Hbxip is essential for murine embryogenesis and regulates embryonic stem cell differentiation through activating mTORC1.

HBXIP, also named LAMTOR5, has been well characterized as a transcriptional co-activator in various cancers. However, the role of Hbxip in normal development remains unexplored. Here, we demonstrated that homozygous knockout of Hbxip leads to embryonic lethality, with retarded growth around E7.5, and that depletion of Hbxip compromises the self-renewal of embryonic stem cells (ESCs), with reduced expression of pluripotency genes, reduced cell proliferation and decreased colony-forming capacity. In addition, both Hbxip-/- ESCs and E7.5 embryos displayed defects in ectodermal and mesodermal differentiation. Mechanistically, Hbxip interacts with other components of the Ragulator complex, which is required for mTORC1 activation by amino acids. Importantly, ESCs depleted of Ragulator subunits, Lamtor3 or Lamtor4, displayed differentiation defects similar to those of Hbxip-/- ESCs. Moreover, Hbxip-/-, p14-/- and p18-/- mice, lacking subunits of the Ragulator complex, also shared similar phenotypes, embryonic lethality and retarded growth around E7-E8. Thus, we conclude that Hbxip plays a pivotal role in the development and differentiation of the epiblast, as well as the self-renewal and differentiation of ESCs, through activating mTORC1 signaling.

TransGEM: a molecule generation model based on Transformer with gene expression data.

MOTIVATION: It is difficult to generate new molecules with desirable bioactivity through ligand-based de novo drug design, and receptor-based de novo drug design is constrained by disease target information availability. The combination of artificial intelligence and phenotype-based de novo drug design can generate new bioactive molecules, independent from disease target information. Gene expression profiles can be used to characterize biological phenotypes. The Transformer model can be utilized to capture the associations between gene expression profiles and molecular structures due to its remarkable ability in processing contextual information. RESULTS: We propose TransGEM (Transformer-based model from gene expression to molecules), which is a phenotype-based de novo drug design model. A specialized gene expression encoder is used to embed gene expression difference values between diseased cell lines and their corresponding normal tissue cells into TransGEM model. The results demonstrate that the TransGEM model can generate molecules with desirable evaluation metrics and property distributions. Case studies illustrate that TransGEM model can generate structurally novel molecules with good binding affinity to disease target proteins. The majority of genes with high attention scores obtained from TransGEM model are associated with the onset of the disease, indicating the potential of these genes as disease targets. Therefore, this study provides a new paradigm for de novo drug design, and it will promote phenotype-based drug discovery. AVAILABILITY AND IMPLEMENTATION: The code is available at https://github.com/hzauzqy/TransGEM.

The Role of Antisolvents with Different Functional Groups in the Formation of Cs4PbBr6 and CsPbBr3 Particles.

Compared to the high-temperature hot injection (HI) technique, the room-temperature supersaturated recrystallization (SR) approach is more hopeful to realize the industrialized production of CsPbX3 (X = Cl, Br, and I) nanomaterials. However, accurate compositional control of the product is still difficult, and the role and underlying mechanism of antisolvents in the reprecipitation process remain unclear. Herein, CsPbBr3 particles and CsPbBr3/Cs4PbBr6 composites with certain proportions are synthesized using different antisolvents with the SR method. By adjustment of the polarity or functional group of antisolvents, it is found that the functional groups of antisolvents have a major impact on the composition of the products. Furthermore, the influential mechanism of different antisolvents on the compositions of products is investigated by combining electrostatic potential calculations and ultraviolet-visible absorption spectroscopy. It suggests that the interaction between functional groups of antisolvents and organic ligands influences the coordination status of the intermediate Pb-complex and further affects the separating rate of the Pb(II)-intermediate, leading to the formation of products with different compositions. A physicochemical mechanism is proposed to explain the formation of Cs4PbBr6 and CsPbBr3. This work deepens the understanding of the formation mechanism of all-inorganic metal halide perovskite-related materials based on the SR method and provides new routes to achieve their controllable preparation.

