Evidence map›Paper›PMID 32430805›Full record

ArticleBiological trace element research2021

Impact of Dietary Selenium on Modulation of Expression of Several Non-Selenoprotein Genes Related to Key Ovarian Functions, Female Fertility, and Proteostasis: a Transcriptome-Based Analysis of the Aging Mice Ovaries.

Izhar Hyder Qazi, Yutao Cao, Haoxuan Yang, Christiana Angel, Bo Pan, Guangbin Zhou, Hongbing Han

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Article in Biological trace element research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.3field-weighted citation impact, top 22% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 16 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 4 institutions in 2 countries.

Izhar Hyder Qazi *Beijing Key Laboratory for Animal Genetic Improvement, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China.
Yutao Cao *Beijing Key Laboratory for Animal Genetic Improvement, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China.
Haoxuan Yang *Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130, China.
Christiana AngelDepartment of Veterinary Parasitology, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu, 611130, China.
Bo PanFarm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130, China.
Guangbin ZhouFarm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130, China. zguangbin@sicau.edu.cn.ORCID http://orcid.org/0000-0002-4493-8311
Hongbing HanBeijing Key Laboratory for Animal Genetic Improvement, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China. hanhongbing@cau.edu.cn.
Sichuan Agricultural University · CNShaheed Benazir Bhutto University · PKBeijing University of Agriculture · CNChina Agricultural University · CN

Funding

The National Key R&D Program 2016YFD0501202
6 · The paper itself

Abstract

Female reproductive (ovarian) aging is characterized by a marked decline in quantity and quality of follicles and oocytes, as well as alterations in the surrounding ovarian stroma. In our previous report, we have shown that dietary selenium (Se) insufficiency and supplementation differentially impacted the reproductive efficiency in aging mice; however, the precise understanding of such modulation is still incomplete. In the present study, we sought to determine the impact of low (mildly low level) and moderately high (medium level) Se diets on expression profile of non-selenoprotein genes in the ovaries of aging mice. For this purpose, the aged mice were divided in two groups and fed either a low Se (Se-L; 0.08 mg Se/kg) diet or a moderately high Se (Se-M; 0.33 mg Se/kg) diet. RNA-seq analysis revealed that a total of 168 genes were differentially expressed between the two groups. From these, 72 and 96 differentially expressed genes (DEGs) were found to be upregulated and downregulated, respectively. Gene Ontology (GO) and pathways enrichment (KEGG) analyses revealed that these DEGs were enriched in several key GO terms and biological pathways including PI3K-Akt signaling pathway, steroid hormone biosynthesis, signaling pathways regulating pluripotency of stem cells, Hippo signaling pathway, ovarian steroidogenesis, and Wnt signaling pathway. Further filtering of RNA-seq data revealed that several DEGs such as Star, Hsd3b6, Scd1, Bmp7, Aqp8, Gas1, Fzd1, and Wwc1 were implicated in key ovarian- and fertility-related functions. In addition, some of the DEGs were related to ER homeostasis and/or proteostasis. These results highlight that dietary low and moderately high (medium level) Se diets, in addition to modulation of selenoproteins, can also have an impact on expression of several non-selenoprotein genes in the ovaries of aging mice. To sum up, these findings add more value to our understanding of Se modulation of ovarian functions and female fertility and will pave a way for the focused mechanistic and functional studies in this domain.

Indexed as

SeleniumAgingAnimalsCell Cycle ProteinsFemaleFertilityGPI-Linked ProteinsMiceOvaryPhosphatidylinositol 3-KinasesProteostasisSelenoproteinsTranscriptomeCell Cycle ProteinsGas1 protein, mouseGPI-Linked ProteinsPhosphatidylinositol 3-KinasesSeleniumSelenoproteinsFertilityNon-selenoprotein genesOvarian agingSelenium

Identifiers

PMID32430805
OpenAlexW3027440054

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.