Biology subjects

Pedersen, A.-K.

Publications and source records attributed to Pedersen, A.-K..

3 recordsLinked to original sources

bioRxiv2023

Multi-layered proteomics identifies insulin-induced upregulation of the EphA2 receptor via the ERK pathway and dependent on low IGF1R level

Insulin resistance impairs the cellular insulin response and frequently precedes metabolic disorders, like type 2 diabetes, which are affecting an increasing number of people globally. Given the critical role of the liver in glucose and lipid metabolism, understanding the molecular mechanisms in hepatic insulin resistance is essential for early preventive treatments. To elucidate changes in insulin signal transduction associated with hepatocellular resistance, we employed a multi-layered mass spectrometry-based proteomics approach focusing on insulin receptor (IR) signaling at the interactome, phosphoproteome, and proteome levels.in a long-term hyperinsulinemia-induced insulin-resistant HepG2 cell line with a knockout of the insulin-like growth factor 1 receptor (IGF1R KO). Analysis of the dynamic insulin-induced IR interactome revealed recruitment of the PI3K complex in both insulin-sensitive and -resistant cells. From the phosphoproteomics dataset, a change in insulin-stimulated signaling responses in insulin resistance was observed and showed attenuated signaling via the metabolic PI3K-AKT pathway but sustained extracellular signal-regulated kinase (ERK) activity. At the proteome level, the ephrin type-A receptor 2 (EphA2) showed an insulin-induced increase in expression. This receptor belongs to the Eph receptor family and participates in various cellular processes, such as cell adhesion, migration, and tissue development. The protein abundance regulation of EphA2 occurred through the ERK signaling pathway and was concordantly independent of insulin resistance. Induction of EphA2 by insulin was confirmed in other cell lines and observed uniquely in cells with high levels of IR compared to IGF1R. The multi-layered proteomics dataset provided insights into insulin signaling in general and in the context of insulin resistance, and it can going forward serve as a resource to generate and test hypotheses, leading to an improved understanding of insulin resistance.

cell biology↗

bioRxiv2023

Optimizing linear ion trap data independent acquisition towards single cell proteomics

A linear ion trap (LIT) is an affordable, robust mass spectrometer that proves fast scanning speed and high sensitivity, where its primary disadvantage is inferior mass accuracy compared to more commonly used time-of-flight (TOF) or orbitrap (OT) mass analyzers. Previous efforts to utilize the LIT for low-input proteomics analysis still rely on either built-in OTs for collecting precursor data or OT-based library generation. Here, we demonstrate the potential versatility of the LIT for low-input proteomics as a stand-alone mass analyzer for all mass spectrometry measurements, including library generation. To test this approach, we first optimized LIT data acquisition methods and performed library-free searches with and without entrapment peptides to evaluate both the detection and quantification accuracy. We then generated matrix-matched calibration curves to estimate the lower limit of quantification using only 10 ng of starting material. While LIT-MS1 measurements provided poor quantitative accuracy, LIT-MS2 measurements were quantitatively accurate down to 0.5 ng on column. Finally, we optimized a suitable strategy for spectral library generation from low-input material, which we used to analyze single-cell samples by LIT-DIA using LIT-based libraries generated from as few as 40 cells.

biochemistry↗

bioRxiv2022

Repurposing of Amisulpride, a known antipsychotic drug, to target synovial fibroblasts activation in arthritis.

Synovial Fibroblasts (SFs) are key pathogenic drivers in arthritis and their in vivo activation by TNF is sufficient to orchestrate full arthritic pathogenesis in animal models. TNF blockade has been efficacious for a large percentage of Rheumatoid Arthritis (RA) patients, although characterized by a plethora of side effects. Novel therapeutic discoveries remain however challenging, especially in optimizing drug safety, side effects, longer-term responses, costs and administration routes. Aiming to find new potent therapeutics, we applied the L1000CDS2 search engine, in order to identify compounds that could potentially reverse the pathogenic expression signature of arthritogenic SFs, derived from the human TNF transgenic mouse model (hTNFtg). We identified a neuroleptic drug, namely Amisulpride, which was validated to reduce SFs inflammatory potential while decreasing the clinical score of hTNFtg polyarthritis. Notably, we found that Amisulpride did not exert its biological activities through its known targets Dopamine receptors 2 and 3 and Serotonin Receptor 7, nor through TNF-TNFRI binding inhibition. By applying a click chemistry approach, novel potential targets of Amisulpride were identified, which were further validated to repress hTNFtg SFs inflammatory potential in vitro (Ascc3 and Sec62), while phosphoproteomics analysis revealed important fibroblast activation pathways, such as adhesion, to be altered upon treatment. Our data support that Amisulpride could provide an additive beneficial effect to patients suffering from RA and comorbid dysthymia, as it may reduce SFs pathogenicity in parallel with its anti-depressive activity. Importantly, Amisulpride may also serve as a "lead" compound for the development of novel, more potent therapeutics against chronic inflammatory diseases.

pharmacology and toxicology↗