USP3 plays a critical role in the induction of innate immune tolerance.

Microbial products, such as lipopolysaccharide (LPS), can elicit efficient innate immune responses against invading pathogens. However, priming with LPS can induce a form of innate immune memory, termed innate immune "tolerance", which blunts subsequent NF-κB signaling. Although epigenetic and transcriptional reprogramming has been shown to play a role in innate immune memory, the involvement of post-translational regulation remains unclear. Here, we report that ubiquitin-specific protease 3 (USP3) participates in establishing "tolerance" innate immune memory through non-transcriptional feedback. Upon NF-κB signaling activation, USP3 is stabilized and exits the nucleus. The cytoplasmic USP3 specifically removes the K63-linked polyubiquitin chains on MyD88, thus negatively regulating TLR/IL1ß-induced inflammatory signaling activation. Importantly, cytoplasmic translocation is a prerequisite step for USP3 to deubiquitinate MyD88. Additionally, LPS priming could induce cytoplasmic retention and faster and stronger cytoplasmic translocation of USP3, enabling it to quickly shut down NF-κB signaling upon the second LPS challenge. This work identifies a previously unrecognized post-translational feedback loop in the MyD88-USP3 axis, which is critical for inducing normal "tolerance" innate immune memory.

Targeting RPA promotes autophagic flux and the antitumor response to radiation in nasopharyngeal carcinoma.

BACKGROUND: Autophagy is involved in nasopharyngeal carcinoma (NPC) radioresistance. Replication protein A 1 (RPA1) and RPA3, substrates of the RPA complex, are potential therapeutic targets for reversing NPC radioresistance. Nevertheless, the role of RPA in autophagy is not adequately understood. This investigation was performed to reveal the cytotoxic mechanism of a pharmacologic RPA inhibitor (RPAi) in NPC cells and the underlying mechanism by which RPAi-mediated autophagy regulates NPC radiosensitivity. METHODS AND RESULTS: We characterized a potent RPAi (HAMNO) that was substantially correlated with radiosensitivity enhancement and proliferative inhibition of in vivo and in NPC cell lines in vitro. We show that the RPAi induced autophagy at multiple levels by inducing autophagic flux, AMPK/mTOR pathway activation, and autophagy-related gene transcription by decreasing glycolytic function. We hypothesized that RPA inhibition impaired glycolysis and increased NPC dependence on autophagy. We further demonstrated that combining autophagy inhibition with chloroquine (CQ) treatment or genetic inhibition of the autophagy regulator ATG5 and RPAi treatment was more effective than either approach alone in enhancing the antitumor response of NPC to radiation. CONCLUSIONS: Our study suggests that HAMNO is a potent RPAi that enhances radiosensitivity and induces autophagy in NPC cell lines by decreasing glycolytic function and activating autophagy-related genes. We suggest a novel treatment strategy in which pharmacological inhibitors that simultaneously disrupt RPA and autophagic processes improve NPC responsiveness to radiation.

USP2 promotes tumor immune evasion via deubiquitination and stabilization of PD-L1.

The abnormal upregulation of programmed death ligand-1 (PD-L1) on tumor cells impedes T-cell mediated cytotoxicity through PD-1 engagement, and further exploring the mechanisms regulation of PD-L1 in cancers may enhance the clinical efficacy of PD-L1 blockade. Here, using single-guide RNAs (sgRNAs) screening system, we identify ubiquitin-specific processing protease 2 (USP2) as a novel regulator of PD-L1 stabilization for tumor immune evasion. USP2 directly interacts with and increases PD-L1 abundance in colorectal and prostate cancer cells. Our results show that Thr288, Arg292 and Asp293 at USP2 control its binding to PD-L1 through deconjugating the K48-linked polyubiquitination at lysine 270 of PD-L1. Depletion of USP2 causes endoplasmic reticulum (ER)-associated degradation of PD-L1, thus attenuates PD-L1/PD-1 interaction and sensitizes cancer cells to T cell-mediated killing. Meanwhile, USP2 ablation-induced PD-L1 clearance enhances antitumor immunity in mice via increasing CD8+ T cells infiltration and reducing immunosuppressive infiltration of myeloid-derived suppressor cells (MDSCs) and regulatory T cells (Tregs), whereas PD-L1 overexpression reverses the tumor growth suppression by USP2 silencing. USP2-depletion combination with anti-PD-1 also exhibits a synergistic anti-tumor effect. Furthermore, analysis of clinical tissue samples indicates that USP2 is positively associated with PD-L1 expression in cancer. Collectively, our data reveal a crucial role of USP2 for controlling PD-L1 stabilization in tumor cells, and highlight USP2 as a potential therapeutic target for cancer immunotherapy.

