The Mevalonate Pathway Is a Druggable Target for Vaccine Adjuvant Discovery.

Motivated by the clinical observation that interruption of the mevalonate pathway stimulates immune responses, we hypothesized that this pathway may function as a druggable target for vaccine adjuvant discovery. We found that lipophilic statin drugs and rationally designed bisphosphonates that target three distinct enzymes in the mevalonate pathway have potent adjuvant activities in mice and cynomolgus monkeys. These inhibitors function independently of conventional "danger sensing." Instead, they inhibit the geranylgeranylation of small GTPases, including Rab5 in antigen-presenting cells, resulting in arrested endosomal maturation, prolonged antigen retention, enhanced antigen presentation, and T cell activation. Additionally, inhibiting the mevalonate pathway enhances antigen-specific anti-tumor immunity, inducing both Th1 and cytolytic T cell responses. As demonstrated in multiple mouse cancer models, the mevalonate pathway inhibitors are robust for cancer vaccinations and synergize with anti-PD-1 antibodies. Our research thus defines the mevalonate pathway as a druggable target for vaccine adjuvants and cancer immunotherapies.

A Structural Change in Butyrophilin upon Phosphoantigen Binding Underlies Phosphoantigen-Mediated Vγ9Vδ2 T Cell Activation.

Human Vγ9Vδ2 T cells respond to microbial infections and malignancy by sensing diphosphate-containing metabolites called phosphoantigens, which bind to the intracellular domain of butyrophilin 3A1, triggering extracellular interactions with the Vγ9Vδ2 T cell receptor (TCR). Here, we examined the molecular basis of this "inside-out" triggering mechanism. Crystal structures of intracellular butyrophilin 3A proteins alone or in complex with the potent microbial phosphoantigen HMBPP or a synthetic analog revealed key features of phosphoantigens and butyrophilins required for γδ T cell activation. Analyses with chemical probes and molecular dynamic simulations demonstrated that dimerized intracellular proteins cooperate in sensing HMBPP to enhance the efficiency of γδ T cell activation. HMBPP binding to butyrophilin doubled the binding force between a γδ T cell and a target cell during "outside" signaling, as measured by single-cell force microscopy. Our findings provide insight into the "inside-out" triggering of Vγ9Vδ2 T cell activation by phosphoantigen-bound butyrophilin, facilitating immunotherapeutic drug design.

The transcription factor Foxm1 is essential for the quiescence and maintenance of hematopoietic stem cells.

Foxm1 is known as a typical proliferation-associated transcription factor. Here we found that Foxm1 was essential for maintenance of the quiescence and self-renewal capacity of hematopoietic stem cells (HSCs) in vivo in mice. Reducing expression of FOXM1 also decreased the quiescence of human CD34(+) HSCs and progenitor cells, and its downregulation was associated with a subset of myelodysplastic syndrome (MDS). Mechanistically, Foxm1 directly bound to the promoter region of the gene encoding the receptor Nurr1 (Nr4a2; called 'Nurr1' here), inducing transcription, while forced expression of Nurr1 reversed the loss of quiescence observed in Foxm1-deficient cells in vivo. Thus, our studies reveal a previously unrecognized role for Foxm1 as a critical regulator of the quiescence and self-renewal of HSCs mediated at least in part by control of Nurr1 expression.

Correction: Farnesyl pyrophosphate is a new danger signal inducing acute cell death.

[This corrects the article DOI: 10.1371/journal.pbio.3001134.].

Cytosolic GDH1 degradation restricts protein synthesis to sustain tumor cell survival following amino acid deprivation.

The mTORC1 pathway plays key roles in regulating various biological processes, including sensing amino acid deprivation and driving expression of ribosomal protein (RP)-coding genes. In this study, we observed that depletion of glutamate dehydrogenase 1 (GDH1), an enzyme that converts glutamate to α-ketoglutarate (αKG), confers resistance to amino acid deprivation on kidney renal clear cell carcinoma (KIRC) cells. Mechanistically, under conditions of adequate nutrition, GDH1 maintains RP gene expression in a manner dependent on its enzymatic activity. Following amino acid deprivation or mTORC1 inhibition, GDH1 translocates from mitochondria to the cytoplasm, where it becomes ubiquitinated and degraded via the E3 ligase RNF213. GDH1 degradation reduces intracellular αKG levels by more than half and decreases the activity of αKG-dependent lysine demethylases (KDMs). Reduced KDM activity in turn leads to increased histone H3 lysine 9 and 27 methylation, further suppressing RP gene expression and preserving nutrition to support cell survival. In summary, our study exemplifies an economical and efficient strategy of solid tumor cells for coping with amino acid deficiency, which might in the future be targeted to block renal carcinoma progression.

