Manoj Bhasin

ESLpred: SVM-based method for subcellular localization of eukaryotic proteins using dipeptide composition and PSI-BLAST

Automated prediction of subcellular localization of proteins is an important step in the functional annotation of genomes. The existing subcellular localization prediction methods are based on either amino acid composition or N-terminal characteristics of the proteins. In this paper, support vector machine (SVM) has been used to predict the subcellular location of eukaryotic proteins from their different features such as amino acid composition, dipeptide composition and physico-chemical properties. The SVM module based on dipeptide composition performed better than the SVM modules based on amino acid composition or physico-chemical properties. In addition, PSI-BLAST was also used to search the query sequence against the dataset of proteins (experimentally annotated proteins) to predict its subcellular location. In order to improve the prediction accuracy, we developed a hybrid module using all features of a protein, which consisted of an input vector of 458 dimensions (400 dipeptide compositions, 33 properties, 20 amino acid compositions of the protein and 5 from PSI-BLAST output). Using this hybrid approach, the prediction accuracies of nuclear, cytoplasmic, mitochondrial and extracellular proteins reached 95.3, 85.2, 68.2 and 88.9%, respectively. The overall prediction accuracy of SVM modules based on amino acid composition, physico-chemical properties, dipeptide composition and the hybrid approach was 78.1, 77.8, 82.9 and 88.0%, respectively. The accuracy of all the modules was evaluated using a 5-fold cross-validation technique. Assigning a reliability index (reliability index > or =3), 73.5% of prediction can be made with an accuracy of 96.4%. Based on the above approach, an online web server ESLpred was developed, which is available at http://www.imtech.res.in/raghava/eslpred/.

Notch1 Contributes to Mouse T-Cell Leukemia by Directly Inducing the Expression of c-myc

ABSTRACT Recent work with mouse models and human leukemic samples has shown that gain-of-function mutation(s) in Notch1 is a common genetic event in T-cell acute lymphoblastic leukemia (T-ALL). The Notch1 receptor signals through a γ-secretase-dependent process that releases intracellular Notch1 from the membrane to the nucleus, where it forms part of a transcriptional activator complex. To identify Notch1 target genes in leukemia, we developed mouse T-cell leukemic lines that express intracellular Notch1 in a doxycycline-dependent manner. Using gene expression profiling and chromatin immunoprecipitation, we identified c-myc as a novel, direct, and critical Notch1 target gene in T-cell leukemia. c-myc mRNA levels are increased in primary mouse T-cell tumors that harbor Notch1 mutations, and Notch1 inhibition decreases c-myc mRNA levels and inhibits leukemic cell growth. Retroviral expression of c-myc, like intracellular Notch1, rescues the growth arrest and apoptosis associated with γ-secretase inhibitor treatment or Notch1 inhibition. Consistent with these findings, retroviral insertional mutagenesis screening of our T-cell leukemia mouse model revealed common insertions in either notch1 or c-myc genes. These studies define the Notch1 molecular signature in mouse T-ALL and importantly provide mechanistic insight as to how Notch1 contributes to human T-ALL.

Support Vector Machine-based Method for Subcellular Localization of Human Proteins Using Amino Acid Compositions, Their Order, and Similarity Search

Here we report a systematic approach for predicting subcellular localization (cytoplasm, mitochondrial, nuclear, and plasma membrane) of human proteins. First, support vector machine (SVM)-based modules for predicting subcellular localization using traditional amino acid and dipeptide (i + 1) composition achieved overall accuracy of 76.6 and 77.8%, respectively. PSI-BLAST, when carried out using a similarity-based search against a nonredundant data base of experimentally annotated proteins, yielded 73.3% accuracy. To gain further insight, a hybrid module (hybrid1) was developed based on amino acid composition, dipeptide composition, and similarity information and attained better accuracy of 84.9%. In addition, SVM modules based on a different higher order dipeptide i.e. i + 2, i + 3, and i + 4 were also constructed for the prediction of subcellular localization of human proteins, and overall accuracy of 79.7, 77.5, and 77.1% was accomplished, respectively. Furthermore, another SVM module hybrid2 was developed using traditional dipeptide (i + 1) and higher order dipeptide (i + 2, i + 3, and i + 4) compositions, which gave an overall accuracy of 81.3%. We also developed SVM module hybrid3 based on amino acid composition, traditional and higher order dipeptide compositions, and PSI-BLAST output and achieved an overall accuracy of 84.4%. A Web server HSLPred (www.imtech.res.in/raghava/hslpred/ or bioinformatics.uams.edu/raghava/hslpred/) has been designed to predict subcellular localization of human proteins using the above approaches.

