Reconstruction and applications of consensus yeast metabolic network based on RNA sequencing

Abstract

One practical application of genome‐scale metabolic reconstructions is to interrogate multispecies relationships. Here, we report a consensus metabolic model in four yeast species ( Saccharomyces cerevisiae , S. paradoxus , S. mikatae , and S. bayanus ) by integrating metabolic network simulations with RNA sequencing ( RNA ‐seq) datasets. We generated high‐resolution transcriptome maps of four yeast species through de novo assembly and genome‐guided approaches. The transcriptomes were annotated and applied to build the consensus metabolic network, which was verified using independent RNA ‐seq experiments. The expression profiles reveal that the genes involved in amino acid and lipid metabolism are highly coexpressed. The diverse phenotypic characteristics, such as cellular growth and gene deletions, can be simulated using the metabolic model. We also explored the applications of the consensus model in metabolic engineering using yeast‐specific reactions and biofuel production as examples. Similar strategies will benefit communities studying genome‐scale metabolic networks of other organisms.

High throughput gene expression profiling of yeast colonies with microgel-culture Drop-seq

“…Key to the method is the capture, growth, and sequencing of single cells in agarose hydrogel spheres, which affords a facile route towards generating and analyzing large numbers of distinct colonies. Because gene expression is a universal readout, ICO-seq can be applied to a broad range of phenotypes such as metabolic flux prediction (45)(46)(47). It can also be applied to dissect a heterogeneous response from single colonies when they are perturbed (e.g.…”

Genome-Wide Analysis of Yeast Metabolic Cycle through Metabolic Network Models Reveals Superiority of Integrated ATAC-seq Data over RNA-seq Data

“…In this regard, we investigated the efficiency of these data for the prediction of differential flux profiles and assessed their predictive capacity in a comparative manner with RNA-seq data. Context-specific GMN models have been extensively used to characterize yeast metabolism ( 18 – 22 ). Therefore, we surveyed altered yeast phenotype through the reconstruction of YMC models.…”

“…GMNs are mathematical representations of the cellular metabolism based on all known stoichiometry-based chemical reactions, metabolites, and their associated genes. Omics data-integrated yeast models have been reported to be beneficial in the characterization of yeast metabolism, phenotype predictions, and metabolic engineering studies under diverse growth conditions ( 18 – 22 ). Incorporation of different omics layers dramatically contributes to the understanding of the condition-specific cellular processes, and hence, this approach is used to develop next-generation genome-scale models of S. cerevisiae ( 23 ).…”

Reconstruction and analysis of genome-scale metabolic model of weak Crabtree positive yeast Lachancea kluyveri

“…GIMME tries to maximize the incorporation of reactions having expression of their constituent genes above the threshold while minimizing the reactions below it. We used a gene expression cutoff of 25th percentile as conducted in earlier studies 65 , 66 with proven results.…”

“…19 compared to other growth conditions i.e. glucose minimal (65), galactose minimal (69) and ethanol minimal (73). The gene essentiality prediction statistics for S. cerevisiae (iMM904) 36 for YPD and glucose minimal media has been added for comparison to L. kluyveri (Fig.…”