The spatial binding model of the pioneer factor Oct4 with its target genes during cell reprogramming

Identification of DNase I hypersensitive sites in the human genome by multiple sequence descriptors

DNase I hypersensitive sites (DHSs) are chromatin regions highly sensitive to DNase I enzymes. Studying DHSs is crucial for understanding complex transcriptional regulation mechanisms and localizing cis-regulatory elements (CREs). Numerous studies have indicated that disease-related loci are often enriched in DHSs regions, underscoring the importance of identifying DHSs. Although wet experiments exist for DHSs identification, they are often labor-intensive. Therefore, there is a strong need to develop computational methods for this purpose. In this study, we used experimental data to construct a benchmark dataset. Seven feature extraction methods were employed to capture information about human DHSs. The F-score was applied to filter the features. By comparing the prediction performance of various classification algorithms through five-fold cross-validation, random forest was proposed to perform the final model construction. The model could produce an overall prediction accuracy of 0.859 with an AUC value of 0.837. We hope that this model can assist scholars conducting DNase research in identifying these sites.

Highly cooperative chimeric super-SOX induces naive pluripotency across species

Our understanding of pluripotency remains limited: iPSC generation has only been established for a few model species, pluripotent stem cell lines exhibit inconsistent developmental potential, and germline transmission has only been demonstrated for mice and rats. By swapping structural elements between Sox2 and Sox17, we built a chimeric super-SOX factor, Sox2-17, that enhanced iPSC generation in five tested species: mouse, human, cynomolgus monkey, cow, and pig. A swap of alanine to valine at the interface between Sox2 and Oct4 delivered a gain of function by stabilizing Sox2/Oct4 dimerization on DNA, enabling generation of high-quality OSKM iPSCs capable of supporting the development of healthy all-iPSC mice. Sox2/Oct4 dimerization emerged as the core driver of naive pluripotency with its levels diminished upon priming. Transient overexpression of the SK cocktail (Sox+Klf4) restored the dimerization and boosted the developmental potential of pluripotent stem cells across species, providing a universal method for naive reset in mammals.

Deciphering the decisive factors driving fate bifurcations in somatic cell reprogramming

Single-cell studies have demonstrated that somatic cell reprogramming is a continuous process of cell fates transition. Only partial reprogramming intermediates can overcome the molecular bottlenecks to acquire pluripotency. To decipher the underlying decisive factors driving cell fate, we identified induced pluripotent stem cells or stromal-like cells (iPSCs/SLCs) and iPSCs or trophoblast-like cells (iPSCs/TLCs) fate bifurcations by reconstructing cellular trajectory. The mesenchymal-epithelial transition and the activation of pluripotency networks are the main molecular series in successful reprogramming. Correspondingly, intermediates diverge into SLCs accompanied by the inhibition of cell cycle genes and the activation of extracellular matrix genes, whereas the TLCs fate is characterized by the up-regulation of placenta development genes. Combining putative gene regulatory networks, seven (Taf7, Ezh2, Klf2, etc.) and three key factors (Cdc5l, Klf4, and Nanog) were individually identified as drivers of the successful reprogramming by triggering downstream pluripotent networks during iPSCs/SLCs and iPSCs/TLCs fate bifurcation. Conversely, 11 factors (Cebpb, Sox4, Junb, etc.) and four factors (Gata2, Jund, Ctnnb1, etc.) drive SLCs fate and TLCs fate, respectively. Our study sheds new light on the understanding of decisive factors driving cell fate, which is helpful for improving reprogramming efficiency through manipulating cell fates to avoid alternative fates.

Integrated analysis of ovarian cancer patients from prospective transcription factor activity reveals subtypes of prognostic significance

Transcription factors are protein molecules that act as regulators of gene expression. Aberrant protein activity of transcription factors can have a significant impact on tumor progression and metastasis in tumor patients. In this study, 868 immune-related transcription factors were identified from the transcription factor activity profile of 1823 ovarian cancer patients. The prognosis-related transcription factors were identified through univariate Cox analysis and random survival tree analysis, and two distinct clustering subtypes were subsequently derived based on these transcription factors. We assessed the clinical significance and genomics landscape of the two clustering subtypes and found statistically significant differences in prognosis, response to immunotherapy, and chemotherapy among ovarian cancer patients with different subtypes. Multi-scale Embedded Gene Co-expression Network Analysis was used to identify differential gene modules between the two clustering subtypes, which allowed us to conduct further analysis of biological pathways that exhibited significant differences between them. Finally, a ceRNA network was constructed to analyze lncRNA-miRNA-mRNA regulatory pairs with differential expression levels between two clustering subtypes. We expected that our study may provide some useful references for stratifying and treating patients with ovarian cancer.

