1The Brown Foundation Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, USA
2The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, Texas, USA 3Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
4Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, Texas, USA
5Department of Genomic Medicine, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA 6School of Pharmaceutical Sciences, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada
7Department of Pathology, University of Utah, Salt Lake City, USA
Correspondence
Ru Chen, PhD, Baylor College of Medicine, Houston, Texas 77030, USA.
Email: ru.chen@bcm.edu
Sheng Pan, PhD, The Brown Foundation Institute of Molecular Medicine, McGovern Medical School/ The University of Texas Health Science Center at Houston, Houston, Texas 77030, USA.
Email: sheng.pan@uth.tmc.edu
Funding information
NIH, Grant/Award Numbers: R01CA211892, R01CA276173, P30CA125123

www.proteomics-journal.com

© 2024 Wiley-VCH GmbH. 1of11

Accepted: 22 May 2024

Received: 8 March 2024 Revised: 17 May 2024
DOI: 10.1002/pmic.202400078
RESEARCH ARTICLE
A complementary metaproteomic approach to interrogate microbiome cultivated from clinical colon biopsies
Van-An Duong1 Altai Enkhbayar2 Nobel Bhasin3	Lakmini Senavirathna1
Eva C. Preisner4	Kristi L. Hoffman4	Richa Shukla3	Robert R. Jenq2,5
Kai Cheng6	Mary P. Bronner7	Daniel Figeys6	Robert A. Britton4
Sheng Pan1,2	Ru Chen3
Abstract
The human gut microbiome plays a vital role in preserving individual health and is intricately involved in essential functions. Imbalances or dysbiosis within the microbiome can significantly impact human health and are associated with many diseases. Several metaproteomics platforms are currently available to study microbial proteins within complex microbial communities. In this study, we attempted to develop an integrated pipeline to provide deeper insights into both the taxonomic and functional aspects of the cultivated human gut microbiomes derived from clinical colon biopsies. We combined a rapid peptide search by MSFragger against the Unified Human Gastrointestinal Protein database and the taxonomic and functional analyses with Unipept Desktop and MetaLab-MAG. Across seven samples, we identified and matched nearly 36,000 unique peptides to approximately 300 species and 11 phyla. Unipept Desktop provided gene ontology, InterPro entries, and enzyme commission number annotations, facilitating the identification of relevant metabolic pathways. MetaLab-MAG contributed functional annotations through Clusters of Orthologous Genes and Non-supervised Orthologous Groups categories. These results unveiled functional similarities and differences among the samples. This integrated pipeline holds the potential to provide deeper insights into the taxonomy and functions of the human gut microbiome for interrogating the intricate connections between microbiome balance and diseases.
KEYWORDS
data processing and analysis, gut microbiome, mass spectrometry, metaproteomics, proteomics, taxonomy
Abbreviations: COG, clusters of orthologous genes; EC, enzyme commission; eggNOG, evolutionary genealogy of genes non-supervised orthologous GOgene ontology Groups; LCA, lowest common ancestor; MAGs, metagenome-assembled genomes; SCFAs, short-chain fatty acids; UHGG, unified human gastrointestinal genome; UHGP, unified human gastrointestinal protein.
Proteomics 2024, 24:2400078
https://doi.org/10.1002/pmic.202400078

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1		INTRODUCTION
The human microbiome constitutes a vast and intricate community of microorganisms, including bacteria, viruses, fungi, and other microbial species [1]. They reside within the human body and considerably influence human health and disease [2]. Their prevalence and impact are noteworthy within the gastrointestinal tract, where they perform multiple essential functions, such as the breakdown and absorption of nutrients, the production of essential vitamins, and the fortification of the immune system by defending against harmful pathogens [3]. Imbalances or dysbiosis within the microbiome can significantly impact human health and are associated with various diseases, including inflammatory bowel disease, cancer, obesity, diabetes, and even neurological conditions [4, 5]. Metaproteomics is a burgeoning field in studying microbial proteins within complex microbial communities [6, 7]. Different from conventional proteomics that primarily examines individual organisms, metaproteomics focuses on the simultaneous analysis of the collective proteome of diverse microorganisms [8]. Metaproteomics provides qualitative and quantitative insights into expressed proteins, shedding light on microbial activities and their relationship with the human host [9, 10].