Glycolysis-Stimulated Esrrb Lactylation Promotes the Self-Renewal and Extraembryonic Endoderm Stem Cell Differentiation of Embryonic Stem Cells.

Embryonic stem cells (ESCs) favor glycolysis over oxidative phosphorylation for energy production, and glycolytic metabolism is critical for pluripotency establishment, maintenance, and exit. However, an understanding of how glycolysis regulates the self-renewal and differentiation of ESCs remains elusive. Here, we demonstrated that protein lactylation, regulated by intracellular lactate, contributes to the self-renewal of ESCs. We further showed that Esrrb, an orphan nuclear receptor involved in pluripotency maintenance and extraembryonic endoderm stem cell (XEN) differentiation, is lactylated on K228 and K232. The lactylation of Esrrb enhances its activity in promoting ESC self-renewal in the absence of the LIF and XEN differentiation of ESCs by increasing its binding at target genes. Our studies reveal the importance of protein lactylation in the self-renewal and XEN differentiation of ESCs, and the underlying mechanism of glycolytic metabolism regulating cell fate choice.

Reconstructed Genome-Scale Metabolic Model Characterizes Adaptive Metabolic Flux Changes in Peripheral Blood Mononuclear Cells in Severe COVID-19 Patients.

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) poses a mortal threat to human health. The elucidation of the relationship between peripheral immune cells and the development of inflammation is essential for revealing the pathogenic mechanism of COVID-19 and developing related antiviral drugs. The immune cell metabolism-targeting therapies exhibit a desirable anti-inflammatory effect in some treatment cases. In this study, based on differentially expressed gene (DEG) analysis, a genome-scale metabolic model (GSMM) was reconstructed by integrating transcriptome data to characterize the adaptive metabolic changes in peripheral blood mononuclear cells (PBMCs) in severe COVID-19 patients. Differential flux analysis revealed that metabolic changes such as enhanced aerobic glycolysis, impaired oxidative phosphorylation, fluctuating biogenesis of lipids, vitamins (folate and retinol), and nucleotides played important roles in the inflammation adaptation of PBMCs. Moreover, the main metabolic enzymes such as the solute carrier (SLC) family 2 member 3 (SLC2A3) and fatty acid synthase (FASN), responsible for the reactions with large differential fluxes, were identified as potential therapeutic targets. Our results revealed the inflammation regulation potentials of partial metabolic reactions with differential fluxes and their metabolites. This study provides a reference for developing potential PBMC metabolism-targeting therapy strategies against COVID-19.

An analysis of the mechanism underlying photocatalytic disinfection based on integrated metabolic networks and transcriptional data.

Photocatalytic disinfection has long been used to combat pathogenic bacteria. However, the specific mechanism underlying photocatalytic disinfection and its corresponding targets remain unclear. In this study, an analysis of the potential mechanism underlying photocatalytic disinfection was performed based on integrated metabolic networks and transcriptional data. Two sets of RNA-seq data (wild type and a photocatalysis-resistant mutant mediated by titanium dioxide (TiO2)) were processed to constrain the genome scale metabolic models (GSMM) of E. coli. By analyzing the metabolic network, the differential metabolic flux of every reaction was computed in constrained GSMM, and several significantly differential metabolic fluxes in reactions were extracted and analyzed. Most of these reactions were involved in the transmembrane transport of substances and occurred on the inner membrane or were an important component of the cell membrane. These results, which are consistent with the reported information, validated our analysis process. In addition, our work also identified other new and valuable metabolic pathways, such as the reaction ALCD2x, which has a great effect on the energy production process under bacterial anaerobic conditions. The DHAK reaction is also related to the metabolic process of ATP. These reactions with large differential metabolic fluxes merit further research. Additionally, to provide a strategy to address photocatalysis-resistant mutant bacteria, a metabolic compensation analysis was also performed. The metabolic compensation analysis results provided suggestions for a combined method that can effectively combat resistant bacteria. This method could also be used to explore the mechanisms of drug resistance in other microorganisms.

