Manoj Bhasin
Harvard Medical School, Medicine, Faculty Member
Research Interests: Metabolism, Proteomics, Western blotting, Fatty acids, Ischemia, and 14 moreAnimals, Medical Physiology, Microarray Analysis, Muscle contraction, Proteome analysis, Rabbits, Base Sequence, Gene Expression Regulation, Gene expression profiling, Physiological Genomics, Cycloheximide, Mitochondrion, Mitochondrial Proteins, and Molecular Sequence Data
Research Interests: Metabolism, Proteomics, Western blotting, Fatty acids, Ischemia, and 15 moreAnimals, Medical Physiology, Microarray Analysis, Muscle contraction, Proteome analysis, Rabbits, Base Sequence, Gene Expression Regulation, Gene expression profiling, Physiological Genomics, Cycloheximide, Mitochondrion, Mitochondrial Proteins, Molecular Sequence Data, and ALPHA AMANITIN
Research Interests: Algorithms, Proteomics, Mitochondria, Biological Chemistry, Biological Sciences, and 16 moreHigher Order Thinking, Software, Humans, Peptides, Similarity Search, Proteins, Support vector machine, Subcellular Localization, CHEMICAL SCIENCES, Cell nucleus, Theoretical Models, Reproducibility of Results, Amino Acid Profile, Cytoplasm, Internet, and Plasma Membrane
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Research Interests: Flow Cytometry, Transcription Regulation, Stem Cell, Transcription Factors, Molecular Mechanics, and 10 moreBiological Sciences, Cell Differentiation, Endothelial Cells, Mice, Animals, Embryonic Stem Cell, Embryonic Stem Cells, Endothelial cell, ES cell, and Vascular endothelial growth factor receptor
Diagnosis of soft tissue sarcomas (STS) is challenging. Many remain unclassified (not-otherwise-specified, NOS) or grouped in controversial categories such as malignant fibrous histiocytoma (MFH), with unclear therapeutic value. We... more
Diagnosis of soft tissue sarcomas (STS) is challenging. Many remain unclassified (not-otherwise-specified, NOS) or grouped in controversial categories such as malignant fibrous histiocytoma (MFH), with unclear therapeutic value. We analyzed several independent microarray datasets, to identify a predictor, use it to classify unclassifiable sarcomas, and assess oncogenic pathway activation and chemotherapy response.
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Research Interests: Computational Biology, MicroRNA, Transcription Factors, Cardiovascular disease, Biological Sciences, and 15 moreCell line, Cell Differentiation, Humans, Endothelial Cells, Disease, Gene ontology, Transcription Factor, Genes, microRNAs, Endothelial Function, Pathway Analysis, Endothelial cell, Very high throughput, Binding Site, and Gene expression profiling
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Research Interests: Engineering, Physics, Chemistry, Immune response, Biology, and 30 moreInflammation, System Biology, Transcription Factors, Medicine, Multidisciplinary, Energy Metabolism, real time PCR, Humans, Liver, Mice, Animals, Cytochrome c oxidase, Promoter Analysis, Liver Function, Lipid metabolism, Gene Regulatory Networks, Transcription Factor, Scale free network, PLoS one, Energy Production, Liver regeneration, Hepatocytes, Biological Process, Reproducibility of Results, Liver Resection, Cell Proliferation, Enzymatic Activity, DNA binding proteins, Metabolic control, and Gene Expression Regulation
Resistance to antiangiogenic therapy is an important clinical problem. We examined whether resistance occurs at least in part via reversible, physiologic changes in the tumor, or results solely from stable genetic changes in resistant... more
Resistance to antiangiogenic therapy is an important clinical problem. We examined whether resistance occurs at least in part via reversible, physiologic changes in the tumor, or results solely from stable genetic changes in resistant tumor cells.
