Evidence map›Paper›PMID 36959243›Full record

ArticleScientific reports2023

Comparison of hepatic responses to glucose perturbation between healthy and obese mice based on the edge type of network structures.

Yuki Ito, Shinsuke Uda, Toshiya Kokaji, Akiyoshi Hirayama, Tomoyoshi Soga, Yutaka Suzuki, Shinya Kuroda, Hiroyuki Kubota

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Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Yuki ItoDivision of Integrated Omics, Medical Research Center for High Depth Omics, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan.
Shinsuke UdaDivision of Integrated Omics, Medical Research Center for High Depth Omics, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan. uda@bioreg.kyushu-u.ac.jp.
Toshiya KokajiDepartment of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8562, Japan.
Akiyoshi HirayamaInstitute for Advanced Biosciences, Keio University, 246-2 Mizukami, Kakuganji, Tsuruoka, Yamagata, 997-0052, Japan.
Tomoyoshi SogaInstitute for Advanced Biosciences, Keio University, 246-2 Mizukami, Kakuganji, Tsuruoka, Yamagata, 997-0052, Japan.
Yutaka SuzukiDepartment of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8562, Japan.
Shinya KurodaDepartment of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8562, Japan.
Hiroyuki KubotaDivision of Integrated Omics, Medical Research Center for High Depth Omics, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Interactions between various molecular species in biological phenomena give rise to numerous networks. The investigation of these networks, including their statistical and biochemical interactions, supports a deeper understanding of biological phenomena. The clustering of nodes associated with molecular species and enrichment analysis is frequently applied to examine the biological significance of such network structures. However, these methods focus on delineating the function of a node. As such, in-depth investigations of the edges, which are the connections between the nodes, are rarely explored. In the current study, we aimed to investigate the functions of the edges rather than the nodes. To accomplish this, for each network, we categorized the edges and defined the edge type based on their biological annotations. Subsequently, we used the edge type to compare the network structures of the metabolome and transcriptome in the livers of healthy (wild-type) and obese (ob/ob) mice following oral glucose administration (OGTT). The findings demonstrate that the edge type can facilitate the characterization of the state of a network structure, thereby reducing the information available through datasets containing the OGTT response in the metabolome and transcriptome.

Indexed as

GlucoseMetabolomeAnimalsLiverMiceMice, ObeseGlucose

Identifiers

PMID36959243
PMCPMC10036622

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