Cell
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CKIα ablation induces p53 activation, and CKIα degradation underlies the therapeutic effect of lenalidomide in a pre-leukemia syndrome. Here we describe the development of CKIα inhibitors, which co-target the transcriptional kinases CDK7 and CDK9, thereby augmenting CKIα-induced p53 activation and its anti-leukemic activity. ⋯ We show that blocking CKIα together with CDK7 and/or CDK9 synergistically stabilize p53, deprive leukemia cells of survival and proliferation-maintaining SE-driven oncogenes, and induce apoptosis. Leukemia progenitors are selectively eliminated by the inhibitors, explaining their therapeutic efficacy with preserved hematopoiesis and leukemia cure potential; they eradicate leukemia in MLL-AF9 and Tet2-/-;Flt3ITD AML mouse models and in several patient-derived AML xenograft models, supporting their potential efficacy in curing human leukemia.
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Together with David Allis, Michael Grunstein just received the Lasker Basic Medical research award. The article that follows is a transcript of a conversation with Jacques Deguine, scientific editor at Cell, that was edited for length. Annotated excerpts from this conversation are presented below, and the full conversation is available with the article online.
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Joan Steitz radiates a passion for science. Whether she's teaching an undergraduate course, mentoring a grad student or post-doc, or speaking at a scientific conference, her enthusiasm and curiosity for all things RNA is infectious. Joan, the recipient of the 2018 Lasker-Koshland Special Achievement Award in Medical Science, spoke with Cell editor (and her former post-doc) Lara Szewczak about how she came to be an advocate for women in science and shared advice for young scientists entering the research community today. Annotated excerpts from this conversation are presented below, and the full conversation is available with the article online.
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Machine learning, a collection of data-analytical techniques aimed at building predictive models from multi-dimensional datasets, is becoming integral to modern biological research. By enabling one to generate models that learn from large datasets and make predictions on likely outcomes, machine learning can be used to study complex cellular systems such as biological networks. Here, we provide a primer on machine learning for life scientists, including an introduction to deep learning. We discuss opportunities and challenges at the intersection of machine learning and network biology, which could impact disease biology, drug discovery, microbiome research, and synthetic biology.
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Corticospinal neurons (CSNs) represent the direct cortical outputs to the spinal cord and play important roles in motor control across different species. However, their organizational principle remains unclear. By using a retrograde labeling system, we defined the requirement of CSNs in the execution of a skilled forelimb food-pellet retrieval task in mice. ⋯ Region-specific manipulations indicate that CSNs from caudal or rostral forelimb area control reaching or grasping, respectively, and both are required in the transitional pronation step. These region-specific CSNs terminate in different spinal levels and locations, therefore preferentially connecting with the premotor neurons of muscles engaged in different steps of the task. Together, our findings suggest that spatially defined groups of CSNs encode different movement modules, providing a logic for parallel-ordered corticospinal circuits to orchestrate multistep motor skills.