Exploring mixed microbial community functioning: recent advances in metaproteomics

System approaches to elucidate ecosystem functioning constitute an emerging area of research within microbial ecology. Such approaches aim at investigating all levels of biological information (DNA, RNA, proteins and metabolites) to capture the functional interactions occurring in a given ecosystem and track down characteristics that could not be accessed by the study of isolated components. In this context, the study of the proteins collectively expressed by all the microorganisms present within an ecosystem (metaproteomics) is not only crucial but can also provide insights into microbial functionality. Overall, the success of metaproteomics is closely linked to metagenomics, and with the exponential increase in the availability of metagenome sequences, this field of research is starting to experience generation of an overwhelming amount of data, which requires systematic analysis. Metaproteomics has been employed in very diverse environments, and this review discusses the recent advances achieved in the context of human biology, soil, marine and freshwater environments as well as natural and bioengineered systems.     

Metaproteomics Analysis Reveals the Adaptation Process for the Chicken Gut Microbiota

The animal gastrointestinal tract houses a large microbial community, the gut microbiota, that confers many benefits to its host, such as protection from pathogens and provision of essential metabolites. Metagenomic approaches have defined the chicken fecal microbiota in other studies, but here, we wished to assess the correlation between the metagenome and the bacterial proteome in order to better understand the healthy chicken gut microbiota. Here, we performed high-throughput sequencing of 16S rRNA gene amplicons and metaproteomics analysis of fecal samples to determine microbial gut composition and protein expression. 16 rRNA gene sequencing analysis identified Clostridiales, Bacteroidaceae, and Lactobacillaceae species as the most abundant species in the gut. For metaproteomics analysis, peptides were generated by using the Fasp method and subsequently fractionated by strong anion exchanges. Metaproteomics analysis identified 3,673 proteins. Among the most frequently identified proteins, 380 proteins belonged to Lactobacillus spp., 155 belonged to Clostridium spp., and 66 belonged to Streptococcus spp. The most frequently identified proteins were heat shock chaperones, including 349 GroEL proteins, from many bacterial species, whereas the most abundant enzymes were pyruvate kinases, as judged by the number of peptides identified per protein (spectral counting). Gene ontology and KEGG pathway analyses revealed the functions and locations of the identified proteins. The findings of both metaproteomics and 16S rRNA sequencing analyses are discussed.     

Metaproteomic Survey of Six Aquatic Habitats: Discovering the Identities of Microbial Populations Active in Biogeochemical Cycling

Our goal is to strengthen the foundations of metaproteomics as a microbial community analysis tool that links the functional identity of actively expressed gene products with host phylogeny. We used shotgun metaproteomics to survey waters in six disparate aquatic habitats (Cayuga Lake, NY; Oneida Lake, NY; Gulf of Maine; Chesapeake Bay, MD; Gulf of Mexico; and the South Pacific). Peptide pools prepared from filter-gathered microbial biomass, analyzed by nano-liquid chromatography–mass spectrometry (MS/MS) generating 9,693?±?1,073 mass spectra identified 326?±?107 bacterial proteins per sample. Distribution of proteobacterial (Alpha and Beta) and cyanobacterial (Prochlorococcus and Synechococcus spp.) protein hosts across all six samples was consistent with the previously published biogeography for these microorganisms. Marine samples were enriched in transport proteins (TRAP-type for dicarboxylates and ATP binding cassette (ABC)-type for amino acids and carbohydrates) compared with the freshwater samples. We were able to match in situ expression of many key proteins catalyzing C-, N-, and S-cycle processes with their bacterial hosts across all six habitats. Pelagibacter was identified as the host of ABC-type sugar-, organic polyanion-, and glycine betaine-transport proteins; this extends previously published studies of Pelagibacter's in situ biogeochemical role in marine C- and N-metabolism. Proteins matched to Ruegeria confirmed these organism's role in marine waters oxidizing both carbon monoxide and sulfide. By documenting both processes expressed in situ and the identity of host cells, metaproteomics tested several existing hypotheses about ecophysiological processes and provided fodder for new ones.     

元蛋白质组学在微生物功能研究中的应用

后基因组时代,仅依靠基因组方法来研究原位微生物群落的功能已远远不够,在这种背景下元蛋白质组学研究逐渐兴起。应用元蛋白质组学技术可大规模研究原位微生物群落的蛋白质表达,分析生态系统中微生物的功能,寻找新的功能基因和代谢通路,为微生物群体的基因和功能多样性研究提供数据。同时,还可鉴定与微生物功能相关的蛋白质,这些蛋白质未来可以作为生物标记物为环境可持续发展铺路。综述了元蛋白质组学的发展概况及其在微生物功能研究中的重大作用,强调了元蛋白质组学方法在分析新功能基因及其相关基因,揭示微生物多样性与微生物群体功能之间的关系等方面起到的作用,并对其应用前景进行了展望。     

