Endometrial cancer accounts for ~76,000 deaths among women each year worldwide. Disease mortality and the increasing number of new diagnoses make endometrial cancer an important consideration in ...women's health, particularly in industrialized countries, where the incidence of this tumour type is highest. Most endometrial cancers are carcinomas, with the remainder being sarcomas. Endometrial carcinomas can be classified into several histological subtypes, including endometrioid, serous and clear cell carcinomas. Histological subtyping is currently used routinely to guide prognosis and treatment decisions for endometrial cancer patients, while ongoing studies are evaluating the potential clinical utility of molecular subtyping. In this Review, we summarize the overarching molecular features of endometrial cancers and highlight recent studies assessing the potential clinical utility of specific molecular features for early detection, disease risk stratification and directing targeted therapies.
Endometrial cancer is the most commonly diagnosed gynecologic malignancy in the United States. Endometrioid endometrial carcinomas constitute approximately 85% of newly diagnosed cases; serous ...carcinomas represent approximately 3-10% of diagnoses; clear cell carcinoma accounts for <5% of diagnoses; and uterine carcinosarcomas are rare, biphasic tumors. Longstanding molecular observations implicate
PTEN
inactivation as a major driver of endometrioid carcinomas;
TP53
inactivation as a major driver of most serous carcinomas, some high-grade endometrioid carcinomas, and many uterine carcinosarcomas; and inactivation of either gene as drivers of some clear cell carcinomas. In the past decade, targeted gene and exome sequencing have uncovered additional pathogenic aberrations in each histotype. Moreover, an integrated genomic analysis by The Cancer Genome Atlas (TCGA) resulted in the molecular classification of endometrioid and serous carcinomas into four distinct subgroups,
POLE
(ultramutated), microsatellite instability (hypermutated), copy number low (endometrioid), and copy number high (serous-like). In this review, we provide an overview of the major molecular features of the aforementioned histopathological subtypes and TCGA subgroups and discuss potential prognostic and therapeutic implications for endometrial carcinoma.
The lowest winter‐maximum areal sea‐ice coverage on record (1980–2019) in the Bering Sea occurred in the winter of 2017/2018. Sea ice arrived late due to warm southerly winds in November. More ...typical northerly winds (albeit warm) in December and January advanced the ice, but strong, warm southerlies in February and March forced the ice to retreat. The cold pool (shelf region with bottom water < 2 °C) was the smallest on record, because of two related mechanisms: (1) lack of direct cooling in winter by melting sea ice and (2) weaker vertical stratification (no ice melt reduced the vertical salinity gradient) allowing surface heating to penetrate into the near bottom water during summer. February 2019 exhibited another outbreak of warm southerly winds forcing ice to retreat. The number of >31‐day outbreaks of southerly winds in winter has increased since 2016.
Key Points
Wind patterns have changed in the Bering Sea, with a marked increase in warm southerly winds since 2016
These changes in winds have resulted in decrease in sea ice, with the lowest extent ever observed in the Bering Sea occurring in 2017/2018
The low ice extent restructured the horizontal temperature patterns, resulting in an almost nonexistent cold pool in August 2018
Comparison of ligand poses generated by protein–ligand docking programs has often been carried out with the assumption of direct atomic correspondence between ligand structures. However, this ...correspondence is not necessarily chemically relevant for symmetric molecules and can lead to an artificial inflation of ligand pose distance metrics, particularly those that depend on receptor superposition (rather than ligand superposition), such as docking root mean square deviation (RMSD). Several of the commonly-used RMSD calculation algorithms that correct for molecular symmetry do not take into account the bonding structure of molecules and can therefore result in non-physical atomic mapping. Here, we present DockRMSD, a docking pose distance calculator that converts the symmetry correction to a graph isomorphism searching problem, in which the optimal atomic mapping and RMSD calculation are performed by an exhaustive and fast matching search of all isomorphisms of the ligand structure graph. We show through evaluation of docking poses generated by AutoDock Vina on the CSAR Hi-Q set that DockRMSD is capable of deterministically identifying the minimum symmetry-corrected RMSD and is able to do so without significant loss of computational efficiency compared to other methods. The open-source DockRMSD program can be conveniently integrated with various docking pipelines to assist with accurate atomic mapping and RMSD calculations, which can therefore help improve docking performance, especially for ligand molecules with complicated structural symmetry.
Nanopores are a versatile technique for the detection and characterization of single molecules in solution. An ongoing challenge in the field is to find methods to selectively detect specific ...biomolecules. In this work we describe a new technique for sensing specific proteins using unmodified solid-state nanopores. We engineered a double strand of DNA by hybridizing nearly two hundred oligonucleotides to a linearized version of the m13mp18 virus genome. This engineered double strand, which we call a DNA carrier, allows positioning of protein binding sites at nanometer accurate intervals along its contour via DNA conjugation chemistry. We measure the ionic current signal of translocating DNA carriers as a function of the number of binding sites and show detection down to the single protein level. Furthermore, we use DNA carriers to develop an assay for identifying a single protein species within a protein mixture.
As the infection of 2019-nCoV coronavirus is quickly developing into a global pneumonia epidemic, the careful analysis of its transmission and cellular mechanisms is sorely needed. In this ...Communication, we first analyzed two recent studies that concluded that snakes are the intermediate hosts of 2019-nCoV and that the 2019-nCoV spike protein insertions share a unique similarity to HIV-1. However, the reimplementation of the analyses, built on larger scale data sets using state-of-the-art bioinformatics methods and databases, presents clear evidence that rebuts these conclusions. Next, using metagenomic samples from
, we assembled a draft genome of the 2019-nCoV-like coronavirus, which shows 73% coverage and 91% sequence identity to the 2019-nCoV genome. In particular, the alignments of the spike surface glycoprotein receptor binding domain revealed four times more variations in the bat coronavirus RaTG13 than in the
coronavirus compared with 2019-nCoV, suggesting the pangolin as a missing link in the transmission of 2019-nCoV from bats to human.
