Proteomic and Metaproteomic Approaches to Understand Host

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Proteomic and metaproteomic approaches to understand host-microbe interactions Amanda E. Starr, Shelley A. Deeke, Leyuan Li, Xu Zhang, Rachid Daoud, James Ryan, Zhibin Ning, Kai Cheng, Linh V.H. Nguyen, Elias Abou Samra, Mathieu Lavallee-Adam, and Daniel Figeys Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.7b04340 • Publication Date (Web): 23 Oct 2017 Downloaded from http://pubs.acs.org on October 24, 2017

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Analytical Chemistry

Proteomic and metaproteomic approaches to understand host-microbe interactions Amanda E. Starr1, Shelley A. Deeke1, Leyuan Li1, Xu Zhang1, Rachid Daoud1, James Ryan1, Zhibin Ning1, Kai Cheng1, Linh V. H. Nguyen1, Elias Abou Samra1, Mathieu Lavallée-Adam1, and Daniel Figeys1,2,3 1

Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and

Immunology, University of Ottawa, Ottawa, Canada. 2

Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, Canada

3Molecular Architecture of Life Program, Canadian Institute for Advanced Research, Toronto, Ontario, Canada. Correspondence to: Dr. Amanda Starr, Dr. Daniel Figeys, Roger Guidon Hall, 451 Smyth Road, University of Ottawa, Ottawa, Ontario, Canada, K1H 8M5; [email protected]; [email protected];

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INTRODUCTION The human body is composed not only of human cells, but is occupied by bacteria, archaea, fungi and viruses; this ensemble of organisms (microbiota) and their expressed genes are termed the microbiome. Despite their small size, the human-associated microbiota have a genetic composition that is at least two orders of magnitude greater than the human genome 12

and it outnumbers the cells of human host; the bacterial component alone is estimated to be

equal in number to that of human cells3-4. Different areas of our body have distinct microbial compositions that are reflective of that microenvironment2. For example, the epidermis is the most exposed to the external environment, with separate microbiomes in areas reflecting the local conditions such as at the skin surface, genitalia, armpit, hair etc5-6. Likewise, mucosal surfaces, including the mouth, intestines, vagina and lung also provide niche environments in which different microbiomes flourish. In a healthy state, these microbiomes form symbiotic relationships with the host; the microbes are within a stable and nourishing environment, whereas the host benefits in terms of metabolism, immune system priming, and protection from other more pathogenic organisms7. While there is variability in the microbes inhabiting different individuals 5, 8, the microbiomes between individuals have shared core functionalities that are relevant to the symbiotic relationship that exists between the microbiome and its host9-10. A disturbance of the levels and function of the microbiome, termed dysbiosis, can lead to systemic problems with serious impacts on human health 11. The role of the microbiome as an important contributor to human health has long been understudied, in part due to limited technologies. Early research relied on propagation of microbes, often in monoculture, and thus limited the study of unculturable species, which represent a significant proportion of human-inhabiting microbes10. Recent advances in genomic technologies have made it possible to rapidly sequence the microbiota, and to measure bacterial abundance and gene expression. Genomic studies have contributed to the increased recognition of the association between changes in the microbiota and a number of diseases, including metabolic diseases12, inflammatory bowel disease (IBD)13, and those related to the gut-brain axis14. We now appreciate that changes in lifestyles, diet, and medicine are associated with drastic changes in microbiome composition and diversity15. Concomitantly, there is an increase in diseases in many countries of the world that are undergoing lifestyle and dietary transitions, such as the observed increased incidence of IBD with rural to urban transitions16. 2 ACS Paragon Plus Environment

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Genomics technologies have led the way in establishing the importance of the microbiome in human health, and represent the vast majority of microbiome studies to date. However, genomics provides limited information on the functional aspect of the microbiome, including which bacteria and metabolic pathways are active, and what are the interacting interspecies or transkingdom networks that are involved in health and disease. Mass spectrometry (MS)-based proteomic technologies have the capacity to provide the deeper functional information of host-microbe interactions that is restricted in genomic studies. Unfortunately, microbiome studies have had limited uptake by the proteomic research community; one hand would be sufficient to count the number of microbiome-related posters at the 2017 Human Proteome Organisation World Congress. This is due in part to difficulties in obtaining appropriate samples for study, be it from culture or clinical samples, and the limitations of current technologies and software packages in fully assessing community level proteomics, termed metaproteomics. The concurrent study of a single microbe and the human proteome to understand their interactions are complicated by biases that result from differing abundances, sample preparation, completeness of the genome and associated database generation. These difficulties are compounded in microbiome studies where hundreds of species may be present in varying amounts, with protein homology existing at different taxonomic levels. Here we review some of the recent developments that address these difficulties in microbiome studies, including the analytical aspects, technologies and software tools available for proteomics and metaproteomics, and highlight the role of these in improving our understanding and modifying host-microbe interactions (Figure 1).

