Bad to the bone? - Genomic analysis of Enterococcus isolates from diverse environments reveals that most are safe and display potential as food fermentation microorganisms.

Enterococci comprise a group of lactic acid bacteria (LAB) with considerable potential to serve as food fermentation microorganisms. Unfortunately, enterococci have received a lot of negative attention, due to the occurrence of pathogenic and multidrug resistant strains. In this study, we used genomics to select safe candidates among the forty-four studied enterococcal isolates. The genomes of the forty-four strains were fully sequenced and assessed for presence of virulence and antibiotic resistance genes. Nineteen isolates belonging to the species Enterococcus lactis, Enterococcus faecium, Enterococcus durans, and Enterococcus thailandicus, were deemed safe from the genome analysis. The presence of secondary metabolite gene clusters for bacteriocins was assessed, and twelve candidates were found to secrete antimicrobial compounds effective against Listeria monocytogenes isolated from cheese and Staphylococcus aureus. Physiological characterization revealed nineteen industrial potentials; all strains grew well at 42 °C and acidified 1.5 hours faster than their mesophilic counterpart Lactococcus lactis, with which they share metabolism and flavor forming ability. We conclude that a large fraction of the examined enterococci were safe and could serve as excellent food fermentation microorganisms with inherent bioprotective abilities.

N-Terminal chemical derivatization of peptides with 4-formyl-benzenesulfonic acid and electrospray positive and negative tandem mass spectrometry with high abundance of b-ion series.

RATIONALE: Selective derivatization of peptide N-terminus with 4-formyl-benzenesulfonic acid (FBSA) enables chemically activated fragmentation in positive and negative ion modes (ESI+/-) under charge reduction conditions. Overlapped positive and negative tandem mass spectra show b-ions making the assignment of b-ion series fragments easy and accurate. METHODS: We developed an FBSA-peptide microwave-assisted derivatization procedure. Derivatized and nonderivatized bovine serum albumin tryptic peptides and insulin non-tryptic peptide were compared after tandem mass spectrometry (MS/MS) analysis in positive and negative ion modes. A high-quality data set of sulfonated b-ions obtained in negative tandem mass spectra of singly charged FBSA-peptides were matched to detected b-ions in positive MS/MS spectra. Moreover, negative spectra signals were converted and matched against y-ions in positive tandem mass spectra to identify complete peptide sequences. RESULTS: The FBSA derivatization procedure produced a significantly improved MS/MS data set (populated by high-intensity signals of b- and y-ions) compared to commonly used N-terminal sulfonation reagents. Undesired side reactions almost do not occur, and the procedure reduces the derivatization time. It was found that b-ion intensities comprise 15% and 13% compared to combined ion intensities generated in positive- and negative ion modes, respectively. High visibility of b-ion series in negative ion mode can be attributed to N-terminal sulfonation that had no negative effect on the production of b- and y-ion series in positive ion mode. CONCLUSIONS: The FBSA derivatization and de novo sequencing approach outlined here is a reliable method for accurate peptide sequence assignment. Increased production of b-ions in positive- and negative ion modes greatly improves peak assignment and thus enables accurate sequence reconstruction. Implementation of the named methodology would improve the quality of de novo sequencing data and reduce the number of misinterpreted spectra.

Direct Identification of Urinary Tract Pathogens by MALDI-TOF/TOF Analysis and De Novo Peptide Sequencing.

For mass spectrometry-based diagnostics of microorganisms, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) is currently routinely used to identify urinary tract pathogens. However, it requires a lengthy culture step for accurate pathogen identification, and is limited by a relatively small number of available species in peptide spectral libraries (≤3329). Here, we propose a method for pathogen identification that overcomes the above limitations, and utilizes the MALDI-TOF/TOF MS instrument. Tandem mass spectra of the analyzed peptides were obtained by chemically activated fragmentation, which allowed mass spectrometry analysis in negative and positive ion modes. Peptide sequences were elucidated de novo, and aligned with the non-redundant National Center for Biotechnology Information Reference Sequence Database (NCBInr). For data analysis, we developed a custom program package that predicted peptide sequences from the negative and positive MS/MS spectra. The main advantage of this method over a conventional MALDI-TOF MS peptide analysis is identification in less than 24 h without a cultivation step. Compared to the limited identification with peptide spectra libraries, the NCBI database derived from genome sequencing currently contains 20,917 bacterial species, and is constantly expanding. This paper presents an accurate method that is used to identify pathogens grown on agar plates, and those isolated directly from urine samples, with high accuracy.

