Showing papers by "Cold Spring Harbor Laboratory published in 2019"
TL;DR: A new population of CAFs that express MHC class II and CD74, but do not express classical co-stimulatory molecules are described, and it is found that they activate CD4+ T cells in an antigen-specific fashion in a model system, confirming their putative immune-modulatory capacity.
Abstract: Cancer-associated fibroblasts (CAF) are major players in the progression and drug resistance of pancreatic ductal adenocarcinoma (PDAC). CAFs constitute a diverse cell population consisting of several recently described subtypes, although the extent of CAF heterogeneity has remained undefined. Here we use single-cell RNA sequencing to thoroughly characterize the neoplastic and tumor microenvironment content of human and mouse PDAC tumors. We corroborate the presence of myofibroblastic CAFs and inflammatory CAFs and define their unique gene signatures in vivo. Moreover, we describe a new population of CAFs that express MHC class II and CD74, but do not express classic costimulatory molecules. We term this cell population "antigen-presenting CAFs" and find that they activate CD4+ T cells in an antigen-specific fashion in a model system, confirming their putative immune-modulatory capacity. Our cross-species analysis paves the way for investigating distinct functions of CAF subtypes in PDAC immunity and progression. SIGNIFICANCE: Appreciating the full spectrum of fibroblast heterogeneity in pancreatic ductal adenocarcinoma is crucial to developing therapies that specifically target tumor-promoting CAFs. This work identifies MHC class II-expressing CAFs with a capacity to present antigens to CD4+ T cells, and potentially to modulate the immune response in pancreatic tumors.See related commentary by Belle and DeNardo, p. 1001.This article is highlighted in the In This Issue feature, p. 983.
900 citations
TL;DR: An IL1-induced signaling cascade that leads to JAK/STAT activation and promotes an inflammatory CAF state is identified, suggesting multiple strategies to target these cells in vivo and illuminating strategies to selectively target CAFs that support tumor growth.
Abstract: Pancreatic ductal adenocarcinoma (PDAC) is poorly responsive to therapies and histologically contains a paucity of neoplastic cells embedded within a dense desmoplastic stroma. Within the stroma, cancer-associated fibroblasts (CAFs) secrete tropic factors and extracellular matrix components, and have been implicated in PDAC progression and chemotherapy resistance. We recently identified two distinct CAF subtypes characterized by either myofibroblastic or inflammatory phenotypes; however, the mechanisms underlying their diversity and their roles in PDAC remain unknown. Here, we use organoid and mouse models to identify TGF-beta and IL-1 as tumor-secreted ligands that promote CAF heterogeneity. We show that IL-1 induces LIF expression and downstream JAK/STAT activation to generate inflammatory CAFs, and demonstrate that TGF-beta antagonizes this process by downregulating IL-1R1 expression and promoting differentiation into myofibroblasts. Our results provide a mechanism through which distinct fibroblast niches are established in the PDAC microenvironment and illuminate strategies to selectively target CAFs that support tumor growth.
668 citations
University College London1, Canadian Institute for Advanced Research2, Université de Montréal3, University of Oxford4, Stanford University5, Imperial College London6, University of Bristol7, University of Bern8, Google9, University of Toronto10, Cold Spring Harbor Laboratory11, Columbia University12, University of Ottawa13, McGill University14, Foundation for Research & Technology – Hellas15, Netherlands Institute for Neuroscience16, University of Zurich17, University of Pennsylvania18, Friedrich Miescher Institute for Biomedical Research19, York University20
TL;DR: It is argued that a deep network is best understood in terms of components used to design it—objective functions, architecture and learning rules—rather than unit-by-unit computation.
Abstract: Systems neuroscience seeks explanations for how the brain implements a wide variety of perceptual, cognitive and motor tasks. Conversely, artificial intelligence attempts to design computational systems based on the tasks they will have to solve. In artificial neural networks, the three components specified by design are the objective functions, the learning rules and the architectures. With the growing success of deep learning, which utilizes brain-inspired architectures, these three designed components have increasingly become central to how we model, engineer and optimize complex artificial learning systems. Here we argue that a greater focus on these components would also benefit systems neuroscience. We give examples of how this optimization-based framework can drive theoretical and experimental progress in neuroscience. We contend that this principled perspective on systems neuroscience will help to generate more rapid progress.
