Raya Khanin - New York City Metropolitan Area | Professional Profile | LinkedIn (original) (raw)

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molecular cancer therapeutics June 4, 2014

the majority of uveal melanomas carry oncogenic mutations in the g proteins gnaq and gna11, with consequent activation of the mapk pathway. selective mek inhibitors, such as selumetinib, have shown clinical benefit in uveal melanoma. however, mechanisms of drug resistance limit their efficacy in some patients. analysis of mek inhibitor-resistant uveal melanoma cell lines revealed the induction of ras protein expression and activity. this effect was mediated by the rna helicase ddx43, which was…
the majority of uveal melanomas carry oncogenic mutations in the g proteins gnaq and gna11, with consequent activation of the mapk pathway. selective mek inhibitors, such as selumetinib, have shown clinical benefit in uveal melanoma. however, mechanisms of drug resistance limit their efficacy in some patients. analysis of mek inhibitor-resistant uveal melanoma cell lines revealed the induction of ras protein expression and activity. this effect was mediated by the rna helicase ddx43, which was remarkably overexpressed in these cells. depletion of ddx43 in mek inhibitor-resistant cells decreased ras proteins and inhibited erk and akt pathways. on the contrary, ectopic expression of ddx43 in parental uveal melanoma cells induced ras protein levels and rendered cells resistant to mek inhibition. similar to ddx43 depletion, downregulation of kras, hras, and nras inhibited downstream pathways in the resistant cells, overcoming mutant gnaq signaling. we also analyzed the expression of ddx43 in liver metastases of patients with uveal melanoma by rt-pcr, and found a significant overexpression of ddx43 in patients who did not benefit from selumetinib therapy. in conclusion, ddx43 induces ras protein expression and signaling, mediating a novel mechanism of mek inhibitor resistance. the detection of ddx43 in patients with uveal melanoma could lead to more targeted therapies for this disease.
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September 15, 2013

PURPOSE: To investigate the relationship between lactate dehydrogenase A (LDH-A) expression, lactate concentration, cell metabolism, and metastases in murine 4T1 breast tumors.
EXPERIMENTAL DESIGN: Inhibition of LDH-A expression and protein levels were achieved in a metastatic breast cancer cell line (4T1) using short hairpin RNA (shRNA) technology. The relationship between tumor LDH-A protein levels and lactate concentration (measured by magnetic resonance spectroscopic imaging, MRSI)…
PURPOSE: To investigate the relationship between lactate dehydrogenase A (LDH-A) expression, lactate concentration, cell metabolism, and metastases in murine 4T1 breast tumors.
EXPERIMENTAL DESIGN: Inhibition of LDH-A expression and protein levels were achieved in a metastatic breast cancer cell line (4T1) using short hairpin RNA (shRNA) technology. The relationship between tumor LDH-A protein levels and lactate concentration (measured by magnetic resonance spectroscopic imaging, MRSI) and metastases was assessed.
RESULTS: LDH-A knockdown cells (KD9) showed a significant reduction in LDH-A protein and LDH activity, less acid production, decreased transwell migration and invasion, lower proliferation, reduced glucose consumption and glycolysis, and increase in oxygen consumption, reactive oxygen species (ROS), and cellular ATP levels, compared with control (NC) cells cultured in 25 mmol/L glucose. In vivo studies showed lower lactate levels in KD9, KD5, and KD317 tumors than in NC or 4T1 wild-type tumors (P < 0.01), and a linear relationship between tumor LDH-A protein expression and lactate concentration. Metastases were delayed and primary tumor growth rate decreased.
CONCLUSIONS: We show for the first time that LDH-A knockdown inhibited the formation of metastases, and was accompanied by in vivo changes in tumor cell metabolism. Lactate MRSI can be used as a surrogate to monitor targeted inhibition of LDH-A in a preclinical setting and provides a noninvasive imaging strategy to monitor LDH-A-targeted therapy. This imaging strategy can be translated to the clinic to identify and monitor patients who are at high risk of developing metastatic disease. Clin Cancer Res; 19(18); 5158-69. ©2013 AACR.
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Genome Biology September 10, 2013

A large number of computational methods have been developed for analyzing differential gene expression in RNA-seq data. We describe a comprehensive evaluation of common methods using the SEQC benchmark dataset and ENCODE data. We consider a number of key features, including normalization, accuracy of differential expression detection and differential expression analysis when one condition has no detectable expression. We find significant differences among the methods, but note that array-based…
A large number of computational methods have been developed for analyzing differential gene expression in RNA-seq data. We describe a comprehensive evaluation of common methods using the SEQC benchmark dataset and ENCODE data. We consider a number of key features, including normalization, accuracy of differential expression detection and differential expression analysis when one condition has no detectable expression. We find significant differences among the methods, but note that array-based methods adapted to RNA-seq data perform comparably to methods designed for RNA-seq. Our results demonstrate that increasing the number of replicate samples significantly improves detection power over increased sequencing depth.
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J Exp Med May 7, 2012

