Evolutionary Bioinformatics最新文献

Protein-protein interactions (PPIs) in plants are essential for understanding the regulation of biological processes. Although high-throughput technologies have been widely used to identify PPIs, they are usually laborious, expensive, and suffer from high false-positive rates. Therefore, it is imperative to develop novel computational approaches as a supplement tool to detect PPIs in plants. In this work, we presented a method, namely DST-RoF, to identify PPIs in plants by combining an ensemble learning classifier-Rotation Forest (RoF) with discrete sine transformation (DST). Specifically, plant protein sequence is firstly converted into Position-Specific Scoring Matrix (PSSM). Then, the discrete sine transformation was employed to extract effective features for obtaining the evolutionary information of proteins. Finally, these optimal features were fed into the RoF classifier for training and prediction. When performed on the plant datasets Arabidopsis, Rice, and Maize, DST-RoF yielded high prediction accuracy of 82.95%, 88.82%, and 93.70%, respectively. To further evaluate the prediction ability of our approach, we compared it with 4 state-of-the-art classifiers and 3 different feature extraction methods. Comprehensive experimental results anticipated that our method is feasible and robust for predicting potential plant-protein interacted pairs.

SARS-CoV-2 needs to efficiently make use of the resources from hosts in order to survive and propagate. Among the multiple layers of regulatory network, mRNA translation is the rate-limiting step in gene expression. Synonymous codon usage usually conforms with tRNA concentration to allow fast decoding during translation. It is acknowledged that SARS-CoV-2 has adapted to the codon usage of human lungs so that the virus could rapidly proliferate in the lung environment. While this notion seems to nicely explain the adaptation of SARS-CoV-2 to lungs, it is unable to tell why other viruses do not have this advantage. In this study, we retrieve the GTEx RNA-seq data for 30 tissues (belonging to over 17 000 individuals). We calculate the RSCU (relative synonymous codon usage) weighted by gene expression in each human sample, and investigate the correlation of RSCU between the human tissues and SARS-CoV-2 or RaTG13 (the closest coronavirus to SARS-CoV-2). Lung has the highest correlation of RSCU to SARS-CoV-2 among all tissues, suggesting that the lung environment is generally suitable for SARS-CoV-2. Interestingly, for most tissues, SARS-CoV-2 has higher correlations with the human samples compared with the RaTG13-human correlation. This difference is most significant for lungs. In conclusion, the codon usage of SARS-CoV-2 has adapted to human lungs to allow fast decoding and translation. This adaptation probably took place after SARS-CoV-2 split from RaTG13 because RaTG13 is less perfectly correlated with human. This finding depicts the trajectory of adaptive evolution from ancestral sequence to SARS-CoV-2, and also well explains why SARS-CoV-2 rather than other viruses could perfectly adapt to human lung environment.

Atherosclerosis is a multifaceted disease characterized by the formation and accumulation of plaques that attach to arteries and cause cardiovascular disease and vascular embolism. A range of diagnostic techniques, including selective coronary angiography, stress tests, computerized tomography, and nuclear scans, assess cardiovascular disease risk and treatment targets. However, there is currently no simple blood biochemical index or biological target for the diagnosis of atherosclerosis. Therefore, it is of interest to find a biochemical blood marker for atherosclerosis. Three datasets from the Gene Expression Omnibus (GEO) database were analyzed to obtain differentially expressed genes (DEG) and the results were integrated using the Robustrankaggreg algorithm. The genes considered more critical by the Robustrankaggreg algorithm were put into their own data set and the data set system with cell classification information for verification. Twenty-one possible genes were screened out. Interestingly, we found a good correlation between RPS4Y1, EIF1AY, and XIST. In addition, we know the general expression of these genes in different cell types and whole blood cells. In this study, we identified BTNL8 and BLNK as having good clinical significance. These results will contribute to the analysis of the underlying genes involved in the progression of atherosclerosis and provide insights for the discovery of new diagnostic and evaluation methods.

[This corrects the article DOI: 10.1177/1176934320901721.].

