Étude de l'impact des microARNs sur la carcinogenèse des cancers colorectaux instables sur les séquences répétées microsatellites du génome

La progression tumorale MSI (Microsatellite Instable) est un processus multi-étapes résultant de mutations générées par un processus d'instabilité génétique qui affecte en majorité les motifs répétés en tandem de l'ADN (microsatellites). Ces mutations contribuent à l'oncogenèse lorsqu'elles perturbent la fonction d'oncogènes ou de gènes suppresseurs de tumeurs. Le trait phénotypique MSI est consécutif à l'inactivation du système de réparation des mésappariements de l'ADN (système MMR). Dans ce travail, je me suis intéressé au rôle des microARNs dans l'oncogenèse MSI. Les microARNs régulent l'expression de nombreux gènes pouvant avoir un rôle clé dans le cancer. J'ai donc fait l'hypothèse d'un rôle de ces microARNs lors des différentes étapes du processus tumorigénique MSI. Tout d'abord nous avons mis en évidence une surexpression du miR-155 (ciblant les principales protéines MMR) au niveau de la muqueuse colique non transformée des malades atteints d'une Maladie Inflammatoire Chronique Intestinale, qui pourrait constituer un évènement pré-tumoral favorisant l'émergence de clones MMR-déficients (notion d'effet de champs). Dans une deuxième partie, nous avons pu identifier la première mutation somatique touchant un microARN. Il s'agit du miR-3613 dont la répétition microsatellite est entièrement localisée dans le miR mature. L'instabilité au niveau de ce miR conduit à des changements de séquence à l'extrémité 3' du miR (notion d'IsomiRs). Les isomiRs produits ont un répertoire de cibles qui pour certaines sont communes à la forme sauvage et pour d'autres spécifiques à chacun des variants.MSI tumor progression (Microsatellite Instability) is depicted as a multistage process that results from mutations generated by a process of genetic instability affecting mostly DNA tandem repeats (known as microsatellites). These mutations contribute to tumorigenesis when they disrupt the function of oncogenes or tumor suppressor genes. As a phenotypic trait, MSI is the consequence of DNA mismatch repair inactivation (MMR). This work focused on the role microRNAs might play in MSI tumorigenesis. MicroRNAs regulate the expression of numerous genes and are deregulated in cancer. I have hypothesized a role of theses microRNAs during the various stages of the MSI tumorigenic process, choosing colorectal cancers (CRC) as a working model. First we demonstrated that overexpression of miR-155 (targeting core MMR proteins) in the non-transformed colonic mucosa of patients with Inflammatory Bowel Disease, might constitute a pre-tumoral event promoting the emergence of MMR-deficient clones (a concept known as ?field effect?). In a second part, we were able to identify the first somatic mutation affecting a mature microRNA sequence. A DNA microsatellite repeat is indeed fully embedded within the mature sequence of miR-3613. Instability at this DNA repeat leads to sequence modifications at the 3?end of miR-3613-5p (IsomiRs). IsomiRs display a signature among which some mRNA targets are common to the wild form, while others are specific to each variant.PARIS-JUSSIEU-Bib.électronique (751059901) / SudocSudocFranceF

PhAST: Physics-Aware, Scalable, and Task-specific GNNs for Accelerated Catalyst Design

Mitigating the climate crisis requires a rapid transition towards lower carbon energy. Catalyst materials play a crucial role in the electrochemical reactions involved in a great number of industrial processes key to this transition, such as renewable energy storage and electrofuel synthesis. To reduce the amount of energy spent on such processes, we must quickly discover more efficient catalysts to drive the electrochemical reactions. Machine learning (ML) holds the potential to efficiently model the properties of materials from large amounts of data, and thus to accelerate electrocatalyst design. The Open Catalyst Project OC20 data set was constructed to that end. However, most existing ML models trained on OC20 are still neither scalable nor accurate enough for practical applications. Here, we propose several task-specific innovations, applicable to most architectures, which increase both computational efficiency and accuracy. In particular, we propose improvements in (1) the graph creation step, (2) atom representations and (3) the energy prediction head. We describe these contributions and evaluate them on several architectures, showing up to 5$\times$ reduction in inference time without sacrificing accuracy.Comment: Accepted at the NeurIPS 2022 AI for Accelerated Materials Design Worksho

Investigation of Rare Earth Element Binding to a Surface-Bound Affinity Peptide Derived from EF-Hand Loop I of Lanmodulin

