Exploring the functional diversity and metabolic activities of the human gut microbiome in Thai adults in response to a prebiotic diet

Abstract

Exploring dietary methods to alter microbial communities and metabolic functions is becoming an increasingly fascinating strategy for improving health. Copra meal hydrolysate (CMH) is alternatively used as a gut health supplement. However, the functional diversity and metabolic activities in gut microbiome in relation to CMH treatment remain largely unknown. Therefore, this study aimed to identify key predominant groups of bacterial species toward diversified metabolic functions, activities, and routes using metaproteomics. As a result, the integrative analysis of metaproteomic data revealed that seven key families across 11 dominant gut bacterial species were concerted. Consistently, across 76,206 proteins assigned to the metabolism of the 255,964 annotated proteins, short-chain fatty acid (SCFA) biosynthesis, lipopolysaccharide (LPS) biosynthesis, and bile acid (BA) metabolism were positively associated with CMH. Further identification of cooperative metabolic routes promisingly highlighted the importance of glycolysis/gluconeogenesis, tricarboxylic acid (TCA) cycle, inositol phosphate metabolism, steroid hormone biosynthesis, O-antigen repeat unit biosynthesis, and chloroalkane and chloroalkene degradation. This work presents an initial study of metaproteomics associated with prebiotic diet in a Thai population-based cohort in a developing Southeast Asian country.

IMPORTANCE
Studies primarily focused on the impact of CMH on gastrointestinal symptoms and gut microbial compositions. However, as the field moves toward understanding the relationship between microbiome and diet in relation to gut health, it is critical to evaluate how changes in metabolic activities relate to cooperative metabolic routes in the gut microbiome for promoting human health. Through the use of metaproteomics, our findings highlighted the key predominant groups of bacterial species, potential proteins, and their metabolic routes involved in gut metabolism. This study provides comprehensive insights into the fundamental relationship between microbiome and dietary supplements and suggests that metaproteomics is a powerful method for monitoring metabolic functions, activities, and routes in the gut microbiome.

Full text

| Systems Biology | Research Article
Exploring the functional diversity and metabolic activities of
the human gut microbiome in Thai adults in response to a
prebioticdiet
Amornthep Kingkaw,1 Preecha Patumcharoenpol,1 Narissara Suratannon,2 Massalin Nakphaichit,3 Sittiruk Roytrakul,4 Wanwipa
Vongsangnak5,6
AUTHOR AFFILIATIONS See aliation list on p. 14.
ABSTRACT Exploring dietary methods to alter microbial communities and metabolic
functions is becoming an increasingly fascinating strategy for improving health. Copra
meal hydrolysate (CMH) is alternatively used as a gut health supplement. However,
the functional diversity and metabolic activities in gut microbiome in relation to CMH
treatment remain largely unknown. Therefore, this study aimed to identify key predomi
nant groups of bacterial species toward diversied metabolic functions, activities, and
routes using metaproteomics. As a result, the integrative analysis of metaproteomic
data revealed that seven key families across 11 dominant gut bacterial species were
concerted. Consistently, across 76,206 proteins assigned to the metabolism of the
255,964 annotated proteins, short-chain fatty acid (SCFA) biosynthesis, lipopolysacchar
ide (LPS) biosynthesis, and bile acid (BA) metabolism were positively associated with
CMH. Further identication of cooperative metabolic routes promisingly highlighted
the importance of glycolysis/gluconeogenesis, tricarboxylic acid (TCA) cycle, inositol
phosphate metabolism, steroid hormone biosynthesis, O-antigen repeat unit biosynthe
sis, and chloroalkane and chloroalkene degradation. This work presents an initial study
of metaproteomics associated with prebiotic diet in a Thai population-based cohort in a
developing Southeast Asian country.
IMPORTANCE Studies primarily focused on the impact of CMH on gastrointestinal
symptoms and gut microbial compositions. However, as the eld moves toward
understanding the relationship between microbiome and diet in relation to gut health, it
is critical to evaluate how changes in metabolic activities relate to cooperative meta
bolic routes in the gut microbiome for promoting human health. Through the use
of metaproteomics, our ndings highlighted the key predominant groups of bacterial
species, potential proteins, and their metabolic routes involved in gut metabolism.
This study provides comprehensive insights into the fundamental relationship between
microbiome and dietary supplements and suggests that metaproteomics is a powerful
method for monitoring metabolic functions, activities, and routes in the gut microbiome.
KEYWORDS copra meal hydrolysate, human gut microbiome, metaproteomics, gut
metabolism, functional and metabolic activity
The gastrointestinal (GI) tract harbors a dynamic and complex community of gut
microbiomes that plays a crucial role in human health (1). Several factors, such as
host genetics, diet, lifestyle, medication, and environment, exert inuence on the gut
microbiome. Moreover, gut microbial compositions can be modulated by the consump
tion of specic nutritional substances, such as prebiotics (2, 3). One dietary strategy for
modulating the gut microbiome is the utilization of prebiotics, which benecially aect
February 2025 Volume 13 Issue 2 10.1128/spectrum.01599-24 1
Editor Jennifer M. Auchtung, University of Nebraska-
Lincoln, Lincoln, Nebraska, USA
Address correspondence to Wanwipa Vongsangnak,
wanwipa.v@ku.ac.th.
The authors declare no conict of interest.
See the funding table on p. 15.
Received 1 July 2024
Accepted 19 November 2024
Published 13 December 2024
Copyright © 2024 Kingkaw et al. This is an open-
access article distributed under the terms of the
Creative Commons Attribution 4.0 International
license.

the host by selectively stimulating the growth and/or activity of the microbiome in the
GI tract (3). Additionally, prebiotics can alter the microbial communities and their
functions, providing energy and maintaining gut homeostasis (4, 5).
