Current Understanding of Human Metaproteome Association and

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Review

Current Understanding of Human Metaproteome Association and Modulation Shahd Ezzeldin, Aya El-Wazir, Shymaa Enany, Abdelrahman Muhammad, Dina Johar, Aya Osama, Eman Ahmed, and Sameh Magdeldin J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.9b00301 • Publication Date (Web): 02 Jul 2019 Downloaded from pubs.acs.org on July 24, 2019

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Journal of Proteome Research

Current Understanding of Human Metaproteome Association and Modulation Shahd Ezzeldin1, Aya El-Wazir2,3, Shymaa Enany4, Abdelrahman Muhammad5, Dina Johar6, Aya Osama1, Eman Ahmed1,7, Sameh Magdeldin1,8* 1Proteomics

and Metabolomics Unit, Department of Basic Research, Children’s Cancer Hospital Egypt

57357, 11441, Cairo, Egypt 2Genetics

Unit, Department of Histology and Cell Biology, Faculty of Medicine, Suez Canal University,

41522, Ismailia, Egypt 3Center

of Excellence of Molecular and Cellular Medicine, Suez Canal University, 41522, Ismailia, Egypt

4Department

of Microbiology and Immunology, Faculty of Pharmacy, Suez Canal University, 41522,

Ismailia, Egypt 5Department 6Biomedical

of Biomedical Engineering, Higher Technological Institute, 44634, Sharqia, Egypt Sciences Program, University of Science and Technology, Zewail City of Science and

Technology, 12588, Giza, Egypt 7Department

of Pharmacology, Faculty of Veterinary Medicine, Suez Canal University, Ismailia, 41522,

Egypt 8Department

of Physiology, Faculty of Veterinary Medicine, Suez Canal University, Ismailia 41522,

Egypt

Sameh Magdeldin, Ph.D* (Corresponding address) Proteomics and Metabolomics Unit, Department of Basic Research, Children’s Cancer Hospital Egypt 57357, Cairo, Egypt Email: [email protected]

ACS Paragon Plus Environment

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Abstract During last decade, metaproteomics provided better understanding and functional characterization of microbiome. A large body of evidence now revealed interspecies, species of bacterial-host interaction via secreted-modulatory microbial proteins “metaproteome”. Although high throughput state-of-art mass spectrometry has empowered metaproteomics recently, its profile remains unclear, and most importantly, the exact consequences and underlying mechanism of these protein molecules on host are insufficiently understood. Here we address the current progress in the study of the human metaproteome suggesting possible modulation, metaproteome dysbiotic signature, challenges, and future perspectives.

Keywords: gut microbiota, dysbiosis, microbiome, metaproteomics, protein function, human


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Introduction During the last decade, several studies have provided strong evidence of microbial diversity in the human gastrointestinal tract. Evidence-based data revealed that the balanced gut microbiota forms a symbiotic relationship with the host. Benefits provided by gut microbes to the host include immune priming, effects on metabolism, and protection from pathogens 1. In contrast, perturbation of the balance of these microbial communities, dysbiosis, is associated with several disorders, such as inflammatory bowel disease (IBD), and type 1 diabetes (T1D) 2. Targeting of bacterial 16S rRNA and metagenomic approaches have laid the foundation for construction of core microbial taxonomic databases 2. Analysis of these databases has provided insight into not only the complexity of the microbial colonies within the human gut but also the unambiguous potential effects of this diversity on the host. Metaproteomics, which is the study of the entire proteins of the microbial community in a given sample, has been developed as a potential omics approach to gain functional information regarding the dynamic interactions between the microbiota and host; such information is limited in metagenomic studies 3. Hence, host-microbiota crosstalk and its impact on the development of several diseases can be comprehended using metaproteomic technologies. However, due to the great complexity of microbial environments, there are several challenges including database construction, taxonomic and functional characterization, validation of identified proteins and, most importantly, standardization of the workflow. In this perspective, the objective of this review is to update the scientific community on the mutual relationships between host and gut microbiota, the alteration of the microbial diversity and the impact of the metaproteome on host profile in different disease states. Additionally, the review provides a holistic picture on metaproteomics highlighting the available metaproteomic analysis tools, current challenges and future perspectives. The review further elucidates the diverse application of metaproteomics in studying human gut microbiota in health and diseased state.

The Triad of Metagenomics, Metatranscriptomics and Metaproteomics


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Metagenomics is the study of the entire genetic material of microorganisms from complex microbial communities, and hence it is used for exploration of the microbial diversity in a specific environment 4. This omics technology is more sophisticated than other phylogenetic methods as it identifies all the microbial genes present in any complex ecological sample. The growing number of metagenomic studies has improved the current understanding of the structure, diversity, and abundance of microbial communities in various environments such as the human intestine. The latter contains high abundance of microorganisms, a small proportion of which has been identified as harbouring more genes than humans, collectively referred to as microbiome 5. Recently, in-depth investigations revealed why some bacteria are associated with diseases, while others are crucial for the human immune system and other aspects of health. On the downside, shotgun sequencing cannot be used to differentiate active microbes from either dead or dormant, as the genes of all mixed microbial cells are sequenced 3. Furthermore, metagenomics provides no data on the activity of the identified bacteria. Measurement of DNA content is not sufficient for identification of the main functional forms of the protein as not all protein-encoding genes are equally expressed 6. To some extent, metatranscriptomics provides a solution to this challenge; RNA expression can act as an indicator of gene activity. However, whether the expressed RNAs continue to produce functional protein products, are degraded or epigenetically silenced, is highly dependent on the regulatory machinery and could consequently result in a false peptide count 7. In addition, owing to alternative splicing, every gene can encode multiple proteins, the functions of which may differ 8. Therefore, metatranscriptomics still does not fully indicate the functions, providing only an inventory of human genes associated communities 9. Furthermore, metatranscriptomics relies on identification of ribonucleotides, which are less stable than prokaryotic proteins 9. The metaproteomics insights on protein functions advantage them over metagenomics and metatranscriptomics. It allows interrogation of the in situ pathophysiology of microbial communities and ultimately elucidates functional operations along the digestive tract 9. Additionally, it monitors the activities in host and microbes and the mutualistic interactions between them. Several metaproteomic studies have


