Expression Of Regulatory Elements Research Articles

Recent advances in synthetic biology toolkits and metabolic engineering of Ralstonia eutropha H16 for production of value-added chemicals.

Ralstonia eutropha H16, a facultative chemolithoautotrophic Gram-negative bacterium, demonstrates remarkable metabolic flexibility by utilizing either diverse organic substrates or CO2 as the sole carbon source, with H2 serving as the electron donor under aerobic conditions. The capacity of carbon and energy metabolism of R. eutropha H16 enabled development of synthetic biology technologies and strategies to engineer its metabolism for biosynthesis of value-added chemicals. This review firstly outlines the development of synthetic biology tools tailored for R. eutropha H16, including construction of expression vectors, regulatory elements, and transformation techniques. The availability of comprehensive omics data (i.e., transcriptomic, proteomic, and metabolomic) combined with the fully annotated genome sequence provides a robust genetic framework for advanced metabolic engineering. These advancements facilitate efficient reprogramming metabolic network of R. eutropha. The potential of R. eutropha as a versatile microbial platform for industrial biotechnology is further underscored by its ability to utilize a wide range of carbon sources for the production of value-added chemicals through both autotrophic and heterotrophic pathways. The integration of state-of-the-art genetic and genomic engineering tools and strategies with high cell-density fermentation processes enables engineered R. eutropha as promising microbial cell factories for optimizing carbon fluxes and expanding the portfolio of bio-based products.

Unraveling molecular mechanisms in growth plate development advancing pediatric orthopedic interventions

Dynamic Growth Plate based on Molecular Profiling (DGMP) represents a pioneering approach to understanding the intricate molecular processes governing growth plate development, essential for advancing pediatric orthopedic treatments. Growth plates, located at the ends of long bones, are pivotal for bone elongation during childhood and adolescence, but disruptions in their molecular and cellular regulation can result in developmental abnormalities and orthopedic deformities. DGMP integrates genomic, transcriptomic, proteomic, and epigenomic analyses to uncover cell-specific expression patterns and regulatory elements critical to growth plate formation. By leveraging high-throughput sequencing, single-cell RNA (Ribonucleic Acid) sequencing, and spatial transcriptomics, DGMP facilitates the identification of diagnostic biomarkers and enables the development of targeted pharmacological therapies tailored to children with defective growth plates. Furthermore, in silico models simulating cellular differentiation and pathway interactions provide predictive insights into the long-term effects of interventions on bone development. This innovative framework not only enhances early and accurate detection of growth-related disorders but also supports the design of personalized treatments, ultimately improving clinical outcomes for affected children. As a precision medicine tool, DGMP has the potential to transform pediatric orthopedics by resolving challenges related to molecular data integration, spatiotemporal dynamics, and therapeutic application, thereby advancing the understanding and treatment of growth plate disorders.

Genome-Wide Identification and Analysis of Phospholipase C Gene Family Reveals Orthologs, Co-Expression Networks, and Expression Profiling Under Abiotic Stress in Sorghum bicolor.

Phospholipase C (PLC) is an essential enzyme involved in lipid signaling pathways crucial for regulating plant growth and responding to environmental stress. In sorghum, 11 PLC genes have been identified, comprising 6 PI-PLCs and 5 NPCs. Through phylogenetic and interspecies collinearity analyses, structural similarities between SbPLCs and ZmPLCs proteins have been observed, with a particularly strong collinearity between SbPLCs and OsPLCs. Promoter function analysis has shown that SbPLCs are significantly enriched under abiotic stress and hormonal stimuli, like ABA, jasmonic acid, drought, high temperature, and salt. Gene co-expression networks, constructed using a weighted gene co-expression network analysis (WGCNA), highlight distinct expression patterns of SbPLC1, SbPLC3a, and SbPLC4 in response to abiotic stress, providing further insights into the expression patterns and interactions of SbPLCs under various environmental stimuli. qRT-PCR results reveal variations in expression levels among most SbPLCs members under different stress conditions (drought, NaCl, NaHCO3), hormone treatments (ABA), and developmental stages, indicating both specific and overlapping expression patterns. This comprehensive analysis offers valuable insights into the roles of SbPLCs in sorghum, shedding light on their specific expression patterns, regulatory elements, and protein interactions across different environmental stimuli and developmental stages.

