Technical controls MiCA
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB30293 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR3287885_1.fastq.gz | Fastqsanger.gz | |||
| ERR3287885_2.fastq.gz | Fastqsanger.gz | |||
| ERR4674613_1.fastq.gz | Fastqsanger.gz | |||
| ERR4674613_2.fastq.gz | Fastqsanger.gz | |||
| ERR5418250_1.fastq.gz | Fastqsanger.gz |
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Similar Datasets
Project description:Background: Studies recently support that non-HLA antigens could be additional targets of injury in organ transplant recipients, and MICA was associated with an increased risk of graft loss. Methods: A ProtoArray platform was used to study 37 serum samples from 22 unique patients (15 renal recipients and 7 healthy controls). Thirty paired pre- and post-transplant serum samples were analyzed for detection of de novo post-transplant antibody responses in the 15 patients (10 acute rejection (AR), 5 Stable). Probes on ProtoArray and cDNA platforms (GSE: 3931) were re-annotated and compartment specific gene lists were analyzed using the integrated genomics method. Normal and transplant kidney IHC were performed for MICA antigen localization. Results: Mean MICA-Ab (antibody) signal intensity was significantly higher in transplant patients compared with healthy controls and de novo MICA-Ab were detected in 73% transplant patients. The mean post-transplant signal intensity of MICA-Ab was the highest in C4d+AR. Detection of MICA-Ab responses did not correlate with time post-transplantation, but significantly correlated with decline in graft function over the subsequent year. Integrative genomics predicted localization of the MICA antigen to the glomerulus. IHC confirmed cytoplasmic MICA staining solely in glomerular podocytes in normal kidney. In the transplant kidney, infiltrating mononuclear lymphocytes (T, B and NK) in AR had additional MICA staining. Conclusions: MICA can be highly detected regardless of graft dysfunction or AR. The intensity signal of the MICA antibody correlates with subsequent decline in graft function. The MICA antigen localizes to the glomerulus and infiltrating mononuclear cells in AR.
2009-01-16 | GSE14329 | GEO
Project description:Background: Studies recently support that non-HLA antigens could be additional targets of injury in organ transplant recipients, and MICA was associated with an increased risk of graft loss. Methods: A ProtoArray platform was used to study 37 serum samples from 22 unique patients (15 renal recipients and 7 healthy controls). Thirty paired pre- and post-transplant serum samples were analyzed for detection of de novo post-transplant antibody responses in the 15 patients (10 acute rejection (AR), 5 Stable). Probes on ProtoArray and cDNA platforms (GSE: 3931) were re-annotated and compartment specific gene lists were analyzed using the integrated genomics method. Normal and transplant kidney IHC were performed for MICA antigen localization. Results: Mean MICA-Ab (antibody) signal intensity was significantly higher in transplant patients compared with healthy controls and de novo MICA-Ab were detected in 73% transplant patients. The mean post-transplant signal intensity of MICA-Ab was the highest in C4d+AR. Detection of MICA-Ab responses did not correlate with time post-transplantation, but significantly correlated with decline in graft function over the subsequent year. Integrative genomics predicted localization of the MICA antigen to the glomerulus. IHC confirmed cytoplasmic MICA staining solely in glomerular podocytes in normal kidney. In the transplant kidney, infiltrating mononuclear lymphocytes (T, B and NK) in AR had additional MICA staining. Conclusions: MICA can be highly detected regardless of graft dysfunction or AR. The intensity signal of the MICA antibody correlates with subsequent decline in graft function. The MICA antigen localizes to the glomerulus and infiltrating mononuclear cells in AR. Pre- and post-transplant serum antibodies were profiled for each patient, using the Invitrogen ProtoArray® Human Protein Microarray v3.0 platform (Invitrogen, Carlsbad, CA). This platform contains 5,056 non-redundant human proteins expressed in a baculovirus system, purified from insect cells and printed in duplicate onto a nitrocellulose-coated glass slide. Each protein is spotted twice on each array, to measure the quality of the signal intensity. Details for experiment processing and analysis follow the previous publication from our group (13). Prospector software was used to retrieve the expression based on immune response profiling of the .gal files.
2010-05-06 | E-GEOD-14329 | biostudies-arrayexpress
Project description:MICA and MICB (MICA/B) are expressed by many human cancers due to cellular stress and tag cells for elimination by cytotoxic lymphocytes through NKG2D receptor activation. However, tumors evade this immune recognition pathway through proteolytic shedding of MICA/B proteins. We rationally designed antibodies targeting the MICA α3 domain, the site of proteolytic shedding, and found that these antibodies prevented loss of cell surface MICA/B by human cancer cells. These antibodies inhibited tumor growth in multiple fully immuno-competent mouse models and also reduced human melanoma metastases in a humanized mouse model. Anti-tumor immunity was mediated mainly by NK cells through activation of NKG2D and CD16 Fc receptors. This novel approach prevents the loss of important immunostimulatory ligands by human cancers and reactivates antitumor immunity.
