Prediction of multi-relational drug-gene interaction via Dynamic hyperGraph Contrastive Learning.

Drug-gene interaction prediction occupies a crucial position in various areas of drug discovery, such as drug repurposing, lead discovery and off-target detection. Previous studies show good performance, but they are limited to exploring the binding interactions and ignoring the other interaction relationships. Graph neural networks have emerged as promising approaches owing to their powerful capability of modeling correlations under drug-gene bipartite graphs. Despite the widespread adoption of graph neural network-based methods, many of them experience performance degradation in situations where high-quality and sufficient training data are unavailable. Unfortunately, in practical drug discovery scenarios, interaction data are often sparse and noisy, which may lead to unsatisfactory results. To undertake the above challenges, we propose a novel Dynamic hyperGraph Contrastive Learning (DGCL) framework that exploits local and global relationships between drugs and genes. Specifically, graph convolutions are adopted to extract explicit local relations among drugs and genes. Meanwhile, the cooperation of dynamic hypergraph structure learning and hypergraph message passing enables the model to aggregate information in a global region. With flexible global-level messages, a self-augmented contrastive learning component is designed to constrain hypergraph structure learning and enhance the discrimination of drug/gene representations. Experiments conducted on three datasets show that DGCL is superior to eight state-of-the-art methods and notably gains a 7.6% performance improvement on the DGIdb dataset. Further analyses verify the robustness of DGCL for alleviating data sparsity and over-smoothing issues.

Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction.

Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs). DDIs refer to a change in the effect of one drug to the presence of another drug in the human body, which plays an essential role in drug discovery and clinical research. DDIs prediction through traditional clinical trials and experiments is an expensive and time-consuming process. To correctly apply the advanced AI and deep learning, the developer and user meet various challenges such as the availability and encoding of data resources, and the design of computational methods. This review summarizes chemical structure based, network based, natural language processing based and hybrid methods, providing an updated and accessible guide to the broad researchers and development community with different domain knowledge. We introduce widely used molecular representation and describe the theoretical frameworks of graph neural network models for representing molecular structures. We present the advantages and disadvantages of deep and graph learning methods by performing comparative experiments. We discuss the potential technical challenges and highlight future directions of deep and graph learning models for accelerating DDIs prediction.

Suitable Isolation Side Chains: A Simple Strategy for Simultaneously Improving the Phototherapy Efficacy and Biodegradation Capacities of Conjugated Polymer Nanoparticles.

Utilizing one molecule to realize combinational photodynamic and photothermal therapy upon single-wavelength laser excitation, which relies on a multifunctional phototherapy agent, is one of the most cutting-edge research directions in tumor therapy owing to the high efficacy achieved over a short course of treatment. Herein, a simple strategy of "suitable isolation side chains" is proposed to collectively improve the fluorescence intensity, reactive oxygen species production, photothermal conversion efficiency, and biodegradation capacity. Both in vitro and in vivo results reveal the practical value and huge potential of the designed biodegradable conjugated polymer PTD-C16 with suitable isolation side chains in fluorescence image-guided combinational photodynamic and photothermal therapy. These improvements are achieved through manipulation of aggregated states by only side chain modification without changing any conjugated structure, providing new insight into the design of biodegradable high-performance phototherapy agents.

Ectopic CXCR2 expression cells improve the anti-tumor efficiency of CAR-T cells and remodel the immune microenvironment of pancreatic ductal adenocarcinoma.

BACKGROUND: Recent progressions in CAR-T cell therapy against pancreatic ductal adenocarcinoma (PDAC) remain disappointing, which are partially attributed to the immunosuppressive microenvironment including macrophage-mediated T cell repletion. METHODS: We first characterized the expression patterns of macrophage-relevant chemokines and identified CXCR2 as the key factor regulating T cell trafficking and tumor-specific accumulation in PDAC microenvironment. After that, we synthesized and introduced a CXCR2 expression cascade into Claudin18.2 CAR-T cells and compared the behaviors of CAR-T cells in vitro and in vivo. The therapeutic potential of CXCR2 CAR-T was evaluated in two different allogeneic models: subcutaneous allografts and metastatic PDAC models. RESULTS: The results showed that CXCR2 CAR-T not only reduced the size of allografted PDAC tumors, but also completely eliminated the formation of metastases. Lastly, we investigated the tumor tissues and found that expression of ectopic CXCR2 significantly improved tumor-targeted infiltration and residence of T cells and reduced the presence of MDSCs and CXCR2 + macrophages in PDAC microenvironment. CONCLUSION: Our studies suggested that ectopic CXCR2 played a significant and promising role in improving the efficiency of CAR-T therapy against primary and metastatic PDAC and partially reversed the immune-suppressive microenvironment.