Design, synthesis and biological evaluation of novel N-nitrophenyl derivatives based on the structure of acetohydroxyacid synthase.

Acetohydroxyacid synthase (AHAS, EC: 2.2.1.6) is a target for the development of novel herbicides. Two series of N-nitrophenyl derivatives, type-A and type-B, were designed and synthesized based on the active site of the AHAS structure. All the structures of newly prepared compounds were thorough characterized by IR, and 1H NMR spectrums. The IC50 values of all synthesized target compounds against AHAS enzyme and EC50 values for herbicidal activity against Brassica campestris L., Amaranthus mangostanus L. and Sorghum sudanense were determined. The bioactive assay results showed that the type-B compounds exhibited highly improved inhibitory activity against the AHAS enzyme and the tested plants comparing to type-A compounds. The IC50 values of most type-B compounds against the AHAS enzyme were between 25-177µM. The EC50 values of several type-B compounds against Sorghum sudanense reached 5.0mg/L. The differences in the biological activity between type-A and type-B compounds were attributed to two structural features - the orthogonal bend at the N-nitro amides group and the common plane structure of another phenyl with chain bridge. With the structure of the target compounds and the IC50 values for AHAS enzyme, a statistically significant CoMFA model with high predict abilities (q2=0.606, r2=0.982, N=4, SEE=0.058, F=280.255) was obtained, and its reliability was verified. The model will provide a theoretical basis for the further structural optimization.

Drug Repurposing for Japanese Encephalitis Virus Infection by Systems Biology Methods.

Japanese encephalitis is a zoonotic disease caused by the Japanese encephalitis virus (JEV). It is mainly epidemic in Asia with an estimated 69,000 cases occurring per year. However, no approved agents are available for the treatment of JEV infection, and existing vaccines cannot control various types of JEV strains. Drug repurposing is a new concept for finding new indication of existing drugs, and, recently, the concept has been used to discover new antiviral agents. Identifying host proteins involved in the progress of JEV infection and using these proteins as targets are the center of drug repurposing for JEV infection. In this study, based on the gene expression data of JEV infection and the phenome-wide association study (PheWAS) data, we identified 286 genes that participate in the progress of JEV infection using systems biology methods. The enrichment analysis of these genes suggested that the genes identified by our methods were predominantly related to viral infection pathways and immune response-related pathways. We found that bortezomib, which can target these genes, may have an effect on the treatment of JEV infection. Subsequently, we evaluated the antiviral activity of bortezomib using a JEV-infected mouse model. The results showed that bortezomib can lower JEV-induced lethality in mice, alleviate suffering in JEV-infected mice and reduce the damage in brains caused by JEV infection. This work provides an agent with new indication to treat JEV infection.

Identification of Non-Electrophilic Nrf2 Activators from Approved Drugs.

Oxidative damage can lead to a wide range of diseases. Nrf2 is an important transcription factor that regulates many of the cytoprotective enzymes involved in the oxidative stress response. Therefore, targeting the regulation of Nrf2 activation is one logical and effective strategy to prevent or lower the risk of oxidative stress-related diseases. Until now, most research has focused on electrophilic indirect Nrf2 activators, but the risk of 'off-target' effects may be associated with these activators. To find novel small non-electrophilic modulators of Nrf2, we started from chemical agents derived from a connectivity map (cMap) and identified 22 non-electrophilic potential Nrf2-activating drugs through a drug repositioning tactic. By determining the expression changes of antioxidant genes in MCF7 cells that were treated with the potential Nrf2 activators using quantitative real-time polymerase chain reaction RT-PCR (real-time polymerase chain reaction) (qRT-PCR), astemizole was found to have a greater scale of upregulating antioxidant genes NQO1, HO-1, and GCLM than the positive control d,l-sulforaphane, although the testing concentration was lower than that of the control. Astemizole is a good potential redox regulator and deserves more pharmacodynamic experimentation to test and verify its feasibility for use as an Nrf2 activator.

