Metabolomics As a Tool for the Characterization of Drug-Resistant Epilepsy.

PURPOSE: Drug resistance is a critical issue in the treatment of epilepsy, contributing to clinical emergencies and increasing both serious social and economic burdens on the health system. The wide variety of potential drug combinations followed by often failed consecutive attempts to match drugs to an individual patient may mean that this treatment stage may last for years with suboptimal benefit to the patient. Given these challenges, it is valuable to explore the availability of new methodologies able to shorten the period of determining a rationale pharmacologic treatment. Metabolomics could provide such a tool to investigate possible markers of drug resistance in subjects with epilepsy. METHODS: Blood samples were collected from (1) controls (C) (n = 35), (2) patients with epilepsy "responder" (R) (n = 18), and (3) patients with epilepsy "non-responder" (NR) (n = 17) to the drug therapy. The samples were analyzed using nuclear magnetic resonance spectroscopy, followed by multivariate statistical analysis. KEY FINDINGS: A different metabolic profile based on metabolomics analysis of the serum was observed between C and patients with epilepsy and also between R and NR patients. It was possible to identify the discriminant metabolites for the three classes under investigation. Serum from patients with epilepsy were characterized by increased levels of 3-OH-butyrate, 2-OH-valerate, 2-OH-butyrate, acetoacetate, acetone, acetate, choline, alanine, glutamate, scyllo-inositol (C  R > NR). SIGNIFICANCE: In conclusion, metabolomics may represent an important tool for discovery of differences between subjects affected by epilepsy responding or resistant to therapies and for the study of its pathophysiology, optimizing the therapeutic resources and the quality of life of patients

ipaPy2: Integrated Probabilistic Annotation (IPA) 2.0-an improved Bayesian-based method for the annotation of LC-MS/MS untargeted metabolomics data.

SummaryThe Integrated Probabilistic Annotation (IPA) is an automated annotation method for LC-MS-based untargeted metabolomics experiments that provides statistically rigorous estimates of the probabilities associated with each annotation. Here, we introduce ipaPy2, a substantially improved and completely refactored Python implementation of the IPA method. The revised method is now able to integrate tandem MS fragmentation data, which increases the accuracy of the identifications. Moreover, ipaPy2 provides a much more user-friendly interface, and isotope peaks are no longer treated as individual features but integrated into isotope fingerprints, greatly speeding up the calculations. The method has also been fully integrated with the mzMatch pipeline, so that the results of the annotation can be explored through the newly developed PeakMLViewerPy tool available at https://github.com/UoMMIB/PeakMLViewerPy.Availability and implementationThe source code, extensive documentation, and tutorials are freely available on GitHub at https://github.com/francescodc87/ipaPy2

1H NMR spectroscopy-based metabolomics analysis for the diagnosis of symptomatic E. coli-associated urinary tract infection (UTI).

BACKGROUND: Urinary tract infection (UTI) is one of the most common diagnoses in girls and women, and to a lesser extent in boys and men younger than 50 years. Escherichia coli, followed by Klebsiella spp. and Proteus spp., cause 75-90% of all infections. Infection of the urinary tract is identified by growth of a significant number of a single species in the urine, in the presence of symptoms. Urinary culture is an accurate diagnostic method but takes several hours or days to be carried out. Metabolomics analysis aims to identify biomarkers that are capable of speeding up diagnosis. METHODS: Urine samples from 51 patients with a prior diagnosis of Escherichia coli-associated UTI, from 21 patients with UTI caused by other pathogens (bacteria and fungi), and from 61 healthy controls were analyzed. The 1H-NMR spectra were acquired and processed. Multivariate statistical models were applied and their performance was validated using permutation test and ROC curve. RESULTS: Orthogonal Partial Least Squares-discriminant Analysis (OPLS-DA) showed good separation (R2Y = 0.76, Q2=0.45, p < 0.001) between UTI caused by Escherichia coli and healthy controls. Acetate and trimethylamine were identified as discriminant metabolites. The concentrations of both metabolites were calculated and used to build the ROC curves. The discriminant metabolites identified were also evaluated in urine samples from patients with other pathogens infections to test their specificity. CONCLUSIONS: Acetate and trimethylamine were identified as optimal candidates for biomarkers for UTI diagnosis. The conclusions support the possibility of a fast diagnostic test for Escherichia coli-associated UTI using acetate and trimethylamine concentrations