A typical metaproteomic workflow comprises three key parts: sample collection and preparation, data acquisition by LC-MS/MS, and data analysis. The metaproteomic data analysis involves (1) a database search for peptide and protein identification and (2) the interpretation of taxonomy and function [11]. The Human Microbiome Project (HMP) database stands as a pioneering resource for this purpose [12, 13]. The HMP’s human gut microbiome database comprises 457 bacterial species identified from fecal samples. Subsequently, the integration of HMP’s sequence data with the Metagenomics of the Human Intestinal Tract consortium [14] has yielded the Integrated nonredundant Gene Catalog, containing 9.9 million genes from the human gut microbiome [15]. Recently, the Unified Human Gastrointestinal Genome (UHGG) collection was built [16] and is available on the MGnify website [17]. The UHGG v2.0.1 comprises 4,744 species-level clusters with 289,232 genomes encoding over 170 million protein sequences, which has been used to construct the Unified Human Gastrointestinal Protein (UHGP) catalog with more than 10 million protein sequences [16]. Following the database search, taxonomy, and function analyses are carried out using publicly available software tools, including Unipept and MetaLabMAG. Unipept is one of the pioneering tools in metaproteomic analysis, which searches a list of peptides against a peptide database constructed through in-silico tryptic digestion of all UniProtKB proteins [18]. It then assigns a taxon from the NCBI taxonomy to each peptide based on the lowest common ancestor (LCA) algorithm [19]. A list of tryptic peptides from the database search can be input to Unipept to obtain taxonomic and functional annotations. MetaLab-MAG, another recently developed software, operates through two distinct steps: (1) peptide and protein identification using pFind and (2) taxonomic and functional annotations. It relies on metagenome-assembled genomes (MAGs) as the resource for both steps [20]. For the human gut microbiome, MetaLab-MAG restricts both steps within UHGP and UHGG databases, enhancing the efficiency and precision of database search.
SIGNIFICANCE STATEMENT
This study presents the first attempt to integrate MSFragger, Unipept Desktop, and MetaLab-MAG within a metaproteomics pipeline to unveil the taxonomic and functional annotations of microbiomes cultivated from clinical colon biopsies. MSFragger is paired with the expansive UHGP database containing over 10 million protein sequences from the human gastrointestinal tract microbiota. MetaLab-MAG provides functional annotations of microbiomes using COGs and NOGs, which are complemented by the GO, Inter- Pro, and EC annotations of Unipept Desktop. They enable the identification of relevant metabolic pathways within the microbiome. This integrated pipeline can be applied to interrogate the taxonomic and functional characteristics of the human gut microbiome and unveil the impacts of the microbiome on diseases.
Here, we have applied a metaproteomic workflow for the analysis of the human gut microbiome, using in vitro cultivation of colon mucosa adherent microbiota as a demonstration of the workflow. Our approach involves searching raw MS/MS files using MSFragger [21] against the UHGP database, followed by taxonomic and functional analyses performed by both Unipept Desktop and MetaLab-MAG. This is the first study combining Unipept Desktop and MetaLab-MAG to provide more insight into the taxonomy and function of the microbiome in the samples. It is valuable for studying the human gut microbiome in clinical samples.
2		EXPERIMENTAL PROCEDURES
2.1		Sample collection
The frozen colon samples were obtained as de-identified samples at the University of Washington, which were originally collected in accordance with approved Human Subject’s guidelines at the University of Washington [22]. The specimens obtained at the time of colonoscopy or from surgical resections were placed in frozen media containing Minimal Essential Medium (Thermo Fisher Scientific, Waltham, MA, USA) with 10% DMSO and kept frozen at ‑70◦C until use. Clinical information of the samples is shown in Table S1.