Characterizing Cellular Differentiation Potency and Waddington Landscape via Energy Indicator

The precise characterization of cellular differentiation potency remains an open question, which is fundamentally important for deciphering the dynamics mechanism related to cell fate transition. We quantitatively evaluated the differentiation potency of different stem cells based on the Hopfield neural network (HNN). The results emphasized that cellular differentiation potency can be approximated by Hopfield energy values. We then profiled the Waddington energy landscape of embryogenesis and cell reprogramming processes. The energy landscape at single-cell resolution further confirmed that cell fate decision is progressively specified in a continuous process. Moreover, the transition of cells from one steady state to another in embryogenesis and cell reprogramming processes was dynamically simulated on the energy ladder. These two processes can be metaphorized as the motion of descending and ascending ladders, respectively. We further deciphered the dynamics of the gene regulatory network (GRN) for driving cell fate transition. Our study proposes a new energy indicator to quantitatively characterize cellular differentiation potency without prior knowledge, facilitating the further exploration of the potential mechanism of cellular plasticity.

Competitive binding of TET1 and DNMT3A/B cooperates the DNA methylation pattern in human embryonic stem cells

DNMT3A/B and TET1 play indispensable roles in regulating DNA methylation that undergoes extensive reprogramming during mammalian embryogenesis. Yet the competitive and cooperative relationships between TET1 and DNMT3A/B remain largely unknown in the human embryonic stem cells. Here, we revealed that the main DNA-binding domain of TET1 contains more positive charges by using charge reduction of amino acid alphabet, followed by DNMT3A and DNMT3B. The genome-wide binding profiles showed that TET1 prefers binding to the proximal promoters and CpG islands compared with DNMT3A/B. Moreover, the binding regions of these three transcription factors can be divided into specific and co-binding regions. And a stronger inhibitory effect of DNMT3A on TET1 demethylation was observed in co-binding regions. Furthermore, we integrated TET1 knockout data to further discuss the competitive binding patterns of TET1 and DNMT3A/B. The lack of TET1 increased the occupation of DNMT3A/B at the specific binding regions of TET1 causing focal hypermethylation. The knockout of TET1 was also accompanied by a reduction of DNMT3A/B binding in the co-binding regions, further confirming the cooperative binding function between TET1 and DNMT3A/B. In conclusion, our studies found that the competitive binding of TET1 and DNMT3A/B cooperatively shapes the global DNA methylation pattern in human embryonic stem cells.

The Cumulative Formation of R-loop Interacts with Histone Modifications to Shape Cell Reprogramming

R-loop, a three-stranded RNA/DNA structure, plays important roles in modulating genome stability and gene expression, but the molecular mechanism of R-loops in cell reprogramming remains elusive. Here, we comprehensively profiled the genome-wide landscape of R-loops during cell reprogramming. The results showed that the R-loop formation on most different types of repetitive elements is stage-specific in cell reprogramming. We unveiled that the cumulative deposition of an R-loop subset is positively correlated with gene expression during reprogramming. More importantly, the dynamic turnover of this R-loop subset is accompanied by the activation of the pluripotent transcriptional regulatory network (TRN). Moreover, the large accumulation of the active histone marker H3K4me3 and the reduction in H3K27me3 were also observed in these R-loop regions. Finally, we characterized the dynamic network of R-loops that facilitates cell fate transitions in reprogramming. Together, our study provides a new clue for deciphering the interplay mechanism between R-loops and HMs to control cell reprogramming.

Identify DNA-Binding Proteins Through the Extreme Gradient Boosting Algorithm

The exploration of DNA-binding proteins (DBPs) is an important aspect of studying biological life activities. Research on life activities requires the support of scientific research results on DBPs. The decline in many life activities is closely related to DBPs. Generally, the detection method for identifying DBPs is achieved through biochemical experiments. This method is inefficient and requires considerable manpower, material resources and time. At present, several computational approaches have been developed to detect DBPs, among which machine learning (ML) algorithm-based computational techniques have shown excellent performance. In our experiments, our method uses fewer features and simpler recognition methods than other methods and simultaneously obtains satisfactory results. First, we use six feature extraction methods to extract sequence features from the same group of DBPs. Then, this feature information is spliced together, and the data are standardized. Finally, the extreme gradient boosting (XGBoost) model is used to construct an effective predictive model. Compared with other excellent methods, our proposed method has achieved better results. The accuracy achieved by our method is 78.26% for PDB2272 and 85.48% for PDB186. The accuracy of the experimental results achieved by our strategy is similar to that of previous detection methods.

