Rhinovirus upregulates transient receptor potential channels in a human neuronal cell line: Implications for respiratory virus-induced cough reflex sensitivity

ABSTRACT (250 words)
BACKGROUND: The mechanism underlying respiratory virus-induced cough hypersensitivity is unknown. Up-regulation of airway neuronal receptors responsible for sensing physical and chemical stimuli is one possibility and the transient receptor potential (TRP) channel family are potential candidates. We have used an in vitro model of sensory neurones and human rhinovirus (HRV-16) to study the effect of virus infection on TRP expression.
METHODS: IMR32 neuroblastoma cells were differentiated in culture to express three TRP channels, TRPV1, TRPA1 and TRPM8. Flow cytometry and qRT-PCR were used to measure TRP channel protein and mRNA levels following inoculation with live virus, inactivated virus, virus- induced soluble factors or pelleted virus particles. Multiplex bioassay was used to determine nerve growth factor (NGF), interleukin (IL)-1ß, IL-6 and IL-8 levels in response to infection.
RESULTS: Early up-regulation of TRPA1 and TRPV1 expression occurred 2 to4 hours post infection. This was independent of replicating virus as virus induced soluble factors alone were sufficient to increase channel expression 50 and 15 fold, respectively. NGF, IL-6 and IL-8 levels, increased in infected cell supernatants, represent possible candidates. In contrast, TRPM8 expression was maximal at 48 hours (9.6 fold) and required virus replication rather than soluble factors
CONCLUSIONS We show for the first time that rhinovirus can infect neuronal cells. Furthermore, infection causes up-regulation of TRP channels by channel specific mechanisms. Increase in TRPA1 and TRPV1 levels can be mediated by soluble factors induced by infection whereas TRPM8 requires replicating virus. TRP channels may be novel therapeutic targets for controlling virus-induced cough.

Low Complexity Spectral Analysis of Heart-Rate-Variability through a Wavelet based FFT

In this paper, a low complexity system for spectral analysis of heart rate variability (HRV) is presented. The main idea of the proposed approach is the implementation of the Fast-Lomb periodogram that is a ubiquitous tool in spectral analysis, using a wavelet based Fast Fourier transform. Interestingly we show that the proposed approach enables the classification of processed data into more and less significant based on their contribution to output quality. Based on such a classification a percentage of less-significant data is being pruned leading to a significant reduction of algorithmic complexity with minimal quality degradation. Indeed, our results indicate that the proposed system can achieve up-to 45% reduction in number of computations with only 4.9% average error in the output quality compared to a conventional FFT based HRV system.