Biology subjects

Ye, Q.

Publications and source records attributed to Ye, Q..

77 records · Page 5Linked to original sources

bioRxiv2018

Enhancement of Macrophage Function by the Antimicrobial Peptide Sublancin Protects Mice from Methicillin-Resistant Staphylococcus aureus

Methicillin-resistant Staphylococcus aureus (MRSA) is the major pathogen responsible for community and hospital bacterial infections. Sublancin, a glocosylated antimicrobial peptide isolated from Bacillus subtilis 168, possesses anti-bacterial infective effects. In this study, we investigated the role and anti-infection mechanism of sublancin in a mouse model of MRSA-induced sublethal infection. Sublancin could modulate innate immunity by inducing the production of IL-1{beta}, IL-6, TNF- and nitric oxide, enhancing phagocytosis and MRSA-killing activity in both RAW264.7 cells and peritoneal macrophages. The enhanced macrophage function by the peptide in vitro correlated with stronger protective activity in vivo in the MRSA-invasive sublethal infection model. Macrophages activation by sublancin was found to be mediated through the TLR4 and the NF-{kappa}B and MAPK signaling pathways. Moreover, oral administration of sublancin increased the frequencies of CD4+ and CD8+ T cells in mesenteric lymph nodes. The protective activity of sublancin was associated with in vivo augmenting phagocytotic activity of peritoneal macrophages and partly improving T cell-mediated immunity. Macrophages thus represent a potentially pivotal and novel target for future development of innate defense regulator therapeutics againt S. aureus infection.

immunology↗

bioRxiv2018

Deciphering signaling specificity with interpretable deep neural networks

Protein kinase phosphorylation is a prevalent post-translational modification (PTM) regulating protein function and transmitting signals throughout the cell. Defective signal transductions, which are associated with protein phosphorylation, have been revealed to link to many human diseases, such as cancer. Defining the organization of the phosphorylation-based signaling network and, in particular, identifying kinase-specific substrates can help reveal the molecular mechanism of the signaling network. Here, we present DeepSignal, a deep learning framework for predicting the substrate specificity for kinase/SH2 sequences with or without mutations. Empowered by the memory and selection mechanism of recurrent neural network, DeepSignal can identify important specificity-defining residues to predict kinase specificity and changes upon mutations. Evaluated on several public benchmark datasets, DeepSignal significantly outperforms current methods on predicting substrate specificity on both kinase and SH2 domains. Further analysis in The Cancer Genome Atlas (TCGA) demonstrated that DeepSignal is able to aggregate mutations on both kinase/SH2 domains and substrates to quantify binding specificity changes, predict cancer genes related to signaling transduction, and identify novel perturbed pathways.\n\nAvailabilityImplementation of DeepSignal is at https://github.com/luoyunan/DeepSignal

bioinformatics↗

bioRxiv2018

Causal evidence for mnemonic metacognition in human precuneus

Metacognition is the capacity to introspectively monitor and control ones own cognitive processes. Previous anatomical and functional neuroimaging findings implicated the important role of precuneus in metacognition processing, especially during mnemonic tasks. However, the issue of whether this medial parietal cortex is a domain-specific region that supports mnemonic metacognition remains controversial. Here, we focally disrupted this parietal area with repetitive transcranial magnetic stimulation in healthy participants of both sexes, seeking to ascertain its functional necessity for metacognition for memory versus perceptual decisions. Perturbing the precuneal activity impaired the metacognitive efficiency selectively in the memory judgment of temporal-order, but not in perceptual discrimination. Moreover, the correlation in individuals metacognitive efficiency between the domains disappeared when the precuneus was perturbed. Together with the previous finding that lesion to the anterior prefrontal cortex impairs perceptual but not mnemonic metacognition, we double dissociated the macro-anatomical underpinnings for the two kinds of metacognitive capacity in an interconnected network of brain regions.\n\nSIGNIFICANCE STATEMENTTheories on the neural basis of metacognition have thus far largely centered on the role of prefrontal cortex. Here we refined the theoretical framework through characterizing a unique precuneal involvement in mnemonic metacognition with a noninvasive but inferentially powerful method: transcranial magnetic stimulation. By quantifying meta-cognitive efficiency across two distinct domains (memory vs. perception) that are matched for stimulus characteristics, we reveal an instrumental - and highly selective - role of the precuneus in mnemonic metacognition. These causal evidence corroborate ample clinical reports that parietal lobe lesions often produce inaccurate self-reports of confidence in memory recollection and establish that the precuneus as a nexus for the introspective ability to evaluate the success of memory judgment in humans.

neuroscience↗

bioRxiv2018

Locally distributed abstraction of temporal distance in human parietal cortex

An enduring puzzle in the neuroscience of memory is how the brain parsimoniously situates past events by their order in relation to time. By combining functional MRI, and representational similarity analysis, we reveal a multivoxel representation of time intervals separating pairs of episodic event-moments in the posterior medial memory system, especially when the events were experienced within a similar temporal context. We further show such multivoxel representations to be vulnerable to disruption through targeted repetitive transcranial magnetic stimulation and that perturbation to the mnemonic abstraction alters the neural--behavior relationship across the wider parietal memory network. Our findings establish a mnemonic \"pattern-based\" code of temporal distances in the human brain, a fundamental neural mechanism for supporting the temporal structure of past events, assigning the precuneus as a locus of flexibly effecting the manipulation of physical time during episodic memory retrieval.

neuroscience↗

bioRxiv2017

Learning Structural Motif Representations For Efficient Protein Structure Search

MotivationUnderstanding the relationship between protein structure and function is a fundamental problem in protein science. Given a protein of unknown function, fast identification of similar protein structures from the Protein Data Bank (PDB) is a critical step for inferring its biological function. Such structural neighbors can provide evolutionary insights into protein conformation, interfaces and binding sites that are not detectable from sequence similarity. However, the computational cost of performing pairwise structural alignment against all structures in PDB is prohibitively expensive. Alignment-free approaches have been introduced to enable fast but coarse comparisons by representing each protein as a vector of structure features or fingerprints and only computing similarity between vectors. As a notable example, FragBag represents each protein by a \"bag of fragments\", which is a vector of frequencies of contiguous short backbone fragments from a predetermined library.\n\nResultsHere we present a new approach to learning effective structural motif presentations using deep learning. We develop DeepFold, a deep convolutional neural network model to extract structural motif features of a protein structure. Similar to FragBag, DeepFold represents each protein structure or fold using a vector of learned structural motif features. We demonstrate that DeepFold substantially outperforms FragBag on protein structural search on a non-redundant protein structure database and a set of newly released structures. Remarkably, DeepFold not only extracts meaningful backbone segments but also finds important long-range interacting motifs for structural comparison. We expect that DeepFold will provide new insights into the evolution and hierarchical organization of protein structural motifs.\n\nAvailabilityhttps://github.com/largelymfs/DeepFold\n\nContactjianpeng@illinois.edu

bioinformatics↗