Integration of Prior Expectations and Suppression of Prediction Errors During Expectancy-Induced Pain Modulation: The Influence of Anxiety and Pleasantness.

Pain perception arises from the integration of prior expectations with sensory information. Although recent work has demonstrated that treatment expectancy effects (e.g., placebo hypoalgesia) can be explained by a Bayesian integration framework incorporating the precision level of expectations and sensory inputs, the key factor modulating this integration in stimulus expectancy-induced pain modulation remains unclear. In a stimulus expectancy paradigm combining emotion regulation in healthy male and female adults, we found that participants' voluntary reduction in anticipatory anxiety and pleasantness monotonically reduced the magnitude of pain modulation by negative and positive expectations, respectively, indicating a role of emotion. For both types of expectations, Bayesian model comparisons confirmed that an integration model using the respective emotion of expectations and sensory inputs explained stimulus expectancy effects on pain better than using their respective precision. For negative expectations, the role of anxiety is further supported by our fMRI findings that (1) functional coupling within anxiety-processing brain regions (amygdala and anterior cingulate) reflected the integration of expectations with sensory inputs and (2) anxiety appeared to impair the updating of expectations via suppressed prediction error signals in the anterior cingulate, thus perpetuating negative expectancy effects. Regarding positive expectations, their integration with sensory inputs relied on the functional coupling within brain structures processing positive emotion and inhibiting threat responding (medial orbitofrontal cortex and hippocampus). In summary, different from treatment expectancy, pain modulation by stimulus expectancy emanates from emotion-modulated integration of beliefs with sensory evidence and inadequate belief updating.

Application of Nanohybrid Substrates with Layer-by-Layer Self-Assembling Properties to High-Sensitivity Surface-Enhanced Raman Scattering Detection.

The present study was conducted to prepare and investigate large-area, high-sensitivity surface-enhanced Raman scattering (SERS) substrates. Organic/inorganic nanohybrid dispersants consisting of an amphiphilic triblock copolymer (hereafter referred to simply as "copolymer") and graphene oxide (GO) were used to stabilize the growth and size of gold nanoparticles (AuNPs). Ion-dipole forces were present between the AuNPs and copolymer dispersants, while the hydrogen bonds between GO and the copolymer prevented the aggregation of GO, thereby stabilizing the AuNP/GO nanohybrids. Transmission electron microscopy (TEM) revealed that the AuNPs had particle sizes of 25-35 nm and a relatively uniform size distribution. The AuNP/GO nanohybrids were deposited onto the glass substrate by using the solution drop-casting method and employed for SERS detection. The self-assembling properties of two-dimensional sheet-like GO led to a regular lamellar arrangement of AuNP/GO nanohybrids, which could be used for the preparation of large-area SERS substrates. Following removal of the copolymer by annealing at 300 °C for 2 h, measurements were obtained under scanning electron microscopy. The results confirmed that 2D GO nanosheets were capable of stabilizing AuNPs, with the final size reaching approximately 40 nm. These AuNPs were adsorbed on both sides of the GO nanosheets. Because the GO nanosheets were merely 5 nm-thick, a good three-dimensional hot-junction effect was generated along the z-axis of the AuNPs. Lastly, the prepared material was used for the SERS detection of rhodamine 6G (R6G), a commonly used highly fluorescent dye. An enhancement factor (EF) of up to 3.5 × 106 was achieved, and the limit of detection was approximately 10-10 M. Detection limits of 10-10 M and < 10-10 M were also observed with the detection of Direct Blue 200 and the biological molecule adenine. It is therefore evident that AuNP/copolymer/GO nanohybrids are large-area flexible SERS substrates that hold great potential in environmental monitoring and biological system detection applications.

A Low-Cost, Sensitive Reporter System Using Membrane-Tethered Horseradish Peroxidase for Efficient Gene Expression Analysis.