Bioinformatics Identification of Drug Resistance-Associated Gene Pairs in Mycobacterium tuberculosis.

Tuberculosis is a chronic infectious disease caused by Mycobacterium tuberculosis (Mtb). Due to the extensive use of anti-tuberculosis drugs and the development of mutations, the emergence and spread of multidrug-resistant tuberculosis is recognized as one of the most dangerous threats to global tuberculosis control. Some single mutations have been identified to be significantly linked with drug resistance. However, the prior research did not take gene-gene interactions into account, and the emergence of transmissible drug resistance is connected with multiple genetic mutations. In this study we use the bioinformatics software GBOOST (The Hong Kong University, Clear Water Bay, Kowloon, Hong Kong, China) to calculate the interactions of Single Nucleotide Polymorphism (SNP) pairs and identify gene pairs associated with drug resistance. A large part of the non-synonymous mutations in the drug target genes that were included in the screened gene pairs were confirmed by previous reports, which lent sound solid credits to the effectiveness of our method. Notably, most of the identified gene pairs containing drug targets also comprise Pro-Pro-Glu (PPE) family proteins, suggesting that PPE family proteins play important roles in the drug resistance of Mtb. Therefore, this study provides deeper insights into the mechanisms underlying anti-tuberculosis drug resistance, and the present method is useful for exploring the drug resistance mechanisms for other microorganisms.

Biodegradation of Phenanthrene by Pseudomonas sp. JPN2 and Structure-Based Degrading Mechanism Study.

The strain Pseudomonas sp. JPN2 had a high potential to degrade phenanthrene degrading 98.52 % of the initial amount of 100 mg L-1 after 10 days incubation. The analysis of metabolites demonstrated that the cleavage of phenanthrene started at the C9 and C10 positions on the aromatic ring by the dioxygenation reaction, and then further degraded via a phthalate pathway. To understand the interaction between phenanthrene and the amino acid residues in the active site of the target enzyme, a molecular docking simulation was performed. The results showed that the distances of C9-O1 and C10-O2 atoms were 3.47 and 3.67 Å, respectively. The C9 and C10 positions of the phenanthrene ring are much closer to the dioxygen molecule in the active site relative to the other atoms. Therefore, the C9 and C10 positions are vulnerable to attack in the initial oxygenation process.

Novel lead compound optimization and synthesized based on the target structure of Xanthomonas oryzae pv. oryzae GlmU.

Bacterial leaf blight, caused by Xanthomonas oryzae pv. oryzae, is one of the most destructive diseases of rice worldwide. N-acetylglucosamine-1-phosphate uridyltransferase (GlmU) was an attractive target for the development of antimicrobial agents. To develop novel, more potent and even more selective inhibitors of the uridyltransferase activity of Xanthomonas oryzae pv. oryzae GlmU (Xo-GlmU), three types of novel target compounds were optimized and synthesized based on the Xo-GlmU structure in this study. The biological testing results showed that all of the target compounds displayed the higher inhibition than the lead compound with the IC50 values in the 10.82-23.31 µM range, and the inhibition rates were increased by 30%-67%. The binding mode and the possible inhibitory mechanism of the target compounds in the active site were also analyzed by the molecular docking based on the uridyltransferase active site of Xo-GlmU.

Characterization of brain resilience in Alzheimer's disease using polygenic risk scores and further improvement by integrating mitochondria-associated loci.

