This is an open access article published under an ACS AuthorChoice License, which permits copying and redistribution of the article or any adaptations for non-commercial purposes.
Review pubs.acs.org/ac
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 Lavallée-Adam,† and Daniel Figeys*,†,‡,§ †
Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, University of Ottawa, Ottawa, Ontario, K1N 6N5, Canada ‡ Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, Ontario, K1N 6N5, Canada § Molecular Architecture of Life Program, Canadian Institute for Advanced Research, Toronto, Ontario, M5G 1M1, Canada
■
CONTENTS
Model Systems to Study Bacteria, Host−Bacteria Interactions, and Microbiome In Vitro Systems Membrane and Surfaceome: Protein-Protein Interactions Membrane and Surfaceome: Cell Shaving Exosomes Ex Vivo Systems Organoids Explant Organ Culture Microbiome Community Culture Host−Microbiota Coculturing Model Organisms Nonmammalian Model Organisms Rodent Model Organisms Human Sampling: Where to Look for Host−Microbe Interactions Saliva and Plaque Intestinal Mucosal Luminal Interface Stool Urine Technical Considerations for Metaproteomics Protein Extraction and Digestion Considerations for Metaproteomics New Quantitative Metaproteomics Approaches Post-Translational Modifications in the Host− Microbe Interaction Databases and Searching Strategies to Minimize Time Constraints Public Databases Sample-Specific Databases Confidence Assessment of Spectrum, Peptide, and Protein Identifications in Metaproteomes Bioinformatics Tools in Metaproteomics Integrating Metaproteomics with Metagenomics/Metatranscriptomics for Microbiome Study Tools for Multiomics Integration Applications of Proteomics and Metaproteomics in Health Functional Characterization of the Early Development Microbiome Disease Pathogenesis and Biomarker Identification © 2017 American Chemical Society
Clinical Immunoproteomics for Vaccine Development and Diagnosis Development of New Antimicrobial PeptideBased Antibiotic Treatments Perspective and Concluding Remarks Author Information Corresponding Authors ORCID Notes Biographies Acknowledgments References
87 88 88 88 89 89 89 89 89 90 90 90 91
102 102 103 103 103 103 103 103 104 104
T
he 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 humanassociated microbiota have a genetic composition that is at least 2 orders of magnitude greater than the human genome1,2and it outnumbers the cells of human host; the bacterial component alone is estimated to be equal in number to that of human cells.3,4 Different areas of our body have distinct microbial compositions that are reflective of that microenvironment.2 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 etc.5,6 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 organisms.7 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 host.9,10 A disturbance of the levels and function of the microbiome, termed dysbiosis, can lead to systemic problems with serious impacts on human health.11
91 91 92 92 92 92 92 93 94 95 95 95 98 99 100 101 101
Special Issue: Fundamental and Applied Reviews in Analytical Chemistry 2018
101 101
Published: October 23, 2017 86
DOI: 10.1021/acs.analchem.7b04340 Anal. Chem. 2018, 90, 86−109
Analytical Chemistry
Review
Figure 1. Overview of the processes for evaluating host−microbe interactions by proteomics and metaproteomics.
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 of host−microbe interactions (Figure 1).
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 humaninhabiting microbes.10 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 diseases,12 inflammatory bowel disease (IBD),13 and those related to the gut-brain axis.14 We now appreciate that changes in lifestyles, diet, and medicine are associated with drastic changes in microbiome composition and diversity.15 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 transitions.16 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 on
■
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. 87
DOI: 10.1021/acs.analchem.7b04340 Anal. Chem. 2018, 90, 86−109
Analytical Chemistry
Review
were cross-linked 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. Membrane and Surfaceome: 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 exposure.39,40 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 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 interactions.41−43 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 pathogenesis.44 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 interactions based on previous findings.43 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, respectively.42 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.
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 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: ProteinProtein Interactions. Many features of host−microbe interactions occur through direct and indirect interaction of membrane proteins.30 Kumar and Ting showed the interrelationships that exist within microbes that alter membrane protein expression, which can impact the host.31 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. aeruginosa.35 Using proteomics, Kumar and Ting found that seven classes of proteins were elevated on the surface of S. aureus upon coculturing with P. aeruginosa, including those related to host−microbe interactions such as virulence, adhesion, and resistance.31 In addition to highlighting the value of coculture 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 nonclassical plasma proteins involved in cell adhesion, intracellular proteins, extracellular matrix components, and secreted proteins. Building upon this study, the authors performed absolute quantification to determine protein ratios between wildtype or M1-protein (a critical surface-expressed virulence factor)deficient S. pyogenes with adherent human plasma proteins, identifying the M1 virulence factor and host protein interactors.38 Further, utilizing host protein tertiary structures, a stoichiometric surface density model was developed to visualize the host− pathogen 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 nonphysiological 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 88
DOI: 10.1021/acs.analchem.7b04340 Anal. Chem. 2018, 90, 86−109
Analytical Chemistry
Review
the transkingdom cross-talk mediated by exosomes and other extracellular vesicles. Ex Vivo Systems. In vitro monoculture systems provide utility in assessing direct interactions between the host and microbome but do not provide value at the level of interplay, which can be achieved with more complex systems.43 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. 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 intestine,19,20 stomach,21 brain,22,59 and lung.60 This model has been applied to study host−microbe interactions using a variety of infectious pathogens including Salmonella enterica,19 Clostridium dif f icile,20 Helicobacter pylori,21 and Zika virus,22 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 organoids61−64 in the field of cancer62,63 and following chemical exposure;64 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. dif f icile was shown only up to 12 h.20 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 h in a microfabricated device with six-parallel chambers.65 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 h) and identified immune and nonimmune 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. Ex vivo models of the hostoriginated 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 h23 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.
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. 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 interactions.45 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 exosomes.46 Furthermore, exosomes have also been reported to contain pathogen components,45 highlighting their role in host−microbe interaction. Analogous to exosomes, bacterial outer membrane vesicles (OMVs) represent important vehicles of host−microbe interactions, are able to elicit host immune responses,47 and disseminate virulent factors.48,49 Exosomes and OMVs are commonly isolated by differential ultracentrifugation, a low throughput method that produces low yields.50 Numerous groups have developed alternative methods to reduce time requirements and increase throughput for exosome isolation,51,52 including the utilization of immune-affinity capture that is directed toward 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 proteins by use of a two-step labeling strategy.55 First, surface proteins were labeled with cell impermeable NHS-Sulfo-LC-LC-Biotin (452 Da); after exosome lysis, Sulfo-NHS-Biotin (226 Da) 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 noninfected 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 according to the number of cells plated57 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 aid in elucidating the targeting of exosomes to recipient cells and thus shed light on 89
DOI: 10.1021/acs.analchem.7b04340 Anal. Chem. 2018, 90, 86−109
Analytical Chemistry mucin-coated membrane for MLI study >12 h >2 weeks
>24 h
√ √
√ Caco-2 cells and gut bacteria, e.g. Lactobacillus rhamnosus GG, Bacteroides caccae
host− microbiome host−bacteria host− microbiome host− microbiome HuMiX28
oral epithelial cell, and multispecies biofilm
microbiome
continuous flow culturing24,25,70,71 periodontal biofilm model26 transwell model27 gut on a chip29
Caco-2 cells and Faecalibacterium prausnitzii Caco-2 cells and gut microbiome
transwells two parallel microchannels, 1 mm × 150 μm × 1 cm W × H × L spiral-shaped microchannels, 200 × 4 × 0.5 mm3 L × W × H, volume 400 μL per channel
>3 days √
gut microbiome
host microbiome organoids272 batch culturing
intestinal stem cell gut microbiome
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 coculturing 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 of the Human Intestinal Microbial Ecosystem (SHIME24 and M-SHIME25), and Chemostat71 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 Coculturing. A periodontal biofilm model has been used for coculturing host cells and microbiota from the oral cavity.26 In this model, epithelial cells (OKF6-TERT2) are seeded in 24 well plates, and multispecies 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 cost-effective coculturing 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 coculturing system, and it is high-throughput in contrast with the continuous flow models for microbiome culturing. The HuMiX (humanmicrobial crosstalk) model28 is a sandwich-structure device with three colaminar microchannels, which 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-a-chip model72 realizes host−gut microbiome coculture 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. Rodent83 and nonmammalian73,74 models have been used to 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. Nonmammalian Model Organisms. Nonmammalian 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, respectively.75−77 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 gut,78
static, artificial saliva replaced daily static microfluidic capillary flow and lumen flow microfluidic flows
>1 month ×
continuous flow
cost-and-time effective option for high-throughput testing M-SHIME for MLI study25 √ √
100 μm magnitude culture tube, multichannel well plate, multichannel fermenter tandem bioreactors mimicking different regions of the GI tract hanging basket coculture model
static static/stirring/mixing
additional features lumen flow and external medium flow
flow feature viable duration
24 h (epithelial degradation after 24 h) >7 days273 24−48 h23 √ six parallel chambers, 8 × 25 mm2 each mouse intestinal organ (including epithelial, immune, neural cells) host mouse intestinal organ culture system65
high throughput model structure/size cultured subject(s) model classification
Table 1. Summary of Advances in ex Vivo Models for Host and Microbiome Studies
Review
90
DOI: 10.1021/acs.analchem.7b04340 Anal. Chem. 2018, 90, 86−109
Analytical Chemistry
Review
but germ-free D. melanogaster are easily produced and 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 that 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, singlespecies 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 coincubation, 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 coincubated, 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 habitat where they are exposed to a larger variety of food sources than the standard ones used in most studies.82 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 elsewhere.83 Despite 99% genetic similarity between mice and humans, and key similarities between the two at the phylum and family levels of gut microbiota,84 several factors have prevented the routine use of gnotobiotic and humanized mouse models in host−microbe 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 conditions,85 ex vivo host proteomics of alveolar epithelial cells from mice that underwent intratracheal infection with A. f umigatus,86 and 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 overexpressed in A. baumannii. To evaluate gut microbiota, a mouse model was utilized that 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 host−microbes 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 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 epidermis.87 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 response88 or cultured microbes isolated from skin17 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 proteomicsbased 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 explants90 or animal models (see above) have dominated the study of respiratory infectious agents in proteomics. The lack of proteomicbased 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 noninvasive 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 mammals;91 however, sample collection from discrete areas of this mucous membrane represents an important challenge as it requires invasive endoscopic means. Saliva and Plaque. 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 saliva92 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 plaque93 and saliva94,95 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 2 × 103 metagenomes (HMP)276 Integrated Gene catalog1 > 9.8 × 106 ORFs Genomic Encyclopedia of > 3 × 106 predicted protein-coding sequences Bacteria and Archaea277
√ √ √ 974 reference √ 29 reference genomes genomes
√ > 1.7 × 106 √ > 0.16 × √ > 62 × 106 106 √ > 93 × 106 protein sequences
level. Raw data155 were downloaded and searched against the combined database. The identified LCA peptides were assigned the quantification information which is the peptide intensity. It is clear that there is a bias in the database toward research-focused species with significant sequencing data, such as E. coli and Saccharomyces cerevisiae (Figure 3). However, this bias does not correlate well with the taxon readout by the LCA strategy, where the number of E. coli peptides was among the lowest. The variation might come from proteome expression, proteome extraction, or even the biochemical characteristic of the whole proteome/peptidome. The dynamic range of the results reminds us to be cautious when inferring the taxonomic compositions of the microbiome using proteomics data when using public curated databases. Timmins-Schiffman et al. described how the increased size of experimentally derived databases decreases search sensitivity and causes a loss of statistical power from multiple hypothesis testing against the vast number of sequences unrepresented in their experimentally derived metaproteome,156 in agreement with the findings of other researchers.155,157,158 To address the large database size, we adopted an iterative database search strategy, termed MetaPro-IQ, which constructs a shrunken sample-specific nonredundant database.67 A pseudometaproteome derived from gut microbial gene catalogs forms an initial sequence database, which is searched first without and then a second time with FDR filtering against experimental-specific sample databases. The resulting reduced database was used to obtain protein identifications in the analysis of gut microbiome samples. In another method, Timmins-Schiffman et al. proposed a database construction that combines a metapeptide database generated from an experimentally derived metagenome and a database consisting of the peptides predicted by metagenomics.156 Through an iterative search strategy that annotates sequences with the best consensus sequence obtained with BLASTp, their approach can achieve better metaproteome coverage. Further, Tang et al. implemented a graph-centric approach to generate a tryptic peptide database from metagenomic and metatranscriptomic sources, called Graph2Pep/Graph2Pro.159 This de Bruijn graph structure is constructed by abstracting contigs as nodes and their putative adjacent sequence connections as edges. Then, their algorithm performs a depth-first search on this graph and outputs a database that contains all of the in silico tryptic peptides resulting from ORFs found in the search. These putative peptides are used to remove sparsity from the original graph, and a second depthfirst search on the now-constrained graph yields putative protein sequences. Their results showed that predictions from Graph2Pep/Graph2Pro show a nearly 4-fold increase in PSMs and unique peptide identifications compared to proteins predicted from FragGeneScan,160 a protein-coding gene predictor used as the basis for Graph2Pep. These approaches constitute effective ways to increase the utility of parallel metagenomics sequencing when performing metaproteomics experiments, but choosing the right database may be tricky. LiDSiM (Limits of Detection Simulation for Microbes)161 provides a means for researchers to assess the suitability of any reference database for their experiment by estimating the ratio of MS/MS spectra that are identifiable at various taxonomic levels. Here, all of an organism’s known proteins are extracted from a sequence database and in silico digested into peptides, which are then searched against a subset of the database. The absence of an organism or taxonomic branch is simulated by removing it from the database and searching peptides against the remaining database, giving an indication of
derived from >1200 fecal samples derived from cultivatable species
derived from >30,000 samples from >15 body sites
√ > 4.7 × 106 b
√ > 0.9 × 106 √ > 6 × 103 values represent combined Swiss-Prot and TrEMBL entries
√ √ > 1.1 × 106
additional features and notes Protists
√ √
√ √ √ √ > 3.9 × 106
Plants Fungi Virus
√ √ √ √ √ √ √ > 0.3 × 106 √ > 2.2 × 106 √ > 2.7 × 106 √ > 7.4 × 106 √
2619 peptides 17611 peptides 10247 peptides > 88 × 106 protein sequences
APD3263 DRAMP264 CAMPR3265 NCBI Reference Sequence Database274 Uniprot275
Human/ Animal
Bacteria
√ √ √ √ √ √ √ > 4.4 × 106 √ > 75 × 106
Size
Archaea
Review
Database
Table 2. Public databases.
