Biology subjects

Yu, W.

Publications and source records attributed to Yu, W..

189 records · Page 11Linked to original sources

bioRxiv2018

Understanding the limit of open search in the identification of peptides with post-translational modifications -- A simulation-based study

MotivationAnalyzing tandem mass spectrometry data to recognize peptides in a sample is the fundamental task in computational proteomics. Traditional peptide identification algorithms perform well when identifying unmodified peptides. However, when peptides have post-translational modifications (PTMs), these methods cannot provide satisfactory results. Recently, Chick et al., 2015 and Yu et al., 2016 proposed the spectrum-based and tag-based open search methods, respectively, to identify peptides with PTMs. While the performance of these two methods is promising, the identification results vary greatly with respect to the quality of tandem mass spectra and the number of PTMs in peptides. This motivates us to systematically study the relationship between the performance of open search methods and quality parameters of tandem mass spectrum data, as well as the number of PTMs in peptides.\n\nResultsThrough large-scale simulations, we obtain the performance trend when simulated tandem mass spectra are of different quality. We propose an analytical model to describe the relationship between the probability of obtaining correct identifications and the spectrum quality as well as the number of PTMs. Based on the analytical model, we can quantitatively describe the necessary condition to effectively apply open search methods.\n\nAvailabilitySource codes of the simulation are available at http://bioinformatics.ust.hk/PST.html.\n\nContactboningli@ust.hk or eeyu@ust.hk\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2018

Common antigenic motif recognized by human VH5-51/VL4-1 tau antibodies with distinct functionalities

Misfolding and aggregation of tau protein are closely associated with the onset and progression of Alzheimers Disease (AD). By interrogating IgG+ memory B cells from asymptomatic donors with tau peptides, we have identified two somatically mutated VH5-51/VL4-1 antibodies. One of these, CBTAU-27.1, binds to the aggregation motif in the R3 repeat domain and blocks the aggregation of tau into paired helical filaments (PHFs) by sequestering monomeric tau. The other, CBTAU-28.1, binds to the N-terminal insert region and inhibits the spreading of tau seeds and mediates the uptake of tau aggregates into microglia by binding PHFs. Crystal structures revealed that the combination of VH5-51 and VL4-1 recognizes a common Pro-Xn-Lys motif driven by germline-encoded hotspot interactions while the specificity and thereby functionality of the antibodies are defined by the CDR3 regions. Affinity improvement led to improvement in functionality, identifying their epitopes as new targets for therapy and prevention of AD.

neuroscience↗

bioRxiv2018

Septal Secretion of Protein A in Staphylococcus aureus Requires SecA and Lipoteichoic Acid Synthesis

Surface proteins of Staphylococcus aureus are secreted across septal membranes for assembly into the bacterial cross-wall. This localized secretion requires the YSIRK/GXXS motif signal peptide, however the mechanisms supporting precursor trafficking are not known. We show here that the signal peptide of staphylococcal protein A (SpA) is cleaved at the YSIRK/GXXS motif. A signal peptide mutant defective for cleavage can be crosslinked to SecA, SecDF and LtaS. SecA depletion blocks precursor targeting to septal membranes, whereas deletion of secDF diminishes SpA secretion into the cross-wall. Depletion of LtaS blocks lipoteichoic acid synthesis and promotes precursor trafficking to peripheral membranes. We propose a model whereby SecA directs SpA precursors to lipoteichoic acid-rich septal membranes for YSIRK/GXXS motif cleavage and secretion into the cross-wall.

microbiology↗

bioRxiv2017

Resting-state connectivity predicts patient-specific effects of deep brain stimulation for Parkinson’s disease