GAGE-seq concurrently profiles multiscale 3D genome organization and gene expression in single cells.

The organization of mammalian genomes features a complex, multiscale three-dimensional (3D) architecture, whose functional significance remains elusive because of limited single-cell technologies that can concurrently profile genome organization and transcriptional activities. Here, we introduce genome architecture and gene expression by sequencing (GAGE-seq), a scalable, robust single-cell co-assay measuring 3D genome structure and transcriptome simultaneously within the same cell. Applied to mouse brain cortex and human bone marrow CD34+ cells, GAGE-seq characterized the intricate relationships between 3D genome and gene expression, showing that multiscale 3D genome features inform cell-type-specific gene expression and link regulatory elements to target genes. Integration with spatial transcriptomic data revealed in situ 3D genome variations in mouse cortex. Observations in human hematopoiesis unveiled discordant changes between 3D genome organization and gene expression, underscoring a complex, temporal interplay at the single-cell level. GAGE-seq provides a powerful, cost-effective approach for exploring genome structure and gene expression relationships at the single-cell level across diverse biological contexts.

Synthetic Biology Tools for Engineering Aspergillus oryzae.

For more than a thousand years, Aspergillus oryzae has been used in traditional culinary industries, including for food fermentation, brewing, and flavoring. In recent years, A. oryzae has been extensively used in deciphering the pathways of natural product synthesis and value-added compound bioproduction. Moreover, it is increasingly being used in modern biotechnology industries, such as for the production of enzymes and recombinant proteins. The investigation of A. oryzae has been significantly accelerated through the successive application of a diverse array of synthetic biology techniques and methodologies. In this review, the advancements in biological tools for the synthesis of A. oryzae, including DNA assembly technologies, gene expression regulatory elements, and genome editing systems, are discussed. Additionally, the challenges associated with the heterologous expression of A. oryzae are addressed.

In silico comparative transcriptome analysis of Papaver somniferum cultivars

Papaver somniferum is a medicinal plant of the Papaveraceae family that has traditionally been used for diet or its therapeutic value for thousands of years. Mainly, morphine and noscapine alkaloids exhibit anti-analgesic and anti-cancer effects. However, gene expression patterns and regulatory elements, such as transcription factors between different tissues, still need to be detected. In this study, comparative in silico transcriptome analyses were conducted to examine the tissue-specificity of the benzylisoquinoline alkaloids (BIAs) biosynthetic genes and transcription factors (TFs) between morphine and noscapine cultivars. Analysis showed that BIA biosynthetic genes are expressed in a different pattern between two varieties. Results showed that some members of plant-specific secondary metabolites related to TF families, such as MYB, MADS-box, bHLH, NAC, and WRKY, are differentially expressed between tissues and varieties.

