Reconstruction and Validation of a Genome-Scale Metabolic Model for the Filamentous Fungus Neurospora crassa Using FARM

Abstract

The filamentous fungus Neurospora crassa played a central role in the development of twentieth-century genetics, biochemistry and molecular biology, and continues to serve as a model organism for eukaryotic biology. Here, we have reconstructed a genome-scale model of its metabolism. This model consists of 836 metabolic genes, 257 pathways, 6 cellular compartments, and is supported by extensive manual curation of 491 literature citations. To aid our reconstruction, we developed three optimization-based algorithms, which together comprise Fast Automated Reconstruction of Metabolism (FARM). These algorithms are: LInear MEtabolite Dilution Flux Balance Analysis (limed-FBA), which predicts flux while linearly accounting for metabolite dilution; One-step functional Pruning (OnePrune), which removes blocked reactions with a single compact linear program; and Consistent Reproduction Of growth/no-growth Phenotype (CROP), which reconciles differences between in silico and experimental gene essentiality faster than previous approaches. Against an independent test set of more than 300 essential/non-essential genes that were not used to train the model, the model displays 93% sensitivity and specificity. We also used the model to simulate the biochemical genetics experiments originally performed on Neurospora by comprehensively predicting nutrient rescue of essential genes and synthetic lethal interactions, and we provide detailed pathway-based mechanistic explanations of our predictions. Our model provides a reliable computational framework for the integration and interpretation of ongoing experimental efforts in Neurospora, and we anticipate that our methods will substantially reduce the manual effort required to develop high-quality genome-scale metabolic models for other organisms.

Functional Metabolic Map of Faecalibacterium prausnitzii, a Beneficial Human Gut Microbe

“…We, and others, have previously shown that automatically generated GENREs, based solely on genome annotation, do not adequately capture biochemical characteristics and phenotypic properties of the target organisms (28,47,48). However, as poorly characterized organisms have a limited amount of published biochemical data available, a key ingredient for traditional reconstruction of high-quality metabolic reconstructions (23), the genome becomes the main source of metabolic information.…”

The circadian system as an organizer of metabolism

“…A filamentous fungus, Neurospora is originally best known for the one gene, one enzyme hypothesis (Beadle and Tatum, 1941). With the manifold similarities between the Neurospora and animal circadian systems, a fully sequenced and well annotated genome (Galagan et al, 2003), facile recombineering with 98% efficiency, functional genomics, a genome scale metabolic model, a high throughput knock out project (Colot et al, 2006; Dreyfuss et al, 2013; Dunlap et al, 2007), and the development of a CRISPR system (Matsu-ura et al, 2015), Neurospora has become one of the most tractable model organisms for the study of chronobiology at the level of the cell.…”

Genome-scale reconstruction of the metabolic network in Pseudomonas stutzeri A1501

“…It is now possible to automatically extract the information required for metabolic network reconstruction from genomic sequences 3,4 . Combining the genomic data with the biochemical knowledge leads to the emergence of genomescale metabolic network models for a wide range of organisms, from bacteria [8][9][10][11][12][13][14][15][16] to archaea [17][18][19] to eukaryotic cells [20][21][22][23][24][25][26] . Combining the genomic data with the biochemical knowledge leads to the emergence of genomescale metabolic network models for a wide range of organisms, from bacteria [8][9][10][11][12][13][14][15][16] to archaea [17][18][19] to eukaryotic cells [20][21][22][23][24][25][26] .…”