Regulation of intestinal inflammation by microbiota following allogeneic bone marrow transplantation
Robert R. Jenq, Carles Ubeda, Ying Taur, Clarissa C. Menezes, Raya Khanin, Jarrod A. Dudakov, Chen Liu, Mallory L. West, Natalie V. Singer, Michele J. Equinda, Asia Gobourne, Lauren Lipuma, Lauren F. Young, Odette M. Smith, Arnab Ghosh, Alan M. Hanash, Jenna D. Goldberg, Kazutoshi Aoyama, Bruce R. Blazar, Eric G. Pamer, Marcel R.M. van den Brink
J Exp Med. 2012 May 7; 209(5): 903–911. doi:…
Regulation of intestinal inflammation by microbiota following allogeneic bone marrow transplantation
Robert R. Jenq, Carles Ubeda, Ying Taur, Clarissa C. Menezes, Raya Khanin, Jarrod A. Dudakov, Chen Liu, Mallory L. West, Natalie V. Singer, Michele J. Equinda, Asia Gobourne, Lauren Lipuma, Lauren F. Young, Odette M. Smith, Arnab Ghosh, Alan M. Hanash, Jenna D. Goldberg, Kazutoshi Aoyama, Bruce R. Blazar, Eric G. Pamer, Marcel R.M. van den Brink
J Exp Med. 2012 May 7; 209(5): 903–911. doi: 10.1084/jem.20112408
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Nature February 15, 2012

IDH mutation impairs histone demethylation and results in a block to cell differentiation
Chao Lu, Patrick S. Ward, Gurpreet S. Kapoor, Dan Rohle, Sevin Turcan, Omar Abdel-Wahab, Christopher R. Edwards, Raya Khanin, Maria E. Figueroa, Ari Melnick, Kathryn E. Wellen, Donald M. O’Rourke, Shelley L. Berger, Timothy A. Chan, Ross L. Levine, Ingo K. Mellinghoff, Craig B. Thompson. Nature. Author manuscript; available in PMC 2012 October 23.
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Genes Chromosomes & Cancer October 27, 2011

Cancer gene fusions that encode a chimeric protein are often characterized by an intragenic discontinuity in the RNA\expression levels of the exons that are 5' or 3' to the fusion point in one or both of the fusion partners due to differences in the levels of activation of their respective promoters. Based on this, we developed an unbiased, genome-wide bioinformatic screen for gene fusions using Affymetrix Exon array expression data. Using a training set of 46 samples with different known gene…
Cancer gene fusions that encode a chimeric protein are often characterized by an intragenic discontinuity in the RNA\expression levels of the exons that are 5' or 3' to the fusion point in one or both of the fusion partners due to differences in the levels of activation of their respective promoters. Based on this, we developed an unbiased, genome-wide bioinformatic screen for gene fusions using Affymetrix Exon array expression data. Using a training set of 46 samples with different known gene fusions, we developed a data analysis pipeline, the "Fusion Score (FS) model", to score and rank genes for intragenic changes in expression. In a separate discovery set of 41 tumor samples with possible unknown gene fusions, the FS model generated a list of 552 candidate genes. The transcription factor gene NCOA2 was one of the candidates identified in a mesenchymal chondrosarcoma. A novel HEY1-NCOA2 fusion was identified by 5' RACE, representing an in-frame fusion of HEY1 exon 4 to NCOA2 exon 13. RT-PCR or FISH evidence of this HEY1-NCOA2 fusion was present in all additional mesenchymal chondrosarcomas tested with a definitive histologic diagnosis and adequate material for analysis (n = 9) but was absent in 15 samples of other subtypes of chondrosarcomas. We also identified a NUP107-LGR5 fusion in a dedifferentiated liposarcoma but analysis of 17 additional samples did not confirm it as a recurrent event in this sarcoma type. The novel HEY1-NCOA2 fusion appears to be the defining and diagnostic gene fusion in mesenchymal chondrosarcomas.
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Cancer Research Sep 2011

