Peripheral T cell expansion predicts tumour infiltration and clinical response.

Despite the resounding clinical success in cancer treatment of antibodies that block the interaction of PD1 with its ligand PDL11, the mechanisms involved remain unknown. A major limitation to understanding the origin and fate of T cells in tumour immunity is the lack of quantitative information on the distribution of individual clonotypes of T cells in patients with cancer. Here, by performing deep single-cell sequencing of RNA and T cell receptors in patients with different types of cancer, we survey the profiles of various populations of T cells and T cell receptors in tumours, normal adjacent tissue, and peripheral blood. We find clear evidence of clonotypic expansion of effector-like T cells not only within the tumour but also in normal adjacent tissue. Patients with gene signatures of such clonotypic expansion respond best to anti-PDL1 therapy. Notably, expanded clonotypes found in the tumour and normal adjacent tissue can also typically be detected in peripheral blood, which suggests a convenient approach to patient identification. Analyses of our data together with several external datasets suggest that intratumoural T cells, especially in responsive patients, are replenished with fresh, non-exhausted replacement cells from sites outside the tumour, suggesting continued activity of the cancer immunity cycle in these patients, the acceleration of which may be associated with clinical response.

A machine learning-based quantitative model (LogBB_Pred) to predict the blood-brain barrier permeability (logBB value) of drug compounds.

MOTIVATION: Efficient assessment of the blood-brain barrier (BBB) penetration ability of a drug compound is one of the major hurdles in central nervous system drug discovery since experimental methods are costly and time-consuming. To advance and elevate the success rate of neurotherapeutic drug discovery, it is essential to develop an accurate computational quantitative model to determine the absolute logBB value (a logarithmic ratio of the concentration of a drug in the brain to its concentration in the blood) of a drug candidate. RESULTS: Here, we developed a quantitative model (LogBB_Pred) capable of predicting a logBB value of a query compound. The model achieved an R2 of 0.61 on an independent test dataset and outperformed other publicly available quantitative models. When compared with the available qualitative (classification) models that only classified whether a compound is BBB-permeable or not, our model achieved the same accuracy (0.85) with the best qualitative model and far-outperformed other qualitative models (accuracies between 0.64 and 0.70). For further evaluation, our model, quantitative models, and the qualitative models were evaluated on a real-world central nervous system drug screening library. Our model showed an accuracy of 0.97 while the other models showed an accuracy in the range of 0.29-0.83. Consequently, our model can accurately classify BBB-permeable compounds as well as predict the absolute logBB values of drug candidates. AVAILABILITY AND IMPLEMENTATION: Web server is freely available on the web at http://ssbio.cau.ac.kr/software/logbb_pred/. The data used in this study are available to download at http://ssbio.cau.ac.kr/software/logbb_pred/dataset.zip.

A multi-layered network model identifies Akt1 as a common modulator of neurodegeneration.

The accumulation of misfolded and aggregated proteins is a hallmark of neurodegenerative proteinopathies. Although multiple genetic loci have been associated with specific neurodegenerative diseases (NDs), molecular mechanisms that may have a broader relevance for most or all proteinopathies remain poorly resolved. In this study, we developed a multi-layered network expansion (MLnet) model to predict protein modifiers that are common to a group of diseases and, therefore, may have broader pathophysiological relevance for that group. When applied to the four NDs Alzheimer's disease (AD), Huntington's disease, and spinocerebellar ataxia types 1 and 3, we predicted multiple members of the insulin pathway, including PDK1, Akt1, InR, and sgg (GSK-3ß), as common modifiers. We validated these modifiers with the help of four Drosophila ND models. Further evaluation of Akt1 in human cell-based ND models revealed that activation of Akt1 signaling by the small molecule SC79 increased cell viability in all models. Moreover, treatment of AD model mice with SC79 enhanced their long-term memory and ameliorated dysregulated anxiety levels, which are commonly affected in AD patients. These findings validate MLnet as a valuable tool to uncover molecular pathways and proteins involved in the pathophysiology of entire disease groups and identify potential therapeutic targets that have relevance across disease boundaries. MLnet can be used for any group of diseases and is available as a web tool at http://ssbio.cau.ac.kr/software/mlnet.

