1School of Pharmaceutical Sciences, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada
2Ottawa Institute of Systems Biology, University of Ottawa, Ottawa, Ontario, Canada
3Center for Biologics Evaluation, Biologic and Radiopharmaceutical Drugs Directorate, Health Products and Food Branch, Health Canada, Ottawa, Canada
Correspondence
Daniel Figeys, School of Pharmaceutical Sciences, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada.
Email: dfigeys@uottawa.ca

Accepted: 28 September 2022

Received: 30 April 2022 Revised: 27 September 2022
DOI: 10.1002/pmic.202100389
REVIEW
An emerging field: Post-translational modification in microbiome
Haonan Duan1,2	Xu Zhang3	Daniel Figeys1,2
Abstract
Post-translational modifications (PTMs) play an essential role in most biological processes. PTMs on human proteins have been extensively studied. Studies on bacterial PTMs are emerging, which demonstrate that bacterial PTMs are different from human PTMs in their types, mechanisms and functions. Few PTM studies have been done on the microbiome. Here, we reviewed several studied PTMs in bacteria including phosphorylation, acetylation, succinylation, glycosylation, and proteases. We discussed the enzymes responsible for each PTM and their functions. We also summarized the current methods used to study microbiome PTMs and the observations demonstrating the roles of PTM in the microbe-microbe interactions within the microbiome and their interactions with the environment or host. Although new methods and tools for PTM studies are still needed, the existing technologies have made great progress enabling a deeper understanding of the functional regulation of the microbiome. Large-scale application of these microbiome-wide PTM studies will provide a better understanding of the microbiome and its roles in the development of human diseases.
KEYWORDS
bioinformatics, cell biology, microbiology, microbiome, metaproteomics, post-translational modifications

Proteomics 2023, 23:2100389
https://doi.org/10.1002/pmic.202100389

www.proteomics-journal.com

© 2022 Wiley-VCH GmbH. 1of17

1		INTRODUCTION
Post-translational modifications (PTMs) are important in both eukaryotes and prokaryotes for the modulation of protein function. PTM refers to the covalent modification of amino acid side chains and C and N terminus as well as proteolytic cleavage of proteins. Currently, over 400 different types of PTMs have been catalogued [1], many of which are dynamic and reversible. Protein functions, localization, interactions, and structure are often modulated through PTMs that cause changes in the chemical properties of the modified sites [2]. Moreover, the addition of PTMs can lead to multiple protein isoforms representing different protein functional states present in a cell at any given time. However, each of these protein isoforms varies in abundance with some PTMs present only at minute levels. Their analysis and identification are challenging, often requiring enrichment using chemical and biochemical methods before detection.
In microbes, PTMs are involved in many aspects of biological activities including metabolism [3], cell growth, stress response [4], and virulence [5–7]. As well, they help microbes acclimatize to special environments and cope with changes [8–10]. The study of PTMs in microbiome is further complicated by our limited understanding of their functions in this complex microbial system. Fortunately, systematic studies of PTM changes in microbiome following environmental changes can help elucidate their functions. For example, a study on the microbiome in deep-sea hydrothermal vent found a connection between modification types and particular functions [11]. Although they did not match the modification to specific mechanisms, the significant enrichment of PTMs helped better understand their functions. PTM patterns also change along ecological succession. For example, microbiome’s PTM pattern in early growth-stage biofilm was found to be different from those in a mature biofilm [12]. Those changes of PTMs are very likely to be used by microbes to deal with different
circumstances. The function of PTMs can also be inferred by similarity across microbes responding to the same environmental changes. For example, succinylation is commonly observed in thermophilic and mesophilic bacteria which can live in high temperature [13]. Because the succinylation sites are conserved across different bacteria, they were suspected of being involved in bacterial heat resistance. Microbial PTMs were usually studied at the single-species level. For example, Escherichia coli at different growth phases showed differences in the PTM level. A higher level of lysine acetylation was observed in E. coli at stationary phase [14]. Differences in PTM can even be observed at the strain level. For example, Erwinia amylovora, strains Ea1189 and Ea273 under different culture conditions have different PTM patterns [15]. Although there are some promising developments, overall, we know very little about the PTMs present in microbiomes, their roles, and dynamic expression. As well, we do not know whether PTMs measured using individual bacterial culture will be present or behavior similarly when the bacteria are in a complex microbiome as the environmental pressures are drastically different. Moreover, it is likely that microbiomes contain unknown PTM types yet to be catalogued.
Rapid advances in sequencing technology have revealed the complexity of microbiomes. Complex microbiomes were found in human, ocean [16], forest [17], and even some extreme environments [11]. A microbiome could harbor hundreds of species that are dynamic and interactional [18, 19]. Microbes in a microbiome are often in competition for resources, interact with other microbes, and can depend on other microbes for specific resources. These are likely reflected through dynamic proteins PTMs. A proteomic study in Mycobacterium tuberculosis, the bacterium that causes tuberculosis, estimated that roughly 1/3rd of the identified proteins carried modifications [20]. Similarly, a metaproteomics study of the microbiome from an acid mine drainage site, reported that 29% of the identified proteins were modified [12]. Assuming these numbers hold true, this would mean for example that a human gut microbiome that encodes ∼800,000 non-redundant genes (based on metagenome sequencing), would have up to 240,000 proteins (if expressed) carrying one or multiple PTMs representing possibly millions of PTM protein isoforms. Clearly, the study of PTMs in a complex microbiome represents an important technical challenge. Here we review microbial PTMs and their functions as well as the methods used for their characterization at a microbiome level.