WGBS combined with RNA-seq analysis revealed that Dnmt1 affects the methylation modification and gene expression changes during mouse oocyte vitrification

Understanding the molecular level changes of oocyte cryopreservation and the subsequent warming process is essential for improving the oocyte cryopreservation technologies. Here, we collected the mature metaphase II (MII) oocytes from mice and vitrified. After thawing, single-cell whole-genome bisulphite sequencing (scWGBS) and single-cell RNA sequencing (scRNA-seq) were used to investigate the molecular attributes of this process. Compared to the fresh oocytes, the vitrified oocytes had lower global methylation and gene expression levels, and 1426 genes up-regulated and 3321 genes down-regulated. The 1426 up-regulated differentially expressed genes (DEGs) in the vitrified oocytes were mainly associated with the histone ubiquitination, while the 3321 down-regulated genes were mainly enriched in the mitochondrion organisation and ATP metabolism processes. The differentially methylated regions (DMRs) were mainly located in promoter, intron and exon region of genes, and the length of DMRs in the vitrified oocytes were also significantly lower than that of the fresh oocytes. Notably, there were no significant difference in the expression levels of DNA demethylases (Tet1, Tet2 and Tet3) and methyltransferases (Dnmt3a and Dnmt3b) between two treatments of oocytes. However, Dnmt1 and kcnq1ot1, which are responsible for maintaining DNA methylation, were significantly down regulated in the vitrified oocytes. Gene regulatory network (GRN) analysis showed the Dnmt1 and kcnq1ot1 play a core role in regulating methylation and expression levels of downstream genes. Moreover, some genes associated with oocyte quality were significantly down-regulated in the vitrified oocytes. The present data provides a new perspective for understanding the impact of vitrification on oocytes.

Nuclear Transfer Arrest Embryos Show Massive Dysregulation of Genes Involved in Transcription Pathways

Somatic cell nuclear transfer (SCNT) technology can reprogram terminally differentiated cell nuclei into a totipotent state. However, the underlying molecular barriers of SCNT embryo development remain incompletely elucidated. Here, we observed that transcription-related pathways were incompletely activated in nuclear transfer arrest (NTA) embryos compared to normal SCNT embryos and in vivo fertilized (WT) embryos, which hinders the development of SCNT embryos. We further revealed the transcription pathway associated gene regulatory networks (GRNs) and found the aberrant transcription pathways can lead to the massive dysregulation of genes in NTA embryos. The predicted target genes of transcription pathways contain a series of crucial factors in WT embryos, which play an important role in catabolic process, pluripotency regulation, epigenetic modification and signal transduction. In NTA embryos, however, these genes were varying degrees of inhibition and show a defect in synergy. Overall, our research found that the incomplete activation of transcription pathways is another potential molecular barrier for SCNT embryos besides the incomplete reprogramming of epigenetic modifications, broadening the understanding of molecular mechanism of SCNT embryonic development.

Sequence-Based Deep Learning Frameworks on Enhancer-Promoter Interactions Prediction

Enhancer-promoter interactions (EPIs) in the human genome are of great significance to transcriptional regulation, which tightly controls gene expression. Identification of EPIs can help us better decipher gene regulation and understand disease mechanisms. However, experimental methods to identify EPIs are constrained by funds, time, and manpower, while computational methods using DNA sequences and genomic features are viable alternatives. Deep learning methods have shown promising prospects in classification and efforts that have been utilized to identify EPIs. In this survey, we specifically focus on sequence-based deep learning methods and conduct a comprehensive review of the literature. First, we briefly introduce existing sequence- based frameworks on EPIs prediction and their technique details. After that, we elaborate on the dataset, pre-processing means, and evaluation strategies. Finally, we concluded with the challenges these methods are confronted with and suggest several future opportunities. We hope this review will provide a useful reference for further studies on enhancer-promoter interactions.

iDHS-Deep: an integrated tool for predicting DNase I hypersensitive sites by deep neural network

DNase I hypersensitive site (DHS) refers to the hypersensitive region of chromatin for the DNase I enzyme. It is an important part of the noncoding region and contains a variety of regulatory elements, such as promoter, enhancer, and transcription factor-binding site, etc. Moreover, the related locus of disease (or trait) are usually enriched in the DHS regions. Therefore, the detection of DHS region is of great significance. In this study, we develop a deep learning-based algorithm to identify whether an unknown sequence region would be potential DHS. The proposed method showed high prediction performance on both training datasets and independent datasets in different cell types and developmental stages, demonstrating that the method has excellent superiority in the identification of DHSs. Furthermore, for the convenience of related wet-experimental researchers, the user-friendly web-server iDHS-Deep was established at http://lin-group.cn/server/iDHS-Deep/, by which users can easily distinguish DHS and non-DHS and obtain the corresponding developmental stage ofDHS.

