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SRP133356
Mus musculus
90 Downloadable Samples
Illumina HiSeq 2000
Description
Pleomorphic adenoma gene 1 (PLAG1) encodes a transcription factor involved in cancer and growth. We study the role of PLAG1 in preimplantation embryos using STRT RNA-seq of single embryos from wild type and knockout mothers (both mated with wild type studs). The lack of maternal Plag1 led to delayed mouse 2-cell stage embryo development, compensatory expression of Plag1 from the paternal allele, and dysregulation of 1,089 genes. Half of these genes displayed a pattern of delayed activation and play roles in ribosome biogenesis and protein synthesis. These mouse genes further showed a significant overlap with human EGA genes with similar ontology, and an enrichment of the PLAG1 de novo motif. We conclude that Plag1 affects EGA through retrotransposons influencing ribosomes and protein synthesis, a mechanism that might also explain its roles in cancer and growth Overall design: Single wild type and maternal Plag1 knockout embryos at MII, 2-cell and 8-cell stage development in 14-16 biologicla replicas per developmental stage and genotype.
Publication Title
Pleomorphic Adenoma Gene 1 Is Needed For Timely Zygotic Genome Activation and Early Embryo Development.
Sample Metadata Fields
Subject
View Samples- Mus musculus
- 6 Downloadable Samples
Affymetrix Mouse Gene 1.1 ST Array (mogene11st)
Description
Obesity is a major risk factor for metabolic disorders like insulin resistance and diabetes. We previously identified GPS2 as a clinical relavant repressor of metaflammation. No animal KO models were used to study its physiological function in vivo. The role of GPS2 in macrophage activation and inflammation is also largely unknown.
Publication Title
Loss of the co-repressor GPS2 sensitizes macrophage activation upon metabolic stress induced by obesity and type 2 diabetes.
Sample Metadata Fields
Sex
View Samples- Homo sapiens
- 4 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
A humanized in vivo APL model has been established utilizing the retroviral transduction of PML-RARA into human CD34+ hematopoietic cells and the transplantation of these cells into immunodeficient mice. The resultant leukemia recapitulated human APL phenotypically, and was clustered in the same category as human APL samples in the gene expression analysis.
Publication Title
Establishment of a humanized APL model via the transplantation of PML-RARA-transduced human common myeloid progenitors into immunodeficient mice.
Sample Metadata Fields
Specimen part
View Samples- Mus musculus
- 12 Downloadable Samples
- Affymetrix Mouse Gene 1.0 ST Array (mogene10st)
Description
Although early developmental processes involve cell fate decisions that define the body axes and establish progenitor cell pools, development does not cease once cells are specified. Instead, most cells undergo specific maturation events where changes in the cell transcriptome ensure that the proper gene products are expressed to carry out unique physiological functions. Pancreatic acinar cells mature post-natally to handle an extensive protein synthetic load, establsih organized apical-basal polarity for zymogen granule trafficking, and assemble gap-junctions to perimt efficient cell-cell communication. Despite significant progress in defining transcriptional networks that control initial acinar cell specification and differentiation decisions, little is know regarding the role of transcription factors in the specification and maintenance of maturation events. One candidate maturation effector is MIST1, a secretory cell-restricted transcription factor that has been implicated in controlling regulated exocytosis events in a number of cell types. Embryonic knock-out of MIST1 generates acinar cells that fail to establish an apical-basal organization, fail to properly localize zymogen granule and fail to communicate intra-cellularly, making the exocrine organ highly suceptible to pancreatic diseases.
Publication Title
Induced Mist1 expression promotes remodeling of mouse pancreatic acinar cells.
