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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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REFERENCES 1. Li, J.; Jia, H.; Cai, X.; Zhong, H.; Feng, Q.; Sunagawa, S.; Arumugam, M.; Kultima, J. R.; Prifti, E.; Nielsen, T.; Juncker, A. S.; Manichanh, C.; Chen, B.; Zhang, W.; Levenez, F.; Wang, J. J. J.; Xu, X.; Xiao, L.; Liang, S.; Zhang, D.; Zhang, Z.; Chen, W.; Zhao, H.; Al-Aama, J. Y.; Edris, S.; Yang, H.; Wang, J. J. J.; Hansen, T.; Nielsen, H. B.; Brunak, S.; Kristiansen, K.; Guarner, F.; Pedersen, O.; Doré, J.; Ehrlich, S. D.; Bork, P.; Wang, J. J. J., An integrated catalog of reference genes in the human gut microbiome. Nat Biotechnol 2014, 32 (8), 834-841. 2. Lloyd-Price, J.; Mahurkar, A.; Rahnavard, G.; Crabtree, J.; Orvis, J.; Hall, A. B.; Brady, A.; Creasy, H. H.; McCracken, C.; Giglio, M. G.; McDonald, D.; Franzosa, E. A.; Knight, R.; White, O.; Huttenhower, C., Strains, functions and dynamics in the expanded Human Microbiome Project. Nature 2017, 550 (7674), 61-66. 3. Sender, R.; Fuchs, S.; Milo, R., Are We Really Vastly Outnumbered? Revisiting the Ratio of Bacterial to Host Cells in Humans. Cell 2016, 164 (3), 337-40. 4. Sender, R.; Fuchs, S.; Milo, R., Revised Estimates for the Number of Human and Bacteria Cells in the Body. PLoS Biol 2016, 14 (8), e1002533. 5. Costello, E. K.; Lauber, C. L.; Hamady, M.; Fierer, N.; Gordon, J. I.; Knight, R., Bacterial community variation in human body habitats across space and time. Science 2009, 326 (5960), 1694-7. 6. Bouslimani, A.; Porto, C.; Rath, C. M.; Wang, M.; Guo, Y.; Gonzalez, A.; Berg-Lyon, D.; Ackermann, G.; Moeller Christensen, G. J.; Nakatsuji, T.; Zhang, L.; Borkowski, A. W.; Meehan, M. J.; Dorrestein, K.; Gallo, R. L.; Bandeira, N.; Knight, R.; Alexandrov, T.; Dorrestein, P. C., Molecular cartography of the human skin surface in 3D. Proceedings of the National Academy of Sciences of the United States of America 2015, 112 (17), E2120-9. 7. Eloe-Fadrosh, E. A.; Rasko, D. A., The human microbiome: from symbiosis to pathogenesis. Annu Rev Med 2013, 64, 145-63. 8. Turnbaugh, P. J.; Quince, C.; Faith, J. J.; McHardy, A. C.; Yatsunenko, T.; Niazi, F.; Affourtit, J.; Egholm, M.; Henrissat, B.; Knight, R.; Gordon, J. I., Organismal, genetic, and transcriptional variation in the deeply sequenced gut microbiomes of identical twins. Proc Natl Acad Sci U S A 2010, 107 (16), 7503-8. 9. Burke, C.; Steinberg, P.; Rusch, D.; Kjelleberg, S.; Thomas, T., Bacterial community assembly based on functional genes rather than species. Proc Natl Acad Sci U S A 2011, 108 (34), 14288-93. 10. Lloyd-Price, J.; Abu-Ali, G.; Huttenhower, C., The healthy human microbiome. Genome Med 2016, 8 (1), 51. 11. Clemente, J. C.; Ursell, L. K.; Parfrey, L. W.; Knight, R., The impact of the gut microbiota on human health: an integrative view. Cell 2012, 148 (6), 1258-70. 12. Li, D.; Kirsop, J.; Tang, W. H., Listening to Our Gut: Contribution of Gut Microbiota and Cardiovascular Risk in Diabetes Pathogenesis. Curr Diab Rep 2015, 15 (9), 63. 13. Ni, J.; Wu, G. D.; Albenberg, L.; Tomov, V. T., Gut microbiota and IBD: causation or correlation? Nat Rev Gastroenterol Hepatol 2017, 14 (10), 573-584. 14. Ghaisas, S.; Maher, J.; Kanthasamy, A., Gut microbiome in health and disease: Linking the microbiome-gut-brain axis and environmental factors in the pathogenesis of systemic and neurodegenerative diseases. Pharmacol Ther 2016, 158, 52-62. 15. Knight, R.; Callewaert, C.; Marotz, C.; Hyde, E. R.; Debelius, J. W.; McDonald, D.; Sogin, M. L., The Microbiome and Human Biology. Annu Rev Genomics Hum Genet 2017, 18, 65-86.

ACS Paragon Plus Environment

Page 50 of 73

Page 51 of 73

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


16. Benchimol, E. I.; Kaplan, G. G.; Otley, A. R.; Nguyen, G. C.; Underwood, F. E.; Guttmann, A.; Jones, J. L.; Potter, B. K.; Catley, C. A.; Nugent, Z. J.; Cui, Y.; Tanyingoh, D.; Mojaverian, N.; Bitton, A.; Carroll, M. W.; deBruyn, J.; Dummer, T. J. B.; El-Matary, W.; Griffiths, A. M.; Jacobson, K.; Kuenzig, M. E.; Leddin, D.; Lix, L. M.; Mack, D. R.; Murthy, S. K.; Sanchez, J. N. P.; Singh, H.; Targownik, L. E.; Vutcovici, M.; Bernstein, C. N., Rural and Urban Residence During Early Life is Associated with Risk of Inflammatory Bowel Disease: A Population-Based Inception and Birth Cohort Study. Am J Gastroenterol 2017, 112 (9), 14121422. 17. Kilsgard, O.; Karlsson, C.; Malmstrom, E.; Malmstrom, J., Differential compartmentalization of Streptococcus pyogenes virulence factors and host protein binding properties as a mechanism for host adaptation. Int J Med Microbiol 2016, 306 (7), 504-516. 18. Dutta, D.; Clevers, H., Organoid culture systems to study host-pathogen interactions. Curr Opin Immunol 2017, 48, 15-22. 19. Forbester, J. L.; Goulding, D.; Vallier, L.; Hannan, N.; Hale, C.; Pickard, D.; Mukhopadhyay, S.; Dougan, G., Interaction of Salmonella enterica Serovar Typhimurium with Intestinal Organoids Derived from Human Induced Pluripotent Stem Cells. Infect Immun 2015, 83 (7), 2926-34. 20. Leslie, J. L.; Huang, S.; Opp, J. S.; Nagy, M. S.; Kobayashi, M.; Young, V. B.; Spence, J. R., Persistence and toxin production by Clostridium difficile within human intestinal organoids result in disruption of epithelial paracellular barrier function. Infect Immun 2015, 83 (1), 138-45. 21. Bartfeld, S.; Clevers, H., Organoids as Model for Infectious Diseases: Culture of Human and Murine Stomach Organoids and Microinjection of Helicobacter Pylori. J Vis Exp 2015, (105). 22. Garcez, P. P.; Loiola, E. C.; Madeiro da Costa, R.; Higa, L. M.; Trindade, P.; Delvecchio, R.; Nascimento, J. M.; Brindeiro, R.; Tanuri, A.; Rehen, S. K., Zika virus impairs growth in human neurospheres and brain organoids. Science 2016, 352 (6287), 816-8. 23. Williams, C. F.; Walton, G. E.; Jiang, L.; Plummer, S.; Garaiova, I.; Gibson, G. R., Comparative Analysis of Intestinal Tract Models. Annual Review of Food Science and Technology 2015, 6 (1), 329-350. 24. Joly, C.; Gay-Quéheillard, J.; Léké, A.; Chardon, K.; Delanaud, S.; Bach, V.; KhorsiCauet, H., Impact of chronic exposure to low doses of chlorpyrifos on the intestinal microbiota in the Simulator of the Human Intestinal Microbial Ecosystem (SHIME®) and in the rat. Environ. Sci. Pollut. Res. 2013, 20 (5), 2726-2734. 25. Van den Abbeele, P.; Belzer, C.; Goossens, M.; Kleerebezem, M.; De Vos, W. M.; Thas, O.; De Weirdt, R.; Kerckhof, F.-M.; Van de Wiele, T., Butyrate-producing Clostridium cluster XIVa species specifically colonize mucins in an in vitro gut model. The ISME journal 2013, 7 (5), 949-961. 26. Millhouse, E.; Jose, A.; Sherry, L.; Lappin, D. F.; Patel, N.; Middleton, A. M.; Pratten, J.; Culshaw, S.; Ramage, G., Development of an in vitro periodontal biofilm model for assessing antimicrobial and host modulatory effects of bioactive molecules. BMC Oral Health 2014, 14, 80-80. 27. Ulluwishewa, D.; Anderson, R. C.; Young, W.; McNabb, W. C.; van Baarlen, P.; Moughan, P. J.; Wells, J. M.; Roy, N. C., Live Faecalibacterium prausnitzii in an apical anaerobic model of the intestinal epithelial barrier. Cellular Microbiology 2015, 17 (2), 226-240.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 52 of 73

