Biology subjects

Wei, Z.

Publications and source records attributed to Wei, Z..

11 recordsLinked to original sources

bioRxiv2018

Proteomic Profile of TGF-β1 treated Lung Fibroblasts identifies Novel Markers of Activated Fibroblasts in the Silica Exposed Rat Lung

We performed liquid chromatography-tandem mass spectrometry (LC-MS/MS) on control and TGF-{beta}1-exposed rat lung fibroblasts to identify proteins differentially expressed between cell populations. A total of 1648 proteins were found to be differentially expressed in response to TGF-{beta}1 treatment and 196 proteins were expressed at [≥] 1.2 fold relative to control. Guided by these results, we next determined whether similar changes in protein expression were detectable in the rat lung after chronic exposure to silica dust. Of the five proteins selected for further analysis, we found that levels of all proteins were markedly increased in the silica-exposed rat lung, including the proteins for the very low density lipoprotein receptor (VLDLR) and the transmembrane (type I) heparin sulfate proteoglycan called syndecan 2 (SDC2). Because VLDLR and SDC2 have not, to our knowledge, been previously linked to the pathobiology of silicosis, we next examined whether knockdown of either gene altered responses to TGF-{beta}1 in MRC-5 lung fibroblasts. Interestingly, we found knockdown of either VLDLR or SDC2 dramatically reduced collagen production to TGF-{beta}1, suggesting that both proteins might play a novel role in myofibroblast biology and pathogenesis of silica-induced pulmonary fibrosis. In summary, our findings suggest that performing LC-MS/MS on TGF-{beta}1 stimulated lung fibroblasts can uncover novel molecular targets of activated myofibroblasts in silica-exposed lung.\n\nHighlightsWe identified 196 proteins differentially expressed between control and TGF-{beta}1 treated fibroblastsby LC-MS/MS.\n\nSeveral proteins identified by LC-MS/MS were also found to be differentially expressed in whole lung tissues and isolated fibroblasts after chronic exposure to silica dust, including the very low density lipoprotein receptor (VLDLR) and the transmembrane type I heparan sulfate proteoglycan called syndecan 2\n\nKnockdown of SDC2 or VLDLR markedly inhibited collagen production in MRC-5 fibroblasts, suggesting a novel pathogenic role for these proteins in myofibroblast biology.

pharmacology and toxicology↗

bioRxiv2018

An improved high-quality genome assembly and annotation of Qingke, Tibetan hulless barley

BackgroundThe Tibetan hulless barley (Hordeum vulgare L. var. nudum), also called \"Qingke\" in Chinese and \"Ne\" in Tibetan, is the staple food for Tibetans and an important livestock feed in the Tibetan Plateau. The Tibetan hulless barley in China has about 3500 years of cultivation history, mainly produced in Tibet, Qinghai, Sichuan, Yunnan and other areas. In addition, Tibetan hulless barley has rich nutritional value and outstanding health effects, including the beta glucan, dietary fiber, amylopectin, the contents of trace elements, which are higher than any other cereal crops.\n\nFindingsHere, we reported an improved high-quality assembly of Tibetan hulless barley genome with 4.0 Gb in size. We employed the falcon assembly package, scaffolding and error correction tools to finish improvement using PacBio long reads sequencing technology, with contig and scaffold N50 lengths of 1.563Mb and 4.006Mb, respectively, representing more continuous than the original Tibetan hulless barley genome nearly two orders of magnitude. We also re-annotated the new assembly, and reported 61,303 stringent confident putative protein-coding genes, of which 40,457 is HC genes. We have developed a new Tibetan hulless barley genome database (THBGD) to download and use friendly, as well as to better manage the information of the Tibetan hulless barley genetic resources.\n\nConclusionsThe availability of new Tibetan hulless barley genome and annotations will take the genetics of Tibetan hulless barley to a new level and will greatly simplify the breeders effort. It will also enrich the granary of the Tibetan people.

genomics↗

bioRxiv2018

An orderly single-trial organization of population dynamics in premotor cortex predicts behavioral variability

Animals are not simple input-output machines. Their responses to even very similar stimuli are variable. A key, long-standing question in neuroscience is understanding the neural correlates of such behavioral variability. To reveal these correlates, behavior and neural population must be related to one another on single trials. Such analysis is challenging due to the dynamical nature of brain function (e.g. decision making), neuronal heterogeneity and signal to noise difficulties. By analyzing population recordings from mouse frontal cortex in perceptual decision-making tasks, we show that an analysis approach tailored to the coarse grain features of the dynamics was able to reveal previously unrecognized structure in the organization of population activity. This structure was similar on error and correct trials, suggesting what may be the underlying circuit mechanisms, was able to predict multiple aspects of behavioral variability and revealed long time-scale modulation of population activity.

neuroscience↗

bioRxiv2018

Deciphering the metabolic perturbation in hepatic alveolar echinococcosis: a 1H NMR-based metabolomics study