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reported the involvement of some metaproteins in certain core functions of the healthy human gut, such as carbohydrate and amino acids metabolism 10, 11. Metaproteomics analysis of healthy human gut from two different datasets demonstrated variable abundances of identified microbiota at different taxonomic ranks (Figure 1) 11, 12. In addition, there is a discrepancy in taxonomic designations between metagenomics and metaproteomics, which can be modelled by a study on cystic fibrosis (CF) patients and healthy controls 12. The metaproteomic study reported increased Bacteroidetes and decreased Firmicutes in CF patients, contrary to metagenomic findings

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Unravelling this variance between both omics data, whether it is

related to biological factors such as gene transcription, RNA regulation, protein translational rate and turnover, or metaproteomics computational challenges, is still missing and requires further investigations. Comparative presentation of these omics technologies is provided in table 1.

Gut Microbiome Stability and Diversity The human gut is a rich source of different microbes and an interesting subject for research focusing on the gut microbial metagenome 14, 15, metatranscriptome 16, 17, and metaproteome 9, 15, 18. Some of these studies have revealed the stability of the intestinal microbiota 17, 19. Mehta et al. characterized the stability of the faecal microbiota in a group of adult men and concluded that the faecal metagenome was more stable than the corresponding metatranscriptome

17.

The inter-individual variations in the metagenome were more

apparent than those within one person, contrary to metatranscriptomic variations, which were higher in the same person over time. The reduced stability of the microbiome for individuals was attributed to the conditions at the time of sampling. For example, the impact of antibiotic treatment and bowel preparation may affect the microbiome composition with a temporal change in the metagenome 17. A longitudinal metaproteomic analysis of newborn stool found a significant increase in the complexity of the microbial community, with decreased levels of human proteins from 96% to 30% of the total identified metaproteome after 21 days from birth 20. This shift in the proportion of microbial to human proteins was


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accompanied by some alterations in the composition, similar to the results of previous metagenomic and 16S rRNA gene sequencing analyses 21. The changes in microbiome profiles over time are illustrated in Figure 2 11, 12, 22. Previous studies have concluded that the intestinal microbial community is conserved from early to late adulthood

10, 18.

To date, 10% of the identified microbiome has been found to be present in all persons,

representing the core of the gut ecological system. Firmicutes, Bacteroidetes, Actinobacteria, and Proteobacteria were found to be the dominant phyla in human gut by 16S rRNA gene examination and metaproteomic analysis 9. A longitudinal study that explored the stability of the microbiome of the famous artist Billy Apple revealed that 45% of the core microbiome was retained over a 45-year interval 19. Although the function of the intestinal microbiota has been reported to be conserved within healthy persons, some studies have found that the composition of the adult microbiome is individual specific 9. Clustering analysis of 16 individuals, at both the oligonucleotide and peptide levels, revealed that functional microbiome is personalized and stable, and this may reflect the health status of individuals. This finding is supported by the fact that hierarchical clustering of individual samples showed subject-specific metaproteomes that were retained for a year 10. Consistent with this observation, our analysis of the KEGG results from two different metaproteomic studies showed that the top 25 metaproteins were personalized with limited variability (Figure 3)

9, 11.

Further insights on gut metaproteome interactions in healthy and

different disease states from metaproteomics-based studies

10, 23-25

are provided in Figure 4 and table S1.

Network analyses of cluster of orthologous classification (COGs) identified in these studies highlighted possible interactions at several diseased states. For example, three functional categories were observed only in T1D group but not the control group; coenzyme transport and metabolism, amino acid transport and metabolism, and post-translational modification, protein turnover and chaperone. Moreover, the interaction network pinpointed higher interconnections between carbohydrate transport and metabolism, and energy production and conversion categories in T1D patients compared to the control group. Such interaction networks could elucidate metaproteome functional profile variations associated with multiple diseases.


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Host-Microbiota Crosstalk in the Gastrointestinal Tract There are accumulating evidence of the symbiotic relationship between the host immune system and the microbiota, with an ultimate goal to maintain host homeostasis 26. Host-microbial interactions can be further explained by the balanced alliance between microbiota and host immune system. The latter cooperates optimally with gut microbiota to induce balanced protective responses against nocuous pathogens, employing both the innate and adaptive systems, including T‐helper, type 1 and type 17, regulatory T cells, immunoglobulin A (IgA), dendritic cells, cytokines, and other immune components

27-32.

A detailed

explanation is provided in Figure 5. Toll-like receptors (TLRs) are key players in the interactions between microbiota and host immunity (Figure 5). TLR5, for example, was shown to be activated by microbial flagellin proteins inducing a series of signalling cascade that mediate innate and adaptive immune responses 33

(Figure 5). Flagellin proteins have been identified as highly conserved core components of the human

gut metaproteome, and are known to increase microbiotal motility by enhancing their ability to gain access to nutrients 10. Needless to mention that TLR5 is the only TLR stimulated by flagellins, and this interaction is boosted by the increased motility 34. Nevertheless, in-depth investigations of this type of interaction are crucial because TLR5 varies extensively between species, which might impact the induced signalling cascade. One of the main contributions of the gut microbiota is the production of short-chain fatty acids (SCFAs) via anaerobic fermentation of carbohydrates by bacterial carbohydrate-active enzymes

35, 36.

