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"Morgan, Michael"
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Roles of RIPK3 in necroptosis, cell signaling, and disease
Receptor-interacting protein kinase-3 (RIPK3, or RIP3) is an essential protein in the “programmed” and “regulated” cell death pathway called necroptosis. Necroptosis is activated by the death receptor ligands and pattern recognition receptors of the innate immune system, and the findings of many reports have suggested that necroptosis is highly significant in health and human disease. This significance is largely because necroptosis is distinguished from other modes of cell death, especially apoptosis, in that it is highly proinflammatory given that cell membrane integrity is lost, triggering the activation of the immune system and inflammation. Here, we discuss the roles of RIPK3 in cell signaling, along with its role in necroptosis and various pathways that trigger RIPK3 activation and cell death. Lastly, we consider pathological situations in which RIPK3/necroptosis may play a role.
Cell death: Multiple roles for enzyme
Further research into the various roles of an enzyme involved in necroptosis, a form of programmed cell death, could present novel therapeutics for multiple diseases. Necroptosis involves the rupture of cellular membranes in response to stress, for example when the body is under attack from pathogens. The contents of the cell spill out, triggering a proinflammatory immune response. You-Sun Kim at Ajou University in Suwon, South Korea, and Michael Morgan at Northeastern State University in Tahlequah, USA, reviewed the activity of receptor-interacting protein kinase 3 (RIPK3) in cell signaling and necroptosis. In initiating necroptosis, RIPK3 forms a complex with another enzyme, which then activates a protein involved in regulating membrane permeability. Disruption of necroptosis is implicated in inflammatory and neurodegenerative diseases, cancers, and conditions involving tissue damage such as heart disease.
Journal Article
The Cambridge introduction to Emmanuel Levinas
\"This book provides a clear and helpful overview of the philosophical core of the thought of Emmanuel Levinas, one of the most significant and interesting philosophers of the late twentieth century\"-- Provided by publisher.
Book
Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors
Differences in gene expression between individual cells of the same type are measured across batches and used to correct technical artifacts in single-cell RNA-sequencing data.
Large-scale single-cell RNA sequencing (scRNA-seq) data sets that are produced in different laboratories and at different times contain batch effects that may compromise the integration and interpretation of the data. Existing scRNA-seq analysis methods incorrectly assume that the composition of cell populations is either known or identical across batches. We present a strategy for batch correction based on the detection of mutual nearest neighbors (MNNs) in the high-dimensional expression space. Our approach does not rely on predefined or equal population compositions across batches; instead, it requires only that a subset of the population be shared between batches. We demonstrate the superiority of our approach compared with existing methods by using both simulated and real scRNA-seq data sets. Using multiple droplet-based scRNA-seq data sets, we demonstrate that our MNN batch-effect-correction method can be scaled to large numbers of cells.
Journal Article
Challenges in measuring and understanding biological noise
Biochemical reactions are intrinsically stochastic, leading to variation in the production of mRNAs and proteins within cells. In the scientific literature, this source of variation is typically referred to as ‘noise’. The observed variability in molecular phenotypes arises from a combination of processes that amplify and attenuate noise. Our ability to quantify cell-to-cell variability in numerous biological contexts has been revolutionized by recent advances in single-cell technology, from imaging approaches through to ‘omics’ strategies. However, defining, accurately measuring and disentangling the stochastic and deterministic components of cell-to-cell variability is challenging. In this Review, we discuss the sources, impact and function of molecular phenotypic variability and highlight future directions to understand its role.Gene expression is subjected to various random processes (referred to as ‘noise’) that contribute to variability in molecular phenotypes. As Eling, Morgan and Marioni describe, there are various challenges to studying this variability, such as disentangling its multilayered sources, distinguishing it from deterministic influences on cellular variability, modelling it with appropriate statistical methods and understanding its practical consequences.
Journal Article
Law and society in imperial Japan : Suehiro Izutarهo and the search for equity
\"This is the first monograph on Suehiro in the English language. As the representative figure of the law and society movement in Japan, a study of Suehiro helps us go much deeper into the often-neglected jurisprudential aspects of Japanese history. Far from being a matter of poring over dusty lawbooks and parsing legalese, law-and-society studies helps us see the kaleidoscopic interaction among elites and common people in the roiling 1920s and 30s, greatly complicating and enriching our view of Japanese society (and law) as a whole. Also, Suehiro is our entrepمot into an intellectual history of law-and-society study in Japan. He was in the thick of the social changes of the mid to late Taishهo and early Shهowa periods, and was able to translate what he saw around him into the theoretical and legal planes with great fluency. He could translate in the other direction with equal facility. For his time as well as for ours, Suehiro was, and is, an adroit interpreter between society and law, and the addition of his voice to the pageant of Japanese history in English is long overdue\"-- Provided by publisher.
Book
Differential abundance testing on single-cell data using k-nearest neighbor graphs
Current computational workflows for comparative analyses of single-cell datasets typically use discrete clusters as input when testing for differential abundance among experimental conditions. However, clusters do not always provide the appropriate resolution and cannot capture continuous trajectories. Here we present Milo, a scalable statistical framework that performs differential abundance testing by assigning cells to partially overlapping neighborhoods on a
k
-nearest neighbor graph. Using simulations and single-cell RNA sequencing (scRNA-seq) data, we show that Milo can identify perturbations that are obscured by discretizing cells into clusters, that it maintains false discovery rate control across batch effects and that it outperforms alternative differential abundance testing strategies. Milo identifies the decline of a fate-biased epithelial precursor in the aging mouse thymus and identifies perturbations to multiple lineages in human cirrhotic liver. As Milo is based on a cell–cell similarity structure, it might also be applicable to single-cell data other than scRNA-seq. Milo is provided as an open-source R software package at
https://github.com/MarioniLab/miloR
.
Milo identifies differentially abundant populations of cells in scRNA-seq data without clustering.
Journal Article
Use of Cell and Genome Modification Technologies to Generate Improved “Off-the-Shelf” CAR T and CAR NK Cells
The broad success of adoptive immunotherapy to treat human cancer has resulted in a paradigm shift in modern medicine. Modification of autologous and allogenic immune cells with chimeric antigen receptors (CAR) designed to target specific antigens on tumor cells has led to production of CAR T and CAR NK cell therapies, which are ever more commonly introduced into cancer patient treatment protocols. While allogenic T cells may offer advantages such as improved anti-tumor activity, they also carry the risk of adverse reactions like graft-versus-host disease. This risk can be mitigated by use of autologous immune cells, however, the time needed for T and/or NK cell isolation, modification and expansion may be too long for some patients. Thus, there is an urgent need for strategies to robustly produce \"off-the-shelf\" CAR T and CAR NK cells, which could be used as a bridging therapy between cancer diagnosis or relapse and allogeneic transplantation. Advances in genome modification technologies have accelerated the generation of designer cell therapy products, including development of \"off-the-shelf\" CAR T cells for cancer immunotherapy. The feasibility and safety of such approaches is currently tested in clinical trials. This review will describe cell sources for CAR-based therapies, provide background of current genome editing techniques and the applicability of these approaches for generation of universal \"off-the-shelf\" CAR T and NK cell therapeutics.
Journal Article