Metabolism and ageing are intimately linked. Compared to ad libitum feeding, dietary restriction (DR) or calorie restriction (CR) consistently extends lifespan and delays age-related diseases in evolutionarily diverse organisms1,2. Similar conditions of nutrient limitation and genetic or pharmacological perturbations of nutrient or energy metabolism also have longevity benefits3,4. Recently, several metabolites have been identified that modulate ageing5,6 with largely undefined molecular mechanisms. Here we show that the tricarboxylic acid (TCA) cycle intermediate α-ketoglutarate (α-KG) extends the lifespan of adult C. elegans. ATP synthase subunit beta is identified as a novel binding protein of α-KG using a small-molecule target identification strategy called DARTS (drug affinity responsive target stability)7. The ATP synthase, also known as Complex V of the mitochondrial electron transport chain (ETC), is the main cellular energy-generating machinery and is highly conserved throughout evolution8,9. Although complete loss of mitochondrial function is detrimental, partial suppression of the ETC has been shown to extend C. elegans lifespan10–13. We show that α-KG inhibits ATP synthase and, similar to ATP synthase knockdown, inhibition by α-KG leads to reduced ATP content, decreased oxygen consumption, and increased autophagy in both C. elegans and mammalian cells. We provide evidence that the lifespan increase by α-KG requires ATP synthase subunit beta and is dependent on the target of rapamycin (TOR) downstream. Endogenous α-KG levels are increased upon starvation and α-KG does not extend the lifespan of DR animals, indicating that α-KG is a key metabolite that mediates longevity by DR. Our analyses uncover new molecular links between a common metabolite, a universal cellular energy generator, and DR in the regulation of organismal lifespan, thus suggesting new strategies for the prevention and treatment of ageing and age-related diseases.
Direct cDNA preamplification protocols developed for single-cell RNA-seq have enabled transcriptome profiling of precious clinical samples and rare cells without sample pooling or RNA extraction. Currently, there is no algorithm optimized to reveal and remove noisy transcripts in limiting-cell RNA-seq (lcRNA-seq) data for downstream analyses. Herein, we present CLEAR, a workflow that identifies reliably quantifiable transcripts in lcRNA-seq data for differentially expressed gene (DEG) analysis. Libraries at three input amounts of FACS-derived CD5+ and CD5-cells from a chronic lymphocytic leukemia patient were used to develop CLEAR. When using CLEAR transcripts vs. using all transcripts, downstream analyses revealed more shared transcripts across different input RNA amounts, improved Principal Component Analysis (PCA) separation, and yielded more DEGs between cell types. As proof-of-principle, CLEAR was applied to an in-house lcRNA-seq dataset and two public datasets. When imputation is used, CLEAR is also adaptable to large clinical studies and for single cell analyses.
AUTHOR CONTRIBUTIONSConceived and designed the experiments: L.A.
Regulated transgene expression is an integral component of gene therapies, cell therapies and biomanufacturing. However, transcription factor-based regulation, upon which most applications are based, suffers from complications such as epigenetic silencing that limit expression longevity and reliability. Constitutive transgene transcription paired with post-transcriptional gene regulation could combat silencing, but few such RNA- or protein-level platforms exist. Here we develop an RNA-regulation platform we call “PERSIST" which consists of nine CRISPR-specific endoRNases as RNA-level activators and repressors as well as modular OFF- and ON-switch regulatory motifs. We show that PERSIST-regulated transgenes exhibit strong OFF and ON responses, resist silencing for at least two months, and can be readily layered to construct cascades, logic functions, switches and other sophisticated circuit topologies. The orthogonal, modular and composable nature of this platform as well as the ease in constructing robust and predictable gene circuits promises myriad applications in gene and cell therapies.
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