Biology subjects

Bates, P. A.

Publications and source records attributed to Bates, P. A..

7 recordsLinked to original sources

bioRxiv2025

Cell migration sculpts evolutionary dynamics favouring therapy resistance in lung cancer

The evolution of cancer undermines the long-term efficacy of therapy. In this study, we combine experimental, computational, and clinical data analysis to investigate factors influencing the competition of subclones in lung cancer. Lineage tracing reveals unexpected variation in the long-term fate of neutral subclones, with subclones arising near the edge of tumours being favoured. Low levels of cell migration and cell mixing lead to high cell densities in the interior of the tumour that suppress proliferation. Using agent-based modelling and in silico analysis we inferred the extent of cell mixing in human tumours from the TRACERx lung cancer study. This reveals correlations between Epithelial to Mesenchymal Transition (EMT), stromal fibroblasts and levels of cell mixing. Experimental analysis confirms that both TGF{beta}-driven EMT and stromal fibroblasts reduce the variability in subclone fate and promote subclone mixing. Moreover, mixing favours clonal sweeps by subclones resistant to therapy-induced cell killing. Together, these analyses demonstrate that EMT and stromal fibroblasts sculpt tumour evolution by promoting cell mixing and thereby favour the rapid dominance of therapy resistant subclones.

cancer biology↗

bioRxiv2021

Interplay of adherens junctions and matrix proteolysis determines the invasive pattern and growth of squamous cell carcinoma

Cancers, such as squamous cell carcinoma, frequently invade as multicellular units. However, these invading units can be organized in a variety of ways, ranging from thin discontinuous strands to thick pushing collectives. Here we employ an integrated experimental and computational approach to identify the factors that determine the mode of collective cancer cell invasion. We find that matrix proteolysis is linked to the formation of wide strands, but has little effect on the maximum extent of invasion. Cell-cell junctions also favour wide strands, but our analysis also reveals a requirement for cell-cell junctions for efficient invasion in response to uniform directional cues. Unexpectedly, the ability to generate wide invasive strands is coupled to the ability to grow effectively when surrounded by ECM in 3D assays. Combinatorial perturbation of both matrix proteolysis and cell-cell adhesion demonstrates that the most aggressive cancer behaviour, both in terms of invasion and growth, is achieved at high levels of cell-cell adhesion and high levels of proteolysis. Contrary to expectation, cells with canonical mesenchymal traits - no cell-cell junctions and high proteolysis - exhibit reduced growth and lymph node metastasis. Thus, we conclude that the ability of squamous cell carcinoma cells to invade effectively is also linked to their ability to generate space for proliferation in confined contexts. These data provide an explanation for the apparent advantage of retaining cell-cell junctions in SCC.

cancer biology↗

bioRxiv2020

Response to thermal and infection stresses in an American vector of visceral leishmaniasis.

The phlebotomine sand fly Lutzomyia longipalpis is the primary insect vector of visceral leishmaniasis in the Americas. For ectothermic organisms such as sand flies, the ambient temperature is a critical factor influencing all aspects of their life. However, the impact of temperature has been ignored in previous investigations of stress-induced responses by the vector, such as taking a blood meal or during Leishmania infection. Therefore, this study explored the interaction of Lu. longipalpis with temperature by evaluating sand fly behaviour across a thermal gradient after sugar or blood-feeding, and infection with Leishmania mexicana. Thermographic recordings of sand fly females fed on mice were analysed, and the gene expression of heat shock proteins HSP70 and HSP90(83) was evaluated when insects were exposed to extreme temperatures or infected. The results showed that 72h after blood ingestion females of Lu. longipalpis became less active and preferred relatively low temperatures. However, at later stages of blood digestion females increased their activity and remained at higher temperatures prior to taking a second blood meal; this behaviour seems to be correlated with the evolution of their oocysts and voracity for a second blood meal. No changes in the temperature preferences of female sand flies were recorded in the presence of a gut infection by Le. mexicana, indicating that this parasite has not triggered behavioural immunity in Lu. longipalpis. Real-time imaging showed that the body temperature of female flies feeding on mice increased to the same temperature as the host within a few seconds after landing. The body temperature of females remained around 35 {+/-} 0.5 {degrees}C until the end of blood-feeding, revealing a lack of thermoregulatory behaviour. Analysis of expression of heat shock proteins revealed insects increased expression of HSP90(83) when exposed to higher temperatures, such as during blood feeding. Our findings suggest that Lu. longipalpis interacts with the environmental temperature by using its behaviour to avoid temperature-related physiological damage during the gonotrophic cycle. However, the expression of certain heat shock proteins might be triggered to mitigate against thermal stress in situations where a behavioural response is not the best option.