Neural circuit-based guidance for optimizing patient screening, target selection and parameter tuning for deep brain stimulation (DBS) remains limited. To this end, we propose a functional brain connectome-based modeling approach that simulates network-spreading effects of stimulating different brain regions and quantifies rectification of abnormal network topology in silico. We validate these analyses by predicting nuclei in basal-ganglia circuits as top-ranked targets for 43 local patients with Parkinsons disease and 90 patients from public database. However, individual connectome-based predictions demonstrate that globus pallidus and subthalamic nucleus (STN) constituted as the best choice for 21.1% and 19.5% of patients, respectively. Notably, the priority rank of STN significantly correlated with motor symptom severity in the local cohort. By introducing whole-brain network diffusion dynamics, these findings unfold a new dimension of brain connectomics and underscore the importance of neural network modeling for personalized DBS therapy, which warrants experimental investigation to validate its clinical utility.

neuroscience↗

bioRxiv2017

Interplay between antibiotic efficacy and drug-induced lysis underlie enhanced biofilm formation at subinhibitory drug concentrations

Subinhibitory concentrations of antibiotics have been shown to enhance biofilm formation in multiple bacterial species. While antibiotic exposure has been associated with modulated expression in many biofilm-related genes, the mechanisms of drug-induced biofilm formation remain a focus of ongoing research efforts and may vary significantly across species. In this work, we investigate antibiotic-induced biofilm formation in E. faecalis, a leading cause of nosocomial infections. We show that biofilm formation is enhanced by subinhibitory concentrations of cell wall synthesis inhibitors, but not by inhibitors of protein, DNA, folic acid, or RNA synthesis. Furthermore, enhanced biofilm is associated with increased cell lysis, an increase in extracellular DNA (eDNA), and an increase in the density of living cells in the biofilm. In addition, we observe similar enhancement of biofilm formation when cells are treated with non-antibiotic surfactants that induce cell lysis. These findings suggest that antibiotic-induced biofilm formation is governed by a trade-off between drug toxicity and the beneficial effects of cell lysis. To understand this trade-off, we developed a simple mathematical model that predicts changes to antibiotic-induced biofilm formation due to external perturbations, and we verify these predictions experimentally. Specifically, we demonstrate that perturbations that reduce eDNA (DNase treatment) or decrease the number of living cells in the planktonic phase (a second antibiotic) decrease biofilm induction, while chemical inhibitors of cell lysis increase relative biofilm induction and shift the peak to higher antibiotic concentrations. Overall, our results offer experimental evidence linking cell wall synthesis inhibitors, cell lysis, increased eDNA, and biofilm formation in E. faecalis while also providing a predictive, quantitative model that sheds light on the interplay between cell lysis and antibiotic efficacy in developing biofilms.

microbiology↗

bioRxiv2017

Mice use robust and common strategies to discriminate natural scenes

Mice use vision to navigate and avoid predators in natural environments. However, the spatial resolution of mouse vision is poor compared to primates, and mice lack a fovea. Thus, it is unclear how well mice can discriminate ethologically relevant scenes. Here, we examined natural scene discrimination in mice using an automated touch-screen system. We estimated the discrimination difficulty using the computational metric structural similarity (SSIM), and constructed psychometric curves. However, the performance of each mouse was better predicted by the population mean than SSIM. This high inter-mouse agreement indicates that mice use common and robust strategies to discriminate natural scenes. We tested several other image metrics to find an alternative to SSIM for predicting discrimination performance. We found that a simple, primary visual cortex (V1)-inspired model predicted mouse performance with fidelity approaching the inter-mouse agreement. The model involved convolving the images with Gabor filters, and its performance varied with the orientation of the Gabor filter. This orientation dependence was driven by the stimuli, rather than an innate biological feature. Together, these results indicate that mice are adept at discriminating natural scenes, and their performance is well predicted by simple models of V1 processing.