Several

microbial genera, such as Bacteroides, Faecalibacterium and Bifidobacterium, mediate this reaction resulting in three dominant SCFAs; propionate, butyrate and acetate. Microbiota-derived SCFAs are involved in maintaining the integrity of the mucus layer and epithelial cell barrier. Acetate, for example, was shown to prevent the passage of Shiga toxin produced by Escherichia coli O157:H7

37

(Figure 5),

although the exact mechanism remains unclear. Gut microbiota also protects the host from pathogen colonization via nutrients competition. Commensal E. coli strains compete with the pathogenic enterohaemorrhagic E. coli for amino acids and organic acids, causing starvation of the latter 37. Another


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gut microbes defense mechanism is induction of antimicrobial peptides production, such as C-type lectins, cathelicidins and pro-defensins by intestinal Paneth cells 35. Additionally, gut microbiota participates in the metabolism of phenolic compounds derived from food consumption, e.g., flavonoids, the final form of which has powerful anti-microbial properties 36. Intestinal microbiota was found also to interact, directly or indirectly, with the host enteric nervous system modulating several functions 38. For instance, a recent study by Reigstad et al. has pointed out the role of microbiota-derived metabolites, SCFAs, in the induction of serotonin by enterochromaffin cells regulating gut motility 39.

Post-Translational Modification (PTM) of the Metaproteome Protein PTM is a mechanism that is adopted by both eukaryotes and prokaryotes to chemically alter protein function and activity after translation 40. Several PTMs have been characterized, including phosphorylation, acetylation, and glycosylation. These modifications represent a potential survival strategy to microbial pathogens. Pathogens were shown to adopt several mechanisms to interfere with host PTMs, modifying key functions that are required for infection. This action is mediated by bacterial effectors that are either secreted through secretion systems, or are present at the bacterial cell surface. Three Yersinia species, Y. pseudotuberculosis, Y. enterocolitica and Y. pestis, were shown to secrete YopH effector with tyrosine phosphatase activity, dephosphorylating specific host proteins to alter host immunity

40.

This

dephosphorylation event results in resistance of Yersinia against phagocytosis by macrophages, and disruption of the host cytoskeleton required for bacterial engulfment. Although the role of PTMs in bacterial pathogenicity has been established, the potential functions of PTMs in host-microbial interactions remain unclear. In 2017, Brown et al. attempted to study multiple PTMs in E. coli using a MS/MS proteomic dataset 41. The authors were able to generate a time-based unbiased map of all post-translationally modified proteins, identifying novel temporal patterns associated with C-terminal glutamylation, N-terminal acetylation and


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asparagine deamidation. Recently, using an optimized Fe3+-immobilized metal affinity chromatography column-based method, a research group constructed a phosphoproteomic E. coli dataset with an extensive focus on histidine phosphorylation

42.

The number of phospho-sites identified by this method was

approximately 10-fold higher than those identified by previously published phosphoproteomic datasets 42. Interestingly, 10% of the phosphoproteins were histidine-phosphorylated proteins, indicating the crucial role of this PTM in bacterial survival and biological function. However, most studies have attempted to characterize PTMs in either single microbial organisms or microbial communities in extreme environments, such as hydrothermal vents

43.

While global screening of metaproteome PTMs remains untouched,

exploration of these bacterial response is of great importance in the context of our growing understanding of the mutual relationship between gut microbiota and host systematic response.

Metaproteome Profile in Dysbiosis Dysbiosis is a term that describes alterations in the predominance, localization or diversity of the microbial flora affecting their functional contributions to the host. The outcome of dysbiosis might be damaging., i.e., drugs and diseases are effectors that potentiate the causes and/or consequences of dysbiosis at the metaproteomic level. Diet alone might account for more than half of the alterations in the configuration of the microbiota 44. Long-term consumption of a high-fat diet triggers the expression of microbial proteins involved in inflammation, mucus secretion, epithelial junction stability, and insulin release 45. Diets rich in resistant starch, carbohydrates resistant to amylase degradation in the small intestine, are also linked to a characteristic dysbiotic metaproteome and to an increased Firmicutes to Bacteroidetes ratio 15. The gut metaproteome is dramatically affected by antibiotic therapy. For example, extreme shifts in taxonomy and function were reported by a multi-omics study following a two-week course of β-lactam 46. The study also reported a reduction in the abundance of gram-negative bacteria during the first days of treatment and an increase in the gram-positive population by the end of the two weeks. There was a transient elevation in proteins involved in glucose breakdown, and pyruvic and glutamic acid metabolism.


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Additionally, Hernández et al. demonstrated the impact of antibiotics on the metaproteome of obese patients 47. The authors reported doubling of glucosidase activity after three days of antibiotic usage that declined later. Individuals treated with antibiotics exhibited enhanced sugar metabolism, particularly pentose phosphate metabolism, and altered enzymes that control the density, configuration and consistency of mucus in the gut. In addition, multiple non-antibiotic drugs have been implicated in dysbiosis, including anti-fungal, anti-viral and anti-parasitic drugs, in addition to the anti-rheumatic agent auranofin and the ovulatory stimulant clomiphene 48. Probiotics, on the other hand, were reported to have no significant effect on the faecal metaproteome 9.

Evidence of Stress-induced Microbiome Modification Despite the existing individual variability of the microbiome and its alteration by the different factors mentioned above, various studies have linked certain gut microbial compositions with specific disease phenotypes. Figure 6 shows some of the gut microbial alterations linked to disease states 12, 23-25, 49-53. Dysbiosis Linked to Inflammatory Bowel Disease (IBD) To date, IBD is the most widely studied dysbiosis. This disease comprises two clinical entities: Crohn’s disease (CD) and ulcerative colitis (UC), both of which share a common pathogenesis; chronic inflammation of the intestinal wall and a defective mucosal barrier 54. According to metagenomics 55 and metaproteomic studies 23, the main feature of dysbiosis in IBD is a reduction in Firmicutes, particularly Faecalibacterium prausnitzii, an organism that generates butyrate, an anti-inflammatory fatty acid and consequently reduces inflammation

55.

