Redefining the differences in gene content between Yersinia pestis and Yersinia pseudotuberculosis using large-scale comparative genomics

Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in Silico Peptide Mass Libraries

Over the past decade, modern methods of MS (MS) have emerged that allow reliable, fast and cost-effective identification of pathogenic microorganisms. Although MALDI-TOF MS has already revolutionized the way microorganisms are identified, recent years have witnessed also substantial progress in the development of liquid chromatography (LC)-MS based proteomics for microbiological applications. For example, LC-tandem MS (LC-MS2) has been proposed for microbial characterization by means of multiple discriminative peptides that enable identification at the species, or sometimes at the strain level. However, such investigations can be laborious and time-consuming, especially if the experimental LC-MS2 data are tested against sequence databases covering a broad panel of different microbiological taxa. In this proof of concept study, we present an alternative bottom-up proteomics method for microbial identification. The proposed approach involves efficient extraction of proteins from cultivated microbial cells, digestion by trypsin and LC-MS measurements. Peptide masses are then extracted from MS1 data and systematically tested against an in silico library of all possible peptide mass data compiled in-house. The library has been computed from the UniProt Knowledgebase covering Swiss-Prot and TrEMBL databases and comprises more than 12,000 strain-specific in silico profiles, each containing tens of thousands of peptide mass entries. Identification analysis involves computation of score values derived from correlation coefficients between experimental and strain-specific in silico peptide mass profiles and compilation of score ranking lists. The taxonomic positions of the microbial samples are then determined by using the best-matching database entries. The suggested method is computationally efficient - less than 2 mins per sample - and has been successfully tested by a test set of 39 LC-MS1 peak lists obtained from 19 different microbial pathogens. The proposed method is rapid, simple and automatable and we foresee wide application potential for future microbiological applications.

Crohn's Disease: Is the Cold Chain Hypothesis Still Hot?

Crohn's disease [CD] is an inflammatory bowel disease of unknown aetiology. During recent decades, significant technological advances led to development of -omic datasets allowing a detailed description of the disease. Unfortunately these have not, to date, resolved the question of the aetiology of CD. Thus, it may be necessary to [re]consider hypothesis-driven approaches to resolve the aetiology of CD. According to the cold chain hypothesis, the development of industrial and domestic refrigeration has led to frequent exposure of human populations to bacteria capable of growing in the cold. These bacteria, at low levels of exposure, particularly those of the genus Yersinia, are believed to be capable of inducing exacerbated inflammation of the intestine in genetically predisposed subjects. We discuss the consistency of this working hypothesis in light of recent data from epidemiological, clinical, pathological, microbiological, and molecular studies.

The Effects of Signal Erosion and Core Genome Reduction on the Identification of Diagnostic Markers

Whole-genome sequence (WGS) data are commonly used to design diagnostic targets for the identification of bacterial pathogens. To do this effectively, genomics databases must be comprehensive to identify the strict core genome that is specific to the target pathogen. As additional genomes are analyzed, the core genome size is reduced and there is erosion of the target-specific regions due to commonality with related species, potentially resulting in the identification of false positives and/or false negatives.

A comparative analysis of 1,130 Burkholderia genomes identified unique markers for many named species, including the human pathogens B. pseudomallei and B. mallei. Due to core genome reduction and signature erosion, only 38 targets specific to B. pseudomallei/mallei were identified. By using only public genomes, a larger number of markers were identified, due to undersampling, and this larger number represents the potential for false positives. This analysis has implications for the design of diagnostics for other species where the genomic space of the target and/or closely related species is not well defined.