Significance: Photosynthesis metabolites are quickly labeled when ¹³ CO ₂ is fed to leaves, but the time course of labeling reveals additional contributing processes involved in the metabolic dynamics of photosynthesis. The existence of three such processes is demonstrated, and a metabolic flux model is developed to explore and characterize them. The model is consistent with a slow return of carbon from cytosolic and vacuolar sugars into the Calvin–Benson cycle through the oxidative pentose phosphate pathway. Our results provide insight into how carbon assimilation is integrated into the metabolic network of photosynthetic cells with implications for global carbon fluxes.

13C‐Metabolic Flux Analysis (13C‐MFA) and Flux Balance Analysis (FBA) are widely used to investigate the operation of biochemical networks in both biological and biotechnological research. Both methods use metabolic reaction network models of metabolism operating at steady state so that reaction rates (fluxes) and the levels of metabolic intermediates are constrained to be invariant. They provide estimated (MFA) or predicted (FBA) values of the fluxes through the network in vivo, which cannot be measured directly. These fluxes can shed light on basic biology and have been successfully used to inform metabolic engineering strategies. Several approaches have been taken to test the reliability of estimates and predictions from constraint‐based methods and to compare alternative model architectures. Despite advances in other areas of the statistical evaluation of metabolic models, such as the quantification of flux estimate uncertainty, validation and model selection methods have been underappreciated and underexplored. We review the history and state‐of‐the‐art in constraint‐based metabolic model validation and model selection. Applications and limitations of the χ²‐test of goodness‐of‐fit, the most widely used quantitative validation and selection approach in 13C‐MFA, are discussed, and complementary and alternative forms of validation and selection are proposed. A combined model validation and selection framework for 13C‐MFA incorporating metabolite pool size information that leverages new developments in the field is presented and advocated for. Finally, we discuss how adopting robust validation and selection procedures can enhance confidence in constraint‐based modeling as a whole and ultimately facilitate more widespread use of FBA in biotechnology.

Since they emerged approximately 125 million years ago, flowering plants have evolved to dominate the terrestrial landscape and survive in the most inhospitable environments on earth. At their core, these adaptations have been shaped by changes in numerous, interconnected pathways and genes that collectively give rise to emergent biological phenomena. Linking gene expression to morphological outcomes remains a grand challenge in biology, and new approaches are needed to begin to address this gap. Here, we implemented topological data analysis (TDA) to summarize the high dimensionality and noisiness of gene expression data using lens functions that delineate plant tissue and stress responses. Using this framework, we created a topological representation of the shape of gene expression across plant evolution, development, and environment for the phylogenetically diverse flowering plants. The TDA-based Mapper graphs form a well-defined gradient of tissues from leaves to seeds, or from healthy to stressed samples, depending on the lens function. This suggests that there are distinct and conserved expression patterns across angiosperms that delineate different tissue types or responses to biotic and abiotic stresses. Genes that correlate with the tissue lens function are enriched in central processes such as photosynthetic, growth and development, housekeeping, or stress responses. Together, our results highlight the power of TDA for analyzing complex biological data and reveal a core expression backbone that defines plant form and function.

Epichloë species (Clavicipitaceae, Ascomycota) are endophytic symbionts of many cool-season grasses. Many interactions between Epichloë and their host grasses contribute to plant growth promotion, protection from many pathogens and insect pests, and tolerance to drought stress. Resistance to insect herbivores by endophytes associated with Hordeum species has been previously shown to vary depending on the endophyte-grass-insect combination. We explored the genetic and chemotypic diversity of endophytes present in wild Hordeum species. We analyzed seeds of Hordeum bogdanii, H. brevisubulatum, and H. comosum obtained from the US Department of Agriculture’s (USDA) National Plant Germplasm System (NPGS), of which some have been reported as endophyte-infected. Using polymerase chain reaction (PCR) with primers specific to Epichloë species, we were able to identify endophytes in seeds from 17 of the 56 Plant Introduction (PI) lines, of which only 9 lines yielded viable seed. Phylogenetic analyses of housekeeping, alkaloid biosynthesis, and mating type genes suggest that the endophytes of the infected PI lines separate into five taxa: Epichloë bromicola, Epichloë tembladerae, and three unnamed interspecific hybrid species. One PI line contained an endophyte that is considered a new taxonomic group, Epichloë sp. HboTG-3 (H. bogdanii Taxonomic Group 3). Phylogenetic analyses of the interspecific hybrid endophytes from H. bogdanii and H. brevisubulatum indicate that these taxa all have an E. bromicola allele but the second allele varies. We verified in planta alkaloid production from the five genotypes yielding viable seed. Morphological characteristics of the isolates from the viable Hordeum species were analyzed for their features in culture and in planta. In the latter, we observed epiphyllous growth and in some cases sporulation on leaves of infected plants.

The accurate prediction of complex phenotypes such as metabolic fluxes in living systems is a grand challenge for systems biology and central to efficiently identifying biotechnological interventions that can address pressing industrial needs. The application of gene expression data to improve the accuracy of metabolic flux predictions using mechanistic modeling methods such as flux balance analysis (FBA) has not been previously demonstrated in multi-tissue systems, despite their biotechnological importance. We hypothesized that a method for generating metabolic flux predictions informed by relative expression levels between tissues would improve prediction accuracy. Relative gene expression levels derived from multiple transcriptomic and proteomic datasets were integrated into FBA predictions of a multi-tissue, diel model of Arabidopsis thaliana’s central metabolism. This integration dramatically improved the agreement of flux predictions with experimentally based flux maps from ¹³C metabolic flux analysis compared with a standard parsimonious FBA approach. Disagreement between FBA predictions and MFA flux maps was measured using weighted averaged percent error values, and for parsimonious FBA this was169%–180% for high light conditions and 94%–103% for low light conditions, depending on the gene expression dataset used. This fell to 10%-13% and 9%-11% upon incorporating expression data into the modeling process, which also substantially altered the predicted carbon and energy economy of the plant. Code and data generated as part of this study are available from https://github.com/Gibberella/ArabidopsisGeneExpressionWeights.