Kathryn Dempsey

CD4+ Regulatory and Effector/Memory T Cell Subsets Profile Motor Dysfunction in Parkinson’s Disease

Animal models and clinical studies have linked the innate and adaptive immune system to the pathology of Parkinson’s disease (PD). Despite such progress, the specific immune responses that influence disease progression have eluded investigators. Herein, we assessed relationships between T cell phenotype and function with PD progression. Peripheral blood lymphocytes from two separate cohorts, a discovery cohort and a validation cohort, totaling 113 PD patients and 96 age- and environment-matched caregivers were examined by flow cytometric analysis and T cell proliferation assays. Increased effector/memory T cells (Tem), defined as CD45RO+ and FAS+ CD4+ T cells and decreased CD31+ and α4β7+ CD4+ T cells were associated with progressive Unified Parkinson’s Disease Rating Scale III scores. However, no associations were seen between immune biomarkers and increased age or disease duration. Impaired abilities of regulatory T cells (Treg) from PD patients to suppress effector T cell function was observed. These data support the concept that chronic immune stimulation, notably Tem activation and Treg dysfunction is linked to PD pathobiology and disease severity, but not disease duration. The association of T cell phenotypes with motor symptoms provides fresh avenues for novel biomarkers and therapeutic designs.

A Parallel Graph Sampling Algorithm for Analyzing Gene Correlation Networks

Abstract Effcient analysis of complex networks is often a challenging task due to its large size and the noise inherent in the system. One popular method of overcoming this problem is through graph sampling, that is extracting a representative subgraph from the larger network. The accuracy of the sample is validated by comparing the combinatorial properties of the subgraph and the original network. However, there has been little study in comparing networks based on the applications that they represent. Furthermore, sampling methods are generally applied agnostically, without mapping to the requirements of the underlying analysis. In this paper,we introduce a parallel graph sampling algorithm focusing on gene correlation networks. Densely connected subgraphs indicate important functional units of gene products. In our sampling algorithm, we emphasize maintaining highly connected regions of the network through parallel sampling based on extracting the maximal chordal subgraph of the network. We validate our methods by comparing both combinatorial properties and functional units of the subgraphs and larger networks. Our results show that even with significant reduction of the network (on average 20% to 40%), we obtain reliable samplings and many of the relevant combinatorial and functional properties are retained in the subgraphs.

A noise reducing sampling approach for uncovering critical properties in large scale biological networks

A correlation network is a graph-based representation of relationships among genes or gene products, such as proteins. The advent of high-throughput bioinformatics has resulted in the generation of volumes of data that require sophisticated in silico models, such as the correlation network, for in-depth analysis. Each element in our network represents expression levels of multiple samples of one gene and an edge connecting two nodes reflects the correlation level between the two corresponding genes in the network according to the Pearson correlation coefficient. Biological networks made in this manner are generally found to adhere to a scale-free structural nature, that is, it is modular and adheres to a power-law degree distribution. Filtering these structures to remove noise and coincidental edges in the network is a necessity for network theorists because unfortunately, when examining entire genomes at once, network size and complexity can act as a bottleneck for network manageability. Our previous work demonstrated that chordal graph based sampling of network results in viable models. In this paper, we extend our research to investigate how different orderings affect the results of our sampling, and maintain the viability of resulting network structures. Our results show that chordal graph based sampling not only conserves clusters that are present within the original networks, but by reducing noise can also help uncover additional functional clusters that were previously not obtainable from the original network.

MTAP: The Motif Tool Assessment Platform

BackgroundIn recent years, substantial effort has been applied to de novo regulatory motif discovery. At this time, more than 150 software tools exist to detect regulatory binding sites given a set of genomic sequences. As the number of software packages increases, it becomes more important to identify the tools with the best performance characteristics for specific problem domains. Identifying the correct tool is difficult because of the great variability in motif detection software. Consequently, many labs spend considerable effort testing methods to find one that works well in their problem of interest.ResultsIn this work, we propose a method (MTAP) that substantially reduces the effort required to assess de novo regulatory motif discovery software. MTAP differs from previous attempts at regulatory motif assessment in that it automates motif discovery tool pipelines (something that traditionally required many manual steps), automatically constructs orthologous upstream sequences, and provides automated benchmarks for many popular tools. As a proof of concept, we have run benchmarks over human, mouse, fly, yeast, E. coli and B. subtilis.ConclusionMTAP presents a new approach to the challenging problem of assessing regulatory motif discovery methods. The most current version of MTAP can be downloaded from http://biobase.ist.unomaha.edu/

Identifying aging-related genes in mouse hippocampus using gateway nodes

BackgroundHigh-throughput studies continue to produce volumes of metadata representing valuable sources of information to better guide biological research. With a stronger focus on data generation, analysis models that can readily identify actual signals have not received the same level of attention. This is due in part to high levels of noise and data heterogeneity, along with a lack of sophisticated algorithms for mining useful information. Networks have emerged as a powerful tool for modeling high-throughput data because they are capable of representing not only individual biological elements but also different types of relationships en masse. Moreover, well-established graph theoretic methodology can be applied to network models to increase efficiency and speed of analysis. In this project, we propose a network model that examines temporal data from mouse hippocampus at the transcriptional level via correlation of gene expression. Using this model, we formally define the concept of “gateway” nodes, loosely defined as nodes representing genes co-expressed in multiple states. We show that the proposed network model allows us to identify target genes implicated in hippocampal aging-related processes.ResultsBy mining gateway genes related to hippocampal aging from networks made from gene expression in young and middle-aged mice, we provide a proof-of-concept of existence and importance of gateway nodes. Additionally, these results highlight how network analysis can act as a supplement to traditional statistical analysis of differentially expressed genes. Finally, we use the gateway nodes identified by our method as well as functional databases and literature to propose new targets for study of aging in the mouse hippocampus.Conclu s ionsThis research highlights the need for methods of temporal comparison using network models and provides a systems biology approach to extract information from correlation networks of gene expression. Our results identify a number of genes previously implicated in the aging mouse hippocampus related to synaptic plasticity and apoptosis. Additionally, this model identifies a novel set of aging genes previously uncharacterized in the hippocampus. This research can be viewed as a first-step for identifying the processes behind comparative experiments in aging that is applicable to any type of temporal multi-state network.

