Seipin is involved in oxygen-glucose deprivation/reoxygenation induced neuroinflammation by regulating the TLR3/TRAF3/NF-κB pathway.

Seipin plays a crucial role in lipid metabolism and is involved in neurological disorders. However, the function and mechanism of action of seipin in acute ischemic stroke have not yet been elucidated. Here, we aimed to investigate the effect of seipin on neuroinflammation induced by oxygen-glucose deprivation/reoxygenation (OGD/R) and further explore the molecular mechanism by functional experiments. Our results revealed a significant decrease in seipin mRNA levels, accompanied by enhanced expression of TNF-α in patients with AIS, and a significant negative correlation between seipin and TNF-α was observed. Additionally, there was a negative correlation between seipin levels and the National Institutes of Health Stroke Scale (NIHSS) score. Furthermore, seipin levels were also decreased in middle cerebral artery occlusion/reperfusion (MCAO/R) mice and OGD/R-treated BV2 cells. RNA sequencing analysis showed that seipin knockdown altered the Toll-like receptor 3 (TLR3) signaling pathway. It was further confirmed in vitro that seipin knockdown caused significantly increased secretion of inflammatory factors including TNF-α, interleukin (IL)-1ß, and interferon (IFN)-ß. Meanwhile, seipin knockdown activated the Tlr3 signal pathway while this effect could be reversed by Tlr3 inhibitor in OGD/R treated BV2 cells. Furthermore, neuroinflammation induced by OGD/R was significantly reduced by seipin overexpression. Overall, our study demonstrate that seipin deficiency aggravates neuroinflammation by activating the TLR3/TRAF3/NF-κB signaling pathway after OGD/R stimuli, and suggest that seipin may be a potential therapeutic target for AIS.

Oxidized Dextran/Chitosan Hydrogel Engineered with Tetrasulfide-Bridged Silica Nanoparticles for Postsurgical Treatment.

Tumor recurrence and wound microbial infection after tumor excision are serious threats to patients. Thus, the strategy to supply a sufficient and sustained release of cancer drugs and simultaneously engineer antibacterial properties and satisfactory mechanical strength is highly desired for tumor postsurgical treatment. Herein, A novel double-sensitive composite hydrogel embedded with tetrasulfide-bridged mesoporous silica (4S-MSNs) is developed. The incorporation of 4S-MSNs into oxidized dextran/chitosan hydrogel network, not only enhances the mechanical properties of hydrogels, but also can increase the specificity of drug with dual pH/redox sensitivity, thereby allowing more efficient and safer therapy. Besides, 4S-MSNs hydrogel preserves the favorable physicochemical properties of polysaccharide hydrogel, such as high hydrophilicity, satisfactory antibacterial activity, and excellent biocompatibility. Thus, the prepared 4S-MSNs hydrogel can be served as an efficient strategy for postsurgical bacterial infection and inhibition of tumor recurrence.

Therapeutic improvements of nifedipine controlled-release tablets combined with sacubitril valsartan on patients with diabetic nephropathy complicated with hypertension.

This study aimed to evaluate the efficacy of nifedipine controlled-release tablets combined with sacubitril valsartan in diabetic nephropathy (DN) patients with hypertension. One hundred and twelve DN patients with hypertension were enrolled. They were randomly divided into the control group (treated with nifedipine controlled-release tablets combined with valsartan) and the observation group (treated with nifedipine controlled-release tablets combined with sacubitril valsartan). Renal function, endothelial function and inflammatory response were examined. After three-months treatment, the levels of clinical indexes (glycosylated hemoglobin, fasting blood glucose, systolic and diastolic blood pressure), renal function indicators (urinary albumin excretion rate, blood urea nitrogen, serum creatinine and cystatin C), endothelial function indicators (microalbumin, angiotensin II, thrombomodulin and cartilage oligomeric matrix protein) and inflammatory response factors (interleukin-6 and tumor necrosis factor-α) in the observation group were significantly lower than those in the control group. Nifedipine controlled-release tablets combined with sacubitril valsartan could effectively alleviate the progression of DN combined with hypertension.

Low serum magnesium is associated with poor functional outcome in acute ischemic stroke or transient ischemic attack patients.

