Genetic source completeness of HIV-1 circulating recombinant forms (CRFs) predicted by multi-label learning.

MOTIVATION: Infection with strains of different subtypes and the subsequent crossover reading between the two strands of genomic RNAs by host cells' reverse transcriptase are the main causes of the vast HIV-1 sequence diversity. Such inter-subtype genomic recombinants can become circulating recombinant forms (CRFs) after widespread transmissions in a population. Complete prediction of all the subtype sources of a CRF strain is a complicated machine learning problem. It is also difficult to understand whether a strain is an emerging new subtype and if so, how to accurately identify the new components of the genetic source. RESULTS: We introduce a multi-label learning algorithm for the complete prediction of multiple sources of a CRF sequence as well as the prediction of its chronological number. The prediction is strengthened by a voting of various multi-label learning methods to avoid biased decisions. In our steps, frequency and position features of the sequences are both extracted to capture signature patterns of pure subtypes and CRFs. The method was applied to 7185 HIV-1 sequences, comprising 5530 pure subtype sequences and 1655 CRF sequences. Results have demonstrated that the method can achieve very high accuracy (reaching 99%) in the prediction of the complete set of labels of HIV-1 recombinant forms. A few wrong predictions are actually incomplete predictions, very close to the complete set of genuine labels. AVAILABILITY AND IMPLEMENTATION: https://github.com/Runbin-tang/The-source-of-HIV-CRFs-prediction. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

lncRNA RNCR2 facilitates cell proliferation and epithelial-mesenchymal transition in melanoma through HK2-mediated Warburg effect via targeting miR-495-3p.

Melanoma is a potentially lethal skin cancer with a high death rate. LncRNAs were reported to be implicated in melanoma progression. However, the function and mechanisms of lncRNA RNCR2 in melanoma are little known. In this study, RNCR2, miR-495-3p, and HK2 expression levels were measured in melanoma tissue specimens and cell lines by qPCR. EdU and CCK-8 assays were performed to assess cell proliferation. Enolase activity, ATP level, lactate production, and glucose consumption measurement kits were used to evaluate the glycolysis of tumor cells. Immunofluorescence and western blot were used to detect the expression of epithelial-mesenchymal transition (EMT) and glycolysis-related proteins. Luciferase reporter assay was applied to confirm the target relationships. The role of RNCR2 in tumorigenesis was examined using murine xenograft models. LncRNA RNCR2 was upregulated in melanoma tissues and cell lines. Cell function detection showed that RNCR2 knockdown remarkably inhibited cell proliferation and EMT via glycolysis, as well as reduced the growth of a tumor. Mechanically, RNCR2 was confirmed to bind to miR-495-3p and positively regulated HK2 expression level, and the miR-495-3p level was negatively correlated with RNCR2 or HK2 in melanoma tissues. Further, miR-495-3p downregulation or HK2 upregulation partially reversed RNCR2 knockdown-induced inhibition of melanoma cell growth, EMT, and glycolysis. Collectively, RNCR2 might be an oncogenic lncRNA to promote tumor cell glycolysis and accelerate tumor growth via the miR-495-3p/HK2 axis, providing a promising treatment target for melanoma.

PRIP: A Protein-RNA Interface Predictor Based on Semantics of Sequences.

RNA-protein interactions play an indispensable role in many biological processes. Growing evidence has indicated that aberration of the RNA-protein interaction is associated with many serious human diseases. The precise and quick detection of RNA-protein interactions is crucial to finding new functions and to uncovering the mechanism of interactions. Although many methods have been presented to recognize RNA-binding sites, there is much room left for the improvement of predictive accuracy. We present a sequence semantics-based method (called PRIP) for predicting RNA-binding interfaces. The PRIP extracted semantic embedding by pre-training the Word2vec with the corpus. Extreme gradient boosting was employed to train a classifier. The PRIP obtained a SN of 0.73 over the five-fold cross validation and a SN of 0.67 over the independent test, outperforming the state-of-the-art methods. Compared with other methods, this PRIP learned the hidden relations between words in the context. The analysis of the semantics relationship implied that the semantics of some words were specific to RNA-binding interfaces. This method is helpful to explore the mechanism of RNA-protein interactions from a semantics point of view.

