Gulsah Altun

A bioinformatic assay for pluripotency in human cells.

Pluripotent stem cells (PSCs) are defined by their potential to generate all cell types of an organism. The standard assay for pluripotency of mouse PSCs is cell transmission through the germline, but for human PSCs researchers depend on indirect methods such as differentiation into teratomas in immunodeficient mice. Here we report PluriTest, a robust open-access bioinformatic assay of pluripotency in human cells based on their gene expression profiles.

Recurrent Variations in DNA Methylation in Human Pluripotent Stem Cells and Their Differentiated Derivatives

Human pluripotent stem cells (hPSCs) are potential sources of cells for modeling disease and development, drug discovery, and regenerative medicine. However, it is important to identify factors that may impact the utility of hPSCs for these applications. In an unbiased analysis of 205 hPSC and 130 somatic samples, we identified hPSC-specific epigenetic and transcriptional aberrations in genes subject to X chromosome inactivation (XCI) and genomic imprinting, which were not corrected during directed differentiation. We also found that specific tissue types were distinguished by unique patterns of DNA hypomethylation, which were recapitulated by DNA demethylation during in vitro directed differentiation. Our results suggest that verification of baseline epigenetic status is critical for hPSC-based disease models in which the observed phenotype depends on proper XCI or imprinting and that tissue-specific DNA methylation patterns can be accurately modeled during directed differentiation of hPSCs, even in the presence of variations in XCI or imprinting.

Improved K-means clustering algorithm for exploring local protein sequence motifs representing common structural property

Information about local protein sequence motifs is very important to the analysis of biologically significant conserved regions of protein sequences. These conserved regions can potentially determine the diverse conformation and activities of proteins. In this work, recurring sequence motifs of proteins are explored with an improved K-means clustering algorithm on a new dataset. The structural similarity of these recurring sequence clusters to produce sequence motifs is studied in order to evaluate the relationship between sequence motifs and their structures. To the best of our knowledge, the dataset used by our research is the most updated dataset among similar studies for sequence motifs. A new greedy initialization method for the K-means algorithm is proposed to improve traditional K-means clustering techniques. The new initialization method tries to choose suitable initial points, which are well separated and have the potential to form high-quality clusters. Our experiments indicate that the improved K-means algorithm satisfactorily increases the percentage of sequence segments belonging to clusters with high structural similarity. Careful comparison of sequence motifs obtained by the improved and traditional algorithms also suggests that the improved K-means clustering algorithm may discover some relatively weak and subtle sequence motifs, which are undetectable by the traditional K-means algorithms. Many biochemical tests reported in the literature show that these sequence motifs are biologically meaningful. Experimental results also indicate that the improved K-means algorithm generates more detailed sequence motifs representing common structures than previous research. Furthermore, these motifs are universally conserved sequence patterns across protein families, overcoming some weak points of other popular sequence motifs. The satisfactory result of the experiment suggests that this new K-means algorithm may be applied to other areas of bioinformatics research in order to explore the underlying relationships between data samples more effectively.

Specific lectin biomarkers for isolation of human pluripotent stem cells identified through array-based glycomic analysis

Rapid and dependable methods for isolating human pluripotent stem cell (hPSC) populations are urgently needed for quality control in basic research and in cell-based therapy applications. Using lectin arrays, we analyzed glycoproteins extracted from 26 hPSC samples and 22 differentiated cell samples, and identified a small group of lectins with distinctive binding signatures that were sufficient to distinguish hPSCs from a variety of non-pluripotent cell types. These specific biomarkers were shared by all the 12 human embryonic stem cell and the 14 human induced pluripotent stem cell samples examined, regardless of the laboratory of origin, the culture conditions, the somatic cell type reprogrammed, or the reprogramming method used. We demonstrated a practical application of specific lectin binding by detecting hPSCs within a differentiated cell population with lectin-mediated staining followed by fluorescence microscopy and flow cytometry, and by enriching and purging viable hPSCs from mixed cell populations using lectin-mediated cell separation. Global gene expression analysis showed pluripotency-associated differential expression of specific fucosyltransferases and sialyltransferases, which may underlie these differences in protein glycosylation and lectin binding. Taken together, our results show that protein glycosylation differs considerably between pluripotent and non-pluripotent cells, and demonstrate that lectins may be used as biomarkers to monitor pluripotency in stem cell populations and for removal of viable hPSCs from mixed cell populations.

