Johannes M. Freudenberg

Effect of Roux-en-Y Gastric Bypass Surgery on Bile Acid Metabolism in Normal and Obese Diabetic Rats

In addition to classic functions of facilitating hepatobiliary secretion and intestinal absorption of lipophilic nutrients, bile acids (BA) are also endocrine factors and regulate glucose and lipid metabolism. Recent data indicate that antiobesity bariatric procedures e.g. Roux-en-Y gastric bypass surgery (RYGB), which also remit diabetes, increase plasma BAs in humans, leading to the hypothesis that BAs may play a role in diabetes resolution following surgery. To investigate the effect of RYGB on BA physiology and its relationship with glucose homeostasis, we undertook RYGB and SHAM surgery in Zucker diabetic fatty (ZDF) and normoglycemic Sprague Dawley (SD) rats and measured plasma and fecal BA levels, as well as plasma glucose, insulin, Glucagon like peptide 1 (GLP-1) and Peptide YY (PYY), 2 days before and 3, 7, 14 and 28 days after surgery. RYGB decreased body weight and increased plasma GLP-1 in both SD and ZDF rats while decreasing plasma insulin and glucose in ZDF rats starting from the first week. Compared to SHAM groups, both SD-RYGB and ZDF-RYGB groups started to have increases in plasma total BAs in the second week, which might not contribute to early post-surgery metabolic changes. While there was no significant difference in fecal BA excretion between SD-RYGB and SD-SHAM groups, the ZDF-RYGB group had a transient 4.2-fold increase (P<0.001) in 24-hour fecal BA excretion on post-operative day 3 compared to ZDF-SHAM, which paralleled a significant increase in plasma PYY. Ratios of plasma and fecal cholic acid/chenodeoxycholic acid derived BAs were decreased in RYGB groups. In addition, tissue mRNA expression analysis suggested early intestinal BA reabsorption and potentially reduced hepatic cholic acid production in RYGB groups. In summary, we present novel data on RYGB-mediated changes in BA metabolism to further understand the role of BAs in RYGB-induced metabolic effects in humans.

Human and Mouse Skeletal Muscle Stem Cells: Convergent and Divergent Mechanisms of Myogenesis

Satellite cells are the chief contributor to skeletal muscle growth and regeneration. The study of mouse satellite cells has accelerated in recent years due to technical advancements in the isolation of these cells. The study of human satellite cells has lagged and thus little is known about how the biology of mouse and human satellite cells compare. We developed a flow cytometry-based method to prospectively isolate human skeletal muscle progenitors from the satellite cell pool using positive and negative selection markers. Results show that this pool is enriched in PAX7 expressing cells that possess robust myogenic potential including the ability to give rise to de novo muscle in vivo. We compared mouse and human satellite cells in culture and identify differences in the elaboration of the myogenic genetic program and in the sensitivity of the cells to cytokine stimulation. These results indicate that not all mechanisms regulating mouse satellite cell activation are conserved in human satellite cells and that such differences may impact the clinical translation of therapeutics validated in mouse models. Thus, the findings of this study are relevant to developing therapies to combat muscle disease.

Evaluation of analytical methods for connectivity map data.

Connectivity map data and associated methodologies have become a valuable tool in understanding drug mechanism of action (MOA) and discovering new indications for drugs. However, few systematic evaluations have been done to assess the accuracy of these methodologies. One of the difficulties has been the lack of benchmarking data sets. Iskar et al. (PLoS. Comput. Biol. 6, 2010) predicted the Anatomical Therapeutic Chemical (ATC) drug classification based on drug-induced gene expression profile similarity (DIPS), and quantified the accuracy of their method by computing the area under the curve (AUC) of the Receiver Operating Characteristic (ROC) curve. We adopt the same data and extend the methodology, by using a simpler eXtreme cosine (XCos) method, and find it does better in this limited setting than the Kolmogorov-Smirnov (KS) statistic. In fact, for partial AUC (a more relevant statistic for actual application to repositioning) XCos does 17% better than the DIPS method (p=1.2e-7). We also observe that smaller gene signatures (with 100 probes) do better than larger ones (with 500 probes), and that DMSO controls from within the same batch obviate the need for mean centering. As expected there is heterogeneity in the prediction accuracy amongst the various ATC codes. We find that good transcriptional response to drug treatment appears necessary but not sufficient to achieve high AUCs. Certain ATC codes, such as those corresponding to corticosteroids, had much higher AUCs possibly due to strong transcriptional responses and consistency in MOA.

