Biology subjects

Feng, D.

Publications and source records attributed to Feng, D..

54 records · Page 3Linked to original sources

bioRxiv2021

TCR signaling promotes the assembly of RanBP2/RanGAP1-SUMO1/Ubc9 NPC subcomplex via phosphorylation of RanGAP1 by PKC-θ

The nuclear pore complex (NPC) is the sole and selective gateway for nuclear transport and its dysfunction has been associated with many diseases. The NPC subcomplex RanBP2, which consists of RanBP2 (Nup358), RanGAP1-SUMO1 and Ubc9, regulates the assembly and function of the NPC. The roles of immune signaling in NPC assembly remain poorly understood. Here, we show that, following TCR stimulation, protein kinase C-{theta} (PKC-{theta}) directly phosphorylates RanGAP1 to facilitate RanBP2 subcomplex assembly and nuclear import and, thus, the nuclear translocation of AP-1 transcription factor. Mechanistically, TCR stimulation induces the translocation of activated PKC-{theta} to the NPC, where it interacts with and phosphorylates RanGAP1 on Ser504 and Ser506. RanGAP1 phosphorylation increases its binding affinity for Ubc9, thereby promoting sumoylation of RanGAP1 and, finally, assembly of the RanBP2 subcomplex. Our findings reveal an unexpected role of PKC-{theta} as a direct regulator of nuclear import and uncover a phosphorylation-dependent sumoylation of RanGAP1, delineating a novel link between TCR signaling and assembly of the RanBP2 NPC subcomplex.

cell biology↗

bioRxiv2021

Thinopyrum intermedium TiAP1 interacts with a chitin deacetylase from Blumeria graminis f. sp. tritici and increases the resistance to Bgt in wheat

The biotrophic fungal pathogen Blumeria graminis f. sp. tritici (Bgt) is a crucial factor causing reduction of global wheat production. Wild wheat relatives, e.g. Thinopyrum intermedium, is one of the wild-used parents in wheat disease-resistant breeding. From T. intermedium line, we identified the aspartic acid protein gene, TiAP1, which involved in resistance against Bgt. TiAP1 is a secreted protein that accumulates in large amounts at the infection sites of powdery mildew and extends to the intercellular space. Yeast two-hybrid showed that it interacted with the chitin deacetylase (BgtCDA1) of Bgt. The yeast expression, purification, and invitro test confirmed the chitin deacetylase activity of BgtCDA1. The bombardment and VIGS mediated host-induced gene silencing showed BgtCDA1 promotes the invasion of Bgt. Transcriptome analysis showed the cell wall xylan metabolism, lignin biosynthesis-related, and defence genes involved in the signal transduction were upregulated in the transgenic TiAP1 wheat induced by Bgt. The TiAP1 in wheat may inactivate the deacetylation function of BgtCDA1, cause chitin oligomers expose to wheat chitin receptor, then trigger the wheat immune response to inhibit the growth and penetration of Bgt, and thereby enhance the tolerance of wheat to pathogens.

plant biology↗

bioRxiv2020

Modeling of symbiotic bacterial biofilm growth with an example of the Streptococcus-Veillonella sp. system

We present a multi-dimensional continuum mathematical model for modeling the growth of a symbiotic biofilm system. We take a dual-species namely, the Streptococcus - Veillonella sp. biofilm system as an example for numerical investigations. The presented model describes both the cooperation and competition between these species of bacteria. The coupled partial differential equations are solved by using an integrative finite element numerical strategy. Numerical examples are carried out for studying the evolution and distribution of the bio-components. The results demonstrate that the presented model is capable of describing the symbiotic behavior of the biofilm system. However, homogenized numerical solutions are observed locally. To study the homogenization behavior of the model, numerical investigations regarding on how random initial biomass distribution influences the homogenization process are carried out. We found that a smaller correlation length of the initial biomass distribution leads to faster homogenization of the solution globally, however, shows more fluctuated biomass profiles along the biofilm thickness direction. More realistic scenarios with bacteria in patches are also investigated numerically in this study.

bioengineering↗

bioRxiv2020

MicroRNA-224 Promotes Cell Migration and Invasion by Targeting HOXA5 expression in Hepatocellular Carcinoma