Reporter gene assays are essential for high-throughput analysis, such as drug screening or determining downstream signaling activation/inhibition. However, use of this technology has been hampered by the high cost of the substrate (e.g., d-Luciferin (d-Luc)) in the most common firefly luciferase (FLuc) reporter gene assay. Although alternate luciferase is available worldwide, its substrate has remained expensive, and a more affordable option is still in demand. Here, we present a membrane-tethered horseradish peroxidase (mHRP), a new reporter system composed of a cell membrane expressing HRP that can preserve its enzymatic function on the cell surface, facilitates contact with HRP substrates (e.g., ABTS and TMB), and avoids the cell lysis process and the use of the high-priced luciferase substrate. An evaluation of the light signal sensitivity of mHRP compared to FLuc showed that both had comparable signal sensitivity. We also identified an extended substrate half-life of more than 5-fold that of d-Luc. Of note, this strategy provided a more stable detection signal, and the cell lysis process is not mandatory. Furthermore, with this strategy, we decreased the total amount of time taken for analysis and increased the time of detection limit of the reporter assay. Pricing analysis showed a one-third to one twenty-eighth price drop per single test of reporter assay. Given the convenience and stability of the mHRP reporter system, we believe that our strategy is suitable for use as an alternative to the luciferase reporter assay.

Simulation of highly efficient GeSe-based solar cells with SCAPS-1D.

Recently GeSe has developed as a promising light harvesting material by enjoying to its optical and electrical features as well as earth-abundant and low-toxic constituent elements. Nevertheless, the power conversion efficiency of GeSe-based solar cells yet lags far behind the Shockley-Queisser limit. In this work, we systematically designed, simulated and analyzed the highly efficient GeSe thin-film solar cells by SCAPS-1D. The influence of thickness and defect density of light harvest material, GeSe/CdS interface defect density, electron transport layer (ETL), electrode work function and hole transport layer (HTL) on the device output are carefully analyzed. By optimizing the parameters (thickness, defect, concentration, work function, ETL and HTL), an impressive PCE of 17.98% is delivered along with Jsc of 37.11 mA/cm2, FF of 75.53%, Voc of 0.61 V. This work offers theoretical guidance for the design of highly efficient GeSe thin film solar cells.

Development of NS2B-NS3 protease inhibitor that impairs Zika virus replication.

Zika virus (ZIKV) is a mosquito-borne flavivirus that causes severe neurological disorders, such as microcephaly in fetuses. Most recently, an outbreak of ZIKV started in Brazil in 2015. To date, no therapeutic agents have been approved to treat ZIKV infection in the clinic. Here, we screened a small molecule inhibitor that can inhibit the function of ZIKV non-structural protein 2B (NS2B)-NS3 protease (ZIKV NS2B-NS3 protease), thereby interfering with viral replication and spread. First, we identified the half maximal inhibitory concentration (IC50) of compound 3 (14.01 µM), 8 (6.85 µM), and 9 (14.2 µM) and confirmed that they are all non-competitive inhibitors. In addition, we have used the blind molecular docking method to simulate the inhibition area of three non-competitive inhibitors (compound 3, 8, and 9) with the ZIKV NS2B-NS3 protease. The results indicated that the four allosteric binding residues (Gln139, Trp148, Leu150, and Val220) could form hydrogen bonds or non-bonding interactions most frequently with the three compounds. The interaction might induce the reaction center conformation change of NS2B-NS3 protease to reduce catalyzed efficiency. The concentration of compounds required to reduce cell viability by 50% (CC50), and the concentration of compounds required to inhibit virus-induced cytopathic effect by 50% (EC50) of three potential compounds are >200 µM, 2.15 µM (compound 3), > 200 µM, 0.52 µM (compound 8) and 61.48 µM, 3.52 µM (compound 9), and Temoporfin are 61.05 µM, 2 µM, respectively. To select candidate compounds for further animal experiments, we analyzed the selectivity index (SI) of compound 3 (93.02), 8 (384.61), 9 (17.46), and Temoporfin (30.53, FDA-approved drug against cancer). Compound 8 has the highest SI value. Therefore, compound 8 was selected for verification in animal models. In vivo, compound 8 significantly delayed ZIKV-induced lethality and illness symptoms and decreased ZIKV-induced weight loss in a ZIKV-infected suckling mouse model. We conclude that compound 8 is worth further investigation for use as a potential future therapeutic agent against ZIKV infection.

