Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
6,379
result(s) for
"Ling, Yun"
Sort by:
iSNO-PseAAC: Predict Cysteine S-Nitrosylation Sites in Proteins by Incorporating Position Specific Amino Acid Propensity into Pseudo Amino Acid Composition
Posttranslational modifications (PTMs) of proteins are responsible for sensing and transducing signals to regulate various cellular functions and signaling events. S-nitrosylation (SNO) is one of the most important and universal PTMs. With the avalanche of protein sequences generated in the post-genomic age, it is highly desired to develop computational methods for timely identifying the exact SNO sites in proteins because this kind of information is very useful for both basic research and drug development. Here, a new predictor, called iSNO-PseAAC, was developed for identifying the SNO sites in proteins by incorporating the position-specific amino acid propensity (PSAAP) into the general form of pseudo amino acid composition (PseAAC). The predictor was implemented using the conditional random field (CRF) algorithm. As a demonstration, a benchmark dataset was constructed that contains 731 SNO sites and 810 non-SNO sites. To reduce the homology bias, none of these sites were derived from the proteins that had [Formula: see text] pairwise sequence identity to any other. It was observed that the overall cross-validation success rate achieved by iSNO-PseAAC in identifying nitrosylated proteins on an independent dataset was over 90%, indicating that the new predictor is quite promising. Furthermore, a user-friendly web-server for iSNO-PseAAC was established at http://app.aporc.org/iSNO-PseAAC/, by which users can easily obtain the desired results without the need to follow the mathematical equations involved during the process of developing the prediction method. It is anticipated that iSNO-PseAAC may become a useful high throughput tool for identifying the SNO sites, or at the very least play a complementary role to the existing methods in this area.
Journal Article
Bike Sharing and the Economy, the Environment, and Health-Related Externalities
In recent years, bike-sharing has experienced rapid development; however, controversies about the externalities of bike-sharing programs have arisen as well. While bike-sharing programs have impacts on traffic, the environment, and public health, the social impacts, the management, and sustainable development of bike-sharing has also been of interest. The debate regards whether there are externalities, as well as whether and how such externalities can be determined. Based on the rapidly diffused bike-sharing in China, this paper quantitatively explores bike-sharing externalities. Specifically, this paper estimates the impacts of bike-sharing on the economy, energy use, the environment, and public health. The empirical results show that bike-sharing programs have significant positive externalities. The bike-sharing systems can provide urban residents with a convenient and time-saving travel mode. We find that the bike-sharing dramatically decreases traffic, reduces energy consumption, decreasing harmful gas emissions, improves public health generally, and promotes economic growth. This study contributes to a better comprehension of the externalities of bike-sharing and provides empirical evidence of the impacts of bike-sharing. Findings suggest that bike-sharing can play a critical role in the process of urban transportation development and provide information useful for urban transportation policies.
Journal Article
IPCARF: improving lncRNA-disease association prediction using incremental principal component analysis feature selection and a random forest classifier
Background
Identifying lncRNA-disease associations not only helps to better comprehend the underlying mechanisms of various human diseases at the lncRNA level but also speeds up the identification of potential biomarkers for disease diagnoses, treatments, prognoses, and drug response predictions. However, as the amount of archived biological data continues to grow, it has become increasingly difficult to detect potential human lncRNA-disease associations from these enormous biological datasets using traditional biological experimental methods. Consequently, developing new and effective computational methods to predict potential human lncRNA diseases is essential.
Results
Using a combination of incremental principal component analysis (IPCA) and random forest (RF) algorithms and by integrating multiple similarity matrices, we propose a new algorithm (IPCARF) based on integrated machine learning technology for predicting lncRNA-disease associations. First, we used two different models to compute a semantic similarity matrix of diseases from a directed acyclic graph of diseases. Second, a characteristic vector for each lncRNA-disease pair is obtained by integrating disease similarity, lncRNA similarity, and Gaussian nuclear similarity. Then, the best feature subspace is obtained by applying IPCA to decrease the dimension of the original feature set. Finally, we train an RF model to predict potential lncRNA-disease associations. The experimental results show that the IPCARF algorithm effectively improves the AUC metric when predicting potential lncRNA-disease associations. Before the parameter optimization procedure, the AUC value predicted by the IPCARF algorithm under 10-fold cross-validation reached 0.8529; after selecting the optimal parameters using the grid search algorithm, the predicted AUC of the IPCARF algorithm reached 0.8611.