An Information-Entropy Position-Weighted K-Mer Relative Measure for Whole Genome Phylogeny Reconstruction.

Alignment methods have faced disadvantages in sequence comparison and phylogeny reconstruction due to their high computational costs in handling time and space complexity. On the other hand, alignment-free methods incur low computational costs and have recently gained popularity in the field of bioinformatics. Here we propose a new alignment-free method for phylogenetic tree reconstruction based on whole genome sequences. A key component is a measure called information-entropy position-weighted k-mer relative measure (IEPWRMkmer), which combines the position-weighted measure of k-mers proposed by our group and the information entropy of frequency of k-mers. The Manhattan distance is used to calculate the pairwise distance between species. Finally, we use the Neighbor-Joining method to construct the phylogenetic tree. To evaluate the performance of this method, we perform phylogenetic analysis on two datasets used by other researchers. The results demonstrate that the IEPWRMkmer method is efficient and reliable. The source codes of our method are provided at https://github.com/ wuyaoqun37/IEPWRMkmer.

An Information Entropy-Based Approach for Computationally Identifying Histone Lysine Butyrylation.

Butyrylation plays a crucial role in the cellular processes. Due to limit of techniques, it is a challenging task to identify histone butyrylation sites on a large scale. To fill the gap, we propose an approach based on information entropy and machine learning for computationally identifying histone butyrylation sites. The proposed method achieves 0.92 of area under the receiver operating characteristic (ROC) curve over the training set by 3-fold cross validation and 0.80 over the testing set by independent test. Feature analysis implies that amino acid residues in the down/upstream of butyrylation sites would exhibit specific sequence motif to a certain extent. Functional analysis suggests that histone butyrylation was most possibly associated with four pathways (systemic lupus erythematosus, alcoholism, viral carcinogenesis and transcriptional misregulation in cancer), was involved in binding with other molecules, processes of biosynthesis, assembly, arrangement or disassembly and was located in such complex as consists of DNA, RNA, protein, etc. The proposed method is useful to predict histone butyrylation sites. Analysis of feature and function improves understanding of histone butyrylation and increases knowledge of functions of butyrylated histones.

[Analysis of gene mutations in two patients with tuberous sclerosis complex].

OBJECTIVE: To analyze the mutation of TSC gene in two sporadic patients with tuberous sclerosis complex (TSC). METHODS: All the coding exons of TSC1 and TSC2 genes of these two patients, unaffected member in the two families, and 100 unrelated population-matched controls were amplified by polymerase chain reaction. The products were analyzed by direct sequencing. RESULT: Two TSC2 gene mutations (c. 268C > T, c. 5 227C > T) were identified in two patients, but not in their family members and in 100 unrelated population-matched controls. CONCLUSION: These two mutations are the cause of the clinical phenotypes of these two sporadic patients with TSC.

Effects of demethoxycurcumin on the viability and apoptosis of skin cancer cells.

The present study investigated the effects and mechanisms of demethoxycurcumin (DMC) on a human skin squamous cell carcinoma cell line, A431, and a human keratinocyte cell line, HaCaT. A431 and HaCaT cells were cultured in vitro. The effects of DMC treatment on cell viability were analyzed using the Cell Counting kit­8 (CCK­8) assay; cell cycle distribution was analyzed by flow cytometry; apoptosis was assessed by flow cytometry and Hoechst 33258 staining; and the protein expression levels of cytochrome c, B­cell lymphoma 2 (Bcl­2), Bcl­2­associated X protein (BAX), caspase­9 and caspase­3 were evaluated by western blotting. CCK­8 assay results demonstrated that DMC treatment significantly inhibited viability of A431 and HaCaT cells in a dose­dependent manner. Flow cytometric analysis confirmed that DMC treatment induced apoptosis in a dose­dependent manner, and significantly increased the proportion of cells in G2/M phase. Western blot analysis indicated that the protein expression levels of Bcl­2 were decreased, whereas the expression levels of BAX, caspase­9, caspase­3 and cytochrome c were increased following DMC treatment compared with in untreated cells. In conclusion, DMC treatment significantly inhibited viability of A431 and HaCaT cells, and induced cell cycle arrest in G2/M phase. The present study indicated that DMC may induce apoptosis of skin cancer cells through a caspase­dependent pathway.