Reconstruction of the yeast Snf1 kinase regulatory network reveals its role as a global energy regulator

Highly conserved among eukaryotic cells, the AMP-activated kinase (AMPK) is a central regulator of carbon metabolism. To map the complete network of interactions around AMPK in yeast (Snf1) and to evaluate the role of its regulatory subunit Snf4, we measured global mRNA, protein and metabolite levels in wild type, Δsnf1, Δsnf4, and Δsnf1Δsnf4 knockout strains. Using four newly developed computational tools, including novel DOGMA sub-network analysis, we showed the benefits of three-level ome-data integration to uncover the global Snf1 kinase role in yeast. We for the first time identified Snf1's global regulation on gene and protein expression levels, and showed that yeast Snf1 has a far more extensive function in controlling energy metabolism than reported earlier. Additionally, we identified complementary roles of Snf1 and Snf4. Similar to the function of AMPK in humans, our findings showed that Snf1 is a low-energy checkpoint and that yeast can be used more extensively as a model system for studying the molecular mechanisms underlying the global regulation of AMPK in mammals, failure of which leads to metabolic diseases

Transcription factor control of growth rate dependent genes in Saccharomyces cerevisiae: A three factor design

<p>Abstract</p> <p>Background</p> <p>Characterization of cellular growth is central to understanding living systems. Here, we applied a three-factor design to study the relationship between specific growth rate and genome-wide gene expression in 36 steady-state chemostat cultures of <it>Saccharomyces cerevisiae</it>. The three factors we considered were specific growth rate, nutrient limitation, and oxygen availability.</p> <p>Results</p> <p>We identified 268 growth rate dependent genes, independent of nutrient limitation and oxygen availability. The transcriptional response was used to identify key areas in metabolism around which mRNA expression changes are significantly associated. Among key metabolic pathways, this analysis revealed <it>de novo </it>synthesis of pyrimidine ribonucleotides and ATP producing and consuming reactions at fast cellular growth. By scoring the significance of overlap between growth rate dependent genes and known transcription factor target sets, transcription factors that coordinate balanced growth were also identified. Our analysis shows that Fhl1, Rap1, and Sfp1, regulating protein biosynthesis, have significantly enriched target sets for genes up-regulated with increasing growth rate. Cell cycle regulators, such as Ace2 and Swi6, and stress response regulators, such as Yap1, were also shown to have significantly enriched target sets.</p> <p>Conclusion</p> <p>Our work, which is the first genome-wide gene expression study to investigate specific growth rate and consider the impact of oxygen availability, provides a more conservative estimate of growth rate dependent genes than previously reported. We also provide a global view of how a small set of transcription factors, 13 in total, contribute to control of cellular growth rate. We anticipate that multi-factorial designs will play an increasing role in elucidating cellular regulation.</p

Lipid biosynthesis monitored at the single-cell level in Saccharomyces cerevisiae

There is increasing interest in bioengineering of lipids for use in functional foods, pharmaceuticals, and biofuels. Saccharomyces cerevisiae is a widely utilized cell factory for biotechnological production, thus a tempting alternative. Herein, we show how its neutral lipid accumulation varies throughout metabolic phases under nutritional conditions relevant for large-scale fermentation. Population-averaged metabolic data were correlated with lipid storage at the single-cell level monitored at submicron resolution by label-free coherent anti-Stokes Raman scattering (CARS) microscopy. While lipid droplet sizes are fairly constant, the number of droplets is a dynamic parameter determined by glucose and ethanol levels. The lowest number of lipid droplets is observed in the transition phase between glucose and ethanol fermentation. It is followed by a buildup during the ethanol phase. The surplus of accumulated lipids is then mobilized at concurrent glucose and ethanol starvation in the subsequent stationary phase. Thus, the highest amount of lipids is found in the ethanol phase, which is about 0.3 fL/cell. Our results indicate that the budding yeast, S. cerevisiae, can be used for the biosynthesis of lipids and demonstrate the strength of CARS microscopy for monitoring the dynamics of lipid metabolism at the single-cell level of importance for optimized lipid production

The evolutionary rewiring of ubiquitination targets has reprogrammed the regulation of carbon assimilation in the pathogenic yeast Candida albicans

Date of Acceptance: 13/11/2012 This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-ShareAlike 3.0 Unported license, which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original author and source are credited. Correction for Sandai et al., The Evolutionary Rewiring of Ubiquitination Targets Has Reprogrammed the Regulation of Carbon Assimilation in the Pathogenic Yeast Candida albicans published 20-01-2015 DOI: 10.1128/mBio.02489-14Peer reviewedPublisher PD

Quantifying the impact of immunotherapy on RNA dynamics in cancer

BACKGROUND: Checkpoint inhibitor (CPI) immunotherapies have provided durable clinical responses across a range of solid tumor types for some patients with cancer. Nonetheless, response rates to CPI vary greatly between cancer types. Resolving intratumor transcriptomic changes induced by CPI may improve our understanding of the mechanisms of sensitivity and resistance. METHODS: We assembled a cohort of longitudinal pre-therapy and on-therapy samples from 174 patients treated with CPI across six cancer types by leveraging transcriptomic sequencing data from five studies. RESULTS: Meta-analyses of published RNA markers revealed an on-therapy pattern of immune reinvigoration in patients with breast cancer, which was not discernible pre-therapy, providing biological insight into the impact of CPI on the breast cancer immune microenvironment. We identified 98 breast cancer-specific correlates of CPI response, including 13 genes which are known IO targets, such as toll-like receptors TLR1, TLR4, and TLR8, that could hold potential as combination targets for patients with breast cancer receiving CPI treatment. Furthermore, we demonstrate that a subset of response genes identified in breast cancer are already highly expressed pre-therapy in melanoma, and additionally we establish divergent RNA dynamics between breast cancer and melanoma following CPI treatment, which may suggest distinct immune microenvironments between the two cancer types. CONCLUSIONS: Overall, delineating longitudinal RNA dynamics following CPI therapy sheds light on the mechanisms underlying diverging response trajectories, and identifies putative targets for combination therapy

Proteomics: Challenges, Techniques and Possibilities to Overcome Biological Sample Complexity

Proteomics is the large-scale study of the structure and function of proteins in complex biological sample. Such an approach has the potential value to understand the complex nature of the organism. Current proteomic tools allow large-scale, high-throughput analyses for the detection, identification, and functional investigation of proteome. Advances in protein fractionation and labeling techniques have improved protein identification to include the least abundant proteins. In addition, proteomics has been complemented by the analysis of posttranslational modifications and techniques for the quantitative comparison of different proteomes. However, the major limitation of proteomic investigations remains the complexity of biological structures and physiological processes, rendering the path of exploration paved with various difficulties and pitfalls. The quantity of data that is acquired with new techniques places new challenges on data processing and analysis. This article provides a brief overview of currently available proteomic techniques and their applications, followed by detailed description of advantages and technical challenges. Some solutions to circumvent technical difficulties are proposed