Immunomodulatory Function of Pien Tze Huang in T Cell-Mediated Anti-tumor Activity against B16-F10, MC38 and Hep1-6 Tumor Models.

OBJECTIVE: To investigate the anti-tumor effects of Pien Tze Huang (PZH) in mouse models of B16-F10 melanoma, MC38 colorectal cancer, Hep1-6 hepatocellular carcinoma and chemically induced hepatocellular carcinoma model. METHODS: Various tumor models, including B16-F10, MC38 and Hep1-6 tumor hypodermic inoculation models, B16-F10 and Hep1-6 pulmonary metastasis models, Hep1-6 orthotopic implantation model, and chemically induced hepatocellular carcinoma model, were utilized to evaluate the anti-tumor function of PZH. Tumor growth was assessed by measuring tumor size and weight of solid tumors isolated from C57BL/6 mice. For cell proliferation and death of tumor cells in vitro, as well as T cell activation markers, cytokine production and immune checkpoints analysis, single-cell suspensions were prepared from mouse spleen, lymph nodes, and tumors after PZH treatment. RESULTS: PZH demonstrated significant therapeutic efficacy in inhibiting tumor growth (P<0.01). Treatment with PZH resulted in a reduction in tumor size in subcutaneous MC38 colon adenocarcinoma and B16-F10 melanoma models, and decreased pulmonary metastasis of B16-F10 melanoma and Hep1-6 hepatoma (P<0.01). However, in vitro experiments showed that PZH only had slight impact on the cell proliferation and survival of tumor cells (P>0.05). Nevertheless, PZH exhibited a remarkable ability to enhance T cell activation and the production of interferon gamma, tumor necrosis factor alpha, and interleukin 2 in CD4+ T cells in vitro (P<0.01 or P<0.05). Importantly, PZH substantially inhibited T cell exhaustion and boosted cytokine production by tumor-infiltrating CD8+ T cells (P<0.01 or P<0.05). CONCLUSION: This study has confirmed a novel immunomodulatory function of PZH in T cell-mediated anti-tumor immunity, indicating that PZH holds promise as a potential therapeutic agent for cancer treatment.

Glycogen synthase kinase 3 controls T-cell exhaustion by regulating NFAT activation.

Cellular immunity mediated by CD8+ T cells plays an indispensable role in bacterial and viral clearance and cancers. However, persistent antigen stimulation of CD8+ T cells leads to an exhausted or dysfunctional cellular state characterized by the loss of effector function and high expression of inhibitory receptors during chronic viral infection and in tumors. Numerous studies have shown that glycogen synthase kinase 3 (GSK3) controls the function and development of immune cells, but whether GSK3 affects CD8+ T cells is not clearly elucidated. Here, we demonstrate that mice with deletion of Gsk3α and Gsk3ß in activated CD8+ T cells (DKO) exhibited decreased CTL differentiation and effector function during acute and chronic viral infection. In addition, DKO mice failed to control tumor growth due to the upregulated expression of inhibitory receptors and augmented T-cell exhaustion in tumor-infiltrating CD8+ T cells. Strikingly, anti-PD-1 immunotherapy substantially restored tumor rejection in DKO mice. Mechanistically, GSK3 regulates T-cell exhaustion by suppressing TCR-induced nuclear import of NFAT, thereby in turn dampening NFAT-mediated exhaustion-related gene expression, including TOX/TOX2 and PD-1. Thus, we uncovered the molecular mechanisms underlying GSK3 regulation of CTL differentiation and T-cell exhaustion in anti-tumor immune responses.

Transcriptome analysis of Δmig1Δmig2 mutant reveals their roles in methanol catabolism, peroxisome biogenesis and autophagy in methylotrophic yeast Pichia pastoris.

Two catabolite repressor genes (MIG1 and MIG2) were previously identified in Pichia pastoris, and the derepression of alcohol oxidase (AOX) expression was realized in Δmig1 or Δmig1Δmig2 mutants grown in glycerol, but not in glucose. In this study, genome-wide RNA-seq analysis of Δmig1Δmig2 and the wild-type strain grown in glycerol revealed that the expression of numerous genes was greatly altered. Nearly 7% (357 genes) of approximately 5276 genes annotated in P. pastoris were significantly upregulated, with at least a two-fold differential expression in Δmig1Δmig2; the genes were mainly related to cell metabolism. Approximately 23% (1197 genes) were significantly downregulated; these were mainly correlated with the physiological characteristics of the cell. The methanol catabolism and peroxisome biogenesis pathways were remarkably enhanced, and the genes AOX1 and AOX2 were upregulated higher than 30-fold, which was consistent with the experimental results of AOX expression. The Mig proteins had a slight effect on autophagy when cells were grown in glycerol. The expression analysis of transcription factors showed that deletion of MIG1 and MIG2 significantly upregulated the binding of an essential transcription activator, Mit1p, with the AOX1 promoter, which suggested that Mig proteins might regulate the AOX1 promoter through the regulation of Mit1p. This work provides a reference for the further exploration of the methanol induction and catabolite repression mechanisms of AOX expression in methylotrophic yeasts.