2		PTMS IN PROKARYOTES
2.1		Phosphorylation
Phosphorylations on proteins are regulated through enzymes including kinases and phosphatases with different sequence specificities. The phosphorylation of proteins is catalyzed by kinase whereas phosphatases are responsible for the removal of phosphorylation. According to the different phosphorylated groups, phosphorylation can be classified into four types (Figure 1). Adenosine Triphosphate (ATP) is the common phosphate donor whereas some kinases transfer the phosphate from a phosphorylated protein to another [21]. Most phosphorylation is residual specific. Due to the diverse forms of phosphorylation, it is difficult to study all phosphorylation together instead researches specific to each phosphorylation type is generally performed.
In bacteria, protein kinases have been sub-classified based on their substrate specificity, which are serine/threonine kinases (Hanks-Type), tyrosine kinases, histidine kinases, arginine kinases and atypical serine kinases. Serine and threonine residuals are typically catalyzed by the hanks-type kinase [22]. The kinases are involved in many functions in cell growth and interactions with host. The BY kinases, arginine kinases are responsible for phosphorylation on tyrosine and arginine respectively [23, 24]. Phosphorylations have also been observed on other amino acids. Lysine phosphorylation can be detected in vivo [25], but neither related enzymes nor its functions have been elucidated. Glutamate with a small difference from aspartate is distinctive on phosphorylation. Phosphorylated glutamate is always found as a free amino acid known as γ-glutamyl phosphate which is the intermediate in the biosynthesis of glutamine and proline [26]. No glutamate phosphorylation protein has been found in bacteria. And many analogues of free phosphorylated glutamate are found as inhibitors of glutamine synthetase [26]. Thiols on cysteine are active and can undertake many PTMs like acylation, nitrosylation, and sulfuration [27]. However, phosphorylation on cysteine is rarely studied. No specific kinase/phosphatase pair to cysteine phosphorylation has been found. But it was reported that cysteine phosphorylation can be mediated by serine and threonine kinase/phosphatase, named as stks/stps and is related to bacterial virulence and antibiotic resistance [28].
Dephosphorylation is an important part of signal transduction and is achieved through protein phosphatases that have substrate specificity. There are three types of phosphatases that have been studied in bacteria specific to phosphotyrosine [29], phosphoserine/threonine [22], and phosphohistidine [30]. Phosphatase with dual specificity to phosphoserine/threonine residual and phosphotyrosine residual is only found in eukaryotes and viruses [31, 32]. Besides, many bacteria also harbor nonspecific phosphatases which can be classified into alkaline phosphatases and acid phosphatases according to their optimum pH for their activities [33]. The alkaline phosphatases also show the capacity to perform dephosphorylation of proteins [34]. In the soil ecosystem, those nonspecific phosphatases play an important role in organic phosphorus mineralization which provides inorganic phosphorus for the plant [35]. And soil microorganism is one of the main sources of phosphatases [36]. Similarly, bacteria-derived phosphatase is also a key factor in the marine environment [37, 38].

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Phosphorylation is used for rapid signal transduction in bacteria. This is well illustrated by the two-component regulatory systems (TCS). At its basis, TCS functions by having a membrane-bound sensor with autocatalytic histidine kinase activity. Upon sensing environmental changes (pH, osmotic pressure, phosphate, oxygen, etc), the kinase autophosphorylates the sensor on a histidine residue [39]. The phosphorylation is then transferred onto another protein (DNA binding response regulator) which then regulates specific gene expressions. The signal transduction is realized through the
Phosphoester
Phosphoamidate Phosphoacyl anhydride
Phosphothiolate
FIGURE 1 The structure of each type of phosphorylated amino acids
transfer of the phosphate group from the histidine residual on the sensor protein to the aspartate residual on the regulator protein. The TCS enhances the ability of bacteria to adapt to the environment. For example, some TCSs directly regulate flagellar rotation to help bacteria move toward the environment favoring survival [40]. The gram-negative pathogen Salmonella typhimurium can even survive in host macrophages with an active TCS [41]. In the gut microbiome, the TCS is useful for pathogen’s competitiveness [42]. As well, the TCS is enriched in the lung microbiome of patients with idiopathic pulmonary fibrosis [43]. Phosphorylation of TCS typically happens on the histidine residue, however, the phosphorylation on threonine residue through serine/threonine kinases has also been reported to be involved in TCS regulation [44, 45]. The GlnR is a regulator from a TCS responsible for N-assimilation. Except for the phosphorylation of aspartate, phosphorylation on serine/threonine or acetylation on lysine residues could inhibit the binding of GlnR to its target genes [46].
Studies about serine, threonine, and tyrosine phosphorylation on single species such as E. coli [47] and Clostridioides difficile [48] have been done. Similar to lysine acetylation, the phosphorylation level was found related to the growth phase. An increased level of phosphorylation was observed in C. difficile at the stationary phase. In addition, the activity of many enzymes in E. coli is regulated by phosphorylation. Unfortunately, microbiome scale studies of protein phosphorylations have yet to be reported. Nevertheless, studying protein phosphorylation is important to better understand the regulatory mechanisms on how a complex microbiome responds to environmental changes.
2.2 Acetylation
Protein acetylations are common in bacteria and are involved in the regulation of protein function. Acetylation is a reversible PTM [49]
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and is controlled by acetyltransferase and deacetylase. As well, protein acetylation can also occur non-enzymatically [50]. In bacteria, acetylation can occur on an amino group either at the protein Nterminus or the residues of lysine (Nɛ-acetylation). Lysine acetylation (Kac) has been extensively studied and is conserved from prokaryotes to eukaryotes [51]. Kac neutralizes the positive charge on proteins and may affect protein stability and activity. In E. coli, Kac proteins are mostly found in metabolism, transcription and translation [14, 52, 53] and maybe highly involved in regulating central carbon metabolism [54]. In eukaryotes, acetylation on histone can also loosen chromatin which is necessary for transcription [2]. Alteration of gut microbiome Kac pattern in Crohn’s disease has been observed which indicates it may be involved in the aberrant host-microbiome interactions in human disease [55]. Acetylation can also occur on the hydroxyl groups (O-acetylation) of the amino acid (threonine, serine) and is catalyzed by serine/threonine-protein acetyltransferase. Serine acetyltransferase and threonine acetyltransferase in Uniport (UniprotKB, 3/25/2022, limited to microbes) are only annotated in 20 bacterial species. Although O-acetylation is not prevalent, one of the serine/threonine-protein acetyltransferases, YopJ can be used by Yersinia pestis to acetylate kinase activation in the host and inhibit the host immune signaling [56, 57]. O-acetylation is also found in M. tuberculosis and is related to its virulence [58].