Liposarcoma remains the most common mesenchymal cancer, with a mortality rate of 60% among patients with this disease. To address the present lack of therapeutic options, we embarked upon a study of microRNA (miRNA) expression alterations associated with liposarcomagenesis with the goal of exploiting differentially expressed miRNAs and the gene products they regulate as potential therapeutic targets. MicroRNA expression was profiled in samples of normal adipose tissue, well-differentiated…
Liposarcoma remains the most common mesenchymal cancer, with a mortality rate of 60% among patients with this disease. To address the present lack of therapeutic options, we embarked upon a study of microRNA (miRNA) expression alterations associated with liposarcomagenesis with the goal of exploiting differentially expressed miRNAs and the gene products they regulate as potential therapeutic targets. MicroRNA expression was profiled in samples of normal adipose tissue, well-differentiated liposarcoma, and dedifferentiated liposarcoma by both deep sequencing of small RNA libraries and hybridization-based Agilent microarrays. The expression profiles discriminated liposarcoma from normal adipose tissue and well differentiated from dedifferentiated disease. We defined over 40 miRNAs that were dysregulated in dedifferentiated liposarcomas in both the sequencing and the microarray analysis. The upregulated miRNAs included two cancer-associated species (miR-21 and miR-26a), and the downregulated miRNAs included two species that were highly abundant in adipose tissue (miR-143 and miR-145). Restoring miR-143 expression in dedifferentiated liposarcoma cells inhibited proliferation, induced apoptosis, and decreased expression of BCL2, topoisomerase 2A, protein regulator of cytokinesis 1 (PRC1), and polo-like kinase 1 (PLK1). The downregulation of PRC1 and its docking partner PLK1 suggests that miR-143 inhibits cytokinesis in these cells. In support of this idea, treatment with a PLK1 inhibitor potently induced G(2)-M growth arrest and apoptosis in liposarcoma cells. Taken together, our findings suggest that miR-143 re-expression vectors or selective agents directed at miR-143 or its targets may have therapeutic value in dedifferentiated liposarcoma.
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The American Journal of Pathology Aug 2011

Transforming growth factor (TGF)-β is one of the main fibrogenic cytokines that drives the pathophysiology of progressive renal scarring. MicroRNAs (miRNAs) are endogenous non-coding RNAs that post-transcriptionally regulate gene expression. We examined the role of TGF-β-induced expression of miR-21, miRNAs in cell culture models and miRNA expression in relevant models of renal disease. In vitro, TGF-β changed expression of miR-21, miR-214, and miR-145 in rat mesangial cells (CRL-2753) and…
Transforming growth factor (TGF)-β is one of the main fibrogenic cytokines that drives the pathophysiology of progressive renal scarring. MicroRNAs (miRNAs) are endogenous non-coding RNAs that post-transcriptionally regulate gene expression. We examined the role of TGF-β-induced expression of miR-21, miRNAs in cell culture models and miRNA expression in relevant models of renal disease. In vitro, TGF-β changed expression of miR-21, miR-214, and miR-145 in rat mesangial cells (CRL-2753) and miR-214, miR-21, miR-30c, miR-200b, and miR-200c during induction of epithelial-mesenchymal transition in rat tubular epithelial cells (NRK52E). miR-214 expression was robustly modulated in both cell types, whereas in tubular epithelial cells miR-21 was increased and miR-200b and miR-200c were decreased by 58% and 48%, respectively, in response to TGF-β. TGF-β receptor-1 was found to be a target of miR-200b/c and was down-regulated after overexpression of miR-200c. To assess the differential expression of these miRNAs in vivo, we used the anti-Thy1.1 mesangial glomerulonephritis model and the unilateral ureteral obstruction model in which TGF-β plays a role and also a genetic model of hypertension, the stroke-prone spontaneously hypertensive rat with and without salt loading. The expressions of miR-214 and miR-21 were significantly increased in all in vivo models, showing a possible miRNA signature of renal damage despite differing causes.
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Clinical Cancer Research May 2011

