Dppa2/4 as a trigger of signaling pathways to promote zygote genome activation by binding to CG-rich region

A composite scaling network of EfficientNet for improving spatial domain identification performance

Spatial Transcriptomics leverages gene expression profiling while preserving spatial location and histological images. However, processing the vast and noisy image data in spatial transcriptomics (ST) for precise recognition of spatial domains remains a challenge. In this study, we propose a method of EfNST for recognizing spatial domains, which employs an efficient composite scaling network of EfficientNet to learn multi-scale image features. Compared with other relevant algorithms on six data sets from three sequencing platforms, EfNST exhibits higher accuracy in discerning fine tissue structures, highlighting its strong scalability to data and operational efficiency. Under limited computing resources, the testing results on multiple data sets show that the EfNST algorithm runs faster while maintaining accuracy. The ablation studies of EfNST model demonstrate the significant effectiveness of the EfficientNet. Within the annotated data sets, EfNST showcases the ability to finely identify subregions within tissue structure and discover corresponding marker genes. In the unannotated data sets, EfNST successfully identifies minute regions within complex tissues and elucidated their spatial expression patterns in biological processes. In summary, EfNST presents a novel approach to inferring cellular spatial organization from discrete data spots with significant implications for the exploration of tissue structure and function.

Rewriting cellular fate: epigenetic interventions in obesity and cellular programming

External constraints, such as development, disease, and environment, can induce changes in epigenomic patterns that may profoundly impact the health trajectory of fetuses and neonates into adulthood, influencing conditions like obesity. Epigenetic modifications encompass processes including DNA methylation, covalent histone modifications, and RNA-mediated regulation. Beyond forward cellular differentiation (cell programming), terminally differentiated cells are reverted to a pluripotent or even totipotent state, that is, cellular reprogramming. Epigenetic modulators facilitate or erase histone and DNA modifications both in vivo and in vitro during programming and reprogramming. Noticeably, obesity is a complex metabolic disorder driven by both genetic and environmental factors. Increasing evidence suggests that epigenetic modifications play a critical role in the regulation of gene expression involved in adipogenesis, energy homeostasis, and metabolic pathways. Hence, we discuss the mechanisms by which epigenetic interventions influence obesity, focusing on DNA methylation, histone modifications, and non-coding RNAs. We also analyze the methodologies that have been pivotal in uncovering these epigenetic regulations, i.e., Large-scale screening has been instrumental in identifying genes and pathways susceptible to epigenetic control, particularly in the context of adipogenesis and metabolic homeostasis; Single-cell RNA sequencing (scRNA-seq) provides a high-resolution view of gene expression patterns at the individual cell level, revealing the heterogeneity and dynamics of epigenetic regulation during cellular differentiation and reprogramming; Chromatin immunoprecipitation (ChIP) assays, focused on candidate genes, have been crucial for characterizing histone modifications and transcription factor binding at specific genomic loci, thereby elucidating the epigenetic mechanisms that govern cellular programming; Somatic cell nuclear transfer (SCNT) and cell fusion techniques have been employed to study the epigenetic reprogramming accompanying cloning and the generation of hybrid cells with pluripotent characteristics, etc. These approaches have been instrumental in identifying specific epigenetic marks and pathways implicated in obesity, providing a foundation for developing targeted therapeutic interventions. Understanding the dynamic interplay between epigenetic regulation and cellular programming is crucial for advancing mechanism and clinical management of obesity.