MODEL SYSTEMS TO STUDY BACTERIA, HOST-BACTERIA INTERACTIONS AND MICROBIOME. Different approaches of increasing complexity have been developed to model and better understand host-microbe interactions. First, bacteria can be studied in pure isolate or simple mixtures and stimulated with different factors including host-derived factors 17. Comparably, multicellular models derived from the host, such as organoids, can be challenged with microbes or microbe-derived factors to study their response 18-22. These models provide insight into the molecular components directly involved in the responsiveness of the individual organism, but not into the interplay between multiple organisms. Advances have been made toward more 3 ACS Paragon Plus Environment


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complex model systems, wherein complex microbiomes are cultured alone or in combination with host cells, progressing our understanding of the relationships that exist between microbes and their host and the overall changes in transkingdom protein expression 23-29.

In vitro systems Membrane and surfaceome Many features of host-microbe interactions occur through direct and indirect interaction of membrane proteins30. Kumar and Ting showed the interrelationships that exist within microbes that alter membrane protein expression, which can impact the host31. The dual presence of the two opportunistic pathogens Staphylococcus aureus and Pseudomonas aeruginosa results in increased fatality compared to colonization of the individual bacterial species 32-33, wherein first S. aureus and then P. aeruginosa infects the lungs of adult cystic fibrosis patients 34. However, disease progression can be improved by preventing or postponing infection by P. aeruginosa35. Using proteomics, Kumar and Ting found that seven classes of proteins were elevated on the surface of S. aureus upon co-culturing with P. aeruginosa, including those related to host-microbe interactions such as virulence, adhesion and resistance31. In addition to highlighting the value of co-culture systems, this study emphasizes the importance of the surface protein repertoire, or “surfaceome”. Here we discuss some modifications developed and applied to standard surface profiling techniques that have utility in better understanding molecules involved in host-microbe interactions. Protein-protein interactions To study host proteins which are interacting with the bacterial surface, Karlsson et al. developed a method wherein the bacteria proteome is assessed along with the host proteins bound to its surface 36. In this method, bacteria are incubated with a proteinous fluid (e.g. human plasma or saliva) and after washing unbound proteins, eluted proteins are processed for standard bottom-up MS analysis. Recently, the approach was applied to identify human plasma proteins that interact with the Gram-positive (G+) bacterium Streptococcus pyogenes 37. The authors identified 181 human proteins adsorbed to the bacterial surface, identifying non-classical plasma proteins involved in cell adhesion, intracellular proteins, extracellular matrix components and secreted proteins. Building upon this study, the authors performed absolute quantification to 4 ACS Paragon Plus Environment

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determine protein stoichiometry ratios between wild-type or M1-protein (a critical surfaceexpressed virulence factor) -deficient S. pyogenes with adherent human plasma proteins, identifying the M1 virulence factor and host protein interactors38. Further, utilizing host protein tertiary structures, a stoichiometric surface density model was developed to visualize the hostpathogen interactions. Although this method is valuable in identifying potential host proteins that interact with the surface of bacteria, follow-up validation experiments are necessary to confirm that the observed interaction is not the result of the non-physiological levels of the biological materials utilized. In a proof-of-principle study, Schweppe et al. utilized crosslinking to assess interspecies protein-protein interactions (PPI) 30. Briefly, H292 lung epithelial cells were infected with the highly virulent A. baumannii isolate Ab5075, and interacting proteins were crosslinked using Biotin-aspartate proline-PIR n-hydroxyphthalimide BDP-NHP. Following cell lysis, full-length crosslinked proteins were enriched with monomeric avidin beads and trypsin digested for MS analysis. Although the majority of PPIs identified were between peptides from the same proteins, a total of 46 interspecies PPIs were identified. With this approach the authors identified bacterial virulent factors and their target host proteins, including the bacterial protein OmpA interacting with the human protein desmoplakin, shown for the first time for this pathogen. Modifying this approach to eliminate or reduce the number of PPI between peptides from the same proteins would be beneficial to enable emphasis on interspecies PPIs. Cell shaving Cell shaving is a method wherein surface exposed proteins are proteolytically cleaved from the cell, and resultant peptides can be evaluated by MS. This simple approach does not necessitate the use of MS-incompatible detergents, nor does it entail extensive sample handling, such as in the case of density gradient ultracentrifugation. However, cell lysis can occur during the cell shaving process and therefore intracellular proteins may be falsely identified as surface exposed. To reduce the number of false positives, Solis and Cordwell described a two-step approach to calculate the likelihood of a protein with surface exposure39 40. The authors proposed the parallel processing of a protease-free “false positive control” to account for intracellular contaminant proteins released during the cell shaving incubation. The probability of an identified protein being surface exposed is calculated based upon the number of peptides identified in the 5 ACS Paragon Plus Environment


