Early growth response 1 (EGR-1) is a transcriptional regulator of mitochondrial carrier homolog 1 (MTCH 1)/presenilin 1-associated protein (PSAP).

Attempts to elucidate the cellular function of MTCH1 (mitochondrial carrier homolog 1) have not yet rendered a clear insight into the function of this outer mitochondrial membrane protein. Classical biochemical and cell biology approaches have not produced the expected outcome. In vitro experiments have indicated a likely role in the regulation of cell death by apoptosis, and its reported interaction with presenilin 1 suggests a role in the cellular pathways in which this membrane protease participates, nevertheless in vivo data are missing. In an attempt to identify cellular pathways in which this protein might participate, we have studied its promoter looking for transcriptional regulators. We have identified several putative binding sites for EGR-1 (Early growth response 1; a protein involved in growth, proliferation and differentiation), in the proximal region of the MTCH1 promoter. Chromatin immunoprecipitation showed an enrichment of these sequences in genomic DNA bound to EGR-1 and transient overexpression of EGR-1 in cultured HEK293T cells induces an increase of endogenous MTCH1 levels. We also show that MTCH1 levels increase in response to treatment of cells with doxorubicin, an apoptosis inducer through DNA damage. The endogenous levels of MTCH1 decrease when EGR-1 levels are lowered by RNA interference. Our results indicate that EGR-1 is a transcriptional regulator of MTCH1 and give some clues about the cellular processes in which MTCH1 might participate.

Reducing the standard deviation in multiple-assay experiments where the variation matters but the absolute value does not.

When the value of a quantity x for a number of systems (cells, molecules, people, chunks of metal, DNA vectors, so on) is measured and the aim is to replicate the whole set again for different trials or assays, despite the efforts for a near-equal design, scientists might often obtain quite different measurements. As a consequence, some systems' averages present standard deviations that are too large to render statistically significant results. This work presents a novel correction method of a very low mathematical and numerical complexity that can reduce the standard deviation of such results and increase their statistical significance. Two conditions are to be met: the inter-system variations of x matter while its absolute value does not, and a similar tendency in the values of x must be present in the different assays (or in other words, the results corresponding to different assays must present a high linear correlation). We demonstrate the improvements this method offers with a cell biology experiment, but it can definitely be applied to any problem that conforms to the described structure and requirements and in any quantitative scientific field that deals with data subject to uncertainty.