Genomic Counter-Stress Changes Induced by the Relaxation Response

Background Mind-body practices that elicit the relaxation response (RR) have been used worldwide for millennia to prevent and treat disease. The RR is characterized by decreased oxygen consumption, increased exhaled nitric oxide, and reduced psychological distress. It is believed to be the counterpart of the stress response that exhibits a distinct pattern of physiology and transcriptional profile. We hypothesized that RR elicitation results in characteristic gene expression changes that can be used to measure physiological responses elicited by the RR in an unbiased fashion. Methods/Principal Findings We assessed whole blood transcriptional profiles in 19 healthy, long-term practitioners of daily RR practice (group M), 19 healthy controls (group N1), and 20 N1 individuals who completed 8 weeks of RR training (group N2). 2209 genes were differentially expressed in group M relative to group N1 (p<0.05) and 1561 genes in group N2 compared to group N1 (p<0.05). Importantly, 433 (p<10−10) of 2209 and 1561 differentially expressed genes were shared among long-term (M) and short-term practitioners (N2). Gene ontology and gene set enrichment analyses revealed significant alterations in cellular metabolism, oxidative phosphorylation, generation of reactive oxygen species and response to oxidative stress in long-term and short-term practitioners of daily RR practice that may counteract cellular damage related to chronic psychological stress. A significant number of genes and pathways were confirmed in an independent validation set containing 5 N1 controls, 5 N2 short-term and 6 M long-term practitioners. Conclusions/Significance This study provides the first compelling evidence that the RR elicits specific gene expression changes in short-term and long-term practitioners. Our results suggest consistent and constitutive changes in gene expression resulting from RR may relate to long term physiological effects. Our study may stimulate new investigations into applying transcriptional profiling for accurately measuring RR and stress related responses in multiple disease settings.

PSLpred: prediction of subcellular localization of bacterial proteins

SUMMARY We developed a web server PSLpred for predicting subcellular localization of gram-negative bacterial proteins with an overall accuracy of 91.2%. PSLpred is a hybrid approach-based method that integrates PSI-BLAST and three SVM modules based on compositions of residues, dipeptides and physico-chemical properties. The prediction accuracies of 90.7, 86.8, 90.3, 95.2 and 90.6% were attained for cytoplasmic, extracellular, inner-membrane, outer-membrane and periplasmic proteins, respectively. Furthermore, PSLpred was able to predict approximately 74% of sequences with an average prediction accuracy of 98% at RI = 5. AVAILABILITY PSLpred is available at http://www.imtech.res.in/raghava/pslpred/

PGC-1α promotes recovery after acute kidney injury during systemic inflammation in mice.

Sepsis-associated acute kidney injury (AKI) is a common and morbid condition that is distinguishable from typical ischemic renal injury by its paucity of tubular cell death. The mechanisms underlying renal dysfunction in individuals with sepsis-associated AKI are therefore less clear. Here we have shown that endotoxemia reduces oxygen delivery to the kidney, without changing tissue oxygen levels, suggesting reduced oxygen consumption by the kidney cells. Tubular mitochondria were swollen, and their function was impaired. Expression profiling showed that oxidative phosphorylation genes were selectively suppressed during sepsis-associated AKI and reactivated when global function was normalized. PPARγ coactivator-1α (PGC-1α), a major regulator of mitochondrial biogenesis and metabolism, not only followed this pattern but was proportionally suppressed with the degree of renal impairment. Furthermore, tubular cells had reduced PGC-1α expression and oxygen consumption in response to TNF-α; however, excess PGC-1α reversed the latter effect. Both global and tubule-specific PGC-1α-knockout mice had normal basal renal function but suffered persistent injury following endotoxemia. Our results demonstrate what we believe to be a novel mechanism for sepsis-associated AKI and suggest that PGC-1α induction may be necessary for recovery from this disorder, identifying a potential new target for future therapeutic studies.