chemical modification; prediction tools prediction tools synthetic; structure; target; prediction tools
Analytical Chemistry
96
DOI: 10.1021/acs.analchem.7b04340 Anal. Chem. 2018, 90, 86−109
Analytical Chemistry
Review
Figure 3. Evaluation of a metaproteomic analysis with a public database. (Top) A combined.fasta database that contained sequence information for the nine species indicated was developed from UniProt/TrEMBLE information. The number of FASTA, distinct peptides, Lowest Common Ancestor (LCA) peptides per species in the database is indicated. (Bottom) Raw data from an experiment that combined the nine species in equal amounts prior to digestion and LC analysis155 was analyzed and the identified LCA peptides, and the intensity of LCA peptides is shown for each species.
compared to the next-best algorithm in an open (500 Da) search. Similarly, in the first of three SpecOMS163 tools, SpecXtract compares the number of shared fragment masses for any pair of experimental and theoretical spectra. In contrast to MSFragger, SpecOMS utilizes an efficient tree data structure (SpecTrees) to compare experimental and theoretical spectra, which retains information while reducing the expensive search running time that is traditionally associated with open search algorithms. Finally, SpecOMS applies an algorithm (SpecFit) to determine the mass difference between experimental and theoretical spectral pair sets that accounts for missed cleavages, semitryptic peptides, and PTMs. Because of the number of open search comparisons made in a SpecOMS workflow, it requires highquality PSMs to maintain stringency and minimize the FDR. Further, accounting for missing N-termini proteoforms and amino acid substitutions can expand search spaces. Willems et al. proposed a workflow incorporating metagenomics databases to determine N-terminus-lacking proteins in MS/MS experiments, reconstructing both their genome-level origins and eventual sequences.168 Ischenko et al. described speptide, an ad-hoc method to identify single amino acid polymorphisms in proteomics experiments without the use of genomic databases.169 Here, spectra are abstracted as a spectral “angle”,170 or multidimensional vector, with the cosine similarity between two spectral angles representing these spectra’s similarity. A single amino acid polymorphism’s alteration of the cosine similarity is calculated and compared to verify those polymorphisms. This
the extent to which related organisms or taxonomic branches contain a given peptide. The algorithm’s error-tolerant search allows identification of closely related peptides in these cases, which increases identification sensitivity at lower taxonomic levels. Through this, LiDSiM can evaluate the detection potential and limits of a specific sequence database. Increasing the search space to account for proteoforms and their effects in an MS/MS experiment also increases the peptide identification running time. Protein PTMs affect MS/MS spectra, and must be considered for a metaproteomics workflow to be truly comprehensive in its assessment of a biological sample. To maximize PTM identification, a popular approach is to search using a wide precursor ion mass tolerance window. Such “open search” strategies significantly increase database search running times. MSFragger162 and SpecOMS163 are two new programs that use this open search paradigm to identify modified proteins using sequence databases but which maintain a viable computational speed. MSFragger162 performs in silico digestion of a selected protein database to create a nonredundant peptide index, the data structure of which incorporates mass binning and precursor mass ordering. The resulting theoretical fragment index is a reference for comparison and quick retrieval of matches with experimental fragment ions. In benchmarking tests where similar protein identification sensitivity levels were reported,162 MSFragger completed a conventional narrow-window search markedly faster than X!Tandem,164,165 Comet,166 and SEQUEST,167 while providing a ∼124-fold search speed increase 97
DOI: 10.1021/acs.analchem.7b04340 Anal. Chem. 2018, 90, 86−109
Analytical Chemistry
Review
sequences were in both reverse (decoy) and target databases,150 thereby reducing even more our capabilities of distinguishing true positive from false positive PSMs. A number of groups have used methods that are typically tailored for single organism proteomics data analysis to process metaproteomics data sets. For instance, Chatterjee et al. presented an approach using DTASelect2,179 a widely used filtering algorithm for database search results of single organism data sets, to filter protein identifications from the gut microbiome with a FDR of 1% and considered protein identifications supported by at least 2 peptides, thereby controlling for error propagation.150 Zwittink et al. used a similar strategy to filter peptides and proteins identified from fecal microbiomes with a FDR of 1% and where protein identifications were considered if a minimum of two peptides were identified, of which at least one was unique and at least one was not modified.180 Another group used the ProteinProphet algorithm181 to filter protein identifications from fecal microbiomes that obtained a probability of being a correct match greater than 99%182 and were supported by at least two peptides. Finally, Lassek et al. used a Percolator183 to filter proteins from catheter biofilms and urine that were associated with a FDR of 1% and were identified by at least two distinct peptides.184 The use of such traditional database search filtering tools forces the users to apply highly stringent FDR criteria at the protein level to maximize protein identification confidence to the detriment of protein identification sensitivity. On the other hand, Heintz-Buschart et al. used a more lenient approach for the metaproteomics analysis of fecal samples: considering protein identifications requiring solely one unique peptide while having peptide identifications filtered at a FDR of 1%.185 While the latter methods represent traditional computational proteomics approaches, a few groups have proposed protein identification confidence assessment strategies that are more adapted to the characteristics of metaproteomics data sets. In an attempt to improve our computational ability to differentiate true positive from false positive PSMs in data sets where the sequences in the database share a high similarity, such as those of metaproteomes, Gonnelli et al. proposed a method replacing decoy PSMs by PSMs with low confidence scores as incorrect PSMs in the training procedure of their filtering tool.186 Their algorithm, named Nokoi, classifies PSMs as correct or incorrect based on 47 features. When applied to the Clinical Proteomic Tumor Analysis Consortium (CPTAC) initiative data sets,187 their approach showed a sensitivity that was not as high as the one of Percolator183 but outcompeted Mascot188 in terms of the number of PSMs associated with a FDR of 1%. Finally, Jagtap et al.95 analyzed salivary supernatant metaproteomes using a target-decoy approach and filtered the results from this database search using ProteinPilot to only consider distinct peptides and PSMs with a 5% local FDR. They then used PSMs respecting this 5% local FDR threshold to filter for nonhuman peptides that were detected in the saliva. While such filtering criteria yield more protein identifications, those have a reduced confidence level. Database search filtering tools are only starting to be used in the context of metaproteomics, and there are no clear standards or guidelines to effectively balance out the specificity and sensitivity of protein identifications. Such guidelines are much needed, especially to compare and integrate metaproteomics results from different species with each other. Filtering tools performing protein inference and that are adapted to the characteristics of metaproteomics data sets will be necessary to ensure accurate protein identifications.
