The development of a drug-resistant cell line can take from 3 to 18 months. However, little is published on the methodology of this development process. This article will discuss key decisions to be made prior to starting resistant cell line development; the choice of parent cell line, dose of selecting agent, treatment interval, and optimizing the dose of drug for the parent cell line. Clinically relevant drug-resistant cell lines are developed by mimicking the conditions cancer patients experience during chemotherapy and cell lines display between two- and eight-fold resistance compared to their parental cell line. Doses of drug administered are low, and a pulsed treatment strategy is often used where the cells recover in drug-free media. High-level laboratory models are developed with the aim of understanding potential mechanisms of resistance to chemotherapy agents. Doses of drug are higher and escalated over time. It is common to have difficulty developing stable clinically relevant drug-resistant cell lines. A comparative selection strategy of multiple cell lines or multiple chemotherapeutic agents mitigates this risk and gives insight into which agents or type of cell line develops resistance easily. Successful selection strategies from our research are presented. Pulsed-selection produced platinum or taxane-resistant large cell lung cancer (H1299 and H460) and temozolomide-resistant melanoma (Malme-3M and HT144) cell lines. Continuous selection produced a lapatinib-resistant breast cancer cell line (HCC1954). Techniques for maintaining drug-resistant cell lines are outlined including; maintaining cells with chemotherapy, pulse treating with chemotherapy, or returning to master drug-resistant stocks. The heterogeneity of drug-resistant models produced from the same parent cell line with the same chemotherapy agent is explored with reference to P-glycoprotein. Heterogeneity in drug-resistant cell lines reflects the heterogeneity that can occur in clinical drug resistance.

Anthrax lethal toxin (LT) is a bipartite protease-containing toxin and a key virulence determinant of Bacillus anthracis. In mice, LT causes the rapid lysis of macrophages isolated from certain inbred strains, but the correlation between murine macrophage sensitivity and mouse strain susceptibility to toxin challenge is poor. In rats, LT induces a rapid death in as little as 37 minutes through unknown mechanisms. We used a recombinant inbred (RI) rat panel of 19 strains generated from LT-sensitive and LT-resistant progenitors to map LT sensitivity in rats to a locus on chromosome 10 that includes the inflammasome NOD-like receptor (NLR) sensor, Nlrp1. This gene is the closest rat homolog of mouse Nlrp1b, which was previously shown to control murine macrophage sensitivity to LT. An absolute correlation between in vitro macrophage sensitivity to LT-induced lysis and animal susceptibility to the toxin was found for the 19 RI strains and 12 additional rat strains. Sequencing Nlrp1 from these strains identified five polymorphic alleles. Polymorphisms within the N-terminal 100 amino acids of the Nlrp1 protein were perfectly correlated with LT sensitivity. These data suggest that toxin-mediated lethality in rats as well as macrophage sensitivity in this animal model are controlled by a single locus on chromosome 10 that is likely to be the inflammasome NLR sensor, Nlrp1.

BackgroundTo study the role of microRNA (miRNA) in the regulation of Chinese hamster ovary (CHO) cell growth, qPCR, microarray and quantitative LC-MS/MS analysis were utilised for simultaneous expression profiling of miRNA, mRNA and protein. The sample set under investigation consisted of clones with variable cellular growth rates derived from the same population. In addition to providing a systems level perspective on cell growth, the integration of multiple profiling datasets can facilitate the identification of non-seed miRNA targets, complement computational prediction tools and reduce false positive and false negative rates.Results51 miRNAs were associated with increased growth rate (35 miRNAs upregulated and 16 miRNAs downregulated). Gene ontology (GO) analysis of genes (n=432) and proteins (n=285) found to be differentially expressed (DE) identified biological processes driving proliferation including mRNA processing and translation. To investigate the influence of miRNA on these processes we combined the proteomic and transcriptomic data into two groups. The first set contained candidates where evidence of translational repression was observed (n=158). The second group was a mixture of proteins and mRNAs where evidence of translational repression was less clear (n=515). The TargetScan algorithm was utilised to predict potential targets within these two groups for anti-correlated DE miRNAs.ConclusionsThe evidence presented in this study indicates that biological processes such as mRNA processing and protein synthesis are correlated with growth rate in CHO cells. Through the integration of expression data from multiple levels of the biological system a number of proteins central to these processes including several hnRNPs and components of the ribosome were found to be post-transcriptionally regulated. We utilised the expression data in conjunction with in-silico tools to identify potential miRNA-mediated regulation of mRNA/proteins involved in CHO cell growth rate. These data have allowed us to prioritise candidates for cell engineering and/or biomarkers relevant to industrial cell culture. We also expect the knowledge gained from this study to be applicable to other fields investigating the role of miRNAs in mammalian cell growth.

This study compares the effects of a calcium channel blocker (amlodipine) and an angiotensin converting enzyme inhibitor (enalapril) on in vivo insulin sensitivity in patients with essential hypertension. Forty-six patients with mild and moderate hypertension were studied. After a 2-week single-blind placebo phase, they were randomly assigned to double-blind therapy with either amlodipine (2.5 to 10 mg/day) or enalapril (5 to 40 mg/day) for 16 weeks. Both groups were comparable in terms of demographic characteristics, degree of obesity, metabolic parameters, and arterial blood pressure. Insulin sensitivity was measured at baseline and at week 16 during the active phase using euglycemic hyperinsulinemic clamps. Arterial blood pressure decreased similarly in both groups. Whole body glucose uptake (M-value) increased with amlodipine from 3.63 +/- 0.32 (mean +/- SEM) to 3.97 +/- 0.31 mg/kg/min (P = .02). A similar tendency was observed with enalapril: from 3.59 +/- 0.32 to 3.94 +/- 0.30 mg/kg/min (P = .09). A trend to lower steady-state insulin level during the second clamp (compared to baseline) was observed in both groups. The clamp-derived insulin sensitivity index (that corrects for steady-state insulin levels and glucose levels during the clamp) increased similarly in both groups: from 1.15 +/- 0.11 to 1.39 +/- 0.13 with amlodipine (P = .03) and from 1.25 +/- 0.13 to 1.49 +/- 0.16 with enalapril (P = .01). LDL cholesterol decreased with amlodipine (mean change, -11.3 mg/dL, P = .004). Amlodipine and enalapril were associated with increments in insulin sensitivity. Amlodipine provided an additional benefit with decreased low density lipoprotein cholesterol levels.

Reductions in muscle mitochondrial respiration occur concomitantly with insulin resistance and loss of muscle mass during bed rest and may play a role in the adaptations to physical inactivity. Significantly, we show that RVE is an effective strategy to partially prevent some of the deleterious metabolic effects of bed rest.