Hepatic alveolar echinococcosis (HAE) is a chronic and potentially lethal parasitic disease. It is caused by growth of Echinococcus multilocularis larvae in liver. To date, early-stage diagnosis for the disease is not mature due to its long asymptomatic incubation period. In this study, a proton nuclear magnetic resonance (1H NMR) -based metabolomics approach was applied in conjunction with multivariate statistical analysis to investigate the altered metabolic profiles in blood serum and urine samples from HAE patients and to identify characteristic metabolic markers associated with HAE. The current results identified 21 distinctive metabolic difference between the HAE patients and healthy individuals, which can be associated with perturbations in energy metabolism, amino acid metabolism, oxidative stress, and neurotransmitter imbalance. In addition, the Fischer ratio, which is the molar ratio of branched-chain amino acids to aromatic amino acids was found significantly lower (p<0.001) in blood serum from HAE patients. The ratio, together with changes in other metabolic pathways may provide new insight into mechanistic understanding of HAE pathogenesis, and may be useful for early-stage HAE diagnosis.\n\nAuthor SummaryHepatic alveolar echinococcosis (HAE) is a life-threatening disease caused by Echinococcus multilocularis infection. The disease has a long asymptomatic early stage (5~15 years), which complicates effective diagnosis of early-stage HAE even with advanced imaging techniques. Metabolomics is an emerging analytical platform that comprises of analysis of all small molecule metabolites that are present within an organism. The applications of metabolomics method on HAE may help to reveal the molecular biology mechanisms of HAE. In the current study, we had used 1H NMR-based metabolomics technique to investigate blood serum and urine samples from HAE patients. Altered metabolic responses and characteristic differential metabolites for HAE were identified. The metabolic profiling of human biofluids provided valuable information for early-stage HAE diagnosis and for therapeutic interventions, without having to extract HAE vesicles from patients. By featuring global and comprehensive metabolic status, the metabolomics approach holds considerable promise as a noninvasive, dynamic, and effective tool for probing the underlying mechanism of HAE.

systems biology↗

bioRxiv2018

Deep learning achieves super-resolution in fluorescence microscopy

AbtsractWe present a deep learning-based method for achieving super-resolution in fluorescence microscopy. This data-driven approach does not require any numerical models of the imaging process or the estimation of a point spread function, and is solely based on training a generative adversarial network, which statistically learns to transform low resolution input images into super-resolved ones. Using this method, we super-resolve wide-field images acquired with low numerical aperture objective lenses, matching the resolution that is acquired using high numerical aperture objectives. We also demonstrate that diffraction-limited confocal microscopy images can be transformed by the same framework into super-resolved fluorescence images, matching the image resolution acquired with a stimulated emission depletion (STED) microscope. The deep network rapidly outputs these super-resolution images, without any iterations or parameter search, and even works for types of samples that it was not trained for.

bioengineering↗

bioRxiv2018

Visual physiology of the Layer 4 cortical circuit in silico

Despite advances in experimental techniques and accumulation of large datasets concerning the composition and properties of the cortex, quantitative modeling of cortical circuits under in-vivo-like conditions remains challenging. Here we report and publicly release a biophysically detailed circuit model of layer 4 in the mouse primary visual cortex, receiving thalamo-cortical visual inputs. The 45,000-neuron model was subjected to a battery of visual stimuli, and results were compared to published work and new in vivo experiments. Simulations reproduced a variety of observations, including effects of optogenetic perturbations. Critical to the agreement between responses in silico and in vivo were the rules of functional synaptic connectivity between neurons. Interestingly, after extreme simplification the model still performed satisfactorily on many measurements, although quantitative agreement with experiments suffered. These results emphasize the importance of functional rules of cortical wiring and enable a next generation of data-driven models of in vivo neural activity and computations.\n\nAUTHOR SUMMARYHow can we capture the incredible complexity of brain circuits in quantitative models, and what can such models teach us about mechanisms underlying brain activity? To answer these questions, we set out to build extensive, bio-realistic models of brain circuitry employing systematic datasets on brain structure and function. Here we report the first modeling results of this project, focusing on the layer 4 of the primary visual cortex (V1) of the mouse. Our simulations reproduced a variety of experimental observations in a large battery of visual stimuli. The results elucidated circuit mechanisms determining patters of neuronal activity in layer 4 - in particular, the roles of feedforward thalamic inputs and specific patterns of intracortical connectivity in producing tuning of neuronal responses to the orientation of motion. Simplification of neuronal models led to specific deficiencies in reproducing experimental data, giving insights into how biological details contribute to various aspects of brain activity. To enable future development of more sophisticated models, we make the software code, the model, and simulation results publicly available.

neuroscience↗

bioRxiv2018

Genome-wide prediction of synthetic rescue mediators of resistance to targeted and immunotherapy

