Biology subjects

Feng, J.

Publications and source records attributed to Feng, J..

At least 217 records · Page 12Linked to original sources

bioRxiv2020

Cryo-EM structure of a proton-activated chloride channel TMEM206

TMEM206 has been recently identified as an evolutionarily conserved chloride channel that underlies ubiquitously expressed, proton-activated, outwardly rectifying anion currents. Here we report the cryo-electron microscopy structure of pufferfish TMEM206, which forms a trimeric channel, with each subunit comprising two transmembrane segments, the outer and inner helices, and a large extracellular domain. An ample vestibule in the extracellular region is accessible laterally from the three side portals. The central pore contains multiple constrictions preventing ion conduction. A conserved lysine residue near the cytoplasmic end of the inner helix forms the presumed chloride ion selectivity filter. Unprecedentedly, the core structure and assembly closely resemble those of the epithelial sodium channel/degenerin family of sodium channels that are unrelated in amino acid sequence and conduct cations instead of anions. Together with electrophysiology, this work provides insights into ion conduction and gating for a new class of chloride channels that is architecturally distinct from previously characterized chloride channel families.

biophysics↗

bioRxiv2020

Alisertib exerts KRAS allele specific anticancer effects in colorectal cancer

Colorectal cancer (CRC) is a common cancer causing substantial mortality and morbidity worldwide. Oncogene RAS mutations occur notably in [~]45% of CRCs, associated with a poor prognosis. KRAS is subject to multiple tiers of regulation, including kinase. Aurora kinases has been implicated in many types of tumor onsets and progression, making them as a promising therapeutic targets. Alisertib (ALS), selectively inhibits Aurora kinase A (AURKA) and exerts potent anticancer activities in vitro and in vivo studies, but the latent anticancer effect of ALS on CRC remains unclear in the context of different KRAS mutations. This study aimed to assess the effects of ALS on RAS signaling pathway in a panel of CRC lines expressing different KRAS alleles, including Caco-2 (KRAS WT), Colo-678 (KRAS G12D), SK-CO-1 (KRAS G12V), HCT116 (KRAS G13D), CCCL-18 (KRAS A146T), and HT29 (BRAF V600E). The results showed that ALS modulated the active form of KRAS in a RAS allele specific manner across the panel of CRC lines; ALS differentially regulated RAS signal via PI3K/Akt and MAPK pathways; and ALS induced apoptosis and autophagy in a RAS allele specific manner. Of note, in combination of ALS and MEK inhibitor, selumetinib, enhanced ALS regulatory effects in CRC lines in a RAS allele specific manner on apoptosis, autophagy, and cell growth. Taken together, this study suggests that ALS differentially regulates RAS signaling pathway and manipulates cell apoptosis and autophagy in RAS allele specific manner. The combinatorial approach of ALS and MEK inhibitor may represent a new therapeutic strategy for precision therapy of CRC in a RAS allele manner.

cancer biology↗

bioRxiv2020

Multi-hops functional connectivity improves individual prediction of fusiform face activation via a graph neural network

Brain connectivity plays an important role in determining the brain regions function. Previous researchers proposed that the brain regions function is characterized by that regions input and output connectivity profiles. Following this proposal, numerous studies have investigated the relationship between connectivity and function. However, based on a preliminary analysis, this proposal is deficient in explaining individual differences in the brain regions function. To overcome this problem, we proposed that a brain regions function is characterized by that regions multi-hops connectivity profile. To test this proposal, we used multi-hops functional connectivity to predict the individual face response of the right fusiform face area (rFFA) via a multi-layers graph neural network and showed that the prediction performance is essentially improved. Results also indicated that the 2-layers graph neural network is the best in characterizing rFFAs face response and revealed a hierarchical network for the face processing of rFFA.

neuroscience↗

bioRxiv2020

Age-specific gene signatures underlying the transcriptomes and functional connectomes of human cerebral cortex

