Tongbin Li

SVRMHC prediction server for MHC-binding peptides

BackgroundThe binding between antigenic peptides (epitopes) and the MHC molecule is a key step in the cellular immune response. Accurate in silico prediction of epitope-MHC binding affinity can greatly expedite epitope screening by reducing costs and experimental effort.ResultsRecently, we demonstrated the appealing performance of SVRMHC, an SVR-based quantitative modeling method for peptide-MHC interactions, when applied to three mouse class I MHC molecules. Subsequently, we have greatly extended the construction of SVRMHC models and have established such models for more than 40 class I and class II MHC molecules. Here we present the SVRMHC web server for predicting peptide-MHC binding affinities using these models. Benchmarked percentile scores are provided for all predictions. The larger number of SVRMHC models available allowed for an updated evaluation of the performance of the SVRMHC method compared to other well- known linear modeling methods.ConclusionSVRMHC is an accurate and easy-to-use prediction server for epitope-MHC binding with significant coverage of MHC molecules. We believe it will prove to be a valuable resource for T cell epitope researchers.

ER71 directs mesodermal fate decisions during embryogenesis

Er71 mutant embryos are nonviable and lack hematopoietic and endothelial lineages. To further define the functional role for ER71 in cell lineage decisions, we generated genetically modified mouse models. We engineered an Er71-EYFP transgenic mouse model by fusing the 3.9 kb Er71 promoter to the EYFP reporter gene. Using FACS and transcriptional profiling, we examined the EYFP+ population of cells in Er71 mutant and wild-type littermates. In the absence of ER71, we observed an increase in the number of EYFP-expressing cells, increased expression of the cardiac molecular program and decreased expression of the hemato-endothelial program, as compared with wild-type littermate controls. We also generated a novel Er71-Cre transgenic mouse model using the same 3.9 kb Er71 promoter. Genetic fate-mapping studies revealed that the ER71-expressing cells give rise to the hematopoietic and endothelial lineages in the wild-type background. In the absence of ER71, these cell populations contributed to alternative mesodermal lineages, including the cardiac lineage. To extend these analyses, we used an inducible embryonic stem/embryoid body system and observed that ER71 overexpression repressed cardiogenesis. Together, these studies identify ER71 as a critical regulator of mesodermal fate decisions that acts to specify the hematopoietic and endothelial lineages at the expense of cardiac lineages. This enhances our understanding of the mechanisms that govern mesodermal fate decisions early during embryogenesis.

Meta-prediction of phosphorylation sites with weighted voting and restricted grid search parameter selection

Meta-predictors make predictions by organizing and processing the predictions produced by several other predictors in a defined problem domain. A proficient meta-predictor not only offers better predicting performance than the individual predictors from which it is constructed, but it also relieves experimentally researchers from making difficult judgments when faced with conflicting results made by multiple prediction programs. As increasing numbers of predicting programs are being developed in a large number of fields of life sciences, there is an urgent need for effective meta-prediction strategies to be investigated. We compiled four unbiased phosphorylation site datasets, each for one of the four major serine/threonine (S/T) protein kinase families—CDK, CK2, PKA and PKC. Using these datasets, we examined several meta-predicting strategies with 15 phosphorylation site predictors from six predicting programs: GPS, KinasePhos, NetPhosK, PPSP, PredPhospho and Scansite. Meta-predictors constructed with a generalized weighted voting meta-predicting strategy with parameters determined by restricted grid search possess the best performance, exceeding that of all individual predictors in predicting phosphorylation sites of all four kinase families. Our results demonstrate a useful decision-making tool for analysing the predictions of the various S/T phosphorylation site predictors. An implementation of these meta-predictors is available on the web at: http://MetaPred.umn.edu/MetaPredPS/.

