The Role of Ras Oncogene in Differentiation of 3T3-L1 Preadipocytes

Ras is a proto-oncogene that codes for a small GTPase and is responsible for linking several extracellular signals to intracellular mechanisms that involve cell growth, differentiation and cell-programmed death in normal and diseased cells. In all these processes, Ras has been extensively investigated. However, the role of Ras GTPases is still poorly understood during the differentiation of 3T3-L1 preadipocytes. In this study I investigated the role of the H-Ras defective mutant, Ras:G12V on the differentiation of 3T3-L1 preadipocytes. Preadipocytes were differentiated in vitro to adipocytes (fat cells) by adding an induction medium containing several factors including glucose and insulin. The formation of fat cells evidenced by the visualization of lipid drops as well as by quantifying the accumulation of Oil red O into lipid drops. To examine the role of Ras:G12V mutant, several selective mutations were introduced in order to determine the signaling transduction pathways (i.e., PI3(K)kinase and MAP(K)Kinase) responsible for the Ras-dependent adipogenesis. Cells expressing Ras:G12V mutant stimulated 3T3-L1 preadipocyte differentiation without he need for induction media, suggesting that Ras activation is an essential factor required for 3T3-L1 preadipocyte differentiation. Introduction of a second mutation on Ras:G12V (i.e., Ras:G12V;E37G), which blocks the activation of the MAPKinase pathway, strongly inhibited the 3T3-L1 preadipocyte differentiation. It is also important to note Ras:G12V:E37G double mutant does not inhibit the activation of the PI3kinase pathway. Other Ras double mutants (Ras:G12V;S35T, and V12G;C40Y) showed a modest inhibition of the 3T3-L1 preadipocyte differentiation. Taken together, these observations indicate that Ras plays a selective role in 3T3-L1 preadipocyte differentiation. Thus, understanding which specific pathway Ras employs during preadipocyte differentiation could clarify some of the uncertainties surrounding fat production.

Evaluation of non-contact sampling and detection of explosives using receiver operating characteristic curves

The growing need for fast sampling of explosives in high throughput areas has increased the demand for improved technology for the trace detection of illicit compounds. Detection of the volatiles associated with the presence of the illicit compounds offer a different approach for sensitive trace detection of these compounds without increasing the false positive alarm rate. This study evaluated the performance of non-contact sampling and detection systems using statistical analysis through the construction of Receiver Operating Characteristic (ROC) curves in real-world scenarios for the detection of volatiles in the headspace of smokeless powder, used as the model system for generalizing explosives detection. A novel sorbent coated disk coined planar solid phase microextraction (PSPME) was previously used for rapid, non-contact sampling of the headspace containers. The limits of detection for the PSPME coupled to IMS detection was determined to be 0.5-24 ng for vapor sampling of volatile chemical compounds associated with illicit compounds and demonstrated an extraction efficiency of three times greater than other commercially available substrates, retaining >50% of the analyte after 30 minutes sampling of an analyte spike in comparison to a non-detect for the unmodified filters. Both static and dynamic PSPME sampling was used coupled with two ion mobility spectrometer (IMS) detection systems in which 10-500 mg quantities of smokeless powders were detected within 5-10 minutes of static sampling and 1 minute of dynamic sampling time in 1-45 L closed systems, resulting in faster sampling and analysis times in comparison to conventional solid phase microextraction-gas chromatography-mass spectrometry (SPME-GC-MS) analysis. Similar real-world scenarios were sampled in low and high clutter environments with zero false positive rates. Excellent PSPME-IMS detection of the volatile analytes were visualized from the ROC curves, resulting with areas under the curves (AUC) of 0.85-1.0 and 0.81-1.0 for portable and bench-top IMS systems, respectively. Construction of ROC curves were also developed for SPME-GC-MS resulting with AUC of 0.95-1.0, comparable with PSPME-IMS detection. The PSPME-IMS technique provides less false positive results for non-contact vapor sampling, cutting the cost and providing an effective sampling and detection needed in high-throughput scenarios, resulting in similar performance in comparison to well-established techniques with the added advantage of fast detection in the field.