New long-non coding RNAs related to fat deposition based on pig model

Abstract Obesity has been a problem in the last decades since the development of certain technologies has led to a faster pace of life, resulting in nutritional changes. Domestic pigs are an excellent animal model in recognition of adiposity-related processes, corresponding to the size of individual organs, the distribution of body fat in the organism, and similar metabolism. The present study applied next-generation sequencing to identify adipose tissue (AT) transcriptomic signals related to increased fat content by identifying differentially expressed genes (DEGs), including long non-coding RNAs in Złotnicka White pigs (n=16). Moreover, besides commonly used functional analysis, we applied the Freiburg RNA tool to predict DE lncRNA targets based on calculation hybridisation energy. And in addition, DE lncRNAs were recognized based on information available in databases. The obtained results show that close to 230 gene expressions were found to be dependent on fat content, including 8 lncRNAs. The most interesting was that among identified DE lncRNAs was transcript corresponding to human MALAT1, which was previously considered in the obesity-related context. Moreover, it was determined that in ENSSSCG00000048394, ENSSSCG00000047210, ENSSSCG00000047442 and ENSSSCG00000041577 lncRNAs are contained repeat insertion domains of LncRNAs (RIDLs) considered as important gene expression regulatory elements, and ENSSSCG00000041577 seems to be the host for mir1247(NR_031649.1). The analysis of energy hybridisation between DE lncRNAs and DEGs using the Freiburg IntaRNAv2 tool, including isoforms expressed in AT, showed that ENSSSCG00000047210 lncRNA interacted with the highest number of DEGs and ENSSSCG00000047210 expression was only correlated with positive fat-related DEGs. The functional analysis showed that down-regulated DEGs involved in ECM proteoglycan pathways could be under the control of both positive and negative fat-related lncRNAs. The present study, using pigs as an animal model, expands current knowledge of possible gene expression regulation by lncRNAs in fat tissue and indicates for MALAT1 role in the fat deposition determination, whose function is still often questioned or doubtful.

The snoGloBe interaction predictor reveals a broad spectrum of C/D snoRNA RNA targets.

Box C/D small nucleolar RNAs (snoRNAs) are a conserved class of RNA known for their role in guiding ribosomal RNA 2′-O-ribose methylation. Recently, C/D snoRNAs were also implicated in regulating the expression of non-ribosomal genes through different modes of binding. Large scale RNA–RNA interaction datasets detect many snoRNAs binding messenger RNA, but are limited by specific experimental conditions. To enable a more comprehensive study of C/D snoRNA interactions, we created snoGloBe, a human C/D snoRNA interaction predictor based on a gradient boosting classifier. SnoGloBe considers the target type, position and sequence of the interactions, enabling it to outperform existing predictors. Interestingly, for specific snoRNAs, snoGloBe identifies strong enrichment of interactions near gene expression regulatory elements including splice sites. Abundance and splicing of predicted targets were altered upon the knockdown of their associated snoRNA. Strikingly, the predicted snoRNA interactions often overlap with the binding sites of functionally related RNA binding proteins, reinforcing their role in gene expression regulation. SnoGloBe is also an excellent tool for discovering viral RNA targets, as shown by its capacity to identify snoRNAs targeting the heavily methylated SARS-CoV-2 RNA. Overall, snoGloBe is capable of identifying experimentally validated binding sites and predicting novel sites with shared regulatory function.

Transcriptomic analysis reveals myometrial topologically associated domains linked to the onset of human term labour.

Changes in cell phenotype are thought to occur through the expression of groups of co-regulated genes within topologically associated domains (TADs). In this paper, we allocate genes expressed within the myometrium of the human uterus during the onset of term labour into TADs. Transformation of the myometrial cells of the uterus into a contractile phenotype during term human labour is the result of a complex interaction of different epigenomic and genomic layers. Recent work suggests that the transcription factor (TF) RELA lies at the top of this regulatory network. Using deep RNA sequencing (RNAseq) analysis of myometrial samples (n = 16) obtained at term from women undergoing caesarean section prior to or after the onset of labour, we have identified evidence for how other gene expression regulatory elements interact with TFs in the labour phenotype transition. Gene set enrichment analysis of our RNAseq data identified three modules of enriched genes (M1, M2 and M3), which in gene ontology studies are linked to matrix degradation, smooth muscle and immune gene signatures, respectively. These genes were predominantly located within chromosomal TADs suggesting co-regulation of expression. Our transcriptomic analysis also identified significant differences in the expression of long non-coding RNAs (lncRNA), microRNAs (miRNA) and TFs that were predicted to target genes within the TADs. Additionally, network analysis revealed 15 new lncRNA (MCM3AP-AS1, TUG1, MIR29B2CHG, HCG18, LINC00963, KCNQ1OT1, NEAT1, HELLPAR, SNHG16, NUTM2B-AS1, MALAT1, PSMA3-AS1, GABPB1-AS1, NORAD and NKILA) and 4 miRNA (mir-145, mir-223, mir-let-7a and mir-132) as top gene hubs with three TFs (NFKB1, RELA and ESR1) as master regulators. Together, these factors are likely to be involved in co-regulatory networks driving a myometrial transformation to generate an estrogen-sensitive phenotype. We conclude that lncRNA and miRNA targeting the estrogen receptor 1 and nuclear factor kappa B pathways play a key role in the initiation of human labour. For the first time, we perform an integrative analysis to present a multi-level genomic signature made of mRNA, non-coding RNA and TFs in the myometrium for spontaneous term labour.

