Toward Understanding the Role of Amot80 Lipid Binding in Cellular Proliferation and Migration

poster abstractsAmots are adaptor proteins which coordinate signaling that controls cellular differentiation and proliferation, and their Amot coiled-coil homology (ACCH) domain is able to bind lipids with specificity which leads to membrane deformation and targets transcription factors to the nucleus. Understanding the biophysical mechanisms involved in lipid binding may provide pathways to modulate protein sorting and downstream signaling events inducing cellular differentiation, cancer cell proliferation, and migration. At this time, all work reported on signaling based on Amot expression is unable to distinguish between the role of the Amot80 and the 130 family members as they share a common ACCH domain. The goal of this project is to specifically relate the Amot80 ACCH lipid binding with function related to cancer phenotypes Mutations were carried forward based on lipid sedimentation, FRET, and SAXS assays against the ACCH domain of the protein. Site-directed mutagenesis was then employed to probe the specific contributions of 7 selected lysines and arginines toward lipid head-group binding in the full length protein. The polarity/scaffolding signaling effect of mutations in the Amot80 will be monitored by matrigel, accumulation/cell counting, and titrated thymidine incorporation assays. Cell morphology will be imaged by confocal imaging, and cellular migration will be recorded by video. The effects on YAP1/2 and MAPK activation will be assessed by immunoblot analysis. The changes will then be correlated in extracellular scaffolding and migration with immunoblots and cellular staining. Likewise, effects on proliferation will be monitored by MTT assays. The hypothesis of this aim is that modulation of Amot’s ability to bind selective lipids will interrupt the signaling pathways leading to cellular migration, differentiation, and proliferation. This work was supported by the IUPUI Undergraduate Research Opportunities Program (UROP) and NIH K01CA169078-01

Duplex DNA from Sites of Helicase-Polymerase Uncoupling Links Non-B DNA Structure Formation to Replicative Stress

BACKGROUND: Replication impediments can produce helicase-polymerase uncoupling allowing lagging strand synthesis to continue for as much as 6 kb from the site of the impediment. MATERIALS AND METHODS: We developed a cloning procedure designed to recover fragments from lagging strand near the helicase halt site. RESULTS: A total of 62% of clones from a p53-deficient tumor cell line (PC3) and 33% of the clones from a primary cell line (HPS-19I) were within 5 kb of a G-quadruplex forming sequence. Analyses of a RACK7 gene sequence, that was cloned multiple times from the PC3 line, revealed multiple deletions in region about 1 kb from the cloned region that was present in a non-B conformation. Sequences from the region formed G-quadruplex and i-motif structures under physiological conditions. CONCLUSION: Defects in components of non-B structure suppression systems (e.g. p53 helicase targeting) promote replication-linked damage selectively targeted to sequences prone to G-quadruplex and i-motif formation

Structural validity and reliability of the patient experience measure: A new approach to assessing psychosocial experience of upper limb prosthesis users

Recent advances in upper limb prosthetics include sensory restoration techniques and osseointegration technology that introduce additional risks, higher costs, and longer periods of rehabilitation. To inform regulatory and clinical decision making, validated patient reported outcome measures are required to understand the relative benefits of these interventions. The Patient Experience Measure (PEM) was developed to quantify psychosocial outcomes for research studies on sensory-enabled upper limb prostheses. While the PEM was responsive to changes in prosthesis experience in prior studies, its psychometric properties had not been assessed. Here, the PEM was examined for structural validity and reliability across a large sample of people with upper limb loss (n = 677). The PEM was modified and tested in three phases: initial refinement and cognitive testing, pilot testing, and field testing. Exploratory factor analysis (EFA) was used to discover the underlying factor structure of the PEM items and confirmatory factor analysis (CFA) verified the structure. Rasch partial credit modeling evaluated monotonicity, fit, and magnitude of differential item functioning by age, sex, and prosthesis use for all scales. EFA resulted in a seven-factor solution that was reduced to the following six scales after CFA: social interaction, self-efficacy, embodiment, intuitiveness, wellbeing, and self-consciousness. After removal of two items during Rasch analyses, the overall model fit was acceptable (CFI = 0.973, TLI = 0.979, RMSEA = 0.038). The social interaction, self-efficacy and embodiment scales had strong person reliability (0.81, 0.80 and 0.77), Cronbach\u27s alpha (0.90, 0.80 and 0.71), and intraclass correlation coefficients (0.82, 0.85 and 0.74), respectively. The large sample size and use of contemporary measurement methods enabled identification of unidimensional constructs, differential item functioning by participant characteristics, and the rank ordering of the difficulty of each item in the scales. The PEM enables quantification of critical psychosocial impacts of advanced prosthetic technologies and provides a rigorous foundation for future studies of clinical and prosthetic interventions

Bringing Forecasting Into the Future: Using Google to Predict Visitation in U.S. National Parks

In recent years, visitation to U.S. National Parks has been increasing, with the majority of this increase occurring in a subset of parks. As a result, managers in these parks must respond quickly to increasing visitor-related challenges. Improved visitation forecasting would allow managers to more proactively plan for such increases. In this study, we leverage internet search data that is freely available through Google Trends to create a forecasting model. We compare this Google Trends model to a traditional autoregressive forecasting model. Overall, our Google Trends model accurately predicted 97% of the total visitation variation to all parks one year in advance from 2013 to 2017 and outperformed the autoregressive model by all metrics. While our Google Trends model performs better overall, this was not the case for each park unit individually; the accuracy of this model varied significantly from park to park. We hypothesized that park attributes related to trip planning would correlate with the accuracy of our Google Trends model, but none of the variables tested produced overly compelling results. Future research can continue exploring the utility of Google Trends to forecast visitor use in protected areas, or use methods demonstrated in this paper to explore alternative data sources to improve visitation forecasting in U.S. National Parks

Are Eimeria Genetically Diverse, and Does It Matter?

Eimeria pose a risk to all livestock species as a cause of coccidiosis, reducing productivity and compromising animal welfare. Pressure to reduce drug use in the food chain makes the development of cost-effective vaccines against Eimeria essential. For novel vaccines to be successful, understanding genetic and antigenic diversity in field populations is key. Eimeria species that infect chickens are most significant, with Eimeria tenella among the best studied and most economically important. Genome-wide single nucleotide polymorphism (SNP)-based haplotyping has been used to determine population structure, genotype distribution, and potential for cross-fertilization between E. tenella strains. Here, we discuss recent developments in our understanding of diversity for Eimeria in relation to its specialized life cycle, distribution across the globe, and the challenges posed to vaccine development