Lipidomics – the global assessment of lipids – can be performed using a variety of mass spectrometry (MS)-based approaches. However, choosing the optimal approach in terms of lipid coverage, robustness and throughput can be a challenging task. Here, we compare a novel targeted quantitative lipidomics platform known as the Lipidyzer to a conventional untargeted liquid chromatography (LC)-MS approach. We find that both platforms are efficient in profiling more than 300 lipids across 11 lipid classes in mouse plasma with precision and accuracy below 20% for most lipids. While the untargeted and targeted platforms detect similar numbers of lipids, the former identifies a broader range of lipid classes and can unambiguously identify all three fatty acids in triacylglycerols (TAG). Quantitative measurements from both approaches exhibit a median correlation coefficient (r) of 0.99 using a dilution series of deuterated internal standards and 0.71 using endogenous plasma lipids in the context of aging. Application of both platforms to plasma from aging mouse reveals similar changes in total lipid levels across all major lipid classes and in specific lipid species. Interestingly, TAG is the lipid class that exhibits the most changes with age, suggesting that TAG metabolism is particularly sensitive to the aging process in mice. Collectively, our data show that the Lipidyzer platform provides comprehensive profiling of the most prevalent lipids in plasma in a simple and automated manner.
The lifespan of an organism is strongly influenced by environmental factors, including diet, and by internal factors, notably reproductive status. Lipid metabolism is critical for adaptation to external conditions or reproduction. Interestingly, specific lipid profiles are associated with longevity and increased uptake of certain lipids extends longevity in C. elegans and ameliorates disease phenotypes in humans. How lipids impact longevity, and how lipid metabolism is regulated during aging, is just beginning to be unraveled. This review describes recent advances in the regulation and role of lipids in longevity, focusing on the interaction between lipid metabolism and chromatin states in aging and age-related diseases.
BackgroundProtein aggregation and its pathological effects are the major cause of several neurodegenerative diseases. In Huntington’s disease an elongated stretch of polyglutamines within the protein Huntingtin leads to increased aggregation propensity. This induces cellular defects, culminating in neuronal loss, but the connection between aggregation and toxicity remains to be established.ResultsTo uncover cellular pathways relevant for intoxication we used genome-wide analyses in a yeast model system and identify fourteen genes that, if deleted, result in higher polyglutamine toxicity. Several of these genes, like UGO1, ATP15 and NFU1 encode mitochondrial proteins, implying that a challenged mitochondrial system may become dysfunctional during polyglutamine intoxication. We further employed microarrays to decipher the transcriptional response upon polyglutamine intoxication, which exposes an upregulation of genes involved in sulfur and iron metabolism and mitochondrial Fe-S cluster formation. Indeed, we find that in vivo iron concentrations are misbalanced and observe a reduction in the activity of the prominent Fe-S cluster containing protein aconitase. Like in other yeast strains with impaired mitochondria, non-fermentative growth is impossible after intoxication with the polyglutamine protein. NMR-based metabolic analyses reveal that mitochondrial metabolism is reduced, leading to accumulation of metabolic intermediates in polyglutamine-intoxicated cells.ConclusionThese data show that damages to the mitochondrial system occur in polyglutamine intoxicated yeast cells and suggest an intricate connection between polyglutamine-induced toxicity, mitochondrial functionality and iron homeostasis in this model system.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1831-7) contains supplementary material, which is available to authorized users.
Background:The knockout of the protein UNC-23, a cochaperone of Hsc70, leads to severe motility dysfunction in the model organism C. elegans. Results: UNC-23 interacts with Hsc70, whose uncoupling from DNJ-13 cures the muscular phenotype. Conclusion:The dystrophic muscle phenotype is caused by Hsc70 cycle deregulation. Significance: Hsc70 and its cofactors might be potent targets for therapies against human myopathies.
Hsc70 is a conserved ATP-dependent molecular chaperone, which utilizes the energy of ATP hydrolysis to alter the folding state of its client proteins. In contrast to the Hsc70 systems of bacteria, yeast and humans, the Hsc70 system of C. elegans (CeHsc70) has not been studied to date.We find that CeHsc70 is characterized by a high ATP turnover rate and limited by post-hydrolysis nucleotide exchange. This rate-limiting step is defined by the helical lid domain at the C-terminus. A certain truncation in this domain (CeHsc70-Δ545) reduces the turnover rate and renders the hydrolysis step rate-limiting. The helical lid domain also affects cofactor affinities as the lidless mutant CeHsc70-Δ512 binds more strongly to DNJ-13, forming large protein complexes in the presence of ATP. Despite preserving the ability to hydrolyze ATP and interact with its cofactors DNJ-13 and BAG-1, the truncation of the helical lid domain leads to the loss of all protein folding activity, highlighting the requirement of this domain for the functionality of the nematode's Hsc70 protein.
Neurodegeneration is linked to protein aggregation in several human disorders. In Huntington’s disease, the length of a polyglutamine stretch in Huntingtin is correlated to neuronal death. Here we utilize a model based on glutamine stretches of 0, 30 or 56 residues in Saccharomyces cerevisiae to understand how such toxic proteins interfere with cellular physiology. A toxicity-mimicking cytostatic effect is evident from compromised colony formation upon expression of polyglutamines. Interestingly, diploid cells are insensitive to polyglutamines and haploid cells can escape cytostasis by hyperploidization. Using a genome-wide screen for genes required to obtain the cytostatic effect, we identify a network related to the budding process and cellular division. We observe a striking mislocalization of the septins Cdc10 and Shs1 in cells arrested by polyglutamines, suggesting that the septin ring may be a pivotal structure connecting polyglutamine toxicity and ploidy.
DNA-Microarrays are powerful tools to obtain expression data on the genome-wide scale. We performed microarray experiments to elucidate the transcriptional networks, which are up- or down-regulated in response to the expression of toxic polyglutamine proteins in yeast. Such experiments initially generate hit lists containing differentially expressed genes. To look into transcriptional responses, we constructed networks from these genes. We therefore developed an algorithm, which is capable of dealing with very small numbers of microarrays by clustering the hits based on co-regulatory relationships obtained from the SPELL database. Here, we evaluate this algorithm according to several criteria and further develop its statistical capabilities. Initially, we define how the number of SPELL-derived co-regulated genes and the number of input hits influences the quality of the networks. We then show the ability of our networks to accurately predict further differentially expressed genes. Including these predicted genes into the networks improves the network quality and allows quantifying the predictive strength of the networks based on a newly implemented scoring method. We find that this approach is useful for our own experimental data sets and also for many other data sets which we tested from the SPELL microarray database. Furthermore, the clusters obtained by the described algorithm greatly improve the assignment to biological processes and transcription factors for the individual clusters. Thus, the described clustering approach, which will be available through the ClusterEx web interface, and the evaluation parameters derived from it represent valuable tools for the fast and informative analysis of yeast microarray data.
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