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Metaproteomics Characterizes Human Gut Microbiome Function In www.nature.com/npjbiofilms ARTICLE OPEN Metaproteomics characterizes human gut microbiome function in colorectal cancer ✉ ✉ Shuping Long1,2,3,5, Yi Yang 1,5, Chengpin Shen4, Yiwen Wang 1, Anmei Deng2, Qin Qin2 and Liang Qiao 1 Pathogenesis of colorectal cancer (CRC) is associated with alterations in gut microbiome. Previous studies have focused on the changes of taxonomic abundances by metagenomics. Variations of the function of intestinal bacteria in CRC patients compared to healthy crowds remain largely unknown. Here we collected fecal samples from CRC patients and healthy volunteers and characterized their microbiome using quantitative metaproteomic method. We have identified and quantified 91,902 peptides, 30,062 gut microbial protein groups, and 195 genera of microbes. Among the proteins, 341 were found significantly different in abundance between the CRC patients and the healthy volunteers. Microbial proteins related to iron intake/transport; oxidative stress; and DNA replication, recombination, and repair were significantly alternated in abundance as a result of high local concentration of iron and high oxidative stress in the large intestine of CRC patients. Our study shows that metaproteomics can provide functional information on intestinal microflora that is of great value for pathogenesis research, and can help guide clinical diagnosis in the future. npj Biofilms and Microbiomes (2020) 6:14 ; https://doi.org/10.1038/s41522-020-0123-4 1234567890():,; INTRODUCTION abundances of samples. Nevertheless, biases can exist due to DNA Colorectal cancer (CRC) is the third most commonly diagnosed extraction methods, the use of amplification primers, and cancer and the fourth leading cause of oncological mortality bioinformatic tools12,13. In addition, sequencing cannot distinguish worldwide1. In recent years, CRC incidence rates in developing between live bacteria and transient DNA12. It is also difficult to countries have been rising because of obesity and westernized reveal important functional elements of gut microbiome solely by diet2. In particular, high-level intake of red meat and inadequate metagenomics. Therefore, it has been suggested that functional intake of vegetables and fiber could increase the risk of CRC2. The omics, like metaproteomics and metabolomics, should also be pathogenesis of CRC is a complex multistep process involving involved in the study of gut microbiome, wherein “function first, 3 4 genetic alterations , immune factors , environmental factors (e.g. taxa second” has been proposed14. 5 6 diet and lifestyle) , and human gut microbiome . Metaproteomics was initially used to study the microbial Human gut hosts about 100 trillion microbes. Most microbes function of environmental samples, like soil, activated sludge, 12 colonize the large intestine at a concentration of about 10 cell and acid mine drainage15. In 2009, Verberkmoes et al. first studied 7 per mL . Emerging evidences indicate that microbial dysbiosis is a human gut microbiome using shotgun metaproteomics, wherein 8 driving force in the pathogenesis of intestinal tumor . Studies the samples were feces collected from a pair of monozygotic using metagenomics-based approaches demonstrated that Parvi- twins16. In 2017, Tanca et al. chose a cohort of 15 healthy monas micra, Solobacterium moorei, Fusobacterium nucleatum, and Sardinian populations and studied the function of their gut Peptostreptococcus stomatis are enriched in the gut of CRC microbiome using metaproteomics17. In 2018, Zhang et al. patients9. It has been observed that the enterotoxigenic demonstrated an upregulated expression of human proteins Bacteroides fragilis is increased in the feces and colonic mucosa related to oxidative antimicrobial activity in pediatric inflamma- of CRC patients10,11. Tjalsma et al. presented a bacterial tory bowel disease (IBD) by metaproteomics18. driver–passenger model for CRC pathogenesis, indicating that Herein, we used data-independent acquisition (DIA)-based CRC can be initiated by “driver” bacteria that are eventually label-free quantitative proteomics for a cohort analysis of CRC replaced by “passenger” bacteria during tumorigenesis6. However, patients’ and healthy volunteers’ gut microbiome. A total of the actual function of human gut microbiome in the pathogenesis 30,062 protein groups and 91,902 peptides from 195 genera of of CRC remains largely unexplored. There is an urgent need to fi fi fully understand the impact of microbes in CRC. microbes were identi ed and quanti ed. Three hundred and forty- fi Traditional methods for bacterial characterization are usually one protein groups were found signi cantly different in abun- based on bacterial culture. Culturomics is a bacterial identification dance between the