AIM: The association between magnesium and outcomes after stroke is uncertain. We aimed to investigate the association of serum magnesium with all-cause mortality and poor functional outcome. METHODS: We included patients with acute ischemic stroke (AIS) or transient ischemic attack (TIA) from the China National Stroke Registry III. We used Cox proportional hazards model for all-cause mortality and logistic regression model for poor functional outcome (modified Rankin Scale [mRS] 2-6/3-6) to examine the relationships. RESULTS: Among the 6483 patients, the median (interquartile range) magnesium was 0.87 (0.80-0.93) mmol/L. Patients in the first quartile had a higher risk of mRS score 3-6/2-6 at 3 months (adjusted odds ratio [OR]: 1.30; 95% confidence interval [CI]: 1.02, 1.64; adjusted OR: 1.29; 95% CI: 1.04-1.59) compared with those in the fourth quartile. Similar results were found for mRS score 26 at 1 year. The age- and sex-adjusted hazard ratio (HR) with 95% CI in first quartile magnesium was 1.40 (1.02-1.93) for all-cause mortality within 1 year, but became insignificant (HR: 1.03; 95% CI: 0.71-1.50) after adjusting for potential variables. CONCLUSIONS: Low serum magnesium was associated with a high risk of poor functional outcome in patients with AIS or TIA.

Prediction of protein amidation sites by feature selection and analysis.

Carboxy-terminal α-amidation is a widespread post-translational modification of proteins found widely in vertebrates and invertebrates. The α-amide group is required for full biological activity, since it may render a peptide more hydrophobic and thus better be able to bind to other proteins, preventing ionization of the C-terminus. However, in particular, the C-terminal amidation is very difficult to detect because experimental methods are often labor-intensive, time-consuming and expensive. Therefore, in silico methods may complement due to their high efficiency. In this study, a computational method was developed to predict protein amidation sites, by incorporating the maximum relevance minimum redundancy method and the incremental feature selection method based on the nearest neighbor algorithm. From a total of 735 features, 41 optimal features were selected and were utilized to construct the final predictor. As a result, the predictor achieved an overall Matthews correlation coefficient of 0.8308. Feature analysis showed that PSSM conservation scores and amino acid factors played the most important roles in the α-amidation site prediction. Site-specific feature analyses showed that features derived from the amidation site itself and adjacent sites were most significant. This method presented could be used as an efficient tool to theoretically predict amidated peptides. And the selected features from our study could shed some light on the in-depth understanding of the mechanisms of the amidation modification, providing guidelines for experimental validation.

A sequence-based approach for predicting protein disordered regions.

Protein disordered regions are associated with some critical cellular functions such as transcriptional regulation, translation and cellular signal transduction, and they are responsible for various diseases. Although experimental methods have been developed to determine these regions, they are time-consuming and expensive. Therefore, it is highly desired to develop computational methods that can provide us with this kind information in a rapid and inexpensive manner. Here we propose a sequence-based computational approach for predicting protein disordered regions by means of the Nearest Neighbor algorithm, in which conservation, amino acid factor and secondary structure status of each amino acid in a fixed-length sliding window are taken as the encoding features. Also, the feature selection based on mRMR (maximum Relevancy Minimum Redundancy) is applied to obtain an optimal 51-feature set that includes 39 conservation features and 12 secondary structure features. With the optimal 51 features, our predictor yielded quite promising MCC (Mathew's correlation coefficients): 0.371 on a rigorous benchmark dataset tested by 5-fold cross-validation and 0.219 on an independent test dataset. Our results suggest that conservation and secondary structure play important roles in intrinsically disordered proteins.

A comparison of computational methods for identifying virulence factors.

Bacterial pathogens continue to threaten public health worldwide today. Identification of bacterial virulence factors can help to find novel drug/vaccine targets against pathogenicity. It can also help to reveal the mechanisms of the related diseases at the molecular level. With the explosive growth in protein sequences generated in the postgenomic age, it is highly desired to develop computational methods for rapidly and effectively identifying virulence factors according to their sequence information alone. In this study, based on the protein-protein interaction networks from the STRING database, a novel network-based method was proposed for identifying the virulence factors in the proteomes of UPEC 536, UPEC CFT073, P. aeruginosa PAO1, L. pneumophila Philadelphia 1, C. jejuni NCTC 11168 and M. tuberculosis H37Rv. Evaluated on the same benchmark datasets derived from the aforementioned species, the identification accuracies achieved by the network-based method were around 0.9, significantly higher than those by the sequence-based methods such as BLAST, feature selection and VirulentPred. Further analysis showed that the functional associations such as the gene neighborhood and co-occurrence were the primary associations between these virulence factors in the STRING database. The high success rates indicate that the network-based method is quite promising. The novel approach holds high potential for identifying virulence factors in many other various organisms as well because it can be easily extended to identify the virulence factors in many other bacterial species, as long as the relevant significant statistical data are available for them.

