Biology subjects

Zhu, W.

Publications and source records attributed to Zhu, W..

17 recordsLinked to original sources

bioRxiv2018

Molecular basis of human asparagine synthetase inhibitor specificity

Expression of the enzyme human asparagine synthetase (ASNS) promotes metastatic progression in breast cancer, which affects L-asparagine levels and tumor cell invasiveness. Human ASNS has therefore emerged as a bona fide drug target for cancer therapy. We have reported a slow-onset, tight binding ASNS inhibitor with nanomolar affinity, but our compound exhibits poor cell permeability. On the other hand, we show here that this inhibitor exhibits remarkable selectivity for the human ASNS in HCT-116 cell lysates. By determining the first high-resolution (1.85 [A]) X-ray crystal structure for human ASNS, we have built a computational model of the enzyme complexed to our inhibitor, which provides the first insights into the intermolecular interactions mediating specificity. These findings should facilitate the development of a second generation of ASNS inhibitors, leading to the discovery of drugs to prevent metastasis.

biochemistry↗

bioRxiv2018

Identification of genome-wide nucleotide sites associated with mammalian virulence in influenza A viruses

MotivationThe virulence of influenza viruses is a complex multigenic trait. Previous studies about the virulence determinants of influenza viruses mainly focused on amino acid sites, ignoring the influence of nucleotide mutations.\n\nResultsWe collected more than 200 viral strains from 21 subtypes of influenza A viruses with virulence in mammals and obtained over 100 mammalian virulence-related nucleotide sites across the genome by computational analysis. Interestingly, 50 of these nucleotide sites only experienced synonymous mutations. Further experiments showed that synonymous mutations in the top two of these nucleotide sites, i.e., PB1-2031 and PB1-633, enhanced the pathogenicity of the viruses in mice. Finally, machine-learning models with accepted accuracy for predicting mammalian virulence of influenza A viruses were built. Overall, this study highlighted the importance of nucleotide mutations, especially synonymous mutations in viral virulence, and provided rapid methods for evaluating the virulence of influenza A viruses. It could be helpful for early warning of newly emerging influenza A viruses.

microbiology↗

bioRxiv2018

Human antibody cocktail deploys multiple functions to confer pan-ebolavirus protection

During the unprecedented 2013-2016 Ebola virus disease (EVD) epidemic in Western Africa and in its aftermath, the passive administration of monoclonal antibodies (mAbs) emerged as a promising treatment approach1-7. However, all antibody-based therapeutics currently in advanced development are specific for a single member of the Ebolavirus genus, Ebola virus (EBOV), and ineffective against divergent outbreak-causing ebolaviruses, including Bundibugyo virus (BDBV) and Sudan virus (SUDV)2,3,5,7. Here we advance MBP134, a cocktail of two broadly neutralizing human mAbs targeting the filovirus surface glycoprotein, GP, as a candidate pan-ebolavirus therapeutic. One component of this cocktail is a pan-ebolavirus neutralizing mAb, ADI-15878, isolated from a human EVD survivor8,9. The second, ADI-23774, was derived by affinity maturation of a human mAb8,9 via yeast display to enhance its potency against SUDV. MBP134 afforded exceptionally potent pan-ebolavirus neutralization in vitro and demonstrated greater protective efficacy than ADI-15878 alone in the guinea pig model of lethal EBOV challenge. A second-generation cocktail, MBP134AF, engineered to effectively harness natural killer (NK) cells afforded additional, unprecedented improvements in protective efficacy against EBOV and SUDV in guinea pigs relative to both its precursor and to any mAbs or mAb cocktails tested previously. MBP134AF is a best-in-class mAb cocktail suitable for evaluation as a pan-ebolavirus therapeutic in nonhuman primates.

microbiology↗

bioRxiv2018

Two-antibody pan-ebolavirus cocktail confers broad therapeutic protection in ferrets and nonhuman primates

All available experimental vaccines and immunotherapeutics1,2 against Ebola virus (EBOV), including rVSV-ZEBOV3 and ZMappTM4, lack activity against other ebolaviruses associated with human disease outbreaks. This year, two separate outbreaks of EBOV in the Democratic Republic of Congo underscored the unpredictable nature of ebolavirus reemergence in a region that has historically experienced outbreaks of the divergent ebolaviruses Sudan virus (SUDV) and Bundibugyo virus (BDBV)5. Here we show that MBP134AF, a pan-ebolavirus therapeutic comprising two broadly neutralizing human antibodies (bNAbs)6,7(see companion manuscript, Wec et al.) could protect against lethal EBOV, SUDV, and BDBV infection in ferrets and nonhuman primates (NHPs). MBP134AF not only not only establishes a viable therapeutic countermeasure to outbreaks caused by antigenically diverse ebolaviruses but also affords unprecedented effectiveness and potency--a single 25-mg/kg dose was fully protective in NHPs. This best-in-class antibody cocktail is the culmination of an intensive collaboration spanning academia, industry and government in response to the 2013-2016 EBOV epidemic6,7 and provides a translational research model for the rapid development of immunotherapeutics targeting emerging infectious diseases.