633 citations
Memorial Sloan Kettering Cancer Center1, University of Texas MD Anderson Cancer Center2, University of Manchester3, Case Western Reserve University4, University of Cologne5, University of Texas Southwestern Medical Center6, Vanderbilt University Medical Center7, Fred Hutchinson Cancer Research Center8, Huntsman Cancer Institute9, Stanford University10, Cold Spring Harbor Laboratory11
TL;DR: This Opinion, written by many leading experts in small cell lung cancer (SCLC) research, proposes a new model of SCLC subtypes defined by differential expression of four key transcription regulators that should help to focus and accelerate therapeutic research.
Abstract: Small cell lung cancer (SCLC) is an exceptionally lethal malignancy for which more effective therapies are urgently needed Several lines of evidence, from SCLC primary human tumours, patient-derived xenografts, cancer cell lines and genetically engineered mouse models, appear to be converging on a new model of SCLC subtypes defined by differential expression of four key transcription regulators: achaete-scute homologue 1 (ASCL1; also known as ASH1), neurogenic differentiation factor 1 (NeuroD1), yes-associated protein 1 (YAP1) and POU class 2 homeobox 3 (POU2F3) In this Perspectives article, we review and synthesize these recent lines of evidence and propose a working nomenclature for SCLC subtypes defined by relative expression of these four factors Defining the unique therapeutic vulnerabilities of these subtypes of SCLC should help to focus and accelerate therapeutic research, leading to rationally targeted approaches that may ultimately improve clinical outcomes for patients with this disease
576 citations
TL;DR: This paper found that neurons with similar trial-averaged activity often reflected different combinations of cognitive and movement variables, accounting for trial-by-trial fluctuations that are often considered 'noise'.
Abstract: When experts are immersed in a task, do their brains prioritize task-related activity? Most efforts to understand neural activity during well-learned tasks focus on cognitive computations and task-related movements. We wondered whether task-performing animals explore a broader movement landscape and how this impacts neural activity. We characterized movements using video and other sensors and measured neural activity using widefield and two-photon imaging. Cortex-wide activity was dominated by movements, especially uninstructed movements not required for the task. Some uninstructed movements were aligned to trial events. Accounting for them revealed that neurons with similar trial-averaged activity often reflected utterly different combinations of cognitive and movement variables. Other movements occurred idiosyncratically, accounting for trial-by-trial fluctuations that are often considered 'noise'. This held true throughout task-learning and for extracellular Neuropixels recordings that included subcortical areas. Our observations argue that animals execute expert decisions while performing richly varied, uninstructed movements that profoundly shape neural activity.
571 citations
TL;DR: CaImAn provides automatic and scalable methods to address problems common to pre-processing, including motion correction, neural activity identification, and registration across different sessions of data collection, while requiring minimal user intervention.
Abstract: The human brain contains billions of cells called neurons that rapidly carry information from one part of the brain to another. Progress in medical research and healthcare is hindered by the difficulty in understanding precisely which neurons are active at any given time. New brain imaging techniques and genetic tools allow researchers to track the activity of thousands of neurons in living animals over many months. However, these experiments produce large volumes of data that researchers currently have to analyze manually, which can take a long time and generate irreproducible results. There is a need to develop new computational tools to analyze such data. The new tools should be able to operate on standard computers rather than just specialist equipment as this would limit the use of the solutions to particularly well-funded research teams. Ideally, the tools should also be able to operate in real-time as several experimental and therapeutic scenarios, like the control of robotic limbs, require this. To address this need, Giovannucci et al. developed a new software package called CaImAn to analyze brain images on a large scale. Firstly, the team developed algorithms that are suitable to analyze large sets of data on laptops and other standard computing equipment. These algorithms were then adapted to operate online in real-time. To test how well the new software performs against manual analysis by human researchers, Giovannucci et al. asked several trained human annotators to identify active neurons that were round or donut-shaped in several sets of imaging data from mouse brains. Each set of data was independently analyzed by three or four researchers who then discussed any neurons they disagreed on to generate a ‘consensus annotation’. Giovannucci et al. then used CaImAn to analyze the same sets of data and compared the results to the consensus annotations. This demonstrated that CaImAn is nearly as good as human researchers at identifying active neurons in brain images. CaImAn provides a quicker method to analyze large sets of brain imaging data and is currently used by over a hundred laboratories across the world. The software is open source, meaning that it is freely-available and that users are encouraged to customize it and collaborate with other users to develop it further.
