Rudy, S. | Search bioRxiv

bioRxiv2023

Induced pluripotent stem cell-derived human macrophages as an infection model for Leishmania donovani

The parasite Leishmania donovani is one of the species causing visceral leishmaniasis in humans, a deadly infection claiming up to 40,000 lives each year. The current drugs for leishmaniasis treatment have severe drawbacks and there is an urgent need to find new anti-leishmanial compounds. However, the search for drug candidates is complicated by the intracellular lifestyle of Leishmania. Here, we investigate the use of human induced pluripotent stem cell (iPS)-derived macrophages (iMACs) as host cells for L. donovani. iMACs obtained through embryoid body differentiation were infected with L. donovani promastigotes, and high-content imaging techniques were used to optimise the iMACs seeding density and multiplicity of infection, allowing us to reach infection rates up to 70% five days after infection. IC50 values obtained for miltefosine and amphotericin B using the infected iMACs or mouse peritoneal macrophages as host cells were comparable and in agreement with the literature, showing the potential of iMACs as an infection model for drug screening. Author SummaryYearly, up two million people in poverty-stricken areas contract leishmaniasis, a disease caused by parasites of the genus Leishmania. When an infected sandfly takes a blood meal, Leishmania parasites enter the host where they are taken up by macrophages. Inside the macrophage, Leishmania parasites establish a niche where they can proliferate. Although this infection often leads to disability or death, the drugs currently available are lacking due to toxic side effects, high expenses or difficulties in usage. Drug screening assays that are currently used for compound screening often rely on mouse peritoneal macrophages. We have generated human induced pluripotent stem cell derived macrophages and used these as new host cells for Leishmania donovani in the testing of anti-leishmanial compounds. This model has many advantages. For one, it allows us to work with human cells, mimicking the natural infection more closely than possible with murine cells. Secondly, it allows to obtain bigger batches of uniform cells for screening campaigns. Finally, this approach aligns with the principle of 3R, replacing the use of animals for cultivation of Leishmania and drug screening purposes.

cell biology↗

bioRxiv2020

A Data-driven Surrogate Model for Work Computation of a Periodically Forced Half-Sarcomere

Muscle force generation follows from molecular scale interactions that drive macroscopic behaviors and macroscopic processes that influence those at the molecular scale. A particuarly challenging issue is that models at the molecular level of organization are often quite difficult to apply to larger spatial scales. This is particularly true of moleuclar models driven by Monte-Carlo simulations. This challenge of multiscale dynamics requires methods to extract reduced order behaviors from detailed high-dimensional simulations. In this work we present a novel deterministic simulation method yielding accurate predictions of force-length behaviors of contracting muscle sarcomeres undergoing periodic length changes (work loops). The model maintains interpretability by tracking macroscopic state variables throughout the simulation while using data-driven representations of dynamics. Parameters of the data-driven dynamics are learned from trajectories from Monte-Carlo simulations of a half-sarcomere. Our method significantly reduces computational cost by tracking the state of the sarcomere in a course grained set of variables while maintaining accurate prediction of macroscopic level observables and time series for course grained variables. This allows for rapid sampling of the models output and builds towards the ability to scale to multiple-sarcomere simulations. Author SummaryWe develop a data-driven surrogate model for the dynamics of the half-sarcomere. This model achieves the same behavior with respect to force traces as more sophisticated Monte Carlo models at a substantially lower computational cost. The model is built by finding a course grained description of the full state space of the Monte Carlo simulation and learning dynamical models on the course grained space. Data-driven representations of the dynamics in the course grained space are trained using data from the full model. Data-driven models for forcing are also learned, and the result fed back into the dynamics. In doing so, the model seeks to replicate the effects of filament compliance on macro scale dynamics without explicitly tracking micro scale features. We withhold some input parameter regimes and demonstrate accurate reconstruction of course grained state and force traces using the data-driven model and given only knowledge of the initial condition and input. This work allows for faster computation of the forcing behavior of the half-sarcomere, as well as consistent representations of the course grained state variables. It is therefore promising as a step towards multi-sarcomere or even tissue scale models of skeletal muscle.

biophysics↗