A lesion-selective albumin-CTLA4Ig as a safe and effective treatment for collagen-induced arthritis.

BACKGROUND: CTLA4Ig is a dimeric fusion protein of the extracellular domain of cytotoxic T-lymphocyte protein 4 (CTLA4) and an Fc (Ig) fragment of human IgG1 that is approved for treating rheumatoid arthritis. However, CTLA4Ig may induce adverse effects. Developing a lesion-selective variant of CTLA4Ig may improve safety while maintaining the efficacy of the treatment. METHODS: We linked albumin to the N-terminus of CTLA4Ig (termed Alb-CTLA4Ig) via a substrate sequence of matrix metalloproteinase (MMP). The binding activities and the biological activities of Alb-CTLA4Ig before and after MMP digestion were analyzed by a cell-based ELISA and an in vitro Jurkat T cell activation assay. The efficacy and safety of Alb-CTLA4Ig in treating joint inflammation were tested in mouse collagen-induced arthritis. RESULTS: Alb-CTLA4Ig is stable and inactive under physiological conditions but can be fully activated by MMPs. The binding activity of nondigested Alb-CTLA4Ig was at least 10,000-fold weaker than that of MMP-digested Alb-CTLA4Ig. Nondigested Alb-CTLA4Ig was unable to inhibit Jurkat T cell activation, whereas MMP-digested Alb-CTLA4Ig was as potent as conventional CTLA4Ig in inhibiting the T cells. Alb-CTLA4Ig was converted to CTLA4Ig in the inflamed joints to treat mouse collagen-induced arthritis, showing similar efficacy to that of conventional CTLA4Ig. In contrast to conventional CTLA4Ig, Alb-CTLA4Ig did not inhibit the antimicrobial responses in the spleens of the treated mice. CONCLUSIONS: Our study indicates that Alb-CTLA4Ig can be activated by MMPs to suppress tissue inflammation in situ. Thus, Alb-CTLA4Ig is a safe and effective treatment for collagen-induced arthritis in mice.

Multiview Orthonormalized Partial Least Squares: Regularizations and Deep Extensions.

In this article, we establish a family of subspace-based learning methods for multiview learning using least squares as the fundamental basis. Specifically, we propose a novel unified multiview learning framework called multiview orthonormalized partial least squares (MvOPLSs) to learn a classifier over a common latent space shared by all views. The regularization technique is further leveraged to unleash the power of the proposed framework by providing three types of regularizers on its basic ingredients, including model parameters, decision values, and latent projected points. With a set of regularizers derived from various priors, we not only recast most existing multiview learning methods into the proposed framework with properly chosen regularizers but also propose two novel models. To further improve the performance of the proposed framework, we propose to learn nonlinear transformations parameterized by deep networks. Extensive experiments are conducted on multiview datasets in terms of both feature extraction and cross-modal retrieval. Results show that the subspace-based learning for a common latent space is effective and its nonlinear extension can further boost performance, and more importantly, one of two proposed methods with nonlinear extension can achieve better results than all compared methods.

Engineering a High-Affinity Anti-Methoxy Poly(ethylene glycol) (mPEG) Antibody for Sensitive Immunosensing of mPEGylated Therapeutics and mPEG Molecules.