Abstract
PURPOSE:
Gastric cancer may be subdivided into 3 distinct subtypes--proximal, diffuse, and distal gastric cancer--based on histopathologic and anatomic criteria. Each subtype is associated with unique epidemiology. Our aim is to test the hypothesis that these distinct gastric cancer subtypes may also be distinguished by gene expression analysis.
EXPERIMENTAL DESIGN:
Patients with localized gastric adenocarcinoma being screened for a phase II preoperative clinical trial…
Abstract
PURPOSE:
Gastric cancer may be subdivided into 3 distinct subtypes--proximal, diffuse, and distal gastric cancer--based on histopathologic and anatomic criteria. Each subtype is associated with unique epidemiology. Our aim is to test the hypothesis that these distinct gastric cancer subtypes may also be distinguished by gene expression analysis.
EXPERIMENTAL DESIGN:
Patients with localized gastric adenocarcinoma being screened for a phase II preoperative clinical trial (National Cancer Institute, NCI #5917) underwent endoscopic biopsy for fresh tumor procurement. Four to 6 targeted biopsies of the primary tumor were obtained. Macrodissection was carried out to ensure more than 80% carcinoma in the sample. HG-U133A GeneChip (Affymetrix) was used for cDNA expression analysis, and all arrays were processed and analyzed using the Bioconductor R-package.
RESULTS:
Between November 2003 and January 2006, 57 patients were screened to identify 36 patients with localized gastric cancer who had adequate RNA for expression analysis. Using supervised analysis, we built a classifier to distinguish the 3 gastric cancer subtypes, successfully classifying each into tightly grouped clusters. Leave-one-out cross-validation error was 0.14, suggesting that more than 85% of samples were classified correctly. Gene set analysis with the false discovery rate set at 0.25 identified several pathways that were differentially regulated when comparing each gastric cancer subtype to adjacent normal stomach.
CONCLUSIONS:
Subtypes of gastric cancer that have epidemiologic and histologic distinctions are also distinguished by gene expression data. These preliminary data suggest a new classification of gastric cancer with implications for improving our understanding of disease biology and identification of unique molecular drivers for each gastric cancer subtype.
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New England Journal of Medicine Dec 2010

Abstract
BACKGROUND:
Uveal melanoma is the most common intraocular cancer. There are no effective therapies for metastatic disease. Mutations in GNAQ, the gene encoding an alpha subunit of heterotrimeric G proteins, are found in 40% of uveal melanomas.
METHODS:
We sequenced exon 5 of GNAQ and GNA11, a paralogue of GNAQ, in 713 melanocytic neoplasms of different types (186 uveal melanomas, 139 blue nevi, 106 other nevi, and 282 other melanomas). We sequenced exon 4 of GNAQ and…
Abstract
BACKGROUND:
Uveal melanoma is the most common intraocular cancer. There are no effective therapies for metastatic disease. Mutations in GNAQ, the gene encoding an alpha subunit of heterotrimeric G proteins, are found in 40% of uveal melanomas.
METHODS:
We sequenced exon 5 of GNAQ and GNA11, a paralogue of GNAQ, in 713 melanocytic neoplasms of different types (186 uveal melanomas, 139 blue nevi, 106 other nevi, and 282 other melanomas). We sequenced exon 4 of GNAQ and GNA11 in 453 of these samples and in all coding exons of GNAQ and GNA11 in 97 uveal melanomas and 45 blue nevi.
RESULTS:
We found somatic mutations in exon 5 (affecting Q209) and in exon 4 (affecting R183) in both GNA11 and GNAQ, in a mutually exclusive pattern. Mutations affecting Q209 in GNA11 were present in 7% of blue nevi, 32% of primary uveal melanomas, and 57% of uveal melanoma metastases. In contrast, we observed Q209 mutations in GNAQ in 55% of blue nevi, 45% of uveal melanomas, and 22% of uveal melanoma metastases. Mutations affecting R183 in either GNAQ or GNA11 were less prevalent (2% of blue nevi and 6% of uveal melanomas) than the Q209 mutations. Mutations in GNA11 induced spontaneously metastasizing tumors in a mouse model and activated the mitogen-activated protein kinase pathway.
CONCLUSIONS:
Of the uveal melanomas we analyzed, 83% had somatic mutations in GNAQ or GNA11. Constitutive activation of the pathway involving these two genes appears to be a major contributor to the development of uveal melanoma. (Funded by the National Institutes of Health and others.).
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PLoS ONE Aug 2010

Prior to gastrulation in the mouse, all endodermal cells arise from the primitive endoderm of the blastocyst stage embryo. Primitive endoderm and its derivatives are generally referred to as extra-embryonic endoderm (ExEn) because the majority of these cells contribute to extra-embryonic lineages encompassing the visceral endoderm (VE) and the parietal endoderm (PE). During gastrulation, the definitive endoderm (DE) forms by ingression of cells from the epiblast. The DE comprises most of the…
Prior to gastrulation in the mouse, all endodermal cells arise from the primitive endoderm of the blastocyst stage embryo. Primitive endoderm and its derivatives are generally referred to as extra-embryonic endoderm (ExEn) because the majority of these cells contribute to extra-embryonic lineages encompassing the visceral endoderm (VE) and the parietal endoderm (PE). During gastrulation, the definitive endoderm (DE) forms by ingression of cells from the epiblast. The DE comprises most of the cells of the gut and its accessory organs. Despite their different origins and fates, there is a surprising amount of overlap in marker expression between the ExEn and DE, making it difficult to distinguish between these cell types by marker analysis. This is significant for two main reasons. First, because endodermal organs, such as the liver and pancreas, play important physiological roles in adult animals, much experimental effort has been directed in recent years toward the establishment of protocols for the efficient derivation of endodermal cell types in vitro. Conversely, factors secreted by the VE play pivotal roles that cannot be attributed to the DE in early axis formation, heart formation and the patterning of the anterior nervous system. Thus, efforts in both of these areas have been hampered by a lack of markers that clearly distinguish between ExEn and DE. To further understand the ExEn we have undertaken a comparative analysis of three ExEn-like cell lines (END2, PYS2 and XEN). PYS2 cells are derived from embryonal carcinomas (EC) of 129 strain mice and have been characterized as parietal endoderm-like [1], END2 cells are derived from P19 ECs and described as visceral endoderm-like, while XEN cells are derived from blastocyst stage embryos and are described as primitive endoderm-like. Our analysis suggests that none of these cell lines represent a bona fide single in vivo lineage.
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Clinical Science Jul 2010

