Melbourne Medical School Collected Works - Research Publications
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ItemNo Preview AvailableDirect association between pharyngeal viral secretion and host cytokine response in severe pandemic influenza(BMC, 2011-08-31)BACKGROUND: Severe disease caused by 2009 pandemic influenza A/H1N1virus is characterized by the presence of hypercytokinemia. The origin of the exacerbated cytokine response is unclear. As observed previously, uncontrolled influenza virus replication could strongly influence cytokine production. The objective of the present study was to evaluate the relationship between host cytokine responses and viral levels in pandemic influenza critically ill patients. METHODS: Twenty three patients admitted to the ICU with primary viral pneumonia were included in this study. A quantitative PCR based method targeting the M1 influenza gene was developed to quantify pharyngeal viral load. In addition, by using a multiplex based assay, we systematically evaluated host cytokine responses to the viral infection at admission to the ICU. Correlation studies between cytokine levels and viral load were done by calculating the Spearman correlation coefficient. RESULTS: Fifteen patients needed of intubation and ventilation, while eight did not need of mechanical ventilation during ICU hospitalization. Viral load in pharyngeal swabs was 300 fold higher in the group of patients with the worst respiratory condition at admission to the ICU. Pharyngeal viral load directly correlated with plasma levels of the pro-inflammatory cytokines IL-6, IL-12p70, IFN-γ, the chemotactic factors MIP-1β, GM-CSF, the angiogenic mediator VEGF and also of the immuno-modulatory cytokine IL-1ra (p < 0.05). Correlation studies demonstrated also the existence of a significant positive association between the levels of these mediators, evidencing that they are simultaneously regulated in response to the virus. CONCLUSIONS: Severe respiratory disease caused by the 2009 pandemic influenza virus is characterized by the existence of a direct association between viral replication and host cytokine response, revealing a potential pathogenic link with the severe disease caused by other influenza subtypes such as H5N1.
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ItemAnalysis of the genome content of Lactococcus garvieae by genomic interspecies microarray hybridization(BIOMED CENTRAL LTD, 2010-03-16)BACKGROUND: Lactococcus garvieae is a bacterial pathogen that affects different animal species in addition to humans. Despite the widespread distribution and emerging clinical significance of L. garvieae in both veterinary and human medicine, there is almost a complete lack of knowledge about the genetic content of this microorganism. In the present study, the genomic content of L. garvieae CECT 4531 was analysed using bioinformatics tools and microarray-based comparative genomic hybridization (CGH) experiments. Lactococcus lactis subsp. lactis IL1403 and Streptococcus pneumoniae TIGR4 were used as reference microorganisms. RESULTS: The combination and integration of in silico analyses and in vitro CGH experiments, performed in comparison with the reference microorganisms, allowed establishment of an inter-species hybridization framework with a detection threshold based on a sequence similarity of >or= 70%. With this threshold value, 267 genes were identified as having an analogue in L. garvieae, most of which (n = 258) have been documented for the first time in this pathogen. Most of the genes are related to ribosomal, sugar metabolism or energy conversion systems. Some of the identified genes, such as als and mycA, could be involved in the pathogenesis of L. garvieae infections. CONCLUSIONS: In this study, we identified 267 genes that were potentially present in L. garvieae CECT 4531. Some of the identified genes could be involved in the pathogenesis of L. garvieae infections. These results provide the first insight into the genome content of L. garvieae.
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ItemHost adaptive immunity deficiency in severe pandemic influenza(BMC, 2010)INTRODUCTION: Pandemic A/H1N1/2009 influenza causes severe lower respiratory complications in rare cases. The association between host immune responses and clinical outcome in severe cases is unknown. METHODS: We utilized gene expression, cytokine profiles and generation of antibody responses following hospitalization in 19 critically ill patients with primary pandemic A/H1N1/2009 influenza pneumonia for identifying host immune responses associated with clinical outcome. Ingenuity pathway analysis 8.5 (IPA) (Ingenuity Systems, Redwood City, CA) was used to select, annotate and visualize genes by function and pathway (gene ontology). IPA analysis identified those canonical pathways differentially expressed (P < 0.05) between comparison groups. Hierarchical clustering of those genes differentially expressed between groups by IPA analysis was performed using BRB-Array Tools v.3.8.1. RESULTS: The majority of patients were characterized by the presence of comorbidities and the absence of immunosuppressive conditions. pH1N1 specific antibody production was observed around day 9 from disease onset and defined an early period of innate immune response and a late period of adaptive immune response to the virus. The most severe patients (n = 12) showed persistence of viral secretion. Seven of the most severe patients died. During the late phase, the most severe patient group had impaired expression of a number of genes participating in adaptive immune responses when compared to less severe patients. These genes were involved in antigen presentation, B-cell development, T-helper cell differentiation, CD28, granzyme B signaling, apoptosis and protein ubiquitination. Patients with the poorest outcomes were characterized by proinflammatory hypercytokinemia, along with elevated levels of immunosuppressory cytokines (interleukin (IL)-10 and IL-1ra) in serum. CONCLUSIONS: Our findings suggest an impaired development of adaptive immunity in the most severe cases of pandemic influenza, leading to an unremitting cycle of viral replication and innate cytokine-chemokine release. Interruption of this deleterious cycle may improve disease outcome.