The Emerging Landscape of Long Non-Coding RNAs in Wilms Tumor.

Long noncoding RNAs (LncRNAs) are transcripts of nucleic acid sequences with a length of more than 200 bp, which have only partial coding capabilities. Recent studies have shown that lncRNAs located in the nucleus or cytoplasm can be used as gene expression regulatory elements due to their important regulatory effects in a variety of biological processes. Wilms tumor (WT) is a common abdominal tumor in children whose pathogenesis remains unclear. In recent years, many specifically expressed lncRNAs have been found in WT, which affect the occurrence and development of WT. At the same time, lncRNAs may have the capacity to become novel biomarkers for the diagnosis and prognosis of WT. This article reviews related research progress on the relationship between lncRNAs and WT, to provide a new direction for clinical diagnosis and treatment of WT.

Evaluating the Potential of Applying Machine Learning Tools to Metabolic Pathway Optimization

Successful engineering of a microbial host for efficient production of a target product from a given substrate can be viewed as an extensive optimization task. Such a task involves the selection of high activity enzymes as well as their gene expression regulatory control elements (i.e., promoters and ribosome binding sites). Finally, there is also the need to tune expression of multiple genes along a heterologous pathway to relieve constraints from rate-limiting step and help reduce metabolic burden on cells from unnecessary over-expression of high activity enzymes. While the aforementioned tasks could be performed through combinatorial experiments, such an approach incurs significant cost, time and effort, which is a handicap that can be relieved by application of modern machine learning tools. Such tools could attempt to predict high activity enzymes from sequence, but they are currently most usefully applied in classifying strong promoters from weaker ones as well as combinatorial tuning of expression of multiple genes. This perspective reviews the application of machine learning tools to aid metabolic pathway optimization through identifying challenges in metabolic engineering that could be overcome with the help of machine learning tools.

Cap analysis of gene expression (CAGE) and noncoding regulatory elements.

Cap analysis of gene expression (CAGE) was developed to detect the 5' end of RNA. Trapping of the RNA 5'-cap structure enables the enrichment and selective sequencing of complete transcripts. Upscaled high-throughput versions of CAGE have enabled the genome-wide identification of transcription start sites, including transcriptionally active promoters and enhancers. CAGE sequencing can be exploited to draw comprehensive maps of active genomic regulatory elements in a cell type- and activation-specific manner. The cells of the immune system are among the best candidates to be analyzed in humans, since they are easily accessible. In this review, we discuss how CAGE data are instrumental for integrative analyses with quantitative trait loci and omics data, and their usefulness in the mechanistic interpretation of the effects of genetic variations over the entire human genome. Integrating CAGE data with the currently available omics information will contribute to better understanding of the genome-wide association study variants that lie outside of annotated genes, deepening our knowledge on human diseases, and enabling the targeted design of more specific therapeutic interventions.

MRLocus: Identifying causal genes mediating a trait through Bayesian estimation of allelic heterogeneity.