Reference-grade genome and large linear plasmid of Streptomyces rimosus: pushing the limits of Nanopore sequencing

[EN] Streptomyces rimosus ATCC 10970 is the parental strain of industrial strains used for the commercial production of the important antibiotic oxytetracycline. As an actinobacterium with a large linear chromosome containing numerous long repeat regions, high GC content, and a single giant linear plasmid (GLP), these genomes are challenging to assemble. Here, we apply a hybrid sequencing approach relying on the combination of short- and long-read next-generation sequencing platforms and whole-genome restriction analysis by using pulsed-field gel electrophoresis (PFGE) to produce a high-quality reference genome for this biotechnologically important bacterium. By using PFGE to separate and isolate plasmid DNA from chromosomal DNA, we successfully sequenced the GLP using Nanopore data alone. Using this approach, we compared the sequence of GLP in the parent strain ATCC 10970 with those found in two semi-industrial progenitor strains, R6-500 and M4018. Sequencing of the GLP of these three S. rimosus strains shed light on several rearrangements accompanied by transposase genes, suggesting that transposases play an important role in plasmid and genome plasticity in S. rimosus. The polished annotation of secondary metabolite biosynthetic pathways compared to metabolite analysis in the ATCC 10970 strain also refined our knowledge of the secondary metabolite arsenal of these strains. The proposed methodology is highly applicable to a variety of sequencing projects, as evidenced by the reliable assemblies obtainedSIThis work was supported as part of the European project “Thoroughly Optimised Production Chassis for Advanced Pharmaceutical Ingredients” (grant ID 720793, European Union’s Horizon 2020 Research and Innovation Program) and by the Slovenian Research Agency (P4-0116, P4-0077, and P1-0034). L.S. is supported by a Slovenian Research Agency young researcher grant (35220200570), and M.T. is supported by grant C3330-19-952047 funded by Republic of Slovenia Ministry of Education, Science, and Sport and the European Union European Regional Development Fun

Multi-omics Study of Planobispora rosea, Producer of the Thiopeptide Antibiotic GE2270A

Planobispora rosea is the natural producer of the potent thiopeptide antibiotic GE2270A. Here, we present the results of a metabolomics and transcriptomics analysis of P. rosea during production of GE2270A. The data generated provides useful insights into the biology of this genetically intractable bacterium. We characterize the details of the shutdown of protein biosynthesis and the respiratory chain associated with the end of the exponential growth phase. We also provide the first description of the phosphate regulon in P. rosea. Based on the transcriptomics data, we show that both phosphate and iron are limiting P. rosea growth in our experimental conditions. Additionally, we identified and validated a new biosynthetic gene cluster associated with the production of the siderophores benarthin and dibenarthin in P. rosea. Together, the metabolomics and transcriptomics data are used to inform and refine the very first genome-scale metabolic model for P. rosea, which will be a valuable framework for the interpretation of future studies of the biology of this interesting but poorly characterized species. IMPORTANCE Planobispora rosea is a genetically intractable bacterium used for the production of GE2270A on an industrial scale. GE2270A is a potent thiopeptide antibiotic currently used as a precursor for the synthesis of two compounds under clinical studies for the treatment of Clostridium difficile infection and acne. Here, we present the very first systematic multi-omics investigation of this important bacterium, which provides a much-needed detailed picture of the dynamics of metabolism of P. rosea while producing GE2270A

Reference-Grade Genome and Large Linear Plasmid of Streptomyces rimosus : Pushing the Limits of Nanopore Sequencing

