Evidence map›Paper›PMID 29976239›Full record

ArticleArthritis research & therapy2018

Next-generation sequencing profiling of mitochondrial genomes in gout.

Chia-Chun Tseng, Chung-Jen Chen, Jeng-Hsien Yen, Hsi-Yuan Huang, Jan-Gowth Chang, Shun-Jen Chang, Wei-Ting Liao

Open access · goldAbstract read
In one paragraph

Article in Arthritis research & therapy, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
2.1field-weighted citation impact, top 14% 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

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.

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  10. Detecting Rare Variants and Heteroplasmy of Mitochondrial DNA from High-Throughput Sequencing in Patients with Coronary Artery Disease.Medical science monitor : international medical journal of experimental and clinical research · 2020
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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 6 institutions in 1 country.

Chia-Chun TsengDepartment of Internal Medicine, Kaohsiung Municipal Ta-Tung Hospital, Kaohsiung, Taiwan.
Chung-Jen ChenDepartment of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.
Jeng-Hsien YenDivision of Rheumatology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.
Hsi-Yuan HuangDepartment of Laboratory Medicine and Epigenome Research Center, China Medical University Hospital, China Medical University, Taichung, Taiwan.
Jan-Gowth ChangDepartment of Laboratory Medicine and Epigenome Research Center, China Medical University Hospital, China Medical University, Taichung, Taiwan.
Shun-Jen ChangDepartment of Kinesiology, Health and Leisure Studies, National University of Kaohsiung, Kaohsiung, Taiwan. changsj1104@gmail.com.
Wei-Ting LiaoDepartment of Biotechnology, College of Life Science, Kaohsiung Medical University, Kaohsiung, Taiwan. wtliao@kmu.edu.tw.
Kaohsiung Medical University · TWChina Medical University · TWChina Medical University Hospital · TWKaohsiung Medical University Chung-Ho Memorial Hospital · TWKaohsiung Municipal Ta-Tung Hospital · TWNational University of Kaohsiung · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAccumulating evidence implicates mitochondrial DNA (mtDNA) alleles, which are independent of the nuclear genome, in disease, especially in human metabolic diseases. However, this area of investigation has lagged behind in researching the nuclear alleles in complex traits, for example, in gout.

methodsNext-generation sequencing was utilized to investigate the relationship between mtDNA alleles and phenotypic variations in 52 male patients with gout and 104 age-matched male non-gout controls from the Taiwan Biobank whole-genome sequencing samples. Differences from a reference sequence (GRCh38) were identified. The sequence kernel association test (SKAT) was applied to identify gout-associated alleles in mitochondrial genes. The tools Polymorphism Phenotyping, Sorting Intolerant From Tolerant (SIFT), Predict the pathology of Mutations (PMUT), Human Mitochondrial Genome Database (mtDB), Multiple Alignment using Fast Fourier Transform (MAFFT), and Mammalian Mitochondrial tRNA Genes (Mamit-tRNA) were used to evaluate pathogenicity of alleles. Validation of selected alleles by quantitative polymerase chain reaction of single nucleotide polymorphisms (qPCR SNPs) was also performed.

resultsWe identified 456 alleles in patients with gout and 640 alleles in non-gout controls with 274 alleles shared by both. Mitochondrial genes were associated with gout, with MT-CO3, MT-TA, MT-TC, and MT-TT containing potentially pathogenic gout-associated alleles and displaying evidence of gene-gene interactions. All heteroplasmy levels of potentially pathogenic alleles exceeded metabolic thresholds for pathogenicity. Validation assays confirmed the next-generation sequencing results of selected alleles. Among them, potentially pathogenic MT-CO3 alleles correlated with high-density lipoprotein (HDL) levels (P = 0.034).

conclusionThis study provided two scientific insights. First, this was the most extensive mitochondrial genomic profiling associated with gout. Second, our results supported the roles of mitochondria in gout and HDL, and this comprehensive analysis framework can be applied to other diseases in which mitochondrial dysfunction has been implicated.

Indexed as

AdultAllelesAsian PeopleBase SequenceDNA, MitochondrialGene FrequencyGenome, MitochondrialGoutHigh-Throughput Nucleotide SequencingHumansMaleMiddle AgedMutationPolymorphism, Single NucleotideRNA, TransferTaiwanDNA, MitochondrialRNA, TransferGoutMitochondriaNext-generation sequencing

Identifiers

PMID29976239
PMCPMC6034246
OpenAlexW2866754580

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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.