174 research outputs found

    Investigation into programmability for layer 2 protocol frame delineation architectures

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    Image Processing and Analysis for Preclinical and Clinical Applications

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    Radiomics is one of the most successful branches of research in the field of image processing and analysis, as it provides valuable quantitative information for the personalized medicine. It has the potential to discover features of the disease that cannot be appreciated with the naked eye in both preclinical and clinical studies. In general, all quantitative approaches based on biomedical images, such as positron emission tomography (PET), computed tomography (CT) and magnetic resonance imaging (MRI), have a positive clinical impact in the detection of biological processes and diseases as well as in predicting response to treatment. This Special Issue, “Image Processing and Analysis for Preclinical and Clinical Applications”, addresses some gaps in this field to improve the quality of research in the clinical and preclinical environment. It consists of fourteen peer-reviewed papers covering a range of topics and applications related to biomedical image processing and analysis

    Disordered Proteins: Connecting Sequences to Emergent Properties

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    Many IDPs participate in coupled folding and binding reactions and form alpha helical structures in their bound complexes. Alanine, glycine, or proline scanning mutagenesis approaches are often used to dissect the contributions of intrinsic helicities to coupled folding and binding. These experiments can yield confounding results because the mutagenesis strategy changes the amino acid compositions of IDPs. Therefore, an important next step in mutagenesis-based approaches to mechanistic studies of coupled folding and binding is the design of sequences that satisfy three major constraints. These are (i) achieving a target intrinsic alpha helicity profile; (ii) fixing the positions of residues corresponding to the binding interface; and (iii) maintaining the native amino acid composition. Here, we report the development of a Genetic Algorithm for Design of Intrinsic secondary Structure (GADIS) for designing sequences that satisfy the specified constraints. We describe the algorithm and present results to demonstrate the applicability of GADIS by designing sequence variants of the intrinsically disordered PUMA system that undergoes coupled folding and binding to Mcl-1. Our sequence designs span a range of intrinsic helicity profiles. The predicted variations in sequence-encoded mean helicities are tested against experimental measurements.There is a significant collection of proteins with repeating blocks of oppositely charged residues where the consensus sequence is a block of four Glu residues followed by a block of four Lys or Arg residues, (Glu4(Lys/Arg)4)n. These proteins have been experimentally shown to form long single alpha helices (SAHs) under biologically relevant conditions. However, these results are confounding to disorder predictors and to certain atomistic simulations in that both predict these sequences to be strongly disordered. The current working hypothesis is that SAHs are stabilized by i:i+4 salt bridges between opposite charges in consecutive helical turns. We test the merits of this hypothesis to understand the sequence-encoded preference for SAHs and the logic behind the failure of certain atomistic simulations in anticipating the preference for stable SAHs.In simulations with fixed charges the favorable free energy of solvation of charged residues and the associated loss of sidechain entropy hinders the formation of SAHs. We proposed that alterations to charge states induced by sequence context might play an important role in stabilizing SAHs. We tested this hypothesis using a (Glu4Lys4)n repeat protein and a simulation strategy that permits the substitution of charged residues with neutralized protonated or deprotonated variants of Glu / Lys. Our results predict that stable SAH structures derive from the neutralization of approximately half the Glu residues. These findings explain experimental observations and also provide a coherent rationale for the failure of simulations based on fixed charge models. Large-scale sequence analysis reveals that naturally occurring sequences often include defects in charge patterns such as Gln or Ala substitutions. This sequence-encoded incorporation of uncharged residues combined with neutralization of charged residues might tilt the balance toward alpha helical conformations.Micron-sized, non-membrane bound cellular bodies can form as the result of collective interactions between modules of distinct multidomain proteins. Li et al. have examined the phase diagrams that result for polymers of SH3 domains and proline-rich modules (PRMs) while varying the number of interacting domains. It is noteworthy that flexible, intrinsically disordered linkers connect the interacting units within each polymer. Conventional wisdom holds that linkers play a passive role in determining the phase behavior of multidomain proteins that undergo phase separations. Here, we ask if this view is accurate. The motivation for our work comes from recent studies that have uncovered a rich diversity of composition-to-conformation and sequence-to-conformation relationships for intrinsically disordered proteins. The central finding is that disordered regions of proteins have distinct sequence-encoded conformational preferences. Accordingly, we reasoned that the conformational properties of linkers might be a contributing factor, in addition to polyvalency, to the phase behavior of multidomain proteins.We have developed and deployed a three-dimensional lattice model to arrive at a predictive framework to query the effects of linkers on the phase diagrams of polyvalent systems. We find that the critical concentration for phase transition can be influenced by the conformational properties of linkers. Specifically, our results show that linkers modulate the cooperative binding between domains of polymers that are already bound together. Depending on their conformational properties, linkers can also block access to the binding domains via excluded volume effects. Additionally, we find that the properties of the linkers can lead to controls over the mixing of proteins in these bodies. Specifically, we find that there are large ranges of parameters for three protein systems where the bodies isolate specific proteins to different regions of the bodies instead of uniformly mixing them. This result is validated by recent findings of organization inside some observed bodies