Acetyl-coenzyme A (acetyl-CoA) is believed to be the donor of the acetyl group for lysine acetylation [59, 60]. Acetyl-phosphate, which can be converted to acetyl-CoA, can induce non-enzymatic acetylation of proteins in vitro and is essential to global acetylation in E. coli [61]. Acetate kinase (ACK) and phosphate acetyltransferase (PTA) are the two enzymes that are responsible for the generation of acetylphosphate from acetate and acetyl-CoA respectively. The global effect of acetyl-phosphate indicates that the non-enzymatic acetylation is involved in bacterial viability and pathogenicity. These processes may be regulated in a uniform mechanism rather than site-specific.
In contrast, the enzymatic based protein acetylation has a higher specificity and more diverse acetyltransferases[50]. The enzymatic pathway is regulated by lysine acetyltransferase (KAT). There are four major families in KAT, p300/CBP, MYST, GNAT, and YopJ effector proteins [62]. p300/CBP and MYST are found only in eukaryotes, whereas YopJ effector protein is only found in prokaryotes. As mentioned before, YopJ is mainly responsible for the o-acetylation on serine and threonine and related to bacteria virulence [63]. GNAT is represented by YfiQ and its homologs (Pat, PatZ, and Pka) are widely found and well studied [62]. Acetyl-CoA synthetase (ACS) can convert acetate into acetyl-CoA. Pat in Salmonella enterica can inactivate ACS through acetylation on Lys609 [64]. Because acetylation of Pat needs acetyl- CoA as an acetyl group donor, the negative feedback regulation is realized.
Another notable example of Kac modulation on cell growth is the Ribonuclease R (RNase R) in Trans-translation. Trans-translation is a ubiquitous process in all bacteria. Due to a variety of reasons, bacteria may go wrong during translation resulting in a stalled ribosome. The bacteria need the trans-translation system to rescue the stalled ribosome and ensure the quality of translation [65]. It has been demon- strated that the system is essential for bacterial viability and pathogen virulence [5]. As one of the enzymes in Trans-translation, RNase R is responsible for the degradation of defective RNAs and can be regulated by acetylation on Lys544 [66]. The modification is catalyzed by protein lysine acetyltransferase [67]. In addition, unacetylated RNase R is stable and will accumulate while the bacteria are in the stationary phase. Interestingly, as soon as cultured cells are transferred into a fresh media, the RNase R will be acetylated and the bacteria will enter log-phase growth [14], which indicates that the acetylation of RNase R is involved in the cell growth. Because the trans-translation is absent in eukaryotes and important to bacteria, it can be a potential target for new antibiotics to handle antibiotic resistance [68].
Unlike acetylation, deacetylation is an enzyme-dependent process but not all the acetylated proteins can be deacetylated [62]. Lysine deacetylase (KDAC) is responsible for removing the acetyl group from lysine. The KDACs in human are widely studied and inhibitors that target KDACs have been developed and investigated in clinical trials [69]. However, for prokaryotes, CobB is the only KDAC that has been fully confirmed. Other putative KDACs like YcgC and Sir2E have been reported before [70]. But the YcgC is disputable and Sir2E needs to be further verified [71].
Because Kac has been widely studied in many species, we are able to compare the sequence motifs surrounding Kac. In many eukaryotes like Aspergillus flavus [72], Oryza sativa [73], Botrytis cinerea [74], and human [55, 75], Kac motif is characterized for tyrosine (Y), histidine (H), or phenylalanine (F) at +1 position and tyrosine (Y) or phenylalanine (F) at –1. The Kac motif in prokaryotes is more diverse [76, 77]. However, a significant and distinctive motif around Kac can be observed in the human gut microbiome with glutamic acid (E) and aspartic acid (D) at –1 and phenylalanine (F) at position +155. Except +1 or –1, many significant patterns can be observed from –6 to +6. Interestingly, the bacterial Kac motif is similar to those in mitochondria of some plants [78, 79]. Mitochondria is considered to have evolved from bacteria which could be the reason for the similarity [80]. Although it may be biased by the specificity of immunoaffinity enrichment, the specific pattern of Kac motif in the microbiome may be related to a particular function or activity.
2.3		Succinylation
Lysine succinylation (Ksu) was first found and verified in E. coli [81]. Since then, Ksu has been widely observed in eukaryotes and prokaryotes. Similar to acetylation, succinylation can occur both enzymatically and nonenzymatically and relies on succinyl-coenzyme A (succinyl- CoA) as the donor of the succinyl group [82]. Succinyl-CoA is an intermediate product in the TCA cycle which occurs mainly at mitochondrion in eukaryotes and at cytoplasm in prokaryotes. Kgd1 and Lsc1 are two enzymes which are responsible for the generation and degradation of Succinyl-CoA. The total level of Ksu could be regulated by knocking off Kgd1 and Lsc1 [82]. In addition, supplement with different carbon sources could also increase Ksu level (Table 1) [82–88], likely because those carbon sources promote the TCA cycle.