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ERG is a member of the ETS transcription factor family that is highly enriched in endothelial cells (ECs). To further define the role of ERG in regulating EC function, we evaluated the effect of ERG knock-down on EC lumen formation in 3D... more
ERG is a member of the ETS transcription factor family that is highly enriched in endothelial cells (ECs). To further define the role of ERG in regulating EC function, we evaluated the effect of ERG knock-down on EC lumen formation in 3D collagen matrices. Blockade of ERG using siRNA completely interferes with EC lumen formation. Quantitative PCR (QPCR) was used to identify potential downstream gene targets of ERG. In particular, we identified RhoJ as the Rho GTPase family member that is closely related to Cdc42 as a target of ERG. Knockdown of ERG expression in ECs led to a 75% reduction in the expression of RhoJ. Chromatin immunoprecipitation and transactivation studies demonstrated that ERG could bind to functional sites in the proximal promoter of the RhoJ gene. Knock-down of RhoJ similarly resulted in a marked reduction in the ability of ECs to form lumens. Suppression of either ERG or RhoJ during EC lumen formation was associated with a marked increase in RhoA activation and a decrease in Rac1 and Cdc42 activation and their downstream effectors. Finally, in contrast to other Rho GTPases, RhoJ exhibits a highly EC-restricted expression pattern in several different tissues, including the brain, heart, lung, and liver.
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Research Interests: Engineering, Physics, Chemistry, Systems Biology, Biology, and 20 moreSystem Biology, Medicine, Multidisciplinary, Adipose tissue, Insulin, Mice, Female, Animals, Insulin signaling, Monoclonal Antibodies, Cell Viability, Gene Regulatory Networks, PLoS one, T lymphocytes, Tumor necrosis factor-alpha, Immune Tolerance, Lymph Node, Type 2 Diabetes Mellitus, Alpha-1 Antitrypsin, and Lymph nodes
Research Interests: Engineering, Physics, Chemistry, Immune response, Biology, and 19 moreMedicine, Multidisciplinary, Extracellular Matrix, Quality Control, Transcriptome, real time PCR, Dogs, Laser Capture Microdissection, Animals, Gene ontology, Dendritic cell, PLoS one, Veins, Endothelial cell, Backpropagation, Insulin Receptor, Interaction Network, Differential expression, and Intimal hyperplasia
Coactivator-associated arginine methyltransferase I (CARM1; PRMT4) regulates gene expression by multiple mechanisms including methylation of histones and coactivation of steroid receptor transcription. Mice lacking CARM1 are small, fail... more
Coactivator-associated arginine methyltransferase I (CARM1; PRMT4) regulates gene expression by multiple mechanisms including methylation of histones and coactivation of steroid receptor transcription. Mice lacking CARM1 are small, fail to breathe and die shortly after birth, demonstrating the crucial role of CARM1 in development. In adults, CARM1 is overexpressed in human grade-III breast tumors and prostate adenocarcinomas, and knockdown of CARM1 inhibits proliferation of breast and prostate cancer cell lines. Based on these observations, we hypothesized that loss of CARM1 in mouse embryos would inhibit pulmonary cell proliferation, resulting in respiratory distress. By contrast, we report here that loss of CARM1 results in hyperproliferation of pulmonary epithelial cells during embryonic development. The lungs of newborn mice lacking CARM1 have substantially reduced airspace compared with their wild-type littermates. In the absence of CARM1, alveolar type II cells show increased proliferation. Electron microscopic analyses demonstrate that lungs from mice lacking CARM1 have immature alveolar type II cells and an absence of alveolar type I cells. Gene expression analysis reveals a dysregulation of cell cycle genes and markers of differentiation in the Carm1 knockout lung. Furthermore, there is an overlap in gene expression in the Carm1 knockout and the glucocorticoid receptor knockout lung, suggesting that hyperproliferation and lack of maturation of the alveolar cells are at least in part caused by attenuation of glucocorticoid-mediated signaling. These results demonstrate for the first time that CARM1 inhibits pulmonary cell proliferation and is required for proper differentiation of alveolar cells.