The Development of Parallel Adaptive Sampling Algorithms for Analyzing Biological Networks

The availability of biological data in massive scales continues to represent unlimited opportunities as well as great challenges in bioinformatics research. Developing innovative data mining techniques and efficient parallel computational methods to implement them will be crucial in extracting useful knowledge from this raw unprocessed data, such as in discovering significant cellular subsystems from gene correlation networks. In this paper, we present a scalable combinatorial sampling technique, based on identifying maximum chordal sub graphs, that reduces noise from biological correlation networks, thereby making it possible to find biologically relevant clusters from the filtered network. We show how selecting the appropriate filter is crucial in maintaining the key structures from the original networks and uncovering new ones after removing noisy relationships. We also conduct one of the first comparisons in two important sensitivity criteria - the perturbation due to the vertex numbers of the network and perturbations due to data distribution. We demonstrate that our chordal-graph based filter is effective across many different vertex permutations, as is our parallel implementation of the sampling algorithm.

Function-preserving Filters for Sampling in Biological Networks

Assays created to study systems of disease and aging can offer a whole new set of therapeutic targets. However, with experiments of this immense volume, data becomes unmanageable for many traditional analyses. Enter the biological network, a tool for modeling relationships among high-throughput data that is quickly rising in popularity. Small networks (in the order of hundreds to few thousands of nodes) use relationships between network structure to infer biological function; this relationship has been confirmed and used in many studies to advance the study of model organisms. Networks built for assessing entire genomes, or entire protein repertoires, however, tend to be very large and complex, having tens of thousands of nodes and in some cases upwards of millions of edges. Thus, network sampling techniques take an appropriate step to reduce complexity while modeling data. Here we present a new type of network sampling applied to biological correlations network, the spanning tree, designed to identify critical hub nodes in the model. We compare this filter to others previously used to identify structures in complex networks, chordal-based filters. The results of this work highlight the applicability for multiple filters based upon the graphic structure and biological result desired.

Identifying modular function via edge annotation in gene correlation networks using Gene Ontology search

Correlation networks provide a powerful tool for analyzing large sets of biological information. This method of high-throughput data modeling has important implications in uncovering novel knowledge of cellular function. Previous studies on other types of network modeling (protein-protein interaction networks, metabolomes, etc.) have demonstrated the presence of relationships between network structures and organization of cellular function. Studies with correlation network further confirm the existence of such network structure and biological function relationship. However, correlation networks are typically noisy and the identified network structures, such as clusters, must be further investigated to verify actual cellular function. This is traditionally done using Gene Ontology enrichment of the genes in that cluster. In this study a novel method to identify common cluster functions in correlation networks is proposed, which uses annotations of edges as opposed to the traditional annotation of node analysis. The results obtained using proposed method reveals functional relationships in clusters not visible by the traditional approach.

An intelligent data-centric approach toward identification of conserved motifs in protein sequences

The continued integration of the computational and biological sciences has revolutionized genomic and proteomic studies. However, efficient collaboration between these fields requires the creation of shared standards. A common problem arises when biological input does not properly fit the expectations of the algorithm, which can result in misinterpretation of the output. This potential confounding of input/output is a drawback especially when regarding motif finding software. Here we propose a method for improving output by selecting input based upon evolutionary distance, domain architecture, and known function. This method improved detection of both known and unknown motifs in two separate case studies. By standardizing input considerations, both biologists and bioinformaticians can better interpret and design the evolving sophistication of bioinformatic software.

On the Robustness of the Biological Correlation Network Model

Recent progress in high-throughput technology has resulted in a significant data overload. Determining how to obtain valuable knowledge from such massive raw data has become one of the most challenging issues in biomedical research. As a result, bioinformatics researchers continue to look for advanced data analysis tools to analysis and mine the available data. Correlation network models obtained from various biological assays, such as those measuring gene expression levels, are a powerful method for representing correlated expression. Although correlation does not always imply causation, the correlation network has been shown to be effective in identifying elements of interest in various bioinformatics applications. While these models have found success, little to no investigation has been made into the robustness of relationships in the correlation network with regard to vulnerability of the model according to manipulation of sample values. Particularly, reservations about the correlation network model stem from a lack of testing on the reliability of the model. In this work, we probe the robustness of the model by manipulating samples to create six different expression networks and find a slight inverse relationship between sample count and network size/density. When samples are iteratively removed during model creation, the results suggest that network edges may or may not remain within the statistical parameters of the model, suggesting that there is room for improvement in the filtering of these networks. A cursory investigation into a secondary robustness threshold using these measures confirms the existence of a positive relationship between sample size and edge robustness. This work represents an important step toward better understanding of the critical noise versus signal issue in the correlation network model.