Manno-oligosaccharides (MOS) are prebiotics composed of a linear chain of mannose,
derived from mannan-rich plants, such as copra meal, a by-product of the oil extraction
from dried coconut kernels. In Thailand, an annual production of 25 million metric tons
of copra meal is reported (6). Copra meal hydrolysate (CMH) is obtained as a source
for subsequent MOS production through enzymatic hydrolysis using β-mannanase. CMH
is stable under the human GI tract conditions, particularly in the small intestine, and
readily fermented by Lactobacilli and Bidobacteria (7). Very recently, the impact of CMH
on gastrointestinal symptoms and gut microbiome was revealed to have a positive
relationship with gut health using integrative metagenomics (e.g., 16S rRNA gene and
whole metagenome shotgun [WMGS] sequencing) (8, 9). Currently, metagenomics has
rapidly become a routinely used method for characterizing the functional potential of
diversities, communities, and functions of gut microbiome. Nevertheless, this method
does not possess the capability to directly unveil the functional diversity and activities of
microbial communities.
To overcome this challenge, metaproteomics has recently emerged as an alterna
tive approach; it can identify and quantify proteins from microbial communities at
a large scale, providing direct insights into the functional diversity and activities of
microbial communities at the species level. Recently, there have been a number of
studies investigating the relationship between gut microbiome and several factors, that
is, sex, age, disease, diet, and dierent treatments through metaproteomic approach
(10). Despite earlier studies of the eects of CMH on the gut microbiome, however,
the metabolic diversity and activities of microbial communities in response to prebi
otic diet remain largely unknown. Therefore, this study is proposed to investigate
metabolic diversity and activities of microbial communities in response to prebiotic
diet, for example, CMH with an overall aim at identifying key predominant groups of
bacterial species, metabolic functions, and their routes using metaproteomics. Initially,
fecal samples were obtained from Thai adults with dierent treatments, that is, baseline,
placebo, or CMH. Extracted microbial proteins obtained from fecal samples were
then quantied and identied using liquid chromatography-tandem mass spectrome
try (LC-MS/MS). Thereafter, the data were processed with dierent bioinformatics and
systems biology tools, and databases for diversities, taxonomic proles, and metabolic
functions of the gut microbiome. The dierentially expressed proteins (DEPs) and their
metabolic functions were mapped and then explored. In the end, the key predominant
groups of bacterial species together with metabolic functions of DEPs and their routes
under cooperative gut microbiome networks were targeted in response to CMH. This
study serves as a scaold for monitoring metabolic alterations in gut microbiome in Thai
and Asian cohort studies.
RESULTS
Assessment of metaproteomic data from the gut microbiome of Thai adults
An analysis of metaproteomic-based gut microbiome from 20 fecal samples of Thai
adults yielded a total of 530,757 mass spectral counts. These counts were assigned to
the following four groups: baseline placebo (bPB), baseline CMH (bCMH), treatment
with placebo (tPB), and treatment with CMH (tCMH), with respective spectral counts of
122,783, 148,630, 126,139, and 133,205. The mapping of unique peptide counts revealed
a total of 880,807, distributed as follows: 197,659 in bPB, 219,721 in bCMH, 224,460
in tPB, and 238,967 in tCMH (Table S1.1). Based on the spectral library and proteomic
resources, 255,964 annotated proteins were selected from a total of 262,416 proteins for
further analysis. The summarized results are presented in Table 1.
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Assigning protein functional diversity of the gut microbiome of Thai adults
A total of 255,964 annotated proteins from the assessed metaproteomic data were
assigned functions according to the Kyoto Encyclopedia of Genes and Genomes
(KEGG) database (11). These proteins were categorized into six main functional groups,
with metabolism being the largest category, encompassing 76,206 proteins. This was
followed by genetic information processing (19,882 proteins), environmental information
processing, cellular processes (8,275 proteins), human diseases (7,760 proteins), and
organismal systems (4,474 proteins), as shown in Fig. 1; Table S1.2. Within the metabolic
category, the functions of the proteins were further detailed: carbohydrate metabolism
(20,843 proteins), energy metabolism (6,599 proteins), lipid metabolism (4,567 proteins),
nucleotide metabolism (5,390 proteins), amino acid metabolism (12,800 proteins),
metabolism of other amino acids (3,212 proteins), glycan biosynthesis and metabolism
(8,216 proteins), metabolism of cofactors and vitamins (7,479 proteins), metabolism of
terpenoids and polyketides (1,775 proteins), biosynthesis of other secondary metabolites
(3,571 proteins), and xenobiotics biodegradation and metabolism (1,754 proteins) (Table
S1.2). Based on KEGG database, there is no signicant dierence in microbial protein
between the CMH and PB groups at baseline (Table S1.3).
Investigating the taxonomy, diversity, and composition of microbial com
munities in relation to a prebiotic diet
To preliminarily investigate the taxonomy and diversity of microbial community-assigned
protein functions in relation to a prebiotic diet, alpha and beta diversity analyses were
conducted for the CMH and PB groups. As anticipated, the alpha diversity indices,
including observed species, Shannon index, and Simpson index, showed similar results
between the CMH and PB groups, as illustrated in Fig. 2A. Linear modeling revealed
no signicant dierence in microbial diversity based on observed species. Interestingly,
the Shannon index indicated a statistically signicant dierence in microbial diversity,
whereas the Simpson index exhibited a slightly dierent trend (Table S1.4). Considering
beta diversity, a principal coordinate analysis (PCoA) plot based on Bray-Curtis distance
using ADONIS2 function is presented in Fig. 2B. The results demonstrate a signicant
shift in microbial diversity patterns between the CMH and PB groups (ADONIS2 analysis:
R² = 0.039; P-value = 0.038) (Table S1.5). These ndings suggest that treatment with CMH
impacts gut microbial diversity.
