Kv1.3 expression is upregulated in activated CD8+ T cells and co-localizes with CD8.

<p>(A) Purified CD8+ T cells were stimulated with anti-CD3/CD28 for 3 days. Naïve and stimulated cells were then immunostained for Kv1.3 in combination with CD8 and subsequently viewed by immunofluorescence microscopy. Cellular nuclei were counterstained with DNA dye DAPI (blue). Kv1.3 detected by AF 594 fluorescence is shown in red, while CD8 detected by AF 488 fluorescence is shown in green. Colocalization is indicated by a yellow and/or orange color in the overlay panels. (B) An isotype-matched antibody was used as a negative control. Original magnification, ×100. Image is representative of three different donors. (C) Summary of percentages of activated CD8+ T cells expressing Kv1.3. In brief, 4 view fields/microscopic section were evaluated for Kv1.3+ CD8 cells stimulated with anti-CD3/CD28 or anti-CD3 alone for 3 days. The percentages of Kv1.3+ cells are based on the number of CD8+ T cells counted. Data are mean ± SD from one representative of three independent and reproducible experiments. Values that are significantly different from that of non-stimulated control are indicated as **, <i>p</i><0.01.</p

Kv1.3 channel blocker attenuates GrB mediated neural cell toxicity.

<p>Freshly isolated human CD8+ T cells were simulated with anti-CD3 or anti-CD3/CD28 in the presence or absence of MgTx. Cultured supernatants were collected at 3 days after stimulation. (A). Human neural cells cultured on poly-D-lysine pre-coated 96 well plates were pretreated with supernatants from non-activated CD8 T cells (Unstim.), anti-CD3 or anti-CD3/CD28-activated CD8+ T cells without MgTx (none), and with MgTx (MgTx). After 24 hours of treatment, CellQuanti-blue dye was added in each well for 30 minutes. Fluorescence was then detected using a plate reader. Cell viability was quantified by fluorescence intensity. (B). MgTx (30 nM) was added to culture media and incubated for 3 days. Human NPCs were treated with supernatants without MgTx (Ctrl), MgTx contained sups with vehicle treatment, with GrB alone (GrB) or MgTx containing sups plus GrB treatment (GrB/MgTx sups). Neurotoxicity was evaluated by cell viability quantified by fluorescence intensity. The fluorescence intensity in each group is plotted as percent relative to that in non-activated cells (Unstim.) or control cells (Ctrl). Data are mean of triplicate ± SD of one representative of three independent experiments. Values that are significantly different from that of vehicle treated control are indicated as *, <i>p</i><0.05; **, <i>p</i><0.01; ***, <i>p</i><0.005.</p

K+ channel blockers do not affect CD107a expression on activated CD8+ T cells.

<p>(A) Freshly isolated CD8+ T cells were stimulated with anti-CD3/CD28 or anti-CD3 for 24 hours. Cells were then stained with a CD107a-specific mAb, or an IgG1 isotype control (filled histogram) at the indicated times. (B). CD8+ T cells were pretreated with ShK (10 nM), MgTX (30 nM), ChTX (50 nM) and TRAM-34 (500 nM) for 3 h, followed by stimulation with anti-CD3/CD28 or anti-CD3 alone for 6 hours. Surface expression levels of CD107a were then analyzed by flow cytometry. FACS plots shown are representative data from three separate experiments.</p

Inhibitory effects of Kv1.3 blockade in the differentiation and homeostatic maintenance of T_EM CD8 cells.

<p>(A) Naïve, T_CM, T_EMRA and T_EM CD8 subpopulations were sorted from CD8+ gated cell population based on surface markers of CCR7 and CD45RA. Sorted individual subsets within the respective gates shown were transduced with a DN-Kv1.x and GFP control. After 7 days of transfection, the percentages of each subset in gated GFP+ CD8+ cells were analyzed by flow cytometry. Gate for expression of GFP was established using untransduced controls. (B) FACS profiles are representative of three separate experiments using cells from different donors. The percentage of cells in each quadrant is indicated. (C) The percentages of CD8 subsets displaying GFP fluorescence from each single transfected subpopulation are presented as mean ± SD of three experiments. Values that are significantly different from that of GFP control are indicated as follows: *, <i>p</i><0.05, **, <i>p</i><0.01; ***, <i>p</i><0.005. (D) Representative FACS profiles of phenotypical changes of transduced T_CM and T_EM subsets 21 days after transfection. (E). FACS-sorted CCR7- (T_EM/T_EMRA) were labeled with PKH26 day (2×10–6 M), followed by stimulation with anti-CD3/CD28 for 24 h and then transduced with a lentiviral vector encoding the dominant-negative Kv1.x and GFP control alone at an MOI of ∼8. PKH26 fluorescence was analyzed by flow cytometry at days 0, 5 and 11. FACS plots shown are representative data from two experiments.</p

A Comparison of Supervised Machine Learning Algorithms and Feature Vectors for MS Lesion Segmentation Using Multimodal Structural MRI