Expression quantitative trait loci (eQTL) studies are used to understand the regulatory function of non-coding genome-wide association study (GWAS) risk loci, but colocalization alone does not demonstrate a causal relationship of gene expression affecting a trait. Evidence for mediation, that perturbation of gene expression in a given tissue or developmental context will induce a change in the downstream GWAS trait, can be provided by two-sample Mendelian Randomization (MR). Here, we introduce a new statistical method, MRLocus, for Bayesian estimation of the gene-to-trait effect from eQTL and GWAS summary data for loci with evidence of allelic heterogeneity, that is, containing multiple causal variants. MRLocus makes use of a colocalization step applied to each nearly-LD-independent eQTL, followed by an MR analysis step across eQTLs. Additionally, our method involves estimation of the extent of allelic heterogeneity through a dispersion parameter, indicating variable mediation effects from each individual eQTL on the downstream trait. Our method is evaluated against other state-of-the-art methods for estimation of the gene-to-trait mediation effect, using an existing simulation framework. In simulation, MRLocus often has the highest accuracy among competing methods, and in each case provides more accurate estimation of uncertainty as assessed through interval coverage. MRLocus is then applied to five candidate causal genes for mediation of particular GWAS traits, where gene-to-trait effects are concordant with those previously reported. We find that MRLocus's estimation of the causal effect across eQTLs within a locus provides useful information for determining how perturbation of gene expression or individual regulatory elements will affect downstream traits. The MRLocus method is implemented as an R package available at https://mikelove.github.io/mrlocus.

Single nucleotide variations in ZBTB46 are associated with post-thrombolytic parenchymal haematoma.

Haemorrhagic transformation is a complication of recombinant tissue-plasminogen activator treatment. The most severe form, parenchymal haematoma, can result in neurological deterioration, disability, and death. Our objective was to identify single nucleotide variations associated with a risk of parenchymal haematoma following thrombolytic therapy in patients with acute ischaemic stroke. A fixed-effect genome-wide meta-analysis was performed combining two-stage genome-wide association studies (n = 1904). The discovery stage (three cohorts) comprised 1324 ischaemic stroke individuals, 5.4% of whom had a parenchymal haematoma. Genetic variants yielding a P-value < 0.05 1 × 10-5 were analysed in the validation stage (six cohorts), formed by 580 ischaemic stroke patients with 12.1% haemorrhagic events. All participants received recombinant tissue-plasminogen activator; cases were parenchymal haematoma type 1 or 2 as defined by the European Cooperative Acute Stroke Study (ECASS) criteria. Genome-wide significant findings (P < 5 × 10-8) were characterized by in silico functional annotation, gene expression, and DNA regulatory elements. We analysed 7 989 272 single nucleotide polymorphisms and identified a genome-wide association locus on chromosome 20 in the discovery cohort; functional annotation indicated that the ZBTB46 gene was driving the association for chromosome 20. The top single nucleotide polymorphism was rs76484331 in the ZBTB46 gene [P = 2.49 × 10-8; odds ratio (OR): 11.21; 95% confidence interval (CI): 4.82-26.55]. In the replication cohort (n = 580), the rs76484331 polymorphism was associated with parenchymal haematoma (P = 0.01), and the overall association after meta-analysis increased (P = 1.61 × 10-8; OR: 5.84; 95% CI: 3.16-10.76). ZBTB46 codes the zinc finger and BTB domain-containing protein 46 that acts as a transcription factor. In silico studies indicated that ZBTB46 is expressed in brain tissue by neurons and endothelial cells. Moreover, rs76484331 interacts with the promoter sites located at 20q13. In conclusion, we identified single nucleotide variants in the ZBTB46 gene associated with a higher risk of parenchymal haematoma following recombinant tissue-plasminogen activator treatment.

Isolation and molecular characterization of MYB60 in Solanum lycopersicum.

