Collagen XIα1 and the Stem Cell Theory of Cancer

Cancer stem cell (CSC) theory hypothesizes that heterogeneity within tumors is not a mere consequence of random mutation and clonal evolution, but results from an intrinsic hierarchy of cells. The KeWe cell line was isolated and characterized in Dr. Oxford’s lab. Characterization has included the determination of conditions for maintenance in cell culture for extended periods of time and using different techniques to count the cells to characterize cellular proliferation rates. Collagen XIα1 can be found throughout the body in a variety of places including tendons, skin, ligaments, interstitial tissue, dentin, blood vessels, the cornea, intervertebral discs, muscle, bone, and cartilage. The purpose of this research is to examine the proliferation rates of the KeWe cell line and analyze them to see if they meet the criteria of stem cells and would therefore provide a model system for the investigation of the stem cell theory of cancer. In order to fulfill this research, confirming the high proliferation rate in the cells and identifying the signaling pathways that are active will be the first steps. After confirming the stem cell nature of the KeWe cell line, we propose to use Collagen XIα1 to control stem cell-like behaviors that are important in cancer initiation and progression. Changes in gene and protein expression will be analyzed using high throughput qPCR and mass spectrometry. Collagens are the most abundant protein in the body, and changes in the Collagen XIα1 expression have been identified in cancers and may play a role in disease progression

Cardiac Repair and Regenerative Potential in the Goldfish (Carassius auratus) Heart

The remarkable ability of the heart to regenerate has been demonstrated in the zebrafish and giant danio, two fish members of the cyprinid family. Here we use light and electron microscopy to examine the repair response in the heart of another cyprinid, the goldfish (Carassius auratus), following cautery injury to a small portion of its ventricularmyocardium. We observed a robust inflammatory response in the first two weeks consisting primarily of infiltrating macrophages, heterophils, and melanomacrophages. These inflammatory cells were identified in the lumen of the spongy heart, within the site of the wound, and attached to endocardial cells adjacent to the site of injury. Marked accumulation of collagen fibers and increased connective tissue were also observed during the first and second weeks in a transition zone between healthy and injured myocardium as well as in adjacent sub-epicardial regions. The accumulation of collagen and connective tissue however did not persist. The presence of capillaries was also noted in the injured area during repair. The replacement of the cauterized region of the ventricle by myocardial tissue was achieved in 6 weeks. The presence of ethynyl deoxyuridinepositive cardiac myocytes and partially differentiated cardiac myocytes during repair suggest effective cardiac myocyte driven regeneration mechanisms also operate in the injured goldfish heart, and are similar to those observed in zebrafish and giant danio. Our data suggest the ability for cardiac regeneration may be widely conserved among cyprinids

Development of the InTelligence And Machine LEarning (TAME) Toolkit for Introductory Data Science, Chemical-Biological Analyses, Predictive Modeling, and Database Mining for Environmental Health Research

Research in environmental health is becoming increasingly reliant upon data science and computational methods that can more efficiently extract information from complex datasets. Data science and computational methods can be leveraged to better identify relationships between exposures to stressors in the environment and human disease outcomes, representing critical information needed to protect and improve global public health. Still, there remains a critical gap surrounding the training of researchers on these in silico methods. We aimed to address this gap by developing the inTelligence And Machine lEarning (TAME) Toolkit, promoting trainee-driven data generation, management, and analysis methods to “TAME” data in environmental health studies. Training modules were developed to provide applications-driven examples of data organization and analysis methods that can be used to address environmental health questions. Target audiences for these modules include students, post-baccalaureate and post-doctorate trainees, and professionals that are interested in expanding their skillset to include recent advances in data analysis methods relevant to environmental health, toxicology, exposure science, epidemiology, and bioinformatics/cheminformatics. Modules were developed by study coauthors using annotated script and were organized into three chapters within a GitHub Bookdown site. The first chapter of modules focuses on introductory data science, which includes the following topics: setting up R/RStudio and coding in the R environment; data organization basics; finding and visualizing data trends; high-dimensional data visualizations; and Findability, Accessibility, Interoperability, and Reusability (FAIR) data management practices. The second chapter of modules incorporates chemical-biological analyses and predictive modeling, spanning the following methods: dose-response modeling; machine learning and predictive modeling; mixtures analyses; -omics analyses; toxicokinetic modeling; and read-across toxicity predictions. The last chapter of modules was organized to provide examples on environmental health database mining and integration, including chemical exposure, health outcome, and environmental justice indicators. Training modules and associated data are publicly available online (https://uncsrp.github.io/Data-Analysis-Training-Modules/). Together, this resource provides unique opportunities to obtain introductory-level training on current data analysis methods applicable to 21st century science and environmental health