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Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.08.01.551411v1?rss=1

Authors: Ocana-Tienda, B., Leon-Triana, O., Perez-Beteta, J., Perez-Garcia, V. M.

Abstract:
Metastasis is the process through which cancer cells break away from a primary tumor, travel through the blood or lymph system, and form new tumors in distant tissues. One of the preferred sites for metastatic dissemination is the brain, affecting more than 20% of all cancer patients. This figure is increasing steadily due to improvements in treatments of primary tumors. Stereotactic radiosurgery (SRS) is one of the main treatment options for patients with a small or moderate number of brain metastases (BMs). A frequent adverse event of SRS is radiation necrosis (RN), an inflammatory condition caused by late normal tissue cell death. A major diagnostic problem is that RNs are difficult to distinguish from BM recurrences, due to their similarities on standard magnetic resonance images (MRIs). However, this distinction is key to choosing the best therapeutic approach since RNs resolve often without further interventions, while relapsing BMs may require open brain surgery. Recent research has shown that RNs have a faster growth dynamics than recurrent BMs, providing a way to differentiate the two entities, but no mechanistic explanation has been provided for those observations. In this study, computational frameworks were developed based on mathematical models of increasing complexity, providing mechanistic explanations for the differential growth dynamics of BMs relapse versus RN events and explaining the observed clinical phenomenology. Simulated tumor relapses were found to have growth exponents substantially smaller than the group in which there was inflammation due to damage induced by SRS to normal brain tissue adjacent to the BMs, thus leading to RN. ROC curves with the synthetic data had an optimal threshold that maximized the sensitivity and specificity values for a growth exponent beta* = 1.05, very close to that observed in patient datasets.

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Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.08.03.551833v1?rss=1

Authors: Liu, X., Husby, M., Stahelin, R. V., Pienaar, E.

Abstract:
Ebola virus (EBOV) infection is threatening human health, especially in Central and West Africa. Limited clinical trials and requirement of biosafety level-4 (BSL-4) laboratories hinders experimental work to advance our understanding of EBOV and evaluation of treatment. In this work, we use a computational model to study the assembly and budding process of EBOV and evaluate the effect of fendiline on these processes. Our results indicate that the assembly of VP40 filaments may follow the nucleation-elongation theory, as it is critical to maintain a pool of VP40 dimer for the maturation and production of virus-like particles (VLPs). We further find that the nucleation-elongation process can also be influenced by phosphatidylserine (PS), which can complicate the efficacy of fendiline, a drug that lowers cellular PS levels. We observe that fendiline may increase VLP production at earlier time points (24 h) and under low concentrations ( less than or equal to 2 M). But this effect is transient and does not change the conclusion that fendiline generally decreases VLP production. We also conclude that fendiline can be more efficient at the stage of VLP budding relative to earlier phases. Combination therapy with a VLP budding step-targeted drug may further increase the treatment efficiency of fendiline. Finally, we also show that fendiline has higher efficacy when VP40 expression is high. While these are single-cell level results based on the VP40 system, it points out a potential way of fendiline application affecting EBOV assembly, which can be further tested in experimental studies with multiple EBOV proteins or live virus.

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Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.08.01.551346v1?rss=1

Authors: Joisa, C. U., Chen, K. A., Berginski, M., Beville, S., Stuhlmiller, T., Okumu, D., Golitz, B. T., Johnson, G. L., Gomez, S. M.

Abstract:
Protein kinases are a primary focus in targeted therapy development for cancer, owing to their role as regulators in nearly all areas of cell life. Kinase inhibitors are one of the fastest growing drug classes in oncology, but resistance acquisition to kinase-targeting monotherapies is inevitable due to the dynamic and interconnected nature of the kinome in response to perturbation. Recent strategies targeting the kinome with combination therapies have shown promise, such as the approval of Trametinib and Dabrafenib in advanced melanoma, but similar empirical combination design for less characterized pathways remains a challenge. Computational combination screening is an attractive alternative, allowing in-silico screening prior to in-vitro or in-vivo testing of drastically fewer leads, increasing efficiency and effectiveness of drug development pipelines. In this work, we generate combined kinome inhibition states of 40,000 kinase inhibitor combinations from kinobeads-based kinome profiling across 64 doses. We then integrated these with baseline transcriptomics from CCLE to build robust machine learning models to predict cell line sensitivity from NCI-ALMANAC across nine cancer types, with model accuracy R2 ~ 0.75-0.9 after feature selection using elastic-net regression. We further validated the model's ability to extend to real-world examples by using the best-performing breast cancer model to generate predictions for kinase inhibitor combination sensitivity and synergy in a PDX-derived TNBC cell line and saw reasonable global accuracy in our experimental validation (R2 ~ 0.7) as well as high accuracy in predicting synergy using four popular metrics (R2 ~ 0.9). Additionally, the model was able to predict a highly synergistic combination of Trametinib (MEK inhibitor) and Omipalisib (PI3K inhibitor) for TNBC treatment, which incidentally was recently in phase I clinical trials for TNBC. Our choice of tree-based models over networks for greater interpretability also allowed us to further interrogate which specific kinases were highly predictive of cell sensitivity in each cancer type, and we saw confirmatory strong predictive power in the inhibition of MAPK, CDK, and STK kinases. Overall, these results suggest that kinome inhibition states of kinase inhibitor combinations are strongly predictive of cell line responses and have great potential for integration into computational drug screening pipelines. This approach may facilitate the identification of effective kinase inhibitor combinations and accelerate the development of novel cancer therapies, ultimately improving patient outcomes.

