FOXOs transcription factors not only play key roles in glucose metabolism, muscle atrophy and energy homeostasis but also play crucial transcriptional regulatory roles in the cell's metabolism, orchestrating programs of gene expression that regulate cell apoptosis, cell-cycle progression and oxidative stress resistance. However, the specific function of FOXOs promoting fibroblasts proliferation and apoptosis are still unknown. Thus, we used the High-Resolution Melting (HRM) and RNA interference methods to detect SNPs and function. We found one SNP in the exon of FOXO1, three SNPs were identified in the exon of FOXO3, and three SNPs and production traits were significantly different. The siRNA sequence of yak FOXO1 and FOXO3 were transfected into the yak fibroblasts, and effects were detected by a series of assays to reveal the function in yak fibroblasts. The results demonstrated that down-regulated expression of FOXO1 and FOXO3 resulted in up-regulated the expression of BAX, Caspase9 and Caspase3, and down-regulated the expression level of anti-apoptotic gene of BCL2. The apoptotic situation was consistent with results of the flow cytometry and Tunel test cell cycle and cell vitality results revealed that knockdown FOXO1 and FOXO3 resulted in increased P27 expression level and decreased CyclinD1. Meanwhile, cell vitality was also decreased. These results demonstrated that FOXO1 and FOXO3 are two novel regulatory factors to suppress cells proliferation and promote cells apoptosis. Furthermore, these results provide evidence that FOXO1 and FOXO3 play a functional role in cell apoptosis.
We first conducted time-series analysis of mono- and dinucleotide composition for over 10,000 SARS-CoV-2 genomes, as well as over 1500 Zaire ebolavirus genomes, and found clear time-series changes in the compositions on a monthly basis, which should reflect viral adaptations for efficient growth in human cells. We next developed a sequence alignment free method that extensively searches for advantageous mutations and rank them in an increase level for their intrapopulation frequency. Time-series analysis of occurrences of oligonucleotides of diverse lengths for SARS-CoV-2 genomes revealed seven distinctive mutations that rapidly expanded their intrapopulation frequency and are thought to be candidates of advantageous mutations for the efficient growth in human cells.
BACKGROUND:The accurate identification of the exon/intron boundaries is critical for the correct annotation of genes with multiple exons. Donor and acceptor splice sites (SS) demarcate these boundaries. Therefore, deriving accurate computational models to predict the SS are useful for functional annotation of genes and genomes, and for finding alternative SS associated with different diseases. Although various models have been proposed for the in silico prediction of SS, improving their accuracy is required for reliable annotation. Moreover, models are often derived and tested using the same genome, providing no evidence of broad application, i.e. to other poorly studied genomes.RESULTS:With this in mind, we developed the Splice2Deep models for SS detection. Each model is an ensemble of deep convolutional neural networks. We evaluated the performance of the models based on the ability to detect SS in Homo sapiens, Oryza sativa japonica, Arabidopsis thaliana, Drosophila melanogaster, and Caenorhabditis elegans. Results demonstrate that the models efficiently detect SS in other organisms not considered during the training of the models. Compared to the state-of-the-art tools, Splice2Deep models achieved significantly reduced average error rates of 41.97% and 28.51% for acceptor and donor SS, respectively. Moreover, the Splice2Deep cross-organism validation demonstrates that models correctly identify conserved genomic elements enabling annotation of SS in new genomes by choosing the taxonomically closest model.CONCLUSIONS:The results of our study demonstrated that Splice2Deep both achieved a considerably reduced error rate compared to other state-of-the-art models and the ability to accurately recognize SS in other organisms for which the model was not trained, enabling annotation of poorly studied or newly sequenced genomes. Splice2Deep models are implemented in Python using Keras API; the models and the data are available at https://github.com/SomayahAlbaradei/Splice_Deep.git.
Mouse Ptchd3 (patched domain containing 3) was previously identified as a male germ-cell specific gene. The protein product of this gene has been found on the surface of mouse, rat and human sperm. Since Ptchd3 contains a conserved patched domain, we hypothesize that it functions as a membrane receptor for the hedgehog ligand. Herein, we used a Ptchd3 knockout mouse model to study its function in mouse development and spermatogenesis. We found that Ptchd3 knockout mice were born and lived normally. The fertility and sperm production of knockout males were not changed. Moreover, our data indicated that the expression levels of several hedgehog signaling genes were not affected in mutant testis. Taken together, these findings demonstrate that Ptchd3 is a non-essential gene in mouse development and spermatogenesis.
BACKGROUND:Human mesenchymal stromal cells (MSCs) phenotypically share their positive expression of the International Society for Cell and Gene Therapy (ISCT) markers CD73, CD90 and CD105 with fibroblasts. Fibroblasts are often co-isolated as an unwanted by-product from biopsy and they can rapidly overgrow the MSCs in culture. Indeed, many other surface markers have been proposed, though no unique MSC specific marker has been identified yet. Quantitative PCR (qPCR) is a precise, efficient and rapid method for gene expression analysis. To identify a marker suitable for accurate MSC characterisation, qPCR was exploited.METHODS AND RESULTS:Two commercially obtained bone marrow (BM) derived MSCs and an hTERT immortalised BM-MSC line (MSC-TERT) have been cultured for different days and at different oxygen levels before RNA extraction. Together with RNA samples previous extracted from umbilical cord derived MSCs and MSC-TERT cells cultured in 2D or 3D, this heterogeneous sample set was quantitatively analysed for the expression levels of 18 candidate MSC marker genes. The expression levels in MSCs were compared with the expression levels in fibroblasts to verify the differentiation capability of these genes between MSCs and fibroblasts. None of the ISCT markers could differentiate between fibroblasts and MSCs. A total of six other genes (ALCAM, CLIC1, EDIL3, EPHA2, NECTIN2, and TMEM47) were identified as possible biomarkers for accurate identification of MSCs.CONCLUSION:Justified by considerations on expression level, reliability and specificity, Activated-Leukocyte Cell Adhesion Molecule (ALCAM) was the best candidate for improving the biomarker set of MSC identification.