Comparing genomes of Fructus Amomi-producing species reveals genetic basis of volatile terpenoid divergence.

Wurfbainia longiligularis and Wurfbainia villosa are both rich in volatile terpenoids and are 2 primary plant sources of Fructus Amomi used for curing gastrointestinal diseases. Metabolomic profiling has demonstrated that bornyl diphosphate (BPP)-related terpenoids are more abundant in the W. villosa seeds and have a wider tissue distribution in W. longiligularis. To explore the genetic mechanisms underlying the volatile terpenoid divergence, a high-quality chromosome-level genome of W. longiligularis (2.29 Gb, contig N50 of 80.39 Mb) was assembled. Functional characterization of 17 terpene synthases (WlTPSs) revealed that WlBPPS, along with WlTPS 24/26/28 with bornyl diphosphate synthase (BPPS) activity, contributes to the wider tissue distribution of BPP-related terpenoids in W. longiligularis compared to W. villosa. Furthermore, transgenic Nicotiana tabacum showed that the GCN4-motif element positively regulates seed expression of WvBPPS and thus promotes the enrichment of BPP-related terpenoids in W. villosa seeds. Systematic identification and analysis of candidate TPS in 29 monocot plants from 16 families indicated that substantial expansion of TPS-a and TPS-b subfamily genes in Zingiberaceae may have driven increased diversity and production of volatile terpenoids. Evolutionary analysis and functional identification of BPPS genes showed that BPP-related terpenoids may be distributed only in the Zingiberaceae of monocot plants. This research provides valuable genomic resources for breeding and improving Fructus Amomi with medicinal and edible value and sheds light on the evolution of terpenoid biosynthesis in Zingiberaceae.

Infrared Photodissociation Spectroscopy of Mass-Selected Dinuclear Transition Metal Boride Carbonyl Cluster Cations.

The transition-metal-boron bonding interactions and geometric structures of heterodinuclear transition metal carbonyl cluster cations BM(CO)n+ (M = Co, Ni, and Cu) are studied by a combination of the infrared photodissociation spectroscopy and density functional theory calculations at the B3LYP/def2-TZVP level. The BCu(CO)5+ and BCo(CO)6+ cations are characterized as an (CO)2B-M(CO)3/4+ structure involving an σ-type (OC)2B → M(CO)3,4+ dative bonding with end-on carbonyls, while for BNi(CO)5,6+ complexes with a bridged carbonyl, a 3c-2e bond involving the 5σ electrons of the bridged carbonyl and an electron-sharing bond between the B(CO)2 fragment and the Ni(CO)2,3+ subunits were revealed. Moreover, the fundamental driving force of the exclusive existence of a bridged carbonyl group in the boron-nickel complexes has been demonstrated to stem from the desire of the B and Ni centers for the favorable 8- and 18-electron structures.

Infralimbic YTHDF1 is necessary for the beneficial effects of acute mild exercise on auditory fear extinction retention.

Exposure therapy is the most effective approach of behavioral therapy for anxiety and post-traumatic stress disorder (PTSD). But fear is easy to reappear even after successful extinction. So, identifying novel strategies for augmenting exposure therapy is rather important. It was reported that exercise had beneficial effects on cognitive and memory deficits. However, whether exercise could affect fear memory, especially for fear extinction remained elusive. Here, our results showed that exposure to acute mild exercise 1 or 2 h before extinction training can augment recent fear extinction retention and 2 h for the remote fear extinction retention. These beneficial effects could be attributed to increased YTHDF1 expression in medial prefrontal cortex (mPFC). Furthermore, by using an AAV-shRNA-based approach to silence YTHDF1 expression via stereotactic injection in prelimbic cortex (PL) or infralimbic cortex (IL), respectively, we demonstrated that silence YTHDF1 in IL, but not in PL, blunted augmentation of exposure therapy induced by acute mild exercise and accompanied with decreased NR2B and GluR1 expression. Moreover, YTHDF1 modulated dendritic spines remodeling of pyramidal neuron in IL. Collectively, our findings suggested that acute mild exercise acted as an effective strategy in augmenting exposure therapy with possible implications for understanding new treatment underlying PTSD.

