Faculty Bibliography

To investigate the effects and mechanisms of irisin, a newly discovered myokine, in cartilage development, osteoarthritis (OA) pathophysiology and its therapeutic potential for treating OA we applied the following five strategical analyses using (1) murine joint tissues at different developmental stages; (2) human normal and OA pathological tissue samples; (3) experimental OA mouse model; (4) irisin gene knockout (KO) and knock in (KI) mouse lines and their cartilage cells; (5) in vitro mechanistic experiments. We found that Irisin was involved in all stages of cartilage development. Both human and mouse OA tissues showed a decreased expression of irisin. Intra-articular injection of irisin attenuated ACLT-induced OA progression. Irisin knockout mice developed severe OA while irisin overexpression in both irisin KI mice and intraarticular injection of irisin protein attenuated OA progression. Irisin inhibited inflammation and promoted anabolism in chondrogenic ADTC5 cells. Proliferative potential of primary chondrocytes from KI mice was found to be enhanced, while KO mice showed an inhibition under normal or inflammatory conditions. The primary chondrocytes from irisin KI mice showed reduced expression of inflammatory factors and the chondrocytes isolated from KO mice showed an opposite pattern. In conclusion, it is the first time to show that irisin is involved in cartilage development and OA pathogenesis. Irisin has the potential to ameliorate OA progression by decreasing cartilage degradation and inhibiting inflammation, which could lead to the development of a novel therapeutic target for treating bone and cartilage disorders including osteoarthritis.

Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer death in the United States. Glycans, such as carbohydrate antigen 19-9, are biomarkers of PDAC and are emerging as important modulators of cancer phenotypes. Herein, we used a systems-based approach integrating glycomic analysis of the well-established KC mouse, which models early events in transformation, and analysis of samples from human pancreatic cancer patients to identify glycans with potential roles in cancer formation. We observed both common and distinct patterns of glycosylation in pancreatic cancer across species. Common alterations included increased levels of α-2,3-sialic acid and α-2,6-sialic acid, bisecting GlcNAc and poly-N-acetyllactosamine. However, core fucose, which was increased in human PDAC, was not seen in the mouse, indicating that not all human glycomic changes are observed in the KC mouse model. In silico analysis of bulk and single-cell sequencing data identified ST6 beta-galactoside alpha-2,6-sialyltransferase 1, which underlies α-2,6-sialic acid, as overexpressed in human PDAC, concordant with histological data showing higher levels of this enzyme at the earliest stages. To test whether ST6 beta-galactoside alpha-2,6-sialyltransferase 1 promotes pancreatic cancer, we created a novel mouse in which a pancreas-specific genetic deletion of this enzyme overlays the KC mouse model. The analysis of our new model showed delayed cancer formation and a significant reduction in fibrosis. Our results highlight the importance of a strategic systems approach to identifying glycans whose functions can be modeled in mouse, a crucial step in the development of therapeutics targeting glycosylation in pancreatic cancer.

TNFα/TNFR signaling plays a critical role in the pathogenesis of various inflammatory and autoimmune diseases, and anti-TNFα therapies have been accepted as the effective approaches for treating several autoimmune diseases. Progranulin (PGRN), a multi-faced growth factor-like molecule, directly binds to TNFR1 and TNFR2, particularly to the latter with higher affinity than TNFα. PGRN derivative Atsttrin is composed of three TNFR-binding domain of PGRN and exhibits even better therapeutic effects than PGRN in several inflammatory disease models, including collagen-induced arthritis. Herein we describe the detailed methodology of using (1) ELISA-based solid phase protein-protein interaction assay to demonstrate the direct binding of Atsttrin to TNFR2 and its inhibition of TNFα/TNFR2 interaction; and (2) tartrate-resistant acid phosphatase (TRAP) staining of in vitro osteoclastogenesis to reveal the cell-based anti-TNFα activity of Atsttrin. Using the protocol described here, the investigators should be able to reproducibly detect the physical inhibition of TNFα binding to TNFR and the functional inhibition of TNFα activity by Atsttrin and various kinds of TNF inhibitors.