Effects of rosiglitazone, an antidiabetic drug, on Kv3.1 channels.

Rosiglitazone is a thiazolidinedione-class antidiabetic drug that reduces blood glucose and glycated hemoglobin levels. We here investigated the interaction of rosiglitazone with Kv3.1 expressed in Chinese hamster ovary cells using the whole-cell patch-clamp technique. Rosiglitazone rapidly and reversibly inhibited Kv3.1 currents in a concentration-dependent manner (IC50 = 29.8 µM) and accelerated the decay of Kv3.1 currents without modifying the activation kinetics. The rosiglitazone-mediated inhibition of Kv3.1 channels increased steeply in a sigmoidal pattern over the voltage range of -20 to +30 mV, whereas it was voltage-independent in the voltage range above +30 mV, where the channels were fully activated. The deactivation of Kv3.1 current, measured along with tail currents, was also slowed by the drug. In addition, the steady-state inactivation curve of Kv3.1 by rosiglitazone shifts to a negative potential without significant change in the slope value. All the results with the use dependence of the rosiglitazone-mediated blockade suggest that rosiglitazone acts on Kv3.1 channels as an open channel blocker.

CTLA-4 suppresses the pathogenicity of self antigen-specific T cells by cell-intrinsic and cell-extrinsic mechanisms.

The inhibitory immunoregulatory receptor CTLA-4 is critical in maintaining self-tolerance, but the mechanisms of its actions have remained controversial. Here we examined the antigen specificity of tissue-infiltrating CD4(+) T cells in Ctla4(-/-) mice. After adoptive transfer, T cells isolated from tissues of Ctla4(-/-) mice showed T cell antigen receptor (TCR)-dependent accumulation in the tissues from which they were derived, which suggested reactivity to tissue-specific antigens. We identified the pancreas-specific enzyme PDIA2 as an autoantigen in Ctla4(-/-) mice. CTLA-4 expressed either on PDIA2-specific effector cells or on regulatory T cells was sufficient to control tissue destruction mediated by PDIA2-specific T cells. Our results demonstrate that both cell-intrinsic and non-cell-autonomous actions of CTLA-4 operate to maintain T cell tolerance to a self antigen.

Advanced biotechnology using methyltransferase and its applications in bacteria: a mini review.

Since prokaryotic restriction-modification (RM) systems protect the host by cleaving foreign DNA by restriction endonucleases, it is difficult to introduce engineered plasmid DNAs into newly isolated microorganisms whose RM system is not discovered. The prokaryotes also possess methyltransferases to protect their own DNA from the endonucleases. As those methyltransferases can be utilized to methylate engineered plasmid DNAs before transformation and to enhance the stability within the cells, the study on methyltransferases in newly isolated bacteria is essential for genetic engineering. Here, we introduce the mechanism of the RM system, specifically the methyltransferases and their biotechnological applications. These biotechnological strategies could facilitate plasmid DNA-based genetic engineering in bacteria strains that strongly defend against foreign DNA.

The antidiabetic drug rosiglitazone blocks Kv1.5 potassium channels in an open state.

An antidiabetic drug, rosiglitazone is a member of the drug class of thiazolidinedione. Although restrictions on use due to the possibility of heart toxicity have been removed, it is still a drug that is concerned about side effects on the heart. We here examined, using Chinese hamster ovary cells, the action of rosiglitazone on Kv1.5 channels, which is a major determinant of the duration of cardiac action potential. Rosiglitazone rapidly and reversibly inhibited Kv1.5 currents in a concentration-dependent manner (IC50 = 18.9 µM) and accelerated the decay of Kv1.5 currents without modifying the activation kinetics. In addition, the deactivation of Kv1.5 current, assayed with tail current, was slowed by the drug. All of the results as well as the use-dependence of the rosiglitazone-mediated blockade indicate that rosiglitazone acts on Kv1.5 channels as an open channel blocker. This study suggests that the cardiac side effects of rosiglitazone might be mediated in part by suppression of Kv1.5 channels, and therefore, raises a concern of using the drug for diabetic therapeutics.

