Hannah Tipney

Biomedical Discovery Acceleration, with Applications to Craniofacial Development

The profusion of high-throughput instruments and the explosion of new results in the scientific literature, particularly in molecular biomedicine, is both a blessing and a curse to the bench researcher. Even knowledgeable and experienced scientists can benefit from computational tools that help navigate this vast and rapidly evolving terrain. In this paper, we describe a novel computational approach to this challenge, a knowledge-based system that combines reading, reasoning, and reporting methods to facilitate analysis of experimental data. Reading methods extract information from external resources, either by parsing structured data or using biomedical language processing to extract information from unstructured data, and track knowledge provenance. Reasoning methods enrich the knowledge that results from reading by, for example, noting two genes that are annotated to the same ontology term or database entry. Reasoning is also used to combine all sources into a knowledge network that represents the integration of all sorts of relationships between a pair of genes, and to calculate a combined reliability score. Reporting methods combine the knowledge network with a congruent network constructed from experimental data and visualize the combined network in a tool that facilitates the knowledge-based analysis of that data. An implementation of this approach, called the Hanalyzer, is demonstrated on a large-scale gene expression array dataset relevant to craniofacial development. The use of the tool was critical in the creation of hypotheses regarding the roles of four genes never previously characterized as involved in craniofacial development; each of these hypotheses was validated by further experimental work.

Spatial and Temporal Analysis of Gene Expression during Growth and Fusion of the Mouse Facial Prominences

Orofacial malformations resulting from genetic and/or environmental causes are frequent human birth defects yet their etiology is often unclear because of insufficient information concerning the molecular, cellular and morphogenetic processes responsible for normal facial development. We have, therefore, derived a comprehensive expression dataset for mouse orofacial development, interrogating three distinct regions – the mandibular, maxillary and frontonasal prominences. To capture the dynamic changes in the transcriptome during face formation, we sampled five time points between E10.5–E12.5, spanning the developmental period from establishment of the prominences to their fusion to form the mature facial platform. Seven independent biological replicates were used for each sample ensuring robustness and quality of the dataset. Here, we provide a general overview of the dataset, characterizing aspects of gene expression changes at both the spatial and temporal level. Considerable coordinate regulation occurs across the three prominences during this period of facial growth and morphogenesis, with a switch from expression of genes involved in cell proliferation to those associated with differentiation. An accompanying shift in the expression of polycomb and trithorax genes presumably maintains appropriate patterns of gene expression in precursor or differentiated cells, respectively. Superimposed on the many coordinated changes are prominence-specific differences in the expression of genes encoding transcription factors, extracellular matrix components, and signaling molecules. Thus, the elaboration of each prominence will be driven by particular combinations of transcription factors coupled with specific cell:cell and cell:matrix interactions. The dataset also reveals several prominence-specific genes not previously associated with orofacial development, a subset of which we externally validate. Several of these latter genes are components of bidirectional transcription units that likely share cis-acting sequences with well-characterized genes. Overall, our studies provide a valuable resource for probing orofacial development and a robust dataset for bioinformatic analysis of spatial and temporal gene expression changes during embryogenesis.

Isolation and characterisation of GTF2IRD2, a novel fusion gene and member of the TFII-I family of transcription factors, deleted in Williams-Beuren syndrome.

Williams–Beuren syndrome (WBS) is a developmental disorder with characteristic physical, cognitive and behavioural traits caused by a microdeletion of ∼1.5 Mb on chromosome 7q11.23. In total, 24 genes have been described within the deleted region to date. We have isolated and characterised a novel human gene, GTF2IRD2, mapping to the WBS critical region thought to harbour genes important for the cognitive aspects of the disorder. GTF2IRD2 is the third member of the novel TFII-I family of genes clustered on 7q11.23. The GTF2IRD2 protein contains two putative helix-loop-helix regions (I-repeats) and an unusual C-terminal CHARLIE8 transposon-like domain, thought to have arisen as a consequence of the random insertion of a transposable element generating a functional fusion gene. The retention of a number of conserved transposase-associated motifs within the protein suggests that the CHARLIE8-like region may still have some degree of transposase functionality that could influence the stability of the region in a mechanism similar to that proposed for Charcot–Marie–Tooth neuropathy type 1A. GTF2IRD2 is highly conserved in mammals and the mouse ortholgue (Gtf2ird2) has also been isolated and maps to the syntenic WBS region on mouse chromosome 5G. Deletion mapping studies using somatic cell hybrids show that some WBS patients are hemizygous for this gene, suggesting that it could play a role in the pathogenesis of the disorder.

