This research focuses on dissecting the molecular mechanism of cardiac regeneration in the animal model, zebrafish, upon a myocardial infarction like injury. Zebrafish are one of the few vertebrates that can fully regenerate their hearts after an injury in 30 days. This phenomenon is not seen in humans, who generate scar tissue after this injury with reduced circulatory efficiency. However, there is evidence that neonatal mice under 7 days old can regenerate their hearts, but this is lost upon adulthood. Determining this pathway is the first step to develop therapeutics in order to provide relief to people suffering from cardiac injuries. In this research, we used chemically ablated transgenic zebrafish to generate a 30% injury. We determined that upon an injury, both the Wnt pathway and the mTOR pathway are sequentially activated and upregulated to restart cardiac proliferation to regenerate the heart. Wnt pathway proteins like Axin and β-catenin are activated 3 days post injury and mTOR proteins like pS6 are activated gradually over 7 days post injury. The inhibition of the Wnt pathway using DKK showed a downregulation of the mTOR pathway and downregulation of cardiomyocyte proliferation. Inhibition of the mTOR pathway using Rapamycin also stopped cardiomyocyte proliferation from occurring. Mass spectrometry data showed a decrease in glutamine and an increase in leucine during the proliferative phase. Since leucine is one of the activators of the mTOR pathway, we see that the glutamine-leucine transporter is also upregulated post-injury. Thus, we show that heart regeneration in adult zebrafish occurs via cardiomyocyte proliferation by using the Wnt and mTOR pathways to upregulate cardiomyocyte proliferation upon injury.

Epigenetic proteins modify the chromatin structure to manipulate gene expression, and dysregulated epigenetic modification in cells has been linked to cancer formation. Previous studies on young female Drosophila have shown that after injury, germline stem cells (GSC) are capable of entering and exiting a protective state called quiescence. When exposed to ionizing radiation (IR), the apoptotic differentiating daughter cells send a protective signal to GSC, resulting in GSC quiescence. This survival behavior of GSCs validates them as a potential model for cancer stem cells, which are a subset of tumor cells that are capable of withstanding traditional chemotherapy through reversible quiescence, resulting in future tumor relapse. To identify genes required for GSC survival, we performed a spatially restricted RNA interference (RNAi) screen. Here we show that two members of the repressive epigenetic regulator complex PRC1, Pc and Sce, are required for entry, while demethylase Utx is required for exit of GSC quiescence. Notably, PRC2 dependent H3K27me3 marks are required for PRC1 function, and Utx is required to erase these PRC2 dependent H3K27me3 marks. Importantly, we detected around a 3-fold increase in H3K27me3 marks in GSC following IR, suggesting that the repressive PRC1-PRC2 dependent complex is critical for entry, and elimination of PRC2 dependent marks is critical for exit from the quiescence state. Furthermore, we show that Trx, a writer enzyme which promotes euchromatin formation through H3K4me1 addition, is required for GSC exit from the quiescent. These data suggest that reversible quiescence in GSC is controlled by specific epigenetic states. In the future, more work is needed to investigate gene specificity of the epigenetic regulation.

The Polycomb Repressive Complex 2 (PRC2) is an important epigenetic remodeler in developmental transitions and cell fate determinations. PRC2 is responsible for the addition of H3K27me3 marks that repress developmental gene expression. The catalytic subunit of PRC2 is the methyltransferase (Enhancer of Zeste 2) EZH2 which binds to EED (Embryonic Ectoderm Development) to methylate H3K27 on gene promoter regions. To investigate the requirement of PRC2 in different developmental transitions, a computationally designed protein was utilized to inhibit EED-EZH2 interaction. The novel designed protein is named EED binder (EB) and competes over endogenous EZH2 on the EED binding cleft with 300 times greater affinity than endogenous EZH2. We cloned EB-GFP under heatshock inducible promoter and injected this construct to one cell zebrafish embryos to generate a germ line transmissible insertion. To study the requirement of PRC2 in early developing embryos (0-3dpf), we applied heatshock (HS) on EB-GFP positive and negative embryos. Western blot analysis revealed global downregulation of EZH2 and H3K27me3 in EB-GFP positive, but not control embryos. Additionally, Co-Immunoprecipitation experiments showed EB-GFP binding to EED. Finally, to test the requirement of PRC2 in caudal fin regeneration, adult (5 month old) EB-GFP positive and negative animals were fin-amputated and the regeneration growth rate was measured for 14 days. Our results show that EB-GFP positive fish were able to regenerate their fins faster, resulting in a large fin size compared to either negative or non-HS clutch mate. Overall, we have developed a computer designed inducible PRC2 inhibitory system to study PRC2 function in Zebrafish, at the whole animal level. In the future, we will utilize EB-GFP to explore PRC2 and other epigenetic modifiers that are required for tissue and organ regeneration before and after injury.