Increased inflammation causes widening of the tight junctions

between epithelial cells, resulting in increased permeability of the mucosal barrier and allowing continued stimulation by luminal antigens56. Consistent with this finding, Erikson et al. reported increased expression of microbial antigenic cell surface proteins in CD, such as TonB, outer membrane protein A, Ras-related GTP-binding protein B and starch-binding proteins C and D

23.

Details regarding the impact of gut

metaproteome dysbiosis and the associated inflammatory processes are illustrated in Figure 7.


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The mechanistic differences between microbiota in adults and pediatric diseased guts were highlighted by two metaproteomic studies on pediatric IBD patients 26, 51. The two studies reported increased levels of F. prausnitzii, that were reduced in adults (Figure 6). The metaproteome of IBD patients exhibited changes in carbohydrate, amino acid and lipid metabolism, in addition to enhanced microbial defense and DNA replication and repair 23, 51. Li et al. investigated the IBD-associated metaproteome of the mucosal-luminal interface and reported increased levels of human beta-defensin 1 and 2 in CD patients and increased hepcidin and transferrin in UC patients; proteins with anti-microbial activity 57. Mottawea et al. observed compromised mitochondrial function in new-onset pediatric CD patients, secondary to diminished levels of mitochondrial host proteins involved in hydrogen sulphide (H2S) detoxification and increased proportions of H2S-producing microbes in the gut

58.

Elevated H2S levels, together with reduction in

butyrate levels, lead to increased reactive oxygen species, inflammation and mucosal permeability. Multiple factors should be taken into consideration when assessing the IBD-associated microbiota. For instance, microbial changes are thought to be more pronounced in mucosal samples than in stool samples, suggesting that faecal specimens might not necessarily represent the actual microbiota interacting with host cells 59. The microbial composition associated with IBD is temporally unstable in active and inactive phases of the disease 57, 60. Additionally, some structural differences in the microbiota between non-inflamed and inflamed regions of the intestine have been reported, such as increased Prevotella and decreased Faecalibacterium in inflamed areas 61. Dysbiosis Linked to Cystic Fibrosis (CF) Microbial alterations have been also studied in patients with CF. The latter is caused by loss of function of the cystic fibrosis transmembrane regulator, resulting in defective electrolyte and water secretion from epithelial cells of the lungs, intestine, pancreatic and bile ducts 62-64. The decreased hydration and altered luminal ionic composition lead to reduced dissolvability of the mucus and its consequent accumulation in the lungs and intestine, providing a favourable medium for recurrent infection. The microbiota of CF patients was shown to be markedly different from that of normal individuals (Figure 6) 12.


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CF patients exhibited decreased levels of butyrate, flagellins and proteins that are involved in carbohydrate transport and metabolism 12. Analysis of host proteins indicated increased levels of acute phase reactants and inflammatory cytokines, reflecting an inflammatory process that might have inhibited bacterial proteins, resulting in low bacterial counts in the patients as opposed to their healthy siblings. The study also identified several potential gut inflammatory markers, such as lipocalin-2, zinc-alpha-2 glycoprotein, orosomucoid-1, serpin B1 and deleted in malignant brain tumours 1. Dysbiosis Linked to Cirrhosis Cirrhosis is an end result of chronic inflammation and fibrosis of the liver, secondary to multiple causative factors, including viral hepatitis, alcohol use, and metabolic diseases 65. Intestinal bacterial overgrowth, dysbiosis and cirrhosis-associated portal hypertension affect the permeability of the intestinal barrier, allowing the passage of bacteria and bacterial products from the intestine to the liver via the portal vein, enhancing the inflammatory milieu 66. Metagenomic studies have linked dysbiosis with liver cirrhosis 52, 53. The main changes observed were decreased abundances of the beneficial bacterial families Lachnospiraceae, Clostridiales XIV and Ruminococcaceae and increased abundances of pathogenic families, specifically Enterobacteriaceae, Enterococcaceae and Streptococcaceae (Figure 6). These aberrations were correlated with the progression of cirrhosis and occurrence of complications, including liver failure and hepatic encephalopathy. The dysbiosis associated with liver cirrhosis is not only restricted to the mucosa and stool; in fact, dysbiosis has been detected in samples from the oral cavity, liver, ascitic fluid and even blood as well 67. To date, the only study that has provided a metaproteomic perspective on the cirrhosis-associated microbiota is a study by Wei et al., which demonstrated that cirrhotic patients had significantly higher levels of enzymes involved in glucose and energy production, particularly the glyceraldehyde 3-phosphate dehydrogenase and glutamate dehydrogenase enzymes 18. Moreover, patients with severe disease exhibited elevated levels of enzymes involved in the synthesis of branched-chain amino acids, vitamin B5 and coenzyme A. The most distinctive of these enzymes were ketol-acid reductoisomerase and dihydroxy-acid