Quantitative Metaproteomics and Activity-based Protein Profiling of Patient Fecal Microbiome Identifies Host and Microbial Serine-type Endopeptidase Activity Associated With Ulcerative Colitis

The gut microbiota plays an important yet incompletely understood role in the induction and propagation of ulcerative colitis (UC). Organism-level efforts to identify UC-associated microbes have revealed the importance of community structure, but less is known about the molecular effectors of disease. We performed 16S rRNA gene sequencing in parallel with label-free data-dependent LC-MS/MS proteomics to characterize the stool microbiomes of healthy (n = 8) and UC (n = 10) patients. Comparisons of taxonomic composition between techniques revealed major differences in community structure partially attributable to the additional detection of host, fungal, viral, and food peptides by metaproteomics. Differential expression analysis of metaproteomic data identified 176 significantly enriched protein groups between healthy and UC patients. Gene ontology analysis revealed several enriched functions with serine-type endopeptidase activity overrepresented in UC patients. Using a biotinylated fluorophosphonate probe and streptavidin-based enrichment, we show that serine endopeptidases are active in patient fecal samples and that additional putative serine hydrolases are detectable by this approach compared with unenriched profiling. Finally, as metaproteomic databases expand, they are expected to asymptotically approach completeness. Using ComPIL and de novo peptide sequencing, we estimate the size of the probable peptide space unidentified (“dark peptidome”) by our large database approach to establish a rough benchmark for database sufficiency. Despite high variability inherent in patient samples, our analysis yielded a catalog of differentially enriched proteins between healthy and UC fecal proteomes. This catalog provides a clinically relevant jumping-off point for further molecular-level studies aimed at identifying the microbial underpinnings of UC.     

Verification of automated peptide identifications from proteomic tandem mass spectra

Shotgun proteomics yields tandem mass spectra of peptides that can be identified by database search algorithms. When only a few observed peptides suggest the presence of a protein, establishing the accuracy of the peptide identifications is necessary for accepting or rejecting the protein identification. In this protocol, we describe the properties of peptide identifications that can differentiate legitimately identified peptides from spurious ones. The chemistry of fragmentation, as embodied in the 'mobile proton' and 'pathways in competition' models, informs the process of confirming or rejecting each spectral match. Examples of ion-trap and tandem time-of-flight (TOF/TOF) mass spectra illustrate these principles of fragmentation.     

Speeding up tandem mass spectrometry database search: metric embeddings and fast near neighbor search

MOTIVATION: Due to the recent advances in technology of mass spectrometry, there has been an exponential increase in the amount of data being generated in the past few years. Database searches have not been able to keep with this data explosion. Thus, speeding up the data searches becomes increasingly important in mass-spectrometry-based applications. Traditional database search methods use one-against-all comparisons of a query spectrum against a very large number of peptides generated from in silico digestion of protein sequences in a database, to filter potential candidates from this database followed by a detailed scoring and ranking of those filtered candidates. RESULTS: In this article, we show that we can avoid the one-against-all comparisons. The basic idea is to design a set of hash functions to pre-process peptides in the database such that for each query spectrum we can use the hash functions to find only a small subset of peptide sequences that are most likely to match the spectrum. The construction of each hash function is based on a random spectrum and the hash value of a peptide is the normalized shared peak counts score (cosine) between the random spectrum and the hypothetical spectrum of the peptide. To implement this idea, we first embed each peptide into a unit vector in a high-dimensional metric space. The random spectrum is represented by a random vector, and we use random vectors to construct a set of hash functions called locality sensitive hashing (LSH) for preprocessing. We demonstrate that our mapping is accurate. We show that our method can filter out >95.65% of the spectra without missing any correct sequences, or gain 111 times speedup by filtering out 99.64% of spectra while missing at most 0.19% (2 out of 1014) of the correct sequences. In addition, we show that our method can be effectively used for other mass spectra mining applications such as finding clusters of spectra efficiently and accurately. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Diversity and functions of the sheep faecal microbiota: a multi‐omic characterization

Little is currently known on the microbial populations colonizing the sheep large intestine, despite their expected key role in host metabolism, physiology and immunity. This study reports the first characterization of the sheep faecal microbiota composition and functions, obtained through the application of a multi‐omic strategy. An optimized protocol was first devised for DNA extraction and amplification from sheep stool samples. Then, 16S rDNA sequencing, shotgun metagenomics and shotgun metaproteomics were applied to unravel taxonomy, genetic potential and actively expressed functions and pathways respectively. Under a taxonomic perspective, the sheep faecal microbiota appeared globally comparable to that of other ruminants, with Firmicutes being the main phylum. In functional terms, we detected 2097 gene and 441 protein families, finding that the sheep faecal microbiota was primarily involved in catabolism. We investigated carbohydrate transport and degradation activities and identified phylum‐specific pathways, such as methanogenesis for Euryarchaeota and acetogenesis for Firmicutes. Furthermore, our approach enabled the identification of proteins expressed by the eukaryotic component of the microbiota. Taken together, these findings unveil structure and role of the distal gut microbiota in sheep, and open the way to further studies aimed at elucidating its connections with management and dietary variables in sheep farming.     

