Oral Samples (HOMIM-v.02); A Characterization of the Oral Microbiome in Allogeneic Stem Cell Transplant Patients
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Jennifer Barb
PROVIDER: E-GEOD-34439 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
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Project description:Opportunistic oral infections are ultimately presented in a vast majority of HIV-infected patients, often causing debilitating lesions that also contribute to deterioration in nutritional health. Although appreciation for the role that the microbiota is likely to play in the initiation and/or enhancement of oral infections has grown considerably in recent years, little is known about the impact of HIV infection on host-microbe interactions within the oral cavity. In the current study, we characterize modulations in the bacterial composition of the lingual microbiome in patients with treated and untreated HIV infection. Bacterial species profiles were elucidated by microarray assay and compared between untreated HIV infected patients, HIV infected patients receiving antiretroviral therapy, and healthy HIV negative controls. The relationship between clinical parameters (viral burden and CD4+ T cell depletion) and the loss or gain of bacterial species was evaluated in each HIV patient group. Characterization of modulations in the dorsal tongue (lingual) microbiota that are associated with chronic HIV infection.
2012-06-25 | E-GEOD-38908 | biostudies-arrayexpress
Project description:Analysis of gingival crevicular fluid (GCF) samples may give information of the identity of unattached (planktonic) subgingival bacteria, the 35 forefront candidates for systemic dispersal via ulcerated periodontal pocket epithelium. Our study represents the first one targeting the identity of bacteria in gingival crevicular fluid. Methodology/Principal findings: We determined bacterial species diversity in GCF samples of a group of periodontitis patients and delineated contributing bacterial and host-associated factors. Subgingival paper point (PP) samples from the same sites were taken for comparison. After DNA extraction, 16S rRNA genes were PCR amplified and DNA-DNA hybridization was performed using a microarray for over 300 bacterial species or groups. Altogether 133 species from 41 genera and 8 phyla 45 were detected with 9 to 62 and 18 to 64 species in GCF and PP samples, respectively, 46 per patient. Projection to latent structures by means of partial least squares (PLS) was applied to the multivariate data analysis. PLS regression analysis showed that species of genera including Campylobacter, Selenomonas, Porphyromonas, Catonella, Tannerella, Dialister, Peptostreptococcus, Streptococcus and Eubacterium had significant positive correlations and the number of teeth with low-grade attachment loss a significant negative correlation to species diversity in GCF samples. OPLS/O2PLS discriminant analysis revealed significant positive correlations to GCF sample group membership for species of genera Campylobacter, Leptotrichia, Prevotella, Dialister, Tannerella, Haemophilus, Fusobacterium, Eubacterium, and Actinomyces. Conclusions/Significance: Among a variety of detected species those traditionally classified as Gram-negative anaerobes growing in mature subgingival biofilms were the main predictors for species diversity in GCF samples as well as responsible for distinguishing GCF samples from PP samples. GCF bacteria may provide new prospects for studying dynamic properties of subgingival biofilms. The microbial profiles of GCF and subgingival plaque were analyzed from 17 subjects with periodontal disease.
2010-05-21 | E-GEOD-21929 | biostudies-arrayexpress
Project description:Murine bronchioalveolar stem cells play a key role in pulmonary epithelial maintenance and repair but their molecular profile is poorly described so far. In this study, we used antibodies directed against Sca-1 and CD34, two markers originally ascribed to pulmonary cells harboring regenerative potential, to isolate single putative stem cells from murine lung tissue. The mean detection rate of positive cells was 8 per 106 lung cells. We then isolated and globally amplified the mRNA of positive cells to analyze gene expression in single cells. The resulting amplicons were then used for molecular profiling by transcript specific polymerase chain reaction (PCR) and global gene expression analysis using microarrays. Single marker-positive cells displayed a striking heterogeneity for the expression of epithelial and mesenchymal transcripts on the single cell level. Nevertheless, they could be subdivided into two cell populations: Sca-1+/CD34- and Sca-1+/CD34+ cells. In these subpopulations, transcripts of the epithelial marker Epcam (CD326) were exclusively detected in Sca-1+/CD34- cells (p = 0.03), whereas mRNA of the mesenchymal marker PdgfrM-NM-1 (CD140a) was detected in both subpopulations and more frequently in Sca-1+/CD34+ cells (p = 0.04). FACS analysis confirmed the existence of a PdgfrM-NM-1 positive subpopulation within Epcam+/Sca-1+/CD34- epithelial cells. Gene expression analysis by microarray hybridization identified transcripts differentially expressed between the two cell types as well as between epithelial reference cells and Sca-1+/CD34+ single cells, and selected transcripts were validated by quantitative PCR. Our results suggest a more mesenchymal commitment of Sca-1+/CD34+ cells and a more epithelial commitment of Sca-1+/CD34- cells. In summary, the study shows that single cell analysis enables the identification of novel molecular markers in yet poorly characterized populations of rare cells. Our results could further improve our understanding of Sca-1+/CD34+,- cells in the biology of the murine lung. Single cells of 10 Sca-1+/CD34+/CD31-/CD45-, 7 Sca-1+/CD34-/CD31-/CD45-, and 12 Sca-1-/CD34-/CD31-/CD45- were analyzed. Although the raw data are two channel only Cy5 signal of each file as analyzed. The Cy5 channel for each gene is normalized to the average Cy5 intensity of the gene across all samples.
