Avi Rosenberg

Design of a genome-wide siRNA library using an artificial neural network

The largest gene knock-down experiments performed to date have used multiple short interfering/short hairpin (si/sh)RNAs per gene. To overcome this burden for design of a genome-wide siRNA library, we used the Stuttgart Neural Net Simulator to train algorithms on a data set of 2,182 randomly selected siRNAs targeted to 34 mRNA species, assayed through a high-throughput fluorescent reporter gene system. The algorithm, (BIOPREDsi), reliably predicted activity of 249 siRNAs of an independent test set (Pearson coefficient r = 0.66) and siRNAs targeting endogenous genes at mRNA and protein levels. Neural networks trained on a complementary 21-nucleotide (nt) guide sequence were superior to those trained on a 19-nt sequence. BIOPREDsi was used in the design of a genome-wide siRNA collection with two potent siRNAs per gene. When this collection of 50,000 siRNAs was used to identify genes involved in the cellular response to hypoxia, two of the most potent hits were the key hypoxia transcription factors HIF1A and ARNT.

Discovery and Validation of Novel Peptide Agonists for G-protein-coupled Receptors

G-protein-coupled receptors (GPCRs) represent an important group of targets for pharmaceutical therapeutics. The completion of the human genome revealed a large number of putative GPCRs. However, the identification of their natural ligands, and especially peptides, suffers from low discovery rates, thus impeding development of therapeutics based on these potential drug targets. We describe the discovery of novel GPCR ligands encrypted in the human proteome. Hundreds of potential peptide ligands were predicted by machine learning algorithms. In vitro screening of selected 33 peptides on a set of 152 GPCRs, including a group of designated orphan receptors, was conducted by intracellular calcium measurements and cAMP assays. The screening revealed eight novel peptides as potential agonists that specifically activated six different receptors in a dose-dependent manner. Most of the peptides showed distinct stimulatory patterns targeted at designated and orphan GPCRs. Further analysis demonstrated a significant in vivo effect for one of the peptides in a mouse inflammation model.

Peptides modulating conformational changes in secreted chaperones: From in silico design to preclinical proof of concept

Blocking conformational changes in biologically active proteins holds therapeutic promise. Inspired by the susceptibility of viral entry to inhibition by synthetic peptides that block the formation of helix–helix interactions in viral envelope proteins, we developed a computational approach for predicting interacting helices. Using this approach, which combines correlated mutations analysis and Fourier transform, we designed peptides that target gp96 and clusterin, 2 secreted chaperones known to shift between inactive and active conformations. In human blood mononuclear cells, the gp96-derived peptide inhibited the production of TNFα, IL-1β, IL-6, and IL-8 induced by endotoxin by >80%. When injected into mice, the peptide reduced circulating levels of endotoxin-induced TNFα, IL-6, and IFNγ by >50%. The clusterin-derived peptide arrested proliferation of several neoplastic cell lines, and significantly enhanced the cytostatic activity of taxol in vitro and in a xenograft model of lung cancer. Also, the predicted mode of action of the active peptides was experimentally verified. Both peptides bound to their parent proteins, and their biological activity was abolished in the presence of the peptides corresponding to the counterpart helices. These data demonstrate a previously uncharacterized method for rational design of protein antagonists.

Activation of Relaxin‐Related Receptors by Short, Linear Peptides Derived from a Collagen‐Containing Precursor