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BACKGROUNDTranslation of preclinical studies into effective human cancer therapy is hampered by the lack of defined molecular expression patterns in mouse models that correspond to the human counterpart. We sought to generate an open... more
BACKGROUNDTranslation of preclinical studies into effective human cancer therapy is hampered by the lack of defined molecular expression patterns in mouse models that correspond to the human counterpart. We sought to generate an open source TRAMP mouse microarray dataset and to use this array to identify differentially expressed genes from human prostate cancer (PCa) that have concordant expression in TRAMP tumors, and thereby represent lead targets for preclinical therapy development.Translation of preclinical studies into effective human cancer therapy is hampered by the lack of defined molecular expression patterns in mouse models that correspond to the human counterpart. We sought to generate an open source TRAMP mouse microarray dataset and to use this array to identify differentially expressed genes from human prostate cancer (PCa) that have concordant expression in TRAMP tumors, and thereby represent lead targets for preclinical therapy development.METHODSWe performed microarrays on total RNA extracted and amplified from eight TRAMP tumors and nine normal prostates. A subset of differentially expressed genes was validated by QRT-PCR. Differentially expressed TRAMP genes were analyzed for concordant expression in publicly available human prostate array datasets and a subset of resulting genes was analyzed by QRT-PCR.We performed microarrays on total RNA extracted and amplified from eight TRAMP tumors and nine normal prostates. A subset of differentially expressed genes was validated by QRT-PCR. Differentially expressed TRAMP genes were analyzed for concordant expression in publicly available human prostate array datasets and a subset of resulting genes was analyzed by QRT-PCR.RESULTSCross-referencing differentially expressed TRAMP genes to public human prostate array datasets revealed 66 genes with concordant expression in mouse and human PCa; 56 between metastases and normal and 10 between primary tumor and normal tissues. Of these 10 genes, two, Sox4 and Tubb2a, were validated by QRT-PCR. Our analysis also revealed various dysregulations in major biologic pathways in the TRAMP prostates.Cross-referencing differentially expressed TRAMP genes to public human prostate array datasets revealed 66 genes with concordant expression in mouse and human PCa; 56 between metastases and normal and 10 between primary tumor and normal tissues. Of these 10 genes, two, Sox4 and Tubb2a, were validated by QRT-PCR. Our analysis also revealed various dysregulations in major biologic pathways in the TRAMP prostates.CONCLUSIONSWe report a TRAMP microarray dataset of which a gene subset was validated by QRT-PCR with expression patterns consistent with previous gene-specific TRAMP studies. Concordance analysis between TRAMP and human PCa associated genes supports the utility of the model and suggests several novel molecular targets for preclinical therapy. Prostate 68: 1517–1530, 2008. © 2008 Wiley-Liss, Inc.We report a TRAMP microarray dataset of which a gene subset was validated by QRT-PCR with expression patterns consistent with previous gene-specific TRAMP studies. Concordance analysis between TRAMP and human PCa associated genes supports the utility of the model and suggests several novel molecular targets for preclinical therapy. Prostate 68: 1517–1530, 2008. © 2008 Wiley-Liss, Inc.