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shaved sample and in the false-positive control, and then adjusted for by the predicted localization of identified proteins as established by existing databases; this calculation that can be made with a downloadable program (https://github.com/mehwoot/cellshaving). Several studies have expanded the field of surfaceomics to investigate cell surface proteins on various microbes that are likely to participate in host-microbe interactions41-43. Recently the cell shaving approach was applied to different morphological forms of two pathogenic yeast species, namely Candida parapsilosis and C. tropicalis41 although false-positive controls were not performed. It was observed that the proportion of surface proteins related to adhesins and other virulence factors were dependent on the morphological form of yeast cells (unicellular, yeast-like cells or filamentous pseudohyphae). This observation was consistent with previous studies that described the filamentous morphological form of fungus as essential for pathogenesis44. Cell shaving was also performed on the human commensal microbe, Bifidobacteria, identifying 105 surface proteins from which 15 were deemed to be potentially involved in host-microbe interaction based on previous findings43. The cell shaving approach was also recently applied to the study of two porcine intestinal pathogens Brachyspira hyodysenteriae and Brachyspira pilosicoli, identifying 53 and 139 surface proteins, respectively42. Although the authors did not implement the aforementioned false-positive control they did investigate the peptides and proteins in the extracellular medium, referred to as the exoproteome. Several virulence factors including those related to chemotaxis, flagella-related proteins, adherence, hemolysis, aerotolerance proteins and iron metabolism were identified and differentially distributed between the surfaceome and exoproteome. For example, the chemotaxis-related protein methyl-accepting chemotaxis protein B (McpB) was mainly identified in the surfaceomes whereas the chemotaxis protein CheW displayed higher abundance in the exoproteome. Although the cell shaving method is useful in selecting potential proteins participating in the host-microbe interactions, further validation studies are required to confirm the proteins involvement in host-microbe interaction since its presence on the cell surface does not necessarily indicate it is partaking in interkingdom cross-talk.

Exosomes


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Exosomes are cell-derived vesicles that have gained attention in recent years as they represent vehicles of both long distance and local cell communication, and thus readily participate in host-microbe interactions45. They reflect molecular signatures from the cell type and cell status from which they originated, and thus represent an alternative biomarker source. Infection by various pathogens has been shown to alter the protein composition of exosomes46. Furthermore, exosomes have also been reported to contain pathogen components45, highlighting their role in host-microbe interaction. Analagous to exosomes, bacterial outer membrane vesicles (OMVs) represent important vehicles of host-microbe interactions, are able to elicit host immune responses47 and disseminate virulent factors48-49. Exosomes and OMVs are commonly isolated by differential ultracentrifugation, a low throughput method that produces low yields50. Numerous groups have developed alternative methods to reduce time requirements and increase throughput for exosome isolation51-52, including the utilization of immune-affinity capture that is directed towards proteins involved in exosome biogenesis 52-54. Notably, immune-affinity capture will only isolate the subset of exosomes that express the particular target, and is inapplicable to isolation of bacterial OMVs, which have a biogenesis that is distinct from exosome biogenesis. Host proteins present on the exosomal surface represent likely candidates for interaction with the resident microbe community. In a study by Diaz et al., exosomal surface-associated proteins were differentiated from intraluminal by use of a two-step labeling strategy55. First, surface proteins were labeled with cell impermeable NHS-Sulfo-LC-LC-Biotin (452Da); after exosome lysis, Sulfo-NHS-Biotin (226Da) was used to label intraluminal or internal exosomal membrane leaflet proteins. Applying this approach to exosomes secreted from THP-1 derived macrophages infected with Mycobacterium tuberculosis (Mtb), 41 proteins were deemed significantly elevated in exosomes from Mtb-infected cells compared to non-infected cells, including six that were determined by differential labeling to be surface proteins. While the exosomes outlined above were isolated from a culture system, exosomes can also be isolated from biofluids (detailed below). However, obtaining sufficient amounts of exosomes, particularly from biofluids, can be a challenge. While storage by freezing at -80°C is common among exosome studies, Maroto et al. showed that the vesicle stability is compromised and can result in altered protein content, and suggest the use of exosomes immediately upon isolation 56. In experimental conditions, exosome sample normalization must be carefully considered. For proteomic analysis, exosomes and extracellular vesicles have been normalized 7 ACS Paragon Plus Environment