Sheep litter size heredity basis using genome-wide selective analysis

Sheep are important herbivorous domestic animal globally, and the Chinese indigenous sheep breed has a multitude of economically significant variations due to the diverse geographical and ecological conditions. In particular, certain native breeds exhibit a visible high litter size phenotype due to the selection pressure of natural and artificial for thousands of years, offering an ideal animal model for investigating sheep's fecundity. In this study, selective signal analysis was performed on public whole-genome sequencing data from 60 sheep across eight breeds to identify candidate genes related to litter size. Results revealed that a total of 34,065,017 single-nucleotide polymorphisms (SNPs) were identified from all sheep, and 65 candidate genes (CDGs) were pinpointed from the top 1% of interacted windows and SNPs between the pairwise fixation index (FST, >0.149543) and cross-population extended haplotype homozygosity (XP-EHH, >0.701551). A total of 41 CDGs (e.g. VRTN, EYA2 and MCPH1) were annotated to 576 GO terms, of which seven terms were directly linked to follicular and embryonic development (e.g. TBXT, BMPR1B, and BMP2). In addition, 73 KEGG pathways were enriched by 21 CDGs (e.g. ENTPD5, ABCD4 and RXFP2), mainly related to Hippo (TCF4, BMPR1B and BMP2), TGF-β (BMPR1B and BMP2), PI3K-Akt (ITGB4, IL4R and PPP2R5A) and Jak-STAT signalling pathways (IL20RA and IL4R). Notably, a series of CDGs was under strong selection in sheep with high litter size traits. These findings result could improve the comprehension of the genetic underpinnings of sheep litter size. Furthermore, it provides valuable CDGS for future molecular breeding.

Identification of Key lncRNAs Associated with Immune Infiltration and Prognosis in Gastric Cancer

Long non-coding RNAs (lncRNAs), as promising novel biomarkers for cancer treatment and prognosis, can function as tumor suppressors and oncogenes in the occurrence and development of many types of cancer, including gastric cancer (GC). However, little is known about the complex regulatory system of lncRNAs in GC. In this study, we systematically analyzed lncRNA and miRNA transcriptomic profiles of GC based on bioinformatics methods and experimental validation. An lncRNA-miRNA interaction network related to GC was constructed, and the nine crucial lncRNAs were identified. These 9 lncRNAs were found to be associated with the prognosis of GC patients by Cox proportional hazards regression analysis. Among them, the expression of lncRNA SNHG14 can affect the survival of GC patients as a potential prognostic marker. Moreover, it was shown that SNHG14 was involved in immune-related pathways and significantly correlated with immune cell infiltration in GC. Meanwhile, we found that SNHG14 affected immune function in many cancers, such as breast cancer and esophageal carcinoma. Such information revealed that SNHG14 may serve as a potential target for cancer immunotherapy. As well, our study could provide practical and theoretical guiding significance for clinical application of non-coding RNAs.

DPPA2/4 Promote the Pluripotency and Proliferation of Bovine Extended Pluripotent Stem Cells by Upregulating the PI3K/AKT/GSK3β/β-Catenin Signaling Pathway

Developmental pluripotency-associated 2 (DPPA2) and DPPA4 are crucial transcription factors involved in maintaining pluripotency in humans and mice. However, the role of DPPA2/4 in bovine extended pluripotent stem cells (bEPSCs) has not been investigated. In this study, a subset of bEPSC-related differentially expressed genes (DEGs), including DPPA2 and DPPA4, was identified based on multiomics data (ATAC-seq and RNA-seq). Subsequent investigations revealed that double overexpression of DPPA2/4 facilitates the reprogramming of bovine fetal fibroblasts (BFFs) into bEPSCs, whereas knockout of DPPA2/4 in BFFs leads to inefficient reprogramming. DPPA2/4 overexpression and knockdown experiments revealed that the pluripotency and proliferation capability of bEPSCs were maintained by promoting the transition from the G1 phase to the S phase of the cell cycle. By activating the PI3K/AKT/GSK3β/β-catenin pathway in bEPSCs, DPPA2/4 can increase the nuclear accumulation of β-catenin, which further upregulates lymphoid enhancer binding factor 1 (LEF1) transcription factor activity. Moreover, DPPA2/4 can also regulate the expression of LEF1 by directly binding to its promoter region. Overall, our results demonstrate that DPPA2/4 promote the reprogramming of BFFs into bEPSCs while also maintaining the pluripotency and proliferation capability of bEPSCs by regulating the PI3K/AKT/GSK3β/β-catenin pathway and subsequently activating LEF1. These findings expand our understanding of the gene regulatory network involved in bEPSC pluripotency.

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.