microbiology↗

bioRxiv2020

Computational and experimental analyses of mitotic chromosome formation pathways in fission yeast

Underlying higher order chromatin organization are Structural Maintenance of Chromosomes (SMC) complexes, large protein rings that entrap DNA. The molecular mechanism by which SMC complexes organize chromatin is as yet incompletely understood. Two prominent models posit that SMC complexes actively extrude DNA loops (loop extrusion), or that they sequentially entrap two DNAs that come into proximity by Brownian motion (diffusion capture). To explore the implications of these two mechanisms, we perform biophysical simulations of a 3.76 Mb-long chromatin chain, the size of the long S. pombe chromosome I left arm. On it, the SMC complex condensin is modeled to perform loop extrusion or diffusion capture. We then compare computational to experimental observations of mitotic chromosome formation. Both loop extrusion and diffusion capture can result in native-like contact probability distributions. In addition, the diffusion capture model more readily recapitulates mitotic chromosome axis shortening and chromatin density enrichment. Diffusion capture can also explain why mitotic chromatin shows reduced, as well as more anisotropic, movements, features that lack support from loop extrusion. The condensin distribution within mitotic chromosomes, visualized by stochastic optical reconstruction microscopy (STORM), shows clustering predicted from diffusion capture. Our results inform the evaluation of current models of mitotic chromosome formation.

biophysics↗

bioRxiv2020

A Novel Machine Learning Approach Uncovers New and Distinctive Inhibitors for Cyclin-Dependent Kinase 9

We present a novel combination of generative and predictive machine learning models for discovering unique protein inhibitors. The new method is assessed on its ability to generate unique inhibitors for the cancer associated protein kinase, CDK9. We validate our method by performing biochemical assays, attaining a hit rate of more than 10%, demonstrating the method to be a notable improvement upon a more standard, and somewhat naive approach. Moreover, we imposed the additional challenge of finding inhibitors that are readily synthesized. Importantly, two new inhibitors are found, with one being distinct from reported CDK9 inhibitors. We discuss the results in the context of modern machine learning principles and the desire expressed by the rational drug design community to secure molecules that are structurally different, yet with high binding affinities, to structurally determined protein targets.

bioinformatics↗

bioRxiv2019

A FIJI Macro for quantifying pattern in extracellular matrix

Diverse extracellular matrix patterns are observed in both normal and pathological tissue. However, most current tools for quantitative analysis focus on a single aspect of matrix patterning. Thus, an automated pipeline that simultaneously quantifies a broad range of metrics and enables a comprehensive description of varied matrix patterns is needed. To this end we have developed an ImageJ plugin called TWOMBLI, which stands for The Workflow Of Matrix BioLogy Informatics. TWOMBLI is designed to be quick, versatile and easy-to-use particularly for non-computational scientists. TWOMBLI can be downloaded from https://github.com/wershofe/TWOMBLI together with detailed documentation. Here we present an overview of the pipeline together with examples from a wide range of contexts where matrix patterns are generated.

cancer biology↗

bioRxiv2018

Predicting improved protein conformations with a temporal deep recurrent neural network

Accurate protein structure prediction from amino acid sequence is still an unsolved problem. The most reliable methods centre on template based modelling. However, the accuracy of these models entirely depends on the availability of experimentally resolved homologous template structures. In order to generate more accurate models, extensive physics based molecular dynamics (MD) refinement simulations are performed to sample many different conformations to find improved conformational states. In this study, we propose a deep recurrent network model, called DeepTrajectory, that is able to identify these improved conformational states, with high precision, from a variety of different MD based sampling protocols. The proposed model learns the temporal patterns of features computed from the MD trajectory data in order to classify whether each recorded simulation snapshot is an improved conformational state, decreased conformational state or a none perceivable change in state with respect to the starting conformation. The model is trained and tested on 904 trajectories from 42 different protein systems with a cumulative number of more than 1.7 million snapshots. We show that our model outperforms other state of the art machine-learning algorithms that do not consider temporal dependencies. To our knowledge, DeepTrajectory is the first implementation of a time-dependent deep-learning protocol that is re-trainable and able to adapt to any new MD based sampling procedure, thereby demonstrating how a neural network can be used to learn the latter part of the protein folding funnel.

bioinformatics↗