FifBase: a comprehensive fertility-associated indicators factor database for domestic animals.

Fertility refers to the ability of animals to maintain reproductive function and give birth to offspring, which is an important indicator to measure the productivity of animals. Fertility is affected by many factors, among which environmental factors may also play key roles. During the past years, substantial research studies have been conducted to detect the factors related to fecundity, including genetic factors and environmental factors. However, the identified genes associated with fertility from countless previous studies are randomly dispersed in the literature, whereas some other novel fertility-related genes are needed to detect from omics-based datasets. Here, we constructed a fertility index factor database FifBase based on manually curated published literature and RNA-Seq datasets. During the construction of the literature group, we obtained 3301 articles related to fecundity for 13 species from PubMed, involving 2823 genes, which are related to 75 fecundity indicators or 47 environmental factors. Eventually, 1558 genes associated with fertility were filtered in 10 species, of which 1088 and 470 were from RNA-Seq datasets and text mining data, respectively, involving 2910 fertility-gene pairs and 58 fertility-environmental factors. All these data were cataloged into FifBase (http://www.nwsuaflmz.com/FifBase/), where the fertility-related factor information, including gene annotation and environmental factors, can be browsed, retrieved and downloaded with the user-friendly interface.

CellSim: a novel software to calculate cell similarity and identify their co-regulation networks.

BACKGROUND: Cell direct reprogramming technology has been rapidly developed with its low risk of tumor risk and avoidance of ethical issues caused by stem cells, but it is still limited to specific cell types. Direct reprogramming from an original cell to target cell type needs the cell similarity and cell specific regulatory network. The position and function of cells in vivo, can provide some hints about the cell similarity. However, it still needs further clarification based on molecular level studies. RESULT: CellSim is therefore developed to offer a solution for cell similarity calculation and a tool of bioinformatics for researchers. CellSim is a novel tool for the similarity calculation of different cells based on cell ontology and molecular networks in over 2000 different human cell types and presents sharing regulation networks of part cells. CellSim can also calculate cell types by entering a list of genes, including more than 250 human normal tissue specific cell types and 130 cancer cell types. The results are shown in both tables and spider charts which can be preserved easily and freely. CONCLUSION: CellSim aims to provide a computational strategy for cell similarity and the identification of distinct cell types. Stable CellSim releases (Windows, Linux, and Mac OS/X) are available at: www.cellsim.nwsuaflmz.com , and source code is available at: https://github.com/lileijie1992/CellSim/ .

Dynamic and modular gene regulatory networks drive the development of gametogenesis.

Gametogenesis is a complex process, which includes mitosis and meiosis and results in the production of ovum and sperm. The development of gametogenesis is dynamic and needs many different genes to work synergistically, but it is lack of global perspective research about this process. In this study, we detected the dynamic process of gametogenesis from the perspective of systems biology based on protein-protein interaction networks (PPINs) and functional analysis. Results showed that gametogenesis genes have strong synergistic effects in PPINs within and between different phases during the development. Addition to the synergistic effects on molecular networks, gametogenesis genes showed functional consistency within and between different phases, which provides the further evidence about the dynamic process during the development of gametogenesis. At last, we detected and provided the core molecular modules of different phases about gametogenesis. The gametogenesis genes and related modules can be obtained from our Web site Gametogenesis Molecule Online (GMO, http://gametsonline.nwsuaflmz.com/index.php), which is freely accessible. GMO may be helpful for the reference and application of these genes and modules in the future identification of key genes about gametogenesis. Summary, this work provided a computational perspective and frame to the analysis of the gametogenesis dynamics and modularity in both human and mouse.

miRNA editing landscape reveals miR-34c regulated spermatogenesis through structure and target change in pig and mouse.

Spermatogenesis has a close relationship with male infertility. MicroRNAs (miRNAs) play crucial roles in their regulation of target genes during spermatogenesis. A huge dataset of high-throughput sequencing all over the world provides the basis to dig the cryptic molecular mechanism. But how to take advantage of the big data and unearth the miRNA regulation is still a challenging problem. Here we integrated transcriptome of spermatogenesis and found miRNA regulate spermatogenesis through miRNA editing. We then compared different species and found that the distributions of miRNA editing site number and editing types among different cell types during spermatogenesis are conservative. Interesting, we further found that nearly half of the editing events occurred in the seed region in both mouse and pig. Finally, we foundmiR-34c, which is edited frequently at all stages during spermatogenesis, regulates its target genes through the RNA structure changing and shows dysfunction when it is edited. Summary, we depicted the overall profile of miRNA editing during spermatogenesis in mouse and pig and reveal miR-34c may play its roles through miRNA editing.

Profiling of miRNAs in porcine germ cells during spermatogenesis.