Analysis of the dynamic mechanism of trans-boundary pollution collaborative control in the Yangtze River Economic Belt.

Climate change mitigation requires simultaneous reduction of carbon emissions and air pollution. This study examines the synergy between pollution reduction and carbon reduction, identifying key variables and strategies to achieve this goal. Using a Geographical Detector model and a Coupling Degree of Coordination model, 108 cities in the Yangtze River Economic Belt (YREB) are investigated. Results show that while controlling PM2.5 has been more successful than managing carbon emissions in the YREB, synergy between pollution reduction and carbon emissions increased by an average of 7.2% from 2006 to 2019. Spatial analysis reveals higher synergy in upstream areas, indicating significant spatial diversity. The impact of pollution and emission reduction synergies is influenced by societal and environmental variables, including industry structure, technological innovation, energy structure, human capital quality, and economic basis. Synergy is amplified when natural limits align with high-quality development drivers such as technical innovation and the digital economy. Recommendations include enhancing city-to-city contact, improving energy and industrial structures, and fostering technological innovation to address regional variations in synergy levels.

Visible light irradiation enhanced sulfidated zero-valent iron/peroxymonosulfate process for organic pollutant degradation.

This study developed a novel process named sulfidated zero-valent iron/peroxymonosulfate/visible light irradiation (S-mZVI/PMS/vis) for enhanced organic pollutant degradation. The S-mZVI/PMS/vis process exhibited remarkable catalytic activity, achieving a 99.6% rhodamine B (RhB) removal within 10 min. The degradation rate constant of RhB by the S-mZVI/PMS/vis process was found to be 6.49 and 79.84 times higher than that by the S-mZVI/PMS and PMS/vis processes, respectively. Furthermore, the S-mZVI/PMS/vis process worked efficiently across a wide pH range (3.0-9.0), and the result of five-cycle experiments demonstrated the excellent reusability and stability of S-mZVI. Radical quenching tests and electron paramagnetic resonance analysis indicated that ·O2-, 1O2, and h+ significantly contributed to the degradation of RhB through the S-mZVI/PMS/vis process. The visible light irradiation increased the Fe2+ concentration, improved the Fe3+/Fe2+ cycle, and consequently enhanced the PMS decomposition, reactive species production, and RhB degradation. This work offers a promising strategy to highly efficiently activate PMS for organic pollutants elimination from aqueous solutions.

Type-I Photosensitizer-Triggered Controllable Carbon Monoxide Release for Effective Treatment of Staph Skin Infection.

Staphylococcus aureus (S. aureus) infection is a major infectious skin disease that is highly resistant to conventional antibiotic treatment and host immune defense, leading to recurrence and exacerbation of bacterial infection. Herein, we developed a photoresponsive carbon monoxide (CO)-releasing nanocomposite by integrating anion-π+ type-I photosensitizer (OMeTBP) and organometallic complex (FeCO) for the treatment of planktonic S. aureus and biofilm-associated infections. After optimizing the molar ratio of FeCO and OMeTBP, the prepared nanoparticles, OMeTBP@FeCONPs, not only ensured sufficient loading of CO donors and efficient CO generation but also showed negligible free ROS leakage under light irradiation, which helped to avoid tissue damage caused by excessive ROS. Both in vitro and in vivo results demonstrated that OMeTBP@FeCONPs could effectively inhibit S. aureus methicillin-resistant S. aureus (MRSA), and bacterial biofilm. Our design has the potential to overcome the resistance of conventional antibiotic treatment and provide a more effective option for bacterial infections.

Tissue-resident CXCR4+ macrophage as a poor prognosis signature promotes pancreatic ductal adenocarcinoma progression.

