Protein sequence databases generated from metagenomics and public databases produced similar soil metaproteomic results of microbial taxonomic and functional changes
Section snippets
INTRODUCTION
Soil is a dynamic system with complex and heterogeneous physical, chemical, and biological interactions. Soil microorganisms play critical roles in ecosystems and are heavily involved in a large number of biogeochemical processes, including nutrient acquisition, recycling of elements (carbon, nitrogen, and phosphorus (P)), and organic matter transformation (van der Heijden et al., 2008; Bastida et al., 2009). Recently, several molecular techniques have been applied to explore soil microbial
Data collection
Soil metaproteomic data were obtained from P-rich and P-deficient soils in a 17-year fertilization experiment in a tropical forest by shotgun proteomics measurements (Yao et al., 2018). Soil samples were collected from four plots (40 m × 40 m) with two technical replicates for each treatment: P-rich soils from plots 1 and 30, and P-deficient soils from plots 6 and 36. The MS/MS spectra raw files and FASTA files of the predicted protein sequences were downloaded from the ProteomeXchange dataset
Number of identified proteins and protein sequence coverage
Two typical protein sequence databases, the Meta-database and Public database, were generated (Table I). Overall, the total number of proteins, the total length of proteins, and the protein length distribution were very similar between the two databases. In both databases, most proteins (93%–95%) contained 51–800 amino acids. Moreover, the number of proteins was the highest within the length range of 201–400 amino acids, and gradually decreased for the lengths greater than 400 amino acids or
DISCUSSION
Soil metaproteomics has been applied increasingly in analyzing soil microbial functions with the development of soil protein extraction methods and mass spectrometry technology in recent ten years. However, bioinformatic analyses of complex and unknown microbial communities are still unclear and poorly studied. This study thoroughly and systematically demonstrated the soil metaproteomic workflow and results using two protein sequence databases, the Meta-database and Public database. More
CONCLUSIONS
In this study, we used two strategies to construct protein sequence databases with comparable distribution of their protein lengths in soil metaproteomics and demonstrated similarities and differences in the results of downstream bioinformatic analysis using two kinds of databases. The Meta-database showed some superiority over the Public database in soil metaproteomics, with the identification of more proteins, higher sequence coverage, and even more microbial taxa. However, regardless of the
CONTRIBUTION OF AUTHORS
The first two authors, Yi Xiong and Lu Zheng, contribute equally to this work.
ACKNOWLEDGEMENT
This work was supported by the National Key Research and Development Program of China (No. 2016YFD0200-308), the National Key Basic Research Program of China (No. 2015CB150501), and the Project of Priority and Key Areas, Institute of Soil Science, Chinese Academy of Sciences (Nos. ISSASIP1605 and ISSASIP1640)
SUPPLEMENTARY MATERIAL
Supplementary material for this article can be found in the online version.
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