Sensitive quantification of methoxy poly(ethylene glycol) (mPEG)-conjugated therapeutics for pharmacokinetic determination is critical for mPEGylated drug development. However, sensitive measurement of low-molecular-weight (lmw) mPEG compounds remains challenging due to epitope competition between backbone-specific anti-PEG antibodies. Here, we engineered a high-affinity methoxy-specific anti-mPEG antibody for sensitive quantification of free mPEG molecules and mPEGylated therapeutics. The affinity-enhanced h15-2Y antibody variant shows a 10.3-fold increase in mPEG-binding activity compared to parental h15-2b. h15-2Y-based sandwich ELISA can effectively quantify lmw mPEG5K and high-molecular-weight (hmw) mPEG20K at concentrations as low as 3.4 and 5.1 ng mL-1, respectively. Moreover, lmw mPEG compounds (560, 750, 1000, and 2000 Da) can be efficiently quantified via h15-2Y-based competitive ELISA with detection limits at nanomolar levels. This study provides a promising approach for application in the quantitative analysis of the various sizes of mPEG molecules to accelerate the timeline of mPEG-conjugated drug development.

Micro-PET imaging of hepatitis C virus NS3/4A protease activity using a protease-activatable retention probe.

Hepatitis C virus (HCV) NS3/4A protease is an attractive target for direct-acting antiviral agents. Real-time tracking of the NS3/4A protease distribution and activity is useful for clinical diagnosis and disease management. However, no approach has been developed that can systemically detect NS3/4A protease activity or distribution. We designed a protease-activatable retention probe for tracking HCV NS3/4A protease activity via positron emission topography (PET) imaging. A cell-penetrating probe was designed that consisted of a cell-penetrating Tat peptide, HCV NS3/4A protease substrate, and a hydrophilic domain. The probe was labeled by fluorescein isothiocyanate (FITC) and 124I in the hydrophilic domain to form a TAT-ΔNS3/4A-124I-FITC probe. Upon cleavage at NS3/4A substrate, the non-penetrating hydrophilic domain is released and accumulated in the cytoplasm allowing PET or optical imaging. The TAT-ΔNS3/4A-FITC probe selectively accumulated in NS3/4A-expressing HCC36 (NS3/4A-HCC36) cells/tumors and HCV-infected HCC36 cells. PET imaging showed that the TAT-ΔNS3/4A-124I-FITC probe selectively accumulated in the NS3/4A-HCC36 xenograft tumors and liver-implanted NS3/4A-HCC36 tumors, but not in the control HCC36 tumors. The TAT-ΔNS3/4A-124I-FITC probe can be used to represent NS3/4 protease activity and distribution via a clinical PET imaging system allowing. This strategy may be extended to detect any cellular protease activity for optimization the protease-based therapies.

Correction: Wu et al. Secondary Metabolites with Antimicrobial Activities from Chamaecyparisobtusa var. formosana. Molecules 2022, 27, 429.

The authors wish to make the following changes to their paper [...].

Structural insights into Nirmatrelvir (PF-07321332)-3C-like SARS-CoV-2 protease complexation: a ligand Gaussian accelerated molecular dynamics study.

Coronavirus 3C-like protease (3CLpro) is found in SARS-CoV-2 virus, which causes COVID-19. 3CLpro controls virus replication and is a major target for target-based antiviral discovery. As reported by Pfizer, Nirmatrelvir (PF-07321332) is a competitive protein inhibitor and a clinical candidate for orally delivered medication. However, the binding mechanisms between Nirmatrelvir and 3CLpro complex structures remain unknown. This study incorporated ligand Gaussian accelerated molecular dynamics, the one-dimensional and two-dimensional potential of mean force, normal molecular dynamics, and Kramers' rate theory to determine the binding and dissociation rate constants (koff and kon) associated with the binding of the 3CLpro protein to the Nirmatrelvir inhibitor. The proposed approach addresses the challenges in designing small-molecule antiviral drugs.

Structural determination of an antibody that specifically recognizes polyethylene glycol with a terminal methoxy group.

