Lipidomics in research on yeast membrane lipid homeostasis

Shortening of membrane lipid acyl chains compensates for phosphatidylcholine deficiency in choline-auxotroph yeast

Phosphatidylcholine (PC) is an abundant membrane lipid component in most eukaryotes, including yeast, and has been assigned multiple functions in addition to acting as building block of the lipid bilayer. Here, by isolating S. cerevisiae suppressor mutants that exhibit robust growth in the absence of PC, we show that PC essentiality is subject to cellular evolvability in yeast. The requirement for PC is suppressed by monosomy of chromosome XV or by a point mutation in the ACC1 gene encoding acetyl-CoA carboxylase. Although these two genetic adaptations rewire lipid biosynthesis in different ways, both decrease Acc1 activity, thereby reducing average acyl chain length. Consistently, soraphen A, a specific inhibitor of Acc1, rescues a yeast mutant with deficient PC synthesis. In the aneuploid suppressor, feedback inhibition of Acc1 through acyl-CoA produced by fatty acid synthase (FAS) results from upregulation of lipid synthesis. The results show that budding yeast regulates acyl chain length by fine-tuning the activities of Acc1 and FAS and indicate that PC evolved by benefitting the maintenance of membrane fluidity.

Bioactive Sugarcane Lipids in a Circular Economy Context

Most of the global sugar and ethanol supply trade comes from the harvesting of Saccharum officinarum (i.e., sugarcane). Its industrial processing results in numerous by-products and waste streams, such as tops, straw, filter cake, molasses and bagasse. The recovery of lipids (i.e., octacosanol, phytosterols, long-chain aldehydes and triterpenoids) from these residues is an excellent starting point for the development of new products for various application fields, such as health and well-being, representing an important feature of the circular economy. By selecting green scalable extraction procedures, industry can reduce its environmental impact. Refluxed ethanol extraction methods have been demonstrated to meet these characteristics. On the other hand, effective non-solvent methodologies such as molecular distillation and supercritical CO₂ extraction can fractionate lipids based on high temperature and pressure application with similar yields. Sugarcane lipophilic extracts are usually analyzed through gas chromatography (GC) and liquid chromatography (LC) techniques. In many cases, the identification of such compounds involves the development of high-temperature GC–MS/FID techniques. On the other hand, for the identification and quantification of thermolabile lipids, LC–MS techniques are suitable for the separation and identification of major lipid classes. Generically, its composition includes terpenes, phytosterols, tocopherol, free fatty acids, fatty alcohols, wax esters, triglycerides, diglycerides and monoglycerides. These compounds are already known for their interesting application in various fields such as pharma and cosmetics due to their anti-hypercholesterolemic, anti-hyperglycemic, antioxidant and anti-inflammatory properties.

Fluidity and Lipid Composition of Membranes of Peroxisomes, Mitochondria and the ER From Oleic Acid-Induced Saccharomyces cerevisiae

The maintenance of a fluid lipid bilayer is key for organelle function and cell viability. Given the critical role of lipid compositions in determining membrane properties and organelle identity, it is clear that cells must have elaborate mechanism for membrane maintenance during adaptive responses to environmental conditions. Emphasis of the presented study is on peroxisomes, oleic acid-inducible organelles that are essential for the growth of yeast under conditions of oleic acid as single carbon source. Here, we isolated peroxisomes, mitochondria and ER from oleic acid-induced Saccharomyces cerevisiae and determined the lipid composition of their membranes using shotgun lipidomics and compared it to lipid ordering using fluorescence microscopy. In comparison to mitochondrial and ER membranes, the peroxisomal membranes were slightly more disordered and characterized by a distinct enrichment of phosphaditylinositol, indicating an important role of this phospholipid in peroxisomal membrane associated processes.

