The electron transport chain (ETC) is an important participant in cellular energy conversion, but its biogenesis presents the cell with numerous challenges. To address these complexities, the cell utilizes ETC assembly factors, which include the LYR protein family. Each member of this family interacts with the mitochondrial acyl carrier protein (ACP), the scaffold protein upon which the mitochondrial fatty acid synthesis (mtFAS) pathway builds fatty acyl chains from acetyl-CoA. We demonstrate that the acylated form of ACP is an acetyl-CoA-dependent allosteric activator of the LYR protein family used to stimulate ETC biogenesis. By tuning ETC assembly to the abundance of acetyl-CoA, which is the major fuel of the TCA cycle and ETC, this system could provide an elegant mechanism for coordinating the assembly of ETC complexes with one another and with substrate availability.
Cells harbor two systems for fatty acid synthesis, one in the cytoplasm (catalyzed by fatty acid synthase, FASN) and one in the mitochondria (mtFAS). In contrast to FASN, mtFAS is poorly characterized, especially in higher eukaryotes, with the major product(s), metabolic roles, and cellular function(s) being essentially unknown. Here we show that hypomorphic mtFAS mutant mouse skeletal myoblast cell lines display a severe loss of electron transport chain (ETC) complexes and exhibit compensatory metabolic activities including reductive carboxylation. This effect on ETC complexes appears to be independent of protein lipoylation, the best characterized function of mtFAS, as mutants lacking lipoylation have an intact ETC. Finally, mtFAS impairment blocks the differentiation of skeletal myoblasts in vitro. Together, these data suggest that ETC activity in mammals is profoundly controlled by mtFAS function, thereby connecting anabolic fatty acid synthesis with the oxidation of carbon fuels.
The fate of pyruvate is a defining feature in many cell types. One major fate is mitochondrial entry via the mitochondrial pyruvate carrier (MPC). We found that diffuse large B cell lymphomas (DLBCLs) consume mitochondrial pyruvate via glutamate-pyruvate transaminase 2 to enable α-ketoglutarate production as part of glutaminolysis. This led us to discover that glutamine exceeds pyruvate as a carbon source for the tricarboxylic acid cycle in DLBCLs. As a result, MPC inhibition led to decreased glutaminolysis in DLBCLs, opposite to previous observations in other cell types. We also found that MPC inhibition or genetic depletion decreased DLBCL proliferation in an extracellular matrix (ECM)–like environment and xenografts, but not in a suspension environment. Moreover, the metabolic profile of DLBCL cells in ECM is markedly different from cells in a suspension environment. Thus, we conclude that the synergistic consumption and assimilation of glutamine and pyruvate enables DLBCL proliferation in an extracellular environment-dependent manner.
Ribosome profiling, an application of nucleic acid sequencing for monitoring ribosome activity, has revolutionized our understanding of protein translation dynamics. This technique has been available for a decade, yet the current state and standardization of publicly available computational tools for these data is bleak. We introduce XPRESSyourself, an analytical toolkit that eliminates barriers and bottlenecks associated with this specialized data type by filling gaps in the computational toolset for both experts and non-experts of ribosome profiling. XPRESSyourself automates and standardizes analysis procedures, decreasing time-to-discovery and increasing reproducibility. This toolkit acts as a reference implementation of current best practices in ribosome profiling analysis. We demonstrate this toolkit's performance on publicly available ribosome profiling data by rapidly identifying hypothetical mechanisms related to neurodegenerative phenotypes and neuroprotective mechanisms of the small-molecule ISRIB during acute cellular stress. XPRESSyourself brings robust, rapid analysis of ribosome-profiling data to a broad and ever-expanding audience and will lead to more reproducible and accessible measurements of translation regulation. XPRESSyourselfsoftware is perpetually open-source under the GPL-3.0 license and is hosted at https://github.com/XPRESSyourself, where users can access additional documentation and report software issues.
To further validate the design, reliability, and versatility of the XPRESSpipe pipeline, we processed raw TCGA sequence data using XPRESSpipe and compared the output count values to those publicly available through TCGA [1]. Spearman ρ values for the selected samples ranged from 0.979-0.980 when pseudogenes were excluded (Figure 1), indicating XPRESSpipe performs with similar accuracy to the TCGA RNA-Seq processing standards. The differences in reported counts can be accounted for by a couple of key differences. For instance, the XPRESSpipe-processed files are aligned to the Homo sapiens GRChv98 reference transcriptome, while the original count data are aligned to the GRChv79 reference transcriptome. The use of a different transcriptome reference can result in variance in the final quantified data for several genes (Figure 2) as significant advances have been made in our understanding of transcribed regions of the human genome between versions.Another source of dissimilarity in data processing appears to arise if an Ensembl canonical transcripts-only reference is used during quantification. TCGA-processed data used an unmodified transcriptome reference file (all transcripts); therefore, the use of this modified (Ensembl canonical transcripts only) GTF will produce varied quantification for some genes as quantifications are constrained to a single transcript version of a given gene and a read will not be quantified if mapping to an exon not used by the canonical transcript. Even using XPRESSpipe settings closest to the TCGA pipeline and using the same genome and transcriptome version resulted in some variation (Figure 2, plot enclosed in maroon). By performing a more detailed analysis of these differences, it is clear that virtually all genes exhibiting variance between the processing methods are pseudogenes, with the TCGA pipeline accepting and quantifying more pseudogenes at the time of initial analysis of this dataset. This can be indicative of the difficulty surrounding the recognition of these reads as multi-mapping to both the original gene and pseudogene (Figure 3,4,5; interactive plots accompanying Figure 5 can be accessed at [2].
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