Reduced methanol input induces increased protein output by AOX1 promoter in a trans-acting elements engineered Pichia pastoris.

High oxygen consumption and heat release caused by methanol catabolism usually bring difficulties to industrial scale-up and cost for protein expression driven by methanol-induced AOX1 promoter in Pichia pastoris. Here, reduced methanol feeding levels were investigated for expression of insulin precursor in a trans-acting elements engineered P. pastoris strain MF1-IP. Insulin precursor expression level reached 6.69 g/(L supernatant) at the methanol feeding rate of 6.67 mL/(h·L broth), which was 59% higher than that in the wild-type strain WT-IP at the methanol feeding rate of 12 mL/(h·L broth). Correspondingly, the insulin precursor expression level in fermentation broth and maximum specific insulin precursor production rate was 137 and 77% higher than the WT-IP, respectively. However, oxygen consumption and heat evolution were reduced, and the highest oxygen consumption rate and heat evolution rate of the MF1-IP were 18.0 and 37.7% lower than the WT-IP, respectively.

Methanol-Independent Protein Expression by AOX1 Promoter with trans-Acting Elements Engineering and Glucose-Glycerol-Shift Induction in Pichia pastoris.

The alcohol oxidase 1 promoter (PAOX1) of Pichia pastoris is commonly used for high level expression of recombinant proteins. While the safety risk of methanol and tough process control for methanol induction usually cause problems especially in large-scale fermentation. By testing the functions of trans-acting elements of PAOX1 and combinatorially engineering of them, we successfully constructed a methanol-free PAOX1 start-up strain, in which, three transcription repressors were identified and deleted and, one transcription activator were overexpressed. The strain expressed 77% GFP levels in glycerol compared to the wide-type in methanol. Then, insulin precursor (IP) was expressed, taking which as a model, we developed a novel glucose-glycerol-shift induced PAOX1 start-up for this methanol-free strain. A batch phase with glucose of 40 g/L followed by controlling residual glucose not lower than 20 g/L was compatible for supporting cell growth and suppressing PAOX1. Then, glycerol induction was started after glucose used up. Accordingly, an optimal bioprocess was further determined, generating a high IP production of 2.46 g/L in a 5-L bioreactor with dramatical decrease of oxygen consumption and heat evolution comparing with the wild-type in methanol. This mutant and bioprocess represent a safe and efficient alternative to the traditional glycerol-repressed/methanol-induced PAOX1 system.

Peroxisome-targeted and tandem repeat multimer expressions of human antimicrobial peptide LL37 in Pichia pastoris.

Although the human antimicrobial peptide LL37 has a broad spectrum of antimicrobial activities, it easily damages host cells following heterologous expressions. This study attempted two strategies to alleviate its damage to host cells when expressed in Pichia pastoris using the AOX1 promoter. Tandem repeat multimers of LL37 were first designed, and secretion expression strains GS115-9K-(DPLL37DP)n (n = 2, 4, 6 and 8) containing different copies of the LL37 gene were constructed. However, LL37 tandems still killed the cells after 96 hr of induction. Subsequently, peroxisome-targeted expression was performed by adding a peroxisomal targeting signal 1 (SKL) at the C-terminus of LL37. The LL37 expression strain GS115-3.5K-LL37-SKL showed no significant inhibition in the cells after induction. Antibacterial activity assays showed that the recombinant LL37 expressed in peroxisomes had good antimicrobial activities. Then, a strain GS115-3.5K-LL37-GFP-SKL producing LL37, green fluorescent protein, and SKL fusion proteins was constructed, and the fusion protein was confirmed to be targeting the peroxisomes. However, protein extraction analysis indicated that most of the fusion proteins were still located in the cell debris after cell disruption, and further studies are required to extract more proteins from the peroxisome membrane.

