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T cells need to adapt their cellular metabolism for effector cell differentiation. This relies on alterations in mitochondrial physiology. Which signals and molecules regulate those alterations remains unclear. We recently reported, that the mitochondrial protein TCAIM inhibits activation-induced changes in mitochondrial morphology and function and thus, CD effector T cell formation. Using conditional TCAIM knock-in (KI) and knockout (KO) mice, w now show that it also applies to CD8+ T cells and more importantly, delineate the molecular processes in mitochondria by which TCAIM controls effector cell differentiation. TCAIM KI resulted in reduced activation-induced HIF1α protein expression. Metabolomics and transcriptional data in combination with mathematical flux modeling revealed an impaired induction of anabolic pathways, especially of the mevalonate pathway and cholesterol biosynthesis in TCAIM KI CD8+ T cells. Addition of cholesterol completely rescued HIF1α protein expression, activation and proliferation of TCAIM KI CD8+ T cells. At the molecular level, TCAIM delayed activation-induced mitochondria-ER contact (MERC) formation by binding to MERC promoting proteins such as RMD3 and VDAC2. In summary, we demonstrate that TCAIM suppresses effector cell differentiation by inhibiting MERC formation, which induce HIF1α-mediated increase in cellular metabolism and cholesterol biosynthesis.
One of the great strengths of X-ray computed tomography over conventional inspection methods (ultrasound, thermography, radiography) is that it can image damage in 3D. However for curved ordeformed composite panels it can be difficult to automatically ascribe the damage to specific plies or inter-ply interfaces. An X-ray computed tomography (CT) data processing methodology is developed to extract the through-thickness distribution of damage in curved or deformed composite panels. The method is applied to [(0°/90°)2]s carbon fibre reinforced polymer (CFRP) panels subjected low velocity impact damage (5 J up to 20 J) providing 3D ply-by-ply damage visualisation and analysis. Our distance transform approach allows slices to be taken that approximately follow the composite curvature allowing the impact damage to be separated, visualised and quantified in 3D on a ply-by-ply basis. In this way the interply delaminations have been mapped, showing characteristic peanut shaped delaminations with the major axis oriented with the fibres in the ply below the interface. This registry to the profile of the panel constitutes a significant improvement in our ability to characterise impact damage in composite laminates and extract relevant measurements from X-ray CT datasets.
MACC1 is a prognostic and predictive metastasis biomarker for more than 20 solid Cancer entities. However, its role in cancer metabolism is not sufficiently explored. Here, we report on how MACC1 impacts the use of glucose, glutamine, lactate, pyruvate and fatty acids and show the comprehensive analysis of MACC1-driven metabolic networks. We analyzed concentrationdependent changes in nutrient use, nutrient depletion, metabolic tracing employing 13C-labeled substrates, and in vivo studies. We found that MACC1 permits numerous effects on cancer
metabolism. Most of those effects increased nutrient uptake. Furthermore, MACC1 alters metabolic pathways by affecting metabolite production or turnover from metabolic substrates. MACC1 supports use of glucose, glutamine and pyruvate via their increased depletion or altered distribution within metabolic pathways. In summary, we demonstrate that MACC1 is an important regulator of metabolism in cancer cells.
Pyrotechnic smoke generators fall under the Directive 2013/29/EU in Europe and could potentially belong to all categories except consumer fireworks. These types are especially present in the field of “Other Pyrotechnic Articles” of the categories P1 and P2, e.g. as simulation devices for paintball or airsoft gaming. The specific users of these products are aware of the corresponding smoke liberation during use and usually wear protective equipment to minimize exposure to potentially harmful aerosols. However, such products are often misused against the labelling requirements in locations where these articles are not supposed to be used, like football stadiums and demonstrations. In contrast to the intended use, uninvolved third parties are likely exposed to these reaction products without proper protective equipment. This study aims at identifying the transient particle size range of the aerosols emitted during the functioning of such common smoke generators for simulation purposes. In total four different types of articles were investigated, with five colors per type (white, blue, green, red, orange). Results show that the majority of the particles were emitted in a range between 40 nm and 350 nm, with some variation depending on the smoke color. Particles with diameters of less than 100 nm are generally of specific concern, as they can penetrate the alveolar system of the human lungs and therefore present a specific hazard.
Metastasis is the main cause of death from colorectal cancer (CRC). About 20% of stage II CRC patients develop metastasis during the course of disease. We performed metabolic profiling of plasma samples from non-metastasized and metachronously metastasized stage II CRC patients to assess the potential of plasma metabolites to serve as biomarkers for stratification of stage II CRC patients according to metastasis risk. We compared the metabolic profiles of plasma samples prospectively obtained prior to metastasis formation from non-metastasized vs. metachronously metastasized stage II CRC patients of the German population-based case–control multicenter DACHS study retrospectively. Plasma samples were analyzed from stage II CRC patients for whom follow-up data including the information on metachronous metastasis were available. To identify metabolites distinguishing non-metastasized from metachronously metastasized stage II CRC patients robust supervised classifications using decision trees and support vector machines were performed and verified by 10-fold cross-validation, by nested cross-validation and by traditional validation using training and test sets. We found that metabolic profiles distinguish non-metastasized from metachronously metastasized stage II CRC patients. Classification models from decision trees and support vector machines with 10-fold cross-validation gave average accuracy of 0.75 (sensitivity 0.79, specificity 0.7) and 0.82 (sensitivity 0.85, specificity 0.77), respectively, correctly predicting metachronous metastasis in stage II CRC patients. Taken together, plasma metabolic profiles distinguished non-metastasized and metachronously metastasized stage II CRC patients. The classification models consisting of few metabolites stratify non-invasively stage II CRC patients according to their risk for metachronous metastasis.