Conclusions
We compared IPCARF with the existing LRLSLDA, LRLSLDA-LNCSIM, TPGLDA, NPCMF, and ncPred prediction methods, which have shown excellent performance in predicting lncRNA-disease associations. The compared results of 10-fold cross-validation procedures show that the predictions of the IPCARF method are better than those of the other compared methods.
Journal Article
Identifying phenotype-associated subpopulations by integrating bulk and single-cell sequencing data
Bulk and single cell measurements are integrated to identify phenotype-associated subpopulations of cells.
Single-cell RNA sequencing (scRNA-seq) distinguishes cell types, states and lineages within the context of heterogeneous tissues. However, current single-cell data cannot directly link cell clusters with specific phenotypes. Here we present Scissor, a method that identifies cell subpopulations from single-cell data that are associated with a given phenotype. Scissor integrates phenotype-associated bulk expression data and single-cell data by first quantifying the similarity between each single cell and each bulk sample. It then optimizes a regression model on the correlation matrix with the sample phenotype to identify relevant subpopulations. Applied to a lung cancer scRNA-seq dataset, Scissor identified subsets of cells associated with worse survival and with
TP53
mutations. In melanoma, Scissor discerned a T cell subpopulation with low
PDCD1
/
CTLA4
and high
TCF7
expression associated with an immunotherapy response. Beyond cancer, Scissor was effective in interpreting facioscapulohumeral muscular dystrophy and Alzheimer’s disease datasets. Scissor identifies biologically and clinically relevant cell subpopulations from single-cell assays by leveraging phenotype and bulk-omics datasets.
Journal Article
SIRT1 Promotes M2 Microglia Polarization via Reducing ROS-Mediated NLRP3 Inflammasome Signaling After Subarachnoid Hemorrhage
Mounting evidence has suggested that modulating microglia polarization from pro-inflammatory M1 phenotype to anti-inflammatory M2 state might be a potential therapeutic approach in the treatment of subarachnoid hemorrhage (SAH) injury. Our previous study has indicated that sirtuin 1 (SIRT1) could ameliorate early brain injury (EBI) in SAH by reducing oxidative damage and neuroinflammation. However, the effects of SIRT1 on microglial polarization and the underlying molecular mechanisms after SAH have not been fully illustrated. In the present study, we first observed that EX527, a potent selective SIRT1 inhibitor, enhanced microglial M1 polarization and nod-like receptor pyrin domain-containing 3 (NLRP3) inflammasome activation in microglia after SAH. Administration of SRT1720, an agonist of SIRT1, significantly enhanced SIRT1 expression, improved functional recovery, and ameliorated brain edema and neuronal death after SAH. Moreover, SRT1720 modulated the microglia polarization shift from the M1 phenotype and skewed toward the M2 phenotype. Additionally, SRT1720 significantly decreased acetylation of forkhead box protein O1, inhibited the overproduction of reactive oxygen species (ROS) and suppressed NLRP3 inflammasome signaling. In contrast, EX527 abated the upregulation of SIRT1 and reversed the inhibitory effects of SRT1720 on ROS-NLRP3 inflammasome activation and EBI. Similarly, in vitro , SRT1720 suppressed inflammatory response, oxidative damage, and neuronal degeneration, and improved cell viability in neurons and microglia co-culture system. These effects were associated with the suppression of ROS-NLRP3 inflammasome and stimulation of SIRT1 signaling, which could be abated by EX527. Altogether, these findings indicate that SRT1720, an SIRT1 agonist, can ameliorate EBI after SAH by shifting the microglial phenotype toward M2 via modulation of ROS-mediated NLRP3 inflammasome signaling.
Journal Article
Requirement for p62 acetylation in the aggregation of ubiquitylated proteins under nutrient stress
Autophagy receptor p62/SQSTM1 promotes the assembly and removal of ubiquitylated proteins by forming p62 bodies and mediating their encapsulation in autophagosomes. Here we show that under nutrient-deficient conditions, cellular p62 specifically undergoes acetylation, which is required for the formation and subsequent autophagic clearance of p62 bodies. We identify K420 and K435 in the UBA domain as the main acetylation sites, and TIP60 and HDAC6 as the acetyltransferase and deacetylase. Mechanically, acetylation at both K420 and K435 sites enhances p62 binding to ubiquitin by disrupting UBA dimerization, while K435 acetylation also directly increases the UBA-ubiquitin affinity. Furthermore, we show that acetylation of p62 facilitates polyubiquitin chain-induced p62 phase separation. Our results suggest an essential role of p62 acetylation in the selective degradation of ubiquitylated proteins in cells under nutrient stress, by specifically regulating the assembly of p62 bodies.