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Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.08.03.551714v1?rss=1

Authors: Feng, S., Sanford, J. A., Weber, T. J., Hutchinson-Bunch, C. M., Dakup, P. P., Paurus, V. L., Attah, K., Sauro, H. A., Qian, W.-J., Wiley, H. S.

Abstract:
Building mechanistic models of kinase-driven signaling pathways requires quantitative measurements of protein phosphorylation across physiologically relevant conditions, but this is rarely done because of the insensitivity of traditional technologies. By using a multiplexed deep phosphoproteome profiling workflow, we were able to generate a deep phosphoproteomics dataset of the EGFR-MAPK pathway in non-transformed MCF10A cells across physiological ligand concentrations with a time resolution of less than 12 min and in the presence and absence of multiple kinase inhibitors. An improved phosphosite mapping technique allowed us to reliably identify greater than 46,000 phosphorylation sites on greater than 6600 proteins, of which greater than 4500 sites from 2110 proteins displayed a greater than 2-fold increase in phosphorylation in response to EGF. This data was then placed into a cellular context by linking it to 15 previously published protein databases. We found that our results were consistent with much, but not all previously reported data regarding the activation and negative feedback phosphorylation of core EGFR-ERK pathway proteins. We also found that EGFR signaling is biphasic with substrates downstream of RAS/MAPK activation showing a maximum response at less than 3ng/ml EGF while direct substrates, such as HGS and STAT5B, showing no saturation. We found that RAS activation is mediated by at least 3 parallel pathways, two of which depend on PTPN11. There appears to be an approximately 4-minute delay in pathway activation at the step between RAS and RAF, but subsequent pathway phosphorylation was extremely rapid. Approximately 80 proteins showed a greater than 2-fold increase in phosphorylation across all experiments and these proteins had a significantly higher median number of phosphorylation sites (~18) relative to total cellular phosphoproteins (~4). Over 60% of EGF-stimulated phosphoproteins were downstream of MAPK and included mediators of cellular processes such as gene transcription, transport, signal transduction and cytoskeletal arrangement. Their phosphorylation was either linear with respect to MAPK activation or biphasic, corresponding to the biphasic signaling seen at the level of the EGFR. This deep, integrated phosphoproteomics data resource should be useful in building mechanistic models of EGFR and MAPK signaling and for understanding how downstream responses are regulated.

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Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.08.01.551525v1?rss=1

Authors: Zhu, L., Wang, J.

Abstract:
Recent developments of single-cell sequencing technology enabling acquisition of the whole transcriptome data. To uncover the underlying mechanism of cell cycle function from such data, we reconstruct a continuous vector field based on the discrete single-cell RNA velocity to quantify the global non-equilibrium dynamical landscape and flux. We reveal that biological noise can make the global landscape more complex and less predictable. Genetic perturbations alter landscape-flux, thus identify key genes in maintaining cell cycle dynamics and predict the associated effects on cell cycle behaviour. Cell cycle initiation costs energy and sustaining cell cycle requires dissipation to increase oscillatory phase coherence. This approach enables the inference of cell cycle gene regulatory networks directly from single-cell transcriptomic data, including feedback mechanisms. Our study provides a new framework with insights into cell cycle regulation from single-cell transcriptome data and can be extended to other biological processes, such as differentiation-development and disease pathogenesis

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Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.08.01.551537v1?rss=1

Authors: Wordsworth, J., Yde Nielsen, P., Fielder, E., Chandrasegaran, S., Shanley, D.

Abstract:
Ageing results from the gradual loss of homeostasis, and there are currently many hypotheses for the underlying initial causes, such as molecular damage accumulation. However, few if any theories directly connect comprehensive, underlying biological mechanisms to specific age-related diseases. We recently demonstrated how a specific maintenance system impeding overactivity disorders such as cancer might undergo positive selection while still resulting in a gradual homeostatic shift toward slower metabolism. Here we connect this metabolic slowdown, via a series of unavoidable homeostatic shifts, to the hallmarks of ageing, including mitochondrial dysfunction, insulin resistance (IR), weight gain, basal inflammation, and age-related diseases such as atherosclerosis. We constructed the fuel and energy model (FEM) based on these shifts and found that ageing via metabolic slowdown could explain not only the effects of anti-ageing interventions such as rapamycin and calorie restriction, but many of the paradoxes of ageing that currently defy alternative theories.

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