Macrophage is an essential part of the tumor immune microenvironment of pancreatic ductal adenocarcinoma. In our study, we explored the CXCR4+ macrophages subset on its prognosis value, immune profile and distinct function in pancreatic cancer progression. Specimens from 102 postoperative pancreatic patients were analyzed by flow cytometry or immune-fluorescence, and the prognostic value of CXCR4+ macrophages infiltration was further determined by Cox regression. In silico analysis on TCGA, ICGC database and single-cell sequencing of pancreatic ductal adenocarcinoma further validated our findings. We found that high CXCR4+ macrophages infiltration was associated with poor overall survival (P < .01) and disease-free survival (P < .05) as an independent factor. CXCR4+ macrophages exhibited an M2 protumor phenotype with high expression of CD206. The function of CXCR4+ macrophages was further analyzed in the murine orthotopic PDAC model with its tumor promotion effect and inhibition of CD8+ T cells. Mechanistic and RNA-seq analysis showed that CXCR4+ macrophages participated in extracellular matrix remodeling procedures and especially secreted SPARC through CXCR4/PI3K/Akt pathway promoting tumor proliferation and migration. Our study reveals that CXCR4+ macrophages infiltration is an indicator of poor prognosis of PDAC and targeting these cells was potentially crucial in immunotherapy of PDAC.

In vivo flow cytometry reveals an anti-metastatic effect of Rujifang in triple-negative breast cancer.

Breast cancer is the most common cancer, and triple-negative breast cancer (TNBC) has the highest metastasis and mortality rate among all breast cancer subtypes. Rujifang is a traditional Chinese medicine formula with many years of clinical application in breast cancer treatment. Here, we aim to investigate the effects of Rujifang on circulating tumor cell (CTC) dynamics and the tumor microenvironment in a ZsGreen/luciferase double-labeled TNBC orthotopic model. We report that the number of CTCs monitored by in vivo flow cytometry (IVFC) strongly correlates with disease progression. Rujifang treatment decreased the number of CTCs and suppressed the distant metastasis of TNBC. Moreover, immunofluorescence analysis revealed that Rujifang treatment could affect the tumor microenvironment by downregulating Kindlin-1, which has been reported to promote metastasis of TNBC. Our study provides evidence of the anti-metastatic effect of Rujifang against TNBC in an animal model using fluorescent cell lines. The results suggest the potential therapeutic value of Rujifang as an anti-metastatic drug, however, further clinical trials are needed to validate these findings in humans.

A novel immune checkpoint score system for prognostic evaluation in pancreatic adenocarcinoma.

BACKGROUND: Pancreatic adenocarcinoma (PAAD) remains a lethal malignancy making the detection of novel prognostic biomarkers urgent. Limited studies have investigated the predictive capability of immune checkpoints in PAAD. METHOD: Gene expression data and correlative clinical information of PAAD cohort were obtained from public databases, including TCGA, ICGC, GTEX and GEO databases. Risk factors were screened and used to establish a risk score model through LASSO and Cox regression analyses. The prognostic ability of the risk score model was demonstrated. The association between risk score with immune cells infiltration, immune checkpoint genes expression, immunogenic cell death, somatic mutations and signaling pathways enrichment were analysed. scRNA-seq data were collected to confirmed the immune checkpoints expression in PAAD samples. The prognosis prediction ability of OX40/TNFRSF4 was identified. The mRNA and protein expression of OX40 in our clinical specimens were examined by RT-PCR and IHC method and its prognosis ability was verified. RESULTS: First of all, the difference of immune microenvironment between pancreatic cancer and adjacent tissues was shown. A risk score system based on three immune checkpoints (OX40, TNFSF14 and KIR3DL1) was established. The risk score model was an independent prognostic factor and performed well regarding overall survival (OS) predictions among PAAD patients. A nomogram was established to facilitate the risk model application in clinical prognosis. Immune cells including naive B cells, CD8+ T cells and Tregs were negatively correlated with the risk score. The risk score was associated with expression of immune checkpoint genes, immunogenic cell death related genes and somatic mutations. Glycolysis processes, IL-2-STAT5, IL-6-STAT3, and mTORC1 signaling pathways were enriched in the high-risk score group. Furthermore, scRNA-seq data confirmed that TNFRSF4, TNFSF14 and KIR3DL1 were expressed on immune cells in PAAD samples. We then identified OX40 as an independent prognosis-related gene, and a higher OX40 expression was associated with increased survival rate and immune environment change. In 84 PAAD clinical specimens collected from our center, we confirmed that higher OX40 mRNA expression levels were related to a good prognosis. The protein expression of OX40 on tumor-infiltrating immune cells (TIICs), endothelial cells and tumor cells was verified in PAAD tissues by immunohistochemistry (IHC) method. CONCLUSIONS: Overall, our findings strongly suggested that the three-immune checkpoints score system might be useful in the prognosis and design of personalized treatments for PAAD patients. Finally, we identified OX40 as an independent potential biomarker for PAAD prognosis prediction.