A broad range of self-reactivity drives thymic regulatory T cell selection to limit responses to self.

The degree of T cell self-reactivity considered dangerous by the immune system, thereby requiring thymic selection processes to prevent autoimmunity, is unknown. Here, we analyzed a panel of T cell receptors (TCRs) with a broad range of reactivity to ovalbumin (OVA(323-339)) in the rat insulin promoter (RIP)-mOVA self-antigen model for their ability to trigger thymic self-tolerance mechanisms. Thymic regulatory T (Treg) cell generation in vivo was directly correlated with in vitro TCR reactivity to OVA-peptide in a broad ~1,000-fold range. Interestingly, higher TCR affinity was associated with a larger Treg cell developmental "niche" size, even though the amount of antigen should remain constant. The TCR-reactivity threshold to elicit thymic negative selection and peripheral T cell responses was ~100-fold higher than that of Treg cell differentiation. Thus, these data suggest that the broad range of self-reactivity that elicits thymic Treg cell generation is tuned to secure peripheral tolerance to self.

Molecular cloning and functional analysis of deubiquitinase CYLD in rainbow trout, Oncorhynchus mykiss.

Deubiquitinase cylindromatosis (CYLD) inhibits MAPK and NF-κB activation pathways by deubiquitinating upstream regulatory factors. Although CYLD has been identified and actively studied in mammals, nothing is known about its putative function in fish. In this study, we identified the gene encoding CYLD (OmCYLD) from rainbow trout, Oncorhynchus mykiss, and examined its role during pathogenic infections. The deduced amino acid sequence of OmCYLD contains conserved CAP-Gly and USP domains. In RTH-149 cells, the expression of OmCYLD was increased by stimulation with Edwardsiella tarda and Streptococcus iniae. Gain-of-function and loss-of-function experiments showed that OmCYLD down-regulates the activation of MAPK and NF-κB and the expression of pro-inflammatory cytokines in E. tarda-stimulated RTH-149 cells. These findings suggest that OmCYLD might function like those of mammals to negatively regulate bacteria-triggered signaling pathway in fish.

Computational determination of hERG-related cardiotoxicity of drug candidates.

BACKGROUND: Drug candidates often cause an unwanted blockage of the potassium ion channel of the human ether-a-go-go-related gene (hERG). The blockage leads to long QT syndrome (LQTS), which is a severe life-threatening cardiac side effect. Therefore, a virtual screening method to predict drug-induced hERG-related cardiotoxicity could facilitate drug discovery by filtering out toxic drug candidates. RESULT: In this study, we generated a reliable hERG-related cardiotoxicity dataset composed of 2130 compounds, which were carried out under constant conditions. Based on our dataset, we developed a computational hERG-related cardiotoxicity prediction model. The neural network model achieved an area under the receiver operating characteristic curve (AUC) of 0.764, with an accuracy of 90.1%, a Matthews correlation coefficient (MCC) of 0.368, a sensitivity of 0.321, and a specificity of 0.967, when ten-fold cross-validation was performed. The model was further evaluated using ten drug compounds tested on guinea pigs and showed an accuracy of 80.0%, an MCC of 0.655, a sensitivity of 0.600, and a specificity of 1.000, which were better than the performances of existing hERG-toxicity prediction models. CONCLUSION: The neural network model can predict hERG-related cardiotoxicity of chemical compounds with a high accuracy. Therefore, the model can be applied to virtual high-throughput screening for drug candidates that do not cause cardiotoxicity. The prediction tool is available as a web-tool at http://ssbio.cau.ac.kr/CardPred .

Artificial transformation methodologies for improving the efficiency of plasmid DNA transformation and simplifying its use.

