Evaluating the influence of selection markers on obtaining selected pools and stable cell lines in human cells

Selection markers are common genetic elements used in recombinant cell line development. While several selection systems exist for use in mammalian cell lines, no previous study has comprehensively evaluated their performance in the isolation of recombinant populations and cell lines. Here we examine four antibiotics, hygromycin B, neomycin, puromycin, and Zeocin™, and their corresponding selector genes, using a green fluorescent protein (GFP) as a reporter in two model cell lines, HT1080 and HEK293. We identify Zeocin™ as the best selection agent for cell line development in human cells. In comparison to the other selection systems, Zeocin™ is able to identify populations with higher fluorescence levels, which in turn leads to the isolation of better clonal populations and less false positives. Furthermore, Zeocin™-resistant populations exhibit better transgene stability in the absence of selection pressure compared to other selection agents. All isolated Zeocin™-resistant clones, regardless of cell type, exhibited GFP expression. By comparison, only 79% of hygromycin B-resistant, 47% of neomycin-resistant, and 14% of puromycin-resistant clones expressed GFP. Based on these results, we rank Zeocin™ > hygromycin B ∼ puromycin > neomycin for cell line development in human cells. Furthermore, this study demonstrates that selection marker choice does indeed impact cell line development.     

Systems Metabolic Engineering Meets Machine Learning: A New Era for Data‐Driven Metabolic Engineering

The recent increase in high‐throughput capacity of ‘omics datasets combined with advances and interest in machine learning (ML) have created great opportunities for systems metabolic engineering. In this regard, data‐driven modeling methods have become increasingly valuable to metabolic strain design. In this review, the nature of ‘omics is discussed and a broad introduction to the ML algorithms combining these datasets into predictive models of metabolism and metabolic rewiring is provided. Next, this review highlights recent work in the literature that utilizes such data‐driven methods to inform various metabolic engineering efforts for different classes of application including product maximization, understanding and profiling phenotypes, de novo metabolic pathway design, and creation of robust system‐scale models for biotechnology. Overall, this review aims to highlight the potential and promise of using ML algorithms with metabolic engineering and systems biology related datasets.     

Prazosin unmasks a renin response to intravenous para-chloroamphetamine

When injected intraperitoneally, p-chloroamphetamine (PCA) causes the acute release of catecholamines and serotonin, increases mean arterial pressure (MAP) and increases plasma renin activity (PRA) in rats. Experiments were designed to determine the dose-response and time-course for the effect of PCA administered intravenously on PRA in conscious, unrestrained rats. It was found initially that intravenous doses of PCA ranging from 0.3 - 6.0 mg/kg caused rapid and marked hypertension, but produced variable effects on PRA for up to 30 minutes after injection. In a second study PCA (0.3 - 6.0 mg/kg) did not alter PRA at 30 or 60 minutes after intravenous injection, but did increase PRA 60 minutes after 10 mg/kg, intraperitoneally. When the hypertension elicited by intravenous PCA was abolished by pretreatment with the alpha 1-adrenoceptor antagonist prazosin (100 micrograms/kg, iv), PCA produced marked elevations in PRA from 15 - 60 minutes. Thus it appeared that the renin response to intravenous PCA was masked by an elevation in MAP; when the vascular response to PCA was blocked, a large increase in PRA was observed.     

Carbonic anhydrase inhibitory properties of some uracil derivatives

Inhibitors of carbonic anhydrase (CA) have been carried out in many therapeutic applications, especially antiglaucoma activity. In this study, we investigated some uracil derivatives (4–12) to inhibit human CA I (hCA I) and II (hCA II) isoenzymes. The K_I values of the compounds 4–12 are in the range of 0.085–428?µM for hCA I and of 0.1715–645?µM against hCA II, respectively. It is concluded from the kinetic investigations, all compounds used in the study act as competitive inhibitors with substrate, 4-NPA. Uracil derivatives are emerging agents for the inhibiton of carbonic anhydrase which could be used in biomedicine.