MicroRNAs (miRNAs) are non-coding RNAs of ~22 nucleotides in length, which serve an important role in numerous diseases. Asthma is a chronic airway inflammatory disease, which is the most common chronic disease among children. The role of miRNA (miR)-16 in asthma is unclear. The objective of the present study was to examine the underlying molecular mechanism of the involvement of miR-16 in asthma. A total of 72 volunteers diagnosed with asthma consented to participate in the study, of whom 52 participants were identified to be sensitive to salmeterol and 20 participants were identified to be resistant to salmeterol. Receiver operating characteristic (ROC) curve analysis was performed to compare the expression levels of serum miR-16 between the sensitive and resistant groups, and to confirm the association between the expression level of serum miR-16 and forced expiratory volume in 1 sec (FEV1). In silico analysis, a luciferase assay, reverse transcription-quantitative polymerase chain reaction analysis and western blotting were performed to elucidate the molecular mechanism underlying the role of miR-16 in asthma. ROC results demonstrated that the serum miR-16 level may function as a biomarker to predict the response to salmeterol therapy, and the miR-16 expression level displayed a significant negative correlation with FEV1. According to the in silico analysis, adrenoreceptor β-2 (ADRB2) was a direct target of miR-16, and it was further confirmed by luciferase assay that 25 nM miR-16 mimic had an inhibitory effect on the luciferase activity of the wild-type ADRB2 3′ untranslated region (UTR); the inhibitory effect on the luciferase activity of the wild-type ADRB2 3′UTR was stronger with 50 nM miR-16 mimic, and strongest with 75 nM miR-16 mimic, whereas the luciferase activity of the mutant ADRB2 3′UTR in cells was similar following treatment with 0, 25, 50 or 75 nM miR-16 mimic. miR-16 reduced the mRNA and protein expression levels of ADRB2 in a dose-dependent manner. These results identified that miR-16 may be used as a predictive biomarker of therapeutic response in asthma.
Although the main stem node number of soybean [Glycine max (L.) Merr. ] is an important yield-related trait, there have been limited studies on the effect of plant density on the identification of quantitative trait loci (QTL) for main stem node number (MSNN). To address this issue, here, 144 four-way recombinant inbred lines (FW-RILs) derived from Kenfeng 14, Kenfeng 15, Heinong 48, and Kenfeng 19 were used to identify QTL for MSNN with densities of 2.2 × 105 (D1) and 3 × 105 (D2) plants/ha in five environments by linkage and association studies. As a result, the linkage and association studies identified 40 and 28 QTL in D1 and D2, respectively, indicating the difference in QTL in various densities. Among these QTL, five were common in the two densities; 36 were singly identified for response to density; 12 were repeatedly identified by both response to density and phenotype of two densities. Thirty-one were repeatedly detected across various methods, densities, and environments in the linkage and association studies. Among the 24 common QTL in the linkage and association studies, 15 explained a phenotypic variation of more than 10%. Finally, Glyma.06G094400, Glyma.06G147600, Glyma.19G160800.1, and Glyma.19G161100 were predicted to be associated with MSNN. These findings will help to elucidate the genetic basis of MSNN and improve molecular assistant selection in high-yield soybean breeding.
As the major source of edible protein and oil, the global demand for soybean (Glycine max (L.) Merr.) is increasing. Plant height is closely related to yield; therefore, understanding the genetic basis of plant height will help to improve soybean plant type and increase seed yield. In this study, quantitative trait loci (QTLs) and nucleotides (QTNs) for soybean plant height were detected by linkage analysis and association analysis. A high-density map containing 2225 bin markers was constructed by using 108 342 SNPs of a recombinant inbred line population (named RIL3613) of 120 lines for linkage analysis. In total, 39 QTLs were detected, including 16 QTLs that were repeatedly detected in multiple environments. Association analysis was performed by using 63 306 SNPs from a germplasm population of 455 natural soybean accessions. In total, 62 QTNs were detected, and 26 QTNs were repeatedly detected by multiple methods. Fourteen QTNs were distributed in the intervals of six multiple-environment QTLs by comparing the results of association analysis and linkage analysis. With pathway analysis, six candidate genes were identified as being associated with plant height. These results contribute to analysis of the genetic basis of plant height and will promote marker-assisted selection for breeding ideal plant shape.
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