Accurate mass tag retention time database for urine proteome analysis by chromatography-mass spectrometry

Information about peptides and proteins in urine can be used to search for biomarkers of early stages of various diseases. The main technology currently used for identification of peptides and proteins is tandem mass spectrometry, in which peptides are identified by mass spectra of their fragmentation products. However, the presence of the fragmentation stage decreases sensitivity of analysis and increases its duration. We have developed a method for identification of human urinary proteins and peptides. This method based on the accurate mass and time tag (AMT) method does not use tandem mass spectrometry. The database of AMT tags containing more than 1381 AMT tags of peptides has been constructed. The software for database filling with AMT tags, normalizing the chromatograms, database application for identification of proteins and peptides, and their quantitative estimation has been developed. The new procedures for peptide identification by tandem mass spectra and the AMT tag database are proposed. The paper also lists novel proteins that have been identified in human urine for the first time.     

环境微生物学的组学技术应用和突破（英文稿）

由于研究环境变化和微生物群落的需要,近年来高通量组学技术得到了迅猛开发和应用.其中,基于测序和芯片技术的宏基因组学是一个关键的、最成熟的组学技术,为大多数的其它组学技术提供了支撑.相比较而言,宏转录组学、宏蛋白质组学和宏代谢组学也取得了少数的有限成功,但已经显示出可喜的潜力.所有的组学技术都有赖于生物信息学,使得后者成为组学技术应用的一个主要的技术瓶颈.这些新的组学技术对环境微生物学领域产生了革命性的影响,极大地丰富了我们对于环境微生物基因资源和功能活性的了解.     

Human plasma proteome analysis by reversed sequence database search and molecular weight correlation based on a bacterial proteome analysis

In shotgun proteomics, proteins can be fractionated by 1-D gel electrophoresis and digested into peptides, followed by liquid chromatography to separate the peptide mixture. Mass spectrometry generates hundreds of thousands of tandem mass spectra from these fractions, and proteins are identified by database searching. However, the search scores are usually not sufficient to distinguish the correct peptides. In this study, we propose a confident protein identification method for high-throughput analysis of human proteome. To build a filtering protocol in database search, we chose Pseudomonas putida KT2440 as a reference because this bacterial proteome contains fewer modifications and is simpler than the human proteome. First, the P. putida KT2440 proteome was filtered by reversed sequence database search and correlated by the molecular weight in 1-D-gel band positions. The characterization protocol was then applied to determine the criteria for clustering of the human plasma proteome into three different groups. This protein filtering method, based on bacterial proteome data analysis, represents a rapid way to generate higher confidence protein list of the human proteome, which includes some of heavily modified and cleaved proteins.     

Validation of endogenous peptide identifications using a database of tandem mass spectra

The SwePep database is designed for endogenous peptides and mass spectrometry. It contains information about the peptides such as mass, pl, precursor protein and potential post-translational modifications. Here, we have improved and extended the SwePep database with tandem mass spectra, by adding a locally curated version of the global proteome machine database (GPMDB). In peptidomic experiment practice, many peptide sequences contain multiple tandem mass spectra with different quality. The new tandem mass spectra database in SwePep enables validation of low quality spectra using high quality tandem mass spectra. The validation is performed by comparing the fragmentation patterns of the two spectra using algorithms for calculating the correlation coefficient between the spectra. The present study is the first step in developing a tandem spectrum database for endogenous peptides that can be used for spectrum-to-spectrum identifications instead of peptide identifications using traditional protein sequence database searches.     

Structure and function of the symbiosis partners of the lung lichen (Lobaria pulmonaria L. Hoffm.) analyzed by metaproteomics

Environmental proteomics, also referred to as metaproteomics, is an emerging technology to study the structure and function of microbial communities. Here, we applied semi-quantitative label-free proteomics using one-dimensional gel electrophoresis combined with LC-MS/MS and normalized spectral counting together with fluorescence in situ hybridization and confocal laser scanning microscopy to characterize the metaproteome of the lung lichen symbiosis Lobaria pulmonaria. In addition to the myco- and photobiont, L. pulmonaria harbors proteins from a highly diverse prokaryotic community, which is dominated by Proteobacteria and including also Archaea. While fungal proteins are most dominant (75.4% of all assigned spectra), about the same amount of spectra were assigned to prokaryotic proteins (10%) and to the green algal photobiont (9%). While the latter proteins were found to be mainly associated with energy and carbohydrate metabolism, a major proportion of fungal and bacterial proteins appeared to be involved in PTMs and protein turnover and other diverse functions.     

