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Monoclonal antibody (mAb) therapy directed against CD20 is an important tool in the treatment of B cell disorders. However, variable patient response and acquired resistance remain important clinical challenges. To identify genetic factors that may influence sensitivity to treatment, the cytotoxic activity of three CD20 mAbs: rituximab; ofatumumab; and obinutuzumab, were screened in high-throughput assays using 680 ethnically diverse lymphoblastoid cell lines (LCLs) followed by a pharmacogenomic assessment. GWAS analysis identified several novel gene candidates. The most significant SNP, rs58600101, in the gene MKL1 displayed ethnic stratification, with the variant being significantly more prevalent in the African cohort and resulting in reduced transcript levels as measured by qPCR. Functional validation of MKL1 by shRNA-mediated knockdown of MKL1 resulted in a more resistant phenotype. Gene expression analysis identified the developmentally associated TGFB1I1 as the most significant gene associated with sensitivity. qPCR among a panel of sensitive and resistant LCLs revealed immunoglobulin class-switching as well as differences in the expression of B cell activation markers. Flow cytometry showed heterogeneity within some cell lines relative to surface Ig isotype with a shift to more IgG+ cells among the resistant lines. Pretreatment with prednisolone could partly reverse the resistant phenotype. Results suggest that the efficacy of anti-CD20 mAb therapy may be influenced by B cell developmental status as well as polymorphism in the MKL1 gene. A clinical benefit may be achieved by pretreatment with corticosteroids such as prednisolone followed by mAb therapy. [ABSTRACT FROM AUTHOR]

Oxaliplatin (OXAL) is a commonly used chemotherapy for treating colorectal cancer (CRC). A recent genome wide association study (GWAS) showed that a genetic variant (rs11006706) in the lncRNA gene MKX-AS1 and partnered sense gene MKX could impact the response of genetically varied cell lines to OXAL treatment. This study found that the expression levels of MKX-AS1 and MKX in lymphocytes (LCLs) and CRC cell lines differed between the rs11006706 genotypes, indicating that this gene pair could play a role in OXAL response. Further analysis of patient survival data from the Cancer Genome Atlas (TCGA) and other sources showed that patients with high MKX-AS1 expression status had significantly worse overall survival (HR = 3.2; 95%CI = (1.17–9); p = 0.024) compared to cases with low MKX-AS1 expression status. Alternatively, high MKX expression status had significantly better overall survival (HR = 0.22; 95%CI = (0.07–0.7); p = 0.01) compared to cases with low MKX expression status. These results suggest an association between MKX-AS1 and MKX expression status that could be useful as a prognostic marker of response to OXAL and potential patient outcomes in CRC. [ABSTRACT FROM AUTHOR]

Temozolomide (TMZ) chemotherapy is an important tool in the treatment of glioma brain tumors. However, variable patient response and chemo-resistance remain exceptionally challenging. Our previous genome-wide association study (GWAS) identified a suggestively significant association of SNP rs4470517 in the RYK (receptor-like kinase) gene with TMZ drug response. Functional validation of RYK using lymphocytes and glioma cell lines resulted in gene expression analysis indicating differences in expression status between genotypes of the cell lines and TMZ dose response. We conducted univariate and multivariate Cox regression analyses using publicly available TCGA and GEO datasets to investigate the impact of RYK gene expression status on glioma patient overall (OS) and progression-free survival (PFS). Our results indicated that in IDH mutant gliomas, RYK expression and tumor grade were significant predictors of survival. In IDH wildtype glioblastomas (GBM), MGMT status was the only significant predictor. Despite this result, we revealed a potential benefit of RYK expression in IDH wildtype GBM patients. We found that a combination of RYK expression and MGMT status could serve as an additional biomarker for improved survival. Overall, our findings suggest that RYK expression may serve as an important prognostic or predictor of TMZ response and survival for glioma patients. [ABSTRACT FROM AUTHOR]

Individuals within genetically diverse populations display broad susceptibility differences upon chemical exposures. Understanding the role of gene-environment interactions (GxE) in differential susceptibility to an expanding exposome is key to protecting public health. However, a chemical's potential to elicit GxE is often not considered during risk assessment. Previously, we've leveraged high-throughput zebrafish (Danio rerio) morphology screening data to reveal patterns of potential GxE effects. Here, using a population genetics framework, we apportioned variation in larval behavior and gene expression in three different PFHxA environments via mixed-effect modeling to assess significance of GxE term. We estimated the intraclass correlation (ICC) between full siblings from different families using one-way random-effects model. We found a significant GxE effect upon PFHxA exposure in larval behavior, and the ICC of behavioral responses in the PFHxA exposed population at the lower concentration was 43.7%, while that of the control population was 14.6%. Considering global gene expression data, a total of 3746 genes showed statistically significant GxE. By showing evidence that heritable genetics are directly affecting gene expression and behavioral susceptibility of individuals to PFHxA exposure, we demonstrate how standing genetic variation in a heterogeneous population such as ours can be leveraged to test for potential GxE. [ABSTRACT FROM AUTHOR]

