Hyperacute changes in blood mRNA expression profiles of rats after middle cerebral artery occlusion: Towards a stroke time signature.

Stroke evolution is a highly dynamic but variable disease which makes clinical decision making difficult. Biomarker discovery programs intended to aid clinical decision making have however largely ignored the rapidity of stroke evolution. We have used gene array technology to determine blood mRNA expression changes over the first day after stroke in rats. Blood samples were collected from 8 male spontaneously hypertensive rats at 0, 1, 2, 3, 6 and 24h post stroke induction by middle cerebral artery occlusion. RNA was extracted from whole blood stabilized in PAXgene tubes and mRNA expression was detected by oligonucleotide Affymetrix microarray. Using a pairwise comparison model, 1932 genes were identified to vary significantly over time (p≤0.5x10(-7)) within 24h after stroke. Some of the top20 most changed genes are already known to be relevant to the ischemic stroke physiopathology (e.g. Il-1R, Nos2, Prok2). Cluster analysis showed multiple stereotyped and time dependent profiles of gene expression. Direction and rate of change of expression for some profiles varied dramatically during these 24h. Profiles with potential clinical utility including hyper acute or acute transient upregulation (with expression peaking from 2 to 6h after stroke and normalisation by 24h) were identified. We found that blood gene expression varies rapidly and stereotypically after stroke in rats. Previous researchers have often missed the optimum time for biomarker measurement. Temporally overlapping profiles have the potential to provide a biological "stroke clock" able to tell the clinician how far an individual stroke has evolved

DNA binding-dependent androgen receptor signaling contributes to gender differences and has physiological actions in males and females

We used our genomic androgen receptor (AR) knockout (ARKO) mouse model, in which the AR is unable to bind DNA to: 1) document gender differences between males and females; 2) identify the genomic (DNA-binding-dependent) AR-mediated actions in males; 3) determine the contribution of genomic AR-mediated actions to these gender differences; and 4) identify physiological genomic AR-mediated actions in females. At 9 weeks of age, control males had higher body, heart and kidney mass, lower spleen mass, and longer and larger bones compared to control females. Compared to control males, ARKO males had lower body and kidney mass, higher splenic mass, and reductions in cortical and trabecular bone. Deletion of the AR in ARKO males abolished the gender differences in heart and cortical bone. Compared with control females, ARKO females had normal body weight, but 14% lower heart mass and heart weight/ body weight ratio. Relative kidney mass was also reduced, and relative spleen mass was increased. ARKO females had a significant reduction in cortical bone growth and changes in trabecular architecture, although with no net change in trabecular bone volume. In conclusion, we have shown that androgens acting via the genomic AR signaling pathway mediate, at least in part, the gender differences in body mass, heart, kidney, spleen, and bone, and play a physiological role in the regulation of cardiac, kidney and splenic size, cortical bone growth, and trabecular bone architecture in females.

Supplementary Table 10 - 13 from Tumor Explants Elucidate a Cascade of Paracrine SHH, WNT, and VEGF Signals Driving Pancreatic Cancer Angiosuppression

Supplementary Table 10: Antibodies for IHC and IF.Overview of antibodies used for stainings in human PDAC or KPC-derived tissues.Supplementary Table 11: Primer sequences for qRT-PCR.Primers for qRT-PCR-based quantification of ChIP and mRNA samples.Supplementary Table 12: Freezer dryer settings for sponge production.Specific settings for optimal sponge production.Supplementary Table 13: Explant media composition.List of reagents used for human PDAC and murine explants.</p

Supplementary Table 8 from Tumor Explants Elucidate a Cascade of Paracrine SHH, WNT, and VEGF Signals Driving Pancreatic Cancer Angiosuppression

Lymphoid network.Regulatory network for PDAC-associated lymphoid cells, listing the inferred transcriptional targets (Target) for each regulatory protein (Regulator). Association weight (AW) and association mode (AM) are scores to quantify strength/direction of interaction. Sign indicates directionality of the interaction (1 = transactivating, -1 = transrepressing).</p

Supplementary Figure 5 from Tumor Explants Elucidate a Cascade of Paracrine SHH, WNT, and VEGF Signals Driving Pancreatic Cancer Angiosuppression

SHH secretion in vitro and ex vivo.</p

Supplementary Figure 2 from Tumor Explants Elucidate a Cascade of Paracrine SHH, WNT, and VEGF Signals Driving Pancreatic Cancer Angiosuppression

IPI-926 treatment alters cellular composition in the tumor microenvironment.</p

Supplementary Table 8 from Tumor Explants Elucidate a Cascade of Paracrine SHH, WNT, and VEGF Signals Driving Pancreatic Cancer Angiosuppression

Lymphoid network.Regulatory network for PDAC-associated lymphoid cells, listing the inferred transcriptional targets (Target) for each regulatory protein (Regulator). Association weight (AW) and association mode (AM) are scores to quantify strength/direction of interaction. Sign indicates directionality of the interaction (1 = transactivating, -1 = transrepressing).</p