[Generalized interaction LASSO based on alternating direction method of multipliers for liver disease classification].

Features and interaction between features of liver disease is of great significance for the classification of liver disease. Based on least absolute shrinkage and selection operator (LASSO) and interaction LASSO, the generalized interaction LASSO model is proposed in this paper for liver disease classification and compared with other methods. Firstly, the generalized interaction logistic classification model was constructed and the LASSO penalty constraints were added to the interactive model parameters. Then the model parameters were solved by an efficient alternating directions method of multipliers (ADMM) algorithm. The solutions of model parameters were sparse. Finally, the test samples were fed to the model and the classification results were obtained by the largest statistical probability. The experimental results of liver disorder dataset and India liver dataset obtained by the proposed methods showed that the coefficients of interaction features of the model were not zero, indicating that interaction features were contributive to classification. The accuracy of the generalized interaction LASSO method is better than that of the interaction LASSO method, and it is also better than that of traditional pattern recognition methods. The generalized interaction LASSO method can also be popularized to other disease classification areas.

Mit1 Transcription Factor Mediates Methanol Signaling and Regulates the Alcohol Oxidase 1 (AOX1) Promoter in Pichia pastoris.

The alcohol oxidase 1 (AOX1) promoter (P AOX1) of Pichia pastoris is the most powerful and commonly used promoter for driving protein expression. However, mechanisms regulating its transcriptional activity are unclear. Here, we identified a Zn(II)2Cys6-type methanol-induced transcription factor 1 (Mit1) and elucidated its roles in regulating PAOX1 activity in response to glycerol and methanol. Mit1 regulated the expression of many genes involved in methanol utilization pathway, including AOX1, but did not participate in peroxisome proliferation and transportation of peroxisomal proteins during methanol metabolism. Structural analysis of Mit1 by performing domain deletions confirmed its specific and critical role in the strict repression of P AOX1 in glycerol medium. Importantly, Mit1, Mxr1, and Prm1, which positively regulated P AOX1 in response to methanol, were bound to P AOX1 at different sites and did not interact with each other. However, these factors cooperatively activated P AOX1 through a cascade. Mxr1 mainly functioned during carbon derepression, whereas Mit1 and Prm1 functioned during methanol induction, with Prm1 transmitting methanol signal to Mit1 by binding to the MIT1 promoter (P MIT1), thus increasingly expressing Mit1 and subsequently activating P AOX1.

PpNrg1 is a transcriptional repressor for glucose and glycerol repression of AOX1 promoter in methylotrophic yeast Pichia pastoris.

OBJECTIVE: The regulator in glycerol repression of Pichia pastoris AOX1 promoter (P AOX1 ) is still unclear. RESULTS: A Cys2His2 zinc finger transcriptional repressor PpNrg1 localized to nucleus and participated in the repression of P AOX1 in P. pastoris in glucose and glycerol. Quantitative real-time PCR revealed that PpNrg1 repressed expression of numerous genes involved in methanol utilization and peroxisome biogenesis in 0.02 % glucose and 1 % (v/v) glycerol. Electrophoretic mobility shift assay and DNase I footprinting assay revealed that PpNrg1 bound to five sites of P AOX1 , including two binding sites of PpMxr1, which is an indispensable activator of P AOX1 in P. pastoris. CONCLUSION: Transcriptional repressor PpNrg1 suppresses P AOX1 in glucose and glycerol by directly binding to five sites of P AOX1 , including two binding sites of transcriptional activator PpMxr1.

[Selection and Classification of Elastic Net Feature with Fused Electroencephalogram Features].

Signal classification is a key of brain-computer interface(BCI).In this paper,we present a new method for classifying the electroencephalogram(EEG)signals of which the features are heterogeneous.This method is called wrapped elastic net feature selection and classification.Firstly,we used the joint application of time-domain statistic,power spectral density(PSD),common spatial pattern(CSP)and autoregressive(AR)model to extract high-dimensional fused features of the preprocessed EEG signals.Then we used the wrapped method for feature selection.We fitted the logistic regression model penalized with elastic net on the training data,and obtained the parameter estimation by coordinate descent method.Then we selected best feature subset by using 10-fold cross-validation.Finally,we classified the test sample using the trained model.Data used in the experiment were the EEG data from international BCI CompetitionⅣ.The results showed that the method proposed was suitable for fused feature selection with high-dimension.For identifying EEG signals,it is more effective and faster,and can single out a more relevant subset to obtain a relatively simple model.The average test accuracy reached 81.78%.

