Résultats de recherche

Abstract Primarily described as an alarmone, secondary messenger (p)ppGpp, when accumulated, binds to many targets involved in DNA replication, translation, and transcription. In the asymmetrically-dividing a-proteobacterium Caulobacter crescentus, (p)ppGpp has been shown to strongly impact cell cycle progression and differentiation, promoting the non-replicating G1/swarmer phase. Mutations in the major subunits of transcriptional complex, b or b’ subunits, were able to display the (p)ppGpp-related phenotypes even in the absence of the alarmone. We identified that the transcriptional holo-enzyme, RNA polymerase (RNAP) is a primary target of (p)ppGpp in C. crescentus. Furthermore, mutations that inactivate (p)ppGpp binding to RNAP annihilated the (p)ppGpp-related phenotypes and phenocopied a (p)ppGpp0 strain. Our RNAseq analysis further elucidated the changes in the transcriptional landscape of C. crescentus cells displaying different (p)ppGpp levels and expressing RNAP mutants. Since the DNA replication initiation protein DnaA is required to exit the G1 phase, we observed that it was significantly less abundant in cells accumulating (p)ppGpp. We further explored its proteolysis under the influence of (p)ppGpp. Our work suggests that (p)ppGpp regulates cell cycle and differentiation in C. crescentus by reprogramming transcription and triggering proteolytic degradation of key cell cycle regulators by yet unknown mechanisms. In Part II, we identified two σ factors belonging to the ECF family that might be involved in this (p)ppGpp-accompanied phenotypes. In Part III, we propose an overlapping role of the ω subunit, RpoZ, and the heat shock subunit, RpoH, in carbon metabolism.JuryProf. Gipsi LIMA MENDEZ (UNamur), PresidentProf Régis HALLEZ (UNamur), SecretaryDr Emanuele BIONDI (CNRS-Université Paris-Saclay)Prof. Justine COLLIER (University of Lausanne)Dr Marie DELABY (Université de Montréal)

Abstract Estrogens originating from human and animal excretion, as well as from anthropogenic sources such as cosmetics, plastics, pesticides, detergents, and pharmaceuticals, are among the most concerning endocrine-disrupting compounds in aquatic environments due to their potent estrogenic activity. While their effects on fish reproduction are well documented, their impact on development, particularly metamorphosis, remains poorly studied. This hormonal transition, mainly controlled by the thyroid axis, is essential for the shift from the larval to the juvenile stage in teleosts.The effects of two contraceptive estrogens on zebrafish (Danio rerio) metamorphosis were evaluated: 17α-ethinylestradiol (EE2), a synthetic reference estrogen, and estetrol (E4), a natural estrogen recently introduced in a new combined oral contraceptive formulation. Continuous exposure from fertilization to the end of metamorphosis allowed the assessment of morphological changes, disruptions of the thyroid axis, and modifications of additional molecular pathways potentially involved in metamorphic regulation.EE2 induced significant delays and disturbances in metamorphosis, affecting both internal and external morphological traits, confirming its role as an endocrine disruptor of concern. In contrast, E4 did not cause any detectable morphological alterations even at concentrations far exceeding those expected in the environment, indicating a limited ecotoxicological risk. Molecular analyses showed that EE2 strongly affected thyroid signaling and energy metabolism during metamorphosis, whereas E4 induced only minor transcriptional and proteomic changes.This study provides the first evidence that EE2 can disrupt zebrafish metamorphosis and highlights the importance of including this developmental stage in ecotoxicological assessments. The results also suggest a larger environmental safety margin for E4, although further research is needed to clarify the mechanisms linking estrogen exposure to metamorphic regulation.JuryProf. Frederik DE LAENDER (UNamur), PrésidentProf. Patrick KESTEMONT (UNamur), SecrétaireDr Sébastien BAEKELANDT (UNamur)Dr Valérie CORNET (UNamur)Prof. Jean-Baptiste FINI (Muséum National d’Histoire Naturelle de Paris)Dr Marc MULLER (ULiège)Prof. Veerle DARRAS (KULeuven) 

AbstractThe Simbu serogroup, part of the Peribunyaviridae family, includes arboviruses associated with febrile disease and fetal congenital malformations due to viral neurotropism. These viruses possess a tripartite, negative-sense RNA genome that lacks the poly(A) tail. Notably, the 3' non-translated region of the small (S) genomic segment contains conserved RNA elements, including a stem loop (SL) structure and a sequence-based motif (GC signal) flanking the termination site for messenger RNA (mRNA) synthesis. Although their functions remain unclear, their conservation and specific positions suggest a potential role in mRNA transcription termination and translation initiation. A reverse genetics system for Schmallenberg virus (SBV), a ruminant pathogen, was used to generate a viral recombinant library bearing deliberate mutations. The replication kinetics, S segment transcription termination profile, and nucleoprotein (N) abundance were evaluated in mammalian and insect cell lines. At the same time, the virulence was assessed in an immunocompetent mouse model. Characterization of the mutant viruses indicated that the SL structure is essential for viral production, with the stem length being a key feature; at least five complementary base pairs are necessary between the stem arms. A shorter stem length impaired replicative fitness, N protein abundance, and altered the mRNA to complementary RNA ratio. Point mutations in the GC signal disrupted proper mRNA termination, thereby limiting viral N protein synthesis and, thus, virion assembly. In vivo, attenuated viruses resulted in lower viral loads, reduced neuroinvasion, and improved survival rates compared to the wild type SBV. The GC signal mutants exhibited strong attenuation while still maintaining active transcription. Overall, these findings indicate that the SL and GC signal serve as cis-regulatory elements and are indirect determinants of SBV virulence, regulating viral replication and neuropathogenesis.JuryProf. Patsy RENARD (UNamur), PrésidenteDr Damien COUPEAU (UNamur) SecrétaireProf. Damien VITOUR (Ecole Vétérinaire de Maisons-Alfort)Prof. Ludovic MARTINELLE (ULiège)Prof. Lionel TAFFOREAU (UMons)Prof. Charles NICAISE (UNamur)Prof. Benoît MUYLKENS (UNamur)