MicroRNAs (miRNAs) are involved in the regulation of multiple cellular pathways and play a key role in the development and progression of tumor. Based on the cellular function of their targets, miRNAs play the role of oncogenes or tumor suppressor genes. Multiple studies have shown that abnormal expression of miRNAs has close relation with the incidence of HCC, but the mechanism of miRNAs in HCC still needs further research. In the present study, we showed that the overexpression of miR-224 can reduce the mRNA and protein expression of ADAM17 and HOXA5, the silencing of miR-224 can increase the protein expression of ADAM17 and HOXA5. Dual luciferase reporter assays showed that miR-224 can directly regulate the expression of ADAM17 and HOXA5. Importantly, we found that miR-224 positively regulates cell migration and invasion in HCC, miR-224 overexpression can promote the migration and invasion of BEL-7402 cell, and miR-224 silencing can suppress the migration and invasion of BEL-7402 cell. miR-224 overexpression can result in the redistribution of cell cycle, the cell percentage of S phase was increased significantly, the cell percentage of G1 phase was decreased significantly, and there is no noticeable change for the cell percentage of G2 phase. These results demonstrated that it may be exert the function of oncogenes in a particular link of cancer cell growth. In conclusion, these results suggest that miR-224 will become a promising biological target in the treatment strategy of hepatocellular carcinoma (HCC).

cancer biology↗

bioRxiv2020

Regional, layer, and cell-class specific connectivity of the mouse default mode network

The evolutionarily conserved default mode network (DMN) is characterized by temporally correlated activity between brain regions during resting states. The DMN has emerged as a selectively vulnerable network in multiple disorders, so understanding its anatomical composition will provide fundamental insight into how its function is impacted by disease. Reproducible rodent analogs of the human DMN offer an opportunity to investigate the underlying brain regions and structural connectivity (SC) with high spatial and cell type resolution. Here, we performed systematic analyses using mouse resting state functional magnetic resonance imaging to identify the DMN and whole brain axonal tracing data, co-registered to the 3D Allen Mouse Common Coordinate Framework reference atlas. We identified the specific, predominantly cortical, brain regions comprising the mouse DMN and report preferential SC between these regions. Next, at the cell class level, we report that cortical layer (L) 2/3 neurons in DMN regions project almost exclusively to other DMN regions, whereas L5 neurons project to targets both in and out of the DMN. We then test the hypothesis that in- and out-DMN projection patterns originate from distinct L5 neuron sub-classes using an intersectional viral tracing strategy to label all the axons from neurons defined by a single target. In the ventral retrosplenial cortex, a core DMN region, we found two L5 projection types related to the DMN and mapped them to unique transcriptomically-defined cell types. Together, our results provide a multi-scale description of the anatomical correlates of the mouse DMN.

neuroscience↗

bioRxiv2020

Human cortical expansion involves diversification and specialization of supragranular intratelencephalic-projecting neurons

The neocortex is disproportionately expanded in human compared to mouse, both in its total volume relative to subcortical structures and in the proportion occupied by supragranular layers that selectively make connections within the cortex and other telencephalic structures. Single-cell transcriptomic analyses of human and mouse cortex show an increased diversity of glutamatergic neuron types in supragranular cortex in human and pronounced gradients as a function of cortical depth. To probe the functional and anatomical correlates of this transcriptomic diversity, we describe a robust Patch-seq platform using neurosurgically-resected human tissues. We characterize the morphological and physiological properties of five transcriptomically defined human glutamatergic supragranular neuron types. Three of these types have properties that are specialized compared to the more homogeneous properties of transcriptomically defined homologous mouse neuron types. The two remaining supragranular neuron types, located exclusively in deep layer 3, do not have clear mouse homologues in supragranular cortex but are transcriptionally most similar to deep layer mouse intratelencephalic-projecting neuron types. Furthermore, we reveal the transcriptomic types in deep layer 3 that express high levels of non-phosphorylated heavy chain neurofilament protein that label long-range neurons known to be selectively depleted in Alzheimers disease. Together, these results demonstrate the power of transcriptomic cell type classification, provide a mechanistic underpinning for increased complexity of cortical function in human cortical evolution, and implicate discrete transcriptomic cell types as selectively vulnerable in disease.

neuroscience↗

bioRxiv2020

Meta-analysis of the effects of sleep deprivation on depression in patients and animals