Covalent attachment of methoxy poly(ethylene) glycol (mPEG) to therapeutic molecules is widely employed to improve their systemic circulation time and therapeutic efficacy. mPEG, however, can induce anti-PEG antibodies that negatively impact drug therapeutic effects. However, the underlying mechanism for specific binding of antibodies to mPEG remains unclear. Here, we determined the first co-crystal structure of the humanized 15-2b anti-mPEG antibody in complex with mPEG, which possesses a deep pocket in the antigen-binding site to accommodate the mPEG polymer. Structural and mutational analyses revealed that mPEG binds to h15-2b via Van der Waals and hydrogen bond interactions, whereas the methoxy group of mPEG is stabilized in a hydrophobic environment between the VH:VL interface. Replacement of the heavy chain hydrophobic V37 residue with a neutral polar serine or threonine residue offers additional hydrogen bond interactions with methoxyl and hydroxyl groups, resulting in cross-reactivity to mPEG and OH-PEG. Our findings provide insights into understanding mPEG-binding specificity and antigenicity of anti-mPEG antibodies.

A novel 2-phase residual U-net algorithm combined with optimal mass transportation for 3D brain tumor detection and segmentation.

Utilizing the optimal mass transportation (OMT) technique to convert an irregular 3D brain image into a cube, a required input format for a U-net algorithm, is a brand new idea for medical imaging research. We develop a cubic volume-measure-preserving OMT (V-OMT) model for the implementation of this conversion. The contrast-enhanced histogram equalization grayscale of fluid-attenuated inversion recovery (FLAIR) in a brain image creates the corresponding density function. We then propose an effective two-phase residual U-net algorithm combined with the V-OMT algorithm for training and validation. First, we use the residual U-net and V-OMT algorithms to precisely predict the whole tumor (WT) region. Second, we expand this predicted WT region with dilation and create a smooth function by convolving the step-like function associated with the WT region in the brain image with a [Formula: see text] blur tensor. Then, a new V-OMT algorithm with mesh refinement is constructed to allow the residual U-net algorithm to effectively train Net1-Net3 models. Finally, we propose ensemble voting postprocessing to validate the final labels of brain images. We randomly chose 1000 and 251 brain samples from the Brain Tumor Segmentation (BraTS) 2021 training dataset, which contains 1251 samples, for training and validation, respectively. The Dice scores of the WT, tumor core (TC) and enhanced tumor (ET) regions for validation computed by Net1-Net3 were 0.93705, 0.90617 and 0.87470, respectively. A significant improvement in brain tumor detection and segmentation with higher accuracy is achieved.

Inhibition of gut microbial ß-glucuronidase effectively prevents carcinogen-induced microbial dysbiosis and intestinal tumorigenesis.

The bidirectional interaction between carcinogens and gut microbiota that contributes to colorectal cancer is complicated. Reactivation of carcinogen metabolites by microbial ß-glucuronidase (ßG) in the gut potentially plays an important role in colorectal carcinogenesis. We assessed the chemoprotective effects and associated changes in gut microbiota induced by pre-administration of bacterial-specific ßG inhibitor TCH-3511 in carcinogen azoxymethane (AOM)-treated APCMin/+ mice. AOM induced intestinal ßG activity, which was reflected in increases in the incidence, formation, and number of tumors in the intestine. Notably, inhibition of gut microbial ßG by TCH-3511 significantly reduced AOM-induced intestinal ßG activity, decreased the number of polyps in both the small and large intestine to a frequency that was similar in mice without AOM exposure. AOM also led to lower diversity and altered composition in the gut microbiota with a significant increase in mucin-degrading Akkermansia genus. Conversely, mice treated with TCH-3511 and AOM exhibited a more similar gut microbiota structure as mice without AOM administration. Importantly, TCH-3511 treatment significant decreased Akkermansia genus and produced a concomitant increase in short-chain fatty acid butyrate-producing gut commensal microbes Lachnoospiraceae NK4A136 group genus in AOM-treated mice. Taken together, our results reveal a key role of gut microbial ßG in promoting AOM-induced gut microbial dysbiosis and intestinal tumorigenesis, indicating the chemoprotective benefit of gut microbial ßG inhibition against carcinogens via maintaining the gut microbiota balance and preventing cancer-associated gut microbial dysbiosis. Thus, the bacterial-specific ßG inhibitor TCH-3511 is a potential chemoprevention agent for colorectal cancer.

