Future of fundamental discovery in US biomedical research.
Ontology highlight
ABSTRACT:
SUBMITTER: Levitt M
PROVIDER: S-EPMC5488913 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Analyses of publicly available structural data reveal interesting insights into the impact of the three-dimensional (3D) structures of protein targets important for discovery of new drugs (e.g., G-protein-coupled receptors, voltage-gated ion channels, ligand-gated ion channels, transporters, and E3 ubiquitin ligases). The Protein Data Bank (PDB) archive currently holds > 155,000 atomic-level 3D structures of biomolecules experimentally determined using crystallography, nuclear magnetic resonance spectroscopy, and electron microscopy. The PDB was established in 1971 as the first open-access, digital-data resource in biology, and is now managed by the Worldwide PDB partnership (wwPDB; wwPDB.org). US PDB operations are the responsibility of the Research Collaboratory for Structural Bioinformatics PDB (RCSB PDB). The RCSB PDB serves millions of RCSB.org users worldwide by delivering PDB data integrated with ∼40 external biodata resources, providing rich structural views of fundamental biology, biomedicine, and energy sciences. Recently published work showed that the PDB archival holdings facilitated discovery of ∼90% of the 210 new drugs approved by the US Food and Drug Administration 2010-2016. We review user-driven development of RCSB PDB services, examine growth of the PDB archive in terms of size and complexity, and present examples and opportunities for structure-guided drug discovery for challenging targets (e.g., integral membrane proteins).
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Project description:Lung cancer cell lines have made a substantial contribution to lung cancer translational research and biomedical discovery. A systematic approach to initiating and characterizing cell lines from small cell and non-small cell lung carcinomas has led to the current collection of more than 200 lung cancer cell lines, a number that exceeds those for other common epithelial cancers combined. The ready availability and widespread dissemination of the lines to investigators worldwide have resulted in more than 9000 citations, including multiple examples of important biomedical discoveries. The high (but not perfect) genomic similarities between lung cancer cell lines and the lung tumor type from which they were derived provide evidence of the relevance of their use. However, major problems including misidentification or cell line contamination remain. Ongoing studies and new approaches are expected to reveal the full potential of the lung cancer cell line panel.
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Project description:Recent years have witnessed the emergence of powerful new tools for assaying the brain and a remarkable acceleration of research focused on the interplay of emotion and cognition. This work has begun to yield new insights into fundamental questions about the nature of the mind and important clues about the origins of mental illness. In particular, this research demonstrates that stress, anxiety, and other kinds of emotion can profoundly influence key elements of cognition, including selective attention, working memory, and cognitive control. Often, this influence persists beyond the duration of transient emotional challenges, partially reflecting the slower molecular dynamics of catecholamine and hormonal neurochemistry. In turn, circuits involved in attention, executive control, and working memory contribute to the regulation of emotion. The distinction between the 'emotional' and the 'cognitive' brain is fuzzy and context-dependent. Indeed, there is compelling evidence that brain territories and psychological processes commonly associated with cognition, such as the dorsolateral prefrontal cortex and working memory, play a central role in emotion. Furthermore, putatively emotional and cognitive regions influence one another via a complex web of connections in ways that jointly contribute to adaptive and maladaptive behavior. This work demonstrates that emotion and cognition are deeply interwoven in the fabric of the brain, suggesting that widely held beliefs about the key constituents of 'the emotional brain' and 'the cognitive brain' are fundamentally flawed. We conclude by outlining several strategies for enhancing future research. Developing a deeper understanding of the emotional-cognitive brain is important, not just for understanding the mind but also for elucidating the root causes of its disorders.
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Project description:Historically, spontaneous mutations in mice have served as valuable models of heritable human diseases, contributing substantially to our understanding of both disease mechanisms and basic biological pathways. While advances in molecular technologies have improved our ability to create mouse models of human disease through targeted mutagenesis and transgenesis, spontaneous mutations continue to provide valuable research tools for discovery of novel genes and functions. In addition, the genetic defects caused by spontaneous mutations are molecularly similar to mutations in the human genome and, therefore often produce phenotypes that more closely resemble those characteristic of human disease than do genetically engineered mutations. Due to the rarity with which spontaneous mutations arise and the animal intensive nature of their genetic analysis, large-scale spontaneous mutation analysis has traditionally been limited to large mammalian genetics institutes. More recently, ENU mutagenesis and new screening methods have increased the rate of mutant strain discovery, and high-throughput DNA sequencing has enabled rapid identification of the underlying genes and their causative mutations. Here, we discuss the continued value of spontaneous mutations for biomedical research.
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Project description:Sturge-Weber syndrome (SWS) is a vascular neurocutaneous disorder that results from a somatic mosaic mutation in GNAQ, which is also responsible for isolated port-wine birthmarks. Infants with SWS are born with a cutaneous capillary malformation (port-wine birthmark) of the forehead or upper eyelid which can signal an increased risk of brain and/or eye involvement prior to the onset of specific symptoms. This symptom-free interval represents a time when a targeted intervention could help to minimize the neurological and ophthalmologic manifestations of the disorder. This paper summarizes a 2015 SWS workshop in Bethesda, Maryland that was sponsored by the National Institutes of Health. Meeting attendees included a diverse group of clinical and translational researchers with a goal of establishing research priorities for the next few years. The initial portion of the meeting included a thorough review of the recent genetic discovery and what is known of the pathogenesis of SWS. Breakout sessions related to neurology, dermatology, and ophthalmology aimed to establish SWS research priorities in each field. Key priorities for future development include the need for clinical consensus guidelines, further work to develop a clinical trial network, improvement of tissue banking for research purposes, and the need for multiple animal and cell culture models of SWS.
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Project description:BackgroundAdvances in biotechnology have created "big-data" situations in molecular and cellular biology. Several sophisticated algorithms have been developed that process big data to generate hundreds of biomedical hypotheses (or predictions). The bottleneck to translating this large number of biological hypotheses is that each of them needs to be studied by experimentation for interpreting its functional significance. Even when the predictions are estimated to be very accurate, from a biologist's perspective, the choice of which of these predictions is to be studied further is made based on factors like availability of reagents and resources and the possibility of formulating some reasonable hypothesis about its biological relevance. When viewed from a global perspective, say from that of a federal funding agency, ideally the choice of which prediction should be studied would be made based on which of them can make the most translational impact.ResultsWe propose that algorithms be developed to identify which of the computationally generated hypotheses have potential for high translational impact; this way, funding agencies and scientific community can invest resources and drive the research based on a global view of biomedical impact without being deterred by local view of feasibility. In short, data-analytic algorithms analyze big-data and generate hypotheses; in contrast, the proposed inference-analytic algorithms analyze these hypotheses and rank them by predicted biological impact. We demonstrate this through the development of an algorithm to predict biomedical impact of protein-protein interactions (PPIs) which is estimated by the number of future publications that cite the paper which originally reported the PPI.ConclusionsThis position paper describes a new computational problem that is relevant in the era of big-data and discusses the challenges that exist in studying this problem, highlighting the need for the scientific community to engage in this line of research. The proposed class of algorithms, namely inference-analytic algorithms, is necessary to ensure that resources are invested in translating those computational outcomes that promise maximum biological impact. Application of this concept to predict biomedical impact of PPIs illustrates not only the concept, but also the challenges in designing these algorithms.
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