Hackettsmed0429

Cancer patients exhibit a broad range of inter-individual variability in response and toxicity to widely used anticancer drugs, and genetic variation is a major contributor to this variability. To identify new genes that influence the response of 44 FDA-approved anticancer drug treatments widely used to treat various types of cancer, we conducted high-throughput screening and genome-wide association mapping using 680 lymphoblastoid cell lines from the 1000 Genomes Project. The drug treatments considered in this study represent nine drug classes widely used in the treatment of cancer in addition to the paclitaxel + epirubicin combination therapy commonly used for breast cancer patients. Our genome-wide association study (GWAS) found several significant and suggestive associations. We prioritized consistent associations for functional follow-up using gene-expression analyses. The NAD(P)H quinone dehydrogenase 1 (NQO1) gene was found to be associated with the dose-response of arsenic trioxide, erlotinib, trametinib, and a combination treatment of paclitaxel + epirubicin. NQO1 has previously been shown as a biomarker of epirubicin response, but our results reveal novel associations with these additional treatments. Baseline gene expression of NQO1 was positively correlated with response for 43 of the 44 treatments surveyed. By interrogating the functional mechanisms of this association, the results demonstrate differences in both baseline and drug-exposed induction.Inherited genetic variation contributes to individual risk for many complex diseases and is increasingly being used for predictive patient stratification. Previous work has shown that genetic factors are not equally relevant to human traits across age and other contexts, though the reasons for such variation are not clear. Here, we introduce methods to infer the form of the longitudinal relationship between genetic relative risk for disease and age and to test whether all genetic risk factors behave similarly. We use a proportional hazards model within an interval-based censoring methodology to estimate age-varying individual variant contributions to genetic relative risk for 24 common diseases within the British ancestry subset of UK Biobank, applying a Bayesian clustering approach to group variants by their relative risk profile over age and permutation tests for age dependency and multiplicity of profiles. We find evidence for age-varying relative risk profiles in nine diseases, including hypertension, skin cancer, atherosclerotic heart disease, hypothyroidism and calculus of gallbladder, several of which show evidence, albeit weak, for multiple distinct profiles of genetic relative risk. see more The predominant pattern shows genetic risk factors having the greatest relative impact on risk of early disease, with a monotonic decrease over time, at least for the majority of variants, although the magnitude and form of the decrease varies among diseases. As a consequence, for diseases where genetic relative risk decreases over age, genetic risk factors have stronger explanatory power among younger populations, compared to older ones. We show that these patterns cannot be explained by a simple model involving the presence of unobserved covariates such as environmental factors. We discuss possible models that can explain our observations and the implications for genetic risk prediction.Tumor necrosis factor receptor-1 (TNFR1) signaling, apart from its pleiotropic functions in inflammation, plays a role in embryogenesis as deficiency of varieties of its downstream molecules leads to embryonic lethality in mice. Caspase-8 noncleavable receptor interacting serine/threonine kinase 1 (RIPK1) mutations occur naturally in humans, and the corresponding D325A mutation in murine RIPK1 leads to death at early midgestation. It is known that both the demise of Ripk1D325A/D325A embryos and the death of Casp8-/- mice are initiated by TNFR1, but they are mediated by apoptosis and necroptosis, respectively. Here, we show that the defects in Ripk1D325A/D325A embryos occur at embryonic day 10.5 (E10.5), earlier than that caused by Casp8 knockout. By analyzing a series of genetically mutated mice, we elucidated a mechanism that leads to the lethality of Ripk1D325A/D325A embryos and compared it with that underlies Casp8 deletion-mediated lethality. We revealed that the apoptosis in Ripk1D325A/D325A embryos requires a scaffold function of RIPK3 and enzymatically active caspase-8. Unexpectedly, caspase-1 and caspase-11 are downstream of activated caspase-8, and concurrent depletion of Casp1 and Casp11 postpones the E10.5 lethality to embryonic day 13.5 (E13.5). Moreover, caspase-3 is an executioner of apoptosis at E10.5 in Ripk1D325A/D325A mice as its deletion extends life of Ripk1D325A/D325A mice to embryonic day 11.5 (E11.5). Hence, an unexpected death pathway of TNFR1 controls RIPK1 D325A mutation-induced lethality at E10.5.Growing evidence suggests that internal factors influence how we perceive the world. However, it remains unclear whether and how motivational states, such as hunger and satiety, regulate perceptual decision-making in the olfactory domain. Here, we developed a novel behavioral task involving mixtures of food and nonfood odors (i.e., cinnamon bun and cedar; pizza and pine) to assess olfactory perceptual decision-making in humans. Participants completed the task before and after eating a meal that matched one of the food odors, allowing us to compare perception of meal-matched and non-matched odors across fasted and sated states. We found that participants were less likely to perceive meal-matched, but not non-matched, odors as food dominant in the sated