ObjectiveResearch on the antidepressant effects of sleep deprivation (SD) is lagging and has not produced completely uniform results in humans and animals. The present study aimed to reassess the effect of SD on patients and animals by meta-analysis based on updated research. MethodsWe searched PubMed, Embase and Cochrane Library for articles since the first relevant literature published up to June 10th, 2019. Data on sample characteristics, features of SD, and tests for depression were extracted. ResultsFourteen articles were included, eight on humans and six on animals. We found that when the duration of SD in patients was 7-14 days, it reflected antidepression [-1.52 (-2.07, -0.97); I2=19.6%]. In animals, the results of sucrose consumption experiments showed that SD has depressogenic effects [-1.06 (-1.63, -0.49); I2=81.1%], while the results of forced swimming experiments showed that SD treated depression [-1.17 (-2.19, -0.16); I2=80.1%], regardless of the duration of sleep deprivation. ConclusionSD can be an effective antidepressant measure when the duration is 7-14 days in patients. In animal studies, SD has shown more antidepressant effects when measured by forced swimming experiments, whereas using sucrose consumption tests had the effect of worsening depression.

neuroscience↗

bioRxiv2020

Toward an integrated classification of neuronal cell types: morphoelectric and transcriptomic characterization of individual GABAergic cortical neurons

Neurons are frequently classified into distinct groups or cell types on the basis of structural, physiological, or genetic attributes. To better constrain the definition of neuronal cell types, we characterized the transcriptomes and intrinsic physiological properties of over 3,700 GABAergic mouse visual cortical neurons and reconstructed the local morphologies of 350 of those neurons. We found that most transcriptomic types (t-types) occupy specific laminar positions within mouse visual cortex, and many of those t-types exhibit consistent electrophysiological and morphological features. We observed that these properties could vary continuously between t-types, which limited the ability to predict specific t-types from other data modalities. Despite that, the data support the presence of at least 20 interneuron met-types that have congruent morphological, electrophysiological, and transcriptomic properties. HighlightsO_LIPatch-seq data obtained from >3,700 GABAergic cortical interneurons C_LIO_LIComprehensive characterization of morpho-electric features of transcriptomic types C_LIO_LI20 interneuron met-types that have congruent properties across data modalities C_LIO_LIDifferent Sst met-types preferentially innervate different cortical layers C_LI

neuroscience↗

bioRxiv2019

Single-Cell Map of Diverse Immune Phenotypes in the Metastatic Brain Tumor Microenvironment of Non Small Cell Lung Cancer

Cancer immunotherapies have shown sustained clinical success in treating primary non-small-cell lung cancer (NSCLC). However, patients with brain metastasis are excluded from the trials because the brain is viewed traditionally as an immune-privileged organ. The composition and properties of tumor-infiltrating myeloid cells in metastatic brain tumors are mostly unknown. To depict the baseline landscape of the composition, gene signature, and functional states of these immune cells, we performed - single-cell RNA sequencing (scRNAseq) for 12,196cellsafter data preprocessing, including 2,241 immunecells from three surgically removed brain lesions of treatment-naive NSCLC patients. We found a lack of T lymphocyte infiltration and activation, as well as the vast expansion of tumor-associated macrophage(TAM) in the brain lesions of NSCLC patients. By comparing our scRNAseq dataset with published data from early and late-stage primary NSCLC tumors, we showed that this compromised T cell response is unique to brain lesions. We identified a unique alternative activation (M2) gene expression pattern of the TAM in the brain metastasis and a lack of known T cell co-stimulator expression. Accumulation of M2 polarized TAM may, therefore, cause the comprised anti-tumor T cell response in metastatic brain lesions. These findings can contribute to the design of new immunotherapy strategies for NSCLC patients with brain metastasis.

cancer biology↗

bioRxiv2019

A survey of spiking activity reveals a functional hierarchy of mouse corticothalamic visual areas

The mammalian visual system, from retina to neocortex, has been extensively studied at both anatomical and functional levels. Anatomy indicates the cortico-thalamic system is hierarchical, but characterization of cellular-level functional interactions across multiple levels of this hierarchy is lacking, partially due to the challenge of simultaneously recording activity across numerous regions. Here, we describe a large, open dataset (part of the Allen Brain Observatory) that surveys spiking from units in six cortical and two thalamic regions responding to a battery of visual stimuli. Using spike cross-correlation analysis, we find that inter-area functional connectivity mirrors the anatomical hierarchy from the Allen Mouse Brain Connectivity Atlas. Classical functional measures of hierarchy, including visual response latency, receptive field size, phase-locking to a drifting grating stimulus, and autocorrelation timescale are all correlated with the anatomical hierarchy. Moreover, recordings during a visual task support the behavioral relevance of hierarchical processing. Overall, this dataset and the hierarchy we describe provide a foundation for understanding coding and dynamics in the mouse cortico-thalamic visual system.

