Project description:In schistosomiasis japonica, the egg-induced granulomatous response and the development of extensive hepatic fibrosis is the main pathology. Information regarding the specific mechanisms associated with granuloma regression and the subsequent recovery events in the host liver are still limited. In this study, a murine model of schistosomiasis japonica was used to characterise the multicellular pathways occurring during liver regeneration. Schistosoma japonicum-infected C57BL/6 mice were administered with the drug praziquantel (PZQ), on a daily basis for five consecutive days to eliminate all adult parasites. The pathological changes of PZQ-treated groups after 3, 6 and 7 weeks post PZQ treatment were examined, along with the assessment of cellular infiltration to the liver. PZQ treatment significantly reduced the degree of splenomegaly, granuloma density and the collagen deposition of liver fibrosis. The infiltration of inflammatory cells, including neutrophils, eosinophils and macrophages to the liver were as well significantly decreased. Transcriptomic analysis revealed the significant up-regulation of fatty acid metabolism genes and the identification of peroxisome proliferator-activated receptor alpha (PPAR-α) as the upstream regulator during the process of liver recovery. Aryl hydrocarbon receptor (AhR) signalling pathway that is involved primarily in the regulation of hepatic enzymes responsible for xenobiotic metabolism was as well differentially up-regulated. These findings indicate that schistosome egg-induced fibrogenesis process is reversible, and provide a better understanding of the regression mechanisms associated with hepatic schistosomiasis. These results hold important implications for the future alleviation of this and other fibrotic diseases of clinical significance. C57BL/6 murine model infected with S. japonicum were treated orally with Praziquantel (PZQ) after 7 weeks post infection to examine the hepatic regression process following drug treatment. These PZQ-treated, non PZQ-treated and uninfected mice were then euthanised at 10, 13, and 14 weeks p.i. Livers were collected from each mice, and subjected to total RNA isolation and gene expression analysis. Microarray analysis of this study was performed using samples derived from 3 individual mice per group/time-point.

Project description:In patients with metastatic melanoma, the identification and validation of accurate prognostic biomarkers will assist rational treatment planning. Studies based on "-omics" technologies have focussed on a single high-throughput data type such as gene or microRNA transcripts. Occasionally, these features were evaluated in conjunction with limited clinico-pathologic data. With the increased availability of multiple data types, there is a pressing need to tease apart which of these sources contain the most valuable prognostic information. We evaluated and integrated several data types derived from the same tumor specimens in AJCC stage III melanoma patients - gene, protein, and microRNA expression as well as clinical, pathologic and mutation information - to determine their relative impact on prognosis. We used classification frameworks based on pre-validation and bootstrap multiple imputation classification to compare the prognostic power of each data source, both individually as well as integratively. We found that the prognostic utility of clinico-pathologic information was not out-performed by various "-omics" platforms. Rather, a combination of clinico-pathologic variables and mRNA expression data performed best. Furthermore, a patient-based classification analysis revealed that the prognostic accuracy of various data types was not the same for different patients, providing useful insights for ongoing developments in the individualized treatment of melanomas patients. SPECIAL NOTE: In this study, survival data were re-extracted from the MIA research database for all patients and brought up to date, revealing discrepancies affecting survival class in the case of four patients compared with the previous dataset (GSE53118: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE53118). The current survival data are considered to be more accurate although the expression information has not changed. In addition, there were 5 samples (122, 144, 195, 264, and 358) for which gene expression information were not available at the time of analysis. However, the associated clinical information for these samples is provided since it was analysed elsewhere in the accompanying publication. Samples eligible for this study (n=84) were obtained from lymph node specimens (Melanoma Institute Australia (MIA) Biospecimen Bank) in which macroscopic tumor was observed, obtained from patients believed to be without distant metastases at the time of tumor banking based on clinical examination and computerised axial tomographic scanning of the brain, chest, abdomen and pelvis. Specimens were macro-dissected at time of banking and subsequently reviewed to meet minimum criteria for tumor cell content (>80%) and amount of necrosis (<30%). Linked clinical and pathologic data were obtained from the MIA research database. We previously analyzed the distribution of survival times in these samples and identified more favorable and less favorable groups as patients having time from surgery to death from melanoma greater than 4 years with no sign of relapse (n=25) or less than 1 year (n=22), respectively (Mann et al. 2013, PMID: 22931913). Since that publication, survival data have been re-extracted from the MIA research database for all patients and brought up to date, revealing discrepancies affecting survival class in the case of four patients compared with the previous dataset. The current survival data are considered to be more accurate. MRNA expression profiling and somatic mutation profiling, were performed as previously described in Mann et al. 2013 (PMID: 22931913).