Increased Risk of Genetic and Epigenetic Instability in Human Embryonic Stem Cells Associated with Specific Culture Conditions

The self-renewal and differentiation capacities of human pluripotent stem cells (hPSCs) make them a promising source of material for cell transplantation therapy, drug development, and studies of cellular differentiation and development. However, the large numbers of cells necessary for many of these applications require extensive expansion of hPSC cultures, a process that has been associated with genetic and epigenetic alterations. We have performed a combinatorial study on both hESCs and hiPSCs to compare the effects of enzymatic vs. mechanical passaging, and feeder-free vs. mouse embryonic fibroblast feeder substrate, on the genetic and epigenetic stability and the phenotypic characteristics of hPSCs. In extensive experiments involving over 100 continuous passages, we observed that both enzymatic passaging and feeder-free culture were associated with genetic instability, higher rates of cell proliferation, and persistence of OCT4/POU5F1-positive cells in teratomas, with enzymatic passaging having the stronger effect. In all combinations of culture conditions except for mechanical passaging on feeder layers, we noted recurrent deletions in the genomic region containing the tumor suppressor gene TP53, which was associated with decreased mRNA expression of TP53, as well as alterations in the expression of several downstream genes consistent with a decrease in the activity of the TP53 pathway. Among the hESC cultures, we also observed culture-associated variations in global gene expression and DNA methylation. The effects of enzymatic passaging and feeder-free conditions were also observed in hiPSC cultures. Our results highlight the need for careful assessment of the effects of culture conditions on cells intended for clinical therapies.

DNA methylation in embryonic stem cells

Embryonic stem cells (ESCs) are pluripotent, self‐renewing cells. These cells can be used in applications such as cell therapy, drug development, disease modeling, and the study of cellular differentiation. Investigating the interplay of epigenetics, genetics, and gene expression in control of pluripotence and differentiation could give important insights on how these cells function. One of the best known epigenetic factors is DNA methylation, which is a major mechanism for regulation of gene expression. This phenomenon is mostly seen in imprinted genes and X‐chromosome inactivation where DNA methylation of promoter regions leads to repression of gene expression. Differential DNA methylation of pluripotence‐associated genes such as Nanog and Oct4/Pou5f1 has been observed between pluripotent and differentiated cells. It is clear that tight regulation of DNA methylation is necessary for normal development. As more associations between aberrant DNA methylation and disease are reported, the demand for high‐throughput approaches for DNA methylation analysis has increased. In this article, we highlight these methods and discuss recent DNA methylation studies on ESCs. J. Cell. Biochem. 109: 1–6, 2010.

Equivalence of conventionally-derived and parthenote-derived human embryonic stem cells.