INTRODUCTION: Identification of high-risk people for Alzheimer's disease (AD) is critical for prognosis and early management. Longitudinal epidemiologic studies have observed heterogeneity in the brain and cognitive aging. Brain resilience was described as above-expected cognitive function. The "resilience" framework has been shown to correlate with individual characteristics such as genetic factors and age. Besides, accumulative evidence has confirmed the association of mitochondria with the pathogenesis of AD. However, it is challenging to assess resilience through genetic metrics, in particular incorporating mitochondria-associated loci. OBJECTIVES: In this paper, we first demonstrated that polygenic risk scores (PRS) could characterize individuals' resilience levels. Then, we indicated that mitochondria-associated loci could improve the performance of PRSs, providing more reliable measurements for the prevention and diagnosis of AD. METHODS: The discovery (N = 1,550) and independent validation samples (N = 2,090) were used to construct nine types of PRSs containing mitochondria-related loci (PRSMT) from both biological and statistical aspects and combined them with known AD risk loci derived from genome-wide association studies (GWAS).Individuals' levels of brain resilience were comprehensively measured by linear regression models using eight pathological characteristics. RESULTS: It was found that PRSs could characterize brain resilience levels (e.g., Pearson correlation test Pmin = 7.96×10-9). Moreover, the performance of PRS models could be efficiently improved by incorporating a small number of mitochondria-related loci (e.g., Pearson correlation test P improved from 1.41×10-3 to 6.09×10-6). PRSs' ability to characterize brain resilience was validated. More importantly, by incorporating some mitochondria-related loci, the performance of PRSs in measuring brain resilience could be significantly improved. CONCLUSION: Our findings imply that mitochondria may play an important role in brain resilience, and targeting mitochondria may open a new door to AD prevention and therapy.

Metabolites of pathogenic microorganisms database (MPMdb) and its seed metabolite applications.

Seed metabolites are the combination of essential compounds required by an organism across various potential environmental conditions. The seed metabolites screening framework based on the network topology approach can capture important biological information of species. This study aims to identify comprehensively the relationship between seed metabolites and pathogenic bacteria. A large-scale data set was compiled, describing the seed metabolite sets and metabolite sets of 124,192 pathogenic strains from 34 genera, by constructing genome-scale metabolic models. The enrichment analysis method was used to screen the specific seed metabolites of each species/genus of pathogenic bacteria. The metabolites of pathogenic microorganisms database (MPMdb) (http://qyzhanglab.hzau.edu.cn/MPMdb/) was established for browsing, searching, predicting, or downloading metabolites and seed metabolites of pathogenic microorganisms. Based on the MPMdb, taxonomic and phylogenetic analyses of pathogenic bacteria were performed according to the function of seed metabolites and metabolites. The results showed that the seed metabolites could be used as a feature for microorganism chemotaxonomy, and they could mirror the phylogeny of pathogenic bacteria. In addition, our screened specific seed metabolites of pathogenic bacteria can be used not only for further tapping the nutritional resources and identifying auxotrophies of pathogenic bacteria but also for designing targeted bactericidal compounds by combining with existing antimicrobial agents.IMPORTANCEMetabolites serve as key communication links between pathogenic microorganisms and hosts, with seed metabolites being crucial for microbial growth, reproduction, external communication, and host infection. However, the large-scale screening of metabolites and the identification of seed metabolites have always been the main technical bottleneck due to the low throughput and costly analysis. Genome-scale metabolic models have become a recognized research paradigm to investigate the metabolic characteristics of species. The developed metabolites of pathogenic microorganisms database in this study is committed to systematically predicting and identifying the metabolites and seed metabolites of pathogenic microorganisms, which could provide a powerful resource platform for pathogenic bacteria research.

An integrated method for studying the biodegradation of benzo[a]pyrene by Citrobacter sp. HJS-1 and interaction mechanism based on the structural model of the initial dioxygenase.

