| 1. |
- Bompada, Pradeep, et al.
(författare)
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Epigenome-Wide Histone Acetylation Changes in Peripheral Blood Mononuclear Cells in Patients with Type 2 Diabetes and Atherosclerotic Disease
- 2021
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Ingår i: Biomedicines. - : MDPI AG. - 2227-9059. ; 9:12
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Tidskriftsartikel (refereegranskat)abstract
- There is emerging evidence of an association between epigenetic modifications, glycemic control and atherosclerosis risk. In this study, we mapped genome-wide epigenetic changes in patients with type 2 diabetes (T2D) and advanced atherosclerotic disease. We performed chromatin immunoprecipitation sequencing (ChIP-seq) using a histone 3 lysine 9 acetylation (H3K9ac) mark in peripheral blood mononuclear cells from patients with atherosclerosis with T2D (n = 8) or without T2D (ND, n = 10). We mapped epigenome changes and identified 23,394 and 13,133 peaks in ND and T2D individuals, respectively. Out of all the peaks, 753 domains near the transcription start site (TSS) were unique to T2D. We found that T2D in atherosclerosis leads to an H3K9ac increase in 118, and loss in 63 genomic regions. Furthermore, we discovered an association between the genomic locations of significant H3K9ac changes with genetic variants identified in previous T2D GWAS. The transcription factor 7-like 2 (TCF7L2) rs7903146, together with several human leukocyte antigen (HLA) variants, were among the domains with the most dramatic changes of H3K9ac enrichments. Pathway analysis revealed multiple activated pathways involved in immunity, including type 1 diabetes. Our results present novel evidence on the interaction between genetics and epigenetics, as well as epigenetic changes related to immunity in patients with T2D and advanced atherosclerotic disease.
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| 2. |
- Wu, Chuanyan, et al.
(författare)
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A novel model for protein sequence similarity analysis based on spectral radius
- 2018
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Ingår i: Journal of Theoretical Biology. - : Elsevier BV. - 0022-5193. ; 446, s. 61-70
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Tidskriftsartikel (refereegranskat)abstract
- Advances in sequencing technologies led to rapid increase in the number and diversity of biological sequences, which facilitated development in the sequence research. In this paper, we present a new method for analyzing protein sequence similarity. We calculated the spectral radii of 20 amino acids (AAs) and put forward a novel 2-D graphical representation of protein sequences. To characterize protein sequences numerically, three groups of features were extracted and related to statistical, dynamics measurements and fluctuation complexity of the sequences. With the obtained feature vector, two models utilizing Gaussian Kernel similarity and Cosine similarity were built to measure the similarity between sequences. We applied our method to analyze the similarities/dissimilarities of four data sets. Both proposed models received consistent results with improvements when compared to that obtained by the ClustalW analysis. The novel approach we present in this study may therefore benefit protein research in medical and scientific fields.
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| 3. |
- Wu, Chuanyan, et al.
(författare)
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Elevated circulating follistatin associates with an increased risk of type 2 diabetes
- 2021
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 12, s. 1-10
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Tidskriftsartikel (refereegranskat)abstract
- The hepatokine follistatin is elevated in patients with type 2 diabetes (T2D) and promotes hyperglycemia in mice. Here we explore the relationship of plasma follistatin levels with incident T2D and mechanisms involved. Adjusted hazard ratio (HR) per standard deviation (SD) increase in follistatin levels for T2D is 1.24 (CI: 1.04-1.47, p < 0.05) during 19-year follow-up (n = 4060, Sweden); and 1.31 (CI: 1.09-1.58, p < 0.01) during 4-year follow-up (n = 883, Finland). High circulating follistatin associates with adipose tissue insulin resistance and non-alcoholic fatty liver disease (n = 210, Germany). In human adipocytes, follistatin dose-dependently increases free fatty acid release. In genome-wide association study (GWAS), variation in the glucokinase regulatory protein gene (GCKR) associates with plasma follistatin levels (n = 4239, Sweden; n = 885, UK, Italy and Sweden) and GCKR regulates follistatin secretion in hepatocytes in vitro. Our findings suggest that GCKR regulates follistatin secretion and that elevated circulating follistatin associates with an increased risk of T2D by inducing adipose tissue insulin resistance.
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| 4. |
- Wu, Chuanyan, et al.
