Elovanoid-N34 modulates TXNRD1 key in protection against oxidative stress-related diseases.

The thioredoxin (TXN) system is an NADPH + H+/FAD redox-triggered effector that sustains homeostasis, bioenergetics, detoxifying drug networks, and cell survival in oxidative stress-related diseases. Elovanoid (ELV)-N34 is an endogenously formed lipid mediator in neural cells from omega-3 fatty acid precursors that modulate neuroinflammation and senescence gene programming when reduction-oxidation (redox) homeostasis is disrupted, enhancing cell survival. Limited proteolysis (LiP) screening of human retinal pigment epithelial (RPE) cells identified TXNRD1 isoforms 2, 3, or 5, the reductase of the TXN system, as an intracellular target of ELV-N34. TXNRD1 silencing confirmed that the ELV-N34 target was isoform 2 or 3. This lipid mediator induces TXNRD1 structure changes that modify the FAD interface domain, leading to its activity modulation. The addition of ELV-N34 decreased membrane and cytosolic TXNRD1 activity, suggesting localizations for the targeted reductase. These results show for the first time that the lipid mediator ELV-N34 directly modulates TXNRD1 activity, underling its protection in several pathologies when uncompensated oxidative stress (UOS) evolves.

Discovering the sluggishness of triathlon running - using the attractor method to quantify the impact of the bike-run transition.

Running in a triathlon, a so-called brick run, is uniquely influenced by accumulated load from its preceding disciplines. Crucially, however, and irrespective of race type, the demands of a triathlon always exceed the sum of its parts. Triathletes of all levels commonly report subjectively perceived incoordination within the initial stages of the cycle run transition (T2). Although minimizing it, and its influence on running kinematics, can positively impact running and overall triathlon performance, the mechanisms behind the T2 effect remain unclear. In the present study, we assessed the influence of the pre-load exercise mode focusing on the biomechanical perspective. To analyze inertial sensor-based raw data from both legs, the so-called Attractor Method was applied. The latter represents a sensitive approach, allowing to quantify subtle changes of cyclic motions to uncover the transient effect, a potentially detrimental transient phase at the beginning of a run. The purpose was to analyze the impact of a pre-load on the biomechanics of a brick run during a simulated Olympic Distance triathlon (without the swimming section). Therefore, we assessed the influence of pre-load exercise mode on running pattern (δM) and precision (δD), and on the length of the transient effect (tT) within a 10 km field-based run in 22 well-trained triathletes. We found that δD, but not δM, differed significantly between an isolated run (IRun) and when it was preceded by a 40 km cycle (TRun) or an energetically matched run (RRun). The average distance ran until overcoming the transient phase (tT) was 679 m for TRun, 450 m for RRun, and 29 4 m for IRun. The results demonstrated that especially the first kilometer of a triathlon run is prone to an uncoordinated running sensation, which is also commonly reported by athletes. That is, i) the T2 effect appeared more linked to variability in running style than to running style per se ii) run tT distance was influenced by preceding exercise load mode, being greater for a TRun than for the RRun condition, and iii) the Attractor Method seemed to be a potentially promising method of sensitively monitoring T2 adaptation under ecologically valid conditions.

Surpassing 10 000 identified and quantified proteins in a single run by optimizing current LC-MS instrumentation and data analysis strategy.

Comprehensive proteome quantification is crucial for a better understanding of underlying mechanisms of diseases. Liquid chromatography mass spectrometry (LC-MS) has become the method of choice for comprehensive proteome quantification due to its power and versatility. Even though great advances have been made in recent years, full proteome coverage for complex samples remains challenging due to the high dynamic range of protein expression. Additionally, when studying disease regulatory proteins, biomarkers or potential drug targets are often low abundant, such as for instance kinases and transcription factors. Here, we show that with improvements in chromatography and data analysis the single shot proteome coverage can go beyond 10 000 proteins in human tissue. In a testis cancer study, we quantified 11 200 proteins using data independent acquisition (DIA). This depth was achieved with a false discovery rate of 1% which was experimentally validated using a two species test. We introduce the concept of hybrid libraries which combines the strength of direct searching of DIA data as well as the use of large project-specific or published DDA data sets. Remarkably deep proteome coverage is possible using hybrid libraries without the additional burden of creating a project-specific library. Within the testis cancer set, we found a large proportion of proteins in an altered expression (in total: 3351; 1453 increased in cancer). Many of these proteins could be linked to the hallmarks of cancer. For example, the complement system was downregulated which helps to evade the immune response and chromosomal replication was upregulated indicating a dysregulated cell cycle.

Chemoproteomics-Aided Medicinal Chemistry for the Discovery of EPHA2 Inhibitors.

The receptor tyrosine kinase EPHA2 has gained attention as a therapeutic drug target for cancer and infectious diseases. However, EPHA2 research and EPHA2-based therapies have been hampered by the lack of selective small-molecule inhibitors. Herein we report the synthesis and evaluation of dedicated EPHA2 inhibitors based on the clinical BCR-ABL/SRC inhibitor dasatinib as a lead structure. We designed hybrid structures of dasatinib and the previously known EPHA2 binders CHEMBL249097, PD-173955, and a known EPHB4 inhibitor in order to exploit both the ATP pocket entrance as well as the ribose pocket as binding epitopes in the kinase EPHA2. Medicinal chemistry and inhibitor design were guided by a chemical proteomics approach, allowing early selectivity profiling of the newly synthesized inhibitor candidates. Concomitant protein crystallography of 17 inhibitor co-crystals delivered detailed insight into the atomic interactions that underlie the structure-affinity relationship. Finally, the anti-proliferative effect of the inhibitor candidates was confirmed in the glioblastoma cell line SF-268. In this work, we thus discovered a novel EPHA2 inhibitor candidate that features an improved selectivity profile while maintaining potency against EPHA2 and anticancer activity in SF-268 cells.