MicroRNAs (miRNAs) are 21-24-nucleotide non-coding RNAs found in diverse organisms. Although hundreds of miRNAs have been cloned or predicted, only very few miRNAs have been functionally characterized. Embryo implantation is a crucial step in mammalian reproduction. Many genes have been shown to be significantly changed in mouse uterus during embryo implantation. However, miRNA expression profiles in the mouse uterus between implantation sites and inter-implantation sites are still unknown. In this study, miRNA microarray was used to examine differential expression of miRNAs in the mouse uterus between implantation sites and inter-implantation sites. Compared with inter-implantation sites, there were 8 up-regulated miRNAs at implantation sites, which were confirmed by both Northern blot and in situ hybridization. miR-21 was highly expressed in the subluminal stromal cells at implantation sites on day 5 of pregnancy. Because miR-21 was not detected in mouse uterus during pseudopregnancy and under delayed implantation, miR-21 expression at implantation sites was regulated by active blastocysts. Furthermore, we showed that Reck was the target gene of miR-21. Our data suggest that miR-21 may play a key role during embryo implantation.

Cysteine dioxygenase is a non-heme mononuclear iron metalloenzyme that catalyzes the oxidation of cysteine to cysteine sulfinic acid with addition of molecular dioxygen. This irreversible oxidative catabolism of cysteine initiates several important metabolic pathways related to diverse sulfurate compounds. Cysteine dioxygenase is therefore very important for maintaining the proper hepatic concentration of intracellular free cysteine. Mechanisms for mouse and rat cysteine dioxygenases have recently been reported based on their crystal structures in the absence of substrates, although there is still a lack of direct evidence. Here we report the first crystal structure of human cysteine dioxygenase in complex with its substrate L-cysteine to 2.7 Å , together with enzymatic activity and metal content assays of several single point mutants. Our results provide an insight into a new mechanism of cysteine thiol dioxygenation catalyzed by cysteine dioxygenase, which is tightly associated with a thioether-bonded tyrosine-cysteine cofactor involving Tyr-157 and Cys-93. This cross-linked protein-derived cofactor plays several key roles different from those in galactose oxidase. This report provides a new potential target for therapy of diseases related to human cysteine dioxygenase, including neurodegenerative and autoimmune diseases.Cysteine dioxygenase (CDO, 2 EC 1.13.11.20) is a non-heme mononuclear iron metalloenzyme that catalyzes the irreversible oxidation of cysteine to cysteine sulfinic acid (CSA) with addition of molecular oxygen (1) (Structure 1). This oxidative catabolism of cysteine initiates several important metabolic pathways related to pyruvate and several sulfurate compounds, including sulfate, hypotaurine, and taurine. CDO is expressed at appreciable levels in the brain, kidney, and lung, with extremely high levels in liver tissue (2-5), where CDO plays an important role in maintaining the hepatic concentration of intracellular free cysteine within a proper narrow range (6). When the levels of cysteine decrease below this range, the increase of CDO ubiquitination rate results in rapid degradation of the ubiquitinated portion by the 26 S proteasome system (7,8). However, the precise means by which cysteine regulates CDO ubiquitination remain unknown.Intracellular free cysteine is cytotoxic and neuroexcitotoxic due to oxidative damage via formation of free radicals in the presence of iron (9 -11). Elevated cysteine levels were reported previously in relation to several neurodegenerative diseases, including the well known Parkinson and Alzheimer diseases (12-14), and autoimmune diseases such as systemic lupus erythematosus and rheumatoid arthritis (15, 16). CDO is considered to be involved in these diseases due to its function in regulating free cysteine levels.Sequence alignment classifies CDO as a member of the cupin superfamily (see Fig. 1), whose members possess what may be the most diverse range of functions, encompassing ϳ18 subclasses. Nonetheless, neither of the exact characteristic conserved sequenc...

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BackgroundSynonymous codon usage bias (SCUB) is an inevitable phenomenon in organismic taxa, generally referring to differences in the occurrence frequency of codons across different species or within the genome of the same species. SCUB happens in various degrees under pressure from nature selection, mutation bias and other factors in different ways. It also attaches great significance to gene expression and species evolution, however, a systematic investigation towards the codon usage in Bombyx mori (B. mori) has not been reported yet. Moreover, it is still indistinct about the reasons contributing to the bias or the relationship between the bias and the evolution of B. mori.ResultsThe comparison of the codon usage pattern between the genomic DNA (gDNA) and the mitochondrial DNA (mtDNA) from B. mori suggests that mtDNA has a higher level of codon bias. Furthermore, the correspondence analysis suggests that natural selection, such as gene length, gene function and translational selection, dominates the codon preference of mtDNA, while the composition constraints for mutation bias only plays a minor role. Additionally, the clustering results of the silkworm superfamily suggest a lack of explicitness in the relationship between the codon usage of mitogenome and species evolution.ConclusionsAmong the complicated influence factors leading to codon bias, natural selection is found to play a major role in shaping the high bias in the mtDNA of B. mori from our current data. Although the cluster analysis reveals that codon bias correlates little with the species evolution, furthermore, a detailed analysis of codon usage of mitogenome provides better insight into the evolutionary relationships in Lepidoptera. However, more new methods and data are needed to investigate the relationship between the mtDNA bias and evolution.