LAD1 promotes malignant progression by diminishing ubiquitin-dependent degradation of vimentin in gastric cancer.

BACKGROUND: Ladinin-1 (LAD1), an anchoring filament protein, has been associated with several cancer types, including cancers of the colon, lungs, and breast. However, it is still unclear how and why LAD1 causes gastric cancer (GC). METHODS: Multiple in vitro and in vivo, functional gains and loss experiments were carried out in the current study to confirm the function of LAD1. Mass spectrometry was used to find the proteins that interact with LAD1. Immunoprecipitation analyses revealed the mechanism of LAD1 involved in promoting aggressiveness. RESULTS: The results revealed that the LAD1 was overexpressed in GC tissues, and participants with increased LAD1 expression exhibited poorer disease-free survival (DFS) and overall survival (OS). Functionally, LAD1 promotes cellular invasion, migration, proliferation, and chemoresistance in vivo and in vitro in the subcutaneous patient-and cell-derived xenograft (PDX and CDX) tumor models. Mechanistically, LAD1 competitively bound to Vimentin, preventing it from interacting with the E3 ubiquitin ligase macrophage erythroblast attacher (MAEA), which led to a reduction in K48-linked ubiquitination of Vimentin and an increase in Vimentin protein levels in GC cells. CONCLUSIONS: In conclusion, the current investigation indicated that LAD1 has been predicted as a possible prognostic biomarker and therapeutic target for GC due to its ability to suppress Vimentin-MAEA interaction.

Knockdown LIMP2 inhibits colorectal cancer cells migration, invasion, and metastasis.

Colorectal cancer (CRC) is a common malignancy worldwide nowadays and liver metastasis is the primary cause of death in patients with CRC. Although lysosomal integral membrane protein 2 (LIMP2) has been reported to play important roles in gastric cancer and prostate cancer, its role in CRC remains unclear. The aim of this study was to investigate the function of LIMP2 in CRC invasion and migration, along with the potential underlying molecular mechanisms. We found that LIMP2 levels were higher in CRC tissues compared to adjacent normal tissues. Kaplan-Meier survival analysis showed that high expression of LIMP2 was associated with worse prognosis in CRC patients. Knockdown of LIMP2 significantly inhibited invasion, migration, and wound healing abilities of CRC cells in vitro, and inhibited CRC liver metastasis in vivo. Additionally, LIMP2 knockdown inhibited autophagy in CRC. Therefore, LIMP2 plays an important role in CRC progression. High expression of LIMP2 was associated with worse prognosis in CRC patients. Knockdown LIMP2 can effectively inhibit CRC cell migration and invasion in vitro and prevent liver metastasis in vivo. These findings suggest that LIMP2 may serve as an independent prognostic factor and potential therapeutic target for CRC.

RBFOX2/GOLIM4 Splicing Axis Activates Vesicular Transport Pathway to Promote Nasopharyngeal Carcinogenesis.

