Assembly and maturation of calf gut microbiome from neonate to post-puberty.

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-03-04 DOI:10.1038/s41597-025-04677-7
Hyun Sik Kim, Jae-Yun Lee, Tae Woong Whon, Jin-Woo Bae
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Abstract

With the help of rumen bacteria, ruminants can feed on indigestible plant materials and produce over 70% of their energy as fatty acids. However, during lactation, ruminants exhibit characteristics of monogastric animals due to an undeveloped rumen; therefore, understanding gut microbiome changes in growing calves is essential. Our understanding of the gut microbiome in growing calves remains limited in large populations with the same diet, breed, and period. Here, we describe 16S rRNA gene amplicon sequencing data from 420 faecal samples, 20 rumen contents, 17 small intestine contents, and 18 large intestine contents collected from 57 healthy, antibiotic-free Korean beef cattle from neonatal to post-pubertal age. Eight 16S rRNA gene amplicon datasets from the host diet samples were obtained. Approximately 148 million raw reads, averaging 153,352 ± 96,050 (mean ± SD) reads per sample, and 51,596 unique amplicon sequence variants (381-368 per sample) were identified in the 483 samples. These shareable datasets can be reused by researchers to assess gut microbiome-related functions in growing calves and improve ruminant production and health.

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从新生儿到青春期后犊牛肠道微生物群的组装和成熟。
在瘤胃细菌的帮助下,反刍动物可以以难以消化的植物材料为食,并以脂肪酸的形式产生超过70%的能量。然而,在哺乳期间,由于瘤胃不发达,反刍动物表现出单胃动物的特征;因此,了解犊牛生长过程中肠道菌群的变化是至关重要的。我们对正在生长的小牛肠道微生物群的了解仍然有限,因为它们的饮食、品种和时期都是相同的。在这里,我们描述了从57头从新生儿到青春期后未使用抗生素的健康韩国肉牛收集的420份粪便样本、20份瘤胃内容物、17份小肠内容物和18份大肠内容物的16S rRNA基因扩增子测序数据。从宿主饮食样本中获得8个16S rRNA基因扩增子数据集。在483个样本中鉴定出了51,596个独特的扩增子序列变异(381-368个),平均每个样本153,352±96,050(平均±SD)个原始reads。这些可共享的数据集可以被研究人员重用,以评估生长中的小牛肠道微生物组相关功能,并改善反刍动物的生产和健康。
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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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