Body fat distribution is a strong determinant of health and exhibits, as a specific feature of human beings, a marked sexual dimorphism. Women have more subcutaneous adipose tissue (SAT), while men have fat predominantly distributed to the central body visceral adipose tissue (VAT). The central accumulation of fat is associated with metabolic complications that trigger a low-grade chronic inflammation. Conversely, the SAT is rather associated with very little inflammation even in obesity.

Every organ is a combination of a functional compartment, the parenchyma, and a stromal compartment, the stroma, supporting the parenchymal cells of the organ. Characterizing its involvement and defining whether and how stroma evolution can be a marker of frailty and/or can be therapeutically targeted is worthy particularly in the context of aging.

Adipose Tissue (AT) is a dynamic organ, made up of a heterogeneous stroma that is poorly understood. The stroma of AT is the richest bodily reservoir of a peculiar cell type the Adipose Stromal Cells (ASCs) that exhibit high regenerative potential. AT dysfunction is thought to be a predictor of frailty and declining health span. Here, we want to investigate the age-dependent heterogeneity of the AT stroma as a predictive marker of frailty. To address this issue methods using antibody labelling are very useful but also time consuming.

Moreover, spectral overlap inherently limits the number of simultaneous labels. Based on our preliminary data, we hypothesized that microscopic images of AT where only nuclei are stained allow the characterization of stroma’s heterogeneity using deep neural networks.

On the deep learning side, this raises many interesting problems such as the handling of pseudo-3D data, the scarcity of the data, and the prediction’s explainability that can take advantage of the strong geometric properties of the nuclei.

The position of nuclear chromatin domains in human cells impacts genome stability and gene regulation. The nuclear envelope (NE), which defines nuclear volume boundaries, is a keyplayer in organizing nuclear architecture as nuclear lamina protein scaffold, at its inner side, associates with large chromatin domains. During postmitotic nuclear assembly, physical connections occur between the NE and about 40 to 50% of telomeres, the chromosome ends. While suggesting a novel role for telomeres in nuclear organization, the functional significanceof this anchoring remains to be unraveled.

This proposal first intends to reveal the interplay between chromosome and telomere organization during postmitotic nuclear assembly. Interactions between telomeres and NE also likely regulate heterochromatin formation/maintenance. In telomerase-negative cancer cells that undergo recombinationmediated alternative lengthening of telomeres (ALT), telomeric heterochromatin is dysregulated.

Here, we will test the hypothesis that telomere-NE interaction may be disrupted in ALT cells to facilitate unwanted recombination events between telomeric repeats. To do this, a large panel of telomerase-expressing or ALT cancer cell lines will be used to characterize telomere-NE attachment and its impact on telomere maintenance.

Live cell imaging will be employed to investigate the kinetics of events. Artificial tethering and/or depletion of proteins involved in telomere tethering will be instrumental to characterize the functional significance of this interaction.

Extracellular Vesicles (EVs) are nanoparticles released by cells, key players in intercellular communication and in the maintenance of tissue homeostasis and in pathogenesis¹. EVs contribute to cancer initiation and progression: they modulate cancer-associated processes (immunosuppression, angiogenesis, invasion, and metastasis). Hence, EVs and their biomolecular cargo can be detected in body fluids and exploited as biomarkers in cancer.

Our group within the Institut de Recherche en Informatique de Toulouse (IRIT) has developed a simulation platform that provides all the tools necessary to simulate multicellular cell microtissues growth under different environmental or treatment conditions (1). Such platform is key to easily develop models of complex biological systems by proposing various interaction paradigms such as visual programming, real-time simulation generation, simulation analysis in 2D/3D and virtual reality among others. For this PhD program, we want to model oncolytic virus infection and killing of cancer cells, to identify an optimal dose of treatment with a given schedule in silico for best therapeutic efficacy. Briefly, oncolytic virus (OV) are novel therapeutics that specifically infect, kill, replicate and propagate in cancer cells and tumors, to induce cancer cell death via multiple pathways and to recruit an immune response against tumors. Our partner team, led by Dr P. Cordelier within the cancer research center of Toulouse (CRCT), recently demonstrated that OV derived from myxoma virus (MYXV) are effective in killing primary cells derived from pancreatic cancer (PDAC), a disease with no cure (2), and are promising therapies for patients with this disease (3). However, the method and timing of administration of a given total dose of virus in a treatment cycle in complex 3D tumoroïds is still to be identified, to foster clinical valorization of this research program.