Modular arrangements of sequence motifs determine the functional diversity of KDM proteins

Histone lysine demethylases (KDMs) play a vital role in regulating chromatin dynamics and transcription. KDM proteins are given modular activities by its sequence motifs with obvious roles division, which endow the complex and diverse functions. In our review, according to functional features, we classify sequence motifs into four classes: catalytic motifs, targeting motifs, regulatory motifs and potential motifs. JmjC, as the main catalytic motif, combines to Fe2+ and α-ketoglutarate by residues H-D/E-H and S-N-N/Y-K-N/Y-T/S. Targeting motifs make catalytic motifs recognize specific methylated lysines, such as PHD that helps KDM5 to demethylate H3K4me3. Regulatory motifs consist of a functional network. For example, NLS, Ser-rich, TPR and JmjN motifs regulate the nuclear localization. And interactions through the CW-type-C4H2C2-SWIRM are necessary to the demethylase activity of KDM1B. Additionally, many conservative domains that have potential functions but no deep exploration are reviewed for the first time. These conservative domains are usually amino acid-rich regions, which have great research value. The arrangements of four types of sequence motifs generate that KDM proteins diversify toward modular activities and biological functions. Finally, we draw a blueprint of functional mechanisms to discuss the modular activity of KDMs.

Characterization of DNA Methylation Patterns and Mining of Epigenetic Markers During Genomic Reprogramming in SCNT Embryos

Somatic cell nuclear transfer (SCNT), also known as somatic cell cloning, is a commonly used technique to study epigenetic reprogramming. Although SCNT has the advantages of being safe and able to obtain pluripotent cells, early developmental arrest happens in most SCNT embryos. Overcoming epigenetic barriers is currently the primary strategy for improving reprogramming efficiency and improving developmental rate in SCNT embryos. In this study, we analyzed DNA methylation profiles of in vivo fertilized embryos and SCNT embryos with different developmental fates. Overall DNA methylation level was higher in SCNT embryos during global de-methylation process compared to in vivo fertilized embryos. In addition, promoter region, first intron and 3′UTR were found to be the major genomic regions that were hyper-methylated in SCNT embryos. Surprisingly, we found the length of re-methylated region was directly related to the change of methylation level. Furthermore, a number of genes including Dppa2 and Dppa4 which are important for early zygotic genome activation (ZGA) were not properly activated in SCNT embryos. This study comprehensively analyzed genome-wide DNA methylation patterns in SCNT embryos and provided candidate target genes for improving efficiency of genomic reprogramming in SCNT embryos. Since SCNT technology has been widely used in agricultural and pastoral production, protection of endangered animals, and therapeutic cloning, the findings of this study have significant importance for all these fields.

Predicting Preference of Transcription Factors for Methylated DNA Using Sequence Information

Transcription factors play key roles in cell-fate decisions by regulating 3D genome conformation and gene expression. The traditional view is that methylation of DNA hinders transcription factors binding to them, but recent research has shown that many transcription factors prefer to bind to methylated DNA. Therefore, identifying such transcription factors and understanding their functions is a stepping-stone for studying methylation-mediated biological processes. In this paper, a two-step discriminated method was proposed to recognize transcription factors and their preference for methylated DNA based only on sequences information. In the first step, the proposed model was used to discriminate transcription factors from non-transcription factors. The areas under the curve (AUCs) are 0.9183 and 0.9116, respectively, for the 5-fold cross-validation test and independent dataset test. Subsequently, for the classification of transcription factors that prefer methylated DNA and transcription factors that prefer non-methylated DNA, our model could produce the AUCs of 0.7744 and 0.7356, respectively, for the 5-fold cross-validation test and independent dataset test. Based on the proposed model, a user-friendly web server called TFPred was built, which can be freely accessed at http://lin-group.cn/server/TFPred/.