Regarding microbial composition proles, the relative abundance of 15 bacterial
families was comparable between the CMH and PB groups (see Materials and Methods)
as shown in Fig. 3. Notably, Bacteroidaceae exhibited the highest relative abundance in
the CMH and PB groups, accounting for 20.41% and 23.74%, respectively. Interestingly,
seven predominant bacterial families, that is, Bacteroidaceae, Clostridiaceae, Erysipelotri
chaceae, Eubacteriaceae, Lachnospiraceae, Ruminococcaceae, and Veillonellaceae were
positively associated with CMH, as illustrated in Fig. 4A; Table S1.6. Notably, three
TABLE 1 Assigned spectral counts, unique peptide counts, and number of proteinsb
Protein
annotation
Number of
proteins
Total spectral counts
(Mean ± SD)
Total unique peptide counts
(Mean ± SD)
bPB bCMH tPB tCMH bPB bCMH tPB tCMH
Annotated
proteinsa
255,964 119,781 145,168 123,264 130,118 192,759 214,286 219,208 233,480
Unannotated
proteins
6,452 3,002 3,462 2,875 3,087 4,900 5,435 5,252 5,487
Total 262,416 122,783 148,630 126,139 133,205 197,659 219,721 224,460 238,967
12,278.30 ±
3,023.17
14,863.00 ±
2,332.96
12,613.90 ±
1,830.08
13,320.50 ±
2,120.85
19,765.90 ±
2,943.82
21,972.10 ±
2,908.68
22,446.00 ±
3,541.86
23,896.70 ±
1,989.81
aAnnotated protein is based on a protein ID with assigned function from the UniProt database.
bbPB, bCMH, tPB, tCMH, and SD represent baseline placebo, baseline CMH, treatment with placebo, treatment with CMH, and standard deviation, respectively.
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signicant species,that is, Clostridium bovifaecis, Clostridium putrefaciens, and Eubacte
rium pyruvativorans, showed a statistical dierence between the CMH and PB groups (P
adj <0.05), as illustrated in Fig. 4B; Table S1.7.
Exploring metabolic functional activities in association with prebiotic diet
through dierentially expressed protein analysis
Of 76,206 proteins associated with metabolism, the top 20 pathways of 146 metabolic
pathways with a high number of protein functional activities were identied (Fig.
5; Table S1.2). They were namely starch and sucrose metabolism (3,248 proteins),
amino sugar and nucleotide sugar metabolism (2,839 proteins), glycolysis/gluconeo
genesis (2,181 proteins), galactose metabolism (2,120 proteins), pyruvate metabolism
(2,050 proteins), pentose phosphate pathway (1,395 proteins), fructose and mannose
metabolism (1,164 proteins), glyoxylate and dicarboxylate metabolism (1,140 proteins),
butanoate metabolism (1,031 proteins), citrate cycle (TCA cycle) (906 proteins), pentose
and glucuronate interconversions (905 proteins), propanoate metabolism (894 proteins),
lipopolysaccharide biosynthesis (599 proteins), ascorbate and aldarate metabolism
(419 proteins), C5-branched dibasic acid metabolism (373 proteins), chloroalkane and
chloroalkene degradation (252 proteins), inositol phosphate metabolism (178 proteins),
O-antigen repeat unit biosynthesis (61 proteins), secondary bile acid biosynthesis (16
proteins), and steroid hormone biosynthesis (5 proteins).
After protein functional categories, pathway and functional enrichment analysis
was performed based on DEPs. Interestingly, six enriched metabolic pathways were
highlighted, including the TCA cycle, O-antigen repeat unit biosynthesis, glycolysis/glu
coneogenesis, inositol phosphate metabolism, steroid hormone biosynthesis, and
chloroalkane and chloroalkene degradation. (Fig. 5; Table S1.8). Moreover, observably
453 signicant proteins of 76,206 proteins were identied under P adj <0.1 (Table
S1.9). These are essential for nutrient processing, immune modulation, host metabolism
regulation, gut integrity maintenance, and ecosystem balance. Understanding these
pathways enhances our ability to manipulate the microbiome for health and therapeutic
benets. Focusing on the top enriched signicant pathways, 127 KO IDs were identi
ed, and ve targeted KO IDs, that is, K00627, K06606, K12343, K02851, and K02586,
were signicant (P < 0.05) (Table S1.10). Across targeted KO IDs against 453 signicant
proteins, nine key proteins were positively associated with CMH. The results are listed in
Table 2; Table S1.11.
Elaborately, we identied the dihydrolipoamide acetyltransferase component of
pyruvate dehydrogenase complex (G5H8J6 and A0A1H0BQ85, K00627) involved in
glycolysis/gluconeogenesis and citrate cycle (TCA cycle) from Alistipes indistinctus and
FIG 1 Functional diversity of proteins in the gut microbiome of Thai adults spans six main categories.