microbiology↗

bioRxiv2018

AnatomyNet: Deep 3D Squeeze-and-excitation U-Nets for fast and fully automated whole-volume anatomical segmentation

PurposeRadiation therapy (RT) is a common treatment for head and neck (HaN) cancer where therapists are often required to manually delineate boundaries of the organs-at-risks (OARs). The radiation therapy planning is time-consuming as each computed tomography (CT) volumetric data set typically consists of hundreds to thousands of slices and needs to be individually inspected. Automated head and neck anatomical segmentation provides a way to speed up and improve the reproducibility of radiation therapy planning. Previous work on anatomical segmentation is primarily based on atlas registrations, which takes up to hours for one patient and requires sophisticated atlas creation. In this work, we propose the AnatomyNet, an end-to-end and atlas-free three dimensional squeeze-and-excitation U-Net (3D SE U-Net), for fast and fully automated whole-volume HaN anatomical segmentation.\n\nMethodsThere are two main challenges for fully automated HaN OARs segmentation: 1) challenge in segmenting small anatomies (i.e., optic chiasm and optic nerves) occupying only a few slices, and 2) training model with inconsistent data annotations with missing ground truth for some anatomical structures because of different RT planning. We propose the AnatomyNet that has one down-sampling layer with the trade-off between GPU memory and feature representation capacity, and 3D SE residual blocks for effective feature learning to alleviate these challenges. Moreover, we design a hybrid loss function with the Dice loss and the focal loss. The Dice loss is a class level distribution loss that depends less on the number of voxels in the anatomy, and the focal loss is designed to deal with highly unbalanced segmentation. For missing annotations, we propose masked loss and weighted loss for accurate and balanced weights updating in the learning of the AnatomyNet.\n\nResultsWe collect 261 HaN CT images to train the AnatomyNet, and use MICCAI Head and Neck Auto Segmentation Challenge 2015 as the benchmark dataset to evaluate the performance of the AnatomyNet. The objective is to segment nine anatomies: brain stem, chiasm, mandible, optic nerve left, optic nerve right, parotid gland left, parotid gland right, submandibular gland left, and submandibular gland right. Compared to previous state-of-the-art methods for each anatomy from the MICCAI 2015 competition, the AnatomyNet increases Dice similarity coefficient (DSC) by 3.3% on average. The proposed AnatomyNet takes only 0.12 seconds on average to segment a whole-volume HaN CT image of an average dimension of 178 x 302 x 225. All the data and code will be availablea.\n\nConclusion1We propose an end-to-end, fast and fully automated deep convolutional network, AnatomyNet, for accurate and whole-volume HaN anatomical segmentation. The proposed Anato-myNet outperforms previous state-of-the-art methods on the benchmark dataset. Extensive experiments demonstrate the effectiveness and good generalization ability of the components in the AnatomyNet.

bioengineering↗

bioRxiv2018

DeepEM: Deep 3D ConvNets With EM For Weakly Supervised Pulmonary Nodule Detection

Recently deep learning has been witnessing widespread adoption in various medical image applications. However, training complex deep neural nets requires large-scale datasets labeled with ground truth, which are often unavailable in many medical image domains. For instance, to train a deep neural net to detect pulmonary nodules in lung computed tomography (CT) images, current practice is to manually label nodule locations and sizes in many CT images to construct a sufficiently large training dataset, which is costly and difficult to scale. On the other hand, electronic medical records (EMR) contain plenty of partial information on the content of each medical image. In this work, we explore how to tap this vast, but currently unexplored data source to improve pulmonary nodule detection. We propose DeepEM, a novel deep 3D ConvNet framework augmented with expectation-maximization (EM), to mine weakly supervised labels in EMRs for pulmonary nodule detection. Experimental results show that DeepEM can lead to 1.5% and 3.9% average improvement in free-response receiver operating characteristic (FROC) scores on LUNA16 and Tianchi datasets, respectively, demonstrating the utility of incomplete information in EMRs for improving deep learning algorithms.1

bioinformatics↗

bioRxiv2018

Modulation of ACD6 dependent hyperimmunity by natural alleles of an Arabidopsis thaliana NLR resistance gene

