Identification of bacterial sRNA regulatory targets using ribosome profiling

Bacteria express large numbers of non-coding, regulatory RNAs known as ‘small RNAs’ (sRNAs). sRNAs typically regulate expression of multiple target messenger RNAs (mRNAs) through base-pairing interactions. sRNA:mRNA base-pairing often results in altered mRNA stability and/or altered translation initiation. Computational identification of sRNA targets is challenging due to the requirement for only short regions of base-pairing that can accommodate mismatches. Experimental approaches have been applied to identify sRNA targets on a genomic scale, but these focus only on those targets regulated at the level of mRNA stability. Here, we utilize ribosome profiling (Ribo-seq) to experimentally identify regulatory targets of the Escherichia coli sRNA RyhB. We not only validate a majority of known RyhB targets using the Ribo-seq approach, but also discover many novel ones. We further confirm regulation of a selection of known and novel targets using targeted reporter assays. By mutating nucleotides in the mRNA of a newly discovered target, we demonstrate direct regulation of this target by RyhB. Moreover, we show that Ribo-seq distinguishes between mRNAs regulated at the level of RNA stability and those regulated at the level of translation. Thus, Ribo-seq represents a powerful approach for genome-scale identification of sRNA targets.     

Selecting targets for therapeutic validation through differential protein expression using chromatography-mass spectrometry

The identification of potential targets for therapeutic intervention can be accomplished on a systematic basis by a variety of techniques that include quantitative analysis of gene-specific mRNA levels and expressed proteins in normal and diseased cells. Differences in the expression levels of nucleic acid and protein gene products could suggest protein drug targets that are directly causative of disease, or reveal biochemical pathways that could be modulated by therapeutic molecules. Any effort based on mRNA or protein expression level comparisons could be confounded by a number of factors: level in steady-state may not be correlated with actual encoded protein levels; differentially expressed protein levels might be a result of disease process, and not causative of the process, and therapeutic intervention based on such a difference will be unproductive and the differential expression of mRNA or protein may be the result of biological variation unrelated to the disease process under study. In order to address these possibly confounding factors, it is necessary to validate potential targets by establishing their firm association with disease, and their minimal distribution in non-diseased tissues of any type. This requirement suggests that emphasis on true and reproducible quantitation of protein expression levels in a variety of samples will be an effective and highly efficient method of generating drug targets with a high degree of utility. To achieve this aim, we have established an industrial-scale proteomics-based discovery platform consisting of cell biology, protein chemistry, and mass spectrometry technical groups together with bioinformatics groups. The analytical method used for quantitation employs isotope labeling for differential analysis (ICATTM, Applied Biosystems, Inc.). With this technique, tryptic peptides are generated from labeled proteins that have been specifically captured from various subcellular locations or protein families. The resulting peptides are identified and quantified by mass spectrometry. To evaluate this approach on a large-scale, we have applied it to a study of continuous cell lines derived from human pancreatic adenocarcinomas. We have been able to establish processes for target discovery for small molecule drug targets as well as therapeutic antibody target identification for cell surface proteins. In addition, we have developed a process for identification of serum markers of this disease based upon standardized fractionation procedures. The results of these analyses will be presented together with the some of the issues from both the wet and dry (computational) lab that need to be addressed in such an undertaking.     

MicroRNA regulation of molecular networks mapped by global microRNA, mRNA, and protein expression in activated T lymphocytes

MicroRNAs (miRNAs) regulate specific immune mechanisms, but their genome-wide regulation of T lymphocyte activation is largely unknown. We performed a multidimensional functional genomics analysis to integrate genome-wide differential mRNA, miRNA, and protein expression as a function of human T lymphocyte activation and time. We surveyed expression of 420 human miRNAs in parallel with genome-wide mRNA expression. We identified a unique signature of 71 differentially expressed miRNAs, 57 of which were previously not known as regulators of immune activation. The majority of miRNAs are upregulated, mRNA expression of these target genes is downregulated, and this is a function of binding multiple miRNAs (combinatorial targeting). Our data reveal that consideration of this complex signature, rather than single miRNAs, is necessary to construct a full picture of miRNA-mediated regulation. Molecular network mapping of miRNA targets revealed the regulation of activation-induced immune signaling. In contrast, pathways populated by genes that are not miRNA targets are enriched for metabolism and biosynthesis. Finally, we specifically validated miR-155 (known) and miR-221 (novel in T lymphocytes) using locked nucleic acid inhibitors. Inhibition of these two highly upregulated miRNAs in CD4(+) T cells was shown to increase proliferation by removing suppression of four target genes linked to proliferation and survival. Thus, multiple lines of evidence link top functional networks directly to T lymphocyte immunity, underlining the value of mapping global gene, protein, and miRNA expression.     

