Person:

Mootha, Vamsi

The cellular content of mitochondria changes dynamically during development and in response to external stimuli, but the underlying mechanisms remain obscure. To systematically identify molecular probes and pathways that control mitochondrial abundance, we developed a high-throughput imaging assay that tracks both the per cell mitochondrial content and the cell size in confluent human umbilical vein endothelial cells. We screened 28,786 small molecules and observed that hundreds of small molecules are capable of increasing or decreasing the cellular content of mitochondria in a manner proportionate to cell size, revealing stereotyped control of these parameters. However, only a handful of compounds dissociate this relationship. We focus on one such compound, BRD6897, and demonstrate through secondary assays that it increases the cellular content of mitochondria as evidenced by fluorescence microscopy, mitochondrial protein content, and respiration, even after rigorous correction for cell size, cell volume, or total protein content. BRD6897 increases uncoupled respiration 1.6-fold in two different, non-dividing cell types. Based on electron microscopy, BRD6897 does not alter the percent of cytoplasmic area occupied by mitochondria, but instead, induces a striking increase in the electron density of existing mitochondria. The mechanism is independent of known transcriptional programs and is likely to be related to a blockade in the turnover of mitochondrial proteins. At present the molecular target of BRD6897 remains to be elucidated, but if identified, could reveal an important additional mechanism that governs mitochondrial biogenesis and turnover.

Mitochondrial calcium uptake is present in nearly all vertebrate tissues and is believed to be critical in shaping calcium signaling, regulating ATP synthesis and controlling cell death. Calcium uptake occurs through a channel called the uniporter that resides in the inner mitochondrial membrane. Recently, we used comparative genomics to identify MICU1 and MCU as the key regulatory and putative pore-forming subunits of this channel, respectively. Using bioinformatics, we now report that the human genome encodes two additional paralogs of MICU1, which we call MICU2 and MICU3, each of which likely arose by gene duplication and exhibits distinct patterns of organ expression. We demonstrate that MICU1 and MICU2 are expressed in HeLa and HEK293T cells, and provide multiple lines of biochemical evidence that MCU, MICU1 and MICU2 reside within a complex and cross-stabilize each other's protein expression in a cell-type dependent manner. Using in vivo RNAi technology to silence MICU1, MICU2 or both proteins in mouse liver, we observe an additive impairment in calcium handling without adversely impacting mitochondrial respiration or membrane potential. The results identify MICU2 as a new component of the uniporter complex that may contribute to the tissue-specific regulation of this channel.

In recent years, there has been a huge rise in the number of publicly available transcriptional profiling datasets. These massive compendia comprise billions of measurements and provide a special opportunity to predict the function of unstudied genes based on co-expression to well-studied pathways. Such analyses can be very challenging, however, since biological pathways are modular and may exhibit co-expression only in specific contexts. To overcome these challenges we introduce CLIC, CLustering by Inferred Co-expression. CLIC accepts as input a pathway consisting of two or more genes. It then uses a Bayesian partition model to simultaneously partition the input gene set into coherent co-expressed modules (CEMs), while assigning the posterior probability for each dataset in support of each CEM. CLIC then expands each CEM by scanning the transcriptome for additional co-expressed genes, quantified by an integrated log-likelihood ratio (LLR) score weighted for each dataset. As a byproduct, CLIC automatically learns the conditions (datasets) within which a CEM is operative. We implemented CLIC using a compendium of 1774 mouse microarray datasets (28628 microarrays) or 1887 human microarray datasets (45158 microarrays). CLIC analysis reveals that of 910 canonical biological pathways, 30% consist of strongly co-expressed gene modules for which new members are predicted. For example, CLIC predicts a functional connection between protein C7orf55 (FMC1) and the mitochondrial ATP synthase complex that we have experimentally validated. CLIC is freely available at www.gene-clic.org. We anticipate that CLIC will be valuable both for revealing new components of biological pathways as well as the conditions in which they are active.