Name	Type	Ksu peptide	Location	Ksu sites shared with
Kac	Top enriched function	Increased by	References	
Pseudomonas aeruginosa	Prokaryote	1522 sites	Mainly cytoplasmic	40%	Amino acid
metabolism/Translation/Central carbohydrate metabolism	Citrate	[83]	
Escherichia coli	Prokaryote	2580 sites	Mainly cytoplasmic	66%	Translation/Oxo acid metabolism/Cellular ketone metabolism/Amino acid metabolism/Nucleotide metabolism/Gene expression	Glucose/ Succinate/ Pyruvate	[84]	
Saccharomyces cerevisiae	Eukaryote	1345 sites	Cytoplasm (86.2%)	56%	NA	Galactose	[82]	
Homo sapiens (Hela)	Eukaryote	2004 sites	Mitochondria (46.0%)	57%	NA	NA		
Mus musculus (Mouse)	Eukaryote	2140 sites	Mitochondria (70.6%)	27%	TCA cycle/Urea cycle/Fatty acid oxidation	NA		
Corynebacterium glutamicum	Prokaryote	651 sites	NA	NA	Carbohydrate
metabolic/Translation/Amino
acid metabolism/Nucleotide metabolism	Glutamate	[85]	
Bacillus subtilis	Prokaryote	327 sites	NA	NA	TCA cycle	Citrate/Glucose/
Glycerol/
Pyruvate	[86]	
Mycobacterium tuberculosis	Prokaryote	1739 sites	Mitochondria (89%)	14%	Carbon metabolism/Biosynthesis of amino acids/Ribosome	NA	[87]	
Aeromonas hydrophila	Prokaryote	2174 peptides	Mitochondria (80%)	55%	Ribosome/Carbon metabolism	NA	[88]	

TABLE 1 Studies specific to Lysine succinylation (Ksu)

However, neither lysine succinyltransferase nor desuccinylase has been confirmed. The lysine deacetylase CobB was found to catalyze the desuccinylation of lysine in Aeromonas hydrophila [89]. However, deficiency of CobB in E. coli won’t increase the global Ksu level [82].
The Ksu often occurs at sites that can also be acetylated. The percent of Ksu sites that could be acetylated ranges from 14% to 66% across different species (Table 1). In the human gut microbiome, the value is 57% [90]. The motif of Ksu is conserved usually with charged amino acids lysine (K) or aspartate (D) at +1. Ksu is also a conserved modification in functional distribution. The functions of succinylated proteins are mainly found in carbohydrate metabolism, translation, and amino acid metabolism from prokaryotes to eukaryotes, from unicellular organisms to multicellular organisms (Table 1). Succinylated proteins are even shared in different species that are from different phyla. Up to 105 succinylated proteins are found both in M. tuberculosis (phylum: Actinobacteria) and E. coli (phylum: Proteobacteria) [87], which may indicate they play some fundamental functions in different species. Although Ksu and Kac share many sites, they modified the lysine residue in very different ways. Kac neutralizes the positive charge of the amino group whereas Ksu adds a negative charge. Those differences could lead to different protein functions. S- ribosylhomocysteine lyase (LuxS) is an enzyme in Aeromonas hydrophila which harbors three Ksu sites. One of the sites, K165 could either be acetylated or succinylated. It was found that compared with the unmodified protein, acetylation on K165 could slightly inhibit the enzyme activity whereas succinylation could strongly increase the enzyme activity [89]. Ksu on homoserine transuccinylase in E. coli is necessary for its activity [91].
2.4		Glycosylation

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Glycosylation can occur at multiple sites on proteins. In addition to usual –OH, –NH2, and –SH side chains, glycan can also bind to the C-terminal –COOH through esterification. Together with the diversity of glycan structures, this makes glycosylation perhaps the most diverse protein modification and we have limited understanding of their structural effects on protein function [92]. The process of protein glycosylation in eukaryotes mainly occurs in the endoplasmic reticulum and Golgi complex. In prokaryotes, due to the lack of organelle, the cytoplasm is the main location for glycosylation. The high diversity of glycoproteins plays an important role in cell recognition, and signal transduction. For example, many Bacteroidales species are able to synthesize polysaccharides and glycoproteins [93]. Bacteroides fragilis NCTC 9343 is has a general protein O-glycosylation system in which numerous proteins are glycosylated [94]. The mutants with protein glycosylation defects have impaired in vitro growth [94]. However, we have limited understanding of its function in crosstalk between species
within complex microbiome or between microbiomes and their host such as the human gut microbiome with the host.
There are many methods, such as hydrazide chemistry, that are used in combination with mass spectrometry to study O-linked glycosylation or N-linked glycosylation [95, 96]. However, limited studies have been done on the role of protein glycosylation in the microbiome due to the extremely high complexity of both glycan and microbiomes. A few studies focusing on specific species did find that glycosylation was essential to bacterial virulence. It has been reported that the cell surface glycoprotein HMW1 in Haemophilus influenzae is necessary for its attachment to the host cell [7]. Recently, a study used MALDI-TOF- MS and PGC-LC-MSMS methods to study the glycans released from gut microbiome samples collected from pediatric ulcerative colitis (UC) patients [97]. Distinctive patterns were observed between UC and healthy patients indicating the importance of intestinal glycoproteins in human disease.
2.5		Others
In addition to the more commonly studied PTMs, an increasing number of PTMs have been discovered and demonstrated to be functionally important in bacteria. These include S-sulfhydration [98], β-lysine modification [99], propionylation [90], malonylation [100], butyrylation [101], and glutarylation [102]. Although less common, they play important roles. For example, diet can modulate renal function through PTM of microbiome proteins [98]. A high level of uremic toxins can accumulate in the blood and cause damage to the kidneys [103]. Some of the uremic toxins are derived from the gut microbiome through tryptophan metabolisms leading to indole compounds. Tryptophan can be degraded by the bacteria-secreted enzyme, tryptophanase (TnaA). It was found that hydrogen sulfide (H2S) can inhibit TnaA activity through S-sulfhydration [98]. H2S is produced by the gut microbiome, and the level of H2S can be increased by supplementing with dietary sulfurcontaining amino acids like methionine and cysteine. High sulfur amino acid-containing diet could mitigate kidney disease through PTM of microbiome proteins.
