Biomedical subjects

Richard Desper

Publications and source records attributed to Richard Desper.

7 recordsLinked to original sources

PubMed2005

A newly discovered human alpha-globin gene.

A previously undefined transcript with significant homology to the pseudo-alpha2 region of the alpha-globin locus on human chromosome 16 was detected as part of an effort to better define the transcriptional profiles of human reticulocytes. Cloning and sequencing of that transcript (GenBank AY698022; named mu-globin) revealed an insert with a 423-nucleotide open reading frame. BLASTP and ClustalW and phylogenetic analyses of the predicted protein demonstrated a high level of homology with the avian alpha-D globin. In addition, the heme- and globin-binding amino acids of mu-globin and avian alpha-D globin are largely conserved. Using quantitative real-time polymerase chain reaction (PCR), mu-globin was detected at a level of approximately 0.1% that measured for alpha-globin in erythroid tissues. Erythroid-specific expression was detected by Northern blot analysis, and maximal expression during the erythroblast terminal differentiation was also detected. Despite this highly regulated pattern of mu-globin gene transcription, mu-globin protein was not detected by mass spectrometry. These results suggest the human genome encodes a previously unrecognized globin member of the avian alpha-D family that is transcribed in a highly regulated pattern in erythroid cells.

Cell Differentiation↗

PubMed2005

A comprehensive continuous-time model for the appearance of CGH signal due to chromosomal missegregations during mitosis.

Aneuploidy, the gain or loss of large regions of the genome, is a common feature in cancer cells. Irregularities in chromosomal copy number caused by missegregations of chromosomes during mitosis can be visualized by cytogenetic techniques including fluorescence in situ hybridization (FISH), spectral karyotyping (SKY) and comparative genomic hybridization (CGH). In the current work, we consider the propagation of irregular copy numbers throughout a cell population as the individual cells progress through ordinary mitotic cell cycles. We use an algebraic model to track the different copy numbers as states in a stochastic process, based on the model of chromosome instability of Gusev, Kagansky, and Dooley, and consider the average copy number of a particular chromosome within a cell population as a function of the cell division rate. We review a number of mathematical models for determining the length of the cell cycle, including the Smith-Martin transition probability model and the 'sloppy size' model of Wheals, Tyson and Diekmann. The program MITOSIM simulates the growth of a population of cells using the aforementioned models of the cell cycle. MITOSIM allows the cell population to grow, with occasional resampling, until the average copy number of a given chromosome in the population reaches a preset threshold signifying a positive copy number alteration in this region. MITOSIM calculates the relationship between the missegregation rate and the growth rate of the cell population. This allows the user to test hypotheses regarding the effect chromosomal aberrations have upon the cell cycle, cell growth rates, and time to population dominance.

Aneuploidy↗

PubMed2004

Tumor classification using phylogenetic methods on expression data.

Tumor classification is a well-studied problem in the field of bioinformatics. Developments in the field of DNA chip design have now made it possible to measure the expression levels of thousands of genes in sample tissue from healthy cell lines or tumors. A number of studies have examined the problems of tumor classification: class discovery, the problem of defining a number of classes of tumors using the data from a DNA chip, and class prediction, the problem of accurately classifying an unknown tumor, given expression data from the unknown tumor and from a learning set. The current work has applied phylogenetic methods to both problems. To solve the class discovery problem, we impose a metric on a set of tumors as a function of their gene expression levels, and impose a tree structure on this metric, using standard tree fitting methods borrowed from the field of phylogenetics. Phylogenetic methods provide a simple way of imposing a clear hierarchical relationship on the data, with branch lengths in the classification tree representing the degree of separation witnessed. We tested our method for class discovery on two data sets: a data set of 87 tissues, comprised mostly of small, round, blue-cell tumors (SRBCTs), and a data set of 22 breast tumors. We fit the 87 samples of the first set to a classification tree, which neatly separated into four major clusters corresponding exactly to the four groups of tumors, namely neuroblastomas, rhabdomyosarcomas, Burkitt's lymphomas, and the Ewing's family of tumors. The classification tree built using the breast cancer data separated tumors with BRCA1 mutations from those with BRCA2 mutations, with sporadic tumors separated from both groups and from each other. We also demonstrate the flexibility of the class discovery method with regard to standard resampling methodology such as jackknifing and noise perturbation. To solve the class prediction problem, we built a classification tree on the learning set, and then sought the optimal placement of each test sample within the classification tree. We tested this method on the SRBCT data set, and classified each tumor successfully.

Algorithms↗

PubMed2004

Construction of tree models for pathogenesis of nasopharyngeal carcinoma.

Pathogenesis of nasopharyngeal carcinoma (NPC) is a multistep and multipathway process that cannot be fully explained by a fixed linear progression model. We used distance-based and branching-tree methods to construct more general tree-like models for NPC carcinogenesis from 170 comparative genomic hybridization (CGH) samples previously published in five smaller studies. Imbalances were classified into "overlap regions," each containing the most commonly gained or lost band on each chromosome arm as well as adjacent bands that were gained or lost almost as often. The chromosome abnormalities associated with NPC were -3p26-13 (48.9%), -11q22-25 (38.1%), -16q12-24 (38.1%), -14q24-32 (32.4%), -13q21-32 (22.3%), -9p23-21(21.6%), +12p12 (46%), +12q13-15 (43.9%), +1q22-32 (33.1%), +3q13.1-26.2 (30.2%), and +8q22.1-24.2 (27.3%). NPC can be classified into two groups, one marked by +12p12 and +8q22.1-24.2 and the other by -3p26-13, -11q22-25, -14q24-32, and +1q22-32. The tree models predicted -3p26-13 and +12p12 as early events and +8q22.1-24.2 as a late event. The predictions for -3p26-13 and +8q22.1-24.2 were consistent with previous studies. The prediction for +12p12 is being reported for the first time. Many known NPC-related genes on chromosomal regions of these tree models are discussed, some of which may merit additional study. The potential applications of tree models are also discussed.

