Metabolic flux partitioning between the TCA cycle and glyoxylate shunt combined with a reversible methyl citrate cycle provide nutritional flexibility for Mycobacterium tuberculosis

Borah, K.; Mendum, T. A.; Hawkins, N.; Ward, J.; Beale, M.; Larrouy-Maumus, G.; Bhatt, A.; Pichler, H.; MOULIN, M.; Haertlein, M.; Forsyth, T.; Noack, S.; Goulding, C.; McFadden, J.; Beste, D. J.

doi:10.1101/2021.01.29.428863

bioRxiv · 10.1101/2021.01.29.428863

Metabolic flux partitioning between the TCA cycle and glyoxylate shunt combined with a reversible methyl citrate cycle provide nutritional flexibility for Mycobacterium tuberculosis

Borah, K.·Mendum, T. A.·Hawkins, N.·Ward, J.·Beale, M.·Larrouy-Maumus, G.·Bhatt, A.·Pichler, H.·MOULIN, M.·Haertlein, M.·Forsyth, T.·Noack, S.·Goulding, C.·McFadden, J.·Beste, D. J.

View original source ↗Export metadata ↓

Abstract

The utilisation of multiple host-derived carbon substrates is required by Mycobacterium tuberculosis (Mtb) to successfully sustain a tuberculosis infection thereby identifying the Mtb specific metabolic pathways and enzymes required for carbon co-metabolism as potential drug targets. Metabolic flux represents the final integrative outcome of many different levels of cellular regulation that contribute to the flow of metabolites through the metabolic network. It is therefore critical that we have an in-depth understanding of the rewiring of metabolic fluxes in different conditions. Here, we employed 13C-metabolic flux analysis using stable isotope tracers (13C and 2H) and lipid fingerprinting to investigate the metabolic network of Mtb growing slowly on physiologically relevant carbon sources in a steady state chemostat. We demonstrate that Mtb is able to efficiently co-metabolise combinations of either cholesterol or glycerol along with C2 generating carbon substrates. The uniform assimilation of the carbon sources by Mtb throughout the network indicated no compartmentalization of metabolism in these conditions however there were substrate specific differences in metabolic fluxes. This work identified that partitioning of flux between the TCA cycle and the glyoxylate shunt combined with a reversible methyl citrate cycle as the critical metabolic nodes which underlie the nutritional flexibility of Mtb. These findings provide new insights into the metabolic architecture that affords adaptability of Mtb to divergent carbon substrates. ImportanceEach year more than 1 million people die of tuberculosis (TB). Many more are infected but successfully diagnosed and treated with antibiotics, however antibiotic-resistant TB isolates are becoming ever more prevalent and so novel therapies are urgently needed that can effectively kill the causative agent. Mtb specific metabolic pathways have been identified as an important drug target in TB. However the apparent metabolic plasticity of this pathogen presents a major obstacle to efficient targeting of Mtb specific vulnerabilities and therefore it is critical to define the metabolic fluxes that Mtb utilises in different conditions. Here, we used 13C-metabolic flux analysis to measure the metabolic fluxes that Mtb uses whilst growing on potential in vivo nutrients. Our analysis identified selective use of the metabolic network that included the TCA cycle, glyoxylate shunt and methyl citrate cycle. The metabolic flux phenotypes determined in this study improves our understanding about the co-metabolism of multiple carbon substrates by Mtb identifying a reversible methyl citrate cycle and the glyoxylate shunt as the critical metabolic nodes which underlie the nutritional flexibility of Mtb.

