Gene expression in obese humans before and during treatment with rimonabant
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE68055 | GEO | 2015/04/21
SECONDARY ACCESSION(S): PRJNA281622
REPOSITORIES: GEO
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Project description:Rimonabant was prescribed to eight patients, admitted to the Overweight Study Unit at the Department of Medicine, Karolinska University Hospital. All patients had a BMI > 35 kg/m2, were treated on clinical indications (metabolic and mechanical disability) and without mental disturbances. Blood samples were collected before treatment and at the first clinical control, when the patients had received rimonabant, 20 mg daily, for 4 weeks. Blood mononuclear cells were isolated by Ficoll separation (Ficoll-Paque PLUS, GE Healthcare). From the cell-pellet total RNA was prepared using Qiagen midi plus kit (Qiagen GmbH, Hilden Germany) as recommended by the manufacturer and was quality controlled on an Agilent Bioanalyzer (Agilent Technologies, Inc. Palo Alto, CA). Six pretreatment samples and seven samples obtained after rimonabant treatment passed the quality control. The cRNA synthesis for microarray experiments and the hybridizations were carried out using Affymetrix Human Gene 1.0 ST Array (Affymetrix, Inc., Santa Clara, CA) according to standard Affymetrix protocols at the core facility for Bioinformatics and Expression Analysis, Department of Biosciences and Nutrition, Karolinska Institutet.
2015-04-21 | E-GEOD-68055 | biostudies-arrayexpress
Project description:In the present study we aimed to investigate the systemic response to a rupture of intracranial aneurysms by an analysis of global gene expression profiles in peripheral blood cells. In addition, we sought to determine whether this approach could provide biomarkers related to clinical status of subarachnoid hemorrhage patients. Patients with subarachnoid hemorrhage from ruptured aneurysm were prospectively recruited from patients consecutively admitted to the Departments of Neurology or Neurosurgery and Neurotraumatology, University Hospital, Krakow, Poland in 2010 and 2011. Control subjects were recruited from patients of the Department of Neurology suffered from headaches.
2014-01-01 | E-GEOD-36791 | biostudies-arrayexpress
Project description:To investigate the corelation of genes regulated by TXX-1-10 and Rimonabant , we treated MDA-MB-231 cells with TXX-1-10 and Rimonabant.
2021-02-09 | GSE166371 | GEO
Project description:Gene expression data from whole-blood collected from healthy Vietnamese subjects (n=8) or from patients admitted to hospital with acute uncomplicated (n=9) or complicated (n=20) Plasmodium falciparum malaria infection. A clinical history design type is where the organisms clinical history of diagnosis, treatments, e.g. vaccinations, surgery etc. Disease State: patient with complicated or uncomplicated Plasmodium falciparum malaria infection or Healthy control
2016-02-15 | GSE35858 | GEO
Project description:Gene expression data from whole-blood collected from healthy Vietnamese subjects (n=8) or from patients admitted to hospital with acute uncomplicated (n=9) or complicated (n=20) Plasmodium falciparum malaria infection. A clinical history design type is where the organisms clinical history of diagnosis, treatments, e.g. vaccinations, surgery etc. Disease State: patient with complicated or uncomplicated Plasmodium falciparum malaria infection or Healthy control clinical_history_design
2016-02-15 | E-GEOD-35858 | biostudies-arrayexpress
Project description:Sepsis is a frequent complication in critically ill patients and highly heterogeneous that is associated with high morbidity and mortality rates, especially in the elderly population. Utilizing RNA-Sequencing (RNA-Seq) to analysis biological pathways is widely used in clinical and molecular genetics studies, but in elderly patients with sepsis are still lacking. Hence, we aim to investigate the mortality-relevant biological features and transcriptomic features in elderly patients who were admitted to intensive care unit (ICU) for sepsis.
2022-11-03 | GSE216902 | GEO
Project description:Specific early diagnosis of renal allograft rejection is gaining importance in the current trend to minimize and individualize immunosuppression. Gene expression analyses could contribute significantly by defining âmolecular Banffâ signatures. Several previous studies have applied transcriptomics to distinguish different classes of kidney biopsies. However, the heterogeneity of microarray platforms, clinical samples and data analysis methods complicates the identification of robust signatures for the different types and grades of rejection. To address these issues, a comparative meta-analysis was performed across five different microarray datasets of heterogeneous sample collections from two published clinical datasets and three own datasets including biopsies for clinical indications, protocol biopsies, as well as comparative samples from non-human primates (NHP). This work identified conserved gene expression signatures that can differentiate groups with different histopathological findings in both human and NHP, regardless of the technical platform used. The marker panels comprise genes that clearly support the biological changes known to be involved in allograft rejection. A characteristic dynamic expression change of genes associated with immune and kidney functions was observed across samples with different grades of CAN. In addition, differences between human and NHP rejection were essentially limited to genes reflecting interstitial fibrosis progression. This data set comprises all renal allograft biopsies for clinical indications from patients at Hôpital Tenon, Paris (February 2003 until September 2004) and few respective patients from Hôpital Bicêtre, Paris, Hôpital Pellegrin, Bordeaux, and Hôpital Dupuytren, Limoges, plus control normal kidney samples from Hôpital Tenon, Paris, France (first batch). We used microarrays to identify different gene expression signatures of renal allograft biopsies that can classify them according to different types of allograft rejection or CAN. Experiment Overall Design: 47 renal allograft core biopsies for clinical indications with different histopathological diagnoses according to BANFF'97 criteria, and 13 normal kidney samples as controls.