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Megasphaera paucivorans; sugar phosphate isomerase/epimerase (A0A6N9PAY8, K06606)
involved in inositol phosphate metabolism from Clostridiaceae bacterium; 3-oxo-5-
alpha-steroid 4-dehydrogenase (A0A3B9CP90, K12343) involved in steroid hormone
biosynthesis from Alistipes sp.; undecaprenyl/decaprenyl-phosphate alpha-N-acetylglu
cosaminyl 1-phosphate transferase (A0A8B3C6B7, K02851) involved in O-antigen repeat
unit biosynthesis from Alistipes sp.; UDP-N-acetylmuramyl pentapeptide phosphotrans
ferase/UDP-N-acetylglucosamine-1-phosphate transferase (R7JPN7, K02851) involved
in O-antigen repeat unit biosynthesis from Alistipes putredinis; UDP-N-acetylglucosa
mine:undecaprenyl-P N-acetylglucosaminyl 1 P transferase (F2BWI1, K02851) involved
in O-antigen repeat unit biosynthesis from Dialister micraerophilus; oxidoreductase
nitrogenase component 1 (R6I712, K02586) involved in chloroalkane and chloroalkene
degradation from Phascolarctobacterium faecium; and nitrogenase protein alpha chain
(A0A170NNM0, K02586) involved in chloroalkane and chloroalkene degradation from
Clostridium coskatii. Remarkably, these eight bacterial species were manually curated and
associated with short-chain fatty acids (SCFAs) as shown in Fig. 6.
Identifying cooperative metabolic routes analysis in association with
prebiotic diet
To identify the cooperative metabolic routes related to human gut, the key predominant
bacterial species and crucial metabolic activities were integrated upon treatment with
prebiotic diet, that is, CMH. Hereby, the cooperative metabolic routes are identied
as shown in Fig. 7. Promisingly, cooperative routes of SCFAs biosynthesis, lipopolysac
charide (LPS) biosynthesis, and bile acid (BA) metabolism were communicated through
eleven predominant species (A. indistinctus, A. putredinis, Alistipes sp., Clostridiaceae
FIG 2 Comparison of gut microbial diversity between the CMH and PB groups. (A) Dot plots with regression lines depicting alpha diversity (i.e. observed species,
Shannon index, and Simpson index). (B) PCoA plot of beta diversity based on Bray-Curtis distances across bPB, bCMH, tPB, and tCMH. Note: bPB represents
baseline placebo, bCMH represents baseline CMH, tPB represents treatment with placebo, and tCMH represents treatment with CMH.
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bacterium, C. bovifaecis, C. coskatii, C. putrefaciens, D. micraerophilus, E. pyruvativorans, M.
paucivorans, and P. faecium).
Regarding SCFAs biosynthesis, the three metabolic pathways were highlighted, for
example, inositol phosphate metabolic pathway, chloroalkane and chloroalkene
degradation pathway, as well as glycolysis/gluconeogenesis and TCA cycle pathway,
which are responsible for acetate production. For inositol phosphate metabolic pathway,
we found key enzyme 2-keto-myo-inositol isomerase (K06606, EC:5.3.99.11) identied in
Clostridiaceae bacterium, which is capable of converting 1-keto-D-chiro-inositol to scyllo-
inosose. This is important for generating key precursors like malonate semialdehyde and
glyceraldehyde 3-phosphate for acetyl-CoA. As acetyl-CoA formation, we also found key
enzyme (K02586, EC: 1.18.6.1) in chloroalkane and chloroalkene degradation pathway,
which is capable of converting acetylene to ethylene associating with nitrogenase
molybdenum-iron protein alpha chain in both P. faecium and C. coskatii. This study shows
that acetyl-CoA is a key precursor for further acetate formation.
In glycolysis/gluconeogenesis and TCA cycle pathway, the key enzyme pyruvate
dehydrogenase, dihydrolipoamide acetyltransferase (K00627, EC:2.3.1.12) across A.
indistinctus and M. paucivorans, was found. This enzyme is a second component of the
pyruvate dehydrogenase complex, responsible for transferring the acetyl group to
coenzyme A (CoA) during the conversion of pyruvate into acetyl CoA (12).
Focusing on LPS biosynthesis, O-antigen repeat unit biosynthetic pathway is also
illustrated (Fig. 7). There was a key enzyme, that is, UDP-GlcNAc: undecaprenyl-phos
phate/decaprenyl-phosphate GlcNAc-1-phosphate transferase (K02851, EC: 2.7.8.33, EC:
2.7.8.35), across Alistipes sp. A. putredinis and D. micraerophilus. This enzyme transfers
GlcNAc-1-P from UDP-GlcNAc to membrane-bound P-Und, forming GlcNAc-PP-Und.
Subsequently, it is extended to form the repeating unit by sequential addition of sugars
by specic glycosyltransferases (GTs) (13, 14).
Considering the BA metabolism, the steroid hormone biosynthetic pathway involved
in gut microbiome-shaped BAs is also depicted (Fig. 7). A key enzyme involved in steroid
dehydrogenase: 3-oxo-5α-steroid 4-dehydrogenase 1 (K12343, EC:1.3.1.22) in Alistipes sp.
This can convert a secondary BA metabolite 3-oxolithocholic acid (3-oxoLCA) to isoal
loLCA. The 3-oxoLCA is converted to isoalloLCA by sequential actions of three enzymes:
Δ4-3-oxosteroid 5β-reductase (5β-reductase), 3-oxo-5α-steroid 4-dehydrogenase (5α-
reductase), and 3β-hydroxysteroid dehydrogenase (3β-HSDH), which were found in
Odoribacter spp. and Alistipes spp (15).
FIG 3 Comparison of microbial composition proles between CMH and PB groups. Note: Stacked column illustrates the relative abundance of bacterial families
across the four groups: bPB, bCMH, tPB, and tCMH.
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Concerning isoalloLCA, it maintains intestinal homeostasis by promoting dierentia-
tion of naive T-cells to regulatory T cells in gut lamina propria (15, 16). IsoalloLCA was also
observed to inhibit the growth of Clostridium dicile and other gram-positive species,
such as Bidobacterium, Faecalibacterium, and Streptococcus. This might lower the risk of
infection and contribute to maintaining intestinal homeostasis for healthy aging. The
results of this study show that key predominant bacterial species are strongly linked to
the utilization of prebiotic diet, for example, CMH. These ndings uncover concerted
microbial communities utilizing sugars (e.g., glucose or mannose) and dietary bers (e.g.,
CMH) in relation to SCFAs biosynthesis, LPS biosynthesis, and BA metabolism. These
FIG 4 Comparison of bacterial families and species between CMH and PB groups. (A) Seven bacterial families associated with
CMH. (B) Three signicant species between CMH and PB groups (P adj < 0.05). Note: Dot plots with regression lines depict the
log2 observed abundance corrected by DESeq2.