2.2		In vitro cultivation of gut microbiota
A minibioreactor arrays system (bioreactor) that allows simultaneous cultivation of complex microbial communities in a single anaerobic chamber was previously developed [23]. The bioreactors have been shown to be able to grow stable and reproducible microbial com-

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munities that recapitulate key features of human fecal communities [24–27]. In the current study, colon biopsy samples were inoculated into this minibioreactor arrays system containing 15 mL bioreactor medium 3 [28] for in vitro cultivation of gut microbiota as described previously [23]. Briefly, frozen biopsy samples were vortexed for 1 min, and the whole sample volume was added to the reactor. A continuous flow was initiated 16 h post inoculation at a flow rate of 0.9375 mL/h and 16 h retention time. After 4 days of continuous flow, an aliquot of 1 mL was removed from each reactor, and 100 μL was plated on Brain Heart Infusion agar plates for determination of microbial growth. On the final day of cultivation (day seven), all samples from the bioreactors that had microbial growth showed similar CFUs, which were ∼109 CFU/mL. At the end of the cultivation, 13 mL was removed from each reactor and spun down at 4000 rpm for 5 min. Cell pellets were frozen immediately at -80◦C until proteomic analysis.
2.3		Proteomic sample preparation
Each bacterial pellet was resuspended in 200–500 μL of BugBuster Master Mix (MilliporeSigma, MA, USA) with the addition of protease inhibitor cocktail set II (MilliporeSigma, MA, USA). The samples were incubated at room temperature for 20 min on a rotating platform shaker. The homogenate was centrifuged at 16,000 × g for 20 min at 4◦C. The resulting aqueous supernatant was collected, and protein concentration was determined with the Pierce Rapid Gold Assay Kit (Thermo Fisher Scientific, Waltham, MA, USA). For each sample, 50 μg of protein was used for proteomic sample preparation. The in-solution digestion process was carried out as previously described [29]. Subsequently, the digested sample was dried down and re-suspended in 0.1% formic acid to achieve a final concentration of 1 μg/μL for LC-MS/MS analysis.
2.4		LC-MS/MS analysis
The tryptic digested samples were analyzed by LC-MS/MS using a Q Exactive HF-X Orbitrap mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA) interfaced with an UltiMate 3000 Binary RSLC- nano HPLC system. Initially, the samples were loaded onto a 5-mm trap column packed with 5 μm/100 Å C18 material (Thermo Fisher Scientific, Waltham, MA, USA) using 98% buffer A (0.1% formic acid in water) and 2% buffer B (0.1% formic acid in acetonitrile) at a flow rate of 3.0 μL/min. The samples were separated in a self-packed C18 analytical column (100 μm × 30 cm, 3 μm Reprosil-Pur C18-AQ material (Dr Maisch, Germany)) using a 90-min linear gradient from 2% to 35% for buffer B at a flow rate of 0.3 μL/min. Mass spectrometric analysis was performed using a data dependent acquisition (DDA) mode. The survey scan was acquired with a resolution of 60,000, spanning from 400 to 1600 m/z, with an Automatic Gain Control (AGC) target of 3 × 106 and a maximum injection time of 50 ms. Monoisotopic masses were then selected for further fragmentation for the 25 most abundant precursor ions within a dynamic exclusion range of 15 s and a minimum intensity threshold of 2.3 × 105. Precursor ions were isolated using the quadrupole with an isolation window of 1.6 m/z. Higher energy collisional dissociation (HCD) was applied with a normalized collision energy of 28%. MS/MS scans were performed with a resolution of 7500, with an AGC target of 5 × 105 and a maximum injection time of 22 ms.
2.5		Data analysis
The MS/MS data were searched against the UHGP database (10,231,988 sequences) using MSFragger v3.8 on FragPipe v20.0 [21]. The search parameters are as follows: fixed modification, car- bamidomethylation of cysteine; variable modifications, oxidation of methionine and acetylation at protein N-terminal; precursor mass tolerance, 20 ppm; fragment mass tolerance, 20 ppm; missed cleavages, ≤2; maximum variable modifications on a peptide, 3; and split of the database, 8. Peptide validation was carried out using PeptideProphet on FragPipe with an FDR set at ≤ 1% [30]. Subsequently, protein identifications were performed using the Protein Prophet algorithm with an FDR ≤ 1% [31]. The identified peptides were input into Unipept Desktop v2.0.0, while the identified peptides and proteins were input into MetaLab MAG v1.0.2 for metapro- teome data analysis. Sunburst and 3D scatter plot were prepared using packages sunburstR and scatterplot3d, respectively, in R 4.3.1. UpSet plots were plotted by https://www.bioinformatics.com.cn/en (accessed on September 19, 2023). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [32] partner repository with the dataset identifier PXD047418.
