A comprehensive,sensitive and economical approach for the detection of mutations in the <Emphasis Type="Italic">RB1</Emphasis> gene in retinoblastoma

Retinoblastoma (Rb) is the most common primary intraocular malignancy in children. It is brought about by the mutational inactivation of both alleles of RB1 gene in the developing retina. To identify the RB1 mutations, we analysed 74 retinoblastoma patients by screening the exons and the promoter region of RB1. The strategy used was to detect large deletions/duplications by fluorescent quantitative multiplex PCR; small deletions/insertions by fluorescent genotyping of RB1 alleles, and point mutations by PCR-RFLP and sequencing. Genomic DNA from the peripheral blood leucocytes of 74 Rb patients (53 with bilateral Rb, 21 with unilateral Rb; 4 familial cases) was screened for mutations. Recurrent mutations were identified in five patients with bilateral Rb, large deletions in 11 patients (nine with bilateral Rb and two with unilateral Rb), small deletions/insertions were found in 12 patients all with bilateral Rb, and point mutations in 26 patients (14 nonsense, six splice site, five substitution and one silent change). Three mutations were associated with variable expressivity of the disease in different family members. Using this method, the detection rates achieved in patients with bilateral Rb were 44/53 (83%) and with unilateral Rb, 5/21 (23.8%). This approach may be feasible for clinical genetic testing and counselling of patients.     

Predicting DNA-binding sites of proteins from amino acid sequence

Background  

Understanding the molecular details of protein-DNA interactions is critical for deciphering the mechanisms of gene regulation. We present a machine learning approach for the identification of amino acid residues involved in protein-DNA interactions.     

On evaluating MHC-II binding peptide prediction methods

Choice of one method over another for MHC-II binding peptide prediction is typically based on published reports of their estimated performance on standard benchmark datasets. We show that several standard benchmark datasets of unique peptides used in such studies contain a substantial number of peptides that share a high degree of sequence identity with one or more other peptide sequences in the same dataset. Thus, in a standard cross-validation setup, the test set and the training set are likely to contain sequences that share a high degree of sequence identity with each other, leading to overly optimistic estimates of performance. Hence, to more rigorously assess the relative performance of different prediction methods, we explore the use of similarity-reduced datasets. We introduce three similarity-reduced MHC-II benchmark datasets derived from MHCPEP, MHCBN, and IEDB databases. The results of our comparison of the performance of three MHC-II binding peptide prediction methods estimated using datasets of unique peptides with that obtained using their similarity-reduced counterparts shows that the former can be rather optimistic relative to the performance of the same methods on similarity-reduced counterparts of the same datasets. Furthermore, our results demonstrate that conclusions regarding the superiority of one method over another drawn on the basis of performance estimates obtained using commonly used datasets of unique peptides are often contradicted by the observed performance of the methods on the similarity-reduced versions of the same datasets. These results underscore the importance of using similarity-reduced datasets in rigorously comparing the performance of alternative MHC-II peptide prediction methods.     

Predicting linear B-cell epitopes using string kernels

The identification and characterization of B-cell epitopes play an important role in vaccine design, immunodiagnostic tests, and antibody production. Therefore, computational tools for reliably predicting linear B-cell epitopes are highly desirable. We evaluated Support Vector Machine (SVM) classifiers trained utilizing five different kernel methods using fivefold cross-validation on a homology-reduced data set of 701 linear B-cell epitopes, extracted from Bcipep database, and 701 non-epitopes, randomly extracted from SwissProt sequences. Based on the results of our computational experiments, we propose BCPred, a novel method for predicting linear B-cell epitopes using the subsequence kernel. We show that the predictive performance of BCPred (AUC = 0.758) outperforms 11 SVM-based classifiers developed and evaluated in our experiments as well as our implementation of AAP (AUC = 0.7), a recently proposed method for predicting linear B-cell epitopes using amino acid pair antigenicity. Furthermore, we compared BCPred with AAP and ABCPred, a method that uses recurrent neural networks, using two data sets of unique B-cell epitopes that had been previously used to evaluate ABCPred. Analysis of the data sets used and the results of this comparison show that conclusions about the relative performance of different B-cell epitope prediction methods drawn on the basis of experiments using data sets of unique B-cell epitopes are likely to yield overly optimistic estimates of performance of evaluated methods. This argues for the use of carefully homology-reduced data sets in comparing B-cell epitope prediction methods to avoid misleading conclusions about how different methods compare to each other. Our homology-reduced data set and implementations of BCPred as well as the APP method are publicly available through our web-based server, BCPREDS, at: http://ailab.cs.iastate.edu/bcpreds/.     

Neomycin- and spectinomycin-resistance replacement vectors for Bacillus subtilis

A plasmid is described for Bacillus subtilis that facilitates replacement of the widely used neomycin resistance gene (neo) with a spectinomycin resistance (spc_E) gene. A second plasmid is described that facilitates replacement of spc_S, associated with mini-Tn10 mutagenesis in B. subtilis, with neo. These plasmids can also function as integrative vectors for B. subtilis. They expand the scope of strain construction and gene analysis in B. subtilis.     

Evidence for Patterns of Selective Urban Migration in the Greater Indus Valley (2600-1900 BC): A Lead and Strontium Isotope Mortuary Analysis

Just as modern nation-states struggle to manage the cultural and economic impacts of migration, ancient civilizations dealt with similar external pressures and set policies to regulate people’s movements. In one of the earliest urban societies, the Indus Civilization, mechanisms linking city populations to hinterland groups remain enigmatic in the absence of written documents. However, isotopic data from human tooth enamel associated with Harappa Phase (2600-1900 BC) cemetery burials at Harappa (Pakistan) and Farmana (India) provide individual biogeochemical life histories of migration. Strontium and lead isotope ratios allow us to reinterpret the Indus tradition of cemetery inhumation as part of a specific and highly regulated institution of migration. Intra-individual isotopic shifts are consistent with immigration from resource-rich hinterlands during childhood. Furthermore, mortuary populations formed over hundreds of years and composed almost entirely of first-generation immigrants suggest that inhumation was the final step in a process linking certain urban Indus communities to diverse hinterland groups. Additional multi disciplinary analyses are warranted to confirm inferred patterns of Indus mobility, but the available isotopic data suggest that efforts to classify and regulate human movement in the ancient Indus region likely helped structure socioeconomic integration across an ethnically diverse landscape.     

Acyl-CoAs from coenzyme ribozymes.

We describe in vitro selection of two novel ribozymes that mediate coenzyme reactions. The first is a trans-capping ribozyme that attaches coenzyme A (CoA) at the 5' end of any RNA with the proper short terminal sequence, including RNAs with randomized internal sequences. From such a trans-capped CoA-RNA pool, we derive ribozymes that attack biotinyl-AMP using the SH group of CoA. These ribozymes, selected to acylate CoA with the valeryl side chain of biotin, also produce the crucial metabolic intermediates acetyl-CoA and butyryl-CoA with substantial velocities. Thus, we argue that RNAs might have used the chemical functionality offered by coenzymes to support an RNA world metabolism. In particular, we can combine our results with those of other labs to argue that simple chemistry and RNA catalysis suffice to proceed from simple chemicals to catalysis with acyl-CoAs. The trans-capping method can be generalized for production of varied coenzyme ribozymes using a single catalytic RNA subunit. Finally, the long-suggested RNA origin for CoA itself appears to be chemically feasible.