Our goal is to predictively engineer bio/nanomaterial hybrid systems with targeted functionality in a wide range of practical, technical, and medical applications. The open literature provides datasets of the functional properties of crystals, aqueous chemicals, and biological macromolecules, but the design of hybrid systems necessitates the modeling of all of these molecular species in a single common framework. Molecular graph convolutional networks and other deep learning methods are capable to train on datasets from multiple disciplines simultaneously, but in order to build these networks, a far-reaching data infrastructure is needed. We have created this infrastructure for three data sets: The Immune Epitope Database (IEDB) of MHC-I binding peptides, the Quantum-Machine.org QM9 dataset (QM9), and results extracted from the Materials Project. The IEDB provides binding affinities between biological macromolecules (peptide sequences in association with multiple MHC-I alleles); QM9 consists of 140,000 small organic molecules encoded as SMILES strings and 17 associated properties (including thermodynamic, energetic, geometric, and electronic information). The Materials Project dataset provides band gaps and formation energies for 70,000 crystal structures. We present a standardized train/test split and machine-learning-ready import interface for each of these datasets, as well as early results on co- and cross-training of deep neural networks across multiple datasets. The framework is expandable to new datasets and provides a strong foundation for ongoing efforts to build universal molecular encoding neural networks.

The ease of data retrieval, analysis, and distribution can accelerate the pace of scientific research. A difference in philosophies and methodologies in the manner of data-collection and storage leads to a lack of semantic-consistency in naming-conventions across disciplines. Previous studies have opened this discussion within various research disciplines, however there has yet to be a study accomplished within a Convergence Science Lab such ours, Genetically Engineered Materials Science and Engineering Center (GEMSEC). Well-defined naming-conventions, clear-cut data standards, and set programmatic interfaces are required to provide improved data access to researchers and the public. This consistency is necessary to create a robust database with the ability to query large amounts of related information. The key point is that integrating design (of variables, research questions, etc.) with traditional, empirical research approaches in the natural sciences will result in more robust data and clearer analysis. Here we discuss the need for standardized protocol and metadata standards for data-collection and storage. We began by creating a data analysis and storage pipeline for two computationally involved experiments, Molecular Dynamics (MD) and Next Generation Sequencing (NGS), within GEMSEC. A database schema storing metadata associated with raw, cleaned, and analyzed files was created. We found homogenous collections of metadata with inconsistencies, especially in peptide naming conventions, between experiments. The target-database includes those from other labs some of which may store unconventional file types, use other naming schemes for key variables such as sequences, and have different standards for what metadata is stored and what is computationally retrieved at a later time. This schema is formed via interviewing various other researchers, determining similarities and differences within metadata, and creating a standard for all to use based on this collected information.

Major Histocompatibility Complexes (MHC) are transmembrane proteins that utilize a selective binding domain to recognize peptide fragments in the cell environment and display these antigens on the cell surface. This selectivity of binding to different substrates is a feature that would be highly useful to mimic in the realm of genetically engineered peptide-solid surface binding, with broad implications applicable to engineered biomimetic systems. Our goal is to engineer selective binding biological molecules by mimicking the characteristics of the MHC-1 protein binding slot. The conventional approach to this problem applies directed evolution in a lab setting, selecting mutant MHCs with higher binding affinity against an antigen of interest. This method is resource and time intensive. Instead, we propose a machine learning approach to this problem via molecular graph convolutional neural networks (MGCNs) which are structured just like the connected atoms of input molecules. To explicitly model the MHC-peptide binding event as a graph, it is necessary to find computationally tenable representations of the MHC binding site. Prior attempts represented MHC binding alleles using only the critical contact residue positions of the MHC. This method omits the protein architecture, making it untenable as a graph encoding strategy. In this study, we evaluate alternate approaches to generate graph encodings of the two actively-binding alpha helices in the MHC-I complex and evaluate their performance on the task of predicting antigen binding affinity. We present an open Python toolset for generating graphs of MHC-I alpha helices and preliminary evaluation of their performance on a regression task on the Immune Epitope Database MHC-I dataset. The ability to generate de novo binding molecules for unique surfaces such as cancer surface proteins, viral spike proteins, metallic surfaces etc. has various use cases in: diagnostics, therapeutics, and engineered biomimetic systems.