3		RESULTS AND DISCUSSION
3.1		Establishment of a pipeline for metaproteomic analysis
A typical metaproteomics workflow shares similarities with a conventional proteomics workflow. However, whereas proteomics focuses on a single species, metaproteomics may deal with complex samples containing hundreds of different species [33]. Consequently, in metaproteomics, after peptide and protein identification, we need to perform taxonomic analysis and functional annotations using specialized metaproteomics tools [18].
We have established a pipeline for metaproteomics data analysis of the human gut microbiome cultivated from clinical colon biopsies. This pipeline includes a database search against the UHGP database using MSFragger within the FragPipe platform, followed by taxonomic and functional analyses using a combination of Unipept Desktop and MetaLab-MAG (Figure 1). The selection of the UHGP database was deliberate, as it is currently the most extensive microbiome database with over 10 million protein sequences [16]. Typically, a microbiome protein database can be sourced from either a public microbiome

FIGURE 1 Flow chart illustrating the devised pipeline for metaproteomic analysis of microbiome samples cultivated from clinical colon

biopsies.
gene catalog database or through metagenomic/metatranscriptomic sequencing of a sample (which results in a sample-specific database) [4]. While a sample-specific microbiome protein database is efficient and confines the search to a smaller and more specific database, its quality hinges on the quality of the sequencing data. Any missed genes during sequencing will result in the corresponding proteins not being identified in the search. Therefore, we opted for the comprehensive yet relatively large UHGP public database for the search.
Our search employed MSFragger within FragPipe, a notably rapid search algorithm capable of dividing large databases into smaller segments for searches, making it ideal for large databases such as UHGP [21]. Subsequently, taxonomic analysis and functional annotations were conducted using both Unipept Desktop and MetaLab-MAG. MetaLab-MAG is specially designed to characterize microbiomes from the MAGs databases [20]. In our current pipeline, we used the UHGP database for both peptide/protein identification and taxonomic/functional annotation. MetaLab-MAG provides functional annotation of microbiomes by Clusters of Ortholo- gous Genes (COG) [34] and evolutionary genealogy of genes Nonsupervised Orthologous Groups (eggNOG) categories [35]. On the other hand, Unipept Desktop searches peptides against the Uniprot database and retrieves taxonomic information from the NCBI taxonomy [36, 37], and offers additional protein metadata, including gene ontology (GO) [38], InterPro [39], and enzyme commission (EC) numbers annotations [40]. Thus, combining outputs from both MetaLab-MAG and Unipept Desktop enriches our understanding of the taxonomic and functional annotation of the microbiome in samples.
3.2		
Taxonomic compositions of the microbiome in cultivated samples

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We successfully identified a total of 35,893 peptides across all seven samples through the MSFragger search engine. The peptide and protein identifications in the samples are presented in Table S2. Notably, samples S7 and S10 exhibited a considerably higher number of peptides, with more than 13,000 and 17,000 peptides, respectively. In contrast, the remaining samples fell within the range of 4100 to 5000 peptides (Figure 2A). These disparities in peptide counts across samples underscore the heterogeneity in the microbiome compositions of each sample. We set a threshold that taxa were considered “identified” if they constituted ≥ 0.05% of the total spectral count in each sample. MetaLab-MAG identified 9–11 phyla and 195–287 species across seven samples (Figure 2B). Because MetaLab-MAG utilizes the same database for peptide and protein searches, the resulting peptides and proteins tend to be able to assign to lower taxonomic levels. Sunburst plots in Figure 2C and Figure S1 provide a visual representation of the microbiome taxonomy across the seven samples using data from MetaLab-MAG and Unipept Desktop, respectively. Within Unipept Desktop, 34%–72% of the peptides were matched to the kingdom (Bacteria) or the root (Organism). Consequently, a substantial number of peptides, particularly in samples like S5 and S9, could not be assigned to any taxonomic level ranging from phylum to species, which is due to the large Uniprot-based peptide-to-taxon database. For a more detailed examination of the microbiome taxonomy across the seven samples, we provide a tree view in Figure S2. MetaLab-MAG demonstrates a high level of precision, with 95%–98% of the peptides successfully matched to the species level (Figure 2C), underscoring
FIGURE 2 Taxonomic identification of microbiome compositions. (A) Peptide identification and spectral count obtained from MSFragger. (B) Numbers of taxa id—entified in metaproteomic analysis by MetaLab-MAG with ≥ 0.05% total spectral count. (C) Sunburst plots illustrating the taxonomy of microbiomes in seven samples using MetaLab-MAG data. (D) Alpha diversity by Shannon-Wiener index using MetaLab-MAG metaproteomic data and (E) beta diversity by Bray Curtis dissimilarity using MetaLab-MAG data at the species level and visualized by Principal Coordinates Analysis.
