MicroRNA-143 and-145 modulate the phenotype of synovial fibroblasts in rheumatoid arthritis

Fibroblast-like synoviocytes (FLSs) constitute a major cell subset of rheumatoid arthritis (RA) synovia. Dysregulation of microRNAs (miRNAs) has been implicated in activation and proliferation of RA-FLSs. However, the functional association of various miRNAs with their targets that are characteristic of the RA-FLS phenotype has not been globally elucidated. In this study, we performed microarray analyses of miRNAs and mRNAs in RA-FLSs and osteoarthritis FLSs (OA-FLSs), simultaneously, to validate how dysregulated miRNAs may be associated with the RA-FLS phenotype. Global miRNA profiling revealed that miR-143 and miR-145 were differentially upregulated in RA-FLSs compared to OA-FLSs. miR-143 and miR-145 were highly expressed in independent RA-FLSs. The miRNA-target prediction and network model of the predicted targets identified insulin-like growth factor binding protein 5 (IGFBP5) and semaphorin 3A (SEMA3A) as potential target genes downregulated by miR-143 and miR-145, respectively. IGFBP5 level was inversely correlated with miR-143 expression, and its deficiency rendered RA-FLSs more sensitive to TNFα stimulation, promoting IL-6 production and NF-κB activity. Moreover, SEMA3A was a direct target of miR-145, as determined by a luciferase reporter assay, antagonizing VEGF165-induced increases in the survival, migration and invasion of RA-FLSs. Taken together, our data suggest that enhanced expression of miR-143 and miR-145 renders RA-FLSs susceptible to TNFα and VEGF165 stimuli by downregulating IGFBP5 and SEMA3A, respectively, and that these miRNAs could be therapeutic targets. © 2017 KSBMB4

Analytical model of IEEE 802.15.4 non-beacon mode with download traffic by the piggyback method

Abstract. We analyze the MAC performance of the IEEE 802.15.4 LR-WPAN non-beacon mode with the piggyback method in non-saturated condition. Our approach is to model a stochastic behavior of one device as a discrete time Markov chain. We propose an analytical model describing the download behavior of a device using piggyback method. We obtain the performance measures such as throughput, packet delay, energy consumption and packet loss probability of a device. Numerical results and simulation results show that the piggyback method which removes a backoff procedure in the backoff method can reduce the delay, loss probability and energy consumption compared with backoff method. Our results can be used to find the optimal number of devices with some constraints on packet delay and packet loss probability