501 citations
TL;DR: Emerging evidence indicates that organoids can be used to accurately predict drug responses in a personalized treatment setting, and the current state and future prospects of the rapidly evolving tumor organoid field are reviewed.
Abstract: Organoids are microscopic self-organizing, three-dimensional structures that are grown from stem cells in vitro. They recapitulate many structural and functional aspects of their in vivo counterpart organs. This versatile technology has led to the development of many novel human cancer models. It is now possible to create indefinitely expanding organoids starting from tumor tissue of individuals suffering from a range of carcinomas. Alternatively, CRISPR-based gene modification allows the engineering of organoid models of cancer through the introduction of any combination of cancer gene alterations to normal organoids. When combined with immune cells and fibroblasts, tumor organoids become models for the cancer microenvironment enabling immune-oncology applications. Emerging evidence indicates that organoids can be used to accurately predict drug responses in a personalized treatment setting. Here, we review the current state and future prospects of the rapidly evolving tumor organoid field.
494 citations
TL;DR: It is shown that deletion of fibroblast activation protein-α (FAPα)+ fibroblasts suppressed both inflammation and bone erosions in mouse models of resolving and persistent arthritis.
Abstract: The identification of lymphocyte subsets with non-overlapping effector functions has been pivotal to the development of targeted therapies in immune-mediated inflammatory diseases (IMIDs)1,2. However, it remains unclear whether fibroblast subclasses with non-overlapping functions also exist and are responsible for the wide variety of tissue-driven processes observed in IMIDs, such as inflammation and damage3-5. Here we identify and describe the biology of distinct subsets of fibroblasts responsible for mediating either inflammation or tissue damage in arthritis. We show that deletion of fibroblast activation protein-α (FAPα)+ fibroblasts suppressed both inflammation and bone erosions in mouse models of resolving and persistent arthritis. Single-cell transcriptional analysis identified two distinct fibroblast subsets within the FAPα+ population: FAPα+THY1+ immune effector fibroblasts located in the synovial sub-lining, and FAPα+THY1- destructive fibroblasts restricted to the synovial lining layer. When adoptively transferred into the joint, FAPα+THY1- fibroblasts selectively mediate bone and cartilage damage with little effect on inflammation, whereas transfer of FAPα+ THY1+ fibroblasts resulted in a more severe and persistent inflammatory arthritis, with minimal effect on bone and cartilage. Our findings describing anatomically discrete, functionally distinct fibroblast subsets with non-overlapping functions have important implications for cell-based therapies aimed at modulating inflammation and tissue damage.
476 citations
TL;DR: A molecular approach is presented that reveals the comprehensive transcriptional landscape of cell types populating the embryonic heart at three developmental stages and that maps cell-type-specific gene expression to specific anatomical domains.
412 citations
TL;DR: A comprehensive pipeline called Extensive de-novo TE Annotator (EDTA) is created that produces a filtered non-redundant TE library for annotation of structurally intact and fragmented elements and will greatly facilitate TE annotation in eukaryotic genomes.