Heat-Shock Factor 1 (HSF1), master regulator of the heat-shock response, facilitates malignant transformation, cancer cell survival, and proliferation in model systems. The common assumption is that ...these effects are mediated through regulation of heat-shock protein (HSP) expression. However, the transcriptional network that HSF1 coordinates directly in malignancy and its relationship to the heat-shock response have never been defined. By comparing cells with high and low malignant potential alongside their nontransformed counterparts, we identify an HSF1-regulated transcriptional program specific to highly malignant cells and distinct from heat shock. Cancer-specific genes in this program support oncogenic processes: cell-cycle regulation, signaling, metabolism, adhesion and translation. HSP genes are integral to this program, however, many are uniquely regulated in malignancy. This HSF1 cancer program is active in breast, colon and lung tumors isolated directly from human patients and is strongly associated with metastasis and death. Thus, HSF1 rewires the transcriptome in tumorigenesis, with prognostic and therapeutic implications.
Display omitted
► Comprehensive study of the direct transactivating effects of HSF1 in cancer ► HSF1 regulates diverse cellular processes that extend far beyond heat-shock genes ► Fundamental differences in HSF1 program in cancer versus heat shock ► HSF1 activation in multiple cancers is strongly associated with metastasis and death
The purview of the transcription factor HSF1 extends far beyond heat shock in tumor cells, and the newly identified targets appear to play a key role in determining cancer aggressiveness.
MicroRNA targets are often recognized through pairing between the miRNA seed region and complementary sites within target mRNAs, but not all of these canonical sites are equally effective, and both ...computational and in vivo UV-crosslinking approaches suggest that many mRNAs are targeted through non-canonical interactions. Here, we show that recently reported non-canonical sites do not mediate repression despite binding the miRNA, which indicates that the vast majority of functional sites are canonical. Accordingly, we developed an improved quantitative model of canonical targeting, using a compendium of experimental datasets that we pre-processed to minimize confounding biases. This model, which considers site type and another 14 features to predict the most effectively targeted mRNAs, performed significantly better than existing models and was as informative as the best high-throughput in vivo crosslinking approaches. It drives the latest version of TargetScan (v7.0; targetscan.org), thereby providing a valuable resource for placing miRNAs into gene-regulatory networks.
The topology of protein folds can be specified by the inter-residue contact-maps and accurate contact-map prediction can help ab initio structure folding. We developed TripletRes to deduce protein ...contact-maps from discretized distance profiles by end-to-end training of deep residual neural-networks. Compared to previous approaches, the major advantage of TripletRes is in its ability to learn and directly fuse a triplet of coevolutionary matrices extracted from the whole-genome and metagenome databases and therefore minimize the information loss during the course of contact model training. TripletRes was tested on a large set of 245 non-homologous proteins from CASP 11&12 and CAMEO experiments and outperformed other top methods from CASP12 by at least 58.4% for the CASP 11&12 targets and 44.4% for the CAMEO targets in the top-L long-range contact precision. On the 31 FM targets from the latest CASP13 challenge, TripletRes achieved the highest precision (71.6%) for the top-L/5 long-range contact predictions. It was also shown that a simple re-training of the TripletRes model with more proteins can lead to further improvement with precisions comparable to state-of-the-art methods developed after CASP13. These results demonstrate a novel efficient approach to extend the power of deep convolutional networks for high-accuracy medium- and long-range protein contact-map predictions starting from primary sequences, which are critical for constructing 3D structure of proteins that lack homologous templates in the PDB library.
Structure prediction for proteins lacking homologous templates in the Protein Data Bank (PDB) remains a significant unsolved problem. We developed a protocol, C-I-TASSER, to integrate interresidue ...contact maps from deep neural-network learning with the cutting-edge I-TASSER fragment assembly simulations. Large-scale benchmark tests showed that C-I-TASSER can fold more than twice the number of non-homologous proteins than the I-TASSER, which does not use contacts. When applied to a folding experiment on 8,266 unsolved Pfam families, C-I-TASSER successfully folded 4,162 domain families, including 504 folds that are not found in the PDB. Furthermore, it created correct folds for 85% of proteins in the SARS-CoV-2 genome, despite the quick mutation rate of the virus and sparse sequence profiles. The results demonstrated the critical importance of coupling whole-genome and metagenome-based evolutionary information with optimal structure assembly simulations for solving the problem of non-homologous protein structure prediction.
Display omitted
•C-I-TASSER adds deep-learning contact prediction to fragment assembly simulations•C-I-TASSER enables ab initio folding of proteins lacking homology in the PDB•The inherent force field is critical for proteins with poor templates and sparse MSAs•Half of unsolved Pfam families are foldable by C-I-TASSER
Taking advantage of the rapid progress in deep-learning technologies, residue-residue contact-map prediction recently achieved impressive breakthroughs. However, how to efficiently convert the binary contact maps into atomic-level structure models remains an important unsolved problem in ab initio protein structure prediction. In this work, we integrated the deep-learning contact-map predictions with cutting-edge threading assembly simulations and found that the inherent force field of the structural folding simulations is essential to maximize the potential of contact-assisted protein structure prediction, especially for the targets and regions that lack spatial restraints and sufficient evolutionary data.
Zheng et al. develop C-I-TASSER, which integrates interresidue contact maps from deep neural-network learning with the cutting-edge I-TASSER fragment assembly simulations, for high-accuracy protein structure prediction. C-I-TASSER folds more than twice the number of proteins without homology than I-TASSER and has successfully folded 50% of Pfam families without solved experimental structures.
PDF