Cooperativity among short amyloid stretches in long amyloidogenic sequences.

Amyloid fibrillar aggregates of polypeptides are associated with many neurodegenerative diseases. Short peptide segments in protein sequences may trigger aggregation. Identifying these stretches and examining their behavior in longer protein segments is critical for understanding these diseases and obtaining potential therapies. In this study, we combined machine learning and structure-based energy evaluation to examine and predict amyloidogenic segments. Our feature selection method discovered that windows consisting of long amino acid segments of ~30 residues, instead of the commonly used short hexapeptides, provided the highest accuracy. Weighted contributions of an amino acid at each position in a 27 residue window revealed three cooperative regions of short stretch, resemble the ß-strand-turn-ß-strand motif in A-ßpeptide amyloid and ß-solenoid structure of HET-s(218-289) prion (C). Using an in-house energy evaluation algorithm, the interaction energy between two short stretches in long segment is computed and incorporated as an additional feature. The algorithm successfully predicted and classified amyloid segments with an overall accuracy of 75%. Our study revealed that genome-wide amyloid segments are not only dependent on short high propensity stretches, but also on nearby residues.

Prediction of protein domain with mRMR feature selection and analysis.

The domains are the structural and functional units of proteins. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop effective methods for predicting the protein domains according to the sequences information alone, so as to facilitate the structure prediction of proteins and speed up their functional annotation. However, although many efforts have been made in this regard, prediction of protein domains from the sequence information still remains a challenging and elusive problem. Here, a new method was developed by combing the techniques of RF (random forest), mRMR (maximum relevance minimum redundancy), and IFS (incremental feature selection), as well as by incorporating the features of physicochemical and biochemical properties, sequence conservation, residual disorder, secondary structure, and solvent accessibility. The overall success rate achieved by the new method on an independent dataset was around 73%, which was about 28-40% higher than those by the existing method on the same benchmark dataset. Furthermore, it was revealed by an in-depth analysis that the features of evolution, codon diversity, electrostatic charge, and disorder played more important roles than the others in predicting protein domains, quite consistent with experimental observations. It is anticipated that the new method may become a high-throughput tool in annotating protein domains, or may, at the very least, play a complementary role to the existing domain prediction methods, and that the findings about the key features with high impacts to the domain prediction might provide useful insights or clues for further experimental investigations in this area. Finally, it has not escaped our notice that the current approach can also be utilized to study protein signal peptides, B-cell epitopes, HIV protease cleavage sites, among many other important topics in protein science and biomedicine.

Predicting protein oxidation sites with feature selection and analysis approach.

Protein oxidation is a ubiquitous post-translational modification that plays important roles in various physiological and pathological processes. Owing to the fact that protein oxidation can also take place as an experimental artifact or caused by oxygen in the air during the process of sample collection and analysis, and that it is both time-consuming and expensive to determine the protein oxidation sites purely by biochemical experiments, it would be of great benefit to develop in silico methods for rapidly and effectively identifying protein oxidation sites. In this study, we developed a computational method to address this problem. Our method was based on the nearest neighbor algorithm in which, however, the maximum relevance minimum redundancy and incremental feature selection approaches were incorporated. From the initial 735 features, 16 features were selected as the optimal feature set. Of such 16 optimized features, 10 features were associated with the position-specific scoring matrix conservation scores, three with the amino acid factors, one with the propensity of conservation of residues on protein surface, one with the side chain count of carbon atom deviation from mean, and one with the solvent accessibility. It was observed that our prediction model achieved an overall success rate of 75.82%, indicating that it is quite encouraging and promising for practical applications. Also, the 16 optimal features obtained through this study may provide useful clues and insights for in-depth understanding the action mechanism of protein oxidation.

Using protein-protein interaction network information to predict the subcellular locations of proteins in budding yeast.

