An Evolutionary Conserved Role for Anaplastic Lymphoma Kinase in Behavioral Responses to Ethanol

Anaplastic lymphoma kinase (Alk) is a gene expressed in the nervous system that encodes a receptor tyrosine kinase commonly known for its oncogenic function in various human cancers. We have determined that Alk is associated with altered behavioral responses to ethanol in the fruit fly Drosophila melanogaster, in mice, and in humans. Mutant flies containing transposon insertions in dAlk demonstrate increased resistance to the sedating effect of ethanol. Database analyses revealed that Alk expression levels in the brains of recombinant inbred mice are negatively correlated with ethanol-induced ataxia and ethanol consumption. We therefore tested Alk gene knockout mice and found that they sedate longer in response to high doses of ethanol and consume more ethanol than wild-type mice. Finally, sequencing of human ALK led to the discovery of four polymorphisms associated with a low level of response to ethanol, an intermediate phenotype that is predictive of future alcohol use disorders (AUDs). These results suggest that Alk plays an evolutionary conserved role in ethanol-related behaviors. Moreover, ALK may be a novel candidate gene conferring risk for AUDs as well as a potential target for pharmacological intervention

The Receptor Tyrosine Kinase Alk Controls Neurofibromin Functions in Drosophila Growth and Learning

Anaplastic Lymphoma Kinase (Alk) is a Receptor Tyrosine Kinase (RTK) activated in several cancers, but with largely unknown physiological functions. We report two unexpected roles for the Drosophila ortholog dAlk, in body size determination and associative learning. Remarkably, reducing neuronal dAlk activity increased body size and enhanced associative learning, suggesting that its activation is inhibitory in both processes. Consistently, dAlk activation reduced body size and caused learning deficits resembling phenotypes of null mutations in dNf1, the Ras GTPase Activating Protein-encoding conserved ortholog of the Neurofibromatosis type 1 (NF1) disease gene. We show that dAlk and dNf1 co-localize extensively and interact functionally in the nervous system. Importantly, genetic or pharmacological inhibition of dAlk rescued the reduced body size, adult learning deficits, and Extracellular-Regulated-Kinase (ERK) overactivation dNf1 mutant phenotypes. These results identify dAlk as an upstream activator of dNf1-regulated Ras signaling responsible for several dNf1 defects, and they implicate human Alk as a potential therapeutic target in NF1

Computational Methods for Protein Identification from Mass Spectrometry Data

Protein identification using mass spectrometry is an indispensable computational tool in the life sciences. A dramatic increase in the use of proteomic strategies to understand the biology of living systems generates an ongoing need for more effective, efficient, and accurate computational methods for protein identification. A wide range of computational methods, each with various implementations, are available to complement different proteomic approaches. A solid knowledge of the range of algorithms available and, more critically, the accuracy and effectiveness of these techniques is essential to ensure as many of the proteins as possible, within any particular experiment, are correctly identified. Here, we undertake a systematic review of the currently available methods and algorithms for interpreting, managing, and analyzing biological data associated with protein identification. We summarize the advances in computational solutions as they have responded to corresponding advances in mass spectrometry hardware. The evolution of scoring algorithms and metrics for automated protein identification are also discussed with a focus on the relative performance of different techniques. We also consider the relative advantages and limitations of different techniques in particular biological contexts. Finally, we present our perspective on future developments in the area of computational protein identification by considering the most recent literature on new and promising approaches to the problem as well as identifying areas yet to be explored and the potential application of methods from other areas of computational biology