At both levels (genomic and phenotypic), the production of siderophores, indoleacetic acid-derived growth promoters, gluconic acid, and enzyme activities related to the degradation of complex organic compounds were found. Extracellular gluconic acid production under aerobic conditions (up to 8 g/l) is probably the result of glucose oxidation by the membrane-bound pyrroloquinoline quinone-dependent enzyme glucose dehydrogenase. The strain has a number of properties potentially beneficial to the hop plant and its closest relatives include the strains also isolated from the aerial parts of plants, yet its safety profile needs to be addressed in follow-up research.</p