Clustering of MS spectra for improved protein identification rate and screening for protein variants and modifications by MALDI-MS/MS

Prevalence and Characteristics of Bacillus cereus Group Isolated from Raw and Pasteurised Milk

The elimination of spore-forming bacteria is not guaranteed by current pasteurisation processes and is a challenging problem for the dairy industry. Given that Bacillus cereus sensu lato (B. cereus group) is an important foodborne pathogen and spoiler in the dairy industry, this study aimed at evaluating the prevalence and characteristics of B. cereus group in raw and pasteurised milk samples collected in Victoria, Australia. Isolated B. cereus group were tested for antimicrobial susceptibility, biofilm formation and virulence properties. Genetic diversity was assessed using ERIC-PCR. Proteomic profiling using MALDI-TOF MS and chemical profiling using Fourier-transform infrared (FTIR) spectroscopy were also applied for clustering of the isolates. Results showed 42.3% of milk samples contained B. cereus group, with a higher contamination level for pasteurised milk. Virulence studies identified genes nheA, nheB, hblA and nheC in most isolates and cyk gene in 46% of all isolates. Antimicrobial susceptibility testing showed a high prevalence of resistance towards ampicillin, ceftriaxone and penicillin. The biofilm-forming capacity of our isolates showed that most (53.7%) had the ability to form a biofilm. Genetic profiling using ERIC-PCR placed most B. cereus group isolates from pasteurised milk in the same cluster, indicating that they probably originated from a similar source. Raw milk isolates showed greater diversity indicating various sources. FTIR spectroscopy showed high agreement with genetic profiling. In contrast, low agreement between proteomic (MALDI-TOF MS) and genetic typing was observed. The present study showed that the FTIR spectroscopy could be adopted as a rapid tool for the typing of B. cereus group. Overall, the virulence and antimicrobial resistance characteristics, together with the ability of isolates to produce biofilm, indicate the importance of B. cereus group in the Australian dairy industry.

Mass-Up: an all-in-one open software application for MALDI-TOF mass spectrometry knowledge discovery

BACKGROUND

Mass spectrometry is one of the most important techniques in the field of proteomics. MALDI-TOF mass spectrometry has become popular during the last decade due to its high speed and sensitivity for detecting proteins and peptides. MALDI-TOF-MS can be also used in combination with Machine Learning techniques and statistical methods for knowledge discovery. Although there are many software libraries and tools that can be combined for these kind of analysis, there is still a need for all-in-one solutions with graphical user-friendly interfaces and avoiding the need of programming skills.

RESULTS

Mass-Up, an open software multiplatform application for MALDI-TOF-MS knowledge discovery is herein presented. Mass-Up software allows data preprocessing, as well as subsequent analysis including (i) biomarker discovery, (ii) clustering, (iii) biclustering, (iv) three-dimensional PCA visualization and (v) classification of large sets of spectra data.

CONCLUSIONS

Mass-Up brings knowledge discovery within reach of MALDI-TOF-MS researchers. Mass-Up is distributed under license GPLv3 and it is open and free to all users at http://sing.ei.uvigo.es/mass-up.

Fluorescent protein tagging as a tool to define the subcellular distribution of proteins in plants

Fluorescent protein (FP) tagging approaches are widely used to determine the subcellular location of plant proteins. Here we give a brief overview of FP approaches, highlight potential technical problems, and discuss what to consider when designing FP/protein fusion constructs and performing transformation assays. We analyze published FP tagging data sets along with data from proteomics studies collated in SUBA3, a subcellular location database for Arabidopsis proteins, and assess the reliability of these data sets by comparing them. We also outline the limitations of the FP tagging approach for defining protein location and investigate multiple localization claims by FP tagging. We conclude that the collation of localization datasets in databases like SUBA3 is helpful for revealing discrepancies in location attributions by different techniques and/or by different research groups.