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dehydratase, reflecting increased protein and fatty acid synthesis and metabolism. The authors suggested that increased production of metabolically active proteins is a mechanism by which the microbiota adapts to the intestinal changes associated with cirrhosis. Likewise, this mechanism was advantageous to the host, providing increased energy supply to compensate for the disease-associated malnutrition. Despite this interesting observation by Wei, further metaproteomic studies on cirrhosis-associated taxonomic alterations and host-microbial interactions are needed. Dysbiosis Linked to Type 1 Diabetes (T1D) Recently, the effect of T1D on the metaproteome was studied in a couple of studies 24, 50. One of them found that proteins expressed in children with T1D originated from Eubacterium, Faecalibacterium and Bacteroides 24. The microbial metaproteome of patients was exclusively involved in functional categories associated with amino acid transport and metabolism, transcription, PTM, protein turnover and chaperones. T1D patients also exhibited increased expression of host mucin-2. In parallel, the other study described alterations in both host and microbial proteins in children and adult T1D patients 50. Five human proteins exhibited lower levels in new-onset diabetics (NODs) and seropositive individuals (positive for islet autoantibodies) than in control subjects. These proteins included IgA1 heavy-chain constant region, involved in the synthesis of IgA; calcium-activated chloride channel regulator-1, which regulates mucus production in goblet cells; and three proteins secreted by the exocrine pancreas, namely CUB and zona pellucida-like domains-containing protein 1 (CUZD1), chymotrypsin-like elastase family member 3A, and neutral ceramidase. On the other hand, two human proteins associated with inflammation, fibrillin-1 and galectin3, were overexpressed. Multiple microbial proteins were expressed differently in diseased and control individuals, the most significant of which were assigned to KEGG orthology groups associated with phosphotransferase systems, thermo-unstable elongation factors, and ferredoxin hydrogenase. Taken together, the data revealed that proteins that were altered in NODs and seropositive individuals caused inflammation, increased mucus secretion and defective mucosal barrier function, mainly due to imbalance in the abundances of Prevotella, Alistipes and F. prausnitzii.


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Dysbiosis Linked to Obesity Another disease that is linked to dysbiosis is obesity. By performing an integrated metagenomic and metaproteomic study, Ferrer et al. reported increased Firmicutes and decreased Bacteroidetes in obese patient compared to lean individual 25. However, the levels of the proteins expressed from both phyla were very similar, showing that the Bacteroidetes species in obese individual were highly active, a result that was confirmed recently in another metaproteomic study 68. Obese and lean individuals exhibited differences in the levels of proteins involved in cell motility, butyrate metabolism and vitamin synthesis, particularly vitamins B6 and B12. Most of the alterations were associated with augmented energy production in the obese gut, as indicated by the increased butyrate production and expression of specific pili-forming proteins and flagellins that might facilitate bacterial attachment to carbohydrates for fermentation 25. In addition, there was significant expression of proteins such as alkaline phosphatase, serpins and alpha-amylase, which could reflect metabolic derangement, intestinal damage, or inflammation similar to that observed in IBD

68.

Furthermore, the levels of some proteins, such as sensor kinases and pectate lyase, were found to be significantly correlated with insulin resistance, which is closely associated with obesity. Dysbiosis Linked to Major Depressive Disorder (MDD) Interestingly, the effect of the microbiota is not restricted to nearby organs but extends to distant regions as well. An in silico metaproteomic study suggested that the microbiota can impact ion transport, neurotransmitter release and synapse organization in the basal ganglia, triggering Parkinson’s disease 69. Additionally, the microbiota can regulate the immune system, particularly major histocompatibility complex 1, and exert epigenetic effects on gene expression, leading to other CNS diseases such as depression and autism. A new metaproteomic study investigated the relationship between the metaproteome and MDD 49. The study reported key differences between normal and MDD-associated gut, with the latter exhibiting more Firmicutes and Actinobacteria and less Bacteroides and Proteobacteria (Figure 6). Once again, this result is inconsistent with the results of a former metagenomics study which reported decreased Firmicutes and increased counts of the other three major phyla 70. Despite the abundance of Firmicutes


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observed by Chen et al., there was a decrease in the abundance of the anti-inflammatory Faecalibacterium, which is consistent with the inflammatory hypothesis of depression

71.

The imbalance of microbiota in

MDD caused variations in proteins involved in translation, ribosome synthesis and inorganic ion and carbohydrate transport and metabolism 49.

Experimental Challenges in Metaproteomics Complex microbial samples present some challenges to the metaproteomic field and require special considerations in terms of sample preparation prior to mass spectrometric analysis. Stool represents one of the most complex microbial environments of the gastrointestinal tract. It was estimated that one gram of human faeces contains approximately 1011 bacterial cells 72. This rich specimen is widely used as a noninvasive method for examination of samples with high microbial content. Several microbial enrichment methods have been proposed for processing of stool samples, including double filtration and differential centrifugation 1, 72. In the latter, microbial cells are separated by repeated low-speed centrifugation, followed by high-speed centrifugation. Via a double-filtration method, microbial cells are enriched based on differences in cell size between bacteria (0.2–2 μm) and humans (10–100 μm). Although this non-microbial cell depletion method allows enrichment of low-abundance proteins, it is associated with possible loss of microbial proteins adhered to food surfaces or host cells and exoproteins, including meta-exoproteins 1, 73, 74. The complexity and high diversity of microbial samples makes protein extraction a challenging task. One of the technical concerns is the difference in the efficiency of protein extraction between gram-negative and gram-positive bacteria 1. The latter are surrounded by thick peptidoglycan layer that resists lysis 75. The impact of different extraction methods on metaproteomic analysis, including protein identification, taxonomic diversity and functional analysis, was studied recently 75. Three lysis buffers were compared, the non-ionic detergent-based B-Per buffer, ionic detergent-based SDS, and non-detergent-based urea


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buffer. Moreover, two mechanical lysis methods were used, bead beating and ultrasonication. The study demonstrated that SDS and ultrasonication were associated with relatively high levels of peptide and protein identification. Furthermore, taxonomic composition varied with the extraction method. For example, Firmicutes and Actinobacteria were observed at high abundance with bead beating, while ultrasonication led to increased and decreased Firmicutes and Actinobacteria, respectively. Similarly, the abundance of eggNOG functional categories was affected by different extraction protocols. Taken together, the data indicate that a combination of SDS and ultrasonication might be an optimal extraction method for improvement of protein identification and yield. However, a standard extraction protocol for metaproteomic analysis needs to be established.