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the advantages of restricting taxonomic annotation within the UHGP database.
Alpha diversity, as measured by the Shannon-Wiener index using MetaLab-MAG metaproteomic data, is depicted in Figure 2D. The Shannon-Wiener index incorporates both richness and evenness in its calculation. Higher values indicate a greater number of taxa and a more balanced distribution among those taxa [41]. At the phylum level, S7 exhibits the highest Shannon-Wiener index. Given the similar numbers of phyla across samples, this suggests a more even distribution of phyla in S7. At the species level, Shannon-Wiener index values range from 4.288 (S9) to 5.070 (S4), indicating variations in species richness and evenness among the samples. S2, S4, and S7 stand out with higher Shannon-Wiener index values attributed to their higher numbers of species. Beta diversity is assessed using Bray Curtis dissimilarity [42] with MetaLab-MAG data at the species level, and the results are visualized through Principal Coordinates Analysis (Figure 2E). Due to the small sample size, we did not attempt to do statistical analysis based on the clinical diagnosis or tissue histology of these samples (Table S1).
Table S3 presents the taxonomic compositions (%) of microbiomes in each sample at the phylum, class, and order levels. Both MetaLabMAG and Unipept Desktop provide relatively similar identifications of the most abundant phyla, classes, and orders. The most prevalent phyla, classes, and orders in each sample are similar between MetaLabMAG and Unipept Desktop. The correlation between metaproteomic compositions at the phylum level of microbiomes is > 0.98 for S2, S4, S5, S9, and S1, and > 0.84 for S3 and S7. According to both tools, Bacteroidota, Pseudomonadota and Bacillota emerge as the three most prevalent phyla in most of the samples. In addition, S7 exhibits a notably higher percentage (12.6%–13.9%) of Fusobacteriota compared to the other samples. Within this phylum, we identified five genera (Fusobacterium A, B, C, Fusobacterium, and Cetobacterium A), and 23 species, including Fusobacterium nucleatum. F. nucleatum, in particular, has been associated with increased formate secretion and cancer glutamine metabolism [43]. It is considered a colorectal cancer-associated bacterium [44].
3.3		Functional enrichment of microbiome in cultivated samples
Using Unipept Desktop, we identified a total of 1144 biological processes, 278 cellular components, and 1613 molecular functions across all samples. UpSet plots in Figure S3 provide a visualization of the GO terms that are commonly shared among the samples, including 224 biological processes, 40 cellular components, and 295 molecular functions. Furthermore, Figure S3 illustrates the unique GO terms in each sample. Samples S7 and S10 exhibit the highest number of unique biological processes (128 and 121, respectively), likely due to the much greater peptide identifications in these two samples. In terms of cellular components, S9, S10, and S7 display 38, 29, and 24 unique components, respectively. Additionally, S10 and S7 possess 160 and 141 unique molecular functions, respectively. Moreover, they share 84 molecular functions that are not present in the other samples.
Table 1 and Table S4 present the top 10 most abundant biological processes, cellular components, and molecular functions within the microbiome of each sample. Common biological processes that appear across all samples include translation, carbohydrate metabolic process, glycolytic process, protein folding, gluconeogenesis, glucose metabolic process, and DNA-templated transcription. Typical shared molecular functions are ATP binding, metal ion binding, structural constituents of ribosome, DNA binding, GTP binding, translation elongation factor activity, and GTPase activity.