    Multi-scale models of ovarian cancer

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    In ovarian cancer, disease and treatment can be examined across multiple spatial scales including molecules, cells, intra-tumor vasculature, and body-scale dynamics of circulating drugs. Survival of primary tumor cells and their development into disseminated tumors is related to adhesion between the cells, attachment, and invasion. Growth of new tumors depends on the delivery of nutrients, which depends on the tumor diameter and the tumors vasculature. Drug delivery also depends on tumor diameter and vasculature, and molecular- and gross-scale drug processes. A cellular Potts simulation integrated data at these multiple scales to model microscopic residual disease during relapse after a primary surgery. The model generated new hypotheses about tumor cell behavior, and the effectiveness of drug delivery to tumors disseminated in the peritoneal cavity. First, the model required high intra-tumor adhesion in ovarian tumors, the existence of an unknown factor that drew tumor cells to vessels, a threshold of vascular endothelial growth factor (VEGF) for initiation of endothelial sprouting, and constitutive expression of angiogenic chemical messengers by tumor cells prior to needing oxygen. Alteration of the model incorporated drug delivery by the two standard routes, intraperitoneal and intravenous, from tumor vasculature parameterized from real patient data. Delivery of both small- and large-molecular weight therapies was superior during intraperitoneal therapy. Finally, empirical and theoretical distributions of vessel radii were considered. Samples from tumors with each type of vascular morphology were run as though too distant from the peritoneal cavity to receive peritoneal delivery, with three results: first, intravenous delivery was superior to the secondary delivery into the circulatory system from a primary intraperitoneal delivery. Second, small molecules penetrated homogeneously across all cells, regardless of vascular volume or morphology, while antibodies penetrated heterogeneously, particularly in low-vessel-volume samples. Third, when each of the whole tumors was considered, this heterogeneity resulted in a large sub-population of cells that accumulated non-therapeutic levels of antibody, even during the best delivery scenario (IV). Fourth, delivery of antibodies was poorest in the empirical distribution. Finally, hypotheses were generated about the impact of heterogeneity of drug delivery, to be addressed as future questions

    Dopaminergic Modulation Shapes Sensorimotor Processing in the Drosophila Mushroom Body

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    To survive in a complex and dynamic environment, animals must adapt their behavior based on their current needs and prior experiences. This flexibility is often mediated by neuromodulation within neural circuits that link sensory representations to alternative behavioral responses depending on contextual cues and learned associations. In Drosophila, the mushroom body is a prominent neural structure essential for olfactory learning. Dopaminergic neurons convey salient information about reward and punishment to the mushroom body in order to adjust synaptic connectivity between Kenyon cells, the neurons representing olfactory stimuli, and the mushroom body output neurons that ultimately influence behavior. However, we still lack a mechanistic understanding of how the dopaminergic neurons represent the moment-tomoment experience of a fly and drive changes in this sensory-to-motor transformation. Furthermore, very little is known about how the output neuron pathways lead to the execution of appropriate odor-related behaviors. We took advantage of the mushroom body’s modular circuit organization to investigate how the dopaminergic neuron population encodes different contextual cues. In vivo functional imaging of the dopaminergic neurons reveals that they represent both external reinforcement stimuli, like sugar rewards or punitive electric shock, as well as the fly’s motor state, through coordinated and partially antagonistic activity patterns across the population. This multiplexing of motor and reward signals by the dopaminergic neurons parallels the dual roles of dopaminergic inputs to the vertebrate basal ganglia, thus demonstrating a conserved link between these distantly related neural circuits. We proceed to demonstrate that this dopaminergic signal in the mushroom body modifies neurotransmission with synaptic specificity and temporal precision to coordinately regulate the propagation of sensory signals through the output neurons. To explore how these output pathways ultimately influence olfactory navigation we have developed a closed loop olfactory paradigm in which we can monitor and manipulate the mushroom body output neurons as a fly navigates in a virtual olfactory environment. We have begun to probe the mushroom body circuitry in the context of olfactory navigation. These preliminary investigations have led to the identification of putative pathways for linking mushroom body output with the circuits that implement odor-tracking behavior and the characterization of the complex sensorimotor representations in the dopaminergic network. Our work reveals that the Drosophila dopaminergic system modulates mushroom body output at both acute and enduring timescales to guide immediate behaviors and learned responses

    Computational Image Analysis For Axonal Transport, Phenotypic Profiling, And Digital Pathology