Abstract: Sequencing technology and assembly algorithms have matured to the point that high-quality de novo assembly is possible for large, repetitive genomes. Current assemblies traverse transposable elements (TEs) and provide an opportunity for comprehensive annotation of TEs. Numerous methods exist for annotation of each class of TEs, but their relative performances have not been systematically compared. Moreover, a comprehensive pipeline is needed to produce a non-redundant library of TEs for species lacking this resource to generate whole-genome TE annotations. We benchmark existing programs based on a carefully curated library of rice TEs. We evaluate the performance of methods annotating long terminal repeat (LTR) retrotransposons, terminal inverted repeat (TIR) transposons, short TIR transposons known as miniature inverted transposable elements (MITEs), and Helitrons. Performance metrics include sensitivity, specificity, accuracy, precision, FDR, and F1. Using the most robust programs, we create a comprehensive pipeline called Extensive de-novo TE Annotator (EDTA) that produces a filtered non-redundant TE library for annotation of structurally intact and fragmented elements. EDTA also deconvolutes nested TE insertions frequently found in highly repetitive genomic regions. Using other model species with curated TE libraries (maize and Drosophila), EDTA is shown to be robust across both plant and animal species. The benchmarking results and pipeline developed here will greatly facilitate TE annotation in eukaryotic genomes. These annotations will promote a much more in-depth understanding of the diversity and evolution of TEs at both intra- and inter-species levels. EDTA is open-source and freely available: https://github.com/oushujun/EDTA.
410 citations
TL;DR: It is suggested that stringent genetic validation of the mechanism of action of cancer drugs in the preclinical setting may decrease the number of therapies tested in human patients that fail to provide any clinical benefit.
Abstract: Ninety-seven percent of drug-indication pairs that are tested in clinical trials in oncology never advance to receive U.S. Food and Drug Administration approval. While lack of efficacy and dose-limiting toxicities are the most common causes of trial failure, the reason(s) why so many new drugs encounter these problems is not well understood. Using CRISPR-Cas9 mutagenesis, we investigated a set of cancer drugs and drug targets in various stages of clinical testing. We show that-contrary to previous reports obtained predominantly with RNA interference and small-molecule inhibitors-the proteins ostensibly targeted by these drugs are nonessential for cancer cell proliferation. Moreover, the efficacy of each drug that we tested was unaffected by the loss of its putative target, indicating that these compounds kill cells via off-target effects. By applying a genetic target-deconvolution strategy, we found that the mischaracterized anticancer agent OTS964 is actually a potent inhibitor of the cyclin-dependent kinase CDK11 and that multiple cancer types are addicted to CDK11 expression. We suggest that stringent genetic validation of the mechanism of action of cancer drugs in the preclinical setting may decrease the number of therapies tested in human patients that fail to provide any clinical benefit.
TL;DR: This work presents RaGOO, a reference-guided contig ordering and orienting tool that leverages the speed and sensitivity of Minimap2 to accurately achieve chromosome-scale assemblies in minutes and demonstrates the scalability and utility of the tool.
Abstract: We present RaGOO, a reference-guided contig ordering and orienting tool that leverages the speed and sensitivity of Minimap2 to accurately achieve chromosome-scale assemblies in minutes. After the pseudomolecules are constructed, RaGOO identifies structural variants, including those spanning sequencing gaps. We show that RaGOO accurately orders and orients 3 de novo tomato genome assemblies, including the widely used M82 reference cultivar. We then demonstrate the scalability and utility of RaGOO with a pan-genome analysis of 103 Arabidopsis thaliana accessions by examining the structural variants detected in the newly assembled pseudomolecules. RaGOO is available open source at https://github.com/malonge/RaGOO .
TL;DR: It is suggested that for AI to learn from animal brains, it is important to consider that animal behaviour results from brain connectivity specified in the genome through evolution, and not due to unique learning algorithms.
Abstract: Artificial neural networks (ANNs) have undergone a revolution, catalyzed by better supervised learning algorithms. However, in stark contrast to young animals (including humans), training such networks requires enormous numbers of labeled examples, leading to the belief that animals must rely instead mainly on unsupervised learning. Here we argue that most animal behavior is not the result of clever learning algorithms-supervised or unsupervised-but is encoded in the genome. Specifically, animals are born with highly structured brain connectivity, which enables them to learn very rapidly. Because the wiring diagram is far too complex to be specified explicitly in the genome, it must be compressed through a "genomic bottleneck". The genomic bottleneck suggests a path toward ANNs capable of rapid learning.
TL;DR: The lower accuracy of de novo assembly-based methods notwithstanding, they are useful for reconstructing fusion isoforms and tumor viruses, both of which are important in cancer research.
