RNA exosome-regulated long non-coding RNA transcription controls super-enhancer activity.

We have ablated the cellular RNA degradation machinery in differentiated B cells and pluripotent embryonic stem cells (ESCs) by conditional mutagenesis of core (Exosc3) and nuclear RNase (Exosc10) components of RNA exosome and identified a vast number of long non-coding RNAs (lncRNAs) and enhancer RNAs (eRNAs) with emergent functionality. Unexpectedly, eRNA-expressing regions accumulate R-loop structures upon RNA exosome ablation, thus demonstrating the role of RNA exosome in resolving deleterious DNA/RNA hybrids arising from active enhancers. We have uncovered a distal divergent eRNA-expressing element (lncRNA-CSR) engaged in long-range DNA interactions and regulating IgH 3' regulatory region super-enhancer function. CRISPR-Cas9-mediated ablation of lncRNA-CSR transcription decreases its chromosomal looping-mediated association with the IgH 3' regulatory region super-enhancer and leads to decreased class switch recombination efficiency. We propose that the RNA exosome protects divergently transcribed lncRNA expressing enhancers by resolving deleterious transcription-coupled secondary DNA structures, while also regulating long-range super-enhancer chromosomal interactions important for cellular function.

Critical Role for the DNA Sensor AIM2 in Stem Cell Proliferation and Cancer.

Colorectal cancer is a leading cause of cancer-related deaths. Mutations in the innate immune sensor AIM2 are frequently identified in patients with colorectal cancer, but how AIM2 modulates colonic tumorigenesis is unknown. Here, we found that Aim2-deficient mice were hypersusceptible to colonic tumor development. Production of inflammasome-associated cytokines and other inflammatory mediators was largely intact in Aim2-deficient mice; however, intestinal stem cells were prone to uncontrolled proliferation. Aberrant Wnt signaling expanded a population of tumor-initiating stem cells in the absence of AIM2. Susceptibility of Aim2-deficient mice to colorectal tumorigenesis was enhanced by a dysbiotic gut microbiota, which was reduced by reciprocal exchange of gut microbiota with healthy wild-type mice. These findings uncover a synergy between a specific host genetic factor and gut microbiota in determining the susceptibility to colorectal cancer. Therapeutic modulation of AIM2 expression and microbiota has the potential to prevent colorectal cancer.

Mechanism of noncoding RNA-associated N6-methyladenosine recognition by an RNA processing complex during IgH DNA recombination.

Immunoglobulin heavy chain (IgH) locus-associated G-rich long noncoding RNA (SµGLT) is important for physiological and pathological B cell DNA recombination. We demonstrate that the METTL3 enzyme-catalyzed N6-methyladenosine (m6A) RNA modification drives recognition and 3' end processing of SµGLT by the RNA exosome, promoting class switch recombination (CSR) and suppressing chromosomal translocations. The recognition is driven by interaction of the MPP6 adaptor protein with nuclear m6A reader YTHDC1. MPP6 and YTHDC1 promote CSR by recruiting AID and the RNA exosome to actively transcribe SµGLT. Direct suppression of m6A modification of SµGLT or of m6A reader YTHDC1 reduces CSR. Moreover, METTL3, an essential gene for B cell development in the bone marrow and germinal center, suppresses IgH-associated aberrant DNA breaks and prevents genomic instability. Taken together, we propose coordinated and central roles for MPP6, m6A modification, and m6A reader proteins in controlling long noncoding RNA processing, DNA recombination, and development in B cells.

LogicGep: Boolean networks inference using symbolic regression from time-series transcriptomic profiling data.