Background As human embryonic stem cell (hESC) lines can be derived via multiple means, it is important to determine particular characteristics of individual lines that may dictate the applications to which they are best suited. The objective of this work was to determine points of equivalence and differences between conventionally-derived hESC and parthenote-derived hESC lines (phESC) in the undifferentiated state and during neural differentiation. Methodology/Principal Findings hESC and phESC were exposed to the same expansion conditions and subsequent neural and retinal pigmented epithelium (RPE) differentiation protocols. Growth rates and gross morphology were recorded during expansion. RTPCR for developmentally relevant genes and global DNA methylation profiling were used to compare gene expression and epigenetic characteristics. Parthenote lines proliferated more slowly than conventional hESC lines and yielded lower quantities of less mature differentiated cells in a neural progenitor cell (NPC) differentiation protocol. However, the cell lines performed similarly in a RPE differentiation protocol. The DNA methylation analysis showed similar general profiles, but the two cell types differed in methylation of imprinted genes. There were no major differences in gene expression between the lines before differentiation, but when differentiated into NPCs, the two cell types differed in expression of extracellular matrix (ECM) genes. Conclusions/Significance These data show that hESC and phESC are similar in the undifferentiated state, and both cell types are capable of differentiation along neural lineages. The differences between the cell types, in proliferation and extent of differentiation, may be linked, in part, to the observed differences in ECM synthesis and methylation of imprinted genes.

Parallel protein secondary structure prediction schemes using Pthread and OpenMP over hyper-threading technology

Abstract Protein secondary structure prediction has a fundamental influence on today’s bioinformatics research. In this work, tertiary classifiers for the protein secondary structure prediction are implemented on Denoeux Belief Neural Network (DBNN) architecture. Hydrophobicity matrix, orthogonal matrix, BLOSUM62 matrix and PSSM matrix are experimented separately as the encoding schemes for DBNN. Hydrophobicity matrix, BLOSUM62 matrix and PSSM matrix are applied to DBNN architecture for the first time. The experimental results contribute to the design of new encoding schemes. Our accuracy of the tertiary classifier with PSSM encoding scheme reaches 72.01%, which is almost 10% better than the previous results obtained in 2003. Due to the time consuming task of training the neural networks, Pthread and OpenMP are employed to parallelize DBNN in the Hyper-Threading enabled Intel architecture. Speedup for 16 Pthreads is 4.9 and speedup for 16 OpenMP threads is 4 in the 4 processors shared memory architecture. Both speedup performance of OpenMP and Pthread is superior to that of other research. With the new parallel training algorithm, thousands of amino acids can be processed in reasonable amount of time. Our research also shows that Hyper-Threading technology for Intel architecture is efficient for parallel biological algorithms.

Efficient, Secure, Dynamic Source Routing for Ad-hoc Networks

Routing is a must for networks that do not have a fixed point-to-point infrastructure, such as in an ad hoc wireless network that offers unrestricted mobility. A source node in such a network can communicate with a distant destination node after finding a route, relying on the intermediate nodes to transfer the packets. However, some intermediate nodes might act selfishly and drop packets for other nodes in order to save their own battery power.In this paper, we propose an algorithm to find selfish nodes and deal with them, using a modified Dynamic Source Routing (DSR) protocol, that we call Efficient Secure Dynamic Source Routing (ESDSR). Our results show an increase in the packet delivery ratio in a network containing selfish/unreliable nodes when we compare DSR with our protocol.

Mean squared residue based biclustering algorithms

The availability of large microarray data has brought along many challengesfor biological data mining. Following Cheng and Church [4], many differentbiclustering methods have been widely used to find appropriate subsets ofexperimental conditions. Still no paper directly optimizes or bounds the MeanSquared Residue (MSR) originally suggested by Cheng and Church. Their algorithm,for a given expression matrix A and an upper bound on MSR, finds kalmost non overlapping biclusters whose sizes are not predefined thus making itdifficult to compare with other methods. In this paper, we propose two new Mean Squared Residue (MSR) based biclusteringmethods. The first method is a dual biclustering algorithm which finds(k × l)-bicluster with MSR using a greedy approach. The second method combinesdual biclustering algorithm with quadratic programming. The dual biclusteringalgorithm reduces the size of the matrix, so that the quadratic programcan find an optimal bicluster reasonably fast. We control bicluster overlappingby changing the penalty for reusing cells in biclusters. The average MSR in [4]biclusterings for yeast is almost the same as for the proposed dual biclusteringwhile the median MSR is 1.5 times larger thus implying that the quadratic programfinds much better smaller biclusters.