Localized immune surveillance of primary melanoma in the skin deciphered through executable modeling.

While skin is a site of active immune surveillance, primary melanomas often escape detection. Here, we have developed an in silico model to determine the local cross-talk between melanomas and Langerhans cells (LCs), the primary antigen-presenting cells at the site of melanoma development. The model predicts that melanomas fail to activate LC migration to lymph nodes until tumors reach a critical size, which is determined by a positive TNF-α feedback loop within melanomas, in line with our observations of murine tumors. In silico drug screening, supported by subsequent experimental testing, shows that treatment of primary tumors with MAPK pathway inhibitors may further prevent LC migration. In addition, our in silico model predicts treatment combinations that bypass LC dysfunction. In conclusion, our combined approach of in silico and in vivo studies suggests a molecular mechanism that explains how early melanomas develop under the radar of immune surveillance by LC.

ScreenGarden: a shinyR application for fast and easy analysis of plate-based high-throughput screens.

BACKGROUND: Colony growth on solid media is a simple and effective measure for high-throughput genomic experiments such as yeast two-hybrid, synthetic dosage lethality and Synthetic Physical Interaction screens. The development of robotic pinning tools has facilitated the experimental design of these assays, and different imaging software can be used to automatically measure colony sizes on plates. However, comparison to control plates and statistical data analysis is often laborious and pinning issues or plate specific growth effects can lead to the detection of false-positive growth defects. RESULTS: We have developed ScreenGarden, a shinyR application, to enable easy, quick and robust data analysis of plate-based high throughput assays. The code allows comparisons of different formats of data and different sized arrays of colonies. A comparison of ScreenGarden with previous analysis tools shows that it performs, at least, equivalently. The software can be run either via a website or offline via the RStudio program; the code is available and can be modified by expert uses to customise the analysis. CONCLUSIONS: ScreenGarden provides a simple, fast and effective tool to analyse colony growth data from genomic experiments.

Executable network of SARS-CoV-2-host interaction predicts drug combination treatments.

The COVID-19 pandemic has pushed healthcare systems globally to a breaking point. The urgent need for effective and affordable COVID-19 treatments calls for repurposing combinations of approved drugs. The challenge is to identify which combinations are likely to be most effective and at what stages of the disease. Here, we present the first disease-stage executable signalling network model of SARS-CoV-2-host interactions used to predict effective repurposed drug combinations for treating early- and late stage severe disease. Using our executable model, we performed in silico screening of 9870 pairs of 140 potential targets and have identified nine new drug combinations. Camostat and Apilimod were predicted to be the most promising combination in effectively supressing viral replication in the early stages of severe disease and were validated experimentally in human Caco-2 cells. Our study further demonstrates the power of executable mechanistic modelling to enable rapid pre-clinical evaluation of combination therapies tailored to disease progression. It also presents a novel resource and expandable model system that can respond to further needs in the pandemic.

Unifying the mechanism of mitotic exit control in a spatiotemporal logical model.

The transition from mitosis into the first gap phase of the cell cycle in budding yeast is controlled by the Mitotic Exit Network (MEN). The network interprets spatiotemporal cues about the progression of mitosis and ensures that release of Cdc14 phosphatase occurs only after completion of key mitotic events. The MEN has been studied intensively; however, a unified understanding of how localisation and protein activity function together as a system is lacking. In this paper, we present a compartmental, logical model of the MEN that is capable of representing spatial aspects of regulation in parallel to control of enzymatic activity. We show that our model is capable of correctly predicting the phenotype of the majority of mutants we tested, including mutants that cause proteins to mislocalise. We use a continuous time implementation of the model to demonstrate that Cdc14 Early Anaphase Release (FEAR) ensures robust timing of anaphase, and we verify our findings in living cells. Furthermore, we show that our model can represent measured cell-cell variation in Spindle Position Checkpoint (SPoC) mutants. This work suggests a general approach to incorporate spatial effects into logical models. We anticipate that the model itself will be an important resource to experimental researchers, providing a rigorous platform to test hypotheses about regulation of mitotic exit.

Synthetic Physical Interactions with the Yeast Centrosome.

The yeast centrosome or Spindle Pole Body (SPB) is an organelle situated in the nuclear membrane, where it nucleates spindle microtubules and acts as a signaling hub. Various studies have explored the effects of forcing individual proteins to interact with the yeast SPB, however no systematic study has been performed. We used synthetic physical interactions to detect proteins that inhibit growth when forced to associate with the SPB. We found the SPB to be especially sensitive to relocalization, necessitating a novel data analysis approach. This novel analysis of SPI screening data shows that regions of the cell are locally more sensitive to forced relocalization than previously thought. Furthermore, we found a set of associations that result in elevated SPB number and, in some cases, multi-polar spindles. Since hyper-proliferation of centrosomes is a hallmark of cancer cells, these associations point the way for the use of yeast models in the study of spindle formation and chromosome segregation in cancer.