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Research Interests: RNA, Flow Cytometry, Stem Cell, Molecular Mechanics, Hematopoiesis, and 18 moreBiological Sciences, Cell Differentiation, Endothelial Cells, Mice, Animals, Embryonic Stem Cell, Bone marrow, Embryonic Stem Cells, Microarray Analysis, Time Factors, Model System, Gene Expression Analysis, Embryos, Vascular Injury, ES cell, Gene expression profiling, Vascular endothelial growth factor receptor, and Cadherins
ERG (Ets-related gene) is an ETS transcription factor that has recently been shown to regulate a number of endothelial cell (EC)-restricted genes including VE-cadherin, von Willebrand factor, endoglin, and intercellular adhesion... more
ERG (Ets-related gene) is an ETS transcription factor that has recently been shown to regulate a number of endothelial cell (EC)-restricted genes including VE-cadherin, von Willebrand factor, endoglin, and intercellular adhesion molecule-2. Our preliminary data demonstrate that unlike other ETS factors, ERG exhibits a highly EC-restricted pattern of expression in cultured primary cells and several adult mouse tissues including the heart, lung, and brain. In response to inflammatory stimuli, such as tumor necrosis factor-alpha, we observed a marked reduction of ERG expression in ECs. To further define the role of ERG in the regulation of normal EC function, we used RNA interference to knock down ERG. Microarray analysis of RNA derived from ERG small interfering RNA- or tumor necrosis factor-alpha-treated human umbilical vein (HUV)ECs revealed significant overlap (P<0.01) in the genes that are up- or downregulated. Of particular interest to us was a significant change in expression of interleukin (IL)-8 at both protein and RNA levels. Exposure of ECs to tumor necrosis factor-alpha is known to be associated with increased neutrophil attachment. We observed that knockdown of ERG in HUVECs is similarly associated with increased neutrophil attachment compared to control small interfering RNA-treated cells. This enhanced adhesion could be blocked with IL-8 neutralizing or IL-8 receptor blocking antibodies. ERG can inhibit the activity of the IL-8 promoter in a dose dependent manner. Direct binding of ERG to the IL-8 promoter in ECs was confirmed by chromatin immunoprecipitation. In summary, our findings support a role for ERG in promoting antiinflammatory effects in ECs through repression of inflammatory genes such as IL-8.
Research Interests: Inflammation, Cell Adhesion, Transcription Factors, RNA interference, Humans, and 14 moreEndothelial Cells, Mice, Animals, Male, Clinical Sciences, Neutrophils, Lipopolysaccharides, ENDOTOXEMIA, Endothelial cell, Time Factors, Tumor necrosis factor-alpha, Circulation, Interleukin, and Down-Regulation
The identification of peptides in an antigenic sequence that can bind with high affinity to a wide range of MHC alleles is one of the challenges in subunit vaccine design. The mutation of natural peptides is an alternative to obtaining... more
The identification of peptides in an antigenic sequence that can bind with high affinity to a wide range of MHC alleles is one of the challenges in subunit vaccine design. The mutation of natural peptides is an alternative to obtaining peptides that can bind to a wide range of MHC alleles with high affinity. A large number of experiments are typically necessary to identify mutations that define high-affinity binding peptides. Therefore there is a need to develop a computational method for detecting amino acid mutations in a peptide for making it high-affinity or promiscuous MHC binders. This report describes a high-throughput computer driven solution for the identification of promiscuous and high-affinity mutated binders of 47 MHC class I alleles by introducing mutations in an antigenic sequence. The method implements quantitative matrices for creating optimal mutations in an antigenic sequence. It has two major options: (i) prediction of promiscuous MHC binders and (ii) prediction of high-affinity binders. In case of prediction of promiscuous binders, the server allows a user to select (i) permissible mutations in a peptide; (ii) MHC alleles to whom it should bind; and (iii) positions at which mutation is allowed. In the case of prediction of high-affinity binders, the server allows users to specify the positions that should be conserved in the native protein. In both cases, the method computes the type of mutations and position of mutations in 9-mer peptides required to have the desired results. The web server MMBPred is available at www.imtech.res.in/raghava/mmbpred/.