The human cerebral cortex undergoes profound structural and functional dynamic variations across the lifespan, whereas the underlying molecular mechanisms remain unclear. Here, with a novel method TCA (Transcriptome-connectome Correlation Analysis), which integrates the brain functional MR magnetic resonance images and region-specific transcriptomes, we identify age-specific cortex (ASC) gene signatures for adolescence, early adulthood, and late adulthood. The ASC gene signatures are significantly correlated with the cortical thickness (P-value <2.00e-3) and myelination (P-value <1.00e-3), two key brain structural features that vary in accordance with brain development. In addition to the molecular underpinning of age-related brain functions, the ASC gene signatures allow delineation of the molecular mechanisms of neuropsychiatric disorders, such as the regulation between ARNT2 and its target gene ETF1 involved in Schizophrenia. We further validate the ASC gene signatures with published gene sets associated with the adult cortex, and confirm the robustness of TCA on other brain image datasets.

neuroscience↗

bioRxiv2020

Genome assembly and transcriptome analysis provide insights into the anti-schistosome mechanism of Microtus fortis

Microtus fortis (M. fortis) so far is the only mammal host that exhibits intrinsic resistance against Schistosoma japonicum infection. However, the underlying molecular mechanisms of this intrinsic resistance are not yet known. Here we performed the first de novo genome assembly of M. fortis, comprehensive gene annotation and evolution analysis. Furthermore, we compared the recovery rate of schistosome, pathological change and liver transcriptome between non-permissive host M. fortis and susceptible host mouse at different time points after Schistosome infection. We reveal that Immune response of M. fortis and mouse is different in time and type. M. fortis activates immune and inflammatory responses on the 10th days post infection, involving in multiple pathways, such as leukocyte extravasation, antibody activation (especially IgG3), Fc-gamma receptor mediated phagocytosis, and interferon signaling cascade. The strong immune responses of M. fortis in early stages of infection play important roles in preventing the development of schistosome. On the contrary, intense immune response occurred in mouse in late stages of infection (28~42 days post infection), and cannot eliminate schistosome. Infected mouse suffers severe pathological injury and continuous decrease of important functions such as cell cycle and lipid metabolism. Our findings offer new insights to the intrinsic resistance mechanism of M. fortis against schistosome infection. The genome sequence also provides bases for future studies of other important traits in M. fortis.

bioinformatics↗

bioRxiv2020

The potential function of forehead gland secretions in conflict resolution of the male Great Himalayan leaf-nosed bats

Chemical communication is an important aspect of social behavior in almost all animals. Here, we used gas chromatography-mass spectrometry (GC-MS) to detect the chemical composition, and behavioral tests to evaluate the potential function of forehead gland secretions between adult male Great Himalayan leaf-nosed bats, Hipposideros armiger. Our results showed that the concentrations of compounds and their categories differed significantly among individuals, and behavioral studies indicated that males are capable of utilizing the secretions for individual discrimination. Moreover, paired males that were incapable of gland protrusion showed more physical contact and longer contest duration compared to pairs in which both males could protrude the gland. In trials where only one male could protrude the gland, males with gland protrusion were more likely to win in contests. These findings provide the first behavioral evidence that chemical communication plays a vital role in conflict resolution in non-human mammals.

ecology↗

bioRxiv2020

Dynamic fluctuations of the locus coeruleus-norepinephrine system underlie sleep state transitions

We normally regard sleep and wake as two distinct opposing brain states, where sleep requires silence of wake-promoting structures such as the locus coeruleus (LC)-norepinephrine (NE) system. We set out to investigate how cortical NE dynamics and NE-related astrocytic activity relates to LC population activity during sleep states. We show that LC displays regular phasic activity bouts during NREM sleep leading to a slow oscillatory pattern of prefrontal NE levels of which the majority of NE increases does not lead to awakening. NE troughs link to sleep spindles and continued NE decline transitions into REM sleep. Last, we show that prefrontal astrocytes have reduced sensitivity towards NE during sleep. Our results suggest that dynamic changes in the activity of wake-promoting systems during sleep create alternation between crucial sleep processes and broadening of sensitivity towards incoming sensory input. HighlightsO_LIExtracellular levels of norepinephrine display dynamic changes during NREM and REM sleep C_LIO_LIPhasic activity of locus coeruleus neurons during NREM underlies slow norepinephrine oscillations C_LIO_LISpindles occur at norepinephrine troughs and are abolished by norepinephrine increases C_LIO_LIIncreased spindles prior to REM reflect the beginning of a long-lasting norepinephrine decline C_LIO_LIREM episodes are characterized by a sub-threshold continuous norepinephrine decline C_LIO_LIThe responsiveness of astrocytic Ca2+ to norepinephrine is reduced during sleep C_LI

neuroscience↗

bioRxiv2020

Avalanche criticality in individuals is associated with fluid intelligence and working memory capacity