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according to the number of cells plated 57 and to protein concentration 58. An important consideration is ensuring that the normalization method applied is aligned with the biological question under consideration. Despite challenges in isolation and quantification, exosomes derived from different cell types, and vesicles released from bacteria, and applying the differential labeling strategy to identify the exosomal surface exposed proteins could aide in elucidating the targeting of exosomes to recipient cells and thus shed light on the transkingdom cross-talk mediated by exosomes and other extracellular vesicles.

Ex vivo systems Organoids In vitro monoculture systems provide utility in assessing direct interactions between the host and microbome, but don’t provide value at the level of interplay, which can be achieved with more complex systems43. Compared with in vivo studies, ex vivo systems are cost-effective and highly controlled with minimized interfering factors, thus present highly reproducible results. Advances in recent ex vivo host and microbial models (Table 1) can serve as helpful platforms for proteomic/metaproteomic insights into host-microbiome interaction mechanisms. Organoids are stem cell-derived 3D cultures which express organ-specific cell types, and represent an ex vivo system to evaluate host-microbe interactions 18. Organoids have been generated from a plethora of tissues including the intestine19-20, stomach21, brain22, 59, and lung60. This model has been applied to study host-microbe interactions using a variety of infectious pathogens including Salmonella enterica19, Clostridium difficile20, Helicobacter pylori21 and Zika virus22, although characterization of these has been limited to imaging 19-21, RNA sequencing 19, 21

, or specific functional assays 20. Proteomic techniques have contributed to quantitative

characterization of organoids 61-64 in the field of cancer 62-63 and following chemical exposure64; application of proteomics to organoid models of host-microbe interaction is a logical next step in shedding light on the mechanism of infection and potentially identify targets for therapeutic intervention and infection prevention. Notably, while there is great utility within this system, organoids lack the immune cell component. Further, the utility of these is limited in the study of obligate anaerobes, where microbe viability of vegetative C. difficile was shown only up to 12 hours 20.


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Explant organ culture Culturing of a single host cell type or a 2D culturing of host tissues does not mimic the complex cellular compositions and physical conjunctions of the host organ involved in host-microbiome interactions. To address this, Yissachar et al. established a fluidic mouse intestinal organ culture system, which maintains viable intestinal cell types (epithelial, immune, neural), tissue structure, and dynamic cell-cell interactions for more than 24 hr in a microfabricated device with sixparallel chambers65. The system consists of two independent flow streams, one inside the lumen and a second flow in the external medium, and is suited for short-term responses of gut immune/neuronal system to environmental perturbations. The authors were able to maintain both aerobic and anaerobic bacterial growth for the duration of the study (24 hours), and identified immune and non-immune transcriptional and functional responses to bacterial stimulation; thus, proteomics characterization of the system could be readily applied. While in its present form this system has not yet been developed for human organs; advances in humanized mouse models may permit its application in human cell/tissues responses 66.