[Comparison of catalytic functions and expression patterns of two pinene synthases from Wurfbainia villosa].

Wurfbainia villosa fruit is rich in volatile terpenoids, among which pinene is one of the main components and has anti-inflammatory, antibacterial, anti-tumor, and other pharmacological activities. This research group found that W. villosa fruits were rich in α-pinene by GC-MS, and terpene synthase(WvTPS63, formerly known as AvTPS1) with ß-pinene as the main product was cloned and identified, but α-pinene synthase had not been identified. In this study, based on the genome data of W. villosa, we screened and found WvTPS66 with highly similar sequences to WvTPS63, identified enzyme functions of WvTPS66 in vitro, and performed a comparative analysis of sequence, catalytic function, expression pattern, and promoter with WvTPS63. Multiple sequence alignment showed that the amino acid sequences of WvTPS63 and WvTPS66 were highly similar and the conservative motif of terpene synthase was almost identical. In vitro enzymatic experiments on catalytic functions showed that both could produce pinene, and the main product of WvTPS63 was ß-pinene, while that of WvTPS66 was α-pinene. Expression pattern analysis showed that WvTS63 was highly expressed in flowers, WvTPS66 was expressed in the whole plant, and the highest expression level was found in the pericarp, which indicated that it might be mainly responsible for the synthesis of α-pinene in fruits. In addition, promoter analysis revealed the presence of multiple regulatory elements related to stress response in the promoter regions of both genes. The findings of this study can provide a reference for the functional study of terpene synthase genes and new genetic elements for pinene biosynthesis.

Cell-free DNA methylation markers for differential diagnosis of hepatocellular carcinoma.

BACKGROUND: Aberrant DNA methylation may offer opportunities in revolutionizing cancer screening and diagnosis. We sought to identify a non-invasive DNA methylation-based screening approach using cell-free DNA (cfDNA) for early detection of hepatocellular carcinoma (HCC). METHODS: Differentially, DNA methylation blocks were determined by comparing methylation profiles of biopsy-proven HCC, liver cirrhosis, and normal tissue samples with high throughput DNA bisulfite sequencing. A multi-layer HCC screening model was subsequently constructed based on tissue-derived differentially methylated blocks (DMBs). This model was tested in a cohort consisting of 120 HCC, 92 liver cirrhotic, and 290 healthy plasma samples including 65 hepatitis B surface antigen-seropositive (HBsAg+) samples, independently validated in a cohort consisting of 67 HCC, 111 liver cirrhotic, and 242 healthy plasma samples including 56 HBsAg+ samples. RESULTS: Based on methylation profiling of tissue samples, 2321 DMBs were identified, which were subsequently used to construct a cfDNA-based HCC screening model, achieved a sensitivity of 86% and specificity of 98% in the training cohort and a sensitivity of 84% and specificity of 96% in the independent validation cohort. This model obtained a sensitivity of 76% in 37 early-stage HCC (Barcelona clinical liver cancer [BCLC] stage 0-A) patients. The screening model can effectively discriminate HCC patients from non-HCC controls, including liver cirrhotic patients, asymptomatic HBsAg+ and healthy individuals, achieving an AUC of 0.957(95% CI 0.939-0.975), whereas serum α-fetoprotein (AFP) only achieved an AUC of 0.803 (95% CI 0.758-0.847). Besides detecting patients with early-stage HCC from non-HCC controls, this model showed high capacity for distinguishing early-stage HCC from a high risk population (AUC=0.934; 95% CI 0.905-0.963), also significantly outperforming AFP. Furthermore, our model also showed superior performance in distinguishing HCC with normal AFP (< 20ng ml-1) from high risk population (AUC=0.93; 95% CI 0.892-0.969). CONCLUSIONS: We have developed a sensitive blood-based non-invasive HCC screening model which can effectively distinguish early-stage HCC patients from high risk population and demonstrated its performance through an independent validation cohort. TRIAL REGISTRATION: The study was approved by the ethic committee of The Second Xiangya Hospital of Central South University (KYLL2018072) and Chongqing University Cancer Hospital (2019167). The study is registered at ClinicalTrials.gov(# NCT04383353 ).