GPCRpred: an SVM-based method for prediction of families and subfamilies of G-protein coupled receptors

G-protein coupled receptors (GPCRs) belong to one of the largest superfamilies of membrane proteins and are important targets for drug design. In this study, a support vector machine (SVM)-based method, GPCRpred, has been developed for predicting families and subfamilies of GPCRs from the dipeptide composition of proteins. The dataset used in this study for training and testing was obtained from http://www.soe.ucsc.edu/research/compbio/gpcr/. The method classified GPCRs and non-GPCRs with an accuracy of 99.5% when evaluated using 5-fold cross-validation. The method is further able to predict five major classes or families of GPCRs with an overall Matthews correlation coefficient (MCC) and accuracy of 0.81 and 97.5% respectively. In recognizing the subfamilies of the rhodopsin-like family, the method achieved an average MCC and accuracy of 0.97 and 97.3% respectively. The method achieved overall accuracy of 91.3% and 96.4% at family and subfamily level respectively when evaluated on an independent/blind dataset of 650 GPCRs. A server for recognition and classification of GPCRs based on multiclass SVMs has been set up at http://www.imtech.res.in/raghava/gpcrpred/. We have also suggested subfamilies for 42 sequences which were previously identified as unclassified ClassA GPCRs. The supplementary information is available at http://www.imtech.res.in/raghava/gpcrpred/info.html.

Curative and β cell regenerative effects of α1-antitrypsin treatment in autoimmune diabetic NOD mice

Invasive insulitis is a destructive T cell-dependent autoimmune process directed against insulin-producing β cells that is central to the pathogenesis of type 1 diabetes mellitus (T1DM) in humans and the clinically relevant nonobese diabetic (NOD) mouse model. Few therapies have succeeded in restoring long-term, drug-free euglycemia and immune tolerance to β cells in overtly diabetic NOD mice, and none have demonstrably enabled enlargement of the functional β cell mass. Recent studies have emphasized the impact of inflammatory cytokines on the commitment of antigen-activated T cells to various effector or regulatory T cell phenotypes and insulin resistance and defective insulin signaling. Hence, we tested the hypothesis that inflammatory mechanisms trigger insulitis, insulin resistance, faulty insulin signaling, and the loss of immune tolerance to islets. We demonstrate that treatment with α1-antitrypsin (AAT), an agent that dampens inflammation, does not directly inhibit T cell activation, ablates invasive insulitis, and restores euglycemia, immune tolerance to β cells, normal insulin signaling, and insulin responsiveness in NOD mice with recent-onset T1DM through favorable changes in the inflammation milieu. Indeed, the functional mass of β cells expands in AAT-treated diabetic NOD mice.

Bcipep: A database of B-cell epitopes

BackgroundBcipep is a database of experimentally determined linear B-cell epitopes of varying immunogenicity collected from literature and other publicly available databases.ResultsThe current version of Bcipep database contains 3031 entries that include 763 immunodominant, 1797 immunogenic and 471 null-immunogenic epitopes. It covers a wide range of pathogenic organisms like viruses, bacteria, protozoa, and fungi. The database provides a set of tools for the analysis and extraction of data that includes keyword search, peptide mapping and BLAST search. It also provides hyperlinks to various databases such as GenBank, PDB, SWISS-PROT and MHCBN.ConclusionA comprehensive database of B-cell epitopes called Bcipep has been developed that covers information on epitopes from a wide range of pathogens. The Bcipep will be source of information for investigators involved in peptide-based vaccine design, disease diagnosis and research in allergy. It should also be a promising data source for the development and evaluation of methods for prediction of B-cell epitopes. The database is available at http://www.imtech.res.in/raghava/bcipep.

A metabolic prosurvival role for PML in breast cancer

Cancer cells exhibit an aberrant metabolism that facilitates more efficient production of biomass and hence tumor growth and progression. However, the genetic cues modulating this metabolic switch remain largely undetermined. We identified a metabolic function for the promyelocytic leukemia (PML) gene, uncovering an unexpected role for this bona fide tumor suppressor in breast cancer cell survival. We found that PML acted as both a negative regulator of PPARγ coactivator 1A (PGC1A) acetylation and a potent activator of PPAR signaling and fatty acid oxidation. We further showed that PML promoted ATP production and inhibited anoikis. Importantly, PML expression allowed luminal filling in 3D basement membrane breast culture models, an effect that was reverted by the pharmacological inhibition of fatty acid oxidation. Additionally, immunohistochemical analysis of breast cancer biopsies revealed that PML was overexpressed in a subset of breast cancers and enriched in triple-negative cases. Indeed, PML expression in breast cancer correlated strikingly with reduced time to recurrence, a gene signature of poor prognosis, and activated PPAR signaling. These findings have important therapeutic implications, as PML and its key role in fatty acid oxidation metabolism are amenable to pharmacological suppression, a potential future mode of cancer prevention and treatment.