Pre-clinical assessment of genetic and neurobiochemical markers for depressive behavior and antidepressant response

Efforts to advance health practice and research in the area of depression are hindered by the absence of a biomarker that can be objectively measured to establish diagnosis or monitor and predict treatment response. It has been suggested that multiple cellular and molecular pathways are likely to be involved with major depression. Therefore, it is critical to apply a systems biology approach to identify biomarkers that can aid in diagnosis and treatment selection. Our laboratory has collected behavioral data for over 30 mouse inbred strains for the tail suspension test, in both nave mice and mice chronically-treated with the antidepressant fluoxetine. We have also analyzed whole-genome gene expression in the same inbred strains in multiple brain regions believed to play a role in the regulation of mood. In this application, we propose to quantify 40 biochemical biomarkers in the same three brain regions among all 30 inbred strains in both nave mice and mice that have been chronically-treated with the antidepressant fluoxetine. The biochemical markers, which were chosen based on literature searches and in consultation with experts in the field of psychiatry and psychiatric genetics, assess multiple mechanisms that have been implicated in human depression, including neuronal modulation, neurogenesis, gliogenesis, and hypothalamic-pituitary-mediated immunomodulation. By comparing biochemical and behavioral profiles in both nave and drug-treated mice, we will identify biomarkers that can predict predisposition to depressive-like behavior and treatment response. Furthermore, comparison of these data with inter-strain gene expression differences will provide information regarding the role of gene regulation on depression. Genetic and biochemical markers that are significantly correlated with differences in behavior in the treatment nave group can predict predisposition to depressive-like behavior in mice that may influence response to treatment, while genetic and biochemical markers that are significantly different between response groups can provide a biological explanation for differences in treatment response. By using a multi-faceted approach that investigates connections on genetic, neurobiochemical, and behavioral levels, we were able to identify genetic and neurobiochemical markers that can potentially assess risk for despair and poor treatment outcome. Importantly, our research study provides an innovative and powerful platform for pre-clinical assessment of antidepressant drugs in depressive-like susceptible strains and non-responsive lines

Makerspaces: Supporting an Entrepreneurial System

This Co-Learning Plan provided an overview of the Makerspace movement -- an initiative that supports creative process and innovation through providing a workshop space where amateurs and professionals interested in electronics, robotics, software, wood or metal working or art could expand their skills, invent, and build new products in a collaborative environment. The project also focused on the makerspace programs developed by public libraries and suggests the creation of 'maker' programs at public libraries to support an innovative and entrepreneurial ecosystem

Transitioning Pharmacogenomics into the Clinical Setting: Training Future Pharmacists

Pharmacogenomics, once hailed as a futuristic approach to pharmacotherapy, has transitioned to clinical implementation. Although logistic and economic limitations to clinical pharmacogenomics are being superseded by external measures such as preemptive genotyping, implementation by clinicians has met resistance, partly due to a lack of education. Pharmacists, with extensive training in pharmacology and pharmacotherapy and accessibility to patients, are ideally suited to champion clinical pharmacogenomics. This study aimed to analyze the outcomes of an innovative pharmacogenomic teaching approach. Second-year student pharmacists enrolled in a required, 15-week pharmaceutical care lab course in 2015 completed educational activities including lectures and small group work focusing on practical pharmacogenomics. Reflecting the current landscape of direct-to-consumer (DTC) genomic testing, students were offered 23andMe genotyping. Students completed surveys regarding their attitudes and confidence on pharmacogenomics prior to and following the educational intervention. Paired pre- and post-intervention responses were analyzed with McNemar's test for binary comparisons and the Wilcoxon signed-rank test for Likert items. Responses between genotyped and non-genotyped students were analyzed with Fisher's exact test for binary comparisons and the Mann-Whitney U-test for Likert items. Responses were analyzed for all student pharmacists who voluntarily completed the pre-intervention survey (N = 121, 83% response) and for student pharmacists who completed both pre- and post-intervention surveys (N = 39, 27% response). Of those who completed both pre- and post-intervention surveys, 59% obtained genotyping. Student pharmacists demonstrated a significant increase in their knowledge of pharmacogenomic resources (17.9 vs. 56.4%, p < 0.0001) and confidence in applying pharmacogenomic information to manage patients' drug therapy (28.2 vs. 48.7%, p = 0.01), particularly if the student had received genotyping. Student pharmacists understanding of the risks and benefits of using personal genome testing services significantly increased (55.3 vs. 86.8%, p = 0.001) along with agreement that personal genomics would likely play an important role in their future career (47.4 vs. 76.3%, p = 0.01), particularly among students who participated in genotyping. The educational intervention, including personal genotyping, was feasible, and positively enhanced students' reflections, and attitudes toward pharmacogenomics in a professional pharmacy program

Developing a victorious strategy to the second strong gravitational lensing data challenge