Possible Action of Transition Divalent Metal Ions at the Inter-Pentameric Interface of Inactivated Foot-and-Mouth Disease Virus Provide A Simple but Effective Approach to Enhance Stability.

The structural instability of inactivated foot-and-mouth disease virus (FMDV) hinders the development of vaccine industry. Here we found that some transition metal ions like Cu2+ and Ni2+ could specifically bind to FMDV capsids at capacities about 7089 and 3448 metal ions per capsid, respectively. These values are about 33- and 16-folds of the binding capacity of non-transition metal ion Ca2+ (about 214 per capsid). Further thermodynamic studies indicated that all these three metal ions bound to the capsids in spontaneous enthalpy driving manners (ΔG<0, ΔH<0, ΔS<0), and the Cu2+ binding had the highest affinity. The binding of Cu2+ and Ni2+ could enhance both the thermostability and acid-resistant stability of capsids, while the binding of Ca2+ was helpful only to the thermostability of the capsids. Animal experiments showed that the immunization of FMDV bound with Cu2+ induced the highest specific antibody titers in mice. Coincidently, the FMDV bound with Cu2+ exhibited significantly enhanced affinities to integrin ß6 and heparin sulfate, both of which are important cell surface receptors for FMDV attaching. Finally, the specific interaction between capsids and Cu2+ or Ni2+ was applied to direct purification of FMDV from crude cell culture feedstock by the immobilized metal affinity chromatography. Based on our new findings and structural analysis of the FMDV capsid, a "transition metal ion bridges" mechanism that describes linkage between adjacent histidine and other amino acids at the inter-pentameric interface of the capsids by transition metal ions coordination action was proposed to explain their stabilizing effect imposed on the capsid.IMPORTANCE How to stabilize the inactivated FMDV without affecting virus infectivity and immunogenicity is a big challenge in vaccine industry. The electrostatic repulsion induced by protonation of a large amount of histidine residues at the inter-pentameric interface of viral capsids is one of the major mechanisms causing the dissociation of capsids. In the present work, this structural disadvantage inspired us to stabilize the capsids through coordinating transition metal ions with the adjacent histidine residues in FMDV capsid, instead of removing or substituting them. This approach was proved effective to enhance not only the stability of FMDV, but also enhance the specific antibody responses; thus, providing a new guideline for designing an easy-to-use strategy suitable for large-scale production of FMDV vaccine antigen.

A novel molecular representation with BiGRU neural networks for learning atom.

Molecular representations play critical roles in researching drug design and properties, and effective methods are beneficial to assisting in the calculation of molecules and solving related problem in drug discovery. In previous years, most of the traditional molecular representations are based on hand-crafted features and rely heavily on biological experimentations, which are often costly and time consuming. However, recent researches achieve promising results using machine learning on various domains. In this article, we present a novel method named Smi2Vec-BiGRU that is designed for learning atoms and solving the single- and multitask binary classification problems in the field of drug discovery, which are the basic and also key problems in this field. Specifically, our approach transforms the molecule data in the SMILES format into a set of sample vectors and then feeds them into the bidirectional gated recurrent unit neural networks for training, which learns low-dimensional vector representations for molecular drug. We conduct extensive experiments on several widely used benchmarks including Tox21, SIDER and ClinTox. The experimental results show that our approach can achieve state-of-the-art performance on these benchmarking datasets, demonstrating the feasibility and competitiveness of our proposed approach.

