Andy W. Fulmer

Mucosal cytokine imbalance in irritable bowel syndrome

Objective. To systematically examine mucosal biopsies for differences in cytokine gene expression and protein secretion. Material and methods. The study included 59 females with irritable bowel syndrome (IBS) and 39, otherwise healthy, female volunteers presenting for colonoscopy. Colonic biopsies from subsets were studied by microarray analysis (IBS, n=9; controls, n=8), quantitative reverse transcription-polymerase chain reaction (qRT-PCR) (IBS, n=22; controls, n=21), and ex vivo biopsy culture (IBS, n=28, controls, n=10). Biopsies from patients with active colitis were used as inflammatory disease controls. Results. While gene array analysis revealed extensive overlapping between controls and IBS patients, reduced expression of genes linked to chemokine function was evident among the IBS patients alone. Differential expression was confirmed by qRT-PCR or ex vivo biopsy culture for 5 out of 6 selected genes. Reduced secretion of chemokines (IL-8, CXCL-9 and MCP-1) but not pro-inflammatory cytokines (TNF-α, IL-6 and IL-1β) was established on the basis of the ex vivo biopsy cultures. These findings were in marked contrast to the IBD patients who demonstrated increased production of both chemokines and pro-inflammatory cytokines. Conclusions. Despite the expected heterogeneity of the disorder, differences in mucosal chemokine signalling were evident in this cross-sectional study of IBS patients at the level of both gene expression and protein secretion, with IBS patients demonstrating a consistent deficit in the expression and secretion of chemokines known to play a critical role in mucosal defence.

Gene Expression Profiles during In Vivo Human Rhinovirus Infection Insights into the Host Response

RATIONALE Human rhinovirus infections cause colds and trigger exacerbations of lower airway diseases. OBJECTIVES To define changes in gene expression profiles during in vivo rhinovirus infections. METHODS Nasal epithelial scrapings were obtained before and during experimental rhinovirus infection, and gene expression was evaluated by microarray. Naturally acquired rhinovirus infections, cultured human epithelial cells, and short interfering RNA knockdown were used to further evaluate the role of viperin in rhinovirus infections. MEASUREMENTS AND MAIN RESULTS Symptom scores and viral titers were measured in subjects inoculated with rhinovirus or sham control, and changes in gene expression were assessed 8 and 48 hours after inoculation. Real-time reverse transcription-polymerase chain reaction for viperin and rhinoviruses was used in naturally acquired infections, and viperin mRNA levels and viral titers were measured in cultured cells. Rhinovirus-induced changes in gene expression were not observed 8 hours after viral infection, but 11,887 gene transcripts were significantly altered in scrapings obtained 2 days postinoculation. Major groups of up-regulated genes included chemokines, signaling molecules, interferon-responsive genes, and antivirals. Viperin expression was further examined and also was increased in naturally acquired rhinovirus infections, as well as in cultured human epithelial cells infected with intact, but not replication-deficient, rhinovirus. Knockdown of viperin with short interfering RNA increased rhinovirus replication in infected epithelial cells. CONCLUSIONS Rhinovirus infection significantly alters the expression of many genes associated with the immune response, including chemokines and antivirals. The data obtained provide insights into the host response to rhinovirus infection and identify potential novel targets for further evaluation.

Differential network expression during drug and stress response

MOTIVATION The application of microarray chip technology has led to an explosion of data concerning the expression levels of the genes in an organism under a plethora of conditions. One of the major challenges of systems biology today is to devise generally applicable methods of interpreting this data in a way that will shed light on the complex relationships between multiple genes and their products. The importance of such information is clear, not only as an aid to areas of research like drug design, but also as a contribution to our understanding of the mechanisms behind an organisms ability to react to its environment. RESULTS We detail one computational approach for using gene expression data to identify response networks in an organism. The method is based on the construction of biological networks given different sets of interaction information and the reduction of the said networks to important response sub-networks via the integration of the gene expression data. As an application, the expression data of known stress responders and DNA repair genes in Mycobacterium tuberculosis is used to construct a generic stress response sub-network. This is compared to similar networks constructed from data obtained from subjecting M.tuberculosis to various drugs; we are thus able to distinguish between generic stress response and specific drug response. We anticipate that this approach will be able to accelerate target identification and drug development for tuberculosis in the future. CONTACT chris@lanl.gov SUPPLEMENTARY INFORMATION Supplementary Figures 1 through 6 on drug response networks and differential network analyses on cerulenin, chlorpromazine, ethionamide, ofloxacin, thiolactomycin and triclosan. Supplementary Tables 1 to 3 on predicted protein interactions. http://www.santafe.edu/~chris/DifferentialNW.