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results suggest that CMH evidently inuences the human gut microbiome on diversities,
compositions, and metabolic responses.
DISCUSSION
The impact of CMH has been focused on gastrointestinal symptoms and gut microbial
compositions. The functional diversities and activities toward metabolic routes of
microbial communities in response to prebiotic diet, for example, CMH, were mainly
studied. Regarding microbial diversities, the results revealed no signicant dierence in
gut microbial richness and alpha diversity indices when treated with CMH (8, 9). For beta-
diversity indices, our results demonstrated a distinct microbial community composition
after treatment with CMH. These suggest signicant alterations in microbial composition
structure by prebiotic diet uptake.
In terms of microbial taxonomic proles contributing to these diversity patterns,
noticeably, CMH increases the abundance of bacterial families, that is, Bacteroidaceae,
Clostridiaceae, Erysipelotrichaceae, Eubacteriaceae, Lachnospiraceae, Ruminococcaceae,
and Veillonellaceae. This result aligns with previous studies nding that treatment with
CMH could enhance the abundance of the gut microbiome, including Acidaminococca
ceae, Coriobacteriaceae, Erysipelotrichaceae, Ruminococcaceae, and Veillonellaceae (9). In
addition, the results are consistent with those of earlier reports that these bacterial
families contain enzymes involved in the degradation of hemicellulose (e.g., xylan,
mannan, and galactomannan), cellulose, or dietary ber (17–20). Interestingly, signicant
dierences were observed in the gut microbial composition at the species level, that is, C.
bovifaecis, C. putrefaciens, and E. pyruvativorans in relation to CMH. Consistent with prior
information, members of the Clostridium exhibit both fermentative and electrogenic
activity, enabling them to degrade a wide range of substrates, such as cellulose (21).
FIG 5 Top 20 metabolic pathways with a high number of protein functional activities.
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Additionally, Clostridium has also been reported to ferment glycerol, xylose, or pentosans
into SCFAs, for example, acetate production (22). Moreover, C. bovifaecis and C. putrefa
ciens belonging to the Clostridium are generally involved in SCFA production. Meanwhile,
E. pyruvativorans, belonging to the Eubacterium, is a non-saccharolytic anaerobe that
fermented pyruvate and amino acids known as SCFA production (i.e., caproate and
valerate) and BA metabolism (23–26).
Furthermore, our ndings are also consistent with those of Rowland et al. (27),
demonstrating various microbially mediated modications at the steroid nucleus that led
to secondary bile acids. Clostridium and Eubacterium have the capability to transform
FIG 6 Bubble plot represents the signicant proteins across dierent bacterial species underlying targeted KO IDs. Note: bPB represents baseline placebo, bCMH
represents baseline CMH, tPB represents treatment with placebo, and tCMH represents treatment with CMH. No bubbles mean no protein expression (zero
expression).
TABLE 2 List of key proteins associated with metabolic functional activities which signicantly enriched in CMHa
Protein ID Protein name Category KO ID P adj
G5H8J6 Dihydrolipoamide acetyltransferase component of
pyruvate dehydrogenase complex
Glycolysis/Gluconeogenesis, Citrate cycle
(TCA cycle)
K00627 0.098107
A0A1H0BQ85 Dihydrolipoamide acetyltransferase component of
pyruvate dehydrogenase complex
Glycolysis/Gluconeogenesis, Citrate cycle
(TCA cycle)
K00627 0.051396
A0A6N9PAY8 Sugar phosphate isomerase/epimerase Inositol phosphate metabolism K06606 0.098107
A0A3B9CP90 3-oxo-5-alpha-steroid 4-dehydrogenase Steroid hormone biosynthesis K12343 0.098107
A0A8B3C6B7 Undecaprenyl/decaprenyl-phosphate alpha-N-acetyl
glucosaminyl 1-phosphate transferase
O-Antigen repeat unit biosynthesis K02851 0.098107
R7JPN7 UDP-N-acetylmuramyl pentapeptide phospho
transferase/UDP-N-acetylglucosamine-1-phosphate
transferase
O-Antigen repeat unit biosynthesis K02851 0.098107
F2BWI1 UDP-N-acetylglucosamine: undecaprenyl-P N-acetyl
glucosaminyl 1 P transferase
O-Antigen repeat unit biosynthesis K02851 0.098107
R6I712 Oxidoreductase nitrogenase component 1 Chloroalkane and chloroalkene degradation K02586 0.098107
A0A170NNM0 Nitrogenase protein alpha chain Chloroalkane and chloroalkene degradation K02586 0.098107
aProtein ID was selected under P adj<0.1.
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chenodeoxycholic acid (CDCA) and cholic acid (CA) into the secondary bile acids
lithocholic acid (LCA) and deoxycholic acid (DCA), respectively. These ndings suggest
that the analysis of microbial diversity and composition in the gut is useful for signifying
the impacts of CMH interventions in humans.
A quantitative assessment was conducted of metabolic function across DEPs and
enrichment analysis. An identication of enzymes involved in metabolism that were
signicantly enriched in CMH was of particular interest. Indeed, key enzymes and a
predominant bacterial species associated with SCFA biosynthesis, LPS biosynthesis, and
BA metabolism were signicantly enriched in CMH (28–34).