Bioinspired strategies have been given extensive attention for the recovery of rare earth elements (REEs) from waste streams because of their high selectivity, regeneration potential, and sustainability as well as low cost. Lanmodulin protein is an emerging biotechnology that is highly selective for REE binding. Mimicking lanmodulin with shorter peptides is advantageous because they are simpler and potentially easier to manipulate and optimize. Lanmodulin-derived peptides have been found to bind REEs, but their properties have not been explored when immobilized on solid substrates, which is required for many advanced separation technologies. Here, two peptides, LanM1 and scrambled LanM1, are designed from the EF-hand loop 1 of lanmodulin and investigated for their binding affinity toward different REEs when surface-bound. First, the ability of LanM1 to bind REEs was confirmed and characterized in solution using circular dichroism (CD), nuclear magnetic resonance (NMR), and molecular dynamics (MD) simulations for Ce(III) ions. Isothermal titration calorimetry (ITC) was used to further analyze the binding of the LanM1 to Ce(III), Nd(III), Eu(III), and Y(III) ions and in low-pH conditions. The performance of the immobilized peptides on a model gold surface was examined using a quartz crystal microbalance with dissipation (QCM-D). The studies show that the LanM1 peptide has a stronger REE binding affinity than that of scrambled LanM1 when in solution and when immobilized on a gold surface. QCM-D data were fit to the Langmuir adsorption model to estimate the surface-bound dissociation constant (Kd) of LanM1 with Ce(III) and Nd(III). The results indicate that LanM1 peptides maintain a high affinity for REEs when immobilized, and surface-bound LanM1 has no affinity for potential competitor calcium and copper ions. The utility of surface-bound LanM1 peptides was further demonstrated by immobilizing them to gold nanoparticles (GNPs) and capturing REEs from solution in experiments utilizing an Arsenazo III-based colorimetric dye displacement assay and ultraviolet-visible (UV-vis) spectrophotometry. The saturated adsorption capacity of GNPs was estimated to be around 3.5 μmol REE/g for Ce(III), Nd(III), Eu(III), and Y(III) ions, with no binding of non-REE Ca(II) ions observed

On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions

The use of machine learning for material property prediction and discovery has traditionally centered on graph neural networks that incorporate the geometric configuration of all atoms. However, in practice not all this information may be readily available, e.g.~when evaluating the potentially unknown binding of adsorbates to catalyst. In this paper, we investigate whether it is possible to predict a system's relaxed energy in the OC20 dataset while ignoring the relative position of the adsorbate with respect to the electro-catalyst. We consider SchNet, DimeNet++ and FAENet as base architectures and measure the impact of four modifications on model performance: removing edges in the input graph, pooling independent representations, not sharing the backbone weights and using an attention mechanism to propagate non-geometric relative information. We find that while removing binding site information impairs accuracy as expected, modified models are able to predict relaxed energies with remarkably decent MAE. Our work suggests future research directions in accelerated materials discovery where information on reactant configurations can be reduced or altogether omitted

MiRNA Genes Constitute New Targets for Microsatellite Instability in Colorectal Cancer

Mismatch repair-deficient colorectal cancers (CRC) display widespread instability at DNA microsatellite sequences (MSI). Although MSI has been reported to commonly occur at coding repeats, leading to alterations in the function of a number of genes encoding cancer-related proteins, nothing is known about the putative impact of this process on non-coding microRNAs. In miRbase V15, we identified very few human microRNA genes with mono- or di-nucleotide repeats (n = 27). A mutational analysis of these sequences in a large series of MSI CRC cell lines and primary tumors underscored instability in 15 of the 24 microRNA genes successfully studied at variable frequencies ranging from 2.5% to 100%. Following a maximum likelihood statistical method, microRNA genes were separated into two groups that differed significantly in their mutation frequencies and in their tendency to represent mutations that may or may not be under selective pressures during MSI tumoral progression. The first group included 21 genes that displayed no or few mutations in CRC. The second group contained three genes, i.e., hsa-mir-1273c, hsa-mir-1303 and hsa-mir-567, with frequent (≥80%) and sometimes bi-allelic mutations in MSI tumors. For the only one expressed in colonic tissues, hsa-mir-1303, no direct link was found between the presence or not of mono- or bi-allelic alterations and the levels of mature miR expression in MSI cell lines, as determined by sequencing and quantitative PCR respectively. Overall, our results provide evidence that DNA repeats contained in human miRNA genes are relatively rare and preserved from mutations due to MSI in MMR-deficient cancer cells. Functional studies are now required to conclude whether mutated miRNAs, and especially the miR-1303, might have a role in MSI tumorigenesis