A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth

Metabolic modeling and machine learning are key components in the emerging next generation of systems and synthetic biology tools, targeting the genotype-phenotype-environment relationship. Rather than being used in isolation, it is becoming clear that their value is maximized when they are combined. However, the potential of integrating these two frameworks for omic data augmentation and integration is largely unexplored. We propose, rigorously assess, and compare machine-learning-based data integration techniques, combining gene expression profiles with computationally generated metabolic flux data to predict yeast cell growth. To this end, we create strain-specific metabolic models for 1,143 Saccharomyces cerevisiae mutants and we test 27 machine-learning methods, incorporating state-of-the-art feature selection and multiview learning approaches. We propose a multiview neural network using fluxomic and transcriptomic data, showing that the former increases the predictive accuracy of the latter and reveals functional patterns that are not directly deducible from gene expression alone. We test the proposed neural network on a further 86 strains generated in a different experiment, therefore verifying its robustness to an additional independent dataset. Finally, we show that introducing mechanistic flux features improves the predictions also for knockout strains whose genes were not modeled in the metabolic reconstruction. Our results thus demonstrate that fusing experimental cues with in silico models, based on known biochemistry, can contribute with disjoint information toward biologically informed and interpretable machine learning. Overall, this study provides tools for understanding and manipulating complex phenotypes, increasing both the prediction accuracy and the extent of discernible mechanistic biological insights.

redLips: a comprehensive mechanistic model of the lipid metabolic network of yeast

Over the last decades, yeast has become a key model organism for the study of lipid biochemistry. Because the regulation of lipids has been closely linked to various physiopathologies, the study of these biomolecules could lead to new diagnostics and treatments. Before the field can reach this point, however, sufficient tools for integrating and analyzing the ever-growing availability of lipidomics data will need to be developed. To this end, genome-scale models (GEMs) of metabolic networks are useful tools, though their large size and complexity introduces too much uncertainty in the accuracy of predicted outcomes. Ideally, therefore, a model for studying lipids would contain only the pathways required for the proper analysis of these biomolecules, but would not be an ad hoc reduction. We hereby present a metabolic model that focuses on lipid metabolism constructed through the integration of detailed lipid pathways into an already existing GEM of Saccharomyces cerevisiae. Our model was then systematically reduced around the subsystems defined by these pathways to provide a more manageable model size for complex studies. We show that this model is as consistent and inclusive as other yeast GEMs regarding the focus and detail on the lipid metabolism, and can be used as a scaffold for integrating lipidomics data to improve predictions in studies of lipid-related biological functions.

A Targeted Mass Spectrometric Analysis Reveals the Presence of a Reduced but Dynamic Sphingolipid Metabolic Pathway in an Ancient Protozoan, Giardia lamblia

Giardia lamblia, a single-celled eukaryote, colonizes and thrives in the small intestine of humans. Because of its compact and reduced genome, Giardia has adapted a “minimalistic” life style, as it becomes dependent on available resources of the small intestine. Because Giardia expresses fewer sphingolipid (SL) genes—and glycosphingolipids are critical for encystation—we investigated the SL metabolic cycle in this parasite. A tandem mass spectrometry (MS/MS) analysis reveals that major SLs in Giardia include sphingomyelins, sphingoid bases, ceramides, and glycosylceramides. Many of these lipids are obtained by Giardia from the growth medium, remodeled at their fatty acyl chains and end up in the spent medium. For instance, ceramide-1-phosphate, a proinflammatory molecule that is not present in the culture medium, is generated from sphingosine (abundant in the culture medium) possibly by remodeling reactions. It is then subsequently released into the spent medium. Thus, the secretion of ceramide-1-phospate and other SL derivatives by Giardia could be associated with inflammatory bowel disease observed in acute giardiasis. Additionally, we found that the levels of SLs increase in encysting Giardia and are differentially regulated throughout the encystation cycle. We propose that SL metabolism is important for this parasite and, could serve as potential targets for developing novel anti-giardial agents.

Perturbation of the yeast mitochondrial lipidome and associated membrane proteins following heterologous expression of Artemia-ANT

Heterologous expression is a landmark technique for studying a protein itself or its effect on the expression host, in which membrane-embedded proteins are a common choice. Yet, the impact of inserting a foreign protein to the lipid environment of host membranes, has never been addressed. Here we demonstrated that heterologous expression of the Artemia franciscana adenine nucleotide translocase (ANT) in yeasts altered lipidomic composition of their inner mitochondrial membranes. Along with this, activities of complex II, IV and ATP synthase, all membrane-embedded components, were significantly decreased while their expression levels remained unaffected. Although the results represent an individual case of expressing a crustacean protein in yeast inner mitochondrial membranes, it cannot be excluded that host lipidome alterations is a more widespread epiphenomenon, potentially biasing heterologous expression experiments. Finally, our results raise the possibility that not only lipids modulate protein function, but also membrane-embedded proteins modulate lipid composition, thus revealing a reciprocal mode of regulation for these two biomolecular entities.