The critical brain hypothesis suggests that efficient neural computation can be realized through dynamics of the brain characterized by scale-free avalanche activities. However, the relation between human cognitive performance and the avalanche criticality in large-scale brain networks remains unclear. In this study, we analyzed the mean synchronization and synchronization entropy of blood oxygenation level signals from resting-state fMRI. We found that the scale-free avalanche activity was associated with intermediate synchronization and maximal synchronization entropy. The complexity of functional connectivity, as well as structure-function coupling, is maximized by criticality, which is consistent with theoretical predictions. We observed order-disorder phase transitions in resting-state brain dynamics and found that there were longer times spent in the subcritical regime. These results support the hypothesis that large-scale brain networks lie in the vicinity of a critical point. Finally, we showed evidence that the neural dynamics of human participants with higher fluid intelligence and working memory scores are closer to criticality. We identified brain regions whose critical dynamics showed significant positive correlations with fluid intelligence performance, and found these regions were located in the prefrontal cortex and inferior parietal cortex, which are believed to be important nodes of brain networks underlying human intelligence. Our results reveal the role that avalanche criticality plays in cognitive performance, and provide a simple method to identify the critical point and map cortical states on a spectrum of neural dynamics, ranging from subcriticality to supercriticality.

neuroscience↗

bioRxiv2020

Assessing Study Reproducibility through MMRI: A Novel Approach for Large-scale High-throughput Association Studies

Magnetic Resonance Imaging (MRI) technology has been increasingly used in large-scale association studies. Reproducibility of statistically significant findings generated by MRI-based association studies, especially structural MRI (sMRI) and functional MRI (fMRI), has been recently heavily debated. However, there is still a lack of overall reproducibility assessment for MRI-based association studies. It is also crucial to elucidate the relationship between overall reproducibility and sample size in an experimental design. In this study, we proposed an overall reproducibility index for large-scale high-throughput MRI-based association studies. We performed the overall reproducibility assessments for several recent large sMRI/fMRI databases and observed satisfactory overall reproducibility. Furthermore, we performed the sample size evaluation for the purpose of achieving a desirable overall reproducibility. Additionally, we evaluated the overall reproducibility of GMV changes for UKB vs. PPMI and UKB vs. HCP. We demonstrated that both sample size and some experimental factors play important roles in the overall reproducibility for different experiments. In summary, a systematic assessment of overall reproducibility is fundamental and crucial in the current large-scale high-throughput MRI-based research.

neuroscience↗

bioRxiv2020

Meta-matching: a simple framework to translate phenotypic predictive models from big to small data

There is significant interest in using brain imaging data to predict non-brain-imaging phenotypes in individual participants. However, most prediction studies are underpowered, relying on less than a few hundred participants, leading to low reliability and inflated prediction performance. Yet, small sample sizes are unavoidable when studying clinical populations or addressing focused neuroscience questions. Here, we propose a simple framework - "meta-matching" - to translate predictive models from large-scale datasets to new unseen non-brain-imaging phenotypes in boutique studies. The key observation is that many large-scale datasets collect a wide range inter-correlated phenotypic measures. Therefore, a unique phenotype from a boutique study likely correlates with (but is not the same as) some phenotypes in some large-scale datasets. Meta-matching exploits these correlations to boost prediction in the boutique study. We applied meta-matching to the problem of predicting non-brain-imaging phenotypes using resting-state functional connectivity (RSFC). Using the UK Biobank (N = 36,848), we demonstrated that meta-matching can boost the prediction of new phenotypes in small independent datasets by 100% to 400% in many scenarios. When considering relative prediction performance, meta-matching significantly improved phenotypic prediction even in samples with 10 participants. When considering absolute prediction performance, meta-matching significantly improved phenotypic prediction when there were least 50 participants. With a growing number of large-scale population-level datasets collecting an increasing number of phenotypic measures, our results represent a lower bound on the potential of meta-matching to elevate small-scale boutique studies.

neuroscience↗

bioRxiv2020

Human Embryonic Stem Cell-derived Lung Organoids: a Model for SARS-CoV-2 Infection and Drug Test

