Durmuş, H., Clemen, C.S.,Önay Uçar, E. Hofmann, A., Schlötzer-Schrehardt, U., Mertoğlu, E., Ceylaner, S., Dursun, M., Sewry, C.A., Schröder, R., Parman, Y. (2025) J. Neuromusc. Dis., in press

Homozygous KY mutations cause congenital myopathy and hereditary spastic paraplegia. We report the findings in two families harbouring the homozygous missense NM_178554.4:c.727T>C p.(Cys243Arg) and splice site NM_178554.4:c.710+1G>A KY mutations leading to early-onset myopathy with equinovarus deformity, lateral tongue atrophy, kyphoscoliosis, and contractures. Myopathological examination showed a myopathic pattern in conjunction with fibers containing eosinophilic sarcoplasmic inclusions positive for kyphoscoliosis peptidase and filamin-C but not desmin, myofibrillar degeneration, and focal mitochondrial loss. Kyphoscoliosis peptidase protein expression levels were markedly reduced, and in silico analysis of the predicted protein variants suggested impairment of the kyphoscoliosis peptidase catalytic triad.

Hofmann, A., Rietsch, J., Haase, I. (2025) NPJ Sci. Food 9, 123

Efforts in the filing and organisation of primary data, despite being a pillar of digital transformation, appear to be less pronounced when it comes to unstructured or text-based data. This situation is contrasted by the demand for knowledge management in organisational units operating at the interface between different stakeholders, including, for instance, governmental authorities. Supporting the specific objectives of our institution, we have developed a central platform that serves as an information management system for food. By providing a flexible infrastructure for the management of text-based information, as well as an interactive and intuitive user interface for application and integration into every-day business, we have implemented a system that facilitates building of a data pool of information relevant for food and feed fraud/authenticity. While transforming the institutional information management within the NRZ-Authent, the platform allows access to the data pool by external partners at national and, potentially, European or international level.

Taki, A.C., Kapp, L., Hall, R.S., Byrne, J.J., Sleebs, B.E., Chang, B.C.H., Gasser, R.B., Hofmann, A. (2025) Int. J. Mol. Sci. 26, 3134

The control of socioeconomically important parasitic roundworms (nematodes) of animals has become challenging or ineffective due to problems associated with widespread resistance in these worms to most classes of chemotherapeutic drugs (anthelmintics) currently available. Thus, there is an urgent need to discover and develop novel compounds with unique mechanisms of action to underpin effective parasite control programmes. Here, we evaluated an in silico (computational) approach to accelerate the discovery of new anthelmintics against the parasitic nematode Haemonchus contortus (barber’s pole worm) as a model system. Using a supervised machine learning workflow, we trained and assessed a multi-layer perceptron classifier on a labelled dataset of 15,000 small-molecule compounds, for which extensive bioactivity data were previously obtained for H. contortus via high-throughput screening, as well as evidence-based datasets from the peer-reviewed literature. This model achieved 83% precision and 81% recall on the class of ‘active’ compounds during testing, despite a high imbalance in the training data, with only 1% of compounds carrying this label. The trained model was then used to infer nematocidal candidates by in silico screening of 14.2 million compounds from the ZINC15 database. An experimental assessment of 10 of these candidates showed significant inhibitory effects on the motility and development of H. contortus larvae and adults in vitro, with two compounds exhibiting high potency for further exploration as lead candidates. These findings indicate that the present machine learning-based approach could accelerate the in silico prediction and prioritisation of anthelmintic small molecules for subsequent in vitro and in vivo validations.