Comparison of TFII-I gene family members deleted in Williams-Beuren syndrome

Williams‐Beuren syndrome (WBS) is a neurological disorder resulting from a microdeletion, typically 1.5 megabases in size, at 7q11.23. Atypical patients implicate genes at the telomeric end of this multigene deletion as the main candidates for the pathology of WBS in particular the unequal cognitive profile associated with the condition. We recently identified a gene (GTF2IRD2) that shares homology with other members of a unique family of transcription factors (TFII‐I family), which reside in the critical telomeric region. Using bioinformatics tools this study focuses on the detailed assessment of this gene family, concentrating on their characteristic structural components such as the leucine zipper (LZ) and I‐repeat elements, in an attempt to identify features that could aid functional predictions. Phylogenetic analysis identified distinct I‐repeat clades shared between family members. Linking functional data to one such clade has implicated them in DNA binding. The identification of PEST, synergy control motifs, and sumoylation sites common to all family members suggest a shared mechanism regulating the stability and transcriptional activity of these factors. In addition, the identification/isolation of short truncated isoforms for each TFII‐I family member implies a mode of self‐regulation. The exceptionally high identity shared between GTF2I and GTF2IRD2, suggests that heterodimers as well as homodimers are possible, and indicates overlapping functions between their respective short isoforms. Such cross‐reactivity between GTF2I and GTF2IRD2 short isoforms might have been the evolutionary driving force for the 7q11.23 chromosomal rearrangement not present in the syntenic region in mice.

An introduction to effective use of enrichment analysis software

In recent years, there has been an explosion in the range of software available for annotation enrichment analysis. Three classes of enrichment algorithms and their associated software implementations are introduced here.Their limitations and caveats are discussed, and direction for tool selection is given.

HIGH-PRECISION BIOLOGICAL EVENT EXTRACTION: EFFECTS OF SYSTEM AND OF DATA.

We approached the problems of event detection, argument identification, and negation and speculation detection in the BioNLP’09 information extraction challenge through concept recognition and analysis. Our methodology involved using the OpenDMAP semantic parser with manually written rules. The original OpenDMAP system was updated for this challenge with a broad ontology defined for the events of interest, new linguistic patterns for those events, and specialized coordination handling. We achieved state‐of‐the‐art precision for two of the three tasks, scoring the highest of 24 teams at precision of 71.81 on Task 1 and the highest of 6 teams at precision of 70.97 on Task 2. We provide a detailed analysis of the training data and show that a number of trigger words were ambiguous as to event type, even when their arguments are constrained by semantic class. The data is also shown to have a number of missing annotations. Analysis of a sampling of the comparatively small number of false positives returned by our system shows that major causes of this type of error were failing to recognize second themes in two‐theme events, failing to recognize events when they were the arguments to other events, failure to recognize nontheme arguments, and sentence segmentation errors. We show that specifically handling coordination had a small but important impact on the overall performance of the system. The OpenDMAP system and the rule set are available at http://bionlp.sourceforge.net.

Leveraging existing biological knowledge in the identification of candidate genes for facial dysmorphology.

BackgroundIn response to the frequently overwhelming output of high-throughput microarray experiments, we propose a methodology to facilitate interpretation of biological data in the context of existing knowledge. Through the probabilistic integration of explicit and implicit data sources a functional interaction network can be constructed. Each edge connecting two proteins is weighted by a confidence value capturing the strength and reliability of support for that interaction given the combined data sources. The resulting network is examined in conjunction with expression data to identify groups of genes with significant temporal or tissue specific patterns. In contrast to unstructured gene lists, these networks often represent coherent functional groupings.ResultsBy linking from shared functional categorizations to primary biological resources we apply this method to craniofacial microarray data, generating biologically testable hypotheses and identifying candidate genes for craniofacial development.ConclusionThe novel methodology presented here illustrates how the effective integration of pre-existing biological knowledge and high-throughput experimental data drives biological discovery and hypothesis generation.