(författare)
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MHMDA : Human Microbe-Disease Association Prediction by Matrix Completion and Multi-Source Information
- 2019
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Ingår i: IEEE Access. - 2169-3536. ; 7, s. 106687-106693
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Tidskriftsartikel (refereegranskat)abstract
- Microbes are vital in human health. It is helpful to promote diagnostic and treatment of human disease and drug development by identifying microbe-disease associations. However, knowledge in this area still needs to be further improved. In this paper, a new computational model using matrix completion to predict human microbe-disease associations (mHMDA, Fig. 1) is developed. First, we extract the disease feature by Gaussian kernel-based similarity and symptom-based similarity. Meanwhile, the microbe feature is computed by Gaussian kernel-based similarity. As treating potential association as the missing elements of a matrix, the matrix completion is adopted to get the potential microbe-disease associations. Leave-one-out cross-validation (LOOCV) is carried out which get the AUC (The area under ROC curve) of 0.928 showing the effectiveness of mHMDA. Furthermore, 5-fold CV get the AUCs of 0.8838 ± 0.0044 (mean ± standard deviation). Moreover, through the four case studies (asthma, inflammatory bowel disease (IBD), type 2 diabetes (T2D), and type 1 diabetes (T1D)), we find that nine, ten, nine, and eight of top-ten inferred microorganisms for the four diseases are previously verified by experiments. All these results indicate the effectiveness of mHMDA. mHMDA might be helpful to infer the disease-related microorganisms.
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| 5. |
- Wu, Chuanyan, et al.
(författare)
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PEPRF : Identification of Essential Proteins by Integrating Topological Features of PPI Network and Sequence-based Features via Random Forest
- 2021
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Ingår i: Current Bioinformatics. - : Bentham Science Publishers Ltd.. - 1574-8936. ; 16:9, s. 1161-1168
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Tidskriftsartikel (refereegranskat)abstract
- Background: Essential proteins play an important role in the process of life, which can be identified by experimental methods and computational approaches. Experimental approaches to identify essential proteins are of high accuracy but with the limitation of time and resource-consuming. Objective: Herein, we present a computational model (PEPRF) to identify essential proteins based on machine learning. Methods: Different features of proteins were extracted. Topological features of Protein-Protein Interaction (PPI) network-based are extracted. Based on the protein sequence, graph theory-based features, in-formation-based features, composition and physichemical features, etc., were extracted. Finally, 282 features are constructed. In order to select the features that contributed most to the identification, Re-liefF-based feature selection method was adopted to measure the weights of these features. Results: As a result, 212 features were curated to train random forest classifiers. Finally, PEPRF get the AUC of 0.71 and an accuracy of 0.742. Conclusion: Our results show that PEPRF may be applied as an efficient tool to identify essential pro-teins.
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| 6. |
- Wu, Chuanyan, et al.
(författare)
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PTPD : Predicting therapeutic peptides by deep learning and word2vec
- 2019
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Ingår i: BMC Bioinformatics. - : Springer Science and Business Media LLC. - 1471-2105. ; 20:1
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Tidskriftsartikel (refereegranskat)abstract
- Background In the search for therapeutic peptides for disease treatments, many efforts have been made to identify various functional peptides from large numbers of peptide sequence databases. In this paper, we propose an effective computational model that uses deep learning and word2vec to predict therapeutic peptides (PTPD).∗: Results Representation vectors of all k-mers were obtained through word2vec based on k-mer co-existence information. The original peptide sequences were then divided into k-mers using the windowing method. The peptide sequences were mapped to the input layer by the embedding vector obtained by word2vec. Three types of filters in the convolutional layers, as well as dropout and max-pooling operations, were applied to construct feature maps. These feature maps were concatenated into a fully connected dense layer, and rectified linear units (ReLU) and dropout operations were included to avoid over-fitting of PTPD. The classification probabilities were generated by a sigmoid function. PTPD was then validated using two datasets: an independent anticancer peptide dataset and a virulent protein dataset, on which it achieved accuracies of 96% and 94%, respectively.∗: Conclusions PTPD identified novel therapeutic peptides efficiently, and it is suitable for application as a useful tool in therapeutic peptide design.
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| 7. |
- Zhang, Jialin, et al.
(författare)
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FS-GBDT : identification multicancer-risk module via a feature selection algorithm by integrating Fisher score and GBDT
- 2021
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Ingår i: Briefings in Bioinformatics. - : Oxford University Press (OUP). - 1477-4054 .- 1467-5463. ; 22:3
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Tidskriftsartikel (refereegranskat)abstract
- Cancer is a highly heterogeneous disease caused by dysregulation in different cell types and tissues. However, different cancers may share common mechanisms. It is critical to identify decisive genes involved in the development and progression of cancer, and joint analysis of multiple cancers may help to discover overlapping mechanisms among different cancers. In this study, we proposed a fusion feature selection framework attributed to ensemble method named Fisher score and Gradient Boosting Decision Tree (FS-GBDT) to select robust and decisive feature genes in high-dimensional gene expression datasets. Joint analysis of 11 human cancers types was conducted to explore the key feature genes subset of cancer. To verify the efficacy of FS-GBDT, we compared it with four other common feature selection algorithms by Support Vector Machine (SVM) classifier. The algorithm achieved highest indicators, outperforms other four methods. In addition, we performed gene ontology analysis and literature validation of the key gene subset, and this subset were classified into several functional modules. Functional modules can be used as markers of disease to replace single gene which is difficult to be found repeatedly in applications of gene chip, and to study the core mechanisms of cancer.
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