Microbiome community culture In vitro models of the host-originated microbiota can be used for examining its response to xenobiotics and host-originated molecules, exosomes, etc. Static culture (or batch culture) methods are suitable for detecting acute microbiome responses within a short viable period of less than 48 hours 23, and are the most cost-and-time effective models for high-throughput tests (e.g. for drug screening). Ex vivo cultivation of an entire microbiota continues to be a challenge, due in part to media preparations that are specific for growth of certain phyla, resulting in the loss of the diversity that is observed in vivo. We found that ex vivo experiments permit for observable stimulation-specific changes over a short-term 67-68. We have continued to optimize culture conditions for ex vivo culture of stool microbiota, noting that inorganic salts, bile salts and mucin are the key components that must be considered for the maintenance of the microbiome both functionally and compositionally (Li et al., under review). Studies on ex vivo static co-culturing of gut microbiome with prebiotics have shown agreements with in vivo mechanisms 68-69. Compared with batch culturing, continuous flow culture models are more suitable for observation of long-term responses. Typical three-stage continuous flow culture models consist of tandem bioreactors that simulate different gut regions70, and include Simulator 9 ACS Paragon Plus Environment


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of the Human Intestinal Microbial Ecosystem (SHIME 24 and M-SHIME 25), and Chemostat 71 models. Among these, M-SHIME achieved the simulation of mucosal gut microbiota through enrichment of mucosal butyrate producing bacteria, which is of significance for studying the host-microbe interactions at the mucosal surface 25.

Host-microbiota co-culturing An in vitro periodontal biofilm model has been used for co-culturing host cells and microbiota from the oral cavity 26. In this model, epithelial cells (OKF6-TERT2) are seeded in 24 well plates, and multi-species biofilms grown on Thermanox™ coverslips are attached inversely to Millipore cell culture inserts and are placed over the cell culture. Biofilm viability was maintained through replacing the artificial saliva daily. Similarly, the transwell model27 is a costeffective co-culturing of intestinal Caco-2 cells and gut bacteria, however, without medium replacement such a static system would have a short viable period. Microfluidic models can achieve medium replacement for both host cells and microbes in the co-culturing system, it also enables high-throughput in contrast with the continuous flow models for microbiome culturing. The HuMiX (human-microbial crosstalk) model28 is a sandwich-structure device with three colaminar microchannels, that is a medium perfusion microchamber, a human epithelial cell culture microchamber and a microbial culture microchamber. The microbial culture chamber contains a membrane coated with mucin for simulating the mucosal luminal interface (MLI). The gut-on-achip model72 realizes host-gut microbiome co-culture in a microfabricated device with luminal medium flow for gut microbes and capillary flow for the growth epithelial cells on a flexible porous polydimethylsiloxane membrane. Studies have shown a stable microbial niche is formed on cultured epithelial villi within 2-3 days 29, and viability can be maintained for more than 2 weeks 72. This model is also suitable for other host-microbiome systems such as the oral cavity, skin and urogenital tract 29.

Model organisms Model organisms have several advantages over in vitro and ex vivo culture systems, including the ability to evaluate microbiome flux which may be a result of environmental, chemical or infectious conditions. Rodent 83 and non-mammalian 73 74 models have been used to


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aide in unraveling the complex host-microbe interactions that occur in both healthy and diseased individuals; each organism offers its own unique set of benefits and limitations.

Non-mammalian model organisms Non-mammalian models, including, flies, worms and fish, produce a large number of offspring, with a rapid maturation rate and small size, permitting for increased statistical power and reduced economic burden associated with housing, respectively75 76-77. Furthermore, several genetic mutants have been developed and can be useful in studying host-microbe interactions. Similar to the human gut where Bacteroidetes and Firmicutes dominate, Firmicutes is the dominant phylum in fruit fly gut78, but germ-free D. melanogaster are easily produced and so enable for evaluation of the species-specific effects 79. Notably, D. melanogaster lacks an adaptive immune system limiting its value in the study of host-microbe interactions, but is a cost and time effective organism for preliminary studies which require a high degree of manipulation of the resident microbiota and/or the host. Most fish have a predominance of Proteobacteria80 in their gut, though gnotobiotic animals are readily generated through sterilization after fertilization. In a recent metaproteomic73 study, zebrafish larvae injected with, but not immersed in, P. aeruginosa PAO1 displayed elevated circulating neutrophils. Additionally, while virulent factors related to bacterial-type flagellum and the type III secretion system were enriched in injected animals, single-species biofilm formation and cellular response to starvation were enriched in immersion-infected animals. This study highlighted the importance of infection method, and demonstrated the utility of using fish models to study host-microbe interactions. Introduction of a bacterium of interest to Caenorhabditis elegans can be readily made through co-incubation, since nematodes utilize bacteria as a food source. In a quantitative proteomics study by Treitz et al., C. elegans were grown in the presence of E. coli, or combined with either a non-or -pathogenic Bacillus thuringiensis strain 81. The study identified proteins from both the host and the microbe with which it was co-incubated, though a very limited number of B. thuringiensis proteins were identified. Several C. elegans protein families were differentially expressed upon treatment with the pathogenic B. thuringiensis strain including lectins, lysozymes, and the transthyretin-like proteins. Notably, while many studies have utilized C. elegans for investigation of host-microbe interactions, the majority do so using a single or only a few microbes77 and so underrepresents the community effects that occur in their natural 11 ACS Paragon Plus Environment


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habitat where they are exposed to a larger variety of food sources than the standard ones used in most studies82.