Research Interests: Algorithms, Technology, Computational Biology, Prediction, Quantitative analysis, and 14 moreComputers, Identification, Mutation, High throughput screening, Method, Computer User Interface Design, Protein Sequence Analysis, Major histocompatibility complex, Reproducibility of Results, Quantitative Analysis, Amino Acid Sequence, Allele, Internet, and Subunit
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Research Interests: Gene expression, Multidisciplinary, Signal Transduction, Humans, Stress response, and 15 moreFemale, Male, Gene ontology, PLoS one, Physiological Response, Human Genome, Psychological distress, Exhaled Nitric Oxide, Adult, Oxidative phosphorylation, Physiological Stress Markers, Oxygen Consumption, Psychological Stress, Relaxation Therapy, and Differential expression
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Research Interests: Cancer, Breast Cancer, Cell Adhesion, Prostate Cancer, Transcription Factors, and 15 moreGene expression, Signal Transduction, Humans, Male, Mesenchymal Stem Cell, Transcription Factor, Integrins, Neoplasm Invasiveness, Epithelial cells, Transforming Growth Factor Beta, Androgen Receptor, Transfection, Prostate Specific Antigen, Epithelial to Mesenchymal Transition, and Cell Motility
In the present study, a systematic attempt has been made to develop an accurate method for predicting MHC class I restricted T cell epitopes for a large number of MHC class I alleles. Initially, a quantitative matrix (QM)-based method was... more
In the present study, a systematic attempt has been made to develop an accurate method for predicting MHC class I restricted T cell epitopes for a large number of MHC class I alleles. Initially, a quantitative matrix (QM)-based method was developed for 47 MHC class I alleles having at least 15 binders. A secondary artificial neural network (ANN)-based method was developed for 30 out of 47 MHC alleles having a minimum of 40 binders. Combination of these ANN-and QM-based prediction methods for 30 alleles improved the accuracy of prediction by 6% compared to each individual method. Average accuracy of hybrid method for 30 MHC alleles is 92.8%. This method also allows prediction of binders for 20 additional alleles using QM that has been reported in the literature, thus allowing prediction for 67 MHC class I alleles. The performance of the method was evaluated using jack-knife validation test. The performance of the methods was also evaluated on blind or independent data. Comparison of our method with existing MHC binder prediction methods for alleles studied by both methods shows that our method is superior to other existing methods. This method also identifies proteasomal cleavage sites in antigen sequences by implementing the matrices described earlier. Thus, the method that we discover allows the identification of MHC class I binders (peptides binding with many MHC alleles) having proteasomal cleavage site at C-terminus. The user-friendly result display format (HTML-II) can assist in locating the promiscuous MHC binding regions from antigen sequence. The method is available on the web at www.imtech.res.in/raghava/nhlapred and its mirror site is available at http://bioinformatics.uams.edu/mirror/nhlapred/.
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Research Interests: Algorithms, Artificial Intelligence, Machine Learning, Protein Structure Prediction, Biological Sciences, and 13 moreSoftware, Environmental Sciences, Sequence alignment, Nucleic Acids, Support vector machine, Protein Sequence Analysis, Secondary Structure, Protein Secondary Structure Prediction, Correlation coefficient, Multiple Sequence Alignment, Amino Acid Sequence, Internet, and Artificial Neural Network
Research Interests: Artificial Intelligence, Biological Sciences, Software, Environmental Sciences, G protein-coupled receptors, and 12 moreDopamine Receptors, Nucleic Acids, Cross Validation, Support vector machine, Protein Sequence Analysis, Serotonin Receptors, Amino Acid Profile, Correlation coefficient, Indexation, Amino Acid Sequence, Drug Targeting, and Internet
DNA methylation plays a key role in the regulation of gene expression. The most common type of DNA modification consists of the methylation of cytosine in the CpG dinucleotide. At the present time, there is no method available for the... more
DNA methylation plays a key role in the regulation of gene expression. The most common type of DNA modification consists of the methylation of cytosine in the CpG dinucleotide. At the present time, there is no method available for the prediction of DNA methylation sites. Therefore, in this study we have developed a support vector machine (SVM)-based method for the prediction of cytosine methylation in CpG dinucleotides. Initially a SVM module was developed from human data for the prediction of human-specific methylation sites. This module achieved a MCC and AUC of 0.501 and 0.814, respectively, when evaluated using a 5-fold cross-validation. The performance of this SVM-based module was better than the classifiers built using alternative machine learning and statistical algorithms including artificial neural networks, Bayesian statistics, and decision trees. Additional SVM modules were also developed based on mammalian- and vertebrate-specific methylation patterns. The SVM module based on human methylation patterns was used for genome-wide analysis of methylation sites. This analysis demonstrated that the percentage of methylated CpGs is higher in UTRs as compared to exonic and intronic regions of human genes. This method is available on line for public use under the name of Methylator at http://bio.dfci.harvard.edu/Methylator/.