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ItemA method for automatically extracting infectious disease-related primers and probes from the literature(BMC, 2010-08-03)BACKGROUND: Primer and probe sequences are the main components of nucleic acid-based detection systems. Biologists use primers and probes for different tasks, some related to the diagnosis and prescription of infectious diseases. The biological literature is the main information source for empirically validated primer and probe sequences. Therefore, it is becoming increasingly important for researchers to navigate this important information. In this paper, we present a four-phase method for extracting and annotating primer/probe sequences from the literature. These phases are: (1) convert each document into a tree of paper sections, (2) detect the candidate sequences using a set of finite state machine-based recognizers, (3) refine problem sequences using a rule-based expert system, and (4) annotate the extracted sequences with their related organism/gene information. RESULTS: We tested our approach using a test set composed of 297 manuscripts. The extracted sequences and their organism/gene annotations were manually evaluated by a panel of molecular biologists. The results of the evaluation show that our approach is suitable for automatically extracting DNA sequences, achieving precision/recall rates of 97.98% and 95.77%, respectively. In addition, 76.66% of the detected sequences were correctly annotated with their organism name. The system also provided correct gene-related information for 46.18% of the sequences assigned a correct organism name. CONCLUSIONS: We believe that the proposed method can facilitate routine tasks for biomedical researchers using molecular methods to diagnose and prescribe different infectious diseases. In addition, the proposed method can be expanded to detect and extract other biological sequences from the literature. The extracted information can also be used to readily update available primer/probe databases or to create new databases from scratch.
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ItemColon cancer molecular subtypes identified by expression profiling and associated to stroma, mucinous type and different clinical behavior(BMC Cancer, 2012)BACKGROUND: Colon cancer patients with the same stage show diverse clinical behavior due to tumor heterogeneity. We aimed to discover distinct classes of tumors based on microarray expression patterns, to analyze whether the molecular classification correlated with the histopathological stages or other clinical parameters and to study differences in the survival. METHODS: Hierarchical clustering was performed for class discovery in 88 colon tumors (stages I to IV). Pathways analysis and correlations between clinical parameters and our classification were analyzed. Tumor subtypes were validated using an external set of 78 patients. A 167 gene signature associated to the main subtype was generated using the 3-Nearest-Neighbor method. Coincidences with other prognostic predictors were assesed. RESULTS: Hierarchical clustering identified four robust tumor subtypes with biologically and clinically distinct behavior. Stromal components (p < 0.001), nuclear β-catenin (p = 0.021), mucinous histology (p = 0.001), microsatellite-instability (p = 0.039) and BRAF mutations (p < 0.001) were associated to this classification but it was independent of Dukes stages (p = 0.646). Molecular subtypes were established from stage I. High-stroma-subtype showed increased levels of genes and altered pathways distinctive of tumour-associated-stroma and components of the extracellular matrix in contrast to Low-stroma-subtype. Mucinous-subtype was reflected by the increased expression of trefoil factors and mucins as well as by a higher proportion of MSI and BRAF mutations. Tumor subtypes were validated using an external set of 78 patients. A 167 gene signature associated to the Low-stroma-subtype distinguished low risk patients from high risk patients in the external cohort (Dukes B and C:HR = 8.56(2.53-29.01); Dukes B,C and D:HR = 1.87(1.07-3.25)). Eight different reported survival gene signatures segregated our tumors into two groups the Low-stroma-subtype and the other tumor subtypes. CONCLUSIONS: We have identified novel molecular subtypes in colon cancer with distinct biological and clinical behavior that are established from the initiation of the tumor. Tumor microenvironment is important for the classification and for the malignant power of the tumor. Differential gene sets and biological pathways characterize each tumor subtype reflecting underlying mechanisms of carcinogenesis that may be used for the selection of targeted therapeutic procedures. This classification may contribute to an improvement in the management of the patients with CRC and to a more comprehensive prognosis.
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ItemSYMBIOmatics: Synergies in Medical Informatics and Bioinformatics - exploring current scientific literature for emerging topics(BMC, 2007)BACKGROUND: The SYMBIOmatics Specific Support Action (SSA) is "an information gathering and dissemination activity" that seeks "to identify synergies between the bioinformatics and the medical informatics" domain to improve collaborative progress between both domains (ref. to http://www.symbiomatics.org). As part of the project experts in both research fields will be identified and approached through a survey. To provide input to the survey, the scientific literature was analysed to extract topics relevant to both medical informatics and bioinformatics. RESULTS: This paper presents results of a systematic analysis of the scientific literature from medical informatics research and bioinformatics research. In the analysis pairs of words (bigrams) from the leading bioinformatics and medical informatics journals have been used as indication of existing and emerging technologies and topics over the period 2000-2005 ("recent") and 1990-1990 ("past"). We identified emerging topics that were equally important to bioinformatics and medical informatics in recent years such as microarray experiments, ontologies, open source, text mining and support vector machines. Emerging topics that evolved only in bioinformatics were system biology, protein interaction networks and statistical methods for microarray analyses, whereas emerging topics in medical informatics were grid technology and tissue microarrays. CONCLUSION: We conclude that although both fields have their own specific domains of interest, they share common technological developments that tend to be initiated by new developments in biotechnology and computer science.
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ItemMapping Biomedical Vocabularies: A Semi-Automated Term Matching Approach(IOS PRESS, 2014)Biomedical vocabularies vary in scope, and it is often necessary to utilize multiple vocabularies simultaneously in order to cover the full range of concepts relevant to a given biomedical application. However, as the number and size of these resources grow both redundancy (i.e., different vocabularies containing similar terms) and inconsistency (i.e., different terms in multiple vocabularies referring to the same entity) between the vocabularies increase. Therefore, there is a need for automatically aligning vocabularies. In this paper, we explore and propose new methods for detecting probable matches between two vocabularies. The methods build upon existing string similarity functions, enhancing these functions for the context of semi-automated vocabulary matching.