Stomatal closure is a common adaptation response of plants to the onset of drought condition and its regulation is controlled by transcription factors. MYB60, a transcription factor involved in the regulation of light-induced stomatal opening, has been characterized in arabidopsis and grapevine. In this work, we studied the role of MYB60 homolog SIMYB60 in tomato plants. We identified, isolated, and sequenced the SIMYB60 coding sequence, and found domains and motifs characteristic of other MYB60 proteins. We determined that SlMYB60 is mainly expressed in leaves, and its expression is repressed by abscisic acid. Next, we isolated a putative promoter region containing regulatory elements responsible for guard cell expression and other putative regulatory elements related to ABA repression and vascular tissue expression. Protein localization assays demonstrated that SlMYB60 localizes to the nucleus. Finally, SlMYB60 is able to complement the mutant phenotype of atmyb60-1 in Arabidopsis. Together, these results indicate that SlMYB60 is the homologous gene in tomato and potentially offer a molecular target to improve crops.

Fantastic databases and where to find them: Web applications for researchers in a rush

Public databases are essential to the development of multi-omics resources. The amount of data created by biological technologies needs a systematic and organized form of storage, that can quickly be accessed, and managed. This is the objective of a biological database. Here, we present an overview of human databases with web applications. The databases and tools allow the search of biological sequences, genes and genomes, gene expression patterns, epigenetic variation, protein-protein interactions, variant frequency, regulatory elements, and comparative analysis between human and model organisms. Our goal is to provide an opportunity for exploring large datasets and analyzing the data for users with little or no programming skills. Public user-friendly web-based databases facilitate data mining and the search for information applicable to healthcare professionals. Besides, biological databases are essential to improve biomedical search sensitivity and efficiency and merge multiple datasets needed to share data and build global initiatives for the diagnosis, prognosis, and discovery of new treatments for genetic diseases. To show the databases at work, we present a a case study using ACE2 as example of a gene to be investigated. The analysis and the complete list of databases is available in the following website <https://kur1sutaru.github.io/fantastic_databases_and_where_to_find_them/>.

Fine-Tuning Multi-Gene Clusters via Well-Characterized Gene Expression Regulatory Elements: Case Study of the Arginine Synthesis Pathway in C. glutamicum.

Promoters and ribosome binding sites (RBSs) are routinely applied in gene expression regulation, but their orthogonality and combinatorial effects have not yet been systematically studied in Corynebacterium glutamicum. Here, 17 core promoters and 29 RBSs in C. glutamicum were characterized, which exhibited 470-fold and 430-fold in transcriptional and translational activity, respectively. By comparing the expression of two reporter genes regulated by multiple RBSs, the RBS efficacy showed significant dependence on the gene context, besides the RBSs' strength, reflecting the poor orthogonality of RBSs. Bicistron-modified RBS (referred as bc-RBS) was adapted to C. glutamicum, which improved RBS reliability. By coupling a series of promoters with RBSs/bc-RBSs, a much broader regulation range that spanned 4 orders of magnitude was observed compared with that of a sole element, and the contribution to gene expression of RBS was more than that of promoter. Finally, promoters and RBSs were applied as built-in elements to fine-tune the gene cluster in the arginine synthesis pathway in C. glutamicum. Compared with the original strain, more arginine (1.61-fold) or citrulline (2.35-fold) was accumulated in a 7 L bioreactor by strains with the gene expression regulation system rationally engineered. We demonstrated that, via combination of well-characterized gene elements, and overall consideration for both transcription and translation, the biosynthesis pathway can be effectively balanced, and the yield of a target metabolite can be further improved.

Use Chou's 5-steps rule to identify DNase I hypersensitive sites via dinucleotide property matrix and extreme gradient boosting.

