Recently, IL-17A has been shown to be expressed in higher levels in respiratory secretions from asthmatics and correlated with airway hyperresponsiveness. Although these studies raise the possibility that IL-17A may influence allergic disease, the mechanisms remain unknown. In this study, we investigated the molecular mechanisms involved in IL-17A-mediated CC chemokine (eotaxin-1/CCL11) production from human airway smooth muscle (ASM) cells. We found that incubation of human ASM cells with rIL-17A resulted in a significant increase of eotaxin-1/CCL11 release from ASM cells that was reduced by neutralizing anti-IL-17A mAb. Moreover, IL-17A significantly induced eotaxin-1/CCL11 release and mRNA expression, an effect that was abrogated with cycloheximide and actinomycin D treatment. Furthermore, transfection studies using a luciferase-driven reporter construct containing eotaxin-1/CCL11 proximal promoter showed that IL-17A induced eotaxin-1/CCL11 at the transcriptional level. IL-17A also enhanced significantly IL-1β-mediated eotaxin-1/CCL11 mRNA, protein release, and promoter activity in ASM cells. Primary human ASM cells pretreated with inhibitors of MAPK p38, p42/p44 ERK, JNK, or JAK but not PI3K, showed a significant decrease in eotaxin-1/CCL11 release upon IL-17A treatment. In addition, IL-17A mediated rapid phosphorylation of MAPK (p38, JNK, and p42/44 ERK) and STAT-3 but not STAT-6 or STAT-5 in ASM cells. Taken together, our data provide the first evidence of IL-17A-induced eotaxin-1/CCL11 expression in ASM cells via MAPK (p38, p42/p44 ERK, JNK) signaling pathways. Our results raise the possibility that IL-17A may play a role in allergic asthma by inducing eotaxin-1/CCL11 production.

December 22, 2006; doi:10.1152/ajplung.00306.2006.-Recent studies into the pathogenesis of airway disorders such as asthma have revealed a dynamic role for airway smooth muscle cells in the perpetuation of airway inflammation via secretion of cytokines and chemokines. In this study, we evaluated whether IL-17 could enhance IL-1␤-mediated CXCL-8 release from human airway smooth muscle cells (HASMC) and investigated the upstream and downstream signaling events regulating the induction of CXCL-8. CXCL-8 mRNA and protein induction were assessed by real-time RT-PCR and ELISA from primary HASMC cultures. HASMC transfected with site-mutated activator protein (AP)-1/NF-B CXCL-8 promoter constructs were treated with selective p38, MEK1/2, and phosphatidylinositol 3-kinase (PI3K) inhibitors to determine the importance of MAPK and PI3K signaling pathways as well as AP-1 and NF-B promoter binding sites. We demonstrate IL-17 induced and synergized with IL-1␤ to upregulate CXCL-8 mRNA and protein levels. Erk1/2 and p38 modulated IL-17 and IL-1␤ CXCL-8 promoter activity; however, IL-1␤ also activated the PI3K pathway. The synergistic response mediating CXCL-8 promoter activity was dependent on both MAPK and PI3K signal transduction pathways and required the cooperation of AP-1 and NF-B cis-acting elements upstream of the CXCL-8 gene. Collectively, our observations indicate MAPK and PI3K pathways regulate the synergy of IL-17 and IL-1␤ to enhance CXCL-8 promoter activity, mRNA induction, and protein synthesis in HASMC via the cooperative activation of AP-1 and NF-B trans-acting elements.

Biodegradable polymers are attractive candidates for chondrocyte embedding and transplantation in cartilage tissue engineering. In an attempt to determine the effects of a variety of biodegradable materials on cartilage proliferation and extracellular matrix production, poly-L-lactic acid (PLLA) with a molecular weight of 5,000, polyglycolic acid (PGA) with a molecular weight of 3,000, and copolymer of poly(L-lactic acid-glycolic acid) 50:50 (PLGA) with a molecular weight of 5,000, were dissolved in DMSO and added into the medium for 4 weeks in in vitro high-density micromass culture of multiplied human articular chondrocytes (HAC). PLLA with a molecular weight of 270,000 (PLAO3) was used as thin film. Cell proliferation and differentiation in these biomaterials were compared with tissue culture polystyrene (TCPS) as a control. Alamar blue and alcian blue staining were carried out to determine the chondrocyte proliferation and differentiation, respectively. Samples exposed to these biomaterials promoted cell proliferation in the range of 86-105% of the control proliferation, and a slight but significant increase in cell proliferation was noted only in the culture exposed to PLGA. The sample exposed to PGA elicited a significant 3.7-fold higher (p < 0.01) cell differentiation than controls and was significantly higher than that of the samples exposed to PLLA, PLAO3, and PLGA. After 4 weeks of culture, the cell differentiation from most to least was in the following order PGA > PLAO3 > PLGA = PLLA > Cont. = DMSO. Chondrocyte differentiation of the samples exposed to various biomaterials were significantly higher compared with controls. Thus, serially passage chondrocytes are competent for cell growth and quantifiable matrix production, and biodegradable polymers, especially PGA, hold promise as suitable substrates for scaffolding materials for human cartilage tissue engineering.

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Research in corpus-driven Automatic Speech Recognition (ASR) is advancing rapidly towards building a robust Large Vocabulary Continuous Speech Recognition (LVCSR) system. Under-resourced languages like Bangla require benchmarking large corpora for more research on LVCSR to tackle their limitations and avoid the biased results. In this paper, a publicly published large-scale Bangladeshi Bangla speech corpus is used to implement deep Convolutional Neural Network (CNN) based model and Recurrent Neural Network (RNN) based model with Connectionist Temporal Classification (CTC) loss function for Bangla LVCSR. In experimental evaluations, we find that CNN-based architecture yields superior results over the RNN-based approach. This study also emphasizes assessing the quality of an open-source large-scale Bangladeshi Bangla speech corpus and investigating the effect of the various high-order N-gram Language Models (LM) on a morphologically rich language Bangla. We achieve 36.12% word error rate (WER) using CNN-based acoustic model and 13.93% WER using beam search decoding with 5-gram LM. The findings demonstrate by far the state-of-the-art performance of any Bangla LVCSR system on a specific benchmarked large corpus.