Reconstructing the topology of gene regulatory network from gene expression data has been extensively studied. With the abundance functional transcriptomic data available, it is now feasible to systematically decipher regulatory interaction dynamics in a logic form such as a Boolean network (BN) framework, which qualitatively indicates how multiple regulators aggregated to affect a common target gene. However, inferring both the network topology and gene interaction dynamics simultaneously is still a challenging problem since gene expression data are typically noisy and data discretization is prone to information loss. We propose a new method for BN inference from time-series transcriptional profiles, called LogicGep. LogicGep formulates the identification of Boolean functions as a symbolic regression problem that learns the Boolean function expression and solve it efficiently through multi-objective optimization using an improved gene expression programming algorithm. To avoid overly emphasizing dynamic characteristics at the expense of topology structure ones, as traditional methods often do, a set of promising Boolean formulas for each target gene is evolved firstly, and a feed-forward neural network trained with continuous expression data is subsequently employed to pick out the final solution. We validated the efficacy of LogicGep using multiple datasets including both synthetic and real-world experimental data. The results elucidate that LogicGep adeptly infers accurate BN models, outperforming other representative BN inference algorithms in both network topology reconstruction and the identification of Boolean functions. Moreover, the execution of LogicGep is hundreds of times faster than other methods, especially in the case of large network inference.

IRF7 Exacerbates Candida albicans Infection by Compromising CD209-Mediated Phagocytosis and Autophagy-Mediated Killing in Macrophages.

IFN regulatory factor 7 (IRF7) exerts anti-infective effects by promoting the production of IFNs in various bacterial and viral infections, but its role in highly morbid and fatal Candida albicans infections is unknown. We unexpectedly found that Irf7 gene expression levels were significantly upregulated in tissues or cells after C. albicans infection in humans and mice and that IRF7 actually exacerbates C. albicans infection in mice independent of its classical function in inducing IFNs production. Compared to controls, Irf7-/- mice showed stronger phagocytosis of fungus, upregulation of C-type lectin receptor CD209 expression, and enhanced P53-AMPK-mTOR-mediated autophagic signaling in macrophages after C. albicans infection. The administration of the CD209-neutralizing Ab significantly hindered the phagocytosis of Irf7-/- mouse macrophages, whereas the inhibition of p53 or autophagy impaired the killing function of these macrophages. Thus, IRF7 exacerbates C. albicans infection by compromising the phagocytosis and killing capacity of macrophages via regulating CD209 expression and p53-AMPK-mTOR-mediated autophagy, respectively. This finding reveals a novel function of IRF7 independent of its canonical IFNs production and its unexpected role in enhancing fungal infections, thus providing more specific and effective targets for antifungal therapy.

Type 1 Interferons Induce Changes in Core Metabolism that Are Critical for Immune Function.

Greater understanding of the complex host responses induced by type 1 interferon (IFN) cytokines could allow new therapeutic approaches for diseases in which these cytokines are implicated. We found that in response to the Toll-like receptor-9 agonist CpGA, plasmacytoid dendritic cells (pDC) produced type 1 IFNs, which, through an autocrine type 1 IFN receptor-dependent pathway, induced changes in cellular metabolism characterized by increased fatty acid oxidation (FAO) and oxidative phosphorylation (OXPHOS). Direct inhibition of FAO and of pathways that support this process, such as fatty acid synthesis, prevented full pDC activation. Type 1 IFNs also induced increased FAO and OXPHOS in non-hematopoietic cells and were found to be responsible for increased FAO and OXPHOS in virus-infected cells. Increased FAO and OXPHOS in response to type 1 IFNs was regulated by PPARα. Our findings reveal FAO, OXPHOS and PPARα as potential targets to therapeutically modulate downstream effects of type 1 IFNs.

AIM2 enhances Candida albicans infection through promoting macrophage apoptosis via AKT signaling.