[A New Method for Diagnosing Erythemato-squamous Diseases Based on Virtual Coding and Multinomial Logistic Regression Penalized via Elastic Net].

Erythemato-squamous diseases are a general designation of six common skin diseases, of which the differential diagnosis is a difficult problem in dermatology. This paper presents a new method based on virtual coding for qualitative variables and multinomial logistic regression penalized via elastic net. Considering the attributes of variables, a virtual coding is applied and contributes to avoid the irrationality of calculating nominal values directly. Multinomial logistic regression model penalized via elastic net is thence used to fit the correlation between the features and classification of diseases. At last, parameter estimations can be attained through coordinate descent. This method reached accuracy rate of 98.34% +/- 0.0027% using 10-fold cross validation in the experiments. Our method attained equivalent accuracy rate compared to the results of other methods, but steps are simpler and stability is higher.

[Tensor Feature Extraction Using Multi-linear Principal Component Analysis for Brain Computer Interface].

The brain computer interface (BCI) can be used to control external devices directly through electroencephalogram (EEG) information. A multi-linear principal component analysis (MPCA) framework was used for the limitations of tensor form of multichannel EEG signals processing based on traditional principal component analysis (PCA) and two-dimensional principal component analysis (2DPCA). Based on MPCA, we used the projection of tensor-matrix to achieve the goal of dimensionality reduction and features exaction. Then we used the Fisher linear classifier to classify the features. Furthermore, we used this novel method on the BCI competition II dataset 4 and BCI competition N dataset 3 in the experiment. The second-order tensor representation of time-space EEG data and the third-order tensor representation of time-space-frequency BEG data were used. The best results that were superior to those from other dimensionality reduction methods were obtained by much debugging on parameter P and testQ. For two-order tensor, the highest accuracy rates could be achieved as 81.0% and 40.1%, and for three-order tensor, the highest accuracy rates were 76.0% and 43.5%, respectively.

[A Feature Extraction Method for Brain Computer Interface Based on Multivariate Empirical Mode Decomposition].

This paper presents a feature extraction method based on multivariate empirical mode decomposition (MEMD) combining with the power spectrum feature, and the method aims at the non-stationary electroencephalogram (EEG) or magnetoencephalogram (MEG) signal in brain-computer interface (BCI) system. Firstly, we utilized MEMD algorithm to decompose multichannel brain signals into a series of multiple intrinsic mode function (IMF), which was proximate stationary and with multi-scale. Then we extracted and reduced the power characteristic from each IMF to a lower dimensions using principal component analysis (PCA). Finally, we classified the motor imagery tasks by linear discriminant analysis classifier. The experimental verification showed that the correct recognition rates of the two-class and four-class tasks of the BCI competition III and competition IV reached 92.0% and 46.2%, respectively, which were superior to the winner of the BCI competition. The experimental proved that the proposed method was reasonably effective and stable and it would provide a new way for feature extraction.

[A novel method of multi-channel feature extraction combining multivariate autoregression and multiple-linear principal component analysis].

Brain-computer interface (BCI) systems identify brain signals through extracting features from them. In view of the limitations of the autoregressive model feature extraction method and the traditional principal component analysis to deal with the multichannel signals, this paper presents a multichannel feature extraction method that multivariate autoregressive (MVAR) model combined with the multiple-linear principal component analysis (MPCA), and used for magnetoencephalography (MEG) signals and electroencephalograph (EEG) signals recognition. Firstly, we calculated the MVAR model coefficient matrix of the MEG/EEG signals using this method, and then reduced the dimensions to a lower one, using MPCA. Finally, we recognized brain signals by Bayes Classifier. The key innovation we introduced in our investigation showed that we extended the traditional single-channel feature extraction method to the case of multi-channel one. We then carried out the experiments using the data groups of IV-III and IV - I. The experimental results proved that the method proposed in this paper was feasible.

Simultaneous channel and feature selection of fused EEG features based on Sparse Group Lasso.

