HPTRMF: Collaborative Matrix Factorization-Based Prediction Method for LncRNA-Disease Associations Using High-Order Perturbation and Flexible Trifactor Regularization.

Existing matrix factorization methods face challenges, including the cold start problem and global nonlinear data loss during similarity learning, particularly in predicting associations between long noncoding RNAs (LncRNAs) and diseases. To overcome these issues, we introduce HPTRMF, a matrix factorization approach incorporating high-order perturbation and flexible trifactor regularization. HPTRMF constructs a high-order correlation matrix utilizing the known association matrix, leveraging high-order perturbation to effectively address the cold start problem caused by data sparsity. Additionally, HPTRMF incorporates a flexible trifactor regularization term to capture similarity information on LncRNAs and diseases, enabling the effective handling of global nonlinear data loss by capturing such data in the similarity matrix. Experimental results demonstrate the superiority of HPTRMF over nine state-of-the-art algorithms in Leave-One-Out Cross-Validation (LOOCV) and Five-Fold Cross-Validation (5-Fold CV) on three data sets.HPTRMF and data sets are available in https://github.com/Llvvvv/HPTRMF.

RNA m6A modification regulates cell fate transition between pluripotent stem cells and 2-cell-like cells.

N6-methyladenosine (m6A) exerts essential roles in early embryos, especially in the maternal-to-zygotic transition stage. However, the landscape and roles of RNA m6A modification during the transition between pluripotent stem cells and 2-cell-like (2C-like) cells remain elusive. Here, we utilised ultralow-input RNA m6A immunoprecipitation to depict the dynamic picture of transcriptome-wide m6A modifications during 2C-like transitions. We found that RNA m6A modification was preferentially enriched in zygotic genome activation (ZGA) transcripts and MERVL with high expression levels in 2C-like cells. During the exit of the 2C-like state, m6A facilitated the silencing of ZGA genes and MERVL. Notably, inhibition of m6A methyltransferase METTL3 and m6A reader protein IGF2BP2 is capable of significantly delaying 2C-like state exit and expanding 2C-like cells population. Together, our study reveals the critical roles of RNA m6A modification in the transition between 2C-like and pluripotent states, facilitating the study of totipotency and cell fate decision in the future.

Being a right parent: a narrative review of the theory and practice of parental involvement in sport parenting.

The family is the first classroom for children and adolescents to learn and grow, and parents' behavior plays an important role in influencing their children's development, which is also evident in the process of sport participation. The main purpose of this study is to summarise the specific theoretical and practical experiences of parents in sport parenting based on a comprehensive review of the types and functions that constitute parental involvement in sport parenting and the process of their practice. To this end, this study used narrative research as the main research method and searched the literature related to parents' involvement in parenting through sport using the Web of Science database. Using the theoretical underpinnings of parents' implementation of sport parenting and their role practice, studies were screened and 39 pieces of literature were finally obtained. The study found that in terms of theoretical underpinnings, the existing types of parental involvement in sport parenting can be broadly categorized into four types: authoritative, authoritarian, permissive and rejecting-neglecting. The functions of parental involvement in sport education have two dimensions: promoting sport development and promoting socialization. Based on a review of their theories, we further summarise and conclude the consequences of action and appropriate practices of parental practices in three scenarios: on the sports field, on the way home and in the private space. It is assumed that parents, when participating in sports parenting, need to: (I) regulate their own behavior in order to avoid psychological pressure on their children due to inappropriate behavior; (II) play different roles at different stages of their children's sports development; (III) should not put too much pressure on their children's performance. Based on these reviews of the theory and practice of parental involvement in sport parenting, this study further examines the theoretical limitations of the established research. It is argued that future research should pay attention to the differences between the identities and expectations of parents or children of different genders about their sport parenting, in addition to the differences in parental involvement in sport parenting and different practices in different cultural contexts.

Elucidation of the Reversible Self-Association Interface of a Diabody-Interleukin Fusion Protein Using Hydrogen-Exchange Mass Spectrometry and In Silico Modeling.

