Evidence map›Paper›PMID 32483604›Full record

ArticleThe Journal of clinical endocrinology and metabolism2020

Network-based Transcriptome-wide Expression Study for Postmenopausal Osteoporosis.

Lan Zhang, Tian-Liu Peng, Le Wang, Xiang-He Meng, Wei Zhu, Yong Zeng, Jia-Qiang Zhu, Yu Zhou, Hong-Mei Xiao, Hong-Wen Deng

Open access · bronzeAbstract read
In one paragraph

Article in The Journal of clinical endocrinology and metabolism, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.8field-weighted citation impact, top 29% 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

8 citing papers in PubMed, 13 citations in OpenAlex.

  1. Article
  2. IMPC-based screening revealed thatFrontiers in cell and developmental biology · 2024
    Article
  3. Review
  4. NEDD4 E3 Ligases: Functions and Mechanisms in Bone and Tooth.International journal of molecular sciences · 2022
    Review
  5. Article
  6. Article
  7. Article
  8. Article
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

10 authors at 4 institutions in 2 countries.

Lan ZhangCenter for Biomedical informatics and Genomics, Department of Medicine, Tulane University, New Orleans, Louisiana.
Tian-Liu PengInstitute of Reproduction and Stem Cell Engineering, School of Basic Medical Science, Central South University, Changsha, Hunan, China.
Le WangInstitute of Reproduction and Stem Cell Engineering, School of Basic Medical Science, Central South University, Changsha, Hunan, China.
Xiang-He MengLaboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, Hunan, China.
Wei ZhuCenter for Biomedical informatics and Genomics, Department of Medicine, Tulane University, New Orleans, Louisiana.
Yong ZengCenter for Biomedical informatics and Genomics, Department of Medicine, Tulane University, New Orleans, Louisiana.
Jia-Qiang ZhuDepartment of Biostatistics, University of Michigan, Ann Arbor, Michigan.
Yu ZhouCenter for Biomedical informatics and Genomics, Department of Medicine, Tulane University, New Orleans, Louisiana.
Hong-Mei XiaoInstitute of Reproduction and Stem Cell Engineering, School of Basic Medical Science, Central South University, Changsha, Hunan, China.
Hong-Wen DengCenter for Biomedical informatics and Genomics, Department of Medicine, Tulane University, New Orleans, Louisiana.
Tulane University · USCentral South University · CNHunan Normal University · CNUniversity of Michigan · US

Funding

Tulane COBRE in Cardiometabolic Diseases Clinical Research CoreP20GM109036 · NIGMS · TULANE UNIVERSITY OF LOUISIANA · PI Ning Liu · 2016 to 2026
$25.3M
Trans-omics Integration of Multi-omics Studies for OsteoporosisU19AG055373 · NIA · TULANE UNIVERSITY OF LOUISIANA · PI HONG-WEN DENG · 2017 to 2026
$24.3M
Epigenomewide DNA Methylation Study for Osteoporosis RiskR01AR059781 · NIAMS · TULANE UNIVERSITY OF LOUISIANA · PI DENG, HONG-WEN · 2012 to 2016
$3.1M
Decoding Methylation Mediated Epigenomic Contributions to Male OsteoporosisR01AR069055 · NIAMS · TULANE UNIVERSITY OF LOUISIANA · PI DENG, HONG-WEN · 2017 to 2021
$2.9M
Integration of brain imaging with genomic and epigenomic dataR01MH104680 · NIMH · TULANE UNIVERSITY OF LOUISIANA · PI CALHOUN, VINCE D, DENG, HONG-WEN · 2014 to 2017
$2.1M
Integration of fMRI imaging, genomics, network and biological knowledgeR01MH107354 · NIMH · TULANE UNIVERSITY OF LOUISIANA · PI WANG, YU-PING · 2015 to 2018
$1.9M
Integration of multiscale genomic data for comprehensive analysis of complex diseR01GM109068 · NIGMS · TULANE UNIVERSITY OF LOUISIANA · PI WANG, YU-PING · 2014 to 2018
$1.6M
NIAMS NIH HHS R01 AR059781NIAMS NIH HHS R01 AR069055NIA NIH HHS U19 AG055373NIGMS NIH HHS P20 GM109036NIGMS NIH HHS R01 GM109068NIMH NIH HHS R01 MH104680NIMH NIH HHS R01 MH107354
6 · The paper itself

Abstract

purposeMenopause is a crucial physiological transition during a woman's life, and it occurs with growing risks of health issues like osteoporosis. To identify postmenopausal osteoporosis-related genes, we performed transcriptome-wide expression analyses for human peripheral blood monocytes (PBMs) using Affymetrix 1.0 ST arrays in 40 Caucasian postmenopausal women with discordant bone mineral density (BMD) levels.

methodsWe performed multiscale embedded gene coexpression network analysis (MEGENA) to study functionally orchestrating clusters of differentially expressed genes in the form of functional networks. Gene sets net correlations analysis (GSNCA) was applied to assess how the coexpression structure of a predefined gene set differs in high and low BMD groups. Bayesian network (BN) analysis was used to identify important regulation patterns between potential risk genes for osteoporosis. A small interfering ribonucleic acid (siRNA)-based gene silencing in vitro experiment was performed to validate the findings from BN analysis.

resultMEGENA showed that the "T cell receptor signaling pathway" and the "osteoclast differentiation pathway" were significantly enriched in the identified compact network, which is significantly correlated with BMD variation. GSNCA revealed that the coexpression structure of the "Signaling by TGF-beta receptor complex pathway" is significantly different between the 2 BMD discordant groups; the hub genes in the postmenopausal low and high BMD group are FURIN and SMAD3 respectively. With siRNA in vitro experiments, we confirmed the regulation relationship of TGFBR2-SMAD7 and TGFBR1-SMURF2. MAIN

conclusionThe present study suggests that biological signals involved in monocyte recruitment, monocyte/macrophage lineage development, osteoclast formation, and osteoclast differentiation might function together in PBMs that contribute to the pathogenesis of postmenopausal osteoporosis.

Indexed as

Gene Regulatory NetworksAbsorptiometry, PhotonBone DensityCohort StudiesComputational BiologyFemaleFurinGene Expression ProfilingHumansOsteoporosis, PostmenopausalReceptor, Transforming Growth Factor-beta Type IReceptor, Transforming Growth Factor-beta Type IIRNA, Small InterferingSignal TransductionSmad3 ProteinSmad7 ProteinFurinFURIN protein, humanReceptor, Transforming Growth Factor-beta Type IReceptor, Transforming Growth Factor-beta Type IIRNA, Small InterferingSmad3 ProteinSMAD3 protein, humanSmad7 ProteinSMAD7 protein, humanSMURF2 protein, humanTGFBR1 protein, humanTGFBR2 protein, humanUbiquitin-Protein Ligasescoexpression analysisosteoclastpostmenopausal osteoporosissiRNAtranscriptomics

Identifiers

PMID32483604
PMCPMC7320836
OpenAlexW3030248596

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.