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Lactation-related metabolic mechanism investigated based on mammary gland metabolomics and 4 biofluids’ metabolomics relationships in dairy cows

Overview of attention for article published in BMC Genomics, December 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

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Lactation-related metabolic mechanism investigated based on mammary gland metabolomics and 4 biofluids’ metabolomics relationships in dairy cows
Published in
BMC Genomics, December 2017
DOI 10.1186/s12864-017-4314-1
Pubmed ID

Hui-Zeng Sun, Kai Shi, Xue-Hui Wu, Ming-Yuan Xue, Zi-Hai Wei, Jian-Xin Liu, Hong-Yun Liu


Lactation is extremely important for dairy cows; however, the understanding of the underlying metabolic mechanisms is very limited. This study was conducted to investigate the inherent metabolic patterns during lactation using the overall biofluid metabolomics and the metabolic differences from non-lactation periods, as determined using partial tissue-metabolomics. We analyzed the metabolomic profiles of four biofluids (rumen fluid, serum, milk and urine) and their relationships in six mid-lactation Holstein cows and compared their mammary gland (MG) metabolomic profiles with those of six non-lactating cows by using gas chromatography-time of flight/mass spectrometry. In total, 33 metabolites were shared among the four biofluids, and 274 metabolites were identified in the MG tissues. The sub-clusters of the hierarchical clustering analysis revealed that the rumen fluid and serum metabolomics profiles were grouped together and highly correlated but were separate from those for milk. Urine had the most different profile compared to the other three biofluids. Creatine was identified as the most different metabolite among the four biofluids (VIP = 1.537). Five metabolic pathways, including gluconeogenesis, pyruvate metabolism, the tricarboxylic acid cycle (TCA cycle), glycerolipid metabolism, and aspartate metabolism, showed the most functional enrichment among the four biofluids (false discovery rate < 0.05, fold enrichment >2). Clear discriminations were observed in the MG metabolomics profiles between the lactating and non-lactating cows, with 54 metabolites having a significantly higher abundance (P < 0.05, VIP > 1) in the lactation group. Lactobionic acid, citric acid, orotic acid and oxamide were extracted by the S-plot as potential biomarkers of the metabolic difference between lactation and non-lactation. The TCA cycle, glyoxylate and dicarboxylate metabolism, glutamate metabolism and glycine metabolism were determined to be pathways that were significantly impacted (P < 0.01, impact value >0.1) in the lactation group. Among them, the TCA cycle was the most up-regulated pathway (P < 0.0001), with 7 of the 10 related metabolites increased in the MG tissues of the lactating cows. The overall biofluid and MG tissue metabolic mechanisms in the lactating cows were interpreted in this study. Our findings are the first to provide an integrated insight and a better understanding of the metabolic mechanism of lactation, which is beneficial for developing regulated strategies to improve the metabolic status of lactating dairy cows.

Mendeley readers

The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 31 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 23%
Researcher 5 16%
Student > Doctoral Student 4 13%
Student > Master 3 10%
Student > Bachelor 2 6%
Other 1 3%
Unknown 9 29%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 32%
Medicine and Dentistry 3 10%
Veterinary Science and Veterinary Medicine 2 6%
Biochemistry, Genetics and Molecular Biology 2 6%
Chemistry 2 6%
Other 2 6%
Unknown 10 32%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 December 2017.
All research outputs
of 12,253,439 outputs
Outputs from BMC Genomics
of 7,182 outputs
Outputs of similar age
of 341,978 outputs
Outputs of similar age from BMC Genomics
of 548 outputs
Altmetric has tracked 12,253,439 research outputs across all sources so far. Compared to these this one has done well and is in the 77th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,182 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done well, scoring higher than 79% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 341,978 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 548 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.