The epigenetic signature of systemic insulin resistance in obese women

Aims/hypothesisInsulin resistance (IR) links obesity to type 2 diabetes. The aim of this study was to explore whether white adipose tissue (WAT) epigenetic dysregulation is associated with systemic IR by genome-wide CG dinucleotide (CpG) methylation and gene expression profiling in WAT from insulin-resistant and insulin-sensitive women. A secondary aim was to determine whether the DNA methylation signature in peripheral blood mononuclear cells (PBMCs) reflects WAT methylation and, if so, can be used as a marker for systemic IR.MethodsFrom 220 obese women, we selected a total of 80 individuals from either of the extreme ends of the distribution curve of HOMA-IR, an indirect measure of systemic insulin sensitivity. Genome-wide transcriptome and DNA CpG methylation profiling by array was performed on subcutaneous (SAT) and visceral (omental) adipose tissue (VAT). CpG methylation in PBMCs was assayed in the same cohort.ResultsThere were 647 differentially expressed genes (false discovery rate [FDR] 10%) in SAT, all of which displayed directionally consistent associations in VAT. This suggests that IR is associated with dysregulated expression of a common set of genes in SAT and VAT. The average degree of DNA methylation did not differ between the insulin-resistant and insulin-sensitive group in any of the analysed tissues/cells. There were 223 IR-associated genes in SAT containing a total of 336 nominally significant differentially methylated sites (DMS). The 223 IR-associated genes were over-represented in pathways related to integrin cell surface interactions and insulin signalling and included COL5A1, GAB1, IRS2, PFKFB3 and PTPRJ. In VAT there were a total of 51 differentially expressed genes (FDR 10%); 18 IR-associated genes contained a total of 29 DMS.Conclusions/interpretationIn individuals discordant for insulin sensitivity, the average DNA CpG methylation in SAT and VAT is similar, although specific genes, particularly in SAT, display significantly altered expression and DMS in IR, possibly indicating that epigenetic regulation of these genes influences metabolism.

Mining Emerging Biomedical Literature for Understanding Disease Associations in Drug Discovery

Systematically evaluating the exponentially growing body of scientific literature has become a critical task that every drug discovery organization must engage in in order to understand emerging trends for scientific investment and strategy development. Developing trends analysis uses the number of publications within a 3-year window to determine concepts derived from well-established disease and gene ontologies to aid in recognizing and predicting emerging areas of scientific discoveries relevant to that space. In this chapter, we describe such a method and use obesity and psoriasis as use-case examples by analyzing the frequency of disease-related MeSH terms in PubMed abstracts over time. We share how our system can be used to predict emerging trends at a relatively early stage and we analyze the literature-identified genes for genetic associations, druggability, and biological pathways to explore any potential biological connections between the two diseases that could be utilized for drug discovery.

Significant obesity‐associated gene expression changes occur in the stomach but not intestines in obese mice

The gastrointestinal (GI) tract can have significant impact on the regulation of the whole‐body metabolism and may contribute to the development of obesity and diabetes. To systemically elucidate the role of the GI tract in obesity, we performed a transcriptomic analysis in different parts of the GI tract of two obese mouse models: ob/ob and high‐fat diet (HFD) fed mice. Compared to their lean controls, significant changes in the gene expression were observed in both obese mouse groups in the stomach (ob/ob: 959; HFD: 542). In addition, these changes were quantitatively much higher than in the intestine. Despite the difference in genetic background, the two mouse models shared 296 similar gene expression changes in the stomach. Among those genes, some had known associations to obesity, diabetes, and insulin resistance. In addition, the gene expression profiles strongly suggested an increased gastric acid secretion in both obese mouse models, probably through an activation of the gastrin pathway. In conclusion, our data reveal a previously unknown dominant connection between the stomach and obesity in murine models extensively used in research.