method was applied by the researchers to differentiate bacteria at the strain level, allowing them to reconstruct a phylogenetic tree of E. coli strains from four different microbial environments; however, validation at a genomic level was not performed, and strains clustered on their similarities to each other through a neighbor-joining tree construction method. The proper use of databases, and effective algorithms to search them, is an imperative part of experimental planning during a metaproteomics investigation. Challenges to be met in the future include overcoming computational limitations to the massive search space involved in protein identification in microbiome samples. Cloud computing has gained traction as a means for researchers to analyze MS/MS data in a high-throughput, distributed manner171−175 and may address computational limitations’ effect on the time constraint, albeit the aforementioned platforms’ applications in a mixed-sample metaproteomics context have not fully materialized. There remains a significant space for metaproteomics workflows to enter the realm of cloud computing. Nonetheless, with the expected continued increase in computational power that researchers have at their disposal, large-scale metaproteomics experiments with accurate and comprehensive results continue to become more realistic. Confidence Assessment of Spectrum, Peptide, and Protein Identifications in Metaproteomes. In addition to increasing database search running time, large metaproteome protein sequence databases also impact the sensitivity of protein identifications. Traditional database search algorithms employing target-decoy strategies to filter PSMs as well as peptide and protein identifications at a given FDR176 have been mostly developed for protein samples originating from a single organism and are poorly adapted to metaproteomics. Muth et. al demonstrated that the increasing size of a protein sequence database negatively affects the sensitivity of protein identifications.149 They showed that the distribution of PSM confidence scores (obtained using two standard database search algorithms; OMSSA177 and X! TANDEM165) of decoy hits overlaps more with the one from target hits in larger sequence databases. Similarly, Xiong et al. demonstrated that when analyzing gut metaproteomes, the usage of a matched metagenome sequence database derived from the analyzed sample yields a better separation of the confidence scores of false positive PSMs and likely true positive PSMs than when the same data was analyzed using a similar metagenome database but not specific to the analyzed sample.178 The similarity of decoy and target peptide sequences is greater in metaproteome databases than in traditional proteome databases due to the large number of decoy and target peptide sequences; therefore, PSMs are more likely to be matched to decoy peptide sequences with high confidence scores. This hinders the ability of filtering algorithms to discriminate true positive from false positive PSMs, ultimately resulting in a reduction of protein identifications at a given FDR. In addition, because of the peptide sequence homology, FDRs typically increase when evaluated at the peptide or protein level as errors in identification propagate through the protein inference process.150 Indeed, two target PSMs are more likely to involve peptides from the same protein than two decoy PSMs since, in theory, decoy PSMs should be biologically unrelated. This behavior forces the adoption of extremely stringent classification criteria that usually involve filtering data sets at the protein level and often result in a reduced sensitivity in protein identification. Moreover, when building a metaproteomics sequence database of gut bacteria for a target-decoy database search approach, Chatterjee et al. reported that 0.7% of 98
DOI: 10.1021/acs.analchem.7b04340 Anal. Chem. 2018, 90, 86−109
Analytical Chemistry
Review
Table 3. Bioinformatics Tools for Metaproteomic Analysisa preprocessing
MetaPro-IQ67 ComPIL & Blazmass150 Graph2Pep and Graph2Pro159 MetaProSIP118 Unipept190−192 TCUP193 COGs194 STRING v10196 eggNOG 4.5195 MEGAN CE197 MicrobiomeAnalyst198 MetaProteomeAnalyzer200 Galaxy202,203 MetaLab a
core processing
postprocessing
database construction
protein identification
protein quantification
√ √ √
+ √ + +
+
taxonomy analysis
functional annotation
√ √
+
statistics and visualization
√
√ √ √ √
* √
√ * √
√ * √
√ * √
√ √
√ *
* √
√, Applicable for this issue; +, applicable in the workflow but other tools are required; *, user-defined workflows are required.
Bioinformatics Tools in Metaproteomics. Beyond database selection for accurate peptide identification and its confidence assessment, the investigation of the functional profiles, pathways, and determination of the statistical significance of these are also important topics in metaproteomics studies. Therefore, a multistep processing workflow is required in metaproteomics data analysis. Here we have listed recently developed/updated bioinformatics tools addressing specific issues in metaproteomics (Table 3). Quantitative metaproteomics aiming to reveal the biological variation between microorganisms is a promising strategy for understanding overall microbial differences in health and disease. Because of the complexity and associated difficulty of labeling strategies for microbiota samples, label-free quantification strategies implemented by existing proteomic tools such as MaxQuant189 are often adopted.67,103 For the protein-SIP quantification strategy, a specific tool named MetaProSIP was developed,118 which provides a workflow that first identifies the unlabeled peptides and then extracts features for both unlabeled and labeled peptides to obtain the relative isotopic abundance. Obtaining the taxonomic composition from the peptide/ protein identification is an essential part in the metaproteomics data analysis workflow. Unipept is a commonly used tool that provides both a Web site version and an application program interface (API) version for the mapping of tryptic peptides to Uniprot (http://www.uniprot.org/uniprot/) entries and taxonomic lineages.190 The Web site version providing several types of visualizations is easy to use.191 The API version is appropriate for processing multiple data sets and readily being integrated into user-defined workflows.192 Typing and Characterization of bacteria Using bottom-up tandem mass spectrometry Proteomics (TCUP) adopted a different way that aligns peptides to reference genome databases to determine the bacterial taxonomic compositions.193 Metaproteomics analysis also typically attempts to characterize the functional profile of microbial communities. A set of databases providing functional information for proteins has published new releases in recent years. The Clusters of Orthologous Groups of proteins (COGs) database brings expanded genome coverage and updated annotations in the new version.194 EggNOG v4.5 provides a user-friendly Web interface, which
allows users to search proteins or Orthologous Groups (OGs) of proteins on the Web site. Meanwhile an API is available to access the database.195 The STRING v10 database also provides a Web interface and an API for retrieving PPIs and functional annotations.196 For the comprehensive analysis of microbiome data sets, downstream statistical analyses, especially comparative analyses, are indispensable. In metagenomics studies, generally an operational taxonomic units (OTU) table of taxa or genes containing the abundance information across samples will be generated for further statistical analysis, including distribution and abundance. In metaproteomics, after the taxonomic profiling is obtained, a similar data matrix of taxa can be created that can utilize quantification information from the MS data as the values in the table. Typically, data filtering, normalization, and transformation are adopted before the significance testing; visualization provides an intuitive interpretation of the result. Software tools which can perform these tasks include MEGAN Community Edition197 and MicrobiomeAnalyst.198 MEGAN is metagenomic data processing tool developed a decade ago for taxa assignment and statistical analysis.199 The usability and continuous upgrades make it a still widely used microbiome data analysis tool. As an extended version of MEGAN, MEGAN Community Edition is designed to process very large scale data sets. MicrobiomeAnalyst is a Web-based program providing various useful features such as comparative statistical tests, clustering analysis, and visualization of data. Generally, multiple tools are needed to accomplish a whole metaproteomics workflow, which brings numerous challenges to the researchers. To address this, Muth et al. developed a comprehensive software platform MetaProteomeAnalyzer, which covers a major part of the metaproteomics workflow.200 It implements multiple search engines for peptide identification and spectral count based quantification methods for the measurement of the abundance of proteins. Taxonomy analysis and functional annotation are also supported. Similarly, the Galaxy platform is a comprehensive framework for the computational analyses of various types of data sets.201 There are currently over 4 800 valid tools in the Galaxy Tool Shed (https://toolshed.g2. bx.psu.edu/). Users can define their own data processing workflows based on the tools in the Galaxy repository. This feature makes Galaxy a promising solution for tackling the 99
DOI: 10.1021/acs.analchem.7b04340 Anal. Chem. 2018, 90, 86−109
Analytical Chemistry
Review
problems in metaproteomics data analysis.202 While the Galaxy framework provides great flexibility, the utilization requires some background in bioinformatics. Jagtap et al. established a complete workflow using the Galaxy framework for metaproteomics data analysis, which can also easily be shared with other researchers.203 Our group developed a fully automated data processing software platform, termed MetaLab, which covers the major components of the bioinformatics pipeline for metaproteomics (http://dashboard.imetalab.ca/account). MetaLab provides an integrated workflow which allows the user to obtain the qualitative and quantitative information on the taxa from mass spectrometry raw data directly. Users can upload their raw data directly to the iMetaLab server for online analysis or alternatively can download a stand-alone version that is available for local use. Using the complex set of tools required for metaproteomics analysis remains a significant challenge. Future development of comprehensive workflows which provide superior combination of versatility and easy utilization are required for continued successes in the metaproteomics field. Integrating Metaproteomics with Metagenomics/ Metatranscriptomics for Microbiome Study. Analogous to the more complete understanding that is obtained when moving from experiments on a single species to a complex biome, combining proteomics with additional -omics data can provide a more complete system-level picture of dynamic interactions and system modulations. In addition, the integration of multiomics data benefits metaproteomics in many aspects, including protein identification as well functional and taxonomic analysis. However, the increased complexity of multiple data levels (i.e., gene, transcript, protein) is associated with an increased complexity in data integration. Notably, the different levels are not independent but are functionally related. For this reason, data integration most often includes correlation analysis, wherein differentially expressed genes/proteins are compared, and thus both shared and unique features of each data set are identified. In evaluating host−microbe interactions, transkingdom correlation analysis204,205 can be utilized to predict causal relationships. Complementary to this, construction of gene regulatory networks through common nodes is a means of integrating data levels. While the principle ideas are simple, in practice the integration of data levels is very complicated. Increasingly, studies have been performed with parallel metaproteomics and metagenomics (either shotgun metagenomics or 16S rRNA gene sequencing) analyses on the same samples, deciphering the similarity and differences of the multiomic layers. Zwittink et al. combined 16S rRNA gene sequencing and metaproteomics to study the microbiota development in preterm infants, finding generally similar observations from the two approaches.180 However, taxonomic observations were different between the two approaches, which was mainly a result of the limited resolution of 16S rRNA gene sequencing on genera with similar rRNA gene sequences such as Enterobacter and Klebsiella. Lluch-Senar et al. compared the proteome of two Mycoplasm pneumoniae strains with RNA seq data, identifying a protein with altered relative protein abundance despite a lack of transcriptlevel change, proposing a role for this protein in strain virulence.206 Cernava et al. studied the functional diversification of lichen microbiota using metatranscriptomics, metagenomic, metaproteomic, and 16S rRNA gene sequencing.207 They found that the lichen microbiota was similar as revealed by different omics approaches at higher phylogenetic levels but not at finer taxonomic resolution. Therefore, by combining multiomics data,
