Evolutionary Bioinformatics最新文献

Infections caused by multidrug-resistant (MDR) Pseudomonas aeruginosa and Klebsiella pneumoniae are a serious worldwide public health concern due to the ineffectiveness of empirical antibiotic therapy. Therefore, research and the development of new antibiotic alternatives are urgently needed to control these bacteria. The use of cationic antimicrobial peptides (CAMPs) is a promising candidate alternative therapeutic strategy to antibiotics because they exhibit antibacterial activity against both antibiotic susceptible and MDR strains. In this study, we aimed to investigate the in vitro antibacterial effect of a short synthetic CAMP derived from the ΔM2 analog of Cec D-like (CAMP-CecD) against clinical isolates of K pneumoniae (n = 30) and P aeruginosa (n = 30), as well as its hemolytic activity. Minimal inhibitory concentrations (MICs) and minimal bactericidal concentrations (MBCs) of CAMP-CecD against wild-type and MDR strains were determined by the broth microdilution test. In addition, an in silico molecular dynamic simulation was performed to predict the interaction between CAMP-CecD and membrane models of K pneumoniae and P aeruginosa. The results revealed a bactericidal effect of CAMP-CecD against both wild-type and resistant strains, but MDR P aeruginosa showed higher susceptibility to this peptide with MIC values between 32 and >256 μg/mL. CAMP-CecD showed higher stability in the P aeruginosa membrane model compared with the K pneumoniae model due to the greater number of noncovalent interactions with phospholipid 1-Palmitoyl-2-oleyl-sn-glycero-3-(phospho-rac-(1-glycerol)) (POPG). This may be related to the boosted effectiveness of the peptide against P aeruginosa clinical isolates. Given the antibacterial activity of CAMP-CecD against wild-type and MDR clinical isolates of P aeruginosa and K pneumoniae and its nonhemolytic effects on human erythrocytes, CAMP-CecD may be a promising alternative to conventional antibiotics.

Computational prediction of gene-gene associations is one of the productive directions in the study of bioinformatics. Many tools are developed to infer the relation between genes using different biological data sources. The association of a pair of genes deduced from the analysis of biological data becomes meaningful when it reflects the directionality and the type of reaction between genes. In this work, we follow another method to construct a causal gene co-expression network while identifying transcription factors in each pair of genes using microarray expression data. We adopt a machine learning technique based on a logistic regression model to tackle the sparsity of the network and to improve the quality of the prediction accuracy. The proposed system classifies each pair of genes into either connected or nonconnected class using the data of the correlation between these genes in the whole Saccharomyces cerevisiae genome. The accuracy of the classification model in predicting related genes was evaluated using several data sets for the yeast regulatory network. Our system achieves high performance in terms of several statistical measures.

The 3 biological domains delineated based on small subunit ribosomal RNAs (SSU rRNAs) are confronted by uncertainties regarding the relationship between Archaea and Bacteria, and the origin of Eukarya. The similarities between the paralogous valyl-tRNA and isoleucyl-tRNA synthetases in 5398 species estimated by BLASTP, which decreased from Archaea to Bacteria and further to Eukarya, were consistent with vertical gene transmission from an archaeal root of life close to Methanopyrus kandleri through a Primitive Archaea Cluster to an Ancestral Bacteria Cluster, and to Eukarya. The predominant similarities of the ribosomal proteins (rProts) of eukaryotes toward archaeal rProts relative to bacterial rProts established that an archaeal parent rather than a bacterial parent underwent genome merger with bacteria to generate eukaryotes with mitochondria. Eukaryogenesis benefited from the predominantly archaeal accelerated gene adoption (AGA) phenotype pertaining to horizontally transferred genes from other prokaryotes and expedited genome evolution via both gene-content mutations and nucleotidyl mutations. Archaeons endowed with substantial AGA activity were accordingly favored as candidate archaeal parents. Based on the top similarity bitscores displayed by their proteomes toward the eukaryotic proteomes of Giardia and Trichomonas, and high AGA activity, the Aciduliprofundum archaea were identified as leading candidates of the archaeal parent. The Asgard archaeons and a number of bacterial species were among the foremost potential contributors of eukaryotic-like proteins to Eukarya.

Transposable elements (TEs) are known to play a role in genome evolution, gene regulation, and epigenetics, representing potential tools for genetics research in and breeding of conifers. Recently, thanks to the development of high-throughput sequencing, more conifer genomes have been reported. Using bioinformatics tools, the TEs of 3 important conifers (Picea abies, Picea glauce, and Pinus taeda) were identified in our previous study, which provided a foundation for accelerating the use of TEs in conifer breeding and genetic study. Here, we review recent studies on the functional biology of TEs and discuss the potential applications for TEs in conifers.

