Genome-scale metabolic modelling identifies reactions mediated by SNP-SNP interactions associated with yeast sporulation

Elucidating genotype-phenotype or variant-to-function relationships remains a pivotal challenge in genetics. For quantitative traits, causal SNPs act either additively or epistatically, resulting in complex interactions which are difficult to dissect molecularly. Here, we developed gene co-expression networks and genome-scale metabolic models for all combinations of causal SNPs of yeast sporulation efficiency, a quantitative trait, to determine how genetic interactions drive phenotypic variation. Analysis of expression networks identified SNP-SNP interaction-dependent changes in the connectivity of crucial metabolic regulators. These regulators revealed how some SNP combinations added to higher phenotypic value. Genome-scale differential flux analysis was used to identify differentially activated metabolic reactions across multiple metabolic pathways in each SNP-specific model. This analysis identified causal reactions explaining the observed sporulation efficiency differences. Finally, the differential regulation of the pentose phosphate pathway in specific SNP combinations suggested autophagy as a pentose phosphate pathway-dependent compensatory mechanism for increasing sporulation efficiency. Our study presents a modelling framework that has the potential to unravel specific causal metabolic pathways and reactions regulated by SNPs and their combinations, thus providing insights into GWAS variants of metabolic diseases.

Multi-SNP combination genome-scale metabolic models identify molecular mechanisms determining genetic interactions in a quantitative trait

Genetic interactions are prevalent across species, from yeast to humans, and play a crucial role in shaping complex traits. However, the molecular mechanisms underlying these interactions remain largely unexplored. Here, we answer the question of whether interactions between genetic variants can activate novel unique pathways and if such pathways can be targeted to modulate phenotypic outcomes. The model organism Saccharomyces cerevisiae was used to investigate the interaction between two key SNPs, MKT1 89G and TAO3 4477C , during sporulation, a complex dynamic metabolic process. By integrating temporal gene expression and proteomics data, we show that these SNPs, in combination, activate amino acid metabolism, particularly the arginine biosynthesis pathway, while downregulating ribosome biogenesis, an energy-intensive process in response to nutrient starvation. This metabolic trade-off reveals a key mechanism by which SNP interactions modulate cellular responses to environmental stress, thereby enhancing sporulation. Further, the essential role of the arginine biosynthetic pathway in maintaining mitochondrial activity when these SNPs interact has been validated experimentally with deletion mutants. Our findings illustrate a broader impact beyond yeast, especially on complex human metabolic diseases like cancer, where specific SNP combinations hyperactivate ribosomal pathways while suppressing metabolic pathways that usually control cell growth, leading to uncontrolled proliferation. 

Interaction of Genetic Variants Activates Latent Metabolic Pathways in Yeast

Reactive species (RS) are known to play significant roles in cancer development as well as in treating or managing cancer. On the other hand, genome scale metabolic models are being used to understand cell metabolism in disease contexts including cancer, and also in planning strategies to handle diseases. Despite their crucial roles in cancers, the reactive species have not been adequately modeled in the genome scale metabolic models (GSMMs) when probing disease models for their metabolism or detection of drug targets. In this work, we have developed a module of reactive species reactions, which is scalable - it can be integrated with any human metabolic model as it is, or with any metabolic model with fine-tuning. When integrated with a cancer (colorectal cancer in this case) metabolic model, the RS module highlighted the deregulation occurring in important CRC pathways such as fatty acid metabolism, cholesterol metabolism, arachidonic acid and eicosanoid metabolism. We show that the RS module helps in better deciphering crucial metabolic targets for devising better therapeutics such as FDFT1, FADS2 and GUK1 by taking into account the effects mediated by reactive species during colorectal cancer progression. The results from this reactive species integrated CRC metabolic model reinforces ferroptosis as a potential target for colorectal cancer therapy.

Modeling Reactive Species Metabolism in Colorectal Cancer for Identifying Metabolic Targets and Devising Therapeutics

Molecular property prediction using a molecule’s structure is a crucial step in drug and novel material discovery, as computational screening approaches rely on predicted properties to refine the existing design of molecules. Although the problem has existed for decades, it has recently gained attention due to the advent of big data and deep learning. On average, one FDA drug is approved for 250 compounds entering the preclinical research stage, requiring screening of chemical libraries containing more than 20000 compounds. In-silico property prediction approaches using learnable representations increase the pace of development and reduce the cost of discovery. We propose developing molecule representations using functional groups in chemistry to address the problem of deciphering the relationship between a molecule’s structure and property. Functional groups are substructures in a molecule with distinctive chemical properties that influence its chemical characteristics. These substructures are found by (i) curating functional groups annotated by chemists and (ii) mining a large corpus of molecules to extract frequent substructures using a pattern-mining algorithm. We show that the Functional Group Representation (FGR) framework beats state-of-the-art models on several benchmark datasets while ensuring explainability between the predicted property and molecular structure to experimentalists.

Chemically Interpretable Molecular Representation for Property Prediction

Genome-scale metabolic models (GEMs) are valuable tools for investigating normal and disease phenotypes of biological systems through the prediction of fluxes in biochemical reactions. However, in specific contexts such as different cell lines, tissues, or diseases, only a subset of reactions is active. To address this, several model extraction methods (MeMs) have been developed to filter the reactions in GEMs and extract context-specific models. These methods utilize gene expression data as a source of context-specific information. To construct context-specific models, MeMs require core reactions specific to the given context as input. Typically, core reactions are derived using a single threshold applied to gene expression data. Reactions associated with genes whose expression values exceed the threshold are considered as core reactions. However, it is important to note that enzyme activity is not solely determined by gene expression levels. This approach based on a single threshold may inadvertently exclude reactions that require enzymes in smaller quantities. In this study, we propose a novel thresholding algorithm called ‘Localgini’, which leverages the Gini coefficient and transcriptomics data to derive gene-specific thresholds. Localgini is implemented as a pre-processing step to obtain core reactions for MeMs. To demonstrate the effectiveness of Localgini, we construct context-specific models for NCI-60 cancer cell lines and human tissues using different MeMs. We compare the performance of Localgini with existing thresholding methods, namely LocalT2 and StanDep. The results show that the models derived using Localgini recover a greater number of housekeeping functionalities compared to the other thresholding methods. Moreover, the Localgini-derived core reactions exhibit increased self-consistency and display enhanced consensus among models built using different MeMs. By incorporating transcriptomic support, Localgini includes low-expression reactions in the core reaction list, enhancing the comprehensiveness of the resulting models. Codes used in this study, compatible with COBRA toolbox are available at https://github.com/NiravBhattLab/Localgini Author summary Genome-scale models are becoming a desirable tool to understand the metabolism of a biological system and hence find applications in the fields of systems and synthetic biology. These models are often integrated with transcriptomics data to improve prediction accuracy. Algorithms developed to integrate transcriptomics data with genome-scale models require core reactions to be derived from omics data using a threshold. In this work, we propose a thresholding method that uses an inequality-based metric to derive thresholds. We implied the proposed method and other existing methods to datasets of cancer cell lines and human tissue. We showed that our method improves the inclusion of reactions required for basic cellular maintenance. Furthermore, we validated the built models for the reduction in variance owing to the model-extraction algorithms. Overall, the proposed method improves the quality of metabolic models by inferring inequality in the distribution of gene expression levels across samples/contexts.

Localgini: A method for harnessing inequality in gene expression to improve the quality of context-specific models

Model maintenance, monitoring, and control framework using In-Situ NIR spectroscopy and offline process data for Lactococcus lactis fermentation

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