Candida albicans is among the most prevalent invasive fungal pathogens for immunocompromised individuals and novel therapeutic approaches that involve immune response modulation are imperative. Absent in melanoma 2 (AIM2), a pattern recognition receptor for DNA sensing, is well recognized for its involvement in inflammasome formation and its crucial role in safeguarding the host against various pathogenic infections. However, the role of AIM2 in host defense against C. albicans infection remains uncertain. This study reveals that the gene expression of AIM2 is induced in human and mouse innate immune cells or tissues after C. albicans infection. Furthermore, compared to their wild-type (WT) counterparts, Aim2-/- mice surprisingly exhibit resistance to C. albicans infection, along with reduced inflammation in the kidneys post-infection. The resistance of Aim2-/- mice to C. albicans infection is not reliant on inflammasome or type I interferon production. Instead, Aim2-/- mice display lower levels of apoptosis in kidney tissues following infection than WT mice. The deficiency of AIM2 in macrophages, but not in dendritic cells, results in a phenocopy of the resistance observed in Aim2-/- mice against C. albican infection. The treatment of Clodronate Liposome, a reagent that depletes macrophages, also shows the critical role of macrophages in host defense against C. albican infection in Aim2-/- mice. Furthermore, the reduction in apoptosis is observed in Aim2-/- mouse macrophages following infection or treatment of DNA from C. albicans in comparison with controls. Additionally, higher levels of AKT activation are observed in Aim2-/- mice, and treatment with an AKT inhibitor reverses the host resistance to C. albicans infection. The findings collectively demonstrate that AIM2 exerts a negative regulatory effect on AKT activation and enhances macrophage apoptosis, ultimately compromising host defense against C. albicans infection. This suggests that AIM2 and AKT may represent promising therapeutic targets for the management of fungal infections.

GeoBind: segmentation of nucleic acid binding interface on protein surface with geometric deep learning.

Unveiling the nucleic acid binding sites of a protein helps reveal its regulatory functions in vivo. Current methods encode protein sites from the handcrafted features of their local neighbors and recognize them via a classification, which are limited in expressive ability. Here, we present GeoBind, a geometric deep learning method for predicting nucleic binding sites on protein surface in a segmentation manner. GeoBind takes the whole point clouds of protein surface as input and learns the high-level representation based on the aggregation of their neighbors in local reference frames. Testing GeoBind on benchmark datasets, we demonstrate GeoBind is superior to state-of-the-art predictors. Specific case studies are performed to show the powerful ability of GeoBind to explore molecular surfaces when deciphering proteins with multimer formation. To show the versatility of GeoBind, we further extend GeoBind to five other types of ligand binding sites prediction tasks and achieve competitive performances.

Clinical signature and associated immune metabolism of NLRP1 in pan-cancer.

Inflammations have been linked to tumours, suggesting a potential association between NLRP1 and cancer. Nevertheless, a systematic assessment of NLRP1's role across various cancer types currently absent. A comprehensive bioinformatic analysis was conducted to determine whether NLRP1 exhibits prognostic relevance linked to immune metabolism across various cancers. The study leveraged data from the TCGA and GTEx databases to explore the clinical significance, metabolic features, and immunological characteristics of NLRP1, employing various tools such as R, GEPIA, STRING and TISIDB. NLRP1 exhibited differential expression patterns across various cancers, with elevated expression correlating with a more favourable prognosis in lung adenocarcinoma (LUAD) and pancreatic adenocarcinoma (PAAD). Downregulation of NLRP1 reduced tumour metabolic activity in LUAD. Moreover, the mutational signature of NLRP1 was linked to a favourable prognosis. Interestingly, high NLRP1 expression inversely correlated with tumour stemness while positively correlating with tumour immune infiltration in various cancers including LUAD and PAAD. Through extensive big data analysis, we delved into the role of NLRP1 across various tumour types, constructing a comprehensive role map of its involvement in pan-cancer scenarios. Our findings highlight the potential of NLRP1 as a promising therapeutic target specifically in LUAD and PAAD.

Assessment of SWI/SNF chromatin remodeling complex related genes as potential biomarkers and therapeutic targets in pan-cancer.