they identified the unrecognized groups of lichen microbiome as important players of the symbiotic interactions in lichens. As outlined previously, metaproteomics studies benefit from multiomic integration through the improvement in peptide/ protein identification. Unlike single organisms, such as human or mouse proteomics, the microbial community studied by metaproteomics contains a large number of genes that are unknown or not well studied. Erickson et al.114 have suggested the use of a matched metagenome sequencing-derived database for database searching in metaproteomics. Tanca et al.152 recently evaluated the impact of different sequence databases on metaproteomic results, suggesting that the use of multiple databases is advantageous, with the greatest benefits in using the experimental metagenomic database. To compare cecal and fecal microbiomes, Tanca et al.208 performed 16S genomics, shotgun metagenomics, and metaproteomics on samples collected from mice. Through a metaproteogenomics approach, differential functional and taxonomic expressions were determined at each level, with little overlap in differential genera being detected. The authors further compared changes in taxon-specific function and pathway data from metagenomics and metaproteomics to identify commonality between layers and pathways that differ between cecal and fecal microbiomes. A similar approach was applied in revealing the diversity and functions of sheep and human fecal microbiota.209,210 Xiong et al. reported a metagenomic-informed metaproteomic analysis on the gut microbiota of preterm infants, which allows species/strain-specific characterization of gut microbial functions and pathways.211 Species-associated gut metabolic modules (GMMs) were inferred through a recently developed GOmixer approach,212 which suggested the major contribution of nutrient utilization and shortchain fatty acid production pathways in the early life gut microbial dynamics of preterm infants. Utilizing an approach developed for analysis of environmental microbial communities124,213 that has now been incorporated into the analysis pipeline called IMP,214 Heintz-Buschart et al. reported the coassembly of metagenomics and metatranscriptomics data to develop a database for the metaproteomic peptide/protein identification; individual human SNPs and variants identified by host genome sequencing were incorporated into the database search.185 With this multiomic approach, the authors evaluated the structural and functional compositions of gut microbiome of Type 1 diabetic patients within and between families. Metagenomics provides taxonomic value and functional potential, whereas metatranscriptomics and metaproteomics are important for revealing the functional activities of microbiome. Accordingly, the authors revealed that the metaproteomic data showed greater correlation with metatranscriptomics data than with metagenomics data, highlighting the importance of assessing protein or transcript levels when attempting to elucidate the functional effect of the microbiome. In addition, through transkingdom correlation analysis, the authors identified differential expression of host-generated pancreatic enzymes that correlate with microbial functions, which may contribute to the disrupted host−microbiome interactions in Type 1 diabetes. Similarly, Morgun et al. applied transkingdom analysis to evaluate the effects of antibiotics on both the gut microbiome and host ileal tissue in mice by shotgun metagenomics and transcriptomics, respectively.204 Differentially expressed genes as a result of normal microbiota depletion, antibiotic-resistant microbes, and direct antibiotic responses were functionally annotated and statistically evaluated for enrichment. The authors constructed a network of the differentially expressed host and 100
DOI: 10.1021/acs.analchem.7b04340 Anal. Chem. 2018, 90, 86−109
Analytical Chemistry
Review
with the microbial communities.227 As a result, the maintenance of a healthy mutualistic interplay between resident microbes and immune cells is critical for preventing pathological threats. To evaluate the microbiome during development, Young et al. built upon their previous metagenomics-based study, by utilizing metaproteomics to evaluate both the microbiome and human proteins in stool from a preterm infant over the first 3 weeks of life.99 By metaproteomics, they observed a shift in the microbial community from growth (cell division, protein production) toward metabolic function (carbohydrate catabolism), and a concurrent increase of human mucins that suggested development of the intestinal barrier. Similar work by Xiong et al. evaluated the metaproteome of four preterm infants over 3 months, finding interindividual variation in microbial composition but conserved metabolic functions of the microbiome, with host proteins involved in immune regulation and intestinal barrier function.211 In an experimental breastfeeding mouse model, Mortera et al. utilized IgA-producing and -deficient mice to evaluate the effects of the immune system on gut microbiome development.228 Through metaproteomics, they identified the role of IgA in limiting opportunistic bacterial pathogen growth. Disease Pathogenesis and Biomarker Identification. A great challenge exists in the identification of microbes and proteins that contribute to, and can be therapeutically targeted or act as biomarkers of, intestinal and nonintestinal diseases such as IBD229,230 diabetes,185,231 and liver cirrhosis.100 While metagenomics studies have contributed to the identification of microbes in disease, metaproteomics provides a functional signature of the microbiome through mapping microbiota-expressed proteins against their original microbial communities. Importantly, such mapping strategies could reveal communication networks underlying host−microbe cross-talk and emphasis on the microbial community that is primarily responsible in affecting the progression of associated diseases. Proteomic analysis of single-species infection models has provided insight into the mechanisms by which microbes evade or dampen the host defense mechanisms. Salmonella proteins that were differentially expressed when bacteria were isolated from infected epithelial cells indicated pathogen adaptation to the environment and upregulation of virulence factors as well as a mechanism whereby the microbe was depleting host metal levels, which would limit the defense mechanisms of the host cell.232 Separate phosphoproteomic analyses found that a fungus and a virus, namely, Cryptococcus neoformans and HIV, alter host kinase phosphorylation and activation, which may contribute to intracellular survival and replication.233,234 By using an aminoterminal enrichment strategy, Jagdeo et al. found that proteases produced by picornaviruses process and redirect a host protein from its role in RNA splicing toward facilitation of viral infection.235 While these studies represent the pathogenic effects of single species, there exists a community effect wherein the sum of the whole must be considered. Comparison of the fecal microbiomes from three liver cirrhosis patients with that of their spouses indicated a core metaproteome, including 21 microbial proteins that were unique or differentially expressed in the cirrhotic patients.100 Analysis of this identified unique biosynthetic pathways could contribute to cirrhosis pathogenesis and identified proteins with potential utility as biomarkers. In a metaproteome study of patients with dental caries, Belda-Ferre et al. identified altered host defense mechanisms from plaque samples obtained from diseased patients when compared with healthy controls.93 Moreover,
bacterial genes, identifying two major subnetworks, including one that indicated a role for microbes in inducing suppression of host mitochondrial function. While the authors did not use proteomics in this study, the incorporation of proteomic data could be utilized in the same way as the host transcriptome data. In our recent study of pediatric Crohn’s disease patients, we applied transkingdom correlation analysis to compare differentially abundant host mitochondrial proteins from colon biopsies with mucosal-luminal interface-obtained OTUs.205 Proteomic analysis identified a reduction of mitochondrial function in Crohn’s disease patients, which positively correlated with butyrate producers in the microbiota. Tools for Multiomics Integration. MixOmics is a comprehensive R package developed for omics data exploration, integration, and visualization.215 Among the many R packages, mixDIABLO is dedicated for the multiomics data integration and biomarker discovery,216 which utilized the Projection to Latent Structure (PLS) models to identify optimal multiomic key variables for group classifications. GOmixer is a human gut-specific metabolic pathway analysis tool for different types of metaomics data including metagenomics and metaproteomics. The GOmixer tool quantifies metabolic functions of human gut microbiome using a predefined GMM database; the differential GMMs can be statistically determined and displayed on a global metabolic pathway map, which is promising for integrative functional analysis of multiomics data of human gut microbiome.212 As outlined above, Galaxy is a Web-based data analysis platform that has been widely applied in proteomics/metaproteomics studies.202,217,218 Recently, Fan et al. developed the Galaxy Integrated Omics (GIO) platform for facilitating the genome/ transcriptome informed proteomics protein identification.219 Galaxy-P (Galaxy for Proteomics) is a platform for integrative analysis of proteomics data with genomic or transcriptomic data.220,221 The application of Galaxy-P in metaproteomics has provided a comprehensive solution including database generation from sequencing data, iterative database search, and downstream taxonomic and functional analysis using external tools such as Unipept and MEGAN.203 Public literature and databases, including transcriptome, proteome, metabolome, and fluxome data, were compiled by Kim et al. to develop a multiomics integrated database for E. coli for use in machine learning.222 Each data layer was preprocessed separately to remove noise, correct for platform biases, and conversion of relative expression to absolute-level quantification. The authors then developed a multiomics model and analytics (MOMA) prediction tool utilizing the interaction data from each layer. Similar multiomic databases exist for Listeria,223 Mycoplasma pneumoniae,224,225 and zika virus.226 The establishment of such a database for human microbiome is challenging and still lacking due to the complexity and limited functional knowledge on host−microbiome interactions. Along with the increasing number of microbiome samples characterized with multiomic information, and gut microbial species isolated and functionally elucidated, we anticipate that human microbiome-wide association and protein−protein interaction databases may be generated and will greatly enhance the microbiome studies in the future.
■
APPLICATIONS OF PROTEOMICS AND METAPROTEOMICS IN HEALTH Functional Characterization of the Early Development Microbiome. It is widely recognized that the gut microbiome is critical to development, contributing to shaping the mammalian immune system, while host immunity maintains homeostasis 101
DOI: 10.1021/acs.analchem.7b04340 Anal. Chem. 2018, 90, 86−109
Analytical Chemistry
Review
of clinical sample required for screening. However, issues including antibody quality, selected assay conditions, and extensive biostatistical analyses are ongoing challenges. Recently, Delfani et al. provided technical refinement (including optimization of blocking solution and sample incubation time) as well as essential improvements linked to biomarker panel condensation and array data normalization.253 Despite the current technical advances, the risk of including false negatives and positives signals is still evident. Further interdisciplinary studies will be required to promote protein profiling assays as clinical diagnostic tools. Other significant aspects of immunoproteomics are occurring in biomedical research, specifically in the field of in silico prediction of putative vaccine candidates (also called reverse vaccinology). In contrast to conventional vaccine development methodologies that use in vitro pathogenic cultivation, in silico pipeline prediction for therapeutic targets is not bound to pathogens with high antigen expression or to microbial activity. Newly designed software (VacSoI) by Rizwan et al.,254 which screens for therapeutic vaccine agents from the bacterial pathogen proteome, exhibited a unique integrated algorithm to reduce false positive vaccine candidate hits. This feature gives VacSoI an advantage over existing software tools that provide limited information on pathogenicity (Vaceed) or which focus on the antigenicity of adhesion proteins (NERVE). Together, immunoproteomics significantly decreases the time and the cost for vaccine development compared to current resources. However, challenges in the prediction of functional mutations or bacterial− host adaptation strategies remain. Development of New Antimicrobial Peptide-Based Antibiotic Treatments. Antimicrobial peptides (AMPs) are universal short amino acid sequences (5−150 amino acids) that are positively charged with hydrophobic sequences and are produced by organisms of most kingdoms of life.255 Their widespread distribution suggests that they have a fundamental role in host defense and in intramicrobial competition for nutrients. While not fully established, AMPs utilize different mechanisms of action that include disruption of the cell membrane, targeting and inhibition of intracellular molecules, and induction of cell lysis or apoptosis and thus may be detected extracellularly, at the membrane, and intracellularly.255 AMPs derived from multicellular organisms have a large spectrum of antimicrobial activity against pathogenic viruses, bacteria, fungi, and protozoan but unexpectedly can also contribute to cellular-specific functions including apoptosis and iron homeostasis.256 As such, AMPs represent a potential source for the development on new antibiotics. The majority of AMPs produced by multicellular organisms are encoded by the genome and, after transcription and ribosomal translation, result from PTM of the protein precursor including the proteolytic processing,257 glycosylation,258 amino acid isomerization, and complex modification (e.g., defensin cyclization).259 AMPs produced by microorganisms contain several uncommon amino acids including D-amino acids and hydroxy acids.260 Several AMPs are nonribosomally synthesized and subsequently modified;261 nonribosomally synthesized AMPs result from by a large multimodular biocatalysts, namely, nonribosomal peptide synthetases, which utilizes complex reactions to assemble structurally and functionally diverse molecules including proteinogenic and nonproteinogenic amino acids, fatty acids, and hydroxyl acids.262 Because of the significant PTMs involved in AMP generation and the presence of noncoded protein biosynthesis, significant challenges exist in identifying AMPs by standard database analyses.