InterPro provides a functional classification of protein families by predicting domains and important sites [39]. Utilizing Unipept Desktop, we found a total of 7375 InterPro entries across all samples, with 1345 entries commonly shared among them (Figure S3). Samples S10 and S7 show the highest count of unique InterPro entries (789 and 697, respectively). For a more detailed overview, Table S5 presents the top 20 most abundant InterPro entries within the microbiome of each sample.
We utilized MetaLab-MAG to identify the functional characteristics of microbiomes using COG [34] and eggNOG categories [35] (Figures 3A, 3B, and Figure S4). In total, we identified 1942 COG features, with 267 of them present in all samples, and 5275 NOG features, with 135 shared across all samples. The most enriched functions across all samples correspond to COG and NOG categories J (translation, ribosomal structure, and biogenesis), G (carbohydrate transport and metabolism), and C (energy production and conversion). These results align with the GO data obtained from Unipept Desktop.
The UpSet plots in Figure S5 provide an overview of the shared and unique COGs and NOGs in each sample. Samples S10 and S7 exhibit very high numbers of unique COGs and NOGs that do not appear in the other samples, also likely due to the much greater peptide identifications in these two samples. Clustering (heatmap) and principal component analysis (PCA) of the top 100 COG and NOG features are presented in Figures S6 and S7, respectively.
3.4		EC enrichment in cultivated samples
From Unipept, we identified a total of 1461 ECs across the samples. Among these, 232 ECs were consistently found in all samples (Figure 4A). ECs are categorized into seven groups, which include oxi- doreductase (EC 1), transferase (EC 2), hydrolase (EC 3), lyase (EC 4), isomerase (EC 5), ligase (EC 6), and translocase (EC 7) [40]. Table S6 presents the top 10 most abundant ECs in each group (top 2 for EC 7). Among the 232 ECs commonly present in all samples, the most abundant ECs are oxidoreductases utilizing NAD(+) or NADP(+) as acceptors, DNA-directed RNA polymerase, and protein-synthesizing GTPase based on average microbial peptide percent spectral count. We then utilized these 232 common ECs to investigate commonly enriched pathways across all samples using String v12.0 [45]. With Escherichia coli ATCC 8739 selected as a representative bacterial species, we identified 26 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways with an FDR < 0.05 (Table S7). Metabolic pathways and biosynthesis of secondary metabolites were the two top enriched pathways [46].
Name	Identifier	% spectral count	
		S2	S3	S4	S5	S7	S9	S10	
Translation	GO:0006412	13.2	8.3	14.8	12.2	9.9	5.9	6.0	
Carbohydrate metabolic process	GO:0005975	6.2	4.7	6.1	6.3	4.0	5.4	4.8	
Glycolytic process	GO:0006096	5.2	5.3	6.4	5.0	3.7	4.9	3.8	
Protein folding	GO:0006457	2.7	2.8	3.2	2.7	2.8	3.3	2.5	
Gluconeogenesis	GO:0006094	3.8		3.1	2.1	1.8	2.4	2.7	
Glucose metabolic process	GO:0006006	2.9	1.9	4.1	1.9		1.6	2.4	
DNA-templated transcription	GO:0006351	1.9			2.6	1.7	2.7	1.3	
Monoatomic ion transmembrane transport	GO:0034220	2.2	2.0	4.0	2.4	1.6	3.4		
Tricarboxylic acid cycle	GO:0006099	2.1	2.1	1.9	2.0		2.5	1.3	
Protein refolding	GO:0042026		2.1	2.3			2.0	1.1	
Phosphoenolpyruvate-dependent sugar phosphotransferase system	GO:0009401		2.9		1.8	1.9			
Amino acid metabolic process	GO:0006520		2.5			1.9			
Protein transport	GO:0015031	1.5				1.6		1.3	
Obsolete conjugation	GO:0000746			2.0					
TABLE 1 The most abundant biological processes in each sample.
