Genotype‒Structurotype‒Phenotype Correlations in Patients with Pachyonychia Congenita.

Pachyonychia congenita (PC) is a genetic disorder of keratin that presents with nail dystrophy, painful palmoplantar keratoderma, and other clinical manifestations. We investigated the genotype‒structurotype‒phenotype correlations seen with mutations in keratin genes (keratin [K]6A, K6B, K6C, K16, K17) and utilized protein structure modeling of high-frequency mutations to examine the functional importance of keratin structural domains in PC pathogenesis. Participants of the International PC Research Registry underwent genetic testing and completed a standardized survey on their symptoms. Our results support previous reports associating oral leukokeratosis with K6A mutations and cutaneous cysts, follicular hyperkeratosis, and natal teeth with K17 mutations. Painful keratoderma was prominent with K6A and K16 mutations. Nail involvement was most common in patients with K6A mutation and least common in those with K6C mutation. Across keratin subtypes, patients with coil 2B mutations had the greatest impairment in ambulation, and patients with coil 1A mutations reported more emotional issues. Molecular modeling demonstrated that hotspot missense mutations in PC largely disrupted hydrophobic interactions or surface charge. The former may destabilize keratin dimers/tetramers, whereas the latter likely interferes with higher-order keratin filament formation. Understanding the pathologic alterations in keratin structure improves our knowledge of how PC genotype correlates with clinical phenotype, advancing insight into disease pathogenesis and therapeutic development.

Molecular Modeling of Pathogenic Mutations in the Keratin 1B Domain.

Keratin intermediate filaments constitute the primary cytoskeletal component of epithelial cells. Numerous human disease phenotypes related to keratin mutation remain mechanistically elusive. Our recent crystal structures of the helix 1B heterotetramer from keratin 1/10 enabled further investigation of the effect of pathologic 1B domain mutations on keratin structure. We used our highest resolution keratin 1B structure as a template for homology-modeling the 1B heterotetramers of keratin 5/14 (associated with blistering skin disorders), keratin 8/18 (associated with liver disease), and keratin 74/28 (associated with hair disorder). Each structure was examined for the molecular alterations caused by incorporating pathogenic 1B keratin mutations. Structural modeling indicated keratin 1B mutations can harm the heterodimer interface (R265PK5, L311RK5, R211PK14, I150VK18), the tetramer interface (F231LK1, F274SK74), or higher-order interactions needed for mature filament formation (S233LK1, L311RK5, Q169EK8, H128LK18). The biochemical changes included altered hydrophobic and electrostatic interactions, and altered surface charge, hydrophobicity or contour. Together, these findings advance the genotype-structurotype-phenotype correlation for keratin-based human diseases.

Structural Basis for How Biologic Medicines Bind their Targets in Psoriasis Therapy.

As biologic therapies become first line treatments for many inflammatory disorders, it becomes increasingly important for the practicing physician to be familiar with how these drugs function at the molecular level. This information is useful in making therapeutic decisions and helping patients understand their treatment options. It is critical to patient safety and clinical response that the molecular differences between these drugs inform prescribing practices. To this end, we present and analyze the available structural biology information about the biologics used in the treatment of psoriasis including inhibitors of tumor necrosis factor alpha (TNFα), interleukin-17 (IL-17), and interleukin-23 (IL-23). We describe and analyze the molecular surface character of known binding epitopes for medications in these classes, showing that significant differences exist in epitope location, hydrophobicity, and charge. Some of these differences can be correlated with clinical data, but our analysis ultimately points to the need for more structural information to allow for a better understanding of the structure-function relationship of biologic therapies.