Research Interests: Algorithms, Artificial Intelligence, Machine Learning, Prediction, DNA, and 17 moreHumans, Regulation of Gene Expression, Methylation, Bayesian statistics, DNA methylation, CpG islands, Decision Tree, Cross Validation, Support vector machine, Human Genome, Svm, Base Sequence, CPG, Artificial Neural Network, Biochemistry and cell biology, DNA sequence, and MCC
The generation of cytotoxic T lymphocyte (CTL) epitopes from an antigenic sequence involves number of intracellular processes, including production of peptide fragments by proteasome and transport of peptides to endoplasmic reticulum... more
The generation of cytotoxic T lymphocyte (CTL) epitopes from an antigenic sequence involves number of intracellular processes, including production of peptide fragments by proteasome and transport of peptides to endoplasmic reticulum through transporter associated with antigen processing (TAP). In this study, 409 peptides that bind to human TAP transporter with varying affinity were analyzed to explore the selectivity and specificity of TAP transporter. The abundance of each amino acid from P1 to P9 positions in high-, intermediate-, and low-affinity TAP binders were examined. The rules for predicting TAP binding regions in an antigenic sequence were derived from the above analysis. The quantitative matrix was generated on the basis of contribution of each position and residue in binding affinity. The correlation of r = 0.65 was obtained between experimentally determined and predicted binding affinity by using a quantitative matrix. Further a support vector machine (SVM)-based method has been developed to model the TAP binding affinity of peptides. The correlation (r = 0.80) was obtained between the predicted and experimental measured values by using sequence-based SVM. The reliability of prediction was further improved by cascade SVM that uses features of amino acids along with sequence. An extremely good correlation (r = 0.88) was obtained between measured and predicted values, when the cascade SVM-based method was evaluated through jackknife testing. A Web service, TAPPred (http://www.imtech.res.in/raghava/tappred/ or http://bioinformatics.uams.edu/mirror/tappred/), has been developed based on this approach.
Research Interests: Artificial Intelligence, Computational Biology, Protein Science, Humans, Endoplasmic Reticulum, and 13 moreAntigen Processing, Support vector machine, Amino Acids, Web Service, Reproducibility of Results, Amino Acid Profile, Protein Conformation, Amino Acid Sequence, Experimental Measurement, Protein Binding, Static Electricity, Biochemistry and cell biology, and Binding affinity
Cytotoxic T lymphocyte (CTL) epitopes are potential candidates for subunit vaccine design for various diseases. Most of the existing T cell epitope prediction methods are indirect methods that predict MHC class I binders instead of CTL... more
Cytotoxic T lymphocyte (CTL) epitopes are potential candidates for subunit vaccine design for various diseases. Most of the existing T cell epitope prediction methods are indirect methods that predict MHC class I binders instead of CTL epitopes. In this study, a systematic attempt has been made to develop a direct method for predicting CTL epitopes from an antigenic sequence. This method is based on quantitative matrix (QM) and machine learning techniques such as Support Vector Machine (SVM) and Artificial Neural Network (ANN). This method has been trained and tested on non-redundant dataset of T cell epitopes and non-epitopes that includes 1137 experimentally proven MHC class I restricted T cell epitopes. The accuracy of QM-, ANN- and SVM-based methods was 70.0, 72.2 and 75.2%, respectively. The performance of these methods has been evaluated through Leave One Out Cross-Validation (LOOCV) at a cutoff score where sensitivity and specificity was nearly equal. Finally, both machine-learning methods were used for consensus and combined prediction of CTL epitopes. The performances of these methods were evaluated on blind dataset where machine learning-based methods perform better than QM-based method. We also demonstrated through subgroup analysis that our methods can discriminate between T-cell epitopes and MHC binders (non-epitopes). In brief this method allows prediction of CTL epitopes using QM, SVM, ANN approaches. The method also facilitates prediction of MHC restriction in predicted T cell epitopes. The method is available at http://www.imtech.res.in/raghava/ctlpred/.