Except for the modified proteins, the ribosomal synthesized and post-translationally modified peptide (RiPPs) have many biological functions. The molecular weight of RiPPs is limited to less than 10 kDa to distinguish them from proteins [104]. The biosynthesis of RiPPs starts from the precursor peptides produced by ribosome. The precursor peptide contains a core region that will transform into mature RiPP after modifications. The types of modifications on RiPPs include almost all types of modifications on proteins such as acylation, dehydration, and sulfuration, as well as other modifications that are less often seen in proteins, such as prenylation [105], macrocyclization [106], and nucleotidylation [104, 107]. Over 20 types of RiPPs expressed by bacteria, fungi, plants, and even insects and reptiles have been identified [104]. The highly diverse structures lead to a wide range of biological functions and make the RiPPs a great source of nature products. Many RiPPs are found in complex ecosystems like human and soil microbiome and have important functions [108]. Ruminococcin C, one of the RiPPs produce by Ruminococcus gnavus E1 (a human commensal bacterium) showed inhibitory effect to multidrug-resistant pathogens and no toxicity towards eukaryotic cells, which makes it a promising antibiotic [109]. E. coli Nissle 1917 (Mutaflor) is a probiotic strain that has been used commercially. The siderophore peptides microcins M and H47 are two RiPPs produced by E. coli Nissle 1917 with potent bactericidal properties and are used for niche competition against Enterobacteriaceae [110]. Beijerinckin and Ljungdin are two putative RiPPs in Clostridium beijerinckii and Clostridium ljungdahlii, respectively. The bacterial growth was affected after removing the genes related with the two RiPPs, which indicates that the two RiPPs may be involved in quorum sensing and cellular metabolism [111].
Moreover, due to the conserved features of genes related with RiPPs, putative RiPPs can be predicated from genomic sequence data. So far, RiPPs have been predicted to be widespread in the human microbiome [112] and anaerobic bacteria [113], but their functions in microbiome remains to be assessed.
Moreover, due to the conserved features of genes related with RiPPs, putative RiPPs can be predicated from genome sequence data. So far, RiPPs have been predicted to be widespread in the human microbiome [112] and anaerobic bacteria [113], but their functional effects remains to be assessed.
3		PTMS IN MICROBE–MICROBE AND HOST–MICROBE INTERACTIONS

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Interactions between the gut microbiome and host cell are achieved through metabolites and proteins [98]. These molecules either secreted from the microbiome or the host can affect the functions of the microbiome and host cells. For example, short-chain fatty acids (SCFAs), produced by the human gut microbiome through fermentation of dietary fiber can reach different human cells and regulate the function of the specific host protein. SCFAs were found to regulate specific host gene expressions through an increase in the host histone acetylation [114]. SCFAs appear to modulate histone acetylation through at least two mechanisms. First, SCFAs have histone deacetylases(HDAC) inhibitory activities with butyrate as the most potent inhibitor [114]. HDACs are responsible for the specific removal of acetylation on histone and their inhibition would lead to increased histone acetylation. Secondly, a recent study found that butyrate and propionate can also activate a broad-spectrum histone acetyl transferase (HAT) [115]. Auto-acylation on an autoinhibitory loop is required for activation of P300 [116]. Butyrate and propionate can increase corresponding intracellular acyl-CoA thus boosting auto-acylation [115] and therefore histone acetylation. Interestingly, histone hyperacetylation is associated with the amelioration of colitis [117]. As well, SCFAs treatment can ameliorate colitis disease [118], which could be possibly explained by their acylation-promoting effects [119]. Some microbes can suppress other microbes by altering PTM proteins. Fungus Fusarium graminearum is potential pathogen in wheat. It has been found that bacterium Streptomyces hygroscopicus can secret rapamycin to inducing degradation of the fungal histone acetyltransferase, which will
subsequently result in autophagy induction autophagy induction in F. graminearum [120].
The gut microbiome also secretes enzymes that can impact host protein PTM and are suspected to be involved in host disease [121]. For example, peptidylarginine deiminase is expressed in oral commensal porphyromonas gingivalis and was reported to catalyze the citrullination of human proteins [122]. The enzyme is responsible for converting arginine residue into citrulline residue. The presence of citrullinated epitopes can stimulate the immune system and induce systemic inflammation [123]. Over citrullination is related to autoimmune diseases, rheumatoid arthritis [124] and periodontitis [125]. Proteases or peptidases are enzymes that hydrolyze peptide bonds in protein. A well-known example of protease is the trypsin secreted by the pancreas to digest food proteins. However, proteases can also stimulate host inflammatory response pathways through proteolytic cleavage of N-terminal domain of protease-activated receptors (PARs) expressed in gut cells [126, 127]. Proteases can be secreted by the pancreas, intestinal epithelial cells and immune cells [128]. As well, the gut microbiome is another important source of proteases [129–132]. The colonic proteases activity in mice can be significantly inhibited by the administration of antibiotics [133]. Moreover, it is likely that many yet to be discovered proteases are present in the microbiome. For example, after searching against the human gut metagenome based on the similarity with known bacterial protease sequences, 285 putative proteases were found [134]. The pathogenic bacterium Pseudomonas aeruginosa can produce a metalloproteinase to enhance its virulence through activating PARs [135]. B. fragilis can secrete a metalloproteinase which is identified as enterotoxin and associated with diarrheal disease and gut inflammatory [136, 137].