Beyond our metaproteomics with integrated metagenomics (i.e., 16S rRNA gene
sequence and WMGS data sets), this nding reveals the cooperative metabolic routes
facilitated by 23 predominant bacterial species, including A. butyriciproducens, A. hadrus,
A. indistinctus, A. intestini, A. putredinis, Alistipes sp., B. dorei, B. massiliensis, B. vulgatus,
C. bovifaecis, C. coskatii, C. putrefaciens, C. saccharolyticum, Clostridiaceae bacterium, D.
micraerophilus, D. piger, E. coli, E. pyruvativorans, E. siraeum, M. paucivorans, P. faecium, R.
hominis, and R. intestinalis. These predominant bacterial species participated in potential
metabolic pathways, including glycolysis/gluconeogenesis, the TCA cycle, pentose
and glucuronate interconversions, propanoate metabolism, C5-branched dibasic acid
metabolism, inositol phosphate metabolism, steroid hormone biosynthesis, O-antigen
FIG 7 The concerted microbial communities and associated metabolic activities involved in SCFA biosynthesis, LPS biosynthesis, and BA metabolism pathways.
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repeat unit biosynthesis, and chloroalkane and chloroalkene degradation. Regarding the
predominant bacterial species and potential metabolic pathways, we interestingly found
that the genus Clostridium and glycolysis/gluconeogenesis as well as TCA cycle pathways
were commonly observed in both metaproteomics and metagenomics (Table S1.12). This
integrative nding not only uncovers the cooperative metabolic routes underpinning
gut health but also specically identies critical bacterial species and potential metabolic
pathways that may serve as targets for therapeutic interventions, highlighting their roles
in sustaining gut homeostasis and inuencing host health.
In light of our study, the relationship between gut microbiome and dietary sup
plements is revealed to be cooperative networks within gut microbial communities,
providing insight into the metabolic diversity and activities of microbial communities
and the complexities of the diet-microbiome relationship.
Conclusions
Metaproteomics-based analysis reveals functional diversity and metabolic activities of
the human gut microbiome in Thai Adults. In response to prebiotic diet, for exam
ple, CMH, cooperative metabolic routes within gut microbial communities under the
utilization of sugars and dietary bers were unveiled. CMH is thus proposed to be the
alternative intervention as a potential prebiotic diet for modulating and maintaining
gut metabolism. This study serves as a roadmap for monitoring metabolic functional
diversity and activities of gut microbiome toward its implications for human health.
MATERIALS AND METHODS
Participant enrollment and fecal sample collection
Twenty Thai adults residing in Bangkok and neighborhood aged between 22 and
39 years with a body mass index of 18.5–24.8 kg/m2 were enrolled in double-blin
ded, placebo-controlled trials. Regarding the recruitment process, the participants
were informed at King Chulalongkorn Memorial Hospital in Bangkok, Thailand, under
stringent inclusion and exclusion criteria, including considerations, such as dietary
intake, age, and health status. Importantly, the participants had no history of intesti
nal diseases or diarrheal symptoms in the months preceding sampling, and none had
a family history of colorectal cancer. Additionally, participants refrained from taking
antibiotics for at least 3 months, as well as avoiding probiotics, prebiotics, and synbiotics
for a minimum of 1 month before sampling. Participants with allergies to coconuts or
food intolerance were excluded. The demographic characteristics of the study partici
pants in this cohort are presented in Table 3; Table S1.13. The preparation of CMH and
placebo drinks, study design, randomization and allocation concealment, and interven
tion were detailed according to the methodology outlined by Sathitkowitchai et al. (8).
For fecal sample collection, the participants were assigned into two groups (CMH and
PB) at baseline and treatment, resulting in a total of 40 samples: bPB for 10 samples,
bCMH for 10 samples, tPB for 10 samples, and tCMH for 10 samples. Notably, baseline
means that they were not treated with either CMH or PB, whereas treatment means that
they were subjected to CMH or PB for 21 days. Fecal samples (20 g) were immediately
TABLE 3 Demographic characteristics of study participants at baselinea
Variable Placebo CMH P-value
Number 10 10 1.000
Age (year) 29 ± 4.32 31 ± 5.95 0.286
Weight (Kg) 54 ± 7.76 49 ± 7.31 0.198
Height (cm) 159 ± 4.29 156 ± 4.92 0.182
Body mass index 21 ± 2.30 20 ± 1.55 0.436
aAll values are expressed as mean ± SD. The P-values were calculated using the Wilcoxon rank-sum test.
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collected at the time of defecation and placed into a collection tube in a cooler bag. The
fecal samples were stored at −80°C for further analysis.
This study was approved by the Thai Clinical Trials Registry (trial identication
number: TCTR20190426003) and the Ethics Committee of King Chulalongkorn Memorial
Hospital, Bangkok, Thailand (IRB No. 388/61). All methods were performed in accordance
with relevant guidelines and regulations. Written informed consent was obtained from
all participants.
Microbial protein extraction
Sample preparation was performed as previously described by Losuwannarak et al.
(35). Briey, frozen fecal samples were reconstituted in 50 mM phosphate buer pH
7.0 and then vortexed well. After centrifugation for 10 min at 12,000 rpm to remove
debris and some large particles (36), the solubilized protein remaining in the clear
supernatant was collected. Total soluble protein was measured with a Lowry assay using
bovine serum albumin as a standard (37). In 5 mg protein samples, disulde bonds
were reduced using 5 mM dithiothreitol in 10 mM ammonium bicarbonate at 60°C for
1 h, followed by the alkylation of sulfhydryl groups by 15 mM iodoacetamide in 10 mM
ammonium bicarbonate for 45 min in the dark at room temperature. For digestion, the
protein samples were mixed with sequencing-grade trypsin (ratio of 1:20) (Promega,
Germany) and incubated at 37°C overnight. Prior to LC-MS/MS analysis, the digested
protein (tryptic peptide) samples were dried and protonated with 0.1% formic acid
before injection into the LC-MS/MS system.