20-30% of patients with nasopharyngeal carcinoma (NPC) develop distant metastasis or recurrence leading to poor survival, of which the underlying key molecular events have yet to be addressed. Here alternative splicing events in 85 NPC samples are profiled using transcriptome analysis and it is revealed that the long isoform of GOLIM4 (-L) with exon-7 is highly expressed in NPC and associated with poor prognosis. Lines of evidence demonstrate the pro-tumorigenic function of GOLIM4-L in NPC cells. It is further revealed that RBFOX2 binds to a GGAA motif in exon-7 and promotes its inclusion forming GOLIM4-L. RBFOX2 knockdown suppresses the tumorigenesis of NPC cells, phenocopying GOLIM4-L knockdown, which is significantly rescued by GOLIM4-L overexpression. High expression of RBFOX2 is correlated with the exon-7 inclusion of GOLIM4 in NPC biopsies and associated with worse prognosis. It is observed that RBFOX2 and GOLIM4 can influence vesicle-mediated transport through maintaining the organization of Golgi apparatus. Finally, it is revealed that RAB26 interacts with GOLIM4 and mediates its tumorigenic potentials in NPC cells. Taken together, the findings provide insights into how alternative splicing contributes to NPC development, by highlighting a functional link between GOLIM4-L and its splicing regulator RBFOX2 activating vesicle-mediated transport involving RAB26.

Identification of Multi-Class Drugs Based on Near Infrared Spectroscopy and Bidirectional Generative Adversarial Networks.

Drug detection and identification technology are of great significance in drug supervision and management. To determine the exact source of drugs, it is often necessary to directly identify multiple varieties of drugs produced by multiple manufacturers. Near-infrared spectroscopy (NIR) combined with chemometrics is generally used in these cases. However, existing NIR classification modeling methods have great limitations in dealing with a large number of categories and spectra, especially under the premise of insufficient samples, unbalanced samples, and sensitive identification error cost. Therefore, this paper proposes a NIR multi-classification modeling method based on a modified Bidirectional Generative Adversarial Networks (Bi-GAN). It makes full utilization of the powerful feature extraction ability and good sample generation quality of Bi-GAN and uses the generated samples with obvious features, an equal number between classes, and a sufficient number within classes to replace the unbalanced and insufficient real samples in the courses of spectral classification. 1721 samples of four kinds of drugs produced by 29 manufacturers were used as experimental materials, and the results demonstrate that this method is superior to other comparative methods in drug NIR classification scenarios, and the optimal accuracy rate is even more than 99% under ideal conditions.

Germline Polymorphisms and Length of Survival of Nasopharyngeal Carcinoma: An Exome-Wide Association Study in Multiple Cohorts.

Germline polymorphisms are linked with differential survival outcomes in cancers but are not well studied in nasopharyngeal carcinoma (NPC). Here, a two-phase association study is conducted to discover germline polymorphisms that are associated with the prognosis of NPC. The discovery phase includes two consecutive hospital cohorts of patients with NPC from Southern China. Exome-wide genotypes at 246 173 single nucleotide polymorphisms (SNPs) are determined, followed by survival analysis for each SNP under Cox proportional hazard regression model. Candidate SNP is replicated in another two independent cohorts from Southern China and Singapore. Meta-analysis of all samples (n = 5553) confirms that the presence of rs1131636-T, located in the 3'-UTR of RPA1, confers an inferior overall survival (HR = 1.33, 95% CI = 1.20-1.47, P = 6.31 × 10-8). Bioinformatics and biological assays show that rs1131636 has regulatory effects on upstream RPA1. Functional studies further demonstrate that RPA1 promotes the growth, invasion, migration, and radioresistance of NPC cells. Additionally, miR-1253 is identified as a suppressor for RPA1 expression, likely through regulation of its binding affinity to rs1131636 locus. Collectively, these findings provide a promising biomarker aiding in stratifying patients with poor survival, as well as a potential drug target for NPC.

Effect of abscisic acid (ABA) and chitosan/nano-silica/sodium alginate composite film on the color development and quality of postharvest Chinese winter jujube (Zizyphus jujuba Mill. cv. Dongzao).

