Generation of rat forebrain tissues in mice.

Interspecies blastocyst complementation (IBC) provides a unique platform to study development and holds the potential to overcome worldwide organ shortages. Despite recent successes, brain tissue has not been achieved through IBC. Here, we developed an optimized IBC strategy based on C-CRISPR, which facilitated rapid screening of candidate genes and identified that Hesx1 deficiency supported the generation of rat forebrain tissue in mice via IBC. Xenogeneic rat forebrain tissues in adult mice were structurally and functionally intact. Cross-species comparative analyses revealed that rat forebrain tissues developed at the same pace as the mouse host but maintained rat-like transcriptome profiles. The chimeric rate of rat cells gradually decreased as development progressed, suggesting xenogeneic barriers during mid-to-late pre-natal development. Interspecies forebrain complementation opens the door for studying evolutionarily conserved and divergent mechanisms underlying brain development and cognitive function. The C-CRISPR-based IBC strategy holds great potential to broaden the study and application of interspecies organogenesis.

Modeling post-implantation stages of human development into early organogenesis with stem-cell-derived peri-gastruloids.

In vitro stem cell models that replicate human gastrulation have been generated, but they lack the essential extraembryonic cells needed for embryonic development, morphogenesis, and patterning. Here, we describe a robust and efficient method that prompts human extended pluripotent stem cells to self-organize into embryo-like structures, termed peri-gastruloids, which encompass both embryonic (epiblast) and extraembryonic (hypoblast) tissues. Although peri-gastruloids are not viable due to the exclusion of trophoblasts, they recapitulate critical stages of human peri-gastrulation development, such as forming amniotic and yolk sac cavities, developing bilaminar and trilaminar embryonic discs, specifying primordial germ cells, initiating gastrulation, and undergoing early neurulation and organogenesis. Single-cell RNA-sequencing unveiled transcriptomic similarities between advanced human peri-gastruloids and primary peri-gastrulation cell types found in humans and non-human primates. This peri-gastruloid platform allows for further exploration beyond gastrulation and may potentially aid in the development of human fetal tissues for use in regenerative medicine.

HBV-infected hepatocellular carcinoma can be robustly classified into three clinically relevant subgroups by a novel analytical protocol.

Liver cancer is the third leading cause of cancer-related death worldwide, and hepatocellular carcinoma (HCC) accounts for a relatively large proportion of all primary liver malignancies. Among the several known risk factors, hepatitis B virus (HBV) infection is one of the important causes of HCC. In this study, we demonstrated that the HBV-infected HCC patients could be robustly classified into three clinically relevant subgroups, i.e. Cluster1, Cluster2 and Cluster3, based on consistent differentially expressed mRNAs and proteins, which showed better generalization. The proposed three subgroups showed different molecular characteristics, immune microenvironment and prognostic survival characteristics. The Cluster1 subgroup had near-normal levels of metabolism-related proteins, low proliferation activity and good immune infiltration, which were associated with its good liver function, smaller tumor size, good prognosis, low alpha-fetoprotein (AFP) levels and lower clinical stage. In contrast, the Cluster3 subgroup had the lowest levels of metabolism-related proteins, which corresponded with its severe liver dysfunction. Also, high proliferation activity and poor immune microenvironment in Cluster3 subgroup were associated with its poor prognosis, larger tumor size, high AFP levels, high incidence of tumor thrombus and higher clinical stage. The characteristics of the Cluster2 subgroup were between the Cluster1 and Cluster3 groups. In addition, MCM2-7, RFC2-5, MSH2, MSH6, SMC2, SMC4, NCPAG and TOP2A proteins were significantly upregulated in the Cluster3 subgroup. Meanwhile, abnormally high phosphorylation levels of these proteins were associated with high levels of DNA repair, telomere maintenance and proliferative features. Therefore, these proteins could be identified as potential diagnostic and prognostic markers. In general, our research provided a novel analytical protocol and insights for the robust classification, treatment and prevention of HBV-infected HCC.

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.

Bovine blastocyst-like structures derived from stem cell cultures.

Understanding the mechanisms of blastocyst formation and implantation is critical for improving farm animal reproduction but is hampered by a limited supply of embryos. Here, we developed an efficient method to generate bovine blastocyst-like structures (termed blastoids) via assembling bovine trophoblast stem cells and expanded potential stem cells. Bovine blastoids resemble blastocysts in morphology, cell composition, single-cell transcriptomes, in vitro growth, and the ability to elicit maternal recognition of pregnancy following transfer to recipient cows. Bovine blastoids represent an accessible in vitro model for studying embryogenesis and improving reproductive efficiency in livestock species.

Pan-Cancer Single-Cell Analysis Reveals the Core Factors and Pathway in Specific Cancer Stem Cells of Upper Gastrointestinal Cancer.

Upper gastrointestinal cancer (UGIC) is an aggressive carcinoma with increasing incidence and poor outcomes worldwide. Here, we collected 39,057 cells, and they were annotated into nine cell types. By clustering cancer stem cells (CSCs), we discovered the ubiquitous existence of sub-cluster CSCs in all UGICs, which is named upper gastrointestinal cancer stem cells (UGCSCs). The identification of UGCSC function is coincident with the carcinogen of UGICs. We compared the UGCSC expression profile with 215,291 single cells from six other cancers and discovered that UGCSCs are specific tumor stem cells in UGIC. Exploration of the expression network indicated that inflammatory genes (CXCL8, CXCL3, PIGR, and RNASE1) and Wnt pathway genes (GAST, REG1A, TFF3, and ZG16B) are upregulated in tumor stem cells of UGICs. These results suggest a new mechanism for carcinogenesis in UGIC: mucosa damage and repair caused by poor eating habits lead to chronic inflammation, and the persistent chronic inflammation triggers the Wnt pathway; ultimately, this process induces UGICs. These findings establish the core signal pathway that connects poor eating habits and UGIC. Our system provides deeper insights into UGIC carcinogens and a platform to promote gastrointestinal cancer diagnosis and therapy.