A feature extraction method based on noise reduction for circRNA-miRNA interaction prediction combining multi-structure features in the association networks.

MOTIVATION: A large number of studies have shown that circular RNA (circRNA) affects biological processes by competitively binding miRNA, providing a new perspective for the diagnosis, and treatment of human diseases. Therefore, exploring the potential circRNA-miRNA interactions (CMIs) is an important and urgent task at present. Although some computational methods have been tried, their performance is limited by the incompleteness of feature extraction in sparse networks and the low computational efficiency of lengthy data. RESULTS: In this paper, we proposed JSNDCMI, which combines the multi-structure feature extraction framework and Denoising Autoencoder (DAE) to meet the challenge of CMI prediction in sparse networks. In detail, JSNDCMI integrates functional similarity and local topological structure similarity in the CMI network through the multi-structure feature extraction framework, then forces the neural network to learn the robust representation of features through DAE and finally uses the Gradient Boosting Decision Tree classifier to predict the potential CMIs. JSNDCMI produces the best performance in the 5-fold cross-validation of all data sets. In the case study, seven of the top 10 CMIs with the highest score were verified in PubMed. AVAILABILITY: The data and source code can be found at https://github.com/1axin/JSNDCMI.

DksA is a conserved master regulator of stress response in Acinetobacter baumannii.

Coordination of bacterial stress response mechanisms is critical for long-term survival in harsh environments for successful host infection. The general and specific stress responses of well-studied Gram-negative pathogens like Escherichia coli are controlled by alternative sigma factors, archetypically RpoS. The deadly hospital pathogen Acinetobacter baumannii is notoriously resistant to environmental stresses, yet it lacks RpoS, and the molecular mechanisms driving this incredible stress tolerance remain poorly defined. Here, using functional genomics, we identified the transcriptional regulator DksA as a master regulator for broad stress protection and virulence in A. baumannii. Transcriptomics, phenomics and in vivo animal studies revealed that DksA controls ribosomal protein expression, metabolism, mutation rates, desiccation, antibiotic resistance, and host colonization in a niche-specific manner. Phylogenetically, DksA was highly conserved and well-distributed across Gammaproteobacteria, with 96.6% containing DksA, spanning 88 families. This study lays the groundwork for understanding DksA as a major regulator of general stress response and virulence in this important pathogen.

MHESMMR: a multilevel model for predicting the regulation of miRNAs expression by small molecules.

According to the expression of miRNA in pathological processes, miRNAs can be divided into oncogenes or tumor suppressors. Prediction of the regulation relations between miRNAs and small molecules (SMs) becomes a vital goal for miRNA-target therapy. But traditional biological approaches are laborious and expensive. Thus, there is an urgent need to develop a computational model. In this study, we proposed a computational model to predict whether the regulatory relationship between miRNAs and SMs is up-regulated or down-regulated. Specifically, we first use the Large-scale Information Network Embedding (LINE) algorithm to construct the node features from the self-similarity networks, then use the General Attributed Multiplex Heterogeneous Network Embedding (GATNE) algorithm to extract the topological information from the attribute network, and finally utilize the Light Gradient Boosting Machine (LightGBM) algorithm to predict the regulatory relationship between miRNAs and SMs. In the fivefold cross-validation experiment, the average accuracies of the proposed model on the SM2miR dataset reached 79.59% and 80.37% for up-regulation pairs and down-regulation pairs, respectively. In addition, we compared our model with another published model. Moreover, in the case study for 5-FU, 7 of 10 candidate miRNAs are confirmed by related literature. Therefore, we believe that our model can promote the research of miRNA-targeted therapy.

