Biomedical subjects

D C Tam

Publications and source records attributed to D C Tam.

7 recordsLinked to original sources

PubMed2001

Human eosinophils induce mucin production in airway epithelial cells via epidermal growth factor receptor activation.

Eosinophil recruitment and mucus hypersecretion are characteristic of asthmatic airway inflammation, but eosinophils have not been shown to induce mucin production. Because an epidermal growth factor receptor (EGFR) cascade induces MUC5AC mucin in airways, and because EGFR is up-regulated in asthmatic airways, we examined the effect of eosinophils on MUC5AC mucin production in NCI-H292 cells (a human airway epithelial cell line that produces mucins). Eosinophils were isolated from the peripheral blood of allergic patients, and their effects on MUC5AC mucin gene and protein synthesis were assessed using in situ hybridization and ELISAs. When IL-3 plus GM-CSF or IL-3 plus IL-5 were added to eosinophils cultured with NCI-H292 cells, MUC5AC mucin production increased; eosinophils or cytokines alone had no effect. Eosinophil supernatant obtained by culturing eosinophils with IL-3 plus GM-CSF or IL-3 plus IL-5 also increased MUC5AC synthesis in NCI-H292 cells, an effect that was prevented by selective EGFR inhibitors (AG1478, BIBX1522). Supernatant of activated eosinophils induced EGFR phosphorylation in NCI-H292 cells. Supernatant of activated eosinophils contained increased concentrations of TGF-alpha protein (an EGFR ligand) and induced up-regulation of TGF-alpha expression and release in NCI-H292 cells. A blocking Ab to TGF-alpha reduced activated eosinophil-induced MUC5AC synthesis in NCI-H292 cells. These results show that activated eosinophils induce mucin synthesis in human airway epithelial cells via EGFR activation, and they implicate TGF-alpha produced by eosinophils and epithelial cells in the EGFR activation that results in mucin production in human airway epithelium.

Adult↗

PubMed2001

IL-13 induces mucin production by stimulating epidermal growth factor receptors and by activating neutrophils.

Mucus hypersecretion contributes to the morbidity and mortality in acute asthma. Both T helper 2 (Th2) cytokines and epidermal growth factor receptor (EGFR) signaling have been implicated in allergen-induced goblet cell (GC) metaplasia. Present results show that a cascade of EGFR involving neutrophils is implicated in interleukin (IL)-13-induced mucin expression in GC. Treatment with a selective EGFR tyrosine kinase inhibitor prevented IL-13-induced GC metaplasia dose dependently and completely. Instillation of IL-13 also induced tumor necrosis factor-alpha protein expression, mainly in infiltrating neutrophils. Control airway epithelium contained few leukocytes, but intratracheal instillation of IL-13 resulted in time-dependent leukocyte recruitment by IL-13-induced IL-8-like chemoattractant expression in airway epithelium. Pretreatment with an inhibitor of leukocytes in the bone marrow (cyclophosphamide) or with a blocking antibody to IL-8 prevented both IL-13-induced leukocyte recruitment and GC metaplasia. These findings indicate that EGFR signaling is involved in IL-13-induced mucin production. They suggest a potential therapeutic role for inhibitors of the EGFR cascade in the hypersecretion that occurs in acute asthma.

Animals↗

PubMed2000

Identification of reliable spike templates in multi-unit extracellular recordings using fuzzy clustering.

A method for extracting single-unit spike trains from extracellular recordings containing the activity of several simultaneously active cells is presented. The technique is particularly effective when spikes overlap temporally. It is capable of identifying the exact number of neurons contributing to a recording and of creating reliable spike templates. The procedure is based on fuzzy clustering and its performance is controlled by minimizing a cluster-validity index which optimizes the compactness and separation of the identified clusters. Application examples with synthetic spike trains generated from real spikes and segments of background noise show the advantage of the fuzzy method over conventional template-creation approaches in a wide range of signal-to-noise ratios.