Strong lensing is a powerful probe of the matter distribution in galaxies and clusters and a relevant tool for cosmography. Analyses of strong gravitational lenses with deep learning have become a popular approach due to these astronomical objects’ rarity and image complexity. Next-generation surveys will provide more opportunities to derive science from these objects and an increasing data volume to be analysed. However, finding strong lenses is challenging, as their number densities are orders of magnitude below those of galaxies. Therefore, specific strong lensing search algorithms are required to discover the highest number of systems possible with high purity and low false alarm rate. The need for better algorithms has prompted the development of an open community data science competition named strong gravitational lensing challenge (SGLC). This work presents the deep learning strategies and methodology used to design the highest scoring algorithm in the second SGLC (II SGLC). We discuss the approach used for this data set, the choice of a suitable architecture, particularly the use of a network with two branches to work with images in different resolutions, and its optimization. We also discuss the detectability limit, the lessons learned, and prospects for defining a tailor-made architecture in a survey in contrast to a general one. Finally, we release the models and discuss the best choice to easily adapt the model to a data set representing a survey with a different instrument. This work helps to take a step towards efficient, adaptable, and accurate analyses of strong lenses with deep learning frameworks

PD-L1 has a heterogeneous and dynamic expression in gastric cancer with implications for immunoPET

IntroductionThis study aimed to investigate the dynamics of programmed death-ligand 1 (PD-L1) expression, spatial heterogeneity, and binding affinity of FDA-approved anti-PD-L1 antibodies (avelumab and atezolizumab) in gastric cancer. Additionally, we determined how PD-L1 glycosylation impacts antibody accumulation in gastric cancer cells.MethodsDynamic PD-L1 expression was examined in NCIN87 gastric cancer cells. Comparative binding studies of avelumab and atezolizumab were conducted in gastric cancer models, both in vitro and in vivo. Antibody uptake in tumors was visualized through positron emission tomography (PET) imaging. PD-L1 glycosylation status was determined via Western blot analyses before and after PNGase F treatment. ResultsConsistent findings revealed time-dependent PD-L1 induction in NCIN87 gastric cancer cells and spatial heterogeneity in tumors, as shown by PET imaging and immunofluorescence. Avelumab displayed superior binding affinity to NCIN87 cells compared to atezolizumab, confirmed by in vivo PET imaging and ex vivo biodistribution analyses. Notably, PD-L1 glycosylation at approximately 50 kDa was observed, with PNGase F treatment inducing a shift to 35 kDa in molecular weight. Tissue samples from patient-derived xenografts (PDXs) validated the presence of both glycosylated and deglycosylated PD-L1 (degPD-L1) forms in gastric cancer. Immunofluorescence microscopy and binding assays demonstrated enhanced avelumab binding post-deglycosylation. DiscussionThis study provides an understanding of dynamic and spatially heterogeneous PD-L1 expression in gastric cancer. Anti-PD-L1 immunoPET was able to visualize gastric tumors, and PD-L1 glycosylation has significant implications for antibody recognition. These insights contribute to demonstrating the complexities of PD-L1 in gastric cancer, holding relevance for refining PD-L1 imaging-based approaches

Identifying genes that mediate anthracyline toxicity in immune cells

The role of the immune system in response to chemotherapeutic agents remains elusive. The interpatient variability observed in immune and chemotherapeutic cytotoxic responses is likely, at least in part, due to complex genetic differences. Through the use of a panel of genetically diverse mouse inbred strains, we developed a drug screening platform aimed at identifying genes underlying these chemotherapeutic cytotoxic effects on immune cells. Using genome-wide association studies (GWAS), we identified four genome-wide significant quantitative trait loci (QTL) that contributed to the sensitivity of doxorubicin and idarubicin in immune cells. Of particular interest, a locus on chromosome 16 was significantly associated with cell viability following idarubicin administration (p = 5.01 × 10−8). Within this QTL lies App, which encodes amyloid beta precursor protein. Comparison of dose-response curves verified that T-cells in App knockout mice were more sensitive to idarubicin than those of C57BL/6J control mice (p < 0.05). In conclusion, the cellular screening approach coupled with GWAS led to the identification and subsequent validation of a gene involved in T-cell viability after idarubicin treatment. Previous studies have suggested a role for App in in vitro and in vivo cytotoxicity to anticancer agents; the overexpression of App enhances resistance, while the knockdown of this gene is deleterious to cell viability. Further investigations should include performing mechanistic studies, validating additional genes from the GWAS, including Ppfia1 and Ppfibp1, and ultimately translating the findings to in vivo and human studies

A cellular genetics approach identifies gene-drug interactions and pinpoints drug toxicity pathway nodes

New approaches to toxicity testing have incorporated high-throughput screening across a broad-range of in vitro assays to identify potential key events in response to chemical or drug treatment. To date, these approaches have primarily utilized repurposed drug discovery assays. In this study, we describe an approach that combines in vitro screening with genetic approaches for the experimental identification of genes and pathways involved in chemical or drug toxicity. Primary embryonic fibroblasts isolated from 32 genetically-characterized inbred mouse strains were treated in concentration-response format with 65 compounds, including pharmaceutical drugs, environmental chemicals, and compounds with known modes-of-action. Integrated cellular responses were measured at 24 and 72 h using high-content imaging and included cell loss, membrane permeability, mitochondrial function, and apoptosis. Genetic association analysis of cross-strain differences in the cellular responses resulted in a collection of candidate loci potentially underlying the variable strain response to each chemical. As a demonstration of the approach, one candidate gene involved in rotenone sensitivity, Cybb, was experimentally validated in vitro and in vivo. Pathway analysis on the combined list of candidate loci across all chemicals identified a number of over-connected nodes that may serve as core regulatory points in toxicity pathways