Luminescence-Monitored Progressive Chemical Pressure Implementation Realized through Successive Y3+ and Mg2+ Doping into Ca10.5(PO4)7:Eu2.

Chemical pressure generated through ion doping into crystal lattices has been proven to be conducive to exploration of new matter, development of novel functionalities, and realization of unprecedented performances. However, studies are focusing on one-time doping, and there is a lack of both advanced investigations for multiple doping and sophisticated strategies to precisely and quantitatively track the gradual functionality evolution along with progressive chemical pressure implementation. Herein, high-valent Y3+ and equal-valent Mg2+ is successively doped to replace multiple Ca sites in Ca10.5(PO4)7:Eu2+. The luminescence evolution of Eu2+ serves as an optical probe, allowing step-by-step and atomic-level tracking of the site occupation of Y3+ and Mg2+, interassociation of Ca sites, and ultimately functionality improvement. The resulting Ca8MgY(PO4)7:Eu2+ displays a record-high relative sensitivity for optical thermometry. Utilization of the environment-sensitive emission of Eu2+ as a luminescent probe has offered a unique approach to monitoring structure-functionality evolution in vivo with atomic precision, which shall also be extended to optimization of other functionalities such as ferroelectricity, conductivity, thermoelectricity, and catalytic activity through precise control over atomic diffusion in other types of substances.

Catalytic Regio- and Enantioselective Boracarboxylation of Arylalkenes with CO2 and Diboron.

Catalytic asymmetric carboxylation of readily available alkenes with CO2, an abundant and sustainable one-carbon building block, that gives access to value-added α-stereogenic carboxylic acids in an atom- and step-economic manner is highly attractive. However, it has remained a formidable challenge for the synthetic community. Here, the first example of Cu-catalyzed highly regio- and enantioselective boracarboxylation reaction on various arylalkenes with diboron under an atmospheric pressure of CO2 is described, which afforded a variety of chiral ß-boron-functionalized α-aryl carboxylic acids with up to 87% yield and 97% ee under mild conditions. Importantly, α-substituted arylalkenes could also be subject to this protocol with excellent enantiopurities, thereby rendering an efficient approach for the generation of enantioenriched carboxylic acids with an α-chiral all-carbon quaternary center. Moreover, high functional group tolerance, scalable synthesis, and facile access to bioactive compounds, like (-)-scopolamine, (-)-anisodamine, and (-)-tropicamide, further demonstrated the synthetic utility of this strategy.

Catalytic Regio- and Enantioselective Remote Hydrocarboxylation of Unactivated Alkenes with CO2.

The catalytic regio- and enantioselective hydrocarboxylation of alkenes with carbon dioxide is a straightforward strategy to construct enantioenriched α-chiral carboxylic acids but remains a big challenge. Herein we report the first example of catalytic highly enantio- and site-selective remote hydrocarboxylation of a wide range of readily available unactivated alkenes with abundant and renewable CO2 under mild conditions enabled by the SaBOX/Ni catalyst. The key to this success is utilizing the chiral SaBOX ligand, which combines with nickel to simultaneously control both chain-walking and the enantioselectivity of carboxylation. This process directly furnishes a range of different alkyl-chain-substituted or benzo-fused α-chiral carboxylic acids bearing various functional groups in high yields and regio- and enantioselectivities. Furthermore, the synthetic utility of this methodology was demonstrated by the concise synthesis of the antiplatelet aggregation drug (R)-indobufen from commercial starting materials.

An Unexpected Decrease in Vibrational Entropy of Multicomponent Rutile Oxides.