Project description:Prediction of outcome for melanoma patients with surgically-resected macroscopic nodal metastases is very imprecise. We performed a comprehensive clinicopathologic assessment of fresh-frozen macroscopic nodal metastases and the preceding primary melanoma, somatic mutation profiling and gene expression profiling to identify determinants of outcome in 79 melanoma patients. In addition to disease stage <II at initial presentation, the following clinical and pathologic factors were independent predictors of improved outcome (ORs for survival >4yr, 90%CI): presence of a nodular component in the primary melanoma (6.8, 0.6-76.0), and small cell size (11.1, 0.8-100.0) or low pigmentation (3.0, 0.8-100.0) in the nodal metastases. Absence of BRAF mutation (20.0, 1.0-1000.0) or NRAS mutation (16.7, 0.6-1000.0) were both favourable prognostic factors. A 46-gene expression signature with strong over-representation of immune response genes was predictive of better survival (10.9, 0.4-325.6); in the full cohort median survival was >100 months in those with the signature, but 10 months in those without. This relationship was validated in two previously published independent stage III melanoma datasets. We conclude that the presence of BRAF mutation, NRAS mutation and absence of an immune-related expressed gene profile predict poor outcome in melanoma patients with macroscopic stage III disease. Samples eligible for this study (n=79) were obtained from lymph node specimens (Melanoma Institute Australia (MIA) Biospecimen Bank) in which macroscopic tumor was observed, obtained from patients believed to be without distant metastases at the time of tumor banking based on clinical examination and computerised axial tomographic scanning of the brain, chest, abdomen and pelvis. Specimens were macro-dissected at time of banking and subsequently reviewed to meet minimum criteria for tumor cell content (>80%) and amount of necrosis (<30%). Linked clinical and pathologic data were obtained from the MIA research database. Total RNA was extracted from 20-30mg of fresh frozen tissue. Tissue samples were homogenized using a high-speed agitation polytron blender (Kinematica, Luzern, Switzerland) in the presence of Trizol. The RNA was isolated and purified with an RNeasy purification kit (Qiagen RNeasy purification kit- Qiagen Pty Ltd., Clifton Hill, Victoria, Australia) with DNAse I digestion on the column. The quality of the RNA preparations was assessed using the Agilent 2100 Bioanalyser (Agilent Technologies, Palo Alto, CA, USA). RNA integrity scores were >8 for all samples analyzed. cRNA amplification and labelling with biotin were performed using Illumina TotalPrep RNA amplification kit according to the manufacturer’s directions (Ambion, Inc., Austin, TX, USA) with 250ng total RNA as input material. Gene expression analysis was performed using the Sentrix Human-6 v3 Expression BeadChips (Illumina, Inc., San Diego, CA, USA), and BeadStation system from Illumina as per manufacturer’s instructions. Expression BeadChip using array annotation based on R-2.11.0 and illuminaHumanv3.db. Quality control was performed on all chips using R/Bioconductor and the lumi package (www.bioconductor.org). Data normalization was performed using a variance-stabilizing transform (VST) and quantile normalization as implemented in the lumi package for R/Bioconductor.

Project description:Stem cells are defined as self-renewing cell populations that can differentiate into multiple distinct cell types. However, hundreds of different human cell lines from embryonic, fetal, and adult sources have been called stem cells, even though they range from pluripotent cells, typified by embryonic stem cells, which are capable of virtually unlimited proliferation and differentiation, to adult stem cell lines, which can generate a far more limited repertory of differentiated cell types. The rapid increase in reports of new sources of stem cells and their anticipated value to regenerative medicine have highlighted the need for a general, reproducible method for classification of these cells. We report here the creation and analysis of a database of global gene expression profiles (“Stem Cell Matrix”) that enables the classification of cultured human stem cells in the context of a wide variety of pluripotent, multipotent, and differentiated cell types. Using an unsupervised clustering method to categorize a collection of ~150 cell samples, we discovered that pluripotent stem cell lines group together, while other cell types, including brain-derived neural stem cell lines, are very diverse. Using further bioinformatic analysis we uncovered a protein-protein network (“PluriNet”) that is shared by the pluripotent cells (embryonic stem cells, embryonal carcinomas, and induced pluripotent cells). Analysis of published data showed that the PluriNet appears to be a common characteristic of pluripotent cells, including mouse ES and iPS cells and human oocytes. Our results offer a new strategy for classifying stem cells and support the idea that pluripotence and self-renewal are under tight control by specific molecular networks. Keywords: cell type comparison, clustering, classification, systems analysis, gene networks, protein-protein interactions, taxonomy, reference based classification In vitro stem cell preparations were first grouped with an unsupervised clustering mechanism and then we identified differentially expressed, connected subnetworks by a systems analysis with differential MATISSE (Ulitsky & Schamir 2007), which integrated information on a) co-expression patterns b) topological data from protein-protein interaction networks of the respective gene products and c) group memberships from the Stem Cell Matrix clustering step.

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Gene expression during neuronal differentiation of embryonic stem cells

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