Pheochromocytoma in rats with multiple endocrine neoplasia (MENX) shares gene expression patterns with human pheochromocytoma

Pheochromocytomas are rare neoplasias of neural crest origin arising from chromaffin cells of the adrenal medulla and sympathetic ganglia (extra-adrenal pheochromocytoma). Pheochromocytoma that develop in rats homozygous for a loss-of-function mutation in p27Kip1 (MENX syndrome) show a clear progression from hyperplasia to tumor, offering the possibility to gain insight into tumor pathobiology. We compared the gene-expression signatures of both adrenomedullary hyperplasia and pheochromocytoma with normal rat adrenal medulla. Hyperplasia and tumor show very similar transcriptome profiles, indicating early determination of the tumorigenic signature. Overrepresentation of developmentally regulated neural genes was a feature of the rat lesions. Quantitative RT-PCR validated the up-regulation of 11 genes, including some involved in neural development: Cdkn2a, Cdkn2c, Neurod1, Gal, Bmp7, and Phox2a. Overexpression of these genes precedes histological changes in affected adrenal glands. Their presence at early stages of tumorigenesis indicates they are not acquired during progression and may be a result of the lack of functional p27Kip1. Adrenal and extra-adrenal pheochromocytoma development clearly follows diverged molecular pathways in MENX rats. To correlate these findings to human pheochromocytoma, we studied nine genes overexpressed in the rat lesions in 46 sporadic and familial human pheochromocytomas. The expression of GAL, DGKH, BMP7, PHOX2A, L1CAM, TCTE1, EBF3, SOX4, and HASH1 was up-regulated, although with different frequencies. Immunohistochemical staining detected high L1CAM expression selectively in 27 human pheochromocytomas but not in 140 nonchromaffin neuroendocrine tumors. These studies reveal clues to the molecular pathways involved in rat and human pheochromocytoma and identify previously unexplored biomarkers for clinical use.

Abacavir induced T cell reactivity from drug naïve individuals shares features of allo-immune responses.

Abacavir hypersensitivity is a severe hypersensitivity reaction which occurs exclusively in carriers of the HLA-B*57∶01 allele. In vitro culture of PBMC with abacavir results in the outgrowth of abacavir-reacting CD8+ T cells, which release IFNγ and are cytotoxic. How this immune response is induced and what is recognized by these T cells is still a matter of debate. We analyzed the conditions required to develop an abacavir-dependent T cell response in vitro. The abacavir reactivity was independent of co-stimulatory signals, as neither DC maturation nor release of inflammatory cytokines were observed upon abacavir exposure. Abacavir induced T cells arose in the absence of professional APC and stemmed from naïve and memory compartments. These features are reminiscent of allo-reactivity. Screening for allo-reactivity revealed that about 5% of generated T cell clones (n = 136) from three donors were allo-reactive exclusively to the related HLA-B*58∶01. The addition of peptides which can bind to the HLA-B*57∶01-abacavir complex and to HLA-B*58∶01 during the induction phase increased the proportion of HLA-B*58∶01 allo-reactive T cell clones from 5% to 42%. In conclusion, abacavir can alter the HLA-B*57∶01-peptide complex in a way that mimics an allo-allele ('altered self-allele') and create the potential for robust T cell responses.

How quota-based procedures affect the self-ascribed fit and motivation of European women and men striving for leadership positions

The consequences of quota-based hiring policies on women’s and men’s self-ascribed fit and motivation to apply for leadership positions were investigated in the European context. 389 management students (195 women, 194 men) from Switzerland, Germany, and Austria received job advertisements varying in their gender policies. Interestingly, women’s and men’s self-ascribed fit and motivation to apply did not differ when no policy was included. Instead, participant’s agency was the relevant predictor leading to higher perceived fit, resulting in higher inclination to apply. When women were either explicitly invited to apply or preferentially treated when equally qualified, they reported higher fit and inclination to apply compared to both the control condition and men. In contrast, when a quota of 40% women should be established, neither women’s fit nor motivation to apply were increased beyond the levels of the control or men’s. Men were not affected by the different policies.

PSHARE: Stata module to compute and graph percentile shares

-pshare- computes and graphs percentile shares from individual level data. Percentile shares are often used in inequality research to study the distribution of income or wealth. They are defined as differences between Lorenz ordinates of the outcome variable. Technically, the observations are sorted in increasing order of the outcome variable and the specified percentiles are computed from the running sum of the outcomes. Percentile shares are then computed as differences between percentiles, divided by total outcome. pshare requires moremata to be installed on the system; see ssc describe moremata.

A new Stata command for computing and graphing percentile shares

Percentile shares provide an intuitive and easy-to-understand way for analyzing income or wealth distributions. A celebrated example are the top income shares sported by the works of Thomas Piketty and colleagues. Moreover, series of percentile shares, defined as differences between Lorenz ordinates, can be used to visualize whole distributions or changes in distributions. In this talk, I present a new command called pshare that computes and graphs percentile shares (or changes in percentile shares) from individual level data. The command also provides confidence intervals and supports survey estimation.

A new Stata command for computing and graphing percentile shares

Percentile shares provide an intuitive and easy-to-understand way for analyzing income or wealth distributions. A celebrated example is the top income shares sported by the works of Thomas Piketty and colleagues. Moreover, series of percentile shares, defined as differences between Lorenz ordinates, can be used to visualize whole distributions or changes in distributions. In this talk, I present a new command called pshare that computes and graphs percentile shares (or changes in percentile shares) from individual level data. The command also provides confidence intervals and supports survey estimation.

Assessing inequality using percentile shares

At least since Thomas Piketty's best-selling \Capital in the Twenty- First Century" (2014, Cambridge, MA: The Belknap Press), percentile shares have become a popular approach for analyzing distributional inequalities. In their work on the development of top incomes, Piketty and collaborators typically report top- percentage shares, using varying percentages as thresholds (top 10%, top 1%, top 0.1%, etc.). However, analysis of percentile shares at other positions in the distri- bution may also be of interest. In this paper I present a new Stata command called pshare that estimates percentile shares from individual-level data and displays the results using histograms or stacked bar charts.

Assessing inequality using percentile shares

At least since Thomas Piketty's best-selling "Capital in the Twenty-First Century" (2014, Cambridge, MA: The Belknap Press), percentile shares have become a popular approach for analyzing distributional inequalities. In their work on the development of top incomes, Piketty and collaborators typically report top-percentage shares, using varying percentages as thresholds (top 10%, top 1%, top 0.1%, etc.). However, analysis of percentile shares at other positions in the distribution may also be of interest. In this paper I present a new Stata command called -pshare- that estimates percentile shares from individual-level data and displays the results using histograms or stacked bar charts.