they identified seven bacterial and four human proteins that are potential biomarkers of the disease. While these studies of metaproteome comparisons between healthy and diseased individuals provide a snapshot of the microbial and host activities, challenges in elucidating causation, rather than correlation, remain. Clinical Immunoproteomics for Vaccine Development and Diagnosis. Immunoproteomics, first termed in 2001,236 is a rapidly growing research field that permits for both the identification of microbial antigens as well as characterization of the host antibody response.237 In essence, immunoproteomics techniques rely upon detection of immunoreactive antigens by use of antibody-containing sera. Potential antigens can be separated for analysis by gel electrophoresis, fractionated and utilized on a protein array, or recombinant forms utilized in suspension arrays, and antigen targets can be identified by MS. Separately, the peptide repertoire that are processed and expressed by host MHC molecules can be identified by MS. Serological Proteome Analysis (SERPA) is a classical and still widely used immunoproteomics approach that combines twodimensionally gel electrophoresis (2D-GE) separation of potentially pathogenic proteins with immunoblotting and probing with patient sera. SERPA followed by MALDI-TOF MS was recently employed to evaluate immunoreactivity and identify proteins that may be associated with multidrug resistance (MDR) in E. coli and K. pneumoniae,238 finding that the E. coli protein OmpA had greater reactivity and levels in the MDR strain. SERPA is a robust method for comparing protein patterns or strength of immunoreactivity of different cellular conditions. However, 2D-GE has several limitations related to the difficulties in separating and resolving proteins of varied sizes, acidities, or those that are of low abundance and detects epitopes in a denatured state. Protein or suspension arrays can overcome some of the limitations of SERPA, though require prior knowledge of the proteome. Following identification of Staphylococcus aureus proteins by MS, Schmidt et al. evaluated the immunogenic potential of these by use of infected patient sera or polyp extracts against a suspension array of 115 recombinant proteins or peptides.239 Notably, the most immunogenic proteins were not necessarily detected by shot-gun proteomics. Currently, techniques like nucleic acid based serological analysis of antigens by recombinant cDNA expression libraries (SEREX)240 and phage immunoprecipitation sequencing241 are widely used. In addition, to obtain a reliable result, MS was efficiently combined to different techniques like SERPA, antibody mediated identification of antigens (AMIDA),242 and circulating immune complexome (CIC) analysis.243 These approaches have been used to identify novel antigens, for absolute quantification of specific peptides,244,245 and for characterization of PTMs.246 Using an immunoproteomic approach previously applied for Chlamydia,247 Karunakaran et al. successfully purified and evaluated MHC-bound peptides originating from Salmonella from in vitro infected bone-marrow mononuclear cells.248 They identified antigenic potential of cytosolic and membrane proteins and proposed the use of these peptides for vaccine development. In addition, Cotham et al. developed a middle-down immunoproteomics approach, which uses restricted Lys-C enzymatic digestion method for analyzing peptides uniquely derived from single antibody species through increasing the average mass of proteolytic IgG peptides (≥4.5 kDa).249 Antibody-based microarrays have recently been considered as potential diagnostic tools to decipher disease-associated biomarkers from clinical samples.250−252 One of the great advantages underlying this technical approach is the minimal amount 102
DOI: 10.1021/acs.analchem.7b04340 Anal. Chem. 2018, 90, 86−109
Analytical Chemistry
Review
microbiome. However, the true value of metaproteomics comes through integration with other -omic technologies, including genomics, viromics, epigenomics, lipidomics, and metabolomics. A complex interplay exists between the microbiome and its host, a true comprehension of which will only be obtained through combining knowledge gained in each of these fields.
Dedicated databases have been developed to list and characterize AMPs, which can aid in MS-based search strategies. Currently, there are databases dedicated for specific organisms (e.g., PHytAMP for plant and Bactibase for bacteria), activities (e.g., Hemolytik for hemolytic activity and YADAMP for antibacterial activity), or for AMPs structures (DBAASP). In addition, the most common AMPs databases like APD3,263 DRAMP,264 CAMPR3,265 and LAMP3266 are dedicated for a multispecies analysis (Table 2). Prior to the recent developments in the procedures of AMP purification, time-consuming and low recovery techniques like ion-exchange and size exclusion chromatography (e.g., Sephadex G-50) combined with the antimicrobial assays were applied.267,268 Currently, standard reversed-phase high-performance liquid chromatography (HPLC) has become the main method for highresolution separation and recovery of AMP from the single bacteria to the multicellular organisms, having high resolution and recovery.269 A comparative study of human, pig and mouse saliva utilized molecular weight cutoff filtration and HPLC−MALDITOF MSMS to isolate and identify peptides, respectively, and used the CAMP database to predict AMP peptides; the AMP activity of two peptides was confirmed by in vitro methods.270 Currently, the identification of AMPs is difficult due to the diversity of the nonribosomally synthesized AMPs, their posttranslational modification, and their hydrophobic sequences limiting the use of proteases. However, the physicochemical parameters and sequences information derived from the collection of AMPs has proven to be a powerful resource for the design of novel AMPs. For example, the Nebraska Medical Center has developed a novel anti-MRSA peptide that is based on the peptide characteristics in the APD database.271 Increasingly, studies involving computational approaches have been applied in AMP research and new algorithms to predict the AMPs structure, activity, and function were developed.
■
AUTHOR INFORMATION
Corresponding Authors
*E-mail:
[email protected]. *E-mail: dfi
[email protected]. ORCID
Amanda E. Starr: 0000-0002-3697-1154 Daniel Figeys: 0000-0002-5373-7546 Notes
The authors declare no competing financial interest. Biographies Amanda E. Starr completed both a B.Sc. degree in Biology and M.Sc. degree in Biomedical Sciences at the University of Guelph. She obtained her Ph.D. degree in Biochemistry from the University of British Columbia before working as a postdoctoral fellow under the supervision of Professor Daniel Figeys. Currently, she is a research associate at the Ottawa Institute of Systems Biology (OISB), University of Ottawa, where she is developing and applying proteomics techniques to study inflammatory disease. Shelley A. Deeke completed her B.Sc. degree in Biochemistry and M.Sc. degree in Biochemistry in 2008 and 2011, respectively, at the University of Ottawa. Since 2014, she has been pursuing her Ph.D. in Biochemistry at the OISB, University of Ottawa. Her current research focuses on biomarker discovery and extracellular vesicle molecular signatures in children and youth affected by inflammatory bowel disease.
■
Leyuan Li completed her B.Eng. degree in Bioengineering and her Ph.D. degree in Biomedical Engineering in 2010 and 2016, both at Beihang University (previously known as Beijing University of Aeronautics and Astronautics), Beijing, China. She is currently a postdoctoral fellow at the OISB, University of Ottawa, under the supervision of Professor Daniel Figeys. Her research focuses on the development and application of in vitro gut microbiome models and high-throughput metaproteomic processes for Rapidly Assay of Individual’s Microbiome (RapidAIM) against xenobiotics.
PERSPECTIVE AND CONCLUDING REMARKS Advances in next generation sequencing technologies have been transformational to our understanding of the microbiome and its link to health or disease.15 However, genomics studies alone are not sufficient to determine the interactions that are occurring between host and microbe. Recent improvements in model systems, including ex vivo culture systems that more fully approximate the microbiome and host components, and in technology that facilitates deeper metaproteome analysis,115 have made it possible for MS-based proteomics and metaproteomics studies to provide complementary information to elucidate the functional microbiome. Nevertheless, significant challenges remain. In particular, accurate protein identification and taxonomic classification is hampered by the complexity, different abundances, and close homology of microbes. Fortunately, ongoing sequencing will lead to improved database generation. Further, appropriate algorithms and search strategies must be developed to enable for efficient identification and quantification of PTMs, which significantly contribute to host− microbe interactions. Reflected by the citations in this review, metaproteomic research has been dominated by gut- and bacteria-centric studies, perhaps due to the ease of sampling (e.g., stool) and relative abundance (bacteria vs archea), respectively. Nevertheless, we hope that future work will build upon the studies outlined herein to more fully investigate the entire human microbiome. Metaproteomics has benefited from advances in the proteomics field and is an important tool in understanding the functional
Xu Zhang completed a B.Sc. degree in Biotechnology (2006) and M.Sc. degree in Microbiology (2009) in Lanzhou University, China. He obtained his Ph.D. degree in Microbiology from the Shanghai Jiao Tong University in 2013. He is currently a postdoctoral fellow at the OISB, University of Ottawa, under the supervision of Professors Daniel Figeys and Alain Stintzi. His research focuses on the host−gut microbe interactions with multiomic strategies including proteomics/metaproteomics, metagenomics, and metatranscriptomics, in the context of human disease including metabolic syndrome and inflammatory bowel disease. Rachid Daoud studied structural biochemistry and biotechnology at the University of Sciences and Technologies (France), where he received his Master and Post-Master diploma (DEA). After that, he completed his Ph.D. degree on comparative complexomics and proteomics of mitochondria at the University of Montreal (Canada) in 2012. He worked for 2 years as a postdoctoral researcher at the Robert-Cedergren Center for Bioinformatics and Genomics at the University of Montreal (Canada). Since 2016 he has been a postdoctoral fellow at the OISB at the University of Ottawa (Canada). 103
DOI: 10.1021/acs.analchem.7b04340 Anal. Chem. 2018, 90, 86−109
Analytical Chemistry
Review
spectrometry, including protein quantitation and post-translational modifications. In terms of applications, his laboratory focuses on using omics technologies to study human−microbiome interactions. They also study the circadian clock and its role in diseases. Finally, they study regulation of LDLR and cholesterol.
James Ryan completed his B.Sc. (Combined Honours) in Biochemistry & Molecular Biology and Computer Science at Dalhousie University in 2016 and is currently a M.Sc. Biochemistry (specializing in Bioinformatics) candidate in the Figeys and Lavallée-Adam laboratories at the University of Ottawa. James’ current research activities focus on metaproteomics, metagenomics, and bioinformatics approaches to investigate the effects of prebiotics on the human gut microbiome.
■
ACKNOWLEDGMENTS We thank J. Mayne for discussion and advice in the preparation of this manuscript. D.F. acknowledges a Canada Research Chair in Proteomics and Systems Biology and funding from the Natural Sciences and Engineering Research Council of Canada (NSERC), Canadian Institutes of Health Research (CIHR), Genome Canada, and the Province of Ontario. M.L.-A. acknowledges a Discovery Grant from NSERC and University of Ottawa start-up funds.
Zhibin Ning obtained his B.S. degree in Life Science at Shandong Normal University, China, in 2003. He received his Ph.D. degree in Biotechnology and Biochemistry from Shanghai Institutes for Biological Sciences in 2008 for the development and applications of liquid-based separation strategies for proteomics. After his postdoctoral training in the OISB, University of Ottawa, under the guidance of Professor Daniel Figeys, he continued his career as a research associate in the same lab. Presently he is focusing on technology development and applications in proteomics and bioinformatics, especially data visualization.
■
Kai Cheng studied analytical chemistry at Dalian Institute of Chemistry Physics, Chinese Academy of Sciences (China), where he received his Ph.D. degree in 2015. He has worked as a postdoctoral researcher in the Department of Biochemistry, Microbiology and Immunology at the University of Ottawa, Ottawa (Canada) since 2015. His major research field is bioinformatics, including software development for proteomics data analysis.
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. 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. Nature 2017, 550 (7674), 61−66. (3) Sender, R.; Fuchs, S.; Milo, R. Cell 2016, 164 (3), 337−40. (4) Sender, R.; Fuchs, S.; Milo, R. PLoS Biol. 2016, 14 (8), e1002533. (5) Costello, E. K.; Lauber, C. L.; Hamady, M.; Fierer, N.; Gordon, J. I.; Knight, R. 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. Proc. Natl. Acad. Sci. U. S. A. 2015, 112 (17), E2120−9. (7) Eloe-Fadrosh, E. A.; Rasko, D. A. 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. Proc. Natl. Acad. Sci. U. S. A. 2010, 107 (16), 7503−8. (9) Burke, C.; Steinberg, P.; Rusch, D.; Kjelleberg, S.; Thomas, T. Proc. Natl. Acad. Sci. U. S. A. 2011, 108 (34), 14288−93. (10) Lloyd-Price, J.; Abu-Ali, G.; Huttenhower, C. Genome Med. 2016, 8 (1), 51. (11) Clemente, J. C.; Ursell, L. K.; Parfrey, L. W.; Knight, R. Cell 2012, 148 (6), 1258−70. (12) Li, D.; Kirsop, J.; Tang, W. H. Curr. Diabetes Rep. 2015, 15 (9), 63. (13) Ni, J.; Wu, G. D.; Albenberg, L.; Tomov, V. T. Nat. Rev. Gastroenterol. Hepatol. 2017, 14 (10), 573−584. (14) Ghaisas, S.; Maher, J.; Kanthasamy, A. Pharmacol. Ther. 2016, 158, 52−62. (15) Knight, R.; Callewaert, C.; Marotz, C.; Hyde, E. R.; Debelius, J. W.; McDonald, D.; Sogin, M. L. Annu. Rev. Genomics Hum. Genet. 2017, 18, 65−86. (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. Am. J. Gastroenterol. 2017, 112 (9), 1412−1422. (17) Kilsgard, O.; Karlsson, C.; Malmstrom, E.; Malmstrom, J. Int. J. Med. Microbiol. 2016, 306 (7), 504−516. (18) Dutta, D.; Clevers, H. Curr. Opin. Immunol. 2017, 48, 15−22.