Liver X receptors regulate adrenal steroidogenesis and hypothalamic-pituitary-adrenal feedback

The nuclear hormone receptors liver X receptor alpha (LXRalpha) (NR1H3) and LXRbeta (NR1H2) are established regulators of cholesterol, lipid, and glucose metabolism and are attractive drug targets for the treatment of diabetes and cardiovascular disease. Adrenal steroid hormones including glucocorticoids and mineralocorticoids are known to interfere with glucose metabolism, insulin signaling, and blood pressure regulation. Here we present genome-wide expression profiles of LXR-responsive genes in both the adrenal and the pituitary gland. LXR activation in cultured adrenal cells inhibited expression of multiple steroidogenic genes and consequently decreased adrenal steroid hormone production. In addition, LXR agonist treatment elevated ACTH mRNA expression and hormone secretion from pituitary cells both in vitro and in vivo. Reduced expression of the glucocortioid-activating enzyme 11beta-hydroxysteroid dehydrogenase 1 in pituitary cells upon LXR activation suggests blunting of the negative feedback of glucocorticoids by LXRs. In conclusion, LXRs independently interfere with the hypothalamic-pituitary-adrenal axis regulation at the level of the pituitary and the adrenal gland.     

TREAP: A New Topological Approach to Drug Target Inference

Over 50% of drugs fail in stage 3 clinical trials, many because of a poor understanding of the drug’s mechanisms of action (MoA). A better comprehension of drug MoA will significantly improve research and development (R&D). Current proposed algorithms, such as ProTINA and DeMAND, can be overly complex. Additionally, they are unable to predict whether the drug-induced gene expression or the topology of the networks used to model gene regulation primarily impacts accurate drug target inference. In this work, we evaluate how network and gene expression data affect ProTINA’s accuracy. We find that network topology predominantly determines the accuracy of ProTINA’s predictions. We further show that the size of an interaction network and/or selecting cell-specific networks has a limited effect on accuracy. We then demonstrate that a specific network topology measure, betweenness, can be used to improve drug target prediction. Based on these results, we create a new algorithm, TREAP, that combines betweenness values and adjusted p-values for target inference. TREAP offers an alternative approach to drug target inference and is advantageous because it is not computationally demanding, provides easy-to-interpret results, and is often more accurate at predicting drug targets than current state-of-the-art approaches.     

Autoregulation of transformer-2 alternative splicing is necessary for normal male fertility in Drosophila.

In the male germline of Drosophila the transformer-2 protein is required for differential splicing of pre-mRNAs from the exuperantia and att genes and autoregulates alternative splicing of its own pre-mRNA. Autoregulation of TRA-2 splicing results in production of two mRNAs that differ by the splicing/retention of the M1 intron and encode functionally distinct protein isoforms. Splicing of the intron produces an mRNA encoding TRA-2(226), which is necessary and sufficient for both male fertility and regulation of downstream target RNAs. When the intron is retained, an mRNA is produced encoding TRA-2(179), a protein with no known function. We have previously shown that repression of M1 splicing is dependent on TRA-2(226), suggesting that this protein quantitatively limits its own expression through a negative feedback mechanism at the level of splicing. Here we examine this idea, by testing the effect that variations in the level of tra-2 expression have on the splicing of M1 and on male fertility. Consistent with our hypothesis, we observe that as tra-2 gene dosage is increased, smaller proportions of TRA-2(226) mRNA are produced, limiting expression of this isoform. Feedback regulation is critical for male fertility, since it is significantly decreased by a transgene in which repression of M1 splicing cannot occur and TRA-2(226) mRNA is constitutively produced. The effect of this transgene becomes more severe as its dosage is increased, indicating that fertility is sensitive to an excess of TRA-2(226). Our results suggest that autoregulation of TRA-2(226) expression in male germ cells is necessary for normal spermatogenesis.     

Cataloguing the phosphorylation sites of tristetraprolin (TTP): Functional implications for inflammatory diseases

Tristetraprolin (TTP) is a destabilizing mRNA binding protein known to regulate gene expression of a wide variety of targets, including those that control inflammation. TTP expression, regulation and function is controlled by phosphorylation. While the importance of key serine (S) sites (S⁵² and S¹⁷⁸ in mice and S¹⁸⁶ in humans) has been recognized, other sites on the hyperphosphorylated TTP protein have more recently emerged as playing an important role in regulating cellular signalling and downstream functions of TTP. In order to propel investigation of TTP and fully exploit its potential as a drug target in inflammatory disease, this review will catalogue TTP phosphorylation sites in both the murine and human TTP protein, the known and unknown roles and functions of these sites, the kinases and phosphatases that act upon TTP and overview methodological approaches to increase our knowledge of this important protein regulated by phosphorylation.     

Translational regulation by ribosomal protein S8 in Escherichia coli: structural homology between rRNA binding site and feedback target on mRNA.

It has been previously shown that ribosomal protein synthesis in Escherichia coli is regulated at the level of translation by certain key ribosomal proteins. In the spc operon, S8 regulates the expression of L5 and some of the subsequent genes, while the first two genes (L14 and L24) are regulated independently. We therefore determined the DNA sequence at the junction of the L24 and L5 genes, which corresponds to the putative feedback target for S8. We show that there is a striking homology between the structure of the mRNA for this region and the known binding site for S8 on 16S rRNA. These results support the theory that the regulation of ribosomal protein synthesis is based on competition between rRNA and mRNA for regulatory ribosomal proteins.