Recent research has uncovered a surprisingly high occurrence of aberrant expression and mutations in the genes that encode subunits of the SWI/SNF chromatin-remodeling complexes (SCRC). Nevertheless, the carcinogenic effects of aberrant expression and mutations in SWI/SNF genes have only been acknowledged in recent times, resulting in a comparatively limited understanding of these modifications. In this study, we comprehensively analyzed the expression difference, somatic mutation, potential biological pathways, stromal or immune cell infiltration, and drug sensitivity of SCRC-related genes (SCRGs) in pan-cancer. Furthermore, the evolutionary trend, prognostic signature, and immunotherapy response of SCRGs in kidney renal clear cell carcinoma (KIRC) were also evaluated. The expression of SCRGs was changed in 13 out of 14 tumor types, strongly linked to prognosis, and mutated in 30.9% of tumor patients. SCRGs were also closely associated with immune-related pathways and tumor metastasis pathways. The expression of SCRGs was positively associated with the immune score or stromal score but negatively correlated with Tumor purity. Three potential drugs (FK866, Ispinesib mesylate, and WZ3105) were identified to target the SCRGs. In KIRC, scRNA-seq analysis showed that the enrichment of SCRC and the communication frequency with immune cells were significantly declined during tumor cell progression. A prognostic signature was constructed in KIRC and was effective in predicting the prognosis for KIRC. Aberrant expression of eleven prognostic genes identified from the KIRC prognostic signature and the cytotoxicity of FK866 and Ispinesib mesylate to KIRC were verified by qRT-PCR and CCK-8 assay, respectively. Our study identified SCRGs as potential biomarker and therapeutic targets, providing new insights into SCRC for tumor-targeted therapy.

Hydrophobicity Promoted Efficient Hydroxyl Radical Generation in Visible-Light-Driven Photocatalytic Oxidation.

Hydroxyl radical (•OH) with strong oxidation capability is one of the most important reactive oxygen species. The generation of •OH from superoxide radicals (•O2 -) is an important process in visible-light-driven photocatalysis, but the conversion generally suffers from slow reaction kinetics. Here, a hydrophobicity promoted efficient •OH generation in a visible-light-driven semiconductor-mediated photodegradation reaction is reported. Hydrophobic TiO2 that is synthesized by modifying the TiO2 surface with a thin polydimethylsiloxane (PDMS) layer and rhodamine B (RhB) are used as model semiconductors and dye molecules, respectively. The surface hydrophobicity resulted in the formation of a solid-liquid-air triphase interface microenvironment, which increased the local concentration of O2. In the meanwhile, the saturated adsorption quantity of RhB on hydrophobic TiO2 is improved by five-fold than that on untreated TiO2. These advantages increased the density of the conduction band photoelectrons and •O2 - generation, and stimulated the conversion of •O2 - to •OH. This consequently not only increased the kinetics of the photocatalytic reaction by an order of magnitude, but also altered the oxidation route from conventional decolorization to mineralization. This study highlights the importance of surface wettability modulation in boosting •OH generation in visible-light-driven photocatalysis.

ActivePPI: quantifying protein-protein interaction network activity with Markov random fields.

MOTIVATION: Protein-protein interactions (PPI) are crucial components of the biomolecular networks that enable cells to function. Biological experiments have identified a large number of PPI, and these interactions are stored in knowledge bases. However, these interactions are often restricted to specific cellular environments and conditions. Network activity can be characterized as the extent of agreement between a PPI network (PPIN) and a distinct cellular environment measured by protein mass spectrometry, and it can also be quantified as a statistical significance score. Without knowing the activity of these PPI in the cellular environments or specific phenotypes, it is impossible to reveal how these PPI perform and affect cellular functioning. RESULTS: To calculate the activity of PPIN in different cellular conditions, we proposed a PPIN activity evaluation framework named ActivePPI to measure the consistency between network architecture and protein measurement data. ActivePPI estimates the probability density of protein mass spectrometry abundance and models PPIN using a Markov-random-field-based method. Furthermore, empirical P-value is derived based on a nonparametric permutation test to quantify the likelihood significance of the match between PPIN structure and protein abundance data. Extensive numerical experiments demonstrate the superior performance of ActivePPI and result in network activity evaluation, pathway activity assessment, and optimal network architecture tuning tasks. To summarize it succinctly, ActivePPI is a versatile tool for evaluating PPI network that can uncover the functional significance of protein interactions in crucial cellular biological processes and offer further insights into physiological phenomena. AVAILABILITY AND IMPLEMENTATION: All source code and data are freely available at https://github.com/zpliulab/ActivePPI.

LogBTF: gene regulatory network inference using Boolean threshold network model from single-cell gene expression data.