Explore related subjects

systems biology Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Borah, K., Mendum, T. A., Hawkins, N., Ward, J., Beale, M., Larrouy-Maumus, G., Bhatt, A., Pichler, H., MOULIN, M., Haertlein, M., Forsyth, T., Noack, S., Goulding, C., McFadden, J., Beste, D. J.. 2021-01-31. Metabolic flux partitioning between the TCA cycle and glyoxylate shunt combined with a reversible methyl citrate cycle provide nutritional flexibility for Mycobacterium tuberculosis. https://doi.org/10.1101/2021.01.29.428863

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

KEEP EXPLORING

Related preprints

bioRxiv2026

Low-fidelity simulator selection for multi-fidelity inference of epithelial vertex models

Vertex models link cell division, neighbour exchange, and intercellular forces to epithelial tissue form through parameters such as junctional line tension and perimeter contractility. Bayesian inference of these parameters from data quantifies their uncertainty, but for stochastic models of proliferating tissues it requires many costly simulations. Multi-fidelity simulation-based inference reduces this cost by training on many runs of a cheaper, less accurate ('low-fidelity') simulator before refining with a few full ('high-fidelity') runs. Low-fidelity versions can be constructed in several ways, for example by stopping simulations early, coarsening the timestep, or averaging fewer replicates, but it is unclear which to choose before committing to costly training. To address this, we introduce PRISM (Paired Ranking Index for Simulator Matching), a screen that compares each candidate with the full simulator on paired runs, scoring agreement in cell-packing statistics against cost. For a vertex model of a proliferating tissue, PRISM flags stopping early as unreliable before training; once trained, this choice can produce confidently wrong parameter estimates. By contrast, a coarser timestep, within the range tested, is cheap and accurate; while fewer replicates are accurate but too costly. Using a screened low-fidelity simulator, multi-fidelity inference outperforms approximate Bayesian computation and neural-network inference trained on full simulations alone, at matched cost and with well-calibrated uncertainty. The same pattern recurs for a second vertex model fitted to published Drosophila pupal-wing imaging data, although there stopping halfway proves safe. Together, our findings suggest that which approximations are safe depends on the model, and that screening them before training makes multi-fidelity inference of tissue mechanics practical.

systems biology↗

bioRxiv2026

Calibrating developmental dynamics from single-cell atlases: lineage paths outrun least-action routes

Single-cell atlases infer developmental dynamics from destructive snapshots, yet how far realized lineage paths depart from such inferred dynamics has never been measured with controls. We introduce LADD (Least-Action Developmental Dynamics), a measurement framework that learns a stochastic developmental vector field from time-series single-cell RNA-seq and computes minimum-action and minimum-time paths, together with a three-layer calibration protocol that separates genuine path deviation from the generic cost of centroid measurement, and we compare its forecasting component with optimal-transport and flow-matching methods. In the lineage-resolved Caenorhabditis elegans atlas, the raw action ratio of observed lineage-centroid trajectories to optimal paths is 3.79, yet forward-simulated ensembles of the learned model itself score 2.07, leaving a genuine excess of about two-fold. In zebrafish (Danio rerio), the observed ratio sits entirely at the model's floor. Three mechanisms that could explain the residual deviation (detours, flow-opposition, and fate-map complexity) are each excluded; what remains is speed, with lineages moving 30-43% faster along the learned flow. We report validity boundaries for temporal resolution, cross-species comparison, and energetic interpretation, delimiting where action-based trajectory comparisons are meaningful.

systems biology↗

bioRxiv2026

Choice of annotation data significantly impacts automated reconstruction of genome-scale metabolic networks

Genome-scale metabolic models are valuable tools for studying metabolism in many applications, such as metabolic engineering or understanding human disease. However, curated models are available for only a limited number of organisms, and generating high-quality models for additional organisms remains a challenge. To help address this challenge, several software tools have been developed that can produce draft models in an automated fashion, but the quality of these draft models lags behind manually curated ones. Thus, there is still a need to improve these tools. Previous work has focused primarily on developing better methods for each step in the reconstruction process, such as gene annotation and gap-filling. Here, we focus on the data that underlies these methods, such as known connections between protein sequence and function. We find that changing the source of reconstruction data has a substantial impact on the contents and predictions of generated models. In fact, the source of data can have a bigger impact than the choice of reconstruction method. Additionally, we show that the reconstruction process is sensitive to the accuracy of gene annotations in the reconstruction data. Finally, we demonstrate the importance of having functional annotation data from an organism that is closely related to the one for which a model is being built. These observations highlight the importance of reconstruction data and identify principles that should be considered when selecting data for reconstruction tools.

systems biology↗