Secondary Metabolites with Antimicrobial Activities from Chamaecyparis obtusa var. formosana.

Seven new compounds, including one dimer novel skeleton, chamaecyformosanin A (1); three diterpenes, chamaecyformosanins B-D (2-4); one sesquiterpene, chamaecyformosanin E (5); and two monoterpenes, chamaecyformosanins F and G (6 and 7) were isolated from the methanol extract of the bark of Chamaecyparis obtusa var. formosana. Their structures were established by the mean of spectroscopic analysis and the comparison of NMR data with those of known analogues. Their structures were elucidated on the basis of physicochemical evidence, in-depth NMR spectroscopic analysis, and high-resolution mass spectrometry. Furthermore, the isolated compounds were subjected to an evaluation of their antimicrobial activity. Metabolites 1, 3, and 4 present antibacterial activities. It is worth mentioning that the chemical composition of the bark of C. obtusa var. formosana has never been studied in the past. This is the first time the barks from C. obtusa var. formosana were studied and two new skeleton compounds, 1 and 7, were obtained.

3D brain tumor segmentation using a two-stage optimal mass transport algorithm.

Optimal mass transport (OMT) theory, the goal of which is to move any irregular 3D object (i.e., the brain) without causing significant distortion, is used to preprocess brain tumor datasets for the first time in this paper. The first stage of a two-stage OMT (TSOMT) procedure transforms the brain into a unit solid ball. The second stage transforms the unit ball into a cube, as it is easier to apply a 3D convolutional neural network to rectangular coordinates. Small variations in the local mass-measure stretch ratio among all the brain tumor datasets confirm the robustness of the transform. Additionally, the distortion is kept at a minimum with a reasonable transport cost. The original [Formula: see text] dataset is thus reduced to a cube of [Formula: see text], which is a 76.6% reduction in the total number of voxels, without losing much detail. Three typical U-Nets are trained separately to predict the whole tumor (WT), tumor core (TC), and enhanced tumor (ET) from the cube. An impressive training accuracy of 0.9822 in the WT cube is achieved at 400 epochs. An inverse TSOMT method is applied to the predicted cube to obtain the brain results. The conversion loss from the TSOMT method to the inverse TSOMT method is found to be less than one percent. For training, good Dice scores (0.9781 for the WT, 0.9637 for the TC, and 0.9305 for the ET) can be obtained. Significant improvements in brain tumor detection and the segmentation accuracy are achieved. For testing, postprocessing (rotation) is added to the TSOMT, U-Net prediction, and inverse TSOMT methods for an accuracy improvement of one to two percent. It takes 200 seconds to complete the whole segmentation process on each new brain tumor dataset.

Selective activation of pro-anti-IL-1ß antibody enhances specificity for autoinflammatory disorder therapy.

Canakinumab is a fully human monoclonal antibody that specifically neutralizes human interleukin (IL)-1ß and has been approved by the US Food and Drug Administration for treating different types of autoinflammatory disorders such as cryopyrin-associated periodic syndrome, tumor necrosis factor receptor-associated periodic syndrome and systemic juvenile idiopathic arthritis. However, long-term systemic neutralization of IL-1ß by Canakinumab may cause severe adverse events such as serious upper respiratory tract infections and inflammation, thereby decreasing the quality of life of patients. Here, we used an IgG1 hinge as an Ab lock to cover the IL-1ß-binding site of Canakinumab by linking with matrix metalloprotease 9 (MMP-9) substrate to generate pro-Canakinumab that can be specifically activated in the inflamed regions in autoinflammatory diseases to enhance the selectivity and safety of treatment. The Ab lock significantly inhibited the IL-1ß-binding by 68-fold compared with Canakinumab, and MMP-9 completely restored the IL-1ß neutralizing ability of pro-Canakinumab within 60 min and blocked IL-1ß-downstream signaling and IL-1ß-regulated genes (i.e., IL-6). It is expected that MMP-9 cleavable and efficient Ab lock will be able to significantly enhance the selective reaction of Canakinumab at the disease site and reduce the on-target toxicities of Canakinumab during systemic circulation, thereby showing potential for development to improve the safety and quality of life of patients with autoinflammatory disorders in the future.

