Biology subjects

Francois, P.

Publications and source records attributed to Francois, P..

27 records · Page 2Linked to original sources

bioRxiv2021

Across-population genomic prediction in grapevine opens up promising prospects for breeding

Crop breeding involves two selection steps: choosing progenitors and selecting offspring within progenies. Genomic prediction, based on genome-wide marker estimation of genetic values, could facilitate these steps. However, its potential usefulness in grapevine (Vitis vinifera L.) has only been evaluated in non-breeding contexts mainly through cross-validation within a single population. We tested across-population genomic prediction in a more realistic breeding configuration, from a diversity panel to ten bi-parental crosses connected within a half-diallel mating design. Prediction quality was evaluated over 15 traits of interest (related to yield, berry composition, phenology and vigour), for both the average genetic value of each cross (cross mean) and the genetic values of individuals within each cross (individual values). Genomic prediction in these conditions was found useful: for cross mean, average per-trait predictive ability was 0.6, while per-cross predictive ability was halved on average, but reached a maximum of 0.7. Mean predictive ability for individual values within crosses was 0.26, about half the within-half-diallel value taken as a reference. For some traits and/or crosses, these across-population predictive ability values are promising for implementing genomic selection in grapevine breeding. This study also provided key insights on variables affecting predictive ability. Per-cross predictive ability was well predicted by genetic distance between parents and when this predictive ability was below 0.6, it was improved by training set optimization. For individual values, predictive ability mostly depended on trait-related variables (magnitude of the cross effect and heritability). These results will greatly help designing grapevine breeding programs assisted by genomic prediction.

genetics↗

bioRxiv2021

Pre-existing chromatin accessibility and gene expression differences among naïve CD4+ T cells influence effector potential

CD4+ T cells have a remarkable potential to differentiate into diverse effector lineages following activation. Here, we probed the heterogeneity present among naive CD4+ T cells before encountering their cognate antigen to ask whether their effector potential is modulated by pre-existing transcriptional and epigenetic differences. Using single-cell RNA sequencing, we showed that key drivers of variability are genes involved in T cell receptor (TCR) signaling. Using CD5 expression as a read-out of the strength of tonic TCR interactions with self-peptide MHC, and sorting on the ends of this self-reactivity spectrum, we find that pre-existing transcriptional differences among naive CD4+ T cells impact follicular helper cell (TFH) versus non-TFH effector lineage choice. Moreover, our data implicate TCR signal strength during thymic development in establishing differences in naive CD4 T cell chromatin landscapes that ultimately shape their effector potential.

immunology↗

bioRxiv2020

Geometric models for robust encoding of dynamical information into embryonic patterns

During development, cells gradually assume specialized fates via changes of transcriptional dynamics, sometimes even within the same developmental stage. For anterior-posterior (AP) patterning in metazoans, it has been suggested that the gradual transition from a dynamic genetic regime to a static one is encoded by different transcriptional modules. In that case, the static regime has an essential role in pattern formation in addition to its maintenance function. In this work, we introduce a geometric approach to study such transition. We exhibit two types of genetic regime transitions, respectively arising through local or global bifurcations. We find that the global bifurcation type is more generic, more robust, and better preserves dynamical information. This could parsimoniously explain common features of metazoan segmentation, such as changes of periods leading to waves of gene expressions, "speed/frequency-gradient" dynamics, and changes of wave patterns. Geometric approaches appear as possible alternatives to gene regulatory networks to understand development.

biophysics↗

bioRxiv2019

RpiRc regulates RsbU to modulate eDNA-dependent biofilm formation and in vivo virulence of Staphylococcus aureus in a mouse model of catheter infection

