No abstract

Domoic acid toxicosis (DAT) in California sea lions (Zalophus californianus) is caused by exposure to the marine biotoxin domoic acid and has been linked to massive stranding events and mortality. Diagnosis is based on clinical signs in addition to the presence of domoic acid in body fluids. Chronic DAT further is characterized by reoccurring seizures progressing to status epilepticus. Diagnosis of chronic DAT is often slow and problematic, and minimally invasive tests for DAT have been the focus of numerous recent biomarker studies. The goal of this study was to retrospectively profile plasma proteins in a population of sea lions with chronic DAT and those without DAT using two dimensional gel electrophoresis to discover whether individual, multiple, or combinations of protein and clinical data could be utilized to identify sea lions with DAT. Using a training set of 32 sea lion sera, 20 proteins and their isoforms were identified that were significantly different between the two groups (p<0.05). Interestingly, 11 apolipoprotein E (ApoE) charge forms were decreased in DAT samples, indicating that ApoE charge form distributions may be important in the progression of DAT. In order to develop a classifier of chronic DAT, an independent blinded test set of 20 sea lions, seven with chronic DAT, was used to validate models utilizing ApoE charge forms and eosinophil counts. The resulting support vector machine had high sensitivity (85.7% with 92.3% negative predictive value) and high specificity (92.3% with 85.7% positive predictive value). These results suggest that ApoE and eosinophil counts along with machine learning can perform as a robust and accurate tool to diagnose chronic DAT. Although this analysis is specifically focused on blood biomarkers and routine clinical data, the results demonstrate promise for future studies combining additional variables in multidimensional space to create robust classifiers.

Proteomic studies including marine mammals are rare, largely due to the lack of fully sequenced genomes. This has hampered the application of these techniques toward biomarker discovery efforts for monitoring of health and disease in these animals. We conducted a pilot label-free LC-MS/MS study to profile and compare the cerebrospinal fluid from California sea lions with domoic acid toxicosis (DAT) and without DAT. Across 11 samples, a total of 206 proteins were identified (FDR<0.1) using a composite mammalian database. Several peptide identifications were validated using stable isotope labeled peptides. Comparison of spectral counts revealed seven proteins that were elevated in the cerebrospinal fluid from sea lions with DAT: complement C3, complement factor B, dickkopf-3, malate dehydrogenase 1, neuron cell adhesion molecule 1, gelsolin, and neuronal cell adhesion molecule. Immunoblot analysis found reelin to be depressed in the cerebrospinal fluid from California sea lions with DAT. Mice administered domoic acid also had lower hippocampal reelin protein levels suggesting that domoic acid depresses reelin similar to kainic acid. In summary, proteomic analysis of cerebrospinal fluid in marine mammals is a useful tool to characterize the underlying molecular pathology of neurodegenerative disease. All MS data have been deposited in the ProteomeXchange with identifier PXD002105 (http://proteomecentral.proteomexchange.org/dataset/PXD002105).

BackgroundThere are currently no reliable markers of acute domoic acid toxicosis (DAT) for California sea lions. We investigated whether patterns of serum peptides could diagnose acute DAT. Serum peptides were analyzed by MALDI-TOF mass spectrometry from 107 sea lions (acute DAT n = 34; non-DAT n = 73). Artificial neural networks (ANN) were trained using MALDI-TOF data. Individual peaks and neural networks were qualified using an independent test set (n = 20).ResultsNo single peak was a good classifier of acute DAT, and ANN models were the best predictors of acute DAT. Performance measures for a single median ANN were: sensitivity, 100%; specificity, 60%; positive predictive value, 71%; negative predictive value, 100%. When 101 ANNs were combined and allowed to vote for the outcome, the performance measures were: sensitivity, 30%; specificity, 100%; positive predictive value, 100%; negative predictive value, 59%.ConclusionsThese results suggest that MALDI-TOF peptide profiling and neural networks can perform either as a highly sensitive (100% negative predictive value) or a highly specific (100% positive predictive value) diagnostic tool for acute DAT. This also suggests that machine learning directed by populations of predictive models offer the ability to modulate the predictive effort into a specific type of error.