Enhancement of tumor tropism of mPEGylated nanoparticles by anti-mPEG bispecific antibody for ovarian cancer therapy.

Ovarian cancer is highly metastatic, with a high frequency of relapse, and is the most fatal gynecologic malignancy in women worldwide. It is important to elevate the drug susceptibility and cytotoxicity of ovarian cancer cells, thereby eliminating resident cancer cells for more effective therapeutic efficacy. Here, we developed a bispecific antibody (BsAb; mPEG × HER2) that can easily provide HER2+ tumor tropism to mPEGylated liposomal doxorubicin (PLD) and further increase the drug accumulation in cancer cells via receptor-mediated endocytosis, and improve the cytotoxicity and therapeutic efficacy of HER2+ ovarian tumors. The mPEG × HER2 can simultaneously bind to mPEG molecules on the surface of PLD and HER2 antigen on the surface of ovarian cancer cells. Simply mixing the mPEG × HER2 with PLD was able to confer HER2 specificity of PLD to HER2+ ovarian cancer cells and efficiently trigger endocytosis and enhance cytotoxicity by 5.4-fold as compared to non-targeted PLD. mPEG × HER2-modified PLD was able to significantly increase the targeting and accumulation of HER2+ ovarian tumor by 220% as compared with non-targeted PLD. It could also significantly improve the anti-tumor activity of PLD (P < 0.05) with minimal obvious toxicity in a tumor-bearing mouse model. We believe that the mPEG × HER2 can significantly improve the therapeutic efficacy, potentially reduce the relapse freqency and thereby achieve good prognosis in ovarian cancer patients.

Fibrinogen-Like Protein 1 Serves as an Anti-Inflammatory Agent for Collagen-Induced Arthritis Therapy in Mice.

Fibrinogen-like protein 1 (FGL1) was recently identified as a major ligand of lymphocyte-activation gene-3 (LAG-3) on activated T cells and serves as an immune suppressive molecule for regulation of immune homeostasis. However, whether FGL1 has therapeutic potential for use in the T cell-induced the autoimmune disease, rheumatoid arthritis (RA), is still unknown. Here, we attempted to evaluate the effect of FGL1 protein on arthritis progression. We also evaluated potential adverse events in a collagen-induced arthritis (CIA) mouse model. We first confirmed that soluble Fgl1 protein could specifically bind to surface Lag-3 receptor on 3T3-Lag-3 cells and further inhibit interleukin (IL-2) and interferon gamma (IFNγ) secretion from activated primary mouse T cells by 95% and 43%, respectively. Intraperitoneal administration of Fgl1 protein significantly decreased the inflammatory cytokine level (i.e., IL-1ß and IL-6) in local paw tissue, and prevented joint inflammation, cellular infiltration, bone deformation and attenuated collagen-induced arthritis progression in vivo. We further demonstrated that exogenous Fgl1 does not cause obvious adverse events during treatment by monitoring body weight and liver weight, and assessing the morphology of several organs (i.e., heart, liver, spleen, lung and kidney) by pathological studies. We expect that Fgl1 protein may be suitable to serve as a potential therapeutic agent for treatment of RA or even other types of T cell-induced autoimmune or inflammatory diseases in the future.