Reversible self-association (RSA) of therapeutic proteins presents major challenges in the development of high-concentration formulations, especially those intended for subcutaneous administration. Understanding self-association mechanisms is therefore critical to the design and selection of candidates with acceptable developability to advance to clinical trials. The combination of experiments and in silico modeling presents a powerful tool to elucidate the interface of self-association. RSA of monoclonal antibodies has been studied extensively under different solution conditions and have been shown to involve interactions for both the antigen-binding fragment and the crystallizable fragment. Novel modalities such as bispecific antibodies, antigen-binding fragments, single-chain-variable fragments, and diabodies constitute a fast-growing class of antibody-based therapeutics that have unique physiochemical properties compared to monoclonal antibodies. In this study, the RSA interface of a diabody-interleukin 22 fusion protein (FP-1) was studied using hydrogen-deuterium exchange coupled with mass spectrometry (HDX-MS) in combination with in silico modeling. Taken together, the results show that a complex solution behavior underlies the self-association of FP-1 and that the interface thereof can be attributed to a specific segment in the variable light chain of the diabody. These findings also demonstrate that the combination of HDX-MS with in silico modeling is a powerful tool to guide the design and candidate selection of novel biotherapeutic modalities.

4 mC site recognition algorithm based on pruned pre-trained DNABert-Pruning model and fused artificial feature encoding.

DNA 4 mC plays a crucial role in the genetic expression process of organisms. However, existing deep learning algorithms have shortcomings in the ability to represent DNA sequence features. In this paper, we propose a 4 mC site identification algorithm, DNABert-4mC, based on a fusion of the pruned pre-training DNABert-Pruning model and artificial feature encoding to identify 4 mC sites. The algorithm prunes and compresses the DNABert model, resulting in the pruned pre-training model DNABert-Pruning. This model reduces the number of parameters and removes redundancy from output features, yielding more precise feature representations while upholding accuracy.Simultaneously, the algorithm constructs an artificial feature encoding module to assist the DNABert-Pruning model in feature representation, effectively supplementing the information that is missing from the pre-trained features. The algorithm also introduces the AFF-4mC fusion strategy, which combines artificial feature encoding with the DNABert-Pruning model, to improve the feature representation capability of DNA sequences in multi-semantic spaces and better extract 4 mC sites and the distribution of nucleotide importance within the sequence. In experiments on six independent test sets, the DNABert-4mC algorithm achieved an average AUC value of 93.81%, outperforming seven other advanced algorithms with improvements of 2.05%, 5.02%, 11.32%, 5.90%, 12.02%, 2.42% and 2.34%, respectively.

Predicting Microbe-Disease Associations Based on a Linear Neighborhood Label Propagation Method with Multi-order Similarity Fusion Learning.

Computational approaches employed for predicting potential microbe-disease associations often rely on similarity information between microbes and diseases. Therefore, it is important to obtain reliable similarity information by integrating multiple types of similarity information. However, existing similarity fusion methods do not consider multi-order fusion of similarity networks. To address this problem, a novel method of linear neighborhood label propagation with multi-order similarity fusion learning (MOSFL-LNP) is proposed to predict potential microbe-disease associations. Multi-order fusion learning comprises two parts: low-order global learning and high-order feature learning. Low-order global learning is used to obtain common latent features from multiple similarity sources. High-order feature learning relies on the interactions between neighboring nodes to identify high-order similarities and learn deeper interactive network structures. Coefficients are assigned to different high-order feature learning modules to balance the similarities learned from different orders and enhance the robustness of the fusion network. Overall, by combining low-order global learning with high-order feature learning, multi-order fusion learning can capture both the shared and unique features of different similarity networks, leading to more accurate predictions of microbe-disease associations. In comparison to six other advanced methods, MOSFL-LNP exhibits superior prediction performance in the leave-one-out cross-validation and 5-fold validation frameworks. In the case study, the predicted 10 microbes associated with asthma and type 1 diabetes have an accuracy rate of up to 90% and 100%, respectively.

Prediction of miRNA-disease associations based on strengthened hypergraph convolutional autoencoder.

Most existing graph neural network-based methods for predicting miRNA-disease associations rely on initial association matrices to pass messages, but the sparsity of these matrices greatly limits performance. To address this issue and predict potential associations between miRNAs and diseases, we propose a method called strengthened hypergraph convolutional autoencoder (SHGAE). SHGAE leverages multiple layers of strengthened hypergraph neural networks (SHGNN) to obtain robust node embeddings. Within SHGNN, we design a strengthened hypergraph convolutional network module (SHGCN) that enhances original graph associations and reduces matrix sparsity. Additionally, SHGCN expands node receptive fields by utilizing hyperedge features as intermediaries to obtain high-order neighbor embeddings. To improve performance, we also incorporate attention-based fusion of self-embeddings and SHGCN embeddings. SHGAE predicts potential miRNA-disease associations using a multilayer perceptron as the decoder. Across multiple metrics, SHGAE outperforms other state-of-the-art methods in five-fold cross-validation. Furthermore, we evaluate SHGAE on colon and lung neoplasms cases to demonstrate its ability to predict potential associations. Notably, SHGAE also performs well in the analysis of gastric neoplasms without miRNA associations.