Integrative clinical transcriptomics analyses for new therapeutic intervention strategies: a psoriasis case study

Psoriasis is a chronic inflammatory skin disease with complex pathological features and unmet pharmacotherapy needs. Here, we present a framework for developing new therapeutic intervention strategies for psoriasis by utilizing publicly available clinical transcriptomics data sets. By exploring the underlying molecular mechanisms of psoriasis, the effects of subsequent perturbation of these mechanisms by drugs and an integrative analysis, we propose a psoriasis disease signature, identify potential drug repurposing opportunities and present novel target selection methodologies. We anticipate that the outlined methodology or similar approaches will further support biomarker discovery and the development of new drugs for psoriasis.

Gastrointestinal weight-loss surgery: glimpses at the molecular level

Pharmacotherapy for obesity remains a key challenge, and gastrointestinal weight-loss surgery remains a preferred option to help reduce excess body weight along with resolution of several comorbidities associated with obesity. This offers a unique opportunity to study the underlying mechanisms of gastro-intestinal weight-loss surgery to develop effective and less invasive long-term therapeutic interventions potentially mimicking the benefits of gastrointestinal weight-loss surgery. Here, we present an integrative analysis of currently available human transcriptomics data sets pre- and post-surgery and propose a computational biology strategy for selecting putative drug targets. We anticipate that approaches similar to the one that we outline here, would help elucidate underlying mechanisms that result in metabolic improvements and provide guidance on pharmaceutical targets to develop effective and less invasive therapies for obesity and related comorbidities.

Erratum to: The epigenetic signature of systemic insulin resistance in obese women

The number of differentially methylated sites (DMS) found to display directionally consistent differences in methylation in the cohorts studied by both Nilsson et al [10] and Arner et al was incorrectly reported to be 592, not 591. The sentence concerned should read: ‘Nilsson et al reported, in a cohort of 56 individuals, 15,627 DMS (q<0.15) in WAT associated with type 2 diabetes [10]; 671 of the DMS overlapped with those in the present study, of which 591 displayed directionally consistent differences in methylation in both cohorts (p<2.7×10) (ESM Table 4) [10].’ The second footnote to ESM Table 4 incorrectly referred to transcriptome profiles not methylome profiles. It should have read: ‘b. comparison with published methylome profiles on SAT; see references in main manuscript for details.’

Assessment of rosacea symptom severity by genome-wide association study and expression analysis highlights immuno-inflammatory and skin pigmentation genes

Abstract Rosacea is a common, chronic skin disease of variable severity with limited treatment options. The cause of rosacea is unknown, but it is believed to be due to a combination of hereditary and environmental factors. Little is known about the genetics of the disease. We performed a genome-wide association study (GWAS) of rosacea symptom severity with data from 73 265 research participants of European ancestry from the 23andMe customer base. Seven loci had variants associated with rosacea at the genome-wide significance level (P < 5 × 10−8). Further analyses highlighted likely gene regions or effector genes including IRF4 (P = 1.5 × 10−17), a human leukocyte antigen (HLA) region flanked by PSMB9 and HLA-DMB (P = 2.2 × 10−15), HERC2-OCA2 (P = 4.2 × 10−12), SLC45A2 (P = 1.7 × 10−10), IL13 (P = 2.8 × 10−9), a region flanked by NRXN3 and DIO2 (P = 4.1 × 10−9), and a region flanked by OVOL1and SNX32 (P = 1.2 × 10−8). All associations with rosacea were novel except for the HLA locus. Two of these loci (HERC-OCA2 and SLC45A2) and another precedented variant (rs1805007 in melanocortin 1 receptor) with an association P value just below the significance threshold (P = 1.3 × 10−7) have been previously associated with skin phenotypes and pigmentation, two of these loci are linked to immuno-inflammation phenotypes (IL13 and PSMB9-HLA-DMA) and one has been associated with both categories (IRF4). Genes within three loci (PSMB9-HLA-DMA, HERC-OCA2 and NRX3-DIO2) were differentially expressed in a previously published clinical rosacea transcriptomics study that compared lesional to non-lesional samples. The identified loci provide specificity of inflammatory mechanisms in rosacea, and identify potential pathways for therapeutic intervention.