FIGURE 3 Functional analysis of microbiomes by Clusters of Orthologous Genes (COG) categories and evolutionary genealogy of genes Non-supervised Orthologous Groups (eggNOG) categories using MetaLab-MAG. The functions of the microbiome are illustrated by (A) Voronoi plots using combined data of all samples and (B) bar plots using normalized data of each sample.
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FIGURE 4 (A) UpSet plot illustrating the unique and shared enzyme commissions (ECs) among samples. (B) Metabolisms of acetate, butanoate, and propanoate and (C) the percentage spectral counts of enzymes in samples. The enzyme commissions are in orange. The drawing of the pathways was adopted from the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways (pyruvate metabolism, butanoate metabolism, and propanoate metabolism). Only some steps of the original pathways are presented.
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Among the 1461 ECs identified across the samples, 24 ECs were involved in the metabolic pathways of short-chain fatty acids (SCFAs), with multiple enzymes identified in each pathway (including acetate, butanoate, and propanoate). Acetate, butanoate, and propanoate are common SCFAs produced by the gut microbiome during the fermentation of partially and non-digestible polysaccharides. They collectively make up about 80% of all SCFAs generated by the gut microbiome and are typically found in a molar ratio of 3:1:1 (acetate: propanoate: butyrate) [47]. These SCFAs play a crucial role in enhancing the integrity of the gut barrier, regulating glucose and lipid metabolism, modulating the immune system and inflammatory responses, and controlling blood pressure [48]. Adequate production of SCFAs is critical in preventing various diseases, including inflammatory bowel disease, cancer, obesity, diabetes, kidney diseases, liver diseases, and immune system disorders [49]. Figures 4B and 4C exhibited the SCFA pathways and the percentage of spectral counts attributed to enzymes, respectively. Most of the enzymes were consistently detected across all samples, with variations in their abundances, likely attributed to the highly heterogeneous gut microbiome across different individuals as well as influences from dietary intakes [29]. The identification of these enzymes within the metabolic pathways of acetate, butanoate, and propanoate suggests the potential of metaproteomics in assessing enzyme activity and pathways. These results can be integrated with metabolomics data to yield more comprehensive and valuable results to understand the metabolism of SCFAs in gut microbiomes.
4		CONCLUSIONS
In this study, we have shown the feasibility of integrating publicly available software tools, including MSFragger, Unipept Desktop, and MetaLab-MAG within a metaproteomics pipeline designed to unveil the taxonomic compositions and functions of microbiomes cultivated from clinical colon biopsies. This pipeline is based on the rapid search capabilities of MSFragger, paired with the expansive UHGP database containing over 10 million protein sequences from the human gastrointestinal tract microbiota. Unipept Desktop and MetaLab-MAG offer valuable taxonomic insights and annotations for GO, InterPro, EC numbers, COGs, and NOGs, enabling the identification of relevant metabolic pathways within the microbiome. While limitations still exist, we have demonstrated an effective complementary approach to challenge current obstacles in metaproteomic analysis towards an in-depth interrogation of the intricate link between microbiome composition and functional activities.
ACKNOWLEDGMENTS
The authors thank the Texas Medical Center Digestive Disease Center Core Facilities, Professors Antone Robert Opekun and Zhe Lyu for technical support. This research is supported by the National Institutes of Health (NIH) under grants R01CA211892 and R01CA276173, and in part by NIH grant P30 CA125123.
CONFLICT OF INTEREST STATEMENT
The authors declare no conflict of interest.
DATA AVAILABILITY STATEMENT
The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD047418.
ORCID
Van-An Duong https://orcid.org/0000-0002-4676-1804
Daniel Figeys https://orcid.org/0000-0002-5373-7546
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SUPPORTING INFORMATION
Additional supporting information may be found online https://doi.org/10.1002/pmic.202400078 in the Supporting Information section at the end of the article.
How to cite this article: Duong, V.-A., Enkhbayar, A., Bhasin, N., Senavirathna, L., Preisner, E. C., Hoffman, K. L., Shukla, R., Jenq, R. R., Cheng, K., Bronner, M. P., Figeys, D., Britton, R. A., Pan, S., & Chen, R. (2024). A complementary metaproteomic approach to interrogate microbiome cultivated from clinical colon biopsies. Proteomics, 24, e2400078. https://doi.org/10.1002/pmic.202400078