Owing to the dynamic nature of the transcriptome, gene expression profiling is a promising tool for discovery of disease-related genes and biological pathways. In the present study, we examined gene expression in whole blood of 12 patients with CAD (coronary artery disease) and 12 healthy control subjects. Furthermore, ten patients with CAD underwent whole-blood gene expression analysis before and after the completion of a cardiac rehabilitation programme following surgical coronary…
Owing to the dynamic nature of the transcriptome, gene expression profiling is a promising tool for discovery of disease-related genes and biological pathways. In the present study, we examined gene expression in whole blood of 12 patients with CAD (coronary artery disease) and 12 healthy control subjects. Furthermore, ten patients with CAD underwent whole-blood gene expression analysis before and after the completion of a cardiac rehabilitation programme following surgical coronary revascularization. mRNA and miRNA (microRNA) were isolated for expression profiling. Gene expression analysis identified 365 differentially expressed genes in patients with CAD compared with healthy controls (175 up- and 190 down-regulated in CAD), and 645 in CAD rehabilitation patients (196 up- and 449 down-regulated post-rehabilitation). Biological pathway analysis identified a number of canonical pathways, including oxidative phosphorylation and mitochondrial function, as being significantly and consistently modulated across the groups. Analysis of miRNA expression revealed a number of differentially expressed miRNAs, including hsa-miR-140-3p (control compared with CAD, P=0.017), hsa-miR-182 (control compared with CAD, P=0.093), hsa-miR-92a and hsa-miR-92b (post- compared with pre-exercise, P<0.01). Global analysis of predicted miRNA targets found significantly reduced expression of genes with target regions compared with those without: hsa-miR-140-3p (P=0.002), hsa-miR-182 (P=0.001), hsa-miR-92a and hsa-miR-92b (P=2.2x10-16). In conclusion, using whole blood as a 'surrogate tissue' in patients with CAD, we have identified differentially expressed miRNAs, differentially regulated genes and modulated pathways which warrant further investigation in the setting of cardiovascular function. This approach may represent a novel non-invasive strategy to unravel potentially modifiable pathways and possible therapeutic targets in cardiovascular disease.
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Arteriosclerosis, Thrombosis, and Vascular Biology Apr 2010

Abstract
OBJECTIVE:
MicroRNAs (miRNAs) are small noncoding RNAs that have the capacity to control protein production through binding "seed" sequences within a target mRNA. Each miRNA is capable of potentially controlling hundreds of genes. The regulation of miRNAs in the lung during the development of pulmonary arterial hypertension (PAH) is unknown.
METHODS AND RESULTS:
We screened lung miRNA profiles in a longitudinal and crossover design during the development of PAH caused…
Abstract
OBJECTIVE:
MicroRNAs (miRNAs) are small noncoding RNAs that have the capacity to control protein production through binding "seed" sequences within a target mRNA. Each miRNA is capable of potentially controlling hundreds of genes. The regulation of miRNAs in the lung during the development of pulmonary arterial hypertension (PAH) is unknown.
METHODS AND RESULTS:
We screened lung miRNA profiles in a longitudinal and crossover design during the development of PAH caused by chronic hypoxia or monocrotaline in rats. We identified reduced expression of Dicer, involved in miRNA processing, during the onset of PAH after hypoxia. MiR-22, miR-30, and let-7f were downregulated, whereas miR-322 and miR-451 were upregulated significantly during the development of PAH in both models. Differences were observed between monocrotaline and chronic hypoxia. For example, miR-21 and let-7a were significantly reduced only in monocrotaline-treated rats. MiRNAs that were significantly regulated were validated by quantitative polymerase chain reaction. By using in vitro studies, we demonstrated that hypoxia and growth factors implicated in PAH induced similar changes in miRNA expression. Furthermore, we confirmed miR-21 downregulation in human lung tissue and serum from patients with idiopathic PAH.
CONCLUSIONS:
Defined miRNAs are regulated during the development of PAH in rats. Therefore, miRNAs may contribute to the pathogenesis of PAH and represent a novel opportunity for therapeutic intervention.
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Nature Sep 2009