Action Potentials↗

PubMed1999

A joint interspike interval difference stochastic spike train analysis: detecting local trends in the temporal firing patterns of single neurons.

We introduce a stochastic spike train analysis method called joint interspike interval difference (JISID) analysis. By design, this method detects changes in firing interspike intervals (ISIs), called local trends, within a 4-spike pattern in a spike train. This analysis classifies 4-spike patterns that have similar incremental changes. It characterizes the higher-order serial dependence in spike firing relative to changes in the firing history. Mathematically, this spike train analysis describes the statistical joint distribution of consecutive changes in ISIs, from which the serial dependence of the changes in higher-order intervals can be determined. It is similar to the joint interspike interval (JISI) analysis, except that the joint distribution of consecutive ISI differences (ISIDs) is quantified. The graphical location of points in the JISID scatter plot reveals the local trends in firing (i.e., monotonically increasing, monotonically decreasing, or transitional firing). The trajectory of these points in the serial-JISID plot traces the time evolution of these trends represented by a 5-spike pattern, while points in the JISID scatter plot represent trends of a 4-spike pattern. We provide complete theoretical interpretations of the JISID analysis. We also demonstrate that this method indeed identifies firing trends in both simulated spike trains and spike trains recorded from cultured neurons.

Animals↗

PubMed1998

A cross-interval spike train analysis: the correlation between spike generation and temporal integration of doublets.

A stochastic spike train analysis technique is introduced to reveal the correlation between the firing of the next spike and the temporal integration period of two consecutive spikes (i.e., a doublet). Statistics of spike firing times between neurons are established to obtain the conditional probability of spike firing in relation to the integration period. The existence of a temporal integration period is deduced from the time interval between two consecutive spikes fired in a reference neuron as a precondition to the generation of the next spike in a compared neuron. This analysis can show whether the coupled spike firing in the compared neuron is correlated with the last or the second-to-last spike in the reference neuron. Analysis of simulated and experimentally recorded biological spike trains shows that the effects of excitatory and inhibitory temporal integration are extracted by this method without relying on any subthreshold potential recordings. The analysis also shows that, with temporal integration, a neuron driven by random firing patterns can produce fairly regular firing patterns under appropriate conditions. This regularity in firing can be enhanced by temporal integration of spikes in a chain of polysynaptically connected neurons. The bandpass filtering of spike firings by temporal integration is discussed. The results also reveal that signal transmission delays may be attributed not just to conduction and synaptic delays, but also to the delay time needed for temporal integration.

Action Potentials↗

PubMed1997

Multi-unit spike discrimination using wavelet transforms.

A new spike discrimination procedure addressing the specific problem of spike superposition is described. The method, based on a shift-invariant wavelet transform and its amplitude-and-phase representation, has the advantage of both reducing the effect of noise present in the data and correcting the latency of specific components in a waveform. When spikes overlap and produce unknown patterns, the procedure extracts the constituent spikes and also estimates their exact time of occurrence. Fast implementation algorithms, having complexity of at most O (N log N), allow the use of the method in real-time applications.

Action Potentials↗

PubMed1988

Conditional cross-interval correlation analyses with applications to simultaneously recorded cerebellar Purkinje neurons.

Two conditional cross-correlation techniques are described for the analysis of two simultaneously recorded neuronal spike trains. The conditional interspike interval histogram describes the distribution of interspike intervals of a neuron conditioned by a preceding spike in another neuron. The conditional cross-interval histogram describes the distribution of cross-intervals of two neurons conditioned by a preceding spike in one of the neurons. These techniques could be used to reveal the temporal coupling in the discharge of two neurons recorded simultaneously. The techniques augment the description of the correlation obtained with conventional cross-correlation measures. When applied to the simple spike discharge of simultaneously recorded cerebellar Purkinje neurons, the methods reveal temporal interactions between neurons that are not readily apparent from conventional cross-correlograms. The patterns observed suggest a tightly coupled, temporal surround-inhibition among nearby Purkinje neurons.

Animals↗