High-entropy oxides (HEOs), featuring infinite chemical composition and exceptional physicochemical properties, are attracting much attention. The configurational entropy caused by a component disorder of HEOs is popularly believed to be the main driving force for thermal stability, while the role of vibrational entropy in the thermodynamic landscape has been neglected. In this study, we systematically investigated the vibrational entropy of multicomponent rutile oxides (including Fe0.5Ta0.5O2, Fe0.333Ti0.333Ta0.333O2, Fe0.25Ti0.25Ta0.25Sn0.25O2, and Fe0.21Ti0.21Ta0.21Sn0.21Ge0.16O2) by precise heat capacity measurements. It is found that vibrational entropy gradually decreases with increasing component disorder, beyond what one could expect from an equilibrium thermodynamics perspective. Moreover, all multicomponent rutile oxides exhibit a positive excess vibrational entropy at 298.15 K. Upon examinations of configuration disorder, size mismatch, phase transition, and polyhedral distortions, we demonstrate that the excess vibrational entropy plays a pivotal role in lowering the crystallization temperature of multicomponent rutile oxides. These findings represent the first experimental confirmation of the role of lattice vibrations in the thermodynamic landscape of rutile HEOs. In particular, vibrational entropy could serve as a novel descriptor to guide the predictive design of multicomponent oxide materials.

Predictions of photophysical properties of phosphorescent platinum(II) complexes based on ensemble machine learning approach.

Cyclometalated Pt(II) complexes are popular phosphorescent emitters with color-tunable emissions. To render their practical applications as organic light-emitting diodes emitters, it is required to develop Pt(II) complexes with high radiative decay rate constant and photoluminescence (PL) quantum yield. Here, a general protocol is developed for accurate predictions of emission wavelength, radiative decay rate constant, and PL quantum yield based on the combination of first-principles quantum mechanical method, machine learning, and experimental calibration. A new dataset concerning phosphorescent Pt(II) emitters is constructed, with more than 200 samples collected from the literature. Features containing pertinent electronic properties of the complexes are chosen and ensemble learning models combined with stacking-based approaches exhibit the best performance, where the values of squared correlation coefficients are 0.96, 0.81, and 0.67 for the predictions of emission wavelength, PL quantum yield and radiative decay rate constant, respectively. The accuracy of the protocol is further confirmed using 24 recently reported Pt(II) complexes, which demonstrates its reliability for a broad palette of Pt(II) emitters.

Amorphous MoS2 Decorated Ni3 S2 with a Core-shell Structure of Urchin-Like on Nickel-Foam Efficient Hydrogen Evolution in Acidic and Alkaline Media.

The large-scale commercialization of the hydrogen evolution reaction (HER) necessitates the development of cost-effective and highly efficient electrocatalysts. Although transition metal sulfides, such as MoS2 and Ni3 S2 , hold great potential in the field of HER, their catalytic performance has been unsatisfactory due to incomplete exposure of active sites and poor electrical conductivity. In this work, via a simple hydrothermal strategy, amorphous MoS2 nanoshells in the form of urchin-like MoS2 -Ni3 S2 core-shell heterogeneous structure is realized and in situ loaded on nickel foam (A-MoS2 -Ni3 S2 -NF). In particular, XPS analysis results show that the coupling of amorphous MoS2 and Ni3 S2 makes the electrode surface exhibit electron-abundant property, which will have a positive impact on HER catalytic activity. In addition, the fully exposed active site of amorphous MoS2 is another crucial factor contributing to its high catalytic performance of A-MoS2 -Ni3 S2 -NF electrode. In particular, at a current density of 10 mA cm⁻2 , the overpotential of electrode is 95 mV (1.0 m KOH) and 145 mV (0.5 m H2 SO4 ). This work highlights the importance of amorphous MoS2 and MoS2 -Ni3 S2 of sea-urchin core-shell structure in optimizing HER performance, which provides an important reference for HER research.

Yolk-Shell Gradient-Structured SiOx Anodes Derived from Periodic Mesoporous Organosilicas Enable High-Performance Lithium-Ion Batteries.

Gradient-structured materials hold great promise in the areas of batteries and electrocatalysis. Here, yolk-shell gradient-structured SiOx -based anode (YSG-SiOx /C@C) derived from periodic mesoporous organosilica spheres (PMOs) through a selective etching method is reported. Capitalizing on the poor hydrothermal stability of inorganic silica in organic-inorganic hybrid silica spheres, the inorganic silica component in the hybrid spheres is selectively etched to obtain yolk-shell-structured PMOs. Subsequently, the yolk-shell PMOs are coated with carbon to fabricate YSG-SiOx /C@C. YSG-SiOx /C@C is comprised of a core with uniform distribution of SiOx and carbon at the atomic scale, a middle void layer, and outer layers of SiOx and amorphous carbon. This unique gradient structure and composition from inside to outside not only enhances the electrical conductivity of the SiOx anode and reduces the side reactions, but also reserves void space for the expansion of SiOx , thereby effectively mitigating the stress caused by volumetric effect. As a result, YSG-SiOx /C@C exhibits exceptional cycling stability and rate capability. Specifically, YSG-SiOx /C@C maintains a specific capacity of 627 mAh g-1 after 400 cycles at 0.5 A g-1 , and remains stable even after 550 cycles at current density of 2 A g-1 , achieving a specific capacity of 519 mAh g-1 .