Abstract: Accurate fusion transcript detection is essential for comprehensive characterization of cancer transcriptomes. Over the last decade, multiple bioinformatic tools have been developed to predict fusions from RNA-seq, based on either read mapping or de novo fusion transcript assembly. We benchmark 23 different methods including applications we develop, STAR-Fusion and TrinityFusion, leveraging both simulated and real RNA-seq. Overall, STAR-Fusion, Arriba, and STAR-SEQR are the most accurate and fastest for fusion detection on cancer transcriptomes. The lower accuracy of de novo assembly-based methods notwithstanding, they are useful for reconstructing fusion isoforms and tumor viruses, both of which are important in cancer research.
TL;DR: This work underlines the continuum from normal to aberrant perception, encouraging a more empathic approach to clinical hallucinations, and highlights the role of prior beliefs as a critical elicitor of hallucinations.
TL;DR: This review suggests three major computational roles of dopamine in cognitive control: (i) gating sensory input, (ii) maintaining and manipulating working memory contents, and (iii) relaying motor commands.
TL;DR: It is demonstrated that repression of the mevalonate pathway is a crucial component of p53-mediated liver tumor suppression and the mechanism by which this occurs is outlined.
TL;DR: An update of the most recent findings in the tumour microenvironment in PDA is provided and their translational and clinical implications for basic scientists and clinicians alike are discussed.
Abstract: Pancreatic ductal adenocarcinoma (PDA) is notoriously aggressive and hard to treat. The tumour microenvironment (TME) in PDA is highly dynamic and has been found to promote tumour progression, metastasis niche formation and therapeutic resistance. Intensive research of recent years has revealed an incredible heterogeneity and complexity of the different components of the TME, including cancer-associated fibroblasts, immune cells, extracellular matrix components, tumour vessels and nerves. It has been hypothesised that paracrine interactions between neoplastic epithelial cells and TME compartments may result in either tumour-promoting or tumour-restraining consequences. A better preclinical understanding of such complex and dynamic network systems is required to develop more powerful treatment strategies for patients. Scientific activity and the number of compelling findings has virtually exploded during recent years. Here, we provide an update of the most recent findings in this area and discuss their translational and clinical implications for basic scientists and clinicians alike.
TL;DR: The most comprehensive analyses of extracellular matrix (ECM) of pancreatic ductal adenocarcinoma (PDAC) yet available are described, which detected previously unknown molecular changes during PDAC progression in both mouse models and human patients, and suggest more precise ECM manipulations as PDAC treatments.
Abstract: Pancreatic ductal adenocarcinoma (PDAC) has prominent extracellular matrix (ECM) that compromises treatments yet cannot be nonselectively disrupted without adverse consequences. ECM of PDAC, despite the recognition of its importance, has not been comprehensively studied in patients. In this study, we used quantitative mass spectrometry (MS)-based proteomics to characterize ECM proteins in normal pancreas and pancreatic intraepithelial neoplasia (PanIN)- and PDAC-bearing pancreas from both human patients and mouse genetic models, as well as chronic pancreatitis patient samples. We describe detailed changes in both abundance and complexity of matrisome proteins in the course of PDAC progression. We reveal an early up-regulated group of matrisome proteins in PanIN, which are further up-regulated in PDAC, and we uncover notable similarities in matrix changes between pancreatitis and PDAC. We further assigned cellular origins to matrisome proteins by performing MS on multiple lines of human-to-mouse xenograft tumors. We found that, although stromal cells produce over 90% of the ECM mass, elevated levels of ECM proteins derived from the tumor cells, but not those produced exclusively by stromal cells, tend to correlate with poor patient survival. Furthermore, distinct pathways were implicated in regulating expression of matrisome proteins in cancer cells and stromal cells. We suggest that, rather than global suppression of ECM production, more precise ECM manipulations, such as targeting tumor-promoting ECM proteins and their regulators in cancer cells, could be more effective therapeutically.
TL;DR: RaGOO is presented, an open-source reference-guided contig ordering and orienting tool that leverages the speed and sensitivity of Minimap2 to accurately achieve chromosome-scale assemblies in just minutes and outperforms error-prone reference-free methods and enable rapid pan-genome analysis.