2018-02-01 | GSE109542 | GEO
Project description:We focused on how mica fine particle influences macrophage activities. We analyzed altered transcription in RAW 264.7 cells at 0, 12, and 48 h following the stimulation with 100 M-BM-5g/mL of mica fine particles.
2014-12-04 | E-GEOD-63827 | biostudies-arrayexpress
Project description:We focused on how mica fine particle influences macrophage activities.
2014-12-04 | GSE63827 | GEO
Project description:MUC1-C regulates expression of MICA/B NKG2D LIGAND AND enhance EXOSOME SECRETION IN HUMAN CANCER CELLS
2022-10-17 | GSE215308 | GEO
Project description:To study the regulatory outcome of Hfq-mediated sRNA-target interactions, we measured the change in gene expression following overexpression of each of five well-established sRNAs: GcvB, MicA, ArcZ, RyhB and CyaR. For each of the studied sRNAs we applied RNA-seq to two E. coli K-12 MG1655 strains: (1) a WT strain or a strain deleted of the sRNA gene; (2) a strain overexpressing the sRNA, either artificially from a plasmid or from the endogenous sRNA gene by changing the growth condition. For GcvB induction, the E. coli K-12 MG1655 Z1 gcvB::Cm, pZA12-gcvB and the E. coli K-12 MG1655 Z1 gcvB::Cm, pTP-011 strains were used. For MicA overexpression the E. coli K-12 MG1655lacIq pBRplac, pEF21-Hfq and the E. coli K-12 MG1655lacIq pMicA, pEF21-Hfq strains were used. For ArcZ induction the E. coli K-12 MG1655 Z1 arcZ::Cm, pZE12-ArcZ and the E. coli K-12 MG1655 Z1 arcZ::Cm, pJV300 strains were used. For RyhB overexpression the E. coli K-12 MG1655 ryhB::Cm and the E. coli K-12 MG1655 were used. For CyaR induction the E. coli K-12 MG1655 Z1 cyaR::Cm, pZE12-CyaR and the E. coli K-12 MG1655 Z1 cyaR::Cm, pJV300 strains were used. All E. coli strains used in this study were grown overnight in Luria Bertani (LB) medium at 37 °C with shaking (200 r.p.m.), diluted 100-fold in fresh LB medium, and re-grown with shaking at 37 °C to exponential phase, for GcvB (OD600 = 0.3) and for RyhB (OD600 = 0.5), or to stationary phase, for ArcZ WT, ArcZ M1, ArcZ M2 and CyaR (OD600 = 1.0) and for MicA (grown for 6 hr). For induction of ArcZ and GcvB, IPTG was added (1mM, 20 min). MicA was constitutively expressed and further induced at the end of growth with IPTG (1mM, 20 min). For induction of RyhB, the iron chelator 2,2'-Dipyridyl was added (200 μM, 30 min).
2020-03-03 | E-MTAB-8229 | biostudies-arrayexpress
Project description:2-4-8 cell porcine embryo technical repeat dye-swap for Agilent EMPV1 (4x44k Format)
2013-02-25 | GSE34157 | GEO
Project description:RNA-seq: technical variability and sampling
| PRJNA234515 | ENA
Project description:Background: RNA-seq is revolutionizing the way we study transcriptomes. mRNA can be surveyed without prior knowledge of gene transcripts. Alternative splicing of transcript isoforms and the identification of previously unknown exons are being reported. Initial reports of differences in exon usage, and splicing between samples as well as quantitative differences among samples are beginning to surface. Biological variation has been reported to be larger than technical variation. In addition, technical variation has been reported to be in line with expectations due to random sampling. However, strategies for dealing with technical variation will differ depending on the magnitude. The size of technical variance, and the role of sampling are examined in this manuscript. Results: Independent Solexa/Illumina experiments containing technical replicates are analyzed. When coverage is low, large disagreements between technical replicates are apparent. Exon detection between technical replicates is highly variable when the coverage is less than 5 reads per nucleotide and estimates of gene expression are more likely to disagree when coverage is low. Although large disagreements in the estimates of expression are observed at all levels of coverage. Conclusions: Technical variability is too high to ignore. Technical variability results in inconsistent detection of exons at low levels of coverage. Further, the estimate of the relative abundance of a transcript can substantially disagree, even when coverage levels are high. This may be due to the low sampling fraction and if so, it will persist as an issue needing to be addressed in experimental design even as the next wave of technology produces larger numbers of reads. We provide practical recommendations for dealing with the technical variability, without dramatic cost increases.
2014-01-15 | GSE54068 | GEO