Pseudouridine (Ψ) is the first discovered and the most prevalent posttranscriptional modification, which has been widely studied during the past decades. Pseudouridine was observed in almost all kinds of RNAs and shown to have important biological functions. Currently, the time-consuming and high-cost procedures of experimental approaches limit its uses in real-life Ψ site detection. Alternatively, by taking advantage of the explosive growth of Ψ sequencing data, the computational methods may provide a more cost-effective avenue. To date, the existing mouse Ψ site predictors were all developed based on sequence-derived features, and their performance can be further improved by adding the domain knowledge derived feature. Therefore, it is highly desirable to propose a genomic feature-based computational method to increase the accuracy and efficiency of the identification of Ψ RNA modification in the mouse transcriptome. In our study, a predictive framework PSI-MOUSE was built. Besides the conventional sequence-based features, PSI-MOUSE first introduced 38 additional genomic features derived from the mouse genome, which achieved a satisfactory improvement in the prediction performance, compared with other existing models. Moreover, PSI-MOUSE also features in automatically annotating the putative Ψ sites with diverse types of posttranscriptional regulations (RNA-binding protein [RBP]-binding regions, miRNA-RNA interactions, and splicing sites), which can serve as a useful research tool for the study of Ψ RNA modification in the mouse genome. Finally, 3282 experimentally validated mouse Ψ sites were also collected in a database with customized query functions. For the convenience of academic users, a website was built to provide a user-friendly interface for the query and analysis on the database. The website is freely accessible at www.xjtlu.edu.cn/biologicalsciences/psimouse and http://psimouse.rnamd.com. We introduced the genome-derived features to mouse for the first time, and we achieved a good performance in mouse Ψ site prediction. Compared with the existing state-of-art methods, our newly developed approach PSI-MOUSE obtained a substantial improvement in prediction accuracy, marking the reliable contributions of genomic features for the prediction of RNA modifications in a species other than human.

Self-interacting proteins (SIPs) play crucial roles in biological activities of organisms. Many high-throughput methods can be used to identify SIPs. However, these methods are both time-consuming and expensive. How to develop effective computational approaches for identifying SIPs is a challenging task. In the article, we present a novel computational method called RRN-SIFT, which combines the recurrent neural network (RNN) with scale invariant feature transform (SIFT) to predict SIPs based on protein evolutionary information. The main advantage of the proposed RNN-SIFT model is that it uses SIFT for extracting key feature by exploring the evolutionary information embedded in Position-Specific Iterated BLAST-constructed position-specific scoring matrix and employs an RNN classifier to perform classification based on extracted features. Extensive experiments show that the RRN-SIFT obtained average accuracy of 94.34% and 97.12% on the yeast and human dataset, respectively. We also compared our performance with the back propagation neural network (BPNN), the state-of-the-art support vector machine (SVM), and other existing methods. By comparing with experimental results, the performance of RNN-SIFT is significantly better than that of the BPNN, SVM, and other previous methods in the domain. Therefore, we conclude that the proposed RNN-SIFT model is a useful tool for predicting SIPs, as well to solve other bioinformatics tasks. To facilitate widely studies and encourage future proteomics research, a freely available web server called RNN-SIFT-SIPs was developed at http://219.219.62.123:8888/RNNSIFT/ including the source code and the SIP datasets.

Tyrosine kinases (TKs) play key roles in the regulation of multicellularity in organisms and involved primarily in cell growth, differentiation, and cell-to-cell communication. Genome-wide characterization of TKs has been conducted in many metazoans; however, systematic information regarding this superfamily in Electrophorus electricus (electric eel) is still lacking. In this study, we identified 114 TK genes in the E electricus genome and investigated their evolution, molecular features, and domain architecture using phylogenetic profiling to gain a better understanding of their similarities and specificity. Our results suggested that the electric eel TK (EeTK) repertoire was shaped by whole-genome duplications (WGDs) and tandem duplication events. Compared with other vertebrate TKs, gene members in Jak, Src, and EGFR subfamily duplicated specifically, but with members lost in Eph, Axl, and Ack subfamily in electric eel. We also conducted an exhaustive survey of TK genes in genomic databases, identifying 1674 TK proteins in 31 representative species covering all the main metazoan lineages. Extensive evolutionary analysis indicated that TK repertoire in vertebrates tended to be remarkably conserved, but the gene members in each subfamily were very variable. Comparative expression profile analysis showed that electric organ tissues and muscle shared a similar pattern with specific highly expressed TKs (ie, epha7, musk, jak1, and pdgfra), suggesting that regulation of TKs might play an important role in specifying an electric organ identity from its muscle precursor. We further identified TK genes exhibiting tissue-specific expression patterns, indicating that members in TKs participated in subfunctionalization representing an evolutionary divergence required for the performance of different tissues. This work generates valuable information for further gene function analysis and identifying candidate TK genes reflecting their unique tissue-function specializations in electric eel.