The color change of the jujube fruit after harvest is used as a quality indicator. The effects of abscisic acid (ABA) and a chitosan/nano-silica/sodium alginate composite film on the color development and qualitative properties of harvested jujube in cold storage were investigated. The results indicate that a composite film could prolong the shelf life of post-harvested winter jujube for approximately 1 month, while the ABA treatment induced ripening and reduced the quality. A significant positive correlation between the a∗/b∗ values, water loss and the malondialdehyde (MDA) content was found for fruits in cold storage. In comparison with the control and ABA-treated fruit, superoxide dismutase (SOD), polyphenol oxidase (PPO) and peroxidase (POD) activities were lower than those in a composite film-treated fruit. In addition, the dihydroflavonol-4-reductase (DFR) is the primary gene that regulates the expression of anthocyanin.

LRRC25 inhibits type I IFN signaling by targeting ISG15-associated RIG-I for autophagic degradation.

The RIG-I-like receptors (RLRs) are critical for protection against RNA virus infection, and their activities must be stringently controlled to maintain immune homeostasis. Here, we report that leucine-rich repeat containing protein 25 (LRRC25) is a key negative regulator of RLR-mediated type I interferon (IFN) signaling. Upon RNA virus infection, LRRC25 specifically binds to ISG15-associated RIG-I to promote interaction between RIG-I and the autophagic cargo receptor p62 and to mediate RIG-I degradation via selective autophagy. Depletion of either LRRC25 or ISG15 abrogates RIG-I-p62 interaction as well as the autophagic degradation of RIG-I. Collectively, our findings identify a previously unrecognized role of LRRC25 in type I IFN signaling activation by which LRRC25 acts as a secondary receptor to assist RIG-I delivery to autophagosomes for degradation in a p62-dependent manner.

LRRC25 Functions as an Inhibitor of NF-κB Signaling Pathway by Promoting p65/RelA for Autophagic Degradation.

Nuclear factor κB (NF-κB) is a family of critical transcription factors that play a critical role in innate immune responses and inflammation, yet the molecular mechanisms responsible for its tight regulation is not fully understood. In this study, we identified LRRC25, a member of leucine-rich repeat (LRR)-containing protein family, as a negative regulator in the NF-κB signaling pathway. Ectopic expression of LRRC25 impaired NF-κB activation, whereas knockout of LRRC25 potentiated NF-κB activation and enhanced the production of inflammatory cytokines. Further study demonstrated that the LRR domain of LRRC25 interacted with the Rel Homology domain (RHD) of p65/RelA and promotes the degradation of p65/RelA. Furthermore, LRRC25 enhanced the interaction between p65/RelA and cargo receptor p62, thus facilitating the degradation of p65/RelA through autophagy pathway. Our study has not only identified LRRC25 as a novel inhibitor of NF-κB signaling pathway, but also uncovers a new mechanism of crosstalk between NF-κB signaling and autophagy pathways.

[Drug Discrimination Method Based on Near Infrared Reflectance Spectrum and Balance Cascading Sparse Representation Based Classification].

The combination of near infrared spectrum and pattern recognition methods has a wide application prospect in rapid and nondestructive supervision and management of drugs. The traditional identification methods regard the smallest error rate as the goal while the imbalance of classes is ignored. This makes the positive class is overwhelming covered by the negative class and reduces its effect for the classifier, so that the classification results tend to recognize the negative class correctly, which severely affects the identification accuracy. In this paper, we mainly studied the class imbalance problems of true or false drugs via infrared spectral data of its, and then propose a balance cascading and sparse representation based classification method (BC-SRC) by combining the Balance Cascading with SRC. We sampling majority samples from the majority class for several times, which has the same size as minority samples and the majority samples we sampled can contain all the majority class samples entirely (sampling times is ceiling the result of majority samples number divide minority samples number). We can get sets of results, and then obtain the final predict labels form those results. Experiments of three databases achieved on Matlab2012a shows that the method is effective. From the experimental results, it can be seen that the method is superior to the commonly used Partial Least Squares (PLS), Extreme Learning Machine (ELM) and BP. Particularly, for the imbalanced databases, when the imbalance factor is greater than 10, the proposed method has more stable performance with higher classification accuracy than the existing ones mentioned above.