Machine Learning of Single-Cell Transcriptome Highly Identifies mRNA Signature by Comparing F-Score Selection with DGE Analysis

Human preimplantation development is a complex process involving dramatic changes in transcriptional architecture. For a better understanding of their time-spatial development, it is indispensable to identify key genes. Although the single-cell RNA sequencing (RNA-seq) techniques could provide detailed clustering signatures, the identification of decisive factors remains difficult. Additionally, it requires high experimental cost and a long experimental period. Thus, it is highly desired to develop computational methods for identifying effective genes of development signature. In this study, we first developed a predictor called EmPredictor to identify developmental stages of human preimplantation embryogenesis. First, we compared the F-score of feature selection algorithms with differential gene expression (DGE) analysis to find specific signatures of the development stage. In addition, by training the support vector machine (SVM), four types of signature subsets were comprehensively discussed. The prediction results showed that a feature subset with 1,881 genes from the F-score algorithm obtained the best predictive performance, which achieved the highest accuracy of 93.3% on the cross-validation set. Further function enrichment demonstrated that the gene set selected by the feature selection method was involved in more development-related pathways and cell fate determination biomarkers. This indicates that the F-score algorithm should be preferentially proposed for detecting key genes of multi-period data in mammalian early development.

Computational identification of N6-methyladenosine sites in multiple tissues of mammals

N6-methyladenosine (m6A) is the methylation of the adenosine at the nitrogen-6 position, which is the most abundant RNA methylation modification and involves a series of important biological processes. Accurate identification of m6A sites in genome-wide is invaluable for better understanding their biological functions. In this work, an ensemble predictor named iRNA-m6A was established to identify m6A sites in multiple tissues of human, mouse and rat based on the data from high-throughput sequencing techniques. In the proposed predictor, RNA sequences were encoded by physical-chemical property matrix, mono-nucleotide binary encoding and nucleotide chemical property. Subsequently, these features were optimized by using minimum Redundancy Maximum Relevance (mRMR) feature selection method. Based on the optimal feature subset, the best m6A classification models were trained by Support Vector Machine (SVM) with 5-fold cross-validation test. Prediction results on independent dataset showed that our proposed method could produce the excellent generalization ability. We also established a user-friendly webserver called iRNA-m6A which can be freely accessible at http://lin-group.cn/server/iRNA-m6A. This tool will provide more convenience to users for studying m6A modification in different tissues.

Immune cell infiltration-based signature for prognosis and immunogenomic analysis in breast cancer

Breast cancer is one of the most human malignant diseases and the leading cause of cancer-related death in the world. However, the prognostic and therapeutic benefits of breast cancer patients cannot be predicted accurately by the current stratifying system. In this study, an immune-related prognostic score was established in 22 breast cancer cohorts with a total of 6415 samples. An extensive immunogenomic analysis was conducted to explore the relationships between immune score, prognostic significance, infiltrating immune cells, cancer genotypes and potential immune escape mechanisms. Our analysis revealed that this immune score was a promising biomarker for estimating overall survival in breast cancer. This immune score was associated with important immunophenotypic factors, such as immune escape and mutation load. Further analysis revealed that patients with high immune scores exhibited therapeutic benefits from chemotherapy and immunotherapy. Based on these results, we can conclude that this immune score may be a useful tool for overall survival prediction and treatment guidance for patients with breast cancer.

A Comparative Analysis of Single-Cell Transcriptome Identifies Reprogramming Driver Factors for Efficiency Improvement

Terminally differentiated somatic cells can be reprogrammed into a totipotent state through somatic cell nuclear transfer (SCNT). The incomplete reprogramming is the major reason for developmental arrest of SCNT embryos at early stages. In our studies, we found that pathways for autophagy, endocytosis, and apoptosis were incompletely activated in nuclear transfer (NT) 2-cell arrest embryos, whereas extensively inhibited pathways for stem cell pluripotency maintenance, DNA repair, cell cycle, and autophagy may result in NT 4-cell embryos arrest. As for NT normal embryos, a significant shift in expression of developmental transcription factors (TFs) Id1, Pou6f1, Cited1, and Zscan4c was observed. Compared with pluripotent gene Ascl2 being activated only in NT 2-cell, Nanog, Dppa2, and Sall4 had major expression waves in normal development of both NT 2-cell and 4-cell embryos. Additionally, Kdm4b/4d and Kdm5b had been confirmed as key markers in NT 2-cell and 4-cell embryos, respectively. Histone acetylases Kat8, Elp6, and Eid1 were co-activated in NT 2-cell and 4-cell embryos to facilitate normal development. Gadd45a as a key driver functions with Tet1 and Tet2 to improve the efficiency of NT reprogramming. Taken together, our findings provided an important theoretical basis for elucidating the potential molecular mechanisms and identified reprogramming driver factor to improve the efficiency of SCNT reprogramming.