neuroscience↗

bioRxiv2018

Automatic navigation system for the mouse brain

Identification and delineation of brain regions in histologic mouse brain sections is especially pivotal for many neurogenomics, transcriptomics, proteomics and connectomics studies, yet this process is prone to observer error and bias. Here we present a novel brain navigation system, named NeuroInfo, whose general principle is similar to that of a global positioning system (GPS) in a car. NeuroInfo automatically navigates an investigator through the complex microscopic anatomy of histologic sections of mouse brains (thereafter: \"experimental mouse brain sections\"). This is achieved by automatically registering a digital image of an experimental mouse brain section with a three-dimensional (3D) digital mouse brain atlas that is essentially based on the third version of the Allen Mouse Brain Common Coordinate Framework (CCF v3), retrieving graphical region delineations and annotations from the 3D digital mouse brain atlas, and superimposing this information onto the digital image of the experimental mouse brain section on a computer screen. By doing so, NeuroInfo helps in solving the long-standing problem faced by researchers investigating experimental mouse brain sections under a light microscope--that of correctly identifying the distinct brain regions contained within the experimental mouse brain sections. Specifically, NeuroInfo provides an intuitive, readily-available computer microscopy tool to enhance researchers ability to correctly identify specific brain regions in experimental mouse brain sections. Extensive validation studies of NeuroInfo demonstrated that this novel technology performs remarkably well in accurately delineating regions that are large and/or located in the dorsal parts of mouse brains, independent on whether the sections were imaged with fluorescence or brightfield microscopy. This novel navigation system provides a highly efficient way for registering a digital image of an experimental mouse brain section with the 3D digital mouse brain atlas in a minute and accurate delineation of the image in real-time.

neuroscience↗

bioRxiv2018

Biological variation in the sizes, shapes and locations of visual cortical areas in the mouse

Visual cortex is organized into discrete sub-regions or areas that are arranged into a hierarchy and serve different functions in the processing of visual information. In our previous work, we noted that retinotopic maps of cortical visual areas differed between mice, but did not quantify these differences or determine the relative contributions of biological variation and measurement noise. Here we quantify the biological variation in the size, shape and locations of 11 visual areas in the mouse. We find that there is substantial biological variation in the sizes of visual areas, with some visual areas varying in size by two-fold across the population of mice.

neuroscience↗

bioRxiv2018

Classification of electrophysiological and morphological types in mouse visual cortex

Understanding the diversity of cell types in the brain has been an enduring challenge and requires detailed characterization of individual neurons in multiple dimensions. To profile morpho-electric properties of mammalian neurons systematically, we established a single cell characterization pipeline using standardized patch clamp recordings in brain slices and biocytin-based neuronal reconstructions. We built a publicly-accessible online database, the Allen Cell Types Database, to display these data sets. Intrinsic physiological and morphological properties were measured from over 1,800 neurons from the adult laboratory mouse visual cortex. Quantitative features were used to classify neurons into distinct types using unsupervised methods. We establish a taxonomy of morphologically- and electrophysiologically-defined cell types for this region of cortex with 17 e-types and 35 m-types, as well as an initial correspondence with previously-defined transcriptomic cell types using the same transgenic mouse lines.

neuroscience↗

bioRxiv2018

A large-scale, standardized physiological survey reveals higher order coding throughout the mouse visual cortex

To understand how the brain processes sensory information to guide behavior, we must know how stimulus representations are transformed throughout the visual cortex. Here we report an open, large-scale physiological survey of neural activity in the awake mouse visual cortex: the Allen Brain Observatory Visual Coding dataset. This publicly available dataset includes cortical activity from nearly 60,000 neurons collected from 6 visual areas, 4 layers, and 12 transgenic mouse lines from 221 adult mice, in response to a systematic set of visual stimuli. Using this dataset, we reveal functional differences across these dimensions and show that visual cortical responses are sparse but correlated. Surprisingly, responses to different stimuli are largely independent, e.g. whether a neuron responds to natural scenes provides no information about whether it responds to natural movies or to gratings. We show that these phenomena cannot be explained by standard local filter-based models, but are consistent with multi-layer hierarchical computation, as found in deeper layers of standard convolutional neural networks.

neuroscience↗

bioRxiv2018

The organization of intracortical connections by layer and cell class in the mouse brain