MOTIVATION: From a systematic perspective, it is crucial to infer and analyze gene regulatory network (GRN) from high-throughput single-cell RNA sequencing data. However, most existing GRN inference methods mainly focus on the network topology, only few of them consider how to explicitly describe the updated logic rules of regulation in GRNs to obtain their dynamics. Moreover, some inference methods also fail to deal with the over-fitting problem caused by the noise in time series data. RESULTS: In this article, we propose a novel embedded Boolean threshold network method called LogBTF, which effectively infers GRN by integrating regularized logistic regression and Boolean threshold function. First, the continuous gene expression values are converted into Boolean values and the elastic net regression model is adopted to fit the binarized time series data. Then, the estimated regression coefficients are applied to represent the unknown Boolean threshold function of the candidate Boolean threshold network as the dynamical equations. To overcome the multi-collinearity and over-fitting problems, a new and effective approach is designed to optimize the network topology by adding a perturbation design matrix to the input data and thereafter setting sufficiently small elements of the output coefficient vector to zeros. In addition, the cross-validation procedure is implemented into the Boolean threshold network model framework to strengthen the inference capability. Finally, extensive experiments on one simulated Boolean value dataset, dozens of simulation datasets, and three real single-cell RNA sequencing datasets demonstrate that the LogBTF method can infer GRNs from time series data more accurately than some other alternative methods for GRN inference. AVAILABILITY AND IMPLEMENTATION: The source data and code are available at https://github.com/zpliulab/LogBTF.

Inter-eye asymmetry of microvascular density in patients on hydroxychloroquine therapy by optical coherence tomography angiography.

AIMS: To explore the inter-eye retinal microvascular density asymmetry of patients on hydroxychloroquine (HCQ) therapy through optical coherence tomography angiography (OCTA). METHODS: 40 subjects were enrolled in this cross-sectional study, including 20 systemic lupus erythematasus patients currently treated with HCQ (40 eyes) and 20 age- and sex-matched normal controls (NCs, 40 eyes). OCTA images were obtained to measure macular and peripapillary mircrovasculatures and microstructures, including vessel density, retinal nerver fiber layer thickness, and peripapillary ganglion cell-inner plexiform layer thickness. The absolute values of the difference between right and left eyes were taken as a measure of inter-eye asymmetry. RESULTS: Macular whole image vessel density (wiVD-M) and perifoveal vessel density (pfVD) of superficial capillary plexus (SCP) were notably reduced in both the right and left eyes of the HCQ treatment group compared with NCs. Specifically, SLE patients treated with HCQ have higher inter-eye asymmetry of wiVD-M of SCP (2.28 ± 1.03 vs 1.27 ± 0.79, p < 0.01) and pfVD of SCP (2.55 ± 1.26 vs 1.78 ± 1.06, p = 0.04) compared with NCs. There were no significant differences in inter-eye asymmetry of structure parameters. Inter-eye asymmetry of wiVD-M of SCP (AUC = 0.80, p < 0.01) and pfVD of SCP (AUC = 0.71, p = 0.02) exhibited greater discrimination power. CONCLUSION: SLE Patients treated with HCQ exhibited a notably higher inter-eye vessel density asymmetry compared to that of NCs. Thus, inter-eye vessel density asymmetry could be used to screen for HCQ retinal toxicity.

Limitations of Classically Simulable Measurements for Quantum State Discrimination.

In the realm of fault-tolerant quantum computing, stabilizer operations play a pivotal role, characterized by their remarkable efficiency in classical simulation. This efficiency sets them apart from nonstabilizer operations within the quantum computational theory. In this Letter, we investigate the limitations of classically simulable measurements in distinguishing quantum states. We demonstrate that any pure magic state and its orthogonal complement of odd prime dimensions cannot be unambiguously distinguished by stabilizer operations, regardless of how many copies of the states are supplied. We also reveal intrinsic similarities and distinctions between the quantum resource theories of magic states and entanglement in quantum state discrimination. The results emphasize the inherent limitations of classically simulable measurements and contribute to a deeper understanding of the quantum-classical boundary.

The RNA-binding proteins regulate innate antiviral immune signaling by modulating pattern recognition receptors.

