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Each year, the life-sciences and healthcare industry kicks start the year with the J.P. Morgan Healthcare Conference in San Francisco. Like always, it gave a glimpse into the mood of the industry and which sectors are going to play a vital role in the near future. The JPM conference demonstrated that 'obesity' will drive the market and capture global biopharma's imagination, with a 2030 market projection of over $80 billion. The market is bullish on the obesity sector after the commercial success of obesity drugs and indication expansion campaigns (cardiovascular mortality, nonfatal MI, and stroke) by Novo and Lilly. Not surprisingly, the highlight of the JPM this year was Glucagon-like peptide 1 (GLP-1).
Elucidata is enabling data-centric approaches to fast-track obesity research in one of the top 2 pharma players in the space worldwide. In this monthly dataset roundup, we present you some of high-quality multi-modal datasets (lipidomics, metabolomics, Bulk RNA-seq, single-cell, and proteomics) that have been delivered to the pharma companies in their quest for obesity research. These multi-modal datasets offer insights for target identification and a deeper understanding of the molecular mechanisms behind obesity.
Integration of genetic colocalizations with physiological and pharmacological perturbations identifies cardiometabolic disease genes.
Dataset ID: GSE179347_GPL20301
Year of Publication: 2022
Experiment Type: Bulk RNA Sequencing
Total Samples: 83
Organism: Homo sapiens
Reference Link: Publication
Complex traits and diseases can be influenced by both genetics and environment. However, given the large number of environmental stimuli and power challenges for gene-by-environment testing, it remains a critical challenge to identify and prioritize specific disease-relevant environmental exposures. The authors proposed a framework for leveraging signals from transcriptional responses to environmental perturbations to identify disease-relevant perturbations that can modulate genetic risk for complex traits and inform the functions of genetic variants associated with complex traits.
The authors used 21 relevant physiological and pharmacological perturbations (drugs, transcription factors, and metabolites) in human adipocytes, hepatocytes, and skeletal muscle cells and mapped their protein-protein interactions to observe the transcriptional response of genes involved in complex traits. These perturbations affected insulin resistance, glucose homeostasis, and metabolic regulation in humans and identified thousands of environmentally responsive genes. By combining these data with GWASs from 31 distinct polygenic traits, the authors show that the heritability of multiple traits is enriched in regions surrounding genes responsive to specific perturbations and, further, that environmentally responsive genes are enriched for associations with specific diseases and phenotypes from the GWAS Catalog.
Overall, this study demonstrates the advantages of large-scale characterization of transcriptional changes in diversely stimulated and pathologically relevant cells to identify disease-relevant perturbations.
Global analyses of selective insulin resistance in hepatocytes caused by palmitate lipotoxicity.
Dataset ID: MTBLS582
Year of Publication: 2018
Experiment Type: Lipidomics
Total Samples: 12
Organism: Homo sapiens
Reference Link: Publication
Obesity is tightly linked to hepatic steatosis and insulin resistance. One feature of this association is the paradox of selective insulin resistance: insulin fails to suppress hepatic gluconeogenesis but activates lipid synthesis in the liver. How lipid accumulation interferes selectively with some branches of hepatic insulin signaling is not well understood. Here, the authors provide a resource based on unbiased approaches and established in a simple cell culture system to enable investigations of the phenomenon of selective insulin resistance.
The authors analyzed the phosphoproteome of insulin-treated human hepatoma cells and identified sites in which palmitate selectively impairs insulin signaling. The authors, with their lipidome study, show that palmitate interferes with insulin signaling to FoxO1, a key transcription factor regulating gluconeogenesis. They also identified altered FoxO1 cellular compartmentalization as a contributing mechanism for selective insulin resistance. The comprehensive characterization of the proteome, phosphoproteome, and lipidome changes in response to palmitate treatment provides a novel and useful resource for unraveling the mechanisms underlying selective insulin resistance.
Deletion of glycerol channel aquaporin-9 (Aqp9) impairs long-term blood glucose control in C57BL/6 leptin receptor-deficient (db/db) obese mice.
Dataset ID: MTBLS219
Year of Publication: 2015
Experiment Type: Metabolomics
Total Samples: 62
Organism: Mus musculus
Reference Link: Publication
Aquaporin-9 (Aqp9), a member of the major intrinsic protein family of transmembrane channels, is permeable to glycerol, urea, and some other small, neutral solutes. The aim of this study was to investigate the metabolic effects of Aqp9 deletion in coisogenic db/db mice of the C57BL/6 background. Aqp9wt db/db and Aqp9−/− db/db mice did not differ in body weight and liver triacylglycerol contents. The authors observed elevated plasma glucose in Aqp9−/− db/db mice (+1.1 mmol/L, lifetime average) in C57BL/6 mice, while plasma insulin concentration was reduced at the time of death.
Liver transcriptional profiling did not detect differential gene expression between genotypes. Metabolite profiling revealed a sex-independent increase in plasma glycerol (+55%) and glucose (+24%) and a reduction in threonate (all at q < 0.1) in Aqp9−/− db/db mice compared to controls. Metabolite profiling thus confirms the role of AQP9 in glycerol metabolism of obese C57BL/6 db/db mice. This study found that Aqp9 gene deletion elevates plasma glucose and does not alleviate hepatosteatosis in C57BL/6 mice.
Molecular integration of incretin and glucocorticoid action reverses immuno-metabolic dysfunction and obesity.
