Pool size measurements improve precision of flux estimates but increase sensitivity to unmodeled reactions outside the core network in isotopically nonstationary metabolic flux analysis (INST-MFA).

Metabolic flux analysis (MFA) involves model-based estimation of metabolic reaction rates (i.e., fluxes) and, in some cases, metabolite content (i.e., pool sizes) from experimental measurements. Applying MFA to biological data helps determine the fate of substrates and the activity of specific pathways within metabolic networks. However, reliably estimating fluxes by using simplified "core" models to predict the dynamics of larger metabolic networks remains a challenge. One point of uncertainty relates to the advantages and potential pitfalls of including pool size measurements as experimental inputs for isotopically nonstationary MFA (INST-MFA). Here, we directly assessed the role of pool sizes using various core models and simulated datasets. To investigate the effects of pool size measurements on INST-MFA, we assessed the accuracy and precision of flux estimates obtained using different subsets of data (e.g., with or without pool size measurements) and simple network models that either matched or differed from the true network. The inclusion of pool size measurements provided incremental improvements to the precision of the flux estimates. However, adding pool size measurements increased the sensitivity of the flux solution to unmodeled reactions outside the core network. These results were confirmed using a large Escherichia coli model that is representative of realistic metabolic networks examined in MFA studies. Our findings indicate that accurate flux estimates can be obtained in the absence of pool size measurements, even when using core models that lack full network coverage. Addition of pool size measurements to INST-MFA datasets may reveal the activity of non-core reactions that influence the labeling dynamics and therefore necessitate network expansion in order to reconcile all available data to the model. Our findings also emphasize the key role that goodness-of-fit testing plays in assessing the quality of model fits obtained with INST-MFA.

Widespread bacterial diversity within the bacteriome of fungi.

Knowledge of associations between fungal hosts and their bacterial associates has steadily grown in recent years as the number and diversity of examinations have increased, but current knowledge is predominantly limited to a small number of fungal taxa and bacterial partners. Here, we screened for potential bacterial associates in over 700 phylogenetically diverse fungal isolates, representing 366 genera, or a tenfold increase compared with previously examined fungal genera, including isolates from several previously unexplored phyla. Both a 16 S rDNA-based exploration of fungal isolates from four distinct culture collections spanning North America, South America and Europe, and a bioinformatic screen for bacterial-specific sequences within fungal genome sequencing projects, revealed that a surprisingly diverse array of bacterial associates are frequently found in otherwise axenic fungal cultures. We demonstrate that bacterial associations with diverse fungal hosts appear to be the rule, rather than the exception, and deserve increased consideration in microbiome studies and in examinations of microbial interactions.

Systematic identification and elimination of flux bottlenecks in the aldehyde production pathway of Synechococcus elongatus PCC 7942.

Isotopically nonstationary metabolic flux analysis (INST-MFA) provides a versatile platform to quantitatively assess in vivo metabolic activities of autotrophic systems. By applying INST-MFA to recombinant aldehyde-producing cyanobacteria, we identified metabolic alterations that correlated with increased strain performance in order to guide rational metabolic engineering. We identified four reactions adjacent to the pyruvate node that varied significantly with increasing aldehyde production: pyruvate kinase (PK) and acetolactate synthase (ALS) fluxes were directly correlated with product formation, while pyruvate dehydrogenase (PDH) and phosphoenolpyruvate carboxylase (PPC) fluxes were inversely correlated. Overexpression of enzymes for PK or ALS did not result in further improvements to the previous best-performing strain, while downregulation of PDH expression (through antisense RNA expression) or PPC flux (through expression of the reverse reaction, phosphoenolpyruvate carboxykinase) provided significant improvements. These results illustrate the potential of INST-MFA to enable a systematic approach for iterative identification and removal of pathway bottlenecks in autotrophic host cells.