[Study on the Selection of Parameters for Evaluating Drug NIR Universal Quantitative Models].

In order to find out the optimum combination of the evaluation parameters for the selection of the best drug near infrared (NIR) universal quantitative model during model optimization, 13 common evaluation parameters of NIR quantitative models were collected and arranged from commercial chemometrics software or References based on the requirements of validation of quantitative analytical procedures of ICH (International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use). Then all these parameters of 92 drug NIR universal quantitative models were calculated and analyzed. By studying the correlation of these parameters, the optimum combination of evaluation parameters for drug NIR universal quantitative models was determined. And the value range of these parameters in the optimum combination was also obtained. Root mean square error of cross-validation(RMSECV)/root mean square error of prediction (RMSEP), average relative deviation (ARD) and ratio of (standard error of) prediction (validation) to (standard) deviation (RPD) were used as the key parameters to evaluate the model accuracy. Most of RMSECV/RMSEP was within 3%, and the value of RMSECV was roughly equivalent to the average absolute deviation of the corresponding model. Most of RPD was more than 2. The value of ARD was related to the type of universal models (such as the drug preparation and packing) and the content range which the test sample belonged to. Determination coefficient (R2) was used as the key parameter to evaluate the model linearity and most of its values were from 80% to 100%. The ratio of RMSEP to RMSECV was selected as the key evaluation parameter of model robustness and its value was usually within 1.5. The standard deviation of repeated measurement data was chosen to evaluate model precision. And it was an important parameter for standardizing operation of NIR instruments and studying the feasibility of model transfer in different instruments. However, the parameter for NIR universal quantitative models received much less attention in previous studies and it was difficult to give a value range for this parameter at present. All the results can not only provide evidence for evaluation of drug NIR universal quantitative models for the model builders or users, but also supply basic data to establish and improve the parameter evaluation system of drug NIR universal quantitative models.

Identification of a new isomer from a reversible isomerization of ceftriaxone in aqueous solution.

A reversible isomerization of ceftriaxone in aqueous solution was observed, and the structure of the isomer was determined by mass spectrometry and various 1D and 2D NMR techniques. The mechanism of isomerization was also discussed. Finally, molecular docking simulations were performed and the antimicrobial activities of the isomers were measured. This showed that the biological activity of ceftriaxone was stronger than that of its isomer. The results reported in this article may be important to quality control requirements and to the stability of ceftriaxone products.

Isolation, identification and characterization of related substances in furbenicillin.

Furbenicillin is a broad-spectrum semisynthetic penicillin with strong antibacterial activity against Gram-negative bacteria. In this study, three impurities in furbenicillin, including an unknown epimer, were determined. On the basis of a complete analysis of the spectrum (MS, (1)H,(13)C, 2D NMR and CD) and the results of chemical methods, the unknown epimer impurity was identified as 10-epi-furbenicillin (impurity 1). Isolation and structure elucidation of impurity 1 was also reported here for the first time.

[Construction of the quantitative structure retention relationship of cefdinir related substances].

The molecular descriptors of impurities with known structure in cefdinir were calculated, selected and associated with the chromatographic retention behavior to establish a model. This quantitative structure retention relationships (QSRR) model for the related substances of cefdinir was established under specific chromatographic condition and verified by other impurities. 12 molecular descriptors were used to establish the QSRR model, F_AFRBWF, Blbn_J, SsCH3, SssCH2, SsNH2, SssNH, SssS, SHdCH2, EEM_AFc, EEM_AFpl, EEM_XFpl and Pi_MaxQ. The relativity between true values and predictions in QSRR of cefdinir is R2 = 0.9836 (n = 18), ΔRRT is no more than 0.154, as 10.17% in RRT. The results indicate that the QSRR model for the related substances of cefdinir can be used to evaluate the analysis methods for related substances and predict the chromatographic behavior of new impurities, which will provide a new way for the evaluation of the effectiveness for drug quality control.