It has been found that the increases of enteric proteases level and activity are related with intestinal disease such as inflammatory bowel diseases (IBD) [129, 138] and irritable bowel syndrome (IBS) [139, 140]. The activity of serine protease whose active site contain serine residue was 10-fold higher in fecal samples of IBD patients compared to healthy controls [138]. The increased proteases can lead to the damage of intestinal barrier function and the increased epithelium permeability [128]. Oral administration of the serine protease SP-1 produced by human gut microbiome was reported to damage epithelial barrier function and cause colitis [132]. Interestingly, the gut microbiome can also secret protease inhibitors [128]. It is suspected that the inhibitive effect on the proteases produced by other microorganisms can help bacteria survive in the microbiome [141]. Overall, there are clear indications that host and microbiome proteases are important in host-microbiome interactions and that there remain proteases that have yet to be identified in the microbiome.
It is worthy to note that the key component of the gut barrier, mucin, is a host-producing glycoprotein and highly regulated by the microbiome (Figure 2). The mucin is produced by enterocytes and is mainly constructed through O-glycosylation. 80% of the total mass is contributed by the O-glycan part. The glycan coat can protect the protein core and form a gel to separate the intestinal environment from the host [142]. However, mucin generation is highly modulated by and interacts with the gut microbiome. Compared with conventionally raised mice, germ-free mice have a low-quality mucus layer characterized as thicker and stickier to the intestinal wall (Figure 2). However, the thicker mucus layer in germ-free mice did not show a better protective function instead was more penetrable [143]. The molecular composition of glycoprotein was also affected. Moreover, the properties of the mucus were transmissible between mice through fecal bacteria transplantation [144] even though mucin is not produced by the microbiome. This demonstrates the importance of gut microbiome to a healthy, integral mucus layer. The mechanism through which the microbiome regulates mucus generation is not fully understood. It is estimated that up to 40% of gut bacteria encode glycan-degrading enzymes able to degrade mucin [145]. The released glycan can be consumed by the bacteria itself or by other bacteria. As well, the gut microbiome can use glycan to produce short-chain fatty acid which supplies 80% of energy to the colonocytes [146, 147]. A fiber-free diet which leads the microbiome to overexploit the mucus resulting in gut barrier dysfunction (Figure 2) [148]. Therefore, there might be dynamic homeostasis between the generation and degradation of the healthy mucus layer. Both degradation and regeneration are necessary for healthy mucus. In addition to the gut, the respiratory tract is also covered with mucin which interacts with the microbiome [149].
As we mentioned above, many PTMs can be regulated by molecules or proteins from other organisms. Thus, the interactions between microbe and host or microbe and microbe are important regulators of PTMs. Those interactions often signal through PTM or depend on PTMs to be active [150]. Experimental methods such as yeast two-hybrid assay (Y2H) and co-immunoprecipitation assay have been extensively used to study protein-protein interactions (PPIs) [151]. However, their applications for microbiome studies are limited and they often have inherent limitations in terms of time-requirements, low throughput and missing low-affinity interactions [151]. The study of PPIs and PTMs in microbiome is complex and most PPIs in microbiome are unknown. Fortunately, computational methods have been developed to use the existing protein interaction databases to infer putative interactions in microbes (Table 2).

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Pathogen-Host Interaction Search Tool (PHISTO) [152] and Pathosystems Resource Integration Center (PATRIC) [153] (now transitioned into BV-BBRC) are two bioinformatic tools to search experimental confirmed PPIs between pathogen and host. VirRBase [154] is a search tool dedicated to the interactions related to noncoding RNA between virus and host. In addition to the confirmed interactions, many tools use the confirmed interactions to infer new interactions or help understand the relationships between host and microbe, such as HPIDB 3.0 [155]. NetCooperate [156] is a tool developed to determine the cooperative potential between the host and microbe or between different microbes. Short linear motifs are a distinct class of protein functional modules that participate in PPIs and act as sites of PTMs. Almost 4000 short linear motifs have been confirmed and documented in the Eukaryotic Linear Motif Database [157]. However, the domain-motif interactions are typically low affinity, which will easily give false positive result. SLiMEnrich [151] is a tool to assess the domain-motif interactions from experimental or predicted interactions. MicrobioLink [158] can predict the PPIs
+/
FIGURE 2 The dynamic homeostasis between microbiome and mucus. In the healthy mucus (left), mucus is secreted by host cells. The bacteria consume both the mucus and diet fibers. In the germ-free mucus (top right), the mucus is accumulated. In the fiber-free mucus (top right), bacteria overexploit the mucus
Searching tools	Interaction type	Organisms	Evidence	References	
PHISTO	Protein–protein interaction	Pathogen-host	Experimental	[152]	
PATRIC	Protein–protein interaction	Pathogen-host;
Bacterium-bacterium	Experimental	[153]	
ViRBase v3.0	RNA–RNA, RNA-protein	Virus-host	Experimental, predicted	[154]	
HPIDB 3.0	Protein–protein interaction	Pathogen-host	Experimental, predicted	[155]	
Other tools	Description			References	
TABLE 2 Bioinformatic methods used for microbe-microbe and microbe-host interactions
MicrobioLink	A computational pipeline to integrate predicted interactions between microbial and host proteins together with host molecular networks.	[158]	
SLiMEnrich	A tool to assess domain-motif interactions based on the protein-protein interactions.	[151]	
NetCooperate	A software package for determining host-microbe and microbe-microbe cooperation.	[156]
among different organisms based on protein secondary structures. Those putative interactions will further be used to build signaling pathways to better understand how microbial proteins influence the cellular processes in a certain context. In a case study, eight bacterial species relevant to Crohn’s disease were selected for microbe-host PPI prediction. 739 orthologous groups with the potential ability to bind to human proteins were inferred. Functional enrichment analysis showed that many of the predicted host-binding bacterial proteins may elicit PTMs on host receptors. Nevertheless, more experimental and bioinformatic tools are needed to map and better understand the role of protein interactions and PTMs in the microbiome.