This study was aimed at revealing the dynamic regulation of mRNAs, long noncoding RNAs (lncRNAs), and microRNAs (miRNAs) in hepatocellular carcinoma (HCC) and to identify HCC biomarkers capable of predicting prognosis. Differentially expressed mRNAs (DEmRNAs), lncRNAs, and miRNAs were acquired by comparing expression profiles of HCC with normal samples, using an expression data set from The Cancer Genome Atlas. Altered biological functions and pathways in HCC were analyzed by subjecting DEmRNAs to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis. Gene modules significantly associated with disease status were identified by weighted gene coexpression network analysis. An lncRNA-mRNA and an miRNA-mRNA coexpression network were constructed for genes in disease-related modules, followed by the identification of prognostic biomarkers using Kaplan-Meier survival analysis. Differential expression and association with the prognosis of 4 miRNAs were verified in independent data sets. A total of 1220 differentially expressed genes were identified between HCC and normal samples. Differentially expressed mRNAs were significantly enriched in functions and pathways related to "plasma membrane structure," "sensory perception," "metabolism," and "cell proliferation." Two disease-associated gene modules were identified. Among genes in lncRNA-mRNA and miRNA-mRNA coexpression networks, 9 DEmRNAs and 7 DEmiRNAs were identified to be potential prognostic biomarkers. MIMAT0000102, MIMAT0003882, and MIMAT0004677 were successfully validated in independent data sets. Our results may advance our understanding of molecular mechanisms underlying HCC. The biomarkers may contribute to diagnosis in future clinical practice.

Despite advances in the treatment of cervical cancer (CC), the prognosis of patients with CC remains to be improved. This study aimed to explore candidate gene targets for CC. CC datasets were downloaded from the Gene Expression Omnibus database. Genes with similar expression trends in varying steps of CC development were clustered using Short Time-series Expression Miner (STEM) software. Gene functions were then analyzed using the Gene Ontology (GO) database and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Protein interactions among genes of interest were predicted, followed by drug-target genes and prognosis-associated genes. The expressions of the predicted genes were determined using real-time quantitative polymerase chain reaction (RT-qPCR) and Western blotting. Red and green profiles with upward and downward gene expressions, respectively, were screened using STEM software. Genes with increased expression were significantly enriched in DNA replication, cell-cycle-related biological processes, and the p53 signaling pathway. Based on the predicted results of the Drug-Gene Interaction database, 17 drug-gene interaction pairs, including 3 red profile genes (TOP2A, RRM2, and POLA1) and 16 drugs, were obtained. The Cancer Genome Atlas data analysis showed that high POLA1 expression was significantly correlated with prolonged survival, indicating that POLA1 is protective against CC. RT-qPCR and Western blotting showed that the expressions of TOP2A, RRM2, and POLA1 gradually increased in the multistep process of CC. TOP2A, RRM2, and POLA1 may be targets for the treatment of CC. However, many studies are needed to validate our findings.

MicroRNAs (miRNAs) are small single-stranded noncoding RNAs that have shown to play a critical role in regulating gene expression. In past decades, cumulative experimental studies have verified that miRNAs are implicated in many complex human diseases and might be potential biomarkers for various types of diseases. With the increase of miRNA-related data and the development of analysis methodologies, some computational methods have been developed for predicting miRNA-disease associations, which are more economical and time-saving than traditional biological experimental approaches. In this study, a novel computational model, deep belief network (DBN)-based matrix factorization (DBN-MF), is proposed for miRNA-disease association prediction. First, the raw interaction features of miRNAs and diseases were obtained from the miRNA-disease adjacent matrix. Second, 2 DBNs were used for unsupervised learning of the features of miRNAs and diseases, respectively, based on the raw interaction features. Finally, a classifier consisting of 2 DBNs and a cosine score function was trained with the initial weights of DBN from the last step. During the training, the miRNA-disease adjacent matrix was factorized into 2 feature matrices for the representation of miRNAs and diseases, and the final prediction label was obtained according to the feature matrices. The experimental results show that the proposed model outperforms the state-of-the-art approaches in miRNA-disease association prediction based on the 10-fold cross-validation. Besides, the effectiveness of our model was further demonstrated by case studies.