Biology subjects

Amann, J.

Publications and source records attributed to Amann, J..

3 recordsLinked to original sources

bioRxiv2024

An in vivo 'turning model' reveals new RanBP9 interactions in lung macrophages.

The biological functions of the scaffold protein Ran Binding Protein 9 (RanBP9) remain elusive in macrophages or any other cell type where this protein is expressed together with its CTLH (C-terminal to LisH) complex partners. We have engineered a new mouse model, named RanBP9-TurnX, where RanBP9 fused to three copies of the HA tag (RanBP9-3xHA) can be turned into RanBP9-V5 tagged upon Cre-mediated recombination. We created this model to enable stringent biochemical studies at cell type specific level throughout the entire organism. Here, we have used this tool crossed with LysM-Cre transgenic mice to identify RanBP9 interactions in lung macrophages. We show that RanBP9-V5 and RanBP9-3xHA can be both co-immunoprecipitated with the known members of the CTLH complex from the same whole lung lysates. However, more than ninety percent of the proteins pulled down by RanBP9-V5 differ from those pulled-down by RanBP9-HA. The lung RanBP9-V5 associated proteome includes previously unknown interactions with macrophage-specific proteins as well as with players of the innate immune response, DNA damage response, metabolism, and mitochondrial function. This work provides the first lung specific RanBP9-associated interactome in physiological conditions and reveals that RanBP9 and the CTLH complex could be key regulators of macrophage bioenergetics and immune functions.

cell biology↗

bioRxiv2022

Digital twins and hybrid modelling for simulation of physiological variables and stroke risk

One of the more interesting ideas for achieving personalized, preventive, and participatory medicine is the concept of a digital twin. A digital twin is a personalized computer model of a patient. So far, digital twins have been constructed using either mechanistic models, which can simulate the trajectory of physiological and biochemical processes in a person, or using machine learning models, which for example can be used to estimate the risk of having a stroke given a cross-section profile at a given timepoint. These two modelling approaches have complementary strengths which can be combined into a hybrid model. However, even though hybrid modelling combining mechanistic modelling and machine learning have been proposed, there are few, if any, real examples of hybrid digital twins available. We now present such a hybrid model for the simulation of ischemic stroke. On the mechanistic side, we develop a new model for blood pressure and integrate this with an existing multi-level and multi-timescale model for the development of type 2 diabetes. This mechanistic model can simulate the evolution of known physiological risk factors (such as weight, diabetes development, and blood pressure) through time, under different intervention scenarios, involving a change in diet, exercise, and certain medications. These forecast trajectories of the physiological risk factors are then used by a machine learning model to calculate the 5-year risk of stroke, which thus also can be calculated for each timepoint in the simulated scenarios. We discuss and illustrate practical issues with clinical implementation, such as data gathering and harmonization. By improving patients understanding of their body and health, the digital twin can serve as a valuable tool for patient education and as a conversation aid during the clinical encounter. As such, it can facilitate shared decision-making, promote behavior change towards a healthy lifestyle, and improve adherence to prescribed medications.

systems biology↗

bioRxiv2021

An Immunogold Single Extracellular Vesicular RNA and Protein (AuSERP) Biochip to Predict Responses to Immunotherapy in Non-Small Cell Lung Cancer Patients

Conventional PD-L1 immunohistochemical tissue biopsies only predict 20~40% of non-small cell lung cancer (NSCLC) patients that will respond positively to anti-PD-1/PD-L1 immunotherapy. Herein, we present an immunogold biochip to quantify single extracellular vesicular RNA and protein (AuSERP) as a non-invasive alternative. With only 3 L of serum, PD-1/PD-L1 proteins on the surface of extracellular vesicles (EVs) and EV PD-1/PD-L1 messenger RNA (mRNA) cargo were detected at a single-vesicle resolution and exceeded the sensitivities of ELISA and qRT-PCR by 1000 times. By testing a cohort of 27 non-responding and 27 responding NSCLC patients, AuSERP indicated that the single-EV mRNA biomarkers surpass the single-EV protein biomarkers at predicting patient responses to immunotherapy. Dual single-EV PD-1/PD-L1 mRNA detection differentiated responders from non-responders with an accuracy of 72.2% and achieved an NSCLC diagnosis accuracy of 93.2%, suggesting the potential for AuSERP to provide enhanced immunotherapy predictions and cancer diagnoses within the clinical setting.

immunology↗