Biology subjects

Le Sueur, C.

Publications and source records attributed to Le Sueur, C..

4 recordsLinked to original sources

bioRxiv2024

Subcellular thermal profiling enables the deep functional exploration of the mitochondrial proteome

Mitochondria are membrane-bound organelle hubs of cellular metabolism and signaling. The dysregulation of mitochondria is related to the genesis of several highly prevalent diseases, including cancer and cardiovascular disorders, urging the development of novel technologies to systematically study this organelle and its dynamics. Thermal proteome profiling (TPP) allows the unbiased study of the interactions of proteins with drugs, metabolites, and other proteins, providing a unique understanding of the state of the proteome. Here, we develop and introduce an optimized TPP workflow, mito-TPP, for the direct and extensive study of this organelle. We demonstrate that our approach detects both direct mitochondrial small molecule-protein and metabolite-protein interactions, as well as indirect downstream effects. We also show that mito-TPP preserves features from whole-cell systems, such as the coaggregation of interacting proteins. Finally, we explore the mitochondrial proteoform map, detecting more than 180 proteins with multiple proteoform groups. Overall, we demonstrate that mito-TPP is a powerful new tool for the functional study of the mitochondrial proteome.

molecular biology↗

bioRxiv2023

Hierarchical Gaussian process models explore the dark meltome of thermal proteome profiling experiments.

Thermal proteome profiling (TPP) is a proteome wide technology that enables unbiased detection of protein drug interactions as well as changes in post-translational state of proteins between different biological conditions. Statistical analysis of temperature range TPP (TPP-TR) datasets relies on comparing protein melting curves, describing the amount of non-denatured proteins as a function of temperature, between different conditions (e.g. presence or absence of a drug). However, state-of-the-art models are restricted to sigmoidal melting behaviours while unconventional melting curves, representing up to 50% of TPP-TR datasets, have recently been shown to carry important biological information. We present a novel statistical framework, based on hierarchical Gaussian process models and named GPMelt, to make TPP-TR datasets analysis unbiased with respect to the melting profiles of proteins. GPMelt scales to multiple conditions, and extension of the model to deeper hierarchies (i.e. with additional sub-levels) allows to deal with complex TPP-TR protocols. Collectively, our statistical framework extends the analysis of TPP-TR datasets for both protein and peptide level melting curves, offering access to thousands of previously excluded melting curves and thus substantially increasing the coverage and the ability of TPP to uncover new biology. Author summaryProteins interactions with other proteins, nucleic acids or metabolites, are key to all biological processes. Being able to detect these interactions is essential to understand biological systems. Thermal proteome profiling is a proteome-wide biological assay able to capture these interactions. It consists in analysing the effect of heat treatment on proteins. Indeed, proteins, under physiological conditions, are folded. This folding gets disrupted as the temperature increases. The way this unfolding happens, called the melting profile of the protein, informs on the interactions of proteins. For example, the interaction of a protein with another protein can increase (thermally stabilise) or decrease (thermally destabilise) the temperature at which this protein starts unfolding. In this work, we present a new statistical method, named GPMelt, to analyse these melting profiles. Notably, GPMelt allows to analyse any melting profiles, independently of their shapes. The proposed improvements over previously published methods allow to investigate more robustly the melting profiles of more proteins, hence increasing the ability of thermal proteome profiling assays to discover new protein interactions. We anticipate that these advancements will aid in unravelling complex biological phenomena.

bioinformatics↗

bioRxiv2021

Robust, Universal Tree Balance Indices

Balance indices that quantify the symmetry of branching events and the compactness of trees are widely used to compare evolutionary processes or tree-generating algorithms. Yet existing indices have important shortcomings, including that they are unsuited to the tree types commonly used to describe the evolution of tumours, microbial populations, and cell lines. The contributions of this article are twofold. First, we define a new class of robust, universal tree balance indices. These indices take a form similar to Colless index but account for node sizes, are defined for trees with any degree distribution, and enable more meaningful comparison of trees with different numbers of leaves. Second, we show that for bifurcating and all other full m-ary cladograms (in which every internal node has the same out-degree), one such Colless-like index is equivalent to the normalised reciprocal of Sackins index. Hence we both unify and generalise the two most popular existing tree balance indices. Our indices are intrinsically normalised and can be computed in linear time. We conclude that these more widely applicable indices have potential to supersede those in current use.

evolutionary biology↗

bioRxiv2019

When, why and how clonal diversity predicts future tumour growth

Intratumour heterogeneity holds promise as a prognostic biomarker in multiple cancer types. However, the relationship between this marker and its clinical impact is mediated by an evolutionary process that is not well understood. Here we employ a spatial computational model of tumour evolution to assess when, why and how intratumour heterogeneity can be used to forecast tumour growth rate, an important predictor of clinical progression. We identify three conditions that can lead to a positive correlation between clonal diversity and subsequent growth rate: diversity is measured early in tumour development; selective sweeps are rare; and/or tumours vary in the rate at which they acquire driver mutations. Opposite conditions typically lead to negative correlation. Our results further suggest that prognosis can be better predicted on the basis of both clonal diversity and genomic instability than either factor alone. Nevertheless, we find that, for predicting tumour growth, clonal diversity is likely to perform worse than conventional measures of tumour stage and grade. We thus offer explanations - grounded in evolutionary theory - for empirical findings in various cancers. Our work informs the search for new prognostic biomarkers and contributes to the development of predictive oncology.

cancer biology↗