Highlights d A new methodology to detect and quantify most translation errors in proteomes d Most amino acid substitutions result from mis-pairing between codons and anti-codons d Proteins' error rates are reduced at conserved and highly expressed proteins d Translation speed is negatively correlated with error rates

The pool of transfer RNA (tRNA) molecules in cells allows the ribosome to decode genetic information. This repertoire of molecular decoders is positioned in the crossroad of the genome, the transcriptome, and the proteome. Omics and systems biology now allow scientists to explore the entire repertoire of tRNAs of many organisms, revealing basic exciting biology. The tRNA gene set of hundreds of species is now characterized, in addition to the tRNA genes of organelles and viruses. Genes encoding tRNAs for certain anticodon types appear in dozens of copies in a genome, while others are universally absent from any genome. Transcriptome measurement of tRNAs is challenging, but in recent years new technologies have allowed researchers to determine the dynamic expression patterns of tRNAs. These advances reveal that availability of ready-to-translate tRNA molecules is highly controlled by several transcriptional and posttranscriptional regulatory processes. This regulation shapes the proteome according to the cellular state. The tRNA pool profoundly impacts many aspects of cellular and organismal life, including protein expression level, translation accuracy, adequacy of folding, and even mRNA stability. As a result, the shape of the tRNA pool affects organismal health and may participate in causing conditions such as cancer and neurological conditions.

Codon usage bias affects protein translation because tRNAs that recognize synonymous codons differ in their abundance. Although the current dogma states that tRNA expression is exclusively regulated by intrinsic control elements (A- and B-box sequences), we revealed, using a reporter that monitors the levels of individual tRNA genes in Caenorhabditis elegans, that eight tryptophan tRNA genes, 100% identical in sequence, are expressed in different tissues and change their expression dynamically. Furthermore, the expression levels of the sup-7 tRNA gene at day 6 were found to predict the animal’s lifespan. We discovered that the expression of tRNAs that reside within introns of protein-coding genes is affected by the host gene’s promoter. Pairing between specific Pol II genes and the tRNAs that are contained in their introns is most likely adaptive, since a genome-wide analysis revealed that the presence of specific intronic tRNAs within specific orthologous genes is conserved across Caenorhabditis species.

Neurofibromin regulates cell motility via three distinct GTPase pathways acting through two different domains, the Ras GTPase-activating protein-related domain (GRD) and the pre-GRD domain. First, the GRD domain inhibits Ras-dependent changes in cell motility through the mitogen activated protein cascade. Second, it also regulates Rho-dependent (Ras-independent) changes by activating LIM kinase 2 (LIMK2), an enzyme that phosphorylates and inactivates cofilin (an actin-depolymerizing factor). Third, the pre-GRD domain acts through the Rac1 GTPase, that activate the P21 activated kinase 1 (PAK1)-LIMK1-cofilin pathway. We employed molecular modeling to identify a novel inhibitor of LIMK1/2. The active sites of an ephrin-A receptor (EphA3) and LIMK2 showed marked similarity (60%). On testing a known inhibitor of EphA3, we found that it fits to the LIMK1/2-ATP binding site and to the latter's substrate-binding pockets. We identified a similar compound, T56-LIMKi, and found that it inhibits LIMK1/2 kinase activities. It blocked the phosphorylation of cofilin which led to actin severance and inhibition of tumor cell migration, tumor cell growth, and anchorage-independent colony formation in soft agar. Because modulation of LIMK by neurofibromin is not affected by the Ras inhibitor Salirasib, we examined the combined effect of Salirasib and T56-LIMKi each of which can affect cell motility by a distinct pathway. We found that their combined action on cell proliferation and stress-fiber formation in neurofibromin-deficient cells was synergistic. We suggest that this drug combination may be developed for treatment of neurofibromatosis and cancer.

LIM kinases (LIMKs) are important cell cytoskeleton regulators that play a prominent role in cancer manifestation and neuronal diseases. The LIMK family consists of two homologues, LIMK1 and LIMK2, which differ from one another in expression profile, intercellular localization, and function. The main substrate of LIMK is cofilin, a member of the actin-depolymerizing factor (ADF) protein family. When phosphorylated by LIMK, cofilin is inactive. LIMKs play a contributory role in several neurodevelopmental disorders and in cancer growth and metastasis. We recently reported the development and validation of a novel LIMK inhibitor, referred to here as T56-LIMKi, using a combination of computational methods and classical biochemistry techniques. Here we report that T56-LIMKi inhibits LIMK2 with high specificity, and shows little or no cross-reactivity with LIMK1. We found that T56-LIMKi decreases phosphorylated cofilin (p-cofilin) levels and thus inhibits growth of several cancerous cell lines, including those of pancreatic cancer, glioma and schwannoma. Because the most promising in-vitro effect of T56-LIMKi was observed in the pancreatic cancer cell line Panc-1, we tested the inhibitor on a nude mouse Panc-1 xenograft model. T56-LIMKi reduced tumor size and p-cofilin levels in the Panc-1 tumors, leading us to propose T56-LIMKi as a candidate drug for cancer therapy.