Début septembre, l’Université de Namur a accueilli la première Main-Group Elements Reactivity Conference (MG-ERC). Plus de 100 chercheurs venus de 12 pays et 32 institutions se sont réunis autour du Professeur Guillaume Berionni. Un événement salué comme « l’une des meilleures conférences de chimie » par ses prestigieux invités.

RésuméAlors qu'un courant de recherches s'efforçait de reformuler la mécanique quantique en abrogeant les opérateurs et en leur substituant des fonctions, Wigner et Szilard proposèrent en 1932 une quasi-distribution de probabilités définie sur l'espace de phase grâce à des fonctions d'onde. Ils n'expliquèrent pas quelle en avait été la genèse.La première partie de notre thèse propose une genèse de cette quasi-distribution, fondée sur les conditions naturelles qu'elles doit remplir. Elle se penche brièvement sur une pathologie dont elle est affectée : présenter dans certains sous-domaines de l'espace de phase des valeurs négatives (d'où le « quasi »), pathologie qui ne porte aucun préjudice au calcul des valeurs moyennes. Elle montre ensuite comment, si on tient compte du spin, les fonctions d'onde cédant la place aux spineurs, on est amené, grâce au calcul des valeurs moyennes d'observables, à une généralisation de cette quasi-distribution sous la forme d'une matrice hermitique. Cette démarche est étendue à la transformée croisée de Wigner, c'est-à-dire aux valeurs faibles.Un important théorème, qui a fait l'objet d'une publication, est démontré dans la deuxième partie de notre thèse. Utilisant l’analyse harmonique, ce résultat exprime les valeurs faibles en terme d’une intégrale sur un groupe Lie agissant sur l’espace de Hilbert considéré. Nous donnons deux exemples particuliers : SU(2) et SO(3). Le cas d'un groupe quotient est brièvement évoqué.Dans une troisième partie, nous rappelons le lien bien connu entre les algèbres de Clifford et deux équations importantes de la physique quantique : celle de Klein-Gordon et celle de Dirac, et sa généralisation aux espace-temps riemanniens.Nous introduisons enfin dans une quatrième partie les groupes de spin, et utilisons le groupe de spin Spin(3,2) dans le contexte de la transformée croisée de Wigner traitée dans la première partie.JuryProf. André FÜZFA (UNamur), PrésidentProf. Yves CAUDANO (UNamur), secrétaireDr Thomas DURT (Institut Fresnel et Ecole Centrale Marseille, Marseille, France)Prof. Romain MURENZI (Worcester Polytecnic Institute)Prof. Dominique LAMBERT (UNamur)Prof. Bertrand HESPEL (UNamur)Prof. André HARDY (UNamur)

Une équipe de chercheurs américains, avec l’aide de Frédérik De Laender, professeur au Département de biologie de l’UNamur, vient de publier dans la prestigieuse revue Nature. Leur étude décrit comment l’évolution des températures des cours d’eau et les introductions de poissons par l’humain peuvent modifier la biodiversité des rivières aux États-Unis.

Abstract Deep learning has become an extremely important technology in numerous domains such as computer vision, natural language processing, and autonomous systems. As neural networks grow in size and complexity to meet the demands of these applications, the cost of designing and training efficient models continues to rise in computation and energy consumption. Neural Architecture Search (NAS) has emerged as a promising solution to automate the design of performant neural networks. However, conventional NAS methods often require evaluating thousands of architectures, making them extremely resource-intensive and environmentally costly.This thesis introduces a novel, energy-aware NAS pipeline that operates at the intersection of Software Engineering and Machine Learning. We present CNNGen, a domain-specific generator for convolutional architectures, combined with performance and energy predictors to drastically reduce the number of architectures that need full training. These predictors are integrated into a multi-objective genetic algorithm (NSGA-II), enabling an efficient search for architectures that balance accuracy and energy consumption.Our approach explores a variety of prediction strategies, including sequence-based models, image-based representations, and deep metric learning, to estimate model quality from partial or symbolic representations. We validate our framework across three benchmark datasets, CIFAR-10, CIFAR-100, and Fashion-MNIST, demonstrating that it can produce results comparable to state-of-the-art architectures with significantly lower computational cost. By reducing the environmental footprint of NAS while maintaining high performance, this work contributes to the growing field of Green AI and highlights the value of predictive modelling in scalable and sustainable deep learning workflows. Jury Prof. Wim Vanhoof - University of Namur, BelgiumProf. Gilles Perrouin - University of Namur, BelgiumProf. Benoit Frénay - University of Namur, BelgiumProf. Pierre-Yves Schobbens - University of Namur, BelgiumProf. Clément Quinton - University of Lille, FranceProf. Paul Temple- University of Rennes, FranceProf. Schin’ichi Satoh - National Institute of Informatics, Japon