BackgroundPrevious studies showed that the majority of developmental genes are devoid of DNA methylation at promoters even when they are repressed. Such hypomethylated regions at developmental genes are unusually large and extend well beyond proximal promoters, forming DNA methylation valleys (DMVs) or DNA methylation canyons. However, it remains elusive how most developmental genes can evade DNA methylation regardless of their transcriptional states.ResultsWe show that DMVs are hypomethylated in development and are highly conserved across vertebrates. Importantly, DMVs are hotspots of regulatory regions for key developmental genes and show low levels of deamination mutation rates. By analyzing a panel of DNA methylomes from mouse tissues, we identify a subset of DMVs that are dynamically methylated. These DMVs are strongly enriched for Polycomb-deposited H3K27me3 when the associated genes are silenced, and surprisingly show elevated DNA methylation upon gene activation. 4C-seq analyses indicates that Polycomb-bound DMVs form insulated and self-interacting chromatin domains. Further investigations show that DNA hypomethylation is better correlated with the binding of Polycomb than with H3K27me3. In support of a role of Polycomb in DMV hypomethylation, we observe aberrant methylation in DMVs in mouse embryonic stem cells deficient in the EED protein. Finally, we show that Polycomb regulates hypomethylation of DMVs likely through ten-eleven translocation (TET) proteins.ConclusionsWe show that Polycomb promotes the hypomethylation of DMVs near key developmental genes. These data reveal a delicate interplay between histone modifiers and DNA methylation, which contributes to their division at distinct gene targets, allowing lineage-specifying genes to largely maintain DNA methylation-free at regulatory elements.Electronic supplementary materialThe online version of this article (10.1186/s13059-018-1390-8) contains supplementary material, which is available to authorized users.

Abstract Promoter design remains one of the most important considerations in metabolic engineering and synthetic biology applications. Theoretically, there are 450 possible sequences for a 50-nt promoter, of which naturally occurring promoters make up only a small subset. To explore the vast number of potential sequences, we report a novel AI-based framework for de novo promoter design in Escherichia coli. The model, which was guided by sequence features learned from natural promoters, could capture interactions between nucleotides at different positions and design novel synthetic promoters in silico. We combined a deep generative model that guides the search for artificial sequences with a predictive model to preselect the most promising promoters. The AI-designed promoters were optimized based on the promoter activity in E. coli and the predictive model. After two rounds of optimization, up to 70.8% of the AI-designed promoters were experimentally demonstrated to be functional, and few of them shared significant sequence similarity with the E. coli genome. Our work provided an end-to-end approach to the de novo design of novel promoter elements, indicating the potential to apply deep learning methods to de novo genetic element design.

In recent years, coverage-based greybox fuzzing has proven itself to be one of the most effective techniques for finding security bugs in practice. Particularly, American Fuzzy Lop (AFL for short) is deemed to be a great success in fuzzing relatively simple test inputs. Unfortunately, when it meets structured test inputs such as XML and JavaScript, those grammar-blind trimming and mutation strategies in AFL hinder the effectiveness and efficiency.To this end, we propose a grammar-aware coverage-based greybox fuzzing approach to fuzz programs that process structured inputs. Given the grammar (which is often publicly available) of test inputs, we introduce a grammar-aware trimming strategy to trim test inputs at the tree level using the abstract syntax trees (ASTs) of parsed test inputs. Further, we introduce two grammar-aware mutation strategies (i.e., enhanced dictionary-based mutation and tree-based mutation). Specifically, tree-based mutation works via replacing subtrees using the ASTs of parsed test inputs. Equipped with grammar-awareness, our approach can carry the fuzzing exploration into width and depth.We implemented our approach as an extension to AFL, named Superion; and evaluated the effectiveness of Superion on real-life large-scale programs (a XML engine libplist and three JavaScript engines WebKit, Jerryscript and ChakraCore). Our results have demonstrated that Superion can improve the code coverage (i.e., 16.7% and 8.8% in line and function coverage) and bug-finding capability (i.e., 31 new bugs, among which we discovered 21 new vulnerabilities with 16 CVEs assigned and 3.2K USD bug bounty rewards received) over AFL and jsfunfuzz. We also demonstrated the effectiveness of our grammar-aware trimming and mutation.

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