2009-02-28 | E-GEOD-9489 | biostudies-arrayexpress
Project description:<p><strong>BACKGROUND:</strong> There is a critical need for rapid viral infection diagnostics to enable prompt case identification in pandemic settings and support targeted antimicrobial prescribing.</p><p><strong>METHODS:</strong> Using untargeted high-resolution liquid chromatography coupled with mass spectrometry, we compared the admission serum metabolome of emergency department patients with acute viral infections, including COVID-19, bacterial infections, inflammatory conditions and healthy controls. Sera from an independent cohort of emergency department patients admitted with viral or bacterial infections underwent profiling to validate findings. Associations between whole-blood gene expression and the identified metabolite of interest were examined.</p><p><strong>FINDINGS:</strong> 3'-Deoxy-3',4'-didehydro-cytidine (ddhC), a free base of the only known human antiviral small molecule ddhC-triphosphate (ddhCTP), was detected for the first time in serum. When comparing 60 viral with 101 non-viral cases in the discovery cohort, ddhC was the most differentially abundant metabolite, generating an area under the receiver operating characteristic curve (AUC) of 0.954 (95% CI: 0.923-0.986). In the validation cohort, ddhC was again the most significantly differentially abundant metabolite when comparing 40 viral with 40 bacterial cases, generating an AUC of 0.81 (95% CI: 0.708-0.915). Transcripts of viperin and <em>CMPK2</em>, enzymes responsible for ddhCTP synthesis, were amongst the 5 genes most highly correlated with ddhC abundance.</p><p><strong>CONCLUSIONS: </strong>The antiviral precursor molecule ddhC is detectable in serum and an accurate marker for acute viral infection. Interferon-inducible genes, viperin and <em>CMPK2</em> are implicated in ddhC production <em>in vivo</em>. These findings highlight a future diagnostic role for ddhC in viral diagnosis, pandemic preparedness and acute infection management.</p><p><strong>FUNDING:</strong> National Institute for Health Research Imperial Biomedical Research Centre; Medical Research Council.</p>
2022-02-10 | MTBLS718 | MetaboLights
Project description:B cell chronic lymphocytic leukemia - A model with immune response
Seema Nanda 1, , Lisette dePillis 2, and Ami Radunskaya 3,
1.
Tata Institute of Fundamental Research, Centre for Applicable Mathematics, Bangalore 560065, India
2.
Department of Mathematics, Harvey Mudd College, Claremont, CA 91711
3.
Department of Mathematics, Pomona College, Claremont, CA, 91711, United States
Abstract
B cell chronic lymphocytic leukemia (B-CLL) is known to have substantial clinical heterogeneity. There is no cure, but treatments allow for disease management. However, the wide range of clinical courses experienced by B-CLL patients makes prognosis and hence treatment a significant challenge. In an attempt to study disease progression across different patients via a unified yet flexible approach, we present a mathematical model of B-CLL with immune response, that can capture both rapid and slow disease progression. This model includes four different cell populations in the peripheral blood of humans: B-CLL cells, NK cells, cytotoxic T cells and helper T cells. We analyze existing data in the medical literature, determine ranges of values for parameters of the model, and compare our model outcomes to clinical patient data. The goal of this work is to provide a tool that may shed light on factors affecting the course of disease progression in patients. This modeling tool can serve as a foundation upon which future treatments can be based.
Keywords: NK cell, chronic lymphocytic leukemia, mathematical model, T cell., B-CLL.
2020-01-09 | MODEL2001090002 | BioModels
Project description:Specific early diagnosis of renal allograft rejection is gaining importance in the current trend to minimize and individualize immunosuppression. Gene expression analyses could contribute significantly by defining M-bM-^@M-^\molecular BanffM-bM-^@M-^] signatures. Several previous studies have applied transcriptomics to distinguish different classes of kidney biopsies. However, the heterogeneity of microarray platforms, clinical samples and data analysis methods complicates the identification of robust signatures for the different types and grades of rejection. To address these issues, a comparative meta-analysis was performed across five different microarray datasets of heterogeneous sample collections from two published clinical datasets and three own datasets including biopsies for clinical indications, protocol biopsies, as well as comparative samples from non-human primates (NHP). This work identified conserved gene expression signatures that can differentiate groups with different histopathological findings in both human and NHP, regardless of the technical platform used. The marker panels comprise genes that clearly support the biological changes known to be involved in allograft rejection. A characteristic dynamic expression change of genes associated with immune and kidney functions was observed across samples with different grades of CAN. In addition, differences between human and NHP rejection were essentially limited to genes reflecting interstitial fibrosis progression. This data set comprises all renal allograft biopsies for clinical indications from patients at HM-CM-4pital Tenon, Paris (February 2003 until September 2004) and few respective patients from HM-CM-4pital BicM-CM-*tre, Paris, HM-CM-4pital Pellegrin, Bordeaux, and HM-CM-4pital Dupuytren, Limoges, plus control normal kidney samples from HM-CM-4pital Tenon, Paris, France (first batch). We used microarrays to identify different gene expression signatures of renal allograft biopsies that can classify them according to different types of allograft rejection or CAN. Keywords: disease state analysis Keywords: Expression profiling by array 16 renal allograft core biopsies for clinical indications with different histopathological diagnoses according to BANFF'97 criteria (additional samples associated with GSE9489)
2009-08-27 | E-GEOD-17861 | biostudies-arrayexpress