Feature extraction and classification of EEG signals are core parts of brain computer interfaces (BCIs). Due to the high dimension of the EEG feature vector, an effective feature selection algorithm has become an integral part of research studies. In this paper, we present a new method based on a wrapped Sparse Group Lasso for channel and feature selection of fused EEG signals. The high-dimensional fused features are firstly obtained, which include the power spectrum, time-domain statistics, AR model, and the wavelet coefficient features extracted from the preprocessed EEG signals. The wrapped channel and feature selection method is then applied, which uses the logistical regression model with Sparse Group Lasso penalized function. The model is fitted on the training data, and parameter estimation is obtained by modified blockwise coordinate descent and coordinate gradient descent method. The best parameters and feature subset are selected by using a 10-fold cross-validation. Finally, the test data is classified using the trained model. Compared with existing channel and feature selection methods, results show that the proposed method is more suitable, more stable, and faster for high-dimensional feature fusion. It can simultaneously achieve channel and feature selection with a lower error rate. The test accuracy on the data used from international BCI Competition IV reached 84.72%.

[Features Interaction Lasso for Liver Disease Classification].

To solve the complex interaction problems of hepatitis disease classification, we proposed a lasso method (least absolute shrinkage and selection operator method) with feature interaction. First, lasso penalized function and hierarchical convex constraint were added to the interactive model which is newly defined. Then the model was solved with the convex optimal method combining Karush-Kuhn-Tucker (KKT) condition with generalized gradient descent. Finally, the sparse solution of the main effect features and interactive features were derived, and the classification model was implemented. The experiments were performed on two liver data sets and proved that features interaction contributed to the classification of liver diseases. The experimental results showed that the feature interaction lasso method was of strong explanatory ability, and its effectiveness and efficiency were superior to those of lasso, of all pair-wise lasso, support vector machine (SVM) method, K nearest neighbor (KNN) method, linear discriminant analysis (LDA) classification method, etc.

[Group Lasso Penalized Classifier for Diagnosis of Diseases with Categorical Data].

Six kinds of erythemato-squamous diseases have been common skin diseases, but the diagnosis of them has always been a problem. The quantitative data processing method is not suitable for erythemato-squamous data because they are categorical qualitative data. This paper proposed a new method based on group lasso penalized classification for the feature selection and classification for erythemato-squamous data with categorical qualitative data. The first categorical data of 33 dimensions were changed by the virtual code, and then 34th dimension age data were discretized and changed by the virtual code. Then the encoded data were grouped according to class group and variable group. Lastly Group Lasso penalized classification was executed. The classified accuracy of 10-fold cross validation was 98.88% ± 0.002 3%. Compared with those of other method in the literature, this new method is simpler, and better for effect and efficiency, and has stronger interpretability and stronger stability.

[Research of controlling of smart home system based on P300 brain-computer interface].

Using electroencephalogram (EEG) signal to control external devices has always been the research focus in the field of brain-computer interface (BCI). This is especially significant for those disabilities who have lost capacity of movements. In this paper, the P300-based BCI and the microcontroller-based wireless radio frequency (RF) technology are utilized to design a smart home control system, which can be used to control household appliances, lighting system, and security devices directly. Experiment results showed that the system was simple, reliable and easy to be populirised.

[Application of semi-supervised sparse representation classifier based on help training in EEG classification].

Electroencephalogram (EEG) classification for brain-computer interface (BCI) is a new way of realizing human-computer interreaction. In this paper the application of semi-supervised sparse representation classifier algorithms based on help training to EEG classification for BCI is reported. Firstly, the correlation information of the unlabeled data is obtained by sparse representation classifier and some data with high correlation selected. Secondly, the boundary information of the selected data is produced by discriminative classifier, which is the Fisher linear classifier. The final unlabeled data with high confidence are selected by a criterion containing the information of distance and direction. We applied this novel method to the three benchmark datasets, which were BCI I, BCI II_IV and USPS. The classification rate were 97%, 82% and 84.7%, respectively. Moreover the fastest arithmetic rate was just about 0. 2 s. The classification rate and efficiency results of the novel method are both better than those of S3VM and SVM, proving that the proposed method is effective.

[Design and implementation of controlling smart car systems using P300 brain-computer interface].

Using human electroencephalogram (EEG) to control external devices in order to achieve a variety of functions has been focus of the field of brain-computer interface (BCI) research. P300 is experiments which stimulate the eye to produce EEG by using letters flashing, and then identify the corresponding letters. In this paper, some improvements based on the P300 experiments were made??. Firstly, the matrix of flashing letters were modified into words which represent a certain sense. Secondly, the BCI2000 procedures were added with the corresponding source code. Thirdly, the smart car systems were designed using the radiofrequency signal. Finally it was realized that the evoked potentials were used to control the state of the smart car.