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    Recent advances in fluorescent probes, microscopy, and imaging platforms have revolutionized biology and medicine, generating multi-dimensional image datasets at unprecedented scales. Traditional, low-throughput methods of image analysis are inadequate to handle the increased “volume, velocity, and variety” that characterize the realm of big data. Thus, biomedical imaging requires a new set of tools, which include advanced computer vision and machine learning algorithms. In this work, we develop computational image analysis solutions to biological questions at the level of single-molecules, cells, and tissues. At the molecular level, we dissect the regulation of dynein-dynactin transport initiation using in vitro reconstitution, single-particle tracking, super-resolution microscopy, live-cell imaging in neurons, and computational modeling. We show that at least two mechanisms regulate dynein transport initiation neurons: (1) cytoplasmic linker proteins, which are regulated by phosphorylation, increase the capture radius around the microtubule, thus reducing the time cargo spends in a diffusive search; and (2) a spatial gradient of tyrosinated alpha-tubulin enriched in the distal axon increases the affinity of dynein-dynactin for microtubules. Together, these mechanisms support a multi-modal recruitment model where interacting layers of regulation provide efficient, robust, and spatiotemporal control of transport initiation. At the cellular level, we develop and train deep residual convolutional neural networks on a large and diverse set of cellular microscopy images. Then, we apply networks trained for one task as deep feature extractors for unsupervised phenotypic profiling in a different task. We show that neural networks trained on one dataset encode robust image phenotypes that are sufficient to cluster subcellular structures by type and separate drug compounds by the mechanism of action, without additional training, supporting the strength and flexibility of this approach. Future applications include phenotypic profiling in image-based screens, where clustering genetic or drug treatments by image phenotypes may reveal novel relationships among genetic or pharmacologic pathways. Finally, at the tissue level, we apply deep learning pipelines in digital pathology to segment cardiac tissue and classify clinical heart failure using whole-slide images of cardiac histopathology. Together, these results demonstrate the power and promise of computational image analysis, computer vision, and deep learning in biological image analysis

    Telepresence and Transgenic Art

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    Nanobody-Based Interactomic Studies of Single Transcripts During mRNA Maturation

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    During and after transcription in the nucleus, messenger RNAs (mRNAs) undergo a variety of processing events before being exported to the cytoplasm through the nuclear pore complex. mRNA processing and nuclear export require a wide range of protein factors, which interact with maturing transcripts and each other to form dynamic mRNP complexes. While there are many core, essential mRNP factors, the pathways governing mRNA maturation are not uniform, and different transcripts can be associated with mRNP complexes of dramatically different composition or kinetics. To date though, it has been difficult to study RNP complexes specific to any single mRNA species, as each transcript is relatively unabundant in the cell, and few robust techniques exist to specifically purify a particular mRNP for proteomic analysis. We thus sought to develop a method to isolate mRNPs from a single transcript, allowing us to study the dynamic RNP compositions of individual mRNA maturation pathways. To optimize purifications of the protein tags required for RNP isolations, we first generated high affinity reagents targeting key tags like GFP and mCherry. Instead of traditional antibodies, we chose to use nanobodies: recombinant single domain derivatives of a heavy chain-only antibody variant found in camelids. The recombinant nature and small size of nanobodies make them ideal reagents for affinity isolations. We developed an improved pipeline for the identification of nanobody repertoires against any antigen of interest, which provided us with 25 nanobodies against GFP, the most common and robust protein tag in use. This pipeline has also allowed us to develop nanobodies against a variety of other antigens of biomedical interest. With the help of optimized reagents, we developed a two-step purification method allowing highly targeted isolations of mRNPs, starting in a budding yeast model system. In our approach, a single target transcript is tagged with MS2 hairpin sequences – these hairpins are bound specifically and with high affinity by the bacteriophage MS2 coat protein (MS2CP). In the first purification step, a chosen RNP protein known to be associated with a particular mRNA processing step of interest is Protein A-tagged and affinity isolated. From this material, anti-GFP nanobodies are used in the second step to isolate the MS2-tagged transcript of interest, through purification of MS2CP-GFP fusion proteins bound to the tag. This approach is able to efficiently and cleanly isolate a particular transcript at a chosen step of mRNP maturation. The use of an RNP factor as a separate purification target both improves overall purity and simplifies analysis by limiting heterogeneity of the mRNP mixture. Using this novel method for single mRNP isolations, we have performed a preliminary survey of transcripts with distinct sequence elements suspected to be associated with unique processing machinery. Mass spectrometric (MS) analysis of RNPs co-purified with these transcripts revealed several RNA-specific changes in composition. Most notably, introns from either a house keeping ACT1 gene or the RPS30b ribosomal protein gene led to dramatically different levels of various splicing-related proteins. These differences provide mechanistic insight into changes in the kinetics of spliceosome assembly determined by intron sequence
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