The activity of the anti-apoptotic fragment generated by the caspase-3/p120 RasGAP stress-sensing module displays strict Akt isoform specificity.

The caspase-3/p120 RasGAP module acts as a stress sensor that promotes pro-survival or pro-death signaling depending on the intensity and the duration of the stressful stimuli. Partial cleavage of p120 RasGAP generates a fragment, called fragment N, which protects stressed cells by activating Akt signaling. Akt family members regulate many cellular processes including proliferation, inhibition of apoptosis and metabolism. These cellular processes are regulated by three distinct Akt isoforms: Akt1, Akt2 and Akt3. However, which of these isoforms are required for fragment N mediated protection have not been defined. In this study, we investigated the individual contribution of each isoform in fragment N-mediated cell protection against Fas ligand induced cell death. To this end, DLD1 and HCT116 isogenic cell lines lacking specific Akt isoforms were used. It was found that fragment N could activate Akt1 and Akt2 but that only the former could mediate the protective activity of the RasGAP-derived fragment. Even overexpression of Akt2 or Akt3 could not rescue the inability of fragment N to protect cells lacking Akt1. These results demonstrate a strict Akt isoform requirement for the anti-apoptotic activity of fragment N.

Persistent spatial clusters of high body mass index in a Swiss urban population as revealed by the 5-year GeoCoLaus longitudinal study.

OBJECTIVE: Body mass index (BMI) may cluster in space among adults and be spatially dependent. Whether and how BMI clusters evolve over time in a population is currently unknown. We aimed to determine the spatial dependence of BMI and its 5-year evolution in a Swiss general adult urban population, taking into account the neighbourhood-level and individual-level characteristics. DESIGN: Cohort study. SETTING: Swiss general urban population. PARTICIPANTS: 6481 georeferenced individuals from the CoLaus cohort at baseline (age range 35-74 years, period=2003-2006) and 4460 at follow-up (period=2009-2012). OUTCOME MEASURES: Body weight and height were measured by trained healthcare professionals with participants standing without shoes in light indoor clothing. BMI was calculated as weight (kg) divided by height squared (m(2)). Participants were geocoded using their postal address (geographic coordinates of the place of residence). Getis-Ord Gi statistic was used to measure the spatial dependence of BMI values at baseline and its evolution at follow-up. RESULTS: BMI was not randomly distributed across the city. At baseline and at follow-up, significant clusters of high versus low BMIs were identified and remained stable during the two periods. These clusters were meaningfully attenuated after adjustment for neighbourhood-level income but not individual-level characteristics. Similar results were observed among participants who showed a significant weight gain. CONCLUSIONS: To the best of our knowledge, this is the first study to report longitudinal changes in BMI clusters in adults from a general population. Spatial clusters of high BMI persisted over a 5-year period and were mainly influenced by neighbourhood-level income.