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Gene expression in GLN versus PDLN in NOD mice

ABSTRACT: Comparison of the gene expression in the two pancreatic lymph nodes: gastric lymph node (GLN) and pancreatico-duodenal lymph node (PDLN) in NOD mice. The analysis was done in an attempt to explain the preferential homing of bone marrow-derived dendritic cells to the GLN after intravenous injection. GLN and PDLN were excised from 4 individual 12-wk old female NOD mice and RNA was extracted after tissue homogenization in Trizol for processing onto microarray (Agilent 4x44K). Each array corresponds to one single mouse, thus this study comprise 4 biological replicates.

ORGANISM(S): Mus musculus

SUBMITTER: Garry Fathman

PROVIDER: E-GEOD-15753 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Lymphoid-tissue-specific homing of bone-marrow-derived dendritic cells.

Creusot Rémi J RJ Yaghoubi Shahriar S SS Chang Pearl P Chia Justine J Contag Christopher H CH Gambhir Sanjiv S SS Fathman C Garrison CG

Blood 20090410 26

Because of their potent immunoregulatory capacity, dendritic cells (DCs) have been exploited as therapeutic tools to boost immune responses against tumors or pathogens, or dampen autoimmune or allergic responses. Murine bone marrow-derived DCs (BM-DCs) are the closest known equivalent of the blood monocyte-derived DCs that have been used for human therapy. Current imaging methods have proven unable to properly address the migration of injected DCs to small and deep tissues in mice and humans. Th ...[more]

PMID: 19363220

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Lymphoid tissue specific homing of bone marrow-derived dendritic cells

Project description:Because of their potent immunoregulatory capacity, dendritic cells (DCs) have been exploited as therapeutic tools to boost immune responses against tumors or pathogens, or dampen autoimmune or allergic responses. Murine bone marrow-derived DCs (BM-DCs) are the closest known equivalent of the blood monocyte-derived DCs that have been used for human therapy. Current imaging methods have proven unable to properly address the migration of injected DCs to small and deep tissues in mice and humans. This study presents the first extensive analysis of BM-DC homing to lymph nodes (and other selected tissues) after intravenous and intraperitoneal inoculation. Following intravenous delivery, DCs accumulated in the spleen, and preferentially in the pancreatic and lung-draining lymph nodes. In contrast, DCs injected intraperitoneally were found predominantly in peritoneal lymph nodes (pancreatic in particular), and in omentum-associated lymphoid tissue. This uneven distribution of BM-DCs, independent of the mouse strain and also observed within pancreatic lymph nodes, resulted in the uneven induction of an immune response in different lymphoid tissues. These data have important implications for the design of systemic cellular therapy with DCs, and in particular underlie a previously unsuspected potential for specific treatment of diseases such as autoimmune diabetes and pancreatic cancer. This SuperSeries is composed of the following subset Series: GSE15748: Gene expression in PLN vs. ILN or MLN in mice GSE15753: Gene expression in GLN versus PDLN in NOD mice Refer to individual Series

2009-04-23 | E-GEOD-15784 | biostudies-arrayexpress

Gene expression in PLN vs. ILN or MLN in mice

Project description:Normal gene expression in pancreatic lymph nodes compared to inguinal or mesenteric lymph nodes across different strains of mice (BALB/c, FVB, NOD, NOD.B10). Lymph nodes were excised from 12 wk old female mice (5 mice per group), and total RNA was extracted for dual dye microarray analysis.

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SGEV_LRS

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Identification of Hipk2 as a central regulator of white fat development

Project description:Homeodomain interacting protein kinase 2 (Hipk2) has previously been implicated in control of several transcription factors involved in embryonic development, apoptosis, cell proliferation and tumour development1–3. Analysis of gene expression in tissues from genetically heterogeneous mouse or human populations can reveal motifs associated with the structural or functional components of the tissue, and may predict roles for genes of unknown function4,5. Here we have applied this network strategy to uncover a novel role for the Hipk2 gene in the transcriptional system controlling adipogenesis. Both in vitro and in vivo models were used to show that knockdown or loss of Hipk2 specifically inhibits white adipose cell differentiation and tissue development. In addition, loss of Hipk2 leads to induction of pockets of multilocular brown fat-like cells in remaining white adipose depots. These cells express markers of brown and beige fat such as uncoupling protein 1 (Ucp1) and transmembrane protein 26 (Tmem26), and thermogenic genes including PPAR-γ coactivator 1a (Ppargc1a), and cell death-inducing DFFA-like effector a (Cidea). These changes are accompanied by increased insulin sensitivity in Hipk2 knock-out mice and reduced high fat diet-induced weight gain, highlighting a potential role for this kinase in diseases such as diabetes and obesity. Our study underscores the versatility and power of a readily available tissue, such as skin, for network modelling of systemic transcriptional programs involved in multiple pathways, including lipid metabolism and adipogenesis. Mammary fat pads from 115 distinct untreated 8 week old [FVB/N x Spret/Ei] x FVB/N Backcross mice

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Gene expression in GLN versus PDLN in NOD mice

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2009-04-23 | GSE15753 | GEO

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2013-08-21 | E-GEOD-49614 | biostudies-arrayexpress

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Cancer tissue classication using machine learning applied to MALDI MSI and high resolution H&E stained images

Project description:To identify a panel of m/z markers for the early detection of colorectal cancer (CRC). Identication of a molecular pattern that can distinguish the primary tumours of colorectal cancer with lymph node metastasis compared to those without. Using MALDI MSI data,we developed and validated a machine learning model that can be used for early screening of CRC. Our model yields high sensitivity and specicity in distinguishing normal tissue from the cancerous. Model described here, can be a used in clinical labs for early diagnosis of colorectal cancer.

2022-02-15 | PXD019653 | Pride

Cancer tissue classification using machine learning applied to MALDI MSI and high resolution H&E stained images

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Gene expression patterns and TP53 mutations are associated with HPV RNA status, lymph node metastasis, and survival in head and neck cancer

Project description:A growing proportion of head and neck squamous cell carcinomas (HNSCC) is associated with the human papilloma virus (HPV), particularly HPV16. We compare tumors with different HPV16 DNA and RNA (E6*I) status from 290 consecutively recruited HNSCC patients by gene expression profiling and targeted sequencing of 50 genes. We confirm that the HPV16 DNA+ RNA+ tumors are molecularly distinct from the HPV-negative (DNA-) HNSCC and have elevated expression of cell cycle genes and rare TP53 mutations (3.6%, 1/28). We show that tumors with non-transcriptionally active HPV16 (DNA+ RNA-) are similar to HPV DNA- tumors regarding gene expression and frequency of TP53 mutations (47%, 8/17 and 43%, 72/167, respectively). Furthermore, we identify four gene expression clusters. They moderately but significantly differ in overall survival. One cluster exhibits high expression of immune response genes (IR) and contains most of the HPV16 DNA+ RNA+ patients. The IR cluster and disruptive TP53 mutations are associated with lymph node metastasis independent of HPV16 status and we validate each of these associations in another large data set. Consistent with earlier studies, disruptive TP53 mutations are prognostically unfavorable. Our findings underscore the importance of measuring the HPV16 RNA (E6*I) and TP53-mutation status for patient stratification and for the first time identify associations of an immune response-related gene expression cluster and TP53 mutations with lymph node metastasis in HNSCC. A total of 300 samples were considered. Quality control procedures were applied to microarray probe-level intensity files. A total of 270 tumor arrays remained after removing low-quality arrays, duplicate arrays, and arrays from non-HNSCC samples. Expression values were log2-transformed and normalized using RSN.

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