The information of protein subcellular localization is vitally important for in-depth understanding the intricate pathways that regulate biological processes at the cellular level. With the rapidly increasing number of newly found protein sequence in the Post-Genomic Age, many automated methods have been developed attempting to help annotate their subcellular locations in a timely manner. However, very few of them were developed using the protein-protein interaction (PPI) network information. In this paper, we have introduced a new concept called "tethering potential" by which the PPI information can be effectively fused into the formulation for protein samples. Based on such a network frame, a new predictor called Yeast-PLoc has been developed for identifying budding yeast proteins among their 19 subcellular location sites. Meanwhile, a purely sequence-based approach, called the "hybrid-property" method, is integrated into Yeast-PLoc as a fall-back to deal with those proteins without sufficient PPI information. The overall success rate by the jackknife test on the 4,683 yeast proteins in the training dataset was 70.25%. Furthermore, it was shown that the success rate by Yeast- PLoc on an independent dataset was remarkably higher than those by some other existing predictors, indicating that the current approach by incorporating the PPI information is quite promising. As a user-friendly web-server, Yeast-PLoc is freely accessible at http://yeastloc.biosino.org/.

Deciphering the effects of gene deletion on yeast longevity using network and machine learning approaches.

Longevity is one of the most basic and one of the most essential properties of all living organisms. Identification of genes that regulate longevity would increase understanding of the mechanisms of aging, so as to help facilitate anti-aging intervention and extend the life span. In this study, based on the network features and the biochemical/physicochemical features of the deletion network and deletion genes, as well as their functional features, a two-layer model was developed for predicting the deletion effects on yeast longevity. The first stage of our prediction approach was to identify whether the deletion of one gene would change the life span of yeast; if it did, the second stage of our procedure would automatically proceed to predict whether the deletion of one gene would increase or decrease the life span. It was observed by analyzing the predicted results that the functional features (such as mitochondrial function and chromatin silencing), the network features (such as the edge density and edge weight density of the deletion network), and the local centrality of deletion gene, would have important impact for predicting the deletion effects on longevity. It is anticipated that our model may become a useful tool for studying longevity from the angle of genes and networks. Moreover, it has not escaped our notice that, after some modification, the current model can also be used to study many other phenotype prediction problems from the angle of systems biology.

Prediction of lysine ubiquitination with mRMR feature selection and analysis.

Ubiquitination, one of the most important post-translational modifications of proteins, occurs when ubiquitin (a small 76-amino acid protein) is attached to lysine on a target protein. It often commits the labeled protein to degradation and plays important roles in regulating many cellular processes implicated in a variety of diseases. Since ubiquitination is rapid and reversible, it is time-consuming and labor-intensive to identify ubiquitination sites using conventional experimental approaches. To efficiently discover lysine-ubiquitination sites, a sequence-based predictor of ubiquitination site was developed based on nearest neighbor algorithm. We used the maximum relevance and minimum redundancy principle to identify the key features and the incremental feature selection procedure to optimize the prediction engine. PSSM conservation scores, amino acid factors and disorder scores of the surrounding sequence formed the optimized 456 features. The Mathew's correlation coefficient (MCC) of our ubiquitination site predictor achieved 0.142 by jackknife cross-validation test on a large benchmark dataset. In independent test, the MCC of our method was 0.139, higher than the existing ubiquitination site predictor UbiPred and UbPred. The MCCs of UbiPred and UbPred on the same test set were 0.135 and 0.117, respectively. Our analysis shows that the conservation of amino acids at and around lysine plays an important role in ubiquitination site prediction. What's more, disorder and ubiquitination have a strong relevance. These findings might provide useful insights for studying the mechanisms of ubiquitination and modulating the ubiquitination pathway, potentially leading to potential therapeutic strategies in the future.

Predict and analyze S-nitrosylation modification sites with the mRMR and IFS approaches.

S-nitrosylation (SNO) is one of the most important and universal post-translational modifications (PTMs) which regulates various cellular functions and signaling events. Identification of the exact S-nitrosylation sites in proteins may facilitate the understanding of the molecular mechanisms and biological function of S-nitrosylation. Unfortunately, traditional experimental approaches used for detecting S-nitrosylation sites are often laborious and time-consuming. However, computational methods could overcome this demerit. In this work, we developed a novel predictor based on nearest neighbor algorithm (NNA) with the maximum relevance minimum redundancy (mRMR) method followed by incremental feature selection (IFS). The features of physicochemical/biochemical properties, sequence conservation, residual disorder, amino acid occurrence frequency, second structure and the solvent accessibility were utilized to represent the peptides concerned. Feature analysis showed that the features except residual disorder affected identification of the S-nitrosylation sites. It was also shown via the site-specific feature analysis that the features of sites away from the central cysteine might contribute to the S-nitrosylation site determination through a subtle manner. It is anticipated that our prediction method may become a useful tool for identifying the protein S-nitrosylation sites and that the features analysis described in this paper may provide useful insights for in-depth investigation into the mechanism of S-nitrosylation.