Integrated siRNA design based on surveying of features associated with high RNAi effectiveness

BackgroundShort interfering RNAs have allowed the development of clean and easily regulated methods for disruption of gene expression. However, while these methods continue to grow in popularity, designing effective siRNA experiments can be challenging. The various existing siRNA design guidelines suffer from two problems: they differ considerably from each other, and they produce high levels of false-positive predictions when tested on data of independent origins.ResultsUsing a distinctly large set of siRNA efficacy data assembled from a vast diversity of origins (the siRecords data, containing records of 3,277 siRNA experiments targeting 1,518 genes, derived from 1,417 independent studies), we conducted extensive analyses of all known features that have been implicated in increasing RNAi effectiveness. A number of features having positive impacts on siRNA efficacy were identified. By performing quantitative analyses on cooperative effects among these features, then applying a disjunctive rule merging (DRM) algorithm, we developed a bundle of siRNA design rule sets with the false positive problem well curbed. A comparison with 15 online siRNA design tools indicated that some of the rule sets we developed surpassed all of these design tools commonly used in siRNA design practice in positive predictive values (PPVs).ConclusionThe availability of the large and diverse siRNA dataset from siRecords and the approach we describe in this report have allowed the development of highly effective and generally applicable siRNA design rule sets. Together with ever improving RNAi lab techniques, these design rule sets are expected to make siRNAs a more useful tool for molecular genetics, functional genomics, and drug discovery studies.

siRecords: an extensive database of mammalian siRNAs with efficacy ratings

UNLABELLED Short interfering RNAs (siRNAs) have been gaining popularity as the gene knock-down tool of choice by many researchers because of the clean nature of their workings as well as the technical simplicity and cost efficiency in their applications. We have constructed siRecords, a database of siRNAs experimentally tested by researchers with consistent efficacy ratings. This database will help siRNA researchers develop more reliable siRNA design rules; in the mean time, siRecords will benefit experimental researchers directly by providing them with information about the siRNAs that have been experimentally tested against the genes of their interest. Currently, more than 4100 carefully annotated siRNA sequences obtained from more than 1200 published siRNA studies are hosted in siRecords. This database will continue to expand as more experimentally tested siRNAs are published. AVAILABILITY The siRecords database can be accessed at http://siRecords.umn.edu/siRecords/

Cortical metabolites as biomarkers in the R6/2 model of Huntington's disease.

To improve the ability to move from preclinical trials in mouse models of Huntingtons disease (HD) to clinical trials in humans, biomarkers are needed that can track similar aspects of disease progression across species. Brain metabolites, detectable by magnetic resonance spectroscopy (MRS), have been suggested as potential biomarkers in HD. In this study, the R6/2 transgenic mouse model of HD was used to investigate the relative sensitivity of the metabolite profiling and the brain volumetry to anticipate the disease progression. Magnetic resonance imaging (MRI) and 1H MRS data were acquired at 9.4 T from the R6/2 mice and wild-type littermates at 4, 8, 12, and 15 weeks. Brain shrinkage was detectable in striatum, cortex, thalamus, and hypothalamus by 12 weeks. Metabolite changes in cortex paralleled and sometimes preceded those in striatum. The entire set of metabolite changes was compressed into principal components (PCs) using Partial Least Squares-Discriminant Analysis (PLS-DA) to increase the sensitivity for monitoring disease progression. In comparing the efficacy of volume and metabolite measurements, the cortical PC1 emerged as the most sensitive single biomarker, distinguishing R6/2 mice from littermates at all time points. Thus, neurochemical changes precede volume shrinkage and become potential biomarkers for HD mouse models.

PepCyber:P∼PEP: a database of human protein–protein interactions mediated by phosphoprotein-binding domains

Phosphoprotein-binding domains (PPBDs) mediate many important cellular and molecular processes. Ten PPBDs have been known to exist in the human proteome, namely, 14-3-3, BRCT, C2, FHA, MH2, PBD, PTB, SH2, WD-40 and WW. PepCyber:P∼PEP is a newly constructed database specialized in documenting human PPBD-containing proteins and PPBD-mediated interactions. Our motivation is to provide the research community with a rich information source emphasizing the reported, experimentally validated data for specific PPBD–PPEP interactions. This information is not only useful for designing, comparing and validating the relevant experiments, but it also serves as a knowledge-base for computationally constructing systems signaling pathways and networks. PepCyber:P∼PEP is accessible through the URL, http://www.pepcyber.org/PPEP/. The current release of the database contains 7044 PPBD-mediated interactions involving 337 PPBD-containing proteins and 1123 substrate proteins.

Etv2 is expressed in the yolk sac hematopoietic and endothelial progenitors and regulates Lmo2 gene expression.