28. Shah, P.; Fritz, J. V.; Glaab, E.; Desai, M. S.; Greenhalgh, K.; Frachet, A.; Niegowska, M.; Estes, M.; Jäger, C.; Seguin-Devaux, C.; Zenhausern, F.; Wilmes, P., A microfluidics-based in vitro model of the gastrointestinal human–microbe interface. 2016, 7, 11535. 29. Kim, H. J.; Lee, J.; Choi, J. H.; Bahinski, A.; Ingber, D. E., Co-culture of Living Microbiome with Microengineered Human Intestinal Villi in a Gut-on-a-Chip Microfluidic Device. . J. Vis. Exp. 2016, 114, e54344. 30. Schweppe, D. K.; Harding, C.; Chavez, J. D.; Wu, X.; Ramage, E.; Singh, P. K.; Manoil, C.; Bruce, J. E., Host-Microbe Protein Interactions during Bacterial Infection. Chem Biol 2015, 22 (11), 1521-30. 31. Kumar, A.; Ting, Y. P., Presence of Pseudomonas aeruginosa influences biofilm formation and surface protein expression of Staphylococcus aureus. Environ Microbiol 2015, 17 (11), 4459-68. 32. Fazli, M.; Bjarnsholt, T.; Kirketerp-Moller, K.; Jorgensen, B.; Andersen, A. S.; Krogfelt, K. A.; Givskov, M.; Tolker-Nielsen, T., Nonrandom distribution of Pseudomonas aeruginosa and Staphylococcus aureus in chronic wounds. J Clin Microbiol 2009, 47 (12), 4084-9. 33. Mainz, J. G.; Naehrlich, L.; Schien, M.; Kading, M.; Schiller, I.; Mayr, S.; Schneider, G.; Wiedemann, B.; Wiehlmann, L.; Cramer, N.; Pfister, W.; Kahl, B. C.; Beck, J. F.; Tummler, B., Concordant genotype of upper and lower airways P aeruginosa and S aureus isolates in cystic fibrosis. Thorax 2009, 64 (6), 535-40. 34. Kahl, B. C.; Duebbers, A.; Lubritz, G.; Haeberle, J.; Koch, H. G.; Ritzerfeld, B.; Reilly, M.; Harms, E.; Proctor, R. A.; Herrmann, M.; Peters, G., Population dynamics of persistent Staphylococcus aureus isolated from the airways of cystic fibrosis patients during a 6-year prospective study. J Clin Microbiol 2003, 41 (9), 4424-7. 35. Silva Filho, L. V.; Ferreira Fde, A.; Reis, F. J.; Britto, M. C.; Levy, C. E.; Clark, O.; Ribeiro, J. D., Pseudomonas aeruginosa infection in patients with cystic fibrosis: scientific evidence regarding clinical impact, diagnosis, and treatment. J Bras Pneumol 2013, 39 (4), 495512. 36. Karlsson, C.; Teleman, J.; Malmstrom, J., Analysis of Bacterial Surface Interactions with Mass Spectrometry-Based Proteomics. Methods Mol Biol 2017, 1535, 17-24. 37. Sjoholm, K.; Karlsson, C.; Linder, A.; Malmstrom, J., A comprehensive analysis of the Streptococcus pyogenes and human plasma protein interaction network. Mol Biosyst 2014, 10 (7), 1698-708. 38. Sjoholm, K.; Kilsgard, O.; Teleman, J.; Happonen, L.; Malmstrom, L.; Malmstrom, J., Targeted Proteomics and Absolute Protein Quantification for the Construction of a Stoichiometric Host-Pathogen Surface Density Model. Mol Cell Proteomics 2017, 16 (4 suppl 1), S29-S41. 39. Solis, N.; Cordwell, S. J., Cell Shaving and False-Positive Control Strategies Coupled to Novel Statistical Tools to Profile Gram-Positive Bacterial Surface Proteomes. Methods Mol Biol 2016, 1440, 47-55. 40. Solis, N.; Larsen, M. R.; Cordwell, S. J., Improved accuracy of cell surface shaving proteomics in Staphylococcus aureus using a false-positive control. Proteomics 2010, 10 (10), 2037-49. 41. Karkowska-Kuleta, J.; Zajac, D.; Bochenska, O.; Kozik, A., Surfaceome of pathogenic yeasts, Candida parapsilosis and Candida tropicalis, revealed with the use of cell surface shaving method and shotgun proteomic approach. Acta Biochim Pol 2015, 62 (4), 807-19.


Page 53 of 73

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


42. Casas, V.; Vadillo, S.; San Juan, C.; Carrascal, M.; Abian, J., The Exposed Proteomes of Brachyspira hyodysenteriae and B. pilosicoli. Front Microbiol 2016, 7, 1103. 43. Zhu, D.; Sun, Y.; Liu, F.; Li, A.; Yang, L.; Meng, X. C., Identification of surfaceassociated proteins of Bifidobacterium animalis ssp. lactis KLDS 2.0603 by enzymatic shaving. J Dairy Sci 2016, 99 (7), 5155-72. 44. Thompson, D. S.; Carlisle, P. L.; Kadosh, D., Coevolution of morphology and virulence in Candida species. Eukaryot Cell 2011, 10 (9), 1173-82. 45. Bhatnagar, S.; Shinagawa, K.; Castellino, F. J.; Schorey, J. S., Exosomes released from macrophages infected with intracellular pathogens stimulate a proinflammatory response in vitro and in vivo. Blood 2007, 110 (9), 3234-44. 46. Hassani, K.; Olivier, M., Immunomodulatory Impact of Leishmania-Induced Macrophage Exosomes: A Comparative Proteomic and Functional Analysis. Plos Neglect Trop D 2013, 7 (5). 47. Park, K. S.; Lee, J.; Jang, S. C.; Kim, S. R.; Jang, M. H.; Lotvall, J.; Kim, Y. K.; Gho, Y. S., Pulmonary inflammation induced by bacteria-free outer membrane vesicles from Pseudomonas aeruginosa. Am J Respir Cell Mol Biol 2013, 49 (4), 637-45. 48. Fernandez-Rojas, M. A.; Vaca, S.; Reyes-Lopez, M.; de la Garza, M.; Aguilar-Romero, F.; Zenteno, E.; Soriano-Vargas, E.; Negrete-Abascal, E., Outer membrane vesicles of Pasteurella multocida contain virulence factors. Microbiologyopen 2014, 3 (5), 711-7. 49. Veith, P. D.; Chen, Y. Y.; Gorasia, D. G.; Chen, D.; Glew, M. D.; O'Brien-Simpson, N. M.; Cecil, J. D.; Holden, J. A.; Reynolds, E. C., Porphyromonas gingivalis outer membrane vesicles exclusively contain outer membrane and periplasmic proteins and carry a cargo enriched with virulence factors. J Proteome Res 2014, 13 (5), 2420-32. 50. Momen-Heravi, F.; Balaj, L.; Alian, S.; Mantel, P. Y.; Halleck, A. E.; Trachtenberg, A. J.; Soria, C. E.; Oquin, S.; Bonebreak, C. M.; Saracoglu, E.; Skog, J.; Kuo, W. P., Current methods for the isolation of extracellular vesicles. Biol Chem 2013, 394 (10), 1253-62. 51. Nordin, J. Z.; Lee, Y.; Vader, P.; Mager, I.; Johansson, H. J.; Heusermann, W.; Wiklander, O. P.; Hallbrink, M.; Seow, Y.; Bultema, J. J.; Gilthorpe, J.; Davies, T.; Fairchild, P. J.; Gabrielsson, S.; Meisner-Kober, N. C.; Lehtio, J.; Smith, C. I.; Wood, M. J.; El Andaloussi, S., Ultrafiltration with size-exclusion liquid chromatography for high yield isolation of extracellular vesicles preserving intact biophysical and functional properties. Nanomedicine 2015, 11 (4), 879-83. 52. Nakai, W.; Yoshida, T.; Diez, D.; Miyatake, Y.; Nishibu, T.; Imawaka, N.; Naruse, K.; Sadamura, Y.; Hanayama, R., A novel affinity-based method for the isolation of highly purified extracellular vesicles. Sci Rep 2016, 6, 33935. 53. Ueda, K.; Ishikawa, N.; Tatsuguchi, A.; Saichi, N.; Fujii, R.; Nakagawa, H., Antibodycoupled monolithic silica microtips for highthroughput molecular profiling of circulating exosomes. Sci Rep 2014, 4, 6232. 54. Zarovni, N.; Corrado, A.; Guazzi, P.; Zocco, D.; Lari, E.; Radano, G.; Muhhina, J.; Fondelli, C.; Gavrilova, J.; Chiesi, A., Integrated isolation and quantitative analysis of exosome shuttled proteins and nucleic acids using immunocapture approaches. Methods 2015, 87, 46-58. 55. Diaz, G.; Wolfe, L. M.; Kruh-Garcia, N. A.; Dobos, K. M., Changes in the MembraneAssociated Proteins of Exosomes Released from Human Macrophages after Mycobacterium tuberculosis Infection. Sci Rep 2016, 6, 37975. 56. Maroto, R.; Zhao, Y.; Jamaluddin, M.; Popov, V. L.; Wang, H.; Kalubowilage, M.; Zhang, Y.; Luisi, J.; Sun, H.; Culbertson, C. T.; Bossmann, S. H.; Motamedi, M.; Brasier, A. R.,



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 54 of 73

Effects of storage temperature on airway exosome integrity for diagnostic and functional analyses. J Extracell Vesicles 2017, 6 (1), 1359478. 57. Kowal, J.; Arras, G.; Colombo, M.; Jouve, M.; Morath, J. P.; Primdal-Bengtson, B.; Dingli, F.; Loew, D.; Tkach, M.; Thery, C., Proteomic comparison defines novel markers to characterize heterogeneous populations of extracellular vesicle subtypes. Proceedings of the National Academy of Sciences of the United States of America 2016, 113 (8), E968-77. 58. Jia, X.; Chen, J.; Megger, D. A.; Zhang, X.; Kozlowski, M.; Zhang, L.; Fang, Z.; Li, J.; Chu, Q.; Wu, M.; Li, Y.; Sitek, B.; Yuan, Z., Label-free Proteomic Analysis of Exosomes Derived from Inducible Hepatitis B Virus-Replicating HepAD38 Cell Line. Mol Cell Proteomics 2017, 16 (4 suppl 1), S144-S160. 59. Lancaster, M. A.; Knoblich, J. A., Generation of cerebral organoids from human pluripotent stem cells. Nature protocols 2014, 9 (10), 2329-40. 60. Dye, B. R.; Hill, D. R.; Ferguson, M. A.; Tsai, Y. H.; Nagy, M. S.; Dyal, R.; Wells, J. M.; Mayhew, C. N.; Nattiv, R.; Klein, O. D.; White, E. S.; Deutsch, G. H.; Spence, J. R., In vitro generation of human pluripotent stem cell derived lung organoids. Elife 2015, 4. 61. Gonneaud, A.; Jones, C.; Turgeon, N.; Levesque, D.; Asselin, C.; Boudreau, F.; Boisvert, F. M., A SILAC-Based Method for Quantitative Proteomic Analysis of Intestinal Organoids. Sci Rep 2016, 6, 38195. 62. Cristobal, A.; van den Toorn, H. W. P.; van de Wetering, M.; Clevers, H.; Heck, A. J. R.; Mohammed, S., Personalized Proteome Profiles of Healthy and Tumor Human Colon Organoids Reveal Both Individual Diversity and Basic Features of Colorectal Cancer. Cell Rep 2017, 18 (1), 263-274. 63. Boj, S. F.; Hwang, C. I.; Baker, L. A.; Chio, II; Engle, D. D.; Corbo, V.; Jager, M.; PonzSarvise, M.; Tiriac, H.; Spector, M. S.; Gracanin, A.; Oni, T.; Yu, K. H.; van Boxtel, R.; Huch, M.; Rivera, K. D.; Wilson, J. P.; Feigin, M. E.; Ohlund, D.; Handly-Santana, A.; ArditoAbraham, C. M.; Ludwig, M.; Elyada, E.; Alagesan, B.; Biffi, G.; Yordanov, G. N.; Delcuze, B.; Creighton, B.; Wright, K.; Park, Y.; Morsink, F. H.; Molenaar, I. Q.; Borel Rinkes, I. H.; Cuppen, E.; Hao, Y.; Jin, Y.; Nijman, I. J.; Iacobuzio-Donahue, C.; Leach, S. D.; Pappin, D. J.; Hammell, M.; Klimstra, D. S.; Basturk, O.; Hruban, R. H.; Offerhaus, G. J.; Vries, R. G.; Clevers, H.; Tuveson, D. A., Organoid models of human and mouse ductal pancreatic cancer. Cell 2015, 160 (1-2), 324-38. 64. Williams, K. E.; Lemieux, G. A.; Hassis, M. E.; Olshen, A. B.; Fisher, S. J.; Werb, Z., Quantitative proteomic analyses of mammary organoids reveals distinct signatures after exposure to environmental chemicals. Proceedings of the National Academy of Sciences of the United States of America 2016, 113 (10), E1343-51. 65. Yissachar, N.; Zhou, Y.; Ung, L.; Lai, N. Y.; Mohan, J. F.; Ehrlicher, A.; Weitz, D. A.; Kasper, D. L.; Chiu, I. M.; Mathis, D.; Benoist, C., An Intestinal Organ Culture System Uncovers a Role for the Nervous System in Microbe-Immune Crosstalk. Cell 2017, 168 (6), 1135-1148.e12. 66. Ivanov, I. I., Mucosal Bioengineering: Gut in a Dish. Trends in Immunology 2017, 38 (8), 537-539. 67. Zhang, X.; Ning, Z. B.; Mayne, J.; Moore, J. I.; Li, J.; Butcher, J.; Deeke, S. A.; Chen, R.; Chiang, C. K.; Wen, M.; Mack, D.; Stintzi, A.; Figeys, D., MetaPro-IQ: a universal metaproteomic approach to studying human and mouse gut microbiota. Microbiome 2016, 4.