Linh V. H. Nguyen is completing her B.Sc (Honours) in Translational and Molecular Medicine in the Department of Biochemistry, Microbiology and Immunology at the University of Ottawa in 2018. Her Honours project, under the supervision of Dr. Mathieu Lavallée-Adam, focuses on developing novel statistical methods for the accurate significance assessment of protein differential expression quantified using mass spectrometry. Elias Abou-Samra studied Microbiology and Immunology at the University of Ottawa (Canada), where he received his Ph.D. degree in 2016. He is currently working as a postdoctoral researcher in the Department of Biochemistry, Microbiology and Immunology at the University of Ottawa (Canada). His research activities focus on the in vivo validation of a new in vitro bacterial culturing system that allows the rapid screening of candidate drugs against potential adverse microbiome effects. His research interests also cover the role of convertases in gut mucosal functions. Mathieu Lavallée-Adam is an Assistant Professor at the University of Ottawa in the Department of Biochemistry, Microbiology and Immunology and has been affiliated with the OISB since 2016. He performed his postdoctoral research in John R. Yates III’s laboratory in the Department of Chemical Physiology at The Scripps Research Institute from 2013 to 2016. He obtained a B.Sc. in Computer Science (2008) and a Ph.D. in Computer Science, Bioinformatics option (2013), from McGill University under the supervision of Mathieu Blanchette and Benoit Coulombe. His research focuses on the development of statistical and machine learning algorithms for the analysis of quantitative proteomics, intact protein mass spectrometry, and protein− protein interaction network datasets. Daniel Figeys is the Chair and Professor in the Department of Biochemistry, Microbiology and Immunology, at the University of Ottawa, and a Tier-1 Canada Research Chair in Proteomics and Systems Biology. He was the inaugural Director of the OISB from 2004 to 2013. He is also a Visiting Professor for Senior International Scientists, Chinese Academy of Sciences. Daniel obtained a B.Sc. and a M.Sc. in Chemistry from the Université de Montréal. He obtained a Ph.D. in Chemistry from the University of Alberta and did his postdoctoral studies at the University of Washington. His laboratory is developing proteomics and metaproteomics technologies based primarily on mass 104
DOI: 10.1021/acs.analchem.7b04340 Anal. Chem. 2018, 90, 86−109
Analytical Chemistry
Review
(19) Forbester, J. L.; Goulding, D.; Vallier, L.; Hannan, N.; Hale, C.; Pickard, D.; Mukhopadhyay, S.; Dougan, G. 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. Infect. Immun. 2015, 83 (1), 138−45. (21) Bartfeld, S.; Clevers, H. J. Visualized Exp. 2015, No. 105, e53359. (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. Science 2016, 352 (6287), 816−8. (23) Williams, C. F.; Walton, G. E.; Jiang, L.; Plummer, S.; Garaiova, I.; Gibson, G. R. Annu. Rev. Food Sci. Technol. 2015, 6 (1), 329−350. (24) Joly, C.; Gay-Quéheillard, J.; Léké, A.; Chardon, K.; Delanaud, S.; Bach, V.; Khorsi-Cauet, H. 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. ISME J. 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. 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. Cell. Microbiol. 2015, 17 (2), 226−240. (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. Nat. Commun. 2016, 7, 11535. (29) Kim, H. J.; Lee, J.; Choi, J. H.; Bahinski, A.; Ingber, D. E. J. Visualized 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. Chem. Biol. 2015, 22 (11), 1521−30. (31) Kumar, A.; Ting, Y. P. Environ. Microbiol. 2015, 17 (11), 4459−68. (32) Fazli, M.; Bjarnsholt, T.; Kirketerp-M?ller, K.; Jorgensen, B.; Andersen, A. S.; Krogfelt, K. A.; Givskov, M.; Tolker-Nielsen, T. 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. 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. 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. J. Bras. Pneumol. 2013, 39 (4), 495−512. (36) Karlsson, C.; Teleman, J.; Malmstrom, J. Methods Mol. Biol. 2017, 1535, 17−24. (37) Sjoholm, K.; Karlsson, C.; Linder, A.; Malmstrom, J. Mol. BioSyst. 2014, 10 (7), 1698−708. (38) Sjoholm, K.; Kilsgard, O.; Teleman, J.; Happonen, L.; Malmstrom, L.; Malmstrom, J. Mol. Cell. Proteomics 2017, 16 (4 suppl1), S29−S41. (39) Solis, N.; Cordwell, S. J. Methods Mol. Biol. 2016, 1440, 47−55. (40) Solis, N.; Larsen, M. R.; Cordwell, S. J. Proteomics 2010, 10 (10), 2037−49. (41) Karkowska-Kuleta, J.; Zajac, D.; Bochenska, O.; Kozik, A. Acta Biochim Pol 2015, 62 (4), 807−19. (42) Casas, V.; Vadillo, S.; San Juan, C.; Carrascal, M.; Abian, J. Front. Microbiol. 2016, 7, 1103. (43) Zhu, D.; Sun, Y.; Liu, F.; Li, A.; Yang, L.; Meng, X. C. J. Dairy Sci. 2016, 99 (7), 5155−72. (44) Thompson, D. S.; Carlisle, P. L.; Kadosh, D. Eukaryotic Cell 2011, 10 (9), 1173−82. (45) Bhatnagar, S.; Shinagawa, K.; Castellino, F. J.; Schorey, J. S. Blood 2007, 110 (9), 3234−44. (46) Hassani, K.; Olivier, M. PLoS Neglected Trop. Dis. 2013, 7 (5), e2185. (47) Park, K. S.; Lee, J.; Jang, S. C.; Kim, S. R.; Jang, M. H.; Lotvall, J.; Kim, Y. K.; Gho, Y. S. 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.; NegreteAbascal, E. 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. 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. 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.; MeisnerKober, N. C.; Lehtio, J.; Smith, C. I.; Wood, M. J.; El Andaloussi, S. 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. Sci. Rep. 2016, 6, 33935. (53) Ueda, K.; Ishikawa, N.; Tatsuguchi, A.; Saichi, N.; Fujii, R.; Nakagawa, H. Sci. Rep. 2015, 4, 6232. (54) Zarovni, N.; Corrado, A.; Guazzi, P.; Zocco, D.; Lari, E.; Radano, G.; Muhhina, J.; Fondelli, C.; Gavrilova, J.; Chiesi, A. Methods 2015, 87, 46−58. (55) Diaz, G.; Wolfe, L. M.; Kruh-Garcia, N. A.; Dobos, K. M. 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. 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. Proc. Natl. Acad. Sci. U. S. A. 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. Mol. Cell. Proteomics 2017, 16 (4 suppl 1), S144−S160. (59) Lancaster, M. A.; Knoblich, J. A. Nat. Protoc. 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. eLife 2015, 4, e05098. (61) Gonneaud, A.; Jones, C.; Turgeon, N.; Levesque, D.; Asselin, C.; Boudreau, F.; Boisvert, F. M. 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. 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.; Ponz-Sarvise, 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.; Ardito-Abraham, 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.; IacobuzioDonahue, 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. 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. Proc. Natl. Acad. Sci. U. S. A. 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. Cell 2017, 168 (6), 1135−1148.e12. (66) Ivanov, I. I. Trends Immunol. 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. Microbiome 2016, 4, 31. (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. 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. PLoS One 2016, 11 (8), e0160533. 105
DOI: 10.1021/acs.analchem.7b04340 Anal. Chem. 2018, 90, 86−109
Analytical Chemistry
Review
(70) Gibson, G. R.; Cummings, J. H.; Macfarlane, G. T. 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. J. Microbiol. Methods 2015, 108, 36−44. (72) Kim, H. J.; Huh, D.; Hamilton, G.; Ingber, D. E. Lab Chip 2012, 12 (12), 2165−2174. (73) Diaz-Pascual, F.; Ortiz-Severin, J.; Varas, M. A.; Allende, M. L.; Chavez, F. P. Front. Cell. Infect. Microbiol. 2017, 7, 334. (74) Ma, D.; Storelli, G.; Mitchell, M.; Leulier, F. Biomed J. 2015, 38 (4), 285−293. (75) Lescak, E. A.; Milligan-Myhre, K. C. J. Bacteriol. 2017, 199 (15), e00868-16. (76) Trinder, M.; Daisley, B. A.; Dube, J. S.; Reid, G. Front. Microbiol. 2017, 8, 751. (77) Zhang, R.; Hou, A. ISRN Microbiol. 2013, 2013, 356451. (78) Chaston, J. M.; Newell, P. D.; Douglas, A. E. mBio 2014, 5 (5), e01631-14. (79) Koyle, M. L.; Veloz, M.; Judd, A. M.; Wong, A. C.; Newell, P. D.; Douglas, A. E.; Chaston, J. M. J. Visualized Exp. 2016, No. 113, e54219. (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. Mol. Ecol. 2012, 21 (13), 3363−3378. (81) Treitz, C.; Cassidy, L.; Hockendorf, A.; Leippe, M.; Tholey, A. J. Proteomics 2015, 113, 337−50. (82) Samuel, B. S.; Rowedder, H.; Braendle, C.; Felix, M. A.; Ruvkun, G. Proc. Natl. Acad. Sci. U. S. A. 2016, 113 (27), E3941−9. (83) Cho, I.; Blaser, M. J. Nat. Rev. Genet. 2012, 13 (4), 260−270. (84) Kostic, A. D.; Howitt, M. R.; Garrett, W. S. Genes Dev. 2013, 27 (7), 701−18. (85) Boehme, J. D.; Stegemann-Koniszewski, S.; Autengruber, A.; Peters, N.; Wissing, J.; Jansch, L.; Jeron, A.; Bruder, D. Sci. Rep. 2017, 7, 4972. (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. Mol. Cell. Proteomics 2017, mcp.000072.2017. (87) Belkaid, Y.; Tamoutounour, S. 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. 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. 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. Front. Cell. Infect. Microbiol. 