Survivin 2α: a novel Survivin splice variant expressed in human malignancies

BACKGROUND: Survivin and its alternative splice forms are involved in critical cellular processes, including cell division and programmed cell death. Survivin is expressed in the majority of human cancers, but minimally in differentiated normal tissues. Expression levels correlate with tumor aggressiveness and resistance to therapy. RESULTS: In the present study, we identify and characterize a novel survivin isoform that we designate survivin 2α. Structurally, the transcript consists of 2 exons: exon 1 and exon 2, as well as a 3' 197 bp region of intron 2. Acquisition of a new in-frame stop codon within intron 2 results in an open reading frame of 225 nucleotides, predicting a truncated 74 amino acid protein. Survivin 2α is expressed at high levels in several malignant cell lines and primary tumors. Functional assays show that survivin 2α attenuates the anti-apoptotic activity of survivin. Subcellular localization and immunoprecipitation of survivin 2α suggests a physical interaction with survivin. CONCLUSION: We characterized a novel survivin splice variant that we designated survivin 2α. We hypothesize that survivin 2α can alter the anti-apoptotic functions of survivin in malignant cells. Thus survivin 2α may be useful as a therapeutic tool in sensitizing chemoresistant tumor cells to chemotherapy

Data from: Accurate estimates of age at maturity from the growth trajectories of fishes and other ectotherms

Age-at-maturity (AAM) is a key life history trait that provides insight into ecology, evolution, and population dynamics. However, maturity data can be costly to collect or may not be available. Life history theory suggests that growth is biphasic for many organisms, with a change-point in growth occurring at maturity. If so, then it should be possible to use a biphasic growth model to estimate AAM from growth data. To test this prediction, we used the Lester biphasic growth model in a likelihood profiling framework to estimate AAM from length-at-age data. We fit our model to simulated growth trajectories to determine minimum data requirements (in terms of sample size, precision in length-at-age, and the cost to somatic growth of maturity) for accurate AAM estimates. We then applied our method to a large walleye Sander vitreus data set and show that our AAM estimates are in close agreement with conventional estimates when our model fits well. Finally, we highlight the potential of our method by applying it to length-at-age data for a variety of ectotherms. Our method shows promise as a tool for estimating AAM and other life history traits from contemporary and historical samples