A vector projection similarity-based method for miRNA-disease association prediction.

[S U M M A R Y] Many miRNA-disease association prediction models incorporate Gaussian interaction profile kernel similarity (GIPS). However, the GIPS fails to consider the specificity of the miRNA-disease association matrix, where matrix elements with a value of 0 represent miRNA and disease relationships that have not been discovered yet. To address this issue and better account for the impact of known and unknown miRNA-disease associations on similarity, we propose a method called vector projection similarity-based method for miRNA-disease association prediction (VPSMDA). In VPSMDA, we introduce three projection rules and combined with logistic functions for the miRNA-disease association matrix and propose a vector projection similarity measure for miRNAs and diseases. By integrating the vector projection similarity matrix with the original one, we obtain the improved miRNA and disease similarity matrix. Additionally, we construct a weight matrix using different numbers of neighbors to reduce the noise in the similarity matrix. In performance evaluation, both LOOCV and 5-fold CV experiments demonstrate that VPSMDA outperforms seven other state-of-the-art methods in AUC. Furthermore, in a case study, VPSMDA successfully predicted 10, 9, and 10 out of the top 10 associations for three important human diseases, respectively, and these predictions were confirmed by recent biomedical resources.

Research on a collaborative evolution model of multi-airport route network considering subsidy strategies.

To efficiently utilize subsidy strategies for optimizing multi-airport route networks and promoting collaborative development among multiple airports, we delve into the tripartite strategic interactions between passengers, airlines and airports. A dual-layer game-theoretic model is constructed to optimize subsidy strategies, facilitating a synergistic alignment between multi-airport positioning and route networks. In the upper-layer game-theoretic model, Fermi rules are employed to analyze the interplay between pricing strategies of distinct airline brands and passenger travel preferences, aiding in determining optimal pricing strategies for airlines. The lower-layer game-theoretic model introduces an asymmetric stochastic best response equilibrium (QRE) model, drawing insights from optimal airline pricing and the impact of airport subsidies on airline route adjustments to formulate effective multi-airport subsidy strategies. The results reveal that: (ⅰ) Airline revenues display varying peaks based on travel distances, with optimal fare discount intervals clustering between 0.6 and 0.9, contingent upon travel distances and passenger rationality; (ⅱ) dynamic monopolistic intervals and inefficient ranges characterize airport subsidy strategies due to diverse competitive strategies employed by rivals; (ⅲ) targeted airport subsidy strategies can enhance inter-airport route coordination in alignment with their functional positioning. This research provides decision-making insights into collaborative airport group development, encompassing airport subsidy strategies and considerations for airline pricing.

Improving diagnostic efficacy of primary prostate cancer with combined 99mTc-PSMA SPECT/CT and multiparametric-MRI and quantitative parameters.

Purpose: This prospective study aimed to evaluate the difference between 99mTc-PSMA single-photon emission computed tomography (SPECT)/CT and multiparametric magnetic resonance imaging (mpMRI) in the detection of primary prostate cancer (PCa). Materials and methods: Fifty-six men with suspected PCa between October 2019 and November 2022 were prospectively enrolled in this study. The median age of the patients was 70 years (range, 29-87 years). Patients were divided into high-(Gleason score>7, n=31), medium- (Gleason score=7, n=6) and low-risk groups (Gleason score < 7, n=6). All patients underwent 99mTc-PSMA SPECT/CT and mpMRI at an average interval of 3 days (range, 1-7 days). The maximum standardized uptake value (SUVmax), the minimum apparent diffusion coefficient (ADCmin), and their ratio (SUVmax/ADCmin) were used as imaging parameters to distinguish benign from malignant prostatic lesions. Results: Of the 56 patients, 12 were pathologically diagnosed with a benign disease, and 44 were diagnosed with PCa. 99mTc-PSMA SPECT/CT and mpMRI showed no significant difference in the detection of primary PCa (kappa =0.401, P=0.002), with sensitivities of 97.7% (43/44) and 90.9% (40/44), specificities of 75.0% (9/12) and 75.0% (9/12), and AUC of 97.4% and 95.1%, respectively. The AUC of SUVmax/ADCmin was better than those of SUVmax or ADCmin alone. When SUVmax/ADCmin in the prostatic lesion was >7.0×103, the lesion was more likely to be malignant. When SUVmax/ADCmin in the prostatic lesion is >27.0×103, the PCa patient may have lymph node and bone metastases. SUVmax was positively correlated with the Gleason score (r=0.61, P=0.008), whereas ADCmin was negatively correlated with the Gleason score (r=-0.35, P=0.023). SUVmax/ADCmin was positively correlated with the Gleason score (r=0.59, P=0.023). SUVmax/ADCmin was the main predictor of the high-risk group, with an optimal cut-off value of 15.0×103. Conclusions: The combination of 99mTc-PSMA SPECT/CT and mpMRI can improve the diagnostic efficacy for PCa compared with either modality alone; SUVmax/ADCmin is a valuable differential diagnostic imaging parameter.

