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The helminth endoparasites of many European amphibian species are often known exclusively from morphological descriptions. A molecular library of DNA sequence data linked to morphological identifications is still in its infancy. In this paper, we aim to contribute to such a library on the smooth newt Lissotriton vulgaris, the intermediate and definitive host of 31 helminth parasites, according to evidence published so far. Newts (n = 69) were collected at two study sites in western Germany and examined for the presence of helminths. A total of five helminth species were detected in 56 (81%) of the newts, but only one or two species infected a single host. Four out of five helminth species were identified morphologically and based on DNA sequences as Parastrigea robusta (metacercariae), Oswaldocruzia filiformis, Megalobatrachonema terdentatum (adults and larvae) and Cosmocerca longicauda, and the corresponding sequences were provided subsequently. Oswaldocruzia molgeta was confirmed to be a junior synonym of O. filiformis. Molecular data on a fifth species (a cosmocercid nematode) that could not be identified at species level were added to GenBank. These findings increased the molecular library on morphologically identified smooth newt parasites significantly, from 12 to 15 entries.

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Computational methods that allow predicting the effects of nonsynonymous substitutions are an integral part of exome studies. Here, we validated and improved their specificity by performing a comprehensive bioinformatics analysis combined with experimental and clinical data on a model of glucokinase (GCK): 8835 putative variations, including 515 disease-associated variations from 1596 families with diagnoses of monogenic diabetes (GCK-MODY) or persistent hyperinsulinemic hypoglycemia of infancy (PHHI), and 126 variations with available or newly reported (19 variations) data on enzyme kinetics. We also proved that high frequency of disease-associated variations found in patients is closely related to their evolutionary conservation. The default set prediction methods predicted correctly the effects of only a part of the GCK-MODY-associated variations and completely failed to predict the normoglycemic or PHHI-associated variations. Therefore, we calculated evidence-based thresholds that improved significantly the specificity of predictions (≤75%). The combined prediction analysis even allowed to distinguish activating from inactivating variations and identified a group of putatively highly pathogenic variations (EVmutation score <−7.5 and SNAP2 score >70), which were surprisingly underrepresented among MODY patients and thus under negative selection during molecular evolution. We suggested and validated the first robust evidence-based thresholds, which allow improved, highly specific predictions of disease-associated GCK variations.