Page 55 of 73

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68. Zhang, X.; Ning, Z.; Mayne, J.; Deeke, S. A.; Li, J.; Starr, A. E.; Chen, R.; Singleton, R.; Butcher, J.; Mack, D. R.; Stintzi, A.; Figeys, D., In Vitro Metabolic Labeling of Intestinal Microbiota for Quantitative Metaproteomics. Anal Chem 2016, 88 (12), 6120-5. 69. Takagi, R.; Sasaki, K.; Sasaki, D.; Fukuda, I.; Tanaka, K.; Yoshida, K.-i.; Kondo, A.; Osawa, R., A Single-Batch Fermentation System to Simulate Human Colonic Microbiota for High-Throughput Evaluation of Prebiotics. PLoS ONE 2016, 11 (8), e0160533. 70. Gibson, G. R.; Cummings, J. H.; Macfarlane, G. T., Use of a three-stage continuous culture system to study the effect of mucin on dissimilatory sulfate reduction and methanogenesis by mixed populations of human gut bacteria. Appl. Environ. Microbiol. 1988, 54 (11), 2750-2755. 71. McDonald, J. A. K.; Fuentes, S.; Schroeter, K.; Heikamp-deJong, I.; Khursigara, C. M.; de Vos, W. M.; Allen-Vercoe, E., Simulating distal gut mucosal and luminal communities using packed-column biofilm reactors and an in vitro chemostat model. J. Microbiol. Methods 2015, 108, 36-44. 72. Kim, H. J.; Huh, D.; Hamilton, G.; Ingber, D. E., Human gut-on-a-chip inhabited by microbial flora that experiences intestinal peristalsis-like motions and flow. Lab on a Chip 2012, 12 (12), 2165-2174. 73. Diaz-Pascual, F.; Ortiz-Severin, J.; Varas, M. A.; Allende, M. L.; Chavez, F. P., In vivo Host-Pathogen Interaction as Revealed by Global Proteomic Profiling of Zebrafish Larvae. Front Cell Infect Microbiol 2017, 7, 334. 74. Ma, D.; Storelli, G.; Mitchell, M.; Leulier, F., Studying host-microbiota mutualism in Drosophila: Harnessing the power of gnotobiotic flies. Biomed J 2015, 38 (4), 285-293. 75. Lescak, E. A.; Milligan-Myhre, K. C., Teleosts as Model Organisms To Understand Host-Microbe Interactions. Journal of Bacteriology 2017, 199 (15). 76. Trinder, M.; Daisley, B. A.; Dube, J. S.; Reid, G., Drosophila melanogaster as a HighThroughput Model for Host-Microbiota Interactions. Front Microbiol 2017, 8. 77. Zhang, R.; Hou, A., Host-Microbe Interactions in Caenorhabditis elegans. ISRN Microbiol 2013, 2013, 356451. 78. Chaston, J. M.; Newell, P. D.; Douglas, A. E., Metagenome-Wide Association of Microbial Determinants of Host Phenotype in Drosophila melanogaster. Mbio 2014, 5 (5). 79. Koyle, M. L.; Veloz, M.; Judd, A. M.; Wong, A. C.; Newell, P. D.; Douglas, A. E.; Chaston, J. M., Rearing the Fruit Fly Drosophila melanogaster Under Axenic and Gnotobiotic Conditions. J Vis Exp 2016, (113). 80. Sullam, K. E.; Essinger, S. D.; Lozupone, C. A.; O'Connor, M. P.; Rosen, G. L.; Knight, R.; Kilham, S. S.; Russell, J. A., Environmental and ecological factors that shape the gut bacterial communities of fish: a meta-analysis. Mol Ecol 2012, 21 (13), 3363-3378. 81. Treitz, C.; Cassidy, L.; Hockendorf, A.; Leippe, M.; Tholey, A., Quantitative proteome analysis of Caenorhabditis elegans upon exposure to nematicidal Bacillus thuringiensis. J Proteomics 2015, 113, 337-50. 82. Samuel, B. S.; Rowedder, H.; Braendle, C.; Felix, M. A.; Ruvkun, G., Caenorhabditis elegans responses to bacteria from its natural habitats. Proceedings of the National Academy of Sciences of the United States of America 2016, 113 (27), E3941-9. 83. Cho, I.; Blaser, M. J., APPLICATIONS OF NEXT-GENERATION SEQUENCING The human microbiome: at the interface of health and disease. Nat Rev Genet 2012, 13 (4), 260-270. 84. Kostic, A. D.; Howitt, M. R.; Garrett, W. S., Exploring host-microbiota interactions in animal models and humans. Genes Dev 2013, 27 (7), 701-18. 55 ACS Paragon Plus Environment


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 56 of 73

85. Boehme, J. D.; Stegemann-Koniszewski, S.; Autengruber, A.; Peters, N.; Wissing, J.; Jansch, L.; Jeron, A.; Bruder, D., Chronic lung inflammation primes humoral immunity and augments antipneumococcal resistance. Scientific Reports 2017, 7. 86. Seddigh, P.; Bracht, T.; Molinier-Frenkel, V.; Castellano, F.; Kniemeyer, O.; Schuster, M.; Weski, J.; Hasenberg, A.; Kraus, A.; Poschet, G.; Hager, T.; Theegarte, D.; Opitz, C.; Brakhage, A. A.; Sitek, B.; Hasenberg, M.; Gunzer, M., Quantitative Analysis of Proteome Modulations in Alveolar Epithelial Type II Cells in Response to Pulmonary Aspergillus fumigatus Infection. Mol Cell Proteomics 2017. 87. Belkaid, Y.; Tamoutounour, S., The influence of skin microorganisms on cutaneous immunity. Nat Rev Immunol 2016, 16 (6), 353-66. 88. Burian, M.; Velic, A.; Matic, K.; Gunther, S.; Kraft, B.; Gonser, L.; Forchhammer, S.; Tiffert, Y.; Naumer, C.; Krohn, M.; Berneburg, M.; Yazdi, A. S.; Macek, B.; Schittek, B., Quantitative proteomics of the human skin secretome reveal a reduction in immune defense mediators in ectodermal dysplasia patients. J Invest Dermatol 2015, 135 (3), 759-767. 89. Yu, Y.; Jin, D.; Hu, S.; Zhang, Y.; Zheng, X.; Zheng, J.; Liao, M.; Chen, X.; Graner, M.; Liu, H.; Jin, Q., A novel tuberculosis antigen identified from human tuberculosis granulomas. Mol Cell Proteomics 2015, 14 (4), 1093-103. 90. Hoppe, J.; Unal, C. M.; Thiem, S.; Grimpe, L.; Goldmann, T.; Gassler, N.; Richter, M.; Shevchuk, O.; Steinert, M., PilY1 Promotes Legionella pneumophila Infection of Human Lung Tissue Explants and Contributes to Bacterial Adhesion, Host Cell Invasion, and Twitching Motility. Frontiers in cellular and infection microbiology 2017, 7, 63. 91. Lawley, T. D.; Walker, A. W., Intestinal colonization resistance. Immunology 2013, 138 (1), 1-11. 92. Rudney, J. D.; Xie, H.; Rhodus, N. L.; Ondrey, F. G.; Griffin, T. J., A metaproteomic analysis of the human salivary microbiota by three-dimensional peptide fractionation and tandem mass spectrometry. Mol Oral Microbiol 2010, 25 (1), 38-49. 93. Belda-Ferre, P.; Williamson, J.; Simon-Soro, A.; Artacho, A.; Jensen, O. N.; Mira, A., The human oral metaproteome reveals potential biomarkers for caries disease. Proteomics 2015, 15 (20), 3497-507. 94. Grassl, N.; Kulak, N. A.; Pichler, G.; Geyer, P. E.; Jung, J.; Schubert, S.; Sinitcyn, P.; Cox, J.; Mann, M., Ultra-deep and quantitative saliva proteome reveals dynamics of the oral microbiome. Genome Med 2016, 8 (1), 44. 95. Jagtap, P.; McGowan, T.; Bandhakavi, S.; Tu, Z. J.; Seymour, S.; Griffin, T. J.; Rudney, J. D., Deep metaproteomic analysis of human salivary supernatant. Proteomics 2012, 12 (7), 992-1001. 96. Li, X.; LeBlanc, J.; Truong, A.; Vuthoori, R.; Chen, S. S.; Lustgarten, J. L.; Roth, B.; Allard, J.; Ippoliti, A.; Presley, L. L.; Borneman, J.; Bigbee, W. L.; Gopalakrishnan, V.; Graeber, T. G.; Elashoff, D.; Braun, J.; Goodglick, L., A metaproteomic approach to study humanmicrobial ecosystems at the mucosal luminal interface. PLoS One 2011, 6 (11), e26542. 97. Presley, L. L.; Ye, J.; Li, X.; Leblanc, J.; Zhang, Z.; Ruegger, P. M.; Allard, J.; McGovern, D.; Ippoliti, A.; Roth, B.; Cui, X.; Jeske, D. R.; Elashoff, D.; Goodglick, L.; Braun, J.; Borneman, J., Host-microbe relationships in inflammatory bowel disease detected by bacterial and metaproteomic analysis of the mucosal-luminal interface. Inflamm Bowel Dis 2012, 18 (3), 409-17. 98. Lichtman, J. S.; Alsentzer, E.; Jaffe, M.; Sprockett, D.; Masutani, E.; Ikwa, E.; Fragiadakis, G. K.; Clifford, D.; Huang, B. E.; Sonnenburg, J. L.; Huang, K. C.; Elias, J. E., The 56 ACS Paragon Plus Environment