Viral infections pose significant threats to human health, leading to a diverse spectrum of infectious diseases. The innate immune system serves as the primary barrier against viruses and bacteria in the early stages of infection. A rapid and forceful antiviral innate immune response is triggered by distinguishing between self-nucleic acids and viral nucleic acids. RNA-binding proteins (RBPs) are a diverse group of proteins which contain specific structural motifs or domains for binding RNA molecules. In the last decade, numerous of studies have outlined that RBPs influence viral replication via diverse mechanisms, directly recognizing viral nucleic acids and modulating the activity of pattern recognition receptors (PRRs). In this review, we summarize the functions of RBPs in regulation of host-virus interplay by controlling the activation of PRRs, such as RIG-I, MDA5, cGAS and TLR3. RBPs are instrumental in facilitating the identification of viral RNA or DNA, as well as viral structural proteins within the cellular cytoplasm and nucleus, functioning as co-receptor elements. On the other hand, RBPs are capable of orchestrating the activation of PRRs and facilitating the transmission of antiviral signals to downstream adaptor proteins by post-translational modifications or aggregation. Gaining a deeper comprehension of the interaction between the host and viruses is crucial for the development of novel therapeutics targeting viral infections.

E3 ubiquitin ligase RNF128 negatively regulates the IL-3/STAT5 signaling pathway by facilitating K27-linked polyubiquitination of IL-3Rα.

IL-3/STAT5 signaling pathway is crucial for the development and activation of immune cells, contributing to the cellular response to infections and inflammatory stimuli. Dysregulation of the IL-3/STAT5 signaling have been associated with inflammatory and autoimmune diseases characterized by inflammatory cell infiltration and organ damage. IL-3 receptor α (IL-3Rα) specifically binds to IL-3 and initiates intracellular signaling, resulting in the phosphorylation of STAT5. However, the regulatory mechanisms of IL-3Rα remain unclear. Here, we identified the E3 ubiquitin ligase RNF128 as a negative regulator of IL-3/STAT5 signaling by targeting IL-3Rα for lysosomal degradation. RNF128 was shown to selectively bind to IL-3Rα, without interacting with the common beta chain IL-3Rß, which shares the subunit with GM-CSF. The deficiency of Rnf128 had no effect on GM-CSF-induced phosphorylation of Stat5, but it resulted in heightened Il-3-triggered activation of Stat5 and increased transcription of the Id1, Pim1, and Cd69 genes. Furthermore, we found that RNF128 promoted the K27-linked polyubiquitination of IL-3Rα in a ligase activity-dependent manner, ultimately facilitating its degradation through the lysosomal pathway. RNF128 inhibited the activation and chemotaxis of macrophages in response to LPS stimulation, thereby attenuating excessive inflammatory responses. Collectively, these results reveal that RNF128 negatively regulates the IL-3/STAT5 signaling pathway by facilitating K27-linked polyubiquitination of IL-3Rα. This study uncovers E3 ubiquitin ligase RNF128 as a novel regulator of the IL-3/STAT5 signaling pathway, providing potential molecular targets for the treatment of inflammatory diseases.

Blockade of endothelial adenosine receptor 2 A suppresses atherosclerosis in vivo through inhibiting CREB-ALK5-mediated endothelial to mesenchymal transition.