The coronavirus disease 2019 (COVID-19) pandemic is caused by infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is spread primary via respiratory droplets and infects the lungs. Currently widely used cell lines and animals are unable to accurately mimic human physiological conditions because of the abnormal status of cell lines (transformed or cancer cells) and species differences between animals and humans. Organoids are stem cell-derived self-organized three-dimensional culture in vitro and model the physiological conditions of natural organs. Here we demonstrated that SARS-CoV-2 infected and extensively replicated in human embryonic stem cells (hESCs)-derived lung organoids, including airway and alveolar organoids. Ciliated cells, alveolar type 2 (AT2) cells and rare club cells were virus target cells. Electron microscopy captured typical replication, assembly and release ultrastructures and revealed the presence of viruses within lamellar bodies in AT2 cells. Virus infection induced more severe cell death in alveolar organoids than in airway organoids. Additionally, RNA-seq revealed early cell response to SARS-CoV-2 infection and an unexpected downregulation of ACE2 mRNA. Further, compared to the transmembrane protease, serine 2 (TMPRSS2) inhibitor camostat, the nucleotide analog prodrug Remdesivir potently inhibited SARS-CoV-2 replication in lung organoids. Therefore, human lung organoids can serve as a pathophysiological model for SARS-CoV-2 infection and drug discovery.

microbiology↗

bioRxiv2020

Long-term Reconstruction of Human Airway Epithelium-like Structure in Vivo with hESCs-derived organoid cells

Human embryonic stem cells (hESCs) derived lung organoids (HLOs) provide a promising model to study human lung development and disease. However, whether HLO cells could reconstitute airway epithelial structure in vivo remains unclear. Here we established an orthotopic xenograft system for hESCs-derived HLOs, enabling stable reconstruction of human airway epithelium in vivo. Removal of the mouse airway epithelium by naphthalene (NA) treatment enabled xenografted organoid cells survival, differentiation, and reconstruction of airway pseudostratified epithelium in immune-compromised NSG mice. Compared to unsorted pool cells, CD47high cells generated more ciliated cells and possessed thicker pseudostratified epithelium. RNA-seq data revealed that CD47high cells highly expressed epithelial cell, lung progenitor, lung proximal cell and embryonic lung development associated genes. These data reveal that HLOs hold cell therapy potential in regenerative medicine by long-term reconstituting airway epithelium.

cell biology↗

bioRxiv2020

Numerical Model for Formation and Evolution of the Bleb

ABSTRACTThe bleb morphology and its changes are an important mechanism of cell’s amoeboid migration. By releasing bonds between the membrane and the cortex of a cell, the formation of bleb can be observed experimentally, but the mechanism that affects the size and shape of this kind of bleb is waiting for further study. In this paper, a two-dimensional fluid-solid coupling model is established to describe a cell with membrane, cortex and cytoplasm in a solution, and a numerical solving method for the fluid-solid coupling model is developed to simulate the behaviors of cell bleb. The effects of parameters, such as the number of broken bonds, the viscosity coefficient of the cortex, and the cell’s membrane modulus on the size and the shape of the bleb were investigated. Numerical results show that the model is effective to simulate the formation and evolution of cell’s bleb, and derive the contribution of several affecting factors to the bleb shape and size clearly.SIGNIFICANCE To understand the process of cell migration with bleb pseudopods in the amoeba cell migration, it is necessary to study the formation mechanism of cells protruding bleb. In this paper, we propose a reasonable and reliable cell numerical model. With this model we successfully simulate the bleb phenomenon consistent with the experimental phenomenon by changing the key impact factors. The method in this paper is applicable to the cell model of amoeba cell migration pattern, which helps to understand the important role of blebs in the process of cell migration.View Full Text

cell biology↗

bioRxiv2020

Structural basis for potent neutralization of SARS-CoV-2 and role of antibody affinity maturation

SARS-CoV-2 is a betacoronavirus virus responsible for the COVID-19 pandemic. Here, we determined the X-ray crystal structure of a potent neutralizing monoclonal antibody, CV30, isolated from a patient infected with SARS-CoV-2, in complex with the receptor binding domain (RBD). The structure reveals CV30s epitope overlaps with the human ACE2 receptor binding site thus providing the structural basis for its neutralization by preventing ACE2 binding.