Associations of PNPLA3 rs738409 Polymorphism with Plasma Lipid Levels: A Systematic Review and Meta-Analysis.

Accumulating evidence has shown that the rs738409 polymorphism of patatin-like phospholipase domain-containing 3 (PNPLA3) is associated with non-alcoholic fatty liver disease (NAFLD). Since NAFLD has been reported to be associated with lipid metabolism, this study is conducted to explore whether the rs738409 polymorphism of PNPLA3 was associated with lipid levels. By searching PubMed and the Cochrane database from May 31, 2020, to June 30, 2021. Sixty-three studies (81 003 subjects) were included for the analysis. The consistent findings for the associations of rs738409 polymorphism with lipid levels were the significantly decreased triglycerides (TG) (SMD=-0.04, 95% CI=-0.07 to -0.01, p=0.02) and total cholesterol (TC) (SMD=-0.03, 95% CI=-0.05 to -0.01, p<0.01) levels. Subgroup analysis indicated that the associations of rs738409 polymorphism with TG and TC levels were stronger in Caucasians, obesity patients, and adult subjects than in Asians, T2DM patients, and children subjects. The rs738409 polymorphism of PNPLA3 was associated with lower TG and TC levels in Caucasians, obese and adult subjects, which may contribute to the reduced coronary artery disease (CAD) risk between PNPLA3 rs738409 polymorphism and CAD.

Reciprocal positive regulation between BRD4 and YAP in GNAQ-mutant uveal melanoma cells confers sensitivity to BET inhibitors.

Uveal melanoma (UM) is the most common intraocular cancer in adults. UMs are usually initiated by a mutation in GNAQ or GNA11 (encoding Gq or G11, respectively), unlike cutaneous melanomas (CMs), which usually carry a BRAF or NRAS mutation. Currently, there are no clinically effective targeted therapies for UM carrying Gq/11 mutations. Here, we identified a causal link between Gq activating mutations and hypersensitivity to bromodomain and extra-terminal (BET) inhibitors. BET inhibitors transcriptionally repress YAP via BRD4 regardless of Gq mutation status, independently of Hippo core components LATS1/2. In contrast, YAP/TAZ downregulation reduces BRD4 transcription exclusively in Gq-mutant cells and LATS1/2 double knockout cells, both of which are featured by constitutively active YAP/TAZ. The transcriptional interdependency between BRD4 and YAP identified in Gq-mutated cells is responsible for the preferential inhibitory effect of BET inhibitors on the growth and dissemination of Gq-mutated UM cells compared to BRAF-mutated CM cells in both culture cells and animal models. Our findings suggest BRD4 as a viable therapeutic target for Gq-driven UMs that are addicted to unrestrained YAP function.

Integrating Genome-Resolved Metagenomics with Trait-Based Process Modeling to Determine Biokinetics of Distinct Nitrifying Communities within Activated Sludge.

Conventional bioprocess models for wastewater treatment are based on aggregated bulk biomass concentrations and do not incorporate microbial physiological diversity. Such a broad aggregation of microbial functional groups can fail to predict ecosystem dynamics when high levels of physiological diversity exist within trophic guilds. For instance, functional diversity among nitrite-oxidizing bacteria (NOB) can obfuscate engineering strategies for their out-selection in activated sludge (AS), which is desirable to promote energy-efficient nitrogen removal. Here, we hypothesized that different NOB populations within AS can have different physiological traits that drive process performance, which we tested by estimating biokinetic growth parameters using a combination of highly replicated respirometry, genome-resolved metagenomics, and process modeling. A lab-scale AS reactor subjected to a selective pressure for over 90 days experienced resilience of NOB activity. We recovered three coexisting Nitrospira population genomes belonging to two sublineages, which exhibited distinct growth strategies and underwent a compositional shift following the selective pressure. A trait-based process model calibrated at the NOB genus level better predicted nitrite accumulation than a conventional process model calibrated at the NOB guild level. This work demonstrates that trait-based modeling can be leveraged to improve our prediction, control, and design of functionally diverse microbiomes driving key environmental biotechnologies.