Extracting biochemical interactions from MEDLINE using a link grammar parser

Many natural language processing approaches at various complexity levels have been reported for extracting biochemical interactions from MEDLINE. While some algorithms using simple template matching are unable to deal with the complex syntactic structures, others exploiting sophisticated parsing techniques are hindered by greater computational cost. This study investigates link grammar parsing for extracting biochemical interactions. Link grammar parsing can handle many syntactic structures and is computationally relatively efficient. We experimented on a sample MEDLINE corpus. Although the parser was originally developed for conversational English and made many mistakes in parsing sentences from the biochemical domain, it nevertheless achieved better overall performance than a co-occurrence-only method. Customizing the parser for the biomedical domain is expected to improve its performance further.

Gingival Transcriptome Patterns During Induction and Resolution of Experimental Gingivitis in Humans

BACKGROUND To our knowledge, changes in the patterns of whole-transcriptome gene expression that occur during the induction and resolution of experimental gingivitis in humans were not previously explored using bioinformatic tools. METHODS Gingival biopsy samples collected from 14 subjects during a 28-day stent-induced experimental gingivitis model, followed by treatment, and resolution at days 28 through 35 were analyzed using gene-expression arrays. Biopsy samples were collected at different sites within each subject at baseline (day 0), at the peak of gingivitis (day 28), and at resolution (day 35) and processed using whole-transcriptome gene-expression arrays. Gene-expression data were analyzed to identify biologic themes and pathways associated with changes in gene-expression profiles that occur during the induction and resolution of experimental gingivitis using bioinformatic tools. RESULTS During disease induction and resolution, the dominant expression pathway was the immune response, with 131 immune response genes significantly up- or downregulated during induction, during resolution, or during both at P <0.05. During induction, there was significant transient increase in the expression of inflammatory and oxidative stress mediators, including interleukin (IL)-1 alpha (IL1A), IL-1 beta (IL1B), IL8, RANTES, colony stimulating factor 3 (CSF3), and superoxide dismutase 2 (SOD2), and a decreased expression of IP10, interferon inducible T-cell alpha chemoattractant (ITAC), matrix metalloproteinase 10 (MMP10), and beta 4 defensin (DEFB4). These genes reversed expression patterns upon resolution in parallel with the reversal of gingival inflammation. CONCLUSIONS A relatively small subset (11.9%) of the immune response genes analyzed by array was transiently activated in response to biofilm overgrowth, suggesting a degree of specificity in the transcriptome-expression response. The fact that this same subset demonstrates a reversal in expression patterns during clinical resolution implicates these genes as being critical for maintaining tissue homeostasis at the biofilm-gingival interface. In addition to the immune response pathway as the dominant response theme, new candidate genes and pathways were identified as being selectively modulated in experimental gingivitis, including neural processes, epithelial defenses, angiogenesis, and wound healing.

Creating metabolic and regulatory network models using fuzzy cognitive maps

This paper describes a model of metabolic networks that uses fuzzy cognitive maps. Nodes of the map represent specific biochemicals such as proteins, RNA, and small molecules, or stimuli, such as light, heat, or nutrients. Edges of the map capture regulatory and metabolic relationships found in biological systems. These relationships are established by a domain expert, the biological literature, and extracted from RNA microarray data. This work is part of the development of a software tool, FCModeler, which models and visualizes metabolic networks. A model of the metabolism of the plant hormone gibberellin in Arabidopsis is used to show the capabilities of the fuzzy model.

Using the biological taxonomy to access biological literature with PathBinderH

UNLABELLED PathBinderH allows users to make queries that retrieve sentences and the abstracts containing them from PubMed. Another aspect of PathBinderH is that users can specify biological taxa in order to limit searches by mentioning either the specified taxa, or their subordinate taxa, in the biological taxonomy. Although the current project requires this function only for plant taxa, the principle is extensible to the entire taxonomy. AVAILABILITY www.plantgenomics.iastate.edu/PathBinderH. Source code and databases on request.

PubMed Assistant: a biologist-friendly interface for enhanced PubMed search

MEDLINE is one of the most important bibliographical information sources for biologists and medical workers. Its PubMed interface supports Boolean queries, which are potentially expressive and exact. However, PubMed is also designed to support simplicity of use at the expense of query expressiveness and exactness. Many PubMed users have never tried explicit Boolean queries. We developed a Java program, PubMed Assistant, to make literature access easier in several ways. PubMed Assistant provides an interface that efficiently displays information about the citations and includes useful functions such as keyword highlighting, export to citation managers, clickable links to Google Scholar and others that are lacking in PubMed.