Selection of reprogramming factors of induced pluripotent stem cells based on the protein interaction network and functional profiles.

Induced pluripotent stem cells have displayed great potential in disease investigation and drug development applications. However, selection of reprogramming factors in each cell type or disease state is both expensive and time consuming. To deal with this kind of situation, a fast computational framework was developed by optimize the reprogramming factors via the protein interaction network and gene functional profiles. It can be used to select reprogramming factors from millions of possibilities. It is anticipated that the novel approach will become a very useful tool for both basic research and drug development.

Prediction of the functional roles of small molecules in lipid metabolism based on ensemble learning.

As many diseases like high cholesterol are referred to lipid metabolism, studying the lipid metabolic pathway has a positive effect on finding the knowledge about interactions between different elements within high complex living systems. Here, we employed a typical ensemble learning method, Bagging learner, to study and predict the possible sub lipid metabolic pathway of small molecules based on physical and chemical features of the compounds. As a result, jackknife cross validation test and independent set test on the model reached 89.85% and 91.46%, respectively. Therefore, our predictor may be used for finding the new compounds which participate in lipid metabolic procedures.

A nearest neighbor algorithm based predictor for the prediction of enzyme-small molecule interaction.

It is of great use to find out and clear up the interactions between enzymes and small molecules, for understanding the molecular and cellular functions of organisms. In this study, we developed a novel method for the prediction of enzyme-small molecules interactions based on machine learning approach. The biochemical and physicochemical description of proteins and the functional group composition of small molecules are used for representing enzyme-small molecules pairs. Tested by jackknife cross-validation, our predictor achieved an overall accuracy of 87.47%, showing an acceptable efficiency. The 39 features selected by feature selection were analyzed for further understanding of enzyme-small molecule interactions.

Prediction of protein quaternary structure with feature selection and analysis based on protein biological features.

Information of protein quaternary structure can help to understand the biological functions of proteins. Because wet-lab experiments are both time-consuming and costly, we adopt a novel computational approach to assign proteins into 10 kinds of quaternary structures. By coding each protein using its biochemical and physicochemical properties, feature selection was carried out using Incremental Feature Selection (IFS) method. The thus obtained optimal feature set consisted of 97 features, with which the prediction model was built. As a result, the overall prediction success rate is 74.90% evaluated by Jackknife test, much higher than the overall correct rate of a random guess 10% (1/10). The further feature analysis indicates that protein secondary structure is the most contributed feature in the prediction of protein quaternary structure.

PSCL: predicting protein subcellular localization based on optimal functional domains.

It is well known that protein subcellular localizations are closely related to their functions. Although many computational methods and tools are available from Internet, it is still necessary to develop new algorithms in this filed to gain a better understanding of the complex mechanism of plant subcellular localization. Here, we provide a new web server named PSCL for plant protein subcellular localization prediction by employing optimized functional domains. After feature optimization, 848 optimal functional domains from InterPro were obtained to represent each protein. By calculating the distances to each of the seven categories, PSCL showing the possibilities of a protein located into each of those categories in ascending order. Toward our dataset, PSCL achieved a first-order predicted accuracy of 75.7% by jackknife test. Gene Ontology enrichment analysis showing that catalytic activity, cellular process and metabolic process are strongly correlated with the localization of plant proteins. Finally, PSCL, a Linux Operate System based web interface for the predictor was designed and is accessible for public use at http://pscl.biosino.org/.

Prediction of body fluids where proteins are secreted into based on protein interaction network.

Determining the body fluids where secreted proteins can be secreted into is important for protein function annotation and disease biomarker discovery. In this study, we developed a network-based method to predict which kind of body fluids human proteins can be secreted into. For a newly constructed benchmark dataset that consists of 529 human-secreted proteins, the prediction accuracy for the most possible body fluid location predicted by our method via the jackknife test was 79.02%, significantly higher than the success rate by a random guess (29.36%). The likelihood that the predicted body fluids of the first four orders contain all the true body fluids where the proteins can be secreted into is 62.94%. Our method was further demonstrated with two independent datasets: one contains 57 proteins that can be secreted into blood; while the other contains 61 proteins that can be secreted into plasma/serum and were possible biomarkers associated with various cancers. For the 57 proteins in first dataset, 55 were correctly predicted as blood-secrete proteins. For the 61 proteins in the second dataset, 58 were predicted to be most possible in plasma/serum. These encouraging results indicate that the network-based prediction method is quite promising. It is anticipated that the method will benefit the relevant areas for both basic research and drug development.