Computational toxicology combines data from high-throughput test methods, chemical structure analyses and other biological domains (e.g., genes, proteins, cells, tissues) with the goals of predicting and understanding the underlying mechanistic causes of chemical toxicity and for predicting toxicity of new chemicals and products. A key feature of such approaches is their reliance on knowledge extracted from large collections of data and data sets in computable formats. The U.S. Environmental Protection Agency (EPA) has developed a large data resource called ACToR (Aggregated Computational Toxicology Resource) to support these data-intensive efforts. ACToR comprises four main repositories: core ACToR (chemical identifiers and structures, and summary data on hazard, exposure, use, and other domains), ToxRefDB (Toxicity Reference Database, a compilation of detailed in vivo toxicity data from guideline studies), ExpoCastDB (detailed human exposure data from observational studies of selected chemicals), and ToxCastDB (data from high-throughput screening programs, including links to underlying biological information related to genes and pathways). The EPA DSSTox (Distributed Structure-Searchable Toxicity) program provides expert-reviewed chemical structures and associated information for these and other high-interest public inventories. Overall, the ACToR system contains information on about 400,000 chemicals from 1100 different sources. The entire system is built using open source tools and is freely available to download. This review describes the organization of the data repository and provides selected examples of use cases. [ABSTRACT FROM AUTHOR]

Exposure to endocrine-disrupting chemicals (EDCs) is linked to myriad disorders, characterized by the disruption of the complex endocrine signaling pathways that govern development, physiology, and even behavior across the entire body. The mechanisms of endocrine disruption involve a complex system of pathways that communicate across the body to stimulate specific receptors that bind DNA and regulate the expression of a suite of genes. These mechanisms, including gene regulation, DNA binding, and protein binding, can be tied to differences in individual susceptibility across a genetically diverse population. In this review, we posit that EDCs causing such differential responses may be identified by looking for a signal of population variability after exposure. We begin by summarizing how the biology of EDCs has implications for genetically diverse populations. We then describe how gene-environment interactions (GxE) across the complex pathways of endocrine signaling could lead to differences in susceptibility. We survey examples in the literature of individual susceptibility differences to EDCs, pointing to a need for research in this area, especially regarding the exceedingly complex thyroid pathway. Following a discussion of experimental designs to better identify and study GxE across EDCs, we present a case study of a high-throughput screening signal of putative GxE within known endocrine disruptors. We conclude with a call for further, deeper analysis of the EDCs, particularly the thyroid disruptors, to identify if these chemicals participate in GxE leading to differences in susceptibility. [ABSTRACT FROM AUTHOR]

Computational thinking is an essential skill in the modern global workforce. The current public health crisis has highlighted the need for students and educators to have a deeper understanding of epidemiology. While existing STEM curricula has addressed these topics in the past, current events present an opportunity for new curricula that can be designed to present epidemiology, the science of public health, as a modern topic for students that embeds the problem-solving and mathematics skills of computational thinking practices authentically. Using the Computational Thinking Taxonomy within the informal education setting of a STEM outreach program, a curriculum was developed to introduce middle school students to epidemiological concepts while developing their problem-solving skills, a subset of their computational thinking and mathematical thinking practices, in a contextually rich environment. The informal education setting at a Research I Institution provides avenues to connect diverse learners to visually engaging computational thinking and data science curricula to understand emerging teaching and learning approaches. This paper documents the theory and design approach used by researchers and practitioners to create a Pandemic Awareness STEM Curriculum and future implications for teaching and learning computational thinking practices through engaging with data science. [ABSTRACT FROM AUTHOR]

Increasing numbers of chemicals are on the market and present in consumer products. Emerging evidence on the relationship between environmental contributions and prevalent diseases suggests associations between early-life exposure to manufactured chemicals and a wide range of children's health outcomes. Using current assessment methodologies, public health and chemical management decisionmakers face challenges in evaluating and anticipating the potential impacts of exposure to chemicals on children's health in the broader context of their physical (built and natural) and social environments. Here, we consider a systems approach to address the complexity of children's environmental health and the role of exposure to chemicals during early life, in the context of nonchemical stressors, on health outcomes. By advancing the tools for integrating this more complex information, the scope of considerations that support chemical management decisions can be extended to include holistic impacts on children's health.