Genomic characterization and assessment of pathogenic potential of Legionella spp. isolates from environmental monitoring.

Several species in the genus Legionella are known to cause an acute pneumonia when the aerosols containing the bacteria from man-made water systems are inhaled. The disease is usually caused by Legionella pneumophila, but other species have been implicated in the infection. The disease is frequently manifested as an outbreak, which means several people are affected when exposed to the common source of Legionella contamination. Therefor environmental surveillance which includes isolation and identification of Legionella is performed routinely. However, usually no molecular or genome-based methods are employed in further characterization of the isolates during routine environmental monitoring. During several years of such monitoring, isolates from different geographical locations were collected and 39 of them were sequenced by hybrid de novo approach utilizing short and long sequencing reads. In addition, the isolates were typed by standard culture and MALDI-TOF method. The sequencing reads were assembled and annotated to produce high-quality genomes. By employing discriminatory genome typing, four potential new species in the Legionella genus were identified, which are yet to be biochemically and morphologically characterized. Moreover, functional annotations concerning virulence and antimicrobial resistance were performed on the sequenced genomes. The study contributes to the knowledge on little-known non-pneumophila species present in man-made water systems and establishes support for future genetic relatedness studies as well as understanding of their pathogenic potential.

NanoUPLC-QTOF-MS/MS Determination of Major Rosuvastatin Degradation Products Generated by Gamma Radiation in Aqueous Solution.

Rosuvastatin, a member of the statin family of drugs, is used to regulate high cholesterol levels in the human body. Moreover, rosuvastatin and other statins demonstrate a protective role against free radical-induced oxidative stress. Our research aimed to investigate the end-products of free radical-induced degradation of rosuvastatin. To induce the radical degradation, an aqueous solution of rosuvastatin was irradiated using different doses of gamma radiation (50-1000 Gy) under oxidative conditions. Rosuvastatin and related degradation products were separated on nanoC18 column under gradient elution, and identification was carried out on hyphenated nanoUPLC and nanoESI-QTOF mass spectrometer system. Elemental composition analysis using highly accurate mass measurements together with isotope fitting algorithm identified nine major degradation products. This is the first study of gamma radiation-induced degradation of rosuvastatin, where chemical structures, MS/MS fragmentation pathways and formation mechanisms of the resulting degradation products are detailly described. The presented results contribute to the understanding of the degradation pathway of rosuvastatin and possibly other statins under gamma radiation conditions.

Modeling of Urinary Microbiota Associated With Cystitis.

A decade ago, when the Human Microbiome Project was starting, urinary tract (UT) was not included because the bladder and urine were considered to be sterile. Today, we are presented with evidence that healthy UT possesses native microbiota and any major event disrupting its "equilibrium" can impact the host also. This dysbiosis often leads to cystitis symptoms, which is the most frequent lower UT complaint, especially among women. Cystitis is one of the most common causes of antimicrobial drugs prescriptions in primary and secondary care and an important contributor to the problem of antimicrobial resistance. Despite this fact, we still have trouble distinguishing whether the primary cause of majority of cystitis cases is a single pathogen overgrowth, or a systemic disorder affecting entire UT microbiota. There are relatively few studies monitoring changes and dynamics of UT microbiota in cystitis patients, making this field of research still an unknown. In this study variations to the UT microbiota of cystitis patients were identified and microbial dynamics has been modeled. The microbial genetic profile of urine samples from 28 patients was analyzed by 16S rDNA Illumina sequencing and bioinformatics analysis. One patient with bacterial cystitis symptoms was prescribed therapy based on national guideline recommendations on antibacterial treatment of urinary tract infections (UTI) and UT microbiota change was monitored by 16S rDNA sequencing on 24 h basis during the entire therapy duration. The results of sequencing implied that a particular class of bacteria is associated with majority of cystitis cases in this study. The contributing role of this class of bacteria - Gammaproteobacteria, was further predicted by generalized Lotka-Volterra modeling (gLVM). Longitudinal microbiota insight obtained from a single patient under prescribed antimicrobial therapy revealed rapid and extensive changes in microbial composition and emphasized the need for current guidelines revision in regards to therapy duration. Models based on gLVM indicated protective role of two taxonomic classes of bacteria, Actinobacteria and Bacteroidia class, which appear to actively suppress pathogen overgrowth.