Aims/UNASSIGNED:Histology, the microscopic study of normal tissues, is a crucial element of most medical curricula. Learning tools focused on histology are very important to learners who seek diagnostic competency within this important diagnostic arena. Recent developments in machine learning (ML) suggest that certain ML tools may be able to benefit this histology learning platform. Here, we aim to explore how one such tool based on a convolutional neural network, can be used to build a generalizable multi-classification model capable of classifying microscopic images of human tissue samples with the ultimate goal of providing a differential diagnosis (a list of look-alikes) for each entity. Methods/UNASSIGNED:We obtained three institutional training datasets and one generalizability test dataset, each containing images of histologic tissues in 38 categories. Models were trained on data from single institutions, low quantity combinations of multiple institutions, and high quantity combinations of multiple institutions. Models were tested against withheld validation data, external institutional data, and generalizability test images obtained from Google image search. Performance was measured with macro and micro accuracy, sensitivity, specificity, and f1-score. Results/UNASSIGNED:In this study, we were able to show that such a model's generalizability is dependent on both the training data source variety and the total number of training images used. Models which were trained on 760 images from only a single institution performed well on withheld internal data but poorly on external data (lower generalizability). Increasing data source diversity improved generalizability, even when decreasing data quantity: models trained on 684 images, but from three sources improved generalization accuracy between 4.05% and 18.59%. Maintaining this diversity and increasing the quantity of training images to 2280 further improved generalization accuracy between 16.51% and 32.79%. Conclusions/UNASSIGNED:This pilot study highlights the significance of data diversity within such studies. As expected, optimal models are those that incorporate both diversity and quantity into their platforms.s.

Tyrosinase (TYR) is a copper-containing monooxygenase central to the function of melanocytes. Alterations in its expression or activity contribute to variations in skin, hair and eye color, and underlie a variety of pathogenic pigmentary phenotypes, including several forms of oculocutaneous albinism (OCA). Many of these phenotypes are linked to individual missense mutations causing single nucleotide variants and polymorphisms (SNVs) in TYR. We previously showed that two TYR homologues, TYRP1 and TYRP2, modulate TYR activity and stabilize the TYR protein. Accordingly, to investigate whether TYR, TYRP1, and TYRP2 are biophysically compatible with various heterocomplexes, we computationally docked a high-quality 3D model of TYR to the crystal structure of TYRP1 and to a high-quality 3D model of TYRP2. Remarkably, the resulting TYR-TYRP1 heterodimer was complementary in structure and energy with the TYR-TYRP2 heterodimer, with TYRP1 and TYRP2 docking to different adjacent surfaces on TYR that apposed a third realistic protein interface between TYRP1-TYRP2. Hence, the 3D models are compatible with a heterotrimeric TYR-TYRP1-TYRP2 complex. In addition, this heterotrimeric TYR-TYRP1-TYRP2 positioned the C-terminus of each folded enzymatic domain in an ideal position to allow their C-terminal transmembrane helices to form a putative membrane embedded three-helix bundle. Finally, pathogenic TYR mutations causing OCA1A, which also destabilize TYR biochemically, cluster on an unoccupied protein interface at the periphery of the heterotrimeric complex, suggesting that this may be a docking site for OCA2, an anion channel. Pathogenic OCA2 mutations result in similar phenotypes to those produced by OCA1A TYR mutations. While this complex may be difficult to detect in vitro, due to the complex environment of the vertebrate cellular membranous system, our results support the existence of a heterotrimeric complex in melanogenesis.

Purpose: A critical gap in the adoption of genomic medicine into medical practice is the need for the rigorous evaluation of the utility of genomic medicine interventions.
Method(s): The Implementing Genomics in Practice Pragmatic Trials Network (IGNITE PTN) was formed in 2018 to measure the clinical utility and cost-effectiveness of genomic medicine interventions, to assess approaches for real-world application of genomic medicine in diverse clinical settings, and to produce generalizable knowledge on clinical trials using genomic interventions. Five clinical sites and a coordinating center evaluated trial proposals and developed working groups to enable their implementation.
Result(s): Two pragmatic clinical trials (PCTs) have been initiated, one evaluating genetic risk APOL1 variants in African Americans in the management of their hypertension, and the other to evaluate the use of pharmacogenetic testing for medications to manage acute and chronic pain as well as depression.
Conclusion(s): IGNITE PTN is a network that carries out PCTs in genomic medicine; it is focused on diversity and inclusion of underrepresented minority trial participants; it uses electronic health records and clinical decision support to deliver the interventions. IGNITE PTN will develop the evidence to support (or oppose) the adoption of genomic medicine interventions by patients, providers, and payers.
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