Page 57 of 73

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


effect of microbial colonization on the host proteome varies by gastrointestinal location. The ISME journal 2016, 10 (5), 1170-81. 99. Young, J. C.; Pan, C.; Adams, R. M.; Brooks, B.; Banfield, J. F.; Morowitz, M. J.; Hettich, R. L., Metaproteomics reveals functional shifts in microbial and human proteins during a preterm infant gut colonization case. Proteomics 2015, 15 (20), 3463-73. 100. Wei, X.; Jiang, S.; Chen, Y.; Zhao, X.; Li, H.; Lin, W.; Li, B.; Wang, X.; Yuan, J.; Sun, Y., Cirrhosis related functionality characteristic of the fecal microbiota as revealed by a metaproteomic approach. BMC Gastroenterol 2016, 16 (1), 121. 101. Hepburn, S.; Cairns, D. A.; Jackson, D.; Craven, R. A.; Riley, B.; Hutchinson, M.; Wood, S.; Smith, M. W.; Thompson, D.; Banks, R. E., An analysis of the impact of pre-analytical factors on the urine proteome: Sample processing time, temperature, and proteolysis. Proteomics Clin Appl 2015, 9 (5-6), 507-21. 102. Santucci, L.; Candiano, G.; Petretto, A.; Bruschi, M.; Lavarello, C.; Inglese, E.; Righetti, P. G.; Ghiggeri, G. M., From hundreds to thousands: Widening the normal human Urinome (1). J Proteomics 2015, 112, 53-62. 103. Yu, Y.; Sikorski, P.; Smith, M.; Bowman-Gholston, C.; Cacciabeve, N.; Nelson, K. E.; Pieper, R., Comprehensive Metaproteomic Analyses of Urine in the Presence and Absence of Neutrophil-Associated Inflammation in the Urinary Tract. Theranostics 2017, 7 (2), 238-252. 104. Yu, Y.; Sikorski, P.; Bowman-Gholston, C.; Cacciabeve, N.; Nelson, K. E.; Pieper, R., Diagnosing inflammation and infection in the urinary system via proteomics. J Transl Med 2015, 13, 111. 105. Zhang, Y.; Fonslow, B. R.; Shan, B.; Baek, M.-C.; Yates, J. R., Protein Analysis by Shotgun/Bottom-up Proteomics. Chemical Reviews 2013, 113 (4), 2343-2394. 106. Wisniewski, J. R.; Zougman, A.; Nagaraj, N.; Mann, M., Universal sample preparation method for proteome analysis. Nat Meth 2009, 6 (5), 359-362. 107. Leary, D. H.; Hervey, W. J.; Deschamps, J. R.; Kusterbeck, A. W.; Vora, G. J., Which metaproteome? The impact of protein extraction bias on metaproteomic analyses. Molecular and Cellular Probes 2013, 27 (5), 193-199. 108. Speda, J.; Johansson, M. A.; Carlsson, U.; Karlsson, M., Assessment of sample preparation methods for metaproteomics of extracellular proteins. Analytical Biochemistry 2017, 516, 23-36. 109. Tanca, A.; Palomba, A.; Pisanu, S.; Addis, M. F.; Uzzau, S., Enrichment or depletion? The impact of stool pretreatment on metaproteomic characterization of the human gut microbiota. PROTEOMICS 2015, 15 (20), 3474-3485. 110. Xiong, W.; Giannone, R. J.; Morowitz, M. J.; Banfield, J. F.; Hettich, R. L., Development of an Enhanced Metaproteomic Approach for Deepening the Microbiome Characterization of the Human Infant Gut. Journal of Proteome Research 2015, 14 (1), 133-141. 111. Bojanova, D. P.; Bordenstein, S. R., Fecal Transplants: What Is Being Transferred? PLoS Biol 2016, 14 (7), e1002503. 112. Zhang, X.; Li, L.; Mayne, J.; Ning, Z.; Stintzi, A.; Figeys, D., Assessing the impact of protein extraction methods for human gut metaproteomics. Journal of Proteomics 2017. 113. Armengaud, J.; Christie-Oleza, J. A.; Clair, G.; Malard, V.; Duport, C., Exoproteomics: exploring the world around biological systems. Expert Review of Proteomics 2012, 9 (5), 561575. 114. Erickson, A. R.; Cantarel, B. L.; Lamendella, R.; Darzi, Y.; Mongodin, E. F.; Pan, C.; Shah, M.; Halfvarson, J.; Tysk, C.; Henrissat, B.; Raes, J.; Verberkmoes, N. C.; Fraser, C. M.; 57 ACS Paragon Plus Environment


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 58 of 73

Hettich, R. L.; Jansson, J. K., Integrated Metagenomics/Metaproteomics Reveals Human HostMicrobiota Signatures of Crohn's Disease. PLOS ONE 2012, 7 (11), e49138. 115. Zhang, X.; Chen, W.; Ning, Z.; Mayne, J.; Mack, D. R.; Stintzi, A.; Tian, R.; Figeys, D., A deep metaproteomics approach for the study of human microbiomes. Analytical Chemistry 2017. 116. Mayers, M. D.; Moon, C.; Stupp, G. S.; Su, A. I.; Wolan, D. W., Quantitative Metaproteomics and Activity-Based Probe Enrichment Reveals Significant Alterations in Protein Expression from a Mouse Model of Inflammatory Bowel Disease. J Proteome Res 2017, 16 (2), 1014-1026. 117. Jehmlich, N.; Schmidt, F.; Taubert, M.; Seifert, J.; Bastida, F.; von Bergen, M.; Richnow, H. H.; Vogt, C., Protein-based stable isotope probing. Nature protocols 2010, 5 (12), 1957-66. 118. Sachsenberg, T.; Herbst, F. A.; Taubert, M.; Kermer, R.; Jehmlich, N.; von Bergen, M.; Seifert, J.; Kohlbacher, O., MetaProSIP: automated inference of stable isotope incorporation rates in proteins for functional metaproteomics. J Proteome Res 2015, 14 (2), 619-27. 119. Oberbach, A.; Haange, S. B.; Schlichting, N.; Heinrich, M.; Lehmann, S.; Till, H.; Hugenholtz, F.; Kullnick, Y.; Smidt, H.; Frank, K.; Seifert, J.; Jehmlich, N.; von Bergen, M., Metabolic in Vivo Labeling Highlights Differences of Metabolically Active Microbes from the Mucosal Gastrointestinal Microbiome between High-Fat and Normal Chow Diet. J Proteome Res 2017, 16 (4), 1593-1604. 120. Gillet, L. C.; Navarro, P.; Tate, S.; Rost, H.; Selevsek, N.; Reiter, L.; Bonner, R.; Aebersold, R., Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol Cell Proteomics 2012, 11 (6), O111 016717. 121. Venable, J. D.; Dong, M. Q.; Wohlschlegel, J.; Dillin, A.; Yates, J. R., Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra. Nature methods 2004, 1 (1), 39-45. 122. Croft, N. P.; de Verteuil, D. A.; Smith, S. A.; Wong, Y. C.; Schittenhelm, R. B.; Tscharke, D. C.; Purcell, A. W., Simultaneous Quantification of Viral Antigen Expression Kinetics Using Data-Independent (DIA) Mass Spectrometry. Mol Cell Proteomics 2015, 14 (5), 1361-72. 123. Ayllon, N.; Jimenez-Marin, A.; Arguello, H.; Zaldivar-Lopez, S.; Villar, M.; Aguilar, C.; Moreno, A.; De La Fuente, J.; Garrido, J. J., Comparative Proteomics Reveals Differences in Host-Pathogen Interaction between Infectious and Commensal Relationship with Campylobacter jejuni. Frontiers in cellular and infection microbiology 2017, 7, 145. 124. Muller, E. E.; Pinel, N.; Laczny, C. C.; Hoopmann, M. R.; Narayanasamy, S.; Lebrun, L. A.; Roume, H.; Lin, J.; May, P.; Hicks, N. D.; Heintz-Buschart, A.; Wampach, L.; Liu, C. M.; Price, L. B.; Gillece, J. D.; Guignard, C.; Schupp, J. M.; Vlassis, N.; Baliga, N. S.; Moritz, R. L.; Keim, P. S.; Wilmes, P., Community-integrated omics links dominance of a microbial generalist to fine-tuned resource usage. Nat Commun 2014, 5, 5603. 125. Thompson, A.; Schafer, J.; Kuhn, K.; Kienle, S.; Schwarz, J.; Schmidt, G.; Neumann, T.; Johnstone, R.; Mohammed, A. K.; Hamon, C., Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal Chem 2003, 75 (8), 1895-904. 126. Ross, P. L.; Huang, Y. N.; Marchese, J. N.; Williamson, B.; Parker, K.; Hattan, S.; Khainovski, N.; Pillai, S.; Dey, S.; Daniels, S.; Purkayastha, S.; Juhasz, P.; Martin, S.; BartletJones, M.; He, F.; Jacobson, A.; Pappin, D. J., Multiplexed protein quantitation in 58 ACS Paragon Plus Environment

Page 59 of 73

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol Cell Proteomics 2004, 3 (12), 1154-69. 127. Kitahara, N.; Morisaka, H.; Aoki, W.; Takeda, Y.; Shibasaki, S.; Kuroda, K.; Ueda, M., Description of the interaction between Candida albicans and macrophages by mixed and quantitative proteome analysis without isolation. AMB Express 2015, 5 (1), 127. 128. Hebert, A. S.; Merrill, A. E.; Bailey, D. J.; Still, A. J.; Westphall, M. S.; Strieter, E. R.; Pagliarini, D. J.; Coon, J. J., Neutron-encoded mass signatures for multiplexed proteome quantification. Nature methods 2013, 10 (4), 332-4. 129. Merrill, A. E.; Hebert, A. S.; MacGilvray, M. E.; Rose, C. M.; Bailey, D. J.; Bradley, J. C.; Wood, W. W.; El Masri, M.; Westphall, M. S.; Gasch, A. P.; Coon, J. J., NeuCode labels for relative protein quantification. Mol Cell Proteomics 2014, 13 (9), 2503-12. 130. Potts, G. K.; Voigt, E. A.; Bailey, D. J.; Rose, C. M.; Westphall, M. S.; Hebert, A. S.; Yin, J.; Coon, J. J., Neucode Labels for Multiplexed, Absolute Protein Quantification. Anal Chem 2016, 88 (6), 3295-303. 131. Khoury, G. A.; Baliban, R. C.; Floudas, C. A., Proteome-wide post-translational modification statistics: frequency analysis and curation of the swiss-prot database. Sci Rep 2011, 1. 132. Goto, Y.; Uematsu, S.; Kiyono, H., Epithelial glycosylation in gut homeostasis and inflammation. Nat Immunol 2016, 17 (11), 1244-1251. 133. Ribet, D.; Cossart, P., Post-translational modifications in host cells during bacterial infection. FEBS letters 2010, 584 (13), 2748-58. 134. Ribet, D.; Cossart, P., Pathogen-mediated posttranslational modifications: A re-emerging field. Cell 2010, 143 (5), 694-702. 135. Brown, C. W.; Sridhara, V.; Boutz, D. R.; Person, M. D.; Marcotte, E. M.; Barrick, J. E.; Wilke, C. O., Large-scale analysis of post-translational modifications in E. coli under glucoselimiting conditions. Bmc Genomics 2017, 18 (1), 301. 136. Carabetta, V. J.; Cristea, I. M., Regulation, Function, and Detection of Protein Acetylation in Bacteria. J Bacteriol 2017, 199 (16). 137. Bastos, P. A.; da Costa, J. P.; Vitorino, R., A glimpse into the modulation of posttranslational modifications of human-colonizing bacteria. J Proteomics 2017, 152, 254-275. 138. Zhang, W.; Sun, J.; Cao, H.; Tian, R.; Cai, L.; Ding, W.; Qian, P. Y., Post-translational modifications are enriched within protein functional groups important to bacterial adaptation within a deep-sea hydrothermal vent environment. Microbiome 2016, 4 (1), 49. 139. Lai, S. J.; Tu, I. F.; Wu, W. L.; Yang, J. T.; Luk, L. Y. P.; Lai, M. C.; Tsai, Y. H.; Wu, S. H., Site-specific His/Asp phosphoproteomic analysis of prokaryotes reveals putative targets for drug resistance. BMC microbiology 2017, 17 (1), 123. 140. Kim, M. S.; Zhong, J.; Pandey, A., Common errors in mass spectrometry-based analysis of post-translational modifications. Proteomics 2016, 16 (5), 700-14. 141. Giansanti, P.; Aye, T. T.; van den Toorn, H.; Peng, M.; van Breukelen, B.; Heck, A. J., An Augmented Multiple-Protease-Based Human Phosphopeptide Atlas. Cell Rep 2015, 11 (11), 1834-43. 142. Keller, A.; Bader, S. L.; Kusebauch, U.; Shteynberg, D.; Hood, L.; Moritz, R. L., Opening a SWATH Window on Posttranslational Modifications: Automated Pursuit of Modified Peptides. Mol Cell Proteomics 2016, 15 (3), 1151-63.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 60 of 73

