Biology subjects

Giroux, B.

Publications and source records attributed to Giroux, B..

4 recordsLinked to original sources

bioRxiv2025

Gene editing of Nicotiana benthamiana architecture for space-efficient production of recombinant proteins in controlled environments

Indoor vertical farming (VF) offers several practical advantages for the cultivation of plant protein bio-factories, including plant uniformity, product consistency, water/nutrient recycling and production cycles on a year-round basis. Much progress has been achieved in recent years toward the development of innovative systems for artificial lighting, automated irrigation, plant handling, environment control and space use optimization in VF systems. Here, we used a CRISPR-Cas9 gene editing approach to generate mutant lines of transient protein expression host Nicotiana benthamiana presenting a compact, space-efficient phenotype compared to the so-called LAB strain commonly used for protein production. Our strategy consisted of altering apical dominance by suppressing the biosynthesis of strigolactone, a negative regulator of axillary bud outgrowth-promoting cytokinins. Strigolactone-depleted lines were generated by knocking-down the expression of either Carotenoid cleavage dioxygenase 7 (CCD7) or Carotenoid cleavage dioxygenase 8 (CCD8), two key enzymes of the metabolic pathway leading to strigolactone synthesis. Knocking-down the genes of either enzyme had no impact on the overall growth rate of the plant but drastically influenced its leaf proteome, auxin/cytokinin ratio and overall architecture. More specifically, the {Delta}CCD mutants exhibited altered glycolytic and malate-processing enzyme fluxes driving the production of pyruvate and cytokinins in leaf tissue, an axillary growth-oriented development pattern and, most importantly, a spatial footprint reduced by 45% to 50% compared to the LAB strain. Most importantly, recombinant protein yields per plant were maintained in the mutant lines, as here illustrated for the model protein GFP and for rituximab, a chimeric monoclonal antibody of confirmed clinical value in humans. Our data demonstrate the usefulness of {Delta}CCD7 and {Delta}CCD8 knockout leading to strigolactone depletion for the generation of compact, space-efficient N. benthamiana lines well suited to VF systems intended for biopharmaceutical production.

plant biology↗

bioRxiv2024

The Roboscope: Smart and Fast Microscopy for Generic Event-Driven Acquisition

Automation of fluorescence microscopy is a challenge for capturing rare or transient events in biology and medicine. It relies on smart devices that integrate and interpret the observed data, and react to the targeted biological event. We report on the Roboscope, a novel autonomous microscope combining sequence interruption and deep learning integration, allowing generic event-driven acquisitions. This system distinguishes itself by its adaptability to various experiments, quick capture of dynamic events, and minimal data greediness - training with less than 100 images per class. The Roboscopes capability is demonstrated in non-synchronized cells by capturing the metaphase, a 20-minute event happening once per day or less. Conversely, double thymidine-block synchronisation, despite occurring during DNA replication, may perturb mitotic-spindle mechanics. The Roboscopes versatility and efficiency offer significant advancements to tackle the current challenges of cell biology, spreading out advanced microscopy methods to fundamental research as well as high content screening and precision medicine.

bioengineering↗

bioRxiv2024

Different outcomes of endurance and resistance exercise in skeletal muscles of Oculopharyngeal muscular dystrophy.

BackgroundExercise is widely considered to have beneficial impact on skeletal muscle aging. In addition, there are also several studies demonstrating a positive effect of exercise on muscular dystrophies. Oculopharyngeal muscular dystrophy (OPMD) is a late-onset autosomal dominant inherited neuromuscular disorder caused by mutations in the PAPBN1 gene. These mutations consist in short (1-8) and meiotically stable GCN trinucleotide repeat expansions in its coding region responsible for the formation of PAPBN1 intranuclear aggregates. This study aims to characterize the effects of two types of chronic exercise, resistance and endurance, on the OPMD skeletal muscle phenotype using a relevant murine model of OPMD. MethodsIn this study, we tested two protocols of exercise. In the first, based on endurance exercise, FvB (wild-type) and A17 (OPMD) mice underwent a 6-week-long motorized treadmill protocol consisting in 3 sessions per week of running 20cm/s for 20 minutes. In the second protocol, based on resistance exercise generated by chronic mechanical overload (OVL), surgical removal of gastrocnemius and soleus muscles was performed, inducing hypertrophy of the plantaris muscle. In both types of exercise, muscles of A17 and FvB mice were compared to those of respective sedentary mice. For all the groups, force measurement, muscle histology and molecular analyses were conducted. ResultsFollowing the endurance exercise protocol, we did not observe any major changes in the muscle physiological parameters, but an increase in the number of PABPN1 intranuclear aggregates and enhanced collagen deposition in the exercised A17 OPMD mice. In the resistance overload protocol, we also observed an increased collagen deposition in the A17 OPMD mice which was associated with larger muscle mass and fiber cross sectional area and increased absolute maximal force as well as a reduction in PABPN1 aggregate number. ConclusionsRunning exercise and mechanical overload led to very different outcome in skeletal muscles of A17 mice. Both types of exercise enhanced collagen deposition but while the running protocol increased aggregates, the OVL reduced them. More importantly OVL reversed muscle atrophy and maximal force in the A17 mice. Our study performed in a relevant model gives an indication of the effect of different types of exercise on OPMD muscle which should be further evaluated in humans for future recommendations as a part of the lifestyle of individuals with OPMD.

pathology↗

bioRxiv2020

Neural network fast-classifies biological images using features selected after their random-forests-importance to power smart microscopy.

Artificial intelligence is nowadays used for cell detection and classification in optical microscopy, during post-acquisition analysis. The microscopes are now fully automated and next expected to be smart, to make acquisition decisions based on the images. It calls for analysing them on the fly. Biology further imposes training on a reduced dataset due to cost and time to prepare the samples and have the datasets annotated by experts. We propose here a real-time image processing, compliant with these specifications by balancing accurate detection and execution performance. We characterised the images using a generic, high-dimensional feature extractor. We then classified the images using machine learning for the sake of understanding the contribution of each feature in decision and execution time. We found that the non-linear-classifier random forests outperformed Fishers linear discriminant. More importantly, the most discriminant and time-consuming features could be excluded without any significant loss in accuracy, offering a substantial gain in execution time. It suggests a feature-group redundancy likely related to the biology of the observed cells. We offer a method to select fast and discriminant features. In our assay, a 79.6 {+/-} 2.4 % accurate classification of a cell took 68.7 {+/-} 3.5 ms (mean {+/-} SD, 5-fold cross-validation nested in 10 bootstrap repeats), corresponding to 14 cells per second, dispatched into 8 phases of the cell cycle using 12 feature-groups and operating a consumer market ARM-based embedded system. Interestingly, a simple neural network offered similar performances paving the way to faster training and classification, using parallel execution on a general-purpose graphic processing unit. Finally, this strategy is also usable for deep neural networks paving the way to optimising these algorithms for smart microscopy.

bioinformatics↗