immunology↗

bioRxiv2020

IGD: high-performance search for large-scale genomic interval datasets

Databases of large-scale genome projects now contain thousands of genomic interval datasets. These data are a critical resource for understanding the function of DNA. However, our ability to examine and integrate interval data of this scale is limited. Here, we introduce the integrated genome database (IGD), a method and tool for searching genome interval datasets more than three orders of magnitude faster than existing approaches, while using only one hundredth of the memory. IGD uses a novel linear binning method that allows us to scale analysis to billions of genomic regions. Availabilityhttps://github.com/databio/IGD

bioinformatics↗

bioRxiv2020

How antiporters exchange substrates across the cell membrane? An atomic-level description of the complete exchange cycle in NarK

Major facilitator superfamily (MFS) proteins operate via three different mechanisms: uniport, symport, and antiport. Despite extensive investigations, molecular understanding of antiporters is less advanced than other transporters due to the complex coupling between two substrates and the lack of distinct structures. We employ extensive (~300 s) all-atom molecular dynamics simulations to dissect the complete substrate exchange cycle of the bacterial [Formula] antiporter, NarK. We show that paired basic residues in the binding site prevent the closure of unbound protein and ensure the exchange of two substrates. Conformational transition only occurs in the presence of substrate, which weakens the electrostatic repulsion and stabilizes the transporter by ~1.5 {+/-} 0.1 kcal/mol. Furthermore, we propose a state-dependent substrate exchange model, in which the relative spacing between the paired basic residues determines whether [Formula] and [Formula] bind simultaneously or sequentially. Overall, this work presents a general working model for the antiport mechanism within MFS family.

biophysics↗

bioRxiv2020

Characterization of neutralizing antibodies from a SARS-CoV-2 infected individual

B cells specific for the SARS-CoV-2 S envelope glycoprotein spike were isolated from a COVID-19-infected subject using a stabilized spike-derived ectodomain (S2P) twenty-one days post-infection. Forty-four S2P-specific monoclonal antibodies were generated, three of which bound to the receptor binding domain (RBD). The antibodies were minimally mutated from germline and were derived from different B cell lineages. Only two antibodies displayed neutralizing activity against SARS-CoV-2 pseudo-virus. The most potent antibody bound the RBD in a manner that prevented binding to the ACE2 receptor, while the other bound outside the RBD. Our study indicates that the majority of antibodies against the viral envelope spike that were generated during the first weeks of COVID-19 infection are non-neutralizing and target epitopes outside the RBD. Antibodies that disrupt the SARS-CoV-2 spike-ACE2 interaction can potently neutralize the virus without undergoing extensive maturation. Such antibodies have potential preventive/therapeutic potential and can serve as templates for vaccine-design. IN BRIEFSARS-CoV-2 infection leads to expansion of diverse B cells clones against the viral spike glycoprotein (S). The antibodies bind S with high affinity despite being minimally mutated. Thus, the development of neutralizing antibody responses by vaccination will require the activation of certain naive B cells without requiring extensive somatic mutation. HighlightsO_LIAnalysis of early B cell response to SARS-CoV-2 spike protein C_LIO_LIMost antibodies target non-neutralizing epitopes C_LIO_LIPotent neutralizing mAb blocks the interaction of the S protein with ACE2 C_LIO_LINeutralizing antibodies are minimally mutated C_LI

immunology↗

bioRxiv2020

FingerprintContacts: Predicting Alternative Conformations of Proteins from Coevolution

Proteins are dynamic molecules which perform diverse molecular functions by adopting different three-dimensional structures. Recent progress in residue-residue contacts prediction opens up new avenues for the de novo protein structure prediction from sequence information. However, it is still difficult to predict more than one conformation from residue-residue contacts alone. This is due to the inability to deconvolve the complex signals of residue-residue contacts, i.e. spatial contacts relevant for protein folding, conformational diversity, and ligand binding. Here, we introduce a machine learning based method, called FingerprintContacts, for extending the capabilities of residue-residue contacts. This algorithm leverages the features of residue-residue contacts, that is, (1) a single conformation outperforms the others in the structural prediction using all the top ranking residue-residue contacts as structural constraints, and (2) conformation specific contacts rank lower and constitute a small fraction of residue-residue contacts. We demonstrate the capabilities of FingerprintContacts on eight ligand binding proteins with varying conformational motions. Furthermore, FingerprintContacts identifies small clusters of residue-residue contacts which are preferentially located in the dynamically fluctuating regions. With the rapid growth in protein sequence information, we expect FingerprintContacts to be a powerful first step in structural understanding of protein functional mechanisms.

bioinformatics↗