Liquid chromatography-tandem mass spectrometry analysis
LC-MS/MS was conducted as previously described in Losuwannarak et al. (35). Speci-
cally, the tryptic peptide samples (100 ng) were injected in triplicates into an UltimateTM
3000 Nano/Capillary LC System (Thermo Scientic) coupled to a Hybrid quadrupole
Q-TOF impact II (Bruker Daltonics) equipped with a Nano-captive spray ionization (CSI)
source. Here, the peptides underwent an enrichment step utilizing a μ-Precolumn
(300 mm i.d. × 5 mm) packed with C18 PepMap 100, 5 mm, 100 A° (Thermo Scientic)
and separated on a column (75 mm I.D. × 15 cm) lled with Acclaim PepMap RSLC C18,
2 mm, 100, nanoViper (Thermo Scientic). A mobile phase of solvent A (0.1% formic
acid) and solvent B (80% acetonitrile and 0.1% formic acid) were applied to the analytical
column. A linear gradient ranging from 5% to 55% solvent B was utilized to elute the
peptides at a constant ow rate of 0.30 mL/min for 30 min. Electrospray ionization
was performed at 1.6 kV using the CaptiveSpray system. Mass spectra (MS) and MS/MS
spectra were acquired in the positive ion mode at a frequency of 2 Hz, covering the
range of m/z 150–2,200.
Quantication and identication of microbial proteins and database search
For the quantication of proteins, MaxQuant (version 2.1.4.0) was used to quantify
individual samples and submit their MS/MS spectra to match with the UniProt bacterial
databases by using the Andromeda search engine (38). The parameters for label-free
quantitation and identication with MaxQuant were (ⅰ) a maximum of two missed
cleavages, (ⅱ) mass tolerance of 0.6 Daltons for the main search, (ⅲ) trypsin as the
digestion enzyme, (ⅳ) carbamidomethylation of cysteine residues as a xed modi-
cation, and (ⅴ) oxidation of methionine and acetylation of the protein N-terminus
as variable modications. The signicance threshold for protein identication was
established with a P-value < 0.05 and a false discovery rate (FDR) of 1%. For searches in
FASTA les, a protein database of 15 candidate bacterial families—Acidaminococcaceae,
Bacteroidaceae, Bidobacteriaceae, Clostridiaceae, Coriobacteriaceae, Enterobacteriaceae,
Erysipelotrichaceae, Eubacteriaceae, Lachnospiraceae, Lactobacillaceae, Prevotellaceae,
Rikenellaceae, Ruminococcaceae, Streptococcaceae, and Veillonellaceae, selected from
earlier reports of gut microbiome data from Thailand (9, 39, 40)—was downloaded from
UniProt. Database with potential contaminants included in MaxQuant was automatically
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added. The MaxQuant ProteinGroups.txt le was subsequently obtained in conjunction
with the use of Perseus software (version 1.6.6.0) for importing peptide sequences into
the metaproteome data set (41). Exact peptides for which a unique protein sequence
was matched to a single bacterial strain were classied as bacterial strain-specic
sequences for taxonomic classication (42, 43). The remaining peptides for which a
unique protein sequence was not matched to a single bacterial strain were discarded.
The protein sequences assigned with protein IDs with known/putative functions from
the UniProt bacterial database were denoted as annotated proteins. In contrast, the
protein sequences assigned an ID corresponding to a hypothetical protein/uncharacter
ized protein were designated as unannotated proteins. For quality control, the mean
and standard deviation (SD) values were calculated for the total spectral counts and
total unique peptide counts. Maximum peptide intensities were selected, providing the
protein expression levels (PELs). For further functional diversity, annotated proteins were
assigned and categorized using the KEGG database.
Microbial diversity, taxonomy, and metabolic functional analysis
Metaproteome data were analyzed for microbial diversity, community composition, and
metabolic functional analysis for both CMH and PB groups. Alpha- and beta-diversity
analyses were conducted using a vegan package in the R program (version 2.5–6).
For alpha-diversity, the observed species, Shannon’s index, and Simpson’s index were
used to calculate species richness and abundance. Beta-diversity was assessed using
Bray-Curtis distances with the metaMDS function in the vegan R package (44). PCoA
was performed to visually evaluate the dierences in microbial community structure
across sample conditions using the ggplot2 R package (45). Statistical dierences in
associations between diversity values and sample groups, that is, CMH and PB were
calculated using linear regression for alpha-diversity and permutational multivariate
analysis of variance (ADONIS) for beta-diversity.
Concerning dierential abundance of bacteria and proteins, PELs were used to
infer taxonomic levels and DEPs. For microbial taxonomy according to the 15 selec
ted bacterial families, the PELs were grouped based on taxonomic information in the
sequence database and then were summed to obtain total PELs for taxa at each level
(i.e., family, genus, and species) in each sample. The relative abundance was plotted
using the ggplot2 package in R program. The DEPs analysis was afterward performed
between CMH and PB groups using total PELs for each abundant protein. The dierence
in abundance of bacterial taxa and proteins was examined using negative binomial
generalized linear models (DESeq2) (46). The likelihood-ratio tests (LRT) in DESeq2 model
were used to determine the statistical signicance of dierent treatments, which allows
us to estimate the treatment eect on protein expression by normalizing for variations
between samples, ensuring that the analysis reected genuine biological changes. The
apeglm method for log2FC shrinkage was used to account for dispersion and varia
tion of eect size across individuals and treatments. Only bacteria and proteins with
independent hypothesis weighted log2FC > 0 were regarded as positively related to
CMH. Benjamini-Hochberg correction was used for multiple testing to dene dieren-
tially abundant bacteria and proteins (FDR < 0.05).