Sample Metadata Fields
Age, Specimen part
View Samples- Mus musculus
- 79 Downloadable Samples
- Illumina HiSeq 2500
Description
Purpose: Eliciting effective anti-tumor immune responses in patients who fail checkpoint inhibitor therapy is a critical challenge in cancer immunotherapy, and in such patients, tumor-associated myeloid cells and macrophages (TAMs) are promising therapeutic targets. We demonstrate in an autochthonous, poorly immunogenic mouse model of melanoma that combination therapy with an agonistic anti-CD40 mAb and CSF1R inhibitor potently suppressed tumor growth. Microwell assays to measure multiplex protein secretion by single cells identified that untreated tumors have distinct TAM subpopulations secreting MMP9 or co-secreting CCL17/22, characteristic of an M2-like state. Combination therapy reduced the frequency of these subsets, while simultaneously inducing a separate polyfunctional inflammatory TAM subset co-secreting TNF?, IL-6, and IL-12. Tumor suppression by this combined therapy was partially dependent on T cells, TNF? and IFN?. Together, this study demonstrates the potential for targeting TAMs to convert a “cold” into an “inflamed” tumor microenvironment capable of eliciting protective T cell responses. Methods: Total RNA was purified with the use of QIAzol and RNeasy Mini kit (QIAGEN), in which an on-column DNase treatment was included. Purified RNA was submitted to the Yale Center for Genomic Analysis where it was subjected to mRNA isolation and library preparation. Non-strand specific libraries were generated from 50ng total RNA using the SMARTer Ultra Low Input RNA for Illumina Sequencing kit. Libraries were pooled, six samples per lane, and sequenced on an Illumina HiSeq 2500 (75-bp paired end reads), and aligned using STAR to the GRCm38 (mm10) reference genome. A count-based differential expression protocol was adapted for this analysis(Anders et al., 2013); mappable data were counted using HTSeq, and imported into R for differential expression analysis using the DESeq2.To find differentially regulated sets of genes for signature generation, a 1.5-Log2 fold-change difference between samples and p-adjusted (Holm-Sidak) = 0.01 was used. Results: To begin to understand how these treatments modulated T cells to control tumor growth, and to possibly illuminate additional biomarkers of response, we examined the transcriptomes of CD11b+ Ly6G- cells treated with CD40 or CSF1Ri, alone or in combination, relative to control, using high throughput RNA-sequencing. Principal components analysis (PCA) on the genome-wide dataset demonstrated that treating with CD40 and CSF1Ri individually caused largely non-overlapping changes in transcription, as indicated by their movement along orthogonal principal components (PC) relative to the control. Importantly, combination therapy was visualized as a systems-level combination of each individual treatment in PC space. We then examined the mRNAs most altered by either treatment alone or in combination relative to Controls (Log2FC>1.5, p<.01) by unsupervised hierarchical clustering. Five major gene patterns emerged from the clustering of genes. Cluster #1 comprises genes that are upregulated by CD40 and CSF1Ri+CD40 treatment but are mostly unaffected by CSF1Ri, suggesting that CD40 is the primary driver of this cluster in the combination treatment. Notable genes in this cluster include Tnfa, Ifng??Il12b and Cxcl9; interestingly, for Tnfa and Il12b, CSF1Ri+CD40 appears to have a synergistic effect on expression. In contrast to Cluster #1, Cluster #5 contains genes substantially downregulated by CSF1Ri and CSF1Ri+CD40 treatments, but are largely unaffected by CD40, suggesting that CSF1Ri is the driver of this cluster in the combination treatment. Cluster #5 genes include Cd36 and Fabp4, suggesting alterations in lipid homeostasis in the TAMs after treatment. Cluster #2 includes genes that are modestly upregulated by CD40 and CSF1Ri individually, leading to a stronger upregulation when combined. Finally, Clusters #3 and #4 include, for the most part, genes that are differentially affected by CD40 versus CSF1Ri and for which the combination treatment yields an intermediate response. In summary, these data show that CSF1Ri and CD40 agonism elicit predominantly distinct changes in gene expression in the CD11b+ cells, indicating they target different biological processes in myeloid cells. The net result of the changes in myeloid gene expression from the combination of CSF1Ri+CD40 treatment reveal additive effects by the individual treatments, but also synergy in the expression of several pro-inflammatory genes (e.g., Tnfa, Ifng, Il6 and Il12b). We further examined our dataset with Gene Set Enrichment Analysis (GSEA). Although CSF1Ri and CD40 treatments did not closely match any immunological signatures in the immunological database of MSigDb, combined CSF1Ri+CD40 had a strikingly similar signature to myeloid cells exposed to a variety of inflammatory stimulants, most closely reflected by BMDMs treated with lipopolysaccharide (LPS). This motivated us to look specifically at categories of NF-?B target genes that are significantly affected by LPS treatment, including transcription factors, cytokines and chemokines. Indeed, most of these NF-?B target genes associated with inflammation were strongly upregulated by CSF1Ri+CD40 treatment. Finally, Ingenuity Pathway Analysis identified TNFR1 and TNFR2 signaling and Acute phase response signaling among the top genetic signatures produced by the CSF1Ri+CD40 treatment combination, matching what we observed with GSEA. Thus, gene expression analysis not only revealed several biomarkers of response that may be relevant for assessing therapeutic activity in ongoing clinical trials using these drugs, but illuminated lead biological factors that may cause tumor regression. Conclusions: myeloid-targeted immunotherapies anti-CD40+CSF1R inhibition synergistically induce a pro-inflammatory microenviroment Overall design: mRNA profiles of tumor infiltrating lymphocytes (TILs) in mice were generated by deep sequencing, in triplicate, using Illumina.
Publication Title
Myeloid-targeted immunotherapies act in synergy to induce inflammation and antitumor immunity.
Sample Metadata Fields
Specimen part, Cell line, Subject
View Samples- Mus musculus
- 13 Downloadable Samples
- Affymetrix Mouse Gene 1.0 ST Array (mogene10st)
Description
Expression profiling was performed using uncultured melanocytes and melanoma cell from various mouse models of BrafV600E induced melanocytic proliferation
Publication Title
mTORC1 activation blocks BrafV600E-induced growth arrest but is insufficient for melanoma formation.
