Evidence map›Paper›PMID 26950864›Full record

ArticleAsian-Australasian journal of animal sciences2016

Characterizing Milk Production Related Genes in Holstein Using RNA-seq.

Minseok Seo, Hyun-Jeong Lee, Kwondo Kim, Kelsey Caetano-Anolles, Jin Young Jeong, Sungkwon Park, Young Kyun Oh, Seoae Cho, Heebal Kim

Abstract read
In one paragraph

Article in Asian-Australasian journal of animal sciences, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

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  10. Identification of novel molecular markers of mastitis caused byJournal of animal science and biotechnology · 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

9 authors.

Minseok SeoInterdisciplinary Program in Bioinformatics, Seoul National University, Seoul 151-741, Korea ; CHO&KIM genomics, Seoul 151-919, Korea .
Hyun-Jeong LeeInterdisciplinary Program in Bioinformatics, Seoul National University, Seoul 151-741, Korea ; Animal Nutritional & Physiology Team, National Institute of Animal Science, Jeonju 565-851, Korea .
Kwondo KimDepartment of Agricultural Biotechnology, Animal Biotechnology Major, and Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul 151-921, Korea .
Kelsey Caetano-AnollesDepartment of Animal Sciences, University of Illinois, Urbana, IL 61801, USA .
Jin Young JeongDivision of Animal Products R&D, National Institute of Animal Science, Jeonju 565-851, Korea .
Sungkwon ParkAnimal Nutritional & Physiology Team, National Institute of Animal Science, Jeonju 565-851, Korea . ; Department of Food Science and Technology, Sejong University, Seoul 143-747, Korea .
Young Kyun OhAnimal Nutritional & Physiology Team, National Institute of Animal Science, Jeonju 565-851, Korea .
Seoae ChoCHO&KIM genomics, Seoul 151-919, Korea .
Heebal KimInterdisciplinary Program in Bioinformatics, Seoul National University, Seoul 151-741, Korea ; CHO&KIM genomics, Seoul 151-919, Korea . ; Department of Animal Sciences, University of Illinois, Urbana, IL 61801, USA .

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although the chemical, physical, and nutritional properties of bovine milk have been extensively studied, only a few studies have attempted to characterize milk-synthesizing genes using RNA-seq data. RNA-seq data was collected from 21 Holstein samples, along with group information about milk production ability; milk yield; and protein, fat, and solid contents. Meta-analysis was employed in order to generally characterize genes related to milk production. In addition, we attempted to investigate the relationship between milk related traits, parity, and lactation period. We observed that milk fat is highly correlated with lactation period; this result indicates that this effect should be considered in the model in order to accurately detect milk production related genes. By employing our developed model, 271 genes were significantly (false discovery rate [FDR] adjusted p-value<0.1) detected as milk production related differentially expressed genes. Of these genes, five (albumin, nitric oxide synthase 3, RNA-binding region (RNP1, RRM) containing 3, secreted and transmembrane 1, and serine palmitoyltransferase, small subunit B) were technically validated using quantitative real-time polymerase chain reaction (qRT-PCR) in order to check the accuracy of RNA-seq analysis. Finally, 83 gene ontology biological processes including several blood vessel and mammary gland development related terms, were significantly detected using DAVID gene-set enrichment analysis. From these results, we observed that detected milk production related genes are highly enriched in the circulation system process and mammary gland related biological functions. In addition, we observed that detected genes including caveolin 1, mammary serum amyloid A3.2, lingual antimicrobial peptide, cathelicidin 4 (CATHL4), cathelicidin 6 (CATHL6) have been reported in other species as milk production related gene. For this reason, we concluded that our detected 271 genes would be strong candidates for determining milk production.

Indexed as

Differentially Expressed GeneHolsteinMeta-analysisMilk ProductionMilk YieldRNA-seq

Identifiers

PMID26950864
PMCPMC4811784

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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.