Staphylococcus aureus is a major human pathogen. Despite high incidence and morbidity, molecular mechanisms occurring during infection remain largely unknown. Under defined conditions, biofilm formation contributes to the severity of S. aureus related infections. Extracellular DNA (eDNA), a component of biofilm matrix released from apoptotic bacteria, is involved in biofilm structure and stability. In many bacterial biofilms, eDNA originates from cell lysis although eDNA can also be actively secreted or exported by bacterial membrane vesicles. By screening the Nebraska transposon library, we identified rpiRc as a biofilm regulator involved in eDNA regulation. RpiRc is a transcription factor from the pentose phosphate pathway (PPP) whose product is a polysaccharide intercellular adhesin (PIA) precursor. However, rpiRc mutant strain showed neither susceptibility to DispersinB(R) (a commercially available enzyme disrupting PIA biofilms) nor alteration of ica transcription (the operon regulating PIA production). Decreased biofilm formation was linked to Sln, an extracellular compound degrading eDNA in an autolysis independent pathway. Biofilm susceptibility to antibiotics in wt and mutant strains was tested using a similar protocol as the Calgary biofilm device. Involvement of RpiRc in S. aureus virulence was assessed ex vivo by internalization experiments into HEK293 cells and in vivo in a mouse model of subcutaneous catheter infection. While minimum inhibitory concentrations (MICs) of planktonic cells were not affected in the mutant strain, we observed increased biofilm susceptibility to almost all tested antibiotics, regardless of their mode of action. More importantly, the rpiRc mutant showed reduced virulence in both ex vivo and in vivo experiments related to decreased fnbpA-B transcription and eDNA production. RpiRc is an important regulator involved in eDNA degradation inside the matrix of mature PIA independent biofilms. These results illustrate that RpiRc contributes to increased antibiotic tolerance in mature bacterial biofilm and also to S. aureus cell adhesion and virulence during subcutaneous infection.\n\nAuthor summaryBiofilm formation contributes to the severity of Staphylococcus aureus related infections. Biofilm matrix is mainly composed by polysaccharide intercellular adhesion (PIA), proteins and extracellular DNA (eDNA). By screening a mutant library of S. aureus, RpiRc was identified as a new regulator of eDNA dependent biofilm formation. How RpiRc regulates biofilm and its role in S. aureus virulence was studied in four different S. aureus strains. Deletion of RpiRc resulted in a pronounced decreased eDNA dependent biofilm formation, but not PIA dependent biofilm formation. Decreased biofilm formation was not related to increased autolysis, but was linked to extracellular compounds found in the supernatant of mutant biofilms. Sln was identified as one of this compound. RpiRc deletion also decreased biofilm recalcitrance (resistance) to selected antibiotics. Involvement of RpiRc in S. aureus pathogenesis was investigated ex vivo by internalization into HEK293 cells and in vivo in a mouse model of catheter infection. RpiRc deletion resulted in decreased virulence related to decreased expression of surface proteins like the fibronectin binding proteins A and B (FnbpA-B). These results illustrate that RpiRc contributes to increased antibiotic tolerance in mature bacterial biofilm and also to S. aureus cell adhesion and virulence during subcutaneous infection.

microbiology↗

bioRxiv2018

Numerical parameter space compression and its application to microtubule dynamic instability

Physical models of biological systems can become difficult to interpret when they have a large number of parameters. But the models themselves actually depend on (i.e. are sensitive to) only a subset of those parameters. Rigorously identifying this subset of \"stiff\" parameters has been made possible by the development of parameter space compression (PSC). However, PSC has only been applied to analytically-solvable physical models. We have generalized this powerful method by developing a numerical approach to PSC that can be applied to any computational model. We validated our method against analytically-solvable models of random walk with drift and protein production and degradation. We then applied our method to an active area of biophysics research, namely to a simple computational model of microtubule dynamic instability. Such models have become increasingly complex, perhaps unnecessarily. By adding two new parameters that account for prominent structural features of microtubules, we identify one that can be \"compressed away\" (the \"seam\" in the microtubule) and another that is essential to model performance (the \"tapering\" of microtubule ends). Furthermore, we show that the microtubule model has an underlying, low-dimensional structure that explains the vast majority of our experimental data. We argue that numerical PSC can identify the low-dimensional structure of any computational model in biophysics. The low-dimensional structure of a model is easier to interpret and identifies the mechanisms and experiments that best characterize the system.

biophysics↗

bioRxiv2018

Bipolar spindle formation is a fast, irreversible process requiring Kinesin-5 crosslinking and sliding modalities