There is no doubt that many more microbiome effects on host protein PTMs will be characterized along with more microbiome research being focused on functions. These effects are likely modulated through the secretion by the microbiome of specific metabolites, enzymes and
peptides. Understanding the crosstalk between the microbiome and the host will require exploring different PTMs.
4 MICROBIOME-WIDE PTM ANALYSIS
4.1		Challenges and methods
Microbiome analyses are challenging due to the complexity of the samples in terms of the number of different but related species that are present in a given sample. It means that sequence homology can be very high between species, with bacterial strains having as little as 5% sequence difference that does not necessarily cause changes in amino acids. The peptides derived from microbiome proteins can be common between species (i.e., they do not identify a specific protein), unique to
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a protein present in different species (they are not unique to a specie), or distinct to a bacterial species (uniquely present in a specie). This also applies to the identification of PTMs.
Most PTM studies are performed using high-resolution mass spectrometry with the data-dependent acquisition (DDA). Because PTMs are usually present at a low level across multiple proteins [21], the bias of DDA favoring the high abundant peptides poses a challenge for the characterization of lower abundance PTMs. These are particularly challenging in microbiome-wide PTM analysis due to the microbiome complexity. In most studies, immunoaffinity enrichment before mass spectrometry analysis is needed to detect low abundant modified pro- teins/peptides [159]. As well, a higher amount of sample is needed for the enrichment experiment. For some clinical studies, the small amount of sample available may not meet the requirement for enrichment. Multiplexing approaches, such as tandem mass tag (TMT) and isobaric tags for relative and absolute quantitation (iTRAQ), which combine multiple samples can not only increase the throughput but deal with limited amounts per sample for enrichment [160]. Otherwise, longer chromatographic time is required for increasing the depth of measurement. Li et al. [12] used a 22-hour LC method and obtained comparable PTMs number with studies using the enrichment strategy.
One challenge with immunoaffinity enrichment strategies is that they rely on the development of antibodies and often focus on some specific types of modifications. Moreover, enrichment after digestion discards all the peptides that are not carrying a specific PTM. Theoretically, data-independent acquisition (DIA) collects peptide information without bias. All the peptide information including PTMs could be recorded in the raw data. But the peptide identification from complex MS2 spectra hinders its development. Recently, some advanced software specific for peptide identification from DIA data, such as, DIA- Umpire DIA-NN and MaxDIA, have been published which could be a future direction for microbiome PTM analysis [161–164].
Confident bioinformatic identification of modified pep- tides/proteins is another challenge for PTM study on microbiome especially the high diversity ones like human gut microbiome. Even though only a fraction of the gut bacteria identified to date are present in an individual gut microbiome, all of their sequences are included in metagenomic databases [165]. Database searching methods developed for traditional proteomics have difficulties handling large databases in which the vast majorities of the species are not present in specific samples. They tend to be prone to high false-discovery rates (FDR) when dealing with complex and large metagenomic databases. Instead, multi-step database search strategies have been widely used to reduce searching time and address FDR issues [166–169]. In this strategy, a reduced database is firstly extracted from the metagenome based on a preliminary survey of the acquired MS spectra then the resulting smaller database is used for the second search. The strategy dramatically reduces the searching time and makes large database searches feasible. In addition, when considering multi-modifications simultaneously, a close search with many variable modifications will vastly expand searching space which needs significant computational power. For example, searching 12 modifications simultaneously used up to 35,000 central processing unit cores of the supercomputer Titan for 1000s [12]. To address this problem, Kong et al. [170] designed an ultrafast searching tool named MSFragger for open search. The searching speed has been increased over 100-fold by MSFragger. As well, Chi et al. [171] upgraded their searching engine Pfind 3.0 to support open search. The search engine performs an open search in the same amount of time as a close search and it is very suitable for PTM studies. Several software specific to metaproteomics has been launched. The Galaxy is a highly flexible framework which allows metaproteomics analysis including the taxonomic and functional analysis [172]. The software suite MetaProteomeAna- lyzer is specific to the metaproteomics analysis containing a graph database [173]. And Metalab is a user-friendly software combining both advantages of multi-step strategy and open search [167]. The software is dedicated to metaproteomics analysis not only enabling accurate PTMs identification but providing taxonomy and function analysis.
Some PTMs lead to nearly similar or identical mass differences when added to proteins/peptides. These PTMs can be misidentified by search engines and further complicate PTM identification. For example, the lysine trimethylation (42.047 Da) has a similar mass to lysine acetylation (42.011 Da). This could be distinguished by characteristic fragments like the neutral loss of ion (MH+, –59 Da) [174] that is particular to the trimethylation and the derivative ion at m/z 126.0913 [175] that is particular to lysine acetylation. Similarly, succinylation adds identical mass to the peptide as methylmalonylation. But the latter has a significant neutral loss of CO2 peaks (-43.9898) [81]. More common potential misidentifications have been reported previously [176]. In addition, many modifications would hinder enzymatic digestion [159, 177], and therefore the N-terminal modification in the result peptide list should be confirmed manually.
Many PTMs are reversible or are thermodynamically unstable, which makes the detection of these types of PTMs challenging. Phosphorylation on histidine is known to be very unstable under acidic conditions which are usually used in spectrometry-based proteomics. Thus, the pH should be adjusted for those studies [178]. As well, N- phosphorylated peptides are labile which could lead to false-positive results due to phosphate-related neutral losses and gas-phase rearrangements [179].
4		.2 Applications of microbiome-wide PTMomics
Although most PTMomics studies focus on single species, several attempts on the microbiome have been done. In 2014, Li et al. [12] adopted a very long gradient MS analysis (22-hour) and greater computing power to study the development of microbiome on biofilm. They identified ∼3600 unique PTM events including 8 PTM types and found that there was a significant difference between the biofilm microbiome at different stages. Zhang et al. [11] also used the enrichmentindependent technique to study the PTMs in microbiomes of the deep-sea hydrothermal vent. Totally, 1306 unique PTMs across 7 PTM types were identified in their study. They found that PTM proteins played roles in energy metabolism, signal transduction, and inorganic 
ion transport and may relate with microbiome’s ability to survive in extreme conditions.
