A network of RNA-binding proteins controls translation efficiency to activate anaerobic metabolism

Abstract

Protein expression evolves under greater evolutionary constraint than mRNA levels, and translation efficiency represents a primary determinant of protein levels during stimuli adaptation. This raises the question as to the translatome remodelers that titrate protein output from mRNA populations. Here, we uncover a network of RNA-binding proteins (RBPs) that enhances the translation efficiency of glycolytic proteins in cells responding to oxygen deprivation. A system-wide proteomic survey of translational engagement identifies a family of oxygen-regulated RBPs that functions as a switch of glycolytic intensity. Tandem mass tagpulse SILAC (TMT-pSILAC) and RNA sequencing reveals that each RBP controls a unique but overlapping portfolio of hypoxic responsive proteins. These RBPs collaborate with the hypoxic protein synthesis apparatus, operating as a translation efficiency checkpoint that integrates upstream mRNA signals to activate anaerobic metabolism. This system allows anoxiaresistant animals and mammalian cells to initiate anaerobic glycolysis and survive hypoxia. We suggest that an oxygen-sensitive RBP cluster controls anaerobic metabolism to confer hypoxia tolerance.

Proteomics reveal cap-dependent translation inhibitors remodel the translation machinery and translatome

“…Furthermore, pSILAC allows us to exclude newly synthesized peptides as a confounding signal during analysis of the factors that constitute the machinery (and not the products) of protein synthesis. Using the ratio of protein abundance in the polysome-to-ribosome-free fractions as a primary readout as previously established (Balukoff et al, 2020;Ho et al, 2018Ho et al, , 2020, MATRIX revealed substantial reorganization of the translation machinery, confirming downregulation of eIF4E-dependent translation (Figure 5B, blue) and an overall decrease in ribosomal engagement (Figure 5C). Alternatively, the translation elongation factor eEF1ε1, which participates in tRNA charging, demonstrated the highest increase in translational engagement out of known detected canonical translation factors (Figure 5B, red).…”

“…As the most energy-consuming cellular investment (Li et al, 2014), the complexity of eukaryotic protein synthesis machinery (Jackson et al, 2010) provides intricate, precise control over protein production during cellular state transition (Ho and Lee, 2016;Liu et al, 2016). Accumulating evidence (Cai et al, 2020;de la Parra et al, 2018;Landon et al, 2014;Lee et al, 2015;Liu et al, 2016;Schwanha ¨usser et al, 2011;Vogel and Marcotte, 2012), including ours (Balukoff et al, 2020;Ho et al, , 2018Ho et al, , 2020, demonstrates the predominance of translation efficiency (TE) and translation machinery adaptations over transcript-level fluctuations in determining protein output (translatome) and phenotype in human cells responding to physiologic stimuli. In this study, we address whether this paradigm also applies to therapeutic interventions, especially those traditionally thought to elicit translational inhibition as their sole mode of action.…”

“…The potency of these classic translation “inhibitors” across cancers raises key questions as to whether results inferred from RNA-based approaches provide a complete picture ( Alachkar et al, 2013 ; Bordeleau et al, 2008 ; Cencic et al, 2009 ; Lucas et al, 2009 ; Schatz et al, 2011 ). In this study, we employed proteome-centric strategies to address this limitation, directly measuring protein output and adaptations in the translation machinery using tandem mass tag-pulse stable isotope labeling with amino acids in cell culture mass spectrometry (TMT-pSI-LAC) and our recently developed MATRIX (mass spectrometry analysis of active translation factors using ribosome density fractionation and isotopic labeling experiments) platform ( Balukoff et al, 2020 ; Ho et al, 2018 , 2020 ), respectively.…”

Translational remodeling by RNA‐binding proteins and noncoding RNAs

“…Expanding beyond this traditional target-specific strategy, recent developments in high throughput technologies (discussed below) have provided the ability to dissect the intricate relationships between translatome remodelers and their target mRNAs from a systems-level perspective (Achsel & Bagni, 2016;Ho, Balukoff, et al, 2020). This unbiased approach has revealed key principles of post-transcriptional regulation, including the fresh insight that in response to different stresses, translatome remodelers are dynamically repurposed and reorganized into unique networks, each targeting a distinct but overlapping selection of mRNAs to fully activate adaptive cellular pathways (Hogan et al, 2008;Mukherjee et al, 2019;Quattrone & Dassi, 2019;Sternburg & Karginov, 2020).…”

The construction and validation of an RNA binding protein-related prognostic model for bladder cancer

“…Recently, various RBP-related mechanisms in cancer onset and progression have been clarified, including genomic alterations, transcriptional and post-transcriptional control, and posttranslational modifications [ 5 ]. In addition, RBPs directly or indirectly affected oncogenic and tumour-suppressive signalling pathways [ 6 ]. However, only a few RBPs have been completely studied and identified as vital players in human cancers.…”