Li, Jiaqi; Department of Physiology (Augusta University, 2021-07)
      Drug combination therapies can improve drug efficacy, reduce drug dosage, and overcome drug resistance with respect to cancer treatments. Current research strategies to determine which drug combinations have a synergistic effect rely mainly on clinical or empirical experience and screening predefined pools of drugs. Given the number of possible drug combinations, the speed and scope to find new drug combinations are very limited using these methods. Due to the exponential growth in these combinatorials, it is difficult to test all possible outcomes in the lab. Several large-scale public genomic and phenotypic resources that provide data from single drug-treated cells as well as data from small molecules deliver a wealth of cellular response information. This data gives opportunity to overcome limitations of the current methods. The development of a new strategy for advanced data processing and analysis that includes a computational prediction algorithm is highly desirable. Because of this, a program was written that predicts synergistic drug combinations using gene regulatory network knowledge and an operational module unit (OMU) system generated from single drug genomic and phenotypic data. As a proof of principle, we applied the pipeline to a group of anticancer drugs and demonstrated how the algorithm could help researchers efficiently find possible synergistic drug combinations using single drug data to evaluate all possible drug pairs.

      Kodeboyina, Sai Karthik; Department of Physiology (Augusta University, 2021-05)
      Aqueous humor (AH) is a fluid in the anterior and posterior chambers of the eye that contains proteins associated with vision disorders including glaucoma. We performed comprehensive characterization of AH proteins and evaluated their association with optic nerve and visual field changes in glaucoma patients. AH reference database was developed to include proteomic and clinical information from cataract and glaucoma patients. Aqueous humor samples from 251 cataract and glaucoma patients were analyzed using Liquid-Chromatography Mass spectrometry (LC-MS/MS). Retinal nerve fiber layer (RNFL) thickness was evaluated with the SPECTRALIS Tracking Laser Tomography. Optic nerve head imaging was performed using Heidelberg Retinal Tomograph (HRT). Visual fields were analyzed with the Humphrey Visual Field analyzer. Statistical analyses were performed to discover the relationship between AH proteins and demographic characteristics, RNFL, optic nerve, and visual field parameters. AH reference database and website was developed using standard software tools including Visual Studio, ASP.NET, SQL, C#, and HTML. A total of 1774 unique proteins were identified in 251 AH samples of which 233 proteins were expressed in at least half of samples. Five protein families were discovered including apolipoproteins, complements, immunoglobulins, serine protease inhibitors (SERPINS) and insulin like growth factors (IGF). A total of 38 proteins significantly correlated with at least one RNFL thickness measure including average, inferior and superior thicknesses. Similarly, 62 proteins significantly associated with at least one HRT parameter such as cup shape measure, cup-disc area ratio and rim area. A total of 11, 9, 7, and 6 proteins were significantly correlated with pattern standard deviation, visual field index, mean deviation, and glaucoma hemifield test respectively. Strongly associated proteins include APOD, APOH, C4A, C4B, C7, IKHV3-9, IGKV2-28, SERPINA1, SERPINF1, IGFBP6, and IGFBP7. They are involved in immune responses, signaling, binding, and metabolic functions. These findings provide targets for future studies investigating molecular mechanisms and new therapies for glaucoma. Moreover, the database would serve as a resource for researchers pursuing AH proteomic and glaucoma studies.