143. Ke, M.; Shen, H.; Wang, L.; Luo, S.; Lin, L.; Yang, J.; Tian, R., Identification, Quantification, and Site Localization of Protein Posttranslational Modifications via Mass Spectrometry-Based Proteomics. Adv Exp Med Biol 2016, 919, 345-382. 144. Qing, G.; Lu, Q.; Xiong, Y.; Zhang, L.; Wang, H.; Li, X.; Liang, X.; Sun, T., New Opportunities and Challenges of Smart Polymers in Post-Translational Modification Proteomics. Adv Mater 2017, 29 (20). 145. Leney, A. C.; El Atmioui, D.; Wu, W.; Ovaa, H.; Heck, A. J. R., Elucidating crosstalk mechanisms between phosphorylation and O-GlcNAcylation. Proceedings of the National Academy of Sciences of the United States of America 2017, 114 (35), E7255-E7261. 146. Bekker-Jensen, D. B.; Kelstrup, C. D.; Batth, T. S.; Larsen, S. C.; Haldrup, C.; Bramsen, J. B.; Sorensen, K. D.; Hoyer, S.; Orntoft, T. F.; Andersen, C. L.; Nielsen, M. L.; Olsen, J. V., An Optimized Shotgun Strategy for the Rapid Generation of Comprehensive Human Proteomes. Cell Syst 2017, 4 (6), 587-599 e4. 147. Sommer, F.; Backhed, F., The gut microbiota--masters of host development and physiology. Nat Rev Microbiol 2013, 11 (4), 227-38. 148. Vaudel, M.; Burkhart, J. M.; Sickmann, A.; Martens, L.; Zahedi, R. P., Peptide identification quality control. Proteomics 2011, 11 (10), 2105-14. 149. Muth, T.; Kolmeder, C. A.; Salojarvi, J.; Keskitalo, S.; Varjosalo, M.; Verdam, F. J.; Rensen, S. S.; Reichl, U.; de Vos, W. M.; Rapp, E.; Martens, L., Navigating through metaproteomics data: a logbook of database searching. Proteomics 2015, 15 (20), 3439-53. 150. Chatterjee, S.; Stupp, G. S.; Park, S. K. R.; Ducom, J. C.; Yates, J. R.; Su, A. I.; Wolan, D. W., A comprehensive and scalable database search system for metaproteomics. Bmc Genomics 2016, 17. 151. Muth, T.; Renard, B. Y.; Martens, L., Metaproteomic data analysis at a glance: advances in computational microbial community proteomics. Expert Review of Proteomics 2016, 13 (8), 757-769. 152. Tanca, A.; Palomba, A.; Fraumene, C.; Pagnozzi, D.; Manghina, V.; Deligios, M.; Muth, T.; Rapp, E.; Martens, L.; Addis, M. F.; Uzzau, S., The impact of sequence database choice on metaproteomic results in gut microbiota studies. Microbiome 2016, 4. 153. Jovel, J.; Patterson, J.; Wang, W.; Hotte, N.; O'Keefe, S.; Mitchel, T.; Perry, T.; Kao, D.; Mason, A. L.; Madsen, K. L.; Wong, G. K. S., Characterization of the gut microbiome using 16S or shotgun metagenomics. Front Microbiol 2016, 7 (APR), 1-17. 154. Heyer, R.; Schallert, K.; Zoun, R.; Becher, B.; Saake, G.; Benndorf, D., Challenges and perspectives of metaproteomic data analysis. Journal of Biotechnology 2017, (February), 0-1. 155. Tanca, A.; Palomba, A.; Deligios, M.; Cubeddu, T.; Fraumene, C.; Biosa, G.; Pagnozzi, D.; Addis, M. F.; Uzzau, S., Evaluating the impact of different sequence databases on metaproteome analysis: Insights from a lab-assembled microbial mixture. PLoS ONE 2013, 8 (12). 156. Timmins-Schiffman, E.; May, D. H.; Mikan, M.; Riffle, M.; Frazar, C.; Harvey, H. R.; Noble, W. S.; Nunn, B. L., Critical decisions in metaproteomics: achieving high confidence protein annotations in a sea of unknowns. The ISME journal 2017, 11 (2), 309-314. 157. Jagtap, P.; Goslinga, J.; Kooren, J. A.; McGowan, T.; Wroblewski, M. S.; Seymour, S. L.; Griffin, T. J., A two-step database search method improves sensitivity in peptide sequence matches for metaproteomics and proteogenomics studies. Proteomics 2013, 13 (8), 1352-1357.


Page 61 of 73

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


158. Nesvizhskii, A., A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics. Journal of Proteomics 2010, 73 (11), 2092-2123. 159. Tang, H. X.; Li, S. J.; Ye, Y. Z., A Graph-Centric Approach for Metagenome-Guided Peptide and Protein Identification in Metaproteomics. Plos Comput Biol 2016, 12 (12). 160. Rho, M.; Tang, H.; Ye, Y., FragGeneScan: Predicting genes in short and error-prone reads. Nucleic Acids Research 2010, 38 (20), 1-12. 161. Kuhring, M.; Renard, B. Y., Estimating the computational limits of detection of microbial non-model organisms. Proteomics 2015, 15 (20), 3580-3584. 162. Kong, A. T.; Leprevost, F. V.; Avtonomov, D. M.; Mellacheruvu, D.; Nesvizhskii, A. I., MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry–based proteomics. Nature methods 2017, 14 (5), 513-520. 163. David, M.; Fertin, G.; Rogniaux, H.; Tessier, D., SpecOMS: A Full Open Modification Search Method Performing All-to-All Spectra Comparisons within Minutes. J Proteome Res 2017, 16 (8), 3030-3038. 164. Craig, R.; Beavis, R. C., A method for reducing the time required to match protein sequences with tandem mass spectra. Rapid Communications in Mass Spectrometry 2003, 17 (20), 2310-2316. 165. Craig, R.; Beavis, R. C., TANDEM: matching proteins with tandem mass spectra. Bioinformatics 2004, 20 (9), 1466-7. 166. Eng, J. K.; Jahan, T. A.; Hoopmann, M. R., Comet: An open-source MS/MS sequence database search tool. Proteomics 2013, 13 (1), 22-24. 167. Eng, J. K.; McCormack, A. L.; Yates, J. R., An Approach to Correlate Tandem Mass Spectral Data of Peptides with Amino Acid Sequences in a Protein Database. American society for Mass Spectrometry 1994, 5, 976-989. 168. Willems, P.; Ndah, E.; Jonckheere, V.; Stael, S.; Sticker, A.; Martens, L.; Van Breusegem, F.; Gevaert, K.; Van Damme, P., N-terminal Proteomics Assisted Profiling of the Unexplored Translation Initiation Landscape in Arabidopsis thaliana. Molecular & cellular proteomics : MCP 2017, 16 (6), 1064-1080. 169. Ischenko, D.; Alexeev, D.; Shitikov, E.; Kanygina, A.; Malakhova, M.; Kostryukova, E.; Larin, A.; Kovalchuk, S.; Pobeguts, O.; Butenko, I.; Anikanov, N.; Altukhov, I.; Ilina, E.; Govorun, V., Large scale analysis of amino acid substitutions in bacterial proteomics. BMC Bioinformatics 2016, 17 (1), 450-450. 170. Wan, K. X.; Vidavsky, I.; Gross, M. L., Comparing similar spectra: From similarity index to spectral contrast angle. Journal of the American Society for Mass Spectrometry 2002, 13 (1), 85-88. 171. Afgan, E.; Chapman, B.; Taylor, J., CloudMan as a platform for tool, data, and analysis distribution. BMC bioinformatics 2012, 13, 315-315. 172. Deutsch, E. W.; Mendoza, L.; Shteynberg, D.; Slagel, J.; Sun, Z.; Moritz, R. L., TransProteomic Pipeline, a standardized data processing pipeline for large-scale reproducible proteomics informatics. Proteomics - Clinical Applications 2015, 9 (7-8), 745-754. 173. Halligan, B. D.; Geiger, J. F.; Vallejos, A. K.; Greene, A. S.; Twigger, S. N., Low Cost, Scalable Proteomics Data Analysis Using Amazon's Cloud Computing Services and Open Source Search Algorithms. Journal of proteome research 2009, 8 (6), 3148-3153. 174. Muth, T.; Peters, J.; Blackburn, J.; Rapp, E.; Martens, L., Proteocloud: A full-featured open source proteomics cloud computing pipeline. Journal of Proteomics 2013, 88, 104-108. 61 ACS Paragon Plus Environment