DNase I hypersensitive sites (DHSs) are highly sensitive active chromatin regions to DNase I enzymes, which provide the basis for the study of gene transcriptional regulation mechanism and play an important role in the analysis of gene expression regulatory elements. The identification of DHSs has contributed to biomedical research and genome analysis. There are already southern blotting technology and high-throughput sequencing technology to identify DHSs, but these experimental methods are often time-consuming and expensive, thus, novel and powerful computational methods are needed to predict DHSs. It is understood that researchers in related fields have proposed many feasible methods for the identification of DNase I hypersensitive sites. However, the accuracy of these methods is not satisfactory, so it is necessary to use more effective methods to predict DHSs. Therefore, on the basis of previous studies, we design a novel predictor called iDHS-DXG. First of all, we choose three sequence-derived feature representation methods to extract features, including kmer, mismatch and the dinucleotide property matrix based on Moran coefficient. Truncated singular value decomposition is selected for reducing the dimensionality of the benchmark dataset, and the optimal dimension is obtained through the test. Then, synthetic minority over-sampling technique is utilized to balance the positive and negative samples. After that, we introduce extreme gradient boosting ensemble classifier to predict DHSs. Compared with the previous research results, the main performance evaluation metrics of our method have been improved after five-fold cross-validation test. DHSs were identified on two human genome datasets with an accuracy of 90.84% and 91.27% respectively. This result shows that our method is a feasible, effective and competitive tool for the analysis of gene regulatory elements. Our research is helpful for biologists and geneticists to study genome analysis and gene regulation mechanism. Meanwhile, it is also of great significance to the development of human disease and drug design. Furthermore, the datasets and codes of iDHS-DXG can be obtained from the website: http://github.com/Xtian-696/iDHS-DXG/ .

Isolation and characterization of the promoter of SEPALLATA1-like gene from Platanus acerifolia

London plane (Platanus acerifolia Wild.) is a famous landscape plant because of its numerous desirable traits except the abundant pollens and seed hairs, which not only pollute the environment but also affect human health. To resolve these problems, we herein isolated and functionally analyzed the promoter of PlacSEP1.1, an orthologous gene of Arabidopsis SEPALLATA1, and investigated the potential usability for cell ablation strategies to engineer reproductive sterility in plants. A 2130 bp 5' upstream region of PlacSEP1.1 was isolated and termed pPlacSEP1.1. Putative motif detections show that there were several types of motifs in pPlacSEP1.1 including core promoter elements, tissue-specific expression regulatory elements, and some negative regulatory elements. β-Glucuronidase histochemical and quantitative assay showed that pPlacSEP1.1 of all deletions was active in all detected tissues except the shortest deletion D5 in roots. In order to test whether pPlacSEP1.1 could be used for London plane sterility breeding with a cytotoxic gene Barnase, the pPlacSEP1.1::Barnase and pPlacSEP1.1::Barnase-mic35S-Barstar vectors were constructed and transformed into tobacco. The pPlacSEP1.1::Barnase transgenic tobacco showed serious defects with respect to vegetative development and died within a couple of weeks after transplantation. On the other hand, most pPlacSEP1.1::Barnase-mic35S-Barstar transgenic tobacco showed normal vegetative growth and inflorescence, and flower development prevented phenotype.

Endogenous Retroviruses Function as Gene Expression Regulatory Elements During Mammalian Pre-implantation Embryo Development.

Pre-implantation embryo development encompasses several key developmental events, especially the activation of zygotic genome activation (ZGA)-related genes. Endogenous retroviruses (ERVs), which are regarded as “deleterious genomic parasites”, were previously considered to be “junk DNA”. However, it is now known that ERVs, with limited conservatism across species, mediate conserved developmental processes (e.g., ZGA). Transcriptional activation of ERVs occurs during the transition from maternal control to zygotic genome control, signifying ZGA. ERVs are versatile participants in rewiring gene expression networks during epigenetic reprogramming. Particularly, a subtle balance exists between ERV activation and ERV repression in host–virus interplay, which leads to stage-specific ERV expression during pre-implantation embryo development. A large portion of somatic cell nuclear transfer (SCNT) embryos display developmental arrest and ZGA failure during pre-implantation embryo development. Furthermore, because of the close relationship between ERV activation and ZGA, exploring the regulatory mechanism underlying ERV activation may also shed more light on the enigma of SCNT embryo development in model animals.