The mammalian cortex is a laminar structure composed of many cell types densely interconnected in complex ways. Recent systematic efforts to map the mouse mesoscale connectome provide comprehensive projection data on interareal connections, but not at the level of specific cell classes or layers within cortical areas. We present here a significant expansion of the Allen Mouse Brain Connectivity Atlas, with [~]1,000 new axonal projection mapping experiments across nearly all isocortical areas in 49 Cre driver lines. Using 13 lines selective for cortical layer-specific projection neuron classes, we identify the differential contribution of each layer/class to the overall intracortical connectivity patterns. We find layer 5 (L5) projection neurons account for essentially all intracortical outputs. L2/3, L4, and L6 neurons contact a subset of the L5 cortical targets. We also describe the most common axon lamination patterns in cortical targets. Most patterns are consistent with previous anatomical rules used to determine hierarchical position between cortical areas (feedforward, feedback), with notable exceptions. While diverse target lamination patterns arise from every source layer/class, L2/3 and L4 neurons are primarily associated with feedforward type projection patterns and L6 with feedback. L5 has both feedforward and feedback projection patterns. Finally, network analyses revealed a modular organization of the intracortical connectome. By labeling interareal and intermodule connections as feedforward or feedback, we present an integrated view of the intracortical connectome as a hierarchical network.

neuroscience↗

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↗

bioRxiv2017

Epidemic Potential for Human Infection with Influenza A (H7N9) Virus in China through Web Search Behaviors: A Data-Driven Study

Since the beginning of September 2016, a steep upsurge of the human cases of avian influenza A (H7N9) virus has been reported in China, which are alarming public concern for the pandemic potential of the H7N9 virus. In this study, we collected the data from H7N9 epidemics and H7N9-related Baidu Search Index (BSI) in China between September 2013 and June 2017. And we observed a strong correlation between the numbers of Influenza A (H7N9) cases and H7N9-related BSI in Guangdong province and Shanghai municipality (p<0.001). Autoregressive integrated moving average (ARIMA) models were constructed for the dynamic estimation of seasonal H7N9 outbreaks in 2016-2017 and the online search data acted as an external regressor with the historical H7N9 epidemic data in the forecasting model to improve the quality of predictions. Predictions by the models closely matched the actual numbers of reported cases during current H7N9 epidemic season. Especially, the estimated numbers of reported cases sharply increased to reach 49.88 (95% CI: 0-194.05) in Guangdong and 9.05 (95% CI: 0-37.43) in Shanghai from December 2016 to June 2017. Moreover, this accessible and flexible dynamic forecast model could be used in the monitoring of H7N9 virus to provide advanced warning of future emerging infection diseases.\n\nAuthor summaryAs the availability and popularity of the internet has greatly increased in recent years, an increasing number of cyber users, including patients and their family members, search online for health information on personal computers (PCs) and mobile phones (MPs) before seeking medical attention, making it possible to investigate the influenza prevalence by monitoring changes in frequencies of uses of particular search terms. In this study, we collected the data from H7N9 epidemics and H7N9-related Baidu Search Index (BSI) in China between September 2013 and June 2017. And then, we showed a strong correlation between the numbers of Influenza A (H7N9) cases and H7N9-related BSI in Guangdong province and Shanghai municipality (p<0.001). Furthermore, we reconstructed an improved dynamic forecasting method for outbreaks of H7N9 influenza using Autoregressive integrated moving average (ARIMA) models to predict future patterns of H7N9 transmission and the online search data acted as an external regressor with the historical H7N9 epidemic data in the forecasting model to improve the quality of predictions. Our results suggest that data from the Baidu search engine, combed with data from a traditional disease surveillance system, may be considered for early detection of H7N9 influenza outbreaks in mainland China.

epidemiology↗

bioRxiv2017

Generalized Leaky Integrate-And-Fire Models Classify Multiple Neuron Types

In the mammalian neocortex, there is a high diversity of neuronal types. To facilitate construction of system models with multiple cell types, we generate a database of point models associated with the Allen Cell Types Database. We construct a series of generalized integrate-and-fire (GLIF) models of increasing complexity aiming to reproduce the spiking behaviors of the recorded neurons. We test the performance of these GLIF models on data from 771 neurons from 14 transgenic lines, with increasing model performance for more complex models. To answer how complex a model needs to be to reproduce the number of electrophysiological cell types, we perform unsupervised clustering on the parameters extracted from these models. The number of clusters is smaller for individual model types, but when combining all GLIF parameters 18 clusters are obtained, while 11 clusters are obtained using 16 electrophysiological features. Therefore, these low dimensional models have the capacity to characterize the diversity of cell types without the need for a priori defined features.

neuroscience↗