2017, 7, 63. (91) Lawley, T. D.; Walker, A. W. Immunology 2013, 138 (1), 1−11. (92) Rudney, J. D.; Xie, H.; Rhodus, N. L.; Ondrey, F. G.; Griffin, T. J. Mol. Oral Microbiol. 2010, 25 (1), 38−49. (93) Belda-Ferre, P.; Williamson, J.; Simon-Soro, A.; Artacho, A.; Jensen, O. N.; Mira, A. 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. Genome Med. 2016, 8 (1), 44. (95) Jagtap, P.; McGowan, T.; Bandhakavi, S.; Tu, Z. J.; Seymour, S.; Griffin, T. J.; Rudney, J. D. 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. 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. 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. ISME J. 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. 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. 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. 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. J. Proteomics 2015, 112, 53−62. (103) Yu, Y.; Sikorski, P.; Smith, M.; Bowman-Gholston, C.; Cacciabeve, N.; Nelson, K. E.; Pieper, R. Theranostics 2017, 7 (2), 238−252. (104) Yu, Y.; Sikorski, P.; Bowman-Gholston, C.; Cacciabeve, N.; Nelson, K. E.; Pieper, R. J. Transl. Med. 2015, 13, 111. (105) Zhang, Y.; Fonslow, B. R.; Shan, B.; Baek, M.-C.; Yates, J. R. Chem. Rev. 2013, 113 (4), 2343−2394. (106) Wisniewski, J. R.; Zougman, A.; Nagaraj, N.; Mann, M. Nat. Methods 2009, 6 (5), 359−362. (107) Leary, D. H.; Hervey, W. J.; Deschamps, J. R.; Kusterbeck, A. W.; Vora, G. J. Mol. Cell. Probes 2013, 27 (5), 193−199. (108) Speda, J.; Johansson, M. A.; Carlsson, U.; Karlsson, M. Anal. Biochem. 2017, 516, 23−36. (109) Tanca, A.; Palomba, A.; Pisanu, S.; Addis, M. F.; Uzzau, S. Proteomics 2015, 15 (20), 3474−3485. (110) Xiong, W.; Giannone, R. J.; Morowitz, M. J.; Banfield, J. F.; Hettich, R. L. J. Proteome Res. 2015, 14 (1), 133−141. (111) Bojanova, D. P.; Bordenstein, S. R. PLoS Biol. 2016, 14 (7), e1002503. (112) Zhang, X.; Li, L.; Mayne, J.; Ning, Z.; Stintzi, A.; Figeys, D. J. Proteomics 2017, DOI: 10.1016/j.jprot.2017.07.001. (113) Armengaud, J.; Christie-Oleza, J. A.; Clair, G.; Malard, V.; Duport, C. Expert Rev. Proteomics 2012, 9 (5), 561−575. (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.; Hettich, R. L.; Jansson, J. K. PLoS One 2012, 7 (11), e49138. (115) Zhang, X.; Chen, W.; Ning, Z.; Mayne, J.; Mack, D. R.; Stintzi, A.; Tian, R.; Figeys, D. Anal. Chem. 2017, 89, 9407. (116) Mayers, M. D.; Moon, C.; Stupp, G. S.; Su, A. I.; Wolan, D. W. 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. Nat. Protoc. 2010, 5 (12), 1957−66. (118) Sachsenberg, T.; Herbst, F. A.; Taubert, M.; Kermer, R.; Jehmlich, N.; von Bergen, M.; Seifert, J.; Kohlbacher, O. 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. 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. Mol. Cell. Proteomics 2012, 11 (6), O111.016717. (121) Venable, J. D.; Dong, M. Q.; Wohlschlegel, J.; Dillin, A.; Yates, J. R. Nat. 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. 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. Front. Cell. Infect. Microbiol. 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. 106
DOI: 10.1021/acs.analchem.7b04340 Anal. Chem. 2018, 90, 86−109
Analytical Chemistry
Review
D.; Guignard, C.; Schupp, J. M.; Vlassis, N.; Baliga, N. S.; Moritz, R. L.; Keim, P. S.; Wilmes, P. 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. Anal. Chem. 2003, 75 (8), 1895−1904. (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.; Bartlet-Jones, M.; He, F.; Jacobson, A.; Pappin, D. J. Mol. Cell. Proteomics 2004, 3 (12), 1154−69. (127) Kitahara, N.; Morisaka, H.; Aoki, W.; Takeda, Y.; Shibasaki, S.; Kuroda, K.; Ueda, M. 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. Nat. 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. 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. Anal. Chem. 2016, 88 (6), 3295−303. (131) Khoury, G. A.; Baliban, R. C.; Floudas, C. A. Sci. Rep. 2011, 1, 90. (132) Goto, Y.; Uematsu, S.; Kiyono, H. Nat. Immunol. 2016, 17 (11), 1244−1251. (133) Ribet, D.; Cossart, P. FEBS Lett. 2010, 584 (13), 2748−58. (134) Ribet, D.; Cossart, P. 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. BMC Genomics 2017, 18 (1), 301. (136) Carabetta, V. J.; Cristea, I. M. J. Bacteriol. 2017, 199 (16), e00107-17. (137) Bastos, P. A.; da Costa, J. P.; Vitorino, R. J. Proteomics 2017, 152, 254−275. (138) Zhang, W.; Sun, J.; Cao, H.; Tian, R.; Cai, L.; Ding, W.; Qian, P. Y. 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. BMC Microbiol. 2017, 17 (1), 123. (140) Kim, M. S.; Zhong, J.; Pandey, A. Proteomics 2016, 16 (5), 700− 14. (141) Giansanti, P.; Aye, T. T.; van den Toorn, H.; Peng, M.; van Breukelen, B.; Heck, A. J. Cell Rep. 2015, 11 (11), 1834−43. (142) Keller, A.; Bader, S. L.; Kusebauch, U.; Shteynberg, D.; Hood, L.; Moritz, R. L. Mol. Cell. Proteomics 2016, 15 (3), 1151−63. (143) Ke, M.; Shen, H.; Wang, L.; Luo, S.; Lin, L.; Yang, J.; Tian, R. 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. Adv. Mater. 2017, DOI: 10.1002/adma.201770140. (145) Leney, A. C.; El Atmioui, D.; Wu, W.; Ovaa, H.; Heck, A. J. R. Proc. Natl. Acad. Sci. U. S. A. 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. Cell Systems 2017, 4 (6), 587−599. (147) Sommer, F.; Backhed, F. Nat. Rev. Microbiol. 2013, 11 (4), 227− 38. (148) Vaudel, M.; Burkhart, J. M.; Sickmann, A.; Martens, L.; Zahedi, R. P. 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. 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. BMC Genomics 2016, 17, 642. (151) Muth, T.; Renard, B. Y.; Martens, L. Expert Rev. 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. Microbiome 2016, 4, 51. (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. Front. Microbiol. 2016, 7, 459.
(154) Heyer, R.; Schallert, K.; Zoun, R.; Becher, B.; Saake, G.; Benndorf, D. J. Biotechnol. 2017, 261, 24. (155) Tanca, A.; Palomba, A.; Deligios, M.; Cubeddu, T.; Fraumene, C.; Biosa, G.; Pagnozzi, D.; Addis, M. F.; Uzzau, S. PLoS One 2013, 8 (12), e82981. (156) Timmins-Schiffman, E.; May, D. H.; Mikan, M.; Riffle, M.; Frazar, C.; Harvey, H. R.; Noble, W. S.; Nunn, B. L. ISME J. 2017, 11 (2), 309−314. (157) Jagtap, P.; Goslinga, J.; Kooren, J. A.; McGowan, T.; Wroblewski, M. S.; Seymour, S. L.; Griffin, T. J. Proteomics 2013, 13 (8), 1352−1357. (158) Nesvizhskii, A. J. Proteomics 2010, 73 (11), 2092−2123. (159) Tang, H. X.; Li, S. J.; Ye, Y. Z. PLoS Comput. Biol. 2016, 12 (12), e1005224. (160) Rho, M.; Tang, H.; Ye, Y. Nucleic Acids Res. 2010, 38 (20), e191. (161) Kuhring, M.; Renard, B. Y. Proteomics 2015, 15 (20), 3580− 3584. (162) Kong, A. T.; Leprevost, F. V.; Avtonomov, D. M.; Mellacheruvu, D.; Nesvizhskii, A. I. Nat. Methods 2017, 14 (5), 513−520. (163) David, M.; Fertin, G.; Rogniaux, H.; Tessier, D. J. Proteome Res. 2017, 16 (8), 3030−3038. (164) Craig, R.; Beavis, R. C. Rapid Commun. Mass Spectrom. 2003, 17 (20), 2310−2316. (165) Craig, R.; Beavis, R. C. Bioinformatics 2004, 20 (9), 1466−7. (166) Eng, J. K.; Jahan, T. A.; Hoopmann, M. R. Proteomics 2013, 13 (1), 22−24. (167) Eng, J. K.; McCormack, A. L.; Yates, J. R. J. Am. Soc. Mass Spectrom. 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. Mol. Cell. Proteomics 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. BMC Bioinf. 2016, 17 (1), 450−450. (170) Wan, K. X.; Vidavsky, I.; Gross, M. L. J. Am. Soc. Mass Spectrom. 2002, 13 (1), 85−88. (171) Afgan, E.; Chapman, B.; Taylor, J. BMC Bioinf. 2012, 13, 315− 315. (172) Deutsch, E. W.; Mendoza, L.; Shteynberg, D.; Slagel, J.; Sun, Z.; Moritz, R. L. Proteomics: Clin. Appl. 2015, 9 (7−8), 745−754. (173) Halligan, B. D.; Geiger, J. F.; Vallejos, A. K.; Greene, A. S.; Twigger, S. N. J. Proteome Res. 2009, 8 (6), 3148−3153. (174) Muth, T.; Peters, J.; Blackburn, J.; Rapp, E.; Martens, L. J. Proteomics 2013, 88, 104−108. (175) Trudgian, D. C.; Mirzaei, H. J. Proteome Res. 2012, 11 (12), 6282−6290. (176) Elias, J. E.; Gygi, S. P. Nat. 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. J. Proteome Res. 2004, 3 (5), 958−64. (178) Xiong, W.; Abraham, P. E.; Li, Z.; Pan, C.; Hettich, R. L. Proteomics 2015, 15 (20), 3424−38. (179) Cociorva, D.; Tabb, D. L.; Yates, J. R., Validation of tandem mass spectrometry database search results using DTASelect. In Current Protocols in Bioinformatics; John Wiley & Sons, Inc.: Hoboken, NJ, 2007; Chapter 13, Unit 13 4, DOI: 10.1002/0471250953.bi1304s16. (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. Mol. Cell. Proteomics 2017, 16, 1610. (181) Nesvizhskii, A. I.; Keller, A.; Kolker, E.; Aebersold, R. 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. J. Cystic Fibrosis 2016, 15 (2), 242−50. (183) Käll, L.; Canterbury, J. D.; Weston, J.; Noble, W. S.; MacCoss, M. J. Nat. 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.; 107
DOI: 10.1021/acs.analchem.7b04340 Anal. Chem. 2018, 90, 86−109
Analytical Chemistry
Review