A bacterial strain Citrobacter sp. HJS-1 was discovered from the sludge in a drainage canal of a coal mine. Firstly, its biodegradation capacity for benzo[a]pyrene (BaP) was detected under different concentrations. The results proved that the strain possessed excellent biodegradation capacity for BaP with high-efficiency degradation rates ranging from 78.9 to 86.8%. The highest degradation rate was observed in the low-concentration sample, and the high-concentration BaP had a slight influence on the biodegradation capacity due to the potential toxicity of BaP and its oxygen-containing derivatives. Meanwhile, the degradation test for the other five aromatic hydrocarbons (2- to 4-ring) proved that the strain had a comprehensive degradation potential. To clarify the biodegradation mechanism of BaP, a dioxygenase structure was constructed by homology modeling. Then, the interactions between dioxygenase and BaP were researched by molecular simulation. Combined with the identification of the vital BaP-cis-7,8-dihydrodiol intermediate and the interaction analysis, the initial oxidation mode and the binding site of BaP were revealed in the dioxygenase. Taken together, this study has offered a way to understand the biodegradation process of BaP and its interaction mechanism based on experimental and theoretical analysis.

Biotransformation of benzo[a]pyrene by Pannonibacter sp. JPA3 and the degradation mechanism through the initially oxidized benzo[a]pyrene-4,5-dihydrodiol to downstream metabolites.

Owing to its adverse effects on the environment and human health, benzo[a]pyrene (BaP) has attracted considerable attention and has been used as a model compound in ecotoxicology. In this study, Pannonibacter sp. JPA3 as a BaP-degrading strain was isolated from the production water of an oil well. The strain could remove 80% of BaP at an initial concentration of 100 mg L-1 after 35 d culture. The BaP-4,5-dihydrodiol, BaP-4,5-epoxide, 5-hydroxychrysene, and 2-hydroxy-1-naphthoic acid metabolites were identified in the biodegradation process. Simultaneously, the gene sequence coding for dioxygenase in the strain was amplified and a dioxygenase model was built by homology modeling. Combined with the identification of the metabolites, the interaction mechanism of BaP with dioxygenase was investigated using molecular docking. It was assumed that BaP was initially oxidized at the C4-C5 positions in the active cavity of dioxygenase. Moreover, a hypothesis for the progressive degradation mechanism of BaP by this strain was proposed via the identification of the downstream metabolites. In conclusion, our study provided an efficient BaP degrader and a comprehensive reference for the study of the degradation mechanism in terms of the degrading metabolites and theoretical research at the molecular level.

Evolution-strengthened knowledge graph enables predicting the targetability and druggability of genes.

Identifying promising targets is a critical step in modern drug discovery, with causative genes of diseases that are an important source of successful targets. Previous studies have found that the pathogeneses of various diseases are closely related to the evolutionary events of organisms. Accordingly, evolutionary knowledge can facilitate the prediction of causative genes and further accelerate target identification. With the development of modern biotechnology, massive biomedical data have been accumulated, and knowledge graphs (KGs) have emerged as a powerful approach for integrating and utilizing vast amounts of data. In this study, we constructed an evolution-strengthened knowledge graph (ESKG) and validated applications of ESKG in the identification of causative genes. More importantly, we developed an ESKG-based machine learning model named GraphEvo, which can effectively predict the targetability and the druggability of genes. We further investigated the explainability of the ESKG in druggability prediction by dissecting the evolutionary hallmarks of successful targets. Our study highlights the importance of evolutionary knowledge in biomedical research and demonstrates the potential power of ESKG in promising target identification. The data set of ESKG and the code of GraphEvo can be downloaded from https://github.com/Zhankun-Xiong/GraphEvo.

Dissecting Mitochondrial Mechanisms of Alzheimer's Disease Using Gene Dependency Network and Its Implications for Discovering Nutrients Combatting the Disease.