Carcinoma↗

PubMed2003

Theoretical foundation of the balanced minimum evolution method of phylogenetic inference and its relationship to weighted least-squares tree fitting.

Due to its speed, the distance approach remains the best hope for building phylogenies on very large sets of taxa. Recently (R. Desper and O. Gascuel, J. Comp. Biol. 9:687-705, 2002), we introduced a new "balanced" minimum evolution (BME) principle, based on a branch length estimation scheme of Y. Pauplin (J. Mol. Evol. 51:41-47, 2000). Initial simulations suggested that FASTME, our program implementing the BME principle, was more accurate than or equivalent to all other distance methods we tested, with running time significantly faster than Neighbor-Joining (NJ). This article further explores the properties of the BME principle, and it explains and illustrates its impressive topological accuracy. We prove that the BME principle is a special case of the weighted least-squares approach, with biologically meaningful variances of the distance estimates. We show that the BME principle is statistically consistent. We demonstrate that FASTME only produces trees with positive branch lengths, a feature that separates this approach from NJ (and related methods) that may produce trees with branches with biologically meaningless negative lengths. Finally, we consider a large simulated data set, with 5,000 100-taxon trees generated by the Aldous beta-splitting distribution encompassing a range of distributions from Yule-Harding to uniform, and using a covarion-like model of sequence evolution. FASTME produces trees faster than NJ, and much faster than WEIGHBOR and the weighted least-squares implementation of PAUP*. Moreover, FASTME trees are consistently more accurate at all settings, ranging from Yule-Harding to uniform distributions, and all ranges of maximum pairwise divergence and departure from molecular clock. Interestingly, the covarion parameter has little effect on the tree quality for any of the algorithms. FASTME is freely available on the web.

Algorithms↗

PubMed2002

Genetic differences detected by comparative genomic hybridization in head and neck squamous cell carcinomas from different tumor sites: construction of oncogenetic trees for tumor progression.

For a better understanding of genetic alterations in head and neck squamous cell carcinoma (HNSCC), we applied comparative genomic hybridization (CGH) in the analysis of 75 HNSCCs, comprised of 18 pharyngeal squamous cell carcinomas (PSCCs), 23 laryngeal squamous cell carcinomas (LSCCs), and 34 oral squamous cell carcinomas (OSCCs). The three subgroups of HNSCC showed significant differences in genetic alteration patterns. Overall, PSCC and LSCC had more copy number aberrations (CNAs) per tumor than did OSCC. Apparent differing patterns of high-level amplification were also observed. The smallest recurrent chromosomal regions of high-level amplification (> or = 15% of cases) were 7q22, 8q24.1, and 11q12-13 in PSCC and 3q26.1-29 in OSCC. According to single frequency and combined frequencies of CNAs, we concluded that the most important chromosomal events for progression of head and neck cancer were +3q, +5p, +8q, and -3p for all subgroups of HNSCC; additionally, +7q, +17q, -9p, and -13q for PSCC; +7p, +9q, +11q12-13, +14q, and +17q for LSCC; and +1p and +11q12-13 for OSCC. To identify further important genetic alterations and the relationships among the alterations, we constructed oncogenetic tree models for tumor progression of HNSCC from CGH data using branching and distance-based tree models. The tree models predicted that: (1) +3q21-29 was the most important early chromosomal event, and -3p, which occurred after +3q21-29, was also an important chromosomal event for all subsites of HNSCC; (2) +8q is the second most important early chromosomal event; (3) there may be at least three subgroups of HNSCC: one characterized by -3p, -9p, +7p, and -13q; another by +5p, +9qter, and +17p; and the other by +8q and +18p. These results suggest that different chromosomal aberrations may play a role in the initiation and/or progression of different subgroups of HNSCC.

Carcinoma, Squamous Cell↗

PubMed2002

Fast and accurate phylogeny reconstruction algorithms based on the minimum-evolution principle.

The Minimum Evolution (ME) approach to phylogeny estimation has been shown to be statistically consistent when it is used in conjunction with ordinary least-squares (OLS) fitting of a metric to a tree structure. The traditional approach to using ME has been to start with the Neighbor Joining (NJ) topology for a given matrix and then do a topological search from that starting point. The first stage requires O(n(3)) time, where n is the number of taxa, while the current implementations of the second are in O(p n(3)) or more, where p is the number of swaps performed by the program. In this paper, we examine a greedy approach to minimum evolution which produces a starting topology in O(n(2)) time. Moreover, we provide an algorithm that searches for the best topology using nearest neighbor interchanges (NNIs), where the cost of doing p NNIs is O(n(2) + p n), i.e., O(n(2)) in practice because p is always much smaller than n. The Greedy Minimum Evolution (GME) algorithm, when used in combination with NNIs, produces trees which are fairly close to NJ trees in terms of topological accuracy. We also examine ME under a balanced weighting scheme, where sibling subtrees have equal weight, as opposed to the standard "unweighted" OLS, where all taxa have the same weight so that the weight of a subtree is equal to the number of its taxa. The balanced minimum evolution scheme (BME) runs slower than the OLS version, requiring O(n(2) x diam(T)) operations to build the starting tree and O(p n x diam(T)) to perform the NNIs, where diam(T) is the topological diameter of the output tree. In the usual Yule-Harding distribution on phylogenetic trees, the diameter expectation is in log(n), so our algorithms are in practice faster that NJ. Moreover, this BME scheme yields a very significant improvement over NJ and other distance-based algorithms, especially with large trees, in terms of topological accuracy.

Algorithms↗