Becher, D.; Pieper, D. H.; Jahn, M.; Jahn, D.; Riedel, K. Mol. Cell. Proteomics 2015, 14 (4), 989−1008. (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. Nat. Microbiol 2016, 2, 16180. (186) Gonnelli, G.; Stock, M.; Verwaeren, J.; Maddelein, D.; De Baets, B.; Martens, L.; Degroeve, S. 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. J. Proteome Res. 2015, 14 (6), 2707−13. (188) Perkins, D. N.; Pappin, D. J.; Creasy, D. M.; Cottrell, J. S. Electrophoresis 1999, 20 (18), 3551−67. (189) Cox, J.; Hein, M. Y.; Luber, C. A.; Paron, I.; Nagaraj, N.; Mann, M. Mol. Cell. Proteomics 2014, 13 (9), 2513−2526. (190) Mesuere, B.; Debyser, G.; Aerts, M.; Devreese, B.; Vandamme, P.; Dawyndt, P. Proteomics 2015, 15 (8), 1437−1442. (191) Mesuere, B.; Willems, T.; Van der Jeugt, F.; Devreese, B.; Vandamme, P.; Dawyndt, P. Bioinformatics 2016, 32 (11), 1746−1748. (192) Mesuere, B.; Van der Jeugt, F.; Willems, T.; Naessens, T.; Devreese, B.; Martens, L.; Dawyndt, P. J. Proteomics 2017, DOI: 10.1016/j.jprot.2017.05.022. (193) Boulund, F.; Karlsson, R.; Gonzales-Siles, L.; Johnning, A.; Karami, N.; Al-Bayati, O.; Ahren, C.; Moore, E. R. B.; Kristiansson, E. Mol. Cell. Proteomics 2017, 16 (6), 1052−1063. (194) Galperin, M. Y.; Makarova, K. S.; Wolf, Y. I.; Koonin, E. V. Nucleic Acids Res. 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. Nucleic Acids Res. 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. Nucleic Acids Res. 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. PLoS Comput. Biol. 2016, 12 (6), e1004957. (198) Dhariwal, A.; Chong, J.; Habib, S.; King, I. L.; Agellon, L. B.; Xia, J. Nucleic Acids Res. 2017, 45, W180. (199) Huson, D. H.; Auch, A. F.; Qi, J.; Schuster, S. C. Genome Res. 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. J. Proteome Res. 2015, 14 (3), 1557−1565. (201) Goecks, J.; Nekrutenko, A.; The Galaxy Team. Genome Biology 2010, 11 (8), R86. (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. 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. Proteomics 2015, 15 (20), 3553−3565. (204) Morgun, A.; Dzutsev, A.; Dong, X.; Greer, R. L.; Sexton, D. J.; Ravel, J.; Schuster, M.; Hsiao, W.; Matzinger, P.; Shulzhenko, N. 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. Nat. Commun. 2016, 7, 13419. (206) Lluch-Senar, M.; Cozzuto, L.; Cano, J.; Delgado, J.; LlorensRico, V.; Pereyre, S.; Bebear, C.; Serrano, L. PLoS One 2015, 10 (9), e0137354. (207) Cernava, T.; Erlacher, A.; Aschenbrenner, I. A.; Krug, L.; Lassek, C.; Riedel, K.; Grube, M.; Berg, G. Microbiome 2017, 5 (1), 82. (208) Tanca, A.; Manghina, V.; Fraumene, C.; Palomba, A.; Abbondio, M.; Deligios, M.; Silverman, M.; Uzzau, S. Front. Microbiol. 2017, 8, 391. (209) Tanca, A.; Abbondio, M.; Palomba, A.; Fraumene, C.; Manghina, V.; Cucca, F.; Fiorillo, E.; Uzzau, S. 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. Microb. Biotechnol. 2017, 10 (3), 541−554. (211) Xiong, W.; Brown, C. T.; Morowitz, M. J.; Banfield, J. F.; Hettich, R. L. Microbiome 2017, 5 (1), 72. (212) Darzi, Y.; Falony, G.; Vieira-Silva, S.; Raes, J. ISME J. 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. 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. Genome Biol. 2016, 17 (1), 260. (215) Rohart, F.; Gautier, B.; Singh, A.; Le Cao, K.-A. bioRxiv 2017, DOI: 10.1101/108597. (216) Singh, A.; Gautier, B.; Shannon, C. P.; Vacher, M.; Rohart, F.; Tebutt, S. J.; Le Cao, K.-A. bioRxiv 2016, DOI: 10.1101/067611. (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. Genome Res. 2005, 15 (10), 1451−1455. (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, J.; Von Kuster, G.; Rasche, E.; Soranzo, N.; Turaga, N.; Taylor, J.; Nekrutenko, A.; Goecks, J. 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. 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. 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. J. Proteome Res. 2014, 13 (12), 5898−908. (222) Kim, M.; Rai, N.; Zorraquino, V.; Tagkopoulos, I. 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. mSystems 2017, 2 (2), e00186-16. (224) Wodke, J. A.; Alibes, A.; Cozzuto, L.; Hermoso, A.; Yus, E.; Lluch-Senar, M.; Serrano, L.; Roma, G. Nucleic Acids Res. 2015, 43 (D1), D618−D623. (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. Nucleic Acids Res. 2016, 44 (3), 1192−1202. (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. Sci. Rep. 2016, 6, 32713. (227) Sanchez, B.; Gueimonde, M.; Pena, A. S.; Bernardo, D. 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. 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. Sci. Rep. 2016, 6, 35216. (230) Fukuda, K.; Fujita, Y. BMC Gastroenterol. 2014, 14, 49. (231) Cai, L.; Wu, H.; Li, D.; Zhou, K.; Zou, F. PLoS One 2015, 10 (10), e0140827. (232) Liu, Y.; Zhang, Q.; Hu, M.; Yu, K.; Fu, J.; Zhou, F.; Liu, X. 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. 108
DOI: 10.1021/acs.analchem.7b04340 Anal. Chem. 2018, 90, 86−109
Analytical Chemistry
Review
J.; Rice-Ficht, A.; Russell, W. K.; Ficht, T. A.; de Figueiredo, P. Cell Host Microbe 2017, 21 (5), 637−649. (234) Lapek, J. D., Jr.; Lewinski, M. K.; Wozniak, J. M.; Guatelli, J.; Gonzalez, D. J. 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. J. Virol. 2015, 89 (14), 7064−78. (236) Jungblut, P. R. Microbes Infect. 2001, 3 (10), 831−40. (237) Fulton, K. M.; Twine, S. M. Methods Mol. Biol. 2013, 1061, 21− 57. (238) Magalhaes, S.; Aroso, M.; Roxo, I.; Ferreira, S.; Cerveira, F.; Ramalheira, E.; Ferreira, R.; Vitorino, R. 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. J. Proteomics 2017, 155, 31−39. (240) Lee, S. Y.; Jeoung, D. J. Microbiol. Biotechnol. 2007, 17 (6), 879− 890. (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. 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. Cell. Mol. Life Sci. 2004, 61 (10), 1198−207. (243) Roozendaal, R.; Carroll, M. C. Immunol. Rev. 2007, 219, 157−66. (244) Testa, J. S.; Shetty, V.; Hafner, J.; Nickens, Z.; Kamal, S.; Sinnathamby, G.; Philip, R. PLoS One 2012, 7 (11), e48484. (245) Weinzierl, A. O.; Maurer, D.; Altenberend, F.; SchneiderhanMarra, N.; Klingel, K.; Schoor, O.; Wernet, D.; Joos, T.; Rammensee, H. G.; Stevanovic, S. Cancer Res. 2008, 68 (7), 2447−54. (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. J. Mol. Biol. 2010, 399 (4), 596−603. (247) Karunakaran, K. P.; Yu, H.; Foster, L. J.; Brunham, R. C. 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. J. Proteome Res. 2017, 16 (1), 298−306. (249) Cotham, V. C.; Horton, A. P.; Lee, J.; Georgiou, G.; Brodbelt, J. S. Anal. Chem. 2017, 89 (12), 6498−6504. (250) Pauly, F.; Smedby, K. E.; Jerkeman, M.; Hjalgrim, H.; Ohlsson, M.; Rosenquist, R.; Borrebaeck, C. A.; Wingren, C. Leuk. Res. 2014, 38 (6), 682−90. (251) Sandstrom, A.; Andersson, R.; Segersvard, R.; Lohr, M.; Borrebaeck, C. A.; Wingren, C. 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. 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. PLoS One 2016, 11 (7), e0159138. (254) Rizwan, M.; Naz, A.; Ahmad, J.; Naz, K.; Obaid, A.; Parveen, T.; Ahsan, M.; Ali, A. BMC Bioinf. 2017, 18 (1), 106. (255) Ageitos, J. M.; Sanchez-Perez, A.; Calo-Mata, P.; Villa, T. G. Biochem. Pharmacol. 2017, 133, 117−138. (256) Peters, B. M.; Shirtliff, M. E.; Jabra-Rizk, M. A. PLoS Pathog. 2010, 6 (10), e1001067. (257) Bastos, P.; Trindade, F.; Ferreira, R.; Casteleiro, M. A.; Stevens, R.; Klein, J.; Vitorino, R. J. Proteomics 2017, DOI: 10.1016/ j.jprot.2017.02.016. (258) Selsted, M. E.; Novotny, M. J.; Morris, W. L.; Tang, Y. Q.; Smith, W.; Cullor, J. S. J. Biol. Chem. 1992, 267 (7), 4292−4295. (259) Tang, Y. Q.; Yuan, J.; Osapay, G.; Osapay, K.; Tran, D.; Miller, C. J.; Ouellette, A. J.; Selsted, M. E. Science 1999, 286 (5439), 498−502. (260) Mishra, B.; Wang, G. Front. Immunol. 2012, 3, 221. (261) Kleinkauf, H.; von Dohren, H. Eur. J. Biochem. 1990, 192 (1), 1− 15.
(262) Strieker, M.; Tanovic, A.; Marahiel, M. A. Curr. Opin. Struct. Biol. 2010, 20 (2), 234−40. (263) Wang, G.; Li, X.; Wang, Z. Nucleic Acids Res. 2016, 44 (D1), D1087−93. (264) Fan, L.; Sun, J.; Zhou, M.; Zhou, J.; Lao, X.; Zheng, H.; Xu, H. Sci. Rep. 2016, 6, 24482. (265) Waghu, F. H.; Barai, R. S.; Gurung, P.; Idicula-Thomas, S. Nucleic Acids Res. 2016, 44 (D1), D1094−7. (266) Zhao, X.; Wu, H.; Lu, H.; Li, G.; Huang, Q. PLoS One 2013, 8 (6), e66557. (267) Wang, M.; Wang, Y.; Wang, A.; Song, Y.; Ma, D.; Yang, H.; Ma, Y.; Lai, R. Comp. Biochem. Physiol., Part B: Biochem. Mol. Biol. 2010, 155 (1), 72−6. (268) Zasloff, M. Nature 2002, 415 (6870), 389−95. (269) Conlon, J. M. 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. J. Proteomics 2015, 115, 49− 57. (271) Mishra, B.; Wang, G. J. Am. Chem. Soc. 2012, 134 (30), 12426−9. (272) Sato, T.; Clevers, H. Science 2013, 340 (6137), 1190. (273) Grabinger, T.; Luks, L.; Kostadinova, F.; Zimberlin, C.; Medema, J. P.; Leist, M.; Brunner, T. Cell Death Dis. 2014, 5 (5), e1228. (274) Pruitt, K. D.; Tatusova, T.; Maglott, D. R. Nucleic Acids Res. 2007, 35 (suppl_1), D61−D65. (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.; GruazGumowski, 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.; 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. Nucleic Acids Res. 2015, 43 (D1), D204− D212. (276) Turnbaugh, P. J.; Ley, R. E.; Hamady, M.; Fraser-liggett, C.; Knight, R.; Gordon, J. I. 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. Nat. Biotechnol. 2017, 35 (7), 676−683.
109
DOI: 10.1021/acs.analchem.7b04340 Anal. Chem. 2018, 90, 86−109