For the metabolic functional annotation, the DEPs were searched against the KEGG
database and then assigned to KEGG Orthology (KO) IDs using GhostKOALA (47). After
the identication of KO IDs, they were categorized into main functions, subfunctions,
and pathways. Statistical dierences in microbial protein between CMH and PB at
baseline were calculated using permutational multivariate analysis of variance (ADONIS).
Additionally, pathway and functional enrichment analysis was performed using GSEA
(48) in the R package piano based on DEPs between CMH and PB groups. Pathways
and functions with a distinct updirectional P-value < 0.05 were considered signicantly
enriched.
Research Article Microbiology Spectrum
February 2025 Volume 13 Issue 2 10.1128/spectrum.01599-24 13

Identication of key predominant groups of bacterial species, potential
proteins, and their metabolic routes in relation to prebiotic diet
To identify the key predominant groups of bacterial species, potential proteins,
and associated routes, the results from microbial taxonomy and metabolic functions
obtained from metaproteomic data were integrated. Subsequently, abundant bacterial
species (P adj <0.05) and enriched KO IDs (P adj <0.1) were then considered. The targets
of enriched KO IDs across abundant bacterial species were mapped to target metabolic
pathways using the KEGG database. Upon mapping abundant bacterial species onto
target metabolic pathways, the abundant species in each pathway were considered to
have the ability to act together in a community as key predominant groups of bacterial
species. Furthermore, manual curation and literature mining were also conducted to
reveal the key predominant groups of bacterial species and a list of potential proteins
as well as associated routes for generating cooperative networks within microbial
communities.
ACKNOWLEDGMENTS
This project is supported by National Research Council of Thailand (NRCT) (N42A650235)
and the High-Quality Research Graduate Development Cooperation Project between
Kasetsart University and the National Science and Technology Development Agency
(NSTDA). The authors would also like to thank Professor Gianni Panagiotou from
Microbiome Dynamics Unit, Leibniz Institute for Natural Product Research and Infection
Biology–Hans Knöll Institute, Jena, Germany for valuable suggestion. A.K. would like to
thank the Interdisciplinary Graduate Program in Bioscience, Faculty of Science, Kasetsart
University. W.V. would like to thank Department of Zoology, SciKU Biodata Server, and
International SciKU Branding (ISB), Faculty of Science, Kasetsart University for supports.
This research was funded by the National Research Council of Thailand (NRCT) under
Grant N42A650235, and the High-Quality Research Graduate Development Coopera
tion Project between Kasetsart University and the National Science and Technology
Development Agency (NSTDA).
A.K. analyzed the data, prepared tables and gures, and wrote the manuscript; P.P.
assisted in analyzed the data; M.N., N.R., and S.R. conducted the cohort study and sample
preparation; N.S. collected clinical data and fecal samples; W.V. conceived and designed
all experiments, interpreted all results, supervised throughout the study, and wrote the
manuscript. All authors revised the manuscript. All authors have read and agreed to the
published version of the manuscript.
AUTHOR AFFILIATIONS
1Interdisciplinary Graduate Program in Bioscience, Faculty of Science, Kasetsart
University, Bangkok, Thailand
2Pediatric Allergy & Clinical Immunology Research Unit, Division of Allergy and Immu
nology, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, King
Chulalongkorn Memorial Hospital, the Thai Red Cross Society, Bangkok, Thailand
3Department of Biotechnology, Faculty of Agro-Industry, Kasetsart University, Bangkok,
Thailand
4Functional Proteomics Technology Laboratory, National Center for Genetic Engineering
and Biotechnology, National Science and Technology Development Agency, Pathum
Thani, Thailand
5Department of Zoology, Faculty of Sciences, Kasetsart University, Bangkok, Thailand
6Omics Center for Agriculture, Bioresources, Food, and Health, Kasetsart University
(OmiKU), Bangkok, Thailand
AUTHOR ORCIDs
Amornthep Kingkaw http://orcid.org/0000-0001-6111-3568
Research Article Microbiology Spectrum
February 2025 Volume 13 Issue 2 10.1128/spectrum.01599-24 14

FUNDING
Funder Grant(s) Author(s)
The High-Quality Research Graduate Development
Cooperation Project between Kasetsart Univer
sity and the National Science and Technology
Development Agency (NSTDA)
Amornthep Kingkaw
National Research Council of Thailand (NRCT) N42A650235 Wanwipa Vongsang
nak
AUTHOR CONTRIBUTIONS
Amornthep Kingkaw, Conceptualization, Formal analysis, Investigation, Validation,
Visualization, Writing – original draft, Writing – review and editing | Preecha Patum
charoenpol, Formal analysis, Writing – review and editing | Narissara Suratannon, Data
curation, Resources, Writing – review and editing | Massalin Nakphaichit, Data curation,
Resources, Writing – review and editing | Sittiruk Roytrakul, Data curation, Software,
Writing – review and editing | Wanwipa Vongsangnak, Conceptualization, Funding
acquisition, Investigation, Project administration, Supervision, Writing – original draft,
Writing – review and editing
DATA AVAILABILITY
The mass spectrometry proteomic data have been deposited with the ProteomeX
change: PXD052903 and JPST003162.
ETHICS APPROVAL
This study was approved by the Thai Clinical Trials Registry (trial identication number
TCTR20190426003) and the Ethics Committee of King Chulalongkorn Memorial Hospital,
Bangkok, Thailand (IRB number 388/61).
ADDITIONAL FILES
The following material is available online.
Supplemental Material
Supplemental material (Spectrum01599-24-s0001.xlsx). Tables S1 to S13.
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