Cushing's and Acromegaly Dataset (Carr et al. Submission)

This is the dataset at the time of initial submission to medRxiv and Clinical Endocrinology. This repository contains the raw and processed data for several projects related to glucocorticoid and growth hormone action. The preprint is available at:

Treyton Carr, Irit Hochberg, and Dave Bridges. Differential Metabolic Signatures of Cushing’s Disease Patients Dependent on their Obesity Status medRxiv 2026.02.25.26346994 doi: 10.64898/2026.02.25.26346994

Full Changelog: Muscle-v1.0.0...Cushings-Obesity-v0.1.0

Muscle Manuscript Submission

This is the state of the repository and manuscript at time of submission of the preprint to bioRxiv and to biomedicines. This repository contains the raw and processed data for several projects related to glucocorticoid and growth hormone action. The reference for the newest preprint is:

Gunder, L.C.; Harvey, I.; Redd, J.R.; Davis, C.S.; AL-Tamimi, A.; Brooks, S. V.; Bridges, D. Obesity promotes glucocorticoid-dependent muscle atrophy in male C57BL/6J mice. bioRxiv 2020, doi:10.1101/2020.08.11.247106.

Dataset for Harvey et al Endocrinology 2018 Manuscript

This dataset contains the raw data and analysis code for the studies described in the manuscript:

Harvey, I. et al. Glucocorticoid-Induced Metabolic Disturbances are Exacerbated in Obese Male Mice. Endocrinology 159, 1–19 (2018). doi:10.1210/en.2018-00147

Dataset for bioRxiv Submission

This dataset is for the manuscript submission titled Glucocorticoid-Induced Metabolic Disturbances are Exacerbated in Obesity. This preprint can be found at doi:10.1101/184507. This dataset includes all raw and calculated data and scripts used for generating figures and the statistical tests. Some key findings of this manuscript include:

• Obese Cushing's disease patients have worse NAFLD and insulin resistance than either obese or Cushingoid patients alone.
• A mouse model in which HFD-induced obese animals were exposed to dexamethasone had substantial insulin resistance, hyperglycemia and NAFLD.
• Glucocorticoids induce lipolysis and expression of the key gene Pnpla2 encoding for ATGL.
• In obese animals, glucocorticoids enhance the expression of ATGL, and have increased lipolysis.

Dataset for Hochberg et al 2015 Journal of Molecular Endocrinology Manuscript

This dataset contains the raw data and analysis code for the studies described in this manuscript:

I. Hochberg, I. Harvey, Q. T. Tran, E. J. Stephenson, A. L. Barkan, A. Saltiel, W. F. Chandler, D. Bridges, Gene expression changes in subcutaneous adipose tissue due to Cushing’s disease., J. Mol. Endocrinol. Accepted (2015). doi:10.1530/JME-15-0119.

Dataset for Hochberg et al 2015 PLOS One Manuscript

This dataset contains the raw data and analysis code for the studies described in this manuscript:

Hochberg, Q. T. Tran, A. L. Barkan, A. R. Saltiel, W. F. Chandler, D. Bridges, Gene Expression Signature in Adipose Tissue of Acromegaly Patients, PLoS One 10, e0129359 (2015). doi:10.1371/journal.pone.0129359

Journal of Endocrinology Submission

This version of the repository corresponded to the initial submission of the Cushing's manuscript to the Journal of Endocrinology.

PLOS One Revised Version

This version was the revised version based on the reviews from PLOS One. The reviewer comments are grouped into issues #39 #40 #41 #42 #43 #44 #45

The major change in this release is the switch to an age-adjusted model for transcript abundance. We grouped subjects into two groups, under 60 or 60 and above and then corrected based on that grouping. This affected several q-values. We also observed that older subjects had lower IGF-1 levels and lower magnitude transcriptional changes. This was addressed in issue #40

Journal of Endocrinology Submission

Relative to the previous submission, this version only has formatting changes in the manuscript.

Submission to Molecular Endocrinolgoy

This is the code for the second submission. The major changes from the first submission include

Switch from DESeq to DESeq2

DESeq2 controls for the false positive rate differently, allowing for a more powerful statistical analysis. We switched all the analyses over to DESeq2. This dramatically changed the number of statistically significant genes. This was done in issue #9 for the code and issue #14 for the manuscript. We also upgraded to GRCh37.74 version of the human genome in issue #13.

Counts Mapping Algoritm

Based on utility, and avoiding the need to filter out extra transcripts that we are not analysing we mapped genes using HTSseq rather than Genomic Ranges. This was done in issue #1.

Clinical Measurements

We noted that some of the clinical measurements seemed to be non-normally distributed. We adjusted the clinical analysis script to first test for normality, then test for equal variance. The resulting p-values are now based on either a Wilcoxon, Welch or Student's T-Test. This was done in issue #16 (commit 6143ff1).

Gene Set Enrichment

Added analysis using GSEA to replace/complement the GOseq analysis. GSEA (http://www.broadinstitute.org/gsea/index.jsp) incorporates the rank of genes in the entire data set, rather than just the top significant hits. This allowed us to analyse more data sets including miRNA, TRANSFAC, Reactome. This was done in issue #12 and largely in commit 1e25f3b. These data are presented in Supplementary Tables 2,3 and 4 as done in issue #20