GRAPHICAL ABSTRACT\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=128 SRC=\"FIGDIR/small/452821_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (30K):\norg.highwire.dtl.DTLVardef@ad6c3corg.highwire.dtl.DTLVardef@19cf957org.highwire.dtl.DTLVardef@b5c2a8org.highwire.dtl.DTLVardef@fece04_HPS_FORMAT_FIGEXP M_FIG C_FIG Separation of duplicated spindle poles is the first step in forming the mitotic spindle. Kinesin-5 crosslinks and slides anti-parallel microtubules, but it is unclear how these two activities contribute to the first steps in spindle formation. In this study we report that in monopolar spindles, the duplicated spindle poles snap apart in a fast and irreversible step that produces a nascent bipolar spindle. Using mutations in Kinesin-5 that inhibit microtubule sliding, we show crosslinking alone drives the fast, irreversible pole separation. Electron tomography revealed microtubule pairs in monopolar spindles have short overlaps that intersect at high angles and are unsuited for ensemble Kinesin-5 sliding. However, maximal extension of a subset of microtubule pairs approaches the length of nascent bipolar spindles and is consistent with a Kinesin-5 crosslinking driven transition. Finally, stochastic microtubule sliding by Kinesin-5 stabilizes the nascent spindle and sets a stereotyped equilibrium length.

biophysics↗

bioRxiv2018

Fooling the classifier: Ligand antagonism and adversarial examples

Machine learning algorithms can be fooled by small well-designed adversarial perturbations. This is reminiscent of cellular decision-making where ligands (called antagonists) prevent correct signalling, like in early immune recognition. We draw a formal analogy between neural networks used in machine learning and models of cellular decision-making (adaptive proofreading). We apply attacks from machine learning to simple decision-making models, and show explicitly the correspondence to antagonism by weakly bound ligands. Such antagonism is absent in more nonlinear models, which inspired us to implement a biomimetic defence in neural networks filtering out adversarial perturbations. We then apply a gradient-descent approach from machine learning to different cellular decision-making models, and we reveal the existence of two regimes characterized by the presence or absence of a critical point for the gradient. This critical point causes the strongest antagonists to lie close to the decision boundary. This is validated in the loss landscapes of robust neural networks and cellular decision-making models, and observed experimentally for immune cells. For both regimes, we explain how associated defence mechanisms shape the geometry of the loss landscape, and why different adversarial attacks are effective in different regimes. Our work connects evolved cellular decision-making to machine learning, and motivates the design of a general theory of adversarial perturbations, both for in vivo and in silico systems.

biophysics↗

bioRxiv2018

A dual sRNA in Staphylococcus aureus induces a metabolic switch responding to glucose consumption

Pathogenic bacteria must rapidly adapt to ever-changing environmental signals or nutrient availability resulting in metabolism remodeling. The carbon catabolite repression represents a global regulatory system, allowing the bacteria to express genes involved in carbon utilization and metabolization of the preferred carbon source. In Staphylococcus aureus, regulation of catabolite repressing genes is mediated by the carbon catabolite protein A (CcpA). Here, we have identified a CcpA-dependent small non-coding RNA, RsaI that is inhibited by high glucose concentrations. RsaI represses the translation of mRNAs encoding a major permease of glucose uptake, the FN3K enzyme that protects proteins against damages caused by high glucose concentrations, and IcaR, the transcriptional repressor of exopolysaccharide production. Besides, RsaI regulates the activities of other sRNAs responding to the uptake of glucose-6 phosphate or NO. Finally, RsaI inhibits the expression of several enzymes involved in carbon catabolism pathway, and activates genes involved in energy production, fermentation and NO detoxification when the glucose concentration decreases. This multifunctional RNA provides a signature for a metabolic switch when glucose is scarce and growth is arrested.

microbiology↗

bioRxiv2017

Endocytosis caused by liquid-liquid phase separation of proteins

Membrane invagination and vesicle formation are key steps in endocytosis and cellular trafficking. Here, we show that endocytic coat proteins with prion-like domains (PLDs) form hemispherical puncta in the budding yeast, S. cerevisiae. These puncta have the hallmarks of biomolecular condensates and enable membrane remodeling to drive actin-independent endocytosis. The puncta, which we refer to as endocytic condensates, form and dissolve reversibly in response to changes in temperature and solution conditions. The condensates are organized around dynamic protein-protein interaction networks, which involve interactions among PLDs with high glutamine contents. The endocytic coat protein Sla1 is at the hub of the protein-protein interaction network. Using active rheology, we indirectly characterized the material properties of endocytic condensates. These experiments show that endocytic condensates are viscoelastic materials and allow us to estimate the interfacial tension between endocytic condensates and their surroundings. We then adapt the physics of contact mechanics, specifically the contact theory of Hertz, to develop a quantitative framework for describing how interfacial tensions among condensates, the membrane, and the cytosol can deform the plasma membrane to enable actin independent endocytosis.

cell biology↗