Biomedical subjects

Sunduz Keles

Publications and source records attributed to Sunduz Keles.

5 recordsLinked to original sources

PubMed2025

Oncogenic DEAD-box ATPase DDX41 establishes transcript ensembles via CLK3-dependent and -independent mechanisms.

Post-transcriptional diversification of RNA transcripts mediated by complex processing machinery, including DEAD-box ATPases, establishes and maintains cellular phenotypes. For example, DDX41 controls RNA splicing, innate immune signaling, and genome stability. Although heterozygous DDX41 germline genetic variation occurs in familial myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML), the DDX41 contributions to splicing globally, biological processes, and pathogenic mechanisms are incompletely defined. Using a genetic rescue system with Ddx41+/- myeloid progenitors, we established global wildtype DDX41 and pathogenic variant mechanisms. Differing from pathogenic variants of other RNA splicing regulators, DDX41 deficiency compromised multiple splicing steps. DDX41-regulated transcripts encoded factors controlling RNA splicing, including Cdc2-like kinase 3 (CLK3). DDX41 regulated Clk3 transcripts, and elevated CLK3 during myeloid differentiation. Loss-of-function analysis revealed DDX41-regulated splicing commonly, but not always, required CLK3. Thus, through a mechanism utilizing a splicing factor kinase that itself is DDX41-regulated, DDX41 establishes transcript ensembles in myeloid progenitors.

DEAD-box RNA Helicases↗

PubMed2005

Expression profiling of GABAergic motor neurons in Caenorhabditis elegans.

Neurons constitute the most diverse cell types and acquire their identity by the activity of particular genetic programs . The GABAergic nervous system in C. elegans consists of 26 neurons that fall into six classes . Animals that are defective in GABAergic neuron function and development display "shrinker" movement , abnormal foraging and defecation . Among the known shrinker genes, unc-25 and unc-47 encode the GABA biosynthetic enzyme glutamic acid decarboxylase and vesicular transporter, respectively . unc-30 encodes a homeodomain protein of the Pitx family and regulates the differentiation of the D-type GABAergic neurons . unc-46 probably functions in presynaptic GABA release , but its identity has not been reported. By cell-based microarray analysis, we identified over 250 genes with enriched expression in GABAergic neurons. The highly enriched gene set included all known genes. In vivo expression study with computational predictions further identified six new genes that are potential transcriptional targets of UNC-30. Behavioral studies of a deletion mutant implicate a function of a nicotinic receptor subunit in D-type neurons. Our analysis demonstrates the utility of neuron-specific genomics in identifying cell-specific genes and regulatory networks.

Animals↗

PubMed2004

Asymptotic optimality of likelihood-based cross-validation.

Likelihood-based cross-validation is a statistical tool for selecting a density estimate based on n i.i.d. observations from the true density among a collection of candidate density estimators. General examples are the selection of a model indexing a maximum likelihood estimator, and the selection of a bandwidth indexing a nonparametric (e.g. kernel) density estimator. In this article, we establish a finite sample result for a general class of likelihood-based cross-validation procedures (as indexed by the type of sample splitting used, e.g. V-fold cross-validation). This result implies that the cross-validation selector performs asymptotically as well (w.r.t. to the Kullback-Leibler distance to the true density) as a benchmark model selector which is optimal for each given dataset and depends on the true density. Crucial conditions of our theorem are that the size of the validation sample converges to infinity, which excludes leave-one-out cross-validation, and that the candidate density estimates are bounded away from zero and infinity. We illustrate these asymptotic results and the practical performance of likelihood-based cross-validation for the purpose of bandwidth selection with a simulation study. Moreover, we use likelihood-based cross-validation in the context of regulatory motif detection in DNA sequences.

Journal Article↗

PubMed2003

Exploratory and confirmatory gene expression profiling of mac1Delta.

Exploratory outlier identification methods and confirmatory gene expression studies showed induction of the iron regulon in Saccharomyces cerevisiae lacking Mac1p, a copper-responsive transcription factor. The Aft1p/Aft2p binding motif was the most discriminating motif between up- and down-regulated genes, and we identified new genes potentially regulated by Aft1p/Aft2p. In addition, multiple genes encoding proteins containing Fe-S clusters were down-regulated suggesting metabolic reorganization to conserve iron in mac1Delta. Null mutants of each of the differentially expressed genes were characterized for copper- or iron-related phenotypes. New or additional support for a role in copper and iron homeostasis is provided in this study for the gene products of AKR1, MRS4, PCA1, SSU1, TIS11, YBR047W, YHL035C, YHR045W, YLR047C, YLR126C, and YTP1.

Base Sequence↗

PubMed2003

Supervised detection of regulatory motifs in DNA sequences.

Identification of transcription factor binding sites (regulatory motifs) is a major interest in contemporary biology. We propose a new likelihood based method, COMODE, for identifying structural motifs in DNA sequences. Commonly used methods (e.g. MEME, Gibbs motif sampler) model binding sites as families of sequences described by a position weight matrix (PWM) and identify PWMs that maximize the likelihood of observed sequence data under a simple multinomial mixture model. This model assumes that the positions of the PWM correspond to independent multinomial distributions with four cell probabilities. We address supervising the search for DNA binding sites using the information derived from structural characteristics of protein-DNA interactions. We extend the simple multinomial mixture model to a constrained multinomial mixture model by incorporating constraints on the information content profiles or on specific parameters of the motif PWMs. The parameters of this extended model are estimated by maximum likelihood using a nonlinear constraint optimization method. Likelihood-based cross-validation is used to select model parameters such as motif width and constraint type. The performance of COMODE is compared with existing motif detection methods on simulated data that incorporate real motif examples from Saccharomyces cerevisiae. The proposed method is especially effective when the motif of interest appears as a weak signal in the data. Some of the transcription factor binding data of Lee et al. (2002) were also analyzed using COMODE and biologically verified sites were identified.

Journal Article↗