Plants defend themselves against pathogens by activating an array of immune responses. Unfortunately, immunity programs may also cause unintended collateral damage to the plant itself. The quantitative disease resistance gene ACCELERATED CELL DEATH 6 (ACD6) serves as a nexus for the trade-off between growth and pathogen resistance in wild populations of Arabidopsis thaliana. An autoimmune allele, ACD6-Est, first identified in the natural accession Est-1, is found in over 10% of wild strains, even though it causes a clear fitness penalty under optimal growth conditions. There is, however, extensive variation in the strength of the autoimmune phenotype expressed by strains with an ACD6-Est allele, indicative of genetic modifiers. Quantitative genetic analysis suggests that the population genetic basis of ACD6 modulation is complex, with different strains often carrying different large-effect modifiers. One modifier is SUPPRESSOR OF NPR1-1, CONSTITUTIVE 1 (SNC1), located in a highly polymorphic cluster of nucleotide-binding domain and leucine-rich repeat (NLR) immune receptor genes, which are prototypes for qualitative disease resistance genes. Allelic variation at SNC1 correlates with ACD6-Est activity in multiple accessions, and a common structural variant affecting the NL linker sequence can explain differences in SNC1 activity. Taken together, we find that an NLR gene can mask the activity of an ACD6 autoimmune allele in natural A. thaliana populations, thereby linking different arms of the plant immune system.\n\nAuthor summaryPlants defend themselves against pathogens by activating immune responses. Unfortunately, these can cause unintended collateral damage to the plant itself. Nevertheless, some wild plants have genetic variants that confer a low threshold for the activation of immunity. While these enable a plant to respond particularly quickly to pathogen attack, such variants might be potentially dangerous. We are investigating one such variant of the immune gene ACCELERATED CELL DEATH 6 (ACD6) in the plant Arabidopsis thaliana. We discovered that there are variants at other genetic loci that can mask the effects of an overly active ACD6 gene. One of these genes, SUPPRESSOR OF NPR1-1, CONSTITUTIVE 1 (SNC1), codes for a known immune receptor. The SNC1 variant that attenuates ACD6 activity is rather common in A. thaliana populations, suggesting that new combinations of the hyperactive ACD6 variant and this antagonistic SNC1 variant will often arise by natural crosses. Similarly, because the two genes are unlinked, outcrossing will often lead to the hyperactive ACD6 variants being unmasked again. We propose that allelic diversity at SNC1 contributes to the maintenance of the hyperactive ACD6 variant in natural A. thaliana populations.

plant biology↗

bioRxiv2018

The plasma miR-122 basal levels respond to circulating catecholamine in rats

miR-122 in circulation is a promising non-invasive biomarker as a replacement or supplement of current serum biomarkers for liver injuries. But the concept was questioned by recent studies, mainly due to its release from hepatocytes in absence of overt cellular injuries. In this study, we reported that the hepatic metabolism of circulating catecholamines resulted in the release of hepatocyte-specific miR-122. Acute stress-induced hepatocellular deformation was histopathologically different from drug-induced liver injury with significant increases of plasma miR-122 levels. The basal levels of human plasma miR-122 could be significantly altered by emotional responses. Interday variances of plasma miR-122 measurements were reduced effectively by stress-relief measures. The metabolism of basal circulating norepinephrine and epinephrine in liver might contribute to the basal levels of plasma miRNAs expressed in hepatocytes.

molecular biology↗

bioRxiv2018

The brown alga Saccharina japonica features distinct vanadium-dependent bromoperoxidases and iodoperoxidases

Marine algae have an extraordinary ability to absorb halogens which provide algae with an inorganic antioxidant impacting atmospheric chemistry. Although brown algal Laminariales species are the most efficient iodine accumulators among all living systems, and Saccharina japonica is the primary material used for iodine extraction, the functions and regulatory mechanisms of these species have not been fully documented. In this study, a functional genomics analysis of the algal vanadium-dependent haloperoxidase (vHPO) gene family was conducted; there genes can introduce halogen atoms into organic compounds. The comprehensive analyses regarding the bioinformatics and phylogenetics of novel genomic and transcriptomic sequencing data of 21 Rhodophyta and 19 Ochrophyta marine algal species revealed that brown algal vHPOs have two gene types, vanadium-dependent bromoperoxidase (vBPO) and vanadium-dependent iodoperoxidase (vIPO), with secondary endosymbiotic host origin. The enzyme activity of S. japonica vBPO and vIPO were verified for the first time and were quite stable in a wide range of temperature and pH values. However, the specific activity and optimal conditions were considerably different between vBPO and vIPO. The transcript expression analysis in different S. japonica tissues (including rhizoids), generations (sporophytes and gametophytes), sexes (male and female), and stress conditions (hyposaline and hyperthermia) also showed great differences between vBPOs and vIPOs. Most of the vBPOs were constitutively expressed with higher expression dose, which may be responsible for basal halogen metabolism. On the contrary, vIPOs mainly showed specific expression, which may be involved in tissue differentiation, generation differentiation, sex differentiation, and stress regulation. Comprehensive analysis of gene family evolution, enzyme biochemical characteristics, and complex transcriptional mechanisms were conducive to the environmental adaptation and sophisticated system evolution of Laminariales. The successful bromination of small-molecule compound substrate by SjavBPO provided high activity and efficient enzymatic tools for artificial synthesis of halogenated compounds.