Comparative metaproteomics and diversity analysis of human intestinal microbiota testifies for its temporal stability and expression of core functions

The human intestinal tract is colonized by microbial communities that show a subject-specific composition and a high-level temporal stability in healthy adults. To determine whether this is reflected at the functional level, we compared the faecal metaproteomes of healthy subjects over time using a novel high-throughput approach based on denaturing polyacrylamide gel electrophoresis and liquid chromatography-tandem mass spectrometry. The developed robust metaproteomics workflow and identification pipeline was used to study the composition and temporal stability of the intestinal metaproteome using faecal samples collected from 3 healthy subjects over a period of six to twelve months. The same samples were also subjected to DNA extraction and analysed for their microbial composition and diversity using the Human Intestinal Tract Chip, a validated phylogenetic microarray. Using metagenome and single genome sequence data out of the thousands of mass spectra generated per sample, approximately 1,000 peptides per sample were identified. Our results indicate that the faecal metaproteome is subject-specific and stable during a one-year period. A stable common core of approximately 1,000 proteins could be recognized in each of the subjects, indicating a common functional core that is mainly involved in carbohydrate transport and degradation. Additionally, a variety of surface proteins could be identified, including potential microbes-host interacting components such as flagellins and pili. Altogether, we observed a highly comparable subject-specific clustering of the metaproteomic and phylogenetic profiles, indicating that the distinct microbial activity is reflected by the individual composition.     

Protein-based stable isotope probing

We describe a stable isotope probing (SIP) technique that was developed to link microbe-specific metabolic function to phylogenetic information. Carbon ((13)C)- or nitrogen ((15)N)-labeled substrates (typically with >98% heavy label) were used in cultivation experiments and the heavy isotope incorporation into proteins (protein-SIP) on growth was determined. The amount of incorporation provides a measure for assimilation of a substrate, and the sequence information from peptide analysis obtained by mass spectrometry delivers phylogenetic information about the microorganisms responsible for the metabolism of the particular substrate. In this article, we provide guidelines for incubating microbial cultures with labeled substrates and a protocol for protein-SIP. The protocol guides readers through the proteomics pipeline, including protein extraction, gel-free and gel-based protein separation, the subsequent mass spectrometric analysis of peptides and the calculation of the incorporation of stable isotopes into peptides. Extraction of proteins and the mass fingerprint measurements of unlabeled and labeled fractions can be performed in 2-3 d.     

A new chemical approach to differentiate carboxy terminal peptide fragments in cyanogen bromide digests of proteins

We present a novel approach to perform C-terminal sequence analysis by discriminating the C-terminal peptide in a mass spectral analysis of a CNBr digest. During CNBr cleavage, all Met-Xxx peptide bonds are cleaved and the generated internal peptides all end with a homoserine lactone (hsl)-derivative. The partial opening of the hsl-derivatives, by using a slightly basic buffer solution, results in the formation of m/z doublets (Δm = 18 Da) for all internal peptides and allows to identify the C-terminal peptide which appears as a singlet in the mass spectra. Using two model proteins we demonstrate that this approach can be applied to study proteins purified in gel or in solution. The chemical opening of the hsl-derivative does not require any sample clean-up and therefore, the sensitivity of the C-terminal sequencing approach is increased significantly. Finally, the new protocol was applied to characterize the C-terminal sequence of two recombinant proteins. Tandem mass spectrometry by MALDI-TOF/TOF allowed to identify the sequence of the C-terminal peptides. This novel approach will allow to perform a proteome-wide study of C-terminal proteolytic processing events in a high-throughput fashion.     

Peptide identification by database search of mixture tandem mass spectra

In high-throughput proteomics the development of computational methods and novel experimental strategies often rely on each other. In certain areas, mass spectrometry methods for data acquisition are ahead of computational methods to interpret the resulting tandem mass spectra. Particularly, although there are numerous situations in which a mixture tandem mass spectrum can contain fragment ions from two or more peptides, nearly all database search tools still make the assumption that each tandem mass spectrum comes from one peptide. Common examples include mixture spectra from co-eluting peptides in complex samples, spectra generated from data-independent acquisition methods, and spectra from peptides with complex post-translational modifications. We propose a new database search tool (MixDB) that is able to identify mixture tandem mass spectra from more than one peptide. We show that peptides can be reliably identified with up to 95% accuracy from mixture spectra while considering only a 0.01% of all possible peptide pairs (four orders of magnitude speedup). Comparison with current database search methods indicates that our approach has better or comparable sensitivity and precision at identifying single-peptide spectra while simultaneously being able to identify 38% more peptides from mixture spectra at significantly higher precision.