The uptake of exogenous DNA materials through the cell membrane by bacteria, known as transformation, is essential for the genetic manipulation of bacteria and, thus, plays key roles in biotechnological and biological research. The efficiency of natural transformation is very low; therefore, various artificial transformation methods have been developed for simple and efficient bacterial transformation. The basic bacterial transformation method is based on chemical, physical, and electrical processes and other means to permeabilize the bacterial cell membrane to allow plasmid DNA uptake. With the introduction of novel chemicals, materials, and devices and the optimization of protocols, new transformation methods have become simpler, cheaper, and more reproducible for use in diverse bacterial species compared with conventional methods. In this review, artificial transformation methods have been classified according to the membrane-permeabilizing mechanisms employed by them. Their influential factors, transformation efficiency, advantages, disadvantages, and practical applications are briefly illustrated. Finally, physicochemical transformation as a new bacterial transformation technique has also been described.

Effects of an ethanolic extract of mulberry fruit on blood pressure and vascular remodeling in spontaneous hypertensive rats.

Mulberry (Morus alba) has been used in traditional oriental medicine since ages. Recently, it has been reported that mulberry produces hypotensive effects through the eNOS signaling pathway. However, the mechanism underlying the hypotensive effects of mulberry is not entirely clear. Moreover, the effects of mulberry on vascular remodeling events such as hyperplasia, an important etiology in the pathogenesis of hypertension and arteriosclerosis, are also ambiguous. Here, we hypothesized that an ethanolic extract of mulberry fruit (EMF) has beneficial effects on vascular remodeling and produces hypotensive effects. The effects of a 6-week oral administration of EMF were examined in spontaneously hypertensive rats (SHRs). The animals were divided into four groups: normotensive control (Wistar Kyoto rats), non-treated SHR, low-dose (100 mg/kg) EMF-treated SHR, and high-dose (300 mg/kg) EMF-treated SHR. Our results showed that the EMF-diet normalizes hypertension in SHRs in a dose-dependent manner, by preventing smooth muscle proliferation, thickening of the tunica media, and vascular hyper-reactivity. The endothelial functions were not substantially affected by the EMF diet in our experimental setting. In conclusion, we suggest that the mulberry fruit could act as a food supplement for reducing blood pressure in hypertensive subjects through its effects on smooth muscle proliferation and vascular contractility.

In silico prediction of potential chemical reactions mediated by human enzymes.

BACKGROUND: Administered drugs are often converted into an ineffective or activated form by enzymes in our body. Conventional in silico prediction approaches focused on therapeutically important enzymes such as CYP450. However, there are more than thousands of different cellular enzymes that potentially convert administered drug into other forms. RESULT: We developed an in silico model to predict which of human enzymes including metabolic enzymes as well as CYP450 family can catalyze a given chemical compound. The prediction is based on the chemical and physical similarity between known enzyme substrates and a query chemical compound. Our in silico model was developed using multiple linear regression and the model showed high performance (AUC = 0.896) despite of the large number of enzymes. When evaluated on a test dataset, it also showed significantly high performance (AUC = 0.746). Interestingly, evaluation with literature data showed that our model can be used to predict not only enzymatic reactions but also drug conversion and enzyme inhibition. CONCLUSION: Our model was able to predict enzymatic reactions of a query molecule with a high accuracy. This may foster to discover new metabolic routes and to accelerate the computational development of drug candidates by enabling the prediction of the potential conversion of administered drugs into active or inactive forms.

Antidepressant drug paroxetine blocks the open pore of Kv3.1 potassium channel.

In patients with epilepsy, depression is a common comorbidity but difficult to be treated because many antidepressants cause pro-convulsive effects. Thus, it is important to identify the risk of seizures associated with antidepressants. To determine whether paroxetine, a very potent selective serotonin reuptake inhibitor (SSRI), interacts with ion channels that modulate neuronal excitability, we examined the effects of paroxetine on Kv3.1 potassium channels, which contribute to highfrequency firing of interneurons, using the whole-cell patch-clamp technique. Kv3.1 channels were cloned from rat neurons and expressed in Chinese hamster ovary cells. Paroxetine reversibly reduced the amplitude of Kv3.1 current, with an IC50 value of 9.43 µM and a Hill coefficient of 1.43, and also accelerated the decay of Kv3.1 current. The paroxetine-induced inhibition of Kv3.1 channels was voltage-dependent even when the channels were fully open. The binding (k+1) and unbinding (k-1) rate constants for the paroxetine effect were 4.5 µM-1s-1 and 35.8 s-1, respectively, yielding a calculated KD value of 7.9 µM. The analyses of Kv3.1 tail current indicated that paroxetine did not affect ion selectivity and slowed its deactivation time course, resulting in a tail crossover phenomenon. Paroxetine inhibited Kv3.1 channels in a usedependent manner. Taken together, these results suggest that paroxetine blocks the open state of Kv3.1 channels. Given the role of Kv3.1 in fast spiking of interneurons, our data imply that the blockade of Kv3.1 by paroxetine might elevate epileptic activity of neural networks by interfering with repetitive firing of inhibitory neurons.

