BackgroundThe investigation of the interconnections between the molecular and genetic events that govern biological systems is essential if we are to understand the development of disease and design effective novel treatments. Microarray and next-generation sequencing technologies have the potential to provide this information. However, taking full advantage of these approaches requires that biological connections be made across large quantities of highly heterogeneous genomic datasets. Leveraging the increasingly huge quantities of genomic data in the public domain is fast becoming one of the key challenges in the research community today.Methodology/ResultsWe have developed a novel data mining framework that enables researchers to use this growing collection of public high-throughput data to investigate any set of genes or proteins. The connectivity between molecular states across thousands of heterogeneous datasets from microarrays and other genomic platforms is determined through a combination of rank-based enrichment statistics, meta-analyses, and biomedical ontologies. We address data quality concerns through dataset replication and meta-analysis and ensure that the majority of the findings are derived using multiple lines of evidence. As an example of our strategy and the utility of this framework, we apply our data mining approach to explore the biology of brown fat within the context of the thousands of publicly available gene expression datasets.ConclusionsOur work presents a practical strategy for organizing, mining, and correlating global collections of large-scale genomic data to explore normal and disease biology. Using a hypothesis-free approach, we demonstrate how a data-driven analysis across very large collections of genomic data can reveal novel discoveries and evidence to support existing hypothesis.

A complete description of the serological response following exposure of humans to complex pathogens is lacking and approaches suitable for accomplishing this are limited. Here we report, using malaria as a model, a method which elucidates the profile of antibodies that develop after natural or experimental infection or after vaccination with attenuated organisms, and which identifies immunoreactive antigens of interest for vaccine development or other applications. Expression vectors encoding 250 Plasmodium falciparum (Pf) proteins were generated by PCR/ recombination cloning; the proteins were individually expressed with >90% efficiency in E. coli cell-free in vitro transcription and translation reactions, and printed directly without purification onto microarray slides. The protein microarrays were probed with human sera from one of four groups which differed in immune status: sterile immunity or no immunity against experimental challenge following vaccination with radiation-attenuated Pf sporozoites, partial immunity acquired by natural exposure, and no previous exposure to Pf. Overall, 72 highly reactive Pf antigens were identified. Proteomic features associated with immunoreactivity were identified. AUTHOR CONTRIBUTIONSDLD and PLF conceived and designed the study, assisted in data analysis and interpretation, and wrote the manuscript. PLF and DHD contributed to the supervision and execution of the research. YM, BU, CV executed the research and assisted in data analysis and preparation of the manuscript figures. DM and XL provided the protein microarrays. SS, SH, AR and PB were responsible for the bioinformatic and statistical analysis. PLB and JCA assisted in the initial selected of open reading frames for analysis. DAF was responsible for the studies with irradiated sporozoite immunized volunteers that provided key specimens for analysis. JAO was responsible for the field studies with Kenyan volunteers that provided key specimens for analysis. COMPETING INTERESTSThe authors declare that no competing interests exist. NIH Public Access Author ManuscriptProteomics. Author manuscript; available in PMC 2011 January 16. Importantly, antibody profiles were distinct for each donor group. Information obtained from such analyses will facilitate identifying antigens for vaccine development, dissecting the molecular basis of immunity, monitoring the outcome of whole-organism vaccine trials, and identifying immune correlates of protection.

Enhanced stress responsiveness has been implicated as a potential mechanism contributing to the pathophysiology of irritable bowel syndrome (IBS), and should be reflected in altered function of the hypothalamic-pituitary-adrenal (HPA) axis and the sympathetic nervous system. Both of these systems can modulate mucosal immune function. The aims of this study were: (i) to characterize the basal circadian rhythm of adrenocorticotropin hormone (ACTH) and cortisol in IBS vs healthy controls; (ii) to compare stimulated ACTH, cortisol and noradrenaline responses to a pelvic visceral stressor (sigmoidoscopy) in IBS and controls; and (iii) to correlate neuroendocrine responses with colonic mucosal cytokine expression and symptoms in IBS. Two separate studies were conducted in women. In Study 1, basal cortisol levels were analysed in 41 IBS and 25 controls using 24-h collections of plasma ACTH and cortisol (q10 min sampling). In Study 2, 10 IBS patients with diarrhoea (IBS-D) and 10 controls underwent sigmoidoscopy with measurements of stimulated neuroendocrine responses and cytokine mRNA expression in colonic tissue. Basal ACTH levels were significantly blunted (P < 0.05), while basal and stimulated plasma cortisol levels were higher in patients. Basal cortisol levels prior to an experimental visceral stressor positively correlated with anxiety symptoms (P < 0.004), but not IBS symptoms. Irritable bowel syndrome patients with diarrhoea had significantly decreased mRNA expression of mucosal cytokines [interleukin (IL)-2, IL-6] in the sigmoid colon vs controls (P < 0.05). Although dysregulations in stress-responsive systems such as the HPA axis and mucosal immune function are demonstrated in IBS, they do not appear to have a primary role in modulating IBS severity and abdominal pain.

Supplementary data are available at Bioinformatics online.