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Find video protocols related to scientific articles indexed in Pubmed.
Transcription factor binding site analysis identifies FOXO transcription factors as regulators of the cutaneous wound healing process.
PLoS ONE
PUBLISHED: 01-01-2014
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The search for significantly overrepresented and co-occurring transcription factor binding sites in the promoter regions of the most differentially expressed genes in microarray data sets could be a powerful approach for finding key regulators of complex biological processes. To test this concept, two previously published independent data sets on wounded human epidermis were re-analyzed. The presence of co-occurring transcription factor binding sites for FOXO1, FOXO3 and FOXO4 in the majority of the promoter regions of the most significantly differentially expressed genes between non-wounded and wounded epidermis implied an important role for FOXO transcription factors during wound healing. Expression levels of FOXO transcription factors during wound healing in vivo in both human and mouse skin were analyzed and a decrease for all FOXOs in human wounded skin was observed, with FOXO3 having the highest expression level in non wounded skin. Impaired re-epithelialization was found in cultures of primary human keratinocytes expressing a constitutively active variant of FOXO3. Conversely knockdown of FOXO3 in keratinocytes had the opposite effect and in an in vivo mouse model with FOXO3 knockout mice we detected significantly accelerated wound healing. This article illustrates that the proposed approach is a viable method for identifying important regulators of complex biological processes using in vivo samples. FOXO3 has not previously been implicated as an important regulator of wound healing and its exact function in this process calls for further investigation.
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What is Visualize?

JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.

How does it work?

We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.

Video X seems to be unrelated to Abstract Y...

In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.