molecular biology↗

bioRxiv2017

Improving the calling of non-invasive prenatal testing on 13-/18-/21-trisomy by support vector machine discrimination

With the advance of next-generation sequencing technologies, non-invasive prenatal testing (NIPT) has been developed and employed in fetal aneuploidy screening on 13-/18-/21-trisomies through detecting cell-free fetal DNA (cffDNA) in maternal blood. Although Z test is widely used in NIPT nowadays, there is still necessity to improve its accuracy for removing a) false negatives and false positives, and b) the ratio of unclassified data, so as to reduce the potential harm to patients caused by these inaccuracies as well as the induced cost of retests.\n\nEmploying multiple Z tests with machine-learning algorithm could provide a better prediction on NIPT data. Combining the multiple Z values with indexes of clinical signs and quality control, features were collected from the known samples and scaled for model training in support vector machine (SVM) discrimination. The trained model was applied to predict the unknown samples, which showed significant improvement. In 4752 qualified NIPT data, our method reached 100% accuracies on all three chromosomes, including 151 data that were grouped as unclassified by one-Z-value based method. Moreover, four false positives and four false negatives were corrected by using this machine-learning model.\n\nTo our knowledge, this is the first study to employ support vector machine in NIPT data analysis. It is expected to replace the current one-Z-value based NIPT analysis in clinical use.

bioinformatics↗

bioRxiv2017

DeepLung: 3D Deep Convolutional Nets for Automated Pulmonary Nodule Detection and Classification

In this work, we present a fully automated lung CT cancer diagnosis system, DeepLung. DeepLung contains two parts, nodule detection and classification. Considering the 3D nature of lung CT data, two 3D networks are designed for the nodule detection and classification respectively. Specifically, a 3D Faster R-CNN is designed for nodule detection with a U-net-like encoder-decoder structure to effectively learn nodule features. For nodule classification, gradient boosting machine (GBM) with 3D dual path network (DPN) features is proposed. The nodule classification subnetwork is validated on a public dataset from LIDC-IDRI, on which it achieves better performance than state-of-the-art approaches, and surpasses the average performance of four experienced doctors. For the DeepLung system, candidate nodules are detected first by the nodule detection subnetwork, and nodule diagnosis is conducted by the classification subnetwork. Extensive experimental results demonstrate the DeepLung is comparable to the experienced doctors both for the nodule-level and patient-level diagnosis on the LIDC-IDRI dataset.

bioinformatics↗

bioRxiv2017

A rainfall-manipulation experiment with 517 Arabidopsis thaliana accessions

The gold standard for studying natural selection and adaptation in the wild is to quantify lifetime fitness of individuals from natural populations that have been grown together in a common garden, or that have been reciprocally transplanted. By combining fitness values with species traits and genome sequences, one can infer selection coefficients at the genetic level. Here we present a rainfall-manipulation experiment with 517 whole-genome sequenced natural accessions of the plant Arabidopsis thaliana spanning the global distribution of the species. The experiments were conducted in two field stations in contrasting climates, in the Mediterranean and in Central Europe, where we built rainout shelters and simulated high and low rainfall. Using custom image analysis we quantified fitness- and phenology-related traits for 23,154 pots, which contained about 14,500 plants growing independently, and over 310,000 plants growing in small populations (max. 30 plants). This large field experiment dataset, which associates fitness and ecologically-relevant traits with genomes, will provide an important resource to test eco-evolutionary genetic theories and to understand the potential evolutionary impacts of future climates on an important plant model species.

evolutionary biology↗

bioRxiv2017

Aging-associated dysbiosis increases susceptibility to enteric viral infection in Drosophila