Most patients with advanced cancer eventually acquire resistance to targeted therapies, spurring extensive efforts to identify molecular events mediating therapy resistance. Many of these events involve synthetic rescue (SR) interactions, where the reduction in cancer cell viability caused by targeted gene inactivation is rescued by an adaptive alteration of another gene (the rescuer). Here we perform a genome-wide prediction of SR rescuer genes by analyzing tumor transcriptomics and survival data of 10,000 TCGA cancer patients. Predicted SR interactions are validated in new experimental screens. We show that SR interactions can successfully predict cancer patients response and emerging resistance. Inhibiting predicted rescuer genes sensitizes resistant cancer cells to therapies synergistically, providing initial leads for developing combinatorial approaches to overcome resistance proactively. Finally, we show that the SR analysis of melanoma patients successfully identifies known mediators of resistance to immunotherapy and predicts novel rescuers.

cancer biology↗

bioRxiv2018

A learning-based framework for miRNA-disease association prediction using neural networks

MotivationA microRNA (miRNA) is a type of non-coding RNA, which plays important roles in many bio-logical processes. Lots of studies have shown that miRNAs were implicated in human diseases, indicating that miRNAs might be potential biomarkers for various types of diseases. Therefore, it is important to reveal the relationships between miRNAs and diseases/phenotypes.\n\nResultsWe proposed a novel learning-based framework, MDA-CNN, for miRNA-disease association identification. The model first captures richer interaction features between diseases and miRNAs based on a three-layer network with an additional gene layer. An auto-encoder is then employed to identify the essential feature combination for each pair of miRNA and disease automatically. A final label is given by a convolutional neural network taking the reduced feature representation as input. The evaluation results showed that the proposed framework outperformed some state-of-the-art approaches in a large margin on both tasks of miRNA-disease associations prediction and miRNA-phenotype associations prediction.\n\nAvailabilityThe software will be available after the manuscript is accepted.\n\nContactjiajiepeng@nwpu.edu.cn;zywei@fudan.edu.cn;shang@nwpu.edu.cn\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2018

MicroRNA-132 provides neuroprotection for tauopathies via multiple signaling pathways

MicroRNAs (miRNA) regulate fundamental biological processes, including neuronal plasticity, stress response, and survival. Here we describe a neuroprotective function of miR-132, the miRNA most significantly down-regulated in Alzheimers disease. miR-132 protects mouse and human wild-type neurons and more vulnerable Tau-mutant primary neurons against amyloid {beta}-peptide (A{beta}) and glutamate excitotoxicity. It lowers the levels of total, phosphorylated, acetylated, and cleaved forms of Tau implicated in tauopathies, promotes neurite elongation and branching, and reduces neuronal death. Similarly, miR-132 attenuates PHF Tau pathology and neurodegeneration and enhances long-term potentiation in the P301S Tau transgenic mice. The neuroprotective effects are mediated by direct regulation of the Tau modifiers acetyltransferase EP300, kinase GSK3{beta}, RNA-binding protein Rbfox1, and proteases Calpain 2 and Caspases 3/7. These data suggest miR-132 as a master regulator of neuronal health and indicate that miR-132 supplementation could be of therapeutic benefit for the treatment of Tau-associated neurodegenerative disorders.

neuroscience↗

bioRxiv2017

Unexpected CRISPR off-target mutation pattern in vivo are not typicallygermline-like

To the EditorSchaefer et al.1 (referred to as Study_1) recently presented the provocative conclusion that CRISPR-Cas9 nuclease can induce many unexpected off-target mutations across the genome that arise from the sites with poor homology to the gRNA. As Wilson et al.2 pointed out, however, the selection of a co-housed mouse as the control is insufficient to attribute the observed mutation differences between the CRISPR-treated mice and control mice. Therefore, the causes of these mutations need to be further investigated. In 2015, Iyer et al.3 (referred to as Study_2) used Cas9 and a pair of sgRNAs to mutate the Ar gene in vivo and off-target mutations were investigated by comparison the control mice and the offspring of the modified mice. After analyzing the whole genome sequencing (WGS) of the offspring and the control mice, they claimed that off-target mutations are rare from CRISPR-Cas9 engineering. Notably, their study only focused on indel off-target mutations. We re-analyzed the WGS data of these two studies and detected both single nucleotide variants (SNVs) and indel mutations.

genetics↗

bioRxiv2017

Approach To Insect Wing Shape And Deformation Field Measurement

Summary StatementA fine shape and deformation field measurement of insect wing is achieved by a self-developed setup. This measurement could foster investigation of insect wing stiffness distribution.\n\nAbstractFor measuring the shape and deformation of insect wing, a scanning setup adopting line laser and coaxial LED light is developed. Wing shape can be directly acquired from the line laser images by triangulation. Yet the wing deformation field can also be obtained by a self-devised algorithm that processes the images from line laser and coaxial LED simultaneously. During the experiment, three wing samples from termite and mosquito under concentrated force are scanned. The venation and corrugation could be significantly identified from shape measurement result. The deformation field is sufficiently accurate to demonstrate its variation from wing base to tip. The load conditions in experiments are also be discussed. For softer wings, local deformation is apparent if pinhead is employed to impose force. The similarity analysis is better than 5% deformation ratio as a static criterion, if the wing is simplified as a cantilever beam. The setup is proved to be effective and versatile. The shape and deformation fields would give enough details for the measurement of wing stiffness distribution.

zoology↗