PTM-specific immunoaffinity enrichment methods drastically increased the identification of PTMs. One recent study using an immunoaffinity enrichment method specific to Kac coupled with a high-resolution mass spectrometer on the human gut microbiome identified 35,200 Kac peptides corresponding to 20,030 Kac proteins [55]. In contrast, in single bacteria using the same method, only 6700 Kac peptides were identified [180]. The increase in peptides with PTMs not only reflects the complexity of the gut microbiome but also showed the widespread presence of Kac in different species. Microbiome-wide PTM studies enable the comparison of PTMs across different taxa. In the human gut microbiome, the overall level of Kac is positively related with ACK and PTA [55], and taxa containing more ACK and PTA, such as Bacteroides, Blautia, Faecalibacterium, Lachnospira, Eubacterium rectale, butyrate-producing bacterium SS3/4 and Prevotella, have higher levels of Kac in their proteomes. Similar to prokaryotes [14, 52, 53], the functional distribution of the human gut microbiome Kac proteins is mainly enriched in proteins involved in translation and metabolism [55]. Many (51) gut microbial metabolic pathways are fully covered using identified Kac proteins, including SCFAs-producing pathway. Application of this lysine acetylomics approach in the study of microbiomes from pediatric Crohn’s disease patients identified 136 taxon-specific microbial protein Kac sites that are differentially abundant in patients when compared to controls, which provides additional information for better understanding the roles of microbiome in the development of Crohn’s disease. Recently a serial immunoaffinity enrichment strategy was integrated with an open search strategy. In total, >60,000 lysine acetylation, 17,089 lysine succinylation, and 20,914 lysine propionylation peptides were identified from the same microbiome samples [90]. This approach established a meta-PTMomics framework for the study of multiple PTMs of the human gut microbiome [90]. Further analysis of the identified lysine acylations demonstrated the presence of extensive cross-talks between PTMs. They also demonstrated species- and PTM type-specific distribution of lysine acylated proteins in different metabolic pathways. These examples demonstrated the feasibility of PTM studies on the microbiome and indicated the importance of PTMs in regulating the microbiome functions and influencing the development of human diseases.
5 CONCLUSION AND PERSPECTIVES
Our understanding of the microbiome has evolved from initially simply cataloging the species present in a microbiome and predicting their functions to measuring the protein expressed in a microbiome and understanding their functions. PTMs which directly regulate the proteins’ conformation and activities are keys in defining and regulating protein functions. Our understanding of PTMs on a microbiome scale is limited as only a few manuscripts on microbiome scale PTMs analysis have been published [11, 12, 55, 90]. Although many types of PTMs have been found, their mechanisms and functions are still unknown. Nevertheless, we can postulate that PTMs are not only important for the roles and functions of individual species present in a microbiome, but also for organizations and communications across species in a microbiome and exchange between the microbiome and the host. To further understand microbiome PTMs, microbiome under different situations such as disease or drug stimulations should be studied.
Assigning PTMs to specific proteins in specific species is a challenge due to the sequence homology between species. Therefore, novel approaches are needed to solve this issue either through bacteria fractionations, specific metabolic labeling, molecular approaches that target specific bacteria, or other approaches. As well, more sensitive affinity reagents will be needed to probe more microbiome level PTMs in a deeper way. Finally, bioinformatics tools are further needed for PTMs analysis combined with other information.
ACKNOWLEDGMENTS
H.		D. is a trainee in Natural Sciences and Engineering Research Council of Canada (NSERC)-CREATE TECHNOMISE program.
CONFLICT OF INTEREST
D.F. has a co-founded MedBiome, a clinical microbiome company. The remaining authors declare no competing interests.
DATA AVAILABILITY STATEMENT
Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.
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AUTHOR BIOGRAPHIES
Haonan Duan obtained his BSc and MSc degrees at China Pharmaceutical University and University of Chinese Academy of Sciences in medicinal chemistry respectively. Now he is a PhD candidate in biochemistry at University of Ottawa under the guidance of Dr. Daniel Figeys. He has been focusing the technology of metabolomics and metaproteomics using the mass spectrometry.
Dr. Xu Zhang obtained his MSc and PhD degrees in microbiology and a B.Sc. degree in biotechnology in Shanghai Jiao Tong University and Lanzhou University, respectively. He obtained his postdoctoral training on microbiome and mass spectrometry with Dr. Daniel Figeys at the University of Ottawa. Since 2009, his research has been focusing on the intestinal microbiota, their interactions with therapeutics, and their roles in the development of chronic diseases. Currently, he is leading a research team consisting of chemists and biologists at Health Canada Regulatory research division to develop high-resolution mass spectrometrybased analytical methods and bio-functional assays to evaluate microbiota-directed therapeutics.
Dr. Daniel Figeys is the distinguished research chair and professor in the Faculty of Medicine, University of Ottawa. He was the inaugural director of the Ottawa Institute of Systems Biology and director of the SIMM–uOttawa Joint Research Center. He was recently appointed Director of the new School of Pharmaceutical Sci- ences at the Faculty of Medicine. He obtained a BSc and an MSc in Chemistry from the Universitè de Montrèal and a PhD in Chemistry from the University of Alberta. His laboratory is developing proteomics and metaproteomics technologies based primarily on mass spectrometry to study the human microbiome, circadian clock, proprotein convertases, and their roles in health
and diseases.
How to cite this article: Duan, H., Zhang, X., & Figeys, D. (2023). An emerging field: Post-translational modification in microbiome. Proteomics, 23, e2100389.

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https://doi.org/10.1002/pmic.202100389