A meta-learning approach to improving radiation response prediction in cancers.

PURPOSE: Predicting the efficacy of radiotherapy in individual patients has drawn widespread attention, but the limited sample size remains a bottleneck for utilizing high-dimensional multi-omics data to guide personalized radiotherapy. We hypothesize the recently developed meta-learning framework could address this limitation. METHODS AND MATERIALS: By combining gene expression, DNA methylation, and clinical data of 806 patients who had received radiotherapy from The Cancer Genome Atlas (TCGA), we applied the Model-Agnostic Meta-Learning (MAML) framework to tasks consisting of pan-cancer data, to obtain the best initial parameters of a neural network for a specific cancer with smaller number of samples. The performance of meta-learning framework was compared with four traditional machine learning methods based on two training schemes, and tested on Cancer Cell Line Encyclopedia (CCLE) and Chinese Glioma Genome Atlas (CGGA) datasets. Moreover, biological significance of the models was investigated by survival analysis and feature interpretation. RESULTS: The mean AUC (Area under the ROC Curve) [95% confidence interval] of our models across nine cancer types was 0.702 [0.691-0.713], which improved by 0.166 on average over other the four machine learning methods on two training schemes. Our models performed significantly better (p < 0.05) in seven cancer types and performed comparable to the other predictors in the rest of two cancer types. The more pan-cancer samples were used to transfer meta-knowledge, the greater the performance improved (p < 0.05). The predicted response scores that our models generated were negatively correlated with cell radiosensitivity index in four cancer types (p < 0.05), while not statistically significant in the other three cancer types. Moreover, the predicted response scores were shown to be prognostic factors in seven cancer types and eight potential radiosensitivity-related genes were identified. CONCLUSIONS: For the first time, we established the meta-learning approach to improving individual radiation response prediction by transferring common knowledge from pan-cancer data with MAML framework. The results demonstrated the superiority, generalizability, and biological significance of our approach.

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.

ATD: a comprehensive bioinformatics resource for deciphering the association of autophagy and diseases.

Autophagy is the natural, regulated, destructive mechanism of the eukaryotes cell that disassembles unnecessary or dysfunctional components. In recent years, the association between autophagy and diseases has attracted more and more attention, but our understanding of the molecular mechanism about the association in the system perspective is limited and ambiguous. Hence, we developed the comprehensive bioinformatics resource Autophagy To Disease (ATD, http://auto2disease.nwsuaflmz.com) to archive autophagy-associated diseases. This resource provides bioinformatics annotation system about genes and chemicals about autophagy and human diseases by extracting results from previous studies with text mining technology. Based on the big data from ATD, we found that some classes of disease tend to be related with autophagy, including respiratory disease, cancer, urogenital disease and digestive system disease. We also found that some classes of autophagy-related diseases have a strong association among each other and constitute modules. Furthermore, we extracted the autophagy-disease-related genes (ADGs) from ATD and provided a novel algorithm Optimized Random Forest with Label model to predict potential ADGs. This bioinformatics annotation system about autophagy and human diseases may provide a basic resource for the further detection of the molecular mechanisms of autophagy pathway to disease.

Genome-wide targets identification of "core" pluripotency transcription factors with integrated features in human embryonic stem cells.

Embryonic stem cells (ESCs) play an important role in developmental biology which is still lacking clear molecular mechanisms. The "core" transcription factors (TFs) including OCT4, SOX2 and NANOG are essential for maintaining the stemness of ESCs. But the downstream targets of these "core" TFs are still ambiguous. Based on support vector machine (SVM) technology, this study develops a label method algorithm (LMA) for genome-wide target identification of "core" TFs in humans, which eliminates the need for negative training samples. This method integrates histone modifications and TF binding motifs as identification features. Compared with a previous mapping-convergence (M-C) algorithm, the LMA can provide more stable and reliable predictions. 4796, 3166 and 4384 target genes of OCT4, SOX2 and NANOG, respectively, were identified with the LMA model. Then verifications of the predicted targets were carried out based on their functional consistency and their connection degree in networks from a computational system biology perspective. The results showed that the targets of "core" TFs present higher gene functional similarity and closer connection distance than background levels.

Synergistic transcriptional and post-transcriptional regulation of ESC characteristics by core pluripotency transcription factors in protein-protein interaction networks.

The molecular mechanism that maintains the pluripotency of embryonic stem cells (ESCs) is not well understood but may be reflected in complex biological networks. However, there have been few studies on the effects of transcriptional and post-transcriptional regulation during the development of ESCs from the perspective of computational systems biology. In this study, we analyzed the topological properties of the "core" pluripotency transcription factors (TFs) OCT4, SOX2 and NANOG in protein-protein interaction networks (PPINs). Further, we identified synergistic interactions between these TFs and microRNAs (miRNAs) in PPINs during ESC development. Results show that there were significant differences in centrality characters between TF-targets and non-TF-targets in PPINs. We also found that there was consistent regulation of multiple "core" pluripotency TFs. Based on the analysis of shortest path length, we found that the module properties were not only within the targets regulated by common or multiple "core" pluripotency TFs but also between the groups of targets regulated by different TFs. Finally, we identified synergistic regulation of these TFs and miRNAs. In summary, the synergistic effects of "core" pluripotency TFs and miRNAs were analyzed using computational methods in both human and mouse PPINs.