LUNCRW: Prediction of potential lncRNA-disease associations based on unbalanced neighborhood constraint random walk.

Accumulating evidence suggests that long non-coding RNAs (lncRNAs) are associated with various complex human diseases. They can serve as disease biomarkers and hold considerable promise for the prevention and treatment of various diseases. The traditional random walk algorithms generally exclude the effect of non-neighboring nodes on random walking. In order to overcome the issue, the neighborhood constraint (NC) approach is proposed in this study for regulating the direction of the random walk by computing the effects of both neighboring nodes and non-neighboring nodes. Then the association matrix is updated by matrix multiplication for minimizing the effect of the false negative data. The heterogeneous lncRNA-disease network is finally analyzed using an unbalanced random walk method for predicting the potential lncRNA-disease associations. The LUNCRW model is therefore developed for predicting potential lncRNA-disease associations. The area under the curve (AUC) values of the LUNCRW model in leave-one-out cross-validation and five-fold cross-validation were 0.951 and 0.9486 ± 0.0011, respectively. Data from published case studies on three diseases, including squamous cell carcinoma, hepatocellular carcinoma, and renal cell carcinoma, confirmed the predictive potential of the LUNCRW model. Altogether, the findings indicated that the performance of the LUNCRW method is superior to that of existing methods in predicting potential lncRNA-disease associations.

Genome-wide identification of the AcMADS-box family and functional validation of AcMADS32 involved in carotenoid biosynthesis in Actinidia.

MADS-box is a large transcription factor family in plants and plays a crucial role in various plant developmental processes; however, it has not been systematically analyzed in kiwifruit. In the present study, 74 AcMADS genes were identified in the Red5 kiwifruit genome, including 17 type-I and 57 type-II members according to the conserved domains. The AcMADS genes were randomly distributed across 25 chromosomes and were predicted to be mostly located in the nucleus. A total of 33 fragmental duplications were detected in the AcMADS genes, which might be the main force driving the family expansion. Many hormone-associated cis-acting elements were detected in the promoter region. Expression profile analysis showed that AcMADS members had tissue specificity and different responses to dark, low temperature, drought, and salt stress. Two genes in the AG group, AcMADS32 and AcMADS48, had high expression levels during fruit development, and the role of AcMADS32 was further verified by stable overexpression in kiwifruit seedlings. The content of α-carotene and the ratio of zeaxanthin/ß-carotene was increased in transgenic kiwifruit seedlings, and the expression level of AcBCH1/2 was significantly increased, suggesting that AcMADS32 plays an important role in regulating carotenoid accumulation. These results have enriched our understanding of the MADS-box gene family and laid a foundation for further research of the functions of its members during kiwifruit development.

AcMADS32 positively regulates carotenoid biosynthesis in kiwifruit by activating AcBCH1/2 expression.