<div><p>Machine learning is a popular method for mining and analyzing large collections of medical data. We focus on a particular problem from medical research, supervised multiple sclerosis (MS) lesion segmentation in structural magnetic resonance imaging (MRI). We examine the extent to which the choice of machine learning or classification algorithm and feature extraction function impacts the performance of lesion segmentation methods. As quantitative measures derived from structural MRI are important clinical tools for research into the pathophysiology and natural history of MS, the development of automated lesion segmentation methods is an active research field. Yet, little is known about what drives performance of these methods. We evaluate the performance of automated MS lesion segmentation methods, which consist of a supervised classification algorithm composed with a feature extraction function. These feature extraction functions act on the observed T1-weighted (T1-w), T2-weighted (T2-w) and fluid-attenuated inversion recovery (FLAIR) MRI voxel intensities. Each MRI study has a manual lesion segmentation that we use to train and validate the supervised classification algorithms. Our main finding is that the differences in predictive performance are due more to differences in the feature vectors, rather than the machine learning or classification algorithms. Features that incorporate information from neighboring voxels in the brain were found to increase performance substantially. For lesion segmentation, we conclude that it is better to use simple, interpretable, and fast algorithms, such as logistic regression, linear discriminant analysis, and quadratic discriminant analysis, and to develop the features to improve performance.</p></div

Kv1.3 blockade suppresses proliferation and differentiation of anti-CD3 stimulated CD8+ T cells. <i>(</i>

<p>A) Freshly isolated CD8+ T cells were pretreated with Kv channel blockers, ShK (10 nM), MgTx (30 nM) and ChTx (50 nM), for 3 hours, then stimulated with anti-CD3 alone or anti-CD3/CD28. After 4 days of culture, proliferation was measured by [3H] thymidine uptake. Data show the mean ± SD of three experiments. Significant differences are marked as follows: (*, <i>p</i><0.05; **, <i>p</i><0.01; ***, <i>p</i><0.005). (B). Isolated CD8+ T cells were labeled with PKH26 stimulated with anti-CD3/CD28 for 24 h, and then transduced with a lentiviral vector encoding the dominant-negative Kv1.x or the GFP control alone. PKH26 fluorescence was analyzed by flow cytometry at baseline and 5 and 11 days later as shown. Quantification of proliferating cells was evaluated by gating on PKH26high PKH26dim and PKH26low among GFP+ cells. (C). Transduced CD8+ T cells were stained with anti-CD8, anti-CCR7 or anti-CD45RA mAbs seven days after transduction and analyzed for the percentages of naïve, T_CM, T_EMRA and T_EM cells in gated GFP+ CD8+ cells. FACS plots shown are representative data from three separate experiments using cells from different donors. (D) The percentages of each CD8+ subset displaying GFP fluorescence are presented as mean ± SD of three experiments. Values that are significantly different from that of GFP control are indicated as follows: **, <i>p</i><0.01; ***, <i>p</i><0.005.</p

K+ channel blockers inhibit GrB production by activated CD8+ T cells.

<p>Freshly isolated CD8+ T cells were pretreated with a Kv1.3 channel blocker, ShK at various concentrations (A) or with ShK (10 nM), MgTX (30 nM), ChTX (50 nM) and TRAM-34 (500 nM) (B) for 3 h, followed by stimulation with anti-CD3/CD28 or anti-CD3 alone. The levels of GrB were measured in cell supernatants by ELISA at 6 h (A) and indicated times (B and C). Data are mean of triplicate ± SD of one representative of three independent and reproducible experiments. Values that are significantly different from that of non-blocker vehicle treated control are indicated as follows: *, <i>p</i><0.05; **, <i>p</i><0.01.</p

The super learner coefficient versus the number of voxels the algorithm is fit on for the (A) unnormalized and the (B) smoothed and moments feature vectors.

<p>As the number of voxels used to fit the algorithm changes, the super learner consistently assigns large weights to the same small number of algorithms. For the unnormalized feature vector, high coefficient weights are selected for the logistic regression, one of the random forest tuning parameters, and the Gaussian mixture model. On the smoothed and moments feature vector, the super learner favors the less complex algorithms: logistic regression, the quadratic discriminant analysis, and the linear discriminant analysis. Some weight is also assigned to the Gaussian mixture model and the random forest.</p

GrB induces neurotoxicity.

<p>(A) Human fetal neurons cultured in 96-well plates were treated with GrB (0.3–10 nM) for 24 hr and cell viability was measured using Cellquanti-blue assay. Results represent average ± SEM from four independent fetal cultures. (B) Human fetal neurons on coverslips in 24-well plates were treated with GrB (4 nM) for 24 hr and neurons were immunostained for beta-III-tubulin. Average neurite lengths were measured as described in the methods section. Results represent average ± SEM from three independent experiments.</p

A summary of the training set, training set after the voxel selection procedure has been applied, and the validation set.

<p>Subjects were randomly assigned to the training or validation set. All training, including tuning of algorithm parameters with 10-fold cross validation, was performed on the training set.</p