Preimplantation embryo gene expression: 56 years of discovery, and counting

The biology of preimplantation embryo gene expression began 56 years ago with studies of the effects of protein synthesis inhibition and discovery of changes in embryo metabolism and related enzyme activities. The field accelerated rapidly with the emergence of embryo culture systems and progressively evolving methodologies that have allowed early questions to be re-addressed in new ways and in greater detail, leading to deeper understanding and progressively more targeted studies to discover ever more fine details. The advent of technologies for assisted reproduction, preimplantation genetic testing, stem cell manipulations, artificial gametes, and genetic manipulation, particularly in experimental animal models and livestock species, has further elevated the desire to understand preimplantation development in greater detail. The questions that drove enquiry from the earliest years of the field remain drivers of enquiry today. Our understanding of the crucial roles of oocyte-expressed RNA and proteins in early embryos, temporal patterns of embryonic gene expression, and mechanisms controlling embryonic gene expression has increased exponentially over the past five and a half decades as new analytical methods emerged. This review combines early and recent discoveries on gene regulation and expression in mature oocytes and preimplantation stage embryos to provide a comprehensive understanding of preimplantation embryo biology and to anticipate exciting future advances that will build upon and extend what has been discovered so far.

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.

Characterized the diversity of ABCB1 subtypes in immunogenomic landscape for predicting the drug response in breast cancer

ABCB1 is an important gene that closely related to analgesic tolerance to opioids, and plays an important role in their postoperative treatment. Recent studies have demonstrated that ABCB1 genotype is significantly associated with the chemico-resistance and chemical sensitivity in breast cancer patients. So, it is become very important to investigate the important role of ABCB1 for predicting drug response in breast cancer patients. In this study, by conducting the Cox proportional hazards regression analysis in breast cancer patients, significant differences were found in prognosis between the ABCB1 high- and low-expression subtypes. Meanwhile, by using immune infiltration profiles as well as transcriptomics datasets, the ABCB1 high subtype was found to be significantly enriched in many immune-related KEGG pathways and biological processes, and was characterized by the high infiltration levels of immune cell types. Furthermore, bioinformatics inference revealed that the ABCB1 subtypes were associated with the therapeutic effect of immunotherapy, which would be important for patient prognosis. In conclusion, these findings may provide useful help for recognizing the diversity between ABCB1 subtypes in tumor immune microenvironment, and may unravel prognosis outcomes and immunotherapy utility for ABCB1 in breast cancer.

DPPA2, DPPA4, and other DPPA factor epigenomic functions in cell fate and cancer

Many gene networks are shared between pluripotent stem cells and cancer; a concept exemplified by several DPPA factors such as DPPA2 and DPPA4, which are highly and selectively expressed in stem cells but also found to be reactivated in cancer. Despite their striking expression pattern, for many years the function of DPPA2 and DPPA4 remained a mystery; knockout of Dppa2 and Dppa4 did not affect pluripotency, but caused lung and skeletal defects late in development, long after Dppa2 and Dppa4 expression had been turned off. A number of recent papers have further clarified and defined the roles of these important factors, identifying roles in priming the chromatin and maintaining developmental competency through regulating both H3K4me3 and H3K27me3 at bivalent chromatin domains, and acting to remodel chromatin and facilitate reprogramming of somatic cells to induced pluripotency. These findings highlight an important regulatory role for DPPA2 and DPPA4 at the transitional boundary between pluripotency and differentiation and may have relevance to the functions of DPPA2 and 4 in the context of cancer cells as well.

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.

Application of Machine Learning for Drug-Target Interaction Prediction

Exploring drug–target interactions by biomedical experiments requires a lot of human, financial, and material resources. To save time and cost to meet the needs of the present generation, machine learning methods have been introduced into the prediction of drug–target interactions. The large amount of available drug and target data in existing databases, the evolving and innovative computer technologies, and the inherent characteristics of various types of machine learning have made machine learning techniques the mainstream method for drug–target interaction prediction research. In this review, details of the specific applications of machine learning in drug–target interaction prediction are summarized, the characteristics of each algorithm are analyzed, and the issues that need to be further addressed and explored for future research are discussed. The aim of this review is to provide a sound basis for the construction of high-performance models.