In silico pharmacogenetic approach: The natalizumab case study

A Structural Study on the Listeria Monocytogenes Internalin A-Human E-cadherin Interaction: A Molecular Tool to Investigate the Effects of Missense Mutations

Listeria monocytogenes is a widespread foodborne pathogen of high concern and internalin A is an important virulence factor that mediates cell invasion upon the interaction with the host protein E-cadherin. Nonsense mutations of internalin A are known to reduce virulence. Although missense mutations are largely overlooked, they need to be investigated in respect to their effects in cell invasion processes. This work presented a computational workflow to early characterize internalin A missense mutations. The method reliably estimated the effects of a set of engineered missense mutations in terms of their effects on internalin A–E-cadherin interaction. Then, the effects of mutations of an internalin A variant from a L. monocytogenes isolate were calculated. Mutations showed impairing effects on complex stability providing a mechanistic explanation of the low cells invasion capacity previously observed. Overall, our results provided a rational approach to explain the effects of internalin A missense mutations. Moreover, our findings highlighted that the strength of interaction may not directly relate to the cell invasion capacity reflecting the non-exclusive role of internalin A in determining the virulence of L. monocytogenes. The workflow could be extended to other virulence factors providing a promising platform to support a better molecular understanding of L. monocytogenes epidemiology.

Identification of CD4+ T cell biomarkers for predicting the response of patients with relapsing‑remitting multiple sclerosis to natalizumab treatment

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system of autoimmune etiopathogenesis, and is characterized by various neurological symptoms. Glatiramer acetate and interferon-β are administered as first-line treatments for this disease. In non-responsive patients, several second-line therapies are available, including natalizumab; however, a percentage of MS patients does not respond, or respond partially. Therefore, it is of the utmost importance to develop a diagnostic test for the prediction of drug response in patients suffering from complex diseases, such as MS, where several therapeutic options are already available. By a machine learning approach, the UnCorrelated Shrunken Centroid algorithm was applied to identify a subset of genes of CD4⁺ T cells that may predict the pharmacological response of relapsing-remitting MS patients to natalizumab, before the initiation of therapy. The results from the present study may provide a basis for the design of personalized therapeutic strategies for patients with MS.