Rodent model organisms Several different mouse models have been developed in order to study host-microbe interactions, including standard inbred mice, gnotobiotic mice, humanized mice and conventionalized gnotobiotic mice for which the advantages and disadvantages of each model have been reviewed elsewhere83. Despite 99% genetic similarity between mice and humans, and key similarities between the two at the phylum and family levels of gut microbiota84, several factors have prevented the routine use of gnotobiotic and humanized mouse models in hostmicrobe studies, including the elevated cost, facility limitations and prolonged study times. Recently, proteomics has been applied to several mouse models of different lung disorders and infection models, including proteomic analysis of bronchoalveolar lavage fluid (BALF) from a mouse model infected with Streptococcus pneumoniae with pre-existing inflammatory conditions85, ex vivo host proteomics of alveolar epithelial cells from mice that underwent intratracheal infection with A. fumigatus86, proteome analysis of A. baumannii grown in BALF from infected rats and evaluated by LC-MALDI-TOF/TOF. While the first two studies examined the host proteome, the latter evaluated the pathogen proteome and identified proteins related to pathogenesis and virulence, cell wall/membrane/envelope biogenesis, energy production and conversion and translation to be over-expressed in A. baumannii grown in BALF from infected rats. To evaluate gut microbiota a mouse model was utilized, which studied the influence of genetic background (IgA-producing vs IgA-deficient) and feeding origin (nursed either by their own or wet-nursing mothers) by applying both shotgun metaproteomics and MALDI-TOF. Interestingly milk that was deficient in IgA resulted in an increase in opportunistic bacterial pathogens.

HUMAN SAMPLING: WHERE TO LOOK FOR HOST-MICROBE INTERACTIONS Biological specimens that are collected from anatomical regions at the interface of hostmicrobes are valuable sources for deciphering the complex interplay between the host and its associated residential microbial community. A great advantage of these biological samples is that


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they can be used for the concurrent evaluation of both host and microbe proteomes that result from their ‘natural’ interacting environment. The skin is the largest organ of the human body, and has the greatest surface area and microbe-hosting environment. As the first line of defense from environmental microbes, both the innate and adaptive immune systems are highly active within the dermis and epidermis87. Despite the significant host-microbe interaction and potential for discovery from skin biological sampling, there are very few metaproteomics studies directly from skin samples. Rather, the host response 88 or cultured microbes isolated from skin 17 have been evaluated in seclusion. Similarly, lung tissue obtained at biopsy, expectorated sputum and BALF are useful biological samples for investigation of respiratory disease. However, the majority of proteomics-based studies using these samples are related to carcinoma, with a limited number for infection-related disorders. For example, frozen biopsy samples obtained from Mycobacterium tuberculosis patients were homogenized and evaluated by proteomics to identify M. tuberculosis antigens 89. Healthy lung explants 90 or animal models (see above) have dominated the study of respiratory infectious agents in proteomics. The lack of proteomic-based studies to investigate host-pathogen interactions from skin and lung biological samples may be due to the dominance of a single pathogen, rather than a significant change in the microbiome, in these two tissues as well as the invasive measures required for sample collection from the lower respiratory tract. Second to the dermis, the mucosa represents the greatest interaction site between the host and microbes, and provides for multiple sample types of biological interest. In fact, gastrointestinal tract biological samples dominate the metaproteomic field. Saliva, gastric secretions and stool samples each have different characteristics that create both opportunities and challenges in evaluating host-microbe interactions. Salivary and stool samples offer the advantage of non-invasive collection; the former is readily available, whereas the latter has a limited window of availability for fresh sample collection. The large intestine is the most densely colonized microbial surface area in mammals91, however, sample collection from discrete areas of this mucous membrane represents an important challenge as it requires invasive endoscopic means. Saliva and plaque