Abstract: Background As the number of new genome assemblies continues to grow, there is increasing demand for methods to coalesce contigs from draft assemblies into pseudomolecules. Most current methods use genetic maps, optical maps, chromatin conformation (Hi-C), or other long-range linking data, however these data are expensive and analysis methods often fail to accurately order and orient a high percentage of assembly contigs. Other approaches utilize alignments to a reference genome for ordering and orienting, however these tools rely on slow aligners and are not robust to repetitive contigs. Results We present RaGOO, an open-source reference-guided contig ordering and orienting tool that leverages the speed and sensitivity of Minimap2 to accurately achieve chromosome-scale assemblies in just minutes. With the pseudomolecules constructed, RaGOO identifies structural variants, including those spanning sequencing gaps that are not reported by alternative methods. We show that RaGOO accurately orders and orients contigs into nearly complete chromosomes based on de novo assemblies of Oxford Nanopore long-read sequencing from three wild and domesticated tomato genotypes, including the widely used M82 reference cultivar. We then demonstrate the scalability and utility of RaGOO with a pan-genome analysis of 103 Arabidopsis thaliana accessions by examining the structural variants detected in the newly assembled pseudomolecules. RaGOO is available open-source with an MIT license at https://github.com/malonge/RaGOO. Conclusions We demonstrate that with a highly contiguous assembly and a structurally accurate reference genome, reference-guided scaffolding with RaGOO outperforms error-prone reference-free methods and enable rapid pan-genome analysis.
TL;DR: Conditions for long-term culture of human mucosal organoids derived from head and neck squamous cell carcinoma (HNSCC) are described and responses to therapies both currently used in the treatment of HNSCC and those not used in clinical practice are observed.
Abstract: Previous studies have described that tumor organoids can capture the diversity of defined human carcinoma types. Here, we describe conditions for long-term culture of human mucosal organoids. Using this protocol, a panel of 31 head and neck squamous cell carcinoma (HNSCC)-derived organoid lines was established. This panel recapitulates genetic and molecular characteristics previously described for HNSCC. Organoids retain their tumorigenic potential upon xenotransplantation. We observe differential responses to a panel of drugs including cisplatin, carboplatin, cetuximab and radiotherapy in vitro. Drug screens reveals selective sensitivity to targeted drugs that are not normally used in the treatment of HNSCC patients. These observations may inspire a personalized approach to the management of HNSCC and expand the repertoire of HNSCC drugs.
TL;DR: A network of physically interacting RBPs upregulated in acute myeloid leukemia (AML) and crucial for maintaining RNA splicing and AML survival is uncovered, with genetic or pharmacologic targeting of one key member, RBM39, providing a strategy for treatment of AML bearing RBP splicing mutations.
TL;DR: Antisense oligonucleotides represent a novel therapeutic platform for the discovery of medicines that have the potential to treat most neurodegenerative diseases, including amyotrophic lateral sclerosis, Huntington's disease, and Alzheimer's disease.
Abstract: Antisense oligonucleotides represent a novel therapeutic platform for the discovery of medicines that have the potential to treat most neurodegenerative diseases. Antisense drugs are currently in development for the treatment of amyotrophic lateral sclerosis, Huntington's disease, and Alzheimer's disease, and multiple research programs are underway for additional neurodegenerative diseases. One antisense drug, nusinersen, has been approved for the treatment of spinal muscular atrophy. Importantly, nusinersen improves disease symptoms when administered to symptomatic patients rather than just slowing the progression of the disease. In addition to the benefit to spinal muscular atrophy patients, there are discoveries from nusinersen that can be applied to other neurological diseases, including method of delivery, doses, tolerability of intrathecally delivered antisense drugs, and the biodistribution of intrathecal dosed antisense drugs. Based in part on the early success of nusinersen, antisense drugs hold great promise as a therapeutic platform for the treatment of neurological diseases.
TL;DR: A high-quality reference genome of the maize SK inbred line and analyses between the tropical SK line and two other maize genomes, B73 and Mo17, provide insights into structural variation and crop improvement.
