shRNA-Induced Knockdown of a Bioinformatically Predicted Target IL10 Influences Functional Parameters in Spontaneously Hypertensive Rats with Asthma.
Tatiana DrevytskaRoman MorhachovLesya TumanovskaGeorgiy PortnichenkoVasyl NagibinOleksiy BoldyrievTatiana Lapikova-BryhinskaVeronika GurianovaBorys Dons'koiMaxim FreidinVladimir IvanisenkoElena Yu BraginaRalf HofestädtVictor DosenkoPublished in: Journal of integrative bioinformatics (2018)
One of the most common comorbid pathology is asthma and arterial hypertension. For experimental modeling of comorbidity we have used spontaneously hypertensive rats with ovalbumin (OVA)-induced asthma. Rats were randomly divided into three groups: control group, OVA-induced asthma group; OVA-induced asthma + IL10 shRNA interference group. Target gene (IL10) was predicted by ANDSystem. We have demonstrated that RNA-interference of IL10 affected cardiovascular (tested using Millar microcatheter system) as well as respiratory functions (tested using force-oscillation technique, Flexivent) in rats. We have shown that during RNA-interference of IL10 gene in vivo there were changes in both cardiac and lung function parameters. These changes in the cardiovascular parameters can be described as positive. But the more intensive heart workload can lead to exhaust and decompensation of the heart functions. Knockdown of IL10 gene in asthma modeling induces some positive changes in respiratory functions of asthmatic animals such as decreased elastance and increased compliance of the lungs, as well as less pronounced pathomorphological changes in the lung tissue. Thus, we provide the data about experimentally confirmed functionality changes of the target which was in silico predicted to be associated with both asthma and hypertension - in our new experimental model of comorbid pathology.
Keyphrases
- lung function
- chronic obstructive pulmonary disease
- cystic fibrosis
- air pollution
- allergic rhinitis
- diabetic rats
- drug induced
- blood pressure
- heart failure
- arterial hypertension
- genome wide
- atrial fibrillation
- genome wide identification
- gene expression
- artificial intelligence
- deep learning
- nucleic acid
- molecular dynamics simulations