A blood test to monitor bee health across a European network of agricultural sites of different land-use by MALDI BeeTyping mass spectrometry

There are substantial concerns about impaired honey bee health and colony losses due to several poorly understood factors. We used MALDI profiling (MALDI BeeTyping®) analysis to investigate how some environmental and management factors under field conditions across Europe affected the honey bee haemolymph peptidome (all peptides in the circulatory fluid), as a profile of molecular markers representing the immune status of Apis mellifera. Honey bees were exposed to a range of environmental stressors in 128 agricultural sites across eight European countries in four biogeographic zones, with each country contributing eight sites each for two different cropping systems: oilseed rape (OSR) and apple (APP). The full haemolymph peptide profiles, including the presence and levels of three key immunity markers, namely the antimicrobial peptides (AMPs) Apidaecin, Abaecin and Defensin-1, allowed the honey bee responses to environmental variables to be discriminated by country, crop type and site. When considering just the AMPs, it was not possible to distinguish between countries by the prevalence of each AMP in the samples. However, it was possible to discriminate between countries on the amounts of the AMPs, with the Swedish samples in particular expressing high amounts of all AMPs. A machine learning model was developed to discriminate the haemolymphs of bees from APP and OSR sites. The model was 90.6 % accurate in identifying the crop type from the samples used to build the model. Overall, MALDI BeeTyping® of bee haemolymph represents a promising and cost-effective "blood test" for simultaneously monitoring dozens of peptide markers affected by environmental stressors at the landscape scale, thus providing policymakers with new diagnostic and regulatory tools for monitoring bee health.

MALDI-MS Profiling to Address Honey Bee Health Status under Bacterial Challenge through Computational Modeling

Honey bees play a critical role in the maintenance of plant biodiversity and sustainability of food webs. In the past few decades, bees have been subjected to biotic and abiotic threats causing various colony disorders. Therefore, monitoring solutions to help beekeepers to improve bee health are necessary. Matrix-assisted laser desorption ionization-mass spectrometry (MALDI-MS) profiling has emerged within this decade as a powerful tool to identify in routine micro-organisms and is currently used in real-time clinical diagnosis. MALDI BeeTyping is developed to monitor significant hemolymph molecular changes in honey bees upon infection with a series of entomopathogenic Gram-positive and -negative bacteria. A Serratia marcescens strain isolated from one naturally infected honey bee collected from the field is also considered. A series of hemolymph molecular mass fingerprints is individually recorded and to the authors' knowledge, the first computational model harboring a predictive score of 97.92% and made of nine molecular signatures that discriminate and classify the honey bees' systemic response to the bacteria is built. Hence, the model is challenged by classifying a training set of hemolymphs and an overall recognition of 91.93% is obtained. Through this work, a novel, time and cost saving high-throughput strategy that addresses honey bee health on an individual scale is introduced.

Fast detection of Piscirickettsia salmonis in Salmo salar serum through MALDI-TOF-MS profiling

Piscirickettsia salmonis is a pathogenic bacteria known as the aetiological agent of the salmonid rickettsial syndrome and causes a high mortality in farmed salmonid fishes. Detection of P. salmonis in farmed fishes is based mainly on molecular biology and immunohistochemistry techniques. These techniques are in most of the cases expensive and time consuming. In the search of new alternatives to detect the presence of P. salmonis in salmonid fishes, this work proposed the use of MALDI-TOF-MS to compare serum protein profiles from Salmo salar fish, including experimentally infected and non-infected fishes using principal component analysis (PCA). Samples were obtained from a controlled bioassay where S. salar was challenged with P. salmonis in a cohabitation model and classified according to the presence or absence of the bacteria by real time PCR analysis. MALDI spectra of the fish serum samples showed differences in its serum protein composition. These differences were corroborated with PCA analysis. The results demonstrated that the use of both MALDI-TOF-MS and PCA represents a useful tool to discriminate the fish status through the analysis of salmonid serum samples.

A PSA-guided approach for a better diagnosis of prostatic adenocarcinoma based on MALDI profiling and peptide identification

BACKGROUND

Prostate cancer (PCa) is the second cause of mortality in men worldwide. The prostate-specific antigen (PSA) test is routinely adopted in diagnosis; nevertheless more reliable biomarkers are continuously under investigation by monitoring the release of molecules into the bloodstream. The serum protein profiles appear to provide cancer-specific fingerprints that help to discriminate patients (especially with low PSA level) from controls, improving the performance of existing clinical tests.

METHODS

Samples from healthy controls and PCa patients with low (≤4 ng/mL) and high PSA (>4 ng/mL) levels were analyzed by MALDI profiling, and by a multi fractionation approach coupled to ESI-MS for peaks identification.

RESULTS

MALDI profiling achieved to detect 10 and 14 changed peaks (p-value <0.05), respectively, in PCa patients with low and high PSA versus controls. In particular, a peak identified as C3f fragment, resulted overexpressed in low PSA PCa patients.

CONCLUSIONS

PSA test, coupled to MALDI profiling, is able to detect changes, specifically related to PCa, in low molecular weight protein range. Furthermore, for the first time in prostate cancer research, the identification and quantification of the small peptide C3f appears to be relevant for the detection of false negatives, providing an additive diagnostic power to PSA (p<0.01) and suggesting its use in clinical tests.