Cardiovascular diseases (CVDs) are the leading cause of death worldwide, and morbidity and mortality rates continue to rise. Atherosclerosis constitutes the principal etiology of CVDs. Endothelial injury, inflammation, and dysfunction are the initiating factors of atherosclerosis. Recently, we reported that endothelial adenosine receptor 2 A (ADORA2A), a G protein-coupled receptor (GPCR), plays critical roles in neovascularization disease and cerebrovascular disease. However, the precise role of endothelial ADORA2A in atherosclerosis is still not fully understood. Here, we showed that ADORA2A expression was markedly increased in the aortic endothelium of humans with atherosclerosis or Apoe-/- mice fed a high-cholesterol diet. In vivo studies unraveled that endothelial-specific Adora2a deficiency alleviated endothelial-to-mesenchymal transition (EndMT) and prevented the formation and instability of atherosclerotic plaque in Apoe-/- mice. Moreover, pharmacologic inhibition of ADORA2A with KW6002 recapitulated the anti-atherogenic phenotypes observed in genetically Adora2a-deficient mice. In cultured human aortic endothelial cells (HAECs), siRNA knockdown of ADORA2A or KW6002 inhibition of ADORA2A decreased EndMT, whereas adenoviral overexpression of ADORA2A induced EndMT. Mechanistically, ADORA2A upregulated ALK5 expression via a cAMP/PKA/CREB axis, leading to TGFß-Smad2/3 signaling activation, thereby promoting EndMT. In conclusion, these findings, for the first time, demonstrate that blockade of ADORA2A attenuated atherosclerosis via inhibition of EndMT induced by the CREB1-ALK5 axis. This study discloses a new link between endothelial ADORA2A and EndMT and indicates that inhibiting endothelial ADORA2A could be an effective novel strategy for the prevention and treatment of atherosclerotic CVDs.

Synthesis, antitumor activity evaluation of 2-selenocyano-3-selenocyanoalkyloxyestradiols with a bisselenocyanate structure.

The combination of steroid structure and selenocyano group offers high potential for the design and synthesis of new potential anti-tumor drugs. Beginning with estradiol, a series of 2-selenocyano-3-selenocyanoalkyloxyestradiol derivatives with remarkable antiproliferative activity was synthesized. Additionally, a 2,4-bisselenocyanoestradiol was synthesized by directly selenocyanating estradiol diacetate. It was found that the cytotoxicity of 2-selenocyano-3-selenocyanoalkyloxyestradiol derivatives was significantly increased in comparison to the corresponding monoselenocyanate precursor, whereas the cytotoxicity of the 2, 4-bisselenocyanoestradiol derivative was significantly reduced compared to the respective monosubstituted precursor. The introduction of the second selenocyano group at different locations of estradiol shows a various impact on the cytotoxicity of the compounds. Among them, compound 3e showed the best cytotoxicity, with an IC50 value of less than 5 µM against the tested tumor cells, and strong inhibitory activities against HeLa and MCF-7 cell xenograft tumors in zebrafish, suppressing tumor cell migration and neovascularization. Notably, compound 3e was more effective at inhibiting neovascularization of MCF-7 cell xenograft tumors than the positive control 2-methoxyestradiol. Furthermore, compound 3e showed excellent anti-oxidative stress effect in zebrafish. Therefore, these estrogen bisselenocyanate compounds may be promising anti-tumor agents, warranting further investigation.

Targeting CDK12 obviates the malignant phenotypes of colorectal cancer through the Wnt/ß-catenin signaling pathway.

Colorectal cancer (CRC) is the second most common cause of cancer-related mortality and lies third in terms of morbidity due to the limited number of effective druggable targets. Since cancer stem cells (CSCs) are considered to be one of the roots of tumorigenesis, outgrowth and metastasis, targeting CSCs may be a promising strategy to reverse the malignant phenotypes of CRC. Cyclin-dependent kinase 12 (CDK12) has been reported to be involved in the self-renewal of CSCs in various cancers, rendering it an attractive potential target against CSCs to consequently limit the malignant phenotypes in CRC. In the present study, we aimed to investigate whether CDK12 can be a potential therapeutic target for patients with CRC and clarify its underlying mechanism. We found that CDK12, but not CDK13 is required for CRC survival. CDK12 was found to drive tumor initiation according to the colitis-associated colorectal cancer mouse model. In addition, CDK12 promoted CRC outgrowth and hepatic metastasis in the subcutaneous allograft and liver metastasis mouse models, respectively. In particular, CDK12 was able to induce the self-renewal of CRC CSCs. Mechanistically, the activation of Wnt/ß-catenin signaling mediated by CDK12 was implicated in stemness regulation and malignant phenotype maintenance. These findings indicate that CDK12 is a candidate druggable target in CRC. Therefore, the CDK12 inhibitor SR-4835 warrants clinical trial testing in patients with CRC.