The autophagy receptor p62 mediates the assembly and removal of ubiquitylated protein aggregates by forming p62 bodies. Here, the authors identify an acetylation-dependent mechanism that regulates formation and autophagic clearance of p62 bodies under nutrient-deficient conditions.
Journal Article
Hepatocyte nuclear factor 4α and cancer-related cell signaling pathways: a promising insight into cancer treatment
Hepatocyte nuclear factor 4α (HNF4α), a member of the nuclear receptor superfamily, is described as a protein that binds to the promoters of specific genes. It controls the expression of functional genes and is also involved in the regulation of numerous cellular processes. A large number of studies have demonstrated that HNF4α is involved in many human malignancies. Abnormal expression of HNF4α is emerging as a critical factor in cancer cell proliferation, apoptosis, invasion, dedifferentiation, and metastasis. In this review, we present emerging insights into the roles of HNF4α in the occurrence, progression, and treatment of cancer; reveal various mechanisms of HNF4α in cancer (e.g., the Wnt/β-catenin, nuclear factor-κB, signal transducer and activator of transcription 3, and transforming growth factor β signaling pathways); and highlight potential clinical uses of HNF4α as a biomarker and therapeutic target for cancer.
Cancer diagnosis: Possible protein biomarker
A regulatory protein that plays complex and varied roles in different cancers may provide a diagnostic biomarker and prove useful in future treatments. Hong Tang and co-workers at Sichuan University in Chengdu, China, reviewed current understanding of the functional role of the hepatocyte nuclear factor 4α (HNF4α) protein. Abnormal expression of HNF4α appears to be involved in cell proliferation, invasion and metastasis in some cancers, but may suppress tumor growth in other cancers. HNF4α has been found to influence signaling pathways involved in cancer induction and progression in early-stage liver and colorectal cancers. It may also help build resistance to chemotherapy in gastric cancer. The presence of abnormal HNF4α expression may provide a diagnostic and prognostic biomarker for certain cancers. Further investigations are needed, but drugs that target HNF4α could be valuable in combination cancer treatments.
Journal Article
Two and three pseudoscalar production in e+e− annihilation and their contributions to (g − 2)μ
A
bstract
A coherent study of
e
+
e
−
annihilation into two (
π
+
π
−
,
K
+
K
−
) and three (
π
+
π
−
π
0
, π
+
π
−
η
) pseudoscalar meson production is carried out within the framework of resonance chiral theory in energy region
E
≲ 2 GeV. The work of [L.Y. Dai, J. Portolés, and O. Shekhovtsova,
Phys. Rev. D
88
(2013) 056001] is revisited with the latest experimental data and a joint analysis of two pseudoscalar meson production. Hence, we evaluate the lowest order hadronic vacuum polarization contributions of those two and three pseudoscalar processes to the anomalous magnetic moment of the muon. We also estimate some higher-order additions led by the same hadronic vacuum polarization. Combined with the other contributions from the standard model, the theoretical prediction differs still by (21
.
6 ± 7
.
4) × 10
−
10
(2.9
σ
) from the experimental value.
Journal Article
iNitro-Tyr: Prediction of Nitrotyrosine Sites in Proteins with General Pseudo Amino Acid Composition
Nitrotyrosine is one of the post-translational modifications (PTMs) in proteins that occurs when their tyrosine residue is nitrated. Compared with healthy people, a remarkably increased level of nitrotyrosine is detected in those suffering from rheumatoid arthritis, septic shock, and coeliac disease. Given an uncharacterized protein sequence that contains many tyrosine residues, which one of them can be nitrated and which one cannot? This is a challenging problem, not only directly related to in-depth understanding the PTM's mechanism but also to the nitrotyrosine-based drug development. Particularly, with the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop a high throughput tool in this regard. Here, a new predictor called \"iNitro-Tyr\" was developed by incorporating the position-specific dipeptide propensity into the general pseudo amino acid composition for discriminating the nitrotyrosine sites from non-nitrotyrosine sites in proteins. It was demonstrated via the rigorous jackknife tests that the new predictor not only can yield higher success rate but also is much more stable and less noisy. A web-server for iNitro-Tyr is accessible to the public at http://app.aporc.org/iNitro-Tyr/. For the convenience of most experimental scientists, we have further provided a protocol of step-by-step guide, by which users can easily get their desired results without the need to follow the complicated mathematics that were presented in this paper just for the integrity of its development process. It has not escaped our notice that the approach presented here can be also used to deal with the other PTM sites in proteins.
Journal Article