Evidence map›Paper›PMID 38892221›Full record

ArticleInternational journal of molecular sciences2024

Multi-Omic Analysis Reveals Genetic Determinants and Therapeutic Targets of Chronic Kidney Disease and Kidney Function.

Yao-Qi Lu, Yirong Wang

Abstract read
In one paragraph

Article in International journal of molecular sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
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

7 citing papers in PubMed.

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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

2 authors.

Yao-Qi LuSchool of Biology & Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou 215123, China.
Yirong WangSchool of Biology & Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou 215123, China.

Funding

Jiangsu Funding Program for Excellent Postdoctoral Talent 2022ZB553National Key Research and Development Program of China 2022YFA1305500The Young Scientists Fund of the National Natural Science Foundation of China 82303232
6 · The paper itself

Abstract

Chronic kidney disease (CKD) presents a significant global health challenge, characterized by complex pathophysiology. This study utilized a multi-omic approach, integrating genomic data from the CKDGen consortium alongside transcriptomic, metabolomic, and proteomic data to elucidate the genetic underpinnings and identify therapeutic targets for CKD and kidney function. We employed a range of analytical methods including cross-tissue transcriptome-wide association studies (TWASs), Mendelian randomization (MR), summary-based MR (SMR), and molecular docking. These analyses collectively identified 146 cross-tissue genetic associations with CKD and kidney function. Key Golgi apparatus-related genes (GARGs) and 41 potential drug targets were highlighted, with MAP3K11 emerging as a significant gene from the TWAS and MR data, underscoring its potential as a therapeutic target. Capsaicin displayed promising drug-target interactions in molecular docking analyses. Additionally, metabolome- and proteome-wide MR (PWMR) analyses revealed 33 unique metabolites and critical inflammatory proteins such as FGF5 that are significantly linked to and colocalized with CKD and kidney function. These insights deepen our understanding of CKD pathogenesis and highlight novel targets for treatment and prevention.

Indexed as

Molecular Docking SimulationRenal Insufficiency, ChronicGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansKidneyMendelian Randomization AnalysisMetabolomeMetabolomicsMultiomicsProteomeProteomicsTranscriptomeProteomechronic kidney diseasedrug targetgenomicsGolgi apparatus-related genesinflammatory proteinskidney functionMendelian randomizationmulti-omic analysisproteomicstranscriptomics

Identifiers

PMID38892221
PMCPMC11172763

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Registered trials

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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.