Sample Metadata Fields
Specimen part
View Samples- Mus musculus
- 11 Downloadable Samples
- Illumina MouseWG-6 v2.0 expression beadchip
Description
The goals were to investigate differences in gene expression between wild type and Gpr120 knockout mouse interscapular brown adipose tissue
Publication Title
The GPR120 agonist TUG-891 promotes metabolic health by stimulating mitochondrial respiration in brown fat.
Sample Metadata Fields
Sex, Age, Specimen part
View Samples- Homo sapiens
- 8 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
Borrelia burgdorferi, the agent of Lyme disease, promotes pro-inflammatory changes in endothelium that lead to the recruitment of leukocytes. The host immune response to infection results in increased levels of IFN-gamma in the serum and lesions of Lyme disease patients that correlate with greater severity of disease. Therefore, the effect of IFN-gamma on the gene expression profile of primary human endothelial cells exposed to B. burgdorferi was determined. B. burgdorferi and IFN-gamma synergistically augmented the expression of 34 genes, seven of which encode chemokines. Six of these (CCL7, CCL8, CX3CL1, CXCL9, CXCL10, and CXCL11) attract T lymphocytes, and one (CXCL2) is specific for neutrophils. Synergistic production of the attractants for T cells was confirmed at the protein level. IL-1beta, TNF-alpha, and LPS also cooperated with IFN-gamma to induce synergistic production of CXCL10 by endothelium, indicating that IFN-gamma potentiates inflammation in concert with a variety of mediators. An in vitro model of the blood vessel wall revealed that an increased number of human T lymphocytes traversed endothelium exposed to B. burgdorferi and IFN-gamma, as compared to unstimulated endothelial monolayers. In contrast, addition of IFN-gamma diminished the migration of neutrophils across B. burgdorferi-activated endothelium. IFN-gamma thus alters gene expression by endothelium exposed to B. burgdorferi in a manner that promotes recruitment of T cells and suppresses that of neutrophils. This modulation may facilitate the development of chronic inflammatory lesions in Lyme disease.
Publication Title
IFN-gamma alters the response of Borrelia burgdorferi-activated endothelium to favor chronic inflammation.
Sample Metadata Fields
No sample metadata fields
View Samples- Rattus norvegicus
- 8 Downloadable Samples
- Affymetrix Rat Genome 230 2.0 Array (rat2302)
Description
In the growth plate, the reserve and perichondral zones have been hypothesized to have similar functions, but their exact functions are poorly understood. Our hypothesis was that significant differential gene expression exists between perichondral and reserve chondrocytes that may differentiate the respective functions of these two zones. Normal Sprague-Dawley rat growth plate chondrocytes from the perichondral zone (PC), reserve zone (RZ), proliferative zone (PZ), and hypertrophic zone (HZ) were isolated by laser microdissection and then subjected to microarray analysis. In order to most comprehensively capture the unique features of the two zones, we analyzed both the most highly expressed genes and those that were most significantly different from the proliferative zone (PZ) as a single comparator.
Publication Title
Microarray analysis of perichondral and reserve growth plate zones identifies differential gene expressions and signal pathways.
Sample Metadata Fields
No sample metadata fields
View Samples- Pseudomonas aeruginosa
- 6 Downloadable Samples
- Affymetrix Pseudomonas aeruginosa Array (paeg1a)
Description
Pseudomonas aeruginosa is an opportunistic pathogen that can adapt to changing environments and can secrete an exopolysaccharide known as alginate as a protection response resulting in a colony morphology and phenotype referred to as mucoid. However how P. aeruginosa senses its environment and activates alginate overproduction is not fully understood. Previously, we showed that Pseudomonas isolation agar (PIA) supplemented with ammonium metavanadate (PIAAMV) induces P. aeruginosa to overproduce alginate. Vanadate is a phosphate mimic and causes protein misfolding by disruption of disulfide bonds. Here we used PIAAMV to characterize the pathways involved in inducible alginate production and tested the global effects of P. aeruginosa growth on PIAAMV by a mutant library screen, transcriptomics, and in a murine acute virulence model. The PA14 non-redundant mutant library was screened on PIAAMV to identify new genes that are required for the inducible alginate stress response. A functionally diverse set of genes encoding products involved in cell envelope biogenesis, peptidoglycan, uptake of phosphate and iron, phenazines biosynthesis, and other processes were identified as positive regulators of the mucoid phenotype on PIAAMV. Transcriptome analysis of P. aeruginosa growing in the presence of vanadate caused differential expression of genes involved in virulence, envelope biogenesis, and cell stress pathways. In this study, it was observed that growth on PIAAMV attenuates P. aeruginosa in a mouse pneumonia model. Induction of alginate overproduction occurs as a stress response to protect P. aeruginosa but it may be possible to modulate and inhibit these pathways based on the new genes identified in this study.
Publication Title
Genes required for and effects of alginate overproduction induced by growth of Pseudomonas aeruginosa on Pseudomonas isolation agar supplemented with ammonium metavanadate.
Sample Metadata Fields
No sample metadata fields
View Samples