As of Jan 2013, cited over 1200 times according to Google Scholar
Featured in Nature News and Views:
Small RNAs: The seeds of silence by Z.Mourelatos
www.nature.com/nature/journal/v455/n7209/full/455044a.html
Abstract:
Animal microRNAs (miRNAs) regulate gene expression by inhibiting translation and/or by inducing degradation of target messenger RNAs. It is unknown how much translational control is exerted by miRNAs on a genome-wide scale. We used a new proteomic approach to…
As of Jan 2013, cited over 1200 times according to Google Scholar
Featured in Nature News and Views:
Small RNAs: The seeds of silence by Z.Mourelatos
www.nature.com/nature/journal/v455/n7209/full/455044a.html
Abstract:
Animal microRNAs (miRNAs) regulate gene expression by inhibiting translation and/or by inducing degradation of target messenger RNAs. It is unknown how much translational control is exerted by miRNAs on a genome-wide scale. We used a new proteomic approach to measure changes in synthesis of several thousand proteins in response to miRNA transfection or endogenous miRNA knockdown. In parallel, we quantified mRNA levels using microarrays. Here we show that a single miRNA can repress the production of hundreds of proteins, but that this repression is typically relatively mild. A number of known features of the miRNA-binding site such as the seed sequence also govern repression of human protein synthesis, and we report additional target sequence characteristics. We demonstrate that, in addition to downregulating mRNA levels, miRNAs also directly repress translation of hundreds of genes. Finally, our data suggest that a miRNA can, by direct or indirect effects, tune protein synthesis from thousands of genes.
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Pan Stanford Publishing, 2009. 2009

Mathematical models and computational simulations have proved valuable in many areas of cell biology, including gene regulatory networks. When properly calibrated against experimental data,
kinetic models can be used to describe how the concentrations of key species evolve over time. A reliable model allows `what if' scenarios to be investigated quantitatively in silico, and also provides a means to compare competing hypotheses about the underlying biological mechanisms at work. Moreover…
Mathematical models and computational simulations have proved valuable in many areas of cell biology, including gene regulatory networks. When properly calibrated against experimental data,
kinetic models can be used to describe how the concentrations of key species evolve over time. A reliable model allows `what if' scenarios to be investigated quantitatively in silico, and also provides a means to compare competing hypotheses about the underlying biological mechanisms at work. Moreover, models at different scales of resolution can be merged into a bigger picture `systems' level description. In the case where gene regulation is post-transcriptionally affected by microRNAs, biological understanding and experimental techniques have only recently matured to the extent that we can postulate and test kinetic models. In this chapter, we summarize some recent work that takes the first steps towards realistic modelling,
focusing on the contributions of the authors. Using a deterministic ordinary differential equation framework, we derive models from first principles and test them for consistency with recent experimental data, including microarray and mass spectrometry measurements. We first consider typical mis-expression experiments, where the microRNA level is instantaneously boosted or depleted and thereafter remains at a fixed level.
We then move on to a more general setting where the microRNA is simply treated as another species in the reaction network, with
microRNA-mRNA binding forming the basis for the post-transcriptional repression. We include some speculative comments about the potential for kinetic modelling to contribute to the more widespread sequence and network based approaches in the qualitative investigation of microRNA based gene regulation.
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Journal of Computational Biology Apr 2008

Prediction of our computational model has now been experimentally verified .
Abstract
MicroRNAs (miRNAs) have recently emerged as a new complex layer of gene regulation. MiRNAs act post-transcriptionally, influencing the stability, compartmentalization, and translation of their target mRNAs. Computational efforts to understand the post-transcriptional gene regulation by miRNAs have been focused on the target prediction tools, while quantitative kinetic models of gene regulation by…
Prediction of our computational model has now been experimentally verified .
Abstract
MicroRNAs (miRNAs) have recently emerged as a new complex layer of gene regulation. MiRNAs act post-transcriptionally, influencing the stability, compartmentalization, and translation of their target mRNAs. Computational efforts to understand the post-transcriptional gene regulation by miRNAs have been focused on the target prediction tools, while quantitative kinetic models of gene regulation by miRNAs have so far largely been overlooked. We here develop a kinetic model of post-transcriptional gene regulation by miRNAs, focusing on the miRNAs' effect on increasing the target mRNAs degradation rates. The model is fitted to a temporal microarray dataset where human mRNAs are measured upon transfection with a specific miRNA (miRNA124a). The proposed model exhibits good fit with many target mRNA profiles, indicating that such type of models can be used for studying post-transcriptional gene regulation by miRNA. In particular, the proposed kinetic model can be used for quantifying the miRNA-mediated effects on its targets in the miRNA mis-expression experiments. The model makes an experimentally verifiable prediction of the miRNA124a decay rate, quantifies the miRNA-mediated effect on the target mRNAs degradation, and yields a good correspondence between the inferred and experimentally measured decay rates of human target mRNAs.
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Biometrics Sep 2007

