کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8506032 | 1555622 | 2018 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
An ex vivo abomasal ovine model to study the immediate immune response in the context of Haemonchus contortus larval-stage
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کلمات کلیدی
موضوعات مرتبط
علوم زیستی و بیوفناوری
علوم کشاورزی و بیولوژیک
علوم دامی و جانورشناسی
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چکیده انگلیسی
We have set up an ex vivo ovine abomasal model, which can mimic the multicellular process to explore the early steps in haemonchine nematode infection using RNA-seq technology. Ovine abomasal explants were collected for histological and transcriptional analysis and supernatants collected to quantitate lactate dehydrogenase (LDH) enzymes. Atotal of 233 were substantially induced genes between L4-inoculated and uninoculated-control tissues, respectively. However, a total of 14 were considerably down-regulated genes between the 51 aforementioned tissues. Fifteen pathways were annotated by Kyoto Encyclopedia of Genes, and Genomes pathway analysis accounted for the significant percentage in immediate response to larval-stage of H. contortus. Key genes upregulated in response to the addition of L4-inoculum of H. contortus were IL-6, IL-8, C1q, Atypical chemokine receptor-3, chemokine ligand-2, manganese superoxide dismutase, integrin alpha-7, -8, -9, integrin subunit beta-1, integrin subunit beta 6, intercellular adhesion molecule-1 and actin alpha-1. This study shows for the first time that galectin-1 is up-regulated in an ex vivo abomasal segment model exposed to L4-inoculum of H. contortus following 6â¯h of incubation. The abomasal segment model has been shown to be a suitable tool to study the haemonchine larval-stage effects on the ovine abomasal tissues prior to in vivo assessment.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Veterinary Parasitology - Volume 254, 30 April 2018, Pages 105-113
Journal: Veterinary Parasitology - Volume 254, 30 April 2018, Pages 105-113
نویسندگان
Saeed El-Ashram, Cuiping Li, Fathi Abouhajer, Rashid Mehmood, Ibrahim Al Nasr, Yinghui Zhang, Tang Lu, Ding Yili, Xun Suo, Zhang Haoji, Zhili Li, Shujian Huang,