The CCAAT/enhancer binding protein (C/EBP) transcription factors (TFs) regulate many important biological processes, such as energy metabolism, inflammation, cell proliferation etc. A genome-wide gene identification revealed the presence of a total of 99 C/EBP genes in pig and 19 eukaryote genomes. Phylogenetic analysis showed that all C/EBP TFs were classified into 6 subgroups named C/EBPα, C/EBPβ, C/EBPδ, C/EBPε, C/EBPγ, and C/EBPζ. Gene expression analysis showed that the C/EBPα, C/EBPβ, C/EBPδ, C/EBPγ, and C/EBPζ genes were expressed ubiquitously with inconsistent expression patterns in various pig tissues. Moreover, a pig C/EBP regulatory network was constructed, including C/EBP genes, TFs and miRNAs. A total of 27 feed-forward loop (FFL) motifs were detected in the pig C/EBP regulatory network. Based on the RNA-seq data, gene expression patterns related to FFL sub-network were analyzed in 27 adult pig tissues. Certain FFL motifs may be tissue specific. Functional enrichment analysis indicated that C/EBP and its target genes are involved in many important biological pathways. These results provide valuable information that clarifies the evolutionary relationships of the C/EBP family and contributes to the understanding of the biological function of C/EBP genes.

In recent times, diverse agriculturally important endophytic bacteria colonizing plant endosphere have been identified. Harnessing the potential of Bacillus species from sunflower could reveal their biotechnological and agricultural importance. Here, we present genomic insights into B. cereus T4S isolated from sunflower sourced from Lichtenburg, South Africa. Genome analysis revealed a sequence read count of 7 255 762, a genome size of 5 945 881 bp, and G + C content of 34.8%. The genome contains various protein-coding genes involved in various metabolic pathways. The detection of genes involved in the metabolism of organic substrates and chemotaxis could enhance plant-microbe interactions in the synthesis of biological products with biotechnological and agricultural importance.

In humans, taste genes are responsible for perceiving at least 5 different taste qualities. Human taste genes' evolutionary mechanisms need to be explored. We compiled a list of 69 human taste-related genes and divided them into 7 functional groups. We carried out comparative genomic and evolutionary analyses for these taste genes based on 8 vertebrate species. We found that relative to other groups of human taste genes, human TAS2R genes have a higher proportion of tandem duplicates, suggesting that tandem duplications have contributed significantly to the expansion of the human TAS2R gene family. Human TAS2R genes tend to have fewer collinear genes in outgroup species and evolve faster, suggesting that human TAS2R genes have experienced more gene relocations. Moreover, human TAS2R genes tend to be under more relaxed purifying selection than other genes. Our study sheds new insights into diverse and contrasting evolutionary patterns among human taste genes.

The RNA G-quadruplex (rG4) is a kind of non-canonical high-order secondary structure with important biological functions and is enriched in untranslated regions (UTRs) of protein-coding genes. However, how rG4 structures evolve is largely unknown. Here, we systematically investigated the evolution of RNA sequences around UTR rG4 structures in 5 eukaryotic organisms. We found universal selection on UTR sequences, which facilitated rG4 formation in all the organisms that we analyzed. While G-rich sequences were preferred in the rG4 structural region, C-rich sequences were selectively not preferred. The selective pressure acting on rG4 structures in the UTRs of genes with higher G content was significantly smaller. Furthermore, we found that rG4 structures experienced smaller evolutionary selection near the translation initiation region in the 5' UTR, near the polyadenylation signals in the 3' UTR, and in regions flanking the miRNA targets in the 3' UTR. These results suggest universal selection for rG4 formation in the UTRs of eukaryotic genomes and the selection may be related to the biological functions of rG4s.

Gefitinib resistance is a serious threat in the treatment of patients with non-small cell lung cancer (NSCLC). Elucidating the underlying mechanisms and developing effective therapies to overcome gefitinib resistance is urgently needed. The differentially expressed genes (DEGs) were screened from the gene expression profile GSE122005 between gefitinib-sensitive and resistant samples. GO and KEGG analyses were performed with DAVID. The protein-protein interaction (PPI) network was established to visualize DEGs and screen hub genes. The functional roles of CCL20 in lung adenocarcinoma (LUAD) were examined using gene set enrichment analysis (GSEA). Functional analysis revealed that the DEGs were mainly concentrated in inflammatory, cell chemotaxis, and PI3K signal regulation. Ten hub genes were identified based on the PPI network. The survival analysis of the hub genes showed that CCL20 had a significant effect on the prognosis of LUAD patients. GSEA analysis showed that CCL20 high expression group was mainly enriched in cytokine-related signaling pathways. In conclusion, our analysis suggests that changes in inflammation and cytokine-related signaling pathways are closely related to gefitinib resistance in patients with lung cancer. The CCL20 gene may promote the formation of gefitinib resistance, which may serve as a new biomarker for predicting gefitinib resistance in patients with lung cancer.