Negative regulation in correct tissue-specific expression of mouse mammary tumor virus in transgenic mice. is a research paper published in Molecular and Cellular Biology (1990). On theSindex it has a DataRank of 2.8. It has been cited 60 times, with 44 citing works in its 1-hop citation network.
Mouse mammary tumor virus (MMTV) is an endogenous murine retrovirus that is expressed in the epithelial cells of the mammary and salivary glands, lungs, kidneys, and seminal vesicles and in the lymphoid cells of the spleen and thymus. Several studies have shown that the long terminal repeat (LTR) of this virus can direct the expression of reporter genes to the same tissues in transgenic mice. To determine whether multiple regulatory elements within the LTR are involved in this tissue-specific expression, we have established lines of transgenic mice containing transgenes that have deletions in the MMTV LTR. Deletions of all LTR sequences upstream of -364 or of LTR sequences from -165 to -665 both result in the expression of linked reporter genes such as the simian virus 40 early region or the bacterial enzyme chloramphenicol acetyltransferase in novel sites, such as the heart, brain, and skeletal muscle; expression of endogenous MMTV and transgenes containing the full-length LTR is not detected in these organs. Negative regulation appears to involve more than one region, since deletion of sequences between either -201 and -471 or -201 and -344, as well as sequences upstream of -364, results in inappropriate expression in heart, brain, and skeletal muscle. Therefore, a negative regulatory element(s) in the MMTV LTR can suppress transcription from the viral promoter in several different organs. This represents the first example of generalized negative regulatory elements that act in many different tissues in transgenic mice to prevent inappropriate expression of a gene.
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Base Score Contribution
0.617
From this paper's citation signal
Citation Network Contribution
2.2
From 41 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 22% comes from its base citations and 78% from the citation network (41 citing papers contributed measurable signal).
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