A biomedical knowledge graph-based method for drug-drug interactions prediction through combining local and global features with deep neural networks.

Drug-drug interactions (DDIs) prediction is a challenging task in drug development and clinical application. Due to the extremely large complete set of all possible DDIs, computer-aided DDIs prediction methods are getting lots of attention in the pharmaceutical industry and academia. However, most existing computational methods only use single perspective information and few of them conduct the task based on the biomedical knowledge graph (BKG), which can provide more detailed and comprehensive drug lateral side information flow. To this end, a deep learning framework, namely DeepLGF, is proposed to fully exploit BKG fusing local-global information to improve the performance of DDIs prediction. More specifically, DeepLGF first obtains chemical local information on drug sequence semantics through a natural language processing algorithm. Then a model of BFGNN based on graph neural network is proposed to extract biological local information on drug through learning embedding vector from different biological functional spaces. The global feature information is extracted from the BKG by our knowledge graph embedding method. In DeepLGF, for fusing local-global features well, we designed four aggregating methods to explore the most suitable ones. Finally, the advanced fusing feature vectors are fed into deep neural network to train and predict. To evaluate the prediction performance of DeepLGF, we tested our method in three prediction tasks and compared it with state-of-the-art models. In addition, case studies of three cancer-related and COVID-19-related drugs further demonstrated DeepLGF's superior ability for potential DDIs prediction. The webserver of the DeepLGF predictor is freely available at http://120.77.11.78/DeepLGF/.

HMGA1 chromatin regulators induce transcriptional networks involved in GATA2 and proliferation during MPN progression.

Myeloproliferative neoplasms (MPNs) transform to myelofibrosis (MF) and highly lethal acute myeloid leukemia (AML), although the actionable mechanisms driving progression remain elusive. Here, we elucidate the role of the high mobility group A1 (HMGA1) chromatin regulator as a novel driver of MPN progression. HMGA1 is upregulated in MPN, with highest levels after transformation to MF or AML. To define HMGA1 function, we disrupted gene expression via CRISPR/Cas9, short hairpin RNA, or genetic deletion in MPN models. HMGA1 depletion in JAK2V617F AML cell lines disrupts proliferation, clonogenicity, and leukemic engraftment. Surprisingly, loss of just a single Hmga1 allele prevents progression to MF in JAK2V617F mice, decreasing erythrocytosis, thrombocytosis, megakaryocyte hyperplasia, and expansion of stem and progenitors, while preventing splenomegaly and fibrosis within the spleen and BM. RNA-sequencing and chromatin immunoprecipitation sequencing revealed HMGA1 transcriptional networks and chromatin occupancy at genes that govern proliferation (E2F, G2M, mitotic spindle) and cell fate, including the GATA2 master regulatory gene. Silencing GATA2 recapitulates most phenotypes observed with HMGA1 depletion, whereas GATA2 re-expression partially rescues leukemogenesis. HMGA1 transactivates GATA2 through sequences near the developmental enhancer (+9.5), increasing chromatin accessibility and recruiting active histone marks. Further, HMGA1 transcriptional networks, including proliferation pathways and GATA2, are activated in human MF and MPN leukemic transformation. Importantly, HMGA1 depletion enhances responses to the JAK2 inhibitor, ruxolitinib, preventing MF and prolonging survival in murine models of JAK2V617F AML. These findings illuminate HMGA1 as a key epigenetic switch involved in MPN transformation and a promising therapeutic target to treat or prevent disease progression.