CRC patients and the healthy volunteers. method that combines multiple culture strategies, matrix-assisted Among the 341 proteins, 27 are related to iron intake and laser desorption/ionization–time of flight mass spectrometry (MS) transport, and 42 are related to oxidative stress, which can be identification, and 16S rRNA typing12. However, most microbes in resulted from the high local concentration of iron and high the gut are difficult to culture. Metagenomics has recently been oxidative stress in the large intestine of CRC patients. The results widely used to characterize gut microbiome without bacterial show that not only taxonomic abundances but also function of culture13. The methods can provide information on the taxonomic gut microbiome is changed during the pathogenesis of CRC. 1Department of Chemistry, Shanghai Stomatological Hospital, Fudan University, Shanghai, China. 2Changhai Hospital, The Naval Military Medical University, Shanghai, China. 3Department of Clinical Laboratory Medicine, Shanghai Tenth People’s Hospital of Tongji University, Shanghai, China. 4Shanghai Omicsolution Co., Ltd., Shanghai, China. 5These ✉ authors contributed equally: Shuping Long, Yi Yang. email: [email protected]; [email protected] Published in partnership with Nanyang Technological University S. Long et al. 2 a 14 Healthy control 14 CRC Protein extracon Sample collecon Microbial separaon Trypsin digeson Stascs Taxonomy Database Funcon Data analysis Database searching LC-MS/MS bcdProtein Groups Pepdes Genera H P H P H P 4,151 22,879 3,032 15,883 63,926 12,093 25 157 13 1234567890():,; e 3 2 (p-value) 10 1 log 0 -10 -5 0 5 10 log2(P/H) Fig. 1 Metaproteomic characterization of the gut microbiome of CRC patients and healthy crowds. a Experimental design and workflow. The numbers of protein groups (b), peptides (c), and genera (d) identified from the CRC patient group (P) and the healthy volunteer group (H). e Volcano plot indicating the differential proteins. Protein groups with P/H fold change (FC(P/H)) ≥ 2 and P value < 0.05 were colored red, while those with FC(P/H) ≤ 0.5 and P value < 0.05 were colored blue. Source data are provided as a Source Data file. RESULTS combining the National Center for Biotechnology Information Metaproteomic characterization of the gut microbiome of CRC (NCBI) non-redundant (nr) bacteria (containing >78 million protein patients and healthy crowds entries) and the SwissProt human (>20,000 protein entries). At the fi In the study, we enrolled 14 CRC patients and 14 healthy protein group level, 15,685 protein groups were identi ed, volunteers. There was no significant difference in ages or body including 11,391 (72.6%) from the HMP database and 3920 weights between the two groups (Supplementary Table 1). As (25.0%) from the NCBI nr database (Supplementary Fig. 1a and illustrated in Fig. 1a, gut microbes were enriched using differential Supplementary Data 1). Most of the identified protein groups are centrifugation. After protein extraction and trypsin digestion, from microbes, indicating a good sample pretreatment by the label-free DIA was used to identify and quantify proteins in each differential centrifugation. Then the DDA and DIA data were sample using a merged spectral library generated by data- searched against a database combined from the HMP and the dependent acquisition (DDA) experiments performed on a pool identified NCBI nr proteins (11,994 entries) by SpectroMine, and from every sample and spectrum-centric analysis of the DIA data. the results are shown in Supplementary Fig. 1b and Supplemen- The workflow of library generation is described in detail in the tary Data 2. Consequently, 36,053 protein groups and 103,444 “Methods” section and Supplementary Fig. 1. De novo peptides were identified from the pooled DDA data with an sequencing-assisted database searching by PEAKS19 was con- identification rate of MS/MS spectra of 26.7% (178,300 peptide- ducted on the pooled fractionated DDA data against successively spectrum matches (PSMs) from 668,162 MS/MS spectra). From the a database of stool from Human Microbiome Project (HMP) DIA data, 12,463 protein groups and 39,319 peptides were (containing >4.8 million protein entries) and a database identified with an identification rate of 33.3% (794,028 PSMs from npj Biofilms and Microbiomes (2020) 14 Published in partnership with Nanyang Technological University S. Long et al. 3 2,386,773 MS/MS spectra) by spectrum-centric database search- in the large intestine can contribute to the pathogenesis of CRC27. ing. The search results were merged to generate spectral library At the species level, we found that B. fragilis and Peptostrepto- for peptide-centric DIA analysis, and finally the library contained coccus anaerobius were more abundant in the CRC group, (Fig. 2b), 37,416 protein groups
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