Rational Design of a DNA-Scaffolded High-Affinity Binder for Langerin.

Binders of langerin could target vaccines to Langerhans cells for improved therapeutic effect. Since langerin has low affinity for monovalent glycan ligands, highly multivalent presentation has previously been key for targeting. Aiming to reduce the amount of ligand required, we rationally designed molecularly defined high-affinity binders based on the precise display of glycomimetic ligands (Glc2NTs) on DNA-PNA scaffolds. Rather than mimicking langerin's homotrimeric structure with a C3-symmetric scaffold, we developed readily accessible, easy-to-design bivalent binders. The method considers the requirements for bridging sugar binding sites and statistical rebinding as a means to both strengthen the interactions at single binding sites and amplify the avidity enhancement provided by chelation. This gave a 1150-fold net improvement over the affinity of the free ligand and provided a nanomolar binder (IC50 =300 nM) for specific internalization by langerin-expressing cells.

Identification of Multiple Druggable Secondary Sites by Fragment Screening against DC-SIGN.

DC-SIGN is a cell-surface receptor for several pathogenic threats, such as HIV, Ebola virus, or Mycobacterium tuberculosis. Multiple attempts to develop inhibitors of the underlying carbohydrate-protein interactions have been undertaken in the past fifteen years. Still, drug-like DC-SIGN ligands are sparse, which is most likely due to its hydrophilic, solvent-exposed carbohydrate-binding site. Herein, we report on a parallel fragment screening against DC-SIGN applying SPR and a reporter displacement assay, which complements previous screenings using 19 F NMR spectroscopy and chemical fragment microarrays. Hit validation by SPR and 1 H-15 N HSQC NMR spectroscopy revealed that although no fragment bound in the primary carbohydrate site, five secondary sites are available to harbor drug-like molecules. Building on key interactions of the reported fragment hits, these pockets will be targeted in future approaches to accelerate the development of DC-SIGN inhibitors.

Targeting Longevity Gene SLC13A5: A Novel Approach to Prevent Age-Related Bone Fragility and Osteoporosis.

Reduced expression of the plasma membrane citrate transporter SLC13A5, also known as INDY, has been linked to increased longevity and mitigated age-related cardiovascular and metabolic diseases. Citrate, a vital component of the tricarboxylic acid cycle, constitutes 1-5% of bone weight, binding to mineral apatite surfaces. Our previous research highlighted osteoblasts' specialized metabolic pathway facilitated by SLC13A5 regulating citrate uptake, production, and deposition within bones. Disrupting this pathway impairs bone mineralization in young mice. New Mendelian randomization analysis using UK Biobank data indicated that SNPs linked to reduced SLC13A5 function lowered osteoporosis risk. Comparative studies of young (10 weeks) and middle-aged (52 weeks) osteocalcin-cre-driven osteoblast-specific Slc13a5 knockout mice (Slc13a5cKO) showed a sexual dimorphism: while middle-aged females exhibited improved elasticity, middle-aged males demonstrated enhanced bone strength due to reduced SLC13A5 function. These findings suggest reduced SLC13A5 function could attenuate age-related bone fragility, advocating for SLC13A5 inhibition as a potential osteoporosis treatment.

Exploring disease-causing traits for drug repurposing in critically ill COVID-19 patients: A causal inference approach.

Despite recent development of vaccines to prevent SARS-CoV-2 infection, treatment of critically ill COVID-19 patients remains an important goal. In principle, genome-wide association studies (GWASs) provide a shortcut to the clinical evidence needed to repurpose existing drugs; however, genes identified frequently lack a causal disease link. We report an alternative method for finding drug repurposing targets, focusing on disease-causing traits beyond immediate disease genetics. Sixty blood cell types and biochemistries, and body mass index, were screened on a cohort of critically ill COVID-19 cases and controls that exhibited mild symptoms after infection, yielding high neutrophil cell count as a possible causal trait for critical illness. Our methodology identified CDK6 and janus kinase (JAK) inhibitors as treatment targets that were validated in an ex vivo neutrophil extracellular trap (NET) formation assay. Our methodology demonstrates the increased power for drug target identification by leveraging large disease-causing trait datasets.

Interaction-Based Feature Selection Algorithm Outperforms Polygenic Risk Score in Predicting Parkinson's Disease Status.

Polygenic risk scores (PRS) aggregating results from genome-wide association studies are the state of the art in the prediction of susceptibility to complex traits or diseases, yet their predictive performance is limited for various reasons, not least of which is their failure to incorporate the effects of gene-gene interactions. Novel machine learning algorithms that use large amounts of data promise to find gene-gene interactions in order to build models with better predictive performance than PRS. Here, we present a data preprocessing step by using data-mining of contextual information to reduce the number of features, enabling machine learning algorithms to identify gene-gene interactions. We applied our approach to the Parkinson's Progression Markers Initiative (PPMI) dataset, an observational clinical study of 471 genotyped subjects (368 cases and 152 controls). With an AUC of 0.85 (95% CI = [0.72; 0.96]), the interaction-based prediction model outperforms the PRS (AUC of 0.58 (95% CI = [0.42; 0.81])). Furthermore, feature importance analysis of the model provided insights into the mechanism of Parkinson's disease. For instance, the model revealed an interaction of previously described drug target candidate genes TMEM175 and GAPDHP25. These results demonstrate that interaction-based machine learning models can improve genetic prediction models and might provide an answer to the missing heritability problem.

CellFy: A Cell-Based Fragment Screen against C-Type Lectins.

Fragment-based drug discovery is a powerful complement to conventional high-throughput screening, especially for difficult targets. Screening low-molecular-weight fragments usually requires highly sensitive biophysical methods, because of the generally low affinity of the identified ligands. Here, we developed a cell-based fragment screening assay (cellFy) that allows sensitive identification of fragment hits in a physiologically more relevant environment, in contrast to isolated target screenings in solution. For this, a fluorescently labeled multivalent reporter was employed, enabling direct measurement of displacement by low-molecular-weight fragments without requiring enzymatic reactions or receptor activation. We applied this technique to identify hits against two challenging targets of the C-type lectin receptor (CLR) family: Dendritic Cell-Specific Intercellular adhesion molecule-3-Grabbing Nonintegrin (DC-SIGN) and Langerin. Both receptors are involved in pathogen recognition and initiation of an immune response, which renders them attractive targets for immune modulation. Because of their shallow and hydrophilic primary binding site, hit identification for CLRs is challenging and druglike ligands for CLRs are sparse. Screening of a fragment library followed by hit validation identified several promising candidates for further fragment evolution for DC-SIGN. In addition, a multiplexed assay format was developed for simultaneous screening against multiple CLRs, allowing a selectivity counterscreening. Overall, this sensitive cell-based fragment screening assay provides a powerful tool for rapid identification of bioactive fragments, even for difficult targets.