Conjugation of growth factors to a carrier is a favorable method to improve their efficacy as therapeutic molecules.

Different growth factors can regulate stem cell differentiation. We used keratinocyte growth factor (KGF) to direct adipose-derived stem cells (ASCs) differentiation into keratinocytes. To enhance KGF bioavailability, we targeted KGF for collagen by fusing it to collagen-binding domain from Vibrio mimicus metalloprotease (vibrioCBD-KGF). KGF and vibrioCBD-KGF were expressed in Escherichia coli and purified to homogeneity. Both proteins displayed comparable activities in stimulating proliferation of HEK-293 and MCF-7 cells. vibrioCBD-KGF demonstrated enhanced collagen-binding affinity in immunofluorescence and ELISA. KGF and vibrioCBD-KGF at different concentrations (2, 10, and 20 ng/ml) were applied for 21 days on ASCs cultured on collagen-coated plates. Keratinocyte differentiation was assessed based on morphological changes, the expression of keratinocyte markers (Keratin-10 and Involucrin), and stem cell markers (Collagen-I and Vimentin) by real-time PCR or immunofluorescence. Our results indicated that the expression of keratinocyte markers was substantially increased at all concentrations of vibrioCBD-KGF, while it was observed for KGF only at 20 ng/ml. Immunofluorescence staining approved this finding. Moreover, down-regulation of Collagen-I, an indicator of differentiation commitment, was more significant in samples treated with vibrioCBD-KGF. The present study showed that vibrioCBD-KGF is more potent in inducing the ASCs differentiation into keratinocytes compared to KGF. Our results have important implications for effective skin regeneration using collagen-based biomaterials.

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One of the main issues in solid tumours is progressive mutation in epidermal growth factor receptors (EGFR) gene, which activates signalling pathways that create new blood vessels. In this study, it was attempted to find new a therapeutic candidate to inhibit EGFR. One of the cost-effective alternative methods to find new inhibitors has been the repositioning approach of existing drugs. The critical point of computational drug repositioning method is saving time and cost to remove the pre-clinical step and accelerate the drug discovery process. Hence, an ensemble computational-experimental approach, consisting of three different steps, a machine learning model, simulation of drug-target interaction and experimental characterization, was developed. The machine learning type used here was different tree classification method, which is one of the best randomize machine learning model to identify potential inhibitors from weak inhibitors. The machine learning step aimed to discover the approved drugs with the highest possible success rate in the experimental step. Finally, out of the nine chosen drugs, seven compounds had been confirmed to inhibit EGFR in the published articles since 2019. Hence, two identified compounds, in addition to gefitinib, as a positive control, and one neutral, were considered via molecular docking study. Finally, eight proposed drugs, including gefitinib, were investigated using MTT assay and In-Cell ELISA to characterise the drugs effect on A431 cell growth and EGFR-signaling. From our experiments, we could conclude that salicylic acid and piperazine could play an EGFR-inhibitor role like gefitinib.

The study was aimed at design a good fusion construct that would successfully express the recombinant proteins and produce peptides in Escherichia coli. Two different constructs including human epidermal growth factor (hEGF) gene were designed to obtain an effi cient expression level of hEGF. The hEGF sequence was inserted in pET32a vector containing thioredoxin (Trx) sequence and modifi ed pET15b vector containing intein and elastin-like polypeptide (ELP). METHODS: The vectors were transformed into E. coli TOP10F′ for multiplication and further into E. coli BL21 (DE3) to express protein. The hEGF expression was induced by isopropyl β-D-1-thiogalactopyranoside (IPTG) while the expression levels were evaluated by SDS-PAGE and western blotting and compared by ImageJ analysis, BCA and Elisa assays. RESULTS: The expression level after 2 hours of IPTG induction was signifi cantly higher than after other induction times. ImageJ, BCA and Elisa analyses demonstrated that the Trx presence enhanced protein expression signifi cantly when compared to ELP-intein-based construct. CONCLUSION: The pET32a-Trx-hEGF construct had a higher expression than pET15b-ELP-intein-hEGF. Overall, considering Trx, the fusion protein in construct design can make it suitable to signifi cantly express hEGF compared to ELP-intein while its combination with ELP-intein may improve the expression of the ELPintein construct (Tab. 2, Fig. 7, Ref. 34).