2014-01-01 | E-GEOD-52215 | biostudies-arrayexpress
Project description:Biallelic inactivating mutations of the transcription factor 1 gene (TCF1), encoding hepatocyte nuclear factor 1a (HNF1a), were identified in 50% of hepatocellular adenomas (HCA) phenotypically characterized by a striking steatosis. To understand the molecular basis of this aberrant lipid storage, we performed a microarray transcriptome analysis validated by quantitative RT-PCR, western-blotting and lipid profiling. In mutated HCA, we showed a repression of gluconeogenesis coordinated with an activation of glycolysis, citrate shuttle and fatty acid synthesis predicting elevated rates of lipogenesis. Moreover, the strong dowregulation of L-FABP suggests that impaired fatty acid trafficking may also contribute to the fatty phenotype. In addition, transcriptional profile analysis of the observed deregulated genes in non-HNF1a-mutated HCA as well as in non-tumor livers allowed us to define a specific signature of the HNF1a-mutated HCA. In theses tumors, lipid composition was dramatically modified according to the transcriptional deregulations identified in the fatty acid synthetic pathway. Surprisingly, lipogenesis activation did not operate through SREBP-1 and ChREBP that were repressed. We conclude that steatosis in HNF1a-mutated HCA results mainly from an aberrant promotion of lipogenesis that is linked to HNF1a inactivation and that is independent of both SREBP-1 and ChREBP activation. Finally, our findings have potential clinical implications since lipogenesis can be efficiently inhibited by targeted therapies. Keywords: Disease state analysis In a first experiment, microarray analyses were applied to 8 HNF1-alpha mutated HCA and their corresponding non-tumor livers using cDNA in-house manufactured arrays following a randomized and blinded unbalanced design. RNA labelling, hybridization and analysis of fluorescence was carried out, as described in [Graudens et al., Genome Biol 2006, 7: R19; PMID: 16542501], considering that uneven numbers of samples were randomly allocated to each of the engineers who were not aware of sample phenotypes. To assess data reproducibility and minimize dye bias effects, each of the samples was measured four times, twice with Cy3 and twice with Cy5. To ensure robustness and flexibility in data analysis, a reference design was used with a universal reference sample (Stratagene, USA) serving as a baseline for the comparisons of tumor samples. Hybridizations were performed onto an 11K human array (11K_VJF-ARRAY, GPL3282), which provides a genome-wide coverage of functional pathways. Raw data were obtained using the ArrayVisionM-bM-^DM-" 7.0 software (Imaging Research Inc., USA); the resulting 3.2x106 hybridization data points collected from 32 arrays were stored in a MIAME-compliant database and pre-processed for normalization and filtering as described in [Graudens et al., Genome Biol 2006, 7: R19; PMID: 16542501]. In a second experiment, HG-U133A Affymetrix GeneChipTM arrays were used to compare the expression profiles of 5 HNF1-alpha mutated HCA and 4 non related non-tumor livers. RNA labelling, hybridization and analysis were carried out following the manufacturerM-bM-^@M-^Ys instructions(Affymetrix, Santa Clara, CA). Raw data were obtained by using Microarray Suite 5.0 (MAS5) software, embedded in the Affymetrix GeneChip Operating Software (Santa Clara, USA); the resulting raw numerical data (CEL files) - available as supplementary files - collected from 9 Affymetrix GeneChips were pre-processed for normalization and filtering as described in [Rebouissou et al., J Biol Chem 2007, in press; PMID: 17379603]. A platform-dependant statistical comparison was performed considering HNF1 alpha-mutated HCA versus non tumor liver tissues, using multiple testing procedures to evaluate statistical significance for differentially expressed genes, as described in [Graudens et al., Genome Biol 2006, 7: R19; PMID: 16542501] and in the supplemental experimental procedures in [Rebouissou et al., J Biol Chem 2007, in press; PMID: 17379603]. Thus, a combined cross-platform analysis was done considering the UGCluster Identifiants as a common primary key (Unigene Build184-June05). Additional information on the procedures is provided in the supplemental experimental procedures in [Rebouissou et al., J Biol Chem 2007, in press; PMID: 17379603].
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