In a screening effort based on algorithmic predictions for novel G‐protein‐coupled receptor (GPCR) peptide activators, we were able to identify and examine two novel peptides (P59 and P74) which are short, linear, and derived from a natural, previously unidentified precursor protein containing a collagen‐like repeat. Both peptides seemed to show an apparent cAMP‐related effect on CHO‐K1 cells transiently transfected with either LGR7 or LGR8, usually after treatment with cAMP‐generating forskolin, compared to the same cells treated with forskolin plus relaxin. This activation was not found for the relaxin‐3 receptor (GPR135). In a set of follow‐up experiments, both peptides were found to stimulate cAMP production, mostly upon initial stimulation of cAMP production by 5 μM forskolin in cells transfected with either LGR7 or LGR8. In a dye‐free cell impedance GPCR activation assay, we were able to show that these peptides were also able to activate a cellular response mediated by these receptors. Although untransfected CHO‐K1 cells showed some cellular activation by both relaxin and at least one of our newly discovered peptides, both LGR7‐ and LGR8‐transfected cells showed a stronger response, indicating stimulation of a cellular pathway through activation of these receptors. In conclusion, we were able to show that these newly discovered peptides, which have no similarity to any member of the relaxin–insulin‐like peptide family, are potential ligands for the relaxin‐related family of receptors and as such might serve as novel candidates for relaxin‐related therapeutic indications. Both peptides are linear and were found to be active after being chemically synthesized.

Differential expression of novel adiponectin receptor-1 transcripts in skeletal muscle of subjects with normal glucose tolerance and type 2 diabetes.

OBJECTIVE Adiponectin receptor-1 (AdipoR1) expression in skeletal muscle has been suggested to play an important role in insulin resistance and diabetes. We aimed at evaluating the presence of novel AdiopR1 splice variants in human muscle and their regulation under physiological and pathophysiological states. RESEARCH DESIGN AND METHODS AdipoR1 5′UTR mRNA transcripts, predicted from bioinformatics data, were evaluated in fetal and adult human tissues. Expression and function of the identified transcripts were assessed in cultured human skeletal muscle cells and in muscle biopsies obtained from individuals with normal glucose tolerance (NGT) and type 2 diabetes (n = 49). RESULTS Screening of potential AdipoR1 5′UTR splice variants revealed a novel highly abundant muscle transcript (R1T3) in addition to the previously described transcript (R1T1). Unlike R1T1, R1T3 expression was significantly increased during fetal development and myogenesis, paralleled with increased AdipoR1 protein expression. The 5′UTR of R1T3 was found to contain upstream open reading frames that repress translation of downstream coding sequences. Conversely, AdipoR1 3′UTR was associated with enhanced translation efficiency during myoblast-myotube differentiation. A marked reduction in muscle expression of R1T3, R1T1, and R1T3-to-R1T1 ratio was observed in individuals with type 2 diabetes compared with expression levels of NGT subjects, paralleled with decreased expression of the differentiation marker myogenin. Among NGT subjects, R1T3 expression was positively correlated with insulin sensitivity. CONCLUSIONS These results indicate that AdipoR1 receptor expression in human skeletal muscle is subjected to posttranscriptional regulation, including alternative splicing and translational control. These mechanisms play an important role during myogenesis and may be important for whole-body insulin sensitivity.

Corrigendum: Design of a genome-wide siRNA library using an artificial neural network

Nat. Biotechnol. 23, 995–1001 (2005), published online 17 July 2005; corrected after print 25 July 2006 In the Methods section, p. 1,000, col. 2, paragraph 2, the text beginning: “The whole plate was discarded if for any time point...” and ending, “The final data set contained 2,431 sequences.” inaccurately described the procedure.

Peptides modulating conformational changes: From in silico design to in vivo proof of concept

From in-Silico Design to in-Vivo Proof of Concept Yossef Kliger, Ofer Levy, Anat Oren, Haim Ashkenazy, Zohar Tiran, Amit Novik, Avi Rosenberg, Anat Amir, Assaf Wool, Amir Toporik, Ehud Schreiber, Dani Eshel, Zurit Levine, Yossi Cohen, Claudia Nold-Petry, Charles A. Dinarello, Itamar Borukhov 1Compugen Ltd. Tel Aviv 69512, Israel. (www.cgen.com) *Correspondence: kliger@cgen.com, itamarb@cgen.com 2The Mina and Everard Goodman Faculty of Life Sciences, Bar Ilan University, Ramat Gan, Israel 3University of Colorado Denver, Aurora, CO 80045, USA