The basic building block of a gene regulatory network consists of a gene encoding a transcription factor (TF) and the gene(s) it regulates. Considerable efforts have been directed recently at devising experiments and algorithms to determine TFs and their corresponding target genes using gene expression and other types of data. The underlying problem is that the expression of a gene coding for the TF provides only limited information about the activity of the TF, which can also be controlled…
The basic building block of a gene regulatory network consists of a gene encoding a transcription factor (TF) and the gene(s) it regulates. Considerable efforts have been directed recently at devising experiments and algorithms to determine TFs and their corresponding target genes using gene expression and other types of data. The underlying problem is that the expression of a gene coding for the TF provides only limited information about the activity of the TF, which can also be controlled posttranscriptionally. In the absence of a reliable technology to routinely measure the activity of regulators, it is of great importance to understand whether this activity can be inferred from gene expression data. We here develop a statistical framework to reconstruct the activity of a TF from gene expression data of the target genes in its regulatory module. The novelty of our approach is that we embed the deterministic Michaelis-Menten model of gene regulation in this statistical framework. The kinetic parameters of the gene regulation model are inferred together with the profile of the TF regulator. We also obtain a goodness-of-fit test to verify the fit of the model. The model is applied to a time series involving the Streptomyces coelicolor bacterium. We focus on the transcriptional activator cdaR, which is partly responsible for the production of a particular type of antibiotic. The aim is to reconstruct the activity profile of this regulator. Our approach can be extended to include more complex regulatory relationships, such as multiple regulatory factors, competition, and cooperativity.
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BMC Bioinformatics May 2007

Abstract
BACKGROUND:
In many approaches to the inference and modeling of regulatory interactions using microarray data, the expression of the gene coding for the transcription factor is considered to be an accurate surrogate for the true activity of the protein it produces. There are many instances where this is inaccurate due to post-translational modifications of the transcription factor protein. Inference of the activity of the transcription factor from the expression of its targets…
Abstract
BACKGROUND:
In many approaches to the inference and modeling of regulatory interactions using microarray data, the expression of the gene coding for the transcription factor is considered to be an accurate surrogate for the true activity of the protein it produces. There are many instances where this is inaccurate due to post-translational modifications of the transcription factor protein. Inference of the activity of the transcription factor from the expression of its targets has predominantly involved linear models that do not reflect the nonlinear nature of transcription. We extend a recent approach to inferring the transcription factor activity based on nonlinear Michaelis-Menten kinetics of transcription from maximum likelihood to fully Bayesian inference and give an example of how the model can be further developed.
RESULTS:
We present results on synthetic and real microarray data. Additionally, we illustrate how gene and replicate specific delays can be incorporated into the model.
CONCLUSION:
We demonstrate that full Bayesian inference is appropriate in this application and has several benefits over the maximum likelihood approach, especially when the volume of data is limited. We also show the benefits of using a non-linear model over a linear model, particularly in the case of repression.
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Proceedings of National Academy of Science USA Dec 2006

Abstract
The basic underlying problem in reverse engineering of gene regulatory networks from gene expression data is that the expression of a gene encoding the regulator provides only limited information about its protein activity. The proteins, which result from translation, are subject to stringent posttranscriptional control and modification. Often, it is only the modified version of the protein that is capable of activating or repressing its regulatory targets. At present there exists…
Abstract
The basic underlying problem in reverse engineering of gene regulatory networks from gene expression data is that the expression of a gene encoding the regulator provides only limited information about its protein activity. The proteins, which result from translation, are subject to stringent posttranscriptional control and modification. Often, it is only the modified version of the protein that is capable of activating or repressing its regulatory targets. At present there exists no reliable high-throughput technology to measure the protein activity levels in real-time, and therefore they are, so-to-say, lost in translation. However, these activity levels can be recovered by studying the gene expression of their targets. Here, we describe a computational approach to predict temporal regulator activity levels from the gene expression of its transcriptional targets in a network motif with one regulator and many targets. We consider an example of an SOS repair system, and computationally infer the regulator activity of its master repressor, LexA. The reconstructed activity profile of LexA exhibits a behavior that is similar to the experimentally measured profile of this repressor: after UV irradiation, the amount of LexA substantially decreases within a few minutes, followed by a recovery to its normal level. Our approach can easily be applied to known single-input motifs in other organisms.
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Bulletin of Mathematical Biology Jul 2006