Abstract: Maize is one of the most important crops globally, and it shows remarkable genetic diversity. Knowledge of this diversity could help in crop improvement; however, gold-standard genomes have been elucidated only for modern temperate varieties. Here, we present a high-quality reference genome (contig N50 of 15.78 megabases) of the maize small-kernel inbred line, which is derived from a tropical landrace. Using haplotype maps derived from B73, Mo17 and SK, we identified 80,614 polymorphic structural variants across 521 diverse lines. Approximately 22% of these variants could not be detected by traditional single-nucleotide-polymorphism-based approaches, and some of them could affect gene expression and trait performance. To illustrate the utility of the diverse SK line, we used it to perform map-based cloning of a major effect quantitative trait locus controlling kernel weight—a key trait selected during maize improvement. The underlying candidate gene ZmBARELY ANY MERISTEM1d provides a target for increasing crop yields. A high-quality reference genome of the maize SK inbred line and analyses between the tropical SK line and two other maize genomes, B73 and Mo17, provide insights into structural variation and crop improvement.
TL;DR: It is argued that in the short term, more legumes such as beans are the best option, and in such plants targeted changes in antiflorigen genes were and will continue to be key for large-scale production.
Abstract: The dominance of the major crops that feed humans and their livestock arose from agricultural revolutions that increased productivity and adapted plants to large-scale farming practices. Two hormone systems that universally control flowering and plant architecture, florigen and gibberellin, were the source of multiple revolutions that modified reproductive transitions and proportional growth among plant parts. While step-changes based on serendipitous mutations in these hormone systems laid the foundation, genetic and agronomic tuning was required for broad agricultural benefits. We propose that generating targeted genetic variation in core components of both systems would elicit a wider range of phenotypic variation. Incorporating this enhanced diversity into breeding programs of conventional and underutilized crops can help meet future needs of the human diet and promote sustainable agriculture.
TL;DR: The bioRxiv server has brought preprint practice to the life sciences and recently posted its 64,000th manuscript, and is encouraging new initiatives that experiment with the peer review process and the development of novel approaches to literature filtering and assessment.
Abstract: The traditional publication process delays dissemination of new research, often by months, sometimes by years. Preprint servers decouple dissemination of research papers from their evaluation and certification by journals, allowing researchers to share work immediately, receive feedback from a much larger audience, and provide evidence of productivity long before formal publication. Launched in 2013 as a non-profit community service, the bioRxiv server has brought preprint practice to the life sciences and recently posted its 64,000th manuscript. The server now receives more than four million views per month and hosts papers spanning all areas of biology. Initially dominated by evolutionary biology, genetics/genomics and computational biology, bioRxiv has been increasingly populated by papers in neuroscience, cell and developmental biology, and many other fields. Changes in journal and funder policies that encourage preprint posting have helped drive adoption, as has the development of bioRxiv technologies that allow authors to transfer papers easily between the server and journals. A bioRxiv user survey found that 42% of authors post their preprints prior to journal submission whereas 37% post concurrently with journal submission. Authors are motivated by a desire to share work early; they value the feedback they receive, and very rarely experience any negative consequences of preprint posting. Rapid dissemination via bioRxiv is also encouraging new initiatives that experiment with the peer review process and the development of novel approaches to literature filtering and assessment.
TL;DR: It is posited that cardinal interneuron types can be defined by their synaptic communication properties, which are encoded in key transcriptional signatures, and a framework in which cell types are transcriptionally defined communication elements with characteristic input–output properties is proposed.
Abstract: The phenotypic diversity of cortical GABAergic neurons is probably necessary for their functional versatility in shaping the spatiotemporal dynamics of neural circuit operations underlying cognition. Deciphering the logic of this diversity requires comprehensive analysis of multi-modal cell features and a framework of neuronal identity that reflects biological mechanisms and principles. Recent high-throughput single-cell analyses have generated unprecedented data sets characterizing the transcriptomes, morphology and electrophysiology of interneurons. We posit that cardinal interneuron types can be defined by their synaptic communication properties, which are encoded in key transcriptional signatures. This conceptual framework integrates multi-modal cell features, captures neuronal input-output properties fundamental to circuit operation and may advance understanding of the appropriate granularity of neuron types, towards a biologically grounded and operationally useful interneuron taxonomy.