Crystal-GFN: sampling crystals with desirable properties and constraints

Accelerating material discovery holds the potential to greatly help mitigate the climate crisis. Discovering new solid-state materials such as electrocatalysts, super-ionic conductors or photovoltaic materials can have a crucial impact, for instance, in improving the efficiency of renewable energy production and storage. In this paper, we introduce Crystal-GFN, a generative model of crystal structures that sequentially samples structural properties of crystalline materials, namely the space group, composition and lattice parameters. This domain-inspired approach enables the flexible incorporation of physical and structural hard constraints, as well as the use of any available predictive model of a desired physicochemical property as an objective function. To design stable materials, one must target the candidates with the lowest formation energy. Here, we use as objective the formation energy per atom of a crystal structure predicted by a new proxy machine learning model trained on MatBench. The results demonstrate that Crystal-GFN is able to sample highly diverse crystals with low (median -3.1 eV/atom) predicted formation energy.Comment: Main paper (10 pages) + references + appendi

Chromosomal Instability in Near-Diploid Colorectal Cancer: A Link between Numbers and Structure

Chromosomal instability (CIN) plays a crucial role in tumor development and occurs mainly as the consequence of either missegregation of normal chromosomes (MSG) or structural rearrangement (SR). However, little is known about the respective chromosomal targets of MSG and SR and the way these processes combined within tumors to generate CIN. To address these questions, we karyotyped a consecutive series of 96 near-diploid colorectal cancers (CRCs) and distinguished chromosomal changes generated by either MSG or SR in tumor cells. Eighty-three tumors (86%) presented with chromosomal abnormalities that contained both MSGs and SRs to varying degrees whereas all 13 others (14%) showed normal karyotype. Using a maximum likelihood statistical method, chromosomes affected by MSG or SR and likely to represent changes that are selected for during tumor progression were found to be different and mostly mutually exclusive. MSGs and SRs were not randomly associated within tumors, delineating two major pathways of chromosome alterations that consisted of either chromosome gains by MSG or chromosomal losses by both MSG and SR. CRCs showing microsatellite instability (MSI) presented with either normal karyotype or chromosome gains whereas MSS (microsatellite stable) CRCs exhibited a combination of the two pathways. Taken together, these data provide new insights into the respective involvement of MSG and SR in near-diploid colorectal cancers, showing how these processes target distinct portions of the genome and result in specific patterns of chromosomal changes according to MSI status

Development of a prototype Lateral Flow Immunoassay (LFI) for the rapid diagnosis of melioidosis

Burkholderia pseudomallei is a soil-dwelling bacterium and the causative agent of melioidosis. Isolation of B. pseudomallei from clinical samples is the “gold standard” for the diagnosis of melioidosis; results can take 3–7 days to produce. Alternatively, antibody-based tests have low specificity due to a high percentage of seropositive individuals in endemic areas. There is a clear need to develop a rapid point-of-care antigen detection assay for the diagnosis of melioidosis. Previously, we employed In vivo Microbial Antigen Discovery (InMAD) to identify potential B. pseudomallei diagnostic biomarkers. The B. pseudomallei capsular polysaccharide (CPS) and numerous protein antigens were identified as potential candidates. Here, we describe the development of a diagnostic immunoassay based on the detection of CPS. Following production of a CPS-specific monoclonal antibody (mAb), an antigen-capture immunoassay was developed to determine the concentration of CPS within a panel of melioidosis patient serum and urine samples. The same mAb was used to produce a prototype Active Melioidosis Detect Lateral Flow Immunoassay (AMD LFI); the limit of detection of the LFI for CPS is comparable to the antigen-capture immunoassay (~0.2 ng/ml). The analytical reactivity (inclusivity) of the AMD LFI was 98.7% (76/77) when tested against a large panel of B. pseudomallei isolates. Analytical specificity (cross-reactivity) testing determined that 97.2% of B. pseudomallei near neighbor species (35/36) were not reactive. The non-reactive B. pseudomallei strain and the reactive near neighbor strain can be explained through genetic sequence analysis. Importantly, we show the AMD LFI is capable of detecting CPS in a variety of patient samples. The LFI is currently being evaluated in Thailand and Australia; the focus is to optimize and validate testing procedures on melioidosis patient samples prior to initiation of a large, multisite pre-clinical evaluation

Ice fabric in an Antarctic ice stream interpreted from seismic anisotropy

Here we present new measurements of an anisotropic ice fabric in a fast moving (377 ma−1) ice stream in West Antarctica. We use ∼6000 measurements of shear wave splitting observed in microseismic signals from the bed of Rutford Ice Stream, to show that in contrast to large-scale ice flow models, which assume that ice is isotropic, the ice in Rutford Ice Stream is dominated by a previously unobserved type of partial girdle fabric. This fabric has a strong directional contrast in mechanical properties, shearing 9.1 times more easily along the ice flow direction than across flow. This observed fabric is likely to be widespread and representative of fabrics in other ice streams and large glaciers, suggesting it is essential to consider anisotropy in data-driven models to correctly predict ice loss and future flow in these regions. We show how passive microseismic monitoring can be effectively used to provide these data