Claudin 13, a Member of the Claudin Family Regulated in Mouse Stress Induced Erythropoiesis

Mammals are able to rapidly produce red blood cells in response to stress. The molecular pathways used in this process are important in understanding responses to anaemia in multiple biological settings. Here we characterise the novel gene Claudin 13 (Cldn13), a member of the Claudin family of tight junction proteins using RNA expression, microarray and phylogenetic analysis. We present evidence that Cldn13 appears to be co-ordinately regulated as part of a stress induced erythropoiesis pathway and is a mouse-specific gene mainly expressed in tissues associated with haematopoietic function. CLDN13 phylogenetically groups with its genomic neighbour CLDN4, a conserved tight junction protein with a putative role in epithelial to mesenchymal transition, suggesting a recent duplication event. Mechanisms of mammalian stress erythropoiesis are of importance in anaemic responses and expression microarray analyses demonstrate that Cldn13 is the most abundant Claudin in spleen from mice infected with Trypanosoma congolense. In mice prone to anaemia (C57BL/6), its expression is reduced compared to strains which display a less severe anaemic response (A/J and BALB/c) and is differentially regulated in spleen during disease progression. Genes clustering with Cldn13 on microarrays are key regulators of erythropoiesis (Tal1, Trim10, E2f2), erythrocyte membrane proteins (Rhd and Gypa), associated with red cell volume (Tmcc2) and indirectly associated with erythropoietic pathways (Cdca8, Cdkn2d, Cenpk). Relationships between genes appearing co-ordinately regulated with Cldn13 post-infection suggest new insights into the molecular regulation and pathways involved in stress induced erythropoiesis and suggest a novel, previously unreported role for claudins in correct cell polarisation and protein partitioning prior to erythroblast enucleation.

Visualization and language processing for supporting analysis across the biomedical literature

Finding relevant publications in the large and rapidly growing body of biomedical literature is challenging. Search queries on PubMed often return thousands of publications and it can be a tedious task to filter out irrelevant publications and choose a manageable set to read. We have developed a visual analytics system, named Bio-Jigsaw, which acts like a visual index on a document collection and supports biologists in investigating and understanding connections between biological entities. We apply natural language processing techniques to identify biological entities such as genes and pathways and visualize connections among them via multiple representations. Connections are based on co-occurrence in abstracts and also are drawn from ontologies or annotations in digital libraries. We demonstrate how Bio-Jigsaw can be used to analyze a PubMed search query on a gene related to breast cancer resulting in over 1500 primary papers.

Profiling Impaired Hepatic Endoplasmic Reticulum Glycosylation as a Consequence of Ethanol Ingestion

Alcoholic liver disease (ALD) is a prominent cause of morbidity and mortality in the United States. Alterations in protein folding occur in numerous disease states, including ALD. The endoplasmic reticulum (ER) is the primary site of post-translational modifications (PTM) within the cell. Glycosylation, the most abundant PTM, affects protein stability, structure, localization, and activity. Decreases in hepatic glycosylation machinery have been observed in rodent models of ALD, but specific protein targets have not been identified. Utilizing two-dimensional gel electrophoresis and liquid chromatography-tandem mass spectrometry, glycoproteins were identified in hepatic microsomal fractions from control and ethanol-fed mice. This study reports for the first time a global decrease in ER glycosylation. Additionally, the identification of 30 glycoproteins within this fraction elucidates pathway-specific alterations in ALD impaired glycosylation. Among the identified proteins, triacylglycerol hydrolase (TGH) is positively affected by glycosylation, showing increased activity following the addition of sugar moieties. Impaired TGH activity is associated with increased cellular storage of lipids and provides a potential mechanism for the observed pathologies associated with ALD.