A C-terminal peptide from type I interferon protects the retina in a mouse model of autoimmune uveitis

Recent and future perspectives on engineering interferons and other cytokines as therapeutics

As crucial mediators and regulators of our immune system, cytokines are involved in a broad range of biological processes and are implicated in various disease pathologies. The field of cytokine therapeutics has gained much momentum from the maturation of conventional protein engineering methodologies such as structure-based designs and/or directed evolution, which is further aided by the advent of in silico protein designs and characterization. Just within the past 5 years, there has been an explosion of proof-of-concept, preclinical, and clinical studies that utilize an armory of protein engineering methods to develop cytokine-based drugs. Here, we highlight the key engineering strategies undertaken by recent studies that aim to improve the pharmacodynamic and pharmacokinetic profile of interferons and other cytokines as therapeutics.

Low-dose melittin is safe for intravitreal administration and ameliorates inflammation in an experimental model of uveitis

Uveitis is a group of sight-threatening ocular inflammatory disorders, whose mainstay of therapy is associated with severe adverse events, prompting the investigation of alternative treatments. The peptide melittin (MEL) is the major component of Apis mellifera bee venom and presents anti-inflammatory and antiangiogenic activities, with possible application in ophthalmology. This work aims to investigate the potential of intravitreal MEL in the treatment of ocular diseases involving inflammatory processes, especially uveitis. Safety of MEL was assessed in retinal cells, chick embryo chorioallantoic membranes, and rats. MEL at concentrations safe for intravitreal administration showed an antiangiogenic activity in the chorioallantoic membrane model comparable to bevacizumab, used as positive control. A protective anti-inflammatory effect in retinal cells stimulated with lipopolysaccharide (LPS) was also observed, without toxic effects. Finally, rats with bacille Calmette-Guerin- (BCG) induced uveitis treated with intravitreal MEL showed attenuated disease progression and improvement of clinical, morphological, and functional parameters, in addition to decreased levels of proinflammatory mediators in the posterior segment of the eye. These effects were comparable to the response observed with corticosteroid treatment. Therefore, MEL presents adequate safety profile for intraocular administration and has therapeutic potential as an anti-inflammatory and antiangiogenic agent for ocular diseases.

•

Melittin at low concentration is safe for intravitreal administration.

•

The antiangiogenic effect of melittin on the chorioallantoic membrane model is comparable to bevacizumab.

•

Melittin protects retinal cells from inflammatory response induced by lipopolysaccharide.

•

Melittin improves clinical, functional and morphological signs of inflammation in rats with BCG-induced uveitis.

Corneal Application of R9-SOCS1-KIR Peptide Alleviates Endotoxin-Induced Uveitis

Purpose

Uveitis is an ocular inflammation that can affect individuals of all ages and is a major cause of blindness. We have tested the therapeutic efficacy of a cell penetrating peptide from the kinase inhibitory region of suppressor of cytokine signaling 1, denoted as R9-SOCS1-KIR.

Methods

We stimulated J774A.1 cells with lipopolysaccharide (LPS) in the presence of R9-SOCS1-KIR or its inactive control peptide. Effect on inflammatory pathways was followed by the nuclear translocation of nuclear factor κB p65 subunit and phosphorylated-p38. Synthesis of inflammatory markers induced by LPS was tested by reverse transcriptase polymerase chain reaction, Western blot analysis, and ELISA of cell supernatants. We monitored effects on the barrier properties of a differentiated ARPE-19 monolayer treated with LPS. We treated C57BL/6 mice topically with either R9-SOCS1-KIR or vehicle and injected their eyes intravitreally with LPS. Eyes were analyzed by fundoscopy, fluorescein angiography, optical coherence tomography, histology, Western blotting, multiplex enzyme-linked immunosorbent assay, and flow cytometry.

Results

Treatment with R9-SOCS1-KIR resulted in suppression of signaling through nuclear factor κB and p-p38 pathways. R9-SOCS1-KIR suppressed the expression of inflammatory genes, the secretion of inflammatory makers such as nitric oxide, and IL-1β induced by LPS. Increased permeability of retinal pigment epithelial cell monolayers was prevented. Corneal administration of R9-SOCS1-KIR blocked the acute inflammation observed in LPS-injected mouse eyes.

Conclusions

Treatment with R9-SOCS1-KIR alleviated the inflammatory responses in cell culture. Topical delivery of this peptide on mouse eyes protected against LPS-induced damage.

Translational Relevance

Topical delivery of R9-SOCS1-KIR peptide allows the patient to self-administer the drug, while preventing any systemic effects on unrelated organs.

Identifying Mouse Autoimmune Uveitis from Fundus Photographs Using Deep Learning

Purpose

To develop a deep learning model for objective evaluation of experimental autoimmune uveitis (EAU), the animal model of posterior uveitis that reveals its essential pathological features via fundus photographs.

Methods

We developed a deep learning construct to identify uveitis using reference mouse fundus images and further categorized the severity levels of disease into mild and severe EAU. We evaluated the performance of the model using the area under the receiver operating characteristic curve (AUC) and confusion matrices. We further assessed the clinical relevance of the model by visualizing the principal components of features at different layers and through the use of gradient-weighted class activation maps, which presented retinal regions having the most significant influence on the model.

Results

Our model was trained, validated, and tested on 1500 fundus images (training, 1200; validation, 150; testing, 150) and achieved an average AUC of 0.98 for identifying the normal, trace (small and local lesions), and disease classes (large and spreading lesions). The AUCs of the model using an independent subset with 180 images were 1.00 (95% confidence interval [CI], 0.99-1.00), 0.97 (95% CI, 0.94-0.99), and 0.96 (95% CI, 0.90-1.00) for the normal, trace and disease classes, respectively.

Conclusions

The proposed deep learning model is able to identify three severity levels of EAU with high accuracy. The model also achieved high accuracy on independent validation subsets, reflecting a substantial degree of generalizability.

Translational Relevance

The proposed model represents an important new tool for use in animal medical research and provides a step toward clinical uveitis identification in clinical practice.