Computational Challenges in Metaproteomic Analysis The complexity and heterogeneity of data analysis are the two main obstacles in metaproteomics. These challenges arise from the overwhelming microbial diversity; there are estimated to be approximately one trillion microbes on Earth 76. The rapid mutations in the bacterial genome represented by transposons and small plasmids compromise 20% of all species and impose constraints on construction of a well-annotated database 77. Furthermore, the high dynamic range of microbial proteins interferes with accurate protein identification and increases the FDR of prokaryotic, food and host proteins. Redundancy of peptide sequences is another unresolved concern in metaproteomic data analysis 76. Bacterial genetic overlap and homologous prokaryotic proteins are the primary causes of redundancy, increased search duration, and ambiguous results. Special care should be taken to avoid cross-species detection due to high protein sequence homology 78. Hence, selection of an appropriate database is highly challenging for metaproteomic data analysis. Comprehensive public databases, such as UniProt, NCBI, RefSeq, and Ensembl, offer large search space of protein sequences of diverse biological origins

1, 78.

However, the large size of such databases is often


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associated with increased search duration and decreased peptide identification sensitivity due to the high FDR 76. Furthermore, the results derived from public databases can be biased due to overrepresentation of some species or strains 78. Alternatively, shotgun DNA sequencing or 16S rRNA genomic sequencing of the same metaproteomic sample might be beneficial for generation of a conserved reference database of the identified species for subsequent metaproteomic analysis. Nonetheless, a high proportion of microorganisms are either non-culturable or have not been fully sequenced, limiting the matched metagenome databases 78, 79. The latter could also be subject to sequencing and assembly errors, affecting protein sequence annotation, and hence, identification 79. Knowledge of the taxonomic composition of a sample could allow construction of a database containing all the sequences of the predicted taxa, that is, a pseudo-metagenome database, which is a recommended alternative to matched metagenome 80. Tanca et al. highlighted the impact of using different databases on the results of metaproteomic analyses. The Firmicutes/Bacteroidetes ratio, for example, was found to be highly variable when comparing the metagenomic database to the UniProt database, possibly due to bias in the latter, as explained above. Each type of database generates unique identification profiles, suggesting that the use of multiple databases would lead to an enhanced yield. A two-step search strategy in which MS/MS data are parsed against a large database to generate a refined database might be another alternative strategy for overcoming the large search space. The output can then be used for a second search after applying the target-decoy approach 81. This method exhibits substantial accuracy over the standard search method, with increased levels of both sensitivity and confidence in peptide identification. Recently, a comprehensive database containing ~ 10 million genes of the human gut microbiota derived by metagenomic sequencing, called the integrated gene catalog (IGC), was constructed 82.

This comprehensive database will improve our understanding of the human gut microbiome and will

provide further insight into microbial variations among different populations – Asian, American and European. Another approach was developed to provide a universal workflow for gut metaproteomic studies, named MetaPro-IQ, which is an iterative database search strategy 83. A first search against the IGC database


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generates a pseudo-metaproteomic database for each sample. The target-decoy version of the generated database is then used for a second search with FDR filtering of 0.01. Finally, the identified proteins for each sample are combined, resulting in a non-redundant database that can be used for accurate quantification. In parallel,

multiple

algorithms-based

software

programs

have

been

developed,

such

as

MetaProteomeAnalyzer 84 and MetaLab 85, to enhance protein quantification in human gut metaproteomic studies. Unlike proteomic analysis, most metaproteomic specimens lack comprehensive taxonomic and functional information. The presence of protein isoforms from non-sequenced microorganisms or several proteins with conserved domains hampers identification in shotgun metaproteomic analyses

76, 78.

In such analyses, the

unique identification percentage can be predicted to be minimal, with subsequent misinterpretation of taxon-specific and function-related results. Initial efforts have been proposed to address this concern, including incorporation of the lowest common ancestor, which returns two or more identification nodes to the nearest ancestor node of close daughter nodes. The principle behind this method is based on weighing identified peptides by their uniqueness or spectral counts 76. While this approach simplifies the results, it loses resolution on the other hand and renders identification difficult at strain level. Nesvizhskii et al. suggested that a razor protein could be used to identify all peptides. Although feasible for reduction of database complexity, this approach ignores the homologous proteins that exist in different species. Similar grouping approaches have been proposed based on one identified peptide or identical peptide set 76. Metaproteome Quantification Strategies Several methods have been developed to enhance protein quantification in human gut metaproteomic studies. The most common quantification method is label-free quantification, using either spectral counts, where protein abundance is correlated with the number of identified spectra, or signal intensity, where quantification is based on peak area 86. Labelled quantification, on the other hand, represents a potential strategy to metaproteomics analyses. Metabolic protein labeling, for example, is based on stable isotopes


18

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labeling by amino acids in cell culture (SILAC) or mammals (SILAM)

87, 88.

In SILAC, essential amino

acids are supplied to the cell culture medium, inducing their incorporation into all newly synthesized proteins 88. Mass spectrometeric analysis of the differentially labeled cultures allows quantification of the unlabeled to labeled proteins’ ratio. Recently, using isotope 15N labeling strategy, Zang et al. established a methodology to metabolically label human gut microbiota, referred to as stable isotopically labeled microbiota (SILAMi) 87. Nevertheless, this approach may be limited due to variations between in vitro and in vivo conditions that might affect microbiotal composition during labeling. Protein-based stable isotope probing (protein-SIP) represents another promising labeled-based quantification method such as

13C

and

15N,

86, 89.

The method is based on the incorporation of isotopically labeled substrates,

into metaproteins in microbial community. Recently, Sachsenberg et al. have

developed MetaProSIP, an automated open source protein-SIP-based tool for metaproteomics studies 90. Using MetaPro-SIP, Oberbach et al. identified differences in the metabolically active microbiota associated with diet, chow diet or high fat diet, in rat intestine

91,

opening the door for further protein-SIP

metaproteomics studies to reveal the metabolic activity of microbiota within the human gut. In addition to metabolic labeling, isobaric labeling techniques, including tandem mass tag and isobaric tag for relative and absolute quantitation (iTRAQ), could be suggested as potential approaches in metaproteomics

87.

