ABSTRACTmRNA profiling of pathogens during the course of human infections gives detailed information on the expression levels of relevant genes that drive pathogenicity and adaptation and at the same time allows for the delineation of phylogenetic relatedness of pathogens that cause specific diseases. In this study, we used mRNA sequencing to acquire information on the expression of Escherichia coli pathogenicity genes during urinary tract infections (UTI) in humans and to assign the UTI-associated E. coli isolates to different phylogenetic groups. Whereas the in vivo gene expression profiles of the majority of genes were conserved among 21 E. coli strains in the urine of elderly patients suffering from an acute UTI, the specific gene expression profiles of the flexible genomes was diverse and reflected phylogenetic relationships. Furthermore, genes transcribed in vivo relative to laboratory media included well-described virulence factors, small regulatory RNAs, as well as genes not previously linked to bacterial virulence. Knowledge on relevant transcriptional responses that drive pathogenicity and adaptation of isolates to the human host might lead to the introduction of a virulence typing strategy into clinical microbiology, potentially facilitating management and prevention of the disease.

BackgroundAmyotrophic lateral sclerosis (ALS) is a fatal disorder of the motor neuron system with poor prognosis and marginal therapeutic options. Current clinical diagnostic criteria are based on electrophysiological examination and exclusion of other ALS-mimicking conditions. Neuroprotective treatments are, however, most promising in early disease stages. Identification of disease-specific CSF biomarkers and associated biochemical pathways is therefore most relevant to monitor disease progression, response to neuroprotective agents and to enable early inclusion of patients into clinical trials.Methods and FindingsCSF from 35 patients with ALS diagnosed according to the revised El Escorial criteria and 23 age-matched controls was processed using paramagnetic bead chromatography for protein isolation and subsequently analyzed by MALDI-TOF mass spectrometry. CSF protein profiles were integrated into a Random Forest model constructed from 153 mass peaks. After reducing this peak set to the top 25%, a classifier was built which enabled prediction of ALS with high accuracy, sensitivity and specificity. Further analysis of the identified peptides resulted in a panel of five highly sensitive ALS biomarkers. Upregulation of secreted phosphoprotein 1 in ALS-CSF samples was confirmed by univariate analysis of ELISA and mass spectrometry data. Further quantitative validation of the five biomarkers was achieved in an 80-plex Multiple Reaction Monitoring mass spectrometry assay.ConclusionsALS classification based on the CSF biomarker panel proposed in this study could become a valuable predictive tool for early clinical risk stratification. Of the numerous CSF proteins identified, many have putative roles in ALS-related metabolic processes, particularly in chromogranin-mediated secretion signaling pathways. While a stand-alone clinical application of this classifier will only be possible after further validation and a multicenter trial, it could be readily used to complement current ALS diagnostics and might also provide new insights into the pathomechanisms of this disease in the future.

Monitoring the bronchoalveolar lavage fluid levels of seven polypeptides detected by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry allows a reliable prediction of early BOS using a Random Forest decision tree-based classifier model. The high accuracy of this robust model and its synergistic potential in combination with established forced expiratory volume-based diagnostics could make it an effective tool to supplement the current diagnostic regime after multicentric validation.

No abstract

Background Pseudomonas aeruginosa is a gram-negative bacterium that is ubiquitously present in the aerobic biosphere. As an antibiotic-resistant facultative pathogen, it is a major cause of hospital-acquired infections. Its rapid and accurate identification is crucial in clinical and therapeutic environments.MethodsIn a large-scale MALDI-TOF mass spectrometry-based screen of the Harvard transposon insertion mutant library of P. aeruginosa strain PA14, intact-cell proteome profile spectra of 5547 PA14 transposon mutants exhibiting a plethora of different phenotypes were acquired and analyzed.ResultsOf all P. aeruginosa PA14 mutant profiles 99.7% were correctly identified as P. aeruginosa with the Biotyper software on the species level. On the strain level, 99.99% of the profiles were mapped to five different individual P. aeruginosa Biotyper database entries. A principal component analysis-based approach was used to determine the most important discriminatory mass features between these Biotyper groups. Although technical replicas were consistently categorized to specific Biotyper groups in 94.2% of the mutant profiles, biological replicas were not, indicating that the distinct proteotypes are affected by growth conditions.ConclusionsThe PA14 mutant profile collection presented here constitutes the largest coherent P. aeruginosa MALDI-TOF spectral dataset publicly available today. Transposon insertions in thousands of different P. aeruginosa genes did not affect species identification from MALDI-TOF mass spectra, clearly demonstrating the robustness of the approach. However, the assignment of the individual spectra to sub-groups proved to be non-consistent in biological replicas, indicating that the differentiation between biotyper groups in this nosocomial pathogen is unassured.