Prokaryotic cellulase gene clusters derived from 2,305 metagenomes.

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-02-05 DOI:10.1038/s41597-025-04524-9
Bing Song, Fernando D K Tria, Josip Skejo
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Abstract

Cellulose is a carbon source widespread in nature. However, it is a difficult task for any organism to get carbon atoms from the cellulose as it has a highly complex structure. Only a few taxonomic groups are known to decompose cellulose. They do it by producing cellulases, the various enzymes which break beta-glycosidic bonds in the cellulose. Cellulases were identified in 1,735 metagenomes from 225 bioprojects. The set of 12,837 metagenome-derived cellulases encompass three catalytic functions: exoglucanases (CBH, 1,042), endoglucanases (EG, 5,685), and beta-glucosidases (βG, 6,110). All three enzymatic functions are thought to be necessary for driving cellulase to a cascade of reactions that can make cellulose available as glucose. These metagenome-derived cellulases were clustered into protein families for each EC category individually, resulting in a total of 136 clusters, with the majority observed for EG (97 clusters), followed by βG (19 clusters) and CBH (19 clusters). These clusters provided a useful cellulase dataset for future research on cellulase utilization.

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原核纤维素酶基因簇来源于2305个宏基因组。
纤维素是自然界中广泛存在的碳源。然而,对于任何生物体来说,从纤维素中获得碳原子都是一项艰巨的任务,因为它具有高度复杂的结构。已知只有少数分类类群能分解纤维素。它们通过产生纤维素酶来实现这一点,纤维素酶是一种破坏纤维素中β -糖苷键的酶。在225个生物项目的1735个宏基因组中鉴定出纤维素酶。这套12,837元基因组衍生的纤维素酶包括三种催化功能:外葡萄糖酶(CBH, 1,042),内切葡聚糖酶(EG, 5,685)和β -葡萄糖苷酶(βG, 6,110)。所有三种酶的功能都被认为是驱动纤维素酶进行一连串反应的必要条件,这些反应可以使纤维素转化为葡萄糖。这些宏基因组衍生的纤维素酶被分别聚集到每个EC类别的蛋白质家族中,总共有136个簇,其中EG(97个簇)最多,其次是βG(19个簇)和CBH(19个簇)。这些集群为今后纤维素酶利用的研究提供了有用的数据集。
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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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