Streptomyces rimosus ATCC 10970 is the parental strain of industrial strains used for the commercial production of the important antibiotic oxytetracycline. As an actinobacterium with a large linear chromosome containing numerous long repeat regions, high GC content, and a single giant linear plasmid (GLP), these genomes are challenging to assemble. Here, we apply a hybrid sequencing approach relying on the combination of short- and long-read next-generation sequencing platforms and whole-genome restriction analysis by using pulsed-field gel electrophoresis (PFGE) to produce a high-quality reference genome for this biotechnologically important bacterium. By using PFGE to separate and isolate plasmid DNA from chromosomal DNA, we successfully sequenced the GLP using Nanopore data alone. Using this approach, we compared the sequence of GLP in the parent strain ATCC 10970 with those found in two semi-industrial progenitor strains, R6-500 and M4018. Sequencing of the GLP of these three S. rimosus strains shed light on several rearrangements accompanied by transposase genes, suggesting that transposases play an important role in plasmid and genome plasticity in S. rimosus. The polished annotation of secondary metabolite biosynthetic pathways compared to metabolite analysis in the ATCC 10970 strain also refined our knowledge of the secondary metabolite arsenal of these strains. The proposed methodology is highly applicable to a variety of sequencing projects, as evidenced by the reliable assemblies obtained. IMPORTANCE The genomes of Streptomyces species are difficult to assemble due to long repeats, extrachromosomal elements (giant linear plasmids [GLPs]), rearrangements, and high GC content. To improve the quality of the S. rimosus ATCC 10970 genome, producer of oxytetracycline, we validated the assembly of GLPs by applying a new approach to combine pulsed-field gel electrophoresis separation and GLP isolation and sequenced the isolated GLP with Oxford Nanopore technology. By examining the sequenced plasmids of ATCC 10970 and two industrial progenitor strains, R6-500 and M4018, we identified large GLP rearrangements. Analysis of the assembled plasmid sequences shed light on the role of transposases in genome plasticity of this species. The new methodological approach developed for Nanopore sequencing is highly applicable to a variety of sequencing projects. In addition, we present the annotated reference genome sequence of ATCC 10970 with a detailed analysis of the biosynthetic gene clusters

Biotechnological application of Streptomyces for the production of clinical drugs and other bioactive molecules

Streptomyces is one of the most relevant genera in biotechnology, and its rich secondary metabolism is responsible for the biosynthesis of a plethora of bioactive compounds, including several clinically relevant drugs. The use of Streptomyces species for the manufacture of natural products has been established for more than half a century; however, the tremendous advances observed in recent years in genetic engineering and molecular biology have revolutionised the optimisation of Streptomyces as cell factories and drastically expanded the biotechnological potential of these bacteria. Here, we illustrate the most exciting advances reported in the past few years, with a particular focus on the approaches significantly improving the biotechnological capacity of Streptomyces to produce clinical drugs and other valuable secondary metabolites

RankProd 2.0: a refactored Bioconductor package for detecting differentially expressed features in molecular profiling datasets

Motivation: The Rank Products is a statistical technique widely used to detect differentially expressed features in molecular profiling experiments such as transcriptomics, metabolomics and proteomics studies. An implementation of the Rank Product (RP) and the closely related Rank Sum (RS) statistics has been available in the RankProd Bioconductor package for several years. However, several recent advances in the understanding of the statistical foundations of the method have made a complete refactoring of the existing package desirable. Results: We implemented a completely refactored version of the RankProd package, which provides a more principled implementation of the statistics for unpaired datasets. Moreover, the permutation-based p-value estimation methods have been replaced by exact methods, providing faster and more accurate results. Availability: RankProd 2.0 is available at Bioconductor (https://www.bioconductor.org/packages/devel/bioc/html/RankProd.html) and as part of the mzMatch pipeline (http://www.mzmatch.sourceforge.net)