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 62 of 73

175. Trudgian, D. C.; Mirzaei, H., Cloud CPFP: A shotgun proteomics data analysis pipeline using cloud and high performance computing. Journal of Proteome Research 2012, 11 (12), 6282-6290. 176. Elias, J. E.; Gygi, S. P., Target-decoy search strategy for increased confidence in largescale protein identifications by mass spectrometry. Nature methods 2007, 4 (3), 207-14. 177. Geer, L. Y.; Markey, S. P.; Kowalak, J. A.; Wagner, L.; Xu, M.; Maynard, D. M.; Yang, X.; Shi, W.; Bryant, S. H., Open mass spectrometry search algorithm. J Proteome Res 2004, 3 (5), 958-64. 178. Xiong, W.; Abraham, P. E.; Li, Z.; Pan, C.; Hettich, R. L., Microbial metaproteomics for characterizing the range of metabolic functions and activities of human gut microbiota. Proteomics 2015, 15 (20), 3424-38. 179. Cociorva, D.; D, L. T.; Yates, J. R., Validation of tandem mass spectrometry database search results using DTASelect. Curr Protoc Bioinformatics 2007, Chapter 13, Unit 13 4. 180. Zwittink, R. D.; van Zoeren-Grobben, D.; Martin, R.; van Lingen, R. A.; Groot Jebbink, L. J.; Boeren, S.; Renes, I. B.; van Elburg, R. M.; Belzer, C.; Knol, J., Metaproteomics reveals functional differences in intestinal microbiota development of preterm infants. Mol Cell Proteomics 2017. 181. Nesvizhskii, A. I.; Keller, A.; Kolker, E.; Aebersold, R., A statistical model for identifying proteins by tandem mass spectrometry. Anal Chem 2003, 75 (17), 4646-58. 182. Debyser, G.; Mesuere, B.; Clement, L.; Van de Weygaert, J.; Van Hecke, P.; Duytschaever, G.; Aerts, M.; Dawyndt, P.; De Boeck, K.; Vandamme, P.; Devreese, B., Faecal proteomics: A tool to investigate dysbiosis and inflammation in patients with cystic fibrosis. J Cyst Fibros 2016, 15 (2), 242-50. 183. Käll, L.; Canterbury, J. D.; Weston, J.; Noble, W. S.; MacCoss, M. J., Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nature methods 2007, 4 (11), 923-925. 184. Lassek, C.; Burghartz, M.; Chaves-Moreno, D.; Otto, A.; Hentschker, C.; Fuchs, S.; Bernhardt, J.; Jauregui, R.; Neubauer, R.; Becher, D.; Pieper, D. H.; Jahn, M.; Jahn, D.; Riedel, K., A metaproteomics approach to elucidate host and pathogen protein expression during catheter-associated urinary tract infections (CAUTIs). Mol Cell Proteomics 2015, 14 (4), 9891008. 185. Heintz-Buschart, A.; May, P.; Laczny, C. C.; Lebrun, L. A.; Bellora, C.; Krishna, A.; Wampach, L.; Schneider, J. G.; Hogan, A.; de Beaufort, C.; Wilmes, P., Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes. Nat Microbiol 2016, 2, 16180. 186. Gonnelli, G.; Stock, M.; Verwaeren, J.; Maddelein, D.; De Baets, B.; Martens, L.; Degroeve, S., A decoy-free approach to the identification of peptides. J Proteome Res 2015, 14 (4), 1792-8. 187. Edwards, N. J.; Oberti, M.; Thangudu, R. R.; Cai, S.; McGarvey, P. B.; Jacob, S.; Madhavan, S.; Ketchum, K. A., The CPTAC Data Portal: A Resource for Cancer Proteomics Research. J Proteome Res 2015, 14 (6), 2707-13. 188. Perkins, D. N.; Pappin, D. J.; Creasy, D. M.; Cottrell, J. S., Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 1999, 20 (18), 3551-67.


Page 63 of 73

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


189. Cox, J.; Hein, M. Y.; Luber, C. A.; Paron, I.; Nagaraj, N.; Mann, M., Accurate Proteomewide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ. Molecular & Cellular Proteomics 2014, 13 (9), 2513-2526. 190. Mesuere, B.; Debyser, G.; Aerts, M.; Devreese, B.; Vandamme, P.; Dawyndt, P., The Unipept metaproteomics analysis pipeline. Proteomics 2015, 15 (8), 1437-1442. 191. Mesuere, B.; Willems, T.; Van der Jeugt, F.; Devreese, B.; Vandamme, P.; Dawyndt, P., Unipept web services for metaproteomics analysis. Bioinformatics 2016, 32 (11), 1746-1748. 192. Mesuere, B.; Van der Jeugt, F.; Willems, T.; Naessens, T.; Devreese, B.; Martens, L.; Dawyndt, P., High-throughput metaproteomics data analysis with Unipept: A tutorial. J Proteomics 2017. 193. Boulund, F.; Karlsson, R.; Gonzales-Siles, L.; Johnning, A.; Karami, N.; Al-Bayati, O.; Ahren, C.; Moore, E. R. B.; Kristiansson, E., Typing and Characterization of Bacteria Using Bottom-up Tandem Mass Spectrometry Proteomics. Molecular & Cellular Proteomics 2017, 16 (6), 1052-1063. 194. Galperin, M. Y.; Makarova, K. S.; Wolf, Y. I.; Koonin, E. V., Expanded microbial genome coverage and improved protein family annotation in the COG database. Nucleic Acids Research 2015, 43 (D1), D261-D269. 195. Huerta-Cepas, J.; Szklarczyk, D.; Forslund, K.; Cook, H.; Heller, D.; Walter, M. C.; Rattei, T.; Mende, D. R.; Sunagawa, S.; Kuhn, M.; Jensen, L. J.; von Mering, C.; Bork, P., eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences. Nucleic Acids Research 2016, 44 (D1), D286-D293. 196. Szklarczyk, D.; Franceschini, A.; Wyder, S.; Forslund, K.; Heller, D.; Huerta-Cepas, J.; Simonovic, M.; Roth, A.; Santos, A.; Tsafou, K. P.; Kuhn, M.; Bork, P.; Jensen, L. J.; von Mering, C., STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Research 2015, 43 (D1), D447-D452. 197. Huson, D. H.; Beier, S.; Flade, I.; Gorska, A.; El-Hadidi, M.; Mitra, S.; Ruscheweyh, H. J.; Tappu, R., MEGAN Community Edition - Interactive Exploration and Analysis of LargeScale Microbiome Sequencing Data. Plos Comput Biol 2016, 12 (6). 198. Dhariwal, A.; Chong, J.; Habib, S.; King, I. L.; Agellon, L. B.; Xia, J., MicrobiomeAnalyst: a web-based tool for comprehensive statistical, visual and meta-analysis of microbiome data. Nucleic Acids Res 2017. 199. Huson, D. H.; Auch, A. F.; Qi, J.; Schuster, S. C., MEGAN analysis of metagenomic data. Genome research 2007, 17 (3), 377-86. 200. Muth, T.; Behne, A.; Heyer, R.; Kohrs, F.; Benndorf, D.; Hoffmann, M.; Lehteva, M.; Reichl, U.; Martens, L.; Rapp, E., The MetaProteomeAnalyzer: A Powerful Open-Source Software Suite for Metaproteomics Data Analysis and Interpretation. Journal of Proteome Research 2015, 14 (3), 1557-1565. 201. Goecks, J.; Nekrutenko, A.; Taylor, J.; Team, G., Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biology 2010, 11 (8). 202. Boekel, J.; Chilton, J. M.; Cooke, I. R.; Horvatovich, P. L.; Jagtap, P. D.; Kall, L.; Lehtio, J.; Lukasse, P.; Moerland, P. D.; Griffin, T. J., Multi-omic data analysis using Galaxy. Nat Biotechnol 2015, 33 (2), 137-139. 203. Jagtap, P. D.; Blakely, A.; Murray, K.; Stewart, S.; Kooren, J.; Johnson, J. E.; Rhodus, N. L.; Rudney, J.; Griffin, T. J., Metaproteomic analysis using the Galaxy framework. Proteomics 2015, 15 (20), 3553-3565. 63 ACS Paragon Plus Environment


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 64 of 73

204. Morgun, A.; Dzutsev, A.; Dong, X.; Greer, R. L.; Sexton, D. J.; Ravel, J.; Schuster, M.; Hsiao, W.; Matzinger, P.; Shulzhenko, N., Uncovering effects of antibiotics on the host and microbiota using transkingdom gene networks. Gut 2015, 64 (11), 1732-43. 205. Mottawea, W.; Chiang, C. K.; Muhlbauer, M.; Starr, A. E.; Butcher, J.; Abujamel, T.; Deeke, S. A.; Brandel, A.; Zhou, H.; Shokralla, S.; Hajibabaei, M.; Singleton, R.; Benchimol, E. I.; Jobin, C.; Mack, D. R.; Figeys, D.; Stintzi, A., Altered intestinal microbiota-host mitochondria crosstalk in new onset Crohn's disease. Nat Commun 2016, 7, 13419. 206. Lluch-Senar, M.; Cozzuto, L.; Cano, J.; Delgado, J.; Llorens-Rico, V.; Pereyre, S.; Bebear, C.; Serrano, L., Comparative "-omics" in Mycoplasma pneumoniae Clinical Isolates Reveals Key Virulence Factors. PLoS One 2015, 10 (9), e0137354. 207. Cernava, T.; Erlacher, A.; Aschenbrenner, I. A.; Krug, L.; Lassek, C.; Riedel, K.; Grube, M.; Berg, G., Deciphering functional diversification within the lichen microbiota by meta-omics. Microbiome 2017, 5 (1), 82. 208. Tanca, A.; Manghina, V.; Fraumene, C.; Palomba, A.; Abbondio, M.; Deligios, M.; Silverman, M.; Uzzau, S., Metaproteogenomics Reveals Taxonomic and Functional Changes between Cecal and Fecal Microbiota in Mouse. Front Microbiol 2017, 8, 391. 209. Tanca, A.; Abbondio, M.; Palomba, A.; Fraumene, C.; Manghina, V.; Cucca, F.; Fiorillo, E.; Uzzau, S., Potential and active functions in the gut microbiota of a healthy human cohort. Microbiome 2017, 5 (1), 79. 210. Tanca, A.; Fraumene, C.; Manghina, V.; Palomba, A.; Abbondio, M.; Deligios, M.; Pagnozzi, D.; Addis, M. F.; Uzzau, S., Diversity and functions of the sheep faecal microbiota: a multi-omic characterization. Microbial biotechnology 2017, 10 (3), 541-554. 211. Xiong, W.; Brown, C. T.; Morowitz, M. J.; Banfield, J. F.; Hettich, R. L., Genomeresolved metaproteomic characterization of preterm infant gut microbiota development reveals species-specific metabolic shifts and variabilities during early life. Microbiome 2017, 5 (1), 72. 212. Darzi, Y.; Falony, G.; Vieira-Silva, S.; Raes, J., Towards biome-specific analysis of meta-omics data. The ISME journal 2016, 10 (5), 1025-8. 213. Roume, H.; Heintz-Buschart, A.; Muller, E. E. L.; May, P.; Satagopam, V. P.; Laczny, C. C.; Narayanasamy, S.; Lebrun, L. A.; Hoopmann, M. R.; Schupp, J. M.; Gillece, J. D.; Hicks, N. D.; Engelthaler, D. M.; Sauter, T.; Keim, P. S.; Moritz, R. L.; Wilmes, P., Comparative integrated omics: identification of key functionalities in microbial community-wide metabolic networks. NPJ Biofilms Microbiomes 2015, 1, 15007. 214. Narayanasamy, S.; Jarosz, Y.; Muller, E. E.; Heintz-Buschart, A.; Herold, M.; Kaysen, A.; Laczny, C. C.; Pinel, N.; May, P.; Wilmes, P., IMP: a pipeline for reproducible referenceindependent integrated metagenomic and metatranscriptomic analyses. Genome Biol 2016, 17 (1), 260. 215. Rohart, F.; Gautier, B.; Singh, A.; Le Cao, K.-A., mixOmics: an R package for 'omics feature selection and multiple data integration. bioRxiv 2017. 216. Singh, A.; Gautier, B.; Shannon, C. P.; Vacher, M.; Rohart, F.; Tebutt, S. J.; Le Cao, K.A., DIABLO - an integrative, multi-omics, multivariate method for multi-group classification. bioRxiv 2016. 217. Giardine, B.; Riemer, C.; Hardison, R. C.; Burhans, R.; Elnitski, L.; Shah, P.; Zhang, Y.; Blankenberg, D.; Albert, I.; Taylor, J.; Miller, W.; Kent, W. J.; Nekrutenko, A., Galaxy: a platform for interactive large-scale genome analysis. Genome research 2005, 15 (10), 1451-5. 218. Afgan, E.; Baker, D.; van den Beek, M.; Blankenberg, D.; Bouvier, D.; Cech, M.; Chilton, J.; Clements, D.; Coraor, N.; Eberhard, C.; Gruning, B.; Guerler, A.; Hillman-Jackson, 64 ACS Paragon Plus Environment