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The oral mucosa is known to have a diverse microbiome, and localized bacteria contribute to oral diseases including dental caries and periodontitis. The first metaproteomic analysis of saliva 92 was a follow-up study that had prepared samples with the intent of evaluating host proteins, and so did not exclude cellular debris; their findings were overwhelmed by host proteins, and limited to 139 microbial proteins from 34 different species. More recently, metaproteome-focused studies have been undertaken which used dental plaque 93 and saliva 94-95 as a sample source. The microbial component of the dental plaque accounted for nearly 90% (7771 microbial, 874 human) of the total proteins identified, whereas it accounted for 7 days 24-48 hr

273 23

Multi-channel well plate,

Static Static/stirring/mi

Cost-and-time effective

xing

option for high-

multi-channel fermenter Continuous flow culturing

Microbiome

Gut microbiome

24-25, 70-71

Tandem bioreactors

Additional features

throughput testing ×

> 1 month

Continuous flow

√

> 3 days

Static, artificial

mimicking different

M-SHIME for MLI study 25

regions of the GI tract Periodontal biofilm model

26

Host-

Oral epithelial cell, and

Hanging basket co-

Microbiome

multi-species biofilm

culture model

saliva replaced daily

Transwell model27

Host-bacteria

Caco-2 cells and

Transwells

√

> 12 hr

Static

√

> 2 weeks

microfluidic

Faecalibacterium prausnitzii Gut on a chip

HuMiX

28

29

Host-

Caco-2 cells and gut

Two parallel

Microbiome

microbiome

microchannels, 1 mm ×

capillary flow

150 µm × 1 cm W×H×L

and lumen flow

Host-

Caco-2 cells and gut

spiral-shaped

Microbiome

bacteria, e.g. Lactobacillus

microchannels,

√ 3

rhamnosus GG,

200×4×0.5 mm

Bacteroides caccae

L×W×H, volume 400 µl

> 24 hr

Microfluidic

Mucin-coated

flows

membrane for MLI study

per channel.



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Table 2: Public databases Size APD3263

2619 AMPs

DRAMP264 CAMPR3265

17611 AMPs 10247 sequences 757 structures 114 familyspecific signatures 5547 >88.3 million protein sequences >62.3 million protein sequences

LAMP3266 NCBI Reference Sequence Database274 Uniprot275 Human Microbiome Project (HMP)276 Integrated Gene catalog1 Genomic Encyclopedia of Bacteria and Archaea (GEBA)277

>9.8 million ORFs > 3 million predicted protein-coding sequences

Human, Animal √

Bacteria

Archaea

Virus

Fungi

Plants

Protists

Additional Features

√

√

√

√

√

√

Chemical modification; Prediction; External links; Distribution by target

√ √

√ √

√ √

√

√ √

√

√

√ ~4.4 million √ ~0.16 million

√ ~69 million √ >58 million 823 reference genomes

√ >1.7 million

√

√

Synthetic; Structure; Target; Prediction

Synthetic

√ >2.2 million Derived from 30708 samples from human nasal passages, oral cavity, skin, GI tract and uorogenital tract Derived from 1267 fecal samples from 1070 individuals

1003 reference genomes

*Rows in grey indicate antimicrobial peptide-specific databases.


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Table 3: Bioinformatics tools for metaproteomic analysis Pre-processing

Core processing

Post-processing

Database

Protein

Protein

Taxonomy

Functional

Statistics &

construction

identification

quantification

analysis

annotation

visualization

√

+

+

ComPIL & Blazmass 150

√

√

Graph2Pep & Graph2Pro

√

+

√

+

MetaPro-IQ

67

159

+

MetaProSIP 118 Unipept

√

190-192

√

TCUP 193 COGs

√

194

√

STRING v10 196 eggNOG 4.5

√

195

√

MEGAN CE 197 MicrobiomeAnalyst

√

√

√

√

*

*

*

*

*

√

√

√

√

MetaProteomeAnalyzer200 Galaxy

202-203

MetaLab

√ √

198

* √

√: applicable for this issue; +: applicable in the workﬂow but other tools are required; *: user-defined workflows are requi



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For Table of Contents Only


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Host cell/tissue proteome

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Metaproteome ACS Paragon Plus Environment

Microbial Meta-exoproteome

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Proteomic and Metaproteomic Approaches to Understand Host

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