Structure of the thrombopoietin-MPL receptor complex is a blueprint for biasing hematopoiesis

Thrombopoietin (THPO or TPO) is an essential cytokine for hematopoietic stem cell (HSC) maintenance and megakaryocyte differentiation. Here, we report the 3.4 Å resolution cryoelectron microscopy structure of the extracellular TPO-TPO receptor (TpoR or MPL) signaling complex, revealing the basis for homodimeric MPL activation and providing a structural rationalization for genetic loss-of-function thrombocytopenia mutations. The structure guided the engineering of TPO variants (TPOmod) with a spectrum of signaling activities, from neutral antagonists to partial- and super-agonists. Partial agonist TPOmod decoupled JAK/STAT from ERK/AKT/CREB activation, driving a bias for megakaryopoiesis and platelet production without causing significant HSC expansion in mice and showing superior maintenance of human HSCs in vitro. These data demonstrate the functional uncoupling of the two primary roles of TPO, highlighting the potential utility of TPOmod in hematology research and clinical HSC transplantation

Gibberellin-mediated RGA-LIKE1 degradation regulates embryo sac development in Arabidopsis

[EN] Ovule development is essential for plant survival, as it allows correct embryo and seed development upon fertilization. The female gametophyte is formed in the central area of the nucellus during ovule development, in a complex developmental programme that involves key regulatory genes and the plant hormones auxins and brassinosteroids. Here we provide novel evidence of the role of gibberellins (GAs) in the control of megagametogenesis and embryo sac development, via the GA-dependent degradation of RGA-LIKE1 (RGL1) in the ovule primordia. YPet-rgl1.17 plants, which express a dominant version of RGL1, showed reduced fertility, mainly due to altered embryo sac formation that varied from partial to total ablation. YPet-rgl1.17 ovules followed normal development of the megaspore mother cell, meiosis, and formation of the functional megaspore, but YPet-rgl1.17 plants had impaired mitotic divisions of the functional megaspore. This phenotype is RGL1-specific, as it is not observed in any other dominant mutants of the DELLA proteins. Expression analysis of YPet-rgl1.17 coupled to in situ localization of bioactive GAs in ovule primordia led us to propose a mechanism of GA-mediated RGL1 degradation that allows proper embryo sac development. Taken together, our data unravel a novel specific role of GAs in the control of female gametophyte development.We wish to thank the IBMCP microscopy facility, and Ms J. Yun for technical assistance. We also thank Jennifer Nemhauser (University of Washington, USA) for the HACR sensor. Cambridge proofreading (https://proofreading.org/order/) provided proofreading and editing of this manuscript. This work was supported by grants from the Spanish Ministry for Science and Innovation-FEDER [BIO2017-83138R] to MAP-A and National Science Foundation [MCB-0923727] to JMA. 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Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Integrated Genomic Analysis of the Ubiquitin Pathway across Cancer Types

Protein ubiquitination is a dynamic and reversibleprocess of adding single ubiquitin molecules orvarious ubiquitin chains to target proteins. Here,using multidimensional omic data of 9,125 tumorsamples across 33 cancer types from The CancerGenome Atlas, we perform comprehensive molecu-lar characterization of 929 ubiquitin-related genesand 95 deubiquitinase genes. Among them, we sys-tematically identify top somatic driver candidates,including mutatedFBXW7with cancer-type-specificpatterns and amplifiedMDM2showing a mutuallyexclusive pattern withBRAFmutations. Ubiquitinpathway genes tend to be upregulated in cancermediated by diverse mechanisms. By integratingpan-cancer multiomic data, we identify a group oftumor samples that exhibit worse prognosis. Thesesamples are consistently associated with the upre-gulation of cell-cycle and DNA repair pathways, char-acterized by mutatedTP53,MYC/TERTamplifica-tion, andAPC/PTENdeletion. Our analysishighlights the importance of the ubiquitin pathwayin cancer development and lays a foundation fordeveloping relevant therapeutic strategies

Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.

Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR) machine-learning algorithm to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Using OCLR, we were able to identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of the tumor microenvironment revealed unanticipated correlation of cancer stemness with immune checkpoint expression and infiltrating immune cells. We found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors. Application of our stemness indices to single-cell data revealed patterns of intra-tumor molecular heterogeneity. Finally, the indices allowed for the identification of novel targets and possible targeted therapies aimed at tumor differentiation