Biomedical subjects

Eleazar Eskin

Publications and source records attributed to Eleazar Eskin.

6 recordsLinked to original sources

PubMed2007

Catecholamine release-inhibitory peptide catestatin (chromogranin A(352-372)): naturally occurring amino acid variant Gly364Ser causes profound changes in human autonomic activity and alters risk for hypertension.

BACKGROUND: Chromogranin A, coreleased with catecholamines by exocytosis, is cleaved to the catecholamine release-inhibitory fragment catestatin. We identified a natural nonsynonymous variant of catestatin, Gly364Ser, that alters human autonomic function and blood pressure. METHODS AND RESULTS: Gly364Ser heterozygotes and controls underwent physiological and biochemical phenotyping, including catecholamine production, chromogranin A precursor, and its catestatin product. Case-control studies replicated effects of the gene on blood pressure in the population. Gly364Ser displayed diminished inhibition of catecholamine secretion from cultured neurons. Gly/Ser heterozygotes displayed increased baroreceptor slope during upward deflections (by approximately 47%) and downward deflections (by approximately 44%), increased cardiac parasympathetic index (by approximately 2.4-fold), and decreased cardiac sympathetic index (by approximately 26%). Renal norepinephrine excretion was diminished by approximately 26% and epinephrine excretion by approximately 34% in Gly/Ser heterozygotes. The coalescent dated emergence of the variant to approximately 70,000 years ago. Gly364Ser was in linkage disequilibrium with 1 major Chromogranin A promoter haplotype, although promoter haplotypes did not predict autonomic phenotypes. The 364Ser variant was associated with lower diastolic blood pressure in 2 independent/confirmatory groups of patients with hypertension; genotype groups differed by approximately 5 to 6 mm Hg, and the polymorphism accounted for approximately 1.8% of population diastolic blood pressure variance, although a significant gene-by-sex interaction existed, with an enhanced effect in men. CONCLUSIONS: The catestatin Gly364Ser variant causes profound changes in human autonomic activity, both parasympathetic and sympathetic, and seems to reduce risk of developing hypertension, especially in men. A model for catestatin action in the baroreceptor center of the nucleus of the tractus solitarius accounts for these actions.

Amino Acid Sequence↗

PubMed2003

Protein family classification using sparse markov transducers.

We present a method for classifying proteins into families based on short subsequences of amino acids using a new probabilistic model called sparse Markov transducers (SMT). We classify a protein by estimating probability distributions over subsequences of amino acids from the protein. Sparse Markov transducers, similar to probabilistic suffix trees, estimate a probability distribution conditioned on an input sequence. SMTs generalize probabilistic suffix trees by allowing for wild-cards in the conditioning sequences. Since substitutions of amino acids are common in protein families, incorporating wild-cards into the model significantly improves classification performance. We present two models for building protein family classifiers using SMTs. As protein databases become larger, data driven learning algorithms for probabilistic models such as SMTs will require vast amounts of memory. We therefore describe and use efficient data structures to improve the memory usage of SMTs. We evaluate SMTs by building protein family classifiers using the Pfam and SCOP databases and compare our results to previously published results and state-of-the-art protein homology detection methods. SMTs outperform previous probabilistic suffix tree methods and under certain conditions perform comparably to state-of-the-art protein homology methods.

Algorithms↗

PubMed2003

Genome-wide analysis of bacterial promoter regions.

Identifying prokaryotic promoter sequences is notoriously difficult and for most sequenced bacterial genomes the promoter sequences are still unknown. Since experimental analysis trails behind sequencing, genome-wide computational promoter discovery is often the only realistic way to discover these sequences in newly sequenced bacterial genomes. However, genome-wide samples for promoter discovery may be very large and corrupted complicating promoter discovery. We discuss three aspects of genome-wide promoter discovery: sample generation, signal finding algorithms, and scoring signals. We applied our new MITRA algorithm to analyze samples of divergent and convergent genes in 20 bacterial genomes and found strong putative dyad signals in 17 out of the 20 genomes. Moreover, in 12 out of 20 genomes the found signals are identical or similar to the known regulatory patterns (Pribnow-Gilbert boxes and CRP binding sites). Since many of putative signals correspond to previously known elements of bacterial transcriptional regulation, the remaining discovered signals are good candidates for unknown regulatory elements.

Algorithms↗

PubMed2002

Using substitution matrices to estimate probability distributions for biological sequences.

Accurately estimating probabilities from observations is important for probabilistic-based approaches to problems in computational biology. In this paper we present a biologically-motivated method for estimating probability distributions over discrete alphabets from observations using a mixture model of common ancestors. The method is an extension of substitution matrix-based probability estimation methods. In contrast to previous such methods, our method has a simple Bayesian interpretation and has the advantage over Dirichlet mixtures that it is both effective and simple to compute for large alphabets. The method is applied to estimate amino acid probabilities based on observed counts in an alignment and is shown to perform comparably to previous methods. The method is also applied to estimate probability distributions over protein families and improves protein classification accuracy.

Amino Acid Sequence↗

PubMed2002

Finding composite regulatory patterns in DNA sequences.

Pattern discovery in unaligned DNA sequences is a fundamental problem in computational biology with important applications in finding regulatory signals. Current approaches to pattern discovery focus on monad patterns that correspond to relatively short contiguous strings. However, many of the actual regulatory signals are composite patterns that are groups of monad patterns that occur near each other. A difficulty in discovering composite patterns is that one or both of the component monad patterns in the group may be 'too weak'. Since the traditional monad-based motif finding algorithms usually output one (or a few) high scoring patterns, they often fail to find composite regulatory signals consisting of weak monad parts. In this paper, we present a MITRA (MIsmatch TRee Algorithm) approach for discovering composite signals. We demonstrate that MITRA performs well for both monad and composite patterns by presenting experiments over biological and synthetic data.

Algorithms↗

PubMed2002

The spectrum kernel: a string kernel for SVM protein classification.

We introduce a new sequence-similarity kernel, the spectrum kernel, for use with support vector machines (SVMs) in a discriminative approach to the protein classification problem. Our kernel is conceptually simple and efficient to compute and, in experiments on the SCOP database, performs well in comparison with state-of-the-art methods for homology detection. Moreover, our method produces an SVM classifier that allows linear time classification of test sequences. Our experiments provide evidence that string-based kernels, in conjunction with SVMs, could offer a viable and computationally efficient alternative to other methods of protein classification and homology detection.

Artificial Intelligence↗