Fruits provide abundant carotenoid nutrients for humans, whereas the understanding of the transcriptional regulatory mechanisms of carotenoids in fruits is still limited. Here, we identified a transcription factor AcMADS32 in kiwifruit, which was highly expressed in the fruit, correlated with carotenoid content and localized in the nucleus. The silencing expression of AcMADS32 significantly reduced the content of ß-carotene and zeaxanthin and expression of ß-carotene hydroxylase gene AcBCH1/2 in kiwifruit, while transient overexpression increased the accumulation of zeaxanthin, suggesting that AcMADS32 was an activator involved in the transcriptional regulation of carotenoid in fruit. When AcMADS32 was further stably transformed into kiwifruit, the content of total carotenoid and components in the leaves of transgenic lines significantly increased, and the expression level of carotenogenic genes was up-regulated. Moreover, Y1H and dual luciferase reporter experiments confirmed that AcMADS32 directly bound the AcBCH1/2 promoter and activated its expression. Through Y2H assays, AcMADS32 can interact with other MADS transcription factor AcMADS30, AcMADS64 and AcMADS70. These findings will contribute to our understanding of the transcriptional regulation mechanisms underlying carotenoid biosynthesis in plants.

Image Adversarial Example Generation Method Based on Adaptive Parameter Adjustable Differential Evolution.

Adversarial example generation techniques for neural network models have exploded in recent years. In the adversarial attack scheme for image recognition models, it is challenging to achieve a high attack success rate with very few pixel modifications. To address this issue, this paper proposes an adversarial example generation method based on adaptive parameter adjustable differential evolution. The method realizes the dynamic adjustment of the algorithm performance by adjusting the control parameters and operation strategies of the adaptive differential evolution algorithm, while searching for the optimal perturbation. Finally, the method generates adversarial examples with a high success rate, modifying just a very few pixels. The attack effectiveness of the method is confirmed in CIFAR10 and MNIST datasets. The experimental results show that our method has a greater attack success rate than the One Pixel Attack based on the conventional differential evolution. In addition, it requires significantly less perturbation to be successful compared to global or local perturbation attacks, and is more resistant to perception and detection.

Highly sensitive detection of thyroglobulin based on sandwich-type electrochemical immunoassay.

As a dimeric protein, thyroglobulin (Tg) is an important biomarker for different thyroid cancer (DTC), so designing effective method to detect Tg is of great significance. In this work, by preparing ß-cyclodextrin (CD) functionalized carbon nanotubes (CNTs) nanohybrid (CD-CNTs) as carrier to immobilize primary antibody (Ab1) of Tg, assembling sulfydryl ferrocene (Fc) and secondary antibody (Ab2) on the surface of nanogold (Au) as signaling amplifier (Ab2-Au-Fc), a simple and sensitive sandwich-type electrochemical immunoassay (STEM) of Tg was designed herein for the first time. In brief, CNTs show large surface area and conductivity, while CD offers superior host-guest recognition capability that can bound with Ab1; meanwhile, Fc probe can offer stable electrochemical signal that is proportionable to the concentration of Tg. Under the optimum conditions, the proposed STEM platform shows excellent sensing results for Tg detection: a considerable low analytical detection (0.5 ng mL-1) and wide linearity (2 to 200 ng mL-1), suggesting the designed STEM platform offers potential real applications for detect Tg.

Can 99 Tc m -3PRGD 2 (α ν ß 3 ) and 18 F-FDG dual-tracer molecular imaging change the therapeutic strategy for progressive refractory differentiated thyroid cancer: Case report.

RATIONALE: The management of radioiodine refractory differentiated thyroid cancer (RAIR-DTC) represents a major challenge in thyroid cancer. The American Thyroid Association guidelines recommend the use of tyrosine kinase inhibitors (TKIs) for RAIR-DTC that does not respond to conventional treatment. Currently, imaging modalities that predict the response to TKI treatment based on morphological and functional features are lacking. we report a case of a patient with progressive RAIR lung metastases who underwent 2-deoxy-2-[ 18 F]fluoro-D-glucose and 99technetiumm-three polyethylene glycol spacers-arginine-glycine-aspartic acid ( 99 Tc m -3PRGD 2 ) dual-tracer imaging and investigate the value of this imaging strategy for determining subsequent therapeutic schedules. PATIENT CONCERNS: A 52-year-old man with advanced RAIR-DTC and progressive lung metastasis. After TKI treatment [sorafenib] lost its clinical benefits, the patient's therapeutic response was evaluated as progressive disease. 2-deoxy-2-[ 18 F]fluoro-D-glucose PET/CT and 99 Tc m -3PRGD 2 SPECT/CT were performed. There were multiple FDG-positive lesions in the lung. However, 99 Tc m -3PRGD 2 SPECT/CT showed only 1 lesion in the right middle pulmonary lobe with arginine-glycine-aspartic positivity. DIAGNOSIS: RAIR-DTC. INTERVENTIONS: Radiofrequency ablation was performed for only the lesion with RDG and FDG positivity. OUTCOMES: The patient quickly achieved partial response. LESSONS: This case indicates that for progressive RAIR metastases, patients can benefit more from prioritizing treatment for lesions that are both arginine-glycine-aspartic and FDG positive.