neuroscience↗

bioRxiv2017

Xolik: finding cross-linked peptides with maximum paired scores in linear time

MotivationCross-linking technique coupled with mass spectrometry (MS) is widely used in the analysis of protein structures and protein-protein interactions. In order to identify cross-linked peptides from MS data, we need to consider all pairwise combinations of peptides, which is computationally prohibitive when the sequence database is large. To alleviate this problem, some heuristic screening strategies are used to reduce the number of peptide pairs during the identification. However, heuristic screening criteria may ignore true findings.\n\nResultsWe directly tackle the combination challenge without using any screening strategies. With the additive scoring function and the data structure of double-ended queue, the proposed algorithm reduces the quadratic time complexity of exhaustive searching down to the linear time complexity. We implement the algorithm in a tool named Xolik, and the running time of Xolik is validated using databases with different number of proteins. Experiments using synthetic and empirical datasets show that Xolik outperforms existing tools in terms of running time and statistical power.\n\nAvailabilitySource code and binaries of Xolik are freely available at http://bioinformatics.ust.hk/Xolik.html.\n\nContacteeyu@ust.hk\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2017

Genome-Scale Mutational Signatures Of Aflatoxin In Cells, Mice And Human Tumors

Aflatoxin B1 (AFB1) is a mutagen and IARC Group 1 carcinogen that causes hepatocellular carcinoma (HCC). Here we present the first whole genome data on the mutational signatures of AFB1 exposure from a total of > 40,000 mutations in four experimental systems: two different human cell lines, and in liver tumors in wild-type mice and in mice that carried a hepatitis B surface antigen transgene - this to model the multiplicative effects of aflatoxin exposure and hepatitis B in causing HCC. AFB1 mutational signatures from all four experimental systems were remarkably similar. We integrated the experimental mutational signatures with data from newly-sequenced HCCs from Qidong County, China, a region of well-studied aflatoxin exposure. This indicated that COSMIC mutational signature 24, previously hypothesized to stem from aflatoxin exposure, indeed likely represents AFB1 exposure, possibly combined with other exposures. Among published somatic mutation data, we found evidence of AFB1 exposure in 0.7% of HCCs treated in North America, 1% of HCCs from Japan, but 16% of HCCs from Hong Kong. Thus, aflatoxin exposure apparently remains a substantial public health issue in some areas. This aspect of our study exemplifies the promise of future widespread resequencing of tumor genomes in providing new insights into the contribution of mutagenic exposures to cancer incidence.

cancer biology↗

bioRxiv2016

ECL 2.0: Exhaustively Identifying Cross-Linked Peptides with a Linear Computational Complexity

Chemical cross-linking coupled with mass spectrometry is a powerful tool to study protein-protein interactions and protein conformations. Two linked peptides are ionized and fragmented to produce a tandem mass spectrum. In such an experiment, a tandem mass spectrum contains ions from two peptides. The peptide identification problem becomes a peptide-peptide pair identification problem. Currently, most existing tools dont search all possible pairs due to the quadratic time complexity. Consequently, a significant percentage of linked peptides are missed. In our earlier work, we developed a tool named ECL to search all pairs of peptides exhaustively. While ECL does not miss any linked peptides, it is very slow due to the quadratic computational complexity, especially when the database is large. Furthermore, ECL uses a score function without statistical calibration, while researchers1,2 have demonstrated that using a statistical calibrated score function can achieve a higher sensitivity than using an uncalibrated one.\n\nHere, we propose an advanced version of ECL, named ECL 2.0. It achieves a linear time and space complexity by taking advantage of the additive property of a score function. It can analyze a typical data set containing tens of thousands of spectra using a large-scale database containing thousands of proteins in a few hours. Comparison with other five state-of-the-art tools shows that ECL 2.0 is much faster than pLink, StavroX, ProteinProspector, and ECL. Kojak is the only one tool that is faster than ECL 2.0. But Kojak does not exhaustively search all possible peptide pairs. We also adopt an e-value estimation method to calibrate the original score. Comparison shows that ECL 2.0 has the highest sensitivity among the state-of-the-art tools. The experiment using a large-scale in vivo cross-linking data set demonstrates that ECL 2.0 is the only tool that can find PSMs passing the false discovery rate threshold. The result illustrates that exhaustive search and well calibrated score function are useful to find PSMs from a huge search space.

bioinformatics↗