Duygu Ucar

Transgenerational epigenetic inheritance of longevity in Caenorhabditis elegans

Chromatin modifiers regulate lifespan in several organisms, raising the question of whether changes in chromatin states in the parental generation could be incompletely reprogrammed in the next generation and thereby affect the lifespan of descendants. The histone H3 lysine 4 trimethylation (H3K4me3) complex, composed of ASH-2, WDR-5 and the histone methyltransferase SET-2, regulates Caenorhabditis elegans lifespan. Here we show that deficiencies in the H3K4me3 chromatin modifiers ASH-2, WDR-5 or SET-2 in the parental generation extend the lifespan of descendants up until the third generation. The transgenerational inheritance of lifespan extension by members of the ASH-2 complex is dependent on the H3K4me3 demethylase RBR-2, and requires the presence of a functioning germline in the descendants. Transgenerational inheritance of lifespan is specific for the H3K4me3 methylation complex and is associated with epigenetic changes in gene expression. Thus, manipulation of specific chromatin modifiers only in parents can induce an epigenetic memory of longevity in descendants.

H3K4me3 Breadth Is Linked to Cell Identity and Transcriptional Consistency

Trimethylation of histone H3 at lysine 4 (H3K4me3) is a chromatin modification known to mark the transcription start sites of active genes. Here, we show that H3K4me3 domains that spread more broadly over genes in a given cell type preferentially mark genes that are essential for the identity and function of that cell type. Using the broadest H3K4me3 domains as a discovery tool in neural progenitor cells, we identify novel regulators of these cells. Machine learning models reveal that the broadest H3K4me3 domains represent a distinct entity, characterized by increased marks of elongation. The broadest H3K4me3 domains also have more paused polymerase at their promoters, suggesting a unique transcriptional output. Indeed, genes marked by the broadest H3K4me3 domains exhibit enhanced transcriptional consistency and [corrected] increased transcriptional levels, and perturbation of H3K4me3 breadth leads to changes in transcriptional consistency. Thus, H3K4me3 breadth contains information that could ensure transcriptional precision at key cell identity/function genes.

An ensemble framework for clustering protein–protein interaction networks

MOTIVATION Protein-Protein Interaction (PPI) networks are believed to be important sources of information related to biological processes and complex metabolic functions of the cell. The presence of biologically relevant functional modules in these networks has been theorized by many researchers. However, the application of traditional clustering algorithms for extracting these modules has not been successful, largely due to the presence of noisy false positive interactions as well as specific topological challenges in the network. RESULTS In this article, we propose an ensemble clustering framework to address this problem. For base clustering, we introduce two topology-based distance metrics to counteract the effects of noise. We develop a PCA-based consensus clustering technique, designed to reduce the dimensionality of the consensus problem and yield informative clusters. We also develop a soft consensus clustering variant to assign multifaceted proteins to multiple functional groups. We conduct an empirical evaluation of different consensus techniques using topology-based, information theoretic and domain-specific validation metrics and show that our approaches can provide significant benefits over other state-of-the-art approaches. Our analysis of the consensus clusters obtained demonstrates that ensemble clustering can (a) produce improved biologically significant functional groupings; and (b) facilitate soft clustering by discovering multiple functional associations for proteins. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

An event-based framework for characterizing the evolutionary behavior of interaction graphs

Interaction graphs are ubiquitous in many fields such as bioinformatics, sociology and physical sciences. There have been many studies in the literature targeted at studying and mining these graphs. However, almost all of them have studied these graphs from a static point of view. The study of the evolution of these graphs over time can provide tremendous insight on the behavior of entities, communities and the flow of information among them. In this work, we present an event-based characterization of critical behavioral patterns for temporally varying interaction graphs. We use nonoverlapping snapshots of interaction graphs and develop a framework for capturing and identifying interesting events from them. We use these events to characterize complex behavioral patterns of individuals and communities over time. We show how semantic information can be incorporated to reason about community-behavior events. We also demonstrate the application of behavioral patterns for the purposes of modeling evolution, link prediction and influence maximization. Finally, we present a diffusion model for evolving networks, based on our framework.

FOXO3 Shares Common Targets with ASCL1 Genome-wide and Inhibits ASCL1-Dependent Neurogenesis

FOXO transcription factors are central regulators of longevity from worms to humans. FOXO3, the FOXO isoform associated with exceptional human longevity, preserves adult neural stem cell pools. Here, we identify FOXO3 direct targets genome-wide in primary cultures of adult neural progenitor cells (NPCs). Interestingly, FOXO3-bound sites are enriched for motifs for bHLH transcription factors, and FOXO3 shares common targets with the proneuronal bHLH transcription factor ASCL1/MASH1 in NPCs. Analysis of the chromatin landscape reveals that FOXO3 and ASCL1 are particularly enriched at the enhancers of genes involved in neurogenic pathways. Intriguingly, FOXO3 inhibits ASCL1-dependent neurogenesis in NPCs and direct neuronal conversion in fibroblasts. FOXO3 also restrains neurogenesis in vivo. Our study identifies a genome-wide interaction between the prolongevity transcription factor FOXO3 and the cell-fate determinant ASCL1 and raises the possibility that FOXO3s ability to restrain ASCL1-dependent neurogenesis may help preserve the neural stem cell pool.