Macrophages do not only play a beneficial role in our immunity. Indeed, the massive tissue infiltration of macrophages derived from blood monocytes has a deleterious effect in most cancers and chronic inflammations, among other pathologies. It is therefore important to dissect the molecular mechanisms involved in the deleterious infiltration of tumor-associated macrophages in order to modulate it. In dense tumors, macrophages use the mesenchymal mode of migration, which requires proteolysis of the extracellular matrix, and the formation of adhesion structures called podosomes.

The proposed project is part of an interdisciplinary research program that combines cutting-edge and innovative techniques in optical and electron imaging, cell mechanics and materials science. It will build on our complementary expertise in podosome mechanobiology and cryo-electron tomography in situ to decipher the influence of nanoscale substrate topography on macrophage podosomes, the molecular mechanism involved in macrophage topography sensing and its importance for the infiltration of tumor-associated macrophages.

DNA double-strand breaks (DSBs) are toxic lesions, holding the potential to generate mutations and translocations. DSBs were historically seen as rare, mostly therapy-induced DNA lesions. However, recent work unequivocally revealed that endogenous DSBs occur far more frequently that initially believed, including in physiological conditions such as neuron stimulation, and mainly fall within transcribed loci (Transcription Coupled DSB, TC-DSB). Notably, altered repair of TC-DSBs is not only emerging as a driver of oncogenesis but also of many developmental, neurological and aging-associated diseases.

Using high throughput genomic and an original dedicated cell system (the DIvA cell line) our lab pioneered the discovery of a dedicated pathway mobilized to repair such DSBs in transcribed loci, coined as Transcription Coupled DSB repair (TC-DSBR). Grounding on our previous work, the present project aims at investigating TC-DSBR in stimulated neurons where endogenous TC-DSBs are generated as a consequence of early responsive genes activation.

Holding a strong background in bioinformatics the PhD student will:

1. Analyze and integrate the vast amount of high-throughput genomics data already obtained in DIvA cells (ATAC-seq, RNA-seq, ChIP-seq and HiC) to get a comprehensive view on the chromatin restructuration induced by TC-DSBs, to stage up the stage for the second aim.
2. Investigate using stimulated primary mouse neurons and human minibrains the repair processes at work at these endogenous TC-DSBs, using bulk and single cell Cut&Tag approaches. Sc Cut&Tag will be performed in Jop Kind’s lab (Hubrecht institute) who is a leader in the development of single cell genomics approaches.

The use of digital biomarkers for early detection of functional or cognitive decline is receiving increasing attention. For example, sensor-derived mobility indicators have been studied to predict an upcoming fall in the elderly. Currently, assessment of an individual’s food intake behavior relies primarily on self-report measures with a major recall bias. Continuous real-time sensor monitoring may provide a more sensitive and environmentally valid method. It is well demonstrated that the detection of subclinical alteration of nutritional habits (e.g. meal time, frequency, meal preparation…) combined with physiological parameters (weight, body composition, mobility) can prevent disease or social isolation.

In this work, we propose to develop digital biomarkers to track nutrition-related outcomes. We hypothesize that changes in meal intake or meal preparation outcomes correlated to physiological health characteristics (e.g. weight) could give predictive signs of deterioration but also that the monitoring of these parameters could give useful information on the dynamics of recovery after a hospitalization or a surgery for example. The proposed system is composed of a set of low cost sensors and embedded algorithms installed in the home to detect subtle changes on different time scales (weekly to yearly).

Finally, the development of digital biomarkers should allow better specification of the innovative services to be implemented to ensure their scalability and integration into the care pathway. This project associating a technological research center and a clinical center of excellence will benefit from the observational cohort of 100 elderly people continuously monitored with sensors at home for 5 years (INSPIRE-T project).