Page 65 of 73

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


J.; Von Kuster, G.; Rasche, E.; Soranzo, N.; Turaga, N.; Taylor, J.; Nekrutenko, A.; Goecks, J., The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update. Nucleic Acids Res 2016, 44 (W1), W3-W10. 219. Fan, J.; Saha, S.; Barker, G.; Heesom, K. J.; Ghali, F.; Jones, A. R.; Matthews, D. A.; Bessant, C., Galaxy Integrated Omics: Web-based Standards-Compliant Workflows for Proteomics Informed by Transcriptomics. Mol Cell Proteomics 2015, 14 (11), 3087-93. 220. Sheynkman, G. M.; Johnson, J. E.; Jagtap, P. D.; Shortreed, M. R.; Onsongo, G.; Frey, B. L.; Griffin, T. J.; Smith, L. M., Using Galaxy-P to leverage RNA-Seq for the discovery of novel protein variations. Bmc Genomics 2014, 15, 703. 221. Jagtap, P. D.; Johnson, J. E.; Onsongo, G.; Sadler, F. W.; Murray, K.; Wang, Y.; Shenykman, G. M.; Bandhakavi, S.; Smith, L. M.; Griffin, T. J., Flexible and accessible workflows for improved proteogenomic analysis using the Galaxy framework. J Proteome Res 2014, 13 (12), 5898-908. 222. Kim, M.; Rai, N.; Zorraquino, V.; Tagkopoulos, I., Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli. Nat Commun 2016, 7, 13090. 223. Becavin, C.; Koutero, M.; Tchitchek, N.; Cerutti, F.; Lechat, P.; Maillet, N.; Hoede, C.; Chiapello, H.; Gaspin, C.; Cossart, P., Listeriomics: an Interactive Web Platform for Systems Biology of Listeria. mSystems 2017, 2 (2). 224. Wodke, J. A.; Alibes, A.; Cozzuto, L.; Hermoso, A.; Yus, E.; Lluch-Senar, M.; Serrano, L.; Roma, G., MyMpn: a database for the systems biology model organism Mycoplasma pneumoniae. Nucleic Acids Res 2015, 43 (Database issue), D618-23. 225. Chen, W. H.; van Noort, V.; Lluch-Senar, M.; Hennrich, M. L.; Wodke, J. A.; Yus, E.; Alibes, A.; Roma, G.; Mende, D. R.; Pesavento, C.; Typas, A.; Gavin, A. C.; Serrano, L.; Bork, P., Integration of multi-omics data of a genome-reduced bacterium: Prevalence of posttranscriptional regulation and its correlation with protein abundances. Nucleic Acids Res 2016, 44 (3), 1192-202. 226. Gupta, A. K.; Kaur, K.; Rajput, A.; Dhanda, S. K.; Sehgal, M.; Khan, M. S.; Monga, I.; Dar, S. A.; Singh, S.; Nagpal, G.; Usmani, S. S.; Thakur, A.; Kaur, G.; Sharma, S.; Bhardwaj, A.; Qureshi, A.; Raghava, G. P.; Kumar, M., ZikaVR: An Integrated Zika Virus Resource for Genomics, Proteomics, Phylogenetic and Therapeutic Analysis. Sci Rep 2016, 6, 32713. 227. Sanchez, B.; Gueimonde, M.; Pena, A. S.; Bernardo, D., Intestinal microbiota as modulators of the immune system. J Immunol Res 2015, 2015, 159094. 228. Levi Mortera, S.; Del Chierico, F.; Vernocchi, P.; Rosado, M. M.; Cavola, A.; Chierici, M.; Pieroni, L.; Urbani, A.; Carsetti, R.; Lante, I.; Dallapiccola, B.; Putignani, L., Monitoring Perinatal Gut Microbiota in Mouse Models by Mass Spectrometry Approaches: Parental Genetic Background and Breastfeeding Effects. Front Microbiol 2016, 7, 1523. 229. Tedjo, D. I.; Smolinska, A.; Savelkoul, P. H.; Masclee, A. A.; van Schooten, F. J.; Pierik, M. J.; Penders, J.; Jonkers, D. M., The fecal microbiota as a biomarker for disease activity in Crohn's disease. Sci Rep 2016, 6, 35216. 230. Fukuda, K.; Fujita, Y., Determination of the discriminant score of intestinal microbiota as a biomarker of disease activity in patients with ulcerative colitis. BMC Gastroenterol 2014, 14, 49. 231. Cai, L.; Wu, H.; Li, D.; Zhou, K.; Zou, F., Type 2 Diabetes Biomarkers of Human Gut Microbiota Selected via Iterative Sure Independent Screening Method. PLoS One 2015, 10 (10), e0140827. 65 ACS Paragon Plus Environment


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 66 of 73

232. Liu, Y.; Zhang, Q.; Hu, M.; Yu, K.; Fu, J.; Zhou, F.; Liu, X., Proteomic Analyses of Intracellular Salmonella enterica Serovar Typhimurium Reveal Extensive Bacterial Adaptations to Infected Host Epithelial Cells. Infect Immun 2015, 83 (7), 2897-906. 233. Pandey, A.; Ding, S. L.; Qin, Q. M.; Gupta, R.; Gomez, G.; Lin, F.; Feng, X.; Fachini da Costa, L.; Chaki, S. P.; Katepalli, M.; Case, E. D.; van Schaik, E. J.; Sidiq, T.; Khalaf, O.; Arenas, A.; Kobayashi, K. S.; Samuel, J. E.; Rivera, G. M.; Alaniz, R. C.; Sze, S. H.; Qian, X.; Brown, W. J.; Rice-Ficht, A.; Russell, W. K.; Ficht, T. A.; de Figueiredo, P., Global Reprogramming of Host Kinase Signaling in Response to Fungal Infection. Cell Host Microbe 2017, 21 (5), 637-649 e6. 234. Lapek, J. D., Jr.; Lewinski, M. K.; Wozniak, J. M.; Guatelli, J.; Gonzalez, D. J., Quantitative Temporal Viromics of an Inducible HIV-1 Model Yields Insight to Global Host Targets and Phospho-Dynamics Associated with Protein Vpr. Mol Cell Proteomics 2017, 16 (8), 1447-1461. 235. Jagdeo, J. M.; Dufour, A.; Fung, G.; Luo, H.; Kleifeld, O.; Overall, C. M.; Jan, E., Heterogeneous Nuclear Ribonucleoprotein M Facilitates Enterovirus Infection. J Virol 2015, 89 (14), 7064-78. 236. Jungblut, P. R., Proteome analysis of bacterial pathogens. Microbes and infection 2001, 3 (10), 831-40. 237. Fulton, K. M.; Twine, S. M., Immunoproteomics: current technology and applications. Methods Mol Biol 2013, 1061, 21-57. 238. Magalhaes, S.; Aroso, M.; Roxo, I.; Ferreira, S.; Cerveira, F.; Ramalheira, E.; Ferreira, R.; Vitorino, R., Proteomic profile of susceptible and multidrug-resistant clinical isolates of Escherichia coli and Klebsiella pneumoniae using label-free and immunoproteomic strategies. Res Microbiol 2017, 168 (3), 222-233. 239. Schmidt, F.; Meyer, T.; Sundaramoorthy, N.; Michalik, S.; Surmann, K.; Depke, M.; Dhople, V.; Gesell Salazar, M.; Holtappels, G.; Zhang, N.; Broker, B. M.; Bachert, C.; Volker, U., Characterization of human and Staphylococcus aureus proteins in respiratory mucosa by in vivo- and immunoproteomics. J Proteomics 2017, 155, 31-39. 240. Lee, S. Y.; Jeoung, D., The reverse proteomics for identification of tumor antigens. Journal of microbiology and biotechnology 2007, 17 (6), 879-90. 241. Larman, H. B.; Zhao, Z.; Laserson, U.; Li, M. Z.; Ciccia, A.; Gakidis, M. A.; Church, G. M.; Kesari, S.; Leproust, E. M.; Solimini, N. L.; Elledge, S. J., Autoantigen discovery with a synthetic human peptidome. Nat Biotechnol 2011, 29 (6), 535-41. 242. Gires, O.; Munz, M.; Schaffrik, M.; Kieu, C.; Rauch, J.; Ahlemann, M.; Eberle, D.; Mack, B.; Wollenberg, B.; Lang, S.; Hofmann, T.; Hammerschmidt, W.; Zeidler, R., Profile identification of disease-associated humoral antigens using AMIDA, a novel proteomics-based technology. Cellular and molecular life sciences : CMLS 2004, 61 (10), 1198-207. 243. Roozendaal, R.; Carroll, M. C., Complement receptors CD21 and CD35 in humoral immunity. Immunological reviews 2007, 219, 157-66. 244. Testa, J. S.; Shetty, V.; Hafner, J.; Nickens, Z.; Kamal, S.; Sinnathamby, G.; Philip, R., MHC class I-presented T cell epitopes identified by immunoproteomics analysis are targets for a cross reactive influenza-specific T cell response. PLoS One 2012, 7 (11), e48484. 245. Weinzierl, A. O.; Maurer, D.; Altenberend, F.; Schneiderhan-Marra, N.; Klingel, K.; Schoor, O.; Wernet, D.; Joos, T.; Rammensee, H. G.; Stevanovic, S., A cryptic vascular endothelial growth factor T-cell epitope: identification and characterization by mass spectrometry and T-cell assays. Cancer research 2008, 68 (7), 2447-54. 66 ACS Paragon Plus Environment