99mTc-rituximab tracer injection for guiding internal mammary sentinel lymph nodes biopsy in primary breast cancer: A prospective observational study.

Objective: To explore the use of 99mTc-rituximab tracer injection for internal mammary sentinel lymph node (IM-SLN) detection in patients with primary breast cancer. Methods: This prospective observational study enrolled female patients with primary breast cancer between September 2017 and June 2022 at Fujian Provincial Hospital. The participants were divided into the peritumoral group (two subcutaneous injection points on the surface of the tumor), two-site group (injections into the glands at 6 and 12 o'clock around the areola area), and four-site group (injections into the gland at 3, 6, 9, and 12 o'clock around the areola area). The outcomes were the detection rates of the IM-SLNs and axillary sentinel lymph nodes (A-SLNs). Results: Finally, 133 patients were enrolled, including 53 in the peritumoral group, 60 in the two-site group, and 20 in the four-site group. The detection rate of the IM-SLNs in the peritumoral group (9.4% [5/53]) was significantly lower than in the two-site (61.7% [37/60], P<0.001) and four-site (50.0% [10/20], P<0.001) groups. The detection rates of A-SLNs among the three groups were comparable (P=0.436). Conclusion: The two-site or four-site intra-gland injection of 99mTc-rituximab tracer might achieve a higher detection rate of IM-SLNs and a comparable detection rate of A-SLNs compared with the peritumoral method. The location of the primary focus has no impact on the detection rate of the IM-SLNs.

Predicting lncRNA-disease associations based on combining selective similarity matrix fusion and bidirectional linear neighborhood label propagation.

Recent studies have revealed that long noncoding RNAs (lncRNAs) are closely linked to several human diseases, providing new opportunities for their use in detection and therapy. Many graph propagation and similarity fusion approaches can be used for predicting potential lncRNA-disease associations. However, existing similarity fusion approaches suffer from noise and self-similarity loss in the fusion process. To address these problems, a new prediction approach, termed SSMF-BLNP, based on organically combining selective similarity matrix fusion (SSMF) and bidirectional linear neighborhood label propagation (BLNP), is proposed in this paper to predict lncRNA-disease associations. In SSMF, self-similarity networks of lncRNAs and diseases are obtained by selective preprocessing and nonlinear iterative fusion. The fusion process assigns weights to each initial similarity network and introduces a unit matrix that can reduce noise and compensate for the loss of self-similarity. In BLNP, the initial lncRNA-disease associations are employed in both lncRNA and disease directions as label information for linear neighborhood label propagation. The propagation was then performed on the self-similarity network obtained from SSMF to derive the scoring matrix for predicting the relationships between lncRNAs and diseases. Experimental results showed that SSMF-BLNP performed better than seven other state of-the-art approaches. Furthermore, a case study demonstrated up to 100% and 80% accuracy in 10 lncRNAs associated with hepatocellular carcinoma and 10 lncRNAs associated with renal cell carcinoma, respectively. The source code and datasets used in this paper are available at: https://github.com/RuiBingo/SSMF-BLNP.

Remodeling of H3K9me3 during the pluripotent to totipotent-like state transition.

Multiple chromatin modifiers associated with H3K9me3 play important roles in the transition from embryonic stem cells to 2-cell (2C)-like cells. However, it remains elusive how H3K9me3 is remodeled and its association with totipotency. Here, we integrated transcriptome and H3K9me3 profiles to conduct a detailed comparison of 2C embryos and 2C-like cells. Globally, H3K9me3 is highly preserved and H3K9me3 dynamics within the gene locus is not associated with gene expression change during 2C-like transition. Promoter-deposited H3K9me3 plays non-repressive roles in the activation of genes during 2C-like transition. In contrast, transposable elements, residing in the nearby regions of up-regulated genes, undergo extensive elimination of H3K9me3 and are tended to be induced in 2C-like transitions. Furthermore, a large fraction of trophoblast stem cell-specific enhancers undergo loss of H3K9me3 exclusively in MERVL+/Zscan4+ cells. Our study therefore reveals the unique H3K9me3 profiles of 2C-like cells, facilitating the further exploration of totipotency.