Applying iTRAQ, significant differences in human gut metaproteins in MDD patients were observed compared to the control group, highlighting the link between dysbiosis and depression 91.

Future Perspectives The complexity of sample, high diversity of microbial species and presence of non-microbial proteins, e.g. host and food proteins, present some limitations to metaproteomics analyses. A complete picture of the exact host-microbial crosstalk is still missing at this current time. As the future of metaproteomics unfolds, significant development of dedicated metaproteomics software with robust ability to stratify patient population based on their metaproteome signature is required. Special considerations should be taken when


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designing such software including the ability to handle large data in shorter time period and retrieve metaproteome taxonomic and functional information from different available databases. The nowadays trend of cloud computing might be a promising solution to the metaproteomics field. Accurate protein identification and quantification is a necessity especially with the high complexity and heterogeneity of gut microbiota. The new emerging data-independent acquisition method – sequential windowed acquisition of all theoretical fragment ions MS Spectrometry (SWATH-MS) – has shown an outstanding performance in conventional proteomics with increased accurate and reproducible quantification even for low abundant proteins 1. The application of such accurate quantification approach might benefit the metaproteomics field. Additionally, huge amount of non-culturable microorganisms limits the database search step as discussed in this review. Hence, de novo sequencing approach might be a promising solution to overcome such challenge. The complexity of human microorganisms and the high dynamic range of interactions between host and microbiota impose challenges in the characterization of protein PTMs that further requires the development of MS/MS tools for accurate PTM site identification and quantification in complex environments. Integration of metaproteomics with multiple omics approaches, such as metagenomics, metatranscriptomics, and metabolomics, will further reinforce data analysis by providing a more comprehensive understanding of microbiota within their complex environments. The multi-omics integration at different levels will expand our knowledge on the dynamic interactions between host and microbiota and provide a deeper elucidation of the contribution of microbial dysbiosis to several diseases. Conclusions Omics technologies, such as metagenomics, have provided insights on the possible link between microbiota and several diseases. Yet a deeper analysis of the complex dynamic interactions between the microbiota and host was still missing. The newbie metaproteomics field has extended our understanding of the hostmicrobial crosstalk by providing the functional profile of metaproteome within their environment. Metaproteomics studies of human gut have pinpointed the potential cross talk between host and microbiome highlighting the role of gut metaproteome in healthy and diseased status. Interestingly, the altered


20

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metaproteome was also associated with non- gastrointestinal disorders including central nervous system diseases such as major depressive disorder and Parkinson’s disease. The recently emerged omics technique has elucidated the hypothetical scenario of the possible impact of microbiotal diversity – either in healthy status or dysbiotic condition – on host. Competing interests The authors declare that they have no competing interests. Acknowledgment This work was supported by the Egyptian Cancer Network, USA (ECN) and the Children’s Cancer Hospital Egypt 57357. Funding Sources This work was funded by the Egyptian Cancer Network, USA (ECN) and the Children’s Cancer Hospital Egypt 57357. Glossary of Abbreviations IBD

Inflammatory bowel disease

T1D

Type 1 diabetes

CF

Cystic fibrosis

IgA

Immunoglobulin A

TLRs

Toll-like receptors

SCFAs

Short-chain fatty acids

PTM

Post-Translational Modification

CD

Crohn’s disease

UC

Ulcerative colitis

H2S

Hydrogen sulphide


21


NODs

New-onset diabetics

CUZD1

CUB and zona pellucida-like domains-containing protein 1

MDD

Major depressive disorder

IGC

Integrated gene catalog

SILAC

Stable isotopes labeling by amino acids in cell culture

SILAM

Stable isotopes labeling by amino acids in mammals

SILAMi

Stable isotopically labeled microbiota

Protein-SIP

Protein-based stable isotope probing

iTRAQ

Isobaric tag for relative and absolute quantitation

SWATH-MS

Sequential windowed acquisition of all theoretical fragment ions MS Spectrometry

Page 22 of 40

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Figures

Figure 1 Metaproteomics-based microbial abundance in healthy human gut. Taxonomic cladogram constructed from 2 cohort metaproteomics based studies11,

12

from 15 normal individuals per study

highlighting common microbial communities in healthy gut with their corresponding abundance (based on peptide identification). The taxonomic classification was based on lowest common ancestor (LCA). Five internal rings of the cladogram represent superkingdom (outermost), kingdom, phylum, class, and subclass (innermost). The end taxonomic rank of the tree is marked with nodes shape (star for genus and circle for species level). The external rings represent taxa abundance based on extracted ion chromatography (XIC)


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or spectral counting (SC). The bar’s width12 and length11 is proportional to protein abundance but not comparable.

Figure 2 Global overview of the microbiotal composition across different ages. The figure describes metaproteomics- associated phyla abundance percentage. Upper pie diagram represents percentage of major phyla across age in normal individuals. Lower line chart represents changes of major phyla and genera levels across time.


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Figure 3 Metaproteome functional enzymes in the healthy human gut. Heatmap analysis of normal faecal individual from 2 independent metaproteomics cohorts showing top 25 functional KEGG orthologs (Kyoto Encyclopedia of Genes and Genomes) in one cohort with their corresponding match in the other cohort (upper panel), or vice versa (lower panel). Color intensities represents normalized metaproteome abundance.