state. Moreover, functional magnetic resonance imaging (fMRI) data revealed neural changes that paralleled these behavioral effects. Namely, odor-evoked fMRI responses in olfactory/limbic brain regions were altered after the meal, such that neural patterns for meal-matched odor pairs were less discriminable and less food-like than their non-matched counterparts. Our findings demonstrate that olfactory perceptual decision-making is biased by motivational state in an odor-specific manner and highlight a potential brain mechanism underlying this adaptive behavior.Drug resistance mutations (DRMs) appear in HIV under treatment pressure. DRMs are commonly transmitted to naive patients. The standard approach to reveal new DRMs is to test for significant frequency differences of mutations between treated and naive patients. However, we then consider each mutation individually and cannot hope to study interactions between several mutations. Here, we aim to leverage the ever-growing quantity of high-quality sequence data and machine learning methods to study such interactions (i.e. epistasis), as well as try to find new DRMs. We trained classifiers to discriminate between Reverse Transcriptase Inhibitor (RTI)-experienced and RTI-naive samples on a large HIV-1 reverse transcriptase (RT) sequence dataset from the UK (n ≈ 55, 000), using all observed mutations as binary representation features. To assess the robustness of our findings, our classifiers were evaluated on independent data sets, both from the UK and Africa. Important representation features for each classifier wereignal of further, more subtle epistasis combining several mutations which individually do not seem to confer any resistance.The COVID-19 epidemic has forced most countries to impose contact-limiting restrictions at workplaces, universities, schools, and more broadly in our societies. Yet, the effectiveness of these unprecedented interventions in containing the virus spread remain largely unquantified. Here, we develop a simulation study to analyze COVID-19 outbreaks on three real-life contact networks stemming from a workplace, a primary school and a high school in France. Our study provides a fine-grained analysis of the impact of contact-limiting strategies at workplaces, schools and high schools, including (1) Rotating strategies, in which workers are evenly split into two shifts that alternate on a daily or weekly basis; and (2) On-Off strategies, where the whole group alternates periods of normal work interactions with complete telecommuting. We model epidemics spread in these different setups using a stochastic discrete-time agent-based transmission model that includes the coronavirus most salient features super-spreaders, infectious asymptomatic individuals, and pre-symptomatic infectious periods. Our study yields clear results the ranking of the strategies, based on their ability to mitigate epidemic propagation in the network from a first index case, is the same for all network topologies (workplace, primary school and high school). Namely, from best to worst Rotating week-by-week, Rotating day-by-day, On-Off week-by-week, and On-Off day-by-day. Moreover, our results show that below a certain threshold for the original local reproduction number [Formula see text] within the network ( less then 1.52 for primary schools, less then 1.30 for the workplace, less then 1.38 for the high school, and less then 1.55 for the random graph), all four strategies efficiently control outbreak by decreasing effective local reproduction number to [Formula see text] less then 1. These results can provide guidance for public health decisions related to telecommuting.Cryo-electron tomography (cryo-ET) and subtomogram averaging (STA) are increasingly used for macromolecular structure determination in situ. Here, we introduce a set of computational tools and resources designed to enable flexible approaches to STA through increased automation and simplified metadata handling. We create a bidirectional interface between the Dynamo software package and the Warp-Relion-M pipeline, providing a framework for ab initio and geometrical approaches to multiparticle refinement in M. We illustrate the power of working within this framework by applying it to EMPIAR-10164, a publicly available dataset containing immature HIV-1 virus-like particles (VLPs), and a challenging in situ dataset containing chemosensory arrays in bacterial minicells. Additionally, we provide a comprehensive, step-by-step guide to obtaining a 3.4-Å reconstruction from EMPIAR-10164. The guide is hosted on https//teamtomo.org/, a collaborative online platform we establish for sharing knowledge about cryo-ET.Subtomogram averaging (STA) is a powerful image processing technique in electron tomography used to determine the 3D structure of macromolecular complexes in their native environments. It is a fast growing technique with increasing importance in structural biology. The computational aspect of STA is very complex and depends on a large number of variables. We noticed a lack of detailed guides for STA processing. Also, current publications in this field often lack a documentation that is practical enough to reproduce the results with reasonable effort, which is necessary for the scientific community to grow. We therefore provide a complete, detailed, and fully reproducible processing protocol that covers all aspects of particle picking and particle alignment in STA. The command line-based workflow is fully based on the popular Dynamo software for STA. Within this workflow, we also demonstrate how large parts of the processing pipeline can be streamlined and automatized for increased throughput. This protocol is aimed at users on all levels.