BACKGROUND: Alzheimer's disease (AD) is the leading cause of dementia, with its prevalence increasing as the global population ages. AD is a multifactorial and intricate neurodegenerative disease with pathological changes varying from person to person. Because the mechanism of AD is highly controversial, effective treatments remain a distant prospect. Currently, one of the most promising hypotheses posits mitochondrial dysfunction as an early event in AD diagnosis and a potential therapeutic target. OBJECTIVE: Here, we adopted a systems medicine strategy to explore the mitochondria-related mechanisms of AD. Then, its implications for discovering nutrients combatting the disease were demonstrated. METHODS: We employed conditional mutual information (CMI) to construct AD gene dependency networks. Furthermore, the GeneRank algorithm was applied to prioritize the gene importance of AD patients and identify potential anti-AD nutrients targeting crucial genes. RESULTS: The results suggested that two highly interconnected networks of mitochondrial ribosomal proteins (MRPs) play an important role in the regulation of AD pathology. The close association between mitochondrial ribosome dysfunction and AD was identified. Additionally, we proposed seven nutrients with potential preventive and ameliorative effects on AD, five of which have been supported by experimental reports. CONCLUSIONS: Our study explored the important regulatory role of MRP genes in AD, which has significant implications for AD prevention and treatment.

Transporter proteins knowledge graph construction and its application in drug development.

Transporters are the main determinant for pharmacokinetics characteristics of drugs, such as absorption, distribution, and excretion of drugs in humans. However, it is difficult to perform drug transporter validation and structure analysis of membrane transporter proteins by experimental methods. Many studies have demonstrated that knowledge graphs (KG) could effectively excavate potential association information between different entities. To improve the effectiveness of drug discovery, a transporter-related KG was constructed in this study. Meanwhile, a predictive frame (AutoInt_KG) and a generative frame (MolGPT_KG) were established based on the heterogeneity information obtained from the transporter-related KG by the RESCAL model. Natural product Luteolin with known transporters was selected to verify the reliability of the AutoInt_KG frame, its ROC-AUC (1:1), ROC-AUC (1:10), PR-AUC (1:1), PR-AUC (1:10) are 0.91, 0.94, 0.91 and 0.78, respectively. Subsequently, the MolGPT_KG frame was constructed to implement efficient drug design based on transporter structure. The evaluation results showed that the MolGPT_KG could generate novel and valid molecules and that these molecules were further confirmed by molecular docking analysis. The docking results showed that they could bind to important amino acids at the active site of the target transporter. Our findings will provide rich information resources and guidance for the further development of the transporter-related drugs.

The leader proteinase of foot-and-mouth disease virus negatively regulates the type I interferon pathway by acting as a viral deubiquitinase.

The leader proteinase (L(pro)) of foot-and-mouth disease virus (FMDV) is a papain-like proteinase that plays an important role in FMDV pathogenesis. Previously, it has been shown that L(pro) is involved in the inhibition of the type I interferon (IFN) response by FMDV. However, the underlying mechanisms remain unclear. Here we demonstrate that FMDV Lb(pro), a shorter form of L(pro), has deubiquitinating activity. Sequence alignment and structural bioinformatics analyses revealed that the catalytic residues (Cys51 and His148) are highly conserved in FMDV Lb(pro) of all seven serotypes and that the topology of FMDV Lb(pro) is remarkably similar to that of ubiquitin-specific protease 14 (USP14), a cellular deubiquitylation enzyme (DUB), and to that of severe acute respiratory syndrome coronavirus (SARS-CoV) papain-like protease (PLpro), a coronaviral DUB. Both purified Lb(pro) protein and in vivo ectopically expressed Lb(pro) removed ubiquitin (Ub) moieties from cellular substrates, acting on both lysine-48- and lysine-63-linked polyubiquitin chains. Furthermore, Lb(pro) significantly inhibited ubiquitination of retinoic acid-inducible gene I (RIG-I), TANK-binding kinase 1 (TBK1), TNF receptor-associated factor 6 (TRAF6), and TRAF3, key signaling molecules in activation of type I IFN response. Mutations in Lb(pro) that ablate the catalytic activity (C51A or D163N/D164N) or disrupt the SAP (for SAF-A/B, Acinus, and PIAS) domain (I83A/L86A) abrogated the DUB activity of Lb(pro) as well as its ability to block signaling to the IFN-ß promoter. Collectively, these results demonstrate that FMDV Lb(pro) possesses DUB activity in addition to serving as a viral proteinase and describe a novel mechanism evolved by FMDV to counteract host innate antiviral responses.