Dataset ID: GSE102415_GPL17021
Year of Publication: 2017
Experiment Type: Bulk RNA Sequencing
Total Samples: 30
Organism: Mus musculus
Reference Link: Publication
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Chronic inflammation has been proposed to contribute to the pathogenesis of diet-induced obesity. However, scarce therapeutic options are available to treat obesity and the associated immuno-metabolic complications. Glucocorticoids are routinely employed for the management of inflammatory diseases, but their pleiotropic nature leads to detrimental metabolic side effects. The authors developed a glucagon-like peptide-1 (GLP-1)-dexamethasone co-agonist in which GLP-1 selectively delivers dexamethasone to GLP-1 receptor-expressing cells. GLP-1-dexamethasone lowers body weight by up to 25% in obese mice by targeting the hypothalamic control of feeding and by increasing energy expenditure.
This strategy reverses hypothalamic and systemic inflammation while improving glucose tolerance and insulin sensitivity. The selective preference for the GLP-1 receptor bypasses the deleterious effects of dexamethasone on glucose handling, bone integrity, and hypothalamus-pituitary-adrenal axis activity. Thus, GLP-1-directed glucocorticoid pharmacology represents a safe and efficacious therapy option for diet-induced immuno-metabolic derangements and the resulting obesity.
To elucidate the in vivo signaling properties of GLP-1/Dexa, the authors performed unbiased transcriptional profiling (mRNA-seq) of hypothalami from DIO mice treated for 5 days with vehicle, GLP-1/Dexa, Dexamethasone (Dexa) or with GLP-1 at the equimolar doses of 100 nmol/kg. A group of calorie-restricted mice with body weight matched to that of GLP-1/Dexa-treated mice was included in order to dissect whether the molecular signatures governed by the conjugate are independent of the induced weight loss. N=5 hypothalamic samples per experimental group were used.
Phosphoproteomic analysis of platelets in severe obesity uncovers platelet reactivity and signaling pathway alterations.
Dataset ID: PXD020204
Year of Publication: 2022
Experiment Type: Proteomics
Total Samples: 24
Organism: Homo sapiens
Reference Link: Publication
Obesity is associated with a proinflammatory and prothrombotic state that supports atherosclerosis progression. The goal of this study was to gain insights into the phosphorylation events related to platelet reactivity in obesity and identify platelet biomarkers and altered activation pathways in this clinical condition. The authors performed a comparative phosphoproteomic analysis of resting platelets from obese patients and their age- and gender-matched lean controls. The phosphoproteomic data were validated by mechanistic, functional, and biochemical assays.
The authors identified 220 differentially regulated phosphopeptides from at least 175 proteins; interestingly, all were up-regulated in obesity. Most of the altered phosphoproteins are involved in SFKs (Src-family kinases)-related signaling pathways, cytoskeleton reorganization, and vesicle transport, some of them validated by targeted mass spectrometry.
To confirm platelet dysfunction, flow cytometry assays were performed in whole blood, indicating higher surface levels of glycoprotein VI (GPVI) and C-type lectin-like receptor 2 in platelets from obese patients correlating positively with body mass index. Receiver operator characteristics curves analysis suggested a much higher sensitivity for GPVI to discriminate between obese and lean individuals. Indeed, this study also found that obese platelets displayed more adhesion to collagen-coated plates. In line with the above data, soluble GPVI levels—indicative of higher GPVI signaling activation—were almost double in plasma from obese patients.
A single-cell atlas of human and mouse white adipose tissue.
Dataset ID: SCP1376_1
Year of Publication: 2022
Experiment Type: Single-cell
Total Samples: 137684
Organism: Homo sapiens
Reference Link: Publication
White adipose tissue, once regarded as morphologically and functionally bland, is now recognized to be dynamic, plastic, and heterogenous and is involved in a wide array of biological processes, including energy homeostasis, glucose and lipid handling, blood pressure control, and host defense. High-fat feeding and other metabolic stressors cause marked changes in adipose morphology, physiology, and cellular composition, and alterations in adiposity are associated with insulin resistance, dyslipidemia, and type 2 diabetes. Here, the authors provide detailed cellular atlases of human and mouse subcutaneous and visceral white fat at single-cell resolution across a range of body weights.
The authors identify subpopulations of adipocytes, adipose stem and progenitor cells, and vascular and immune cells and demonstrate commonalities and differences across species and dietary conditions. This study links specific cell types to increased risk of metabolic disease and provides an initial blueprint for a comprehensive set of interactions between individual cell types in the adipose niche in leanness and obesity. These datasets are an extensive resource for the exploration of genes, traits, and cell types in the function of white adipose tissue across species, depots, and nutritional conditions.
GLP-1 emerged as a central theme during discussions at JPM, signifying a notable shift in the Pharma and Healthcare industry's focus towards it as a key target for addressing obesity and metabolic diseases. This trend underscores the industry's dedication to exploring similar targets for the development of new therapies. We can expect the emergence of more effective treatments aimed at targeting GLP-1 and other genes to address weight loss and metabolic diseases in the upcoming and current decade.
The race to the finish line in drug discovery requires data-centric approaches. We believe that high-quality data leads to better, faster, and more reliable insights. We presented here high-quality, ML-ready obesity datasets across lipidomics, metabolomics, transcriptomics, proteomics, and single-cell. These multi-modal datasets enhance the statistical robustness for target identification, which in turn increases the predictive efficiency.
Connect with us to explore how Polly can expedite your research journey or reach us at info@elucidata.io to learn more.
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