This review is dedicated to the memory of Lee Segel. Lee was great leader, a leading scholar in Applied mathematics and a pioneer in modern Mathematical Biology. I was privileged to be his PhD student.
Abstract
This review describes the development of the molecular level Ca(2+)-voltage hypothesis. Theoretical considerations and feedback between theory and experiments played a key role in its development. The theory, backed by experiments, states that at fast synapses, membrane…
This review is dedicated to the memory of Lee Segel. Lee was great leader, a leading scholar in Applied mathematics and a pioneer in modern Mathematical Biology. I was privileged to be his PhD student.
Abstract
This review describes the development of the molecular level Ca(2+)-voltage hypothesis. Theoretical considerations and feedback between theory and experiments played a key role in its development. The theory, backed by experiments, states that at fast synapses, membrane potential by means of presynaptic inhibitory autoreceptors controls initiation and termination of neurotransmitter release. A molecular kinetic scheme which depicts initiation and termination of evoked release is discussed. This scheme is able to account for both spontaneous release and evoked release. The physiological implications of this scheme are enumerated.
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Journal of Computational Biology Apr 2006

This article, featured in Faculty 1000, http://f1000.com/ hit the nerve by addressing one of the myths of modern computational biology about scale-freenes of biological networks, and whether it actually matters for biology.
Abstract
The concept of scale-free network has emerged as a powerful unifying paradigm in the study of complex systems in biology and in physical and social studies. Metabolic, protein, and gene interaction networks have been reported to exhibit scale-free…
This article, featured in Faculty 1000, http://f1000.com/ hit the nerve by addressing one of the myths of modern computational biology about scale-freenes of biological networks, and whether it actually matters for biology.
Abstract
The concept of scale-free network has emerged as a powerful unifying paradigm in the study of complex systems in biology and in physical and social studies. Metabolic, protein, and gene interaction networks have been reported to exhibit scale-free behavior based on the analysis of the distribution of the number of connections of the network nodes. Here we study 10 published datasets of various biological interactions and perform goodness-of-fit tests to determine whether the given data is drawn from the power-law distribution. Our analysis did not identify a single interaction network that has a nonzero probability of being drawn from the power-law distribution.
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Bioinformatics Feb 2005

Abstract
MOTIVATION:
Despite theoretical arguments that so-called 'loop designs' for two-channel DNA microarray experiments are more efficient, biologists continue to use 'reference designs'. We describe two sets of microarray experiments with RNA from two different biological systems (TPA-stimulated mammalian cells and Streptomyces coelicolor). In each case, both a loop and a reference design were used with the same RNA preparations with the aim of studying their relative…
Abstract
MOTIVATION:
Despite theoretical arguments that so-called 'loop designs' for two-channel DNA microarray experiments are more efficient, biologists continue to use 'reference designs'. We describe two sets of microarray experiments with RNA from two different biological systems (TPA-stimulated mammalian cells and Streptomyces coelicolor). In each case, both a loop and a reference design were used with the same RNA preparations with the aim of studying their relative efficiency.
RESULTS:
The results of these experiments show that (1) the loop design attains a much higher precision than the reference design, (2) multiplicative spot effects are a large source of variability, and if they are not accounted for in the mathematical model, for example, by taking log-ratios or including spot effects, then the model will perform poorly. The first result is reinforced by a simulation study. Practical recommendations are given on how simple loop designs can be extended to more realistic experimental designs and how standard statistical methods allow the experimentalist to use and interpret the results from loop designs in practice.
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Applied Bioinformatics 2005

Abstract
In this article we propose two practical types of designs for large time-course, dual-channel microarray experiments. One type consists of several interwoven loops, and the other type combines reference and loop designs. By representing the experiment as a graph, where the timepoints are nodes and the arrays are edges, we demonstrate how the time contrasts between any two timepoints can be estimated, provided that there is a path of edges linking them. In addition, we give a general…
Abstract
In this article we propose two practical types of designs for large time-course, dual-channel microarray experiments. One type consists of several interwoven loops, and the other type combines reference and loop designs. By representing the experiment as a graph, where the timepoints are nodes and the arrays are edges, we demonstrate how the time contrasts between any two timepoints can be estimated, provided that there is a path of edges linking them. In addition, we give a general formula for the variance of such contrasts. The efficiency of the proposed designs is evaluated by estimating the variances of the log-ratios of the comparisons of interest.
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