Neuroactive small molecules are indispensable tools for treating mental illnesses and dissecting nervous system function. However, it has been difficult to discover novel neuroactive drugs. Here, we describe a high—throughput (HT) behavior—based approach to neuroactive small molecule discovery in the zebrafish. We use automated screening assays to evaluate thousands of chemical compounds and find that diverse classes of neuroactive molecules cause distinct patterns of behavior. These `behavioral barcodes' can be used to rapidly identify novel psychotropic chemicals and to predict their molecular targets. For example, we identify novel acetylcholinesterase and monoamine oxidase inhibitors using phenotypic comparisons and computational techniques. By combining HT screening technologies with behavioral phenotyping in vivo, behavior—based chemical screens may accelerate the pace of neuroactive drug discovery and provide small—molecule tools for understanding vertebrate behavior.

Although the ribosome is a very general catalyst, it cannot synthesize all protein sequences equally well. For example, ribosomes stall on the secretion monitor (SecM) leader peptide to regulate expression of a downstream gene. Using a genetic selection in Escherichia coli, we identified additional nascent peptide motifs that stall ribosomes. Kinetic studies show that some nascent peptides dramatically inhibit rates of peptide release by release factors. We find that residues upstream of the minimal stalling motif can either enhance or suppress this effect. In other stalling motifs, peptidyl transfer to certain aminoacyl-tRNAs is inhibited. In particular, three consecutive Pro codons pose a challenge for elongating ribosomes. The translation factor elongation factor P, which alleviates pausing at polyproline sequences, has little or no effect on other stalling peptides. The motifs that we identified are underrepresented in bacterial proteomes and show evidence of stalling on endogenous E. coli proteins.EF-P | proline | ribosome stalling | tmRNA

Bacground & Aims Hepatitis C virus (HCV) infection is associated with an increased prevalence of diabetes and insulin resistance (IR); whether this is a causal relationship has not been established. Methods We performed a longitudinal study within the lead-in phase of the Hepatitis C Antiviral Long-Term Treatment against Cirrhosis (HALT-C) Trial to evaluate whether suppression of hepatitis C is associated with improvement in IR. Participants had advanced hepatic fibrosis and carried non-3 HCV genotypes (n=96). Patients underwent 24 weeks of pegylated interferon (PEG IFN) and ribavirin therapy and were categorized into HCV clearance groups at week 20 based on HCV RNA levels: null responders (NR) had < 1 log10 decline (n=38); partial responders (PR) had ≥1 log10 decline (n=37), but detectable HCV RNA, and complete responders (CR) had no detectable HCV RNA (n=21). The primary outcomewas change (week 20 minus week 0) in IR using the homeostasis model assessment (HOMA2-IR). Results Adjusting only for baseline HOMA2-IR, mean HOMA2-IR differences were −2.23 (CR), −0.90 (PR), and +0.18 (NR) (p =0.036). The observed differences in mean HOMA2-IR scores were ordered in a linear fashion across response groups (p=0.01). The association between HCV clearance and improvement in HOMA2-IR could not be accounted for by adiponectin or tumor necrosis alfa, and was independent of potential confounders including age, gender, ethnicity, BMI, duration of infection, medications used, and fibrosis. Conclusion HCV suppression correlates with improvement in insulin resistance. These data provide further support for a role of HCV in the development of insulin resistance.