Oregon Health & Science University1, Fred Hutchinson Cancer Research Center2, Stanford University3, Northwestern University4, University of South Florida5, Vanderbilt University6, Indiana University7, Mayo Clinic8, Duke University9, University of Southern California10, Cold Spring Harbor Laboratory11
TL;DR: The study met its primary end point, demonstrating activity of regorafenib in patients with progressive metastatic osteosarcoma, and should be considered a treatment option for patients with relapsed metastatic arthritis.
Abstract: PURPOSESARC024 is a phase II clinical trial of the multikinase inhibitor regorafenib in specific sarcoma subtypes, including advanced osteosarcoma. We hypothesized that regorafenib would improve pr...
TL;DR: CA19-9 may be more than an innocent bystander that marks the presence of pancreatic disease; it may play a causal role in disease and be nominated as a therapeutic target.
Abstract: Glycosylation alterations are indicative of tissue inflammation and neoplasia, but whether these alterations contribute to disease pathogenesis is largely unknown. To study the role of glycan changes in pancreatic disease, we inducibly expressed human fucosyltransferase 3 and β1,3-galactosyltransferase 5 in mice, reconstituting the glycan sialyl-Lewisa, also known as carbohydrate antigen 19-9 (CA19-9). Notably, CA19-9 expression in mice resulted in rapid and severe pancreatitis with hyperactivation of epidermal growth factor receptor (EGFR) signaling. Mechanistically, CA19-9 modification of the matricellular protein fibulin-3 increased its interaction with EGFR, and blockade of fibulin-3, EGFR ligands, or CA19-9 prevented EGFR hyperactivation in organoids. CA19-9-mediated pancreatitis was reversible and could be suppressed with CA19-9 antibodies. CA19-9 also cooperated with the KrasG12D oncogene to produce aggressive pancreatic cancer. These findings implicate CA19-9 in the etiology of pancreatitis and pancreatic cancer and nominate CA19-9 as a therapeutic target.
TL;DR: A pathogenic crosstalk between altered epigenetic state and splicing in a subset of leukaemias is identified, providing functional evidence that mutations in splicing factors drive myeloid malignancy development, and identifying spliceosomal changes as a mediator of IDH2-mutantLeukaemogenesis.
Abstract: Transcription and pre-mRNA splicing are key steps in the control of gene expression and mutations in genes regulating each of these processes are common in leukaemia1,2. Despite the frequent overlap of mutations affecting epigenetic regulation and splicing in leukaemia, how these processes influence one another to promote leukaemogenesis is not understood and, to our knowledge, there is no functional evidence that mutations in RNA splicing factors initiate leukaemia. Here, through analyses of transcriptomes from 982 patients with acute myeloid leukaemia, we identified frequent overlap of mutations in IDH2 and SRSF2 that together promote leukaemogenesis through coordinated effects on the epigenome and RNA splicing. Whereas mutations in either IDH2 or SRSF2 imparted distinct splicing changes, co-expression of mutant IDH2 altered the splicing effects of mutant SRSF2 and resulted in more profound splicing changes than either mutation alone. Consistent with this, co-expression of mutant IDH2 and SRSF2 resulted in lethal myelodysplasia with proliferative features in vivo and enhanced self-renewal in a manner not observed with either mutation alone. IDH2 and SRSF2 double-mutant cells exhibited aberrant splicing and reduced expression of INTS3, a member of the integrator complex3, concordant with increased stalling of RNA polymerase II (RNAPII). Aberrant INTS3 splicing contributed to leukaemogenesis in concert with mutant IDH2 and was dependent on mutant SRSF2 binding to cis elements in INTS3 mRNA and increased DNA methylation of INTS3. These data identify a pathogenic crosstalk between altered epigenetic state and splicing in a subset of leukaemias, provide functional evidence that mutations in splicing factors drive myeloid malignancy development, and identify spliceosomal changes as a mediator of IDH2-mutant leukaemogenesis. Analyses of transcriptomes from patients with acute myeloid leukaemia identified frequently co-occurring mutations of IDH2 and SRSF2, which functional analyses showed to have distinct and coordinated leukaemogenic effects on the epigenome and RNA splicing.