[Abstract unavailable

No abstract

Fat is stored or mobilized according to food availability. Malfunction of the mechanisms that ensure this coordination underlie metabolic diseases in humans. In mammals, lysosomal and autophagic function is required for normal fat storage and mobilization in the presence or absence of food. Autophagy is tightly linked to nutrients. However, if and how lysosomal lipolysis is coupled to nutritional status remains to be determined. Here we identify MXL-3 and HLH-30 as transcriptional switches coupling lysosomal lipolysis and autophagy to nutrient availability and controlling fat storage and ageing in Caenorhabditis elegans. Transcriptional coupling of lysosomal lipolysis and autophagy to nutrients is also observed in mammals. Thus, MXL-3 and HLH-30 orchestrate an adaptive and conserved cellular response to nutritional status and regulate lifespan.

Diverse algorithms can integrate transcriptomics with genome-scale metabolic models (GEMs) to build context-specific metabolic models. These algorithms require identification of a list of high confidence (core) reactions from transcriptomics, but parameters related to identification of core reactions, such as thresholding of expression profiles, can significantly change model content. Importantly, current thresholding approaches are burdened with setting singular arbitrary thresholds for all genes; thus, resulting in removal of enzymes needed in small amounts and even many housekeeping genes. Here, we describe StanDep, a novel heuristic method for using transcriptomics to identify core reactions prior to building contextspecific metabolic models. StanDep clusters gene expression data based on their expression pattern across different contexts and determines thresholds for each cluster using data-dependent statistics, specifically standard deviation and mean. To demonstrate the use of StanDep, we built hundreds of models for the NCI-60 cancer cell lines. These models successfully increased the inclusion of housekeeping reactions, which are often lost in models built using standard thresholding approaches. Further, StanDep also provided a transcriptomic explanation for inclusion of lowly expressed reactions that were otherwise only supported by model extraction methods. Our study also provides novel insights into how cells may deal with context-specific and ubiquitous functions. StanDep, as a MATLAB tool