IFNγ signaling endows DCs with the capacity to control type I inflammation during parasitic infection through promoting T-bet+ regulatory T cells.

IFNγ signaling drives dendritic cells (DCs) to promote type I T cell (Th1) immunity. Here, we show that activation of DCs by IFNγ is equally crucial for the differentiation of a population of T-bet+ regulatory T (Treg) cells specialized to inhibit Th1 immune responses. Conditional deletion of IFNγ receptor in DCs but not in Treg cells resulted in a severe defect in this specific Treg cell subset, leading to exacerbated immune pathology during parasitic infections. Mechanistically, IFNγ-unresponsive DCs failed to produce sufficient amount of IL-27, a cytokine required for optimal T-bet induction in Treg cells. Thus, IFNγ signalling endows DCs with the ability to efficiently control a specific type of T cell immunity through promoting a corresponding Treg cell population.

Blockade of Kv1.5 channels by the antidepressant drug sertraline.

Sertraline, a selective serotonin reuptake inhibitor (SSRI), has been reported to lead to cardiac toxicity even at therapeutic doses including sudden cardiac death and ventricular arrhythmia. And in a SSRI-independent manner, sertraline has been known to inhibit various voltage-dependent channels, which play an important role in regulation of cardiovascular system. In the present study, we investigated the action of sertraline on Kv1.5, which is one of cardiac ion channels. The eff ect of sertraline on the cloned neuronal rat Kv1.5 channels stably expressed in Chinese hamster ovary cells was investigated using the whole-cell patch-clamp technique. Sertraline reduced Kv1.5 whole-cell currents in a reversible concentration-dependent manner, with an IC 50 value and a Hill coefficient of 0.71 µM and 1.29, respectively. Sertraline accelerated the decay rate of inactivation of Kv1.5 currents without modifying the kinetics of current activation. The inhibition increased steeply between -20 and 0 mV, which corresponded with the voltage range for channel opening. In the voltage range positive to +10 mV, inhibition displayed a weak voltage dependence, consistent with an electrical distance δ of 0.16. Sertraline slowed the deactivation time course, resulting in a tail crossover phenomenon when the tail currents, recorded in the presence and absence of sertraline, were superimposed. Inhibition of Kv1.5 by sertraline was use-dependent. The present results suggest that sertraline acts on Kv1.5 currents as an open-channel blocker.

Blockade of Kv1.5 by paroxetine, an antidepressant drug.

Paroxetine, a selective serotonin reuptake inhibitor (SSRI), has been reported to have an effect on several ion channels including human ether-a-go-go-related gene in a SSRI-independent manner. These results suggest that paroxetine may cause side effects on cardiac system. In this study, we investigated the effect of paroxetine on Kv1.5, which is one of cardiac ion channels. The action of paroxetine on the cloned neuronal rat Kv1.5 channels stably expressed in Chinese hamster ovary cells was investigated using the whole-cell patch-clamp technique. Paroxetine reduced Kv1.5 whole-cell currents in a reversible concentration-dependent manner, with an IC 50 value and a Hill coefficient of 4.11 µM and 0.98, respectively. Paroxetine accelerated the decay rate of inactivation of Kv1.5 currents without modifying the kinetics of current activation. The inhibition increased steeply between -30 and 0 mV, which corresponded with the voltage range for channel opening. In the voltage range positive to 0 mV, inhibition displayed a weak voltage dependence, consistent with an electrical distance δ of 0.32. The binding (k+1) and unbinding (k-1) rate constants for paroxetine-induced block of Kv1.5 were 4.9 µM(-1)s(-1) and 16.1 s(-1), respectively. The theoretical K D value derived by k-1/k+1 yielded 3.3 µM. Paroxetine slowed the deactivation time course, resulting in a tail crossover phenomenon when the tail currents, recorded in the presence and absence of paroxetine, were superimposed. Inhibition of Kv1.5 by paroxetine was use-dependent. The present results suggest that paroxetine acts on Kv1.5 currents as an open-channel blocker.