Discover regulatory DNA elements using chromatin signatures and artificial neural network

MOTIVATION Recent large-scale chromatin states mapping efforts have revealed characteristic chromatin modification signatures for various types of functional DNA elements. Given the important influence of chromatin states on gene regulation and the rapid accumulation of genome-wide chromatin modification data, there is a pressing need for computational methods to analyze these data in order to identify functional DNA elements. However, existing computational tools do not exploit data transformation and feature extraction as a means to achieve a more accurate prediction. RESULTS We introduce a new computational framework for identifying functional DNA elements using chromatin signatures. The framework consists of a data transformation and a feature extraction step followed by a classification step using time-delay neural network. We implemented our framework in a software tool CSI-ANN (chromatin signature identification by artificial neural network). When applied to predict transcriptional enhancers in the ENCODE region, CSI-ANN achieved a 65.5% sensitivity and 66.3% positive predictive value, a 5.9% and 11.6% improvement, respectively, over the previously best approach. AVAILABILITY AND IMPLEMENTATION CSI-ANN is implemented in Matlab. The source code is freely available at http://www.medicine.uiowa.edu/Labs/tan/CSIANNsoft.zip CONTACT kai-tan@uiowa.edu SUPPLEMENTARY INFORMATION Supplementary Materials are available at Bioinformatics online.

FoxO6 regulates memory consolidation and synaptic function

The FoxO family of transcription factors is known to slow aging downstream from the insulin/IGF (insulin-like growth factor) signaling pathway. The most recently discovered FoxO isoform in mammals, FoxO6, is highly enriched in the adult hippocampus. However, the importance of FoxO factors in cognition is largely unknown. Here we generated mice lacking FoxO6 and found that these mice display normal learning but impaired memory consolidation in contextual fear conditioning and novel object recognition. Using stereotactic injection of viruses into the hippocampus of adult wild-type mice, we found that FoxO6 activity in the adult hippocampus is required for memory consolidation. Genome-wide approaches revealed that FoxO6 regulates a program of genes involved in synaptic function upon learning in the hippocampus. Consistently, FoxO6 deficiency results in decreased dendritic spine density in hippocampal neurons in vitro and in vivo. Thus, FoxO6 may promote memory consolidation by regulating a program coordinating neuronal connectivity in the hippocampus, which could have important implications for physiological and pathological age-dependent decline in memory.

Improving functional modularity in protein-protein interactions graphs using hub-induced subgraphs

Dense subgraphs of Protein-Protein Interaction (PPI) graphs are believed to be potential functional modules and play an important role in inferring the functional behavior of proteins. PPI graphs are known to exhibit the scale-free property in which a few nodes (hubs) are highly connected. This scale-free topology of PPI graphs makes it hard to isolate dense subgraphs effectively. In this paper, we propose a novel refinement method based on neighborhoods and the biological importance of hub proteins. We show that this refinement improves the functional modularity of the PPI graph and leads to effective clustering into dense components. A detailed comparison of these dense components with the ones obtained from the original PPI graph reveal three major benefits of the refinement: i) Enhancement of existing functional groupings; ii) Isolation of new functional groupings; and iii) Soft clustering of multifunctional hub proteins to multiple functional groupings.

Developmental enhancers are marked independently of zygotic Nodal signals in Xenopus.

To determine the hierarchy of transcriptional regulation within the in vivo vertebrate embryo, we examined whether developmental enhancers were influenced by Nodal signaling during early embryogenesis in Xenopus tropicalis. We find that developmental enhancers, defined by the active enhancer chromatin marks H3K4me1 and H3K27ac, are established as early as blastula stage and that Smad2/3 only strongly associates with these regions at gastrula stages. Significantly, when we perturb Nodal signaling using the drug SB431542, most enhancers remain marked, including at genes known to be sensitive to Nodal signaling. Overall, as enhancers are in an active conformation prior to Nodal signaling and are established independently of Nodal signaling, we suggest that many developmental enhancers are marked maternally, prior to exposure to extrinsic signals.

Markov clustering of protein interaction networks with improved balance and scalability

Markov Clustering (MCL) is a popular algorithm for clustering networks in bioinformatics such as protein-protein interaction networks and protein similarity networks. An important requirement when clustering protein networks is minimizing the number of big clusters, since it is generally understood that protein complexes tend not to have more than 15--30 nodes. Similarly, it is important to not output too many singleton clusters, since they do not provide much useful information. In this paper, we show how MCL may be modified so as to better respect these two requirements, while also taking the link structure in the graph into account. We design our algorithm on top of Regularized MCL (R-MCL) [16], a previously proposed modification of MCL. Our proposed variation computes a new regularization matrix at each iteration that penalizes big cluster sizes, with the size of the penalty being tunable using a balance parameter. This algorithm also naturally fits in a Multi level framework that allows great improvements in speed. We perform experiments on three real protein interaction networks and show significant improvements over MCL in quality, balance and execution speed.