[Research progress in using near infrared for monitoring drug quality in domestic circulation].

Drugs are special goods that are directly related to public health, so their quality should be monitored in any link of their whole lifecycle. With nondestructive, rapid and environmentally friendly characteristics, near infrared technique is highly suitable for monitoring drug quality in the open market as well as the distribution channels. The present paper reviewed the current situation (analytical objects, methods and instruments) about the application of near infrared spectroscopy in monitoring the quality of the final drug products, Chinese crude drug or decoction pieces in domestic circulation since 1997, expounded the unsolved problems and future prospects.

Rapid identification of illegal synthetic adulterants in herbal anti-diabetic medicines using near infrared spectroscopy.

We created a rapid detection procedure for identifying herbal medicines illegally adulterated with synthetic drugs using near infrared spectroscopy. This procedure includes a reverse correlation coefficient method (RCCM) and comparison of characteristic peaks. Moreover, we made improvements to the RCCM based on new strategies for threshold settings. Any tested herbal medicine must meet two criteria to be identified with our procedure as adulterated. First, the correlation coefficient between the tested sample and the reference must be greater than the RCCM threshold. Next, the NIR spectrum of the tested sample must contain the same characteristic peaks as the reference. In this study, four pure synthetic anti-diabetic drugs (i.e., metformin, gliclazide, glibenclamide and glimepiride), 174 batches of laboratory samples and 127 batches of herbal anti-diabetic medicines were used to construct and validate the procedure. The accuracy of this procedure was greater than 80%. Our data suggest that this protocol is a rapid screening tool to identify synthetic drug adulterants in herbal medicines on the market.

[Update of near-infrared models for testing ceftazidime, water and arginine in ceftazidime for injection].

UNLABELLED: To find a more reasonable index to decide whether the universal quantitative NIR model needs to be updated and to develop a general method to update universal quantitative NIR models, the quantitative models for testing ceftazidime, water and arginine contents in ceftazidime for injection were taken as example. The study was performed by analyzing the similarity between new sample spectra and the training set spectra of the original models. At first, new samples of ceftazidime for injection were divided into five groups by cluster analysis. Then representative samples of each group were selected by sample selection strategy. Spectra of those samples were used to update the original quantitative models. The prediction deviation of the new ceftazidime powder injection samples by the models before and after updating was calculated. Decreasing the prediction deviation was regarded as the standard to decide if the updating was effective. At the same time, the correlation coefficient of new sample spectra and reference sample spectra was defined as the index to study the general method for model updating. (Reference sample refers to training set sample) Finally, the proposed method was validated by updating universal models for testing ceftazidime, water and arginine contents in ceftazidime powder injections. Results show that the correlation coefficient of new sample spectra and training set sample spectra of the original model was calculated within modeling wavelength range. It was proved that when correlation coefficient rT < 96.5%, the model needs to be updated. Accordingly, rT = 96.5% was set as the threshold. The quantitative models were updated by the method mentioned above. As a result, when testing ceftazidime for injection containing sodium carbonate using newly updated models, the average predicting deviation of ceftazidime contents decreased from 8.1% to 2.3%. And the average predicting deviation of water contents decreased from 2.2% to 0.3%. Meanwhile, with regard to samples containing arginine using the updated models, the average predicting deviation of ceftazidime contents decreased from 7.0% to 1.9%. The average predicting deviation of water contents decreased from 0.6% to 0.3%. And that of arginine contents de- creased from 2.3% to 0.4%. CONCLUSION: The newly updated models can be used for testing ceftazidime, water and arginine contens in ceftazidime for injection samples of domestic market. It is reasonable to set rT as the index to decide whether the model needs updating. Moreover, it is necessary to take PCA scores graph of new sample spectra and training set spectra of the original model into account. The proposed method for updating models can be used as a usual approach. And rT = 96.5% can be set as the threshold to determine whether the model needs to be updated.