Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.64898/2026.09.29.755377

Reducing growth-medium complexity reveals nutrient-responsive programs in a near-minimal bacterium

Duval, A.·Gagnon, J.·Jeanneau, S.·Matteau, D.·Jacques, P.-E.·Rodrigue, S.
View original source ↗Export metadata ↓

Abstract

Mesoplasma florum is a fast-growing, near-minimal bacterium and an emerging model for systems and synthetic biology. However, its dependence on complex serum-containing media limits experimental control and complicates the interpretation of cellular phenotypes. Here, we developed CMRL-AT, a serum-free, quasi-defined medium that supports rapid growth comparable to the commonly used ATCC 1161 medium. Despite supporting similar biomass, CMRL-AT profoundly reshaped the M. florum transcriptome, with approximately one-third of the annotated protein-encoding genes being differentially expressed relative to ATCC 1161. These changes revealed distinct physiological programs associated with rapid growth in complex medium and higher nutrient acquisition in CMRL-AT, illustrating how medium composition alters the functional priorities of a near-minimal cell. Transcriptome profiling across six energy sources further uncovered distinct sugar-responsive expression programs. Combining these responses with transcription-unit organization, protein-domain predictions, and metabolic context resolved fructose- and sucrose-responsive modules, and allowed the assignment of previously ambiguous phosphotransferase system components to specific sugar-utilization pathways. CMRL-AT provides an experimental framework to help resolving gene functions, refining metabolic models, and designing reduced genomes adapted to defined environments.

Explore related subjects

systems biologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Duval, A., Gagnon, J., Jeanneau, S., Matteau, D., Jacques, P.-E., Rodrigue, S.. 2026-09-30. Reducing growth-medium complexity reveals nutrient-responsive programs in a near-minimal bacterium. https://doi.org/10.64898/2026.09.29.755377

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Cyclic uremia without kidney injury drives cardiovascular disease with rapid onset of immunosenescence and heart failure

End-stage kidney disease (ESKD) is a major driver of cardiovascular disease (CVD) and requires dialysis treatment leading to a unique metabolic phenotype with cyclic transitions between accumulation and clearance of uremic toxins. Although ESKD patients display the highest cardiovascular mortality among CKD patients, the effect of cyclic uremia on cardiovascular disease remains subject to discussion. Existing mouse models rely on induction of kidney injury and do not resolve the interconnected pathophysiological effects of metabolic, hypertensive and endocrine renal failure. They are therefore unable to distinguish uremia-driven effects from well-established drivers of cardiovascular disease such as hypertension. Here, we established a simple, reproducible, and sex-inclusive mouse model of uremia in absence of kidney injury or hypertension leveraging a bistable vesico-peritoneal shunt (VPS). Strikingly, we identify cyclic uremia as an independent driver of ESKD-induced CVD, that induces heart failure with preserved ejection fraction (HFpEF), vascular inflammation and immunosenescence. As such, the VPS represents the first preclinical model that enables investigation of cyclic uremia as a driver of cardiovascular disease.

systems biology↗
bioRxiv2026

A Reduced Mechanistic Model for Aquatic Decomposition of a Human Body

Estimating the postmortem submersion interval (PMSI) remains challenging due to complex biological and environmental interactions during aquatic decomposition. This study proposes a reduced mechanistic ordinary differential equation (ODE) model linking tissue degradation, microbial activity, and dissolved oxygen dynamics. Calibrated against the total aquatic decomposition score (TADS) trajectory for Northern Adriatic Sea cases, the model preserves a strictly monotone TADS--time relationship, ensuring unique numerical inversion from observed TADS to PMSI. Synthetic observation experiments evaluated how data selection influences parameter recovery, practical identifiability, and robustness under measurement uncertainty. Microbial measurements improved parameter recovery for microbial dynamics, whereas dissolved oxygen measurements constrained oxygen-consumption and renewal processes. Combining both types of observation optimized the recovery of coupled parameters. Trajectory reconstruction, profile likelihood analysis, boundary-hit frequencies, and Monte Carlo simulations confirmed these findings. Importantly, while all observation strategies reproduced TADS with comparable accuracy, they differed substantially in recovering underlying biological parameters and latent-state dynamics. Thus, accurately reproducing decomposition scores alone is insufficient for reliable parameter inference. This framework provides a transparent and biologically interpretable foundation for the mechanistic estimation of PMSI. Future applications to case-level and multi-site datasets may enable site-specific recalibration and improved predictive evaluation across diverse aquatic environments.

systems biology↗
bioRxiv2026

Mapping a genome-scale in vivo knockout screen to a mechanistic network model identifies VAV2, RASA1, and LEPR as regulators of cardiomyocyte hypertrophy

Cardiomyocyte hypertrophy is a leading clinical predictor of heart failure, yet newly identified candidate genes often remain disconnected from the signaling mechanisms that govern cardiomyocyte growth. We developed a computational-experimental pipeline that integrates genome-scale mouse knockout phenotypes with a logic-based differential equation model of hypertrophic signaling. Among 9,605 genes evaluated by the International Mouse Phenotyping Consortium, 939 knockout lines induced abnormal heart morphology. Directional curation of hypertrophy-related sub-phenotypes followed by interaction-based network expansion mapped 37 genes to the signaling model. Virtual knockdown screening identified five candidates with concordant in vivo and in silico effects: LRIG1 and CBL as predicted negative regulators and VAV2, RASA1, and LEPR as predicted positive regulators. Mechanistic subnetwork analysis linked these candidates to distinct receptor-proximal, Ras, PI3K-AKT, and MAPK signaling axes. In neonatal rat cardiomyocytes, siRNA-mediated depletion of VAV2, RASA1, or LEPR reduced phenylephrine-induced cell growth, supporting their cell-autonomous contribution to hypertrophy. Quantitative phenotyping further validated the predicted decreased cardiac hypertrophy for VAV2 and LEPR knockouts but identified potential age-dependent mechanisms for RASA1 knockout. Overall, this study establishes the application of network models to translate from in vivo phenotypic screens into pathway mechanisms.

systems biology↗