Spermatogenesis includes mitosis of spermatogonia, meiosis of pachytene spermatocytes and spermiogenesis of round spermatids. MiRNAs as a ~22 nt small noncoding RNA are involved in regulating spermatogenesis at post-transcriptional level. However, the dynamic miRNAs expression in the developmental porcine male germ cells remains largely undefined. In this study, we purified porcine spermatogonia, pachytene spermatocytes and round spermatids using a STA-PUT apparatus. A small RNA deep sequencing and analysis were conducted to establish a miRNAs profiling in these male germ cells. We found that 19 miRNAs were differentially expressed between spermatogonia and pachytene spermatocytes, and 74 miRNAs differentially expressed between pachytene spermatocytes and round spermatids. Furthermore, 91 miRNAs were upregulated, while 108 miRNAs were downregulated in spermatozoa. We demonstrated that ssc-miR-10a-5p, ssc-miR-125b, ssc-let-7f and ssc-miR-186 were highly expressed in spermatogonia, pachytene spermatocytes, round spermatids and spermatozoa respectively. The findings could provide novel insights into roles of miRNAs in regulation of porcine spermatogenesis.

The Clinical Implications of Tumor Mutational Burden in Osteosarcoma.

BACKGROUND: Osteosarcoma (OTS) is aggressive bone malignancy without well-recognized prognosis biomarker. Tumor mutational burden (TMB) has been proved as effective biomarker in predicting clinical outcomes in several cancer types. However, its prognostic value in OTS remains unknown. In this study, we aim to evaluate the implication of TMB in OTS patients. METHODS: To depict the landscape of somatic mutations in OTS, we performed Whole-Exome Sequencing (WES) on 31 OTS tissue samples and corresponding White Blood Cells (WBCs) as matched control. TMB was calculated as the total number of somatic alterations in coding regions normalized to the per sequenced genomic megabase (~30.4Mb in WES). The prognostic values of TMB were evaluated by Kaplan-Meier methods and Cox regression models. RESULTS: The median age was 16.0 years at diagnosis, and 54.8% of patients were male. The most common genetic alterations were mainly involved in cell cycle and DNA damage response and repair, including H3F3A, TP53, MYC, and CDKN2A/B. The median progression-free survival (PFS) was 775.5 days in TMB-High (defined as third quartile of TMB value, <2.565) versus 351 days in TMB-Low (<2.565). All patients with TMB-High are PFS-Long (>400 days), while 36.4% of all patients with TMB-Low were PFS-Long (P=0.003). TMB were significantly greater in PFS-Long than in PFS-Short (<400 days) (P=0.002). Moreover, the median overall survival (OS) was 1,307 days in TMB-High versus 672.5 days in TMB-Low. Furthermore, TMB-High group had significantly improved PFS (P=0.04) and OS (P=0.03). CONCLUSIONS: TMB-High can be used as prognostic marker for OTS. Our findings demonstrate that TMB may be helpful in combination with traditionally clinicopathologic risk factors to optimize risk stratification and guide treatment decisions.

Enhancer recognition and prediction during spermatogenesis based on deep convolutional neural networks.

MOTIVATION: enhancers play an important role in the regulation of gene expression during spermatogenesis. The development of ChIP-Chip and ChIP-Seq sequencing technology has enabled researchers to focus on the relationship between enhancers and DNA sequences and histone protein modifications. However, the prediction of enhancers based on the locally conserved DNA sequence and similar histone modification features is still unknown. Here, the present study proposed a convolutional neural network (CNN) model to predict enhancers that can regulate gene expression during spermatogenesis. RESULTS: we have obtained a positive set of enhancers using the P300 locus, verified by experiments, while a negative set was constructed using the promoter as a non-enhancer locus. The model was trained on all types of specific cells during spermatogenesis independently, and the transfer learning strategy was used to fine-tune the model based on which the model can be trained and adapted to other cells quickly. We visualized the convolution layer of the trained model and aligned the predicted enhancer with the JASPAR database. The results showed that the model was highly matched with some important transcription factors during spermatogenesis, signifying the reliability of the model. Finally, we compared the CNN algorithm with the gkmSVM algorithm (Support Vector Machine). It is well known that CNN has better performance than the gkmSVM algorithm, especially in the generalization ability. Our work demonstrated their strong learning ability and the low CPU requirements for the experiment, with a small number of convolution layers and simple network structure, while avoiding overfitting the training data. At the end of the experiment, we used the trained model to build an enhancer recognition website for further research and communication.

Landscape of RNA editing reveals new insights into the dynamic gene regulation of spermatogenesis.

Spermatogenesis is an important physiological process associated with male infertility. As a kind of post-transcriptional regulation, RNA editings (REs) change the genetic information at the mRNA level. But whether there are REs and what's the role of REs during the process are still unclear. In this study, we integrated published RNA-Seq datasets and established a landscape of RNA REs during the development of mouse spermatogenesis. Totally, 7530 editing sites occurred in 2012 genes among all types of male germ cells were found, these sites enrich on some regions of chromosomes, including chromosome 17 and both ends of chromosome Y. We also found about half of the REs in CDSs can cause amino acids changes. Some non-synonymous REs which exist in specific genes may play important roles in spermatogenesis. Finally, we verified a non-synonymous A-to-I RNA editing site in Cog3 and a stoploss editing in Tssk6 during spermatogenesis. In short, we systematically analyzed the dynamic landscape of RNA editing at different stages of spermatogenesis.