Age is associated with increased susceptibility to enteric infections, but the molecular mechanisms are unclear. We find that aged Drosophila are more susceptible to enteric viral infections and that this increase in susceptibility is due to the aged microbiota, since depletion of the microbiota or reconstitution with a young microbiome suppressed infection. Metagenomic analysis of the aged microbiome revealed dysbiosis with an increased abundance in reactive oxygen species (ROS) producing pathways. This aged microbiota drives intestinal ROS production and we could restore immune function in old flies by reducing ROS genetically or pharmacologically. Moreover, we found that reconstitution of old flies with a cocktail of commensals, including L. fructivorans and heat-killed A. pomorum, could fully restore immunity. Altogether, these findings provide a mechanistic link between age-dependent dysbiosis and antiviral immunity and show that we can restore innate protection in aged animals, suggesting that this is a treatable and reversible state.

immunology↗

bioRxiv2017

Faster carbon accumulation in global forest soils

Comparing soil organic carbon (SOC) stocks across space and time is a fundamental issue in global ecology. However, the conventional approach fails to determine SOC stock in an equivalent volume of mineral-soil, and therefore, SOC stock changes can be under- or overestimates if soils swell or shrink during forest development or degradation. Here, we propose to estimate SOC stock as the product of mineral-soil mass in an equivalent mineral-soil volume and SOC concentration expressed as g C Kg-1 mineral-soil. This method enables researchers to compare SOC stocks across space and time. Our results show an unaccounted SOC accumulation of 2.4 - 10.1 g C m-2 year-1 in the 1m surface mineral-soils in global forests. This unaccounted SOC amounts to an additional C sink of 0.12 - 0.25 Pg C year-1, which equals 30 - 62% of the previously estimated annual SOC accumulation in global forests. This finding suggests that forest soils are stronger C sinks than previously recognized.

ecology↗

bioRxiv2017

Daam2 Driven Degradation Of VHL Promotes Gliomagenesis

Von Hippel-Landau (VHL) protein is a potent tumor suppressor regulating numerous pathways that drive cancer, but mutations in VHL are restricted to limited subsets of malignancies. Here we identified a novel mechanism for VHL suppression in tumors that do not have inactivating mutations. Using developmental processes to uncover new pathways contributing to tumorigenesis, we found that Daam2 promotes glioma formation. Protein expression screening identified an inverse correlation between Daam2 and VHL expression across a host of cancers, including glioma. These in silico insights guided corroborating functional studies, which revealed that Daam2 promotes tumorigenesis by suppressing VHL expression. Furthermore, biochemical analyses demonstrate that Daam2 associates with VHL and facilitates its ubiquitination and degradation. Together, these studies are the first to define an upstream mechanism regulating VHL suppression in cancer and describe the role of Daam2 in tumorigenesis.\n\nStatement of SignificanceWe found that the glial developmental factor Daam2 promotes glioma tumorigenesis by suppressing VHL expression. Our studies show, for the first time, a regulatory mechanism that operates upstream of VHL in cancer and provides an explanation for how VHL expression is extinguished in tumors that do not have inactivating mutations.

cancer biology↗

bioRxiv2016

Adversarial Deep Structural Networks for Mammographic Mass Segmentation

Mass segmentation is an important task in mammogram analysis, providing effective morphological features and regions of interest (ROI) for mass detection and classification. Inspired by the success of using deep convolutional features for natural image analysis and conditional random fields (CRF) for structural learning, we propose an end-to-end network for mammographic mass segmentation. The network employs a fully convolutional network (FCN) to model potential function, followed by a CRF to perform structural learning. Because the mass distribution varies greatly with pixel position, the FCN is combined with position priori for the task. Due to the small size of mammogram datasets, we use adversarial training to control over-fitting. Four models with different convolutional kernels are further fused to improve the segmentation results. Experimental results on two public datasets, INbreast and DDSM-BCRP, show that our end-to-end network combined with adversarial training achieves the-state-of-the-art results.

bioinformatics↗

bioRxiv2016

Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification

Mammogram classification is directly related to computer-aided diagnosis of breast cancer. Traditional methods requires great effort to annotate the training data by costly manual labeling and specialized computational models to detect these annotations during test. Inspired by the success of using deep convolutional features for natural image analysis and multi-instance learning for labeling a set of instances/patches, we propose end-to-end trained deep multiinstance networks for mass classification based on whole mammogram without the aforementioned costly need to annotate the training data. We explore three different schemes to construct deep multi-instance networks for whole mammogram classification. Experimental results on the INbreast dataset demonstrate the robustness of proposed deep networks compared to previous work using segmentation and detection annotations in the training.

bioinformatics↗