Enzyme Replacement Therapy for Mucopolysaccharidosis IIID using Recombinant Human α-N-Acetylglucosamine-6-Sulfatase in Neonatal Mice

There is currently no cure or effective treatment available for mucopolysaccharidosis type IIID (MPS IIID, Sanfilippo syndrome type D), a lysosomal storage disorder (LSD) caused by the deficiency of α-N-acetylglucosamine-6-sulfatase (GNS). The clinical symptoms of MPS IIID, like other subtypes of Sanfilippo syndrome, are largely localized to the central nervous system (CNS), and any treatments aiming to ameliorate or reverse the catastrophic and fatal neurologic decline caused by this disease need to be delivered across the blood–brain barrier. Here, we report a proof-of-concept enzyme replacement therapy (ERT) for MPS IIID using recombinant human α-N-acetylglucosamine-6-sulfatase (rhGNS) via intracerebroventricular (ICV) delivery in a neonatal MPS IIID mouse model. We overexpressed and purified rhGNS from CHO cells with a specific activity of 3.9 × 10⁴ units/mg protein and a maximal enzymatic activity at lysosomal pH (pH 5.6), which was stable for over one month at 4 °C in artificial cerebrospinal fluid (CSF). We demonstrated that rhGNS was taken up by MPS IIID patient fibroblasts via the mannose 6-phosphate (M6P) receptor and reduced intracellular glycosaminoglycans to normal levels. The delivery of 5 μg of rhGNS into the lateral cerebral ventricle of neonatal MPS IIID mice resulted in normalization of the enzymatic activity in brain tissues; rhGNS was found to be enriched in lysosomes in MPS IIID-treated mice relative to the control. Furthermore, a single dose of rhGNS was able to reduce the accumulated heparan sulfate and β-hexosaminidase. Our results demonstrate that rhGNS delivered into CSF is a potential therapeutic option for MPS IIID that is worthy of further development

Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning

Understanding the spatial organization of tissues is of critical importance for both basic and translational research. While recent advances in tissue imaging are opening an exciting new window into the biology of human tissues, interpreting the data that they create is a significant computational challenge. Cell segmentation, the task of uniquely identifying each cell in an image, remains a substantial barrier for tissue imaging, as existing approaches are inaccurate or require a substantial amount of manual curation to yield useful results. Here, we addressed the problem of cell segmentation in tissue imaging data through large-scale data annotation and deep learning. We constructed TissueNet, an image dataset containing >1 million paired whole-cell and nuclear annotations for tissue images from nine organs and six imaging platforms. We created Mesmer, a deep learning-enabled segmentation algorithm trained on TissueNet that performs nuclear and whole-cell segmentation in tissue imaging data. We demonstrated that Mesmer has better speed and accuracy than previous methods, generalizes to the full diversity of tissue types and imaging platforms in TissueNet, and achieves human-level performance for whole-cell segmentation. Mesmer enabled the automated extraction of key cellular features, such as subcellular localization of protein signal, which was challenging with previous approaches. We further showed that Mesmer could be adapted to harness cell lineage information present in highly multiplexed datasets. We used this enhanced version to quantify cell morphology changes during human gestation. All underlying code and models are released with permissive licenses as a community resource

Species Interactions during Diversification and Community Assembly in an Island Radiation of Shrews

Closely related, ecologically similar species often have adjacent distributions, suggesting competitive exclusion may contribute to the structure of some natural communities. In systems such as island archipelagos, where speciation is often tightly associated with dispersal over oceanic barriers, competitive exclusion may prevent population establishment following inter-island dispersal and subsequent cladogenesis.) species in the Philippines are the result of competitive exclusion preventing secondary invasion of occupied islands. We first compare ecological niche models between two widespread, allopatric species and find statistical support for their ecological similarity, implying that competition for habitat between these species is possible. We then examine dispersion patterns among sympatric species and find some signal for overdispersion of body size, but not for phylogenetic branch length. Finally, we simulate the process of inter-island colonization under a stochastic model of dispersal lacking ecological forces. Results are dependent on the geographic scope and colonization probability employed. However, some combinations suggest that the number of inter-island dispersal events necessary to populate the archipelago may be much higher than the minimum number of colonization events necessary to explain current estimates of species richness and phylogenetic relationships. If our model is appropriate, these results imply that alternative factors, such as competitive exclusion, may have influenced the process of inter-island colonization and subsequent cladogenesis.We interpret the combined results as providing tenuous evidence that similarity in body size may prevent co-occurrence in Philippine shrews and that competitive exclusion among ecologically similar species, rather than an inability to disperse among islands, may have limited diversification in this group, and, possibly other clades endemic to island archipelagos

Nestedness of Ectoparasite-Vertebrate Host Networks

Determining the structure of ectoparasite-host networks will enable disease ecologists to better understand and predict the spread of vector-borne diseases. If these networks have consistent properties, then studying the structure of well-understood networks could lead to extrapolation of these properties to others, including those that support emerging pathogens. Borrowing a quantitative measure of network structure from studies of mutualistic relationships between plants and their pollinators, we analyzed 29 ectoparasite-vertebrate host networks—including three derived from molecular bloodmeal analysis of mosquito feeding patterns—using measures of nestedness to identify non-random interactions among species. We found significant nestedness in ectoparasite-vertebrate host lists for habitats ranging from tropical rainforests to polar environments. These networks showed non-random patterns of nesting, and did not differ significantly from published estimates of nestedness from mutualistic networks. Mutualistic and antagonistic networks appear to be organized similarly, with generalized ectoparasites interacting with hosts that attract many ectoparasites and more specialized ectoparasites usually interacting with these same “generalized” hosts. This finding has implications for understanding the network dynamics of vector-born pathogens. We suggest that nestedness (rather than random ectoparasite-host associations) can allow rapid transfer of pathogens throughout a network, and expand upon such concepts as the dilution effect, bridge vectors, and host switching in the context of nested ectoparasite-vertebrate host networks

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

Background The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. Conclusion We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.Peer reviewe

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

BackgroundThe Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function.ResultsHere, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory.ConclusionWe conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.</p

Hemodynamics in a giant intracranial aneurysm characterized by in vitro 4D flow MRI

Experimental and computational data suggest that hemodynamics play a critical role in the development, growth, and rupture of cerebral aneurysms. The flow structure, especially in aneurysms with a large sac, is highly complex and three-dimensional. Therefore, volumetric and time-resolved measurements of the flow properties are crucial to fully characterize the hemodynamics. In this study, phase-contrast Magnetic Resonance Imaging is used to assess the fluid dynamics inside a 3D-printed replica of a giant intracranial aneurysm, whose hemodynamics was previously simulated by multiple research groups. The physiological inflow waveform is imposed in a flow circuit with realistic cardiovascular impedance. Measurements are acquired with sub-millimeter spatial resolution for 16 time steps over a cardiac cycle, allowing for the detailed reconstruction of the flow evolution. Moreover, the three-dimensional and time-resolved pressure distribution is calculated from the velocity field by integrating the fluid dynamics equations, and is validated against differential pressure measurements using precision transducers. The flow structure is characterized by vortical motions that persist within the aneurysm sac for most of the cardiac cycle. All the main flow statistics including velocity, vorticity, pressure, and wall shear stress suggest that the flow pattern is dictated by the aneurysm morphology and is largely independent of the pulsatility of the inflow, at least for the flow regimes investigated here. Comparisons are carried out with previous computational simulations that used the same geometry and inflow conditions, both in terms of cycle-averaged and systolic quantities.ISSN:1932-620