Blockade of HERG human K+ channels by the antidepressant drug paroxetine.

The effects of paroxetine, a selective serotonin reuptake inhibitor, on human ether-a-go-go-related gene (HERG) channels were investigated using the whole-cell patch-clamp technique. The HERG channels were stably expressed in human embryonic kidney cells. Paroxetine inhibited the peak tail currents of the HERG channel in a concentration-dependent manner, with an IC50 value of 0.45 µM and a Hill coefficient of 0.85. These effects were reversible after wash-out of the drug. The paroxetine-induced inhibition of the HERG channels was voltage-dependent. There was a steep increase in inhibition over the voltage range of the channel opening. Also, a shallow voltage-dependent inhibition was detected over the voltage range in which the channels were fully activated. The fractional electrical distance was estimated to be 0.11. Paroxetine induced a leftward shift in the voltage-dependence of the steady-state activation of the HERG channels. Before and after application of the 1 µM paroxetine, the half-maximum activation was -14.21 mV and -27.04 mV, respectively, with no shift in the slope value. The HERG channel block was not use-dependent. The characteristics of the block were dependent on open and inactivated channel states rather than closed state. Paroxetine had no effect on activation and deactivation kinetics, steady-state inactivation. These results suggest that paroxetine blocks the HERG channels by binding to these channels in the open and inactivated states.

Engineered T Cell Receptor for Cancer Immunotherapy.

Among the therapeutic strategies in cancer immunotherapy-such as immune-modulating antibodies, cancer vaccines, or adoptive T cell transfer-T cells have been an attractive target due to their cytotoxicity toward tumor cells and the tumor antigen-specific binding of their receptors. Leveraging the unique properties of T cells, chimeric antigen receptor-T cells and T cell receptor (TCR)-T cells were developed through genetic modification of their receptors, enhancing the specificity and effectiveness of T cell therapy. Adoptive cell transfer of chimeric antigen receptor-T cells has been successful for the treatment of hematological malignancies. To expand T cell therapy to solid tumors, T cells are modified to express defined TCR targeting tumor associated antigen, which is called TCR-T therapy. This review discusses anti-tumor T cell therapies, with a focus on engineered TCR-T cell therapy. We outline the characteristics of TCR-T cell therapy and its clinical application to non-hematological malignancies.

Corydalis ternata Nakai Alleviates Cognitive Decline in Alzheimer's Disease by Reducing ß-Amyloid and Neuroinflammation.

Recently, natural herbs have gained increasing attention owing to their comparatively low toxicity levels and the abundance of historical medical documentation regarding their use. Nevertheless, owing to a lack of knowledge regarding these herbs and their compounds, attempts to find those that could be beneficial for treating diseases have often been ad hoc; thus, there is now a growing demand for an in silico method to identify beneficial herbs. In this study, we present a computational approach for identifying natural herbs specifically effective in treating cognitive decline in Alzheimer's disease (AD) sufferers, which analyzes the similarities between herbal compounds and known drugs targeting AD-related proteins. Our in silico method suggests that Corydalis ternata can improve cognitive decline in AD sufferers. Behavioral tests with an AD mouse model for the confirmation of the in silico prediction reveals that C. ternata significantly alleviated the cognitive decline (memory and motor functions) caused by neurodegeneration. Further pathology analyses reveal that C. ternata decreases the level of Aß plaques, reduces neuroinflammation, and promotes autophagy flux, and thus C. ternata can be clinically effective for preventing mild cognitive impairment during the early stages of AD. These findings highlight the potential utility of our in silico method and the potential clinical application of the identified natural herb in treating and preventing AD.