Transcriptome-wide high-throughput m6 A sequencing of differential m6 A methylation patterns in the decidual tissues from RSA patients.

Recurrent spontaneous abortion (RSA) is characterized by two or more consecutive pregnancy losses in the first trimester of pregnancy, experienced by 5% of women during their reproductive age. As a complex pathological process, the etiology of RSA remains poorly understood. Recent studies have established that gene expression changes dramatically in human endometrial stromal cells (ESCs) during decidualization. N6-methyladenosine (m6 A) modification is the most prevalent epigenetic modification of mRNA in eukaryotic cells and it is closely related to the occurrence and development of many pathophysiological phenomena. In this study, we first confirmed that high levels of m6 A mRNA methylation in decidual tissues are associated with RSA. Then, we used m6 A-modified RNA immunoprecipitation sequence (m6 A-seq) and RNA sequence (RNA-seq) to identify the differentially expressed m6 A methylation in decidual tissues from RSA patients and identified the key genes involved in abnormal decidualization by bioinformatics analysis. Using m6 A-seq, we identified a total of 2169 genes with differentially expressed m6 A methylation, of which 735 m6 A hypermethylated genes and 1434 m6 A hypomethylated genes were identified. Further joint analysis of m6 A-seq and RNA-seq revealed that 133 genes were m6 A modified with mRNA expression. GO and KEGG analyses indicated that these unique genes were mainly enriched in environmental information processing pathways, including the cytokine-cytokine receptor interaction and PI3K-Akt signaling pathway. In summary, this study uncovered the transcriptome-wide m6 A modification pattern in decidual tissue of RSA, which provides a theoretical basis for further research into m6 A modification and new therapeutic strategies for RSA.

The development of probiotics and prebiotics therapy to ulcerative colitis: a therapy that has gained considerable momentum.

Ulcerative colitis (UC) is increasingly common, and it is gradually become a kind of global epidemic. UC is a type of inflammatory bowel disease (IBD), and it is a lifetime recurrent disease. UC as a common disease has become a financial burden for many people and has the potential to develop into cancer if not prevented or treated. There are multiple factors such as genetic factors, host immune system disorders, and environmental factors to cause UC. A growing body of research have suggested that intestinal microbiota as an environmental factor play an important role in the occurrence and development of UC. Meanwhile, evidence to date suggests that manipulating the gut microbiome may represent effective treatment for the prevention or management of UC. In addition, the main clinical drugs to treat UC are amino salicylate and corticosteroid. These clinical drugs always have some side effects and low success rate when treating patients with UC. Therefore, there is an urgent need for safe and efficient methods to treat UC. Based on this, probiotics and prebiotics may be a valuable treatment for UC. In order to promote the wide clinical application of probiotics and prebiotics in the treatment of UC. This review aims to summarize the recent literature as an aid to better understanding how the probiotics and prebiotics contributes to UC while evaluating and prospecting the therapeutic effect of the probiotics and prebiotics in the treatment of UC based on previous publications.

Farnesyl pyrophosphate is a new danger signal inducing acute cell death.

Cell death is a vital event in life. Infections and injuries cause lytic cell death, which gives rise to danger signals that can further induce cell death, inflammation, and tissue damage. The mevalonate (MVA) pathway is an essential, highly conserved and dynamic metabolic pathway. Here, we discover that farnesyl pyrophosphate (FPP), a metabolic intermediate of the MVA pathway, functions as a newly identified danger signal to trigger acute cell death leading to neuron loss in stroke. Harboring both a hydrophobic 15-carbon isoprenyl chain and a heavily charged pyrophosphate head, FPP leads to acute cell death independent of its downstream metabolic pathways. Mechanistically, extracellular calcium influx and the cation channel transient receptor potential melastatin 2 (TRPM2) exhibit essential roles in FPP-induced cell death. FPP activates TRPM2 opening for ion influx. Furthermore, in terms of a mouse model constructing by middle cerebral artery occlusion (MCAO), FPP accumulates in the brain, which indicates the function of the FPP and TRPM2 danger signal axis in ischemic injury. Overall, our data have revealed a novel function of the MVA pathway intermediate metabolite FPP as a danger signal via transient receptor potential cation channels.