During embryogenesis, the endothelial and the hematopoietic lineages first appear during gastrulation in the blood island of the yolk sac. We have previously reported that an Ets variant gene 2 (Etv2/ER71) mutant embryo lacks hematopoietic and endothelial lineages; however, the precise roles of Etv2 in yolk sac development remains unclear. In this study, we define the role of Etv2 in yolk sac blood island development using the Etv2 mutant and a novel Etv2‐EYFP reporter transgenic line. Both the hematopoietic and the endothelial lineages are absent in the Etv2 mutant yolk sac. In the Etv2‐EYFP transgenic mouse, the EYFP reporter is activated in the nascent mesoderm, expressed in the endothelial and blood progenitors, and in the Tie2+, c‐kit+, and CD41+ hematopoietic population. The hematopoietic activity in the E7.75 yolk sac was exclusively localized to the Etv2‐EYFP+ population. In the Etv2 mutant yolk sac, Tie2+ cells are present but do not express hematopoietic or endothelial markers. In addition, these cells do not form hematopoietic colonies, indicating an essential role of Etv2 in the specification of the hematopoietic lineage. Forced overexpression of Etv2 during embryoid body differentiation induces the hematopoietic and the endothelial lineages, and transcriptional profiling in this context identifies Lmo2 as a downstream target. Using electrophoretic mobility shift assay, chromatin immunoprecipitation, transcriptional assays, and mutagenesis, we demonstrate that Etv2 binds to the Lmo2 enhancer and transactivates its expression. Collectively, our studies demonstrate that Etv2 is expressed during and required for yolk sac hematoendothelial development, and that Lmo2 is one of the downstream targets of Etv2. STEM CELLS2012;30:1611–1623

Meta-prediction of protein subcellular localization with reduced voting

Meta-prediction seeks to harness the combined strengths of multiple predicting programs with the hope of achieving predicting performance surpassing that of all existing predictors in a defined problem domain. We investigated meta-prediction for the four-compartment eukaryotic subcellular localization problem. We compiled an unbiased subcellular localization dataset of 1693 nuclear, cytoplasmic, mitochondrial and extracellular animal proteins from Swiss-Prot 50.2. Using this dataset, we assessed the predicting performance of 12 predictors from eight independent subcellular localization predicting programs: ELSPred, LOCtree, PLOC, Proteome Analyst, PSORT, PSORT II, SubLoc and WoLF PSORT. Gorodkin correlation coefficient (GCC) was one of the performance measures. Proteome Analyst is the best individual subcellular localization predictor tested in this four-compartment prediction problem, with GCC = 0.811. A reduced voting strategy eliminating six of the 12 predictors yields a meta-predictor (RAW-RAG-6) with GCC = 0.856, substantially better than all tested individual subcellular localization predictors (P = 8.2 × 10−6, Fishers Z-transformation test). The improvement in performance persists when the meta-predictor is tested with data not used in its development. This and similar voting strategies, when properly applied, are expected to produce meta-predictors with outstanding performance in other life sciences problem domains.

Homeostatic adaptations in brain energy metabolism in mouse models of Huntington disease

Impairment of energy metabolism is a key feature of Huntington disease (HD). Recently, we reported longitudinal neurochemical changes in R6/2 mice measured by in-vivo proton magnetic resonance spectroscopy (1H MRS; Zacharoff et al, 2012). Here, we present similar 1H MRS measurements at an early stage in the milder Q111 mouse model. In addition, we measured the concentration of ATP and inorganic phosphate (Pi), key energy metabolites not accessible with 1H MRS, using 31P MRS both in Q111 and in R6/2 mice. Significant changes in striatal creatine and phosphocreatine were observed in Q111 mice at 6 weeks relative to control, and these changes were largely reversed at 13 weeks. No significant change was detected in ATP concentration, in either HD mouse, compared with control. Calculated values of [ADP], phosphorylation potential, relative rate of ATP synthase (v/Vmax(ATP)), and relative rate of creatine kinase (v/Vmax(CK)) were calculated from the measured data. ADP concentration and v/Vmax(ATP) were increased in Q111 mice at 6 weeks, and returned close to normal at 13 weeks. In contrast, these parameters were normal in R6/2 mice. These results suggest that early changes in brain energy metabolism are followed by compensatory shifts to maintain energetic homeostasis from early ages through manifest disease.