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Figure 4 Metaproteome interactions in healthy and disease states. Microbial protein interaction networks showing the top 10-20 COGs, from four metaproteomics studies, in A) healthy individuals, B) controls and Crohn’s patients, C) controls and type 1 diabetic pediatric patients, and D) lean and obese adolescents. Each colored node represents a cluster of orthologous classification (COGs). Lines represent interactions between the different COGs and circles show the functional categories related to each COG or


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group of COGs. The figure was generated using STRING version 10.5 (https://string-db.org). The annotation of each node and the score of interaction for all networks is provided in Table S1.

Figure 5 Impact of gut microbiota on the intestinal immune system. The gut microbiota interacts with the host immune system at several lines, including immunoglobulin A (IgA), T‐helper type 1 (Th1) and type 17 (Th17), and regulatory T cells (Treg), maintaining a balanced defense mechanism against pathogens during homeostasis status. Intestinal microbiota induces the production of IgA via activation of Toll-like receptor (TLR). In the lumen, secreted IgA (s-IgA) prevents conjunction of pathogens to the epithelium. Flagellins, secreted by segmented filamentous bacteria (SFB), stimulate dendritic cells (DC) to induce Th17 and Th1 via TLR5 signaling and interleukins (IL) production. SFB can also induce Th1 cells via TLRs. Proximity of SFB to intestinal epithelial cells and antigens presentation on major histocompatibility


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complex II (MHC II) by DC stimulate Th17. Th17 cells promote antimicrobial peptide production, regenerating islet-derived protein 3γ (REGIIIγ), while Th1 cells promote macrophages activation contributing to host defense. Short chain fatty acids (SCFA) induces Foxp3+ Treg via G protein-coupled receptor (GPR109a). Butyrate, secreted by specific Clostridium clusters induces also Foxp3+ Treg via transforming growth factor β (TGF-β) production. Polysaccharide A (PSA), from Bacteroides fragilis, stimulates Foxp3+ Treg through TLR2 signaling. Foxp3+ Treg produces IL-10 required to maintain gut homeostasis. Butyrate induces IL-18 production through GPR, triggering the proliferation of Treg cells and the maintenance of homeostasis. Bifidobacterium species can produce acetate that enhance the epithelial integrity and inhibit the translocation of Shiga toxin of the enterohaemorrhagic E. coli. FDC: Follicular dendritic cells; BAFF: B cell-activating factor; APRIL: a proliferation-inducing ligand; SAA: serum amyloid A; MMPs: matrix metalloproteinases.


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Figure 6 Dysbiotic gut microbiota associated with diseased states. The altered gut microbiota composition, based on metaproteomics studies, in A) major depressive disorder (MDD), inflammatory bowel disease (IBD), type 1 diabetes (T1D), and cirrhosis adult patients, B) cystic fibrosis, IBD, T1D and obesity pediatric patients. Darker bar indicates mixed cohort, children and adults. *Taxonomic annotation based on integrated metaproteomics-metagenomics studies.

Figure 7 Metaproteome microbial-host interactions associated with dysbiotic profile of IBD. Gut dysbiosis leads to cascade of events between host and microbiota. The host attempts to restore homeostasis by limiting iron availability to pathogens and producing defensive proteins increasing the oxidative stress against microbes. Such stress is confronted by significant increase of metaproteins involved in DNA damage repair and defense and elevated antigenic surface metaproteins, such as Outer Membrane Protein A (OmpA). Elevated human enzymatic proteins pinpoint possible pancreatitis. Increased inflammation


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along with a reduction in host proteins crucial for mucosal integrity lead to widening of epithelial tight junctions and increased permeability. At the immune level, dysbiosis in IBD is associated with uncontrolled host immune response. MDH: malate dehydrogenase; DsrA: dissimilatory sulfite reductase alpha subunit; AST: aspartate aminotransferase; TST: thiosulfate sulfurtransferase; ETHE1: ethylmalonic encephalopathy 1 protein; HNP: human neutrophil peptide; HD5: human alpha defensin 5; HβD: human beta defensing; ITGB2: integrin beta-2; ITGAM: integrin alpha M; MPO: peroxidase myeloperoxidase; UDG: uracil DNA glycosylase; MutS: DNA mismatch repair MutS; CRISPR/CAS: CRISPR/CAS system associated endonuclease/helicase Cas3; AhpC: alkyl hydro peroxidase reductase subunit C; RagB: ras-related GTPbinding protein B; SusC/D: starch-binding protein C and D; CTRC: chymotrypsin C; CTRB: chymotrypsinogen B; CPA1: carboxypeptidase A1; CPB1: Carboxypeptidase B1; AMY2A: alpha-amylase; PCLKC: protocadherin LKC; COL1A2: collagen type 1 alpha 2 chain; Treg: T regulatory cells; Th: T helper cells. Tables Table 1 Comparative analysis of metagenomics and metaproteomics approaches. Metagenomics

Metaproteomics

Target

DNA (whole genome sequence)

Proteins (peptide sequences)

Identification of bacterial organisms Priori-defined technique

Easy

Difficult (requires unique proteins) No

Ability to detect switched on/off genes Microbial functional activity

Active protein coding genes

Host-microbiota interactions

All genes in a sample, active or dead Prediction (not all genes are necessarily expressed) No

Dynamic range

Less

High

Diversity

To bacterial species composition

Complexity

Less

More to proteins population within bacterial species High

Environmental effect

High

High

Specificity

More

Less

Yes


Yes Yes

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Amplification

Possible

Not possible

Quantification

Copy number based

Cost

More expensive

LFQ (spectral count or intensity) Labeled (metabolic and chemical) Less expensive

Supporting Information Table S1. Supplementary table for figure 4 providing nodes annotation and interactions score for network analyses of gut metaproteome from different metaproteomics studies representing healthy and diseased cohorts; type-1 diabetes (T1D), obesity, and crohn’s disease (CD) patients. All networks are generated by STRING version 10.5 (https://string-db.org).


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For Table of Contents Only (TOC)


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Current Understanding of Human Metaproteome Association and

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