Assessment of transcriptomic constraint-based methods for central carbon flux inference

PLOS ONE RESEARCH ARTICLE Assessment of transcriptomic constraintbased methods for central carbon flux inference Siddharth Bhadra-Lobo ID1*, Min Kyung Kim1, Desmond S. Lun1,2,3 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 1 Center for Computational and Integrative Biology, Rutgers, The State University of New Jersey, Camden, NJ, United States of America, 2 Department of Computer Science, Rutgers, The State University of New Jersey, Camden, NJ, United States of America, 3 Department of Plant Biology, Rutgers, The State University of New Jersey, New Brunswick, NJ, United States of America * Abstract OPEN ACCESS Citation: Bhadra-Lobo S, Kim MK, Lun DS (2020) Assessment of transcriptomic constraint-based methods for central carbon flux inference. PLoS ONE 15(9): e0238689. https://doi.org/10.1371/ journal.pone.0238689 Editor: Rajagopal Subramanyam, University of Hyderabad School of Life Sciences, INDIA Received: August 8, 2019 Motivation Determining intracellular metabolic flux through isotope labeling techniques such as 13C metabolic flux analysis (13C-MFA) incurs significant cost and effort. Previous studies have shown transcriptomic data coupled with constraint-based metabolic modeling can determine intracellular fluxes that correlate highly with 13C-MFA measured fluxes and can achieve higher accuracy than constraint-based metabolic modeling alone. These studies, however, used validation data limited to E. coli and S. cerevisiae grown on glucose, with significantly similar flux distribution for central metabolism. It is unclear whether those results apply to more diverse metabolisms, and therefore further, extensive validation is needed. Accepted: August 21, 2020 Published: September 9, 2020 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pone.0238689 Copyright: © 2020 Bhadra-Lobo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the manuscript and its Supporting Information files. Results In this paper, we formed a dataset of transcriptomic data coupled with corresponding 13CMFA flux data for 21 experimental conditions in different unicellular organisms grown on varying carbon substrates and conditions. Three computational flux-balance analysis (FBA) methods were comparatively assessed. The results show when uptake rates of carbon sources and key metabolites are known, transcriptomic data provides no significant advantage over constraint-based metabolic modeling (average correlation coefficients, transcriptomic E-Flux2 0.725 and SPOT 0.650 vs non-transcriptomic pFBA 0.768). When uptake rates are unknown, however, predictions obtained utilizing transcriptomic data are generally good and significantly better than those obtained using constraint-based metabolic modeling alone (E-Flux2 0.385 and SPOT 0.583 vs pFBA 0.237). Thus, transcriptomic data coupled with constraint-based metabolic modeling is a promising method to obtain intracellular flux estimates in microorganisms, particularly in cases where uptake rates of key metabolites cannot be easily determined, such as for growth in complex media or in vivo conditions. Funding: This work was supported in part by NSF award no. 1515511 (MKK). PLOS ONE | https://doi.org/10.1371/journal.pone.0238689 September 9, 2020 1 / 16 PLOS ONE Competing interests: The authors have declared that no competing interests exist. Transcriptomic coupled FBA validation Introduction Computational tools integrating transcriptomic data into genome-scale metabolic models can predict system-level and condition specific metabolic flux distributions. Many methods for inferring metabolic fluxes from gene expression data have been, and continue to be, developed [1–3]. However, the comparative performance of these methods lacks diverse experimental flux data for validation. Existing validation was performed exclusively against flux data generated from E. coli and S. cerevisiae (yeast) cultures grown on glucose as the sole carbon source [3, 4]. Cells cultured on identical substrates utilize highly similar metabolic pathways [5]. This carbon source bias presents significant similarities in the measured metabolic flux distribution across previous validation datasets which may have been inadequate in assessing predictive performance. Carbon source availability and relative uptake rates influence cellular metabolism. In nature, heterotrophic microorganisms can encounter a wide set of possible carbon sources to support growth, including sugars, polyols, alcohols, organic acids, and amino acids [6]. Heterotrophs such as E. coli and Bacillus subtilis have been widely studied and cultured on a variety of substrates including monosaccharides (e.g. glucose, fructose, galactose), disaccharides (e.g. sucrose), and two-carbon compounds (e.g. acetate) [7–11]. Thus, under a multitude of possible carbon sources, an incorrectly constrained heterotrophic model can reduce the predictive accuracy of central carbon fluxes from conventional FBA methods. Gene expression may be useful to impute model constraints based on transcript abundance in the absence of specific carbon source and uptake rate data. Growth condition encompasses the availability of metabolic state-determining metabolites, both organic and inorganic (e.g. glucose, CO2, photons, NO3). Missing or incorrect growth condition information can change flux predictions to alternate metabolic states of the cell. Photoautotrophic unicellular metabolic models are generally well characterized and therefore simpler to constrain with respect to carbon source. The depletion of non-carbon metabolites may metabolically adapt the cell to alternate metabolic states. For example, light inhibition can shift metabolism from either autotrophic, heterotrophic, or a combination of both as mixotrophic in Synechocystis sp. PCC 6803 [12]. A substrate void of nitrate can induce replenishing of nitrogen from metabolic sinks such as amino acids for Synechococcus sp. PCC 7002 [13]. In the lack of environmental condition specificity, informational deficit may be overcome with gene expression data such as key pathways being allocated flux values based on the upregulation of associated transcripts. Previous studies [2, 3] have extensively evaluated the predictive capability of in silico flux prediction using measured extracellular and intracellular fluxes in multiple experimental conditions, but under single carbon source bias (glucose) in two organisms. To address the limitations of the previous dataset, w (...truncated)