Page 67 of 73

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246. Li, Y.; Depontieu, F. R.; Sidney, J.; Salay, T. M.; Engelhard, V. H.; Hunt, D. F.; Sette, A.; Topalian, S. L.; Mariuzza, R. A., Structural basis for the presentation of tumor-associated MHC class II-restricted phosphopeptides to CD4+ T cells. Journal of molecular biology 2010, 399 (4), 596-603. 247. Karunakaran, K. P.; Yu, H.; Foster, L. J.; Brunham, R. C., Using MHC Molecules to Define a Chlamydia T Cell Vaccine. Methods Mol Biol 2016, 1403, 419-32. 248. Karunakaran, K. P.; Yu, H.; Jiang, X.; Chan, Q.; Goldberg, M. F.; Jenkins, M. K.; Foster, L. J.; Brunham, R. C., Identification of MHC-Bound Peptides from Dendritic Cells Infected with Salmonella enterica Strain SL1344: Implications for a Nontyphoidal Salmonella Vaccine. J Proteome Res 2017, 16 (1), 298-306. 249. Cotham, V. C.; Horton, A. P.; Lee, J.; Georgiou, G.; Brodbelt, J. S., Middle-Down 193nm Ultraviolet Photodissociation for Unambiguous Antibody Identification and its Implications for Immunoproteomic Analysis. Analytical Chemistry 2017, 89 (12), 6498-6504. 250. Pauly, F.; Smedby, K. E.; Jerkeman, M.; Hjalgrim, H.; Ohlsson, M.; Rosenquist, R.; Borrebaeck, C. A.; Wingren, C., Identification of B-cell lymphoma subsets by plasma protein profiling using recombinant antibody microarrays. Leuk Res 2014, 38 (6), 682-90. 251. Sandstrom, A.; Andersson, R.; Segersvard, R.; Lohr, M.; Borrebaeck, C. A.; Wingren, C., Serum proteome profiling of pancreatitis using recombinant antibody microarrays reveals disease-associated biomarker signatures. Proteomics Clin Appl 2012, 6 (9-10), 486-96. 252. Kristensson, M.; Olsson, K.; Carlson, J.; Wullt, B.; Sturfelt, G.; Borrebaeck, C. A.; Wingren, C., Design of recombinant antibody microarrays for urinary proteomics. Proteomics Clin Appl 2012, 6 (5-6), 291-6. 253. Delfani, P.; Dexlin Mellby, L.; Nordstrom, M.; Holmer, A.; Ohlsson, M.; Borrebaeck, C. A.; Wingren, C., Technical Advances of the Recombinant Antibody Microarray Technology Platform for Clinical Immunoproteomics. PLoS One 2016, 11 (7), e0159138. 254. Rizwan, M.; Naz, A.; Ahmad, J.; Naz, K.; Obaid, A.; Parveen, T.; Ahsan, M.; Ali, A., VacSol: a high throughput in silico pipeline to predict potential therapeutic targets in prokaryotic pathogens using subtractive reverse vaccinology. BMC Bioinformatics 2017, 18 (1), 106. 255. Ageitos, J. M.; Sanchez-Perez, A.; Calo-Mata, P.; Villa, T. G., Antimicrobial peptides (AMPs): Ancient compounds that represent novel weapons in the fight against bacteria. Biochemical pharmacology 2017, 133, 117-138. 256. Peters, B. M.; Shirtliff, M. E.; Jabra-Rizk, M. A., Antimicrobial peptides: primeval molecules or future drugs? PLoS pathogens 2010, 6 (10), e1001067. 257. Bastos, P.; Trindade, F.; Ferreira, R.; Casteleiro, M. A.; Stevens, R.; Klein, J.; Vitorino, R., Unveiling antimicrobial peptide-generating human proteases using PROTEASIX. J Proteomics 2017. 258. Selsted, M. E.; Novotny, M. J.; Morris, W. L.; Tang, Y. Q.; Smith, W.; Cullor, J. S., Indolicidin, a novel bactericidal tridecapeptide amide from neutrophils. The Journal of biological chemistry 1992, 267 (7), 4292-5. 259. Tang, Y. Q.; Yuan, J.; Osapay, G.; Osapay, K.; Tran, D.; Miller, C. J.; Ouellette, A. J.; Selsted, M. E., A cyclic antimicrobial peptide produced in primate leukocytes by the ligation of two truncated alpha-defensins. Science 1999, 286 (5439), 498-502. 260. Mishra, B.; Wang, G., The Importance of Amino Acid Composition in Natural AMPs: An Evolutional, Structural, and Functional Perspective. Front Immunol 2012, 3, 221. 261. Kleinkauf, H.; von Dohren, H., Nonribosomal biosynthesis of peptide antibiotics. European journal of biochemistry 1990, 192 (1), 1-15. 67 ACS Paragon Plus Environment


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Page 68 of 73

262. Strieker, M.; Tanovic, A.; Marahiel, M. A., Nonribosomal peptide synthetases: structures and dynamics. Current opinion in structural biology 2010, 20 (2), 234-40. 263. Wang, G.; Li, X.; Wang, Z., APD3: the antimicrobial peptide database as a tool for research and education. Nucleic Acids Res 2016, 44 (D1), D1087-93. 264. Fan, L.; Sun, J.; Zhou, M.; Zhou, J.; Lao, X.; Zheng, H.; Xu, H., DRAMP: a comprehensive data repository of antimicrobial peptides. Sci Rep 2016, 6, 24482. 265. Waghu, F. H.; Barai, R. S.; Gurung, P.; Idicula-Thomas, S., CAMPR3: a database on sequences, structures and signatures of antimicrobial peptides. Nucleic Acids Res 2016, 44 (D1), D1094-7. 266. Zhao, X.; Wu, H.; Lu, H.; Li, G.; Huang, Q., LAMP: A Database Linking Antimicrobial Peptides. PLoS One 2013, 8 (6), e66557. 267. Wang, M.; Wang, Y.; Wang, A.; Song, Y.; Ma, D.; Yang, H.; Ma, Y.; Lai, R., Five novel antimicrobial peptides from skin secretions of the frog, Amolops loloensis. Comparative biochemistry and physiology. Part B, Biochemistry & molecular biology 2010, 155 (1), 72-6. 268. Zasloff, M., Antimicrobial peptides of multicellular organisms. Nature 2002, 415 (6870), 389-95. 269. Conlon, J. M., Purification of naturally occurring peptides by reversed-phase HPLC. Nature protocols 2007, 2 (1), 191-7. 270. Trindade, F.; Amado, F.; Pinto da Costa, J.; Ferreira, R.; Maia, C.; Henriques, I.; Colaco, B.; Vitorino, R., Salivary peptidomic as a tool to disclose new potential antimicrobial peptides. J Proteomics 2015, 115, 49-57. 271. Mishra, B.; Wang, G., Ab initio design of potent anti-MRSA peptides based on database filtering technology. J Am Chem Soc 2012, 134 (30), 12426-9. 272. Sato, T.; Clevers, H., Growing Self-Organizing Mini-Guts from a Single Intestinal Stem Cell: Mechanism and Applications. Science 2013, 340 (6137), 1190. 273. Grabinger, T.; Luks, L.; Kostadinova, F.; Zimberlin, C.; Medema, J. P.; Leist, M.; Brunner, T., Ex vivo culture of intestinal crypt organoids as a model system for assessing cell death induction in intestinal epithelial cells and enteropathy. Cell Death & Disease 2014, 5 (5), e1228. 274. Pruitt, K. D.; Tatusova, T.; Maglott, D. R., NCBI reference sequences (RefSeq): A curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Research 2007, 35 (SUPPL. 1), 61-65. 275. Bateman, A.; Martin, M. J.; O'Donovan, C.; Magrane, M.; Apweiler, R.; Alpi, E.; Antunes, R.; Arganiska, J.; Bely, B.; Bingley, M.; Bonilla, C.; Britto, R.; Bursteinas, B.; Chavali, G.; Cibrian-Uhalte, E.; Da Silva, A.; De Giorgi, M.; Dogan, T.; Fazzini, F.; Gane, P.; Castro, L. G.; Garmiri, P.; Hatton-Ellis, E.; Hieta, R.; Huntley, R.; Legge, D.; Liu, W.; Luo, J.; Macdougall, A.; Mutowo, P.; Nightingale, A.; Orchard, S.; Pichler, K.; Poggioli, D.; Pundir, S.; Pureza, L.; Qi, G.; Rosanoff, S.; Saidi, R.; Sawford, T.; Shypitsyna, A.; Turner, E.; Volynkin, V.; Wardell, T.; Watkins, X.; Zellner, H.; Cowley, A.; Figueira, L.; Li, W.; McWilliam, H.; Lopez, R.; Xenarios, I.; Bougueleret, L.; Bridge, A.; Poux, S.; Redaschi, N.; Aimo, L.; Argoud-Puy, G.; Auchincloss, A.; Axelsen, K.; Bansal, P.; Baratin, D.; Blatter, M. C.; Boeckmann, B.; Bolleman, J.; Boutet, E.; Breuza, L.; Casal-Casas, C.; De Castro, E.; Coudert, E.; Cuche, B.; Doche, M.; Dornevil, D.; Duvaud, S.; Estreicher, A.; Famiglietti, L.; Feuermann, M.; Gasteiger, E.; Gehant, S.; Gerritsen, V.; Gos, A.; Gruaz-Gumowski, N.; Hinz, U.; Hulo, C.; Jungo, F.; Keller, G.; Lara, V.; Lemercier, P.; Lieberherr, D.; Lombardot, T.; Martin, X.; Masson, P.; Morgat, A.; Neto, T.; Nouspikel, N.; Paesano, S.; Pedruzzi, I.; Pilbout, S.; Pozzato, M.; Pruess, M.; Rivoire, C.; 68 ACS Paragon Plus Environment

Page 69 of 73

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Roechert, B.; Schneider, M.; Sigrist, C.; Sonesson, K.; Staehli, S.; Stutz, A.; Sundaram, S.; Tognolli, M.; Verbregue, L.; Veuthey, A. L.; Wu, C. H.; Arighi, C. N.; Arminski, L.; Chen, C.; Chen, Y.; Garavelli, J. S.; Huang, H.; Laiho, K.; McGarvey, P.; Natale, D. A.; Suzek, B. E.; Vinayaka, C. R.; Wang, Q.; Wang, Y.; Yeh, L. S.; Yerramalla, M. S.; Zhang, J., UniProt: A hub for protein information. Nucleic Acids Research 2015, 43 (D1), D204-D212. 276. Turnbaugh, P. J.; Ley, R. E.; Hamady, M.; Fraser-liggett, C.; Knight, R.; Gordon, J. I., The human microbiome project: exploring the microbial part of ourselves in a changing world. Nature 2007, 449 (7164), 804-810. 277. Mukherjee, S.; Seshadri, R.; Varghese, N. J.; Eloe-Fadrosh, E. A.; Meier-Kolthoff, J. P.; Goker, M.; Coates, R. C.; Hadjithomas, M.; Pavlopoulos, G. A.; Paez-Espino, D.; Yoshikuni, Y.; Visel, A.; Whitman, W. B.; Garrity, G. M.; Eisen, J. A.; Hugenholtz, P.; Pati, A.; Ivanova, N. N.; Woyke, T.; Klenk, H. P.; Kyrpides, N. C., 1,003 reference genomes of bacterial and archaeal isolates expand coverage of the tree of life. Nat Biotechnol 2017, 35 (7), 676-683.



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For Table of Contents Only


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Host cell/tissue proteome

Host proteins in microbiome samples

Metaproteome ACS Paragon Plus Environment

Microbial Meta-exoproteome

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Proteomic and Metaproteomic Approaches to ... - ACS Publications

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